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Theta = (theta_1, theta_2, ... theta_M)
Likelihood of mixture parameters given data: L(Theta | X) = product_i P(x_i | Theta)
log likelihood: log L(Theta | X) = sum_i log(P(x_i | Theta))
and note that p(x_i | Theta) = sum_j prior_j * p(x_i | theta_j)
Probability of sample x being generated fro... |
Validation Check for M.L. paramateres
def _validate_params(priors, means, covars):
""" Validation Check for M.L. paramateres
"""
for i,(p,m,cv) in enumerate(zip(priors, means, covars)):
if np.any(np.isinf(p)) or np.any(np.isnan(p)):
raise ValueError("Component %d of priors is not valid... |
Update class parameters as below:
priors: P(w_i) = sum_x P(w_i | x) ==> Then normalize to get in [0,1]
Class means: center_w_i = sum_x P(w_i|x)*x / sum_i sum_x P(w_i|x)
def _maximization_step(X, posteriors):
"""
Update class parameters as below:
priors: P(w_i) = sum_x P(w_i | x) ==> ... |
Fit mixture-density parameters with EM algorithm
def fit(self, X):
""" Fit mixture-density parameters with EM algorithm
"""
params_dict = _fit_gmm_params(X=X, n_mixtures=self.n_clusters, \
n_init=self.n_trials, init_method=self.init_method, \
n_it... |
Initialize k=n_clusters centroids randomly
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)
else:
#print('Gen... |
Assignment Step:
assign each point to the closet cluster center
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... |
Calculate the SSE to the cluster center
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)) |
Update Cluster Centers:
calculate the mean of feature vectors for each cluster
def _update_centers(X, membs, n_clusters):
""" Update Cluster Centers:
calculate the mean of feature vectors for each cluster
"""
centers = np.empty(shape=(n_clusters, X.shape[1]), dtype=float)
sse = np... |
Run a single trial of k-means clustering
on dataset X, and given number of clusters
def _kmeans_run(X, n_clusters, max_iter, tol):
""" Run a single trial of k-means clustering
on dataset X, and given number of clusters
"""
membs = np.empty(shape=X.shape[0], dtype=int)
centers = _kmeans_... |
Run multiple trials of k-means clustering,
and outputt he best centers, and cluster labels
def _kmeans(X, n_clusters, max_iter, n_trials, tol):
""" Run multiple trials of k-means clustering,
and outputt he best centers, and cluster labels
"""
n_samples, n_features = X.shape[0], X.shape[1]
... |
Apply KMeans Clustering
X: dataset with feature vectors
def fit(self, X):
""" Apply KMeans Clustering
X: dataset with feature vectors
"""
self.centers_, self.labels_, self.sse_arr_, self.n_iter_ = \
_kmeans(X, self.n_clusters, self.max_iter, self.n_tria... |
Cut the tree to get desired number of clusters as n_clusters
2 <= n_desired <= n_clusters
def _cut_tree(tree, n_clusters, membs):
""" Cut the tree to get desired number of clusters as n_clusters
2 <= n_desired <= n_clusters
"""
## starting from root,
## a node is added to the cu... |
Add a node to the tree
if parent is not known, the node is a root
The nodes of this tree keep properties of each cluster/subcluster:
size --> cluster size as the number of points in the cluster
center --> mean of the cluster
label --> cluster label
sse ... |
Apply Bisecting Kmeans clustering
to reach n_clusters number of clusters
def _bisect_kmeans(X, n_clusters, n_trials, max_iter, tol):
""" Apply Bisecting Kmeans clustering
to reach n_clusters number of clusters
"""
membs = np.empty(shape=X.shape[0], dtype=int)
centers = dict() #np.empty(... |
r""" Returns the corrected Deviance Information Criterion (DIC) for all chains loaded into ChainConsumer.
If a chain does not have a posterior, this method will return `None` for that chain. **Note that
the DIC metric is only valid on posterior surfaces which closely resemble multivariate normals!**
... |
r""" Returns the corrected Bayesian Information Criterion (BIC) for all chains loaded into ChainConsumer.
If a chain does not have a posterior, number of data points, and number of free parameters
loaded, this method will return `None` for that chain. Formally, the BIC is defined as
.. math::
... |
r""" Returns the corrected Akaike Information Criterion (AICc) for all chains loaded into ChainConsumer.
If a chain does not have a posterior, number of data points, and number of free parameters
loaded, this method will return `None` for that chain. Formally, the AIC is defined as
.. math::
... |
Return a LaTeX ready table of model comparisons.
Parameters
----------
caption : str, optional
The table caption to insert.
label : str, optional
The table label to insert.
hlines : bool, optional
Whether to insert hlines in the table or not.
... |
Estimate un-normalised probability density at target points
Parameters
----------
data : np.ndarray
A `(num_targets, num_dim)` array of points to investigate.
Returns
-------
np.ndarray
A `(num_targets)` length array of estimates... |
Plot the chain!
Parameters
----------
figsize : str|tuple(float)|float, optional
The figure size to generate. Accepts a regular two tuple of size in inches,
or one of several key words. The default value of ``COLUMN`` creates a figure
of appropriate size of i... |
Plots the chain walk; the parameter values as a function of step index.
This plot is more for a sanity or consistency check than for use with final results.
Plotting this before plotting with :func:`plot` allows you to quickly see if the
chains are well behaved, or if certain parameters are sus... |
Plots the 1D parameter distributions for verification purposes.
This plot is more for a sanity or consistency check than for use with final results.
Plotting this before plotting with :func:`plot` allows you to quickly see if the
chains give well behaved distributions, or if certain parameters ... |
Plots parameter summaries
This plot is more for a sanity or consistency check than for use with final results.
Plotting this before plotting with :func:`plot` allows you to quickly see if the
chains give well behaved distributions, or if certain parameters are suspect
or require a great... |
r""" Runs the Gelman Rubin 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 c... |
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)... |
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... |
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 ... |
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
Squeeze the summa... |
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
The list of parameters to compute correlations. Defaults to all ... |
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... |
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
... |
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... |
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 ... |
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... |
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... |
r""" Configure the general plotting parameters common across the bar
and contour plots.
If you do not call this explicitly, the :func:`plot`
method will invoke this method automatically.
Please ensure that you call this method *after* adding all the relevant data to the
chain c... |
Configure the arguments passed to the ``axvline`` and ``axhline``
methods when plotting truth values.
If you do not call this explicitly, the :func:`plot` method will
invoke this method automatically.
Recommended to set the parameters ``linestyle``, ``color`` and/or ``alpha``
i... |
Returns a ChainConsumer instance containing all the walks of a given chain
as individual chains themselves.
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... |
Calculate motif score threshold for a given FPR.
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... |
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... |
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
... |
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).
... |
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).
... |
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).
... |
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... |
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... |
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.
... |
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... |
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
def pr_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):
"""
C... |
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
... |
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... |
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-measure.
def max_fmeasure(fg_vals, bg_vals):
... |
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.
de... |
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
-log1... |
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
Images and labels ... |
Create a compiled Keras model.
Parameters
----------
input_shape : tuple, len=3
Shape of each image sample.
Returns
-------
model : keras.Model
Constructed model.
def build_model(input_shape):
"""Create a compiled Keras model.
Parameters
----------
input_shape... |
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}
def... |
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
Updat... |
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
def parse_denovo_params(user_params=None):
"""Return default Gim... |
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... |
Return aggregated ranks.
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 : pandas.DataFrame
DataFrame with value... |
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 learning problem, with X as featu... |
Parallel calculation of motif statistics.
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)))
... |
Parallel motif prediction.
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 |
Parallel prediction of motifs.
Utility function for gimmemotifs.denovo.gimme_motifs. Probably better to
use that, instead of this function directly.
def pp_predict_motifs(fastafile, outfile, analysis="small", organism="hg18", single=False, background="", tools=None, job_server=None, ncpus=8, max_time=-1, sta... |
Predict motifs, input is a FASTA-file
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",
"genome", "use_strand", ... |
Add motifs to the result object.
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:
job = args[0]
logger.wa... |
Make sure all jobs are finished.
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) |
Callback to add motif statistics.
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] ... |
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... |
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... |
Create all the FASTA files for de novo motif prediction and validation.
Parameters
----------
def prepare_denovo_input_fa(inputfile, params, outdir):
"""Create all the FASTA files for de novo motif prediction and validation.
Parameters
----------
"""
fraction = float(params["fract... |
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... |
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... |
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... |
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.
... |
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.
... |
Rename motifs to GimmeMotifs_1..GimmeMotifs_N.
If stats object is passed, stats will be copied.
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):... |
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
Name of output directory.
params : dict, optional
Optional parameters.
filter_significant : ... |
Register method to keep list of dbs.
def register_db(cls, dbname):
"""Register method to keep list of dbs."""
def decorator(subclass):
"""Register as decorator function."""
cls._dbs[dbname] = subclass
subclass.name = dbname
return subclass
return ... |
Run a single motif activity prediction algorithm.
Parameters
----------
inputfile : str
:1File with regions (chr:start-end) in first column and either cluster
name in second column or a table with values.
method : str, optional
Motif activity method to use. Any of 'hyp... |
Create a Moap instance based on the predictor name.
Parameters
----------
name : str
Name of the predictor (eg. Xgboost, BayesianRidge, ...)
ncpus : int, optional
Number of threads. Default is the number specified in the config.
Returns
... |
Register method to keep list of predictors.
def register_predictor(cls, name):
"""Register method to keep list of predictors."""
def decorator(subclass):
"""Register as decorator function."""
cls._predictors[name.lower()] = subclass
subclass.name = name.lower()
... |
List available classification predictors.
def list_classification_predictors(self):
"""List available classification predictors."""
preds = [self.create(x) for x in self._predictors.keys()]
return [x.name for x in preds if x.ptype == "classification"] |
Activates the stream.
def _activate(self):
"""Activates the stream."""
if six.callable(self.streamer):
# If it's a function, create the stream.
self.stream_ = self.streamer(*(self.args), **(self.kwargs))
else:
# If it's iterable, use it directly.
... |
Instantiate an iterator.
Parameters
----------
max_iter : None or int > 0
Maximum number of iterations to yield.
If ``None``, exhaust the stream.
Yields
------
obj : Objects yielded by the streamer provided on init.
See Also
----... |
Iterate from the streamer infinitely.
This function will force an infinite stream, restarting
the streamer even if a StopIteration is raised.
Parameters
----------
max_iter : None or int > 0
Maximum number of iterations to yield.
If `None`, iterate indef... |
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 : ... |
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 : ... |
Determine mean rank of motifs based on metrics.
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()
for metric in metrics:
... |
write motif statistics to text file.
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:
f.write(header)
stat_ke... |
Calculate ROC AUC values for ROC plots.
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, ... |
Make ROC plots for all motifs.
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 bac... |
Create text report of motifs with statistics and database match.
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(m... |
Create main gimme_motifs output html report.
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 ReportMoti... |
Create text and graphical (.html) motif reports.
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
... |
Get rid of all axis ticks, lines, etc.
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) |
Plot list of motifs with database match and p-value
"param plotdata: list of (motif, dbmotif, pval)
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)
n... |
Plot a "phylogenetic" tree
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 functiona... |
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