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savvastj/nbashots | nbashots/api.py | TeamLog.update_params | def update_params(self, parameters):
"""Pass in a dictionary to update url parameters for NBA stats API
Parameters
----------
parameters : dict
A dict containing key, value pairs that correspond with NBA stats
API parameters.
Returns
-------
... | python | def update_params(self, parameters):
"""Pass in a dictionary to update url parameters for NBA stats API
Parameters
----------
parameters : dict
A dict containing key, value pairs that correspond with NBA stats
API parameters.
Returns
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savvastj/nbashots | nbashots/api.py | Shots.get_shots | def get_shots(self):
"""Returns the shot chart data as a pandas DataFrame."""
shots = self.response.json()['resultSets'][0]['rowSet']
headers = self.response.json()['resultSets'][0]['headers']
return pd.DataFrame(shots, columns=headers) | python | def get_shots(self):
"""Returns the shot chart data as a pandas DataFrame."""
shots = self.response.json()['resultSets'][0]['rowSet']
headers = self.response.json()['resultSets'][0]['headers']
return pd.DataFrame(shots, columns=headers) | [
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mcuadros/pynats | pynats/connection.py | Connection.connect | def connect(self):
"""
Connect will attempt to connect to the NATS server. The url can
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"""
self._build_socket()
self._connect_socket()
self._build_file_socket()
self._send_connect_msg() | python | def connect(self):
"""
Connect will attempt to connect to the NATS server. The url can
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self._build_socket()
self._connect_socket()
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mcuadros/pynats | pynats/connection.py | Connection.subscribe | def subscribe(self, subject, callback, queue=''):
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Subscribe will express interest in the given subject. The subject can
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subject (string): a string with the subject
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mcuadros/pynats | pynats/connection.py | Connection.unsubscribe | def unsubscribe(self, subscription, max=None):
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mcuadros/pynats | pynats/connection.py | Connection.publish | def publish(self, subject, msg, reply=None):
"""
Publish publishes the data argument to the given subject.
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subject (string): a string with the subject
msg (string): payload string
reply (string): subject used in the reply
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Publish publishes the data argument to the given subject.
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subject (string): a string with the subject
msg (string): payload string
reply (string): subject used in the reply
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mcuadros/pynats | pynats/connection.py | Connection.request | def request(self, subject, callback, msg=None):
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ublish a message with an implicit inbox listener as the reply.
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subject (string): a string with the subject
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mcuadros/pynats | pynats/connection.py | Connection.wait | def wait(self, duration=None, count=0):
"""
Publish publishes the data argument to the given subject.
Args:
duration (float): will wait for the given number of seconds
count (count): stop of wait after n messages from any subject
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"""
Publish publishes the data argument to the given subject.
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savvastj/nbashots | nbashots/charts.py | draw_court | def draw_court(ax=None, color='gray', lw=1, outer_lines=False):
"""Returns an axes with a basketball court drawn onto to it.
This function draws a court based on the x and y-axis values that the NBA
stats API provides for the shot chart data. For example the center of the
hoop is located at the (0,0) ... | python | def draw_court(ax=None, color='gray', lw=1, outer_lines=False):
"""Returns an axes with a basketball court drawn onto to it.
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savvastj/nbashots | nbashots/charts.py | shot_chart | def shot_chart(x, y, kind="scatter", title="", color="b", cmap=None,
xlim=(-250, 250), ylim=(422.5, -47.5),
court_color="gray", court_lw=1, outer_lines=False,
flip_court=False, kde_shade=True, gridsize=None, ax=None,
despine=False, **kwargs):
"""
Retur... | python | def shot_chart(x, y, kind="scatter", title="", color="b", cmap=None,
xlim=(-250, 250), ylim=(422.5, -47.5),
court_color="gray", court_lw=1, outer_lines=False,
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savvastj/nbashots | nbashots/charts.py | shot_chart_jointgrid | def shot_chart_jointgrid(x, y, data=None, joint_type="scatter", title="",
joint_color="b", cmap=None, xlim=(-250, 250),
ylim=(422.5, -47.5), court_color="gray", court_lw=1,
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joint_color="b", cmap=None, xlim=(-250, 250),
ylim=(422.5, -47.5), court_color="gray", court_lw=1,
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savvastj/nbashots | nbashots/charts.py | shot_chart_jointplot | def shot_chart_jointplot(x, y, data=None, kind="scatter", title="", color="b",
cmap=None, xlim=(-250, 250), ylim=(422.5, -47.5),
court_color="gray", court_lw=1, outer_lines=False,
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court_color="gray", court_lw=1, outer_lines=False,
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savvastj/nbashots | nbashots/charts.py | heatmap | def heatmap(x, y, z, title="", cmap=plt.cm.YlOrRd, bins=20,
xlim=(-250, 250), ylim=(422.5, -47.5),
facecolor='lightgray', facecolor_alpha=0.4,
court_color="black", court_lw=0.5, outer_lines=False,
flip_court=False, ax=None, **kwargs):
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xlim=(-250, 250), ylim=(422.5, -47.5),
facecolor='lightgray', facecolor_alpha=0.4,
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savvastj/nbashots | nbashots/charts.py | bokeh_draw_court | def bokeh_draw_court(figure, line_color='gray', line_width=1):
"""Returns a figure with the basketball court lines drawn onto it
This function draws a court based on the x and y-axis values that the NBA
stats API provides for the shot chart data. For example the center of the
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savvastj/nbashots | nbashots/charts.py | bokeh_shot_chart | def bokeh_shot_chart(data, x="LOC_X", y="LOC_Y", fill_color="#1f77b4",
scatter_size=10, fill_alpha=0.4, line_alpha=0.4,
court_line_color='gray', court_line_width=1,
hover_tool=False, tooltips=None, **kwargs):
# TODO: Settings for hover tooltip
"""
... | python | def bokeh_shot_chart(data, x="LOC_X", y="LOC_Y", fill_color="#1f77b4",
scatter_size=10, fill_alpha=0.4, line_alpha=0.4,
court_line_color='gray', court_line_width=1,
hover_tool=False, tooltips=None, **kwargs):
# TODO: Settings for hover tooltip
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vmirly/pyclust | pyclust/_kmedoids.py | _update_centers | def _update_centers(X, membs, n_clusters, distance):
""" Update Cluster Centers:
calculate the mean of feature vectors for each cluster.
distance can be a string or callable.
"""
centers = np.empty(shape=(n_clusters, X.shape[1]), dtype=float)
sse = np.empty(shape=n_clusters, dtype=fl... | python | def _update_centers(X, membs, n_clusters, distance):
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calculate the mean of feature vectors for each cluster.
distance can be a string or callable.
"""
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vmirly/pyclust | pyclust/_kmedoids.py | _kmedoids_run | def _kmedoids_run(X, n_clusters, distance, max_iter, tol, rng):
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"""
membs = np.empty(shape=X.shape[0], dtype=int)
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... | python | def _kmedoids_run(X, n_clusters, distance, max_iter, tol, rng):
""" Run a single trial of k-medoids clustering
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"""
membs = np.empty(shape=X.shape[0], dtype=int)
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vmirly/pyclust | pyclust/_kmedoids.py | KMedoids.fit | def fit(self, X):
""" Apply KMeans Clustering
X: dataset with feature vectors
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X: dataset with feature vectors
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vmirly/pyclust | pyclust/_kernel_kmeans.py | _kernelized_dist2centers | def _kernelized_dist2centers(K, n_clusters, wmemb, kernel_dist):
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vmirly/pyclust | pyclust/_gaussian_mixture_model.py | _init_mixture_params | def _init_mixture_params(X, n_mixtures, init_method):
"""
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random means
identity covariance matrices
"""
init_priors = np.ones(shape=n_mixtures, dtype=float) / n_mixtures
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Initialize mixture density parameters with
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vmirly/pyclust | pyclust/_gaussian_mixture_model.py | __log_density_single | def __log_density_single(x, mean, covar):
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vmirly/pyclust | pyclust/_gaussian_mixture_model.py | _log_multivariate_density | def _log_multivariate_density(X, means, covars):
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log P(x | mu, Sigma) = -1/2*(d*log(2pi) + log(|Sigma|) + (x-mu)^T * Sigma^-1 * (x-m... | python | def _log_multivariate_density(X, means, covars):
"""
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vmirly/pyclust | pyclust/_kmeans.py | _kmeans_init | def _kmeans_init(X, n_clusters, method='balanced', rng=None):
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"""
n_samples = X.shape[0]
if rng is None:
cent_idx = np.random.choice(n_samples, replace=False, size=n_clusters)
else:
#print('Generate random centers using RNG')
cen... | python | def _kmeans_init(X, n_clusters, method='balanced', rng=None):
""" Initialize k=n_clusters centroids randomly
"""
n_samples = X.shape[0]
if rng is None:
cent_idx = np.random.choice(n_samples, replace=False, size=n_clusters)
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vmirly/pyclust | pyclust/_kmeans.py | _assign_clusters | def _assign_clusters(X, centers):
""" Assignment Step:
assign each point to the closet cluster center
"""
dist2cents = scipy.spatial.distance.cdist(X, centers, metric='seuclidean')
membs = np.argmin(dist2cents, axis=1)
return(membs) | python | def _assign_clusters(X, centers):
""" Assignment Step:
assign each point to the closet cluster center
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vmirly/pyclust | pyclust/_kmeans.py | _cal_dist2center | def _cal_dist2center(X, center):
""" Calculate the SSE to the cluster center
"""
dmemb2cen = scipy.spatial.distance.cdist(X, center.reshape(1,X.shape[1]), metric='seuclidean')
return(np.sum(dmemb2cen)) | python | def _cal_dist2center(X, center):
""" Calculate the SSE to the cluster center
"""
dmemb2cen = scipy.spatial.distance.cdist(X, center.reshape(1,X.shape[1]), metric='seuclidean')
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vmirly/pyclust | pyclust/_kmeans.py | _update_centers | def _update_centers(X, membs, n_clusters):
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vmirly/pyclust | pyclust/_kmeans.py | _kmeans_run | def _kmeans_run(X, n_clusters, max_iter, tol):
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vmirly/pyclust | pyclust/_kmeans.py | _kmeans | def _kmeans(X, n_clusters, max_iter, n_trials, tol):
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vmirly/pyclust | pyclust/_kmeans.py | KMeans.fit | def fit(self, X):
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X: dataset with feature vectors
"""
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""" Apply KMeans Clustering
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vmirly/pyclust | pyclust/_bisect_kmeans.py | _cut_tree | def _cut_tree(tree, n_clusters, membs):
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vmirly/pyclust | pyclust/_bisect_kmeans.py | _add_tree_node | def _add_tree_node(tree, label, ilev, X=None, size=None, center=None, sse=None, parent=None):
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vmirly/pyclust | pyclust/_bisect_kmeans.py | _bisect_kmeans | def _bisect_kmeans(X, n_clusters, n_trials, max_iter, tol):
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"""
membs = np.empty(shape=X.shape[0], dtype=int)
centers = dict() #np.empty(shape=(n_clusters,X.shape[1]), dtype=float)
sse_arr = dict() #-1.0*np.ones(sha... | python | def _bisect_kmeans(X, n_clusters, n_trials, max_iter, tol):
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membs = np.empty(shape=X.shape[0], dtype=int)
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Samreay/ChainConsumer | chainconsumer/comparisons.py | Comparison.dic | def dic(self):
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Samreay/ChainConsumer | chainconsumer/comparisons.py | Comparison.comparison_table | def comparison_table(self, caption=None, label="tab:model_comp", hlines=True,
aic=True, bic=True, dic=True, sort="bic", descending=True): # pragma: no cover
"""
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Samreay/ChainConsumer | chainconsumer/kde.py | MegKDE.evaluate | def evaluate(self, data):
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Samreay/ChainConsumer | chainconsumer/plotter.py | Plotter.plot | def plot(self, figsize="GROW", parameters=None, chains=None, extents=None, filename=None,
display=False, truth=None, legend=None, blind=None, watermark=None): # pragma: no cover
""" Plot the chain!
Parameters
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figsize : str|tuple(float)|float, optional
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Samreay/ChainConsumer | chainconsumer/plotter.py | Plotter.plot_walks | def plot_walks(self, parameters=None, truth=None, extents=None, display=False,
filename=None, chains=None, convolve=None, figsize=None,
plot_weights=True, plot_posterior=True, log_weight=None): # pragma: no cover
""" Plots the chain walk; the parameter values as a function... | python | def plot_walks(self, parameters=None, truth=None, extents=None, display=False,
filename=None, chains=None, convolve=None, figsize=None,
plot_weights=True, plot_posterior=True, log_weight=None): # pragma: no cover
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Samreay/ChainConsumer | chainconsumer/plotter.py | Plotter.plot_distributions | def plot_distributions(self, parameters=None, truth=None, extents=None, display=False,
filename=None, chains=None, col_wrap=4, figsize=None, blind=None): # pragma: no cover
""" Plots the 1D parameter distributions for verification purposes.
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Samreay/ChainConsumer | chainconsumer/plotter.py | Plotter.plot_summary | def plot_summary(self, parameters=None, truth=None, extents=None, display=False,
filename=None, chains=None, figsize=1.0, errorbar=False, include_truth_chain=True,
blind=None, watermark=None, extra_parameter_spacing=0.5,
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filename=None, chains=None, figsize=1.0, errorbar=False, include_truth_chain=True,
blind=None, watermark=None, extra_parameter_spacing=0.5,
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Samreay/ChainConsumer | chainconsumer/diagnostic.py | Diagnostic.gelman_rubin | def gelman_rubin(self, chain=None, threshold=0.05):
r""" Runs the Gelman Rubin diagnostic on the supplied chains.
Parameters
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chain : int|str, optional
Which chain to run the diagnostic on. By default, this is `None`,
which will run the diagnostic on al... | python | def gelman_rubin(self, chain=None, threshold=0.05):
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Samreay/ChainConsumer | chainconsumer/diagnostic.py | Diagnostic.geweke | def geweke(self, chain=None, first=0.1, last=0.5, threshold=0.05):
""" Runs the Geweke diagnostic on the supplied chains.
Parameters
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chain : int|str, optional
Which chain to run the diagnostic on. By default, this is `None`,
which will run the diagnost... | python | def geweke(self, chain=None, first=0.1, last=0.5, threshold=0.05):
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chain : int|str, optional
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Samreay/ChainConsumer | chainconsumer/analysis.py | Analysis.get_latex_table | def get_latex_table(self, parameters=None, transpose=False, caption=None,
label="tab:model_params", hlines=True, blank_fill="--"): # pragma: no cover
""" Generates a LaTeX table from parameter summaries.
Parameters
----------
parameters : list[str], optional
... | python | def get_latex_table(self, parameters=None, transpose=False, caption=None,
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""" Generates a LaTeX table from parameter summaries.
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Samreay/ChainConsumer | chainconsumer/analysis.py | Analysis.get_summary | def get_summary(self, squeeze=True, parameters=None, chains=None):
""" Gets a summary of the marginalised parameter distributions.
Parameters
----------
squeeze : bool, optional
Squeeze the summaries. If you only have one chain, squeeze will not return
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Samreay/ChainConsumer | chainconsumer/analysis.py | Analysis.get_max_posteriors | def get_max_posteriors(self, parameters=None, squeeze=True, chains=None):
""" Gets the maximum posterior point in parameter space from the passed parameters.
Requires the chains to have set `posterior` values.
Parameters
----------
parameters : str|list[str]
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parameters : str|list[str]
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Samreay/ChainConsumer | chainconsumer/analysis.py | Analysis.get_correlations | def get_correlations(self, chain=0, parameters=None):
"""
Takes a chain and returns the correlation between chain parameters.
Parameters
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chain : int|str, optional
The chain index or name. Defaults to first chain.
parameters : list[str], optional
... | python | def get_correlations(self, chain=0, parameters=None):
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Takes a chain and returns the correlation between chain parameters.
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chain : int|str, optional
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Samreay/ChainConsumer | chainconsumer/analysis.py | Analysis.get_covariance | def get_covariance(self, chain=0, parameters=None):
"""
Takes a chain and returns the covariance between chain parameters.
Parameters
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chain : int|str, optional
The chain index or name. Defaults to first chain.
parameters : list[str], optional
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Samreay/ChainConsumer | chainconsumer/analysis.py | Analysis.get_correlation_table | def get_correlation_table(self, chain=0, parameters=None, caption="Parameter Correlations",
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Samreay/ChainConsumer | chainconsumer/analysis.py | Analysis.get_covariance_table | def get_covariance_table(self, chain=0, parameters=None, caption="Parameter Covariance",
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Samreay/ChainConsumer | chainconsumer/analysis.py | Analysis.get_parameter_text | def get_parameter_text(self, lower, maximum, upper, wrap=False):
""" Generates LaTeX appropriate text from marginalised parameter bounds.
Parameters
----------
lower : float
The lower bound on the parameter
maximum : float
The value of the parameter with ... | python | def get_parameter_text(self, lower, maximum, upper, wrap=False):
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Samreay/ChainConsumer | chainconsumer/chainconsumer.py | ChainConsumer.add_chain | def add_chain(self, chain, parameters=None, name=None, weights=None, posterior=None, walkers=None,
grid=False, num_eff_data_points=None, num_free_params=None, color=None, linewidth=None,
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Samreay/ChainConsumer | chainconsumer/chainconsumer.py | ChainConsumer.remove_chain | def remove_chain(self, chain=-1):
"""
Removes a chain from ChainConsumer. Calling this will require any configurations set to be redone!
Parameters
----------
chain : int|str, list[str|int]
The chain(s) to remove. You can pass in either the chain index, or the chain ... | python | def remove_chain(self, chain=-1):
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Removes a chain from ChainConsumer. Calling this will require any configurations set to be redone!
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Samreay/ChainConsumer | chainconsumer/chainconsumer.py | ChainConsumer.configure | def configure(self, statistics="max", max_ticks=5, plot_hists=True, flip=True,
serif=True, sigma2d=False, sigmas=None, summary=None, bins=None, rainbow=None,
colors=None, linestyles=None, linewidths=None, kde=False, smooth=None,
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colors=None, linestyles=None, linewidths=None, kde=False, smooth=None,
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"""
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"""
Returns a ChainConsumer instance containing all the walks of a given chain
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vanheeringen-lab/gimmemotifs | gimmemotifs/commands/threshold.py | threshold | def threshold(args):
"""Calculate motif score threshold for a given FPR."""
if args.fpr < 0 or args.fpr > 1:
print("Please specify a FPR between 0 and 1")
sys.exit(1)
motifs = read_motifs(args.pwmfile)
s = Scanner()
s.set_motifs(args.pwmfile)
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"""Calculate motif score threshold for a given FPR."""
if args.fpr < 0 or args.fpr > 1:
print("Please specify a FPR between 0 and 1")
sys.exit(1)
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vanheeringen-lab/gimmemotifs | gimmemotifs/rocmetrics.py | values_to_labels | def values_to_labels(fg_vals, bg_vals):
"""
Convert two arrays of values to an array of labels and an array of scores.
Parameters
----------
fg_vals : array_like
The list of values for the positive set.
bg_vals : array_like
The list of values for the negative set.
Returns
... | python | def values_to_labels(fg_vals, bg_vals):
"""
Convert two arrays of values to an array of labels and an array of scores.
Parameters
----------
fg_vals : array_like
The list of values for the positive set.
bg_vals : array_like
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vanheeringen-lab/gimmemotifs | gimmemotifs/rocmetrics.py | recall_at_fdr | def recall_at_fdr(fg_vals, bg_vals, fdr_cutoff=0.1):
"""
Computes the recall at a specific FDR (default 10%).
Parameters
----------
fg_vals : array_like
The list of values for the positive set.
bg_vals : array_like
The list of values for the negative set.
fdr : float, ... | python | def recall_at_fdr(fg_vals, bg_vals, fdr_cutoff=0.1):
"""
Computes the recall at a specific FDR (default 10%).
Parameters
----------
fg_vals : array_like
The list of values for the positive set.
bg_vals : array_like
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vanheeringen-lab/gimmemotifs | gimmemotifs/rocmetrics.py | matches_at_fpr | def matches_at_fpr(fg_vals, bg_vals, fpr=0.01):
"""
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Parameters
----------
fg_vals : array_like
The list of values for the positive set.
bg_vals : array_like
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fpr... | python | def matches_at_fpr(fg_vals, bg_vals, fpr=0.01):
"""
Computes the hypergeometric p-value at a specific FPR (default 1%).
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fg_vals : array_like
The list of values for the positive set.
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vanheeringen-lab/gimmemotifs | gimmemotifs/rocmetrics.py | phyper_at_fpr | def phyper_at_fpr(fg_vals, bg_vals, fpr=0.01):
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vanheeringen-lab/gimmemotifs | gimmemotifs/rocmetrics.py | fraction_fpr | def fraction_fpr(fg_vals, bg_vals, fpr=0.01):
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Parameters
----------
fg_vals : array_like
The list of values for the positive set.
bg_vals : array_like
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Computes the fraction positives at a specific FPR (default 1%).
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vanheeringen-lab/gimmemotifs | gimmemotifs/rocmetrics.py | score_at_fpr | def score_at_fpr(fg_vals, bg_vals, fpr=0.01):
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Parameters
----------
fg_vals : array_like
The list of values for the positive set.
bg_vals : array_like
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"""
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fg_vals : array_like
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vanheeringen-lab/gimmemotifs | gimmemotifs/rocmetrics.py | enr_at_fpr | def enr_at_fpr(fg_vals, bg_vals, fpr=0.01):
"""
Computes the enrichment at a specific FPR (default 1%).
Parameters
----------
fg_vals : array_like
The list of values for the positive set.
bg_vals : array_like
The list of values for the negative set.
fpr : float, option... | python | def enr_at_fpr(fg_vals, bg_vals, fpr=0.01):
"""
Computes the enrichment at a specific FPR (default 1%).
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fg_vals : array_like
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bg_vals : array_like
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vanheeringen-lab/gimmemotifs | gimmemotifs/rocmetrics.py | max_enrichment | def max_enrichment(fg_vals, bg_vals, minbg=2):
"""
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Parameters
----------
fg_vals : array_like
The list of values for the positive set.
bg_vals : array_like
The list of values for the negative set.
minbg : int, optional
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"""
Computes the maximum enrichment.
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fg_vals : array_like
The list of values for the positive set.
bg_vals : array_like
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vanheeringen-lab/gimmemotifs | gimmemotifs/rocmetrics.py | mncp | def mncp(fg_vals, bg_vals):
"""
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MNCP is described in Clarke & Granek, Bioinformatics, 2003.
Parameters
----------
fg_vals : array_like
The list of values for the positive set.
bg_vals : array_like
The list of val... | python | def mncp(fg_vals, bg_vals):
"""
Computes the Mean Normalized Conditional Probability (MNCP).
MNCP is described in Clarke & Granek, Bioinformatics, 2003.
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fg_vals : array_like
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bg_vals : array_like
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vanheeringen-lab/gimmemotifs | gimmemotifs/rocmetrics.py | pr_auc | def pr_auc(fg_vals, bg_vals):
"""
Computes the Precision-Recall Area Under Curve (PR AUC)
Parameters
----------
fg_vals : array_like
list of values for positive set
bg_vals : array_like
list of values for negative set
Returns
-------
score : float
PR AU... | python | def pr_auc(fg_vals, bg_vals):
"""
Computes the Precision-Recall Area Under Curve (PR AUC)
Parameters
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fg_vals : array_like
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vanheeringen-lab/gimmemotifs | gimmemotifs/rocmetrics.py | roc_auc | def roc_auc(fg_vals, bg_vals):
"""
Computes the ROC Area Under Curve (ROC AUC)
Parameters
----------
fg_vals : array_like
list of values for positive set
bg_vals : array_like
list of values for negative set
Returns
-------
score : float
ROC AUC score
... | python | def roc_auc(fg_vals, bg_vals):
"""
Computes the ROC Area Under Curve (ROC AUC)
Parameters
----------
fg_vals : array_like
list of values for positive set
bg_vals : array_like
list of values for negative set
Returns
-------
score : float
ROC AUC score
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vanheeringen-lab/gimmemotifs | gimmemotifs/rocmetrics.py | roc_auc_xlim | def roc_auc_xlim(x_bla, y_bla, xlim=0.1):
"""
Computes the ROC Area Under Curve until a certain FPR value.
Parameters
----------
fg_vals : array_like
list of values for positive set
bg_vals : array_like
list of values for negative set
xlim : float, optional
FPR val... | python | def roc_auc_xlim(x_bla, y_bla, xlim=0.1):
"""
Computes the ROC Area Under Curve until a certain FPR value.
Parameters
----------
fg_vals : array_like
list of values for positive set
bg_vals : array_like
list of values for negative set
xlim : float, optional
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vanheeringen-lab/gimmemotifs | gimmemotifs/rocmetrics.py | roc_values | def roc_values(fg_vals, bg_vals):
"""
Return fpr (x) and tpr (y) of the ROC curve.
Parameters
----------
fg_vals : array_like
The list of values for the positive set.
bg_vals : array_like
The list of values for the negative set.
Returns
-------
fpr : array
... | python | def roc_values(fg_vals, bg_vals):
"""
Return fpr (x) and tpr (y) of the ROC curve.
Parameters
----------
fg_vals : array_like
The list of values for the positive set.
bg_vals : array_like
The list of values for the negative set.
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fpr : array
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vanheeringen-lab/gimmemotifs | gimmemotifs/rocmetrics.py | max_fmeasure | def max_fmeasure(fg_vals, bg_vals):
"""
Computes the maximum F-measure.
Parameters
----------
fg_vals : array_like
The list of values for the positive set.
bg_vals : array_like
The list of values for the negative set.
Returns
-------
f : float
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"""
Computes the maximum F-measure.
Parameters
----------
fg_vals : array_like
The list of values for the positive set.
bg_vals : array_like
The list of values for the negative set.
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f : float
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vanheeringen-lab/gimmemotifs | gimmemotifs/rocmetrics.py | ks_pvalue | def ks_pvalue(fg_pos, bg_pos=None):
"""
Computes the Kolmogorov-Smirnov p-value of position distribution.
Parameters
----------
fg_pos : array_like
The list of values for the positive set.
bg_pos : array_like, optional
The list of values for the negative set.
Returns
... | python | def ks_pvalue(fg_pos, bg_pos=None):
"""
Computes the Kolmogorov-Smirnov p-value of position distribution.
Parameters
----------
fg_pos : array_like
The list of values for the positive set.
bg_pos : array_like, optional
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vanheeringen-lab/gimmemotifs | gimmemotifs/rocmetrics.py | ks_significance | def ks_significance(fg_pos, bg_pos=None):
"""
Computes the -log10 of Kolmogorov-Smirnov p-value of position distribution.
Parameters
----------
fg_pos : array_like
The list of values for the positive set.
bg_pos : array_like, optional
The list of values for the negative set.
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Computes the -log10 of Kolmogorov-Smirnov p-value of position distribution.
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fg_pos : array_like
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bg_pos : array_like, optional
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pescadores/pescador | examples/frameworks/keras_example.py | setup_data | def setup_data():
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Returns
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input_shape : tuple, len=3
Shape of each sample; adapts to channel configuration of Keras.
X_train, y_train : np.ndarrays
Images and labels for training.
X_test, y_test : np.ndarrays
... | python | def setup_data():
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Returns
-------
input_shape : tuple, len=3
Shape of each sample; adapts to channel configuration of Keras.
X_train, y_train : np.ndarrays
Images and labels for training.
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pescadores/pescador | examples/frameworks/keras_example.py | build_model | def build_model(input_shape):
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Parameters
----------
input_shape : tuple, len=3
Shape of each image sample.
Returns
-------
model : keras.Model
Constructed model.
"""
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"""Create a compiled Keras model.
Parameters
----------
input_shape : tuple, len=3
Shape of each image sample.
Returns
-------
model : keras.Model
Constructed model.
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pescadores/pescador | examples/frameworks/keras_example.py | sampler | def sampler(X, y):
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Parameters
----------
X : np.ndarray, len=n_samples, ndim=4
Image data.
y : np.ndarray, len=n_samples, ndim=2
One-hot encoded class vectors.
Yields
------
data : dict
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'''A basic generator for sampling data.
Parameters
----------
X : np.ndarray, len=n_samples, ndim=4
Image data.
y : np.ndarray, len=n_samples, ndim=2
One-hot encoded class vectors.
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Parameters
----------
stream : iterable
A stream that yields data objects.
key : string, default='X'
Name of the field to add noise.
scale : float, default=0.1
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Parameters
----------
stream : iterable
A stream that yields data objects.
key : string, default='X'
Name of the field to add noise.
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vanheeringen-lab/gimmemotifs | gimmemotifs/config.py | parse_denovo_params | def parse_denovo_params(user_params=None):
"""Return default GimmeMotifs parameters.
Defaults will be replaced with parameters defined in user_params.
Parameters
----------
user_params : dict, optional
User-defined parameters.
Returns
-------
params : dict
"""
config ... | python | def parse_denovo_params(user_params=None):
"""Return default GimmeMotifs parameters.
Defaults will be replaced with parameters defined in user_params.
Parameters
----------
user_params : dict, optional
User-defined parameters.
Returns
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params : dict
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vanheeringen-lab/gimmemotifs | gimmemotifs/rank.py | rankagg_R | def rankagg_R(df, method="stuart"):
"""Return aggregated ranks as implemented in the RobustRankAgg R package.
This function is now deprecated.
References:
Kolde et al., 2012, DOI: 10.1093/bioinformatics/btr709
Stuart et al., 2003, DOI: 10.1126/science.1087447
Parameters
--------... | python | def rankagg_R(df, method="stuart"):
"""Return aggregated ranks as implemented in the RobustRankAgg R package.
This function is now deprecated.
References:
Kolde et al., 2012, DOI: 10.1093/bioinformatics/btr709
Stuart et al., 2003, DOI: 10.1126/science.1087447
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vanheeringen-lab/gimmemotifs | gimmemotifs/rank.py | rankagg | def rankagg(df, method="stuart"):
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Implementation is ported from the RobustRankAggreg R package
References:
Kolde et al., 2012, DOI: 10.1093/bioinformatics/btr709
Stuart et al., 2003, DOI: 10.1126/science.1087447
Parameters
----------
df : pand... | python | def rankagg(df, method="stuart"):
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Implementation is ported from the RobustRankAggreg R package
References:
Kolde et al., 2012, DOI: 10.1093/bioinformatics/btr709
Stuart et al., 2003, DOI: 10.1126/science.1087447
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pescadores/pescador | examples/zmq_example.py | data_gen | def data_gen(n_ops=100):
"""Yield data, while optionally burning compute cycles.
Parameters
----------
n_ops : int, default=100
Number of operations to run between yielding data.
Returns
-------
data : dict
A object which looks like it might come from some
machine l... | python | def data_gen(n_ops=100):
"""Yield data, while optionally burning compute cycles.
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n_ops : int, default=100
Number of operations to run between yielding data.
Returns
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vanheeringen-lab/gimmemotifs | gimmemotifs/prediction.py | mp_calc_stats | def mp_calc_stats(motifs, fg_fa, bg_fa, bg_name=None):
"""Parallel calculation of motif statistics."""
try:
stats = calc_stats(motifs, fg_fa, bg_fa, ncpus=1)
except Exception as e:
raise
sys.stderr.write("ERROR: {}\n".format(str(e)))
stats = {}
if not bg_name:
bg... | python | def mp_calc_stats(motifs, fg_fa, bg_fa, bg_name=None):
"""Parallel calculation of motif statistics."""
try:
stats = calc_stats(motifs, fg_fa, bg_fa, ncpus=1)
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raise
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vanheeringen-lab/gimmemotifs | gimmemotifs/prediction.py | _run_tool | def _run_tool(job_name, t, fastafile, params):
"""Parallel motif prediction."""
try:
result = t.run(fastafile, params, mytmpdir())
except Exception as e:
result = ([], "", "{} failed to run: {}".format(job_name, e))
return job_name, result | python | def _run_tool(job_name, t, fastafile, params):
"""Parallel motif prediction."""
try:
result = t.run(fastafile, params, mytmpdir())
except Exception as e:
result = ([], "", "{} failed to run: {}".format(job_name, e))
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vanheeringen-lab/gimmemotifs | gimmemotifs/prediction.py | pp_predict_motifs | def pp_predict_motifs(fastafile, outfile, analysis="small", organism="hg18", single=False, background="", tools=None, job_server=None, ncpus=8, max_time=-1, stats_fg=None, stats_bg=None):
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vanheeringen-lab/gimmemotifs | gimmemotifs/prediction.py | predict_motifs | def predict_motifs(infile, bgfile, outfile, params=None, stats_fg=None, stats_bg=None):
""" Predict motifs, input is a FASTA-file"""
# Parse parameters
required_params = ["tools", "available_tools", "analysis",
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... | python | def predict_motifs(infile, bgfile, outfile, params=None, stats_fg=None, stats_bg=None):
""" Predict motifs, input is a FASTA-file"""
# Parse parameters
required_params = ["tools", "available_tools", "analysis",
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vanheeringen-lab/gimmemotifs | gimmemotifs/prediction.py | PredictionResult.add_motifs | def add_motifs(self, args):
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# Callback function for motif programs
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vanheeringen-lab/gimmemotifs | gimmemotifs/prediction.py | PredictionResult.wait_for_stats | def wait_for_stats(self):
"""Make sure all jobs are finished."""
logging.debug("waiting for statistics to finish")
for job in self.stat_jobs:
job.get()
sleep(2) | python | def wait_for_stats(self):
"""Make sure all jobs are finished."""
logging.debug("waiting for statistics to finish")
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job.get()
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vanheeringen-lab/gimmemotifs | gimmemotifs/prediction.py | PredictionResult.add_stats | def add_stats(self, args):
"""Callback to add motif statistics."""
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logger.debug("Stats: %s %s", bg_name, stats)
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if motif_id not in self.stats:
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self.stat... | python | def add_stats(self, args):
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bg_name, stats = args
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vanheeringen-lab/gimmemotifs | gimmemotifs/denovo.py | prepare_denovo_input_narrowpeak | def prepare_denovo_input_narrowpeak(inputfile, params, outdir):
"""Prepare a narrowPeak file for de novo motif prediction.
All regions to same size; split in test and validation set;
converted to FASTA.
Parameters
----------
inputfile : str
BED file with input regions.
params : di... | python | def prepare_denovo_input_narrowpeak(inputfile, params, outdir):
"""Prepare a narrowPeak file for de novo motif prediction.
All regions to same size; split in test and validation set;
converted to FASTA.
Parameters
----------
inputfile : str
BED file with input regions.
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vanheeringen-lab/gimmemotifs | gimmemotifs/denovo.py | prepare_denovo_input_bed | def prepare_denovo_input_bed(inputfile, params, outdir):
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All regions to same size; split in test and validation set;
converted to FASTA.
Parameters
----------
inputfile : str
BED file with input regions.
params : dict
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All regions to same size; split in test and validation set;
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Parameters
----------
inputfile : str
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vanheeringen-lab/gimmemotifs | gimmemotifs/denovo.py | prepare_denovo_input_fa | def prepare_denovo_input_fa(inputfile, params, outdir):
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Parameters
----------
"""
fraction = float(params["fraction"])
abs_max = int(params["abs_max"])
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"""Create all the FASTA files for de novo motif prediction and validation.
Parameters
----------
"""
fraction = float(params["fraction"])
abs_max = int(params["abs_max"])
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vanheeringen-lab/gimmemotifs | gimmemotifs/denovo.py | create_background | def create_background(bg_type, fafile, outfile, genome="hg18", width=200, nr_times=10, custom_background=None):
"""Create background of a specific type.
Parameters
----------
bg_type : str
Name of background type.
fafile : str
Name of input FASTA file.
outfile : str
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Parameters
----------
bg_type : str
Name of background type.
fafile : str
Name of input FASTA file.
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vanheeringen-lab/gimmemotifs | gimmemotifs/denovo.py | create_backgrounds | def create_backgrounds(outdir, background=None, genome="hg38", width=200, custom_background=None):
"""Create different backgrounds for motif prediction and validation.
Parameters
----------
outdir : str
Directory to save results.
background : list, optional
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Parameters
----------
outdir : str
Directory to save results.
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vanheeringen-lab/gimmemotifs | gimmemotifs/denovo.py | _is_significant | def _is_significant(stats, metrics=None):
"""Filter significant motifs based on several statistics.
Parameters
----------
stats : dict
Statistics disctionary object.
metrics : sequence
Metric with associated minimum values. The default is
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"""Filter significant motifs based on several statistics.
Parameters
----------
stats : dict
Statistics disctionary object.
metrics : sequence
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vanheeringen-lab/gimmemotifs | gimmemotifs/denovo.py | filter_significant_motifs | def filter_significant_motifs(fname, result, bg, metrics=None):
"""Filter significant motifs based on several statistics.
Parameters
----------
fname : str
Filename of output file were significant motifs will be saved.
result : PredictionResult instance
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"""Filter significant motifs based on several statistics.
Parameters
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fname : str
Filename of output file were significant motifs will be saved.
result : PredictionResult instance
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vanheeringen-lab/gimmemotifs | gimmemotifs/denovo.py | best_motif_in_cluster | def best_motif_in_cluster(single_pwm, clus_pwm, clusters, fg_fa, background, stats=None, metrics=("roc_auc", "recall_at_fdr")):
"""Return the best motif per cluster for a clustering results.
The motif can be either the average motif or one of the clustered motifs.
Parameters
----------
single_pwm ... | python | def best_motif_in_cluster(single_pwm, clus_pwm, clusters, fg_fa, background, stats=None, metrics=("roc_auc", "recall_at_fdr")):
"""Return the best motif per cluster for a clustering results.
The motif can be either the average motif or one of the clustered motifs.
Parameters
----------
single_pwm ... | [
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"# combine original and clustered mot... | Return the best motif per cluster for a clustering results.
The motif can be either the average motif or one of the clustered motifs.
Parameters
----------
single_pwm : str
Filename of motifs.
clus_pwm : str
Filename of motifs.
clusters :
Motif clustering result.
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] | train | https://github.com/vanheeringen-lab/gimmemotifs/blob/1dc0572179e5d0c8f96958060133c1f8d92c6675/gimmemotifs/denovo.py#L395-L466 |
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