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cggh/scikit-allel | allel/model/ndarray.py | SortedIndex.is_unique | def is_unique(self):
"""True if no duplicate entries."""
if self._is_unique is None:
t = self.values[:-1] == self.values[1:] # type: np.ndarray
self._is_unique = ~np.any(t)
return self._is_unique | python | def is_unique(self):
"""True if no duplicate entries."""
if self._is_unique is None:
t = self.values[:-1] == self.values[1:] # type: np.ndarray
self._is_unique = ~np.any(t)
return self._is_unique | True if no duplicate entries. | https://github.com/cggh/scikit-allel/blob/3c979a57a100240ba959dd13f98839349530f215/allel/model/ndarray.py#L3404-L3409 |
cggh/scikit-allel | allel/model/ndarray.py | SortedIndex.locate_key | def locate_key(self, key):
"""Get index location for the requested key.
Parameters
----------
key : object
Value to locate.
Returns
-------
loc : int or slice
Location of `key` (will be slice if there are duplicate entries).
Examples
--------
>>> import allel
>>> idx = allel.SortedIndex([3, 6, 6, 11])
>>> idx.locate_key(3)
0
>>> idx.locate_key(11)
3
>>> idx.locate_key(6)
slice(1, 3, None)
>>> try:
... idx.locate_key(2)
... except KeyError as e:
... print(e)
...
2
"""
left = bisect.bisect_left(self, key)
right = bisect.bisect_right(self, key)
diff = right - left
if diff == 0:
raise KeyError(key)
elif diff == 1:
return left
else:
return slice(left, right) | python | def locate_key(self, key):
"""Get index location for the requested key.
Parameters
----------
key : object
Value to locate.
Returns
-------
loc : int or slice
Location of `key` (will be slice if there are duplicate entries).
Examples
--------
>>> import allel
>>> idx = allel.SortedIndex([3, 6, 6, 11])
>>> idx.locate_key(3)
0
>>> idx.locate_key(11)
3
>>> idx.locate_key(6)
slice(1, 3, None)
>>> try:
... idx.locate_key(2)
... except KeyError as e:
... print(e)
...
2
"""
left = bisect.bisect_left(self, key)
right = bisect.bisect_right(self, key)
diff = right - left
if diff == 0:
raise KeyError(key)
elif diff == 1:
return left
else:
return slice(left, right) | Get index location for the requested key.
Parameters
----------
key : object
Value to locate.
Returns
-------
loc : int or slice
Location of `key` (will be slice if there are duplicate entries).
Examples
--------
>>> import allel
>>> idx = allel.SortedIndex([3, 6, 6, 11])
>>> idx.locate_key(3)
0
>>> idx.locate_key(11)
3
>>> idx.locate_key(6)
slice(1, 3, None)
>>> try:
... idx.locate_key(2)
... except KeyError as e:
... print(e)
...
2 | https://github.com/cggh/scikit-allel/blob/3c979a57a100240ba959dd13f98839349530f215/allel/model/ndarray.py#L3429-L3470 |
cggh/scikit-allel | allel/model/ndarray.py | SortedIndex.locate_intersection | def locate_intersection(self, other):
"""Locate the intersection with another array.
Parameters
----------
other : array_like, int
Array of values to intersect.
Returns
-------
loc : ndarray, bool
Boolean array with location of intersection.
loc_other : ndarray, bool
Boolean array with location in `other` of intersection.
Examples
--------
>>> import allel
>>> idx1 = allel.SortedIndex([3, 6, 11, 20, 35])
>>> idx2 = allel.SortedIndex([4, 6, 20, 39])
>>> loc1, loc2 = idx1.locate_intersection(idx2)
>>> loc1
array([False, True, False, True, False])
>>> loc2
array([False, True, True, False])
>>> idx1[loc1]
<SortedIndex shape=(2,) dtype=int64>
[6, 20]
>>> idx2[loc2]
<SortedIndex shape=(2,) dtype=int64>
[6, 20]
"""
# check inputs
other = SortedIndex(other, copy=False)
# find intersection
assume_unique = self.is_unique and other.is_unique
loc = np.in1d(self, other, assume_unique=assume_unique)
loc_other = np.in1d(other, self, assume_unique=assume_unique)
return loc, loc_other | python | def locate_intersection(self, other):
"""Locate the intersection with another array.
Parameters
----------
other : array_like, int
Array of values to intersect.
Returns
-------
loc : ndarray, bool
Boolean array with location of intersection.
loc_other : ndarray, bool
Boolean array with location in `other` of intersection.
Examples
--------
>>> import allel
>>> idx1 = allel.SortedIndex([3, 6, 11, 20, 35])
>>> idx2 = allel.SortedIndex([4, 6, 20, 39])
>>> loc1, loc2 = idx1.locate_intersection(idx2)
>>> loc1
array([False, True, False, True, False])
>>> loc2
array([False, True, True, False])
>>> idx1[loc1]
<SortedIndex shape=(2,) dtype=int64>
[6, 20]
>>> idx2[loc2]
<SortedIndex shape=(2,) dtype=int64>
[6, 20]
"""
# check inputs
other = SortedIndex(other, copy=False)
# find intersection
assume_unique = self.is_unique and other.is_unique
loc = np.in1d(self, other, assume_unique=assume_unique)
loc_other = np.in1d(other, self, assume_unique=assume_unique)
return loc, loc_other | Locate the intersection with another array.
Parameters
----------
other : array_like, int
Array of values to intersect.
Returns
-------
loc : ndarray, bool
Boolean array with location of intersection.
loc_other : ndarray, bool
Boolean array with location in `other` of intersection.
Examples
--------
>>> import allel
>>> idx1 = allel.SortedIndex([3, 6, 11, 20, 35])
>>> idx2 = allel.SortedIndex([4, 6, 20, 39])
>>> loc1, loc2 = idx1.locate_intersection(idx2)
>>> loc1
array([False, True, False, True, False])
>>> loc2
array([False, True, True, False])
>>> idx1[loc1]
<SortedIndex shape=(2,) dtype=int64>
[6, 20]
>>> idx2[loc2]
<SortedIndex shape=(2,) dtype=int64>
[6, 20] | https://github.com/cggh/scikit-allel/blob/3c979a57a100240ba959dd13f98839349530f215/allel/model/ndarray.py#L3472-L3515 |
cggh/scikit-allel | allel/model/ndarray.py | SortedIndex.locate_keys | def locate_keys(self, keys, strict=True):
"""Get index locations for the requested keys.
Parameters
----------
keys : array_like
Array of keys to locate.
strict : bool, optional
If True, raise KeyError if any keys are not found in the index.
Returns
-------
loc : ndarray, bool
Boolean array with location of values.
Examples
--------
>>> import allel
>>> idx1 = allel.SortedIndex([3, 6, 11, 20, 35])
>>> idx2 = allel.SortedIndex([4, 6, 20, 39])
>>> loc = idx1.locate_keys(idx2, strict=False)
>>> loc
array([False, True, False, True, False])
>>> idx1[loc]
<SortedIndex shape=(2,) dtype=int64>
[6, 20]
"""
# check inputs
keys = SortedIndex(keys, copy=False)
# find intersection
loc, found = self.locate_intersection(keys)
if strict and np.any(~found):
raise KeyError(keys[~found])
return loc | python | def locate_keys(self, keys, strict=True):
"""Get index locations for the requested keys.
Parameters
----------
keys : array_like
Array of keys to locate.
strict : bool, optional
If True, raise KeyError if any keys are not found in the index.
Returns
-------
loc : ndarray, bool
Boolean array with location of values.
Examples
--------
>>> import allel
>>> idx1 = allel.SortedIndex([3, 6, 11, 20, 35])
>>> idx2 = allel.SortedIndex([4, 6, 20, 39])
>>> loc = idx1.locate_keys(idx2, strict=False)
>>> loc
array([False, True, False, True, False])
>>> idx1[loc]
<SortedIndex shape=(2,) dtype=int64>
[6, 20]
"""
# check inputs
keys = SortedIndex(keys, copy=False)
# find intersection
loc, found = self.locate_intersection(keys)
if strict and np.any(~found):
raise KeyError(keys[~found])
return loc | Get index locations for the requested keys.
Parameters
----------
keys : array_like
Array of keys to locate.
strict : bool, optional
If True, raise KeyError if any keys are not found in the index.
Returns
-------
loc : ndarray, bool
Boolean array with location of values.
Examples
--------
>>> import allel
>>> idx1 = allel.SortedIndex([3, 6, 11, 20, 35])
>>> idx2 = allel.SortedIndex([4, 6, 20, 39])
>>> loc = idx1.locate_keys(idx2, strict=False)
>>> loc
array([False, True, False, True, False])
>>> idx1[loc]
<SortedIndex shape=(2,) dtype=int64>
[6, 20] | https://github.com/cggh/scikit-allel/blob/3c979a57a100240ba959dd13f98839349530f215/allel/model/ndarray.py#L3517-L3556 |
cggh/scikit-allel | allel/model/ndarray.py | SortedIndex.intersect | def intersect(self, other):
"""Intersect with `other` sorted index.
Parameters
----------
other : array_like, int
Array of values to intersect with.
Returns
-------
out : SortedIndex
Values in common.
Examples
--------
>>> import allel
>>> idx1 = allel.SortedIndex([3, 6, 11, 20, 35])
>>> idx2 = allel.SortedIndex([4, 6, 20, 39])
>>> idx1.intersect(idx2)
<SortedIndex shape=(2,) dtype=int64>
[6, 20]
"""
loc = self.locate_keys(other, strict=False)
return self.compress(loc, axis=0) | python | def intersect(self, other):
"""Intersect with `other` sorted index.
Parameters
----------
other : array_like, int
Array of values to intersect with.
Returns
-------
out : SortedIndex
Values in common.
Examples
--------
>>> import allel
>>> idx1 = allel.SortedIndex([3, 6, 11, 20, 35])
>>> idx2 = allel.SortedIndex([4, 6, 20, 39])
>>> idx1.intersect(idx2)
<SortedIndex shape=(2,) dtype=int64>
[6, 20]
"""
loc = self.locate_keys(other, strict=False)
return self.compress(loc, axis=0) | Intersect with `other` sorted index.
Parameters
----------
other : array_like, int
Array of values to intersect with.
Returns
-------
out : SortedIndex
Values in common.
Examples
--------
>>> import allel
>>> idx1 = allel.SortedIndex([3, 6, 11, 20, 35])
>>> idx2 = allel.SortedIndex([4, 6, 20, 39])
>>> idx1.intersect(idx2)
<SortedIndex shape=(2,) dtype=int64>
[6, 20] | https://github.com/cggh/scikit-allel/blob/3c979a57a100240ba959dd13f98839349530f215/allel/model/ndarray.py#L3558-L3584 |
cggh/scikit-allel | allel/model/ndarray.py | SortedIndex.locate_range | def locate_range(self, start=None, stop=None):
"""Locate slice of index containing all entries within `start` and
`stop` values **inclusive**.
Parameters
----------
start : int, optional
Start value.
stop : int, optional
Stop value.
Returns
-------
loc : slice
Slice object.
Examples
--------
>>> import allel
>>> idx = allel.SortedIndex([3, 6, 11, 20, 35])
>>> loc = idx.locate_range(4, 32)
>>> loc
slice(1, 4, None)
>>> idx[loc]
<SortedIndex shape=(3,) dtype=int64>
[6, 11, 20]
"""
# locate start and stop indices
if start is None:
start_index = 0
else:
start_index = bisect.bisect_left(self, start)
if stop is None:
stop_index = len(self)
else:
stop_index = bisect.bisect_right(self, stop)
if stop_index - start_index == 0:
raise KeyError(start, stop)
loc = slice(start_index, stop_index)
return loc | python | def locate_range(self, start=None, stop=None):
"""Locate slice of index containing all entries within `start` and
`stop` values **inclusive**.
Parameters
----------
start : int, optional
Start value.
stop : int, optional
Stop value.
Returns
-------
loc : slice
Slice object.
Examples
--------
>>> import allel
>>> idx = allel.SortedIndex([3, 6, 11, 20, 35])
>>> loc = idx.locate_range(4, 32)
>>> loc
slice(1, 4, None)
>>> idx[loc]
<SortedIndex shape=(3,) dtype=int64>
[6, 11, 20]
"""
# locate start and stop indices
if start is None:
start_index = 0
else:
start_index = bisect.bisect_left(self, start)
if stop is None:
stop_index = len(self)
else:
stop_index = bisect.bisect_right(self, stop)
if stop_index - start_index == 0:
raise KeyError(start, stop)
loc = slice(start_index, stop_index)
return loc | Locate slice of index containing all entries within `start` and
`stop` values **inclusive**.
Parameters
----------
start : int, optional
Start value.
stop : int, optional
Stop value.
Returns
-------
loc : slice
Slice object.
Examples
--------
>>> import allel
>>> idx = allel.SortedIndex([3, 6, 11, 20, 35])
>>> loc = idx.locate_range(4, 32)
>>> loc
slice(1, 4, None)
>>> idx[loc]
<SortedIndex shape=(3,) dtype=int64>
[6, 11, 20] | https://github.com/cggh/scikit-allel/blob/3c979a57a100240ba959dd13f98839349530f215/allel/model/ndarray.py#L3586-L3630 |
cggh/scikit-allel | allel/model/ndarray.py | SortedIndex.intersect_range | def intersect_range(self, start=None, stop=None):
"""Intersect with range defined by `start` and `stop` values
**inclusive**.
Parameters
----------
start : int, optional
Start value.
stop : int, optional
Stop value.
Returns
-------
idx : SortedIndex
Examples
--------
>>> import allel
>>> idx = allel.SortedIndex([3, 6, 11, 20, 35])
>>> idx.intersect_range(4, 32)
<SortedIndex shape=(3,) dtype=int64>
[6, 11, 20]
"""
try:
loc = self.locate_range(start=start, stop=stop)
except KeyError:
return self.values[0:0]
else:
return self[loc] | python | def intersect_range(self, start=None, stop=None):
"""Intersect with range defined by `start` and `stop` values
**inclusive**.
Parameters
----------
start : int, optional
Start value.
stop : int, optional
Stop value.
Returns
-------
idx : SortedIndex
Examples
--------
>>> import allel
>>> idx = allel.SortedIndex([3, 6, 11, 20, 35])
>>> idx.intersect_range(4, 32)
<SortedIndex shape=(3,) dtype=int64>
[6, 11, 20]
"""
try:
loc = self.locate_range(start=start, stop=stop)
except KeyError:
return self.values[0:0]
else:
return self[loc] | Intersect with range defined by `start` and `stop` values
**inclusive**.
Parameters
----------
start : int, optional
Start value.
stop : int, optional
Stop value.
Returns
-------
idx : SortedIndex
Examples
--------
>>> import allel
>>> idx = allel.SortedIndex([3, 6, 11, 20, 35])
>>> idx.intersect_range(4, 32)
<SortedIndex shape=(3,) dtype=int64>
[6, 11, 20] | https://github.com/cggh/scikit-allel/blob/3c979a57a100240ba959dd13f98839349530f215/allel/model/ndarray.py#L3632-L3663 |
cggh/scikit-allel | allel/model/ndarray.py | SortedIndex.locate_intersection_ranges | def locate_intersection_ranges(self, starts, stops):
"""Locate the intersection with a set of ranges.
Parameters
----------
starts : array_like, int
Range start values.
stops : array_like, int
Range stop values.
Returns
-------
loc : ndarray, bool
Boolean array with location of entries found.
loc_ranges : ndarray, bool
Boolean array with location of ranges containing one or more
entries.
Examples
--------
>>> import allel
>>> import numpy as np
>>> idx = allel.SortedIndex([3, 6, 11, 20, 35])
>>> ranges = np.array([[0, 2], [6, 17], [12, 15], [31, 35],
... [100, 120]])
>>> starts = ranges[:, 0]
>>> stops = ranges[:, 1]
>>> loc, loc_ranges = idx.locate_intersection_ranges(starts, stops)
>>> loc
array([False, True, True, False, True])
>>> loc_ranges
array([False, True, False, True, False])
>>> idx[loc]
<SortedIndex shape=(3,) dtype=int64>
[6, 11, 35]
>>> ranges[loc_ranges]
array([[ 6, 17],
[31, 35]])
"""
# check inputs
starts = asarray_ndim(starts, 1)
stops = asarray_ndim(stops, 1)
check_dim0_aligned(starts, stops)
# find indices of start and stop values in idx
start_indices = np.searchsorted(self, starts)
stop_indices = np.searchsorted(self, stops, side='right')
# find intervals overlapping at least one value
loc_ranges = start_indices < stop_indices
# find values within at least one interval
loc = np.zeros(self.shape, dtype=np.bool)
for i, j in zip(start_indices[loc_ranges], stop_indices[loc_ranges]):
loc[i:j] = True
return loc, loc_ranges | python | def locate_intersection_ranges(self, starts, stops):
"""Locate the intersection with a set of ranges.
Parameters
----------
starts : array_like, int
Range start values.
stops : array_like, int
Range stop values.
Returns
-------
loc : ndarray, bool
Boolean array with location of entries found.
loc_ranges : ndarray, bool
Boolean array with location of ranges containing one or more
entries.
Examples
--------
>>> import allel
>>> import numpy as np
>>> idx = allel.SortedIndex([3, 6, 11, 20, 35])
>>> ranges = np.array([[0, 2], [6, 17], [12, 15], [31, 35],
... [100, 120]])
>>> starts = ranges[:, 0]
>>> stops = ranges[:, 1]
>>> loc, loc_ranges = idx.locate_intersection_ranges(starts, stops)
>>> loc
array([False, True, True, False, True])
>>> loc_ranges
array([False, True, False, True, False])
>>> idx[loc]
<SortedIndex shape=(3,) dtype=int64>
[6, 11, 35]
>>> ranges[loc_ranges]
array([[ 6, 17],
[31, 35]])
"""
# check inputs
starts = asarray_ndim(starts, 1)
stops = asarray_ndim(stops, 1)
check_dim0_aligned(starts, stops)
# find indices of start and stop values in idx
start_indices = np.searchsorted(self, starts)
stop_indices = np.searchsorted(self, stops, side='right')
# find intervals overlapping at least one value
loc_ranges = start_indices < stop_indices
# find values within at least one interval
loc = np.zeros(self.shape, dtype=np.bool)
for i, j in zip(start_indices[loc_ranges], stop_indices[loc_ranges]):
loc[i:j] = True
return loc, loc_ranges | Locate the intersection with a set of ranges.
Parameters
----------
starts : array_like, int
Range start values.
stops : array_like, int
Range stop values.
Returns
-------
loc : ndarray, bool
Boolean array with location of entries found.
loc_ranges : ndarray, bool
Boolean array with location of ranges containing one or more
entries.
Examples
--------
>>> import allel
>>> import numpy as np
>>> idx = allel.SortedIndex([3, 6, 11, 20, 35])
>>> ranges = np.array([[0, 2], [6, 17], [12, 15], [31, 35],
... [100, 120]])
>>> starts = ranges[:, 0]
>>> stops = ranges[:, 1]
>>> loc, loc_ranges = idx.locate_intersection_ranges(starts, stops)
>>> loc
array([False, True, True, False, True])
>>> loc_ranges
array([False, True, False, True, False])
>>> idx[loc]
<SortedIndex shape=(3,) dtype=int64>
[6, 11, 35]
>>> ranges[loc_ranges]
array([[ 6, 17],
[31, 35]]) | https://github.com/cggh/scikit-allel/blob/3c979a57a100240ba959dd13f98839349530f215/allel/model/ndarray.py#L3665-L3724 |
cggh/scikit-allel | allel/model/ndarray.py | SortedIndex.locate_ranges | def locate_ranges(self, starts, stops, strict=True):
"""Locate items within the given ranges.
Parameters
----------
starts : array_like, int
Range start values.
stops : array_like, int
Range stop values.
strict : bool, optional
If True, raise KeyError if any ranges contain no entries.
Returns
-------
loc : ndarray, bool
Boolean array with location of entries found.
Examples
--------
>>> import allel
>>> import numpy as np
>>> idx = allel.SortedIndex([3, 6, 11, 20, 35])
>>> ranges = np.array([[0, 2], [6, 17], [12, 15], [31, 35],
... [100, 120]])
>>> starts = ranges[:, 0]
>>> stops = ranges[:, 1]
>>> loc = idx.locate_ranges(starts, stops, strict=False)
>>> loc
array([False, True, True, False, True])
>>> idx[loc]
<SortedIndex shape=(3,) dtype=int64>
[6, 11, 35]
"""
loc, found = self.locate_intersection_ranges(starts, stops)
if strict and np.any(~found):
raise KeyError(starts[~found], stops[~found])
return loc | python | def locate_ranges(self, starts, stops, strict=True):
"""Locate items within the given ranges.
Parameters
----------
starts : array_like, int
Range start values.
stops : array_like, int
Range stop values.
strict : bool, optional
If True, raise KeyError if any ranges contain no entries.
Returns
-------
loc : ndarray, bool
Boolean array with location of entries found.
Examples
--------
>>> import allel
>>> import numpy as np
>>> idx = allel.SortedIndex([3, 6, 11, 20, 35])
>>> ranges = np.array([[0, 2], [6, 17], [12, 15], [31, 35],
... [100, 120]])
>>> starts = ranges[:, 0]
>>> stops = ranges[:, 1]
>>> loc = idx.locate_ranges(starts, stops, strict=False)
>>> loc
array([False, True, True, False, True])
>>> idx[loc]
<SortedIndex shape=(3,) dtype=int64>
[6, 11, 35]
"""
loc, found = self.locate_intersection_ranges(starts, stops)
if strict and np.any(~found):
raise KeyError(starts[~found], stops[~found])
return loc | Locate items within the given ranges.
Parameters
----------
starts : array_like, int
Range start values.
stops : array_like, int
Range stop values.
strict : bool, optional
If True, raise KeyError if any ranges contain no entries.
Returns
-------
loc : ndarray, bool
Boolean array with location of entries found.
Examples
--------
>>> import allel
>>> import numpy as np
>>> idx = allel.SortedIndex([3, 6, 11, 20, 35])
>>> ranges = np.array([[0, 2], [6, 17], [12, 15], [31, 35],
... [100, 120]])
>>> starts = ranges[:, 0]
>>> stops = ranges[:, 1]
>>> loc = idx.locate_ranges(starts, stops, strict=False)
>>> loc
array([False, True, True, False, True])
>>> idx[loc]
<SortedIndex shape=(3,) dtype=int64>
[6, 11, 35] | https://github.com/cggh/scikit-allel/blob/3c979a57a100240ba959dd13f98839349530f215/allel/model/ndarray.py#L3726-L3767 |
cggh/scikit-allel | allel/model/ndarray.py | SortedIndex.intersect_ranges | def intersect_ranges(self, starts, stops):
"""Intersect with a set of ranges.
Parameters
----------
starts : array_like, int
Range start values.
stops : array_like, int
Range stop values.
Returns
-------
idx : SortedIndex
Examples
--------
>>> import allel
>>> import numpy as np
>>> idx = allel.SortedIndex([3, 6, 11, 20, 35])
>>> ranges = np.array([[0, 2], [6, 17], [12, 15], [31, 35],
... [100, 120]])
>>> starts = ranges[:, 0]
>>> stops = ranges[:, 1]
>>> idx.intersect_ranges(starts, stops)
<SortedIndex shape=(3,) dtype=int64>
[6, 11, 35]
"""
loc = self.locate_ranges(starts, stops, strict=False)
return self.compress(loc, axis=0) | python | def intersect_ranges(self, starts, stops):
"""Intersect with a set of ranges.
Parameters
----------
starts : array_like, int
Range start values.
stops : array_like, int
Range stop values.
Returns
-------
idx : SortedIndex
Examples
--------
>>> import allel
>>> import numpy as np
>>> idx = allel.SortedIndex([3, 6, 11, 20, 35])
>>> ranges = np.array([[0, 2], [6, 17], [12, 15], [31, 35],
... [100, 120]])
>>> starts = ranges[:, 0]
>>> stops = ranges[:, 1]
>>> idx.intersect_ranges(starts, stops)
<SortedIndex shape=(3,) dtype=int64>
[6, 11, 35]
"""
loc = self.locate_ranges(starts, stops, strict=False)
return self.compress(loc, axis=0) | Intersect with a set of ranges.
Parameters
----------
starts : array_like, int
Range start values.
stops : array_like, int
Range stop values.
Returns
-------
idx : SortedIndex
Examples
--------
>>> import allel
>>> import numpy as np
>>> idx = allel.SortedIndex([3, 6, 11, 20, 35])
>>> ranges = np.array([[0, 2], [6, 17], [12, 15], [31, 35],
... [100, 120]])
>>> starts = ranges[:, 0]
>>> stops = ranges[:, 1]
>>> idx.intersect_ranges(starts, stops)
<SortedIndex shape=(3,) dtype=int64>
[6, 11, 35] | https://github.com/cggh/scikit-allel/blob/3c979a57a100240ba959dd13f98839349530f215/allel/model/ndarray.py#L3769-L3800 |
cggh/scikit-allel | allel/model/ndarray.py | UniqueIndex.locate_intersection | def locate_intersection(self, other):
"""Locate the intersection with another array.
Parameters
----------
other : array_like
Array to intersect.
Returns
-------
loc : ndarray, bool
Boolean array with location of intersection.
loc_other : ndarray, bool
Boolean array with location in `other` of intersection.
Examples
--------
>>> import allel
>>> idx1 = allel.UniqueIndex(['A', 'C', 'B', 'F'], dtype=object)
>>> idx2 = allel.UniqueIndex(['X', 'F', 'G', 'C', 'Z'], dtype=object)
>>> loc1, loc2 = idx1.locate_intersection(idx2)
>>> loc1
array([False, True, False, True])
>>> loc2
array([False, True, False, True, False])
>>> idx1[loc1]
<UniqueIndex shape=(2,) dtype=object>
['C', 'F']
>>> idx2[loc2]
<UniqueIndex shape=(2,) dtype=object>
['F', 'C']
"""
# check inputs
other = UniqueIndex(other)
# find intersection
assume_unique = True
loc = np.in1d(self, other, assume_unique=assume_unique)
loc_other = np.in1d(other, self, assume_unique=assume_unique)
return loc, loc_other | python | def locate_intersection(self, other):
"""Locate the intersection with another array.
Parameters
----------
other : array_like
Array to intersect.
Returns
-------
loc : ndarray, bool
Boolean array with location of intersection.
loc_other : ndarray, bool
Boolean array with location in `other` of intersection.
Examples
--------
>>> import allel
>>> idx1 = allel.UniqueIndex(['A', 'C', 'B', 'F'], dtype=object)
>>> idx2 = allel.UniqueIndex(['X', 'F', 'G', 'C', 'Z'], dtype=object)
>>> loc1, loc2 = idx1.locate_intersection(idx2)
>>> loc1
array([False, True, False, True])
>>> loc2
array([False, True, False, True, False])
>>> idx1[loc1]
<UniqueIndex shape=(2,) dtype=object>
['C', 'F']
>>> idx2[loc2]
<UniqueIndex shape=(2,) dtype=object>
['F', 'C']
"""
# check inputs
other = UniqueIndex(other)
# find intersection
assume_unique = True
loc = np.in1d(self, other, assume_unique=assume_unique)
loc_other = np.in1d(other, self, assume_unique=assume_unique)
return loc, loc_other | Locate the intersection with another array.
Parameters
----------
other : array_like
Array to intersect.
Returns
-------
loc : ndarray, bool
Boolean array with location of intersection.
loc_other : ndarray, bool
Boolean array with location in `other` of intersection.
Examples
--------
>>> import allel
>>> idx1 = allel.UniqueIndex(['A', 'C', 'B', 'F'], dtype=object)
>>> idx2 = allel.UniqueIndex(['X', 'F', 'G', 'C', 'Z'], dtype=object)
>>> loc1, loc2 = idx1.locate_intersection(idx2)
>>> loc1
array([False, True, False, True])
>>> loc2
array([False, True, False, True, False])
>>> idx1[loc1]
<UniqueIndex shape=(2,) dtype=object>
['C', 'F']
>>> idx2[loc2]
<UniqueIndex shape=(2,) dtype=object>
['F', 'C'] | https://github.com/cggh/scikit-allel/blob/3c979a57a100240ba959dd13f98839349530f215/allel/model/ndarray.py#L3904-L3947 |
cggh/scikit-allel | allel/model/ndarray.py | UniqueIndex.locate_keys | def locate_keys(self, keys, strict=True):
"""Get index locations for the requested keys.
Parameters
----------
keys : array_like
Array of keys to locate.
strict : bool, optional
If True, raise KeyError if any keys are not found in the index.
Returns
-------
loc : ndarray, bool
Boolean array with location of keys.
Examples
--------
>>> import allel
>>> idx = allel.UniqueIndex(['A', 'C', 'B', 'F'])
>>> idx.locate_keys(['F', 'C'])
array([False, True, False, True])
>>> idx.locate_keys(['X', 'F', 'G', 'C', 'Z'], strict=False)
array([False, True, False, True])
"""
# check inputs
keys = UniqueIndex(keys)
# find intersection
loc, found = self.locate_intersection(keys)
if strict and np.any(~found):
raise KeyError(keys[~found])
return loc | python | def locate_keys(self, keys, strict=True):
"""Get index locations for the requested keys.
Parameters
----------
keys : array_like
Array of keys to locate.
strict : bool, optional
If True, raise KeyError if any keys are not found in the index.
Returns
-------
loc : ndarray, bool
Boolean array with location of keys.
Examples
--------
>>> import allel
>>> idx = allel.UniqueIndex(['A', 'C', 'B', 'F'])
>>> idx.locate_keys(['F', 'C'])
array([False, True, False, True])
>>> idx.locate_keys(['X', 'F', 'G', 'C', 'Z'], strict=False)
array([False, True, False, True])
"""
# check inputs
keys = UniqueIndex(keys)
# find intersection
loc, found = self.locate_intersection(keys)
if strict and np.any(~found):
raise KeyError(keys[~found])
return loc | Get index locations for the requested keys.
Parameters
----------
keys : array_like
Array of keys to locate.
strict : bool, optional
If True, raise KeyError if any keys are not found in the index.
Returns
-------
loc : ndarray, bool
Boolean array with location of keys.
Examples
--------
>>> import allel
>>> idx = allel.UniqueIndex(['A', 'C', 'B', 'F'])
>>> idx.locate_keys(['F', 'C'])
array([False, True, False, True])
>>> idx.locate_keys(['X', 'F', 'G', 'C', 'Z'], strict=False)
array([False, True, False, True]) | https://github.com/cggh/scikit-allel/blob/3c979a57a100240ba959dd13f98839349530f215/allel/model/ndarray.py#L3949-L3985 |
cggh/scikit-allel | allel/model/ndarray.py | SortedMultiIndex.locate_key | def locate_key(self, k1, k2=None):
"""
Get index location for the requested key.
Parameters
----------
k1 : object
Level 1 key.
k2 : object, optional
Level 2 key.
Returns
-------
loc : int or slice
Location of requested key (will be slice if there are duplicate
entries).
Examples
--------
>>> import allel
>>> chrom = ['chr1', 'chr1', 'chr2', 'chr2', 'chr2', 'chr3']
>>> pos = [1, 4, 2, 5, 5, 3]
>>> idx = allel.SortedMultiIndex(chrom, pos)
>>> idx.locate_key('chr1')
slice(0, 2, None)
>>> idx.locate_key('chr1', 4)
1
>>> idx.locate_key('chr2', 5)
slice(3, 5, None)
>>> try:
... idx.locate_key('chr3', 4)
... except KeyError as e:
... print(e)
...
('chr3', 4)
"""
loc1 = self.l1.locate_key(k1)
if k2 is None:
return loc1
if isinstance(loc1, slice):
offset = loc1.start
try:
loc2 = SortedIndex(self.l2[loc1], copy=False).locate_key(k2)
except KeyError:
# reraise with more information
raise KeyError(k1, k2)
else:
if isinstance(loc2, slice):
loc = slice(offset + loc2.start, offset + loc2.stop)
else:
# assume singleton
loc = offset + loc2
else:
# singleton match in l1
v = self.l2[loc1]
if v == k2:
loc = loc1
else:
raise KeyError(k1, k2)
return loc | python | def locate_key(self, k1, k2=None):
"""
Get index location for the requested key.
Parameters
----------
k1 : object
Level 1 key.
k2 : object, optional
Level 2 key.
Returns
-------
loc : int or slice
Location of requested key (will be slice if there are duplicate
entries).
Examples
--------
>>> import allel
>>> chrom = ['chr1', 'chr1', 'chr2', 'chr2', 'chr2', 'chr3']
>>> pos = [1, 4, 2, 5, 5, 3]
>>> idx = allel.SortedMultiIndex(chrom, pos)
>>> idx.locate_key('chr1')
slice(0, 2, None)
>>> idx.locate_key('chr1', 4)
1
>>> idx.locate_key('chr2', 5)
slice(3, 5, None)
>>> try:
... idx.locate_key('chr3', 4)
... except KeyError as e:
... print(e)
...
('chr3', 4)
"""
loc1 = self.l1.locate_key(k1)
if k2 is None:
return loc1
if isinstance(loc1, slice):
offset = loc1.start
try:
loc2 = SortedIndex(self.l2[loc1], copy=False).locate_key(k2)
except KeyError:
# reraise with more information
raise KeyError(k1, k2)
else:
if isinstance(loc2, slice):
loc = slice(offset + loc2.start, offset + loc2.stop)
else:
# assume singleton
loc = offset + loc2
else:
# singleton match in l1
v = self.l2[loc1]
if v == k2:
loc = loc1
else:
raise KeyError(k1, k2)
return loc | Get index location for the requested key.
Parameters
----------
k1 : object
Level 1 key.
k2 : object, optional
Level 2 key.
Returns
-------
loc : int or slice
Location of requested key (will be slice if there are duplicate
entries).
Examples
--------
>>> import allel
>>> chrom = ['chr1', 'chr1', 'chr2', 'chr2', 'chr2', 'chr3']
>>> pos = [1, 4, 2, 5, 5, 3]
>>> idx = allel.SortedMultiIndex(chrom, pos)
>>> idx.locate_key('chr1')
slice(0, 2, None)
>>> idx.locate_key('chr1', 4)
1
>>> idx.locate_key('chr2', 5)
slice(3, 5, None)
>>> try:
... idx.locate_key('chr3', 4)
... except KeyError as e:
... print(e)
...
('chr3', 4) | https://github.com/cggh/scikit-allel/blob/3c979a57a100240ba959dd13f98839349530f215/allel/model/ndarray.py#L4102-L4164 |
cggh/scikit-allel | allel/model/ndarray.py | SortedMultiIndex.locate_range | def locate_range(self, key, start=None, stop=None):
"""Locate slice of index containing all entries within the range
`key`:`start`-`stop` **inclusive**.
Parameters
----------
key : object
Level 1 key value.
start : object, optional
Level 2 start value.
stop : object, optional
Level 2 stop value.
Returns
-------
loc : slice
Slice object.
Examples
--------
>>> import allel
>>> chrom = ['chr1', 'chr1', 'chr2', 'chr2', 'chr2', 'chr3']
>>> pos = [1, 4, 2, 5, 5, 3]
>>> idx = allel.SortedMultiIndex(chrom, pos)
>>> idx.locate_range('chr1')
slice(0, 2, None)
>>> idx.locate_range('chr1', 1, 4)
slice(0, 2, None)
>>> idx.locate_range('chr2', 3, 7)
slice(3, 5, None)
>>> try:
... idx.locate_range('chr3', 4, 9)
... except KeyError as e:
... print(e)
('chr3', 4, 9)
"""
loc1 = self.l1.locate_key(key)
if start is None and stop is None:
loc = loc1
elif isinstance(loc1, slice):
offset = loc1.start
idx = SortedIndex(self.l2[loc1], copy=False)
try:
loc2 = idx.locate_range(start, stop)
except KeyError:
raise KeyError(key, start, stop)
else:
loc = slice(offset + loc2.start, offset + loc2.stop)
else:
# singleton match in l1
v = self.l2[loc1]
if start <= v <= stop:
loc = loc1
else:
raise KeyError(key, start, stop)
# ensure slice is always returned
if not isinstance(loc, slice):
loc = slice(loc, loc + 1)
return loc | python | def locate_range(self, key, start=None, stop=None):
"""Locate slice of index containing all entries within the range
`key`:`start`-`stop` **inclusive**.
Parameters
----------
key : object
Level 1 key value.
start : object, optional
Level 2 start value.
stop : object, optional
Level 2 stop value.
Returns
-------
loc : slice
Slice object.
Examples
--------
>>> import allel
>>> chrom = ['chr1', 'chr1', 'chr2', 'chr2', 'chr2', 'chr3']
>>> pos = [1, 4, 2, 5, 5, 3]
>>> idx = allel.SortedMultiIndex(chrom, pos)
>>> idx.locate_range('chr1')
slice(0, 2, None)
>>> idx.locate_range('chr1', 1, 4)
slice(0, 2, None)
>>> idx.locate_range('chr2', 3, 7)
slice(3, 5, None)
>>> try:
... idx.locate_range('chr3', 4, 9)
... except KeyError as e:
... print(e)
('chr3', 4, 9)
"""
loc1 = self.l1.locate_key(key)
if start is None and stop is None:
loc = loc1
elif isinstance(loc1, slice):
offset = loc1.start
idx = SortedIndex(self.l2[loc1], copy=False)
try:
loc2 = idx.locate_range(start, stop)
except KeyError:
raise KeyError(key, start, stop)
else:
loc = slice(offset + loc2.start, offset + loc2.stop)
else:
# singleton match in l1
v = self.l2[loc1]
if start <= v <= stop:
loc = loc1
else:
raise KeyError(key, start, stop)
# ensure slice is always returned
if not isinstance(loc, slice):
loc = slice(loc, loc + 1)
return loc | Locate slice of index containing all entries within the range
`key`:`start`-`stop` **inclusive**.
Parameters
----------
key : object
Level 1 key value.
start : object, optional
Level 2 start value.
stop : object, optional
Level 2 stop value.
Returns
-------
loc : slice
Slice object.
Examples
--------
>>> import allel
>>> chrom = ['chr1', 'chr1', 'chr2', 'chr2', 'chr2', 'chr3']
>>> pos = [1, 4, 2, 5, 5, 3]
>>> idx = allel.SortedMultiIndex(chrom, pos)
>>> idx.locate_range('chr1')
slice(0, 2, None)
>>> idx.locate_range('chr1', 1, 4)
slice(0, 2, None)
>>> idx.locate_range('chr2', 3, 7)
slice(3, 5, None)
>>> try:
... idx.locate_range('chr3', 4, 9)
... except KeyError as e:
... print(e)
('chr3', 4, 9) | https://github.com/cggh/scikit-allel/blob/3c979a57a100240ba959dd13f98839349530f215/allel/model/ndarray.py#L4166-L4227 |
cggh/scikit-allel | allel/model/ndarray.py | ChromPosIndex.locate_key | def locate_key(self, chrom, pos=None):
"""
Get index location for the requested key.
Parameters
----------
chrom : object
Chromosome or contig.
pos : int, optional
Position within chromosome or contig.
Returns
-------
loc : int or slice
Location of requested key (will be slice if there are duplicate
entries).
Examples
--------
>>> import allel
>>> chrom = ['chr2', 'chr2', 'chr1', 'chr1', 'chr1', 'chr3']
>>> pos = [1, 4, 2, 5, 5, 3]
>>> idx = allel.ChromPosIndex(chrom, pos)
>>> idx.locate_key('chr1')
slice(2, 5, None)
>>> idx.locate_key('chr2', 4)
1
>>> idx.locate_key('chr1', 5)
slice(3, 5, None)
>>> try:
... idx.locate_key('chr3', 4)
... except KeyError as e:
... print(e)
...
('chr3', 4)
"""
if pos is None:
# we just want the region for a chromosome
if chrom in self.chrom_ranges:
# return previously cached result
return self.chrom_ranges[chrom]
else:
loc_chrom = np.nonzero(self.chrom == chrom)[0]
if len(loc_chrom) == 0:
raise KeyError(chrom)
slice_chrom = slice(min(loc_chrom), max(loc_chrom) + 1)
# cache the result
self.chrom_ranges[chrom] = slice_chrom
return slice_chrom
else:
slice_chrom = self.locate_key(chrom)
pos_chrom = SortedIndex(self.pos[slice_chrom])
try:
idx_within_chrom = pos_chrom.locate_key(pos)
except KeyError:
raise KeyError(chrom, pos)
if isinstance(idx_within_chrom, slice):
return slice(slice_chrom.start + idx_within_chrom.start,
slice_chrom.start + idx_within_chrom.stop)
else:
return slice_chrom.start + idx_within_chrom | python | def locate_key(self, chrom, pos=None):
"""
Get index location for the requested key.
Parameters
----------
chrom : object
Chromosome or contig.
pos : int, optional
Position within chromosome or contig.
Returns
-------
loc : int or slice
Location of requested key (will be slice if there are duplicate
entries).
Examples
--------
>>> import allel
>>> chrom = ['chr2', 'chr2', 'chr1', 'chr1', 'chr1', 'chr3']
>>> pos = [1, 4, 2, 5, 5, 3]
>>> idx = allel.ChromPosIndex(chrom, pos)
>>> idx.locate_key('chr1')
slice(2, 5, None)
>>> idx.locate_key('chr2', 4)
1
>>> idx.locate_key('chr1', 5)
slice(3, 5, None)
>>> try:
... idx.locate_key('chr3', 4)
... except KeyError as e:
... print(e)
...
('chr3', 4)
"""
if pos is None:
# we just want the region for a chromosome
if chrom in self.chrom_ranges:
# return previously cached result
return self.chrom_ranges[chrom]
else:
loc_chrom = np.nonzero(self.chrom == chrom)[0]
if len(loc_chrom) == 0:
raise KeyError(chrom)
slice_chrom = slice(min(loc_chrom), max(loc_chrom) + 1)
# cache the result
self.chrom_ranges[chrom] = slice_chrom
return slice_chrom
else:
slice_chrom = self.locate_key(chrom)
pos_chrom = SortedIndex(self.pos[slice_chrom])
try:
idx_within_chrom = pos_chrom.locate_key(pos)
except KeyError:
raise KeyError(chrom, pos)
if isinstance(idx_within_chrom, slice):
return slice(slice_chrom.start + idx_within_chrom.start,
slice_chrom.start + idx_within_chrom.stop)
else:
return slice_chrom.start + idx_within_chrom | Get index location for the requested key.
Parameters
----------
chrom : object
Chromosome or contig.
pos : int, optional
Position within chromosome or contig.
Returns
-------
loc : int or slice
Location of requested key (will be slice if there are duplicate
entries).
Examples
--------
>>> import allel
>>> chrom = ['chr2', 'chr2', 'chr1', 'chr1', 'chr1', 'chr3']
>>> pos = [1, 4, 2, 5, 5, 3]
>>> idx = allel.ChromPosIndex(chrom, pos)
>>> idx.locate_key('chr1')
slice(2, 5, None)
>>> idx.locate_key('chr2', 4)
1
>>> idx.locate_key('chr1', 5)
slice(3, 5, None)
>>> try:
... idx.locate_key('chr3', 4)
... except KeyError as e:
... print(e)
...
('chr3', 4) | https://github.com/cggh/scikit-allel/blob/3c979a57a100240ba959dd13f98839349530f215/allel/model/ndarray.py#L4322-L4386 |
cggh/scikit-allel | allel/model/ndarray.py | ChromPosIndex.locate_range | def locate_range(self, chrom, start=None, stop=None):
"""Locate slice of index containing all entries within the range
`key`:`start`-`stop` **inclusive**.
Parameters
----------
chrom : object
Chromosome or contig.
start : int, optional
Position start value.
stop : int, optional
Position stop value.
Returns
-------
loc : slice
Slice object.
Examples
--------
>>> import allel
>>> chrom = ['chr2', 'chr2', 'chr1', 'chr1', 'chr1', 'chr3']
>>> pos = [1, 4, 2, 5, 5, 3]
>>> idx = allel.ChromPosIndex(chrom, pos)
>>> idx.locate_range('chr1')
slice(2, 5, None)
>>> idx.locate_range('chr2', 1, 4)
slice(0, 2, None)
>>> idx.locate_range('chr1', 3, 7)
slice(3, 5, None)
>>> try:
... idx.locate_range('chr3', 4, 9)
... except KeyError as e:
... print(e)
('chr3', 4, 9)
"""
slice_chrom = self.locate_key(chrom)
if start is None and stop is None:
return slice_chrom
else:
pos_chrom = SortedIndex(self.pos[slice_chrom])
try:
slice_within_chrom = pos_chrom.locate_range(start, stop)
except KeyError:
raise KeyError(chrom, start, stop)
loc = slice(slice_chrom.start + slice_within_chrom.start,
slice_chrom.start + slice_within_chrom.stop)
return loc | python | def locate_range(self, chrom, start=None, stop=None):
"""Locate slice of index containing all entries within the range
`key`:`start`-`stop` **inclusive**.
Parameters
----------
chrom : object
Chromosome or contig.
start : int, optional
Position start value.
stop : int, optional
Position stop value.
Returns
-------
loc : slice
Slice object.
Examples
--------
>>> import allel
>>> chrom = ['chr2', 'chr2', 'chr1', 'chr1', 'chr1', 'chr3']
>>> pos = [1, 4, 2, 5, 5, 3]
>>> idx = allel.ChromPosIndex(chrom, pos)
>>> idx.locate_range('chr1')
slice(2, 5, None)
>>> idx.locate_range('chr2', 1, 4)
slice(0, 2, None)
>>> idx.locate_range('chr1', 3, 7)
slice(3, 5, None)
>>> try:
... idx.locate_range('chr3', 4, 9)
... except KeyError as e:
... print(e)
('chr3', 4, 9)
"""
slice_chrom = self.locate_key(chrom)
if start is None and stop is None:
return slice_chrom
else:
pos_chrom = SortedIndex(self.pos[slice_chrom])
try:
slice_within_chrom = pos_chrom.locate_range(start, stop)
except KeyError:
raise KeyError(chrom, start, stop)
loc = slice(slice_chrom.start + slice_within_chrom.start,
slice_chrom.start + slice_within_chrom.stop)
return loc | Locate slice of index containing all entries within the range
`key`:`start`-`stop` **inclusive**.
Parameters
----------
chrom : object
Chromosome or contig.
start : int, optional
Position start value.
stop : int, optional
Position stop value.
Returns
-------
loc : slice
Slice object.
Examples
--------
>>> import allel
>>> chrom = ['chr2', 'chr2', 'chr1', 'chr1', 'chr1', 'chr3']
>>> pos = [1, 4, 2, 5, 5, 3]
>>> idx = allel.ChromPosIndex(chrom, pos)
>>> idx.locate_range('chr1')
slice(2, 5, None)
>>> idx.locate_range('chr2', 1, 4)
slice(0, 2, None)
>>> idx.locate_range('chr1', 3, 7)
slice(3, 5, None)
>>> try:
... idx.locate_range('chr3', 4, 9)
... except KeyError as e:
... print(e)
('chr3', 4, 9) | https://github.com/cggh/scikit-allel/blob/3c979a57a100240ba959dd13f98839349530f215/allel/model/ndarray.py#L4388-L4441 |
cggh/scikit-allel | allel/model/ndarray.py | VariantTable.set_index | def set_index(self, index):
"""Set or reset the index.
Parameters
----------
index : string or pair of strings, optional
Names of columns to use for positional index, e.g., 'POS' if table
contains a 'POS' column and records from a single
chromosome/contig, or ('CHROM', 'POS') if table contains records
from multiple chromosomes/contigs.
"""
if index is None:
pass
elif isinstance(index, str):
index = SortedIndex(self[index], copy=False)
elif isinstance(index, (tuple, list)) and len(index) == 2:
index = SortedMultiIndex(self[index[0]], self[index[1]],
copy=False)
else:
raise ValueError('invalid index argument, expected string or '
'pair of strings, found %s' % repr(index))
self.index = index | python | def set_index(self, index):
"""Set or reset the index.
Parameters
----------
index : string or pair of strings, optional
Names of columns to use for positional index, e.g., 'POS' if table
contains a 'POS' column and records from a single
chromosome/contig, or ('CHROM', 'POS') if table contains records
from multiple chromosomes/contigs.
"""
if index is None:
pass
elif isinstance(index, str):
index = SortedIndex(self[index], copy=False)
elif isinstance(index, (tuple, list)) and len(index) == 2:
index = SortedMultiIndex(self[index[0]], self[index[1]],
copy=False)
else:
raise ValueError('invalid index argument, expected string or '
'pair of strings, found %s' % repr(index))
self.index = index | Set or reset the index.
Parameters
----------
index : string or pair of strings, optional
Names of columns to use for positional index, e.g., 'POS' if table
contains a 'POS' column and records from a single
chromosome/contig, or ('CHROM', 'POS') if table contains records
from multiple chromosomes/contigs. | https://github.com/cggh/scikit-allel/blob/3c979a57a100240ba959dd13f98839349530f215/allel/model/ndarray.py#L4541-L4563 |
cggh/scikit-allel | allel/model/ndarray.py | VariantTable.query_position | def query_position(self, chrom=None, position=None):
"""Query the table, returning row or rows matching the given genomic
position.
Parameters
----------
chrom : string, optional
Chromosome/contig.
position : int, optional
Position (1-based).
Returns
-------
result : row or VariantTable
"""
if self.index is None:
raise ValueError('no index has been set')
if isinstance(self.index, SortedIndex):
# ignore chrom
loc = self.index.locate_key(position)
else:
loc = self.index.locate_key(chrom, position)
return self[loc] | python | def query_position(self, chrom=None, position=None):
"""Query the table, returning row or rows matching the given genomic
position.
Parameters
----------
chrom : string, optional
Chromosome/contig.
position : int, optional
Position (1-based).
Returns
-------
result : row or VariantTable
"""
if self.index is None:
raise ValueError('no index has been set')
if isinstance(self.index, SortedIndex):
# ignore chrom
loc = self.index.locate_key(position)
else:
loc = self.index.locate_key(chrom, position)
return self[loc] | Query the table, returning row or rows matching the given genomic
position.
Parameters
----------
chrom : string, optional
Chromosome/contig.
position : int, optional
Position (1-based).
Returns
-------
result : row or VariantTable | https://github.com/cggh/scikit-allel/blob/3c979a57a100240ba959dd13f98839349530f215/allel/model/ndarray.py#L4565-L4589 |
cggh/scikit-allel | allel/model/ndarray.py | VariantTable.query_region | def query_region(self, chrom=None, start=None, stop=None):
"""Query the table, returning row or rows within the given genomic
region.
Parameters
----------
chrom : string, optional
Chromosome/contig.
start : int, optional
Region start position (1-based).
stop : int, optional
Region stop position (1-based).
Returns
-------
result : VariantTable
"""
if self.index is None:
raise ValueError('no index has been set')
if isinstance(self.index, SortedIndex):
# ignore chrom
loc = self.index.locate_range(start, stop)
else:
loc = self.index.locate_range(chrom, start, stop)
return self[loc] | python | def query_region(self, chrom=None, start=None, stop=None):
"""Query the table, returning row or rows within the given genomic
region.
Parameters
----------
chrom : string, optional
Chromosome/contig.
start : int, optional
Region start position (1-based).
stop : int, optional
Region stop position (1-based).
Returns
-------
result : VariantTable
"""
if self.index is None:
raise ValueError('no index has been set')
if isinstance(self.index, SortedIndex):
# ignore chrom
loc = self.index.locate_range(start, stop)
else:
loc = self.index.locate_range(chrom, start, stop)
return self[loc] | Query the table, returning row or rows within the given genomic
region.
Parameters
----------
chrom : string, optional
Chromosome/contig.
start : int, optional
Region start position (1-based).
stop : int, optional
Region stop position (1-based).
Returns
-------
result : VariantTable | https://github.com/cggh/scikit-allel/blob/3c979a57a100240ba959dd13f98839349530f215/allel/model/ndarray.py#L4591-L4616 |
cggh/scikit-allel | allel/model/ndarray.py | VariantTable.to_vcf | def to_vcf(self, path, rename=None, number=None, description=None,
fill=None, write_header=True):
r"""Write to a variant call format (VCF) file.
Parameters
----------
path : string
File path.
rename : dict, optional
Rename these columns in the VCF.
number : dict, optional
Override the number specified in INFO headers.
description : dict, optional
Descriptions for the INFO and FILTER headers.
fill : dict, optional
Fill values used for missing data in the table.
write_header : bool, optional
If True write VCF header.
Examples
--------
Setup a variant table to write out::
>>> import allel
>>> chrom = [b'chr1', b'chr1', b'chr2', b'chr2', b'chr3']
>>> pos = [2, 6, 3, 8, 1]
>>> ids = ['a', 'b', 'c', 'd', 'e']
>>> ref = [b'A', b'C', b'T', b'G', b'N']
>>> alt = [(b'T', b'.'),
... (b'G', b'.'),
... (b'A', b'C'),
... (b'C', b'A'),
... (b'X', b'.')]
>>> qual = [1.2, 2.3, 3.4, 4.5, 5.6]
>>> filter_qd = [True, True, True, False, False]
>>> filter_dp = [True, False, True, False, False]
>>> dp = [12, 23, 34, 45, 56]
>>> qd = [12.3, 23.4, 34.5, 45.6, 56.7]
>>> flg = [True, False, True, False, True]
>>> ac = [(1, -1), (3, -1), (5, 6), (7, 8), (9, -1)]
>>> xx = [(1.2, 2.3), (3.4, 4.5), (5.6, 6.7), (7.8, 8.9),
... (9.0, 9.9)]
>>> columns = [chrom, pos, ids, ref, alt, qual, filter_dp,
... filter_qd, dp, qd, flg, ac, xx]
>>> records = list(zip(*columns))
>>> dtype = [('CHROM', 'S4'),
... ('POS', 'u4'),
... ('ID', 'S1'),
... ('REF', 'S1'),
... ('ALT', ('S1', 2)),
... ('qual', 'f4'),
... ('filter_dp', bool),
... ('filter_qd', bool),
... ('dp', int),
... ('qd', float),
... ('flg', bool),
... ('ac', (int, 2)),
... ('xx', (float, 2))]
>>> vt = allel.VariantTable(records, dtype=dtype)
Now write out to VCF and inspect the result::
>>> rename = {'dp': 'DP', 'qd': 'QD', 'filter_qd': 'QD'}
>>> fill = {'ALT': b'.', 'ac': -1}
>>> number = {'ac': 'A'}
>>> description = {'ac': 'Allele counts', 'filter_dp': 'Low depth'}
>>> vt.to_vcf('example.vcf', rename=rename, fill=fill,
... number=number, description=description)
>>> print(open('example.vcf').read())
##fileformat=VCFv4.1
##fileDate=...
##source=...
##INFO=<ID=DP,Number=1,Type=Integer,Description="">
##INFO=<ID=QD,Number=1,Type=Float,Description="">
##INFO=<ID=ac,Number=A,Type=Integer,Description="Allele counts">
##INFO=<ID=flg,Number=0,Type=Flag,Description="">
##INFO=<ID=xx,Number=2,Type=Float,Description="">
##FILTER=<ID=QD,Description="">
##FILTER=<ID=dp,Description="Low depth">
#CHROM POS ID REF ALT QUAL FILTER INFO
chr1 2 a A T 1.2 QD;dp DP=12;QD=12.3;ac=1;flg;xx=...
chr1 6 b C G 2.3 QD DP=23;QD=23.4;ac=3;xx=3.4,4.5
chr2 3 c T A,C 3.4 QD;dp DP=34;QD=34.5;ac=5,6;flg;x...
chr2 8 d G C,A 4.5 PASS DP=45;QD=45.6;ac=7,8;xx=7...
chr3 1 e N X 5.6 PASS DP=56;QD=56.7;ac=9;flg;xx=...
"""
write_vcf(path, callset=self, rename=rename, number=number,
description=description, fill=fill,
write_header=write_header) | python | def to_vcf(self, path, rename=None, number=None, description=None,
fill=None, write_header=True):
r"""Write to a variant call format (VCF) file.
Parameters
----------
path : string
File path.
rename : dict, optional
Rename these columns in the VCF.
number : dict, optional
Override the number specified in INFO headers.
description : dict, optional
Descriptions for the INFO and FILTER headers.
fill : dict, optional
Fill values used for missing data in the table.
write_header : bool, optional
If True write VCF header.
Examples
--------
Setup a variant table to write out::
>>> import allel
>>> chrom = [b'chr1', b'chr1', b'chr2', b'chr2', b'chr3']
>>> pos = [2, 6, 3, 8, 1]
>>> ids = ['a', 'b', 'c', 'd', 'e']
>>> ref = [b'A', b'C', b'T', b'G', b'N']
>>> alt = [(b'T', b'.'),
... (b'G', b'.'),
... (b'A', b'C'),
... (b'C', b'A'),
... (b'X', b'.')]
>>> qual = [1.2, 2.3, 3.4, 4.5, 5.6]
>>> filter_qd = [True, True, True, False, False]
>>> filter_dp = [True, False, True, False, False]
>>> dp = [12, 23, 34, 45, 56]
>>> qd = [12.3, 23.4, 34.5, 45.6, 56.7]
>>> flg = [True, False, True, False, True]
>>> ac = [(1, -1), (3, -1), (5, 6), (7, 8), (9, -1)]
>>> xx = [(1.2, 2.3), (3.4, 4.5), (5.6, 6.7), (7.8, 8.9),
... (9.0, 9.9)]
>>> columns = [chrom, pos, ids, ref, alt, qual, filter_dp,
... filter_qd, dp, qd, flg, ac, xx]
>>> records = list(zip(*columns))
>>> dtype = [('CHROM', 'S4'),
... ('POS', 'u4'),
... ('ID', 'S1'),
... ('REF', 'S1'),
... ('ALT', ('S1', 2)),
... ('qual', 'f4'),
... ('filter_dp', bool),
... ('filter_qd', bool),
... ('dp', int),
... ('qd', float),
... ('flg', bool),
... ('ac', (int, 2)),
... ('xx', (float, 2))]
>>> vt = allel.VariantTable(records, dtype=dtype)
Now write out to VCF and inspect the result::
>>> rename = {'dp': 'DP', 'qd': 'QD', 'filter_qd': 'QD'}
>>> fill = {'ALT': b'.', 'ac': -1}
>>> number = {'ac': 'A'}
>>> description = {'ac': 'Allele counts', 'filter_dp': 'Low depth'}
>>> vt.to_vcf('example.vcf', rename=rename, fill=fill,
... number=number, description=description)
>>> print(open('example.vcf').read())
##fileformat=VCFv4.1
##fileDate=...
##source=...
##INFO=<ID=DP,Number=1,Type=Integer,Description="">
##INFO=<ID=QD,Number=1,Type=Float,Description="">
##INFO=<ID=ac,Number=A,Type=Integer,Description="Allele counts">
##INFO=<ID=flg,Number=0,Type=Flag,Description="">
##INFO=<ID=xx,Number=2,Type=Float,Description="">
##FILTER=<ID=QD,Description="">
##FILTER=<ID=dp,Description="Low depth">
#CHROM POS ID REF ALT QUAL FILTER INFO
chr1 2 a A T 1.2 QD;dp DP=12;QD=12.3;ac=1;flg;xx=...
chr1 6 b C G 2.3 QD DP=23;QD=23.4;ac=3;xx=3.4,4.5
chr2 3 c T A,C 3.4 QD;dp DP=34;QD=34.5;ac=5,6;flg;x...
chr2 8 d G C,A 4.5 PASS DP=45;QD=45.6;ac=7,8;xx=7...
chr3 1 e N X 5.6 PASS DP=56;QD=56.7;ac=9;flg;xx=...
"""
write_vcf(path, callset=self, rename=rename, number=number,
description=description, fill=fill,
write_header=write_header) | r"""Write to a variant call format (VCF) file.
Parameters
----------
path : string
File path.
rename : dict, optional
Rename these columns in the VCF.
number : dict, optional
Override the number specified in INFO headers.
description : dict, optional
Descriptions for the INFO and FILTER headers.
fill : dict, optional
Fill values used for missing data in the table.
write_header : bool, optional
If True write VCF header.
Examples
--------
Setup a variant table to write out::
>>> import allel
>>> chrom = [b'chr1', b'chr1', b'chr2', b'chr2', b'chr3']
>>> pos = [2, 6, 3, 8, 1]
>>> ids = ['a', 'b', 'c', 'd', 'e']
>>> ref = [b'A', b'C', b'T', b'G', b'N']
>>> alt = [(b'T', b'.'),
... (b'G', b'.'),
... (b'A', b'C'),
... (b'C', b'A'),
... (b'X', b'.')]
>>> qual = [1.2, 2.3, 3.4, 4.5, 5.6]
>>> filter_qd = [True, True, True, False, False]
>>> filter_dp = [True, False, True, False, False]
>>> dp = [12, 23, 34, 45, 56]
>>> qd = [12.3, 23.4, 34.5, 45.6, 56.7]
>>> flg = [True, False, True, False, True]
>>> ac = [(1, -1), (3, -1), (5, 6), (7, 8), (9, -1)]
>>> xx = [(1.2, 2.3), (3.4, 4.5), (5.6, 6.7), (7.8, 8.9),
... (9.0, 9.9)]
>>> columns = [chrom, pos, ids, ref, alt, qual, filter_dp,
... filter_qd, dp, qd, flg, ac, xx]
>>> records = list(zip(*columns))
>>> dtype = [('CHROM', 'S4'),
... ('POS', 'u4'),
... ('ID', 'S1'),
... ('REF', 'S1'),
... ('ALT', ('S1', 2)),
... ('qual', 'f4'),
... ('filter_dp', bool),
... ('filter_qd', bool),
... ('dp', int),
... ('qd', float),
... ('flg', bool),
... ('ac', (int, 2)),
... ('xx', (float, 2))]
>>> vt = allel.VariantTable(records, dtype=dtype)
Now write out to VCF and inspect the result::
>>> rename = {'dp': 'DP', 'qd': 'QD', 'filter_qd': 'QD'}
>>> fill = {'ALT': b'.', 'ac': -1}
>>> number = {'ac': 'A'}
>>> description = {'ac': 'Allele counts', 'filter_dp': 'Low depth'}
>>> vt.to_vcf('example.vcf', rename=rename, fill=fill,
... number=number, description=description)
>>> print(open('example.vcf').read())
##fileformat=VCFv4.1
##fileDate=...
##source=...
##INFO=<ID=DP,Number=1,Type=Integer,Description="">
##INFO=<ID=QD,Number=1,Type=Float,Description="">
##INFO=<ID=ac,Number=A,Type=Integer,Description="Allele counts">
##INFO=<ID=flg,Number=0,Type=Flag,Description="">
##INFO=<ID=xx,Number=2,Type=Float,Description="">
##FILTER=<ID=QD,Description="">
##FILTER=<ID=dp,Description="Low depth">
#CHROM POS ID REF ALT QUAL FILTER INFO
chr1 2 a A T 1.2 QD;dp DP=12;QD=12.3;ac=1;flg;xx=...
chr1 6 b C G 2.3 QD DP=23;QD=23.4;ac=3;xx=3.4,4.5
chr2 3 c T A,C 3.4 QD;dp DP=34;QD=34.5;ac=5,6;flg;x...
chr2 8 d G C,A 4.5 PASS DP=45;QD=45.6;ac=7,8;xx=7...
chr3 1 e N X 5.6 PASS DP=56;QD=56.7;ac=9;flg;xx=... | https://github.com/cggh/scikit-allel/blob/3c979a57a100240ba959dd13f98839349530f215/allel/model/ndarray.py#L4618-L4708 |
cggh/scikit-allel | allel/model/ndarray.py | FeatureTable.to_mask | def to_mask(self, size, start_name='start', stop_name='end'):
"""Construct a mask array where elements are True if the fall within
features in the table.
Parameters
----------
size : int
Size of chromosome/contig.
start_name : string, optional
Name of column with start coordinates.
stop_name : string, optional
Name of column with stop coordinates.
Returns
-------
mask : ndarray, bool
"""
m = np.zeros(size, dtype=bool)
for start, stop in self[[start_name, stop_name]]:
m[start-1:stop] = True
return m | python | def to_mask(self, size, start_name='start', stop_name='end'):
"""Construct a mask array where elements are True if the fall within
features in the table.
Parameters
----------
size : int
Size of chromosome/contig.
start_name : string, optional
Name of column with start coordinates.
stop_name : string, optional
Name of column with stop coordinates.
Returns
-------
mask : ndarray, bool
"""
m = np.zeros(size, dtype=bool)
for start, stop in self[[start_name, stop_name]]:
m[start-1:stop] = True
return m | Construct a mask array where elements are True if the fall within
features in the table.
Parameters
----------
size : int
Size of chromosome/contig.
start_name : string, optional
Name of column with start coordinates.
stop_name : string, optional
Name of column with stop coordinates.
Returns
-------
mask : ndarray, bool | https://github.com/cggh/scikit-allel/blob/3c979a57a100240ba959dd13f98839349530f215/allel/model/ndarray.py#L4734-L4757 |
cggh/scikit-allel | allel/model/ndarray.py | FeatureTable.from_gff3 | def from_gff3(path, attributes=None, region=None, score_fill=-1, phase_fill=-1,
attributes_fill='.', dtype=None):
"""Read a feature table from a GFF3 format file.
Parameters
----------
path : string
File path.
attributes : list of strings, optional
List of columns to extract from the "attributes" field.
region : string, optional
Genome region to extract. If given, file must be position
sorted, bgzipped and tabix indexed. Tabix must also be installed
and on the system path.
score_fill : int, optional
Value to use where score field has a missing value.
phase_fill : int, optional
Value to use where phase field has a missing value.
attributes_fill : object or list of objects, optional
Value(s) to use where attribute field(s) have a missing value.
dtype : numpy dtype, optional
Manually specify a dtype.
Returns
-------
ft : FeatureTable
"""
a = gff3_to_recarray(path, attributes=attributes, region=region,
score_fill=score_fill, phase_fill=phase_fill,
attributes_fill=attributes_fill, dtype=dtype)
if a is None:
return None
else:
return FeatureTable(a, copy=False) | python | def from_gff3(path, attributes=None, region=None, score_fill=-1, phase_fill=-1,
attributes_fill='.', dtype=None):
"""Read a feature table from a GFF3 format file.
Parameters
----------
path : string
File path.
attributes : list of strings, optional
List of columns to extract from the "attributes" field.
region : string, optional
Genome region to extract. If given, file must be position
sorted, bgzipped and tabix indexed. Tabix must also be installed
and on the system path.
score_fill : int, optional
Value to use where score field has a missing value.
phase_fill : int, optional
Value to use where phase field has a missing value.
attributes_fill : object or list of objects, optional
Value(s) to use where attribute field(s) have a missing value.
dtype : numpy dtype, optional
Manually specify a dtype.
Returns
-------
ft : FeatureTable
"""
a = gff3_to_recarray(path, attributes=attributes, region=region,
score_fill=score_fill, phase_fill=phase_fill,
attributes_fill=attributes_fill, dtype=dtype)
if a is None:
return None
else:
return FeatureTable(a, copy=False) | Read a feature table from a GFF3 format file.
Parameters
----------
path : string
File path.
attributes : list of strings, optional
List of columns to extract from the "attributes" field.
region : string, optional
Genome region to extract. If given, file must be position
sorted, bgzipped and tabix indexed. Tabix must also be installed
and on the system path.
score_fill : int, optional
Value to use where score field has a missing value.
phase_fill : int, optional
Value to use where phase field has a missing value.
attributes_fill : object or list of objects, optional
Value(s) to use where attribute field(s) have a missing value.
dtype : numpy dtype, optional
Manually specify a dtype.
Returns
-------
ft : FeatureTable | https://github.com/cggh/scikit-allel/blob/3c979a57a100240ba959dd13f98839349530f215/allel/model/ndarray.py#L4760-L4795 |
cggh/scikit-allel | allel/stats/distance.py | pairwise_distance | def pairwise_distance(x, metric, chunked=False, blen=None):
"""Compute pairwise distance between individuals (e.g., samples or
haplotypes).
Parameters
----------
x : array_like, shape (n, m, ...)
Array of m observations (e.g., samples or haplotypes) in a space
with n dimensions (e.g., variants). Note that the order of the first
two dimensions is **swapped** compared to what is expected by
scipy.spatial.distance.pdist.
metric : string or function
Distance metric. See documentation for the function
:func:`scipy.spatial.distance.pdist` for a list of built-in
distance metrics.
chunked : bool, optional
If True, use a block-wise implementation to avoid loading the entire
input array into memory. This means that a distance matrix will be
calculated for each block of the input array, and the results will
be summed to produce the final output. For some distance metrics
this will return a different result from the standard implementation.
blen : int, optional
Block length to use for chunked implementation.
Returns
-------
dist : ndarray, shape (m * (m - 1) / 2,)
Distance matrix in condensed form.
Examples
--------
>>> import allel
>>> g = allel.GenotypeArray([[[0, 0], [0, 1], [1, 1]],
... [[0, 1], [1, 1], [1, 2]],
... [[0, 2], [2, 2], [-1, -1]]])
>>> d = allel.pairwise_distance(g.to_n_alt(), metric='cityblock')
>>> d
array([3., 4., 3.])
>>> import scipy.spatial
>>> scipy.spatial.distance.squareform(d)
array([[0., 3., 4.],
[3., 0., 3.],
[4., 3., 0.]])
"""
import scipy.spatial
# check inputs
if not hasattr(x, 'ndim'):
x = np.asarray(x)
if x.ndim < 2:
raise ValueError('array with at least 2 dimensions expected')
if x.ndim == 2:
# use scipy to calculate distance, it's most efficient
def f(b):
# transpose as pdist expects (m, n) for m observations in an
# n-dimensional space
t = b.T
# compute the distance matrix
return scipy.spatial.distance.pdist(t, metric=metric)
else:
# use our own implementation, it handles multidimensional observations
def f(b):
return pdist(b, metric=metric)
if chunked:
# use block-wise implementation
blen = get_blen_array(x, blen)
dist = None
for i in range(0, x.shape[0], blen):
j = min(x.shape[0], i+blen)
block = x[i:j]
if dist is None:
dist = f(block)
else:
dist += f(block)
else:
# standard implementation
dist = f(x)
return dist | python | def pairwise_distance(x, metric, chunked=False, blen=None):
"""Compute pairwise distance between individuals (e.g., samples or
haplotypes).
Parameters
----------
x : array_like, shape (n, m, ...)
Array of m observations (e.g., samples or haplotypes) in a space
with n dimensions (e.g., variants). Note that the order of the first
two dimensions is **swapped** compared to what is expected by
scipy.spatial.distance.pdist.
metric : string or function
Distance metric. See documentation for the function
:func:`scipy.spatial.distance.pdist` for a list of built-in
distance metrics.
chunked : bool, optional
If True, use a block-wise implementation to avoid loading the entire
input array into memory. This means that a distance matrix will be
calculated for each block of the input array, and the results will
be summed to produce the final output. For some distance metrics
this will return a different result from the standard implementation.
blen : int, optional
Block length to use for chunked implementation.
Returns
-------
dist : ndarray, shape (m * (m - 1) / 2,)
Distance matrix in condensed form.
Examples
--------
>>> import allel
>>> g = allel.GenotypeArray([[[0, 0], [0, 1], [1, 1]],
... [[0, 1], [1, 1], [1, 2]],
... [[0, 2], [2, 2], [-1, -1]]])
>>> d = allel.pairwise_distance(g.to_n_alt(), metric='cityblock')
>>> d
array([3., 4., 3.])
>>> import scipy.spatial
>>> scipy.spatial.distance.squareform(d)
array([[0., 3., 4.],
[3., 0., 3.],
[4., 3., 0.]])
"""
import scipy.spatial
# check inputs
if not hasattr(x, 'ndim'):
x = np.asarray(x)
if x.ndim < 2:
raise ValueError('array with at least 2 dimensions expected')
if x.ndim == 2:
# use scipy to calculate distance, it's most efficient
def f(b):
# transpose as pdist expects (m, n) for m observations in an
# n-dimensional space
t = b.T
# compute the distance matrix
return scipy.spatial.distance.pdist(t, metric=metric)
else:
# use our own implementation, it handles multidimensional observations
def f(b):
return pdist(b, metric=metric)
if chunked:
# use block-wise implementation
blen = get_blen_array(x, blen)
dist = None
for i in range(0, x.shape[0], blen):
j = min(x.shape[0], i+blen)
block = x[i:j]
if dist is None:
dist = f(block)
else:
dist += f(block)
else:
# standard implementation
dist = f(x)
return dist | Compute pairwise distance between individuals (e.g., samples or
haplotypes).
Parameters
----------
x : array_like, shape (n, m, ...)
Array of m observations (e.g., samples or haplotypes) in a space
with n dimensions (e.g., variants). Note that the order of the first
two dimensions is **swapped** compared to what is expected by
scipy.spatial.distance.pdist.
metric : string or function
Distance metric. See documentation for the function
:func:`scipy.spatial.distance.pdist` for a list of built-in
distance metrics.
chunked : bool, optional
If True, use a block-wise implementation to avoid loading the entire
input array into memory. This means that a distance matrix will be
calculated for each block of the input array, and the results will
be summed to produce the final output. For some distance metrics
this will return a different result from the standard implementation.
blen : int, optional
Block length to use for chunked implementation.
Returns
-------
dist : ndarray, shape (m * (m - 1) / 2,)
Distance matrix in condensed form.
Examples
--------
>>> import allel
>>> g = allel.GenotypeArray([[[0, 0], [0, 1], [1, 1]],
... [[0, 1], [1, 1], [1, 2]],
... [[0, 2], [2, 2], [-1, -1]]])
>>> d = allel.pairwise_distance(g.to_n_alt(), metric='cityblock')
>>> d
array([3., 4., 3.])
>>> import scipy.spatial
>>> scipy.spatial.distance.squareform(d)
array([[0., 3., 4.],
[3., 0., 3.],
[4., 3., 0.]]) | https://github.com/cggh/scikit-allel/blob/3c979a57a100240ba959dd13f98839349530f215/allel/stats/distance.py#L17-L106 |
cggh/scikit-allel | allel/stats/distance.py | pdist | def pdist(x, metric):
"""Alternative implementation of :func:`scipy.spatial.distance.pdist`
which is slower but more flexible in that arrays with >2 dimensions can be
passed, allowing for multidimensional observations, e.g., diploid
genotype calls or allele counts.
Parameters
----------
x : array_like, shape (n, m, ...)
Array of m observations (e.g., samples or haplotypes) in a space
with n dimensions (e.g., variants). Note that the order of the first
two dimensions is **swapped** compared to what is expected by
scipy.spatial.distance.pdist.
metric : string or function
Distance metric. See documentation for the function
:func:`scipy.spatial.distance.pdist` for a list of built-in
distance metrics.
Returns
-------
dist : ndarray
Distance matrix in condensed form.
"""
if isinstance(metric, str):
import scipy.spatial
if hasattr(scipy.spatial.distance, metric):
metric = getattr(scipy.spatial.distance, metric)
else:
raise ValueError('metric name not found')
m = x.shape[1]
dist = list()
for i, j in itertools.combinations(range(m), 2):
a = x[:, i, ...]
b = x[:, j, ...]
d = metric(a, b)
dist.append(d)
return np.array(dist) | python | def pdist(x, metric):
"""Alternative implementation of :func:`scipy.spatial.distance.pdist`
which is slower but more flexible in that arrays with >2 dimensions can be
passed, allowing for multidimensional observations, e.g., diploid
genotype calls or allele counts.
Parameters
----------
x : array_like, shape (n, m, ...)
Array of m observations (e.g., samples or haplotypes) in a space
with n dimensions (e.g., variants). Note that the order of the first
two dimensions is **swapped** compared to what is expected by
scipy.spatial.distance.pdist.
metric : string or function
Distance metric. See documentation for the function
:func:`scipy.spatial.distance.pdist` for a list of built-in
distance metrics.
Returns
-------
dist : ndarray
Distance matrix in condensed form.
"""
if isinstance(metric, str):
import scipy.spatial
if hasattr(scipy.spatial.distance, metric):
metric = getattr(scipy.spatial.distance, metric)
else:
raise ValueError('metric name not found')
m = x.shape[1]
dist = list()
for i, j in itertools.combinations(range(m), 2):
a = x[:, i, ...]
b = x[:, j, ...]
d = metric(a, b)
dist.append(d)
return np.array(dist) | Alternative implementation of :func:`scipy.spatial.distance.pdist`
which is slower but more flexible in that arrays with >2 dimensions can be
passed, allowing for multidimensional observations, e.g., diploid
genotype calls or allele counts.
Parameters
----------
x : array_like, shape (n, m, ...)
Array of m observations (e.g., samples or haplotypes) in a space
with n dimensions (e.g., variants). Note that the order of the first
two dimensions is **swapped** compared to what is expected by
scipy.spatial.distance.pdist.
metric : string or function
Distance metric. See documentation for the function
:func:`scipy.spatial.distance.pdist` for a list of built-in
distance metrics.
Returns
-------
dist : ndarray
Distance matrix in condensed form. | https://github.com/cggh/scikit-allel/blob/3c979a57a100240ba959dd13f98839349530f215/allel/stats/distance.py#L109-L148 |
cggh/scikit-allel | allel/stats/distance.py | pairwise_dxy | def pairwise_dxy(pos, gac, start=None, stop=None, is_accessible=None):
"""Convenience function to calculate a pairwise distance matrix using
nucleotide divergence (a.k.a. Dxy) as the distance metric.
Parameters
----------
pos : array_like, int, shape (n_variants,)
Variant positions.
gac : array_like, int, shape (n_variants, n_samples, n_alleles)
Per-genotype allele counts.
start : int, optional
Start position of region to use.
stop : int, optional
Stop position of region to use.
is_accessible : array_like, bool, shape (len(contig),), optional
Boolean array indicating accessibility status for all positions in the
chromosome/contig.
Returns
-------
dist : ndarray
Distance matrix in condensed form.
See Also
--------
allel.model.ndarray.GenotypeArray.to_allele_counts
"""
if not isinstance(pos, SortedIndex):
pos = SortedIndex(pos, copy=False)
gac = asarray_ndim(gac, 3)
# compute this once here, to avoid repeated evaluation within the loop
gan = np.sum(gac, axis=2)
m = gac.shape[1]
dist = list()
for i, j in itertools.combinations(range(m), 2):
ac1 = gac[:, i, ...]
an1 = gan[:, i]
ac2 = gac[:, j, ...]
an2 = gan[:, j]
d = sequence_divergence(pos, ac1, ac2, an1=an1, an2=an2,
start=start, stop=stop,
is_accessible=is_accessible)
dist.append(d)
return np.array(dist) | python | def pairwise_dxy(pos, gac, start=None, stop=None, is_accessible=None):
"""Convenience function to calculate a pairwise distance matrix using
nucleotide divergence (a.k.a. Dxy) as the distance metric.
Parameters
----------
pos : array_like, int, shape (n_variants,)
Variant positions.
gac : array_like, int, shape (n_variants, n_samples, n_alleles)
Per-genotype allele counts.
start : int, optional
Start position of region to use.
stop : int, optional
Stop position of region to use.
is_accessible : array_like, bool, shape (len(contig),), optional
Boolean array indicating accessibility status for all positions in the
chromosome/contig.
Returns
-------
dist : ndarray
Distance matrix in condensed form.
See Also
--------
allel.model.ndarray.GenotypeArray.to_allele_counts
"""
if not isinstance(pos, SortedIndex):
pos = SortedIndex(pos, copy=False)
gac = asarray_ndim(gac, 3)
# compute this once here, to avoid repeated evaluation within the loop
gan = np.sum(gac, axis=2)
m = gac.shape[1]
dist = list()
for i, j in itertools.combinations(range(m), 2):
ac1 = gac[:, i, ...]
an1 = gan[:, i]
ac2 = gac[:, j, ...]
an2 = gan[:, j]
d = sequence_divergence(pos, ac1, ac2, an1=an1, an2=an2,
start=start, stop=stop,
is_accessible=is_accessible)
dist.append(d)
return np.array(dist) | Convenience function to calculate a pairwise distance matrix using
nucleotide divergence (a.k.a. Dxy) as the distance metric.
Parameters
----------
pos : array_like, int, shape (n_variants,)
Variant positions.
gac : array_like, int, shape (n_variants, n_samples, n_alleles)
Per-genotype allele counts.
start : int, optional
Start position of region to use.
stop : int, optional
Stop position of region to use.
is_accessible : array_like, bool, shape (len(contig),), optional
Boolean array indicating accessibility status for all positions in the
chromosome/contig.
Returns
-------
dist : ndarray
Distance matrix in condensed form.
See Also
--------
allel.model.ndarray.GenotypeArray.to_allele_counts | https://github.com/cggh/scikit-allel/blob/3c979a57a100240ba959dd13f98839349530f215/allel/stats/distance.py#L151-L196 |
cggh/scikit-allel | allel/stats/distance.py | pcoa | def pcoa(dist):
"""Perform principal coordinate analysis of a distance matrix, a.k.a.
classical multi-dimensional scaling.
Parameters
----------
dist : array_like
Distance matrix in condensed form.
Returns
-------
coords : ndarray, shape (n_samples, n_dimensions)
Transformed coordinates for the samples.
explained_ratio : ndarray, shape (n_dimensions)
Variance explained by each dimension.
"""
import scipy.linalg
# This implementation is based on the skbio.math.stats.ordination.PCoA
# implementation, with some minor adjustments.
# check inputs
dist = ensure_square(dist)
# perform scaling
e_matrix = (dist ** 2) / -2
row_means = np.mean(e_matrix, axis=1, keepdims=True)
col_means = np.mean(e_matrix, axis=0, keepdims=True)
matrix_mean = np.mean(e_matrix)
f_matrix = e_matrix - row_means - col_means + matrix_mean
eigvals, eigvecs = scipy.linalg.eigh(f_matrix)
# deal with eigvals close to zero
close_to_zero = np.isclose(eigvals, 0)
eigvals[close_to_zero] = 0
# sort descending
idxs = eigvals.argsort()[::-1]
eigvals = eigvals[idxs]
eigvecs = eigvecs[:, idxs]
# keep only positive eigenvalues
keep = eigvals >= 0
eigvecs = eigvecs[:, keep]
eigvals = eigvals[keep]
# compute coordinates
coords = eigvecs * np.sqrt(eigvals)
# compute ratio explained
explained_ratio = eigvals / eigvals.sum()
return coords, explained_ratio | python | def pcoa(dist):
"""Perform principal coordinate analysis of a distance matrix, a.k.a.
classical multi-dimensional scaling.
Parameters
----------
dist : array_like
Distance matrix in condensed form.
Returns
-------
coords : ndarray, shape (n_samples, n_dimensions)
Transformed coordinates for the samples.
explained_ratio : ndarray, shape (n_dimensions)
Variance explained by each dimension.
"""
import scipy.linalg
# This implementation is based on the skbio.math.stats.ordination.PCoA
# implementation, with some minor adjustments.
# check inputs
dist = ensure_square(dist)
# perform scaling
e_matrix = (dist ** 2) / -2
row_means = np.mean(e_matrix, axis=1, keepdims=True)
col_means = np.mean(e_matrix, axis=0, keepdims=True)
matrix_mean = np.mean(e_matrix)
f_matrix = e_matrix - row_means - col_means + matrix_mean
eigvals, eigvecs = scipy.linalg.eigh(f_matrix)
# deal with eigvals close to zero
close_to_zero = np.isclose(eigvals, 0)
eigvals[close_to_zero] = 0
# sort descending
idxs = eigvals.argsort()[::-1]
eigvals = eigvals[idxs]
eigvecs = eigvecs[:, idxs]
# keep only positive eigenvalues
keep = eigvals >= 0
eigvecs = eigvecs[:, keep]
eigvals = eigvals[keep]
# compute coordinates
coords = eigvecs * np.sqrt(eigvals)
# compute ratio explained
explained_ratio = eigvals / eigvals.sum()
return coords, explained_ratio | Perform principal coordinate analysis of a distance matrix, a.k.a.
classical multi-dimensional scaling.
Parameters
----------
dist : array_like
Distance matrix in condensed form.
Returns
-------
coords : ndarray, shape (n_samples, n_dimensions)
Transformed coordinates for the samples.
explained_ratio : ndarray, shape (n_dimensions)
Variance explained by each dimension. | https://github.com/cggh/scikit-allel/blob/3c979a57a100240ba959dd13f98839349530f215/allel/stats/distance.py#L199-L252 |
cggh/scikit-allel | allel/stats/distance.py | condensed_coords | def condensed_coords(i, j, n):
"""Transform square distance matrix coordinates to the corresponding
index into a condensed, 1D form of the matrix.
Parameters
----------
i : int
Row index.
j : int
Column index.
n : int
Size of the square matrix (length of first or second dimension).
Returns
-------
ix : int
"""
# guard conditions
if i == j or i >= n or j >= n or i < 0 or j < 0:
raise ValueError('invalid coordinates: %s, %s' % (i, j))
# normalise order
i, j = sorted([i, j])
# calculate number of items in rows before this one (sum of arithmetic
# progression)
x = i * ((2 * n) - i - 1) / 2
# add on previous items in current row
ix = x + j - i - 1
return int(ix) | python | def condensed_coords(i, j, n):
"""Transform square distance matrix coordinates to the corresponding
index into a condensed, 1D form of the matrix.
Parameters
----------
i : int
Row index.
j : int
Column index.
n : int
Size of the square matrix (length of first or second dimension).
Returns
-------
ix : int
"""
# guard conditions
if i == j or i >= n or j >= n or i < 0 or j < 0:
raise ValueError('invalid coordinates: %s, %s' % (i, j))
# normalise order
i, j = sorted([i, j])
# calculate number of items in rows before this one (sum of arithmetic
# progression)
x = i * ((2 * n) - i - 1) / 2
# add on previous items in current row
ix = x + j - i - 1
return int(ix) | Transform square distance matrix coordinates to the corresponding
index into a condensed, 1D form of the matrix.
Parameters
----------
i : int
Row index.
j : int
Column index.
n : int
Size of the square matrix (length of first or second dimension).
Returns
-------
ix : int | https://github.com/cggh/scikit-allel/blob/3c979a57a100240ba959dd13f98839349530f215/allel/stats/distance.py#L255-L288 |
cggh/scikit-allel | allel/stats/distance.py | condensed_coords_within | def condensed_coords_within(pop, n):
"""Return indices into a condensed distance matrix for all
pairwise comparisons within the given population.
Parameters
----------
pop : array_like, int
Indices of samples or haplotypes within the population.
n : int
Size of the square matrix (length of first or second dimension).
Returns
-------
indices : ndarray, int
"""
return [condensed_coords(i, j, n)
for i, j in itertools.combinations(sorted(pop), 2)] | python | def condensed_coords_within(pop, n):
"""Return indices into a condensed distance matrix for all
pairwise comparisons within the given population.
Parameters
----------
pop : array_like, int
Indices of samples or haplotypes within the population.
n : int
Size of the square matrix (length of first or second dimension).
Returns
-------
indices : ndarray, int
"""
return [condensed_coords(i, j, n)
for i, j in itertools.combinations(sorted(pop), 2)] | Return indices into a condensed distance matrix for all
pairwise comparisons within the given population.
Parameters
----------
pop : array_like, int
Indices of samples or haplotypes within the population.
n : int
Size of the square matrix (length of first or second dimension).
Returns
-------
indices : ndarray, int | https://github.com/cggh/scikit-allel/blob/3c979a57a100240ba959dd13f98839349530f215/allel/stats/distance.py#L291-L309 |
cggh/scikit-allel | allel/stats/distance.py | condensed_coords_between | def condensed_coords_between(pop1, pop2, n):
"""Return indices into a condensed distance matrix for all pairwise
comparisons between two populations.
Parameters
----------
pop1 : array_like, int
Indices of samples or haplotypes within the first population.
pop2 : array_like, int
Indices of samples or haplotypes within the second population.
n : int
Size of the square matrix (length of first or second dimension).
Returns
-------
indices : ndarray, int
"""
return [condensed_coords(i, j, n)
for i, j in itertools.product(sorted(pop1), sorted(pop2))] | python | def condensed_coords_between(pop1, pop2, n):
"""Return indices into a condensed distance matrix for all pairwise
comparisons between two populations.
Parameters
----------
pop1 : array_like, int
Indices of samples or haplotypes within the first population.
pop2 : array_like, int
Indices of samples or haplotypes within the second population.
n : int
Size of the square matrix (length of first or second dimension).
Returns
-------
indices : ndarray, int
"""
return [condensed_coords(i, j, n)
for i, j in itertools.product(sorted(pop1), sorted(pop2))] | Return indices into a condensed distance matrix for all pairwise
comparisons between two populations.
Parameters
----------
pop1 : array_like, int
Indices of samples or haplotypes within the first population.
pop2 : array_like, int
Indices of samples or haplotypes within the second population.
n : int
Size of the square matrix (length of first or second dimension).
Returns
-------
indices : ndarray, int | https://github.com/cggh/scikit-allel/blob/3c979a57a100240ba959dd13f98839349530f215/allel/stats/distance.py#L312-L332 |
cggh/scikit-allel | allel/stats/distance.py | plot_pairwise_distance | def plot_pairwise_distance(dist, labels=None, colorbar=True, ax=None,
imshow_kwargs=None):
"""Plot a pairwise distance matrix.
Parameters
----------
dist : array_like
The distance matrix in condensed form.
labels : sequence of strings, optional
Sample labels for the axes.
colorbar : bool, optional
If True, add a colorbar to the current figure.
ax : axes, optional
The axes on which to draw. If not provided, a new figure will be
created.
imshow_kwargs : dict-like, optional
Additional keyword arguments passed through to
:func:`matplotlib.pyplot.imshow`.
Returns
-------
ax : axes
The axes on which the plot was drawn
"""
import matplotlib.pyplot as plt
# check inputs
dist_square = ensure_square(dist)
# set up axes
if ax is None:
# make a square figure
x = plt.rcParams['figure.figsize'][0]
fig, ax = plt.subplots(figsize=(x, x))
fig.tight_layout()
# setup imshow arguments
if imshow_kwargs is None:
imshow_kwargs = dict()
imshow_kwargs.setdefault('interpolation', 'none')
imshow_kwargs.setdefault('cmap', 'jet')
imshow_kwargs.setdefault('vmin', np.min(dist))
imshow_kwargs.setdefault('vmax', np.max(dist))
# plot as image
im = ax.imshow(dist_square, **imshow_kwargs)
# tidy up
if labels:
ax.set_xticks(range(len(labels)))
ax.set_yticks(range(len(labels)))
ax.set_xticklabels(labels, rotation=90)
ax.set_yticklabels(labels, rotation=0)
else:
ax.set_xticks([])
ax.set_yticks([])
if colorbar:
plt.gcf().colorbar(im, shrink=.5)
return ax | python | def plot_pairwise_distance(dist, labels=None, colorbar=True, ax=None,
imshow_kwargs=None):
"""Plot a pairwise distance matrix.
Parameters
----------
dist : array_like
The distance matrix in condensed form.
labels : sequence of strings, optional
Sample labels for the axes.
colorbar : bool, optional
If True, add a colorbar to the current figure.
ax : axes, optional
The axes on which to draw. If not provided, a new figure will be
created.
imshow_kwargs : dict-like, optional
Additional keyword arguments passed through to
:func:`matplotlib.pyplot.imshow`.
Returns
-------
ax : axes
The axes on which the plot was drawn
"""
import matplotlib.pyplot as plt
# check inputs
dist_square = ensure_square(dist)
# set up axes
if ax is None:
# make a square figure
x = plt.rcParams['figure.figsize'][0]
fig, ax = plt.subplots(figsize=(x, x))
fig.tight_layout()
# setup imshow arguments
if imshow_kwargs is None:
imshow_kwargs = dict()
imshow_kwargs.setdefault('interpolation', 'none')
imshow_kwargs.setdefault('cmap', 'jet')
imshow_kwargs.setdefault('vmin', np.min(dist))
imshow_kwargs.setdefault('vmax', np.max(dist))
# plot as image
im = ax.imshow(dist_square, **imshow_kwargs)
# tidy up
if labels:
ax.set_xticks(range(len(labels)))
ax.set_yticks(range(len(labels)))
ax.set_xticklabels(labels, rotation=90)
ax.set_yticklabels(labels, rotation=0)
else:
ax.set_xticks([])
ax.set_yticks([])
if colorbar:
plt.gcf().colorbar(im, shrink=.5)
return ax | Plot a pairwise distance matrix.
Parameters
----------
dist : array_like
The distance matrix in condensed form.
labels : sequence of strings, optional
Sample labels for the axes.
colorbar : bool, optional
If True, add a colorbar to the current figure.
ax : axes, optional
The axes on which to draw. If not provided, a new figure will be
created.
imshow_kwargs : dict-like, optional
Additional keyword arguments passed through to
:func:`matplotlib.pyplot.imshow`.
Returns
-------
ax : axes
The axes on which the plot was drawn | https://github.com/cggh/scikit-allel/blob/3c979a57a100240ba959dd13f98839349530f215/allel/stats/distance.py#L335-L396 |
cggh/scikit-allel | allel/stats/misc.py | jackknife | def jackknife(values, statistic):
"""Estimate standard error for `statistic` computed over `values` using
the jackknife.
Parameters
----------
values : array_like or tuple of array_like
Input array, or tuple of input arrays.
statistic : function
The statistic to compute.
Returns
-------
m : float
Mean of jackknife values.
se : float
Estimate of standard error.
vj : ndarray
Statistic values computed for each jackknife iteration.
"""
if isinstance(values, tuple):
# multiple input arrays
n = len(values[0])
masked_values = [np.ma.asarray(v) for v in values]
for m in masked_values:
assert m.ndim == 1, 'only 1D arrays supported'
assert m.shape[0] == n, 'input arrays not of equal length'
m.mask = np.zeros(m.shape, dtype=bool)
else:
n = len(values)
masked_values = np.ma.asarray(values)
assert masked_values.ndim == 1, 'only 1D arrays supported'
masked_values.mask = np.zeros(masked_values.shape, dtype=bool)
# values of the statistic calculated in each jackknife iteration
vj = list()
for i in range(n):
if isinstance(values, tuple):
# multiple input arrays
for m in masked_values:
m.mask[i] = True
x = statistic(*masked_values)
for m in masked_values:
m.mask[i] = False
else:
masked_values.mask[i] = True
x = statistic(masked_values)
masked_values.mask[i] = False
vj.append(x)
# convert to array for convenience
vj = np.array(vj)
# compute mean of jackknife values
m = vj.mean()
# compute standard error
sv = ((n - 1) / n) * np.sum((vj - m) ** 2)
se = np.sqrt(sv)
return m, se, vj | python | def jackknife(values, statistic):
"""Estimate standard error for `statistic` computed over `values` using
the jackknife.
Parameters
----------
values : array_like or tuple of array_like
Input array, or tuple of input arrays.
statistic : function
The statistic to compute.
Returns
-------
m : float
Mean of jackknife values.
se : float
Estimate of standard error.
vj : ndarray
Statistic values computed for each jackknife iteration.
"""
if isinstance(values, tuple):
# multiple input arrays
n = len(values[0])
masked_values = [np.ma.asarray(v) for v in values]
for m in masked_values:
assert m.ndim == 1, 'only 1D arrays supported'
assert m.shape[0] == n, 'input arrays not of equal length'
m.mask = np.zeros(m.shape, dtype=bool)
else:
n = len(values)
masked_values = np.ma.asarray(values)
assert masked_values.ndim == 1, 'only 1D arrays supported'
masked_values.mask = np.zeros(masked_values.shape, dtype=bool)
# values of the statistic calculated in each jackknife iteration
vj = list()
for i in range(n):
if isinstance(values, tuple):
# multiple input arrays
for m in masked_values:
m.mask[i] = True
x = statistic(*masked_values)
for m in masked_values:
m.mask[i] = False
else:
masked_values.mask[i] = True
x = statistic(masked_values)
masked_values.mask[i] = False
vj.append(x)
# convert to array for convenience
vj = np.array(vj)
# compute mean of jackknife values
m = vj.mean()
# compute standard error
sv = ((n - 1) / n) * np.sum((vj - m) ** 2)
se = np.sqrt(sv)
return m, se, vj | Estimate standard error for `statistic` computed over `values` using
the jackknife.
Parameters
----------
values : array_like or tuple of array_like
Input array, or tuple of input arrays.
statistic : function
The statistic to compute.
Returns
-------
m : float
Mean of jackknife values.
se : float
Estimate of standard error.
vj : ndarray
Statistic values computed for each jackknife iteration. | https://github.com/cggh/scikit-allel/blob/3c979a57a100240ba959dd13f98839349530f215/allel/stats/misc.py#L15-L82 |
cggh/scikit-allel | allel/stats/misc.py | plot_variant_locator | def plot_variant_locator(pos, step=None, ax=None, start=None,
stop=None, flip=False,
line_kwargs=None):
"""
Plot lines indicating the physical genome location of variants from a
single chromosome/contig. By default the top x axis is in variant index
space, and the bottom x axis is in genome position space.
Parameters
----------
pos : array_like
A sorted 1-dimensional array of genomic positions from a single
chromosome/contig.
step : int, optional
Plot a line for every `step` variants.
ax : axes, optional
The axes on which to draw. If not provided, a new figure will be
created.
start : int, optional
The start position for the region to draw.
stop : int, optional
The stop position for the region to draw.
flip : bool, optional
Flip the plot upside down.
line_kwargs : dict-like
Additional keyword arguments passed through to `plt.Line2D`.
Returns
-------
ax : axes
The axes on which the plot was drawn
"""
import matplotlib.pyplot as plt
# check inputs
pos = SortedIndex(pos, copy=False)
# set up axes
if ax is None:
x = plt.rcParams['figure.figsize'][0]
y = x / 7
fig, ax = plt.subplots(figsize=(x, y))
fig.tight_layout()
# determine x axis limits
if start is None:
start = np.min(pos)
if stop is None:
stop = np.max(pos)
loc = pos.locate_range(start, stop)
pos = pos[loc]
if step is None:
step = len(pos) // 100
ax.set_xlim(start, stop)
# plot the lines
if line_kwargs is None:
line_kwargs = dict()
# line_kwargs.setdefault('linewidth', .5)
n_variants = len(pos)
for i, p in enumerate(pos[::step]):
xfrom = p
xto = (
start +
((i * step / n_variants) * (stop-start))
)
line = plt.Line2D([xfrom, xto], [0, 1], **line_kwargs)
ax.add_line(line)
# invert?
if flip:
ax.invert_yaxis()
ax.xaxis.tick_top()
else:
ax.xaxis.tick_bottom()
# tidy up
ax.set_yticks([])
ax.xaxis.set_tick_params(direction='out')
for spine in 'left', 'right':
ax.spines[spine].set_visible(False)
return ax | python | def plot_variant_locator(pos, step=None, ax=None, start=None,
stop=None, flip=False,
line_kwargs=None):
"""
Plot lines indicating the physical genome location of variants from a
single chromosome/contig. By default the top x axis is in variant index
space, and the bottom x axis is in genome position space.
Parameters
----------
pos : array_like
A sorted 1-dimensional array of genomic positions from a single
chromosome/contig.
step : int, optional
Plot a line for every `step` variants.
ax : axes, optional
The axes on which to draw. If not provided, a new figure will be
created.
start : int, optional
The start position for the region to draw.
stop : int, optional
The stop position for the region to draw.
flip : bool, optional
Flip the plot upside down.
line_kwargs : dict-like
Additional keyword arguments passed through to `plt.Line2D`.
Returns
-------
ax : axes
The axes on which the plot was drawn
"""
import matplotlib.pyplot as plt
# check inputs
pos = SortedIndex(pos, copy=False)
# set up axes
if ax is None:
x = plt.rcParams['figure.figsize'][0]
y = x / 7
fig, ax = plt.subplots(figsize=(x, y))
fig.tight_layout()
# determine x axis limits
if start is None:
start = np.min(pos)
if stop is None:
stop = np.max(pos)
loc = pos.locate_range(start, stop)
pos = pos[loc]
if step is None:
step = len(pos) // 100
ax.set_xlim(start, stop)
# plot the lines
if line_kwargs is None:
line_kwargs = dict()
# line_kwargs.setdefault('linewidth', .5)
n_variants = len(pos)
for i, p in enumerate(pos[::step]):
xfrom = p
xto = (
start +
((i * step / n_variants) * (stop-start))
)
line = plt.Line2D([xfrom, xto], [0, 1], **line_kwargs)
ax.add_line(line)
# invert?
if flip:
ax.invert_yaxis()
ax.xaxis.tick_top()
else:
ax.xaxis.tick_bottom()
# tidy up
ax.set_yticks([])
ax.xaxis.set_tick_params(direction='out')
for spine in 'left', 'right':
ax.spines[spine].set_visible(False)
return ax | Plot lines indicating the physical genome location of variants from a
single chromosome/contig. By default the top x axis is in variant index
space, and the bottom x axis is in genome position space.
Parameters
----------
pos : array_like
A sorted 1-dimensional array of genomic positions from a single
chromosome/contig.
step : int, optional
Plot a line for every `step` variants.
ax : axes, optional
The axes on which to draw. If not provided, a new figure will be
created.
start : int, optional
The start position for the region to draw.
stop : int, optional
The stop position for the region to draw.
flip : bool, optional
Flip the plot upside down.
line_kwargs : dict-like
Additional keyword arguments passed through to `plt.Line2D`.
Returns
-------
ax : axes
The axes on which the plot was drawn | https://github.com/cggh/scikit-allel/blob/3c979a57a100240ba959dd13f98839349530f215/allel/stats/misc.py#L85-L171 |
cggh/scikit-allel | allel/stats/misc.py | tabulate_state_transitions | def tabulate_state_transitions(x, states, pos=None):
"""Construct a dataframe where each row provides information about a state transition.
Parameters
----------
x : array_like, int
1-dimensional array of state values.
states : set
Set of states of interest. Any state value not in this set will be ignored.
pos : array_like, int, optional
Array of positions corresponding to values in `x`.
Returns
-------
df : DataFrame
Notes
-----
The resulting dataframe includes one row at the start representing the first state
observation and one row at the end representing the last state observation.
Examples
--------
>>> import allel
>>> x = [1, 1, 0, 1, 1, 2, 2, 0, 2, 1, 1]
>>> df = allel.tabulate_state_transitions(x, states={1, 2})
>>> df
lstate rstate lidx ridx
0 -1 1 -1 0
1 1 2 4 5
2 2 1 8 9
3 1 -1 10 -1
>>> pos = [2, 4, 7, 8, 10, 14, 19, 23, 28, 30, 31]
>>> df = allel.tabulate_state_transitions(x, states={1, 2}, pos=pos)
>>> df
lstate rstate lidx ridx lpos rpos
0 -1 1 -1 0 -1 2
1 1 2 4 5 10 14
2 2 1 8 9 28 30
3 1 -1 10 -1 31 -1
"""
# check inputs
x = asarray_ndim(x, 1)
check_integer_dtype(x)
x = memoryview_safe(x)
# find state transitions
switch_points, transitions, _ = state_transitions(x, states)
# start to build a dataframe
items = [('lstate', transitions[:, 0]),
('rstate', transitions[:, 1]),
('lidx', switch_points[:, 0]),
('ridx', switch_points[:, 1])]
# deal with optional positions
if pos is not None:
pos = asarray_ndim(pos, 1)
check_dim0_aligned(x, pos)
check_integer_dtype(pos)
# find switch positions
switch_positions = np.take(pos, switch_points)
# deal with boundary transitions
switch_positions[0, 0] = -1
switch_positions[-1, 1] = -1
# add columns into dataframe
items += [('lpos', switch_positions[:, 0]),
('rpos', switch_positions[:, 1])]
import pandas
return pandas.DataFrame.from_dict(OrderedDict(items)) | python | def tabulate_state_transitions(x, states, pos=None):
"""Construct a dataframe where each row provides information about a state transition.
Parameters
----------
x : array_like, int
1-dimensional array of state values.
states : set
Set of states of interest. Any state value not in this set will be ignored.
pos : array_like, int, optional
Array of positions corresponding to values in `x`.
Returns
-------
df : DataFrame
Notes
-----
The resulting dataframe includes one row at the start representing the first state
observation and one row at the end representing the last state observation.
Examples
--------
>>> import allel
>>> x = [1, 1, 0, 1, 1, 2, 2, 0, 2, 1, 1]
>>> df = allel.tabulate_state_transitions(x, states={1, 2})
>>> df
lstate rstate lidx ridx
0 -1 1 -1 0
1 1 2 4 5
2 2 1 8 9
3 1 -1 10 -1
>>> pos = [2, 4, 7, 8, 10, 14, 19, 23, 28, 30, 31]
>>> df = allel.tabulate_state_transitions(x, states={1, 2}, pos=pos)
>>> df
lstate rstate lidx ridx lpos rpos
0 -1 1 -1 0 -1 2
1 1 2 4 5 10 14
2 2 1 8 9 28 30
3 1 -1 10 -1 31 -1
"""
# check inputs
x = asarray_ndim(x, 1)
check_integer_dtype(x)
x = memoryview_safe(x)
# find state transitions
switch_points, transitions, _ = state_transitions(x, states)
# start to build a dataframe
items = [('lstate', transitions[:, 0]),
('rstate', transitions[:, 1]),
('lidx', switch_points[:, 0]),
('ridx', switch_points[:, 1])]
# deal with optional positions
if pos is not None:
pos = asarray_ndim(pos, 1)
check_dim0_aligned(x, pos)
check_integer_dtype(pos)
# find switch positions
switch_positions = np.take(pos, switch_points)
# deal with boundary transitions
switch_positions[0, 0] = -1
switch_positions[-1, 1] = -1
# add columns into dataframe
items += [('lpos', switch_positions[:, 0]),
('rpos', switch_positions[:, 1])]
import pandas
return pandas.DataFrame.from_dict(OrderedDict(items)) | Construct a dataframe where each row provides information about a state transition.
Parameters
----------
x : array_like, int
1-dimensional array of state values.
states : set
Set of states of interest. Any state value not in this set will be ignored.
pos : array_like, int, optional
Array of positions corresponding to values in `x`.
Returns
-------
df : DataFrame
Notes
-----
The resulting dataframe includes one row at the start representing the first state
observation and one row at the end representing the last state observation.
Examples
--------
>>> import allel
>>> x = [1, 1, 0, 1, 1, 2, 2, 0, 2, 1, 1]
>>> df = allel.tabulate_state_transitions(x, states={1, 2})
>>> df
lstate rstate lidx ridx
0 -1 1 -1 0
1 1 2 4 5
2 2 1 8 9
3 1 -1 10 -1
>>> pos = [2, 4, 7, 8, 10, 14, 19, 23, 28, 30, 31]
>>> df = allel.tabulate_state_transitions(x, states={1, 2}, pos=pos)
>>> df
lstate rstate lidx ridx lpos rpos
0 -1 1 -1 0 -1 2
1 1 2 4 5 10 14
2 2 1 8 9 28 30
3 1 -1 10 -1 31 -1 | https://github.com/cggh/scikit-allel/blob/3c979a57a100240ba959dd13f98839349530f215/allel/stats/misc.py#L174-L248 |
cggh/scikit-allel | allel/stats/misc.py | tabulate_state_blocks | def tabulate_state_blocks(x, states, pos=None):
"""Construct a dataframe where each row provides information about continuous state blocks.
Parameters
----------
x : array_like, int
1-dimensional array of state values.
states : set
Set of states of interest. Any state value not in this set will be ignored.
pos : array_like, int, optional
Array of positions corresponding to values in `x`.
Returns
-------
df : DataFrame
Examples
--------
>>> import allel
>>> x = [1, 1, 0, 1, 1, 2, 2, 0, 2, 1, 1]
>>> df = allel.tabulate_state_blocks(x, states={1, 2})
>>> df
state support start_lidx ... size_min size_max is_marginal
0 1 4 -1 ... 5 -1 True
1 2 3 4 ... 4 4 False
2 1 2 8 ... 2 -1 True
[3 rows x 9 columns]
>>> pos = [2, 4, 7, 8, 10, 14, 19, 23, 28, 30, 31]
>>> df = allel.tabulate_state_blocks(x, states={1, 2}, pos=pos)
>>> df
state support start_lidx ... stop_rpos length_min length_max
0 1 4 -1 ... 14 9 -1
1 2 3 4 ... 30 15 19
2 1 2 8 ... -1 2 -1
[3 rows x 15 columns]
"""
# check inputs
x = asarray_ndim(x, 1)
check_integer_dtype(x)
x = memoryview_safe(x)
# find state transitions
switch_points, transitions, observations = state_transitions(x, states)
# setup some helpers
t = transitions[1:, 0]
o = observations[1:]
s1 = switch_points[:-1]
s2 = switch_points[1:]
is_marginal = (s1[:, 0] < 0) | (s2[:, 1] < 0)
size_min = s2[:, 0] - s1[:, 1] + 1
size_max = s2[:, 1] - s1[:, 0] - 1
size_max[is_marginal] = -1
# start to build a dataframe
items = [
('state', t),
('support', o),
('start_lidx', s1[:, 0]),
('start_ridx', s1[:, 1]),
('stop_lidx', s2[:, 0]),
('stop_ridx', s2[:, 1]),
('size_min', size_min),
('size_max', size_max),
('is_marginal', is_marginal)
]
# deal with optional positions
if pos is not None:
pos = asarray_ndim(pos, 1)
check_dim0_aligned(x, pos)
check_integer_dtype(pos)
# obtain switch positions
switch_positions = np.take(pos, switch_points)
# deal with boundary transitions
switch_positions[0, 0] = -1
switch_positions[-1, 1] = -1
# setup helpers
p1 = switch_positions[:-1]
p2 = switch_positions[1:]
length_min = p2[:, 0] - p1[:, 1] + 1
length_max = p2[:, 1] - p1[:, 0] - 1
length_max[is_marginal] = -1
items += [
('start_lpos', p1[:, 0]),
('start_rpos', p1[:, 1]),
('stop_lpos', p2[:, 0]),
('stop_rpos', p2[:, 1]),
('length_min', length_min),
('length_max', length_max),
]
import pandas
return pandas.DataFrame.from_dict(OrderedDict(items)) | python | def tabulate_state_blocks(x, states, pos=None):
"""Construct a dataframe where each row provides information about continuous state blocks.
Parameters
----------
x : array_like, int
1-dimensional array of state values.
states : set
Set of states of interest. Any state value not in this set will be ignored.
pos : array_like, int, optional
Array of positions corresponding to values in `x`.
Returns
-------
df : DataFrame
Examples
--------
>>> import allel
>>> x = [1, 1, 0, 1, 1, 2, 2, 0, 2, 1, 1]
>>> df = allel.tabulate_state_blocks(x, states={1, 2})
>>> df
state support start_lidx ... size_min size_max is_marginal
0 1 4 -1 ... 5 -1 True
1 2 3 4 ... 4 4 False
2 1 2 8 ... 2 -1 True
[3 rows x 9 columns]
>>> pos = [2, 4, 7, 8, 10, 14, 19, 23, 28, 30, 31]
>>> df = allel.tabulate_state_blocks(x, states={1, 2}, pos=pos)
>>> df
state support start_lidx ... stop_rpos length_min length_max
0 1 4 -1 ... 14 9 -1
1 2 3 4 ... 30 15 19
2 1 2 8 ... -1 2 -1
[3 rows x 15 columns]
"""
# check inputs
x = asarray_ndim(x, 1)
check_integer_dtype(x)
x = memoryview_safe(x)
# find state transitions
switch_points, transitions, observations = state_transitions(x, states)
# setup some helpers
t = transitions[1:, 0]
o = observations[1:]
s1 = switch_points[:-1]
s2 = switch_points[1:]
is_marginal = (s1[:, 0] < 0) | (s2[:, 1] < 0)
size_min = s2[:, 0] - s1[:, 1] + 1
size_max = s2[:, 1] - s1[:, 0] - 1
size_max[is_marginal] = -1
# start to build a dataframe
items = [
('state', t),
('support', o),
('start_lidx', s1[:, 0]),
('start_ridx', s1[:, 1]),
('stop_lidx', s2[:, 0]),
('stop_ridx', s2[:, 1]),
('size_min', size_min),
('size_max', size_max),
('is_marginal', is_marginal)
]
# deal with optional positions
if pos is not None:
pos = asarray_ndim(pos, 1)
check_dim0_aligned(x, pos)
check_integer_dtype(pos)
# obtain switch positions
switch_positions = np.take(pos, switch_points)
# deal with boundary transitions
switch_positions[0, 0] = -1
switch_positions[-1, 1] = -1
# setup helpers
p1 = switch_positions[:-1]
p2 = switch_positions[1:]
length_min = p2[:, 0] - p1[:, 1] + 1
length_max = p2[:, 1] - p1[:, 0] - 1
length_max[is_marginal] = -1
items += [
('start_lpos', p1[:, 0]),
('start_rpos', p1[:, 1]),
('stop_lpos', p2[:, 0]),
('stop_rpos', p2[:, 1]),
('length_min', length_min),
('length_max', length_max),
]
import pandas
return pandas.DataFrame.from_dict(OrderedDict(items)) | Construct a dataframe where each row provides information about continuous state blocks.
Parameters
----------
x : array_like, int
1-dimensional array of state values.
states : set
Set of states of interest. Any state value not in this set will be ignored.
pos : array_like, int, optional
Array of positions corresponding to values in `x`.
Returns
-------
df : DataFrame
Examples
--------
>>> import allel
>>> x = [1, 1, 0, 1, 1, 2, 2, 0, 2, 1, 1]
>>> df = allel.tabulate_state_blocks(x, states={1, 2})
>>> df
state support start_lidx ... size_min size_max is_marginal
0 1 4 -1 ... 5 -1 True
1 2 3 4 ... 4 4 False
2 1 2 8 ... 2 -1 True
[3 rows x 9 columns]
>>> pos = [2, 4, 7, 8, 10, 14, 19, 23, 28, 30, 31]
>>> df = allel.tabulate_state_blocks(x, states={1, 2}, pos=pos)
>>> df
state support start_lidx ... stop_rpos length_min length_max
0 1 4 -1 ... 14 9 -1
1 2 3 4 ... 30 15 19
2 1 2 8 ... -1 2 -1
[3 rows x 15 columns] | https://github.com/cggh/scikit-allel/blob/3c979a57a100240ba959dd13f98839349530f215/allel/stats/misc.py#L251-L349 |
cggh/scikit-allel | allel/io/vcf_write.py | write_vcf | def write_vcf(path, callset, rename=None, number=None, description=None,
fill=None, write_header=True):
"""Preliminary support for writing a VCF file. Currently does not support sample data.
Needs further work."""
names, callset = normalize_callset(callset)
with open(path, 'w') as vcf_file:
if write_header:
write_vcf_header(vcf_file, names, callset=callset, rename=rename,
number=number, description=description)
write_vcf_data(vcf_file, names, callset=callset, rename=rename, fill=fill) | python | def write_vcf(path, callset, rename=None, number=None, description=None,
fill=None, write_header=True):
"""Preliminary support for writing a VCF file. Currently does not support sample data.
Needs further work."""
names, callset = normalize_callset(callset)
with open(path, 'w') as vcf_file:
if write_header:
write_vcf_header(vcf_file, names, callset=callset, rename=rename,
number=number, description=description)
write_vcf_data(vcf_file, names, callset=callset, rename=rename, fill=fill) | Preliminary support for writing a VCF file. Currently does not support sample data.
Needs further work. | https://github.com/cggh/scikit-allel/blob/3c979a57a100240ba959dd13f98839349530f215/allel/io/vcf_write.py#L50-L61 |
cggh/scikit-allel | allel/util.py | asarray_ndim | def asarray_ndim(a, *ndims, **kwargs):
"""Ensure numpy array.
Parameters
----------
a : array_like
*ndims : int, optional
Allowed values for number of dimensions.
**kwargs
Passed through to :func:`numpy.array`.
Returns
-------
a : numpy.ndarray
"""
allow_none = kwargs.pop('allow_none', False)
kwargs.setdefault('copy', False)
if a is None and allow_none:
return None
a = np.array(a, **kwargs)
if a.ndim not in ndims:
if len(ndims) > 1:
expect_str = 'one of %s' % str(ndims)
else:
# noinspection PyUnresolvedReferences
expect_str = '%s' % ndims[0]
raise TypeError('bad number of dimensions: expected %s; found %s' %
(expect_str, a.ndim))
return a | python | def asarray_ndim(a, *ndims, **kwargs):
"""Ensure numpy array.
Parameters
----------
a : array_like
*ndims : int, optional
Allowed values for number of dimensions.
**kwargs
Passed through to :func:`numpy.array`.
Returns
-------
a : numpy.ndarray
"""
allow_none = kwargs.pop('allow_none', False)
kwargs.setdefault('copy', False)
if a is None and allow_none:
return None
a = np.array(a, **kwargs)
if a.ndim not in ndims:
if len(ndims) > 1:
expect_str = 'one of %s' % str(ndims)
else:
# noinspection PyUnresolvedReferences
expect_str = '%s' % ndims[0]
raise TypeError('bad number of dimensions: expected %s; found %s' %
(expect_str, a.ndim))
return a | Ensure numpy array.
Parameters
----------
a : array_like
*ndims : int, optional
Allowed values for number of dimensions.
**kwargs
Passed through to :func:`numpy.array`.
Returns
-------
a : numpy.ndarray | https://github.com/cggh/scikit-allel/blob/3c979a57a100240ba959dd13f98839349530f215/allel/util.py#L32-L61 |
cggh/scikit-allel | allel/util.py | hdf5_cache | def hdf5_cache(filepath=None, parent=None, group=None, names=None, typed=False,
hashed_key=False, **h5dcreate_kwargs):
"""HDF5 cache decorator.
Parameters
----------
filepath : string, optional
Path to HDF5 file. If None a temporary file name will be used.
parent : string, optional
Path to group within HDF5 file to use as parent. If None the root
group will be used.
group : string, optional
Path to group within HDF5 file, relative to parent, to use as
container for cached data. If None the name of the wrapped function
will be used.
names : sequence of strings, optional
Name(s) of dataset(s). If None, default names will be 'f00', 'f01',
etc.
typed : bool, optional
If True, arguments of different types will be cached separately.
For example, f(3.0) and f(3) will be treated as distinct calls with
distinct results.
hashed_key : bool, optional
If False (default) the key will not be hashed, which makes for
readable cache group names. If True the key will be hashed, however
note that on Python >= 3.3 the hash value will not be the same between
sessions unless the environment variable PYTHONHASHSEED has been set
to the same value.
Returns
-------
decorator : function
Examples
--------
Without any arguments, will cache using a temporary HDF5 file::
>>> import allel
>>> @allel.util.hdf5_cache()
... def foo(n):
... print('executing foo')
... return np.arange(n)
...
>>> foo(3)
executing foo
array([0, 1, 2])
>>> foo(3)
array([0, 1, 2])
>>> foo.cache_filepath # doctest: +SKIP
'/tmp/tmp_jwtwgjz'
Supports multiple return values, including scalars, e.g.::
>>> @allel.util.hdf5_cache()
... def bar(n):
... print('executing bar')
... a = np.arange(n)
... return a, a**2, n**2
...
>>> bar(3)
executing bar
(array([0, 1, 2]), array([0, 1, 4]), 9)
>>> bar(3)
(array([0, 1, 2]), array([0, 1, 4]), 9)
Names can also be specified for the datasets, e.g.::
>>> @allel.util.hdf5_cache(names=['z', 'x', 'y'])
... def baz(n):
... print('executing baz')
... a = np.arange(n)
... return a, a**2, n**2
...
>>> baz(3)
executing baz
(array([0, 1, 2]), array([0, 1, 4]), 9)
>>> baz(3)
(array([0, 1, 2]), array([0, 1, 4]), 9)
"""
# initialise HDF5 file path
if filepath is None:
import tempfile
filepath = tempfile.mktemp(prefix='scikit_allel_', suffix='.h5')
atexit.register(os.remove, filepath)
# initialise defaults for dataset creation
h5dcreate_kwargs.setdefault('chunks', True)
def decorator(user_function):
# setup the name for the cache container group
if group is None:
container = user_function.__name__
else:
container = group
def wrapper(*args, **kwargs):
# load from cache or not
no_cache = kwargs.pop('no_cache', False)
# compute a key from the function arguments
key = _make_key(args, kwargs, typed)
if hashed_key:
key = str(hash(key))
else:
key = str(key).replace('/', '__slash__')
return _hdf5_cache_act(filepath, parent, container, key, names,
no_cache, user_function, args, kwargs,
h5dcreate_kwargs)
wrapper.cache_filepath = filepath
return update_wrapper(wrapper, user_function)
return decorator | python | def hdf5_cache(filepath=None, parent=None, group=None, names=None, typed=False,
hashed_key=False, **h5dcreate_kwargs):
"""HDF5 cache decorator.
Parameters
----------
filepath : string, optional
Path to HDF5 file. If None a temporary file name will be used.
parent : string, optional
Path to group within HDF5 file to use as parent. If None the root
group will be used.
group : string, optional
Path to group within HDF5 file, relative to parent, to use as
container for cached data. If None the name of the wrapped function
will be used.
names : sequence of strings, optional
Name(s) of dataset(s). If None, default names will be 'f00', 'f01',
etc.
typed : bool, optional
If True, arguments of different types will be cached separately.
For example, f(3.0) and f(3) will be treated as distinct calls with
distinct results.
hashed_key : bool, optional
If False (default) the key will not be hashed, which makes for
readable cache group names. If True the key will be hashed, however
note that on Python >= 3.3 the hash value will not be the same between
sessions unless the environment variable PYTHONHASHSEED has been set
to the same value.
Returns
-------
decorator : function
Examples
--------
Without any arguments, will cache using a temporary HDF5 file::
>>> import allel
>>> @allel.util.hdf5_cache()
... def foo(n):
... print('executing foo')
... return np.arange(n)
...
>>> foo(3)
executing foo
array([0, 1, 2])
>>> foo(3)
array([0, 1, 2])
>>> foo.cache_filepath # doctest: +SKIP
'/tmp/tmp_jwtwgjz'
Supports multiple return values, including scalars, e.g.::
>>> @allel.util.hdf5_cache()
... def bar(n):
... print('executing bar')
... a = np.arange(n)
... return a, a**2, n**2
...
>>> bar(3)
executing bar
(array([0, 1, 2]), array([0, 1, 4]), 9)
>>> bar(3)
(array([0, 1, 2]), array([0, 1, 4]), 9)
Names can also be specified for the datasets, e.g.::
>>> @allel.util.hdf5_cache(names=['z', 'x', 'y'])
... def baz(n):
... print('executing baz')
... a = np.arange(n)
... return a, a**2, n**2
...
>>> baz(3)
executing baz
(array([0, 1, 2]), array([0, 1, 4]), 9)
>>> baz(3)
(array([0, 1, 2]), array([0, 1, 4]), 9)
"""
# initialise HDF5 file path
if filepath is None:
import tempfile
filepath = tempfile.mktemp(prefix='scikit_allel_', suffix='.h5')
atexit.register(os.remove, filepath)
# initialise defaults for dataset creation
h5dcreate_kwargs.setdefault('chunks', True)
def decorator(user_function):
# setup the name for the cache container group
if group is None:
container = user_function.__name__
else:
container = group
def wrapper(*args, **kwargs):
# load from cache or not
no_cache = kwargs.pop('no_cache', False)
# compute a key from the function arguments
key = _make_key(args, kwargs, typed)
if hashed_key:
key = str(hash(key))
else:
key = str(key).replace('/', '__slash__')
return _hdf5_cache_act(filepath, parent, container, key, names,
no_cache, user_function, args, kwargs,
h5dcreate_kwargs)
wrapper.cache_filepath = filepath
return update_wrapper(wrapper, user_function)
return decorator | HDF5 cache decorator.
Parameters
----------
filepath : string, optional
Path to HDF5 file. If None a temporary file name will be used.
parent : string, optional
Path to group within HDF5 file to use as parent. If None the root
group will be used.
group : string, optional
Path to group within HDF5 file, relative to parent, to use as
container for cached data. If None the name of the wrapped function
will be used.
names : sequence of strings, optional
Name(s) of dataset(s). If None, default names will be 'f00', 'f01',
etc.
typed : bool, optional
If True, arguments of different types will be cached separately.
For example, f(3.0) and f(3) will be treated as distinct calls with
distinct results.
hashed_key : bool, optional
If False (default) the key will not be hashed, which makes for
readable cache group names. If True the key will be hashed, however
note that on Python >= 3.3 the hash value will not be the same between
sessions unless the environment variable PYTHONHASHSEED has been set
to the same value.
Returns
-------
decorator : function
Examples
--------
Without any arguments, will cache using a temporary HDF5 file::
>>> import allel
>>> @allel.util.hdf5_cache()
... def foo(n):
... print('executing foo')
... return np.arange(n)
...
>>> foo(3)
executing foo
array([0, 1, 2])
>>> foo(3)
array([0, 1, 2])
>>> foo.cache_filepath # doctest: +SKIP
'/tmp/tmp_jwtwgjz'
Supports multiple return values, including scalars, e.g.::
>>> @allel.util.hdf5_cache()
... def bar(n):
... print('executing bar')
... a = np.arange(n)
... return a, a**2, n**2
...
>>> bar(3)
executing bar
(array([0, 1, 2]), array([0, 1, 4]), 9)
>>> bar(3)
(array([0, 1, 2]), array([0, 1, 4]), 9)
Names can also be specified for the datasets, e.g.::
>>> @allel.util.hdf5_cache(names=['z', 'x', 'y'])
... def baz(n):
... print('executing baz')
... a = np.arange(n)
... return a, a**2, n**2
...
>>> baz(3)
executing baz
(array([0, 1, 2]), array([0, 1, 4]), 9)
>>> baz(3)
(array([0, 1, 2]), array([0, 1, 4]), 9) | https://github.com/cggh/scikit-allel/blob/3c979a57a100240ba959dd13f98839349530f215/allel/util.py#L283-L401 |
cggh/scikit-allel | allel/stats/decomposition.py | pca | def pca(gn, n_components=10, copy=True, scaler='patterson', ploidy=2):
"""Perform principal components analysis of genotype data, via singular
value decomposition.
Parameters
----------
gn : array_like, float, shape (n_variants, n_samples)
Genotypes at biallelic variants, coded as the number of alternate
alleles per call (i.e., 0 = hom ref, 1 = het, 2 = hom alt).
n_components : int, optional
Number of components to keep.
copy : bool, optional
If False, data passed to fit are overwritten.
scaler : {'patterson', 'standard', None}
Scaling method; 'patterson' applies the method of Patterson et al
2006; 'standard' scales to unit variance; None centers the data only.
ploidy : int, optional
Sample ploidy, only relevant if 'patterson' scaler is used.
Returns
-------
coords : ndarray, float, shape (n_samples, n_components)
Transformed coordinates for the samples.
model : GenotypePCA
Model instance containing the variance ratio explained and the stored
components (a.k.a., loadings). Can be used to project further data
into the same principal components space via the transform() method.
Notes
-----
Genotype data should be filtered prior to using this function to remove
variants in linkage disequilibrium.
See Also
--------
randomized_pca, allel.stats.ld.locate_unlinked
"""
# set up the model
model = GenotypePCA(n_components, copy=copy, scaler=scaler, ploidy=ploidy)
# fit the model and project the input data onto the new dimensions
coords = model.fit_transform(gn)
return coords, model | python | def pca(gn, n_components=10, copy=True, scaler='patterson', ploidy=2):
"""Perform principal components analysis of genotype data, via singular
value decomposition.
Parameters
----------
gn : array_like, float, shape (n_variants, n_samples)
Genotypes at biallelic variants, coded as the number of alternate
alleles per call (i.e., 0 = hom ref, 1 = het, 2 = hom alt).
n_components : int, optional
Number of components to keep.
copy : bool, optional
If False, data passed to fit are overwritten.
scaler : {'patterson', 'standard', None}
Scaling method; 'patterson' applies the method of Patterson et al
2006; 'standard' scales to unit variance; None centers the data only.
ploidy : int, optional
Sample ploidy, only relevant if 'patterson' scaler is used.
Returns
-------
coords : ndarray, float, shape (n_samples, n_components)
Transformed coordinates for the samples.
model : GenotypePCA
Model instance containing the variance ratio explained and the stored
components (a.k.a., loadings). Can be used to project further data
into the same principal components space via the transform() method.
Notes
-----
Genotype data should be filtered prior to using this function to remove
variants in linkage disequilibrium.
See Also
--------
randomized_pca, allel.stats.ld.locate_unlinked
"""
# set up the model
model = GenotypePCA(n_components, copy=copy, scaler=scaler, ploidy=ploidy)
# fit the model and project the input data onto the new dimensions
coords = model.fit_transform(gn)
return coords, model | Perform principal components analysis of genotype data, via singular
value decomposition.
Parameters
----------
gn : array_like, float, shape (n_variants, n_samples)
Genotypes at biallelic variants, coded as the number of alternate
alleles per call (i.e., 0 = hom ref, 1 = het, 2 = hom alt).
n_components : int, optional
Number of components to keep.
copy : bool, optional
If False, data passed to fit are overwritten.
scaler : {'patterson', 'standard', None}
Scaling method; 'patterson' applies the method of Patterson et al
2006; 'standard' scales to unit variance; None centers the data only.
ploidy : int, optional
Sample ploidy, only relevant if 'patterson' scaler is used.
Returns
-------
coords : ndarray, float, shape (n_samples, n_components)
Transformed coordinates for the samples.
model : GenotypePCA
Model instance containing the variance ratio explained and the stored
components (a.k.a., loadings). Can be used to project further data
into the same principal components space via the transform() method.
Notes
-----
Genotype data should be filtered prior to using this function to remove
variants in linkage disequilibrium.
See Also
--------
randomized_pca, allel.stats.ld.locate_unlinked | https://github.com/cggh/scikit-allel/blob/3c979a57a100240ba959dd13f98839349530f215/allel/stats/decomposition.py#L11-L60 |
cggh/scikit-allel | allel/stats/decomposition.py | randomized_pca | def randomized_pca(gn, n_components=10, copy=True, iterated_power=3,
random_state=None, scaler='patterson', ploidy=2):
"""Perform principal components analysis of genotype data, via an
approximate truncated singular value decomposition using randomization
to speed up the computation.
Parameters
----------
gn : array_like, float, shape (n_variants, n_samples)
Genotypes at biallelic variants, coded as the number of alternate
alleles per call (i.e., 0 = hom ref, 1 = het, 2 = hom alt).
n_components : int, optional
Number of components to keep.
copy : bool, optional
If False, data passed to fit are overwritten.
iterated_power : int, optional
Number of iterations for the power method.
random_state : int or RandomState instance or None (default)
Pseudo Random Number generator seed control. If None, use the
numpy.random singleton.
scaler : {'patterson', 'standard', None}
Scaling method; 'patterson' applies the method of Patterson et al
2006; 'standard' scales to unit variance; None centers the data only.
ploidy : int, optional
Sample ploidy, only relevant if 'patterson' scaler is used.
Returns
-------
coords : ndarray, float, shape (n_samples, n_components)
Transformed coordinates for the samples.
model : GenotypeRandomizedPCA
Model instance containing the variance ratio explained and the stored
components (a.k.a., loadings). Can be used to project further data
into the same principal components space via the transform() method.
Notes
-----
Genotype data should be filtered prior to using this function to remove
variants in linkage disequilibrium.
Based on the :class:`sklearn.decomposition.RandomizedPCA` implementation.
See Also
--------
pca, allel.stats.ld.locate_unlinked
"""
# set up the model
model = GenotypeRandomizedPCA(n_components, copy=copy,
iterated_power=iterated_power,
random_state=random_state, scaler=scaler,
ploidy=ploidy)
# fit the model and project the input data onto the new dimensions
coords = model.fit_transform(gn)
return coords, model | python | def randomized_pca(gn, n_components=10, copy=True, iterated_power=3,
random_state=None, scaler='patterson', ploidy=2):
"""Perform principal components analysis of genotype data, via an
approximate truncated singular value decomposition using randomization
to speed up the computation.
Parameters
----------
gn : array_like, float, shape (n_variants, n_samples)
Genotypes at biallelic variants, coded as the number of alternate
alleles per call (i.e., 0 = hom ref, 1 = het, 2 = hom alt).
n_components : int, optional
Number of components to keep.
copy : bool, optional
If False, data passed to fit are overwritten.
iterated_power : int, optional
Number of iterations for the power method.
random_state : int or RandomState instance or None (default)
Pseudo Random Number generator seed control. If None, use the
numpy.random singleton.
scaler : {'patterson', 'standard', None}
Scaling method; 'patterson' applies the method of Patterson et al
2006; 'standard' scales to unit variance; None centers the data only.
ploidy : int, optional
Sample ploidy, only relevant if 'patterson' scaler is used.
Returns
-------
coords : ndarray, float, shape (n_samples, n_components)
Transformed coordinates for the samples.
model : GenotypeRandomizedPCA
Model instance containing the variance ratio explained and the stored
components (a.k.a., loadings). Can be used to project further data
into the same principal components space via the transform() method.
Notes
-----
Genotype data should be filtered prior to using this function to remove
variants in linkage disequilibrium.
Based on the :class:`sklearn.decomposition.RandomizedPCA` implementation.
See Also
--------
pca, allel.stats.ld.locate_unlinked
"""
# set up the model
model = GenotypeRandomizedPCA(n_components, copy=copy,
iterated_power=iterated_power,
random_state=random_state, scaler=scaler,
ploidy=ploidy)
# fit the model and project the input data onto the new dimensions
coords = model.fit_transform(gn)
return coords, model | Perform principal components analysis of genotype data, via an
approximate truncated singular value decomposition using randomization
to speed up the computation.
Parameters
----------
gn : array_like, float, shape (n_variants, n_samples)
Genotypes at biallelic variants, coded as the number of alternate
alleles per call (i.e., 0 = hom ref, 1 = het, 2 = hom alt).
n_components : int, optional
Number of components to keep.
copy : bool, optional
If False, data passed to fit are overwritten.
iterated_power : int, optional
Number of iterations for the power method.
random_state : int or RandomState instance or None (default)
Pseudo Random Number generator seed control. If None, use the
numpy.random singleton.
scaler : {'patterson', 'standard', None}
Scaling method; 'patterson' applies the method of Patterson et al
2006; 'standard' scales to unit variance; None centers the data only.
ploidy : int, optional
Sample ploidy, only relevant if 'patterson' scaler is used.
Returns
-------
coords : ndarray, float, shape (n_samples, n_components)
Transformed coordinates for the samples.
model : GenotypeRandomizedPCA
Model instance containing the variance ratio explained and the stored
components (a.k.a., loadings). Can be used to project further data
into the same principal components space via the transform() method.
Notes
-----
Genotype data should be filtered prior to using this function to remove
variants in linkage disequilibrium.
Based on the :class:`sklearn.decomposition.RandomizedPCA` implementation.
See Also
--------
pca, allel.stats.ld.locate_unlinked | https://github.com/cggh/scikit-allel/blob/3c979a57a100240ba959dd13f98839349530f215/allel/stats/decomposition.py#L126-L187 |
cggh/scikit-allel | allel/stats/admixture.py | h_hat | def h_hat(ac):
"""Unbiased estimator for h, where 2*h is the heterozygosity
of the population.
Parameters
----------
ac : array_like, int, shape (n_variants, 2)
Allele counts array for a single population.
Returns
-------
h_hat : ndarray, float, shape (n_variants,)
Notes
-----
Used in Patterson (2012) for calculation of various statistics.
"""
# check inputs
ac = asarray_ndim(ac, 2)
assert ac.shape[1] == 2, 'only biallelic variants supported'
# compute allele number
an = ac.sum(axis=1)
# compute estimator
x = (ac[:, 0] * ac[:, 1]) / (an * (an - 1))
return x | python | def h_hat(ac):
"""Unbiased estimator for h, where 2*h is the heterozygosity
of the population.
Parameters
----------
ac : array_like, int, shape (n_variants, 2)
Allele counts array for a single population.
Returns
-------
h_hat : ndarray, float, shape (n_variants,)
Notes
-----
Used in Patterson (2012) for calculation of various statistics.
"""
# check inputs
ac = asarray_ndim(ac, 2)
assert ac.shape[1] == 2, 'only biallelic variants supported'
# compute allele number
an = ac.sum(axis=1)
# compute estimator
x = (ac[:, 0] * ac[:, 1]) / (an * (an - 1))
return x | Unbiased estimator for h, where 2*h is the heterozygosity
of the population.
Parameters
----------
ac : array_like, int, shape (n_variants, 2)
Allele counts array for a single population.
Returns
-------
h_hat : ndarray, float, shape (n_variants,)
Notes
-----
Used in Patterson (2012) for calculation of various statistics. | https://github.com/cggh/scikit-allel/blob/3c979a57a100240ba959dd13f98839349530f215/allel/stats/admixture.py#L14-L43 |
cggh/scikit-allel | allel/stats/admixture.py | patterson_f2 | def patterson_f2(aca, acb):
"""Unbiased estimator for F2(A, B), the branch length between populations
A and B.
Parameters
----------
aca : array_like, int, shape (n_variants, 2)
Allele counts for population A.
acb : array_like, int, shape (n_variants, 2)
Allele counts for population B.
Returns
-------
f2 : ndarray, float, shape (n_variants,)
Notes
-----
See Patterson (2012), Appendix A.
"""
# check inputs
aca = AlleleCountsArray(aca, copy=False)
assert aca.shape[1] == 2, 'only biallelic variants supported'
acb = AlleleCountsArray(acb, copy=False)
assert acb.shape[1] == 2, 'only biallelic variants supported'
check_dim0_aligned(aca, acb)
# compute allele numbers
sa = aca.sum(axis=1)
sb = acb.sum(axis=1)
# compute heterozygosities
ha = h_hat(aca)
hb = h_hat(acb)
# compute sample frequencies for the alternate allele
a = aca.to_frequencies()[:, 1]
b = acb.to_frequencies()[:, 1]
# compute estimator
x = ((a - b) ** 2) - (ha / sa) - (hb / sb)
return x | python | def patterson_f2(aca, acb):
"""Unbiased estimator for F2(A, B), the branch length between populations
A and B.
Parameters
----------
aca : array_like, int, shape (n_variants, 2)
Allele counts for population A.
acb : array_like, int, shape (n_variants, 2)
Allele counts for population B.
Returns
-------
f2 : ndarray, float, shape (n_variants,)
Notes
-----
See Patterson (2012), Appendix A.
"""
# check inputs
aca = AlleleCountsArray(aca, copy=False)
assert aca.shape[1] == 2, 'only biallelic variants supported'
acb = AlleleCountsArray(acb, copy=False)
assert acb.shape[1] == 2, 'only biallelic variants supported'
check_dim0_aligned(aca, acb)
# compute allele numbers
sa = aca.sum(axis=1)
sb = acb.sum(axis=1)
# compute heterozygosities
ha = h_hat(aca)
hb = h_hat(acb)
# compute sample frequencies for the alternate allele
a = aca.to_frequencies()[:, 1]
b = acb.to_frequencies()[:, 1]
# compute estimator
x = ((a - b) ** 2) - (ha / sa) - (hb / sb)
return x | Unbiased estimator for F2(A, B), the branch length between populations
A and B.
Parameters
----------
aca : array_like, int, shape (n_variants, 2)
Allele counts for population A.
acb : array_like, int, shape (n_variants, 2)
Allele counts for population B.
Returns
-------
f2 : ndarray, float, shape (n_variants,)
Notes
-----
See Patterson (2012), Appendix A. | https://github.com/cggh/scikit-allel/blob/3c979a57a100240ba959dd13f98839349530f215/allel/stats/admixture.py#L46-L89 |
cggh/scikit-allel | allel/stats/admixture.py | patterson_f3 | def patterson_f3(acc, aca, acb):
"""Unbiased estimator for F3(C; A, B), the three-population test for
admixture in population C.
Parameters
----------
acc : array_like, int, shape (n_variants, 2)
Allele counts for the test population (C).
aca : array_like, int, shape (n_variants, 2)
Allele counts for the first source population (A).
acb : array_like, int, shape (n_variants, 2)
Allele counts for the second source population (B).
Returns
-------
T : ndarray, float, shape (n_variants,)
Un-normalized f3 estimates per variant.
B : ndarray, float, shape (n_variants,)
Estimates for heterozygosity in population C.
Notes
-----
See Patterson (2012), main text and Appendix A.
For un-normalized f3 statistics, ignore the `B` return value.
To compute the f3* statistic, which is normalized by heterozygosity in
population C to remove numerical dependence on the allele frequency
spectrum, compute ``np.sum(T) / np.sum(B)``.
"""
# check inputs
aca = AlleleCountsArray(aca, copy=False)
assert aca.shape[1] == 2, 'only biallelic variants supported'
acb = AlleleCountsArray(acb, copy=False)
assert acb.shape[1] == 2, 'only biallelic variants supported'
acc = AlleleCountsArray(acc, copy=False)
assert acc.shape[1] == 2, 'only biallelic variants supported'
check_dim0_aligned(aca, acb, acc)
# compute allele number and heterozygosity in test population
sc = acc.sum(axis=1)
hc = h_hat(acc)
# compute sample frequencies for the alternate allele
a = aca.to_frequencies()[:, 1]
b = acb.to_frequencies()[:, 1]
c = acc.to_frequencies()[:, 1]
# compute estimator
T = ((c - a) * (c - b)) - (hc / sc)
B = 2 * hc
return T, B | python | def patterson_f3(acc, aca, acb):
"""Unbiased estimator for F3(C; A, B), the three-population test for
admixture in population C.
Parameters
----------
acc : array_like, int, shape (n_variants, 2)
Allele counts for the test population (C).
aca : array_like, int, shape (n_variants, 2)
Allele counts for the first source population (A).
acb : array_like, int, shape (n_variants, 2)
Allele counts for the second source population (B).
Returns
-------
T : ndarray, float, shape (n_variants,)
Un-normalized f3 estimates per variant.
B : ndarray, float, shape (n_variants,)
Estimates for heterozygosity in population C.
Notes
-----
See Patterson (2012), main text and Appendix A.
For un-normalized f3 statistics, ignore the `B` return value.
To compute the f3* statistic, which is normalized by heterozygosity in
population C to remove numerical dependence on the allele frequency
spectrum, compute ``np.sum(T) / np.sum(B)``.
"""
# check inputs
aca = AlleleCountsArray(aca, copy=False)
assert aca.shape[1] == 2, 'only biallelic variants supported'
acb = AlleleCountsArray(acb, copy=False)
assert acb.shape[1] == 2, 'only biallelic variants supported'
acc = AlleleCountsArray(acc, copy=False)
assert acc.shape[1] == 2, 'only biallelic variants supported'
check_dim0_aligned(aca, acb, acc)
# compute allele number and heterozygosity in test population
sc = acc.sum(axis=1)
hc = h_hat(acc)
# compute sample frequencies for the alternate allele
a = aca.to_frequencies()[:, 1]
b = acb.to_frequencies()[:, 1]
c = acc.to_frequencies()[:, 1]
# compute estimator
T = ((c - a) * (c - b)) - (hc / sc)
B = 2 * hc
return T, B | Unbiased estimator for F3(C; A, B), the three-population test for
admixture in population C.
Parameters
----------
acc : array_like, int, shape (n_variants, 2)
Allele counts for the test population (C).
aca : array_like, int, shape (n_variants, 2)
Allele counts for the first source population (A).
acb : array_like, int, shape (n_variants, 2)
Allele counts for the second source population (B).
Returns
-------
T : ndarray, float, shape (n_variants,)
Un-normalized f3 estimates per variant.
B : ndarray, float, shape (n_variants,)
Estimates for heterozygosity in population C.
Notes
-----
See Patterson (2012), main text and Appendix A.
For un-normalized f3 statistics, ignore the `B` return value.
To compute the f3* statistic, which is normalized by heterozygosity in
population C to remove numerical dependence on the allele frequency
spectrum, compute ``np.sum(T) / np.sum(B)``. | https://github.com/cggh/scikit-allel/blob/3c979a57a100240ba959dd13f98839349530f215/allel/stats/admixture.py#L93-L147 |
cggh/scikit-allel | allel/stats/admixture.py | patterson_d | def patterson_d(aca, acb, acc, acd):
"""Unbiased estimator for D(A, B; C, D), the normalised four-population
test for admixture between (A or B) and (C or D), also known as the
"ABBA BABA" test.
Parameters
----------
aca : array_like, int, shape (n_variants, 2),
Allele counts for population A.
acb : array_like, int, shape (n_variants, 2)
Allele counts for population B.
acc : array_like, int, shape (n_variants, 2)
Allele counts for population C.
acd : array_like, int, shape (n_variants, 2)
Allele counts for population D.
Returns
-------
num : ndarray, float, shape (n_variants,)
Numerator (un-normalised f4 estimates).
den : ndarray, float, shape (n_variants,)
Denominator.
Notes
-----
See Patterson (2012), main text and Appendix A.
For un-normalized f4 statistics, ignore the `den` return value.
"""
# check inputs
aca = AlleleCountsArray(aca, copy=False)
assert aca.shape[1] == 2, 'only biallelic variants supported'
acb = AlleleCountsArray(acb, copy=False)
assert acb.shape[1] == 2, 'only biallelic variants supported'
acc = AlleleCountsArray(acc, copy=False)
assert acc.shape[1] == 2, 'only biallelic variants supported'
acd = AlleleCountsArray(acd, copy=False)
assert acd.shape[1] == 2, 'only biallelic variants supported'
check_dim0_aligned(aca, acb, acc, acd)
# compute sample frequencies for the alternate allele
a = aca.to_frequencies()[:, 1]
b = acb.to_frequencies()[:, 1]
c = acc.to_frequencies()[:, 1]
d = acd.to_frequencies()[:, 1]
# compute estimator
num = (a - b) * (c - d)
den = (a + b - (2 * a * b)) * (c + d - (2 * c * d))
return num, den | python | def patterson_d(aca, acb, acc, acd):
"""Unbiased estimator for D(A, B; C, D), the normalised four-population
test for admixture between (A or B) and (C or D), also known as the
"ABBA BABA" test.
Parameters
----------
aca : array_like, int, shape (n_variants, 2),
Allele counts for population A.
acb : array_like, int, shape (n_variants, 2)
Allele counts for population B.
acc : array_like, int, shape (n_variants, 2)
Allele counts for population C.
acd : array_like, int, shape (n_variants, 2)
Allele counts for population D.
Returns
-------
num : ndarray, float, shape (n_variants,)
Numerator (un-normalised f4 estimates).
den : ndarray, float, shape (n_variants,)
Denominator.
Notes
-----
See Patterson (2012), main text and Appendix A.
For un-normalized f4 statistics, ignore the `den` return value.
"""
# check inputs
aca = AlleleCountsArray(aca, copy=False)
assert aca.shape[1] == 2, 'only biallelic variants supported'
acb = AlleleCountsArray(acb, copy=False)
assert acb.shape[1] == 2, 'only biallelic variants supported'
acc = AlleleCountsArray(acc, copy=False)
assert acc.shape[1] == 2, 'only biallelic variants supported'
acd = AlleleCountsArray(acd, copy=False)
assert acd.shape[1] == 2, 'only biallelic variants supported'
check_dim0_aligned(aca, acb, acc, acd)
# compute sample frequencies for the alternate allele
a = aca.to_frequencies()[:, 1]
b = acb.to_frequencies()[:, 1]
c = acc.to_frequencies()[:, 1]
d = acd.to_frequencies()[:, 1]
# compute estimator
num = (a - b) * (c - d)
den = (a + b - (2 * a * b)) * (c + d - (2 * c * d))
return num, den | Unbiased estimator for D(A, B; C, D), the normalised four-population
test for admixture between (A or B) and (C or D), also known as the
"ABBA BABA" test.
Parameters
----------
aca : array_like, int, shape (n_variants, 2),
Allele counts for population A.
acb : array_like, int, shape (n_variants, 2)
Allele counts for population B.
acc : array_like, int, shape (n_variants, 2)
Allele counts for population C.
acd : array_like, int, shape (n_variants, 2)
Allele counts for population D.
Returns
-------
num : ndarray, float, shape (n_variants,)
Numerator (un-normalised f4 estimates).
den : ndarray, float, shape (n_variants,)
Denominator.
Notes
-----
See Patterson (2012), main text and Appendix A.
For un-normalized f4 statistics, ignore the `den` return value. | https://github.com/cggh/scikit-allel/blob/3c979a57a100240ba959dd13f98839349530f215/allel/stats/admixture.py#L150-L202 |
cggh/scikit-allel | allel/stats/admixture.py | moving_patterson_f3 | def moving_patterson_f3(acc, aca, acb, size, start=0, stop=None, step=None,
normed=True):
"""Estimate F3(C; A, B) in moving windows.
Parameters
----------
acc : array_like, int, shape (n_variants, 2)
Allele counts for the test population (C).
aca : array_like, int, shape (n_variants, 2)
Allele counts for the first source population (A).
acb : array_like, int, shape (n_variants, 2)
Allele counts for the second source population (B).
size : int
The window size (number of variants).
start : int, optional
The index at which to start.
stop : int, optional
The index at which to stop.
step : int, optional
The number of variants between start positions of windows. If not
given, defaults to the window size, i.e., non-overlapping windows.
normed : bool, optional
If False, use un-normalised f3 values.
Returns
-------
f3 : ndarray, float, shape (n_windows,)
Estimated value of the statistic in each window.
"""
# calculate per-variant values
T, B = patterson_f3(acc, aca, acb)
# calculate value of statistic within each block
if normed:
T_bsum = moving_statistic(T, statistic=np.nansum, size=size,
start=start, stop=stop, step=step)
B_bsum = moving_statistic(B, statistic=np.nansum, size=size,
start=start, stop=stop, step=step)
f3 = T_bsum / B_bsum
else:
f3 = moving_statistic(T, statistic=np.nanmean, size=size,
start=start, stop=stop, step=step)
return f3 | python | def moving_patterson_f3(acc, aca, acb, size, start=0, stop=None, step=None,
normed=True):
"""Estimate F3(C; A, B) in moving windows.
Parameters
----------
acc : array_like, int, shape (n_variants, 2)
Allele counts for the test population (C).
aca : array_like, int, shape (n_variants, 2)
Allele counts for the first source population (A).
acb : array_like, int, shape (n_variants, 2)
Allele counts for the second source population (B).
size : int
The window size (number of variants).
start : int, optional
The index at which to start.
stop : int, optional
The index at which to stop.
step : int, optional
The number of variants between start positions of windows. If not
given, defaults to the window size, i.e., non-overlapping windows.
normed : bool, optional
If False, use un-normalised f3 values.
Returns
-------
f3 : ndarray, float, shape (n_windows,)
Estimated value of the statistic in each window.
"""
# calculate per-variant values
T, B = patterson_f3(acc, aca, acb)
# calculate value of statistic within each block
if normed:
T_bsum = moving_statistic(T, statistic=np.nansum, size=size,
start=start, stop=stop, step=step)
B_bsum = moving_statistic(B, statistic=np.nansum, size=size,
start=start, stop=stop, step=step)
f3 = T_bsum / B_bsum
else:
f3 = moving_statistic(T, statistic=np.nanmean, size=size,
start=start, stop=stop, step=step)
return f3 | Estimate F3(C; A, B) in moving windows.
Parameters
----------
acc : array_like, int, shape (n_variants, 2)
Allele counts for the test population (C).
aca : array_like, int, shape (n_variants, 2)
Allele counts for the first source population (A).
acb : array_like, int, shape (n_variants, 2)
Allele counts for the second source population (B).
size : int
The window size (number of variants).
start : int, optional
The index at which to start.
stop : int, optional
The index at which to stop.
step : int, optional
The number of variants between start positions of windows. If not
given, defaults to the window size, i.e., non-overlapping windows.
normed : bool, optional
If False, use un-normalised f3 values.
Returns
-------
f3 : ndarray, float, shape (n_windows,)
Estimated value of the statistic in each window. | https://github.com/cggh/scikit-allel/blob/3c979a57a100240ba959dd13f98839349530f215/allel/stats/admixture.py#L206-L252 |
cggh/scikit-allel | allel/stats/admixture.py | moving_patterson_d | def moving_patterson_d(aca, acb, acc, acd, size, start=0, stop=None,
step=None):
"""Estimate D(A, B; C, D) in moving windows.
Parameters
----------
aca : array_like, int, shape (n_variants, 2),
Allele counts for population A.
acb : array_like, int, shape (n_variants, 2)
Allele counts for population B.
acc : array_like, int, shape (n_variants, 2)
Allele counts for population C.
acd : array_like, int, shape (n_variants, 2)
Allele counts for population D.
size : int
The window size (number of variants).
start : int, optional
The index at which to start.
stop : int, optional
The index at which to stop.
step : int, optional
The number of variants between start positions of windows. If not
given, defaults to the window size, i.e., non-overlapping windows.
Returns
-------
d : ndarray, float, shape (n_windows,)
Estimated value of the statistic in each window.
"""
# calculate per-variant values
num, den = patterson_d(aca, acb, acc, acd)
# N.B., nans can occur if any of the populations have completely missing
# genotype calls at a variant (i.e., allele number is zero). Here we
# assume that is rare enough to be negligible.
# compute the numerator and denominator within each window
num_sum = moving_statistic(num, statistic=np.nansum, size=size,
start=start, stop=stop, step=step)
den_sum = moving_statistic(den, statistic=np.nansum, size=size,
start=start, stop=stop, step=step)
# calculate the statistic values in each block
d = num_sum / den_sum
return d | python | def moving_patterson_d(aca, acb, acc, acd, size, start=0, stop=None,
step=None):
"""Estimate D(A, B; C, D) in moving windows.
Parameters
----------
aca : array_like, int, shape (n_variants, 2),
Allele counts for population A.
acb : array_like, int, shape (n_variants, 2)
Allele counts for population B.
acc : array_like, int, shape (n_variants, 2)
Allele counts for population C.
acd : array_like, int, shape (n_variants, 2)
Allele counts for population D.
size : int
The window size (number of variants).
start : int, optional
The index at which to start.
stop : int, optional
The index at which to stop.
step : int, optional
The number of variants between start positions of windows. If not
given, defaults to the window size, i.e., non-overlapping windows.
Returns
-------
d : ndarray, float, shape (n_windows,)
Estimated value of the statistic in each window.
"""
# calculate per-variant values
num, den = patterson_d(aca, acb, acc, acd)
# N.B., nans can occur if any of the populations have completely missing
# genotype calls at a variant (i.e., allele number is zero). Here we
# assume that is rare enough to be negligible.
# compute the numerator and denominator within each window
num_sum = moving_statistic(num, statistic=np.nansum, size=size,
start=start, stop=stop, step=step)
den_sum = moving_statistic(den, statistic=np.nansum, size=size,
start=start, stop=stop, step=step)
# calculate the statistic values in each block
d = num_sum / den_sum
return d | Estimate D(A, B; C, D) in moving windows.
Parameters
----------
aca : array_like, int, shape (n_variants, 2),
Allele counts for population A.
acb : array_like, int, shape (n_variants, 2)
Allele counts for population B.
acc : array_like, int, shape (n_variants, 2)
Allele counts for population C.
acd : array_like, int, shape (n_variants, 2)
Allele counts for population D.
size : int
The window size (number of variants).
start : int, optional
The index at which to start.
stop : int, optional
The index at which to stop.
step : int, optional
The number of variants between start positions of windows. If not
given, defaults to the window size, i.e., non-overlapping windows.
Returns
-------
d : ndarray, float, shape (n_windows,)
Estimated value of the statistic in each window. | https://github.com/cggh/scikit-allel/blob/3c979a57a100240ba959dd13f98839349530f215/allel/stats/admixture.py#L255-L302 |
cggh/scikit-allel | allel/stats/admixture.py | average_patterson_f3 | def average_patterson_f3(acc, aca, acb, blen, normed=True):
"""Estimate F3(C; A, B) and standard error using the block-jackknife.
Parameters
----------
acc : array_like, int, shape (n_variants, 2)
Allele counts for the test population (C).
aca : array_like, int, shape (n_variants, 2)
Allele counts for the first source population (A).
acb : array_like, int, shape (n_variants, 2)
Allele counts for the second source population (B).
blen : int
Block size (number of variants).
normed : bool, optional
If False, use un-normalised f3 values.
Returns
-------
f3 : float
Estimated value of the statistic using all data.
se : float
Estimated standard error.
z : float
Z-score (number of standard errors from zero).
vb : ndarray, float, shape (n_blocks,)
Value of the statistic in each block.
vj : ndarray, float, shape (n_blocks,)
Values of the statistic from block-jackknife resampling.
Notes
-----
See Patterson (2012), main text and Appendix A.
See Also
--------
allel.stats.admixture.patterson_f3
"""
# calculate per-variant values
T, B = patterson_f3(acc, aca, acb)
# N.B., nans can occur if any of the populations have completely missing
# genotype calls at a variant (i.e., allele number is zero). Here we
# assume that is rare enough to be negligible.
# calculate overall value of statistic
if normed:
f3 = np.nansum(T) / np.nansum(B)
else:
f3 = np.nanmean(T)
# calculate value of statistic within each block
if normed:
T_bsum = moving_statistic(T, statistic=np.nansum, size=blen)
B_bsum = moving_statistic(B, statistic=np.nansum, size=blen)
vb = T_bsum / B_bsum
_, se, vj = jackknife((T_bsum, B_bsum),
statistic=lambda t, b: np.sum(t) / np.sum(b))
else:
vb = moving_statistic(T, statistic=np.nanmean, size=blen)
_, se, vj = jackknife(vb, statistic=np.mean)
# compute Z score
z = f3 / se
return f3, se, z, vb, vj | python | def average_patterson_f3(acc, aca, acb, blen, normed=True):
"""Estimate F3(C; A, B) and standard error using the block-jackknife.
Parameters
----------
acc : array_like, int, shape (n_variants, 2)
Allele counts for the test population (C).
aca : array_like, int, shape (n_variants, 2)
Allele counts for the first source population (A).
acb : array_like, int, shape (n_variants, 2)
Allele counts for the second source population (B).
blen : int
Block size (number of variants).
normed : bool, optional
If False, use un-normalised f3 values.
Returns
-------
f3 : float
Estimated value of the statistic using all data.
se : float
Estimated standard error.
z : float
Z-score (number of standard errors from zero).
vb : ndarray, float, shape (n_blocks,)
Value of the statistic in each block.
vj : ndarray, float, shape (n_blocks,)
Values of the statistic from block-jackknife resampling.
Notes
-----
See Patterson (2012), main text and Appendix A.
See Also
--------
allel.stats.admixture.patterson_f3
"""
# calculate per-variant values
T, B = patterson_f3(acc, aca, acb)
# N.B., nans can occur if any of the populations have completely missing
# genotype calls at a variant (i.e., allele number is zero). Here we
# assume that is rare enough to be negligible.
# calculate overall value of statistic
if normed:
f3 = np.nansum(T) / np.nansum(B)
else:
f3 = np.nanmean(T)
# calculate value of statistic within each block
if normed:
T_bsum = moving_statistic(T, statistic=np.nansum, size=blen)
B_bsum = moving_statistic(B, statistic=np.nansum, size=blen)
vb = T_bsum / B_bsum
_, se, vj = jackknife((T_bsum, B_bsum),
statistic=lambda t, b: np.sum(t) / np.sum(b))
else:
vb = moving_statistic(T, statistic=np.nanmean, size=blen)
_, se, vj = jackknife(vb, statistic=np.mean)
# compute Z score
z = f3 / se
return f3, se, z, vb, vj | Estimate F3(C; A, B) and standard error using the block-jackknife.
Parameters
----------
acc : array_like, int, shape (n_variants, 2)
Allele counts for the test population (C).
aca : array_like, int, shape (n_variants, 2)
Allele counts for the first source population (A).
acb : array_like, int, shape (n_variants, 2)
Allele counts for the second source population (B).
blen : int
Block size (number of variants).
normed : bool, optional
If False, use un-normalised f3 values.
Returns
-------
f3 : float
Estimated value of the statistic using all data.
se : float
Estimated standard error.
z : float
Z-score (number of standard errors from zero).
vb : ndarray, float, shape (n_blocks,)
Value of the statistic in each block.
vj : ndarray, float, shape (n_blocks,)
Values of the statistic from block-jackknife resampling.
Notes
-----
See Patterson (2012), main text and Appendix A.
See Also
--------
allel.stats.admixture.patterson_f3 | https://github.com/cggh/scikit-allel/blob/3c979a57a100240ba959dd13f98839349530f215/allel/stats/admixture.py#L306-L373 |
cggh/scikit-allel | allel/stats/admixture.py | average_patterson_d | def average_patterson_d(aca, acb, acc, acd, blen):
"""Estimate D(A, B; C, D) and standard error using the block-jackknife.
Parameters
----------
aca : array_like, int, shape (n_variants, 2),
Allele counts for population A.
acb : array_like, int, shape (n_variants, 2)
Allele counts for population B.
acc : array_like, int, shape (n_variants, 2)
Allele counts for population C.
acd : array_like, int, shape (n_variants, 2)
Allele counts for population D.
blen : int
Block size (number of variants).
Returns
-------
d : float
Estimated value of the statistic using all data.
se : float
Estimated standard error.
z : float
Z-score (number of standard errors from zero).
vb : ndarray, float, shape (n_blocks,)
Value of the statistic in each block.
vj : ndarray, float, shape (n_blocks,)
Values of the statistic from block-jackknife resampling.
Notes
-----
See Patterson (2012), main text and Appendix A.
See Also
--------
allel.stats.admixture.patterson_d
"""
# calculate per-variant values
num, den = patterson_d(aca, acb, acc, acd)
# N.B., nans can occur if any of the populations have completely missing
# genotype calls at a variant (i.e., allele number is zero). Here we
# assume that is rare enough to be negligible.
# calculate overall estimate
d_avg = np.nansum(num) / np.nansum(den)
# compute the numerator and denominator within each block
num_bsum = moving_statistic(num, statistic=np.nansum, size=blen)
den_bsum = moving_statistic(den, statistic=np.nansum, size=blen)
# calculate the statistic values in each block
vb = num_bsum / den_bsum
# estimate standard error
_, se, vj = jackknife((num_bsum, den_bsum),
statistic=lambda n, d: np.sum(n) / np.sum(d))
# compute Z score
z = d_avg / se
return d_avg, se, z, vb, vj | python | def average_patterson_d(aca, acb, acc, acd, blen):
"""Estimate D(A, B; C, D) and standard error using the block-jackknife.
Parameters
----------
aca : array_like, int, shape (n_variants, 2),
Allele counts for population A.
acb : array_like, int, shape (n_variants, 2)
Allele counts for population B.
acc : array_like, int, shape (n_variants, 2)
Allele counts for population C.
acd : array_like, int, shape (n_variants, 2)
Allele counts for population D.
blen : int
Block size (number of variants).
Returns
-------
d : float
Estimated value of the statistic using all data.
se : float
Estimated standard error.
z : float
Z-score (number of standard errors from zero).
vb : ndarray, float, shape (n_blocks,)
Value of the statistic in each block.
vj : ndarray, float, shape (n_blocks,)
Values of the statistic from block-jackknife resampling.
Notes
-----
See Patterson (2012), main text and Appendix A.
See Also
--------
allel.stats.admixture.patterson_d
"""
# calculate per-variant values
num, den = patterson_d(aca, acb, acc, acd)
# N.B., nans can occur if any of the populations have completely missing
# genotype calls at a variant (i.e., allele number is zero). Here we
# assume that is rare enough to be negligible.
# calculate overall estimate
d_avg = np.nansum(num) / np.nansum(den)
# compute the numerator and denominator within each block
num_bsum = moving_statistic(num, statistic=np.nansum, size=blen)
den_bsum = moving_statistic(den, statistic=np.nansum, size=blen)
# calculate the statistic values in each block
vb = num_bsum / den_bsum
# estimate standard error
_, se, vj = jackknife((num_bsum, den_bsum),
statistic=lambda n, d: np.sum(n) / np.sum(d))
# compute Z score
z = d_avg / se
return d_avg, se, z, vb, vj | Estimate D(A, B; C, D) and standard error using the block-jackknife.
Parameters
----------
aca : array_like, int, shape (n_variants, 2),
Allele counts for population A.
acb : array_like, int, shape (n_variants, 2)
Allele counts for population B.
acc : array_like, int, shape (n_variants, 2)
Allele counts for population C.
acd : array_like, int, shape (n_variants, 2)
Allele counts for population D.
blen : int
Block size (number of variants).
Returns
-------
d : float
Estimated value of the statistic using all data.
se : float
Estimated standard error.
z : float
Z-score (number of standard errors from zero).
vb : ndarray, float, shape (n_blocks,)
Value of the statistic in each block.
vj : ndarray, float, shape (n_blocks,)
Values of the statistic from block-jackknife resampling.
Notes
-----
See Patterson (2012), main text and Appendix A.
See Also
--------
allel.stats.admixture.patterson_d | https://github.com/cggh/scikit-allel/blob/3c979a57a100240ba959dd13f98839349530f215/allel/stats/admixture.py#L376-L439 |
cggh/scikit-allel | allel/model/dask.py | get_chunks | def get_chunks(data, chunks=None):
"""Try to guess a reasonable chunk shape to use for block-wise
algorithms operating over `data`."""
if chunks is None:
if hasattr(data, 'chunklen') and hasattr(data, 'shape'):
# bcolz carray, chunk first dimension only
return (data.chunklen,) + data.shape[1:]
elif hasattr(data, 'chunks') and hasattr(data, 'shape') and \
len(data.chunks) == len(data.shape):
# h5py dataset or zarr array
return data.chunks
else:
# fall back to something simple, ~4Mb chunks of first dimension
row = np.asarray(data[0])
chunklen = max(1, (2**22) // row.nbytes)
if row.shape:
chunks = (chunklen,) + row.shape
else:
chunks = (chunklen,)
return chunks
else:
return chunks | python | def get_chunks(data, chunks=None):
"""Try to guess a reasonable chunk shape to use for block-wise
algorithms operating over `data`."""
if chunks is None:
if hasattr(data, 'chunklen') and hasattr(data, 'shape'):
# bcolz carray, chunk first dimension only
return (data.chunklen,) + data.shape[1:]
elif hasattr(data, 'chunks') and hasattr(data, 'shape') and \
len(data.chunks) == len(data.shape):
# h5py dataset or zarr array
return data.chunks
else:
# fall back to something simple, ~4Mb chunks of first dimension
row = np.asarray(data[0])
chunklen = max(1, (2**22) // row.nbytes)
if row.shape:
chunks = (chunklen,) + row.shape
else:
chunks = (chunklen,)
return chunks
else:
return chunks | Try to guess a reasonable chunk shape to use for block-wise
algorithms operating over `data`. | https://github.com/cggh/scikit-allel/blob/3c979a57a100240ba959dd13f98839349530f215/allel/model/dask.py#L47-L74 |
cggh/scikit-allel | allel/io/gff.py | iter_gff3 | def iter_gff3(path, attributes=None, region=None, score_fill=-1,
phase_fill=-1, attributes_fill='.', tabix='tabix'):
"""Iterate over records in a GFF3 file.
Parameters
----------
path : string
Path to input file.
attributes : list of strings, optional
List of columns to extract from the "attributes" field.
region : string, optional
Genome region to extract. If given, file must be position
sorted, bgzipped and tabix indexed. Tabix must also be installed
and on the system path.
score_fill : int, optional
Value to use where score field has a missing value.
phase_fill : int, optional
Value to use where phase field has a missing value.
attributes_fill : object or list of objects, optional
Value(s) to use where attribute field(s) have a missing value.
tabix : string
Tabix command.
Returns
-------
Iterator
"""
# prepare fill values for attributes
if attributes is not None:
attributes = list(attributes)
if isinstance(attributes_fill, (list, tuple)):
if len(attributes) != len(attributes_fill):
raise ValueError('number of fills does not match attributes')
else:
attributes_fill = [attributes_fill] * len(attributes)
# open input stream
if region is not None:
cmd = [tabix, path, region]
buffer = subprocess.Popen(cmd, stdout=subprocess.PIPE).stdout
elif path.endswith('.gz') or path.endswith('.bgz'):
buffer = gzip.open(path, mode='rb')
else:
buffer = open(path, mode='rb')
try:
for line in buffer:
if line[0] == b'>':
# assume begin embedded FASTA
return
if line[0] == b'#':
# skip comment lines
continue
vals = line.split(b'\t')
if len(vals) == 9:
# unpack for processing
fseqid, fsource, ftype, fstart, fend, fscore, fstrand, fphase, fattrs = vals
# convert numerics
fstart = int(fstart)
fend = int(fend)
if fscore == b'.':
fscore = score_fill
else:
fscore = float(fscore)
if fphase == b'.':
fphase = phase_fill
else:
fphase = int(fphase)
if not PY2:
fseqid = str(fseqid, 'ascii')
fsource = str(fsource, 'ascii')
ftype = str(ftype, 'ascii')
fstrand = str(fstrand, 'ascii')
fattrs = str(fattrs, 'ascii')
rec = (fseqid, fsource, ftype, fstart, fend, fscore, fstrand, fphase)
if attributes is not None:
dattrs = gff3_parse_attributes(fattrs)
vattrs = tuple(
dattrs.get(k, f)
for k, f in zip(attributes, attributes_fill)
)
rec += vattrs
yield rec
finally:
buffer.close() | python | def iter_gff3(path, attributes=None, region=None, score_fill=-1,
phase_fill=-1, attributes_fill='.', tabix='tabix'):
"""Iterate over records in a GFF3 file.
Parameters
----------
path : string
Path to input file.
attributes : list of strings, optional
List of columns to extract from the "attributes" field.
region : string, optional
Genome region to extract. If given, file must be position
sorted, bgzipped and tabix indexed. Tabix must also be installed
and on the system path.
score_fill : int, optional
Value to use where score field has a missing value.
phase_fill : int, optional
Value to use where phase field has a missing value.
attributes_fill : object or list of objects, optional
Value(s) to use where attribute field(s) have a missing value.
tabix : string
Tabix command.
Returns
-------
Iterator
"""
# prepare fill values for attributes
if attributes is not None:
attributes = list(attributes)
if isinstance(attributes_fill, (list, tuple)):
if len(attributes) != len(attributes_fill):
raise ValueError('number of fills does not match attributes')
else:
attributes_fill = [attributes_fill] * len(attributes)
# open input stream
if region is not None:
cmd = [tabix, path, region]
buffer = subprocess.Popen(cmd, stdout=subprocess.PIPE).stdout
elif path.endswith('.gz') or path.endswith('.bgz'):
buffer = gzip.open(path, mode='rb')
else:
buffer = open(path, mode='rb')
try:
for line in buffer:
if line[0] == b'>':
# assume begin embedded FASTA
return
if line[0] == b'#':
# skip comment lines
continue
vals = line.split(b'\t')
if len(vals) == 9:
# unpack for processing
fseqid, fsource, ftype, fstart, fend, fscore, fstrand, fphase, fattrs = vals
# convert numerics
fstart = int(fstart)
fend = int(fend)
if fscore == b'.':
fscore = score_fill
else:
fscore = float(fscore)
if fphase == b'.':
fphase = phase_fill
else:
fphase = int(fphase)
if not PY2:
fseqid = str(fseqid, 'ascii')
fsource = str(fsource, 'ascii')
ftype = str(ftype, 'ascii')
fstrand = str(fstrand, 'ascii')
fattrs = str(fattrs, 'ascii')
rec = (fseqid, fsource, ftype, fstart, fend, fscore, fstrand, fphase)
if attributes is not None:
dattrs = gff3_parse_attributes(fattrs)
vattrs = tuple(
dattrs.get(k, f)
for k, f in zip(attributes, attributes_fill)
)
rec += vattrs
yield rec
finally:
buffer.close() | Iterate over records in a GFF3 file.
Parameters
----------
path : string
Path to input file.
attributes : list of strings, optional
List of columns to extract from the "attributes" field.
region : string, optional
Genome region to extract. If given, file must be position
sorted, bgzipped and tabix indexed. Tabix must also be installed
and on the system path.
score_fill : int, optional
Value to use where score field has a missing value.
phase_fill : int, optional
Value to use where phase field has a missing value.
attributes_fill : object or list of objects, optional
Value(s) to use where attribute field(s) have a missing value.
tabix : string
Tabix command.
Returns
-------
Iterator | https://github.com/cggh/scikit-allel/blob/3c979a57a100240ba959dd13f98839349530f215/allel/io/gff.py#L31-L118 |
cggh/scikit-allel | allel/io/gff.py | gff3_to_recarray | def gff3_to_recarray(path, attributes=None, region=None, score_fill=-1,
phase_fill=-1, attributes_fill='.', tabix='tabix', dtype=None):
"""Load data from a GFF3 into a NumPy recarray.
Parameters
----------
path : string
Path to input file.
attributes : list of strings, optional
List of columns to extract from the "attributes" field.
region : string, optional
Genome region to extract. If given, file must be position
sorted, bgzipped and tabix indexed. Tabix must also be installed
and on the system path.
score_fill : int, optional
Value to use where score field has a missing value.
phase_fill : int, optional
Value to use where phase field has a missing value.
attributes_fill : object or list of objects, optional
Value(s) to use where attribute field(s) have a missing value.
tabix : string, optional
Tabix command.
dtype : dtype, optional
Override dtype.
Returns
-------
np.recarray
"""
# read records
recs = list(iter_gff3(path, attributes=attributes, region=region,
score_fill=score_fill, phase_fill=phase_fill,
attributes_fill=attributes_fill, tabix=tabix))
if not recs:
return None
# determine dtype
if dtype is None:
dtype = [('seqid', object),
('source', object),
('type', object),
('start', int),
('end', int),
('score', float),
('strand', object),
('phase', int)]
if attributes:
for n in attributes:
dtype.append((n, object))
a = np.rec.fromrecords(recs, dtype=dtype)
return a | python | def gff3_to_recarray(path, attributes=None, region=None, score_fill=-1,
phase_fill=-1, attributes_fill='.', tabix='tabix', dtype=None):
"""Load data from a GFF3 into a NumPy recarray.
Parameters
----------
path : string
Path to input file.
attributes : list of strings, optional
List of columns to extract from the "attributes" field.
region : string, optional
Genome region to extract. If given, file must be position
sorted, bgzipped and tabix indexed. Tabix must also be installed
and on the system path.
score_fill : int, optional
Value to use where score field has a missing value.
phase_fill : int, optional
Value to use where phase field has a missing value.
attributes_fill : object or list of objects, optional
Value(s) to use where attribute field(s) have a missing value.
tabix : string, optional
Tabix command.
dtype : dtype, optional
Override dtype.
Returns
-------
np.recarray
"""
# read records
recs = list(iter_gff3(path, attributes=attributes, region=region,
score_fill=score_fill, phase_fill=phase_fill,
attributes_fill=attributes_fill, tabix=tabix))
if not recs:
return None
# determine dtype
if dtype is None:
dtype = [('seqid', object),
('source', object),
('type', object),
('start', int),
('end', int),
('score', float),
('strand', object),
('phase', int)]
if attributes:
for n in attributes:
dtype.append((n, object))
a = np.rec.fromrecords(recs, dtype=dtype)
return a | Load data from a GFF3 into a NumPy recarray.
Parameters
----------
path : string
Path to input file.
attributes : list of strings, optional
List of columns to extract from the "attributes" field.
region : string, optional
Genome region to extract. If given, file must be position
sorted, bgzipped and tabix indexed. Tabix must also be installed
and on the system path.
score_fill : int, optional
Value to use where score field has a missing value.
phase_fill : int, optional
Value to use where phase field has a missing value.
attributes_fill : object or list of objects, optional
Value(s) to use where attribute field(s) have a missing value.
tabix : string, optional
Tabix command.
dtype : dtype, optional
Override dtype.
Returns
-------
np.recarray | https://github.com/cggh/scikit-allel/blob/3c979a57a100240ba959dd13f98839349530f215/allel/io/gff.py#L124-L178 |
cggh/scikit-allel | allel/io/gff.py | gff3_to_dataframe | def gff3_to_dataframe(path, attributes=None, region=None, score_fill=-1,
phase_fill=-1, attributes_fill='.', tabix='tabix', **kwargs):
"""Load data from a GFF3 into a pandas DataFrame.
Parameters
----------
path : string
Path to input file.
attributes : list of strings, optional
List of columns to extract from the "attributes" field.
region : string, optional
Genome region to extract. If given, file must be position
sorted, bgzipped and tabix indexed. Tabix must also be installed
and on the system path.
score_fill : int, optional
Value to use where score field has a missing value.
phase_fill : int, optional
Value to use where phase field has a missing value.
attributes_fill : object or list of objects, optional
Value(s) to use where attribute field(s) have a missing value.
tabix : string, optional
Tabix command.
Returns
-------
pandas.DataFrame
"""
import pandas
# read records
recs = list(iter_gff3(path, attributes=attributes, region=region,
score_fill=score_fill, phase_fill=phase_fill,
attributes_fill=attributes_fill, tabix=tabix))
# load into pandas
columns = ['seqid', 'source', 'type', 'start', 'end', 'score', 'strand', 'phase']
if attributes:
columns += list(attributes)
df = pandas.DataFrame.from_records(recs, columns=columns, **kwargs)
return df | python | def gff3_to_dataframe(path, attributes=None, region=None, score_fill=-1,
phase_fill=-1, attributes_fill='.', tabix='tabix', **kwargs):
"""Load data from a GFF3 into a pandas DataFrame.
Parameters
----------
path : string
Path to input file.
attributes : list of strings, optional
List of columns to extract from the "attributes" field.
region : string, optional
Genome region to extract. If given, file must be position
sorted, bgzipped and tabix indexed. Tabix must also be installed
and on the system path.
score_fill : int, optional
Value to use where score field has a missing value.
phase_fill : int, optional
Value to use where phase field has a missing value.
attributes_fill : object or list of objects, optional
Value(s) to use where attribute field(s) have a missing value.
tabix : string, optional
Tabix command.
Returns
-------
pandas.DataFrame
"""
import pandas
# read records
recs = list(iter_gff3(path, attributes=attributes, region=region,
score_fill=score_fill, phase_fill=phase_fill,
attributes_fill=attributes_fill, tabix=tabix))
# load into pandas
columns = ['seqid', 'source', 'type', 'start', 'end', 'score', 'strand', 'phase']
if attributes:
columns += list(attributes)
df = pandas.DataFrame.from_records(recs, columns=columns, **kwargs)
return df | Load data from a GFF3 into a pandas DataFrame.
Parameters
----------
path : string
Path to input file.
attributes : list of strings, optional
List of columns to extract from the "attributes" field.
region : string, optional
Genome region to extract. If given, file must be position
sorted, bgzipped and tabix indexed. Tabix must also be installed
and on the system path.
score_fill : int, optional
Value to use where score field has a missing value.
phase_fill : int, optional
Value to use where phase field has a missing value.
attributes_fill : object or list of objects, optional
Value(s) to use where attribute field(s) have a missing value.
tabix : string, optional
Tabix command.
Returns
-------
pandas.DataFrame | https://github.com/cggh/scikit-allel/blob/3c979a57a100240ba959dd13f98839349530f215/allel/io/gff.py#L181-L223 |
cggh/scikit-allel | allel/stats/selection.py | ehh_decay | def ehh_decay(h, truncate=False):
"""Compute the decay of extended haplotype homozygosity (EHH)
moving away from the first variant.
Parameters
----------
h : array_like, int, shape (n_variants, n_haplotypes)
Haplotype array.
truncate : bool, optional
If True, the return array will exclude trailing zeros.
Returns
-------
ehh : ndarray, float, shape (n_variants, )
EHH at successive variants from the first variant.
"""
# check inputs
# N.B., ensure int8 so we can use cython optimisation
h = HaplotypeArray(np.asarray(h), copy=False)
if h.min() < 0:
raise NotImplementedError('missing calls are not supported')
# initialise
n_variants = h.n_variants # number of rows, i.e., variants
n_haplotypes = h.n_haplotypes # number of columns, i.e., haplotypes
n_pairs = (n_haplotypes * (n_haplotypes - 1)) // 2
# compute the shared prefix length between all pairs of haplotypes
spl = pairwise_shared_prefix_lengths(memoryview_safe(np.asarray(h)))
# compute EHH by counting the number of shared prefixes extending beyond
# each variant
minlength = None if truncate else n_variants + 1
b = np.bincount(spl, minlength=minlength)
c = np.cumsum(b[::-1])[:-1]
ehh = (c / n_pairs)[::-1]
return ehh | python | def ehh_decay(h, truncate=False):
"""Compute the decay of extended haplotype homozygosity (EHH)
moving away from the first variant.
Parameters
----------
h : array_like, int, shape (n_variants, n_haplotypes)
Haplotype array.
truncate : bool, optional
If True, the return array will exclude trailing zeros.
Returns
-------
ehh : ndarray, float, shape (n_variants, )
EHH at successive variants from the first variant.
"""
# check inputs
# N.B., ensure int8 so we can use cython optimisation
h = HaplotypeArray(np.asarray(h), copy=False)
if h.min() < 0:
raise NotImplementedError('missing calls are not supported')
# initialise
n_variants = h.n_variants # number of rows, i.e., variants
n_haplotypes = h.n_haplotypes # number of columns, i.e., haplotypes
n_pairs = (n_haplotypes * (n_haplotypes - 1)) // 2
# compute the shared prefix length between all pairs of haplotypes
spl = pairwise_shared_prefix_lengths(memoryview_safe(np.asarray(h)))
# compute EHH by counting the number of shared prefixes extending beyond
# each variant
minlength = None if truncate else n_variants + 1
b = np.bincount(spl, minlength=minlength)
c = np.cumsum(b[::-1])[:-1]
ehh = (c / n_pairs)[::-1]
return ehh | Compute the decay of extended haplotype homozygosity (EHH)
moving away from the first variant.
Parameters
----------
h : array_like, int, shape (n_variants, n_haplotypes)
Haplotype array.
truncate : bool, optional
If True, the return array will exclude trailing zeros.
Returns
-------
ehh : ndarray, float, shape (n_variants, )
EHH at successive variants from the first variant. | https://github.com/cggh/scikit-allel/blob/3c979a57a100240ba959dd13f98839349530f215/allel/stats/selection.py#L20-L59 |
cggh/scikit-allel | allel/stats/selection.py | voight_painting | def voight_painting(h):
"""Paint haplotypes, assigning a unique integer to each shared haplotype
prefix.
Parameters
----------
h : array_like, int, shape (n_variants, n_haplotypes)
Haplotype array.
Returns
-------
painting : ndarray, int, shape (n_variants, n_haplotypes)
Painting array.
indices : ndarray, int, shape (n_hapotypes,)
Haplotype indices after sorting by prefix.
"""
# check inputs
# N.B., ensure int8 so we can use cython optimisation
h = HaplotypeArray(np.asarray(h), copy=False)
if h.max() > 1:
raise NotImplementedError('only biallelic variants are supported')
if h.min() < 0:
raise NotImplementedError('missing calls are not supported')
# sort by prefix
indices = h.prefix_argsort()
h = np.take(h, indices, axis=1)
# paint
painting = paint_shared_prefixes(memoryview_safe(np.asarray(h)))
return painting, indices | python | def voight_painting(h):
"""Paint haplotypes, assigning a unique integer to each shared haplotype
prefix.
Parameters
----------
h : array_like, int, shape (n_variants, n_haplotypes)
Haplotype array.
Returns
-------
painting : ndarray, int, shape (n_variants, n_haplotypes)
Painting array.
indices : ndarray, int, shape (n_hapotypes,)
Haplotype indices after sorting by prefix.
"""
# check inputs
# N.B., ensure int8 so we can use cython optimisation
h = HaplotypeArray(np.asarray(h), copy=False)
if h.max() > 1:
raise NotImplementedError('only biallelic variants are supported')
if h.min() < 0:
raise NotImplementedError('missing calls are not supported')
# sort by prefix
indices = h.prefix_argsort()
h = np.take(h, indices, axis=1)
# paint
painting = paint_shared_prefixes(memoryview_safe(np.asarray(h)))
return painting, indices | Paint haplotypes, assigning a unique integer to each shared haplotype
prefix.
Parameters
----------
h : array_like, int, shape (n_variants, n_haplotypes)
Haplotype array.
Returns
-------
painting : ndarray, int, shape (n_variants, n_haplotypes)
Painting array.
indices : ndarray, int, shape (n_hapotypes,)
Haplotype indices after sorting by prefix. | https://github.com/cggh/scikit-allel/blob/3c979a57a100240ba959dd13f98839349530f215/allel/stats/selection.py#L62-L95 |
cggh/scikit-allel | allel/stats/selection.py | plot_voight_painting | def plot_voight_painting(painting, palette='colorblind', flank='right',
ax=None, height_factor=0.01):
"""Plot a painting of shared haplotype prefixes.
Parameters
----------
painting : array_like, int, shape (n_variants, n_haplotypes)
Painting array.
ax : axes, optional
The axes on which to draw. If not provided, a new figure will be
created.
palette : string, optional
A Seaborn palette name.
flank : {'right', 'left'}, optional
If left, painting will be reversed along first axis.
height_factor : float, optional
If no axes provided, determine height of figure by multiplying
height of painting array by this number.
Returns
-------
ax : axes
"""
import seaborn as sns
from matplotlib.colors import ListedColormap
import matplotlib.pyplot as plt
if flank == 'left':
painting = painting[::-1]
n_colors = painting.max()
palette = sns.color_palette(palette, n_colors)
# use white for singleton haplotypes
cmap = ListedColormap(['white'] + palette)
# setup axes
if ax is None:
w = plt.rcParams['figure.figsize'][0]
h = height_factor*painting.shape[1]
fig, ax = plt.subplots(figsize=(w, h))
sns.despine(ax=ax, bottom=True, left=True)
ax.pcolormesh(painting.T, cmap=cmap)
ax.set_xticks([])
ax.set_yticks([])
ax.set_xlim(0, painting.shape[0])
ax.set_ylim(0, painting.shape[1])
return ax | python | def plot_voight_painting(painting, palette='colorblind', flank='right',
ax=None, height_factor=0.01):
"""Plot a painting of shared haplotype prefixes.
Parameters
----------
painting : array_like, int, shape (n_variants, n_haplotypes)
Painting array.
ax : axes, optional
The axes on which to draw. If not provided, a new figure will be
created.
palette : string, optional
A Seaborn palette name.
flank : {'right', 'left'}, optional
If left, painting will be reversed along first axis.
height_factor : float, optional
If no axes provided, determine height of figure by multiplying
height of painting array by this number.
Returns
-------
ax : axes
"""
import seaborn as sns
from matplotlib.colors import ListedColormap
import matplotlib.pyplot as plt
if flank == 'left':
painting = painting[::-1]
n_colors = painting.max()
palette = sns.color_palette(palette, n_colors)
# use white for singleton haplotypes
cmap = ListedColormap(['white'] + palette)
# setup axes
if ax is None:
w = plt.rcParams['figure.figsize'][0]
h = height_factor*painting.shape[1]
fig, ax = plt.subplots(figsize=(w, h))
sns.despine(ax=ax, bottom=True, left=True)
ax.pcolormesh(painting.T, cmap=cmap)
ax.set_xticks([])
ax.set_yticks([])
ax.set_xlim(0, painting.shape[0])
ax.set_ylim(0, painting.shape[1])
return ax | Plot a painting of shared haplotype prefixes.
Parameters
----------
painting : array_like, int, shape (n_variants, n_haplotypes)
Painting array.
ax : axes, optional
The axes on which to draw. If not provided, a new figure will be
created.
palette : string, optional
A Seaborn palette name.
flank : {'right', 'left'}, optional
If left, painting will be reversed along first axis.
height_factor : float, optional
If no axes provided, determine height of figure by multiplying
height of painting array by this number.
Returns
-------
ax : axes | https://github.com/cggh/scikit-allel/blob/3c979a57a100240ba959dd13f98839349530f215/allel/stats/selection.py#L98-L148 |
cggh/scikit-allel | allel/stats/selection.py | fig_voight_painting | def fig_voight_painting(h, index=None, palette='colorblind',
height_factor=0.01, fig=None):
"""Make a figure of shared haplotype prefixes for both left and right
flanks, centred on some variant of choice.
Parameters
----------
h : array_like, int, shape (n_variants, n_haplotypes)
Haplotype array.
index : int, optional
Index of the variant within the haplotype array to centre on. If not
provided, the middle variant will be used.
palette : string, optional
A Seaborn palette name.
height_factor : float, optional
If no axes provided, determine height of figure by multiplying
height of painting array by this number.
fig : figure
The figure on which to draw. If not provided, a new figure will be
created.
Returns
-------
fig : figure
Notes
-----
N.B., the ordering of haplotypes on the left and right flanks will be
different. This means that haplotypes on the right flank **will not**
correspond to haplotypes on the left flank at the same vertical position.
"""
import matplotlib.pyplot as plt
from matplotlib.gridspec import GridSpec
import seaborn as sns
# check inputs
h = asarray_ndim(h, 2)
if index is None:
# use midpoint
index = h.shape[0] // 2
# divide data into two flanks
hl = h[:index+1][::-1]
hr = h[index:]
# paint both flanks
pl, il = voight_painting(hl)
pr, ir = voight_painting(hr)
# compute ehh decay for both flanks
el = ehh_decay(hl, truncate=False)
er = ehh_decay(hr, truncate=False)
# setup figure
# fixed height for EHH decay subplot
h_ehh = plt.rcParams['figure.figsize'][1] // 3
# add height for paintings
h_painting = height_factor*h.shape[1]
if fig is None:
w = plt.rcParams['figure.figsize'][0]
h = h_ehh + h_painting
fig = plt.figure(figsize=(w, h))
# setup gridspec
gs = GridSpec(2, 2,
width_ratios=[hl.shape[0], hr.shape[0]],
height_ratios=[h_painting, h_ehh])
# plot paintings
ax = fig.add_subplot(gs[0, 0])
sns.despine(ax=ax, left=True, bottom=True)
plot_voight_painting(pl, palette=palette, flank='left', ax=ax)
ax = fig.add_subplot(gs[0, 1])
sns.despine(ax=ax, left=True, bottom=True)
plot_voight_painting(pr, palette=palette, flank='right', ax=ax)
# plot ehh
ax = fig.add_subplot(gs[1, 0])
sns.despine(ax=ax, offset=3)
x = np.arange(el.shape[0])
y = el
ax.fill_between(x, 0, y)
ax.set_ylim(0, 1)
ax.set_yticks([0, 1])
ax.set_ylabel('EHH')
ax.invert_xaxis()
ax = fig.add_subplot(gs[1, 1])
sns.despine(ax=ax, left=True, right=False, offset=3)
ax.yaxis.tick_right()
ax.set_ylim(0, 1)
ax.set_yticks([0, 1])
x = np.arange(er.shape[0])
y = er
ax.fill_between(x, 0, y)
# tidy up
fig.tight_layout()
return fig | python | def fig_voight_painting(h, index=None, palette='colorblind',
height_factor=0.01, fig=None):
"""Make a figure of shared haplotype prefixes for both left and right
flanks, centred on some variant of choice.
Parameters
----------
h : array_like, int, shape (n_variants, n_haplotypes)
Haplotype array.
index : int, optional
Index of the variant within the haplotype array to centre on. If not
provided, the middle variant will be used.
palette : string, optional
A Seaborn palette name.
height_factor : float, optional
If no axes provided, determine height of figure by multiplying
height of painting array by this number.
fig : figure
The figure on which to draw. If not provided, a new figure will be
created.
Returns
-------
fig : figure
Notes
-----
N.B., the ordering of haplotypes on the left and right flanks will be
different. This means that haplotypes on the right flank **will not**
correspond to haplotypes on the left flank at the same vertical position.
"""
import matplotlib.pyplot as plt
from matplotlib.gridspec import GridSpec
import seaborn as sns
# check inputs
h = asarray_ndim(h, 2)
if index is None:
# use midpoint
index = h.shape[0] // 2
# divide data into two flanks
hl = h[:index+1][::-1]
hr = h[index:]
# paint both flanks
pl, il = voight_painting(hl)
pr, ir = voight_painting(hr)
# compute ehh decay for both flanks
el = ehh_decay(hl, truncate=False)
er = ehh_decay(hr, truncate=False)
# setup figure
# fixed height for EHH decay subplot
h_ehh = plt.rcParams['figure.figsize'][1] // 3
# add height for paintings
h_painting = height_factor*h.shape[1]
if fig is None:
w = plt.rcParams['figure.figsize'][0]
h = h_ehh + h_painting
fig = plt.figure(figsize=(w, h))
# setup gridspec
gs = GridSpec(2, 2,
width_ratios=[hl.shape[0], hr.shape[0]],
height_ratios=[h_painting, h_ehh])
# plot paintings
ax = fig.add_subplot(gs[0, 0])
sns.despine(ax=ax, left=True, bottom=True)
plot_voight_painting(pl, palette=palette, flank='left', ax=ax)
ax = fig.add_subplot(gs[0, 1])
sns.despine(ax=ax, left=True, bottom=True)
plot_voight_painting(pr, palette=palette, flank='right', ax=ax)
# plot ehh
ax = fig.add_subplot(gs[1, 0])
sns.despine(ax=ax, offset=3)
x = np.arange(el.shape[0])
y = el
ax.fill_between(x, 0, y)
ax.set_ylim(0, 1)
ax.set_yticks([0, 1])
ax.set_ylabel('EHH')
ax.invert_xaxis()
ax = fig.add_subplot(gs[1, 1])
sns.despine(ax=ax, left=True, right=False, offset=3)
ax.yaxis.tick_right()
ax.set_ylim(0, 1)
ax.set_yticks([0, 1])
x = np.arange(er.shape[0])
y = er
ax.fill_between(x, 0, y)
# tidy up
fig.tight_layout()
return fig | Make a figure of shared haplotype prefixes for both left and right
flanks, centred on some variant of choice.
Parameters
----------
h : array_like, int, shape (n_variants, n_haplotypes)
Haplotype array.
index : int, optional
Index of the variant within the haplotype array to centre on. If not
provided, the middle variant will be used.
palette : string, optional
A Seaborn palette name.
height_factor : float, optional
If no axes provided, determine height of figure by multiplying
height of painting array by this number.
fig : figure
The figure on which to draw. If not provided, a new figure will be
created.
Returns
-------
fig : figure
Notes
-----
N.B., the ordering of haplotypes on the left and right flanks will be
different. This means that haplotypes on the right flank **will not**
correspond to haplotypes on the left flank at the same vertical position. | https://github.com/cggh/scikit-allel/blob/3c979a57a100240ba959dd13f98839349530f215/allel/stats/selection.py#L151-L251 |
cggh/scikit-allel | allel/stats/selection.py | compute_ihh_gaps | def compute_ihh_gaps(pos, map_pos, gap_scale, max_gap, is_accessible):
"""Compute spacing between variants for integrating haplotype
homozygosity.
Parameters
----------
pos : array_like, int, shape (n_variants,)
Variant positions (physical distance).
map_pos : array_like, float, shape (n_variants,)
Variant positions (genetic map distance).
gap_scale : int, optional
Rescale distance between variants if gap is larger than this value.
max_gap : int, optional
Do not report scores if EHH spans a gap larger than this number of
base pairs.
is_accessible : array_like, bool, optional
Genome accessibility array. If provided, distance between variants
will be computed as the number of accessible bases between them.
Returns
-------
gaps : ndarray, float, shape (n_variants - 1,)
"""
# check inputs
if map_pos is None:
# integrate over physical distance
map_pos = pos
else:
map_pos = asarray_ndim(map_pos, 1)
check_dim0_aligned(pos, map_pos)
# compute physical gaps
physical_gaps = np.diff(pos)
# compute genetic gaps
gaps = np.diff(map_pos).astype('f8')
if is_accessible is not None:
# compute accessible gaps
is_accessible = asarray_ndim(is_accessible, 1)
assert is_accessible.shape[0] > pos[-1], \
'accessibility array too short'
accessible_gaps = np.zeros_like(physical_gaps)
for i in range(1, len(pos)):
# N.B., expect pos is 1-based
n_access = np.count_nonzero(is_accessible[pos[i-1]-1:pos[i]-1])
accessible_gaps[i-1] = n_access
# adjust using accessibility
scaling = accessible_gaps / physical_gaps
gaps = gaps * scaling
elif gap_scale is not None and gap_scale > 0:
scaling = np.ones(gaps.shape, dtype='f8')
loc_scale = physical_gaps > gap_scale
scaling[loc_scale] = gap_scale / physical_gaps[loc_scale]
gaps = gaps * scaling
if max_gap is not None and max_gap > 0:
# deal with very large gaps
gaps[physical_gaps > max_gap] = -1
return gaps | python | def compute_ihh_gaps(pos, map_pos, gap_scale, max_gap, is_accessible):
"""Compute spacing between variants for integrating haplotype
homozygosity.
Parameters
----------
pos : array_like, int, shape (n_variants,)
Variant positions (physical distance).
map_pos : array_like, float, shape (n_variants,)
Variant positions (genetic map distance).
gap_scale : int, optional
Rescale distance between variants if gap is larger than this value.
max_gap : int, optional
Do not report scores if EHH spans a gap larger than this number of
base pairs.
is_accessible : array_like, bool, optional
Genome accessibility array. If provided, distance between variants
will be computed as the number of accessible bases between them.
Returns
-------
gaps : ndarray, float, shape (n_variants - 1,)
"""
# check inputs
if map_pos is None:
# integrate over physical distance
map_pos = pos
else:
map_pos = asarray_ndim(map_pos, 1)
check_dim0_aligned(pos, map_pos)
# compute physical gaps
physical_gaps = np.diff(pos)
# compute genetic gaps
gaps = np.diff(map_pos).astype('f8')
if is_accessible is not None:
# compute accessible gaps
is_accessible = asarray_ndim(is_accessible, 1)
assert is_accessible.shape[0] > pos[-1], \
'accessibility array too short'
accessible_gaps = np.zeros_like(physical_gaps)
for i in range(1, len(pos)):
# N.B., expect pos is 1-based
n_access = np.count_nonzero(is_accessible[pos[i-1]-1:pos[i]-1])
accessible_gaps[i-1] = n_access
# adjust using accessibility
scaling = accessible_gaps / physical_gaps
gaps = gaps * scaling
elif gap_scale is not None and gap_scale > 0:
scaling = np.ones(gaps.shape, dtype='f8')
loc_scale = physical_gaps > gap_scale
scaling[loc_scale] = gap_scale / physical_gaps[loc_scale]
gaps = gaps * scaling
if max_gap is not None and max_gap > 0:
# deal with very large gaps
gaps[physical_gaps > max_gap] = -1
return gaps | Compute spacing between variants for integrating haplotype
homozygosity.
Parameters
----------
pos : array_like, int, shape (n_variants,)
Variant positions (physical distance).
map_pos : array_like, float, shape (n_variants,)
Variant positions (genetic map distance).
gap_scale : int, optional
Rescale distance between variants if gap is larger than this value.
max_gap : int, optional
Do not report scores if EHH spans a gap larger than this number of
base pairs.
is_accessible : array_like, bool, optional
Genome accessibility array. If provided, distance between variants
will be computed as the number of accessible bases between them.
Returns
-------
gaps : ndarray, float, shape (n_variants - 1,) | https://github.com/cggh/scikit-allel/blob/3c979a57a100240ba959dd13f98839349530f215/allel/stats/selection.py#L255-L322 |
cggh/scikit-allel | allel/stats/selection.py | ihs | def ihs(h, pos, map_pos=None, min_ehh=0.05, min_maf=0.05, include_edges=False,
gap_scale=20000, max_gap=200000, is_accessible=None, use_threads=True):
"""Compute the unstandardized integrated haplotype score (IHS) for each
variant, comparing integrated haplotype homozygosity between the
reference (0) and alternate (1) alleles.
Parameters
----------
h : array_like, int, shape (n_variants, n_haplotypes)
Haplotype array.
pos : array_like, int, shape (n_variants,)
Variant positions (physical distance).
map_pos : array_like, float, shape (n_variants,)
Variant positions (genetic map distance).
min_ehh: float, optional
Minimum EHH beyond which to truncate integrated haplotype
homozygosity calculation.
min_maf : float, optional
Do not compute integrated haplotype homozogysity for variants with
minor allele frequency below this value.
include_edges : bool, optional
If True, report scores even if EHH does not decay below `min_ehh`
before reaching the edge of the data.
gap_scale : int, optional
Rescale distance between variants if gap is larger than this value.
max_gap : int, optional
Do not report scores if EHH spans a gap larger than this number of
base pairs.
is_accessible : array_like, bool, optional
Genome accessibility array. If provided, distance between variants
will be computed as the number of accessible bases between them.
use_threads : bool, optional
If True use multiple threads to compute.
Returns
-------
score : ndarray, float, shape (n_variants,)
Unstandardized IHS scores.
Notes
-----
This function will calculate IHS for all variants. To exclude variants
below a given minor allele frequency, filter the input haplotype array
before passing to this function.
This function computes IHS comparing the reference and alternate alleles.
These can be polarised by switching the sign for any variant where the
reference allele is derived.
This function returns NaN for any IHS calculations where haplotype
homozygosity does not decay below `min_ehh` before reaching the first or
last variant. To disable this behaviour, set `include_edges` to True.
Note that the unstandardized score is returned. Usually these scores are
then standardized in different allele frequency bins.
See Also
--------
standardize_by_allele_count
"""
# check inputs
h = asarray_ndim(h, 2)
check_integer_dtype(h)
pos = asarray_ndim(pos, 1)
check_dim0_aligned(h, pos)
h = memoryview_safe(h)
pos = memoryview_safe(pos)
# compute gaps between variants for integration
gaps = compute_ihh_gaps(pos, map_pos, gap_scale, max_gap, is_accessible)
# setup kwargs
kwargs = dict(min_ehh=min_ehh, min_maf=min_maf, include_edges=include_edges)
if use_threads and multiprocessing.cpu_count() > 1:
# run with threads
# create pool
pool = ThreadPool(2)
# scan forward
result_fwd = pool.apply_async(ihh01_scan, (h, gaps), kwargs)
# scan backward
result_rev = pool.apply_async(ihh01_scan, (h[::-1], gaps[::-1]), kwargs)
# wait for both to finish
pool.close()
pool.join()
# obtain results
ihh0_fwd, ihh1_fwd = result_fwd.get()
ihh0_rev, ihh1_rev = result_rev.get()
# cleanup
pool.terminate()
else:
# run without threads
# scan forward
ihh0_fwd, ihh1_fwd = ihh01_scan(h, gaps, **kwargs)
# scan backward
ihh0_rev, ihh1_rev = ihh01_scan(h[::-1], gaps[::-1], **kwargs)
# handle reverse scan
ihh0_rev = ihh0_rev[::-1]
ihh1_rev = ihh1_rev[::-1]
# compute unstandardized score
ihh0 = ihh0_fwd + ihh0_rev
ihh1 = ihh1_fwd + ihh1_rev
score = np.log(ihh1 / ihh0)
return score | python | def ihs(h, pos, map_pos=None, min_ehh=0.05, min_maf=0.05, include_edges=False,
gap_scale=20000, max_gap=200000, is_accessible=None, use_threads=True):
"""Compute the unstandardized integrated haplotype score (IHS) for each
variant, comparing integrated haplotype homozygosity between the
reference (0) and alternate (1) alleles.
Parameters
----------
h : array_like, int, shape (n_variants, n_haplotypes)
Haplotype array.
pos : array_like, int, shape (n_variants,)
Variant positions (physical distance).
map_pos : array_like, float, shape (n_variants,)
Variant positions (genetic map distance).
min_ehh: float, optional
Minimum EHH beyond which to truncate integrated haplotype
homozygosity calculation.
min_maf : float, optional
Do not compute integrated haplotype homozogysity for variants with
minor allele frequency below this value.
include_edges : bool, optional
If True, report scores even if EHH does not decay below `min_ehh`
before reaching the edge of the data.
gap_scale : int, optional
Rescale distance between variants if gap is larger than this value.
max_gap : int, optional
Do not report scores if EHH spans a gap larger than this number of
base pairs.
is_accessible : array_like, bool, optional
Genome accessibility array. If provided, distance between variants
will be computed as the number of accessible bases between them.
use_threads : bool, optional
If True use multiple threads to compute.
Returns
-------
score : ndarray, float, shape (n_variants,)
Unstandardized IHS scores.
Notes
-----
This function will calculate IHS for all variants. To exclude variants
below a given minor allele frequency, filter the input haplotype array
before passing to this function.
This function computes IHS comparing the reference and alternate alleles.
These can be polarised by switching the sign for any variant where the
reference allele is derived.
This function returns NaN for any IHS calculations where haplotype
homozygosity does not decay below `min_ehh` before reaching the first or
last variant. To disable this behaviour, set `include_edges` to True.
Note that the unstandardized score is returned. Usually these scores are
then standardized in different allele frequency bins.
See Also
--------
standardize_by_allele_count
"""
# check inputs
h = asarray_ndim(h, 2)
check_integer_dtype(h)
pos = asarray_ndim(pos, 1)
check_dim0_aligned(h, pos)
h = memoryview_safe(h)
pos = memoryview_safe(pos)
# compute gaps between variants for integration
gaps = compute_ihh_gaps(pos, map_pos, gap_scale, max_gap, is_accessible)
# setup kwargs
kwargs = dict(min_ehh=min_ehh, min_maf=min_maf, include_edges=include_edges)
if use_threads and multiprocessing.cpu_count() > 1:
# run with threads
# create pool
pool = ThreadPool(2)
# scan forward
result_fwd = pool.apply_async(ihh01_scan, (h, gaps), kwargs)
# scan backward
result_rev = pool.apply_async(ihh01_scan, (h[::-1], gaps[::-1]), kwargs)
# wait for both to finish
pool.close()
pool.join()
# obtain results
ihh0_fwd, ihh1_fwd = result_fwd.get()
ihh0_rev, ihh1_rev = result_rev.get()
# cleanup
pool.terminate()
else:
# run without threads
# scan forward
ihh0_fwd, ihh1_fwd = ihh01_scan(h, gaps, **kwargs)
# scan backward
ihh0_rev, ihh1_rev = ihh01_scan(h[::-1], gaps[::-1], **kwargs)
# handle reverse scan
ihh0_rev = ihh0_rev[::-1]
ihh1_rev = ihh1_rev[::-1]
# compute unstandardized score
ihh0 = ihh0_fwd + ihh0_rev
ihh1 = ihh1_fwd + ihh1_rev
score = np.log(ihh1 / ihh0)
return score | Compute the unstandardized integrated haplotype score (IHS) for each
variant, comparing integrated haplotype homozygosity between the
reference (0) and alternate (1) alleles.
Parameters
----------
h : array_like, int, shape (n_variants, n_haplotypes)
Haplotype array.
pos : array_like, int, shape (n_variants,)
Variant positions (physical distance).
map_pos : array_like, float, shape (n_variants,)
Variant positions (genetic map distance).
min_ehh: float, optional
Minimum EHH beyond which to truncate integrated haplotype
homozygosity calculation.
min_maf : float, optional
Do not compute integrated haplotype homozogysity for variants with
minor allele frequency below this value.
include_edges : bool, optional
If True, report scores even if EHH does not decay below `min_ehh`
before reaching the edge of the data.
gap_scale : int, optional
Rescale distance between variants if gap is larger than this value.
max_gap : int, optional
Do not report scores if EHH spans a gap larger than this number of
base pairs.
is_accessible : array_like, bool, optional
Genome accessibility array. If provided, distance between variants
will be computed as the number of accessible bases between them.
use_threads : bool, optional
If True use multiple threads to compute.
Returns
-------
score : ndarray, float, shape (n_variants,)
Unstandardized IHS scores.
Notes
-----
This function will calculate IHS for all variants. To exclude variants
below a given minor allele frequency, filter the input haplotype array
before passing to this function.
This function computes IHS comparing the reference and alternate alleles.
These can be polarised by switching the sign for any variant where the
reference allele is derived.
This function returns NaN for any IHS calculations where haplotype
homozygosity does not decay below `min_ehh` before reaching the first or
last variant. To disable this behaviour, set `include_edges` to True.
Note that the unstandardized score is returned. Usually these scores are
then standardized in different allele frequency bins.
See Also
--------
standardize_by_allele_count | https://github.com/cggh/scikit-allel/blob/3c979a57a100240ba959dd13f98839349530f215/allel/stats/selection.py#L325-L443 |
cggh/scikit-allel | allel/stats/selection.py | xpehh | def xpehh(h1, h2, pos, map_pos=None, min_ehh=0.05, include_edges=False,
gap_scale=20000, max_gap=200000, is_accessible=None,
use_threads=True):
"""Compute the unstandardized cross-population extended haplotype
homozygosity score (XPEHH) for each variant.
Parameters
----------
h1 : array_like, int, shape (n_variants, n_haplotypes)
Haplotype array for the first population.
h2 : array_like, int, shape (n_variants, n_haplotypes)
Haplotype array for the second population.
pos : array_like, int, shape (n_variants,)
Variant positions on physical or genetic map.
map_pos : array_like, float, shape (n_variants,)
Variant positions (genetic map distance).
min_ehh: float, optional
Minimum EHH beyond which to truncate integrated haplotype
homozygosity calculation.
include_edges : bool, optional
If True, report scores even if EHH does not decay below `min_ehh`
before reaching the edge of the data.
gap_scale : int, optional
Rescale distance between variants if gap is larger than this value.
max_gap : int, optional
Do not report scores if EHH spans a gap larger than this number of
base pairs.
is_accessible : array_like, bool, optional
Genome accessibility array. If provided, distance between variants
will be computed as the number of accessible bases between them.
use_threads : bool, optional
If True use multiple threads to compute.
Returns
-------
score : ndarray, float, shape (n_variants,)
Unstandardized XPEHH scores.
Notes
-----
This function will calculate XPEHH for all variants. To exclude variants
below a given minor allele frequency, filter the input haplotype arrays
before passing to this function.
This function returns NaN for any EHH calculations where haplotype
homozygosity does not decay below `min_ehh` before reaching the first or
last variant. To disable this behaviour, set `include_edges` to True.
Note that the unstandardized score is returned. Usually these scores are
then standardized genome-wide.
Haplotype arrays from the two populations may have different numbers of
haplotypes.
See Also
--------
standardize
"""
# check inputs
h1 = asarray_ndim(h1, 2)
check_integer_dtype(h1)
h2 = asarray_ndim(h2, 2)
check_integer_dtype(h2)
pos = asarray_ndim(pos, 1)
check_dim0_aligned(h1, h2, pos)
h1 = memoryview_safe(h1)
h2 = memoryview_safe(h2)
pos = memoryview_safe(pos)
# compute gaps between variants for integration
gaps = compute_ihh_gaps(pos, map_pos, gap_scale, max_gap, is_accessible)
# setup kwargs
kwargs = dict(min_ehh=min_ehh, include_edges=include_edges)
if use_threads and multiprocessing.cpu_count() > 1:
# use multiple threads
# setup threadpool
pool = ThreadPool(min(4, multiprocessing.cpu_count()))
# scan forward
res1_fwd = pool.apply_async(ihh_scan, (h1, gaps), kwargs)
res2_fwd = pool.apply_async(ihh_scan, (h2, gaps), kwargs)
# scan backward
res1_rev = pool.apply_async(ihh_scan, (h1[::-1], gaps[::-1]), kwargs)
res2_rev = pool.apply_async(ihh_scan, (h2[::-1], gaps[::-1]), kwargs)
# wait for both to finish
pool.close()
pool.join()
# obtain results
ihh1_fwd = res1_fwd.get()
ihh2_fwd = res2_fwd.get()
ihh1_rev = res1_rev.get()
ihh2_rev = res2_rev.get()
# cleanup
pool.terminate()
else:
# compute without threads
# scan forward
ihh1_fwd = ihh_scan(h1, gaps, **kwargs)
ihh2_fwd = ihh_scan(h2, gaps, **kwargs)
# scan backward
ihh1_rev = ihh_scan(h1[::-1], gaps[::-1], **kwargs)
ihh2_rev = ihh_scan(h2[::-1], gaps[::-1], **kwargs)
# handle reverse scans
ihh1_rev = ihh1_rev[::-1]
ihh2_rev = ihh2_rev[::-1]
# compute unstandardized score
ihh1 = ihh1_fwd + ihh1_rev
ihh2 = ihh2_fwd + ihh2_rev
score = np.log(ihh1 / ihh2)
return score | python | def xpehh(h1, h2, pos, map_pos=None, min_ehh=0.05, include_edges=False,
gap_scale=20000, max_gap=200000, is_accessible=None,
use_threads=True):
"""Compute the unstandardized cross-population extended haplotype
homozygosity score (XPEHH) for each variant.
Parameters
----------
h1 : array_like, int, shape (n_variants, n_haplotypes)
Haplotype array for the first population.
h2 : array_like, int, shape (n_variants, n_haplotypes)
Haplotype array for the second population.
pos : array_like, int, shape (n_variants,)
Variant positions on physical or genetic map.
map_pos : array_like, float, shape (n_variants,)
Variant positions (genetic map distance).
min_ehh: float, optional
Minimum EHH beyond which to truncate integrated haplotype
homozygosity calculation.
include_edges : bool, optional
If True, report scores even if EHH does not decay below `min_ehh`
before reaching the edge of the data.
gap_scale : int, optional
Rescale distance between variants if gap is larger than this value.
max_gap : int, optional
Do not report scores if EHH spans a gap larger than this number of
base pairs.
is_accessible : array_like, bool, optional
Genome accessibility array. If provided, distance between variants
will be computed as the number of accessible bases between them.
use_threads : bool, optional
If True use multiple threads to compute.
Returns
-------
score : ndarray, float, shape (n_variants,)
Unstandardized XPEHH scores.
Notes
-----
This function will calculate XPEHH for all variants. To exclude variants
below a given minor allele frequency, filter the input haplotype arrays
before passing to this function.
This function returns NaN for any EHH calculations where haplotype
homozygosity does not decay below `min_ehh` before reaching the first or
last variant. To disable this behaviour, set `include_edges` to True.
Note that the unstandardized score is returned. Usually these scores are
then standardized genome-wide.
Haplotype arrays from the two populations may have different numbers of
haplotypes.
See Also
--------
standardize
"""
# check inputs
h1 = asarray_ndim(h1, 2)
check_integer_dtype(h1)
h2 = asarray_ndim(h2, 2)
check_integer_dtype(h2)
pos = asarray_ndim(pos, 1)
check_dim0_aligned(h1, h2, pos)
h1 = memoryview_safe(h1)
h2 = memoryview_safe(h2)
pos = memoryview_safe(pos)
# compute gaps between variants for integration
gaps = compute_ihh_gaps(pos, map_pos, gap_scale, max_gap, is_accessible)
# setup kwargs
kwargs = dict(min_ehh=min_ehh, include_edges=include_edges)
if use_threads and multiprocessing.cpu_count() > 1:
# use multiple threads
# setup threadpool
pool = ThreadPool(min(4, multiprocessing.cpu_count()))
# scan forward
res1_fwd = pool.apply_async(ihh_scan, (h1, gaps), kwargs)
res2_fwd = pool.apply_async(ihh_scan, (h2, gaps), kwargs)
# scan backward
res1_rev = pool.apply_async(ihh_scan, (h1[::-1], gaps[::-1]), kwargs)
res2_rev = pool.apply_async(ihh_scan, (h2[::-1], gaps[::-1]), kwargs)
# wait for both to finish
pool.close()
pool.join()
# obtain results
ihh1_fwd = res1_fwd.get()
ihh2_fwd = res2_fwd.get()
ihh1_rev = res1_rev.get()
ihh2_rev = res2_rev.get()
# cleanup
pool.terminate()
else:
# compute without threads
# scan forward
ihh1_fwd = ihh_scan(h1, gaps, **kwargs)
ihh2_fwd = ihh_scan(h2, gaps, **kwargs)
# scan backward
ihh1_rev = ihh_scan(h1[::-1], gaps[::-1], **kwargs)
ihh2_rev = ihh_scan(h2[::-1], gaps[::-1], **kwargs)
# handle reverse scans
ihh1_rev = ihh1_rev[::-1]
ihh2_rev = ihh2_rev[::-1]
# compute unstandardized score
ihh1 = ihh1_fwd + ihh1_rev
ihh2 = ihh2_fwd + ihh2_rev
score = np.log(ihh1 / ihh2)
return score | Compute the unstandardized cross-population extended haplotype
homozygosity score (XPEHH) for each variant.
Parameters
----------
h1 : array_like, int, shape (n_variants, n_haplotypes)
Haplotype array for the first population.
h2 : array_like, int, shape (n_variants, n_haplotypes)
Haplotype array for the second population.
pos : array_like, int, shape (n_variants,)
Variant positions on physical or genetic map.
map_pos : array_like, float, shape (n_variants,)
Variant positions (genetic map distance).
min_ehh: float, optional
Minimum EHH beyond which to truncate integrated haplotype
homozygosity calculation.
include_edges : bool, optional
If True, report scores even if EHH does not decay below `min_ehh`
before reaching the edge of the data.
gap_scale : int, optional
Rescale distance between variants if gap is larger than this value.
max_gap : int, optional
Do not report scores if EHH spans a gap larger than this number of
base pairs.
is_accessible : array_like, bool, optional
Genome accessibility array. If provided, distance between variants
will be computed as the number of accessible bases between them.
use_threads : bool, optional
If True use multiple threads to compute.
Returns
-------
score : ndarray, float, shape (n_variants,)
Unstandardized XPEHH scores.
Notes
-----
This function will calculate XPEHH for all variants. To exclude variants
below a given minor allele frequency, filter the input haplotype arrays
before passing to this function.
This function returns NaN for any EHH calculations where haplotype
homozygosity does not decay below `min_ehh` before reaching the first or
last variant. To disable this behaviour, set `include_edges` to True.
Note that the unstandardized score is returned. Usually these scores are
then standardized genome-wide.
Haplotype arrays from the two populations may have different numbers of
haplotypes.
See Also
--------
standardize | https://github.com/cggh/scikit-allel/blob/3c979a57a100240ba959dd13f98839349530f215/allel/stats/selection.py#L446-L571 |
cggh/scikit-allel | allel/stats/selection.py | nsl | def nsl(h, use_threads=True):
"""Compute the unstandardized number of segregating sites by length (nSl)
for each variant, comparing the reference and alternate alleles,
after Ferrer-Admetlla et al. (2014).
Parameters
----------
h : array_like, int, shape (n_variants, n_haplotypes)
Haplotype array.
use_threads : bool, optional
If True use multiple threads to compute.
Returns
-------
score : ndarray, float, shape (n_variants,)
Notes
-----
This function will calculate nSl for all variants. To exclude variants
below a given minor allele frequency, filter the input haplotype array
before passing to this function.
This function computes nSl by comparing the reference and alternate
alleles. These can be polarised by switching the sign for any variant where
the reference allele is derived.
This function does nothing about nSl calculations where haplotype
homozygosity extends up to the first or last variant. There may be edge
effects.
Note that the unstandardized score is returned. Usually these scores are
then standardized in different allele frequency bins.
See Also
--------
standardize_by_allele_count
"""
# check inputs
h = asarray_ndim(h, 2)
check_integer_dtype(h)
h = memoryview_safe(h)
# # check there are no invariant sites
# ac = h.count_alleles()
# assert np.all(ac.is_segregating()), 'please remove non-segregating sites'
if use_threads and multiprocessing.cpu_count() > 1:
# create pool
pool = ThreadPool(2)
# scan forward
result_fwd = pool.apply_async(nsl01_scan, args=(h,))
# scan backward
result_rev = pool.apply_async(nsl01_scan, args=(h[::-1],))
# wait for both to finish
pool.close()
pool.join()
# obtain results
nsl0_fwd, nsl1_fwd = result_fwd.get()
nsl0_rev, nsl1_rev = result_rev.get()
else:
# scan forward
nsl0_fwd, nsl1_fwd = nsl01_scan(h)
# scan backward
nsl0_rev, nsl1_rev = nsl01_scan(h[::-1])
# handle backwards
nsl0_rev = nsl0_rev[::-1]
nsl1_rev = nsl1_rev[::-1]
# compute unstandardized score
nsl0 = nsl0_fwd + nsl0_rev
nsl1 = nsl1_fwd + nsl1_rev
score = np.log(nsl1 / nsl0)
return score | python | def nsl(h, use_threads=True):
"""Compute the unstandardized number of segregating sites by length (nSl)
for each variant, comparing the reference and alternate alleles,
after Ferrer-Admetlla et al. (2014).
Parameters
----------
h : array_like, int, shape (n_variants, n_haplotypes)
Haplotype array.
use_threads : bool, optional
If True use multiple threads to compute.
Returns
-------
score : ndarray, float, shape (n_variants,)
Notes
-----
This function will calculate nSl for all variants. To exclude variants
below a given minor allele frequency, filter the input haplotype array
before passing to this function.
This function computes nSl by comparing the reference and alternate
alleles. These can be polarised by switching the sign for any variant where
the reference allele is derived.
This function does nothing about nSl calculations where haplotype
homozygosity extends up to the first or last variant. There may be edge
effects.
Note that the unstandardized score is returned. Usually these scores are
then standardized in different allele frequency bins.
See Also
--------
standardize_by_allele_count
"""
# check inputs
h = asarray_ndim(h, 2)
check_integer_dtype(h)
h = memoryview_safe(h)
# # check there are no invariant sites
# ac = h.count_alleles()
# assert np.all(ac.is_segregating()), 'please remove non-segregating sites'
if use_threads and multiprocessing.cpu_count() > 1:
# create pool
pool = ThreadPool(2)
# scan forward
result_fwd = pool.apply_async(nsl01_scan, args=(h,))
# scan backward
result_rev = pool.apply_async(nsl01_scan, args=(h[::-1],))
# wait for both to finish
pool.close()
pool.join()
# obtain results
nsl0_fwd, nsl1_fwd = result_fwd.get()
nsl0_rev, nsl1_rev = result_rev.get()
else:
# scan forward
nsl0_fwd, nsl1_fwd = nsl01_scan(h)
# scan backward
nsl0_rev, nsl1_rev = nsl01_scan(h[::-1])
# handle backwards
nsl0_rev = nsl0_rev[::-1]
nsl1_rev = nsl1_rev[::-1]
# compute unstandardized score
nsl0 = nsl0_fwd + nsl0_rev
nsl1 = nsl1_fwd + nsl1_rev
score = np.log(nsl1 / nsl0)
return score | Compute the unstandardized number of segregating sites by length (nSl)
for each variant, comparing the reference and alternate alleles,
after Ferrer-Admetlla et al. (2014).
Parameters
----------
h : array_like, int, shape (n_variants, n_haplotypes)
Haplotype array.
use_threads : bool, optional
If True use multiple threads to compute.
Returns
-------
score : ndarray, float, shape (n_variants,)
Notes
-----
This function will calculate nSl for all variants. To exclude variants
below a given minor allele frequency, filter the input haplotype array
before passing to this function.
This function computes nSl by comparing the reference and alternate
alleles. These can be polarised by switching the sign for any variant where
the reference allele is derived.
This function does nothing about nSl calculations where haplotype
homozygosity extends up to the first or last variant. There may be edge
effects.
Note that the unstandardized score is returned. Usually these scores are
then standardized in different allele frequency bins.
See Also
--------
standardize_by_allele_count | https://github.com/cggh/scikit-allel/blob/3c979a57a100240ba959dd13f98839349530f215/allel/stats/selection.py#L574-L658 |
cggh/scikit-allel | allel/stats/selection.py | xpnsl | def xpnsl(h1, h2, use_threads=True):
"""Cross-population version of the NSL statistic.
Parameters
----------
h1 : array_like, int, shape (n_variants, n_haplotypes)
Haplotype array for the first population.
h2 : array_like, int, shape (n_variants, n_haplotypes)
Haplotype array for the second population.
use_threads : bool, optional
If True use multiple threads to compute.
Returns
-------
score : ndarray, float, shape (n_variants,)
Unstandardized XPNSL scores.
"""
# check inputs
h1 = asarray_ndim(h1, 2)
check_integer_dtype(h1)
h2 = asarray_ndim(h2, 2)
check_integer_dtype(h2)
check_dim0_aligned(h1, h2)
h1 = memoryview_safe(h1)
h2 = memoryview_safe(h2)
if use_threads and multiprocessing.cpu_count() > 1:
# use multiple threads
# setup threadpool
pool = ThreadPool(min(4, multiprocessing.cpu_count()))
# scan forward
res1_fwd = pool.apply_async(nsl_scan, args=(h1,))
res2_fwd = pool.apply_async(nsl_scan, args=(h2,))
# scan backward
res1_rev = pool.apply_async(nsl_scan, args=(h1[::-1],))
res2_rev = pool.apply_async(nsl_scan, args=(h2[::-1],))
# wait for both to finish
pool.close()
pool.join()
# obtain results
nsl1_fwd = res1_fwd.get()
nsl2_fwd = res2_fwd.get()
nsl1_rev = res1_rev.get()
nsl2_rev = res2_rev.get()
# cleanup
pool.terminate()
else:
# compute without threads
# scan forward
nsl1_fwd = nsl_scan(h1)
nsl2_fwd = nsl_scan(h2)
# scan backward
nsl1_rev = nsl_scan(h1[::-1])
nsl2_rev = nsl_scan(h2[::-1])
# handle reverse scans
nsl1_rev = nsl1_rev[::-1]
nsl2_rev = nsl2_rev[::-1]
# compute unstandardized score
nsl1 = nsl1_fwd + nsl1_rev
nsl2 = nsl2_fwd + nsl2_rev
score = np.log(nsl1 / nsl2)
return score | python | def xpnsl(h1, h2, use_threads=True):
"""Cross-population version of the NSL statistic.
Parameters
----------
h1 : array_like, int, shape (n_variants, n_haplotypes)
Haplotype array for the first population.
h2 : array_like, int, shape (n_variants, n_haplotypes)
Haplotype array for the second population.
use_threads : bool, optional
If True use multiple threads to compute.
Returns
-------
score : ndarray, float, shape (n_variants,)
Unstandardized XPNSL scores.
"""
# check inputs
h1 = asarray_ndim(h1, 2)
check_integer_dtype(h1)
h2 = asarray_ndim(h2, 2)
check_integer_dtype(h2)
check_dim0_aligned(h1, h2)
h1 = memoryview_safe(h1)
h2 = memoryview_safe(h2)
if use_threads and multiprocessing.cpu_count() > 1:
# use multiple threads
# setup threadpool
pool = ThreadPool(min(4, multiprocessing.cpu_count()))
# scan forward
res1_fwd = pool.apply_async(nsl_scan, args=(h1,))
res2_fwd = pool.apply_async(nsl_scan, args=(h2,))
# scan backward
res1_rev = pool.apply_async(nsl_scan, args=(h1[::-1],))
res2_rev = pool.apply_async(nsl_scan, args=(h2[::-1],))
# wait for both to finish
pool.close()
pool.join()
# obtain results
nsl1_fwd = res1_fwd.get()
nsl2_fwd = res2_fwd.get()
nsl1_rev = res1_rev.get()
nsl2_rev = res2_rev.get()
# cleanup
pool.terminate()
else:
# compute without threads
# scan forward
nsl1_fwd = nsl_scan(h1)
nsl2_fwd = nsl_scan(h2)
# scan backward
nsl1_rev = nsl_scan(h1[::-1])
nsl2_rev = nsl_scan(h2[::-1])
# handle reverse scans
nsl1_rev = nsl1_rev[::-1]
nsl2_rev = nsl2_rev[::-1]
# compute unstandardized score
nsl1 = nsl1_fwd + nsl1_rev
nsl2 = nsl2_fwd + nsl2_rev
score = np.log(nsl1 / nsl2)
return score | Cross-population version of the NSL statistic.
Parameters
----------
h1 : array_like, int, shape (n_variants, n_haplotypes)
Haplotype array for the first population.
h2 : array_like, int, shape (n_variants, n_haplotypes)
Haplotype array for the second population.
use_threads : bool, optional
If True use multiple threads to compute.
Returns
-------
score : ndarray, float, shape (n_variants,)
Unstandardized XPNSL scores. | https://github.com/cggh/scikit-allel/blob/3c979a57a100240ba959dd13f98839349530f215/allel/stats/selection.py#L661-L736 |
cggh/scikit-allel | allel/stats/selection.py | haplotype_diversity | def haplotype_diversity(h):
"""Estimate haplotype diversity.
Parameters
----------
h : array_like, int, shape (n_variants, n_haplotypes)
Haplotype array.
Returns
-------
hd : float
Haplotype diversity.
"""
# check inputs
h = HaplotypeArray(h, copy=False)
# number of haplotypes
n = h.n_haplotypes
# compute haplotype frequencies
f = h.distinct_frequencies()
# estimate haplotype diversity
hd = (1 - np.sum(f**2)) * n / (n - 1)
return hd | python | def haplotype_diversity(h):
"""Estimate haplotype diversity.
Parameters
----------
h : array_like, int, shape (n_variants, n_haplotypes)
Haplotype array.
Returns
-------
hd : float
Haplotype diversity.
"""
# check inputs
h = HaplotypeArray(h, copy=False)
# number of haplotypes
n = h.n_haplotypes
# compute haplotype frequencies
f = h.distinct_frequencies()
# estimate haplotype diversity
hd = (1 - np.sum(f**2)) * n / (n - 1)
return hd | Estimate haplotype diversity.
Parameters
----------
h : array_like, int, shape (n_variants, n_haplotypes)
Haplotype array.
Returns
-------
hd : float
Haplotype diversity. | https://github.com/cggh/scikit-allel/blob/3c979a57a100240ba959dd13f98839349530f215/allel/stats/selection.py#L739-L766 |
cggh/scikit-allel | allel/stats/selection.py | moving_haplotype_diversity | def moving_haplotype_diversity(h, size, start=0, stop=None, step=None):
"""Estimate haplotype diversity in moving windows.
Parameters
----------
h : array_like, int, shape (n_variants, n_haplotypes)
Haplotype array.
size : int
The window size (number of variants).
start : int, optional
The index at which to start.
stop : int, optional
The index at which to stop.
step : int, optional
The number of variants between start positions of windows. If not
given, defaults to the window size, i.e., non-overlapping windows.
Returns
-------
hd : ndarray, float, shape (n_windows,)
Haplotype diversity.
"""
hd = moving_statistic(values=h, statistic=haplotype_diversity, size=size,
start=start, stop=stop, step=step)
return hd | python | def moving_haplotype_diversity(h, size, start=0, stop=None, step=None):
"""Estimate haplotype diversity in moving windows.
Parameters
----------
h : array_like, int, shape (n_variants, n_haplotypes)
Haplotype array.
size : int
The window size (number of variants).
start : int, optional
The index at which to start.
stop : int, optional
The index at which to stop.
step : int, optional
The number of variants between start positions of windows. If not
given, defaults to the window size, i.e., non-overlapping windows.
Returns
-------
hd : ndarray, float, shape (n_windows,)
Haplotype diversity.
"""
hd = moving_statistic(values=h, statistic=haplotype_diversity, size=size,
start=start, stop=stop, step=step)
return hd | Estimate haplotype diversity in moving windows.
Parameters
----------
h : array_like, int, shape (n_variants, n_haplotypes)
Haplotype array.
size : int
The window size (number of variants).
start : int, optional
The index at which to start.
stop : int, optional
The index at which to stop.
step : int, optional
The number of variants between start positions of windows. If not
given, defaults to the window size, i.e., non-overlapping windows.
Returns
-------
hd : ndarray, float, shape (n_windows,)
Haplotype diversity. | https://github.com/cggh/scikit-allel/blob/3c979a57a100240ba959dd13f98839349530f215/allel/stats/selection.py#L769-L795 |
cggh/scikit-allel | allel/stats/selection.py | garud_h | def garud_h(h):
"""Compute the H1, H12, H123 and H2/H1 statistics for detecting signatures
of soft sweeps, as defined in Garud et al. (2015).
Parameters
----------
h : array_like, int, shape (n_variants, n_haplotypes)
Haplotype array.
Returns
-------
h1 : float
H1 statistic (sum of squares of haplotype frequencies).
h12 : float
H12 statistic (sum of squares of haplotype frequencies, combining
the two most common haplotypes into a single frequency).
h123 : float
H123 statistic (sum of squares of haplotype frequencies, combining
the three most common haplotypes into a single frequency).
h2_h1 : float
H2/H1 statistic, indicating the "softness" of a sweep.
"""
# check inputs
h = HaplotypeArray(h, copy=False)
# compute haplotype frequencies
f = h.distinct_frequencies()
# compute H1
h1 = np.sum(f**2)
# compute H12
h12 = np.sum(f[:2])**2 + np.sum(f[2:]**2)
# compute H123
h123 = np.sum(f[:3])**2 + np.sum(f[3:]**2)
# compute H2/H1
h2 = h1 - f[0]**2
h2_h1 = h2 / h1
return h1, h12, h123, h2_h1 | python | def garud_h(h):
"""Compute the H1, H12, H123 and H2/H1 statistics for detecting signatures
of soft sweeps, as defined in Garud et al. (2015).
Parameters
----------
h : array_like, int, shape (n_variants, n_haplotypes)
Haplotype array.
Returns
-------
h1 : float
H1 statistic (sum of squares of haplotype frequencies).
h12 : float
H12 statistic (sum of squares of haplotype frequencies, combining
the two most common haplotypes into a single frequency).
h123 : float
H123 statistic (sum of squares of haplotype frequencies, combining
the three most common haplotypes into a single frequency).
h2_h1 : float
H2/H1 statistic, indicating the "softness" of a sweep.
"""
# check inputs
h = HaplotypeArray(h, copy=False)
# compute haplotype frequencies
f = h.distinct_frequencies()
# compute H1
h1 = np.sum(f**2)
# compute H12
h12 = np.sum(f[:2])**2 + np.sum(f[2:]**2)
# compute H123
h123 = np.sum(f[:3])**2 + np.sum(f[3:]**2)
# compute H2/H1
h2 = h1 - f[0]**2
h2_h1 = h2 / h1
return h1, h12, h123, h2_h1 | Compute the H1, H12, H123 and H2/H1 statistics for detecting signatures
of soft sweeps, as defined in Garud et al. (2015).
Parameters
----------
h : array_like, int, shape (n_variants, n_haplotypes)
Haplotype array.
Returns
-------
h1 : float
H1 statistic (sum of squares of haplotype frequencies).
h12 : float
H12 statistic (sum of squares of haplotype frequencies, combining
the two most common haplotypes into a single frequency).
h123 : float
H123 statistic (sum of squares of haplotype frequencies, combining
the three most common haplotypes into a single frequency).
h2_h1 : float
H2/H1 statistic, indicating the "softness" of a sweep. | https://github.com/cggh/scikit-allel/blob/3c979a57a100240ba959dd13f98839349530f215/allel/stats/selection.py#L798-L841 |
cggh/scikit-allel | allel/stats/selection.py | moving_garud_h | def moving_garud_h(h, size, start=0, stop=None, step=None):
"""Compute the H1, H12, H123 and H2/H1 statistics for detecting signatures
of soft sweeps, as defined in Garud et al. (2015), in moving windows,
Parameters
----------
h : array_like, int, shape (n_variants, n_haplotypes)
Haplotype array.
size : int
The window size (number of variants).
start : int, optional
The index at which to start.
stop : int, optional
The index at which to stop.
step : int, optional
The number of variants between start positions of windows. If not
given, defaults to the window size, i.e., non-overlapping windows.
Returns
-------
h1 : ndarray, float, shape (n_windows,)
H1 statistics (sum of squares of haplotype frequencies).
h12 : ndarray, float, shape (n_windows,)
H12 statistics (sum of squares of haplotype frequencies, combining
the two most common haplotypes into a single frequency).
h123 : ndarray, float, shape (n_windows,)
H123 statistics (sum of squares of haplotype frequencies, combining
the three most common haplotypes into a single frequency).
h2_h1 : ndarray, float, shape (n_windows,)
H2/H1 statistics, indicating the "softness" of a sweep.
"""
gh = moving_statistic(values=h, statistic=garud_h, size=size, start=start,
stop=stop, step=step)
h1 = gh[:, 0]
h12 = gh[:, 1]
h123 = gh[:, 2]
h2_h1 = gh[:, 3]
return h1, h12, h123, h2_h1 | python | def moving_garud_h(h, size, start=0, stop=None, step=None):
"""Compute the H1, H12, H123 and H2/H1 statistics for detecting signatures
of soft sweeps, as defined in Garud et al. (2015), in moving windows,
Parameters
----------
h : array_like, int, shape (n_variants, n_haplotypes)
Haplotype array.
size : int
The window size (number of variants).
start : int, optional
The index at which to start.
stop : int, optional
The index at which to stop.
step : int, optional
The number of variants between start positions of windows. If not
given, defaults to the window size, i.e., non-overlapping windows.
Returns
-------
h1 : ndarray, float, shape (n_windows,)
H1 statistics (sum of squares of haplotype frequencies).
h12 : ndarray, float, shape (n_windows,)
H12 statistics (sum of squares of haplotype frequencies, combining
the two most common haplotypes into a single frequency).
h123 : ndarray, float, shape (n_windows,)
H123 statistics (sum of squares of haplotype frequencies, combining
the three most common haplotypes into a single frequency).
h2_h1 : ndarray, float, shape (n_windows,)
H2/H1 statistics, indicating the "softness" of a sweep.
"""
gh = moving_statistic(values=h, statistic=garud_h, size=size, start=start,
stop=stop, step=step)
h1 = gh[:, 0]
h12 = gh[:, 1]
h123 = gh[:, 2]
h2_h1 = gh[:, 3]
return h1, h12, h123, h2_h1 | Compute the H1, H12, H123 and H2/H1 statistics for detecting signatures
of soft sweeps, as defined in Garud et al. (2015), in moving windows,
Parameters
----------
h : array_like, int, shape (n_variants, n_haplotypes)
Haplotype array.
size : int
The window size (number of variants).
start : int, optional
The index at which to start.
stop : int, optional
The index at which to stop.
step : int, optional
The number of variants between start positions of windows. If not
given, defaults to the window size, i.e., non-overlapping windows.
Returns
-------
h1 : ndarray, float, shape (n_windows,)
H1 statistics (sum of squares of haplotype frequencies).
h12 : ndarray, float, shape (n_windows,)
H12 statistics (sum of squares of haplotype frequencies, combining
the two most common haplotypes into a single frequency).
h123 : ndarray, float, shape (n_windows,)
H123 statistics (sum of squares of haplotype frequencies, combining
the three most common haplotypes into a single frequency).
h2_h1 : ndarray, float, shape (n_windows,)
H2/H1 statistics, indicating the "softness" of a sweep. | https://github.com/cggh/scikit-allel/blob/3c979a57a100240ba959dd13f98839349530f215/allel/stats/selection.py#L844-L885 |
cggh/scikit-allel | allel/stats/selection.py | plot_haplotype_frequencies | def plot_haplotype_frequencies(h, palette='Paired', singleton_color='w',
ax=None):
"""Plot haplotype frequencies.
Parameters
----------
h : array_like, int, shape (n_variants, n_haplotypes)
Haplotype array.
palette : string, optional
A Seaborn palette name.
singleton_color : string, optional
Color to paint singleton haplotypes.
ax : axes, optional
The axes on which to draw. If not provided, a new figure will be
created.
Returns
-------
ax : axes
"""
import matplotlib.pyplot as plt
import seaborn as sns
# check inputs
h = HaplotypeArray(h, copy=False)
# setup figure
if ax is None:
width = plt.rcParams['figure.figsize'][0]
height = width / 10
fig, ax = plt.subplots(figsize=(width, height))
sns.despine(ax=ax, left=True)
# count distinct haplotypes
hc = h.distinct_counts()
# setup palette
n_colors = np.count_nonzero(hc > 1)
palette = sns.color_palette(palette, n_colors)
# paint frequencies
x1 = 0
for i, c in enumerate(hc):
x2 = x1 + c
if c > 1:
color = palette[i]
else:
color = singleton_color
ax.axvspan(x1, x2, color=color)
x1 = x2
# tidy up
ax.set_xlim(0, h.shape[1])
ax.set_yticks([])
return ax | python | def plot_haplotype_frequencies(h, palette='Paired', singleton_color='w',
ax=None):
"""Plot haplotype frequencies.
Parameters
----------
h : array_like, int, shape (n_variants, n_haplotypes)
Haplotype array.
palette : string, optional
A Seaborn palette name.
singleton_color : string, optional
Color to paint singleton haplotypes.
ax : axes, optional
The axes on which to draw. If not provided, a new figure will be
created.
Returns
-------
ax : axes
"""
import matplotlib.pyplot as plt
import seaborn as sns
# check inputs
h = HaplotypeArray(h, copy=False)
# setup figure
if ax is None:
width = plt.rcParams['figure.figsize'][0]
height = width / 10
fig, ax = plt.subplots(figsize=(width, height))
sns.despine(ax=ax, left=True)
# count distinct haplotypes
hc = h.distinct_counts()
# setup palette
n_colors = np.count_nonzero(hc > 1)
palette = sns.color_palette(palette, n_colors)
# paint frequencies
x1 = 0
for i, c in enumerate(hc):
x2 = x1 + c
if c > 1:
color = palette[i]
else:
color = singleton_color
ax.axvspan(x1, x2, color=color)
x1 = x2
# tidy up
ax.set_xlim(0, h.shape[1])
ax.set_yticks([])
return ax | Plot haplotype frequencies.
Parameters
----------
h : array_like, int, shape (n_variants, n_haplotypes)
Haplotype array.
palette : string, optional
A Seaborn palette name.
singleton_color : string, optional
Color to paint singleton haplotypes.
ax : axes, optional
The axes on which to draw. If not provided, a new figure will be
created.
Returns
-------
ax : axes | https://github.com/cggh/scikit-allel/blob/3c979a57a100240ba959dd13f98839349530f215/allel/stats/selection.py#L888-L945 |
cggh/scikit-allel | allel/stats/selection.py | moving_hfs_rank | def moving_hfs_rank(h, size, start=0, stop=None):
"""Helper function for plotting haplotype frequencies in moving windows.
Parameters
----------
h : array_like, int, shape (n_variants, n_haplotypes)
Haplotype array.
size : int
The window size (number of variants).
start : int, optional
The index at which to start.
stop : int, optional
The index at which to stop.
Returns
-------
hr : ndarray, int, shape (n_windows, n_haplotypes)
Haplotype rank array.
"""
# determine windows
windows = np.asarray(list(index_windows(h, size=size, start=start,
stop=stop, step=None)))
# setup output
hr = np.zeros((windows.shape[0], h.shape[1]), dtype='i4')
# iterate over windows
for i, (window_start, window_stop) in enumerate(windows):
# extract haplotypes for the current window
hw = h[window_start:window_stop]
# count haplotypes
hc = hw.distinct_counts()
# ensure sorted descending
hc.sort()
hc = hc[::-1]
# compute ranks for non-singleton haplotypes
cp = 0
for j, c in enumerate(hc):
if c > 1:
hr[i, cp:cp+c] = j+1
cp += c
return hr | python | def moving_hfs_rank(h, size, start=0, stop=None):
"""Helper function for plotting haplotype frequencies in moving windows.
Parameters
----------
h : array_like, int, shape (n_variants, n_haplotypes)
Haplotype array.
size : int
The window size (number of variants).
start : int, optional
The index at which to start.
stop : int, optional
The index at which to stop.
Returns
-------
hr : ndarray, int, shape (n_windows, n_haplotypes)
Haplotype rank array.
"""
# determine windows
windows = np.asarray(list(index_windows(h, size=size, start=start,
stop=stop, step=None)))
# setup output
hr = np.zeros((windows.shape[0], h.shape[1]), dtype='i4')
# iterate over windows
for i, (window_start, window_stop) in enumerate(windows):
# extract haplotypes for the current window
hw = h[window_start:window_stop]
# count haplotypes
hc = hw.distinct_counts()
# ensure sorted descending
hc.sort()
hc = hc[::-1]
# compute ranks for non-singleton haplotypes
cp = 0
for j, c in enumerate(hc):
if c > 1:
hr[i, cp:cp+c] = j+1
cp += c
return hr | Helper function for plotting haplotype frequencies in moving windows.
Parameters
----------
h : array_like, int, shape (n_variants, n_haplotypes)
Haplotype array.
size : int
The window size (number of variants).
start : int, optional
The index at which to start.
stop : int, optional
The index at which to stop.
Returns
-------
hr : ndarray, int, shape (n_windows, n_haplotypes)
Haplotype rank array. | https://github.com/cggh/scikit-allel/blob/3c979a57a100240ba959dd13f98839349530f215/allel/stats/selection.py#L948-L996 |
cggh/scikit-allel | allel/stats/selection.py | plot_moving_haplotype_frequencies | def plot_moving_haplotype_frequencies(pos, h, size, start=0, stop=None, n=None,
palette='Paired', singleton_color='w',
ax=None):
"""Plot haplotype frequencies in moving windows over the genome.
Parameters
----------
pos : array_like, int, shape (n_items,)
Variant positions, using 1-based coordinates, in ascending order.
h : array_like, int, shape (n_variants, n_haplotypes)
Haplotype array.
size : int
The window size (number of variants).
start : int, optional
The index at which to start.
stop : int, optional
The index at which to stop.
n : int, optional
Color only the `n` most frequent haplotypes (by default, all
non-singleton haplotypes are colored).
palette : string, optional
A Seaborn palette name.
singleton_color : string, optional
Color to paint singleton haplotypes.
ax : axes, optional
The axes on which to draw. If not provided, a new figure will be
created.
Returns
-------
ax : axes
"""
import matplotlib as mpl
import matplotlib.pyplot as plt
import seaborn as sns
# setup figure
if ax is None:
fig, ax = plt.subplots()
# compute haplotype frequencies
# N.B., here we use a haplotype rank data structure to enable the use of
# pcolormesh() which is a lot faster than any other type of plotting
# function
hr = moving_hfs_rank(h, size=size, start=start, stop=stop)
# truncate to n most common haplotypes
if n:
hr[hr > n] = 0
# compute window start and stop positions
windows = moving_statistic(pos, statistic=lambda v: (v[0], v[-1]),
size=size, start=start, stop=stop)
# create color map
colors = [singleton_color] + sns.color_palette(palette, n_colors=hr.max())
cmap = mpl.colors.ListedColormap(colors)
# draw colors
x = np.append(windows[:, 0], windows[-1, -1])
y = np.arange(h.shape[1]+1)
ax.pcolormesh(x, y, hr.T, cmap=cmap)
# tidy up
ax.set_xlim(windows[0, 0], windows[-1, -1])
ax.set_ylim(0, h.shape[1])
ax.set_ylabel('haplotype count')
ax.set_xlabel('position (bp)')
return ax | python | def plot_moving_haplotype_frequencies(pos, h, size, start=0, stop=None, n=None,
palette='Paired', singleton_color='w',
ax=None):
"""Plot haplotype frequencies in moving windows over the genome.
Parameters
----------
pos : array_like, int, shape (n_items,)
Variant positions, using 1-based coordinates, in ascending order.
h : array_like, int, shape (n_variants, n_haplotypes)
Haplotype array.
size : int
The window size (number of variants).
start : int, optional
The index at which to start.
stop : int, optional
The index at which to stop.
n : int, optional
Color only the `n` most frequent haplotypes (by default, all
non-singleton haplotypes are colored).
palette : string, optional
A Seaborn palette name.
singleton_color : string, optional
Color to paint singleton haplotypes.
ax : axes, optional
The axes on which to draw. If not provided, a new figure will be
created.
Returns
-------
ax : axes
"""
import matplotlib as mpl
import matplotlib.pyplot as plt
import seaborn as sns
# setup figure
if ax is None:
fig, ax = plt.subplots()
# compute haplotype frequencies
# N.B., here we use a haplotype rank data structure to enable the use of
# pcolormesh() which is a lot faster than any other type of plotting
# function
hr = moving_hfs_rank(h, size=size, start=start, stop=stop)
# truncate to n most common haplotypes
if n:
hr[hr > n] = 0
# compute window start and stop positions
windows = moving_statistic(pos, statistic=lambda v: (v[0], v[-1]),
size=size, start=start, stop=stop)
# create color map
colors = [singleton_color] + sns.color_palette(palette, n_colors=hr.max())
cmap = mpl.colors.ListedColormap(colors)
# draw colors
x = np.append(windows[:, 0], windows[-1, -1])
y = np.arange(h.shape[1]+1)
ax.pcolormesh(x, y, hr.T, cmap=cmap)
# tidy up
ax.set_xlim(windows[0, 0], windows[-1, -1])
ax.set_ylim(0, h.shape[1])
ax.set_ylabel('haplotype count')
ax.set_xlabel('position (bp)')
return ax | Plot haplotype frequencies in moving windows over the genome.
Parameters
----------
pos : array_like, int, shape (n_items,)
Variant positions, using 1-based coordinates, in ascending order.
h : array_like, int, shape (n_variants, n_haplotypes)
Haplotype array.
size : int
The window size (number of variants).
start : int, optional
The index at which to start.
stop : int, optional
The index at which to stop.
n : int, optional
Color only the `n` most frequent haplotypes (by default, all
non-singleton haplotypes are colored).
palette : string, optional
A Seaborn palette name.
singleton_color : string, optional
Color to paint singleton haplotypes.
ax : axes, optional
The axes on which to draw. If not provided, a new figure will be
created.
Returns
-------
ax : axes | https://github.com/cggh/scikit-allel/blob/3c979a57a100240ba959dd13f98839349530f215/allel/stats/selection.py#L999-L1070 |
cggh/scikit-allel | allel/stats/selection.py | moving_delta_tajima_d | def moving_delta_tajima_d(ac1, ac2, size, start=0, stop=None, step=None):
"""Compute the difference in Tajima's D between two populations in
moving windows.
Parameters
----------
ac1 : array_like, int, shape (n_variants, n_alleles)
Allele counts array for the first population.
ac2 : array_like, int, shape (n_variants, n_alleles)
Allele counts array for the second population.
size : int
The window size (number of variants).
start : int, optional
The index at which to start.
stop : int, optional
The index at which to stop.
step : int, optional
The number of variants between start positions of windows. If not
given, defaults to the window size, i.e., non-overlapping windows.
Returns
-------
delta_d : ndarray, float, shape (n_windows,)
Standardized delta Tajima's D.
See Also
--------
allel.stats.diversity.moving_tajima_d
"""
d1 = moving_tajima_d(ac1, size=size, start=start, stop=stop, step=step)
d2 = moving_tajima_d(ac2, size=size, start=start, stop=stop, step=step)
delta = d1 - d2
delta_z = (delta - np.mean(delta)) / np.std(delta)
return delta_z | python | def moving_delta_tajima_d(ac1, ac2, size, start=0, stop=None, step=None):
"""Compute the difference in Tajima's D between two populations in
moving windows.
Parameters
----------
ac1 : array_like, int, shape (n_variants, n_alleles)
Allele counts array for the first population.
ac2 : array_like, int, shape (n_variants, n_alleles)
Allele counts array for the second population.
size : int
The window size (number of variants).
start : int, optional
The index at which to start.
stop : int, optional
The index at which to stop.
step : int, optional
The number of variants between start positions of windows. If not
given, defaults to the window size, i.e., non-overlapping windows.
Returns
-------
delta_d : ndarray, float, shape (n_windows,)
Standardized delta Tajima's D.
See Also
--------
allel.stats.diversity.moving_tajima_d
"""
d1 = moving_tajima_d(ac1, size=size, start=start, stop=stop, step=step)
d2 = moving_tajima_d(ac2, size=size, start=start, stop=stop, step=step)
delta = d1 - d2
delta_z = (delta - np.mean(delta)) / np.std(delta)
return delta_z | Compute the difference in Tajima's D between two populations in
moving windows.
Parameters
----------
ac1 : array_like, int, shape (n_variants, n_alleles)
Allele counts array for the first population.
ac2 : array_like, int, shape (n_variants, n_alleles)
Allele counts array for the second population.
size : int
The window size (number of variants).
start : int, optional
The index at which to start.
stop : int, optional
The index at which to stop.
step : int, optional
The number of variants between start positions of windows. If not
given, defaults to the window size, i.e., non-overlapping windows.
Returns
-------
delta_d : ndarray, float, shape (n_windows,)
Standardized delta Tajima's D.
See Also
--------
allel.stats.diversity.moving_tajima_d | https://github.com/cggh/scikit-allel/blob/3c979a57a100240ba959dd13f98839349530f215/allel/stats/selection.py#L1073-L1108 |
cggh/scikit-allel | allel/stats/selection.py | make_similar_sized_bins | def make_similar_sized_bins(x, n):
"""Utility function to create a set of bins over the range of values in `x`
such that each bin contains roughly the same number of values.
Parameters
----------
x : array_like
The values to be binned.
n : int
The number of bins to create.
Returns
-------
bins : ndarray
An array of bin edges.
Notes
-----
The actual number of bins returned may be less than `n` if `x` contains
integer values and any single value is represented more than len(x)//n
times.
"""
# copy and sort the array
y = np.array(x).flatten()
y.sort()
# setup bins
bins = [y[0]]
# determine step size
step = len(y) // n
# add bin edges
for i in range(step, len(y), step):
# get value at this index
v = y[i]
# only add bin edge if larger than previous
if v > bins[-1]:
bins.append(v)
# fix last bin edge
bins[-1] = y[-1]
return np.array(bins) | python | def make_similar_sized_bins(x, n):
"""Utility function to create a set of bins over the range of values in `x`
such that each bin contains roughly the same number of values.
Parameters
----------
x : array_like
The values to be binned.
n : int
The number of bins to create.
Returns
-------
bins : ndarray
An array of bin edges.
Notes
-----
The actual number of bins returned may be less than `n` if `x` contains
integer values and any single value is represented more than len(x)//n
times.
"""
# copy and sort the array
y = np.array(x).flatten()
y.sort()
# setup bins
bins = [y[0]]
# determine step size
step = len(y) // n
# add bin edges
for i in range(step, len(y), step):
# get value at this index
v = y[i]
# only add bin edge if larger than previous
if v > bins[-1]:
bins.append(v)
# fix last bin edge
bins[-1] = y[-1]
return np.array(bins) | Utility function to create a set of bins over the range of values in `x`
such that each bin contains roughly the same number of values.
Parameters
----------
x : array_like
The values to be binned.
n : int
The number of bins to create.
Returns
-------
bins : ndarray
An array of bin edges.
Notes
-----
The actual number of bins returned may be less than `n` if `x` contains
integer values and any single value is represented more than len(x)//n
times. | https://github.com/cggh/scikit-allel/blob/3c979a57a100240ba959dd13f98839349530f215/allel/stats/selection.py#L1111-L1157 |
cggh/scikit-allel | allel/stats/selection.py | standardize | def standardize(score):
"""Centre and scale to unit variance."""
score = asarray_ndim(score, 1)
return (score - np.nanmean(score)) / np.nanstd(score) | python | def standardize(score):
"""Centre and scale to unit variance."""
score = asarray_ndim(score, 1)
return (score - np.nanmean(score)) / np.nanstd(score) | Centre and scale to unit variance. | https://github.com/cggh/scikit-allel/blob/3c979a57a100240ba959dd13f98839349530f215/allel/stats/selection.py#L1160-L1163 |
cggh/scikit-allel | allel/stats/selection.py | standardize_by_allele_count | def standardize_by_allele_count(score, aac, bins=None, n_bins=None,
diagnostics=True):
"""Standardize `score` within allele frequency bins.
Parameters
----------
score : array_like, float
The score to be standardized, e.g., IHS or NSL.
aac : array_like, int
An array of alternate allele counts.
bins : array_like, int, optional
Allele count bins, overrides `n_bins`.
n_bins : int, optional
Number of allele count bins to use.
diagnostics : bool, optional
If True, plot some diagnostic information about the standardization.
Returns
-------
score_standardized : ndarray, float
Standardized scores.
bins : ndarray, int
Allele count bins used for standardization.
"""
from scipy.stats import binned_statistic
# check inputs
score = asarray_ndim(score, 1)
aac = asarray_ndim(aac, 1)
check_dim0_aligned(score, aac)
# remove nans
nonan = ~np.isnan(score)
score_nonan = score[nonan]
aac_nonan = aac[nonan]
if bins is None:
# make our own similar sized bins
# how many bins to make?
if n_bins is None:
# something vaguely reasonable
n_bins = np.max(aac) // 2
# make bins
bins = make_similar_sized_bins(aac_nonan, n_bins)
else:
# user-provided bins
bins = asarray_ndim(bins, 1)
mean_score, _, _ = binned_statistic(aac_nonan, score_nonan,
statistic=np.mean,
bins=bins)
std_score, _, _ = binned_statistic(aac_nonan, score_nonan,
statistic=np.std,
bins=bins)
if diagnostics:
import matplotlib.pyplot as plt
x = (bins[:-1] + bins[1:]) / 2
plt.figure()
plt.fill_between(x,
mean_score - std_score,
mean_score + std_score,
alpha=.5,
label='std')
plt.plot(x, mean_score, marker='o', label='mean')
plt.grid(axis='y')
plt.xlabel('Alternate allele count')
plt.ylabel('Unstandardized score')
plt.title('Standardization diagnostics')
plt.legend()
# apply standardization
score_standardized = np.empty_like(score)
for i in range(len(bins) - 1):
x1 = bins[i]
x2 = bins[i + 1]
if i == 0:
# first bin
loc = (aac < x2)
elif i == len(bins) - 2:
# last bin
loc = (aac >= x1)
else:
# middle bins
loc = (aac >= x1) & (aac < x2)
m = mean_score[i]
s = std_score[i]
score_standardized[loc] = (score[loc] - m) / s
return score_standardized, bins | python | def standardize_by_allele_count(score, aac, bins=None, n_bins=None,
diagnostics=True):
"""Standardize `score` within allele frequency bins.
Parameters
----------
score : array_like, float
The score to be standardized, e.g., IHS or NSL.
aac : array_like, int
An array of alternate allele counts.
bins : array_like, int, optional
Allele count bins, overrides `n_bins`.
n_bins : int, optional
Number of allele count bins to use.
diagnostics : bool, optional
If True, plot some diagnostic information about the standardization.
Returns
-------
score_standardized : ndarray, float
Standardized scores.
bins : ndarray, int
Allele count bins used for standardization.
"""
from scipy.stats import binned_statistic
# check inputs
score = asarray_ndim(score, 1)
aac = asarray_ndim(aac, 1)
check_dim0_aligned(score, aac)
# remove nans
nonan = ~np.isnan(score)
score_nonan = score[nonan]
aac_nonan = aac[nonan]
if bins is None:
# make our own similar sized bins
# how many bins to make?
if n_bins is None:
# something vaguely reasonable
n_bins = np.max(aac) // 2
# make bins
bins = make_similar_sized_bins(aac_nonan, n_bins)
else:
# user-provided bins
bins = asarray_ndim(bins, 1)
mean_score, _, _ = binned_statistic(aac_nonan, score_nonan,
statistic=np.mean,
bins=bins)
std_score, _, _ = binned_statistic(aac_nonan, score_nonan,
statistic=np.std,
bins=bins)
if diagnostics:
import matplotlib.pyplot as plt
x = (bins[:-1] + bins[1:]) / 2
plt.figure()
plt.fill_between(x,
mean_score - std_score,
mean_score + std_score,
alpha=.5,
label='std')
plt.plot(x, mean_score, marker='o', label='mean')
plt.grid(axis='y')
plt.xlabel('Alternate allele count')
plt.ylabel('Unstandardized score')
plt.title('Standardization diagnostics')
plt.legend()
# apply standardization
score_standardized = np.empty_like(score)
for i in range(len(bins) - 1):
x1 = bins[i]
x2 = bins[i + 1]
if i == 0:
# first bin
loc = (aac < x2)
elif i == len(bins) - 2:
# last bin
loc = (aac >= x1)
else:
# middle bins
loc = (aac >= x1) & (aac < x2)
m = mean_score[i]
s = std_score[i]
score_standardized[loc] = (score[loc] - m) / s
return score_standardized, bins | Standardize `score` within allele frequency bins.
Parameters
----------
score : array_like, float
The score to be standardized, e.g., IHS or NSL.
aac : array_like, int
An array of alternate allele counts.
bins : array_like, int, optional
Allele count bins, overrides `n_bins`.
n_bins : int, optional
Number of allele count bins to use.
diagnostics : bool, optional
If True, plot some diagnostic information about the standardization.
Returns
-------
score_standardized : ndarray, float
Standardized scores.
bins : ndarray, int
Allele count bins used for standardization. | https://github.com/cggh/scikit-allel/blob/3c979a57a100240ba959dd13f98839349530f215/allel/stats/selection.py#L1166-L1260 |
cggh/scikit-allel | allel/stats/selection.py | pbs | def pbs(ac1, ac2, ac3, window_size, window_start=0, window_stop=None,
window_step=None, normed=True):
"""Compute the population branching statistic (PBS) which performs a comparison
of allele frequencies between three populations to detect genome regions that are
unusually differentiated in one population relative to the other two populations.
Parameters
----------
ac1 : array_like, int
Allele counts from the first population.
ac2 : array_like, int
Allele counts from the second population.
ac3 : array_like, int
Allele counts from the third population.
window_size : int
The window size (number of variants) within which to compute PBS values.
window_start : int, optional
The variant index at which to start windowed calculations.
window_stop : int, optional
The variant index at which to stop windowed calculations.
window_step : int, optional
The number of variants between start positions of windows. If not given, defaults
to the window size, i.e., non-overlapping windows.
normed : bool, optional
If True (default), use the normalised version of PBS, also known as PBSn1 [2]_.
Otherwise, use the PBS statistic as originally defined in [1]_.
Returns
-------
pbs : ndarray, float
Windowed PBS values.
Notes
-----
The F:sub:`ST` calculations use Hudson's estimator.
References
----------
.. [1] Yi et al., "Sequencing of Fifty Human Exomes Reveals Adaptation to High
Altitude", Science, 329(5987): 75–78, 2 July 2010.
.. [2] Malaspinas et al., "A genomic history of Aboriginal Australia", Nature. volume
538, pages 207–214, 13 October 2016.
"""
# normalise and check inputs
ac1 = AlleleCountsArray(ac1)
ac2 = AlleleCountsArray(ac2)
ac3 = AlleleCountsArray(ac3)
check_dim0_aligned(ac1, ac2, ac3)
# compute fst
fst12 = moving_hudson_fst(ac1, ac2, size=window_size, start=window_start,
stop=window_stop, step=window_step)
fst13 = moving_hudson_fst(ac1, ac3, size=window_size, start=window_start,
stop=window_stop, step=window_step)
fst23 = moving_hudson_fst(ac2, ac3, size=window_size, start=window_start,
stop=window_stop, step=window_step)
# clip fst values to avoid infinite if fst is 1
for x in fst12, fst13, fst23:
np.clip(x, a_min=0, a_max=0.99999, out=x)
# compute fst transform
t12 = -np.log(1 - fst12)
t13 = -np.log(1 - fst13)
t23 = -np.log(1 - fst23)
# compute pbs
ret = (t12 + t13 - t23) / 2
if normed:
# compute pbs normalising constant
norm = 1 + (t12 + t13 + t23) / 2
ret = ret / norm
return ret | python | def pbs(ac1, ac2, ac3, window_size, window_start=0, window_stop=None,
window_step=None, normed=True):
"""Compute the population branching statistic (PBS) which performs a comparison
of allele frequencies between three populations to detect genome regions that are
unusually differentiated in one population relative to the other two populations.
Parameters
----------
ac1 : array_like, int
Allele counts from the first population.
ac2 : array_like, int
Allele counts from the second population.
ac3 : array_like, int
Allele counts from the third population.
window_size : int
The window size (number of variants) within which to compute PBS values.
window_start : int, optional
The variant index at which to start windowed calculations.
window_stop : int, optional
The variant index at which to stop windowed calculations.
window_step : int, optional
The number of variants between start positions of windows. If not given, defaults
to the window size, i.e., non-overlapping windows.
normed : bool, optional
If True (default), use the normalised version of PBS, also known as PBSn1 [2]_.
Otherwise, use the PBS statistic as originally defined in [1]_.
Returns
-------
pbs : ndarray, float
Windowed PBS values.
Notes
-----
The F:sub:`ST` calculations use Hudson's estimator.
References
----------
.. [1] Yi et al., "Sequencing of Fifty Human Exomes Reveals Adaptation to High
Altitude", Science, 329(5987): 75–78, 2 July 2010.
.. [2] Malaspinas et al., "A genomic history of Aboriginal Australia", Nature. volume
538, pages 207–214, 13 October 2016.
"""
# normalise and check inputs
ac1 = AlleleCountsArray(ac1)
ac2 = AlleleCountsArray(ac2)
ac3 = AlleleCountsArray(ac3)
check_dim0_aligned(ac1, ac2, ac3)
# compute fst
fst12 = moving_hudson_fst(ac1, ac2, size=window_size, start=window_start,
stop=window_stop, step=window_step)
fst13 = moving_hudson_fst(ac1, ac3, size=window_size, start=window_start,
stop=window_stop, step=window_step)
fst23 = moving_hudson_fst(ac2, ac3, size=window_size, start=window_start,
stop=window_stop, step=window_step)
# clip fst values to avoid infinite if fst is 1
for x in fst12, fst13, fst23:
np.clip(x, a_min=0, a_max=0.99999, out=x)
# compute fst transform
t12 = -np.log(1 - fst12)
t13 = -np.log(1 - fst13)
t23 = -np.log(1 - fst23)
# compute pbs
ret = (t12 + t13 - t23) / 2
if normed:
# compute pbs normalising constant
norm = 1 + (t12 + t13 + t23) / 2
ret = ret / norm
return ret | Compute the population branching statistic (PBS) which performs a comparison
of allele frequencies between three populations to detect genome regions that are
unusually differentiated in one population relative to the other two populations.
Parameters
----------
ac1 : array_like, int
Allele counts from the first population.
ac2 : array_like, int
Allele counts from the second population.
ac3 : array_like, int
Allele counts from the third population.
window_size : int
The window size (number of variants) within which to compute PBS values.
window_start : int, optional
The variant index at which to start windowed calculations.
window_stop : int, optional
The variant index at which to stop windowed calculations.
window_step : int, optional
The number of variants between start positions of windows. If not given, defaults
to the window size, i.e., non-overlapping windows.
normed : bool, optional
If True (default), use the normalised version of PBS, also known as PBSn1 [2]_.
Otherwise, use the PBS statistic as originally defined in [1]_.
Returns
-------
pbs : ndarray, float
Windowed PBS values.
Notes
-----
The F:sub:`ST` calculations use Hudson's estimator.
References
----------
.. [1] Yi et al., "Sequencing of Fifty Human Exomes Reveals Adaptation to High
Altitude", Science, 329(5987): 75–78, 2 July 2010.
.. [2] Malaspinas et al., "A genomic history of Aboriginal Australia", Nature. volume
538, pages 207–214, 13 October 2016. | https://github.com/cggh/scikit-allel/blob/3c979a57a100240ba959dd13f98839349530f215/allel/stats/selection.py#L1263-L1339 |
cggh/scikit-allel | allel/stats/window.py | moving_statistic | def moving_statistic(values, statistic, size, start=0, stop=None, step=None, **kwargs):
"""Calculate a statistic in a moving window over `values`.
Parameters
----------
values : array_like
The data to summarise.
statistic : function
The statistic to compute within each window.
size : int
The window size (number of values).
start : int, optional
The index at which to start.
stop : int, optional
The index at which to stop.
step : int, optional
The distance between start positions of windows. If not given,
defaults to the window size, i.e., non-overlapping windows.
kwargs
Additional keyword arguments are passed through to the `statistic`
function.
Returns
-------
out : ndarray, shape (n_windows,)
Examples
--------
>>> import allel
>>> values = [2, 5, 8, 16]
>>> allel.moving_statistic(values, np.sum, size=2)
array([ 7, 24])
>>> allel.moving_statistic(values, np.sum, size=2, step=1)
array([ 7, 13, 24])
"""
windows = index_windows(values, size, start, stop, step)
# setup output
out = np.array([statistic(values[i:j], **kwargs) for i, j in windows])
return out | python | def moving_statistic(values, statistic, size, start=0, stop=None, step=None, **kwargs):
"""Calculate a statistic in a moving window over `values`.
Parameters
----------
values : array_like
The data to summarise.
statistic : function
The statistic to compute within each window.
size : int
The window size (number of values).
start : int, optional
The index at which to start.
stop : int, optional
The index at which to stop.
step : int, optional
The distance between start positions of windows. If not given,
defaults to the window size, i.e., non-overlapping windows.
kwargs
Additional keyword arguments are passed through to the `statistic`
function.
Returns
-------
out : ndarray, shape (n_windows,)
Examples
--------
>>> import allel
>>> values = [2, 5, 8, 16]
>>> allel.moving_statistic(values, np.sum, size=2)
array([ 7, 24])
>>> allel.moving_statistic(values, np.sum, size=2, step=1)
array([ 7, 13, 24])
"""
windows = index_windows(values, size, start, stop, step)
# setup output
out = np.array([statistic(values[i:j], **kwargs) for i, j in windows])
return out | Calculate a statistic in a moving window over `values`.
Parameters
----------
values : array_like
The data to summarise.
statistic : function
The statistic to compute within each window.
size : int
The window size (number of values).
start : int, optional
The index at which to start.
stop : int, optional
The index at which to stop.
step : int, optional
The distance between start positions of windows. If not given,
defaults to the window size, i.e., non-overlapping windows.
kwargs
Additional keyword arguments are passed through to the `statistic`
function.
Returns
-------
out : ndarray, shape (n_windows,)
Examples
--------
>>> import allel
>>> values = [2, 5, 8, 16]
>>> allel.moving_statistic(values, np.sum, size=2)
array([ 7, 24])
>>> allel.moving_statistic(values, np.sum, size=2, step=1)
array([ 7, 13, 24]) | https://github.com/cggh/scikit-allel/blob/3c979a57a100240ba959dd13f98839349530f215/allel/stats/window.py#L12-L57 |
cggh/scikit-allel | allel/stats/window.py | index_windows | def index_windows(values, size, start, stop, step):
"""Convenience function to construct windows for the
:func:`moving_statistic` function.
"""
# determine step
if stop is None:
stop = len(values)
if step is None:
# non-overlapping
step = size
# iterate over windows
for window_start in range(start, stop, step):
window_stop = window_start + size
if window_stop > stop:
# ensure all windows are equal sized
return
yield (window_start, window_stop) | python | def index_windows(values, size, start, stop, step):
"""Convenience function to construct windows for the
:func:`moving_statistic` function.
"""
# determine step
if stop is None:
stop = len(values)
if step is None:
# non-overlapping
step = size
# iterate over windows
for window_start in range(start, stop, step):
window_stop = window_start + size
if window_stop > stop:
# ensure all windows are equal sized
return
yield (window_start, window_stop) | Convenience function to construct windows for the
:func:`moving_statistic` function. | https://github.com/cggh/scikit-allel/blob/3c979a57a100240ba959dd13f98839349530f215/allel/stats/window.py#L75-L96 |
cggh/scikit-allel | allel/stats/window.py | position_windows | def position_windows(pos, size, start, stop, step):
"""Convenience function to construct windows for the
:func:`windowed_statistic` and :func:`windowed_count` functions.
"""
last = False
# determine start and stop positions
if start is None:
start = pos[0]
if stop is None:
stop = pos[-1]
if step is None:
# non-overlapping
step = size
windows = []
for window_start in range(start, stop, step):
# determine window stop
window_stop = window_start + size
if window_stop >= stop:
# last window
window_stop = stop
last = True
else:
window_stop -= 1
windows.append([window_start, window_stop])
if last:
break
return np.asarray(windows) | python | def position_windows(pos, size, start, stop, step):
"""Convenience function to construct windows for the
:func:`windowed_statistic` and :func:`windowed_count` functions.
"""
last = False
# determine start and stop positions
if start is None:
start = pos[0]
if stop is None:
stop = pos[-1]
if step is None:
# non-overlapping
step = size
windows = []
for window_start in range(start, stop, step):
# determine window stop
window_stop = window_start + size
if window_stop >= stop:
# last window
window_stop = stop
last = True
else:
window_stop -= 1
windows.append([window_start, window_stop])
if last:
break
return np.asarray(windows) | Convenience function to construct windows for the
:func:`windowed_statistic` and :func:`windowed_count` functions. | https://github.com/cggh/scikit-allel/blob/3c979a57a100240ba959dd13f98839349530f215/allel/stats/window.py#L99-L132 |
cggh/scikit-allel | allel/stats/window.py | window_locations | def window_locations(pos, windows):
"""Locate indices in `pos` corresponding to the start and stop positions
of `windows`.
"""
start_locs = np.searchsorted(pos, windows[:, 0])
stop_locs = np.searchsorted(pos, windows[:, 1], side='right')
locs = np.column_stack((start_locs, stop_locs))
return locs | python | def window_locations(pos, windows):
"""Locate indices in `pos` corresponding to the start and stop positions
of `windows`.
"""
start_locs = np.searchsorted(pos, windows[:, 0])
stop_locs = np.searchsorted(pos, windows[:, 1], side='right')
locs = np.column_stack((start_locs, stop_locs))
return locs | Locate indices in `pos` corresponding to the start and stop positions
of `windows`. | https://github.com/cggh/scikit-allel/blob/3c979a57a100240ba959dd13f98839349530f215/allel/stats/window.py#L135-L143 |
cggh/scikit-allel | allel/stats/window.py | windowed_count | def windowed_count(pos, size=None, start=None, stop=None, step=None,
windows=None):
"""Count the number of items in windows over a single chromosome/contig.
Parameters
----------
pos : array_like, int, shape (n_items,)
The item positions in ascending order, using 1-based coordinates..
size : int, optional
The window size (number of bases).
start : int, optional
The position at which to start (1-based).
stop : int, optional
The position at which to stop (1-based).
step : int, optional
The distance between start positions of windows. If not given,
defaults to the window size, i.e., non-overlapping windows.
windows : array_like, int, shape (n_windows, 2), optional
Manually specify the windows to use as a sequence of (window_start,
window_stop) positions, using 1-based coordinates. Overrides the
size/start/stop/step parameters.
Returns
-------
counts : ndarray, int, shape (n_windows,)
The number of items in each window.
windows : ndarray, int, shape (n_windows, 2)
The windows used, as an array of (window_start, window_stop) positions,
using 1-based coordinates.
Notes
-----
The window stop positions are included within a window.
The final window will be truncated to the specified stop position,
and so may be smaller than the other windows.
Examples
--------
Non-overlapping windows::
>>> import allel
>>> pos = [1, 7, 12, 15, 28]
>>> counts, windows = allel.windowed_count(pos, size=10)
>>> counts
array([2, 2, 1])
>>> windows
array([[ 1, 10],
[11, 20],
[21, 28]])
Half-overlapping windows::
>>> counts, windows = allel.windowed_count(pos, size=10, step=5)
>>> counts
array([2, 3, 2, 0, 1])
>>> windows
array([[ 1, 10],
[ 6, 15],
[11, 20],
[16, 25],
[21, 28]])
"""
# assume sorted positions
if not isinstance(pos, SortedIndex):
pos = SortedIndex(pos, copy=False)
# setup windows
if windows is None:
windows = position_windows(pos, size, start, stop, step)
else:
windows = asarray_ndim(windows, 2)
# find window locations
locs = window_locations(pos, windows)
# count number of items in each window
counts = np.diff(locs, axis=1).reshape(-1)
return counts, windows | python | def windowed_count(pos, size=None, start=None, stop=None, step=None,
windows=None):
"""Count the number of items in windows over a single chromosome/contig.
Parameters
----------
pos : array_like, int, shape (n_items,)
The item positions in ascending order, using 1-based coordinates..
size : int, optional
The window size (number of bases).
start : int, optional
The position at which to start (1-based).
stop : int, optional
The position at which to stop (1-based).
step : int, optional
The distance between start positions of windows. If not given,
defaults to the window size, i.e., non-overlapping windows.
windows : array_like, int, shape (n_windows, 2), optional
Manually specify the windows to use as a sequence of (window_start,
window_stop) positions, using 1-based coordinates. Overrides the
size/start/stop/step parameters.
Returns
-------
counts : ndarray, int, shape (n_windows,)
The number of items in each window.
windows : ndarray, int, shape (n_windows, 2)
The windows used, as an array of (window_start, window_stop) positions,
using 1-based coordinates.
Notes
-----
The window stop positions are included within a window.
The final window will be truncated to the specified stop position,
and so may be smaller than the other windows.
Examples
--------
Non-overlapping windows::
>>> import allel
>>> pos = [1, 7, 12, 15, 28]
>>> counts, windows = allel.windowed_count(pos, size=10)
>>> counts
array([2, 2, 1])
>>> windows
array([[ 1, 10],
[11, 20],
[21, 28]])
Half-overlapping windows::
>>> counts, windows = allel.windowed_count(pos, size=10, step=5)
>>> counts
array([2, 3, 2, 0, 1])
>>> windows
array([[ 1, 10],
[ 6, 15],
[11, 20],
[16, 25],
[21, 28]])
"""
# assume sorted positions
if not isinstance(pos, SortedIndex):
pos = SortedIndex(pos, copy=False)
# setup windows
if windows is None:
windows = position_windows(pos, size, start, stop, step)
else:
windows = asarray_ndim(windows, 2)
# find window locations
locs = window_locations(pos, windows)
# count number of items in each window
counts = np.diff(locs, axis=1).reshape(-1)
return counts, windows | Count the number of items in windows over a single chromosome/contig.
Parameters
----------
pos : array_like, int, shape (n_items,)
The item positions in ascending order, using 1-based coordinates..
size : int, optional
The window size (number of bases).
start : int, optional
The position at which to start (1-based).
stop : int, optional
The position at which to stop (1-based).
step : int, optional
The distance between start positions of windows. If not given,
defaults to the window size, i.e., non-overlapping windows.
windows : array_like, int, shape (n_windows, 2), optional
Manually specify the windows to use as a sequence of (window_start,
window_stop) positions, using 1-based coordinates. Overrides the
size/start/stop/step parameters.
Returns
-------
counts : ndarray, int, shape (n_windows,)
The number of items in each window.
windows : ndarray, int, shape (n_windows, 2)
The windows used, as an array of (window_start, window_stop) positions,
using 1-based coordinates.
Notes
-----
The window stop positions are included within a window.
The final window will be truncated to the specified stop position,
and so may be smaller than the other windows.
Examples
--------
Non-overlapping windows::
>>> import allel
>>> pos = [1, 7, 12, 15, 28]
>>> counts, windows = allel.windowed_count(pos, size=10)
>>> counts
array([2, 2, 1])
>>> windows
array([[ 1, 10],
[11, 20],
[21, 28]])
Half-overlapping windows::
>>> counts, windows = allel.windowed_count(pos, size=10, step=5)
>>> counts
array([2, 3, 2, 0, 1])
>>> windows
array([[ 1, 10],
[ 6, 15],
[11, 20],
[16, 25],
[21, 28]]) | https://github.com/cggh/scikit-allel/blob/3c979a57a100240ba959dd13f98839349530f215/allel/stats/window.py#L146-L231 |
cggh/scikit-allel | allel/stats/window.py | windowed_statistic | def windowed_statistic(pos, values, statistic, size=None, start=None,
stop=None, step=None, windows=None, fill=np.nan):
"""Calculate a statistic from items in windows over a single
chromosome/contig.
Parameters
----------
pos : array_like, int, shape (n_items,)
The item positions in ascending order, using 1-based coordinates..
values : array_like, int, shape (n_items,)
The values to summarise. May also be a tuple of values arrays,
in which case each array will be sliced and passed through to the
statistic function as separate arguments.
statistic : function
The statistic to compute.
size : int, optional
The window size (number of bases).
start : int, optional
The position at which to start (1-based).
stop : int, optional
The position at which to stop (1-based).
step : int, optional
The distance between start positions of windows. If not given,
defaults to the window size, i.e., non-overlapping windows.
windows : array_like, int, shape (n_windows, 2), optional
Manually specify the windows to use as a sequence of (window_start,
window_stop) positions, using 1-based coordinates. Overrides the
size/start/stop/step parameters.
fill : object, optional
The value to use where a window is empty, i.e., contains no items.
Returns
-------
out : ndarray, shape (n_windows,)
The value of the statistic for each window.
windows : ndarray, int, shape (n_windows, 2)
The windows used, as an array of (window_start, window_stop) positions,
using 1-based coordinates.
counts : ndarray, int, shape (n_windows,)
The number of items in each window.
Notes
-----
The window stop positions are included within a window.
The final window will be truncated to the specified stop position,
and so may be smaller than the other windows.
Examples
--------
Count non-zero (i.e., True) items in non-overlapping windows::
>>> import allel
>>> pos = [1, 7, 12, 15, 28]
>>> values = [True, False, True, False, False]
>>> nnz, windows, counts = allel.windowed_statistic(
... pos, values, statistic=np.count_nonzero, size=10
... )
>>> nnz
array([1, 1, 0])
>>> windows
array([[ 1, 10],
[11, 20],
[21, 28]])
>>> counts
array([2, 2, 1])
Compute a sum over items in half-overlapping windows::
>>> values = [3, 4, 2, 6, 9]
>>> x, windows, counts = allel.windowed_statistic(
... pos, values, statistic=np.sum, size=10, step=5, fill=0
... )
>>> x
array([ 7, 12, 8, 0, 9])
>>> windows
array([[ 1, 10],
[ 6, 15],
[11, 20],
[16, 25],
[21, 28]])
>>> counts
array([2, 3, 2, 0, 1])
"""
# assume sorted positions
if not isinstance(pos, SortedIndex):
pos = SortedIndex(pos, copy=False)
# check lengths are equal
if isinstance(values, tuple):
# assume multiple values arrays
check_equal_length(pos, *values)
else:
# assume a single values array
check_equal_length(pos, values)
# setup windows
if windows is None:
windows = position_windows(pos, size, start, stop, step)
else:
windows = asarray_ndim(windows, 2)
# find window locations
locs = window_locations(pos, windows)
# setup outputs
out = []
counts = []
# iterate over windows
for start_idx, stop_idx in locs:
# calculate number of values in window
n = stop_idx - start_idx
if n == 0:
# window is empty
s = fill
else:
if isinstance(values, tuple):
# assume multiple values arrays
wv = [v[start_idx:stop_idx] for v in values]
s = statistic(*wv)
else:
# assume a single values array
wv = values[start_idx:stop_idx]
s = statistic(wv)
# store outputs
out.append(s)
counts.append(n)
# convert to arrays for output
return np.asarray(out), windows, np.asarray(counts) | python | def windowed_statistic(pos, values, statistic, size=None, start=None,
stop=None, step=None, windows=None, fill=np.nan):
"""Calculate a statistic from items in windows over a single
chromosome/contig.
Parameters
----------
pos : array_like, int, shape (n_items,)
The item positions in ascending order, using 1-based coordinates..
values : array_like, int, shape (n_items,)
The values to summarise. May also be a tuple of values arrays,
in which case each array will be sliced and passed through to the
statistic function as separate arguments.
statistic : function
The statistic to compute.
size : int, optional
The window size (number of bases).
start : int, optional
The position at which to start (1-based).
stop : int, optional
The position at which to stop (1-based).
step : int, optional
The distance between start positions of windows. If not given,
defaults to the window size, i.e., non-overlapping windows.
windows : array_like, int, shape (n_windows, 2), optional
Manually specify the windows to use as a sequence of (window_start,
window_stop) positions, using 1-based coordinates. Overrides the
size/start/stop/step parameters.
fill : object, optional
The value to use where a window is empty, i.e., contains no items.
Returns
-------
out : ndarray, shape (n_windows,)
The value of the statistic for each window.
windows : ndarray, int, shape (n_windows, 2)
The windows used, as an array of (window_start, window_stop) positions,
using 1-based coordinates.
counts : ndarray, int, shape (n_windows,)
The number of items in each window.
Notes
-----
The window stop positions are included within a window.
The final window will be truncated to the specified stop position,
and so may be smaller than the other windows.
Examples
--------
Count non-zero (i.e., True) items in non-overlapping windows::
>>> import allel
>>> pos = [1, 7, 12, 15, 28]
>>> values = [True, False, True, False, False]
>>> nnz, windows, counts = allel.windowed_statistic(
... pos, values, statistic=np.count_nonzero, size=10
... )
>>> nnz
array([1, 1, 0])
>>> windows
array([[ 1, 10],
[11, 20],
[21, 28]])
>>> counts
array([2, 2, 1])
Compute a sum over items in half-overlapping windows::
>>> values = [3, 4, 2, 6, 9]
>>> x, windows, counts = allel.windowed_statistic(
... pos, values, statistic=np.sum, size=10, step=5, fill=0
... )
>>> x
array([ 7, 12, 8, 0, 9])
>>> windows
array([[ 1, 10],
[ 6, 15],
[11, 20],
[16, 25],
[21, 28]])
>>> counts
array([2, 3, 2, 0, 1])
"""
# assume sorted positions
if not isinstance(pos, SortedIndex):
pos = SortedIndex(pos, copy=False)
# check lengths are equal
if isinstance(values, tuple):
# assume multiple values arrays
check_equal_length(pos, *values)
else:
# assume a single values array
check_equal_length(pos, values)
# setup windows
if windows is None:
windows = position_windows(pos, size, start, stop, step)
else:
windows = asarray_ndim(windows, 2)
# find window locations
locs = window_locations(pos, windows)
# setup outputs
out = []
counts = []
# iterate over windows
for start_idx, stop_idx in locs:
# calculate number of values in window
n = stop_idx - start_idx
if n == 0:
# window is empty
s = fill
else:
if isinstance(values, tuple):
# assume multiple values arrays
wv = [v[start_idx:stop_idx] for v in values]
s = statistic(*wv)
else:
# assume a single values array
wv = values[start_idx:stop_idx]
s = statistic(wv)
# store outputs
out.append(s)
counts.append(n)
# convert to arrays for output
return np.asarray(out), windows, np.asarray(counts) | Calculate a statistic from items in windows over a single
chromosome/contig.
Parameters
----------
pos : array_like, int, shape (n_items,)
The item positions in ascending order, using 1-based coordinates..
values : array_like, int, shape (n_items,)
The values to summarise. May also be a tuple of values arrays,
in which case each array will be sliced and passed through to the
statistic function as separate arguments.
statistic : function
The statistic to compute.
size : int, optional
The window size (number of bases).
start : int, optional
The position at which to start (1-based).
stop : int, optional
The position at which to stop (1-based).
step : int, optional
The distance between start positions of windows. If not given,
defaults to the window size, i.e., non-overlapping windows.
windows : array_like, int, shape (n_windows, 2), optional
Manually specify the windows to use as a sequence of (window_start,
window_stop) positions, using 1-based coordinates. Overrides the
size/start/stop/step parameters.
fill : object, optional
The value to use where a window is empty, i.e., contains no items.
Returns
-------
out : ndarray, shape (n_windows,)
The value of the statistic for each window.
windows : ndarray, int, shape (n_windows, 2)
The windows used, as an array of (window_start, window_stop) positions,
using 1-based coordinates.
counts : ndarray, int, shape (n_windows,)
The number of items in each window.
Notes
-----
The window stop positions are included within a window.
The final window will be truncated to the specified stop position,
and so may be smaller than the other windows.
Examples
--------
Count non-zero (i.e., True) items in non-overlapping windows::
>>> import allel
>>> pos = [1, 7, 12, 15, 28]
>>> values = [True, False, True, False, False]
>>> nnz, windows, counts = allel.windowed_statistic(
... pos, values, statistic=np.count_nonzero, size=10
... )
>>> nnz
array([1, 1, 0])
>>> windows
array([[ 1, 10],
[11, 20],
[21, 28]])
>>> counts
array([2, 2, 1])
Compute a sum over items in half-overlapping windows::
>>> values = [3, 4, 2, 6, 9]
>>> x, windows, counts = allel.windowed_statistic(
... pos, values, statistic=np.sum, size=10, step=5, fill=0
... )
>>> x
array([ 7, 12, 8, 0, 9])
>>> windows
array([[ 1, 10],
[ 6, 15],
[11, 20],
[16, 25],
[21, 28]])
>>> counts
array([2, 3, 2, 0, 1]) | https://github.com/cggh/scikit-allel/blob/3c979a57a100240ba959dd13f98839349530f215/allel/stats/window.py#L234-L376 |
cggh/scikit-allel | allel/stats/window.py | per_base | def per_base(x, windows, is_accessible=None, fill=np.nan):
"""Calculate the per-base value of a windowed statistic.
Parameters
----------
x : array_like, shape (n_windows,)
The statistic to average per-base.
windows : array_like, int, shape (n_windows, 2)
The windows used, as an array of (window_start, window_stop)
positions using 1-based coordinates.
is_accessible : array_like, bool, shape (len(contig),), optional
Boolean array indicating accessibility status for all positions in the
chromosome/contig.
fill : object, optional
Use this value where there are no accessible bases in a window.
Returns
-------
y : ndarray, float, shape (n_windows,)
The input array divided by the number of (accessible) bases in each
window.
n_bases : ndarray, int, shape (n_windows,)
The number of (accessible) bases in each window
"""
# calculate window sizes
if is_accessible is None:
# N.B., window stops are included
n_bases = np.diff(windows, axis=1).reshape(-1) + 1
else:
n_bases = np.array([np.count_nonzero(is_accessible[i-1:j])
for i, j in windows])
# deal with multidimensional x
if x.ndim == 1:
pass
elif x.ndim == 2:
n_bases = n_bases[:, None]
else:
raise NotImplementedError('only arrays of 1 or 2 dimensions supported')
# calculate density per-base
with ignore_invalid():
y = np.where(n_bases > 0, x / n_bases, fill)
# restore to 1-dimensional
if n_bases.ndim > 1:
n_bases = n_bases.reshape(-1)
return y, n_bases | python | def per_base(x, windows, is_accessible=None, fill=np.nan):
"""Calculate the per-base value of a windowed statistic.
Parameters
----------
x : array_like, shape (n_windows,)
The statistic to average per-base.
windows : array_like, int, shape (n_windows, 2)
The windows used, as an array of (window_start, window_stop)
positions using 1-based coordinates.
is_accessible : array_like, bool, shape (len(contig),), optional
Boolean array indicating accessibility status for all positions in the
chromosome/contig.
fill : object, optional
Use this value where there are no accessible bases in a window.
Returns
-------
y : ndarray, float, shape (n_windows,)
The input array divided by the number of (accessible) bases in each
window.
n_bases : ndarray, int, shape (n_windows,)
The number of (accessible) bases in each window
"""
# calculate window sizes
if is_accessible is None:
# N.B., window stops are included
n_bases = np.diff(windows, axis=1).reshape(-1) + 1
else:
n_bases = np.array([np.count_nonzero(is_accessible[i-1:j])
for i, j in windows])
# deal with multidimensional x
if x.ndim == 1:
pass
elif x.ndim == 2:
n_bases = n_bases[:, None]
else:
raise NotImplementedError('only arrays of 1 or 2 dimensions supported')
# calculate density per-base
with ignore_invalid():
y = np.where(n_bases > 0, x / n_bases, fill)
# restore to 1-dimensional
if n_bases.ndim > 1:
n_bases = n_bases.reshape(-1)
return y, n_bases | Calculate the per-base value of a windowed statistic.
Parameters
----------
x : array_like, shape (n_windows,)
The statistic to average per-base.
windows : array_like, int, shape (n_windows, 2)
The windows used, as an array of (window_start, window_stop)
positions using 1-based coordinates.
is_accessible : array_like, bool, shape (len(contig),), optional
Boolean array indicating accessibility status for all positions in the
chromosome/contig.
fill : object, optional
Use this value where there are no accessible bases in a window.
Returns
-------
y : ndarray, float, shape (n_windows,)
The input array divided by the number of (accessible) bases in each
window.
n_bases : ndarray, int, shape (n_windows,)
The number of (accessible) bases in each window | https://github.com/cggh/scikit-allel/blob/3c979a57a100240ba959dd13f98839349530f215/allel/stats/window.py#L379-L431 |
cggh/scikit-allel | allel/stats/window.py | equally_accessible_windows | def equally_accessible_windows(is_accessible, size, start=0, stop=None, step=None):
"""Create windows each containing the same number of accessible bases.
Parameters
----------
is_accessible : array_like, bool, shape (n_bases,)
Array defining accessible status of all bases on a contig/chromosome.
size : int
Window size (number of accessible bases).
start : int, optional
The genome position at which to start.
stop : int, optional
The genome position at which to stop.
step : int, optional
The number of accessible sites between start positions
of windows. If not given, defaults to the window size, i.e.,
non-overlapping windows. Use half the window size to get
half-overlapping windows.
Returns
-------
windows : ndarray, int, shape (n_windows, 2)
Window start/stop positions (1-based).
"""
pos_accessible, = np.nonzero(is_accessible)
pos_accessible += 1 # convert to 1-based coordinates
# N.B., need some care in handling start and stop positions, these are
# genomic positions at which to start and stop the windows
if start:
pos_accessible = pos_accessible[pos_accessible >= start]
if stop:
pos_accessible = pos_accessible[pos_accessible <= stop]
# now construct moving windows
windows = moving_statistic(pos_accessible, lambda v: [v[0], v[-1]],
size=size, step=step)
return windows | python | def equally_accessible_windows(is_accessible, size, start=0, stop=None, step=None):
"""Create windows each containing the same number of accessible bases.
Parameters
----------
is_accessible : array_like, bool, shape (n_bases,)
Array defining accessible status of all bases on a contig/chromosome.
size : int
Window size (number of accessible bases).
start : int, optional
The genome position at which to start.
stop : int, optional
The genome position at which to stop.
step : int, optional
The number of accessible sites between start positions
of windows. If not given, defaults to the window size, i.e.,
non-overlapping windows. Use half the window size to get
half-overlapping windows.
Returns
-------
windows : ndarray, int, shape (n_windows, 2)
Window start/stop positions (1-based).
"""
pos_accessible, = np.nonzero(is_accessible)
pos_accessible += 1 # convert to 1-based coordinates
# N.B., need some care in handling start and stop positions, these are
# genomic positions at which to start and stop the windows
if start:
pos_accessible = pos_accessible[pos_accessible >= start]
if stop:
pos_accessible = pos_accessible[pos_accessible <= stop]
# now construct moving windows
windows = moving_statistic(pos_accessible, lambda v: [v[0], v[-1]],
size=size, step=step)
return windows | Create windows each containing the same number of accessible bases.
Parameters
----------
is_accessible : array_like, bool, shape (n_bases,)
Array defining accessible status of all bases on a contig/chromosome.
size : int
Window size (number of accessible bases).
start : int, optional
The genome position at which to start.
stop : int, optional
The genome position at which to stop.
step : int, optional
The number of accessible sites between start positions
of windows. If not given, defaults to the window size, i.e.,
non-overlapping windows. Use half the window size to get
half-overlapping windows.
Returns
-------
windows : ndarray, int, shape (n_windows, 2)
Window start/stop positions (1-based). | https://github.com/cggh/scikit-allel/blob/3c979a57a100240ba959dd13f98839349530f215/allel/stats/window.py#L434-L472 |
bartTC/django-attachments | attachments/templatetags/attachments_tags.py | attachment_form | def attachment_form(context, obj):
"""
Renders a "upload attachment" form.
The user must own ``attachments.add_attachment permission`` to add
attachments.
"""
if context['user'].has_perm('attachments.add_attachment'):
return {
'form': AttachmentForm(),
'form_url': add_url_for_obj(obj),
'next': context.request.build_absolute_uri(),
}
else:
return {'form': None} | python | def attachment_form(context, obj):
"""
Renders a "upload attachment" form.
The user must own ``attachments.add_attachment permission`` to add
attachments.
"""
if context['user'].has_perm('attachments.add_attachment'):
return {
'form': AttachmentForm(),
'form_url': add_url_for_obj(obj),
'next': context.request.build_absolute_uri(),
}
else:
return {'form': None} | Renders a "upload attachment" form.
The user must own ``attachments.add_attachment permission`` to add
attachments. | https://github.com/bartTC/django-attachments/blob/012b7168f9342e07683a54ceab57696e0072962e/attachments/templatetags/attachments_tags.py#L12-L26 |
bartTC/django-attachments | attachments/templatetags/attachments_tags.py | attachment_delete_link | def attachment_delete_link(context, attachment):
"""
Renders a html link to the delete view of the given attachment. Returns
no content if the request-user has no permission to delete attachments.
The user must own either the ``attachments.delete_attachment`` permission
and is the creator of the attachment, that he can delete it or he has
``attachments.delete_foreign_attachments`` which allows him to delete all
attachments.
"""
if context['user'].has_perm('attachments.delete_foreign_attachments') or (
context['user'] == attachment.creator
and context['user'].has_perm('attachments.delete_attachment')
):
return {
'next': context.request.build_absolute_uri(),
'delete_url': reverse(
'attachments:delete', kwargs={'attachment_pk': attachment.pk}
),
}
return {'delete_url': None} | python | def attachment_delete_link(context, attachment):
"""
Renders a html link to the delete view of the given attachment. Returns
no content if the request-user has no permission to delete attachments.
The user must own either the ``attachments.delete_attachment`` permission
and is the creator of the attachment, that he can delete it or he has
``attachments.delete_foreign_attachments`` which allows him to delete all
attachments.
"""
if context['user'].has_perm('attachments.delete_foreign_attachments') or (
context['user'] == attachment.creator
and context['user'].has_perm('attachments.delete_attachment')
):
return {
'next': context.request.build_absolute_uri(),
'delete_url': reverse(
'attachments:delete', kwargs={'attachment_pk': attachment.pk}
),
}
return {'delete_url': None} | Renders a html link to the delete view of the given attachment. Returns
no content if the request-user has no permission to delete attachments.
The user must own either the ``attachments.delete_attachment`` permission
and is the creator of the attachment, that he can delete it or he has
``attachments.delete_foreign_attachments`` which allows him to delete all
attachments. | https://github.com/bartTC/django-attachments/blob/012b7168f9342e07683a54ceab57696e0072962e/attachments/templatetags/attachments_tags.py#L30-L50 |
bartTC/django-attachments | attachments/models.py | attachment_upload | def attachment_upload(instance, filename):
"""Stores the attachment in a "per module/appname/primary key" folder"""
return 'attachments/{app}_{model}/{pk}/{filename}'.format(
app=instance.content_object._meta.app_label,
model=instance.content_object._meta.object_name.lower(),
pk=instance.content_object.pk,
filename=filename,
) | python | def attachment_upload(instance, filename):
"""Stores the attachment in a "per module/appname/primary key" folder"""
return 'attachments/{app}_{model}/{pk}/{filename}'.format(
app=instance.content_object._meta.app_label,
model=instance.content_object._meta.object_name.lower(),
pk=instance.content_object.pk,
filename=filename,
) | Stores the attachment in a "per module/appname/primary key" folder | https://github.com/bartTC/django-attachments/blob/012b7168f9342e07683a54ceab57696e0072962e/attachments/models.py#L13-L20 |
csurfer/pyheat | pyheat/pyheat.py | PyHeat.show_heatmap | def show_heatmap(self, blocking=True, output_file=None, enable_scroll=False):
"""Method to actually display the heatmap created.
@param blocking: When set to False makes an unblocking plot show.
@param output_file: If not None the heatmap image is output to this
file. Supported formats: (eps, pdf, pgf, png, ps, raw, rgba, svg,
svgz)
@param enable_scroll: Flag used add a scroll bar to scroll long files.
"""
if output_file is None:
if enable_scroll:
# Add a new axes which will be used as scroll bar.
axpos = plt.axes([0.12, 0.1, 0.625, 0.03])
spos = Slider(axpos, "Scroll", 10, len(self.pyfile.lines))
def update(val):
"""Method to update position when slider is moved."""
pos = spos.val
self.ax.axis([0, 1, pos, pos - 10])
self.fig.canvas.draw_idle()
spos.on_changed(update)
plt.show(block=blocking)
else:
plt.savefig(output_file) | python | def show_heatmap(self, blocking=True, output_file=None, enable_scroll=False):
"""Method to actually display the heatmap created.
@param blocking: When set to False makes an unblocking plot show.
@param output_file: If not None the heatmap image is output to this
file. Supported formats: (eps, pdf, pgf, png, ps, raw, rgba, svg,
svgz)
@param enable_scroll: Flag used add a scroll bar to scroll long files.
"""
if output_file is None:
if enable_scroll:
# Add a new axes which will be used as scroll bar.
axpos = plt.axes([0.12, 0.1, 0.625, 0.03])
spos = Slider(axpos, "Scroll", 10, len(self.pyfile.lines))
def update(val):
"""Method to update position when slider is moved."""
pos = spos.val
self.ax.axis([0, 1, pos, pos - 10])
self.fig.canvas.draw_idle()
spos.on_changed(update)
plt.show(block=blocking)
else:
plt.savefig(output_file) | Method to actually display the heatmap created.
@param blocking: When set to False makes an unblocking plot show.
@param output_file: If not None the heatmap image is output to this
file. Supported formats: (eps, pdf, pgf, png, ps, raw, rgba, svg,
svgz)
@param enable_scroll: Flag used add a scroll bar to scroll long files. | https://github.com/csurfer/pyheat/blob/cc0ee3721aea70a1da4918957500131aa7077afe/pyheat/pyheat.py#L53-L77 |
csurfer/pyheat | pyheat/pyheat.py | PyHeat.__profile_file | def __profile_file(self):
"""Method used to profile the given file line by line."""
self.line_profiler = pprofile.Profile()
self.line_profiler.runfile(
open(self.pyfile.path, "r"), {}, self.pyfile.path
) | python | def __profile_file(self):
"""Method used to profile the given file line by line."""
self.line_profiler = pprofile.Profile()
self.line_profiler.runfile(
open(self.pyfile.path, "r"), {}, self.pyfile.path
) | Method used to profile the given file line by line. | https://github.com/csurfer/pyheat/blob/cc0ee3721aea70a1da4918957500131aa7077afe/pyheat/pyheat.py#L83-L88 |
csurfer/pyheat | pyheat/pyheat.py | PyHeat.__get_line_profile_data | def __get_line_profile_data(self):
"""Method to procure line profiles.
@return: Line profiles if the file has been profiles else empty
dictionary.
"""
if self.line_profiler is None:
return {}
# the [0] is because pprofile.Profile.file_dict stores the line_dict
# in a list so that it can be modified in a thread-safe way
# see https://github.com/vpelletier/pprofile/blob/da3d60a1b59a061a0e2113bf768b7cb4bf002ccb/pprofile.py#L398
return self.line_profiler.file_dict[self.pyfile.path][0].line_dict | python | def __get_line_profile_data(self):
"""Method to procure line profiles.
@return: Line profiles if the file has been profiles else empty
dictionary.
"""
if self.line_profiler is None:
return {}
# the [0] is because pprofile.Profile.file_dict stores the line_dict
# in a list so that it can be modified in a thread-safe way
# see https://github.com/vpelletier/pprofile/blob/da3d60a1b59a061a0e2113bf768b7cb4bf002ccb/pprofile.py#L398
return self.line_profiler.file_dict[self.pyfile.path][0].line_dict | Method to procure line profiles.
@return: Line profiles if the file has been profiles else empty
dictionary. | https://github.com/csurfer/pyheat/blob/cc0ee3721aea70a1da4918957500131aa7077afe/pyheat/pyheat.py#L90-L102 |
csurfer/pyheat | pyheat/pyheat.py | PyHeat.__fetch_heatmap_data_from_profile | def __fetch_heatmap_data_from_profile(self):
"""Method to create heatmap data from profile information."""
# Read lines from file.
with open(self.pyfile.path, "r") as file_to_read:
for line in file_to_read:
# Remove return char from the end of the line and add a
# space in the beginning for better visibility.
self.pyfile.lines.append(" " + line.strip("\n"))
# Total number of lines in file.
self.pyfile.length = len(self.pyfile.lines)
# Fetch line profiles.
line_profiles = self.__get_line_profile_data()
# Creating an array of data points. As the profile keys are 1 indexed
# we should range from 1 to line_count + 1 and not 0 to line_count.
arr = []
for line_num in range(1, self.pyfile.length + 1):
if line_num in line_profiles:
# line_profiles[i] will have multiple entries if line i is
# invoked from multiple places in the code. Here we sum over
# each invocation to get the total time spent on that line.
line_times = [
ltime for _, ltime in line_profiles[line_num].values()
]
arr.append([sum(line_times)])
else:
arr.append([0.0])
# Create nd-array from list of data points.
self.pyfile.data = np.array(arr) | python | def __fetch_heatmap_data_from_profile(self):
"""Method to create heatmap data from profile information."""
# Read lines from file.
with open(self.pyfile.path, "r") as file_to_read:
for line in file_to_read:
# Remove return char from the end of the line and add a
# space in the beginning for better visibility.
self.pyfile.lines.append(" " + line.strip("\n"))
# Total number of lines in file.
self.pyfile.length = len(self.pyfile.lines)
# Fetch line profiles.
line_profiles = self.__get_line_profile_data()
# Creating an array of data points. As the profile keys are 1 indexed
# we should range from 1 to line_count + 1 and not 0 to line_count.
arr = []
for line_num in range(1, self.pyfile.length + 1):
if line_num in line_profiles:
# line_profiles[i] will have multiple entries if line i is
# invoked from multiple places in the code. Here we sum over
# each invocation to get the total time spent on that line.
line_times = [
ltime for _, ltime in line_profiles[line_num].values()
]
arr.append([sum(line_times)])
else:
arr.append([0.0])
# Create nd-array from list of data points.
self.pyfile.data = np.array(arr) | Method to create heatmap data from profile information. | https://github.com/csurfer/pyheat/blob/cc0ee3721aea70a1da4918957500131aa7077afe/pyheat/pyheat.py#L104-L135 |
csurfer/pyheat | pyheat/pyheat.py | PyHeat.__create_heatmap_plot | def __create_heatmap_plot(self):
"""Method to actually create the heatmap from profile stats."""
# Define the heatmap plot.
height = len(self.pyfile.lines) / 3
width = max(map(lambda x: len(x), self.pyfile.lines)) / 8
self.fig, self.ax = plt.subplots(figsize=(width, height))
# Set second sub plot to occupy bottom 20%
plt.subplots_adjust(bottom=0.20)
# Heat scale orange to red
heatmap = self.ax.pcolor(self.pyfile.data, cmap="OrRd")
# X Axis
# Remove X axis.
self.ax.xaxis.set_visible(False)
# Y Axis
# Create lables for y-axis ticks
row_labels = range(1, self.pyfile.length + 1)
# Set y-tick labels.
self.ax.set_yticklabels(row_labels, minor=False)
# Put y-axis major ticks at the middle of each cell.
self.ax.set_yticks(np.arange(self.pyfile.data.shape[0]) + 0.5, minor=False)
# Inver y-axis to have top down line numbers
self.ax.invert_yaxis()
# Plot definitions
# Set plot y-axis label.
plt.ylabel("Line Number")
# Annotate each cell with lines in file in order.
max_time_spent_on_a_line = max(self.pyfile.data)
for i, line in enumerate(self.pyfile.lines):
# In order to ensure easy readability of the code, we need to
# invert colour of text display for darker colours which
# correspond to higher amount of time spent on the line.
if self.pyfile.data[i] >= 0.7 * max_time_spent_on_a_line:
color = (1.0, 1.0, 1.0) # White text
else:
color = (0.0, 0.0, 0.0) # Black text
plt.text(
0.0,
i + 0.5,
line,
ha="left",
va="center",
color=color,
clip_on=True,
)
# Define legend
cbar = plt.colorbar(heatmap)
cbar.set_label("# of seconds") | python | def __create_heatmap_plot(self):
"""Method to actually create the heatmap from profile stats."""
# Define the heatmap plot.
height = len(self.pyfile.lines) / 3
width = max(map(lambda x: len(x), self.pyfile.lines)) / 8
self.fig, self.ax = plt.subplots(figsize=(width, height))
# Set second sub plot to occupy bottom 20%
plt.subplots_adjust(bottom=0.20)
# Heat scale orange to red
heatmap = self.ax.pcolor(self.pyfile.data, cmap="OrRd")
# X Axis
# Remove X axis.
self.ax.xaxis.set_visible(False)
# Y Axis
# Create lables for y-axis ticks
row_labels = range(1, self.pyfile.length + 1)
# Set y-tick labels.
self.ax.set_yticklabels(row_labels, minor=False)
# Put y-axis major ticks at the middle of each cell.
self.ax.set_yticks(np.arange(self.pyfile.data.shape[0]) + 0.5, minor=False)
# Inver y-axis to have top down line numbers
self.ax.invert_yaxis()
# Plot definitions
# Set plot y-axis label.
plt.ylabel("Line Number")
# Annotate each cell with lines in file in order.
max_time_spent_on_a_line = max(self.pyfile.data)
for i, line in enumerate(self.pyfile.lines):
# In order to ensure easy readability of the code, we need to
# invert colour of text display for darker colours which
# correspond to higher amount of time spent on the line.
if self.pyfile.data[i] >= 0.7 * max_time_spent_on_a_line:
color = (1.0, 1.0, 1.0) # White text
else:
color = (0.0, 0.0, 0.0) # Black text
plt.text(
0.0,
i + 0.5,
line,
ha="left",
va="center",
color=color,
clip_on=True,
)
# Define legend
cbar = plt.colorbar(heatmap)
cbar.set_label("# of seconds") | Method to actually create the heatmap from profile stats. | https://github.com/csurfer/pyheat/blob/cc0ee3721aea70a1da4918957500131aa7077afe/pyheat/pyheat.py#L137-L189 |
csurfer/pyheat | pyheat/commandline.py | main | def main():
"""Starting point for the program execution."""
# Create command line parser.
parser = argparse.ArgumentParser()
# Adding command line arguments.
parser.add_argument("-o", "--out", help="Output file", default=None)
parser.add_argument(
"pyfile", help="Python file to be profiled", default=None
)
# Parse command line arguments.
arguments = parser.parse_args()
if arguments.pyfile is not None:
# Core functionality.
pyheat = PyHeat(arguments.pyfile)
pyheat.create_heatmap()
pyheat.show_heatmap(output_file=arguments.out, enable_scroll=True)
pyheat.close_heatmap()
else:
# Print command help
parser.print_help() | python | def main():
"""Starting point for the program execution."""
# Create command line parser.
parser = argparse.ArgumentParser()
# Adding command line arguments.
parser.add_argument("-o", "--out", help="Output file", default=None)
parser.add_argument(
"pyfile", help="Python file to be profiled", default=None
)
# Parse command line arguments.
arguments = parser.parse_args()
if arguments.pyfile is not None:
# Core functionality.
pyheat = PyHeat(arguments.pyfile)
pyheat.create_heatmap()
pyheat.show_heatmap(output_file=arguments.out, enable_scroll=True)
pyheat.close_heatmap()
else:
# Print command help
parser.print_help() | Starting point for the program execution. | https://github.com/csurfer/pyheat/blob/cc0ee3721aea70a1da4918957500131aa7077afe/pyheat/commandline.py#L31-L50 |
limist/py-moneyed | moneyed/classes.py | Money.round | def round(self, ndigits=0):
"""
Rounds the amount using the current ``Decimal`` rounding algorithm.
"""
if ndigits is None:
ndigits = 0
return self.__class__(
amount=self.amount.quantize(Decimal('1e' + str(-ndigits))),
currency=self.currency) | python | def round(self, ndigits=0):
"""
Rounds the amount using the current ``Decimal`` rounding algorithm.
"""
if ndigits is None:
ndigits = 0
return self.__class__(
amount=self.amount.quantize(Decimal('1e' + str(-ndigits))),
currency=self.currency) | Rounds the amount using the current ``Decimal`` rounding algorithm. | https://github.com/limist/py-moneyed/blob/1822e9f77edc6608b429e54c8831b873af9a4de6/moneyed/classes.py#L158-L166 |
klen/muffin | muffin/manage.py | run | def run():
"""CLI endpoint."""
sys.path.insert(0, os.getcwd())
logging.basicConfig(level=logging.INFO, handlers=[logging.StreamHandler()])
parser = argparse.ArgumentParser(description="Manage Application", add_help=False)
parser.add_argument('app', metavar='app',
type=str, help='Application module path')
parser.add_argument('--config', type=str, help='Path to configuration.')
parser.add_argument('--version', action="version", version=__version__)
args_, subargs_ = parser.parse_known_args(sys.argv[1:])
if args_.config:
os.environ[CONFIGURATION_ENVIRON_VARIABLE] = args_.config
from gunicorn.util import import_app
app_uri = args_.app
if ':' not in app_uri:
app_uri += ':app'
try:
app = import_app(app_uri)
app.uri = app_uri
app.logger.info('Application is loaded: %s' % app.name)
except Exception as exc:
logging.exception(exc)
raise sys.exit(1)
app.manage(*subargs_, prog='muffin %s' % args_.app) | python | def run():
"""CLI endpoint."""
sys.path.insert(0, os.getcwd())
logging.basicConfig(level=logging.INFO, handlers=[logging.StreamHandler()])
parser = argparse.ArgumentParser(description="Manage Application", add_help=False)
parser.add_argument('app', metavar='app',
type=str, help='Application module path')
parser.add_argument('--config', type=str, help='Path to configuration.')
parser.add_argument('--version', action="version", version=__version__)
args_, subargs_ = parser.parse_known_args(sys.argv[1:])
if args_.config:
os.environ[CONFIGURATION_ENVIRON_VARIABLE] = args_.config
from gunicorn.util import import_app
app_uri = args_.app
if ':' not in app_uri:
app_uri += ':app'
try:
app = import_app(app_uri)
app.uri = app_uri
app.logger.info('Application is loaded: %s' % app.name)
except Exception as exc:
logging.exception(exc)
raise sys.exit(1)
app.manage(*subargs_, prog='muffin %s' % args_.app) | CLI endpoint. | https://github.com/klen/muffin/blob/7bc891e174e08b62d1ae232b5d45f8cd8bc82112/muffin/manage.py#L250-L278 |
klen/muffin | muffin/manage.py | Manager.command | def command(self, init=False):
"""Define CLI command."""
def wrapper(func):
header = '\n'.join([s for s in (func.__doc__ or '').split('\n')
if not s.strip().startswith(':')])
parser = self.parsers.add_parser(func.__name__, description=header)
args, vargs, kw, defs, kwargs, kwdefs, anns = inspect.getfullargspec(func)
defs = defs or []
kwargs_ = dict(zip(args[-len(defs):], defs))
docs = dict(PARAM_RE.findall(func.__doc__ or ""))
def process_arg(name, *, value=..., **opts):
argname = name.replace('_', '-').lower()
arghelp = docs.get(vargs, '')
if value is ...:
return parser.add_argument(argname, help=arghelp, **opts)
if isinstance(value, bool):
if value:
return parser.add_argument(
"--no-" + argname, dest=name, action="store_false",
help="Disable %s" % (arghelp or name).lower())
return parser.add_argument(
"--" + argname, dest=name, action="store_true",
help="Enable %s" % (arghelp or name).lower())
if isinstance(value, list):
return parser.add_argument(
"--" + argname, action="append", default=value, help=arghelp)
return parser.add_argument(
"--" + argname, type=anns.get(name, type(value)),
default=value, help=arghelp + ' [%s]' % repr(value))
if vargs:
process_arg('*', nargs="*", metavar=vargs)
for name, value in (kwdefs or {}).items():
process_arg(name, value=value)
for name in args:
process_arg(name, value=kwargs_.get(name, ...))
self.handlers[func.__name__] = func
func.parser = parser
return func
if callable(init):
init.__init__ = True
return wrapper(init)
def decorator(func):
func.__init__ = bool(init)
return wrapper(func)
return decorator | python | def command(self, init=False):
"""Define CLI command."""
def wrapper(func):
header = '\n'.join([s for s in (func.__doc__ or '').split('\n')
if not s.strip().startswith(':')])
parser = self.parsers.add_parser(func.__name__, description=header)
args, vargs, kw, defs, kwargs, kwdefs, anns = inspect.getfullargspec(func)
defs = defs or []
kwargs_ = dict(zip(args[-len(defs):], defs))
docs = dict(PARAM_RE.findall(func.__doc__ or ""))
def process_arg(name, *, value=..., **opts):
argname = name.replace('_', '-').lower()
arghelp = docs.get(vargs, '')
if value is ...:
return parser.add_argument(argname, help=arghelp, **opts)
if isinstance(value, bool):
if value:
return parser.add_argument(
"--no-" + argname, dest=name, action="store_false",
help="Disable %s" % (arghelp or name).lower())
return parser.add_argument(
"--" + argname, dest=name, action="store_true",
help="Enable %s" % (arghelp or name).lower())
if isinstance(value, list):
return parser.add_argument(
"--" + argname, action="append", default=value, help=arghelp)
return parser.add_argument(
"--" + argname, type=anns.get(name, type(value)),
default=value, help=arghelp + ' [%s]' % repr(value))
if vargs:
process_arg('*', nargs="*", metavar=vargs)
for name, value in (kwdefs or {}).items():
process_arg(name, value=value)
for name in args:
process_arg(name, value=kwargs_.get(name, ...))
self.handlers[func.__name__] = func
func.parser = parser
return func
if callable(init):
init.__init__ = True
return wrapper(init)
def decorator(func):
func.__init__ = bool(init)
return wrapper(func)
return decorator | Define CLI command. | https://github.com/klen/muffin/blob/7bc891e174e08b62d1ae232b5d45f8cd8bc82112/muffin/manage.py#L145-L202 |
klen/muffin | muffin/urls.py | routes_register | def routes_register(app, handler, *paths, methods=None, router=None, name=None):
"""Register routes."""
if router is None:
router = app.router
handler = to_coroutine(handler)
resources = []
for path in paths:
# Register any exception to app
if isinstance(path, type) and issubclass(path, BaseException):
app._error_handlers[path] = handler
continue
# Ensure that names are unique
name = str(name or '')
rname, rnum = name, 2
while rname in router:
rname = "%s%d" % (name, rnum)
rnum += 1
path = parse(path)
if isinstance(path, RETYPE):
resource = RawReResource(path, name=rname)
router.register_resource(resource)
else:
resource = router.add_resource(path, name=rname)
for method in methods or [METH_ANY]:
method = method.upper()
resource.add_route(method, handler)
resources.append(resource)
return resources | python | def routes_register(app, handler, *paths, methods=None, router=None, name=None):
"""Register routes."""
if router is None:
router = app.router
handler = to_coroutine(handler)
resources = []
for path in paths:
# Register any exception to app
if isinstance(path, type) and issubclass(path, BaseException):
app._error_handlers[path] = handler
continue
# Ensure that names are unique
name = str(name or '')
rname, rnum = name, 2
while rname in router:
rname = "%s%d" % (name, rnum)
rnum += 1
path = parse(path)
if isinstance(path, RETYPE):
resource = RawReResource(path, name=rname)
router.register_resource(resource)
else:
resource = router.add_resource(path, name=rname)
for method in methods or [METH_ANY]:
method = method.upper()
resource.add_route(method, handler)
resources.append(resource)
return resources | Register routes. | https://github.com/klen/muffin/blob/7bc891e174e08b62d1ae232b5d45f8cd8bc82112/muffin/urls.py#L95-L132 |
klen/muffin | muffin/urls.py | parse | def parse(path):
"""Parse URL path and convert it to regexp if needed."""
parsed = re.sre_parse.parse(path)
for case, _ in parsed:
if case not in (re.sre_parse.LITERAL, re.sre_parse.ANY):
break
else:
return path
path = path.strip('^$')
def parse_(match):
[part] = match.groups()
match = DYNR_RE.match(part)
params = match.groupdict()
return '(?P<%s>%s)' % (params['var'], params['re'] or '[^{}/]+')
return re.compile('^%s$' % DYNS_RE.sub(parse_, path)) | python | def parse(path):
"""Parse URL path and convert it to regexp if needed."""
parsed = re.sre_parse.parse(path)
for case, _ in parsed:
if case not in (re.sre_parse.LITERAL, re.sre_parse.ANY):
break
else:
return path
path = path.strip('^$')
def parse_(match):
[part] = match.groups()
match = DYNR_RE.match(part)
params = match.groupdict()
return '(?P<%s>%s)' % (params['var'], params['re'] or '[^{}/]+')
return re.compile('^%s$' % DYNS_RE.sub(parse_, path)) | Parse URL path and convert it to regexp if needed. | https://github.com/klen/muffin/blob/7bc891e174e08b62d1ae232b5d45f8cd8bc82112/muffin/urls.py#L135-L152 |
klen/muffin | muffin/urls.py | RawReResource.url_for | def url_for(self, *subgroups, **groups):
"""Build URL."""
parsed = re.sre_parse.parse(self._pattern.pattern)
subgroups = {n:str(v) for n, v in enumerate(subgroups, 1)}
groups_ = dict(parsed.pattern.groupdict)
subgroups.update({
groups_[k0]: str(v0)
for k0, v0 in groups.items()
if k0 in groups_
})
path = ''.join(str(val) for val in Traverser(parsed, subgroups))
return URL.build(path=path, encoded=True) | python | def url_for(self, *subgroups, **groups):
"""Build URL."""
parsed = re.sre_parse.parse(self._pattern.pattern)
subgroups = {n:str(v) for n, v in enumerate(subgroups, 1)}
groups_ = dict(parsed.pattern.groupdict)
subgroups.update({
groups_[k0]: str(v0)
for k0, v0 in groups.items()
if k0 in groups_
})
path = ''.join(str(val) for val in Traverser(parsed, subgroups))
return URL.build(path=path, encoded=True) | Build URL. | https://github.com/klen/muffin/blob/7bc891e174e08b62d1ae232b5d45f8cd8bc82112/muffin/urls.py#L38-L49 |
klen/muffin | muffin/urls.py | Traverser.state_not_literal | def state_not_literal(self, value):
"""Parse not literal."""
value = negate = chr(value)
while value == negate:
value = choice(self.literals)
yield value | python | def state_not_literal(self, value):
"""Parse not literal."""
value = negate = chr(value)
while value == negate:
value = choice(self.literals)
yield value | Parse not literal. | https://github.com/klen/muffin/blob/7bc891e174e08b62d1ae232b5d45f8cd8bc82112/muffin/urls.py#L185-L190 |
klen/muffin | muffin/urls.py | Traverser.state_max_repeat | def state_max_repeat(self, value):
"""Parse repeatable parts."""
min_, max_, value = value
value = [val for val in Traverser(value, self.groups)]
if not min_ and max_:
for val in value:
if isinstance(val, required):
min_ = 1
break
for val in value * min_:
yield val | python | def state_max_repeat(self, value):
"""Parse repeatable parts."""
min_, max_, value = value
value = [val for val in Traverser(value, self.groups)]
if not min_ and max_:
for val in value:
if isinstance(val, required):
min_ = 1
break
for val in value * min_:
yield val | Parse repeatable parts. | https://github.com/klen/muffin/blob/7bc891e174e08b62d1ae232b5d45f8cd8bc82112/muffin/urls.py#L192-L203 |
klen/muffin | muffin/urls.py | Traverser.state_in | def state_in(self, value):
"""Parse ranges."""
value = [val for val in Traverser(value, self.groups)]
if not value or not value[0]:
for val in self.literals - set(value):
return (yield val)
yield value[0] | python | def state_in(self, value):
"""Parse ranges."""
value = [val for val in Traverser(value, self.groups)]
if not value or not value[0]:
for val in self.literals - set(value):
return (yield val)
yield value[0] | Parse ranges. | https://github.com/klen/muffin/blob/7bc891e174e08b62d1ae232b5d45f8cd8bc82112/muffin/urls.py#L207-L213 |
klen/muffin | muffin/urls.py | Traverser.state_category | def state_category(value):
"""Parse categories."""
if value == re.sre_parse.CATEGORY_DIGIT:
return (yield '0')
if value == re.sre_parse.CATEGORY_WORD:
return (yield 'x') | python | def state_category(value):
"""Parse categories."""
if value == re.sre_parse.CATEGORY_DIGIT:
return (yield '0')
if value == re.sre_parse.CATEGORY_WORD:
return (yield 'x') | Parse categories. | https://github.com/klen/muffin/blob/7bc891e174e08b62d1ae232b5d45f8cd8bc82112/muffin/urls.py#L218-L224 |
klen/muffin | muffin/urls.py | Traverser.state_subpattern | def state_subpattern(self, value):
"""Parse subpatterns."""
num, *_, parsed = value
if num in self.groups:
return (yield required(self.groups[num]))
yield from Traverser(parsed, groups=self.groups) | python | def state_subpattern(self, value):
"""Parse subpatterns."""
num, *_, parsed = value
if num in self.groups:
return (yield required(self.groups[num]))
yield from Traverser(parsed, groups=self.groups) | Parse subpatterns. | https://github.com/klen/muffin/blob/7bc891e174e08b62d1ae232b5d45f8cd8bc82112/muffin/urls.py#L226-L232 |
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