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BerkeleyAutomation/autolab_core | autolab_core/experiment_logger.py | ExperimentLogger.gen_experiment_ref | def gen_experiment_ref(experiment_tag, n=10):
""" Generate a random string for naming.
Parameters
----------
experiment_tag : :obj:`str`
tag to prefix name with
n : int
number of random chars to use
Returns
-------
:obj:`str`
string experiment ref
"""
experiment_id = gen_experiment_id(n=n)
return '{0}_{1}'.format(experiment_tag, experiment_id) | python | def gen_experiment_ref(experiment_tag, n=10):
""" Generate a random string for naming.
Parameters
----------
experiment_tag : :obj:`str`
tag to prefix name with
n : int
number of random chars to use
Returns
-------
:obj:`str`
string experiment ref
"""
experiment_id = gen_experiment_id(n=n)
return '{0}_{1}'.format(experiment_tag, experiment_id) | [
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BerkeleyAutomation/autolab_core | autolab_core/tensor_dataset.py | Tensor.add | def add(self, datapoint):
""" Adds the datapoint to the tensor if room is available. """
if not self.is_full:
self.set_datapoint(self.cur_index, datapoint)
self.cur_index += 1 | python | def add(self, datapoint):
""" Adds the datapoint to the tensor if room is available. """
if not self.is_full:
self.set_datapoint(self.cur_index, datapoint)
self.cur_index += 1 | [
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BerkeleyAutomation/autolab_core | autolab_core/tensor_dataset.py | Tensor.add_batch | def add_batch(self, datapoints):
""" Adds a batch of datapoints to the tensor if room is available. """
num_datapoints_to_add = datapoints.shape[0]
end_index = self.cur_index + num_datapoints_to_add
if end_index <= self.num_datapoints:
self.data[self.cur_index:end_index,...] = datapoints
self.cur_index = end_index | python | def add_batch(self, datapoints):
""" Adds a batch of datapoints to the tensor if room is available. """
num_datapoints_to_add = datapoints.shape[0]
end_index = self.cur_index + num_datapoints_to_add
if end_index <= self.num_datapoints:
self.data[self.cur_index:end_index,...] = datapoints
self.cur_index = end_index | [
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BerkeleyAutomation/autolab_core | autolab_core/tensor_dataset.py | Tensor.datapoint | def datapoint(self, ind):
""" Returns the datapoint at the given index. """
if self.height is None:
return self.data[ind]
return self.data[ind, ...].copy() | python | def datapoint(self, ind):
""" Returns the datapoint at the given index. """
if self.height is None:
return self.data[ind]
return self.data[ind, ...].copy() | [
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BerkeleyAutomation/autolab_core | autolab_core/tensor_dataset.py | Tensor.set_datapoint | def set_datapoint(self, ind, datapoint):
""" Sets the value of the datapoint at the given index. """
if ind >= self.num_datapoints:
raise ValueError('Index %d out of bounds! Tensor has %d datapoints' %(ind, self.num_datapoints))
self.data[ind, ...] = np.array(datapoint).astype(self.dtype) | python | def set_datapoint(self, ind, datapoint):
""" Sets the value of the datapoint at the given index. """
if ind >= self.num_datapoints:
raise ValueError('Index %d out of bounds! Tensor has %d datapoints' %(ind, self.num_datapoints))
self.data[ind, ...] = np.array(datapoint).astype(self.dtype) | [
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BerkeleyAutomation/autolab_core | autolab_core/tensor_dataset.py | Tensor.data_slice | def data_slice(self, slice_ind):
""" Returns a slice of datapoints """
if self.height is None:
return self.data[slice_ind]
return self.data[slice_ind, ...] | python | def data_slice(self, slice_ind):
""" Returns a slice of datapoints """
if self.height is None:
return self.data[slice_ind]
return self.data[slice_ind, ...] | [
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BerkeleyAutomation/autolab_core | autolab_core/tensor_dataset.py | Tensor.save | def save(self, filename, compressed=True):
""" Save a tensor to disk. """
# check for data
if not self.has_data:
return False
# read ext and save accordingly
_, file_ext = os.path.splitext(filename)
if compressed:
if file_ext != COMPRESSED_TENSOR_EXT:
raise ValueError('Can only save compressed tensor with %s extension' %(COMPRESSED_TENSOR_EXT))
np.savez_compressed(filename,
self.data[:self.cur_index,...])
else:
if file_ext != TENSOR_EXT:
raise ValueError('Can only save tensor with .npy extension')
np.save(filename, self.data[:self.cur_index,...])
return True | python | def save(self, filename, compressed=True):
""" Save a tensor to disk. """
# check for data
if not self.has_data:
return False
# read ext and save accordingly
_, file_ext = os.path.splitext(filename)
if compressed:
if file_ext != COMPRESSED_TENSOR_EXT:
raise ValueError('Can only save compressed tensor with %s extension' %(COMPRESSED_TENSOR_EXT))
np.savez_compressed(filename,
self.data[:self.cur_index,...])
else:
if file_ext != TENSOR_EXT:
raise ValueError('Can only save tensor with .npy extension')
np.save(filename, self.data[:self.cur_index,...])
return True | [
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BerkeleyAutomation/autolab_core | autolab_core/tensor_dataset.py | Tensor.load | def load(filename, compressed=True, prealloc=None):
""" Loads a tensor from disk. """
# switch load based on file ext
_, file_ext = os.path.splitext(filename)
if compressed:
if file_ext != COMPRESSED_TENSOR_EXT:
raise ValueError('Can only load compressed tensor with %s extension' %(COMPRESSED_TENSOR_EXT))
data = np.load(filename)['arr_0']
else:
if file_ext != TENSOR_EXT:
raise ValueError('Can only load tensor with .npy extension')
data = np.load(filename)
# fill prealloc tensor
if prealloc is not None:
prealloc.reset()
prealloc.add_batch(data)
return prealloc
# init new tensor
tensor = Tensor(data.shape, data.dtype, data=data)
return tensor | python | def load(filename, compressed=True, prealloc=None):
""" Loads a tensor from disk. """
# switch load based on file ext
_, file_ext = os.path.splitext(filename)
if compressed:
if file_ext != COMPRESSED_TENSOR_EXT:
raise ValueError('Can only load compressed tensor with %s extension' %(COMPRESSED_TENSOR_EXT))
data = np.load(filename)['arr_0']
else:
if file_ext != TENSOR_EXT:
raise ValueError('Can only load tensor with .npy extension')
data = np.load(filename)
# fill prealloc tensor
if prealloc is not None:
prealloc.reset()
prealloc.add_batch(data)
return prealloc
# init new tensor
tensor = Tensor(data.shape, data.dtype, data=data)
return tensor | [
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BerkeleyAutomation/autolab_core | autolab_core/tensor_dataset.py | TensorDataset.datapoint_indices_for_tensor | def datapoint_indices_for_tensor(self, tensor_index):
""" Returns the indices for all datapoints in the given tensor. """
if tensor_index >= self._num_tensors:
raise ValueError('Tensor index %d is greater than the number of tensors (%d)' %(tensor_index, self._num_tensors))
return self._file_num_to_indices[tensor_index] | python | def datapoint_indices_for_tensor(self, tensor_index):
""" Returns the indices for all datapoints in the given tensor. """
if tensor_index >= self._num_tensors:
raise ValueError('Tensor index %d is greater than the number of tensors (%d)' %(tensor_index, self._num_tensors))
return self._file_num_to_indices[tensor_index] | [
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BerkeleyAutomation/autolab_core | autolab_core/tensor_dataset.py | TensorDataset.tensor_index | def tensor_index(self, datapoint_index):
""" Returns the index of the tensor containing the referenced datapoint. """
if datapoint_index >= self._num_datapoints:
raise ValueError('Datapoint index %d is greater than the number of datapoints (%d)' %(datapoint_index, self._num_datapoints))
return self._index_to_file_num[datapoint_index] | python | def tensor_index(self, datapoint_index):
""" Returns the index of the tensor containing the referenced datapoint. """
if datapoint_index >= self._num_datapoints:
raise ValueError('Datapoint index %d is greater than the number of datapoints (%d)' %(datapoint_index, self._num_datapoints))
return self._index_to_file_num[datapoint_index] | [
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BerkeleyAutomation/autolab_core | autolab_core/tensor_dataset.py | TensorDataset.generate_tensor_filename | def generate_tensor_filename(self, field_name, file_num, compressed=True):
""" Generate a filename for a tensor. """
file_ext = TENSOR_EXT
if compressed:
file_ext = COMPRESSED_TENSOR_EXT
filename = os.path.join(self.filename, 'tensors', '%s_%05d%s' %(field_name, file_num, file_ext))
return filename | python | def generate_tensor_filename(self, field_name, file_num, compressed=True):
""" Generate a filename for a tensor. """
file_ext = TENSOR_EXT
if compressed:
file_ext = COMPRESSED_TENSOR_EXT
filename = os.path.join(self.filename, 'tensors', '%s_%05d%s' %(field_name, file_num, file_ext))
return filename | [
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BerkeleyAutomation/autolab_core | autolab_core/tensor_dataset.py | TensorDataset._allocate_tensors | def _allocate_tensors(self):
""" Allocates the tensors in the dataset. """
# init tensors dict
self._tensors = {}
# allocate tensor for each data field
for field_name, field_spec in self._config['fields'].items():
# parse attributes
field_dtype = np.dtype(field_spec['dtype'])
# parse shape
field_shape = [self._datapoints_per_file]
if 'height' in field_spec.keys():
field_shape.append(field_spec['height'])
if 'width' in field_spec.keys():
field_shape.append(field_spec['width'])
if 'channels' in field_spec.keys():
field_shape.append(field_spec['channels'])
# create tensor
self._tensors[field_name] = Tensor(field_shape, field_dtype) | python | def _allocate_tensors(self):
""" Allocates the tensors in the dataset. """
# init tensors dict
self._tensors = {}
# allocate tensor for each data field
for field_name, field_spec in self._config['fields'].items():
# parse attributes
field_dtype = np.dtype(field_spec['dtype'])
# parse shape
field_shape = [self._datapoints_per_file]
if 'height' in field_spec.keys():
field_shape.append(field_spec['height'])
if 'width' in field_spec.keys():
field_shape.append(field_spec['width'])
if 'channels' in field_spec.keys():
field_shape.append(field_spec['channels'])
# create tensor
self._tensors[field_name] = Tensor(field_shape, field_dtype) | [
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BerkeleyAutomation/autolab_core | autolab_core/tensor_dataset.py | TensorDataset.add | def add(self, datapoint):
""" Adds a datapoint to the file. """
# check access level
if self._access_mode == READ_ONLY_ACCESS:
raise ValueError('Cannot add datapoints with read-only access')
# read tensor datapoint ind
tensor_ind = self._num_datapoints // self._datapoints_per_file
# check datapoint fields
for field_name in datapoint.keys():
if field_name not in self.field_names:
raise ValueError('Field %s not specified in dataset' %(field_name))
# store data in tensor
cur_num_tensors = self._num_tensors
new_num_tensors = cur_num_tensors
for field_name in self.field_names:
if tensor_ind < cur_num_tensors:
# load tensor if it was previously allocated
self._tensors[field_name] = self.tensor(field_name, tensor_ind)
else:
# clear tensor if this is a new tensor
self._tensors[field_name].reset()
self._tensor_cache_file_num[field_name] = tensor_ind
new_num_tensors = cur_num_tensors + 1
self._has_unsaved_data = True
self._tensors[field_name].add(datapoint[field_name])
cur_size = self._tensors[field_name].size
# update num tensors
if new_num_tensors > cur_num_tensors:
self._num_tensors = new_num_tensors
# update file indices
self._index_to_file_num[self._num_datapoints] = tensor_ind
self._file_num_to_indices[tensor_ind] = tensor_ind * self._datapoints_per_file + np.arange(cur_size)
# save if tensors are full
field_name = self.field_names[0]
if self._tensors[field_name].is_full:
# save next tensors to file
logging.info('Dataset %s: Writing tensor %d to disk' %(self.filename, tensor_ind))
self.write()
# increment num datapoints
self._num_datapoints += 1 | python | def add(self, datapoint):
""" Adds a datapoint to the file. """
# check access level
if self._access_mode == READ_ONLY_ACCESS:
raise ValueError('Cannot add datapoints with read-only access')
# read tensor datapoint ind
tensor_ind = self._num_datapoints // self._datapoints_per_file
# check datapoint fields
for field_name in datapoint.keys():
if field_name not in self.field_names:
raise ValueError('Field %s not specified in dataset' %(field_name))
# store data in tensor
cur_num_tensors = self._num_tensors
new_num_tensors = cur_num_tensors
for field_name in self.field_names:
if tensor_ind < cur_num_tensors:
# load tensor if it was previously allocated
self._tensors[field_name] = self.tensor(field_name, tensor_ind)
else:
# clear tensor if this is a new tensor
self._tensors[field_name].reset()
self._tensor_cache_file_num[field_name] = tensor_ind
new_num_tensors = cur_num_tensors + 1
self._has_unsaved_data = True
self._tensors[field_name].add(datapoint[field_name])
cur_size = self._tensors[field_name].size
# update num tensors
if new_num_tensors > cur_num_tensors:
self._num_tensors = new_num_tensors
# update file indices
self._index_to_file_num[self._num_datapoints] = tensor_ind
self._file_num_to_indices[tensor_ind] = tensor_ind * self._datapoints_per_file + np.arange(cur_size)
# save if tensors are full
field_name = self.field_names[0]
if self._tensors[field_name].is_full:
# save next tensors to file
logging.info('Dataset %s: Writing tensor %d to disk' %(self.filename, tensor_ind))
self.write()
# increment num datapoints
self._num_datapoints += 1 | [
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BerkeleyAutomation/autolab_core | autolab_core/tensor_dataset.py | TensorDataset.datapoint | def datapoint(self, ind, field_names=None):
""" Loads a tensor datapoint for a given global index.
Parameters
----------
ind : int
global index in the tensor
field_names : :obj:`list` of str
field names to load
Returns
-------
:obj:`TensorDatapoint`
the desired tensor datapoint
"""
# flush if necessary
if self._has_unsaved_data:
self.flush()
# check valid input
if ind >= self._num_datapoints:
raise ValueError('Index %d larger than the number of datapoints in the dataset (%d)' %(ind, self._num_datapoints))
# load the field names
if field_names is None:
field_names = self.field_names
# return the datapoint
datapoint = TensorDatapoint(field_names)
file_num = self._index_to_file_num[ind]
for field_name in field_names:
tensor = self.tensor(field_name, file_num)
tensor_index = ind % self._datapoints_per_file
datapoint[field_name] = tensor.datapoint(tensor_index)
return datapoint | python | def datapoint(self, ind, field_names=None):
""" Loads a tensor datapoint for a given global index.
Parameters
----------
ind : int
global index in the tensor
field_names : :obj:`list` of str
field names to load
Returns
-------
:obj:`TensorDatapoint`
the desired tensor datapoint
"""
# flush if necessary
if self._has_unsaved_data:
self.flush()
# check valid input
if ind >= self._num_datapoints:
raise ValueError('Index %d larger than the number of datapoints in the dataset (%d)' %(ind, self._num_datapoints))
# load the field names
if field_names is None:
field_names = self.field_names
# return the datapoint
datapoint = TensorDatapoint(field_names)
file_num = self._index_to_file_num[ind]
for field_name in field_names:
tensor = self.tensor(field_name, file_num)
tensor_index = ind % self._datapoints_per_file
datapoint[field_name] = tensor.datapoint(tensor_index)
return datapoint | [
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BerkeleyAutomation/autolab_core | autolab_core/tensor_dataset.py | TensorDataset.tensor | def tensor(self, field_name, tensor_ind):
""" Returns the tensor for a given field and tensor index.
Parameters
----------
field_name : str
the name of the field to load
tensor_index : int
the index of the tensor
Returns
-------
:obj:`Tensor`
the desired tensor
"""
if tensor_ind == self._tensor_cache_file_num[field_name]:
return self._tensors[field_name]
filename = self.generate_tensor_filename(field_name, tensor_ind, compressed=True)
Tensor.load(filename, compressed=True,
prealloc=self._tensors[field_name])
self._tensor_cache_file_num[field_name] = tensor_ind
return self._tensors[field_name] | python | def tensor(self, field_name, tensor_ind):
""" Returns the tensor for a given field and tensor index.
Parameters
----------
field_name : str
the name of the field to load
tensor_index : int
the index of the tensor
Returns
-------
:obj:`Tensor`
the desired tensor
"""
if tensor_ind == self._tensor_cache_file_num[field_name]:
return self._tensors[field_name]
filename = self.generate_tensor_filename(field_name, tensor_ind, compressed=True)
Tensor.load(filename, compressed=True,
prealloc=self._tensors[field_name])
self._tensor_cache_file_num[field_name] = tensor_ind
return self._tensors[field_name] | [
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BerkeleyAutomation/autolab_core | autolab_core/tensor_dataset.py | TensorDataset.delete_last | def delete_last(self, num_to_delete=1):
""" Deletes the last N datapoints from the dataset.
Parameters
----------
num_to_delete : int
the number of datapoints to remove from the end of the dataset
"""
# check access level
if self._access_mode == READ_ONLY_ACCESS:
raise ValueError('Cannot delete datapoints with read-only access')
# check num to delete
if num_to_delete > self._num_datapoints:
raise ValueError('Cannot remove more than the number of datapoints in the dataset')
# compute indices
last_datapoint_ind = self._num_datapoints - 1
last_tensor_ind = last_datapoint_ind // self._datapoints_per_file
new_last_datapoint_ind = self._num_datapoints - 1 - num_to_delete
new_num_datapoints = new_last_datapoint_ind + 1
new_last_datapoint_ind = max(new_last_datapoint_ind, 0)
new_last_tensor_ind = new_last_datapoint_ind // self._datapoints_per_file
# delete all but the last tensor
delete_tensor_ind = range(new_last_tensor_ind+1, last_tensor_ind+1)
for tensor_ind in delete_tensor_ind:
for field_name in self.field_names:
filename = self.generate_tensor_filename(field_name, tensor_ind)
os.remove(filename)
# update last tensor
dataset_empty = False
target_tensor_size = new_num_datapoints % self._datapoints_per_file
if target_tensor_size == 0:
if new_num_datapoints > 0:
target_tensor_size = self._datapoints_per_file
else:
dataset_empty = True
for field_name in self.field_names:
new_last_tensor = self.tensor(field_name, new_last_tensor_ind)
while new_last_tensor.size > target_tensor_size:
new_last_tensor.delete_last()
filename = self.generate_tensor_filename(field_name, new_last_tensor_ind)
new_last_tensor.save(filename, compressed=True)
if not new_last_tensor.has_data:
os.remove(filename)
new_last_tensor.reset()
# update num datapoints
if self._num_datapoints - 1 - num_to_delete >= 0:
self._num_datapoints = new_num_datapoints
else:
self._num_datapoints = 0
# handle deleted tensor
self._num_tensors = new_last_tensor_ind + 1
if dataset_empty:
self._num_tensors = 0 | python | def delete_last(self, num_to_delete=1):
""" Deletes the last N datapoints from the dataset.
Parameters
----------
num_to_delete : int
the number of datapoints to remove from the end of the dataset
"""
# check access level
if self._access_mode == READ_ONLY_ACCESS:
raise ValueError('Cannot delete datapoints with read-only access')
# check num to delete
if num_to_delete > self._num_datapoints:
raise ValueError('Cannot remove more than the number of datapoints in the dataset')
# compute indices
last_datapoint_ind = self._num_datapoints - 1
last_tensor_ind = last_datapoint_ind // self._datapoints_per_file
new_last_datapoint_ind = self._num_datapoints - 1 - num_to_delete
new_num_datapoints = new_last_datapoint_ind + 1
new_last_datapoint_ind = max(new_last_datapoint_ind, 0)
new_last_tensor_ind = new_last_datapoint_ind // self._datapoints_per_file
# delete all but the last tensor
delete_tensor_ind = range(new_last_tensor_ind+1, last_tensor_ind+1)
for tensor_ind in delete_tensor_ind:
for field_name in self.field_names:
filename = self.generate_tensor_filename(field_name, tensor_ind)
os.remove(filename)
# update last tensor
dataset_empty = False
target_tensor_size = new_num_datapoints % self._datapoints_per_file
if target_tensor_size == 0:
if new_num_datapoints > 0:
target_tensor_size = self._datapoints_per_file
else:
dataset_empty = True
for field_name in self.field_names:
new_last_tensor = self.tensor(field_name, new_last_tensor_ind)
while new_last_tensor.size > target_tensor_size:
new_last_tensor.delete_last()
filename = self.generate_tensor_filename(field_name, new_last_tensor_ind)
new_last_tensor.save(filename, compressed=True)
if not new_last_tensor.has_data:
os.remove(filename)
new_last_tensor.reset()
# update num datapoints
if self._num_datapoints - 1 - num_to_delete >= 0:
self._num_datapoints = new_num_datapoints
else:
self._num_datapoints = 0
# handle deleted tensor
self._num_tensors = new_last_tensor_ind + 1
if dataset_empty:
self._num_tensors = 0 | [
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BerkeleyAutomation/autolab_core | autolab_core/tensor_dataset.py | TensorDataset.write | def write(self):
""" Writes all tensors to the next file number. """
# write the next file for all fields
for field_name in self.field_names:
filename = self.generate_tensor_filename(field_name, self._num_tensors-1)
self._tensors[field_name].save(filename, compressed=True)
# write the current metadata to file
json.dump(self._metadata, open(self.metadata_filename, 'w'),
indent=JSON_INDENT,
sort_keys=True)
# update
self._has_unsaved_data = False | python | def write(self):
""" Writes all tensors to the next file number. """
# write the next file for all fields
for field_name in self.field_names:
filename = self.generate_tensor_filename(field_name, self._num_tensors-1)
self._tensors[field_name].save(filename, compressed=True)
# write the current metadata to file
json.dump(self._metadata, open(self.metadata_filename, 'w'),
indent=JSON_INDENT,
sort_keys=True)
# update
self._has_unsaved_data = False | [
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BerkeleyAutomation/autolab_core | autolab_core/tensor_dataset.py | TensorDataset.open | def open(dataset_dir, access_mode=READ_ONLY_ACCESS):
""" Opens a tensor dataset. """
# check access mode
if access_mode == WRITE_ACCESS:
raise ValueError('Cannot open a dataset with write-only access')
# read config
try:
# json load
config_filename = os.path.join(dataset_dir, 'config.json')
config = json.load(open(config_filename, 'r'))
except:
# YAML load
config_filename = os.path.join(dataset_dir, 'config.yaml')
config = YamlConfig(config_filename)
# open dataset
dataset = TensorDataset(dataset_dir, config, access_mode=access_mode)
return dataset | python | def open(dataset_dir, access_mode=READ_ONLY_ACCESS):
""" Opens a tensor dataset. """
# check access mode
if access_mode == WRITE_ACCESS:
raise ValueError('Cannot open a dataset with write-only access')
# read config
try:
# json load
config_filename = os.path.join(dataset_dir, 'config.json')
config = json.load(open(config_filename, 'r'))
except:
# YAML load
config_filename = os.path.join(dataset_dir, 'config.yaml')
config = YamlConfig(config_filename)
# open dataset
dataset = TensorDataset(dataset_dir, config, access_mode=access_mode)
return dataset | [
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BerkeleyAutomation/autolab_core | autolab_core/tensor_dataset.py | TensorDataset.split | def split(self, split_name):
""" Return the training and validation indices for the requested split.
Parameters
----------
split_name : str
name of the split
Returns
-------
:obj:`numpy.ndarray`
array of training indices in the global dataset
:obj:`numpy.ndarray`
array of validation indices in the global dataset
dict
metadata about the split
"""
if not self.has_split(split_name):
raise ValueError('Split %s does not exist!' %(split_name))
metadata_filename = self.split_metadata_filename(split_name)
train_filename = self.train_indices_filename(split_name)
val_filename = self.val_indices_filename(split_name)
metadata = json.load(open(metadata_filename, 'r'))
train_indices = np.load(train_filename)['arr_0']
val_indices = np.load(val_filename)['arr_0']
return train_indices, val_indices, metadata | python | def split(self, split_name):
""" Return the training and validation indices for the requested split.
Parameters
----------
split_name : str
name of the split
Returns
-------
:obj:`numpy.ndarray`
array of training indices in the global dataset
:obj:`numpy.ndarray`
array of validation indices in the global dataset
dict
metadata about the split
"""
if not self.has_split(split_name):
raise ValueError('Split %s does not exist!' %(split_name))
metadata_filename = self.split_metadata_filename(split_name)
train_filename = self.train_indices_filename(split_name)
val_filename = self.val_indices_filename(split_name)
metadata = json.load(open(metadata_filename, 'r'))
train_indices = np.load(train_filename)['arr_0']
val_indices = np.load(val_filename)['arr_0']
return train_indices, val_indices, metadata | [
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BerkeleyAutomation/autolab_core | autolab_core/tensor_dataset.py | TensorDataset.delete_split | def delete_split(self, split_name):
""" Delete a split of the dataset.
Parameters
----------
split_name : str
name of the split to delete
"""
if self.has_split(split_name):
shutil.rmtree(os.path.join(self.split_dir, split_name)) | python | def delete_split(self, split_name):
""" Delete a split of the dataset.
Parameters
----------
split_name : str
name of the split to delete
"""
if self.has_split(split_name):
shutil.rmtree(os.path.join(self.split_dir, split_name)) | [
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BerkeleyAutomation/autolab_core | autolab_core/yaml_config.py | YamlConfig._load_config | def _load_config(self, filename):
"""Loads a yaml configuration file from the given filename.
Parameters
----------
filename : :obj:`str`
The filename of the .yaml file that contains the configuration.
"""
# Read entire file for metadata
fh = open(filename, 'r')
self.file_contents = fh.read()
# Replace !include directives with content
config_dir = os.path.split(filename)[0]
include_re = re.compile('^(.*)!include\s+(.*)$', re.MULTILINE)
def recursive_load(matchobj, path):
first_spacing = matchobj.group(1)
other_spacing = first_spacing.replace('-', ' ')
fname = os.path.join(path, matchobj.group(2))
new_path, _ = os.path.split(fname)
new_path = os.path.realpath(new_path)
text = ''
with open(fname) as f:
text = f.read()
text = first_spacing + text
text = text.replace('\n', '\n{}'.format(other_spacing), text.count('\n') - 1)
return re.sub(include_re, lambda m : recursive_load(m, new_path), text)
# def include_repl(matchobj):
# first_spacing = matchobj.group(1)
# other_spacing = first_spacing.replace('-', ' ')
# fname = os.path.join(config_dir, matchobj.group(2))
# text = ''
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# text = text.replace('\n', '\n{}'.format(other_spacing), text.count('\n') - 1)
# return text
self.file_contents = re.sub(include_re, lambda m : recursive_load(m, config_dir), self.file_contents)
# Read in dictionary
self.config = self.__ordered_load(self.file_contents)
# Convert functions of other params to true expressions
for k in self.config.keys():
self.config[k] = YamlConfig.__convert_key(self.config[k])
fh.close()
# Load core configuration
return self.config | python | def _load_config(self, filename):
"""Loads a yaml configuration file from the given filename.
Parameters
----------
filename : :obj:`str`
The filename of the .yaml file that contains the configuration.
"""
# Read entire file for metadata
fh = open(filename, 'r')
self.file_contents = fh.read()
# Replace !include directives with content
config_dir = os.path.split(filename)[0]
include_re = re.compile('^(.*)!include\s+(.*)$', re.MULTILINE)
def recursive_load(matchobj, path):
first_spacing = matchobj.group(1)
other_spacing = first_spacing.replace('-', ' ')
fname = os.path.join(path, matchobj.group(2))
new_path, _ = os.path.split(fname)
new_path = os.path.realpath(new_path)
text = ''
with open(fname) as f:
text = f.read()
text = first_spacing + text
text = text.replace('\n', '\n{}'.format(other_spacing), text.count('\n') - 1)
return re.sub(include_re, lambda m : recursive_load(m, new_path), text)
# def include_repl(matchobj):
# first_spacing = matchobj.group(1)
# other_spacing = first_spacing.replace('-', ' ')
# fname = os.path.join(config_dir, matchobj.group(2))
# text = ''
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# text = first_spacing + text
# text = text.replace('\n', '\n{}'.format(other_spacing), text.count('\n') - 1)
# return text
self.file_contents = re.sub(include_re, lambda m : recursive_load(m, config_dir), self.file_contents)
# Read in dictionary
self.config = self.__ordered_load(self.file_contents)
# Convert functions of other params to true expressions
for k in self.config.keys():
self.config[k] = YamlConfig.__convert_key(self.config[k])
fh.close()
# Load core configuration
return self.config | [
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BerkeleyAutomation/autolab_core | autolab_core/yaml_config.py | YamlConfig.__convert_key | def __convert_key(expression):
"""Converts keys in YAML that reference other keys.
"""
if type(expression) is str and len(expression) > 2 and expression[1] == '!':
expression = eval(expression[2:-1])
return expression | python | def __convert_key(expression):
"""Converts keys in YAML that reference other keys.
"""
if type(expression) is str and len(expression) > 2 and expression[1] == '!':
expression = eval(expression[2:-1])
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BerkeleyAutomation/autolab_core | autolab_core/learning_analysis.py | ClassificationResult.make_summary_table | def make_summary_table(train_result, val_result, plot=True, save_dir=None, prepend="", save=False):
"""
Makes a matplotlib table object with relevant data.
Thanks to Lucas Manuelli for the contribution.
Parameters
----------
train_result: ClassificationResult
result on train split
val_result: ClassificationResult
result on validation split
save_dir: str
path pointing to where to save results
Returns
-------
dict
dict with stored values, can be saved to a yaml file
:obj:`matplotlibt.pyplot.fig`
a figure containing the table
"""
table_key_list = ['error_rate', 'recall_at_99_precision', 'average_precision', 'precision', 'recall']
num_fields = len(table_key_list)
import matplotlib.pyplot as plt
ax = plt.subplot(111, frame_on=False)
ax.xaxis.set_visible(False)
ax.yaxis.set_visible(False)
data = np.zeros([num_fields, 2])
data_dict = dict()
names = ['train', 'validation']
for name, result in zip(names, [train_result, val_result]):
data_dict[name] = {}
data_dict[name]['error_rate'] = result.error_rate
data_dict[name]['average_precision'] = result.ap_score * 100
data_dict[name]['precision'] = result.precision * 100
data_dict[name]['recall'] = result.recall * 100
precision_array, recall_array, _ = result.precision_recall_curve()
recall_at_99_precision = recall_array[np.argmax(precision_array > 0.99)] * 100 # to put it in percentage terms
data_dict[name]['recall_at_99_precision'] = recall_at_99_precision
for i, key in enumerate(table_key_list):
data_dict[name][key] = float("{0:.2f}".format(data_dict[name][key]))
j = names.index(name)
data[i, j] = data_dict[name][key]
table = plt.table(cellText=data, rowLabels=table_key_list, colLabels=names)
fig = plt.gcf()
fig.subplots_adjust(bottom=0.15)
if plot:
plt.show()
# save the results
if save_dir is not None and save:
fig_filename = os.path.join(save_dir, prepend + 'summary.png')
yaml_filename = os.path.join(save_dir, prepend + 'summary.yaml')
yaml.dump(data_dict, open(yaml_filename, 'w'), default_flow_style=False)
fig.savefig(fig_filename, bbox_inches="tight")
return data_dict, fig | python | def make_summary_table(train_result, val_result, plot=True, save_dir=None, prepend="", save=False):
"""
Makes a matplotlib table object with relevant data.
Thanks to Lucas Manuelli for the contribution.
Parameters
----------
train_result: ClassificationResult
result on train split
val_result: ClassificationResult
result on validation split
save_dir: str
path pointing to where to save results
Returns
-------
dict
dict with stored values, can be saved to a yaml file
:obj:`matplotlibt.pyplot.fig`
a figure containing the table
"""
table_key_list = ['error_rate', 'recall_at_99_precision', 'average_precision', 'precision', 'recall']
num_fields = len(table_key_list)
import matplotlib.pyplot as plt
ax = plt.subplot(111, frame_on=False)
ax.xaxis.set_visible(False)
ax.yaxis.set_visible(False)
data = np.zeros([num_fields, 2])
data_dict = dict()
names = ['train', 'validation']
for name, result in zip(names, [train_result, val_result]):
data_dict[name] = {}
data_dict[name]['error_rate'] = result.error_rate
data_dict[name]['average_precision'] = result.ap_score * 100
data_dict[name]['precision'] = result.precision * 100
data_dict[name]['recall'] = result.recall * 100
precision_array, recall_array, _ = result.precision_recall_curve()
recall_at_99_precision = recall_array[np.argmax(precision_array > 0.99)] * 100 # to put it in percentage terms
data_dict[name]['recall_at_99_precision'] = recall_at_99_precision
for i, key in enumerate(table_key_list):
data_dict[name][key] = float("{0:.2f}".format(data_dict[name][key]))
j = names.index(name)
data[i, j] = data_dict[name][key]
table = plt.table(cellText=data, rowLabels=table_key_list, colLabels=names)
fig = plt.gcf()
fig.subplots_adjust(bottom=0.15)
if plot:
plt.show()
# save the results
if save_dir is not None and save:
fig_filename = os.path.join(save_dir, prepend + 'summary.png')
yaml_filename = os.path.join(save_dir, prepend + 'summary.yaml')
yaml.dump(data_dict, open(yaml_filename, 'w'), default_flow_style=False)
fig.savefig(fig_filename, bbox_inches="tight")
return data_dict, fig | [
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Thanks to Lucas Manuelli for the contribution.
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train_result: ClassificationResult
result on train split
val_result: ClassificationResult
result on validation split
save_dir: str
path pointing to where to save results
Returns
-------
dict
dict with stored values, can be saved to a yaml file
:obj:`matplotlibt.pyplot.fig`
a figure containing the table | [
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BerkeleyAutomation/autolab_core | autolab_core/learning_analysis.py | BinaryClassificationResult.app_score | def app_score(self):
""" Computes the area under the app curve. """
# compute curve
precisions, pct_pred_pos, taus = self.precision_pct_pred_pos_curve(interval=False)
# compute area
app = 0
total = 0
for k in range(len(precisions)-1):
# read cur data
cur_prec = precisions[k]
cur_pp = pct_pred_pos[k]
cur_tau = taus[k]
# read next data
next_prec = precisions[k+1]
next_pp = pct_pred_pos[k+1]
next_tau = taus[k+1]
# approximate with rectangles
mid_prec = (cur_prec + next_prec) / 2.0
width_pp = np.abs(next_pp - cur_pp)
app += mid_prec * width_pp
total += width_pp
return app | python | def app_score(self):
""" Computes the area under the app curve. """
# compute curve
precisions, pct_pred_pos, taus = self.precision_pct_pred_pos_curve(interval=False)
# compute area
app = 0
total = 0
for k in range(len(precisions)-1):
# read cur data
cur_prec = precisions[k]
cur_pp = pct_pred_pos[k]
cur_tau = taus[k]
# read next data
next_prec = precisions[k+1]
next_pp = pct_pred_pos[k+1]
next_tau = taus[k+1]
# approximate with rectangles
mid_prec = (cur_prec + next_prec) / 2.0
width_pp = np.abs(next_pp - cur_pp)
app += mid_prec * width_pp
total += width_pp
return app | [
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BerkeleyAutomation/autolab_core | autolab_core/learning_analysis.py | BinaryClassificationResult.accuracy_curve | def accuracy_curve(self, delta_tau=0.01):
""" Computes the relationship between probability threshold
and classification accuracy. """
# compute thresholds based on the sorted probabilities
orig_thresh = self.threshold
sorted_labels, sorted_probs = self.sorted_values
scores = []
taus = []
tau = 0
for k in range(len(sorted_labels)):
# compute new accuracy
self.threshold = tau
scores.append(self.accuracy)
taus.append(tau)
# update threshold
tau = sorted_probs[k]
# add last datapoint
tau = 1.0
self.threshold = tau
scores.append(self.accuracy)
taus.append(tau)
self.threshold = orig_thresh
return scores, taus | python | def accuracy_curve(self, delta_tau=0.01):
""" Computes the relationship between probability threshold
and classification accuracy. """
# compute thresholds based on the sorted probabilities
orig_thresh = self.threshold
sorted_labels, sorted_probs = self.sorted_values
scores = []
taus = []
tau = 0
for k in range(len(sorted_labels)):
# compute new accuracy
self.threshold = tau
scores.append(self.accuracy)
taus.append(tau)
# update threshold
tau = sorted_probs[k]
# add last datapoint
tau = 1.0
self.threshold = tau
scores.append(self.accuracy)
taus.append(tau)
self.threshold = orig_thresh
return scores, taus | [
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BerkeleyAutomation/autolab_core | autolab_core/learning_analysis.py | BinaryClassificationResult.f1_curve | def f1_curve(self, delta_tau=0.01):
""" Computes the relationship between probability threshold
and classification F1 score. """
# compute thresholds based on the sorted probabilities
orig_thresh = self.threshold
sorted_labels, sorted_probs = self.sorted_values
scores = []
taus = []
tau = 0
for k in range(len(sorted_labels)):
# compute new accuracy
self.threshold = tau
scores.append(self.f1_score)
taus.append(tau)
# update threshold
tau = sorted_probs[k]
# add last datapoint
tau = 1.0
self.threshold = tau
scores.append(self.f1_score)
taus.append(tau)
self.threshold = orig_thresh
return scores, taus | python | def f1_curve(self, delta_tau=0.01):
""" Computes the relationship between probability threshold
and classification F1 score. """
# compute thresholds based on the sorted probabilities
orig_thresh = self.threshold
sorted_labels, sorted_probs = self.sorted_values
scores = []
taus = []
tau = 0
for k in range(len(sorted_labels)):
# compute new accuracy
self.threshold = tau
scores.append(self.f1_score)
taus.append(tau)
# update threshold
tau = sorted_probs[k]
# add last datapoint
tau = 1.0
self.threshold = tau
scores.append(self.f1_score)
taus.append(tau)
self.threshold = orig_thresh
return scores, taus | [
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BerkeleyAutomation/autolab_core | autolab_core/learning_analysis.py | BinaryClassificationResult.phi_coef_curve | def phi_coef_curve(self, delta_tau=0.01):
""" Computes the relationship between probability threshold
and classification phi coefficient. """
# compute thresholds based on the sorted probabilities
orig_thresh = self.threshold
sorted_labels, sorted_probs = self.sorted_values
scores = []
taus = []
tau = 0
for k in range(len(sorted_labels)):
# compute new accuracy
self.threshold = tau
scores.append(self.phi_coef)
taus.append(tau)
# update threshold
tau = sorted_probs[k]
# add last datapoint
tau = 1.0
self.threshold = tau
scores.append(self.phi_coef)
taus.append(tau)
self.threshold = orig_thresh
return scores, taus | python | def phi_coef_curve(self, delta_tau=0.01):
""" Computes the relationship between probability threshold
and classification phi coefficient. """
# compute thresholds based on the sorted probabilities
orig_thresh = self.threshold
sorted_labels, sorted_probs = self.sorted_values
scores = []
taus = []
tau = 0
for k in range(len(sorted_labels)):
# compute new accuracy
self.threshold = tau
scores.append(self.phi_coef)
taus.append(tau)
# update threshold
tau = sorted_probs[k]
# add last datapoint
tau = 1.0
self.threshold = tau
scores.append(self.phi_coef)
taus.append(tau)
self.threshold = orig_thresh
return scores, taus | [
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BerkeleyAutomation/autolab_core | autolab_core/learning_analysis.py | BinaryClassificationResult.precision_pct_pred_pos_curve | def precision_pct_pred_pos_curve(self, interval=False, delta_tau=0.001):
""" Computes the relationship between precision
and the percent of positively classified datapoints . """
# compute thresholds based on the sorted probabilities
orig_thresh = self.threshold
sorted_labels, sorted_probs = self.sorted_values
precisions = []
pct_pred_pos = []
taus = []
tau = 0
if not interval:
for k in range(len(sorted_labels)):
# compute new accuracy
self.threshold = tau
precisions.append(self.precision)
pct_pred_pos.append(self.pct_pred_pos)
taus.append(tau)
# update threshold
tau = sorted_probs[k]
else:
while tau < 1.0:
# compute new accuracy
self.threshold = tau
precisions.append(self.precision)
pct_pred_pos.append(self.pct_pred_pos)
taus.append(tau)
# update threshold
tau += delta_tau
# add last datapoint
tau = 1.0
self.threshold = tau
precisions.append(self.precision)
pct_pred_pos.append(self.pct_pred_pos)
taus.append(tau)
precisions.append(1.0)
pct_pred_pos.append(0.0)
taus.append(1.0 + 1e-12)
self.threshold = orig_thresh
return precisions, pct_pred_pos, taus | python | def precision_pct_pred_pos_curve(self, interval=False, delta_tau=0.001):
""" Computes the relationship between precision
and the percent of positively classified datapoints . """
# compute thresholds based on the sorted probabilities
orig_thresh = self.threshold
sorted_labels, sorted_probs = self.sorted_values
precisions = []
pct_pred_pos = []
taus = []
tau = 0
if not interval:
for k in range(len(sorted_labels)):
# compute new accuracy
self.threshold = tau
precisions.append(self.precision)
pct_pred_pos.append(self.pct_pred_pos)
taus.append(tau)
# update threshold
tau = sorted_probs[k]
else:
while tau < 1.0:
# compute new accuracy
self.threshold = tau
precisions.append(self.precision)
pct_pred_pos.append(self.pct_pred_pos)
taus.append(tau)
# update threshold
tau += delta_tau
# add last datapoint
tau = 1.0
self.threshold = tau
precisions.append(self.precision)
pct_pred_pos.append(self.pct_pred_pos)
taus.append(tau)
precisions.append(1.0)
pct_pred_pos.append(0.0)
taus.append(1.0 + 1e-12)
self.threshold = orig_thresh
return precisions, pct_pred_pos, taus | [
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BerkeleyAutomation/autolab_core | autolab_core/utils.py | gen_experiment_id | def gen_experiment_id(n=10):
"""Generate a random string with n characters.
Parameters
----------
n : int
The length of the string to be generated.
Returns
-------
:obj:`str`
A string with only alphabetic characters.
"""
chrs = 'abcdefghijklmnopqrstuvwxyz'
inds = np.random.randint(0,len(chrs), size=n)
return ''.join([chrs[i] for i in inds]) | python | def gen_experiment_id(n=10):
"""Generate a random string with n characters.
Parameters
----------
n : int
The length of the string to be generated.
Returns
-------
:obj:`str`
A string with only alphabetic characters.
"""
chrs = 'abcdefghijklmnopqrstuvwxyz'
inds = np.random.randint(0,len(chrs), size=n)
return ''.join([chrs[i] for i in inds]) | [
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BerkeleyAutomation/autolab_core | autolab_core/utils.py | histogram | def histogram(values, num_bins, bounds, normalized=True, plot=False, color='b'):
"""Generate a histogram plot.
Parameters
----------
values : :obj:`numpy.ndarray`
An array of values to put in the histogram.
num_bins : int
The number equal-width bins in the histogram.
bounds : :obj:`tuple` of float
Two floats - a min and a max - that define the lower and upper
ranges of the histogram, respectively.
normalized : bool
If True, the bins will show the percentage of elements they contain
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plot : bool
If True, this function uses pyplot to plot the histogram.
color : :obj:`str`
The color identifier for the plotted bins.
Returns
-------
:obj:`tuple of `:obj:`numpy.ndarray`
The values of the histogram and the bin edges as ndarrays.
"""
hist, bins = np.histogram(values, bins=num_bins, range=bounds)
width = (bins[1] - bins[0])
if normalized:
if np.sum(hist) > 0:
hist = hist.astype(np.float32) / np.sum(hist)
if plot:
import matplotlib.pyplot as plt
plt.bar(bins[:-1], hist, width=width, color=color)
return hist, bins | python | def histogram(values, num_bins, bounds, normalized=True, plot=False, color='b'):
"""Generate a histogram plot.
Parameters
----------
values : :obj:`numpy.ndarray`
An array of values to put in the histogram.
num_bins : int
The number equal-width bins in the histogram.
bounds : :obj:`tuple` of float
Two floats - a min and a max - that define the lower and upper
ranges of the histogram, respectively.
normalized : bool
If True, the bins will show the percentage of elements they contain
rather than raw counts.
plot : bool
If True, this function uses pyplot to plot the histogram.
color : :obj:`str`
The color identifier for the plotted bins.
Returns
-------
:obj:`tuple of `:obj:`numpy.ndarray`
The values of the histogram and the bin edges as ndarrays.
"""
hist, bins = np.histogram(values, bins=num_bins, range=bounds)
width = (bins[1] - bins[0])
if normalized:
if np.sum(hist) > 0:
hist = hist.astype(np.float32) / np.sum(hist)
if plot:
import matplotlib.pyplot as plt
plt.bar(bins[:-1], hist, width=width, color=color)
return hist, bins | [
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BerkeleyAutomation/autolab_core | autolab_core/utils.py | skew | def skew(xi):
"""Return the skew-symmetric matrix that can be used to calculate
cross-products with vector xi.
Multiplying this matrix by a vector `v` gives the same result
as `xi x v`.
Parameters
----------
xi : :obj:`numpy.ndarray` of float
A 3-entry vector.
Returns
-------
:obj:`numpy.ndarray` of float
The 3x3 skew-symmetric cross product matrix for the vector.
"""
S = np.array([[0, -xi[2], xi[1]],
[xi[2], 0, -xi[0]],
[-xi[1], xi[0], 0]])
return S | python | def skew(xi):
"""Return the skew-symmetric matrix that can be used to calculate
cross-products with vector xi.
Multiplying this matrix by a vector `v` gives the same result
as `xi x v`.
Parameters
----------
xi : :obj:`numpy.ndarray` of float
A 3-entry vector.
Returns
-------
:obj:`numpy.ndarray` of float
The 3x3 skew-symmetric cross product matrix for the vector.
"""
S = np.array([[0, -xi[2], xi[1]],
[xi[2], 0, -xi[0]],
[-xi[1], xi[0], 0]])
return S | [
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BerkeleyAutomation/autolab_core | autolab_core/utils.py | deskew | def deskew(S):
"""Converts a skew-symmetric cross-product matrix to its corresponding
vector. Only works for 3x3 matrices.
Parameters
----------
S : :obj:`numpy.ndarray` of float
A 3x3 skew-symmetric matrix.
Returns
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:obj:`numpy.ndarray` of float
A 3-entry vector that corresponds to the given cross product matrix.
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x = np.zeros(3)
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x[1] = S[0,2]
x[2] = S[1,0]
return x | python | def deskew(S):
"""Converts a skew-symmetric cross-product matrix to its corresponding
vector. Only works for 3x3 matrices.
Parameters
----------
S : :obj:`numpy.ndarray` of float
A 3x3 skew-symmetric matrix.
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:obj:`numpy.ndarray` of float
A 3-entry vector that corresponds to the given cross product matrix.
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x = np.zeros(3)
x[0] = S[2,1]
x[1] = S[0,2]
x[2] = S[1,0]
return x | [
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BerkeleyAutomation/autolab_core | autolab_core/utils.py | reverse_dictionary | def reverse_dictionary(d):
""" Reverses the key value pairs for a given dictionary.
Parameters
----------
d : :obj:`dict`
dictionary to reverse
Returns
-------
:obj:`dict`
dictionary with keys and values swapped
"""
rev_d = {}
[rev_d.update({v:k}) for k, v in d.items()]
return rev_d | python | def reverse_dictionary(d):
""" Reverses the key value pairs for a given dictionary.
Parameters
----------
d : :obj:`dict`
dictionary to reverse
Returns
-------
:obj:`dict`
dictionary with keys and values swapped
"""
rev_d = {}
[rev_d.update({v:k}) for k, v in d.items()]
return rev_d | [
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BerkeleyAutomation/autolab_core | autolab_core/utils.py | filenames | def filenames(directory, tag='', sorted=False, recursive=False):
""" Reads in all filenames from a directory that contain a specified substring.
Parameters
----------
directory : :obj:`str`
the directory to read from
tag : :obj:`str`
optional tag to match in the filenames
sorted : bool
whether or not to sort the filenames
recursive : bool
whether or not to search for the files recursively
Returns
-------
:obj:`list` of :obj:`str`
filenames to read from
"""
if recursive:
f = [os.path.join(directory, f) for directory, _, filename in os.walk(directory) for f in filename if f.find(tag) > -1]
else:
f = [os.path.join(directory, f) for f in os.listdir(directory) if f.find(tag) > -1]
if sorted:
f.sort()
return f | python | def filenames(directory, tag='', sorted=False, recursive=False):
""" Reads in all filenames from a directory that contain a specified substring.
Parameters
----------
directory : :obj:`str`
the directory to read from
tag : :obj:`str`
optional tag to match in the filenames
sorted : bool
whether or not to sort the filenames
recursive : bool
whether or not to search for the files recursively
Returns
-------
:obj:`list` of :obj:`str`
filenames to read from
"""
if recursive:
f = [os.path.join(directory, f) for directory, _, filename in os.walk(directory) for f in filename if f.find(tag) > -1]
else:
f = [os.path.join(directory, f) for f in os.listdir(directory) if f.find(tag) > -1]
if sorted:
f.sort()
return f | [
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tag : :obj:`str`
optional tag to match in the filenames
sorted : bool
whether or not to sort the filenames
recursive : bool
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BerkeleyAutomation/autolab_core | autolab_core/utils.py | sph2cart | def sph2cart(r, az, elev):
""" Convert spherical to cartesian coordinates.
Attributes
----------
r : float
radius
az : float
aziumth (angle about z axis)
elev : float
elevation from xy plane
Returns
-------
float
x-coordinate
float
y-coordinate
float
z-coordinate
"""
x = r * np.cos(az) * np.sin(elev)
y = r * np.sin(az) * np.sin(elev)
z = r * np.cos(elev)
return x, y, z | python | def sph2cart(r, az, elev):
""" Convert spherical to cartesian coordinates.
Attributes
----------
r : float
radius
az : float
aziumth (angle about z axis)
elev : float
elevation from xy plane
Returns
-------
float
x-coordinate
float
y-coordinate
float
z-coordinate
"""
x = r * np.cos(az) * np.sin(elev)
y = r * np.sin(az) * np.sin(elev)
z = r * np.cos(elev)
return x, y, z | [
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BerkeleyAutomation/autolab_core | autolab_core/utils.py | cart2sph | def cart2sph(x, y, z):
""" Convert cartesian to spherical coordinates.
Attributes
----------
x : float
x-coordinate
y : float
y-coordinate
z : float
z-coordinate
Returns
-------
float
radius
float
aziumth
float
elevation
"""
r = np.sqrt(x**2 + y**2 + z**2)
if x > 0 and y > 0:
az = np.arctan(y / x)
elif x > 0 and y < 0:
az = 2*np.pi - np.arctan(-y / x)
elif x < 0 and y > 0:
az = np.pi - np.arctan(-y / x)
elif x < 0 and y < 0:
az = np.pi + np.arctan(y / x)
elif x == 0 and y > 0:
az = np.pi / 2
elif x == 0 and y < 0:
az = 3 * np.pi / 2
elif y == 0 and x > 0:
az = 0
elif y == 0 and x < 0:
az = np.pi
elev = np.arccos(z / r)
return r, az, elev | python | def cart2sph(x, y, z):
""" Convert cartesian to spherical coordinates.
Attributes
----------
x : float
x-coordinate
y : float
y-coordinate
z : float
z-coordinate
Returns
-------
float
radius
float
aziumth
float
elevation
"""
r = np.sqrt(x**2 + y**2 + z**2)
if x > 0 and y > 0:
az = np.arctan(y / x)
elif x > 0 and y < 0:
az = 2*np.pi - np.arctan(-y / x)
elif x < 0 and y > 0:
az = np.pi - np.arctan(-y / x)
elif x < 0 and y < 0:
az = np.pi + np.arctan(y / x)
elif x == 0 and y > 0:
az = np.pi / 2
elif x == 0 and y < 0:
az = 3 * np.pi / 2
elif y == 0 and x > 0:
az = 0
elif y == 0 and x < 0:
az = np.pi
elev = np.arccos(z / r)
return r, az, elev | [
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BerkeleyAutomation/autolab_core | autolab_core/utils.py | keyboard_input | def keyboard_input(message, yesno=False):
""" Get keyboard input from a human, optionally reasking for valid
yes or no input.
Parameters
----------
message : :obj:`str`
the message to display to the user
yesno : :obj:`bool`
whether or not to enforce yes or no inputs
Returns
-------
:obj:`str`
string input by the human
"""
# add space for readability
message += ' '
# add yes or no to message
if yesno:
message += '[y/n] '
# ask human
human_input = input(message)
if yesno:
while human_input.lower() != 'n' and human_input.lower() != 'y':
logging.info('Did not understand input. Please answer \'y\' or \'n\'')
human_input = input(message)
return human_input | python | def keyboard_input(message, yesno=False):
""" Get keyboard input from a human, optionally reasking for valid
yes or no input.
Parameters
----------
message : :obj:`str`
the message to display to the user
yesno : :obj:`bool`
whether or not to enforce yes or no inputs
Returns
-------
:obj:`str`
string input by the human
"""
# add space for readability
message += ' '
# add yes or no to message
if yesno:
message += '[y/n] '
# ask human
human_input = input(message)
if yesno:
while human_input.lower() != 'n' and human_input.lower() != 'y':
logging.info('Did not understand input. Please answer \'y\' or \'n\'')
human_input = input(message)
return human_input | [
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BerkeleyAutomation/autolab_core | autolab_core/dual_quaternion.py | DualQuaternion.interpolate | def interpolate(dq0, dq1, t):
"""Return the interpolation of two DualQuaternions.
This uses the Dual Quaternion Linear Blending Method as described by Matthew Smith's
'Applications of Dual Quaternions in Three Dimensional Transformation and Interpolation'
https://www.cosc.canterbury.ac.nz/research/reports/HonsReps/2013/hons_1305.pdf
Parameters
----------
dq0 : :obj:`DualQuaternion`
The first DualQuaternion.
dq1 : :obj:`DualQuaternion`
The second DualQuaternion.
t : float
The interpolation step in [0,1]. When t=0, this returns dq0, and
when t=1, this returns dq1.
Returns
-------
:obj:`DualQuaternion`
The interpolated DualQuaternion.
Raises
------
ValueError
If t isn't in [0,1].
"""
if not 0 <= t <= 1:
raise ValueError("Interpolation step must be between 0 and 1! Got {0}".format(t))
dqt = dq0 * (1-t) + dq1 * t
return dqt.normalized | python | def interpolate(dq0, dq1, t):
"""Return the interpolation of two DualQuaternions.
This uses the Dual Quaternion Linear Blending Method as described by Matthew Smith's
'Applications of Dual Quaternions in Three Dimensional Transformation and Interpolation'
https://www.cosc.canterbury.ac.nz/research/reports/HonsReps/2013/hons_1305.pdf
Parameters
----------
dq0 : :obj:`DualQuaternion`
The first DualQuaternion.
dq1 : :obj:`DualQuaternion`
The second DualQuaternion.
t : float
The interpolation step in [0,1]. When t=0, this returns dq0, and
when t=1, this returns dq1.
Returns
-------
:obj:`DualQuaternion`
The interpolated DualQuaternion.
Raises
------
ValueError
If t isn't in [0,1].
"""
if not 0 <= t <= 1:
raise ValueError("Interpolation step must be between 0 and 1! Got {0}".format(t))
dqt = dq0 * (1-t) + dq1 * t
return dqt.normalized | [
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BerkeleyAutomation/autolab_core | autolab_core/csv_model.py | CSVModel._save | def _save(self):
"""Save the model to a .csv file
"""
# if not first time saving, copy .csv to a backup
if os.path.isfile(self._full_filename):
shutil.copyfile(self._full_filename, self._full_backup_filename)
# write to csv
with open(self._full_filename, 'w') as file:
writer = csv.DictWriter(file, fieldnames=self._headers)
writer.writeheader()
for row in self._table:
writer.writerow(row) | python | def _save(self):
"""Save the model to a .csv file
"""
# if not first time saving, copy .csv to a backup
if os.path.isfile(self._full_filename):
shutil.copyfile(self._full_filename, self._full_backup_filename)
# write to csv
with open(self._full_filename, 'w') as file:
writer = csv.DictWriter(file, fieldnames=self._headers)
writer.writeheader()
for row in self._table:
writer.writerow(row) | [
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BerkeleyAutomation/autolab_core | autolab_core/csv_model.py | CSVModel.insert | def insert(self, data):
"""Insert a row into the .csv file.
Parameters
----------
data : :obj:`dict`
A dictionary mapping keys (header strings) to values.
Returns
-------
int
The UID for the new row.
Raises
------
Exception
If the value for a given header is not of the appropriate type.
"""
row = {key:self._default_entry for key in self._headers}
row['_uid'] = self._get_new_uid()
for key, val in data.items():
if key in ('_uid', '_default'):
logging.warn("Cannot manually set columns _uid or _default of a row! Given data: {0}".format(data))
continue
if not isinstance(val, CSVModel._KNOWN_TYPES_MAP[self._headers_types[key]]):
raise Exception('Data type mismatch for column {0}. Expected: {1}, got: {2}'.format(key,
CSVModel._KNOWN_TYPES_MAP[self._headers_types[key]], type(val)))
row[key] = val
self._table.append(row)
self._save()
return row['_uid'] | python | def insert(self, data):
"""Insert a row into the .csv file.
Parameters
----------
data : :obj:`dict`
A dictionary mapping keys (header strings) to values.
Returns
-------
int
The UID for the new row.
Raises
------
Exception
If the value for a given header is not of the appropriate type.
"""
row = {key:self._default_entry for key in self._headers}
row['_uid'] = self._get_new_uid()
for key, val in data.items():
if key in ('_uid', '_default'):
logging.warn("Cannot manually set columns _uid or _default of a row! Given data: {0}".format(data))
continue
if not isinstance(val, CSVModel._KNOWN_TYPES_MAP[self._headers_types[key]]):
raise Exception('Data type mismatch for column {0}. Expected: {1}, got: {2}'.format(key,
CSVModel._KNOWN_TYPES_MAP[self._headers_types[key]], type(val)))
row[key] = val
self._table.append(row)
self._save()
return row['_uid'] | [
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BerkeleyAutomation/autolab_core | autolab_core/csv_model.py | CSVModel.update_by_uid | def update_by_uid(self, uid, data):
"""Update a row with the given data.
Parameters
----------
uid : int
The UID of the row to update.
data : :obj:`dict`
A dictionary mapping keys (header strings) to values.
Raises
------
Exception
If the value for a given header is not of the appropriate type.
"""
row = self._table[uid+1]
for key, val in data.items():
if key == '_uid' or key == '_default':
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if key not in self._headers:
logging.warn("Unknown column name: {0}".format(key))
continue
if not isinstance(val, CSVModel._KNOWN_TYPES_MAP[self._headers_types[key]]):
raise Exception('Data type mismatch for column {0}. Expected: {1}, got: {2}'.format(key,
CSVModel._KNOWN_TYPES_MAP[self._headers_types[key]], type(val)))
row[key] = val
self._save() | python | def update_by_uid(self, uid, data):
"""Update a row with the given data.
Parameters
----------
uid : int
The UID of the row to update.
data : :obj:`dict`
A dictionary mapping keys (header strings) to values.
Raises
------
Exception
If the value for a given header is not of the appropriate type.
"""
row = self._table[uid+1]
for key, val in data.items():
if key == '_uid' or key == '_default':
continue
if key not in self._headers:
logging.warn("Unknown column name: {0}".format(key))
continue
if not isinstance(val, CSVModel._KNOWN_TYPES_MAP[self._headers_types[key]]):
raise Exception('Data type mismatch for column {0}. Expected: {1}, got: {2}'.format(key,
CSVModel._KNOWN_TYPES_MAP[self._headers_types[key]], type(val)))
row[key] = val
self._save() | [
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BerkeleyAutomation/autolab_core | autolab_core/csv_model.py | CSVModel.get_col | def get_col(self, col_name, filter = lambda _ : True):
"""Return all values in the column corresponding to col_name that satisfies filter, which is
a function that takes in a value of the column's type and returns True or False
Parameters
----------
col_name : str
Name of desired column
filter : function, optional
A function that takes in a value of the column's type and returns True or False
Defaults to a function that always returns True
Returns
-------
list
A list of values in the desired columns by order of their storage in the model
Raises
------
ValueError
If the desired column name is not found in the model
"""
if col_name not in self._headers:
raise ValueError("{} not found! Model has headers: {}".format(col_name, self._headers))
col = []
for i in range(self.num_rows):
row = self._table[i + 1]
val = row[col_name]
if filter(val):
col.append(val)
return col | python | def get_col(self, col_name, filter = lambda _ : True):
"""Return all values in the column corresponding to col_name that satisfies filter, which is
a function that takes in a value of the column's type and returns True or False
Parameters
----------
col_name : str
Name of desired column
filter : function, optional
A function that takes in a value of the column's type and returns True or False
Defaults to a function that always returns True
Returns
-------
list
A list of values in the desired columns by order of their storage in the model
Raises
------
ValueError
If the desired column name is not found in the model
"""
if col_name not in self._headers:
raise ValueError("{} not found! Model has headers: {}".format(col_name, self._headers))
col = []
for i in range(self.num_rows):
row = self._table[i + 1]
val = row[col_name]
if filter(val):
col.append(val)
return col | [
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BerkeleyAutomation/autolab_core | autolab_core/csv_model.py | CSVModel.get_by_cols | def get_by_cols(self, cols, direction=1):
"""Return the first or last row that satisfies the given col value constraints,
or None if no row contains the given value.
Parameters
----------
cols: :obj:'dict'
Dictionary of col values for a specific row.
direction: int, optional
Either 1 or -1. 1 means find the first row, -1 means find the last row.
Returns
-------
:obj:`dict`
A dictionary mapping keys (header strings) to values, which
represents a row of the table. This row contains the given value in
the specified column.
"""
if direction == 1:
iterator = range(self.num_rows)
elif direction == -1:
iterator = range(self.num_rows-1, -1, -1)
else:
raise ValueError("Direction can only be 1 (first) or -1 (last). Got: {0}".format(direction))
for i in iterator:
row = self._table[i+1]
all_sat = True
for key, val in cols.items():
if row[key] != val:
all_sat = False
break
if all_sat:
return row.copy()
return None | python | def get_by_cols(self, cols, direction=1):
"""Return the first or last row that satisfies the given col value constraints,
or None if no row contains the given value.
Parameters
----------
cols: :obj:'dict'
Dictionary of col values for a specific row.
direction: int, optional
Either 1 or -1. 1 means find the first row, -1 means find the last row.
Returns
-------
:obj:`dict`
A dictionary mapping keys (header strings) to values, which
represents a row of the table. This row contains the given value in
the specified column.
"""
if direction == 1:
iterator = range(self.num_rows)
elif direction == -1:
iterator = range(self.num_rows-1, -1, -1)
else:
raise ValueError("Direction can only be 1 (first) or -1 (last). Got: {0}".format(direction))
for i in iterator:
row = self._table[i+1]
all_sat = True
for key, val in cols.items():
if row[key] != val:
all_sat = False
break
if all_sat:
return row.copy()
return None | [
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BerkeleyAutomation/autolab_core | autolab_core/csv_model.py | CSVModel.get_rows_by_cols | def get_rows_by_cols(self, matching_dict):
"""Return all rows where the cols match the elements given in the matching_dict
Parameters
----------
matching_dict: :obj:'dict'
Desired dictionary of col values.
Returns
-------
:obj:`list`
A list of rows that satisfy the matching_dict
"""
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for i in range(self.num_rows):
row = self._table[i+1]
matching = True
for key, val in matching_dict.items():
if row[key] != val:
matching = False
break
if matching:
result.append(row)
return result | python | def get_rows_by_cols(self, matching_dict):
"""Return all rows where the cols match the elements given in the matching_dict
Parameters
----------
matching_dict: :obj:'dict'
Desired dictionary of col values.
Returns
-------
:obj:`list`
A list of rows that satisfy the matching_dict
"""
result = []
for i in range(self.num_rows):
row = self._table[i+1]
matching = True
for key, val in matching_dict.items():
if row[key] != val:
matching = False
break
if matching:
result.append(row)
return result | [
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BerkeleyAutomation/autolab_core | autolab_core/csv_model.py | CSVModel.next | def next(self):
""" Returns the next row in the CSV, for iteration """
if self._cur_row >= len(self._table):
raise StopIteration
data = self._table[self._cur_row].copy()
self._cur_row += 1
return data | python | def next(self):
""" Returns the next row in the CSV, for iteration """
if self._cur_row >= len(self._table):
raise StopIteration
data = self._table[self._cur_row].copy()
self._cur_row += 1
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BerkeleyAutomation/autolab_core | autolab_core/csv_model.py | CSVModel.load | def load(full_filename):
"""Load a .csv file into a CSVModel.
Parameters
----------
full_filename : :obj:`str`
The file path to a .csv file.
Returns
-------
:obj:`CSVModel`
The CSVModel initialized with the data in the given file.
Raises
------
Excetpion
If the CSV file does not exist or is malformed.
"""
with open(full_filename, 'r') as file:
reader = csv.DictReader(file)
headers = reader.fieldnames
if '_uid' not in headers or '_default' not in headers:
raise Exception("Malformed CSVModel file!")
all_rows = [row for row in reader]
types = all_rows[0]
table = [types]
default_entry = table[0]['_default']
for i in range(1, len(all_rows)):
raw_row = all_rows[i]
row = {}
for column_name in headers:
if raw_row[column_name] != default_entry and column_name != '':
if types[column_name] == 'bool':
row[column_name] = CSVModel._str_to_bool(raw_row[column_name])
else:
try:
row[column_name] = CSVModel._KNOWN_TYPES_MAP[types[column_name]](raw_row[column_name])
except:
logging.error('{}, {}, {}'.format(column_name, types[column_name], raw_row[column_name]))
row[column_name] = CSVModel._KNOWN_TYPES_MAP[types[column_name]](bool(raw_row[column_name]))
else:
row[column_name] = default_entry
table.append(row)
if len(table) == 1:
next_valid_uid = 0
else:
next_valid_uid = int(table[-1]['_uid']) + 1
headers_init = headers[1:-1]
types_init = [types[column_name] for column_name in headers_init]
headers_types_list = zip(headers_init, types_init)
csv_model = CSVModel(full_filename, headers_types_list, default_entry=default_entry)
csv_model._uid = next_valid_uid
csv_model._table = table
csv_model._save()
return csv_model | python | def load(full_filename):
"""Load a .csv file into a CSVModel.
Parameters
----------
full_filename : :obj:`str`
The file path to a .csv file.
Returns
-------
:obj:`CSVModel`
The CSVModel initialized with the data in the given file.
Raises
------
Excetpion
If the CSV file does not exist or is malformed.
"""
with open(full_filename, 'r') as file:
reader = csv.DictReader(file)
headers = reader.fieldnames
if '_uid' not in headers or '_default' not in headers:
raise Exception("Malformed CSVModel file!")
all_rows = [row for row in reader]
types = all_rows[0]
table = [types]
default_entry = table[0]['_default']
for i in range(1, len(all_rows)):
raw_row = all_rows[i]
row = {}
for column_name in headers:
if raw_row[column_name] != default_entry and column_name != '':
if types[column_name] == 'bool':
row[column_name] = CSVModel._str_to_bool(raw_row[column_name])
else:
try:
row[column_name] = CSVModel._KNOWN_TYPES_MAP[types[column_name]](raw_row[column_name])
except:
logging.error('{}, {}, {}'.format(column_name, types[column_name], raw_row[column_name]))
row[column_name] = CSVModel._KNOWN_TYPES_MAP[types[column_name]](bool(raw_row[column_name]))
else:
row[column_name] = default_entry
table.append(row)
if len(table) == 1:
next_valid_uid = 0
else:
next_valid_uid = int(table[-1]['_uid']) + 1
headers_init = headers[1:-1]
types_init = [types[column_name] for column_name in headers_init]
headers_types_list = zip(headers_init, types_init)
csv_model = CSVModel(full_filename, headers_types_list, default_entry=default_entry)
csv_model._uid = next_valid_uid
csv_model._table = table
csv_model._save()
return csv_model | [
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Parameters
----------
full_filename : :obj:`str`
The file path to a .csv file.
Returns
-------
:obj:`CSVModel`
The CSVModel initialized with the data in the given file.
Raises
------
Excetpion
If the CSV file does not exist or is malformed. | [
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BerkeleyAutomation/autolab_core | autolab_core/csv_model.py | CSVModel.get_or_create | def get_or_create(full_filename, headers_types=None, default_entry=''):
"""Load a .csv file into a CSVModel if the file exists, or create
a new CSVModel with the given filename if the file does not exist.
Parameters
----------
full_filename : :obj:`str`
The file path to a .csv file.
headers_types : :obj:`list` of :obj:`tuple` of :obj:`str`, :obj:`str`
A list of tuples, where the first element in each tuple is the
string header for a column and the second element is that column's
data type as a string.
default_entry : :obj:`str`
The default entry for cells in the CSV.
Returns
-------
:obj:`CSVModel`
The CSVModel initialized with the data in the given file, or a new
CSVModel tied to the filename if the file doesn't currently exist.
"""
# convert dictionaries to list
if isinstance(headers_types, dict):
headers_types_list = [(k,v) for k,v in headers_types.items()]
headers_types = headers_types_list
if os.path.isfile(full_filename):
return CSVModel.load(full_filename)
else:
return CSVModel(full_filename, headers_types, default_entry=default_entry) | python | def get_or_create(full_filename, headers_types=None, default_entry=''):
"""Load a .csv file into a CSVModel if the file exists, or create
a new CSVModel with the given filename if the file does not exist.
Parameters
----------
full_filename : :obj:`str`
The file path to a .csv file.
headers_types : :obj:`list` of :obj:`tuple` of :obj:`str`, :obj:`str`
A list of tuples, where the first element in each tuple is the
string header for a column and the second element is that column's
data type as a string.
default_entry : :obj:`str`
The default entry for cells in the CSV.
Returns
-------
:obj:`CSVModel`
The CSVModel initialized with the data in the given file, or a new
CSVModel tied to the filename if the file doesn't currently exist.
"""
# convert dictionaries to list
if isinstance(headers_types, dict):
headers_types_list = [(k,v) for k,v in headers_types.items()]
headers_types = headers_types_list
if os.path.isfile(full_filename):
return CSVModel.load(full_filename)
else:
return CSVModel(full_filename, headers_types, default_entry=default_entry) | [
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BerkeleyAutomation/autolab_core | autolab_core/transformations.py | projection_matrix | def projection_matrix(point, normal, direction=None,
perspective=None, pseudo=False):
"""Return matrix to project onto plane defined by point and normal.
Using either perspective point, projection direction, or none of both.
If pseudo is True, perspective projections will preserve relative depth
such that Perspective = dot(Orthogonal, PseudoPerspective).
>>> P = projection_matrix((0, 0, 0), (1, 0, 0))
>>> numpy.allclose(P[1:, 1:], numpy.identity(4)[1:, 1:])
True
>>> point = numpy.random.random(3) - 0.5
>>> normal = numpy.random.random(3) - 0.5
>>> direct = numpy.random.random(3) - 0.5
>>> persp = numpy.random.random(3) - 0.5
>>> P0 = projection_matrix(point, normal)
>>> P1 = projection_matrix(point, normal, direction=direct)
>>> P2 = projection_matrix(point, normal, perspective=persp)
>>> P3 = projection_matrix(point, normal, perspective=persp, pseudo=True)
>>> is_same_transform(P2, numpy.dot(P0, P3))
True
>>> P = projection_matrix((3, 0, 0), (1, 1, 0), (1, 0, 0))
>>> v0 = (numpy.random.rand(4, 5) - 0.5) * 20.0
>>> v0[3] = 1.0
>>> v1 = numpy.dot(P, v0)
>>> numpy.allclose(v1[1], v0[1])
True
>>> numpy.allclose(v1[0], 3.0-v1[1])
True
"""
M = numpy.identity(4)
point = numpy.array(point[:3], dtype=numpy.float64, copy=False)
normal = unit_vector(normal[:3])
if perspective is not None:
# perspective projection
perspective = numpy.array(perspective[:3], dtype=numpy.float64,
copy=False)
M[0, 0] = M[1, 1] = M[2, 2] = numpy.dot(perspective-point, normal)
M[:3, :3] -= numpy.outer(perspective, normal)
if pseudo:
# preserve relative depth
M[:3, :3] -= numpy.outer(normal, normal)
M[:3, 3] = numpy.dot(point, normal) * (perspective+normal)
else:
M[:3, 3] = numpy.dot(point, normal) * perspective
M[3, :3] = -normal
M[3, 3] = numpy.dot(perspective, normal)
elif direction is not None:
# parallel projection
direction = numpy.array(direction[:3], dtype=numpy.float64, copy=False)
scale = numpy.dot(direction, normal)
M[:3, :3] -= numpy.outer(direction, normal) / scale
M[:3, 3] = direction * (numpy.dot(point, normal) / scale)
else:
# orthogonal projection
M[:3, :3] -= numpy.outer(normal, normal)
M[:3, 3] = numpy.dot(point, normal) * normal
return M | python | def projection_matrix(point, normal, direction=None,
perspective=None, pseudo=False):
"""Return matrix to project onto plane defined by point and normal.
Using either perspective point, projection direction, or none of both.
If pseudo is True, perspective projections will preserve relative depth
such that Perspective = dot(Orthogonal, PseudoPerspective).
>>> P = projection_matrix((0, 0, 0), (1, 0, 0))
>>> numpy.allclose(P[1:, 1:], numpy.identity(4)[1:, 1:])
True
>>> point = numpy.random.random(3) - 0.5
>>> normal = numpy.random.random(3) - 0.5
>>> direct = numpy.random.random(3) - 0.5
>>> persp = numpy.random.random(3) - 0.5
>>> P0 = projection_matrix(point, normal)
>>> P1 = projection_matrix(point, normal, direction=direct)
>>> P2 = projection_matrix(point, normal, perspective=persp)
>>> P3 = projection_matrix(point, normal, perspective=persp, pseudo=True)
>>> is_same_transform(P2, numpy.dot(P0, P3))
True
>>> P = projection_matrix((3, 0, 0), (1, 1, 0), (1, 0, 0))
>>> v0 = (numpy.random.rand(4, 5) - 0.5) * 20.0
>>> v0[3] = 1.0
>>> v1 = numpy.dot(P, v0)
>>> numpy.allclose(v1[1], v0[1])
True
>>> numpy.allclose(v1[0], 3.0-v1[1])
True
"""
M = numpy.identity(4)
point = numpy.array(point[:3], dtype=numpy.float64, copy=False)
normal = unit_vector(normal[:3])
if perspective is not None:
# perspective projection
perspective = numpy.array(perspective[:3], dtype=numpy.float64,
copy=False)
M[0, 0] = M[1, 1] = M[2, 2] = numpy.dot(perspective-point, normal)
M[:3, :3] -= numpy.outer(perspective, normal)
if pseudo:
# preserve relative depth
M[:3, :3] -= numpy.outer(normal, normal)
M[:3, 3] = numpy.dot(point, normal) * (perspective+normal)
else:
M[:3, 3] = numpy.dot(point, normal) * perspective
M[3, :3] = -normal
M[3, 3] = numpy.dot(perspective, normal)
elif direction is not None:
# parallel projection
direction = numpy.array(direction[:3], dtype=numpy.float64, copy=False)
scale = numpy.dot(direction, normal)
M[:3, :3] -= numpy.outer(direction, normal) / scale
M[:3, 3] = direction * (numpy.dot(point, normal) / scale)
else:
# orthogonal projection
M[:3, :3] -= numpy.outer(normal, normal)
M[:3, 3] = numpy.dot(point, normal) * normal
return M | [
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Using either perspective point, projection direction, or none of both.
If pseudo is True, perspective projections will preserve relative depth
such that Perspective = dot(Orthogonal, PseudoPerspective).
>>> P = projection_matrix((0, 0, 0), (1, 0, 0))
>>> numpy.allclose(P[1:, 1:], numpy.identity(4)[1:, 1:])
True
>>> point = numpy.random.random(3) - 0.5
>>> normal = numpy.random.random(3) - 0.5
>>> direct = numpy.random.random(3) - 0.5
>>> persp = numpy.random.random(3) - 0.5
>>> P0 = projection_matrix(point, normal)
>>> P1 = projection_matrix(point, normal, direction=direct)
>>> P2 = projection_matrix(point, normal, perspective=persp)
>>> P3 = projection_matrix(point, normal, perspective=persp, pseudo=True)
>>> is_same_transform(P2, numpy.dot(P0, P3))
True
>>> P = projection_matrix((3, 0, 0), (1, 1, 0), (1, 0, 0))
>>> v0 = (numpy.random.rand(4, 5) - 0.5) * 20.0
>>> v0[3] = 1.0
>>> v1 = numpy.dot(P, v0)
>>> numpy.allclose(v1[1], v0[1])
True
>>> numpy.allclose(v1[0], 3.0-v1[1])
True | [
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] | 8f3813f6401972868cc5e3981ba1b4382d4418d5 | https://github.com/BerkeleyAutomation/autolab_core/blob/8f3813f6401972868cc5e3981ba1b4382d4418d5/autolab_core/transformations.py#L437-L496 | train |
BerkeleyAutomation/autolab_core | autolab_core/transformations.py | projection_from_matrix | def projection_from_matrix(matrix, pseudo=False):
"""Return projection plane and perspective point from projection matrix.
Return values are same as arguments for projection_matrix function:
point, normal, direction, perspective, and pseudo.
>>> point = numpy.random.random(3) - 0.5
>>> normal = numpy.random.random(3) - 0.5
>>> direct = numpy.random.random(3) - 0.5
>>> persp = numpy.random.random(3) - 0.5
>>> P0 = projection_matrix(point, normal)
>>> result = projection_from_matrix(P0)
>>> P1 = projection_matrix(*result)
>>> is_same_transform(P0, P1)
True
>>> P0 = projection_matrix(point, normal, direct)
>>> result = projection_from_matrix(P0)
>>> P1 = projection_matrix(*result)
>>> is_same_transform(P0, P1)
True
>>> P0 = projection_matrix(point, normal, perspective=persp, pseudo=False)
>>> result = projection_from_matrix(P0, pseudo=False)
>>> P1 = projection_matrix(*result)
>>> is_same_transform(P0, P1)
True
>>> P0 = projection_matrix(point, normal, perspective=persp, pseudo=True)
>>> result = projection_from_matrix(P0, pseudo=True)
>>> P1 = projection_matrix(*result)
>>> is_same_transform(P0, P1)
True
"""
M = numpy.array(matrix, dtype=numpy.float64, copy=False)
M33 = M[:3, :3]
l, V = numpy.linalg.eig(M)
i = numpy.where(abs(numpy.real(l) - 1.0) < 1e-8)[0]
if not pseudo and len(i):
# point: any eigenvector corresponding to eigenvalue 1
point = numpy.real(V[:, i[-1]]).squeeze()
point /= point[3]
# direction: unit eigenvector corresponding to eigenvalue 0
l, V = numpy.linalg.eig(M33)
i = numpy.where(abs(numpy.real(l)) < 1e-8)[0]
if not len(i):
raise ValueError("no eigenvector corresponding to eigenvalue 0")
direction = numpy.real(V[:, i[0]]).squeeze()
direction /= vector_norm(direction)
# normal: unit eigenvector of M33.T corresponding to eigenvalue 0
l, V = numpy.linalg.eig(M33.T)
i = numpy.where(abs(numpy.real(l)) < 1e-8)[0]
if len(i):
# parallel projection
normal = numpy.real(V[:, i[0]]).squeeze()
normal /= vector_norm(normal)
return point, normal, direction, None, False
else:
# orthogonal projection, where normal equals direction vector
return point, direction, None, None, False
else:
# perspective projection
i = numpy.where(abs(numpy.real(l)) > 1e-8)[0]
if not len(i):
raise ValueError(
"no eigenvector not corresponding to eigenvalue 0")
point = numpy.real(V[:, i[-1]]).squeeze()
point /= point[3]
normal = - M[3, :3]
perspective = M[:3, 3] / numpy.dot(point[:3], normal)
if pseudo:
perspective -= normal
return point, normal, None, perspective, pseudo | python | def projection_from_matrix(matrix, pseudo=False):
"""Return projection plane and perspective point from projection matrix.
Return values are same as arguments for projection_matrix function:
point, normal, direction, perspective, and pseudo.
>>> point = numpy.random.random(3) - 0.5
>>> normal = numpy.random.random(3) - 0.5
>>> direct = numpy.random.random(3) - 0.5
>>> persp = numpy.random.random(3) - 0.5
>>> P0 = projection_matrix(point, normal)
>>> result = projection_from_matrix(P0)
>>> P1 = projection_matrix(*result)
>>> is_same_transform(P0, P1)
True
>>> P0 = projection_matrix(point, normal, direct)
>>> result = projection_from_matrix(P0)
>>> P1 = projection_matrix(*result)
>>> is_same_transform(P0, P1)
True
>>> P0 = projection_matrix(point, normal, perspective=persp, pseudo=False)
>>> result = projection_from_matrix(P0, pseudo=False)
>>> P1 = projection_matrix(*result)
>>> is_same_transform(P0, P1)
True
>>> P0 = projection_matrix(point, normal, perspective=persp, pseudo=True)
>>> result = projection_from_matrix(P0, pseudo=True)
>>> P1 = projection_matrix(*result)
>>> is_same_transform(P0, P1)
True
"""
M = numpy.array(matrix, dtype=numpy.float64, copy=False)
M33 = M[:3, :3]
l, V = numpy.linalg.eig(M)
i = numpy.where(abs(numpy.real(l) - 1.0) < 1e-8)[0]
if not pseudo and len(i):
# point: any eigenvector corresponding to eigenvalue 1
point = numpy.real(V[:, i[-1]]).squeeze()
point /= point[3]
# direction: unit eigenvector corresponding to eigenvalue 0
l, V = numpy.linalg.eig(M33)
i = numpy.where(abs(numpy.real(l)) < 1e-8)[0]
if not len(i):
raise ValueError("no eigenvector corresponding to eigenvalue 0")
direction = numpy.real(V[:, i[0]]).squeeze()
direction /= vector_norm(direction)
# normal: unit eigenvector of M33.T corresponding to eigenvalue 0
l, V = numpy.linalg.eig(M33.T)
i = numpy.where(abs(numpy.real(l)) < 1e-8)[0]
if len(i):
# parallel projection
normal = numpy.real(V[:, i[0]]).squeeze()
normal /= vector_norm(normal)
return point, normal, direction, None, False
else:
# orthogonal projection, where normal equals direction vector
return point, direction, None, None, False
else:
# perspective projection
i = numpy.where(abs(numpy.real(l)) > 1e-8)[0]
if not len(i):
raise ValueError(
"no eigenvector not corresponding to eigenvalue 0")
point = numpy.real(V[:, i[-1]]).squeeze()
point /= point[3]
normal = - M[3, :3]
perspective = M[:3, 3] / numpy.dot(point[:3], normal)
if pseudo:
perspective -= normal
return point, normal, None, perspective, pseudo | [
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Return values are same as arguments for projection_matrix function:
point, normal, direction, perspective, and pseudo.
>>> point = numpy.random.random(3) - 0.5
>>> normal = numpy.random.random(3) - 0.5
>>> direct = numpy.random.random(3) - 0.5
>>> persp = numpy.random.random(3) - 0.5
>>> P0 = projection_matrix(point, normal)
>>> result = projection_from_matrix(P0)
>>> P1 = projection_matrix(*result)
>>> is_same_transform(P0, P1)
True
>>> P0 = projection_matrix(point, normal, direct)
>>> result = projection_from_matrix(P0)
>>> P1 = projection_matrix(*result)
>>> is_same_transform(P0, P1)
True
>>> P0 = projection_matrix(point, normal, perspective=persp, pseudo=False)
>>> result = projection_from_matrix(P0, pseudo=False)
>>> P1 = projection_matrix(*result)
>>> is_same_transform(P0, P1)
True
>>> P0 = projection_matrix(point, normal, perspective=persp, pseudo=True)
>>> result = projection_from_matrix(P0, pseudo=True)
>>> P1 = projection_matrix(*result)
>>> is_same_transform(P0, P1)
True | [
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BerkeleyAutomation/autolab_core | autolab_core/transformations.py | unit_vector | def unit_vector(data, axis=None, out=None):
"""Return ndarray normalized by length, i.e. eucledian norm, along axis.
>>> v0 = numpy.random.random(3)
>>> v1 = unit_vector(v0)
>>> numpy.allclose(v1, v0 / numpy.linalg.norm(v0))
True
>>> v0 = numpy.random.rand(5, 4, 3)
>>> v1 = unit_vector(v0, axis=-1)
>>> v2 = v0 / numpy.expand_dims(numpy.sqrt(numpy.sum(v0*v0, axis=2)), 2)
>>> numpy.allclose(v1, v2)
True
>>> v1 = unit_vector(v0, axis=1)
>>> v2 = v0 / numpy.expand_dims(numpy.sqrt(numpy.sum(v0*v0, axis=1)), 1)
>>> numpy.allclose(v1, v2)
True
>>> v1 = numpy.empty((5, 4, 3), dtype=numpy.float64)
>>> unit_vector(v0, axis=1, out=v1)
>>> numpy.allclose(v1, v2)
True
>>> list(unit_vector([]))
[]
>>> list(unit_vector([1.0]))
[1.0]
"""
if out is None:
data = numpy.array(data, dtype=numpy.float64, copy=True)
if data.ndim == 1:
data /= math.sqrt(numpy.dot(data, data))
return data
else:
if out is not data:
out[:] = numpy.array(data, copy=False)
data = out
length = numpy.atleast_1d(numpy.sum(data*data, axis))
numpy.sqrt(length, length)
if axis is not None:
length = numpy.expand_dims(length, axis)
data /= length
if out is None:
return data | python | def unit_vector(data, axis=None, out=None):
"""Return ndarray normalized by length, i.e. eucledian norm, along axis.
>>> v0 = numpy.random.random(3)
>>> v1 = unit_vector(v0)
>>> numpy.allclose(v1, v0 / numpy.linalg.norm(v0))
True
>>> v0 = numpy.random.rand(5, 4, 3)
>>> v1 = unit_vector(v0, axis=-1)
>>> v2 = v0 / numpy.expand_dims(numpy.sqrt(numpy.sum(v0*v0, axis=2)), 2)
>>> numpy.allclose(v1, v2)
True
>>> v1 = unit_vector(v0, axis=1)
>>> v2 = v0 / numpy.expand_dims(numpy.sqrt(numpy.sum(v0*v0, axis=1)), 1)
>>> numpy.allclose(v1, v2)
True
>>> v1 = numpy.empty((5, 4, 3), dtype=numpy.float64)
>>> unit_vector(v0, axis=1, out=v1)
>>> numpy.allclose(v1, v2)
True
>>> list(unit_vector([]))
[]
>>> list(unit_vector([1.0]))
[1.0]
"""
if out is None:
data = numpy.array(data, dtype=numpy.float64, copy=True)
if data.ndim == 1:
data /= math.sqrt(numpy.dot(data, data))
return data
else:
if out is not data:
out[:] = numpy.array(data, copy=False)
data = out
length = numpy.atleast_1d(numpy.sum(data*data, axis))
numpy.sqrt(length, length)
if axis is not None:
length = numpy.expand_dims(length, axis)
data /= length
if out is None:
return data | [
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>>> v0 = numpy.random.random(3)
>>> v1 = unit_vector(v0)
>>> numpy.allclose(v1, v0 / numpy.linalg.norm(v0))
True
>>> v0 = numpy.random.rand(5, 4, 3)
>>> v1 = unit_vector(v0, axis=-1)
>>> v2 = v0 / numpy.expand_dims(numpy.sqrt(numpy.sum(v0*v0, axis=2)), 2)
>>> numpy.allclose(v1, v2)
True
>>> v1 = unit_vector(v0, axis=1)
>>> v2 = v0 / numpy.expand_dims(numpy.sqrt(numpy.sum(v0*v0, axis=1)), 1)
>>> numpy.allclose(v1, v2)
True
>>> v1 = numpy.empty((5, 4, 3), dtype=numpy.float64)
>>> unit_vector(v0, axis=1, out=v1)
>>> numpy.allclose(v1, v2)
True
>>> list(unit_vector([]))
[]
>>> list(unit_vector([1.0]))
[1.0] | [
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BerkeleyAutomation/autolab_core | autolab_core/json_serialization.py | json_numpy_obj_hook | def json_numpy_obj_hook(dct):
"""Decodes a previously encoded numpy ndarray with proper shape and dtype.
Parameters
----------
dct : :obj:`dict`
The encoded dictionary.
Returns
-------
:obj:`numpy.ndarray`
The ndarray that `dct` was encoding.
"""
if isinstance(dct, dict) and '__ndarray__' in dct:
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return data.reshape(dct['shape'])
return dct | python | def json_numpy_obj_hook(dct):
"""Decodes a previously encoded numpy ndarray with proper shape and dtype.
Parameters
----------
dct : :obj:`dict`
The encoded dictionary.
Returns
-------
:obj:`numpy.ndarray`
The ndarray that `dct` was encoding.
"""
if isinstance(dct, dict) and '__ndarray__' in dct:
data = np.asarray(dct['__ndarray__'], dtype=dct['dtype'])
return data.reshape(dct['shape'])
return dct | [
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BerkeleyAutomation/autolab_core | autolab_core/json_serialization.py | dump | def dump(*args, **kwargs):
"""Dump a numpy.ndarray to file stream.
This works exactly like the usual `json.dump()` function,
but it uses our custom serializer.
"""
kwargs.update(dict(cls=NumpyEncoder,
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indent=4,
separators=(',', ': ')))
return _json.dump(*args, **kwargs) | python | def dump(*args, **kwargs):
"""Dump a numpy.ndarray to file stream.
This works exactly like the usual `json.dump()` function,
but it uses our custom serializer.
"""
kwargs.update(dict(cls=NumpyEncoder,
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BerkeleyAutomation/autolab_core | autolab_core/json_serialization.py | load | def load(*args, **kwargs):
"""Load an numpy.ndarray from a file stream.
This works exactly like the usual `json.load()` function,
but it uses our custom deserializer.
"""
kwargs.update(dict(object_hook=json_numpy_obj_hook))
return _json.load(*args, **kwargs) | python | def load(*args, **kwargs):
"""Load an numpy.ndarray from a file stream.
This works exactly like the usual `json.load()` function,
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"""
kwargs.update(dict(object_hook=json_numpy_obj_hook))
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BerkeleyAutomation/autolab_core | autolab_core/json_serialization.py | NumpyEncoder.default | def default(self, obj):
"""Converts an ndarray into a dictionary for efficient serialization.
The dict has three keys:
- dtype : The datatype of the array as a string.
- shape : The shape of the array as a tuple.
- __ndarray__ : The data of the array as a list.
Parameters
----------
obj : :obj:`numpy.ndarray`
The ndarray to encode.
Returns
-------
:obj:`dict`
The dictionary serialization of obj.
Raises
------
TypeError
If obj isn't an ndarray.
"""
if isinstance(obj, np.ndarray):
return dict(__ndarray__=obj.tolist(),
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shape=obj.shape)
# Let the base class default method raise the TypeError
return _json.JSONEncoder(self, obj) | python | def default(self, obj):
"""Converts an ndarray into a dictionary for efficient serialization.
The dict has three keys:
- dtype : The datatype of the array as a string.
- shape : The shape of the array as a tuple.
- __ndarray__ : The data of the array as a list.
Parameters
----------
obj : :obj:`numpy.ndarray`
The ndarray to encode.
Returns
-------
:obj:`dict`
The dictionary serialization of obj.
Raises
------
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If obj isn't an ndarray.
"""
if isinstance(obj, np.ndarray):
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BerkeleyAutomation/autolab_core | autolab_core/random_variables.py | RandomVariable._preallocate_samples | def _preallocate_samples(self):
"""Preallocate samples for faster adaptive sampling.
"""
self.prealloc_samples_ = []
for i in range(self.num_prealloc_samples_):
self.prealloc_samples_.append(self.sample()) | python | def _preallocate_samples(self):
"""Preallocate samples for faster adaptive sampling.
"""
self.prealloc_samples_ = []
for i in range(self.num_prealloc_samples_):
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BerkeleyAutomation/autolab_core | autolab_core/random_variables.py | RandomVariable.rvs | def rvs(self, size=1, iteration=1):
"""Sample the random variable, using the preallocated samples if
possible.
Parameters
----------
size : int
The number of samples to generate.
iteration : int
The location in the preallocated sample array to start sampling
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Returns
-------
:obj:`numpy.ndarray` of float or int
The samples of the random variable. If `size == 1`, then
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samples.append(self.prealloc_samples_[(iteration + i) % self.num_prealloc_samples_])
if size == 1:
return samples[0]
return samples
# generate a new sample
return self.sample(size=size) | python | def rvs(self, size=1, iteration=1):
"""Sample the random variable, using the preallocated samples if
possible.
Parameters
----------
size : int
The number of samples to generate.
iteration : int
The location in the preallocated sample array to start sampling
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:obj:`numpy.ndarray` of float or int
The samples of the random variable. If `size == 1`, then
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iteration : int
The location in the preallocated sample array to start sampling
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Returns
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:obj:`numpy.ndarray` of float or int
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BerkeleyAutomation/autolab_core | autolab_core/random_variables.py | IsotropicGaussianRigidTransformRandomVariable.sample | def sample(self, size=1):
""" Sample rigid transform random variables.
Parameters
----------
size : int
number of sample to take
Returns
-------
:obj:`list` of :obj:`RigidTransform`
sampled rigid transformations
"""
samples = []
for i in range(size):
# sample random pose
xi = self._r_xi_rv.rvs(size=1)
S_xi = skew(xi)
R_sample = scipy.linalg.expm(S_xi)
t_sample = self._t_rv.rvs(size=1)
samples.append(RigidTransform(rotation=R_sample,
translation=t_sample,
from_frame=self._from_frame,
to_frame=self._to_frame))
# not a list if only 1 sample
if size == 1 and len(samples) > 0:
return samples[0]
return samples | python | def sample(self, size=1):
""" Sample rigid transform random variables.
Parameters
----------
size : int
number of sample to take
Returns
-------
:obj:`list` of :obj:`RigidTransform`
sampled rigid transformations
"""
samples = []
for i in range(size):
# sample random pose
xi = self._r_xi_rv.rvs(size=1)
S_xi = skew(xi)
R_sample = scipy.linalg.expm(S_xi)
t_sample = self._t_rv.rvs(size=1)
samples.append(RigidTransform(rotation=R_sample,
translation=t_sample,
from_frame=self._from_frame,
to_frame=self._to_frame))
# not a list if only 1 sample
if size == 1 and len(samples) > 0:
return samples[0]
return samples | [
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BerkeleyAutomation/autolab_core | autolab_core/data_stream_recorder.py | DataStreamRecorder._flush | def _flush(self):
""" Returns a list of all current data """
if self._recording:
raise Exception("Cannot flush data queue while recording!")
if self._saving_cache:
logging.warn("Flush when using cache means unsaved data will be lost and not returned!")
self._cmds_q.put(("reset_data_segment",))
else:
data = self._extract_q(0)
return data | python | def _flush(self):
""" Returns a list of all current data """
if self._recording:
raise Exception("Cannot flush data queue while recording!")
if self._saving_cache:
logging.warn("Flush when using cache means unsaved data will be lost and not returned!")
self._cmds_q.put(("reset_data_segment",))
else:
data = self._extract_q(0)
return data | [
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BerkeleyAutomation/autolab_core | autolab_core/data_stream_recorder.py | DataStreamRecorder._stop | def _stop(self):
""" Stops recording. Returns all recorded data and their timestamps. Destroys recorder process."""
self._pause()
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try:
self._recorder.terminate()
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self._recording = False | python | def _stop(self):
""" Stops recording. Returns all recorded data and their timestamps. Destroys recorder process."""
self._pause()
self._cmds_q.put(("stop",))
try:
self._recorder.terminate()
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BerkeleyAutomation/autolab_core | autolab_core/completer.py | Completer._listdir | def _listdir(self, root):
"List directory 'root' appending the path separator to subdirs."
res = []
for name in os.listdir(root):
path = os.path.join(root, name)
if os.path.isdir(path):
name += os.sep
res.append(name)
return res | python | def _listdir(self, root):
"List directory 'root' appending the path separator to subdirs."
res = []
for name in os.listdir(root):
path = os.path.join(root, name)
if os.path.isdir(path):
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res.append(name)
return res | [
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BerkeleyAutomation/autolab_core | autolab_core/completer.py | Completer.complete_extra | def complete_extra(self, args):
"Completions for the 'extra' command."
# treat the last arg as a path and complete it
if len(args) == 0:
return self._listdir('./')
return self._complete_path(args[-1]) | python | def complete_extra(self, args):
"Completions for the 'extra' command."
# treat the last arg as a path and complete it
if len(args) == 0:
return self._listdir('./')
return self._complete_path(args[-1]) | [
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BerkeleyAutomation/autolab_core | autolab_core/completer.py | Completer.complete | def complete(self, text, state):
"Generic readline completion entry point."
# dexnet entity tab completion
results = [w for w in self.words if w.startswith(text)] + [None]
if results != [None]:
return results[state]
buffer = readline.get_line_buffer()
line = readline.get_line_buffer().split()
# dexnet entity tab completion
results = [w for w in self.words if w.startswith(text)] + [None]
if results != [None]:
return results[state]
# account for last argument ending in a space
if RE_SPACE.match(buffer):
line.append('')
return (self.complete_extra(line) + [None])[state] | python | def complete(self, text, state):
"Generic readline completion entry point."
# dexnet entity tab completion
results = [w for w in self.words if w.startswith(text)] + [None]
if results != [None]:
return results[state]
buffer = readline.get_line_buffer()
line = readline.get_line_buffer().split()
# dexnet entity tab completion
results = [w for w in self.words if w.startswith(text)] + [None]
if results != [None]:
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BerkeleyAutomation/autolab_core | autolab_core/data_stream_syncer.py | DataStreamSyncer.stop | def stop(self):
""" Stops syncer operations. Destroys syncer process. """
self._cmds_q.put(("stop",))
for recorder in self._data_stream_recorders:
recorder._stop()
try:
self._syncer.terminate()
except Exception:
pass | python | def stop(self):
""" Stops syncer operations. Destroys syncer process. """
self._cmds_q.put(("stop",))
for recorder in self._data_stream_recorders:
recorder._stop()
try:
self._syncer.terminate()
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BerkeleyAutomation/autolab_core | autolab_core/logger.py | configure_root | def configure_root():
"""Configure the root logger."""
root_logger = logging.getLogger()
# clear any existing handles to streams because we don't want duplicate logs
# NOTE: we assume that any stream handles we find are to ROOT_LOG_STREAM, which is usually the case(because it is stdout). This is fine because we will be re-creating that handle. Otherwise we might be deleting a handle that won't be re-created, which could result in dropped logs.
for hdlr in root_logger.handlers:
if isinstance(hdlr, logging.StreamHandler):
root_logger.removeHandler(hdlr)
# configure the root logger
root_logger.setLevel(ROOT_LOG_LEVEL)
hdlr = logging.StreamHandler(ROOT_LOG_STREAM)
formatter = colorlog.ColoredFormatter(
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reset=True,
log_colors={
'DEBUG': 'cyan',
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'WARNING': 'yellow',
'ERROR': 'red',
'CRITICAL': 'red,bg_white',
}
)
hdlr.setFormatter(formatter)
root_logger.addHandler(hdlr) | python | def configure_root():
"""Configure the root logger."""
root_logger = logging.getLogger()
# clear any existing handles to streams because we don't want duplicate logs
# NOTE: we assume that any stream handles we find are to ROOT_LOG_STREAM, which is usually the case(because it is stdout). This is fine because we will be re-creating that handle. Otherwise we might be deleting a handle that won't be re-created, which could result in dropped logs.
for hdlr in root_logger.handlers:
if isinstance(hdlr, logging.StreamHandler):
root_logger.removeHandler(hdlr)
# configure the root logger
root_logger.setLevel(ROOT_LOG_LEVEL)
hdlr = logging.StreamHandler(ROOT_LOG_STREAM)
formatter = colorlog.ColoredFormatter(
'%(purple)s%(name)-10s %(log_color)s%(levelname)-8s%(reset)s %(white)s%(message)s',
reset=True,
log_colors={
'DEBUG': 'cyan',
'INFO': 'green',
'WARNING': 'yellow',
'ERROR': 'red',
'CRITICAL': 'red,bg_white',
}
)
hdlr.setFormatter(formatter)
root_logger.addHandler(hdlr) | [
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BerkeleyAutomation/autolab_core | autolab_core/logger.py | add_root_log_file | def add_root_log_file(log_file):
"""
Add a log file to the root logger.
Parameters
----------
log_file :obj:`str`
The path to the log file.
"""
root_logger = logging.getLogger()
# add a file handle to the root logger
hdlr = logging.FileHandler(log_file)
formatter = logging.Formatter('%(asctime)s %(name)-10s %(levelname)-8s %(message)s', datefmt='%m-%d %H:%M:%S')
hdlr.setFormatter(formatter)
root_logger.addHandler(hdlr)
root_logger.info('Root logger now logging to {}'.format(log_file)) | python | def add_root_log_file(log_file):
"""
Add a log file to the root logger.
Parameters
----------
log_file :obj:`str`
The path to the log file.
"""
root_logger = logging.getLogger()
# add a file handle to the root logger
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formatter = logging.Formatter('%(asctime)s %(name)-10s %(levelname)-8s %(message)s', datefmt='%m-%d %H:%M:%S')
hdlr.setFormatter(formatter)
root_logger.addHandler(hdlr)
root_logger.info('Root logger now logging to {}'.format(log_file)) | [
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----------
log_file :obj:`str`
The path to the log file. | [
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BerkeleyAutomation/autolab_core | autolab_core/logger.py | Logger.add_log_file | def add_log_file(logger, log_file, global_log_file=False):
"""
Add a log file to this logger. If global_log_file is true, log_file will be handed the root logger, otherwise it will only be used by this particular logger.
Parameters
----------
logger :obj:`logging.Logger`
The logger.
log_file :obj:`str`
The path to the log file to log to.
global_log_file :obj:`bool`
Whether or not to use the given log_file for this particular logger or for the root logger.
"""
if global_log_file:
add_root_log_file(log_file)
else:
hdlr = logging.FileHandler(log_file)
formatter = logging.Formatter('%(asctime)s %(name)-10s %(levelname)-8s %(message)s', datefmt='%m-%d %H:%M:%S')
hdlr.setFormatter(formatter)
logger.addHandler(hdlr) | python | def add_log_file(logger, log_file, global_log_file=False):
"""
Add a log file to this logger. If global_log_file is true, log_file will be handed the root logger, otherwise it will only be used by this particular logger.
Parameters
----------
logger :obj:`logging.Logger`
The logger.
log_file :obj:`str`
The path to the log file to log to.
global_log_file :obj:`bool`
Whether or not to use the given log_file for this particular logger or for the root logger.
"""
if global_log_file:
add_root_log_file(log_file)
else:
hdlr = logging.FileHandler(log_file)
formatter = logging.Formatter('%(asctime)s %(name)-10s %(levelname)-8s %(message)s', datefmt='%m-%d %H:%M:%S')
hdlr.setFormatter(formatter)
logger.addHandler(hdlr) | [
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log_file :obj:`str`
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googlefonts/fontbakery | Lib/fontbakery/checkrunner.py | get_module_profile | def get_module_profile(module, name=None):
"""
Get or create a profile from a module and return it.
If the name `module.profile` is present the value of that is returned.
Otherwise, if the name `module.profile_factory` is present, a new profile
is created using `module.profile_factory` and then `profile.auto_register`
is called with the module namespace.
If neither name is defined, the module is not considered a profile-module
and None is returned.
TODO: describe the `name` argument and better define the signature of `profile_factory`.
The `module` argument is expected to behave like a python module.
The optional `name` argument is used when `profile_factory` is called to
give a name to the default section of the new profile. If name is not
present `module.__name__` is the fallback.
`profile_factory` is called like this:
`profile = module.profile_factory(default_section=default_section)`
"""
try:
# if profile is defined we just use it
return module.profile
except AttributeError: # > 'module' object has no attribute 'profile'
# try to create one on the fly.
# e.g. module.__name__ == "fontbakery.profiles.cmap"
if 'profile_factory' not in module.__dict__:
return None
default_section = Section(name or module.__name__)
profile = module.profile_factory(default_section=default_section)
profile.auto_register(module.__dict__)
return profile | python | def get_module_profile(module, name=None):
"""
Get or create a profile from a module and return it.
If the name `module.profile` is present the value of that is returned.
Otherwise, if the name `module.profile_factory` is present, a new profile
is created using `module.profile_factory` and then `profile.auto_register`
is called with the module namespace.
If neither name is defined, the module is not considered a profile-module
and None is returned.
TODO: describe the `name` argument and better define the signature of `profile_factory`.
The `module` argument is expected to behave like a python module.
The optional `name` argument is used when `profile_factory` is called to
give a name to the default section of the new profile. If name is not
present `module.__name__` is the fallback.
`profile_factory` is called like this:
`profile = module.profile_factory(default_section=default_section)`
"""
try:
# if profile is defined we just use it
return module.profile
except AttributeError: # > 'module' object has no attribute 'profile'
# try to create one on the fly.
# e.g. module.__name__ == "fontbakery.profiles.cmap"
if 'profile_factory' not in module.__dict__:
return None
default_section = Section(name or module.__name__)
profile = module.profile_factory(default_section=default_section)
profile.auto_register(module.__dict__)
return profile | [
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googlefonts/fontbakery | Lib/fontbakery/checkrunner.py | CheckRunner.iterargs | def iterargs(self):
""" uses the singular name as key """
iterargs = OrderedDict()
for name in self._iterargs:
plural = self._profile.iterargs[name]
iterargs[name] = tuple(self._values[plural])
return iterargs | python | def iterargs(self):
""" uses the singular name as key """
iterargs = OrderedDict()
for name in self._iterargs:
plural = self._profile.iterargs[name]
iterargs[name] = tuple(self._values[plural])
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googlefonts/fontbakery | Lib/fontbakery/checkrunner.py | CheckRunner._exec_check | def _exec_check(self, check: FontbakeryCallable, args: Dict[str, Any]):
""" Yields check sub results.
Each check result is a tuple of: (<Status>, mixed message)
`status`: must be an instance of Status.
If one of the `status` entries in one of the results
is FAIL, the whole check is considered failed.
WARN is most likely a PASS in a non strict mode and a
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`message`:
* If it is an `Exception` type we expect `status`
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* If it is a `string` it's a description of what passed
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* we'll think of an AdvancedMessageType as well, so that
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knowledge from the check definition.
"""
try:
# A check can be either a normal function that returns one Status or a
# generator that yields one or more. The latter will return a generator
# object that we can detect with types.GeneratorType.
result = check(**args) # Might raise.
if isinstance(result, types.GeneratorType):
# Iterate over sub-results one-by-one, list(result) would abort on
# encountering the first exception.
for sub_result in result: # Might raise.
yield self._check_result(sub_result)
return # Do not fall through to rest of method.
except Exception as e:
error = FailedCheckError(e)
result = (ERROR, error)
yield self._check_result(result) | python | def _exec_check(self, check: FontbakeryCallable, args: Dict[str, Any]):
""" Yields check sub results.
Each check result is a tuple of: (<Status>, mixed message)
`status`: must be an instance of Status.
If one of the `status` entries in one of the results
is FAIL, the whole check is considered failed.
WARN is most likely a PASS in a non strict mode and a
FAIL in a strict mode.
`message`:
* If it is an `Exception` type we expect `status`
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* If it is a `string` it's a description of what passed
or failed.
* we'll think of an AdvancedMessageType as well, so that
we can connect the check result with more in depth
knowledge from the check definition.
"""
try:
# A check can be either a normal function that returns one Status or a
# generator that yields one or more. The latter will return a generator
# object that we can detect with types.GeneratorType.
result = check(**args) # Might raise.
if isinstance(result, types.GeneratorType):
# Iterate over sub-results one-by-one, list(result) would abort on
# encountering the first exception.
for sub_result in result: # Might raise.
yield self._check_result(sub_result)
return # Do not fall through to rest of method.
except Exception as e:
error = FailedCheckError(e)
result = (ERROR, error)
yield self._check_result(result) | [
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googlefonts/fontbakery | Lib/fontbakery/checkrunner.py | CheckRunner.check_order | def check_order(self, order):
"""
order must be a subset of self.order
"""
own_order = self.order
for item in order:
if item not in own_order:
raise ValueError(f'Order item {item} not found.')
return order | python | def check_order(self, order):
"""
order must be a subset of self.order
"""
own_order = self.order
for item in order:
if item not in own_order:
raise ValueError(f'Order item {item} not found.')
return order | [
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googlefonts/fontbakery | Lib/fontbakery/checkrunner.py | Section.add_check | def add_check(self, check):
"""
Please use rather `register_check` as a decorator.
"""
if self._add_check_callback is not None:
if not self._add_check_callback(self, check):
# rejected, skip!
return False
self._checkid2index[check.id] = len(self._checks)
self._checks.append(check)
return True | python | def add_check(self, check):
"""
Please use rather `register_check` as a decorator.
"""
if self._add_check_callback is not None:
if not self._add_check_callback(self, check):
# rejected, skip!
return False
self._checkid2index[check.id] = len(self._checks)
self._checks.append(check)
return True | [
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googlefonts/fontbakery | Lib/fontbakery/checkrunner.py | Section.merge_section | def merge_section(self, section, filter_func=None):
"""
Add section.checks to self, if not skipped by self._add_check_callback.
order, description, etc. are not updated.
"""
for check in section.checks:
if filter_func and not filter_func(check):
continue
self.add_check(check) | python | def merge_section(self, section, filter_func=None):
"""
Add section.checks to self, if not skipped by self._add_check_callback.
order, description, etc. are not updated.
"""
for check in section.checks:
if filter_func and not filter_func(check):
continue
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googlefonts/fontbakery | Lib/fontbakery/checkrunner.py | Profile.validate_values | def validate_values(self, values):
"""
Validate values if they are registered as expected_values and present.
* If they are not registered they shouldn't be used anywhere at all
because profile can self check (profile.check_dependencies) for
missing/undefined dependencies.
* If they are not present in values but registered as expected_values
either the expected value has a default value OR a request for that
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"""
format_message = '{}: {} (value: {})'.format
messages = []
for name, value in values.items():
if name not in self.expected_values:
continue
valid, message = self.expected_values[name].validate(value)
if valid:
continue
messages.append(format_message(name, message, value))
if len(messages):
return False, '\n'.join(messages)
return True, None | python | def validate_values(self, values):
"""
Validate values if they are registered as expected_values and present.
* If they are not registered they shouldn't be used anywhere at all
because profile can self check (profile.check_dependencies) for
missing/undefined dependencies.
* If they are not present in values but registered as expected_values
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"""
format_message = '{}: {} (value: {})'.format
messages = []
for name, value in values.items():
if name not in self.expected_values:
continue
valid, message = self.expected_values[name].validate(value)
if valid:
continue
messages.append(format_message(name, message, value))
if len(messages):
return False, '\n'.join(messages)
return True, None | [
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googlefonts/fontbakery | Lib/fontbakery/checkrunner.py | Profile._get_aggregate_args | def _get_aggregate_args(self, item, key):
"""
Get all arguments or mandatory arguments of the item.
Item is a check or a condition, which means it can be dependent on
more conditions, this climbs down all the way.
"""
if not key in ('args', 'mandatoryArgs'):
raise TypeError('key must be "args" or "mandatoryArgs", got {}').format(key)
dependencies = list(getattr(item, key))
if hasattr(item, 'conditions'):
dependencies += [name for negated, name in map(is_negated, item.conditions)]
args = set()
while dependencies:
name = dependencies.pop()
if name in args:
continue
args.add(name)
# if this is a condition, expand its dependencies
c = self.conditions.get(name, None)
if c is None:
continue
dependencies += [dependency for dependency in getattr(c, key)
if dependency not in args]
return args | python | def _get_aggregate_args(self, item, key):
"""
Get all arguments or mandatory arguments of the item.
Item is a check or a condition, which means it can be dependent on
more conditions, this climbs down all the way.
"""
if not key in ('args', 'mandatoryArgs'):
raise TypeError('key must be "args" or "mandatoryArgs", got {}').format(key)
dependencies = list(getattr(item, key))
if hasattr(item, 'conditions'):
dependencies += [name for negated, name in map(is_negated, item.conditions)]
args = set()
while dependencies:
name = dependencies.pop()
if name in args:
continue
args.add(name)
# if this is a condition, expand its dependencies
c = self.conditions.get(name, None)
if c is None:
continue
dependencies += [dependency for dependency in getattr(c, key)
if dependency not in args]
return args | [
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googlefonts/fontbakery | Lib/fontbakery/checkrunner.py | Profile.get_iterargs | def get_iterargs(self, item):
""" Returns a tuple of all iterags for item, sorted by name."""
# iterargs should always be mandatory, unless there's a good reason
# not to, which I can't think of right now.
args = self._get_aggregate_args(item, 'mandatoryArgs')
return tuple(sorted([arg for arg in args if arg in self.iterargs])) | python | def get_iterargs(self, item):
""" Returns a tuple of all iterags for item, sorted by name."""
# iterargs should always be mandatory, unless there's a good reason
# not to, which I can't think of right now.
args = self._get_aggregate_args(item, 'mandatoryArgs')
return tuple(sorted([arg for arg in args if arg in self.iterargs])) | [
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googlefonts/fontbakery | Lib/fontbakery/checkrunner.py | Profile.auto_register | def auto_register(self, symbol_table, filter_func=None, profile_imports=None):
"""
Register items from `symbol_table` in the profile.
Get all items from `symbol_table` dict and from `symbol_table.profile_imports`
if it is present. If they an item is an instance of FontBakeryCheck,
FontBakeryCondition or FontBakeryExpectedValue and register it in
the default section.
If an item is a python module, try to get a profile using `get_module_profile(item)`
and then using `merge_profile`;
If the profile_imports kwarg is given, it is used instead of the one taken from
the module namespace.
To register the current module use explicitly:
`profile.auto_register(globals())`
OR maybe: `profile.auto_register(sys.modules[__name__].__dict__)`
To register an imported module explicitly:
`profile.auto_register(module.__dict__)`
if filter_func is defined it is called like:
filter_func(type, name_or_id, item)
where
type: one of "check", "module", "condition", "expected_value", "iterarg",
"derived_iterable", "alias"
name_or_id: the name at which the item will be registered.
if type == 'check': the check.id
if type == 'module': the module name (module.__name__)
item: the item to be registered
if filter_func returns a falsy value for an item, the item will
not be registered.
"""
if profile_imports:
symbol_table = symbol_table.copy() # Avoid messing with original table
symbol_table['profile_imports'] = profile_imports
all_items = list(symbol_table.values()) + self._load_profile_imports(symbol_table)
namespace_types = (FontBakeryCondition, FontBakeryExpectedValue)
namespace_items = []
for item in all_items:
if isinstance(item, namespace_types):
# register these after all modules have been registered. That way,
# "local" items can optionally force override items registered
# previously by modules.
namespace_items.append(item)
elif isinstance(item, FontBakeryCheck):
if filter_func and not filter_func('check', item.id, item):
continue
self.register_check(item)
elif isinstance(item, types.ModuleType):
if filter_func and not filter_func('module', item.__name__, item):
continue
profile = get_module_profile(item)
if profile:
self.merge_profile(profile, filter_func=filter_func)
for item in namespace_items:
if isinstance(item, FontBakeryCondition):
if filter_func and not filter_func('condition', item.name, item):
continue
self.register_condition(item)
elif isinstance(item, FontBakeryExpectedValue):
if filter_func and not filter_func('expected_value', item.name, item):
continue
self.register_expected_value(item) | python | def auto_register(self, symbol_table, filter_func=None, profile_imports=None):
"""
Register items from `symbol_table` in the profile.
Get all items from `symbol_table` dict and from `symbol_table.profile_imports`
if it is present. If they an item is an instance of FontBakeryCheck,
FontBakeryCondition or FontBakeryExpectedValue and register it in
the default section.
If an item is a python module, try to get a profile using `get_module_profile(item)`
and then using `merge_profile`;
If the profile_imports kwarg is given, it is used instead of the one taken from
the module namespace.
To register the current module use explicitly:
`profile.auto_register(globals())`
OR maybe: `profile.auto_register(sys.modules[__name__].__dict__)`
To register an imported module explicitly:
`profile.auto_register(module.__dict__)`
if filter_func is defined it is called like:
filter_func(type, name_or_id, item)
where
type: one of "check", "module", "condition", "expected_value", "iterarg",
"derived_iterable", "alias"
name_or_id: the name at which the item will be registered.
if type == 'check': the check.id
if type == 'module': the module name (module.__name__)
item: the item to be registered
if filter_func returns a falsy value for an item, the item will
not be registered.
"""
if profile_imports:
symbol_table = symbol_table.copy() # Avoid messing with original table
symbol_table['profile_imports'] = profile_imports
all_items = list(symbol_table.values()) + self._load_profile_imports(symbol_table)
namespace_types = (FontBakeryCondition, FontBakeryExpectedValue)
namespace_items = []
for item in all_items:
if isinstance(item, namespace_types):
# register these after all modules have been registered. That way,
# "local" items can optionally force override items registered
# previously by modules.
namespace_items.append(item)
elif isinstance(item, FontBakeryCheck):
if filter_func and not filter_func('check', item.id, item):
continue
self.register_check(item)
elif isinstance(item, types.ModuleType):
if filter_func and not filter_func('module', item.__name__, item):
continue
profile = get_module_profile(item)
if profile:
self.merge_profile(profile, filter_func=filter_func)
for item in namespace_items:
if isinstance(item, FontBakeryCondition):
if filter_func and not filter_func('condition', item.name, item):
continue
self.register_condition(item)
elif isinstance(item, FontBakeryExpectedValue):
if filter_func and not filter_func('expected_value', item.name, item):
continue
self.register_expected_value(item) | [
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Get all items from `symbol_table` dict and from `symbol_table.profile_imports`
if it is present. If they an item is an instance of FontBakeryCheck,
FontBakeryCondition or FontBakeryExpectedValue and register it in
the default section.
If an item is a python module, try to get a profile using `get_module_profile(item)`
and then using `merge_profile`;
If the profile_imports kwarg is given, it is used instead of the one taken from
the module namespace.
To register the current module use explicitly:
`profile.auto_register(globals())`
OR maybe: `profile.auto_register(sys.modules[__name__].__dict__)`
To register an imported module explicitly:
`profile.auto_register(module.__dict__)`
if filter_func is defined it is called like:
filter_func(type, name_or_id, item)
where
type: one of "check", "module", "condition", "expected_value", "iterarg",
"derived_iterable", "alias"
name_or_id: the name at which the item will be registered.
if type == 'check': the check.id
if type == 'module': the module name (module.__name__)
item: the item to be registered
if filter_func returns a falsy value for an item, the item will
not be registered. | [
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] | b355aea2e619a4477769e060d24c32448aa65399 | https://github.com/googlefonts/fontbakery/blob/b355aea2e619a4477769e060d24c32448aa65399/Lib/fontbakery/checkrunner.py#L1417-L1481 | train |
googlefonts/fontbakery | Lib/fontbakery/checkrunner.py | Profile.merge_profile | def merge_profile(self, profile, filter_func=None):
"""Copy all namespace items from profile to self.
Namespace items are: 'iterargs', 'derived_iterables', 'aliases',
'conditions', 'expected_values'
Don't change any contents of profile ever!
That means sections are cloned not used directly
filter_func: see description in auto_register
"""
# 'iterargs', 'derived_iterables', 'aliases', 'conditions', 'expected_values'
for ns_type in self._valid_namespace_types:
# this will raise a NamespaceError if an item of profile.{ns_type}
# is already registered.
ns_dict = getattr(profile, ns_type)
if filter_func:
ns_type_singular = self._valid_namespace_types[ns_type]
ns_dict = {name:item for name,item in ns_dict.items()
if filter_func(ns_type_singular, name, item)}
self._add_dict_to_namespace(ns_type, ns_dict)
check_filter_func = None if not filter_func else \
lambda check: filter_func('check', check.id, check)
for section in profile.sections:
my_section = self._sections.get(str(section), None)
if not len(section.checks):
continue
if my_section is None:
# create a new section: don't change other module/profile contents
my_section = section.clone(check_filter_func)
self.add_section(my_section)
else:
# order, description are not updated
my_section.merge_section(section, check_filter_func) | python | def merge_profile(self, profile, filter_func=None):
"""Copy all namespace items from profile to self.
Namespace items are: 'iterargs', 'derived_iterables', 'aliases',
'conditions', 'expected_values'
Don't change any contents of profile ever!
That means sections are cloned not used directly
filter_func: see description in auto_register
"""
# 'iterargs', 'derived_iterables', 'aliases', 'conditions', 'expected_values'
for ns_type in self._valid_namespace_types:
# this will raise a NamespaceError if an item of profile.{ns_type}
# is already registered.
ns_dict = getattr(profile, ns_type)
if filter_func:
ns_type_singular = self._valid_namespace_types[ns_type]
ns_dict = {name:item for name,item in ns_dict.items()
if filter_func(ns_type_singular, name, item)}
self._add_dict_to_namespace(ns_type, ns_dict)
check_filter_func = None if not filter_func else \
lambda check: filter_func('check', check.id, check)
for section in profile.sections:
my_section = self._sections.get(str(section), None)
if not len(section.checks):
continue
if my_section is None:
# create a new section: don't change other module/profile contents
my_section = section.clone(check_filter_func)
self.add_section(my_section)
else:
# order, description are not updated
my_section.merge_section(section, check_filter_func) | [
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googlefonts/fontbakery | Lib/fontbakery/checkrunner.py | Profile.serialize_identity | def serialize_identity(self, identity):
""" Return a json string that can also be used as a key.
The JSON is explicitly unambiguous in the item order
entries (dictionaries are not ordered usually)
Otherwise it is valid JSON
"""
section, check, iterargs = identity
values = map(
# separators are without space, which is the default in JavaScript;
# just in case we need to make these keys in JS.
partial(json.dumps, separators=(',', ':'))
# iterargs are sorted, because it doesn't matter for the result
# but it gives more predictable keys.
# Though, arguably, the order generated by the profile is also good
# and conveys insights on how the order came to be (clustering of
# iterargs). `sorted(iterargs)` however is more robust over time,
# the keys will be the same, even if the sorting order changes.
, [str(section), check.id, sorted(iterargs)]
)
return '{{"section":{},"check":{},"iterargs":{}}}'.format(*values) | python | def serialize_identity(self, identity):
""" Return a json string that can also be used as a key.
The JSON is explicitly unambiguous in the item order
entries (dictionaries are not ordered usually)
Otherwise it is valid JSON
"""
section, check, iterargs = identity
values = map(
# separators are without space, which is the default in JavaScript;
# just in case we need to make these keys in JS.
partial(json.dumps, separators=(',', ':'))
# iterargs are sorted, because it doesn't matter for the result
# but it gives more predictable keys.
# Though, arguably, the order generated by the profile is also good
# and conveys insights on how the order came to be (clustering of
# iterargs). `sorted(iterargs)` however is more robust over time,
# the keys will be the same, even if the sorting order changes.
, [str(section), check.id, sorted(iterargs)]
)
return '{{"section":{},"check":{},"iterargs":{}}}'.format(*values) | [
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] | b355aea2e619a4477769e060d24c32448aa65399 | https://github.com/googlefonts/fontbakery/blob/b355aea2e619a4477769e060d24c32448aa65399/Lib/fontbakery/checkrunner.py#L1550-L1570 | train |
googlefonts/fontbakery | Lib/fontbakery/commands/check_profile.py | get_profile | def get_profile():
""" Prefetch the profile module, to fill some holes in the help text."""
argument_parser = ThrowingArgumentParser(add_help=False)
argument_parser.add_argument('profile')
try:
args, _ = argument_parser.parse_known_args()
except ArgumentParserError:
# silently fails, the main parser will show usage string.
return Profile()
imported = get_module(args.profile)
profile = get_module_profile(imported)
if not profile:
raise Exception(f"Can't get a profile from {imported}.")
return profile | python | def get_profile():
""" Prefetch the profile module, to fill some holes in the help text."""
argument_parser = ThrowingArgumentParser(add_help=False)
argument_parser.add_argument('profile')
try:
args, _ = argument_parser.parse_known_args()
except ArgumentParserError:
# silently fails, the main parser will show usage string.
return Profile()
imported = get_module(args.profile)
profile = get_module_profile(imported)
if not profile:
raise Exception(f"Can't get a profile from {imported}.")
return profile | [
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googlefonts/fontbakery | Lib/fontbakery/commands/generate_glyphdata.py | collate_fonts_data | def collate_fonts_data(fonts_data):
"""Collate individual fonts data into a single glyph data list."""
glyphs = {}
for family in fonts_data:
for glyph in family:
if glyph['unicode'] not in glyphs:
glyphs[glyph['unicode']] = glyph
else:
c = glyphs[glyph['unicode']]['contours']
glyphs[glyph['unicode']]['contours'] = c | glyph['contours']
return glyphs.values() | python | def collate_fonts_data(fonts_data):
"""Collate individual fonts data into a single glyph data list."""
glyphs = {}
for family in fonts_data:
for glyph in family:
if glyph['unicode'] not in glyphs:
glyphs[glyph['unicode']] = glyph
else:
c = glyphs[glyph['unicode']]['contours']
glyphs[glyph['unicode']]['contours'] = c | glyph['contours']
return glyphs.values() | [
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googlefonts/fontbakery | Lib/fontbakery/profiles/adobefonts.py | com_adobe_fonts_check_family_consistent_upm | def com_adobe_fonts_check_family_consistent_upm(ttFonts):
"""Fonts have consistent Units Per Em?"""
upm_set = set()
for ttFont in ttFonts:
upm_set.add(ttFont['head'].unitsPerEm)
if len(upm_set) > 1:
yield FAIL, ("Fonts have different units per em: {}."
).format(sorted(upm_set))
else:
yield PASS, "Fonts have consistent units per em." | python | def com_adobe_fonts_check_family_consistent_upm(ttFonts):
"""Fonts have consistent Units Per Em?"""
upm_set = set()
for ttFont in ttFonts:
upm_set.add(ttFont['head'].unitsPerEm)
if len(upm_set) > 1:
yield FAIL, ("Fonts have different units per em: {}."
).format(sorted(upm_set))
else:
yield PASS, "Fonts have consistent units per em." | [
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googlefonts/fontbakery | Lib/fontbakery/profiles/adobefonts.py | com_adobe_fonts_check_find_empty_letters | def com_adobe_fonts_check_find_empty_letters(ttFont):
"""Letters in font have glyphs that are not empty?"""
cmap = ttFont.getBestCmap()
passed = True
# http://unicode.org/reports/tr44/#General_Category_Values
letter_categories = {
'Ll', 'Lm', 'Lo', 'Lt', 'Lu',
}
invisible_letters = {
0x115F, 0x1160, 0x3164, 0xFFA0, # Hangul filler chars (category='Lo')
}
for unicode_val, glyph_name in cmap.items():
category = unicodedata.category(chr(unicode_val))
if (_quick_and_dirty_glyph_is_empty(ttFont, glyph_name)) \
and (category in letter_categories) \
and (unicode_val not in invisible_letters):
yield FAIL, \
("U+%04X should be visible, but its glyph ('%s') is empty."
% (unicode_val, glyph_name))
passed = False
if passed:
yield PASS, "No empty glyphs for letters found." | python | def com_adobe_fonts_check_find_empty_letters(ttFont):
"""Letters in font have glyphs that are not empty?"""
cmap = ttFont.getBestCmap()
passed = True
# http://unicode.org/reports/tr44/#General_Category_Values
letter_categories = {
'Ll', 'Lm', 'Lo', 'Lt', 'Lu',
}
invisible_letters = {
0x115F, 0x1160, 0x3164, 0xFFA0, # Hangul filler chars (category='Lo')
}
for unicode_val, glyph_name in cmap.items():
category = unicodedata.category(chr(unicode_val))
if (_quick_and_dirty_glyph_is_empty(ttFont, glyph_name)) \
and (category in letter_categories) \
and (unicode_val not in invisible_letters):
yield FAIL, \
("U+%04X should be visible, but its glyph ('%s') is empty."
% (unicode_val, glyph_name))
passed = False
if passed:
yield PASS, "No empty glyphs for letters found." | [
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googlefonts/fontbakery | Lib/fontbakery/profiles/name.py | com_adobe_fonts_check_name_empty_records | def com_adobe_fonts_check_name_empty_records(ttFont):
"""Check name table for empty records."""
failed = False
for name_record in ttFont['name'].names:
name_string = name_record.toUnicode().strip()
if len(name_string) == 0:
failed = True
name_key = tuple([name_record.platformID, name_record.platEncID,
name_record.langID, name_record.nameID])
yield FAIL, ("'name' table record with key={} is "
"empty and should be removed."
).format(name_key)
if not failed:
yield PASS, ("No empty name table records found.") | python | def com_adobe_fonts_check_name_empty_records(ttFont):
"""Check name table for empty records."""
failed = False
for name_record in ttFont['name'].names:
name_string = name_record.toUnicode().strip()
if len(name_string) == 0:
failed = True
name_key = tuple([name_record.platformID, name_record.platEncID,
name_record.langID, name_record.nameID])
yield FAIL, ("'name' table record with key={} is "
"empty and should be removed."
).format(name_key)
if not failed:
yield PASS, ("No empty name table records found.") | [
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googlefonts/fontbakery | Lib/fontbakery/profiles/name.py | com_google_fonts_check_name_no_copyright_on_description | def com_google_fonts_check_name_no_copyright_on_description(ttFont):
"""Description strings in the name table must not contain copyright info."""
failed = False
for name in ttFont['name'].names:
if 'opyright' in name.string.decode(name.getEncoding())\
and name.nameID == NameID.DESCRIPTION:
failed = True
if failed:
yield FAIL, ("Namerecords with ID={} (NameID.DESCRIPTION)"
" should be removed (perhaps these were added by"
" a longstanding FontLab Studio 5.x bug that"
" copied copyright notices to them.)"
"").format(NameID.DESCRIPTION)
else:
yield PASS, ("Description strings in the name table"
" do not contain any copyright string.") | python | def com_google_fonts_check_name_no_copyright_on_description(ttFont):
"""Description strings in the name table must not contain copyright info."""
failed = False
for name in ttFont['name'].names:
if 'opyright' in name.string.decode(name.getEncoding())\
and name.nameID == NameID.DESCRIPTION:
failed = True
if failed:
yield FAIL, ("Namerecords with ID={} (NameID.DESCRIPTION)"
" should be removed (perhaps these were added by"
" a longstanding FontLab Studio 5.x bug that"
" copied copyright notices to them.)"
"").format(NameID.DESCRIPTION)
else:
yield PASS, ("Description strings in the name table"
" do not contain any copyright string.") | [
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googlefonts/fontbakery | Lib/fontbakery/profiles/name.py | com_google_fonts_check_monospace | def com_google_fonts_check_monospace(ttFont, glyph_metrics_stats):
"""Checking correctness of monospaced metadata.
There are various metadata in the OpenType spec to specify if
a font is monospaced or not. If the font is not trully monospaced,
then no monospaced metadata should be set (as sometimes
they mistakenly are...)
Monospace fonts must:
* post.isFixedWidth "Set to 0 if the font is proportionally spaced,
non-zero if the font is not proportionally spaced (monospaced)"
www.microsoft.com/typography/otspec/post.htm
* hhea.advanceWidthMax must be correct, meaning no glyph's
width value is greater.
www.microsoft.com/typography/otspec/hhea.htm
* OS/2.panose.bProportion must be set to 9 (monospace). Spec says:
"The PANOSE definition contains ten digits each of which currently
describes up to sixteen variations. Windows uses bFamilyType,
bSerifStyle and bProportion in the font mapper to determine
family type. It also uses bProportion to determine if the font
is monospaced."
www.microsoft.com/typography/otspec/os2.htm#pan
monotypecom-test.monotype.de/services/pan2
* OS/2.xAverageWidth must be set accurately.
"OS/2.xAverageWidth IS used when rendering monospaced fonts,
at least by Windows GDI"
http://typedrawers.com/discussion/comment/15397/#Comment_15397
Also we should report an error for glyphs not of average width
"""
from fontbakery.constants import (IsFixedWidth,
PANOSE_Proportion)
failed = False
# Note: These values are read from the dict here only to
# reduce the max line length in the check implementation below:
seems_monospaced = glyph_metrics_stats["seems_monospaced"]
most_common_width = glyph_metrics_stats["most_common_width"]
width_max = glyph_metrics_stats['width_max']
if ttFont['hhea'].advanceWidthMax != width_max:
failed = True
yield FAIL, Message("bad-advanceWidthMax",
("Value of hhea.advanceWidthMax"
" should be set to {} but got"
" {} instead."
"").format(width_max, ttFont['hhea'].advanceWidthMax))
if seems_monospaced:
if ttFont['post'].isFixedPitch == IsFixedWidth.NOT_MONOSPACED:
failed = True
yield FAIL, Message("mono-bad-post-isFixedPitch",
("On monospaced fonts, the value of"
" post.isFixedPitch must be set to a non-zero value"
" (meaning 'fixed width monospaced'),"
" but got {} instead."
"").format(ttFont['post'].isFixedPitch))
if ttFont['OS/2'].panose.bProportion != PANOSE_Proportion.MONOSPACED:
failed = True
yield FAIL, Message("mono-bad-panose-proportion",
("On monospaced fonts, the value of"
" OS/2.panose.bProportion must be set to {}"
" (proportion: monospaced), but got"
" {} instead."
"").format(PANOSE_Proportion.MONOSPACED,
ttFont['OS/2'].panose.bProportion))
num_glyphs = len(ttFont['glyf'].glyphs)
unusually_spaced_glyphs = [
g for g in ttFont['glyf'].glyphs
if g not in ['.notdef', '.null', 'NULL'] and
ttFont['hmtx'].metrics[g][0] != most_common_width
]
outliers_ratio = float(len(unusually_spaced_glyphs)) / num_glyphs
if outliers_ratio > 0:
failed = True
yield WARN, Message("mono-outliers",
("Font is monospaced but {} glyphs"
" ({}%) have a different width."
" You should check the widths of:"
" {}").format(
len(unusually_spaced_glyphs),
100.0 * outliers_ratio, unusually_spaced_glyphs))
if not failed:
yield PASS, Message("mono-good", ("Font is monospaced and all"
" related metadata look good."))
else:
# it is a non-monospaced font, so lets make sure
# that all monospace-related metadata is properly unset.
if ttFont['post'].isFixedPitch != IsFixedWidth.NOT_MONOSPACED:
failed = True
yield FAIL, Message("bad-post-isFixedPitch",
("On non-monospaced fonts, the"
" post.isFixedPitch value must be set to {}"
" (not monospaced), but got {} instead."
"").format(IsFixedWidth.NOT_MONOSPACED,
ttFont['post'].isFixedPitch))
if ttFont['OS/2'].panose.bProportion == PANOSE_Proportion.MONOSPACED:
failed = True
yield FAIL, Message("bad-panose-proportion",
("On non-monospaced fonts, the"
" OS/2.panose.bProportion value can be set to "
" any value except 9 (proportion: monospaced)"
" which is the bad value we got in this font."))
if not failed:
yield PASS, Message("good", ("Font is not monospaced and"
" all related metadata look good.")) | python | def com_google_fonts_check_monospace(ttFont, glyph_metrics_stats):
"""Checking correctness of monospaced metadata.
There are various metadata in the OpenType spec to specify if
a font is monospaced or not. If the font is not trully monospaced,
then no monospaced metadata should be set (as sometimes
they mistakenly are...)
Monospace fonts must:
* post.isFixedWidth "Set to 0 if the font is proportionally spaced,
non-zero if the font is not proportionally spaced (monospaced)"
www.microsoft.com/typography/otspec/post.htm
* hhea.advanceWidthMax must be correct, meaning no glyph's
width value is greater.
www.microsoft.com/typography/otspec/hhea.htm
* OS/2.panose.bProportion must be set to 9 (monospace). Spec says:
"The PANOSE definition contains ten digits each of which currently
describes up to sixteen variations. Windows uses bFamilyType,
bSerifStyle and bProportion in the font mapper to determine
family type. It also uses bProportion to determine if the font
is monospaced."
www.microsoft.com/typography/otspec/os2.htm#pan
monotypecom-test.monotype.de/services/pan2
* OS/2.xAverageWidth must be set accurately.
"OS/2.xAverageWidth IS used when rendering monospaced fonts,
at least by Windows GDI"
http://typedrawers.com/discussion/comment/15397/#Comment_15397
Also we should report an error for glyphs not of average width
"""
from fontbakery.constants import (IsFixedWidth,
PANOSE_Proportion)
failed = False
# Note: These values are read from the dict here only to
# reduce the max line length in the check implementation below:
seems_monospaced = glyph_metrics_stats["seems_monospaced"]
most_common_width = glyph_metrics_stats["most_common_width"]
width_max = glyph_metrics_stats['width_max']
if ttFont['hhea'].advanceWidthMax != width_max:
failed = True
yield FAIL, Message("bad-advanceWidthMax",
("Value of hhea.advanceWidthMax"
" should be set to {} but got"
" {} instead."
"").format(width_max, ttFont['hhea'].advanceWidthMax))
if seems_monospaced:
if ttFont['post'].isFixedPitch == IsFixedWidth.NOT_MONOSPACED:
failed = True
yield FAIL, Message("mono-bad-post-isFixedPitch",
("On monospaced fonts, the value of"
" post.isFixedPitch must be set to a non-zero value"
" (meaning 'fixed width monospaced'),"
" but got {} instead."
"").format(ttFont['post'].isFixedPitch))
if ttFont['OS/2'].panose.bProportion != PANOSE_Proportion.MONOSPACED:
failed = True
yield FAIL, Message("mono-bad-panose-proportion",
("On monospaced fonts, the value of"
" OS/2.panose.bProportion must be set to {}"
" (proportion: monospaced), but got"
" {} instead."
"").format(PANOSE_Proportion.MONOSPACED,
ttFont['OS/2'].panose.bProportion))
num_glyphs = len(ttFont['glyf'].glyphs)
unusually_spaced_glyphs = [
g for g in ttFont['glyf'].glyphs
if g not in ['.notdef', '.null', 'NULL'] and
ttFont['hmtx'].metrics[g][0] != most_common_width
]
outliers_ratio = float(len(unusually_spaced_glyphs)) / num_glyphs
if outliers_ratio > 0:
failed = True
yield WARN, Message("mono-outliers",
("Font is monospaced but {} glyphs"
" ({}%) have a different width."
" You should check the widths of:"
" {}").format(
len(unusually_spaced_glyphs),
100.0 * outliers_ratio, unusually_spaced_glyphs))
if not failed:
yield PASS, Message("mono-good", ("Font is monospaced and all"
" related metadata look good."))
else:
# it is a non-monospaced font, so lets make sure
# that all monospace-related metadata is properly unset.
if ttFont['post'].isFixedPitch != IsFixedWidth.NOT_MONOSPACED:
failed = True
yield FAIL, Message("bad-post-isFixedPitch",
("On non-monospaced fonts, the"
" post.isFixedPitch value must be set to {}"
" (not monospaced), but got {} instead."
"").format(IsFixedWidth.NOT_MONOSPACED,
ttFont['post'].isFixedPitch))
if ttFont['OS/2'].panose.bProportion == PANOSE_Proportion.MONOSPACED:
failed = True
yield FAIL, Message("bad-panose-proportion",
("On non-monospaced fonts, the"
" OS/2.panose.bProportion value can be set to "
" any value except 9 (proportion: monospaced)"
" which is the bad value we got in this font."))
if not failed:
yield PASS, Message("good", ("Font is not monospaced and"
" all related metadata look good.")) | [
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] | Checking correctness of monospaced metadata.
There are various metadata in the OpenType spec to specify if
a font is monospaced or not. If the font is not trully monospaced,
then no monospaced metadata should be set (as sometimes
they mistakenly are...)
Monospace fonts must:
* post.isFixedWidth "Set to 0 if the font is proportionally spaced,
non-zero if the font is not proportionally spaced (monospaced)"
www.microsoft.com/typography/otspec/post.htm
* hhea.advanceWidthMax must be correct, meaning no glyph's
width value is greater.
www.microsoft.com/typography/otspec/hhea.htm
* OS/2.panose.bProportion must be set to 9 (monospace). Spec says:
"The PANOSE definition contains ten digits each of which currently
describes up to sixteen variations. Windows uses bFamilyType,
bSerifStyle and bProportion in the font mapper to determine
family type. It also uses bProportion to determine if the font
is monospaced."
www.microsoft.com/typography/otspec/os2.htm#pan
monotypecom-test.monotype.de/services/pan2
* OS/2.xAverageWidth must be set accurately.
"OS/2.xAverageWidth IS used when rendering monospaced fonts,
at least by Windows GDI"
http://typedrawers.com/discussion/comment/15397/#Comment_15397
Also we should report an error for glyphs not of average width | [
"Checking",
"correctness",
"of",
"monospaced",
"metadata",
"."
] | b355aea2e619a4477769e060d24c32448aa65399 | https://github.com/googlefonts/fontbakery/blob/b355aea2e619a4477769e060d24c32448aa65399/Lib/fontbakery/profiles/name.py#L66-L177 | train |
googlefonts/fontbakery | Lib/fontbakery/profiles/name.py | com_google_fonts_check_name_line_breaks | def com_google_fonts_check_name_line_breaks(ttFont):
"""Name table entries should not contain line-breaks."""
failed = False
for name in ttFont["name"].names:
string = name.string.decode(name.getEncoding())
if "\n" in string:
failed = True
yield FAIL, ("Name entry {} on platform {} contains"
" a line-break.").format(NameID(name.nameID).name,
PlatformID(name.platformID).name)
if not failed:
yield PASS, ("Name table entries are all single-line"
" (no line-breaks found).") | python | def com_google_fonts_check_name_line_breaks(ttFont):
"""Name table entries should not contain line-breaks."""
failed = False
for name in ttFont["name"].names:
string = name.string.decode(name.getEncoding())
if "\n" in string:
failed = True
yield FAIL, ("Name entry {} on platform {} contains"
" a line-break.").format(NameID(name.nameID).name,
PlatformID(name.platformID).name)
if not failed:
yield PASS, ("Name table entries are all single-line"
" (no line-breaks found).") | [
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] | Name table entries should not contain line-breaks. | [
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] | b355aea2e619a4477769e060d24c32448aa65399 | https://github.com/googlefonts/fontbakery/blob/b355aea2e619a4477769e060d24c32448aa65399/Lib/fontbakery/profiles/name.py#L183-L195 | train |
googlefonts/fontbakery | Lib/fontbakery/profiles/name.py | com_google_fonts_check_name_match_familyname_fullfont | def com_google_fonts_check_name_match_familyname_fullfont(ttFont):
"""Does full font name begin with the font family name?"""
from fontbakery.utils import get_name_entry_strings
familyname = get_name_entry_strings(ttFont, NameID.FONT_FAMILY_NAME)
fullfontname = get_name_entry_strings(ttFont, NameID.FULL_FONT_NAME)
if len(familyname) == 0:
yield FAIL, Message("no-font-family-name",
("Font lacks a NameID.FONT_FAMILY_NAME"
" entry in the 'name' table."))
elif len(fullfontname) == 0:
yield FAIL, Message("no-full-font-name",
("Font lacks a NameID.FULL_FONT_NAME"
" entry in the 'name' table."))
else:
# we probably should check all found values are equivalent.
# and, in that case, then performing the rest of the check
# with only the first occurences of the name entries
# will suffice:
fullfontname = fullfontname[0]
familyname = familyname[0]
if not fullfontname.startswith(familyname):
yield FAIL, Message(
"does-not", (" On the 'name' table, the full font name"
" (NameID {} - FULL_FONT_NAME: '{}')"
" does not begin with font family name"
" (NameID {} - FONT_FAMILY_NAME:"
" '{}')".format(NameID.FULL_FONT_NAME, familyname,
NameID.FONT_FAMILY_NAME, fullfontname)))
else:
yield PASS, "Full font name begins with the font family name." | python | def com_google_fonts_check_name_match_familyname_fullfont(ttFont):
"""Does full font name begin with the font family name?"""
from fontbakery.utils import get_name_entry_strings
familyname = get_name_entry_strings(ttFont, NameID.FONT_FAMILY_NAME)
fullfontname = get_name_entry_strings(ttFont, NameID.FULL_FONT_NAME)
if len(familyname) == 0:
yield FAIL, Message("no-font-family-name",
("Font lacks a NameID.FONT_FAMILY_NAME"
" entry in the 'name' table."))
elif len(fullfontname) == 0:
yield FAIL, Message("no-full-font-name",
("Font lacks a NameID.FULL_FONT_NAME"
" entry in the 'name' table."))
else:
# we probably should check all found values are equivalent.
# and, in that case, then performing the rest of the check
# with only the first occurences of the name entries
# will suffice:
fullfontname = fullfontname[0]
familyname = familyname[0]
if not fullfontname.startswith(familyname):
yield FAIL, Message(
"does-not", (" On the 'name' table, the full font name"
" (NameID {} - FULL_FONT_NAME: '{}')"
" does not begin with font family name"
" (NameID {} - FONT_FAMILY_NAME:"
" '{}')".format(NameID.FULL_FONT_NAME, familyname,
NameID.FONT_FAMILY_NAME, fullfontname)))
else:
yield PASS, "Full font name begins with the font family name." | [
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googlefonts/fontbakery | Lib/fontbakery/profiles/name.py | com_google_fonts_check_family_naming_recommendations | def com_google_fonts_check_family_naming_recommendations(ttFont):
"""Font follows the family naming recommendations?"""
# See http://forum.fontlab.com/index.php?topic=313.0
import re
from fontbakery.utils import get_name_entry_strings
bad_entries = []
# <Postscript name> may contain only a-zA-Z0-9
# and one hyphen
bad_psname = re.compile("[^A-Za-z0-9-]")
for string in get_name_entry_strings(ttFont,
NameID.POSTSCRIPT_NAME):
if bad_psname.search(string):
bad_entries.append({
'field':
'PostScript Name',
'value':
string,
'rec': ('May contain only a-zA-Z0-9'
' characters and an hyphen.')
})
if string.count('-') > 1:
bad_entries.append({
'field': 'Postscript Name',
'value': string,
'rec': ('May contain not more'
' than a single hyphen')
})
for string in get_name_entry_strings(ttFont,
NameID.FULL_FONT_NAME):
if len(string) >= 64:
bad_entries.append({
'field': 'Full Font Name',
'value': string,
'rec': 'exceeds max length (63)'
})
for string in get_name_entry_strings(ttFont,
NameID.POSTSCRIPT_NAME):
if len(string) >= 30:
bad_entries.append({
'field': 'PostScript Name',
'value': string,
'rec': 'exceeds max length (29)'
})
for string in get_name_entry_strings(ttFont,
NameID.FONT_FAMILY_NAME):
if len(string) >= 32:
bad_entries.append({
'field': 'Family Name',
'value': string,
'rec': 'exceeds max length (31)'
})
for string in get_name_entry_strings(ttFont,
NameID.FONT_SUBFAMILY_NAME):
if len(string) >= 32:
bad_entries.append({
'field': 'Style Name',
'value': string,
'rec': 'exceeds max length (31)'
})
for string in get_name_entry_strings(ttFont,
NameID.TYPOGRAPHIC_FAMILY_NAME):
if len(string) >= 32:
bad_entries.append({
'field': 'OT Family Name',
'value': string,
'rec': 'exceeds max length (31)'
})
for string in get_name_entry_strings(ttFont,
NameID.TYPOGRAPHIC_SUBFAMILY_NAME):
if len(string) >= 32:
bad_entries.append({
'field': 'OT Style Name',
'value': string,
'rec': 'exceeds max length (31)'
})
if len(bad_entries) > 0:
table = "| Field | Value | Recommendation |\n"
table += "|:----- |:----- |:-------------- |\n"
for bad in bad_entries:
table += "| {} | {} | {} |\n".format(bad["field"],
bad["value"],
bad["rec"])
yield INFO, ("Font does not follow "
"some family naming recommendations:\n\n"
"{}").format(table)
else:
yield PASS, "Font follows the family naming recommendations." | python | def com_google_fonts_check_family_naming_recommendations(ttFont):
"""Font follows the family naming recommendations?"""
# See http://forum.fontlab.com/index.php?topic=313.0
import re
from fontbakery.utils import get_name_entry_strings
bad_entries = []
# <Postscript name> may contain only a-zA-Z0-9
# and one hyphen
bad_psname = re.compile("[^A-Za-z0-9-]")
for string in get_name_entry_strings(ttFont,
NameID.POSTSCRIPT_NAME):
if bad_psname.search(string):
bad_entries.append({
'field':
'PostScript Name',
'value':
string,
'rec': ('May contain only a-zA-Z0-9'
' characters and an hyphen.')
})
if string.count('-') > 1:
bad_entries.append({
'field': 'Postscript Name',
'value': string,
'rec': ('May contain not more'
' than a single hyphen')
})
for string in get_name_entry_strings(ttFont,
NameID.FULL_FONT_NAME):
if len(string) >= 64:
bad_entries.append({
'field': 'Full Font Name',
'value': string,
'rec': 'exceeds max length (63)'
})
for string in get_name_entry_strings(ttFont,
NameID.POSTSCRIPT_NAME):
if len(string) >= 30:
bad_entries.append({
'field': 'PostScript Name',
'value': string,
'rec': 'exceeds max length (29)'
})
for string in get_name_entry_strings(ttFont,
NameID.FONT_FAMILY_NAME):
if len(string) >= 32:
bad_entries.append({
'field': 'Family Name',
'value': string,
'rec': 'exceeds max length (31)'
})
for string in get_name_entry_strings(ttFont,
NameID.FONT_SUBFAMILY_NAME):
if len(string) >= 32:
bad_entries.append({
'field': 'Style Name',
'value': string,
'rec': 'exceeds max length (31)'
})
for string in get_name_entry_strings(ttFont,
NameID.TYPOGRAPHIC_FAMILY_NAME):
if len(string) >= 32:
bad_entries.append({
'field': 'OT Family Name',
'value': string,
'rec': 'exceeds max length (31)'
})
for string in get_name_entry_strings(ttFont,
NameID.TYPOGRAPHIC_SUBFAMILY_NAME):
if len(string) >= 32:
bad_entries.append({
'field': 'OT Style Name',
'value': string,
'rec': 'exceeds max length (31)'
})
if len(bad_entries) > 0:
table = "| Field | Value | Recommendation |\n"
table += "|:----- |:----- |:-------------- |\n"
for bad in bad_entries:
table += "| {} | {} | {} |\n".format(bad["field"],
bad["value"],
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yield INFO, ("Font does not follow "
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yield PASS, "Font follows the family naming recommendations." | [
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googlefonts/fontbakery | Lib/fontbakery/profiles/name.py | com_google_fonts_check_name_rfn | def com_google_fonts_check_name_rfn(ttFont):
"""Name table strings must not contain the string 'Reserved Font Name'."""
failed = False
for entry in ttFont["name"].names:
string = entry.toUnicode()
if "reserved font name" in string.lower():
yield WARN, ("Name table entry (\"{}\")"
" contains \"Reserved Font Name\"."
" This is an error except in a few specific"
" rare cases.").format(string)
failed = True
if not failed:
yield PASS, ("None of the name table strings"
" contain \"Reserved Font Name\".") | python | def com_google_fonts_check_name_rfn(ttFont):
"""Name table strings must not contain the string 'Reserved Font Name'."""
failed = False
for entry in ttFont["name"].names:
string = entry.toUnicode()
if "reserved font name" in string.lower():
yield WARN, ("Name table entry (\"{}\")"
" contains \"Reserved Font Name\"."
" This is an error except in a few specific"
" rare cases.").format(string)
failed = True
if not failed:
yield PASS, ("None of the name table strings"
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googlefonts/fontbakery | Lib/fontbakery/profiles/name.py | com_adobe_fonts_check_family_max_4_fonts_per_family_name | def com_adobe_fonts_check_family_max_4_fonts_per_family_name(ttFonts):
"""Verify that each group of fonts with the same nameID 1
has maximum of 4 fonts"""
from collections import Counter
from fontbakery.utils import get_name_entry_strings
failed = False
family_names = list()
for ttFont in ttFonts:
names_list = get_name_entry_strings(ttFont, NameID.FONT_FAMILY_NAME)
# names_list will likely contain multiple entries, e.g. multiple copies
# of the same name in the same language for different platforms, but
# also different names in different languages, we use set() below
# to remove the duplicates and only store the unique family name(s)
# used for a given font
names_set = set(names_list)
family_names.extend(names_set)
counter = Counter(family_names)
for family_name, count in counter.items():
if count > 4:
failed = True
yield FAIL, ("Family '{}' has {} fonts (should be 4 or fewer)."
).format(family_name, count)
if not failed:
yield PASS, ("There were no more than 4 fonts per family name.") | python | def com_adobe_fonts_check_family_max_4_fonts_per_family_name(ttFonts):
"""Verify that each group of fonts with the same nameID 1
has maximum of 4 fonts"""
from collections import Counter
from fontbakery.utils import get_name_entry_strings
failed = False
family_names = list()
for ttFont in ttFonts:
names_list = get_name_entry_strings(ttFont, NameID.FONT_FAMILY_NAME)
# names_list will likely contain multiple entries, e.g. multiple copies
# of the same name in the same language for different platforms, but
# also different names in different languages, we use set() below
# to remove the duplicates and only store the unique family name(s)
# used for a given font
names_set = set(names_list)
family_names.extend(names_set)
counter = Counter(family_names)
for family_name, count in counter.items():
if count > 4:
failed = True
yield FAIL, ("Family '{}' has {} fonts (should be 4 or fewer)."
).format(family_name, count)
if not failed:
yield PASS, ("There were no more than 4 fonts per family name.") | [
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googlefonts/fontbakery | Lib/fontbakery/profiles/cmap.py | com_google_fonts_check_family_equal_unicode_encodings | def com_google_fonts_check_family_equal_unicode_encodings(ttFonts):
"""Fonts have equal unicode encodings?"""
encoding = None
failed = False
for ttFont in ttFonts:
cmap = None
for table in ttFont['cmap'].tables:
if table.format == 4:
cmap = table
break
# Could a font lack a format 4 cmap table ?
# If we ever find one of those, it would crash the check here.
# Then we'd have to yield a FAIL regarding the missing table entry.
if not encoding:
encoding = cmap.platEncID
if encoding != cmap.platEncID:
failed = True
if failed:
yield FAIL, "Fonts have different unicode encodings."
else:
yield PASS, "Fonts have equal unicode encodings." | python | def com_google_fonts_check_family_equal_unicode_encodings(ttFonts):
"""Fonts have equal unicode encodings?"""
encoding = None
failed = False
for ttFont in ttFonts:
cmap = None
for table in ttFont['cmap'].tables:
if table.format == 4:
cmap = table
break
# Could a font lack a format 4 cmap table ?
# If we ever find one of those, it would crash the check here.
# Then we'd have to yield a FAIL regarding the missing table entry.
if not encoding:
encoding = cmap.platEncID
if encoding != cmap.platEncID:
failed = True
if failed:
yield FAIL, "Fonts have different unicode encodings."
else:
yield PASS, "Fonts have equal unicode encodings." | [
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googlefonts/fontbakery | Lib/fontbakery/profiles/cmap.py | com_google_fonts_check_all_glyphs_have_codepoints | def com_google_fonts_check_all_glyphs_have_codepoints(ttFont):
"""Check all glyphs have codepoints assigned."""
failed = False
for subtable in ttFont['cmap'].tables:
if subtable.isUnicode():
for item in subtable.cmap.items():
codepoint = item[0]
if codepoint is None:
failed = True
yield FAIL, ("Glyph {} lacks a unicode"
" codepoint assignment").format(codepoint)
if not failed:
yield PASS, "All glyphs have a codepoint value assigned." | python | def com_google_fonts_check_all_glyphs_have_codepoints(ttFont):
"""Check all glyphs have codepoints assigned."""
failed = False
for subtable in ttFont['cmap'].tables:
if subtable.isUnicode():
for item in subtable.cmap.items():
codepoint = item[0]
if codepoint is None:
failed = True
yield FAIL, ("Glyph {} lacks a unicode"
" codepoint assignment").format(codepoint)
if not failed:
yield PASS, "All glyphs have a codepoint value assigned." | [
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googlefonts/fontbakery | Lib/fontbakery/reporters/__init__.py | FontbakeryReporter.run | def run(self, order=None):
"""
self.runner must be present
"""
for event in self.runner.run(order=order):
self.receive(event) | python | def run(self, order=None):
"""
self.runner must be present
"""
for event in self.runner.run(order=order):
self.receive(event) | [
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googlefonts/fontbakery | Lib/fontbakery/profiles/universal.py | com_google_fonts_check_name_trailing_spaces | def com_google_fonts_check_name_trailing_spaces(ttFont):
"""Name table records must not have trailing spaces."""
failed = False
for name_record in ttFont['name'].names:
name_string = name_record.toUnicode()
if name_string != name_string.strip():
failed = True
name_key = tuple([name_record.platformID, name_record.platEncID,
name_record.langID, name_record.nameID])
shortened_str = name_record.toUnicode()
if len(shortened_str) > 20:
shortened_str = shortened_str[:10] + "[...]" + shortened_str[-10:]
yield FAIL, (f"Name table record with key = {name_key} has"
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f" '{shortened_str}'")
if not failed:
yield PASS, ("No trailing spaces on name table entries.") | python | def com_google_fonts_check_name_trailing_spaces(ttFont):
"""Name table records must not have trailing spaces."""
failed = False
for name_record in ttFont['name'].names:
name_string = name_record.toUnicode()
if name_string != name_string.strip():
failed = True
name_key = tuple([name_record.platformID, name_record.platEncID,
name_record.langID, name_record.nameID])
shortened_str = name_record.toUnicode()
if len(shortened_str) > 20:
shortened_str = shortened_str[:10] + "[...]" + shortened_str[-10:]
yield FAIL, (f"Name table record with key = {name_key} has"
" trailing spaces that must be removed:"
f" '{shortened_str}'")
if not failed:
yield PASS, ("No trailing spaces on name table entries.") | [
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googlefonts/fontbakery | Lib/fontbakery/profiles/universal.py | com_google_fonts_check_family_single_directory | def com_google_fonts_check_family_single_directory(fonts):
"""Checking all files are in the same directory.
If the set of font files passed in the command line is not all in the
same directory, then we warn the user since the tool will interpret
the set of files as belonging to a single family (and it is unlikely
that the user would store the files from a single family spreaded in
several separate directories).
"""
directories = []
for target_file in fonts:
directory = os.path.dirname(target_file)
if directory not in directories:
directories.append(directory)
if len(directories) == 1:
yield PASS, "All files are in the same directory."
else:
yield FAIL, ("Not all fonts passed in the command line"
" are in the same directory. This may lead to"
" bad results as the tool will interpret all"
" font files as belonging to a single"
" font family. The detected directories are:"
" {}".format(directories)) | python | def com_google_fonts_check_family_single_directory(fonts):
"""Checking all files are in the same directory.
If the set of font files passed in the command line is not all in the
same directory, then we warn the user since the tool will interpret
the set of files as belonging to a single family (and it is unlikely
that the user would store the files from a single family spreaded in
several separate directories).
"""
directories = []
for target_file in fonts:
directory = os.path.dirname(target_file)
if directory not in directories:
directories.append(directory)
if len(directories) == 1:
yield PASS, "All files are in the same directory."
else:
yield FAIL, ("Not all fonts passed in the command line"
" are in the same directory. This may lead to"
" bad results as the tool will interpret all"
" font files as belonging to a single"
" font family. The detected directories are:"
" {}".format(directories)) | [
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googlefonts/fontbakery | Lib/fontbakery/profiles/universal.py | com_google_fonts_check_ftxvalidator | def com_google_fonts_check_ftxvalidator(font):
"""Checking with ftxvalidator."""
import plistlib
try:
import subprocess
ftx_cmd = [
"ftxvalidator",
"-t",
"all", # execute all checks
font
]
ftx_output = subprocess.check_output(ftx_cmd, stderr=subprocess.STDOUT)
ftx_data = plistlib.loads(ftx_output)
# we accept kATSFontTestSeverityInformation
# and kATSFontTestSeverityMinorError
if 'kATSFontTestSeverityFatalError' \
not in ftx_data['kATSFontTestResultKey']:
yield PASS, "ftxvalidator passed this file"
else:
ftx_cmd = [
"ftxvalidator",
"-T", # Human-readable output
"-r", # Generate a full report
"-t",
"all", # execute all checks
font
]
ftx_output = subprocess.check_output(ftx_cmd, stderr=subprocess.STDOUT)
yield FAIL, f"ftxvalidator output follows:\n\n{ftx_output}\n"
except subprocess.CalledProcessError as e:
yield ERROR, ("ftxvalidator returned an error code. Output follows:"
"\n\n{}\n").format(e.output.decode('utf-8'))
except OSError:
yield ERROR, "ftxvalidator is not available!" | python | def com_google_fonts_check_ftxvalidator(font):
"""Checking with ftxvalidator."""
import plistlib
try:
import subprocess
ftx_cmd = [
"ftxvalidator",
"-t",
"all", # execute all checks
font
]
ftx_output = subprocess.check_output(ftx_cmd, stderr=subprocess.STDOUT)
ftx_data = plistlib.loads(ftx_output)
# we accept kATSFontTestSeverityInformation
# and kATSFontTestSeverityMinorError
if 'kATSFontTestSeverityFatalError' \
not in ftx_data['kATSFontTestResultKey']:
yield PASS, "ftxvalidator passed this file"
else:
ftx_cmd = [
"ftxvalidator",
"-T", # Human-readable output
"-r", # Generate a full report
"-t",
"all", # execute all checks
font
]
ftx_output = subprocess.check_output(ftx_cmd, stderr=subprocess.STDOUT)
yield FAIL, f"ftxvalidator output follows:\n\n{ftx_output}\n"
except subprocess.CalledProcessError as e:
yield ERROR, ("ftxvalidator returned an error code. Output follows:"
"\n\n{}\n").format(e.output.decode('utf-8'))
except OSError:
yield ERROR, "ftxvalidator is not available!" | [
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googlefonts/fontbakery | Lib/fontbakery/profiles/universal.py | com_google_fonts_check_ots | def com_google_fonts_check_ots(font):
"""Checking with ots-sanitize."""
import ots
try:
process = ots.sanitize(font, check=True, capture_output=True)
except ots.CalledProcessError as e:
yield FAIL, (
"ots-sanitize returned an error code ({}). Output follows:\n\n{}{}"
).format(e.returncode, e.stderr.decode(), e.stdout.decode())
else:
if process.stderr:
yield WARN, (
"ots-sanitize passed this file, however warnings were printed:\n\n{}"
).format(process.stderr.decode())
else:
yield PASS, "ots-sanitize passed this file" | python | def com_google_fonts_check_ots(font):
"""Checking with ots-sanitize."""
import ots
try:
process = ots.sanitize(font, check=True, capture_output=True)
except ots.CalledProcessError as e:
yield FAIL, (
"ots-sanitize returned an error code ({}). Output follows:\n\n{}{}"
).format(e.returncode, e.stderr.decode(), e.stdout.decode())
else:
if process.stderr:
yield WARN, (
"ots-sanitize passed this file, however warnings were printed:\n\n{}"
).format(process.stderr.decode())
else:
yield PASS, "ots-sanitize passed this file" | [
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googlefonts/fontbakery | Lib/fontbakery/profiles/universal.py | com_google_fonts_check_fontbakery_version | def com_google_fonts_check_fontbakery_version():
"""Do we have the latest version of FontBakery installed?"""
try:
import subprocess
installed_str = None
latest_str = None
is_latest = False
failed = False
pip_cmd = ["pip", "search", "fontbakery"]
pip_output = subprocess.check_output(pip_cmd, stderr=subprocess.STDOUT)
for line in pip_output.decode().split('\n'):
if 'INSTALLED' in line:
installed_str = line.split('INSTALLED')[1].strip()
if 'LATEST' in line:
latest_str = line.split('LATEST')[1].strip()
if '(latest)' in line:
is_latest = True
if not (is_latest or is_up_to_date(installed_str, latest_str)):
failed = True
yield FAIL, (f"Current Font Bakery version is {installed_str},"
f" while a newer {latest_str} is already available."
" Please upgrade it with 'pip install -U fontbakery'")
yield INFO, pip_output.decode()
except subprocess.CalledProcessError as e:
yield ERROR, ("Running 'pip search fontbakery' returned an error code."
" Output follows :\n\n{}\n").format(e.output.decode())
if not failed:
yield PASS, "Font Bakery is up-to-date" | python | def com_google_fonts_check_fontbakery_version():
"""Do we have the latest version of FontBakery installed?"""
try:
import subprocess
installed_str = None
latest_str = None
is_latest = False
failed = False
pip_cmd = ["pip", "search", "fontbakery"]
pip_output = subprocess.check_output(pip_cmd, stderr=subprocess.STDOUT)
for line in pip_output.decode().split('\n'):
if 'INSTALLED' in line:
installed_str = line.split('INSTALLED')[1].strip()
if 'LATEST' in line:
latest_str = line.split('LATEST')[1].strip()
if '(latest)' in line:
is_latest = True
if not (is_latest or is_up_to_date(installed_str, latest_str)):
failed = True
yield FAIL, (f"Current Font Bakery version is {installed_str},"
f" while a newer {latest_str} is already available."
" Please upgrade it with 'pip install -U fontbakery'")
yield INFO, pip_output.decode()
except subprocess.CalledProcessError as e:
yield ERROR, ("Running 'pip search fontbakery' returned an error code."
" Output follows :\n\n{}\n").format(e.output.decode())
if not failed:
yield PASS, "Font Bakery is up-to-date" | [
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googlefonts/fontbakery | Lib/fontbakery/profiles/universal.py | com_google_fonts_check_fontforge_stderr | def com_google_fonts_check_fontforge_stderr(font, fontforge_check_results):
"""FontForge validation outputs error messages?"""
if "skip" in fontforge_check_results:
yield SKIP, fontforge_check_results["skip"]
return
filtered_err_msgs = ""
for line in fontforge_check_results["ff_err_messages"].split('\n'):
if ('The following table(s) in the font'
' have been ignored by FontForge') in line:
continue
if "Ignoring 'DSIG' digital signature table" in line:
continue
filtered_err_msgs += line + '\n'
if len(filtered_err_msgs.strip()) > 0:
yield WARN, ("FontForge seems to dislike certain aspects of this font file."
" The actual meaning of the log messages below is not always"
" clear and may require further investigation.\n\n"
"{}").format(filtered_err_msgs)
else:
yield PASS, "FontForge validation did not output any error message." | python | def com_google_fonts_check_fontforge_stderr(font, fontforge_check_results):
"""FontForge validation outputs error messages?"""
if "skip" in fontforge_check_results:
yield SKIP, fontforge_check_results["skip"]
return
filtered_err_msgs = ""
for line in fontforge_check_results["ff_err_messages"].split('\n'):
if ('The following table(s) in the font'
' have been ignored by FontForge') in line:
continue
if "Ignoring 'DSIG' digital signature table" in line:
continue
filtered_err_msgs += line + '\n'
if len(filtered_err_msgs.strip()) > 0:
yield WARN, ("FontForge seems to dislike certain aspects of this font file."
" The actual meaning of the log messages below is not always"
" clear and may require further investigation.\n\n"
"{}").format(filtered_err_msgs)
else:
yield PASS, "FontForge validation did not output any error message." | [
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googlefonts/fontbakery | Lib/fontbakery/profiles/universal.py | com_google_fonts_check_mandatory_glyphs | def com_google_fonts_check_mandatory_glyphs(ttFont):
"""Font contains .notdef as first glyph?
The OpenType specification v1.8.2 recommends that the first glyph is the
.notdef glyph without a codepoint assigned and with a drawing.
https://docs.microsoft.com/en-us/typography/opentype/spec/recom#glyph-0-the-notdef-glyph
Pre-v1.8, it was recommended that a font should also contain a .null, CR and
space glyph. This might have been relevant for applications on MacOS 9.
"""
from fontbakery.utils import glyph_has_ink
if (
ttFont.getGlyphOrder()[0] == ".notdef"
and ".notdef" not in ttFont.getBestCmap().values()
and glyph_has_ink(ttFont, ".notdef")
):
yield PASS, (
"Font contains the .notdef glyph as the first glyph, it does "
"not have a Unicode value assigned and contains a drawing."
)
else:
yield WARN, (
"Font should contain the .notdef glyph as the first glyph, "
"it should not have a Unicode value assigned and should "
"contain a drawing."
) | python | def com_google_fonts_check_mandatory_glyphs(ttFont):
"""Font contains .notdef as first glyph?
The OpenType specification v1.8.2 recommends that the first glyph is the
.notdef glyph without a codepoint assigned and with a drawing.
https://docs.microsoft.com/en-us/typography/opentype/spec/recom#glyph-0-the-notdef-glyph
Pre-v1.8, it was recommended that a font should also contain a .null, CR and
space glyph. This might have been relevant for applications on MacOS 9.
"""
from fontbakery.utils import glyph_has_ink
if (
ttFont.getGlyphOrder()[0] == ".notdef"
and ".notdef" not in ttFont.getBestCmap().values()
and glyph_has_ink(ttFont, ".notdef")
):
yield PASS, (
"Font contains the .notdef glyph as the first glyph, it does "
"not have a Unicode value assigned and contains a drawing."
)
else:
yield WARN, (
"Font should contain the .notdef glyph as the first glyph, "
"it should not have a Unicode value assigned and should "
"contain a drawing."
) | [
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"getBestCmap",
"(",
")",
".",
"values",
"(",
")",
"and",
"glyph_has_ink",
"(",
"ttFont",
",",
"\".notdef\"",
")",
")",
":",
"yield",
"PASS",
",",
"(",
"\"Font contains the .notdef glyph as the first glyph, it does \"",
"\"not have a Unicode value assigned and contains a drawing.\"",
")",
"else",
":",
"yield",
"WARN",
",",
"(",
"\"Font should contain the .notdef glyph as the first glyph, \"",
"\"it should not have a Unicode value assigned and should \"",
"\"contain a drawing.\"",
")"
] | Font contains .notdef as first glyph?
The OpenType specification v1.8.2 recommends that the first glyph is the
.notdef glyph without a codepoint assigned and with a drawing.
https://docs.microsoft.com/en-us/typography/opentype/spec/recom#glyph-0-the-notdef-glyph
Pre-v1.8, it was recommended that a font should also contain a .null, CR and
space glyph. This might have been relevant for applications on MacOS 9. | [
"Font",
"contains",
".",
"notdef",
"as",
"first",
"glyph?"
] | b355aea2e619a4477769e060d24c32448aa65399 | https://github.com/googlefonts/fontbakery/blob/b355aea2e619a4477769e060d24c32448aa65399/Lib/fontbakery/profiles/universal.py#L559-L586 | train |
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