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values | func_name stringlengths 1 134 | docstring stringlengths 1 46.9k | path stringlengths 4 223 | original_string stringlengths 75 104k | code stringlengths 75 104k | docstring_tokens listlengths 1 1.97k | repo stringlengths 7 55 | language stringclasses 1
value | url stringlengths 87 315 | code_tokens listlengths 19 28.4k | sha stringlengths 40 40 |
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train | Diagnostic.geweke | Runs the Geweke diagnostic on the supplied chains.
Parameters
----------
chain : int|str, optional
Which chain to run the diagnostic on. By default, this is `None`,
which will run the diagnostic on all chains. You can also
supply and integer (the chain index)... | chainconsumer/diagnostic.py | def geweke(self, chain=None, first=0.1, last=0.5, threshold=0.05):
""" Runs the Geweke diagnostic on the supplied chains.
Parameters
----------
chain : int|str, optional
Which chain to run the diagnostic on. By default, this is `None`,
which will run the diagnost... | def geweke(self, chain=None, first=0.1, last=0.5, threshold=0.05):
""" Runs the Geweke diagnostic on the supplied chains.
Parameters
----------
chain : int|str, optional
Which chain to run the diagnostic on. By default, this is `None`,
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train | Analysis.get_latex_table | Generates a LaTeX table from parameter summaries.
Parameters
----------
parameters : list[str], optional
A list of what parameters to include in the table. By default, includes all parameters
transpose : bool, optional
Defaults to False, which gives each column a... | chainconsumer/analysis.py | 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
... | 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
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train | Analysis.get_summary | 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
a length one list, just the single summary. If this is false, you will
get a ... | chainconsumer/analysis.py | 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
a length ... | 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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train | Analysis.get_max_posteriors | 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]
The parameters to find
squeeze : bool, optional
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""" 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]
... | 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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train | Analysis.get_correlations | Takes a chain and returns the correlation between chain parameters.
Parameters
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The chain index or name. Defaults to first chain.
parameters : list[str], optional
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"""
Takes a chain and returns the correlation between chain parameters.
Parameters
----------
chain : int|str, optional
The chain index or name. Defaults to first chain.
parameters : list[str], optional
... | def get_correlations(self, chain=0, parameters=None):
"""
Takes a chain and returns the correlation between chain parameters.
Parameters
----------
chain : int|str, optional
The chain index or name. Defaults to first chain.
parameters : list[str], optional
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train | Analysis.get_covariance | Takes a chain and returns the covariance between chain parameters.
Parameters
----------
chain : int|str, optional
The chain index or name. Defaults to first chain.
parameters : list[str], optional
The list of parameters to compute correlations. Defaults to all p... | chainconsumer/analysis.py | def get_covariance(self, chain=0, parameters=None):
"""
Takes a chain and returns the covariance between chain parameters.
Parameters
----------
chain : int|str, optional
The chain index or name. Defaults to first chain.
parameters : list[str], optional
... | def get_covariance(self, chain=0, parameters=None):
"""
Takes a chain and returns the covariance between chain parameters.
Parameters
----------
chain : int|str, optional
The chain index or name. Defaults to first chain.
parameters : list[str], optional
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train | Analysis.get_correlation_table | Gets a LaTeX table of parameter correlations.
Parameters
----------
chain : int|str, optional
The chain index or name. Defaults to first chain.
parameters : list[str], optional
The list of parameters to compute correlations. Defaults to all parameters
... | chainconsumer/analysis.py | def get_correlation_table(self, chain=0, parameters=None, caption="Parameter Correlations",
label="tab:parameter_correlations"):
"""
Gets a LaTeX table of parameter correlations.
Parameters
----------
chain : int|str, optional
The chain ... | def get_correlation_table(self, chain=0, parameters=None, caption="Parameter Correlations",
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Gets a LaTeX table of parameter correlations.
Parameters
----------
chain : int|str, optional
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train | Analysis.get_covariance_table | Gets a LaTeX table of parameter covariance.
Parameters
----------
chain : int|str, optional
The chain index or name. Defaults to first chain.
parameters : list[str], optional
The list of parameters to compute correlations. Defaults to all parameters
f... | chainconsumer/analysis.py | def get_covariance_table(self, chain=0, parameters=None, caption="Parameter Covariance",
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"""
Gets a LaTeX table of parameter covariance.
Parameters
----------
chain : int|str, optional
The chain index o... | def get_covariance_table(self, chain=0, parameters=None, caption="Parameter Covariance",
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Gets a LaTeX table of parameter covariance.
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train | Analysis.get_parameter_text | 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 maximum probability
upper : float
The upper bound on the ... | chainconsumer/analysis.py | 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 ... | def get_parameter_text(self, lower, maximum, upper, wrap=False):
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----------
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The lower bound on the parameter
maximum : float
The value of the parameter with ... | [
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train | ChainConsumer.add_chain | Add a chain to the consumer.
Parameters
----------
chain : str|ndarray|dict
The chain to load. Normally a ``numpy.ndarray``. If a string is found, it
interprets the string as a filename and attempts to load it in. If a ``dict``
is passed in, it assumes the di... | chainconsumer/chainconsumer.py | def add_chain(self, chain, parameters=None, name=None, weights=None, posterior=None, walkers=None,
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linestyle=None, kde=None, shade=None, shade_alpha=None, power=None, marker_style=None, marker_siz... | def add_chain(self, chain, parameters=None, name=None, weights=None, posterior=None, walkers=None,
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train | ChainConsumer.remove_chain | 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 name, to remove it.
By default removes the... | chainconsumer/chainconsumer.py | 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 ... | def remove_chain(self, chain=-1):
"""
Removes a chain from ChainConsumer. Calling this will require any configurations set to be redone!
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----------
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train | ChainConsumer.configure | r""" Configure the general plotting parameters common across the bar
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If you do not call this explicitly, the :func:`plot`
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Please ensure that you call this method *after* adding all the relevant data to the
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colors=None, linestyles=None, linewidths=None, kde=False, smooth=None,
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train | ChainConsumer.configure_truth | Configure the arguments passed to the ``axvline`` and ``axhline``
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If you do not call this explicitly, the :func:`plot` method will
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Recommended to set the parameters ``linestyle``, ``color`` and/or ``alpha``
i... | chainconsumer/chainconsumer.py | def configure_truth(self, **kwargs): # pragma: no cover
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If you do not call this explicitly, the :func:`plot` method will
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train | ChainConsumer.divide_chain | Returns a ChainConsumer instance containing all the walks of a given chain
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This method might be useful if, for example, your chain was made using
MCMC with 4 walkers. To check the sampling of all 4 walkers agree, you could
call this to get a ChainConsume... | chainconsumer/chainconsumer.py | def divide_chain(self, chain=0):
"""
Returns a ChainConsumer instance containing all the walks of a given chain
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This method might be useful if, for example, your chain was made using
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"""
Returns a ChainConsumer instance containing all the walks of a given chain
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train | threshold | Calculate motif score threshold for a given FPR. | gimmemotifs/commands/threshold.py | 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)
s.set_threshold(args.fpr, filen... | 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)
s.set_threshold(args.fpr, filen... | [
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train | values_to_labels | 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
-------
y_true : array
Labels.
y... | gimmemotifs/rocmetrics.py | 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
... | 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
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train | recall_at_fdr | 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, optional
The FDR (between 0.0 and 1.0).
Returns
... | gimmemotifs/rocmetrics.py | 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, ... | 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, ... | [
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train | matches_at_fpr | Computes the hypergeometric p-value 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, optional
The FPR (between 0.0 and 1.0).
... | gimmemotifs/rocmetrics.py | def matches_at_fpr(fg_vals, bg_vals, fpr=0.01):
"""
Computes the hypergeometric p-value 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... | def matches_at_fpr(fg_vals, bg_vals, fpr=0.01):
"""
Computes the hypergeometric p-value 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.
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train | phyper_at_fpr | Computes the hypergeometric p-value 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, optional
The FPR (between 0.0 and 1.0).
... | gimmemotifs/rocmetrics.py | def phyper_at_fpr(fg_vals, bg_vals, fpr=0.01):
"""
Computes the hypergeometric p-value 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 ... | def phyper_at_fpr(fg_vals, bg_vals, fpr=0.01):
"""
Computes the hypergeometric p-value 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.
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train | fraction_fpr | Computes the fraction positives 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, optional
The FPR (between 0.0 and 1.0).
... | gimmemotifs/rocmetrics.py | def fraction_fpr(fg_vals, bg_vals, fpr=0.01):
"""
Computes the fraction positives 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 : flo... | def fraction_fpr(fg_vals, bg_vals, fpr=0.01):
"""
Computes the fraction positives 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.
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train | score_at_fpr | Returns the motif score 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, optional
The FPR (between 0.0 and 1.0).
Retur... | gimmemotifs/rocmetrics.py | def score_at_fpr(fg_vals, bg_vals, fpr=0.01):
"""
Returns the motif score 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, opti... | def score_at_fpr(fg_vals, bg_vals, fpr=0.01):
"""
Returns the motif score 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, opti... | [
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train | enr_at_fpr | 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, optional
The FPR (between 0.0 and 1.0).
Retur... | gimmemotifs/rocmetrics.py | 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... | 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... | [
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train | max_enrichment | Computes the maximum enrichment.
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
Minimum number of matches in background. The default is 2.
... | gimmemotifs/rocmetrics.py | def max_enrichment(fg_vals, bg_vals, minbg=2):
"""
Computes the maximum enrichment.
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
Minimum n... | def max_enrichment(fg_vals, bg_vals, minbg=2):
"""
Computes the maximum enrichment.
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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train | mncp | Computes the Mean Normalized Conditional Probability (MNCP).
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 values for the negative set.
Retur... | gimmemotifs/rocmetrics.py | def mncp(fg_vals, bg_vals):
"""
Computes the Mean Normalized Conditional Probability (MNCP).
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... | def mncp(fg_vals, bg_vals):
"""
Computes the Mean Normalized Conditional Probability (MNCP).
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
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train | pr_auc | 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 AUC score | gimmemotifs/rocmetrics.py | 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... | 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
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train | roc_auc | 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 | gimmemotifs/rocmetrics.py | 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
... | 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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train | roc_auc_xlim | 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 value
Returns
-------
score : float
... | gimmemotifs/rocmetrics.py | 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... | 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... | [
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train | roc_values | 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
False positive rate.
tpr : array
T... | gimmemotifs/rocmetrics.py | 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
... | 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
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train | max_fmeasure | Computes the maximum F-measure.
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----------
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.
Returns
-------
f : float
Maximum f... | 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.
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-------
f : float
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train | ks_pvalue | 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
-------
p : float
KS p-value. | gimmemotifs/rocmetrics.py | 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
... | 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.
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train | ks_significance | 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.
Returns
-------
p : float
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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.
... | def ks_significance(fg_pos, bg_pos=None):
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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
The list of values for the negative set.
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train | setup_data | Load and shape data for training with Keras + Pescador.
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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
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"""Load and shape data for training with Keras + Pescador.
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.
X_test, y_test : np.ndarrays
... | def setup_data():
"""Load and shape data for training with Keras + Pescador.
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.
X_test, y_test : np.ndarrays
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train | build_model | Create a compiled Keras model.
Parameters
----------
input_shape : tuple, len=3
Shape of each image sample.
Returns
-------
model : keras.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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model = Sequential()
model.add(Conv2D(32, kernel_size=(3, 3),... | def build_model(input_shape):
"""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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train | sampler | 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.
Yields
------
data : dict
Single image sample, like {X: np.ndarray, y: np.ndarray} | examples/frameworks/keras_example.py | def sampler(X, y):
'''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.
Yields
------
data : dict
Single image sample, like {X: np.nd... | 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.
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------
data : dict
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train | additive_noise | Add noise to a data stream.
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
Scale factor for gaussian noise.
Yields
------
data : dict
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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
Scale factor for gaussian noi... | def additive_noise(stream, key='X', scale=1e-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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Scale factor for gaussian noi... | [
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train | parse_denovo_params | 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 | gimmemotifs/config.py | 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 ... | 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
"""
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train | rankagg_R | 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
----------
df : pandas.DataFrame
DataFr... | gimmemotifs/rank.py | 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
--------... | 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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train | rankagg | Return aggregated ranks.
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References:
Kolde et al., 2012, DOI: 10.1093/bioinformatics/btr709
Stuart et al., 2003, DOI: 10.1126/science.1087447
Parameters
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References:
Kolde et al., 2012, DOI: 10.1093/bioinformatics/btr709
Stuart et al., 2003, DOI: 10.1126/science.1087447
Parameters
----------
df : pand... | def rankagg(df, method="stuart"):
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Kolde et al., 2012, DOI: 10.1093/bioinformatics/btr709
Stuart et al., 2003, DOI: 10.1126/science.1087447
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train | data_gen | 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
-------
data : dict
A object which looks like it might come from some
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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
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----------
n_ops : int, default=100
Number of operations to run between yielding data.
Returns
-------
data : dict
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train | mp_calc_stats | Parallel calculation of motif statistics. | gimmemotifs/prediction.py | 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... | 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
sys.stderr.write("ERROR: {}\n".format(str(e)))
stats = {}
if not bg_name:
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train | _run_tool | Parallel motif prediction. | gimmemotifs/prediction.py | 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 | 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 | [
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train | pp_predict_motifs | Parallel prediction of motifs.
Utility function for gimmemotifs.denovo.gimme_motifs. Probably better to
use that, instead of this function directly. | gimmemotifs/prediction.py | 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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Utility function for gimmemotifs.denovo.gimme_motifs. Probably better to
use that, i... | 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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train | predict_motifs | Predict motifs, input is a FASTA-file | gimmemotifs/prediction.py | 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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if params is None:
... | 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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if params is None:
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train | PredictionResult.add_motifs | Add motifs to the result object. | gimmemotifs/prediction.py | def add_motifs(self, args):
"""Add motifs to the result object."""
self.lock.acquire()
# Callback function for motif programs
if args is None or len(args) != 2 or len(args[1]) != 3:
try:
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... | def add_motifs(self, args):
"""Add motifs to the result object."""
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# Callback function for motif programs
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try:
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train | PredictionResult.wait_for_stats | Make sure all jobs are finished. | gimmemotifs/prediction.py | 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) | def wait_for_stats(self):
"""Make sure all jobs are finished."""
logging.debug("waiting for statistics to finish")
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job.get()
sleep(2) | [
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train | PredictionResult.add_stats | Callback to add motif statistics. | gimmemotifs/prediction.py | def add_stats(self, args):
"""Callback to add motif statistics."""
bg_name, stats = args
logger.debug("Stats: %s %s", bg_name, stats)
for motif_id in stats.keys():
if motif_id not in self.stats:
self.stats[motif_id] = {}
self.stat... | def add_stats(self, args):
"""Callback to add motif statistics."""
bg_name, stats = args
logger.debug("Stats: %s %s", bg_name, stats)
for motif_id in stats.keys():
if motif_id not in self.stats:
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train | prepare_denovo_input_narrowpeak | 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 : dict
Dictionary with parameters.
outdir : str
Output... | gimmemotifs/denovo.py | 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... | 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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train | prepare_denovo_input_bed | Prepare a BED 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 : dict
Dictionary with parameters.
outdir : str
Output direct... | gimmemotifs/denovo.py | def prepare_denovo_input_bed(inputfile, params, outdir):
"""Prepare a BED 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 : dict
Dic... | def prepare_denovo_input_bed(inputfile, params, outdir):
"""Prepare a BED 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 : dict
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train | prepare_denovo_input_fa | Create all the FASTA files for de novo motif prediction and validation.
Parameters
---------- | gimmemotifs/denovo.py | def prepare_denovo_input_fa(inputfile, params, outdir):
"""Create all the FASTA files for de novo motif prediction and validation.
Parameters
----------
"""
fraction = float(params["fraction"])
abs_max = int(params["abs_max"])
logger.info("preparing input (FASTA)")
pred_fa = os.pa... | def prepare_denovo_input_fa(inputfile, params, outdir):
"""Create all the FASTA files for de novo motif prediction and validation.
Parameters
----------
"""
fraction = float(params["fraction"])
abs_max = int(params["abs_max"])
logger.info("preparing input (FASTA)")
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train | create_background | Create background of a specific type.
Parameters
----------
bg_type : str
Name of background type.
fafile : str
Name of input FASTA file.
outfile : str
Name of output FASTA file.
genome : str, optional
Genome name.
width : int, optional
Size of re... | gimmemotifs/denovo.py | 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
Na... | 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
Na... | [
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train | create_backgrounds | Create different backgrounds for motif prediction and validation.
Parameters
----------
outdir : str
Directory to save results.
background : list, optional
Background types to create, default is 'random'.
genome : str, optional
Genome name (for genomic and gc backgroun... | gimmemotifs/denovo.py | 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
Background types to ... | 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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train | _is_significant | Filter significant motifs based on several statistics.
Parameters
----------
stats : dict
Statistics disctionary object.
metrics : sequence
Metric with associated minimum values. The default is
(("max_enrichment", 3), ("roc_auc", 0.55), ("enr_at_fpr", 0.55))
Return... | gimmemotifs/denovo.py | 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
(("max_enrichment", 3), ("roc_a... | 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
(("max_enrichment", 3), ("roc_a... | [
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train | filter_significant_motifs | Filter significant motifs based on several statistics.
Parameters
----------
fname : str
Filename of output file were significant motifs will be saved.
result : PredictionResult instance
Contains motifs and associated statistics.
bg : str
Name of background type to use.
... | gimmemotifs/denovo.py | 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
Contains motifs and associat... | 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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train | best_motif_in_cluster | 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.
... | gimmemotifs/denovo.py | 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 ... | 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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train | rename_motifs | Rename motifs to GimmeMotifs_1..GimmeMotifs_N.
If stats object is passed, stats will be copied. | gimmemotifs/denovo.py | def rename_motifs(motifs, stats=None):
"""Rename motifs to GimmeMotifs_1..GimmeMotifs_N.
If stats object is passed, stats will be copied."""
final_motifs = []
for i, motif in enumerate(motifs):
old = str(motif)
motif.id = "GimmeMotifs_{}".format(i + 1)
final_motifs.append(mo... | def rename_motifs(motifs, stats=None):
"""Rename motifs to GimmeMotifs_1..GimmeMotifs_N.
If stats object is passed, stats will be copied."""
final_motifs = []
for i, motif in enumerate(motifs):
old = str(motif)
motif.id = "GimmeMotifs_{}".format(i + 1)
final_motifs.append(mo... | [
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train | gimme_motifs | De novo motif prediction based on an ensemble of different tools.
Parameters
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inputfile : str
Filename of input. Can be either BED, narrowPeak or FASTA.
outdir : str
Name of output directory.
params : dict, optional
Optional parameters.
filter_significant : ... | gimmemotifs/denovo.py | def gimme_motifs(inputfile, outdir, params=None, filter_significant=True, cluster=True, create_report=True):
"""De novo motif prediction based on an ensemble of different tools.
Parameters
----------
inputfile : str
Filename of input. Can be either BED, narrowPeak or FASTA.
outdir : str
... | def gimme_motifs(inputfile, outdir, params=None, filter_significant=True, cluster=True, create_report=True):
"""De novo motif prediction based on an ensemble of different tools.
Parameters
----------
inputfile : str
Filename of input. Can be either BED, narrowPeak or FASTA.
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train | MotifDb.register_db | Register method to keep list of dbs. | gimmemotifs/db/__init__.py | def register_db(cls, dbname):
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train | moap | Run a single motif activity prediction algorithm.
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method : str, optional
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train | Moap.list_classification_predictors | List available classification predictors. | gimmemotifs/moap.py | def list_classification_predictors(self):
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preds = [self.create(x) for x in self._predictors.keys()]
return [x.name for x in preds if x.ptype == "classification"] | def list_classification_predictors(self):
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train | Streamer._activate | Activates the stream. | pescador/core.py | def _activate(self):
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Maximum number of iterations to yield.
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Maximum number of iterations to yield.
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Yields
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Maximum number of iterations to yield.
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max_iter : None or int > 0
Maximum number of iterations to yield.
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train | calc_stats_iterator | Calculate motif enrichment metrics.
Parameters
----------
motifs : str, list or Motif instance
A file with motifs in pwm format, a list of Motif instances or a
single Motif instance.
fg_file : str
Filename of a FASTA, BED or region file with positive sequences.
bg_file : ... | gimmemotifs/stats.py | def calc_stats_iterator(motifs, fg_file, bg_file, genome=None, stats=None, ncpus=None):
"""Calculate motif enrichment metrics.
Parameters
----------
motifs : str, list or Motif instance
A file with motifs in pwm format, a list of Motif instances or a
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fg_file... | def calc_stats_iterator(motifs, fg_file, bg_file, genome=None, stats=None, ncpus=None):
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----------
motifs : str, list or Motif instance
A file with motifs in pwm format, a list of Motif instances or a
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train | calc_stats | Calculate motif enrichment metrics.
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fg_file : str
Filename of a FASTA, BED or region file with positive sequences.
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Parameters
----------
motifs : str, list or Motif instance
A file with motifs in pwm format, a list of Motif instances or a
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motifs : str, list or Motif instance
A file with motifs in pwm format, a list of Motif instances or a
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train | rank_motifs | Determine mean rank of motifs based on metrics. | gimmemotifs/stats.py | def rank_motifs(stats, metrics=("roc_auc", "recall_at_fdr")):
"""Determine mean rank of motifs based on metrics."""
rank = {}
combined_metrics = []
motif_ids = stats.keys()
background = list(stats.values())[0].keys()
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mean_metric_stats = [np.mean(
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"""Determine mean rank of motifs based on metrics."""
rank = {}
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motif_ids = stats.keys()
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train | write_stats | write motif statistics to text file. | gimmemotifs/stats.py | def write_stats(stats, fname, header=None):
"""write motif statistics to text file."""
# Write stats output to file
for bg in list(stats.values())[0].keys():
f = open(fname.format(bg), "w")
if header:
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stat_keys = sorted(list(list(stats.values())[... | def write_stats(stats, fname, header=None):
"""write motif statistics to text file."""
# Write stats output to file
for bg in list(stats.values())[0].keys():
f = open(fname.format(bg), "w")
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train | get_roc_values | Calculate ROC AUC values for ROC plots. | gimmemotifs/report.py | def get_roc_values(motif, fg_file, bg_file):
"""Calculate ROC AUC values for ROC plots."""
#print(calc_stats(motif, fg_file, bg_file, stats=["roc_values"], ncpus=1))
#["roc_values"])
try:
# fg_result = motif.pwm_scan_score(Fasta(fg_file), cutoff=0.0, nreport=1)
# fg_vals = [sorted(x)[... | def get_roc_values(motif, fg_file, bg_file):
"""Calculate ROC AUC values for ROC plots."""
#print(calc_stats(motif, fg_file, bg_file, stats=["roc_values"], ncpus=1))
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try:
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train | create_roc_plots | Make ROC plots for all motifs. | gimmemotifs/report.py | def create_roc_plots(pwmfile, fgfa, background, outdir):
"""Make ROC plots for all motifs."""
motifs = read_motifs(pwmfile, fmt="pwm", as_dict=True)
ncpus = int(MotifConfig().get_default_params()['ncpus'])
pool = Pool(processes=ncpus)
jobs = {}
for bg,fname in background.items():
for m_i... | def create_roc_plots(pwmfile, fgfa, background, outdir):
"""Make ROC plots for all motifs."""
motifs = read_motifs(pwmfile, fmt="pwm", as_dict=True)
ncpus = int(MotifConfig().get_default_params()['ncpus'])
pool = Pool(processes=ncpus)
jobs = {}
for bg,fname in background.items():
for m_i... | [
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train | _create_text_report | Create text report of motifs with statistics and database match. | gimmemotifs/report.py | def _create_text_report(inputfile, motifs, closest_match, stats, outdir):
"""Create text report of motifs with statistics and database match."""
my_stats = {}
for motif in motifs:
match = closest_match[motif.id]
my_stats[str(motif)] = {}
for bg in list(stats.values())[0].keys():
... | def _create_text_report(inputfile, motifs, closest_match, stats, outdir):
"""Create text report of motifs with statistics and database match."""
my_stats = {}
for motif in motifs:
match = closest_match[motif.id]
my_stats[str(motif)] = {}
for bg in list(stats.values())[0].keys():
... | [
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train | _create_graphical_report | Create main gimme_motifs output html report. | gimmemotifs/report.py | def _create_graphical_report(inputfile, pwm, background, closest_match, outdir, stats, best_id=None):
"""Create main gimme_motifs output html report."""
if best_id is None:
best_id = {}
logger.debug("Creating graphical report")
class ReportMotif(object):
"""Placeholder for motif st... | def _create_graphical_report(inputfile, pwm, background, closest_match, outdir, stats, best_id=None):
"""Create main gimme_motifs output html report."""
if best_id is None:
best_id = {}
logger.debug("Creating graphical report")
class ReportMotif(object):
"""Placeholder for motif st... | [
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train | create_denovo_motif_report | Create text and graphical (.html) motif reports. | gimmemotifs/report.py | def create_denovo_motif_report(inputfile, pwmfile, fgfa, background, locfa, outdir, params, stats=None):
"""Create text and graphical (.html) motif reports."""
logger.info("creating reports")
motifs = read_motifs(pwmfile, fmt="pwm")
# ROC plots
create_roc_plots(pwmfile, fgfa, background, outdi... | def create_denovo_motif_report(inputfile, pwmfile, fgfa, background, locfa, outdir, params, stats=None):
"""Create text and graphical (.html) motif reports."""
logger.info("creating reports")
motifs = read_motifs(pwmfile, fmt="pwm")
# ROC plots
create_roc_plots(pwmfile, fgfa, background, outdi... | [
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train | axes_off | Get rid of all axis ticks, lines, etc. | gimmemotifs/plot.py | def axes_off(ax):
"""Get rid of all axis ticks, lines, etc.
"""
ax.set_frame_on(False)
ax.axes.get_yaxis().set_visible(False)
ax.axes.get_xaxis().set_visible(False) | def axes_off(ax):
"""Get rid of all axis ticks, lines, etc.
"""
ax.set_frame_on(False)
ax.axes.get_yaxis().set_visible(False)
ax.axes.get_xaxis().set_visible(False) | [
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train | match_plot | Plot list of motifs with database match and p-value
"param plotdata: list of (motif, dbmotif, pval) | gimmemotifs/plot.py | def match_plot(plotdata, outfile):
"""Plot list of motifs with database match and p-value
"param plotdata: list of (motif, dbmotif, pval)
"""
fig_h = 2
fig_w = 7
nrows = len(plotdata)
ncols = 2
fig = plt.figure(figsize=(fig_w, nrows * fig_h))
for i, (motif, dbmotif, pval) in e... | def match_plot(plotdata, outfile):
"""Plot list of motifs with database match and p-value
"param plotdata: list of (motif, dbmotif, pval)
"""
fig_h = 2
fig_w = 7
nrows = len(plotdata)
ncols = 2
fig = plt.figure(figsize=(fig_w, nrows * fig_h))
for i, (motif, dbmotif, pval) in e... | [
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train | motif_tree_plot | Plot a "phylogenetic" tree | gimmemotifs/plot.py | def motif_tree_plot(outfile, tree, data, circle=True, vmin=None, vmax=None, dpi=300):
"""
Plot a "phylogenetic" tree
"""
try:
from ete3 import Tree, faces, AttrFace, TreeStyle, NodeStyle
except ImportError:
print("Please install ete3 to use this functionality")
sys.exit(1)
... | def motif_tree_plot(outfile, tree, data, circle=True, vmin=None, vmax=None, dpi=300):
"""
Plot a "phylogenetic" tree
"""
try:
from ete3 import Tree, faces, AttrFace, TreeStyle, NodeStyle
except ImportError:
print("Please install ete3 to use this functionality")
sys.exit(1)
... | [
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train | check_bed_file | Check if the inputfile is a valid bed-file | gimmemotifs/validation.py | def check_bed_file(fname):
""" Check if the inputfile is a valid bed-file """
if not os.path.exists(fname):
logger.error("Inputfile %s does not exist!", fname)
sys.exit(1)
for i, line in enumerate(open(fname)):
if line.startswith("#") or line.startswith("track") or line.startswith("... | def check_bed_file(fname):
""" Check if the inputfile is a valid bed-file """
if not os.path.exists(fname):
logger.error("Inputfile %s does not exist!", fname)
sys.exit(1)
for i, line in enumerate(open(fname)):
if line.startswith("#") or line.startswith("track") or line.startswith("... | [
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train | check_denovo_input | Check if an input file is valid, which means BED, narrowPeak or FASTA | gimmemotifs/validation.py | def check_denovo_input(inputfile, params):
"""
Check if an input file is valid, which means BED, narrowPeak or FASTA
"""
background = params["background"]
input_type = determine_file_type(inputfile)
if input_type == "fasta":
valid_bg = FA_VALID_BGS
elif input_type in ["... | def check_denovo_input(inputfile, params):
"""
Check if an input file is valid, which means BED, narrowPeak or FASTA
"""
background = params["background"]
input_type = determine_file_type(inputfile)
if input_type == "fasta":
valid_bg = FA_VALID_BGS
elif input_type in ["... | [
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train | scan_to_best_match | Scan a FASTA file with motifs.
Scan a FASTA file and return a dictionary with the best match per motif.
Parameters
----------
fname : str
Filename of a sequence file in FASTA format.
motifs : list
List of motif instances.
Returns
-------
result : dict
Dictiona... | gimmemotifs/scanner.py | def scan_to_best_match(fname, motifs, ncpus=None, genome=None, score=False):
"""Scan a FASTA file with motifs.
Scan a FASTA file and return a dictionary with the best match per motif.
Parameters
----------
fname : str
Filename of a sequence file in FASTA format.
motifs : list
... | def scan_to_best_match(fname, motifs, ncpus=None, genome=None, score=False):
"""Scan a FASTA file with motifs.
Scan a FASTA file and return a dictionary with the best match per motif.
Parameters
----------
fname : str
Filename of a sequence file in FASTA format.
motifs : list
... | [
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train | Scanner.set_background | Set the background to use for FPR and z-score calculations.
Background can be specified either as a genome name or as the
name of a FASTA file.
Parameters
----------
fname : str, optional
Name of FASTA file to use as background.
genome : str, optio... | gimmemotifs/scanner.py | def set_background(self, fname=None, genome=None, length=200, nseq=10000):
"""Set the background to use for FPR and z-score calculations.
Background can be specified either as a genome name or as the
name of a FASTA file.
Parameters
----------
fname : str, opti... | def set_background(self, fname=None, genome=None, length=200, nseq=10000):
"""Set the background to use for FPR and z-score calculations.
Background can be specified either as a genome name or as the
name of a FASTA file.
Parameters
----------
fname : str, opti... | [
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train | Scanner.set_threshold | Set motif scanning threshold based on background sequences.
Parameters
----------
fpr : float, optional
Desired FPR, between 0.0 and 1.0.
threshold : float or str, optional
Desired motif threshold, expressed as the fraction of the
difference between... | gimmemotifs/scanner.py | def set_threshold(self, fpr=None, threshold=None):
"""Set motif scanning threshold based on background sequences.
Parameters
----------
fpr : float, optional
Desired FPR, between 0.0 and 1.0.
threshold : float or str, optional
Desired motif threshold, ex... | def set_threshold(self, fpr=None, threshold=None):
"""Set motif scanning threshold based on background sequences.
Parameters
----------
fpr : float, optional
Desired FPR, between 0.0 and 1.0.
threshold : float or str, optional
Desired motif threshold, ex... | [
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train | Scanner.count | count the number of matches above the cutoff
returns an iterator of lists containing integer counts | gimmemotifs/scanner.py | def count(self, seqs, nreport=100, scan_rc=True):
"""
count the number of matches above the cutoff
returns an iterator of lists containing integer counts
"""
for matches in self.scan(seqs, nreport, scan_rc):
counts = [len(m) for m in matches]
yield counts | def count(self, seqs, nreport=100, scan_rc=True):
"""
count the number of matches above the cutoff
returns an iterator of lists containing integer counts
"""
for matches in self.scan(seqs, nreport, scan_rc):
counts = [len(m) for m in matches]
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train | Scanner.total_count | count the number of matches above the cutoff
returns an iterator of lists containing integer counts | gimmemotifs/scanner.py | def total_count(self, seqs, nreport=100, scan_rc=True):
"""
count the number of matches above the cutoff
returns an iterator of lists containing integer counts
"""
count_table = [counts for counts in self.count(seqs, nreport, scan_rc)]
return np.sum(np.array(coun... | def total_count(self, seqs, nreport=100, scan_rc=True):
"""
count the number of matches above the cutoff
returns an iterator of lists containing integer counts
"""
count_table = [counts for counts in self.count(seqs, nreport, scan_rc)]
return np.sum(np.array(coun... | [
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train | Scanner.best_score | give the score of the best match of each motif in each sequence
returns an iterator of lists containing floats | gimmemotifs/scanner.py | def best_score(self, seqs, scan_rc=True, normalize=False):
"""
give the score of the best match of each motif in each sequence
returns an iterator of lists containing floats
"""
self.set_threshold(threshold=0.0)
if normalize and len(self.meanstd) == 0:
self.se... | def best_score(self, seqs, scan_rc=True, normalize=False):
"""
give the score of the best match of each motif in each sequence
returns an iterator of lists containing floats
"""
self.set_threshold(threshold=0.0)
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train | Scanner.best_match | give the best match of each motif in each sequence
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"""
give the best match of each motif in each sequence
returns an iterator of nested lists containing tuples:
(score, position, strand)
"""
self.set_threshold(threshold=0.0)
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train | Scanner.scan | scan a set of regions / sequences | gimmemotifs/scanner.py | def scan(self, seqs, nreport=100, scan_rc=True, normalize=False):
"""
scan a set of regions / sequences
"""
if not self.threshold:
sys.stderr.write(
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"""
scan a set of regions / sequences
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train | roc | Calculate ROC_AUC and other metrics and optionally plot ROC curve. | gimmemotifs/commands/roc.py | def roc(args):
""" Calculate ROC_AUC and other metrics and optionally plot ROC curve."""
outputfile = args.outfile
# Default extension for image
if outputfile and not outputfile.endswith(".png"):
outputfile += ".png"
motifs = read_motifs(args.pwmfile, fmt="pwm")
ids = []
if arg... | def roc(args):
""" Calculate ROC_AUC and other metrics and optionally plot ROC curve."""
outputfile = args.outfile
# Default extension for image
if outputfile and not outputfile.endswith(".png"):
outputfile += ".png"
motifs = read_motifs(args.pwmfile, fmt="pwm")
ids = []
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train | ssd | Calculates motif position similarity based on sum of squared distances.
Parameters
----------
p1 : list
Motif position 1.
p2 : list
Motif position 2.
Returns
-------
score : float | gimmemotifs/comparison.py | def ssd(p1, p2):
"""Calculates motif position similarity based on sum of squared distances.
Parameters
----------
p1 : list
Motif position 1.
p2 : list
Motif position 2.
Returns
-------
score : float
"""
return 2 - np.sum([(a-b)**2 for a,b in zip(p1... | def ssd(p1, p2):
"""Calculates motif position similarity based on sum of squared distances.
Parameters
----------
p1 : list
Motif position 1.
p2 : list
Motif position 2.
Returns
-------
score : float
"""
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train | seqcor | Calculates motif similarity based on Pearson correlation of scores.
Based on Kielbasa (2015) and Grau (2015).
Scores are calculated based on scanning a de Bruijn sequence of 7-mers.
This sequence is taken from ShortCAKE (Orenstein & Shamir, 2015).
Optionally another sequence can be given as an argumen... | gimmemotifs/comparison.py | def seqcor(m1, m2, seq=None):
"""Calculates motif similarity based on Pearson correlation of scores.
Based on Kielbasa (2015) and Grau (2015).
Scores are calculated based on scanning a de Bruijn sequence of 7-mers.
This sequence is taken from ShortCAKE (Orenstein & Shamir, 2015).
Optionally anothe... | def seqcor(m1, m2, seq=None):
"""Calculates motif similarity based on Pearson correlation of scores.
Based on Kielbasa (2015) and Grau (2015).
Scores are calculated based on scanning a de Bruijn sequence of 7-mers.
This sequence is taken from ShortCAKE (Orenstein & Shamir, 2015).
Optionally anothe... | [
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train | MotifComparer.compare_motifs | Compare two motifs.
The similarity metric can be any of seqcor, pcc, ed, distance, wic,
chisq, akl or ssd. If match is 'total' the similarity score is
calculated for the whole match, including positions that are not
present in both motifs. If match is partial or subtotal, onl... | gimmemotifs/comparison.py | def compare_motifs(self, m1, m2, match="total", metric="wic", combine="mean", pval=False):
"""Compare two motifs.
The similarity metric can be any of seqcor, pcc, ed, distance, wic,
chisq, akl or ssd. If match is 'total' the similarity score is
calculated for the whole match, ... | def compare_motifs(self, m1, m2, match="total", metric="wic", combine="mean", pval=False):
"""Compare two motifs.
The similarity metric can be any of seqcor, pcc, ed, distance, wic,
chisq, akl or ssd. If match is 'total' the similarity score is
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train | MotifComparer.get_all_scores | Pairwise comparison of a set of motifs compared to reference motifs.
Parameters
----------
motifs : list
List of Motif instances.
dbmotifs : list
List of Motif instances.
match : str
Match can be "partial", "subtotal" or "total". Not all met... | gimmemotifs/comparison.py | def get_all_scores(self, motifs, dbmotifs, match, metric, combine,
pval=False, parallel=True, trim=None, ncpus=None):
"""Pairwise comparison of a set of motifs compared to reference motifs.
Parameters
----------
motifs : list
List of Motif instan... | def get_all_scores(self, motifs, dbmotifs, match, metric, combine,
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"""Pairwise comparison of a set of motifs compared to reference motifs.
Parameters
----------
motifs : list
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train | MotifComparer.get_closest_match | Return best match in database for motifs.
Parameters
----------
motifs : list or str
Filename of motifs or list of motifs.
dbmotifs : list or str, optional
Database motifs, default will be used if not specified.
match : str, optional
metric : s... | gimmemotifs/comparison.py | def get_closest_match(self, motifs, dbmotifs=None, match="partial", metric="wic",combine="mean", parallel=True, ncpus=None):
"""Return best match in database for motifs.
Parameters
----------
motifs : list or str
Filename of motifs or list of motifs.
dbmotifs : list... | def get_closest_match(self, motifs, dbmotifs=None, match="partial", metric="wic",combine="mean", parallel=True, ncpus=None):
"""Return best match in database for motifs.
Parameters
----------
motifs : list or str
Filename of motifs or list of motifs.
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train | list_regions | List regions for the service | aws/main.py | def list_regions(service):
"""
List regions for the service
"""
for region in service.regions():
print '%(name)s: %(endpoint)s' % {
'name': region.name,
'endpoint': region.endpoint,
} | def list_regions(service):
"""
List regions for the service
"""
for region in service.regions():
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'name': region.name,
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train | elb_table | Print nice looking table of information from list of load balancers | aws/main.py | def elb_table(balancers):
"""
Print nice looking table of information from list of load balancers
"""
t = prettytable.PrettyTable(['Name', 'DNS', 'Ports', 'Zones', 'Created'])
t.align = 'l'
for b in balancers:
ports = ['%s: %s -> %s' % (l[2], l[0], l[1]) for l in b.listeners]
por... | def elb_table(balancers):
"""
Print nice looking table of information from list of load balancers
"""
t = prettytable.PrettyTable(['Name', 'DNS', 'Ports', 'Zones', 'Created'])
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train | ec2_table | Print nice looking table of information from list of instances | aws/main.py | def ec2_table(instances):
"""
Print nice looking table of information from list of instances
"""
t = prettytable.PrettyTable(['ID', 'State', 'Monitored', 'Image', 'Name', 'Type', 'SSH key', 'DNS'])
t.align = 'l'
for i in instances:
name = i.tags.get('Name', '')
t.add_row([i.id, i... | def ec2_table(instances):
"""
Print nice looking table of information from list of instances
"""
t = prettytable.PrettyTable(['ID', 'State', 'Monitored', 'Image', 'Name', 'Type', 'SSH key', 'DNS'])
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train | ec2_image_table | Print nice looking table of information from images | aws/main.py | def ec2_image_table(images):
"""
Print nice looking table of information from images
"""
t = prettytable.PrettyTable(['ID', 'State', 'Name', 'Owner', 'Root device', 'Is public', 'Description'])
t.align = 'l'
for i in images:
t.add_row([i.id, i.state, i.name, i.ownerId, i.root_device_type... | def ec2_image_table(images):
"""
Print nice looking table of information from images
"""
t = prettytable.PrettyTable(['ID', 'State', 'Name', 'Owner', 'Root device', 'Is public', 'Description'])
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train | ec2_fab | Run Fabric commands against EC2 instances | aws/main.py | def ec2_fab(service, args):
"""
Run Fabric commands against EC2 instances
"""
instance_ids = args.instances
instances = service.list(elb=args.elb, instance_ids=instance_ids)
hosts = service.resolve_hosts(instances)
fab.env.hosts = hosts
fab.env.key_filename = settings.get('SSH', 'KEY_FI... | def ec2_fab(service, args):
"""
Run Fabric commands against EC2 instances
"""
instance_ids = args.instances
instances = service.list(elb=args.elb, instance_ids=instance_ids)
hosts = service.resolve_hosts(instances)
fab.env.hosts = hosts
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train | main | AWS support script's main method | aws/main.py | def main():
"""
AWS support script's main method
"""
p = argparse.ArgumentParser(description='Manage Amazon AWS services',
prog='aws',
version=__version__)
subparsers = p.add_subparsers(help='Select Amazon AWS service to use')
# Au... | def main():
"""
AWS support script's main method
"""
p = argparse.ArgumentParser(description='Manage Amazon AWS services',
prog='aws',
version=__version__)
subparsers = p.add_subparsers(help='Select Amazon AWS service to use')
# Au... | [
"AWS",
"support",
"script",
"s",
"main",
"method"
] | eofs/aws | python | https://github.com/eofs/aws/blob/479cbe27a9f289b43f32f8e3de7d048a4a8993fe/aws/main.py#L222-L330 | [
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train | buffer_stream | Buffer "data" from an stream into one data object.
Parameters
----------
stream : stream
The stream to buffer
buffer_size : int > 0
The number of examples to retain per batch.
partial : bool, default=False
If True, yield a final partial batch on under-run.
axis : int ... | pescador/maps.py | def buffer_stream(stream, buffer_size, partial=False, axis=None):
'''Buffer "data" from an stream into one data object.
Parameters
----------
stream : stream
The stream to buffer
buffer_size : int > 0
The number of examples to retain per batch.
partial : bool, default=False
... | def buffer_stream(stream, buffer_size, partial=False, axis=None):
'''Buffer "data" from an stream into one data object.
Parameters
----------
stream : stream
The stream to buffer
buffer_size : int > 0
The number of examples to retain per batch.
partial : bool, default=False
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] | pescadores/pescador | python | https://github.com/pescadores/pescador/blob/786e2b5f882d13ea563769fbc7ad0a0a10c3553d/pescador/maps.py#L34-L84 | [
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train | tuples | Reformat data as tuples.
Parameters
----------
stream : iterable
Stream of data objects.
*keys : strings
Keys to use for ordering data.
Yields
------
items : tuple of np.ndarrays
Data object reformated as a tuple.
Raises
------
DataError
If the... | pescador/maps.py | def tuples(stream, *keys):
"""Reformat data as tuples.
Parameters
----------
stream : iterable
Stream of data objects.
*keys : strings
Keys to use for ordering data.
Yields
------
items : tuple of np.ndarrays
Data object reformated as a tuple.
Raises
-... | def tuples(stream, *keys):
"""Reformat data as tuples.
Parameters
----------
stream : iterable
Stream of data objects.
*keys : strings
Keys to use for ordering data.
Yields
------
items : tuple of np.ndarrays
Data object reformated as a tuple.
Raises
-... | [
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] | pescadores/pescador | python | https://github.com/pescadores/pescador/blob/786e2b5f882d13ea563769fbc7ad0a0a10c3553d/pescador/maps.py#L87-L117 | [
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