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dremio/arrow | python/pyarrow/tests/test_array.py | 2 | 23432 | # Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
# under the License.
import datetime
import pytest
import struct
import sys
import numpy as np
import pandas as pd
import pandas.util.testing as tm
import pyarrow as pa
from pyarrow.pandas_compat import get_logical_type
import pyarrow.formatting as fmt
def test_total_bytes_allocated():
assert pa.total_allocated_bytes() == 0
def test_repr_on_pre_init_array():
arr = pa.Array()
assert len(repr(arr)) > 0
def test_getitem_NA():
arr = pa.array([1, None, 2])
assert arr[1] is pa.NA
def test_list_format():
arr = pa.array([[1], None, [2, 3, None]])
result = fmt.array_format(arr)
expected = """\
[
[1],
NA,
[2,
3,
NA]
]"""
assert result == expected
def test_string_format():
arr = pa.array(['', None, 'foo'])
result = fmt.array_format(arr)
expected = """\
[
'',
NA,
'foo'
]"""
assert result == expected
def test_long_array_format():
arr = pa.array(range(100))
result = fmt.array_format(arr, window=2)
expected = """\
[
0,
1,
...
98,
99
]"""
assert result == expected
def test_to_pandas_zero_copy():
import gc
arr = pa.array(range(10))
for i in range(10):
np_arr = arr.to_pandas()
assert sys.getrefcount(np_arr) == 2
np_arr = None # noqa
assert sys.getrefcount(arr) == 2
for i in range(10):
arr = pa.array(range(10))
np_arr = arr.to_pandas()
arr = None
gc.collect()
# Ensure base is still valid
# Because of py.test's assert inspection magic, if you put getrefcount
# on the line being examined, it will be 1 higher than you expect
base_refcount = sys.getrefcount(np_arr.base)
assert base_refcount == 2
np_arr.sum()
def test_array_getitem():
arr = pa.array(range(10, 15))
lst = arr.to_pylist()
for idx in range(-len(arr), len(arr)):
assert arr[idx].as_py() == lst[idx]
for idx in range(-2 * len(arr), -len(arr)):
with pytest.raises(IndexError):
arr[idx]
for idx in range(len(arr), 2 * len(arr)):
with pytest.raises(IndexError):
arr[idx]
def test_array_slice():
arr = pa.array(range(10))
sliced = arr.slice(2)
expected = pa.array(range(2, 10))
assert sliced.equals(expected)
sliced2 = arr.slice(2, 4)
expected2 = pa.array(range(2, 6))
assert sliced2.equals(expected2)
# 0 offset
assert arr.slice(0).equals(arr)
# Slice past end of array
assert len(arr.slice(len(arr))) == 0
with pytest.raises(IndexError):
arr.slice(-1)
# Test slice notation
assert arr[2:].equals(arr.slice(2))
assert arr[2:5].equals(arr.slice(2, 3))
assert arr[-5:].equals(arr.slice(len(arr) - 5))
with pytest.raises(IndexError):
arr[::-1]
with pytest.raises(IndexError):
arr[::2]
n = len(arr)
for start in range(-n * 2, n * 2):
for stop in range(-n * 2, n * 2):
assert arr[start:stop].to_pylist() == arr.to_pylist()[start:stop]
def test_struct_array_slice():
# ARROW-2311: slicing nested arrays needs special care
ty = pa.struct([pa.field('a', pa.int8()),
pa.field('b', pa.float32())])
arr = pa.array([(1, 2.5), (3, 4.5), (5, 6.5)], type=ty)
assert arr[1:].to_pylist() == [{'a': 3, 'b': 4.5},
{'a': 5, 'b': 6.5}]
def test_array_factory_invalid_type():
arr = np.array([datetime.timedelta(1), datetime.timedelta(2)])
with pytest.raises(ValueError):
pa.array(arr)
def test_array_ref_to_ndarray_base():
arr = np.array([1, 2, 3])
refcount = sys.getrefcount(arr)
arr2 = pa.array(arr) # noqa
assert sys.getrefcount(arr) == (refcount + 1)
def test_array_eq_raises():
# ARROW-2150: we are raising when comparing arrays until we define the
# behavior to either be elementwise comparisons or data equality
arr1 = pa.array([1, 2, 3], type=pa.int32())
arr2 = pa.array([1, 2, 3], type=pa.int32())
with pytest.raises(NotImplementedError):
arr1 == arr2
def test_array_from_buffers():
values_buf = pa.py_buffer(np.int16([4, 5, 6, 7]))
nulls_buf = pa.py_buffer(np.uint8([0b00001101]))
arr = pa.Array.from_buffers(pa.int16(), 4, [nulls_buf, values_buf])
assert arr.type == pa.int16()
assert arr.to_pylist() == [4, None, 6, 7]
arr = pa.Array.from_buffers(pa.int16(), 4, [None, values_buf])
assert arr.type == pa.int16()
assert arr.to_pylist() == [4, 5, 6, 7]
arr = pa.Array.from_buffers(pa.int16(), 3, [nulls_buf, values_buf],
offset=1)
assert arr.type == pa.int16()
assert arr.to_pylist() == [None, 6, 7]
with pytest.raises(TypeError):
pa.Array.from_buffers(pa.int16(), 3, [u'', u''], offset=1)
def test_dictionary_from_numpy():
indices = np.repeat([0, 1, 2], 2)
dictionary = np.array(['foo', 'bar', 'baz'], dtype=object)
mask = np.array([False, False, True, False, False, False])
d1 = pa.DictionaryArray.from_arrays(indices, dictionary)
d2 = pa.DictionaryArray.from_arrays(indices, dictionary, mask=mask)
for i in range(len(indices)):
assert d1[i].as_py() == dictionary[indices[i]]
if mask[i]:
assert d2[i] is pa.NA
else:
assert d2[i].as_py() == dictionary[indices[i]]
def test_dictionary_from_boxed_arrays():
indices = np.repeat([0, 1, 2], 2)
dictionary = np.array(['foo', 'bar', 'baz'], dtype=object)
iarr = pa.array(indices)
darr = pa.array(dictionary)
d1 = pa.DictionaryArray.from_arrays(iarr, darr)
for i in range(len(indices)):
assert d1[i].as_py() == dictionary[indices[i]]
def test_dictionary_from_arrays_boundscheck():
indices1 = pa.array([0, 1, 2, 0, 1, 2])
indices2 = pa.array([0, -1, 2])
indices3 = pa.array([0, 1, 2, 3])
dictionary = pa.array(['foo', 'bar', 'baz'])
# Works fine
pa.DictionaryArray.from_arrays(indices1, dictionary)
with pytest.raises(pa.ArrowException):
pa.DictionaryArray.from_arrays(indices2, dictionary)
with pytest.raises(pa.ArrowException):
pa.DictionaryArray.from_arrays(indices3, dictionary)
# If we are confident that the indices are "safe" we can pass safe=False to
# disable the boundschecking
pa.DictionaryArray.from_arrays(indices2, dictionary, safe=False)
def test_dictionary_with_pandas():
indices = np.repeat([0, 1, 2], 2)
dictionary = np.array(['foo', 'bar', 'baz'], dtype=object)
mask = np.array([False, False, True, False, False, False])
d1 = pa.DictionaryArray.from_arrays(indices, dictionary)
d2 = pa.DictionaryArray.from_arrays(indices, dictionary, mask=mask)
pandas1 = d1.to_pandas()
ex_pandas1 = pd.Categorical.from_codes(indices, categories=dictionary)
tm.assert_series_equal(pd.Series(pandas1), pd.Series(ex_pandas1))
pandas2 = d2.to_pandas()
ex_pandas2 = pd.Categorical.from_codes(np.where(mask, -1, indices),
categories=dictionary)
tm.assert_series_equal(pd.Series(pandas2), pd.Series(ex_pandas2))
def test_list_from_arrays():
offsets_arr = np.array([0, 2, 5, 8], dtype='i4')
offsets = pa.array(offsets_arr, type='int32')
pyvalues = [b'a', b'b', b'c', b'd', b'e', b'f', b'g', b'h']
values = pa.array(pyvalues, type='binary')
result = pa.ListArray.from_arrays(offsets, values)
expected = pa.array([pyvalues[:2], pyvalues[2:5], pyvalues[5:8]])
assert result.equals(expected)
# With nulls
offsets = [0, None, 2, 6]
values = ['a', 'b', 'c', 'd', 'e', 'f']
result = pa.ListArray.from_arrays(offsets, values)
expected = pa.array([values[:2], None, values[2:]])
assert result.equals(expected)
# Another edge case
offsets2 = [0, 2, None, 6]
result = pa.ListArray.from_arrays(offsets2, values)
expected = pa.array([values[:2], values[2:], None])
assert result.equals(expected)
def test_union_from_dense():
binary = pa.array([b'a', b'b', b'c', b'd'], type='binary')
int64 = pa.array([1, 2, 3], type='int64')
types = pa.array([0, 1, 0, 0, 1, 1, 0], type='int8')
value_offsets = pa.array([0, 0, 2, 1, 1, 2, 3], type='int32')
result = pa.UnionArray.from_dense(types, value_offsets, [binary, int64])
assert result.to_pylist() == [b'a', 1, b'c', b'b', 2, 3, b'd']
def test_union_from_sparse():
binary = pa.array([b'a', b' ', b'b', b'c', b' ', b' ', b'd'],
type='binary')
int64 = pa.array([0, 1, 0, 0, 2, 3, 0], type='int64')
types = pa.array([0, 1, 0, 0, 1, 1, 0], type='int8')
result = pa.UnionArray.from_sparse(types, [binary, int64])
assert result.to_pylist() == [b'a', 1, b'b', b'c', 2, 3, b'd']
def test_string_from_buffers():
array = pa.array(["a", None, "b", "c"])
buffers = array.buffers()
copied = pa.StringArray.from_buffers(
len(array), buffers[1], buffers[2], buffers[0], array.null_count,
array.offset)
assert copied.to_pylist() == ["a", None, "b", "c"]
copied = pa.StringArray.from_buffers(
len(array), buffers[1], buffers[2], buffers[0])
assert copied.to_pylist() == ["a", None, "b", "c"]
sliced = array[1:]
buffers = sliced.buffers()
copied = pa.StringArray.from_buffers(
len(sliced), buffers[1], buffers[2], buffers[0], -1, sliced.offset)
assert copied.to_pylist() == [None, "b", "c"]
assert copied.null_count == 1
# Slice but exclude all null entries so that we don't need to pass
# the null bitmap.
sliced = array[2:]
buffers = sliced.buffers()
copied = pa.StringArray.from_buffers(
len(sliced), buffers[1], buffers[2], None, -1, sliced.offset)
assert copied.to_pylist() == ["b", "c"]
assert copied.null_count == 0
def _check_cast_case(case, safe=True):
in_data, in_type, out_data, out_type = case
in_arr = pa.array(in_data, type=in_type)
casted = in_arr.cast(out_type, safe=safe)
expected = pa.array(out_data, type=out_type)
assert casted.equals(expected)
def test_cast_integers_safe():
safe_cases = [
(np.array([0, 1, 2, 3], dtype='i1'), 'int8',
np.array([0, 1, 2, 3], dtype='i4'), pa.int32()),
(np.array([0, 1, 2, 3], dtype='i1'), 'int8',
np.array([0, 1, 2, 3], dtype='u4'), pa.uint16()),
(np.array([0, 1, 2, 3], dtype='i1'), 'int8',
np.array([0, 1, 2, 3], dtype='u1'), pa.uint8()),
(np.array([0, 1, 2, 3], dtype='i1'), 'int8',
np.array([0, 1, 2, 3], dtype='f8'), pa.float64())
]
for case in safe_cases:
_check_cast_case(case)
unsafe_cases = [
(np.array([50000], dtype='i4'), 'int32', 'int16'),
(np.array([70000], dtype='i4'), 'int32', 'uint16'),
(np.array([-1], dtype='i4'), 'int32', 'uint16'),
(np.array([50000], dtype='u2'), 'uint16', 'int16')
]
for in_data, in_type, out_type in unsafe_cases:
in_arr = pa.array(in_data, type=in_type)
with pytest.raises(pa.ArrowInvalid):
in_arr.cast(out_type)
def test_cast_column():
arrays = [pa.array([1, 2, 3]), pa.array([4, 5, 6])]
col = pa.column('foo', arrays)
target = pa.float64()
casted = col.cast(target)
expected = pa.column('foo', [x.cast(target) for x in arrays])
assert casted.equals(expected)
def test_cast_integers_unsafe():
# We let NumPy do the unsafe casting
unsafe_cases = [
(np.array([50000], dtype='i4'), 'int32',
np.array([50000], dtype='i2'), pa.int16()),
(np.array([70000], dtype='i4'), 'int32',
np.array([70000], dtype='u2'), pa.uint16()),
(np.array([-1], dtype='i4'), 'int32',
np.array([-1], dtype='u2'), pa.uint16()),
(np.array([50000], dtype='u2'), pa.uint16(),
np.array([50000], dtype='i2'), pa.int16())
]
for case in unsafe_cases:
_check_cast_case(case, safe=False)
def test_cast_timestamp_unit():
# ARROW-1680
val = datetime.datetime.now()
s = pd.Series([val])
s_nyc = s.dt.tz_localize('tzlocal()').dt.tz_convert('America/New_York')
us_with_tz = pa.timestamp('us', tz='America/New_York')
arr = pa.Array.from_pandas(s_nyc, type=us_with_tz)
# ARROW-1906
assert arr.type == us_with_tz
arr2 = pa.Array.from_pandas(s, type=pa.timestamp('us'))
assert arr[0].as_py() == s_nyc[0]
assert arr2[0].as_py() == s[0]
# Disallow truncation
arr = pa.array([123123], type='int64').cast(pa.timestamp('ms'))
expected = pa.array([123], type='int64').cast(pa.timestamp('s'))
target = pa.timestamp('s')
with pytest.raises(ValueError):
arr.cast(target)
result = arr.cast(target, safe=False)
assert result.equals(expected)
def test_cast_signed_to_unsigned():
safe_cases = [
(np.array([0, 1, 2, 3], dtype='i1'), pa.uint8(),
np.array([0, 1, 2, 3], dtype='u1'), pa.uint8()),
(np.array([0, 1, 2, 3], dtype='i2'), pa.uint16(),
np.array([0, 1, 2, 3], dtype='u2'), pa.uint16())
]
for case in safe_cases:
_check_cast_case(case)
def test_unique_simple():
cases = [
(pa.array([1, 2, 3, 1, 2, 3]), pa.array([1, 2, 3])),
(pa.array(['foo', None, 'bar', 'foo']),
pa.array(['foo', 'bar']))
]
for arr, expected in cases:
result = arr.unique()
assert result.equals(expected)
def test_dictionary_encode_simple():
cases = [
(pa.array([1, 2, 3, None, 1, 2, 3]),
pa.DictionaryArray.from_arrays(
pa.array([0, 1, 2, None, 0, 1, 2], type='int32'),
[1, 2, 3])),
(pa.array(['foo', None, 'bar', 'foo']),
pa.DictionaryArray.from_arrays(
pa.array([0, None, 1, 0], type='int32'),
['foo', 'bar']))
]
for arr, expected in cases:
result = arr.dictionary_encode()
assert result.equals(expected)
def test_cast_time32_to_int():
arr = pa.array(np.array([0, 1, 2], dtype='int32'),
type=pa.time32('s'))
expected = pa.array([0, 1, 2], type='i4')
result = arr.cast('i4')
assert result.equals(expected)
def test_cast_time64_to_int():
arr = pa.array(np.array([0, 1, 2], dtype='int64'),
type=pa.time64('us'))
expected = pa.array([0, 1, 2], type='i8')
result = arr.cast('i8')
assert result.equals(expected)
def test_cast_timestamp_to_int():
arr = pa.array(np.array([0, 1, 2], dtype='int64'),
type=pa.timestamp('us'))
expected = pa.array([0, 1, 2], type='i8')
result = arr.cast('i8')
assert result.equals(expected)
def test_cast_date32_to_int():
arr = pa.array([0, 1, 2], type='i4')
result1 = arr.cast('date32')
result2 = result1.cast('i4')
expected1 = pa.array([
datetime.date(1970, 1, 1),
datetime.date(1970, 1, 2),
datetime.date(1970, 1, 3)
]).cast('date32')
assert result1.equals(expected1)
assert result2.equals(arr)
def test_cast_date64_to_int():
arr = pa.array(np.array([0, 1, 2], dtype='int64'),
type=pa.date64())
expected = pa.array([0, 1, 2], type='i8')
result = arr.cast('i8')
assert result.equals(expected)
def test_simple_type_construction():
result = pa.lib.TimestampType()
with pytest.raises(TypeError):
str(result)
@pytest.mark.parametrize(
('type', 'expected'),
[
(pa.null(), 'empty'),
(pa.bool_(), 'bool'),
(pa.int8(), 'int8'),
(pa.int16(), 'int16'),
(pa.int32(), 'int32'),
(pa.int64(), 'int64'),
(pa.uint8(), 'uint8'),
(pa.uint16(), 'uint16'),
(pa.uint32(), 'uint32'),
(pa.uint64(), 'uint64'),
(pa.float16(), 'float16'),
(pa.float32(), 'float32'),
(pa.float64(), 'float64'),
(pa.date32(), 'date'),
(pa.date64(), 'date'),
(pa.binary(), 'bytes'),
(pa.binary(length=4), 'bytes'),
(pa.string(), 'unicode'),
(pa.list_(pa.list_(pa.int16())), 'list[list[int16]]'),
(pa.decimal128(18, 3), 'decimal'),
(pa.timestamp('ms'), 'datetime'),
(pa.timestamp('us', 'UTC'), 'datetimetz'),
(pa.time32('s'), 'time'),
(pa.time64('us'), 'time')
]
)
def test_logical_type(type, expected):
assert get_logical_type(type) == expected
def test_array_uint64_from_py_over_range():
arr = pa.array([2 ** 63], type=pa.uint64())
expected = pa.array(np.array([2 ** 63], dtype='u8'))
assert arr.equals(expected)
def test_array_conversions_no_sentinel_values():
arr = np.array([1, 2, 3, 4], dtype='int8')
refcount = sys.getrefcount(arr)
arr2 = pa.array(arr) # noqa
assert sys.getrefcount(arr) == (refcount + 1)
assert arr2.type == 'int8'
arr3 = pa.array(np.array([1, np.nan, 2, 3, np.nan, 4], dtype='float32'),
type='float32')
assert arr3.type == 'float32'
assert arr3.null_count == 0
def test_array_from_numpy_datetimeD():
arr = np.array([None, datetime.date(2017, 4, 4)], dtype='datetime64[D]')
result = pa.array(arr)
expected = pa.array([None, datetime.date(2017, 4, 4)], type=pa.date32())
assert result.equals(expected)
def test_array_from_py_float32():
data = [[1.2, 3.4], [9.0, 42.0]]
t = pa.float32()
arr1 = pa.array(data[0], type=t)
arr2 = pa.array(data, type=pa.list_(t))
expected1 = np.array(data[0], dtype=np.float32)
expected2 = pd.Series([np.array(data[0], dtype=np.float32),
np.array(data[1], dtype=np.float32)])
assert arr1.type == t
assert arr1.equals(pa.array(expected1))
assert arr2.equals(pa.array(expected2))
def test_array_from_numpy_ascii():
arr = np.array(['abcde', 'abc', ''], dtype='|S5')
arrow_arr = pa.array(arr)
assert arrow_arr.type == 'binary'
expected = pa.array(['abcde', 'abc', ''], type='binary')
assert arrow_arr.equals(expected)
mask = np.array([False, True, False])
arrow_arr = pa.array(arr, mask=mask)
expected = pa.array(['abcde', None, ''], type='binary')
assert arrow_arr.equals(expected)
# Strided variant
arr = np.array(['abcde', 'abc', ''] * 5, dtype='|S5')[::2]
mask = np.array([False, True, False] * 5)[::2]
arrow_arr = pa.array(arr, mask=mask)
expected = pa.array(['abcde', '', None, 'abcde', '', None, 'abcde', ''],
type='binary')
assert arrow_arr.equals(expected)
# 0 itemsize
arr = np.array(['', '', ''], dtype='|S0')
arrow_arr = pa.array(arr)
expected = pa.array(['', '', ''], type='binary')
assert arrow_arr.equals(expected)
def test_array_from_numpy_unicode():
dtypes = ['<U5', '>U5']
for dtype in dtypes:
arr = np.array(['abcde', 'abc', ''], dtype=dtype)
arrow_arr = pa.array(arr)
assert arrow_arr.type == 'utf8'
expected = pa.array(['abcde', 'abc', ''], type='utf8')
assert arrow_arr.equals(expected)
mask = np.array([False, True, False])
arrow_arr = pa.array(arr, mask=mask)
expected = pa.array(['abcde', None, ''], type='utf8')
assert arrow_arr.equals(expected)
# Strided variant
arr = np.array(['abcde', 'abc', ''] * 5, dtype=dtype)[::2]
mask = np.array([False, True, False] * 5)[::2]
arrow_arr = pa.array(arr, mask=mask)
expected = pa.array(['abcde', '', None, 'abcde', '', None,
'abcde', ''], type='utf8')
assert arrow_arr.equals(expected)
# 0 itemsize
arr = np.array(['', '', ''], dtype='<U0')
arrow_arr = pa.array(arr)
expected = pa.array(['', '', ''], type='utf8')
assert arrow_arr.equals(expected)
def test_buffers_primitive():
a = pa.array([1, 2, None, 4], type=pa.int16())
buffers = a.buffers()
assert len(buffers) == 2
null_bitmap = buffers[0].to_pybytes()
assert 1 <= len(null_bitmap) <= 64 # XXX this is varying
assert bytearray(null_bitmap)[0] == 0b00001011
# Slicing does not affect the buffers but the offset
a_sliced = a[1:]
buffers = a_sliced.buffers()
a_sliced.offset == 1
assert len(buffers) == 2
null_bitmap = buffers[0].to_pybytes()
assert 1 <= len(null_bitmap) <= 64 # XXX this is varying
assert bytearray(null_bitmap)[0] == 0b00001011
assert struct.unpack('hhxxh', buffers[1].to_pybytes()) == (1, 2, 4)
a = pa.array(np.int8([4, 5, 6]))
buffers = a.buffers()
assert len(buffers) == 2
# No null bitmap from Numpy int array
assert buffers[0] is None
assert struct.unpack('3b', buffers[1].to_pybytes()) == (4, 5, 6)
a = pa.array([b'foo!', None, b'bar!!'])
buffers = a.buffers()
assert len(buffers) == 3
null_bitmap = buffers[0].to_pybytes()
assert bytearray(null_bitmap)[0] == 0b00000101
offsets = buffers[1].to_pybytes()
assert struct.unpack('4i', offsets) == (0, 4, 4, 9)
values = buffers[2].to_pybytes()
assert values == b'foo!bar!!'
def test_buffers_nested():
a = pa.array([[1, 2], None, [3, None, 4, 5]], type=pa.list_(pa.int64()))
buffers = a.buffers()
assert len(buffers) == 4
# The parent buffers
null_bitmap = buffers[0].to_pybytes()
assert bytearray(null_bitmap)[0] == 0b00000101
offsets = buffers[1].to_pybytes()
assert struct.unpack('4i', offsets) == (0, 2, 2, 6)
# The child buffers
null_bitmap = buffers[2].to_pybytes()
assert bytearray(null_bitmap)[0] == 0b00110111
values = buffers[3].to_pybytes()
assert struct.unpack('qqq8xqq', values) == (1, 2, 3, 4, 5)
a = pa.array([(42, None), None, (None, 43)],
type=pa.struct([pa.field('a', pa.int8()),
pa.field('b', pa.int16())]))
buffers = a.buffers()
assert len(buffers) == 5
# The parent buffer
null_bitmap = buffers[0].to_pybytes()
assert bytearray(null_bitmap)[0] == 0b00000101
# The child buffers: 'a'
null_bitmap = buffers[1].to_pybytes()
assert bytearray(null_bitmap)[0] == 0b00000001
values = buffers[2].to_pybytes()
assert struct.unpack('bxx', values) == (42,)
# The child buffers: 'b'
null_bitmap = buffers[3].to_pybytes()
assert bytearray(null_bitmap)[0] == 0b00000100
values = buffers[4].to_pybytes()
assert struct.unpack('4xh', values) == (43,)
def test_invalid_tensor_constructor_repr():
t = pa.Tensor([1])
assert repr(t) == '<invalid pyarrow.Tensor>'
def test_invalid_tensor_operation():
t = pa.Tensor()
with pytest.raises(TypeError):
t.to_numpy()
| apache-2.0 |
alanjschoen/hikkit | src/trail_map.py | 1 | 2538 | # -*- coding: utf-8 -*-
"""
Created on Thu Apr 7 17:30:40 2016
@author: alanschoen
"""
import matplotlib.patches as mpatches
import matplotlib.pyplot as plt
import shapely.geometry as sgeom
import cartopy.crs as ccrs
import cartopy.io.shapereader as shpreader
import cartopy
from readatc import CenterLine
import numpy as np
def plotSeg(seg):
if len(seg)>1:
for s in seg:
plt.plot(s.points[:,0], s.points[:,1])
else:
plt.plot(seg.points[:,0], seg.points[:,1])
def plotMap(line):
plt.figure(num=None, figsize=(12, 9), dpi=150, facecolor='w', edgecolor='k')
# Plot AT centerline
ax = plt.axes(projection=cartopy.crs.PlateCarree())
ax.set_extent([-88, -66.5, 20, 50]) # US East Coast
ax.add_feature(cartopy.feature.LAND)
ax.add_feature(cartopy.feature.OCEAN)
ax.add_feature(cartopy.feature.COASTLINE)
ax.add_feature(cartopy.feature.BORDERS, linestyle=':')
ax.add_feature(cartopy.feature.LAKES, alpha=0.5)
ax.add_feature(cartopy.feature.RIVERS)
shapename = 'admin_1_states_provinces_lakes_shp'
states_shp = shpreader.natural_earth(resolution='110m',
category='cultural', name=shapename)
# convert to Shapely type
track = sgeom.LineString(line)
for state in shpreader.Reader(states_shp).geometries():
# pick a default color for the land with a black outline,
# this will change if the storm intersects with our track
facecolor = [0.9375, 0.9375, 0.859375]
edgecolor = 'black'
if state.intersects(track):
facecolor = [0.9, 0.9, 0.92]
ax.add_geometries([state], ccrs.PlateCarree(),
facecolor=facecolor, edgecolor=edgecolor)
# Plot AT Course
ax.add_geometries([track], ccrs.PlateCarree(),facecolor='none', edgecolor='red', linewidth=2)
# # Show Springer and Katahdin
# # Coords of springer: 34.6272° N, 84.1939° W
# # Coords of Katahdin: 45.9044° N, 68.9216° W
# plt.plot([-84.1939],[34.6272], 'bo', [-68.9216],[45.9044], 'bo')
shapefile_name = "../testdata/AT_Centerline_12-23-2014/at_centerline"
cLine = CenterLine(shapefile_name)
npts = len(cLine.data)
segBegins = np.zeros([npts,2])
for (i,s) in enumerate(cLine.data):
segBegins[i,:] = s.points[0,:]
plotMap(segBegins)
cLine.autoHike()
npts = len(cLine.data)
segBegins = np.zeros([npts,2])
for (i,s) in enumerate(cLine.data):
segBegins[i,:] = s.points[0,:]
plotMap(segBegins)
| gpl-3.0 |
AlexRobson/scikit-learn | sklearn/utils/tests/test_utils.py | 215 | 8100 | import warnings
import numpy as np
import scipy.sparse as sp
from scipy.linalg import pinv2
from itertools import chain
from sklearn.utils.testing import (assert_equal, assert_raises, assert_true,
assert_almost_equal, assert_array_equal,
SkipTest, assert_raises_regex)
from sklearn.utils import check_random_state
from sklearn.utils import deprecated
from sklearn.utils import resample
from sklearn.utils import safe_mask
from sklearn.utils import column_or_1d
from sklearn.utils import safe_indexing
from sklearn.utils import shuffle
from sklearn.utils import gen_even_slices
from sklearn.utils.extmath import pinvh
from sklearn.utils.mocking import MockDataFrame
def test_make_rng():
# Check the check_random_state utility function behavior
assert_true(check_random_state(None) is np.random.mtrand._rand)
assert_true(check_random_state(np.random) is np.random.mtrand._rand)
rng_42 = np.random.RandomState(42)
assert_true(check_random_state(42).randint(100) == rng_42.randint(100))
rng_42 = np.random.RandomState(42)
assert_true(check_random_state(rng_42) is rng_42)
rng_42 = np.random.RandomState(42)
assert_true(check_random_state(43).randint(100) != rng_42.randint(100))
assert_raises(ValueError, check_random_state, "some invalid seed")
def test_resample_noarg():
# Border case not worth mentioning in doctests
assert_true(resample() is None)
def test_deprecated():
# Test whether the deprecated decorator issues appropriate warnings
# Copied almost verbatim from http://docs.python.org/library/warnings.html
# First a function...
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always")
@deprecated()
def ham():
return "spam"
spam = ham()
assert_equal(spam, "spam") # function must remain usable
assert_equal(len(w), 1)
assert_true(issubclass(w[0].category, DeprecationWarning))
assert_true("deprecated" in str(w[0].message).lower())
# ... then a class.
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always")
@deprecated("don't use this")
class Ham(object):
SPAM = 1
ham = Ham()
assert_true(hasattr(ham, "SPAM"))
assert_equal(len(w), 1)
assert_true(issubclass(w[0].category, DeprecationWarning))
assert_true("deprecated" in str(w[0].message).lower())
def test_resample_value_errors():
# Check that invalid arguments yield ValueError
assert_raises(ValueError, resample, [0], [0, 1])
assert_raises(ValueError, resample, [0, 1], [0, 1], n_samples=3)
assert_raises(ValueError, resample, [0, 1], [0, 1], meaning_of_life=42)
def test_safe_mask():
random_state = check_random_state(0)
X = random_state.rand(5, 4)
X_csr = sp.csr_matrix(X)
mask = [False, False, True, True, True]
mask = safe_mask(X, mask)
assert_equal(X[mask].shape[0], 3)
mask = safe_mask(X_csr, mask)
assert_equal(X_csr[mask].shape[0], 3)
def test_pinvh_simple_real():
a = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 10]], dtype=np.float64)
a = np.dot(a, a.T)
a_pinv = pinvh(a)
assert_almost_equal(np.dot(a, a_pinv), np.eye(3))
def test_pinvh_nonpositive():
a = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]], dtype=np.float64)
a = np.dot(a, a.T)
u, s, vt = np.linalg.svd(a)
s[0] *= -1
a = np.dot(u * s, vt) # a is now symmetric non-positive and singular
a_pinv = pinv2(a)
a_pinvh = pinvh(a)
assert_almost_equal(a_pinv, a_pinvh)
def test_pinvh_simple_complex():
a = (np.array([[1, 2, 3], [4, 5, 6], [7, 8, 10]])
+ 1j * np.array([[10, 8, 7], [6, 5, 4], [3, 2, 1]]))
a = np.dot(a, a.conj().T)
a_pinv = pinvh(a)
assert_almost_equal(np.dot(a, a_pinv), np.eye(3))
def test_column_or_1d():
EXAMPLES = [
("binary", ["spam", "egg", "spam"]),
("binary", [0, 1, 0, 1]),
("continuous", np.arange(10) / 20.),
("multiclass", [1, 2, 3]),
("multiclass", [0, 1, 2, 2, 0]),
("multiclass", [[1], [2], [3]]),
("multilabel-indicator", [[0, 1, 0], [0, 0, 1]]),
("multiclass-multioutput", [[1, 2, 3]]),
("multiclass-multioutput", [[1, 1], [2, 2], [3, 1]]),
("multiclass-multioutput", [[5, 1], [4, 2], [3, 1]]),
("multiclass-multioutput", [[1, 2, 3]]),
("continuous-multioutput", np.arange(30).reshape((-1, 3))),
]
for y_type, y in EXAMPLES:
if y_type in ["binary", 'multiclass', "continuous"]:
assert_array_equal(column_or_1d(y), np.ravel(y))
else:
assert_raises(ValueError, column_or_1d, y)
def test_safe_indexing():
X = [[1, 2, 3], [4, 5, 6], [7, 8, 9]]
inds = np.array([1, 2])
X_inds = safe_indexing(X, inds)
X_arrays = safe_indexing(np.array(X), inds)
assert_array_equal(np.array(X_inds), X_arrays)
assert_array_equal(np.array(X_inds), np.array(X)[inds])
def test_safe_indexing_pandas():
try:
import pandas as pd
except ImportError:
raise SkipTest("Pandas not found")
X = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
X_df = pd.DataFrame(X)
inds = np.array([1, 2])
X_df_indexed = safe_indexing(X_df, inds)
X_indexed = safe_indexing(X_df, inds)
assert_array_equal(np.array(X_df_indexed), X_indexed)
# fun with read-only data in dataframes
# this happens in joblib memmapping
X.setflags(write=False)
X_df_readonly = pd.DataFrame(X)
with warnings.catch_warnings(record=True):
X_df_ro_indexed = safe_indexing(X_df_readonly, inds)
assert_array_equal(np.array(X_df_ro_indexed), X_indexed)
def test_safe_indexing_mock_pandas():
X = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
X_df = MockDataFrame(X)
inds = np.array([1, 2])
X_df_indexed = safe_indexing(X_df, inds)
X_indexed = safe_indexing(X_df, inds)
assert_array_equal(np.array(X_df_indexed), X_indexed)
def test_shuffle_on_ndim_equals_three():
def to_tuple(A): # to make the inner arrays hashable
return tuple(tuple(tuple(C) for C in B) for B in A)
A = np.array([[[1, 2], [3, 4]], [[5, 6], [7, 8]]]) # A.shape = (2,2,2)
S = set(to_tuple(A))
shuffle(A) # shouldn't raise a ValueError for dim = 3
assert_equal(set(to_tuple(A)), S)
def test_shuffle_dont_convert_to_array():
# Check that shuffle does not try to convert to numpy arrays with float
# dtypes can let any indexable datastructure pass-through.
a = ['a', 'b', 'c']
b = np.array(['a', 'b', 'c'], dtype=object)
c = [1, 2, 3]
d = MockDataFrame(np.array([['a', 0],
['b', 1],
['c', 2]],
dtype=object))
e = sp.csc_matrix(np.arange(6).reshape(3, 2))
a_s, b_s, c_s, d_s, e_s = shuffle(a, b, c, d, e, random_state=0)
assert_equal(a_s, ['c', 'b', 'a'])
assert_equal(type(a_s), list)
assert_array_equal(b_s, ['c', 'b', 'a'])
assert_equal(b_s.dtype, object)
assert_equal(c_s, [3, 2, 1])
assert_equal(type(c_s), list)
assert_array_equal(d_s, np.array([['c', 2],
['b', 1],
['a', 0]],
dtype=object))
assert_equal(type(d_s), MockDataFrame)
assert_array_equal(e_s.toarray(), np.array([[4, 5],
[2, 3],
[0, 1]]))
def test_gen_even_slices():
# check that gen_even_slices contains all samples
some_range = range(10)
joined_range = list(chain(*[some_range[slice] for slice in gen_even_slices(10, 3)]))
assert_array_equal(some_range, joined_range)
# check that passing negative n_chunks raises an error
slices = gen_even_slices(10, -1)
assert_raises_regex(ValueError, "gen_even_slices got n_packs=-1, must be"
" >=1", next, slices)
| bsd-3-clause |
S2H-Mobile/RoboND-Perception-Project | scripts/features.py | 1 | 2031 | import matplotlib.colors
import matplotlib.pyplot as plt
import numpy as np
from pcl_helper import *
def rgb_to_hsv(rgb_list):
rgb_normalized = [1.0*rgb_list[0]/255, 1.0*rgb_list[1]/255, 1.0*rgb_list[2]/255]
hsv_normalized = matplotlib.colors.rgb_to_hsv([[rgb_normalized]])[0][0]
return hsv_normalized
def histogram(ch_0, ch_1, ch_2, nbins, bins_range):
# Compute the histogram of each channel separately
hist_0 = np.histogram(ch_0, bins=nbins, range=bins_range)
hist_1 = np.histogram(ch_1, bins=nbins, range=bins_range)
hist_2 = np.histogram(ch_1, bins=nbins, range=bins_range)
# Concatenate the histograms into a single feature vector
features = np.concatenate((hist_0[0], hist_1[0], hist_2[0])).astype(np.float64)
# Normalize the result and return
return features / np.sum(features)
def compute_color_histograms(cloud, using_hsv=True):
# Compute histograms for the clusters
point_colors_list = []
# Step through each point in the point cloud
for point in pc2.read_points(cloud, skip_nans=True):
rgb_list = float_to_rgb(point[3])
if using_hsv:
point_colors_list.append(rgb_to_hsv(rgb_list) * 255)
else:
point_colors_list.append(rgb_list)
# Populate lists with color values
ch_0 = [color[0] for color in point_colors_list]
ch_1 = [color[1] for color in point_colors_list]
ch_2 = [color[2] for color in point_colors_list]
return histogram(ch_0, ch_1, ch_2, nbins=32, bins_range=(0, 256))
def compute_normal_histograms(normal_cloud):
# Convert point cloud to array
fields = ('normal_x', 'normal_y', 'normal_z')
normals = pc2.read_points(normal_cloud, field_names=fields, skip_nans=True):
# Create component arrays for each feature vector
norm_x = [vector[0] for vector in normals]
norm_y = [vector[1] for vector in normals]
norm_z = [vector[2] for vector in normals]
return histogram(norm_x, norm_y, norm_z, nbins=32, bins_range=(-1, 1))
| mit |
lucidfrontier45/scikit-learn | examples/exercises/plot_cv_diabetes.py | 2 | 2384 | """
===============================================
Cross-validation on diabetes Dataset Exercise
===============================================
This exercise is used in the :ref:`cv_estimators_tut` part of the
:ref:`model_selection_tut` section of the :ref:`stat_learn_tut_index`.
"""
print __doc__
import numpy as np
import pylab as pl
from sklearn import cross_validation, datasets, linear_model
diabetes = datasets.load_diabetes()
X = diabetes.data[:150]
y = diabetes.target[:150]
lasso = linear_model.Lasso()
alphas = np.logspace(-4, -.5, 30)
scores = list()
scores_std = list()
for alpha in alphas:
lasso.alpha = alpha
this_scores = cross_validation.cross_val_score(lasso, X, y, n_jobs=1)
scores.append(np.mean(this_scores))
scores_std.append(np.std(this_scores))
pl.figure(figsize=(4, 3))
pl.semilogx(alphas, scores)
# plot error lines showing +/- std. errors of the scores
pl.semilogx(alphas, np.array(scores) + np.array(scores_std) / np.sqrt(len(X)),
'b--')
pl.semilogx(alphas, np.array(scores) - np.array(scores_std) / np.sqrt(len(X)),
'b--')
pl.ylabel('CV score')
pl.xlabel('alpha')
pl.axhline(np.max(scores), linestyle='--', color='.5')
##############################################################################
# Bonus: how much can you trust the selection of alpha?
# To answer this question we use the LassoCV object that sets its alpha
# parameter automatically from the data by internal cross-validation (i.e. it
# performs cross-validation on the training data it receives).
# We use external cross-validation to see how much the automatically obtained
# alphas differ across different cross-validation folds.
lasso_cv = linear_model.LassoCV(alphas=alphas)
k_fold = cross_validation.KFold(len(X), 3)
print "Answer to the bonus question: how much can you trust"
print "the selection of alpha?"
print
print "Alpha parameters maximising the generalization score on different"
print "subsets of the data:"
for k, (train, test) in enumerate(k_fold):
lasso_cv.fit(X[train], y[train])
print "[fold {0}] alpha: {1:.5f}, score: {2:.5f}".\
format(k, lasso_cv.alpha_, lasso_cv.score(X[test], y[test]))
print
print "Answer: Not very much since we obtained different alphas for different"
print "subsets of the data and moreover, the scores for these alphas differ"
print "quite substantially."
pl.show()
| bsd-3-clause |
treycausey/scikit-learn | sklearn/setup.py | 11 | 3207 | import os
from os.path import join
import warnings
def configuration(parent_package='', top_path=None):
from numpy.distutils.misc_util import Configuration
from numpy.distutils.system_info import get_info, BlasNotFoundError
import numpy
libraries = []
if os.name == 'posix':
libraries.append('m')
config = Configuration('sklearn', parent_package, top_path)
config.add_subpackage('__check_build')
config.add_subpackage('svm')
config.add_subpackage('datasets')
config.add_subpackage('datasets/tests')
config.add_subpackage('feature_extraction')
config.add_subpackage('feature_extraction/tests')
config.add_subpackage('cluster')
config.add_subpackage('cluster/tests')
config.add_subpackage('cluster/bicluster')
config.add_subpackage('cluster/bicluster/tests')
config.add_subpackage('covariance')
config.add_subpackage('covariance/tests')
config.add_subpackage('cross_decomposition')
config.add_subpackage('decomposition')
config.add_subpackage('decomposition/tests')
config.add_subpackage("ensemble")
config.add_subpackage("ensemble/tests")
config.add_subpackage('feature_selection')
config.add_subpackage('feature_selection/tests')
config.add_subpackage('utils')
config.add_subpackage('utils/tests')
config.add_subpackage('externals')
config.add_subpackage('mixture')
config.add_subpackage('mixture/tests')
config.add_subpackage('gaussian_process')
config.add_subpackage('gaussian_process/tests')
config.add_subpackage('neighbors')
config.add_subpackage('neural_network')
config.add_subpackage('preprocessing')
config.add_subpackage('manifold')
config.add_subpackage('metrics')
config.add_subpackage('semi_supervised')
config.add_subpackage("tree")
config.add_subpackage("tree/tests")
config.add_subpackage('metrics/tests')
config.add_subpackage('metrics/cluster')
config.add_subpackage('metrics/cluster/tests')
config.add_subpackage('metrics/cluster/bicluster')
config.add_subpackage('metrics/cluster/bicluster/tests')
# add cython extension module for hmm
config.add_extension(
'_hmmc',
sources=['_hmmc.c'],
include_dirs=[numpy.get_include()],
libraries=libraries,
)
config.add_extension(
'_isotonic',
sources=['_isotonic.c'],
include_dirs=[numpy.get_include()],
libraries=libraries,
)
# some libs needs cblas, fortran-compiled BLAS will not be sufficient
blas_info = get_info('blas_opt', 0)
if (not blas_info) or (
('NO_ATLAS_INFO', 1) in blas_info.get('define_macros', [])):
config.add_library('cblas',
sources=[join('src', 'cblas', '*.c')])
warnings.warn(BlasNotFoundError.__doc__)
# the following packages depend on cblas, so they have to be build
# after the above.
config.add_subpackage('linear_model')
config.add_subpackage('utils')
# add the test directory
config.add_subpackage('tests')
return config
if __name__ == '__main__':
from numpy.distutils.core import setup
setup(**configuration(top_path='').todict())
| bsd-3-clause |
CooperLuan/yoka_bot | views.py | 1 | 1545 | import re
from pymongo import MongoClient
import pandas as pd
db = MongoClient('mongodb://127.0.0.1/yoka_bot').yoka_bot
rows = list(db.webpages.find({
'item_name': 'YokaBotBrandListItem',
}))
df_brand_list = pd.DataFrame(rows)
rows = list(db.webpages.find({
'item_name': 'YokaBotBrandItem',
}))
df_brand = pd.DataFrame(rows)
df_brand['alias'] = df_brand['url'].apply(lambda x: x.partition('com/')[-1].strip('/ '))
rows = list(db.webpages.find({
'item_name': 'YokaBotProductListItem',
}))
df_product_list = pd.DataFrame(rows)
df_product_list['alias'] = df_product_list['url'].apply(lambda x: re.search(r'\.com/(.+?)/', x).group(1))
df_product_list['product_id'] = df_product_list['product_url'].apply(lambda x: re.search(r'detail(\d+)', x).group(1))
rows = list(db.webpages.find({
'item_name': 'YokaBotProductItem',
}))
df_products = pd.DataFrame(rows)
df_products['categories'] = df_products['breadcrumb'].apply(lambda x: x and '-'.join(x).partition(u'首页-')[-1] or x)
df_products['attrib2'] = df_products['attrib'].apply(
lambda x: [(
isinstance(t[0], list) and ''.join(t[0]).strip(u'\uff1a') or t[0],
isinstance(t[1], list) and ''.join(t[1]).strip(u'\uff1a') or t[1],
) for t in x
if isinstance(t, list) and len(t) == 2]
)
df = df_brand.merge(
df_product_list, how='inner',
on='alias',
)[['brand_cn', 'brand_en', 'title', 'product_id']].merge(
df_products, how='inner',
on='product_id', suffixes=('', '_2'),
)[['brand_cn', 'brand_en', 'categories', 'title', 'product_id']]
| mit |
riverma/climate | docs/source/conf.py | 3 | 9229 | # -*- coding: utf-8 -*-
#
# Apache Open Climate Workbench documentation build configuration file, created by
# sphinx-quickstart on Fri Oct 25 07:58:45 2013.
#
# This file is execfile()d with the current directory set to its containing dir.
#
# Note that not all possible configuration values are present in this
# autogenerated file.
#
# All configuration values have a default; values that are commented out
# serve to show the default.
import sys, os
# If extensions (or modules to document with autodoc) are in another directory,
# add these directories to sys.path here. If the directory is relative to the
# documentation root, use os.path.abspath to make it absolute, like shown here.
#sys.path.insert(0, os.path.abspath('.'))
#
# Assuming a fresh checkout of trunk, this will point us to the OCW package
sys.path.insert(0, os.path.abspath('../../ocw'))
sys.path.insert(0, os.path.abspath('../../ocw/data_source'))
sys.path.insert(0, os.path.abspath('../../ocw-ui/backend'))
sys.path.insert(0, os.path.abspath('../../ocw-config-runner'))
# -- General configuration -----------------------------------------------------
# If your documentation needs a minimal Sphinx version, state it here.
#needs_sphinx = '1.0'
# Add any Sphinx extension module names here, as strings. They can be extensions
# coming with Sphinx (named 'sphinx.ext.*') or your custom ones.
extensions = [
'sphinx.ext.autodoc',
'sphinx.ext.intersphinx',
'sphinx.ext.ifconfig',
'sphinxcontrib.autohttp.bottle',
]
# Add any paths that contain templates here, relative to this directory.
templates_path = ['_templates']
# The suffix of source filenames.
source_suffix = '.rst'
# The encoding of source files.
#source_encoding = 'utf-8-sig'
# The master toctree document.
master_doc = 'index'
# General information about the project.
project = u'Apache Open Climate Workbench'
copyright = u'2013, Apache Software Foundation'
# The version info for the project you're documenting, acts as replacement for
# |version| and |release|, also used in various other places throughout the
# built documents.
#
# The short X.Y version.
version = '1.0.0'
# The full version, including alpha/beta/rc tags.
release = '1.0.0'
# The language for content autogenerated by Sphinx. Refer to documentation
# for a list of supported languages.
#language = None
# There are two options for replacing |today|: either, you set today to some
# non-false value, then it is used:
#today = ''
# Else, today_fmt is used as the format for a strftime call.
#today_fmt = '%B %d, %Y'
# List of patterns, relative to source directory, that match files and
# directories to ignore when looking for source files.
exclude_patterns = []
# The reST default role (used for this markup: `text`) to use for all documents.
#default_role = None
# If true, '()' will be appended to :func: etc. cross-reference text.
#add_function_parentheses = True
# If true, the current module name will be prepended to all description
# unit titles (such as .. function::).
#add_module_names = True
# If true, sectionauthor and moduleauthor directives will be shown in the
# output. They are ignored by default.
#show_authors = False
# The name of the Pygments (syntax highlighting) style to use.
pygments_style = 'sphinx'
# A list of ignored prefixes for module index sorting.
#modindex_common_prefix = []
# If true, keep warnings as "system message" paragraphs in the built documents.
#keep_warnings = False
# -- Options for HTML output ---------------------------------------------------
# The theme to use for HTML and HTML Help pages. See the documentation for
# a list of builtin themes.
html_theme = 'default'
# Theme options are theme-specific and customize the look and feel of a theme
# further. For a list of options available for each theme, see the
# documentation.
#html_theme_options = {}
# Add any paths that contain custom themes here, relative to this directory.
#html_theme_path = []
# The name for this set of Sphinx documents. If None, it defaults to
# "<project> v<release> documentation".
#html_title = None
# A shorter title for the navigation bar. Default is the same as html_title.
#html_short_title = None
# The name of an image file (relative to this directory) to place at the top
# of the sidebar.
#html_logo = None
# The name of an image file (within the static path) to use as favicon of the
# docs. This file should be a Windows icon file (.ico) being 16x16 or 32x32
# pixels large.
#html_favicon = None
# Add any paths that contain custom static files (such as style sheets) here,
# relative to this directory. They are copied after the builtin static files,
# so a file named "default.css" will overwrite the builtin "default.css".
html_static_path = ['_static']
# If not '', a 'Last updated on:' timestamp is inserted at every page bottom,
# using the given strftime format.
#html_last_updated_fmt = '%b %d, %Y'
# If true, SmartyPants will be used to convert quotes and dashes to
# typographically correct entities.
#html_use_smartypants = True
# Custom sidebar templates, maps document names to template names.
#html_sidebars = {}
# Additional templates that should be rendered to pages, maps page names to
# template names.
#html_additional_pages = {}
# If false, no module index is generated.
#html_domain_indices = True
# If false, no index is generated.
#html_use_index = True
# If true, the index is split into individual pages for each letter.
#html_split_index = False
# If true, links to the reST sources are added to the pages.
#html_show_sourcelink = True
# If true, "Created using Sphinx" is shown in the HTML footer. Default is True.
#html_show_sphinx = True
# If true, "(C) Copyright ..." is shown in the HTML footer. Default is True.
#html_show_copyright = True
# If true, an OpenSearch description file will be output, and all pages will
# contain a <link> tag referring to it. The value of this option must be the
# base URL from which the finished HTML is served.
#html_use_opensearch = ''
# This is the file name suffix for HTML files (e.g. ".xhtml").
#html_file_suffix = None
# Output file base name for HTML help builder.
htmlhelp_basename = 'ApacheOpenClimateWorkbenchdoc'
# -- Options for LaTeX output --------------------------------------------------
latex_elements = {
# The paper size ('letterpaper' or 'a4paper').
#'papersize': 'letterpaper',
# The font size ('10pt', '11pt' or '12pt').
#'pointsize': '10pt',
# Additional stuff for the LaTeX preamble.
#'preamble': '',
}
# Grouping the document tree into LaTeX files. List of tuples
# (source start file, target name, title, author, documentclass [howto/manual]).
latex_documents = [
('index', 'ApacheOpenClimateWorkbench.tex', u'Apache Open Climate Workbench Documentation',
u'Michael Joyce', 'manual'),
]
# The name of an image file (relative to this directory) to place at the top of
# the title page.
#latex_logo = None
# For "manual" documents, if this is true, then toplevel headings are parts,
# not chapters.
#latex_use_parts = False
# If true, show page references after internal links.
#latex_show_pagerefs = False
# If true, show URL addresses after external links.
#latex_show_urls = False
# Documents to append as an appendix to all manuals.
#latex_appendices = []
# If false, no module index is generated.
#latex_domain_indices = True
# -- Options for manual page output --------------------------------------------
# One entry per manual page. List of tuples
# (source start file, name, description, authors, manual section).
man_pages = [
('index', 'apacheopenclimateworkbench', u'Apache Open Climate Workbench Documentation',
[u'Michael Joyce'], 1)
]
# If true, show URL addresses after external links.
#man_show_urls = False
# -- Options for Texinfo output ------------------------------------------------
# Grouping the document tree into Texinfo files. List of tuples
# (source start file, target name, title, author,
# dir menu entry, description, category)
texinfo_documents = [
('index', 'ApacheOpenClimateWorkbench', u'Apache Open Climate Workbench Documentation',
u'Michael Joyce', 'ApacheOpenClimateWorkbench', 'One line description of project.',
'Miscellaneous'),
]
# Documents to append as an appendix to all manuals.
#texinfo_appendices = []
# If false, no module index is generated.
#texinfo_domain_indices = True
# How to display URL addresses: 'footnote', 'no', or 'inline'.
#texinfo_show_urls = 'footnote'
# If true, do not generate a @detailmenu in the "Top" node's menu.
#texinfo_no_detailmenu = False
# Example configuration for intersphinx: refer to the Python standard library.
intersphinx_mapping = {'python': ('http://docs.python.org/2', None),
'numpy': ('http://docs.scipy.org/doc/numpy/', None),
'scipy': ('http://docs.scipy.org/doc/scipy/reference/', None),
'matplotlib': ('http://matplotlib.sourceforge.net/', None)}
# Autodoc config
#
# Select which content is inserted into the main body of an autoclass directive
# "class" - Only the class' docstring
# "both" - The class' and __init__ method's docstring
# "init" - Only __init__'s docstring
autoclass_content = "both"
| apache-2.0 |
ARM-software/lisa | lisa/tests/hotplug.py | 1 | 18395 | # SPDX-License-Identifier: Apache-2.0
#
# Copyright (C) 2021, Arm Limited and contributors.
#
# Licensed under the Apache License, Version 2.0 (the "License"); you may
# not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
import abc
import sys
import random
import os.path
import operator
import collections
import time
from time import sleep
from threading import Thread
from functools import partial
from itertools import chain
import pandas as pd
from devlib.module.hotplug import HotplugModule
from devlib.exception import TargetNotRespondingError, TargetStableError
from lisa.datautils import df_merge
from lisa.tests.base import TestMetric, ResultBundle, TestBundle, DmesgTestBundle, FtraceTestBundle
from lisa.target import Target
from lisa.trace import requires_events
from lisa.utils import ArtifactPath
class CPUHPSequenceError(Exception):
pass
class HotplugDmesgTestBundle(DmesgTestBundle):
DMESG_IGNORED_PATTERNS = [
*DmesgTestBundle.DMESG_IGNORED_PATTERNS,
DmesgTestBundle.CANNED_DMESG_IGNORED_PATTERNS['hotplug-irq-affinity'],
DmesgTestBundle.CANNED_DMESG_IGNORED_PATTERNS['hotplug-irq-affinity-failed'],
]
class HotplugBase(HotplugDmesgTestBundle, TestBundle):
def __init__(self, res_dir, plat_info, target_alive, hotpluggable_cpus, live_cpus):
super().__init__(res_dir, plat_info)
self.target_alive = target_alive
self.hotpluggable_cpus = hotpluggable_cpus
self.live_cpus = live_cpus
@classmethod
def _check_cpuhp_seq_consistency(cls, nr_operations, hotpluggable_cpus,
max_cpus_off, sequence):
"""
Check that a hotplug sequence given by :meth:`cpuhp_seq`
is consistent. Parameters are the same as for :meth:`cpuhp_seq`,
with the addition of:
:param sequence: A hotplug sequence, consisting of a sequence of
2-tuples (CPU and hot plug way)
:type sequence: Sequence
"""
if len(sequence) != nr_operations:
raise CPUHPSequenceError(f'{nr_operations} operations requested, but got {len(sequence)}')
# Assume als CPUs are plugged in at the beginning
state = collections.defaultdict(lambda: 1)
for step, (cpu, plug_way) in enumerate(sequence):
if cpu not in hotpluggable_cpus:
raise CPUHPSequenceError('CPU {cpu} is plugged {way} but is not part of hotpluggable CPUs: {cpu_list}'.format(
cpu=cpu,
way='in' if plug_way else 'out',
cpu_list=str(hotpluggable_cpus),
))
# Forbid plugging OFF offlined CPUs and plugging IN online CPUs
if plug_way == state[cpu]:
raise CPUHPSequenceError('Cannot plug {way} a CPU that is already plugged {way}'.format(
way='in' if plug_way else 'out'
))
state[cpu] = plug_way
cpus_off = [cpu for cpu, state in state.items() if state == 0]
if len(cpus_off) > max_cpus_off:
raise CPUHPSequenceError(f'A maximum of {max_cpus_off} CPUs is allowed to be plugged out, but {len(cpus_off)} CPUs were plugged out at step {step}')
for cpu, state in state.items():
if state != 1:
raise CPUHPSequenceError(f'CPU {cpu} is plugged out but not plugged in at the end of the sequence')
@classmethod
@abc.abstractmethod
def cpuhp_seq(cls, nr_operations, hotpluggable_cpus, max_cpus_off, random_gen):
"""
Yield a consistent random sequence of CPU hotplug operations
:param nr_operations: Number of operations in the sequence
:param max_cpus_off: Max number of CPUs plugged-off
:param random_gen: A random generator instance
:type random_gen: ``random.Random``
"Consistent" means that a CPU will be plugged-in only if it was
plugged-off before (and vice versa). Moreover the state of the CPUs
once the sequence has completed should the same as it was before.
"""
pass
@classmethod
def _cpuhp_func(cls, target, res_dir, sequence, sleep_min_ms,
sleep_max_ms, random_gen):
"""
Generate a script consisting of a random sequence of hotplugs operations
Two consecutive hotplugs can be separated by a random sleep in the script.
"""
def make_sleep():
if sleep_max_ms:
return random_gen.randint(sleep_min_ms, sleep_max_ms) / 1000
else:
return 0
sequence = [
dict(
path=HotplugModule._cpu_path(target, cpu),
sleep=make_sleep(),
way=plug_way,
)
for cpu, plug_way in sequence
]
# The main contributor to the execution time are sleeps, so set a
# timeout to 10 times the total sleep time. This should be enough to
# take into account sysfs writes too
timeout = 10 * sum(map(operator.itemgetter('sleep'), sequence))
# This function will be executed on the target directly to avoid the
# overhead of executing the calls one by one, which could mask
# concurrency issues in the kernel
@target.remote_func(timeout=timeout, as_root=True)
def do_hotplug():
for desc in sequence:
with open(desc['path'], 'w') as f:
f.write(str(desc['way']))
sleep = desc['sleep']
if sleep:
time.sleep(sleep)
return do_hotplug
@classmethod
def _from_target(cls, target: Target, *, res_dir: ArtifactPath = None, seed=None,
nr_operations=100, sleep_min_ms=10, sleep_max_ms=100,
max_cpus_off=sys.maxsize, collector=None) -> 'HotplugBase':
"""
:param seed: Seed of the RNG used to create the hotplug sequences
:type seed: int
:param nr_operations: Number of operations in the sequence
:type nr_operations: int
:param sleep_min_ms: Minimum sleep duration between hotplug operations
:type sleep_min_ms: int
:param sleep_max_ms: Maximum sleep duration between hotplug operations
(0 would lead to no sleep)
:type sleep_max_ms: int
:param max_cpus_off: Maximum number of CPUs hotplugged out at any given
moment
:type max_cpus_off: int
"""
# Instantiate a generator so we can change the seed without any global
# effect
random_gen = random.Random()
random_gen.seed(seed)
target.hotplug.online_all()
hotpluggable_cpus = target.hotplug.list_hotpluggable_cpus()
sequence = list(cls.cpuhp_seq(
nr_operations, hotpluggable_cpus, max_cpus_off, random_gen))
cls._check_cpuhp_seq_consistency(nr_operations, hotpluggable_cpus,
max_cpus_off, sequence)
do_hotplug = cls._cpuhp_func(
target, res_dir, sequence, sleep_min_ms, sleep_max_ms, random_gen)
# We don't want a timeout but we do want to detect if/when the target
# stops responding. So handle the hotplug remote func in a separate
# thread and keep polling the target
thread = Thread(target=do_hotplug, daemon=True)
with collector:
try:
thread.start()
while thread.is_alive():
# We might have a thread hanging off in that case, but there is
# not much we can do since the remote func cannot really be
# canceled. Since it was spawned with a timeout, it will
# eventually die.
if not target.check_responsive():
break
sleep(0.1)
finally:
target_alive = bool(target.check_responsive())
target.hotplug.online_all()
live_cpus = target.list_online_cpus() if target_alive else []
return cls(res_dir, target.plat_info, target_alive, hotpluggable_cpus, live_cpus)
def test_target_alive(self) -> ResultBundle:
"""
Test that the hotplugs didn't leave the target in an unusable state
"""
return ResultBundle.from_bool(self.target_alive)
def test_cpus_alive(self) -> ResultBundle:
"""
Test that all CPUs came back online after the hotplug operations
"""
res = ResultBundle.from_bool(self.hotpluggable_cpus == self.live_cpus)
dead_cpus = sorted(set(self.hotpluggable_cpus) - set(self.live_cpus))
res.add_metric("dead CPUs", dead_cpus)
res.add_metric("number of dead CPUs", len(dead_cpus))
return res
class HotplugTorture(HotplugBase):
@classmethod
def cpuhp_seq(cls, nr_operations, hotpluggable_cpus, max_cpus_off, random_gen):
"""
FIXME: is that actually still true ?
The actual length of the sequence might differ from the requested one
by 1 because it's easier to implement and it shouldn't be an issue for
most test cases.
"""
cur_on_cpus = hotpluggable_cpus[:]
cur_off_cpus = []
i = 0
while i < nr_operations - len(cur_off_cpus):
if not (1 < len(cur_on_cpus) < max_cpus_off):
# Force plug IN when only 1 CPU is on or too many are off
plug_way = 1
elif not cur_off_cpus:
# Force plug OFF if all CPUs are on
plug_way = 0 # Plug OFF
else:
plug_way = random_gen.randint(0, 1)
src = cur_off_cpus if plug_way else cur_on_cpus
dst = cur_on_cpus if plug_way else cur_off_cpus
cpu = random_gen.choice(src)
src.remove(cpu)
dst.append(cpu)
i += 1
yield cpu, plug_way
# Re-plug offline cpus to come back to original state
for cpu in cur_off_cpus:
yield cpu, 1
class HotplugRollback(TestBundle, HotplugDmesgTestBundle, FtraceTestBundle):
@classmethod
def _online(cls, target, cpu, online, verify=True):
try:
if online:
target.hotplug.online(cpu)
else:
target.hotplug.offline(cpu)
except TargetStableError as e:
if verify:
raise e
@classmethod
def _reset_fail(cls, target, cpu):
target.hotplug.fail(cpu, -1)
@classmethod
def _state_can_fail(cls, target, cpu, state, up):
"""
There are no way of probing the kernel for a list of hotplug states
that can fail and for which we can test the rollback. We need therefore
to try:
- If we can set the state in the kernel 'fail' interface.
- If the hotplug is reset actually failing (some states can fail only
when going up or down)
"""
try:
target.hotplug.fail(cpu, state)
except TargetStableError:
return False
try:
cls._online(target, cpu, up)
cls._reset_fail(target, cpu)
cls._online(target, cpu, not up)
#If we can go up/down without a failure, that's because this state
#doesn't have a up/down callback and can't fail.
return False
except TargetStableError:
return True
@classmethod
def _prepare_hotplug(cls, target, cpu, up):
cls._reset_fail(target, cpu)
cls._online(target, cpu, not up)
@classmethod
def _get_states(cls, target, cpu, up):
states = target.hotplug.get_states()
cls._prepare_hotplug(target, cpu, not up)
return [
state
for state in states
if cls._state_can_fail(target, cpu, state, up)
]
@classmethod
def _mark_trace(cls, target, collector, start=True,
expected=False, up=False, failing_state=0):
"""
Convert start, expected and up to int for a lighter trace
"""
target.write_value(
collector['ftrace'].marker_file,
"hotplug_rollback: test={} expected={} up={} failing_state={}".format(
int(start), int(expected), int(up), failing_state),
verify=False
)
@classmethod
def _test_rollback(cls, target, collector, cpu, failing_state, up):
cls._prepare_hotplug(target, cpu, up=up)
target.hotplug.fail(cpu, failing_state)
cls._mark_trace(target, collector, up=up,
failing_state=failing_state)
cls._online(target, cpu, online=up, verify=False)
cls._mark_trace(target, collector, start=False)
@classmethod
def _do_from_target(cls, target, res_dir, collector, cpu):
# Get the list of each state that can fail
states_down = cls._get_states(target, cpu, up=False)
states_up = cls._get_states(target, cpu, up=True)
cls._prepare_hotplug(target, cpu, up=False)
with collector:
# Get the expected list of states for a complete Hotplug
cls._mark_trace(target, collector, expected=True, up=False)
cls._online(target, cpu, online=False)
cls._mark_trace(target, collector, expected=True, up=True)
cls._online(target, cpu, online=True)
cls._mark_trace(target, collector, start=False)
# Test hotunplug rollback for each possible state failure
for failing_state in states_down:
cls._test_rollback(target, collector, cpu=cpu,
failing_state=failing_state, up=False)
# Test hotplug rollback for each possible state failure
for failing_state in states_up:
cls._test_rollback(target, collector, cpu=cpu,
failing_state=failing_state, up=True)
# TODO: trace-cmd is relying on _SC_NPROCESSORS_CONF to know how
# many CPUs are present in the system and what to flush from the
# ftrace buffer to the trace.dat file. The problem is that the Musl
# libc that we use to build trace-cmd in LISA is returning, for
# _SC_NPROCESSORS_CONF, the number of CPUs _online_. We then need,
# until this problem is fixed to set the CPU back online before
# collecting the trace, or some data would be missing.
cls._online(target, cpu, online=True)
return cls(res_dir, target.plat_info)
@classmethod
def _from_target(cls, target, *,
res_dir: ArtifactPath = None, collector=None) -> 'HotplugRollback':
cpu = min(target.hotplug.list_hotpluggable_cpus())
cls._online(target, cpu, online=True)
try:
return cls._do_from_target(target, res_dir, collector, cpu)
finally:
cls._reset_fail(target, cpu)
cls._online(target, cpu, online=True)
@classmethod
def check_from_target(cls, target):
super().check_from_target(target)
try:
cls._reset_fail(target, 0)
except TargetStableError:
ResultBundle.raise_skip(
"Target can't reset the hotplug fail interface")
@classmethod
def _get_expected_states(cls, df, up):
df = df[(df['expected']) & (df['up'] == up)]
return df['idx'].dropna()
@requires_events('userspace@hotplug_rollback', 'cpuhp_enter')
def test_hotplug_rollback(self) -> ResultBundle:
"""
Test that the hotplug can rollback to its previous state after a
failure. All possible steps, up/down combinations will be tested. For
each combination, also verify that the hotplug is going through all the
steps it is supposed to.
"""
df = df_merge([
self.trace.df_event('userspace@hotplug_rollback'),
self.trace.df_event('cpuhp_enter')
])
# Keep only the states delimited by _mark_trace()
df['test'].ffill(inplace=True)
df = df[df['test'] == 1]
df.drop(columns='test', inplace=True)
df['up'].ffill(inplace=True)
df['up'] = df['up'].astype(bool)
# Read the expected states from full hot(un)plug
df['expected'].ffill(inplace=True)
df['expected'] = df['expected'].astype(bool)
expected_down = self._get_expected_states(df, up=False)
expected_up = self._get_expected_states(df, up=True)
df = df[~df['expected']]
df.drop(columns='expected', inplace=True)
def _get_expected_rollback(up, failing_state):
return list(
filter(
partial(
operator.gt if up else operator.lt,
failing_state,
),
chain(expected_up, expected_down) if up else
chain(expected_down, expected_up)
)
)
def _verify_rollback(df):
failing_state = df['failing_state'].iloc[0]
up = df['up'].iloc[0]
expected = _get_expected_rollback(up, failing_state)
return pd.DataFrame(data={
'failing_state': df['failing_state'],
'up': up,
'result': df['idx'].tolist() == expected
})
df['failing_state'].ffill(inplace=True)
df.dropna(inplace=True)
df = df.groupby(['up', 'failing_state'],
observed=True).apply(_verify_rollback)
df.drop_duplicates(inplace=True)
res = ResultBundle.from_bool(df['result'].all())
res.add_metric('Failed rollback states',
df[~df['result']]['failing_state'].tolist())
return res
| apache-2.0 |
Dwii/Master-Thesis | implementation/Cuda/lbm_simple_lbmcuda/plot_domain_size.py | 1 | 1279 |
import sys
import os
import matplotlib.pyplot as plt
import numpy as np
import operator
if not (len(sys.argv) > 2 and (len(sys.argv)-2) % 2 == 0) :
print("usage: python3 {0} <image path> <dat1> <legend1> [<dat2> <legend2>] .. [<datN> <legendN>] ) ".format(os.path.basename(sys.argv[0])))
exit(1)
fig, ax = plt.subplots(figsize=(10,5))
image_path=sys.argv[1]
dats = int(len(sys.argv)/2)-1
legends = [None] * dats
max_mlups = 0
for i, argi in enumerate(range(2, len(sys.argv), 2)):
file = sys.argv[argi]
legends[i] = sys.argv[argi+1]
mlups_by_ds = ()
first = None
last = None
# Load benchark
print(file)
for j, lups in enumerate(open(file,'r')):
mlups_by_ds += ( (int(j+30), int(lups) / 1E6), )
max_mlups = max(max_mlups, int(lups) / 1E6)
p=plt.plot(*zip(*mlups_by_ds), label=legends[i])
import math
xticks = list(range(32, 257, 16))
xticks_labels = [ r"{0}$^3$".format(tick) for tick in xticks]
plt.xticks(xticks, xticks_labels)
ax.set_xlim(16, 256+16)
ax.set_ylabel('MLUPS')
ax.set_xlabel('Taille du domaine')
plt.legend(loc="upper center", ncol=len(legends), prop={'size': 12} ) #, bbox_to_anchor=(0.5,-0.1))
ax.set_ylim(0,max_mlups*1.2)
plt.savefig(image_path, bbox_inches='tight')
plt.grid()
plt.show()
| mit |
pazeshun/jsk_apc | demos/grasp_fusion/examples/grasp_fusion/instance_segmentation/train_common.py | 2 | 10993 | # DO NOT EDIT THIS.
# https://github.com/wkentaro/chainer-mask-rcnn/blob/master/examples/train_common.py # NOQA
from __future__ import print_function
import argparse
import datetime
import functools
import os.path as osp
import random
import socket
import sys
import matplotlib.pyplot as plt
plt.switch_backend('agg') # NOQA
import chainer
from chainer import training
from chainer.training import extensions
import fcn
import numpy as np
import chainer_mask_rcnn as cmr
def get_parser():
parser = argparse.ArgumentParser(
formatter_class=argparse.ArgumentDefaultsHelpFormatter,
)
parser.add_argument(
'--model',
'-m',
choices=['vgg16', 'resnet50', 'resnet101'],
default='resnet50',
help='base model',
)
parser.add_argument(
'--pooling-func',
'-p',
choices=['pooling', 'align', 'resize'],
default='align',
help='pooling function',
)
parser.add_argument('--gpu', '-g', type=int, help='gpu id')
parser.add_argument(
'--multi-node',
'-n',
action='store_true',
help='use multi node',
)
parser.add_argument(
'--roi-size',
'-r',
type=int,
default=14,
help='roi size',
)
parser.add_argument(
'--initializer',
choices=['normal', 'he_normal'],
default='normal',
help='initializer',
)
# (180e3 * 8) / len(coco_trainval)
default_max_epoch = (180e3 * 8) / 118287
parser.add_argument(
'--max-epoch',
type=float,
default=default_max_epoch,
help='max epoch',
)
parser.add_argument(
'--batch-size-per-gpu',
type=int,
default=1,
help='batch size / gpu',
)
return parser
def train(args, train_data, test_data, evaluator_type):
required_args = [
'dataset',
'class_names',
'logs_dir',
'min_size',
'max_size',
'anchor_scales',
]
for arg_key in required_args:
if not hasattr(args, arg_key):
raise ValueError(
'args must contain required key: {}'.format(arg_key)
)
assert evaluator_type in ['voc', 'coco'], \
'Unsupported evaluator_type: {}'.format(evaluator_type)
if args.multi_node:
import chainermn
comm = chainermn.create_communicator('hierarchical')
device = comm.intra_rank
args.n_node = comm.inter_size
args.n_gpu = comm.size
chainer.cuda.get_device_from_id(device).use()
else:
if args.gpu is None:
print(
'Option --gpu is required without --multi-node.',
file=sys.stderr,
)
sys.exit(1)
args.n_node = 1
args.n_gpu = 1
chainer.cuda.get_device_from_id(args.gpu).use()
device = args.gpu
args.seed = 0
now = datetime.datetime.now()
args.timestamp = now.isoformat()
args.out = osp.join(args.logs_dir, now.strftime('%Y%m%d_%H%M%S'))
args.batch_size = args.batch_size_per_gpu * args.n_gpu
# lr: 0.00125 * 8 = 0.01 in original
args.lr = 0.00125 * args.batch_size
args.weight_decay = 0.0001
# lr / 10 at 120k iteration with
# 160k iteration * 16 batchsize in original
args.step_size = [
(120e3 / 180e3) * args.max_epoch,
(160e3 / 180e3) * args.max_epoch,
]
random.seed(args.seed)
np.random.seed(args.seed)
if args.pooling_func == 'align':
pooling_func = cmr.functions.roi_align_2d
elif args.pooling_func == 'pooling':
pooling_func = cmr.functions.roi_pooling_2d
elif args.pooling_func == 'resize':
pooling_func = cmr.functions.crop_and_resize
else:
raise ValueError(
'Unsupported pooling_func: {}'.format(args.pooling_func)
)
if args.initializer == 'normal':
mask_initialW = chainer.initializers.Normal(0.01)
elif args.initializer == 'he_normal':
mask_initialW = chainer.initializers.HeNormal(fan_option='fan_out')
else:
raise ValueError(
'Unsupported initializer: {}'.format(args.initializer)
)
if args.model == 'vgg16':
mask_rcnn = cmr.models.MaskRCNNVGG16(
n_fg_class=len(args.class_names),
pretrained_model='imagenet',
pooling_func=pooling_func,
anchor_scales=args.anchor_scales,
roi_size=args.roi_size,
min_size=args.min_size,
max_size=args.max_size,
mask_initialW=mask_initialW,
)
elif args.model in ['resnet50', 'resnet101']:
n_layers = int(args.model.lstrip('resnet'))
mask_rcnn = cmr.models.MaskRCNNResNet(
n_layers=n_layers,
n_fg_class=len(args.class_names),
pooling_func=pooling_func,
anchor_scales=args.anchor_scales,
roi_size=args.roi_size,
min_size=args.min_size,
max_size=args.max_size,
mask_initialW=mask_initialW,
)
else:
raise ValueError('Unsupported model: {}'.format(args.model))
model = cmr.models.MaskRCNNTrainChain(mask_rcnn)
if args.multi_node or args.gpu >= 0:
model.to_gpu()
optimizer = chainer.optimizers.MomentumSGD(lr=args.lr, momentum=0.9)
if args.multi_node:
optimizer = chainermn.create_multi_node_optimizer(optimizer, comm)
optimizer.setup(model)
optimizer.add_hook(chainer.optimizer.WeightDecay(rate=args.weight_decay))
if args.model in ['resnet50', 'resnet101']:
# ResNetExtractor.freeze_at is not enough to freeze params
# since WeightDecay updates the param little by little.
mask_rcnn.extractor.conv1.disable_update()
mask_rcnn.extractor.bn1.disable_update()
mask_rcnn.extractor.res2.disable_update()
for link in mask_rcnn.links():
if isinstance(link, cmr.links.AffineChannel2D):
link.disable_update()
train_data = chainer.datasets.TransformDataset(
train_data,
cmr.datasets.MaskRCNNTransform(mask_rcnn),
)
test_data = chainer.datasets.TransformDataset(
test_data,
cmr.datasets.MaskRCNNTransform(mask_rcnn, train=False),
)
if args.multi_node:
if comm.rank != 0:
train_data = None
test_data = None
train_data = chainermn.scatter_dataset(train_data, comm, shuffle=True)
test_data = chainermn.scatter_dataset(test_data, comm)
# FIXME: MultiProcessIterator sometimes hangs
train_iter = chainer.iterators.SerialIterator(
train_data,
batch_size=args.batch_size_per_gpu,
)
test_iter = chainer.iterators.SerialIterator(
test_data,
batch_size=args.batch_size_per_gpu,
repeat=False,
shuffle=False,
)
converter = functools.partial(
cmr.datasets.concat_examples,
padding=0,
# img, bboxes, labels, masks, scales
indices_concat=[0, 2, 3, 4], # img, _, labels, masks, scales
indices_to_device=[0, 1], # img, bbox
)
updater = chainer.training.updater.StandardUpdater(
train_iter,
optimizer,
device=device,
converter=converter,
)
trainer = training.Trainer(
updater,
(args.max_epoch, 'epoch'),
out=args.out,
)
trainer.extend(
extensions.ExponentialShift('lr', 0.1),
trigger=training.triggers.ManualScheduleTrigger(
args.step_size,
'epoch',
),
)
eval_interval = 1, 'epoch'
log_interval = 20, 'iteration'
plot_interval = 0.1, 'epoch'
print_interval = 20, 'iteration'
if evaluator_type == 'voc':
evaluator = cmr.extensions.InstanceSegmentationVOCEvaluator(
test_iter,
model.mask_rcnn,
device=device,
use_07_metric=True,
label_names=args.class_names,
)
elif evaluator_type == 'coco':
evaluator = cmr.extensions.InstanceSegmentationCOCOEvaluator(
test_iter,
model.mask_rcnn,
device=device,
label_names=args.class_names,
)
else:
raise ValueError(
'Unsupported evaluator_type: {}'.format(evaluator_type)
)
if args.multi_node:
evaluator = chainermn.create_multi_node_evaluator(evaluator, comm)
trainer.extend(evaluator, trigger=eval_interval)
if not args.multi_node or comm.rank == 0:
# Save snapshot.
trainer.extend(
extensions.snapshot_object(model.mask_rcnn, 'snapshot_model.npz'),
trigger=training.triggers.MaxValueTrigger(
'validation/main/map',
eval_interval,
),
)
# Dump params.yaml.
args.git_hash = cmr.utils.git_hash()
args.hostname = socket.gethostname()
trainer.extend(fcn.extensions.ParamsReport(args.__dict__))
# Visualization.
trainer.extend(
cmr.extensions.InstanceSegmentationVisReport(
test_iter,
model.mask_rcnn,
label_names=args.class_names,
),
trigger=eval_interval,
)
# Logging.
trainer.extend(
chainer.training.extensions.observe_lr(),
trigger=log_interval,
)
trainer.extend(extensions.LogReport(trigger=log_interval))
trainer.extend(
extensions.PrintReport(
[
'iteration',
'epoch',
'elapsed_time',
'lr',
'main/loss',
'main/roi_loc_loss',
'main/roi_cls_loss',
'main/roi_mask_loss',
'main/rpn_loc_loss',
'main/rpn_cls_loss',
'validation/main/map',
],
),
trigger=print_interval,
)
trainer.extend(extensions.ProgressBar(update_interval=10))
# Plot.
assert extensions.PlotReport.available()
trainer.extend(
extensions.PlotReport(
[
'main/loss',
'main/roi_loc_loss',
'main/roi_cls_loss',
'main/roi_mask_loss',
'main/rpn_loc_loss',
'main/rpn_cls_loss',
],
file_name='loss.png',
trigger=plot_interval,
),
trigger=plot_interval,
)
trainer.extend(
extensions.PlotReport(
['validation/main/map'],
file_name='accuracy.png',
trigger=plot_interval,
),
trigger=eval_interval,
)
trainer.extend(extensions.dump_graph('main/loss'))
trainer.run()
| bsd-3-clause |
trichter/sito | bin/dayplots_overview.py | 1 | 2796 | #!/usr/bin/env python
# by TR
"""
Creates a matrix of dayplots (different stations, different dates)
"""
from sito.data import IPOC
from obspy.core import UTCDateTime as UTC
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
from progressbar import ProgressBar
redraw = False
# 13 stations, 7 days, 2007, 2008
# days = '2006-12-01 2007-02-01 2007-04-01 2007-06-01 2007-08-01 2007-10-01 2007-12-01'
days = '2008-01-01 2008-02-01 2008-04-01 2008-06-01 2008-08-01 2008-10-01 2008-12-01'
days = '%d-01-01 %d-02-01 %d-04-01 %d-06-01 %d-08-01 %d-10-01 %d-12-01' % ((2012,) * 7)
stations = 'PB01 PB02 PB03 PB04 PB05 PB06 PB07 PB08 HMBCX MNMCX PATCX PSGCX LVC'
# 20 stations, 10 days, 2008-2012
# days = '2008-01-01 2008-07-01 2009-01-01 2009-07-01 2010-01-01 2010-07-01 2011-01-01 2011-07-01 2012-01-01 2012-07-01'
# stations = 'PB01 PB02 PB03 PB04 PB05 PB06 PB07 PB08 PB09 PB10 PB11 PB12 PB13 PB14 PB15 HMBCX MNMCX PATCX PSGCX LVC'
pic_dir = '/home/richter/Data/IPOC/raw_pictures/'
output = '/home/richter/Results/IPOC/raw_pics/test.pdf'
output = '/home/richter/Results/IPOC/raw_pics/13stations_7days_2012.pdf'
scale_range = 2000
scale_LVC = 20000
days = days.split()
stations = stations.split()
dx = 0.98 / len(days)
dy = 0.98 / len(stations)
data = IPOC()
figsize = (11.69, 16.53) # A3 # 8.27, 11.69 # A4
fig = plt.figure(figsize=figsize)
for i, station in ProgressBar(len(stations))(enumerate(stations)):
for j, day in enumerate(days):
x0 = 0.01 + dx * (j + 0.02)
y0 = 0.99 - dy * (1 + i)
if i == 0:
fig.text(x0 + 0.98 * dx / 2, 0.98, day, ha='center', va='center')
if j == 0:
fig.text(0.02, y0 + (0.98 / 2) * dy, station, va='center', ha='center', rotation=90)
t_day = UTC(day)
fname = '%s/%s-%s.png' % (pic_dir, station, day)
try:
if redraw:
raise IOError
img = mpimg.imread(fname)
except IOError:
try:
stream = data.getRawStreamFromClient(t_day, t_day + 24 * 3600,
station, component='Z')
except ValueError:
continue
fig2 = stream.plot(type='dayplot',
vertical_scaling_range=scale_range if station != 'LVC' else scale_LVC,
interval=30, handle=True)
fig2.axes[0].set_position((0, 0, 1, 1))
fig2.axes[1].set_visible(False)
fig2.texts[0].set_visible(False)
fig2.canvas.draw()
fig2.savefig(fname)
img = mpimg.imread(fname)
ax = fig.add_axes([x0, y0, dx * 0.98, dy * 0.98])
ax.imshow(img)
ax.set_xticklabels([])
ax.set_yticklabels([])
fig.savefig(output, dpi=300)
| mit |
nikitasingh981/scikit-learn | examples/svm/plot_svm_nonlinear.py | 62 | 1119 | """
==============
Non-linear SVM
==============
Perform binary classification using non-linear SVC
with RBF kernel. The target to predict is a XOR of the
inputs.
The color map illustrates the decision function learned by the SVC.
"""
print(__doc__)
import numpy as np
import matplotlib.pyplot as plt
from sklearn import svm
xx, yy = np.meshgrid(np.linspace(-3, 3, 500),
np.linspace(-3, 3, 500))
np.random.seed(0)
X = np.random.randn(300, 2)
Y = np.logical_xor(X[:, 0] > 0, X[:, 1] > 0)
# fit the model
clf = svm.NuSVC()
clf.fit(X, Y)
# plot the decision function for each datapoint on the grid
Z = clf.decision_function(np.c_[xx.ravel(), yy.ravel()])
Z = Z.reshape(xx.shape)
plt.imshow(Z, interpolation='nearest',
extent=(xx.min(), xx.max(), yy.min(), yy.max()), aspect='auto',
origin='lower', cmap=plt.cm.PuOr_r)
contours = plt.contour(xx, yy, Z, levels=[0], linewidths=2,
linetypes='--')
plt.scatter(X[:, 0], X[:, 1], s=30, c=Y, cmap=plt.cm.Paired,
edgecolors='k')
plt.xticks(())
plt.yticks(())
plt.axis([-3, 3, -3, 3])
plt.show()
| bsd-3-clause |
rishikksh20/scikit-learn | examples/linear_model/plot_ransac.py | 103 | 1797 | """
===========================================
Robust linear model estimation using RANSAC
===========================================
In this example we see how to robustly fit a linear model to faulty data using
the RANSAC algorithm.
"""
import numpy as np
from matplotlib import pyplot as plt
from sklearn import linear_model, datasets
n_samples = 1000
n_outliers = 50
X, y, coef = datasets.make_regression(n_samples=n_samples, n_features=1,
n_informative=1, noise=10,
coef=True, random_state=0)
# Add outlier data
np.random.seed(0)
X[:n_outliers] = 3 + 0.5 * np.random.normal(size=(n_outliers, 1))
y[:n_outliers] = -3 + 10 * np.random.normal(size=n_outliers)
# Fit line using all data
lr = linear_model.LinearRegression()
lr.fit(X, y)
# Robustly fit linear model with RANSAC algorithm
ransac = linear_model.RANSACRegressor()
ransac.fit(X, y)
inlier_mask = ransac.inlier_mask_
outlier_mask = np.logical_not(inlier_mask)
# Predict data of estimated models
line_X = np.arange(X.min(), X.max())[:, np.newaxis]
line_y = lr.predict(line_X)
line_y_ransac = ransac.predict(line_X)
# Compare estimated coefficients
print("Estimated coefficients (true, linear regression, RANSAC):")
print(coef, lr.coef_, ransac.estimator_.coef_)
lw = 2
plt.scatter(X[inlier_mask], y[inlier_mask], color='yellowgreen', marker='.',
label='Inliers')
plt.scatter(X[outlier_mask], y[outlier_mask], color='gold', marker='.',
label='Outliers')
plt.plot(line_X, line_y, color='navy', linewidth=lw, label='Linear regressor')
plt.plot(line_X, line_y_ransac, color='cornflowerblue', linewidth=lw,
label='RANSAC regressor')
plt.legend(loc='lower right')
plt.xlabel("Input")
plt.ylabel("Response")
plt.show()
| bsd-3-clause |
hughdbrown/QSTK-nohist | src/qstklearn/mldiagnostics.py | 1 | 2596 | # (c) 2011, 2012 Georgia Tech Research Corporation
# This source code is released under the New BSD license. Please see
# http://wiki.quantsoftware.org/index.php?title=QSTK_License
# for license details.
#
# Created on Month day, Year
#
# @author: Vishal Shekhar
# @contact: [email protected]
# @summary: ML Algo Diagnostic Utility (plots performance of the Algo on Train Vs CV sets)
#
import copy
import numpy as np
import matplotlib.pyplot as plt
from pylab import *
class MLDiagnostics:
"""
This class can be used to produce learning curves.
These are plots of evolution of Training Error and Cross Validation Error across lambda(in general a control param for model complexity).
This plot can help diagnose if the ML algorithmic model has high bias or a high variance problem and can
thus help decide the next course of action.
In general, ML Algorithm is of the form,
Y=f(t,X) + lambdaVal*|t|
where Y is the output, t is the model parameter vector, lambdaVal is the regularization parameter.
|t| is the size of model parameter vector.
"""
def __init__(self, learner, Xtrain, Ytrain, Xcv, Ycv, lambdaArray):
self.learner = learner
self.Xtrain = Xtrain
self.Ytrain = Ytrain
self.Xcv = Xcv
self.Ycv = Ycv
self.lambdaArray = lambdaArray
self.ErrTrain = np.zeros((len(lambdaArray), 1))
self.ErrCV = copy.copy(self.ErrTrain)
def avgsqerror(self, Y, Ypred):
return np.sum((Y - Ypred) ** 2) / len(Y)
def plotCurves(self, filename):
Xrange = [i * self.step for i in range(1, len(self.ErrTrain) + 1)]
plt.plot(Xrange, self.ErrTrain, label="Train Error")
plt.plot(Xrange, self.ErrCV, label="CV Error")
plt.title('Learning Curves')
plt.xlabel('# of Training Examples')
plt.ylabel('Average Error')
plt.draw()
savefig(filename, format='pdf')
def runDiagnostics(self, filename):
for i, lambdaVal in zip(range(len(self.lambdaArray)), self.lambdaArray):
learner = copy.copy(self.learner()) # is deep copy required
# setLambda needs to be a supported function for all ML strategies.
learner.setLambda(lambdaVal)
learner.addEvidence(self.Xtrain, self.Ytrain)
YtrPred = learner.query(self.Xtrain)
self.ErrTrain[i] = self.avgsqerror(self.Ytrain, YtrPred)
YcvPred = learner.query(self.Xcv)
self.ErrCV[i] = self.avgsqerror(self.Ycv, YcvPred)
self.plotCurves(filename)
| bsd-3-clause |
ammarkhann/FinalSeniorCode | lib/python2.7/site-packages/matplotlib/tri/triangulation.py | 10 | 8331 | from __future__ import (absolute_import, division, print_function,
unicode_literals)
import six
import matplotlib._tri as _tri
import matplotlib._qhull as _qhull
import numpy as np
class Triangulation(object):
"""
An unstructured triangular grid consisting of npoints points and
ntri triangles. The triangles can either be specified by the user
or automatically generated using a Delaunay triangulation.
Parameters
----------
x, y : array_like of shape (npoints)
Coordinates of grid points.
triangles : integer array_like of shape (ntri, 3), optional
For each triangle, the indices of the three points that make
up the triangle, ordered in an anticlockwise manner. If not
specified, the Delaunay triangulation is calculated.
mask : boolean array_like of shape (ntri), optional
Which triangles are masked out.
Attributes
----------
`edges`
`neighbors`
is_delaunay : bool
Whether the Triangulation is a calculated Delaunay
triangulation (where `triangles` was not specified) or not.
Notes
-----
For a Triangulation to be valid it must not have duplicate points,
triangles formed from colinear points, or overlapping triangles.
"""
def __init__(self, x, y, triangles=None, mask=None):
self.x = np.asarray(x, dtype=np.float64)
self.y = np.asarray(y, dtype=np.float64)
if self.x.shape != self.y.shape or len(self.x.shape) != 1:
raise ValueError("x and y must be equal-length 1-D arrays")
self.mask = None
self._edges = None
self._neighbors = None
self.is_delaunay = False
if triangles is None:
# No triangulation specified, so use matplotlib._qhull to obtain
# Delaunay triangulation.
self.triangles, self._neighbors = _qhull.delaunay(x, y)
self.is_delaunay = True
else:
# Triangulation specified. Copy, since we may correct triangle
# orientation.
self.triangles = np.array(triangles, dtype=np.int32, order='C')
if self.triangles.ndim != 2 or self.triangles.shape[1] != 3:
raise ValueError('triangles must be a (?,3) array')
if self.triangles.max() >= len(self.x):
raise ValueError('triangles max element is out of bounds')
if self.triangles.min() < 0:
raise ValueError('triangles min element is out of bounds')
if mask is not None:
self.mask = np.asarray(mask, dtype=np.bool)
if (len(self.mask.shape) != 1 or
self.mask.shape[0] != self.triangles.shape[0]):
raise ValueError('mask array must have same length as '
'triangles array')
# Underlying C++ object is not created until first needed.
self._cpp_triangulation = None
# Default TriFinder not created until needed.
self._trifinder = None
def calculate_plane_coefficients(self, z):
"""
Calculate plane equation coefficients for all unmasked triangles from
the point (x,y) coordinates and specified z-array of shape (npoints).
Returned array has shape (npoints,3) and allows z-value at (x,y)
position in triangle tri to be calculated using
z = array[tri,0]*x + array[tri,1]*y + array[tri,2].
"""
return self.get_cpp_triangulation().calculate_plane_coefficients(z)
@property
def edges(self):
"""
Return integer array of shape (nedges,2) containing all edges of
non-masked triangles.
Each edge is the start point index and end point index. Each
edge (start,end and end,start) appears only once.
"""
if self._edges is None:
self._edges = self.get_cpp_triangulation().get_edges()
return self._edges
def get_cpp_triangulation(self):
# Return the underlying C++ Triangulation object, creating it
# if necessary.
if self._cpp_triangulation is None:
self._cpp_triangulation = _tri.Triangulation(
self.x, self.y, self.triangles, self.mask, self._edges,
self._neighbors, not self.is_delaunay)
return self._cpp_triangulation
def get_masked_triangles(self):
"""
Return an array of triangles that are not masked.
"""
if self.mask is not None:
return self.triangles.compress(1 - self.mask, axis=0)
else:
return self.triangles
@staticmethod
def get_from_args_and_kwargs(*args, **kwargs):
"""
Return a Triangulation object from the args and kwargs, and
the remaining args and kwargs with the consumed values removed.
There are two alternatives: either the first argument is a
Triangulation object, in which case it is returned, or the args
and kwargs are sufficient to create a new Triangulation to
return. In the latter case, see Triangulation.__init__ for
the possible args and kwargs.
"""
if isinstance(args[0], Triangulation):
triangulation = args[0]
args = args[1:]
else:
x = args[0]
y = args[1]
args = args[2:] # Consumed first two args.
# Check triangles in kwargs then args.
triangles = kwargs.pop('triangles', None)
from_args = False
if triangles is None and len(args) > 0:
triangles = args[0]
from_args = True
if triangles is not None:
try:
triangles = np.asarray(triangles, dtype=np.int32)
except ValueError:
triangles = None
if triangles is not None and (triangles.ndim != 2 or
triangles.shape[1] != 3):
triangles = None
if triangles is not None and from_args:
args = args[1:] # Consumed first item in args.
# Check for mask in kwargs.
mask = kwargs.pop('mask', None)
triangulation = Triangulation(x, y, triangles, mask)
return triangulation, args, kwargs
def get_trifinder(self):
"""
Return the default :class:`matplotlib.tri.TriFinder` of this
triangulation, creating it if necessary. This allows the same
TriFinder object to be easily shared.
"""
if self._trifinder is None:
# Default TriFinder class.
from matplotlib.tri.trifinder import TrapezoidMapTriFinder
self._trifinder = TrapezoidMapTriFinder(self)
return self._trifinder
@property
def neighbors(self):
"""
Return integer array of shape (ntri,3) containing neighbor
triangles.
For each triangle, the indices of the three triangles that
share the same edges, or -1 if there is no such neighboring
triangle. neighbors[i,j] is the triangle that is the neighbor
to the edge from point index triangles[i,j] to point index
triangles[i,(j+1)%3].
"""
if self._neighbors is None:
self._neighbors = self.get_cpp_triangulation().get_neighbors()
return self._neighbors
def set_mask(self, mask):
"""
Set or clear the mask array. This is either None, or a boolean
array of shape (ntri).
"""
if mask is None:
self.mask = None
else:
self.mask = np.asarray(mask, dtype=np.bool)
if (len(self.mask.shape) != 1 or
self.mask.shape[0] != self.triangles.shape[0]):
raise ValueError('mask array must have same length as '
'triangles array')
# Set mask in C++ Triangulation.
if self._cpp_triangulation is not None:
self._cpp_triangulation.set_mask(self.mask)
# Clear derived fields so they are recalculated when needed.
self._edges = None
self._neighbors = None
# Recalculate TriFinder if it exists.
if self._trifinder is not None:
self._trifinder._initialize()
| mit |
tosolveit/scikit-learn | benchmarks/bench_plot_omp_lars.py | 266 | 4447 | """Benchmarks of orthogonal matching pursuit (:ref:`OMP`) versus least angle
regression (:ref:`least_angle_regression`)
The input data is mostly low rank but is a fat infinite tail.
"""
from __future__ import print_function
import gc
import sys
from time import time
import numpy as np
from sklearn.linear_model import lars_path, orthogonal_mp
from sklearn.datasets.samples_generator import make_sparse_coded_signal
def compute_bench(samples_range, features_range):
it = 0
results = dict()
lars = np.empty((len(features_range), len(samples_range)))
lars_gram = lars.copy()
omp = lars.copy()
omp_gram = lars.copy()
max_it = len(samples_range) * len(features_range)
for i_s, n_samples in enumerate(samples_range):
for i_f, n_features in enumerate(features_range):
it += 1
n_informative = n_features / 10
print('====================')
print('Iteration %03d of %03d' % (it, max_it))
print('====================')
# dataset_kwargs = {
# 'n_train_samples': n_samples,
# 'n_test_samples': 2,
# 'n_features': n_features,
# 'n_informative': n_informative,
# 'effective_rank': min(n_samples, n_features) / 10,
# #'effective_rank': None,
# 'bias': 0.0,
# }
dataset_kwargs = {
'n_samples': 1,
'n_components': n_features,
'n_features': n_samples,
'n_nonzero_coefs': n_informative,
'random_state': 0
}
print("n_samples: %d" % n_samples)
print("n_features: %d" % n_features)
y, X, _ = make_sparse_coded_signal(**dataset_kwargs)
X = np.asfortranarray(X)
gc.collect()
print("benchmarking lars_path (with Gram):", end='')
sys.stdout.flush()
tstart = time()
G = np.dot(X.T, X) # precomputed Gram matrix
Xy = np.dot(X.T, y)
lars_path(X, y, Xy=Xy, Gram=G, max_iter=n_informative)
delta = time() - tstart
print("%0.3fs" % delta)
lars_gram[i_f, i_s] = delta
gc.collect()
print("benchmarking lars_path (without Gram):", end='')
sys.stdout.flush()
tstart = time()
lars_path(X, y, Gram=None, max_iter=n_informative)
delta = time() - tstart
print("%0.3fs" % delta)
lars[i_f, i_s] = delta
gc.collect()
print("benchmarking orthogonal_mp (with Gram):", end='')
sys.stdout.flush()
tstart = time()
orthogonal_mp(X, y, precompute=True,
n_nonzero_coefs=n_informative)
delta = time() - tstart
print("%0.3fs" % delta)
omp_gram[i_f, i_s] = delta
gc.collect()
print("benchmarking orthogonal_mp (without Gram):", end='')
sys.stdout.flush()
tstart = time()
orthogonal_mp(X, y, precompute=False,
n_nonzero_coefs=n_informative)
delta = time() - tstart
print("%0.3fs" % delta)
omp[i_f, i_s] = delta
results['time(LARS) / time(OMP)\n (w/ Gram)'] = (lars_gram / omp_gram)
results['time(LARS) / time(OMP)\n (w/o Gram)'] = (lars / omp)
return results
if __name__ == '__main__':
samples_range = np.linspace(1000, 5000, 5).astype(np.int)
features_range = np.linspace(1000, 5000, 5).astype(np.int)
results = compute_bench(samples_range, features_range)
max_time = max(np.max(t) for t in results.values())
import pylab as pl
fig = pl.figure('scikit-learn OMP vs. LARS benchmark results')
for i, (label, timings) in enumerate(sorted(results.iteritems())):
ax = fig.add_subplot(1, 2, i)
vmax = max(1 - timings.min(), -1 + timings.max())
pl.matshow(timings, fignum=False, vmin=1 - vmax, vmax=1 + vmax)
ax.set_xticklabels([''] + map(str, samples_range))
ax.set_yticklabels([''] + map(str, features_range))
pl.xlabel('n_samples')
pl.ylabel('n_features')
pl.title(label)
pl.subplots_adjust(0.1, 0.08, 0.96, 0.98, 0.4, 0.63)
ax = pl.axes([0.1, 0.08, 0.8, 0.06])
pl.colorbar(cax=ax, orientation='horizontal')
pl.show()
| bsd-3-clause |
JulianPasc/Final-Project-Julien-Pascal | src/Calculate_volatility_wages.py | 1 | 14775 | ##########################################################
# Calculate the volatility of the wages deciles in the US
##########################################################
# Use data from Jonathan Heathcote, Fabrizio Perri, Giovanni L. Violante
# Unequal We Stand: An Empirical Analysis of Economic Inequality in the United States, 1967-2006
# http://www.nber.org/papers/w15483
# I follow the procedure described by Robin 2011 in
# http://onlinelibrary.wiley.com/doi/10.3982/ECTA9070/abstract
import numpy as np
import os
import pandas as pd
import matplotlib.pyplot as plt
from more_itertools import unique_everseen
import pylab
import matplotlib.dates as mdates
from math import exp, log
import math
from datetime import date, timedelta as td
import statsmodels.api as sm
import matplotlib.cm as cm
from tabulate import tabulate
import csv
path = '/home/julien/master-s_thesis/data' #path to the data folder
path_figure = '/home/julien/master-s_thesis/figures/' #where to save the figures
path_table = '/home/julien/master-s_thesis/tables/' #where to save the tables
os.chdir(path) #locate in the data folder
######################
# I. Data 2000 to 2015
######################
df = pd.read_csv("LEU.csv")
starting_year = 2000 #first quarter 1951
starting_month = 1
starting_date = date(starting_year, starting_month, 1)
stop_year = 2015 #third quarter 2010
stop_month = 10
stop_date = date(stop_year, stop_month, 1)
# 1. Create a column with dates objects:
df['Date'] = 0
for i in range(0,len(df['Year'])):
df.ix[i,'Date'] = date(df.ix[i,'Year'], df.ix[i,'Month'], 1)
#keep the selected time period:
df = df.loc[df['Date'] <= stop_date]
df = df.loc[df['Date'] >= starting_date]
#Create
dateList = []
for index, row in df.iterrows():
print(index)
dateList.append(date(df.ix[index,'Year'], df.ix[index,'Month'], 1))
# Usual weekly earnings - in current dollars, first decile, both sexes: LEU0252911200
First_decile = df['LEU0252911200'].values
# Usual weekly earnings, first quartile, Employed full time, Wage and salary workers LEU0252911300
First_quartile = df['LEU0252911300'].values
#Median usual weekly earnings (second quartile), Employed full time, Wage and salary workers LEU0252881500
Median = df['LEU0252881500'].values
# Usual weekly earnings, third quartile, Employed full time, Wage and salary workers LEU0252911400
Third_quartile = df['LEU0252911400'].values
# Usual weekly earnings, ninth decile, Employed full time, Wage and salary workers LEU0252911500
Ninth_decile = df['LEU0252911500'].values
##################
# Plot the series:
##################
plt.plot(dateList, First_decile, color = 'b')
plt.plot(dateList, First_quartile, 'k')
plt.plot(dateList, Median, color = 'r')
plt.plot(dateList, Third_quartile, color = 'g')
plt.plot(dateList, Ninth_decile, color = 'navy')
plt.title('Wages')
plt.legend(['First decile', 'First quartile', 'Median', 'Third quartile', 'Ninth decile'], loc='best', fancybox = True, framealpha=0.5)
plt.savefig(path_figure + 'Wages_2000_2015')
plt.show()
######################
# Plot ratio of series
######################
plt.plot(dateList, Ninth_decile/First_decile, color = 'b')
plt.plot(dateList, Median/First_decile, 'k')
plt.plot(dateList, Ninth_decile/Median, color = 'r')
plt.legend(['P90/P10','P50/P10','P90/P50'] , loc='best', fancybox = True, framealpha=0.2)
plt.title('Wages')
plt.savefig(path_figure + 'Wage_ratios_2000_2015')
plt.show()
#############################
# Plot the log of the series:
#############################
log_First_decile = np.log(First_decile)
log_First_quartile = np.log(First_quartile)
log_Median = np.log(Median)
log_Third_quartile = np.log(Third_quartile)
log_Ninth_decile = np.log(Ninth_decile)
plt.plot(dateList, log_First_decile, color = 'b')
plt.plot(dateList, log_First_quartile , 'k')
plt.plot(dateList, log_Median, color = 'r')
plt.plot(dateList, log_Third_quartile, color = 'g')
plt.plot(dateList, log_Ninth_decile , color = 'navy')
plt.title('Log Wages')
plt.savefig('Log_Wages_2000_2015')
plt.show()
#####################
#Plot ratios of logs
plt.plot(dateList, log_Ninth_decile/log_First_decile, color = 'b')
plt.plot(dateList, log_Median/log_First_decile, 'k')
plt.plot(dateList, log_Ninth_decile/log_Median, color = 'r')
plt.legend(['P90/P10','P50/P10','P90/P50'] , loc='best', fancybox = True, framealpha=0.2)
plt.title('Log Wages Ratios')
plt.savefig(path_figure + 'Log_Wage_ratios_2000_2015')
plt.show()
#####################################
# Get rid of a linear trend on wages:
#####################################
z = np.polyfit(df['Year'].values, log_First_decile, 1)
p = np.poly1d(z)
detrended_log_First_decile = log_First_decile - p(df['Year'].values) + np.mean(p(df['Year'].values)) #remove the trend and add the mean
z = np.polyfit(df['Year'].values, log_First_quartile, 1)
p = np.poly1d(z)
detrended_log_First_quartile = log_First_quartile - p(df['Year'].values) + np.mean(p(df['Year'].values))
z = np.polyfit(df['Year'].values, log_Median, 1)
p = np.poly1d(z)
detrended_log_Median = log_Median - p(df['Year'].values) + np.mean(p(df['Year'].values))
z = np.polyfit(df['Year'].values, log_Third_quartile, 1)
p = np.poly1d(z)
detrended_log_Third_quartile = log_Third_quartile - p(df['Year'].values) + np.mean(p(df['Year'].values))
z = np.polyfit(df['Year'].values, log_Ninth_decile, 1)
p = np.poly1d(z)
detrended_log_Ninth_decile = log_Ninth_decile - p(df['Year'].values) + np.mean(p(df['Year'].values))
#############################
# Plot detrented wage ratios:
#############################
# P90/P10: 90th to 10th percentile ratio
P_90_to_P10 = detrended_log_Ninth_decile/detrended_log_First_decile
# P50/P10
P_50_to_P10 = detrended_log_Median/detrended_log_First_decile
# P90/P50
P_90_to_50 = detrended_log_Ninth_decile/detrended_log_Median
plt.plot(df['Year'].values, P_90_to_P10)
plt.plot(df['Year'].values, P_50_to_P10)
plt.plot(df['Year'].values, P_90_to_50)
plt.legend(['P90/P10','P50/P10','P90/P50'] , loc='best', fancybox = True, framealpha=0.2)
plt.savefig(path_figure + 'delinearized_log_wage_decile_ratios_2000_2015')
plt.show()
######################################
# Standard deviations of wage deciles;
######################################
print(np.std(detrended_log_First_decile))
print(np.std(detrended_log_First_quartile))
print(np.std(detrended_log_Median))
print(np.std(detrended_log_Third_quartile))
print(np.std(detrended_log_Ninth_decile))
########################
# II. Data 1967 to 2005:
########################
df2 = pd.read_csv("individ_pctiles_work_hrs_wage.csv")
#Plot the deciles with the trend:
legend_list = []
#list_color = color_list = plt.cm.Set3(np.linspace(0, 1,13))
a = 0
cmap = plt.cm.Accent
line_colors = cmap(np.linspace(0,1,9))
for i in np.arange(10, 100, 10):
variable = 'avg_wage_%s' %i
legend_name = '%sth percentile' %i
plt.plot(df2['true_year'].values, df2[variable], color = line_colors[a])
legend_list.append(legend_name)
a=a+1
plt.title('Dynamics of wage deciles')
plt.savefig(path_figure + 'trended_wage_deciles')
plt.legend(legend_list, loc='best', fancybox = True, framealpha=0.2)
plt.show()
#######################################
# A. HP Filter the log of the deciles :
#######################################
smoothing_parameter = 100 #Paramter for yearly data
cycle_avg_wage = {}
trend_avg_wage = {}
detrended_avg_wage = {}
# Decompose the trend and the cycle:
for i in np.arange(10, 100, 10):
variable = 'avg_wage_%s' %i
cycle_avg_wage[i], trend_avg_wage[i] = sm.tsa.filters.hpfilter(np.log(df2[variable]), smoothing_parameter)
detrended_avg_wage[i] = np.exp(cycle_avg_wage[i] + np.mean(trend_avg_wage[i])) # add the mean of the trend and take the exponential
#Plot the trend:
a = 0
for i in np.arange(10, 100, 10):
plt.plot(df2['true_year'].values, trend_avg_wage[i], color = line_colors[a])
a=a+1
plt.legend(legend_list, loc='best', fancybox = True, framealpha=0.2)
plt.title('Trend in wage deciles')
plt.savefig(path_figure + 'Trend_wage_deciles')
plt.show()
#Plot the cycle component
a = 0
for i in np.arange(10, 100, 10):
plt.plot(df2['true_year'].values, cycle_avg_wage[i], color = line_colors[a])
a=a+1
plt.title('Cycle component in wage deciles')
plt.savefig(path_figure + 'Cycle_wage_deciles')
plt.legend(legend_list, loc='best', fancybox = True, framealpha=0.2)
plt.show()
#Plot the untrended data:
a = 0
for i in np.arange(10, 100, 10):
plt.plot(df2['true_year'].values, detrended_avg_wage[i], color = line_colors[a])
a=a+1
plt.title('Detrended wage deciles HP Filter smoothing parameter = 100')
plt.savefig(path_figure + 'Detrended_wage_deciles')
plt.legend(legend_list, loc='best', fancybox = True, framealpha=0.2)
plt.show()
# Table for the volatility of the HP filtered deciles:
table = [] #create a table to store the volatility
a = 0
for i in np.arange(10, 100, 10):
variable = '%sth Decile' %i
print(np.std(detrended_avg_wage[i]))
table.append([variable, np.std(detrended_avg_wage[i])])
a=a+1
# Output table of standard errors of detrended data in latek:
print(tabulate(table, headers=['Decile', 'Volatility'], tablefmt="latex"))
#save the table in a csv format:
with open(path_table + 'table_volatility_detrended_wages.csv', 'w') as csvfile:
writer = csv.writer(csvfile)
[writer.writerow(r) for r in table]
#####################################################
# B. Remove a linear trend, as in the original paper:
#####################################################
linear_trend_avg_wage = {}
delinear_avg_wage = {}
deviation_from_linear_trend = {}
for i in np.arange(10, 100, 10):
variable = 'avg_wage_%s' %i
log_wage_decile = np.log(df2[variable]) #take the log
z = np.polyfit(df2['true_year'].values, log_wage_decile , 1) #calculate the least squares line
p = np.poly1d(z)
linear_trend_avg_wage[i] = p(df2['true_year'])
delinear_avg_wage[i] = np.exp(log_wage_decile - linear_trend_avg_wage[i] + np.mean(linear_trend_avg_wage[i])) # add the mean of the trend and take the exponential
deviation_from_linear_trend[i] = np.divide(log_wage_decile - linear_trend_avg_wage[i],linear_trend_avg_wage[i])
# Plot the untrended data:
a = 0
for i in np.arange(10, 100, 10):
plt.plot(df2['true_year'].values, delinear_avg_wage[i], color = line_colors[a])
a=a+1
#plt.title('Detrended wage deciles, removing a linear trend')
plt.legend(legend_list, loc='best', fancybox = True, framealpha=0.2)
plt.savefig(path_figure + 'delinearized_wage_deciles')
plt.show()
#Volatility of the delinearized deciles:
table = [] #create a table to store the volatility
a = 0
for i in np.arange(10, 100, 10):
variable = '%sth Decile' %i
print(np.std(delinear_avg_wage[i]))
table.append([variable, np.std(delinear_avg_wage[i])])
a=a+1
# Output table of standard errors of delinearized data in latek:
print(tabulate(table, headers=['Decile', 'Volatility'], tablefmt="latex"))
#save the table in a csv format:
with open(path_table + 'table_volatility_delinearized_wages.csv', 'w') as csvfile:
writer = csv.writer(csvfile)
[writer.writerow(r) for r in table]
#######################################
# Plot deviations from the linear trend
#######################################
a = 0
for i in np.arange(10, 100, 10):
plt.plot(df2['true_year'].values, deviation_from_linear_trend[i], color = line_colors[a])
a=a+1
#plt.title('Deviations of wage deciles from linear trend')
plt.legend(legend_list, loc='best', fancybox = True, framealpha=0.2)
plt.savefig(path_figure + 'Deviations_wages_deciles_from_linear_trend')
plt.show()
#####################
# Plot earning ratios
#####################
# P90/P10: 90th to 10th percentile ratio
P_90_to_P10 = delinear_avg_wage[90]/delinear_avg_wage[10] #indexing starts at 0
# P50/P10
P_50_to_P10 = delinear_avg_wage[50]/delinear_avg_wage[10]
# P90/P50
P_90_to_50 = delinear_avg_wage[90]/delinear_avg_wage[50]
plt.plot(df2['true_year'].values, P_90_to_P10, color = line_colors[1])
plt.plot(df2['true_year'].values, P_50_to_P10, color = line_colors[2])
plt.plot(df2['true_year'].values, P_90_to_50, color = line_colors[7])
plt.legend(['P90/P10','P50/P10','P90/P50'] , loc='best', fancybox = True, framealpha=0.2)
plt.savefig(path_figure + 'delinearized_wage_decile_ratios')
plt.show()
#######################################################
# III. Plot earning by educational attainment 2000-2015
#######################################################
df_wage = pd.read_csv("LEU_wage_education.csv")
W = df_wage.loc[df_wage['Series ID'] == 'LEU0252887700','Value'] #Median wage for every type of educational attainment
W1 = df_wage.loc[df_wage['Series ID'] == 'LEU0252916700','Value']#Median wage for Less than a high school diploma
W2 = df_wage.loc[df_wage['Series ID'] == 'LEU0252917300','Value']#Median wage for High school graduates, no college
W3 = df_wage.loc[df_wage['Series ID'] == 'LEU0254929400','Value']#Median wage for Some college or associate degree
W4 = df_wage.loc[df_wage['Series ID'] == 'LEU0252918500','Value'] #Median wage for Bachelor's degree or higher
###############
# Scatter point
##############
plt.scatter(W, W1, color = 'b', alpha=0.5)
plt.scatter(W, W2, color = 'k', alpha=0.5)
plt.scatter(W, W3, color = 'r', alpha=0.5)
plt.scatter(W, W4, color = 'g', alpha=0.5)
#plt.scatter(Unemployment_rate_selected_years, degree_line, color = 'grey', alpha=0.2)
plt.legend(['Less than a High School Diploma', 'With a High School Diploma', 'Some College or Associate Degree','Bachelors Degree and Higher'], loc='upper left', fancybox = True, framealpha=0.5)
plt.xlabel('Median usual weekly earnings - in current dollars')
plt.ylabel('Median usual weekly earnings by educational attainment')
plt.savefig(path_figure + 'Overall_vs_group_edu_median_weekly_earning')
plt.show()
########
# Lines
#######
dateListWage = []
for i in range(0,len(W)):
dateListWage.append(date(df_wage.loc[df_wage['Series ID'] == 'LEU0252887700','Year'].values[i],df_wage.loc[df_wage['Series ID'] == 'LEU0252887700','Month'].values[i], 1))
plt.plot(dateListWage, W1, color = 'b')
plt.plot(dateListWage, W2, '--k')
plt.plot(dateListWage, W3, color = 'r')
plt.plot(dateListWage, W4, color = 'g')
#fill in between:
plt.fill_between(dateListWage, 0, W1, color='b', alpha=0.2)
plt.fill_between(dateListWage, W1, W2, color='k', alpha=0.2)
plt.fill_between(dateListWage, W2, W3, color='r', alpha=0.2)
plt.fill_between(dateListWage, W3, W4, color='g', alpha=0.2)
#plt.title('Unemployment educational attainment')
plt.ylabel('Median usual weekly earnings in current dollars')
plt.legend(['Less than a High School Diploma', 'With a High School Diploma', 'Some College or Associate Degree','Bachelors Degree and Higher'], loc='upper left', fancybox = True, framealpha=0.5)
plt.savefig(path_figure + 'Wages_by_education')
plt.show()
| mit |
ryandougherty/mwa-capstone | MWA_Tools/build/matplotlib/lib/matplotlib/tests/test_cbook.py | 3 | 2869 | import numpy as np
from numpy.testing.utils import assert_array_equal
import matplotlib.cbook as cbook
import matplotlib.colors as mcolors
from nose.tools import assert_equal, raises
from datetime import datetime
def test_is_string_like():
y = np.arange( 10 )
assert_equal( cbook.is_string_like( y ), False )
y.shape = 10, 1
assert_equal( cbook.is_string_like( y ), False )
y.shape = 1, 10
assert_equal( cbook.is_string_like( y ), False )
assert cbook.is_string_like( "hello world" )
assert_equal( cbook.is_string_like(10), False )
def test_restrict_dict():
d = {'foo': 'bar', 1: 2}
d1 = cbook.restrict_dict(d, ['foo', 1])
assert_equal(d1, d)
d2 = cbook.restrict_dict(d, ['bar', 2])
assert_equal(d2, {})
d3 = cbook.restrict_dict(d, {'foo': 1})
assert_equal(d3, {'foo': 'bar'})
d4 = cbook.restrict_dict(d, {})
assert_equal(d4, {})
d5 = cbook.restrict_dict(d, set(['foo',2]))
assert_equal(d5, {'foo': 'bar'})
# check that d was not modified
assert_equal(d, {'foo': 'bar', 1: 2})
from matplotlib.cbook import delete_masked_points as dmp
class Test_delete_masked_points:
def setUp(self):
self.mask1 = [False, False, True, True, False, False]
self.arr0 = np.arange(1.0,7.0)
self.arr1 = [1,2,3,np.nan,np.nan,6]
self.arr2 = np.array(self.arr1)
self.arr3 = np.ma.array(self.arr2, mask=self.mask1)
self.arr_s = ['a', 'b', 'c', 'd', 'e', 'f']
self.arr_s2 = np.array(self.arr_s)
self.arr_dt = [datetime(2008, 1, 1), datetime(2008, 1, 2),
datetime(2008, 1, 3), datetime(2008, 1, 4),
datetime(2008, 1, 5), datetime(2008, 1, 6)]
self.arr_dt2 = np.array(self.arr_dt)
self.arr_colors = ['r', 'g', 'b', 'c', 'm', 'y']
self.arr_rgba = mcolors.colorConverter.to_rgba_array(self.arr_colors)
@raises(ValueError)
def test_bad_first_arg(self):
dmp('a string', self.arr0)
def test_string_seq(self):
actual = dmp(self.arr_s, self.arr1)
ind = [0, 1, 2, 5]
expected = (self.arr_s2.take(ind), self.arr2.take(ind))
assert_array_equal(actual[0], expected[0])
assert_array_equal(actual[1], expected[1])
def test_datetime(self):
actual = dmp(self.arr_dt, self.arr3)
ind = [0, 1, 5]
expected = (self.arr_dt2.take(ind),
self.arr3.take(ind).compressed())
assert_array_equal(actual[0], expected[0])
assert_array_equal(actual[1], expected[1])
def test_rgba(self):
actual = dmp(self.arr3, self.arr_rgba)
ind = [0, 1, 5]
expected = (self.arr3.take(ind).compressed(),
self.arr_rgba.take(ind, axis=0))
assert_array_equal(actual[0], expected[0])
assert_array_equal(actual[1], expected[1])
| gpl-2.0 |
nhuntwalker/astroML | book_figures/chapter9/fig_rrlyrae_lda.py | 3 | 4178 | """
LDA Classification of photometry
--------------------------------
Figure 9.4
The linear discriminant boundary for RR Lyrae stars (see caption of figure 9.3
for details). With all four colors, LDA achieves a completeness of 0.672 and a
contamination of 0.806.
"""
# Author: Jake VanderPlas
# License: BSD
# The figure produced by this code is published in the textbook
# "Statistics, Data Mining, and Machine Learning in Astronomy" (2013)
# For more information, see http://astroML.github.com
# To report a bug or issue, use the following forum:
# https://groups.google.com/forum/#!forum/astroml-general
from __future__ import print_function
import numpy as np
from matplotlib import pyplot as plt
from matplotlib import colors
from sklearn.lda import LDA
from astroML.datasets import fetch_rrlyrae_combined
from astroML.utils import split_samples
from astroML.utils import completeness_contamination
#----------------------------------------------------------------------
# This function adjusts matplotlib settings for a uniform feel in the textbook.
# Note that with usetex=True, fonts are rendered with LaTeX. This may
# result in an error if LaTeX is not installed on your system. In that case,
# you can set usetex to False.
from astroML.plotting import setup_text_plots
setup_text_plots(fontsize=8, usetex=True)
#----------------------------------------------------------------------
# get data and split into training & testing sets
X, y = fetch_rrlyrae_combined()
X = X[:, [1, 0, 2, 3]] # rearrange columns for better 1-color results
(X_train, X_test), (y_train, y_test) = split_samples(X, y, [0.75, 0.25],
random_state=0)
N_tot = len(y)
N_st = np.sum(y == 0)
N_rr = N_tot - N_st
N_train = len(y_train)
N_test = len(y_test)
N_plot = 5000 + N_rr
#----------------------------------------------------------------------
# perform LDA
classifiers = []
predictions = []
Ncolors = np.arange(1, X.shape[1] + 1)
for nc in Ncolors:
clf = LDA()
clf.fit(X_train[:, :nc], y_train)
y_pred = clf.predict(X_test[:, :nc])
classifiers.append(clf)
predictions.append(y_pred)
completeness, contamination = completeness_contamination(predictions, y_test)
print("completeness", completeness)
print("contamination", contamination)
#------------------------------------------------------------
# Compute the decision boundary
clf = classifiers[1]
xlim = (0.7, 1.35)
ylim = (-0.15, 0.4)
xx, yy = np.meshgrid(np.linspace(xlim[0], xlim[1], 71),
np.linspace(ylim[0], ylim[1], 81))
Z = clf.predict_proba(np.c_[yy.ravel(), xx.ravel()])
Z = Z[:, 1].reshape(xx.shape)
#----------------------------------------------------------------------
# plot the results
fig = plt.figure(figsize=(5, 2.5))
fig.subplots_adjust(bottom=0.15, top=0.95, hspace=0.0,
left=0.1, right=0.95, wspace=0.2)
# left plot: data and decision boundary
ax = fig.add_subplot(121)
im = ax.scatter(X[-N_plot:, 1], X[-N_plot:, 0], c=y[-N_plot:],
s=4, lw=0, cmap=plt.cm.binary, zorder=2)
im.set_clim(-0.5, 1)
im = ax.imshow(Z, origin='lower', aspect='auto',
cmap=plt.cm.binary, zorder=1,
extent=xlim + ylim)
im.set_clim(0, 1.5)
ax.contour(xx, yy, Z, [0.5], colors='k')
ax.set_xlim(xlim)
ax.set_ylim(ylim)
ax.set_xlabel('$u-g$')
ax.set_ylabel('$g-r$')
# plot completeness vs Ncolors
ax = fig.add_subplot(222)
ax.plot(Ncolors, completeness, 'o-k', ms=6, label='unweighted')
ax.xaxis.set_major_locator(plt.MultipleLocator(1))
ax.yaxis.set_major_locator(plt.MultipleLocator(0.2))
ax.xaxis.set_major_formatter(plt.NullFormatter())
ax.set_ylabel('completeness')
ax.set_xlim(0.5, 4.5)
ax.set_ylim(-0.1, 1.1)
ax.grid(True)
# plot contamination vs Ncolors
ax = fig.add_subplot(224)
ax.plot(Ncolors, contamination, 'o-k', ms=6, label='unweighted')
ax.xaxis.set_major_locator(plt.MultipleLocator(1))
ax.yaxis.set_major_locator(plt.MultipleLocator(0.2))
ax.xaxis.set_major_formatter(plt.FormatStrFormatter('%i'))
ax.set_xlabel('N colors')
ax.set_ylabel('contamination')
ax.set_xlim(0.5, 4.5)
ax.set_ylim(-0.1, 1.1)
ax.grid(True)
plt.show()
| bsd-2-clause |
endolith/scipy | doc/source/tutorial/stats/plots/kde_plot4.py | 142 | 1457 | from functools import partial
import numpy as np
from scipy import stats
import matplotlib.pyplot as plt
def my_kde_bandwidth(obj, fac=1./5):
"""We use Scott's Rule, multiplied by a constant factor."""
return np.power(obj.n, -1./(obj.d+4)) * fac
loc1, scale1, size1 = (-2, 1, 175)
loc2, scale2, size2 = (2, 0.2, 50)
x2 = np.concatenate([np.random.normal(loc=loc1, scale=scale1, size=size1),
np.random.normal(loc=loc2, scale=scale2, size=size2)])
x_eval = np.linspace(x2.min() - 1, x2.max() + 1, 500)
kde = stats.gaussian_kde(x2)
kde2 = stats.gaussian_kde(x2, bw_method='silverman')
kde3 = stats.gaussian_kde(x2, bw_method=partial(my_kde_bandwidth, fac=0.2))
kde4 = stats.gaussian_kde(x2, bw_method=partial(my_kde_bandwidth, fac=0.5))
pdf = stats.norm.pdf
bimodal_pdf = pdf(x_eval, loc=loc1, scale=scale1) * float(size1) / x2.size + \
pdf(x_eval, loc=loc2, scale=scale2) * float(size2) / x2.size
fig = plt.figure(figsize=(8, 6))
ax = fig.add_subplot(111)
ax.plot(x2, np.zeros(x2.shape), 'b+', ms=12)
ax.plot(x_eval, kde(x_eval), 'k-', label="Scott's Rule")
ax.plot(x_eval, kde2(x_eval), 'b-', label="Silverman's Rule")
ax.plot(x_eval, kde3(x_eval), 'g-', label="Scott * 0.2")
ax.plot(x_eval, kde4(x_eval), 'c-', label="Scott * 0.5")
ax.plot(x_eval, bimodal_pdf, 'r--', label="Actual PDF")
ax.set_xlim([x_eval.min(), x_eval.max()])
ax.legend(loc=2)
ax.set_xlabel('x')
ax.set_ylabel('Density')
plt.show()
| bsd-3-clause |
mjgrav2001/scikit-learn | sklearn/calibration.py | 137 | 18876 | """Calibration of predicted probabilities."""
# Author: Alexandre Gramfort <[email protected]>
# Balazs Kegl <[email protected]>
# Jan Hendrik Metzen <[email protected]>
# Mathieu Blondel <[email protected]>
#
# License: BSD 3 clause
from __future__ import division
import inspect
import warnings
from math import log
import numpy as np
from scipy.optimize import fmin_bfgs
from .base import BaseEstimator, ClassifierMixin, RegressorMixin, clone
from .preprocessing import LabelBinarizer
from .utils import check_X_y, check_array, indexable, column_or_1d
from .utils.validation import check_is_fitted
from .isotonic import IsotonicRegression
from .svm import LinearSVC
from .cross_validation import check_cv
from .metrics.classification import _check_binary_probabilistic_predictions
class CalibratedClassifierCV(BaseEstimator, ClassifierMixin):
"""Probability calibration with isotonic regression or sigmoid.
With this class, the base_estimator is fit on the train set of the
cross-validation generator and the test set is used for calibration.
The probabilities for each of the folds are then averaged
for prediction. In case that cv="prefit" is passed to __init__,
it is it is assumed that base_estimator has been
fitted already and all data is used for calibration. Note that
data for fitting the classifier and for calibrating it must be disjpint.
Read more in the :ref:`User Guide <calibration>`.
Parameters
----------
base_estimator : instance BaseEstimator
The classifier whose output decision function needs to be calibrated
to offer more accurate predict_proba outputs. If cv=prefit, the
classifier must have been fit already on data.
method : 'sigmoid' | 'isotonic'
The method to use for calibration. Can be 'sigmoid' which
corresponds to Platt's method or 'isotonic' which is a
non-parameteric approach. It is not advised to use isotonic calibration
with too few calibration samples (<<1000) since it tends to overfit.
Use sigmoids (Platt's calibration) in this case.
cv : integer or cross-validation generator or "prefit", optional
If an integer is passed, it is the number of folds (default 3).
Specific cross-validation objects can be passed, see
sklearn.cross_validation module for the list of possible objects.
If "prefit" is passed, it is assumed that base_estimator has been
fitted already and all data is used for calibration.
Attributes
----------
classes_ : array, shape (n_classes)
The class labels.
calibrated_classifiers_: list (len() equal to cv or 1 if cv == "prefit")
The list of calibrated classifiers, one for each crossvalidation fold,
which has been fitted on all but the validation fold and calibrated
on the validation fold.
References
----------
.. [1] Obtaining calibrated probability estimates from decision trees
and naive Bayesian classifiers, B. Zadrozny & C. Elkan, ICML 2001
.. [2] Transforming Classifier Scores into Accurate Multiclass
Probability Estimates, B. Zadrozny & C. Elkan, (KDD 2002)
.. [3] Probabilistic Outputs for Support Vector Machines and Comparisons to
Regularized Likelihood Methods, J. Platt, (1999)
.. [4] Predicting Good Probabilities with Supervised Learning,
A. Niculescu-Mizil & R. Caruana, ICML 2005
"""
def __init__(self, base_estimator=None, method='sigmoid', cv=3):
self.base_estimator = base_estimator
self.method = method
self.cv = cv
def fit(self, X, y, sample_weight=None):
"""Fit the calibrated model
Parameters
----------
X : array-like, shape (n_samples, n_features)
Training data.
y : array-like, shape (n_samples,)
Target values.
sample_weight : array-like, shape = [n_samples] or None
Sample weights. If None, then samples are equally weighted.
Returns
-------
self : object
Returns an instance of self.
"""
X, y = check_X_y(X, y, accept_sparse=['csc', 'csr', 'coo'],
force_all_finite=False)
X, y = indexable(X, y)
lb = LabelBinarizer().fit(y)
self.classes_ = lb.classes_
# Check that we each cross-validation fold can have at least one
# example per class
n_folds = self.cv if isinstance(self.cv, int) \
else self.cv.n_folds if hasattr(self.cv, "n_folds") else None
if n_folds and \
np.any([np.sum(y == class_) < n_folds for class_ in self.classes_]):
raise ValueError("Requesting %d-fold cross-validation but provided"
" less than %d examples for at least one class."
% (n_folds, n_folds))
self.calibrated_classifiers_ = []
if self.base_estimator is None:
# we want all classifiers that don't expose a random_state
# to be deterministic (and we don't want to expose this one).
base_estimator = LinearSVC(random_state=0)
else:
base_estimator = self.base_estimator
if self.cv == "prefit":
calibrated_classifier = _CalibratedClassifier(
base_estimator, method=self.method)
if sample_weight is not None:
calibrated_classifier.fit(X, y, sample_weight)
else:
calibrated_classifier.fit(X, y)
self.calibrated_classifiers_.append(calibrated_classifier)
else:
cv = check_cv(self.cv, X, y, classifier=True)
arg_names = inspect.getargspec(base_estimator.fit)[0]
estimator_name = type(base_estimator).__name__
if (sample_weight is not None
and "sample_weight" not in arg_names):
warnings.warn("%s does not support sample_weight. Samples"
" weights are only used for the calibration"
" itself." % estimator_name)
base_estimator_sample_weight = None
else:
base_estimator_sample_weight = sample_weight
for train, test in cv:
this_estimator = clone(base_estimator)
if base_estimator_sample_weight is not None:
this_estimator.fit(
X[train], y[train],
sample_weight=base_estimator_sample_weight[train])
else:
this_estimator.fit(X[train], y[train])
calibrated_classifier = _CalibratedClassifier(
this_estimator, method=self.method)
if sample_weight is not None:
calibrated_classifier.fit(X[test], y[test],
sample_weight[test])
else:
calibrated_classifier.fit(X[test], y[test])
self.calibrated_classifiers_.append(calibrated_classifier)
return self
def predict_proba(self, X):
"""Posterior probabilities of classification
This function returns posterior probabilities of classification
according to each class on an array of test vectors X.
Parameters
----------
X : array-like, shape (n_samples, n_features)
The samples.
Returns
-------
C : array, shape (n_samples, n_classes)
The predicted probas.
"""
check_is_fitted(self, ["classes_", "calibrated_classifiers_"])
X = check_array(X, accept_sparse=['csc', 'csr', 'coo'],
force_all_finite=False)
# Compute the arithmetic mean of the predictions of the calibrated
# classfiers
mean_proba = np.zeros((X.shape[0], len(self.classes_)))
for calibrated_classifier in self.calibrated_classifiers_:
proba = calibrated_classifier.predict_proba(X)
mean_proba += proba
mean_proba /= len(self.calibrated_classifiers_)
return mean_proba
def predict(self, X):
"""Predict the target of new samples. Can be different from the
prediction of the uncalibrated classifier.
Parameters
----------
X : array-like, shape (n_samples, n_features)
The samples.
Returns
-------
C : array, shape (n_samples,)
The predicted class.
"""
check_is_fitted(self, ["classes_", "calibrated_classifiers_"])
return self.classes_[np.argmax(self.predict_proba(X), axis=1)]
class _CalibratedClassifier(object):
"""Probability calibration with isotonic regression or sigmoid.
It assumes that base_estimator has already been fit, and trains the
calibration on the input set of the fit function. Note that this class
should not be used as an estimator directly. Use CalibratedClassifierCV
with cv="prefit" instead.
Parameters
----------
base_estimator : instance BaseEstimator
The classifier whose output decision function needs to be calibrated
to offer more accurate predict_proba outputs. No default value since
it has to be an already fitted estimator.
method : 'sigmoid' | 'isotonic'
The method to use for calibration. Can be 'sigmoid' which
corresponds to Platt's method or 'isotonic' which is a
non-parameteric approach based on isotonic regression.
References
----------
.. [1] Obtaining calibrated probability estimates from decision trees
and naive Bayesian classifiers, B. Zadrozny & C. Elkan, ICML 2001
.. [2] Transforming Classifier Scores into Accurate Multiclass
Probability Estimates, B. Zadrozny & C. Elkan, (KDD 2002)
.. [3] Probabilistic Outputs for Support Vector Machines and Comparisons to
Regularized Likelihood Methods, J. Platt, (1999)
.. [4] Predicting Good Probabilities with Supervised Learning,
A. Niculescu-Mizil & R. Caruana, ICML 2005
"""
def __init__(self, base_estimator, method='sigmoid'):
self.base_estimator = base_estimator
self.method = method
def _preproc(self, X):
n_classes = len(self.classes_)
if hasattr(self.base_estimator, "decision_function"):
df = self.base_estimator.decision_function(X)
if df.ndim == 1:
df = df[:, np.newaxis]
elif hasattr(self.base_estimator, "predict_proba"):
df = self.base_estimator.predict_proba(X)
if n_classes == 2:
df = df[:, 1:]
else:
raise RuntimeError('classifier has no decision_function or '
'predict_proba method.')
idx_pos_class = np.arange(df.shape[1])
return df, idx_pos_class
def fit(self, X, y, sample_weight=None):
"""Calibrate the fitted model
Parameters
----------
X : array-like, shape (n_samples, n_features)
Training data.
y : array-like, shape (n_samples,)
Target values.
sample_weight : array-like, shape = [n_samples] or None
Sample weights. If None, then samples are equally weighted.
Returns
-------
self : object
Returns an instance of self.
"""
lb = LabelBinarizer()
Y = lb.fit_transform(y)
self.classes_ = lb.classes_
df, idx_pos_class = self._preproc(X)
self.calibrators_ = []
for k, this_df in zip(idx_pos_class, df.T):
if self.method == 'isotonic':
calibrator = IsotonicRegression(out_of_bounds='clip')
elif self.method == 'sigmoid':
calibrator = _SigmoidCalibration()
else:
raise ValueError('method should be "sigmoid" or '
'"isotonic". Got %s.' % self.method)
calibrator.fit(this_df, Y[:, k], sample_weight)
self.calibrators_.append(calibrator)
return self
def predict_proba(self, X):
"""Posterior probabilities of classification
This function returns posterior probabilities of classification
according to each class on an array of test vectors X.
Parameters
----------
X : array-like, shape (n_samples, n_features)
The samples.
Returns
-------
C : array, shape (n_samples, n_classes)
The predicted probas. Can be exact zeros.
"""
n_classes = len(self.classes_)
proba = np.zeros((X.shape[0], n_classes))
df, idx_pos_class = self._preproc(X)
for k, this_df, calibrator in \
zip(idx_pos_class, df.T, self.calibrators_):
if n_classes == 2:
k += 1
proba[:, k] = calibrator.predict(this_df)
# Normalize the probabilities
if n_classes == 2:
proba[:, 0] = 1. - proba[:, 1]
else:
proba /= np.sum(proba, axis=1)[:, np.newaxis]
# XXX : for some reason all probas can be 0
proba[np.isnan(proba)] = 1. / n_classes
# Deal with cases where the predicted probability minimally exceeds 1.0
proba[(1.0 < proba) & (proba <= 1.0 + 1e-5)] = 1.0
return proba
def _sigmoid_calibration(df, y, sample_weight=None):
"""Probability Calibration with sigmoid method (Platt 2000)
Parameters
----------
df : ndarray, shape (n_samples,)
The decision function or predict proba for the samples.
y : ndarray, shape (n_samples,)
The targets.
sample_weight : array-like, shape = [n_samples] or None
Sample weights. If None, then samples are equally weighted.
Returns
-------
a : float
The slope.
b : float
The intercept.
References
----------
Platt, "Probabilistic Outputs for Support Vector Machines"
"""
df = column_or_1d(df)
y = column_or_1d(y)
F = df # F follows Platt's notations
tiny = np.finfo(np.float).tiny # to avoid division by 0 warning
# Bayesian priors (see Platt end of section 2.2)
prior0 = float(np.sum(y <= 0))
prior1 = y.shape[0] - prior0
T = np.zeros(y.shape)
T[y > 0] = (prior1 + 1.) / (prior1 + 2.)
T[y <= 0] = 1. / (prior0 + 2.)
T1 = 1. - T
def objective(AB):
# From Platt (beginning of Section 2.2)
E = np.exp(AB[0] * F + AB[1])
P = 1. / (1. + E)
l = -(T * np.log(P + tiny) + T1 * np.log(1. - P + tiny))
if sample_weight is not None:
return (sample_weight * l).sum()
else:
return l.sum()
def grad(AB):
# gradient of the objective function
E = np.exp(AB[0] * F + AB[1])
P = 1. / (1. + E)
TEP_minus_T1P = P * (T * E - T1)
if sample_weight is not None:
TEP_minus_T1P *= sample_weight
dA = np.dot(TEP_minus_T1P, F)
dB = np.sum(TEP_minus_T1P)
return np.array([dA, dB])
AB0 = np.array([0., log((prior0 + 1.) / (prior1 + 1.))])
AB_ = fmin_bfgs(objective, AB0, fprime=grad, disp=False)
return AB_[0], AB_[1]
class _SigmoidCalibration(BaseEstimator, RegressorMixin):
"""Sigmoid regression model.
Attributes
----------
a_ : float
The slope.
b_ : float
The intercept.
"""
def fit(self, X, y, sample_weight=None):
"""Fit the model using X, y as training data.
Parameters
----------
X : array-like, shape (n_samples,)
Training data.
y : array-like, shape (n_samples,)
Training target.
sample_weight : array-like, shape = [n_samples] or None
Sample weights. If None, then samples are equally weighted.
Returns
-------
self : object
Returns an instance of self.
"""
X = column_or_1d(X)
y = column_or_1d(y)
X, y = indexable(X, y)
self.a_, self.b_ = _sigmoid_calibration(X, y, sample_weight)
return self
def predict(self, T):
"""Predict new data by linear interpolation.
Parameters
----------
T : array-like, shape (n_samples,)
Data to predict from.
Returns
-------
T_ : array, shape (n_samples,)
The predicted data.
"""
T = column_or_1d(T)
return 1. / (1. + np.exp(self.a_ * T + self.b_))
def calibration_curve(y_true, y_prob, normalize=False, n_bins=5):
"""Compute true and predicted probabilities for a calibration curve.
Read more in the :ref:`User Guide <calibration>`.
Parameters
----------
y_true : array, shape (n_samples,)
True targets.
y_prob : array, shape (n_samples,)
Probabilities of the positive class.
normalize : bool, optional, default=False
Whether y_prob needs to be normalized into the bin [0, 1], i.e. is not
a proper probability. If True, the smallest value in y_prob is mapped
onto 0 and the largest one onto 1.
n_bins : int
Number of bins. A bigger number requires more data.
Returns
-------
prob_true : array, shape (n_bins,)
The true probability in each bin (fraction of positives).
prob_pred : array, shape (n_bins,)
The mean predicted probability in each bin.
References
----------
Alexandru Niculescu-Mizil and Rich Caruana (2005) Predicting Good
Probabilities With Supervised Learning, in Proceedings of the 22nd
International Conference on Machine Learning (ICML).
See section 4 (Qualitative Analysis of Predictions).
"""
y_true = column_or_1d(y_true)
y_prob = column_or_1d(y_prob)
if normalize: # Normalize predicted values into interval [0, 1]
y_prob = (y_prob - y_prob.min()) / (y_prob.max() - y_prob.min())
elif y_prob.min() < 0 or y_prob.max() > 1:
raise ValueError("y_prob has values outside [0, 1] and normalize is "
"set to False.")
y_true = _check_binary_probabilistic_predictions(y_true, y_prob)
bins = np.linspace(0., 1. + 1e-8, n_bins + 1)
binids = np.digitize(y_prob, bins) - 1
bin_sums = np.bincount(binids, weights=y_prob, minlength=len(bins))
bin_true = np.bincount(binids, weights=y_true, minlength=len(bins))
bin_total = np.bincount(binids, minlength=len(bins))
nonzero = bin_total != 0
prob_true = (bin_true[nonzero] / bin_total[nonzero])
prob_pred = (bin_sums[nonzero] / bin_total[nonzero])
return prob_true, prob_pred
| bsd-3-clause |
PyPSA/PyPSA | examples/new_components/add_components_from_library.py | 1 | 1494 |
#Use the component_library to add components.
import component_library
import pandas as pd
n = component_library.Network()
n.set_snapshots(list(range(4)))
places = pd.Index(["Aarhus","Aalborg","Copenhagen"])
n.madd("Bus", places)
n.madd("Load", places, bus=places, p_set=10.5)
n.madd("Bus",places + " heat")
n.madd("Load", places+ " heat", bus=places+ " heat", p_set=4.2)
n.loads_t.p_set["Aalborg heat"] = 3.7
n.madd("Bus",places + " gas")
n.madd("Store",places + " gas",
bus=places + " gas",
e_min_pu=-1,
marginal_cost=50,
e_nom_extendable=True)
#Some HVDC transmission lines
n.madd("Link",
[0,1],
bus0="Aarhus",
bus1=["Aalborg","Copenhagen"],
p_nom_extendable=True,
p_min_pu=-1,
capital_cost=1e2)
n.add("Generator",
"wind",
p_nom_extendable=True,
capital_cost=1e2,
bus="Aalborg")
n.madd("CHP",
places + " CHP",
bus_source=places + " gas",
bus_elec=places,
bus_heat=places + " heat",
p_nom_extendable=True,
capital_cost=1.4e4,
eta_elec=0.468,
c_v=0.15,
c_m=0.75,
p_nom_ratio=1.5)
print("\nn.chps:\n")
print(n.chps)
n.lopf()
print("\nGenerator capacity:\n")
print(n.generators.p_nom_opt)
print("\nLine capacities:\n")
print(n.links.loc[["0","1"],"p_nom_opt"])
print("\nCHP electrical output:\n")
print(n.links.loc[places + " CHP electric","p_nom_opt"]*n.links.loc[places + " CHP electric","efficiency"])
| gpl-3.0 |
keflavich/mpld3 | doc/sphinxext/numpy_ext/docscrape_sphinx.py | 22 | 7924 | import re, inspect, textwrap, pydoc
import sphinx
from docscrape import NumpyDocString, FunctionDoc, ClassDoc
class SphinxDocString(NumpyDocString):
def __init__(self, docstring, config=None):
config = {} if config is None else config
self.use_plots = config.get('use_plots', False)
NumpyDocString.__init__(self, docstring, config=config)
# string conversion routines
def _str_header(self, name, symbol='`'):
return ['.. rubric:: ' + name, '']
def _str_field_list(self, name):
return [':' + name + ':']
def _str_indent(self, doc, indent=4):
out = []
for line in doc:
out += [' '*indent + line]
return out
def _str_signature(self):
return ['']
if self['Signature']:
return ['``%s``' % self['Signature']] + ['']
else:
return ['']
def _str_summary(self):
return self['Summary'] + ['']
def _str_extended_summary(self):
return self['Extended Summary'] + ['']
def _str_param_list(self, name):
out = []
if self[name]:
out += self._str_field_list(name)
out += ['']
for param,param_type,desc in self[name]:
out += self._str_indent(['**%s** : %s' % (param.strip(),
param_type)])
out += ['']
out += self._str_indent(desc,8)
out += ['']
return out
@property
def _obj(self):
if hasattr(self, '_cls'):
return self._cls
elif hasattr(self, '_f'):
return self._f
return None
def _str_member_list(self, name):
"""
Generate a member listing, autosummary:: table where possible,
and a table where not.
"""
out = []
if self[name]:
out += ['.. rubric:: %s' % name, '']
prefix = getattr(self, '_name', '')
if prefix:
prefix = '~%s.' % prefix
autosum = []
others = []
for param, param_type, desc in self[name]:
param = param.strip()
if not self._obj or hasattr(self._obj, param):
autosum += [" %s%s" % (prefix, param)]
else:
others.append((param, param_type, desc))
if autosum:
# GAEL: Toctree commented out below because it creates
# hundreds of sphinx warnings
# out += ['.. autosummary::', ' :toctree:', '']
out += ['.. autosummary::', '']
out += autosum
if others:
maxlen_0 = max([len(x[0]) for x in others])
maxlen_1 = max([len(x[1]) for x in others])
hdr = "="*maxlen_0 + " " + "="*maxlen_1 + " " + "="*10
fmt = '%%%ds %%%ds ' % (maxlen_0, maxlen_1)
n_indent = maxlen_0 + maxlen_1 + 4
out += [hdr]
for param, param_type, desc in others:
out += [fmt % (param.strip(), param_type)]
out += self._str_indent(desc, n_indent)
out += [hdr]
out += ['']
return out
def _str_section(self, name):
out = []
if self[name]:
out += self._str_header(name)
out += ['']
content = textwrap.dedent("\n".join(self[name])).split("\n")
out += content
out += ['']
return out
def _str_see_also(self, func_role):
out = []
if self['See Also']:
see_also = super(SphinxDocString, self)._str_see_also(func_role)
out = ['.. seealso::', '']
out += self._str_indent(see_also[2:])
return out
def _str_warnings(self):
out = []
if self['Warnings']:
out = ['.. warning::', '']
out += self._str_indent(self['Warnings'])
return out
def _str_index(self):
idx = self['index']
out = []
if len(idx) == 0:
return out
out += ['.. index:: %s' % idx.get('default','')]
for section, references in idx.iteritems():
if section == 'default':
continue
elif section == 'refguide':
out += [' single: %s' % (', '.join(references))]
else:
out += [' %s: %s' % (section, ','.join(references))]
return out
def _str_references(self):
out = []
if self['References']:
out += self._str_header('References')
if isinstance(self['References'], str):
self['References'] = [self['References']]
out.extend(self['References'])
out += ['']
# Latex collects all references to a separate bibliography,
# so we need to insert links to it
if sphinx.__version__ >= "0.6":
out += ['.. only:: latex','']
else:
out += ['.. latexonly::','']
items = []
for line in self['References']:
m = re.match(r'.. \[([a-z0-9._-]+)\]', line, re.I)
if m:
items.append(m.group(1))
out += [' ' + ", ".join(["[%s]_" % item for item in items]), '']
return out
def _str_examples(self):
examples_str = "\n".join(self['Examples'])
if (self.use_plots and 'import matplotlib' in examples_str
and 'plot::' not in examples_str):
out = []
out += self._str_header('Examples')
out += ['.. plot::', '']
out += self._str_indent(self['Examples'])
out += ['']
return out
else:
return self._str_section('Examples')
def __str__(self, indent=0, func_role="obj"):
out = []
out += self._str_signature()
out += self._str_index() + ['']
out += self._str_summary()
out += self._str_extended_summary()
for param_list in ('Parameters', 'Returns', 'Raises'):
out += self._str_param_list(param_list)
out += self._str_warnings()
out += self._str_see_also(func_role)
out += self._str_section('Notes')
out += self._str_references()
out += self._str_examples()
for param_list in ('Attributes', 'Methods'):
out += self._str_member_list(param_list)
out = self._str_indent(out,indent)
return '\n'.join(out)
class SphinxFunctionDoc(SphinxDocString, FunctionDoc):
def __init__(self, obj, doc=None, config={}):
self.use_plots = config.get('use_plots', False)
FunctionDoc.__init__(self, obj, doc=doc, config=config)
class SphinxClassDoc(SphinxDocString, ClassDoc):
def __init__(self, obj, doc=None, func_doc=None, config={}):
self.use_plots = config.get('use_plots', False)
ClassDoc.__init__(self, obj, doc=doc, func_doc=None, config=config)
class SphinxObjDoc(SphinxDocString):
def __init__(self, obj, doc=None, config=None):
self._f = obj
SphinxDocString.__init__(self, doc, config=config)
def get_doc_object(obj, what=None, doc=None, config={}):
if what is None:
if inspect.isclass(obj):
what = 'class'
elif inspect.ismodule(obj):
what = 'module'
elif callable(obj):
what = 'function'
else:
what = 'object'
if what == 'class':
return SphinxClassDoc(obj, func_doc=SphinxFunctionDoc, doc=doc,
config=config)
elif what in ('function', 'method'):
return SphinxFunctionDoc(obj, doc=doc, config=config)
else:
if doc is None:
doc = pydoc.getdoc(obj)
return SphinxObjDoc(obj, doc, config=config)
| bsd-3-clause |
shijx12/DeepSim | lib/roi_data_layer/minibatch2.py | 5 | 13350 | # --------------------------------------------------------
# Fast R-CNN
# Copyright (c) 2015 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Ross Girshick
# --------------------------------------------------------
"""Compute minibatch blobs for training a Fast R-CNN network."""
import numpy as np
import numpy.random as npr
import cv2
# TODO: make fast_rcnn irrelevant
# >>>> obsolete, because it depends on sth outside of this project
from ..fast_rcnn.config import cfg
# <<<< obsolete
from ..utils.blob import prep_im_for_blob, im_list_to_blob
def get_minibatch(roidb, num_classes):
"""Given a roidb, construct a minibatch sampled from it."""
num_images = len(roidb)
assert(cfg.TRAIN.BATCH_SIZE % num_images == 0), \
'num_images ({}) must divide BATCH_SIZE ({})'. \
format(num_images, cfg.TRAIN.BATCH_SIZE)
rois_per_image = cfg.TRAIN.BATCH_SIZE / num_images
fg_rois_per_image = np.round(cfg.TRAIN.FG_FRACTION * rois_per_image)
if cfg.IS_MULTISCALE:
im_blob, im_scales = _get_image_blob_multiscale(roidb)
else:
# Get the input image blob, formatted for caffe
# Sample random scales to use for each image in this batch
random_scale_inds = npr.randint(0, high=len(cfg.TRAIN.SCALES_BASE), size=num_images)
im_blob, im_scales = _get_image_blob(roidb, random_scale_inds)
blobs = {'data': im_blob}
if cfg.TRAIN.HAS_RPN:
assert len(im_scales) == 1, "Single batch only"
assert len(roidb) == 1, "Single batch only"
# gt boxes: (x1, y1, x2, y2, cls)
gt_inds = np.where(roidb[0]['gt_classes'] != 0)[0]
gt_boxes = np.empty((len(gt_inds), 5), dtype=np.float32)
gt_boxes[:, 0:4] = roidb[0]['boxes'][gt_inds, :] * im_scales[0]
gt_boxes[:, 4] = roidb[0]['gt_classes'][gt_inds]
blobs['gt_boxes'] = gt_boxes
blobs['im_info'] = np.array(
[[im_blob.shape[1], im_blob.shape[2], im_scales[0]]],
dtype=np.float32)
else:
# Now, build the region of interest and label blobs
rois_blob = np.zeros((0, 5), dtype=np.float32)
labels_blob = np.zeros((0), dtype=np.float32)
bbox_targets_blob = np.zeros((0, 4 * num_classes), dtype=np.float32)
bbox_inside_blob = np.zeros(bbox_targets_blob.shape, dtype=np.float32)
# all_overlaps = []
for im_i in xrange(num_images):
labels, overlaps, im_rois, bbox_targets, bbox_inside_weights, sublabels \
= _sample_rois(roidb[im_i], fg_rois_per_image, rois_per_image, num_classes)
# Add to RoIs blob
if cfg.IS_MULTISCALE:
if cfg.IS_EXTRAPOLATING:
rois, levels = _project_im_rois_multiscale(im_rois, cfg.TRAIN.SCALES)
batch_ind = im_i * len(cfg.TRAIN.SCALES) + levels
else:
rois, levels = _project_im_rois_multiscale(im_rois, cfg.TRAIN.SCALES_BASE)
batch_ind = im_i * len(cfg.TRAIN.SCALES_BASE) + levels
else:
rois = _project_im_rois(im_rois, im_scales[im_i])
batch_ind = im_i * np.ones((rois.shape[0], 1))
rois_blob_this_image = np.hstack((batch_ind, rois))
rois_blob = np.vstack((rois_blob, rois_blob_this_image))
# Add to labels, bbox targets, and bbox loss blobs
labels_blob = np.hstack((labels_blob, labels))
bbox_targets_blob = np.vstack((bbox_targets_blob, bbox_targets))
bbox_inside_blob = np.vstack((bbox_inside_blob, bbox_inside_weights))
# all_overlaps = np.hstack((all_overlaps, overlaps))
# For debug visualizations
# _vis_minibatch(im_blob, rois_blob, labels_blob, all_overlaps, sublabels_blob, view_targets_blob, view_inside_blob)
# _vis_minibatch(im_blob, rois_blob, labels_blob, all_overlaps, sublabels_blob)
blobs['rois'] = rois_blob
blobs['labels'] = labels_blob
if cfg.TRAIN.BBOX_REG:
blobs['bbox_targets'] = bbox_targets_blob
blobs['bbox_inside_weights'] = bbox_inside_blob
blobs['bbox_outside_weights'] = np.array(bbox_inside_blob > 0).astype(np.float32)
return blobs
def _sample_rois(roidb, fg_rois_per_image, rois_per_image, num_classes):
"""Generate a random sample of RoIs comprising foreground and background
examples.
"""
# label = class RoI has max overlap with
labels = roidb['max_classes']
overlaps = roidb['max_overlaps']
rois = roidb['boxes']
# Select foreground RoIs as those with >= FG_THRESH overlap
fg_inds = []
for i in xrange(1, num_classes):
fg_inds.extend(np.where((labels == i) & (overlaps >= cfg.TRAIN.FG_THRESH))[0])
fg_inds = np.array(fg_inds)
# Guard against the case when an image has fewer than fg_rois_per_image
# foreground RoIs
fg_rois_per_this_image = np.minimum(fg_rois_per_image, fg_inds.size)
# Sample foreground regions without replacement
if fg_inds.size > 0:
fg_inds = npr.choice(fg_inds, size=fg_rois_per_this_image,
replace=False)
bg_rois_per_this_image = rois_per_image - fg_rois_per_this_image
# Select background RoIs as those within [BG_THRESH_LO, BG_THRESH_HI)
bg_inds = []
for i in xrange(1, num_classes):
bg_inds.extend( np.where((labels == i) & (overlaps < cfg.TRAIN.BG_THRESH_HI) &
(overlaps >= cfg.TRAIN.BG_THRESH_LO))[0] )
if len(bg_inds) < bg_rois_per_this_image:
for i in xrange(1, num_classes):
bg_inds.extend( np.where((labels == i) & (overlaps < cfg.TRAIN.BG_THRESH_HI))[0] )
if len(bg_inds) < bg_rois_per_this_image:
bg_inds.extend( np.where(overlaps < cfg.TRAIN.BG_THRESH_HI)[0] )
bg_inds = np.array(bg_inds, dtype=np.int32)
# Compute number of background RoIs to take from this image (guarding
# against there being fewer than desired)
bg_rois_per_this_image = np.minimum(bg_rois_per_this_image,
bg_inds.size)
# Sample foreground regions without replacement
if bg_inds.size > 0:
bg_inds = npr.choice(bg_inds, size=bg_rois_per_this_image,
replace=False)
# The indices that we're selecting (both fg and bg)
keep_inds = np.append(fg_inds, bg_inds).astype(int)
# print '{} foregrounds and {} backgrounds'.format(fg_inds.size, bg_inds.size)
# Select sampled values from various arrays:
labels = labels[keep_inds]
# Clamp labels for the background RoIs to 0
labels[fg_rois_per_this_image:] = 0
overlaps = overlaps[keep_inds]
rois = rois[keep_inds]
sublabels = sublabels[keep_inds]
sublabels[fg_rois_per_this_image:] = 0
bbox_targets, bbox_loss_weights = \
_get_bbox_regression_labels(roidb['bbox_targets'][keep_inds, :],
num_classes)
if cfg.TRAIN.VIEWPOINT or cfg.TEST.VIEWPOINT:
viewpoints = viewpoints[keep_inds]
view_targets, view_loss_weights = \
_get_viewpoint_estimation_labels(viewpoints, labels, num_classes)
return labels, overlaps, rois, bbox_targets, bbox_loss_weights, sublabels, view_targets, view_loss_weights
return labels, overlaps, rois, bbox_targets, bbox_loss_weights, sublabels
def _get_image_blob(roidb, scale_inds):
"""Builds an input blob from the images in the roidb at the specified
scales.
"""
num_images = len(roidb)
processed_ims = []
im_scales = []
for i in xrange(num_images):
im = cv2.imread(roidb[i]['image'])
if roidb[i]['flipped']:
im = im[:, ::-1, :]
im_orig = im.astype(np.float32, copy=True)
im_orig -= cfg.PIXEL_MEANS
im_scale = cfg.TRAIN.SCALES_BASE[scale_inds[i]]
im = cv2.resize(im_orig, None, None, fx=im_scale, fy=im_scale, interpolation=cv2.INTER_LINEAR)
im_scales.append(im_scale)
processed_ims.append(im)
# Create a blob to hold the input images
blob = im_list_to_blob(processed_ims)
return blob, im_scales
def _get_image_blob_multiscale(roidb):
"""Builds an input blob from the images in the roidb at multiscales.
"""
num_images = len(roidb)
processed_ims = []
im_scales = []
scales = cfg.TRAIN.SCALES_BASE
for i in xrange(num_images):
im = cv2.imread(roidb[i]['image'])
if roidb[i]['flipped']:
im = im[:, ::-1, :]
im_orig = im.astype(np.float32, copy=True)
im_orig -= cfg.PIXEL_MEANS
for im_scale in scales:
im = cv2.resize(im_orig, None, None, fx=im_scale, fy=im_scale, interpolation=cv2.INTER_LINEAR)
im_scales.append(im_scale)
processed_ims.append(im)
# Create a blob to hold the input images
blob = im_list_to_blob(processed_ims)
return blob, im_scales
def _project_im_rois(im_rois, im_scale_factor):
"""Project image RoIs into the rescaled training image."""
rois = im_rois * im_scale_factor
return rois
def _project_im_rois_multiscale(im_rois, scales):
"""Project image RoIs into the image pyramid built by _get_image_blob.
Arguments:
im_rois (ndarray): R x 4 matrix of RoIs in original image coordinates
scales (list): scale factors as returned by _get_image_blob
Returns:
rois (ndarray): R x 4 matrix of projected RoI coordinates
levels (list): image pyramid levels used by each projected RoI
"""
im_rois = im_rois.astype(np.float, copy=False)
scales = np.array(scales)
if len(scales) > 1:
widths = im_rois[:, 2] - im_rois[:, 0] + 1
heights = im_rois[:, 3] - im_rois[:, 1] + 1
areas = widths * heights
scaled_areas = areas[:, np.newaxis] * (scales[np.newaxis, :] ** 2)
diff_areas = np.abs(scaled_areas - 224 * 224)
levels = diff_areas.argmin(axis=1)[:, np.newaxis]
else:
levels = np.zeros((im_rois.shape[0], 1), dtype=np.int)
rois = im_rois * scales[levels]
return rois, levels
def _get_bbox_regression_labels(bbox_target_data, num_classes):
"""Bounding-box regression targets are stored in a compact form in the
roidb.
This function expands those targets into the 4-of-4*K representation used
by the network (i.e. only one class has non-zero targets). The loss weights
are similarly expanded.
Returns:
bbox_target_data (ndarray): N x 4K blob of regression targets
bbox_loss_weights (ndarray): N x 4K blob of loss weights
"""
clss = bbox_target_data[:, 0]
bbox_targets = np.zeros((clss.size, 4 * num_classes), dtype=np.float32)
bbox_loss_weights = np.zeros(bbox_targets.shape, dtype=np.float32)
inds = np.where(clss > 0)[0]
for ind in inds:
cls = clss[ind]
start = 4 * cls
end = start + 4
bbox_targets[ind, start:end] = bbox_target_data[ind, 1:]
bbox_loss_weights[ind, start:end] = [1., 1., 1., 1.]
return bbox_targets, bbox_loss_weights
def _get_viewpoint_estimation_labels(viewpoint_data, clss, num_classes):
"""Bounding-box regression targets are stored in a compact form in the
roidb.
This function expands those targets into the 4-of-4*K representation used
by the network (i.e. only one class has non-zero targets). The loss weights
are similarly expanded.
Returns:
view_target_data (ndarray): N x 3K blob of regression targets
view_loss_weights (ndarray): N x 3K blob of loss weights
"""
view_targets = np.zeros((clss.size, 3 * num_classes), dtype=np.float32)
view_loss_weights = np.zeros(view_targets.shape, dtype=np.float32)
inds = np.where( (clss > 0) & np.isfinite(viewpoint_data[:,0]) & np.isfinite(viewpoint_data[:,1]) & np.isfinite(viewpoint_data[:,2]) )[0]
for ind in inds:
cls = clss[ind]
start = 3 * cls
end = start + 3
view_targets[ind, start:end] = viewpoint_data[ind, :]
view_loss_weights[ind, start:end] = [1., 1., 1.]
assert not np.isinf(view_targets).any(), 'viewpoint undefined'
return view_targets, view_loss_weights
def _vis_minibatch(im_blob, rois_blob, labels_blob, overlaps, sublabels_blob, view_targets_blob=None, view_inside_blob=None):
"""Visualize a mini-batch for debugging."""
import matplotlib.pyplot as plt
import math
for i in xrange(min(rois_blob.shape[0], 10)):
rois = rois_blob[i, :]
im_ind = rois[0]
roi = rois[1:]
im = im_blob[im_ind, :, :, :].transpose((1, 2, 0)).copy()
im += cfg.PIXEL_MEANS
im = im[:, :, (2, 1, 0)]
im = im.astype(np.uint8)
cls = labels_blob[i]
subcls = sublabels_blob[i]
plt.imshow(im)
print 'class: ', cls, ' subclass: ', subcls, ' overlap: ', overlaps[i]
start = 3 * cls
end = start + 3
# print 'view: ', view_targets_blob[i, start:end] * 180 / math.pi
# print 'view weights: ', view_inside_blob[i, start:end]
plt.gca().add_patch(
plt.Rectangle((roi[0], roi[1]), roi[2] - roi[0],
roi[3] - roi[1], fill=False,
edgecolor='r', linewidth=3)
)
plt.show()
| mit |
joshrule/LOTlib | LOTlib/Projects/FormalLanguageTheory/parse_hypothesis.py | 2 | 47335 | from pickle import load, dump
from collections import Counter
from LOTlib.Eval import register_primitive
from LOTlib.Miscellaneous import flatten2str
import numpy as np
from LOTlib.Projects.FormalLanguageTheory.Language.SimpleEnglish import SimpleEnglish
register_primitive(flatten2str)
import matplotlib.pyplot as plt
from os import listdir
from LOTlib.Projects.FormalLanguageTheory.Language.AnBn import AnBn
from LOTlib.Projects.FormalLanguageTheory.Language.LongDependency import LongDependency
import time
from LOTlib.DataAndObjects import FunctionData
from LOTlib.Miscellaneous import logsumexp
from LOTlib.Miscellaneous import Infinity
from math import log
import sys
from mpi4py import MPI
from Simulation.utils import uniform_data
from optparse import OptionParser
from Language.Index import instance
import pstats, cProfile
import os
from copy import deepcopy
comm = MPI.COMM_WORLD
rank = comm.Get_rank()
size = comm.Get_size()
suffix = time.strftime('_%m%d_%H%M%S', time.localtime())
fff = sys.stdout.flush
parser = OptionParser()
parser.add_option("--jump", dest="JUMP", type="int", default=2, help="")
parser.add_option("--temp", dest="TEMP", type="int", default=1, help="")
parser.add_option("--plot", dest="PLOT", type="string", default='yes', help="")
parser.add_option("--file", dest="FILE", type="string", default='', help="")
parser.add_option("--mode", dest="MODE", type="string", default='', help="")
parser.add_option("--axb", dest="AXB", type="float", default=0.3, help="")
parser.add_option("--x", dest="X", type="float", default=0.08, help="")
parser.add_option("--language", dest="LANG", type="string", default='An', help="")
parser.add_option("--finite", dest="FINITE", type="int", default=10, help="")
parser.add_option("--wfs", dest="WFS", type="string", default='yes', help="")
parser.add_option("--prob", dest="PROB", type="string", default='yes', help="")
parser.add_option("--stype", dest="STYPE", type="string", default='cube', help="")
parser.add_option("--dtype", dest="DTYPE", type="string", default='staged', help="")
(options, args) = parser.parse_args()
def slice_list(input, size):
input_size = len(input)
slice_size = input_size / size
remain = input_size % size
result = []
iterator = iter(input)
for i in range(size):
result.append([])
for j in range(int(slice_size)):
result[i].append(iterator.next())
if remain:
result[i].append(iterator.next())
remain -= 1
return result
def load_hypo(_dir, keys):
"""
1. read raw output file
run: serial
"""
rec = []
for name in listdir(_dir):
flag = False
for e in keys:
if e in name: flag = True; break
if not flag: continue
print name; fff()
rec.append([int(name.split('_')[1]), load(open(_dir+name, 'r'))])
return rec
# ===========================================================================
# prase SimpleEnglish
# ===========================================================================
def most_prob(suffix):
topn = load(open('out/SimpleEnglish/hypotheses__' + suffix))
Z = logsumexp([h.posterior_score for h in topn])
h_set = [h for h in topn]; h_set.sort(key=lambda x: x.posterior_score, reverse=True)
for i in xrange(10):
print h_set[i]
print 'prob`: ', np.exp(h_set[i].posterior_score - Z)
print Counter([h_set[i]() for _ in xrange(512)])
# ===========================================================================
# staged & skewed
# ===========================================================================
def pos_seq(rec, infos):
"""
1. evaluate posterior probability on different data sizes
run: serial
"""
seq = []
seen = set()
for e in rec:
for h in e[1]:
if h in seen: continue
seen.add(h)
for e in infos:
prob_dict = {}
language = AnBn(max_length=e[1])
eval_data = language.sample_data_as_FuncData(e[0])
for h in seen: prob_dict[h] = h.compute_posterior(eval_data)
seq.append(prob_dict)
print e, 'done'; fff()
print '='*50
return seq
def compute_kl(current_dict, next_dict):
current_Z = logsumexp([v for h, v in current_dict.iteritems()])
next_Z = logsumexp([v for h, v in next_dict.iteritems()])
kl = 0.0
for h, v in current_dict.iteritems():
p = np.exp(v - current_Z)
if p == 0: continue
kl += p * (v - next_dict[h] + next_Z - current_Z)
return kl
def get_kl_seq():
"""
1. read posterior sequences
2. compute KL-divergence between adjacent distribution
3. plot
run: serial
"""
print 'loading..'; fff()
seq_set = [load(open('seq0_0825_234606')), load(open('seq1_0825_234606')), load(open('seq2_0825_234606'))]
# seq_set = load(open('seq0_0825_195540')) + load(open('seq1_0825_195540')) + load(open('seq2_0825_195540'))
kl_seq_set = []
print 'compute prior..'; fff()
# add posterior set that without observing any data
from copy import deepcopy
dict_0 = deepcopy(seq_set[0][0])
for h in dict_0:
dict_0[h] = h.compute_posterior([FunctionData(input=[], output=Counter())])
print 'making plot..'; fff()
for seq in seq_set:
kl_seq = []
# # add posterior set that without observing any data
# from copy import deepcopy
# dict_0 = deepcopy(seq[0])
# for h in dict_0:
# dict_0[h] = h.compute_posterior([FunctionData(input=[], output=Counter())])
seq.insert(0, dict_0)
# compute kl
for i in xrange(len(seq)-1):
current_dict = seq[i]
next_dict = seq[i+1]
kl = compute_kl(current_dict, next_dict)
kl_seq.append(log(kl))
print 'KL from %i to %i: ' % (i, i+1), kl; fff()
kl_seq_set.append(kl_seq)
print '='*50; fff()
staged, = plt.plot(range(12, 145, 2), kl_seq_set[0], label='staged')
normal, = plt.plot(range(12, 145, 2), kl_seq_set[1], label='normal')
uniform, = plt.plot(range(12, 145, 2), kl_seq_set[2], label='uniform')
plt.legend(handles=[normal, staged, uniform])
plt.ylabel('KL-divergence')
plt.xlabel('data')
plt.show()
def get_kl_seq2(jump, is_plot, file_name):
print 'loading..'; fff()
# TODO
seq_set = [load(open('seq%i' % i + file_name)) for i in xrange(3)]
kl_seq_set = []
# print 'prior'
# from copy import deepcopy
# dict_0 = deepcopy(seq_set[0][0])
# for h in dict_0:
# dict_0[h] = h.compute_posterior([FunctionData(input=[], output=Counter())])
# avg_seq_set = []
# for i in [4, 8, 12]:
# for j in xrange(i-4, i):
# sub = [seq_set[j] for j in xrange(i-4, i)]
# avg_seq_set.append([(a+b+c+d)/4 for a, b, c, d in zip(*sub)])
for seq in seq_set:
# seq.insert(0, dict_0)
kl_seq = []
kl_sum = 0
for i in xrange(len(seq)-1):
current_dict = seq[i]
next_dict = seq[i+1]
kl = compute_kl(current_dict, next_dict)
kl_sum += kl; kl_seq.append(kl_sum)
print 'KL from %i to %i: ' % (i, i+1), kl; fff()
f = open('tmp_out', 'a')
print >> f, kl_sum
kl_seq_set.append(kl_seq)
print '='*50; fff()
f = open('tmp_out', 'a')
print >> f, '='*50
# print 'avging'; fff()
# avg_seq_set = []
# for i in [4, 8, 12]:
# sub = [kl_seq_set[j] for j in xrange(i-4, i)]
# avg_seq_set.append([(a+b+c+d)/4 for a, b, c, d in zip(*sub)])
#
# for avg_seq in avg_seq_set:
# for i in xrange(1, len(avg_seq)):
# avg_seq[i] = logsumexp([avg_seq[i], avg_seq[i-1]])
#
# dump(kl_seq_set, open('kl_seq_set'+suffix, 'w'))
# dump(avg_seq_set, open('avg_seq_set'+suffix, 'w'))
#
if is_plot == 'yes':
staged, = plt.plot([10**e for e in np.arange(0, 2.5, 0.1)], kl_seq_set[0], label='SI')
normal, = plt.plot([10**e for e in np.arange(0, 2.5, 0.1)], kl_seq_set[1], label='SF')
uniform, = plt.plot([10**e for e in np.arange(0, 2.5, 0.1)], kl_seq_set[2], label='RC')
plt.legend(handles=[normal, staged, uniform])
plt.ylabel('Cumulative KL-divergence')
plt.xlabel('Size of Data')
# plt.title('SS and SF')
plt.show()
def make_staged_seq():
"""
1. read raw output
2. evaluate posterior probability on different data sizes
3. dump the sequence
run: mpiexec -n 3
"""
infos = [[i, 4*((i-1)/48+1)] for i in xrange(12, 145, 2)]
# infos = [[i, 12] for i in xrange(145, 278, 2)]
work_list = [['staged'], ['normal0'], ['normal1']]
rec = load_hypo('out/simulations/staged/', work_list[rank])
seq = pos_seq(rec, infos)
dump([seq], open('seq'+str(rank)+suffix, 'w'))
def make_staged_seq2(jump, temp):
"""
run: mpiexec -n 12
"""
rec = load_hypo('out/simulations/staged/', ['staged', 'normal0', 'normal1'])
seen = set()
work_list = slice_list(range(size), 3)
for e in rec:
for h in e[1]:
if h in seen: continue
seen.add(h)
if rank in work_list[0]:
seq = []
infos = [[i, min(4*((int(i)-1)/48+1), 12)] for i in [10**e for e in np.arange(0, 2.2, 0.1)]]
for e in infos:
prob_dict = {}
language = AnBn(max_length=e[1]+(e[1]%2!=0))
eval_data = language.sample_data_as_FuncData(e[0])
for h in seen:
h.likelihood_temperature = temp
prob_dict[h] = h.compute_posterior(eval_data)
seq.append(prob_dict)
print 'rank: ', rank, e, 'done'; fff()
elif rank in work_list[1]:
seq = []
infos = [[i, 12] for i in [10**e for e in np.arange(0, 2.2, 0.1)]]
for e in infos:
prob_dict = {}
language = AnBn(max_length=e[1])
eval_data = language.sample_data_as_FuncData(e[0])
for h in seen:
h.likelihood_temperature = temp
prob_dict[h] = h.compute_posterior(eval_data)
seq.append(prob_dict)
print 'rank: ', rank, e, 'done'; fff()
else:
seq = []
infos = [[i, 12] for i in [10**e for e in np.arange(0, 2.2, 0.1)]]
for e in infos:
prob_dict = {}
eval_data = uniform_data(e[0], e[1])
for h in seen:
h.likelihood_temperature = temp
prob_dict[h] = h.compute_posterior(eval_data)
seq.append(prob_dict)
print 'rank: ', rank, e, 'done'; fff()
# TODO no need ?
from copy import deepcopy
dict_0 = deepcopy(seq[0])
for h in dict_0:
dict_0[h] = h.compute_posterior([FunctionData(input=[], output=Counter())])
seq.insert(0, dict_0)
dump(seq, open('seq'+str(rank)+suffix, 'w'))
def test_sto():
"""
objective: test if our posterior distribution is stable for each time of estimation
run: mpiexec -n 12
"""
rec = load_hypo('out/simulations/staged/', ['staged', 'normal0', 'normal1'])
# rec = load_hypo('out/simulations/staged/', ['staged'])
seen = set()
for e in rec:
for h in e[1]:
if h in seen: continue
seen.add(h)
print rank, 'hypo len: ', len(seen); fff()
seq = []
inner_kl_seq = []
infos = [[i, 4*((i-1)/48+1)] for i in xrange(12, 145, 2)]
for e in infos:
prob_dict = {}
language = AnBn(max_length=e[1])
eval_data = language.sample_data_as_FuncData(e[0])
for h in seen:prob_dict[h] = h.compute_posterior(eval_data)
seq.append(prob_dict)
if len(seq) > 1: inner_kl_seq.append(compute_kl(seq[-2], seq[-1]))
print 'rank: ', rank, e, 'done'; fff()
dump(seq, open('seq_'+str(rank)+suffix,'w'))
dump(inner_kl_seq, open('inner_kl_seq_'+str(rank)+suffix, 'w'))
if rank != 0:
comm.send(seq, dest=0)
print rank, 'send'; fff()
sys.exit(0)
else:
seq_set = [seq]
for i_s in xrange(size - 1):
seq_set.append(comm.recv(source=i_s+1))
print rank, 'recv:', i_s; fff()
cross_kl_seq = []
for i_s in xrange(len(seq_set[0])):
tmp = []
for i_ss in xrange(len(seq_set)-1):
current_dict = seq_set[i_ss][i_s]
next_dict = seq[i_ss+1][i_s]
tmp.append(compute_kl(current_dict, next_dict))
print 'row %i column %i done' % (i_ss, i_s); fff()
cross_kl_seq.append(tmp)
dump(cross_kl_seq, open('cross_kl_seq_'+str(rank)+suffix, 'w'))
for e in cross_kl_seq:
print e; fff()
def test_hypo_stat():
"""
objective: test how does those high prob hypotheses look like
run: mpiexec -n 12
"""
seq = load(open('seq_'+str(rank)+''))
cnt = 0
for e in seq:
Z = logsumexp([p for h, p in e.iteritems()])
e_list = [[h, p] for h, p in e.iteritems()]; e_list.sort(key=lambda x:x[1], reverse=True)
f = open('hypo_stat_'+str(rank)+suffix, 'a')
print >> f, '='*40
for iii in xrange(4):
print >> f, 'rank: %i' % rank, 'prob', np.exp(e_list[iii][1] - Z)
print >> f, Counter([e_list[iii][0]() for _ in xrange(512)])
print >> f, str(e_list[iii][0])
print cnt, 'done'; cnt += 1
f.close()
# ===========================================================================
# nonadjacent
# ===========================================================================
def make_pos(jump, temp):
"""
1. read raw output
2. compute precision & recall on nonadjacent and adjacent contents
3. evaluate posterior probability on different data sizes
4. dump the sequence
run: mpiexec -n 4
"""
print 'loading..'; fff()
rec = load_hypo('out/simulations/nonadjacent/', ['0'])
print 'estimating pr'; fff()
pr_dict = {}
_set = set()
cnt_tmp = {}
for e in rec:
for h in e[1]:
if h in _set: continue
cnt = Counter([h() for _ in xrange(256)])
# cnt = Counter([h() for _ in xrange(10)])
cnt_tmp[h] = cnt
base = sum(cnt.values())
num = 0
for k, v in cnt.iteritems():
if k is None or len(k) < 2: continue
if k[0] == 'a' and k[-1] == 'b': num += v
pr_dict[h] = float(num) / base
_set.add(h)
work_list = range(2, 24, jump)
space_seq = []
for i in work_list:
language = LongDependency(max_length=i)
eval_data = {}
for e in language.str_sets:
eval_data[e] = 144.0 / len(language.str_sets)
eval_data = [FunctionData(input=[], output=eval_data)]
prob_dict = {}
ada_dict = {}
test_list = []
for h in _set:
h.likelihood_temperature = temp
prob_dict[h] = h.compute_posterior(eval_data)
p, r = language.estimate_precision_and_recall(h, cnt_tmp[h])
ada_dict[h] = 2*p*r/(p+r) if p+r != 0 else 0
test_list.append([h.posterior_score, ada_dict[h], pr_dict[h], cnt_tmp[h], str(h)])
Z = logsumexp([h.posterior_score for h in _set])
test_list.sort(key=lambda x:x[0], reverse=True)
weighted_x = 0
weighted_axb = 0
for e in test_list:
weighted_x += np.exp(e[0] - Z) * e[1]
weighted_axb += np.exp(e[0] - Z) * e[2]
f = open('non_w'+suffix, 'a')
print >> f, weighted_x, weighted_axb
f.close()
# print rank, i, '='*50
# for i_t in xrange(3):
# print 'prob: ', np.exp(test_list[i_t][0] - Z), 'x_f-score', test_list[i_t][1], 'axb_f-score', test_list[i_t][2]
# print test_list[i_t][3]
# print test_list[i_t][5].compute_posterior(eval_data)
# print language.estimate_precision_and_recall(test_list[i_t][5], cnt_tmp[test_list[i_t][5]])
# fff()
# dump(test_list, open('test_list_'+str(rank)+'_'+str(i)+suffix, 'w'))
# space_seq.append([prob_dict, ada_dict])
print 'rank', rank, i, 'done'; fff()
dump([space_seq, pr_dict], open('non_seq'+str(rank)+suffix, 'w'))
def make_pos2(jump, temp):
"""
1. read raw output
2. compute precision & recall on nonadjacent and adjacent contents
3. evaluate posterior probability on different data sizes
4. dump the sequence
run: mpiexec -n 4
"""
print 'loading..'; fff()
rec = load_hypo('out/simulations/nonadjacent/', ['0'])
# TODO one do this
print 'estimating pr'; fff()
pr_dict = {}
_set = set()
cnt_tmp = {}
for e in rec:
for h in e[1]:
if h in _set: continue
cnt = Counter([h() for _ in xrange(1024)])
cnt_tmp[h] = cnt
base = sum(cnt.values())
num = 0
for k, v in cnt.iteritems():
if k is None or len(k) < 2: continue
if k[0] + k[-1] in ['ab', 'cd', 'ef']: num += v
pr_dict[h] = float(num) / base
# fix the h_output
h.h_output = cnt
_set.add(h)
work_list = range(2, 17, jump)
for i in work_list:
language = LongDependency(max_length=i)
eval_data = {}
for e in language.str_sets:
eval_data[e] = 144.0 / len(language.str_sets)
eval_data = [FunctionData(input=[], output=eval_data)]
score = np.zeros(len(_set), dtype=np.float64)
prec = np.zeros(len(_set), dtype=np.float64)
# prob_dict = {}
# test_list = []
for ind, h in enumerate(_set):
h.likelihood_temperature = temp
score[ind] = h.compute_posterior(eval_data)
prec[ind] = pr_dict[h]
# prob_dict[h] = h.compute_posterior(eval_data)
# test_list.append([h.posterior_score, pr_dict[h], cnt_tmp[h], str(h), h])
# test_list.sort(key=lambda x: x[0], reverse=True)
# Z = logsumexp([h.posterior_score for h in _set])
#
# weighted_axb = sum([np.exp(e[0] - Z) * e[1] for e in test_list])
# print i, weighted_axb
# for i_t in xrange(3):
# print 'prob: ', np.exp(test_list[i_t][0] - Z), 'axb_f-score', test_list[i_t][1]
# print test_list[i_t][2]
# # print test_list[i_t][4].compute_posterior(eval_data)
# # print language.estimate_precision_and_recall(test_list[i_t][5], cnt_tmp[test_list[i_t][5]])
# print '='*50
# fff()
#
# f = open('non_w'+suffix, 'a')
# print >> f, Z, weighted_axb
# print
# f.close()
#
# print 'size: %i' % i, Z, weighted_axb; fff()
if rank != 0:
comm.send(score, dest=0)
comm.send(prec, dest=0)
sys.exit(0)
else:
for r in xrange(size - 1):
score += comm.recv(source=r+1)
prec += comm.recv(source=r+1)
score /= size
prec /= size
Z = logsumexp(score)
weighted_axb = np.sum(np.exp(score-Z) * prec)
f = open('non_w'+suffix, 'a')
print >> f, Z, weighted_axb
print i, Z, weighted_axb; fff()
f.close()
# dump([space_seq, pr_dict], open('non_seq'+str(rank)+suffix, 'w'))
def parse_nonadjacent(temperature):
"""
load the hypothesis space and compute weighted F-scores of nonadjacent dependency on different pool sizes.
replace the make_pos function
example script:
mpiexec -n 12 python parse_hypothesis.py --mode=nonadjacent_mk --temp=100
"""
eval_data_size = 1024
global size
global rank
pr_dict = {}
_set = set()
if rank == 0:
print 'loading..'; fff()
rec = load_hypo('out/simulations/nonadjacent/', ['_'])
print 'estimating pr'; fff()
for e in rec:
for h in e[1]:
if h in _set: continue
cnt = Counter([h() for _ in xrange(eval_data_size)])
num = 0
for k, v in cnt.iteritems():
if k is None or len(k) < 2: continue
if k[0] + k[-1] in ['ab', 'cd', 'ef']: num += v
pr_dict[h] = float(num) / eval_data_size
_set.add(h)
#debug
_list = [[h, pr] for h, pr in pr_dict.iteritems()]; _list.sort(key=lambda x: x[1], reverse=True)
for i in xrange(10):
print 'p,r: ', _list[i][1],
print Counter([_list[i][0]() for _ in xrange(256)])
print _list[i][0]
print '='*50; fff()
print "sync..."; fff()
pr_dict = comm.bcast(pr_dict, root=0)
_set = comm.bcast(_set, root=0)
# work_list = slice_list(np.arange(2, 65, 2), size)
work_list = slice_list(np.arange(10, 66, 5), size)
seq = []
for s in work_list[rank]:
wfs = 0.0
language = LongDependency(max_length=s)
eval_data = [FunctionData(input=[], output={e: float(eval_data_size)/s for e in language.str_sets})]
for h in _set:
h.likelihood_temperature = temperature
h.compute_posterior(eval_data)
Z = logsumexp([h.posterior_score for h in _set])
seq.append([s, sum([pr_dict[h]*np.exp(h.posterior_score - Z) for h in _set])])
#debug
_list = [h for h in _set]; _list.sort(key=lambda x: x.posterior_score, reverse=True)
print 'pool size: ', s
for i in xrange(3):
print 'prob: ', np.exp(_list[i].posterior_score - Z), 'p,r: ', pr_dict[_list[i]],
print Counter([_list[i]() for _ in xrange(256)])
print _list[i]
print '='*50; fff()
if rank == 0:
for i in xrange(1, size):
seq += comm.recv(source=i)
else:
comm.send(seq, dest=0)
sys.exit(0)
seq.sort(key=lambda x: x[0])
f = open('nonadjacent_wfs_seq'+suffix, 'w')
for s, wfs in seq:
print >> f, s, wfs
f.close()
def dis_pos(jump, is_plot, file_name, axb_bound, x_bound):
"""
1. read posterior sequence
2. set bound for axb and x hypotheses
3. plot
run: serial
"""
# space_seq, pr_dict = load(open('non_seq%i_' % i + file_name))
print 'loading..'; fff()
_set = [load(open('non_seq%i_' % i + file_name)) for i in xrange(4)]
print 'avging..'; fff()
avg_space_seq, avg_pr_dict = _set.pop(0)
for space_seq, pr_dict in _set:
for i in xrange(len(space_seq)):
prob_dict, ada_dict = space_seq[i]
avg_prob_dict, avg_ada_dict = avg_space_seq[i]
for h in prob_dict:
avg_prob_dict[h] = logsumexp([avg_prob_dict[h], prob_dict[h]])
avg_ada_dict[h] += ada_dict[h]
for h in pr_dict:
avg_pr_dict[h] += pr_dict[h]
for prob_dict, ada_dict in avg_space_seq:
for h in prob_dict:
prob_dict[h] -= 4
ada_dict[h] /= 4
for h in avg_pr_dict:
avg_pr_dict[h] /= 4
for axb_bound in np.arange(0.1, 1, 0.1):
for x_bound in np.arange(0.1, 1, 0.1):
seq = []
seq1 = []
seq2 = []
for seen in avg_space_seq:
Z = logsumexp([p for h, p in seen[0].iteritems()])
axb_prob = -Infinity
x_prob = -Infinity
for h, v in seen[0].iteritems():
if avg_pr_dict[h] > axb_bound: axb_prob = logsumexp([axb_prob, v])
if seen[1][h] > x_bound: x_prob = logsumexp([x_prob, v])
seq.append(np.exp(axb_prob - Z))
seq1.append(np.exp(x_prob - Z))
seq2.append(np.exp(axb_prob - Z) - np.exp(x_prob - Z))
print 'done'; fff()
flag = True
for i in xrange(len(seq2) - 1):
if seq2[i] - seq2[i+1] > 1e-4:
flag = False; break
if not flag: continue
print axb_bound, x_bound, '='*50
print 'axb_prob: ', seq
print 'x_prob: ', seq1
print 'difference_prob: ', seq2; fff()
dump([seq, seq1, seq2], open('nonadjacent_%.2f_%.2f' % (axb_bound, x_bound)+suffix, 'w'))
if is_plot == 'yes':
f, axarr = plt.subplots(1, 3)
axarr[0].plot(range(2, 65, jump), seq)
axarr[1].plot(range(2, 65, jump), seq1)
axarr[2].plot(range(2, 65, jump), seq2)
# plt.legend(handles=[x])
plt.ylabel('posterior')
plt.xlabel('poo_size')
plt.show()
def test_lis_disp(names):
ll = [load(open(name)) for name in names]
for li in ll:
print '='*50
Z = logsumexp([h[0] for h in li])
for i in xrange(3):
print 'p ', np.exp(li[i][0] -Z), 'x_f-score ', li[i][1], 'axb_f-score', li[i][2]
print li[i][4]
# ===========================================================================
# parse & plot
# ===========================================================================
def parse_plot(lang_name, finite, is_plot):
"""
run: mpi supported
example:
mpiexec -n 12 python parse_hypothesis.py --mode=parse_plot --language=An --finite=3 --plot=yes --wfs=yes
"""
_dir = 'out/final/'
global size
global rank
topn = set()
prf_dict = {}
language = instance(lang_name, finite)
if rank == 0:
print 'loading..'; fff()
for file_name in listdir(_dir):
if lang_name + '_' in file_name:
_set = load(open(_dir+file_name))
topn.update([h for h in _set])
print 'getting p&r..'; fff()
pr_data = language.sample_data_as_FuncData(1024)
for h in topn:
p, r = language.estimate_precision_and_recall(h, pr_data)
prf_dict[h] = [p, r, 0 if p+r == 0 else 2*p*r/(p+r)]
dump(prf_dict, open(lang_name+'_prf_dict'+suffix, 'w'))
topn = comm.bcast(topn, root=0)
prf_dict = comm.bcast(prf_dict, root=0)
print rank, 'getting posterior'; fff()
work_list = slice_list(np.arange(235, 300, 5), size)
seq = []
pnt_str = 'Weighted F-score' if options.WFS == 'yes' else 'Posterior Probability'
for s in work_list[rank]:
eval_data = language.sample_data_as_FuncData(s)
for h in topn:
h.likelihood_temperature = 100
h.compute_posterior(eval_data)
Z = logsumexp([h.posterior_score for h in topn])
if options.WFS == 'yes': tmp = sum([prf_dict[h][2]*np.exp(h.posterior_score - Z) for h in topn])
# TODO
# else: tmp = sum([np.exp(h.posterior_score - Z) for h in topn if prf_dict[h][2] > 0.9])
else: tmp = sum([np.exp(h.posterior_score - Z) for h in topn if (prf_dict[h][0] < 0.3 and prf_dict[h][1] > 0.9)])
if options.PROB == 'yes': dump([topn, Z], open(lang_name+'_prob_'+str(s)+suffix, 'w'))
seq.append([s, tmp])
print 'size: %.1f' % s, '%s: %.2f' % (pnt_str, tmp); fff()
#debug
_list = [h for h in topn]; _list.sort(key=lambda x: x.posterior_score, reverse=True)
for i in xrange(3):
print 'prob: ', np.exp(_list[i].posterior_score - Z), 'p,r: ', prf_dict[_list[i]][:2],
print Counter([_list[i]() for _ in xrange(256)])
print _list[i]
print '='*50; fff()
if rank == 0:
for i in xrange(1, size):
seq += comm.recv(source=i)
else:
comm.send(seq, dest=0)
sys.exit(0)
seq.sort(key=lambda x: x[0])
dump(seq, open(lang_name+'_seq'+suffix, 'w'))
if is_plot == 'yes':
x, y = zip(*seq)
plt.plot(x, y)
plt.ylabel(pnt_str)
plt.xlabel('Size of Data')
plt.title(lang_name)
plt.show()
def parse_cube(lang_name, finite):
"""
reads hypotheses of lang_name, estimates the p/r and posterior score, and saves them into a cube (list of tables)
data structure:
stats = [cube, topn]
cube = [[size, Z, table], [size, Z, table], ...]
table = [[ind, score, p, r, f, strs], [ind, score, p, r, f, strs], ...]
NOTE: topn here is a dict, you can use ind to find the h
example script:
mpiexec -n 12 python parse_hypothesis.py --mode=parse_cube --language=An --finite=3/10
"""
_dir = 'out/'
global size
global rank
topn = dict()
prf_dict = {}
language = instance(lang_name, finite)
if rank == 0:
truncate_flag = False if (lang_name == 'An' and finite <= 3) else True
set_topn = set()
print 'loading..'; fff()
for file_name in listdir(_dir):
if lang_name + '_' in file_name:
_set = load(open(_dir+file_name))
set_topn.update([h for h in _set])
print 'getting p&r..'; fff()
pr_data = language.sample_data_as_FuncData(2048)
for h in set_topn:
p, r, h_llcounts = language.estimate_precision_and_recall(h, pr_data, truncate=truncate_flag)
prf_dict[h] = [p, r, 0 if p+r == 0 else 2*p*r/(p+r)]
h.fixed_ll_counts = h_llcounts
topn = dict(enumerate(set_topn))
print 'bcasting..'; fff()
topn = comm.bcast(topn, root=0)
prf_dict = comm.bcast(prf_dict, root=0)
print rank, 'getting posterior'; fff()
# work_list = slice_list(np.arange(0, 72, 6), size)
work_list = slice_list(np.arange(120, 264, 12), size)
cube = []
for s in work_list[rank]:
eval_data = language.sample_data_as_FuncData(s)
for ind, h in topn.iteritems():
h.likelihood_temperature = 100
h.compute_posterior(eval_data)
Z = logsumexp([h.posterior_score for ind, h in topn.iteritems()])
table = [[ind, h.posterior_score, prf_dict[h][0], prf_dict[h][1], prf_dict[h][2], h.fixed_ll_counts] for ind, h in topn.iteritems()]
table.sort(key=lambda x: x[1], reverse=True)
cube += [[s, Z, table]]
print rank, s, 'done'; fff()
if rank == 0:
for i in xrange(1, size):
cube += comm.recv(source=i)
else:
comm.send(cube, dest=0)
print rank, 'table sent'; fff()
sys.exit(0)
cube.sort(key=lambda x: x[0])
dump([cube, topn], open(lang_name+'_stats'+suffix, 'w'))
def cube2graph(file_name, plot):
"""
read the stats file and make graph & table
example script:
python parse_hypothesis.py --mode=cube2graph --file=file --plot=no
"""
cube, topn = load(open('./stats/'+file_name))
# cube, topn = load(open('./'+file_name))
x = []
wfs = []
wp = []
wr = []
top10_f = [[] for _ in xrange(10)]
for size, Z, table in cube:
x.append(size)
# # for anbncn
# for i in xrange(len(table)):
# if -0.02 < table[i][4] - 0.92 < 0.02: table[i][4]=table[i][2]=table[i][3]=1.0
wfs.append(sum([row[4]*np.exp(row[1] - Z) for row in table]))
wp.append(sum([row[2]*np.exp(row[1] - Z) for row in table]))
wr.append(sum([row[3]*np.exp(row[1] - Z) for row in table]))
for i in xrange(10): top10_f[i].append(table[i][4])
MAP_f = top10_f.pop(0)
# fig_other_f = None
# for e in top10_f:
# fig_other_f, = plt.plot(x, e, '#B0B0B0', label='other hypothesis F-score')
# smooth MAP_f
# flag = False
# for i in xrange(len(MAP_f)):
# if -1e-2 < MAP_f[i] - 0.93 < 1e-2: flag = True
# if flag: MAP_f[i] = 1.0
# make latex table
f_table = open(file_name+'_toph_table', 'w')
_, __, table = cube[-1]
for ind, score, p, r, f, strs in table:
print >> f_table, str(topn[ind]), '&', '%.2f' % score, '&', '%.2f' % p, '&', '%.2f' % r, '&', '%.2f' % f, '&', '%.2f' % np.exp(score - __), '&', '$(',
strs = [v for v in strs.keys()]; strs.sort(key=lambda x:len(x))
strs = ['\\mb{'+v+'}' for v in strs]
if strs[0] == '\\mb{}': strs[0] = '\\emptyset'
l = strs.pop()
for s in strs: print >> f_table, s, ',',
print >> f_table, l, ')$', '\\\\'
f_table.close()
dump([x, wfs, wp, wr, MAP_f], open(file_name+'_graph', 'w'))
if plot != 'yes': sys.exit(0)
fig_wfs, = plt.plot(x, wfs, 'r', label='Weighted F-score')
fig_wp, = plt.plot(x, wp, 'r--', label='Weighted precision')
fig_wr, = plt.plot(x, wr, 'r-.', label='Weighted recall')
fig_map_f, = plt.plot(x, MAP_f, label='MAP hypothesis F-score')
# f = open('mod','w')
# for i in xrange(len(wfs)):
# print >> f, x[i], wfs[i], wp[i], wr[i], MAP_f[i]
# f.close()
plt.ylabel('Scores')
plt.xlabel('Amount of data')
plt.legend(handles=[fig_wfs, fig_wp, fig_wr, fig_map_f], loc=5)
# plt.axis([0, 30, 0, 1.05])
plt.axis([0, 240, 0, 1.05])
matplotlib.rcParams.update({'font.size': 16})
plt.show()
def only_plot(file_name, stype):
"""
plot graph based on stats
python parse_hypothesis.py --mode=only_plot --file=file --stype=cube
"""
if stype == 'cube':
x, wfs, wp, wr, MAP_f = load(open(file_name))
fig_wfs, = plt.plot(x, wfs, 'r', label='Weighted F-score')
fig_wp, = plt.plot(x, wp, 'r--', label='Weighted precision')
fig_wr, = plt.plot(x, wr, 'r-.', label='Weighted recall')
fig_map_f, = plt.plot(x, MAP_f, label='MAP hypothesis F-score')
plt.ylabel('Scores')
plt.xlabel('Size of Data')
plt.legend(handles=[fig_wfs, fig_wp, fig_wr, fig_map_f], loc=5)
plt.axis([0, 66, 0, 1.05])
# plt.axis([0, 240, 0, 1.05])
matplotlib.rcParams.update({'font.size': 16})
plt.show()
# x = []; y = []
#
# if is_txt == 'yes':
# f = open(lang_name+suffix)
# for line in f:
# a, b = line.split()
# x.append(float(a))
# y.append(float(b))
# else:
# seq = load(open(lang_name+'_seq'+suffix))
# x, y = zip(*seq)
#
# plt.plot(x, y)
#
# plt.ylabel('Weighted F-score')
# # plt.ylabel('Posterior Probability')
# plt.xlabel('Size of Data')
# plt.title(lang_name)
# plt.show()
def temp():
seq = []
for s in range(0, 61, 2):
print 'loading %i' % s
seq.append(load(open('An_prob_%i_1106_143007' % s)))
_dict = load(open('An_prf_dict_1106_143007'))
return seq, _dict
# _dir = 'out/final/'
# lang_name = 'Dyck'
#
# print 'loading..'; fff()
# topn = set()
# for file_name in listdir(_dir):
# if lang_name in file_name:
# _set = load(open(_dir+file_name))
# topn.update([h for h in _set])
#
# print 'prob..'
# language = instance(lang_name, 8)
# eval_data = language.sample_data_as_FuncData(20)
# for h in topn:
# h.likelihood_temperature = 100
# h.compute_posterior(eval_data)
#
# Z = logsumexp([h.posterior_score for h in topn])
# #debug
# _list = [h for h in topn]; _list.sort(key=lambda x: x.posterior_score, reverse=True)
# for i in xrange(20):
# print 'prob: ', np.exp(_list[i].posterior_score - Z)
# print Counter([_list[i]() for _ in xrange(256)])
# print _list[i]
def inf_prob(seq, _dict, flag, p1, p2):
ind = 0
for topn, Z in seq:
if flag: tmp = sum([np.exp(h.posterior_score - Z) for h in topn if _dict[h][2] > p1])
else: tmp = sum([np.exp(h.posterior_score - Z) for h in topn if (_dict[h][0] < p1 and _dict[h][1] > p2)])
print ind, ': ', tmp; ind += 2
# ==============================================
# parse staged input case, a version made in 2016/04/26
# ==============================================
def make_staged_posterior_seq(_dir, temperature, lang_name, dtype):
"""
script: python parse_hypothesis.py --mode=make_staged_posterior_seq --file=file --temp=1 --language=AnBn --dtype=staged/uniform
1. read raw file
2. compute fixed Counter
3. compute posterior for different amounts
dumped posterior format: [topn, [z,amount,finite,[s1,s2,....]], [], [], ....]
NOTE: if _dir is previously dumped posterior seq, then we use it
"""
if not (os.path.isfile(_dir) and 'posterior_seq' in _dir):
topn = set()
for filename in os.listdir(_dir):
if ('staged' in filename or 'normal' in filename) and 'seq' not in filename:
print 'load', filename
_set = load(open(_dir+filename))
topn.update([h for h in _set])
topn = list(topn)
# fix the llcnts to save time and make curve smooth
print 'get llcnts...'
for h in topn:
llcnts = Counter([h() for _ in xrange(2048)])
h.fixed_ll_counts = llcnts
seq = []
seq.append(topn)
for amount, finite in mk_staged_wlist(0,200,2,[48,96]):
print 'posterior on', amount, finite
if dtype == 'staged':
language = instance(lang_name, finite)
eval_data = language.sample_data_as_FuncData(amount)
elif dtype == 'uniform':
eval_data = uniform_data(amount, 12)
for h in topn:
h.likelihood_temperature = temperature
h.compute_posterior(eval_data)
Z = logsumexp([h.posterior_score for h in topn])
seq.append([Z, amount, finite, [h.posterior_score for h in topn]])
dump(seq,open(dtype + '_posterior_seq' + suffix, 'w'))
else:
seq = load(open(_dir))
# ====================== compute KL based on seq =======================
print 'compute kl seq...'
kl_seq = []
topn = seq.pop(0)
for i in xrange(len(seq)-1):
kl_seq.append([seq[i][1],compute_kl2(seq[i],seq[i+1])])
dump(kl_seq,open(dtype + '_kl_seq' + suffix,'w'))
def compute_kl2(now, nxt):
now_z, _, __, now_scores = now
nxt_z, _, __, nxt_scores = nxt
kl = 0.0
for i in xrange(len(now_scores)):
p = np.exp(now_scores[i] - now_z)
if p == 0: continue
kl += p * (now_scores[i] - nxt_scores[i] + nxt_z - now_z)
return kl
def mk_staged_wlist(left, right, step, stage):
stage1, stage2 = stage
w_list = [[i, 0] for i in xrange(left, right, step)]
ind = 0
for amount, _ in deepcopy(w_list):
if amount < stage1:
w_list[ind][1] = 4
elif amount < stage2:
w_list[ind][1] = 8
else:
w_list[ind][1] = 12
ind += 1
return w_list
def plt_kl_seq(file1, file2):
staged_seq = load(open(file1))
uniform_seq = load(open(file2))
x, y = zip(*staged_seq)
f, (ax1, ax2) = plt.subplots(1,2)
ax1.plot(x, y)
ax1.set_title('staged')
x, y = zip(*uniform_seq)
ax2.plot(x, y)
ax2.set_title('uiform')
plt.show()
# ======================================================
# parse nonadjacent case
# ======================================================
def parse_nonadjacent(_dir,temperature):
"""
1. read raw hypos
2. get fixed llcnts
3. compute posterior given different data pool sizes
NOTE: if _dir is previously dumped topn then load it
"""
if 'nonadjacent_topn' not in _dir:
topn = set()
for filename in os.listdir(_dir):
if 'nonadjacent' in filename and 'seq' not in filename:
print 'load', filename
_set = load(open(_dir+filename))
topn.update([h for h in _set])
topn = list(topn)
# fix the llcnts to save time and make curve smooth
print 'get llcnts...'
topn = gen_fixlen_llcnts(topn, 5)
dump(topn, open(_dir+'_nonadjacent_topn'+suffix, 'w'))
else:
print 'load', _dir
topn = load(open(_dir))
# find all correct hypotheses
topn = list(topn)
correct_set = set()
for i in xrange(len(topn)):
flag = True
for k,v in topn[i].fixed_ll_counts.iteritems():
if len(k) < 2:
continue
elif k[0] == 'a' and k[-1] in 'b':
continue
elif k[0] == 'c' and k[-1] in 'bd':
continue
elif k[0] == 'e' and k[-1] in 'bdf':
continue
flag=False
break
if flag: correct_set.add(i)
print len(correct_set), 'of', len(topn), 'are correct'
# get posterior
w_list = range(2, 25, 1)
amount_list = range(24, 144, 5)
posterior_seq = []
for i in xrange(len(w_list)):
pool_size = w_list[i]
language = LongDependency(max_length=pool_size)
eval_data = [FunctionData(input=[], output={e: float(amount_list[i])/pool_size for e in language.str_sets})]
for h in topn:
h.likelihood_temperature = temperature
h.compute_posterior(eval_data)
Z = logsumexp([h.posterior_score for h in topn])
prob = 0
for i in xrange(len(topn)):
if i in correct_set:
prob += np.exp(topn[i].posterior_score - Z)
print 'pool_size', pool_size, 'prob', prob
posterior_seq.append([pool_size, prob])
#debug
_list = [h for h in topn]; _list.sort(key=lambda x: x.posterior_score, reverse=True)
for i in xrange(3):
print 'prob: ', np.exp(_list[i].posterior_score - Z),
print h.fixed_ll_counts
print _list[i]
print '='*50; fff()
dump(posterior_seq, open('nonadjacent_posterior_seq'+suffix,'w'))
def gen_fixlen_llcnts(topn, _len, num=2048):
"""
force to generate llcnts with a fixed length
"""
for h in topn:
llcnts = Counter([h() for _ in xrange(2048)])
for k,v in deepcopy(llcnts).iteritems():
if len(k) != 5:
del llcnts[k]
now_num = float(sum(llcnts.values()))
for k in deepcopy(llcnts).keys(): llcnts[k] *= (num/now_num)
h.fixed_ll_counts = llcnts
return topn
if __name__ == '__main__':
# if options.MODE == 'staged_mk':
# make_staged_seq2(jump=options.JUMP, temp=options.TEMP)
# elif options.MODE == 'staged_plt':
# get_kl_seq2(jump=options.JUMP, is_plot=options.PLOT, file_name=options.FILE)
# elif options.MODE == 'nonadjacent_mk':
# parse_nonadjacent(temperature=options.TEMP)
# elif options.MODE == 'nonadjacent_plt':
# dis_pos(jump=options.JUMP, is_plot=options.PLOT, file_name=options.FILE, axb_bound=options.AXB, x_bound=options.X)
# elif options.MODE == 'parse_simple':
# most_prob(options.FILE)
if options.MODE == 'make_staged_posterior_seq':
make_staged_posterior_seq(options.FILE, options.TEMP, options.LANG, options.DTYPE)
elif options.MODE == 'parse_nonadjacent':
parse_nonadjacent(options.FILE, options.TEMP)
elif options.MODE == 'parse_plot':
parse_plot(options.LANG, options.FINITE, is_plot=options.PLOT)
elif options.MODE == 'only_plot':
only_plot(options.FILE, options.STYPE)
elif options.MODE == 'parse_cube':
parse_cube(options.LANG, options.FINITE)
elif options.MODE == 'cube2graph':
cube2graph(options.FILE, options.PLOT)
# seq = [[2 ,0.167012421 ], [4 ,0.404128998 ], [6 ,0.408503541], [8 ,0.346094762], [10 ,0.43615293], [12 ,0.995324843], [14 ,0.987169], [16 ,0.979347514], [18 ,0.983990461], [20 ,0.988215573 ], [22 ,0.970604139 ], [24 ,1]]
# x, y = zip(*seq)
# plt.plot(x, y)
#
# plt.ylabel('Weighted F-score')
# plt.xlabel('Size of Data')
# plt.title('Nonadjacent Dependency')
# plt.show()
# test_hypo_stat()
#
# cProfile.runctx("test_sto()", globals(), locals(), "Profile.prof")
# s = pstats.Stats("Profile.prof")
# s.strip_dirs().sort_stats("time").print_stats()
# avg_seq_set = load(open('avg_seq_set_0829_022210'))
# for avg_seq in avg_seq_set:
# for i in xrange(1, len(avg_seq)):
# avg_seq[i] = logsumexp([avg_seq[i], avg_seq[i-1]])
#
# staged, = plt.plot(range(12, 145, 30), avg_seq_set[0], label='staged')
# normal, = plt.plot(range(12, 145, 30), avg_seq_set[1], label='normal')
# uniform, = plt.plot(range(12, 145, 30), avg_seq_set[2], label='uniform')
#
# plt.legend(handles=[normal, staged, uniform])
# plt.ylabel('KL-divergence')
# plt.xlabel('data')
# plt.show()
# nonadjacent case
# seq1 = []
# seq2 = []
# seq3 = []
# seq4 = []
#
# f = open('123.txt')
# for line in f:
# ad, nd, d, x = line.split()
# seq1.append(float(ad))
# seq2.append(float(nd))
# seq3.append(float(d))
# seq4.append(float(x))
#
# adjacent, = plt.plot(seq4, seq1, label='adjacent')
# nonadjacent, = plt.plot(seq4, seq2, label='nonadjacent')
# diff, = plt.plot(seq4, seq3, label='differences')
#
# plt.legend(handles=[adjacent, nonadjacent, diff], loc=5)
# plt.ylabel('Weighted F-score')
# plt.xlabel('Pool size')
# plt.show()
# seq1 = []
# seq2 = []
# seq3 = []
#
# f = open('123.txt')
# for line in f:
# x, y = line.split()
# seq1.append(float(x))
# seq2.append(float(y))
#
# f = open('321.txt')
# for line in f:
# x, y = line.split()
# seq3.append(float(y))
#
#
# for i in xrange(1, len(seq1)):
# seq2[i] = (seq2[i] - seq2[i-1]) / (seq1[i] - seq1[i-1])
# seq2[0] = 0
#
# for i in xrange(1, len(seq1)):
# seq3[i] = (seq3[i] - seq3[i-1]) / (seq1[i] - seq1[i-1])
# seq3[0] = 0
#
# si, = plt.plot(np.log(seq1), seq2, label='SF')
# rc, = plt.plot(np.log(seq1), seq3, label='RC')
# plt.legend(handles=[si, rc], loc=5)
# plt.axis([0, 5.6, 0, 1])
# plt.ylabel('Cumulative KL-divergence')
# plt.xlabel('Data size (log scale)')
# plt.show()
#
# wfs = []
# wp = []
# wr = []
# x = []
# MAP_f = []
#
# fi = open('mod')
# for line in fi:
# para = map(float, line.split())
# x.append(para[0])
# wfs.append(para[1])
# wp.append(para[2])
# wr.append((para[3]))
# MAP_f.append(para[4])
#
# plt.figure(figsize=(10, 6))
# fig_wfs, = plt.plot(x, wfs, 'r', label='Weighted F-score')
# fig_wp, = plt.plot(x, wp, 'r--', label='Weighted precision')
# fig_wr, = plt.plot(x, wr, 'r-.', label='Weighted recall')
# fig_map_f, = plt.plot(x, MAP_f, label='MAP hypothesis F-score')
#
# plt.ylabel('Scores')
# plt.xlabel('Size of Data')
# plt.legend(handles=[fig_wfs, fig_wp, fig_wr, fig_map_f], loc=5)
# plt.axis([0, 30, 0, 1.05])
# matplotlib.rcParams.update({'font.size': 16})
# plt.show()
| gpl-3.0 |
lancezlin/ml_template_py | lib/python2.7/site-packages/sklearn/metrics/tests/test_ranking.py | 46 | 41270 | from __future__ import division, print_function
import numpy as np
from itertools import product
import warnings
from scipy.sparse import csr_matrix
from sklearn import datasets
from sklearn import svm
from sklearn.datasets import make_multilabel_classification
from sklearn.random_projection import sparse_random_matrix
from sklearn.utils.validation import check_array, check_consistent_length
from sklearn.utils.validation import check_random_state
from sklearn.utils.testing import assert_raises, clean_warning_registry
from sklearn.utils.testing import assert_raise_message
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_almost_equal
from sklearn.utils.testing import assert_array_equal
from sklearn.utils.testing import assert_array_almost_equal
from sklearn.utils.testing import assert_warns
from sklearn.metrics import auc
from sklearn.metrics import average_precision_score
from sklearn.metrics import coverage_error
from sklearn.metrics import label_ranking_average_precision_score
from sklearn.metrics import precision_recall_curve
from sklearn.metrics import label_ranking_loss
from sklearn.metrics import roc_auc_score
from sklearn.metrics import roc_curve
from sklearn.exceptions import UndefinedMetricWarning
###############################################################################
# Utilities for testing
def make_prediction(dataset=None, binary=False):
"""Make some classification predictions on a toy dataset using a SVC
If binary is True restrict to a binary classification problem instead of a
multiclass classification problem
"""
if dataset is None:
# import some data to play with
dataset = datasets.load_iris()
X = dataset.data
y = dataset.target
if binary:
# restrict to a binary classification task
X, y = X[y < 2], y[y < 2]
n_samples, n_features = X.shape
p = np.arange(n_samples)
rng = check_random_state(37)
rng.shuffle(p)
X, y = X[p], y[p]
half = int(n_samples / 2)
# add noisy features to make the problem harder and avoid perfect results
rng = np.random.RandomState(0)
X = np.c_[X, rng.randn(n_samples, 200 * n_features)]
# run classifier, get class probabilities and label predictions
clf = svm.SVC(kernel='linear', probability=True, random_state=0)
probas_pred = clf.fit(X[:half], y[:half]).predict_proba(X[half:])
if binary:
# only interested in probabilities of the positive case
# XXX: do we really want a special API for the binary case?
probas_pred = probas_pred[:, 1]
y_pred = clf.predict(X[half:])
y_true = y[half:]
return y_true, y_pred, probas_pred
###############################################################################
# Tests
def _auc(y_true, y_score):
"""Alternative implementation to check for correctness of
`roc_auc_score`."""
pos_label = np.unique(y_true)[1]
# Count the number of times positive samples are correctly ranked above
# negative samples.
pos = y_score[y_true == pos_label]
neg = y_score[y_true != pos_label]
diff_matrix = pos.reshape(1, -1) - neg.reshape(-1, 1)
n_correct = np.sum(diff_matrix > 0)
return n_correct / float(len(pos) * len(neg))
def _average_precision(y_true, y_score):
"""Alternative implementation to check for correctness of
`average_precision_score`."""
pos_label = np.unique(y_true)[1]
n_pos = np.sum(y_true == pos_label)
order = np.argsort(y_score)[::-1]
y_score = y_score[order]
y_true = y_true[order]
score = 0
for i in range(len(y_score)):
if y_true[i] == pos_label:
# Compute precision up to document i
# i.e, percentage of relevant documents up to document i.
prec = 0
for j in range(0, i + 1):
if y_true[j] == pos_label:
prec += 1.0
prec /= (i + 1.0)
score += prec
return score / n_pos
def test_roc_curve():
# Test Area under Receiver Operating Characteristic (ROC) curve
y_true, _, probas_pred = make_prediction(binary=True)
expected_auc = _auc(y_true, probas_pred)
for drop in [True, False]:
fpr, tpr, thresholds = roc_curve(y_true, probas_pred,
drop_intermediate=drop)
roc_auc = auc(fpr, tpr)
assert_array_almost_equal(roc_auc, expected_auc, decimal=2)
assert_almost_equal(roc_auc, roc_auc_score(y_true, probas_pred))
assert_equal(fpr.shape, tpr.shape)
assert_equal(fpr.shape, thresholds.shape)
def test_roc_curve_end_points():
# Make sure that roc_curve returns a curve start at 0 and ending and
# 1 even in corner cases
rng = np.random.RandomState(0)
y_true = np.array([0] * 50 + [1] * 50)
y_pred = rng.randint(3, size=100)
fpr, tpr, thr = roc_curve(y_true, y_pred, drop_intermediate=True)
assert_equal(fpr[0], 0)
assert_equal(fpr[-1], 1)
assert_equal(fpr.shape, tpr.shape)
assert_equal(fpr.shape, thr.shape)
def test_roc_returns_consistency():
# Test whether the returned threshold matches up with tpr
# make small toy dataset
y_true, _, probas_pred = make_prediction(binary=True)
fpr, tpr, thresholds = roc_curve(y_true, probas_pred)
# use the given thresholds to determine the tpr
tpr_correct = []
for t in thresholds:
tp = np.sum((probas_pred >= t) & y_true)
p = np.sum(y_true)
tpr_correct.append(1.0 * tp / p)
# compare tpr and tpr_correct to see if the thresholds' order was correct
assert_array_almost_equal(tpr, tpr_correct, decimal=2)
assert_equal(fpr.shape, tpr.shape)
assert_equal(fpr.shape, thresholds.shape)
def test_roc_curve_multi():
# roc_curve not applicable for multi-class problems
y_true, _, probas_pred = make_prediction(binary=False)
assert_raises(ValueError, roc_curve, y_true, probas_pred)
def test_roc_curve_confidence():
# roc_curve for confidence scores
y_true, _, probas_pred = make_prediction(binary=True)
fpr, tpr, thresholds = roc_curve(y_true, probas_pred - 0.5)
roc_auc = auc(fpr, tpr)
assert_array_almost_equal(roc_auc, 0.90, decimal=2)
assert_equal(fpr.shape, tpr.shape)
assert_equal(fpr.shape, thresholds.shape)
def test_roc_curve_hard():
# roc_curve for hard decisions
y_true, pred, probas_pred = make_prediction(binary=True)
# always predict one
trivial_pred = np.ones(y_true.shape)
fpr, tpr, thresholds = roc_curve(y_true, trivial_pred)
roc_auc = auc(fpr, tpr)
assert_array_almost_equal(roc_auc, 0.50, decimal=2)
assert_equal(fpr.shape, tpr.shape)
assert_equal(fpr.shape, thresholds.shape)
# always predict zero
trivial_pred = np.zeros(y_true.shape)
fpr, tpr, thresholds = roc_curve(y_true, trivial_pred)
roc_auc = auc(fpr, tpr)
assert_array_almost_equal(roc_auc, 0.50, decimal=2)
assert_equal(fpr.shape, tpr.shape)
assert_equal(fpr.shape, thresholds.shape)
# hard decisions
fpr, tpr, thresholds = roc_curve(y_true, pred)
roc_auc = auc(fpr, tpr)
assert_array_almost_equal(roc_auc, 0.78, decimal=2)
assert_equal(fpr.shape, tpr.shape)
assert_equal(fpr.shape, thresholds.shape)
def test_roc_curve_one_label():
y_true = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
y_pred = [0, 1, 0, 1, 0, 1, 0, 1, 0, 1]
# assert there are warnings
w = UndefinedMetricWarning
fpr, tpr, thresholds = assert_warns(w, roc_curve, y_true, y_pred)
# all true labels, all fpr should be nan
assert_array_equal(fpr,
np.nan * np.ones(len(thresholds)))
assert_equal(fpr.shape, tpr.shape)
assert_equal(fpr.shape, thresholds.shape)
# assert there are warnings
fpr, tpr, thresholds = assert_warns(w, roc_curve,
[1 - x for x in y_true],
y_pred)
# all negative labels, all tpr should be nan
assert_array_equal(tpr,
np.nan * np.ones(len(thresholds)))
assert_equal(fpr.shape, tpr.shape)
assert_equal(fpr.shape, thresholds.shape)
def test_roc_curve_toydata():
# Binary classification
y_true = [0, 1]
y_score = [0, 1]
tpr, fpr, _ = roc_curve(y_true, y_score)
roc_auc = roc_auc_score(y_true, y_score)
assert_array_almost_equal(tpr, [0, 1])
assert_array_almost_equal(fpr, [1, 1])
assert_almost_equal(roc_auc, 1.)
y_true = [0, 1]
y_score = [1, 0]
tpr, fpr, _ = roc_curve(y_true, y_score)
roc_auc = roc_auc_score(y_true, y_score)
assert_array_almost_equal(tpr, [0, 1, 1])
assert_array_almost_equal(fpr, [0, 0, 1])
assert_almost_equal(roc_auc, 0.)
y_true = [1, 0]
y_score = [1, 1]
tpr, fpr, _ = roc_curve(y_true, y_score)
roc_auc = roc_auc_score(y_true, y_score)
assert_array_almost_equal(tpr, [0, 1])
assert_array_almost_equal(fpr, [0, 1])
assert_almost_equal(roc_auc, 0.5)
y_true = [1, 0]
y_score = [1, 0]
tpr, fpr, _ = roc_curve(y_true, y_score)
roc_auc = roc_auc_score(y_true, y_score)
assert_array_almost_equal(tpr, [0, 1])
assert_array_almost_equal(fpr, [1, 1])
assert_almost_equal(roc_auc, 1.)
y_true = [1, 0]
y_score = [0.5, 0.5]
tpr, fpr, _ = roc_curve(y_true, y_score)
roc_auc = roc_auc_score(y_true, y_score)
assert_array_almost_equal(tpr, [0, 1])
assert_array_almost_equal(fpr, [0, 1])
assert_almost_equal(roc_auc, .5)
y_true = [0, 0]
y_score = [0.25, 0.75]
# assert UndefinedMetricWarning because of no positive sample in y_true
tpr, fpr, _ = assert_warns(UndefinedMetricWarning, roc_curve, y_true, y_score)
assert_raises(ValueError, roc_auc_score, y_true, y_score)
assert_array_almost_equal(tpr, [0., 0.5, 1.])
assert_array_almost_equal(fpr, [np.nan, np.nan, np.nan])
y_true = [1, 1]
y_score = [0.25, 0.75]
# assert UndefinedMetricWarning because of no negative sample in y_true
tpr, fpr, _ = assert_warns(UndefinedMetricWarning, roc_curve, y_true, y_score)
assert_raises(ValueError, roc_auc_score, y_true, y_score)
assert_array_almost_equal(tpr, [np.nan, np.nan])
assert_array_almost_equal(fpr, [0.5, 1.])
# Multi-label classification task
y_true = np.array([[0, 1], [0, 1]])
y_score = np.array([[0, 1], [0, 1]])
assert_raises(ValueError, roc_auc_score, y_true, y_score, average="macro")
assert_raises(ValueError, roc_auc_score, y_true, y_score,
average="weighted")
assert_almost_equal(roc_auc_score(y_true, y_score, average="samples"), 1.)
assert_almost_equal(roc_auc_score(y_true, y_score, average="micro"), 1.)
y_true = np.array([[0, 1], [0, 1]])
y_score = np.array([[0, 1], [1, 0]])
assert_raises(ValueError, roc_auc_score, y_true, y_score, average="macro")
assert_raises(ValueError, roc_auc_score, y_true, y_score,
average="weighted")
assert_almost_equal(roc_auc_score(y_true, y_score, average="samples"), 0.5)
assert_almost_equal(roc_auc_score(y_true, y_score, average="micro"), 0.5)
y_true = np.array([[1, 0], [0, 1]])
y_score = np.array([[0, 1], [1, 0]])
assert_almost_equal(roc_auc_score(y_true, y_score, average="macro"), 0)
assert_almost_equal(roc_auc_score(y_true, y_score, average="weighted"), 0)
assert_almost_equal(roc_auc_score(y_true, y_score, average="samples"), 0)
assert_almost_equal(roc_auc_score(y_true, y_score, average="micro"), 0)
y_true = np.array([[1, 0], [0, 1]])
y_score = np.array([[0.5, 0.5], [0.5, 0.5]])
assert_almost_equal(roc_auc_score(y_true, y_score, average="macro"), .5)
assert_almost_equal(roc_auc_score(y_true, y_score, average="weighted"), .5)
assert_almost_equal(roc_auc_score(y_true, y_score, average="samples"), .5)
assert_almost_equal(roc_auc_score(y_true, y_score, average="micro"), .5)
def test_roc_curve_drop_intermediate():
# Test that drop_intermediate drops the correct thresholds
y_true = [0, 0, 0, 0, 1, 1]
y_score = [0., 0.2, 0.5, 0.6, 0.7, 1.0]
tpr, fpr, thresholds = roc_curve(y_true, y_score, drop_intermediate=True)
assert_array_almost_equal(thresholds, [1., 0.7, 0.])
# Test dropping thresholds with repeating scores
y_true = [0, 0, 0, 0, 0, 0, 0,
1, 1, 1, 1, 1, 1]
y_score = [0., 0.1, 0.6, 0.6, 0.7, 0.8, 0.9,
0.6, 0.7, 0.8, 0.9, 0.9, 1.0]
tpr, fpr, thresholds = roc_curve(y_true, y_score, drop_intermediate=True)
assert_array_almost_equal(thresholds,
[1.0, 0.9, 0.7, 0.6, 0.])
def test_auc():
# Test Area Under Curve (AUC) computation
x = [0, 1]
y = [0, 1]
assert_array_almost_equal(auc(x, y), 0.5)
x = [1, 0]
y = [0, 1]
assert_array_almost_equal(auc(x, y), 0.5)
x = [1, 0, 0]
y = [0, 1, 1]
assert_array_almost_equal(auc(x, y), 0.5)
x = [0, 1]
y = [1, 1]
assert_array_almost_equal(auc(x, y), 1)
x = [0, 0.5, 1]
y = [0, 0.5, 1]
assert_array_almost_equal(auc(x, y), 0.5)
def test_auc_duplicate_values():
# Test Area Under Curve (AUC) computation with duplicate values
# auc() was previously sorting the x and y arrays according to the indices
# from numpy.argsort(x), which was reordering the tied 0's in this example
# and resulting in an incorrect area computation. This test detects the
# error.
x = [-2.0, 0.0, 0.0, 0.0, 1.0]
y1 = [2.0, 0.0, 0.5, 1.0, 1.0]
y2 = [2.0, 1.0, 0.0, 0.5, 1.0]
y3 = [2.0, 1.0, 0.5, 0.0, 1.0]
for y in (y1, y2, y3):
assert_array_almost_equal(auc(x, y, reorder=True), 3.0)
def test_auc_errors():
# Incompatible shapes
assert_raises(ValueError, auc, [0.0, 0.5, 1.0], [0.1, 0.2])
# Too few x values
assert_raises(ValueError, auc, [0.0], [0.1])
# x is not in order
assert_raises(ValueError, auc, [1.0, 0.0, 0.5], [0.0, 0.0, 0.0])
def test_auc_score_non_binary_class():
# Test that roc_auc_score function returns an error when trying
# to compute AUC for non-binary class values.
rng = check_random_state(404)
y_pred = rng.rand(10)
# y_true contains only one class value
y_true = np.zeros(10, dtype="int")
assert_raise_message(ValueError, "ROC AUC score is not defined",
roc_auc_score, y_true, y_pred)
y_true = np.ones(10, dtype="int")
assert_raise_message(ValueError, "ROC AUC score is not defined",
roc_auc_score, y_true, y_pred)
y_true = -np.ones(10, dtype="int")
assert_raise_message(ValueError, "ROC AUC score is not defined",
roc_auc_score, y_true, y_pred)
# y_true contains three different class values
y_true = rng.randint(0, 3, size=10)
assert_raise_message(ValueError, "multiclass format is not supported",
roc_auc_score, y_true, y_pred)
clean_warning_registry()
with warnings.catch_warnings(record=True):
rng = check_random_state(404)
y_pred = rng.rand(10)
# y_true contains only one class value
y_true = np.zeros(10, dtype="int")
assert_raise_message(ValueError, "ROC AUC score is not defined",
roc_auc_score, y_true, y_pred)
y_true = np.ones(10, dtype="int")
assert_raise_message(ValueError, "ROC AUC score is not defined",
roc_auc_score, y_true, y_pred)
y_true = -np.ones(10, dtype="int")
assert_raise_message(ValueError, "ROC AUC score is not defined",
roc_auc_score, y_true, y_pred)
# y_true contains three different class values
y_true = rng.randint(0, 3, size=10)
assert_raise_message(ValueError, "multiclass format is not supported",
roc_auc_score, y_true, y_pred)
def test_precision_recall_curve():
y_true, _, probas_pred = make_prediction(binary=True)
_test_precision_recall_curve(y_true, probas_pred)
# Use {-1, 1} for labels; make sure original labels aren't modified
y_true[np.where(y_true == 0)] = -1
y_true_copy = y_true.copy()
_test_precision_recall_curve(y_true, probas_pred)
assert_array_equal(y_true_copy, y_true)
labels = [1, 0, 0, 1]
predict_probas = [1, 2, 3, 4]
p, r, t = precision_recall_curve(labels, predict_probas)
assert_array_almost_equal(p, np.array([0.5, 0.33333333, 0.5, 1., 1.]))
assert_array_almost_equal(r, np.array([1., 0.5, 0.5, 0.5, 0.]))
assert_array_almost_equal(t, np.array([1, 2, 3, 4]))
assert_equal(p.size, r.size)
assert_equal(p.size, t.size + 1)
def test_precision_recall_curve_pos_label():
y_true, _, probas_pred = make_prediction(binary=False)
pos_label = 2
p, r, thresholds = precision_recall_curve(y_true,
probas_pred[:, pos_label],
pos_label=pos_label)
p2, r2, thresholds2 = precision_recall_curve(y_true == pos_label,
probas_pred[:, pos_label])
assert_array_almost_equal(p, p2)
assert_array_almost_equal(r, r2)
assert_array_almost_equal(thresholds, thresholds2)
assert_equal(p.size, r.size)
assert_equal(p.size, thresholds.size + 1)
def _test_precision_recall_curve(y_true, probas_pred):
# Test Precision-Recall and aread under PR curve
p, r, thresholds = precision_recall_curve(y_true, probas_pred)
precision_recall_auc = auc(r, p)
assert_array_almost_equal(precision_recall_auc, 0.85, 2)
assert_array_almost_equal(precision_recall_auc,
average_precision_score(y_true, probas_pred))
assert_almost_equal(_average_precision(y_true, probas_pred),
precision_recall_auc, 1)
assert_equal(p.size, r.size)
assert_equal(p.size, thresholds.size + 1)
# Smoke test in the case of proba having only one value
p, r, thresholds = precision_recall_curve(y_true,
np.zeros_like(probas_pred))
precision_recall_auc = auc(r, p)
assert_array_almost_equal(precision_recall_auc, 0.75, 3)
assert_equal(p.size, r.size)
assert_equal(p.size, thresholds.size + 1)
def test_precision_recall_curve_errors():
# Contains non-binary labels
assert_raises(ValueError, precision_recall_curve,
[0, 1, 2], [[0.0], [1.0], [1.0]])
def test_precision_recall_curve_toydata():
with np.errstate(all="raise"):
# Binary classification
y_true = [0, 1]
y_score = [0, 1]
p, r, _ = precision_recall_curve(y_true, y_score)
auc_prc = average_precision_score(y_true, y_score)
assert_array_almost_equal(p, [1, 1])
assert_array_almost_equal(r, [1, 0])
assert_almost_equal(auc_prc, 1.)
y_true = [0, 1]
y_score = [1, 0]
p, r, _ = precision_recall_curve(y_true, y_score)
auc_prc = average_precision_score(y_true, y_score)
assert_array_almost_equal(p, [0.5, 0., 1.])
assert_array_almost_equal(r, [1., 0., 0.])
assert_almost_equal(auc_prc, 0.25)
y_true = [1, 0]
y_score = [1, 1]
p, r, _ = precision_recall_curve(y_true, y_score)
auc_prc = average_precision_score(y_true, y_score)
assert_array_almost_equal(p, [0.5, 1])
assert_array_almost_equal(r, [1., 0])
assert_almost_equal(auc_prc, .75)
y_true = [1, 0]
y_score = [1, 0]
p, r, _ = precision_recall_curve(y_true, y_score)
auc_prc = average_precision_score(y_true, y_score)
assert_array_almost_equal(p, [1, 1])
assert_array_almost_equal(r, [1, 0])
assert_almost_equal(auc_prc, 1.)
y_true = [1, 0]
y_score = [0.5, 0.5]
p, r, _ = precision_recall_curve(y_true, y_score)
auc_prc = average_precision_score(y_true, y_score)
assert_array_almost_equal(p, [0.5, 1])
assert_array_almost_equal(r, [1, 0.])
assert_almost_equal(auc_prc, .75)
y_true = [0, 0]
y_score = [0.25, 0.75]
assert_raises(Exception, precision_recall_curve, y_true, y_score)
assert_raises(Exception, average_precision_score, y_true, y_score)
y_true = [1, 1]
y_score = [0.25, 0.75]
p, r, _ = precision_recall_curve(y_true, y_score)
assert_almost_equal(average_precision_score(y_true, y_score), 1.)
assert_array_almost_equal(p, [1., 1., 1.])
assert_array_almost_equal(r, [1, 0.5, 0.])
# Multi-label classification task
y_true = np.array([[0, 1], [0, 1]])
y_score = np.array([[0, 1], [0, 1]])
assert_raises(Exception, average_precision_score, y_true, y_score,
average="macro")
assert_raises(Exception, average_precision_score, y_true, y_score,
average="weighted")
assert_almost_equal(average_precision_score(y_true, y_score,
average="samples"), 1.)
assert_almost_equal(average_precision_score(y_true, y_score,
average="micro"), 1.)
y_true = np.array([[0, 1], [0, 1]])
y_score = np.array([[0, 1], [1, 0]])
assert_raises(Exception, average_precision_score, y_true, y_score,
average="macro")
assert_raises(Exception, average_precision_score, y_true, y_score,
average="weighted")
assert_almost_equal(average_precision_score(y_true, y_score,
average="samples"), 0.625)
assert_almost_equal(average_precision_score(y_true, y_score,
average="micro"), 0.625)
y_true = np.array([[1, 0], [0, 1]])
y_score = np.array([[0, 1], [1, 0]])
assert_almost_equal(average_precision_score(y_true, y_score,
average="macro"), 0.25)
assert_almost_equal(average_precision_score(y_true, y_score,
average="weighted"), 0.25)
assert_almost_equal(average_precision_score(y_true, y_score,
average="samples"), 0.25)
assert_almost_equal(average_precision_score(y_true, y_score,
average="micro"), 0.25)
y_true = np.array([[1, 0], [0, 1]])
y_score = np.array([[0.5, 0.5], [0.5, 0.5]])
assert_almost_equal(average_precision_score(y_true, y_score,
average="macro"), 0.75)
assert_almost_equal(average_precision_score(y_true, y_score,
average="weighted"), 0.75)
assert_almost_equal(average_precision_score(y_true, y_score,
average="samples"), 0.75)
assert_almost_equal(average_precision_score(y_true, y_score,
average="micro"), 0.75)
def test_score_scale_invariance():
# Test that average_precision_score and roc_auc_score are invariant by
# the scaling or shifting of probabilities
# This test was expanded (added scaled_down) in response to github
# issue #3864 (and others), where overly aggressive rounding was causing
# problems for users with very small y_score values
y_true, _, probas_pred = make_prediction(binary=True)
roc_auc = roc_auc_score(y_true, probas_pred)
roc_auc_scaled_up = roc_auc_score(y_true, 100 * probas_pred)
roc_auc_scaled_down = roc_auc_score(y_true, 1e-6 * probas_pred)
roc_auc_shifted = roc_auc_score(y_true, probas_pred - 10)
assert_equal(roc_auc, roc_auc_scaled_up)
assert_equal(roc_auc, roc_auc_scaled_down)
assert_equal(roc_auc, roc_auc_shifted)
pr_auc = average_precision_score(y_true, probas_pred)
pr_auc_scaled_up = average_precision_score(y_true, 100 * probas_pred)
pr_auc_scaled_down = average_precision_score(y_true, 1e-6 * probas_pred)
pr_auc_shifted = average_precision_score(y_true, probas_pred - 10)
assert_equal(pr_auc, pr_auc_scaled_up)
assert_equal(pr_auc, pr_auc_scaled_down)
assert_equal(pr_auc, pr_auc_shifted)
def check_lrap_toy(lrap_score):
# Check on several small example that it works
assert_almost_equal(lrap_score([[0, 1]], [[0.25, 0.75]]), 1)
assert_almost_equal(lrap_score([[0, 1]], [[0.75, 0.25]]), 1 / 2)
assert_almost_equal(lrap_score([[1, 1]], [[0.75, 0.25]]), 1)
assert_almost_equal(lrap_score([[0, 0, 1]], [[0.25, 0.5, 0.75]]), 1)
assert_almost_equal(lrap_score([[0, 1, 0]], [[0.25, 0.5, 0.75]]), 1 / 2)
assert_almost_equal(lrap_score([[0, 1, 1]], [[0.25, 0.5, 0.75]]), 1)
assert_almost_equal(lrap_score([[1, 0, 0]], [[0.25, 0.5, 0.75]]), 1 / 3)
assert_almost_equal(lrap_score([[1, 0, 1]], [[0.25, 0.5, 0.75]]),
(2 / 3 + 1 / 1) / 2)
assert_almost_equal(lrap_score([[1, 1, 0]], [[0.25, 0.5, 0.75]]),
(2 / 3 + 1 / 2) / 2)
assert_almost_equal(lrap_score([[0, 0, 1]], [[0.75, 0.5, 0.25]]), 1 / 3)
assert_almost_equal(lrap_score([[0, 1, 0]], [[0.75, 0.5, 0.25]]), 1 / 2)
assert_almost_equal(lrap_score([[0, 1, 1]], [[0.75, 0.5, 0.25]]),
(1 / 2 + 2 / 3) / 2)
assert_almost_equal(lrap_score([[1, 0, 0]], [[0.75, 0.5, 0.25]]), 1)
assert_almost_equal(lrap_score([[1, 0, 1]], [[0.75, 0.5, 0.25]]),
(1 + 2 / 3) / 2)
assert_almost_equal(lrap_score([[1, 1, 0]], [[0.75, 0.5, 0.25]]), 1)
assert_almost_equal(lrap_score([[1, 1, 1]], [[0.75, 0.5, 0.25]]), 1)
assert_almost_equal(lrap_score([[0, 0, 1]], [[0.5, 0.75, 0.25]]), 1 / 3)
assert_almost_equal(lrap_score([[0, 1, 0]], [[0.5, 0.75, 0.25]]), 1)
assert_almost_equal(lrap_score([[0, 1, 1]], [[0.5, 0.75, 0.25]]),
(1 + 2 / 3) / 2)
assert_almost_equal(lrap_score([[1, 0, 0]], [[0.5, 0.75, 0.25]]), 1 / 2)
assert_almost_equal(lrap_score([[1, 0, 1]], [[0.5, 0.75, 0.25]]),
(1 / 2 + 2 / 3) / 2)
assert_almost_equal(lrap_score([[1, 1, 0]], [[0.5, 0.75, 0.25]]), 1)
assert_almost_equal(lrap_score([[1, 1, 1]], [[0.5, 0.75, 0.25]]), 1)
# Tie handling
assert_almost_equal(lrap_score([[1, 0]], [[0.5, 0.5]]), 0.5)
assert_almost_equal(lrap_score([[0, 1]], [[0.5, 0.5]]), 0.5)
assert_almost_equal(lrap_score([[1, 1]], [[0.5, 0.5]]), 1)
assert_almost_equal(lrap_score([[0, 0, 1]], [[0.25, 0.5, 0.5]]), 0.5)
assert_almost_equal(lrap_score([[0, 1, 0]], [[0.25, 0.5, 0.5]]), 0.5)
assert_almost_equal(lrap_score([[0, 1, 1]], [[0.25, 0.5, 0.5]]), 1)
assert_almost_equal(lrap_score([[1, 0, 0]], [[0.25, 0.5, 0.5]]), 1 / 3)
assert_almost_equal(lrap_score([[1, 0, 1]], [[0.25, 0.5, 0.5]]),
(2 / 3 + 1 / 2) / 2)
assert_almost_equal(lrap_score([[1, 1, 0]], [[0.25, 0.5, 0.5]]),
(2 / 3 + 1 / 2) / 2)
assert_almost_equal(lrap_score([[1, 1, 1]], [[0.25, 0.5, 0.5]]), 1)
assert_almost_equal(lrap_score([[1, 1, 0]], [[0.5, 0.5, 0.5]]), 2 / 3)
assert_almost_equal(lrap_score([[1, 1, 1, 0]], [[0.5, 0.5, 0.5, 0.5]]),
3 / 4)
def check_zero_or_all_relevant_labels(lrap_score):
random_state = check_random_state(0)
for n_labels in range(2, 5):
y_score = random_state.uniform(size=(1, n_labels))
y_score_ties = np.zeros_like(y_score)
# No relevant labels
y_true = np.zeros((1, n_labels))
assert_equal(lrap_score(y_true, y_score), 1.)
assert_equal(lrap_score(y_true, y_score_ties), 1.)
# Only relevant labels
y_true = np.ones((1, n_labels))
assert_equal(lrap_score(y_true, y_score), 1.)
assert_equal(lrap_score(y_true, y_score_ties), 1.)
# Degenerate case: only one label
assert_almost_equal(lrap_score([[1], [0], [1], [0]],
[[0.5], [0.5], [0.5], [0.5]]), 1.)
def check_lrap_error_raised(lrap_score):
# Raise value error if not appropriate format
assert_raises(ValueError, lrap_score,
[0, 1, 0], [0.25, 0.3, 0.2])
assert_raises(ValueError, lrap_score, [0, 1, 2],
[[0.25, 0.75, 0.0], [0.7, 0.3, 0.0], [0.8, 0.2, 0.0]])
assert_raises(ValueError, lrap_score, [(0), (1), (2)],
[[0.25, 0.75, 0.0], [0.7, 0.3, 0.0], [0.8, 0.2, 0.0]])
# Check that y_true.shape != y_score.shape raise the proper exception
assert_raises(ValueError, lrap_score, [[0, 1], [0, 1]], [0, 1])
assert_raises(ValueError, lrap_score, [[0, 1], [0, 1]], [[0, 1]])
assert_raises(ValueError, lrap_score, [[0, 1], [0, 1]], [[0], [1]])
assert_raises(ValueError, lrap_score, [[0, 1]], [[0, 1], [0, 1]])
assert_raises(ValueError, lrap_score, [[0], [1]], [[0, 1], [0, 1]])
assert_raises(ValueError, lrap_score, [[0, 1], [0, 1]], [[0], [1]])
def check_lrap_only_ties(lrap_score):
# Check tie handling in score
# Basic check with only ties and increasing label space
for n_labels in range(2, 10):
y_score = np.ones((1, n_labels))
# Check for growing number of consecutive relevant
for n_relevant in range(1, n_labels):
# Check for a bunch of positions
for pos in range(n_labels - n_relevant):
y_true = np.zeros((1, n_labels))
y_true[0, pos:pos + n_relevant] = 1
assert_almost_equal(lrap_score(y_true, y_score),
n_relevant / n_labels)
def check_lrap_without_tie_and_increasing_score(lrap_score):
# Check that Label ranking average precision works for various
# Basic check with increasing label space size and decreasing score
for n_labels in range(2, 10):
y_score = n_labels - (np.arange(n_labels).reshape((1, n_labels)) + 1)
# First and last
y_true = np.zeros((1, n_labels))
y_true[0, 0] = 1
y_true[0, -1] = 1
assert_almost_equal(lrap_score(y_true, y_score),
(2 / n_labels + 1) / 2)
# Check for growing number of consecutive relevant label
for n_relevant in range(1, n_labels):
# Check for a bunch of position
for pos in range(n_labels - n_relevant):
y_true = np.zeros((1, n_labels))
y_true[0, pos:pos + n_relevant] = 1
assert_almost_equal(lrap_score(y_true, y_score),
sum((r + 1) / ((pos + r + 1) * n_relevant)
for r in range(n_relevant)))
def _my_lrap(y_true, y_score):
"""Simple implementation of label ranking average precision"""
check_consistent_length(y_true, y_score)
y_true = check_array(y_true)
y_score = check_array(y_score)
n_samples, n_labels = y_true.shape
score = np.empty((n_samples, ))
for i in range(n_samples):
# The best rank correspond to 1. Rank higher than 1 are worse.
# The best inverse ranking correspond to n_labels.
unique_rank, inv_rank = np.unique(y_score[i], return_inverse=True)
n_ranks = unique_rank.size
rank = n_ranks - inv_rank
# Rank need to be corrected to take into account ties
# ex: rank 1 ex aequo means that both label are rank 2.
corr_rank = np.bincount(rank, minlength=n_ranks + 1).cumsum()
rank = corr_rank[rank]
relevant = y_true[i].nonzero()[0]
if relevant.size == 0 or relevant.size == n_labels:
score[i] = 1
continue
score[i] = 0.
for label in relevant:
# Let's count the number of relevant label with better rank
# (smaller rank).
n_ranked_above = sum(rank[r] <= rank[label] for r in relevant)
# Weight by the rank of the actual label
score[i] += n_ranked_above / rank[label]
score[i] /= relevant.size
return score.mean()
def check_alternative_lrap_implementation(lrap_score, n_classes=5,
n_samples=20, random_state=0):
_, y_true = make_multilabel_classification(n_features=1,
allow_unlabeled=False,
random_state=random_state,
n_classes=n_classes,
n_samples=n_samples)
# Score with ties
y_score = sparse_random_matrix(n_components=y_true.shape[0],
n_features=y_true.shape[1],
random_state=random_state)
if hasattr(y_score, "toarray"):
y_score = y_score.toarray()
score_lrap = label_ranking_average_precision_score(y_true, y_score)
score_my_lrap = _my_lrap(y_true, y_score)
assert_almost_equal(score_lrap, score_my_lrap)
# Uniform score
random_state = check_random_state(random_state)
y_score = random_state.uniform(size=(n_samples, n_classes))
score_lrap = label_ranking_average_precision_score(y_true, y_score)
score_my_lrap = _my_lrap(y_true, y_score)
assert_almost_equal(score_lrap, score_my_lrap)
def test_label_ranking_avp():
for fn in [label_ranking_average_precision_score, _my_lrap]:
yield check_lrap_toy, fn
yield check_lrap_without_tie_and_increasing_score, fn
yield check_lrap_only_ties, fn
yield check_zero_or_all_relevant_labels, fn
yield check_lrap_error_raised, label_ranking_average_precision_score
for n_samples, n_classes, random_state in product((1, 2, 8, 20),
(2, 5, 10),
range(1)):
yield (check_alternative_lrap_implementation,
label_ranking_average_precision_score,
n_classes, n_samples, random_state)
def test_coverage_error():
# Toy case
assert_almost_equal(coverage_error([[0, 1]], [[0.25, 0.75]]), 1)
assert_almost_equal(coverage_error([[0, 1]], [[0.75, 0.25]]), 2)
assert_almost_equal(coverage_error([[1, 1]], [[0.75, 0.25]]), 2)
assert_almost_equal(coverage_error([[0, 0]], [[0.75, 0.25]]), 0)
assert_almost_equal(coverage_error([[0, 0, 0]], [[0.25, 0.5, 0.75]]), 0)
assert_almost_equal(coverage_error([[0, 0, 1]], [[0.25, 0.5, 0.75]]), 1)
assert_almost_equal(coverage_error([[0, 1, 0]], [[0.25, 0.5, 0.75]]), 2)
assert_almost_equal(coverage_error([[0, 1, 1]], [[0.25, 0.5, 0.75]]), 2)
assert_almost_equal(coverage_error([[1, 0, 0]], [[0.25, 0.5, 0.75]]), 3)
assert_almost_equal(coverage_error([[1, 0, 1]], [[0.25, 0.5, 0.75]]), 3)
assert_almost_equal(coverage_error([[1, 1, 0]], [[0.25, 0.5, 0.75]]), 3)
assert_almost_equal(coverage_error([[1, 1, 1]], [[0.25, 0.5, 0.75]]), 3)
assert_almost_equal(coverage_error([[0, 0, 0]], [[0.75, 0.5, 0.25]]), 0)
assert_almost_equal(coverage_error([[0, 0, 1]], [[0.75, 0.5, 0.25]]), 3)
assert_almost_equal(coverage_error([[0, 1, 0]], [[0.75, 0.5, 0.25]]), 2)
assert_almost_equal(coverage_error([[0, 1, 1]], [[0.75, 0.5, 0.25]]), 3)
assert_almost_equal(coverage_error([[1, 0, 0]], [[0.75, 0.5, 0.25]]), 1)
assert_almost_equal(coverage_error([[1, 0, 1]], [[0.75, 0.5, 0.25]]), 3)
assert_almost_equal(coverage_error([[1, 1, 0]], [[0.75, 0.5, 0.25]]), 2)
assert_almost_equal(coverage_error([[1, 1, 1]], [[0.75, 0.5, 0.25]]), 3)
assert_almost_equal(coverage_error([[0, 0, 0]], [[0.5, 0.75, 0.25]]), 0)
assert_almost_equal(coverage_error([[0, 0, 1]], [[0.5, 0.75, 0.25]]), 3)
assert_almost_equal(coverage_error([[0, 1, 0]], [[0.5, 0.75, 0.25]]), 1)
assert_almost_equal(coverage_error([[0, 1, 1]], [[0.5, 0.75, 0.25]]), 3)
assert_almost_equal(coverage_error([[1, 0, 0]], [[0.5, 0.75, 0.25]]), 2)
assert_almost_equal(coverage_error([[1, 0, 1]], [[0.5, 0.75, 0.25]]), 3)
assert_almost_equal(coverage_error([[1, 1, 0]], [[0.5, 0.75, 0.25]]), 2)
assert_almost_equal(coverage_error([[1, 1, 1]], [[0.5, 0.75, 0.25]]), 3)
# Non trival case
assert_almost_equal(coverage_error([[0, 1, 0], [1, 1, 0]],
[[0.1, 10., -3], [0, 1, 3]]),
(1 + 3) / 2.)
assert_almost_equal(coverage_error([[0, 1, 0], [1, 1, 0], [0, 1, 1]],
[[0.1, 10, -3], [0, 1, 3], [0, 2, 0]]),
(1 + 3 + 3) / 3.)
assert_almost_equal(coverage_error([[0, 1, 0], [1, 1, 0], [0, 1, 1]],
[[0.1, 10, -3], [3, 1, 3], [0, 2, 0]]),
(1 + 3 + 3) / 3.)
def test_coverage_tie_handling():
assert_almost_equal(coverage_error([[0, 0]], [[0.5, 0.5]]), 0)
assert_almost_equal(coverage_error([[1, 0]], [[0.5, 0.5]]), 2)
assert_almost_equal(coverage_error([[0, 1]], [[0.5, 0.5]]), 2)
assert_almost_equal(coverage_error([[1, 1]], [[0.5, 0.5]]), 2)
assert_almost_equal(coverage_error([[0, 0, 0]], [[0.25, 0.5, 0.5]]), 0)
assert_almost_equal(coverage_error([[0, 0, 1]], [[0.25, 0.5, 0.5]]), 2)
assert_almost_equal(coverage_error([[0, 1, 0]], [[0.25, 0.5, 0.5]]), 2)
assert_almost_equal(coverage_error([[0, 1, 1]], [[0.25, 0.5, 0.5]]), 2)
assert_almost_equal(coverage_error([[1, 0, 0]], [[0.25, 0.5, 0.5]]), 3)
assert_almost_equal(coverage_error([[1, 0, 1]], [[0.25, 0.5, 0.5]]), 3)
assert_almost_equal(coverage_error([[1, 1, 0]], [[0.25, 0.5, 0.5]]), 3)
assert_almost_equal(coverage_error([[1, 1, 1]], [[0.25, 0.5, 0.5]]), 3)
def test_label_ranking_loss():
assert_almost_equal(label_ranking_loss([[0, 1]], [[0.25, 0.75]]), 0)
assert_almost_equal(label_ranking_loss([[0, 1]], [[0.75, 0.25]]), 1)
assert_almost_equal(label_ranking_loss([[0, 0, 1]], [[0.25, 0.5, 0.75]]),
0)
assert_almost_equal(label_ranking_loss([[0, 1, 0]], [[0.25, 0.5, 0.75]]),
1 / 2)
assert_almost_equal(label_ranking_loss([[0, 1, 1]], [[0.25, 0.5, 0.75]]),
0)
assert_almost_equal(label_ranking_loss([[1, 0, 0]], [[0.25, 0.5, 0.75]]),
2 / 2)
assert_almost_equal(label_ranking_loss([[1, 0, 1]], [[0.25, 0.5, 0.75]]),
1 / 2)
assert_almost_equal(label_ranking_loss([[1, 1, 0]], [[0.25, 0.5, 0.75]]),
2 / 2)
# Undefined metrics - the ranking doesn't matter
assert_almost_equal(label_ranking_loss([[0, 0]], [[0.75, 0.25]]), 0)
assert_almost_equal(label_ranking_loss([[1, 1]], [[0.75, 0.25]]), 0)
assert_almost_equal(label_ranking_loss([[0, 0]], [[0.5, 0.5]]), 0)
assert_almost_equal(label_ranking_loss([[1, 1]], [[0.5, 0.5]]), 0)
assert_almost_equal(label_ranking_loss([[0, 0, 0]], [[0.5, 0.75, 0.25]]),
0)
assert_almost_equal(label_ranking_loss([[1, 1, 1]], [[0.5, 0.75, 0.25]]),
0)
assert_almost_equal(label_ranking_loss([[0, 0, 0]], [[0.25, 0.5, 0.5]]),
0)
assert_almost_equal(label_ranking_loss([[1, 1, 1]], [[0.25, 0.5, 0.5]]), 0)
# Non trival case
assert_almost_equal(label_ranking_loss([[0, 1, 0], [1, 1, 0]],
[[0.1, 10., -3], [0, 1, 3]]),
(0 + 2 / 2) / 2.)
assert_almost_equal(label_ranking_loss(
[[0, 1, 0], [1, 1, 0], [0, 1, 1]],
[[0.1, 10, -3], [0, 1, 3], [0, 2, 0]]),
(0 + 2 / 2 + 1 / 2) / 3.)
assert_almost_equal(label_ranking_loss(
[[0, 1, 0], [1, 1, 0], [0, 1, 1]],
[[0.1, 10, -3], [3, 1, 3], [0, 2, 0]]),
(0 + 2 / 2 + 1 / 2) / 3.)
# Sparse csr matrices
assert_almost_equal(label_ranking_loss(
csr_matrix(np.array([[0, 1, 0], [1, 1, 0]])),
[[0.1, 10, -3], [3, 1, 3]]),
(0 + 2 / 2) / 2.)
def test_ranking_appropriate_input_shape():
# Check that y_true.shape != y_score.shape raise the proper exception
assert_raises(ValueError, label_ranking_loss, [[0, 1], [0, 1]], [0, 1])
assert_raises(ValueError, label_ranking_loss, [[0, 1], [0, 1]], [[0, 1]])
assert_raises(ValueError, label_ranking_loss,
[[0, 1], [0, 1]], [[0], [1]])
assert_raises(ValueError, label_ranking_loss, [[0, 1]], [[0, 1], [0, 1]])
assert_raises(ValueError, label_ranking_loss,
[[0], [1]], [[0, 1], [0, 1]])
assert_raises(ValueError, label_ranking_loss, [[0, 1], [0, 1]], [[0], [1]])
def test_ranking_loss_ties_handling():
# Tie handling
assert_almost_equal(label_ranking_loss([[1, 0]], [[0.5, 0.5]]), 1)
assert_almost_equal(label_ranking_loss([[0, 1]], [[0.5, 0.5]]), 1)
assert_almost_equal(label_ranking_loss([[0, 0, 1]], [[0.25, 0.5, 0.5]]),
1 / 2)
assert_almost_equal(label_ranking_loss([[0, 1, 0]], [[0.25, 0.5, 0.5]]),
1 / 2)
assert_almost_equal(label_ranking_loss([[0, 1, 1]], [[0.25, 0.5, 0.5]]), 0)
assert_almost_equal(label_ranking_loss([[1, 0, 0]], [[0.25, 0.5, 0.5]]), 1)
assert_almost_equal(label_ranking_loss([[1, 0, 1]], [[0.25, 0.5, 0.5]]), 1)
assert_almost_equal(label_ranking_loss([[1, 1, 0]], [[0.25, 0.5, 0.5]]), 1)
| mit |
MSeifert04/astropy | astropy/convolution/kernels.py | 6 | 33186 | # Licensed under a 3-clause BSD style license - see LICENSE.rst
import math
import numpy as np
from .core import Kernel1D, Kernel2D, Kernel
from .utils import has_even_axis, raise_even_kernel_exception
from astropy.modeling import models
from astropy.modeling.core import Fittable1DModel, Fittable2DModel
from astropy.utils.decorators import deprecated
__all__ = ['Gaussian1DKernel', 'Gaussian2DKernel', 'CustomKernel',
'Box1DKernel', 'Box2DKernel', 'Tophat2DKernel',
'Trapezoid1DKernel', 'RickerWavelet1DKernel', 'RickerWavelet2DKernel',
'AiryDisk2DKernel', 'Moffat2DKernel', 'Model1DKernel',
'Model2DKernel', 'TrapezoidDisk2DKernel', 'Ring2DKernel']
def _round_up_to_odd_integer(value):
i = math.ceil(value)
if i % 2 == 0:
return i + 1
else:
return i
class Gaussian1DKernel(Kernel1D):
"""
1D Gaussian filter kernel.
The Gaussian filter is a filter with great smoothing properties. It is
isotropic and does not produce artifacts.
Parameters
----------
stddev : number
Standard deviation of the Gaussian kernel.
x_size : odd int, optional
Size of the kernel array. Default = 8 * stddev
mode : str, optional
One of the following discretization modes:
* 'center' (default)
Discretize model by taking the value
at the center of the bin.
* 'linear_interp'
Discretize model by linearly interpolating
between the values at the corners of the bin.
* 'oversample'
Discretize model by taking the average
on an oversampled grid.
* 'integrate'
Discretize model by integrating the
model over the bin. Very slow.
factor : number, optional
Factor of oversampling. Default factor = 10. If the factor
is too large, evaluation can be very slow.
See Also
--------
Box1DKernel, Trapezoid1DKernel, RickerWavelet1DKernel
Examples
--------
Kernel response:
.. plot::
:include-source:
import matplotlib.pyplot as plt
from astropy.convolution import Gaussian1DKernel
gauss_1D_kernel = Gaussian1DKernel(10)
plt.plot(gauss_1D_kernel, drawstyle='steps')
plt.xlabel('x [pixels]')
plt.ylabel('value')
plt.show()
"""
_separable = True
_is_bool = False
def __init__(self, stddev, **kwargs):
self._model = models.Gaussian1D(1. / (np.sqrt(2 * np.pi) * stddev),
0, stddev)
self._default_size = _round_up_to_odd_integer(8 * stddev)
super().__init__(**kwargs)
self._truncation = np.abs(1. - self._array.sum())
class Gaussian2DKernel(Kernel2D):
"""
2D Gaussian filter kernel.
The Gaussian filter is a filter with great smoothing properties. It is
isotropic and does not produce artifacts.
Parameters
----------
x_stddev : float
Standard deviation of the Gaussian in x before rotating by theta.
y_stddev : float
Standard deviation of the Gaussian in y before rotating by theta.
theta : float or :class:`~astropy.units.Quantity`
Rotation angle. If passed as a float, it is assumed to be in radians.
The rotation angle increases counterclockwise.
x_size : odd int, optional
Size in x direction of the kernel array. Default = 8 * stddev.
y_size : odd int, optional
Size in y direction of the kernel array. Default = 8 * stddev.
mode : str, optional
One of the following discretization modes:
* 'center' (default)
Discretize model by taking the value
at the center of the bin.
* 'linear_interp'
Discretize model by performing a bilinear interpolation
between the values at the corners of the bin.
* 'oversample'
Discretize model by taking the average
on an oversampled grid.
* 'integrate'
Discretize model by integrating the
model over the bin.
factor : number, optional
Factor of oversampling. Default factor = 10.
See Also
--------
Box2DKernel, Tophat2DKernel, RickerWavelet2DKernel, Ring2DKernel,
TrapezoidDisk2DKernel, AiryDisk2DKernel, Moffat2DKernel
Examples
--------
Kernel response:
.. plot::
:include-source:
import matplotlib.pyplot as plt
from astropy.convolution import Gaussian2DKernel
gaussian_2D_kernel = Gaussian2DKernel(10)
plt.imshow(gaussian_2D_kernel, interpolation='none', origin='lower')
plt.xlabel('x [pixels]')
plt.ylabel('y [pixels]')
plt.colorbar()
plt.show()
"""
_separable = True
_is_bool = False
def __init__(self, x_stddev, y_stddev=None, theta=0.0, **kwargs):
if y_stddev is None:
y_stddev = x_stddev
self._model = models.Gaussian2D(1. / (2 * np.pi * x_stddev * y_stddev),
0, 0, x_stddev=x_stddev,
y_stddev=y_stddev, theta=theta)
self._default_size = _round_up_to_odd_integer(
8 * np.max([x_stddev, y_stddev]))
super().__init__(**kwargs)
self._truncation = np.abs(1. - self._array.sum())
class Box1DKernel(Kernel1D):
"""
1D Box filter kernel.
The Box filter or running mean is a smoothing filter. It is not isotropic
and can produce artifacts, when applied repeatedly to the same data.
By default the Box kernel uses the ``linear_interp`` discretization mode,
which allows non-shifting, even-sized kernels. This is achieved by
weighting the edge pixels with 1/2. E.g a Box kernel with an effective
smoothing of 4 pixel would have the following array: [0.5, 1, 1, 1, 0.5].
Parameters
----------
width : number
Width of the filter kernel.
mode : str, optional
One of the following discretization modes:
* 'center'
Discretize model by taking the value
at the center of the bin.
* 'linear_interp' (default)
Discretize model by linearly interpolating
between the values at the corners of the bin.
* 'oversample'
Discretize model by taking the average
on an oversampled grid.
* 'integrate'
Discretize model by integrating the
model over the bin.
factor : number, optional
Factor of oversampling. Default factor = 10.
See Also
--------
Gaussian1DKernel, Trapezoid1DKernel, RickerWavelet1DKernel
Examples
--------
Kernel response function:
.. plot::
:include-source:
import matplotlib.pyplot as plt
from astropy.convolution import Box1DKernel
box_1D_kernel = Box1DKernel(9)
plt.plot(box_1D_kernel, drawstyle='steps')
plt.xlim(-1, 9)
plt.xlabel('x [pixels]')
plt.ylabel('value')
plt.show()
"""
_separable = True
_is_bool = True
def __init__(self, width, **kwargs):
self._model = models.Box1D(1. / width, 0, width)
self._default_size = _round_up_to_odd_integer(width)
kwargs['mode'] = 'linear_interp'
super().__init__(**kwargs)
self._truncation = 0
self.normalize()
class Box2DKernel(Kernel2D):
"""
2D Box filter kernel.
The Box filter or running mean is a smoothing filter. It is not isotropic
and can produce artifact, when applied repeatedly to the same data.
By default the Box kernel uses the ``linear_interp`` discretization mode,
which allows non-shifting, even-sized kernels. This is achieved by
weighting the edge pixels with 1/2.
Parameters
----------
width : number
Width of the filter kernel.
mode : str, optional
One of the following discretization modes:
* 'center'
Discretize model by taking the value
at the center of the bin.
* 'linear_interp' (default)
Discretize model by performing a bilinear interpolation
between the values at the corners of the bin.
* 'oversample'
Discretize model by taking the average
on an oversampled grid.
* 'integrate'
Discretize model by integrating the
model over the bin.
factor : number, optional
Factor of oversampling. Default factor = 10.
See Also
--------
Gaussian2DKernel, Tophat2DKernel, RickerWavelet2DKernel, Ring2DKernel,
TrapezoidDisk2DKernel, AiryDisk2DKernel, Moffat2DKernel
Examples
--------
Kernel response:
.. plot::
:include-source:
import matplotlib.pyplot as plt
from astropy.convolution import Box2DKernel
box_2D_kernel = Box2DKernel(9)
plt.imshow(box_2D_kernel, interpolation='none', origin='lower',
vmin=0.0, vmax=0.015)
plt.xlim(-1, 9)
plt.ylim(-1, 9)
plt.xlabel('x [pixels]')
plt.ylabel('y [pixels]')
plt.colorbar()
plt.show()
"""
_separable = True
_is_bool = True
def __init__(self, width, **kwargs):
self._model = models.Box2D(1. / width ** 2, 0, 0, width, width)
self._default_size = _round_up_to_odd_integer(width)
kwargs['mode'] = 'linear_interp'
super().__init__(**kwargs)
self._truncation = 0
self.normalize()
class Tophat2DKernel(Kernel2D):
"""
2D Tophat filter kernel.
The Tophat filter is an isotropic smoothing filter. It can produce
artifacts when applied repeatedly on the same data.
Parameters
----------
radius : int
Radius of the filter kernel.
mode : str, optional
One of the following discretization modes:
* 'center' (default)
Discretize model by taking the value
at the center of the bin.
* 'linear_interp'
Discretize model by performing a bilinear interpolation
between the values at the corners of the bin.
* 'oversample'
Discretize model by taking the average
on an oversampled grid.
* 'integrate'
Discretize model by integrating the
model over the bin.
factor : number, optional
Factor of oversampling. Default factor = 10.
See Also
--------
Gaussian2DKernel, Box2DKernel, RickerWavelet2DKernel, Ring2DKernel,
TrapezoidDisk2DKernel, AiryDisk2DKernel, Moffat2DKernel
Examples
--------
Kernel response:
.. plot::
:include-source:
import matplotlib.pyplot as plt
from astropy.convolution import Tophat2DKernel
tophat_2D_kernel = Tophat2DKernel(40)
plt.imshow(tophat_2D_kernel, interpolation='none', origin='lower')
plt.xlabel('x [pixels]')
plt.ylabel('y [pixels]')
plt.colorbar()
plt.show()
"""
def __init__(self, radius, **kwargs):
self._model = models.Disk2D(1. / (np.pi * radius ** 2), 0, 0, radius)
self._default_size = _round_up_to_odd_integer(2 * radius)
super().__init__(**kwargs)
self._truncation = 0
class Ring2DKernel(Kernel2D):
"""
2D Ring filter kernel.
The Ring filter kernel is the difference between two Tophat kernels of
different width. This kernel is useful for, e.g., background estimation.
Parameters
----------
radius_in : number
Inner radius of the ring kernel.
width : number
Width of the ring kernel.
mode : str, optional
One of the following discretization modes:
* 'center' (default)
Discretize model by taking the value
at the center of the bin.
* 'linear_interp'
Discretize model by performing a bilinear interpolation
between the values at the corners of the bin.
* 'oversample'
Discretize model by taking the average
on an oversampled grid.
* 'integrate'
Discretize model by integrating the
model over the bin.
factor : number, optional
Factor of oversampling. Default factor = 10.
See Also
--------
Gaussian2DKernel, Box2DKernel, Tophat2DKernel, RickerWavelet2DKernel,
Ring2DKernel, AiryDisk2DKernel, Moffat2DKernel
Examples
--------
Kernel response:
.. plot::
:include-source:
import matplotlib.pyplot as plt
from astropy.convolution import Ring2DKernel
ring_2D_kernel = Ring2DKernel(9, 8)
plt.imshow(ring_2D_kernel, interpolation='none', origin='lower')
plt.xlabel('x [pixels]')
plt.ylabel('y [pixels]')
plt.colorbar()
plt.show()
"""
def __init__(self, radius_in, width, **kwargs):
radius_out = radius_in + width
self._model = models.Ring2D(1. / (np.pi * (radius_out ** 2 - radius_in ** 2)),
0, 0, radius_in, width)
self._default_size = _round_up_to_odd_integer(2 * radius_out)
super().__init__(**kwargs)
self._truncation = 0
class Trapezoid1DKernel(Kernel1D):
"""
1D trapezoid kernel.
Parameters
----------
width : number
Width of the filter kernel, defined as the width of the constant part,
before it begins to slope down.
slope : number
Slope of the filter kernel's tails
mode : str, optional
One of the following discretization modes:
* 'center' (default)
Discretize model by taking the value
at the center of the bin.
* 'linear_interp'
Discretize model by linearly interpolating
between the values at the corners of the bin.
* 'oversample'
Discretize model by taking the average
on an oversampled grid.
* 'integrate'
Discretize model by integrating the
model over the bin.
factor : number, optional
Factor of oversampling. Default factor = 10.
See Also
--------
Box1DKernel, Gaussian1DKernel, RickerWavelet1DKernel
Examples
--------
Kernel response:
.. plot::
:include-source:
import matplotlib.pyplot as plt
from astropy.convolution import Trapezoid1DKernel
trapezoid_1D_kernel = Trapezoid1DKernel(17, slope=0.2)
plt.plot(trapezoid_1D_kernel, drawstyle='steps')
plt.xlabel('x [pixels]')
plt.ylabel('amplitude')
plt.xlim(-1, 28)
plt.show()
"""
_is_bool = False
def __init__(self, width, slope=1., **kwargs):
self._model = models.Trapezoid1D(1, 0, width, slope)
self._default_size = _round_up_to_odd_integer(width + 2. / slope)
super().__init__(**kwargs)
self._truncation = 0
self.normalize()
class TrapezoidDisk2DKernel(Kernel2D):
"""
2D trapezoid kernel.
Parameters
----------
radius : number
Width of the filter kernel, defined as the width of the constant part,
before it begins to slope down.
slope : number
Slope of the filter kernel's tails
mode : str, optional
One of the following discretization modes:
* 'center' (default)
Discretize model by taking the value
at the center of the bin.
* 'linear_interp'
Discretize model by performing a bilinear interpolation
between the values at the corners of the bin.
* 'oversample'
Discretize model by taking the average
on an oversampled grid.
* 'integrate'
Discretize model by integrating the
model over the bin.
factor : number, optional
Factor of oversampling. Default factor = 10.
See Also
--------
Gaussian2DKernel, Box2DKernel, Tophat2DKernel, RickerWavelet2DKernel,
Ring2DKernel, AiryDisk2DKernel, Moffat2DKernel
Examples
--------
Kernel response:
.. plot::
:include-source:
import matplotlib.pyplot as plt
from astropy.convolution import TrapezoidDisk2DKernel
trapezoid_2D_kernel = TrapezoidDisk2DKernel(20, slope=0.2)
plt.imshow(trapezoid_2D_kernel, interpolation='none', origin='lower')
plt.xlabel('x [pixels]')
plt.ylabel('y [pixels]')
plt.colorbar()
plt.show()
"""
_is_bool = False
def __init__(self, radius, slope=1., **kwargs):
self._model = models.TrapezoidDisk2D(1, 0, 0, radius, slope)
self._default_size = _round_up_to_odd_integer(2 * radius + 2. / slope)
super().__init__(**kwargs)
self._truncation = 0
self.normalize()
class RickerWavelet1DKernel(Kernel1D):
"""
1D Ricker wavelet filter kernel (sometimes known as a "Mexican Hat"
kernel).
The Ricker wavelet, or inverted Gaussian-Laplace filter, is a
bandpass filter. It smooths the data and removes slowly varying
or constant structures (e.g. Background). It is useful for peak or
multi-scale detection.
This kernel is derived from a normalized Gaussian function, by
computing the second derivative. This results in an amplitude
at the kernels center of 1. / (sqrt(2 * pi) * width ** 3). The
normalization is the same as for `scipy.ndimage.gaussian_laplace`,
except for a minus sign.
.. note::
See https://github.com/astropy/astropy/pull/9445 for discussions
related to renaming of this kernel.
Parameters
----------
width : number
Width of the filter kernel, defined as the standard deviation
of the Gaussian function from which it is derived.
x_size : odd int, optional
Size in x direction of the kernel array. Default = 8 * width.
mode : str, optional
One of the following discretization modes:
* 'center' (default)
Discretize model by taking the value
at the center of the bin.
* 'linear_interp'
Discretize model by linearly interpolating
between the values at the corners of the bin.
* 'oversample'
Discretize model by taking the average
on an oversampled grid.
* 'integrate'
Discretize model by integrating the
model over the bin.
factor : number, optional
Factor of oversampling. Default factor = 10.
See Also
--------
Box1DKernel, Gaussian1DKernel, Trapezoid1DKernel
Examples
--------
Kernel response:
.. plot::
:include-source:
import matplotlib.pyplot as plt
from astropy.convolution import RickerWavelet1DKernel
ricker_1d_kernel = RickerWavelet1DKernel(10)
plt.plot(ricker_1d_kernel, drawstyle='steps')
plt.xlabel('x [pixels]')
plt.ylabel('value')
plt.show()
"""
_is_bool = True
def __init__(self, width, **kwargs):
amplitude = 1.0 / (np.sqrt(2 * np.pi) * width ** 3)
self._model = models.RickerWavelet1D(amplitude, 0, width)
self._default_size = _round_up_to_odd_integer(8 * width)
super().__init__(**kwargs)
self._truncation = np.abs(self._array.sum() / self._array.size)
class RickerWavelet2DKernel(Kernel2D):
"""
2D Ricker wavelet filter kernel (sometimes known as a "Mexican Hat"
kernel).
The Ricker wavelet, or inverted Gaussian-Laplace filter, is a
bandpass filter. It smooths the data and removes slowly varying
or constant structures (e.g. Background). It is useful for peak or
multi-scale detection.
This kernel is derived from a normalized Gaussian function, by
computing the second derivative. This results in an amplitude
at the kernels center of 1. / (pi * width ** 4). The normalization
is the same as for `scipy.ndimage.gaussian_laplace`, except
for a minus sign.
.. note::
See https://github.com/astropy/astropy/pull/9445 for discussions
related to renaming of this kernel.
Parameters
----------
width : number
Width of the filter kernel, defined as the standard deviation
of the Gaussian function from which it is derived.
x_size : odd int, optional
Size in x direction of the kernel array. Default = 8 * width.
y_size : odd int, optional
Size in y direction of the kernel array. Default = 8 * width.
mode : str, optional
One of the following discretization modes:
* 'center' (default)
Discretize model by taking the value
at the center of the bin.
* 'linear_interp'
Discretize model by performing a bilinear interpolation
between the values at the corners of the bin.
* 'oversample'
Discretize model by taking the average
on an oversampled grid.
* 'integrate'
Discretize model by integrating the
model over the bin.
factor : number, optional
Factor of oversampling. Default factor = 10.
See Also
--------
Gaussian2DKernel, Box2DKernel, Tophat2DKernel, Ring2DKernel,
TrapezoidDisk2DKernel, AiryDisk2DKernel, Moffat2DKernel
Examples
--------
Kernel response:
.. plot::
:include-source:
import matplotlib.pyplot as plt
from astropy.convolution import RickerWavelet2DKernel
ricker_2d_kernel = RickerWavelet2DKernel(10)
plt.imshow(ricker_2d_kernel, interpolation='none', origin='lower')
plt.xlabel('x [pixels]')
plt.ylabel('y [pixels]')
plt.colorbar()
plt.show()
"""
_is_bool = False
def __init__(self, width, **kwargs):
amplitude = 1.0 / (np.pi * width ** 4)
self._model = models.RickerWavelet2D(amplitude, 0, 0, width)
self._default_size = _round_up_to_odd_integer(8 * width)
super().__init__(**kwargs)
self._truncation = np.abs(self._array.sum() / self._array.size)
class AiryDisk2DKernel(Kernel2D):
"""
2D Airy disk kernel.
This kernel models the diffraction pattern of a circular aperture. This
kernel is normalized to a peak value of 1.
Parameters
----------
radius : float
The radius of the Airy disk kernel (radius of the first zero).
x_size : odd int, optional
Size in x direction of the kernel array. Default = 8 * radius.
y_size : odd int, optional
Size in y direction of the kernel array. Default = 8 * radius.
mode : str, optional
One of the following discretization modes:
* 'center' (default)
Discretize model by taking the value
at the center of the bin.
* 'linear_interp'
Discretize model by performing a bilinear interpolation
between the values at the corners of the bin.
* 'oversample'
Discretize model by taking the average
on an oversampled grid.
* 'integrate'
Discretize model by integrating the
model over the bin.
factor : number, optional
Factor of oversampling. Default factor = 10.
See Also
--------
Gaussian2DKernel, Box2DKernel, Tophat2DKernel, RickerWavelet2DKernel,
Ring2DKernel, TrapezoidDisk2DKernel, AiryDisk2DKernel, Moffat2DKernel
Examples
--------
Kernel response:
.. plot::
:include-source:
import matplotlib.pyplot as plt
from astropy.convolution import AiryDisk2DKernel
airydisk_2D_kernel = AiryDisk2DKernel(10)
plt.imshow(airydisk_2D_kernel, interpolation='none', origin='lower')
plt.xlabel('x [pixels]')
plt.ylabel('y [pixels]')
plt.colorbar()
plt.show()
"""
_is_bool = False
def __init__(self, radius, **kwargs):
self._model = models.AiryDisk2D(1, 0, 0, radius)
self._default_size = _round_up_to_odd_integer(8 * radius)
super().__init__(**kwargs)
self.normalize()
self._truncation = None
class Moffat2DKernel(Kernel2D):
"""
2D Moffat kernel.
This kernel is a typical model for a seeing limited PSF.
Parameters
----------
gamma : float
Core width of the Moffat model.
alpha : float
Power index of the Moffat model.
x_size : odd int, optional
Size in x direction of the kernel array. Default = 8 * radius.
y_size : odd int, optional
Size in y direction of the kernel array. Default = 8 * radius.
mode : str, optional
One of the following discretization modes:
* 'center' (default)
Discretize model by taking the value
at the center of the bin.
* 'linear_interp'
Discretize model by performing a bilinear interpolation
between the values at the corners of the bin.
* 'oversample'
Discretize model by taking the average
on an oversampled grid.
* 'integrate'
Discretize model by integrating the
model over the bin.
factor : number, optional
Factor of oversampling. Default factor = 10.
See Also
--------
Gaussian2DKernel, Box2DKernel, Tophat2DKernel, RickerWavelet2DKernel,
Ring2DKernel, TrapezoidDisk2DKernel, AiryDisk2DKernel
Examples
--------
Kernel response:
.. plot::
:include-source:
import matplotlib.pyplot as plt
from astropy.convolution import Moffat2DKernel
moffat_2D_kernel = Moffat2DKernel(3, 2)
plt.imshow(moffat_2D_kernel, interpolation='none', origin='lower')
plt.xlabel('x [pixels]')
plt.ylabel('y [pixels]')
plt.colorbar()
plt.show()
"""
_is_bool = False
def __init__(self, gamma, alpha, **kwargs):
# Compute amplitude, from
# https://en.wikipedia.org/wiki/Moffat_distribution
amplitude = (alpha - 1.0) / (np.pi * gamma * gamma)
self._model = models.Moffat2D(amplitude, 0, 0, gamma, alpha)
self._default_size = _round_up_to_odd_integer(4.0 * self._model.fwhm)
super().__init__(**kwargs)
self.normalize()
self._truncation = None
class Model1DKernel(Kernel1D):
"""
Create kernel from 1D model.
The model has to be centered on x = 0.
Parameters
----------
model : `~astropy.modeling.Fittable1DModel`
Kernel response function model
x_size : odd int, optional
Size in x direction of the kernel array. Default = 8 * width.
mode : str, optional
One of the following discretization modes:
* 'center' (default)
Discretize model by taking the value
at the center of the bin.
* 'linear_interp'
Discretize model by linearly interpolating
between the values at the corners of the bin.
* 'oversample'
Discretize model by taking the average
on an oversampled grid.
* 'integrate'
Discretize model by integrating the
model over the bin.
factor : number, optional
Factor of oversampling. Default factor = 10.
Raises
------
TypeError
If model is not an instance of `~astropy.modeling.Fittable1DModel`
See also
--------
Model2DKernel : Create kernel from `~astropy.modeling.Fittable2DModel`
CustomKernel : Create kernel from list or array
Examples
--------
Define a Gaussian1D model:
>>> from astropy.modeling.models import Gaussian1D
>>> from astropy.convolution.kernels import Model1DKernel
>>> gauss = Gaussian1D(1, 0, 2)
And create a custom one dimensional kernel from it:
>>> gauss_kernel = Model1DKernel(gauss, x_size=9)
This kernel can now be used like a usual Astropy kernel.
"""
_separable = False
_is_bool = False
def __init__(self, model, **kwargs):
if isinstance(model, Fittable1DModel):
self._model = model
else:
raise TypeError("Must be Fittable1DModel")
super().__init__(**kwargs)
class Model2DKernel(Kernel2D):
"""
Create kernel from 2D model.
The model has to be centered on x = 0 and y = 0.
Parameters
----------
model : `~astropy.modeling.Fittable2DModel`
Kernel response function model
x_size : odd int, optional
Size in x direction of the kernel array. Default = 8 * width.
y_size : odd int, optional
Size in y direction of the kernel array. Default = 8 * width.
mode : str, optional
One of the following discretization modes:
* 'center' (default)
Discretize model by taking the value
at the center of the bin.
* 'linear_interp'
Discretize model by performing a bilinear interpolation
between the values at the corners of the bin.
* 'oversample'
Discretize model by taking the average
on an oversampled grid.
* 'integrate'
Discretize model by integrating the
model over the bin.
factor : number, optional
Factor of oversampling. Default factor = 10.
Raises
------
TypeError
If model is not an instance of `~astropy.modeling.Fittable2DModel`
See also
--------
Model1DKernel : Create kernel from `~astropy.modeling.Fittable1DModel`
CustomKernel : Create kernel from list or array
Examples
--------
Define a Gaussian2D model:
>>> from astropy.modeling.models import Gaussian2D
>>> from astropy.convolution.kernels import Model2DKernel
>>> gauss = Gaussian2D(1, 0, 0, 2, 2)
And create a custom two dimensional kernel from it:
>>> gauss_kernel = Model2DKernel(gauss, x_size=9)
This kernel can now be used like a usual astropy kernel.
"""
_is_bool = False
_separable = False
def __init__(self, model, **kwargs):
self._separable = False
if isinstance(model, Fittable2DModel):
self._model = model
else:
raise TypeError("Must be Fittable2DModel")
super().__init__(**kwargs)
class PSFKernel(Kernel2D):
"""
Initialize filter kernel from astropy PSF instance.
"""
_separable = False
def __init__(self):
raise NotImplementedError('Not yet implemented')
class CustomKernel(Kernel):
"""
Create filter kernel from list or array.
Parameters
----------
array : list or array
Filter kernel array. Size must be odd.
Raises
------
TypeError
If array is not a list or array.
KernelSizeError
If array size is even.
See also
--------
Model2DKernel, Model1DKernel
Examples
--------
Define one dimensional array:
>>> from astropy.convolution.kernels import CustomKernel
>>> import numpy as np
>>> array = np.array([1, 2, 3, 2, 1])
>>> kernel = CustomKernel(array)
>>> kernel.dimension
1
Define two dimensional array:
>>> array = np.array([[1, 1, 1], [1, 2, 1], [1, 1, 1]])
>>> kernel = CustomKernel(array)
>>> kernel.dimension
2
"""
def __init__(self, array):
self.array = array
super().__init__(self._array)
@property
def array(self):
"""
Filter kernel array.
"""
return self._array
@array.setter
def array(self, array):
"""
Filter kernel array setter
"""
if isinstance(array, np.ndarray):
self._array = array.astype(np.float64)
elif isinstance(array, list):
self._array = np.array(array, dtype=np.float64)
else:
raise TypeError("Must be list or array.")
# Check if array is odd in all axes
if has_even_axis(self):
raise_even_kernel_exception()
# Check if array is bool
ones = self._array == 1.
zeros = self._array == 0
self._is_bool = bool(np.all(np.logical_or(ones, zeros)))
self._truncation = 0.0
@deprecated('4.0', alternative='RickerWavelet1DKernel')
class MexicanHat1DKernel(RickerWavelet1DKernel):
pass
@deprecated('4.0', alternative='RickerWavelet2DKernel')
class MexicanHat2DKernel(RickerWavelet2DKernel):
pass
| bsd-3-clause |
LiaoPan/scikit-learn | sklearn/datasets/tests/test_svmlight_format.py | 228 | 11221 | from bz2 import BZ2File
import gzip
from io import BytesIO
import numpy as np
import os
import shutil
from tempfile import NamedTemporaryFile
from sklearn.externals.six import b
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_array_equal
from sklearn.utils.testing import assert_array_almost_equal
from sklearn.utils.testing import assert_raises
from sklearn.utils.testing import raises
from sklearn.utils.testing import assert_in
import sklearn
from sklearn.datasets import (load_svmlight_file, load_svmlight_files,
dump_svmlight_file)
currdir = os.path.dirname(os.path.abspath(__file__))
datafile = os.path.join(currdir, "data", "svmlight_classification.txt")
multifile = os.path.join(currdir, "data", "svmlight_multilabel.txt")
invalidfile = os.path.join(currdir, "data", "svmlight_invalid.txt")
invalidfile2 = os.path.join(currdir, "data", "svmlight_invalid_order.txt")
def test_load_svmlight_file():
X, y = load_svmlight_file(datafile)
# test X's shape
assert_equal(X.indptr.shape[0], 7)
assert_equal(X.shape[0], 6)
assert_equal(X.shape[1], 21)
assert_equal(y.shape[0], 6)
# test X's non-zero values
for i, j, val in ((0, 2, 2.5), (0, 10, -5.2), (0, 15, 1.5),
(1, 5, 1.0), (1, 12, -3),
(2, 20, 27)):
assert_equal(X[i, j], val)
# tests X's zero values
assert_equal(X[0, 3], 0)
assert_equal(X[0, 5], 0)
assert_equal(X[1, 8], 0)
assert_equal(X[1, 16], 0)
assert_equal(X[2, 18], 0)
# test can change X's values
X[0, 2] *= 2
assert_equal(X[0, 2], 5)
# test y
assert_array_equal(y, [1, 2, 3, 4, 1, 2])
def test_load_svmlight_file_fd():
# test loading from file descriptor
X1, y1 = load_svmlight_file(datafile)
fd = os.open(datafile, os.O_RDONLY)
try:
X2, y2 = load_svmlight_file(fd)
assert_array_equal(X1.data, X2.data)
assert_array_equal(y1, y2)
finally:
os.close(fd)
def test_load_svmlight_file_multilabel():
X, y = load_svmlight_file(multifile, multilabel=True)
assert_equal(y, [(0, 1), (2,), (), (1, 2)])
def test_load_svmlight_files():
X_train, y_train, X_test, y_test = load_svmlight_files([datafile] * 2,
dtype=np.float32)
assert_array_equal(X_train.toarray(), X_test.toarray())
assert_array_equal(y_train, y_test)
assert_equal(X_train.dtype, np.float32)
assert_equal(X_test.dtype, np.float32)
X1, y1, X2, y2, X3, y3 = load_svmlight_files([datafile] * 3,
dtype=np.float64)
assert_equal(X1.dtype, X2.dtype)
assert_equal(X2.dtype, X3.dtype)
assert_equal(X3.dtype, np.float64)
def test_load_svmlight_file_n_features():
X, y = load_svmlight_file(datafile, n_features=22)
# test X'shape
assert_equal(X.indptr.shape[0], 7)
assert_equal(X.shape[0], 6)
assert_equal(X.shape[1], 22)
# test X's non-zero values
for i, j, val in ((0, 2, 2.5), (0, 10, -5.2),
(1, 5, 1.0), (1, 12, -3)):
assert_equal(X[i, j], val)
# 21 features in file
assert_raises(ValueError, load_svmlight_file, datafile, n_features=20)
def test_load_compressed():
X, y = load_svmlight_file(datafile)
with NamedTemporaryFile(prefix="sklearn-test", suffix=".gz") as tmp:
tmp.close() # necessary under windows
with open(datafile, "rb") as f:
shutil.copyfileobj(f, gzip.open(tmp.name, "wb"))
Xgz, ygz = load_svmlight_file(tmp.name)
# because we "close" it manually and write to it,
# we need to remove it manually.
os.remove(tmp.name)
assert_array_equal(X.toarray(), Xgz.toarray())
assert_array_equal(y, ygz)
with NamedTemporaryFile(prefix="sklearn-test", suffix=".bz2") as tmp:
tmp.close() # necessary under windows
with open(datafile, "rb") as f:
shutil.copyfileobj(f, BZ2File(tmp.name, "wb"))
Xbz, ybz = load_svmlight_file(tmp.name)
# because we "close" it manually and write to it,
# we need to remove it manually.
os.remove(tmp.name)
assert_array_equal(X.toarray(), Xbz.toarray())
assert_array_equal(y, ybz)
@raises(ValueError)
def test_load_invalid_file():
load_svmlight_file(invalidfile)
@raises(ValueError)
def test_load_invalid_order_file():
load_svmlight_file(invalidfile2)
@raises(ValueError)
def test_load_zero_based():
f = BytesIO(b("-1 4:1.\n1 0:1\n"))
load_svmlight_file(f, zero_based=False)
def test_load_zero_based_auto():
data1 = b("-1 1:1 2:2 3:3\n")
data2 = b("-1 0:0 1:1\n")
f1 = BytesIO(data1)
X, y = load_svmlight_file(f1, zero_based="auto")
assert_equal(X.shape, (1, 3))
f1 = BytesIO(data1)
f2 = BytesIO(data2)
X1, y1, X2, y2 = load_svmlight_files([f1, f2], zero_based="auto")
assert_equal(X1.shape, (1, 4))
assert_equal(X2.shape, (1, 4))
def test_load_with_qid():
# load svmfile with qid attribute
data = b("""
3 qid:1 1:0.53 2:0.12
2 qid:1 1:0.13 2:0.1
7 qid:2 1:0.87 2:0.12""")
X, y = load_svmlight_file(BytesIO(data), query_id=False)
assert_array_equal(y, [3, 2, 7])
assert_array_equal(X.toarray(), [[.53, .12], [.13, .1], [.87, .12]])
res1 = load_svmlight_files([BytesIO(data)], query_id=True)
res2 = load_svmlight_file(BytesIO(data), query_id=True)
for X, y, qid in (res1, res2):
assert_array_equal(y, [3, 2, 7])
assert_array_equal(qid, [1, 1, 2])
assert_array_equal(X.toarray(), [[.53, .12], [.13, .1], [.87, .12]])
@raises(ValueError)
def test_load_invalid_file2():
load_svmlight_files([datafile, invalidfile, datafile])
@raises(TypeError)
def test_not_a_filename():
# in python 3 integers are valid file opening arguments (taken as unix
# file descriptors)
load_svmlight_file(.42)
@raises(IOError)
def test_invalid_filename():
load_svmlight_file("trou pic nic douille")
def test_dump():
Xs, y = load_svmlight_file(datafile)
Xd = Xs.toarray()
# slicing a csr_matrix can unsort its .indices, so test that we sort
# those correctly
Xsliced = Xs[np.arange(Xs.shape[0])]
for X in (Xs, Xd, Xsliced):
for zero_based in (True, False):
for dtype in [np.float32, np.float64, np.int32]:
f = BytesIO()
# we need to pass a comment to get the version info in;
# LibSVM doesn't grok comments so they're not put in by
# default anymore.
dump_svmlight_file(X.astype(dtype), y, f, comment="test",
zero_based=zero_based)
f.seek(0)
comment = f.readline()
try:
comment = str(comment, "utf-8")
except TypeError: # fails in Python 2.x
pass
assert_in("scikit-learn %s" % sklearn.__version__, comment)
comment = f.readline()
try:
comment = str(comment, "utf-8")
except TypeError: # fails in Python 2.x
pass
assert_in(["one", "zero"][zero_based] + "-based", comment)
X2, y2 = load_svmlight_file(f, dtype=dtype,
zero_based=zero_based)
assert_equal(X2.dtype, dtype)
assert_array_equal(X2.sorted_indices().indices, X2.indices)
if dtype == np.float32:
assert_array_almost_equal(
# allow a rounding error at the last decimal place
Xd.astype(dtype), X2.toarray(), 4)
else:
assert_array_almost_equal(
# allow a rounding error at the last decimal place
Xd.astype(dtype), X2.toarray(), 15)
assert_array_equal(y, y2)
def test_dump_multilabel():
X = [[1, 0, 3, 0, 5],
[0, 0, 0, 0, 0],
[0, 5, 0, 1, 0]]
y = [[0, 1, 0], [1, 0, 1], [1, 1, 0]]
f = BytesIO()
dump_svmlight_file(X, y, f, multilabel=True)
f.seek(0)
# make sure it dumps multilabel correctly
assert_equal(f.readline(), b("1 0:1 2:3 4:5\n"))
assert_equal(f.readline(), b("0,2 \n"))
assert_equal(f.readline(), b("0,1 1:5 3:1\n"))
def test_dump_concise():
one = 1
two = 2.1
three = 3.01
exact = 1.000000000000001
# loses the last decimal place
almost = 1.0000000000000001
X = [[one, two, three, exact, almost],
[1e9, 2e18, 3e27, 0, 0],
[0, 0, 0, 0, 0],
[0, 0, 0, 0, 0],
[0, 0, 0, 0, 0]]
y = [one, two, three, exact, almost]
f = BytesIO()
dump_svmlight_file(X, y, f)
f.seek(0)
# make sure it's using the most concise format possible
assert_equal(f.readline(),
b("1 0:1 1:2.1 2:3.01 3:1.000000000000001 4:1\n"))
assert_equal(f.readline(), b("2.1 0:1000000000 1:2e+18 2:3e+27\n"))
assert_equal(f.readline(), b("3.01 \n"))
assert_equal(f.readline(), b("1.000000000000001 \n"))
assert_equal(f.readline(), b("1 \n"))
f.seek(0)
# make sure it's correct too :)
X2, y2 = load_svmlight_file(f)
assert_array_almost_equal(X, X2.toarray())
assert_array_equal(y, y2)
def test_dump_comment():
X, y = load_svmlight_file(datafile)
X = X.toarray()
f = BytesIO()
ascii_comment = "This is a comment\nspanning multiple lines."
dump_svmlight_file(X, y, f, comment=ascii_comment, zero_based=False)
f.seek(0)
X2, y2 = load_svmlight_file(f, zero_based=False)
assert_array_almost_equal(X, X2.toarray())
assert_array_equal(y, y2)
# XXX we have to update this to support Python 3.x
utf8_comment = b("It is true that\n\xc2\xbd\xc2\xb2 = \xc2\xbc")
f = BytesIO()
assert_raises(UnicodeDecodeError,
dump_svmlight_file, X, y, f, comment=utf8_comment)
unicode_comment = utf8_comment.decode("utf-8")
f = BytesIO()
dump_svmlight_file(X, y, f, comment=unicode_comment, zero_based=False)
f.seek(0)
X2, y2 = load_svmlight_file(f, zero_based=False)
assert_array_almost_equal(X, X2.toarray())
assert_array_equal(y, y2)
f = BytesIO()
assert_raises(ValueError,
dump_svmlight_file, X, y, f, comment="I've got a \0.")
def test_dump_invalid():
X, y = load_svmlight_file(datafile)
f = BytesIO()
y2d = [y]
assert_raises(ValueError, dump_svmlight_file, X, y2d, f)
f = BytesIO()
assert_raises(ValueError, dump_svmlight_file, X, y[:-1], f)
def test_dump_query_id():
# test dumping a file with query_id
X, y = load_svmlight_file(datafile)
X = X.toarray()
query_id = np.arange(X.shape[0]) // 2
f = BytesIO()
dump_svmlight_file(X, y, f, query_id=query_id, zero_based=True)
f.seek(0)
X1, y1, query_id1 = load_svmlight_file(f, query_id=True, zero_based=True)
assert_array_almost_equal(X, X1.toarray())
assert_array_almost_equal(y, y1)
assert_array_almost_equal(query_id, query_id1)
| bsd-3-clause |
bikong2/scikit-learn | examples/model_selection/plot_roc_crossval.py | 247 | 3253 | """
=============================================================
Receiver Operating Characteristic (ROC) with cross validation
=============================================================
Example of Receiver Operating Characteristic (ROC) metric to evaluate
classifier output quality using cross-validation.
ROC curves typically feature true positive rate on the Y axis, and false
positive rate on the X axis. This means that the top left corner of the plot is
the "ideal" point - a false positive rate of zero, and a true positive rate of
one. This is not very realistic, but it does mean that a larger area under the
curve (AUC) is usually better.
The "steepness" of ROC curves is also important, since it is ideal to maximize
the true positive rate while minimizing the false positive rate.
This example shows the ROC response of different datasets, created from K-fold
cross-validation. Taking all of these curves, it is possible to calculate the
mean area under curve, and see the variance of the curve when the
training set is split into different subsets. This roughly shows how the
classifier output is affected by changes in the training data, and how
different the splits generated by K-fold cross-validation are from one another.
.. note::
See also :func:`sklearn.metrics.auc_score`,
:func:`sklearn.cross_validation.cross_val_score`,
:ref:`example_model_selection_plot_roc.py`,
"""
print(__doc__)
import numpy as np
from scipy import interp
import matplotlib.pyplot as plt
from sklearn import svm, datasets
from sklearn.metrics import roc_curve, auc
from sklearn.cross_validation import StratifiedKFold
###############################################################################
# Data IO and generation
# import some data to play with
iris = datasets.load_iris()
X = iris.data
y = iris.target
X, y = X[y != 2], y[y != 2]
n_samples, n_features = X.shape
# Add noisy features
random_state = np.random.RandomState(0)
X = np.c_[X, random_state.randn(n_samples, 200 * n_features)]
###############################################################################
# Classification and ROC analysis
# Run classifier with cross-validation and plot ROC curves
cv = StratifiedKFold(y, n_folds=6)
classifier = svm.SVC(kernel='linear', probability=True,
random_state=random_state)
mean_tpr = 0.0
mean_fpr = np.linspace(0, 1, 100)
all_tpr = []
for i, (train, test) in enumerate(cv):
probas_ = classifier.fit(X[train], y[train]).predict_proba(X[test])
# Compute ROC curve and area the curve
fpr, tpr, thresholds = roc_curve(y[test], probas_[:, 1])
mean_tpr += interp(mean_fpr, fpr, tpr)
mean_tpr[0] = 0.0
roc_auc = auc(fpr, tpr)
plt.plot(fpr, tpr, lw=1, label='ROC fold %d (area = %0.2f)' % (i, roc_auc))
plt.plot([0, 1], [0, 1], '--', color=(0.6, 0.6, 0.6), label='Luck')
mean_tpr /= len(cv)
mean_tpr[-1] = 1.0
mean_auc = auc(mean_fpr, mean_tpr)
plt.plot(mean_fpr, mean_tpr, 'k--',
label='Mean ROC (area = %0.2f)' % mean_auc, lw=2)
plt.xlim([-0.05, 1.05])
plt.ylim([-0.05, 1.05])
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.title('Receiver operating characteristic example')
plt.legend(loc="lower right")
plt.show()
| bsd-3-clause |
mne-tools/mne-tools.github.io | 0.18/_downloads/3af2606b8c96050e95e4212dafcadf19/plot_modifying_data_inplace.py | 4 | 2788 | """
.. _tut_modifying_data_inplace:
Modifying data in-place
=======================
It is often necessary to modify data once you have loaded it into memory.
Common examples of this are signal processing, feature extraction, and data
cleaning. Some functionality is pre-built into MNE-python, though it is also
possible to apply an arbitrary function to the data.
"""
import mne
import os.path as op
import numpy as np
from matplotlib import pyplot as plt
###############################################################################
# Load an example dataset, the preload flag loads the data into memory now
data_path = op.join(mne.datasets.sample.data_path(), 'MEG',
'sample', 'sample_audvis_raw.fif')
raw = mne.io.read_raw_fif(data_path, preload=True)
raw = raw.crop(0, 10)
print(raw)
###############################################################################
# Signal processing
# -----------------
#
# Most MNE objects have in-built methods for filtering:
filt_bands = [(1, 3), (3, 10), (10, 20), (20, 60)]
_, (ax, ax2) = plt.subplots(2, 1, figsize=(15, 10))
data, times = raw[0]
_ = ax.plot(data[0])
for fmin, fmax in filt_bands:
raw_filt = raw.copy()
raw_filt.filter(fmin, fmax, fir_design='firwin')
_ = ax2.plot(raw_filt[0][0][0])
ax2.legend(filt_bands)
ax.set_title('Raw data')
ax2.set_title('Band-pass filtered data')
###############################################################################
# In addition, there are functions for applying the Hilbert transform, which is
# useful to calculate phase / amplitude of your signal.
# Filter signal with a fairly steep filter, then take hilbert transform
raw_band = raw.copy()
raw_band.filter(12, 18, l_trans_bandwidth=2., h_trans_bandwidth=2.,
fir_design='firwin')
raw_hilb = raw_band.copy()
hilb_picks = mne.pick_types(raw_band.info, meg=False, eeg=True)
raw_hilb.apply_hilbert(hilb_picks)
print(raw_hilb[0][0].dtype)
###############################################################################
# Finally, it is possible to apply arbitrary functions to your data to do
# what you want. Here we will use this to take the amplitude and phase of
# the hilbert transformed data.
#
# .. note:: You can also use ``amplitude=True`` in the call to
# :meth:`mne.io.Raw.apply_hilbert` to do this automatically.
#
# Take the amplitude and phase
raw_amp = raw_hilb.copy()
raw_amp.apply_function(np.abs, hilb_picks)
raw_phase = raw_hilb.copy()
raw_phase.apply_function(np.angle, hilb_picks)
_, (a1, a2) = plt.subplots(2, 1, figsize=(15, 10))
a1.plot(raw_band[hilb_picks[0]][0][0].real)
a1.plot(raw_amp[hilb_picks[0]][0][0].real)
a2.plot(raw_phase[hilb_picks[0]][0][0].real)
a1.set_title('Amplitude of frequency band')
a2.set_title('Phase of frequency band')
| bsd-3-clause |
adaptive-learning/robomission | backend/monitoring/visualization.py | 1 | 1855 | """Helper functions for visualization our data in Jupyter notebooks.
"""
from IPython.display import display
import seaborn as sns
VIRIDIS = sns.color_palette('viridis', n_colors=256)
def phase_background(values):
alpha = 0.1
if 'level2' in values:
alpha = values.level2 * 0.1
attr = 'background-color: rgba(0, 0, 0, {alpha});'.format(alpha=alpha)
return [attr for v in values]
def viridis_background(value, vmin, vmax, reverse=False):
norm_value = (value - vmin) / (vmax - vmin)
if reverse:
norm_value = 1 - norm_value
max_index = len(VIRIDIS) - 1
soft_index = norm_value * max_index
index = max(0, min(max_index, int(soft_index)))
color = (int(255*c) for c in VIRIDIS[index])
return 'background-color: rgba({0}, {1}, {2}, 0.6);'.format(*color)
def percentage_background(value):
return viridis_background(value, vmin=0, vmax=1)
def time_background(value):
return viridis_background(value, vmin=0, vmax=300, reverse=True)
def style_level(df, order_by='order'):
df = df.sort_values(by=order_by)
styled_df = (
df.style
.set_table_styles(
[{'selector': 'tr',
'props': [('background-color', 'white')]},
# Workaround to hide the index column:
{'selector': '.row_heading, .blank',
'props': [('display', 'none;')]},
])
#.bar(subset=['n_attempts'], align='mid', color='#d65f5f')
.apply(phase_background, axis=1)
.applymap(percentage_background, subset=['success'])
.applymap(time_background, subset=['time'])
.format({
'success': '{:.0%}'.format,
'time': '{:.0f}s'.format,
}))
return styled_df
def display_level_overview(df, order_by):
styled = style_level(df, order_by=order_by)
display(styled)
| gpl-3.0 |
HeraclesHX/scikit-learn | sklearn/covariance/tests/test_graph_lasso.py | 272 | 5245 | """ Test the graph_lasso module.
"""
import sys
import numpy as np
from scipy import linalg
from sklearn.utils.testing import assert_array_almost_equal
from sklearn.utils.testing import assert_array_less
from sklearn.covariance import (graph_lasso, GraphLasso, GraphLassoCV,
empirical_covariance)
from sklearn.datasets.samples_generator import make_sparse_spd_matrix
from sklearn.externals.six.moves import StringIO
from sklearn.utils import check_random_state
from sklearn import datasets
def test_graph_lasso(random_state=0):
# Sample data from a sparse multivariate normal
dim = 20
n_samples = 100
random_state = check_random_state(random_state)
prec = make_sparse_spd_matrix(dim, alpha=.95,
random_state=random_state)
cov = linalg.inv(prec)
X = random_state.multivariate_normal(np.zeros(dim), cov, size=n_samples)
emp_cov = empirical_covariance(X)
for alpha in (0., .1, .25):
covs = dict()
icovs = dict()
for method in ('cd', 'lars'):
cov_, icov_, costs = graph_lasso(emp_cov, alpha=alpha, mode=method,
return_costs=True)
covs[method] = cov_
icovs[method] = icov_
costs, dual_gap = np.array(costs).T
# Check that the costs always decrease (doesn't hold if alpha == 0)
if not alpha == 0:
assert_array_less(np.diff(costs), 0)
# Check that the 2 approaches give similar results
assert_array_almost_equal(covs['cd'], covs['lars'], decimal=4)
assert_array_almost_equal(icovs['cd'], icovs['lars'], decimal=4)
# Smoke test the estimator
model = GraphLasso(alpha=.25).fit(X)
model.score(X)
assert_array_almost_equal(model.covariance_, covs['cd'], decimal=4)
assert_array_almost_equal(model.covariance_, covs['lars'], decimal=4)
# For a centered matrix, assume_centered could be chosen True or False
# Check that this returns indeed the same result for centered data
Z = X - X.mean(0)
precs = list()
for assume_centered in (False, True):
prec_ = GraphLasso(assume_centered=assume_centered).fit(Z).precision_
precs.append(prec_)
assert_array_almost_equal(precs[0], precs[1])
def test_graph_lasso_iris():
# Hard-coded solution from R glasso package for alpha=1.0
# The iris datasets in R and sklearn do not match in a few places, these
# values are for the sklearn version
cov_R = np.array([
[0.68112222, 0.0, 0.2651911, 0.02467558],
[0.00, 0.1867507, 0.0, 0.00],
[0.26519111, 0.0, 3.0924249, 0.28774489],
[0.02467558, 0.0, 0.2877449, 0.57853156]
])
icov_R = np.array([
[1.5188780, 0.0, -0.1302515, 0.0],
[0.0, 5.354733, 0.0, 0.0],
[-0.1302515, 0.0, 0.3502322, -0.1686399],
[0.0, 0.0, -0.1686399, 1.8123908]
])
X = datasets.load_iris().data
emp_cov = empirical_covariance(X)
for method in ('cd', 'lars'):
cov, icov = graph_lasso(emp_cov, alpha=1.0, return_costs=False,
mode=method)
assert_array_almost_equal(cov, cov_R)
assert_array_almost_equal(icov, icov_R)
def test_graph_lasso_iris_singular():
# Small subset of rows to test the rank-deficient case
# Need to choose samples such that none of the variances are zero
indices = np.arange(10, 13)
# Hard-coded solution from R glasso package for alpha=0.01
cov_R = np.array([
[0.08, 0.056666662595, 0.00229729713223, 0.00153153142149],
[0.056666662595, 0.082222222222, 0.00333333333333, 0.00222222222222],
[0.002297297132, 0.003333333333, 0.00666666666667, 0.00009009009009],
[0.001531531421, 0.002222222222, 0.00009009009009, 0.00222222222222]
])
icov_R = np.array([
[24.42244057, -16.831679593, 0.0, 0.0],
[-16.83168201, 24.351841681, -6.206896552, -12.5],
[0.0, -6.206896171, 153.103448276, 0.0],
[0.0, -12.499999143, 0.0, 462.5]
])
X = datasets.load_iris().data[indices, :]
emp_cov = empirical_covariance(X)
for method in ('cd', 'lars'):
cov, icov = graph_lasso(emp_cov, alpha=0.01, return_costs=False,
mode=method)
assert_array_almost_equal(cov, cov_R, decimal=5)
assert_array_almost_equal(icov, icov_R, decimal=5)
def test_graph_lasso_cv(random_state=1):
# Sample data from a sparse multivariate normal
dim = 5
n_samples = 6
random_state = check_random_state(random_state)
prec = make_sparse_spd_matrix(dim, alpha=.96,
random_state=random_state)
cov = linalg.inv(prec)
X = random_state.multivariate_normal(np.zeros(dim), cov, size=n_samples)
# Capture stdout, to smoke test the verbose mode
orig_stdout = sys.stdout
try:
sys.stdout = StringIO()
# We need verbose very high so that Parallel prints on stdout
GraphLassoCV(verbose=100, alphas=5, tol=1e-1).fit(X)
finally:
sys.stdout = orig_stdout
# Smoke test with specified alphas
GraphLassoCV(alphas=[0.8, 0.5], tol=1e-1, n_jobs=1).fit(X)
| bsd-3-clause |
lzz5235/Code-Segment | AISProject/ais_parse.py | 1 | 9702 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
import os
import xml.etree.ElementTree as ET
import xml.dom.minidom as minidom
import numpy as np
import pandas as pd
def get_data_DY(input_path, all_MMSI):
print input_path
if 0 == int(os.path.getsize(input_path)):
return
et = ET.parse(input_path)
element = et.getroot()
element_Ships = element.findall('Ship')
for ship in element_Ships:
mmsi = long(ship.find("MMSI").text)
DynamicInfo = ship.find("DynamicInfo")
LastTime = DynamicInfo.find("LastTime").text
Latitude = float(DynamicInfo.find("Latitude").text)
Longitude = float(DynamicInfo.find("Longitude").text)
Speed = float(DynamicInfo.find("Speed").text)
course = float(DynamicInfo.find("course").text)
HeadCourse = float(DynamicInfo.find("HeadCourse").text)
AngularRate = float(DynamicInfo.find("AngularRate").text)
NaviStatus = float(DynamicInfo.find("NaviStatus").text)
ShipData = {'MMSI':mmsi, 'DynamicInfo':[]}
ShipData['DynamicInfo'].append({'LastTime':str(LastTime),'Latitude':Latitude,'Longitude':Longitude,
'Speed':Speed,
'course':course,'HeadCourse':HeadCourse,'AngularRate':AngularRate,
'NaviStatus':NaviStatus})
if mmsi < 100000000:
continue
all_MMSI.append(ShipData)
def dump_data_DY_TXT(input_path,all_MMSI):
with open(input_path,'r') as f:
string = f.readlines()
for line in string:
str_part = line.split(',')
Longitude = float(str_part[10][1:])
Latitude = float(str_part[11][:-2])
mmsi = long(str_part[0])
LastTime = str_part[12][:-4]
Speed = float(str_part[17])
course = float(str_part[2])
HeadCourse = float(str_part[2])
AngularRate = float(0.0)
NaviStatus = float(str_part[20])
ShipData = {'MMSI': mmsi, 'DynamicInfo': []}
ShipData['DynamicInfo'].append({'LastTime': LastTime, 'Latitude': Latitude, 'Longitude': Longitude,
'Speed': Speed,
'course': course, 'HeadCourse': HeadCourse, 'AngularRate': AngularRate,
'NaviStatus': NaviStatus})
if mmsi < 100000000:
continue
all_MMSI.append(ShipData)
info = ShipData['DynamicInfo'][0]['LastTime'] + ',' + str(
ShipData['DynamicInfo'][0]['Latitude']) \
+ ',' + str(ShipData['DynamicInfo'][0]['Longitude']) + ',' + str(
ShipData['DynamicInfo'][0]['Speed']) + \
',' + str(ShipData['DynamicInfo'][0]['course']) + ',' + str(
ShipData['DynamicInfo'][0]['NaviStatus'])
with open('./Japan/'+ str(mmsi), 'a') as f:
f.write(info + '\n')
def read_data_DY_TXT(input_path):
colums = np.array([u'LastTime', u'Latitude',u'Longtitude, 'u'Speed', u'course', u'NaviStatus'])
table = pd.read_csv(input_path, sep=',', header=None)
table.set_index(0,drop=True,append=False,inplace=True,verify_integrity=False)
table = table.sort_index()
# print table
# print table[1].values,table[2].values
# data = np.vstack((table[1].values,table[2].values))
# print data.T,data.T.shape
return table
def get_data_ST(input_path,all_MMSI):
print input_path
if 0 == int(os.path.getsize(input_path)):
return
et = ET.parse(input_path)
element = et.getroot()
element_Ships = element.findall('Ship')
for ship in element_Ships:
mmsi = long(ship.find("MMSI").text)
StaticInfo = ship.find("StaticInfo")
LastTime = StaticInfo.find("LastTime").text
ShipType = int(StaticInfo.find("ShipType").text)
Length = float(StaticInfo.find("Length").text)
Width = float(StaticInfo.find("Width").text)
Left = float(StaticInfo.find("Left").text)
Trail = float(StaticInfo.find("Trail").text)
Draught = float(StaticInfo.find("Draught").text)
IMO = long(StaticInfo.find("IMO").text)
CallSign = StaticInfo.find("CallSign").text
ETA = StaticInfo.find("ETA").text
Name = StaticInfo.find("Name").text
Dest = StaticInfo.find("Dest").text
ShipData = {'MMSI': mmsi, 'StaticInfo': []}
ShipData['StaticInfo'].append({'LastTime': str(LastTime), 'ShipType': ShipType, 'Length': Length,
'Width': Width,
'Left': Left, 'Trail': Trail, 'Draught': Draught,
'IMO': IMO,
'CallSign': str(CallSign),'ETA':str(ETA),'Name':str(Name),'Dest':str(Dest)})
if mmsi < 100000000:
continue
all_MMSI.append(ShipData)
def dump_data(all_MMSI_DY,all_MMSI_ST,fileName):
AIS = ET.Element('AIS')
Flag = False
isNavy = False
isGARGO = False # HUO CHUAN
isTANKER = False # YOU LUN
DataST = []
for shipdata in all_MMSI_DY:
Ship = ET.SubElement(AIS,'Ship')
MMSI = ET.SubElement(Ship,'MMSI')
MMSI.text = str(shipdata['MMSI'])
for shipST in all_MMSI_ST:
if Flag ==True:
break
if shipST['MMSI'] == shipdata['MMSI']:
Flag = True
DataST = shipST
DynamicInfo = ET.SubElement(Ship,'DynamicInfo')
LastTime = ET.SubElement(DynamicInfo,'LastTime')
LastTime.text = str(shipdata['DynamicInfo'][0]['LastTime'])
Latitude = ET.SubElement(DynamicInfo, 'Latitude')
Latitude.text = str(shipdata['DynamicInfo'][0]['Latitude'])
Longitude = ET.SubElement(DynamicInfo, 'Longitude')
Longitude.text = str(shipdata['DynamicInfo'][0]['Longitude'])
Speed = ET.SubElement(DynamicInfo, 'Speed')
Speed.text = str(shipdata['DynamicInfo'][0]['Speed'])
course = ET.SubElement(DynamicInfo, 'course')
course.text = str(shipdata['DynamicInfo'][0]['course'])
HeadCourse = ET.SubElement(DynamicInfo, 'HeadCourse')
HeadCourse.text = str(shipdata['DynamicInfo'][0]['HeadCourse'])
AngularRate = ET.SubElement(DynamicInfo, 'AngularRate')
AngularRate.text = str(shipdata['DynamicInfo'][0]['AngularRate'])
NaviStatus = ET.SubElement(DynamicInfo, 'NaviStatus')
NaviStatus.text = str(shipdata['DynamicInfo'][0]['NaviStatus'])
if len(DataST) > 0:
print shipdata['MMSI']
StaticInfo = ET.SubElement(Ship, 'StaticInfo')
LastTime = ET.SubElement(StaticInfo, 'LastTime')
LastTime.text = str(DataST['StaticInfo'][0]['LastTime'])
ShipType = ET.SubElement(StaticInfo, 'ShipType')
ShipType.text = str(DataST['StaticInfo'][0]['ShipType'])
type = DataST['StaticInfo'][0]['ShipType']
if type >= 50 and type < 60:
isNavy = True
elif type >=70 and type < 80:
isGARGO = True
elif type >=80 and type < 90:
isTANKER = True
Length = ET.SubElement(StaticInfo, 'Length')
Length.text = str(DataST['StaticInfo'][0]['Length'])
Width = ET.SubElement(StaticInfo, 'Width')
Width.text = str(DataST['StaticInfo'][0]['Width'])
Left = ET.SubElement(StaticInfo, 'Left')
Left.text = str(DataST['StaticInfo'][0]['Left'])
Trail = ET.SubElement(StaticInfo, 'Trail')
Trail.text = str(DataST['StaticInfo'][0]['Trail'])
Draught = ET.SubElement(StaticInfo, 'Draught')
Draught.text = str(DataST['StaticInfo'][0]['Draught'])
IMO = ET.SubElement(StaticInfo, 'IMO')
IMO.text = str(DataST['StaticInfo'][0]['IMO'])
CallSign = ET.SubElement(StaticInfo, 'CallSign')
CallSign.text = str(DataST['StaticInfo'][0]['CallSign'])
ETA = ET.SubElement(StaticInfo, 'ETA')
ETA.text = str(DataST['StaticInfo'][0]['ETA'])
Name = ET.SubElement(StaticInfo, 'Name')
Name.text = str(DataST['StaticInfo'][0]['Name'])
Dest = ET.SubElement(StaticInfo, 'Dest')
Dest.text = str(DataST['StaticInfo'][0]['Dest'])
tree = ET.tostring(AIS,'utf-8')
tree = minidom.parseString(tree)
result = os.path.split(fileName)
if isNavy == True: # Jun Chuan
newfilepath = os.path.join('./ShipNavy', result[1])
with open(newfilepath,'w+') as f:
tree.writexml(f, addindent=" ", newl="\n", encoding='utf-8')
if isGARGO == True: # Huo Lun
newfilepath = os.path.join('./ShipGARGO', result[1])
with open(newfilepath, 'w+') as f:
tree.writexml(f, addindent=" ", newl="\n", encoding='utf-8')
if isTANKER == True: # You Lun
newfilepath = os.path.join('./ShipTANKER', result[1])
with open(newfilepath, 'w+') as f:
tree.writexml(f, addindent=" ", newl="\n", encoding='utf-8')
if Flag == True:# DY + ST
newfilepath = os.path.join('./ShipDYST', result[1])
with open(newfilepath,'w+') as f:
tree.writexml(f, addindent=" ", newl="\n", encoding='utf-8')
with open(fileName,'w+') as f: # DY
tree.writexml(f,addindent=" ",newl="\n",encoding='utf-8')
if __name__ == "__main__":
all_MMSI_DY=[]
all_MMSI_ST=[]
data_paths_dy = []
data_paths_st = []
read_data_DY_TXT('XXX')
| mit |
asazo/ANN | tarea3/Pregunta2/model_64.py | 1 | 1302 | import numpy as np
from theano.tensor.shared_randomstreams import RandomStreams
from matplotlib import pyplot
from keras.preprocessing import sequence
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import LSTM
from keras.layers.embeddings import Embedding
from keras.datasets import imdb
np.random.seed(3)
srng = RandomStreams(8)
(X_train, y_train), (X_test, y_test) = imdb.load_data(seed=15)
# Concatenamiento de conjuntos de entrenamiento
X = np.concatenate((X_train, X_test), axis=0)
y = np.concatenate((y_train, y_test), axis=0)
# Se cargan las 3000 palabras mas relevantes
top_words = 3000
(X_train, y_train), (X_test, y_test) = imdb.load_data(nb_words=3000, seed=15)
# Se acotan los comentarios a un maximo de 500 palabras
X_train = sequence.pad_sequences(X_train, maxlen=500)
X_test = sequence.pad_sequences(X_test, maxlen=500)
# Tamanio vector generado por embedding
embedding_vector_length = 64
model = Sequential()
model.add(Embedding(top_words, embedding_vector_length, input_length=500))
model.add(LSTM(100))
model.add(Dense(1, activation='sigmoid'))
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
model.fit(X_train, y_train, validation_data=(X_test, y_test), nb_epoch=3, batch_size=64)
model.save('LSTM-64.h5') | mit |
shusenl/scikit-learn | sklearn/datasets/base.py | 196 | 18554 | """
Base IO code for all datasets
"""
# Copyright (c) 2007 David Cournapeau <[email protected]>
# 2010 Fabian Pedregosa <[email protected]>
# 2010 Olivier Grisel <[email protected]>
# License: BSD 3 clause
import os
import csv
import shutil
from os import environ
from os.path import dirname
from os.path import join
from os.path import exists
from os.path import expanduser
from os.path import isdir
from os import listdir
from os import makedirs
import numpy as np
from ..utils import check_random_state
class Bunch(dict):
"""Container object for datasets
Dictionary-like object that exposes its keys as attributes.
>>> b = Bunch(a=1, b=2)
>>> b['b']
2
>>> b.b
2
>>> b.a = 3
>>> b['a']
3
>>> b.c = 6
>>> b['c']
6
"""
def __init__(self, **kwargs):
dict.__init__(self, kwargs)
def __setattr__(self, key, value):
self[key] = value
def __getattr__(self, key):
try:
return self[key]
except KeyError:
raise AttributeError(key)
def __getstate__(self):
return self.__dict__
def get_data_home(data_home=None):
"""Return the path of the scikit-learn data dir.
This folder is used by some large dataset loaders to avoid
downloading the data several times.
By default the data dir is set to a folder named 'scikit_learn_data'
in the user home folder.
Alternatively, it can be set by the 'SCIKIT_LEARN_DATA' environment
variable or programmatically by giving an explicit folder path. The
'~' symbol is expanded to the user home folder.
If the folder does not already exist, it is automatically created.
"""
if data_home is None:
data_home = environ.get('SCIKIT_LEARN_DATA',
join('~', 'scikit_learn_data'))
data_home = expanduser(data_home)
if not exists(data_home):
makedirs(data_home)
return data_home
def clear_data_home(data_home=None):
"""Delete all the content of the data home cache."""
data_home = get_data_home(data_home)
shutil.rmtree(data_home)
def load_files(container_path, description=None, categories=None,
load_content=True, shuffle=True, encoding=None,
decode_error='strict', random_state=0):
"""Load text files with categories as subfolder names.
Individual samples are assumed to be files stored a two levels folder
structure such as the following:
container_folder/
category_1_folder/
file_1.txt
file_2.txt
...
file_42.txt
category_2_folder/
file_43.txt
file_44.txt
...
The folder names are used as supervised signal label names. The
individual file names are not important.
This function does not try to extract features into a numpy array or
scipy sparse matrix. In addition, if load_content is false it
does not try to load the files in memory.
To use text files in a scikit-learn classification or clustering
algorithm, you will need to use the `sklearn.feature_extraction.text`
module to build a feature extraction transformer that suits your
problem.
If you set load_content=True, you should also specify the encoding of
the text using the 'encoding' parameter. For many modern text files,
'utf-8' will be the correct encoding. If you leave encoding equal to None,
then the content will be made of bytes instead of Unicode, and you will
not be able to use most functions in `sklearn.feature_extraction.text`.
Similar feature extractors should be built for other kind of unstructured
data input such as images, audio, video, ...
Read more in the :ref:`User Guide <datasets>`.
Parameters
----------
container_path : string or unicode
Path to the main folder holding one subfolder per category
description: string or unicode, optional (default=None)
A paragraph describing the characteristic of the dataset: its source,
reference, etc.
categories : A collection of strings or None, optional (default=None)
If None (default), load all the categories.
If not None, list of category names to load (other categories ignored).
load_content : boolean, optional (default=True)
Whether to load or not the content of the different files. If
true a 'data' attribute containing the text information is present
in the data structure returned. If not, a filenames attribute
gives the path to the files.
encoding : string or None (default is None)
If None, do not try to decode the content of the files (e.g. for
images or other non-text content).
If not None, encoding to use to decode text files to Unicode if
load_content is True.
decode_error: {'strict', 'ignore', 'replace'}, optional
Instruction on what to do if a byte sequence is given to analyze that
contains characters not of the given `encoding`. Passed as keyword
argument 'errors' to bytes.decode.
shuffle : bool, optional (default=True)
Whether or not to shuffle the data: might be important for models that
make the assumption that the samples are independent and identically
distributed (i.i.d.), such as stochastic gradient descent.
random_state : int, RandomState instance or None, optional (default=0)
If int, random_state is the seed used by the random number generator;
If RandomState instance, random_state is the random number generator;
If None, the random number generator is the RandomState instance used
by `np.random`.
Returns
-------
data : Bunch
Dictionary-like object, the interesting attributes are: either
data, the raw text data to learn, or 'filenames', the files
holding it, 'target', the classification labels (integer index),
'target_names', the meaning of the labels, and 'DESCR', the full
description of the dataset.
"""
target = []
target_names = []
filenames = []
folders = [f for f in sorted(listdir(container_path))
if isdir(join(container_path, f))]
if categories is not None:
folders = [f for f in folders if f in categories]
for label, folder in enumerate(folders):
target_names.append(folder)
folder_path = join(container_path, folder)
documents = [join(folder_path, d)
for d in sorted(listdir(folder_path))]
target.extend(len(documents) * [label])
filenames.extend(documents)
# convert to array for fancy indexing
filenames = np.array(filenames)
target = np.array(target)
if shuffle:
random_state = check_random_state(random_state)
indices = np.arange(filenames.shape[0])
random_state.shuffle(indices)
filenames = filenames[indices]
target = target[indices]
if load_content:
data = []
for filename in filenames:
with open(filename, 'rb') as f:
data.append(f.read())
if encoding is not None:
data = [d.decode(encoding, decode_error) for d in data]
return Bunch(data=data,
filenames=filenames,
target_names=target_names,
target=target,
DESCR=description)
return Bunch(filenames=filenames,
target_names=target_names,
target=target,
DESCR=description)
def load_iris():
"""Load and return the iris dataset (classification).
The iris dataset is a classic and very easy multi-class classification
dataset.
================= ==============
Classes 3
Samples per class 50
Samples total 150
Dimensionality 4
Features real, positive
================= ==============
Read more in the :ref:`User Guide <datasets>`.
Returns
-------
data : Bunch
Dictionary-like object, the interesting attributes are:
'data', the data to learn, 'target', the classification labels,
'target_names', the meaning of the labels, 'feature_names', the
meaning of the features, and 'DESCR', the
full description of the dataset.
Examples
--------
Let's say you are interested in the samples 10, 25, and 50, and want to
know their class name.
>>> from sklearn.datasets import load_iris
>>> data = load_iris()
>>> data.target[[10, 25, 50]]
array([0, 0, 1])
>>> list(data.target_names)
['setosa', 'versicolor', 'virginica']
"""
module_path = dirname(__file__)
with open(join(module_path, 'data', 'iris.csv')) as csv_file:
data_file = csv.reader(csv_file)
temp = next(data_file)
n_samples = int(temp[0])
n_features = int(temp[1])
target_names = np.array(temp[2:])
data = np.empty((n_samples, n_features))
target = np.empty((n_samples,), dtype=np.int)
for i, ir in enumerate(data_file):
data[i] = np.asarray(ir[:-1], dtype=np.float)
target[i] = np.asarray(ir[-1], dtype=np.int)
with open(join(module_path, 'descr', 'iris.rst')) as rst_file:
fdescr = rst_file.read()
return Bunch(data=data, target=target,
target_names=target_names,
DESCR=fdescr,
feature_names=['sepal length (cm)', 'sepal width (cm)',
'petal length (cm)', 'petal width (cm)'])
def load_digits(n_class=10):
"""Load and return the digits dataset (classification).
Each datapoint is a 8x8 image of a digit.
================= ==============
Classes 10
Samples per class ~180
Samples total 1797
Dimensionality 64
Features integers 0-16
================= ==============
Read more in the :ref:`User Guide <datasets>`.
Parameters
----------
n_class : integer, between 0 and 10, optional (default=10)
The number of classes to return.
Returns
-------
data : Bunch
Dictionary-like object, the interesting attributes are:
'data', the data to learn, 'images', the images corresponding
to each sample, 'target', the classification labels for each
sample, 'target_names', the meaning of the labels, and 'DESCR',
the full description of the dataset.
Examples
--------
To load the data and visualize the images::
>>> from sklearn.datasets import load_digits
>>> digits = load_digits()
>>> print(digits.data.shape)
(1797, 64)
>>> import pylab as pl #doctest: +SKIP
>>> pl.gray() #doctest: +SKIP
>>> pl.matshow(digits.images[0]) #doctest: +SKIP
>>> pl.show() #doctest: +SKIP
"""
module_path = dirname(__file__)
data = np.loadtxt(join(module_path, 'data', 'digits.csv.gz'),
delimiter=',')
with open(join(module_path, 'descr', 'digits.rst')) as f:
descr = f.read()
target = data[:, -1]
flat_data = data[:, :-1]
images = flat_data.view()
images.shape = (-1, 8, 8)
if n_class < 10:
idx = target < n_class
flat_data, target = flat_data[idx], target[idx]
images = images[idx]
return Bunch(data=flat_data,
target=target.astype(np.int),
target_names=np.arange(10),
images=images,
DESCR=descr)
def load_diabetes():
"""Load and return the diabetes dataset (regression).
============== ==================
Samples total 442
Dimensionality 10
Features real, -.2 < x < .2
Targets integer 25 - 346
============== ==================
Read more in the :ref:`User Guide <datasets>`.
Returns
-------
data : Bunch
Dictionary-like object, the interesting attributes are:
'data', the data to learn and 'target', the regression target for each
sample.
"""
base_dir = join(dirname(__file__), 'data')
data = np.loadtxt(join(base_dir, 'diabetes_data.csv.gz'))
target = np.loadtxt(join(base_dir, 'diabetes_target.csv.gz'))
return Bunch(data=data, target=target)
def load_linnerud():
"""Load and return the linnerud dataset (multivariate regression).
Samples total: 20
Dimensionality: 3 for both data and targets
Features: integer
Targets: integer
Returns
-------
data : Bunch
Dictionary-like object, the interesting attributes are: 'data' and
'targets', the two multivariate datasets, with 'data' corresponding to
the exercise and 'targets' corresponding to the physiological
measurements, as well as 'feature_names' and 'target_names'.
"""
base_dir = join(dirname(__file__), 'data/')
# Read data
data_exercise = np.loadtxt(base_dir + 'linnerud_exercise.csv', skiprows=1)
data_physiological = np.loadtxt(base_dir + 'linnerud_physiological.csv',
skiprows=1)
# Read header
with open(base_dir + 'linnerud_exercise.csv') as f:
header_exercise = f.readline().split()
with open(base_dir + 'linnerud_physiological.csv') as f:
header_physiological = f.readline().split()
with open(dirname(__file__) + '/descr/linnerud.rst') as f:
descr = f.read()
return Bunch(data=data_exercise, feature_names=header_exercise,
target=data_physiological,
target_names=header_physiological,
DESCR=descr)
def load_boston():
"""Load and return the boston house-prices dataset (regression).
============== ==============
Samples total 506
Dimensionality 13
Features real, positive
Targets real 5. - 50.
============== ==============
Returns
-------
data : Bunch
Dictionary-like object, the interesting attributes are:
'data', the data to learn, 'target', the regression targets,
and 'DESCR', the full description of the dataset.
Examples
--------
>>> from sklearn.datasets import load_boston
>>> boston = load_boston()
>>> print(boston.data.shape)
(506, 13)
"""
module_path = dirname(__file__)
fdescr_name = join(module_path, 'descr', 'boston_house_prices.rst')
with open(fdescr_name) as f:
descr_text = f.read()
data_file_name = join(module_path, 'data', 'boston_house_prices.csv')
with open(data_file_name) as f:
data_file = csv.reader(f)
temp = next(data_file)
n_samples = int(temp[0])
n_features = int(temp[1])
data = np.empty((n_samples, n_features))
target = np.empty((n_samples,))
temp = next(data_file) # names of features
feature_names = np.array(temp)
for i, d in enumerate(data_file):
data[i] = np.asarray(d[:-1], dtype=np.float)
target[i] = np.asarray(d[-1], dtype=np.float)
return Bunch(data=data,
target=target,
# last column is target value
feature_names=feature_names[:-1],
DESCR=descr_text)
def load_sample_images():
"""Load sample images for image manipulation.
Loads both, ``china`` and ``flower``.
Returns
-------
data : Bunch
Dictionary-like object with the following attributes :
'images', the two sample images, 'filenames', the file
names for the images, and 'DESCR'
the full description of the dataset.
Examples
--------
To load the data and visualize the images:
>>> from sklearn.datasets import load_sample_images
>>> dataset = load_sample_images() #doctest: +SKIP
>>> len(dataset.images) #doctest: +SKIP
2
>>> first_img_data = dataset.images[0] #doctest: +SKIP
>>> first_img_data.shape #doctest: +SKIP
(427, 640, 3)
>>> first_img_data.dtype #doctest: +SKIP
dtype('uint8')
"""
# Try to import imread from scipy. We do this lazily here to prevent
# this module from depending on PIL.
try:
try:
from scipy.misc import imread
except ImportError:
from scipy.misc.pilutil import imread
except ImportError:
raise ImportError("The Python Imaging Library (PIL) "
"is required to load data from jpeg files")
module_path = join(dirname(__file__), "images")
with open(join(module_path, 'README.txt')) as f:
descr = f.read()
filenames = [join(module_path, filename)
for filename in os.listdir(module_path)
if filename.endswith(".jpg")]
# Load image data for each image in the source folder.
images = [imread(filename) for filename in filenames]
return Bunch(images=images,
filenames=filenames,
DESCR=descr)
def load_sample_image(image_name):
"""Load the numpy array of a single sample image
Parameters
-----------
image_name: {`china.jpg`, `flower.jpg`}
The name of the sample image loaded
Returns
-------
img: 3D array
The image as a numpy array: height x width x color
Examples
---------
>>> from sklearn.datasets import load_sample_image
>>> china = load_sample_image('china.jpg') # doctest: +SKIP
>>> china.dtype # doctest: +SKIP
dtype('uint8')
>>> china.shape # doctest: +SKIP
(427, 640, 3)
>>> flower = load_sample_image('flower.jpg') # doctest: +SKIP
>>> flower.dtype # doctest: +SKIP
dtype('uint8')
>>> flower.shape # doctest: +SKIP
(427, 640, 3)
"""
images = load_sample_images()
index = None
for i, filename in enumerate(images.filenames):
if filename.endswith(image_name):
index = i
break
if index is None:
raise AttributeError("Cannot find sample image: %s" % image_name)
return images.images[index]
| bsd-3-clause |
habibmasuro/bitex | libs/coinkit/coinkit/words.py | 11 | 726962 | # -*- coding: utf-8 -*-
"""
Coinkit
~~~~~
:copyright: (c) 2014 by Halfmoon Labs
:license: MIT, see LICENSE for more details.
"""
TOP_ENGLISH_WORDS = ["the", "of", "and", "to", "a", "in", "for", "is", "on", "that", "by", "this", "with", "i", "you", "it", "not", "or", "be", "are", "from", "at", "as", "your", "all", "have", "new", "more", "an", "was", "we", "will", "home", "can", "us", "about", "if", "page", "my", "has", "search", "free", "but", "our", "one", "other", "do", "no", "information", "time", "they", "site", "he", "up", "may", "what", "which", "their", "news", "out", "use", "any", "there", "see", "only", "so", "his", "when", "contact", "here", "business", "who", "web", "also", "now", "help", "get", "view", "online", "c", "e", "first", "am", "been", "would", "how", "were", "me", "s", "services", "some", "these", "click", "its", "like", "service", "x", "than", "find", "price", "date", "back", "top", "people", "had", "list", "name", "just", "over", "state", "year", "day", "into", "email", "two", "health", "n", "world", "re", "next", "used", "go", "b", "work", "last", "most", "products", "music", "buy", "data", "make", "them", "should", "product", "system", "post", "her", "city", "t", "add", "policy", "number", "such", "please", "available", "copyright", "support", "message", "after", "best", "software", "then", "jan", "good", "well", "d", "where", "rights", "public", "books", "high", "school", "through", "m", "each", "links", "she", "review", "years", "order", "very", "privacy", "book", "items", "company", "r", "read", "group", "sex", "need", "many", "user", "said", "de", "does", "set", "under", "general", "research", "university", "january", "mail", "full", "map", "reviews", "program", "life", "know", "games", "way", "days", "management", "p", "part", "could", "great", "united", "hotel", "real", "f", "item", "international", "center", "must", "store", "travel", "comments", "made", "development", "report", "off", "member", "details", "line", "terms", "before", "hotels", "did", "send", "right", "type", "because", "local", "those", "using", "results", "office", "education", "national", "car", "design", "take", "posted", "internet", "address", "community", "within", "states", "area", "want", "phone", "shipping", "reserved", "subject", "between", "forum", "family", "l", "long", "based", "w", "code", "show", "o", "even", "black", "check", "special", "prices", "index", "being", "women", "much", "sign", "file", "link", "open", "today", "technology", "south", "case", "project", "same", "pages", "uk", "version", "section", "own", "found", "sports", "house", "related", "security", "both", "g", "county", "american", "photo", "game", "members", "power", "while", "care", "network", "down", "computer", "systems", "three", "total", "place", "end", "following", "download", "h", "him", "without", "per", "access", "think", "north", "resources", "current", "posts", "big", "media", "law", "control", "water", "history", "pictures", "size", "art", "personal", "since", "including", "guide", "shop", "directory", "board", "location", "change", "white", "text", "small", "rating", "rate", "government", "children", "during", "usa", "return", "students", "v", "shopping", "account", "times", "sites", "level", "digital", "profile", "previous", "form", "events", "love", "old", "john", "main", "call", "hours", "image", "department", "title", "description", "non", "k", "y", "insurance", "another", "why", "shall", "property", "class", "cd", "still", "money", "quality", "every", "listing", "content", "country", "private", "little", "visit", "save", "tools", "low", "reply", "customer", "december", "compare", "movies", "include", "college", "value", "article", "york", "man", "card", "jobs", "provide", "j", "food", "source", "author", "different", "press", "u", "learn", "sale", "around", "print", "course", "job", "canada", "process", "teen", "room", "stock", "training", "too", "credit", "point", "join", "science", "men", "categories", "advanced", "west", "sales", "look", "english", "left", "team", "estate", "box", "conditions", "select", "windows", "gay", "thread", "week", "category", "note", "live", "large", "gallery", "table", "register", "however", "june", "october", "november", "market", "library", "really", "action", "start", "series", "model", "features", "air", "industry", "plan", "human", "provided", "tv", "yes", "required", "second", "hot", "accessories", "cost", "movie", "march", "la", "september", "better", "say", "questions", "july", "going", "medical", "test", "friend", "come", "dec", "study", "application", "cart", "staff", "articles", "san", "again", "play", "looking", "issues", "april", "never", "users", "complete", "street", "topic", "comment", "financial", "things", "working", "against", "standard", "tax", "person", "below", "mobile", "less", "got", "party", "payment", "equipment", "login", "student", "let", "programs", "offers", "legal", "above", "recent", "park", "stores", "side", "act", "problem", "red", "give", "memory", "performance", "social", "q", "august", "quote", "language", "story", "sell", "experience", "rates", "create", "key", "body", "young", "america", "important", "field", "few", "east", "paper", "single", "ii", "age", "activities", "club", "example", "girls", "additional", "password", "z", "latest", "something", "road", "gift", "question", "changes", "night", "ca", "hard", "texas", "oct", "pay", "four", "poker", "status", "browse", "issue", "range", "building", "seller", "court", "february", "always", "result", "light", "write", "war", "nov", "offer", "blue", "groups", "al", "easy", "given", "files", "event", "release", "analysis", "request", "china", "making", "picture", "needs", "possible", "might", "professional", "yet", "month", "major", "star", "areas", "future", "space", "committee", "hand", "sun", "cards", "problems", "london", "washington", "meeting", "become", "interest", "id", "child", "keep", "enter", "california", "share", "similar", "garden", "schools", "million", "added", "reference", "companies", "listed", "baby", "learning", "energy", "run", "delivery", "net", "popular", "term", "film", "stories", "put", "computers", "journal", "reports", "co", "try", "welcome", "central", "images", "president", "notice", "god", "original", "head", "radio", "until", "cell", "color", "self", "council", "away", "includes", "track", "australia", "discussion", "archive", "once", "others", "entertainment", "agreement", "format", "least", "society", "months", "log", "safety", "friends", "sure", "trade", "edition", "cars", "messages", "marketing", "tell", "further", "updated", "association", "able", "having", "provides", "david", "fun", "already", "green", "studies", "close", "common", "drive", "specific", "several", "gold", "feb", "living", "collection", "called", "short", "arts", "lot", "ask", "display", "limited", "solutions", "means", "director", "daily", "beach", "past", "natural", "whether", "due", "et", "five", "upon", "period", "planning", "says", "official", "weather", "mar", "land", "average", "done", "technical", "window", "france", "pro", "region", "island", "record", "direct", "conference", "environment", "records", "st", "district", "calendar", "costs", "style", "front", "statement", "parts", "aug", "ever", "early", "miles", "sound", "resource", "present", "applications", "either", "ago", "document", "word", "works", "material", "bill", "written", "talk", "federal", "rules", "final", "adult", "tickets", "thing", "centre", "requirements", "via", "cheap", "nude", "kids", "finance", "true", "minutes", "else", "mark", "third", "rock", "gifts", "europe", "reading", "topics", "bad", "individual", "tips", "plus", "auto", "cover", "usually", "edit", "together", "percent", "fast", "function", "fact", "unit", "getting", "global", "meet", "far", "economic", "en", "player", "projects", "lyrics", "often", "subscribe", "submit", "germany", "amount", "watch", "included", "feel", "though", "bank", "risk", "thanks", "everything", "deals", "various", "words", "jul", "production", "commercial", "james", "weight", "town", "heart", "advertising", "received", "choose", "treatment", "newsletter", "archives", "points", "knowledge", "magazine", "error", "camera", "girl", "currently", "construction", "toys", "registered", "clear", "golf", "receive", "domain", "methods", "chapter", "makes", "protection", "policies", "loan", "wide", "beauty", "manager", "india", "position", "taken", "sort", "models", "michael", "known", "half", "cases", "step", "engineering", "florida", "simple", "quick", "none", "wireless", "license", "paul", "friday", "lake", "whole", "annual", "published", "later", "basic", "shows", "corporate", "church", "method", "purchase", "customers", "active", "response", "practice", "hardware", "figure", "materials", "fire", "holiday", "chat", "enough", "designed", "along", "among", "death", "writing", "speed", "html", "countries", "loss", "face", "brand", "discount", "higher", "effects", "created", "remember", "standards", "oil", "bit", "yellow", "political", "increase", "advertise", "kingdom", "base", "near", "thought", "stuff", "french", "storage", "oh", "japan", "doing", "loans", "shoes", "entry", "stay", "nature", "orders", "availability", "africa", "summary", "turn", "mean", "growth", "notes", "agency", "king", "monday", "european", "activity", "copy", "although", "drug", "western", "income", "force", "cash", "employment", "overall", "bay", "river", "commission", "ad", "package", "contents", "seen", "players", "engine", "port", "album", "regional", "stop", "supplies", "started", "administration", "bar", "institute", "views", "plans", "double", "dog", "build", "screen", "exchange", "types", "soon", "lines", "electronic", "continue", "across", "benefits", "needed", "season", "apply", "someone", "held", "ny", "anything", "printer", "condition", "effective", "believe", "organization", "effect", "asked", "mind", "sunday", "selection", "casino", "lost", "tour", "menu", "volume", "cross", "anyone", "mortgage", "hope", "silver", "corporation", "wish", "inside", "solution", "mature", "role", "rather", "weeks", "addition", "came", "supply", "nothing", "certain", "executive", "running", "lower", "necessary", "union", "jewelry", "according", "dc", "clothing", "mon", "com", "particular", "fine", "names", "robert", "hour", "gas", "skills", "six", "bush", "islands", "advice", "career", "military", "rental", "decision", "leave", "british", "teens", "pre", "huge", "sat", "woman", "facilities", "zip", "bid", "kind", "sellers", "middle", "move", "cable", "opportunities", "taking", "values", "division", "coming", "tuesday", "object", "appropriate", "machine", "length", "actually", "nice", "score", "statistics", "client", "ok", "returns", "capital", "follow", "sample", "investment", "sent", "shown", "saturday", "christmas", "england", "culture", "band", "flash", "ms", "lead", "george", "choice", "went", "starting", "registration", "fri", "thursday", "courses", "consumer", "hi", "foreign", "artist", "outside", "furniture", "levels", "channel", "letter", "mode", "ideas", "wednesday", "structure", "fund", "summer", "allow", "degree", "contract", "button", "releases", "wed", "homes", "super", "male", "matter", "custom", "virginia", "almost", "took", "located", "multiple", "asian", "distribution", "editor", "inn", "industrial", "cause", "potential", "song", "ltd", "los", "focus", "late", "fall", "featured", "idea", "rooms", "female", "responsible", "inc", "communications", "win", "associated", "thomas", "primary", "cancer", "numbers", "reason", "tool", "browser", "spring", "foundation", "answer", "voice", "friendly", "schedule", "documents", "communication", "purpose", "feature", "bed", "comes", "police", "everyone", "independent", "approach", "brown", "physical", "operating", "hill", "maps", "medicine", "deal", "hold", "chicago", "forms", "glass", "happy", "tue", "smith", "wanted", "developed", "thank", "safe", "unique", "survey", "prior", "telephone", "sport", "ready", "feed", "animal", "sources", "mexico", "population", "pa", "regular", "secure", "navigation", "operations", "therefore", "ass", "simply", "evidence", "station", "christian", "round", "favorite", "understand", "option", "master", "valley", "recently", "probably", "sea", "built", "publications", "blood", "cut", "improve", "connection", "publisher", "hall", "larger", "networks", "earth", "parents", "impact", "transfer", "introduction", "kitchen", "strong", "tel", "carolina", "wedding", "properties", "hospital", "ground", "overview", "ship", "accommodation", "owners", "disease", "excellent", "paid", "italy", "perfect", "hair", "opportunity", "kit", "classic", "basis", "command", "cities", "william", "express", "award", "distance", "tree", "peter", "assessment", "ensure", "thus", "wall", "ie", "involved", "el", "extra", "especially", "pussy", "partners", "budget", "rated", "guides", "success", "maximum", "ma", "operation", "existing", "quite", "selected", "boy", "amazon", "patients", "restaurants", "beautiful", "warning", "wine", "locations", "horse", "vote", "forward", "flowers", "stars", "significant", "lists", "owner", "retail", "animals", "useful", "directly", "manufacturer", "ways", "est", "son", "providing", "rule", "mac", "housing", "takes", "iii", "bring", "catalog", "searches", "max", "trying", "mother", "authority", "considered", "told", "traffic", "programme", "joined", "strategy", "feet", "agent", "valid", "bin", "modern", "senior", "ireland", "teaching", "door", "grand", "testing", "trial", "charge", "units", "instead", "canadian", "cool", "normal", "wrote", "enterprise", "ships", "entire", "educational", "md", "leading", "metal", "positive", "fl", "fitness", "chinese", "opinion", "asia", "football", "abstract", "uses", "output", "funds", "mr", "greater", "likely", "develop", "employees", "artists", "alternative", "processing", "responsibility", "resolution", "java", "guest", "seems", "publication", "pass", "relations", "trust", "van", "contains", "session", "photography", "republic", "fees", "components", "vacation", "century", "academic", "assistance", "completed", "skin", "indian", "mary", "il", "expected", "ring", "grade", "dating", "pacific", "mountain", "organizations", "pop", "filter", "mailing", "vehicle", "longer", "consider", "int", "northern", "behind", "panel", "floor", "german", "buying", "match", "proposed", "default", "require", "iraq", "boys", "outdoor", "deep", "morning", "otherwise", "allows", "rest", "protein", "plant", "reported", "hit", "transportation", "mm", "pool", "politics", "partner", "disclaimer", "authors", "boards", "faculty", "parties", "fish", "membership", "mission", "eye", "string", "sense", "modified", "pack", "released", "stage", "internal", "goods", "recommended", "born", "unless", "richard", "detailed", "japanese", "race", "approved", "background", "target", "except", "character", "maintenance", "ability", "maybe", "functions", "ed", "moving", "brands", "places", "pretty", "spain", "southern", "yourself", "etc", "winter", "rape", "battery", "youth", "pressure", "submitted", "boston", "incest", "debt", "medium", "television", "interested", "core", "break", "purposes", "throughout", "sets", "dance", "wood", "itself", "defined", "papers", "playing", "awards", "fee", "studio", "reader", "virtual", "device", "established", "answers", "rent", "las", "remote", "dark", "external", "apple", "le", "regarding", "instructions", "min", "offered", "theory", "enjoy", "remove", "aid", "surface", "minimum", "visual", "host", "variety", "teachers", "martin", "manual", "block", "subjects", "agents", "increased", "repair", "fair", "civil", "steel", "understanding", "songs", "fixed", "wrong", "beginning", "hands", "associates", "finally", "classes", "paris", "ohio", "gets", "sector", "capacity", "requires", "jersey", "un", "fat", "fully", "father", "electric", "saw", "instruments", "quotes", "officer", "driver", "businesses", "dead", "respect", "unknown", "specified", "restaurant", "mike", "trip", "worth", "mi", "procedures", "poor", "teacher", "xxx", "eyes", "relationship", "workers", "farm", "georgia", "peace", "traditional", "campus", "tom", "showing", "creative", "coast", "benefit", "progress", "funding", "devices", "lord", "grant", "sub", "agree", "fiction", "hear", "sometimes", "watches", "careers", "beyond", "goes", "families", "led", "museum", "themselves", "fan", "transport", "interesting", "wife", "accepted", "former", "ten", "hits", "zone", "complex", "th", "cat", "galleries", "references", "die", "presented", "jack", "flat", "flow", "agencies", "literature", "respective", "parent", "spanish", "michigan", "columbia", "setting", "dr", "scale", "stand", "economy", "highest", "helpful", "monthly", "critical", "frame", "musical", "definition", "secretary", "path", "employee", "chief", "gives", "bottom", "magazines", "packages", "detail", "francisco", "laws", "changed", "pet", "heard", "begin", "individuals", "colorado", "royal", "clean", "switch", "russian", "largest", "african", "guy", "titles", "relevant", "guidelines", "justice", "bible", "cup", "basket", "applied", "weekly", "vol", "installation", "described", "demand", "pp", "suite", "na", "square", "chris", "attention", "advance", "skip", "diet", "army", "auction", "gear", "lee", "os", "difference", "allowed", "correct", "charles", "nation", "selling", "lots", "piece", "sheet", "firm", "seven", "older", "illinois", "regulations", "elements", "species", "jump", "cells", "resort", "facility", "random", "certificate", "minister", "motion", "looks", "fashion", "directions", "visitors", "monitor", "trading", "forest", "calls", "whose", "couple", "giving", "chance", "vision", "ball", "ending", "clients", "actions", "listen", "discuss", "accept", "naked", "goal", "successful", "sold", "wind", "communities", "clinical", "situation", "sciences", "markets", "lowest", "highly", "publishing", "appear", "emergency", "lives", "currency", "leather", "determine", "temperature", "palm", "announcements", "patient", "actual", "historical", "stone", "bob", "commerce", "perhaps", "persons", "difficult", "scientific", "satellite", "fit", "tests", "village", "accounts", "amateur", "ex", "met", "pain", "particularly", "factors", "coffee", "cum", "buyer", "cultural", "steve", "easily", "oral", "ford", "poster", "edge", "functional", "root", "au", "fi", "closed", "holidays", "ice", "pink", "zealand", "balance", "graduate", "replies", "shot", "architecture", "initial", "label", "thinking", "scott", "sec", "recommend", "canon", "league", "waste", "minute", "bus", "optional", "dictionary", "cold", "accounting", "manufacturing", "sections", "chair", "fishing", "effort", "phase", "fields", "bag", "fantasy", "po", "letters", "motor", "va", "professor", "context", "install", "shirt", "apparel", "generally", "continued", "foot", "mass", "crime", "count", "breast", "ibm", "johnson", "sc", "quickly", "dollars", "religion", "claim", "driving", "permission", "surgery", "patch", "heat", "wild", "measures", "generation", "kansas", "miss", "chemical", "doctor", "task", "reduce", "brought", "himself", "nor", "component", "enable", 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"findpage", "discourages", "benitez", "boater", "shackleton", "weirdo", "congresswoman", "dalek", "tass", "itrip", "myob", "helloween", "reperfusion", "fieldhouse", "manukau", "libname", "eucharistic", "mong", "homeware", "ckt", "winmx", "mobic", "farts", "rourke", "lackawanna", "villiers", "comercio", "huy", "brooksville", "falwell", "gwb", "donwload", "wrth", "attrs", "knockoffs", "esm", "bionicle", "hygienist", "nichole", "quidditch", "dartmoor", "rowlett", "stapled", "gardenweb", "butternut", "nummer", "groban", "asw", "arora", "yatsura", "warr", "hainan", "esg", "logoff", "cockroach", "xanadu", "computable", "occup", "playgroup", "tintin", "ethnicities", "webposition", "crafter", "roby", "disassemble", "boltzmann", "caos", "abidjan", "anise", "grainy", "hospitalizations", "notizie", "zoek", "sepultura", "walkabout", "pepperoni", "optimising", "cityreview", "boathouse", "katt", "weissman", "siri", "herkimer", "namecite", "refreshingly", "aph", "ryland", "sculptural", "neurophysiology", "gsk", "hermanus", "mocldy", "ngage", "annexure", "ipchains", "yosef", "tlds", "gozo", "pso", "helton", "outflows", "saas", "asthmatic", "guillemot", "realizations", "linguistically", "jaco", "mckinsey", "dezember", "hylafax", "reconstitution", "amateurwebcam", "lumberton", "interviewee", "intereco", "portola", "hematologic", "sgc", "rebbe", "pinup", "transcendence", "surah", "brendon", "farberware", "statisticians", "swatches", "perioperative", "maoist", "henkel", "lilangeni", "trapeze", "lemmings", "extents", "spams", "omagh", "workcentre", "sunbird", "cellophane", "deland", "blevins", "sacha", "cardholders", "dddd", "accessori", "qo", "araujo", "mylist", "pcu", "kloczek", "enet", "seperated", "clusty", "rolfe", "cuttack", "provantage", "dominio", "hyperbaric", "nannofossil", "logansport", "bulldozer", "blacksonblondes", "subprime", "overpayments", "sharpie", "modutils", "whitehaven", "whaley", "currier", "taproot", "topsite", "delorme", "rayner", "aio", "rossum", "urbanism", "colloquia", "ewr", "capillaries", "mountainside", "menthol", "blackouts", "starkey", "eves", "hpux", "canby", "dragonflies", "montrail", "findfont", "aigner", "urusei", "soundblaster", "beatle", "webzine", "propranolol", "inescapable", "swabs", "absorbance", "lbw", "audiofile", "simba", "mohd", "redgoldfish", "cornbread", "jcaho", "appendixes", "aod", "crestview", "keynotes", "fotolia", "subnets", "cau", "espanola", "busnes", "froggy", "decarboxylase", "elfman", "throughs", "prioritise", "oreck", "schottland", "bagpipe", "terns", "erythematosus", "ftrs", "excitatory", "mcevoy", "fujita", "niagra", "yq", "dribble", "hardwired", "hosta", "grambling", "exten", "seeger", "ringgold", "sondheim", "interconnecting", "inkjets", "ebv", "underpinnings", "lazar", "laxatives", "mythos", "soname", "colloid", "hiked", "defrag", "zanesville", "oxidant", "umbra", "poppin", "trebuchet", "pyrite", "partido", "drunks", "submitters", "branes", "mahdi", "agoura", "manchesteronline", "blunkett", "lapd", "kidder", "hotkey", "tirupur", "parkville", "crediting", "tmo"] | gpl-3.0 |
BorisJeremic/Real-ESSI-Examples | analytic_solution/test_cases/Contact/Coupled_Contact/Steady_State_Single_Foundation_Sysytem_Under_Tension/CoupledSoftContact_NonLinHardSoftShear/n_0.3/Plot_Original.py | 12 | 3449 | #!/usr/bin/env python
#!/usr/bin/python
import h5py
from matplotlib import pylab
import matplotlib.pylab as plt
import sys
from matplotlib.font_manager import FontProperties
import math
import numpy as np
#!/usr/bin/python
import h5py
import matplotlib.pylab as plt
import matplotlib as mpl
import sys
import numpy as np;
plt.rcParams.update({'font.size': 30})
# set tick width
mpl.rcParams['xtick.major.size'] = 10
mpl.rcParams['xtick.major.width'] = 5
mpl.rcParams['xtick.minor.size'] = 10
mpl.rcParams['xtick.minor.width'] = 5
plt.rcParams['xtick.labelsize']=28
mpl.rcParams['ytick.major.size'] = 10
mpl.rcParams['ytick.major.width'] = 5
mpl.rcParams['ytick.minor.size'] = 10
mpl.rcParams['ytick.minor.width'] = 5
plt.rcParams['ytick.labelsize']=28
# Plot the figure. Add labels and titles.
plt.figure()
ax = plt.subplot(111)
ax.grid()
ax.set_xlabel("Time [s] ")
ax.set_ylabel(r"Stress [Pa] ")
# Pore Pressure
# #########################################################################
thefile = "Soil_Foundation_System_Surface_Load.h5.feioutput";
finput = h5py.File(thefile)
# Read the time and displacement
times = finput["time"][:]
upU_p = finput["/Model/Nodes/Generalized_Displacements"][3,:]
upU_u = finput["/Model/Nodes/Generalized_Displacements"][2,:]
upU_U = finput["/Model/Nodes/Generalized_Displacements"][6,:]
u_u = finput["/Model/Nodes/Generalized_Displacements"][79,:]
sigma_zz_ = finput["/Model/Elements/Gauss_Outputs"][14,:]
# pore_pressure
ax.plot(times,upU_p,'b',linewidth=2,label=r'Pore Pressure $p$');
ax.hold(True);
# Total Stress
# #########################################################################
# Read the time and displacement
times = finput["time"][:];
T = times[len(times)-1]
sigma_zz = --400/T*times
# kinetic energy
ax.plot(times,sigma_zz,'k',linewidth=2,label=r'Total Stress $\sigma$');
ax.hold(True);
# Effective Stress
# #########################################################################
# Read the time and displacement
times = finput["time"][:];
sigma_zz_ = sigma_zz - upU_p
# kinetic energy
ax.plot(times,sigma_zz_,'r',linewidth=2,label=r'''Effective Stress $\sigma^{\prime}$''');
ax.hold(True);
max_yticks = 5
yloc = plt.MaxNLocator(max_yticks)
ax.yaxis.set_major_locator(yloc)
max_xticks = 5
yloc = plt.MaxNLocator(max_xticks)
ax.xaxis.set_major_locator(yloc)
ax.legend(loc='upper center', bbox_to_anchor=(0.5, 1.35),
ncol=2, fancybox=True, shadow=True, prop={'size': 24})
pylab.savefig("Original_Effective_Stress_Principle_At_Interface.pdf", bbox_inches='tight')
# plt.show()
#
################################### Drainage Condition Verification #############################
ax.hold(False);
fig = plt.figure();
ax = plt.subplot(111)
ax.plot(times,upU_u*1e8,'k',linewidth=3,label=r'$upU\_u$'); ax.hold(True);
ax.plot(times,upU_U*1e8,'b',linewidth=10,label=r'$upU\_U$'); ax.hold(True);
ax.plot(times,u_u*1e8,'r',linewidth=3,label=r'$u\_u$'); ax.hold(True);
ax.grid()
ax.set_xlabel("Time [s] ")
ax.set_ylabel(r"Displacement $\times 1e^{-8}$ [m] ")
ax.legend(loc='upper center', bbox_to_anchor=(0.5, 1.25),
ncol=4, fancybox=True, shadow=True, prop={'size': 24})
max_yticks = 5
yloc = plt.MaxNLocator(max_yticks)
ax.yaxis.set_major_locator(yloc)
max_xticks = 5
yloc = plt.MaxNLocator(max_xticks)
ax.xaxis.set_major_locator(yloc)
pylab.savefig("Original_Displacement_At_Interface.pdf", bbox_inches='tight')
# plt.show()
| cc0-1.0 |
kundajelab/genomedisco | genomedisco/visualization.py | 1 | 1412 |
import matplotlib
matplotlib.use('Agg') # Must be before importing matplotlib.pyplot or pylab!
import matplotlib.pyplot as plt
from matplotlib.backends.backend_pdf import PdfPages
import numpy as np
from pylab import rcParams
def plot_dds(dd_list,dd_names,out,approximation=10000):
assert len(dd_list)==len(dd_names)
rcParams['figure.figsize'] = 7,7
rcParams['font.size']= 30
rcParams['xtick.labelsize'] = 20
rcParams['ytick.labelsize'] = 20
fig, plots = plt.subplots(nrows=1, ncols=1)
fig.set_size_inches(7, 7)
colors=['red','blue']
for dd_idx in range(len(dd_names)):
dd_name=dd_names[dd_idx]
dd=list(dd_list[dd_idx].values())
x=list(dd_list[dd_idx].keys())
sorted_x=np.argsort(np.array(x))
x_plot=[]
dd_plot=[]
x_idx=0
while x_idx<len(x):
x_plot.append(x[sorted_x[x_idx]]*approximation)
dd_plot.append(dd[sorted_x[x_idx]])
x_idx+=1
plots.plot(x_plot[1:],dd_plot[1:],c=colors[dd_idx],label=dd_names[dd_idx])
plots.set_yscale('log',basey=10)
plots.set_xscale('log',basex=10)
plots.set_xlabel('distance (bp)')
plots.set_ylabel('contact probability')
plots.legend(loc=3,fontsize=20)
#fig.tight_layout()
adj=0.2
plt.gcf().subplots_adjust(bottom=adj)
plt.gcf().subplots_adjust(left=adj)
plt.savefig(out+'.png')
| mit |
chrjxj/zipline | zipline/utils/security_list.py | 2 | 4544 | from datetime import datetime
from os import listdir
import os.path
import pandas as pd
import pytz
import zipline
DATE_FORMAT = "%Y%m%d"
zipline_dir = os.path.dirname(zipline.__file__)
SECURITY_LISTS_DIR = os.path.join(zipline_dir, 'resources', 'security_lists')
class SecurityList(object):
def __init__(self, data, current_date_func, asset_finder):
"""
data: a nested dictionary:
knowledge_date -> lookup_date ->
{add: [symbol list], 'delete': []}, delete: [symbol list]}
current_date_func: function taking no parameters, returning
current datetime
"""
self.data = data
self._cache = {}
self._knowledge_dates = self.make_knowledge_dates(self.data)
self.current_date = current_date_func
self.count = 0
self._current_set = set()
self.asset_finder = asset_finder
def make_knowledge_dates(self, data):
knowledge_dates = sorted(
[pd.Timestamp(k) for k in data.keys()])
return knowledge_dates
def __iter__(self):
return iter(self.restricted_list)
def __contains__(self, item):
return item in self.restricted_list
@property
def restricted_list(self):
cd = self.current_date()
for kd in self._knowledge_dates:
if cd < kd:
break
if kd in self._cache:
self._current_set = self._cache[kd]
continue
for effective_date, changes in iter(self.data[kd].items()):
self.update_current(
effective_date,
changes['add'],
self._current_set.add
)
self.update_current(
effective_date,
changes['delete'],
self._current_set.remove
)
self._cache[kd] = self._current_set
return self._current_set
def update_current(self, effective_date, symbols, change_func):
for symbol in symbols:
asset = self.asset_finder.lookup_symbol(
symbol,
as_of_date=effective_date
)
# Pass if no Asset exists for the symbol
if asset is None:
continue
change_func(asset.sid)
class SecurityListSet(object):
# provide a cut point to substitute other security
# list implementations.
security_list_type = SecurityList
def __init__(self, current_date_func, asset_finder):
self.current_date_func = current_date_func
self.asset_finder = asset_finder
self._leveraged_etf = None
@property
def leveraged_etf_list(self):
if self._leveraged_etf is None:
self._leveraged_etf = self.security_list_type(
load_from_directory('leveraged_etf_list'),
self.current_date_func,
asset_finder=self.asset_finder
)
return self._leveraged_etf
def load_from_directory(list_name):
"""
To resolve the symbol in the LEVERAGED_ETF list,
the date on which the symbol was in effect is needed.
Furthermore, to maintain a point in time record of our own maintenance
of the restricted list, we need a knowledge date. Thus, restricted lists
are dictionaries of datetime->symbol lists.
new symbols should be entered as a new knowledge date entry.
This method assumes a directory structure of:
SECURITY_LISTS_DIR/listname/knowledge_date/lookup_date/add.txt
SECURITY_LISTS_DIR/listname/knowledge_date/lookup_date/delete.txt
The return value is a dictionary with:
knowledge_date -> lookup_date ->
{add: [symbol list], 'delete': [symbol list]}
"""
data = {}
dir_path = os.path.join(SECURITY_LISTS_DIR, list_name)
for kd_name in listdir(dir_path):
kd = datetime.strptime(kd_name, DATE_FORMAT).replace(
tzinfo=pytz.utc)
data[kd] = {}
kd_path = os.path.join(dir_path, kd_name)
for ld_name in listdir(kd_path):
ld = datetime.strptime(ld_name, DATE_FORMAT).replace(
tzinfo=pytz.utc)
data[kd][ld] = {}
ld_path = os.path.join(kd_path, ld_name)
for fname in listdir(ld_path):
fpath = os.path.join(ld_path, fname)
with open(fpath) as f:
symbols = f.read().splitlines()
data[kd][ld][fname] = symbols
return data
| apache-2.0 |
jonyroda97/redbot-amigosprovaveis | lib/matplotlib/tests/test_bbox_tight.py | 2 | 3367 | from __future__ import (absolute_import, division, print_function,
unicode_literals)
import numpy as np
from matplotlib.testing.decorators import image_comparison
import matplotlib.pyplot as plt
import matplotlib.path as mpath
import matplotlib.patches as mpatches
from matplotlib.ticker import FuncFormatter
@image_comparison(baseline_images=['bbox_inches_tight'], remove_text=True,
savefig_kwarg=dict(bbox_inches='tight'))
def test_bbox_inches_tight():
#: Test that a figure saved using bbox_inches='tight' is clipped correctly
data = [[66386, 174296, 75131, 577908, 32015],
[58230, 381139, 78045, 99308, 160454],
[89135, 80552, 152558, 497981, 603535],
[78415, 81858, 150656, 193263, 69638],
[139361, 331509, 343164, 781380, 52269]]
colLabels = rowLabels = [''] * 5
rows = len(data)
ind = np.arange(len(colLabels)) + 0.3 # the x locations for the groups
cellText = []
width = 0.4 # the width of the bars
yoff = np.zeros(len(colLabels))
# the bottom values for stacked bar chart
fig, ax = plt.subplots(1, 1)
for row in range(rows):
ax.bar(ind, data[row], width, bottom=yoff, color='b')
yoff = yoff + data[row]
cellText.append([''])
plt.xticks([])
plt.legend([''] * 5, loc=(1.2, 0.2))
# Add a table at the bottom of the axes
cellText.reverse()
the_table = plt.table(cellText=cellText,
rowLabels=rowLabels,
colLabels=colLabels, loc='bottom')
@image_comparison(baseline_images=['bbox_inches_tight_suptile_legend'],
remove_text=False, savefig_kwarg={'bbox_inches': 'tight'})
def test_bbox_inches_tight_suptile_legend():
plt.plot(np.arange(10), label='a straight line')
plt.legend(bbox_to_anchor=(0.9, 1), loc=2, )
plt.title('Axis title')
plt.suptitle('Figure title')
# put an extra long y tick on to see that the bbox is accounted for
def y_formatter(y, pos):
if int(y) == 4:
return 'The number 4'
else:
return str(y)
plt.gca().yaxis.set_major_formatter(FuncFormatter(y_formatter))
plt.xlabel('X axis')
@image_comparison(baseline_images=['bbox_inches_tight_clipping'],
remove_text=True, savefig_kwarg={'bbox_inches': 'tight'})
def test_bbox_inches_tight_clipping():
# tests bbox clipping on scatter points, and path clipping on a patch
# to generate an appropriately tight bbox
plt.scatter(np.arange(10), np.arange(10))
ax = plt.gca()
ax.set_xlim([0, 5])
ax.set_ylim([0, 5])
# make a massive rectangle and clip it with a path
patch = mpatches.Rectangle([-50, -50], 100, 100,
transform=ax.transData,
facecolor='blue', alpha=0.5)
path = mpath.Path.unit_regular_star(5).deepcopy()
path.vertices *= 0.25
patch.set_clip_path(path, transform=ax.transAxes)
plt.gcf().artists.append(patch)
@image_comparison(baseline_images=['bbox_inches_tight_raster'],
remove_text=True, savefig_kwarg={'bbox_inches': 'tight'})
def test_bbox_inches_tight_raster():
"""Test rasterization with tight_layout"""
fig = plt.figure()
ax = fig.add_subplot(111)
ax.plot([1.0, 2.0], rasterized=True)
| gpl-3.0 |
UFABC-AM-2016-1/constrained-k-means | generate_constraints_link.py | 1 | 1649 | import numpy as np
import json
from sklearn.datasets import load_digits, load_iris, load_diabetes
LINK_ARRAY_SIZE = 20
datasets =[
# ("iris", load_iris()),
#("digits", load_digits()),
("diabetes", load_diabetes())
]
def generate(link_array_size):
for name, data_set in datasets:
samples = np.random.choice(len(data_set.data), link_array_size)
must_links = []
cannot_links = []
for sample in samples:
value = data_set.target[sample]
for selected in range(len(data_set.data)):
if value == data_set.target[selected]:
if sample == selected:
continue
must_link = [
np.asarray(data_set.data[sample]),
np.asarray(data_set.data[selected])
]
must_links.append(must_link)
break
else:
continue
samples = np.random.choice(len(data_set.data), link_array_size)
for sample in samples:
value = data_set.target[sample]
for selected in range(len(data_set.data)):
if value != data_set.target[selected]:
cannot_link = [
np.asarray(data_set.data[sample]),
np.asarray(data_set.data[selected])
]
cannot_links.append(cannot_link)
break
else:
continue
links = {'must_link': must_links, 'cannot_link': cannot_links}
np.save(name, links)
| mit |
ua-snap/downscale | snap_scripts/epscor_sc/temp_metrics_cmip5_testing_epscor_sc.py | 1 | 6409 | # # # # # compare tasmin, tas, tasmax in a timeseries of GeoTiff files # # # #
def transform_from_latlon( lat, lon ):
''' simple way to make an affine transform from lats and lons coords '''
from affine import Affine
lat = np.asarray( lat )
lon = np.asarray(lon)
trans = Affine.translation(lon[0], lat[0])
scale = Affine.scale(lon[1] - lon[0], lat[1] - lat[0])
return trans * scale
def rasterize( shapes, coords, latitude='latitude', longitude='longitude', fill=None, **kwargs ):
'''
Rasterize a list of (geometry, fill_value) tuples onto the given
xarray coordinates. This only works for 1d latitude and longitude
arrays.
'''
from rasterio import features
if fill == None:
fill = np.nan
transform = transform_from_latlon( coords[ latitude ], coords[ longitude ] )
out_shape = ( len( coords[ latitude ] ), len( coords[ longitude ] ) )
raster = features.rasterize(shapes, out_shape=out_shape,
fill=fill, transform=transform,
dtype=float, **kwargs)
spatial_coords = {latitude: coords[latitude], longitude: coords[longitude]}
return xr.DataArray(raster, coords=spatial_coords, dims=(latitude, longitude))
def sort_files( files, split_on='_', elem_month=-2, elem_year=-1 ):
'''
sort a list of files properly using the month and year parsed
from the filename. This is useful with SNAP data since the standard
is to name files like '<prefix>_MM_YYYY.tif'. If sorted using base
Pythons sort/sorted functions, things will be sorted by the first char
of the month, which makes thing go 1, 11, ... which sucks for timeseries
this sorts it properly following SNAP standards as the default settings.
ARGUMENTS:
----------
files = [list] list of `str` pathnames to be sorted by month and year. usually from glob.glob.
split_on = [str] `str` character to split the filename on. default:'_', SNAP standard.
elem_month = [int] slice element from resultant split filename list. Follows Python slicing syntax.
default:-2. For SNAP standard.
elem_year = [int] slice element from resultant split filename list. Follows Python slicing syntax.
default:-1. For SNAP standard.
RETURNS:
--------
sorted `list` by month and year ascending.
'''
import pandas as pd
months = [ int(os.path.basename( fn ).split('.')[0].split( split_on )[elem_month]) for fn in files ]
years = [ int(os.path.basename( fn ).split('.')[0].split( split_on )[elem_year]) for fn in files ]
df = pd.DataFrame( {'fn':files, 'month':months, 'year':years} )
df_sorted = df.sort_values( ['year', 'month' ] )
return df_sorted.fn.tolist()
def only_years( files, begin=1901, end=2100, split_on='_', elem_year=-1 ):
'''
return new list of filenames where they are truncated to begin:end
ARGUMENTS:
----------
files = [list] list of `str` pathnames to be sorted by month and year. usually from glob.glob.
begin = [int] four digit integer year of the begin time default:1901
end = [int] four digit integer year of the end time default:2100
split_on = [str] `str` character to split the filename on. default:'_', SNAP standard.
elem_year = [int] slice element from resultant split filename list. Follows Python slicing syntax.
default:-1. For SNAP standard.
RETURNS:
--------
sliced `list` to begin and end year.
'''
import pandas as pd
years = [ int(os.path.basename( fn ).split('.')[0].split( split_on )[elem_year]) for fn in files ]
df = pd.DataFrame( { 'fn':files, 'year':years } )
df_slice = df[ (df.year >= begin ) & (df.year <= end ) ]
return df_slice.fn.tolist()
def masked_mean( fn ):
''' get mean of the full domain since the data are already clipped
mostly used for processing lots of files in parallel.'''
import numpy as np
import rasterio
with rasterio.open( fn ) as rst:
mask = (rst.read_masks( 1 ) == 0)
arr = np.ma.masked_array( rst.read( 1 ), mask=mask )
return np.mean( arr )
if __name__ == '__main__':
import os, glob
import geopandas as gpd
import numpy as np
import xarray as xr
import matplotlib
matplotlib.use( 'agg' )
from matplotlib import pyplot as plt
from pathos.mp_map import mp_map
import pandas as pd
# args / set working dir
base_dir = '/Users/malindgren/Documents/downscale_epscor/august_fix'
os.chdir( base_dir )
model = '5ModelAvg'
scenario = 'rcp45'
begin = 2010
end = 2015
variables = ['tasmax', 'tas', 'tasmin']
out = {}
for v in variables:
path = os.path.join( base_dir,'EPSCOR_SC_DELIVERY_AUG2016','downscaled', model, scenario, v )
# for testing with new downscaler
if v == 'tas':
path = os.path.join( base_dir,'downscaled_tas_pr_epscor_sc', model, scenario, v )
files = glob.glob( os.path.join( path, '*.tif' ) )
files = sort_files( only_years( files, begin=begin, end=end, split_on='_', elem_year=-1 ) )
out[ v ] = mp_map( masked_mean, files, nproc=4 )
plot_df = pd.DataFrame( out )
plot_df.index = pd.date_range( start=str(begin), end=str(end+1), freq='M' )
plot_df = plot_df[['tasmax', 'tas', 'tasmin']] # get em in the order for plotting
# now plot the dataframe
if begin == end:
title = 'EPSCoR SC AOI Temp Metrics {} {} {}'.format( model, scenario, begin )
else:
title = 'EPSCoR SC AOI Temp Metrics {} {} {} - {}'.format( model, scenario, begin, end )
figsize = (13,9)
colors = ['red', 'black', 'blue' ]
ax = plot_df.plot( kind='line', title=title, figsize=figsize, color=colors )
# now plot the dataframe
if begin == end:
plt.savefig( 'mean_temps_epscor_sc_{}_{}_{}.png'.format( model, scenario, begin ), dpi=600 )
else:
plt.savefig( 'mean_temps_epscor_sc_{}_{}_{}_{}.png'.format( model, scenario, begin, end ), dpi=600 )
plt.close()
# # # PRISM TEST VERSION DIFFERENCES # # # # # # #
# import rasterio
# import numpy as np
# import os, glob, itertools
# base_path = '/workspace/Shared/Tech_Projects/EPSCoR_Southcentral/project_data/prism/raw_prism'
# variables = [ 'tmax', 'tmin' ]
# for variable in variables:
# ak_olds = sorted( glob.glob( os.path.join( base_path, 'prism_raw_older', 'ak', variable, '*.asc' ) ) )
# ak_news = sorted( glob.glob( os.path.join( base_path, 'prism_raw_2016', 'ak', variable, '*.asc' ) ) )
# olds = np.array([ rasterio.open( i ).read( 1 ) for i in ak_olds if '_14' not in i ])
# news = np.array([ rasterio.open( i ).read( 1 ) *.10 for i in ak_news if '_14' not in i ])
# out = olds - news
# out[ (olds == -9999.0) | (news == -9999.0) ] = 0
# uniques = np.unique( out )
# uniques[ uniques > 0.01 ]
| mit |
ssaeger/scikit-learn | sklearn/datasets/base.py | 20 | 23481 | """
Base IO code for all datasets
"""
# Copyright (c) 2007 David Cournapeau <[email protected]>
# 2010 Fabian Pedregosa <[email protected]>
# 2010 Olivier Grisel <[email protected]>
# License: BSD 3 clause
import os
import csv
import sys
import shutil
from os import environ
from os.path import dirname
from os.path import join
from os.path import exists
from os.path import expanduser
from os.path import isdir
from os.path import splitext
from os import listdir
from os import makedirs
import numpy as np
from ..utils import check_random_state
class Bunch(dict):
"""Container object for datasets
Dictionary-like object that exposes its keys as attributes.
>>> b = Bunch(a=1, b=2)
>>> b['b']
2
>>> b.b
2
>>> b.a = 3
>>> b['a']
3
>>> b.c = 6
>>> b['c']
6
"""
def __init__(self, **kwargs):
super(Bunch, self).__init__(kwargs)
def __setattr__(self, key, value):
self[key] = value
def __getattr__(self, key):
try:
return self[key]
except KeyError:
raise AttributeError(key)
def __setstate__(self, state):
# Bunch pickles generated with scikit-learn 0.16.* have an non
# empty __dict__. This causes a surprising behaviour when
# loading these pickles scikit-learn 0.17: reading bunch.key
# uses __dict__ but assigning to bunch.key use __setattr__ and
# only changes bunch['key']. More details can be found at:
# https://github.com/scikit-learn/scikit-learn/issues/6196.
# Overriding __setstate__ to be a noop has the effect of
# ignoring the pickled __dict__
pass
def get_data_home(data_home=None):
"""Return the path of the scikit-learn data dir.
This folder is used by some large dataset loaders to avoid
downloading the data several times.
By default the data dir is set to a folder named 'scikit_learn_data'
in the user home folder.
Alternatively, it can be set by the 'SCIKIT_LEARN_DATA' environment
variable or programmatically by giving an explicit folder path. The
'~' symbol is expanded to the user home folder.
If the folder does not already exist, it is automatically created.
"""
if data_home is None:
data_home = environ.get('SCIKIT_LEARN_DATA',
join('~', 'scikit_learn_data'))
data_home = expanduser(data_home)
if not exists(data_home):
makedirs(data_home)
return data_home
def clear_data_home(data_home=None):
"""Delete all the content of the data home cache."""
data_home = get_data_home(data_home)
shutil.rmtree(data_home)
def load_files(container_path, description=None, categories=None,
load_content=True, shuffle=True, encoding=None,
decode_error='strict', random_state=0):
"""Load text files with categories as subfolder names.
Individual samples are assumed to be files stored a two levels folder
structure such as the following:
container_folder/
category_1_folder/
file_1.txt
file_2.txt
...
file_42.txt
category_2_folder/
file_43.txt
file_44.txt
...
The folder names are used as supervised signal label names. The
individual file names are not important.
This function does not try to extract features into a numpy array or
scipy sparse matrix. In addition, if load_content is false it
does not try to load the files in memory.
To use text files in a scikit-learn classification or clustering
algorithm, you will need to use the `sklearn.feature_extraction.text`
module to build a feature extraction transformer that suits your
problem.
If you set load_content=True, you should also specify the encoding of
the text using the 'encoding' parameter. For many modern text files,
'utf-8' will be the correct encoding. If you leave encoding equal to None,
then the content will be made of bytes instead of Unicode, and you will
not be able to use most functions in `sklearn.feature_extraction.text`.
Similar feature extractors should be built for other kind of unstructured
data input such as images, audio, video, ...
Read more in the :ref:`User Guide <datasets>`.
Parameters
----------
container_path : string or unicode
Path to the main folder holding one subfolder per category
description: string or unicode, optional (default=None)
A paragraph describing the characteristic of the dataset: its source,
reference, etc.
categories : A collection of strings or None, optional (default=None)
If None (default), load all the categories.
If not None, list of category names to load (other categories ignored).
load_content : boolean, optional (default=True)
Whether to load or not the content of the different files. If
true a 'data' attribute containing the text information is present
in the data structure returned. If not, a filenames attribute
gives the path to the files.
encoding : string or None (default is None)
If None, do not try to decode the content of the files (e.g. for
images or other non-text content).
If not None, encoding to use to decode text files to Unicode if
load_content is True.
decode_error: {'strict', 'ignore', 'replace'}, optional
Instruction on what to do if a byte sequence is given to analyze that
contains characters not of the given `encoding`. Passed as keyword
argument 'errors' to bytes.decode.
shuffle : bool, optional (default=True)
Whether or not to shuffle the data: might be important for models that
make the assumption that the samples are independent and identically
distributed (i.i.d.), such as stochastic gradient descent.
random_state : int, RandomState instance or None, optional (default=0)
If int, random_state is the seed used by the random number generator;
If RandomState instance, random_state is the random number generator;
If None, the random number generator is the RandomState instance used
by `np.random`.
Returns
-------
data : Bunch
Dictionary-like object, the interesting attributes are: either
data, the raw text data to learn, or 'filenames', the files
holding it, 'target', the classification labels (integer index),
'target_names', the meaning of the labels, and 'DESCR', the full
description of the dataset.
"""
target = []
target_names = []
filenames = []
folders = [f for f in sorted(listdir(container_path))
if isdir(join(container_path, f))]
if categories is not None:
folders = [f for f in folders if f in categories]
for label, folder in enumerate(folders):
target_names.append(folder)
folder_path = join(container_path, folder)
documents = [join(folder_path, d)
for d in sorted(listdir(folder_path))]
target.extend(len(documents) * [label])
filenames.extend(documents)
# convert to array for fancy indexing
filenames = np.array(filenames)
target = np.array(target)
if shuffle:
random_state = check_random_state(random_state)
indices = np.arange(filenames.shape[0])
random_state.shuffle(indices)
filenames = filenames[indices]
target = target[indices]
if load_content:
data = []
for filename in filenames:
with open(filename, 'rb') as f:
data.append(f.read())
if encoding is not None:
data = [d.decode(encoding, decode_error) for d in data]
return Bunch(data=data,
filenames=filenames,
target_names=target_names,
target=target,
DESCR=description)
return Bunch(filenames=filenames,
target_names=target_names,
target=target,
DESCR=description)
def load_iris():
"""Load and return the iris dataset (classification).
The iris dataset is a classic and very easy multi-class classification
dataset.
================= ==============
Classes 3
Samples per class 50
Samples total 150
Dimensionality 4
Features real, positive
================= ==============
Read more in the :ref:`User Guide <datasets>`.
Returns
-------
data : Bunch
Dictionary-like object, the interesting attributes are:
'data', the data to learn, 'target', the classification labels,
'target_names', the meaning of the labels, 'feature_names', the
meaning of the features, and 'DESCR', the
full description of the dataset.
Examples
--------
Let's say you are interested in the samples 10, 25, and 50, and want to
know their class name.
>>> from sklearn.datasets import load_iris
>>> data = load_iris()
>>> data.target[[10, 25, 50]]
array([0, 0, 1])
>>> list(data.target_names)
['setosa', 'versicolor', 'virginica']
"""
module_path = dirname(__file__)
with open(join(module_path, 'data', 'iris.csv')) as csv_file:
data_file = csv.reader(csv_file)
temp = next(data_file)
n_samples = int(temp[0])
n_features = int(temp[1])
target_names = np.array(temp[2:])
data = np.empty((n_samples, n_features))
target = np.empty((n_samples,), dtype=np.int)
for i, ir in enumerate(data_file):
data[i] = np.asarray(ir[:-1], dtype=np.float64)
target[i] = np.asarray(ir[-1], dtype=np.int)
with open(join(module_path, 'descr', 'iris.rst')) as rst_file:
fdescr = rst_file.read()
return Bunch(data=data, target=target,
target_names=target_names,
DESCR=fdescr,
feature_names=['sepal length (cm)', 'sepal width (cm)',
'petal length (cm)', 'petal width (cm)'])
def load_breast_cancer():
"""Load and return the breast cancer wisconsin dataset (classification).
The breast cancer dataset is a classic and very easy binary classification
dataset.
================= ==============
Classes 2
Samples per class 212(M),357(B)
Samples total 569
Dimensionality 30
Features real, positive
================= ==============
Returns
-------
data : Bunch
Dictionary-like object, the interesting attributes are:
'data', the data to learn, 'target', the classification labels,
'target_names', the meaning of the labels, 'feature_names', the
meaning of the features, and 'DESCR', the
full description of the dataset.
The copy of UCI ML Breast Cancer Wisconsin (Diagnostic) dataset is
downloaded from:
https://goo.gl/U2Uwz2
Examples
--------
Let's say you are interested in the samples 10, 50, and 85, and want to
know their class name.
>>> from sklearn.datasets import load_breast_cancer
>>> data = load_breast_cancer()
>>> data.target[[10, 50, 85]]
array([0, 1, 0])
>>> list(data.target_names)
['malignant', 'benign']
"""
module_path = dirname(__file__)
with open(join(module_path, 'data', 'breast_cancer.csv')) as csv_file:
data_file = csv.reader(csv_file)
first_line = next(data_file)
n_samples = int(first_line[0])
n_features = int(first_line[1])
target_names = np.array(first_line[2:4])
data = np.empty((n_samples, n_features))
target = np.empty((n_samples,), dtype=np.int)
for count, value in enumerate(data_file):
data[count] = np.asarray(value[:-1], dtype=np.float64)
target[count] = np.asarray(value[-1], dtype=np.int)
with open(join(module_path, 'descr', 'breast_cancer.rst')) as rst_file:
fdescr = rst_file.read()
feature_names = np.array(['mean radius', 'mean texture',
'mean perimeter', 'mean area',
'mean smoothness', 'mean compactness',
'mean concavity', 'mean concave points',
'mean symmetry', 'mean fractal dimension',
'radius error', 'texture error',
'perimeter error', 'area error',
'smoothness error', 'compactness error',
'concavity error', 'concave points error',
'symmetry error', 'fractal dimension error',
'worst radius', 'worst texture',
'worst perimeter', 'worst area',
'worst smoothness', 'worst compactness',
'worst concavity', 'worst concave points',
'worst symmetry', 'worst fractal dimension'])
return Bunch(data=data, target=target,
target_names=target_names,
DESCR=fdescr,
feature_names=feature_names)
def load_digits(n_class=10):
"""Load and return the digits dataset (classification).
Each datapoint is a 8x8 image of a digit.
================= ==============
Classes 10
Samples per class ~180
Samples total 1797
Dimensionality 64
Features integers 0-16
================= ==============
Read more in the :ref:`User Guide <datasets>`.
Parameters
----------
n_class : integer, between 0 and 10, optional (default=10)
The number of classes to return.
Returns
-------
data : Bunch
Dictionary-like object, the interesting attributes are:
'data', the data to learn, 'images', the images corresponding
to each sample, 'target', the classification labels for each
sample, 'target_names', the meaning of the labels, and 'DESCR',
the full description of the dataset.
Examples
--------
To load the data and visualize the images::
>>> from sklearn.datasets import load_digits
>>> digits = load_digits()
>>> print(digits.data.shape)
(1797, 64)
>>> import pylab as pl #doctest: +SKIP
>>> pl.gray() #doctest: +SKIP
>>> pl.matshow(digits.images[0]) #doctest: +SKIP
>>> pl.show() #doctest: +SKIP
"""
module_path = dirname(__file__)
data = np.loadtxt(join(module_path, 'data', 'digits.csv.gz'),
delimiter=',')
with open(join(module_path, 'descr', 'digits.rst')) as f:
descr = f.read()
target = data[:, -1]
flat_data = data[:, :-1]
images = flat_data.view()
images.shape = (-1, 8, 8)
if n_class < 10:
idx = target < n_class
flat_data, target = flat_data[idx], target[idx]
images = images[idx]
return Bunch(data=flat_data,
target=target.astype(np.int),
target_names=np.arange(10),
images=images,
DESCR=descr)
def load_diabetes():
"""Load and return the diabetes dataset (regression).
============== ==================
Samples total 442
Dimensionality 10
Features real, -.2 < x < .2
Targets integer 25 - 346
============== ==================
Read more in the :ref:`User Guide <datasets>`.
Returns
-------
data : Bunch
Dictionary-like object, the interesting attributes are:
'data', the data to learn and 'target', the regression target for each
sample.
"""
base_dir = join(dirname(__file__), 'data')
data = np.loadtxt(join(base_dir, 'diabetes_data.csv.gz'))
target = np.loadtxt(join(base_dir, 'diabetes_target.csv.gz'))
return Bunch(data=data, target=target)
def load_linnerud():
"""Load and return the linnerud dataset (multivariate regression).
Samples total: 20
Dimensionality: 3 for both data and targets
Features: integer
Targets: integer
Returns
-------
data : Bunch
Dictionary-like object, the interesting attributes are: 'data' and
'targets', the two multivariate datasets, with 'data' corresponding to
the exercise and 'targets' corresponding to the physiological
measurements, as well as 'feature_names' and 'target_names'.
"""
base_dir = join(dirname(__file__), 'data/')
# Read data
data_exercise = np.loadtxt(base_dir + 'linnerud_exercise.csv', skiprows=1)
data_physiological = np.loadtxt(base_dir + 'linnerud_physiological.csv',
skiprows=1)
# Read header
with open(base_dir + 'linnerud_exercise.csv') as f:
header_exercise = f.readline().split()
with open(base_dir + 'linnerud_physiological.csv') as f:
header_physiological = f.readline().split()
with open(dirname(__file__) + '/descr/linnerud.rst') as f:
descr = f.read()
return Bunch(data=data_exercise, feature_names=header_exercise,
target=data_physiological,
target_names=header_physiological,
DESCR=descr)
def load_boston():
"""Load and return the boston house-prices dataset (regression).
============== ==============
Samples total 506
Dimensionality 13
Features real, positive
Targets real 5. - 50.
============== ==============
Returns
-------
data : Bunch
Dictionary-like object, the interesting attributes are:
'data', the data to learn, 'target', the regression targets,
and 'DESCR', the full description of the dataset.
Examples
--------
>>> from sklearn.datasets import load_boston
>>> boston = load_boston()
>>> print(boston.data.shape)
(506, 13)
"""
module_path = dirname(__file__)
fdescr_name = join(module_path, 'descr', 'boston_house_prices.rst')
with open(fdescr_name) as f:
descr_text = f.read()
data_file_name = join(module_path, 'data', 'boston_house_prices.csv')
with open(data_file_name) as f:
data_file = csv.reader(f)
temp = next(data_file)
n_samples = int(temp[0])
n_features = int(temp[1])
data = np.empty((n_samples, n_features))
target = np.empty((n_samples,))
temp = next(data_file) # names of features
feature_names = np.array(temp)
for i, d in enumerate(data_file):
data[i] = np.asarray(d[:-1], dtype=np.float64)
target[i] = np.asarray(d[-1], dtype=np.float64)
return Bunch(data=data,
target=target,
# last column is target value
feature_names=feature_names[:-1],
DESCR=descr_text)
def load_sample_images():
"""Load sample images for image manipulation.
Loads both, ``china`` and ``flower``.
Returns
-------
data : Bunch
Dictionary-like object with the following attributes :
'images', the two sample images, 'filenames', the file
names for the images, and 'DESCR'
the full description of the dataset.
Examples
--------
To load the data and visualize the images:
>>> from sklearn.datasets import load_sample_images
>>> dataset = load_sample_images() #doctest: +SKIP
>>> len(dataset.images) #doctest: +SKIP
2
>>> first_img_data = dataset.images[0] #doctest: +SKIP
>>> first_img_data.shape #doctest: +SKIP
(427, 640, 3)
>>> first_img_data.dtype #doctest: +SKIP
dtype('uint8')
"""
# Try to import imread from scipy. We do this lazily here to prevent
# this module from depending on PIL.
try:
try:
from scipy.misc import imread
except ImportError:
from scipy.misc.pilutil import imread
except ImportError:
raise ImportError("The Python Imaging Library (PIL) "
"is required to load data from jpeg files")
module_path = join(dirname(__file__), "images")
with open(join(module_path, 'README.txt')) as f:
descr = f.read()
filenames = [join(module_path, filename)
for filename in os.listdir(module_path)
if filename.endswith(".jpg")]
# Load image data for each image in the source folder.
images = [imread(filename) for filename in filenames]
return Bunch(images=images,
filenames=filenames,
DESCR=descr)
def load_sample_image(image_name):
"""Load the numpy array of a single sample image
Parameters
-----------
image_name: {`china.jpg`, `flower.jpg`}
The name of the sample image loaded
Returns
-------
img: 3D array
The image as a numpy array: height x width x color
Examples
---------
>>> from sklearn.datasets import load_sample_image
>>> china = load_sample_image('china.jpg') # doctest: +SKIP
>>> china.dtype # doctest: +SKIP
dtype('uint8')
>>> china.shape # doctest: +SKIP
(427, 640, 3)
>>> flower = load_sample_image('flower.jpg') # doctest: +SKIP
>>> flower.dtype # doctest: +SKIP
dtype('uint8')
>>> flower.shape # doctest: +SKIP
(427, 640, 3)
"""
images = load_sample_images()
index = None
for i, filename in enumerate(images.filenames):
if filename.endswith(image_name):
index = i
break
if index is None:
raise AttributeError("Cannot find sample image: %s" % image_name)
return images.images[index]
def _pkl_filepath(*args, **kwargs):
"""Ensure different filenames for Python 2 and Python 3 pickles
An object pickled under Python 3 cannot be loaded under Python 2.
An object pickled under Python 2 can sometimes not be loaded loaded
correctly under Python 3 because some Python 2 strings are decoded as
Python 3 strings which can be problematic for objects that use Python 2
strings as byte buffers for numerical data instead of "real" strings.
Therefore, dataset loaders in scikit-learn use different files for pickles
manages by Python 2 and Python 3 in the same SCIKIT_LEARN_DATA folder so
as to avoid conflicts.
args[-1] is expected to be the ".pkl" filename. Under Python 3, a
suffix is inserted before the extension to s
_pkl_filepath('/path/to/folder', 'filename.pkl') returns:
- /path/to/folder/filename.pkl under Python 2
- /path/to/folder/filename_py3.pkl under Python 3+
"""
py3_suffix = kwargs.get("py3_suffix", "_py3")
basename, ext = splitext(args[-1])
if sys.version_info[0] >= 3:
basename += py3_suffix
new_args = args[:-1] + (basename + ext,)
return join(*new_args)
| bsd-3-clause |
mfjb/scikit-learn | examples/neighbors/plot_digits_kde_sampling.py | 251 | 2022 | """
=========================
Kernel Density Estimation
=========================
This example shows how kernel density estimation (KDE), a powerful
non-parametric density estimation technique, can be used to learn
a generative model for a dataset. With this generative model in place,
new samples can be drawn. These new samples reflect the underlying model
of the data.
"""
import numpy as np
import matplotlib.pyplot as plt
from sklearn.datasets import load_digits
from sklearn.neighbors import KernelDensity
from sklearn.decomposition import PCA
from sklearn.grid_search import GridSearchCV
# load the data
digits = load_digits()
data = digits.data
# project the 64-dimensional data to a lower dimension
pca = PCA(n_components=15, whiten=False)
data = pca.fit_transform(digits.data)
# use grid search cross-validation to optimize the bandwidth
params = {'bandwidth': np.logspace(-1, 1, 20)}
grid = GridSearchCV(KernelDensity(), params)
grid.fit(data)
print("best bandwidth: {0}".format(grid.best_estimator_.bandwidth))
# use the best estimator to compute the kernel density estimate
kde = grid.best_estimator_
# sample 44 new points from the data
new_data = kde.sample(44, random_state=0)
new_data = pca.inverse_transform(new_data)
# turn data into a 4x11 grid
new_data = new_data.reshape((4, 11, -1))
real_data = digits.data[:44].reshape((4, 11, -1))
# plot real digits and resampled digits
fig, ax = plt.subplots(9, 11, subplot_kw=dict(xticks=[], yticks=[]))
for j in range(11):
ax[4, j].set_visible(False)
for i in range(4):
im = ax[i, j].imshow(real_data[i, j].reshape((8, 8)),
cmap=plt.cm.binary, interpolation='nearest')
im.set_clim(0, 16)
im = ax[i + 5, j].imshow(new_data[i, j].reshape((8, 8)),
cmap=plt.cm.binary, interpolation='nearest')
im.set_clim(0, 16)
ax[0, 5].set_title('Selection from the input data')
ax[5, 5].set_title('"New" digits drawn from the kernel density model')
plt.show()
| bsd-3-clause |
JosmanPS/scikit-learn | benchmarks/bench_sparsify.py | 323 | 3372 | """
Benchmark SGD prediction time with dense/sparse coefficients.
Invoke with
-----------
$ kernprof.py -l sparsity_benchmark.py
$ python -m line_profiler sparsity_benchmark.py.lprof
Typical output
--------------
input data sparsity: 0.050000
true coef sparsity: 0.000100
test data sparsity: 0.027400
model sparsity: 0.000024
r^2 on test data (dense model) : 0.233651
r^2 on test data (sparse model) : 0.233651
Wrote profile results to sparsity_benchmark.py.lprof
Timer unit: 1e-06 s
File: sparsity_benchmark.py
Function: benchmark_dense_predict at line 51
Total time: 0.532979 s
Line # Hits Time Per Hit % Time Line Contents
==============================================================
51 @profile
52 def benchmark_dense_predict():
53 301 640 2.1 0.1 for _ in range(300):
54 300 532339 1774.5 99.9 clf.predict(X_test)
File: sparsity_benchmark.py
Function: benchmark_sparse_predict at line 56
Total time: 0.39274 s
Line # Hits Time Per Hit % Time Line Contents
==============================================================
56 @profile
57 def benchmark_sparse_predict():
58 1 10854 10854.0 2.8 X_test_sparse = csr_matrix(X_test)
59 301 477 1.6 0.1 for _ in range(300):
60 300 381409 1271.4 97.1 clf.predict(X_test_sparse)
"""
from scipy.sparse.csr import csr_matrix
import numpy as np
from sklearn.linear_model.stochastic_gradient import SGDRegressor
from sklearn.metrics import r2_score
np.random.seed(42)
def sparsity_ratio(X):
return np.count_nonzero(X) / float(n_samples * n_features)
n_samples, n_features = 5000, 300
X = np.random.randn(n_samples, n_features)
inds = np.arange(n_samples)
np.random.shuffle(inds)
X[inds[int(n_features / 1.2):]] = 0 # sparsify input
print("input data sparsity: %f" % sparsity_ratio(X))
coef = 3 * np.random.randn(n_features)
inds = np.arange(n_features)
np.random.shuffle(inds)
coef[inds[n_features/2:]] = 0 # sparsify coef
print("true coef sparsity: %f" % sparsity_ratio(coef))
y = np.dot(X, coef)
# add noise
y += 0.01 * np.random.normal((n_samples,))
# Split data in train set and test set
n_samples = X.shape[0]
X_train, y_train = X[:n_samples / 2], y[:n_samples / 2]
X_test, y_test = X[n_samples / 2:], y[n_samples / 2:]
print("test data sparsity: %f" % sparsity_ratio(X_test))
###############################################################################
clf = SGDRegressor(penalty='l1', alpha=.2, fit_intercept=True, n_iter=2000)
clf.fit(X_train, y_train)
print("model sparsity: %f" % sparsity_ratio(clf.coef_))
def benchmark_dense_predict():
for _ in range(300):
clf.predict(X_test)
def benchmark_sparse_predict():
X_test_sparse = csr_matrix(X_test)
for _ in range(300):
clf.predict(X_test_sparse)
def score(y_test, y_pred, case):
r2 = r2_score(y_test, y_pred)
print("r^2 on test data (%s) : %f" % (case, r2))
score(y_test, clf.predict(X_test), 'dense model')
benchmark_dense_predict()
clf.sparsify()
score(y_test, clf.predict(X_test), 'sparse model')
benchmark_sparse_predict()
| bsd-3-clause |
CVML/scikit-learn | sklearn/covariance/__init__.py | 389 | 1157 | """
The :mod:`sklearn.covariance` module includes methods and algorithms to
robustly estimate the covariance of features given a set of points. The
precision matrix defined as the inverse of the covariance is also estimated.
Covariance estimation is closely related to the theory of Gaussian Graphical
Models.
"""
from .empirical_covariance_ import empirical_covariance, EmpiricalCovariance, \
log_likelihood
from .shrunk_covariance_ import shrunk_covariance, ShrunkCovariance, \
ledoit_wolf, ledoit_wolf_shrinkage, \
LedoitWolf, oas, OAS
from .robust_covariance import fast_mcd, MinCovDet
from .graph_lasso_ import graph_lasso, GraphLasso, GraphLassoCV
from .outlier_detection import EllipticEnvelope
__all__ = ['EllipticEnvelope',
'EmpiricalCovariance',
'GraphLasso',
'GraphLassoCV',
'LedoitWolf',
'MinCovDet',
'OAS',
'ShrunkCovariance',
'empirical_covariance',
'fast_mcd',
'graph_lasso',
'ledoit_wolf',
'ledoit_wolf_shrinkage',
'log_likelihood',
'oas',
'shrunk_covariance']
| bsd-3-clause |
silgon/rlpy | rlpy/Tools/GeneralTools.py | 2 | 40545 | """General Tools for use throughout RLPy"""
def module_exists(module_name):
try:
__import__(module_name)
except ImportError:
return False
else:
return True
import sys
import numpy as np
# print "Numpy version:", numpy.__version__
# print "Python version:", sys.version_info
import os
__copyright__ = "Copyright 2013, RLPy http://acl.mit.edu/RLPy"
__credits__ = ["Alborz Geramifard", "Robert H. Klein", "Christoph Dann",
"William Dabney", "Jonathan P. How"]
__license__ = "BSD 3-Clause"
__author__ = "Alborz Geramifard"
__rlpy_location__ = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
if os.name == 'nt':
# Anaconda is built with QT4 backend support on Windows
matplotlib_backend = 'qt4agg'
else:
matplotlib_backend = 'tkagg' # 'WX' 'QTAgg' 'QT4Agg'
def available_matplotlib_backends():
def is_backend_module(fname):
"""Identifies if a filename is a matplotlib backend module"""
return fname.startswith('backend_') and fname.endswith('.py')
def backend_fname_formatter(fname):
"""Removes the extension of the given filename, then takes away the leading 'backend_'."""
return os.path.splitext(fname)[0][8:]
# get the directory where the backends live
backends_dir = os.path.dirname(matplotlib.backends.__file__)
# filter all files in that directory to identify all files which provide a
# backend
backend_fnames = filter(is_backend_module, os.listdir(backends_dir))
backends = [backend_fname_formatter(fname) for fname in backend_fnames]
return backends
if module_exists('matplotlib'):
import matplotlib
import matplotlib.backends
import matplotlib.pyplot as plt
mpl_backends = available_matplotlib_backends()
if matplotlib_backend in mpl_backends:
plt.switch_backend(matplotlib_backend)
else:
print "Warning: Matplotlib backend", matplotlib_backend, "not available"
print "Available backends:", mpl_backends
from matplotlib import pylab as pl
import matplotlib.ticker as ticker
from matplotlib import rc, colors
import matplotlib.patches as mpatches
import matplotlib.path as mpath
import matplotlib.cm as cm
from matplotlib import lines
from mpl_toolkits.mplot3d import axes3d
from matplotlib import lines # for plotting lines in pendulum and PST
from matplotlib.patches import ConnectionStyle # for cartpole
pl.ion()
else:
print 'matplotlib is not available => No Graphics'
if module_exists('networkx'):
import networkx as nx
else:
'networkx is not available => No Graphics on SystemAdmin domain'
if module_exists('sklearn'):
from sklearn import svm
else:
'sklearn is not available => No BEBF representation available'
from scipy import stats
from scipy import misc
from scipy import linalg
from scipy.sparse import linalg as slinalg
from scipy import sparse as sp
from time import clock
from hashlib import sha1
import datetime
import csv
from string import lower
# from Sets import ImmutableSet
# from heapq import *
import multiprocessing
from os import path
from decimal import Decimal
# If running on an older version of numpy, check to make sure we have
# defined all required functions.
import numpy as np # We need to be able to reference numpy by name
from select import select
from itertools import combinations, chain
def discrete_sample(p):
cp = np.cumsum(p)
return np.sum(cp <= np.random.rand(1))
def cartesian(arrays, out=None):
"""
Generate a cartesian product of input arrays.
Parameters
----------
arrays : list of array-like
1-D arrays to form the cartesian product of.
out : ndarray
Array to place the cartesian product in.
Returns
-------
out : ndarray
2-D array of shape (M, len(arrays)) containing cartesian products
formed of input arrays.
Examples
--------
>>> cartesian(([1, 2, 3], [4, 5], [6, 7]))
array([[1, 4, 6],
[1, 4, 7],
[1, 5, 6],
[1, 5, 7],
[2, 4, 6],
[2, 4, 7],
[2, 5, 6],
[2, 5, 7],
[3, 4, 6],
[3, 4, 7],
[3, 5, 6],
[3, 5, 7]])
"""
arrays = [np.asarray(x) for x in arrays]
dtype = arrays[0].dtype
n = np.prod([x.size for x in arrays])
if out is None:
out = np.zeros([n, len(arrays)], dtype=dtype)
m = n / arrays[0].size
out[:, 0] = np.repeat(arrays[0], m)
if arrays[1:]:
cartesian(arrays[1:], out=out[0:m, 1:])
for j in xrange(1, arrays[0].size):
out[j * m:(j + 1) * m, 1:] = out[0:m, 1:]
return out
# if numpy.version.version < '2.6.0': # Missing count_nonzero
def count_nonzero(arr):
"""
Custom ``nnz()`` method, moves recursively through any sublists within
*arr*, such that only individual elements are examined. \n
Some versions of numpy's count_nonzero only strictly compare each element;
e.g. ``numpy.count_nonzero([[1,2,3,4,5], [6,7,8,9]])`` returns 2, while
``Tools.count_nonzero([[1,2,3,4,5], [6,7,8,9]])`` returns 9.
"""
nnz = 0
# Is this an instance of a matrix? Use inbuilt nonzero() method and count # of indices returned.
# NOT TESTED with high-dimensional matrices (only 2-dimensional matrices)
if sp.issparse(arr):
return arr.getnnz()
if isinstance(arr, np.matrixlib.defmatrix.matrix):
# Tuple of length = # dimensions (usu. 2) containing indices of nonzero
# elements
nonzero_indices = arr.nonzero()
# Find # of indices in the vector corresponding to any of the
# dimensions (all have same length)
nnz = np.size(nonzero_indices[0])
return nnz
if isinstance(arr, np.ndarray):
# return sum([1 for x in arr.ravel() if x != 0])
return np.count_nonzero(arr.ravel())
if isinstance(arr, list):
for el in arr:
if isinstance(el, list):
nnz += np.count_nonzero(el)
elif el != 0:
nnz += 1
return nnz
print "In tools.py attempted count_nonzero with unsupported type of", type(arr)
return None
def randint(low, high, m=1, n=1):
"""
:param low: Lower bound on possible random ints
:param high: Max possible random int (INCLUSIVE)
:param m: number of rows in output
:param n: number of cols in output
Generates an ``m x n`` whose elements are integers selected uniform random
in the range [low, high].
"""
return np.random.randint(low, high + 1, size=(m, n))
def randSet(x):
"""
:param x: a list, array, or other iterable datatype
Accepts a 1-D vector (list, array, etc) and returns an element from the list
selected uniform random.
"""
# i = random.random_integers(0,size(x)-1)
i = np.random.randint(0, len(x) - 1)
return x[i]
def closestDiscretization(s, num_bins, limits):
"""
:param s: a state. (possibly multidimensional) ndarray, with dimension d =
dimensionality of state space.
:param num_bins: Number of discrete elements in
:param limits: 2 x d ndarray, where row[0] is a row vector of the lower
limit of each discrete dimension, and row[1] are corresponding upper
limits.
Returns the closest point to the state ``s`` based on the discretization
defined by the number of bins and limits. \n
( equivalent to state2bin(x) / (num_bins-1) * width + limits[0] )
"""
# width = limits[1]-limits[0]
# return round((s-limits[0])*num_bins/(width*1.)) / num_bins * width + limits[0]
return bin2state(state2bin(s, num_bins, limits), num_bins, limits)
def bin2state(bin, num_bins, limits):
"""
:param bin: index in the discretization
:param num_bins: the total number of bins in the discretization
:param limits: 2 x d ndarray, where row[0] is a row vector of the lower
limit of each discrete dimension, and row[1] are corresponding upper
limits.
.. note::
This is the inverse of state2bin function.
Given an index ``bin``, the number of the bins ``num_bins``, and the limits
on a single state dimension, this function returns the corresponding value
in the middle of the bin (ie, the average of the discretizations around it)
"""
bin_width = (limits[1] - limits[0]) / (num_bins * 1.)
return bin * bin_width + bin_width / 2.0 + limits[0]
def state2bin(s, num_bins, limits):
"""
:param s: a state. (possibly multidimensional) ndarray, with dimension d =
dimensionality of state space.
:param num_bins: the total number of bins in the discretization
:param limits: 2 x d ndarray, where row[0] is a row vector of the lower
limit of each discrete dimension, and row[1] are corresponding upper
limits.
Returns the bin number (index) corresponding to state s given a
discretization num_bins between each column of limits[0] and limits[1].
The return value has same dimensionality as ``s``. \n
Note that ``s`` may be continuous. \n
\n
Examples: \n
s = 0, limits = [-1,5], num_bins = 6 => 1 \n
s = .001, limits = [-1,5], num_bins = 6 => 1 \n
s = .4, limits = [-.5,.5], num_bins = 3 => 2 \n
"""
if s == limits[1]:
return num_bins - 1
width = limits[1] - limits[0]
if s > limits[1]:
print 'Tools.py: WARNING: ', s, ' > ', limits[1], '. Using the chopped value of s'
print 'Ignoring', limits[1] - s
s = limits[1]
elif s < limits[0]:
print 'Tools.py: WARNING: ', s, ' < ', limits[0], '. Using the chopped value of s'
# print("WARNING: %s is out of limits of %s . Using the chopped value of s" %(str(s),str(limits)))
s = limits[0]
return int((s - limits[0]) * num_bins / (width * 1.))
def deltaT(start_time):
""" Returns the time elapsed since ``start_time`` in seconds. """
return clock() - start_time
def hhmmss(t):
"""
:param t: time elapsed (in seconds)
Returns the string representation of ``t`` in format: ``hhmmss``
"""
return str(datetime.timedelta(seconds=round(t)))
def className(obj):
""" Return the name of a class as a string. """
return obj.__class__.__name__
def createColorMaps():
"""
Create and register the colormaps to be used in domain visualizations.
"""
# Make Grid World ColorMap
mycmap = colors.ListedColormap(
['w', '.75', 'b', 'g', 'r', 'k'], 'GridWorld')
cm.register_cmap(cmap=mycmap)
mycmap = colors.ListedColormap(['r', 'k'], 'fiftyChainActions')
cm.register_cmap(cmap=mycmap)
mycmap = colors.ListedColormap(['b', 'r'], 'FlipBoard')
cm.register_cmap(cmap=mycmap)
mycmap = colors.ListedColormap(
['w', '.75', 'b', 'r'], 'IntruderMonitoring')
cm.register_cmap(cmap=mycmap)
mycmap = colors.ListedColormap(
['w', 'b', 'g', 'r', 'm', (1, 1, 0), 'k'], 'BlocksWorld')
cm.register_cmap(cmap=mycmap)
mycmap = colors.ListedColormap(['.5', 'k'], 'Actions')
cm.register_cmap(cmap=mycmap)
# mycmap = make_colormap({0:(.8,.7,0), 1: 'w', 2:(0,0,1)}) # orange to
# blue
mycmap = make_colormap({0: 'r', 1: 'w', 2: 'g'}) # red to blue
cm.register_cmap(cmap=mycmap, name='ValueFunction')
mycmap = colors.ListedColormap(['r', 'w', 'k'], 'InvertedPendulumActions')
cm.register_cmap(cmap=mycmap)
mycmap = colors.ListedColormap(['r', 'w', 'k'], 'MountainCarActions')
cm.register_cmap(cmap=mycmap)
mycmap = colors.ListedColormap(['r', 'w', 'k', 'b'], '4Actions')
cm.register_cmap(cmap=mycmap)
def make_colormap(colors):
"""
Define a new color map based on values specified in the dictionary
colors, where colors[z] is the color that value z should be mapped to,
with linear interpolation between the given values of z.
The z values (dictionary keys) are real numbers and the values
colors[z] can be either an RGB list, e.g. [1,0,0] for red, or an
html hex string, e.g. "#ff0000" for red.
"""
from matplotlib.colors import LinearSegmentedColormap, ColorConverter
z = np.sort(colors.keys())
n = len(z)
z1 = min(z)
zn = max(z)
x0 = (z - z1) / ((zn - z1) * 1.)
CC = ColorConverter()
R = []
G = []
B = []
for i in xrange(n):
# i'th color at level z[i]:
Ci = colors[z[i]]
if isinstance(Ci, str):
# a hex string of form '#ff0000' for example (for red)
RGB = CC.to_rgb(Ci)
else:
# assume it's an RGB triple already:
RGB = Ci
R.append(RGB[0])
G.append(RGB[1])
B.append(RGB[2])
cmap_dict = {}
cmap_dict['red'] = [(x0[i], R[i], R[i]) for i in xrange(len(R))]
cmap_dict['green'] = [(x0[i], G[i], G[i]) for i in xrange(len(G))]
cmap_dict['blue'] = [(x0[i], B[i], B[i]) for i in xrange(len(B))]
mymap = LinearSegmentedColormap('mymap', cmap_dict)
return mymap
def showcolors(cmap):
"""
:param cmap: A colormap.
Debugging tool: displays all possible values of a colormap.
"""
plt.clf()
x = np.linspace(0, 1, 21)
X, Y = np.meshgrid(x, x)
plt.pcolor(X, Y, 0.5 * (X + Y), cmap=cmap, edgecolors='k')
plt.axis('equal')
plt.colorbar()
plt.title('Plot of x+y using colormap')
def schlieren_colormap(color=[0, 0, 0]):
"""
Creates and returns a colormap suitable for schlieren plots.
"""
if color == 'k':
color = [0, 0, 0]
if color == 'r':
color = [1, 0, 0]
if color == 'b':
color = [0, 0, 1]
if color == 'g':
color = [0, 0.5, 0]
if color == 'y':
color = [1, 1, 0]
color = np.array([1, 1, 1]) - np.array(color)
s = np.linspace(0, 1, 20)
colors = {}
for key in s:
colors[key] = np.array([1, 1, 1]) - key ** 10 * color
schlieren_colors = make_colormap(colors)
return schlieren_colors
def make_amrcolors(nlevels=4):
"""
:param nlevels: maximum number of AMR levels expected.
Make lists of colors useful for distinguishing different grids when
plotting AMR results.
Returns the tuple (linecolors, bgcolors):\n
linecolors = list of nlevels colors for grid lines, contour lines. \n
bgcolors = list of nlevels pale colors for grid background.
"""
# For 4 or less levels:
linecolors = ['k', 'b', 'r', 'g']
# Set bgcolors to white, then light shades of blue, red, green:
bgcolors = ['#ffffff', '#ddddff', '#ffdddd', '#ddffdd']
# Set bgcolors to light shades of yellow, blue, red, green:
# bgcolors = ['#ffffdd','#ddddff','#ffdddd','#ddffdd']
if nlevels > 4:
linecolors = 4 * linecolors # now has length 16
bgcolors = 4 * bgcolors
if nlevels <= 16:
linecolors = linecolors[:nlevels]
bgcolors = bgcolors[:nlevels]
else:
print "*** Warning, suggest nlevels <= 16"
return (linecolors, bgcolors)
def linearMap(x, a, b, A=0, B=1):
"""
.. warning::
``x`` *MUST* be a scalar for truth values to make sense.
This function takes scalar ``x`` in range [a,b] and linearly maps it to
the range [A,B].
Note that ``x`` is truncated to lie in possible boundaries.
"""
if a == b:
res = B
else:
res = (x - a) / (1. * (b - a)) * (B - A) + A
if res < A:
res = A
if res > B:
res = B
return res
def l_norm(x, norm=2):
''' Returns the L infinity norm of a vector '''
return np.linalg.norm(x, norm)
def generalDot(x, y):
"""
Takes the inner product of the inputs x and y.
Defined because of inconsistent or confusing definition of the "dot"
operator for numpy ndarray, matrix, and sparse.matrix.
"""
if sp.issparse(x):
# active_indices = x.nonzero()[0].flatten()
return x.multiply(y).sum()
else:
return np.dot(x, y)
def normpdf(x, mu, sigma):
""" Returns the scalar probability density of Gaussian (mu,sigma) at x. """
return stats.norm.pdf(x, mu, sigma)
def factorial(x):
return misc.factorial(x)
def nchoosek(n, k):
""" Returns combination n choose k. """
return misc.comb(n, k)
def findElemArray1D(x, arr):
"""
:param x: a scalar
:param arr: a 1-dimensional numpy ndarray
Returns an array of indices i in arr where x == arr[i]
or [] if x not in arr.
"""
res = np.where(arr == x)
if len(res[0]):
return res[0].flatten()
else:
return []
def findElemArray2D(x, arr2d):
"""
:param x: a scalar
:param arr2d: a 2-dimensional numpy ndarray or matrix
Returns a tuple of arrays (rVec, cVec), where the corresponding elements in
each are the rows and cols where arr2d[r,c] == x.
Returns [] if x not in arr2d. \n
Example: \n
arr2d = np.array([[1,2],[3,1]]), x = 1
findElemArray2D(x, arr2d) --> ([0, 1], [0, 1]).
i.e., arr2d[0][0] and arr2d[1][1] both == x.
.. note::
The type of each tuple member is the same as type(arr2d)
"""
res = np.where(arr2d == x)
if len(res[0]):
return res[0].flatten(), res[1].flatten()
else:
return [], []
# CURRENTLY not used by any algs
def findRow(rowVec, X):
"""
:param rowVec: a 1-dimensional numpy ndarray
:param X: a 2-d numpy ndarray
Return the indices of the rows of X that are equal to rowVec. \n
NOTE: rowVec and X must have the same number of columns
"""
# return nonzero(any(logical_and.reduce([X[:, i] == r[i] for i in arange(len(r))])))
# return any(logical_and(X[:, 0] == r[0], X[:, 1] == r[1]))
ind = np.nonzero(np.logical_and.reduce([X[:, i] == rowVec[i] for i in xrange(len(rowVec))]))
return ind[0]
def perms(X):
"""
:param X: an iterable type (ndarray, matrix, list).
If a 1-D array, each element e is treated as the number of discrete
elements to use for permutations, [0, e).
If a >1-D array, take permutations between the elements themselves
between dimensions.
Returns all permutations *in numpy array format*. For example: \n
X = [2 3] \n
res = [[0,0],[0,1],[0,2],[1,0],[1,1],[1,2] \n
X = [[1,3],[2,3]] \n
res = [[1,2],[1,3],[3,2],[3,3] \n
"""
allPerms, _ = perms_r(X, perm_sample=np.array([]), allPerms=None, ind=0)
return allPerms
######################################################
def perms_r(X, perm_sample=np.array([]), allPerms=None, ind=0):
""" Recursive helper function for perms(). """
if allPerms is None:
# Get memory
if isinstance(X[0], list):
size = np.prod([len(x) for x in X])
else:
size = np.prod(X, dtype=np.int)
allPerms = np.zeros((size, len(X)))
if len(X) == 0:
allPerms[ind, :] = perm_sample
perm_sample = np.array([])
ind = ind + 1
else:
if isinstance(X[0], list):
for x in X[0]:
allPerms, ind = perms_r(
X[1:], np.hstack((perm_sample, [x])), allPerms, ind)
else:
for x in xrange(X[0]):
allPerms, ind = perms_r(
X[1:], np.hstack((perm_sample, [x])), allPerms, ind)
return allPerms, ind
######################################################
def vec2id2(x, limits):
"""
:param x: A discrete (multidimensional) quantity (often the state vector)
:param limits: The limits of the discrete quantity (often statespace_limits)
Returns a unique id by determining the number of possible values of ``x``
that lie within ``limits``, and then seeing where this particular value of
``x` falls in that spectrum.
.. warning::
This function assumes that (elements of) ``x`` takes integer values,
and that ``limits`` are the lower and upper bounds on ``x``.
.. note::
This implementation is half as fast
as :py:meth:`~rlpy.Tools.GeneralTools.vec2id`.
"""
if isinstance(x, int):
return x
lim_prod = np.cumprod(limits[:-1])
return x[0] + sum(map(lambda x_y: x_y[0] * x_y[1], zip(x[1:], lim_prod)))
def vec2id(x, limits):
"""
:param x: A discrete (multidimensional) quantity (often the state vector)
:param limits: The limits of the discrete quantity (often statespace_limits)
Returns a unique id by determining the number of possible values of ``x``
that lie within ``limits``, and then seeing where this particular value of
``x` falls in that spectrum.
.. note::
See :py:meth:`~rlpy.Tools.GeneralTools.id2vec`, the inverse function.
.. warning::
This function assumes that (elements of) ``x`` takes integer values,
and that ``limits`` are the lower and upper bounds on ``x``.
"""
if isinstance(x, int):
return x
_id = 0
for d in xrange(len(x) - 1, -1, -1):
_id *= limits[d]
_id += x[d]
return _id
######################################################
def id2vec(_id, limits):
"""
:param _id: a unique id, presumably generated using ``vec2id()``.
:param limits: The limits of the discrete quantity (often statespace_limits)
Returns the vector corresponding to the unique ``_id`` by determining the
number of possible values of ``x`` that lie within ``limits``, and then
seeing which particular vector ``x`` lies at the index ``_id``.
.. note::
See :py:meth:`~rlpy.Tools.GeneralTools.vec2id`, the inverse function.
"""
prods = np.cumprod(limits)
s = [0] * len(limits)
for d in xrange(len(prods) - 1, 0, -1):
# s[d] = _id / prods[d-1]
# _id %= prods[d-1]
s[d], _id = divmod(_id, prods[d - 1])
s[0] = _id
return s
def bound_vec(X, limits):
"""
:param X: any (multidimensional) iterable type, eg ndarray or list, len = n.
:param limits: n x 2 iterable type, where limits[i,0] is minimum possible
value for dimension i, and limits[i,1] is maximum possible.
Returns ``X ``with any dimensions that lie outside the bounds of ``limits``
appropriately truncated. \n
i.e limits[i,0] <= output[i] <= limits[i,1]
"""
MIN = limits[:, 0]
MAX = limits[:, 1]
X = np.vstack((X, MIN))
X = np.amax(X, axis=0)
X = np.vstack((X, MAX))
X = np.amin(X, axis=0)
return X
def bound(x, m, M=None):
"""
:param x: scalar
Either have m as scalar, so bound(x,m,M) which returns m <= x <= M *OR*
have m as length 2 vector, bound(x,m, <IGNORED>) returns m[0] <= x <= m[1].
"""
if M is None:
M = m[1]
m = m[0]
# bound x between min (m) and Max (M)
return min(max(x, m), M)
def wrap(x, m, M):
"""
:param x: a scalar
:param m: minimum possible value in range
:param M: maximum possible value in range
Wraps ``x`` so m <= x <= M; but unlike ``bound()`` which
truncates, ``wrap()`` wraps x around the coordinate system defined by m,M.\n
For example, m = -180, M = 180 (degrees), x = 360 --> returns 0.
"""
diff = M - m
while x > M:
x = x - diff
while x < m:
x = x + diff
return x
def powerset(iterable, ascending=1):
"""
:param iterable: an iterable type (list, ndarray)
:param ascending: (boolean) if true, return powerset in ascending order,
else return in descending order.
"""
s = list(iterable)
if ascending:
return (
chain.from_iterable(combinations(s, r) for r in xrange(len(s) + 1))
)
else:
return (
chain.from_iterable(combinations(s, r)
for r in xrange(len(s) + 1, -1, -1))
)
def printClass(obj):
""" Print class name and all attributes of object ``obj``. """
print className(obj)
print '======================================='
for property, value in vars(obj).iteritems():
print property, ": ", value
def addNewElementForAllActions(weight_vec, actions_num, newElem=None):
"""
:param weight_vec: The weight vector (often feature weights from
representation) used for s-a pairs
(i.e, len(weight_vec) = actions_num * numFeats)
:param actions_num: The total number of possible actions
:param newElem: (Optional) The weights associated with each action of the
feature to insert (often newElem = const * np.ones(actions_num, 1)).
If not specified or = None, assume 0 weight on new features.
Adds new elements into ``weight_vec`` in the correct location based on
the number of possible actions.
[[Since the new element (usually feature) is added for all actions,
weight_vec should expand by the number of possible actions as for each
action the feature vector phi(s) is expand by 1 element.]]\n
Example: \n
x = [1,2,3,4], a = 2, newElem = None => [1,2,0,3,4,0] \n
x = [1,2,3], a = 3, newElem = [1,1,1] => [1,1,2,1,3,1] \n
"""
if newElem is None:
newElem = np.zeros((actions_num, 1))
if len(weight_vec) == 0:
return newElem.flatten()
else:
weight_vec = weight_vec.reshape(actions_num, -1) # -1 means figure the other dimension yourself
weight_vec = np.hstack((weight_vec, newElem))
weight_vec = weight_vec.reshape(1, -1).flatten()
return weight_vec
def solveLinear(A, b):
""" Solve the linear equation Ax=b. Return tuple (x, time to solve). """
error = np.inf # just to be safe, initialize error variable here
if sp.issparse(A):
# print 'sparse', type(A)
start_log_time = clock()
result = slinalg.spsolve(A, b)
solve_time = deltaT(start_log_time)
error = linalg.norm((A * result.reshape(-1, 1) - b.reshape(-1, 1))[0])
# For extensive comparision of methods refer to InversionComparison.txt
else:
# print 'not sparse, type',type(A)
if sp.issparse(A):
A = A.todense()
# Regularize A
# result = linalg.lstsq(A,b); result = result[0] # Extract just the
# answer
start_log_time = clock()
result = linalg.solve(A, b)
solve_time = deltaT(start_log_time)
# use numpy matrix multiplication
if isinstance(A, np.matrixlib.defmatrix.matrix):
error = np.linalg.norm(
(A * result.reshape(-1, 1) - b.reshape(-1, 1))[0])
elif isinstance(A, np.ndarray): # use array multiplication
error = np.linalg.norm(
(np.dot(A, result.reshape(-1, 1)) - b.reshape(-1, 1))[0])
else:
print 'Attempted to solve linear equation Ax=b in solveLinear() of Tools.py with a non-numpy (array / matrix) type.'
sys.exit(1)
if error > RESEDUAL_THRESHOLD:
print "||Ax-b|| = %0.1f" % error
return result.ravel(), solve_time
def rank(A, eps=1e-12):
"""
:param A: numpy arrayLike (ndarray, matrix).
:param eps: threshold above which a singular value is considered nonzero.
Returns the rank of matrix ``A``, ie number of eigenvalues > ``eps``.
"""
u, s, v = linalg.svd(A)
return len([x for x in s if abs(x) > eps])
def fromAtoB(x1, y1, x2, y2, color='k', connectionstyle="arc3,rad=-0.4",
shrinkA=10, shrinkB=10, arrowstyle="fancy", ax=None):
"""
Draws an arrow from point A=(x1,y1) to point B=(x2,y2) on the (optional)
axis ``ax``.
.. note::
See matplotlib documentation.
"""
if ax is None:
return pl.annotate("",
xy=(x2, y2), xycoords='data',
xytext=(x1, y1), textcoords='data',
arrowprops=dict(
arrowstyle=arrowstyle, # linestyle="dashed",
color=color,
shrinkA=shrinkA, shrinkB=shrinkB,
patchA=None,
patchB=None,
connectionstyle=connectionstyle),
)
else:
return ax.annotate("",
xy=(x2, y2), xycoords='data',
xytext=(x1, y1), textcoords='data',
arrowprops=dict(
arrowstyle=arrowstyle, # linestyle="dashed",
color=color,
shrinkA=shrinkA, shrinkB=shrinkB,
patchA=None,
patchB=None,
connectionstyle=connectionstyle),
)
def drawHist(data, bins=50, fig=101):
"""
:param data: Data to use in histogram.
:param bins: number of bins to use in histogram
:param fig: The figure number for the plot
Draws a histogram in its own figure using specified parameters.
"""
hist, bins = np.histogram(data, bins=bins)
width = 0.7 * (bins[1] - bins[0])
center = (bins[:-1] + bins[1:]) / 2
plt.figure(fig)
plt.bar(center, hist, align='center', width=width)
def nonZeroIndex(arr):
"""
:param arr: a numpy 1-D array.
Returns the list of indices of nonzero elements in ``arr``. \n
Example: [0,0,0,1] => [4]
"""
return arr.nonzero()[0]
def sp_matrix(m, n=1, dtype='float'):
"""
:param m: number of rows in matrix
:param n: number of cols in matrix
:param dtype: datatype of sparse matrix
Returns an empty sparse matrix with m rows and n columns, with the dtype.
"""
return sp.csr_matrix((m, n), dtype=dtype)
def sp_dot_array(sp_m, arr):
"""
:param sp_m: a sparse 1-D array/matrix (created
with :py:meth:`~rlpy.Tools.GeneralTools.sp_matrix`)
:param arr: a (possibly dense) 1-D iterable type (ndarray, list, matrix)
Returns dot product of 1-by-p matrix ``sp_m`` and length-p array arr.
"""
assert sp_m.shape[1] == len(arr)
ind = sp_m.nonzero()[1]
if len(ind) == 0:
return 0
if sp_m.dtype == 'bool':
# Just sum the corresponding indexes of theta
return sum(arr[ind])
else:
# Multiply by feature values since they are not binary
return sum([arr[i] * sp_m[0, i] for i in ind])
def sp_dot_sp(sp_1, sp_2):
"""
:param sp_1: a sparse 1-D array/matrix (created
with :py:meth:`~rlpy.Tools.GeneralTools.sp_matrix`)
:param sp_2: another sparse 1-D array/matrix, len(sp_2) = len(sp_1).
Returns dot product of 1-by-p matrices ``sp_1`` and ``sp_2``.
"""
assert sp_1.shape[
0] == sp_2.shape[
0] and sp_1.shape[
1] == 1 and sp_2.shape[
1] == 1
ind_1 = sp_1.nonzero()[0]
ind_2 = sp_2.nonzero()[0]
if len(ind_1) * len(ind_2) == 0:
return 0
ind = np.intersect1d(ind_1, ind_2)
# See if they are boolean
if sp_1.dtype == bool and sp_2.dtype == bool:
return len(ind)
sp_bool = None
if sp_1.dtype == bool:
sp_bool = sp_1
sp = sp_2
if sp_2.dtype == bool:
sp_bool = sp_2
sp = sp_1
if sp_bool is None:
# Multiply by feature values since they are not binary
return sum([sp_1[i, 0] * sp_2[i, 0] for i in ind])
else:
return sum([sp[i, 0] for i in ind])
def sp_add2_array(sp, arr):
"""
:param sp: sparse matrix p-by-1 (created
with :py:meth:`~rlpy.Tools.GeneralTools.sp_matrix`)
:param arr: a 1-D iterable type (ndarray, list, matrix) of length p.
Returns ret = arr + sp (with type(ret) = type(arr))
"""
ind = sp.nonzero()[0]
for i in ind:
arr[i] += sp[i, 0]
return arr
def checkNCreateDirectory(fullfilename):
"""
:param fullfilename: root path to desired file/folder.
See if all directories in ``fullfilename`` exist; if not create as required.
"""
path_, _, _ = fullfilename.rpartition('/')
if not os.path.exists(path_):
os.makedirs(path_)
def hasFunction(object, methodname):
""" Test if class of ``object`` has a method called ``methodname``. """
method = getattr(object, methodname, None)
return callable(method)
def pretty(X, format='%0.3f'):
"""
Returns a formatted string for a numpy array ``X``. \n
Example: [1,2,3], %0.3f => 1.000 2.000 3.000
"""
format = format + '\t'
return ''.join(format % x for x in X)
def regularize(A):
""" Regularize the numpy arrayLike object ``A``.
Adds REGULARIZATION*I To A, where I is identity matrix and REGULARIZATION
is defined in GeneralTools.py.\n
This is often done before calling the linearSolver.
.. note::
``A`` must be a square matrix.
"""
x, y = A.shape
assert x == y # Square matrix
if sp.issparse(A):
A = A + REGULARIZATION * sp.eye(x, x)
# print 'REGULARIZE', type(A)
else:
# print 'REGULARIZE', type(A)
for i in xrange(x):
A[i, i] += REGULARIZATION
return A
def sparsity(A):
""" Returns the percentage of nonzero elements in ``A``. """
return (1 - np.count_nonzero(A) / (np.prod(A.shape) * 1.)) * 100
# CURRENTLY UNUSED
def incrementalAverageUpdate(avg, sample, sample_number):
"""
:param avg: the old average
:param sample: the new sample to update the average with
:param sample_number: the current sample number (#samples observed so far+1)
Updates an average incrementally.
"""
return avg + (sample - avg) / (sample_number * 1.)
def padZeros(X, L):
"""
:param X: a 1-D numpy array
:param L: the desired length of ``X`` (integer)
if ``len(X) < L`` pad zeros to X so it will have length ``L``, otherwise
do nothing and return the original ``X``.
"""
if len(X) < L:
new_X = np.zeros(L)
new_X[:len(X)] = X
return new_X
else:
return X
# UNUSED
def expectedPhiNS(p_vec, ns_vec, representation):
# Primarily for use with domain.expectedStep()
# Takes p_vec, probability of each state outcome in ns_vec,
# Returns a vector of length features_num which is the expectation
# over all possible outcomes.
expPhiNS = np.zeros(representation.features_num)
for i, ns in enumerate(ns_vec):
expPhiNS += p_vec[i] * representation.phi_nonTerminal(ns)
return expPhiNS
# p: k-by-1 probability of each transition
# r: k-by-1 rewards
# ns: k-by-|s| next state
# t: k-by-1 terminal values
# UNUSED
def allExpectedPhiNS(domain, representation, policy, allStates=None):
# Returns Phi' matrix with dimensions n x k,
# n: number of possible states, and
# k: number of features
if allStates is None:
allStates = domain.allStates()
allExpPhiNS = np.zeros((len(allStates), representation.features_num))
for i, s in enumerate(allStates):
# print s
# print policy.pi(s)
# print 'looping',i, policy.pi(s)
# print policy.pi(s)
p_vec, r_vec, ns_vec, t_vec = domain.expectedStep(s, policy.pi(s))
allExpPhiNS[i][:] = expectedPhiNS(p_vec, ns_vec, representation)
return allExpPhiNS
def rk4(derivs, y0, t, *args, **kwargs):
"""
Integrate 1D or ND system of ODEs using 4-th order Runge-Kutta.
This is a toy implementation which may be useful if you find
yourself stranded on a system w/o scipy. Otherwise use
:func:`scipy.integrate`.
*y0*
initial state vector
*t*
sample times
*derivs*
returns the derivative of the system and has the
signature ``dy = derivs(yi, ti)``
*args*
additional arguments passed to the derivative function
*kwargs*
additional keyword arguments passed to the derivative function
Example 1 ::
## 2D system
def derivs6(x,t):
d1 = x[0] + 2*x[1]
d2 = -3*x[0] + 4*x[1]
return (d1, d2)
dt = 0.0005
t = arange(0.0, 2.0, dt)
y0 = (1,2)
yout = rk4(derivs6, y0, t)
Example 2::
## 1D system
alpha = 2
def derivs(x,t):
return -alpha*x + exp(-t)
y0 = 1
yout = rk4(derivs, y0, t)
If you have access to scipy, you should probably be using the
scipy.integrate tools rather than this function.
"""
try:
Ny = len(y0)
except TypeError:
yout = np.zeros((len(t),), np.float_)
else:
yout = np.zeros((len(t), Ny), np.float_)
yout[0] = y0
i = 0
for i in np.arange(len(t) - 1):
thist = t[i]
dt = t[i + 1] - thist
dt2 = dt / 2.0
y0 = yout[i]
k1 = np.asarray(derivs(y0, thist, *args, **kwargs))
k2 = np.asarray(derivs(y0 + dt2 * k1, thist + dt2, *args, **kwargs))
k3 = np.asarray(derivs(y0 + dt2 * k2, thist + dt2, *args, **kwargs))
k4 = np.asarray(derivs(y0 + dt * k3, thist + dt, *args, **kwargs))
yout[i + 1] = y0 + dt / 6.0 * (k1 + 2 * k2 + 2 * k3 + k4)
return yout
# # NOT USED
# def findElem(x, lis):
# """
# Searches for the element ``x`` in the list (python built-in type) ``A``
# Returns the index of the first occurrence of ``x``.
#
# .. warning::
#
# ``A`` *MUST* be a list (python built-in type)
#
# """
# if type(lis) is not list:
# print 'ERROR: Tools.findElem() only accepts python lists.
# return []
# elif x in lis:
# return lis.index(x)
# else:
# return []
# def matrix_mult(A, B):
# """
# Multiples the inputs A and B using matrix multiplication.
# Defined because of inconsistent or confusing definition of the "*"
# operator for numpy ndarray, matrix, and sparse.matrix.
#
# """
# if len(A.shape) == 1:
# A = A.reshape(1, -1)
# if len(B.shape) == 1:
# B = B.reshape(1, -1)
# n1, m1 = A.shape
# n2, m2 = B.shape
# if m1 != n2:
# print "Incompatible dimensions: %dx%d and %dx%d" % (n1, m2, n2, m2)
# return None
# else:
# return A.dot(B)
# Setup the latdex path
# if sys.platform == 'darwin':
# os.environ['PATH'] += ':' + TEXPATH
# if sys.platform == 'win32':
# print os.environ['PATH']
# os.environ['PATH'] += ';' + TEXPATH
# def isLatexConfigured():
# return False
# try:
# pl.subplot(1,3,2)
# pl.xlabel(r"$\theta$")
# pl.show()
# pl.draw()
# pl.close()
# print "Latex tested and functioning"
# except:
# print "Matplotlib failed to plot, likely due to a Latex problem."
# print "Check that your TEXPATH is set correctly in config.py,"
# print "and that latex is installed correctly."
# print "\nDisabling latex functionality, using matplotlib native fonts."
if module_exists('matplotlib'):
createColorMaps()
rc('font', family='serif', size=15,
weight="bold", **{"sans-serif": ["Helvetica"]})
rc("axes", labelsize=15)
rc("xtick", labelsize=15)
rc("ytick", labelsize=15)
# rc('text',usetex=False)
# Try to use latex fonts, if available
# rc('text',usetex=True)
# Colors
PURPLE = '\033[95m'
BLUE = '\033[94m'
GREEN = '\033[92m'
YELLOW = '\033[93m'
RED = '\033[91m'
NOCOLOR = '\033[0m'
RESEDUAL_THRESHOLD = 1e-7
REGULARIZATION = 1e-6
FONTSIZE = 15
SEP_LINE = "=" * 60
# Tips:
# array.astype(float) => convert elements
# matlibplot initializes the maping from the values to
# colors on the first time creating unless bounds are set manually.
# Hence you may update color values later but dont see any updates!
# in specifying dimensions for reshape you can put -1 so it will be automatically infered
# [2,2,2] = [2]*3
# [1,2,2,1,2,2,1,2,2] = ([1]+[2]*2)*3
# [[1,2],[1,2],[1,2]] = array([[1,2],]*3)
# apply function foo to all elements of array A: vectorize(foo)(A) (The operation may be unstable! Care!
# Set a property of a class: vars(self)['prop'] = 2
# dont use a=b=zeros((2,3)) because a and b will point to the same array!
# b = A[:,1] does NOT create a new matrix. It is simply a pointer to that row! so if you change b you change A
# DO NOT USE A = B = array() unless you know what you are doing. They will point to the same object!
# Todo:
# Replace vstack and hstack with the trick mentioned here:
# http://stackoverflow.com/questions/4923617/efficient-numpy-2d-array-construction-from-1d-array
# if undo redo does not work in eclipse, you may have an uninfinished
# process. Kill all
| bsd-3-clause |
andyzsf/edx | docs/en_us/platform_api/source/conf.py | 12 | 6815 | # -*- coding: utf-8 -*-
# pylint: disable=invalid-name
# pylint: disable=redefined-builtin
# pylint: disable=protected-access
# pylint: disable=unused-argument
import os
from path import path
import sys
on_rtd = os.environ.get('READTHEDOCS', None) == 'True'
sys.path.append('../../../../')
from docs.shared.conf import *
# Add any paths that contain templates here, relative to this directory.
#templates_path.append('source/_templates')
# Add any paths that contain custom static files (such as style sheets) here,
# relative to this directory. They are copied after the builtin static files,
# so a file named "default.css" will overwrite the builtin "default.css".
#html_static_path.append('source/_static')
if not on_rtd: # only import and set the theme if we're building docs locally
import sphinx_rtd_theme
html_theme = 'sphinx_rtd_theme'
html_theme_path = [sphinx_rtd_theme.get_html_theme_path()]
# If extensions (or modules to document with autodoc) are in another directory,
# add these directories to sys.path here. If the directory is relative to the
# documentation root, use os.path.abspath to make it absolute, like shown here.
root = path('../../../..').abspath()
sys.path.insert(0, root)
sys.path.append(root / "lms/djangoapps/mobile_api")
sys.path.append(root / "lms/djangoapps/mobile_api/course_info")
sys.path.append(root / "lms/djangoapps/mobile_api/users")
sys.path.append(root / "lms/djangoapps/mobile_api/video_outlines")
sys.path.insert(
0,
os.path.abspath(
os.path.normpath(
os.path.dirname(__file__) + '/../../../'
)
)
)
sys.path.append('.')
# django configuration - careful here
if on_rtd:
os.environ['DJANGO_SETTINGS_MODULE'] = 'lms'
else:
os.environ['DJANGO_SETTINGS_MODULE'] = 'lms.envs.test'
# -- General configuration -----------------------------------------------------
# Add any Sphinx extension module names here, as strings. They can be extensions
# coming with Sphinx (named 'sphinx.ext.*') or your custom ones.
extensions = [
'sphinx.ext.autodoc', 'sphinx.ext.doctest', 'sphinx.ext.intersphinx',
'sphinx.ext.todo', 'sphinx.ext.coverage', 'sphinx.ext.pngmath',
'sphinx.ext.mathjax', 'sphinx.ext.viewcode', 'sphinxcontrib.napoleon']
# List of patterns, relative to source directory, that match files and
# directories to ignore when looking for source files.
exclude_patterns = ['build']
# Output file base name for HTML help builder.
htmlhelp_basename = 'edXDocs'
project = u'edX Platform API Version 0.5 Alpha'
copyright = u'2014, edX'
# --- Mock modules ------------------------------------------------------------
# Mock all the modules that the readthedocs build can't import
class Mock(object):
def __init__(self, *args, **kwargs):
pass
def __call__(self, *args, **kwargs):
return Mock()
@classmethod
def __getattr__(cls, name):
if name in ('__file__', '__path__'):
return '/dev/null'
elif name[0] == name[0].upper():
mockType = type(name, (), {})
mockType.__module__ = __name__
return mockType
else:
return Mock()
# The list of modules and submodules that we know give RTD trouble.
# Make sure you've tried including the relevant package in
# docs/share/requirements.txt before adding to this list.
MOCK_MODULES = [
'bson',
'bson.errors',
'bson.objectid',
'dateutil',
'dateutil.parser',
'fs',
'fs.errors',
'fs.osfs',
'lazy',
'mako',
'mako.template',
'matplotlib',
'matplotlib.pyplot',
'mock',
'numpy',
'oauthlib',
'oauthlib.oauth1',
'oauthlib.oauth1.rfc5849',
'PIL',
'pymongo',
'pyparsing',
'pysrt',
'requests',
'scipy.interpolate',
'scipy.constants',
'scipy.optimize',
'yaml',
'webob',
'webob.multidict',
]
if on_rtd:
for mod_name in MOCK_MODULES:
sys.modules[mod_name] = Mock()
# -----------------------------------------------------------------------------
# from http://djangosnippets.org/snippets/2533/
# autogenerate models definitions
import inspect
import types
from HTMLParser import HTMLParser
def force_unicode(s, encoding='utf-8', strings_only=False, errors='strict'):
"""
Similar to smart_unicode, except that lazy instances are resolved to
strings, rather than kept as lazy objects.
If strings_only is True, don't convert (some) non-string-like objects.
"""
if strings_only and isinstance(s, (types.NoneType, int)):
return s
if not isinstance(s, basestring,):
if hasattr(s, '__unicode__'):
s = unicode(s)
else:
s = unicode(str(s), encoding, errors)
elif not isinstance(s, unicode):
s = unicode(s, encoding, errors)
return s
class MLStripper(HTMLParser):
def __init__(self):
self.reset()
self.fed = []
def handle_data(self, d):
self.fed.append(d)
def get_data(self):
return ''.join(self.fed)
def strip_tags(html):
s = MLStripper()
s.feed(html)
return s.get_data()
def process_docstring(app, what, name, obj, options, lines):
"""Autodoc django models"""
# This causes import errors if left outside the function
from django.db import models
# If you want extract docs from django forms:
# from django import forms
# from django.forms.models import BaseInlineFormSet
# Only look at objects that inherit from Django's base MODEL class
if inspect.isclass(obj) and issubclass(obj, models.Model):
# Grab the field list from the meta class
fields = obj._meta._fields()
for field in fields:
# Decode and strip any html out of the field's help text
help_text = strip_tags(force_unicode(field.help_text))
# Decode and capitalize the verbose name, for use if there isn't
# any help text
verbose_name = force_unicode(field.verbose_name).capitalize()
if help_text:
# Add the model field to the end of the docstring as a param
# using the help text as the description
lines.append(u':param %s: %s' % (field.attname, help_text))
else:
# Add the model field to the end of the docstring as a param
# using the verbose name as the description
lines.append(u':param %s: %s' % (field.attname, verbose_name))
# Add the field's type to the docstring
lines.append(u':type %s: %s' % (field.attname, type(field).__name__))
return lines
def setup(app):
"""Setup docsting processors"""
#Register the docstring processor with sphinx
app.connect('autodoc-process-docstring', process_docstring)
| agpl-3.0 |
xzh86/scikit-learn | sklearn/linear_model/tests/test_ransac.py | 216 | 13290 | import numpy as np
from numpy.testing import assert_equal, assert_raises
from numpy.testing import assert_array_almost_equal
from sklearn.utils.testing import assert_raises_regexp
from scipy import sparse
from sklearn.utils.testing import assert_less
from sklearn.linear_model import LinearRegression, RANSACRegressor
from sklearn.linear_model.ransac import _dynamic_max_trials
# Generate coordinates of line
X = np.arange(-200, 200)
y = 0.2 * X + 20
data = np.column_stack([X, y])
# Add some faulty data
outliers = np.array((10, 30, 200))
data[outliers[0], :] = (1000, 1000)
data[outliers[1], :] = (-1000, -1000)
data[outliers[2], :] = (-100, -50)
X = data[:, 0][:, np.newaxis]
y = data[:, 1]
def test_ransac_inliers_outliers():
base_estimator = LinearRegression()
ransac_estimator = RANSACRegressor(base_estimator, min_samples=2,
residual_threshold=5, random_state=0)
# Estimate parameters of corrupted data
ransac_estimator.fit(X, y)
# Ground truth / reference inlier mask
ref_inlier_mask = np.ones_like(ransac_estimator.inlier_mask_
).astype(np.bool_)
ref_inlier_mask[outliers] = False
assert_equal(ransac_estimator.inlier_mask_, ref_inlier_mask)
def test_ransac_is_data_valid():
def is_data_valid(X, y):
assert_equal(X.shape[0], 2)
assert_equal(y.shape[0], 2)
return False
X = np.random.rand(10, 2)
y = np.random.rand(10, 1)
base_estimator = LinearRegression()
ransac_estimator = RANSACRegressor(base_estimator, min_samples=2,
residual_threshold=5,
is_data_valid=is_data_valid,
random_state=0)
assert_raises(ValueError, ransac_estimator.fit, X, y)
def test_ransac_is_model_valid():
def is_model_valid(estimator, X, y):
assert_equal(X.shape[0], 2)
assert_equal(y.shape[0], 2)
return False
base_estimator = LinearRegression()
ransac_estimator = RANSACRegressor(base_estimator, min_samples=2,
residual_threshold=5,
is_model_valid=is_model_valid,
random_state=0)
assert_raises(ValueError, ransac_estimator.fit, X, y)
def test_ransac_max_trials():
base_estimator = LinearRegression()
ransac_estimator = RANSACRegressor(base_estimator, min_samples=2,
residual_threshold=5, max_trials=0,
random_state=0)
assert_raises(ValueError, ransac_estimator.fit, X, y)
ransac_estimator = RANSACRegressor(base_estimator, min_samples=2,
residual_threshold=5, max_trials=11,
random_state=0)
assert getattr(ransac_estimator, 'n_trials_', None) is None
ransac_estimator.fit(X, y)
assert_equal(ransac_estimator.n_trials_, 2)
def test_ransac_stop_n_inliers():
base_estimator = LinearRegression()
ransac_estimator = RANSACRegressor(base_estimator, min_samples=2,
residual_threshold=5, stop_n_inliers=2,
random_state=0)
ransac_estimator.fit(X, y)
assert_equal(ransac_estimator.n_trials_, 1)
def test_ransac_stop_score():
base_estimator = LinearRegression()
ransac_estimator = RANSACRegressor(base_estimator, min_samples=2,
residual_threshold=5, stop_score=0,
random_state=0)
ransac_estimator.fit(X, y)
assert_equal(ransac_estimator.n_trials_, 1)
def test_ransac_score():
X = np.arange(100)[:, None]
y = np.zeros((100, ))
y[0] = 1
y[1] = 100
base_estimator = LinearRegression()
ransac_estimator = RANSACRegressor(base_estimator, min_samples=2,
residual_threshold=0.5, random_state=0)
ransac_estimator.fit(X, y)
assert_equal(ransac_estimator.score(X[2:], y[2:]), 1)
assert_less(ransac_estimator.score(X[:2], y[:2]), 1)
def test_ransac_predict():
X = np.arange(100)[:, None]
y = np.zeros((100, ))
y[0] = 1
y[1] = 100
base_estimator = LinearRegression()
ransac_estimator = RANSACRegressor(base_estimator, min_samples=2,
residual_threshold=0.5, random_state=0)
ransac_estimator.fit(X, y)
assert_equal(ransac_estimator.predict(X), np.zeros(100))
def test_ransac_resid_thresh_no_inliers():
# When residual_threshold=0.0 there are no inliers and a
# ValueError with a message should be raised
base_estimator = LinearRegression()
ransac_estimator = RANSACRegressor(base_estimator, min_samples=2,
residual_threshold=0.0, random_state=0)
assert_raises_regexp(ValueError,
"No inliers.*residual_threshold.*0\.0",
ransac_estimator.fit, X, y)
def test_ransac_sparse_coo():
X_sparse = sparse.coo_matrix(X)
base_estimator = LinearRegression()
ransac_estimator = RANSACRegressor(base_estimator, min_samples=2,
residual_threshold=5, random_state=0)
ransac_estimator.fit(X_sparse, y)
ref_inlier_mask = np.ones_like(ransac_estimator.inlier_mask_
).astype(np.bool_)
ref_inlier_mask[outliers] = False
assert_equal(ransac_estimator.inlier_mask_, ref_inlier_mask)
def test_ransac_sparse_csr():
X_sparse = sparse.csr_matrix(X)
base_estimator = LinearRegression()
ransac_estimator = RANSACRegressor(base_estimator, min_samples=2,
residual_threshold=5, random_state=0)
ransac_estimator.fit(X_sparse, y)
ref_inlier_mask = np.ones_like(ransac_estimator.inlier_mask_
).astype(np.bool_)
ref_inlier_mask[outliers] = False
assert_equal(ransac_estimator.inlier_mask_, ref_inlier_mask)
def test_ransac_sparse_csc():
X_sparse = sparse.csc_matrix(X)
base_estimator = LinearRegression()
ransac_estimator = RANSACRegressor(base_estimator, min_samples=2,
residual_threshold=5, random_state=0)
ransac_estimator.fit(X_sparse, y)
ref_inlier_mask = np.ones_like(ransac_estimator.inlier_mask_
).astype(np.bool_)
ref_inlier_mask[outliers] = False
assert_equal(ransac_estimator.inlier_mask_, ref_inlier_mask)
def test_ransac_none_estimator():
base_estimator = LinearRegression()
ransac_estimator = RANSACRegressor(base_estimator, min_samples=2,
residual_threshold=5, random_state=0)
ransac_none_estimator = RANSACRegressor(None, 2, 5, random_state=0)
ransac_estimator.fit(X, y)
ransac_none_estimator.fit(X, y)
assert_array_almost_equal(ransac_estimator.predict(X),
ransac_none_estimator.predict(X))
def test_ransac_min_n_samples():
base_estimator = LinearRegression()
ransac_estimator1 = RANSACRegressor(base_estimator, min_samples=2,
residual_threshold=5, random_state=0)
ransac_estimator2 = RANSACRegressor(base_estimator,
min_samples=2. / X.shape[0],
residual_threshold=5, random_state=0)
ransac_estimator3 = RANSACRegressor(base_estimator, min_samples=-1,
residual_threshold=5, random_state=0)
ransac_estimator4 = RANSACRegressor(base_estimator, min_samples=5.2,
residual_threshold=5, random_state=0)
ransac_estimator5 = RANSACRegressor(base_estimator, min_samples=2.0,
residual_threshold=5, random_state=0)
ransac_estimator6 = RANSACRegressor(base_estimator,
residual_threshold=5, random_state=0)
ransac_estimator7 = RANSACRegressor(base_estimator,
min_samples=X.shape[0] + 1,
residual_threshold=5, random_state=0)
ransac_estimator1.fit(X, y)
ransac_estimator2.fit(X, y)
ransac_estimator5.fit(X, y)
ransac_estimator6.fit(X, y)
assert_array_almost_equal(ransac_estimator1.predict(X),
ransac_estimator2.predict(X))
assert_array_almost_equal(ransac_estimator1.predict(X),
ransac_estimator5.predict(X))
assert_array_almost_equal(ransac_estimator1.predict(X),
ransac_estimator6.predict(X))
assert_raises(ValueError, ransac_estimator3.fit, X, y)
assert_raises(ValueError, ransac_estimator4.fit, X, y)
assert_raises(ValueError, ransac_estimator7.fit, X, y)
def test_ransac_multi_dimensional_targets():
base_estimator = LinearRegression()
ransac_estimator = RANSACRegressor(base_estimator, min_samples=2,
residual_threshold=5, random_state=0)
# 3-D target values
yyy = np.column_stack([y, y, y])
# Estimate parameters of corrupted data
ransac_estimator.fit(X, yyy)
# Ground truth / reference inlier mask
ref_inlier_mask = np.ones_like(ransac_estimator.inlier_mask_
).astype(np.bool_)
ref_inlier_mask[outliers] = False
assert_equal(ransac_estimator.inlier_mask_, ref_inlier_mask)
def test_ransac_residual_metric():
residual_metric1 = lambda dy: np.sum(np.abs(dy), axis=1)
residual_metric2 = lambda dy: np.sum(dy ** 2, axis=1)
yyy = np.column_stack([y, y, y])
base_estimator = LinearRegression()
ransac_estimator0 = RANSACRegressor(base_estimator, min_samples=2,
residual_threshold=5, random_state=0)
ransac_estimator1 = RANSACRegressor(base_estimator, min_samples=2,
residual_threshold=5, random_state=0,
residual_metric=residual_metric1)
ransac_estimator2 = RANSACRegressor(base_estimator, min_samples=2,
residual_threshold=5, random_state=0,
residual_metric=residual_metric2)
# multi-dimensional
ransac_estimator0.fit(X, yyy)
ransac_estimator1.fit(X, yyy)
ransac_estimator2.fit(X, yyy)
assert_array_almost_equal(ransac_estimator0.predict(X),
ransac_estimator1.predict(X))
assert_array_almost_equal(ransac_estimator0.predict(X),
ransac_estimator2.predict(X))
# one-dimensional
ransac_estimator0.fit(X, y)
ransac_estimator2.fit(X, y)
assert_array_almost_equal(ransac_estimator0.predict(X),
ransac_estimator2.predict(X))
def test_ransac_default_residual_threshold():
base_estimator = LinearRegression()
ransac_estimator = RANSACRegressor(base_estimator, min_samples=2,
random_state=0)
# Estimate parameters of corrupted data
ransac_estimator.fit(X, y)
# Ground truth / reference inlier mask
ref_inlier_mask = np.ones_like(ransac_estimator.inlier_mask_
).astype(np.bool_)
ref_inlier_mask[outliers] = False
assert_equal(ransac_estimator.inlier_mask_, ref_inlier_mask)
def test_ransac_dynamic_max_trials():
# Numbers hand-calculated and confirmed on page 119 (Table 4.3) in
# Hartley, R.~I. and Zisserman, A., 2004,
# Multiple View Geometry in Computer Vision, Second Edition,
# Cambridge University Press, ISBN: 0521540518
# e = 0%, min_samples = X
assert_equal(_dynamic_max_trials(100, 100, 2, 0.99), 1)
# e = 5%, min_samples = 2
assert_equal(_dynamic_max_trials(95, 100, 2, 0.99), 2)
# e = 10%, min_samples = 2
assert_equal(_dynamic_max_trials(90, 100, 2, 0.99), 3)
# e = 30%, min_samples = 2
assert_equal(_dynamic_max_trials(70, 100, 2, 0.99), 7)
# e = 50%, min_samples = 2
assert_equal(_dynamic_max_trials(50, 100, 2, 0.99), 17)
# e = 5%, min_samples = 8
assert_equal(_dynamic_max_trials(95, 100, 8, 0.99), 5)
# e = 10%, min_samples = 8
assert_equal(_dynamic_max_trials(90, 100, 8, 0.99), 9)
# e = 30%, min_samples = 8
assert_equal(_dynamic_max_trials(70, 100, 8, 0.99), 78)
# e = 50%, min_samples = 8
assert_equal(_dynamic_max_trials(50, 100, 8, 0.99), 1177)
# e = 0%, min_samples = 10
assert_equal(_dynamic_max_trials(1, 100, 10, 0), 0)
assert_equal(_dynamic_max_trials(1, 100, 10, 1), float('inf'))
base_estimator = LinearRegression()
ransac_estimator = RANSACRegressor(base_estimator, min_samples=2,
stop_probability=-0.1)
assert_raises(ValueError, ransac_estimator.fit, X, y)
ransac_estimator = RANSACRegressor(base_estimator, min_samples=2,
stop_probability=1.1)
assert_raises(ValueError, ransac_estimator.fit, X, y)
| bsd-3-clause |
lyft/incubator-airflow | airflow/hooks/dbapi_hook.py | 4 | 10836 | #
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
# under the License.
from contextlib import closing
from datetime import datetime
from typing import Optional
from sqlalchemy import create_engine
from airflow.exceptions import AirflowException
from airflow.hooks.base_hook import BaseHook
from airflow.typing_compat import Protocol
class ConnectorProtocol(Protocol):
def connect(host, port, username, schema):
...
class DbApiHook(BaseHook):
"""
Abstract base class for sql hooks.
"""
# Override to provide the connection name.
conn_name_attr = None # type: Optional[str]
# Override to have a default connection id for a particular dbHook
default_conn_name = 'default_conn_id'
# Override if this db supports autocommit.
supports_autocommit = False
# Override with the object that exposes the connect method
connector = None # type: Optional[ConnectorProtocol]
def __init__(self, *args, **kwargs):
if not self.conn_name_attr:
raise AirflowException("conn_name_attr is not defined")
elif len(args) == 1:
setattr(self, self.conn_name_attr, args[0])
elif self.conn_name_attr not in kwargs:
setattr(self, self.conn_name_attr, self.default_conn_name)
else:
setattr(self, self.conn_name_attr, kwargs[self.conn_name_attr])
def get_conn(self):
"""Returns a connection object
"""
db = self.get_connection(getattr(self, self.conn_name_attr))
return self.connector.connect(
host=db.host,
port=db.port,
username=db.login,
schema=db.schema)
def get_uri(self):
conn = self.get_connection(getattr(self, self.conn_name_attr))
login = ''
if conn.login:
login = '{conn.login}:{conn.password}@'.format(conn=conn)
host = conn.host
if conn.port is not None:
host += ':{port}'.format(port=conn.port)
uri = '{conn.conn_type}://{login}{host}/'.format(
conn=conn, login=login, host=host)
if conn.schema:
uri += conn.schema
return uri
def get_sqlalchemy_engine(self, engine_kwargs=None):
if engine_kwargs is None:
engine_kwargs = {}
return create_engine(self.get_uri(), **engine_kwargs)
def get_pandas_df(self, sql, parameters=None):
"""
Executes the sql and returns a pandas dataframe
:param sql: the sql statement to be executed (str) or a list of
sql statements to execute
:type sql: str or list
:param parameters: The parameters to render the SQL query with.
:type parameters: mapping or iterable
"""
import pandas.io.sql as psql
with closing(self.get_conn()) as conn:
return psql.read_sql(sql, con=conn, params=parameters)
def get_records(self, sql, parameters=None):
"""
Executes the sql and returns a set of records.
:param sql: the sql statement to be executed (str) or a list of
sql statements to execute
:type sql: str or list
:param parameters: The parameters to render the SQL query with.
:type parameters: mapping or iterable
"""
with closing(self.get_conn()) as conn:
with closing(conn.cursor()) as cur:
if parameters is not None:
cur.execute(sql, parameters)
else:
cur.execute(sql)
return cur.fetchall()
def get_first(self, sql, parameters=None):
"""
Executes the sql and returns the first resulting row.
:param sql: the sql statement to be executed (str) or a list of
sql statements to execute
:type sql: str or list
:param parameters: The parameters to render the SQL query with.
:type parameters: mapping or iterable
"""
with closing(self.get_conn()) as conn:
with closing(conn.cursor()) as cur:
if parameters is not None:
cur.execute(sql, parameters)
else:
cur.execute(sql)
return cur.fetchone()
def run(self, sql, autocommit=False, parameters=None):
"""
Runs a command or a list of commands. Pass a list of sql
statements to the sql parameter to get them to execute
sequentially
:param sql: the sql statement to be executed (str) or a list of
sql statements to execute
:type sql: str or list
:param autocommit: What to set the connection's autocommit setting to
before executing the query.
:type autocommit: bool
:param parameters: The parameters to render the SQL query with.
:type parameters: mapping or iterable
"""
if isinstance(sql, str):
sql = [sql]
with closing(self.get_conn()) as conn:
if self.supports_autocommit:
self.set_autocommit(conn, autocommit)
with closing(conn.cursor()) as cur:
for s in sql:
if parameters is not None:
self.log.info("{} with parameters {}".format(s, parameters))
cur.execute(s, parameters)
else:
self.log.info(s)
cur.execute(s)
# If autocommit was set to False for db that supports autocommit,
# or if db does not supports autocommit, we do a manual commit.
if not self.get_autocommit(conn):
conn.commit()
def set_autocommit(self, conn, autocommit):
"""
Sets the autocommit flag on the connection
"""
if not self.supports_autocommit and autocommit:
self.log.warning(
"%s connection doesn't support autocommit but autocommit activated.",
getattr(self, self.conn_name_attr)
)
conn.autocommit = autocommit
def get_autocommit(self, conn):
"""
Get autocommit setting for the provided connection.
Return True if conn.autocommit is set to True.
Return False if conn.autocommit is not set or set to False or conn
does not support autocommit.
:param conn: Connection to get autocommit setting from.
:type conn: connection object.
:return: connection autocommit setting.
:rtype: bool
"""
return getattr(conn, 'autocommit', False) and self.supports_autocommit
def get_cursor(self):
"""
Returns a cursor
"""
return self.get_conn().cursor()
def insert_rows(self, table, rows, target_fields=None, commit_every=1000,
replace=False):
"""
A generic way to insert a set of tuples into a table,
a new transaction is created every commit_every rows
:param table: Name of the target table
:type table: str
:param rows: The rows to insert into the table
:type rows: iterable of tuples
:param target_fields: The names of the columns to fill in the table
:type target_fields: iterable of strings
:param commit_every: The maximum number of rows to insert in one
transaction. Set to 0 to insert all rows in one transaction.
:type commit_every: int
:param replace: Whether to replace instead of insert
:type replace: bool
"""
if target_fields:
target_fields = ", ".join(target_fields)
target_fields = "({})".format(target_fields)
else:
target_fields = ''
i = 0
with closing(self.get_conn()) as conn:
if self.supports_autocommit:
self.set_autocommit(conn, False)
conn.commit()
with closing(conn.cursor()) as cur:
for i, row in enumerate(rows, 1):
lst = []
for cell in row:
lst.append(self._serialize_cell(cell, conn))
values = tuple(lst)
placeholders = ["%s", ] * len(values)
if not replace:
sql = "INSERT INTO "
else:
sql = "REPLACE INTO "
sql += "{0} {1} VALUES ({2})".format(
table,
target_fields,
",".join(placeholders))
cur.execute(sql, values)
if commit_every and i % commit_every == 0:
conn.commit()
self.log.info(
"Loaded %s into %s rows so far", i, table
)
conn.commit()
self.log.info("Done loading. Loaded a total of %s rows", i)
@staticmethod
def _serialize_cell(cell, conn=None):
"""
Returns the SQL literal of the cell as a string.
:param cell: The cell to insert into the table
:type cell: object
:param conn: The database connection
:type conn: connection object
:return: The serialized cell
:rtype: str
"""
if cell is None:
return None
if isinstance(cell, datetime):
return cell.isoformat()
return str(cell)
def bulk_dump(self, table, tmp_file):
"""
Dumps a database table into a tab-delimited file
:param table: The name of the source table
:type table: str
:param tmp_file: The path of the target file
:type tmp_file: str
"""
raise NotImplementedError()
def bulk_load(self, table, tmp_file):
"""
Loads a tab-delimited file into a database table
:param table: The name of the target table
:type table: str
:param tmp_file: The path of the file to load into the table
:type tmp_file: str
"""
raise NotImplementedError()
| apache-2.0 |
liberatorqjw/scikit-learn | examples/mixture/plot_gmm_pdf.py | 284 | 1528 | """
=============================================
Density Estimation for a mixture of Gaussians
=============================================
Plot the density estimation of a mixture of two Gaussians. Data is
generated from two Gaussians with different centers and covariance
matrices.
"""
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.colors import LogNorm
from sklearn import mixture
n_samples = 300
# generate random sample, two components
np.random.seed(0)
# generate spherical data centered on (20, 20)
shifted_gaussian = np.random.randn(n_samples, 2) + np.array([20, 20])
# generate zero centered stretched Gaussian data
C = np.array([[0., -0.7], [3.5, .7]])
stretched_gaussian = np.dot(np.random.randn(n_samples, 2), C)
# concatenate the two datasets into the final training set
X_train = np.vstack([shifted_gaussian, stretched_gaussian])
# fit a Gaussian Mixture Model with two components
clf = mixture.GMM(n_components=2, covariance_type='full')
clf.fit(X_train)
# display predicted scores by the model as a contour plot
x = np.linspace(-20.0, 30.0)
y = np.linspace(-20.0, 40.0)
X, Y = np.meshgrid(x, y)
XX = np.array([X.ravel(), Y.ravel()]).T
Z = -clf.score_samples(XX)[0]
Z = Z.reshape(X.shape)
CS = plt.contour(X, Y, Z, norm=LogNorm(vmin=1.0, vmax=1000.0),
levels=np.logspace(0, 3, 10))
CB = plt.colorbar(CS, shrink=0.8, extend='both')
plt.scatter(X_train[:, 0], X_train[:, 1], .8)
plt.title('Negative log-likelihood predicted by a GMM')
plt.axis('tight')
plt.show()
| bsd-3-clause |
Winand/pandas | pandas/io/date_converters.py | 11 | 1901 | """This module is designed for community supported date conversion functions"""
from pandas.compat import range, map
import numpy as np
from pandas._libs.tslibs import parsing
def parse_date_time(date_col, time_col):
date_col = _maybe_cast(date_col)
time_col = _maybe_cast(time_col)
return parsing.try_parse_date_and_time(date_col, time_col)
def parse_date_fields(year_col, month_col, day_col):
year_col = _maybe_cast(year_col)
month_col = _maybe_cast(month_col)
day_col = _maybe_cast(day_col)
return parsing.try_parse_year_month_day(year_col, month_col, day_col)
def parse_all_fields(year_col, month_col, day_col, hour_col, minute_col,
second_col):
year_col = _maybe_cast(year_col)
month_col = _maybe_cast(month_col)
day_col = _maybe_cast(day_col)
hour_col = _maybe_cast(hour_col)
minute_col = _maybe_cast(minute_col)
second_col = _maybe_cast(second_col)
return parsing.try_parse_datetime_components(year_col, month_col, day_col,
hour_col, minute_col,
second_col)
def generic_parser(parse_func, *cols):
N = _check_columns(cols)
results = np.empty(N, dtype=object)
for i in range(N):
args = [c[i] for c in cols]
results[i] = parse_func(*args)
return results
def _maybe_cast(arr):
if not arr.dtype.type == np.object_:
arr = np.array(arr, dtype=object)
return arr
def _check_columns(cols):
if not len(cols):
raise AssertionError("There must be at least 1 column")
head, tail = cols[0], cols[1:]
N = len(head)
for i, n in enumerate(map(len, tail)):
if n != N:
raise AssertionError('All columns must have the same length: {0}; '
'column {1} has length {2}'.format(N, i, n))
return N
| bsd-3-clause |
kapteyn-astro/kapteyn | doc/source/EXAMPLES/kmpfit_example_simple2.py | 1 | 1919 | #!/usr/bin/env python
#------------------------------------------------------------
# Purpose: Demonstrate simple use of fitter routine
#
# Vog, 12 Nov 2011
#------------------------------------------------------------
import numpy
from matplotlib.pyplot import figure, show, rc
from kapteyn import kmpfit
# The model:
def model(p, x):
a,b = p
y = a + b*x
return y
# Define the residual function
#==============================
def residuals(p, d):
x, y = d
return y - model(p,x)
# Artificial data
#================
N = 50 # Number of data points
mean = 0.0; sigma = 0.6 # Characteristics of the noise we add
xstart = 2.0; xend = 10.0
x = numpy.linspace(3.0, 10.0, N)
paramsreal = [1.0, 1.0]
noise = numpy.random.normal(mean, sigma, N)
y = model(paramsreal, x) + noise
# Prepare a 'Fitter' object'
#===========================
arrays = (x, y)
fitobj = kmpfit.Fitter(residuals, data=arrays)
paramsinitial = (0.0, 0.0)
fitobj.fit(params0=paramsinitial)
if (fitobj.status <= 0):
print('Error message = ', fitobj.errmsg)
else:
print("Optimal parameters: ", fitobj.params)
# Plot the result
#================
rc('legend', fontsize=8)
fig = figure()
xp = numpy.linspace(xstart-1, xend+1, 200)
frame = fig.add_subplot(1,1,1, aspect=1.0)
frame.plot(x, y, 'ro', label="Data")
frame.plot(xp, model(fitobj.params,xp), 'm', lw=1, label="Fit with kmpfit")
frame.plot(xp, model(paramsreal,xp), 'g', label="The model")
frame.set_xlabel("X")
frame.set_ylabel("Measurement data")
frame.set_title("Least-squares fit to noisy data using KMPFIT")
s = "Model: Y = a + b*X real (a,b)=(%.2g,%.2g), fit (a,b)=(%.2g,%.2g)"%\
(paramsreal[0],paramsreal[1], fitobj.params[0],fitobj.params[1])
frame.text(0.95, 0.02, s, color='k', fontsize=8,
ha='right', transform=frame.transAxes)
frame.set_xlim(0,12)
frame.set_ylim(0,None)
leg = frame.legend(loc=2)
show()
| bsd-3-clause |
robocomp/learnbot | learnbot_components/laser/python/VL53L0X_example_livegraph.py | 2 | 1899 | #!/usr/bin/python
# MIT License
#
# Copyright (c) 2017 John Bryan Moore, Sampath Vanimisetti
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
import matplotlib.pyplot as plt
import matplotlib.animation as animation
import time
import VL53L0X
fig = plt.figure()
ax1 = fig.add_subplot(1,1,1)
xarr = []
yarr = []
count = 0
def animate(i):
global count
distance = tof.get_distance()
count = count + 1
xarr.append(count)
yarr.append(distance)
time.sleep(timing/1000000.00)
ax1.clear()
ax1.plot(xarr,yarr)
# Create a VL53L0X object
tof = VL53L0X.VL53L0X()
# Start ranging
tof.start_ranging(VL53L0X.VL53L0X_BETTER_ACCURACY_MODE)
timing = tof.get_timing()
if (timing < 20000):
timing = 20000
print("Timing %d ms" % (timing/1000))
print("Press ctrl-c to exit")
ani = animation.FuncAnimation(fig, animate, interval=100)
plt.show()
tof.stop_ranging()
| gpl-3.0 |
teonlamont/mne-python | mne/utils.py | 2 | 96393 | # -*- coding: utf-8 -*-
"""Some utility functions."""
from __future__ import print_function
# Authors: Alexandre Gramfort <[email protected]>
#
# License: BSD (3-clause)
import atexit
from collections import Iterable
from contextlib import contextmanager
from distutils.version import LooseVersion
from functools import wraps
from functools import partial
import hashlib
import inspect
import json
import logging
from math import log, ceil
import multiprocessing
import operator
import os
import os.path as op
import platform
import shutil
from shutil import rmtree
from string import Formatter
import subprocess
import sys
import tempfile
import time
import traceback
from unittest import SkipTest
import warnings
import webbrowser
import numpy as np
from scipy import linalg, sparse
from .externals.six.moves import urllib
from .externals.six import string_types, StringIO, BytesIO, integer_types
from .externals.decorator import decorator
from .fixes import _get_args
logger = logging.getLogger('mne') # one selection here used across mne-python
logger.propagate = False # don't propagate (in case of multiple imports)
def _memory_usage(*args, **kwargs):
if isinstance(args[0], tuple):
args[0][0](*args[0][1], **args[0][2])
elif not isinstance(args[0], int): # can be -1 for current use
args[0]()
return [-1]
try:
from memory_profiler import memory_usage
except ImportError:
memory_usage = _memory_usage
def nottest(f):
"""Mark a function as not a test (decorator)."""
f.__test__ = False
return f
# # # WARNING # # #
# This list must also be updated in doc/_templates/class.rst if it is
# changed here!
_doc_special_members = ('__contains__', '__getitem__', '__iter__', '__len__',
'__call__', '__add__', '__sub__', '__mul__', '__div__',
'__neg__', '__hash__')
###############################################################################
# RANDOM UTILITIES
def _ensure_int(x, name='unknown', must_be='an int'):
"""Ensure a variable is an integer."""
# This is preferred over numbers.Integral, see:
# https://github.com/scipy/scipy/pull/7351#issuecomment-299713159
try:
x = int(operator.index(x))
except TypeError:
raise TypeError('%s must be %s, got %s' % (name, must_be, type(x)))
return x
def _pl(x, non_pl=''):
"""Determine if plural should be used."""
len_x = x if isinstance(x, (integer_types, np.generic)) else len(x)
return non_pl if len_x == 1 else 's'
def _explain_exception(start=-1, stop=None, prefix='> '):
"""Explain an exception."""
# start=-1 means "only the most recent caller"
etype, value, tb = sys.exc_info()
string = traceback.format_list(traceback.extract_tb(tb)[start:stop])
string = (''.join(string).split('\n') +
traceback.format_exception_only(etype, value))
string = ':\n' + prefix + ('\n' + prefix).join(string)
return string
def _get_call_line(in_verbose=False):
"""Get the call line from within a function."""
# XXX Eventually we could auto-triage whether in a `verbose` decorated
# function or not.
# NB This probably only works for functions that are undecorated,
# or decorated by `verbose`.
back = 2 if not in_verbose else 4
call_frame = inspect.getouterframes(inspect.currentframe())[back][0]
context = inspect.getframeinfo(call_frame).code_context
context = 'unknown' if context is None else context[0].strip()
return context
def _sort_keys(x):
"""Sort and return keys of dict."""
keys = list(x.keys()) # note: not thread-safe
idx = np.argsort([str(k) for k in keys])
keys = [keys[ii] for ii in idx]
return keys
def object_hash(x, h=None):
"""Hash a reasonable python object.
Parameters
----------
x : object
Object to hash. Can be anything comprised of nested versions of:
{dict, list, tuple, ndarray, str, bytes, float, int, None}.
h : hashlib HASH object | None
Optional, object to add the hash to. None creates an MD5 hash.
Returns
-------
digest : int
The digest resulting from the hash.
"""
if h is None:
h = hashlib.md5()
if hasattr(x, 'keys'):
# dict-like types
keys = _sort_keys(x)
for key in keys:
object_hash(key, h)
object_hash(x[key], h)
elif isinstance(x, bytes):
# must come before "str" below
h.update(x)
elif isinstance(x, (string_types, float, int, type(None))):
h.update(str(type(x)).encode('utf-8'))
h.update(str(x).encode('utf-8'))
elif isinstance(x, (np.ndarray, np.number, np.bool_)):
x = np.asarray(x)
h.update(str(x.shape).encode('utf-8'))
h.update(str(x.dtype).encode('utf-8'))
h.update(x.tostring())
elif hasattr(x, '__len__'):
# all other list-like types
h.update(str(type(x)).encode('utf-8'))
for xx in x:
object_hash(xx, h)
else:
raise RuntimeError('unsupported type: %s (%s)' % (type(x), x))
return int(h.hexdigest(), 16)
def object_size(x):
"""Estimate the size of a reasonable python object.
Parameters
----------
x : object
Object to approximate the size of.
Can be anything comprised of nested versions of:
{dict, list, tuple, ndarray, str, bytes, float, int, None}.
Returns
-------
size : int
The estimated size in bytes of the object.
"""
# Note: this will not process object arrays properly (since those only)
# hold references
if isinstance(x, (bytes, string_types, int, float, type(None))):
size = sys.getsizeof(x)
elif isinstance(x, np.ndarray):
# On newer versions of NumPy, just doing sys.getsizeof(x) works,
# but on older ones you always get something small :(
size = sys.getsizeof(np.array([])) + x.nbytes
elif isinstance(x, np.generic):
size = x.nbytes
elif isinstance(x, dict):
size = sys.getsizeof(x)
for key, value in x.items():
size += object_size(key)
size += object_size(value)
elif isinstance(x, (list, tuple)):
size = sys.getsizeof(x) + sum(object_size(xx) for xx in x)
elif sparse.isspmatrix_csc(x) or sparse.isspmatrix_csr(x):
size = sum(sys.getsizeof(xx)
for xx in [x, x.data, x.indices, x.indptr])
else:
raise RuntimeError('unsupported type: %s (%s)' % (type(x), x))
return size
def object_diff(a, b, pre=''):
"""Compute all differences between two python variables.
Parameters
----------
a : object
Currently supported: dict, list, tuple, ndarray, int, str, bytes,
float, StringIO, BytesIO.
b : object
Must be same type as x1.
pre : str
String to prepend to each line.
Returns
-------
diffs : str
A string representation of the differences.
"""
out = ''
if type(a) != type(b):
out += pre + ' type mismatch (%s, %s)\n' % (type(a), type(b))
elif isinstance(a, dict):
k1s = _sort_keys(a)
k2s = _sort_keys(b)
m1 = set(k2s) - set(k1s)
if len(m1):
out += pre + ' left missing keys %s\n' % (m1)
for key in k1s:
if key not in k2s:
out += pre + ' right missing key %s\n' % key
else:
out += object_diff(a[key], b[key], pre + '[%s]' % repr(key))
elif isinstance(a, (list, tuple)):
if len(a) != len(b):
out += pre + ' length mismatch (%s, %s)\n' % (len(a), len(b))
else:
for ii, (xx1, xx2) in enumerate(zip(a, b)):
out += object_diff(xx1, xx2, pre + '[%s]' % ii)
elif isinstance(a, (string_types, int, float, bytes)):
if a != b:
out += pre + ' value mismatch (%s, %s)\n' % (a, b)
elif a is None:
if b is not None:
out += pre + ' left is None, right is not (%s)\n' % (b)
elif isinstance(a, np.ndarray):
if not np.array_equal(a, b):
out += pre + ' array mismatch\n'
elif isinstance(a, (StringIO, BytesIO)):
if a.getvalue() != b.getvalue():
out += pre + ' StringIO mismatch\n'
elif sparse.isspmatrix(a):
# sparsity and sparse type of b vs a already checked above by type()
if b.shape != a.shape:
out += pre + (' sparse matrix a and b shape mismatch'
'(%s vs %s)' % (a.shape, b.shape))
else:
c = a - b
c.eliminate_zeros()
if c.nnz > 0:
out += pre + (' sparse matrix a and b differ on %s '
'elements' % c.nnz)
else:
raise RuntimeError(pre + ': unsupported type %s (%s)' % (type(a), a))
return out
def check_random_state(seed):
"""Turn seed into a np.random.RandomState instance.
If seed is None, return the RandomState singleton used by np.random.
If seed is an int, return a new RandomState instance seeded with seed.
If seed is already a RandomState instance, return it.
Otherwise raise ValueError.
"""
if seed is None or seed is np.random:
return np.random.mtrand._rand
if isinstance(seed, (int, np.integer)):
return np.random.RandomState(seed)
if isinstance(seed, np.random.RandomState):
return seed
raise ValueError('%r cannot be used to seed a numpy.random.RandomState'
' instance' % seed)
def split_list(l, n):
"""Split list in n (approx) equal pieces."""
n = int(n)
sz = len(l) // n
for i in range(n - 1):
yield l[i * sz:(i + 1) * sz]
yield l[(n - 1) * sz:]
def create_chunks(sequence, size):
"""Generate chunks from a sequence.
Parameters
----------
sequence : iterable
Any iterable object
size : int
The chunksize to be returned
"""
return (sequence[p:p + size] for p in range(0, len(sequence), size))
def sum_squared(X):
"""Compute norm of an array.
Parameters
----------
X : array
Data whose norm must be found
Returns
-------
value : float
Sum of squares of the input array X
"""
X_flat = X.ravel(order='F' if np.isfortran(X) else 'C')
return np.dot(X_flat, X_flat)
def warn(message, category=RuntimeWarning):
"""Emit a warning with trace outside the mne namespace.
This function takes arguments like warnings.warn, and sends messages
using both ``warnings.warn`` and ``logger.warn``. Warnings can be
generated deep within nested function calls. In order to provide a
more helpful warning, this function traverses the stack until it
reaches a frame outside the ``mne`` namespace that caused the error.
Parameters
----------
message : str
Warning message.
category : instance of Warning
The warning class. Defaults to ``RuntimeWarning``.
"""
import mne
root_dir = op.dirname(mne.__file__)
frame = None
if logger.level <= logging.WARN:
last_fname = ''
frame = inspect.currentframe()
while frame:
fname = frame.f_code.co_filename
lineno = frame.f_lineno
# in verbose dec
if fname == '<string>' and last_fname == 'utils.py':
last_fname = fname
frame = frame.f_back
continue
# treat tests as scripts
# and don't capture unittest/case.py (assert_raises)
if not (fname.startswith(root_dir) or
('unittest' in fname and 'case' in fname)) or \
op.basename(op.dirname(fname)) == 'tests':
break
last_fname = op.basename(fname)
frame = frame.f_back
del frame
# We need to use this instead of warn(message, category, stacklevel)
# because we move out of the MNE stack, so warnings won't properly
# recognize the module name (and our warnings.simplefilter will fail)
warnings.warn_explicit(message, category, fname, lineno,
'mne', globals().get('__warningregistry__', {}))
logger.warning(message)
def check_fname(fname, filetype, endings, endings_err=()):
"""Enforce MNE filename conventions.
Parameters
----------
fname : str
Name of the file.
filetype : str
Type of file. e.g., ICA, Epochs etc.
endings : tuple
Acceptable endings for the filename.
endings_err : tuple
Obligatory possible endings for the filename.
"""
if len(endings_err) > 0 and not fname.endswith(endings_err):
print_endings = ' or '.join([', '.join(endings_err[:-1]),
endings_err[-1]])
raise IOError('The filename (%s) for file type %s must end with %s'
% (fname, filetype, print_endings))
print_endings = ' or '.join([', '.join(endings[:-1]), endings[-1]])
if not fname.endswith(endings):
warn('This filename (%s) does not conform to MNE naming conventions. '
'All %s files should end with %s'
% (fname, filetype, print_endings))
class WrapStdOut(object):
"""Dynamically wrap to sys.stdout.
This makes packages that monkey-patch sys.stdout (e.g.doctest,
sphinx-gallery) work properly.
"""
def __getattr__(self, name): # noqa: D105
# Even more ridiculous than this class, this must be sys.stdout (not
# just stdout) in order for this to work (tested on OSX and Linux)
if hasattr(sys.stdout, name):
return getattr(sys.stdout, name)
else:
raise AttributeError("'file' object has not attribute '%s'" % name)
class _TempDir(str):
"""Create and auto-destroy temp dir.
This is designed to be used with testing modules. Instances should be
defined inside test functions. Instances defined at module level can not
guarantee proper destruction of the temporary directory.
When used at module level, the current use of the __del__() method for
cleanup can fail because the rmtree function may be cleaned up before this
object (an alternative could be using the atexit module instead).
"""
def __new__(self): # noqa: D105
new = str.__new__(self, tempfile.mkdtemp(prefix='tmp_mne_tempdir_'))
return new
def __init__(self): # noqa: D102
self._path = self.__str__()
def __del__(self): # noqa: D105
rmtree(self._path, ignore_errors=True)
def estimate_rank(data, tol='auto', return_singular=False, norm=True):
"""Estimate the rank of data.
This function will normalize the rows of the data (typically
channels or vertices) such that non-zero singular values
should be close to one.
Parameters
----------
data : array
Data to estimate the rank of (should be 2-dimensional).
tol : float | str
Tolerance for singular values to consider non-zero in
calculating the rank. The singular values are calculated
in this method such that independent data are expected to
have singular value around one. Can be 'auto' to use the
same thresholding as ``scipy.linalg.orth``.
return_singular : bool
If True, also return the singular values that were used
to determine the rank.
norm : bool
If True, data will be scaled by their estimated row-wise norm.
Else data are assumed to be scaled. Defaults to True.
Returns
-------
rank : int
Estimated rank of the data.
s : array
If return_singular is True, the singular values that were
thresholded to determine the rank are also returned.
"""
data = data.copy() # operate on a copy
if norm is True:
norms = _compute_row_norms(data)
data /= norms[:, np.newaxis]
s = linalg.svd(data, compute_uv=False, overwrite_a=True)
if isinstance(tol, string_types):
if tol != 'auto':
raise ValueError('tol must be "auto" or float')
eps = np.finfo(float).eps
tol = np.max(data.shape) * np.amax(s) * eps
tol = float(tol)
rank = np.sum(s > tol)
if return_singular is True:
return rank, s
else:
return rank
def _compute_row_norms(data):
"""Compute scaling based on estimated norm."""
norms = np.sqrt(np.sum(data ** 2, axis=1))
norms[norms == 0] = 1.0
return norms
def _reject_data_segments(data, reject, flat, decim, info, tstep):
"""Reject data segments using peak-to-peak amplitude."""
from .epochs import _is_good
from .io.pick import channel_indices_by_type
data_clean = np.empty_like(data)
idx_by_type = channel_indices_by_type(info)
step = int(ceil(tstep * info['sfreq']))
if decim is not None:
step = int(ceil(step / float(decim)))
this_start = 0
this_stop = 0
drop_inds = []
for first in range(0, data.shape[1], step):
last = first + step
data_buffer = data[:, first:last]
if data_buffer.shape[1] < (last - first):
break # end of the time segment
if _is_good(data_buffer, info['ch_names'], idx_by_type, reject,
flat, ignore_chs=info['bads']):
this_stop = this_start + data_buffer.shape[1]
data_clean[:, this_start:this_stop] = data_buffer
this_start += data_buffer.shape[1]
else:
logger.info("Artifact detected in [%d, %d]" % (first, last))
drop_inds.append((first, last))
data = data_clean[:, :this_stop]
if not data.any():
raise RuntimeError('No clean segment found. Please '
'consider updating your rejection '
'thresholds.')
return data, drop_inds
def _get_inst_data(inst):
"""Get data view from MNE object instance like Raw, Epochs or Evoked."""
from .io.base import BaseRaw
from .epochs import BaseEpochs
from . import Evoked
from .time_frequency.tfr import _BaseTFR
_validate_type(inst, (BaseRaw, BaseEpochs, Evoked, _BaseTFR), "Instance")
if not inst.preload:
inst.load_data()
return inst._data
class _FormatDict(dict):
"""Help pformat() work properly."""
def __missing__(self, key):
return "{" + key + "}"
def pformat(temp, **fmt):
"""Format a template string partially.
Examples
--------
>>> pformat("{a}_{b}", a='x')
'x_{b}'
"""
formatter = Formatter()
mapping = _FormatDict(fmt)
return formatter.vformat(temp, (), mapping)
###############################################################################
# DECORATORS
# Following deprecated class copied from scikit-learn
# force show of DeprecationWarning even on python 2.7
warnings.filterwarnings('always', category=DeprecationWarning, module='mne')
class deprecated(object):
"""Mark a function or class as deprecated (decorator).
Issue a warning when the function is called/the class is instantiated and
adds a warning to the docstring.
The optional extra argument will be appended to the deprecation message
and the docstring. Note: to use this with the default value for extra, put
in an empty of parentheses::
>>> from mne.utils import deprecated
>>> deprecated() # doctest: +ELLIPSIS
<mne.utils.deprecated object at ...>
>>> @deprecated()
... def some_function(): pass
Parameters
----------
extra: string
To be added to the deprecation messages.
"""
# Adapted from http://wiki.python.org/moin/PythonDecoratorLibrary,
# but with many changes.
# scikit-learn will not import on all platforms b/c it can be
# sklearn or scikits.learn, so a self-contained example is used above
def __init__(self, extra=''): # noqa: D102
self.extra = extra
def __call__(self, obj): # noqa: D105
"""Call.
Parameters
----------
obj : object
Object to call.
"""
if isinstance(obj, type):
return self._decorate_class(obj)
else:
return self._decorate_fun(obj)
def _decorate_class(self, cls):
msg = "Class %s is deprecated" % cls.__name__
if self.extra:
msg += "; %s" % self.extra
# FIXME: we should probably reset __new__ for full generality
init = cls.__init__
def deprecation_wrapped(*args, **kwargs):
warnings.warn(msg, category=DeprecationWarning)
return init(*args, **kwargs)
cls.__init__ = deprecation_wrapped
deprecation_wrapped.__name__ = '__init__'
deprecation_wrapped.__doc__ = self._update_doc(init.__doc__)
deprecation_wrapped.deprecated_original = init
return cls
def _decorate_fun(self, fun):
"""Decorate function fun."""
msg = "Function %s is deprecated" % fun.__name__
if self.extra:
msg += "; %s" % self.extra
def deprecation_wrapped(*args, **kwargs):
warnings.warn(msg, category=DeprecationWarning)
return fun(*args, **kwargs)
deprecation_wrapped.__name__ = fun.__name__
deprecation_wrapped.__dict__ = fun.__dict__
deprecation_wrapped.__doc__ = self._update_doc(fun.__doc__)
return deprecation_wrapped
def _update_doc(self, olddoc):
newdoc = ".. warning:: DEPRECATED"
if self.extra:
newdoc = "%s: %s" % (newdoc, self.extra)
if olddoc:
newdoc = "%s\n\n %s" % (newdoc, olddoc)
return newdoc
@decorator
def verbose(function, *args, **kwargs):
"""Verbose decorator to allow functions to override log-level.
This decorator is used to set the verbose level during a function or method
call, such as :func:`mne.compute_covariance`. The `verbose` keyword
argument can be 'DEBUG', 'INFO', 'WARNING', 'ERROR', 'CRITICAL', True (an
alias for 'INFO'), or False (an alias for 'WARNING'). To set the global
verbosity level for all functions, use :func:`mne.set_log_level`.
Parameters
----------
function : function
Function to be decorated by setting the verbosity level.
Returns
-------
dec : function
The decorated function
Examples
--------
You can use the ``verbose`` argument to set the verbose level on the fly::
>>> import mne
>>> cov = mne.compute_raw_covariance(raw, verbose='WARNING') # doctest: +SKIP
>>> cov = mne.compute_raw_covariance(raw, verbose='INFO') # doctest: +SKIP
Using up to 49 segments
Number of samples used : 5880
[done]
See Also
--------
set_log_level
set_config
""" # noqa: E501
arg_names = _get_args(function)
default_level = verbose_level = None
if len(arg_names) > 0 and arg_names[0] == 'self':
default_level = getattr(args[0], 'verbose', None)
if 'verbose' in arg_names:
verbose_level = args[arg_names.index('verbose')]
elif 'verbose' in kwargs:
verbose_level = kwargs.pop('verbose')
# This ensures that object.method(verbose=None) will use object.verbose
verbose_level = default_level if verbose_level is None else verbose_level
if verbose_level is not None:
# set it back if we get an exception
with use_log_level(verbose_level):
return function(*args, **kwargs)
return function(*args, **kwargs)
class use_log_level(object):
"""Context handler for logging level.
Parameters
----------
level : int
The level to use.
"""
def __init__(self, level): # noqa: D102
self.level = level
def __enter__(self): # noqa: D105
self.old_level = set_log_level(self.level, True)
def __exit__(self, *args): # noqa: D105
set_log_level(self.old_level)
def has_nibabel(vox2ras_tkr=False):
"""Determine if nibabel is installed.
Parameters
----------
vox2ras_tkr : bool
If True, require nibabel has vox2ras_tkr support.
Returns
-------
has : bool
True if the user has nibabel.
"""
try:
import nibabel
out = True
if vox2ras_tkr: # we need MGHHeader to have vox2ras_tkr param
out = (getattr(getattr(getattr(nibabel, 'MGHImage', 0),
'header_class', 0),
'get_vox2ras_tkr', None) is not None)
return out
except ImportError:
return False
def has_mne_c():
"""Check for MNE-C."""
return 'MNE_ROOT' in os.environ
def has_freesurfer():
"""Check for Freesurfer."""
return 'FREESURFER_HOME' in os.environ
def requires_nibabel(vox2ras_tkr=False):
"""Check for nibabel."""
import pytest
extra = ' with vox2ras_tkr support' if vox2ras_tkr else ''
return pytest.mark.skipif(not has_nibabel(vox2ras_tkr),
reason='Requires nibabel%s' % extra)
def buggy_mkl_svd(function):
"""Decorate tests that make calls to SVD and intermittently fail."""
@wraps(function)
def dec(*args, **kwargs):
try:
return function(*args, **kwargs)
except np.linalg.LinAlgError as exp:
if 'SVD did not converge' in str(exp):
msg = 'Intel MKL SVD convergence error detected, skipping test'
warn(msg)
raise SkipTest(msg)
raise
return dec
def requires_version(library, min_version='0.0'):
"""Check for a library version."""
import pytest
return pytest.mark.skipif(not check_version(library, min_version),
reason=('Requires %s version >= %s'
% (library, min_version)))
def requires_module(function, name, call=None):
"""Skip a test if package is not available (decorator)."""
import pytest
call = ('import %s' % name) if call is None else call
reason = 'Test %s skipped, requires %s.' % (function.__name__, name)
try:
exec(call) in globals(), locals()
except Exception as exc:
if len(str(exc)) > 0 and str(exc) != 'No module named %s' % name:
reason += ' Got exception (%s)' % (exc,)
skip = True
else:
skip = False
return pytest.mark.skipif(skip, reason=reason)(function)
def copy_doc(source):
"""Copy the docstring from another function (decorator).
The docstring of the source function is prepepended to the docstring of the
function wrapped by this decorator.
This is useful when inheriting from a class and overloading a method. This
decorator can be used to copy the docstring of the original method.
Parameters
----------
source : function
Function to copy the docstring from
Returns
-------
wrapper : function
The decorated function
Examples
--------
>>> class A:
... def m1():
... '''Docstring for m1'''
... pass
>>> class B (A):
... @copy_doc(A.m1)
... def m1():
... ''' this gets appended'''
... pass
>>> print(B.m1.__doc__)
Docstring for m1 this gets appended
"""
def wrapper(func):
if source.__doc__ is None or len(source.__doc__) == 0:
raise ValueError('Cannot copy docstring: docstring was empty.')
doc = source.__doc__
if func.__doc__ is not None:
doc += func.__doc__
func.__doc__ = doc
return func
return wrapper
def copy_function_doc_to_method_doc(source):
"""Use the docstring from a function as docstring for a method.
The docstring of the source function is prepepended to the docstring of the
function wrapped by this decorator. Additionally, the first parameter
specified in the docstring of the source function is removed in the new
docstring.
This decorator is useful when implementing a method that just calls a
function. This pattern is prevalent in for example the plotting functions
of MNE.
Parameters
----------
source : function
Function to copy the docstring from
Returns
-------
wrapper : function
The decorated method
Examples
--------
>>> def plot_function(object, a, b):
... '''Docstring for plotting function.
...
... Parameters
... ----------
... object : instance of object
... The object to plot
... a : int
... Some parameter
... b : int
... Some parameter
... '''
... pass
...
>>> class A:
... @copy_function_doc_to_method_doc(plot_function)
... def plot(self, a, b):
... '''
... Notes
... -----
... .. versionadded:: 0.13.0
... '''
... plot_function(self, a, b)
>>> print(A.plot.__doc__)
Docstring for plotting function.
<BLANKLINE>
Parameters
----------
a : int
Some parameter
b : int
Some parameter
<BLANKLINE>
Notes
-----
.. versionadded:: 0.13.0
<BLANKLINE>
Notes
-----
The parsing performed is very basic and will break easily on docstrings
that are not formatted exactly according to the ``numpydoc`` standard.
Always inspect the resulting docstring when using this decorator.
"""
def wrapper(func):
doc = source.__doc__.split('\n')
# Find parameter block
for line, text in enumerate(doc[:-2]):
if (text.strip() == 'Parameters' and
doc[line + 1].strip() == '----------'):
parameter_block = line
break
else:
# No parameter block found
raise ValueError('Cannot copy function docstring: no parameter '
'block found. To simply copy the docstring, use '
'the @copy_doc decorator instead.')
# Find first parameter
for line, text in enumerate(doc[parameter_block:], parameter_block):
if ':' in text:
first_parameter = line
parameter_indentation = len(text) - len(text.lstrip(' '))
break
else:
raise ValueError('Cannot copy function docstring: no parameters '
'found. To simply copy the docstring, use the '
'@copy_doc decorator instead.')
# Find end of first parameter
for line, text in enumerate(doc[first_parameter + 1:],
first_parameter + 1):
# Ignore empty lines
if len(text.strip()) == 0:
continue
line_indentation = len(text) - len(text.lstrip(' '))
if line_indentation <= parameter_indentation:
# Reach end of first parameter
first_parameter_end = line
# Of only one parameter is defined, remove the Parameters
# heading as well
if ':' not in text:
first_parameter = parameter_block
break
else:
# End of docstring reached
first_parameter_end = line
first_parameter = parameter_block
# Copy the docstring, but remove the first parameter
doc = ('\n'.join(doc[:first_parameter]) + '\n' +
'\n'.join(doc[first_parameter_end:]))
if func.__doc__ is not None:
doc += func.__doc__
func.__doc__ = doc
return func
return wrapper
_pandas_call = """
import pandas
version = LooseVersion(pandas.__version__)
if version < '0.8.0':
raise ImportError
"""
_sklearn_call = """
required_version = '0.14'
import sklearn
version = LooseVersion(sklearn.__version__)
if version < required_version:
raise ImportError
"""
_mayavi_call = """
with warnings.catch_warnings(record=True): # traits
from mayavi import mlab
mlab.options.backend = 'test'
"""
_mne_call = """
if not has_mne_c():
raise ImportError
"""
_fs_call = """
if not has_freesurfer():
raise ImportError
"""
_n2ft_call = """
if 'NEUROMAG2FT_ROOT' not in os.environ:
raise ImportError
"""
_fs_or_ni_call = """
if not has_nibabel() and not has_freesurfer():
raise ImportError
"""
requires_pandas = partial(requires_module, name='pandas', call=_pandas_call)
requires_sklearn = partial(requires_module, name='sklearn', call=_sklearn_call)
requires_mayavi = partial(requires_module, name='mayavi', call=_mayavi_call)
requires_mne = partial(requires_module, name='MNE-C', call=_mne_call)
requires_freesurfer = partial(requires_module, name='Freesurfer',
call=_fs_call)
requires_neuromag2ft = partial(requires_module, name='neuromag2ft',
call=_n2ft_call)
requires_fs_or_nibabel = partial(requires_module, name='nibabel or Freesurfer',
call=_fs_or_ni_call)
requires_tvtk = partial(requires_module, name='TVTK',
call='from tvtk.api import tvtk')
requires_pysurfer = partial(requires_module, name='PySurfer',
call="""import warnings
with warnings.catch_warnings(record=True):
from surfer import Brain""")
requires_good_network = partial(
requires_module, name='good network connection',
call='if int(os.environ.get("MNE_SKIP_NETWORK_TESTS", 0)):\n'
' raise ImportError')
requires_nitime = partial(requires_module, name='nitime')
requires_h5py = partial(requires_module, name='h5py')
requires_numpydoc = partial(requires_module, name='numpydoc')
def check_version(library, min_version):
r"""Check minimum library version required.
Parameters
----------
library : str
The library name to import. Must have a ``__version__`` property.
min_version : str
The minimum version string. Anything that matches
``'(\d+ | [a-z]+ | \.)'``. Can also be empty to skip version
check (just check for library presence).
Returns
-------
ok : bool
True if the library exists with at least the specified version.
"""
ok = True
try:
library = __import__(library)
except ImportError:
ok = False
else:
if min_version:
this_version = LooseVersion(library.__version__)
if this_version < min_version:
ok = False
return ok
def _check_mayavi_version(min_version='4.3.0'):
"""Check mayavi version."""
if not check_version('mayavi', min_version):
raise RuntimeError("Need mayavi >= %s" % min_version)
def _check_pyface_backend():
"""Check the currently selected Pyface backend.
Returns
-------
backend : str
Name of the backend.
result : 0 | 1 | 2
0: the backend has been tested and works.
1: the backend has not been tested.
2: the backend not been tested.
Notes
-----
See also http://docs.enthought.com/pyface/.
"""
try:
from traits.trait_base import ETSConfig
except ImportError:
return None, 2
backend = ETSConfig.toolkit
if backend == 'qt4':
status = 0
else:
status = 1
return backend, status
def _import_mlab():
"""Quietly import mlab."""
with warnings.catch_warnings(record=True):
from mayavi import mlab
return mlab
@contextmanager
def traits_test_context():
"""Context to raise errors in trait handlers."""
from traits.api import push_exception_handler
push_exception_handler(reraise_exceptions=True)
yield
push_exception_handler(reraise_exceptions=False)
def traits_test(test_func):
"""Raise errors in trait handlers (decorator)."""
@wraps(test_func)
def dec(*args, **kwargs):
with traits_test_context():
return test_func(*args, **kwargs)
return dec
@verbose
def run_subprocess(command, verbose=None, *args, **kwargs):
"""Run command using subprocess.Popen.
Run command and wait for command to complete. If the return code was zero
then return, otherwise raise CalledProcessError.
By default, this will also add stdout= and stderr=subproces.PIPE
to the call to Popen to suppress printing to the terminal.
Parameters
----------
command : list of str | str
Command to run as subprocess (see subprocess.Popen documentation).
verbose : bool, str, int, or None
If not None, override default verbose level (see :func:`mne.verbose`
and :ref:`Logging documentation <tut_logging>` for more). Defaults to
self.verbose.
*args, **kwargs : arguments
Additional arguments to pass to subprocess.Popen.
Returns
-------
stdout : str
Stdout returned by the process.
stderr : str
Stderr returned by the process.
"""
for stdxxx, sys_stdxxx, thresh in (
['stderr', sys.stderr, logging.ERROR],
['stdout', sys.stdout, logging.WARNING]):
if stdxxx not in kwargs and logger.level >= thresh:
kwargs[stdxxx] = subprocess.PIPE
elif kwargs.get(stdxxx, sys_stdxxx) is sys_stdxxx:
if isinstance(sys_stdxxx, StringIO):
# nose monkey patches sys.stderr and sys.stdout to StringIO
kwargs[stdxxx] = subprocess.PIPE
else:
kwargs[stdxxx] = sys_stdxxx
# Check the PATH environment variable. If run_subprocess() is to be called
# frequently this should be refactored so as to only check the path once.
env = kwargs.get('env', os.environ)
if any(p.startswith('~') for p in env['PATH'].split(os.pathsep)):
warn('Your PATH environment variable contains at least one path '
'starting with a tilde ("~") character. Such paths are not '
'interpreted correctly from within Python. It is recommended '
'that you use "$HOME" instead of "~".')
if isinstance(command, string_types):
command_str = command
else:
command_str = ' '.join(command)
logger.info("Running subprocess: %s" % command_str)
try:
p = subprocess.Popen(command, *args, **kwargs)
except Exception:
if isinstance(command, string_types):
command_name = command.split()[0]
else:
command_name = command[0]
logger.error('Command not found: %s' % command_name)
raise
stdout_, stderr = p.communicate()
stdout_ = u'' if stdout_ is None else stdout_.decode('utf-8')
stderr = u'' if stderr is None else stderr.decode('utf-8')
output = (stdout_, stderr)
if p.returncode:
print(output)
err_fun = subprocess.CalledProcessError.__init__
if 'output' in _get_args(err_fun):
raise subprocess.CalledProcessError(p.returncode, command, output)
else:
raise subprocess.CalledProcessError(p.returncode, command)
return output
###############################################################################
# LOGGING
def set_log_level(verbose=None, return_old_level=False):
"""Set the logging level.
Parameters
----------
verbose : bool, str, int, or None
The verbosity of messages to print. If a str, it can be either DEBUG,
INFO, WARNING, ERROR, or CRITICAL. Note that these are for
convenience and are equivalent to passing in logging.DEBUG, etc.
For bool, True is the same as 'INFO', False is the same as 'WARNING'.
If None, the environment variable MNE_LOGGING_LEVEL is read, and if
it doesn't exist, defaults to INFO.
return_old_level : bool
If True, return the old verbosity level.
"""
if verbose is None:
verbose = get_config('MNE_LOGGING_LEVEL', 'INFO')
elif isinstance(verbose, bool):
if verbose is True:
verbose = 'INFO'
else:
verbose = 'WARNING'
if isinstance(verbose, string_types):
verbose = verbose.upper()
logging_types = dict(DEBUG=logging.DEBUG, INFO=logging.INFO,
WARNING=logging.WARNING, ERROR=logging.ERROR,
CRITICAL=logging.CRITICAL)
if verbose not in logging_types:
raise ValueError('verbose must be of a valid type')
verbose = logging_types[verbose]
logger = logging.getLogger('mne')
old_verbose = logger.level
logger.setLevel(verbose)
return (old_verbose if return_old_level else None)
def set_log_file(fname=None, output_format='%(message)s', overwrite=None):
"""Set the log to print to a file.
Parameters
----------
fname : str, or None
Filename of the log to print to. If None, stdout is used.
To suppress log outputs, use set_log_level('WARN').
output_format : str
Format of the output messages. See the following for examples:
https://docs.python.org/dev/howto/logging.html
e.g., "%(asctime)s - %(levelname)s - %(message)s".
overwrite : bool | None
Overwrite the log file (if it exists). Otherwise, statements
will be appended to the log (default). None is the same as False,
but additionally raises a warning to notify the user that log
entries will be appended.
"""
logger = logging.getLogger('mne')
handlers = logger.handlers
for h in handlers:
# only remove our handlers (get along nicely with nose)
if isinstance(h, (logging.FileHandler, logging.StreamHandler)):
if isinstance(h, logging.FileHandler):
h.close()
logger.removeHandler(h)
if fname is not None:
if op.isfile(fname) and overwrite is None:
# Don't use warn() here because we just want to
# emit a warnings.warn here (not logger.warn)
warnings.warn('Log entries will be appended to the file. Use '
'overwrite=False to avoid this message in the '
'future.', RuntimeWarning, stacklevel=2)
overwrite = False
mode = 'w' if overwrite else 'a'
lh = logging.FileHandler(fname, mode=mode)
else:
""" we should just be able to do:
lh = logging.StreamHandler(sys.stdout)
but because doctests uses some magic on stdout, we have to do this:
"""
lh = logging.StreamHandler(WrapStdOut())
lh.setFormatter(logging.Formatter(output_format))
# actually add the stream handler
logger.addHandler(lh)
class catch_logging(object):
"""Store logging.
This will remove all other logging handlers, and return the handler to
stdout when complete.
"""
def __enter__(self): # noqa: D105
self._data = StringIO()
self._lh = logging.StreamHandler(self._data)
self._lh.setFormatter(logging.Formatter('%(message)s'))
for lh in logger.handlers:
logger.removeHandler(lh)
logger.addHandler(self._lh)
return self._data
def __exit__(self, *args): # noqa: D105
logger.removeHandler(self._lh)
set_log_file(None)
###############################################################################
# CONFIG / PREFS
def get_subjects_dir(subjects_dir=None, raise_error=False):
"""Safely use subjects_dir input to return SUBJECTS_DIR.
Parameters
----------
subjects_dir : str | None
If a value is provided, return subjects_dir. Otherwise, look for
SUBJECTS_DIR config and return the result.
raise_error : bool
If True, raise a KeyError if no value for SUBJECTS_DIR can be found
(instead of returning None).
Returns
-------
value : str | None
The SUBJECTS_DIR value.
"""
if subjects_dir is None:
subjects_dir = get_config('SUBJECTS_DIR', raise_error=raise_error)
return subjects_dir
_temp_home_dir = None
def _get_extra_data_path(home_dir=None):
"""Get path to extra data (config, tables, etc.)."""
global _temp_home_dir
if home_dir is None:
home_dir = os.environ.get('_MNE_FAKE_HOME_DIR')
if home_dir is None:
# this has been checked on OSX64, Linux64, and Win32
if 'nt' == os.name.lower():
if op.isdir(op.join(os.getenv('APPDATA'), '.mne')):
home_dir = os.getenv('APPDATA')
else:
home_dir = os.getenv('USERPROFILE')
else:
# This is a more robust way of getting the user's home folder on
# Linux platforms (not sure about OSX, Unix or BSD) than checking
# the HOME environment variable. If the user is running some sort
# of script that isn't launched via the command line (e.g. a script
# launched via Upstart) then the HOME environment variable will
# not be set.
if os.getenv('MNE_DONTWRITE_HOME', '') == 'true':
if _temp_home_dir is None:
_temp_home_dir = tempfile.mkdtemp()
atexit.register(partial(shutil.rmtree, _temp_home_dir,
ignore_errors=True))
home_dir = _temp_home_dir
else:
home_dir = os.path.expanduser('~')
if home_dir is None:
raise ValueError('mne-python config file path could '
'not be determined, please report this '
'error to mne-python developers')
return op.join(home_dir, '.mne')
def get_config_path(home_dir=None):
r"""Get path to standard mne-python config file.
Parameters
----------
home_dir : str | None
The folder that contains the .mne config folder.
If None, it is found automatically.
Returns
-------
config_path : str
The path to the mne-python configuration file. On windows, this
will be '%USERPROFILE%\.mne\mne-python.json'. On every other
system, this will be ~/.mne/mne-python.json.
"""
val = op.join(_get_extra_data_path(home_dir=home_dir),
'mne-python.json')
return val
def set_cache_dir(cache_dir):
"""Set the directory to be used for temporary file storage.
This directory is used by joblib to store memmapped arrays,
which reduces memory requirements and speeds up parallel
computation.
Parameters
----------
cache_dir: str or None
Directory to use for temporary file storage. None disables
temporary file storage.
"""
if cache_dir is not None and not op.exists(cache_dir):
raise IOError('Directory %s does not exist' % cache_dir)
set_config('MNE_CACHE_DIR', cache_dir, set_env=False)
def set_memmap_min_size(memmap_min_size):
"""Set the minimum size for memmaping of arrays for parallel processing.
Parameters
----------
memmap_min_size: str or None
Threshold on the minimum size of arrays that triggers automated memory
mapping for parallel processing, e.g., '1M' for 1 megabyte.
Use None to disable memmaping of large arrays.
"""
if memmap_min_size is not None:
if not isinstance(memmap_min_size, string_types):
raise ValueError('\'memmap_min_size\' has to be a string.')
if memmap_min_size[-1] not in ['K', 'M', 'G']:
raise ValueError('The size has to be given in kilo-, mega-, or '
'gigabytes, e.g., 100K, 500M, 1G.')
set_config('MNE_MEMMAP_MIN_SIZE', memmap_min_size, set_env=False)
# List the known configuration values
known_config_types = (
'MNE_BROWSE_RAW_SIZE',
'MNE_CACHE_DIR',
'MNE_COREG_COPY_ANNOT',
'MNE_COREG_GUESS_MRI_SUBJECT',
'MNE_COREG_HEAD_HIGH_RES',
'MNE_COREG_HEAD_OPACITY',
'MNE_COREG_INTERACTION',
'MNE_COREG_MARK_INSIDE',
'MNE_COREG_PREPARE_BEM',
'MNE_COREG_PROJECT_EEG',
'MNE_COREG_ORIENT_TO_SURFACE',
'MNE_COREG_SCALE_LABELS',
'MNE_COREG_SCALE_BY_DISTANCE',
'MNE_COREG_SCENE_SCALE',
'MNE_COREG_WINDOW_HEIGHT',
'MNE_COREG_WINDOW_WIDTH',
'MNE_COREG_SUBJECTS_DIR',
'MNE_CUDA_IGNORE_PRECISION',
'MNE_DATA',
'MNE_DATASETS_BRAINSTORM_PATH',
'MNE_DATASETS_EEGBCI_PATH',
'MNE_DATASETS_HF_SEF_PATH',
'MNE_DATASETS_MEGSIM_PATH',
'MNE_DATASETS_MISC_PATH',
'MNE_DATASETS_MTRF_PATH',
'MNE_DATASETS_SAMPLE_PATH',
'MNE_DATASETS_SOMATO_PATH',
'MNE_DATASETS_MULTIMODAL_PATH',
'MNE_DATASETS_SPM_FACE_DATASETS_TESTS',
'MNE_DATASETS_SPM_FACE_PATH',
'MNE_DATASETS_TESTING_PATH',
'MNE_DATASETS_VISUAL_92_CATEGORIES_PATH',
'MNE_DATASETS_KILOWORD_PATH',
'MNE_DATASETS_FIELDTRIP_CMC_PATH',
'MNE_DATASETS_PHANTOM_4DBTI_PATH',
'MNE_FORCE_SERIAL',
'MNE_KIT2FIFF_STIM_CHANNELS',
'MNE_KIT2FIFF_STIM_CHANNEL_CODING',
'MNE_KIT2FIFF_STIM_CHANNEL_SLOPE',
'MNE_KIT2FIFF_STIM_CHANNEL_THRESHOLD',
'MNE_LOGGING_LEVEL',
'MNE_MEMMAP_MIN_SIZE',
'MNE_SKIP_FTP_TESTS',
'MNE_SKIP_NETWORK_TESTS',
'MNE_SKIP_TESTING_DATASET_TESTS',
'MNE_STIM_CHANNEL',
'MNE_USE_CUDA',
'MNE_SKIP_FS_FLASH_CALL',
'SUBJECTS_DIR',
)
# These allow for partial matches, e.g. 'MNE_STIM_CHANNEL_1' is okay key
known_config_wildcards = (
'MNE_STIM_CHANNEL',
)
def _load_config(config_path, raise_error=False):
"""Safely load a config file."""
with open(config_path, 'r') as fid:
try:
config = json.load(fid)
except ValueError:
# No JSON object could be decoded --> corrupt file?
msg = ('The MNE-Python config file (%s) is not a valid JSON '
'file and might be corrupted' % config_path)
if raise_error:
raise RuntimeError(msg)
warn(msg)
config = dict()
return config
def get_config(key=None, default=None, raise_error=False, home_dir=None):
"""Read MNE-Python preferences from environment or config file.
Parameters
----------
key : None | str
The preference key to look for. The os environment is searched first,
then the mne-python config file is parsed.
If None, all the config parameters present in environment variables or
the path are returned.
default : str | None
Value to return if the key is not found.
raise_error : bool
If True, raise an error if the key is not found (instead of returning
default).
home_dir : str | None
The folder that contains the .mne config folder.
If None, it is found automatically.
Returns
-------
value : dict | str | None
The preference key value.
See Also
--------
set_config
"""
_validate_type(key, (string_types, type(None)), "key", 'string or None')
# first, check to see if key is in env
if key is not None and key in os.environ:
return os.environ[key]
# second, look for it in mne-python config file
config_path = get_config_path(home_dir=home_dir)
if not op.isfile(config_path):
config = {}
else:
config = _load_config(config_path)
if key is None:
# update config with environment variables
env_keys = (set(config).union(known_config_types).
intersection(os.environ))
config.update({key: os.environ[key] for key in env_keys})
return config
elif raise_error is True and key not in config:
meth_1 = 'os.environ["%s"] = VALUE' % key
meth_2 = 'mne.utils.set_config("%s", VALUE, set_env=True)' % key
raise KeyError('Key "%s" not found in environment or in the '
'mne-python config file: %s '
'Try either:'
' %s for a temporary solution, or:'
' %s for a permanent one. You can also '
'set the environment variable before '
'running python.'
% (key, config_path, meth_1, meth_2))
else:
return config.get(key, default)
def set_config(key, value, home_dir=None, set_env=True):
"""Set a MNE-Python preference key in the config file and environment.
Parameters
----------
key : str | None
The preference key to set. If None, a tuple of the valid
keys is returned, and ``value`` and ``home_dir`` are ignored.
value : str | None
The value to assign to the preference key. If None, the key is
deleted.
home_dir : str | None
The folder that contains the .mne config folder.
If None, it is found automatically.
set_env : bool
If True (default), update :data:`os.environ` in addition to
updating the MNE-Python config file.
See Also
--------
get_config
"""
if key is None:
return known_config_types
_validate_type(key, 'str', "key")
# While JSON allow non-string types, we allow users to override config
# settings using env, which are strings, so we enforce that here
_validate_type(value, (string_types, type(None)), "value",
"None or string")
if key not in known_config_types and not \
any(k in key for k in known_config_wildcards):
warn('Setting non-standard config type: "%s"' % key)
# Read all previous values
config_path = get_config_path(home_dir=home_dir)
if op.isfile(config_path):
config = _load_config(config_path, raise_error=True)
else:
config = dict()
logger.info('Attempting to create new mne-python configuration '
'file:\n%s' % config_path)
if value is None:
config.pop(key, None)
if set_env and key in os.environ:
del os.environ[key]
else:
config[key] = value
if set_env:
os.environ[key] = value
# Write all values. This may fail if the default directory is not
# writeable.
directory = op.dirname(config_path)
if not op.isdir(directory):
os.mkdir(directory)
with open(config_path, 'w') as fid:
json.dump(config, fid, sort_keys=True, indent=0)
class ProgressBar(object):
"""Generate a command-line progressbar.
Parameters
----------
max_value : int | iterable
Maximum value of process (e.g. number of samples to process, bytes to
download, etc.). If an iterable is given, then `max_value` will be set
to the length of this iterable.
initial_value : int
Initial value of process, useful when resuming process from a specific
value, defaults to 0.
mesg : str
Message to include at end of progress bar.
max_chars : int | str
Number of characters to use for progress bar itself.
This does not include characters used for the message or percent
complete. Can be "auto" (default) to try to set a sane value based
on the terminal width.
progress_character : char
Character in the progress bar that indicates the portion completed.
spinner : bool
Show a spinner. Useful for long-running processes that may not
increment the progress bar very often. This provides the user with
feedback that the progress has not stalled.
max_total_width : int | str
Maximum total message width. Can use "auto" (default) to try to set
a sane value based on the current terminal width.
verbose_bool : bool
If True, show progress.
Example
-------
>>> progress = ProgressBar(13000)
>>> progress.update(3000) # doctest: +SKIP
[......... ] 23.07692 |
>>> progress.update(6000) # doctest: +SKIP
[.................. ] 46.15385 |
>>> progress = ProgressBar(13000, spinner=True)
>>> progress.update(3000) # doctest: +SKIP
[......... ] 23.07692 |
>>> progress.update(6000) # doctest: +SKIP
[.................. ] 46.15385 /
"""
spinner_symbols = ['|', '/', '-', '\\']
template = '\r[{0}{1}] {2:.02f}% {4} {3} '
def __init__(self, max_value, initial_value=0, mesg='', max_chars='auto',
progress_character='.', spinner=False,
max_total_width='auto', verbose_bool=True): # noqa: D102
self.cur_value = initial_value
if isinstance(max_value, Iterable):
self.max_value = len(max_value)
self.iterable = max_value
else:
self.max_value = float(max_value)
self.iterable = None
self.mesg = mesg
self.progress_character = progress_character
self.spinner = spinner
self.spinner_index = 0
self.n_spinner = len(self.spinner_symbols)
self._do_print = verbose_bool
self.cur_time = time.time()
if max_total_width == 'auto':
max_total_width = _get_terminal_width()
self.max_total_width = int(max_total_width)
if max_chars == 'auto':
max_chars = min(max(max_total_width - 40, 10), 60)
self.max_chars = int(max_chars)
self.cur_rate = 0
def update(self, cur_value, mesg=None):
"""Update progressbar with current value of process.
Parameters
----------
cur_value : number
Current value of process. Should be <= max_value (but this is not
enforced). The percent of the progressbar will be computed as
(cur_value / max_value) * 100
mesg : str
Message to display to the right of the progressbar. If None, the
last message provided will be used. To clear the current message,
pass a null string, ''.
"""
cur_time = time.time()
cur_rate = ((cur_value - self.cur_value) /
max(float(cur_time - self.cur_time), 1e-6))
# Smooth the estimate a bit
cur_rate = 0.1 * cur_rate + 0.9 * self.cur_rate
# Ensure floating-point division so we can get fractions of a percent
# for the progressbar.
self.cur_time = cur_time
self.cur_value = cur_value
self.cur_rate = cur_rate
progress = min(float(self.cur_value) / self.max_value, 1.)
num_chars = int(progress * self.max_chars)
num_left = self.max_chars - num_chars
# Update the message
if mesg is not None:
if mesg == 'file_sizes':
mesg = '(%s, %s/s)' % (
sizeof_fmt(self.cur_value).rjust(8),
sizeof_fmt(cur_rate).rjust(8))
self.mesg = mesg
# The \r tells the cursor to return to the beginning of the line rather
# than starting a new line. This allows us to have a progressbar-style
# display in the console window.
bar = self.template.format(self.progress_character * num_chars,
' ' * num_left,
progress * 100,
self.spinner_symbols[self.spinner_index],
self.mesg)
bar = bar[:self.max_total_width]
# Force a flush because sometimes when using bash scripts and pipes,
# the output is not printed until after the program exits.
if self._do_print:
sys.stdout.write(bar)
sys.stdout.flush()
# Increment the spinner
if self.spinner:
self.spinner_index = (self.spinner_index + 1) % self.n_spinner
def update_with_increment_value(self, increment_value, mesg=None):
"""Update progressbar with an increment.
Parameters
----------
increment_value : int
Value of the increment of process. The percent of the progressbar
will be computed as
(self.cur_value + increment_value / max_value) * 100
mesg : str
Message to display to the right of the progressbar. If None, the
last message provided will be used. To clear the current message,
pass a null string, ''.
"""
self.update(self.cur_value + increment_value, mesg)
def __iter__(self):
"""Iterate to auto-increment the pbar with 1."""
if self.iterable is None:
raise ValueError("Must give an iterable to be used in a loop.")
for obj in self.iterable:
yield obj
self.update_with_increment_value(1)
def _get_terminal_width():
"""Get the terminal width."""
if sys.version[0] == '2':
return 80
else:
return shutil.get_terminal_size((80, 20)).columns
def _get_http(url, temp_file_name, initial_size, file_size, timeout,
verbose_bool):
"""Safely (resume a) download to a file from http(s)."""
# Actually do the reading
req = urllib.request.Request(url)
if initial_size > 0:
req.headers['Range'] = 'bytes=%s-' % (initial_size,)
try:
response = urllib.request.urlopen(req, timeout=timeout)
except Exception:
# There is a problem that may be due to resuming, some
# servers may not support the "Range" header. Switch
# back to complete download method
logger.info('Resuming download failed (server '
'rejected the request). Attempting to '
'restart downloading the entire file.')
del req.headers['Range']
response = urllib.request.urlopen(req, timeout=timeout)
total_size = int(response.headers.get('Content-Length', '1').strip())
if initial_size > 0 and file_size == total_size:
logger.info('Resuming download failed (resume file size '
'mismatch). Attempting to restart downloading the '
'entire file.')
initial_size = 0
total_size += initial_size
if total_size != file_size:
raise RuntimeError('URL could not be parsed properly '
'(total size %s != file size %s)'
% (total_size, file_size))
mode = 'ab' if initial_size > 0 else 'wb'
progress = ProgressBar(total_size, initial_value=initial_size,
spinner=True, mesg='file_sizes',
verbose_bool=verbose_bool)
chunk_size = 8192 # 2 ** 13
with open(temp_file_name, mode) as local_file:
while True:
t0 = time.time()
chunk = response.read(chunk_size)
dt = time.time() - t0
if dt < 0.005:
chunk_size *= 2
elif dt > 0.1 and chunk_size > 8192:
chunk_size = chunk_size // 2
if not chunk:
if verbose_bool:
sys.stdout.write('\n')
sys.stdout.flush()
break
local_file.write(chunk)
progress.update_with_increment_value(len(chunk),
mesg='file_sizes')
def _chunk_write(chunk, local_file, progress):
"""Write a chunk to file and update the progress bar."""
local_file.write(chunk)
progress.update_with_increment_value(len(chunk))
@verbose
def _fetch_file(url, file_name, print_destination=True, resume=True,
hash_=None, timeout=30., verbose=None):
"""Load requested file, downloading it if needed or requested.
Parameters
----------
url: string
The url of file to be downloaded.
file_name: string
Name, along with the path, of where downloaded file will be saved.
print_destination: bool, optional
If true, destination of where file was saved will be printed after
download finishes.
resume: bool, optional
If true, try to resume partially downloaded files.
hash_ : str | None
The hash of the file to check. If None, no checking is
performed.
timeout : float
The URL open timeout.
verbose : bool, str, int, or None
If not None, override default verbose level (see :func:`mne.verbose`
and :ref:`Logging documentation <tut_logging>` for more).
"""
# Adapted from NISL:
# https://github.com/nisl/tutorial/blob/master/nisl/datasets.py
if hash_ is not None and (not isinstance(hash_, string_types) or
len(hash_) != 32):
raise ValueError('Bad hash value given, should be a 32-character '
'string:\n%s' % (hash_,))
temp_file_name = file_name + ".part"
verbose_bool = (logger.level <= 20) # 20 is info
try:
# Check file size and displaying it alongside the download url
u = urllib.request.urlopen(url, timeout=timeout)
u.close()
# this is necessary to follow any redirects
url = u.geturl()
u = urllib.request.urlopen(url, timeout=timeout)
try:
file_size = int(u.headers.get('Content-Length', '1').strip())
finally:
u.close()
del u
logger.info('Downloading %s (%s)' % (url, sizeof_fmt(file_size)))
# Triage resume
if not os.path.exists(temp_file_name):
resume = False
if resume:
with open(temp_file_name, 'rb', buffering=0) as local_file:
local_file.seek(0, 2)
initial_size = local_file.tell()
del local_file
else:
initial_size = 0
# This should never happen if our functions work properly
if initial_size > file_size:
raise RuntimeError('Local file (%s) is larger than remote '
'file (%s), cannot resume download'
% (sizeof_fmt(initial_size),
sizeof_fmt(file_size)))
elif initial_size == file_size:
# This should really only happen when a hash is wrong
# during dev updating
warn('Local file appears to be complete (file_size == '
'initial_size == %s)' % (file_size,))
else:
# Need to resume or start over
scheme = urllib.parse.urlparse(url).scheme
if scheme not in ('http', 'https'):
raise NotImplementedError('Cannot use %s' % (scheme,))
_get_http(url, temp_file_name, initial_size, file_size, timeout,
verbose_bool)
# check md5sum
if hash_ is not None:
logger.info('Verifying hash %s.' % (hash_,))
md5 = md5sum(temp_file_name)
if hash_ != md5:
raise RuntimeError('Hash mismatch for downloaded file %s, '
'expected %s but got %s'
% (temp_file_name, hash_, md5))
shutil.move(temp_file_name, file_name)
if print_destination is True:
logger.info('File saved as %s.\n' % file_name)
except Exception:
logger.error('Error while fetching file %s.'
' Dataset fetching aborted.' % url)
raise
def sizeof_fmt(num):
"""Turn number of bytes into human-readable str.
Parameters
----------
num : int
The number of bytes.
Returns
-------
size : str
The size in human-readable format.
"""
units = ['bytes', 'kB', 'MB', 'GB', 'TB', 'PB']
decimals = [0, 0, 1, 2, 2, 2]
if num > 1:
exponent = min(int(log(num, 1024)), len(units) - 1)
quotient = float(num) / 1024 ** exponent
unit = units[exponent]
num_decimals = decimals[exponent]
format_string = '{0:.%sf} {1}' % (num_decimals)
return format_string.format(quotient, unit)
if num == 0:
return '0 bytes'
if num == 1:
return '1 byte'
class SizeMixin(object):
"""Estimate MNE object sizes."""
@property
def _size(self):
"""Estimate the object size."""
try:
size = object_size(self.info)
except Exception:
warn('Could not get size for self.info')
return -1
if hasattr(self, 'data'):
size += object_size(self.data)
elif hasattr(self, '_data'):
size += object_size(self._data)
return size
def __hash__(self):
"""Hash the object.
Returns
-------
hash : int
The hash
"""
from .evoked import Evoked
from .epochs import BaseEpochs
from .io.base import BaseRaw
if isinstance(self, Evoked):
return object_hash(dict(info=self.info, data=self.data))
elif isinstance(self, (BaseEpochs, BaseRaw)):
_check_preload(self, "Hashing ")
return object_hash(dict(info=self.info, data=self._data))
else:
raise RuntimeError('Hashing unknown object type: %s' % type(self))
def _url_to_local_path(url, path):
"""Mirror a url path in a local destination (keeping folder structure)."""
destination = urllib.parse.urlparse(url).path
# First char should be '/', and it needs to be discarded
if len(destination) < 2 or destination[0] != '/':
raise ValueError('Invalid URL')
destination = os.path.join(path,
urllib.request.url2pathname(destination)[1:])
return destination
def _get_stim_channel(stim_channel, info, raise_error=True):
"""Determine the appropriate stim_channel.
First, 'MNE_STIM_CHANNEL', 'MNE_STIM_CHANNEL_1', 'MNE_STIM_CHANNEL_2', etc.
are read. If these are not found, it will fall back to 'STI 014' if
present, then fall back to the first channel of type 'stim', if present.
Parameters
----------
stim_channel : str | list of str | None
The stim channel selected by the user.
info : instance of Info
An information structure containing information about the channels.
Returns
-------
stim_channel : str | list of str
The name of the stim channel(s) to use
"""
if stim_channel is not None:
if not isinstance(stim_channel, list):
_validate_type(stim_channel, 'str', "Stim channel")
stim_channel = [stim_channel]
for channel in stim_channel:
_validate_type(channel, 'str', "Each provided stim channel")
return stim_channel
stim_channel = list()
ch_count = 0
ch = get_config('MNE_STIM_CHANNEL')
while(ch is not None and ch in info['ch_names']):
stim_channel.append(ch)
ch_count += 1
ch = get_config('MNE_STIM_CHANNEL_%d' % ch_count)
if ch_count > 0:
return stim_channel
if 'STI101' in info['ch_names']: # combination channel for newer systems
return ['STI101']
if 'STI 014' in info['ch_names']: # for older systems
return ['STI 014']
from .io.pick import pick_types
stim_channel = pick_types(info, meg=False, ref_meg=False, stim=True)
if len(stim_channel) > 0:
stim_channel = [info['ch_names'][ch_] for ch_ in stim_channel]
elif raise_error:
raise ValueError("No stim channels found. Consider specifying them "
"manually using the 'stim_channel' parameter.")
return stim_channel
def _check_fname(fname, overwrite=False, must_exist=False):
"""Check for file existence."""
_validate_type(fname, 'str', 'fname')
if must_exist and not op.isfile(fname):
raise IOError('File "%s" does not exist' % fname)
if op.isfile(fname):
if not overwrite:
raise IOError('Destination file exists. Please use option '
'"overwrite=True" to force overwriting.')
elif overwrite != 'read':
logger.info('Overwriting existing file.')
def _check_subject(class_subject, input_subject, raise_error=True):
"""Get subject name from class."""
if input_subject is not None:
_validate_type(input_subject, 'str', "subject input")
return input_subject
elif class_subject is not None:
_validate_type(class_subject, 'str',
"Either subject input or class subject attribute")
return class_subject
else:
if raise_error is True:
raise ValueError('Neither subject input nor class subject '
'attribute was a string')
return None
def _check_preload(inst, msg):
"""Ensure data are preloaded."""
from .epochs import BaseEpochs
from .evoked import Evoked
from .time_frequency import _BaseTFR
if isinstance(inst, (_BaseTFR, Evoked)):
pass
else:
name = "epochs" if isinstance(inst, BaseEpochs) else 'raw'
if not inst.preload:
raise RuntimeError(
"By default, MNE does not load data into main memory to "
"conserve resources. " + msg + ' requires %s data to be '
'loaded. Use preload=True (or string) in the constructor or '
'%s.load_data().' % (name, name))
def _check_compensation_grade(inst, inst2, name, name2, ch_names=None):
"""Ensure that objects have same compensation_grade."""
from .io.pick import pick_channels, pick_info
from .io.compensator import get_current_comp
if None in [inst.info, inst2.info]:
return
if ch_names is None:
grade = inst.compensation_grade
grade2 = inst2.compensation_grade
else:
info = inst.info.copy()
info2 = inst2.info.copy()
# pick channels
for t_info in [info, info2]:
if t_info['comps']:
t_info['comps'] = []
picks = pick_channels(t_info['ch_names'], ch_names)
pick_info(t_info, picks, copy=False)
# get compensation grades
grade = get_current_comp(info)
grade2 = get_current_comp(info2)
# perform check
if grade != grade2:
msg = 'Compensation grade of %s (%d) and %s (%d) don\'t match'
raise RuntimeError(msg % (name, inst.compensation_grade,
name2, inst2.compensation_grade))
def _check_pandas_installed(strict=True):
"""Aux function."""
try:
import pandas
return pandas
except ImportError:
if strict is True:
raise RuntimeError('For this functionality to work, the Pandas '
'library is required.')
else:
return False
def _check_pandas_index_arguments(index, defaults):
"""Check pandas index arguments."""
if not any(isinstance(index, k) for k in (list, tuple)):
index = [index]
invalid_choices = [e for e in index if e not in defaults]
if invalid_choices:
options = [', '.join(e) for e in [invalid_choices, defaults]]
raise ValueError('[%s] is not an valid option. Valid index'
'values are \'None\' or %s' % tuple(options))
def _clean_names(names, remove_whitespace=False, before_dash=True):
"""Remove white-space on topo matching.
This function handles different naming
conventions for old VS new VectorView systems (`remove_whitespace`).
Also it allows to remove system specific parts in CTF channel names
(`before_dash`).
Usage
-----
# for new VectorView (only inside layout)
ch_names = _clean_names(epochs.ch_names, remove_whitespace=True)
# for CTF
ch_names = _clean_names(epochs.ch_names, before_dash=True)
"""
cleaned = []
for name in names:
if ' ' in name and remove_whitespace:
name = name.replace(' ', '')
if '-' in name and before_dash:
name = name.split('-')[0]
if name.endswith('_v'):
name = name[:-2]
cleaned.append(name)
return cleaned
def _check_type_picks(picks):
"""Guarantee type integrity of picks."""
err_msg = 'picks must be None, a list or an array of integers'
if picks is None:
pass
elif isinstance(picks, list):
for pick in picks:
_validate_type(pick, 'int', 'Each pick')
picks = np.array(picks)
elif isinstance(picks, np.ndarray):
if not picks.dtype.kind == 'i':
raise TypeError(err_msg)
else:
raise TypeError(err_msg)
return picks
@nottest
def run_tests_if_main(measure_mem=False):
"""Run tests in a given file if it is run as a script."""
local_vars = inspect.currentframe().f_back.f_locals
if not local_vars.get('__name__', '') == '__main__':
return
# we are in a "__main__"
try:
import faulthandler
faulthandler.enable()
except Exception:
pass
with warnings.catch_warnings(record=True): # memory_usage internal dep.
mem = int(round(max(memory_usage(-1)))) if measure_mem else -1
if mem >= 0:
print('Memory consumption after import: %s' % mem)
t0 = time.time()
peak_mem, peak_name = mem, 'import'
max_elapsed, elapsed_name = 0, 'N/A'
count = 0
for name in sorted(list(local_vars.keys()), key=lambda x: x.lower()):
val = local_vars[name]
if name.startswith('_'):
continue
elif callable(val) and name.startswith('test'):
count += 1
doc = val.__doc__.strip() if val.__doc__ else name
sys.stdout.write('%s ... ' % doc)
sys.stdout.flush()
try:
t1 = time.time()
if measure_mem:
with warnings.catch_warnings(record=True): # dep warn
mem = int(round(max(memory_usage((val, (), {})))))
else:
val()
mem = -1
if mem >= peak_mem:
peak_mem, peak_name = mem, name
mem = (', mem: %s MB' % mem) if mem >= 0 else ''
elapsed = int(round(time.time() - t1))
if elapsed >= max_elapsed:
max_elapsed, elapsed_name = elapsed, name
sys.stdout.write('time: %0.3f sec%s\n' % (elapsed, mem))
sys.stdout.flush()
except Exception as err:
if 'skiptest' in err.__class__.__name__.lower():
sys.stdout.write('SKIP (%s)\n' % str(err))
sys.stdout.flush()
else:
raise
elapsed = int(round(time.time() - t0))
sys.stdout.write('Total: %s tests\n• %0.3f sec (%0.3f sec for %s)\n• '
'Peak memory %s MB (%s)\n'
% (count, elapsed, max_elapsed, elapsed_name, peak_mem,
peak_name))
class ArgvSetter(object):
"""Temporarily set sys.argv."""
def __init__(self, args=(), disable_stdout=True,
disable_stderr=True): # noqa: D102
self.argv = list(('python',) + args)
self.stdout = StringIO() if disable_stdout else sys.stdout
self.stderr = StringIO() if disable_stderr else sys.stderr
def __enter__(self): # noqa: D105
self.orig_argv = sys.argv
sys.argv = self.argv
self.orig_stdout = sys.stdout
sys.stdout = self.stdout
self.orig_stderr = sys.stderr
sys.stderr = self.stderr
return self
def __exit__(self, *args): # noqa: D105
sys.argv = self.orig_argv
sys.stdout = self.orig_stdout
sys.stderr = self.orig_stderr
class SilenceStdout(object):
"""Silence stdout."""
def __enter__(self): # noqa: D105
self.stdout = sys.stdout
sys.stdout = StringIO()
return self
def __exit__(self, *args): # noqa: D105
sys.stdout = self.stdout
def md5sum(fname, block_size=1048576): # 2 ** 20
"""Calculate the md5sum for a file.
Parameters
----------
fname : str
Filename.
block_size : int
Block size to use when reading.
Returns
-------
hash_ : str
The hexadecimal digest of the hash.
"""
md5 = hashlib.md5()
with open(fname, 'rb') as fid:
while True:
data = fid.read(block_size)
if not data:
break
md5.update(data)
return md5.hexdigest()
def create_slices(start, stop, step=None, length=1):
"""Generate slices of time indexes.
Parameters
----------
start : int
Index where first slice should start.
stop : int
Index where last slice should maximally end.
length : int
Number of time sample included in a given slice.
step: int | None
Number of time samples separating two slices.
If step = None, step = length.
Returns
-------
slices : list
List of slice objects.
"""
# default parameters
if step is None:
step = length
# slicing
slices = [slice(t, t + length, 1) for t in
range(start, stop - length + 1, step)]
return slices
def _time_mask(times, tmin=None, tmax=None, sfreq=None, raise_error=True):
"""Safely find sample boundaries."""
orig_tmin = tmin
orig_tmax = tmax
tmin = -np.inf if tmin is None else tmin
tmax = np.inf if tmax is None else tmax
if not np.isfinite(tmin):
tmin = times[0]
if not np.isfinite(tmax):
tmax = times[-1]
if sfreq is not None:
# Push to a bit past the nearest sample boundary first
sfreq = float(sfreq)
tmin = int(round(tmin * sfreq)) / sfreq - 0.5 / sfreq
tmax = int(round(tmax * sfreq)) / sfreq + 0.5 / sfreq
if raise_error and tmin > tmax:
raise ValueError('tmin (%s) must be less than or equal to tmax (%s)'
% (orig_tmin, orig_tmax))
mask = (times >= tmin)
mask &= (times <= tmax)
if raise_error and not mask.any():
raise ValueError('No samples remain when using tmin=%s and tmax=%s '
'(original time bounds are [%s, %s])'
% (orig_tmin, orig_tmax, times[0], times[-1]))
return mask
def random_permutation(n_samples, random_state=None):
"""Emulate the randperm matlab function.
It returns a vector containing a random permutation of the
integers between 0 and n_samples-1. It returns the same random numbers
than randperm matlab function whenever the random_state is the same
as the matlab's random seed.
This function is useful for comparing against matlab scripts
which use the randperm function.
Note: the randperm(n_samples) matlab function generates a random
sequence between 1 and n_samples, whereas
random_permutation(n_samples, random_state) function generates
a random sequence between 0 and n_samples-1, that is:
randperm(n_samples) = random_permutation(n_samples, random_state) - 1
Parameters
----------
n_samples : int
End point of the sequence to be permuted (excluded, i.e., the end point
is equal to n_samples-1)
random_state : int | None
Random seed for initializing the pseudo-random number generator.
Returns
-------
randperm : ndarray, int
Randomly permuted sequence between 0 and n-1.
"""
rng = check_random_state(random_state)
idx = rng.rand(n_samples)
randperm = np.argsort(idx)
return randperm
def compute_corr(x, y):
"""Compute pearson correlations between a vector and a matrix."""
if len(x) == 0 or len(y) == 0:
raise ValueError('x or y has zero length')
X = np.array(x, float)
Y = np.array(y, float)
X -= X.mean(0)
Y -= Y.mean(0)
x_sd = X.std(0, ddof=1)
# if covariance matrix is fully expanded, Y needs a
# transpose / broadcasting else Y is correct
y_sd = Y.std(0, ddof=1)[:, None if X.shape == Y.shape else Ellipsis]
return (np.dot(X.T, Y) / float(len(X) - 1)) / (x_sd * y_sd)
def grand_average(all_inst, interpolate_bads=True, drop_bads=True):
"""Make grand average of a list evoked or AverageTFR data.
For evoked data, the function interpolates bad channels based on
`interpolate_bads` parameter. If `interpolate_bads` is True, the grand
average file will contain good channels and the bad channels interpolated
from the good MEG/EEG channels.
For AverageTFR data, the function takes the subset of channels not marked
as bad in any of the instances.
The grand_average.nave attribute will be equal to the number
of evoked datasets used to calculate the grand average.
Note: Grand average evoked should not be used for source localization.
Parameters
----------
all_inst : list of Evoked or AverageTFR data
The evoked datasets.
interpolate_bads : bool
If True, bad MEG and EEG channels are interpolated. Ignored for
AverageTFR.
drop_bads : bool
If True, drop all bad channels marked as bad in any data set.
If neither interpolate_bads nor drop_bads is True, in the output file,
every channel marked as bad in at least one of the input files will be
marked as bad, but no interpolation or dropping will be performed.
Returns
-------
grand_average : Evoked | AverageTFR
The grand average data. Same type as input.
Notes
-----
.. versionadded:: 0.11.0
"""
# check if all elements in the given list are evoked data
from .evoked import Evoked
from .time_frequency import AverageTFR
from .channels.channels import equalize_channels
assert len(all_inst) > 1
inst_type = type(all_inst[0])
_validate_type(all_inst[0], (Evoked, AverageTFR), 'All elements')
for inst in all_inst:
_validate_type(inst, inst_type, 'All elements', 'of the same type')
# Copy channels to leave the original evoked datasets intact.
all_inst = [inst.copy() for inst in all_inst]
# Interpolates if necessary
if isinstance(all_inst[0], Evoked):
if interpolate_bads:
all_inst = [inst.interpolate_bads() if len(inst.info['bads']) > 0
else inst for inst in all_inst]
equalize_channels(all_inst) # apply equalize_channels
from .evoked import combine_evoked as combine
else: # isinstance(all_inst[0], AverageTFR):
from .time_frequency.tfr import combine_tfr as combine
if drop_bads:
bads = list(set((b for inst in all_inst for b in inst.info['bads'])))
if bads:
for inst in all_inst:
inst.drop_channels(bads)
# make grand_average object using combine_[evoked/tfr]
grand_average = combine(all_inst, weights='equal')
# change the grand_average.nave to the number of Evokeds
grand_average.nave = len(all_inst)
# change comment field
grand_average.comment = "Grand average (n = %d)" % grand_average.nave
return grand_average
def _get_root_dir():
"""Get as close to the repo root as possible."""
root_dir = op.abspath(op.dirname(__file__))
up_dir = op.join(root_dir, '..')
if op.isfile(op.join(up_dir, 'setup.py')) and all(
op.isdir(op.join(up_dir, x)) for x in ('mne', 'examples', 'doc')):
root_dir = op.abspath(up_dir)
return root_dir
def sys_info(fid=None, show_paths=False):
"""Print the system information for debugging.
This function is useful for printing system information
to help triage bugs.
Parameters
----------
fid : file-like | None
The file to write to. Will be passed to :func:`print()`.
Can be None to use :data:`sys.stdout`.
show_paths : bool
If True, print paths for each module.
Examples
--------
Running this function with no arguments prints an output that is
useful when submitting bug reports::
>>> import mne
>>> mne.sys_info() # doctest: +SKIP
Platform: Linux-4.2.0-27-generic-x86_64-with-Ubuntu-15.10-wily
Python: 2.7.10 (default, Oct 14 2015, 16:09:02) [GCC 5.2.1 20151010]
Executable: /usr/bin/python
mne: 0.12.dev0
numpy: 1.12.0.dev0+ec5bd81 {lapack=mkl_rt, blas=mkl_rt}
scipy: 0.18.0.dev0+3deede3
matplotlib: 1.5.1+1107.g1fa2697
sklearn: 0.18.dev0
nibabel: 2.1.0dev
mayavi: 4.3.1
pycuda: 2015.1.3
skcuda: 0.5.2
pandas: 0.17.1+25.g547750a
""" # noqa: E501
ljust = 15
out = 'Platform:'.ljust(ljust) + platform.platform() + '\n'
out += 'Python:'.ljust(ljust) + str(sys.version).replace('\n', ' ') + '\n'
out += 'Executable:'.ljust(ljust) + sys.executable + '\n'
out += 'CPU:'.ljust(ljust) + ('%s: %s cores\n' %
(platform.processor(),
multiprocessing.cpu_count()))
out += 'Memory:'.ljust(ljust)
try:
import psutil
except ImportError:
out += 'Unavailable (requires "psutil" package)'
else:
out += '%0.1f GB\n' % (psutil.virtual_memory().total / float(2 ** 30),)
out += '\n'
old_stdout = sys.stdout
capture = StringIO()
try:
sys.stdout = capture
np.show_config()
finally:
sys.stdout = old_stdout
lines = capture.getvalue().split('\n')
libs = []
for li, line in enumerate(lines):
for key in ('lapack', 'blas'):
if line.startswith('%s_opt_info' % key):
lib = lines[li + 1]
if 'NOT AVAILABLE' in lib:
lib = 'unknown'
else:
lib = lib.split('[')[1].split("'")[1]
libs += ['%s=%s' % (key, lib)]
libs = ', '.join(libs)
version_texts = dict(pycuda='VERSION_TEXT')
for mod_name in ('mne', 'numpy', 'scipy', 'matplotlib', '', 'sklearn',
'nibabel', 'mayavi', 'pycuda', 'skcuda', 'pandas'):
if mod_name == '':
out += '\n'
continue
out += ('%s:' % mod_name).ljust(ljust)
try:
mod = __import__(mod_name)
if mod_name == 'mayavi':
# the real test
from mayavi import mlab # noqa, analysis:ignore
except Exception:
out += 'Not found\n'
else:
version = getattr(mod, version_texts.get(mod_name, '__version__'))
extra = (' (%s)' % op.dirname(mod.__file__)) if show_paths else ''
if mod_name == 'numpy':
extra = ' {%s}%s' % (libs, extra)
elif mod_name == 'matplotlib':
extra = ' {backend=%s}%s' % (mod.get_backend(), extra)
elif mod_name == 'mayavi':
try:
from pyface.qt import qt_api
except Exception:
qt_api = 'unknown'
extra = ' {qt_api=%s}%s' % (qt_api, extra)
out += '%s%s\n' % (version, extra)
print(out, end='', file=fid)
class ETSContext(object):
"""Add more meaningful message to errors generated by ETS Toolkit."""
def __enter__(self): # noqa: D105
pass
def __exit__(self, type, value, traceback): # noqa: D105
if isinstance(value, SystemExit) and value.code.\
startswith("This program needs access to the screen"):
value.code += ("\nThis can probably be solved by setting "
"ETS_TOOLKIT=qt4. On bash, type\n\n $ export "
"ETS_TOOLKIT=qt4\n\nand run the command again.")
def open_docs(kind=None, version=None):
"""Launch a new web browser tab with the MNE documentation.
Parameters
----------
kind : str | None
Can be "api" (default), "tutorials", or "examples".
The default can be changed by setting the configuration value
MNE_DOCS_KIND.
version : str | None
Can be "stable" (default) or "dev".
The default can be changed by setting the configuration value
MNE_DOCS_VERSION.
"""
if kind is None:
kind = get_config('MNE_DOCS_KIND', 'api')
help_dict = dict(api='python_reference.html', tutorials='tutorials.html',
examples='auto_examples/index.html')
if kind not in help_dict:
raise ValueError('kind must be one of %s, got %s'
% (sorted(help_dict.keys()), kind))
kind = help_dict[kind]
if version is None:
version = get_config('MNE_DOCS_VERSION', 'stable')
versions = ('stable', 'dev')
if version not in versions:
raise ValueError('version must be one of %s, got %s'
% (version, versions))
webbrowser.open_new_tab('https://martinos.org/mne/%s/%s' % (version, kind))
def _is_numeric(n):
return isinstance(n, (np.integer, np.floating, int, float))
def _validate_type(item, types=None, item_name=None, type_name=None):
"""Validate that `item` is an instance of `types`.
Parameters
----------
item : obj
The thing to be checked.
types : type | tuple of types | str
The types to be checked against. If str, must be one of 'str', 'int',
'numeric'.
"""
if types == "int":
_ensure_int(item, name=item_name)
return # terminate prematurely
elif types == "str":
types = string_types
type_name = "str" if type_name is None else type_name
elif types == "numeric":
types = (np.integer, np.floating, int, float)
type_name = "numeric" if type_name is None else type_name
elif types == "info":
from mne.io import Info as types
type_name = "Info" if type_name is None else type_name
item_name = "Info" if item_name is None else item_name
if type_name is None:
iter_types = ([types] if not isinstance(types, (list, tuple))
else types)
type_name = ', '.join(cls.__name__ for cls in iter_types)
if not isinstance(item, types):
raise TypeError(item_name, ' must be an instance of ', type_name,
', got %s instead.' % (type(item),))
def linkcode_resolve(domain, info):
"""Determine the URL corresponding to a Python object.
Parameters
----------
domain : str
Only useful when 'py'.
info : dict
With keys "module" and "fullname".
Returns
-------
url : str
The code URL.
Notes
-----
This has been adapted to deal with our "verbose" decorator.
Adapted from SciPy (doc/source/conf.py).
"""
import mne
if domain != 'py':
return None
modname = info['module']
fullname = info['fullname']
submod = sys.modules.get(modname)
if submod is None:
return None
obj = submod
for part in fullname.split('.'):
try:
obj = getattr(obj, part)
except Exception:
return None
try:
fn = inspect.getsourcefile(obj)
except Exception:
fn = None
if not fn:
try:
fn = inspect.getsourcefile(sys.modules[obj.__module__])
except Exception:
fn = None
if not fn:
return None
if fn == '<string>': # verbose decorator
fn = inspect.getmodule(obj).__file__
fn = op.relpath(fn, start=op.dirname(mne.__file__))
fn = '/'.join(op.normpath(fn).split(os.sep)) # in case on Windows
try:
source, lineno = inspect.getsourcelines(obj)
except Exception:
lineno = None
if lineno:
linespec = "#L%d-L%d" % (lineno, lineno + len(source) - 1)
else:
linespec = ""
if 'dev' in mne.__version__:
kind = 'master'
else:
kind = 'maint/%s' % ('.'.join(mne.__version__.split('.')[:2]))
return "http://github.com/mne-tools/mne-python/blob/%s/mne/%s%s" % ( # noqa
kind, fn, linespec)
def _check_if_nan(data, msg=" to be plotted"):
"""Raise if any of the values are NaN."""
if not np.isfinite(data).all():
raise ValueError("Some of the values {} are NaN.".format(msg))
| bsd-3-clause |
raymondxyang/tensorflow | tensorflow/contrib/learn/python/learn/tests/dataframe/in_memory_source_test.py | 62 | 3960 | # Copyright 2015 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Tests NumpySource and PandasSource."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
from tensorflow.contrib.learn.python.learn.dataframe.transforms import in_memory_source
from tensorflow.python.client import session
from tensorflow.python.framework import ops
from tensorflow.python.platform import test
from tensorflow.python.training import coordinator
from tensorflow.python.training import queue_runner_impl
# pylint: disable=g-import-not-at-top
try:
import pandas as pd
HAS_PANDAS = True
except ImportError:
HAS_PANDAS = False
def get_rows(array, row_indices):
rows = [array[i] for i in row_indices]
return np.vstack(rows)
class NumpySourceTestCase(test.TestCase):
def testNumpySource(self):
batch_size = 3
iterations = 1000
array = np.arange(32).reshape([16, 2])
numpy_source = in_memory_source.NumpySource(array, batch_size=batch_size)
index_column = numpy_source().index
value_column = numpy_source().value
cache = {}
with ops.Graph().as_default():
value_tensor = value_column.build(cache)
index_tensor = index_column.build(cache)
with session.Session() as sess:
coord = coordinator.Coordinator()
threads = queue_runner_impl.start_queue_runners(sess=sess, coord=coord)
for i in range(iterations):
expected_index = [
j % array.shape[0]
for j in range(batch_size * i, batch_size * (i + 1))
]
expected_value = get_rows(array, expected_index)
actual_index, actual_value = sess.run([index_tensor, value_tensor])
np.testing.assert_array_equal(expected_index, actual_index)
np.testing.assert_array_equal(expected_value, actual_value)
coord.request_stop()
coord.join(threads)
class PandasSourceTestCase(test.TestCase):
def testPandasFeeding(self):
if not HAS_PANDAS:
return
batch_size = 3
iterations = 1000
index = np.arange(100, 132)
a = np.arange(32)
b = np.arange(32, 64)
dataframe = pd.DataFrame({"a": a, "b": b}, index=index)
pandas_source = in_memory_source.PandasSource(
dataframe, batch_size=batch_size)
pandas_columns = pandas_source()
cache = {}
with ops.Graph().as_default():
pandas_tensors = [col.build(cache) for col in pandas_columns]
with session.Session() as sess:
coord = coordinator.Coordinator()
threads = queue_runner_impl.start_queue_runners(sess=sess, coord=coord)
for i in range(iterations):
indices = [
j % dataframe.shape[0]
for j in range(batch_size * i, batch_size * (i + 1))
]
expected_df_indices = dataframe.index[indices]
expected_rows = dataframe.iloc[indices]
actual_value = sess.run(pandas_tensors)
np.testing.assert_array_equal(expected_df_indices, actual_value[0])
for col_num, col in enumerate(dataframe.columns):
np.testing.assert_array_equal(expected_rows[col].values,
actual_value[col_num + 1])
coord.request_stop()
coord.join(threads)
if __name__ == "__main__":
test.main()
| apache-2.0 |
kdebrab/pandas | pandas/core/generic.py | 1 | 339537 | # pylint: disable=W0231,E1101
import collections
import functools
import warnings
import operator
import weakref
import gc
import json
import numpy as np
import pandas as pd
from pandas._libs import tslib, properties
from pandas.core.dtypes.common import (
ensure_int64,
ensure_object,
is_scalar,
is_number,
is_integer, is_bool,
is_bool_dtype,
is_categorical_dtype,
is_numeric_dtype,
is_datetime64_any_dtype,
is_timedelta64_dtype,
is_datetime64tz_dtype,
is_list_like,
is_dict_like,
is_re_compilable,
is_period_arraylike,
is_object_dtype,
pandas_dtype)
from pandas.core.dtypes.cast import maybe_promote, maybe_upcast_putmask
from pandas.core.dtypes.inference import is_hashable
from pandas.core.dtypes.missing import isna, notna
from pandas.core.dtypes.generic import ABCSeries, ABCPanel, ABCDataFrame
from pandas.core.base import PandasObject, SelectionMixin
from pandas.core.index import (Index, MultiIndex, ensure_index,
InvalidIndexError, RangeIndex)
import pandas.core.indexing as indexing
from pandas.core.indexes.datetimes import DatetimeIndex
from pandas.core.indexes.period import PeriodIndex, Period
from pandas.core.internals import BlockManager
import pandas.core.algorithms as algos
import pandas.core.common as com
import pandas.core.missing as missing
from pandas.io.formats.printing import pprint_thing
from pandas.io.formats.format import format_percentiles, DataFrameFormatter
from pandas.tseries.frequencies import to_offset
from pandas import compat
from pandas.compat.numpy import function as nv
from pandas.compat import (map, zip, lzip, lrange, string_types, to_str,
isidentifier, set_function_name, cPickle as pkl)
from pandas.core.ops import _align_method_FRAME
import pandas.core.nanops as nanops
from pandas.util._decorators import (Appender, Substitution,
deprecate_kwarg)
from pandas.util._validators import validate_bool_kwarg, validate_fillna_kwargs
from pandas.core import config
# goal is to be able to define the docs close to function, while still being
# able to share
_shared_docs = dict()
_shared_doc_kwargs = dict(
axes='keywords for axes', klass='NDFrame',
axes_single_arg='int or labels for object',
args_transpose='axes to permute (int or label for object)',
optional_by="""
by : str or list of str
Name or list of names to sort by""")
def _single_replace(self, to_replace, method, inplace, limit):
"""
Replaces values in a Series using the fill method specified when no
replacement value is given in the replace method
"""
if self.ndim != 1:
raise TypeError('cannot replace {0} with method {1} on a {2}'
.format(to_replace, method, type(self).__name__))
orig_dtype = self.dtype
result = self if inplace else self.copy()
fill_f = missing.get_fill_func(method)
mask = missing.mask_missing(result.values, to_replace)
values = fill_f(result.values, limit=limit, mask=mask)
if values.dtype == orig_dtype and inplace:
return
result = pd.Series(values, index=self.index,
dtype=self.dtype).__finalize__(self)
if inplace:
self._update_inplace(result._data)
return
return result
class NDFrame(PandasObject, SelectionMixin):
"""
N-dimensional analogue of DataFrame. Store multi-dimensional in a
size-mutable, labeled data structure
Parameters
----------
data : BlockManager
axes : list
copy : boolean, default False
"""
_internal_names = ['_data', '_cacher', '_item_cache', '_cache', '_is_copy',
'_subtyp', '_name', '_index', '_default_kind',
'_default_fill_value', '_metadata', '__array_struct__',
'__array_interface__']
_internal_names_set = set(_internal_names)
_accessors = frozenset([])
_deprecations = frozenset(['as_blocks', 'blocks',
'consolidate', 'convert_objects', 'is_copy'])
_metadata = []
_is_copy = None
def __init__(self, data, axes=None, copy=False, dtype=None,
fastpath=False):
if not fastpath:
if dtype is not None:
data = data.astype(dtype)
elif copy:
data = data.copy()
if axes is not None:
for i, ax in enumerate(axes):
data = data.reindex_axis(ax, axis=i)
object.__setattr__(self, '_is_copy', None)
object.__setattr__(self, '_data', data)
object.__setattr__(self, '_item_cache', {})
@property
def is_copy(self):
warnings.warn("Attribute 'is_copy' is deprecated and will be removed "
"in a future version.", FutureWarning, stacklevel=2)
return self._is_copy
@is_copy.setter
def is_copy(self, msg):
warnings.warn("Attribute 'is_copy' is deprecated and will be removed "
"in a future version.", FutureWarning, stacklevel=2)
self._is_copy = msg
def _repr_data_resource_(self):
"""
Not a real Jupyter special repr method, but we use the same
naming convention.
"""
if config.get_option("display.html.table_schema"):
data = self.head(config.get_option('display.max_rows'))
payload = json.loads(data.to_json(orient='table'),
object_pairs_hook=collections.OrderedDict)
return payload
def _validate_dtype(self, dtype):
""" validate the passed dtype """
if dtype is not None:
dtype = pandas_dtype(dtype)
# a compound dtype
if dtype.kind == 'V':
raise NotImplementedError("compound dtypes are not implemented"
" in the {0} constructor"
.format(self.__class__.__name__))
return dtype
def _init_mgr(self, mgr, axes=None, dtype=None, copy=False):
""" passed a manager and a axes dict """
for a, axe in axes.items():
if axe is not None:
mgr = mgr.reindex_axis(axe,
axis=self._get_block_manager_axis(a),
copy=False)
# make a copy if explicitly requested
if copy:
mgr = mgr.copy()
if dtype is not None:
# avoid further copies if we can
if len(mgr.blocks) > 1 or mgr.blocks[0].values.dtype != dtype:
mgr = mgr.astype(dtype=dtype)
return mgr
# ----------------------------------------------------------------------
# Construction
@property
def _constructor(self):
"""Used when a manipulation result has the same dimensions as the
original.
"""
raise com.AbstractMethodError(self)
def __unicode__(self):
# unicode representation based upon iterating over self
# (since, by definition, `PandasContainers` are iterable)
prepr = '[%s]' % ','.join(map(pprint_thing, self))
return '%s(%s)' % (self.__class__.__name__, prepr)
def _dir_additions(self):
""" add the string-like attributes from the info_axis.
If info_axis is a MultiIndex, it's first level values are used.
"""
additions = {c for c in self._info_axis.unique(level=0)[:100]
if isinstance(c, string_types) and isidentifier(c)}
return super(NDFrame, self)._dir_additions().union(additions)
@property
def _constructor_sliced(self):
"""Used when a manipulation result has one lower dimension(s) as the
original, such as DataFrame single columns slicing.
"""
raise com.AbstractMethodError(self)
@property
def _constructor_expanddim(self):
"""Used when a manipulation result has one higher dimension as the
original, such as Series.to_frame() and DataFrame.to_panel()
"""
raise NotImplementedError
# ----------------------------------------------------------------------
# Axis
@classmethod
def _setup_axes(cls, axes, info_axis=None, stat_axis=None, aliases=None,
slicers=None, axes_are_reversed=False, build_axes=True,
ns=None, docs=None):
"""Provide axes setup for the major PandasObjects.
Parameters
----------
axes : the names of the axes in order (lowest to highest)
info_axis_num : the axis of the selector dimension (int)
stat_axis_num : the number of axis for the default stats (int)
aliases : other names for a single axis (dict)
slicers : how axes slice to others (dict)
axes_are_reversed : boolean whether to treat passed axes as
reversed (DataFrame)
build_axes : setup the axis properties (default True)
"""
cls._AXIS_ORDERS = axes
cls._AXIS_NUMBERS = {a: i for i, a in enumerate(axes)}
cls._AXIS_LEN = len(axes)
cls._AXIS_ALIASES = aliases or dict()
cls._AXIS_IALIASES = {v: k for k, v in cls._AXIS_ALIASES.items()}
cls._AXIS_NAMES = dict(enumerate(axes))
cls._AXIS_SLICEMAP = slicers or None
cls._AXIS_REVERSED = axes_are_reversed
# typ
setattr(cls, '_typ', cls.__name__.lower())
# indexing support
cls._ix = None
if info_axis is not None:
cls._info_axis_number = info_axis
cls._info_axis_name = axes[info_axis]
if stat_axis is not None:
cls._stat_axis_number = stat_axis
cls._stat_axis_name = axes[stat_axis]
# setup the actual axis
if build_axes:
def set_axis(a, i):
setattr(cls, a, properties.AxisProperty(i, docs.get(a, a)))
cls._internal_names_set.add(a)
if axes_are_reversed:
m = cls._AXIS_LEN - 1
for i, a in cls._AXIS_NAMES.items():
set_axis(a, m - i)
else:
for i, a in cls._AXIS_NAMES.items():
set_axis(a, i)
# addtl parms
if isinstance(ns, dict):
for k, v in ns.items():
setattr(cls, k, v)
def _construct_axes_dict(self, axes=None, **kwargs):
"""Return an axes dictionary for myself."""
d = {a: self._get_axis(a) for a in (axes or self._AXIS_ORDERS)}
d.update(kwargs)
return d
@staticmethod
def _construct_axes_dict_from(self, axes, **kwargs):
"""Return an axes dictionary for the passed axes."""
d = {a: ax for a, ax in zip(self._AXIS_ORDERS, axes)}
d.update(kwargs)
return d
def _construct_axes_dict_for_slice(self, axes=None, **kwargs):
"""Return an axes dictionary for myself."""
d = {self._AXIS_SLICEMAP[a]: self._get_axis(a)
for a in (axes or self._AXIS_ORDERS)}
d.update(kwargs)
return d
def _construct_axes_from_arguments(self, args, kwargs, require_all=False):
"""Construct and returns axes if supplied in args/kwargs.
If require_all, raise if all axis arguments are not supplied
return a tuple of (axes, kwargs).
"""
# construct the args
args = list(args)
for a in self._AXIS_ORDERS:
# if we have an alias for this axis
alias = self._AXIS_IALIASES.get(a)
if alias is not None:
if a in kwargs:
if alias in kwargs:
raise TypeError("arguments are mutually exclusive "
"for [%s,%s]" % (a, alias))
continue
if alias in kwargs:
kwargs[a] = kwargs.pop(alias)
continue
# look for a argument by position
if a not in kwargs:
try:
kwargs[a] = args.pop(0)
except IndexError:
if require_all:
raise TypeError("not enough/duplicate arguments "
"specified!")
axes = {a: kwargs.pop(a, None) for a in self._AXIS_ORDERS}
return axes, kwargs
@classmethod
def _from_axes(cls, data, axes, **kwargs):
# for construction from BlockManager
if isinstance(data, BlockManager):
return cls(data, **kwargs)
else:
if cls._AXIS_REVERSED:
axes = axes[::-1]
d = cls._construct_axes_dict_from(cls, axes, copy=False)
d.update(kwargs)
return cls(data, **d)
def _get_axis_number(self, axis):
axis = self._AXIS_ALIASES.get(axis, axis)
if is_integer(axis):
if axis in self._AXIS_NAMES:
return axis
else:
try:
return self._AXIS_NUMBERS[axis]
except KeyError:
pass
raise ValueError('No axis named {0} for object type {1}'
.format(axis, type(self)))
def _get_axis_name(self, axis):
axis = self._AXIS_ALIASES.get(axis, axis)
if isinstance(axis, string_types):
if axis in self._AXIS_NUMBERS:
return axis
else:
try:
return self._AXIS_NAMES[axis]
except KeyError:
pass
raise ValueError('No axis named {0} for object type {1}'
.format(axis, type(self)))
def _get_axis(self, axis):
name = self._get_axis_name(axis)
return getattr(self, name)
def _get_block_manager_axis(self, axis):
"""Map the axis to the block_manager axis."""
axis = self._get_axis_number(axis)
if self._AXIS_REVERSED:
m = self._AXIS_LEN - 1
return m - axis
return axis
def _get_axis_resolvers(self, axis):
# index or columns
axis_index = getattr(self, axis)
d = dict()
prefix = axis[0]
for i, name in enumerate(axis_index.names):
if name is not None:
key = level = name
else:
# prefix with 'i' or 'c' depending on the input axis
# e.g., you must do ilevel_0 for the 0th level of an unnamed
# multiiindex
key = '{prefix}level_{i}'.format(prefix=prefix, i=i)
level = i
level_values = axis_index.get_level_values(level)
s = level_values.to_series()
s.index = axis_index
d[key] = s
# put the index/columns itself in the dict
if isinstance(axis_index, MultiIndex):
dindex = axis_index
else:
dindex = axis_index.to_series()
d[axis] = dindex
return d
def _get_index_resolvers(self):
d = {}
for axis_name in self._AXIS_ORDERS:
d.update(self._get_axis_resolvers(axis_name))
return d
@property
def _info_axis(self):
return getattr(self, self._info_axis_name)
@property
def _stat_axis(self):
return getattr(self, self._stat_axis_name)
@property
def shape(self):
"""Return a tuple of axis dimensions"""
return tuple(len(self._get_axis(a)) for a in self._AXIS_ORDERS)
@property
def axes(self):
"""Return index label(s) of the internal NDFrame"""
# we do it this way because if we have reversed axes, then
# the block manager shows then reversed
return [self._get_axis(a) for a in self._AXIS_ORDERS]
@property
def ndim(self):
"""
Return an int representing the number of axes / array dimensions.
Return 1 if Series. Otherwise return 2 if DataFrame.
See Also
--------
ndarray.ndim : Number of array dimensions.
Examples
--------
>>> s = pd.Series({'a': 1, 'b': 2, 'c': 3})
>>> s.ndim
1
>>> df = pd.DataFrame({'col1': [1, 2], 'col2': [3, 4]})
>>> df.ndim
2
"""
return self._data.ndim
@property
def size(self):
"""
Return an int representing the number of elements in this object.
Return the number of rows if Series. Otherwise return the number of
rows times number of columns if DataFrame.
See Also
--------
ndarray.size : Number of elements in the array.
Examples
--------
>>> s = pd.Series({'a': 1, 'b': 2, 'c': 3})
>>> s.size
3
>>> df = pd.DataFrame({'col1': [1, 2], 'col2': [3, 4]})
>>> df.size
4
"""
return np.prod(self.shape)
@property
def _selected_obj(self):
""" internal compat with SelectionMixin """
return self
@property
def _obj_with_exclusions(self):
""" internal compat with SelectionMixin """
return self
def _expand_axes(self, key):
new_axes = []
for k, ax in zip(key, self.axes):
if k not in ax:
if type(k) != ax.dtype.type:
ax = ax.astype('O')
new_axes.append(ax.insert(len(ax), k))
else:
new_axes.append(ax)
return new_axes
def set_axis(self, labels, axis=0, inplace=None):
"""
Assign desired index to given axis.
Indexes for column or row labels can be changed by assigning
a list-like or Index.
.. versionchanged:: 0.21.0
The signature is now `labels` and `axis`, consistent with
the rest of pandas API. Previously, the `axis` and `labels`
arguments were respectively the first and second positional
arguments.
Parameters
----------
labels : list-like, Index
The values for the new index.
axis : {0 or 'index', 1 or 'columns'}, default 0
The axis to update. The value 0 identifies the rows, and 1
identifies the columns.
inplace : boolean, default None
Whether to return a new %(klass)s instance.
.. warning::
``inplace=None`` currently falls back to to True, but in a
future version, will default to False. Use inplace=True
explicitly rather than relying on the default.
Returns
-------
renamed : %(klass)s or None
An object of same type as caller if inplace=False, None otherwise.
See Also
--------
pandas.DataFrame.rename_axis : Alter the name of the index or columns.
Examples
--------
**Series**
>>> s = pd.Series([1, 2, 3])
>>> s
0 1
1 2
2 3
dtype: int64
>>> s.set_axis(['a', 'b', 'c'], axis=0, inplace=False)
a 1
b 2
c 3
dtype: int64
The original object is not modified.
>>> s
0 1
1 2
2 3
dtype: int64
**DataFrame**
>>> df = pd.DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]})
Change the row labels.
>>> df.set_axis(['a', 'b', 'c'], axis='index', inplace=False)
A B
a 1 4
b 2 5
c 3 6
Change the column labels.
>>> df.set_axis(['I', 'II'], axis='columns', inplace=False)
I II
0 1 4
1 2 5
2 3 6
Now, update the labels inplace.
>>> df.set_axis(['i', 'ii'], axis='columns', inplace=True)
>>> df
i ii
0 1 4
1 2 5
2 3 6
"""
if is_scalar(labels):
warnings.warn(
'set_axis now takes "labels" as first argument, and '
'"axis" as named parameter. The old form, with "axis" as '
'first parameter and \"labels\" as second, is still supported '
'but will be deprecated in a future version of pandas.',
FutureWarning, stacklevel=2)
labels, axis = axis, labels
if inplace is None:
warnings.warn(
'set_axis currently defaults to operating inplace.\nThis '
'will change in a future version of pandas, use '
'inplace=True to avoid this warning.',
FutureWarning, stacklevel=2)
inplace = True
if inplace:
setattr(self, self._get_axis_name(axis), labels)
else:
obj = self.copy()
obj.set_axis(labels, axis=axis, inplace=True)
return obj
def _set_axis(self, axis, labels):
self._data.set_axis(axis, labels)
self._clear_item_cache()
_shared_docs['transpose'] = """
Permute the dimensions of the %(klass)s
Parameters
----------
args : %(args_transpose)s
copy : boolean, default False
Make a copy of the underlying data. Mixed-dtype data will
always result in a copy
Examples
--------
>>> p.transpose(2, 0, 1)
>>> p.transpose(2, 0, 1, copy=True)
Returns
-------
y : same as input
"""
@Appender(_shared_docs['transpose'] % _shared_doc_kwargs)
def transpose(self, *args, **kwargs):
# construct the args
axes, kwargs = self._construct_axes_from_arguments(args, kwargs,
require_all=True)
axes_names = tuple(self._get_axis_name(axes[a])
for a in self._AXIS_ORDERS)
axes_numbers = tuple(self._get_axis_number(axes[a])
for a in self._AXIS_ORDERS)
# we must have unique axes
if len(axes) != len(set(axes)):
raise ValueError('Must specify %s unique axes' % self._AXIS_LEN)
new_axes = self._construct_axes_dict_from(self, [self._get_axis(x)
for x in axes_names])
new_values = self.values.transpose(axes_numbers)
if kwargs.pop('copy', None) or (len(args) and args[-1]):
new_values = new_values.copy()
nv.validate_transpose_for_generic(self, kwargs)
return self._constructor(new_values, **new_axes).__finalize__(self)
def swapaxes(self, axis1, axis2, copy=True):
"""
Interchange axes and swap values axes appropriately
Returns
-------
y : same as input
"""
i = self._get_axis_number(axis1)
j = self._get_axis_number(axis2)
if i == j:
if copy:
return self.copy()
return self
mapping = {i: j, j: i}
new_axes = (self._get_axis(mapping.get(k, k))
for k in range(self._AXIS_LEN))
new_values = self.values.swapaxes(i, j)
if copy:
new_values = new_values.copy()
return self._constructor(new_values, *new_axes).__finalize__(self)
def pop(self, item):
"""
Return item and drop from frame. Raise KeyError if not found.
Parameters
----------
item : str
Column label to be popped
Returns
-------
popped : Series
Examples
--------
>>> df = pd.DataFrame([('falcon', 'bird', 389.0),
... ('parrot', 'bird', 24.0),
... ('lion', 'mammal', 80.5),
... ('monkey', 'mammal', np.nan)],
... columns=('name', 'class', 'max_speed'))
>>> df
name class max_speed
0 falcon bird 389.0
1 parrot bird 24.0
2 lion mammal 80.5
3 monkey mammal NaN
>>> df.pop('class')
0 bird
1 bird
2 mammal
3 mammal
Name: class, dtype: object
>>> df
name max_speed
0 falcon 389.0
1 parrot 24.0
2 lion 80.5
3 monkey NaN
"""
result = self[item]
del self[item]
try:
result._reset_cacher()
except AttributeError:
pass
return result
def squeeze(self, axis=None):
"""
Squeeze 1 dimensional axis objects into scalars.
Series or DataFrames with a single element are squeezed to a scalar.
DataFrames with a single column or a single row are squeezed to a
Series. Otherwise the object is unchanged.
This method is most useful when you don't know if your
object is a Series or DataFrame, but you do know it has just a single
column. In that case you can safely call `squeeze` to ensure you have a
Series.
Parameters
----------
axis : {0 or 'index', 1 or 'columns', None}, default None
A specific axis to squeeze. By default, all length-1 axes are
squeezed.
.. versionadded:: 0.20.0
Returns
-------
DataFrame, Series, or scalar
The projection after squeezing `axis` or all the axes.
See Also
--------
Series.iloc : Integer-location based indexing for selecting scalars
DataFrame.iloc : Integer-location based indexing for selecting Series
Series.to_frame : Inverse of DataFrame.squeeze for a
single-column DataFrame.
Examples
--------
>>> primes = pd.Series([2, 3, 5, 7])
Slicing might produce a Series with a single value:
>>> even_primes = primes[primes % 2 == 0]
>>> even_primes
0 2
dtype: int64
>>> even_primes.squeeze()
2
Squeezing objects with more than one value in every axis does nothing:
>>> odd_primes = primes[primes % 2 == 1]
>>> odd_primes
1 3
2 5
3 7
dtype: int64
>>> odd_primes.squeeze()
1 3
2 5
3 7
dtype: int64
Squeezing is even more effective when used with DataFrames.
>>> df = pd.DataFrame([[1, 2], [3, 4]], columns=['a', 'b'])
>>> df
a b
0 1 2
1 3 4
Slicing a single column will produce a DataFrame with the columns
having only one value:
>>> df_a = df[['a']]
>>> df_a
a
0 1
1 3
So the columns can be squeezed down, resulting in a Series:
>>> df_a.squeeze('columns')
0 1
1 3
Name: a, dtype: int64
Slicing a single row from a single column will produce a single
scalar DataFrame:
>>> df_0a = df.loc[df.index < 1, ['a']]
>>> df_0a
a
0 1
Squeezing the rows produces a single scalar Series:
>>> df_0a.squeeze('rows')
a 1
Name: 0, dtype: int64
Squeezing all axes wil project directly into a scalar:
>>> df_0a.squeeze()
1
"""
axis = (self._AXIS_NAMES if axis is None else
(self._get_axis_number(axis),))
try:
return self.iloc[
tuple(0 if i in axis and len(a) == 1 else slice(None)
for i, a in enumerate(self.axes))]
except Exception:
return self
def swaplevel(self, i=-2, j=-1, axis=0):
"""
Swap levels i and j in a MultiIndex on a particular axis
Parameters
----------
i, j : int, string (can be mixed)
Level of index to be swapped. Can pass level name as string.
Returns
-------
swapped : same type as caller (new object)
.. versionchanged:: 0.18.1
The indexes ``i`` and ``j`` are now optional, and default to
the two innermost levels of the index.
"""
axis = self._get_axis_number(axis)
result = self.copy()
labels = result._data.axes[axis]
result._data.set_axis(axis, labels.swaplevel(i, j))
return result
# ----------------------------------------------------------------------
# Rename
# TODO: define separate funcs for DataFrame, Series and Panel so you can
# get completion on keyword arguments.
_shared_docs['rename'] = """
Alter axes input function or functions. Function / dict values must be
unique (1-to-1). Labels not contained in a dict / Series will be left
as-is. Extra labels listed don't throw an error. Alternatively, change
``Series.name`` with a scalar value (Series only).
Parameters
----------
%(optional_mapper)s
%(axes)s : scalar, list-like, dict-like or function, optional
Scalar or list-like will alter the ``Series.name`` attribute,
and raise on DataFrame or Panel.
dict-like or functions are transformations to apply to
that axis' values
%(optional_axis)s
copy : boolean, default True
Also copy underlying data
inplace : boolean, default False
Whether to return a new %(klass)s. If True then value of copy is
ignored.
level : int or level name, default None
In case of a MultiIndex, only rename labels in the specified
level.
Returns
-------
renamed : %(klass)s (new object)
See Also
--------
pandas.NDFrame.rename_axis
Examples
--------
>>> s = pd.Series([1, 2, 3])
>>> s
0 1
1 2
2 3
dtype: int64
>>> s.rename("my_name") # scalar, changes Series.name
0 1
1 2
2 3
Name: my_name, dtype: int64
>>> s.rename(lambda x: x ** 2) # function, changes labels
0 1
1 2
4 3
dtype: int64
>>> s.rename({1: 3, 2: 5}) # mapping, changes labels
0 1
3 2
5 3
dtype: int64
Since ``DataFrame`` doesn't have a ``.name`` attribute,
only mapping-type arguments are allowed.
>>> df = pd.DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]})
>>> df.rename(2)
Traceback (most recent call last):
...
TypeError: 'int' object is not callable
``DataFrame.rename`` supports two calling conventions
* ``(index=index_mapper, columns=columns_mapper, ...)``
* ``(mapper, axis={'index', 'columns'}, ...)``
We *highly* recommend using keyword arguments to clarify your
intent.
>>> df.rename(index=str, columns={"A": "a", "B": "c"})
a c
0 1 4
1 2 5
2 3 6
>>> df.rename(index=str, columns={"A": "a", "C": "c"})
a B
0 1 4
1 2 5
2 3 6
Using axis-style parameters
>>> df.rename(str.lower, axis='columns')
a b
0 1 4
1 2 5
2 3 6
>>> df.rename({1: 2, 2: 4}, axis='index')
A B
0 1 4
2 2 5
4 3 6
See the :ref:`user guide <basics.rename>` for more.
"""
@Appender(_shared_docs['rename'] % dict(axes='axes keywords for this'
' object', klass='NDFrame',
optional_mapper='',
optional_axis=''))
def rename(self, *args, **kwargs):
axes, kwargs = self._construct_axes_from_arguments(args, kwargs)
copy = kwargs.pop('copy', True)
inplace = kwargs.pop('inplace', False)
level = kwargs.pop('level', None)
axis = kwargs.pop('axis', None)
if axis is not None:
axis = self._get_axis_number(axis)
if kwargs:
raise TypeError('rename() got an unexpected keyword '
'argument "{0}"'.format(list(kwargs.keys())[0]))
if com._count_not_none(*axes.values()) == 0:
raise TypeError('must pass an index to rename')
# renamer function if passed a dict
def _get_rename_function(mapper):
if isinstance(mapper, (dict, ABCSeries)):
def f(x):
if x in mapper:
return mapper[x]
else:
return x
else:
f = mapper
return f
self._consolidate_inplace()
result = self if inplace else self.copy(deep=copy)
# start in the axis order to eliminate too many copies
for axis in lrange(self._AXIS_LEN):
v = axes.get(self._AXIS_NAMES[axis])
if v is None:
continue
f = _get_rename_function(v)
baxis = self._get_block_manager_axis(axis)
if level is not None:
level = self.axes[axis]._get_level_number(level)
result._data = result._data.rename_axis(f, axis=baxis, copy=copy,
level=level)
result._clear_item_cache()
if inplace:
self._update_inplace(result._data)
else:
return result.__finalize__(self)
rename.__doc__ = _shared_docs['rename']
def rename_axis(self, mapper, axis=0, copy=True, inplace=False):
"""
Alter the name of the index or columns.
Parameters
----------
mapper : scalar, list-like, optional
Value to set as the axis name attribute.
axis : {0 or 'index', 1 or 'columns'}, default 0
The index or the name of the axis.
copy : boolean, default True
Also copy underlying data.
inplace : boolean, default False
Modifies the object directly, instead of creating a new Series
or DataFrame.
Returns
-------
renamed : Series, DataFrame, or None
The same type as the caller or None if `inplace` is True.
Notes
-----
Prior to version 0.21.0, ``rename_axis`` could also be used to change
the axis *labels* by passing a mapping or scalar. This behavior is
deprecated and will be removed in a future version. Use ``rename``
instead.
See Also
--------
pandas.Series.rename : Alter Series index labels or name
pandas.DataFrame.rename : Alter DataFrame index labels or name
pandas.Index.rename : Set new names on index
Examples
--------
**Series**
>>> s = pd.Series([1, 2, 3])
>>> s.rename_axis("foo")
foo
0 1
1 2
2 3
dtype: int64
**DataFrame**
>>> df = pd.DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]})
>>> df.rename_axis("foo")
A B
foo
0 1 4
1 2 5
2 3 6
>>> df.rename_axis("bar", axis="columns")
bar A B
0 1 4
1 2 5
2 3 6
"""
inplace = validate_bool_kwarg(inplace, 'inplace')
non_mapper = is_scalar(mapper) or (is_list_like(mapper) and not
is_dict_like(mapper))
if non_mapper:
return self._set_axis_name(mapper, axis=axis, inplace=inplace)
else:
msg = ("Using 'rename_axis' to alter labels is deprecated. "
"Use '.rename' instead")
warnings.warn(msg, FutureWarning, stacklevel=2)
axis = self._get_axis_name(axis)
d = {'copy': copy, 'inplace': inplace}
d[axis] = mapper
return self.rename(**d)
def _set_axis_name(self, name, axis=0, inplace=False):
"""
Alter the name or names of the axis.
Parameters
----------
name : str or list of str
Name for the Index, or list of names for the MultiIndex
axis : int or str
0 or 'index' for the index; 1 or 'columns' for the columns
inplace : bool
whether to modify `self` directly or return a copy
.. versionadded:: 0.21.0
Returns
-------
renamed : same type as caller or None if inplace=True
See Also
--------
pandas.DataFrame.rename
pandas.Series.rename
pandas.Index.rename
Examples
--------
>>> df._set_axis_name("foo")
A
foo
0 1
1 2
2 3
>>> df.index = pd.MultiIndex.from_product([['A'], ['a', 'b', 'c']])
>>> df._set_axis_name(["bar", "baz"])
A
bar baz
A a 1
b 2
c 3
"""
axis = self._get_axis_number(axis)
idx = self._get_axis(axis).set_names(name)
inplace = validate_bool_kwarg(inplace, 'inplace')
renamed = self if inplace else self.copy()
renamed.set_axis(idx, axis=axis, inplace=True)
if not inplace:
return renamed
# ----------------------------------------------------------------------
# Comparisons
def _indexed_same(self, other):
return all(self._get_axis(a).equals(other._get_axis(a))
for a in self._AXIS_ORDERS)
def __neg__(self):
values = com._values_from_object(self)
if is_bool_dtype(values):
arr = operator.inv(values)
elif (is_numeric_dtype(values) or is_timedelta64_dtype(values)
or is_object_dtype(values)):
arr = operator.neg(values)
else:
raise TypeError("Unary negative expects numeric dtype, not {}"
.format(values.dtype))
return self.__array_wrap__(arr)
def __pos__(self):
values = com._values_from_object(self)
if (is_bool_dtype(values) or is_period_arraylike(values)):
arr = values
elif (is_numeric_dtype(values) or is_timedelta64_dtype(values)
or is_object_dtype(values)):
arr = operator.pos(values)
else:
raise TypeError("Unary plus expects numeric dtype, not {}"
.format(values.dtype))
return self.__array_wrap__(arr)
def __invert__(self):
try:
arr = operator.inv(com._values_from_object(self))
return self.__array_wrap__(arr)
except Exception:
# inv fails with 0 len
if not np.prod(self.shape):
return self
raise
def equals(self, other):
"""
Determines if two NDFrame objects contain the same elements. NaNs in
the same location are considered equal.
"""
if not isinstance(other, self._constructor):
return False
return self._data.equals(other._data)
# -------------------------------------------------------------------------
# Label or Level Combination Helpers
#
# A collection of helper methods for DataFrame/Series operations that
# accept a combination of column/index labels and levels. All such
# operations should utilize/extend these methods when possible so that we
# have consistent precedence and validation logic throughout the library.
def _is_level_reference(self, key, axis=0):
"""
Test whether a key is a level reference for a given axis.
To be considered a level reference, `key` must be a string that:
- (axis=0): Matches the name of an index level and does NOT match
a column label.
- (axis=1): Matches the name of a column level and does NOT match
an index label.
Parameters
----------
key: str
Potential level name for the given axis
axis: int, default 0
Axis that levels are associated with (0 for index, 1 for columns)
Returns
-------
is_level: bool
"""
axis = self._get_axis_number(axis)
if self.ndim > 2:
raise NotImplementedError(
"_is_level_reference is not implemented for {type}"
.format(type=type(self)))
return (key is not None and
is_hashable(key) and
key in self.axes[axis].names and
not self._is_label_reference(key, axis=axis))
def _is_label_reference(self, key, axis=0):
"""
Test whether a key is a label reference for a given axis.
To be considered a label reference, `key` must be a string that:
- (axis=0): Matches a column label
- (axis=1): Matches an index label
Parameters
----------
key: str
Potential label name
axis: int, default 0
Axis perpendicular to the axis that labels are associated with
(0 means search for column labels, 1 means search for index labels)
Returns
-------
is_label: bool
"""
axis = self._get_axis_number(axis)
other_axes = [ax for ax in range(self._AXIS_LEN) if ax != axis]
if self.ndim > 2:
raise NotImplementedError(
"_is_label_reference is not implemented for {type}"
.format(type=type(self)))
return (key is not None and
is_hashable(key) and
any(key in self.axes[ax] for ax in other_axes))
def _is_label_or_level_reference(self, key, axis=0):
"""
Test whether a key is a label or level reference for a given axis.
To be considered either a label or a level reference, `key` must be a
string that:
- (axis=0): Matches a column label or an index level
- (axis=1): Matches an index label or a column level
Parameters
----------
key: str
Potential label or level name
axis: int, default 0
Axis that levels are associated with (0 for index, 1 for columns)
Returns
-------
is_label_or_level: bool
"""
if self.ndim > 2:
raise NotImplementedError(
"_is_label_or_level_reference is not implemented for {type}"
.format(type=type(self)))
return (self._is_level_reference(key, axis=axis) or
self._is_label_reference(key, axis=axis))
def _check_label_or_level_ambiguity(self, key, axis=0, stacklevel=1):
"""
Check whether `key` matches both a level of the input `axis` and a
label of the other axis and raise a ``FutureWarning`` if this is the
case.
Note: This method will be altered to raise an ambiguity exception in
a future version.
Parameters
----------
key: str or object
label or level name
axis: int, default 0
Axis that levels are associated with (0 for index, 1 for columns)
stacklevel: int, default 1
Stack level used when a FutureWarning is raised (see below).
Returns
-------
ambiguous: bool
Raises
------
FutureWarning
if `key` is ambiguous. This will become an ambiguity error in a
future version
"""
axis = self._get_axis_number(axis)
other_axes = [ax for ax in range(self._AXIS_LEN) if ax != axis]
if self.ndim > 2:
raise NotImplementedError(
"_check_label_or_level_ambiguity is not implemented for {type}"
.format(type=type(self)))
if (key is not None and
is_hashable(key) and
key in self.axes[axis].names and
any(key in self.axes[ax] for ax in other_axes)):
# Build an informative and grammatical warning
level_article, level_type = (('an', 'index')
if axis == 0 else
('a', 'column'))
label_article, label_type = (('a', 'column')
if axis == 0 else
('an', 'index'))
msg = ("'{key}' is both {level_article} {level_type} level and "
"{label_article} {label_type} label.\n"
"Defaulting to {label_type}, but this will raise an "
"ambiguity error in a future version"
).format(key=key,
level_article=level_article,
level_type=level_type,
label_article=label_article,
label_type=label_type)
warnings.warn(msg, FutureWarning, stacklevel=stacklevel + 1)
return True
else:
return False
def _get_label_or_level_values(self, key, axis=0, stacklevel=1):
"""
Return a 1-D array of values associated with `key`, a label or level
from the given `axis`.
Retrieval logic:
- (axis=0): Return column values if `key` matches a column label.
Otherwise return index level values if `key` matches an index
level.
- (axis=1): Return row values if `key` matches an index label.
Otherwise return column level values if 'key' matches a column
level
Parameters
----------
key: str
Label or level name.
axis: int, default 0
Axis that levels are associated with (0 for index, 1 for columns)
stacklevel: int, default 1
Stack level used when a FutureWarning is raised (see below).
Returns
-------
values: np.ndarray
Raises
------
KeyError
if `key` matches neither a label nor a level
ValueError
if `key` matches multiple labels
FutureWarning
if `key` is ambiguous. This will become an ambiguity error in a
future version
"""
axis = self._get_axis_number(axis)
other_axes = [ax for ax in range(self._AXIS_LEN) if ax != axis]
if self.ndim > 2:
raise NotImplementedError(
"_get_label_or_level_values is not implemented for {type}"
.format(type=type(self)))
if self._is_label_reference(key, axis=axis):
self._check_label_or_level_ambiguity(key, axis=axis,
stacklevel=stacklevel + 1)
values = self.xs(key, axis=other_axes[0])._values
elif self._is_level_reference(key, axis=axis):
values = self.axes[axis].get_level_values(key)._values
else:
raise KeyError(key)
# Check for duplicates
if values.ndim > 1:
if other_axes and isinstance(
self._get_axis(other_axes[0]), MultiIndex):
multi_message = ('\n'
'For a multi-index, the label must be a '
'tuple with elements corresponding to '
'each level.')
else:
multi_message = ''
label_axis_name = 'column' if axis == 0 else 'index'
raise ValueError(("The {label_axis_name} label '{key}' "
"is not unique.{multi_message}")
.format(key=key,
label_axis_name=label_axis_name,
multi_message=multi_message))
return values
def _drop_labels_or_levels(self, keys, axis=0):
"""
Drop labels and/or levels for the given `axis`.
For each key in `keys`:
- (axis=0): If key matches a column label then drop the column.
Otherwise if key matches an index level then drop the level.
- (axis=1): If key matches an index label then drop the row.
Otherwise if key matches a column level then drop the level.
Parameters
----------
keys: str or list of str
labels or levels to drop
axis: int, default 0
Axis that levels are associated with (0 for index, 1 for columns)
Returns
-------
dropped: DataFrame
Raises
------
ValueError
if any `keys` match neither a label nor a level
"""
axis = self._get_axis_number(axis)
if self.ndim > 2:
raise NotImplementedError(
"_drop_labels_or_levels is not implemented for {type}"
.format(type=type(self)))
# Validate keys
keys = com._maybe_make_list(keys)
invalid_keys = [k for k in keys if not
self._is_label_or_level_reference(k, axis=axis)]
if invalid_keys:
raise ValueError(("The following keys are not valid labels or "
"levels for axis {axis}: {invalid_keys}")
.format(axis=axis,
invalid_keys=invalid_keys))
# Compute levels and labels to drop
levels_to_drop = [k for k in keys
if self._is_level_reference(k, axis=axis)]
labels_to_drop = [k for k in keys
if not self._is_level_reference(k, axis=axis)]
# Perform copy upfront and then use inplace operations below.
# This ensures that we always perform exactly one copy.
# ``copy`` and/or ``inplace`` options could be added in the future.
dropped = self.copy()
if axis == 0:
# Handle dropping index levels
if levels_to_drop:
dropped.reset_index(levels_to_drop, drop=True, inplace=True)
# Handle dropping columns labels
if labels_to_drop:
dropped.drop(labels_to_drop, axis=1, inplace=True)
else:
# Handle dropping column levels
if levels_to_drop:
if isinstance(dropped.columns, MultiIndex):
# Drop the specified levels from the MultiIndex
dropped.columns = dropped.columns.droplevel(levels_to_drop)
else:
# Drop the last level of Index by replacing with
# a RangeIndex
dropped.columns = RangeIndex(dropped.columns.size)
# Handle dropping index labels
if labels_to_drop:
dropped.drop(labels_to_drop, axis=0, inplace=True)
return dropped
# ----------------------------------------------------------------------
# Iteration
def __hash__(self):
raise TypeError('{0!r} objects are mutable, thus they cannot be'
' hashed'.format(self.__class__.__name__))
def __iter__(self):
"""Iterate over infor axis"""
return iter(self._info_axis)
# can we get a better explanation of this?
def keys(self):
"""Get the 'info axis' (see Indexing for more)
This is index for Series, columns for DataFrame and major_axis for
Panel.
"""
return self._info_axis
def iteritems(self):
"""Iterate over (label, values) on info axis
This is index for Series, columns for DataFrame, major_axis for Panel,
and so on.
"""
for h in self._info_axis:
yield h, self[h]
def __len__(self):
"""Returns length of info axis"""
return len(self._info_axis)
def __contains__(self, key):
"""True if the key is in the info axis"""
return key in self._info_axis
@property
def empty(self):
"""
Indicator whether DataFrame is empty.
True if DataFrame is entirely empty (no items), meaning any of the
axes are of length 0.
Returns
-------
bool
If DataFrame is empty, return True, if not return False.
Notes
-----
If DataFrame contains only NaNs, it is still not considered empty. See
the example below.
Examples
--------
An example of an actual empty DataFrame. Notice the index is empty:
>>> df_empty = pd.DataFrame({'A' : []})
>>> df_empty
Empty DataFrame
Columns: [A]
Index: []
>>> df_empty.empty
True
If we only have NaNs in our DataFrame, it is not considered empty! We
will need to drop the NaNs to make the DataFrame empty:
>>> df = pd.DataFrame({'A' : [np.nan]})
>>> df
A
0 NaN
>>> df.empty
False
>>> df.dropna().empty
True
See also
--------
pandas.Series.dropna
pandas.DataFrame.dropna
"""
return any(len(self._get_axis(a)) == 0 for a in self._AXIS_ORDERS)
def __nonzero__(self):
raise ValueError("The truth value of a {0} is ambiguous. "
"Use a.empty, a.bool(), a.item(), a.any() or a.all()."
.format(self.__class__.__name__))
__bool__ = __nonzero__
def bool(self):
"""Return the bool of a single element PandasObject.
This must be a boolean scalar value, either True or False. Raise a
ValueError if the PandasObject does not have exactly 1 element, or that
element is not boolean
"""
v = self.squeeze()
if isinstance(v, (bool, np.bool_)):
return bool(v)
elif is_scalar(v):
raise ValueError("bool cannot act on a non-boolean single element "
"{0}".format(self.__class__.__name__))
self.__nonzero__()
def __abs__(self):
return self.abs()
def __round__(self, decimals=0):
return self.round(decimals)
# ----------------------------------------------------------------------
# Array Interface
def __array__(self, dtype=None):
return com._values_from_object(self)
def __array_wrap__(self, result, context=None):
d = self._construct_axes_dict(self._AXIS_ORDERS, copy=False)
return self._constructor(result, **d).__finalize__(self)
# ideally we would define this to avoid the getattr checks, but
# is slower
# @property
# def __array_interface__(self):
# """ provide numpy array interface method """
# values = self.values
# return dict(typestr=values.dtype.str,shape=values.shape,data=values)
def to_dense(self):
"""Return dense representation of NDFrame (as opposed to sparse)"""
# compat
return self
# ----------------------------------------------------------------------
# Picklability
def __getstate__(self):
meta = {k: getattr(self, k, None) for k in self._metadata}
return dict(_data=self._data, _typ=self._typ, _metadata=self._metadata,
**meta)
def __setstate__(self, state):
if isinstance(state, BlockManager):
self._data = state
elif isinstance(state, dict):
typ = state.get('_typ')
if typ is not None:
# set in the order of internal names
# to avoid definitional recursion
# e.g. say fill_value needing _data to be
# defined
meta = set(self._internal_names + self._metadata)
for k in list(meta):
if k in state:
v = state[k]
object.__setattr__(self, k, v)
for k, v in state.items():
if k not in meta:
object.__setattr__(self, k, v)
else:
self._unpickle_series_compat(state)
elif isinstance(state[0], dict):
if len(state) == 5:
self._unpickle_sparse_frame_compat(state)
else:
self._unpickle_frame_compat(state)
elif len(state) == 4:
self._unpickle_panel_compat(state)
elif len(state) == 2:
self._unpickle_series_compat(state)
else: # pragma: no cover
# old pickling format, for compatibility
self._unpickle_matrix_compat(state)
self._item_cache = {}
# ----------------------------------------------------------------------
# IO
def _repr_latex_(self):
"""
Returns a LaTeX representation for a particular object.
Mainly for use with nbconvert (jupyter notebook conversion to pdf).
"""
if config.get_option('display.latex.repr'):
return self.to_latex()
else:
return None
# ----------------------------------------------------------------------
# I/O Methods
_shared_docs['to_excel'] = """
Write %(klass)s to an excel sheet
%(versionadded_to_excel)s
Parameters
----------
excel_writer : string or ExcelWriter object
File path or existing ExcelWriter
sheet_name : string, default 'Sheet1'
Name of sheet which will contain DataFrame
na_rep : string, default ''
Missing data representation
float_format : string, default None
Format string for floating point numbers
columns : sequence, optional
Columns to write
header : boolean or list of string, default True
Write out the column names. If a list of strings is given it is
assumed to be aliases for the column names
index : boolean, default True
Write row names (index)
index_label : string or sequence, default None
Column label for index column(s) if desired. If None is given, and
`header` and `index` are True, then the index names are used. A
sequence should be given if the DataFrame uses MultiIndex.
startrow :
upper left cell row to dump data frame
startcol :
upper left cell column to dump data frame
engine : string, default None
write engine to use - you can also set this via the options
``io.excel.xlsx.writer``, ``io.excel.xls.writer``, and
``io.excel.xlsm.writer``.
merge_cells : boolean, default True
Write MultiIndex and Hierarchical Rows as merged cells.
encoding: string, default None
encoding of the resulting excel file. Only necessary for xlwt,
other writers support unicode natively.
inf_rep : string, default 'inf'
Representation for infinity (there is no native representation for
infinity in Excel)
freeze_panes : tuple of integer (length 2), default None
Specifies the one-based bottommost row and rightmost column that
is to be frozen
.. versionadded:: 0.20.0
Notes
-----
If passing an existing ExcelWriter object, then the sheet will be added
to the existing workbook. This can be used to save different
DataFrames to one workbook:
>>> writer = pd.ExcelWriter('output.xlsx')
>>> df1.to_excel(writer,'Sheet1')
>>> df2.to_excel(writer,'Sheet2')
>>> writer.save()
For compatibility with to_csv, to_excel serializes lists and dicts to
strings before writing.
"""
def to_json(self, path_or_buf=None, orient=None, date_format=None,
double_precision=10, force_ascii=True, date_unit='ms',
default_handler=None, lines=False, compression=None,
index=True):
"""
Convert the object to a JSON string.
Note NaN's and None will be converted to null and datetime objects
will be converted to UNIX timestamps.
Parameters
----------
path_or_buf : string or file handle, optional
File path or object. If not specified, the result is returned as
a string.
orient : string
Indication of expected JSON string format.
* Series
- default is 'index'
- allowed values are: {'split','records','index'}
* DataFrame
- default is 'columns'
- allowed values are:
{'split','records','index','columns','values'}
* The format of the JSON string
- 'split' : dict like {'index' -> [index],
'columns' -> [columns], 'data' -> [values]}
- 'records' : list like
[{column -> value}, ... , {column -> value}]
- 'index' : dict like {index -> {column -> value}}
- 'columns' : dict like {column -> {index -> value}}
- 'values' : just the values array
- 'table' : dict like {'schema': {schema}, 'data': {data}}
describing the data, and the data component is
like ``orient='records'``.
.. versionchanged:: 0.20.0
date_format : {None, 'epoch', 'iso'}
Type of date conversion. 'epoch' = epoch milliseconds,
'iso' = ISO8601. The default depends on the `orient`. For
``orient='table'``, the default is 'iso'. For all other orients,
the default is 'epoch'.
double_precision : int, default 10
The number of decimal places to use when encoding
floating point values.
force_ascii : boolean, default True
Force encoded string to be ASCII.
date_unit : string, default 'ms' (milliseconds)
The time unit to encode to, governs timestamp and ISO8601
precision. One of 's', 'ms', 'us', 'ns' for second, millisecond,
microsecond, and nanosecond respectively.
default_handler : callable, default None
Handler to call if object cannot otherwise be converted to a
suitable format for JSON. Should receive a single argument which is
the object to convert and return a serialisable object.
lines : boolean, default False
If 'orient' is 'records' write out line delimited json format. Will
throw ValueError if incorrect 'orient' since others are not list
like.
.. versionadded:: 0.19.0
compression : {'infer', 'gzip', 'bz2', 'xz', None}, default None
A string representing the compression to use in the output file,
only used when the first argument is a filename.
.. versionadded:: 0.21.0
index : boolean, default True
Whether to include the index values in the JSON string. Not
including the index (``index=False``) is only supported when
orient is 'split' or 'table'.
.. versionadded:: 0.23.0
See Also
--------
pandas.read_json
Examples
--------
>>> df = pd.DataFrame([['a', 'b'], ['c', 'd']],
... index=['row 1', 'row 2'],
... columns=['col 1', 'col 2'])
>>> df.to_json(orient='split')
'{"columns":["col 1","col 2"],
"index":["row 1","row 2"],
"data":[["a","b"],["c","d"]]}'
Encoding/decoding a Dataframe using ``'records'`` formatted JSON.
Note that index labels are not preserved with this encoding.
>>> df.to_json(orient='records')
'[{"col 1":"a","col 2":"b"},{"col 1":"c","col 2":"d"}]'
Encoding/decoding a Dataframe using ``'index'`` formatted JSON:
>>> df.to_json(orient='index')
'{"row 1":{"col 1":"a","col 2":"b"},"row 2":{"col 1":"c","col 2":"d"}}'
Encoding/decoding a Dataframe using ``'columns'`` formatted JSON:
>>> df.to_json(orient='columns')
'{"col 1":{"row 1":"a","row 2":"c"},"col 2":{"row 1":"b","row 2":"d"}}'
Encoding/decoding a Dataframe using ``'values'`` formatted JSON:
>>> df.to_json(orient='values')
'[["a","b"],["c","d"]]'
Encoding with Table Schema
>>> df.to_json(orient='table')
'{"schema": {"fields": [{"name": "index", "type": "string"},
{"name": "col 1", "type": "string"},
{"name": "col 2", "type": "string"}],
"primaryKey": "index",
"pandas_version": "0.20.0"},
"data": [{"index": "row 1", "col 1": "a", "col 2": "b"},
{"index": "row 2", "col 1": "c", "col 2": "d"}]}'
"""
from pandas.io import json
if date_format is None and orient == 'table':
date_format = 'iso'
elif date_format is None:
date_format = 'epoch'
return json.to_json(path_or_buf=path_or_buf, obj=self, orient=orient,
date_format=date_format,
double_precision=double_precision,
force_ascii=force_ascii, date_unit=date_unit,
default_handler=default_handler,
lines=lines, compression=compression,
index=index)
def to_hdf(self, path_or_buf, key, **kwargs):
"""
Write the contained data to an HDF5 file using HDFStore.
Hierarchical Data Format (HDF) is self-describing, allowing an
application to interpret the structure and contents of a file with
no outside information. One HDF file can hold a mix of related objects
which can be accessed as a group or as individual objects.
In order to add another DataFrame or Series to an existing HDF file
please use append mode and a different a key.
For more information see the :ref:`user guide <io.hdf5>`.
Parameters
----------
path_or_buf : str or pandas.HDFStore
File path or HDFStore object.
key : str
Identifier for the group in the store.
mode : {'a', 'w', 'r+'}, default 'a'
Mode to open file:
- 'w': write, a new file is created (an existing file with
the same name would be deleted).
- 'a': append, an existing file is opened for reading and
writing, and if the file does not exist it is created.
- 'r+': similar to 'a', but the file must already exist.
format : {'fixed', 'table'}, default 'fixed'
Possible values:
- 'fixed': Fixed format. Fast writing/reading. Not-appendable,
nor searchable.
- 'table': Table format. Write as a PyTables Table structure
which may perform worse but allow more flexible operations
like searching / selecting subsets of the data.
append : bool, default False
For Table formats, append the input data to the existing.
data_columns : list of columns or True, optional
List of columns to create as indexed data columns for on-disk
queries, or True to use all columns. By default only the axes
of the object are indexed. See :ref:`io.hdf5-query-data-columns`.
Applicable only to format='table'.
complevel : {0-9}, optional
Specifies a compression level for data.
A value of 0 disables compression.
complib : {'zlib', 'lzo', 'bzip2', 'blosc'}, default 'zlib'
Specifies the compression library to be used.
As of v0.20.2 these additional compressors for Blosc are supported
(default if no compressor specified: 'blosc:blosclz'):
{'blosc:blosclz', 'blosc:lz4', 'blosc:lz4hc', 'blosc:snappy',
'blosc:zlib', 'blosc:zstd'}.
Specifying a compression library which is not available issues
a ValueError.
fletcher32 : bool, default False
If applying compression use the fletcher32 checksum.
dropna : bool, default False
If true, ALL nan rows will not be written to store.
errors : str, default 'strict'
Specifies how encoding and decoding errors are to be handled.
See the errors argument for :func:`open` for a full list
of options.
See Also
--------
DataFrame.read_hdf : Read from HDF file.
DataFrame.to_parquet : Write a DataFrame to the binary parquet format.
DataFrame.to_sql : Write to a sql table.
DataFrame.to_feather : Write out feather-format for DataFrames.
DataFrame.to_csv : Write out to a csv file.
Examples
--------
>>> df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]},
... index=['a', 'b', 'c'])
>>> df.to_hdf('data.h5', key='df', mode='w')
We can add another object to the same file:
>>> s = pd.Series([1, 2, 3, 4])
>>> s.to_hdf('data.h5', key='s')
Reading from HDF file:
>>> pd.read_hdf('data.h5', 'df')
A B
a 1 4
b 2 5
c 3 6
>>> pd.read_hdf('data.h5', 's')
0 1
1 2
2 3
3 4
dtype: int64
Deleting file with data:
>>> import os
>>> os.remove('data.h5')
"""
from pandas.io import pytables
return pytables.to_hdf(path_or_buf, key, self, **kwargs)
def to_msgpack(self, path_or_buf=None, encoding='utf-8', **kwargs):
"""
msgpack (serialize) object to input file path
THIS IS AN EXPERIMENTAL LIBRARY and the storage format
may not be stable until a future release.
Parameters
----------
path : string File path, buffer-like, or None
if None, return generated string
append : boolean whether to append to an existing msgpack
(default is False)
compress : type of compressor (zlib or blosc), default to None (no
compression)
"""
from pandas.io import packers
return packers.to_msgpack(path_or_buf, self, encoding=encoding,
**kwargs)
def to_sql(self, name, con, schema=None, if_exists='fail', index=True,
index_label=None, chunksize=None, dtype=None):
"""
Write records stored in a DataFrame to a SQL database.
Databases supported by SQLAlchemy [1]_ are supported. Tables can be
newly created, appended to, or overwritten.
Parameters
----------
name : string
Name of SQL table.
con : sqlalchemy.engine.Engine or sqlite3.Connection
Using SQLAlchemy makes it possible to use any DB supported by that
library. Legacy support is provided for sqlite3.Connection objects.
schema : string, optional
Specify the schema (if database flavor supports this). If None, use
default schema.
if_exists : {'fail', 'replace', 'append'}, default 'fail'
How to behave if the table already exists.
* fail: Raise a ValueError.
* replace: Drop the table before inserting new values.
* append: Insert new values to the existing table.
index : boolean, default True
Write DataFrame index as a column. Uses `index_label` as the column
name in the table.
index_label : string or sequence, default None
Column label for index column(s). If None is given (default) and
`index` is True, then the index names are used.
A sequence should be given if the DataFrame uses MultiIndex.
chunksize : int, optional
Rows will be written in batches of this size at a time. By default,
all rows will be written at once.
dtype : dict, optional
Specifying the datatype for columns. The keys should be the column
names and the values should be the SQLAlchemy types or strings for
the sqlite3 legacy mode.
Raises
------
ValueError
When the table already exists and `if_exists` is 'fail' (the
default).
See Also
--------
pandas.read_sql : read a DataFrame from a table
References
----------
.. [1] http://docs.sqlalchemy.org
.. [2] https://www.python.org/dev/peps/pep-0249/
Examples
--------
Create an in-memory SQLite database.
>>> from sqlalchemy import create_engine
>>> engine = create_engine('sqlite://', echo=False)
Create a table from scratch with 3 rows.
>>> df = pd.DataFrame({'name' : ['User 1', 'User 2', 'User 3']})
>>> df
name
0 User 1
1 User 2
2 User 3
>>> df.to_sql('users', con=engine)
>>> engine.execute("SELECT * FROM users").fetchall()
[(0, 'User 1'), (1, 'User 2'), (2, 'User 3')]
>>> df1 = pd.DataFrame({'name' : ['User 4', 'User 5']})
>>> df1.to_sql('users', con=engine, if_exists='append')
>>> engine.execute("SELECT * FROM users").fetchall()
[(0, 'User 1'), (1, 'User 2'), (2, 'User 3'),
(0, 'User 4'), (1, 'User 5')]
Overwrite the table with just ``df1``.
>>> df1.to_sql('users', con=engine, if_exists='replace',
... index_label='id')
>>> engine.execute("SELECT * FROM users").fetchall()
[(0, 'User 4'), (1, 'User 5')]
Specify the dtype (especially useful for integers with missing values).
Notice that while pandas is forced to store the data as floating point,
the database supports nullable integers. When fetching the data with
Python, we get back integer scalars.
>>> df = pd.DataFrame({"A": [1, None, 2]})
>>> df
A
0 1.0
1 NaN
2 2.0
>>> from sqlalchemy.types import Integer
>>> df.to_sql('integers', con=engine, index=False,
... dtype={"A": Integer()})
>>> engine.execute("SELECT * FROM integers").fetchall()
[(1,), (None,), (2,)]
"""
from pandas.io import sql
sql.to_sql(self, name, con, schema=schema, if_exists=if_exists,
index=index, index_label=index_label, chunksize=chunksize,
dtype=dtype)
def to_pickle(self, path, compression='infer',
protocol=pkl.HIGHEST_PROTOCOL):
"""
Pickle (serialize) object to file.
Parameters
----------
path : str
File path where the pickled object will be stored.
compression : {'infer', 'gzip', 'bz2', 'zip', 'xz', None}, \
default 'infer'
A string representing the compression to use in the output file. By
default, infers from the file extension in specified path.
.. versionadded:: 0.20.0
protocol : int
Int which indicates which protocol should be used by the pickler,
default HIGHEST_PROTOCOL (see [1]_ paragraph 12.1.2). The possible
values for this parameter depend on the version of Python. For
Python 2.x, possible values are 0, 1, 2. For Python>=3.0, 3 is a
valid value. For Python >= 3.4, 4 is a valid value. A negative
value for the protocol parameter is equivalent to setting its value
to HIGHEST_PROTOCOL.
.. [1] https://docs.python.org/3/library/pickle.html
.. versionadded:: 0.21.0
See Also
--------
read_pickle : Load pickled pandas object (or any object) from file.
DataFrame.to_hdf : Write DataFrame to an HDF5 file.
DataFrame.to_sql : Write DataFrame to a SQL database.
DataFrame.to_parquet : Write a DataFrame to the binary parquet format.
Examples
--------
>>> original_df = pd.DataFrame({"foo": range(5), "bar": range(5, 10)})
>>> original_df
foo bar
0 0 5
1 1 6
2 2 7
3 3 8
4 4 9
>>> original_df.to_pickle("./dummy.pkl")
>>> unpickled_df = pd.read_pickle("./dummy.pkl")
>>> unpickled_df
foo bar
0 0 5
1 1 6
2 2 7
3 3 8
4 4 9
>>> import os
>>> os.remove("./dummy.pkl")
"""
from pandas.io.pickle import to_pickle
return to_pickle(self, path, compression=compression,
protocol=protocol)
def to_clipboard(self, excel=True, sep=None, **kwargs):
r"""
Copy object to the system clipboard.
Write a text representation of object to the system clipboard.
This can be pasted into Excel, for example.
Parameters
----------
excel : bool, default True
- True, use the provided separator, writing in a csv format for
allowing easy pasting into excel.
- False, write a string representation of the object to the
clipboard.
sep : str, default ``'\t'``
Field delimiter.
**kwargs
These parameters will be passed to DataFrame.to_csv.
See Also
--------
DataFrame.to_csv : Write a DataFrame to a comma-separated values
(csv) file.
read_clipboard : Read text from clipboard and pass to read_table.
Notes
-----
Requirements for your platform.
- Linux : `xclip`, or `xsel` (with `gtk` or `PyQt4` modules)
- Windows : none
- OS X : none
Examples
--------
Copy the contents of a DataFrame to the clipboard.
>>> df = pd.DataFrame([[1, 2, 3], [4, 5, 6]], columns=['A', 'B', 'C'])
>>> df.to_clipboard(sep=',')
... # Wrote the following to the system clipboard:
... # ,A,B,C
... # 0,1,2,3
... # 1,4,5,6
We can omit the the index by passing the keyword `index` and setting
it to false.
>>> df.to_clipboard(sep=',', index=False)
... # Wrote the following to the system clipboard:
... # A,B,C
... # 1,2,3
... # 4,5,6
"""
from pandas.io import clipboards
clipboards.to_clipboard(self, excel=excel, sep=sep, **kwargs)
def to_xarray(self):
"""
Return an xarray object from the pandas object.
Returns
-------
a DataArray for a Series
a Dataset for a DataFrame
a DataArray for higher dims
Examples
--------
>>> df = pd.DataFrame({'A' : [1, 1, 2],
'B' : ['foo', 'bar', 'foo'],
'C' : np.arange(4.,7)})
>>> df
A B C
0 1 foo 4.0
1 1 bar 5.0
2 2 foo 6.0
>>> df.to_xarray()
<xarray.Dataset>
Dimensions: (index: 3)
Coordinates:
* index (index) int64 0 1 2
Data variables:
A (index) int64 1 1 2
B (index) object 'foo' 'bar' 'foo'
C (index) float64 4.0 5.0 6.0
>>> df = pd.DataFrame({'A' : [1, 1, 2],
'B' : ['foo', 'bar', 'foo'],
'C' : np.arange(4.,7)}
).set_index(['B','A'])
>>> df
C
B A
foo 1 4.0
bar 1 5.0
foo 2 6.0
>>> df.to_xarray()
<xarray.Dataset>
Dimensions: (A: 2, B: 2)
Coordinates:
* B (B) object 'bar' 'foo'
* A (A) int64 1 2
Data variables:
C (B, A) float64 5.0 nan 4.0 6.0
>>> p = pd.Panel(np.arange(24).reshape(4,3,2),
items=list('ABCD'),
major_axis=pd.date_range('20130101', periods=3),
minor_axis=['first', 'second'])
>>> p
<class 'pandas.core.panel.Panel'>
Dimensions: 4 (items) x 3 (major_axis) x 2 (minor_axis)
Items axis: A to D
Major_axis axis: 2013-01-01 00:00:00 to 2013-01-03 00:00:00
Minor_axis axis: first to second
>>> p.to_xarray()
<xarray.DataArray (items: 4, major_axis: 3, minor_axis: 2)>
array([[[ 0, 1],
[ 2, 3],
[ 4, 5]],
[[ 6, 7],
[ 8, 9],
[10, 11]],
[[12, 13],
[14, 15],
[16, 17]],
[[18, 19],
[20, 21],
[22, 23]]])
Coordinates:
* items (items) object 'A' 'B' 'C' 'D'
* major_axis (major_axis) datetime64[ns] 2013-01-01 2013-01-02 2013-01-03 # noqa
* minor_axis (minor_axis) object 'first' 'second'
Notes
-----
See the `xarray docs <http://xarray.pydata.org/en/stable/>`__
"""
try:
import xarray
except ImportError:
# Give a nice error message
raise ImportError("the xarray library is not installed\n"
"you can install via conda\n"
"conda install xarray\n"
"or via pip\n"
"pip install xarray\n")
if self.ndim == 1:
return xarray.DataArray.from_series(self)
elif self.ndim == 2:
return xarray.Dataset.from_dataframe(self)
# > 2 dims
coords = [(a, self._get_axis(a)) for a in self._AXIS_ORDERS]
return xarray.DataArray(self,
coords=coords,
)
_shared_docs['to_latex'] = r"""
Render an object to a tabular environment table. You can splice
this into a LaTeX document. Requires \\usepackage{booktabs}.
.. versionchanged:: 0.20.2
Added to Series
`to_latex`-specific options:
bold_rows : boolean, default False
Make the row labels bold in the output
column_format : str, default None
The columns format as specified in `LaTeX table format
<https://en.wikibooks.org/wiki/LaTeX/Tables>`__ e.g 'rcl' for 3
columns
longtable : boolean, default will be read from the pandas config module
Default: False.
Use a longtable environment instead of tabular. Requires adding
a \\usepackage{longtable} to your LaTeX preamble.
escape : boolean, default will be read from the pandas config module
Default: True.
When set to False prevents from escaping latex special
characters in column names.
encoding : str, default None
A string representing the encoding to use in the output file,
defaults to 'ascii' on Python 2 and 'utf-8' on Python 3.
decimal : string, default '.'
Character recognized as decimal separator, e.g. ',' in Europe.
.. versionadded:: 0.18.0
multicolumn : boolean, default True
Use \multicolumn to enhance MultiIndex columns.
The default will be read from the config module.
.. versionadded:: 0.20.0
multicolumn_format : str, default 'l'
The alignment for multicolumns, similar to `column_format`
The default will be read from the config module.
.. versionadded:: 0.20.0
multirow : boolean, default False
Use \multirow to enhance MultiIndex rows.
Requires adding a \\usepackage{multirow} to your LaTeX preamble.
Will print centered labels (instead of top-aligned)
across the contained rows, separating groups via clines.
The default will be read from the pandas config module.
.. versionadded:: 0.20.0
"""
@Substitution(header='Write out the column names. If a list of strings '
'is given, it is assumed to be aliases for the '
'column names.')
@Appender(_shared_docs['to_latex'] % _shared_doc_kwargs)
def to_latex(self, buf=None, columns=None, col_space=None, header=True,
index=True, na_rep='NaN', formatters=None, float_format=None,
sparsify=None, index_names=True, bold_rows=False,
column_format=None, longtable=None, escape=None,
encoding=None, decimal='.', multicolumn=None,
multicolumn_format=None, multirow=None):
# Get defaults from the pandas config
if self.ndim == 1:
self = self.to_frame()
if longtable is None:
longtable = config.get_option("display.latex.longtable")
if escape is None:
escape = config.get_option("display.latex.escape")
if multicolumn is None:
multicolumn = config.get_option("display.latex.multicolumn")
if multicolumn_format is None:
multicolumn_format = config.get_option(
"display.latex.multicolumn_format")
if multirow is None:
multirow = config.get_option("display.latex.multirow")
formatter = DataFrameFormatter(self, buf=buf, columns=columns,
col_space=col_space, na_rep=na_rep,
header=header, index=index,
formatters=formatters,
float_format=float_format,
bold_rows=bold_rows,
sparsify=sparsify,
index_names=index_names,
escape=escape, decimal=decimal)
formatter.to_latex(column_format=column_format, longtable=longtable,
encoding=encoding, multicolumn=multicolumn,
multicolumn_format=multicolumn_format,
multirow=multirow)
if buf is None:
return formatter.buf.getvalue()
# ----------------------------------------------------------------------
# Fancy Indexing
@classmethod
def _create_indexer(cls, name, indexer):
"""Create an indexer like _name in the class."""
if getattr(cls, name, None) is None:
_indexer = functools.partial(indexer, name)
setattr(cls, name, property(_indexer, doc=indexer.__doc__))
def get(self, key, default=None):
"""
Get item from object for given key (DataFrame column, Panel slice,
etc.). Returns default value if not found.
Parameters
----------
key : object
Returns
-------
value : same type as items contained in object
"""
try:
return self[key]
except (KeyError, ValueError, IndexError):
return default
def __getitem__(self, item):
return self._get_item_cache(item)
def _get_item_cache(self, item):
"""Return the cached item, item represents a label indexer."""
cache = self._item_cache
res = cache.get(item)
if res is None:
values = self._data.get(item)
res = self._box_item_values(item, values)
cache[item] = res
res._set_as_cached(item, self)
# for a chain
res._is_copy = self._is_copy
return res
def _set_as_cached(self, item, cacher):
"""Set the _cacher attribute on the calling object with a weakref to
cacher.
"""
self._cacher = (item, weakref.ref(cacher))
def _reset_cacher(self):
"""Reset the cacher."""
if hasattr(self, '_cacher'):
del self._cacher
def _iget_item_cache(self, item):
"""Return the cached item, item represents a positional indexer."""
ax = self._info_axis
if ax.is_unique:
lower = self._get_item_cache(ax[item])
else:
lower = self._take(item, axis=self._info_axis_number)
return lower
def _box_item_values(self, key, values):
raise com.AbstractMethodError(self)
def _maybe_cache_changed(self, item, value):
"""The object has called back to us saying maybe it has changed.
"""
self._data.set(item, value, check=False)
@property
def _is_cached(self):
"""Return boolean indicating if self is cached or not."""
return getattr(self, '_cacher', None) is not None
def _get_cacher(self):
"""return my cacher or None"""
cacher = getattr(self, '_cacher', None)
if cacher is not None:
cacher = cacher[1]()
return cacher
@property
def _is_view(self):
"""Return boolean indicating if self is view of another array """
return self._data.is_view
def _maybe_update_cacher(self, clear=False, verify_is_copy=True):
"""
See if we need to update our parent cacher if clear, then clear our
cache.
Parameters
----------
clear : boolean, default False
clear the item cache
verify_is_copy : boolean, default True
provide is_copy checks
"""
cacher = getattr(self, '_cacher', None)
if cacher is not None:
ref = cacher[1]()
# we are trying to reference a dead referant, hence
# a copy
if ref is None:
del self._cacher
else:
try:
ref._maybe_cache_changed(cacher[0], self)
except Exception:
pass
if verify_is_copy:
self._check_setitem_copy(stacklevel=5, t='referant')
if clear:
self._clear_item_cache()
def _clear_item_cache(self, i=None):
if i is not None:
self._item_cache.pop(i, None)
else:
self._item_cache.clear()
def _slice(self, slobj, axis=0, kind=None):
"""
Construct a slice of this container.
kind parameter is maintained for compatibility with Series slicing.
"""
axis = self._get_block_manager_axis(axis)
result = self._constructor(self._data.get_slice(slobj, axis=axis))
result = result.__finalize__(self)
# this could be a view
# but only in a single-dtyped view slicable case
is_copy = axis != 0 or result._is_view
result._set_is_copy(self, copy=is_copy)
return result
def _set_item(self, key, value):
self._data.set(key, value)
self._clear_item_cache()
def _set_is_copy(self, ref=None, copy=True):
if not copy:
self._is_copy = None
else:
if ref is not None:
self._is_copy = weakref.ref(ref)
else:
self._is_copy = None
def _check_is_chained_assignment_possible(self):
"""
Check if we are a view, have a cacher, and are of mixed type.
If so, then force a setitem_copy check.
Should be called just near setting a value
Will return a boolean if it we are a view and are cached, but a
single-dtype meaning that the cacher should be updated following
setting.
"""
if self._is_view and self._is_cached:
ref = self._get_cacher()
if ref is not None and ref._is_mixed_type:
self._check_setitem_copy(stacklevel=4, t='referant',
force=True)
return True
elif self._is_copy:
self._check_setitem_copy(stacklevel=4, t='referant')
return False
def _check_setitem_copy(self, stacklevel=4, t='setting', force=False):
"""
Parameters
----------
stacklevel : integer, default 4
the level to show of the stack when the error is output
t : string, the type of setting error
force : boolean, default False
if True, then force showing an error
validate if we are doing a settitem on a chained copy.
If you call this function, be sure to set the stacklevel such that the
user will see the error *at the level of setting*
It is technically possible to figure out that we are setting on
a copy even WITH a multi-dtyped pandas object. In other words, some
blocks may be views while other are not. Currently _is_view will ALWAYS
return False for multi-blocks to avoid having to handle this case.
df = DataFrame(np.arange(0,9), columns=['count'])
df['group'] = 'b'
# This technically need not raise SettingWithCopy if both are view
# (which is not # generally guaranteed but is usually True. However,
# this is in general not a good practice and we recommend using .loc.
df.iloc[0:5]['group'] = 'a'
"""
if force or self._is_copy:
value = config.get_option('mode.chained_assignment')
if value is None:
return
# see if the copy is not actually referred; if so, then dissolve
# the copy weakref
try:
gc.collect(2)
if not gc.get_referents(self._is_copy()):
self._is_copy = None
return
except Exception:
pass
# we might be a false positive
try:
if self._is_copy().shape == self.shape:
self._is_copy = None
return
except Exception:
pass
# a custom message
if isinstance(self._is_copy, string_types):
t = self._is_copy
elif t == 'referant':
t = ("\n"
"A value is trying to be set on a copy of a slice from a "
"DataFrame\n\n"
"See the caveats in the documentation: "
"http://pandas.pydata.org/pandas-docs/stable/"
"indexing.html#indexing-view-versus-copy"
)
else:
t = ("\n"
"A value is trying to be set on a copy of a slice from a "
"DataFrame.\n"
"Try using .loc[row_indexer,col_indexer] = value "
"instead\n\nSee the caveats in the documentation: "
"http://pandas.pydata.org/pandas-docs/stable/"
"indexing.html#indexing-view-versus-copy"
)
if value == 'raise':
raise com.SettingWithCopyError(t)
elif value == 'warn':
warnings.warn(t, com.SettingWithCopyWarning,
stacklevel=stacklevel)
def __delitem__(self, key):
"""
Delete item
"""
deleted = False
maybe_shortcut = False
if hasattr(self, 'columns') and isinstance(self.columns, MultiIndex):
try:
maybe_shortcut = key not in self.columns._engine
except TypeError:
pass
if maybe_shortcut:
# Allow shorthand to delete all columns whose first len(key)
# elements match key:
if not isinstance(key, tuple):
key = (key, )
for col in self.columns:
if isinstance(col, tuple) and col[:len(key)] == key:
del self[col]
deleted = True
if not deleted:
# If the above loop ran and didn't delete anything because
# there was no match, this call should raise the appropriate
# exception:
self._data.delete(key)
# delete from the caches
try:
del self._item_cache[key]
except KeyError:
pass
_shared_docs['_take'] = """
Return the elements in the given *positional* indices along an axis.
This means that we are not indexing according to actual values in
the index attribute of the object. We are indexing according to the
actual position of the element in the object.
This is the internal version of ``.take()`` and will contain a wider
selection of parameters useful for internal use but not as suitable
for public usage.
Parameters
----------
indices : array-like
An array of ints indicating which positions to take.
axis : int, default 0
The axis on which to select elements. "0" means that we are
selecting rows, "1" means that we are selecting columns, etc.
is_copy : bool, default True
Whether to return a copy of the original object or not.
Returns
-------
taken : same type as caller
An array-like containing the elements taken from the object.
See Also
--------
numpy.ndarray.take
numpy.take
"""
@Appender(_shared_docs['_take'])
def _take(self, indices, axis=0, is_copy=True):
self._consolidate_inplace()
new_data = self._data.take(indices,
axis=self._get_block_manager_axis(axis),
verify=True)
result = self._constructor(new_data).__finalize__(self)
# Maybe set copy if we didn't actually change the index.
if is_copy:
if not result._get_axis(axis).equals(self._get_axis(axis)):
result._set_is_copy(self)
return result
_shared_docs['take'] = """
Return the elements in the given *positional* indices along an axis.
This means that we are not indexing according to actual values in
the index attribute of the object. We are indexing according to the
actual position of the element in the object.
Parameters
----------
indices : array-like
An array of ints indicating which positions to take.
axis : {0 or 'index', 1 or 'columns', None}, default 0
The axis on which to select elements. ``0`` means that we are
selecting rows, ``1`` means that we are selecting columns.
convert : bool, default True
Whether to convert negative indices into positive ones.
For example, ``-1`` would map to the ``len(axis) - 1``.
The conversions are similar to the behavior of indexing a
regular Python list.
.. deprecated:: 0.21.0
In the future, negative indices will always be converted.
is_copy : bool, default True
Whether to return a copy of the original object or not.
**kwargs
For compatibility with :meth:`numpy.take`. Has no effect on the
output.
Returns
-------
taken : same type as caller
An array-like containing the elements taken from the object.
See Also
--------
DataFrame.loc : Select a subset of a DataFrame by labels.
DataFrame.iloc : Select a subset of a DataFrame by positions.
numpy.take : Take elements from an array along an axis.
Examples
--------
>>> df = pd.DataFrame([('falcon', 'bird', 389.0),
... ('parrot', 'bird', 24.0),
... ('lion', 'mammal', 80.5),
... ('monkey', 'mammal', np.nan)],
... columns=['name', 'class', 'max_speed'],
... index=[0, 2, 3, 1])
>>> df
name class max_speed
0 falcon bird 389.0
2 parrot bird 24.0
3 lion mammal 80.5
1 monkey mammal NaN
Take elements at positions 0 and 3 along the axis 0 (default).
Note how the actual indices selected (0 and 1) do not correspond to
our selected indices 0 and 3. That's because we are selecting the 0th
and 3rd rows, not rows whose indices equal 0 and 3.
>>> df.take([0, 3])
name class max_speed
0 falcon bird 389.0
1 monkey mammal NaN
Take elements at indices 1 and 2 along the axis 1 (column selection).
>>> df.take([1, 2], axis=1)
class max_speed
0 bird 389.0
2 bird 24.0
3 mammal 80.5
1 mammal NaN
We may take elements using negative integers for positive indices,
starting from the end of the object, just like with Python lists.
>>> df.take([-1, -2])
name class max_speed
1 monkey mammal NaN
3 lion mammal 80.5
"""
@Appender(_shared_docs['take'])
def take(self, indices, axis=0, convert=None, is_copy=True, **kwargs):
if convert is not None:
msg = ("The 'convert' parameter is deprecated "
"and will be removed in a future version.")
warnings.warn(msg, FutureWarning, stacklevel=2)
nv.validate_take(tuple(), kwargs)
return self._take(indices, axis=axis, is_copy=is_copy)
def xs(self, key, axis=0, level=None, drop_level=True):
"""
Returns a cross-section (row(s) or column(s)) from the
Series/DataFrame. Defaults to cross-section on the rows (axis=0).
Parameters
----------
key : object
Some label contained in the index, or partially in a MultiIndex
axis : int, default 0
Axis to retrieve cross-section on
level : object, defaults to first n levels (n=1 or len(key))
In case of a key partially contained in a MultiIndex, indicate
which levels are used. Levels can be referred by label or position.
drop_level : boolean, default True
If False, returns object with same levels as self.
Examples
--------
>>> df
A B C
a 4 5 2
b 4 0 9
c 9 7 3
>>> df.xs('a')
A 4
B 5
C 2
Name: a
>>> df.xs('C', axis=1)
a 2
b 9
c 3
Name: C
>>> df
A B C D
first second third
bar one 1 4 1 8 9
two 1 7 5 5 0
baz one 1 6 6 8 0
three 2 5 3 5 3
>>> df.xs(('baz', 'three'))
A B C D
third
2 5 3 5 3
>>> df.xs('one', level=1)
A B C D
first third
bar 1 4 1 8 9
baz 1 6 6 8 0
>>> df.xs(('baz', 2), level=[0, 'third'])
A B C D
second
three 5 3 5 3
Returns
-------
xs : Series or DataFrame
Notes
-----
xs is only for getting, not setting values.
MultiIndex Slicers is a generic way to get/set values on any level or
levels. It is a superset of xs functionality, see
:ref:`MultiIndex Slicers <advanced.mi_slicers>`
"""
axis = self._get_axis_number(axis)
labels = self._get_axis(axis)
if level is not None:
loc, new_ax = labels.get_loc_level(key, level=level,
drop_level=drop_level)
# create the tuple of the indexer
indexer = [slice(None)] * self.ndim
indexer[axis] = loc
indexer = tuple(indexer)
result = self.iloc[indexer]
setattr(result, result._get_axis_name(axis), new_ax)
return result
if axis == 1:
return self[key]
self._consolidate_inplace()
index = self.index
if isinstance(index, MultiIndex):
loc, new_index = self.index.get_loc_level(key,
drop_level=drop_level)
else:
loc = self.index.get_loc(key)
if isinstance(loc, np.ndarray):
if loc.dtype == np.bool_:
inds, = loc.nonzero()
return self._take(inds, axis=axis)
else:
return self._take(loc, axis=axis)
if not is_scalar(loc):
new_index = self.index[loc]
if is_scalar(loc):
new_values = self._data.fast_xs(loc)
# may need to box a datelike-scalar
#
# if we encounter an array-like and we only have 1 dim
# that means that their are list/ndarrays inside the Series!
# so just return them (GH 6394)
if not is_list_like(new_values) or self.ndim == 1:
return com._maybe_box_datetimelike(new_values)
result = self._constructor_sliced(
new_values, index=self.columns,
name=self.index[loc], dtype=new_values.dtype)
else:
result = self.iloc[loc]
result.index = new_index
# this could be a view
# but only in a single-dtyped view slicable case
result._set_is_copy(self, copy=not result._is_view)
return result
_xs = xs
def select(self, crit, axis=0):
"""Return data corresponding to axis labels matching criteria
.. deprecated:: 0.21.0
Use df.loc[df.index.map(crit)] to select via labels
Parameters
----------
crit : function
To be called on each index (label). Should return True or False
axis : int
Returns
-------
selection : same type as caller
"""
warnings.warn("'select' is deprecated and will be removed in a "
"future release. You can use "
".loc[labels.map(crit)] as a replacement",
FutureWarning, stacklevel=2)
axis = self._get_axis_number(axis)
axis_name = self._get_axis_name(axis)
axis_values = self._get_axis(axis)
if len(axis_values) > 0:
new_axis = axis_values[
np.asarray([bool(crit(label)) for label in axis_values])]
else:
new_axis = axis_values
return self.reindex(**{axis_name: new_axis})
def reindex_like(self, other, method=None, copy=True, limit=None,
tolerance=None):
"""Return an object with matching indices to myself.
Parameters
----------
other : Object
method : string or None
copy : boolean, default True
limit : int, default None
Maximum number of consecutive labels to fill for inexact matches.
tolerance : optional
Maximum distance between labels of the other object and this
object for inexact matches. Can be list-like.
.. versionadded:: 0.21.0 (list-like tolerance)
Notes
-----
Like calling s.reindex(index=other.index, columns=other.columns,
method=...)
Returns
-------
reindexed : same as input
"""
d = other._construct_axes_dict(axes=self._AXIS_ORDERS, method=method,
copy=copy, limit=limit,
tolerance=tolerance)
return self.reindex(**d)
def drop(self, labels=None, axis=0, index=None, columns=None, level=None,
inplace=False, errors='raise'):
inplace = validate_bool_kwarg(inplace, 'inplace')
if labels is not None:
if index is not None or columns is not None:
raise ValueError("Cannot specify both 'labels' and "
"'index'/'columns'")
axis_name = self._get_axis_name(axis)
axes = {axis_name: labels}
elif index is not None or columns is not None:
axes, _ = self._construct_axes_from_arguments((index, columns), {})
else:
raise ValueError("Need to specify at least one of 'labels', "
"'index' or 'columns'")
obj = self
for axis, labels in axes.items():
if labels is not None:
obj = obj._drop_axis(labels, axis, level=level, errors=errors)
if inplace:
self._update_inplace(obj)
else:
return obj
def _drop_axis(self, labels, axis, level=None, errors='raise'):
"""
Drop labels from specified axis. Used in the ``drop`` method
internally.
Parameters
----------
labels : single label or list-like
axis : int or axis name
level : int or level name, default None
For MultiIndex
errors : {'ignore', 'raise'}, default 'raise'
If 'ignore', suppress error and existing labels are dropped.
"""
axis = self._get_axis_number(axis)
axis_name = self._get_axis_name(axis)
axis = self._get_axis(axis)
if axis.is_unique:
if level is not None:
if not isinstance(axis, MultiIndex):
raise AssertionError('axis must be a MultiIndex')
new_axis = axis.drop(labels, level=level, errors=errors)
else:
new_axis = axis.drop(labels, errors=errors)
result = self.reindex(**{axis_name: new_axis})
# Case for non-unique axis
else:
labels = ensure_object(com._index_labels_to_array(labels))
if level is not None:
if not isinstance(axis, MultiIndex):
raise AssertionError('axis must be a MultiIndex')
indexer = ~axis.get_level_values(level).isin(labels)
# GH 18561 MultiIndex.drop should raise if label is absent
if errors == 'raise' and indexer.all():
raise KeyError('{} not found in axis'.format(labels))
else:
indexer = ~axis.isin(labels)
# Check if label doesn't exist along axis
labels_missing = (axis.get_indexer_for(labels) == -1).any()
if errors == 'raise' and labels_missing:
raise KeyError('{} not found in axis'.format(labels))
slicer = [slice(None)] * self.ndim
slicer[self._get_axis_number(axis_name)] = indexer
result = self.loc[tuple(slicer)]
return result
def _update_inplace(self, result, verify_is_copy=True):
"""
Replace self internals with result.
Parameters
----------
verify_is_copy : boolean, default True
provide is_copy checks
"""
# NOTE: This does *not* call __finalize__ and that's an explicit
# decision that we may revisit in the future.
self._reset_cache()
self._clear_item_cache()
self._data = getattr(result, '_data', result)
self._maybe_update_cacher(verify_is_copy=verify_is_copy)
def add_prefix(self, prefix):
"""
Prefix labels with string `prefix`.
For Series, the row labels are prefixed.
For DataFrame, the column labels are prefixed.
Parameters
----------
prefix : str
The string to add before each label.
Returns
-------
Series or DataFrame
New Series or DataFrame with updated labels.
See Also
--------
Series.add_suffix: Suffix row labels with string `suffix`.
DataFrame.add_suffix: Suffix column labels with string `suffix`.
Examples
--------
>>> s = pd.Series([1, 2, 3, 4])
>>> s
0 1
1 2
2 3
3 4
dtype: int64
>>> s.add_prefix('item_')
item_0 1
item_1 2
item_2 3
item_3 4
dtype: int64
>>> df = pd.DataFrame({'A': [1, 2, 3, 4], 'B': [3, 4, 5, 6]})
>>> df
A B
0 1 3
1 2 4
2 3 5
3 4 6
>>> df.add_prefix('col_')
col_A col_B
0 1 3
1 2 4
2 3 5
3 4 6
"""
new_data = self._data.add_prefix(prefix)
return self._constructor(new_data).__finalize__(self)
def add_suffix(self, suffix):
"""
Suffix labels with string `suffix`.
For Series, the row labels are suffixed.
For DataFrame, the column labels are suffixed.
Parameters
----------
suffix : str
The string to add after each label.
Returns
-------
Series or DataFrame
New Series or DataFrame with updated labels.
See Also
--------
Series.add_prefix: Prefix row labels with string `prefix`.
DataFrame.add_prefix: Prefix column labels with string `prefix`.
Examples
--------
>>> s = pd.Series([1, 2, 3, 4])
>>> s
0 1
1 2
2 3
3 4
dtype: int64
>>> s.add_suffix('_item')
0_item 1
1_item 2
2_item 3
3_item 4
dtype: int64
>>> df = pd.DataFrame({'A': [1, 2, 3, 4], 'B': [3, 4, 5, 6]})
>>> df
A B
0 1 3
1 2 4
2 3 5
3 4 6
>>> df.add_suffix('_col')
A_col B_col
0 1 3
1 2 4
2 3 5
3 4 6
"""
new_data = self._data.add_suffix(suffix)
return self._constructor(new_data).__finalize__(self)
_shared_docs['sort_values'] = """
Sort by the values along either axis
Parameters
----------%(optional_by)s
axis : %(axes_single_arg)s, default 0
Axis to be sorted
ascending : bool or list of bool, default True
Sort ascending vs. descending. Specify list for multiple sort
orders. If this is a list of bools, must match the length of
the by.
inplace : bool, default False
if True, perform operation in-place
kind : {'quicksort', 'mergesort', 'heapsort'}, default 'quicksort'
Choice of sorting algorithm. See also ndarray.np.sort for more
information. `mergesort` is the only stable algorithm. For
DataFrames, this option is only applied when sorting on a single
column or label.
na_position : {'first', 'last'}, default 'last'
`first` puts NaNs at the beginning, `last` puts NaNs at the end
Returns
-------
sorted_obj : %(klass)s
Examples
--------
>>> df = pd.DataFrame({
... 'col1' : ['A', 'A', 'B', np.nan, 'D', 'C'],
... 'col2' : [2, 1, 9, 8, 7, 4],
... 'col3': [0, 1, 9, 4, 2, 3],
... })
>>> df
col1 col2 col3
0 A 2 0
1 A 1 1
2 B 9 9
3 NaN 8 4
4 D 7 2
5 C 4 3
Sort by col1
>>> df.sort_values(by=['col1'])
col1 col2 col3
0 A 2 0
1 A 1 1
2 B 9 9
5 C 4 3
4 D 7 2
3 NaN 8 4
Sort by multiple columns
>>> df.sort_values(by=['col1', 'col2'])
col1 col2 col3
1 A 1 1
0 A 2 0
2 B 9 9
5 C 4 3
4 D 7 2
3 NaN 8 4
Sort Descending
>>> df.sort_values(by='col1', ascending=False)
col1 col2 col3
4 D 7 2
5 C 4 3
2 B 9 9
0 A 2 0
1 A 1 1
3 NaN 8 4
Putting NAs first
>>> df.sort_values(by='col1', ascending=False, na_position='first')
col1 col2 col3
3 NaN 8 4
4 D 7 2
5 C 4 3
2 B 9 9
0 A 2 0
1 A 1 1
"""
def sort_values(self, by=None, axis=0, ascending=True, inplace=False,
kind='quicksort', na_position='last'):
"""
NOT IMPLEMENTED: do not call this method, as sorting values is not
supported for Panel objects and will raise an error.
"""
raise NotImplementedError("sort_values has not been implemented "
"on Panel or Panel4D objects.")
_shared_docs['sort_index'] = """
Sort object by labels (along an axis)
Parameters
----------
axis : %(axes)s to direct sorting
level : int or level name or list of ints or list of level names
if not None, sort on values in specified index level(s)
ascending : boolean, default True
Sort ascending vs. descending
inplace : bool, default False
if True, perform operation in-place
kind : {'quicksort', 'mergesort', 'heapsort'}, default 'quicksort'
Choice of sorting algorithm. See also ndarray.np.sort for more
information. `mergesort` is the only stable algorithm. For
DataFrames, this option is only applied when sorting on a single
column or label.
na_position : {'first', 'last'}, default 'last'
`first` puts NaNs at the beginning, `last` puts NaNs at the end.
Not implemented for MultiIndex.
sort_remaining : bool, default True
if true and sorting by level and index is multilevel, sort by other
levels too (in order) after sorting by specified level
Returns
-------
sorted_obj : %(klass)s
"""
@Appender(_shared_docs['sort_index'] % dict(axes="axes", klass="NDFrame"))
def sort_index(self, axis=0, level=None, ascending=True, inplace=False,
kind='quicksort', na_position='last', sort_remaining=True):
inplace = validate_bool_kwarg(inplace, 'inplace')
axis = self._get_axis_number(axis)
axis_name = self._get_axis_name(axis)
labels = self._get_axis(axis)
if level is not None:
raise NotImplementedError("level is not implemented")
if inplace:
raise NotImplementedError("inplace is not implemented")
sort_index = labels.argsort()
if not ascending:
sort_index = sort_index[::-1]
new_axis = labels.take(sort_index)
return self.reindex(**{axis_name: new_axis})
_shared_docs['reindex'] = """
Conform %(klass)s to new index with optional filling logic, placing
NA/NaN in locations having no value in the previous index. A new object
is produced unless the new index is equivalent to the current one and
copy=False
Parameters
----------
%(optional_labels)s
%(axes)s : array-like, optional (should be specified using keywords)
New labels / index to conform to. Preferably an Index object to
avoid duplicating data
%(optional_axis)s
method : {None, 'backfill'/'bfill', 'pad'/'ffill', 'nearest'}, optional
method to use for filling holes in reindexed DataFrame.
Please note: this is only applicable to DataFrames/Series with a
monotonically increasing/decreasing index.
* default: don't fill gaps
* pad / ffill: propagate last valid observation forward to next
valid
* backfill / bfill: use next valid observation to fill gap
* nearest: use nearest valid observations to fill gap
copy : boolean, default True
Return a new object, even if the passed indexes are the same
level : int or name
Broadcast across a level, matching Index values on the
passed MultiIndex level
fill_value : scalar, default np.NaN
Value to use for missing values. Defaults to NaN, but can be any
"compatible" value
limit : int, default None
Maximum number of consecutive elements to forward or backward fill
tolerance : optional
Maximum distance between original and new labels for inexact
matches. The values of the index at the matching locations most
satisfy the equation ``abs(index[indexer] - target) <= tolerance``.
Tolerance may be a scalar value, which applies the same tolerance
to all values, or list-like, which applies variable tolerance per
element. List-like includes list, tuple, array, Series, and must be
the same size as the index and its dtype must exactly match the
index's type.
.. versionadded:: 0.21.0 (list-like tolerance)
Examples
--------
``DataFrame.reindex`` supports two calling conventions
* ``(index=index_labels, columns=column_labels, ...)``
* ``(labels, axis={'index', 'columns'}, ...)``
We *highly* recommend using keyword arguments to clarify your
intent.
Create a dataframe with some fictional data.
>>> index = ['Firefox', 'Chrome', 'Safari', 'IE10', 'Konqueror']
>>> df = pd.DataFrame({
... 'http_status': [200,200,404,404,301],
... 'response_time': [0.04, 0.02, 0.07, 0.08, 1.0]},
... index=index)
>>> df
http_status response_time
Firefox 200 0.04
Chrome 200 0.02
Safari 404 0.07
IE10 404 0.08
Konqueror 301 1.00
Create a new index and reindex the dataframe. By default
values in the new index that do not have corresponding
records in the dataframe are assigned ``NaN``.
>>> new_index= ['Safari', 'Iceweasel', 'Comodo Dragon', 'IE10',
... 'Chrome']
>>> df.reindex(new_index)
http_status response_time
Safari 404.0 0.07
Iceweasel NaN NaN
Comodo Dragon NaN NaN
IE10 404.0 0.08
Chrome 200.0 0.02
We can fill in the missing values by passing a value to
the keyword ``fill_value``. Because the index is not monotonically
increasing or decreasing, we cannot use arguments to the keyword
``method`` to fill the ``NaN`` values.
>>> df.reindex(new_index, fill_value=0)
http_status response_time
Safari 404 0.07
Iceweasel 0 0.00
Comodo Dragon 0 0.00
IE10 404 0.08
Chrome 200 0.02
>>> df.reindex(new_index, fill_value='missing')
http_status response_time
Safari 404 0.07
Iceweasel missing missing
Comodo Dragon missing missing
IE10 404 0.08
Chrome 200 0.02
We can also reindex the columns.
>>> df.reindex(columns=['http_status', 'user_agent'])
http_status user_agent
Firefox 200 NaN
Chrome 200 NaN
Safari 404 NaN
IE10 404 NaN
Konqueror 301 NaN
Or we can use "axis-style" keyword arguments
>>> df.reindex(['http_status', 'user_agent'], axis="columns")
http_status user_agent
Firefox 200 NaN
Chrome 200 NaN
Safari 404 NaN
IE10 404 NaN
Konqueror 301 NaN
To further illustrate the filling functionality in
``reindex``, we will create a dataframe with a
monotonically increasing index (for example, a sequence
of dates).
>>> date_index = pd.date_range('1/1/2010', periods=6, freq='D')
>>> df2 = pd.DataFrame({"prices": [100, 101, np.nan, 100, 89, 88]},
... index=date_index)
>>> df2
prices
2010-01-01 100
2010-01-02 101
2010-01-03 NaN
2010-01-04 100
2010-01-05 89
2010-01-06 88
Suppose we decide to expand the dataframe to cover a wider
date range.
>>> date_index2 = pd.date_range('12/29/2009', periods=10, freq='D')
>>> df2.reindex(date_index2)
prices
2009-12-29 NaN
2009-12-30 NaN
2009-12-31 NaN
2010-01-01 100
2010-01-02 101
2010-01-03 NaN
2010-01-04 100
2010-01-05 89
2010-01-06 88
2010-01-07 NaN
The index entries that did not have a value in the original data frame
(for example, '2009-12-29') are by default filled with ``NaN``.
If desired, we can fill in the missing values using one of several
options.
For example, to back-propagate the last valid value to fill the ``NaN``
values, pass ``bfill`` as an argument to the ``method`` keyword.
>>> df2.reindex(date_index2, method='bfill')
prices
2009-12-29 100
2009-12-30 100
2009-12-31 100
2010-01-01 100
2010-01-02 101
2010-01-03 NaN
2010-01-04 100
2010-01-05 89
2010-01-06 88
2010-01-07 NaN
Please note that the ``NaN`` value present in the original dataframe
(at index value 2010-01-03) will not be filled by any of the
value propagation schemes. This is because filling while reindexing
does not look at dataframe values, but only compares the original and
desired indexes. If you do want to fill in the ``NaN`` values present
in the original dataframe, use the ``fillna()`` method.
See the :ref:`user guide <basics.reindexing>` for more.
Returns
-------
reindexed : %(klass)s
"""
# TODO: Decide if we care about having different examples for different
# kinds
@Appender(_shared_docs['reindex'] % dict(axes="axes", klass="NDFrame",
optional_labels="",
optional_axis=""))
def reindex(self, *args, **kwargs):
# construct the args
axes, kwargs = self._construct_axes_from_arguments(args, kwargs)
method = missing.clean_reindex_fill_method(kwargs.pop('method', None))
level = kwargs.pop('level', None)
copy = kwargs.pop('copy', True)
limit = kwargs.pop('limit', None)
tolerance = kwargs.pop('tolerance', None)
fill_value = kwargs.pop('fill_value', None)
# Series.reindex doesn't use / need the axis kwarg
# We pop and ignore it here, to make writing Series/Frame generic code
# easier
kwargs.pop("axis", None)
if kwargs:
raise TypeError('reindex() got an unexpected keyword '
'argument "{0}"'.format(list(kwargs.keys())[0]))
self._consolidate_inplace()
# if all axes that are requested to reindex are equal, then only copy
# if indicated must have index names equal here as well as values
if all(self._get_axis(axis).identical(ax)
for axis, ax in axes.items() if ax is not None):
if copy:
return self.copy()
return self
# check if we are a multi reindex
if self._needs_reindex_multi(axes, method, level):
try:
return self._reindex_multi(axes, copy, fill_value)
except Exception:
pass
# perform the reindex on the axes
return self._reindex_axes(axes, level, limit, tolerance, method,
fill_value, copy).__finalize__(self)
def _reindex_axes(self, axes, level, limit, tolerance, method, fill_value,
copy):
"""Perform the reindex for all the axes."""
obj = self
for a in self._AXIS_ORDERS:
labels = axes[a]
if labels is None:
continue
ax = self._get_axis(a)
new_index, indexer = ax.reindex(labels, level=level, limit=limit,
tolerance=tolerance, method=method)
axis = self._get_axis_number(a)
obj = obj._reindex_with_indexers({axis: [new_index, indexer]},
fill_value=fill_value,
copy=copy, allow_dups=False)
return obj
def _needs_reindex_multi(self, axes, method, level):
"""Check if we do need a multi reindex."""
return ((com._count_not_none(*axes.values()) == self._AXIS_LEN) and
method is None and level is None and not self._is_mixed_type)
def _reindex_multi(self, axes, copy, fill_value):
return NotImplemented
_shared_docs[
'reindex_axis'] = ("""Conform input object to new index with optional
filling logic, placing NA/NaN in locations having no value in the
previous index. A new object is produced unless the new index is
equivalent to the current one and copy=False
Parameters
----------
labels : array-like
New labels / index to conform to. Preferably an Index object to
avoid duplicating data
axis : %(axes_single_arg)s
method : {None, 'backfill'/'bfill', 'pad'/'ffill', 'nearest'}, optional
Method to use for filling holes in reindexed DataFrame:
* default: don't fill gaps
* pad / ffill: propagate last valid observation forward to next
valid
* backfill / bfill: use next valid observation to fill gap
* nearest: use nearest valid observations to fill gap
copy : boolean, default True
Return a new object, even if the passed indexes are the same
level : int or name
Broadcast across a level, matching Index values on the
passed MultiIndex level
limit : int, default None
Maximum number of consecutive elements to forward or backward fill
tolerance : optional
Maximum distance between original and new labels for inexact
matches. The values of the index at the matching locations most
satisfy the equation ``abs(index[indexer] - target) <= tolerance``.
Tolerance may be a scalar value, which applies the same tolerance
to all values, or list-like, which applies variable tolerance per
element. List-like includes list, tuple, array, Series, and must be
the same size as the index and its dtype must exactly match the
index's type.
.. versionadded:: 0.21.0 (list-like tolerance)
Examples
--------
>>> df.reindex_axis(['A', 'B', 'C'], axis=1)
See Also
--------
reindex, reindex_like
Returns
-------
reindexed : %(klass)s
""")
@Appender(_shared_docs['reindex_axis'] % _shared_doc_kwargs)
def reindex_axis(self, labels, axis=0, method=None, level=None, copy=True,
limit=None, fill_value=None):
msg = ("'.reindex_axis' is deprecated and will be removed in a future "
"version. Use '.reindex' instead.")
self._consolidate_inplace()
axis_name = self._get_axis_name(axis)
axis_values = self._get_axis(axis_name)
method = missing.clean_reindex_fill_method(method)
warnings.warn(msg, FutureWarning, stacklevel=3)
new_index, indexer = axis_values.reindex(labels, method, level,
limit=limit)
return self._reindex_with_indexers({axis: [new_index, indexer]},
fill_value=fill_value, copy=copy)
def _reindex_with_indexers(self, reindexers, fill_value=None, copy=False,
allow_dups=False):
"""allow_dups indicates an internal call here """
# reindex doing multiple operations on different axes if indicated
new_data = self._data
for axis in sorted(reindexers.keys()):
index, indexer = reindexers[axis]
baxis = self._get_block_manager_axis(axis)
if index is None:
continue
index = ensure_index(index)
if indexer is not None:
indexer = ensure_int64(indexer)
# TODO: speed up on homogeneous DataFrame objects
new_data = new_data.reindex_indexer(index, indexer, axis=baxis,
fill_value=fill_value,
allow_dups=allow_dups,
copy=copy)
if copy and new_data is self._data:
new_data = new_data.copy()
return self._constructor(new_data).__finalize__(self)
def _reindex_axis(self, new_index, fill_method, axis, copy):
new_data = self._data.reindex_axis(new_index, axis=axis,
method=fill_method, copy=copy)
if new_data is self._data and not copy:
return self
else:
return self._constructor(new_data).__finalize__(self)
def filter(self, items=None, like=None, regex=None, axis=None):
"""
Subset rows or columns of dataframe according to labels in
the specified index.
Note that this routine does not filter a dataframe on its
contents. The filter is applied to the labels of the index.
Parameters
----------
items : list-like
List of axis to restrict to (must not all be present).
like : string
Keep axis where "arg in col == True".
regex : string (regular expression)
Keep axis with re.search(regex, col) == True.
axis : int or string axis name
The axis to filter on. By default this is the info axis,
'index' for Series, 'columns' for DataFrame.
Returns
-------
same type as input object
Examples
--------
>>> df = pd.DataFrame(np.array(([1,2,3], [4,5,6])),
... index=['mouse', 'rabbit'],
... columns=['one', 'two', 'three'])
>>> # select columns by name
>>> df.filter(items=['one', 'three'])
one three
mouse 1 3
rabbit 4 6
>>> # select columns by regular expression
>>> df.filter(regex='e$', axis=1)
one three
mouse 1 3
rabbit 4 6
>>> # select rows containing 'bbi'
>>> df.filter(like='bbi', axis=0)
one two three
rabbit 4 5 6
See Also
--------
pandas.DataFrame.loc
Notes
-----
The ``items``, ``like``, and ``regex`` parameters are
enforced to be mutually exclusive.
``axis`` defaults to the info axis that is used when indexing
with ``[]``.
"""
import re
nkw = com._count_not_none(items, like, regex)
if nkw > 1:
raise TypeError('Keyword arguments `items`, `like`, or `regex` '
'are mutually exclusive')
if axis is None:
axis = self._info_axis_name
labels = self._get_axis(axis)
if items is not None:
name = self._get_axis_name(axis)
return self.reindex(
**{name: [r for r in items if r in labels]})
elif like:
def f(x):
return like in to_str(x)
values = labels.map(f)
return self.loc(axis=axis)[values]
elif regex:
def f(x):
return matcher.search(to_str(x)) is not None
matcher = re.compile(regex)
values = labels.map(f)
return self.loc(axis=axis)[values]
else:
raise TypeError('Must pass either `items`, `like`, or `regex`')
def head(self, n=5):
"""
Return the first `n` rows.
This function returns the first `n` rows for the object based
on position. It is useful for quickly testing if your object
has the right type of data in it.
Parameters
----------
n : int, default 5
Number of rows to select.
Returns
-------
obj_head : same type as caller
The first `n` rows of the caller object.
See Also
--------
pandas.DataFrame.tail: Returns the last `n` rows.
Examples
--------
>>> df = pd.DataFrame({'animal':['alligator', 'bee', 'falcon', 'lion',
... 'monkey', 'parrot', 'shark', 'whale', 'zebra']})
>>> df
animal
0 alligator
1 bee
2 falcon
3 lion
4 monkey
5 parrot
6 shark
7 whale
8 zebra
Viewing the first 5 lines
>>> df.head()
animal
0 alligator
1 bee
2 falcon
3 lion
4 monkey
Viewing the first `n` lines (three in this case)
>>> df.head(3)
animal
0 alligator
1 bee
2 falcon
"""
return self.iloc[:n]
def tail(self, n=5):
"""
Return the last `n` rows.
This function returns last `n` rows from the object based on
position. It is useful for quickly verifying data, for example,
after sorting or appending rows.
Parameters
----------
n : int, default 5
Number of rows to select.
Returns
-------
type of caller
The last `n` rows of the caller object.
See Also
--------
pandas.DataFrame.head : The first `n` rows of the caller object.
Examples
--------
>>> df = pd.DataFrame({'animal':['alligator', 'bee', 'falcon', 'lion',
... 'monkey', 'parrot', 'shark', 'whale', 'zebra']})
>>> df
animal
0 alligator
1 bee
2 falcon
3 lion
4 monkey
5 parrot
6 shark
7 whale
8 zebra
Viewing the last 5 lines
>>> df.tail()
animal
4 monkey
5 parrot
6 shark
7 whale
8 zebra
Viewing the last `n` lines (three in this case)
>>> df.tail(3)
animal
6 shark
7 whale
8 zebra
"""
if n == 0:
return self.iloc[0:0]
return self.iloc[-n:]
def sample(self, n=None, frac=None, replace=False, weights=None,
random_state=None, axis=None):
"""
Return a random sample of items from an axis of object.
You can use `random_state` for reproducibility.
Parameters
----------
n : int, optional
Number of items from axis to return. Cannot be used with `frac`.
Default = 1 if `frac` = None.
frac : float, optional
Fraction of axis items to return. Cannot be used with `n`.
replace : boolean, optional
Sample with or without replacement. Default = False.
weights : str or ndarray-like, optional
Default 'None' results in equal probability weighting.
If passed a Series, will align with target object on index. Index
values in weights not found in sampled object will be ignored and
index values in sampled object not in weights will be assigned
weights of zero.
If called on a DataFrame, will accept the name of a column
when axis = 0.
Unless weights are a Series, weights must be same length as axis
being sampled.
If weights do not sum to 1, they will be normalized to sum to 1.
Missing values in the weights column will be treated as zero.
inf and -inf values not allowed.
random_state : int or numpy.random.RandomState, optional
Seed for the random number generator (if int), or numpy RandomState
object.
axis : int or string, optional
Axis to sample. Accepts axis number or name. Default is stat axis
for given data type (0 for Series and DataFrames, 1 for Panels).
Returns
-------
A new object of same type as caller.
Examples
--------
Generate an example ``Series`` and ``DataFrame``:
>>> s = pd.Series(np.random.randn(50))
>>> s.head()
0 -0.038497
1 1.820773
2 -0.972766
3 -1.598270
4 -1.095526
dtype: float64
>>> df = pd.DataFrame(np.random.randn(50, 4), columns=list('ABCD'))
>>> df.head()
A B C D
0 0.016443 -2.318952 -0.566372 -1.028078
1 -1.051921 0.438836 0.658280 -0.175797
2 -1.243569 -0.364626 -0.215065 0.057736
3 1.768216 0.404512 -0.385604 -1.457834
4 1.072446 -1.137172 0.314194 -0.046661
Next extract a random sample from both of these objects...
3 random elements from the ``Series``:
>>> s.sample(n=3)
27 -0.994689
55 -1.049016
67 -0.224565
dtype: float64
And a random 10% of the ``DataFrame`` with replacement:
>>> df.sample(frac=0.1, replace=True)
A B C D
35 1.981780 0.142106 1.817165 -0.290805
49 -1.336199 -0.448634 -0.789640 0.217116
40 0.823173 -0.078816 1.009536 1.015108
15 1.421154 -0.055301 -1.922594 -0.019696
6 -0.148339 0.832938 1.787600 -1.383767
You can use `random state` for reproducibility:
>>> df.sample(random_state=1)
A B C D
37 -2.027662 0.103611 0.237496 -0.165867
43 -0.259323 -0.583426 1.516140 -0.479118
12 -1.686325 -0.579510 0.985195 -0.460286
8 1.167946 0.429082 1.215742 -1.636041
9 1.197475 -0.864188 1.554031 -1.505264
"""
if axis is None:
axis = self._stat_axis_number
axis = self._get_axis_number(axis)
axis_length = self.shape[axis]
# Process random_state argument
rs = com._random_state(random_state)
# Check weights for compliance
if weights is not None:
# If a series, align with frame
if isinstance(weights, pd.Series):
weights = weights.reindex(self.axes[axis])
# Strings acceptable if a dataframe and axis = 0
if isinstance(weights, string_types):
if isinstance(self, pd.DataFrame):
if axis == 0:
try:
weights = self[weights]
except KeyError:
raise KeyError("String passed to weights not a "
"valid column")
else:
raise ValueError("Strings can only be passed to "
"weights when sampling from rows on "
"a DataFrame")
else:
raise ValueError("Strings cannot be passed as weights "
"when sampling from a Series or Panel.")
weights = pd.Series(weights, dtype='float64')
if len(weights) != axis_length:
raise ValueError("Weights and axis to be sampled must be of "
"same length")
if (weights == np.inf).any() or (weights == -np.inf).any():
raise ValueError("weight vector may not include `inf` values")
if (weights < 0).any():
raise ValueError("weight vector many not include negative "
"values")
# If has nan, set to zero.
weights = weights.fillna(0)
# Renormalize if don't sum to 1
if weights.sum() != 1:
if weights.sum() != 0:
weights = weights / weights.sum()
else:
raise ValueError("Invalid weights: weights sum to zero")
weights = weights.values
# If no frac or n, default to n=1.
if n is None and frac is None:
n = 1
elif n is not None and frac is None and n % 1 != 0:
raise ValueError("Only integers accepted as `n` values")
elif n is None and frac is not None:
n = int(round(frac * axis_length))
elif n is not None and frac is not None:
raise ValueError('Please enter a value for `frac` OR `n`, not '
'both')
# Check for negative sizes
if n < 0:
raise ValueError("A negative number of rows requested. Please "
"provide positive value.")
locs = rs.choice(axis_length, size=n, replace=replace, p=weights)
return self.take(locs, axis=axis, is_copy=False)
_shared_docs['pipe'] = (r"""
Apply func(self, \*args, \*\*kwargs)
Parameters
----------
func : function
function to apply to the %(klass)s.
``args``, and ``kwargs`` are passed into ``func``.
Alternatively a ``(callable, data_keyword)`` tuple where
``data_keyword`` is a string indicating the keyword of
``callable`` that expects the %(klass)s.
args : iterable, optional
positional arguments passed into ``func``.
kwargs : mapping, optional
a dictionary of keyword arguments passed into ``func``.
Returns
-------
object : the return type of ``func``.
Notes
-----
Use ``.pipe`` when chaining together functions that expect
Series, DataFrames or GroupBy objects. Instead of writing
>>> f(g(h(df), arg1=a), arg2=b, arg3=c)
You can write
>>> (df.pipe(h)
... .pipe(g, arg1=a)
... .pipe(f, arg2=b, arg3=c)
... )
If you have a function that takes the data as (say) the second
argument, pass a tuple indicating which keyword expects the
data. For example, suppose ``f`` takes its data as ``arg2``:
>>> (df.pipe(h)
... .pipe(g, arg1=a)
... .pipe((f, 'arg2'), arg1=a, arg3=c)
... )
See Also
--------
pandas.DataFrame.apply
pandas.DataFrame.applymap
pandas.Series.map
""")
@Appender(_shared_docs['pipe'] % _shared_doc_kwargs)
def pipe(self, func, *args, **kwargs):
return com._pipe(self, func, *args, **kwargs)
_shared_docs['aggregate'] = ("""
Aggregate using one or more operations over the specified axis.
%(versionadded)s
Parameters
----------
func : function, string, dictionary, or list of string/functions
Function to use for aggregating the data. If a function, must either
work when passed a %(klass)s or when passed to %(klass)s.apply. For
a DataFrame, can pass a dict, if the keys are DataFrame column names.
Accepted combinations are:
- string function name.
- function.
- list of functions.
- dict of column names -> functions (or list of functions).
%(axis)s
*args
Positional arguments to pass to `func`.
**kwargs
Keyword arguments to pass to `func`.
Returns
-------
aggregated : %(klass)s
Notes
-----
`agg` is an alias for `aggregate`. Use the alias.
A passed user-defined-function will be passed a Series for evaluation.
""")
_shared_docs['transform'] = ("""
Call function producing a like-indexed %(klass)s
and return a %(klass)s with the transformed values
.. versionadded:: 0.20.0
Parameters
----------
func : callable, string, dictionary, or list of string/callables
To apply to column
Accepted Combinations are:
- string function name
- function
- list of functions
- dict of column names -> functions (or list of functions)
Returns
-------
transformed : %(klass)s
Examples
--------
>>> df = pd.DataFrame(np.random.randn(10, 3), columns=['A', 'B', 'C'],
... index=pd.date_range('1/1/2000', periods=10))
df.iloc[3:7] = np.nan
>>> df.transform(lambda x: (x - x.mean()) / x.std())
A B C
2000-01-01 0.579457 1.236184 0.123424
2000-01-02 0.370357 -0.605875 -1.231325
2000-01-03 1.455756 -0.277446 0.288967
2000-01-04 NaN NaN NaN
2000-01-05 NaN NaN NaN
2000-01-06 NaN NaN NaN
2000-01-07 NaN NaN NaN
2000-01-08 -0.498658 1.274522 1.642524
2000-01-09 -0.540524 -1.012676 -0.828968
2000-01-10 -1.366388 -0.614710 0.005378
See also
--------
pandas.%(klass)s.aggregate
pandas.%(klass)s.apply
""")
# ----------------------------------------------------------------------
# Attribute access
def __finalize__(self, other, method=None, **kwargs):
"""
Propagate metadata from other to self.
Parameters
----------
other : the object from which to get the attributes that we are going
to propagate
method : optional, a passed method name ; possibly to take different
types of propagation actions based on this
"""
if isinstance(other, NDFrame):
for name in self._metadata:
object.__setattr__(self, name, getattr(other, name, None))
return self
def __getattr__(self, name):
"""After regular attribute access, try looking up the name
This allows simpler access to columns for interactive use.
"""
# Note: obj.x will always call obj.__getattribute__('x') prior to
# calling obj.__getattr__('x').
if (name in self._internal_names_set or name in self._metadata or
name in self._accessors):
return object.__getattribute__(self, name)
else:
if self._info_axis._can_hold_identifiers_and_holds_name(name):
return self[name]
return object.__getattribute__(self, name)
def __setattr__(self, name, value):
"""After regular attribute access, try setting the name
This allows simpler access to columns for interactive use.
"""
# first try regular attribute access via __getattribute__, so that
# e.g. ``obj.x`` and ``obj.x = 4`` will always reference/modify
# the same attribute.
try:
object.__getattribute__(self, name)
return object.__setattr__(self, name, value)
except AttributeError:
pass
# if this fails, go on to more involved attribute setting
# (note that this matches __getattr__, above).
if name in self._internal_names_set:
object.__setattr__(self, name, value)
elif name in self._metadata:
object.__setattr__(self, name, value)
else:
try:
existing = getattr(self, name)
if isinstance(existing, Index):
object.__setattr__(self, name, value)
elif name in self._info_axis:
self[name] = value
else:
object.__setattr__(self, name, value)
except (AttributeError, TypeError):
if isinstance(self, ABCDataFrame) and (is_list_like(value)):
warnings.warn("Pandas doesn't allow columns to be "
"created via a new attribute name - see "
"https://pandas.pydata.org/pandas-docs/"
"stable/indexing.html#attribute-access",
stacklevel=2)
object.__setattr__(self, name, value)
# ----------------------------------------------------------------------
# Getting and setting elements
# ----------------------------------------------------------------------
# Consolidation of internals
def _protect_consolidate(self, f):
"""Consolidate _data -- if the blocks have changed, then clear the
cache
"""
blocks_before = len(self._data.blocks)
result = f()
if len(self._data.blocks) != blocks_before:
self._clear_item_cache()
return result
def _consolidate_inplace(self):
"""Consolidate data in place and return None"""
def f():
self._data = self._data.consolidate()
self._protect_consolidate(f)
def _consolidate(self, inplace=False):
"""
Compute NDFrame with "consolidated" internals (data of each dtype
grouped together in a single ndarray).
Parameters
----------
inplace : boolean, default False
If False return new object, otherwise modify existing object
Returns
-------
consolidated : same type as caller
"""
inplace = validate_bool_kwarg(inplace, 'inplace')
if inplace:
self._consolidate_inplace()
else:
f = lambda: self._data.consolidate()
cons_data = self._protect_consolidate(f)
return self._constructor(cons_data).__finalize__(self)
def consolidate(self, inplace=False):
"""Compute NDFrame with "consolidated" internals (data of each dtype
grouped together in a single ndarray).
.. deprecated:: 0.20.0
Consolidate will be an internal implementation only.
"""
# 15483
warnings.warn("consolidate is deprecated and will be removed in a "
"future release.", FutureWarning, stacklevel=2)
return self._consolidate(inplace)
@property
def _is_mixed_type(self):
f = lambda: self._data.is_mixed_type
return self._protect_consolidate(f)
@property
def _is_numeric_mixed_type(self):
f = lambda: self._data.is_numeric_mixed_type
return self._protect_consolidate(f)
@property
def _is_datelike_mixed_type(self):
f = lambda: self._data.is_datelike_mixed_type
return self._protect_consolidate(f)
def _check_inplace_setting(self, value):
""" check whether we allow in-place setting with this type of value """
if self._is_mixed_type:
if not self._is_numeric_mixed_type:
# allow an actual np.nan thru
try:
if np.isnan(value):
return True
except Exception:
pass
raise TypeError('Cannot do inplace boolean setting on '
'mixed-types with a non np.nan value')
return True
def _get_numeric_data(self):
return self._constructor(
self._data.get_numeric_data()).__finalize__(self)
def _get_bool_data(self):
return self._constructor(self._data.get_bool_data()).__finalize__(self)
# ----------------------------------------------------------------------
# Internal Interface Methods
def as_matrix(self, columns=None):
"""Convert the frame to its Numpy-array representation.
.. deprecated:: 0.23.0
Use :meth:`DataFrame.values` instead.
Parameters
----------
columns: list, optional, default:None
If None, return all columns, otherwise, returns specified columns.
Returns
-------
values : ndarray
If the caller is heterogeneous and contains booleans or objects,
the result will be of dtype=object. See Notes.
Notes
-----
Return is NOT a Numpy-matrix, rather, a Numpy-array.
The dtype will be a lower-common-denominator dtype (implicit
upcasting); that is to say if the dtypes (even of numeric types)
are mixed, the one that accommodates all will be chosen. Use this
with care if you are not dealing with the blocks.
e.g. If the dtypes are float16 and float32, dtype will be upcast to
float32. If dtypes are int32 and uint8, dtype will be upcase to
int32. By numpy.find_common_type convention, mixing int64 and uint64
will result in a float64 dtype.
This method is provided for backwards compatibility. Generally,
it is recommended to use '.values'.
See Also
--------
pandas.DataFrame.values
"""
warnings.warn("Method .as_matrix will be removed in a future version. "
"Use .values instead.", FutureWarning, stacklevel=2)
self._consolidate_inplace()
return self._data.as_array(transpose=self._AXIS_REVERSED,
items=columns)
@property
def values(self):
"""
Return a Numpy representation of the DataFrame.
Only the values in the DataFrame will be returned, the axes labels
will be removed.
Returns
-------
numpy.ndarray
The values of the DataFrame.
Examples
--------
A DataFrame where all columns are the same type (e.g., int64) results
in an array of the same type.
>>> df = pd.DataFrame({'age': [ 3, 29],
... 'height': [94, 170],
... 'weight': [31, 115]})
>>> df
age height weight
0 3 94 31
1 29 170 115
>>> df.dtypes
age int64
height int64
weight int64
dtype: object
>>> df.values
array([[ 3, 94, 31],
[ 29, 170, 115]], dtype=int64)
A DataFrame with mixed type columns(e.g., str/object, int64, float32)
results in an ndarray of the broadest type that accommodates these
mixed types (e.g., object).
>>> df2 = pd.DataFrame([('parrot', 24.0, 'second'),
... ('lion', 80.5, 1),
... ('monkey', np.nan, None)],
... columns=('name', 'max_speed', 'rank'))
>>> df2.dtypes
name object
max_speed float64
rank object
dtype: object
>>> df2.values
array([['parrot', 24.0, 'second'],
['lion', 80.5, 1],
['monkey', nan, None]], dtype=object)
Notes
-----
The dtype will be a lower-common-denominator dtype (implicit
upcasting); that is to say if the dtypes (even of numeric types)
are mixed, the one that accommodates all will be chosen. Use this
with care if you are not dealing with the blocks.
e.g. If the dtypes are float16 and float32, dtype will be upcast to
float32. If dtypes are int32 and uint8, dtype will be upcast to
int32. By :func:`numpy.find_common_type` convention, mixing int64
and uint64 will result in a float64 dtype.
See Also
--------
pandas.DataFrame.index : Retrieve the index labels
pandas.DataFrame.columns : Retrieving the column names
"""
self._consolidate_inplace()
return self._data.as_array(transpose=self._AXIS_REVERSED)
@property
def _values(self):
"""internal implementation"""
return self.values
@property
def _get_values(self):
# compat
return self.values
def get_values(self):
"""
Return an ndarray after converting sparse values to dense.
This is the same as ``.values`` for non-sparse data. For sparse
data contained in a `pandas.SparseArray`, the data are first
converted to a dense representation.
Returns
-------
numpy.ndarray
Numpy representation of DataFrame
See Also
--------
values : Numpy representation of DataFrame.
pandas.SparseArray : Container for sparse data.
Examples
--------
>>> df = pd.DataFrame({'a': [1, 2], 'b': [True, False],
... 'c': [1.0, 2.0]})
>>> df
a b c
0 1 True 1.0
1 2 False 2.0
>>> df.get_values()
array([[1, True, 1.0], [2, False, 2.0]], dtype=object)
>>> df = pd.DataFrame({"a": pd.SparseArray([1, None, None]),
... "c": [1.0, 2.0, 3.0]})
>>> df
a c
0 1.0 1.0
1 NaN 2.0
2 NaN 3.0
>>> df.get_values()
array([[ 1., 1.],
[nan, 2.],
[nan, 3.]])
"""
return self.values
def get_dtype_counts(self):
"""
Return counts of unique dtypes in this object.
Returns
-------
dtype : Series
Series with the count of columns with each dtype.
See Also
--------
dtypes : Return the dtypes in this object.
Examples
--------
>>> a = [['a', 1, 1.0], ['b', 2, 2.0], ['c', 3, 3.0]]
>>> df = pd.DataFrame(a, columns=['str', 'int', 'float'])
>>> df
str int float
0 a 1 1.0
1 b 2 2.0
2 c 3 3.0
>>> df.get_dtype_counts()
float64 1
int64 1
object 1
dtype: int64
"""
from pandas import Series
return Series(self._data.get_dtype_counts())
def get_ftype_counts(self):
"""
Return counts of unique ftypes in this object.
.. deprecated:: 0.23.0
This is useful for SparseDataFrame or for DataFrames containing
sparse arrays.
Returns
-------
dtype : Series
Series with the count of columns with each type and
sparsity (dense/sparse)
See Also
--------
ftypes : Return ftypes (indication of sparse/dense and dtype) in
this object.
Examples
--------
>>> a = [['a', 1, 1.0], ['b', 2, 2.0], ['c', 3, 3.0]]
>>> df = pd.DataFrame(a, columns=['str', 'int', 'float'])
>>> df
str int float
0 a 1 1.0
1 b 2 2.0
2 c 3 3.0
>>> df.get_ftype_counts()
float64:dense 1
int64:dense 1
object:dense 1
dtype: int64
"""
warnings.warn("get_ftype_counts is deprecated and will "
"be removed in a future version",
FutureWarning, stacklevel=2)
from pandas import Series
return Series(self._data.get_ftype_counts())
@property
def dtypes(self):
"""
Return the dtypes in the DataFrame.
This returns a Series with the data type of each column.
The result's index is the original DataFrame's columns. Columns
with mixed types are stored with the ``object`` dtype. See
:ref:`the User Guide <basics.dtypes>` for more.
Returns
-------
pandas.Series
The data type of each column.
See Also
--------
pandas.DataFrame.ftypes : dtype and sparsity information.
Examples
--------
>>> df = pd.DataFrame({'float': [1.0],
... 'int': [1],
... 'datetime': [pd.Timestamp('20180310')],
... 'string': ['foo']})
>>> df.dtypes
float float64
int int64
datetime datetime64[ns]
string object
dtype: object
"""
from pandas import Series
return Series(self._data.get_dtypes(), index=self._info_axis,
dtype=np.object_)
@property
def ftypes(self):
"""
Return the ftypes (indication of sparse/dense and dtype) in DataFrame.
This returns a Series with the data type of each column.
The result's index is the original DataFrame's columns. Columns
with mixed types are stored with the ``object`` dtype. See
:ref:`the User Guide <basics.dtypes>` for more.
Returns
-------
pandas.Series
The data type and indication of sparse/dense of each column.
See Also
--------
pandas.DataFrame.dtypes: Series with just dtype information.
pandas.SparseDataFrame : Container for sparse tabular data.
Notes
-----
Sparse data should have the same dtypes as its dense representation.
Examples
--------
>>> arr = np.random.RandomState(0).randn(100, 4)
>>> arr[arr < .8] = np.nan
>>> pd.DataFrame(arr).ftypes
0 float64:dense
1 float64:dense
2 float64:dense
3 float64:dense
dtype: object
>>> pd.SparseDataFrame(arr).ftypes
0 float64:sparse
1 float64:sparse
2 float64:sparse
3 float64:sparse
dtype: object
"""
from pandas import Series
return Series(self._data.get_ftypes(), index=self._info_axis,
dtype=np.object_)
def as_blocks(self, copy=True):
"""
Convert the frame to a dict of dtype -> Constructor Types that each has
a homogeneous dtype.
.. deprecated:: 0.21.0
NOTE: the dtypes of the blocks WILL BE PRESERVED HERE (unlike in
as_matrix)
Parameters
----------
copy : boolean, default True
Returns
-------
values : a dict of dtype -> Constructor Types
"""
warnings.warn("as_blocks is deprecated and will "
"be removed in a future version",
FutureWarning, stacklevel=2)
return self._to_dict_of_blocks(copy=copy)
@property
def blocks(self):
"""
Internal property, property synonym for as_blocks()
.. deprecated:: 0.21.0
"""
return self.as_blocks()
def _to_dict_of_blocks(self, copy=True):
"""
Return a dict of dtype -> Constructor Types that
each is a homogeneous dtype.
Internal ONLY
"""
return {k: self._constructor(v).__finalize__(self)
for k, v, in self._data.to_dict(copy=copy).items()}
@deprecate_kwarg(old_arg_name='raise_on_error', new_arg_name='errors',
mapping={True: 'raise', False: 'ignore'})
def astype(self, dtype, copy=True, errors='raise', **kwargs):
"""
Cast a pandas object to a specified dtype ``dtype``.
Parameters
----------
dtype : data type, or dict of column name -> data type
Use a numpy.dtype or Python type to cast entire pandas object to
the same type. Alternatively, use {col: dtype, ...}, where col is a
column label and dtype is a numpy.dtype or Python type to cast one
or more of the DataFrame's columns to column-specific types.
copy : bool, default True.
Return a copy when ``copy=True`` (be very careful setting
``copy=False`` as changes to values then may propagate to other
pandas objects).
errors : {'raise', 'ignore'}, default 'raise'.
Control raising of exceptions on invalid data for provided dtype.
- ``raise`` : allow exceptions to be raised
- ``ignore`` : suppress exceptions. On error return original object
.. versionadded:: 0.20.0
raise_on_error : raise on invalid input
.. deprecated:: 0.20.0
Use ``errors`` instead
kwargs : keyword arguments to pass on to the constructor
Returns
-------
casted : same type as caller
Examples
--------
>>> ser = pd.Series([1, 2], dtype='int32')
>>> ser
0 1
1 2
dtype: int32
>>> ser.astype('int64')
0 1
1 2
dtype: int64
Convert to categorical type:
>>> ser.astype('category')
0 1
1 2
dtype: category
Categories (2, int64): [1, 2]
Convert to ordered categorical type with custom ordering:
>>> ser.astype('category', ordered=True, categories=[2, 1])
0 1
1 2
dtype: category
Categories (2, int64): [2 < 1]
Note that using ``copy=False`` and changing data on a new
pandas object may propagate changes:
>>> s1 = pd.Series([1,2])
>>> s2 = s1.astype('int64', copy=False)
>>> s2[0] = 10
>>> s1 # note that s1[0] has changed too
0 10
1 2
dtype: int64
See also
--------
pandas.to_datetime : Convert argument to datetime.
pandas.to_timedelta : Convert argument to timedelta.
pandas.to_numeric : Convert argument to a numeric type.
numpy.ndarray.astype : Cast a numpy array to a specified type.
"""
if is_dict_like(dtype):
if self.ndim == 1: # i.e. Series
if len(dtype) > 1 or self.name not in dtype:
raise KeyError('Only the Series name can be used for '
'the key in Series dtype mappings.')
new_type = dtype[self.name]
return self.astype(new_type, copy, errors, **kwargs)
elif self.ndim > 2:
raise NotImplementedError(
'astype() only accepts a dtype arg of type dict when '
'invoked on Series and DataFrames. A single dtype must be '
'specified when invoked on a Panel.'
)
for col_name in dtype.keys():
if col_name not in self:
raise KeyError('Only a column name can be used for the '
'key in a dtype mappings argument.')
results = []
for col_name, col in self.iteritems():
if col_name in dtype:
results.append(col.astype(dtype[col_name], copy=copy))
else:
results.append(results.append(col.copy() if copy else col))
elif is_categorical_dtype(dtype) and self.ndim > 1:
# GH 18099: columnwise conversion to categorical
results = (self[col].astype(dtype, copy=copy) for col in self)
else:
# else, only a single dtype is given
new_data = self._data.astype(dtype=dtype, copy=copy, errors=errors,
**kwargs)
return self._constructor(new_data).__finalize__(self)
# GH 19920: retain column metadata after concat
result = pd.concat(results, axis=1, copy=False)
result.columns = self.columns
return result
def copy(self, deep=True):
"""
Make a copy of this object's indices and data.
When ``deep=True`` (default), a new object will be created with a
copy of the calling object's data and indices. Modifications to
the data or indices of the copy will not be reflected in the
original object (see notes below).
When ``deep=False``, a new object will be created without copying
the calling object's data or index (only references to the data
and index are copied). Any changes to the data of the original
will be reflected in the shallow copy (and vice versa).
Parameters
----------
deep : bool, default True
Make a deep copy, including a copy of the data and the indices.
With ``deep=False`` neither the indices nor the data are copied.
Returns
-------
copy : Series, DataFrame or Panel
Object type matches caller.
Notes
-----
When ``deep=True``, data is copied but actual Python objects
will not be copied recursively, only the reference to the object.
This is in contrast to `copy.deepcopy` in the Standard Library,
which recursively copies object data (see examples below).
While ``Index`` objects are copied when ``deep=True``, the underlying
numpy array is not copied for performance reasons. Since ``Index`` is
immutable, the underlying data can be safely shared and a copy
is not needed.
Examples
--------
>>> s = pd.Series([1, 2], index=["a", "b"])
>>> s
a 1
b 2
dtype: int64
>>> s_copy = s.copy()
>>> s_copy
a 1
b 2
dtype: int64
**Shallow copy versus default (deep) copy:**
>>> s = pd.Series([1, 2], index=["a", "b"])
>>> deep = s.copy()
>>> shallow = s.copy(deep=False)
Shallow copy shares data and index with original.
>>> s is shallow
False
>>> s.values is shallow.values and s.index is shallow.index
True
Deep copy has own copy of data and index.
>>> s is deep
False
>>> s.values is deep.values or s.index is deep.index
False
Updates to the data shared by shallow copy and original is reflected
in both; deep copy remains unchanged.
>>> s[0] = 3
>>> shallow[1] = 4
>>> s
a 3
b 4
dtype: int64
>>> shallow
a 3
b 4
dtype: int64
>>> deep
a 1
b 2
dtype: int64
Note that when copying an object containing Python objects, a deep copy
will copy the data, but will not do so recursively. Updating a nested
data object will be reflected in the deep copy.
>>> s = pd.Series([[1, 2], [3, 4]])
>>> deep = s.copy()
>>> s[0][0] = 10
>>> s
0 [10, 2]
1 [3, 4]
dtype: object
>>> deep
0 [10, 2]
1 [3, 4]
dtype: object
"""
data = self._data.copy(deep=deep)
return self._constructor(data).__finalize__(self)
def __copy__(self, deep=True):
return self.copy(deep=deep)
def __deepcopy__(self, memo=None):
if memo is None:
memo = {}
return self.copy(deep=True)
def _convert(self, datetime=False, numeric=False, timedelta=False,
coerce=False, copy=True):
"""
Attempt to infer better dtype for object columns
Parameters
----------
datetime : boolean, default False
If True, convert to date where possible.
numeric : boolean, default False
If True, attempt to convert to numbers (including strings), with
unconvertible values becoming NaN.
timedelta : boolean, default False
If True, convert to timedelta where possible.
coerce : boolean, default False
If True, force conversion with unconvertible values converted to
nulls (NaN or NaT)
copy : boolean, default True
If True, return a copy even if no copy is necessary (e.g. no
conversion was done). Note: This is meant for internal use, and
should not be confused with inplace.
Returns
-------
converted : same as input object
"""
return self._constructor(
self._data.convert(datetime=datetime, numeric=numeric,
timedelta=timedelta, coerce=coerce,
copy=copy)).__finalize__(self)
def convert_objects(self, convert_dates=True, convert_numeric=False,
convert_timedeltas=True, copy=True):
"""Attempt to infer better dtype for object columns.
.. deprecated:: 0.21.0
Parameters
----------
convert_dates : boolean, default True
If True, convert to date where possible. If 'coerce', force
conversion, with unconvertible values becoming NaT.
convert_numeric : boolean, default False
If True, attempt to coerce to numbers (including strings), with
unconvertible values becoming NaN.
convert_timedeltas : boolean, default True
If True, convert to timedelta where possible. If 'coerce', force
conversion, with unconvertible values becoming NaT.
copy : boolean, default True
If True, return a copy even if no copy is necessary (e.g. no
conversion was done). Note: This is meant for internal use, and
should not be confused with inplace.
See Also
--------
pandas.to_datetime : Convert argument to datetime.
pandas.to_timedelta : Convert argument to timedelta.
pandas.to_numeric : Convert argument to numeric type.
Returns
-------
converted : same as input object
"""
msg = ("convert_objects is deprecated. To re-infer data dtypes for "
"object columns, use {klass}.infer_objects()\nFor all "
"other conversions use the data-type specific converters "
"pd.to_datetime, pd.to_timedelta and pd.to_numeric."
).format(klass=self.__class__.__name__)
warnings.warn(msg, FutureWarning, stacklevel=2)
return self._constructor(
self._data.convert(convert_dates=convert_dates,
convert_numeric=convert_numeric,
convert_timedeltas=convert_timedeltas,
copy=copy)).__finalize__(self)
def infer_objects(self):
"""
Attempt to infer better dtypes for object columns.
Attempts soft conversion of object-dtyped
columns, leaving non-object and unconvertible
columns unchanged. The inference rules are the
same as during normal Series/DataFrame construction.
.. versionadded:: 0.21.0
See Also
--------
pandas.to_datetime : Convert argument to datetime.
pandas.to_timedelta : Convert argument to timedelta.
pandas.to_numeric : Convert argument to numeric type.
Returns
-------
converted : same type as input object
Examples
--------
>>> df = pd.DataFrame({"A": ["a", 1, 2, 3]})
>>> df = df.iloc[1:]
>>> df
A
1 1
2 2
3 3
>>> df.dtypes
A object
dtype: object
>>> df.infer_objects().dtypes
A int64
dtype: object
"""
# numeric=False necessary to only soft convert;
# python objects will still be converted to
# native numpy numeric types
return self._constructor(
self._data.convert(datetime=True, numeric=False,
timedelta=True, coerce=False,
copy=True)).__finalize__(self)
# ----------------------------------------------------------------------
# Filling NA's
def fillna(self, value=None, method=None, axis=None, inplace=False,
limit=None, downcast=None):
"""
Fill NA/NaN values using the specified method
Parameters
----------
value : scalar, dict, Series, or DataFrame
Value to use to fill holes (e.g. 0), alternately a
dict/Series/DataFrame of values specifying which value to use for
each index (for a Series) or column (for a DataFrame). (values not
in the dict/Series/DataFrame will not be filled). This value cannot
be a list.
method : {'backfill', 'bfill', 'pad', 'ffill', None}, default None
Method to use for filling holes in reindexed Series
pad / ffill: propagate last valid observation forward to next valid
backfill / bfill: use NEXT valid observation to fill gap
axis : %(axes_single_arg)s
inplace : boolean, default False
If True, fill in place. Note: this will modify any
other views on this object, (e.g. a no-copy slice for a column in a
DataFrame).
limit : int, default None
If method is specified, this is the maximum number of consecutive
NaN values to forward/backward fill. In other words, if there is
a gap with more than this number of consecutive NaNs, it will only
be partially filled. If method is not specified, this is the
maximum number of entries along the entire axis where NaNs will be
filled. Must be greater than 0 if not None.
downcast : dict, default is None
a dict of item->dtype of what to downcast if possible,
or the string 'infer' which will try to downcast to an appropriate
equal type (e.g. float64 to int64 if possible)
See Also
--------
interpolate : Fill NaN values using interpolation.
reindex, asfreq
Returns
-------
filled : %(klass)s
Examples
--------
>>> df = pd.DataFrame([[np.nan, 2, np.nan, 0],
... [3, 4, np.nan, 1],
... [np.nan, np.nan, np.nan, 5],
... [np.nan, 3, np.nan, 4]],
... columns=list('ABCD'))
>>> df
A B C D
0 NaN 2.0 NaN 0
1 3.0 4.0 NaN 1
2 NaN NaN NaN 5
3 NaN 3.0 NaN 4
Replace all NaN elements with 0s.
>>> df.fillna(0)
A B C D
0 0.0 2.0 0.0 0
1 3.0 4.0 0.0 1
2 0.0 0.0 0.0 5
3 0.0 3.0 0.0 4
We can also propagate non-null values forward or backward.
>>> df.fillna(method='ffill')
A B C D
0 NaN 2.0 NaN 0
1 3.0 4.0 NaN 1
2 3.0 4.0 NaN 5
3 3.0 3.0 NaN 4
Replace all NaN elements in column 'A', 'B', 'C', and 'D', with 0, 1,
2, and 3 respectively.
>>> values = {'A': 0, 'B': 1, 'C': 2, 'D': 3}
>>> df.fillna(value=values)
A B C D
0 0.0 2.0 2.0 0
1 3.0 4.0 2.0 1
2 0.0 1.0 2.0 5
3 0.0 3.0 2.0 4
Only replace the first NaN element.
>>> df.fillna(value=values, limit=1)
A B C D
0 0.0 2.0 2.0 0
1 3.0 4.0 NaN 1
2 NaN 1.0 NaN 5
3 NaN 3.0 NaN 4
"""
inplace = validate_bool_kwarg(inplace, 'inplace')
value, method = validate_fillna_kwargs(value, method)
self._consolidate_inplace()
# set the default here, so functions examining the signaure
# can detect if something was set (e.g. in groupby) (GH9221)
if axis is None:
axis = 0
axis = self._get_axis_number(axis)
from pandas import DataFrame
if value is None:
if self._is_mixed_type and axis == 1:
if inplace:
raise NotImplementedError()
result = self.T.fillna(method=method, limit=limit).T
# need to downcast here because of all of the transposes
result._data = result._data.downcast()
return result
# > 3d
if self.ndim > 3:
raise NotImplementedError('Cannot fillna with a method for > '
'3dims')
# 3d
elif self.ndim == 3:
# fill in 2d chunks
result = {col: s.fillna(method=method, value=value)
for col, s in self.iteritems()}
new_obj = self._constructor.\
from_dict(result).__finalize__(self)
new_data = new_obj._data
else:
# 2d or less
new_data = self._data.interpolate(method=method, axis=axis,
limit=limit, inplace=inplace,
coerce=True,
downcast=downcast)
else:
if len(self._get_axis(axis)) == 0:
return self
if self.ndim == 1:
if isinstance(value, (dict, ABCSeries)):
from pandas import Series
value = Series(value)
elif not is_list_like(value):
pass
else:
raise TypeError('"value" parameter must be a scalar, dict '
'or Series, but you passed a '
'"{0}"'.format(type(value).__name__))
new_data = self._data.fillna(value=value, limit=limit,
inplace=inplace,
downcast=downcast)
elif isinstance(value, (dict, ABCSeries)):
if axis == 1:
raise NotImplementedError('Currently only can fill '
'with dict/Series column '
'by column')
result = self if inplace else self.copy()
for k, v in compat.iteritems(value):
if k not in result:
continue
obj = result[k]
obj.fillna(v, limit=limit, inplace=True, downcast=downcast)
return result if not inplace else None
elif not is_list_like(value):
new_data = self._data.fillna(value=value, limit=limit,
inplace=inplace,
downcast=downcast)
elif isinstance(value, DataFrame) and self.ndim == 2:
new_data = self.where(self.notna(), value)
else:
raise ValueError("invalid fill value with a %s" % type(value))
if inplace:
self._update_inplace(new_data)
else:
return self._constructor(new_data).__finalize__(self)
def ffill(self, axis=None, inplace=False, limit=None, downcast=None):
"""
Synonym for :meth:`DataFrame.fillna(method='ffill') <DataFrame.fillna>`
"""
return self.fillna(method='ffill', axis=axis, inplace=inplace,
limit=limit, downcast=downcast)
def bfill(self, axis=None, inplace=False, limit=None, downcast=None):
"""
Synonym for :meth:`DataFrame.fillna(method='bfill') <DataFrame.fillna>`
"""
return self.fillna(method='bfill', axis=axis, inplace=inplace,
limit=limit, downcast=downcast)
_shared_docs['replace'] = ("""
Replace values given in `to_replace` with `value`.
Values of the %(klass)s are replaced with other values dynamically.
This differs from updating with ``.loc`` or ``.iloc``, which require
you to specify a location to update with some value.
Parameters
----------
to_replace : str, regex, list, dict, Series, int, float, or None
How to find the values that will be replaced.
* numeric, str or regex:
- numeric: numeric values equal to `to_replace` will be
replaced with `value`
- str: string exactly matching `to_replace` will be replaced
with `value`
- regex: regexs matching `to_replace` will be replaced with
`value`
* list of str, regex, or numeric:
- First, if `to_replace` and `value` are both lists, they
**must** be the same length.
- Second, if ``regex=True`` then all of the strings in **both**
lists will be interpreted as regexs otherwise they will match
directly. This doesn't matter much for `value` since there
are only a few possible substitution regexes you can use.
- str, regex and numeric rules apply as above.
* dict:
- Dicts can be used to specify different replacement values
for different existing values. For example,
``{'a': 'b', 'y': 'z'}`` replaces the value 'a' with 'b' and
'y' with 'z'. To use a dict in this way the `value`
parameter should be `None`.
- For a DataFrame a dict can specify that different values
should be replaced in different columns. For example,
``{'a': 1, 'b': 'z'}`` looks for the value 1 in column 'a'
and the value 'z' in column 'b' and replaces these values
with whatever is specified in `value`. The `value` parameter
should not be ``None`` in this case. You can treat this as a
special case of passing two lists except that you are
specifying the column to search in.
- For a DataFrame nested dictionaries, e.g.,
``{'a': {'b': np.nan}}``, are read as follows: look in column
'a' for the value 'b' and replace it with NaN. The `value`
parameter should be ``None`` to use a nested dict in this
way. You can nest regular expressions as well. Note that
column names (the top-level dictionary keys in a nested
dictionary) **cannot** be regular expressions.
* None:
- This means that the `regex` argument must be a string,
compiled regular expression, or list, dict, ndarray or
Series of such elements. If `value` is also ``None`` then
this **must** be a nested dictionary or Series.
See the examples section for examples of each of these.
value : scalar, dict, list, str, regex, default None
Value to replace any values matching `to_replace` with.
For a DataFrame a dict of values can be used to specify which
value to use for each column (columns not in the dict will not be
filled). Regular expressions, strings and lists or dicts of such
objects are also allowed.
inplace : boolean, default False
If True, in place. Note: this will modify any
other views on this object (e.g. a column from a DataFrame).
Returns the caller if this is True.
limit : int, default None
Maximum size gap to forward or backward fill.
regex : bool or same types as `to_replace`, default False
Whether to interpret `to_replace` and/or `value` as regular
expressions. If this is ``True`` then `to_replace` *must* be a
string. Alternatively, this could be a regular expression or a
list, dict, or array of regular expressions in which case
`to_replace` must be ``None``.
method : {'pad', 'ffill', 'bfill', `None`}
The method to use when for replacement, when `to_replace` is a
scalar, list or tuple and `value` is ``None``.
.. versionchanged:: 0.23.0
Added to DataFrame.
See Also
--------
%(klass)s.fillna : Fill NA values
%(klass)s.where : Replace values based on boolean condition
Series.str.replace : Simple string replacement.
Returns
-------
%(klass)s
Object after replacement.
Raises
------
AssertionError
* If `regex` is not a ``bool`` and `to_replace` is not
``None``.
TypeError
* If `to_replace` is a ``dict`` and `value` is not a ``list``,
``dict``, ``ndarray``, or ``Series``
* If `to_replace` is ``None`` and `regex` is not compilable
into a regular expression or is a list, dict, ndarray, or
Series.
* When replacing multiple ``bool`` or ``datetime64`` objects and
the arguments to `to_replace` does not match the type of the
value being replaced
ValueError
* If a ``list`` or an ``ndarray`` is passed to `to_replace` and
`value` but they are not the same length.
Notes
-----
* Regex substitution is performed under the hood with ``re.sub``. The
rules for substitution for ``re.sub`` are the same.
* Regular expressions will only substitute on strings, meaning you
cannot provide, for example, a regular expression matching floating
point numbers and expect the columns in your frame that have a
numeric dtype to be matched. However, if those floating point
numbers *are* strings, then you can do this.
* This method has *a lot* of options. You are encouraged to experiment
and play with this method to gain intuition about how it works.
* When dict is used as the `to_replace` value, it is like
key(s) in the dict are the to_replace part and
value(s) in the dict are the value parameter.
Examples
--------
**Scalar `to_replace` and `value`**
>>> s = pd.Series([0, 1, 2, 3, 4])
>>> s.replace(0, 5)
0 5
1 1
2 2
3 3
4 4
dtype: int64
>>> df = pd.DataFrame({'A': [0, 1, 2, 3, 4],
... 'B': [5, 6, 7, 8, 9],
... 'C': ['a', 'b', 'c', 'd', 'e']})
>>> df.replace(0, 5)
A B C
0 5 5 a
1 1 6 b
2 2 7 c
3 3 8 d
4 4 9 e
**List-like `to_replace`**
>>> df.replace([0, 1, 2, 3], 4)
A B C
0 4 5 a
1 4 6 b
2 4 7 c
3 4 8 d
4 4 9 e
>>> df.replace([0, 1, 2, 3], [4, 3, 2, 1])
A B C
0 4 5 a
1 3 6 b
2 2 7 c
3 1 8 d
4 4 9 e
>>> s.replace([1, 2], method='bfill')
0 0
1 3
2 3
3 3
4 4
dtype: int64
**dict-like `to_replace`**
>>> df.replace({0: 10, 1: 100})
A B C
0 10 5 a
1 100 6 b
2 2 7 c
3 3 8 d
4 4 9 e
>>> df.replace({'A': 0, 'B': 5}, 100)
A B C
0 100 100 a
1 1 6 b
2 2 7 c
3 3 8 d
4 4 9 e
>>> df.replace({'A': {0: 100, 4: 400}})
A B C
0 100 5 a
1 1 6 b
2 2 7 c
3 3 8 d
4 400 9 e
**Regular expression `to_replace`**
>>> df = pd.DataFrame({'A': ['bat', 'foo', 'bait'],
... 'B': ['abc', 'bar', 'xyz']})
>>> df.replace(to_replace=r'^ba.$', value='new', regex=True)
A B
0 new abc
1 foo new
2 bait xyz
>>> df.replace({'A': r'^ba.$'}, {'A': 'new'}, regex=True)
A B
0 new abc
1 foo bar
2 bait xyz
>>> df.replace(regex=r'^ba.$', value='new')
A B
0 new abc
1 foo new
2 bait xyz
>>> df.replace(regex={r'^ba.$':'new', 'foo':'xyz'})
A B
0 new abc
1 xyz new
2 bait xyz
>>> df.replace(regex=[r'^ba.$', 'foo'], value='new')
A B
0 new abc
1 new new
2 bait xyz
Note that when replacing multiple ``bool`` or ``datetime64`` objects,
the data types in the `to_replace` parameter must match the data
type of the value being replaced:
>>> df = pd.DataFrame({'A': [True, False, True],
... 'B': [False, True, False]})
>>> df.replace({'a string': 'new value', True: False}) # raises
Traceback (most recent call last):
...
TypeError: Cannot compare types 'ndarray(dtype=bool)' and 'str'
This raises a ``TypeError`` because one of the ``dict`` keys is not of
the correct type for replacement.
Compare the behavior of ``s.replace({'a': None})`` and
``s.replace('a', None)`` to understand the peculiarities
of the `to_replace` parameter:
>>> s = pd.Series([10, 'a', 'a', 'b', 'a'])
When one uses a dict as the `to_replace` value, it is like the
value(s) in the dict are equal to the `value` parameter.
``s.replace({'a': None})`` is equivalent to
``s.replace(to_replace={'a': None}, value=None, method=None)``:
>>> s.replace({'a': None})
0 10
1 None
2 None
3 b
4 None
dtype: object
When ``value=None`` and `to_replace` is a scalar, list or
tuple, `replace` uses the method parameter (default 'pad') to do the
replacement. So this is why the 'a' values are being replaced by 10
in rows 1 and 2 and 'b' in row 4 in this case.
The command ``s.replace('a', None)`` is actually equivalent to
``s.replace(to_replace='a', value=None, method='pad')``:
>>> s.replace('a', None)
0 10
1 10
2 10
3 b
4 b
dtype: object
""")
@Appender(_shared_docs['replace'] % _shared_doc_kwargs)
def replace(self, to_replace=None, value=None, inplace=False, limit=None,
regex=False, method='pad'):
inplace = validate_bool_kwarg(inplace, 'inplace')
if not is_bool(regex) and to_replace is not None:
raise AssertionError("'to_replace' must be 'None' if 'regex' is "
"not a bool")
self._consolidate_inplace()
if value is None:
# passing a single value that is scalar like
# when value is None (GH5319), for compat
if not is_dict_like(to_replace) and not is_dict_like(regex):
to_replace = [to_replace]
if isinstance(to_replace, (tuple, list)):
if isinstance(self, pd.DataFrame):
return self.apply(_single_replace,
args=(to_replace, method, inplace,
limit))
return _single_replace(self, to_replace, method, inplace,
limit)
if not is_dict_like(to_replace):
if not is_dict_like(regex):
raise TypeError('If "to_replace" and "value" are both None'
' and "to_replace" is not a list, then '
'regex must be a mapping')
to_replace = regex
regex = True
items = list(compat.iteritems(to_replace))
keys, values = lzip(*items) or ([], [])
are_mappings = [is_dict_like(v) for v in values]
if any(are_mappings):
if not all(are_mappings):
raise TypeError("If a nested mapping is passed, all values"
" of the top level mapping must be "
"mappings")
# passed a nested dict/Series
to_rep_dict = {}
value_dict = {}
for k, v in items:
keys, values = lzip(*v.items()) or ([], [])
if set(keys) & set(values):
raise ValueError("Replacement not allowed with "
"overlapping keys and values")
to_rep_dict[k] = list(keys)
value_dict[k] = list(values)
to_replace, value = to_rep_dict, value_dict
else:
to_replace, value = keys, values
return self.replace(to_replace, value, inplace=inplace,
limit=limit, regex=regex)
else:
# need a non-zero len on all axes
for a in self._AXIS_ORDERS:
if not len(self._get_axis(a)):
return self
new_data = self._data
if is_dict_like(to_replace):
if is_dict_like(value): # {'A' : NA} -> {'A' : 0}
res = self if inplace else self.copy()
for c, src in compat.iteritems(to_replace):
if c in value and c in self:
# object conversion is handled in
# series.replace which is called recursivelly
res[c] = res[c].replace(to_replace=src,
value=value[c],
inplace=False,
regex=regex)
return None if inplace else res
# {'A': NA} -> 0
elif not is_list_like(value):
keys = [(k, src) for k, src in compat.iteritems(to_replace)
if k in self]
keys_len = len(keys) - 1
for i, (k, src) in enumerate(keys):
convert = i == keys_len
new_data = new_data.replace(to_replace=src,
value=value,
filter=[k],
inplace=inplace,
regex=regex,
convert=convert)
else:
raise TypeError('value argument must be scalar, dict, or '
'Series')
elif is_list_like(to_replace): # [NA, ''] -> [0, 'missing']
if is_list_like(value):
if len(to_replace) != len(value):
raise ValueError('Replacement lists must match '
'in length. Expecting %d got %d ' %
(len(to_replace), len(value)))
new_data = self._data.replace_list(src_list=to_replace,
dest_list=value,
inplace=inplace,
regex=regex)
else: # [NA, ''] -> 0
new_data = self._data.replace(to_replace=to_replace,
value=value, inplace=inplace,
regex=regex)
elif to_replace is None:
if not (is_re_compilable(regex) or
is_list_like(regex) or is_dict_like(regex)):
raise TypeError("'regex' must be a string or a compiled "
"regular expression or a list or dict of "
"strings or regular expressions, you "
"passed a"
" {0!r}".format(type(regex).__name__))
return self.replace(regex, value, inplace=inplace, limit=limit,
regex=True)
else:
# dest iterable dict-like
if is_dict_like(value): # NA -> {'A' : 0, 'B' : -1}
new_data = self._data
for k, v in compat.iteritems(value):
if k in self:
new_data = new_data.replace(to_replace=to_replace,
value=v, filter=[k],
inplace=inplace,
regex=regex)
elif not is_list_like(value): # NA -> 0
new_data = self._data.replace(to_replace=to_replace,
value=value, inplace=inplace,
regex=regex)
else:
msg = ('Invalid "to_replace" type: '
'{0!r}').format(type(to_replace).__name__)
raise TypeError(msg) # pragma: no cover
if inplace:
self._update_inplace(new_data)
else:
return self._constructor(new_data).__finalize__(self)
_shared_docs['interpolate'] = """
Please note that only ``method='linear'`` is supported for
DataFrames/Series with a MultiIndex.
Parameters
----------
method : {'linear', 'time', 'index', 'values', 'nearest', 'zero',
'slinear', 'quadratic', 'cubic', 'barycentric', 'krogh',
'polynomial', 'spline', 'piecewise_polynomial',
'from_derivatives', 'pchip', 'akima'}
* 'linear': ignore the index and treat the values as equally
spaced. This is the only method supported on MultiIndexes.
default
* 'time': interpolation works on daily and higher resolution
data to interpolate given length of interval
* 'index', 'values': use the actual numerical values of the index
* 'nearest', 'zero', 'slinear', 'quadratic', 'cubic',
'barycentric', 'polynomial' is passed to
``scipy.interpolate.interp1d``. Both 'polynomial' and 'spline'
require that you also specify an `order` (int),
e.g. df.interpolate(method='polynomial', order=4).
These use the actual numerical values of the index.
* 'krogh', 'piecewise_polynomial', 'spline', 'pchip' and 'akima'
are all wrappers around the scipy interpolation methods of
similar names. These use the actual numerical values of the
index. For more information on their behavior, see the
`scipy documentation
<http://docs.scipy.org/doc/scipy/reference/interpolate.html#univariate-interpolation>`__
and `tutorial documentation
<http://docs.scipy.org/doc/scipy/reference/tutorial/interpolate.html>`__
* 'from_derivatives' refers to BPoly.from_derivatives which
replaces 'piecewise_polynomial' interpolation method in
scipy 0.18
.. versionadded:: 0.18.1
Added support for the 'akima' method
Added interpolate method 'from_derivatives' which replaces
'piecewise_polynomial' in scipy 0.18; backwards-compatible with
scipy < 0.18
axis : {0, 1}, default 0
* 0: fill column-by-column
* 1: fill row-by-row
limit : int, default None.
Maximum number of consecutive NaNs to fill. Must be greater than 0.
limit_direction : {'forward', 'backward', 'both'}, default 'forward'
limit_area : {'inside', 'outside'}, default None
* None: (default) no fill restriction
* 'inside' Only fill NaNs surrounded by valid values (interpolate).
* 'outside' Only fill NaNs outside valid values (extrapolate).
If limit is specified, consecutive NaNs will be filled in this
direction.
.. versionadded:: 0.21.0
inplace : bool, default False
Update the NDFrame in place if possible.
downcast : optional, 'infer' or None, defaults to None
Downcast dtypes if possible.
kwargs : keyword arguments to pass on to the interpolating function.
Returns
-------
Series or DataFrame of same shape interpolated at the NaNs
See Also
--------
reindex, replace, fillna
Examples
--------
Filling in NaNs
>>> s = pd.Series([0, 1, np.nan, 3])
>>> s.interpolate()
0 0
1 1
2 2
3 3
dtype: float64
"""
@Appender(_shared_docs['interpolate'] % _shared_doc_kwargs)
def interpolate(self, method='linear', axis=0, limit=None, inplace=False,
limit_direction='forward', limit_area=None,
downcast=None, **kwargs):
"""
Interpolate values according to different methods.
"""
inplace = validate_bool_kwarg(inplace, 'inplace')
if self.ndim > 2:
raise NotImplementedError("Interpolate has not been implemented "
"on Panel and Panel 4D objects.")
if axis == 0:
ax = self._info_axis_name
_maybe_transposed_self = self
elif axis == 1:
_maybe_transposed_self = self.T
ax = 1
else:
_maybe_transposed_self = self
ax = _maybe_transposed_self._get_axis_number(ax)
if _maybe_transposed_self.ndim == 2:
alt_ax = 1 - ax
else:
alt_ax = ax
if (isinstance(_maybe_transposed_self.index, MultiIndex) and
method != 'linear'):
raise ValueError("Only `method=linear` interpolation is supported "
"on MultiIndexes.")
if _maybe_transposed_self._data.get_dtype_counts().get(
'object') == len(_maybe_transposed_self.T):
raise TypeError("Cannot interpolate with all NaNs.")
# create/use the index
if method == 'linear':
# prior default
index = np.arange(len(_maybe_transposed_self._get_axis(alt_ax)))
else:
index = _maybe_transposed_self._get_axis(alt_ax)
if isna(index).any():
raise NotImplementedError("Interpolation with NaNs in the index "
"has not been implemented. Try filling "
"those NaNs before interpolating.")
data = _maybe_transposed_self._data
new_data = data.interpolate(method=method, axis=ax, index=index,
values=_maybe_transposed_self, limit=limit,
limit_direction=limit_direction,
limit_area=limit_area,
inplace=inplace, downcast=downcast,
**kwargs)
if inplace:
if axis == 1:
new_data = self._constructor(new_data).T._data
self._update_inplace(new_data)
else:
res = self._constructor(new_data).__finalize__(self)
if axis == 1:
res = res.T
return res
# ----------------------------------------------------------------------
# Timeseries methods Methods
def asof(self, where, subset=None):
"""
The last row without any NaN is taken (or the last row without
NaN considering only the subset of columns in the case of a DataFrame)
.. versionadded:: 0.19.0 For DataFrame
If there is no good value, NaN is returned for a Series
a Series of NaN values for a DataFrame
Parameters
----------
where : date or array of dates
subset : string or list of strings, default None
if not None use these columns for NaN propagation
Notes
-----
Dates are assumed to be sorted
Raises if this is not the case
Returns
-------
where is scalar
- value or NaN if input is Series
- Series if input is DataFrame
where is Index: same shape object as input
See Also
--------
merge_asof
"""
if isinstance(where, compat.string_types):
from pandas import to_datetime
where = to_datetime(where)
if not self.index.is_monotonic:
raise ValueError("asof requires a sorted index")
is_series = isinstance(self, ABCSeries)
if is_series:
if subset is not None:
raise ValueError("subset is not valid for Series")
elif self.ndim > 2:
raise NotImplementedError("asof is not implemented "
"for {type}".format(type=type(self)))
else:
if subset is None:
subset = self.columns
if not is_list_like(subset):
subset = [subset]
is_list = is_list_like(where)
if not is_list:
start = self.index[0]
if isinstance(self.index, PeriodIndex):
where = Period(where, freq=self.index.freq).ordinal
start = start.ordinal
if where < start:
if not is_series:
from pandas import Series
return Series(index=self.columns, name=where)
return np.nan
# It's always much faster to use a *while* loop here for
# Series than pre-computing all the NAs. However a
# *while* loop is extremely expensive for DataFrame
# so we later pre-compute all the NAs and use the same
# code path whether *where* is a scalar or list.
# See PR: https://github.com/pandas-dev/pandas/pull/14476
if is_series:
loc = self.index.searchsorted(where, side='right')
if loc > 0:
loc -= 1
values = self._values
while loc > 0 and isna(values[loc]):
loc -= 1
return values[loc]
if not isinstance(where, Index):
where = Index(where) if is_list else Index([where])
nulls = self.isna() if is_series else self[subset].isna().any(1)
if nulls.all():
if is_series:
return self._constructor(np.nan, index=where, name=self.name)
elif is_list:
from pandas import DataFrame
return DataFrame(np.nan, index=where, columns=self.columns)
else:
from pandas import Series
return Series(np.nan, index=self.columns, name=where[0])
locs = self.index.asof_locs(where, ~(nulls.values))
# mask the missing
missing = locs == -1
data = self.take(locs, is_copy=False)
data.index = where
data.loc[missing] = np.nan
return data if is_list else data.iloc[-1]
# ----------------------------------------------------------------------
# Action Methods
_shared_docs['isna'] = """
Detect missing values.
Return a boolean same-sized object indicating if the values are NA.
NA values, such as None or :attr:`numpy.NaN`, gets mapped to True
values.
Everything else gets mapped to False values. Characters such as empty
strings ``''`` or :attr:`numpy.inf` are not considered NA values
(unless you set ``pandas.options.mode.use_inf_as_na = True``).
Returns
-------
%(klass)s
Mask of bool values for each element in %(klass)s that
indicates whether an element is not an NA value.
See Also
--------
%(klass)s.isnull : alias of isna
%(klass)s.notna : boolean inverse of isna
%(klass)s.dropna : omit axes labels with missing values
isna : top-level isna
Examples
--------
Show which entries in a DataFrame are NA.
>>> df = pd.DataFrame({'age': [5, 6, np.NaN],
... 'born': [pd.NaT, pd.Timestamp('1939-05-27'),
... pd.Timestamp('1940-04-25')],
... 'name': ['Alfred', 'Batman', ''],
... 'toy': [None, 'Batmobile', 'Joker']})
>>> df
age born name toy
0 5.0 NaT Alfred None
1 6.0 1939-05-27 Batman Batmobile
2 NaN 1940-04-25 Joker
>>> df.isna()
age born name toy
0 False True False True
1 False False False False
2 True False False False
Show which entries in a Series are NA.
>>> ser = pd.Series([5, 6, np.NaN])
>>> ser
0 5.0
1 6.0
2 NaN
dtype: float64
>>> ser.isna()
0 False
1 False
2 True
dtype: bool
"""
@Appender(_shared_docs['isna'] % _shared_doc_kwargs)
def isna(self):
return isna(self).__finalize__(self)
@Appender(_shared_docs['isna'] % _shared_doc_kwargs)
def isnull(self):
return isna(self).__finalize__(self)
_shared_docs['notna'] = """
Detect existing (non-missing) values.
Return a boolean same-sized object indicating if the values are not NA.
Non-missing values get mapped to True. Characters such as empty
strings ``''`` or :attr:`numpy.inf` are not considered NA values
(unless you set ``pandas.options.mode.use_inf_as_na = True``).
NA values, such as None or :attr:`numpy.NaN`, get mapped to False
values.
Returns
-------
%(klass)s
Mask of bool values for each element in %(klass)s that
indicates whether an element is not an NA value.
See Also
--------
%(klass)s.notnull : alias of notna
%(klass)s.isna : boolean inverse of notna
%(klass)s.dropna : omit axes labels with missing values
notna : top-level notna
Examples
--------
Show which entries in a DataFrame are not NA.
>>> df = pd.DataFrame({'age': [5, 6, np.NaN],
... 'born': [pd.NaT, pd.Timestamp('1939-05-27'),
... pd.Timestamp('1940-04-25')],
... 'name': ['Alfred', 'Batman', ''],
... 'toy': [None, 'Batmobile', 'Joker']})
>>> df
age born name toy
0 5.0 NaT Alfred None
1 6.0 1939-05-27 Batman Batmobile
2 NaN 1940-04-25 Joker
>>> df.notna()
age born name toy
0 True False True False
1 True True True True
2 False True True True
Show which entries in a Series are not NA.
>>> ser = pd.Series([5, 6, np.NaN])
>>> ser
0 5.0
1 6.0
2 NaN
dtype: float64
>>> ser.notna()
0 True
1 True
2 False
dtype: bool
"""
@Appender(_shared_docs['notna'] % _shared_doc_kwargs)
def notna(self):
return notna(self).__finalize__(self)
@Appender(_shared_docs['notna'] % _shared_doc_kwargs)
def notnull(self):
return notna(self).__finalize__(self)
def _clip_with_scalar(self, lower, upper, inplace=False):
if ((lower is not None and np.any(isna(lower))) or
(upper is not None and np.any(isna(upper)))):
raise ValueError("Cannot use an NA value as a clip threshold")
result = self.values
mask = isna(result)
with np.errstate(all='ignore'):
if upper is not None:
result = np.where(result >= upper, upper, result)
if lower is not None:
result = np.where(result <= lower, lower, result)
if np.any(mask):
result[mask] = np.nan
axes_dict = self._construct_axes_dict()
result = self._constructor(result, **axes_dict).__finalize__(self)
if inplace:
self._update_inplace(result)
else:
return result
def _clip_with_one_bound(self, threshold, method, axis, inplace):
inplace = validate_bool_kwarg(inplace, 'inplace')
if axis is not None:
axis = self._get_axis_number(axis)
# method is self.le for upper bound and self.ge for lower bound
if is_scalar(threshold) and is_number(threshold):
if method.__name__ == 'le':
return self._clip_with_scalar(None, threshold, inplace=inplace)
return self._clip_with_scalar(threshold, None, inplace=inplace)
subset = method(threshold, axis=axis) | isna(self)
# GH #15390
# In order for where method to work, the threshold must
# be transformed to NDFrame from other array like structure.
if (not isinstance(threshold, ABCSeries)) and is_list_like(threshold):
if isinstance(self, ABCSeries):
threshold = pd.Series(threshold, index=self.index)
else:
threshold = _align_method_FRAME(self, np.asarray(threshold),
axis)
return self.where(subset, threshold, axis=axis, inplace=inplace)
def clip(self, lower=None, upper=None, axis=None, inplace=False,
*args, **kwargs):
"""
Trim values at input threshold(s).
Assigns values outside boundary to boundary values. Thresholds
can be singular values or array like, and in the latter case
the clipping is performed element-wise in the specified axis.
Parameters
----------
lower : float or array_like, default None
Minimum threshold value. All values below this
threshold will be set to it.
upper : float or array_like, default None
Maximum threshold value. All values above this
threshold will be set to it.
axis : int or string axis name, optional
Align object with lower and upper along the given axis.
inplace : boolean, default False
Whether to perform the operation in place on the data.
.. versionadded:: 0.21.0
*args, **kwargs
Additional keywords have no effect but might be accepted
for compatibility with numpy.
See Also
--------
clip_lower : Clip values below specified threshold(s).
clip_upper : Clip values above specified threshold(s).
Returns
-------
Series or DataFrame
Same type as calling object with the values outside the
clip boundaries replaced
Examples
--------
>>> data = {'col_0': [9, -3, 0, -1, 5], 'col_1': [-2, -7, 6, 8, -5]}
>>> df = pd.DataFrame(data)
>>> df
col_0 col_1
0 9 -2
1 -3 -7
2 0 6
3 -1 8
4 5 -5
Clips per column using lower and upper thresholds:
>>> df.clip(-4, 6)
col_0 col_1
0 6 -2
1 -3 -4
2 0 6
3 -1 6
4 5 -4
Clips using specific lower and upper thresholds per column element:
>>> t = pd.Series([2, -4, -1, 6, 3])
>>> t
0 2
1 -4
2 -1
3 6
4 3
dtype: int64
>>> df.clip(t, t + 4, axis=0)
col_0 col_1
0 6 2
1 -3 -4
2 0 3
3 6 8
4 5 3
"""
if isinstance(self, ABCPanel):
raise NotImplementedError("clip is not supported yet for panels")
inplace = validate_bool_kwarg(inplace, 'inplace')
axis = nv.validate_clip_with_axis(axis, args, kwargs)
if axis is not None:
axis = self._get_axis_number(axis)
# GH 17276
# numpy doesn't like NaN as a clip value
# so ignore
# GH 19992
# numpy doesn't drop a list-like bound containing NaN
if not is_list_like(lower) and np.any(pd.isnull(lower)):
lower = None
if not is_list_like(upper) and np.any(pd.isnull(upper)):
upper = None
# GH 2747 (arguments were reversed)
if lower is not None and upper is not None:
if is_scalar(lower) and is_scalar(upper):
lower, upper = min(lower, upper), max(lower, upper)
# fast-path for scalars
if ((lower is None or (is_scalar(lower) and is_number(lower))) and
(upper is None or (is_scalar(upper) and is_number(upper)))):
return self._clip_with_scalar(lower, upper, inplace=inplace)
result = self
if lower is not None:
result = result.clip_lower(lower, axis, inplace=inplace)
if upper is not None:
if inplace:
result = self
result = result.clip_upper(upper, axis, inplace=inplace)
return result
def clip_upper(self, threshold, axis=None, inplace=False):
"""
Trim values above a given threshold.
Elements above the `threshold` will be changed to match the
`threshold` value(s). Threshold can be a single value or an array,
in the latter case it performs the truncation element-wise.
Parameters
----------
threshold : numeric or array-like
Maximum value allowed. All values above threshold will be set to
this value.
* float : every value is compared to `threshold`.
* array-like : The shape of `threshold` should match the object
it's compared to. When `self` is a Series, `threshold` should be
the length. When `self` is a DataFrame, `threshold` should 2-D
and the same shape as `self` for ``axis=None``, or 1-D and the
same length as the axis being compared.
axis : {0 or 'index', 1 or 'columns'}, default 0
Align object with `threshold` along the given axis.
inplace : boolean, default False
Whether to perform the operation in place on the data.
.. versionadded:: 0.21.0
Returns
-------
clipped
Original data with values trimmed.
See Also
--------
DataFrame.clip : General purpose method to trim DataFrame values to
given threshold(s)
DataFrame.clip_lower : Trim DataFrame values below given
threshold(s)
Series.clip : General purpose method to trim Series values to given
threshold(s)
Series.clip_lower : Trim Series values below given threshold(s)
Examples
--------
>>> s = pd.Series([1, 2, 3, 4, 5])
>>> s
0 1
1 2
2 3
3 4
4 5
dtype: int64
>>> s.clip_upper(3)
0 1
1 2
2 3
3 3
4 3
dtype: int64
>>> t = [5, 4, 3, 2, 1]
>>> t
[5, 4, 3, 2, 1]
>>> s.clip_upper(t)
0 1
1 2
2 3
3 2
4 1
dtype: int64
"""
return self._clip_with_one_bound(threshold, method=self.le,
axis=axis, inplace=inplace)
def clip_lower(self, threshold, axis=None, inplace=False):
"""
Trim values below a given threshold.
Elements below the `threshold` will be changed to match the
`threshold` value(s). Threshold can be a single value or an array,
in the latter case it performs the truncation element-wise.
Parameters
----------
threshold : numeric or array-like
Minimum value allowed. All values below threshold will be set to
this value.
* float : every value is compared to `threshold`.
* array-like : The shape of `threshold` should match the object
it's compared to. When `self` is a Series, `threshold` should be
the length. When `self` is a DataFrame, `threshold` should 2-D
and the same shape as `self` for ``axis=None``, or 1-D and the
same length as the axis being compared.
axis : {0 or 'index', 1 or 'columns'}, default 0
Align `self` with `threshold` along the given axis.
inplace : boolean, default False
Whether to perform the operation in place on the data.
.. versionadded:: 0.21.0
Returns
-------
clipped
Original data with values trimmed.
See Also
--------
DataFrame.clip : General purpose method to trim DataFrame values to
given threshold(s)
DataFrame.clip_upper : Trim DataFrame values above given
threshold(s)
Series.clip : General purpose method to trim Series values to given
threshold(s)
Series.clip_upper : Trim Series values above given threshold(s)
Examples
--------
Series single threshold clipping:
>>> s = pd.Series([5, 6, 7, 8, 9])
>>> s.clip_lower(8)
0 8
1 8
2 8
3 8
4 9
dtype: int64
Series clipping element-wise using an array of thresholds. `threshold`
should be the same length as the Series.
>>> elemwise_thresholds = [4, 8, 7, 2, 5]
>>> s.clip_lower(elemwise_thresholds)
0 5
1 8
2 7
3 8
4 9
dtype: int64
DataFrames can be compared to a scalar.
>>> df = pd.DataFrame({"A": [1, 3, 5], "B": [2, 4, 6]})
>>> df
A B
0 1 2
1 3 4
2 5 6
>>> df.clip_lower(3)
A B
0 3 3
1 3 4
2 5 6
Or to an array of values. By default, `threshold` should be the same
shape as the DataFrame.
>>> df.clip_lower(np.array([[3, 4], [2, 2], [6, 2]]))
A B
0 3 4
1 3 4
2 6 6
Control how `threshold` is broadcast with `axis`. In this case
`threshold` should be the same length as the axis specified by
`axis`.
>>> df.clip_lower([3, 3, 5], axis='index')
A B
0 3 3
1 3 4
2 5 6
>>> df.clip_lower([4, 5], axis='columns')
A B
0 4 5
1 4 5
2 5 6
"""
return self._clip_with_one_bound(threshold, method=self.ge,
axis=axis, inplace=inplace)
def groupby(self, by=None, axis=0, level=None, as_index=True, sort=True,
group_keys=True, squeeze=False, observed=False, **kwargs):
"""
Group series using mapper (dict or key function, apply given function
to group, return result as series) or by a series of columns.
Parameters
----------
by : mapping, function, label, or list of labels
Used to determine the groups for the groupby.
If ``by`` is a function, it's called on each value of the object's
index. If a dict or Series is passed, the Series or dict VALUES
will be used to determine the groups (the Series' values are first
aligned; see ``.align()`` method). If an ndarray is passed, the
values are used as-is determine the groups. A label or list of
labels may be passed to group by the columns in ``self``. Notice
that a tuple is interpreted a (single) key.
axis : int, default 0
level : int, level name, or sequence of such, default None
If the axis is a MultiIndex (hierarchical), group by a particular
level or levels
as_index : boolean, default True
For aggregated output, return object with group labels as the
index. Only relevant for DataFrame input. as_index=False is
effectively "SQL-style" grouped output
sort : boolean, default True
Sort group keys. Get better performance by turning this off.
Note this does not influence the order of observations within each
group. groupby preserves the order of rows within each group.
group_keys : boolean, default True
When calling apply, add group keys to index to identify pieces
squeeze : boolean, default False
reduce the dimensionality of the return type if possible,
otherwise return a consistent type
observed : boolean, default False
This only applies if any of the groupers are Categoricals
If True: only show observed values for categorical groupers.
If False: show all values for categorical groupers.
.. versionadded:: 0.23.0
Returns
-------
GroupBy object
Examples
--------
DataFrame results
>>> data.groupby(func, axis=0).mean()
>>> data.groupby(['col1', 'col2'])['col3'].mean()
DataFrame with hierarchical index
>>> data.groupby(['col1', 'col2']).mean()
Notes
-----
See the `user guide
<http://pandas.pydata.org/pandas-docs/stable/groupby.html>`_ for more.
See also
--------
resample : Convenience method for frequency conversion and resampling
of time series.
"""
from pandas.core.groupby.groupby import groupby
if level is None and by is None:
raise TypeError("You have to supply one of 'by' and 'level'")
axis = self._get_axis_number(axis)
return groupby(self, by=by, axis=axis, level=level, as_index=as_index,
sort=sort, group_keys=group_keys, squeeze=squeeze,
observed=observed, **kwargs)
def asfreq(self, freq, method=None, how=None, normalize=False,
fill_value=None):
"""
Convert TimeSeries to specified frequency.
Optionally provide filling method to pad/backfill missing values.
Returns the original data conformed to a new index with the specified
frequency. ``resample`` is more appropriate if an operation, such as
summarization, is necessary to represent the data at the new frequency.
Parameters
----------
freq : DateOffset object, or string
method : {'backfill'/'bfill', 'pad'/'ffill'}, default None
Method to use for filling holes in reindexed Series (note this
does not fill NaNs that already were present):
* 'pad' / 'ffill': propagate last valid observation forward to next
valid
* 'backfill' / 'bfill': use NEXT valid observation to fill
how : {'start', 'end'}, default end
For PeriodIndex only, see PeriodIndex.asfreq
normalize : bool, default False
Whether to reset output index to midnight
fill_value: scalar, optional
Value to use for missing values, applied during upsampling (note
this does not fill NaNs that already were present).
.. versionadded:: 0.20.0
Returns
-------
converted : same type as caller
Examples
--------
Start by creating a series with 4 one minute timestamps.
>>> index = pd.date_range('1/1/2000', periods=4, freq='T')
>>> series = pd.Series([0.0, None, 2.0, 3.0], index=index)
>>> df = pd.DataFrame({'s':series})
>>> df
s
2000-01-01 00:00:00 0.0
2000-01-01 00:01:00 NaN
2000-01-01 00:02:00 2.0
2000-01-01 00:03:00 3.0
Upsample the series into 30 second bins.
>>> df.asfreq(freq='30S')
s
2000-01-01 00:00:00 0.0
2000-01-01 00:00:30 NaN
2000-01-01 00:01:00 NaN
2000-01-01 00:01:30 NaN
2000-01-01 00:02:00 2.0
2000-01-01 00:02:30 NaN
2000-01-01 00:03:00 3.0
Upsample again, providing a ``fill value``.
>>> df.asfreq(freq='30S', fill_value=9.0)
s
2000-01-01 00:00:00 0.0
2000-01-01 00:00:30 9.0
2000-01-01 00:01:00 NaN
2000-01-01 00:01:30 9.0
2000-01-01 00:02:00 2.0
2000-01-01 00:02:30 9.0
2000-01-01 00:03:00 3.0
Upsample again, providing a ``method``.
>>> df.asfreq(freq='30S', method='bfill')
s
2000-01-01 00:00:00 0.0
2000-01-01 00:00:30 NaN
2000-01-01 00:01:00 NaN
2000-01-01 00:01:30 2.0
2000-01-01 00:02:00 2.0
2000-01-01 00:02:30 3.0
2000-01-01 00:03:00 3.0
See Also
--------
reindex
Notes
-----
To learn more about the frequency strings, please see `this link
<http://pandas.pydata.org/pandas-docs/stable/timeseries.html#offset-aliases>`__.
"""
from pandas.core.resample import asfreq
return asfreq(self, freq, method=method, how=how, normalize=normalize,
fill_value=fill_value)
def at_time(self, time, asof=False):
"""
Select values at particular time of day (e.g. 9:30AM).
Raises
------
TypeError
If the index is not a :class:`DatetimeIndex`
Parameters
----------
time : datetime.time or string
Returns
-------
values_at_time : same type as caller
Examples
--------
>>> i = pd.date_range('2018-04-09', periods=4, freq='12H')
>>> ts = pd.DataFrame({'A': [1,2,3,4]}, index=i)
>>> ts
A
2018-04-09 00:00:00 1
2018-04-09 12:00:00 2
2018-04-10 00:00:00 3
2018-04-10 12:00:00 4
>>> ts.at_time('12:00')
A
2018-04-09 12:00:00 2
2018-04-10 12:00:00 4
See Also
--------
between_time : Select values between particular times of the day
first : Select initial periods of time series based on a date offset
last : Select final periods of time series based on a date offset
DatetimeIndex.indexer_at_time : Get just the index locations for
values at particular time of the day
"""
try:
indexer = self.index.indexer_at_time(time, asof=asof)
return self._take(indexer)
except AttributeError:
raise TypeError('Index must be DatetimeIndex')
def between_time(self, start_time, end_time, include_start=True,
include_end=True):
"""
Select values between particular times of the day (e.g., 9:00-9:30 AM).
By setting ``start_time`` to be later than ``end_time``,
you can get the times that are *not* between the two times.
Raises
------
TypeError
If the index is not a :class:`DatetimeIndex`
Parameters
----------
start_time : datetime.time or string
end_time : datetime.time or string
include_start : boolean, default True
include_end : boolean, default True
Returns
-------
values_between_time : same type as caller
Examples
--------
>>> i = pd.date_range('2018-04-09', periods=4, freq='1D20min')
>>> ts = pd.DataFrame({'A': [1,2,3,4]}, index=i)
>>> ts
A
2018-04-09 00:00:00 1
2018-04-10 00:20:00 2
2018-04-11 00:40:00 3
2018-04-12 01:00:00 4
>>> ts.between_time('0:15', '0:45')
A
2018-04-10 00:20:00 2
2018-04-11 00:40:00 3
You get the times that are *not* between two times by setting
``start_time`` later than ``end_time``:
>>> ts.between_time('0:45', '0:15')
A
2018-04-09 00:00:00 1
2018-04-12 01:00:00 4
See Also
--------
at_time : Select values at a particular time of the day
first : Select initial periods of time series based on a date offset
last : Select final periods of time series based on a date offset
DatetimeIndex.indexer_between_time : Get just the index locations for
values between particular times of the day
"""
try:
indexer = self.index.indexer_between_time(
start_time, end_time, include_start=include_start,
include_end=include_end)
return self._take(indexer)
except AttributeError:
raise TypeError('Index must be DatetimeIndex')
def resample(self, rule, how=None, axis=0, fill_method=None, closed=None,
label=None, convention='start', kind=None, loffset=None,
limit=None, base=0, on=None, level=None):
"""
Convenience method for frequency conversion and resampling of time
series. Object must have a datetime-like index (DatetimeIndex,
PeriodIndex, or TimedeltaIndex), or pass datetime-like values
to the on or level keyword.
Parameters
----------
rule : string
the offset string or object representing target conversion
axis : int, optional, default 0
closed : {'right', 'left'}
Which side of bin interval is closed. The default is 'left'
for all frequency offsets except for 'M', 'A', 'Q', 'BM',
'BA', 'BQ', and 'W' which all have a default of 'right'.
label : {'right', 'left'}
Which bin edge label to label bucket with. The default is 'left'
for all frequency offsets except for 'M', 'A', 'Q', 'BM',
'BA', 'BQ', and 'W' which all have a default of 'right'.
convention : {'start', 'end', 's', 'e'}
For PeriodIndex only, controls whether to use the start or end of
`rule`
kind: {'timestamp', 'period'}, optional
Pass 'timestamp' to convert the resulting index to a
``DateTimeIndex`` or 'period' to convert it to a ``PeriodIndex``.
By default the input representation is retained.
loffset : timedelta
Adjust the resampled time labels
base : int, default 0
For frequencies that evenly subdivide 1 day, the "origin" of the
aggregated intervals. For example, for '5min' frequency, base could
range from 0 through 4. Defaults to 0
on : string, optional
For a DataFrame, column to use instead of index for resampling.
Column must be datetime-like.
.. versionadded:: 0.19.0
level : string or int, optional
For a MultiIndex, level (name or number) to use for
resampling. Level must be datetime-like.
.. versionadded:: 0.19.0
Returns
-------
Resampler object
Notes
-----
See the `user guide
<http://pandas.pydata.org/pandas-docs/stable/timeseries.html#resampling>`_
for more.
To learn more about the offset strings, please see `this link
<http://pandas.pydata.org/pandas-docs/stable/timeseries.html#offset-aliases>`__.
Examples
--------
Start by creating a series with 9 one minute timestamps.
>>> index = pd.date_range('1/1/2000', periods=9, freq='T')
>>> series = pd.Series(range(9), index=index)
>>> series
2000-01-01 00:00:00 0
2000-01-01 00:01:00 1
2000-01-01 00:02:00 2
2000-01-01 00:03:00 3
2000-01-01 00:04:00 4
2000-01-01 00:05:00 5
2000-01-01 00:06:00 6
2000-01-01 00:07:00 7
2000-01-01 00:08:00 8
Freq: T, dtype: int64
Downsample the series into 3 minute bins and sum the values
of the timestamps falling into a bin.
>>> series.resample('3T').sum()
2000-01-01 00:00:00 3
2000-01-01 00:03:00 12
2000-01-01 00:06:00 21
Freq: 3T, dtype: int64
Downsample the series into 3 minute bins as above, but label each
bin using the right edge instead of the left. Please note that the
value in the bucket used as the label is not included in the bucket,
which it labels. For example, in the original series the
bucket ``2000-01-01 00:03:00`` contains the value 3, but the summed
value in the resampled bucket with the label ``2000-01-01 00:03:00``
does not include 3 (if it did, the summed value would be 6, not 3).
To include this value close the right side of the bin interval as
illustrated in the example below this one.
>>> series.resample('3T', label='right').sum()
2000-01-01 00:03:00 3
2000-01-01 00:06:00 12
2000-01-01 00:09:00 21
Freq: 3T, dtype: int64
Downsample the series into 3 minute bins as above, but close the right
side of the bin interval.
>>> series.resample('3T', label='right', closed='right').sum()
2000-01-01 00:00:00 0
2000-01-01 00:03:00 6
2000-01-01 00:06:00 15
2000-01-01 00:09:00 15
Freq: 3T, dtype: int64
Upsample the series into 30 second bins.
>>> series.resample('30S').asfreq()[0:5] #select first 5 rows
2000-01-01 00:00:00 0.0
2000-01-01 00:00:30 NaN
2000-01-01 00:01:00 1.0
2000-01-01 00:01:30 NaN
2000-01-01 00:02:00 2.0
Freq: 30S, dtype: float64
Upsample the series into 30 second bins and fill the ``NaN``
values using the ``pad`` method.
>>> series.resample('30S').pad()[0:5]
2000-01-01 00:00:00 0
2000-01-01 00:00:30 0
2000-01-01 00:01:00 1
2000-01-01 00:01:30 1
2000-01-01 00:02:00 2
Freq: 30S, dtype: int64
Upsample the series into 30 second bins and fill the
``NaN`` values using the ``bfill`` method.
>>> series.resample('30S').bfill()[0:5]
2000-01-01 00:00:00 0
2000-01-01 00:00:30 1
2000-01-01 00:01:00 1
2000-01-01 00:01:30 2
2000-01-01 00:02:00 2
Freq: 30S, dtype: int64
Pass a custom function via ``apply``
>>> def custom_resampler(array_like):
... return np.sum(array_like)+5
>>> series.resample('3T').apply(custom_resampler)
2000-01-01 00:00:00 8
2000-01-01 00:03:00 17
2000-01-01 00:06:00 26
Freq: 3T, dtype: int64
For a Series with a PeriodIndex, the keyword `convention` can be
used to control whether to use the start or end of `rule`.
>>> s = pd.Series([1, 2], index=pd.period_range('2012-01-01',
freq='A',
periods=2))
>>> s
2012 1
2013 2
Freq: A-DEC, dtype: int64
Resample by month using 'start' `convention`. Values are assigned to
the first month of the period.
>>> s.resample('M', convention='start').asfreq().head()
2012-01 1.0
2012-02 NaN
2012-03 NaN
2012-04 NaN
2012-05 NaN
Freq: M, dtype: float64
Resample by month using 'end' `convention`. Values are assigned to
the last month of the period.
>>> s.resample('M', convention='end').asfreq()
2012-12 1.0
2013-01 NaN
2013-02 NaN
2013-03 NaN
2013-04 NaN
2013-05 NaN
2013-06 NaN
2013-07 NaN
2013-08 NaN
2013-09 NaN
2013-10 NaN
2013-11 NaN
2013-12 2.0
Freq: M, dtype: float64
For DataFrame objects, the keyword ``on`` can be used to specify the
column instead of the index for resampling.
>>> df = pd.DataFrame(data=9*[range(4)], columns=['a', 'b', 'c', 'd'])
>>> df['time'] = pd.date_range('1/1/2000', periods=9, freq='T')
>>> df.resample('3T', on='time').sum()
a b c d
time
2000-01-01 00:00:00 0 3 6 9
2000-01-01 00:03:00 0 3 6 9
2000-01-01 00:06:00 0 3 6 9
For a DataFrame with MultiIndex, the keyword ``level`` can be used to
specify on level the resampling needs to take place.
>>> time = pd.date_range('1/1/2000', periods=5, freq='T')
>>> df2 = pd.DataFrame(data=10*[range(4)],
columns=['a', 'b', 'c', 'd'],
index=pd.MultiIndex.from_product([time, [1, 2]])
)
>>> df2.resample('3T', level=0).sum()
a b c d
2000-01-01 00:00:00 0 6 12 18
2000-01-01 00:03:00 0 4 8 12
See also
--------
groupby : Group by mapping, function, label, or list of labels.
"""
from pandas.core.resample import (resample,
_maybe_process_deprecations)
axis = self._get_axis_number(axis)
r = resample(self, freq=rule, label=label, closed=closed,
axis=axis, kind=kind, loffset=loffset,
convention=convention,
base=base, key=on, level=level)
return _maybe_process_deprecations(r,
how=how,
fill_method=fill_method,
limit=limit)
def first(self, offset):
"""
Convenience method for subsetting initial periods of time series data
based on a date offset.
Raises
------
TypeError
If the index is not a :class:`DatetimeIndex`
Parameters
----------
offset : string, DateOffset, dateutil.relativedelta
Examples
--------
>>> i = pd.date_range('2018-04-09', periods=4, freq='2D')
>>> ts = pd.DataFrame({'A': [1,2,3,4]}, index=i)
>>> ts
A
2018-04-09 1
2018-04-11 2
2018-04-13 3
2018-04-15 4
Get the rows for the first 3 days:
>>> ts.first('3D')
A
2018-04-09 1
2018-04-11 2
Notice the data for 3 first calender days were returned, not the first
3 days observed in the dataset, and therefore data for 2018-04-13 was
not returned.
Returns
-------
subset : same type as caller
See Also
--------
last : Select final periods of time series based on a date offset
at_time : Select values at a particular time of the day
between_time : Select values between particular times of the day
"""
if not isinstance(self.index, DatetimeIndex):
raise TypeError("'first' only supports a DatetimeIndex index")
if len(self.index) == 0:
return self
offset = to_offset(offset)
end_date = end = self.index[0] + offset
# Tick-like, e.g. 3 weeks
if not offset.isAnchored() and hasattr(offset, '_inc'):
if end_date in self.index:
end = self.index.searchsorted(end_date, side='left')
return self.iloc[:end]
return self.loc[:end]
def last(self, offset):
"""
Convenience method for subsetting final periods of time series data
based on a date offset.
Raises
------
TypeError
If the index is not a :class:`DatetimeIndex`
Parameters
----------
offset : string, DateOffset, dateutil.relativedelta
Examples
--------
>>> i = pd.date_range('2018-04-09', periods=4, freq='2D')
>>> ts = pd.DataFrame({'A': [1,2,3,4]}, index=i)
>>> ts
A
2018-04-09 1
2018-04-11 2
2018-04-13 3
2018-04-15 4
Get the rows for the last 3 days:
>>> ts.last('3D')
A
2018-04-13 3
2018-04-15 4
Notice the data for 3 last calender days were returned, not the last
3 observed days in the dataset, and therefore data for 2018-04-11 was
not returned.
Returns
-------
subset : same type as caller
See Also
--------
first : Select initial periods of time series based on a date offset
at_time : Select values at a particular time of the day
between_time : Select values between particular times of the day
"""
if not isinstance(self.index, DatetimeIndex):
raise TypeError("'last' only supports a DatetimeIndex index")
if len(self.index) == 0:
return self
offset = to_offset(offset)
start_date = self.index[-1] - offset
start = self.index.searchsorted(start_date, side='right')
return self.iloc[start:]
def rank(self, axis=0, method='average', numeric_only=None,
na_option='keep', ascending=True, pct=False):
"""
Compute numerical data ranks (1 through n) along axis. Equal values are
assigned a rank that is the average of the ranks of those values
Parameters
----------
axis : {0 or 'index', 1 or 'columns'}, default 0
index to direct ranking
method : {'average', 'min', 'max', 'first', 'dense'}
* average: average rank of group
* min: lowest rank in group
* max: highest rank in group
* first: ranks assigned in order they appear in the array
* dense: like 'min', but rank always increases by 1 between groups
numeric_only : boolean, default None
Include only float, int, boolean data. Valid only for DataFrame or
Panel objects
na_option : {'keep', 'top', 'bottom'}
* keep: leave NA values where they are
* top: smallest rank if ascending
* bottom: smallest rank if descending
ascending : boolean, default True
False for ranks by high (1) to low (N)
pct : boolean, default False
Computes percentage rank of data
Returns
-------
ranks : same type as caller
"""
axis = self._get_axis_number(axis)
if self.ndim > 2:
msg = "rank does not make sense when ndim > 2"
raise NotImplementedError(msg)
def ranker(data):
ranks = algos.rank(data.values, axis=axis, method=method,
ascending=ascending, na_option=na_option,
pct=pct)
ranks = self._constructor(ranks, **data._construct_axes_dict())
return ranks.__finalize__(self)
# if numeric_only is None, and we can't get anything, we try with
# numeric_only=True
if numeric_only is None:
try:
return ranker(self)
except TypeError:
numeric_only = True
if numeric_only:
data = self._get_numeric_data()
else:
data = self
return ranker(data)
_shared_docs['align'] = ("""
Align two objects on their axes with the
specified join method for each axis Index
Parameters
----------
other : DataFrame or Series
join : {'outer', 'inner', 'left', 'right'}, default 'outer'
axis : allowed axis of the other object, default None
Align on index (0), columns (1), or both (None)
level : int or level name, default None
Broadcast across a level, matching Index values on the
passed MultiIndex level
copy : boolean, default True
Always returns new objects. If copy=False and no reindexing is
required then original objects are returned.
fill_value : scalar, default np.NaN
Value to use for missing values. Defaults to NaN, but can be any
"compatible" value
method : str, default None
limit : int, default None
fill_axis : %(axes_single_arg)s, default 0
Filling axis, method and limit
broadcast_axis : %(axes_single_arg)s, default None
Broadcast values along this axis, if aligning two objects of
different dimensions
Returns
-------
(left, right) : (%(klass)s, type of other)
Aligned objects
""")
@Appender(_shared_docs['align'] % _shared_doc_kwargs)
def align(self, other, join='outer', axis=None, level=None, copy=True,
fill_value=None, method=None, limit=None, fill_axis=0,
broadcast_axis=None):
from pandas import DataFrame, Series
method = missing.clean_fill_method(method)
if broadcast_axis == 1 and self.ndim != other.ndim:
if isinstance(self, Series):
# this means other is a DataFrame, and we need to broadcast
# self
cons = self._constructor_expanddim
df = cons({c: self for c in other.columns},
**other._construct_axes_dict())
return df._align_frame(other, join=join, axis=axis,
level=level, copy=copy,
fill_value=fill_value, method=method,
limit=limit, fill_axis=fill_axis)
elif isinstance(other, Series):
# this means self is a DataFrame, and we need to broadcast
# other
cons = other._constructor_expanddim
df = cons({c: other for c in self.columns},
**self._construct_axes_dict())
return self._align_frame(df, join=join, axis=axis, level=level,
copy=copy, fill_value=fill_value,
method=method, limit=limit,
fill_axis=fill_axis)
if axis is not None:
axis = self._get_axis_number(axis)
if isinstance(other, DataFrame):
return self._align_frame(other, join=join, axis=axis, level=level,
copy=copy, fill_value=fill_value,
method=method, limit=limit,
fill_axis=fill_axis)
elif isinstance(other, Series):
return self._align_series(other, join=join, axis=axis, level=level,
copy=copy, fill_value=fill_value,
method=method, limit=limit,
fill_axis=fill_axis)
else: # pragma: no cover
raise TypeError('unsupported type: %s' % type(other))
def _align_frame(self, other, join='outer', axis=None, level=None,
copy=True, fill_value=None, method=None, limit=None,
fill_axis=0):
# defaults
join_index, join_columns = None, None
ilidx, iridx = None, None
clidx, cridx = None, None
is_series = isinstance(self, ABCSeries)
if axis is None or axis == 0:
if not self.index.equals(other.index):
join_index, ilidx, iridx = self.index.join(
other.index, how=join, level=level, return_indexers=True)
if axis is None or axis == 1:
if not is_series and not self.columns.equals(other.columns):
join_columns, clidx, cridx = self.columns.join(
other.columns, how=join, level=level, return_indexers=True)
if is_series:
reindexers = {0: [join_index, ilidx]}
else:
reindexers = {0: [join_index, ilidx], 1: [join_columns, clidx]}
left = self._reindex_with_indexers(reindexers, copy=copy,
fill_value=fill_value,
allow_dups=True)
# other must be always DataFrame
right = other._reindex_with_indexers({0: [join_index, iridx],
1: [join_columns, cridx]},
copy=copy, fill_value=fill_value,
allow_dups=True)
if method is not None:
left = left.fillna(axis=fill_axis, method=method, limit=limit)
right = right.fillna(axis=fill_axis, method=method, limit=limit)
# if DatetimeIndex have different tz, convert to UTC
if is_datetime64tz_dtype(left.index):
if left.index.tz != right.index.tz:
if join_index is not None:
left.index = join_index
right.index = join_index
return left.__finalize__(self), right.__finalize__(other)
def _align_series(self, other, join='outer', axis=None, level=None,
copy=True, fill_value=None, method=None, limit=None,
fill_axis=0):
is_series = isinstance(self, ABCSeries)
# series/series compat, other must always be a Series
if is_series:
if axis:
raise ValueError('cannot align series to a series other than '
'axis 0')
# equal
if self.index.equals(other.index):
join_index, lidx, ridx = None, None, None
else:
join_index, lidx, ridx = self.index.join(other.index, how=join,
level=level,
return_indexers=True)
left = self._reindex_indexer(join_index, lidx, copy)
right = other._reindex_indexer(join_index, ridx, copy)
else:
# one has > 1 ndim
fdata = self._data
if axis == 0:
join_index = self.index
lidx, ridx = None, None
if not self.index.equals(other.index):
join_index, lidx, ridx = self.index.join(
other.index, how=join, level=level,
return_indexers=True)
if lidx is not None:
fdata = fdata.reindex_indexer(join_index, lidx, axis=1)
elif axis == 1:
join_index = self.columns
lidx, ridx = None, None
if not self.columns.equals(other.index):
join_index, lidx, ridx = self.columns.join(
other.index, how=join, level=level,
return_indexers=True)
if lidx is not None:
fdata = fdata.reindex_indexer(join_index, lidx, axis=0)
else:
raise ValueError('Must specify axis=0 or 1')
if copy and fdata is self._data:
fdata = fdata.copy()
left = self._constructor(fdata)
if ridx is None:
right = other
else:
right = other.reindex(join_index, level=level)
# fill
fill_na = notna(fill_value) or (method is not None)
if fill_na:
left = left.fillna(fill_value, method=method, limit=limit,
axis=fill_axis)
right = right.fillna(fill_value, method=method, limit=limit)
# if DatetimeIndex have different tz, convert to UTC
if is_series or (not is_series and axis == 0):
if is_datetime64tz_dtype(left.index):
if left.index.tz != right.index.tz:
if join_index is not None:
left.index = join_index
right.index = join_index
return left.__finalize__(self), right.__finalize__(other)
def _where(self, cond, other=np.nan, inplace=False, axis=None, level=None,
errors='raise', try_cast=False):
"""
Equivalent to public method `where`, except that `other` is not
applied as a function even if callable. Used in __setitem__.
"""
inplace = validate_bool_kwarg(inplace, 'inplace')
# align the cond to same shape as myself
cond = com._apply_if_callable(cond, self)
if isinstance(cond, NDFrame):
cond, _ = cond.align(self, join='right', broadcast_axis=1)
else:
if not hasattr(cond, 'shape'):
cond = np.asanyarray(cond)
if cond.shape != self.shape:
raise ValueError('Array conditional must be same shape as '
'self')
cond = self._constructor(cond, **self._construct_axes_dict())
# make sure we are boolean
fill_value = True if inplace else False
cond = cond.fillna(fill_value)
msg = "Boolean array expected for the condition, not {dtype}"
if not isinstance(cond, pd.DataFrame):
# This is a single-dimensional object.
if not is_bool_dtype(cond):
raise ValueError(msg.format(dtype=cond.dtype))
else:
for dt in cond.dtypes:
if not is_bool_dtype(dt):
raise ValueError(msg.format(dtype=dt))
cond = -cond if inplace else cond
# try to align with other
try_quick = True
if hasattr(other, 'align'):
# align with me
if other.ndim <= self.ndim:
_, other = self.align(other, join='left', axis=axis,
level=level, fill_value=np.nan)
# if we are NOT aligned, raise as we cannot where index
if (axis is None and
not all(other._get_axis(i).equals(ax)
for i, ax in enumerate(self.axes))):
raise InvalidIndexError
# slice me out of the other
else:
raise NotImplementedError("cannot align with a higher "
"dimensional NDFrame")
if isinstance(other, np.ndarray):
if other.shape != self.shape:
if self.ndim == 1:
icond = cond.values
# GH 2745 / GH 4192
# treat like a scalar
if len(other) == 1:
other = np.array(other[0])
# GH 3235
# match True cond to other
elif len(cond[icond]) == len(other):
# try to not change dtype at first (if try_quick)
if try_quick:
try:
new_other = com._values_from_object(self)
new_other = new_other.copy()
new_other[icond] = other
other = new_other
except Exception:
try_quick = False
# let's create a new (if we failed at the above
# or not try_quick
if not try_quick:
dtype, fill_value = maybe_promote(other.dtype)
new_other = np.empty(len(icond), dtype=dtype)
new_other.fill(fill_value)
maybe_upcast_putmask(new_other, icond, other)
other = new_other
else:
raise ValueError('Length of replacements must equal '
'series length')
else:
raise ValueError('other must be the same shape as self '
'when an ndarray')
# we are the same shape, so create an actual object for alignment
else:
other = self._constructor(other, **self._construct_axes_dict())
if axis is None:
axis = 0
if self.ndim == getattr(other, 'ndim', 0):
align = True
else:
align = (self._get_axis_number(axis) == 1)
block_axis = self._get_block_manager_axis(axis)
if inplace:
# we may have different type blocks come out of putmask, so
# reconstruct the block manager
self._check_inplace_setting(other)
new_data = self._data.putmask(mask=cond, new=other, align=align,
inplace=True, axis=block_axis,
transpose=self._AXIS_REVERSED)
self._update_inplace(new_data)
else:
new_data = self._data.where(other=other, cond=cond, align=align,
errors=errors,
try_cast=try_cast, axis=block_axis,
transpose=self._AXIS_REVERSED)
return self._constructor(new_data).__finalize__(self)
_shared_docs['where'] = ("""
Replace values where the condition is %(cond_rev)s.
Parameters
----------
cond : boolean %(klass)s, array-like, or callable
Where `cond` is %(cond)s, keep the original value. Where
%(cond_rev)s, replace with corresponding value from `other`.
If `cond` is callable, it is computed on the %(klass)s and
should return boolean %(klass)s or array. The callable must
not change input %(klass)s (though pandas doesn't check it).
.. versionadded:: 0.18.1
A callable can be used as cond.
other : scalar, %(klass)s, or callable
Entries where `cond` is %(cond_rev)s are replaced with
corresponding value from `other`.
If other is callable, it is computed on the %(klass)s and
should return scalar or %(klass)s. The callable must not
change input %(klass)s (though pandas doesn't check it).
.. versionadded:: 0.18.1
A callable can be used as other.
inplace : boolean, default False
Whether to perform the operation in place on the data.
axis : int, default None
Alignment axis if needed.
level : int, default None
Alignment level if needed.
errors : str, {'raise', 'ignore'}, default `raise`
Note that currently this parameter won't affect
the results and will always coerce to a suitable dtype.
- `raise` : allow exceptions to be raised.
- `ignore` : suppress exceptions. On error return original object.
try_cast : boolean, default False
Try to cast the result back to the input type (if possible).
raise_on_error : boolean, default True
Whether to raise on invalid data types (e.g. trying to where on
strings).
.. deprecated:: 0.21.0
Use `errors`.
Returns
-------
wh : same type as caller
Notes
-----
The %(name)s method is an application of the if-then idiom. For each
element in the calling DataFrame, if ``cond`` is ``%(cond)s`` the
element is used; otherwise the corresponding element from the DataFrame
``other`` is used.
The signature for :func:`DataFrame.where` differs from
:func:`numpy.where`. Roughly ``df1.where(m, df2)`` is equivalent to
``np.where(m, df1, df2)``.
For further details and examples see the ``%(name)s`` documentation in
:ref:`indexing <indexing.where_mask>`.
See Also
--------
:func:`DataFrame.%(name_other)s` : Return an object of same shape as
self
Examples
--------
>>> s = pd.Series(range(5))
>>> s.where(s > 0)
0 NaN
1 1.0
2 2.0
3 3.0
4 4.0
dtype: float64
>>> s.mask(s > 0)
0 0.0
1 NaN
2 NaN
3 NaN
4 NaN
dtype: float64
>>> s.where(s > 1, 10)
0 10
1 10
2 2
3 3
4 4
dtype: int64
>>> df = pd.DataFrame(np.arange(10).reshape(-1, 2), columns=['A', 'B'])
>>> m = df %% 3 == 0
>>> df.where(m, -df)
A B
0 0 -1
1 -2 3
2 -4 -5
3 6 -7
4 -8 9
>>> df.where(m, -df) == np.where(m, df, -df)
A B
0 True True
1 True True
2 True True
3 True True
4 True True
>>> df.where(m, -df) == df.mask(~m, -df)
A B
0 True True
1 True True
2 True True
3 True True
4 True True
""")
@Appender(_shared_docs['where'] % dict(_shared_doc_kwargs, cond="True",
cond_rev="False", name='where',
name_other='mask'))
def where(self, cond, other=np.nan, inplace=False, axis=None, level=None,
errors='raise', try_cast=False, raise_on_error=None):
if raise_on_error is not None:
warnings.warn(
"raise_on_error is deprecated in "
"favor of errors='raise|ignore'",
FutureWarning, stacklevel=2)
if raise_on_error:
errors = 'raise'
else:
errors = 'ignore'
other = com._apply_if_callable(other, self)
return self._where(cond, other, inplace, axis, level,
errors=errors, try_cast=try_cast)
@Appender(_shared_docs['where'] % dict(_shared_doc_kwargs, cond="False",
cond_rev="True", name='mask',
name_other='where'))
def mask(self, cond, other=np.nan, inplace=False, axis=None, level=None,
errors='raise', try_cast=False, raise_on_error=None):
if raise_on_error is not None:
warnings.warn(
"raise_on_error is deprecated in "
"favor of errors='raise|ignore'",
FutureWarning, stacklevel=2)
if raise_on_error:
errors = 'raise'
else:
errors = 'ignore'
inplace = validate_bool_kwarg(inplace, 'inplace')
cond = com._apply_if_callable(cond, self)
# see gh-21891
if not hasattr(cond, "__invert__"):
cond = np.array(cond)
return self.where(~cond, other=other, inplace=inplace, axis=axis,
level=level, try_cast=try_cast,
errors=errors)
_shared_docs['shift'] = ("""
Shift index by desired number of periods with an optional time freq
Parameters
----------
periods : int
Number of periods to move, can be positive or negative
freq : DateOffset, timedelta, or time rule string, optional
Increment to use from the tseries module or time rule (e.g. 'EOM').
See Notes.
axis : %(axes_single_arg)s
Notes
-----
If freq is specified then the index values are shifted but the data
is not realigned. That is, use freq if you would like to extend the
index when shifting and preserve the original data.
Returns
-------
shifted : %(klass)s
""")
@Appender(_shared_docs['shift'] % _shared_doc_kwargs)
def shift(self, periods=1, freq=None, axis=0):
if periods == 0:
return self
block_axis = self._get_block_manager_axis(axis)
if freq is None:
new_data = self._data.shift(periods=periods, axis=block_axis)
else:
return self.tshift(periods, freq)
return self._constructor(new_data).__finalize__(self)
def slice_shift(self, periods=1, axis=0):
"""
Equivalent to `shift` without copying data. The shifted data will
not include the dropped periods and the shifted axis will be smaller
than the original.
Parameters
----------
periods : int
Number of periods to move, can be positive or negative
Notes
-----
While the `slice_shift` is faster than `shift`, you may pay for it
later during alignment.
Returns
-------
shifted : same type as caller
"""
if periods == 0:
return self
if periods > 0:
vslicer = slice(None, -periods)
islicer = slice(periods, None)
else:
vslicer = slice(-periods, None)
islicer = slice(None, periods)
new_obj = self._slice(vslicer, axis=axis)
shifted_axis = self._get_axis(axis)[islicer]
new_obj.set_axis(shifted_axis, axis=axis, inplace=True)
return new_obj.__finalize__(self)
def tshift(self, periods=1, freq=None, axis=0):
"""
Shift the time index, using the index's frequency if available.
Parameters
----------
periods : int
Number of periods to move, can be positive or negative
freq : DateOffset, timedelta, or time rule string, default None
Increment to use from the tseries module or time rule (e.g. 'EOM')
axis : int or basestring
Corresponds to the axis that contains the Index
Notes
-----
If freq is not specified then tries to use the freq or inferred_freq
attributes of the index. If neither of those attributes exist, a
ValueError is thrown
Returns
-------
shifted : NDFrame
"""
index = self._get_axis(axis)
if freq is None:
freq = getattr(index, 'freq', None)
if freq is None:
freq = getattr(index, 'inferred_freq', None)
if freq is None:
msg = 'Freq was not given and was not set in the index'
raise ValueError(msg)
if periods == 0:
return self
if isinstance(freq, string_types):
freq = to_offset(freq)
block_axis = self._get_block_manager_axis(axis)
if isinstance(index, PeriodIndex):
orig_freq = to_offset(index.freq)
if freq == orig_freq:
new_data = self._data.copy()
new_data.axes[block_axis] = index.shift(periods)
else:
msg = ('Given freq %s does not match PeriodIndex freq %s' %
(freq.rule_code, orig_freq.rule_code))
raise ValueError(msg)
else:
new_data = self._data.copy()
new_data.axes[block_axis] = index.shift(periods, freq)
return self._constructor(new_data).__finalize__(self)
def truncate(self, before=None, after=None, axis=None, copy=True):
"""
Truncate a Series or DataFrame before and after some index value.
This is a useful shorthand for boolean indexing based on index
values above or below certain thresholds.
Parameters
----------
before : date, string, int
Truncate all rows before this index value.
after : date, string, int
Truncate all rows after this index value.
axis : {0 or 'index', 1 or 'columns'}, optional
Axis to truncate. Truncates the index (rows) by default.
copy : boolean, default is True,
Return a copy of the truncated section.
Returns
-------
type of caller
The truncated Series or DataFrame.
See Also
--------
DataFrame.loc : Select a subset of a DataFrame by label.
DataFrame.iloc : Select a subset of a DataFrame by position.
Notes
-----
If the index being truncated contains only datetime values,
`before` and `after` may be specified as strings instead of
Timestamps.
Examples
--------
>>> df = pd.DataFrame({'A': ['a', 'b', 'c', 'd', 'e'],
... 'B': ['f', 'g', 'h', 'i', 'j'],
... 'C': ['k', 'l', 'm', 'n', 'o']},
... index=[1, 2, 3, 4, 5])
>>> df
A B C
1 a f k
2 b g l
3 c h m
4 d i n
5 e j o
>>> df.truncate(before=2, after=4)
A B C
2 b g l
3 c h m
4 d i n
The columns of a DataFrame can be truncated.
>>> df.truncate(before="A", after="B", axis="columns")
A B
1 a f
2 b g
3 c h
4 d i
5 e j
For Series, only rows can be truncated.
>>> df['A'].truncate(before=2, after=4)
2 b
3 c
4 d
Name: A, dtype: object
The index values in ``truncate`` can be datetimes or string
dates.
>>> dates = pd.date_range('2016-01-01', '2016-02-01', freq='s')
>>> df = pd.DataFrame(index=dates, data={'A': 1})
>>> df.tail()
A
2016-01-31 23:59:56 1
2016-01-31 23:59:57 1
2016-01-31 23:59:58 1
2016-01-31 23:59:59 1
2016-02-01 00:00:00 1
>>> df.truncate(before=pd.Timestamp('2016-01-05'),
... after=pd.Timestamp('2016-01-10')).tail()
A
2016-01-09 23:59:56 1
2016-01-09 23:59:57 1
2016-01-09 23:59:58 1
2016-01-09 23:59:59 1
2016-01-10 00:00:00 1
Because the index is a DatetimeIndex containing only dates, we can
specify `before` and `after` as strings. They will be coerced to
Timestamps before truncation.
>>> df.truncate('2016-01-05', '2016-01-10').tail()
A
2016-01-09 23:59:56 1
2016-01-09 23:59:57 1
2016-01-09 23:59:58 1
2016-01-09 23:59:59 1
2016-01-10 00:00:00 1
Note that ``truncate`` assumes a 0 value for any unspecified time
component (midnight). This differs from partial string slicing, which
returns any partially matching dates.
>>> df.loc['2016-01-05':'2016-01-10', :].tail()
A
2016-01-10 23:59:55 1
2016-01-10 23:59:56 1
2016-01-10 23:59:57 1
2016-01-10 23:59:58 1
2016-01-10 23:59:59 1
"""
if axis is None:
axis = self._stat_axis_number
axis = self._get_axis_number(axis)
ax = self._get_axis(axis)
# GH 17935
# Check that index is sorted
if not ax.is_monotonic_increasing and not ax.is_monotonic_decreasing:
raise ValueError("truncate requires a sorted index")
# if we have a date index, convert to dates, otherwise
# treat like a slice
if ax.is_all_dates:
from pandas.core.tools.datetimes import to_datetime
before = to_datetime(before)
after = to_datetime(after)
if before is not None and after is not None:
if before > after:
raise ValueError('Truncate: %s must be after %s' %
(after, before))
slicer = [slice(None, None)] * self._AXIS_LEN
slicer[axis] = slice(before, after)
result = self.loc[tuple(slicer)]
if isinstance(ax, MultiIndex):
setattr(result, self._get_axis_name(axis),
ax.truncate(before, after))
if copy:
result = result.copy()
return result
def tz_convert(self, tz, axis=0, level=None, copy=True):
"""
Convert tz-aware axis to target time zone.
Parameters
----------
tz : string or pytz.timezone object
axis : the axis to convert
level : int, str, default None
If axis ia a MultiIndex, convert a specific level. Otherwise
must be None
copy : boolean, default True
Also make a copy of the underlying data
Returns
-------
Raises
------
TypeError
If the axis is tz-naive.
"""
axis = self._get_axis_number(axis)
ax = self._get_axis(axis)
def _tz_convert(ax, tz):
if not hasattr(ax, 'tz_convert'):
if len(ax) > 0:
ax_name = self._get_axis_name(axis)
raise TypeError('%s is not a valid DatetimeIndex or '
'PeriodIndex' % ax_name)
else:
ax = DatetimeIndex([], tz=tz)
else:
ax = ax.tz_convert(tz)
return ax
# if a level is given it must be a MultiIndex level or
# equivalent to the axis name
if isinstance(ax, MultiIndex):
level = ax._get_level_number(level)
new_level = _tz_convert(ax.levels[level], tz)
ax = ax.set_levels(new_level, level=level)
else:
if level not in (None, 0, ax.name):
raise ValueError("The level {0} is not valid".format(level))
ax = _tz_convert(ax, tz)
result = self._constructor(self._data, copy=copy)
result.set_axis(ax, axis=axis, inplace=True)
return result.__finalize__(self)
def tz_localize(self, tz, axis=0, level=None, copy=True,
ambiguous='raise'):
"""
Localize tz-naive TimeSeries to target time zone.
Parameters
----------
tz : string or pytz.timezone object
axis : the axis to localize
level : int, str, default None
If axis ia a MultiIndex, localize a specific level. Otherwise
must be None
copy : boolean, default True
Also make a copy of the underlying data
ambiguous : 'infer', bool-ndarray, 'NaT', default 'raise'
- 'infer' will attempt to infer fall dst-transition hours based on
order
- bool-ndarray where True signifies a DST time, False designates
a non-DST time (note that this flag is only applicable for
ambiguous times)
- 'NaT' will return NaT where there are ambiguous times
- 'raise' will raise an AmbiguousTimeError if there are ambiguous
times
Returns
-------
Raises
------
TypeError
If the TimeSeries is tz-aware and tz is not None.
"""
axis = self._get_axis_number(axis)
ax = self._get_axis(axis)
def _tz_localize(ax, tz, ambiguous):
if not hasattr(ax, 'tz_localize'):
if len(ax) > 0:
ax_name = self._get_axis_name(axis)
raise TypeError('%s is not a valid DatetimeIndex or '
'PeriodIndex' % ax_name)
else:
ax = DatetimeIndex([], tz=tz)
else:
ax = ax.tz_localize(tz, ambiguous=ambiguous)
return ax
# if a level is given it must be a MultiIndex level or
# equivalent to the axis name
if isinstance(ax, MultiIndex):
level = ax._get_level_number(level)
new_level = _tz_localize(ax.levels[level], tz, ambiguous)
ax = ax.set_levels(new_level, level=level)
else:
if level not in (None, 0, ax.name):
raise ValueError("The level {0} is not valid".format(level))
ax = _tz_localize(ax, tz, ambiguous)
result = self._constructor(self._data, copy=copy)
result.set_axis(ax, axis=axis, inplace=True)
return result.__finalize__(self)
# ----------------------------------------------------------------------
# Numeric Methods
def abs(self):
"""
Return a Series/DataFrame with absolute numeric value of each element.
This function only applies to elements that are all numeric.
Returns
-------
abs
Series/DataFrame containing the absolute value of each element.
Notes
-----
For ``complex`` inputs, ``1.2 + 1j``, the absolute value is
:math:`\\sqrt{ a^2 + b^2 }`.
Examples
--------
Absolute numeric values in a Series.
>>> s = pd.Series([-1.10, 2, -3.33, 4])
>>> s.abs()
0 1.10
1 2.00
2 3.33
3 4.00
dtype: float64
Absolute numeric values in a Series with complex numbers.
>>> s = pd.Series([1.2 + 1j])
>>> s.abs()
0 1.56205
dtype: float64
Absolute numeric values in a Series with a Timedelta element.
>>> s = pd.Series([pd.Timedelta('1 days')])
>>> s.abs()
0 1 days
dtype: timedelta64[ns]
Select rows with data closest to certain value using argsort (from
`StackOverflow <https://stackoverflow.com/a/17758115>`__).
>>> df = pd.DataFrame({
... 'a': [4, 5, 6, 7],
... 'b': [10, 20, 30, 40],
... 'c': [100, 50, -30, -50]
... })
>>> df
a b c
0 4 10 100
1 5 20 50
2 6 30 -30
3 7 40 -50
>>> df.loc[(df.c - 43).abs().argsort()]
a b c
1 5 20 50
0 4 10 100
2 6 30 -30
3 7 40 -50
See Also
--------
numpy.absolute : calculate the absolute value element-wise.
"""
return np.abs(self)
def describe(self, percentiles=None, include=None, exclude=None):
"""
Generate descriptive statistics that summarize the central tendency,
dispersion and shape of a dataset's distribution, excluding
``NaN`` values.
Analyzes both numeric and object series, as well
as ``DataFrame`` column sets of mixed data types. The output
will vary depending on what is provided. Refer to the notes
below for more detail.
Parameters
----------
percentiles : list-like of numbers, optional
The percentiles to include in the output. All should
fall between 0 and 1. The default is
``[.25, .5, .75]``, which returns the 25th, 50th, and
75th percentiles.
include : 'all', list-like of dtypes or None (default), optional
A white list of data types to include in the result. Ignored
for ``Series``. Here are the options:
- 'all' : All columns of the input will be included in the output.
- A list-like of dtypes : Limits the results to the
provided data types.
To limit the result to numeric types submit
``numpy.number``. To limit it instead to object columns submit
the ``numpy.object`` data type. Strings
can also be used in the style of
``select_dtypes`` (e.g. ``df.describe(include=['O'])``). To
select pandas categorical columns, use ``'category'``
- None (default) : The result will include all numeric columns.
exclude : list-like of dtypes or None (default), optional,
A black list of data types to omit from the result. Ignored
for ``Series``. Here are the options:
- A list-like of dtypes : Excludes the provided data types
from the result. To exclude numeric types submit
``numpy.number``. To exclude object columns submit the data
type ``numpy.object``. Strings can also be used in the style of
``select_dtypes`` (e.g. ``df.describe(include=['O'])``). To
exclude pandas categorical columns, use ``'category'``
- None (default) : The result will exclude nothing.
Returns
-------
Series or DataFrame
Summary statistics of the Series or Dataframe provided.
See Also
--------
DataFrame.count: Count number of non-NA/null observations.
DataFrame.max: Maximum of the values in the object.
DataFrame.min: Minimum of the values in the object.
DataFrame.mean: Mean of the values.
DataFrame.std: Standard deviation of the obersvations.
DataFrame.select_dtypes: Subset of a DataFrame including/excluding
columns based on their dtype.
Notes
-----
For numeric data, the result's index will include ``count``,
``mean``, ``std``, ``min``, ``max`` as well as lower, ``50`` and
upper percentiles. By default the lower percentile is ``25`` and the
upper percentile is ``75``. The ``50`` percentile is the
same as the median.
For object data (e.g. strings or timestamps), the result's index
will include ``count``, ``unique``, ``top``, and ``freq``. The ``top``
is the most common value. The ``freq`` is the most common value's
frequency. Timestamps also include the ``first`` and ``last`` items.
If multiple object values have the highest count, then the
``count`` and ``top`` results will be arbitrarily chosen from
among those with the highest count.
For mixed data types provided via a ``DataFrame``, the default is to
return only an analysis of numeric columns. If the dataframe consists
only of object and categorical data without any numeric columns, the
default is to return an analysis of both the object and categorical
columns. If ``include='all'`` is provided as an option, the result
will include a union of attributes of each type.
The `include` and `exclude` parameters can be used to limit
which columns in a ``DataFrame`` are analyzed for the output.
The parameters are ignored when analyzing a ``Series``.
Examples
--------
Describing a numeric ``Series``.
>>> s = pd.Series([1, 2, 3])
>>> s.describe()
count 3.0
mean 2.0
std 1.0
min 1.0
25% 1.5
50% 2.0
75% 2.5
max 3.0
dtype: float64
Describing a categorical ``Series``.
>>> s = pd.Series(['a', 'a', 'b', 'c'])
>>> s.describe()
count 4
unique 3
top a
freq 2
dtype: object
Describing a timestamp ``Series``.
>>> s = pd.Series([
... np.datetime64("2000-01-01"),
... np.datetime64("2010-01-01"),
... np.datetime64("2010-01-01")
... ])
>>> s.describe()
count 3
unique 2
top 2010-01-01 00:00:00
freq 2
first 2000-01-01 00:00:00
last 2010-01-01 00:00:00
dtype: object
Describing a ``DataFrame``. By default only numeric fields
are returned.
>>> df = pd.DataFrame({'categorical': pd.Categorical(['d','e','f']),
... 'numeric': [1, 2, 3],
... 'object': ['a', 'b', 'c']
... })
>>> df.describe()
numeric
count 3.0
mean 2.0
std 1.0
min 1.0
25% 1.5
50% 2.0
75% 2.5
max 3.0
Describing all columns of a ``DataFrame`` regardless of data type.
>>> df.describe(include='all')
categorical numeric object
count 3 3.0 3
unique 3 NaN 3
top f NaN c
freq 1 NaN 1
mean NaN 2.0 NaN
std NaN 1.0 NaN
min NaN 1.0 NaN
25% NaN 1.5 NaN
50% NaN 2.0 NaN
75% NaN 2.5 NaN
max NaN 3.0 NaN
Describing a column from a ``DataFrame`` by accessing it as
an attribute.
>>> df.numeric.describe()
count 3.0
mean 2.0
std 1.0
min 1.0
25% 1.5
50% 2.0
75% 2.5
max 3.0
Name: numeric, dtype: float64
Including only numeric columns in a ``DataFrame`` description.
>>> df.describe(include=[np.number])
numeric
count 3.0
mean 2.0
std 1.0
min 1.0
25% 1.5
50% 2.0
75% 2.5
max 3.0
Including only string columns in a ``DataFrame`` description.
>>> df.describe(include=[np.object])
object
count 3
unique 3
top c
freq 1
Including only categorical columns from a ``DataFrame`` description.
>>> df.describe(include=['category'])
categorical
count 3
unique 3
top f
freq 1
Excluding numeric columns from a ``DataFrame`` description.
>>> df.describe(exclude=[np.number])
categorical object
count 3 3
unique 3 3
top f c
freq 1 1
Excluding object columns from a ``DataFrame`` description.
>>> df.describe(exclude=[np.object])
categorical numeric
count 3 3.0
unique 3 NaN
top f NaN
freq 1 NaN
mean NaN 2.0
std NaN 1.0
min NaN 1.0
25% NaN 1.5
50% NaN 2.0
75% NaN 2.5
max NaN 3.0
"""
if self.ndim >= 3:
msg = "describe is not implemented on Panel objects."
raise NotImplementedError(msg)
elif self.ndim == 2 and self.columns.size == 0:
raise ValueError("Cannot describe a DataFrame without columns")
if percentiles is not None:
# explicit conversion of `percentiles` to list
percentiles = list(percentiles)
# get them all to be in [0, 1]
self._check_percentile(percentiles)
# median should always be included
if 0.5 not in percentiles:
percentiles.append(0.5)
percentiles = np.asarray(percentiles)
else:
percentiles = np.array([0.25, 0.5, 0.75])
# sort and check for duplicates
unique_pcts = np.unique(percentiles)
if len(unique_pcts) < len(percentiles):
raise ValueError("percentiles cannot contain duplicates")
percentiles = unique_pcts
formatted_percentiles = format_percentiles(percentiles)
def describe_numeric_1d(series):
stat_index = (['count', 'mean', 'std', 'min'] +
formatted_percentiles + ['max'])
d = ([series.count(), series.mean(), series.std(), series.min()] +
series.quantile(percentiles).tolist() + [series.max()])
return pd.Series(d, index=stat_index, name=series.name)
def describe_categorical_1d(data):
names = ['count', 'unique']
objcounts = data.value_counts()
count_unique = len(objcounts[objcounts != 0])
result = [data.count(), count_unique]
if result[1] > 0:
top, freq = objcounts.index[0], objcounts.iloc[0]
if is_datetime64_any_dtype(data):
tz = data.dt.tz
asint = data.dropna().values.view('i8')
names += ['top', 'freq', 'first', 'last']
result += [tslib.Timestamp(top, tz=tz), freq,
tslib.Timestamp(asint.min(), tz=tz),
tslib.Timestamp(asint.max(), tz=tz)]
else:
names += ['top', 'freq']
result += [top, freq]
return pd.Series(result, index=names, name=data.name)
def describe_1d(data):
if is_bool_dtype(data):
return describe_categorical_1d(data)
elif is_numeric_dtype(data):
return describe_numeric_1d(data)
elif is_timedelta64_dtype(data):
return describe_numeric_1d(data)
else:
return describe_categorical_1d(data)
if self.ndim == 1:
return describe_1d(self)
elif (include is None) and (exclude is None):
# when some numerics are found, keep only numerics
data = self.select_dtypes(include=[np.number])
if len(data.columns) == 0:
data = self
elif include == 'all':
if exclude is not None:
msg = "exclude must be None when include is 'all'"
raise ValueError(msg)
data = self
else:
data = self.select_dtypes(include=include, exclude=exclude)
ldesc = [describe_1d(s) for _, s in data.iteritems()]
# set a convenient order for rows
names = []
ldesc_indexes = sorted([x.index for x in ldesc], key=len)
for idxnames in ldesc_indexes:
for name in idxnames:
if name not in names:
names.append(name)
d = pd.concat(ldesc, join_axes=pd.Index([names]), axis=1)
d.columns = data.columns.copy()
return d
def _check_percentile(self, q):
"""Validate percentiles (used by describe and quantile)."""
msg = ("percentiles should all be in the interval [0, 1]. "
"Try {0} instead.")
q = np.asarray(q)
if q.ndim == 0:
if not 0 <= q <= 1:
raise ValueError(msg.format(q / 100.0))
else:
if not all(0 <= qs <= 1 for qs in q):
raise ValueError(msg.format(q / 100.0))
return q
_shared_docs['pct_change'] = """
Percentage change between the current and a prior element.
Computes the percentage change from the immediately previous row by
default. This is useful in comparing the percentage of change in a time
series of elements.
Parameters
----------
periods : int, default 1
Periods to shift for forming percent change.
fill_method : str, default 'pad'
How to handle NAs before computing percent changes.
limit : int, default None
The number of consecutive NAs to fill before stopping.
freq : DateOffset, timedelta, or offset alias string, optional
Increment to use from time series API (e.g. 'M' or BDay()).
**kwargs
Additional keyword arguments are passed into
`DataFrame.shift` or `Series.shift`.
Returns
-------
chg : Series or DataFrame
The same type as the calling object.
See Also
--------
Series.diff : Compute the difference of two elements in a Series.
DataFrame.diff : Compute the difference of two elements in a DataFrame.
Series.shift : Shift the index by some number of periods.
DataFrame.shift : Shift the index by some number of periods.
Examples
--------
**Series**
>>> s = pd.Series([90, 91, 85])
>>> s
0 90
1 91
2 85
dtype: int64
>>> s.pct_change()
0 NaN
1 0.011111
2 -0.065934
dtype: float64
>>> s.pct_change(periods=2)
0 NaN
1 NaN
2 -0.055556
dtype: float64
See the percentage change in a Series where filling NAs with last
valid observation forward to next valid.
>>> s = pd.Series([90, 91, None, 85])
>>> s
0 90.0
1 91.0
2 NaN
3 85.0
dtype: float64
>>> s.pct_change(fill_method='ffill')
0 NaN
1 0.011111
2 0.000000
3 -0.065934
dtype: float64
**DataFrame**
Percentage change in French franc, Deutsche Mark, and Italian lira from
1980-01-01 to 1980-03-01.
>>> df = pd.DataFrame({
... 'FR': [4.0405, 4.0963, 4.3149],
... 'GR': [1.7246, 1.7482, 1.8519],
... 'IT': [804.74, 810.01, 860.13]},
... index=['1980-01-01', '1980-02-01', '1980-03-01'])
>>> df
FR GR IT
1980-01-01 4.0405 1.7246 804.74
1980-02-01 4.0963 1.7482 810.01
1980-03-01 4.3149 1.8519 860.13
>>> df.pct_change()
FR GR IT
1980-01-01 NaN NaN NaN
1980-02-01 0.013810 0.013684 0.006549
1980-03-01 0.053365 0.059318 0.061876
Percentage of change in GOOG and APPL stock volume. Shows computing
the percentage change between columns.
>>> df = pd.DataFrame({
... '2016': [1769950, 30586265],
... '2015': [1500923, 40912316],
... '2014': [1371819, 41403351]},
... index=['GOOG', 'APPL'])
>>> df
2016 2015 2014
GOOG 1769950 1500923 1371819
APPL 30586265 40912316 41403351
>>> df.pct_change(axis='columns')
2016 2015 2014
GOOG NaN -0.151997 -0.086016
APPL NaN 0.337604 0.012002
"""
@Appender(_shared_docs['pct_change'] % _shared_doc_kwargs)
def pct_change(self, periods=1, fill_method='pad', limit=None, freq=None,
**kwargs):
# TODO: Not sure if above is correct - need someone to confirm.
axis = self._get_axis_number(kwargs.pop('axis', self._stat_axis_name))
if fill_method is None:
data = self
else:
data = self.fillna(method=fill_method, limit=limit, axis=axis)
rs = (data.div(data.shift(periods=periods, freq=freq, axis=axis,
**kwargs)) - 1)
rs = rs.reindex_like(data)
if freq is None:
mask = isna(com._values_from_object(data))
np.putmask(rs.values, mask, np.nan)
return rs
def _agg_by_level(self, name, axis=0, level=0, skipna=True, **kwargs):
if axis is None:
raise ValueError("Must specify 'axis' when aggregating by level.")
grouped = self.groupby(level=level, axis=axis, sort=False)
if hasattr(grouped, name) and skipna:
return getattr(grouped, name)(**kwargs)
axis = self._get_axis_number(axis)
method = getattr(type(self), name)
applyf = lambda x: method(x, axis=axis, skipna=skipna, **kwargs)
return grouped.aggregate(applyf)
@classmethod
def _add_numeric_operations(cls):
"""Add the operations to the cls; evaluate the doc strings again"""
axis_descr, name, name2 = _doc_parms(cls)
cls.any = _make_logical_function(
cls, 'any', name, name2, axis_descr,
_any_desc, nanops.nanany, _any_examples, _any_see_also)
cls.all = _make_logical_function(
cls, 'all', name, name2, axis_descr, _all_doc,
nanops.nanall, _all_examples, _all_see_also)
@Substitution(outname='mad',
desc="Return the mean absolute deviation of the values "
"for the requested axis",
name1=name, name2=name2, axis_descr=axis_descr,
min_count='', examples='')
@Appender(_num_doc)
def mad(self, axis=None, skipna=None, level=None):
if skipna is None:
skipna = True
if axis is None:
axis = self._stat_axis_number
if level is not None:
return self._agg_by_level('mad', axis=axis, level=level,
skipna=skipna)
data = self._get_numeric_data()
if axis == 0:
demeaned = data - data.mean(axis=0)
else:
demeaned = data.sub(data.mean(axis=1), axis=0)
return np.abs(demeaned).mean(axis=axis, skipna=skipna)
cls.mad = mad
cls.sem = _make_stat_function_ddof(
cls, 'sem', name, name2, axis_descr,
"Return unbiased standard error of the mean over requested "
"axis.\n\nNormalized by N-1 by default. This can be changed "
"using the ddof argument",
nanops.nansem)
cls.var = _make_stat_function_ddof(
cls, 'var', name, name2, axis_descr,
"Return unbiased variance over requested axis.\n\nNormalized by "
"N-1 by default. This can be changed using the ddof argument",
nanops.nanvar)
cls.std = _make_stat_function_ddof(
cls, 'std', name, name2, axis_descr,
"Return sample standard deviation over requested axis."
"\n\nNormalized by N-1 by default. This can be changed using the "
"ddof argument",
nanops.nanstd)
@Substitution(outname='compounded',
desc="Return the compound percentage of the values for "
"the requested axis", name1=name, name2=name2,
axis_descr=axis_descr,
min_count='', examples='')
@Appender(_num_doc)
def compound(self, axis=None, skipna=None, level=None):
if skipna is None:
skipna = True
return (1 + self).prod(axis=axis, skipna=skipna, level=level) - 1
cls.compound = compound
cls.cummin = _make_cum_function(
cls, 'cummin', name, name2, axis_descr, "minimum",
lambda y, axis: np.minimum.accumulate(y, axis), "min",
np.inf, np.nan, _cummin_examples)
cls.cumsum = _make_cum_function(
cls, 'cumsum', name, name2, axis_descr, "sum",
lambda y, axis: y.cumsum(axis), "sum", 0.,
np.nan, _cumsum_examples)
cls.cumprod = _make_cum_function(
cls, 'cumprod', name, name2, axis_descr, "product",
lambda y, axis: y.cumprod(axis), "prod", 1.,
np.nan, _cumprod_examples)
cls.cummax = _make_cum_function(
cls, 'cummax', name, name2, axis_descr, "maximum",
lambda y, axis: np.maximum.accumulate(y, axis), "max",
-np.inf, np.nan, _cummax_examples)
cls.sum = _make_min_count_stat_function(
cls, 'sum', name, name2, axis_descr,
'Return the sum of the values for the requested axis',
nanops.nansum, _sum_examples)
cls.mean = _make_stat_function(
cls, 'mean', name, name2, axis_descr,
'Return the mean of the values for the requested axis',
nanops.nanmean)
cls.skew = _make_stat_function(
cls, 'skew', name, name2, axis_descr,
'Return unbiased skew over requested axis\nNormalized by N-1',
nanops.nanskew)
cls.kurt = _make_stat_function(
cls, 'kurt', name, name2, axis_descr,
"Return unbiased kurtosis over requested axis using Fisher's "
"definition of\nkurtosis (kurtosis of normal == 0.0). Normalized "
"by N-1\n",
nanops.nankurt)
cls.kurtosis = cls.kurt
cls.prod = _make_min_count_stat_function(
cls, 'prod', name, name2, axis_descr,
'Return the product of the values for the requested axis',
nanops.nanprod, _prod_examples)
cls.product = cls.prod
cls.median = _make_stat_function(
cls, 'median', name, name2, axis_descr,
'Return the median of the values for the requested axis',
nanops.nanmedian)
cls.max = _make_stat_function(
cls, 'max', name, name2, axis_descr,
"""This method returns the maximum of the values in the object.
If you want the *index* of the maximum, use ``idxmax``. This is
the equivalent of the ``numpy.ndarray`` method ``argmax``.""",
nanops.nanmax)
cls.min = _make_stat_function(
cls, 'min', name, name2, axis_descr,
"""This method returns the minimum of the values in the object.
If you want the *index* of the minimum, use ``idxmin``. This is
the equivalent of the ``numpy.ndarray`` method ``argmin``.""",
nanops.nanmin)
@classmethod
def _add_series_only_operations(cls):
"""Add the series only operations to the cls; evaluate the doc
strings again.
"""
axis_descr, name, name2 = _doc_parms(cls)
def nanptp(values, axis=0, skipna=True):
nmax = nanops.nanmax(values, axis, skipna)
nmin = nanops.nanmin(values, axis, skipna)
warnings.warn("Method .ptp is deprecated and will be removed "
"in a future version. Use numpy.ptp instead.",
FutureWarning, stacklevel=4)
return nmax - nmin
cls.ptp = _make_stat_function(
cls, 'ptp', name, name2, axis_descr,
"""
Returns the difference between the maximum value and the
minimum value in the object. This is the equivalent of the
``numpy.ndarray`` method ``ptp``.
.. deprecated:: 0.24.0
Use numpy.ptp instead
""",
nanptp)
@classmethod
def _add_series_or_dataframe_operations(cls):
"""Add the series or dataframe only operations to the cls; evaluate
the doc strings again.
"""
from pandas.core import window as rwindow
@Appender(rwindow.rolling.__doc__)
def rolling(self, window, min_periods=None, center=False,
win_type=None, on=None, axis=0, closed=None):
axis = self._get_axis_number(axis)
return rwindow.rolling(self, window=window,
min_periods=min_periods,
center=center, win_type=win_type,
on=on, axis=axis, closed=closed)
cls.rolling = rolling
@Appender(rwindow.expanding.__doc__)
def expanding(self, min_periods=1, center=False, axis=0):
axis = self._get_axis_number(axis)
return rwindow.expanding(self, min_periods=min_periods,
center=center, axis=axis)
cls.expanding = expanding
@Appender(rwindow.ewm.__doc__)
def ewm(self, com=None, span=None, halflife=None, alpha=None,
min_periods=0, adjust=True, ignore_na=False,
axis=0):
axis = self._get_axis_number(axis)
return rwindow.ewm(self, com=com, span=span, halflife=halflife,
alpha=alpha, min_periods=min_periods,
adjust=adjust, ignore_na=ignore_na, axis=axis)
cls.ewm = ewm
@Appender(_shared_docs['transform'] % _shared_doc_kwargs)
def transform(self, func, *args, **kwargs):
result = self.agg(func, *args, **kwargs)
if is_scalar(result) or len(result) != len(self):
raise ValueError("transforms cannot produce "
"aggregated results")
return result
cls.transform = transform
# ----------------------------------------------------------------------
# Misc methods
_shared_docs['valid_index'] = """
Return index for %(position)s non-NA/null value.
Notes
--------
If all elements are non-NA/null, returns None.
Also returns None for empty %(klass)s.
Returns
--------
scalar : type of index
"""
def _find_valid_index(self, how):
"""Retrieves the index of the first valid value.
Parameters
----------
how : {'first', 'last'}
Use this parameter to change between the first or last valid index.
Returns
-------
idx_first_valid : type of index
"""
assert how in ['first', 'last']
if len(self) == 0: # early stop
return None
is_valid = ~self.isna()
if self.ndim == 2:
is_valid = is_valid.any(1) # reduce axis 1
if how == 'first':
idxpos = is_valid.values[::].argmax()
if how == 'last':
idxpos = len(self) - 1 - is_valid.values[::-1].argmax()
chk_notna = is_valid.iat[idxpos]
idx = self.index[idxpos]
if not chk_notna:
return None
return idx
@Appender(_shared_docs['valid_index'] % {'position': 'first',
'klass': 'NDFrame'})
def first_valid_index(self):
return self._find_valid_index('first')
@Appender(_shared_docs['valid_index'] % {'position': 'last',
'klass': 'NDFrame'})
def last_valid_index(self):
return self._find_valid_index('last')
def _doc_parms(cls):
"""Return a tuple of the doc parms."""
axis_descr = "{%s}" % ', '.join(["{0} ({1})".format(a, i)
for i, a in enumerate(cls._AXIS_ORDERS)])
name = (cls._constructor_sliced.__name__
if cls._AXIS_LEN > 1 else 'scalar')
name2 = cls.__name__
return axis_descr, name, name2
_num_doc = """
%(desc)s
Parameters
----------
axis : %(axis_descr)s
skipna : boolean, default True
Exclude NA/null values when computing the result.
level : int or level name, default None
If the axis is a MultiIndex (hierarchical), count along a
particular level, collapsing into a %(name1)s
numeric_only : boolean, default None
Include only float, int, boolean columns. If None, will attempt to use
everything, then use only numeric data. Not implemented for Series.
%(min_count)s\
Returns
-------
%(outname)s : %(name1)s or %(name2)s (if level specified)
%(examples)s"""
_num_ddof_doc = """
%(desc)s
Parameters
----------
axis : %(axis_descr)s
skipna : boolean, default True
Exclude NA/null values. If an entire row/column is NA, the result
will be NA
level : int or level name, default None
If the axis is a MultiIndex (hierarchical), count along a
particular level, collapsing into a %(name1)s
ddof : int, default 1
Delta Degrees of Freedom. The divisor used in calculations is N - ddof,
where N represents the number of elements.
numeric_only : boolean, default None
Include only float, int, boolean columns. If None, will attempt to use
everything, then use only numeric data. Not implemented for Series.
Returns
-------
%(outname)s : %(name1)s or %(name2)s (if level specified)\n"""
_bool_doc = """
%(desc)s
Parameters
----------
axis : {0 or 'index', 1 or 'columns', None}, default 0
Indicate which axis or axes should be reduced.
* 0 / 'index' : reduce the index, return a Series whose index is the
original column labels.
* 1 / 'columns' : reduce the columns, return a Series whose index is the
original index.
* None : reduce all axes, return a scalar.
skipna : boolean, default True
Exclude NA/null values. If an entire row/column is NA, the result
will be NA.
level : int or level name, default None
If the axis is a MultiIndex (hierarchical), count along a
particular level, collapsing into a %(name1)s.
bool_only : boolean, default None
Include only boolean columns. If None, will attempt to use everything,
then use only boolean data. Not implemented for Series.
**kwargs : any, default None
Additional keywords have no effect but might be accepted for
compatibility with NumPy.
Returns
-------
%(outname)s : %(name1)s or %(name2)s (if level specified)
%(see_also)s
%(examples)s"""
_all_doc = """\
Return whether all elements are True, potentially over an axis.
Returns True if all elements within a series or along a Dataframe
axis are non-zero, not-empty or not-False."""
_all_examples = """\
Examples
--------
Series
>>> pd.Series([True, True]).all()
True
>>> pd.Series([True, False]).all()
False
DataFrames
Create a dataframe from a dictionary.
>>> df = pd.DataFrame({'col1': [True, True], 'col2': [True, False]})
>>> df
col1 col2
0 True True
1 True False
Default behaviour checks if column-wise values all return True.
>>> df.all()
col1 True
col2 False
dtype: bool
Specify ``axis='columns'`` to check if row-wise values all return True.
>>> df.all(axis='columns')
0 True
1 False
dtype: bool
Or ``axis=None`` for whether every value is True.
>>> df.all(axis=None)
False
"""
_all_see_also = """\
See also
--------
pandas.Series.all : Return True if all elements are True
pandas.DataFrame.any : Return True if one (or more) elements are True
"""
_cnum_doc = """
Return cumulative %(desc)s over a DataFrame or Series axis.
Returns a DataFrame or Series of the same size containing the cumulative
%(desc)s.
Parameters
----------
axis : {0 or 'index', 1 or 'columns'}, default 0
The index or the name of the axis. 0 is equivalent to None or 'index'.
skipna : boolean, default True
Exclude NA/null values. If an entire row/column is NA, the result
will be NA.
*args, **kwargs :
Additional keywords have no effect but might be accepted for
compatibility with NumPy.
Returns
-------
%(outname)s : %(name1)s or %(name2)s\n
%(examples)s
See also
--------
pandas.core.window.Expanding.%(accum_func_name)s : Similar functionality
but ignores ``NaN`` values.
%(name2)s.%(accum_func_name)s : Return the %(desc)s over
%(name2)s axis.
%(name2)s.cummax : Return cumulative maximum over %(name2)s axis.
%(name2)s.cummin : Return cumulative minimum over %(name2)s axis.
%(name2)s.cumsum : Return cumulative sum over %(name2)s axis.
%(name2)s.cumprod : Return cumulative product over %(name2)s axis.
"""
_cummin_examples = """\
Examples
--------
**Series**
>>> s = pd.Series([2, np.nan, 5, -1, 0])
>>> s
0 2.0
1 NaN
2 5.0
3 -1.0
4 0.0
dtype: float64
By default, NA values are ignored.
>>> s.cummin()
0 2.0
1 NaN
2 2.0
3 -1.0
4 -1.0
dtype: float64
To include NA values in the operation, use ``skipna=False``
>>> s.cummin(skipna=False)
0 2.0
1 NaN
2 NaN
3 NaN
4 NaN
dtype: float64
**DataFrame**
>>> df = pd.DataFrame([[2.0, 1.0],
... [3.0, np.nan],
... [1.0, 0.0]],
... columns=list('AB'))
>>> df
A B
0 2.0 1.0
1 3.0 NaN
2 1.0 0.0
By default, iterates over rows and finds the minimum
in each column. This is equivalent to ``axis=None`` or ``axis='index'``.
>>> df.cummin()
A B
0 2.0 1.0
1 2.0 NaN
2 1.0 0.0
To iterate over columns and find the minimum in each row,
use ``axis=1``
>>> df.cummin(axis=1)
A B
0 2.0 1.0
1 3.0 NaN
2 1.0 0.0
"""
_cumsum_examples = """\
Examples
--------
**Series**
>>> s = pd.Series([2, np.nan, 5, -1, 0])
>>> s
0 2.0
1 NaN
2 5.0
3 -1.0
4 0.0
dtype: float64
By default, NA values are ignored.
>>> s.cumsum()
0 2.0
1 NaN
2 7.0
3 6.0
4 6.0
dtype: float64
To include NA values in the operation, use ``skipna=False``
>>> s.cumsum(skipna=False)
0 2.0
1 NaN
2 NaN
3 NaN
4 NaN
dtype: float64
**DataFrame**
>>> df = pd.DataFrame([[2.0, 1.0],
... [3.0, np.nan],
... [1.0, 0.0]],
... columns=list('AB'))
>>> df
A B
0 2.0 1.0
1 3.0 NaN
2 1.0 0.0
By default, iterates over rows and finds the sum
in each column. This is equivalent to ``axis=None`` or ``axis='index'``.
>>> df.cumsum()
A B
0 2.0 1.0
1 5.0 NaN
2 6.0 1.0
To iterate over columns and find the sum in each row,
use ``axis=1``
>>> df.cumsum(axis=1)
A B
0 2.0 3.0
1 3.0 NaN
2 1.0 1.0
"""
_cumprod_examples = """\
Examples
--------
**Series**
>>> s = pd.Series([2, np.nan, 5, -1, 0])
>>> s
0 2.0
1 NaN
2 5.0
3 -1.0
4 0.0
dtype: float64
By default, NA values are ignored.
>>> s.cumprod()
0 2.0
1 NaN
2 10.0
3 -10.0
4 -0.0
dtype: float64
To include NA values in the operation, use ``skipna=False``
>>> s.cumprod(skipna=False)
0 2.0
1 NaN
2 NaN
3 NaN
4 NaN
dtype: float64
**DataFrame**
>>> df = pd.DataFrame([[2.0, 1.0],
... [3.0, np.nan],
... [1.0, 0.0]],
... columns=list('AB'))
>>> df
A B
0 2.0 1.0
1 3.0 NaN
2 1.0 0.0
By default, iterates over rows and finds the product
in each column. This is equivalent to ``axis=None`` or ``axis='index'``.
>>> df.cumprod()
A B
0 2.0 1.0
1 6.0 NaN
2 6.0 0.0
To iterate over columns and find the product in each row,
use ``axis=1``
>>> df.cumprod(axis=1)
A B
0 2.0 2.0
1 3.0 NaN
2 1.0 0.0
"""
_cummax_examples = """\
Examples
--------
**Series**
>>> s = pd.Series([2, np.nan, 5, -1, 0])
>>> s
0 2.0
1 NaN
2 5.0
3 -1.0
4 0.0
dtype: float64
By default, NA values are ignored.
>>> s.cummax()
0 2.0
1 NaN
2 5.0
3 5.0
4 5.0
dtype: float64
To include NA values in the operation, use ``skipna=False``
>>> s.cummax(skipna=False)
0 2.0
1 NaN
2 NaN
3 NaN
4 NaN
dtype: float64
**DataFrame**
>>> df = pd.DataFrame([[2.0, 1.0],
... [3.0, np.nan],
... [1.0, 0.0]],
... columns=list('AB'))
>>> df
A B
0 2.0 1.0
1 3.0 NaN
2 1.0 0.0
By default, iterates over rows and finds the maximum
in each column. This is equivalent to ``axis=None`` or ``axis='index'``.
>>> df.cummax()
A B
0 2.0 1.0
1 3.0 NaN
2 3.0 1.0
To iterate over columns and find the maximum in each row,
use ``axis=1``
>>> df.cummax(axis=1)
A B
0 2.0 2.0
1 3.0 NaN
2 1.0 1.0
"""
_any_see_also = """\
See Also
--------
numpy.any : Numpy version of this method.
Series.any : Return whether any element is True.
Series.all : Return whether all elements are True.
DataFrame.any : Return whether any element is True over requested axis.
DataFrame.all : Return whether all elements are True over requested axis.
"""
_any_desc = """\
Return whether any element is True over requested axis.
Unlike :meth:`DataFrame.all`, this performs an *or* operation. If any of the
values along the specified axis is True, this will return True."""
_any_examples = """\
Examples
--------
**Series**
For Series input, the output is a scalar indicating whether any element
is True.
>>> pd.Series([True, False]).any()
True
**DataFrame**
Whether each column contains at least one True element (the default).
>>> df = pd.DataFrame({"A": [1, 2], "B": [0, 2], "C": [0, 0]})
>>> df
A B C
0 1 0 0
1 2 2 0
>>> df.any()
A True
B True
C False
dtype: bool
Aggregating over the columns.
>>> df = pd.DataFrame({"A": [True, False], "B": [1, 2]})
>>> df
A B
0 True 1
1 False 2
>>> df.any(axis='columns')
0 True
1 True
dtype: bool
>>> df = pd.DataFrame({"A": [True, False], "B": [1, 0]})
>>> df
A B
0 True 1
1 False 0
>>> df.any(axis='columns')
0 True
1 False
dtype: bool
Aggregating over the entire DataFrame with ``axis=None``.
>>> df.any(axis=None)
True
`any` for an empty DataFrame is an empty Series.
>>> pd.DataFrame([]).any()
Series([], dtype: bool)
"""
_sum_examples = """\
Examples
--------
By default, the sum of an empty or all-NA Series is ``0``.
>>> pd.Series([]).sum() # min_count=0 is the default
0.0
This can be controlled with the ``min_count`` parameter. For example, if
you'd like the sum of an empty series to be NaN, pass ``min_count=1``.
>>> pd.Series([]).sum(min_count=1)
nan
Thanks to the ``skipna`` parameter, ``min_count`` handles all-NA and
empty series identically.
>>> pd.Series([np.nan]).sum()
0.0
>>> pd.Series([np.nan]).sum(min_count=1)
nan
"""
_prod_examples = """\
Examples
--------
By default, the product of an empty or all-NA Series is ``1``
>>> pd.Series([]).prod()
1.0
This can be controlled with the ``min_count`` parameter
>>> pd.Series([]).prod(min_count=1)
nan
Thanks to the ``skipna`` parameter, ``min_count`` handles all-NA and
empty series identically.
>>> pd.Series([np.nan]).prod()
1.0
>>> pd.Series([np.nan]).prod(min_count=1)
nan
"""
_min_count_stub = """\
min_count : int, default 0
The required number of valid values to perform the operation. If fewer than
``min_count`` non-NA values are present the result will be NA.
.. versionadded :: 0.22.0
Added with the default being 0. This means the sum of an all-NA
or empty Series is 0, and the product of an all-NA or empty
Series is 1.
"""
def _make_min_count_stat_function(cls, name, name1, name2, axis_descr, desc,
f, examples):
@Substitution(outname=name, desc=desc, name1=name1, name2=name2,
axis_descr=axis_descr, min_count=_min_count_stub,
examples=examples)
@Appender(_num_doc)
def stat_func(self, axis=None, skipna=None, level=None, numeric_only=None,
min_count=0,
**kwargs):
nv.validate_stat_func(tuple(), kwargs, fname=name)
if skipna is None:
skipna = True
if axis is None:
axis = self._stat_axis_number
if level is not None:
return self._agg_by_level(name, axis=axis, level=level,
skipna=skipna, min_count=min_count)
return self._reduce(f, name, axis=axis, skipna=skipna,
numeric_only=numeric_only, min_count=min_count)
return set_function_name(stat_func, name, cls)
def _make_stat_function(cls, name, name1, name2, axis_descr, desc, f):
@Substitution(outname=name, desc=desc, name1=name1, name2=name2,
axis_descr=axis_descr, min_count='', examples='')
@Appender(_num_doc)
def stat_func(self, axis=None, skipna=None, level=None, numeric_only=None,
**kwargs):
nv.validate_stat_func(tuple(), kwargs, fname=name)
if skipna is None:
skipna = True
if axis is None:
axis = self._stat_axis_number
if level is not None:
return self._agg_by_level(name, axis=axis, level=level,
skipna=skipna)
return self._reduce(f, name, axis=axis, skipna=skipna,
numeric_only=numeric_only)
return set_function_name(stat_func, name, cls)
def _make_stat_function_ddof(cls, name, name1, name2, axis_descr, desc, f):
@Substitution(outname=name, desc=desc, name1=name1, name2=name2,
axis_descr=axis_descr)
@Appender(_num_ddof_doc)
def stat_func(self, axis=None, skipna=None, level=None, ddof=1,
numeric_only=None, **kwargs):
nv.validate_stat_ddof_func(tuple(), kwargs, fname=name)
if skipna is None:
skipna = True
if axis is None:
axis = self._stat_axis_number
if level is not None:
return self._agg_by_level(name, axis=axis, level=level,
skipna=skipna, ddof=ddof)
return self._reduce(f, name, axis=axis, numeric_only=numeric_only,
skipna=skipna, ddof=ddof)
return set_function_name(stat_func, name, cls)
def _make_cum_function(cls, name, name1, name2, axis_descr, desc,
accum_func, accum_func_name, mask_a, mask_b, examples):
@Substitution(outname=name, desc=desc, name1=name1, name2=name2,
axis_descr=axis_descr, accum_func_name=accum_func_name,
examples=examples)
@Appender(_cnum_doc)
def cum_func(self, axis=None, skipna=True, *args, **kwargs):
skipna = nv.validate_cum_func_with_skipna(skipna, args, kwargs, name)
if axis is None:
axis = self._stat_axis_number
else:
axis = self._get_axis_number(axis)
y = com._values_from_object(self).copy()
if (skipna and
issubclass(y.dtype.type, (np.datetime64, np.timedelta64))):
result = accum_func(y, axis)
mask = isna(self)
np.putmask(result, mask, tslib.iNaT)
elif skipna and not issubclass(y.dtype.type, (np.integer, np.bool_)):
mask = isna(self)
np.putmask(y, mask, mask_a)
result = accum_func(y, axis)
np.putmask(result, mask, mask_b)
else:
result = accum_func(y, axis)
d = self._construct_axes_dict()
d['copy'] = False
return self._constructor(result, **d).__finalize__(self)
return set_function_name(cum_func, name, cls)
def _make_logical_function(cls, name, name1, name2, axis_descr, desc, f,
examples, see_also):
@Substitution(outname=name, desc=desc, name1=name1, name2=name2,
axis_descr=axis_descr, examples=examples, see_also=see_also)
@Appender(_bool_doc)
def logical_func(self, axis=0, bool_only=None, skipna=True, level=None,
**kwargs):
nv.validate_logical_func(tuple(), kwargs, fname=name)
if level is not None:
if bool_only is not None:
raise NotImplementedError("Option bool_only is not "
"implemented with option level.")
return self._agg_by_level(name, axis=axis, level=level,
skipna=skipna)
return self._reduce(f, name, axis=axis, skipna=skipna,
numeric_only=bool_only, filter_type='bool')
return set_function_name(logical_func, name, cls)
# install the indexes
for _name, _indexer in indexing.get_indexers_list():
NDFrame._create_indexer(_name, _indexer)
| bsd-3-clause |
mcdeoliveira/pyctrl | examples/hello_filter_2.py | 3 | 2228 | #!/usr/bin/env python3
def main():
import sys
# import Controller and other blocks from modules
from pyctrl.timer import Controller
from pyctrl.block import Interp, Printer, Constant, Logger
# initialize controller
Ts = 0.01
hello = Controller(period = Ts)
# add pwm signal
hello.add_signal('pwm')
# build interpolated input signal
ts = [0, 1, 2, 3, 4, 5, 5, 6]
us = [0, 0, 100, 100, -50, -50, 0, 0]
# add filter to interpolate data
hello.add_filter('input',
Interp(xp = us, fp = ts),
['clock'],
['pwm'])
# add logger
hello.add_sink('printer',
Printer(message = 'time = {:3.1f} s, motor = {:+6.1f} %',
endln = '\r'),
['clock','pwm'])
# add logger
hello.add_sink('logger',
Logger(),
['clock','pwm'])
# Add a timer to stop the controller
hello.add_timer('stop',
Constant(value = 0),
None, ['is_running'],
period = 6, repeat = False)
# print controller info
print(hello.info('all'))
try:
# run the controller
print('> Run the controller.')
with hello:
# wait for the controller to finish on its own
hello.join()
print('> Done with the controller.')
except KeyboardInterrupt:
pass
# retrieve data from logger
data = hello.get_sink('logger', 'log')
try:
# import matplotlib
import matplotlib.pyplot as plt
except:
print('! Could not load matplotlib, skipping plots')
sys.exit(0)
print('> Will plot')
try:
# start plot
plt.figure()
except:
print('! Could not plot graphics')
print('> Make sure you have a connection to a windows manager')
sys.exit(0)
# plot input
plt.plot(data['clock'], data['motor'], 'b')
plt.ylabel('pwm (%)')
plt.xlabel('time (s)')
plt.ylim((-120,120))
plt.xlim(0,6)
plt.grid()
# show plots
plt.show()
if __name__ == "__main__":
main()
| apache-2.0 |
gavincyi/LightMatchingEngine | setup.py | 1 | 1206 | from setuptools import setup, find_packages, Extension
setup(
name="lightmatchingengine",
url="https://github.com/gavincyi/LightMatchingEngine",
license='MIT',
author="Gavin Chan",
author_email="[email protected]",
description="A light matching engine",
packages=find_packages(exclude=('tests',)),
use_scm_version=True,
install_requires=[],
setup_requires=['setuptools_scm', 'cython'],
ext_modules=[Extension(
'lightmatchingengine.lightmatchingengine',
['lightmatchingengine/lightmatchingengine.pyx'])],
tests_require=[
'pytest'
],
extras_require={
'performance': ['pandas', 'docopt', 'tabulate', 'tqdm']
},
classifiers=[
'Development Status :: 2 - Pre-Alpha',
'License :: OSI Approved :: MIT License',
'Programming Language :: Python',
'Programming Language :: Python :: 2',
'Programming Language :: Python :: 2.7',
'Programming Language :: Python :: 3',
'Programming Language :: Python :: 3.4',
'Programming Language :: Python :: 3.5',
'Programming Language :: Python :: 3.6',
'Programming Language :: Python :: 3.7',
],
)
| mit |
inside-track/pemi | pemi/pipes/pd.py | 1 | 7027 | import pandas as pd
import pemi
import pemi.pipes.patterns
import pemi.transforms
class PdForkPipe(pemi.pipes.patterns.ForkPipe):
def flow(self):
for target in self.targets.values():
target.df = self.sources['main'].df.copy()
class PdConcatPipe(pemi.pipes.patterns.ConcatPipe):
def __init__(self, *, concat_opts=None, **params):
concat_opts = concat_opts or {}
super().__init__(**params)
self.concat_opts = concat_opts
def flow(self):
source_dfs = [source.df for source in self.sources.values() if source.df is not None]
if len(source_dfs) == 0:
self.targets['main'].df = pd.DataFrame()
else:
self.targets['main'].df = pd.concat(source_dfs, **self.concat_opts, sort=False)
class PdLookupJoinPipe(pemi.Pipe):
def __init__(self, main_key, lookup_key,
suffixes=('', '_lkp'),
on_missing='redirect', #redirect, ignore, warn
indicator=None,
lookup_prefix='',
fillna=None,
**kwargs): #pylint: disable=too-many-arguments
super().__init__(**kwargs)
self.main_key = main_key
self.lookup_key = lookup_key
self.suffixes = suffixes
self.on_missing = on_missing
self.indicator = indicator
self.lookup_prefix = lookup_prefix
self.fillna = fillna
self.source(
pemi.PdDataSubject,
name='main'
)
self.source(
pemi.PdDataSubject,
name='lookup'
)
self.target(
pemi.PdDataSubject,
name='main'
)
self.target(
pemi.PdDataSubject,
name='errors'
)
def _drop_lookup_duplicates(self, _na):
return self.sources['lookup'].df.drop_duplicates(self.lookup_key)
def _prefix_lookup_columns(self, lkp_df):
if self.lookup_prefix == '':
return lkp_df
return lkp_df.rename(columns={
col: self.lookup_prefix + col
for col in lkp_df.columns if not col in self.lookup_key
})
def _drop_missing_lookup_keys(self, lkp_df):
missing_keys = lkp_df[self.lookup_key].apply(
lambda v: v.apply(pemi.transforms.isblank).any(), axis=1
)
if len(missing_keys) > 0:
return lkp_df[~missing_keys]
return lkp_df
def _perform_lookup_merge(self, lkp_df):
return pd.merge(
self.sources['main'].df,
lkp_df,
left_on=self.main_key,
right_on=self.lookup_key,
how='left',
suffixes=self.suffixes,
indicator='__indicator__'
)
def _fill_missing(self, merged_df):
if self.fillna:
merged_df['__indicator__'] = merged_df['__indicator__'].astype('str')
merged_df.fillna(**self.fillna, inplace=True)
return merged_df
def _direct_targets(self, merged_df):
matches = (merged_df['__indicator__'] == 'both')
if self.on_missing == 'redirect':
self.targets['main'].df = merged_df[matches].copy()
self.targets['errors'].df = merged_df[~matches].copy()
else:
self.targets['main'].df = merged_df
self.targets['errors'].df = pd.DataFrame([], columns=merged_df.columns)
if self.on_missing == 'warn':
merged_df[~matches][self.main_key].apply(
lambda row: pemi.log.warning('No lookup values found for %s', dict(row)),
axis=1
)
def _remove_indicator(self, _na):
if self.indicator:
indicator_bool = self.targets['main'].df['__indicator__'].apply(
lambda v: v == 'both'
).astype('bool')
self.targets['main'].df[self.indicator] = indicator_bool
del self.targets['main'].df['__indicator__']
del self.targets['errors'].df['__indicator__']
def flow(self):
pemi.log.debug('PdLookupJoinPipe - main source columns: %s',
self.sources['main'].df.columns)
pemi.log.debug('PdLookupJoinPipe - main lookup columns: %s',
self.sources['lookup'].df.columns)
arg = None
for func in [
self._drop_lookup_duplicates,
self._prefix_lookup_columns,
self._drop_missing_lookup_keys,
self._perform_lookup_merge,
self._fill_missing,
self._direct_targets,
self._remove_indicator
]:
arg = func(arg)
pemi.log.debug('PdLookupJoinPipe - main target columns: %s',
self.targets['main'].df.columns)
class PdFieldValueForkPipe(pemi.Pipe):
def __init__(self, field, forks, **kwargs):
super().__init__(**kwargs)
self.field = field
self.forks = forks
self.source(
pemi.PdDataSubject,
name='main'
)
for fork in self.forks:
self.target(
pemi.PdDataSubject,
name=fork
)
self.target(
pemi.PdDataSubject,
name='remainder'
)
def flow(self):
grouped = self.sources['main'].df.groupby(self.field)
for fork in self.forks:
if fork in grouped.groups:
self.targets[fork].df = grouped.get_group(fork).copy()
else:
self.targets[fork].df = pd.DataFrame(columns=self.sources['main'].df.columns)
remainder = set(grouped.groups.keys()) - set(self.forks)
if len(remainder) > 0:
self.targets['remainder'].df = pd.concat(
[grouped.get_group(r) for r in remainder]
).sort_index()
else:
self.targets['remainder'].df = pd.DataFrame(columns=self.sources['main'].df.columns)
class PdLambdaPipe(pemi.Pipe):
'''
This pipe is used to build quick Pandas transformations where building a full pipe class
may feel like overkill. You would use this pipe if you don't need to test it in isolation
(e.g., it only makes sense in a larger context), or you don't need control over the schemas.
Args:
fun (function): A function that accepts a dataframe as argument (source) and returns
a dataframe (target).
:Data Sources:
**main** (*pemi.PdDataSubject*) - The source dataframe that gets pass to ``fun``.
:Data Targets:
**main** (*pemi.PdDataSubject*) - The target dataframe that gets populated from
the return value of ``fun``.
'''
def __init__(self, fun):
super().__init__()
self.fun = fun
self.source(
pemi.PdDataSubject,
name='main'
)
self.target(
pemi.PdDataSubject,
name='main'
)
def flow(self):
self.targets['main'].df = self.fun(self.sources['main'].df)
| mit |
yuyu2172/chainercv | examples/detection/visualize_models.py | 3 | 1840 | from matplotlib.font_manager import FontProperties
import matplotlib.pyplot as plt
from chainercv.experimental.links import YOLOv2Tiny
from chainercv.links import FasterRCNNVGG16
from chainercv.links import SSD300
from chainercv.links import SSD512
from chainercv.links import YOLOv2
from chainercv.links import YOLOv3
from chainercv.datasets import voc_bbox_label_names
from chainercv.datasets import VOCBboxDataset
from chainercv.visualizations import vis_bbox
def main():
dataset = VOCBboxDataset(year='2007', split='test') \
.slice[[29, 301, 189, 229], 'img']
models = [
('Faster R-CNN', FasterRCNNVGG16(pretrained_model='voc07')),
('SSD300', SSD300(pretrained_model='voc0712')),
('SSD512', SSD512(pretrained_model='voc0712')),
('YOLOv2', YOLOv2(pretrained_model='voc0712')),
('YOLOv2 tiny', YOLOv2Tiny(pretrained_model='voc0712')),
('YOLOv3', YOLOv3(pretrained_model='voc0712')),
]
fig = plt.figure(figsize=(30, 20))
for i, img in enumerate(dataset):
for j, (name, model) in enumerate(models):
bboxes, labels, scores = model.predict([img])
bbox, label, score = bboxes[0], labels[0], scores[0]
ax = fig.add_subplot(
len(dataset), len(models), i * len(models) + j + 1)
vis_bbox(
img, bbox, label, score,
label_names=voc_bbox_label_names, ax=ax
)
# Set MatplotLib parameters
ax.set_aspect('equal')
if i == 0:
font = FontProperties()
font.set_family('serif')
font.set_size(35)
ax.set_title(name, y=1.03, fontproperties=font)
plt.axis('off')
plt.tight_layout()
plt.show()
if __name__ == '__main__':
main()
| mit |
jorge2703/scikit-learn | examples/linear_model/lasso_dense_vs_sparse_data.py | 348 | 1862 | """
==============================
Lasso on dense and sparse data
==============================
We show that linear_model.Lasso provides the same results for dense and sparse
data and that in the case of sparse data the speed is improved.
"""
print(__doc__)
from time import time
from scipy import sparse
from scipy import linalg
from sklearn.datasets.samples_generator import make_regression
from sklearn.linear_model import Lasso
###############################################################################
# The two Lasso implementations on Dense data
print("--- Dense matrices")
X, y = make_regression(n_samples=200, n_features=5000, random_state=0)
X_sp = sparse.coo_matrix(X)
alpha = 1
sparse_lasso = Lasso(alpha=alpha, fit_intercept=False, max_iter=1000)
dense_lasso = Lasso(alpha=alpha, fit_intercept=False, max_iter=1000)
t0 = time()
sparse_lasso.fit(X_sp, y)
print("Sparse Lasso done in %fs" % (time() - t0))
t0 = time()
dense_lasso.fit(X, y)
print("Dense Lasso done in %fs" % (time() - t0))
print("Distance between coefficients : %s"
% linalg.norm(sparse_lasso.coef_ - dense_lasso.coef_))
###############################################################################
# The two Lasso implementations on Sparse data
print("--- Sparse matrices")
Xs = X.copy()
Xs[Xs < 2.5] = 0.0
Xs = sparse.coo_matrix(Xs)
Xs = Xs.tocsc()
print("Matrix density : %s %%" % (Xs.nnz / float(X.size) * 100))
alpha = 0.1
sparse_lasso = Lasso(alpha=alpha, fit_intercept=False, max_iter=10000)
dense_lasso = Lasso(alpha=alpha, fit_intercept=False, max_iter=10000)
t0 = time()
sparse_lasso.fit(Xs, y)
print("Sparse Lasso done in %fs" % (time() - t0))
t0 = time()
dense_lasso.fit(Xs.toarray(), y)
print("Dense Lasso done in %fs" % (time() - t0))
print("Distance between coefficients : %s"
% linalg.norm(sparse_lasso.coef_ - dense_lasso.coef_))
| bsd-3-clause |
leftees/BDA_py_demos | demos_ch5/demo5_1.py | 19 | 5055 | """Bayesian Data Analysis, 3rd ed
Chapter 5, demo 1
Hierarchical model for Rats experiment (BDA3, p. 102).
"""
from __future__ import division
import numpy as np
from scipy.stats import beta
from scipy.special import gammaln
import matplotlib.pyplot as plt
# Edit default plot settings (colours from colorbrewer2.org)
plt.rc('font', size=14)
plt.rc('lines', color='#377eb8', linewidth=2)
plt.rc('axes', color_cycle=(plt.rcParams['lines.color'],)) # Disable color cycle
# rat data (BDA3, p. 102)
y = np.array([
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1,
1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 5, 2,
5, 3, 2, 7, 7, 3, 3, 2, 9, 10, 4, 4, 4, 4, 4, 4, 4,
10, 4, 4, 4, 5, 11, 12, 5, 5, 6, 5, 6, 6, 6, 6, 16, 15,
15, 9, 4
])
n = np.array([
20, 20, 20, 20, 20, 20, 20, 19, 19, 19, 19, 18, 18, 17, 20, 20, 20,
20, 19, 19, 18, 18, 25, 24, 23, 20, 20, 20, 20, 20, 20, 10, 49, 19,
46, 27, 17, 49, 47, 20, 20, 13, 48, 50, 20, 20, 20, 20, 20, 20, 20,
48, 19, 19, 19, 22, 46, 49, 20, 20, 23, 19, 22, 20, 20, 20, 52, 46,
47, 24, 14
])
M = len(y)
# plot the separate and pooled models
plt.figure(figsize=(8,10))
x = np.linspace(0, 1, 250)
# separate
plt.subplot(2, 1, 1)
lines = plt.plot(x, beta.pdf(x[:,None], y[:-1] + 1, n[:-1] - y[:-1] + 1),
linewidth=1)
# highlight the last line
line1, = plt.plot(x, beta.pdf(x, y[-1] + 1, n[-1] - y[-1] + 1), 'r')
plt.legend((lines[0], line1),
(r'Posterior of $\theta_j$', r'Posterior of $\theta_{71}$'))
plt.yticks(())
plt.title('separate model')
# pooled
plt.subplot(2, 1, 2)
plt.plot(x, beta.pdf(x, y.sum() + 1, n.sum() - y.sum() + 1),
linewidth=2, label=(r'Posterior of common $\theta$'))
plt.legend()
plt.yticks(())
plt.xlabel(r'$\theta$', fontsize=20)
plt.title('pooled model')
# compute the marginal posterior of alpha and beta in the hierarchical model in a grid
A = np.linspace(0.5, 6, 100)
B = np.linspace(3, 33, 100)
# calculated in logarithms for numerical accuracy
lp = (
- 5/2 * np.log(A + B[:,None])
+ np.sum(
gammaln(A + B[:,None])
- gammaln(A)
- gammaln(B[:,None])
+ gammaln(A + y[:,None,None])
+ gammaln(B[:,None] + (n - y)[:,None,None])
- gammaln(A + B[:,None] + n[:,None,None]),
axis=0
)
)
# subtract the maximum value to avoid over/underflow in exponentation
lp -= lp.max()
p = np.exp(lp)
# plot the marginal posterior
fig = plt.figure()
plt.imshow(p, origin='lower', aspect='auto', extent=(A[0], A[-1], B[0], B[-1]))
plt.xlabel(r'$\alpha$', fontsize=20)
plt.ylabel(r'$\beta$', fontsize=20)
plt.title('The marginal posterior of alpha and beta in hierarchical model')
# sample from the posterior grid of alpha and beta
nsamp = 1000
samp_indices = np.unravel_index(
np.random.choice(p.size, size=nsamp, p=p.ravel()/p.sum()),
p.shape
)
samp_A = A[samp_indices[1]]
samp_B = B[samp_indices[0]]
# add random jitter, see BDA3 p. 76
samp_A += (np.random.rand(nsamp) - 0.5) * (A[1]-A[0])
samp_B += (np.random.rand(nsamp) - 0.5) * (B[1]-B[0])
# Plot samples from the distribution of distributions Beta(alpha,beta),
# that is, plot Beta(alpha,beta) using the posterior samples of alpha and beta
fig = plt.figure(figsize=(8,10))
plt.subplot(2, 1, 1)
plt.plot(x, beta.pdf(x[:,None], samp_A[:20], samp_B[:20]), linewidth=1)
plt.yticks(())
plt.title(r'Posterior samples from the distribution of distributions '
r'Beta($\alpha$,$\beta$)')
# The average of above distributions, is the predictive distribution for a new
# theta, and also the prior distribution for theta_j.
# Plot this.
plt.subplot(2, 1, 2)
plt.plot(x, np.mean(beta.pdf(x, samp_A[:,None], samp_B[:,None]), axis=0))
plt.yticks(())
plt.xlabel(r'$\theta$', fontsize=20)
plt.title(r'Predictive distribution for a new $\theta$ '
r'and prior for $\theta_j$')
# And finally compare the separate model and hierarchical model
plt.figure(figsize=(8,10))
x = np.linspace(0, 1, 250)
# first plot the separate model (same as above)
plt.subplot(2, 1, 1)
# note that for clarity only every 7th distribution is plotted
plt.plot(x, beta.pdf(x[:,None], y[7:-1:7] + 1, n[7:-1:7] - y[7:-1:7] + 1),
linewidth=1)
# highlight the last line
plt.plot(x, beta.pdf(x, y[-1] + 1, n[-1] - y[-1] + 1), 'r')
plt.yticks(())
plt.title('separate model')
# And the hierarchical model. Note that these marginal posteriors for theta_j are
# more narrow than in separate model case, due to borrowed information from
# the other theta_j's.
plt.subplot(2, 1, 2)
# note that for clarity only every 7th distribution is plotted
lines = plt.plot(
x,
np.mean(
beta.pdf(
x[:,None],
y[7::7] + samp_A[:,None,None],
n[7::7] - y[7::7] + samp_B[:,None,None]
),
axis=0
),
linewidth=1,
)
# highlight the last line
lines[-1].set_linewidth(2)
lines[-1].set_color('r')
plt.yticks(())
plt.xlabel(r'$\theta$', fontsize=20)
plt.title('hierarchical model')
plt.show()
| gpl-3.0 |
kristofe/mlskeleton | src/split_data.py | 1 | 3261 | import sys
import numpy as np
#import matplotlib.pyplot as plt
#read the data file
dataFilename = "../mocap_test/labelled_data.txt"
if(len(sys.argv) > 6):
dataFilename = sys.argv[6]
txtfile = open(dataFilename)
lines = txtfile.read().split("\n")
#remove the field names
fieldnames = lines.pop(0).strip().split("\t")
#remove fieldnames of frame, time and valid
fieldnames.pop(0)
fieldnames.pop(0)
fieldnames.pop()
#FIXME: IGNORE THE wand1 DATA. IT SHOULDN'T BE IN HERE
rot_fields = [i for i, j in enumerate(fieldnames) if j.find('Rotation') != -1 and j.find('wand1') == -1]
pos_fields = [i for i, j in enumerate(fieldnames) if j.find('Position') != -1 and j.find('wand1') == -1]
pos_data = []
rot_data = []
pos_labels = []
rot_labels = []
POS_LABEL_DIM = 3
ROT_LABEL_DIM = 4
row_count = 0
for line in lines:
if row_count > 40:
break
fields = line.strip().split("\t")
if(len(fields) != len(fieldnames) + 3):
continue
#take out frame num
fields.pop(0)
#take out time
fields.pop(0)
#now take out the last column - it is the valid indicator
fields.pop()
#this is where to remove any fields such as rotation or position
#doing it the slow way
#position data
tmp = []
for idx in pos_fields:
tmp.append(fields[idx])#this includes the label
pos_data.append(tmp[:len(tmp) - POS_LABEL_DIM])
pos_labels.append(tmp[len(tmp) - POS_LABEL_DIM:])
#Rotation Data
tmp = []
for idx in rot_fields:
tmp.append(fields[idx])#this includes the label
rot_data.append(tmp[:len(tmp) - ROT_LABEL_DIM])
rot_labels.append(tmp[len(tmp) - ROT_LABEL_DIM:])
N = len(pos_data) #number of training examples - around 4K
D = len(pos_data[0]) #dimensionality - around 6*3 = 18
K = 1 #there are no classes as this is regression
X = np.zeros((N*K,D))
y = np.zeros((N*K,POS_LABEL_DIM))
#copy data into X
outfile = open("positions_only_mocap_data.txt", 'w');
for row in range(N):
for column in range(D):
X[row, column] = float(pos_data[row][column]) #Slow way
outfile.write("%s\t" % pos_data[row][column]);
outfile.write("\n")
outfile.close();
outfile = open("positions_only_mocap_labels.txt", 'w');
#copy labels into Y
for row in range(N):
for column in range(POS_LABEL_DIM):
y[row, column] = float(pos_labels[row][column]) #Slow way
outfile.write("%s\t" % pos_labels[row][column]);
outfile.write("\n")
outfile.close();
# ######################################################################
N = len(rot_data) #number of training examples - around 4K
D = len(rot_data[0]) #dimensionality - around 6*4 = 24
K = 1 #there are no classes as this is regression
X = np.zeros((N*K,D))
y = np.zeros((N*K,ROT_LABEL_DIM))
outfile = open("rotations_only_mocap_data.txt", 'w');
#copy rot_data into X
for row in range(N):
for column in range(D):
X[row, column] = float(rot_data[row][column]) #Slow way
outfile.write("%s\t" % rot_data[row][column]);
outfile.write("\n")
outfile.close();
outfile = open("rotations_only_mocap_data_labels.txt", 'w');
#copy labels into Y
for row in range(N):
for column in range(ROT_LABEL_DIM):
y[row, column] = float(rot_labels[row][column]) #Slow way
outfile.write("%s\t" % rot_labels[row][column]);
outfile.write("\n")
outfile.close();
| mit |
zharfi/Cidar | Training_Part/SVM/Giemsa/Tanpa Warna/Cdr_SVM_campur.py | 3 | 5596 | # -*- coding: utf-8 -*-
"""
Created on Wed Jul 26 17:15:15 2017
@author: Visual.Sensor
"""
# Importing the libraries
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import pickle
# Applying Grid Search to find the best hyperparameter
from sklearn.model_selection import GridSearchCV
# Name
filename_noproc = 'SVM_noproc.sav'
filename_pca = 'SVM_pca.sav'
filename_lda = 'SVM_lda.sav'
filename_kpca = 'SVM_kpca.sav'
filename_scale = 'scale.sav'
filename_dr_pca = 'pca.sav'
filename_dr_lda = 'lda.sav'
filename_dr_kpca = 'kpca.sav'
filename_res_noproc = 'SVM_res_noproc.txt'
filename_res_pca = 'SVM_res_pca.txt'
filename_res_lda = 'SVM_res_lda.txt'
filename_res_kpca = 'SVM_res_kpca.txt'
# Grid Searching with Parallel Computing
def cariGrid(clsf, preproc, xtr, ytr, xte, yte, accu, std, test_accu):
parameters = [{'C': [0.1, 1, 10, 100, 1000], 'kernel': ['linear']},
{'C': [0.1, 1, 10, 100, 1000], 'kernel': ['rbf'], 'gamma': [0.5, 0.2, 0.1, 0.05, 0.01, 0.001, 0.0001]}]
grid_search = GridSearchCV(estimator=clsf,
param_grid=parameters,
scoring='accuracy',
cv=10,
n_jobs=-1,
verbose=0)
grid_search = grid_search.fit(xtr, ytr)
best_accuracy = grid_search.best_score_
best_index = grid_search.best_index_
best_std = grid_search.cv_results_['std_test_score'][best_index]
best_parameters = grid_search.best_params_
clsf = SVC(**best_parameters).fit(xtr, ytr)
# Calculate test accuracy with optimized training
test_optimized = grid_search.score(xte, yte)
if preproc == 'noproc':
with open(filename_noproc, 'wb') as f:
pickle.dump(clsf, f)
with open(filename_res_noproc, "w") as text_file:
text_file.write("%f %f %f %f %f %f %s" % (accu, std, test_accu, best_accuracy, best_std, test_optimized, best_parameters))
elif preproc == 'pca':
with open(filename_pca, 'wb') as f:
pickle.dump(clsf, f)
# pickle.dump(clsf, open(filename_pca, 'wb'))
with open(filename_res_pca, "w") as text_file:
text_file.write("%f %f %f %f %f %f %s" % (accu, std, test_accu, best_accuracy, best_std, test_optimized, best_parameters))
elif preproc == 'lda':
with open(filename_lda, 'wb') as f:
pickle.dump(clsf, f)
# pickle.dump(clsf, open(filename_lda, 'wb'))
with open(filename_res_lda, "w") as text_file:
text_file.write("%f %f %f %f %f %f %s" % (accu, std, test_accu, best_accuracy, best_std, test_optimized, best_parameters))
else:
with open(filename_kpca, 'wb') as f:
pickle.dump(clsf, f)
# pickle.dump(clsf, open(filename_kpca, 'wb'))
with open(filename_res_kpca, "w") as text_file:
text_file.write("%f %f %f %f %f %f %s" % (accu, std, test_accu, best_accuracy, best_std, test_optimized, best_parameters))
print(best_accuracy)
print(best_std)
print(best_parameters)
print(test_optimized)
# Importing the dataset
dataset = pd.read_csv('campur_auto_python.csv', sep=';')
X = dataset.iloc[:, 0:23].values
y = dataset.iloc[:, 23].values
# Splitting the dataset into the Training set and Test set
from sklearn.cross_validation import train_test_split
X_train, X_test, y_train, y_test = train_test_split(
X, y, test_size=0.20, random_state=0)
# Feature Scaling
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)
pickle.dump(sc, open(filename_scale, 'wb'))
preprocess = ['noproc', 'pca', 'lda', 'kpca']
for i in preprocess:
if i == 'noproc':
pass
elif i == 'pca':
from sklearn.decomposition import PCA
pca = PCA(n_components = 10)
X_train = pca.fit_transform(X_train)
X_test = pca.transform(X_test)
explained_variance = pca.explained_variance_ratio_
pickle.dump(pca, open(filename_dr_pca, 'wb'))
elif i == 'lda':
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA
lda = LDA(n_components= 5, )
X_train = lda.fit_transform(X_train, y_train)
X_test = lda.transform(X_test)
pickle.dump(lda, open(filename_dr_lda, 'wb'))
else:
from sklearn.decomposition import KernelPCA
kpca = KernelPCA(n_components= 10, kernel='rbf')
X_train = kpca.fit_transform(X_train)
X_test = kpca.transform(X_test)
pickle.dump(kpca, open(filename_dr_kpca, 'wb'))
# Fitting classifier to the Training set
from sklearn.svm import SVC
classifier = SVC(kernel='rbf', random_state=0)
classifier.fit(X_train, y_train)
# Applying k-Fold Cross Validation
from sklearn.model_selection import cross_val_score
accuracies = cross_val_score(estimator=classifier, X=X_train, y=y_train, cv=10)
avg_accuracy = accuracies.mean()
std_accuracy = accuracies.std()
print('Akurasi: ', avg_accuracy)
print('SD: ', std_accuracy)
# Test unoptimized performance
# Predicting the Test set results
test_accuracy = classifier.score(X_test, y_test)
print('Akurasi Tes:', test_accuracy)
# Making the Confusion Matrix
y_pred = classifier.predict(X_test)
from sklearn.metrics import confusion_matrix
cm = confusion_matrix(y_test, y_pred)
if __name__ == '__main__':
cariGrid(classifier, i, X_train, y_train, X_test, y_test, avg_accuracy, std_accuracy, test_accuracy) | mit |
DrSkippy/Data-Science-45min-Intros | Bokeh/bubble_plot.py | 7 | 6845 | from collections import OrderedDict
import pandas as pd
import numpy as np
from jinja2 import Template
from bokeh.embed import components
from bokeh.models import (
ColumnDataSource, Plot, Circle, Range1d,
LinearAxis, HoverTool, Text,
SingleIntervalTicker, Slider, Callback
)
from bokeh.palettes import Spectral6
from bokeh.plotting import vplot, hplot
from bokeh.resources import INLINE, Resources
from bokeh.templates import RESOURCES
def _get_data():
# Get the data
fertility_df = pd.read_csv('assets/fertility.csv', index_col='Country')
life_expectancy_df = pd.read_csv('assets/life_expectancy.csv', index_col='Country')
population_df = pd.read_csv('assets/population.csv', index_col='Country')
regions_df = pd.read_csv('assets/regions.csv', index_col='Country')
columns = list(fertility_df.columns)
years = list(range(int(columns[0]), int(columns[-1])))
rename_dict = dict(zip(columns, years))
fertility_df = fertility_df.rename(columns=rename_dict)
life_expectancy_df = life_expectancy_df.rename(columns=rename_dict)
population_df = population_df.rename(columns=rename_dict)
regions_df = regions_df.rename(columns=rename_dict)
scale_factor = 200
population_df_size = np.sqrt(population_df / np.pi) / scale_factor
min_size = 3
population_df_size = population_df_size.where(population_df_size >= min_size).fillna(min_size)
regions_df.Group = regions_df.Group.astype('category')
regions = list(regions_df.Group.cat.categories)
def get_color(r):
return Spectral6[regions.index(r.Group)]
regions_df['region_color'] = regions_df.apply(get_color, axis=1)
return (years, regions, fertility_df, life_expectancy_df, population_df_size, regions_df)
def _get_plot():
years, regions, fertility_df, life_expectancy_df, population_df_size, regions_df = _get_data()
# Set-up the sources
sources = {}
region_color = regions_df['region_color']
region_color.name = 'region_color'
for year in years:
fertility = fertility_df[year]
fertility.name = 'fertility'
life = life_expectancy_df[year]
life.name = 'life'
population = population_df_size[year]
population.name = 'population'
new_df = pd.concat([fertility, life, population, region_color], axis=1)
sources['_' + str(year)] = ColumnDataSource(new_df)
dictionary_of_sources = dict(zip([x for x in years], ['_%s' % x for x in years]))
js_source_array = str(dictionary_of_sources).replace("'", "")
# Build the plot
# Set up the plot
xdr = Range1d(1, 9)
ydr = Range1d(20, 100)
plot = Plot(
x_range=xdr,
y_range=ydr,
title="",
plot_width=800,
plot_height=400,
outline_line_color=None,
toolbar_location=None,
)
AXIS_FORMATS = dict(
minor_tick_in=None,
minor_tick_out=None,
major_tick_in=None,
major_label_text_font_size="10pt",
major_label_text_font_style="normal",
axis_label_text_font_size="10pt",
axis_line_color='#AAAAAA',
major_tick_line_color='#AAAAAA',
major_label_text_color='#666666',
major_tick_line_cap="round",
axis_line_cap="round",
axis_line_width=1,
major_tick_line_width=1,
)
xaxis = LinearAxis(SingleIntervalTicker(interval=1), axis_label="Children per woman (total fertility)", **AXIS_FORMATS)
yaxis = LinearAxis(SingleIntervalTicker(interval=20), axis_label="Life expectancy at birth (years)", **AXIS_FORMATS)
plot.add_layout(xaxis, 'below')
plot.add_layout(yaxis, 'left')
# Add the year in background (add before circle)
text_source = ColumnDataSource({'year': ['%s' % years[0]]})
text = Text(x=2, y=35, text='year', text_font_size='150pt', text_color='#EEEEEE')
plot.add_glyph(text_source, text)
# Add the circle
renderer_source = sources['_%s' % years[0]]
circle_glyph = Circle(
x='fertility', y='life', size='population',
fill_color='region_color', fill_alpha=0.8,
line_color='#7c7e71', line_width=0.5, line_alpha=0.5)
circle_renderer = plot.add_glyph(renderer_source, circle_glyph)
# Add the hover (only against the circle and not other plot elements)
tooltips = "@index"
plot.add_tools(HoverTool(tooltips=tooltips, renderers=[circle_renderer]))
text_x = 7
text_y = 95
for i, region in enumerate(regions):
plot.add_glyph(Text(x=text_x, y=text_y, text=[region], text_font_size='10pt', text_color='#666666'))
plot.add_glyph(Circle(x=text_x - 0.1, y=text_y + 2, fill_color=Spectral6[i], size=10, line_color=None, fill_alpha=0.8))
text_y = text_y - 5
# Add the slider
code = """
var year = slider.get('value'),
sources = %s,
new_source_data = sources[year].get('data');
renderer_source.set('data', new_source_data);
renderer_source.trigger('change');
text_source.set('data', {'year': [String(year)]});
text_source.trigger('change');
""" % js_source_array
callback = Callback(args=sources, code=code)
slider = Slider(start=years[0], end=years[-1], value=1, step=1, title="Year", callback=callback)
callback.args["slider"] = slider
callback.args["renderer_source"] = renderer_source
callback.args["text_source"] = text_source
# Lay it out
return vplot(plot, hplot(slider))
def get_bubble_html(plot=None):
if plot:
layout = plot
else:
layout = _get_plot()
with open('assets/bubble_template.html', 'r') as f:
template = Template(f.read())
resources = Resources(mode='server', root_url='/tree/')
bokeh_js = RESOURCES.render(js_files=resources.js_files)
script, div = components(layout)
html = template.render(
title="Bokeh - Gapminder demo",
bokeh_js=bokeh_js,
plot_script=script,
plot_div=div,
)
return html
def get_1964_data():
years, regions, fertility_df, life_expectancy_df, population_df_size, regions_df = _get_data()
year = 1964
region_color = regions_df['region_color']
region_color.name = 'region_color'
fertility = fertility_df[year]
fertility.name = 'fertility'
life = life_expectancy_df[year]
life.name = 'life'
population = population_df_size[year]
population.name = 'population'
new_df = pd.concat([fertility, life, population, region_color], axis=1)
return new_df
def get_scatter_data():
years, regions, fertility_df, life_expectancy_df, population_df_size, regions_df = _get_data()
xyvalues = OrderedDict()
xyvalues['1964'] = list(
zip(
fertility_df[1964].dropna().values,
life_expectancy_df[1964].dropna().values
)
)
return xyvalues
| unlicense |
KordingLab/spykes | spykes/utils.py | 2 | 7135 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
from scipy import stats
import matplotlib.pyplot
def train_test_split(*datasets, **split):
'''Splits test data into training and testing data.
This is a replacement for the Scikit Learn version of the function (which
is being deprecated).
Args:
datasets (list of Numpy arrays): The datasets as Numpy arrays, where
the first dimension is the batch dimension.
n (int): Number of test samples to split off (only `n` or `percent`
may be specified).
percent (int): Percentange of test samples to split off.
Returns:
tuple of train / test data, or list of tuples: If only one dataset is
provided, this method returns a tuple of training and testing data;
otherwise, it returns a list of such tuples.
'''
if not datasets:
return [] # Guarentee there's at least one dataset.
num_batches = int(datasets[0].shape[0])
# Checks the input shapes.
if not all(d.shape[0] == num_batches for d in datasets):
raise ValueError('Not all of the datasets have the same batch size. '
'Received batch sizes: {batch_sizes}'
.format(batch_sizes=[d.shape[0] for d in datasets]))
# Gets the split num or split percent.
split_num = split.get('n', None)
split_prct = split.get('percent', None)
# Checks the splits
if (split_num and split_prct) or not (split_num or split_prct):
raise ValueError('Must specify either `split_num` or `split_prct`')
# Splits all of the datasets.
if split_prct is None:
num_test = split_num
else:
num_test = int(num_batches * split_prct)
# Checks that the test number is less than the number of batches.
if num_test >= num_batches:
raise ValueError('Invalid split number: {num_test} There are only '
'{num_batches} samples.'
.format(num_test=num_test, num_batches=num_batches))
# Splits each of the datasets.
idxs = np.arange(num_batches)
np.random.shuffle(idxs)
train_idxs, test_idxs = idxs[num_test:], idxs[:num_test]
datasets = [(d[train_idxs], d[test_idxs]) for d in datasets]
return datasets if len(datasets) > 1 else datasets[0]
def slow_exp(z, eta):
'''Applies a slowly rising exponential function to some data.
This function defines a slowly rising exponential that linearizes above
the threshold parameter :data:`eta`. Mathematically, this is defined as:
.. math::
q = \\begin{cases}
(z + 1 - eta) * \\exp(eta) & \\text{if } z > eta \\\\
\\exp(eta) & \\text{if } z \\leq eta
\\end{cases}
The gradient of this function is defined in :meth:`grad_slow_exp`.
Args:
z (array): The data to apply the :func:`slow_exp` function to.
eta (float): The threshold parameter.
Returns:
array: The resulting slow exponential, with the same shape as
:data:`z`.
'''
qu = np.zeros(z.shape)
slope = np.exp(eta)
intercept = (1 - eta) * slope
qu[z > eta] = z[z > eta] * slope + intercept
qu[z <= eta] = np.exp(z[z <= eta])
return qu
def grad_slow_exp(z, eta):
'''Computes the gradient of a slowly rising exponential function.
This is defined as:
.. math::
\\nabla q = \\begin{cases}
\\exp(eta) & \\text{if } z > eta \\\\
\\exp(z) & \\text{if } z \\leq eta
\\end{cases}
Args:
z (array): The dependent variable, before calling the :func:`slow_exp`
function.
eta (float): The threshold parameter used in the original
:func:`slow_exp` call.
Returns:
array: The gradient with respect to :data:`z` of the output of
:func:`slow_exp`.
'''
dqu_dz = np.zeros(z.shape)
slope = np.exp(eta)
dqu_dz[z > eta] = slope
dqu_dz[z <= eta] = np.exp(z[z <= eta])
return dqu_dz
def log_likelihood(y, yhat):
'''Helper function to compute the log likelihood.'''
eps = np.spacing(1)
return np.nansum(y * np.log(eps + yhat) - yhat)
def circ_corr(alpha1, alpha2):
'''Helper function to compute the circular correlation.'''
alpha1_bar = stats.circmean(alpha1)
alpha2_bar = stats.circmean(alpha2)
num = np.sum(np.sin(alpha1 - alpha1_bar) * np.sin(alpha2 - alpha2_bar))
den = np.sqrt(np.sum(np.sin(alpha1 - alpha1_bar) ** 2) *
np.sum(np.sin(alpha2 - alpha2_bar) ** 2))
rho = num / den
return rho
def get_sort_indices(data, by=None, order='descend'):
'''Helper function to calculate sorting indices given sorting condition.
Args:
data (2-D numpy array): Array with shape :data:`(n_neurons, n_bins)`.
by (str or list): If :data:`rate`, sort by firing rate. If
:data:`latency`, sort by peak latency. If a list or array is
provided, it must correspond to integer indices to be used as
sorting indices. If no sort order is provided, the data is
returned as-is.
order (str): Direction to sort in (either :data:`descend` or
:data:`ascend`).
Returns:
list: The sort indices as a Numpy array, with one index per element in
:data:`data` (i.e. :data:`data[sort_idxs]` gives the sorted data).
'''
# Checks if the by indices are a list or array.
if isinstance(by, list):
by = np.array(by)
if isinstance(by, np.ndarray):
if np.array_equal(np.sort(by), list(range(data.shape[0]))):
return by # Returns if it is a proper permutation.
else:
raise ValueError('The sorting indices not a proper permutation: {}'
.format(by))
# Converts the by array to
if by == 'rate':
sort_idx = np.sum(data, axis=1).argsort()
elif by == 'latency':
sort_idx = np.argmax(data, axis=1).argsort()
elif by is None:
sort_idx = np.arange(data.shape[0])
else:
raise ValueError('Invalid sort preference: "{}". Must be "rate", '
'"latency" or None.'.format(by))
# Checks the sorting order.
if order == 'ascend':
return sort_idx
elif order == 'descend':
return sort_idx[::-1]
else:
raise ValueError('Invalid sort order: {}'.format(order))
def set_matplotlib_defaults(plt=None):
'''Sets publication quality defaults for matplotlib.
Args:
plt (matplotlib.pyplot instance): The plt instance.
'''
if plt is None:
plt = matplotlib.pyplot
plt.rcParams.update({
'font.family': 'sans-serif',
'font.sans-serif': 'Bitsream Vera Sans',
'font.size': 13,
'axes.titlesize': 12,
'xtick.labelsize': 10,
'ytick.labelsize': 10,
'xtick.direction': 'out',
'ytick.direction': 'out',
'xtick.major.size': 6,
'ytick.major.size': 6,
'legend.fontsize': 11,
})
| mit |
m11s/MissionPlanner | Lib/site-packages/scipy/signal/waveforms.py | 55 | 11609 | # Author: Travis Oliphant
# 2003
#
# Feb. 2010: Updated by Warren Weckesser:
# Rewrote much of chirp()
# Added sweep_poly()
from numpy import asarray, zeros, place, nan, mod, pi, extract, log, sqrt, \
exp, cos, sin, polyval, polyint
def sawtooth(t, width=1):
"""
Return a periodic sawtooth waveform.
The sawtooth waveform has a period 2*pi, rises from -1 to 1 on the
interval 0 to width*2*pi and drops from 1 to -1 on the interval
width*2*pi to 2*pi. `width` must be in the interval [0,1].
Parameters
----------
t : array_like
Time.
width : float, optional
Width of the waveform. Default is 1.
Returns
-------
y : ndarray
Output array containing the sawtooth waveform.
Examples
--------
>>> import matplotlib.pyplot as plt
>>> x = np.linspace(0, 20*np.pi, 500)
>>> plt.plot(x, sp.signal.sawtooth(x))
"""
t,w = asarray(t), asarray(width)
w = asarray(w + (t-t))
t = asarray(t + (w-w))
if t.dtype.char in ['fFdD']:
ytype = t.dtype.char
else:
ytype = 'd'
y = zeros(t.shape,ytype)
# width must be between 0 and 1 inclusive
mask1 = (w > 1) | (w < 0)
place(y,mask1,nan)
# take t modulo 2*pi
tmod = mod(t,2*pi)
# on the interval 0 to width*2*pi function is
# tmod / (pi*w) - 1
mask2 = (1-mask1) & (tmod < w*2*pi)
tsub = extract(mask2,tmod)
wsub = extract(mask2,w)
place(y,mask2,tsub / (pi*wsub) - 1)
# on the interval width*2*pi to 2*pi function is
# (pi*(w+1)-tmod) / (pi*(1-w))
mask3 = (1-mask1) & (1-mask2)
tsub = extract(mask3,tmod)
wsub = extract(mask3,w)
place(y,mask3, (pi*(wsub+1)-tsub)/(pi*(1-wsub)))
return y
def square(t, duty=0.5):
"""
Return a periodic square-wave waveform.
The square wave has a period 2*pi, has value +1 from 0 to 2*pi*duty
and -1 from 2*pi*duty to 2*pi. `duty` must be in the interval [0,1].
Parameters
----------
t : array_like
The input time array.
duty : float, optional
Duty cycle.
Returns
-------
y : array_like
The output square wave.
"""
t,w = asarray(t), asarray(duty)
w = asarray(w + (t-t))
t = asarray(t + (w-w))
if t.dtype.char in ['fFdD']:
ytype = t.dtype.char
else:
ytype = 'd'
y = zeros(t.shape,ytype)
# width must be between 0 and 1 inclusive
mask1 = (w > 1) | (w < 0)
place(y,mask1,nan)
# take t modulo 2*pi
tmod = mod(t,2*pi)
# on the interval 0 to duty*2*pi function is
# 1
mask2 = (1-mask1) & (tmod < w*2*pi)
tsub = extract(mask2,tmod)
wsub = extract(mask2,w)
place(y,mask2,1)
# on the interval duty*2*pi to 2*pi function is
# (pi*(w+1)-tmod) / (pi*(1-w))
mask3 = (1-mask1) & (1-mask2)
tsub = extract(mask3,tmod)
wsub = extract(mask3,w)
place(y,mask3,-1)
return y
def gausspulse(t, fc=1000, bw=0.5, bwr=-6, tpr=-60, retquad=False, retenv=False):
"""
Return a gaussian modulated sinusoid: exp(-a t^2) exp(1j*2*pi*fc*t).
If `retquad` is True, then return the real and imaginary parts
(in-phase and quadrature).
If `retenv` is True, then return the envelope (unmodulated signal).
Otherwise, return the real part of the modulated sinusoid.
Parameters
----------
t : ndarray, or the string 'cutoff'
Input array.
fc : int, optional
Center frequency (Hz). Default is 1000.
bw : float, optional
Fractional bandwidth in frequency domain of pulse (Hz).
Default is 0.5.
bwr: float, optional
Reference level at which fractional bandwidth is calculated (dB).
Default is -6.
tpr : float, optional
If `t` is 'cutoff', then the function returns the cutoff
time for when the pulse amplitude falls below `tpr` (in dB).
Default is -60.
retquad : bool, optional
If True, return the quadrature (imaginary) as well as the real part
of the signal. Default is False.
retenv : bool, optional
If True, return the envelope of the signal. Default is False.
"""
if fc < 0:
raise ValueError("Center frequency (fc=%.2f) must be >=0." % fc)
if bw <= 0:
raise ValueError("Fractional bandwidth (bw=%.2f) must be > 0." % bw)
if bwr >= 0:
raise ValueError("Reference level for bandwidth (bwr=%.2f) must "
"be < 0 dB" % bwr)
# exp(-a t^2) <-> sqrt(pi/a) exp(-pi^2/a * f^2) = g(f)
ref = pow(10.0, bwr / 20.0)
# fdel = fc*bw/2: g(fdel) = ref --- solve this for a
#
# pi^2/a * fc^2 * bw^2 /4=-log(ref)
a = -(pi*fc*bw)**2 / (4.0*log(ref))
if t == 'cutoff': # compute cut_off point
# Solve exp(-a tc**2) = tref for tc
# tc = sqrt(-log(tref) / a) where tref = 10^(tpr/20)
if tpr >= 0:
raise ValueError("Reference level for time cutoff must be < 0 dB")
tref = pow(10.0, tpr / 20.0)
return sqrt(-log(tref)/a)
yenv = exp(-a*t*t)
yI = yenv * cos(2*pi*fc*t)
yQ = yenv * sin(2*pi*fc*t)
if not retquad and not retenv:
return yI
if not retquad and retenv:
return yI, yenv
if retquad and not retenv:
return yI, yQ
if retquad and retenv:
return yI, yQ, yenv
def chirp(t, f0, t1, f1, method='linear', phi=0, vertex_zero=True):
"""Frequency-swept cosine generator.
In the following, 'Hz' should be interpreted as 'cycles per time unit';
there is no assumption here that the time unit is one second. The
important distinction is that the units of rotation are cycles, not
radians.
Parameters
----------
t : ndarray
Times at which to evaluate the waveform.
f0 : float
Frequency (in Hz) at time t=0.
t1 : float
Time at which `f1` is specified.
f1 : float
Frequency (in Hz) of the waveform at time `t1`.
method : {'linear', 'quadratic', 'logarithmic', 'hyperbolic'}, optional
Kind of frequency sweep. If not given, `linear` is assumed. See
Notes below for more details.
phi : float, optional
Phase offset, in degrees. Default is 0.
vertex_zero : bool, optional
This parameter is only used when `method` is 'quadratic'.
It determines whether the vertex of the parabola that is the graph
of the frequency is at t=0 or t=t1.
Returns
-------
A numpy array containing the signal evaluated at 't' with the requested
time-varying frequency. More precisely, the function returns:
``cos(phase + (pi/180)*phi)``
where `phase` is the integral (from 0 to t) of ``2*pi*f(t)``.
``f(t)`` is defined below.
See Also
--------
scipy.signal.waveforms.sweep_poly
Notes
-----
There are four options for the `method`. The following formulas give
the instantaneous frequency (in Hz) of the signal generated by
`chirp()`. For convenience, the shorter names shown below may also be
used.
linear, lin, li:
``f(t) = f0 + (f1 - f0) * t / t1``
quadratic, quad, q:
The graph of the frequency f(t) is a parabola through (0, f0) and
(t1, f1). By default, the vertex of the parabola is at (0, f0).
If `vertex_zero` is False, then the vertex is at (t1, f1). The
formula is:
if vertex_zero is True:
``f(t) = f0 + (f1 - f0) * t**2 / t1**2``
else:
``f(t) = f1 - (f1 - f0) * (t1 - t)**2 / t1**2``
To use a more general quadratic function, or an arbitrary
polynomial, use the function `scipy.signal.waveforms.sweep_poly`.
logarithmic, log, lo:
``f(t) = f0 * (f1/f0)**(t/t1)``
f0 and f1 must be nonzero and have the same sign.
This signal is also known as a geometric or exponential chirp.
hyperbolic, hyp:
``f(t) = f0*f1*t1 / ((f0 - f1)*t + f1*t1)``
f1 must be positive, and f0 must be greater than f1.
"""
# 'phase' is computed in _chirp_phase, to make testing easier.
phase = _chirp_phase(t, f0, t1, f1, method, vertex_zero)
# Convert phi to radians.
phi *= pi / 180
return cos(phase + phi)
def _chirp_phase(t, f0, t1, f1, method='linear', vertex_zero=True):
"""
Calculate the phase used by chirp_phase to generate its output.
See `chirp_phase` for a description of the arguments.
"""
f0 = float(f0)
t1 = float(t1)
f1 = float(f1)
if method in ['linear', 'lin', 'li']:
beta = (f1 - f0) / t1
phase = 2*pi * (f0*t + 0.5*beta*t*t)
elif method in ['quadratic','quad','q']:
beta = (f1 - f0)/(t1**2)
if vertex_zero:
phase = 2*pi * (f0*t + beta * t**3/3)
else:
phase = 2*pi * (f1*t + beta * ((t1 - t)**3 - t1**3)/3)
elif method in ['logarithmic', 'log', 'lo']:
if f0*f1 <= 0.0:
raise ValueError("For a geometric chirp, f0 and f1 must be nonzero " \
"and have the same sign.")
if f0 == f1:
phase = 2*pi * f0 * t
else:
beta = t1 / log(f1/f0)
phase = 2*pi * beta * f0 * (pow(f1/f0, t/t1) - 1.0)
elif method in ['hyperbolic', 'hyp']:
if f1 <= 0.0 or f0 <= f1:
raise ValueError("hyperbolic chirp requires f0 > f1 > 0.0.")
c = f1*t1
df = f0 - f1
phase = 2*pi * (f0 * c / df) * log((df*t + c)/c)
else:
raise ValueError("method must be 'linear', 'quadratic', 'logarithmic', "
"or 'hyperbolic', but a value of %r was given." % method)
return phase
def sweep_poly(t, poly, phi=0):
"""Frequency-swept cosine generator, with a time-dependent frequency
specified as a polynomial.
This function generates a sinusoidal function whose instantaneous
frequency varies with time. The frequency at time `t` is given by
the polynomial `poly`.
Parameters
----------
t : ndarray
Times at which to evaluate the waveform.
poly : 1D ndarray (or array-like), or instance of numpy.poly1d
The desired frequency expressed as a polynomial. If `poly` is
a list or ndarray of length n, then the elements of `poly` are
the coefficients of the polynomial, and the instantaneous
frequency is
``f(t) = poly[0]*t**(n-1) + poly[1]*t**(n-2) + ... + poly[n-1]``
If `poly` is an instance of numpy.poly1d, then the
instantaneous frequency is
``f(t) = poly(t)``
phi : float, optional
Phase offset, in degrees. Default is 0.
Returns
-------
A numpy array containing the signal evaluated at 't' with the requested
time-varying frequency. More precisely, the function returns
``cos(phase + (pi/180)*phi)``
where `phase` is the integral (from 0 to t) of ``2 * pi * f(t)``;
``f(t)`` is defined above.
See Also
--------
scipy.signal.waveforms.chirp
Notes
-----
.. versionadded:: 0.8.0
"""
# 'phase' is computed in _sweep_poly_phase, to make testing easier.
phase = _sweep_poly_phase(t, poly)
# Convert to radians.
phi *= pi / 180
return cos(phase + phi)
def _sweep_poly_phase(t, poly):
"""
Calculate the phase used by sweep_poly to generate its output.
See `sweep_poly` for a description of the arguments.
"""
# polyint handles lists, ndarrays and instances of poly1d automatically.
intpoly = polyint(poly)
phase = 2*pi * polyval(intpoly, t)
return phase
| gpl-3.0 |
pprett/scikit-learn | sklearn/cluster/tests/test_birch.py | 342 | 5603 | """
Tests for the birch clustering algorithm.
"""
from scipy import sparse
import numpy as np
from sklearn.cluster.tests.common import generate_clustered_data
from sklearn.cluster.birch import Birch
from sklearn.cluster.hierarchical import AgglomerativeClustering
from sklearn.datasets import make_blobs
from sklearn.linear_model import ElasticNet
from sklearn.metrics import pairwise_distances_argmin, v_measure_score
from sklearn.utils.testing import assert_greater_equal
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_greater
from sklearn.utils.testing import assert_almost_equal
from sklearn.utils.testing import assert_array_equal
from sklearn.utils.testing import assert_raises
from sklearn.utils.testing import assert_warns
def test_n_samples_leaves_roots():
# Sanity check for the number of samples in leaves and roots
X, y = make_blobs(n_samples=10)
brc = Birch()
brc.fit(X)
n_samples_root = sum([sc.n_samples_ for sc in brc.root_.subclusters_])
n_samples_leaves = sum([sc.n_samples_ for leaf in brc._get_leaves()
for sc in leaf.subclusters_])
assert_equal(n_samples_leaves, X.shape[0])
assert_equal(n_samples_root, X.shape[0])
def test_partial_fit():
# Test that fit is equivalent to calling partial_fit multiple times
X, y = make_blobs(n_samples=100)
brc = Birch(n_clusters=3)
brc.fit(X)
brc_partial = Birch(n_clusters=None)
brc_partial.partial_fit(X[:50])
brc_partial.partial_fit(X[50:])
assert_array_equal(brc_partial.subcluster_centers_,
brc.subcluster_centers_)
# Test that same global labels are obtained after calling partial_fit
# with None
brc_partial.set_params(n_clusters=3)
brc_partial.partial_fit(None)
assert_array_equal(brc_partial.subcluster_labels_, brc.subcluster_labels_)
def test_birch_predict():
# Test the predict method predicts the nearest centroid.
rng = np.random.RandomState(0)
X = generate_clustered_data(n_clusters=3, n_features=3,
n_samples_per_cluster=10)
# n_samples * n_samples_per_cluster
shuffle_indices = np.arange(30)
rng.shuffle(shuffle_indices)
X_shuffle = X[shuffle_indices, :]
brc = Birch(n_clusters=4, threshold=1.)
brc.fit(X_shuffle)
centroids = brc.subcluster_centers_
assert_array_equal(brc.labels_, brc.predict(X_shuffle))
nearest_centroid = pairwise_distances_argmin(X_shuffle, centroids)
assert_almost_equal(v_measure_score(nearest_centroid, brc.labels_), 1.0)
def test_n_clusters():
# Test that n_clusters param works properly
X, y = make_blobs(n_samples=100, centers=10)
brc1 = Birch(n_clusters=10)
brc1.fit(X)
assert_greater(len(brc1.subcluster_centers_), 10)
assert_equal(len(np.unique(brc1.labels_)), 10)
# Test that n_clusters = Agglomerative Clustering gives
# the same results.
gc = AgglomerativeClustering(n_clusters=10)
brc2 = Birch(n_clusters=gc)
brc2.fit(X)
assert_array_equal(brc1.subcluster_labels_, brc2.subcluster_labels_)
assert_array_equal(brc1.labels_, brc2.labels_)
# Test that the wrong global clustering step raises an Error.
clf = ElasticNet()
brc3 = Birch(n_clusters=clf)
assert_raises(ValueError, brc3.fit, X)
# Test that a small number of clusters raises a warning.
brc4 = Birch(threshold=10000.)
assert_warns(UserWarning, brc4.fit, X)
def test_sparse_X():
# Test that sparse and dense data give same results
X, y = make_blobs(n_samples=100, centers=10)
brc = Birch(n_clusters=10)
brc.fit(X)
csr = sparse.csr_matrix(X)
brc_sparse = Birch(n_clusters=10)
brc_sparse.fit(csr)
assert_array_equal(brc.labels_, brc_sparse.labels_)
assert_array_equal(brc.subcluster_centers_,
brc_sparse.subcluster_centers_)
def check_branching_factor(node, branching_factor):
subclusters = node.subclusters_
assert_greater_equal(branching_factor, len(subclusters))
for cluster in subclusters:
if cluster.child_:
check_branching_factor(cluster.child_, branching_factor)
def test_branching_factor():
# Test that nodes have at max branching_factor number of subclusters
X, y = make_blobs()
branching_factor = 9
# Purposefully set a low threshold to maximize the subclusters.
brc = Birch(n_clusters=None, branching_factor=branching_factor,
threshold=0.01)
brc.fit(X)
check_branching_factor(brc.root_, branching_factor)
brc = Birch(n_clusters=3, branching_factor=branching_factor,
threshold=0.01)
brc.fit(X)
check_branching_factor(brc.root_, branching_factor)
# Raises error when branching_factor is set to one.
brc = Birch(n_clusters=None, branching_factor=1, threshold=0.01)
assert_raises(ValueError, brc.fit, X)
def check_threshold(birch_instance, threshold):
"""Use the leaf linked list for traversal"""
current_leaf = birch_instance.dummy_leaf_.next_leaf_
while current_leaf:
subclusters = current_leaf.subclusters_
for sc in subclusters:
assert_greater_equal(threshold, sc.radius)
current_leaf = current_leaf.next_leaf_
def test_threshold():
# Test that the leaf subclusters have a threshold lesser than radius
X, y = make_blobs(n_samples=80, centers=4)
brc = Birch(threshold=0.5, n_clusters=None)
brc.fit(X)
check_threshold(brc, 0.5)
brc = Birch(threshold=5.0, n_clusters=None)
brc.fit(X)
check_threshold(brc, 5.)
| bsd-3-clause |
toobaz/pandas | pandas/tests/scalar/timedelta/test_arithmetic.py | 2 | 21988 | """
Tests for scalar Timedelta arithmetic ops
"""
from datetime import datetime, timedelta
import operator
import numpy as np
import pytest
import pandas as pd
from pandas import NaT, Timedelta, Timestamp
from pandas.core import ops
import pandas.util.testing as tm
class TestTimedeltaAdditionSubtraction:
"""
Tests for Timedelta methods:
__add__, __radd__,
__sub__, __rsub__
"""
@pytest.mark.parametrize(
"ten_seconds",
[
Timedelta(10, unit="s"),
timedelta(seconds=10),
np.timedelta64(10, "s"),
np.timedelta64(10000000000, "ns"),
pd.offsets.Second(10),
],
)
def test_td_add_sub_ten_seconds(self, ten_seconds):
# GH#6808
base = Timestamp("20130101 09:01:12.123456")
expected_add = Timestamp("20130101 09:01:22.123456")
expected_sub = Timestamp("20130101 09:01:02.123456")
result = base + ten_seconds
assert result == expected_add
result = base - ten_seconds
assert result == expected_sub
@pytest.mark.parametrize(
"one_day_ten_secs",
[
Timedelta("1 day, 00:00:10"),
Timedelta("1 days, 00:00:10"),
timedelta(days=1, seconds=10),
np.timedelta64(1, "D") + np.timedelta64(10, "s"),
pd.offsets.Day() + pd.offsets.Second(10),
],
)
def test_td_add_sub_one_day_ten_seconds(self, one_day_ten_secs):
# GH#6808
base = Timestamp("20130102 09:01:12.123456")
expected_add = Timestamp("20130103 09:01:22.123456")
expected_sub = Timestamp("20130101 09:01:02.123456")
result = base + one_day_ten_secs
assert result == expected_add
result = base - one_day_ten_secs
assert result == expected_sub
@pytest.mark.parametrize("op", [operator.add, ops.radd])
def test_td_add_datetimelike_scalar(self, op):
# GH#19738
td = Timedelta(10, unit="d")
result = op(td, datetime(2016, 1, 1))
if op is operator.add:
# datetime + Timedelta does _not_ call Timedelta.__radd__,
# so we get a datetime back instead of a Timestamp
assert isinstance(result, Timestamp)
assert result == Timestamp(2016, 1, 11)
result = op(td, Timestamp("2018-01-12 18:09"))
assert isinstance(result, Timestamp)
assert result == Timestamp("2018-01-22 18:09")
result = op(td, np.datetime64("2018-01-12"))
assert isinstance(result, Timestamp)
assert result == Timestamp("2018-01-22")
result = op(td, NaT)
assert result is NaT
@pytest.mark.parametrize("op", [operator.add, ops.radd])
def test_td_add_td(self, op):
td = Timedelta(10, unit="d")
result = op(td, Timedelta(days=10))
assert isinstance(result, Timedelta)
assert result == Timedelta(days=20)
@pytest.mark.parametrize("op", [operator.add, ops.radd])
def test_td_add_pytimedelta(self, op):
td = Timedelta(10, unit="d")
result = op(td, timedelta(days=9))
assert isinstance(result, Timedelta)
assert result == Timedelta(days=19)
@pytest.mark.parametrize("op", [operator.add, ops.radd])
def test_td_add_timedelta64(self, op):
td = Timedelta(10, unit="d")
result = op(td, np.timedelta64(-4, "D"))
assert isinstance(result, Timedelta)
assert result == Timedelta(days=6)
@pytest.mark.parametrize("op", [operator.add, ops.radd])
def test_td_add_offset(self, op):
td = Timedelta(10, unit="d")
result = op(td, pd.offsets.Hour(6))
assert isinstance(result, Timedelta)
assert result == Timedelta(days=10, hours=6)
def test_td_sub_td(self):
td = Timedelta(10, unit="d")
expected = Timedelta(0, unit="ns")
result = td - td
assert isinstance(result, Timedelta)
assert result == expected
def test_td_sub_pytimedelta(self):
td = Timedelta(10, unit="d")
expected = Timedelta(0, unit="ns")
result = td - td.to_pytimedelta()
assert isinstance(result, Timedelta)
assert result == expected
result = td.to_pytimedelta() - td
assert isinstance(result, Timedelta)
assert result == expected
def test_td_sub_timedelta64(self):
td = Timedelta(10, unit="d")
expected = Timedelta(0, unit="ns")
result = td - td.to_timedelta64()
assert isinstance(result, Timedelta)
assert result == expected
result = td.to_timedelta64() - td
assert isinstance(result, Timedelta)
assert result == expected
def test_td_sub_nat(self):
# In this context pd.NaT is treated as timedelta-like
td = Timedelta(10, unit="d")
result = td - NaT
assert result is NaT
def test_td_sub_td64_nat(self):
td = Timedelta(10, unit="d")
td_nat = np.timedelta64("NaT")
result = td - td_nat
assert result is NaT
result = td_nat - td
assert result is NaT
def test_td_sub_offset(self):
td = Timedelta(10, unit="d")
result = td - pd.offsets.Hour(1)
assert isinstance(result, Timedelta)
assert result == Timedelta(239, unit="h")
def test_td_add_sub_numeric_raises(self):
td = Timedelta(10, unit="d")
for other in [2, 2.0, np.int64(2), np.float64(2)]:
with pytest.raises(TypeError):
td + other
with pytest.raises(TypeError):
other + td
with pytest.raises(TypeError):
td - other
with pytest.raises(TypeError):
other - td
def test_td_rsub_nat(self):
td = Timedelta(10, unit="d")
result = NaT - td
assert result is NaT
result = np.datetime64("NaT") - td
assert result is NaT
def test_td_rsub_offset(self):
result = pd.offsets.Hour(1) - Timedelta(10, unit="d")
assert isinstance(result, Timedelta)
assert result == Timedelta(-239, unit="h")
def test_td_sub_timedeltalike_object_dtype_array(self):
# GH#21980
arr = np.array([Timestamp("20130101 9:01"), Timestamp("20121230 9:02")])
exp = np.array([Timestamp("20121231 9:01"), Timestamp("20121229 9:02")])
res = arr - Timedelta("1D")
tm.assert_numpy_array_equal(res, exp)
def test_td_sub_mixed_most_timedeltalike_object_dtype_array(self):
# GH#21980
now = Timestamp.now()
arr = np.array([now, Timedelta("1D"), np.timedelta64(2, "h")])
exp = np.array(
[
now - Timedelta("1D"),
Timedelta("0D"),
np.timedelta64(2, "h") - Timedelta("1D"),
]
)
res = arr - Timedelta("1D")
tm.assert_numpy_array_equal(res, exp)
def test_td_rsub_mixed_most_timedeltalike_object_dtype_array(self):
# GH#21980
now = Timestamp.now()
arr = np.array([now, Timedelta("1D"), np.timedelta64(2, "h")])
with pytest.raises(TypeError):
Timedelta("1D") - arr
@pytest.mark.parametrize("op", [operator.add, ops.radd])
def test_td_add_timedeltalike_object_dtype_array(self, op):
# GH#21980
arr = np.array([Timestamp("20130101 9:01"), Timestamp("20121230 9:02")])
exp = np.array([Timestamp("20130102 9:01"), Timestamp("20121231 9:02")])
res = op(arr, Timedelta("1D"))
tm.assert_numpy_array_equal(res, exp)
@pytest.mark.parametrize("op", [operator.add, ops.radd])
def test_td_add_mixed_timedeltalike_object_dtype_array(self, op):
# GH#21980
now = Timestamp.now()
arr = np.array([now, Timedelta("1D")])
exp = np.array([now + Timedelta("1D"), Timedelta("2D")])
res = op(arr, Timedelta("1D"))
tm.assert_numpy_array_equal(res, exp)
class TestTimedeltaMultiplicationDivision:
"""
Tests for Timedelta methods:
__mul__, __rmul__,
__div__, __rdiv__,
__truediv__, __rtruediv__,
__floordiv__, __rfloordiv__,
__mod__, __rmod__,
__divmod__, __rdivmod__
"""
# ---------------------------------------------------------------
# Timedelta.__mul__, __rmul__
@pytest.mark.parametrize(
"td_nat", [NaT, np.timedelta64("NaT", "ns"), np.timedelta64("NaT")]
)
@pytest.mark.parametrize("op", [operator.mul, ops.rmul])
def test_td_mul_nat(self, op, td_nat):
# GH#19819
td = Timedelta(10, unit="d")
with pytest.raises(TypeError):
op(td, td_nat)
@pytest.mark.parametrize("nan", [np.nan, np.float64("NaN"), float("nan")])
@pytest.mark.parametrize("op", [operator.mul, ops.rmul])
def test_td_mul_nan(self, op, nan):
# np.float64('NaN') has a 'dtype' attr, avoid treating as array
td = Timedelta(10, unit="d")
result = op(td, nan)
assert result is NaT
@pytest.mark.parametrize("op", [operator.mul, ops.rmul])
def test_td_mul_scalar(self, op):
# GH#19738
td = Timedelta(minutes=3)
result = op(td, 2)
assert result == Timedelta(minutes=6)
result = op(td, 1.5)
assert result == Timedelta(minutes=4, seconds=30)
assert op(td, np.nan) is NaT
assert op(-1, td).value == -1 * td.value
assert op(-1.0, td).value == -1.0 * td.value
with pytest.raises(TypeError):
# timedelta * datetime is gibberish
op(td, Timestamp(2016, 1, 2))
with pytest.raises(TypeError):
# invalid multiply with another timedelta
op(td, td)
# ---------------------------------------------------------------
# Timedelta.__div__, __truediv__
def test_td_div_timedeltalike_scalar(self):
# GH#19738
td = Timedelta(10, unit="d")
result = td / pd.offsets.Hour(1)
assert result == 240
assert td / td == 1
assert td / np.timedelta64(60, "h") == 4
assert np.isnan(td / NaT)
def test_td_div_numeric_scalar(self):
# GH#19738
td = Timedelta(10, unit="d")
result = td / 2
assert isinstance(result, Timedelta)
assert result == Timedelta(days=5)
result = td / 5.0
assert isinstance(result, Timedelta)
assert result == Timedelta(days=2)
@pytest.mark.parametrize("nan", [np.nan, np.float64("NaN"), float("nan")])
def test_td_div_nan(self, nan):
# np.float64('NaN') has a 'dtype' attr, avoid treating as array
td = Timedelta(10, unit="d")
result = td / nan
assert result is NaT
result = td // nan
assert result is NaT
# ---------------------------------------------------------------
# Timedelta.__rdiv__
def test_td_rdiv_timedeltalike_scalar(self):
# GH#19738
td = Timedelta(10, unit="d")
result = pd.offsets.Hour(1) / td
assert result == 1 / 240.0
assert np.timedelta64(60, "h") / td == 0.25
# ---------------------------------------------------------------
# Timedelta.__floordiv__
def test_td_floordiv_timedeltalike_scalar(self):
# GH#18846
td = Timedelta(hours=3, minutes=4)
scalar = Timedelta(hours=3, minutes=3)
assert td // scalar == 1
assert -td // scalar.to_pytimedelta() == -2
assert (2 * td) // scalar.to_timedelta64() == 2
def test_td_floordiv_null_scalar(self):
# GH#18846
td = Timedelta(hours=3, minutes=4)
assert td // np.nan is NaT
assert np.isnan(td // NaT)
assert np.isnan(td // np.timedelta64("NaT"))
def test_td_floordiv_offsets(self):
# GH#19738
td = Timedelta(hours=3, minutes=4)
assert td // pd.offsets.Hour(1) == 3
assert td // pd.offsets.Minute(2) == 92
def test_td_floordiv_invalid_scalar(self):
# GH#18846
td = Timedelta(hours=3, minutes=4)
with pytest.raises(TypeError):
td // np.datetime64("2016-01-01", dtype="datetime64[us]")
def test_td_floordiv_numeric_scalar(self):
# GH#18846
td = Timedelta(hours=3, minutes=4)
expected = Timedelta(hours=1, minutes=32)
assert td // 2 == expected
assert td // 2.0 == expected
assert td // np.float64(2.0) == expected
assert td // np.int32(2.0) == expected
assert td // np.uint8(2.0) == expected
def test_td_floordiv_timedeltalike_array(self):
# GH#18846
td = Timedelta(hours=3, minutes=4)
scalar = Timedelta(hours=3, minutes=3)
# Array-like others
assert td // np.array(scalar.to_timedelta64()) == 1
res = (3 * td) // np.array([scalar.to_timedelta64()])
expected = np.array([3], dtype=np.int64)
tm.assert_numpy_array_equal(res, expected)
res = (10 * td) // np.array([scalar.to_timedelta64(), np.timedelta64("NaT")])
expected = np.array([10, np.nan])
tm.assert_numpy_array_equal(res, expected)
def test_td_floordiv_numeric_series(self):
# GH#18846
td = Timedelta(hours=3, minutes=4)
ser = pd.Series([1], dtype=np.int64)
res = td // ser
assert res.dtype.kind == "m"
# ---------------------------------------------------------------
# Timedelta.__rfloordiv__
def test_td_rfloordiv_timedeltalike_scalar(self):
# GH#18846
td = Timedelta(hours=3, minutes=3)
scalar = Timedelta(hours=3, minutes=4)
# scalar others
# x // Timedelta is defined only for timedelta-like x. int-like,
# float-like, and date-like, in particular, should all either
# a) raise TypeError directly or
# b) return NotImplemented, following which the reversed
# operation will raise TypeError.
assert td.__rfloordiv__(scalar) == 1
assert (-td).__rfloordiv__(scalar.to_pytimedelta()) == -2
assert (2 * td).__rfloordiv__(scalar.to_timedelta64()) == 0
def test_td_rfloordiv_null_scalar(self):
# GH#18846
td = Timedelta(hours=3, minutes=3)
assert np.isnan(td.__rfloordiv__(NaT))
assert np.isnan(td.__rfloordiv__(np.timedelta64("NaT")))
def test_td_rfloordiv_offsets(self):
# GH#19738
assert pd.offsets.Hour(1) // Timedelta(minutes=25) == 2
def test_td_rfloordiv_invalid_scalar(self):
# GH#18846
td = Timedelta(hours=3, minutes=3)
dt64 = np.datetime64("2016-01-01", dtype="datetime64[us]")
with pytest.raises(TypeError):
td.__rfloordiv__(dt64)
def test_td_rfloordiv_numeric_scalar(self):
# GH#18846
td = Timedelta(hours=3, minutes=3)
assert td.__rfloordiv__(np.nan) is NotImplemented
assert td.__rfloordiv__(3.5) is NotImplemented
assert td.__rfloordiv__(2) is NotImplemented
with pytest.raises(TypeError):
td.__rfloordiv__(np.float64(2.0))
with pytest.raises(TypeError):
td.__rfloordiv__(np.uint8(9))
with tm.assert_produces_warning(FutureWarning):
# GH-19761: Change to TypeError.
td.__rfloordiv__(np.int32(2.0))
def test_td_rfloordiv_timedeltalike_array(self):
# GH#18846
td = Timedelta(hours=3, minutes=3)
scalar = Timedelta(hours=3, minutes=4)
# Array-like others
assert td.__rfloordiv__(np.array(scalar.to_timedelta64())) == 1
res = td.__rfloordiv__(np.array([(3 * scalar).to_timedelta64()]))
expected = np.array([3], dtype=np.int64)
tm.assert_numpy_array_equal(res, expected)
arr = np.array([(10 * scalar).to_timedelta64(), np.timedelta64("NaT")])
res = td.__rfloordiv__(arr)
expected = np.array([10, np.nan])
tm.assert_numpy_array_equal(res, expected)
def test_td_rfloordiv_numeric_series(self):
# GH#18846
td = Timedelta(hours=3, minutes=3)
ser = pd.Series([1], dtype=np.int64)
res = td.__rfloordiv__(ser)
assert res is NotImplemented
with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
# TODO: GH-19761. Change to TypeError.
ser // td
# ----------------------------------------------------------------
# Timedelta.__mod__, __rmod__
def test_mod_timedeltalike(self):
# GH#19365
td = Timedelta(hours=37)
# Timedelta-like others
result = td % Timedelta(hours=6)
assert isinstance(result, Timedelta)
assert result == Timedelta(hours=1)
result = td % timedelta(minutes=60)
assert isinstance(result, Timedelta)
assert result == Timedelta(0)
result = td % NaT
assert result is NaT
def test_mod_timedelta64_nat(self):
# GH#19365
td = Timedelta(hours=37)
result = td % np.timedelta64("NaT", "ns")
assert result is NaT
def test_mod_timedelta64(self):
# GH#19365
td = Timedelta(hours=37)
result = td % np.timedelta64(2, "h")
assert isinstance(result, Timedelta)
assert result == Timedelta(hours=1)
def test_mod_offset(self):
# GH#19365
td = Timedelta(hours=37)
result = td % pd.offsets.Hour(5)
assert isinstance(result, Timedelta)
assert result == Timedelta(hours=2)
def test_mod_numeric(self):
# GH#19365
td = Timedelta(hours=37)
# Numeric Others
result = td % 2
assert isinstance(result, Timedelta)
assert result == Timedelta(0)
result = td % 1e12
assert isinstance(result, Timedelta)
assert result == Timedelta(minutes=3, seconds=20)
result = td % int(1e12)
assert isinstance(result, Timedelta)
assert result == Timedelta(minutes=3, seconds=20)
def test_mod_invalid(self):
# GH#19365
td = Timedelta(hours=37)
with pytest.raises(TypeError):
td % Timestamp("2018-01-22")
with pytest.raises(TypeError):
td % []
def test_rmod_pytimedelta(self):
# GH#19365
td = Timedelta(minutes=3)
result = timedelta(minutes=4) % td
assert isinstance(result, Timedelta)
assert result == Timedelta(minutes=1)
def test_rmod_timedelta64(self):
# GH#19365
td = Timedelta(minutes=3)
result = np.timedelta64(5, "m") % td
assert isinstance(result, Timedelta)
assert result == Timedelta(minutes=2)
def test_rmod_invalid(self):
# GH#19365
td = Timedelta(minutes=3)
with pytest.raises(TypeError):
Timestamp("2018-01-22") % td
with pytest.raises(TypeError):
15 % td
with pytest.raises(TypeError):
16.0 % td
with pytest.raises(TypeError):
np.array([22, 24]) % td
# ----------------------------------------------------------------
# Timedelta.__divmod__, __rdivmod__
def test_divmod_numeric(self):
# GH#19365
td = Timedelta(days=2, hours=6)
result = divmod(td, 53 * 3600 * 1e9)
assert result[0] == Timedelta(1, unit="ns")
assert isinstance(result[1], Timedelta)
assert result[1] == Timedelta(hours=1)
assert result
result = divmod(td, np.nan)
assert result[0] is NaT
assert result[1] is NaT
def test_divmod(self):
# GH#19365
td = Timedelta(days=2, hours=6)
result = divmod(td, timedelta(days=1))
assert result[0] == 2
assert isinstance(result[1], Timedelta)
assert result[1] == Timedelta(hours=6)
result = divmod(td, 54)
assert result[0] == Timedelta(hours=1)
assert isinstance(result[1], Timedelta)
assert result[1] == Timedelta(0)
result = divmod(td, NaT)
assert np.isnan(result[0])
assert result[1] is NaT
def test_divmod_offset(self):
# GH#19365
td = Timedelta(days=2, hours=6)
result = divmod(td, pd.offsets.Hour(-4))
assert result[0] == -14
assert isinstance(result[1], Timedelta)
assert result[1] == Timedelta(hours=-2)
def test_divmod_invalid(self):
# GH#19365
td = Timedelta(days=2, hours=6)
with pytest.raises(TypeError):
divmod(td, Timestamp("2018-01-22"))
def test_rdivmod_pytimedelta(self):
# GH#19365
result = divmod(timedelta(days=2, hours=6), Timedelta(days=1))
assert result[0] == 2
assert isinstance(result[1], Timedelta)
assert result[1] == Timedelta(hours=6)
def test_rdivmod_offset(self):
result = divmod(pd.offsets.Hour(54), Timedelta(hours=-4))
assert result[0] == -14
assert isinstance(result[1], Timedelta)
assert result[1] == Timedelta(hours=-2)
def test_rdivmod_invalid(self):
# GH#19365
td = Timedelta(minutes=3)
with pytest.raises(TypeError):
divmod(Timestamp("2018-01-22"), td)
with pytest.raises(TypeError):
divmod(15, td)
with pytest.raises(TypeError):
divmod(16.0, td)
with pytest.raises(TypeError):
divmod(np.array([22, 24]), td)
# ----------------------------------------------------------------
@pytest.mark.parametrize(
"op", [operator.mul, ops.rmul, operator.truediv, ops.rdiv, ops.rsub]
)
@pytest.mark.parametrize(
"arr",
[
np.array([Timestamp("20130101 9:01"), Timestamp("20121230 9:02")]),
np.array([Timestamp.now(), Timedelta("1D")]),
],
)
def test_td_op_timedelta_timedeltalike_array(self, op, arr):
with pytest.raises(TypeError):
op(arr, Timedelta("1D"))
| bsd-3-clause |
SUNCAT-Center/catmap | catmap/analyze/analysis_base.py | 2 | 34125 | import catmap
from catmap import ReactionModelWrapper
from catmap.model import ReactionModel as RM
from catmap import griddata
from copy import copy
try:
from scipy.stats import norm
except:
norm = None
from matplotlib.ticker import MaxNLocator
from mpl_toolkits.axes_grid1 import make_axes_locatable
plt = catmap.plt
pickle = catmap.pickle
np = catmap.np
spline = catmap.spline
mtransforms = catmap.mtransforms
basic_colors = [[0,0,0],[0,0,1],[0.1,1,0.1],[1,0,0],[0,1,1],[1,0.5,0],[1,0.9,0],
[1,0,1],[0,0.5,0.5],[0.5,0.25,0.15],[0.5,0.5,0.5]]
#black,blue,green,red,cyan,orange,yellow,magenta,turquoise,brown,gray
def get_colors(n_colors):
"""
Get n colors from basic_colors.
:param n_colors: Number of colors
:type n_colors: int
"""
if n_colors <len(basic_colors):
return basic_colors[0:n_colors]
else:
longlist= basic_colors*n_colors
return longlist[0:n_colors]
def boltzmann_vector(energy_list,vector_list,temperature):
"""
Create a vector which is a Boltzmann average of the vector_list weighted
with energies in the energy_list.
:param energy_list: List of energies
:type energy_list: list
:param vector_list: List of vectors
:type energy_list: list
:param temperature: Temperature
:type energy_list: float
"""
def boltzmann_avg(es,ns,T):
"""
Calculate the Boltzmann average
:param es: energies
:type es: iterable
:param ns:
:type ns: iterable
:param T: temperature
:type T: float
..todo: description for ns
"""
kB = 8.613e-5 #assuming energies are in eV and T is in K
es = [e-min(es) for e in es] #normalize to minimum energy
exp_sum = sum([np.exp(-e/(kB*T)) for e in es])
exp_weighted = [n*np.exp(-e/(kB*T))/exp_sum for n,e in zip(ns,es)]
Z = sum(exp_weighted)
return Z
vars = zip(*vector_list)
boltz_vec = [boltzmann_avg(energy_list,v,temperature) for v in vars]
return boltz_vec
class MapPlot:
"""
Class for generating plots using a dictionary of default plotting attributes.
The following attributes can be modified:
:param resolution_enhancement: Resolution enhancement for interpolated maps
:type resolution_enhancement: int
:param min: Minimum
:type min:
:param max: Maximum
:type max:
:param n_ticks: Number of ticks
:type n_ticks: int
:param descriptor_labels: Label of descriptors
:type descriptor_labels: list
:param default_descriptor_pt_args: Dictionary of descriptor point arguments
:type default_descriptor_pt_args: dict
:param default_descriptor_label_args: Dictionary of descriptor labels
:type default_descriptor_label_args: dict
:param descriptor_pt_args:
:type descriptor_pt_args: dict
:param include_descriptors: Include the descriptors
:type include_descriptors: bool
:param plot_size: Size of the plot
:type plot_size: int
:param aspect:
:type aspect:
:param subplots_adjust_kwargs: Dictionary of keyword arguments for
adjusting matplotlib subplots
:type subplots_adjust_kwargs: dict
.. todo:: Some missing descriptions
"""
def __init__(self):
defaults = dict(resolution_enhancement=1,
min=None,
max=None,
n_ticks=6,
plot_function=None,
colorbar=True,
colormap=plt.cm.YlGnBu_r,
axis_label_decimals=2,
log_scale=False,
descriptor_labels=['X_descriptor', 'Y_descriptor'],
default_descriptor_pt_args={'marker': 'o'},
default_descriptor_label_args={},
descriptor_pt_args={},
descriptor_label_args={},
include_descriptors=False,
plot_size=4,
aspect=None,
subplots_adjust_kwargs={'hspace': 0.35,
'wspace': 0.35,
'bottom': 0.15})
for key in defaults:
val = defaults[key]
if not hasattr(self, key):
setattr(self, key, val)
elif getattr(self,key) is None:
setattr(self,key,val)
def update_descriptor_args(self):
"""
Update descriptor arguments
.. todo:: __doc__
"""
if getattr(self,'descriptor_dict',None):
if self.descriptor_pt_args == {}:
for pt in self.descriptor_dict:
self.descriptor_pt_args[pt] = copy(
self.default_descriptor_pt_args)
if self.descriptor_label_args == {}:
for pt in self.descriptor_dict:
self.descriptor_label_args[pt] = copy(
self.default_descriptor_label_args)
def plot_descriptor_pts(self, mapp, idx, ax, plot_in=None):
"""
Plot descriptor points
:param mapp:
:type mapp:
:param idx:
:type idx:
:param ax: axes object
:param plot_in:
:type plot_in:
.. todo:: __doc__
"""
if getattr(self,'descriptor_dict',None):
self.update_descriptor_args()
xy,rates = zip(*list(mapp))
dim = len(xy[0])
for key in self.descriptor_dict:
pt_kwargs = self.descriptor_pt_args.get(key,
self.default_descriptor_pt_args)
lab_kwargs = self.descriptor_label_args.get(key,
self.default_descriptor_label_args)
if dim == 1: # x will be descriptor values. y will be rate/coverage/etc.
x,y = self.descriptor_dict[key]
y_sp = catmap.spline(plot_in[0], plot_in[1], k=1)
y = y_sp(x)
elif dim == 2:
x,y = self.descriptor_dict[key]
if None not in [x,y]:
if pt_kwargs is not None:
ax.errorbar(x,y,**pt_kwargs)
if lab_kwargs is not None:
ax.annotate(key,[x,y],**lab_kwargs)
if dim == 1:
ax.set_xlim(self.descriptor_ranges[0])
elif dim == 2:
ax.set_xlim(self.descriptor_ranges[0])
ax.set_ylim(self.descriptor_ranges[1])
def plot_single(self, mapp, rxn_index, ax=None,
overlay_map = None, alpha_range=None,
**plot_args):
"""
:param mapp:
:param rxn_index: Index for the reaction
:type rxn_index: int
:param ax: axes object
:param overlay_map:
:type overlay_map:
:type alpha_range:
:type alpha_range:
.. todo:: __doc__
"""
if not ax:
fig = plt.figure()
ax = fig.add_subplot(111)
xy,rates = zip(*list(mapp))
dim = len(xy[0])
if dim == 1:
x = list(zip(*xy))[0]
descriptor_ranges = [[min(x),max(x)]]
if not self.plot_function:
if self.log_scale == True:
self.plot_function = 'semilogy'
else:
self.plot_function = 'plot'
elif dim == 2:
x,y = zip(*xy)
descriptor_ranges = [[min(x),max(x)],[min(y),max(y)]]
if not self.plot_function:
self.plot_function = 'contourf'
if 'cmap' not in plot_args:
plot_args['cmap'] = self.colormap
eff_res =self.resolution*self.resolution_enhancement
if self.min:
minval = self.min
else:
minval = None
maparray = RM.map_to_array(mapp,descriptor_ranges,eff_res,
log_interpolate=self.log_scale,minval=minval)
if self.max is None:
self.max = maparray.T[rxn_index].max()
if self.min is None:
self.min = maparray.T[rxn_index].min()
if dim == 2:
if maparray.min() <= self.min:
plot_args['extend'] = 'min'
if maparray.max() >= self.max:
plot_args['extend'] = 'max'
if maparray.max() >= self.max and maparray.min() <= self.min:
plot_args['extend'] = 'both'
if 'extend' not in plot_args:
plot_args['extend'] = 'neither'
if self.log_scale and dim == 2:
maparray = np.log10(maparray)
min_val = np.log10(float(self.min))
max_val = np.log10(float(self.max))
if min_val < -200:
min_val = max(maparray.min(),-200)
elif max_val == np.inf:
max_val = min(maparray.max(),200)
else:
min_val = self.min
max_val = self.max
maparray = np.clip(maparray,min_val,max_val)
log_scale = self.log_scale
if overlay_map:
overlay_array = RM.map_to_array(overlay_map,
descriptor_ranges,eff_res)
if alpha_range:
alpha_min,alpha_max = alpha_range
else:
alpha_min = overlay_array.min()
alpha_max = overlay_array.max()
overlay_array = (overlay_array - overlay_array.min())
overlay_array = overlay_array/(alpha_max - alpha_min)
overlay_array = np.clip(overlay_array,0,1)
maparray = np.clip(maparray,min_val,max_val)
norm_array = (maparray - maparray.min())
norm_array = norm_array/(maparray.max()-maparray.min())
maparray = norm_array*overlay_array
maparray = (maparray - maparray.min())
maparray = maparray/(maparray.max()-maparray.min())
maparray = maparray*(max_val-min_val) + min_val
maparray=norm_array*overlay_array
norm_array = (maparray - maparray.min())
norm_array = norm_array/(maparray.max()-maparray.min())
maparray = norm_array*(max_val-min_val)+min_val
if dim == 1:
x_range = descriptor_ranges[0]
plot_in = [np.linspace(*x_range+eff_res),maparray[:,rxn_index]]
plot = getattr(ax,self.plot_function)(*plot_in)
elif dim == 2:
x_range,y_range = descriptor_ranges
z = maparray[:,:,rxn_index]
if self.log_scale:
levels = range(int(min_val),int(max_val)+1)
if len(levels) < 3*self.n_ticks:
levels = np.linspace(
int(min_val),int(max_val),3*self.n_ticks)
else:
# python 3 cannot do int < list, thus
# we look at the first element if it is
# a list.
levels = np.linspace(min_val,max_val,min(eff_res if type(eff_res) is int else eff_res[0],25))
plot_in = [np.linspace(*x_range+[eff_res[0]]),
np.linspace(*y_range+[eff_res[1]]),z,levels]
plot = getattr(ax,self.plot_function)(*plot_in,**plot_args)
pos = ax.get_position()
if self.aspect:
ax.set_aspect(self.aspect)
ax.apply_aspect()
if dim == 1:
ax.set_xlim(descriptor_ranges[0])
ax.set_xlabel(self.descriptor_labels[0])
ax.set_ylim([float(self.min), float(self.max)])
elif dim == 2:
if self.colorbar:
if log_scale: #take only integer tick labels
cbar_nums = range(int(min_val),int(max_val)+1)
mod = max(int(len(cbar_nums)/self.n_ticks), 1)
cbar_nums = [n for i,n in enumerate(cbar_nums) if not i%mod]
cbar_nums = np.array(cbar_nums)
else:
cbar_nums = np.linspace(min_val,max_val,self.n_ticks)
formatstring = '%.'+str(self.axis_label_decimals)+'g'
cbar_labels = [formatstring % (s,) for s in cbar_nums]
cbar_labels = [lab.replace('e-0','e-').replace('e+0','e')
for lab in cbar_labels]
plot.set_clim(min_val,max_val)
fig = ax.get_figure()
divider = make_axes_locatable(ax)
cax = divider.append_axes("right", size="5%", pad=0.05)
cbar = fig.colorbar(mappable=plot,ticks=cbar_nums,
cax=cax,extend=plot_args['extend'])
cbar.ax.set_yticklabels(cbar_labels)
if getattr(self,'colorbar_label',None):
cbar_kwargs = getattr(self,'colorbar_label_kwargs',{'rotation':-90})
cbar_ax.set_ylabel(self.colorbar_label,**cbar_kwargs)
if self.descriptor_labels:
ax.set_xlabel(self.descriptor_labels[0])
ax.set_ylabel(self.descriptor_labels[1])
ax.set_xlim(descriptor_ranges[0])
ax.set_ylim(descriptor_ranges[1])
if 'title' in plot_args and plot_args['title']:
if 'title_size' not in plot_args:
n_pts = self.plot_size*72
font_size = min([n_pts/len(plot_args['title']),14])
else:
font_size = plot_args['title_size']
ax.set_title(plot_args['title'],size=font_size)
if getattr(self,'n_xticks',None):
ax.xaxis.set_major_locator(MaxNLocator(self.n_xticks))
if getattr(self,'n_yticks',None):
ax.yaxis.set_major_locator(MaxNLocator(self.n_yticks))
self.plot_descriptor_pts(mapp,rxn_index,ax=ax,plot_in=plot_in)
return ax
def plot_separate(self,mapp,ax_list=None,indices=None,
overlay_map = None,**plot_single_kwargs):
"""
Generate separate plots
.. todo:: __doc__
"""
list_mapp = list(mapp)
pts,rates = list(zip(*list(mapp)))
if indices is None:
indices = range(0,len(rates[0]))
n_plots = len(indices)
if not ax_list:
x = int(np.sqrt(n_plots))
if x*x < n_plots:
y = x+1
else:
y = x
if x*y < n_plots:
x = x+1
if self.colorbar:
fig = plt.figure(
figsize=(y*self.plot_size*1.25,x*self.plot_size))
else:
fig = plt.figure(figsize=(y*self.plot_size,x*self.plot_size))
ax_list = []
for i in range(0,n_plots):
ax_list.append(fig.add_subplot(x,y,i+1))
else:
fig = ax_list[0].get_figure()
if fig:
fig.subplots_adjust(**self.subplots_adjust_kwargs)
else:
fig = plt.gcf()
fig.subplots_adjust(**self.subplots_adjust_kwargs)
plotnum = 0
old_dict = copy(self.__dict__)
if not self.min or not self.max:
for id,i in enumerate(indices):
pts, datas = zip(*list(mapp))
dat_min = 1e99
dat_max = -1e99
for col in zip(*datas):
if min(col) < dat_min:
dat_min = min(col)
if max(col) > dat_max:
dat_max = max(col)
if self.min is None:
self.min = dat_min
if self.max is None:
self.max = dat_max
for id,i in enumerate(indices):
kwargs = plot_single_kwargs
if self.map_plot_labels:
try:
kwargs['title'] = self.map_plot_labels[i]
except IndexError:
kwargs['title'] = ''
kwargs['overlay_map'] = overlay_map
self.__dict__.update(old_dict)
self.plot_single(mapp,i,ax=ax_list[plotnum],**kwargs)
plotnum+=1
return fig
def plot_weighted(self,mapp,ax=None,weighting='linear',
second_map=None,indices=None,**plot_args):
"""
Generate weighted plot
:param mapp:
:type mapp:
:param ax: axes object
:param weighting: weighting function, 'linear' or 'dual'.
:type weighting: str
:param second_map:
:param indices:
.. todo:: __doc__
"""
if ax is None:
fig = plt.figure()
ax = fig.add_subplot(111)
else:
fig = ax.get_figure()
if self.color_list is None:
color_list = get_colors(len(mapp[0][-1])+1)
color_list.pop(0) #remove black
else:
color_list = self.color_list
pts,datas = zip(*list(mapp))
if indices is None:
indices = range(0,len(datas[0]))
rgbs = []
datas = zip(*datas)
datas = [d for id,d in enumerate(datas) if id in indices]
datas = zip(*datas)
if second_map:
pts2,datas2 = zip(*second_map)
datas2 = zip(*datas2)
datas2 = [d for id,d in enumerate(datas2) if id in indices]
datas2 = zip(*datas2)
else:
datas2 = datas
for data,data2 in zip(datas,datas2):
if weighting=='linear':
rs,gs,bs = zip(*color_list)
r = 1 - sum(float((1-ri)*di) for ri,di in zip(rs,data))
g = 1 - sum(float((1-gi)*di) for gi,di in zip(gs,data))
b = 1 - sum(float((1-bi)*di) for bi,di in zip(bs,data))
eff_res = self.resolution*self.resolution_enhancement
rgbs.append([r,g,b])
elif weighting =='dual':
rs,gs,bs = zip(*color_list)
r = 1 - sum(float((1-ri)*di*d2i)
for ri,di,d2i in zip(rs,data,data2))
g = 1 - sum(float((1-gi)*di*d2i)
for gi,di,d2i in zip(gs,data,data2))
b = 1 - sum(float((1-bi)*di*d2i)
for bi,di,d2i in zip(bs,data,data2))
eff_res = 300
rgbs.append([r,g,b])
r,g,b = zip(*rgbs)
x,y = zip(*pts)
xi = np.linspace(min(x),max(x),eff_res)
yi = np.linspace(min(y),max(y),eff_res)
ri = griddata((x,y),r,(xi[None,:],yi[:,None]),method='cubic')
gi = griddata((x,y),g,(xi[None,:],yi[:,None]),method='cubic')
bi = griddata((x,y),b,(xi[None,:],yi[:,None]),method='cubic')
rgb_array = np.zeros((eff_res,eff_res,3))
for i in range(0,eff_res):
for j in range(0,eff_res):
rgb_array[i,j,0] = ri[i,j]
rgb_array[i,j,1] = gi[i,j]
rgb_array[i,j,2] = bi[i,j]
xminmax,yminmax = self.descriptor_ranges
xmin,xmax = xminmax
ymin,ymax = yminmax
ax.imshow(rgb_array,extent=[xmin,xmax,ymin,ymax],origin='lower')
self.plot_descriptor_pts(mapp, i, ax)
if getattr(self,'n_xticks',None):
ax.xaxis.set_major_locator(MaxNLocator(self.n_xticks))
if getattr(self,'n_yticks',None):
ax.yaxis.set_major_locator(MaxNLocator(self.n_yticks))
ax.set_xlabel(self.descriptor_labels[0])
ax.set_ylabel(self.descriptor_labels[1])
if self.aspect:
ax.set_aspect(self.aspect)
ax.apply_aspect()
return fig
def save(self, fig, save=True, default_name='map_plot.pdf'):
"""
:param fig: figure object
:param save: save the figure
:type save: bool
:param default_name: default name for the saved figure.
:type default: str
"""
if save == True:
if not hasattr(self,'output_file'):
save = default_name
else:
save = self.output_file
if save:
fig.savefig(save)
class MechanismPlot:
"""
Class for generating potential energy diagrams
:param energies: list of energies
:type energies: list
:param barriers: list of barriers
:type barriers: list
:param labels: list of labels
:type labels: list
"""
def __init__(self,energies,barriers=[],labels=[]):
self.energies = energies
self.barriers = barriers
self.labels = labels
self.energy_line_args = {'color':'k','lw':2}
self.barrier_line_args = {'color':'k','lw':2}
self.label_args = {'color':'k','size':16,'rotation':45}
self.label_positions= None
self.initial_energy = 0
self.initial_stepnumber = 0
self.energy_mode ='relative' #absolute
self.energy_line_widths = 0.5
def draw(self, ax=None):
"""
Draw the potential energy diagram
.. todo:: __doc__
"""
def attr_to_list(attrname,required_length=len(self.energies)):
"""
Return list of attributes
:param attrname: Name of attributes
:type attrname: list
:param required_length: Required length for the list of attributes
:type required_length: int
.. todo:: __doc__
"""
try:
getattr(self,attrname)[0] #Ensure that it is a list
iter(getattr(self,attrname)) #Ensure that it is a list...
if len(getattr(self,attrname)) == required_length:
pass
else:
raise ValueError(attrname + ' list is of length '+ \
str(len(getattr(self,attrname)))+ \
', but needs to be of length ' + \
str(required_length))
return getattr(self,attrname)
except:
return [getattr(self,attrname)]*required_length
barrier_line_args = attr_to_list('barrier_line_args',
len(self.energies)-1)
energy_line_widths = attr_to_list('energy_line_widths')
energy_line_args = attr_to_list('energy_line_args')
label_args =attr_to_list('label_args')
label_positions=attr_to_list('label_positions')
#plot energy lines
energy_list = np.array(self.energies)
energy_list = (energy_list - energy_list[0])
energy_list = list(energy_list)
if self.energy_mode == 'relative':
cum_energy = [energy_list[0]]
for i,e in enumerate(energy_list[1:]):
last = cum_energy[i]+e
cum_energy.append(last)
energy_list = cum_energy
energy_list = np.array(energy_list) + self.initial_energy
energy_list = list(energy_list)
energy_lines = [
[[i+self.initial_stepnumber,i+width+self.initial_stepnumber],
[energy_list[i]]*2]
for i,width in enumerate(energy_line_widths)]
self.energy_lines = energy_lines
for i,line in enumerate(energy_lines):
ax.plot(*line,**energy_line_args[i])
#create barrier lines
barrier_lines = []
if not self.barriers: self.barriers = [0]*(len(self.energies)-1)
for i,barrier in enumerate(self.barriers):
xi = energy_lines[i][0][1]
xf = energy_lines[i+1][0][0]
yi = energy_lines[i][1][0]
yf = energy_lines[i+1][1][0]
if self.energy_mode == 'relative' and (barrier == 0 or barrier <= yf-yi):
line = [[xi,xf],[yi,yf]]
xts = (xi+xf)/2.
yts = max([yi,yf])
elif self.energy_mode == 'absolute' and (barrier <= yf or barrier <= yi):
line = [[xi,xf],[yi,yf]]
xts = (xi+xf)/2.
yts = max([yi,yf])
else:
if self.energy_mode == 'relative':
yts = yi+barrier
elif self.energy_mode == 'absolute':
yts = barrier
barrier = yts - yi
barrier_rev = barrier + (yi-yf)
if barrier > 0 and barrier_rev > 0:
ratio = np.sqrt(barrier)/(np.sqrt(barrier)+np.sqrt(barrier_rev))
else:
print('Warning: Encountered barrier less than 0')
ratio = 0.0001
yts = max(yi,yf)
xts = xi + ratio*(xf-xi)
xs = [xi,xts,xf]
ys = [yi,yts,yf]
f = spline(xs,ys,k=2)
newxs = np.linspace(xi,xf,20)
newys = f(newxs)
line = [newxs,newys]
barrier_lines.append(line)
self.barrier_lines = barrier_lines
#plot barrier lines
for i,line in enumerate(barrier_lines):
ax.plot(*line,**barrier_line_args[i])
#add labels
trans = ax.get_xaxis_transform()
for i,label in enumerate(self.labels):
xpos = sum(energy_lines[i][0])/len(energy_lines[i][0])
label_position = label_positions[i]
args = label_args[i]
if label_position in ['top','ymax']:
if 'ha' not in args:
args['ha'] = 'left'
if 'va' not in args:
args['va'] = 'bottom'
ypos = 1
args['transform'] = trans
ax.text(xpos,ypos,label,**args)
elif label_position in ['bot','bottom','ymin']:
ypos = -0.1
ax.xaxis.set_ticks([float(sum(line[0])/len(line[0]))
for line in energy_lines])
ax.set_xticklabels(self.labels)
for attr in args.keys():
try:
[getattr(t,'set_'+attr)(args[attr])
for t in ax.xaxis.get_ticklabels()]
except:
pass
elif label_position in ['omit']:
pass
else:
ypos = energy_lines[i][1][0]
if 'ha' not in args:# and 'textcoords' not in args:
args['ha'] = 'left'
if 'va' not in args:# and 'textcoords' not in args:
args['va'] = 'bottom'
ax.annotate(label,[xpos,ypos],**args)
class ScalingPlot:
"""
:param descriptor_names: list of descriptor names
:type descriptor_names: list
:param descriptor_dict: dictionary of descriptors
:type descriptor_dict: dict
:param surface_names: list of the surface names
:type surface_names: list
:param parameter_dict: dictionary of parameters
:type parameter_dict: dict
:param scaling_function: function to project descriptors into energies.
Should take descriptors as an argument and return a
dictionary of {adsorbate:energy} pairs.
:type scaling_function: function
:param x_axis_function: function to project descriptors onto the x-axis.
Should take descriptors as an argument and return a
dictionary of {adsorbate:x_value} pairs.
:type x_axis_function: function
:param scaling_function_kwargs: keyword arguments for scaling_function.
:type scaling_function_kwargs: dict
:param x_axis_function_kwargs: keyword arguments for x_axis_function.
:type x_axis_function_kwargs: dict
"""
def __init__(self,descriptor_names,descriptor_dict,surface_names,
parameter_dict,scaling_function,x_axis_function,
scaling_function_kwargs={},x_axis_function_kwargs={},
):
self.descriptor_names = descriptor_names
self.surface_names = surface_names
self.descriptor_dict = descriptor_dict
self.parameter_dict = parameter_dict
self.scaling_function = scaling_function
self.scaling_function_kwargs = scaling_function_kwargs
self.x_axis_function = x_axis_function
self.x_axis_function_kwargs = x_axis_function_kwargs
self.axis_label_size = 16
self.surface_label_size = 16
self.title_size = 18
self.same_scale = True
self.show_titles = True
self.show_surface_labels = True
self.subplots_adjust_kwargs = {'wspace':0.4,'hspace':0.4}
self.x_label_dict = {}
self.y_label_dict = {}
self.surface_colors = []
self.scaling_line_args = {}
self.label_args = {}
self.line_args = {}
self.include_empty = True
self.include_error_histogram = True
def plot(self, ax_list=None, plot_size=4.0, save=None):
"""
:param ax_list: list of axes objects
:type ax_list: [ax]
:param plot_size: size of the plot
:type plot_size: float
:param save: whether or not to save the plot
:type save: bool
.. todo:: __doc__
"""
all_ads = self.adsorbate_names + self.transition_state_names
all_ads = [a for a in all_ads if a in self.parameter_dict.keys() and
a not in self.echem_transition_state_names]
if self.include_empty:
ads_names = all_ads
else:
ads_names = [n for n in all_ads if
(None in self.parameter_dict[n] or
sum(self.parameter_dict[n])>0.0)]
if not self.surface_colors:
self.surface_colors = get_colors(len(self.surface_names))
if not self.scaling_line_args:
self.scaling_line_args = [{'color':'k'}]*len(ads_names)
elif hasattr(self.scaling_line_args,'update'): #its a dictionary if so.
self.scaling_line_args = [self.scaling_line_args]*len(
self.adsorbate_names)
for d in self.descriptor_names:
if not self.include_descriptors:
if d in ads_names:
ads_names.remove(d)
if self.include_error_histogram:
extra = 1
else:
extra = 0
if not ax_list:
spx = round(np.sqrt(len(ads_names)+extra))
spy = round(np.sqrt(len(ads_names)+extra))
if spy*spx < len(ads_names)+extra:
spy+= 1
fig = plt.figure(figsize=(spy*plot_size,spx*plot_size))
ax_list = [fig.add_subplot(spx,spy,i+1)
for i in range(len(ads_names))]
else:
fig = None
all_xs, all_ys = zip(*[self.descriptor_dict[s]
for s in self.surface_names])
fig.subplots_adjust(**self.subplots_adjust_kwargs)
all_ys = []
maxyrange = 0
ymins = []
all_err = []
for i,ads in enumerate(ads_names):
actual_y_vals = self.parameter_dict[ads]
desc_vals = [self.descriptor_dict[s] for s in self.surface_names]
scaled_x_vals = [self.x_axis_function(
d,**self.x_axis_function_kwargs)[0][ads] for d in desc_vals]
label = self.x_axis_function(
desc_vals[0],**self.x_axis_function_kwargs)[-1][ads]
scaled_y_vals = [self.scaling_function(
d,**self.scaling_function_kwargs)[ads] for d in desc_vals]
diffs = [scaled-actual for scaled,actual
in zip(scaled_y_vals,actual_y_vals) if actual != None]
ax = ax_list[i]
m,b = plt.polyfit(scaled_x_vals,scaled_y_vals,1)
x_vals = np.array([round(min(scaled_x_vals),1)-0.1,
round(max(scaled_x_vals),1)+0.1])
ax.plot(x_vals,m*x_vals+b,**self.scaling_line_args[i])
err = [yi - (m*xi+b) for xi,yi in zip(scaled_x_vals,actual_y_vals) if yi != None]
all_err += err
ax.set_xlabel(label)
ax.set_ylabel('$E_{'+ads+'}$ [eV]')
num_y_vals = []
# for s,c in zip(self.surface_names,self.surface_colors):
# print s, c
for sf,col,x,y in zip(self.surface_names,
self.surface_colors,scaled_x_vals,actual_y_vals):
if y and y != None:
ax.plot(x,y,'o',color=col,markersize=10,mec=None)
if self.show_surface_labels:
ax.annotate(sf,[x,y],color=col,**self.label_args)
num_y_vals.append(y)
if self.show_titles:
ax.set_title('$'+ads+'$',size=self.title_size)
all_ys += num_y_vals
if not num_y_vals: num_y_vals = scaled_y_vals
dy = max(num_y_vals) - min(num_y_vals)
ymins.append([min(num_y_vals),max(num_y_vals)])
if dy > maxyrange:
maxyrange = dy
ax.set_xlim(x_vals)
y_range = [round(min(num_y_vals),1)-0.1,
round(max(num_y_vals),1)+0.1]
self.scaling_error = all_err
if self.same_scale == True:
for i,ax in enumerate(ax_list):
pad = maxyrange - (ymins[i][1]-ymins[i][0])
y_range = [round(ymins[i][0]-pad,1)-0.1,
round(ymins[i][1]+pad,1)+0.1]
ax.set_ylim(y_range)
if self.include_error_histogram:
err_ax = fig.add_subplot(spx,spy,len(ads_names)+1)
err_ax.hist(all_err,bins=15)
err_ax.set_xlabel('$E_{actual} - E_{scaled}$ [eV]')
err_ax.set_ylabel('Counts')
ax_list.append(err_ax)
for ax in ax_list:
if getattr(self,'n_xticks',None):
ax.xaxis.set_major_locator(MaxNLocator(self.n_xticks))
if getattr(self,'n_yticks',None):
ax.yaxis.set_major_locator(MaxNLocator(self.n_yticks))
if save is None:
save = self.model_name+'_scaling.pdf'
if save:
fig.savefig(save)
return fig
| gpl-3.0 |
detrout/debian-statsmodels | statsmodels/sandbox/panel/panelmod.py | 27 | 14526 | """
Sandbox Panel Estimators
References
-----------
Baltagi, Badi H. `Econometric Analysis of Panel Data.` 4th ed. Wiley, 2008.
"""
from __future__ import print_function
from statsmodels.compat.python import range, reduce
from statsmodels.tools.tools import categorical
from statsmodels.regression.linear_model import GLS, WLS
import numpy as np
__all__ = ["PanelModel"]
from pandas import LongPanel, __version__
def group(X):
"""
Returns unique numeric values for groups without sorting.
Examples
--------
>>> X = np.array(['a','a','b','c','b','c'])
>>> group(X)
>>> g
array([ 0., 0., 1., 2., 1., 2.])
"""
uniq_dict = {}
group = np.zeros(len(X))
for i in range(len(X)):
if not X[i] in uniq_dict:
uniq_dict.update({X[i] : len(uniq_dict)})
group[i] = uniq_dict[X[i]]
return group
def repanel_cov(groups, sigmas):
'''calculate error covariance matrix for random effects model
Parameters
----------
groups : array, (nobs, nre) or (nobs,)
array of group/category observations
sigma : array, (nre+1,)
array of standard deviations of random effects,
last element is the standard deviation of the
idiosyncratic error
Returns
-------
omega : array, (nobs, nobs)
covariance matrix of error
omegainv : array, (nobs, nobs)
inverse covariance matrix of error
omegainvsqrt : array, (nobs, nobs)
squareroot inverse covariance matrix of error
such that omega = omegainvsqrt * omegainvsqrt.T
Notes
-----
This does not use sparse matrices and constructs nobs by nobs
matrices. Also, omegainvsqrt is not sparse, i.e. elements are non-zero
'''
if groups.ndim == 1:
groups = groups[:,None]
nobs, nre = groups.shape
omega = sigmas[-1]*np.eye(nobs)
for igr in range(nre):
group = groups[:,igr:igr+1]
groupuniq = np.unique(group)
dummygr = sigmas[igr] * (group == groupuniq).astype(float)
omega += np.dot(dummygr, dummygr.T)
ev, evec = np.linalg.eigh(omega) #eig doesn't work
omegainv = np.dot(evec, (1/ev * evec).T)
omegainvhalf = evec/np.sqrt(ev)
return omega, omegainv, omegainvhalf
class PanelData(LongPanel):
pass
class PanelModel(object):
"""
An abstract statistical model class for panel (longitudinal) datasets.
Parameters
---------
endog : array-like or str
If a pandas object is used then endog should be the name of the
endogenous variable as a string.
# exog
# panel_arr
# time_arr
panel_data : pandas.LongPanel object
Notes
-----
If a pandas object is supplied it is assumed that the major_axis is time
and that the minor_axis has the panel variable.
"""
def __init__(self, endog=None, exog=None, panel=None, time=None,
xtnames=None, equation=None, panel_data=None):
if panel_data == None:
# if endog == None and exog == None and panel == None and \
# time == None:
# raise ValueError("If pandel_data is False then endog, exog, \
#panel_arr, and time_arr cannot be None.")
self.initialize(endog, exog, panel, time, xtnames, equation)
# elif aspandas != False:
# if not isinstance(endog, str):
# raise ValueError("If a pandas object is supplied then endog \
#must be a string containing the name of the endogenous variable")
# if not isinstance(aspandas, LongPanel):
# raise ValueError("Only pandas.LongPanel objects are supported")
# self.initialize_pandas(endog, aspandas, panel_name)
def initialize(self, endog, exog, panel, time, xtnames, equation):
"""
Initialize plain array model.
See PanelModel
"""
#TODO: for now, we are going assume a constant, and then make the first
#panel the base, add a flag for this....
# get names
names = equation.split(" ")
self.endog_name = names[0]
exog_names = names[1:] # this makes the order matter in the array
self.panel_name = xtnames[0]
self.time_name = xtnames[1]
novar = exog.var(0) == 0
if True in novar:
cons_index = np.where(novar == 1)[0][0] # constant col. num
exog_names.insert(cons_index, 'cons')
self._cons_index = novar # used again in fit_fixed
self.exog_names = exog_names
self.endog = np.squeeze(np.asarray(endog))
exog = np.asarray(exog)
self.exog = exog
self.panel = np.asarray(panel)
self.time = np.asarray(time)
self.paneluniq = np.unique(panel)
self.timeuniq = np.unique(time)
#TODO: this structure can possibly be extracted somewhat to deal with
#names in general
#TODO: add some dimension checks, etc.
# def initialize_pandas(self, endog, aspandas):
# """
# Initialize pandas objects.
#
# See PanelModel.
# """
# self.aspandas = aspandas
# endog = aspandas[endog].values
# self.endog = np.squeeze(endog)
# exog_name = aspandas.columns.tolist()
# exog_name.remove(endog)
# self.exog = aspandas.filterItems(exog_name).values
#TODO: can the above be simplified to slice notation?
# if panel_name != None:
# self.panel_name = panel_name
# self.exog_name = exog_name
# self.endog_name = endog
# self.time_arr = aspandas.major_axis
#TODO: is time always handled correctly in fromRecords?
# self.panel_arr = aspandas.minor_axis
#TODO: all of this might need to be refactored to explicitly rely (internally)
# on the pandas LongPanel structure for speed and convenience.
# not sure this part is finished...
#TODO: doesn't conform to new initialize
def initialize_pandas(self, panel_data, endog_name, exog_name):
self.panel_data = panel_data
endog = panel_data[endog_name].values # does this create a copy?
self.endog = np.squeeze(endog)
if exog_name == None:
exog_name = panel_data.columns.tolist()
exog_name.remove(endog_name)
self.exog = panel_data.filterItems(exog_name).values # copy?
self._exog_name = exog_name
self._endog_name = endog_name
self._timeseries = panel_data.major_axis # might not need these
self._panelseries = panel_data.minor_axis
#TODO: this could be pulled out and just have a by kwd that takes
# the panel or time array
#TODO: this also needs to be expanded for 'twoway'
def _group_mean(self, X, index='oneway', counts=False, dummies=False):
"""
Get group means of X by time or by panel.
index default is panel
"""
if index == 'oneway':
Y = self.panel
uniq = self.paneluniq
elif index == 'time':
Y = self.time
uniq = self.timeuniq
else:
raise ValueError("index %s not understood" % index)
#TODO: use sparse matrices
dummy = (Y == uniq[:,None]).astype(float)
if X.ndim > 1:
mean = np.dot(dummy,X)/dummy.sum(1)[:,None]
else:
mean = np.dot(dummy,X)/dummy.sum(1)
if counts == False and dummies == False:
return mean
elif counts == True and dummies == False:
return mean, dummy.sum(1)
elif counts == True and dummies == True:
return mean, dummy.sum(1), dummy
elif counts == False and dummies == True:
return mean, dummy
#TODO: Use kwd arguments or have fit_method methods?
def fit(self, model=None, method=None, effects='oneway'):
"""
method : LSDV, demeaned, MLE, GLS, BE, FE, optional
model :
between
fixed
random
pooled
[gmm]
effects :
oneway
time
twoway
femethod : demeaned (only one implemented)
WLS
remethod :
swar -
amemiya
nerlove
walhus
Notes
------
This is unfinished. None of the method arguments work yet.
Only oneway effects should work.
"""
if method: # get rid of this with default
method = method.lower()
model = model.lower()
if method and method not in ["lsdv", "demeaned", "mle", "gls", "be",
"fe"]: # get rid of if method with default
raise ValueError("%s not a valid method" % method)
# if method == "lsdv":
# self.fit_lsdv(model)
if model == 'pooled':
return GLS(self.endog, self.exog).fit()
if model == 'between':
return self._fit_btwn(method, effects)
if model == 'fixed':
return self._fit_fixed(method, effects)
# def fit_lsdv(self, effects):
# """
# Fit using least squares dummy variables.
#
# Notes
# -----
# Should only be used for small `nobs`.
# """
# pdummies = None
# tdummies = None
def _fit_btwn(self, method, effects):
# group mean regression or WLS
if effects != "twoway":
endog = self._group_mean(self.endog, index=effects)
exog = self._group_mean(self.exog, index=effects)
else:
raise ValueError("%s effects is not valid for the between \
estimator" % s)
befit = GLS(endog, exog).fit()
return befit
def _fit_fixed(self, method, effects):
endog = self.endog
exog = self.exog
demeantwice = False
if effects in ["oneway","twoways"]:
if effects == "twoways":
demeantwice = True
effects = "oneway"
endog_mean, counts = self._group_mean(endog, index=effects,
counts=True)
exog_mean = self._group_mean(exog, index=effects)
counts = counts.astype(int)
endog = endog - np.repeat(endog_mean, counts)
exog = exog - np.repeat(exog_mean, counts, axis=0)
if demeantwice or effects == "time":
endog_mean, dummies = self._group_mean(endog, index="time",
dummies=True)
exog_mean = self._group_mean(exog, index="time")
# This allows unbalanced panels
endog = endog - np.dot(endog_mean, dummies)
exog = exog - np.dot(dummies.T, exog_mean)
fefit = GLS(endog, exog[:,-self._cons_index]).fit()
#TODO: might fail with one regressor
return fefit
class SURPanel(PanelModel):
pass
class SEMPanel(PanelModel):
pass
class DynamicPanel(PanelModel):
pass
if __name__ == "__main__":
import pandas
from pandas import LongPanel
import statsmodels.api as sm
import numpy.lib.recfunctions as nprf
data = sm.datasets.grunfeld.load()
# Baltagi doesn't include American Steel
endog = data.endog[:-20]
fullexog = data.exog[:-20]
# fullexog.sort(order=['firm','year'])
panel_arr = nprf.append_fields(fullexog, 'investment', endog, float,
usemask=False)
panel_panda = LongPanel.fromRecords(panel_arr, major_field='year',
minor_field='firm')
# the most cumbersome way of doing it as far as preprocessing by hand
exog = fullexog[['value','capital']].view(float).reshape(-1,2)
exog = sm.add_constant(exog, prepend=False)
panel = group(fullexog['firm'])
year = fullexog['year']
panel_mod = PanelModel(endog, exog, panel, year, xtnames=['firm','year'],
equation='invest value capital')
# note that equation doesn't actually do anything but name the variables
panel_ols = panel_mod.fit(model='pooled')
panel_be = panel_mod.fit(model='between', effects='oneway')
panel_fe = panel_mod.fit(model='fixed', effects='oneway')
panel_bet = panel_mod.fit(model='between', effects='time')
panel_fet = panel_mod.fit(model='fixed', effects='time')
panel_fe2 = panel_mod.fit(model='fixed', effects='twoways')
#see also Baltagi (3rd edt) 3.3 THE RANDOM EFFECTS MODEL p.35
#for explicit formulas for spectral decomposition
#but this works also for unbalanced panel
#
#I also just saw: 9.4.2 The Random Effects Model p.176 which is
#partially almost the same as I did
#
#this needs to use sparse matrices for larger datasets
#
#"""
#
#import numpy as np
#
groups = np.array([0,0,0,1,1,2,2,2])
nobs = groups.shape[0]
groupuniq = np.unique(groups)
periods = np.array([0,1,2,1,2,0,1,2])
perioduniq = np.unique(periods)
dummygr = (groups[:,None] == groupuniq).astype(float)
dummype = (periods[:,None] == perioduniq).astype(float)
sigma = 1.
sigmagr = np.sqrt(2.)
sigmape = np.sqrt(3.)
#dummyall = np.c_[sigma*np.ones((nobs,1)), sigmagr*dummygr,
# sigmape*dummype]
#exclude constant ?
dummyall = np.c_[sigmagr*dummygr, sigmape*dummype]
# omega is the error variance-covariance matrix for the stacked
# observations
omega = np.dot(dummyall, dummyall.T) + sigma* np.eye(nobs)
print(omega)
print(np.linalg.cholesky(omega))
ev, evec = np.linalg.eigh(omega) #eig doesn't work
omegainv = np.dot(evec, (1/ev * evec).T)
omegainv2 = np.linalg.inv(omega)
omegacomp = np.dot(evec, (ev * evec).T)
print(np.max(np.abs(omegacomp - omega)))
#check
#print(np.dot(omegainv,omega)
print(np.max(np.abs(np.dot(omegainv,omega) - np.eye(nobs))))
omegainvhalf = evec/np.sqrt(ev) #not sure whether ev shouldn't be column
print(np.max(np.abs(np.dot(omegainvhalf,omegainvhalf.T) - omegainv)))
# now we can use omegainvhalf in GLS (instead of the cholesky)
sigmas2 = np.array([sigmagr, sigmape, sigma])
groups2 = np.column_stack((groups, periods))
omega_, omegainv_, omegainvhalf_ = repanel_cov(groups2, sigmas2)
print(np.max(np.abs(omega_ - omega)))
print(np.max(np.abs(omegainv_ - omegainv)))
print(np.max(np.abs(omegainvhalf_ - omegainvhalf)))
# notation Baltagi (3rd) section 9.4.1 (Fixed Effects Model)
Pgr = reduce(np.dot,[dummygr,
np.linalg.inv(np.dot(dummygr.T, dummygr)),dummygr.T])
Qgr = np.eye(nobs) - Pgr
# within group effect: np.dot(Qgr, groups)
# but this is not memory efficient, compared to groupstats
print(np.max(np.abs(np.dot(Qgr, groups))))
| bsd-3-clause |
liyi193328/seq2seq | seq2seq/contrib/learn/learn_io/data_feeder_test.py | 71 | 12923 | # Copyright 2016 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Tests for `DataFeeder`."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import six
from six.moves import xrange # pylint: disable=redefined-builtin
# pylint: disable=wildcard-import
from tensorflow.contrib.learn.python.learn.learn_io import *
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import ops
from tensorflow.python.platform import test
# pylint: enable=wildcard-import
class DataFeederTest(test.TestCase):
# pylint: disable=undefined-variable
"""Tests for `DataFeeder`."""
def _wrap_dict(self, data, prepend=''):
return {prepend + '1': data, prepend + '2': data}
def _assert_raises(self, input_data):
with self.assertRaisesRegexp(TypeError, 'annot convert'):
data_feeder.DataFeeder(input_data, None, n_classes=0, batch_size=1)
def test_input_uint32(self):
data = np.matrix([[1, 2], [3, 4]], dtype=np.uint32)
self._assert_raises(data)
self._assert_raises(self._wrap_dict(data))
def test_input_uint64(self):
data = np.matrix([[1, 2], [3, 4]], dtype=np.uint64)
self._assert_raises(data)
self._assert_raises(self._wrap_dict(data))
def _assert_dtype(self, expected_np_dtype, expected_tf_dtype, input_data):
feeder = data_feeder.DataFeeder(input_data, None, n_classes=0, batch_size=1)
if isinstance(input_data, dict):
for k, v in list(feeder.input_dtype.items()):
self.assertEqual(expected_np_dtype, v)
else:
self.assertEqual(expected_np_dtype, feeder.input_dtype)
with ops.Graph().as_default() as g, self.test_session(g):
inp, _ = feeder.input_builder()
if isinstance(inp, dict):
for k, v in list(inp.items()):
self.assertEqual(expected_tf_dtype, v.dtype)
else:
self.assertEqual(expected_tf_dtype, inp.dtype)
def test_input_int8(self):
data = np.matrix([[1, 2], [3, 4]], dtype=np.int8)
self._assert_dtype(np.int8, dtypes.int8, data)
self._assert_dtype(np.int8, dtypes.int8, self._wrap_dict(data))
def test_input_int16(self):
data = np.matrix([[1, 2], [3, 4]], dtype=np.int16)
self._assert_dtype(np.int16, dtypes.int16, data)
self._assert_dtype(np.int16, dtypes.int16, self._wrap_dict(data))
def test_input_int32(self):
data = np.matrix([[1, 2], [3, 4]], dtype=np.int32)
self._assert_dtype(np.int32, dtypes.int32, data)
self._assert_dtype(np.int32, dtypes.int32, self._wrap_dict(data))
def test_input_int64(self):
data = np.matrix([[1, 2], [3, 4]], dtype=np.int64)
self._assert_dtype(np.int64, dtypes.int64, data)
self._assert_dtype(np.int64, dtypes.int64, self._wrap_dict(data))
def test_input_uint8(self):
data = np.matrix([[1, 2], [3, 4]], dtype=np.uint8)
self._assert_dtype(np.uint8, dtypes.uint8, data)
self._assert_dtype(np.uint8, dtypes.uint8, self._wrap_dict(data))
def test_input_uint16(self):
data = np.matrix([[1, 2], [3, 4]], dtype=np.uint16)
self._assert_dtype(np.uint16, dtypes.uint16, data)
self._assert_dtype(np.uint16, dtypes.uint16, self._wrap_dict(data))
def test_input_float16(self):
data = np.matrix([[1, 2], [3, 4]], dtype=np.float16)
self._assert_dtype(np.float16, dtypes.float16, data)
self._assert_dtype(np.float16, dtypes.float16, self._wrap_dict(data))
def test_input_float32(self):
data = np.matrix([[1, 2], [3, 4]], dtype=np.float32)
self._assert_dtype(np.float32, dtypes.float32, data)
self._assert_dtype(np.float32, dtypes.float32, self._wrap_dict(data))
def test_input_float64(self):
data = np.matrix([[1, 2], [3, 4]], dtype=np.float64)
self._assert_dtype(np.float64, dtypes.float64, data)
self._assert_dtype(np.float64, dtypes.float64, self._wrap_dict(data))
def test_input_bool(self):
data = np.array([[False for _ in xrange(2)] for _ in xrange(2)])
self._assert_dtype(np.bool, dtypes.bool, data)
self._assert_dtype(np.bool, dtypes.bool, self._wrap_dict(data))
def test_input_string(self):
input_data = np.array([['str%d' % i for i in xrange(2)] for _ in xrange(2)])
self._assert_dtype(input_data.dtype, dtypes.string, input_data)
self._assert_dtype(input_data.dtype, dtypes.string,
self._wrap_dict(input_data))
def _assertAllClose(self, src, dest, src_key_of=None, src_prop=None):
def func(x):
val = getattr(x, src_prop) if src_prop else x
return val if src_key_of is None else src_key_of[val]
if isinstance(src, dict):
for k in list(src.keys()):
self.assertAllClose(func(src[k]), dest)
else:
self.assertAllClose(func(src), dest)
def test_unsupervised(self):
def func(feeder):
with self.test_session():
inp, _ = feeder.input_builder()
feed_dict_fn = feeder.get_feed_dict_fn()
feed_dict = feed_dict_fn()
self._assertAllClose(inp, [[1, 2]], feed_dict, 'name')
data = np.matrix([[1, 2], [2, 3], [3, 4]])
func(data_feeder.DataFeeder(data, None, n_classes=0, batch_size=1))
func(
data_feeder.DataFeeder(
self._wrap_dict(data), None, n_classes=0, batch_size=1))
def test_data_feeder_regression(self):
def func(df):
inp, out = df.input_builder()
feed_dict_fn = df.get_feed_dict_fn()
feed_dict = feed_dict_fn()
self._assertAllClose(inp, [[3, 4], [1, 2]], feed_dict, 'name')
self._assertAllClose(out, [2, 1], feed_dict, 'name')
x = np.matrix([[1, 2], [3, 4]])
y = np.array([1, 2])
func(data_feeder.DataFeeder(x, y, n_classes=0, batch_size=3))
func(
data_feeder.DataFeeder(
self._wrap_dict(x, 'in'),
self._wrap_dict(y, 'out'),
n_classes=self._wrap_dict(0, 'out'),
batch_size=3))
def test_epoch(self):
def func(feeder):
with self.test_session():
feeder.input_builder()
epoch = feeder.make_epoch_variable()
feed_dict_fn = feeder.get_feed_dict_fn()
# First input
feed_dict = feed_dict_fn()
self.assertAllClose(feed_dict[epoch.name], [0])
# Second input
feed_dict = feed_dict_fn()
self.assertAllClose(feed_dict[epoch.name], [0])
# Third input
feed_dict = feed_dict_fn()
self.assertAllClose(feed_dict[epoch.name], [0])
# Back to the first input again, so new epoch.
feed_dict = feed_dict_fn()
self.assertAllClose(feed_dict[epoch.name], [1])
data = np.matrix([[1, 2], [2, 3], [3, 4]])
labels = np.array([0, 0, 1])
func(data_feeder.DataFeeder(data, labels, n_classes=0, batch_size=1))
func(
data_feeder.DataFeeder(
self._wrap_dict(data, 'in'),
self._wrap_dict(labels, 'out'),
n_classes=self._wrap_dict(0, 'out'),
batch_size=1))
def test_data_feeder_multioutput_regression(self):
def func(df):
inp, out = df.input_builder()
feed_dict_fn = df.get_feed_dict_fn()
feed_dict = feed_dict_fn()
self._assertAllClose(inp, [[3, 4], [1, 2]], feed_dict, 'name')
self._assertAllClose(out, [[3, 4], [1, 2]], feed_dict, 'name')
x = np.matrix([[1, 2], [3, 4]])
y = np.array([[1, 2], [3, 4]])
func(data_feeder.DataFeeder(x, y, n_classes=0, batch_size=2))
func(
data_feeder.DataFeeder(
self._wrap_dict(x, 'in'),
self._wrap_dict(y, 'out'),
n_classes=self._wrap_dict(0, 'out'),
batch_size=2))
def test_data_feeder_multioutput_classification(self):
def func(df):
inp, out = df.input_builder()
feed_dict_fn = df.get_feed_dict_fn()
feed_dict = feed_dict_fn()
self._assertAllClose(inp, [[3, 4], [1, 2]], feed_dict, 'name')
self._assertAllClose(
out, [[[0, 0, 1, 0, 0], [0, 0, 0, 1, 0], [0, 0, 0, 0, 1]],
[[1, 0, 0, 0, 0], [0, 1, 0, 0, 0], [0, 0, 1, 0, 0]]], feed_dict,
'name')
x = np.matrix([[1, 2], [3, 4]])
y = np.array([[0, 1, 2], [2, 3, 4]])
func(data_feeder.DataFeeder(x, y, n_classes=5, batch_size=2))
func(
data_feeder.DataFeeder(
self._wrap_dict(x, 'in'),
self._wrap_dict(y, 'out'),
n_classes=self._wrap_dict(5, 'out'),
batch_size=2))
def test_streaming_data_feeder(self):
def func(df):
inp, out = df.input_builder()
feed_dict_fn = df.get_feed_dict_fn()
feed_dict = feed_dict_fn()
self._assertAllClose(inp, [[[1, 2]], [[3, 4]]], feed_dict, 'name')
self._assertAllClose(out, [[[1], [2]], [[2], [2]]], feed_dict, 'name')
def x_iter(wrap_dict=False):
yield np.array([[1, 2]]) if not wrap_dict else self._wrap_dict(
np.array([[1, 2]]), 'in')
yield np.array([[3, 4]]) if not wrap_dict else self._wrap_dict(
np.array([[3, 4]]), 'in')
def y_iter(wrap_dict=False):
yield np.array([[1], [2]]) if not wrap_dict else self._wrap_dict(
np.array([[1], [2]]), 'out')
yield np.array([[2], [2]]) if not wrap_dict else self._wrap_dict(
np.array([[2], [2]]), 'out')
func(
data_feeder.StreamingDataFeeder(
x_iter(), y_iter(), n_classes=0, batch_size=2))
func(
data_feeder.StreamingDataFeeder(
x_iter(True),
y_iter(True),
n_classes=self._wrap_dict(0, 'out'),
batch_size=2))
# Test non-full batches.
func(
data_feeder.StreamingDataFeeder(
x_iter(), y_iter(), n_classes=0, batch_size=10))
func(
data_feeder.StreamingDataFeeder(
x_iter(True),
y_iter(True),
n_classes=self._wrap_dict(0, 'out'),
batch_size=10))
def test_dask_data_feeder(self):
if HAS_PANDAS and HAS_DASK:
x = pd.DataFrame(
dict(
a=np.array([.1, .3, .4, .6, .2, .1, .6]),
b=np.array([.7, .8, .1, .2, .5, .3, .9])))
x = dd.from_pandas(x, npartitions=2)
y = pd.DataFrame(dict(labels=np.array([1, 0, 2, 1, 0, 1, 2])))
y = dd.from_pandas(y, npartitions=2)
# TODO(ipolosukhin): Remove or restore this.
# x = extract_dask_data(x)
# y = extract_dask_labels(y)
df = data_feeder.DaskDataFeeder(x, y, n_classes=2, batch_size=2)
inp, out = df.input_builder()
feed_dict_fn = df.get_feed_dict_fn()
feed_dict = feed_dict_fn()
self.assertAllClose(feed_dict[inp.name], [[0.40000001, 0.1],
[0.60000002, 0.2]])
self.assertAllClose(feed_dict[out.name], [[0., 0., 1.], [0., 1., 0.]])
def test_hdf5_data_feeder(self):
def func(df):
inp, out = df.input_builder()
feed_dict_fn = df.get_feed_dict_fn()
feed_dict = feed_dict_fn()
self._assertAllClose(inp, [[3, 4], [1, 2]], feed_dict, 'name')
self.assertAllClose(out, [2, 1], feed_dict, 'name')
try:
import h5py # pylint: disable=g-import-not-at-top
x = np.matrix([[1, 2], [3, 4]])
y = np.array([1, 2])
h5f = h5py.File('test_hdf5.h5', 'w')
h5f.create_dataset('x', data=x)
h5f.create_dataset('y', data=y)
h5f.close()
h5f = h5py.File('test_hdf5.h5', 'r')
x = h5f['x']
y = h5f['y']
func(data_feeder.DataFeeder(x, y, n_classes=0, batch_size=3))
func(
data_feeder.DataFeeder(
self._wrap_dict(x, 'in'),
self._wrap_dict(y, 'out'),
n_classes=self._wrap_dict(0, 'out'),
batch_size=3))
except ImportError:
print("Skipped test for hdf5 since it's not installed.")
class SetupPredictDataFeederTest(DataFeederTest):
"""Tests for `DataFeeder.setup_predict_data_feeder`."""
def test_iterable_data(self):
# pylint: disable=undefined-variable
def func(df):
self._assertAllClose(six.next(df), [[1, 2], [3, 4]])
self._assertAllClose(six.next(df), [[5, 6]])
data = [[1, 2], [3, 4], [5, 6]]
x = iter(data)
x_dict = iter([self._wrap_dict(v) for v in iter(data)])
func(data_feeder.setup_predict_data_feeder(x, batch_size=2))
func(data_feeder.setup_predict_data_feeder(x_dict, batch_size=2))
if __name__ == '__main__':
test.main()
| apache-2.0 |
ningchi/scikit-learn | examples/calibration/plot_calibration_curve.py | 225 | 5903 | """
==============================
Probability Calibration curves
==============================
When performing classification one often wants to predict not only the class
label, but also the associated probability. This probability gives some
kind of confidence on the prediction. This example demonstrates how to display
how well calibrated the predicted probabilities are and how to calibrate an
uncalibrated classifier.
The experiment is performed on an artificial dataset for binary classification
with 100.000 samples (1.000 of them are used for model fitting) with 20
features. Of the 20 features, only 2 are informative and 10 are redundant. The
first figure shows the estimated probabilities obtained with logistic
regression, Gaussian naive Bayes, and Gaussian naive Bayes with both isotonic
calibration and sigmoid calibration. The calibration performance is evaluated
with Brier score, reported in the legend (the smaller the better). One can
observe here that logistic regression is well calibrated while raw Gaussian
naive Bayes performs very badly. This is because of the redundant features
which violate the assumption of feature-independence and result in an overly
confident classifier, which is indicated by the typical transposed-sigmoid
curve.
Calibration of the probabilities of Gaussian naive Bayes with isotonic
regression can fix this issue as can be seen from the nearly diagonal
calibration curve. Sigmoid calibration also improves the brier score slightly,
albeit not as strongly as the non-parametric isotonic regression. This can be
attributed to the fact that we have plenty of calibration data such that the
greater flexibility of the non-parametric model can be exploited.
The second figure shows the calibration curve of a linear support-vector
classifier (LinearSVC). LinearSVC shows the opposite behavior as Gaussian
naive Bayes: the calibration curve has a sigmoid curve, which is typical for
an under-confident classifier. In the case of LinearSVC, this is caused by the
margin property of the hinge loss, which lets the model focus on hard samples
that are close to the decision boundary (the support vectors).
Both kinds of calibration can fix this issue and yield nearly identical
results. This shows that sigmoid calibration can deal with situations where
the calibration curve of the base classifier is sigmoid (e.g., for LinearSVC)
but not where it is transposed-sigmoid (e.g., Gaussian naive Bayes).
"""
print(__doc__)
# Author: Alexandre Gramfort <[email protected]>
# Jan Hendrik Metzen <[email protected]>
# License: BSD Style.
import matplotlib.pyplot as plt
from sklearn import datasets
from sklearn.naive_bayes import GaussianNB
from sklearn.svm import LinearSVC
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import (brier_score_loss, precision_score, recall_score,
f1_score)
from sklearn.calibration import CalibratedClassifierCV, calibration_curve
from sklearn.cross_validation import train_test_split
# Create dataset of classification task with many redundant and few
# informative features
X, y = datasets.make_classification(n_samples=100000, n_features=20,
n_informative=2, n_redundant=10,
random_state=42)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.99,
random_state=42)
def plot_calibration_curve(est, name, fig_index):
"""Plot calibration curve for est w/o and with calibration. """
# Calibrated with isotonic calibration
isotonic = CalibratedClassifierCV(est, cv=2, method='isotonic')
# Calibrated with sigmoid calibration
sigmoid = CalibratedClassifierCV(est, cv=2, method='sigmoid')
# Logistic regression with no calibration as baseline
lr = LogisticRegression(C=1., solver='lbfgs')
fig = plt.figure(fig_index, figsize=(10, 10))
ax1 = plt.subplot2grid((3, 1), (0, 0), rowspan=2)
ax2 = plt.subplot2grid((3, 1), (2, 0))
ax1.plot([0, 1], [0, 1], "k:", label="Perfectly calibrated")
for clf, name in [(lr, 'Logistic'),
(est, name),
(isotonic, name + ' + Isotonic'),
(sigmoid, name + ' + Sigmoid')]:
clf.fit(X_train, y_train)
y_pred = clf.predict(X_test)
if hasattr(clf, "predict_proba"):
prob_pos = clf.predict_proba(X_test)[:, 1]
else: # use decision function
prob_pos = clf.decision_function(X_test)
prob_pos = \
(prob_pos - prob_pos.min()) / (prob_pos.max() - prob_pos.min())
clf_score = brier_score_loss(y_test, prob_pos, pos_label=y.max())
print("%s:" % name)
print("\tBrier: %1.3f" % (clf_score))
print("\tPrecision: %1.3f" % precision_score(y_test, y_pred))
print("\tRecall: %1.3f" % recall_score(y_test, y_pred))
print("\tF1: %1.3f\n" % f1_score(y_test, y_pred))
fraction_of_positives, mean_predicted_value = \
calibration_curve(y_test, prob_pos, n_bins=10)
ax1.plot(mean_predicted_value, fraction_of_positives, "s-",
label="%s (%1.3f)" % (name, clf_score))
ax2.hist(prob_pos, range=(0, 1), bins=10, label=name,
histtype="step", lw=2)
ax1.set_ylabel("Fraction of positives")
ax1.set_ylim([-0.05, 1.05])
ax1.legend(loc="lower right")
ax1.set_title('Calibration plots (reliability curve)')
ax2.set_xlabel("Mean predicted value")
ax2.set_ylabel("Count")
ax2.legend(loc="upper center", ncol=2)
plt.tight_layout()
# Plot calibration cuve for Gaussian Naive Bayes
plot_calibration_curve(GaussianNB(), "Naive Bayes", 1)
# Plot calibration cuve for Linear SVC
plot_calibration_curve(LinearSVC(), "SVC", 2)
plt.show()
| bsd-3-clause |
RWArunde/Mallet | results/nflSparse/vis.py | 10 | 3086 | # -*- coding: utf-8 -*-
# <nbformat>3.0</nbformat>
# ------------------------------------------------------------------------
# Filename : heatmap.py
# Date : 2013-04-19
# Updated : 2014-01-04
# Author : @LotzJoe >> Joe Lotz
# Description: My attempt at reproducing the FlowingData graphic in Python
# Source : http://flowingdata.com/2010/01/21/how-to-make-a-heatmap-a-quick-and-easy-solution/
#
# Other Links:
# http://stackoverflow.com/questions/14391959/heatmap-in-matplotlib-with-pcolor
#
# ------------------------------------------------------------------------
from matplotlib import rcParams
rcParams.update({'figure.autolayout': True})
import matplotlib.pyplot as plt
#import pandas as pd
from urllib2 import urlopen
import numpy as np
import json
import sys
#%pylab inline
page = urlopen("http://datasets.flowingdata.com/ppg2008.csv")
#nba = pd.read_csv(page, index_col=0)
#read in subreddit_topics data
subred = json.load(open("subreddit_topics.json"))
nba = []
ylabels = []
topsub = sys.argv[1]
nba.append(subred[topsub])
ylabels.append(topsub)
for k, v in subred.iteritems():
if k == 'gg' or k == topsub:
continue
ylabels.append(k)
nba.append(v)
#get xlabels
#xlabels = [line.strip().split('\t')[-1] for line in open('topic_words.txt').readlines()]
#hide them
#xlabels = ['' for l in xlabels]
xlabels = []
nba = np.array(nba)
# Plot it out
fig, ax = plt.subplots()
heatmap = ax.pcolor(nba, cmap=plt.cm.Blues, alpha=0.8)
# Format
fig = plt.gcf()
fig.set_size_inches(8,4)
# turn off the frame
ax.set_frame_on(False)
# put the major ticks at the middle of each cell
ax.set_yticks(np.arange(nba.shape[0]) + 0.5, minor=False)
ax.set_xticks(np.arange(nba.shape[1]) + 0.5, minor=False)
# want a more natural, table-like display
ax.invert_yaxis()
ax.xaxis.tick_top()
# Set the labels
labels = xlabels
# note I could have used nba_sort.columns but made "labels" instead
ax.set_xticklabels(labels, minor=False)
ax.set_yticklabels(ylabels, minor=False)
# rotate the x labels
# plt.xticks(rotation=90)
ax.grid(False)
# Turn off all the ticks
ax = plt.gca()
for t in ax.xaxis.get_major_ticks():
t.tick1On = False
t.tick2On = False
for t in ax.yaxis.get_major_ticks():
t.tick1On = False
t.tick2On = False
#fig.subplots_adjust(bottom = 0)
#fig.subplots_adjust(top = .5)
#fig.subplots_adjust(right = 1)
#fig.subplots_adjust(left = 0.1)
plt.savefig('LDA_football.png')
cpy = np.copy(nba)
for c in range(nba.shape[1]):
minn = (nba[:,c]).min()
maxx = (nba[:,c]).max()
mean = (nba[:,c]).mean()
nba[:,c] = (nba[:,c] - minn) / (maxx-minn)
ax.pcolor(nba, cmap=plt.cm.Blues, alpha=0.8)
plt.savefig('LDA_football_normed.png')
nba = cpy
norms = []
for c in range(nba.shape[1]):
norms.append([c,sum(nba[:,c])])
sort_cols = sorted(norms, key=lambda a: -a[1])
nba2 = np.copy(nba)
for c in range(nba.shape[1]):
nba2[:,c] = nba[:,sort_cols[c][0]]
ax.pcolor(nba2, cmap=plt.cm.Blues, alpha=0.8)
plt.savefig('LDA_'+sys.argv[2]+'_sorted.png')
for c in range(nba.shape[0]):
print ylabels[c] + "\t" + str(np.var(nba[c,:]))
| epl-1.0 |
PSFCPlasmaTools/eqtools | eqtools/EFIT.py | 1 | 65199 | # This program is distributed under the terms of the GNU General Purpose License (GPL).
# Refer to http://www.gnu.org/licenses/gpl.txt
#
# This file is part of EqTools.
#
# EqTools is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# EqTools is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with EqTools. If not, see <http://www.gnu.org/licenses/>.
"""Provides class inheriting :py:class:`eqtools.core.Equilibrium` for working
with EFIT data.
"""
import scipy
from collections import namedtuple
from .core import Equilibrium, ModuleWarning, inPolygon
import warnings
try:
import MDSplus
_has_MDS = True
except Exception as _e_MDS:
if isinstance(_e_MDS, ImportError):
warnings.warn("MDSplus module could not be loaded -- classes that use "
"MDSplus for data access will not work.",
ModuleWarning)
else:
warnings.warn("MDSplus module could not be loaded -- classes that use "
"MDSplus for data access will not work. Exception raised "
"was of type %s, message was '%s'."
% (_e_MDS.__class__, _e_MDS.message),
ModuleWarning)
_has_MDS = False
try:
import matplotlib.pyplot as plt
_has_plt = True
except:
warnings.warn("Matplotlib.pyplot module could not be loaded -- classes that "
"use pyplot will not work.",ModuleWarning)
_has_plt = False
class EFITTree(Equilibrium):
"""Inherits :py:class:`Equilibrium <eqtools.core.Equilibrium>` class.
EFIT-specific data handling class for machines using standard EFIT tag
names/tree structure with MDSplus. Constructor and/or data loading may
need overriding in a machine-specific implementation. Pulls EFIT data
from selected MDS tree and shot, stores as object attributes. Each EFIT
variable or set of variables is recovered with a corresponding getter
method. Essential data for EFIT mapping are pulled on initialization
(e.g. psirz grid). Additional data are pulled at the first request and
stored for subsequent usage.
Intializes :py:class:`EFITTree` object. Pulls data from MDS tree for
storage in instance attributes. Core attributes are populated from the MDS
tree on initialization. Additional attributes are initialized as None,
filled on the first request to the object.
Args:
shot (integer): Shot number
tree (string): MDSplus tree to open to fetch EFIT data.
root (string): Root path for EFIT data in MDSplus tree.
Keyword Args:
length_unit (string): Sets the base unit used for any
quantity whose dimensions are length to any power.
Valid options are:
=========== ===========================================================================================
'm' meters
'cm' centimeters
'mm' millimeters
'in' inches
'ft' feet
'yd' yards
'smoot' smoots
'cubit' cubits
'hand' hands
'default' whatever the default in the tree is (no conversion is performed, units may be inconsistent)
=========== ===========================================================================================
Default is 'm' (all units taken and returned in meters).
tspline (boolean): Sets whether or not interpolation in time is
performed using a tricubic spline or nearest-neighbor
interpolation. Tricubic spline interpolation requires at least
four complete equilibria at different times. It is also assumed
that they are functionally correlated, and that parameters do
not vary out of their boundaries (derivative = 0 boundary
condition). Default is False (use nearest neighbor interpolation).
monotonic (boolean): Sets whether or not the "monotonic" form of time
window finding is used. If True, the timebase must be
monotonically increasing. Default is False (use slower,
safer method).
"""
def __init__(self, shot, tree, root, length_unit='m', gfile='g_eqdsk',
afile='a_eqdsk', tspline=False, monotonic=True):
if not _has_MDS:
print("MDSplus module did not load properly. Exception is below:")
print(_e_MDS.__class__)
print(_e_MDS.message)
print(
"Most functionality will not be available! (But pickled data "
"will still be accessible.)"
)
super(EFITTree, self).__init__(length_unit=length_unit, tspline=tspline,
monotonic=monotonic)
self._shot = shot
self._tree = tree
self._root = root
self._gfile = gfile
self._afile = afile
self._MDSTree = MDSplus.Tree(self._tree, self._shot)
self._defaultUnits = {}
# initialize None for non-essential data
# grad-shafranov related parameters
self._fpol = None
self._fluxPres = None # pressure on flux surface (psi,t)
self._ffprim = None
self._pprime = None # pressure derivative on flux surface (t,psi)
# fields
self._btaxp = None # Bt on-axis, with plasma (t)
self._btaxv = None # Bt on-axis, vacuum (t)
self._bpolav = None # avg poloidal field (t)
self._BCentr = None # Bt at RCentr, vacuum (for gfiles) (t)
# plasma current
self._IpCalc = None # EFIT-calculated plasma current (t)
self._IpMeas = None # measured plasma current (t)
self._Jp = None # grid of current density (r,z,t)
self._currentSign = None # sign of current for entire shot (calculated in moderately kludgey manner)
# safety factor parameters
self._q0 = None # q on-axis (t)
self._q95 = None # q at 95% flux (t)
self._qLCFS = None # q at LCFS (t)
self._rq1 = None # outboard-midplane minor radius of q=1 surface (t)
self._rq2 = None # outboard-midplane minor radius of q=2 surface (t)
self._rq3 = None # outboard-midplane minor radius of q=3 surface (t)
# shaping parameters
self._kappa = None # LCFS elongation (t)
self._dupper = None # LCFS upper triangularity (t)
self._dlower = None # LCFS lower triangularity (t)
# (dimensional) geometry parameters
self._rmag = None # major radius, magnetic axis (t)
self._zmag = None # Z magnetic axis (t)
self._aLCFS = None # outboard-midplane minor radius (t)
self._RmidLCFS = None # outboard-midplane major radius (t)
self._areaLCFS = None # LCFS surface area (t)
self._RLCFS = None # R-positions of LCFS (t,n)
self._ZLCFS = None # Z-positions of LCFS (t,n)
self._RCentr = None # Radius for BCentr calculation (for gfiles) (t)
# machine geometry parameters
self._Rlimiter = None # R-positions of vacuum-vessel wall (t)
self._Zlimiter = None # Z-positions of vacuum-vessel wall (t)
# calc. normalized-pressure values
self._betat = None # EFIT-calc toroidal beta (t)
self._betap = None # EFIT-calc avg. poloidal beta (t)
self._Li = None # EFIT-calc internal inductance (t)
# diamagnetic measurements
self._diamag = None # diamagnetic flux (t)
self._betatd = None # diamagnetic toroidal beta (t)
self._betapd = None # diamagnetic poloidal beta (t)
self._WDiamag = None # diamagnetic stored energy (t)
self._tauDiamag = None # diamagnetic energy confinement time (t)
# energy calculations
self._WMHD = None # EFIT-calc stored energy (t)
self._tauMHD = None # EFIT-calc energy confinement time (t)
self._Pinj = None # EFIT-calc injected power (t)
self._Wbdot = None # EFIT d/dt magnetic stored energy (t)
self._Wpdot = None # EFIT d/dt plasma stored energy (t)
# load essential mapping data
# Set the variables to None first so the loading calls will work right:
self._time = None # EFIT timebase
self._psiRZ = None # EFIT flux grid (r,z,t)
self._rGrid = None # EFIT R-axis (t)
self._zGrid = None # EFIT Z-axis (t)
self._psiLCFS = None # flux at LCFS (t)
self._psiAxis = None # flux at magnetic axis (t)
self._fluxVol = None # volume within flux surface (t,psi)
self._volLCFS = None # volume within LCFS (t)
self._qpsi = None # q profile (psi,t)
self._RmidPsi = None # max major radius of flux surface (t,psi)
# Call the get functions to preload the data. Add any other calls you
# want to preload here.
self.getTimeBase()
self.getFluxGrid() # loads _psiRZ, _rGrid and _zGrid at once.
self.getFluxLCFS()
self.getFluxAxis()
self.getVolLCFS()
self.getQProfile()
self.getRmidPsi()
def __str__(self):
"""string formatting for EFITTree class.
"""
try:
nt = len(self._time)
nr = len(self._rGrid)
nz = len(self._zGrid)
mes = (
'EFIT data for shot ' + str(self._shot) + ' from tree ' +
str(self._tree.upper()) + '\n' + 'timebase ' +
str(self._time[0]) + '-' + str(self._time[-1]) + 's in ' +
str(nt) + ' points\n' + str(nr) + 'x' + str(nz) +
' spatial grid'
)
return mes
except TypeError:
return 'tree has failed data load.'
def __getstate__(self):
"""Used to close out the MDSplus.Tree instance to make this class pickleable.
"""
self._MDSTree_internal = None
return super(EFITTree, self).__getstate__()
@property
def _MDSTree(self):
"""Use a property to mask the MDSplus.Tree.
This is needed since it isn't pickleable, so we might need to trash it
and restore it automatically.
You should ALWAYS access _MDSTree since _MDSTree_internal is guaranteed
to be None after pickling/unpickling.
"""
if self._MDSTree_internal is None:
self._MDSTree_internal = MDSplus.Tree(self._tree, self._shot)
return self._MDSTree_internal
@_MDSTree.setter
def _MDSTree(self, v):
self._MDSTree_internal = v
@_MDSTree.deleter
def _MDSTree(self):
del self._MDSTree_internal
def getInfo(self):
"""returns namedtuple of shot information
Returns:
namedtuple containing
===== ===============================
shot C-Mod shot index (long)
tree EFIT tree (string)
nr size of R-axis for spatial grid
nz size of Z-axis for spatial grid
nt size of timebase for flux grid
===== ===============================
"""
try:
nt = len(self._time)
nr = len(self._rGrid)
nz = len(self._zGrid)
except TypeError:
nt, nr, nz = 0, 0, 0
print('tree has failed data load.')
data = namedtuple('Info', ['shot', 'tree', 'nr', 'nz', 'nt'])
return data(shot=self._shot, tree=self._tree, nr=nr, nz=nz, nt=nt)
def getTimeBase(self):
"""returns EFIT time base vector.
Returns:
time (array): [nt] array of time points.
Raises:
ValueError: if module cannot retrieve data from MDS tree.
"""
if self._time is None:
try:
timeNode = self._MDSTree.getNode(
self._root+self._afile+':time'
)
self._time = timeNode.data()
self._defaultUnits['_time'] = str(timeNode.units)
except:
raise ValueError('data retrieval failed.')
return self._time.copy()
def getFluxGrid(self):
"""returns EFIT flux grid.
Note that this method preserves whatever sign convention is used in the
tree. For C-Mod, this means that the result should be multiplied by
-1 * :py:meth:`getCurrentSign()` in most cases.
Returns:
psiRZ (Array): [nt,nz,nr] array of (non-normalized) flux on grid.
Raises:
ValueError: if module cannot retrieve data from MDS tree.
"""
if self._psiRZ is None:
try:
psinode = self._MDSTree.getNode(
self._root + self._gfile + ':psirz'
)
self._psiRZ = psinode.data()
self._rGrid = psinode.dim_of(0).data()
self._zGrid = psinode.dim_of(1).data()
self._defaultUnits['_psiRZ'] = str(psinode.units)
self._defaultUnits['_rGrid'] = str(psinode.dim_of(0).units)
self._defaultUnits['_zGrid'] = str(psinode.dim_of(1).units)
except:
raise ValueError('data retrieval failed.')
return self._psiRZ.copy()
def getRGrid(self, length_unit=1):
"""returns EFIT R-axis.
Returns:
rGrid (Array): [nr] array of R-axis of flux grid.
Raises:
ValueError: if module cannot retrieve data from MDS tree.
"""
if self._rGrid is None:
raise ValueError('data retrieval failed.')
# Default units should be 'm'
unit_factor = self._getLengthConversionFactor(
self._defaultUnits['_rGrid'],
length_unit
)
return unit_factor * self._rGrid.copy()
def getZGrid(self, length_unit=1):
"""returns EFIT Z-axis.
Returns:
zGrid (Array): [nz] array of Z-axis of flux grid.
Raises:
ValueError: if module cannot retrieve data from MDS tree.
"""
if self._zGrid is None:
raise ValueError('data retrieval failed.')
# Default units should be 'm'
unit_factor = self._getLengthConversionFactor(
self._defaultUnits['_zGrid'],
length_unit
)
return unit_factor * self._zGrid.copy()
def getFluxAxis(self):
"""returns psi on magnetic axis.
Returns:
psiAxis (Array): [nt] array of psi on magnetic axis.
Raises:
ValueError: if module cannot retrieve data from MDS tree.
"""
if self._psiAxis is None:
try:
psiAxisNode = self._MDSTree.getNode(
self._root + self._afile + ':simagx'
)
self._psiAxis = psiAxisNode.data()
self._defaultUnits['_psiAxis'] = str(psiAxisNode.units)
except:
raise ValueError('data retrieval failed.')
return self._psiAxis.copy()
def getFluxLCFS(self):
"""returns psi at separatrix.
Returns:
psiLCFS (Array): [nt] array of psi at LCFS.
Raises:
ValueError: if module cannot retrieve data from MDS tree.
"""
if self._psiLCFS is None:
try:
psiLCFSNode = self._MDSTree.getNode(
self._root + self._afile + ':sibdry'
)
self._psiLCFS = psiLCFSNode.data()
self._defaultUnits['_psiLCFS'] = str(psiLCFSNode.units)
except:
raise ValueError('data retrieval failed.')
return self._psiLCFS.copy()
def getFluxVol(self, length_unit=3):
"""returns volume within flux surface.
Keyword Args:
length_unit (String or 3): unit for plasma volume. Defaults to 3,
indicating default volumetric unit (typically m^3).
Returns:
fluxVol (Array): [nt,npsi] array of volume within flux surface.
Raises:
ValueError: if module cannot retrieve data from MDS tree.
"""
if self._fluxVol is None:
try:
fluxVolNode = self._MDSTree.getNode(self._root + 'fitout:volp')
self._fluxVol = fluxVolNode.data()
# Units aren't properly stored in the tree for this one!
if fluxVolNode.units != ' ':
self._defaultUnits['_fluxVol'] = str(fluxVolNode.units)
else:
self._defaultUnits['_fluxVol'] = 'm^3'
except:
raise ValueError('data retrieval failed.')
# Default units are m^3, but aren't stored in the tree!
unit_factor = self._getLengthConversionFactor(
self._defaultUnits['_fluxVol'], length_unit
)
return unit_factor * self._fluxVol.copy()
def getVolLCFS(self, length_unit=3):
"""returns volume within LCFS.
Keyword Args:
length_unit (String or 3): unit for LCFS volume. Defaults to 3,
denoting default volumetric unit (typically m^3).
Returns:
volLCFS (Array): [nt] array of volume within LCFS.
Raises:
ValueError: if module cannot retrieve data from MDS tree.
"""
if self._volLCFS is None:
try:
volLCFSNode = self._MDSTree.getNode(
self._root + self._afile + ':vout'
)
self._volLCFS = volLCFSNode.data()
self._defaultUnits['_volLCFS'] = str(volLCFSNode.units)
except:
raise ValueError('data retrieval failed.')
# Default units should be 'cm^3':
unit_factor = self._getLengthConversionFactor(
self._defaultUnits['_volLCFS'], length_unit
)
return unit_factor * self._volLCFS.copy()
def getRmidPsi(self, length_unit=1):
"""returns maximum major radius of each flux surface.
Keyword Args:
length_unit (String or 1): unit of Rmid. Defaults to 1, indicating
the default parameter unit (typically m).
Returns:
Rmid (Array): [nt,npsi] array of maximum (outboard) major radius of
flux surface psi.
Raises:
Value Error: if module cannot retrieve data from MDS tree.
"""
if self._RmidPsi is None:
try:
RmidPsiNode = self._MDSTree.getNode(
self._root + 'fitout:rpres'
)
self._RmidPsi = RmidPsiNode.data()
# Units aren't properly stored in the tree for this one!
if RmidPsiNode.units != ' ':
self._defaultUnits['_RmidPsi'] = str(RmidPsiNode.units)
else:
self._defaultUnits['_RmidPsi'] = 'm'
except:
raise ValueError('data retrieval failed.')
unit_factor = self._getLengthConversionFactor(
self._defaultUnits['_RmidPsi'], length_unit
)
return unit_factor * self._RmidPsi.copy()
def getRLCFS(self, length_unit=1):
"""returns R-values of LCFS position.
Returns:
RLCFS (Array): [nt,n] array of R of LCFS points.
Raises:
ValueError: if module cannot retrieve data from MDS tree.
"""
if self._RLCFS is None:
try:
RLCFSNode = self._MDSTree.getNode(
self._root + self._gfile + ':rbbbs'
)
self._RLCFS = RLCFSNode.data()
self._defaultUnits['_RLCFS'] = str(RLCFSNode.units)
except:
raise ValueError('data retrieval failed.')
unit_factor = self._getLengthConversionFactor(
self._defaultUnits['_RLCFS'], length_unit
)
return unit_factor * self._RLCFS.copy()
def getZLCFS(self, length_unit=1):
"""returns Z-values of LCFS position.
Returns:
ZLCFS (Array): [nt,n] array of Z of LCFS points.
Raises:
ValueError: if module cannot retrieve data from MDS tree.
"""
if self._ZLCFS is None:
try:
ZLCFSNode = self._MDSTree.getNode(
self._root + self._gfile + ':zbbbs'
)
self._ZLCFS = ZLCFSNode.data()
self._defaultUnits['_ZLCFS'] = str(ZLCFSNode.units)
except:
raise ValueError('data retrieval failed.')
unit_factor = self._getLengthConversionFactor(
self._defaultUnits['_ZLCFS'], length_unit
)
return unit_factor * self._ZLCFS.copy()
def remapLCFS(self, mask=False):
"""Overwrites RLCFS, ZLCFS values pulled from EFIT with
explicitly-calculated contour of psinorm=1 surface. This is then masked
down by the limiter array using core.inPolygon, restricting the contour
to the closed plasma surface and the divertor legs.
Keyword Args:
mask (Boolean): Default False. Set True to mask LCFS path to
limiter outline (using inPolygon). Set False to draw full
contour of psi = psiLCFS.
Raises:
NotImplementedError: if :py:mod:`matplotlib.pyplot` is not loaded.
ValueError: if limiter outline is not available.
"""
if not _has_plt:
raise NotImplementedError(
"Requires matplotlib.pyplot for contour calculation."
)
try:
Rlim, Zlim = self.getMachineCrossSection()
except:
raise ValueError(
"Limiter outline (self.getMachineCrossSection) must be available."
)
plt.ioff()
psiRZ = self.getFluxGrid() # [nt,nZ,nR]
R = self.getRGrid()
Z = self.getZGrid()
psiLCFS = -1.0 * self.getCurrentSign() * self.getFluxLCFS()
RLCFS_stores = []
ZLCFS_stores = []
maxlen = 0
nt = len(self.getTimeBase())
fig = plt.figure()
for i in range(nt):
cs = plt.contour(R, Z, psiRZ[i], [psiLCFS[i]])
paths = cs.collections[0].get_paths()
RLCFS_frame = []
ZLCFS_frame = []
for path in paths:
v = path.vertices
RLCFS_frame.extend(v[:, 0])
ZLCFS_frame.extend(v[:, 1])
RLCFS_frame.append(scipy.nan)
ZLCFS_frame.append(scipy.nan)
RLCFS_frame = scipy.array(RLCFS_frame)
ZLCFS_frame = scipy.array(ZLCFS_frame)
# generate masking array to vessel
if mask:
maskarr = scipy.array([False for i in range(len(RLCFS_frame))])
for i, x in enumerate(RLCFS_frame):
y = ZLCFS_frame[i]
maskarr[i] = inPolygon(Rlim, Zlim, x, y)
RLCFS_frame = RLCFS_frame[maskarr]
ZLCFS_frame = ZLCFS_frame[maskarr]
if len(RLCFS_frame) > maxlen:
maxlen = len(RLCFS_frame)
RLCFS_stores.append(RLCFS_frame)
ZLCFS_stores.append(ZLCFS_frame)
RLCFS = scipy.zeros((nt, maxlen))
ZLCFS = scipy.zeros((nt, maxlen))
for i in range(nt):
RLCFS_frame = RLCFS_stores[i]
ZLCFS_frame = ZLCFS_stores[i]
ni = len(RLCFS_frame)
RLCFS[i, 0:ni] = RLCFS_frame
ZLCFS[i, 0:ni] = ZLCFS_frame
# store final values
self._RLCFS = RLCFS
self._ZLCFS = ZLCFS
# set default unit parameters, based on RZ grid
rUnit = self._defaultUnits['_rGrid']
zUnit = self._defaultUnits['_zGrid']
self._defaultUnits['_RLCFS'] = rUnit
self._defaultUnits['_ZLCFS'] = zUnit
# cleanup
plt.ion()
plt.clf()
plt.close(fig)
plt.ioff()
def getF(self):
r"""returns F=RB_{\Phi}(\Psi), often calculated for grad-shafranov
solutions.
Note that this method preserves whatever sign convention is used in the
tree. For C-Mod, this means that the result should be multiplied by
-1 * :py:meth:`getCurrentSign()` in most cases.
Returns:
F (Array): [nt,npsi] array of F=RB_{\Phi}(\Psi)
Raises:
ValueError: if module cannot retrieve data from MDS tree.
"""
if self._fpol is None:
try:
fNode = self._MDSTree.getNode(self._root+self._gfile+':fpol')
self._fpol = fNode.data()
self._defaultUnits['_fpol'] = str(fNode.units)
except:
raise ValueError('data retrieval failed.')
return self._fpol.copy()
def getFluxPres(self):
"""returns pressure at flux surface.
Returns:
p (Array): [nt,npsi] array of pressure on flux surface psi.
Raises:
ValueError: if module cannot retrieve data from MDS tree.
"""
if self._fluxPres is None:
try:
fluxPresNode = self._MDSTree.getNode(
self._root+self._gfile+':pres'
)
self._fluxPres = fluxPresNode.data()
self._defaultUnits['_fluxPres'] = str(fluxPresNode.units)
except:
raise ValueError('data retrieval failed.')
return self._fluxPres.copy()
def getFFPrime(self):
"""returns FF' function used for grad-shafranov solutions.
Returns:
FFprime (Array): [nt,npsi] array of FF' fromgrad-shafranov solution.
Raises:
ValueError: if module cannot retrieve data from MDS tree.
"""
if self._ffprim is None:
try:
FFPrimeNode = self._MDSTree.getNode(
self._root+self._gfile+':ffprim'
)
self._ffprim = FFPrimeNode.data()
self._defaultUnits['_ffprim'] = str(FFPrimeNode.units)
except:
raise ValueError('data retrieval failed.')
return self._ffprim.copy()
def getPPrime(self):
"""returns plasma pressure gradient as a function of psi.
Returns:
pprime (Array): [nt,npsi] array of pressure gradient on flux surface
psi from grad-shafranov solution.
Raises:
ValueError: if module cannot retrieve data from MDS tree.
"""
if self._pprime is None:
try:
pPrimeNode = self._MDSTree.getNode(
self._root+self._gfile+':pprime'
)
self._pprime = pPrimeNode.data()
self._defaultUnits['_pprime'] = str(pPrimeNode.units)
except:
raise ValueError('data retrieval failed.')
return self._pprime.copy()
def getElongation(self):
"""returns LCFS elongation.
Returns:
kappa (Array): [nt] array of LCFS elongation.
Raises:
ValueError: if module cannot retrieve data from MDS tree.
"""
if self._kappa is None:
try:
kappaNode = self._MDSTree.getNode(
self._root+self._afile+':eout'
)
self._kappa = kappaNode.data()
self._defaultUnits['_kappa'] = str(kappaNode.units)
except:
raise ValueError('data retrieval failed.')
return self._kappa.copy()
def getUpperTriangularity(self):
"""returns LCFS upper triangularity.
Returns:
deltau (Array): [nt] array of LCFS upper triangularity.
Raises:
ValueError: if module cannot retrieve data from MDS tree.
"""
if self._dupper is None:
try:
dupperNode = self._MDSTree.getNode(
self._root+self._afile+':doutu'
)
self._dupper = dupperNode.data()
self._defaultUnits['_dupper'] = str(dupperNode.units)
except:
raise ValueError('data retrieval failed.')
return self._dupper.copy()
def getLowerTriangularity(self):
"""returns LCFS lower triangularity.
Returns:
deltal (Array): [nt] array of LCFS lower triangularity.
Raises:
ValueError: if module cannot retrieve data from MDS tree.
"""
if self._dlower is None:
try:
dlowerNode = self._MDSTree.getNode(
self._root+self._afile+':doutl'
)
self._dlower = dlowerNode.data()
self._defaultUnits['_dlower'] = str(dlowerNode.units)
except:
raise ValueError('data retrieval failed.')
return self._dlower.copy()
def getShaping(self):
"""pulls LCFS elongation and upper/lower triangularity.
Returns:
namedtuple containing (kappa, delta_u, delta_l)
Raises:
ValueError: if module cannot retrieve data from MDS tree.
"""
try:
kap = self.getElongation()
du = self.getUpperTriangularity()
dl = self.getLowerTriangularity()
data = namedtuple('Shaping', ['kappa', 'delta_u', 'delta_l'])
return data(kappa=kap, delta_u=du, delta_l=dl)
except ValueError:
raise ValueError('data retrieval failed.')
def getMagR(self, length_unit=1):
"""returns magnetic-axis major radius.
Returns:
magR (Array): [nt] array of major radius of magnetic axis.
Raises:
ValueError: if module cannot retrieve data from MDS tree.
"""
if self._rmag is None:
try:
rmagNode = self._MDSTree.getNode(
self._root+self._afile+':rmagx'
)
self._rmag = rmagNode.data()
self._defaultUnits['_rmag'] = str(rmagNode.units)
except:
raise ValueError('data retrieval failed.')
unit_factor = self._getLengthConversionFactor(
self._defaultUnits['_rmag'], length_unit
)
return unit_factor * self._rmag.copy()
def getMagZ(self, length_unit=1):
"""returns magnetic-axis Z.
Returns:
magZ (Array): [nt] array of Z of magnetic axis.
Raises:
ValueError: if module cannot retrieve data from MDS tree.
"""
if self._zmag is None:
try:
zmagNode = self._MDSTree.getNode(
self._root+self._afile+':zmagx'
)
self._zmag = zmagNode.data()
self._defaultUnits['_zmag'] = str(zmagNode.units)
except:
raise ValueError('data retrieval failed.')
unit_factor = self._getLengthConversionFactor(
self._defaultUnits['_zmag'], length_unit
)
return unit_factor * self._zmag.copy()
def getAreaLCFS(self, length_unit=2):
"""returns LCFS cross-sectional area.
Keyword Args:
length_unit (String or 2): unit for LCFS area. Defaults to 2,
denoting default areal unit (typically m^2).
Returns:
areaLCFS (Array): [nt] array of LCFS area.
Raises:
ValueError: if module cannot retrieve data from MDS tree.
"""
if self._areaLCFS is None:
try:
areaLCFSNode = self._MDSTree.getNode(
self._root+self._afile+':areao'
)
self._areaLCFS = areaLCFSNode.data()
self._defaultUnits['_areaLCFS'] = str(areaLCFSNode.units)
except:
raise ValueError('data retrieval failed.')
# Units should be cm^2:
unit_factor = self._getLengthConversionFactor(
self._defaultUnits['_areaLCFS'], length_unit
)
return unit_factor * self._areaLCFS.copy()
def getAOut(self, length_unit=1):
"""returns outboard-midplane minor radius at LCFS.
Keyword Args:
length_unit (String or 1): unit for minor radius. Defaults to 1,
denoting default length unit (typically m).
Returns:
aOut (Array): [nt] array of LCFS outboard-midplane minor radius.
Raises:
ValueError: if module cannot retrieve data from MDS tree.
"""
if self._aLCFS is None:
try:
aLCFSNode = self._MDSTree.getNode(
self._root+self._afile+':aout'
)
self._aLCFS = aLCFSNode.data()
self._defaultUnits['_aLCFS'] = str(aLCFSNode.units)
except:
raise ValueError('data retrieval failed.')
unit_factor = self._getLengthConversionFactor(
self._defaultUnits['_aLCFS'], length_unit
)
return unit_factor * self._aLCFS.copy()
def getRmidOut(self, length_unit=1):
"""returns outboard-midplane major radius.
Keyword Args:
length_unit (String or 1): unit for major radius. Defaults to 1,
denoting default length unit (typically m).
Returns:
RmidOut (Array): [nt] array of major radius of LCFS.
Raises:
ValueError: if module cannot retrieve data from MDS tree.
"""
if self._RmidLCFS is None:
try:
RmidLCFSNode = self._MDSTree.getNode(
self._root+self._afile+':rmidout'
)
self._RmidLCFS = RmidLCFSNode.data()
# The units aren't properly stored in the tree for this one!
# Should be meters.
if RmidLCFSNode.units != ' ':
self._defaultUnits['_RmidLCFS'] = str(RmidLCFSNode.units)
else:
self._defaultUnits['_RmidLCFS'] = 'm'
except:
raise ValueError('data retrieval failed.')
unit_factor = self._getLengthConversionFactor(
self._defaultUnits['_RmidLCFS'], length_unit
)
return unit_factor * self._RmidLCFS.copy()
def getGeometry(self, length_unit=None):
"""pulls dimensional geometry parameters.
Returns:
namedtuple containing (magR,magZ,areaLCFS,aOut,RmidOut)
Raises:
ValueError: if module cannot retrieve data from MDS tree.
"""
try:
Rmag = self.getMagR(
length_unit=(length_unit if length_unit is not None else 1)
)
Zmag = self.getMagZ(
length_unit=(length_unit if length_unit is not None else 1)
)
AreaLCFS = self.getAreaLCFS(
length_unit=(length_unit if length_unit is not None else 2)
)
aOut = self.getAOut(
length_unit=(length_unit if length_unit is not None else 1)
)
RmidOut = self.getRmidOut(
length_unit=(length_unit if length_unit is not None else 1)
)
data = namedtuple(
'Geometry', ['Rmag', 'Zmag', 'AreaLCFS', 'aOut', 'RmidOut']
)
return data(
Rmag=Rmag, Zmag=Zmag, AreaLCFS=AreaLCFS, aOut=aOut,
RmidOut=RmidOut
)
except ValueError:
raise ValueError('data retrieval failed.')
def getQProfile(self):
"""returns profile of safety factor q.
Returns:
qpsi (Array): [nt,npsi] array of q on flux surface psi.
Raises:
ValueError: if module cannot retrieve data from MDS tree.
"""
if self._qpsi is None:
try:
qpsiNode = self._MDSTree.getNode(
self._root+self._gfile+':qpsi'
)
self._qpsi = qpsiNode.data()
self._defaultUnits['_qpsi'] = str(qpsiNode.units)
except:
raise ValueError('data retrieval failed.')
return self._qpsi.copy()
def getQ0(self):
"""returns q on magnetic axis,q0.
Returns:
q0 (Array): [nt] array of q(psi=0).
Raises:
ValueError: if module cannot retrieve data from MDS tree.
"""
if self._q0 is None:
try:
q0Node = self._MDSTree.getNode(
self._root+self._afile+':qqmagx'
)
self._q0 = q0Node.data()
self._defaultUnits['_q0'] = str(q0Node.units)
except:
raise ValueError('data retrieval failed.')
return self._q0.copy()
def getQ95(self):
"""returns q at 95% flux surface.
Returns:
q95 (Array): [nt] array of q(psi=0.95).
Raises:
ValueError: if module cannot retrieve data from MDS tree.
"""
if self._q95 is None:
try:
q95Node = self._MDSTree.getNode(
self._root+self._afile+':qpsib'
)
self._q95 = q95Node.data()
self._defaultUnits['_q95'] = str(q95Node.units)
except:
raise ValueError('data retrieval failed.')
return self._q95.copy()
def getQLCFS(self):
"""returns q on LCFS (interpolated).
Returns:
qLCFS (Array): [nt] array of q* (interpolated).
Raises:
ValueError: if module cannot retrieve data from MDS tree.
"""
if self._qLCFS is None:
try:
qLCFSNode = self._MDSTree.getNode(
self._root+self._afile+':qout'
)
self._qLCFS = qLCFSNode.data()
self._defaultUnits['_qLCFS'] = str(qLCFSNode.units)
except:
raise ValueError('data retrieval failed.')
return self._qLCFS.copy()
def getQ1Surf(self, length_unit=1):
"""returns outboard-midplane minor radius of q=1 surface.
Keyword Args:
length_unit (String or 1): unit for minor radius. Defaults to 1,
denoting default length unit (typically m).
Returns:
qr1 (Array): [nt] array of minor radius of q=1 surface.
Raises:
ValueError: if module cannot retrieve data from MDS tree.
"""
if self._rq1 is None:
try:
rq1Node = self._MDSTree.getNode(self._root+self._afile+':aaq1')
self._rq1 = rq1Node.data()
self._defaultUnits['_rq1'] = str(rq1Node.units)
except:
raise ValueError('data retrieval failed.')
unit_factor = self._getLengthConversionFactor(
self._defaultUnits['_rq1'], length_unit
)
return unit_factor * self._rq1.copy()
def getQ2Surf(self, length_unit=1):
"""returns outboard-midplane minor radius of q=2 surface.
Keyword Args:
length_unit (String or 1): unit for minor radius. Defaults to 1,
denoting default length unit (typically m).
Returns:
qr2 (Array): [nt] array of minor radius of q=2 surface.
Raises:
ValueError: if module cannot retrieve data from MDS tree.
"""
if self._rq2 is None:
try:
rq2Node = self._MDSTree.getNode(self._root+self._afile+':aaq2')
self._rq2 = rq2Node.data()
self._defaultUnits['_rq2'] = str(rq2Node.units)
except:
raise ValueError('data retrieval failed.')
unit_factor = self._getLengthConversionFactor(
self._defaultUnits['_rq2'], length_unit
)
return unit_factor * self._rq2.copy()
def getQ3Surf(self, length_unit=1):
"""returns outboard-midplane minor radius of q=3 surface.
Keyword Args:
length_unit (String or 1): unit for minor radius. Defaults to 1,
denoting default length unit (typically m).
Returns:
qr3 (Array): [nt] array of minor radius of q=3 surface.
Raises:
ValueError: if module cannot retrieve data from MDS tree.
"""
if self._rq3 is None:
try:
rq3Node = self._MDSTree.getNode(self._root+self._afile+':aaq3')
self._rq3 = rq3Node.data()
self._defaultUnits['_rq3'] = str(rq3Node.units)
except:
raise ValueError('data retrieval failed.')
unit_factor = self._getLengthConversionFactor(
self._defaultUnits['_rq3'], length_unit
)
return unit_factor * self._rq3.copy()
def getQs(self, length_unit=1):
"""pulls q values.
Returns:
namedtuple containing (q0,q95,qLCFS,rq1,rq2,rq3).
Raises:
ValueError: if module cannot retrieve data from MDS tree.
"""
try:
q0 = self.getQ0()
q95 = self.getQ95()
qLCFS = self.getQLCFS()
rq1 = self.getQ1Surf(length_unit=length_unit)
rq2 = self.getQ2Surf(length_unit=length_unit)
rq3 = self.getQ3Surf(length_unit=length_unit)
data = namedtuple(
'Qs', ['q0', 'q95', 'qLCFS', 'rq1', 'rq2', 'rq3']
)
return data(q0=q0, q95=q95, qLCFS=qLCFS, rq1=rq1, rq2=rq2, rq3=rq3)
except ValueError:
raise ValueError('data retrieval failed.')
def getBtVac(self):
"""Returns vacuum toroidal field on-axis.
Returns:
BtVac (Array): [nt] array of vacuum toroidal field.
Raises:
ValueError: if module cannot retrieve data from MDS tree.
"""
if self._btaxv is None:
try:
btaxvNode = self._MDSTree.getNode(
self._root+self._afile+':btaxv'
)
self._btaxv = btaxvNode.data()
self._defaultUnits['_btaxv'] = str(btaxvNode.units)
except:
raise ValueError('data retrieval failed.')
return self._btaxv.copy()
def getBtPla(self):
"""returns on-axis plasma toroidal field.
Returns:
BtPla (Array): [nt] array of toroidal field including plasma effects.
Raises:
ValueError: if module cannot retrieve data from MDS tree.
"""
if self._btaxp is None:
try:
btaxpNode = self._MDSTree.getNode(
self._root+self._afile+':btaxp'
)
self._btaxp = btaxpNode.data()
self._defaultUnits['_btaxp'] = str(btaxpNode.units)
except:
raise ValueError('data retrieval failed.')
return self._btaxp.copy()
def getBpAvg(self):
"""returns average poloidal field.
Returns:
BpAvg (Array): [nt] array of average poloidal field.
Raises:
ValueError: if module cannot retrieve data from MDS tree.
"""
if self._bpolav is None:
try:
bpolavNode = self._MDSTree.getNode(
self._root+self._afile+':bpolav'
)
self._bpolav = bpolavNode.data()
self._defaultUnits['_bpolav'] = str(bpolavNode.units)
except:
raise ValueError('data retrieval failed.')
return self._bpolav.copy()
def getFields(self):
"""pulls vacuum and plasma toroidal field, avg poloidal field.
Returns:
namedtuple containing (btaxv,btaxp,bpolav).
Raises:
ValueError: if module cannot retrieve data from MDS tree.
"""
try:
btaxv = self.getBtVac()
btaxp = self.getBtPla()
bpolav = self.getBpAvg()
data = namedtuple(
'Fields', ['BtVac', 'BtPla', 'BpAvg']
)
return data(BtVac=btaxv, BtPla=btaxp, BpAvg=bpolav)
except ValueError:
raise ValueError('data retrieval failed.')
def getIpCalc(self):
"""returns EFIT-calculated plasma current.
Returns:
IpCalc (Array): [nt] array of EFIT-reconstructed plasma current.
Raises:
ValueError: if module cannot retrieve data from MDS tree.
"""
if self._IpCalc is None:
try:
IpCalcNode = self._MDSTree.getNode(
self._root+self._afile+':cpasma'
)
self._IpCalc = IpCalcNode.data()
self._defaultUnits['_IpCalc'] = str(IpCalcNode.units)
except:
raise ValueError('data retrieval failed.')
return self._IpCalc.copy()
def getIpMeas(self):
"""returns magnetics-measured plasma current.
Returns:
IpMeas (Array): [nt] array of measured plasma current.
Raises:
ValueError: if module cannot retrieve data from MDS tree.
"""
if self._IpMeas is None:
try:
IpMeasNode = self._MDSTree.getNode(
self._root+self._afile+':pasmat'
)
self._IpMeas = IpMeasNode.data()
self._defaultUnits['_IpMeas'] = str(IpMeasNode.units)
except:
raise ValueError('data retrieval failed.')
return self._IpMeas.copy()
def getJp(self):
"""returns EFIT-calculated plasma current density Jp on flux grid.
Returns:
Jp (Array): [nt,nz,nr] array of current density.
Raises:
ValueError: if module cannot retrieve data from MDS tree.
"""
if self._Jp is None:
try:
JpNode = self._MDSTree.getNode(
self._root+self._gfile+':pcurrt'
)
self._Jp = JpNode.data()
# Units come in as 'a': am I missing something about the
# definition of this quantity?
self._defaultUnits['_Jp'] = str(JpNode.units)
except:
raise ValueError('data retrieval failed.')
return self._Jp.copy()
def getBetaT(self):
"""returns EFIT-calculated toroidal beta.
Returns:
BetaT (Array): [nt] array of EFIT-calculated average toroidal beta.
Raises:
ValueError: if module cannot retrieve data from MDS tree.
"""
if self._betat is None:
try:
betatNode = self._MDSTree.getNode(
self._root+self._afile+':betat'
)
self._betat = betatNode.data()
self._defaultUnits['_betat'] = str(betatNode.units)
except:
raise ValueError('data retrieval failed.')
return self._betat.copy()
def getBetaP(self):
"""returns EFIT-calculated poloidal beta.
Returns:
BetaP (Array): [nt] array of EFIT-calculated average poloidal beta.
Raises:
ValueError: if module cannot retrieve data from MDS tree.
"""
if self._betap is None:
try:
betapNode = self._MDSTree.getNode(
self._root+self._afile+':betap'
)
self._betap = betapNode.data()
self._defaultUnits['_betap'] = str(betapNode.units)
except:
raise ValueError('data retrieval failed.')
return self._betap.copy()
def getLi(self):
"""returns EFIT-calculated internal inductance.
Returns:
Li (Array): [nt] array of EFIT-calculated internal inductance.
Raises:
ValueError: if module cannot retrieve data from MDS tree.
"""
if self._Li is None:
try:
LiNode = self._MDSTree.getNode(self._root+self._afile+':ali')
self._Li = LiNode.data()
self._defaultUnits['_Li'] = str(LiNode.units)
except:
raise ValueError('data retrieval failed.')
return self._Li.copy()
def getBetas(self):
"""pulls calculated betap, betat, internal inductance
Returns:
namedtuple containing (betat,betap,Li)
Raises:
ValueError: if module cannot retrieve data from MDS tree.
"""
try:
betat = self.getBetaT()
betap = self.getBetaP()
Li = self.getLi()
data = namedtuple('Betas', ['betat', 'betap', 'Li'])
return data(betat=betat, betap=betap, Li=Li)
except ValueError:
raise ValueError('data retrieval failed.')
def getDiamagFlux(self):
"""returns measured diamagnetic-loop flux.
Returns:
Flux (Array): [nt] array of diamagnetic-loop flux.
Raises:
ValueError: if module cannot retrieve data from MDS tree.
"""
if self._diamag is None:
try:
diamagNode = self._MDSTree.getNode(
self._root+self._afile+':diamag'
)
self._diamag = diamagNode.data()
self._defaultUnits['_diamag'] = str(diamagNode.units)
except:
raise ValueError('data retrieval failed.')
return self._diamag.copy()
def getDiamagBetaT(self):
"""returns diamagnetic-loop toroidal beta.
Returns:
BetaT (Array): [nt] array of measured toroidal beta.
Raises:
ValueError: if module cannot retrieve data from MDS tree.
"""
if self._betatd is None:
try:
betatdNode = self._MDSTree.getNode(
self._root+self._afile+':betatd'
)
self._betatd = betatdNode.data()
self._defaultUnits['_betatd'] = str(betatdNode.units)
except:
raise ValueError('data retrieval failed.')
return self._betatd.copy()
def getDiamagBetaP(self):
"""returns diamagnetic-loop avg poloidal beta.
Returns:
BetaP (Array): [nt] array of measured poloidal beta.
Raises:
ValueError: if module cannot retrieve data from MDS tree.
"""
if self._betapd is None:
try:
betapdNode = self._MDSTree.getNode(
self._root+self._afile+':betapd'
)
self._betapd = betapdNode.data()
self._defaultUnits['_betapd'] = str(betapdNode.units)
except:
raise ValueError('data retrieval failed.')
return self._betapd.copy()
def getDiamagTauE(self):
"""returns diamagnetic-loop energy confinement time.
Returns:
tauE (Array): [nt] array of measured energy confinement time.
Raises:
ValueError: if module cannot retrieve data from MDS tree.
"""
if self._tauDiamag is None:
try:
tauDiamagNode = self._MDSTree.getNode(
self._root+self._afile+':taudia'
)
self._tauDiamag = tauDiamagNode.data()
self._defaultUnits['_tauDiamag'] = str(tauDiamagNode.units)
except:
raise ValueError('data retrieval failed.')
return self._tauDiamag.copy()
def getDiamagWp(self):
"""returns diamagnetic-loop plasma stored energy.
Returns:
Wp (Array): [nt] array of measured plasma stored energy.
Raises:
ValueError: if module cannot retrieve data from MDS tree.
"""
if self._WDiamag is None:
try:
WDiamagNode = self._MDSTree.getNode(
self._root+self._afile+':wplasmd'
)
self._WDiamag = WDiamagNode.data()
self._defaultUnits['_WDiamag'] = str(WDiamagNode.units)
except:
raise ValueError('data retrieval failed.')
return self._WDiamag.copy()
def getDiamag(self):
"""pulls diamagnetic flux measurements, toroidal and poloidal beta,
energy confinement time and stored energy.
Returns:
namedtuple containing (diamag. flux, betatd, betapd, tauDiamag, WDiamag)
Raises:
ValueError: if module cannot retrieve data from MDS tree.
"""
try:
dFlux = self.getDiamagFlux()
betatd = self.getDiamagBetaT()
betapd = self.getDiamagBetaP()
dTau = self.getDiamagTauE()
dWp = self.getDiamagWp()
data = namedtuple(
'Diamag',
['diaFlux', 'diaBetat', 'diaBetap', 'diaTauE', 'diaWp']
)
return data(
diaFlux=dFlux, diaBetat=betatd, diaBetap=betapd, diaTauE=dTau,
diaWp=dWp
)
except ValueError:
raise ValueError('data retrieval failed.')
def getWMHD(self):
"""returns EFIT-calculated MHD stored energy.
Returns:
WMHD (Array): [nt] array of EFIT-calculated stored energy.
Raises:
ValueError: if module cannot retrieve data from MDS tree.
"""
if self._WMHD is None:
try:
WMHDNode = self._MDSTree.getNode(
self._root+self._afile+':wplasm'
)
self._WMHD = WMHDNode.data()
self._defaultUnits['_WMHD'] = str(WMHDNode.units)
except:
raise ValueError('data retrieval failed.')
return self._WMHD.copy()
def getTauMHD(self):
"""returns EFIT-calculated MHD energy confinement time.
Returns:
tauMHD (Array): [nt] array of EFIT-calculated energy confinement time.
Raises:
ValueError: if module cannot retrieve data from MDS tree.
"""
if self._tauMHD is None:
try:
tauMHDNode = self._MDSTree.getNode(
self._root+self._afile+':taumhd'
)
self._tauMHD = tauMHDNode.data()
self._defaultUnits['_tauMHD'] = str(tauMHDNode.units)
except:
raise ValueError('data retrieval failed.')
return self._tauMHD.copy()
def getPinj(self):
"""returns EFIT-calculated injected power.
Returns:
Pinj (Array): [nt] array of EFIT-reconstructed injected power.
Raises:
ValueError: if module cannot retrieve data from MDS tree.
"""
if self._Pinj is None:
try:
PinjNode = self._MDSTree.getNode(
self._root+self._afile+':pbinj'
)
self._Pinj = PinjNode.data()
self._defaultUnits['_Pinj'] = str(PinjNode.units)
except:
raise ValueError('data retrieval failed.')
return self._Pinj.copy()
def getWbdot(self):
"""returns EFIT-calculated d/dt of magnetic stored energy.
Returns:
dWdt (Array): [nt] array of d(Wb)/dt
Raises:
ValueError: if module cannot retrieve data from MDS tree.
"""
if self._Wbdot is None:
try:
WbdotNode = self._MDSTree.getNode(
self._root+self._afile+':wbdot'
)
self._Wbdot = WbdotNode.data()
self._defaultUnits['_Wbdot'] = str(WbdotNode.units)
except:
raise ValueError('data retrieval failed.')
return self._Wbdot.copy()
def getWpdot(self):
"""returns EFIT-calculated d/dt of plasma stored energy.
Returns:
dWdt (Array): [nt] array of d(Wp)/dt
Raises:
ValueError: if module cannot retrieve data from MDS tree.
"""
if self._Wpdot is None:
try:
WpdotNode = self._MDSTree.getNode(
self._root+self._afile+':wpdot'
)
self._Wpdot = WpdotNode.data()
self._defaultUnits['_Wpdot'] = str(WpdotNode.units)
except:
raise ValueError('data retrieval failed.')
return self._Wpdot.copy()
def getBCentr(self):
"""returns EFIT-Vacuum toroidal magnetic field in Tesla at Rcentr
Returns:
B_cent (Array): [nt] array of B_t at center [T]
Raises:
ValueError: if module cannot retrieve data from MDS tree.
"""
if self._BCentr is None:
try:
BCentrNode = self._MDSTree.getNode(
self._root+self._gfile+':bcentr'
)
self._BCentr = BCentrNode.data()
self._defaultUnits['_BCentr'] = str(BCentrNode.units)
except:
raise ValueError('data retrieval failed.')
return self._BCentr.copy()
def getRCentr(self, length_unit=1):
"""returns EFIT radius where Bcentr evaluated
Returns:
R: Radial position where Bcent calculated [m]
Raises:
ValueError: if module cannot retrieve data from MDS tree.
"""
if self._Rcentr is None:
try:
RCentrNode = self._MDSTree.getNode(
self._root+self._gfile+':rcentr'
)
self._RCentr = RCentrNode.data()
self._defaultUnits['_RCentr'] = str(RCentrNode.units)
except:
raise ValueError('data retrieval failed.')
unit_factor = self._getLengthConversionFactor(
self._defaultUnits['_RCentr'], length_unit
)
return unit_factor * self._RCentr.copy()
def getEnergy(self):
"""pulls EFIT-calculated energy parameters - stored energy, tau_E,
injected power, d/dt of magnetic and plasma stored energy.
Returns:
namedtuple containing (WMHD,tauMHD,Pinj,Wbdot,Wpdot)
Raises:
ValueError: if module cannot retrieve data from MDS tree.
"""
try:
WMHD = self.getWMHD()
tauMHD = self.getTauMHD()
Pinj = self.getPinj()
Wbdot = self.getWbdot()
Wpdot = self.getWpdot()
data = namedtuple(
'Energy', ['WMHD', 'tauMHD', 'Pinj', 'Wbdot', 'Wpdot']
)
return data(
WMHD=WMHD, tauMHD=tauMHD, Pinj=Pinj, Wbdot=Wbdot, Wpdot=Wpdot
)
except ValueError:
raise ValueError('data retrieval failed.')
def getMachineCrossSection(self):
"""Returns R,Z coordinates of vacuum-vessel wall for masking, plotting
routines.
Returns:
(`R_limiter`, `Z_limiter`)
* **R_limiter** (`Array`) - [n] array of x-values for machine cross-section.
* **Z_limiter** (`Array`) - [n] array of y-values for machine cross-section.
"""
if self._Rlimiter is None or self._Zlimiter is None:
try:
limitr = self._MDSTree.getNode(
self._root+self._gfile+':limitr'
).data()
xlim = self._MDSTree.getNode(
self._root+self._gfile+':xlim'
).data()
ylim = self._MDSTree.getNode(
self._root+self._gfile+':ylim'
).data()
npts = len(xlim)
if npts < limitr:
raise ValueError(
"Dimensions inconsistent in limiter array lengths."
)
self._Rlimiter = xlim[0:limitr]
self._Zlimiter = ylim[0:limitr]
except:
raise ValueError("data retrieval failed.")
return (self._Rlimiter, self._Zlimiter)
def getMachineCrossSectionFull(self):
"""Returns R,Z coordinates of vacuum-vessel wall for plotting routines.
Absent additional vector-graphic data on machine cross-section, returns
:py:meth:`getMachineCrossSection`.
Returns:
result from getMachineCrossSection().
"""
try:
return self.getMachineCrossSection()
except:
raise NotImplementedError(
"self.getMachineCrossSection not implemented."
)
def getCurrentSign(self):
"""Returns the sign of the current, based on the check in Steve Wolfe's
IDL implementation efit_rz2psi.pro.
Returns:
currentSign (Integer): 1 for positive-direction current, -1 for negative.
"""
if self._currentSign is None:
self._currentSign = 1 if scipy.mean(self.getIpMeas()) > 1e5 else -1
return self._currentSign
def getParam(self, path):
"""Backup function, applying a direct path input for tree-like data
storage access for parameters not typically found in
:py:class:`Equilbrium <eqtools.core.Equilbrium>` object.
Directly calls attributes read from g/a-files in copy-safe manner.
Args:
name (String): Parameter name for value stored in EqdskReader
instance.
Raises:
AttributeError: raised if no attribute is found.
"""
if self._root in path:
EFITpath = path
else:
EFITpath = self._root+path
try:
var = self._MDSTree.getNode(EFITpath).data()
return var
except AttributeError:
raise ValueError('invalid MDS tree.')
except:
raise ValueError('path '+EFITpath+' is not valid.')
| gpl-3.0 |
chrisburr/scikit-learn | examples/linear_model/plot_lasso_lars.py | 363 | 1080 | #!/usr/bin/env python
"""
=====================
Lasso path using LARS
=====================
Computes Lasso Path along the regularization parameter using the LARS
algorithm on the diabetes dataset. Each color represents a different
feature of the coefficient vector, and this is displayed as a function
of the regularization parameter.
"""
print(__doc__)
# Author: Fabian Pedregosa <[email protected]>
# Alexandre Gramfort <[email protected]>
# License: BSD 3 clause
import numpy as np
import matplotlib.pyplot as plt
from sklearn import linear_model
from sklearn import datasets
diabetes = datasets.load_diabetes()
X = diabetes.data
y = diabetes.target
print("Computing regularization path using the LARS ...")
alphas, _, coefs = linear_model.lars_path(X, y, method='lasso', verbose=True)
xx = np.sum(np.abs(coefs.T), axis=1)
xx /= xx[-1]
plt.plot(xx, coefs.T)
ymin, ymax = plt.ylim()
plt.vlines(xx, ymin, ymax, linestyle='dashed')
plt.xlabel('|coef| / max|coef|')
plt.ylabel('Coefficients')
plt.title('LASSO Path')
plt.axis('tight')
plt.show()
| bsd-3-clause |
HeraclesHX/scikit-learn | examples/mixture/plot_gmm_classifier.py | 250 | 3918 | """
==================
GMM classification
==================
Demonstration of Gaussian mixture models for classification.
See :ref:`gmm` for more information on the estimator.
Plots predicted labels on both training and held out test data using a
variety of GMM classifiers on the iris dataset.
Compares GMMs with spherical, diagonal, full, and tied covariance
matrices in increasing order of performance. Although one would
expect full covariance to perform best in general, it is prone to
overfitting on small datasets and does not generalize well to held out
test data.
On the plots, train data is shown as dots, while test data is shown as
crosses. The iris dataset is four-dimensional. Only the first two
dimensions are shown here, and thus some points are separated in other
dimensions.
"""
print(__doc__)
# Author: Ron Weiss <[email protected]>, Gael Varoquaux
# License: BSD 3 clause
# $Id$
import matplotlib.pyplot as plt
import matplotlib as mpl
import numpy as np
from sklearn import datasets
from sklearn.cross_validation import StratifiedKFold
from sklearn.externals.six.moves import xrange
from sklearn.mixture import GMM
def make_ellipses(gmm, ax):
for n, color in enumerate('rgb'):
v, w = np.linalg.eigh(gmm._get_covars()[n][:2, :2])
u = w[0] / np.linalg.norm(w[0])
angle = np.arctan2(u[1], u[0])
angle = 180 * angle / np.pi # convert to degrees
v *= 9
ell = mpl.patches.Ellipse(gmm.means_[n, :2], v[0], v[1],
180 + angle, color=color)
ell.set_clip_box(ax.bbox)
ell.set_alpha(0.5)
ax.add_artist(ell)
iris = datasets.load_iris()
# Break up the dataset into non-overlapping training (75%) and testing
# (25%) sets.
skf = StratifiedKFold(iris.target, n_folds=4)
# Only take the first fold.
train_index, test_index = next(iter(skf))
X_train = iris.data[train_index]
y_train = iris.target[train_index]
X_test = iris.data[test_index]
y_test = iris.target[test_index]
n_classes = len(np.unique(y_train))
# Try GMMs using different types of covariances.
classifiers = dict((covar_type, GMM(n_components=n_classes,
covariance_type=covar_type, init_params='wc', n_iter=20))
for covar_type in ['spherical', 'diag', 'tied', 'full'])
n_classifiers = len(classifiers)
plt.figure(figsize=(3 * n_classifiers / 2, 6))
plt.subplots_adjust(bottom=.01, top=0.95, hspace=.15, wspace=.05,
left=.01, right=.99)
for index, (name, classifier) in enumerate(classifiers.items()):
# Since we have class labels for the training data, we can
# initialize the GMM parameters in a supervised manner.
classifier.means_ = np.array([X_train[y_train == i].mean(axis=0)
for i in xrange(n_classes)])
# Train the other parameters using the EM algorithm.
classifier.fit(X_train)
h = plt.subplot(2, n_classifiers / 2, index + 1)
make_ellipses(classifier, h)
for n, color in enumerate('rgb'):
data = iris.data[iris.target == n]
plt.scatter(data[:, 0], data[:, 1], 0.8, color=color,
label=iris.target_names[n])
# Plot the test data with crosses
for n, color in enumerate('rgb'):
data = X_test[y_test == n]
plt.plot(data[:, 0], data[:, 1], 'x', color=color)
y_train_pred = classifier.predict(X_train)
train_accuracy = np.mean(y_train_pred.ravel() == y_train.ravel()) * 100
plt.text(0.05, 0.9, 'Train accuracy: %.1f' % train_accuracy,
transform=h.transAxes)
y_test_pred = classifier.predict(X_test)
test_accuracy = np.mean(y_test_pred.ravel() == y_test.ravel()) * 100
plt.text(0.05, 0.8, 'Test accuracy: %.1f' % test_accuracy,
transform=h.transAxes)
plt.xticks(())
plt.yticks(())
plt.title(name)
plt.legend(loc='lower right', prop=dict(size=12))
plt.show()
| bsd-3-clause |
marvinlenk/subsystem_entropy | entPlot.py | 1 | 48685 | import numpy as np
import dft
import os as os
import scipy.integrate as scint
import matplotlib as mpl
mpl.use('Agg')
from matplotlib.pyplot import cm, step
from matplotlib.backends.backend_pdf import PdfPages
import matplotlib.animation as animation
import matplotlib.pyplot as plt
from scipy.signal import savgol_filter
from scipy.integrate import cumtrapz
# searches for closest to value element in array
def find_nearest(array, value):
i = (np.abs(array - value)).argmin()
return int(i)
# This is a workaround until scipy fixes the issue
import warnings
warnings.filterwarnings(action="ignore", module="scipy", message="^internal gelsd")
# noinspection PyStringFormat
def plotData(sysVar):
print("Plotting datapoints to pdf", end='')
avgstyle = 'dashed'
avgsize = 0.6
expectstyle = 'solid'
expectsize = 1
loavgpercent = sysVar.plotLoAvgPerc # percentage of time evolution to start averaging
loavgind = int(loavgpercent * sysVar.dataPoints) # index to start at when calculating average and stddev
loavgtime = np.round(loavgpercent * (sysVar.deltaT * sysVar.steps * sysVar.plotTimeScale), 2)
if sysVar.boolPlotAverages:
print(' with averaging from Jt=%.2f' % loavgtime, end='')
fwidth = sysVar.plotSavgolFrame
ford = sysVar.plotSavgolOrder
params = {
'legend.fontsize': sysVar.plotLegendSize,
'font.size': sysVar.plotFontSize,
'mathtext.default': 'rm' # see http://matplotlib.org/users/customizing.html
}
plt.rcParams['agg.path.chunksize'] = 0
plt.rcParams.update(params)
plt.rc('text', usetex=True)
plt.rc('font', **{'family': 'sans-serif', 'sans-serif': ['Arial']})
pp = PdfPages(sysVar.dataFolder + 'plots/plots.pdf')
if sysVar.boolOccupations:
occfile = sysVar.dataFolder + 'occupation.dat'
occ_array = np.loadtxt(occfile)
normfile = sysVar.dataFolder + 'norm.dat'
norm_array = np.loadtxt(normfile)
# want deviation from 1
norm_array[:, 1] = 1 - norm_array[:, 1]
if sysVar.boolReducedEntropy:
entfile = sysVar.dataFolder + 'entropy.dat'
ent_array = np.loadtxt(entfile)
if sysVar.boolPlotEngy:
engies = np.loadtxt(sysVar.dataFolder + 'hamiltonian_eigvals.dat')
if sysVar.boolPlotDecomp:
stfacts = np.loadtxt(sysVar.dataFolder + 'state.dat')
if sysVar.boolTotalEntropy:
totentfile = sysVar.dataFolder + 'total_entropy.dat'
totent_array = np.loadtxt(totentfile)
if sysVar.boolTotalEnergy:
energyfile = sysVar.dataFolder + 'total_energy.dat'
en_array = np.loadtxt(energyfile)
en0 = en_array[0, 1]
en_array[:, 1] -= en0
# en_micind = find_nearest(engies[:,1], en0)
# print(' - |(E0 - Emicro)/E0|: %.0e - ' % (np.abs((en0 - engies[en_micind,1])/en0)), end='' )
if sysVar.boolReducedEnergy:
redEnergyfile = sysVar.dataFolder + 'reduced_energy.dat'
redEnergy = np.loadtxt(redEnergyfile)
if sysVar.boolPlotOccEnDiag:
occendiag = []
occendiagweighted = []
for i in range(sysVar.m):
occendiag.append(np.loadtxt(sysVar.dataFolder + 'occ_energybasis_diagonals_%i.dat' % i))
occendiagweighted.append(np.loadtxt(sysVar.dataFolder + 'occ_energybasis_diagonals_weighted_%i.dat' % i))
if sysVar.boolPlotOccEnDiagExp:
microexpfile = sysVar.dataFolder + 'occ_energybasis_diagonals_expectation.dat'
microexp = np.loadtxt(microexpfile)
if sysVar.boolPlotOffDiagOcc:
offdiagoccfile = sysVar.dataFolder + 'offdiagocc.dat'
offdiagocc = np.loadtxt(offdiagoccfile)
if sysVar.boolPlotOffDiagDens:
offdiagdensfile = sysVar.dataFolder + 'offdiagdens.dat'
offdiagdens = np.loadtxt(offdiagdensfile)
if sysVar.boolPlotOffDiagDensRed:
offdiagdensredfile = sysVar.dataFolder + 'offdiagdensred.dat'
offdiagdensred = np.loadtxt(offdiagdensredfile)
if sysVar.boolPlotGreen:
greenfile = sysVar.dataFolder + '' + [s for s in os.listdir(sysVar.dataFolder + '') if 'green' in s][0]
greendat = np.loadtxt(greenfile)
def complete_system_enttropy():
return 0
# ### Complete system Entropy
if sysVar.boolTotalEntropy:
plt.plot(totent_array[:, 0] * sysVar.plotTimeScale, totent_array[:, 1] * 1e13, linewidth=0.6, color='r')
plt.grid()
plt.xlabel(r'$J\,t$')
plt.ylabel(r'Total system entropy $/ 10^{-13}$')
plt.tight_layout()
###
pp.savefig()
plt.clf()
print('.', end='', flush=True)
def subsystem_entropy():
return 0
# ## Subsystem Entropy
if sysVar.boolReducedEntropy:
step_array = ent_array[:, 0] * sysVar.plotTimeScale
plt.plot(step_array, ent_array[:, 1], linewidth=0.8, color='r')
plt.grid()
if sysVar.boolPlotAverages:
tavg = savgol_filter(ent_array[:, 1], fwidth, ford)
plt.plot(step_array[loavgind:], tavg[loavgind:], linewidth=avgsize, linestyle=avgstyle, color='black')
plt.xlabel(r'$J\,t$')
plt.ylabel('Subsystem entropy')
plt.tight_layout()
pp.savefig()
plt.clf()
print('.', end='', flush=True)
# Subsystem entropy with inlay
max_time = step_array[-1]
max_ind = int(max_time / step_array[-1] * len(step_array))
avg = np.mean(ent_array[loavgind:, 1])
plt.plot(step_array[:], ent_array[:, 1], linewidth=0.3, color='r')
if sysVar.boolPlotAverages:
tavg = savgol_filter(ent_array[:, 1], fwidth, ford)
plt.plot(step_array[loavgind:], tavg[loavgind:], linewidth=avgsize, linestyle=avgstyle, color='black')
plt.xlabel(r'$J\,t$')
plt.ylabel(r'Subsystem entropy $S\textsubscript{sys}$')
a = plt.axes([.5, .3, .4, .4])
plt.semilogy(step_array[:max_ind], np.abs(avg - ent_array[:max_ind, 1]), linewidth=0.3, color='r')
plt.ylabel(r'$|\,\overline{S}\textsubscript{sys} - S\textsubscript{sys}(t)|$')
plt.yticks([])
pp.savefig()
plt.clf()
print('.', end='', flush=True)
'''
###FFT
print('')
fourier = np.fft.rfft(ent_array[loavgind:,1])
print(fourier[0].real)
freq = np.fft.rfftfreq(np.shape(ent_array[loavgind:,1])[-1], d=step_array[1])
plt.plot(freq[1:],np.abs(fourier[1:]))
print('')
plt.ylabel(r'$A_{\omega}$')
plt.xlabel(r'$\omega$')
plt.grid()
plt.tight_layout()
###
pp.savefig()
plt.clf()
print('.',end='',flush=True)
'''
def single_level_occ():
return 0
# ## Single-level occupation numbers
if sysVar.boolOccupations:
step_array = occ_array[:, 0] * sysVar.plotTimeScale
for i in range(sysVar.m):
plt.plot(step_array, occ_array[:, i + 1], label=r'$n_' + str(i) + '$', linewidth=0.5)
if sysVar.boolPlotAverages:
tavg = savgol_filter(occ_array[:, i + 1], fwidth, ford)
plt.plot(step_array[loavgind:], tavg[loavgind:], linewidth=avgsize, linestyle=avgstyle, color='black')
if sysVar.boolPlotOccEnDiagExp:
plt.axhline(y=microexp[i, 1], color='purple', linewidth=expectsize, linestyle=expectstyle)
plt.ylabel(r'Occupation number')
plt.xlabel(r'$J\,t$')
plt.legend(loc='upper right')
plt.grid()
plt.tight_layout()
###
pp.savefig()
plt.clf()
print('.', end='', flush=True)
'''
###FFT
print('')
for i in range(0,sysVar.m):
plt.xlim(xmax=30)
#GK = -i(2n-1)
fourier = (rfft(occ_array[loavgind:,i+1],norm='ortho'))*2 -1
print(fourier[0].real)
freq = rfftfreq(np.shape(occ_array[loavgind:,i+1])[-1], d=step_array[1])
plt.plot(freq,fourier.real,linewidth = 0.05)
plt.plot(freq,fourier.imag,linewidth = 0.05)
plt.ylabel(r'$G^K_{\omega}$')
plt.xlabel(r'$\omega$')
plt.grid()
plt.tight_layout()
###
pp.savefig()
plt.clf()
print('.',end='',flush=True)
'''
def bath_occ():
return 0
# ## Traced out (bath) occupation numbers
for i in sysVar.kRed:
plt.plot(step_array, occ_array[:, i + 1], label=r'$n_' + str(i) + '$', linewidth=0.6)
if sysVar.boolPlotOccEnDiagExp:
plt.axhline(y=microexp[i, 1], color='purple', linewidth=expectsize, linestyle=expectstyle)
if sysVar.boolPlotAverages:
tavg = savgol_filter(occ_array[:, i + 1], fwidth, ford)
plt.plot(step_array[loavgind:], tavg[loavgind:], linewidth=avgsize, linestyle=avgstyle, color='black')
plt.ylabel(r'Occupation number')
plt.xlabel(r'$J\,t$')
plt.legend(loc='lower right')
plt.grid()
plt.tight_layout()
###
pp.savefig()
plt.clf()
print('.', end='', flush=True)
def system_occ():
return 0
# ## Leftover (system) occupation numbers
for i in np.arange(sysVar.m)[sysVar.mask]:
plt.plot(step_array, occ_array[:, i + 1], label=r'$n_' + str(i) + '$', linewidth=0.6)
if sysVar.boolPlotOccEnDiagExp:
plt.axhline(y=microexp[i, 1], color='purple', linewidth=expectsize, linestyle=expectstyle)
if sysVar.boolPlotAverages:
tavg = savgol_filter(occ_array[:, i + 1], fwidth, ford)
plt.plot(step_array[loavgind:], tavg[loavgind:], linewidth=avgsize, linestyle=avgstyle, color='black')
plt.ylabel(r'Occupation number')
plt.xlabel(r'$J\,t$')
plt.legend(loc='lower right')
plt.grid()
plt.tight_layout()
###
pp.savefig()
plt.clf()
print('.', end='', flush=True)
def subsystem_occupation():
return 0
# ## Subsystems occupation numbers
# store fluctuations in a data
fldat = open(sysVar.dataFolder + 'fluctuation.dat', 'w')
fldat.write('N_tot: %i\n' % (sysVar.N))
tmp = np.zeros(len(step_array))
for i in sysVar.kRed:
tmp += occ_array[:, i + 1]
plt.plot(step_array, tmp, label="bath", linewidth=0.8, color='magenta')
if sysVar.boolPlotAverages:
tavg = savgol_filter(tmp, fwidth, ford)
plt.plot(step_array[loavgind:], tavg[loavgind:], linewidth=avgsize, linestyle=avgstyle, color='black')
if sysVar.boolPlotOccEnDiagExp:
mictmp = 0
for i in sysVar.kRed:
mictmp += microexp[i, 1]
plt.axhline(y=mictmp, color='purple', linewidth=expectsize, linestyle=expectstyle)
avg = np.mean(tmp[loavgind:], dtype=np.float64)
stddev = np.std(tmp[loavgind:], dtype=np.float64)
fldat.write('bath_average: %.16e\n' % avg)
fldat.write('bath_stddev: %.16e\n' % stddev)
# noinspection PyStringFormat
fldat.write("bath_rel._fluctuation: %.16e\n" % (stddev / avg))
tmp.fill(0)
for i in np.arange(sysVar.m)[sysVar.mask]:
tmp += occ_array[:, i + 1]
plt.plot(step_array, tmp, label="system", linewidth=0.8, color='darkgreen')
if sysVar.boolPlotAverages:
tavg = savgol_filter(tmp, fwidth, ford)
plt.plot(step_array[loavgind:], tavg[loavgind:], linewidth=avgsize, linestyle=avgstyle, color='black')
if sysVar.boolPlotOccEnDiagExp:
mictmp = 0
for i in np.arange(sysVar.m)[sysVar.mask]:
mictmp += microexp[i, 1]
plt.axhline(y=mictmp, color='purple', linewidth=expectsize, linestyle=expectstyle)
avg = np.mean(tmp[loavgind:], dtype=np.float64)
stddev = np.std(tmp[loavgind:], dtype=np.float64)
fldat.write('system_average: %.16e\n' % avg)
fldat.write('system_stddev: %.16e\n' % stddev)
fldat.write('system_rel._fluctuation: %.16e\n' % (stddev / avg))
for i in range(sysVar.m):
avg = np.mean(occ_array[loavgind:, i + 1], dtype=np.float64)
stddev = np.std(occ_array[loavgind:, i + 1], dtype=np.float64)
fldat.write('n%i_average: %.16e\n' % (i, avg))
fldat.write('n%i_stddev: %.16e\n' % (i, stddev))
fldat.write('n%i_rel._fluctuation: %.16e\n' % (i, (stddev / avg)))
if sysVar.boolReducedEntropy:
avg = np.mean(ent_array[loavgind:, 1], dtype=np.float64)
stddev = np.std(ent_array[loavgind:, 1], dtype=np.float64)
fldat.write('ssentropy_average: %.16e\n' % avg)
fldat.write('ssentropy_stddev: %.16e\n' % stddev)
fldat.write('ssentropy_rel._fluctuation: %.16e\n' % (stddev / avg))
fldat.close()
plt.ylabel(r'Occupation number')
plt.xlabel(r'$J\,t$')
plt.legend(loc='center right')
plt.grid()
plt.tight_layout()
###
pp.savefig()
plt.clf()
print('.', end='', flush=True)
def occ_distribution():
return 0
# occupation number in levels against level index
occavg = np.loadtxt(sysVar.dataFolder + 'fluctuation.dat', usecols=(1,))
plt.xlim(-0.1, sysVar.m - 0.9)
for l in range(sysVar.m):
plt.errorbar(l, occavg[int(7 + 3 * l)] / sysVar.N, xerr=None, yerr=occavg[int(8 + 3 * l)] / sysVar.N,
marker='o', color=cm.Set1(0))
plt.ylabel(r'Relative level occupation')
plt.xlabel(r'Level index')
plt.grid()
plt.tight_layout()
###
pp.savefig()
plt.clf()
print('.', end='', flush=True)
def sum_offdiagonals():
return 0
# sum of off diagonal elements in energy eigenbasis
if sysVar.boolPlotOffDiagOcc:
for i in range(sysVar.m):
plt.plot(step_array, offdiagocc[:, i + 1], label=r'$n_' + str(i) + '$', linewidth=0.5)
plt.ylabel(r'Sum of off diagonals')
plt.xlabel(r'$J\,t$')
plt.legend(loc='upper right')
plt.grid()
plt.tight_layout()
###
pp.savefig()
plt.clf()
dt = offdiagocc[1, 0] - offdiagocc[0, 0]
nrm = offdiagocc[:, 0] / dt
nrm[1:] = 1 / nrm[1:]
for i in range(sysVar.m):
# ##### only sum (subsystem-thermalization)
plt.ylabel('Sum of off diagonals in $n^{%i}$' % (i))
# start at 10% of the whole x-axis
lox = (offdiagocc[-1, 0] - offdiagocc[0, 0]) / 10 + offdiagocc[0, 0]
hiy = offdiagocc[int(len(offdiagocc[:, 0]) / 10), 0] * 1.1
plt.plot(offdiagocc[:, 0], offdiagocc[:, i + 1], linewidth=0.5)
plt.xlim(xmin=lox)
plt.ylim(ymax=hiy)
plt.grid()
plt.tight_layout()
# ##inlay with the whole deal
a = plt.axes([0.62, 0.6, 0.28, 0.28])
a.plot(offdiagocc[:, 0], offdiagocc[:, i + 1], linewidth=0.8)
a.set_xticks([])
a.set_yticks([])
###
pp.savefig()
plt.clf()
plt.ylabel('Sum of off diagonals in $n^{%i}$' % (i))
plt.semilogy(offdiagocc[:, 0], np.abs(offdiagocc[:, i + 1]), linewidth=0.5)
plt.xlim(xmin=lox)
plt.ylim(ymin=1e-2)
plt.grid()
plt.tight_layout()
# ##inlay with the whole deal
a = plt.axes([0.62, 0.6, 0.28, 0.28])
a.semilogy(offdiagocc[:, 0], offdiagocc[:, i + 1], linewidth=0.8)
a.set_ylim(ymin=1e-2)
a.set_xticks([])
a.set_yticks([])
###
pp.savefig()
plt.clf()
# ##### average (eigenstate-thermalization)
f, (ax1, ax2) = plt.subplots(2, sharex=False, sharey=False)
tmp = cumtrapz(offdiagocc[:, i + 1], offdiagocc[:, 0], initial=offdiagocc[0, i + 1])
tmp = np.multiply(tmp, nrm)
f.text(0.03, 0.5, 'Average of summed off diagonals in $n^{%i}$' % i, ha='center', va='center',
rotation='vertical')
ax1.ticklabel_format(style='sci', axis='y', scilimits=(0, 0))
ax1.plot(offdiagocc[:, 0], tmp, linewidth=0.5)
ax1.grid()
ax2.loglog(offdiagocc[:, 0], np.abs(tmp), linewidth=0.5)
ax2.set_ylim(bottom=1e-4)
ax2.grid()
plt.tight_layout()
plt.subplots_adjust(left=0.12)
###
pp.savefig()
plt.clf()
print('.', end='', flush=True)
def sum_offdiagonalsdens():
return 0
if sysVar.boolPlotOffDiagDens:
plt.plot(step_array, offdiagdens[:, 1], linewidth=0.5)
plt.ylabel(r'Sum of off diagonals (dens. mat.)')
plt.xlabel(r'$J\,t$')
plt.grid()
plt.tight_layout()
###
pp.savefig()
plt.clf()
def sum_offdiagonalsdensred():
return 0
if sysVar.boolPlotOffDiagDensRed:
plt.plot(step_array, offdiagdensred[:, 1], linewidth=0.5)
plt.ylabel(r'Sum of off diagonals (red. dens. mat.)')
plt.xlabel(r'$J\,t$')
plt.grid()
plt.tight_layout()
###
pp.savefig()
plt.clf()
def occupation_energybasis_diagonals():
return 0
if sysVar.boolPlotOccEnDiag:
for i in range(sysVar.m):
plt.plot(occendiag[i][:, 1], occendiag[i][:, 2], marker='o', markersize=0.5, linestyle=None)
plt.ylabel(r'matrix element of $n_%i$' % i)
plt.xlabel(r'$E / J$')
plt.grid()
plt.tight_layout()
###
pp.savefig()
plt.clf()
###
plt.plot(occendiagweighted[i][:, 1], occendiagweighted[i][:, 2], marker='o', markersize=0.5, linestyle=None)
plt.ylabel(r'weighted matrix element of $n_%i$' % i)
plt.xlabel(r'$E / J$')
plt.grid()
plt.tight_layout()
###
pp.savefig()
plt.clf()
def total_energy():
return 0
# ## Total system energy
if sysVar.boolTotalEnergy:
plt.title('$E_{tot}, \; E_0$ = %.2f' % en0)
plt.plot(en_array[:, 0] * sysVar.plotTimeScale, en_array[:, 1] * 1e10, linewidth=0.6)
plt.ylabel(r'$E_{tot} - E_0 / 10^{-10}$')
plt.xlabel(r'$J\,t$')
plt.grid()
plt.tight_layout()
###
pp.savefig()
plt.clf()
print('.', end='', flush=True)
def reduced_energy():
return 0
# ## Total system energy
if sysVar.boolReducedEnergy:
plt.plot(redEnergy[:, 0] * sysVar.plotTimeScale, redEnergy[:, 1], linewidth=0.6)
plt.ylabel(r'$E_{sys}$')
plt.xlabel(r'$J\,t$')
plt.grid()
plt.tight_layout()
###
pp.savefig()
plt.clf()
print('.', end='', flush=True)
def norm_deviation():
return 0
# ## Norm deviation
step_array = norm_array[:, 0] * sysVar.plotTimeScale
plt.plot(step_array, norm_array[:, 1], "ro", ms=0.5)
plt.ylabel('norm deviation from 1')
plt.xlabel(r'$J\,t$')
plt.grid(False)
plt.tight_layout()
###
pp.savefig()
plt.clf()
print('.', end='', flush=True)
###
plt.title('State Norm multiplied (deviation from 1)')
plt.plot(step_array, norm_array[:, 2] - 1, linewidth=0.6, color='r')
plt.ylabel('correction factor - 1')
plt.xlabel(r'$J\,t$')
plt.grid()
plt.tight_layout()
###
pp.savefig()
plt.clf()
print('.', end='', flush=True)
def eigenvalues():
return 0
# ## Hamiltonian eigenvalues (Eigenenergies)
if sysVar.boolPlotEngy:
linearize = False
if linearize:
tap = []
lal = -1
for e in engies[:, 1]:
if lal == -1:
tap.append(e)
lal += 1
elif np.abs(e - tap[lal]) > 1:
lal += 1
tap.append(e)
plt.plot(tap, linestyle='none', marker='o', ms=0.5, color='blue')
else:
plt.plot(engies[:, 0], engies[:, 1], linestyle='none', marker='o', ms=0.5, color='blue')
plt.ylabel(r'Energy / J')
plt.xlabel(r'Eigenvalue Index')
plt.grid(False)
plt.xlim(xmin=-(len(engies[:, 0]) * (2.0 / 100)))
plt.tight_layout()
###
pp.savefig()
plt.clf()
print('.', end='', flush=True)
def density_of_states():
return 0
# ## DOS
if sysVar.boolPlotDOS:
dos = np.zeros(sysVar.dim)
window = 50
iw = window
for i in range(iw, sysVar.dim - iw):
dos[i] = (window) * 2 / (engies[i + iw, 1] - engies[i - iw, 1])
dos /= (sysVar.dim - iw)
print(scint.simps(dos[iw:], engies[iw:, 1]))
plt.plot(engies[:, 1], dos, lw=0.005)
plt.ylabel(r'Density of states')
plt.xlabel(r'Energy / J')
plt.grid(False)
plt.tight_layout()
###
pp.savefig()
plt.clf()
print('.', end='', flush=True)
def green_function():
return 0
# ## Green function
if sysVar.boolPlotGreen:
if False:
if not(os.path.isfile(sysVar.dataFolder + 'spectral_frequency_trace.dat') and
os.path.isfile(sysVar.dataFolder + 'statistical_frequency_trace.dat')):
spectral_time = []
statistical_time = []
spectral_frequency = []
statistical_frequency = []
sample_frequency = 1.0 / ((greendat[-1, 0] - greendat[0, 0]) / (len(greendat[:, 0]) - 1))
for i in range(sysVar.m):
ind = 1 + i * 4
greater = (greendat[:, ind] + 1j * greendat[:, ind + 1]) # greater green function
lesser = (greendat[:, ind + 2] + 1j * greendat[:, ind + 3]) # lesser green function
spectral_time.append(1j * (greater - lesser)) # spectral function in time space
statistical_time.append((greater + lesser) / 2) # statistical function in time space
# spectral function in frequency space
spectral_frequency.append(dft.rearrange(dft.dft(spectral_time[i], sample_frequency)))
# statistical function in frequency space
statistical_frequency.append(dft.rearrange(dft.dft(statistical_time[i], sample_frequency)))
spectral_frequency_trace = spectral_frequency[0]
statistical_frequency_trace = statistical_frequency[0]
for i in range(1, sysVar.m):
spectral_frequency_trace[:, 1] = spectral_frequency_trace[:, 1] + spectral_frequency[i][:, 1]
statistical_frequency_trace[:, 1] = statistical_frequency_trace[:, 1] + statistical_frequency[i][:, 1]
np.savetxt(sysVar.dataFolder + 'spectral_frequency_trace.dat',
np.column_stack((spectral_frequency_trace[:, 0].real, spectral_frequency_trace[:, 1].real,
spectral_frequency_trace[:, 1].imag)))
np.savetxt(sysVar.dataFolder + 'statistical_frequency_trace.dat',
np.column_stack((statistical_frequency_trace[:, 0].real, statistical_frequency_trace[:, 1].real,
statistical_frequency_trace[:, 1].imag)))
else:
spectral_frequency_trace_tmp = np.loadtxt(sysVar.dataFolder + 'spectral_frequency_trace.dat')
statistical_frequency_trace_tmp = np.loadtxt(sysVar.dataFolder + 'statistical_frequency_trace.dat')
spectral_frequency_trace = np.column_stack((
spectral_frequency_trace_tmp[:, 0],
(spectral_frequency_trace_tmp[:, 1] + 1j*spectral_frequency_trace_tmp[:, 2])
))
statistical_frequency_trace = np.column_stack((
statistical_frequency_trace_tmp[:, 0],
(statistical_frequency_trace_tmp[:, 1] + 1j*statistical_frequency_trace_tmp[:, 2])
))
fig = plt.figure()
ax1 = fig.add_subplot(221)
ax1.set_xlim(xmin=-10, xmax=100)
ax1.set_ylabel(r'Re $A(\omega)$')
ax1.set_xlabel(r'$\omega$')
ax1.plot(spectral_frequency_trace[:, 0].real, spectral_frequency_trace[:, 1].real)
ax2 = fig.add_subplot(222)
ax2.set_xlim(xmin=-10, xmax=100)
ax2.set_ylabel(r'Im $A(\omega)$')
ax2.set_xlabel(r'$\omega$')
ax2.plot(spectral_frequency_trace[:, 0].real, spectral_frequency_trace[:, 1].imag)
ax3 = fig.add_subplot(223)
ax3.set_xlim(xmin=-10, xmax=100)
ax3.set_ylabel(r'Re $F(\omega)$')
ax3.set_xlabel(r'$\omega$')
ax3.plot(statistical_frequency_trace[:, 0].real, statistical_frequency_trace[:, 1].real)
ax4 = fig.add_subplot(224)
ax4.set_xlim(xmin=-10, xmax=100)
ax4.set_ylabel(r'Im $F(\omega)$')
ax4.set_xlabel(r'$\omega$')
ax4.plot(statistical_frequency_trace[:, 0].real, statistical_frequency_trace[:, 1].imag)
plt.tight_layout()
pp.savefig()
plt.clf()
# ## ##
fig = plt.figure()
ax1 = fig.add_subplot(121)
ax1.set_xlim(xmin=-10, xmax=80)
ax1.set_ylabel(r'$|A(\omega)|$')
ax1.set_xlabel(r'$\omega$')
ax1.plot(spectral_frequency_trace[:, 0].real, np.abs(spectral_frequency_trace[:, 1]))
ax2 = fig.add_subplot(122)
ax2.set_xlim(xmin=-10, xmax=80)
ax2.set_ylabel(r'$|F(\omega)|$')
ax2.set_xlabel(r'$\omega$')
ax2.plot(statistical_frequency_trace[:, 0].real, np.abs(statistical_frequency_trace[:, 1]))
plt.tight_layout()
pp.savefig()
plt.clf()
print('.', end='', flush=True)
if sysVar.boolPlotDecomp:
def eigendecomposition():
return 0
### Hamiltonian eigenvalues (Eigenenergies) with decomposition
fig, ax1 = plt.subplots()
energy_markersize = 0.7
energy_barsize = 0.06
if sysVar.dim != 1:
energy_markersize *= (2.0 / np.log10(sysVar.dim))
energy_barsize *= (4.0 / np.log10(sysVar.dim))
ax1.plot(engies[:, 0], engies[:, 1], linestyle='none', marker='o', ms=energy_markersize, color='blue')
ax1.set_ylabel(r'Energy / J')
ax1.set_xlabel(r'Eigenvalue index n')
ax2 = ax1.twinx()
ax2.bar(engies[:, 0], engies[:, 2], alpha=0.8, color='red', width=0.03, align='center')
ax2.set_ylabel(r'$|c_n|^2$')
plt.grid(False)
ax1.set_xlim(xmin=-(len(engies[:, 0]) * (5.0 / 100)))
plt.tight_layout()
###
pp.savefig()
plt.clf()
print('.', end='', flush=True)
### Eigenvalue decomposition with energy x-axis
plt.bar(engies[:, 1], engies[:, 2], alpha=0.8, color='red', width=energy_barsize, align='center')
plt.xlabel(r'Energy / J')
plt.ylabel(r'$|c_E|^2$')
plt.grid(False)
plt.xlim(xmin=-(np.abs(engies[0, 1] - engies[-1, 1]) * (5.0 / 100)))
plt.tight_layout()
###
pp.savefig()
plt.clf()
print('.', end='', flush=True)
# omit this in general
'''
### Eigenvalue decomposition en detail
n_rows = 3 #abs**2, phase/2pi, energy on a range from 0 to 1
n_rows += 1 #spacer
n_rows += sysVar.m #occupation numbers
index = np.arange(sysVar.dim)
bar_width = 1
plt.xlim(0,sysVar.dim)
# Initialize the vertical-offset for the stacked bar chart.
y_offset = np.array([0.0] * sysVar.dim)
spacing = np.array([1] * sysVar.dim)
enInt = np.abs(engies[-1,1] - engies[0,1])
cmapVar = plt.cm.OrRd
cmapVar.set_under(color='black')
plt.ylim(0,n_rows)
#energy
plt.bar(index, spacing , bar_width, bottom=y_offset, color=cmapVar((engies[:,1]-engies[0,1])/enInt), linewidth=0.00, edgecolor='gray')
y_offset = y_offset + spacing
#abs squared
plt.bar(index, spacing , bar_width, bottom=y_offset, color=cmapVar(engies[:,2]/np.amax(engies[:,2]) - 1e-16), linewidth=0.00, edgecolor='gray')
y_offset = y_offset + spacing
#phase / 2pi
plt.bar(index, spacing , bar_width, bottom=y_offset, color=cmapVar(engies[:,3] - 1e-16), linewidth=0.00, edgecolor='gray')
y_offset = y_offset + spacing
plt.bar(index, spacing, bar_width, bottom=y_offset, color='white', linewidth=0)
y_offset = y_offset + np.array([1] * sysVar.dim)
#expectation values
for row in range(4, n_rows):
plt.bar(index, spacing , bar_width, bottom=y_offset, color=cmapVar(engies[:,row]/sysVar.N - 1e-16), linewidth=0.00, edgecolor='gray')
y_offset = y_offset + spacing
plt.ylabel("tba")
plt.tight_layout()
###
pp.savefig()
plt.clf()
print('.',end='',flush=True)
### Occupation number basis decomposition en detail
n_rows = 2 #abs**2, phase/2pi
n_rows += 1 #spacer
n_rows += sysVar.m #occupation numbers
index = np.arange(sysVar.dim)
bar_width = 1
plt.xlim(0,sysVar.dim)
# Initialize the vertical-offset for the stacked bar chart.
y_offset = np.array([0.0] * sysVar.dim)
spacing = np.array([1] * sysVar.dim)
cmapVar = plt.cm.OrRd
cmapVar.set_under(color='black')
plt.ylim(0,n_rows)
# abs squared
plt.bar(index, spacing , bar_width, bottom=y_offset, color=cmapVar(stfacts[:,1]/np.amax(stfacts[:,1]) - 1e-16), linewidth=0.00, edgecolor='gray')
y_offset = y_offset + spacing
# phase / 2pi
plt.bar(index, spacing , bar_width, bottom=y_offset, color=cmapVar(stfacts[:,2] - 1e-16), linewidth=0.00, edgecolor='gray')
y_offset = y_offset + spacing
plt.bar(index, spacing, bar_width, bottom=y_offset, color='white', linewidth=0)
y_offset = y_offset + np.array([1] * sysVar.dim)
for row in range(3, n_rows):
plt.bar(index, spacing , bar_width, bottom=y_offset, color=cmapVar(stfacts[:,row]/sysVar.N - 1e-16), linewidth=0.00, edgecolor='gray')
y_offset = y_offset + spacing
plt.ylabel("tba")
plt.tight_layout()
###
pp.savefig()
plt.clf()
print('.',end='',flush=True)
'''
def densmat_spectral():
return 0
####### Density matrix in spectral repesentation
if sysVar.boolPlotSpectralDensity:
###
plt.title('Density matrix spectral repres. abs')
dabs = np.loadtxt(sysVar.dataFolder + 'spectral/dm.dat')
cmapVar = plt.cm.Reds
cmapVar.set_under(color='black')
plt.imshow(dabs, cmap=cmapVar, interpolation='none', vmin=1e-16)
plt.colorbar()
###
pp.savefig()
plt.clf()
print('.', end='', flush=True)
###
pp.close()
print(" done!")
def plotDensityMatrixAnimation(steps, delta_t, files, stepsize=1, red=0, framerate=30):
if files % stepsize != 0:
stepsize = int(files / 100)
if red == 0:
rdstr = ''
rdprstr = ''
else:
rdstr = 'red_'
rdprstr = 'reduced-'
print("Plotting " + rdprstr + "density matrix animation", end='', flush=True)
stor_step = steps / files
fig = plt.figure(num=None, figsize=(30, 10), dpi=300)
ax1 = fig.add_subplot(1, 3, 1)
ax2 = fig.add_subplot(1, 3, 2)
ax3 = fig.add_subplot(1, 3, 3)
cax1 = fig.add_axes([0.06, 0.1, 0.02, 0.8])
cax2 = fig.add_axes([0.93, 0.1, 0.02, 0.8])
cmapVar = plt.cm.Reds
cmapVar.set_under(color='black')
cmapVarIm = plt.cm.seismic
def iterate(n):
ax1.cla()
ax2.cla()
ax3.cla()
plt.suptitle('t = %(time).2f' % {'time': n * stor_step * delta_t * stepsize})
dabsfile = sysVar.dataFolder + rdstr + "density/densmat" + str(int(n)) + ".dat"
dimagfile = sysVar.dataFolder + rdstr + "density/densmat" + str(int(n)) + "_im.dat"
drealfile = sysVar.dataFolder + rdstr + "density/densmat" + str(int(n)) + "_re.dat"
dabs = np.loadtxt(dabsfile)
dimag = np.loadtxt(dimagfile)
dreal = np.loadtxt(drealfile)
ax1.set_xlabel('column')
ax1.set_ylabel('row')
ax1.set_title('absolute value')
im = [ax1.imshow(dabs, cmap=cmapVar, interpolation='none', vmin=1e-16)]
ax2.set_title('real part')
im.append(ax2.imshow(dreal, cmap=cmapVar, interpolation='none', vmin=1e-16))
fig.colorbar(im[1], cax=cax1)
ax3.set_title('imaginary part')
im.append(ax3.imshow(dimag, cmap=cmapVarIm, interpolation='none'))
fig.colorbar(im[2], cax=cax2)
if n % ((files / stepsize) / 10) == 0:
print('.', end='', flush=True)
return im
ani = animation.FuncAnimation(fig, iterate, np.arange(files, stepsize))
# ani.save(sysVar.dataFolder + 'plots/density.gif', writer='imagemagick')
ani.save(sysVar.dataFolder + 'plots/' + rdstr + 'density.mp4', fps=framerate, extra_args=['-vcodec', 'libx264'], bitrate=-1)
plt.close()
print("done!")
def plotHamiltonian():
print("Plotting hamiltonian to pdf.", end='', flush=True)
pp = PdfPages(sysVar.dataFolder + 'plots/hamiltonian.pdf')
plt.figure(num=None, figsize=(10, 10), dpi=300)
plt.title('absolute value of hamiltonian')
dabs = np.loadtxt(sysVar.dataFolder + 'hamiltonian.dat')
plt.xlabel('column')
plt.ylabel('row')
cmapVar = plt.cm.Reds
cmapVar.set_under(color='black')
plt.imshow(dabs, cmap=cmapVar, interpolation='none', vmin=1e-20)
plt.colorbar()
pp.savefig()
print('..', end='', flush=True)
plt.clf()
plt.figure(num=None, figsize=(10, 10), dpi=300)
plt.title('absolute value of time evolution matrix')
dabs = np.loadtxt(sysVar.dataFolder + 'evolutionmatrix.dat')
plt.xlabel('column')
plt.ylabel('row')
cmapVar = plt.cm.Reds
cmapVar.set_under(color='black')
plt.imshow(dabs, cmap=cmapVar, interpolation='none', vmin=1e-20)
plt.colorbar()
pp.savefig()
pp.close()
plt.close()
print(" done!")
def plotOccs(sysVar):
print("Plotting occupations to pdf.", end='', flush=True)
pp = PdfPages(sysVar.dataFolder + 'plots/occs.pdf')
params = {
'mathtext.default': 'rm' # see http://matplotlib.org/users/customizing.html
}
plt.rcParams['agg.path.chunksize'] = 0
plt.rcParams.update(params)
plt.rc('text', usetex=True)
plt.rc('font', **{'family': 'sans-serif', 'sans-serif': ['Arial']})
plt.figure(num=None, figsize=(10, 10), dpi=300)
for i in range(sysVar.m):
plt.title(r'$n_' + str(i) + '$')
dre = np.loadtxt(sysVar.dataFolder + 'occ_energybasis_%i_re.dat' % i)
plt.xlabel('column')
plt.ylabel('row')
cmapVar = plt.cm.seismic
plt.imshow(dre, cmap=cmapVar, interpolation='none', vmin=-sysVar.N, vmax=sysVar.N)
cb = plt.colorbar()
pp.savefig()
cb.remove()
plt.clf
print('.', end='', flush=True)
# now without diagonals and abs only
for i in range(sysVar.m):
plt.title(r'$n_' + str(i) + '$')
dre = np.loadtxt(sysVar.dataFolder + 'occ_energybasis_%i_re.dat' % i)
np.fill_diagonal(dre, 0)
plt.xlabel('column')
plt.ylabel('row')
cmapVar = plt.cm.Reds
cmapVar.set_under(color='black')
plt.imshow(np.abs(dre), cmap=cmapVar, interpolation='none', vmin=1e-6)
cb = plt.colorbar()
pp.savefig()
cb.remove()
plt.clf
print('.', end='', flush=True)
pp.close()
plt.close()
print(" done!")
def plotOffDiagOccSingles(sysVar):
print("Plotting off-diagonal singles.", end='', flush=True)
params = {
'legend.fontsize': sysVar.plotLegendSize,
'font.size': sysVar.plotFontSize,
'mathtext.default': 'rm' # see http://matplotlib.org/users/customizing.html
}
plt.rcParams['agg.path.chunksize'] = 0
plt.rcParams.update(params)
plt.rc('text', usetex=True)
plt.rc('font', **{'family': 'sans-serif', 'sans-serif': ['Arial']})
pp = PdfPages(sysVar.dataFolder + 'plots/offdiagsingles.pdf')
singlesdat = np.loadtxt(sysVar.dataFolder + 'offdiagsingle.dat')
singlesinfo = np.loadtxt(sysVar.dataFolder + 'offdiagsingleinfo.dat')
dt = singlesdat[1, 0] - singlesdat[0, 0]
nrm = singlesdat[:, 0] / dt
nrm[1:] = 1 / nrm[1:]
'''
for i in range(0,sysVar.m):
for j in range(0,sysVar.occEnSingle):
infoind = 1+4*j+2 #so we start at the first energy
f, (ax1, ax2, ax3) = plt.subplots(3, sharex=True, sharey=False)
f.suptitle(r'$n_{%i} \; E_1=%.2e \; E_2=%.2e$' % (i, singlesinfo[i,infoind], singlesinfo[i,infoind+1]))
ind = 1+2*j+(i*sysVar.occEnSingle*2)
comp = singlesdat[:,ind] + 1j*singlesdat[:,ind+1]
ax1.set_ylabel(r'$|A_{n,m}|$')
ax1.plot(singlesdat[:,0], np.abs(comp), linewidth = 0.5)
tmp = cumtrapz(comp,singlesdat[:,0]/dt,initial=comp[0])
tmp = np.multiply(tmp,nrm)
ax2.set_ylabel(r'average $|A_{n,m}|$')
ax2.plot(singlesdat[:,0], np.abs(tmp), linewidth = 0.5)
ax3.set_ylabel(r'arg$/\pi$')
plt.xlabel(r'$J\,t$')
ax3.plot(singlesdat[:,0], np.angle(comp)/(np.pi), linewidth = 0.5)
plt.tight_layout()
plt.subplots_adjust(top=0.9, left=0.1)
pp.savefig(f)
f.clf()
# do the double log plot
de = np.abs(singlesinfo[i,infoind] - singlesinfo[i,infoind+1])
linar = np.zeros(len(singlesdat[:,0]), dtype=np.float64)
linar[0] = 0
linar[1:] = 2/(singlesdat[1:,0] * de)
plt.xlabel(r'$J\,t$')
plt.ylabel(r'relative average $|A_{n,m}|$')
plt.loglog(singlesdat[1:,0], np.abs(tmp/np.abs(comp[0]))[1:], singlesdat[1:,0], linar[1:], lw=0.5)
pp.savefig()
plt.clf()
print('.',end='',flush=True)
'''
for i in range(sysVar.m):
for j in range(sysVar.occEnSingle):
infoind = 1 + 4 * j + 2 # so we start at the first energy
# fetch the exponents. if abs(ordr)==1 set to zero for more readability
f, (ax1, ax2) = plt.subplots(2, sharex=True, sharey=False)
ordr1 = int(np.log10(np.abs(singlesinfo[i, infoind])))
if ordr1 == 1 or ordr1 == -1:
ordr1 = 0
ordr2 = int(np.log10(np.abs(singlesinfo[i, infoind + 1])))
if ordr2 == 1 or ordr2 == -1:
ordr2 = 0
if ordr1 == 0 and ordr2 == 0:
f.suptitle(
r'$n_{%i} \quad E_n=%.2f \; E_m=%.2f$' % (i, singlesinfo[i, infoind], singlesinfo[i, infoind + 1]))
elif ordr1 == 0:
f.suptitle(r'$n_{%i} \quad E_n=%.2f \; E_m=%.2f \cdot 10^{%i}$' % (
i, singlesinfo[i, infoind], singlesinfo[i, infoind + 1] / (10 ** ordr2), ordr2))
elif ordr2 == 0:
f.suptitle(r'$n_{%i} \quad E_n=%.2f \cdot 10^{%i} \; E_m=%.2f$' % (
i, singlesinfo[i, infoind] / (10 ** ordr1), ordr1, singlesinfo[i, infoind + 1]))
else:
f.suptitle(r'$n_{%i} \quad E_n=%.2f \cdot 10^{%i} \; E_m=%.2f \cdot 10^{%i}$' % (
i, singlesinfo[i, infoind] / (10 ** ordr1), ordr1, singlesinfo[i, infoind + 1] / (10 ** ordr2),
ordr2))
#
ind = 1 + 2 * j + (i * sysVar.occEnSingle * 2)
comp = singlesdat[:, ind] + 1j * singlesdat[:, ind + 1]
# order of magnitude of the deviation
if not (np.abs(np.abs(comp[0]) - np.abs(comp[-1])) == 0):
ordr = int(np.log10(np.abs(np.abs(comp[0]) - np.abs(comp[-1])))) - 1
else:
ordr = 0
ax1.set_ylabel(r'$|n_{n,m}^{%i}(t)| - |n_{n,m}^{%i}(0)| / 10^{%i}$' % (i, i, ordr))
ax1.plot(singlesdat[:, 0], (np.abs(comp) - np.abs(comp[0])) / (10 ** ordr), linewidth=0.5)
tmp = cumtrapz(comp, singlesdat[:, 0] / dt, initial=comp[0])
tmp = np.multiply(tmp, nrm)
# order of magnitude of the average
if not (np.abs(tmp[1]) == 0):
ordr = int(np.log10(np.abs(tmp[1]))) - 1
else:
ordr = 0
ax2.set_ylabel(r'$|\overline{n}_{n,m}^{%i}| / 10^{%i}$' % (i, ordr))
ax2.plot(singlesdat[:, 0], np.abs(tmp) / (10 ** ordr), linewidth=0.5)
ax2.set_xlabel(r'$J\,t$')
plt.tight_layout()
pp.savefig(f)
f.clf()
plt.close()
# do the double log plot
de = np.abs(singlesinfo[i, infoind] - singlesinfo[i, infoind + 1])
linar = np.zeros(len(singlesdat[:, 0]), dtype=np.float64)
linar[0] = 0
linar[1:] = 2 / (singlesdat[1:, 0] * de)
plt.xlabel(r'$J\,t$')
plt.ylabel(r'relative average $|n_{n,m}^{%i}|$' % (i))
plt.loglog(singlesdat[1:, 0], np.abs(tmp / np.abs(comp[0]))[1:], singlesdat[1:, 0], linar[1:], lw=0.5)
pp.savefig()
plt.clf()
plt.close()
print('.', end='', flush=True)
diagdat = np.loadtxt(sysVar.dataFolder + 'diagsingles.dat')
if os.path.isfile(sysVar.dataFolder + 'energy.dat') and os.path.isfile(sysVar.dataFolder + 'hamiltonian_eigvals.dat'):
### look for energy - this works because the energies are sorted
engy = np.loadtxt(sysVar.dataFolder + 'energy.dat')
eigengy = np.loadtxt(sysVar.dataFolder + 'hamiltonian_eigvals.dat')
diff = 0
for l in range(sysVar.dim):
if np.abs(eigengy[l, 1] - engy[0, 1]) > diff and l != 0:
eind = l - 1
break
else:
diff = np.abs(eigengy[l, 1] - engy[0, 1])
if eind < 15:
loran = 0
else:
loran = eind - 15
for i in range(sysVar.m):
if os.path.isfile(sysVar.dataFolder + 'energy.dat') and os.path.isfile(sysVar.dataFolder + 'hamiltonian_eigvals.dat'):
plt.title(r'Diagonal weighted elements of $n_{%i}$ in spectral decomp.' % (i))
lo = np.int32(sysVar.dim * i)
hi = np.int32(lo + sysVar.dim)
plt.ylabel(r'$|n%i_{E}|$' % (i))
plt.xlabel(r'$E / J$')
# plt.plot(diagdat[lo:hi,1], diagdat[lo:hi,2],linestyle='none',marker='o',ms=0.5)
plt.plot(diagdat[lo + loran:hi, 1][:30], diagdat[lo + loran:hi, 2][:30], marker='o', ms=2)
plt.axvline(x=engy[0, 1], linewidth=0.8, color='red')
###inlay
a = plt.axes([0.18, 0.6, 0.28, 0.28])
a.plot(diagdat[lo:hi - 300, 1], diagdat[lo:hi - 300, 2], marker='o', ms=0.6, ls='none')
a.set_xticks([])
a.set_yticks([])
pp.savefig()
plt.clf()
if os.path.isfile(sysVar.dataFolder + 'occ' + str(i) + '_re.dat'):
occmat = np.loadtxt(sysVar.dataFolder + 'occ' + str(i) + '_re.dat')
diags = np.zeros(sysVar.dim)
### large plot
plt.title(r'Diagonal elements of $n_{%i}$ in spectral decomposition' % (i))
plt.ylabel(r'$|n%i_{E}|$' % (i))
plt.xlabel(r'$E / J$')
for el in range(sysVar.dim):
diags[el] = occmat[el, el]
plt.plot(diagdat[lo + loran:hi, 1][:30], diags[loran:][:30], marker='o', ms=2)
plt.axvline(x=engy[0, 1], linewidth=0.8, color='red')
### inlay
a = plt.axes([0.18, 0.6, 0.28, 0.28])
a.plot(diagdat[lo:hi - 50, 1], diags[:-50], marker='o', ms=0.5, ls='none')
a.set_xticks([])
a.set_yticks([])
pp.savefig()
plt.clf()
else:
plt.title(r'Diagonal weighted elements of $n_{%i}$ in spectral decomp.' % (i))
lo = np.int32(sysVar.dim * i)
hi = np.int32(lo + sysVar.dim)
plt.ylabel(r'$|n%i_{E}|$' % (i))
plt.xlabel(r'$E / J$')
# plt.plot(diagdat[lo:hi,1], diagdat[lo:hi,2],linestyle='none',marker='o',ms=0.5)
plt.plot(diagdat[lo:hi - 200, 1], diagdat[lo:hi - 200, 2], marker='o', ms=0.6, ls='none')
plt.tight_layout()
pp.savefig()
plt.clf()
print('.', end='', flush=True)
pp.close()
plt.close()
print(' done!')
def plotTimescale(sysVar):
print("Plotting difference to mean.", end='', flush=True)
pp = PdfPages(sysVar.dataFolder + 'plots/lndiff.pdf')
params = {
'mathtext.default': 'rm' # see http://matplotlib.org/users/customizing.html
}
plt.rcParams['agg.path.chunksize'] = 0
plt.rcParams.update(params)
plt.rc('text', usetex=True)
plt.rc('font', **{'family': 'sans-serif', 'sans-serif': ['Arial']})
### get the characteristic energy difference of the system
if sysVar.boolEngyStore:
engys = np.loadtxt(sysVar.dataFolder + 'hamiltonian_eigvals.dat')
enscale = np.abs(engys[0, 1] - engys[-1, 1]) / sysVar.dim
del engys
loavgpercent = sysVar.plotLoAvgPerc # percentage of time evolution to start averaging
loavgind = int(loavgpercent * sysVar.dataPoints) # index to start at when calculating average and stddev
loavgtime = np.round(loavgpercent * (sysVar.deltaT * sysVar.steps * sysVar.plotTimeScale), 2)
occfile = sysVar.dataFolder + 'occupation.dat'
occ_array = np.loadtxt(occfile)
# multiply step array with time scale
step_array = occ_array[:, 0] * sysVar.plotTimeScale
entfile = sysVar.dataFolder + 'entropy.dat'
ent_array = np.loadtxt(entfile)
occavg = []
for i in range(sysVar.m):
occavg.append(np.mean(occ_array[loavgind:, i + 1], dtype=np.float64))
entavg = np.mean(ent_array[loavgind:, 1], dtype=np.float64)
odiff = []
for i in range(sysVar.m):
odiff.append(occ_array[:, i + 1] - occavg[i])
entdiff = ent_array[:, 1] - entavg
for i in range(sysVar.m):
plt.ylabel(r'$\Delta n_%i$' % (i))
plt.xlabel(r'$J\,t$')
plt.plot(occ_array[:, 0], odiff[i], lw=0.5)
if sysVar.boolEngyStore:
plt.axvline(enscale, color='red', lw=0.5)
pp.savefig()
plt.clf()
plt.ylabel(r'$| \Delta n_%i |$' % (i))
plt.xlabel(r'$J\,t$')
plt.ylim(ymin=1e-3)
plt.semilogy(occ_array[:, 0], np.abs(odiff[i]), lw=0.5)
if sysVar.boolEngyStore:
plt.axvline(enscale, color='red', lw=0.5)
plt.tight_layout()
pp.savefig()
plt.clf()
print('.', end='', flush=True)
plt.ylabel(r'$\Delta S_{ss}$')
plt.xlabel(r'$J\,t$')
plt.plot(occ_array[:, 0], entdiff[:], lw=0.5)
if sysVar.boolEngyStore:
plt.axvline(enscale, color='red', lw=0.5)
pp.savefig()
plt.clf()
plt.ylabel(r'$| \Delta S_{ss} |$')
plt.xlabel(r'$J\,t$')
plt.ylim(ymin=1e-3)
plt.semilogy(occ_array[:, 0], np.abs(entdiff[:]), lw=0.5)
if sysVar.boolEngyStore:
plt.axvline(enscale, color='red', lw=0.5)
plt.tight_layout()
pp.savefig()
plt.clf()
print('.', end='', flush=True)
pp.close()
print(" done!")
def plotMatrix(fPath):
fName = fPath.split('/')[-1:][0]
pp = PdfPages(sysVar.dataFolder + 'plots/' + fName[:-4] + '.pdf')
plt.figure(num=None, figsize=(10, 10), dpi=300)
plt.title('absolute value of matrix')
dabs = np.loadtxt(fPath)
plt.xlabel('column')
plt.ylabel('row')
cmapVar = plt.cm.Reds
cmapVar.set_under(color='black')
plt.imshow(dabs, cmap=cmapVar, interpolation='none', vmin=1e-12)
pp.savefig()
pp.close()
plt.close() | bsd-2-clause |
realfastvla/rfpipe | setup.py | 1 | 3993 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
# Note: To use the 'upload' functionality of this file, you must:
# $ pip install twine
import os
import sys
from shutil import rmtree
from setuptools import find_packages, setup, Command
# Package meta-data.
NAME = 'rfpipe'
DESCRIPTION = 'Radio interferometric transient search pipeline for realfast'
URL = 'http://github.com/realfastvla/rfpipe'
EMAIL = '[email protected]'
AUTHOR = 'Casey Law'
# What packages are required for this module to be executed?
REQUIRED = ['numpy', 'scipy', 'sdmpy', 'pyfftw', 'bokeh', 'cython', 'scikit-learn<0.21',
'attrs', 'future', 'astropy', 'pyyaml', 'numba', 'fuzzywuzzy', 'hdbscan', # hdbscan<0.8.19?
'matplotlib', 'kalman_detector', 'seaborn', 'h5py']
# pip install --extra-index-url https://casa-pip.nrao.edu:443/repository/pypi-group/simple casatools
# optional 'rfgpu', 'distributed'
# with conda
# numba
# done manually
# 'evla_mcast', 'vysmaw_reader', 'kalman_detector', 'fetch', 'tensorflow', 'keras'
# The rest you shouldn't have to touch too much :)
# ------------------------------------------------
# Except, perhaps the License and Trove Classifiers!
# If you do change the License, remember to change the Trove Classifier for that!
here = os.path.abspath(os.path.dirname(__file__))
# Import the README and use it as the long-description.
# Note: this will only work if 'README.rst' is present in your MANIFEST.in file!
#with io.open(os.path.join(here, 'README.rst'), encoding='utf-8') as f:
# long_description = '\n' + f.read()
long_description = """rfpipe supports fast transient searching of radio interferometric data.
This is written as part of the realfast project will perform real-time commensal
fast radio transient searches at the Very Large Array. rfpipe supports both
real-time and offline searches, GPU and CPU algorithms, and reproducible analysis of
transients found by realfast. More project info at http://realfast.io.
"""
# Load the package's __version__.py module as a dictionary.
with open(os.path.join(here, NAME, 'version.py')) as f:
exec(f.read())
class PublishCommand(Command):
"""Support setup.py publish."""
description = 'Build and publish the package.'
user_options = []
@staticmethod
def status(s):
"""Prints things in bold."""
print('\033[1m{0}\033[0m'.format(s))
def initialize_options(self):
pass
def finalize_options(self):
pass
def run(self):
try:
self.status('Removing previous buildsâ¦')
rmtree(os.path.join(here, 'dist'))
except FileNotFoundError:
pass
self.status('Building Source and Wheel (universal) distributionâ¦')
os.system('{0} setup.py sdist bdist_wheel --universal'.format(sys.executable))
self.status('Uploading the package to PyPi via Twineâ¦')
os.system('twine upload dist/*')
sys.exit()
# Where the magic happens:
setup(
name=NAME,
version=__version__,
description=DESCRIPTION,
long_description=long_description,
author=AUTHOR,
author_email=EMAIL,
url=URL,
packages=find_packages(exclude=('tests',)),
package_data={NAME: ["tests/data/*xml", "tests/data/realfast.yml"]},
# If your package is a single module, use this instead of 'packages':
# py_modules=['mypackage'],
# entry_points={
# 'console_scripts': ['mycli=mymodule:cli'],
# },
install_requires=REQUIRED,
include_package_data=True,
license='BSD',
classifiers=[
# Trove classifiers
# Full list: https://pypi.python.org/pypi?%3Aaction=list_classifiers
'License :: OSI Approved :: BSD License',
'Programming Language :: Python',
'Programming Language :: Python :: 3.6',
],
# $ setup.py publish support.
cmdclass={
'publish': PublishCommand,
},
)
| bsd-3-clause |
abele/bokeh | bokeh/sampledata/periodic_table.py | 45 | 1542 | '''
This module provides the periodic table as a data set. It exposes an attribute 'elements'
which is a pandas dataframe with the following fields
elements['atomic Number'] (units: g/cm^3)
elements['symbol']
elements['name']
elements['atomic mass'] (units: amu)
elements['CPK'] (convention for molecular modeling color)
elements['electronic configuration']
elements['electronegativity'] (units: Pauling)
elements['atomic radius'] (units: pm)
elements['ionic radius'] (units: pm)
elements['van der waals radius'] (units: pm)
elements['ionization enerygy'] (units: kJ/mol)
elements['electron affinity'] (units: kJ/mol)
elements['phase'] (standard state: solid, liquid, gas)
elements['bonding type']
elements['melting point'] (units: K)
elements['boiling point'] (units: K)
elements['density'] (units: g/cm^3)
elements['type'] (see below)
elements['year discovered']
elements['group']
elements['period']
element types: actinoid, alkali metal, alkaline earth metal, halogen, lanthanoid, metal, metalloid, noble gas, nonmetal, transition metalloid
'''
from __future__ import absolute_import
from os.path import dirname, join
try:
import pandas as pd
except ImportError as e:
raise RuntimeError("elements data requires pandas (http://pandas.pydata.org) to be installed")
elements = pd.read_csv(join(dirname(__file__), 'elements.csv'))
| bsd-3-clause |
scikit-image/skimage-demos | digits_recognition/run_modern_digits.py | 1 | 2223 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
# Author: Francois Boulogne
import os
import os.path
import shutil
import glob
import skimage.io
import matplotlib.pyplot as plt
from segmentation import segment_digit
from machine_learning import load_knowndata, load_unknowndata
from sklearn import svm
if __name__ == '__main__':
data_bundle = 'modern_digits'
output_dir = data_bundle + '_isolated_digits'
results_dir = data_bundle + '_results'
for thisdir in (output_dir, results_dir):
shutil.rmtree(thisdir, ignore_errors=True)
os.makedirs(thisdir)
show = False
for filename in glob.glob(data_bundle + '_to_detect/*.png'):
print(filename)
# Load picture
image = skimage.io.imread(filename, as_grey=True)
# crop picture
image = image[0:200, 47:]
segment_digit(image, filename, output_dir, black_on_white=False, show=False)
filenames = sorted(glob.glob(data_bundle + '_learned/*-*.png'))
training = load_knowndata(filenames, show)
# Create a classifier: a support vector classifier
classifier = svm.SVC(gamma=1e-8)
# We learn the digits on the first half of the digits
classifier.fit(training['data'], training['targets'])
filenames = sorted(glob.glob(os.path.join(output_dir, '*.png')))
unknown = load_unknowndata(filenames)
filenames = set([os.path.splitext(os.path.basename(fn))[0].split('-')[0] for fn in filenames])
for filename in filenames:
fn = sorted(glob.glob(os.path.join(output_dir, filename + '*.png')))
unknown = load_unknowndata(fn)
# Now predict the value of the digit on the second half:
# expected = digits.target[n_samples / 2:]
predicted = classifier.predict(unknown['data'])
result = ''
for pred, image, name in zip(predicted, unknown['images'], unknown['name']):
result += str(pred)
# Check
fn = os.path.join(data_bundle + '_to_detect', filename + '.png')
image = skimage.io.imread(fn)
plt.imshow(image[:150, 56:], cmap=plt.cm.gray_r, interpolation='nearest')
plt.title('Predicted: %s' % result)
plt.savefig(os.path.join(results_dir, filename))
| bsd-3-clause |
tjhei/burnman_old2 | play_withslb.py | 2 | 4808 | # BurnMan - a lower mantle toolkit
# Copyright (C) 2012, 2013, Heister, T., Unterborn, C., Rose, I. and Cottaar, S.
# Released under GPL v2 or later.
"""
Calculates and plots two models for different minerals or methods and plots
the results. Calculates basic percent difference statistics as well.
requires:
- geotherms
- creating minerals
teaches:
- compute seismic velocities and compare
"""
import os, sys, numpy as np, matplotlib.pyplot as plt
#hack to allow scripts to be placed in subdirectories next to burnman:
if not os.path.exists('burnman') and os.path.exists('../burnman'):
sys.path.insert(1,os.path.abspath('..'))
import burnman
from burnman import minerals
from burnman.materials import composite
import matplotlib.image as mpimg
import numpy
if __name__ == "__main__":
###Input Model 1
#INPUT for method_1
""" choose 'slb' (finite-strain 2nd order sheer modulus, stixrude and lithgow-bertelloni, 2005)
or 'mgd' (mie-gruneisen-debeye, matas et al. 2007)
or 'bm' (birch-murnaghan, if you choose to ignore temperature (your choice in geotherm will not matter in this case))
or 'slb3 (finite-strain 3rd order shear modulus, stixrude and lithgow-bertelloni, 2005)"""
method = 'bm3'
# Input for model M_pyrolite as defined in Matas et al. 2007 table 3. Molar proportions are converted to atomic fractions
#weight_percents = {'Mg':0.297882, 'Fe': 0.0489699, 'Si':0.1819, 'Ca':0.0228576, 'Al':0.0116446}
#phase_fractions,relative_molar_percent = burnman.calculate_phase_percents(weight_percents)
rock = burnman.composite( ( (minerals.SLB2011_mg_perovskite(),0.5*1.00),
(minerals.SLB2011_fe_perovskite(), .9*0.0 ),
(minerals.SLB2011_periclase(), 0.5*1.00 ),
(minerals.SLB2011_wuestite(), 0.1*0.0 )))
rock2 = burnman.composite( ( (minerals.mg_perovskite(),.62 ),
(minerals.fe_perovskite(), .078/1.5 ),
(minerals.periclase(), .28 ),
(minerals.wuestite(), .034/1.5 )))
#KD is 2... doesn't match either
#rock2 = burnman.composite( ( (minerals.Matas_mg_perovskite(),.3574 ),
# (minerals.Matas_fe_perovskite(), .0536 ),
# (minerals.Matas_periclase(), .1656 ),
# (minerals.Matas_wuestite(), .0124 )))
#input pressure range for first model. This could be from a seismic model or something you create. For this example we will create an array
rock.set_method(method)
rock2.set_method(method)
seis_p_1 = np.arange(28e9, 128e9, 4.8e9)
temperature_bs = burnman.geotherm.brown_shankland(seis_p_1)
#Now we'll calculate the models.
T0 = 0.
temperature_1 = burnman.geotherm.adiabatic(seis_p_1, T0, rock)
mat_rho_1, mat_vp_1, mat_vs_1, mat_vphi_1, mat_K_1, mat_G_1 = burnman.velocities_from_rock(rock,seis_p_1, temperature_1)
temperature_2 = burnman.geotherm.adiabatic(seis_p_1, T0, rock2)
mat_rho_2, mat_vp_2, mat_vs_2, mat_vphi_2, mat_K_2, mat_G_2 = burnman.velocities_from_rock(rock2,seis_p_1, temperature_2)
prem=burnman.seismic.prem()
depths=map(prem.depth,seis_p_1)
prem_p, prem_rho, prem_vp, prem_vs, prem_vphi = prem.evaluate_all_at(depths)
##Now let's plot the comparison. You can conversely just output to a data file (see example_woutput.py)
plt.subplot(2,2,2)
plt.plot(seis_p_1/1.e9,prem_vs/1.e3,color='g',linestyle='-',label='ak135')
plt.plot(seis_p_1/1.e9,mat_vs_1/1.e3,color='b',linestyle='-',label='rock')
# plt.plot(seis_p_1/1.e9,mat_vs_2/1.e3,color='r',linestyle='-',label='rock2')
#plt.plot(depths,prem_vs/1.e3,color='r')
plt.title("Vs (km/s)")
plt.ylim(5,7.6)
# plt.xlim(29,131)
plt.legend(loc='upper left')
# plot Vp
plt.subplot(2,2,3)
plt.title("Vp (km/s)")
plt.plot(seis_p_1/1.e9,prem_vp/1.e3,color='g',linestyle='-')
plt.plot(seis_p_1/1.e9,mat_vp_1/1.e3,color='b', linestyle='-')
# plt.plot(seis_p_1/1.e9,mat_vp_2/1.e3,color='r', linestyle='-')
plt.ylim(6.25,14.0)
plt.xlim(29,131)
# plot density
plt.subplot(2,2,4)
plt.plot(seis_p_1/1.e9,prem_rho/1.e3,color='b',linestyle='-')
plt.plot(seis_p_1/1.e9,mat_rho_1/1.e3,color='g', linestyle='-')
#plt.plot(seis_p_1/1.e9,mat_rho_2/1.e3,color='r', linestyle='-')
plt.title("Density (kg/m^3)")
plt.xlim(29,131)
plt.subplot(2,2,1)
plt.plot(seis_p_1/1.e9,temperature_1,color='k',linestyle='-',label='adiabat')
plt.ylim(1600,3100)
plt.xlim(29,131)
plt.title("temperature")
plt.legend(loc='upper left')
plt.savefig("output_figures/reproduce_matas.png")
plt.show()
| gpl-2.0 |
michaelpacer/scikit-image | doc/examples/plot_censure.py | 23 | 1079 | """
========================
CENSURE feature detector
========================
The CENSURE feature detector is a scale-invariant center-surround detector
(CENSURE) that claims to outperform other detectors and is capable of real-time
implementation.
"""
from skimage import data
from skimage import transform as tf
from skimage.feature import CENSURE
from skimage.color import rgb2gray
import matplotlib.pyplot as plt
img1 = rgb2gray(data.astronaut())
tform = tf.AffineTransform(scale=(1.5, 1.5), rotation=0.5,
translation=(150, -200))
img2 = tf.warp(img1, tform)
detector = CENSURE()
fig, ax = plt.subplots(nrows=1, ncols=2)
plt.gray()
detector.detect(img1)
ax[0].imshow(img1)
ax[0].axis('off')
ax[0].scatter(detector.keypoints[:, 1], detector.keypoints[:, 0],
2 ** detector.scales, facecolors='none', edgecolors='r')
detector.detect(img2)
ax[1].imshow(img2)
ax[1].axis('off')
ax[1].scatter(detector.keypoints[:, 1], detector.keypoints[:, 0],
2 ** detector.scales, facecolors='none', edgecolors='r')
plt.show()
| bsd-3-clause |
sugiare418/tensorfx | build/tensorflow/scripts/102_gym_fx_180102/fx_env/fx_env.py | 1 | 18455 | # coding:utf-8
import datetime
import os
import gym
import numpy
import pandas
from dateutil import relativedelta
from gym import spaces
import gc
import copy
# import pdb; pdb.set_trace()
# [1] 環境クラス -------------------------------------------------------------------
class FxEnv(gym.Env):
metadata = {'render.modes': ['human', 'ohlc_array']}
# [1.1] 初期化処理 ---------------------------------------------------------------
def __init__(self):
# 定数
self.STAY = 0
self.BUY = 1
self.SELL = 2
self.CLOSE = 3
# 対象となる通貨ペアの最大値
self.MAX_VALUE = 2
# 初期の口座資金
self.initial_balance = 10000
# CSVファイルのパス配列(最低4ヶ月分を昇順で)
self.csv_file_paths = []
now = datetime.datetime.now()
for _ in range(4):
now = now - relativedelta.relativedelta(months=1)
filename = 'DAT_MT_EURUSD_M1_{}.csv'.format(now.strftime('%Y%m'))
if not os.path.exists(filename):
print('ファイルが存在していません。下記からダウンロードしてください。', filename)
print('http://www.histdata.com/download-free-forex-historical-data/?/metatrader/1-minute-bar-quotes/EURUSD/')
else:
self.csv_file_paths.append(filename)
# スプレッド
self.spread = 0.5
# Point(1pipsの値)
self.point = 0.0001
# 利食いpips
self.take_profit_pips = 10
# 損切りpips
self.stop_loss_pips = 15
# ロット数
self.lots = 1
# ロット数
self.lot_base = 10000
# 0～3のアクション。定数に詳細は記載している
self.action_space = gym.spaces.Discrete(4)
# 観測できる足数
self.visible_bar = 32
# CSVを読み込む
self.data = pandas.DataFrame()
for path in self.csv_file_paths:
csv = pandas.read_csv(path,
names=['date', 'time', 'open', 'high', 'low', 'close', 'v'],
parse_dates={'datetime': ['date', 'time']},
)
# csv.index = csv['datetime']
# csv = csv.drop('datetime', axis=1)
csv = csv.drop('v', axis=1)
self.data = self.data.append(csv)
# 最後に読んだCSVのインデックスを開始インデックスとする
self.start_index= len(self.data) - len(csv)
# 1分足、5分足、30分足、4時間足の5時系列データを足数分作る
self._reset()
self.observation_space = spaces.Box(low=0, high=self.MAX_VALUE, shape=numpy.shape(self.make_obs('ohlc_array')))
self.m5 = []
self.m30 = []
self.h4 = []
# [1.2] リセット：為替情報、口座情報などを構築する -----------------------------------------------
def _reset(self):
# ガベージコレクション
gc.collect()
self.info = AccountInformation(self.initial_balance)
self.read_index = self.start_index
# そこから開始位置をランダムにずらす(5日分(7220分)は残す)
# self.read_index += numpy.random.randint(0, (len(csv) - 7220))
# チケット一覧
self.tickets = []
return self.make_obs('ohlc_array')
# [1.3] step：アクション（売買）の実行と観測情報（為替・口座情報）の更新------------------------------
def _step(self, action):
# import pdb; pdb.set_trace()
current_data = self.data.iloc[self.read_index]
ask = current_data['close'] + self.spread * self.point
bid = current_data['close'] - self.spread * self.point
profit = 0
def _set_buy_profit(info, profit):
self.info.balance += profit
self.info.total_pips_buy += profit
if profit > 0:
self.info.total_pips_plus += profit
else:
self.info.total_pips_minus += profit
def _set_sell_profit(info, profit):
self.info.balance += profit
self.info.total_pips_sell += profit
if profit > 0:
self.info.total_pips_plus += profit
else:
self.info.total_pips_minus += profit
if action == self.STAY:
pass
elif action == self.BUY:
ticket = Ticket(self.BUY, ask, ask + self.take_profit_pips * self.point,
ask - self.stop_loss_pips * self.point, self.lots)
self.tickets.append(ticket)
self.info.trade_count += 1
pass
elif action == self.SELL:
ticket = Ticket(self.SELL, bid, bid - self.take_profit_pips * self.point,
bid + self.stop_loss_pips * self.point, self.lots)
self.tickets.append(ticket)
self.info.trade_count += 1
pass
elif action == self.CLOSE:
for ticket in self.tickets[:]:
if ticket.order_type == self.BUY:
# 買いチケットをクローズ
profit = (bid - ticket.open_price) * ticket.lots * self.lot_base
_set_buy_profit(self.info, profit)
self.tickets.remove(ticket)
elif ticket.order_type == self.SELL:
# 売りチケットをクローズ
profit = (ticket.open_price - ask) * ticket.lots * self.lot_base
_set_sell_profit(self.info, profit)
self.tickets.remove(ticket)
# 利確と損切り
for ticket in self.tickets[:]:
if ticket.order_type == self.BUY:
if bid > ticket.take_profit:
# 買いチケットを利確
profit = (bid - ticket.open_price) * ticket.lots * self.lot_base
_set_buy_profit(self.info, profit)
self.tickets.remove(ticket)
elif bid < ticket.stop_loss:
# 買いチケットを損切り
profit = (bid - ticket.open_price) * ticket.lots * self.lot_base
_set_buy_profit(self.info, profit)
self.tickets.remove(ticket)
elif ticket.order_type == self.SELL:
if ask < ticket.take_profit:
# 売りチケットを利確
profit = (ticket.open_price - ask) * ticket.lots * self.lot_base
_set_sell_profit(self.info, profit)
self.tickets.remove(ticket)
elif ask > ticket.stop_loss:
# 売りチケットを損切り
profit = (ticket.open_price - ask) * ticket.lots * self.lot_base
_set_sell_profit(self.info, profit)
self.tickets.remove(ticket)
# インデックスをインクリメント
self.read_index += 1
# obs, reward, done, infoを返す
# return self.make_obs('ohlc_array'), self.info.total_pips_buy + self.info.total_pips_sell, self.info.balance <= 0 or self.read_index > len(self.data), self.info
# import pdb; pdb.set_trace()
done = False
if self.info.balance <= 0:
print('done! balance:{}'.format(self.info.balance))
done = True
pass
elif self.read_index > len(self.data):
print('done! read_index:{}'.format(self.read_index))
done = True
pass
else:
# print('done:{}'.format(done))
pass
# return self.make_obs('ohlc_array'), profit, self.info.balance <= 0 or self.read_index > len(self.data), self.info
return self.make_obs('ohlc_array'), profit, done, self.info
# [1.4] render： --------------------------------------------------------------------
def _render(self, mode='human', close=False):
return self.make_obs(mode)
# [1.5] make_obs : 為替の観測情報を構築する ---------------------------------------------
def make_obs(self, mode):
"""
1分足、5分足、30分足、4時間足の4時系列データを64本分作成する
:return:
"""
target = self.data.iloc[self.read_index - 60 * 4 * 70: self.read_index]
if mode == 'human':
m1 = numpy.array(target.iloc[-1 * self.visible_bar:][target.columns])
m5 = numpy.array(target.resample('5min').agg({'open': 'first',
'high': 'max',
'low': 'min',
'close': 'last'}).dropna().iloc[-1 * self.visible_bar:][target.columns])
m30 = numpy.array(target.resample('30min').agg({'open': 'first',
'high': 'max',
'low': 'min',
'close': 'last'}).dropna().iloc[-1 * self.visible_bar:][target.columns])
h4 = numpy.array(target.resample('4H').agg({'open': 'first',
'high': 'max',
'low': 'min',
'close': 'last'}).dropna().iloc[-1 * self.visible_bar:][target.columns])
return numpy.array([m1, m5, m30, h4])
# # humanの場合はmatplotlibでチャートのimgを作成する?
# fig = plt.figure(figsize=(10, 4))
# # ローソク足は全横幅の太さが1である。表示する足数で割ってさらにその1/3の太さにする
# width = 1.0 / 64 / 3
# # 1分足
# ax = plt.subplot(2, 2, 1)
# # y軸のオフセット表示を無効にする。
# ax.get_yaxis().get_major_formatter().set_useOffset(False)
# data = target.iloc[-1 * self.visible_bar:].values
# mpf.candlestick_ohlc(ax, data, width=width, colorup='g', colordown='r')
# # 5分足
# ax = plt.subplot(2, 2, 2)
# ax.get_yaxis().get_major_formatter().set_useOffset(False)
# data = target['close'].resample('5min').ohlc().dropna().iloc[-1 * self.visible_bar:].values
# mpf.candlestick_ohlc(ax, data, width=width, colorup='g', colordown='r')
# # 30分足
# ax = plt.subplot(2, 2, 3)
# ax.get_yaxis().get_major_formatter().set_useOffset(False)
# data = target['close'].resample('30min').ohlc().dropna().iloc[-1 * self.visible_bar:].values
# mpf.candlestick_ohlc(ax, data, width=width, colorup='g', colordown='r')
# # 4時間足
# ax = plt.subplot(2, 2, 4)
# ax.get_yaxis().get_major_formatter().set_useOffset(False)
# data = target['close'].resample('4H').ohlc().dropna().iloc[-1 * self.visible_bar:].values
# mpf.candlestick_ohlc(ax, data, width=width, colorup='g', colordown='r')
# return fig.canvas.buffer_rgba()
elif mode == 'ohlc_array':
# 移動平均 ------------------------------------------------------------------------
# 移動平均線を求める関数
# ave_day:平均を取る日数
# data:ターゲットのnumpyarayデータ
# target_col:求めた値を設定するカラム
def get_ave(ave_day, data, target_col):
for i in range(ave_day, len(data)):
tmp = data[i-ave_day+1:i+1, 4].astype(numpy.float16)
data[i,target_col] = numpy.mean(tmp)
return data
# m1 = numpy.array(target.iloc[-1 * self.visible_bar:][target.columns])
m1 = numpy.array(target.iloc[-1 * (self.visible_bar+200):]) # 200日移動平均分をプラス
# 5移動平均
m1 = numpy.c_[m1, numpy.zeros((len(m1), 1))]
m1 = get_ave(5, m1, 5)
# 25移動平均
m1 = numpy.c_[m1, numpy.zeros((len(m1), 1))]
m1 = get_ave(25, m1, 6)
# 75移動平均
m1 = numpy.c_[m1, numpy.zeros((len(m1), 1))]
m1 = get_ave(75, m1, 7)
# 200移動平均
m1 = numpy.c_[m1, numpy.zeros((len(m1), 1))]
m1 = get_ave(200, m1, 8)
# 一目均衡表 ---------------------------------------------------------------------
para1 = 9
para2 = 26
para3 = 52
# 転換線 = （過去(para1)日間の高値 + 安値） ÷ 2
m1 = numpy.c_[m1, numpy.zeros((len(m1),1))] # 列の追加
for i in range(para1, len(m1)):
tmp_high =m1[i-para1+1:i+1,2].astype(numpy.float16)
tmp_low =m1[i-para1+1:i+1,3].astype(numpy.float16)
m1[i,9] = (numpy.max(tmp_high) + numpy.min(tmp_low)) / 2
# 基準線 = （過去(para2)日間の高値 + 安値） ÷ 2
m1 = numpy.c_[m1, numpy.zeros((len(m1),1))]
for i in range(para2, len(m1)):
tmp_high =m1[i-para2+1:i+1,2].astype(numpy.float16)
tmp_low =m1[i-para2+1:i+1,3].astype(numpy.float16)
m1[i,10] = (numpy.max(tmp_high) + numpy.min(tmp_low)) / 2
# 先行スパン1 = ｛ （転換値+基準値） ÷ 2 ｝を(para2)日先にずらしたもの
m1 = numpy.c_[m1, numpy.zeros((len(m1),1))]
for i in range(0, len(m1)-para2):
tmp =(m1[i,9] + m1[i,10]) / 2
m1[i+para2,11] = tmp
# 先行スパン2 = ｛ （過去(para3)日間の高値+安値） ÷ 2 ｝を(para2)日先にずらしたもの
m1 = numpy.c_[m1, numpy.zeros((len(m1),1))]
for i in range(para3, len(m1)-para2):
tmp_high =m1[i-para3+1:i+1,2].astype(numpy.float16)
tmp_low =m1[i-para3+1:i+1,3].astype(numpy.float16)
m1[i+para2,12] = (numpy.max(tmp_high) + numpy.min(tmp_low)) / 2
# ボリンジャーバンド ---------------------------------------------------------------
# 25ボリンジャーバンド（±1, 2シグマ）を追加します
parab = 25
m1 = numpy.c_[m1, numpy.zeros((len(m1),4))] # 列の追加
for i in range(parab, len(m1)):
tmp = m1[i-parab+1:i+1,4].astype(numpy.float16)
m1[i,13] = numpy.mean(tmp) + 1.0* numpy.std(tmp)
m1[i,14] = numpy.mean(tmp) - 1.0* numpy.std(tmp)
m1[i,15] = numpy.mean(tmp) + 2.0* numpy.std(tmp)
m1[i,16] = numpy.mean(tmp) - 2.0* numpy.std(tmp)
# 説明変数となる行列Xを作成します
day_ago = self.visible_bar # 何日前までのデータを使用するのかを設定
num_sihyou = 1 + 4 + 4 + 4 # 終値1本、MVave4本、itimoku4本、ボリンジャー4本
X = numpy.zeros((len(m1), day_ago*num_sihyou))
for s in range(0, num_sihyou): # 日にちごとに横向きに並べる
for i in range(0, day_ago):
X[i:len(m1),day_ago*s+i] = m1[0:len(m1)-i,s+4]
# m5 = numpy.array(target.resample('5min').agg({'open': 'first',
# 'high': 'max',
# 'low': 'min',
# 'close': 'last'}).dropna().iloc[-1 * self.visible_bar:][target.columns])
# m30 = numpy.array(target.resample('30min').agg({'open': 'first',
# 'high': 'max',
# 'low': 'min',
# 'close': 'last'}).dropna().iloc[-1 * self.visible_bar:][target.columns])
# h4 = numpy.array(target.resample('4H').agg({'open': 'first',
# 'high': 'max',
# 'low': 'min',
# 'close': 'last'}).dropna().iloc[-1 * self.visible_bar:][target.columns])
# return numpy.array([m1, m5, m30, h4])
# import pdb; pdb.set_trace()
# X[-1]を返すと、メモリを大量に掴んだままになるため、コピーしたものを返す
ret = copy.copy(X[-1])
del X
return ret
# [2] 口座情報クラス -------------------------------------------------------
class AccountInformation(object):
"""
口座情報クラス
"""
def __init__(self, initial_balance):
# 口座資金(含み益含む)
self.balance = initial_balance
# 口座資金
self.fixed_balance = initial_balance
# 総獲得pips(買い)
self.total_pips_buy = 0
# 総獲得pips(売り)
self.total_pips_sell = 0
# 総pips(plus)
self.total_pips_plus = 0
# 総pips(minus)
self.total_pips_minus = 0
# 売買回数
self.trade_count = 0
def items(self):
'''
rl\core.py line 172 で呼び出される
:return: 口座情報
'''
return [('balance', self.balance), ('fixed_balance', self.fixed_balance), ('total_pips_buy', self.total_pips_buy), ('total_pips_sell', self.total_pips_sell)]
class Ticket(object):
"""
チケット
"""
def __init__(self, order_type, open_price, take_profit, stop_loss, lots):
# タイプ
self.order_type = order_type
# 約定価格
self.open_price = open_price
# 利食い価格
self.take_profit = take_profit
# 損切り価格
self.stop_loss = stop_loss
# ロット
self.lots = lots
| mit |
jrmontag/classifier-comp-year2 | build-model.py | 2 | 8125 | #!/usr/bin/env python
# -*- coding: UTF-8 -*-
__author__="Josh Montague"
__license__="MIT License"
import argparse
from datetime import datetime
import logging
import os
import sys
import numpy as np
from sklearn.externals import joblib
from sklearn.cross_validation import cross_val_score, cross_val_predict, train_test_split
from sklearn.metrics import accuracy_score, confusion_matrix
from models import experiment_dict
import utils
parser = argparse.ArgumentParser()
parser.add_argument('expt', help='specify experiment to run (see README)')
parser.add_argument('--expanded', action='store_true',
help='read expanded (perturbation) data files')
parser.add_argument('--ubuntu', action='store_true',
help='modify imports if running on ubuntu')
parser.add_argument('--submission', action='store_true',
help='train model on all of data + create submission file')
parser.add_argument('--cross_val_score', action='store_true',
help='run addl cross val score for uncertainty in accuracy (costly!)')
parser.add_argument('-v', '--verbose', action='store_true',
help='increase output verbosity')
args = parser.parse_args()
# only import mpl when necessary (not a submission)
if not args.submission:
if args.ubuntu:
# ubuntu v. os x shenanigans
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import seaborn as sb
# get the log level from the cmd line
loglevel = logging.DEBUG if args.verbose else logging.INFO
# be explicit here since we also have logs in other modules
logr = logging.getLogger(__name__)
logr.setLevel(loglevel)
sh = logging.StreamHandler(stream=sys.stdout)
formatter = logging.Formatter('%(asctime)s : %(name)s : %(levelname)s : %(message)s')
sh.setFormatter(formatter)
logr.addHandler(sh)
# what are we running?
job = 'submission' if args.submission else 'experiment'
logr.debug('logging enabled for this {}'.format(job))
# read the right files
files = 'expanded' if args.expanded else 'original'
logr.info('reading {} input data from disk'.format(files))
try:
X_train_full, y_train_full, X_test = utils.load_np_arrays(files)
logr.debug('observed data dimensions: {}, {}. {}'.format(
X_train_full.shape, y_train_full.shape, X_test.shape))
except IOError, e:
# let it crash, but give some insight in the log
logr.warn('Error reading data from files (do they exist yet?)')
logr.warn('Error message={}'.format(e))
raise e
# get appropriate pipeline + metadata
pipeline_detail = experiment_dict[args.expt]
logr.debug('specified experiment pipeline={}'.format(pipeline_detail))
pipeline = pipeline_detail['pl']
if args.submission:
# making a submission; train on all given data
logr.info('fitting models to entire training set')
X_train, y_train = X_train_full, y_train_full
else:
# running an experiment - cross validate with train/test split
train_fraction = 0.8
logr.info('fitting models to cv train/test split with train% = {}'.format(train_fraction))
X_train, X_test, y_train, y_test = train_test_split(X_train_full,
y_train_full,
test_size=train_fraction,
random_state=42)
logr.debug('fitting model to array sizes (xtrain, ytrain)={}'.format(
[i.shape for i in [X_train, y_train]]))
# fit
logr.info('fitting experiment pipeline with signature={}'.format(pipeline))
pipeline.fit(X_train, y_train)
if args.submission:
fname_spec = '_submission_'
else:
# gridsearch? log all results + call out the winner
if hasattr(pipeline, 'best_params_'):
logr.info('best gridsearch score={}'.format(pipeline.best_score_))
logr.info('best set of pipeline params={}'.format(pipeline.best_params_))
logr.info('now displaying all pipeline param scores...')
for params, mean_score, scores in pipeline.grid_scores_:
logr.info("{:0.3f} (+/-{:0.03f}) for {}".format(mean_score, scores.std()*2, params))
fname_spec = '_expt_'
# build proper file name so we can reference it in the logs
model_name = utils.short_name(pipeline) + \
fname_spec + \
datetime.utcnow().strftime('%Y-%m-%d_%H%M%S')
logr.info('writing fit {} pipeline to disk as {}'.format(job, model_name))
try:
joblib.dump(pipeline, os.path.join('saved_models', model_name) + '.pkl', compress=3)
except OverflowError, e:
# this is annoying; look into it later
logr.warn('joblib write failed with error={}'.format(e))
logr.info('proceeding with predictions without writing model to disk')
# do something useful with the fit model
if args.submission:
# make predictions for a leaderboard submission
logr.info('writing predictions to formatted submission file')
utils.create_submission(predictions,
pipeline_detail['name'],
comment=pipeline_detail['note'])
else:
# if we already did CV through the gridsearch, then just take
# the best score and make predictions
if hasattr(pipeline, 'best_params_'):
logr.info('predicting test values with best-choice gridsearch params')
predictions = pipeline.predict(X_test)
# fake an array of CV scores to play nice with plot formatting later
scores = np.array([pipeline.best_score_])
# otherwise, do some cross validation
else:
# otherwise, run a cross-validation for test accuracy
cv = 3
logr.info('cross validating model predictions with cv={}'.format(cv))
predictions = cross_val_predict(pipeline, X_test, y_test, cv=cv)
logr.info('obtained accuracy = {:.2f}% with cv={}, pipeline={} '.format(
accuracy_score(y_test,predictions)*100,
cv,
pipeline))
if args.cross_val_score:
# this gives a better idea of uncertainty, but it adds 'cv' more
# fits to the pipeline (expensive!)
logr.info('cross val score flag found')
logr.info('cross validating model accuracy with cv={}'.format(cv))
scores = cross_val_score(pipeline, X_test, y_test, cv=cv)
logr.info('obtained accuracy={:0.2f}% +/- {:0.2f} with cv={}, \
pipeline={} '.format(scores.mean()*100,
scores.std()*100*2,
cv,
pipeline))
# if running an experiment, plot confusion matrix for review
# > TODO: move this figure creation into utils <
logr.info('calculating confusion matrix')
try:
sb.heatmap(confusion_matrix(y_test, predictions))
except RuntimeError, e:
logr.warn('plotting error. matplotlib backend may need to be changed (see readme). error={}'.format(e))
logr.warn('plot may still have been saved, and model has already been saved to disk.')
try:
plt.title(model_name + ' [expt] ({:.2f}%)'.format(scores.mean()*100))
except NameError:
logr.debug('didnt find "scores" from cross_val_score, calculating accuracy by accuracy_score()')
plt.title(model_name + ' [expt] ({:.2f}%)'.format(accuracy_score(y_test,predictions)*100))
plt.xlabel("True")
plt.ylabel("Pred")
#plt.tight_layout()
logr.info('saving confusion matrix')
plt.savefig(os.path.join('saved_models', model_name) + '.pdf',
format='pdf',
bbox_inches='tight')
logr.info('completed {} with pipeline={}'.format(job, pipeline_detail))
| mit |
Divergent914/yakddcup2015 | IO.py | 1 | 3098 | #!/usr/local/bin/python3
# -*- coding:utf-8 -*-
import os
import gzip
import pickle as pkl
import pandas as pd
import Path
def cache(obj, file_path):
with open(file_path, 'wb') as f:
pkl.dump(obj, f)
def fetch_cache(file_path):
if not os.path.exists(file_path):
return None
with open(file_path, 'rb') as f:
data = pkl.load(f)
return data
def __cache__(func):
def cached_func(file_path):
pkl_path = file_path + '.pkl'
data = fetch_cache(pkl_path)
if data is None:
data = func(file_path)
cache(data, pkl_path)
return data
return cached_func
def cache_to(file_path):
def __cache__(func):
def cached_func(*args, **kwargs):
pkl_path = Path.of_cache(file_path + '.pkl')
data = fetch_cache(pkl_path)
if data is None:
data = func(*args, **kwargs)
cache(data, pkl_path)
return data
return cached_func
return __cache__
@__cache__
def __load_log__(file_path):
log_set = pd.read_csv(file_path, parse_dates=['time'])
log_set['event'] = log_set['event'].replace('nagivate', 'navigate')
return log_set
def load_log_train():
return __load_log__(Path.TRAIN_LOG)
def load_log_test():
return __load_log__(Path.TEST_LOG)
@cache_to('log_all')
def load_logs():
log_all = load_log_train().append(load_log_test(), ignore_index=True)
log_all.sort('enrollment_id', inplace=True)
log_all.reset_index(drop=True, inplace=True)
return log_all
@__cache__
def __load_enrollment__(file_path):
return pd.read_csv(file_path)
def load_enrollment_train():
return __load_enrollment__(Path.TRAIN_ENROLL)
def load_enrollment_test():
return __load_enrollment__(Path.TEST_ENROLL)
@cache_to('enroll_all')
def load_enrollments():
enroll_all = load_enrollment_train().append(load_enrollment_test(),
ignore_index=True)
enroll_all.sort('enrollment_id', inplace=True)
enroll_all.reset_index(drop=True, inplace=True)
return enroll_all
@cache_to('object')
def load_object():
obj = pd.read_csv(Path.OBJECT, parse_dates=['start'], na_values=['null'])
obj['children'] = obj['children'].fillna('')
obj.drop_duplicates(inplace=True)
obj.drop([3108, 21274, 26771], inplace=True)
obj.ix[643, 'children'] = obj.ix[643, 'children'].split()[0]
return obj.reset_index(drop=True)
def load_train_y():
return pd.read_csv(Path.TRAIN_Y, header=None, names=['enrollment_id', 'y'])
def dump_submission(clf, filename):
path = filename
if not path.startswith('data/submission/'):
path = 'data/submission/' + path
if not path.endswith('.csv'):
path += '.not-submitted.csv'
enroll_test = load_enrollment_test()['enrollment_id']
import dataset
X_test = dataset.load_test()
y_test = clf.predict_proba(X_test)[:, 1]
lines = ['%d,%f\n' % l for l in zip(enroll_test, y_test)]
with open(path, 'w') as f:
f.writelines(lines)
| gpl-2.0 |
antoinecarme/pyaf | scripts/num_diff.py | 1 | 4003 | import numpy as np
import sys
gSkippedTags = ['_TIME_IN_SECONDS' , "PYAF_SYSTEM_DEPENDENT_", "START_TRAINING", "START_FORECASTING",
"CreationDate" , "Training_Time" , "matplotlib.font_manager"]
if(len(sys.argv) != 3):
print("NUM_DIFF_ERROR_INVALID_ARGS" , sys.argv);
sys.exit(-1)
def is_numeric(x):
try:
val = float(x)
except ValueError:
return False;
return True;
# the goal here is to compare the two words inside a JSON and
# allow some small numeric differences
# (used to compare dataframe.to_json() in the logs).
def compare_words(word_orig, word_new):
if(word_orig == word_new):
return 0;
if(is_numeric(word_orig) and is_numeric(word_new)):
lNumber_orig = float(word_orig);
lNumber_new = float(word_new);
if(np.isclose([lNumber_orig] , [lNumber_new])):
lRelDiff = abs(lNumber_new - lNumber_orig) / (abs(lNumber_orig) + 1e-10);
if(lRelDiff > 1e-12):
print("NUM_DIFF_DEBUG_ALLOWED_SMALL_DIFFERENCE", word_orig, word_new, lNumber_orig, lNumber_new, lRelDiff)
return 0;
else:
print("DIFFERENT_WORDS" , word_orig, word_new)
return 1;
else:
# print("NUM_DIFF_NOT_NUMERIC_DIFF", word_orig, word_new)
return 1;
def compare_lines(line_orig, line_new):
if(line_orig == line_new):
return 0;
import re
lRegex = '[\[?,:"{}\]\= \n\t]'
split_orig = re.split(lRegex, line_orig)
split_new = re.split(lRegex, line_new)
# print("SPLIT_ORIG", split_orig)
# print("SPLIT_NEW", split_new)
N_orig = len(split_orig)
N_new = len(split_new)
if(N_orig != N_new):
print("NUM_DIFF_LINE_WITH_DIFFERENT_NUMBER_OF_WORDS" , N_orig, N_new);
return 1;
N = min(N_orig, N_new);
# compare the first N words of the two lines.
out = 0;
for l in range(N):
result = compare_words(split_orig[l] , split_new[l])
out = out + result;
return out;
def compare_files(file_orig, file_new):
import os.path
if(not os.path.exists(file_orig)):
print("ORIGINAL_FILE_DOES_NOT_EXIST" , file_orig)
print("EXEC_THIS=1 cp ", file_new, file_orig)
return
with open(file_orig) as f:
content_orig = f.readlines()
for tag in gSkippedTags:
content_orig = [x for x in content_orig if tag not in x]
with open(file_new) as f:
content_new = f.readlines()
for tag in gSkippedTags:
content_new = [x for x in content_new if tag not in x]
N_orig = len(content_orig)
N_new = len(content_new)
if(N_orig != N_new):
print("NUM_DIFF_FILES_WITH_DIFFERENT_NUMBER_OF_LINES" , N_orig, N_new);
lDiff = abs(N_orig - N_new)
if(lDiff > 20):
print("NUM_DIFF_FILES_WITH_TOO_MUCH_DIFF" , N_orig, N_new);
print("NUM_DIFF_NUMBER_OF_DIFFERENT_LINES_AT_LEAST" , lDiff)
print("EXEC_THIS=1 cp ", file_new, file_orig)
print("NUM_DIFF_FILES_ARE_DIFFERENT")
return;
N = min(N_orig, N_new);
# compare the first N lines of the two files.
out = 0;
diffs = [];
for l in range(N):
result = compare_lines(content_orig[l] , content_new[l])
if(result > 0):
diffs.append((content_orig[l] , content_new[l]));
out = out + result;
if(out > 0):
print("NUM_DIFF_NUMBER_OF__DIFFERENT_LINES" , out)
print("FIRST_DIFFERENCE_ORIG" , diffs[0][0])
print("FIRST_DIFFERENCE_NEW" , diffs[0][1])
print("LAST_DIFFERENCE_ORIG" , diffs[-1][0])
print("LAST_DIFFERENCE_NEW" , diffs[-1][1])
print("EXEC_THIS=1 cp ", file_new, file_orig)
print("NUM_DIFF_FILES_ARE_DIFFERENT")
file_orig = sys.argv[1]
file_new = sys.argv[2]
try:
compare_files(file_orig, file_new);
except Exception as e:
lStr = str(type(e).__name__) + " " + str(e)
print("FILE_COMPARISON_FAILED_WITH_ERROR" , lStr);
| bsd-3-clause |
bilgili/nest-simulator | pynest/examples/intrinsic_currents_subthreshold.py | 9 | 7172 | # -*- coding: utf-8 -*-
#
# intrinsic_currents_subthreshold.py
#
# This file is part of NEST.
#
# Copyright (C) 2004 The NEST Initiative
#
# NEST is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 2 of the License, or
# (at your option) any later version.
#
# NEST is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with NEST. If not, see <http://www.gnu.org/licenses/>.
'''
Intrinsic currents subthreshold
-------------------------------
This example illustrates how to record from a model with multiple
intrinsic currents and visualize the results. This is illustrated
using the `ht_neuron` which has four intrinsic currents: I_NaP,
I_KNa, I_T, and I_h. It is a slightly simplified implementation of
neuron model proposed in Hill and Tononi (2005) **Modeling Sleep and Wakefulness in the
Thalamocortical System** *J Neurophysiol* 93:1671 http://dx.doi.org/10.1152/jn.00915.2004 .
The neuron is driven by DC current, which is alternated
between depolarizing and hyperpolarizing. Hyperpolarization
intervals become increasingly longer.
See also: intrinsic_currents_spiking.py
'''
'''
We import all necessary modules for simulation, analysis and
plotting. Additionally, we set the verbosity using `set_verbosity` to
suppress info messages. We also reset the kernel to be sure to start
with a clean NEST.
'''
import nest
import numpy as np
import matplotlib.pyplot as plt
nest.set_verbosity("M_WARNING")
nest.ResetKernel()
'''
We define simulation parameters:
- The length of depolarization intervals
- The length of hyperpolarization intervals
- The amplitude for de- and hyperpolarizing currents
- The end of the time window to plot
'''
n_blocks = 5
t_block = 20.
t_dep = [t_block] * n_blocks
t_hyp = [t_block * 2**n for n in range(n_blocks)]
I_dep = 10.
I_hyp = -5.
t_end = 500.
'''
We create the one neuron instance and the DC current generator
and store the returned handles.
'''
nrn = nest.Create('ht_neuron')
dc = nest.Create('dc_generator')
'''
We create a multimeter to record
- membrane potential `V_m`
- threshold value `Theta`
- intrinsic currents `I_NaP`, `I_KNa`, `I_T`, `I_h`
by passing these names in the `record_from` list.
To find out which quantities can be recorded from a given neuron,
run::
nest.GetDefaults('ht_neuron')['recordables']
The result will contain an entry like::
<SLILiteral: V_m>
for each recordable quantity. You need to pass the value of the `SLILiteral`,
in this case `V_m` in the `record_from` list.
We want to record values with 0.1 ms resolution, so we set the
recording interval as well; the default recording resolution is 1 ms.
'''
# create multimeter and configure it to record all information
# we want at 0.1ms resolution
mm = nest.Create('multimeter',
params={'interval': 0.1,
'record_from': ['V_m', 'Theta',
'I_NaP', 'I_KNa', 'I_T', 'I_h']}
)
'''
We connect the DC generator and the multimeter to the neuron.
Note that the multimeter, just like the voltmeter is connected
to the neuron, not the neuron to the multimeter.
'''
nest.Connect(dc, nrn)
nest.Connect(mm, nrn)
'''
We are ready to simulate. We alternate between driving the neuron
with depolarizing and hyperpolarizing currents. Before each simulation
interval, we set the amplitude of the DC generator to the correct value.
'''
for t_sim_dep, t_sim_hyp in zip(t_dep, t_hyp):
nest.SetStatus(dc, {'amplitude': I_dep})
nest.Simulate(t_sim_dep)
nest.SetStatus(dc, {'amplitude': I_hyp})
nest.Simulate(t_sim_hyp)
'''
We now fetch the data recorded by the multimeter. The data are
returned as a dictionary with entry ``'times'`` containing timestamps
for all recorded data, plus one entry per recorded quantity.
All data is contained in the ``'events'`` entry of the status dictionary
returned by the multimeter. Because all NEST function return arrays,
we need to pick out element ``0`` from the result of `GetStatus`.
'''
data = nest.GetStatus(mm)[0]['events']
t = data['times']
'''
The next step is to plot the results. We create a new figure, add a
single subplot and plot at first membrane potential and threshold.
'''
fig = plt.figure()
Vax = fig.add_subplot(111)
Vax.plot(t, data['V_m'], 'b-', lw=2, label=r'$V_m$')
Vax.plot(t, data['Theta'], 'g-', lw=2, label=r'$\Theta$')
Vax.set_ylim(-80., 0.)
Vax.set_ylabel('Voltageinf [mV]')
Vax.set_xlabel('Time [ms]')
'''
To plot the input current, we need to create an input
current trace. We construct it from the durations of the de- and
hyperpolarizing inputs and add the delay in the connection between
DC generator and neuron:
1. We find the delay by checking the status of the dc->nrn connection.
1. We find the resolution of the simulation from the kernel status.
1. Each current interval begins one time step after the previous interval,
is delayed by the delay and effective for the given duration.
1. We build the time axis incrementally. We only add the delay when adding
the first time point after t=0. All subsequent points are then automatically
shifted by the delay.
'''
delay = nest.GetStatus(nest.GetConnections(dc, nrn))[0]['delay']
dt = nest.GetKernelStatus('resolution')
t_dc, I_dc = [0], [0]
for td, th in zip(t_dep, t_hyp):
t_prev = t_dc[-1]
t_start_dep = t_prev + dt if t_prev > 0 else t_prev + dt + delay
t_end_dep = t_start_dep + td
t_start_hyp = t_end_dep + dt
t_end_hyp = t_start_hyp + th
t_dc.extend([t_start_dep, t_end_dep, t_start_hyp, t_end_hyp])
I_dc.extend([I_dep, I_dep, I_hyp, I_hyp])
'''
The following function turns a name such as I_NaP into proper TeX code
$I_{\mathrm{NaP}}$ for a pretty label.
'''
texify_name = lambda name: r'${}_{{\mathrm{{{}}}}}$'.format(*name.split('_'))
'''
Next, we add a right vertical axis and plot the currents with respect
to that axis.
'''
Iax = Vax.twinx()
Iax.plot(t_dc, I_dc, 'k-', lw=2, label=texify_name('I_DC'))
for iname, color in (('I_h', 'maroon'), ('I_T', 'orange'),
('I_NaP', 'crimson'), ('I_KNa', 'aqua')):
Iax.plot(t, data[iname], color=color, lw=2, label=texify_name(iname))
Iax.set_xlim(0, t_end)
Iax.set_ylim(-10., 15.)
Iax.set_ylabel('Current [pA]')
Iax.set_title('ht_neuron driven by DC current')
'''
We need to make a little extra effort to combine lines from the two axis
into one legend.
'''
lines_V, labels_V = Vax.get_legend_handles_labels()
lines_I, labels_I = Iax.get_legend_handles_labels()
try:
Iax.legend(lines_V + lines_I, labels_V + labels_I, fontsize='small')
except TypeError:
Iax.legend(lines_V + lines_I, labels_V + labels_I) # work-around for older Matplotlib versions
'''
Note that I_KNa is not activated in this example because the neuron does
not spike. I_T has only a very small amplitude.
'''
| gpl-2.0 |
njpayne/euclid | python/descriptive_stats.py | 1 | 9093 | import data_work
import scipy
import numpy as np
import csv
import os
from pathlib import Path
import matplotlib.pyplot as plt
import pylab
import seaborn as sns
import pandas as pd
data_location = "../Data" # read data from os.path.join(data_location, <filename>)
results_location = "Results" # save results text/graph to os.path.join(results_location, <filename>)
def main():
#run the two datasets
data_sets = ["basic_data", "basic_data_only_finishers", "basic_data_clean_lecture", "basic_data_piazza", "basic_data_piazza_only_finishers"]
for data_set in data_sets:
#load the data
headings, data = data_work.load_data(data_set + ".csv", conversion_function = data_work.convert_survey_data, max_records = None)
#generate the stats
generate_stats(data, headings, data_set)
#draw charts for the relationships between variables
draw_charts(data, headings, data_set)
#load unaltered data
headings, data = data_work.load_data(data_set + ".csv", conversion_function = None, max_records = None)
#draw some frequency histograms
draw_histograms(data, headings, data_set)
return
def generate_stats(data, headings, data_set):
"""
Runs the basic descriptive stats for the dataset
"""
#get the basic descriptive statistics
basic_descriptive_stats = scipy.stats.describe(data)
#unbundle the results
kurtosis = basic_descriptive_stats.kurtosis
mean = basic_descriptive_stats.mean
skewness = basic_descriptive_stats.skewness
variance = basic_descriptive_stats.variance
covariance = np.cov(data, rowvar = 0)
pearson_correlation = np.corrcoef(data, rowvar = 0)
#create output csv
with open(os.path.join(results_location, 'descriptive_stats' + data_set + '.csv'), 'wb') as output_file:
#establish the csv writer
writer = csv.writer(output_file, delimiter=',')
#write the data
writer.writerow(np.append(np.array(["Statistics"]), headings))
writer.writerow(np.append(np.array(["Kurtosis"]), kurtosis))
writer.writerow(np.append(np.array(["Mean"]), mean))
writer.writerow(np.append(np.array(["Skewness"]), skewness))
writer.writerow(np.append(np.array(["Variance"]), variance))
#write the covariance matrix
writer.writerow([""])
writer.writerow(["Covariance"])
writer.writerow(np.append(np.array([""]), headings))
writer.writerows(np.hstack((np.expand_dims(headings, axis = 1), covariance)))
#write the pearson correlation coefficient
writer.writerow([""])
writer.writerow(["Pearson Correlation"])
writer.writerow(np.append(np.array([""]), headings))
writer.writerows(np.hstack((np.expand_dims(headings, axis = 1), pearson_correlation)))
def draw_charts(data, headings, data_set):
"""
Chart relationships between Variables
"""
#create a folder for the dataset
directory = os.path.dirname(os.path.join(os.getcwd(),"Results",data_set, ""))
if not os.path.exists(directory):
os.makedirs(directory)
#just plot vs course grades
for i in range(data.shape[1]):
for j in np.argwhere(headings == "course_grade")[0].tolist():
#no need to test against itself
#or earlier combos
if(j != i):
x_values = data[ : , i]
y_values = data[ : , j]
x_label = headings[i]
y_label = headings[j]
#make a scatterplot
plt.figure()
plt.scatter(x_values, y_values)
plt.xlabel(x_label)
plt.ylabel(y_label)
#plt.legend(loc='upper right')
plt.title(x_label + " Vs. " + y_label)
#save the graph to file
save_name = x_label + "_" + y_label
save_name = save_name.replace("<", "lt")
save_name = save_name.replace(">", "gt")
save_name = save_name.replace("\\", "")
save_name = save_name.replace("/", "")
save_name = save_name.replace(".", "")
save_name = save_name.replace("I have not used Piazza significantly", "")
pylab.savefig(os.path.join(os.getcwd(),"Results",data_set,save_name))
plt.close()
return
def draw_histograms(data, headings, data_set):
"""
Chart relationships between Variables
"""
chart_categories = ['course_grade', 'Assig_1_full_40', 'Assig_2_full_40', 'Assig_3_full_40', 'proj_1_100', 'proj_2_100', 'proj_3_100', 'final_exam_100', 'peer_feedback_100', 'birth_country', 'residence_country', 'gender', 'age', 'primary_language', 'english_fluency', 'time_zone', 'occupation', 'highest_education', 'expected_hours_spent', 'formal_class_prog_taken', 'C', 'C#', 'C++', 'Java', 'JavaScript', 'Lisp', 'Objective C', 'Perl', 'PHP', 'Python', 'Ruby', 'Shell', 'Swift', 'Visual Basic', 'Other (specify below)', 'years_programming', 'prior_omscs_classes_completed', 'besides_KBAI_how_many_classes', 'moocs_completed_outside_OMSCS', 'qtr_proj1_confidence', 'qtr_proj2_confidence', 'qtr_piazza_opinion', 'qtr_peerfeedback_opinion', 'qtr_on_piazza', 'qtr_email', 'qtr_hipchat', 'qrt_gplus', 'qtr_other_chat', 'qtr_phone', 'qtr_facebook', 'qtr_in_person', 'CS6210_Completed', 'CS8803_Completed', 'CS6250_Completed', 'CS7641_Completed', 'CS6300_Completed', 'CS6310_Completed', 'CS4495_Completed', 'CS6475_Completed', 'CS6505_Completed', 'CS6290_Completed', 'CS8803_Completed', 'CS6440_Completed', 'mid_proj2_confidence', 'mid_proj3_confidence', 'mid_piazza_opinion', 'mid_peerfeedback_opinion', 'mid_on_piazza', 'mid_email', 'mid_hipchat', 'qrt_gplus', 'mid_other_chat', 'mid_phone', 'mid_facebook', 'mid_in_person', 'final_proj3_confidence', 'hours_spent', 'lessons_watched', 'exercises_completed', 'forum_visit_frequency', 'final_on_piazza', 'final_email', 'final_hipchat', 'qrt_gplus', 'final_other_chat', 'final_phone', 'final_facebook', 'final_in_person', 'watch_out_order', 'fall_behind', 'get_ahead', 'rewatch_full_lesson', 'rewatch_partial_lesson', 'view_answer_after_1incorrect', 'repeat_exercise_until_correct', 'skip_exercise', 'correct_first_attempt', 'access_from_mobile', 'download_videos', 'piazza_answers', 'piazza_days', 'piazza_asks', 'piazza_posts', 'piazza_views', 'total_lecture_time', 'overal_lecture_views', 'lecture_1_views', 'lecture_2_views', 'lecture_3_views', 'lecture_4_views', 'lecture_5_views', 'lecture_6_views', 'lecture_7_views', 'lecture_8_views', 'lecture_9_views', 'lecture_10_views', 'lecture_11_views', 'lecture_12_views', 'lecture_13_views', 'lecture_14_views', 'lecture_15_views', 'lecture_16_views', 'lecture_17_views', 'lecture_18_views', 'lecture_19_views', 'lecture_20_views', 'lecture_21_views', 'lecture_22_views', 'lecture_23_views', 'lecture_24_views', 'lecture_25_views', 'lecture_26_views', 'lecture_1_pace', 'lecture_2_pace', 'lecture_3_pace', 'lecture_4_pace', 'lecture_5_pace', 'lecture_6_pace', 'lecture_7_pace', 'lecture_8_pace', 'lecture_9_pace', 'lecture_10_pace', 'lecture_11_pace', 'lecture_12_pace', 'lecture_13_pace', 'lecture_14_pace', 'lecture_15_pace', 'lecture_16_pace', 'lecture_17_pace', 'lecture_18_pace', 'lecture_19_pace', 'lecture_20_pace', 'lecture_21_pace', 'lecture_22_pace', 'lecture_23_pace', 'lecture_24_pace', 'lecture_25_pace', 'lecture_26_pace', 'overall_pace']
#chart_categories = ["Age"]
#create a folder for the dataset
directory = os.path.dirname(os.path.join(os.getcwd(),"Results","Data Counts",data_set, ""))
if not os.path.exists(directory):
os.makedirs(directory)
#convert to a pandas dataset
pandas_data=pd.DataFrame(data = data, columns = headings)
for chart_category in chart_categories:
#get the slice
index = np.argwhere(headings == chart_category)
chart_column = data[ : , index[0][0]]
#get counts
plt.figure()
plt.xlabel(chart_category)
plt.ylabel("Count")
plt.title("%s Count" % chart_category)
try:
#try converting to numbers
chart_column = chart_column.astype(np.float)
#create histogram
hist, bin_edge = np.histogram(chart_column, 10)
bin_middles = bin_edge[:-1] + np.diff(bin_edge)/2
plt.hist(chart_column, 10, normed=False, histtype='bar', rwidth=0.8)
pylab.savefig(os.path.join(os.getcwd(),"Results", "Data Counts",data_set, chart_category))
plt.close()
except:
#get unique values
unique_categories, unique_counts = np.unique(chart_column, return_counts=True)
sns_plot = sns.countplot(x=chart_category, data=pandas_data, palette="Greens_d");
#plt.setp(sns_plot.get_xticklabels(), rotation=45)
sns_plot.figure.savefig(os.path.join(os.getcwd(),"Results", "Data Counts",data_set, chart_category))
plt.close()
if __name__ == "__main__":
main() | gpl-2.0 |
mraspaud/dask | dask/dataframe/tests/test_utils_dataframe.py | 2 | 8502 | import numpy as np
import pandas as pd
import pandas.util.testing as tm
import dask.dataframe as dd
from dask.dataframe.utils import (shard_df_on_index, meta_nonempty, make_meta,
raise_on_meta_error, UNKNOWN_CATEGORIES)
import pytest
def test_shard_df_on_index():
df = pd.DataFrame({'x': [1, 2, 3, 4, 5, 6], 'y': list('abdabd')},
index=[10, 20, 30, 40, 50, 60])
result = list(shard_df_on_index(df, [20, 50]))
assert list(result[0].index) == [10]
assert list(result[1].index) == [20, 30, 40]
assert list(result[2].index) == [50, 60]
def test_make_meta():
df = pd.DataFrame({'a': [1, 2, 3], 'b': list('abc'), 'c': [1., 2., 3.]},
index=[10, 20, 30])
# Pandas dataframe
meta = make_meta(df)
assert len(meta) == 0
assert (meta.dtypes == df.dtypes).all()
assert isinstance(meta.index, type(df.index))
# Pandas series
meta = make_meta(df.a)
assert len(meta) == 0
assert meta.dtype == df.a.dtype
assert isinstance(meta.index, type(df.index))
# Pandas index
meta = make_meta(df.index)
assert isinstance(meta, type(df.index))
assert len(meta) == 0
# Dask object
ddf = dd.from_pandas(df, npartitions=2)
assert make_meta(ddf) is ddf._meta
# Dict
meta = make_meta({'a': 'i8', 'b': 'O', 'c': 'f8'})
assert isinstance(meta, pd.DataFrame)
assert len(meta) == 0
assert (meta.dtypes == df.dtypes).all()
assert isinstance(meta.index, pd.RangeIndex)
# Iterable
meta = make_meta([('a', 'i8'), ('c', 'f8'), ('b', 'O')])
assert (meta.columns == ['a', 'c', 'b']).all()
assert len(meta) == 0
assert (meta.dtypes == df.dtypes[meta.dtypes.index]).all()
assert isinstance(meta.index, pd.RangeIndex)
# Tuple
meta = make_meta(('a', 'i8'))
assert isinstance(meta, pd.Series)
assert len(meta) == 0
assert meta.dtype == 'i8'
assert meta.name == 'a'
# With index
meta = make_meta({'a': 'i8', 'b': 'i4'}, pd.Int64Index([1, 2], name='foo'))
assert isinstance(meta.index, pd.Int64Index)
assert len(meta.index) == 0
meta = make_meta(('a', 'i8'), pd.Int64Index([1, 2], name='foo'))
assert isinstance(meta.index, pd.Int64Index)
assert len(meta.index) == 0
# Categoricals
meta = make_meta({'a': 'category'})
assert len(meta.a.cat.categories) == 1
assert meta.a.cat.categories[0] == UNKNOWN_CATEGORIES
meta = make_meta(('a', 'category'))
assert len(meta.cat.categories) == 1
assert meta.cat.categories[0] == UNKNOWN_CATEGORIES
# Numpy scalar
meta = make_meta(np.float64(1.0))
assert isinstance(meta, np.float64)
# Python scalar
meta = make_meta(1.0)
assert isinstance(meta, np.float64)
# Timestamp
x = pd.Timestamp(2000, 1, 1)
meta = make_meta(x)
assert meta is x
# Dtype expressions
meta = make_meta('i8')
assert isinstance(meta, np.int64)
meta = make_meta(float)
assert isinstance(meta, np.dtype(float).type)
meta = make_meta(np.dtype('bool'))
assert isinstance(meta, np.bool_)
assert pytest.raises(TypeError, lambda: make_meta(None))
def test_meta_nonempty():
df1 = pd.DataFrame({'A': pd.Categorical(['Alice', 'Bob', 'Carol']),
'B': list('abc'),
'C': 'bar',
'D': np.float32(1),
'E': np.int32(1),
'F': pd.Timestamp('2016-01-01'),
'G': pd.date_range('2016-01-01', periods=3,
tz='America/New_York'),
'H': pd.Timedelta('1 hours', 'ms'),
'I': np.void(b' '),
'J': pd.Categorical([UNKNOWN_CATEGORIES] * 3)},
columns=list('DCBAHGFEIJ'))
df2 = df1.iloc[0:0]
df3 = meta_nonempty(df2)
assert (df3.dtypes == df2.dtypes).all()
assert df3['A'][0] == 'Alice'
assert df3['B'][0] == 'foo'
assert df3['C'][0] == 'foo'
assert df3['D'][0] == np.float32(1)
assert df3['D'][0].dtype == 'f4'
assert df3['E'][0] == np.int32(1)
assert df3['E'][0].dtype == 'i4'
assert df3['F'][0] == pd.Timestamp('1970-01-01 00:00:00')
assert df3['G'][0] == pd.Timestamp('1970-01-01 00:00:00',
tz='America/New_York')
assert df3['H'][0] == pd.Timedelta('1', 'ms')
assert df3['I'][0] == 'foo'
assert df3['J'][0] == UNKNOWN_CATEGORIES
s = meta_nonempty(df2['A'])
assert s.dtype == df2['A'].dtype
assert (df3['A'] == s).all()
def test_meta_duplicated():
df = pd.DataFrame(columns=['A', 'A', 'B'])
res = meta_nonempty(df)
exp = pd.DataFrame([['foo', 'foo', 'foo'],
['foo', 'foo', 'foo']],
index=['a', 'b'],
columns=['A', 'A', 'B'])
tm.assert_frame_equal(res, exp)
def test_meta_nonempty_empty_categories():
for dtype in ['O', 'f8', 'M8']:
# Index
idx = pd.CategoricalIndex([], pd.Index([], dtype=dtype),
ordered=True, name='foo')
res = meta_nonempty(idx)
assert type(res) is pd.CategoricalIndex
assert type(res.categories) is type(idx.categories)
assert res.ordered == idx.ordered
assert res.name == idx.name
# Series
s = idx.to_series()
res = meta_nonempty(s)
assert res.dtype == s.dtype
assert type(res.cat.categories) is type(s.cat.categories)
assert res.cat.ordered == s.cat.ordered
assert res.name == s.name
def test_meta_nonempty_index():
idx = pd.RangeIndex(1, name='foo')
res = meta_nonempty(idx)
assert type(res) is pd.RangeIndex
assert res.name == idx.name
idx = pd.Int64Index([1], name='foo')
res = meta_nonempty(idx)
assert type(res) is pd.Int64Index
assert res.name == idx.name
idx = pd.Index(['a'], name='foo')
res = meta_nonempty(idx)
assert type(res) is pd.Index
assert res.name == idx.name
idx = pd.DatetimeIndex(['1970-01-01'], freq='d',
tz='America/New_York', name='foo')
res = meta_nonempty(idx)
assert type(res) is pd.DatetimeIndex
assert res.tz == idx.tz
assert res.freq == idx.freq
assert res.name == idx.name
idx = pd.PeriodIndex(['1970-01-01'], freq='d', name='foo')
res = meta_nonempty(idx)
assert type(res) is pd.PeriodIndex
assert res.freq == idx.freq
assert res.name == idx.name
idx = pd.TimedeltaIndex([np.timedelta64(1, 'D')], freq='d', name='foo')
res = meta_nonempty(idx)
assert type(res) is pd.TimedeltaIndex
assert res.freq == idx.freq
assert res.name == idx.name
idx = pd.CategoricalIndex(['a'], ['a', 'b'], ordered=True, name='foo')
res = meta_nonempty(idx)
assert type(res) is pd.CategoricalIndex
assert (res.categories == idx.categories).all()
assert res.ordered == idx.ordered
assert res.name == idx.name
idx = pd.CategoricalIndex([], [UNKNOWN_CATEGORIES], ordered=True, name='foo')
res = meta_nonempty(idx)
assert type(res) is pd.CategoricalIndex
assert res.ordered == idx.ordered
assert res.name == idx.name
levels = [pd.Int64Index([1], name='a'),
pd.Float64Index([1.0], name='b')]
idx = pd.MultiIndex(levels=levels, labels=[[0], [0]], names=['a', 'b'])
res = meta_nonempty(idx)
assert type(res) is pd.MultiIndex
for idx1, idx2 in zip(idx.levels, res.levels):
assert type(idx1) is type(idx2)
assert idx1.name == idx2.name
assert res.names == idx.names
def test_meta_nonempty_scalar():
meta = meta_nonempty(np.float64(1.0))
assert isinstance(meta, np.float64)
x = pd.Timestamp(2000, 1, 1)
meta = meta_nonempty(x)
assert meta is x
def test_raise_on_meta_error():
try:
with raise_on_meta_error():
raise RuntimeError("Bad stuff")
except Exception as e:
assert e.args[0].startswith("Metadata inference failed.\n")
assert 'RuntimeError' in e.args[0]
else:
assert False, "should have errored"
try:
with raise_on_meta_error("myfunc"):
raise RuntimeError("Bad stuff")
except Exception as e:
assert e.args[0].startswith("Metadata inference failed in `myfunc`.\n")
assert 'RuntimeError' in e.args[0]
else:
assert False, "should have errored"
| bsd-3-clause |
jm-begon/scikit-learn | examples/cluster/plot_adjusted_for_chance_measures.py | 286 | 4353 | """
==========================================================
Adjustment for chance in clustering performance evaluation
==========================================================
The following plots demonstrate the impact of the number of clusters and
number of samples on various clustering performance evaluation metrics.
Non-adjusted measures such as the V-Measure show a dependency between
the number of clusters and the number of samples: the mean V-Measure
of random labeling increases significantly as the number of clusters is
closer to the total number of samples used to compute the measure.
Adjusted for chance measure such as ARI display some random variations
centered around a mean score of 0.0 for any number of samples and
clusters.
Only adjusted measures can hence safely be used as a consensus index
to evaluate the average stability of clustering algorithms for a given
value of k on various overlapping sub-samples of the dataset.
"""
print(__doc__)
# Author: Olivier Grisel <[email protected]>
# License: BSD 3 clause
import numpy as np
import matplotlib.pyplot as plt
from time import time
from sklearn import metrics
def uniform_labelings_scores(score_func, n_samples, n_clusters_range,
fixed_n_classes=None, n_runs=5, seed=42):
"""Compute score for 2 random uniform cluster labelings.
Both random labelings have the same number of clusters for each value
possible value in ``n_clusters_range``.
When fixed_n_classes is not None the first labeling is considered a ground
truth class assignment with fixed number of classes.
"""
random_labels = np.random.RandomState(seed).random_integers
scores = np.zeros((len(n_clusters_range), n_runs))
if fixed_n_classes is not None:
labels_a = random_labels(low=0, high=fixed_n_classes - 1,
size=n_samples)
for i, k in enumerate(n_clusters_range):
for j in range(n_runs):
if fixed_n_classes is None:
labels_a = random_labels(low=0, high=k - 1, size=n_samples)
labels_b = random_labels(low=0, high=k - 1, size=n_samples)
scores[i, j] = score_func(labels_a, labels_b)
return scores
score_funcs = [
metrics.adjusted_rand_score,
metrics.v_measure_score,
metrics.adjusted_mutual_info_score,
metrics.mutual_info_score,
]
# 2 independent random clusterings with equal cluster number
n_samples = 100
n_clusters_range = np.linspace(2, n_samples, 10).astype(np.int)
plt.figure(1)
plots = []
names = []
for score_func in score_funcs:
print("Computing %s for %d values of n_clusters and n_samples=%d"
% (score_func.__name__, len(n_clusters_range), n_samples))
t0 = time()
scores = uniform_labelings_scores(score_func, n_samples, n_clusters_range)
print("done in %0.3fs" % (time() - t0))
plots.append(plt.errorbar(
n_clusters_range, np.median(scores, axis=1), scores.std(axis=1))[0])
names.append(score_func.__name__)
plt.title("Clustering measures for 2 random uniform labelings\n"
"with equal number of clusters")
plt.xlabel('Number of clusters (Number of samples is fixed to %d)' % n_samples)
plt.ylabel('Score value')
plt.legend(plots, names)
plt.ylim(ymin=-0.05, ymax=1.05)
# Random labeling with varying n_clusters against ground class labels
# with fixed number of clusters
n_samples = 1000
n_clusters_range = np.linspace(2, 100, 10).astype(np.int)
n_classes = 10
plt.figure(2)
plots = []
names = []
for score_func in score_funcs:
print("Computing %s for %d values of n_clusters and n_samples=%d"
% (score_func.__name__, len(n_clusters_range), n_samples))
t0 = time()
scores = uniform_labelings_scores(score_func, n_samples, n_clusters_range,
fixed_n_classes=n_classes)
print("done in %0.3fs" % (time() - t0))
plots.append(plt.errorbar(
n_clusters_range, scores.mean(axis=1), scores.std(axis=1))[0])
names.append(score_func.__name__)
plt.title("Clustering measures for random uniform labeling\n"
"against reference assignment with %d classes" % n_classes)
plt.xlabel('Number of clusters (Number of samples is fixed to %d)' % n_samples)
plt.ylabel('Score value')
plt.ylim(ymin=-0.05, ymax=1.05)
plt.legend(plots, names)
plt.show()
| bsd-3-clause |
andyraib/data-storage | python_scripts/env/lib/python3.6/site-packages/matplotlib/colorbar.py | 10 | 50793 | '''
Colorbar toolkit with two classes and a function:
:class:`ColorbarBase`
the base class with full colorbar drawing functionality.
It can be used as-is to make a colorbar for a given colormap;
a mappable object (e.g., image) is not needed.
:class:`Colorbar`
the derived class for use with images or contour plots.
:func:`make_axes`
a function for resizing an axes and adding a second axes
suitable for a colorbar
The :meth:`~matplotlib.figure.Figure.colorbar` method uses :func:`make_axes`
and :class:`Colorbar`; the :func:`~matplotlib.pyplot.colorbar` function
is a thin wrapper over :meth:`~matplotlib.figure.Figure.colorbar`.
'''
from __future__ import (absolute_import, division, print_function,
unicode_literals)
import six
from six.moves import xrange, zip
import warnings
import numpy as np
import matplotlib as mpl
import matplotlib.artist as martist
import matplotlib.cbook as cbook
import matplotlib.collections as collections
import matplotlib.colors as colors
import matplotlib.contour as contour
import matplotlib.cm as cm
import matplotlib.gridspec as gridspec
import matplotlib.patches as mpatches
import matplotlib.path as mpath
import matplotlib.ticker as ticker
import matplotlib.transforms as mtrans
from matplotlib import docstring
make_axes_kw_doc = '''
============= ====================================================
Property Description
============= ====================================================
*orientation* vertical or horizontal
*fraction* 0.15; fraction of original axes to use for colorbar
*pad* 0.05 if vertical, 0.15 if horizontal; fraction
of original axes between colorbar and new image axes
*shrink* 1.0; fraction by which to shrink the colorbar
*aspect* 20; ratio of long to short dimensions
*anchor* (0.0, 0.5) if vertical; (0.5, 1.0) if horizontal;
the anchor point of the colorbar axes
*panchor* (1.0, 0.5) if vertical; (0.5, 0.0) if horizontal;
the anchor point of the colorbar parent axes. If
False, the parent axes' anchor will be unchanged
============= ====================================================
'''
colormap_kw_doc = '''
============ ====================================================
Property Description
============ ====================================================
*extend* [ 'neither' | 'both' | 'min' | 'max' ]
If not 'neither', make pointed end(s) for out-of-
range values. These are set for a given colormap
using the colormap set_under and set_over methods.
*extendfrac* [ *None* | 'auto' | length | lengths ]
If set to *None*, both the minimum and maximum
triangular colorbar extensions with have a length of
5% of the interior colorbar length (this is the
default setting). If set to 'auto', makes the
triangular colorbar extensions the same lengths as
the interior boxes (when *spacing* is set to
'uniform') or the same lengths as the respective
adjacent interior boxes (when *spacing* is set to
'proportional'). If a scalar, indicates the length
of both the minimum and maximum triangular colorbar
extensions as a fraction of the interior colorbar
length. A two-element sequence of fractions may also
be given, indicating the lengths of the minimum and
maximum colorbar extensions respectively as a
fraction of the interior colorbar length.
*extendrect* [ *False* | *True* ]
If *False* the minimum and maximum colorbar extensions
will be triangular (the default). If *True* the
extensions will be rectangular.
*spacing* [ 'uniform' | 'proportional' ]
Uniform spacing gives each discrete color the same
space; proportional makes the space proportional to
the data interval.
*ticks* [ None | list of ticks | Locator object ]
If None, ticks are determined automatically from the
input.
*format* [ None | format string | Formatter object ]
If None, the
:class:`~matplotlib.ticker.ScalarFormatter` is used.
If a format string is given, e.g., '%.3f', that is
used. An alternative
:class:`~matplotlib.ticker.Formatter` object may be
given instead.
*drawedges* [ False | True ] If true, draw lines at color
boundaries.
============ ====================================================
The following will probably be useful only in the context of
indexed colors (that is, when the mappable has norm=NoNorm()),
or other unusual circumstances.
============ ===================================================
Property Description
============ ===================================================
*boundaries* None or a sequence
*values* None or a sequence which must be of length 1 less
than the sequence of *boundaries*. For each region
delimited by adjacent entries in *boundaries*, the
color mapped to the corresponding value in values
will be used.
============ ===================================================
'''
colorbar_doc = '''
Add a colorbar to a plot.
Function signatures for the :mod:`~matplotlib.pyplot` interface; all
but the first are also method signatures for the
:meth:`~matplotlib.figure.Figure.colorbar` method::
colorbar(**kwargs)
colorbar(mappable, **kwargs)
colorbar(mappable, cax=cax, **kwargs)
colorbar(mappable, ax=ax, **kwargs)
arguments:
*mappable*
the :class:`~matplotlib.image.Image`,
:class:`~matplotlib.contour.ContourSet`, etc. to
which the colorbar applies; this argument is mandatory for the
:meth:`~matplotlib.figure.Figure.colorbar` method but optional for the
:func:`~matplotlib.pyplot.colorbar` function, which sets the
default to the current image.
keyword arguments:
*cax*
None | axes object into which the colorbar will be drawn
*ax*
None | parent axes object(s) from which space for a new
colorbar axes will be stolen. If a list of axes is given
they will all be resized to make room for the colorbar axes.
*use_gridspec*
False | If *cax* is None, a new *cax* is created as an instance of
Axes. If *ax* is an instance of Subplot and *use_gridspec* is True,
*cax* is created as an instance of Subplot using the
grid_spec module.
Additional keyword arguments are of two kinds:
axes properties:
%s
colorbar properties:
%s
If *mappable* is a :class:`~matplotlib.contours.ContourSet`, its *extend*
kwarg is included automatically.
Note that the *shrink* kwarg provides a simple way to keep a vertical
colorbar, for example, from being taller than the axes of the mappable
to which the colorbar is attached; but it is a manual method requiring
some trial and error. If the colorbar is too tall (or a horizontal
colorbar is too wide) use a smaller value of *shrink*.
For more precise control, you can manually specify the positions of
the axes objects in which the mappable and the colorbar are drawn. In
this case, do not use any of the axes properties kwargs.
It is known that some vector graphics viewer (svg and pdf) renders white gaps
between segments of the colorbar. This is due to bugs in the viewers not
matplotlib. As a workaround the colorbar can be rendered with overlapping
segments::
cbar = colorbar()
cbar.solids.set_edgecolor("face")
draw()
However this has negative consequences in other circumstances. Particularly
with semi transparent images (alpha < 1) and colorbar extensions and is not
enabled by default see (issue #1188).
returns:
:class:`~matplotlib.colorbar.Colorbar` instance; see also its base class,
:class:`~matplotlib.colorbar.ColorbarBase`. Call the
:meth:`~matplotlib.colorbar.ColorbarBase.set_label` method
to label the colorbar.
''' % (make_axes_kw_doc, colormap_kw_doc)
docstring.interpd.update(colorbar_doc=colorbar_doc)
def _set_ticks_on_axis_warn(*args, **kw):
# a top level function which gets put in at the axes'
# set_xticks set_yticks by _patch_ax
warnings.warn("Use the colorbar set_ticks() method instead.")
class ColorbarBase(cm.ScalarMappable):
'''
Draw a colorbar in an existing axes.
This is a base class for the :class:`Colorbar` class, which is the
basis for the :func:`~matplotlib.pyplot.colorbar` function and the
:meth:`~matplotlib.figure.Figure.colorbar` method, which are the
usual ways of creating a colorbar.
It is also useful by itself for showing a colormap. If the *cmap*
kwarg is given but *boundaries* and *values* are left as None,
then the colormap will be displayed on a 0-1 scale. To show the
under- and over-value colors, specify the *norm* as::
colors.Normalize(clip=False)
To show the colors versus index instead of on the 0-1 scale,
use::
norm=colors.NoNorm.
Useful attributes:
:attr:`ax`
the Axes instance in which the colorbar is drawn
:attr:`lines`
a list of LineCollection if lines were drawn, otherwise
an empty list
:attr:`dividers`
a LineCollection if *drawedges* is True, otherwise None
Useful public methods are :meth:`set_label` and :meth:`add_lines`.
'''
_slice_dict = {'neither': slice(0, None),
'both': slice(1, -1),
'min': slice(1, None),
'max': slice(0, -1)}
n_rasterize = 50 # rasterize solids if number of colors >= n_rasterize
def __init__(self, ax, cmap=None,
norm=None,
alpha=None,
values=None,
boundaries=None,
orientation='vertical',
ticklocation='auto',
extend='neither',
spacing='uniform', # uniform or proportional
ticks=None,
format=None,
drawedges=False,
filled=True,
extendfrac=None,
extendrect=False,
label='',
):
#: The axes that this colorbar lives in.
self.ax = ax
self._patch_ax()
if cmap is None:
cmap = cm.get_cmap()
if norm is None:
norm = colors.Normalize()
self.alpha = alpha
cm.ScalarMappable.__init__(self, cmap=cmap, norm=norm)
self.values = values
self.boundaries = boundaries
self.extend = extend
self._inside = self._slice_dict[extend]
self.spacing = spacing
self.orientation = orientation
self.drawedges = drawedges
self.filled = filled
self.extendfrac = extendfrac
self.extendrect = extendrect
self.solids = None
self.lines = list()
self.outline = None
self.patch = None
self.dividers = None
if ticklocation == 'auto':
ticklocation = 'bottom' if orientation == 'horizontal' else 'right'
self.ticklocation = ticklocation
self.set_label(label)
if cbook.iterable(ticks):
self.locator = ticker.FixedLocator(ticks, nbins=len(ticks))
else:
self.locator = ticks # Handle default in _ticker()
if format is None:
if isinstance(self.norm, colors.LogNorm):
self.formatter = ticker.LogFormatterSciNotation()
elif isinstance(self.norm, colors.SymLogNorm):
self.formatter = ticker.LogFormatterSciNotation(
linthresh=self.norm.linthresh)
else:
self.formatter = ticker.ScalarFormatter()
elif cbook.is_string_like(format):
self.formatter = ticker.FormatStrFormatter(format)
else:
self.formatter = format # Assume it is a Formatter
# The rest is in a method so we can recalculate when clim changes.
self.config_axis()
self.draw_all()
def _extend_lower(self):
"""Returns whether the lower limit is open ended."""
return self.extend in ('both', 'min')
def _extend_upper(self):
"""Returns whether the uper limit is open ended."""
return self.extend in ('both', 'max')
def _patch_ax(self):
# bind some methods to the axes to warn users
# against using those methods.
self.ax.set_xticks = _set_ticks_on_axis_warn
self.ax.set_yticks = _set_ticks_on_axis_warn
def draw_all(self):
'''
Calculate any free parameters based on the current cmap and norm,
and do all the drawing.
'''
self._process_values()
self._find_range()
X, Y = self._mesh()
C = self._values[:, np.newaxis]
self._config_axes(X, Y)
if self.filled:
self._add_solids(X, Y, C)
def config_axis(self):
ax = self.ax
if self.orientation == 'vertical':
ax.xaxis.set_ticks([])
# location is either one of 'bottom' or 'top'
ax.yaxis.set_label_position(self.ticklocation)
ax.yaxis.set_ticks_position(self.ticklocation)
else:
ax.yaxis.set_ticks([])
# location is either one of 'left' or 'right'
ax.xaxis.set_label_position(self.ticklocation)
ax.xaxis.set_ticks_position(self.ticklocation)
self._set_label()
def update_ticks(self):
"""
Force the update of the ticks and ticklabels. This must be
called whenever the tick locator and/or tick formatter changes.
"""
ax = self.ax
ticks, ticklabels, offset_string = self._ticker()
if self.orientation == 'vertical':
ax.yaxis.set_ticks(ticks)
ax.set_yticklabels(ticklabels)
ax.yaxis.get_major_formatter().set_offset_string(offset_string)
else:
ax.xaxis.set_ticks(ticks)
ax.set_xticklabels(ticklabels)
ax.xaxis.get_major_formatter().set_offset_string(offset_string)
def set_ticks(self, ticks, update_ticks=True):
"""
set tick locations. Tick locations are updated immediately unless
update_ticks is *False*. To manually update the ticks, call
*update_ticks* method explicitly.
"""
if cbook.iterable(ticks):
self.locator = ticker.FixedLocator(ticks, nbins=len(ticks))
else:
self.locator = ticks
if update_ticks:
self.update_ticks()
self.stale = True
def set_ticklabels(self, ticklabels, update_ticks=True):
"""
set tick labels. Tick labels are updated immediately unless
update_ticks is *False*. To manually update the ticks, call
*update_ticks* method explicitly.
"""
if isinstance(self.locator, ticker.FixedLocator):
self.formatter = ticker.FixedFormatter(ticklabels)
if update_ticks:
self.update_ticks()
else:
warnings.warn("set_ticks() must have been called.")
self.stale = True
def _config_axes(self, X, Y):
'''
Make an axes patch and outline.
'''
ax = self.ax
ax.set_frame_on(False)
ax.set_navigate(False)
xy = self._outline(X, Y)
ax.update_datalim(xy)
ax.set_xlim(*ax.dataLim.intervalx)
ax.set_ylim(*ax.dataLim.intervaly)
if self.outline is not None:
self.outline.remove()
self.outline = mpatches.Polygon(
xy, edgecolor=mpl.rcParams['axes.edgecolor'],
facecolor='none',
linewidth=mpl.rcParams['axes.linewidth'],
closed=True,
zorder=2)
ax.add_artist(self.outline)
self.outline.set_clip_box(None)
self.outline.set_clip_path(None)
c = mpl.rcParams['axes.facecolor']
if self.patch is not None:
self.patch.remove()
self.patch = mpatches.Polygon(xy, edgecolor=c,
facecolor=c,
linewidth=0.01,
zorder=-1)
ax.add_artist(self.patch)
self.update_ticks()
def _set_label(self):
if self.orientation == 'vertical':
self.ax.set_ylabel(self._label, **self._labelkw)
else:
self.ax.set_xlabel(self._label, **self._labelkw)
self.stale = True
def set_label(self, label, **kw):
'''
Label the long axis of the colorbar
'''
self._label = '%s' % (label, )
self._labelkw = kw
self._set_label()
def _outline(self, X, Y):
'''
Return *x*, *y* arrays of colorbar bounding polygon,
taking orientation into account.
'''
N = X.shape[0]
ii = [0, 1, N - 2, N - 1, 2 * N - 1, 2 * N - 2, N + 1, N, 0]
x = np.take(np.ravel(np.transpose(X)), ii)
y = np.take(np.ravel(np.transpose(Y)), ii)
x = x.reshape((len(x), 1))
y = y.reshape((len(y), 1))
if self.orientation == 'horizontal':
return np.hstack((y, x))
return np.hstack((x, y))
def _edges(self, X, Y):
'''
Return the separator line segments; helper for _add_solids.
'''
N = X.shape[0]
# Using the non-array form of these line segments is much
# simpler than making them into arrays.
if self.orientation == 'vertical':
return [list(zip(X[i], Y[i])) for i in xrange(1, N - 1)]
else:
return [list(zip(Y[i], X[i])) for i in xrange(1, N - 1)]
def _add_solids(self, X, Y, C):
'''
Draw the colors using :meth:`~matplotlib.axes.Axes.pcolormesh`;
optionally add separators.
'''
if self.orientation == 'vertical':
args = (X, Y, C)
else:
args = (np.transpose(Y), np.transpose(X), np.transpose(C))
kw = dict(cmap=self.cmap,
norm=self.norm,
alpha=self.alpha,
edgecolors='None')
# Save, set, and restore hold state to keep pcolor from
# clearing the axes. Ordinarily this will not be needed,
# since the axes object should already have hold set.
_hold = self.ax._hold
self.ax._hold = True
col = self.ax.pcolormesh(*args, **kw)
self.ax._hold = _hold
#self.add_observer(col) # We should observe, not be observed...
if self.solids is not None:
self.solids.remove()
self.solids = col
if self.dividers is not None:
self.dividers.remove()
self.dividers = None
if self.drawedges:
linewidths = (0.5 * mpl.rcParams['axes.linewidth'],)
self.dividers = collections.LineCollection(self._edges(X, Y),
colors=(mpl.rcParams['axes.edgecolor'],),
linewidths=linewidths)
self.ax.add_collection(self.dividers)
elif len(self._y) >= self.n_rasterize:
self.solids.set_rasterized(True)
def add_lines(self, levels, colors, linewidths, erase=True):
'''
Draw lines on the colorbar.
*colors* and *linewidths* must be scalars or
sequences the same length as *levels*.
Set *erase* to False to add lines without first
removing any previously added lines.
'''
y = self._locate(levels)
igood = (y < 1.001) & (y > -0.001)
y = y[igood]
if cbook.iterable(colors):
colors = np.asarray(colors)[igood]
if cbook.iterable(linewidths):
linewidths = np.asarray(linewidths)[igood]
N = len(y)
x = np.array([0.0, 1.0])
X, Y = np.meshgrid(x, y)
if self.orientation == 'vertical':
xy = [list(zip(X[i], Y[i])) for i in xrange(N)]
else:
xy = [list(zip(Y[i], X[i])) for i in xrange(N)]
col = collections.LineCollection(xy, linewidths=linewidths)
if erase and self.lines:
for lc in self.lines:
lc.remove()
self.lines = []
self.lines.append(col)
col.set_color(colors)
self.ax.add_collection(col)
self.stale = True
def _ticker(self):
'''
Return the sequence of ticks (colorbar data locations),
ticklabels (strings), and the corresponding offset string.
'''
locator = self.locator
formatter = self.formatter
if locator is None:
if self.boundaries is None:
if isinstance(self.norm, colors.NoNorm):
nv = len(self._values)
base = 1 + int(nv / 10)
locator = ticker.IndexLocator(base=base, offset=0)
elif isinstance(self.norm, colors.BoundaryNorm):
b = self.norm.boundaries
locator = ticker.FixedLocator(b, nbins=10)
elif isinstance(self.norm, colors.LogNorm):
locator = ticker.LogLocator(subs='all')
elif isinstance(self.norm, colors.SymLogNorm):
# The subs setting here should be replaced
# by logic in the locator.
locator = ticker.SymmetricalLogLocator(
subs=np.arange(1, 10),
linthresh=self.norm.linthresh,
base=10)
else:
if mpl.rcParams['_internal.classic_mode']:
locator = ticker.MaxNLocator()
else:
locator = ticker.AutoLocator()
else:
b = self._boundaries[self._inside]
locator = ticker.FixedLocator(b, nbins=10)
if isinstance(self.norm, colors.NoNorm) and self.boundaries is None:
intv = self._values[0], self._values[-1]
else:
intv = self.vmin, self.vmax
locator.create_dummy_axis(minpos=intv[0])
formatter.create_dummy_axis(minpos=intv[0])
locator.set_view_interval(*intv)
locator.set_data_interval(*intv)
formatter.set_view_interval(*intv)
formatter.set_data_interval(*intv)
b = np.array(locator())
if isinstance(locator, ticker.LogLocator):
eps = 1e-10
b = b[(b <= intv[1] * (1 + eps)) & (b >= intv[0] * (1 - eps))]
else:
eps = (intv[1] - intv[0]) * 1e-10
b = b[(b <= intv[1] + eps) & (b >= intv[0] - eps)]
ticks = self._locate(b)
formatter.set_locs(b)
ticklabels = [formatter(t, i) for i, t in enumerate(b)]
offset_string = formatter.get_offset()
return ticks, ticklabels, offset_string
def _process_values(self, b=None):
'''
Set the :attr:`_boundaries` and :attr:`_values` attributes
based on the input boundaries and values. Input boundaries
can be *self.boundaries* or the argument *b*.
'''
if b is None:
b = self.boundaries
if b is not None:
self._boundaries = np.asarray(b, dtype=float)
if self.values is None:
self._values = 0.5 * (self._boundaries[:-1]
+ self._boundaries[1:])
if isinstance(self.norm, colors.NoNorm):
self._values = (self._values + 0.00001).astype(np.int16)
return
self._values = np.array(self.values)
return
if self.values is not None:
self._values = np.array(self.values)
if self.boundaries is None:
b = np.zeros(len(self.values) + 1, 'd')
b[1:-1] = 0.5 * (self._values[:-1] - self._values[1:])
b[0] = 2.0 * b[1] - b[2]
b[-1] = 2.0 * b[-2] - b[-3]
self._boundaries = b
return
self._boundaries = np.array(self.boundaries)
return
# Neither boundaries nor values are specified;
# make reasonable ones based on cmap and norm.
if isinstance(self.norm, colors.NoNorm):
b = self._uniform_y(self.cmap.N + 1) * self.cmap.N - 0.5
v = np.zeros((len(b) - 1,), dtype=np.int16)
v[self._inside] = np.arange(self.cmap.N, dtype=np.int16)
if self._extend_lower():
v[0] = -1
if self._extend_upper():
v[-1] = self.cmap.N
self._boundaries = b
self._values = v
return
elif isinstance(self.norm, colors.BoundaryNorm):
b = list(self.norm.boundaries)
if self._extend_lower():
b = [b[0] - 1] + b
if self._extend_upper():
b = b + [b[-1] + 1]
b = np.array(b)
v = np.zeros((len(b) - 1,), dtype=float)
bi = self.norm.boundaries
v[self._inside] = 0.5 * (bi[:-1] + bi[1:])
if self._extend_lower():
v[0] = b[0] - 1
if self._extend_upper():
v[-1] = b[-1] + 1
self._boundaries = b
self._values = v
return
else:
if not self.norm.scaled():
self.norm.vmin = 0
self.norm.vmax = 1
self.norm.vmin, self.norm.vmax = mtrans.nonsingular(self.norm.vmin,
self.norm.vmax,
expander=0.1)
b = self.norm.inverse(self._uniform_y(self.cmap.N + 1))
if isinstance(self.norm, colors.LogNorm):
# If using a lognorm, ensure extensions don't go negative
if self._extend_lower():
b[0] = 0.9 * b[0]
if self._extend_upper():
b[-1] = 1.1 * b[-1]
else:
if self._extend_lower():
b[0] = b[0] - 1
if self._extend_upper():
b[-1] = b[-1] + 1
self._process_values(b)
def _find_range(self):
'''
Set :attr:`vmin` and :attr:`vmax` attributes to the first and
last boundary excluding extended end boundaries.
'''
b = self._boundaries[self._inside]
self.vmin = b[0]
self.vmax = b[-1]
def _central_N(self):
'''number of boundaries **before** extension of ends'''
nb = len(self._boundaries)
if self.extend == 'both':
nb -= 2
elif self.extend in ('min', 'max'):
nb -= 1
return nb
def _extended_N(self):
'''
Based on the colormap and extend variable, return the
number of boundaries.
'''
N = self.cmap.N + 1
if self.extend == 'both':
N += 2
elif self.extend in ('min', 'max'):
N += 1
return N
def _get_extension_lengths(self, frac, automin, automax, default=0.05):
'''
Get the lengths of colorbar extensions.
A helper method for _uniform_y and _proportional_y.
'''
# Set the default value.
extendlength = np.array([default, default])
if isinstance(frac, six.string_types):
if frac.lower() == 'auto':
# Use the provided values when 'auto' is required.
extendlength[0] = automin
extendlength[1] = automax
else:
# Any other string is invalid.
raise ValueError('invalid value for extendfrac')
elif frac is not None:
try:
# Try to set min and max extension fractions directly.
extendlength[:] = frac
# If frac is a sequence contaning None then NaN may
# be encountered. This is an error.
if np.isnan(extendlength).any():
raise ValueError()
except (TypeError, ValueError):
# Raise an error on encountering an invalid value for frac.
raise ValueError('invalid value for extendfrac')
return extendlength
def _uniform_y(self, N):
'''
Return colorbar data coordinates for *N* uniformly
spaced boundaries, plus ends if required.
'''
if self.extend == 'neither':
y = np.linspace(0, 1, N)
else:
automin = automax = 1. / (N - 1.)
extendlength = self._get_extension_lengths(self.extendfrac,
automin, automax,
default=0.05)
if self.extend == 'both':
y = np.zeros(N + 2, 'd')
y[0] = 0. - extendlength[0]
y[-1] = 1. + extendlength[1]
elif self.extend == 'min':
y = np.zeros(N + 1, 'd')
y[0] = 0. - extendlength[0]
else:
y = np.zeros(N + 1, 'd')
y[-1] = 1. + extendlength[1]
y[self._inside] = np.linspace(0, 1, N)
return y
def _proportional_y(self):
'''
Return colorbar data coordinates for the boundaries of
a proportional colorbar.
'''
if isinstance(self.norm, colors.BoundaryNorm):
y = (self._boundaries - self._boundaries[0])
y = y / (self._boundaries[-1] - self._boundaries[0])
else:
y = self.norm(self._boundaries.copy())
if self.extend == 'min':
# Exclude leftmost interval of y.
clen = y[-1] - y[1]
automin = (y[2] - y[1]) / clen
automax = (y[-1] - y[-2]) / clen
elif self.extend == 'max':
# Exclude rightmost interval in y.
clen = y[-2] - y[0]
automin = (y[1] - y[0]) / clen
automax = (y[-2] - y[-3]) / clen
else:
# Exclude leftmost and rightmost intervals in y.
clen = y[-2] - y[1]
automin = (y[2] - y[1]) / clen
automax = (y[-2] - y[-3]) / clen
extendlength = self._get_extension_lengths(self.extendfrac,
automin, automax,
default=0.05)
if self.extend in ('both', 'min'):
y[0] = 0. - extendlength[0]
if self.extend in ('both', 'max'):
y[-1] = 1. + extendlength[1]
yi = y[self._inside]
norm = colors.Normalize(yi[0], yi[-1])
y[self._inside] = norm(yi)
return y
def _mesh(self):
'''
Return X,Y, the coordinate arrays for the colorbar pcolormesh.
These are suitable for a vertical colorbar; swapping and
transposition for a horizontal colorbar are done outside
this function.
'''
x = np.array([0.0, 1.0])
if self.spacing == 'uniform':
y = self._uniform_y(self._central_N())
else:
y = self._proportional_y()
self._y = y
X, Y = np.meshgrid(x, y)
if self._extend_lower() and not self.extendrect:
X[0, :] = 0.5
if self._extend_upper() and not self.extendrect:
X[-1, :] = 0.5
return X, Y
def _locate(self, x):
'''
Given a set of color data values, return their
corresponding colorbar data coordinates.
'''
if isinstance(self.norm, (colors.NoNorm, colors.BoundaryNorm)):
b = self._boundaries
xn = x
else:
# Do calculations using normalized coordinates so
# as to make the interpolation more accurate.
b = self.norm(self._boundaries, clip=False).filled()
xn = self.norm(x, clip=False).filled()
# The rest is linear interpolation with extrapolation at ends.
ii = np.searchsorted(b, xn)
i0 = ii - 1
itop = (ii == len(b))
ibot = (ii == 0)
i0[itop] -= 1
ii[itop] -= 1
i0[ibot] += 1
ii[ibot] += 1
db = np.take(b, ii) - np.take(b, i0)
y = self._y
dy = np.take(y, ii) - np.take(y, i0)
z = np.take(y, i0) + (xn - np.take(b, i0)) * dy / db
return z
def set_alpha(self, alpha):
self.alpha = alpha
def remove(self):
"""
Remove this colorbar from the figure
"""
fig = self.ax.figure
fig.delaxes(self.ax)
class Colorbar(ColorbarBase):
"""
This class connects a :class:`ColorbarBase` to a
:class:`~matplotlib.cm.ScalarMappable` such as a
:class:`~matplotlib.image.AxesImage` generated via
:meth:`~matplotlib.axes.Axes.imshow`.
It is not intended to be instantiated directly; instead,
use :meth:`~matplotlib.figure.Figure.colorbar` or
:func:`~matplotlib.pyplot.colorbar` to make your colorbar.
"""
def __init__(self, ax, mappable, **kw):
# Ensure the given mappable's norm has appropriate vmin and vmax set
# even if mappable.draw has not yet been called.
mappable.autoscale_None()
self.mappable = mappable
kw['cmap'] = cmap = mappable.cmap
kw['norm'] = norm = mappable.norm
if isinstance(mappable, contour.ContourSet):
CS = mappable
kw['alpha'] = mappable.get_alpha()
kw['boundaries'] = CS._levels
kw['values'] = CS.cvalues
kw['extend'] = CS.extend
#kw['ticks'] = CS._levels
kw.setdefault('ticks', ticker.FixedLocator(CS.levels, nbins=10))
kw['filled'] = CS.filled
ColorbarBase.__init__(self, ax, **kw)
if not CS.filled:
self.add_lines(CS)
else:
if getattr(cmap, 'colorbar_extend', False) is not False:
kw.setdefault('extend', cmap.colorbar_extend)
if isinstance(mappable, martist.Artist):
kw['alpha'] = mappable.get_alpha()
ColorbarBase.__init__(self, ax, **kw)
def on_mappable_changed(self, mappable):
"""
Updates this colorbar to match the mappable's properties.
Typically this is automatically registered as an event handler
by :func:`colorbar_factory` and should not be called manually.
"""
self.set_cmap(mappable.get_cmap())
self.set_clim(mappable.get_clim())
self.update_normal(mappable)
def add_lines(self, CS, erase=True):
'''
Add the lines from a non-filled
:class:`~matplotlib.contour.ContourSet` to the colorbar.
Set *erase* to False if these lines should be added to
any pre-existing lines.
'''
if not isinstance(CS, contour.ContourSet) or CS.filled:
raise ValueError('add_lines is only for a ContourSet of lines')
tcolors = [c[0] for c in CS.tcolors]
tlinewidths = [t[0] for t in CS.tlinewidths]
# The following was an attempt to get the colorbar lines
# to follow subsequent changes in the contour lines,
# but more work is needed: specifically, a careful
# look at event sequences, and at how
# to make one object track another automatically.
#tcolors = [col.get_colors()[0] for col in CS.collections]
#tlinewidths = [col.get_linewidth()[0] for lw in CS.collections]
#print 'tlinewidths:', tlinewidths
ColorbarBase.add_lines(self, CS.levels, tcolors, tlinewidths,
erase=erase)
def update_normal(self, mappable):
'''
update solid, lines, etc. Unlike update_bruteforce, it does
not clear the axes. This is meant to be called when the image
or contour plot to which this colorbar belongs is changed.
'''
self.draw_all()
if isinstance(self.mappable, contour.ContourSet):
CS = self.mappable
if not CS.filled:
self.add_lines(CS)
self.stale = True
def update_bruteforce(self, mappable):
'''
Destroy and rebuild the colorbar. This is
intended to become obsolete, and will probably be
deprecated and then removed. It is not called when
the pyplot.colorbar function or the Figure.colorbar
method are used to create the colorbar.
'''
# We are using an ugly brute-force method: clearing and
# redrawing the whole thing. The problem is that if any
# properties have been changed by methods other than the
# colorbar methods, those changes will be lost.
self.ax.cla()
# clearing the axes will delete outline, patch, solids, and lines:
self.outline = None
self.patch = None
self.solids = None
self.lines = list()
self.dividers = None
self.set_alpha(mappable.get_alpha())
self.cmap = mappable.cmap
self.norm = mappable.norm
self.config_axis()
self.draw_all()
if isinstance(self.mappable, contour.ContourSet):
CS = self.mappable
if not CS.filled:
self.add_lines(CS)
#if self.lines is not None:
# tcolors = [c[0] for c in CS.tcolors]
# self.lines.set_color(tcolors)
#Fixme? Recalculate boundaries, ticks if vmin, vmax have changed.
#Fixme: Some refactoring may be needed; we should not
# be recalculating everything if there was a simple alpha
# change.
def remove(self):
"""
Remove this colorbar from the figure. If the colorbar was created with
``use_gridspec=True`` then restore the gridspec to its previous value.
"""
ColorbarBase.remove(self)
self.mappable.callbacksSM.disconnect(self.mappable.colorbar_cid)
self.mappable.colorbar = None
self.mappable.colorbar_cid = None
try:
ax = self.mappable.axes
except AttributeError:
return
try:
gs = ax.get_subplotspec().get_gridspec()
subplotspec = gs.get_topmost_subplotspec()
except AttributeError:
# use_gridspec was False
pos = ax.get_position(original=True)
ax.set_position(pos)
else:
# use_gridspec was True
ax.set_subplotspec(subplotspec)
@docstring.Substitution(make_axes_kw_doc)
def make_axes(parents, location=None, orientation=None, fraction=0.15,
shrink=1.0, aspect=20, **kw):
'''
Resize and reposition parent axes, and return a child
axes suitable for a colorbar.
Keyword arguments may include the following (with defaults):
location : [None|'left'|'right'|'top'|'bottom']
The position, relative to **parents**, where the colorbar axes
should be created. If None, the value will either come from the
given ``orientation``, else it will default to 'right'.
orientation : [None|'vertical'|'horizontal']
The orientation of the colorbar. Typically, this keyword shouldn't
be used, as it can be derived from the ``location`` keyword.
%s
Returns (cax, kw), the child axes and the reduced kw dictionary to be
passed when creating the colorbar instance.
'''
locations = ["left", "right", "top", "bottom"]
if orientation is not None and location is not None:
msg = ('position and orientation are mutually exclusive. '
'Consider setting the position to any of '
'{0}'.format(', '.join(locations)))
raise TypeError(msg)
# provide a default location
if location is None and orientation is None:
location = 'right'
# allow the user to not specify the location by specifying the
# orientation instead
if location is None:
location = 'right' if orientation == 'vertical' else 'bottom'
if location not in locations:
raise ValueError('Invalid colorbar location. Must be one '
'of %s' % ', '.join(locations))
default_location_settings = {'left': {'anchor': (1.0, 0.5),
'panchor': (0.0, 0.5),
'pad': 0.10,
'orientation': 'vertical'},
'right': {'anchor': (0.0, 0.5),
'panchor': (1.0, 0.5),
'pad': 0.05,
'orientation': 'vertical'},
'top': {'anchor': (0.5, 0.0),
'panchor': (0.5, 1.0),
'pad': 0.05,
'orientation': 'horizontal'},
'bottom': {'anchor': (0.5, 1.0),
'panchor': (0.5, 0.0),
'pad': 0.15, # backwards compat
'orientation': 'horizontal'},
}
loc_settings = default_location_settings[location]
# put appropriate values into the kw dict for passing back to
# the Colorbar class
kw['orientation'] = loc_settings['orientation']
kw['ticklocation'] = location
anchor = kw.pop('anchor', loc_settings['anchor'])
parent_anchor = kw.pop('panchor', loc_settings['panchor'])
pad = kw.pop('pad', loc_settings['pad'])
# turn parents into a list if it is not already
if not isinstance(parents, (list, tuple)):
parents = [parents]
fig = parents[0].get_figure()
if not all(fig is ax.get_figure() for ax in parents):
raise ValueError('Unable to create a colorbar axes as not all '
'parents share the same figure.')
# take a bounding box around all of the given axes
parents_bbox = mtrans.Bbox.union([ax.get_position(original=True).frozen()
for ax in parents])
pb = parents_bbox
if location in ('left', 'right'):
if location == 'left':
pbcb, _, pb1 = pb.splitx(fraction, fraction + pad)
else:
pb1, _, pbcb = pb.splitx(1 - fraction - pad, 1 - fraction)
pbcb = pbcb.shrunk(1.0, shrink).anchored(anchor, pbcb)
else:
if location == 'bottom':
pbcb, _, pb1 = pb.splity(fraction, fraction + pad)
else:
pb1, _, pbcb = pb.splity(1 - fraction - pad, 1 - fraction)
pbcb = pbcb.shrunk(shrink, 1.0).anchored(anchor, pbcb)
# define the aspect ratio in terms of y's per x rather than x's per y
aspect = 1.0 / aspect
# define a transform which takes us from old axes coordinates to
# new axes coordinates
shrinking_trans = mtrans.BboxTransform(parents_bbox, pb1)
# transform each of the axes in parents using the new transform
for ax in parents:
new_posn = shrinking_trans.transform(ax.get_position())
new_posn = mtrans.Bbox(new_posn)
ax.set_position(new_posn)
if parent_anchor is not False:
ax.set_anchor(parent_anchor)
cax = fig.add_axes(pbcb)
cax.set_aspect(aspect, anchor=anchor, adjustable='box')
return cax, kw
@docstring.Substitution(make_axes_kw_doc)
def make_axes_gridspec(parent, **kw):
'''
Resize and reposition a parent axes, and return a child axes
suitable for a colorbar. This function is similar to
make_axes. Prmary differences are
* *make_axes_gridspec* only handles the *orientation* keyword
and cannot handle the "location" keyword.
* *make_axes_gridspec* should only be used with a subplot parent.
* *make_axes* creates an instance of Axes. *make_axes_gridspec*
creates an instance of Subplot.
* *make_axes* updates the position of the
parent. *make_axes_gridspec* replaces the grid_spec attribute
of the parent with a new one.
While this function is meant to be compatible with *make_axes*,
there could be some minor differences.
Keyword arguments may include the following (with defaults):
*orientation*
'vertical' or 'horizontal'
%s
All but the first of these are stripped from the input kw set.
Returns (cax, kw), the child axes and the reduced kw dictionary to be
passed when creating the colorbar instance.
'''
orientation = kw.setdefault('orientation', 'vertical')
kw['ticklocation'] = 'auto'
fraction = kw.pop('fraction', 0.15)
shrink = kw.pop('shrink', 1.0)
aspect = kw.pop('aspect', 20)
x1 = 1.0 - fraction
# for shrinking
pad_s = (1. - shrink) * 0.5
wh_ratios = [pad_s, shrink, pad_s]
gs_from_subplotspec = gridspec.GridSpecFromSubplotSpec
if orientation == 'vertical':
pad = kw.pop('pad', 0.05)
wh_space = 2 * pad / (1 - pad)
gs = gs_from_subplotspec(1, 2,
subplot_spec=parent.get_subplotspec(),
wspace=wh_space,
width_ratios=[x1 - pad, fraction]
)
gs2 = gs_from_subplotspec(3, 1,
subplot_spec=gs[1],
hspace=0.,
height_ratios=wh_ratios,
)
anchor = (0.0, 0.5)
panchor = (1.0, 0.5)
else:
pad = kw.pop('pad', 0.15)
wh_space = 2 * pad / (1 - pad)
gs = gs_from_subplotspec(2, 1,
subplot_spec=parent.get_subplotspec(),
hspace=wh_space,
height_ratios=[x1 - pad, fraction]
)
gs2 = gs_from_subplotspec(1, 3,
subplot_spec=gs[1],
wspace=0.,
width_ratios=wh_ratios,
)
aspect = 1.0 / aspect
anchor = (0.5, 1.0)
panchor = (0.5, 0.0)
parent.set_subplotspec(gs[0])
parent.update_params()
parent.set_position(parent.figbox)
parent.set_anchor(panchor)
fig = parent.get_figure()
cax = fig.add_subplot(gs2[1])
cax.set_aspect(aspect, anchor=anchor, adjustable='box')
return cax, kw
class ColorbarPatch(Colorbar):
"""
A Colorbar which is created using :class:`~matplotlib.patches.Patch`
rather than the default :func:`~matplotlib.axes.pcolor`.
It uses a list of Patch instances instead of a
:class:`~matplotlib.collections.PatchCollection` because the
latter does not allow the hatch pattern to vary among the
members of the collection.
"""
def __init__(self, ax, mappable, **kw):
# we do not want to override the behaviour of solids
# so add a new attribute which will be a list of the
# colored patches in the colorbar
self.solids_patches = []
Colorbar.__init__(self, ax, mappable, **kw)
def _add_solids(self, X, Y, C):
"""
Draw the colors using :class:`~matplotlib.patches.Patch`;
optionally add separators.
"""
# Save, set, and restore hold state to keep pcolor from
# clearing the axes. Ordinarily this will not be needed,
# since the axes object should already have hold set.
_hold = self.ax._hold
self.ax._hold = True
kw = {'alpha': self.alpha, }
n_segments = len(C)
# ensure there are sufficent hatches
hatches = self.mappable.hatches * n_segments
patches = []
for i in xrange(len(X) - 1):
val = C[i][0]
hatch = hatches[i]
xy = np.array([[X[i][0], Y[i][0]],
[X[i][1], Y[i][0]],
[X[i + 1][1], Y[i + 1][0]],
[X[i + 1][0], Y[i + 1][1]]])
if self.orientation == 'horizontal':
# if horizontal swap the xs and ys
xy = xy[..., ::-1]
patch = mpatches.PathPatch(mpath.Path(xy),
facecolor=self.cmap(self.norm(val)),
hatch=hatch, linewidth=0,
antialiased=False, **kw)
self.ax.add_patch(patch)
patches.append(patch)
if self.solids_patches:
for solid in self.solids_patches:
solid.remove()
self.solids_patches = patches
if self.dividers is not None:
self.dividers.remove()
self.dividers = None
if self.drawedges:
self.dividers = collections.LineCollection(self._edges(X, Y),
colors=(mpl.rcParams['axes.edgecolor'],),
linewidths=(0.5 * mpl.rcParams['axes.linewidth'],))
self.ax.add_collection(self.dividers)
self.ax._hold = _hold
def colorbar_factory(cax, mappable, **kwargs):
"""
Creates a colorbar on the given axes for the given mappable.
Typically, for automatic colorbar placement given only a mappable use
:meth:`~matplotlib.figure.Figure.colorbar`.
"""
# if the given mappable is a contourset with any hatching, use
# ColorbarPatch else use Colorbar
if (isinstance(mappable, contour.ContourSet)
and any([hatch is not None for hatch in mappable.hatches])):
cb = ColorbarPatch(cax, mappable, **kwargs)
else:
cb = Colorbar(cax, mappable, **kwargs)
cid = mappable.callbacksSM.connect('changed', cb.on_mappable_changed)
mappable.colorbar = cb
mappable.colorbar_cid = cid
return cb
| apache-2.0 |
pypot/scikit-learn | examples/svm/plot_separating_hyperplane_unbalanced.py | 329 | 1850 | """
=================================================
SVM: Separating hyperplane for unbalanced classes
=================================================
Find the optimal separating hyperplane using an SVC for classes that
are unbalanced.
We first find the separating plane with a plain SVC and then plot
(dashed) the separating hyperplane with automatically correction for
unbalanced classes.
.. currentmodule:: sklearn.linear_model
.. note::
This example will also work by replacing ``SVC(kernel="linear")``
with ``SGDClassifier(loss="hinge")``. Setting the ``loss`` parameter
of the :class:`SGDClassifier` equal to ``hinge`` will yield behaviour
such as that of a SVC with a linear kernel.
For example try instead of the ``SVC``::
clf = SGDClassifier(n_iter=100, alpha=0.01)
"""
print(__doc__)
import numpy as np
import matplotlib.pyplot as plt
from sklearn import svm
#from sklearn.linear_model import SGDClassifier
# we create 40 separable points
rng = np.random.RandomState(0)
n_samples_1 = 1000
n_samples_2 = 100
X = np.r_[1.5 * rng.randn(n_samples_1, 2),
0.5 * rng.randn(n_samples_2, 2) + [2, 2]]
y = [0] * (n_samples_1) + [1] * (n_samples_2)
# fit the model and get the separating hyperplane
clf = svm.SVC(kernel='linear', C=1.0)
clf.fit(X, y)
w = clf.coef_[0]
a = -w[0] / w[1]
xx = np.linspace(-5, 5)
yy = a * xx - clf.intercept_[0] / w[1]
# get the separating hyperplane using weighted classes
wclf = svm.SVC(kernel='linear', class_weight={1: 10})
wclf.fit(X, y)
ww = wclf.coef_[0]
wa = -ww[0] / ww[1]
wyy = wa * xx - wclf.intercept_[0] / ww[1]
# plot separating hyperplanes and samples
h0 = plt.plot(xx, yy, 'k-', label='no weights')
h1 = plt.plot(xx, wyy, 'k--', label='with weights')
plt.scatter(X[:, 0], X[:, 1], c=y, cmap=plt.cm.Paired)
plt.legend()
plt.axis('tight')
plt.show()
| bsd-3-clause |
Garrett-R/scikit-learn | examples/ensemble/plot_gradient_boosting_regression.py | 20 | 2520 | """
============================
Gradient Boosting regression
============================
Demonstrate Gradient Boosting on the boston housing dataset.
This example fits a Gradient Boosting model with least squares loss and
500 regression trees of depth 4.
"""
print(__doc__)
# Author: Peter Prettenhofer <[email protected]>
#
# License: BSD 3 clause
import numpy as np
import matplotlib.pyplot as plt
from sklearn import ensemble
from sklearn import datasets
from sklearn.utils import shuffle
from sklearn.metrics import mean_squared_error
###############################################################################
# Load data
boston = datasets.load_boston()
X, y = shuffle(boston.data, boston.target, random_state=13)
X = X.astype(np.float32)
offset = int(X.shape[0] * 0.9)
X_train, y_train = X[:offset], y[:offset]
X_test, y_test = X[offset:], y[offset:]
###############################################################################
# Fit regression model
params = {'n_estimators': 500, 'max_depth': 4, 'min_samples_split': 1,
'learning_rate': 0.01, 'loss': 'ls'}
clf = ensemble.GradientBoostingRegressor(**params)
clf.fit(X_train, y_train)
mse = mean_squared_error(y_test, clf.predict(X_test))
print("MSE: %.4f" % mse)
###############################################################################
# Plot training deviance
# compute test set deviance
test_score = np.zeros((params['n_estimators'],), dtype=np.float64)
for i, y_pred in enumerate(clf.staged_decision_function(X_test)):
test_score[i] = clf.loss_(y_test, y_pred)
plt.figure(figsize=(12, 6))
plt.subplot(1, 2, 1)
plt.title('Deviance')
plt.plot(np.arange(params['n_estimators']) + 1, clf.train_score_, 'b-',
label='Training Set Deviance')
plt.plot(np.arange(params['n_estimators']) + 1, test_score, 'r-',
label='Test Set Deviance')
plt.legend(loc='upper right')
plt.xlabel('Boosting Iterations')
plt.ylabel('Deviance')
###############################################################################
# Plot feature importance
feature_importance = clf.feature_importances_
# make importances relative to max importance
feature_importance = 100.0 * (feature_importance / feature_importance.max())
sorted_idx = np.argsort(feature_importance)
pos = np.arange(sorted_idx.shape[0]) + .5
plt.subplot(1, 2, 2)
plt.barh(pos, feature_importance[sorted_idx], align='center')
plt.yticks(pos, boston.feature_names[sorted_idx])
plt.xlabel('Relative Importance')
plt.title('Variable Importance')
plt.show()
| bsd-3-clause |
sahilshah/openface | demos/classifier.py | 2 | 5712 | #!/usr/bin/env python2
#
# Example to classify faces.
# Brandon Amos
# 2015/10/11
#
# Copyright 2015 Carnegie Mellon University
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
import cv2
import itertools
import os
import pickle
from operator import itemgetter
import numpy as np
np.set_printoptions(precision=2)
import pandas as pd
import sys
fileDir = os.path.dirname(os.path.realpath(__file__))
sys.path.append(os.path.join(fileDir, ".."))
import openface
import openface.helper
from openface.data import iterImgs
from sklearn.preprocessing import LabelEncoder
from sklearn.decomposition import PCA
from sklearn.grid_search import GridSearchCV
from sklearn.manifold import TSNE
from sklearn.svm import SVC
modelDir = os.path.join(fileDir, '..', 'models')
dlibModelDir = os.path.join(modelDir, 'dlib')
openfaceModelDir = os.path.join(modelDir, 'openface')
def getRep(imgPath):
img = cv2.imread(imgPath)
if img is None:
raise Exception("Unable to load image: {}".format(imgPath))
if args.verbose:
print(" + Original size: {}".format(img.shape))
bb = align.getLargestFaceBoundingBox(img)
if bb is None:
raise Exception("Unable to find a face: {}".format(imgPath))
alignedFace = align.alignImg("affine", args.imgDim, img, bb)
if alignedFace is None:
raise Exception("Unable to align image: {}".format(imgPath))
rep = net.forwardImage(alignedFace)
return rep
def train(args):
print("Loading embeddings.")
fname = "{}/labels.csv".format(args.workDir)
labels = pd.read_csv(fname, header=None).as_matrix()[:, 1]
labels = map(itemgetter(1),
map(os.path.split,
map(os.path.dirname, labels))) # Get the directory.
fname = "{}/reps.csv".format(args.workDir)
embeddings = pd.read_csv(fname, header=None).as_matrix()
le = LabelEncoder().fit(labels)
labelsNum = le.transform(labels)
param_grid = [
{'C': [1, 10, 100, 1000],
'kernel': ['linear']},
{'C': [1, 10, 100, 1000],
'gamma': [0.001, 0.0001],
'kernel': ['rbf']}
]
svm = GridSearchCV(
SVC(probability=True),
param_grid, verbose=4, cv=5, n_jobs=16
).fit(embeddings, labelsNum)
print("Best estimator: {}".format(svm.best_estimator_))
print("Best score on left out data: {:.2f}".format(svm.best_score_))
with open("{}/classifier.pkl".format(args.workDir), 'w') as f:
pickle.dump((le, svm), f)
def infer(args):
with open(args.classifierModel, 'r') as f:
(le, svm) = pickle.load(f)
rep = getRep(args.img)
predictions = svm.predict_proba(rep)[0]
maxI = np.argmax(predictions)
person = le.inverse_transform(maxI)
confidence = predictions[maxI]
print("Predict {} with {:.2f} confidence.".format(person, confidence))
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--dlibFaceMean', type=str,
help="Path to dlib's face predictor.",
default=os.path.join(dlibModelDir, "mean.csv"))
parser.add_argument('--dlibFacePredictor', type=str,
help="Path to dlib's face predictor.",
default=os.path.join(dlibModelDir,
"shape_predictor_68_face_landmarks.dat"))
parser.add_argument('--dlibRoot', type=str,
default=os.path.expanduser(
"~/src/dlib-18.16/python_examples"),
help="dlib directory with the dlib.so Python library.")
parser.add_argument('--networkModel', type=str,
help="Path to Torch network model.",
default=os.path.join(openfaceModelDir, 'nn4.v1.t7'))
parser.add_argument('--imgDim', type=int,
help="Default image dimension.", default=96)
parser.add_argument('--cuda', action='store_true')
parser.add_argument('--verbose', action='store_true')
subparsers = parser.add_subparsers(dest='mode', help="Mode")
trainParser = subparsers.add_parser('train',
help="Train a new classifier.")
trainParser.add_argument('workDir', type=str,
help="The input work directory containing 'reps.csv' and 'labels.csv'. Obtained from aligning a directory with 'align-dlib' and getting the representations with 'batch-represent'.")
inferParser = subparsers.add_parser('infer',
help='Predict who an image contains from a trained classifier.')
inferParser.add_argument('classifierModel', type=str)
inferParser.add_argument('img', type=str,
help="Input image.")
args = parser.parse_args()
sys.path.append(args.dlibRoot)
import dlib
from openface.alignment import NaiveDlib # Depends on dlib.
align = NaiveDlib(args.dlibFaceMean, args.dlibFacePredictor)
net = openface.TorchWrap(
args.networkModel, imgDim=args.imgDim, cuda=args.cuda)
if args.mode == 'train':
train(args)
elif args.mode == 'infer':
infer(args)
| apache-2.0 |
TinghuiWang/ActivityLearning | actlearn/utils/plot_result.py | 1 | 4280 | import os
import cPickle as pickle
import matplotlib.pyplot as plt
from actlearn.log.logger import actlearn_logger
from actlearn.utils.AlResult import AlResult
from actlearn.utils.classifier_performance import *
def plot_result_time_series(filename_array, fig_fname='', fig_format='pdf',
performance_name='overall_performance', performance_key='exact matching ratio'):
"""
Show the learning result on a time-series plot.
:param filename_array:
:param fig_fname:
:param fig_format:
:param performance_name: overall_performance or per_class_performance
:param performance_key: name listed in overall_performance or per_class_performance
:return:
"""
logger = actlearn_logger.get_logger('casas_learning_curve')
# Load data into array of dictionary
load_error = False
learning_data = []
for fname in filename_array:
if os.path.exists(fname):
cur_data = AlResult()
cur_data.load_from_file(fname=fname)
learning_data.append(cur_data)
else:
logger.error('Cannot find file %s' % fname)
load_error = True
if load_error:
return
# Check the base of all learning curve files
# 1. Same length?
# 2. By week or by day?
result_mode = learning_data[0].get_mode()
curve_length = learning_data[0].get_num_records()
curve_x_label = learning_data[0].get_record_keys()
for result_data in learning_data:
if result_data.get_mode() != result_mode:
load_error = True
cur_length = result_data.get_num_records()
if cur_length != curve_length:
logger.warning('In consistence length of learning curve: %s has %d items' %
(result_data.get_name(), cur_length))
if cur_length < curve_length:
curve_length = cur_length
curve_x_label = result_data.get_record_keys()
if load_error:
logger.error('Error in step size of learning curve')
for curve in learning_data:
logger.error('%20s: by %s' % (curve.get_name(), curve.get_mode()))
return
# Check if performance key is legal
performance_key_id = 0
if performance_name == 'per_class_performance':
if performance_key not in per_class_performance_index:
logger.error('key %s not found in per_class_performance.\nAvailable Keys are: %s' %
(performance_key, str(per_class_performance_index)))
return
else:
performance_key_id = per_class_performance_index.index(performance_key)
elif performance_name == 'overall_performance':
if performance_key not in overall_performance_index:
logger.error('key %s not found in overall_performance.\nAvailable Keys are: %s' %
(performance_key, str(overall_performance_index)))
return
else:
performance_key_id = overall_performance_index.index(performance_key)
else:
logger.error('performance matrix named %s is not available' % performance_name)
return
# Synthesize the selected performance value into an array that can be plotted
curve_value = []
curve_name = []
for result_data in learning_data:
curve_name.append(result_data.get_name())
cur_curve = []
for record_key in result_data.get_record_keys():
cur_curve.append(result_data.get_record_by_key(record_key)[performance_name][performance_key_id])
curve_value.append(cur_curve)
# Plot it using plt
x = range(curve_length)
x_label = curve_x_label
y = np.arange(0, 1.1, 0.1)
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
for i in range(len(curve_name)):
plt.plot(x, curve_value[i], '.-', label=curve_name[i], linewidth=1.0)
ax.set_xticks(x)
ax.set_xticklabels(x_label, rotation='vertical')
ax.set_yticks(y)
handles, labels = ax.get_legend_handles_labels()
ax.legend(handles, labels)
plt.grid(b=True, which='major', linestyle='--', linewidth=1.0, alpha=0.5)
if fig_fname != '':
fig.savefig(fig_fname, transparent=True, format=fig_format, bbox_inches='tight')
else:
plt.show()
return
| bsd-3-clause |
kwailamchan/programming-languages | python/sklearn/examples/general/clustering_text_documents_using_k-means.py | 3 | 6625 | #---------------------------------------------------------------#
# Project: Clustering text documents using k-means
# Author: Kelly Chan
# Date: Apr 25 2014
#---------------------------------------------------------------#
from __future__ import print_function
import sys
import logging
from time import time
from optparse import OptionParser
import numpy as np
from sklearn.datasets import fetch_20newsgroups
from sklearn.decomposition import TruncatedSVD
from sklearn.preprocessing import Normalizer
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.feature_extraction.text import HashingVectorizer
from sklearn.feature_extraction.text import TfidfTransformer
from sklearn import metrics
from sklearn.pipeline import Pipeline
from sklearn.cluster import KMeans, MiniBatchKMeans
# disply progress logs on stdout
logging.basicConfig(level=logging.INFO, \
format='%(asctime)s %(levelname)s %(message)s')
# parse commandline arguments
op = OptionParser()
op.add_option("--lsa", \
dest="n_components", \
type="int", \
help="Preprocess documents with latent semantic analysis.")
op.add_option("--no-minibatch", \
action="store_false", \
dest="minibatch", \
default=True, \
help="Use ordinary k-means algorithm (in batch mode).")
op.add_option("--no-idf", \
action="store_false", \
dest="use_idf", \
default=True, \
help="Disable Inverse Document Frequency feature weighting.")
op.add_option("--use-hashing", \
action="store_true", \
default=False, \
help="Use a hashing feature vectorizer")
op.add_option("--n-features", \
type=int, \
default=10000, \
help="Maximum number of features (dimensions) to extract from text.")
op.add_option("--verbose", \
action="store_true", \
dest="verbose", \
default=False, \
help="Print progress reports inside k-means algorithm.")
(opts, args) = op.parse_args()
if len(args) > 0:
op.error("this script takes no arguments.")
sys.exit(1)
print(__doc__)
op.print_help()
print()
def loadCategories():
# load some categories from the training set
categories = ['alt.atheism', \
'talk.religion.misc', \
'comp.graphics', \
'sci.space', \
]
return categories
def loadData(categories):
# uncomment the following for all categories
# categories = None
print("Loading 20 newsgroups dataset for categories:")
print(categories)
dataset = fetch_20newsgroups(subset='all', \
categories=categories, \
shuffle=True, \
random_state=42)
print("%d documents" % len(dataset.data))
print("%d categories" % len(dataset.target_names))
print()
return dataset
def extractFeatures(dataset):
labels = dataset.target
true_k = np.unique(labels).shape[0]
print("Extracting features from the training dataset using a sparse vectorizer")
t0 = time()
if opts.use_hashing:
if opts.use_idf:
# perform an IDF normalization on the output of HashingVectorizer
hasher = HashingVectorizer(n_features=opts.n_features, \
stop_words='english', \
non_negative=True, \
norm=None, \
binary=False)
vectorizer = Pipeline((('hasher', hasher), \
('tf_idf', TfidTransformer())))
else:
vectorizer = HashingVectorizer(n_features=opts.n_features, \
stop_words='english', \
non_negative=False, \
norm='12', \
binary=False)
else:
vectorizer = TfidfVectorizer(max_df=0.5, \
max_features=opts.n_features, \
stop_words='english', \
use_idf=opts.use_idf)
X = vectorizer.fit_transform(dataset.data)
print("done in %fs" % (time() - t0))
print("n_samples: %d, n_features: %d" % X.shape)
print()
return X, labels
def reduceDimensions(X):
print("Performing dimensionality reduction using LSA")
t0 = time()
lsa = TruncatedSVD(opts.n_components)
X = lsa.fit_transform(X)
print("done in %fs" % (time() - t0))
print()
return X
def normalize(X):
print("Normalizing data for K-Means")
t0 = time()
X = Normalizer(copy=False).fit_transform(X)
print("done in %fs" % (time() - t0))
print()
return X
def clusteringKMeans(X):
if opts.minibatch:
km = MiniBatchKMeans(n_clusters=true_k, \
init='k-means++', \
n_init=1, \
init_size=1000, \
batch_size=1000, \
verbose=opts.verbose)
else:
km = KMeans(n_clusters=true_k, \
init='k-means++', \
max_iter=100, \
n_init=1, \
verbose=opts.verbose)
print("Clustering sparse data with %s" % km)
t0 = time()
y_clust = km.fit(X)
print("done in %.3fs" % (time() - t0))
print()
return y_clust, km
def evaluate(km, labels):
print("Homogeneity: %.3f" % metrics.homogeneity_score(labels, km.labels_))
print("Completeness: %.3f" % metrics.completeness_score(labels, km.labels_))
print("V-measure: %.3f" % metrics.v_measure_score(labels, km.labels_))
print("Adjusted Rand-Index: %.3f" % metrics.adjusted_rand_score(labels, km.labels_))
print("Silhouette Coefficient: %.3f" % metrics.silhouette_score(X, \
labels, \
sample_size=1000))
def main():
categories = loadCategories()
dataset = loadData(categories)
X, labels = extractFeatures(dataset)
X = reduceDimensions(X)
X = normalize(X)
y_clust, km = clusteringKMeans(X)
evaluate(km, labels)
if __name__ == '__main__':
main()
| mit |
jmargeta/scikit-learn | sklearn/feature_selection/rfe.py | 4 | 14193 | # Authors: Alexandre Gramfort <[email protected]>
# Vincent Michel <[email protected]>
# Gilles Louppe <[email protected]>
#
# License: BSD Style.
"""Recursive feature elimination for feature ranking"""
import numpy as np
from ..utils import check_arrays, safe_sqr, safe_mask
from ..base import BaseEstimator
from ..base import MetaEstimatorMixin
from ..base import clone
from ..base import is_classifier
from ..cross_validation import check_cv
class RFE(BaseEstimator, MetaEstimatorMixin):
"""Feature ranking with recursive feature elimination.
Given an external estimator that assigns weights to features (e.g., the
coefficients of a linear model), the goal of recursive feature elimination
(RFE) is to select features by recursively considering smaller and smaller
sets of features. First, the estimator is trained on the initial set of
features and weights are assigned to each one of them. Then, features whose
absolute weights are the smallest are pruned from the current set features.
That procedure is recursively repeated on the pruned set until the desired
number of features to select is eventually reached.
Parameters
----------
estimator : object
A supervised learning estimator with a `fit` method that updates a
`coef_` attribute that holds the fitted parameters. Important features
must correspond to high absolute values in the `coef_` array.
For instance, this is the case for most supervised learning
algorithms such as Support Vector Classifiers and Generalized
Linear Models from the `svm` and `linear_model` modules.
n_features_to_select : int or None (default=None)
The number of features to select. If `None`, half of the features
are selected.
step : int or float, optional (default=1)
If greater than or equal to 1, then `step` corresponds to the (integer)
number of features to remove at each iteration.
If within (0.0, 1.0), then `step` corresponds to the percentage
(rounded down) of features to remove at each iteration.
estimator_params : dict
Parameters for the external estimator.
Useful for doing grid searches.
Attributes
----------
`n_features_` : int
The number of selected features.
`support_` : array of shape [n_features]
The mask of selected features.
`ranking_` : array of shape [n_features]
The feature ranking, such that `ranking_[i]` corresponds to the \
ranking position of the i-th feature. Selected (i.e., estimated \
best) features are assigned rank 1.
`estimator_` : object
The external estimator fit on the reduced dataset.
Examples
--------
The following example shows how to retrieve the 5 right informative
features in the Friedman #1 dataset.
>>> from sklearn.datasets import make_friedman1
>>> from sklearn.feature_selection import RFE
>>> from sklearn.svm import SVR
>>> X, y = make_friedman1(n_samples=50, n_features=10, random_state=0)
>>> estimator = SVR(kernel="linear")
>>> selector = RFE(estimator, 5, step=1)
>>> selector = selector.fit(X, y)
>>> selector.support_ # doctest: +NORMALIZE_WHITESPACE
array([ True, True, True, True, True,
False, False, False, False, False], dtype=bool)
>>> selector.ranking_
array([1, 1, 1, 1, 1, 6, 4, 3, 2, 5])
References
----------
.. [1] Guyon, I., Weston, J., Barnhill, S., & Vapnik, V., "Gene selection
for cancer classification using support vector machines",
Mach. Learn., 46(1-3), 389--422, 2002.
"""
def __init__(self, estimator, n_features_to_select=None, step=1,
estimator_params={}, verbose=0):
self.estimator = estimator
self.n_features_to_select = n_features_to_select
self.step = step
self.estimator_params = estimator_params
self.verbose = verbose
def fit(self, X, y):
"""Fit the RFE model and then the underlying estimator on the selected
features.
Parameters
----------
X : {array-like, sparse matrix}, shape = [n_samples, n_features]
The training input samples.
y : array-like, shape = [n_samples]
The target values.
"""
X, y = check_arrays(X, y, sparse_format="csr")
# Initialization
n_features = X.shape[1]
if self.n_features_to_select is None:
n_features_to_select = n_features / 2
else:
n_features_to_select = self.n_features_to_select
if 0.0 < self.step < 1.0:
step = int(self.step * n_features)
else:
step = int(self.step)
if step <= 0:
raise ValueError("Step must be >0")
support_ = np.ones(n_features, dtype=np.bool)
ranking_ = np.ones(n_features, dtype=np.int)
# Elimination
while np.sum(support_) > n_features_to_select:
# Remaining features
features = np.arange(n_features)[support_]
# Rank the remaining features
estimator = clone(self.estimator)
estimator.set_params(**self.estimator_params)
if self.verbose > 0:
print("Fitting estimator with %d features." % np.sum(support_))
estimator.fit(X[:, features], y)
if estimator.coef_.ndim > 1:
ranks = np.argsort(safe_sqr(estimator.coef_).sum(axis=0))
else:
ranks = np.argsort(safe_sqr(estimator.coef_))
# for sparse case ranks is matrix
ranks = np.ravel(ranks)
# Eliminate the worse features
threshold = min(step, np.sum(support_) - n_features_to_select)
support_[features[ranks][:threshold]] = False
ranking_[np.logical_not(support_)] += 1
# Set final attributes
self.estimator_ = clone(self.estimator)
self.estimator_.set_params(**self.estimator_params)
self.estimator_.fit(X[:, support_], y)
self.n_features_ = support_.sum()
self.support_ = support_
self.ranking_ = ranking_
return self
def predict(self, X):
"""Reduce X to the selected features and then predict using the
underlying estimator.
Parameters
----------
X : array of shape [n_samples, n_features]
The input samples.
Returns
-------
y : array of shape [n_samples]
The predicted target values.
"""
return self.estimator_.predict(X[:, safe_mask(X, self.support_)])
def score(self, X, y):
"""Reduce X to the selected features and then return the score of the
underlying estimator.
Parameters
----------
X : array of shape [n_samples, n_features]
The input samples.
y : array of shape [n_samples]
The target values.
"""
return self.estimator_.score(X[:, safe_mask(X, self.support_)], y)
def transform(self, X):
"""Reduce X to the selected features during the elimination.
Parameters
----------
X : array of shape [n_samples, n_features]
The input samples.
Returns
-------
X_r : array of shape [n_samples, n_selected_features]
The input samples with only the features selected during the \
elimination.
"""
return X[:, safe_mask(X, self.support_)]
def decision_function(self, X):
return self.estimator_.decision_function(self.transform(X))
def predict_proba(self, X):
return self.estimator_.predict_proba(self.transform(X))
class RFECV(RFE, MetaEstimatorMixin):
"""Feature ranking with recursive feature elimination and cross-validated
selection of the best number of features.
Parameters
----------
estimator : object
A supervised learning estimator with a `fit` method that updates a
`coef_` attribute that holds the fitted parameters. Important features
must correspond to high absolute values in the `coef_` array.
For instance, this is the case for most supervised learning
algorithms such as Support Vector Classifiers and Generalized
Linear Models from the `svm` and `linear_model` modules.
step : int or float, optional (default=1)
If greater than or equal to 1, then `step` corresponds to the (integer)
number of features to remove at each iteration.
If within (0.0, 1.0), then `step` corresponds to the percentage
(rounded down) of features to remove at each iteration.
cv : int or cross-validation generator, optional (default=None)
If int, it is the number of folds.
If None, 3-fold cross-validation is performed by default.
Specific cross-validation objects can also be passed, see
`sklearn.cross_validation module` for details.
loss_function : function, optional (default=None)
The loss function to minimize by cross-validation. If None, then the
score function of the estimator is maximized.
estimator_params : dict
Parameters for the external estimator.
Useful for doing grid searches.
verbose : int, default=0
Controls verbosity of output.
Attributes
----------
`n_features_` : int
The number of selected features with cross-validation.
`support_` : array of shape [n_features]
The mask of selected features.
`ranking_` : array of shape [n_features]
The feature ranking, such that `ranking_[i]`
corresponds to the ranking
position of the i-th feature.
Selected (i.e., estimated best)
features are assigned rank 1.
`cv_scores_` : array of shape [n_subsets_of_features]
The cross-validation scores such that
`cv_scores_[i]` corresponds to
the CV score of the i-th subset of features.
`estimator_` : object
The external estimator fit on the reduced dataset.
Examples
--------
The following example shows how to retrieve the a-priori not known 5
informative features in the Friedman #1 dataset.
>>> from sklearn.datasets import make_friedman1
>>> from sklearn.feature_selection import RFECV
>>> from sklearn.svm import SVR
>>> X, y = make_friedman1(n_samples=50, n_features=10, random_state=0)
>>> estimator = SVR(kernel="linear")
>>> selector = RFECV(estimator, step=1, cv=5)
>>> selector = selector.fit(X, y)
>>> selector.support_ # doctest: +NORMALIZE_WHITESPACE
array([ True, True, True, True, True,
False, False, False, False, False], dtype=bool)
>>> selector.ranking_
array([1, 1, 1, 1, 1, 6, 4, 3, 2, 5])
References
----------
.. [1] Guyon, I., Weston, J., Barnhill, S., & Vapnik, V., "Gene selection
for cancer classification using support vector machines",
Mach. Learn., 46(1-3), 389--422, 2002.
"""
def __init__(self, estimator, step=1, cv=None, loss_func=None,
estimator_params={}, verbose=0):
self.estimator = estimator
self.step = step
self.cv = cv
self.loss_func = loss_func
self.estimator_params = estimator_params
self.verbose = verbose
def fit(self, X, y):
"""Fit the RFE model and automatically tune the number of selected
features.
Parameters
----------
X : {array-like, sparse matrix}, shape = [n_samples, n_features]
Training vector, where `n_samples` is the number of samples and
`n_features` is the total number of features.
y : array-like, shape = [n_samples]
Target values (integers for classification, real numbers for
regression).
"""
X, y = check_arrays(X, y, sparse_format="csr")
# Initialization
rfe = RFE(estimator=self.estimator, n_features_to_select=1,
step=self.step, estimator_params=self.estimator_params,
verbose=self.verbose - 1)
cv = check_cv(self.cv, X, y, is_classifier(self.estimator))
scores = np.zeros(X.shape[1])
# Cross-validation
n = 0
for train, test in cv:
# Compute a full ranking of the features
ranking_ = rfe.fit(X[train], y[train]).ranking_
# Score each subset of features
for k in range(0, max(ranking_)):
mask = np.where(ranking_ <= k + 1)[0]
estimator = clone(self.estimator)
estimator.fit(X[train][:, mask], y[train])
if self.loss_func is None:
loss_k = 1.0 - estimator.score(X[test][:, mask], y[test])
else:
loss_k = self.loss_func(
y[test], estimator.predict(X[test][:, mask]))
if self.verbose > 0:
print("Finished fold with %d / %d feature ranks, loss=%f"
% (k, max(ranking_), loss_k))
scores[k] += loss_k
n += 1
# Pick the best number of features on average
best_score = np.inf
best_k = None
for k, score in enumerate(scores):
if score < best_score:
best_score = score
best_k = k + 1
# Re-execute an elimination with best_k over the whole set
rfe = RFE(estimator=self.estimator,
n_features_to_select=best_k,
step=self.step, estimator_params=self.estimator_params)
rfe.fit(X, y)
# Set final attributes
self.estimator_ = clone(self.estimator)
self.estimator_.set_params(**self.estimator_params)
self.estimator_.fit(X[:, safe_mask(X, rfe.support_)], y)
self.n_features_ = rfe.n_features_
self.support_ = rfe.support_
self.ranking_ = rfe.ranking_
self.cv_scores_ = scores / n
return self
| bsd-3-clause |
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