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gophish/api-client-python | gophish/api/campaigns.py | API.summary | def summary(self, campaign_id=None):
""" Returns the campaign summary """
resource_cls = CampaignSummary
single_resource = False
if not campaign_id:
resource_cls = CampaignSummaries
single_resource = True
return super(API, self).get(
resource... | python | def summary(self, campaign_id=None):
""" Returns the campaign summary """
resource_cls = CampaignSummary
single_resource = False
if not campaign_id:
resource_cls = CampaignSummaries
single_resource = True
return super(API, self).get(
resource... | [
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gophish/api-client-python | gophish/api/campaigns.py | API.results | def results(self, campaign_id):
""" Returns just the results for a given campaign """
return super(API, self).get(
resource_id=campaign_id,
resource_action='results',
resource_cls=CampaignResults) | python | def results(self, campaign_id):
""" Returns just the results for a given campaign """
return super(API, self).get(
resource_id=campaign_id,
resource_action='results',
resource_cls=CampaignResults) | [
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gunthercox/jsondb | jsondb/db.py | Database.set_path | def set_path(self, file_path):
"""
Set the path of the database.
Create the file if it does not exist.
"""
if not file_path:
self.read_data = self.memory_read
self.write_data = self.memory_write
elif not is_valid(file_path):
self.write_... | python | def set_path(self, file_path):
"""
Set the path of the database.
Create the file if it does not exist.
"""
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self.read_data = self.memory_read
self.write_data = self.memory_write
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gunthercox/jsondb | jsondb/db.py | Database.delete | def delete(self, key):
"""
Removes the specified key from the database.
"""
obj = self._get_content()
obj.pop(key, None)
self.write_data(self.path, obj) | python | def delete(self, key):
"""
Removes the specified key from the database.
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obj = self._get_content()
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gunthercox/jsondb | jsondb/db.py | Database.data | def data(self, **kwargs):
"""
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A dictionary can be passed in as well.
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gunthercox/jsondb | jsondb/db.py | Database.filter | def filter(self, filter_arguments):
"""
Takes a dictionary of filter parameters.
Return a list of objects based on a list of parameters.
"""
results = self._get_content()
# Filter based on a dictionary of search parameters
if isinstance(filter_arguments, dict):
... | python | def filter(self, filter_arguments):
"""
Takes a dictionary of filter parameters.
Return a list of objects based on a list of parameters.
"""
results = self._get_content()
# Filter based on a dictionary of search parameters
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gunthercox/jsondb | jsondb/db.py | Database.drop | def drop(self):
"""
Remove the database by deleting the JSON file.
"""
import os
if self.path:
if os.path.exists(self.path):
os.remove(self.path)
else:
# Clear the in-memory data if there is no file path
self._data = {} | python | def drop(self):
"""
Remove the database by deleting the JSON file.
"""
import os
if self.path:
if os.path.exists(self.path):
os.remove(self.path)
else:
# Clear the in-memory data if there is no file path
self._data = {} | [
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gunthercox/jsondb | jsondb/file_writer.py | read_data | def read_data(file_path):
"""
Reads a file and returns a json encoded representation of the file.
"""
if not is_valid(file_path):
write_data(file_path, {})
db = open_file_for_reading(file_path)
content = db.read()
obj = decode(content)
db.close()
return obj | python | def read_data(file_path):
"""
Reads a file and returns a json encoded representation of the file.
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write_data(file_path, {})
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db.close()
return obj | [
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gunthercox/jsondb | jsondb/file_writer.py | write_data | def write_data(path, obj):
"""
Writes to a file and returns the updated file content.
"""
with open_file_for_writing(path) as db:
db.write(encode(obj))
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gunthercox/jsondb | jsondb/file_writer.py | is_valid | def is_valid(file_path):
"""
Check to see if a file exists or is empty.
"""
from os import path, stat
can_open = False
try:
with open(file_path) as fp:
can_open = True
except IOError:
return False
is_file = path.isfile(file_path)
return path.exists(fil... | python | def is_valid(file_path):
"""
Check to see if a file exists or is empty.
"""
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can_open = False
try:
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can_open = True
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nschloe/meshzoo | meshzoo/helpers.py | _refine | def _refine(node_coords, cells_nodes, edge_nodes, cells_edges):
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nschloe/meshzoo | meshzoo/helpers.py | create_edges | def create_edges(cells_nodes):
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nschloe/meshzoo | meshzoo/helpers.py | plot2d | def plot2d(points, cells, mesh_color="k", show_axes=False):
"""Plot a 2D mesh using matplotlib.
"""
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from matplotlib.collections import LineCollection
fig = plt.figure()
ax = fig.gca()
plt.axis("equal")
if not show_axes:
ax.set_axis_off()
xmin... | python | def plot2d(points, cells, mesh_color="k", show_axes=False):
"""Plot a 2D mesh using matplotlib.
"""
import matplotlib.pyplot as plt
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fig = plt.figure()
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shazow/workerpool | samples/blockingworker.py | BlockingWorkerPool.put | def put(self, job, result):
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shazow/workerpool | samples/blockingworker.py | BlockingWorkerPool.contract | def contract(self, jobs, result):
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nschloe/meshzoo | meshzoo/cube.py | cube | def cube(
xmin=0.0, xmax=1.0, ymin=0.0, ymax=1.0, zmin=0.0, zmax=1.0, nx=11, ny=11, nz=11
):
"""Canonical tetrahedrization of the cube.
Input:
Edge lenghts of the cube
Number of nodes along the edges.
"""
# Generate suitable ranges for parametrization
x_range = numpy.linspace(xmin, xmax,... | python | def cube(
xmin=0.0, xmax=1.0, ymin=0.0, ymax=1.0, zmin=0.0, zmax=1.0, nx=11, ny=11, nz=11
):
"""Canonical tetrahedrization of the cube.
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Edge lenghts of the cube
Number of nodes along the edges.
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# Generate suitable ranges for parametrization
x_range = numpy.linspace(xmin, xmax,... | [
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jwass/geojsonio.py | geojsonio/geojsonio.py | display | def display(contents, domain=DEFAULT_DOMAIN, force_gist=False):
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Open a web browser pointing to geojson.io with the specified content.
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jwass/geojsonio.py | geojsonio/geojsonio.py | embed | def embed(contents='', width='100%', height=512, *args, **kwargs):
"""
Embed geojson.io in an iframe in Jupyter/IPython notebook.
Parameters
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contents - see make_url()
width - string, default '100%' - width of the iframe
height - string / int, default 512 - height of the iframe
... | python | def embed(contents='', width='100%', height=512, *args, **kwargs):
"""
Embed geojson.io in an iframe in Jupyter/IPython notebook.
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jwass/geojsonio.py | geojsonio/geojsonio.py | make_geojson | def make_geojson(contents):
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GeoJSON string
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jwass/geojsonio.py | geojsonio/geojsonio.py | data_url | def data_url(contents, domain=DEFAULT_DOMAIN):
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Return the URL for embedding the GeoJSON data in the URL hash
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contents - string of GeoJSON
domain - string, default http://geojson.io
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Return the URL for embedding the GeoJSON data in the URL hash
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contents - string of GeoJSON
domain - string, default http://geojson.io
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jwass/geojsonio.py | geojsonio/geojsonio.py | _make_gist | def _make_gist(contents, description='', filename='data.geojson'):
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gist = ghapi.create_gist(description, files)
return gist | python | def _make_gist(contents, description='', filename='data.geojson'):
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nschloe/orthopy | orthopy/line_segment/tools.py | clenshaw | def clenshaw(a, alpha, beta, t):
"""Clenshaw's algorithm for evaluating
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See <https://en.wikipedia.org/wiki/Clenshaw_algorithm> for details.
"""
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"""Clenshaw's algorithm for evaluating
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nschloe/orthopy | orthopy/e1r/orth.py | tree | def tree(X, n, alpha=0, symbolic=False):
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"""
args = recurrence_coefficients(n, alpha=alpha, symbolic=symbolic)
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nschloe/orthopy | orthopy/line_segment/recurrence_coefficients.py | jacobi | def jacobi(n, alpha, beta, standardization, symbolic=False):
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nschloe/orthopy | orthopy/disk/tools.py | plot | def plot(f, lcar=1.0e-1):
"""Plot function over a disk.
"""
import matplotlib
import matplotlib.pyplot as plt
import pygmsh
geom = pygmsh.built_in.Geometry()
geom.add_circle([0.0, 0.0, 0.0], 1.0, lcar, num_sections=4, compound=True)
points, cells, _, _, _ = pygmsh.generate_mesh(geom, ve... | python | def plot(f, lcar=1.0e-1):
"""Plot function over a disk.
"""
import matplotlib
import matplotlib.pyplot as plt
import pygmsh
geom = pygmsh.built_in.Geometry()
geom.add_circle([0.0, 0.0, 0.0], 1.0, lcar, num_sections=4, compound=True)
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nschloe/orthopy | orthopy/ncube/orth.py | tree | def tree(X, n, symbolic=False):
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nschloe/orthopy | orthopy/tools.py | line_evaluate | def line_evaluate(t, p0, a, b, c):
"""Evaluate the orthogonal polynomial defined by its recurrence coefficients
a, b, and c at the point(s) t.
"""
vals1 = numpy.zeros_like(t, dtype=int)
# The order is important here; see
# <https://github.com/sympy/sympy/issues/13637>.
vals2 = numpy.ones_lik... | python | def line_evaluate(t, p0, a, b, c):
"""Evaluate the orthogonal polynomial defined by its recurrence coefficients
a, b, and c at the point(s) t.
"""
vals1 = numpy.zeros_like(t, dtype=int)
# The order is important here; see
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nschloe/orthopy | orthopy/triangle/tools.py | plot | def plot(corners, f, n=100):
"""Plot function over a triangle.
"""
import matplotlib.tri
import matplotlib.pyplot as plt
# discretization points
def partition(boxes, balls):
# <https://stackoverflow.com/a/36748940/353337>
def rec(boxes, balls, parent=tuple()):
if box... | python | def plot(corners, f, n=100):
"""Plot function over a triangle.
"""
import matplotlib.tri
import matplotlib.pyplot as plt
# discretization points
def partition(boxes, balls):
# <https://stackoverflow.com/a/36748940/353337>
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learningequality/le-utils | le_utils/constants/languages.py | _iget | def _iget(key, lookup_dict):
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learningequality/le-utils | le_utils/constants/languages.py | getlang_by_name | def getlang_by_name(name):
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Try to lookup a Language object by name, e.g. 'English', in internal language list.
Returns None if lookup by language name fails in resources/languagelookup.json.
"""
direct_match = _iget(name, _LANGUAGE_NAME_LOOKUP)
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"""
Try to lookup a Language object by name, e.g. 'English', in internal language list.
Returns None if lookup by language name fails in resources/languagelookup.json.
"""
direct_match = _iget(name, _LANGUAGE_NAME_LOOKUP)
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learningequality/le-utils | le_utils/constants/languages.py | getlang_by_native_name | def getlang_by_native_name(native_name):
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Try to lookup a Language object by native_name, e.g. 'English', in internal language list.
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Try to lookup a Language object by native_name, e.g. 'English', in internal language list.
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learningequality/le-utils | le_utils/constants/languages.py | getlang_by_alpha2 | def getlang_by_alpha2(code):
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nschloe/orthopy | orthopy/sphere/tools.py | write | def write(filename, f):
"""Write a function `f` defined in terms of spherical coordinates to a file.
"""
import meshio
import meshzoo
points, cells = meshzoo.iso_sphere(5)
# get spherical coordinates from points
polar = numpy.arccos(points[:, 2])
azimuthal = numpy.arctan2(points[:, 1], ... | python | def write(filename, f):
"""Write a function `f` defined in terms of spherical coordinates to a file.
"""
import meshio
import meshzoo
points, cells = meshzoo.iso_sphere(5)
# get spherical coordinates from points
polar = numpy.arccos(points[:, 2])
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nschloe/orthopy | orthopy/sphere/orth.py | tree_sph | def tree_sph(polar, azimuthal, n, standardization, symbolic=False):
"""Evaluate all spherical harmonics of degree at most `n` at angles `polar`,
`azimuthal`.
"""
cos = numpy.vectorize(sympy.cos) if symbolic else numpy.cos
# Conventions from
# <https://en.wikipedia.org/wiki/Spherical_harmonics#O... | python | def tree_sph(polar, azimuthal, n, standardization, symbolic=False):
"""Evaluate all spherical harmonics of degree at most `n` at angles `polar`,
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"""
cos = numpy.vectorize(sympy.cos) if symbolic else numpy.cos
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nschloe/orthopy | orthopy/line_segment/orth.py | tree_alp | def tree_alp(
x, n, standardization, phi=None, with_condon_shortley_phase=True, symbolic=False
):
"""Evaluates the entire tree of associated Legendre polynomials up to depth
n.
There are many recurrence relations that can be used to construct the
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x, n, standardization, phi=None, with_condon_shortley_phase=True, symbolic=False
):
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nschloe/orthopy | orthopy/disk/orth.py | tree | def tree(X, n, symbolic=False):
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guinslym/pyexifinfo | pyexifinfo/pyexifinfo.py | check_if_this_file_exist | def check_if_this_file_exist(filename):
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This function will check if the given filename
actually exists and if it's not a Directory
Arguments:
filename {string} -- filename
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guinslym/pyexifinfo | pyexifinfo/pyexifinfo.py | command_line | def command_line(cmd):
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keyword arguments:
cmd = a list
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"""
try:
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s = s.stdout.read()
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except subproce... | python | def command_line(cmd):
"""Handle the command line call
keyword arguments:
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guinslym/pyexifinfo | pyexifinfo/pyexifinfo.py | information | def information(filename):
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[JSON] -- Return a JSON object
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[unicode] -- unicode string
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message {str} -- [the message string] (default: {"-"})
"""
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Keyword Arguments:
message {str} -- [the message string] (default: {"-"})
"""
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guinslym/pyexifinfo | setup.py | check_if_exiftool_is_already_installed | def check_if_exiftool_is_already_installed():
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False if not
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brianhie/scanorama | bin/unsupervised.py | silhouette_score | def silhouette_score(X, labels, metric='euclidean', sample_size=None,
random_state=None, **kwds):
"""Compute the mean Silhouette Coefficient of all samples.
The Silhouette Coefficient is calculated using the mean intra-cluster
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brianhie/scanorama | bin/unsupervised.py | silhouette_samples | def silhouette_samples(X, labels, metric='euclidean', **kwds):
"""Compute the Silhouette Coefficient for each sample.
The Silhouette Coefficient is a measure of how well samples are clustered
with samples that are similar to themselves. Clustering models with a high
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brianhie/scanorama | bin/unsupervised.py | calinski_harabaz_score | def calinski_harabaz_score(X, labels):
"""Compute the Calinski and Harabaz score.
The score is defined as ratio between the within-cluster dispersion and
the between-cluster dispersion.
Read more in the :ref:`User Guide <calinski_harabaz_index>`.
Parameters
----------
X : array-like, shap... | python | def calinski_harabaz_score(X, labels):
"""Compute the Calinski and Harabaz score.
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brianhie/scanorama | scanorama/utils.py | handle_zeros_in_scale | def handle_zeros_in_scale(scale, copy=True):
''' Makes sure that whenever scale is zero, we handle it correctly.
This happens in most scalers when we have constant features.
Adapted from sklearn.preprocessing.data'''
# if we are fitting on 1D arrays, scale might be a scalar
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... | python | def handle_zeros_in_scale(scale, copy=True):
''' Makes sure that whenever scale is zero, we handle it correctly.
This happens in most scalers when we have constant features.
Adapted from sklearn.preprocessing.data'''
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brianhie/scanorama | scanorama/t_sne_approx.py | _joint_probabilities | def _joint_probabilities(distances, desired_perplexity, verbose):
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brianhie/scanorama | scanorama/t_sne_approx.py | _joint_probabilities_nn | def _joint_probabilities_nn(distances, neighbors, desired_perplexity, verbose):
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brianhie/scanorama | scanorama/t_sne_approx.py | _kl_divergence | def _kl_divergence(params, P, degrees_of_freedom, n_samples, n_components,
skip_num_points=0):
"""t-SNE objective function: gradient of the KL divergence
of p_ijs and q_ijs and the absolute error.
Parameters
----------
params : array, shape (n_params,)
Unraveled embedding... | python | def _kl_divergence(params, P, degrees_of_freedom, n_samples, n_components,
skip_num_points=0):
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brianhie/scanorama | scanorama/t_sne_approx.py | _kl_divergence_bh | def _kl_divergence_bh(params, P, degrees_of_freedom, n_samples, n_components,
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brianhie/scanorama | scanorama/t_sne_approx.py | _gradient_descent | def _gradient_descent(objective, p0, it, n_iter,
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momentum=0.8, learning_rate=200.0, min_gain=0.01,
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brianhie/scanorama | scanorama/t_sne_approx.py | trustworthiness | def trustworthiness(X, X_embedded, n_neighbors=5, precomputed=False):
"""Expresses to what extent the local structure is retained.
The trustworthiness is within [0, 1]. It is defined as
.. math::
T(k) = 1 - \frac{2}{nk (2n - 3k - 1)} \sum^n_{i=1}
\sum_{j \in U^{(k)}_i} (r(i, j) - k)
... | python | def trustworthiness(X, X_embedded, n_neighbors=5, precomputed=False):
"""Expresses to what extent the local structure is retained.
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.. math::
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brianhie/scanorama | scanorama/t_sne_approx.py | TSNEApprox._fit | def _fit(self, X, skip_num_points=0):
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brianhie/scanorama | scanorama/t_sne_approx.py | TSNEApprox._tsne | def _tsne(self, P, degrees_of_freedom, n_samples, random_state, X_embedded,
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brianhie/scanorama | scanorama/t_sne_approx.py | TSNEApprox.fit_transform | def fit_transform(self, X, y=None):
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Parameters
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X : array, shape (n_samples, n_features) or (n_samples, n_samples)
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brianhie/scanorama | scanorama/scanorama.py | correct | def correct(datasets_full, genes_list, return_dimred=False,
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dimred=DIMRED, approx=APPROX, sigma=SIGMA, alpha=ALPHA, knn=KNN,
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geosketch=False, geosketch_max=20000):
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batch_size=BATCH_SIZE, verbose=VERBOSE, ds_names=None,
dimred=DIMRED, approx=APPROX, sigma=SIGMA, alpha=ALPHA, knn=KNN,
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brianhie/scanorama | scanorama/scanorama.py | integrate | def integrate(datasets_full, genes_list, batch_size=BATCH_SIZE,
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brianhie/scanorama | scanorama/scanorama.py | correct_scanpy | def correct_scanpy(adatas, **kwargs):
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brianhie/scanorama | scanorama/scanorama.py | integrate_scanpy | def integrate_scanpy(adatas, **kwargs):
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Data sets to integrate.
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johntfoster/bspline | bspline/splinelab.py | augknt | def augknt(knots, order):
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Python list or rank-1 array, the original knot vector (without endpoint repeats)
order:
int, >= 0, order of spline
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list_of_knots:
rank-1 array that has (`order` + 1) copies of ``knots[0]``, then ``k... | python | def augknt(knots, order):
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knots:
Python list or rank-1 array, the original knot vector (without endpoint repeats)
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int, >= 0, order of spline
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johntfoster/bspline | bspline/splinelab.py | aveknt | def aveknt(t, k):
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t:
Python list or rank-1 array
k:
int, >= 2, how many successive elements to average
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"""Compute the running average of `k` successive elements of `t`. Return the averaged array.
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t:
Python list or rank-1 array
k:
int, >= 2, how many successive elements to average
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johntfoster/bspline | bspline/splinelab.py | aptknt | def aptknt(tau, order):
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Minimal emulation of MATLAB's ``aptknt``.
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Note that this is only possible when ``len(tau)`` >= `order` + 1.
... | python | def aptknt(tau, order):
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Minimal emulation of MATLAB's ``aptknt``.
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johntfoster/bspline | bspline/splinelab.py | knt2mlt | def knt2mlt(t):
"""Count multiplicities of elements in a sorted list or rank-1 array.
Minimal emulation of MATLAB's ``knt2mlt``.
Parameters:
t:
Python list or rank-1 array. Must be sorted!
Returns:
out
rank-1 array such that
out[k] = #{ t[i] == t[k] for i < k }
Example:
If ``... | python | def knt2mlt(t):
"""Count multiplicities of elements in a sorted list or rank-1 array.
Minimal emulation of MATLAB's ``knt2mlt``.
Parameters:
t:
Python list or rank-1 array. Must be sorted!
Returns:
out
rank-1 array such that
out[k] = #{ t[i] == t[k] for i < k }
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johntfoster/bspline | bspline/splinelab.py | spcol | def spcol(knots, order, tau):
"""Return collocation matrix.
Minimal emulation of MATLAB's ``spcol``.
Parameters:
knots:
rank-1 array, knot vector (with appropriately repeated endpoints; see `augknt`, `aptknt`)
order:
int, >= 0, order of spline
tau:
rank-1 array, collocation sit... | python | def spcol(knots, order, tau):
"""Return collocation matrix.
Minimal emulation of MATLAB's ``spcol``.
Parameters:
knots:
rank-1 array, knot vector (with appropriately repeated endpoints; see `augknt`, `aptknt`)
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int, >= 0, order of spline
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johntfoster/bspline | bspline/bspline.py | Bspline.__basis0 | def __basis0(self, xi):
"""Order zero basis (for internal use)."""
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xi < self.knot_vector[1:]],axis=0), 1.0, 0.0) | python | def __basis0(self, xi):
"""Order zero basis (for internal use)."""
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johntfoster/bspline | bspline/bspline.py | Bspline.__basis | def __basis(self, xi, p, compute_derivatives=False):
"""Recursive Cox - de Boor function (for internal use).
Compute basis functions and optionally their first derivatives.
"""
if p == 0:
return self.__basis0(xi)
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"""Recursive Cox - de Boor function (for internal use).
Compute basis functions and optionally their first derivatives.
"""
if p == 0:
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johntfoster/bspline | bspline/bspline.py | Bspline.d | def d(self, xi):
"""Convenience function to compute first derivative of basis functions. 'Memoized' for speed."""
return self.__basis(xi, self.p, compute_derivatives=True) | python | def d(self, xi):
"""Convenience function to compute first derivative of basis functions. 'Memoized' for speed."""
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johntfoster/bspline | bspline/bspline.py | Bspline.plot | def plot(self):
"""Plot basis functions over full range of knots.
Convenience function. Requires matplotlib.
"""
try:
import matplotlib.pyplot as plt
except ImportError:
from sys import stderr
print("ERROR: matplotlib.pyplot not found, matplo... | python | def plot(self):
"""Plot basis functions over full range of knots.
Convenience function. Requires matplotlib.
"""
try:
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johntfoster/bspline | bspline/bspline.py | Bspline.__diff_internal | def __diff_internal(self):
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This preserves the Bspline object nature of the data, enabling recursive implementation
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The value of the first derivative of `B` at a point `x` ... | python | def __diff_internal(self):
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johntfoster/bspline | bspline/bspline.py | Bspline.diff | def diff(self, order=1):
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Parameters:
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int, >= 0
Returns:
**lambda** `x`: ... that evaluates the `order`-th derivative of `B` at the point `x`.
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"""Differentiate a B-spline `order` number of times.
Parameters:
order:
int, >= 0
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**lambda** `x`: ... that evaluates the `order`-th derivative of `B` at the point `x`.
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johntfoster/bspline | bspline/bspline.py | Bspline.collmat | def collmat(self, tau, deriv_order=0):
"""Compute collocation matrix.
Parameters:
tau:
Python list or rank-1 array, collocation sites
deriv_order:
int, >=0, order of derivative for which to compute the collocation matrix.
The default is 0, which means the function value itself.
... | python | def collmat(self, tau, deriv_order=0):
"""Compute collocation matrix.
Parameters:
tau:
Python list or rank-1 array, collocation sites
deriv_order:
int, >=0, order of derivative for which to compute the collocation matrix.
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disqus/disqus-python | disqusapi/utils.py | build_interfaces_by_method | def build_interfaces_by_method(interfaces):
"""
Create new dictionary from INTERFACES hashed by method then
the endpoints name. For use when using the disqusapi by the
method interface instead of the endpoint interface. For
instance:
'blacklists': {
'add': {
'formats': ['jso... | python | def build_interfaces_by_method(interfaces):
"""
Create new dictionary from INTERFACES hashed by method then
the endpoints name. For use when using the disqusapi by the
method interface instead of the endpoint interface. For
instance:
'blacklists': {
'add': {
'formats': ['jso... | [
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disqus/disqus-python | disqusapi/utils.py | get_normalized_request_string | def get_normalized_request_string(method, url, nonce, params, ext='', body_hash=None):
"""
Returns a normalized request string as described iN OAuth2 MAC spec.
http://tools.ietf.org/html/draft-ietf-oauth-v2-http-mac-00#section-3.3.1
"""
urlparts = urlparse.urlparse(url)
if urlparts.query:
... | python | def get_normalized_request_string(method, url, nonce, params, ext='', body_hash=None):
"""
Returns a normalized request string as described iN OAuth2 MAC spec.
http://tools.ietf.org/html/draft-ietf-oauth-v2-http-mac-00#section-3.3.1
"""
urlparts = urlparse.urlparse(url)
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disqus/disqus-python | disqusapi/utils.py | get_body_hash | def get_body_hash(params):
"""
Returns BASE64 ( HASH (text) ) as described in OAuth2 MAC spec.
http://tools.ietf.org/html/draft-ietf-oauth-v2-http-mac-00#section-3.2
"""
norm_params = get_normalized_params(params)
return binascii.b2a_base64(hashlib.sha1(norm_params).digest())[:-1] | python | def get_body_hash(params):
"""
Returns BASE64 ( HASH (text) ) as described in OAuth2 MAC spec.
http://tools.ietf.org/html/draft-ietf-oauth-v2-http-mac-00#section-3.2
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norm_params = get_normalized_params(params)
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disqus/disqus-python | disqusapi/utils.py | get_mac_signature | def get_mac_signature(api_secret, norm_request_string):
"""
Returns HMAC-SHA1 (api secret, normalized request string)
"""
hashed = hmac.new(str(api_secret), norm_request_string, hashlib.sha1)
return binascii.b2a_base64(hashed.digest())[:-1] | python | def get_mac_signature(api_secret, norm_request_string):
"""
Returns HMAC-SHA1 (api secret, normalized request string)
"""
hashed = hmac.new(str(api_secret), norm_request_string, hashlib.sha1)
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codesardine/Jade-Application-Kit | j/Api.py | Fs.open_file | def open_file(self, access_mode="r"):
"""
input: filename and path.
output: file contents.
"""
try:
with open(self, access_mode, encoding='utf-8') as file:
return file.read()
except IOError:
print(self + " File not found."... | python | def open_file(self, access_mode="r"):
"""
input: filename and path.
output: file contents.
"""
try:
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bhmm/bhmm | bhmm/estimators/maximum_likelihood.py | MaximumLikelihoodEstimator._forward_backward | def _forward_backward(self, itraj):
"""
Estimation step: Runs the forward-back algorithm on trajectory with index itraj
Parameters
----------
itraj : int
index of the observation trajectory to process
Results
-------
logprob : float
... | python | def _forward_backward(self, itraj):
"""
Estimation step: Runs the forward-back algorithm on trajectory with index itraj
Parameters
----------
itraj : int
index of the observation trajectory to process
Results
-------
logprob : float
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bhmm/bhmm | bhmm/estimators/maximum_likelihood.py | MaximumLikelihoodEstimator._update_model | def _update_model(self, gammas, count_matrices, maxiter=10000000):
"""
Maximization step: Updates the HMM model given the hidden state assignment and count matrices
Parameters
----------
gamma : [ ndarray(T,N, dtype=float) ]
list of state probabilities for each traje... | python | def _update_model(self, gammas, count_matrices, maxiter=10000000):
"""
Maximization step: Updates the HMM model given the hidden state assignment and count matrices
Parameters
----------
gamma : [ ndarray(T,N, dtype=float) ]
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bhmm/bhmm | bhmm/estimators/maximum_likelihood.py | MaximumLikelihoodEstimator.compute_viterbi_paths | def compute_viterbi_paths(self):
"""
Computes the viterbi paths using the current HMM model
"""
# get parameters
K = len(self._observations)
A = self._hmm.transition_matrix
pi = self._hmm.initial_distribution
# compute viterbi path for each trajectory
... | python | def compute_viterbi_paths(self):
"""
Computes the viterbi paths using the current HMM model
"""
# get parameters
K = len(self._observations)
A = self._hmm.transition_matrix
pi = self._hmm.initial_distribution
# compute viterbi path for each trajectory
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bhmm/bhmm | bhmm/estimators/maximum_likelihood.py | MaximumLikelihoodEstimator.fit | def fit(self):
"""
Maximum-likelihood estimation of the HMM using the Baum-Welch algorithm
Returns
-------
model : HMM
The maximum likelihood HMM model.
"""
logger().info("=================================================================")
lo... | python | def fit(self):
"""
Maximum-likelihood estimation of the HMM using the Baum-Welch algorithm
Returns
-------
model : HMM
The maximum likelihood HMM model.
"""
logger().info("=================================================================")
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bhmm/bhmm | bhmm/_external/sklearn/mixture/gmm.py | sample_gaussian | def sample_gaussian(mean, covar, covariance_type='diag', n_samples=1,
random_state=None):
"""Generate random samples from a Gaussian distribution.
Parameters
----------
mean : array_like, shape (n_features,)
Mean of the distribution.
covar : array_like, optional
... | python | def sample_gaussian(mean, covar, covariance_type='diag', n_samples=1,
random_state=None):
"""Generate random samples from a Gaussian distribution.
Parameters
----------
mean : array_like, shape (n_features,)
Mean of the distribution.
covar : array_like, optional
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bhmm/bhmm | bhmm/_external/sklearn/mixture/gmm.py | _covar_mstep_diag | def _covar_mstep_diag(gmm, X, responsibilities, weighted_X_sum, norm,
min_covar):
"""Performing the covariance M step for diagonal cases"""
avg_X2 = np.dot(responsibilities.T, X * X) * norm
avg_means2 = gmm.means_ ** 2
avg_X_means = gmm.means_ * weighted_X_sum * norm
return avg... | python | def _covar_mstep_diag(gmm, X, responsibilities, weighted_X_sum, norm,
min_covar):
"""Performing the covariance M step for diagonal cases"""
avg_X2 = np.dot(responsibilities.T, X * X) * norm
avg_means2 = gmm.means_ ** 2
avg_X_means = gmm.means_ * weighted_X_sum * norm
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bhmm/bhmm | bhmm/_external/sklearn/mixture/gmm.py | _covar_mstep_spherical | def _covar_mstep_spherical(*args):
"""Performing the covariance M step for spherical cases"""
cv = _covar_mstep_diag(*args)
return np.tile(cv.mean(axis=1)[:, np.newaxis], (1, cv.shape[1])) | python | def _covar_mstep_spherical(*args):
"""Performing the covariance M step for spherical cases"""
cv = _covar_mstep_diag(*args)
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bhmm/bhmm | bhmm/_external/sklearn/mixture/gmm.py | _covar_mstep_full | def _covar_mstep_full(gmm, X, responsibilities, weighted_X_sum, norm,
min_covar):
"""Performing the covariance M step for full cases"""
# Eq. 12 from K. Murphy, "Fitting a Conditional Linear Gaussian
# Distribution"
n_features = X.shape[1]
cv = np.empty((gmm.n_components, n_fea... | python | def _covar_mstep_full(gmm, X, responsibilities, weighted_X_sum, norm,
min_covar):
"""Performing the covariance M step for full cases"""
# Eq. 12 from K. Murphy, "Fitting a Conditional Linear Gaussian
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bhmm/bhmm | bhmm/_external/sklearn/mixture/gmm.py | GMM._get_covars | def _get_covars(self):
"""Covariance parameters for each mixture component.
The shape depends on `cvtype`::
(`n_states`, 'n_features') if 'spherical',
(`n_features`, `n_features`) if 'tied',
(`n_states`, `n_features`) if 'di... | python | def _get_covars(self):
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The shape depends on `cvtype`::
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bhmm/bhmm | bhmm/_external/sklearn/mixture/gmm.py | GMM._set_covars | def _set_covars(self, covars):
"""Provide values for covariance"""
covars = np.asarray(covars)
_validate_covars(covars, self.covariance_type, self.n_components)
self.covars_ = covars | python | def _set_covars(self, covars):
"""Provide values for covariance"""
covars = np.asarray(covars)
_validate_covars(covars, self.covariance_type, self.n_components)
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bhmm/bhmm | bhmm/_external/sklearn/mixture/gmm.py | GMM.score_samples | def score_samples(self, X):
"""Return the per-sample likelihood of the data under the model.
Compute the log probability of X under the model and
return the posterior distribution (responsibilities) of each
mixture component for each element of X.
Parameters
----------
... | python | def score_samples(self, X):
"""Return the per-sample likelihood of the data under the model.
Compute the log probability of X under the model and
return the posterior distribution (responsibilities) of each
mixture component for each element of X.
Parameters
----------
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bhmm/bhmm | bhmm/_external/sklearn/mixture/gmm.py | GMM.score | def score(self, X, y=None):
"""Compute the log probability under the model.
Parameters
----------
X : array_like, shape (n_samples, n_features)
List of n_features-dimensional data points. Each row
corresponds to a single data point.
Returns
----... | python | def score(self, X, y=None):
"""Compute the log probability under the model.
Parameters
----------
X : array_like, shape (n_samples, n_features)
List of n_features-dimensional data points. Each row
corresponds to a single data point.
Returns
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