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lacava/few | few/variation.py | VariationMixin.mutate | def mutate(self,p_i,func_set,term_set): #, max_depth=2
"""point mutation, addition, removal"""
self.point_mutate(p_i,func_set,term_set) | python | def mutate(self,p_i,func_set,term_set): #, max_depth=2
"""point mutation, addition, removal"""
self.point_mutate(p_i,func_set,term_set) | [
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lacava/few | few/variation.py | VariationMixin.point_mutate | def point_mutate(self,p_i,func_set,term_set):
"""point mutation on individual p_i"""
# point mutation
x = self.random_state.randint(len(p_i))
arity = p_i[x].arity[p_i[x].in_type]
# find eligible replacements based on arity and type
reps = [n for n in func_set+term_set
... | python | def point_mutate(self,p_i,func_set,term_set):
"""point mutation on individual p_i"""
# point mutation
x = self.random_state.randint(len(p_i))
arity = p_i[x].arity[p_i[x].in_type]
# find eligible replacements based on arity and type
reps = [n for n in func_set+term_set
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lacava/few | few/variation.py | VariationMixin.is_valid_program | def is_valid_program(self,p):
"""checks whether program p makes a syntactically valid tree.
checks that the accumulated program length is always greater than the
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alway present for functions. It then checks that ... | python | def is_valid_program(self,p):
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lacava/few | few/population.py | run_MDR | def run_MDR(n,stack_float,labels=None):
"""run utility function for MDR nodes."""
# need to check that tmp is categorical
x1 = stack_float.pop()
x2 = stack_float.pop()
# check data is categorical
if len(np.unique(x1))<=3 and len(np.unique(x2))<=3:
tmp = np.vstack((x1,x2)).transpose()
... | python | def run_MDR(n,stack_float,labels=None):
"""run utility function for MDR nodes."""
# need to check that tmp is categorical
x1 = stack_float.pop()
x2 = stack_float.pop()
# check data is categorical
if len(np.unique(x1))<=3 and len(np.unique(x2))<=3:
tmp = np.vstack((x1,x2)).transpose()
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lacava/few | few/population.py | PopMixin.stack_2_eqn | def stack_2_eqn(self,p):
"""returns equation string for program stack"""
stack_eqn = []
if p: # if stack is not empty
for n in p.stack:
self.eval_eqn(n,stack_eqn)
return stack_eqn[-1]
return [] | python | def stack_2_eqn(self,p):
"""returns equation string for program stack"""
stack_eqn = []
if p: # if stack is not empty
for n in p.stack:
self.eval_eqn(n,stack_eqn)
return stack_eqn[-1]
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lacava/few | few/population.py | PopMixin.stacks_2_eqns | def stacks_2_eqns(self,stacks):
"""returns equation strings from stacks"""
if stacks:
return list(map(lambda p: self.stack_2_eqn(p), stacks))
else:
return [] | python | def stacks_2_eqns(self,stacks):
"""returns equation strings from stacks"""
if stacks:
return list(map(lambda p: self.stack_2_eqn(p), stacks))
else:
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lacava/few | few/population.py | PopMixin.make_program | def make_program(self,stack,func_set,term_set,max_d,ntype):
"""makes a program stack"""
# print("stack:",stack,"max d:",max_d)
if max_d == 0:
ts = [t for t in term_set if t.out_type==ntype]
if not ts:
raise ValueError('no ts. ntype:'+ntype+'. term_set out... | python | def make_program(self,stack,func_set,term_set,max_d,ntype):
"""makes a program stack"""
# print("stack:",stack,"max d:",max_d)
if max_d == 0:
ts = [t for t in term_set if t.out_type==ntype]
if not ts:
raise ValueError('no ts. ntype:'+ntype+'. term_set out... | [
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lacava/few | few/population.py | PopMixin.init_pop | def init_pop(self):
"""initializes population of features as GP stacks."""
pop = Pop(self.population_size)
seed_with_raw_features = False
# make programs
if self.seed_with_ml:
# initial population is the components of the default ml model
if (self.ml_type ... | python | def init_pop(self):
"""initializes population of features as GP stacks."""
pop = Pop(self.population_size)
seed_with_raw_features = False
# make programs
if self.seed_with_ml:
# initial population is the components of the default ml model
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lacava/few | few/few.py | main | def main():
"""Main function that is called when FEW is run on the command line"""
parser = argparse.ArgumentParser(description='A feature engineering wrapper'
' for machine learning algorithms.',
add_help=False)
parser.add_argument(... | python | def main():
"""Main function that is called when FEW is run on the command line"""
parser = argparse.ArgumentParser(description='A feature engineering wrapper'
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add_help=False)
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lacava/few | few/few.py | FEW.fit | def fit(self, features, labels):
"""Fit model to data"""
# setup data
# imputation
if self.clean:
features = self.impute_data(features)
# save the number of features
self.n_features = features.shape[1]
self.n_samples = features.shape[0]
# set ... | python | def fit(self, features, labels):
"""Fit model to data"""
# setup data
# imputation
if self.clean:
features = self.impute_data(features)
# save the number of features
self.n_features = features.shape[1]
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lacava/few | few/few.py | FEW.transform | def transform(self,x,inds=None,labels = None):
"""return a transformation of x using population outputs"""
if inds:
# return np.asarray(Parallel(n_jobs=10)(delayed(self.out)(I,x,labels,self.otype)
# for I in inds)).transpose()
return np.asar... | python | def transform(self,x,inds=None,labels = None):
"""return a transformation of x using population outputs"""
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# return np.asarray(Parallel(n_jobs=10)(delayed(self.out)(I,x,labels,self.otype)
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lacava/few | few/few.py | FEW.impute_data | def impute_data(self,x):
"""Imputes data set containing Nan values"""
imp = Imputer(missing_values='NaN', strategy='mean', axis=0)
return imp.fit_transform(x) | python | def impute_data(self,x):
"""Imputes data set containing Nan values"""
imp = Imputer(missing_values='NaN', strategy='mean', axis=0)
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lacava/few | few/few.py | FEW.clean | def clean(self,x):
"""remove nan and inf rows from x"""
return x[~np.any(np.isnan(x) | np.isinf(x),axis=1)] | python | def clean(self,x):
"""remove nan and inf rows from x"""
return x[~np.any(np.isnan(x) | np.isinf(x),axis=1)] | [
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lacava/few | few/few.py | FEW.clean_with_zeros | def clean_with_zeros(self,x):
""" set nan and inf rows from x to zero"""
x[~np.any(np.isnan(x) | np.isinf(x),axis=1)] = 0
return x | python | def clean_with_zeros(self,x):
""" set nan and inf rows from x to zero"""
x[~np.any(np.isnan(x) | np.isinf(x),axis=1)] = 0
return x | [
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lacava/few | few/few.py | FEW.predict | def predict(self, testing_features):
"""predict on a holdout data set."""
# print("best_inds:",self._best_inds)
# print("best estimator size:",self._best_estimator.coef_.shape)
if self.clean:
testing_features = self.impute_data(testing_features)
if self._best_inds:
... | python | def predict(self, testing_features):
"""predict on a holdout data set."""
# print("best_inds:",self._best_inds)
# print("best estimator size:",self._best_estimator.coef_.shape)
if self.clean:
testing_features = self.impute_data(testing_features)
if self._best_inds:
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lacava/few | few/few.py | FEW.fit_predict | def fit_predict(self, features, labels):
"""Convenience function that fits a pipeline then predicts on the
provided features
Parameters
----------
features: array-like {n_samples, n_features}
Feature matrix
labels: array-like {n_samples}
List of c... | python | def fit_predict(self, features, labels):
"""Convenience function that fits a pipeline then predicts on the
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Parameters
----------
features: array-like {n_samples, n_features}
Feature matrix
labels: array-like {n_samples}
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lacava/few | few/few.py | FEW.score | def score(self, testing_features, testing_labels):
"""estimates accuracy on testing set"""
# print("test features shape:",testing_features.shape)
# print("testing labels shape:",testing_labels.shape)
yhat = self.predict(testing_features)
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# print("test features shape:",testing_features.shape)
# print("testing labels shape:",testing_labels.shape)
yhat = self.predict(testing_features)
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lacava/few | few/few.py | FEW.export | def export(self, output_file_name):
"""exports engineered features
Parameters
----------
output_file_name: string
String containing the path and file name of the desired output file
Returns
-------
None
"""
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"""exports engineered features
Parameters
----------
output_file_name: string
String containing the path and file name of the desired output file
Returns
-------
None
"""
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lacava/few | few/few.py | FEW.print_model | def print_model(self,sep='\n'):
"""prints model contained in best inds, if ml has a coefficient property.
otherwise, prints the features generated by FEW."""
model = ''
# print('ml type:',self.ml_type)
# print('ml:',self._best_estimator)
if self._best_inds:
... | python | def print_model(self,sep='\n'):
"""prints model contained in best inds, if ml has a coefficient property.
otherwise, prints the features generated by FEW."""
model = ''
# print('ml type:',self.ml_type)
# print('ml:',self._best_estimator)
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lacava/few | few/few.py | FEW.valid_loc | def valid_loc(self,F=None):
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valid_locs = self.valid_loc(F)
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valid_locs = self.valid_loc(self.F)
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lacava/few | few/few.py | FEW.get_diversity | def get_diversity(self,X):
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feature_correlations = np.zeros(X.shape[0]-1)
for i in np.arange(1,X.shape[0]-1):
feature_correlations[i] = max(0.0,r2_score(X[0],X[i]))
... | python | def get_diversity(self,X):
"""compute mean diversity of individual outputs"""
# diversity in terms of cosine distances between features
feature_correlations = np.zeros(X.shape[0]-1)
for i in np.arange(1,X.shape[0]-1):
feature_correlations[i] = max(0.0,r2_score(X[0],X[i]))
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lacava/few | few/few.py | FEW.roc_auc_cv | def roc_auc_cv(self,features,labels):
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lacava/few | few/evaluation.py | divs | def divs(x,y):
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lacava/few | few/evaluation.py | r2_score_vec | def r2_score_vec(y_true,y_pred):
""" returns non-aggregate version of r2 score.
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"""
numerator = (y_true - y_pred) ** 2
denominator = (y_true - np.average(y_true)) ** 2
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""" returns non-aggregate version of r2 score.
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lacava/few | few/evaluation.py | inertia | def inertia(X,y,samples=False):
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""" return the within-class squared distance from the centroid"""
# pdb.set_trace()
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lacava/few | few/evaluation.py | separation | def separation(X,y,samples=False):
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# pdb.set_trace()
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total_dist = (X.max()-X.min())**2
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lacava/few | few/evaluation.py | fisher | def fisher(yhat,y,samples=False):
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mu = np.zeros(len(classes))
v = np.zeros(len(classes))
# pdb.set_trace()
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lacava/few | few/evaluation.py | EvaluationMixin.proper | def proper(self,x):
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x[np.isinf(x)] = self.max_fit
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"""cleans fitness vector"""
x[x < 0] = self.max_fit
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x[np.isinf(x)] = self.max_fit
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lacava/few | few/evaluation.py | EvaluationMixin.evaluate | def evaluate(self,n, features, stack_float, stack_bool,labels=None):
"""evaluate node in program"""
np.seterr(all='ignore')
if len(stack_float) >= n.arity['f'] and len(stack_bool) >= n.arity['b']:
if n.out_type == 'f':
stack_float.append(
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"""evaluate node in program"""
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lacava/few | few/evaluation.py | EvaluationMixin.all_finite | def all_finite(self,X):
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lacava/few | few/evaluation.py | EvaluationMixin.out | def out(self,I,features,labels=None,otype='f'):
"""computes the output for individual I"""
stack_float = []
stack_bool = []
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# evaulate stack over rows of features,labels
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lacava/few | few/evaluation.py | EvaluationMixin.calc_fitness | def calc_fitness(self,X,labels,fit_choice,sel):
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yhat: output of a program.
labels: correct outputs
fit_choice: choice of fitness function
"""
if 'lexicase' in sel:
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nion-software/nionswift-io | nionswift_plugin/DM_IO/dm3_image_utils.py | imagedatadict_to_ndarray | def imagedatadict_to_ndarray(imdict):
"""
Converts the ImageData dictionary, imdict, to an nd image.
"""
arr = imdict['Data']
im = None
if isinstance(arr, parse_dm3.array.array):
im = numpy.asarray(arr, dtype=arr.typecode)
elif isinstance(arr, parse_dm3.structarray):
t = tupl... | python | def imagedatadict_to_ndarray(imdict):
"""
Converts the ImageData dictionary, imdict, to an nd image.
"""
arr = imdict['Data']
im = None
if isinstance(arr, parse_dm3.array.array):
im = numpy.asarray(arr, dtype=arr.typecode)
elif isinstance(arr, parse_dm3.structarray):
t = tupl... | [
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nion-software/nionswift-io | nionswift_plugin/DM_IO/dm3_image_utils.py | ndarray_to_imagedatadict | def ndarray_to_imagedatadict(nparr):
"""
Convert the numpy array nparr into a suitable ImageList entry dictionary.
Returns a dictionary with the appropriate Data, DataType, PixelDepth
to be inserted into a dm3 tag dictionary and written to a file.
"""
ret = {}
dm_type = None
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"""
Convert the numpy array nparr into a suitable ImageList entry dictionary.
Returns a dictionary with the appropriate Data, DataType, PixelDepth
to be inserted into a dm3 tag dictionary and written to a file.
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nion-software/nionswift-io | nionswift_plugin/DM_IO/dm3_image_utils.py | load_image | def load_image(file) -> DataAndMetadata.DataAndMetadata:
"""
Loads the image from the file-like object or string file.
If file is a string, the file is opened and then read.
Returns a numpy ndarray of our best guess for the most important image
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"""
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Loads the image from the file-like object or string file.
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nion-software/nionswift-io | nionswift_plugin/DM_IO/dm3_image_utils.py | save_image | def save_image(xdata: DataAndMetadata.DataAndMetadata, file):
"""
Saves the nparray data to the file-like object (or string) file.
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Saves the nparray data to the file-like object (or string) file.
"""
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nion-software/nionswift-io | nionswift_plugin/DM_IO/parse_dm3.py | parse_dm_header | def parse_dm_header(f, outdata=None):
"""
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"""
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nion-software/nionswift-io | nionswift_plugin/DM_IO/parse_dm3.py | get_structdmtypes_for_python_typeorobject | def get_structdmtypes_for_python_typeorobject(typeorobj):
"""
Return structchar, dmtype for the python (or numpy)
type or object typeorobj.
For more complex types we only return the dm type
"""
# not isinstance is probably a bit more lenient than 'is'
# ie isinstance(x,str) is nicer than typ... | python | def get_structdmtypes_for_python_typeorobject(typeorobj):
"""
Return structchar, dmtype for the python (or numpy)
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nion-software/nionswift-io | nionswift_plugin/DM_IO/parse_dm3.py | standard_dm_read | def standard_dm_read(datatype_num, desc):
"""
datatype_num is the number of the data type, see dm_simple_names
above. desc is a (nicename, struct_char) tuple. We return a function
that parses the data for us.
"""
nicename, structchar, types = desc
def dm_read_x(f, outdata=None):
"""... | python | def standard_dm_read(datatype_num, desc):
"""
datatype_num is the number of the data type, see dm_simple_names
above. desc is a (nicename, struct_char) tuple. We return a function
that parses the data for us.
"""
nicename, structchar, types = desc
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nion-software/nionswift-io | nionswift_plugin/TIFF_IO/tifffile.py | imread | def imread(files, **kwargs):
"""Return image data from TIFF file(s) as numpy array.
Refer to the TiffFile and TiffSequence classes and their asarray
functions for documentation.
Parameters
----------
files : str, binary stream, or sequence
File name, seekable binary stream, glob patte... | python | def imread(files, **kwargs):
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nion-software/nionswift-io | nionswift_plugin/TIFF_IO/tifffile.py | imwrite | def imwrite(file, data=None, shape=None, dtype=None, **kwargs):
"""Write numpy array to TIFF file.
Refer to the TiffWriter class and its asarray function for documentation.
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nion-software/nionswift-io | nionswift_plugin/TIFF_IO/tifffile.py | memmap | def memmap(filename, shape=None, dtype=None, page=None, series=0, mode='r+',
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nion-software/nionswift-io | nionswift_plugin/TIFF_IO/tifffile.py | read_tags | def read_tags(fh, byteorder, offsetsize, tagnames, customtags=None,
maxifds=None):
"""Read tags from chain of IFDs and return as list of dicts.
The file handle position must be at a valid IFD header.
"""
if offsetsize == 4:
offsetformat = byteorder+'I'
tagnosize = 2
... | python | def read_tags(fh, byteorder, offsetsize, tagnames, customtags=None,
maxifds=None):
"""Read tags from chain of IFDs and return as list of dicts.
The file handle position must be at a valid IFD header.
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if offsetsize == 4:
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nion-software/nionswift-io | nionswift_plugin/TIFF_IO/tifffile.py | read_exif_ifd | def read_exif_ifd(fh, byteorder, dtype, count, offsetsize):
"""Read EXIF tags from file and return as dict."""
exif = read_tags(fh, byteorder, offsetsize, TIFF.EXIF_TAGS, maxifds=1)
for name in ('ExifVersion', 'FlashpixVersion'):
try:
exif[name] = bytes2str(exif[name])
except Exc... | python | def read_exif_ifd(fh, byteorder, dtype, count, offsetsize):
"""Read EXIF tags from file and return as dict."""
exif = read_tags(fh, byteorder, offsetsize, TIFF.EXIF_TAGS, maxifds=1)
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exif[name] = bytes2str(exif[name])
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nion-software/nionswift-io | nionswift_plugin/TIFF_IO/tifffile.py | read_gps_ifd | def read_gps_ifd(fh, byteorder, dtype, count, offsetsize):
"""Read GPS tags from file and return as dict."""
return read_tags(fh, byteorder, offsetsize, TIFF.GPS_TAGS, maxifds=1) | python | def read_gps_ifd(fh, byteorder, dtype, count, offsetsize):
"""Read GPS tags from file and return as dict."""
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nion-software/nionswift-io | nionswift_plugin/TIFF_IO/tifffile.py | read_interoperability_ifd | def read_interoperability_ifd(fh, byteorder, dtype, count, offsetsize):
"""Read Interoperability tags from file and return as dict."""
tag_names = {1: 'InteroperabilityIndex'}
return read_tags(fh, byteorder, offsetsize, tag_names, maxifds=1) | python | def read_interoperability_ifd(fh, byteorder, dtype, count, offsetsize):
"""Read Interoperability tags from file and return as dict."""
tag_names = {1: 'InteroperabilityIndex'}
return read_tags(fh, byteorder, offsetsize, tag_names, maxifds=1) | [
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nion-software/nionswift-io | nionswift_plugin/TIFF_IO/tifffile.py | read_bytes | def read_bytes(fh, byteorder, dtype, count, offsetsize):
"""Read tag data from file and return as byte string."""
dtype = 'B' if dtype[-1] == 's' else byteorder+dtype[-1]
count *= numpy.dtype(dtype).itemsize
data = fh.read(count)
if len(data) != count:
log.warning('read_bytes: failed to read... | python | def read_bytes(fh, byteorder, dtype, count, offsetsize):
"""Read tag data from file and return as byte string."""
dtype = 'B' if dtype[-1] == 's' else byteorder+dtype[-1]
count *= numpy.dtype(dtype).itemsize
data = fh.read(count)
if len(data) != count:
log.warning('read_bytes: failed to read... | [
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nion-software/nionswift-io | nionswift_plugin/TIFF_IO/tifffile.py | read_utf8 | def read_utf8(fh, byteorder, dtype, count, offsetsize):
"""Read tag data from file and return as unicode string."""
return fh.read(count).decode('utf-8') | python | def read_utf8(fh, byteorder, dtype, count, offsetsize):
"""Read tag data from file and return as unicode string."""
return fh.read(count).decode('utf-8') | [
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nion-software/nionswift-io | nionswift_plugin/TIFF_IO/tifffile.py | read_numpy | def read_numpy(fh, byteorder, dtype, count, offsetsize):
"""Read tag data from file and return as numpy array."""
dtype = 'b' if dtype[-1] == 's' else byteorder+dtype[-1]
return fh.read_array(dtype, count) | python | def read_numpy(fh, byteorder, dtype, count, offsetsize):
"""Read tag data from file and return as numpy array."""
dtype = 'b' if dtype[-1] == 's' else byteorder+dtype[-1]
return fh.read_array(dtype, count) | [
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nion-software/nionswift-io | nionswift_plugin/TIFF_IO/tifffile.py | read_colormap | def read_colormap(fh, byteorder, dtype, count, offsetsize):
"""Read ColorMap data from file and return as numpy array."""
cmap = fh.read_array(byteorder+dtype[-1], count)
cmap.shape = (3, -1)
return cmap | python | def read_colormap(fh, byteorder, dtype, count, offsetsize):
"""Read ColorMap data from file and return as numpy array."""
cmap = fh.read_array(byteorder+dtype[-1], count)
cmap.shape = (3, -1)
return cmap | [
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nion-software/nionswift-io | nionswift_plugin/TIFF_IO/tifffile.py | read_json | def read_json(fh, byteorder, dtype, count, offsetsize):
"""Read JSON tag data from file and return as object."""
data = fh.read(count)
try:
return json.loads(unicode(stripnull(data), 'utf-8'))
except ValueError:
log.warning('read_json: invalid JSON') | python | def read_json(fh, byteorder, dtype, count, offsetsize):
"""Read JSON tag data from file and return as object."""
data = fh.read(count)
try:
return json.loads(unicode(stripnull(data), 'utf-8'))
except ValueError:
log.warning('read_json: invalid JSON') | [
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nion-software/nionswift-io | nionswift_plugin/TIFF_IO/tifffile.py | read_mm_header | def read_mm_header(fh, byteorder, dtype, count, offsetsize):
"""Read FluoView mm_header tag from file and return as dict."""
mmh = fh.read_record(TIFF.MM_HEADER, byteorder=byteorder)
mmh = recarray2dict(mmh)
mmh['Dimensions'] = [
(bytes2str(d[0]).strip(), d[1], d[2], d[3], bytes2str(d[4]).strip(... | python | def read_mm_header(fh, byteorder, dtype, count, offsetsize):
"""Read FluoView mm_header tag from file and return as dict."""
mmh = fh.read_record(TIFF.MM_HEADER, byteorder=byteorder)
mmh = recarray2dict(mmh)
mmh['Dimensions'] = [
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nion-software/nionswift-io | nionswift_plugin/TIFF_IO/tifffile.py | read_uic1tag | def read_uic1tag(fh, byteorder, dtype, count, offsetsize, planecount=None):
"""Read MetaMorph STK UIC1Tag from file and return as dict.
Return empty dictionary if planecount is unknown.
"""
assert dtype in ('2I', '1I') and byteorder == '<'
result = {}
if dtype == '2I':
# pre MetaMorph ... | python | def read_uic1tag(fh, byteorder, dtype, count, offsetsize, planecount=None):
"""Read MetaMorph STK UIC1Tag from file and return as dict.
Return empty dictionary if planecount is unknown.
"""
assert dtype in ('2I', '1I') and byteorder == '<'
result = {}
if dtype == '2I':
# pre MetaMorph ... | [
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nion-software/nionswift-io | nionswift_plugin/TIFF_IO/tifffile.py | read_uic2tag | def read_uic2tag(fh, byteorder, dtype, planecount, offsetsize):
"""Read MetaMorph STK UIC2Tag from file and return as dict."""
assert dtype == '2I' and byteorder == '<'
values = fh.read_array('<u4', 6*planecount).reshape(planecount, 6)
return {
'ZDistance': values[:, 0] / values[:, 1],
'... | python | def read_uic2tag(fh, byteorder, dtype, planecount, offsetsize):
"""Read MetaMorph STK UIC2Tag from file and return as dict."""
assert dtype == '2I' and byteorder == '<'
values = fh.read_array('<u4', 6*planecount).reshape(planecount, 6)
return {
'ZDistance': values[:, 0] / values[:, 1],
'... | [
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nion-software/nionswift-io | nionswift_plugin/TIFF_IO/tifffile.py | read_uic4tag | def read_uic4tag(fh, byteorder, dtype, planecount, offsetsize):
"""Read MetaMorph STK UIC4Tag from file and return as dict."""
assert dtype == '1I' and byteorder == '<'
result = {}
while True:
tagid = struct.unpack('<H', fh.read(2))[0]
if tagid == 0:
break
name, value... | python | def read_uic4tag(fh, byteorder, dtype, planecount, offsetsize):
"""Read MetaMorph STK UIC4Tag from file and return as dict."""
assert dtype == '1I' and byteorder == '<'
result = {}
while True:
tagid = struct.unpack('<H', fh.read(2))[0]
if tagid == 0:
break
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nion-software/nionswift-io | nionswift_plugin/TIFF_IO/tifffile.py | read_uic_tag | def read_uic_tag(fh, tagid, planecount, offset):
"""Read a single UIC tag value from file and return tag name and value.
UIC1Tags use an offset.
"""
def read_int(count=1):
value = struct.unpack('<%iI' % count, fh.read(4*count))
return value[0] if count == 1 else value
try:
... | python | def read_uic_tag(fh, tagid, planecount, offset):
"""Read a single UIC tag value from file and return tag name and value.
UIC1Tags use an offset.
"""
def read_int(count=1):
value = struct.unpack('<%iI' % count, fh.read(4*count))
return value[0] if count == 1 else value
try:
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nion-software/nionswift-io | nionswift_plugin/TIFF_IO/tifffile.py | read_uic_image_property | def read_uic_image_property(fh):
"""Read UIC ImagePropertyEx tag from file and return as dict."""
# TODO: test this
size = struct.unpack('B', fh.read(1))[0]
name = struct.unpack('%is' % size, fh.read(size))[0][:-1]
flags, prop = struct.unpack('<IB', fh.read(5))
if prop == 1:
value = stru... | python | def read_uic_image_property(fh):
"""Read UIC ImagePropertyEx tag from file and return as dict."""
# TODO: test this
size = struct.unpack('B', fh.read(1))[0]
name = struct.unpack('%is' % size, fh.read(size))[0][:-1]
flags, prop = struct.unpack('<IB', fh.read(5))
if prop == 1:
value = stru... | [
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nion-software/nionswift-io | nionswift_plugin/TIFF_IO/tifffile.py | read_cz_lsminfo | def read_cz_lsminfo(fh, byteorder, dtype, count, offsetsize):
"""Read CZ_LSMINFO tag from file and return as dict."""
assert byteorder == '<'
magic_number, structure_size = struct.unpack('<II', fh.read(8))
if magic_number not in (50350412, 67127628):
raise ValueError('invalid CZ_LSMINFO structur... | python | def read_cz_lsminfo(fh, byteorder, dtype, count, offsetsize):
"""Read CZ_LSMINFO tag from file and return as dict."""
assert byteorder == '<'
magic_number, structure_size = struct.unpack('<II', fh.read(8))
if magic_number not in (50350412, 67127628):
raise ValueError('invalid CZ_LSMINFO structur... | [
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nion-software/nionswift-io | nionswift_plugin/TIFF_IO/tifffile.py | read_lsm_floatpairs | def read_lsm_floatpairs(fh):
"""Read LSM sequence of float pairs from file and return as list."""
size = struct.unpack('<i', fh.read(4))[0]
return fh.read_array('<2f8', count=size) | python | def read_lsm_floatpairs(fh):
"""Read LSM sequence of float pairs from file and return as list."""
size = struct.unpack('<i', fh.read(4))[0]
return fh.read_array('<2f8', count=size) | [
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nion-software/nionswift-io | nionswift_plugin/TIFF_IO/tifffile.py | read_lsm_positions | def read_lsm_positions(fh):
"""Read LSM positions from file and return as list."""
size = struct.unpack('<I', fh.read(4))[0]
return fh.read_array('<2f8', count=size) | python | def read_lsm_positions(fh):
"""Read LSM positions from file and return as list."""
size = struct.unpack('<I', fh.read(4))[0]
return fh.read_array('<2f8', count=size) | [
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nion-software/nionswift-io | nionswift_plugin/TIFF_IO/tifffile.py | read_lsm_timestamps | def read_lsm_timestamps(fh):
"""Read LSM time stamps from file and return as list."""
size, count = struct.unpack('<ii', fh.read(8))
if size != (8 + 8 * count):
log.warning('read_lsm_timestamps: invalid LSM TimeStamps block')
return []
# return struct.unpack('<%dd' % count, fh.read(8*cou... | python | def read_lsm_timestamps(fh):
"""Read LSM time stamps from file and return as list."""
size, count = struct.unpack('<ii', fh.read(8))
if size != (8 + 8 * count):
log.warning('read_lsm_timestamps: invalid LSM TimeStamps block')
return []
# return struct.unpack('<%dd' % count, fh.read(8*cou... | [
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nion-software/nionswift-io | nionswift_plugin/TIFF_IO/tifffile.py | read_lsm_eventlist | def read_lsm_eventlist(fh):
"""Read LSM events from file and return as list of (time, type, text)."""
count = struct.unpack('<II', fh.read(8))[1]
events = []
while count > 0:
esize, etime, etype = struct.unpack('<IdI', fh.read(16))
etext = bytes2str(stripnull(fh.read(esize - 16)))
... | python | def read_lsm_eventlist(fh):
"""Read LSM events from file and return as list of (time, type, text)."""
count = struct.unpack('<II', fh.read(8))[1]
events = []
while count > 0:
esize, etime, etype = struct.unpack('<IdI', fh.read(16))
etext = bytes2str(stripnull(fh.read(esize - 16)))
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nion-software/nionswift-io | nionswift_plugin/TIFF_IO/tifffile.py | read_lsm_channelcolors | def read_lsm_channelcolors(fh):
"""Read LSM ChannelColors structure from file and return as dict."""
result = {'Mono': False, 'Colors': [], 'ColorNames': []}
pos = fh.tell()
(size, ncolors, nnames,
coffset, noffset, mono) = struct.unpack('<IIIIII', fh.read(24))
if ncolors != nnames:
log... | python | def read_lsm_channelcolors(fh):
"""Read LSM ChannelColors structure from file and return as dict."""
result = {'Mono': False, 'Colors': [], 'ColorNames': []}
pos = fh.tell()
(size, ncolors, nnames,
coffset, noffset, mono) = struct.unpack('<IIIIII', fh.read(24))
if ncolors != nnames:
log... | [
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nion-software/nionswift-io | nionswift_plugin/TIFF_IO/tifffile.py | read_lsm_scaninfo | def read_lsm_scaninfo(fh):
"""Read LSM ScanInfo structure from file and return as dict."""
block = {}
blocks = [block]
unpack = struct.unpack
if struct.unpack('<I', fh.read(4))[0] != 0x10000000:
# not a Recording sub block
log.warning('read_lsm_scaninfo: invalid LSM ScanInfo structur... | python | def read_lsm_scaninfo(fh):
"""Read LSM ScanInfo structure from file and return as dict."""
block = {}
blocks = [block]
unpack = struct.unpack
if struct.unpack('<I', fh.read(4))[0] != 0x10000000:
# not a Recording sub block
log.warning('read_lsm_scaninfo: invalid LSM ScanInfo structur... | [
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nion-software/nionswift-io | nionswift_plugin/TIFF_IO/tifffile.py | read_sis | def read_sis(fh, byteorder, dtype, count, offsetsize):
"""Read OlympusSIS structure and return as dict.
No specification is avaliable. Only few fields are known.
"""
result = {}
(magic, _, minute, hour, day, month, year, _, name, tagcount
) = struct.unpack('<4s6shhhhh6s32sh', fh.read(60))
... | python | def read_sis(fh, byteorder, dtype, count, offsetsize):
"""Read OlympusSIS structure and return as dict.
No specification is avaliable. Only few fields are known.
"""
result = {}
(magic, _, minute, hour, day, month, year, _, name, tagcount
) = struct.unpack('<4s6shhhhh6s32sh', fh.read(60))
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nion-software/nionswift-io | nionswift_plugin/TIFF_IO/tifffile.py | read_sis_ini | def read_sis_ini(fh, byteorder, dtype, count, offsetsize):
"""Read OlympusSIS INI string and return as dict."""
inistr = fh.read(count)
inistr = bytes2str(stripnull(inistr))
try:
return olympusini_metadata(inistr)
except Exception as exc:
log.warning('olympusini_metadata: %s: %s', ex... | python | def read_sis_ini(fh, byteorder, dtype, count, offsetsize):
"""Read OlympusSIS INI string and return as dict."""
inistr = fh.read(count)
inistr = bytes2str(stripnull(inistr))
try:
return olympusini_metadata(inistr)
except Exception as exc:
log.warning('olympusini_metadata: %s: %s', ex... | [
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nion-software/nionswift-io | nionswift_plugin/TIFF_IO/tifffile.py | read_tvips_header | def read_tvips_header(fh, byteorder, dtype, count, offsetsize):
"""Read TVIPS EM-MENU headers and return as dict."""
result = {}
header = fh.read_record(TIFF.TVIPS_HEADER_V1, byteorder=byteorder)
for name, typestr in TIFF.TVIPS_HEADER_V1:
result[name] = header[name].tolist()
if header['Versi... | python | def read_tvips_header(fh, byteorder, dtype, count, offsetsize):
"""Read TVIPS EM-MENU headers and return as dict."""
result = {}
header = fh.read_record(TIFF.TVIPS_HEADER_V1, byteorder=byteorder)
for name, typestr in TIFF.TVIPS_HEADER_V1:
result[name] = header[name].tolist()
if header['Versi... | [
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nion-software/nionswift-io | nionswift_plugin/TIFF_IO/tifffile.py | read_fei_metadata | def read_fei_metadata(fh, byteorder, dtype, count, offsetsize):
"""Read FEI SFEG/HELIOS headers and return as dict."""
result = {}
section = {}
data = bytes2str(stripnull(fh.read(count)))
for line in data.splitlines():
line = line.strip()
if line.startswith('['):
section ... | python | def read_fei_metadata(fh, byteorder, dtype, count, offsetsize):
"""Read FEI SFEG/HELIOS headers and return as dict."""
result = {}
section = {}
data = bytes2str(stripnull(fh.read(count)))
for line in data.splitlines():
line = line.strip()
if line.startswith('['):
section ... | [
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nion-software/nionswift-io | nionswift_plugin/TIFF_IO/tifffile.py | read_cz_sem | def read_cz_sem(fh, byteorder, dtype, count, offsetsize):
"""Read Zeiss SEM tag and return as dict.
See https://sourceforge.net/p/gwyddion/mailman/message/29275000/ for
unnamed values.
"""
result = {'': ()}
key = None
data = bytes2str(stripnull(fh.read(count)))
for line in data.splitli... | python | def read_cz_sem(fh, byteorder, dtype, count, offsetsize):
"""Read Zeiss SEM tag and return as dict.
See https://sourceforge.net/p/gwyddion/mailman/message/29275000/ for
unnamed values.
"""
result = {'': ()}
key = None
data = bytes2str(stripnull(fh.read(count)))
for line in data.splitli... | [
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nion-software/nionswift-io | nionswift_plugin/TIFF_IO/tifffile.py | read_nih_image_header | def read_nih_image_header(fh, byteorder, dtype, count, offsetsize):
"""Read NIH_IMAGE_HEADER tag from file and return as dict."""
a = fh.read_record(TIFF.NIH_IMAGE_HEADER, byteorder=byteorder)
a = a.newbyteorder(byteorder)
a = recarray2dict(a)
a['XUnit'] = a['XUnit'][:a['XUnitSize']]
a['UM'] = a... | python | def read_nih_image_header(fh, byteorder, dtype, count, offsetsize):
"""Read NIH_IMAGE_HEADER tag from file and return as dict."""
a = fh.read_record(TIFF.NIH_IMAGE_HEADER, byteorder=byteorder)
a = a.newbyteorder(byteorder)
a = recarray2dict(a)
a['XUnit'] = a['XUnit'][:a['XUnitSize']]
a['UM'] = a... | [
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nion-software/nionswift-io | nionswift_plugin/TIFF_IO/tifffile.py | read_scanimage_metadata | def read_scanimage_metadata(fh):
"""Read ScanImage BigTIFF v3 static and ROI metadata from open file.
Return non-varying frame data as dict and ROI group data as JSON.
The settings can be used to read image data and metadata without parsing
the TIFF file.
Raise ValueError if file does not contain... | python | def read_scanimage_metadata(fh):
"""Read ScanImage BigTIFF v3 static and ROI metadata from open file.
Return non-varying frame data as dict and ROI group data as JSON.
The settings can be used to read image data and metadata without parsing
the TIFF file.
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nion-software/nionswift-io | nionswift_plugin/TIFF_IO/tifffile.py | read_micromanager_metadata | def read_micromanager_metadata(fh):
"""Read MicroManager non-TIFF settings from open file and return as dict.
The settings can be used to read image data without parsing the TIFF file.
Raise ValueError if the file does not contain valid MicroManager metadata.
"""
fh.seek(0)
try:
byteo... | python | def read_micromanager_metadata(fh):
"""Read MicroManager non-TIFF settings from open file and return as dict.
The settings can be used to read image data without parsing the TIFF file.
Raise ValueError if the file does not contain valid MicroManager metadata.
"""
fh.seek(0)
try:
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nion-software/nionswift-io | nionswift_plugin/TIFF_IO/tifffile.py | imagej_metadata_tag | def imagej_metadata_tag(metadata, byteorder):
"""Return IJMetadata and IJMetadataByteCounts tags from metadata dict.
The tags can be passed to the TiffWriter.save function as extratags.
The metadata dict may contain the following keys and values:
Info : str
Human-readable information ... | python | def imagej_metadata_tag(metadata, byteorder):
"""Return IJMetadata and IJMetadataByteCounts tags from metadata dict.
The tags can be passed to the TiffWriter.save function as extratags.
The metadata dict may contain the following keys and values:
Info : str
Human-readable information ... | [
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nion-software/nionswift-io | nionswift_plugin/TIFF_IO/tifffile.py | imagej_metadata | def imagej_metadata(data, bytecounts, byteorder):
"""Return IJMetadata tag value as dict.
The 'Info' string can have multiple formats, e.g. OIF or ScanImage,
that might be parsed into dicts using the matlabstr2py or
oiffile.SettingsFile functions.
"""
def _string(data, byteorder):
retu... | python | def imagej_metadata(data, bytecounts, byteorder):
"""Return IJMetadata tag value as dict.
The 'Info' string can have multiple formats, e.g. OIF or ScanImage,
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"""
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nion-software/nionswift-io | nionswift_plugin/TIFF_IO/tifffile.py | imagej_description_metadata | def imagej_description_metadata(description):
"""Return metatata from ImageJ image description as dict.
Raise ValueError if not a valid ImageJ description.
>>> description = 'ImageJ=1.11a\\nimages=510\\nhyperstack=true\\n'
>>> imagej_description_metadata(description) # doctest: +SKIP
{'ImageJ': '... | python | def imagej_description_metadata(description):
"""Return metatata from ImageJ image description as dict.
Raise ValueError if not a valid ImageJ description.
>>> description = 'ImageJ=1.11a\\nimages=510\\nhyperstack=true\\n'
>>> imagej_description_metadata(description) # doctest: +SKIP
{'ImageJ': '... | [
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Raise ValueError if not a valid ImageJ description.
>>> description = 'ImageJ=1.11a\\nimages=510\\nhyperstack=true\\n'
>>> imagej_description_metadata(description) # doctest: +SKIP
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nion-software/nionswift-io | nionswift_plugin/TIFF_IO/tifffile.py | imagej_description | def imagej_description(shape, rgb=None, colormaped=False, version=None,
hyperstack=None, mode=None, loop=None, **kwargs):
"""Return ImageJ image description from data shape.
ImageJ can handle up to 6 dimensions in order TZCYXS.
>>> imagej_description((51, 5, 2, 196, 171)) # doctest... | python | def imagej_description(shape, rgb=None, colormaped=False, version=None,
hyperstack=None, mode=None, loop=None, **kwargs):
"""Return ImageJ image description from data shape.
ImageJ can handle up to 6 dimensions in order TZCYXS.
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ImageJ=1.11a
images=510
channels=2
slices=5
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nion-software/nionswift-io | nionswift_plugin/TIFF_IO/tifffile.py | imagej_shape | def imagej_shape(shape, rgb=None):
"""Return shape normalized to 6D ImageJ hyperstack TZCYXS.
Raise ValueError if not a valid ImageJ hyperstack shape.
>>> imagej_shape((2, 3, 4, 5, 3), False)
(2, 3, 4, 5, 3, 1)
"""
shape = tuple(int(i) for i in shape)
ndim = len(shape)
if 1 > ndim > 6... | python | def imagej_shape(shape, rgb=None):
"""Return shape normalized to 6D ImageJ hyperstack TZCYXS.
Raise ValueError if not a valid ImageJ hyperstack shape.
>>> imagej_shape((2, 3, 4, 5, 3), False)
(2, 3, 4, 5, 3, 1)
"""
shape = tuple(int(i) for i in shape)
ndim = len(shape)
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nion-software/nionswift-io | nionswift_plugin/TIFF_IO/tifffile.py | json_description | def json_description(shape, **metadata):
"""Return JSON image description from data shape and other metadata.
Return UTF-8 encoded JSON.
>>> json_description((256, 256, 3), axes='YXS') # doctest: +SKIP
b'{"shape": [256, 256, 3], "axes": "YXS"}'
"""
metadata.update(shape=shape)
return jso... | python | def json_description(shape, **metadata):
"""Return JSON image description from data shape and other metadata.
Return UTF-8 encoded JSON.
>>> json_description((256, 256, 3), axes='YXS') # doctest: +SKIP
b'{"shape": [256, 256, 3], "axes": "YXS"}'
"""
metadata.update(shape=shape)
return jso... | [
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Return UTF-8 encoded JSON.
>>> json_description((256, 256, 3), axes='YXS') # doctest: +SKIP
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nion-software/nionswift-io | nionswift_plugin/TIFF_IO/tifffile.py | json_description_metadata | def json_description_metadata(description):
"""Return metatata from JSON formated image description as dict.
Raise ValuError if description is of unknown format.
>>> description = '{"shape": [256, 256, 3], "axes": "YXS"}'
>>> json_description_metadata(description) # doctest: +SKIP
{'shape': [256,... | python | def json_description_metadata(description):
"""Return metatata from JSON formated image description as dict.
Raise ValuError if description is of unknown format.
>>> description = '{"shape": [256, 256, 3], "axes": "YXS"}'
>>> json_description_metadata(description) # doctest: +SKIP
{'shape': [256,... | [
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>>> description = '{"shape": [256, 256, 3], "axes": "YXS"}'
>>> json_description_metadata(description) # doctest: +SKIP
{'shape': [256, 256, 3], 'axes': 'YXS'}
>>> json_description_m... | [
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nion-software/nionswift-io | nionswift_plugin/TIFF_IO/tifffile.py | fluoview_description_metadata | def fluoview_description_metadata(description, ignoresections=None):
"""Return metatata from FluoView image description as dict.
The FluoView image description format is unspecified. Expect failures.
>>> descr = ('[Intensity Mapping]\\nMap Ch0: Range=00000 to 02047\\n'
... '[Intensity Mapping... | python | def fluoview_description_metadata(description, ignoresections=None):
"""Return metatata from FluoView image description as dict.
The FluoView image description format is unspecified. Expect failures.
>>> descr = ('[Intensity Mapping]\\nMap Ch0: Range=00000 to 02047\\n'
... '[Intensity Mapping... | [
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The FluoView image description format is unspecified. Expect failures.
>>> descr = ('[Intensity Mapping]\\nMap Ch0: Range=00000 to 02047\\n'
... '[Intensity Mapping End]')
>>> fluoview_description_metadata(descr)
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nion-software/nionswift-io | nionswift_plugin/TIFF_IO/tifffile.py | pilatus_description_metadata | def pilatus_description_metadata(description):
"""Return metatata from Pilatus image description as dict.
Return metadata from Pilatus pixel array detectors by Dectris, created
by camserver or TVX software.
>>> pilatus_description_metadata('# Pixel_size 172e-6 m x 172e-6 m')
{'Pixel_size': (0.0001... | python | def pilatus_description_metadata(description):
"""Return metatata from Pilatus image description as dict.
Return metadata from Pilatus pixel array detectors by Dectris, created
by camserver or TVX software.
>>> pilatus_description_metadata('# Pixel_size 172e-6 m x 172e-6 m')
{'Pixel_size': (0.0001... | [
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Return metadata from Pilatus pixel array detectors by Dectris, created
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>>> pilatus_description_metadata('# Pixel_size 172e-6 m x 172e-6 m')
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nion-software/nionswift-io | nionswift_plugin/TIFF_IO/tifffile.py | svs_description_metadata | def svs_description_metadata(description):
"""Return metatata from Aperio image description as dict.
The Aperio image description format is unspecified. Expect failures.
>>> svs_description_metadata('Aperio Image Library v1.0')
{'Aperio Image Library': 'v1.0'}
"""
if not description.startswit... | python | def svs_description_metadata(description):
"""Return metatata from Aperio image description as dict.
The Aperio image description format is unspecified. Expect failures.
>>> svs_description_metadata('Aperio Image Library v1.0')
{'Aperio Image Library': 'v1.0'}
"""
if not description.startswit... | [
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The Aperio image description format is unspecified. Expect failures.
>>> svs_description_metadata('Aperio Image Library v1.0')
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nion-software/nionswift-io | nionswift_plugin/TIFF_IO/tifffile.py | stk_description_metadata | def stk_description_metadata(description):
"""Return metadata from MetaMorph image description as list of dict.
The MetaMorph image description format is unspecified. Expect failures.
"""
description = description.strip()
if not description:
return []
try:
description = bytes2s... | python | def stk_description_metadata(description):
"""Return metadata from MetaMorph image description as list of dict.
The MetaMorph image description format is unspecified. Expect failures.
"""
description = description.strip()
if not description:
return []
try:
description = bytes2s... | [
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nion-software/nionswift-io | nionswift_plugin/TIFF_IO/tifffile.py | metaseries_description_metadata | def metaseries_description_metadata(description):
"""Return metatata from MetaSeries image description as dict."""
if not description.startswith('<MetaData>'):
raise ValueError('invalid MetaSeries image description')
from xml.etree import cElementTree as etree # delayed import
root = etree.fro... | python | def metaseries_description_metadata(description):
"""Return metatata from MetaSeries image description as dict."""
if not description.startswith('<MetaData>'):
raise ValueError('invalid MetaSeries image description')
from xml.etree import cElementTree as etree # delayed import
root = etree.fro... | [
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nion-software/nionswift-io | nionswift_plugin/TIFF_IO/tifffile.py | scanimage_artist_metadata | def scanimage_artist_metadata(artist):
"""Return metatata from ScanImage artist tag as dict."""
try:
return json.loads(artist)
except ValueError as exc:
log.warning('scanimage_artist_metadata: %s: %s',
exc.__class__.__name__, exc) | python | def scanimage_artist_metadata(artist):
"""Return metatata from ScanImage artist tag as dict."""
try:
return json.loads(artist)
except ValueError as exc:
log.warning('scanimage_artist_metadata: %s: %s',
exc.__class__.__name__, exc) | [
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nion-software/nionswift-io | nionswift_plugin/TIFF_IO/tifffile.py | olympusini_metadata | def olympusini_metadata(inistr):
"""Return OlympusSIS metadata from INI string.
No documentation is available.
"""
def keyindex(key):
# split key into name and index
index = 0
i = len(key.rstrip('0123456789'))
if i < len(key):
index = int(key[i:]) - 1
... | python | def olympusini_metadata(inistr):
"""Return OlympusSIS metadata from INI string.
No documentation is available.
"""
def keyindex(key):
# split key into name and index
index = 0
i = len(key.rstrip('0123456789'))
if i < len(key):
index = int(key[i:]) - 1
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nion-software/nionswift-io | nionswift_plugin/TIFF_IO/tifffile.py | tile_decode | def tile_decode(tile, tileindex, tileshape, tiledshape,
lsb2msb, decompress, unpack, unpredict, out):
"""Decode tile segment bytes into 5D output array."""
_, imagedepth, imagelength, imagewidth, _ = out.shape
tileddepth, tiledlength, tiledwidth = tiledshape
tiledepth, tilelength, tilewi... | python | def tile_decode(tile, tileindex, tileshape, tiledshape,
lsb2msb, decompress, unpack, unpredict, out):
"""Decode tile segment bytes into 5D output array."""
_, imagedepth, imagelength, imagewidth, _ = out.shape
tileddepth, tiledlength, tiledwidth = tiledshape
tiledepth, tilelength, tilewi... | [
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nion-software/nionswift-io | nionswift_plugin/TIFF_IO/tifffile.py | unpack_rgb | def unpack_rgb(data, dtype=None, bitspersample=None, rescale=True):
"""Return array from byte string containing packed samples.
Use to unpack RGB565 or RGB555 to RGB888 format.
Parameters
----------
data : byte str
The data to be decoded. Samples in each pixel are stored consecutively.
... | python | def unpack_rgb(data, dtype=None, bitspersample=None, rescale=True):
"""Return array from byte string containing packed samples.
Use to unpack RGB565 or RGB555 to RGB888 format.
Parameters
----------
data : byte str
The data to be decoded. Samples in each pixel are stored consecutively.
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data : byte str
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nion-software/nionswift-io | nionswift_plugin/TIFF_IO/tifffile.py | delta_encode | def delta_encode(data, axis=-1, out=None):
"""Encode Delta."""
if isinstance(data, (bytes, bytearray)):
data = numpy.frombuffer(data, dtype='u1')
diff = numpy.diff(data, axis=0)
return numpy.insert(diff, 0, data[0]).tobytes()
dtype = data.dtype
if dtype.kind == 'f':
data... | python | def delta_encode(data, axis=-1, out=None):
"""Encode Delta."""
if isinstance(data, (bytes, bytearray)):
data = numpy.frombuffer(data, dtype='u1')
diff = numpy.diff(data, axis=0)
return numpy.insert(diff, 0, data[0]).tobytes()
dtype = data.dtype
if dtype.kind == 'f':
data... | [
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nion-software/nionswift-io | nionswift_plugin/TIFF_IO/tifffile.py | delta_decode | def delta_decode(data, axis=-1, out=None):
"""Decode Delta."""
if out is not None and not out.flags.writeable:
out = None
if isinstance(data, (bytes, bytearray)):
data = numpy.frombuffer(data, dtype='u1')
return numpy.cumsum(data, axis=0, dtype='u1', out=out).tobytes()
if data.dt... | python | def delta_decode(data, axis=-1, out=None):
"""Decode Delta."""
if out is not None and not out.flags.writeable:
out = None
if isinstance(data, (bytes, bytearray)):
data = numpy.frombuffer(data, dtype='u1')
return numpy.cumsum(data, axis=0, dtype='u1', out=out).tobytes()
if data.dt... | [
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nion-software/nionswift-io | nionswift_plugin/TIFF_IO/tifffile.py | bitorder_decode | def bitorder_decode(data, out=None, _bitorder=[]):
"""Reverse bits in each byte of byte string or numpy array.
Decode data where pixels with lower column values are stored in the
lower-order bits of the bytes (TIFF FillOrder is LSB2MSB).
Parameters
----------
data : byte string or ndarray
... | python | def bitorder_decode(data, out=None, _bitorder=[]):
"""Reverse bits in each byte of byte string or numpy array.
Decode data where pixels with lower column values are stored in the
lower-order bits of the bytes (TIFF FillOrder is LSB2MSB).
Parameters
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data : byte string or ndarray
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Decode data where pixels with lower column values are stored in the
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nion-software/nionswift-io | nionswift_plugin/TIFF_IO/tifffile.py | packints_decode | def packints_decode(data, dtype, numbits, runlen=0, out=None):
"""Decompress byte string to array of integers.
This implementation only handles itemsizes 1, 8, 16, 32, and 64 bits.
Install the imagecodecs package for decoding other integer sizes.
Parameters
----------
data : byte str
D... | python | def packints_decode(data, dtype, numbits, runlen=0, out=None):
"""Decompress byte string to array of integers.
This implementation only handles itemsizes 1, 8, 16, 32, and 64 bits.
Install the imagecodecs package for decoding other integer sizes.
Parameters
----------
data : byte str
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nion-software/nionswift-io | nionswift_plugin/TIFF_IO/tifffile.py | apply_colormap | def apply_colormap(image, colormap, contig=True):
"""Return palette-colored image.
The image values are used to index the colormap on axis 1. The returned
image is of shape image.shape+colormap.shape[0] and dtype colormap.dtype.
Parameters
----------
image : numpy.ndarray
Indexes into ... | python | def apply_colormap(image, colormap, contig=True):
"""Return palette-colored image.
The image values are used to index the colormap on axis 1. The returned
image is of shape image.shape+colormap.shape[0] and dtype colormap.dtype.
Parameters
----------
image : numpy.ndarray
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image : numpy.ndarray
Indexes into the colormap.
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nion-software/nionswift-io | nionswift_plugin/TIFF_IO/tifffile.py | reorient | def reorient(image, orientation):
"""Return reoriented view of image array.
Parameters
----------
image : numpy.ndarray
Non-squeezed output of asarray() functions.
Axes -3 and -2 must be image length and width respectively.
orientation : int or str
One of TIFF.ORIENTATION na... | python | def reorient(image, orientation):
"""Return reoriented view of image array.
Parameters
----------
image : numpy.ndarray
Non-squeezed output of asarray() functions.
Axes -3 and -2 must be image length and width respectively.
orientation : int or str
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Non-squeezed output of asarray() functions.
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orientation : int or str
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nion-software/nionswift-io | nionswift_plugin/TIFF_IO/tifffile.py | repeat_nd | def repeat_nd(a, repeats):
"""Return read-only view into input array with elements repeated.
Zoom nD image by integer factors using nearest neighbor interpolation
(box filter).
Parameters
----------
a : array_like
Input array.
repeats : sequence of int
The number of repetit... | python | def repeat_nd(a, repeats):
"""Return read-only view into input array with elements repeated.
Zoom nD image by integer factors using nearest neighbor interpolation
(box filter).
Parameters
----------
a : array_like
Input array.
repeats : sequence of int
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nion-software/nionswift-io | nionswift_plugin/TIFF_IO/tifffile.py | reshape_nd | def reshape_nd(data_or_shape, ndim):
"""Return image array or shape with at least ndim dimensions.
Prepend 1s to image shape as necessary.
>>> reshape_nd(numpy.empty(0), 1).shape
(0,)
>>> reshape_nd(numpy.empty(1), 2).shape
(1, 1)
>>> reshape_nd(numpy.empty((2, 3)), 3).shape
(1, 2, 3)
... | python | def reshape_nd(data_or_shape, ndim):
"""Return image array or shape with at least ndim dimensions.
Prepend 1s to image shape as necessary.
>>> reshape_nd(numpy.empty(0), 1).shape
(0,)
>>> reshape_nd(numpy.empty(1), 2).shape
(1, 1)
>>> reshape_nd(numpy.empty((2, 3)), 3).shape
(1, 2, 3)
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nion-software/nionswift-io | nionswift_plugin/TIFF_IO/tifffile.py | squeeze_axes | def squeeze_axes(shape, axes, skip=None):
"""Return shape and axes with single-dimensional entries removed.
Remove unused dimensions unless their axes are listed in 'skip'.
>>> squeeze_axes((5, 1, 2, 1, 1), 'TZYXC')
((5, 2, 1), 'TYX')
"""
if len(shape) != len(axes):
raise ValueError('... | python | def squeeze_axes(shape, axes, skip=None):
"""Return shape and axes with single-dimensional entries removed.
Remove unused dimensions unless their axes are listed in 'skip'.
>>> squeeze_axes((5, 1, 2, 1, 1), 'TZYXC')
((5, 2, 1), 'TYX')
"""
if len(shape) != len(axes):
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