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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`
... | 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`
... | [
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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,...] ... | 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,...] ... | [
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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.d... | 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))
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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_... | 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_... | [
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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 tenso... | 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)
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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 sel... | 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))
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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))
... | 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))
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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, fil... | 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, fil... | [
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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... | 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... | [
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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._datap... | 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
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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
-------
:ob... | 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
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-------
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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
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-------
:... | 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
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-------
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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_... | 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
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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
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filename = self.generate_tensor_filename(field_name, self._num_tensors-1)
self._tensors[field_name].save(filename, compressed=True... | python | def write(self):
""" Writes all tensors to the next file number. """
# write the next file for all fields
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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_fil... | python | def open(dataset_dir, access_mode=READ_ONLY_ACCESS):
""" Opens a tensor dataset. """
# check access mode
if access_mode == WRITE_ACCESS:
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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):
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""" Delete a split of the dataset.
Parameters
----------
split_name : str
name of the split to delete
"""
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BerkeleyAutomation/autolab_core | autolab_core/yaml_config.py | YamlConfig._load_config | def _load_config(self, filename):
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----------
filename : :obj:`str`
The filename of the .yaml file that contains the configuration.
"""
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filename : :obj:`str`
The filename of the .yaml file that contains the configuration.
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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
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"""
Makes a matplotlib table object with relevant data.
Thanks to Lucas Manuelli for the contribution.
Parameters
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train_result: ClassificationResult
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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
... | 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
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BerkeleyAutomation/autolab_core | autolab_core/learning_analysis.py | BinaryClassificationResult.accuracy_curve | def accuracy_curve(self, delta_tau=0.01):
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# compute thresholds based on the sorted probabilities
orig_thresh = self.threshold
sorted_labels, sorted_probs = self.sorted_values
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# compute thresholds based on the sorted probabilities
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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
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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):
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# compute thresholds based on the sorted probabilities
orig_thresh = self.threshold
sorted_labels, sorted_probs = self.sorted_values
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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):
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orig_thresh = self.threshold
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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... | 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.
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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.
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----------
values : :obj:`numpy.ndarray`
An array of values to put in the histogram.
num_bins : int
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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
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Multiplying this matrix by a vector `v` gives the same result
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Parameters
----------
xi : :obj:`numpy.ndarray` of float
A 3-entry vector.
Returns
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"""Return the skew-symmetric matrix that can be used to calculate
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xi : :obj:`numpy.ndarray` of float
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BerkeleyAutomation/autolab_core | autolab_core/utils.py | deskew | def deskew(S):
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vector. Only works for 3x3 matrices.
Parameters
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S : :obj:`numpy.ndarray` of float
A 3x3 skew-symmetric matrix.
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S : :obj:`numpy.ndarray` of float
A 3x3 skew-symmetric matrix.
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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
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dictionary to reverse
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dictionary with keys and values swapped
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""" Reverses the key value pairs for a given dictionary.
Parameters
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dictionary to reverse
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dictionary with keys and values swapped
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""" Reads in all filenames from a directory that contain a specified substring.
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the directory to read from
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optional tag to match in the filenames
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the directory to read from
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r : float
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elev : float
elevation from xy plane
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x-coordinate
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""" Convert spherical to cartesian coordinates.
Attributes
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r : float
radius
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elev : float
elevation from xy plane
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BerkeleyAutomation/autolab_core | autolab_core/utils.py | cart2sph | def cart2sph(x, y, z):
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z : float
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x : float
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z : float
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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
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Parameters
----------
message : :obj:`str`
the message to display to the user
yesno : :obj:`bool`
whether or not to enforce yes or no inputs
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""" Get keyboard input from a human, optionally reasking for valid
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message : :obj:`str`
the message to display to the user
yesno : :obj:`bool`
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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.... | python | def interpolate(dq0, dq1, t):
"""Return the interpolation of two DualQuaternions.
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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')... | 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)
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BerkeleyAutomation/autolab_core | autolab_core/csv_model.py | CSVModel.insert | def insert(self, data):
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Parameters
----------
data : :obj:`dict`
A dictionary mapping keys (header strings) to values.
Returns
-------
int
The UID for the new row.
Raises
------
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"""Insert a row into the .csv file.
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data : :obj:`dict`
A dictionary mapping keys (header strings) to values.
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int
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BerkeleyAutomation/autolab_core | autolab_core/csv_model.py | CSVModel.update_by_uid | def update_by_uid(self, uid, data):
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----------
uid : int
The UID of the row to update.
data : :obj:`dict`
A dictionary mapping keys (header strings) to values.
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------
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"""Update a row with the given data.
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----------
uid : int
The UID of the row to update.
data : :obj:`dict`
A dictionary mapping keys (header strings) to values.
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BerkeleyAutomation/autolab_core | autolab_core/csv_model.py | CSVModel.get_col | def get_col(self, col_name, filter = lambda _ : True):
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----------
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BerkeleyAutomation/autolab_core | autolab_core/csv_model.py | CSVModel.get_by_cols | def get_by_cols(self, cols, direction=1):
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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):
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matching_dict: :obj:'dict'
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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
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self._cur_row += 1
return data | python | def next(self):
""" Returns the next row in the CSV, for iteration """
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self._cur_row += 1
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BerkeleyAutomation/autolab_core | autolab_core/transformations.py | projection_matrix | def projection_matrix(point, normal, direction=None,
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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:
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>>> point = numpy.random.random(3) - 0.5
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"""Return projection plane and perspective point from projection matrix.
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BerkeleyAutomation/autolab_core | autolab_core/transformations.py | unit_vector | def unit_vector(data, axis=None, out=None):
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>>> v1 = unit_vector(v0)
>>> numpy.allclose(v1, v0 / numpy.linalg.norm(v0))
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>>> v0 = numpy.random.rand(5, 4, 3)
>>> v1 = unit_vector(v0, ... | python | def unit_vector(data, axis=None, out=None):
"""Return ndarray normalized by length, i.e. eucledian norm, along axis.
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>>> v1 = unit_vector(v0)
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BerkeleyAutomation/autolab_core | autolab_core/json_serialization.py | json_numpy_obj_hook | def json_numpy_obj_hook(dct):
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The encoded dictionary.
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The ndarray that `dct` was encoding.
"""
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The encoded dictionary.
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BerkeleyAutomation/autolab_core | autolab_core/json_serialization.py | dump | def dump(*args, **kwargs):
"""Dump a numpy.ndarray to file stream.
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BerkeleyAutomation/autolab_core | autolab_core/json_serialization.py | load | def load(*args, **kwargs):
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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.
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BerkeleyAutomation/autolab_core | autolab_core/random_variables.py | IsotropicGaussianRigidTransformRandomVariable.sample | def sample(self, size=1):
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size : int
number of sample to take
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sampled rigid transformations
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size : int
number of sample to take
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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 """
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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()
self._cmds_q.put(("stop",))
try:
self._recorder.terminate()
except Exception:
pass
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.complete_extra | def complete_extra(self, args):
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if len(args) == 0:
return self._listdir('./')
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BerkeleyAutomation/autolab_core | autolab_core/completer.py | Completer.complete | def complete(self, text, state):
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# dexnet entity tab completion
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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",))
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recorder._stop()
try:
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""" Stops syncer operations. Destroys syncer process. """
self._cmds_q.put(("stop",))
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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 b... | 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
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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.
"""
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# add a file handle to the root logger
hdlr = logging.FileHandler(log_file)
formatter = logg... | python | def add_root_log_file(log_file):
"""
Add a log file to the root logger.
Parameters
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log_file :obj:`str`
The path to the log file.
"""
root_logger = logging.getLogger()
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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.
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----------
logger :obj:`logging.Logger`
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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... | 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
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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.
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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.')
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googlefonts/fontbakery | Lib/fontbakery/checkrunner.py | Section.add_check | def add_check(self, check):
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googlefonts/fontbakery | Lib/fontbakery/checkrunner.py | Section.merge_section | def merge_section(self, section, filter_func=None):
"""
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order, description, etc. are not updated.
"""
for check in section.checks:
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"""
Add section.checks to self, if not skipped by self._add_check_callback.
order, description, etc. are not updated.
"""
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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.
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"""
Validate values if they are registered as expected_values and present.
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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.
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if not key in ('args', 'mandatoryArgs'):
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"""
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.
"""
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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')
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# iterargs should always be mandatory, unless there's a good reason
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args = self._get_aggregate_args(item, 'mandatoryArgs')
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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`
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"""
Register items from `symbol_table` in the profile.
Get all items from `symbol_table` dict and from `symbol_table.profile_imports`
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googlefonts/fontbakery | Lib/fontbakery/checkrunner.py | Profile.merge_profile | def merge_profile(self, profile, filter_func=None):
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Don't change any contents of profile ever!
That means sections are cloned not ... | python | def merge_profile(self, profile, filter_func=None):
"""Copy all namespace items from profile to self.
Namespace items are: 'iterargs', 'derived_iterables', 'aliases',
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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(
# se... | 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(
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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 w... | 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 w... | [
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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 = gly... | 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 = gly... | [
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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(sorte... | 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: {}."
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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 = ... | 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 = ... | [
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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.... | 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.... | [
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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... | 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... | [
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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 mist... | 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 mist... | [
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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"
... | 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"
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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_NA... | 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_NA... | [
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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 ... | 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 ... | [
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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 (\"{}\")"
... | 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 (\"{}\")"
... | [
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] | b355aea2e619a4477769e060d24c32448aa65399 | https://github.com/googlefonts/fontbakery/blob/b355aea2e619a4477769e060d24c32448aa65399/Lib/fontbakery/profiles/name.py#L338-L351 | train |
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:
na... | 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:
na... | [
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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 for... | 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 for... | [
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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 = T... | 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 = T... | [
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] | b355aea2e619a4477769e060d24c32448aa65399 | https://github.com/googlefonts/fontbakery/blob/b355aea2e619a4477769e060d24c32448aa65399/Lib/fontbakery/profiles/cmap.py#L42-L54 | train |
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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] | b355aea2e619a4477769e060d24c32448aa65399 | https://github.com/googlefonts/fontbakery/blob/b355aea2e619a4477769e060d24c32448aa65399/Lib/fontbakery/reporters/__init__.py#L45-L50 | train |
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.plat... | 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.plat... | [
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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 unlik... | 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 unlik... | [
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If the set of font files passed in the command line is not all in the
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the set of files as belonging to a single family (and it is unlikely
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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)
... | 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)
... | [
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] | b355aea2e619a4477769e060d24c32448aa65399 | https://github.com/googlefonts/fontbakery/blob/b355aea2e619a4477769e060d24c32448aa65399/Lib/fontbakery/profiles/universal.py#L258-L292 | train |
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.... | 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.... | [
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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... | 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... | [
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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... | 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... | [
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] | b355aea2e619a4477769e060d24c32448aa65399 | https://github.com/googlefonts/fontbakery/blob/b355aea2e619a4477769e060d24c32448aa65399/Lib/fontbakery/profiles/universal.py#L380-L401 | train |
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-notd... | 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-notd... | [
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The OpenType specification v1.8.2 recommends that the first glyph is the
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https://docs.microsoft.com/en-us/typography/opentype/spec/recom#glyph-0-the-notdef-glyph
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