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MAVENSDC/PyTplot | pytplot/QtPlotter/generate.py | _set_pyqtgraph_title | def _set_pyqtgraph_title(layout):
"""
Private function to add a title to the first row of the window.
Returns True if a Title is set. Else, returns False.
"""
if 'title_size' in pytplot.tplot_opt_glob:
size = pytplot.tplot_opt_glob['title_size']
if 'title_text' in pytplot.tplot_opt_glob... | python | def _set_pyqtgraph_title(layout):
"""
Private function to add a title to the first row of the window.
Returns True if a Title is set. Else, returns False.
"""
if 'title_size' in pytplot.tplot_opt_glob:
size = pytplot.tplot_opt_glob['title_size']
if 'title_text' in pytplot.tplot_opt_glob... | [
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MAVENSDC/PyTplot | pytplot/store_data.py | store_data | def store_data(name, data=None, delete=False, newname=None):
"""
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name : str
Name of the ... | python | def store_data(name, data=None, delete=False, newname=None):
"""
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MAVENSDC/PyTplot | pytplot/get_data.py | get_data | def get_data(name):
"""
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Parameters:
name : str
Name of the tplot variable
Returns:
time_val : pandas dataframe index
data_val : list
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>>> # R... | python | def get_data(name):
"""
This function extracts the data from the Tplot Variables stored in memory.
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name : str
Name of the tplot variable
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time_val : pandas dataframe index
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MAVENSDC/PyTplot | pytplot/QtPlotter/CustomAxis/AxisItem.py | AxisItem.generateDrawSpecs | def generateDrawSpecs(self, p):
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MAVENSDC/PyTplot | pytplot/staticplot.py | static2dplot | def static2dplot(var, time):
""" If the static option is set in tplot, and is supplied with a time, then the spectrogram plot(s) for which
it is set will have another window pop up, with y and z values plotted at the specified time. """
# Grab names of data loaded in as tplot variables.
names = list(py... | python | def static2dplot(var, time):
""" If the static option is set in tplot, and is supplied with a time, then the spectrogram plot(s) for which
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MAVENSDC/PyTplot | pytplot/netcdf_to_tplot.py | netcdf_to_tplot | def netcdf_to_tplot(filenames, time ='', prefix='', suffix='', plot=False, merge=False):
'''
This function will automatically create tplot variables from CDF files.
Parameters:
filenames : str/list of str
The file names and full paths of netCDF files.
time: str
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filenames : str/list of str
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time: str
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MAVENSDC/PyTplot | pytplot/tplot_rename.py | tplot_rename | def tplot_rename(old_name, new_name):
"""
This function will rename tplot variables that are already stored in memory.
Parameters:
old_name : str
Old name of the Tplot Variable
new_name : str
New name of the Tplot Variable
Returns:
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... | python | def tplot_rename(old_name, new_name):
"""
This function will rename tplot variables that are already stored in memory.
Parameters:
old_name : str
Old name of the Tplot Variable
new_name : str
New name of the Tplot Variable
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MAVENSDC/PyTplot | pytplot/__init__.py | TVar._check_spec_bins_ordering | def _check_spec_bins_ordering(self):
"""
This is a private function of the TVar object, this is run during
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"""
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... | python | def _check_spec_bins_ordering(self):
"""
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MAVENSDC/PyTplot | pytplot/tplot_names.py | tplot_names | def tplot_names():
"""
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Parameters:
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list : list of str
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>>> i... | python | def tplot_names():
"""
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list : list of str
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MAVENSDC/PyTplot | pytplot/interactiveplot.py | interactiveplot | def interactiveplot(t_average=None):
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MAVENSDC/PyTplot | pytplot/cdf_to_tplot.py | cdf_to_tplot | def cdf_to_tplot(filenames, varformat=None, get_support_data=False,
prefix='', suffix='', plot=False, merge=False):
"""
This function will automatically create tplot variables from CDF files.
.. note::
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... | python | def cdf_to_tplot(filenames, varformat=None, get_support_data=False,
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.. note::
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MAVENSDC/PyTplot | pytplot/get_timespan.py | get_timespan | def get_timespan(name):
"""
This function extracts the time span from the Tplot Variables stored in memory.
Parameters:
name : str
Name of the tplot variable
Returns:
time_begin : float
The beginning of the time series
time_end : float
... | python | def get_timespan(name):
"""
This function extracts the time span from the Tplot Variables stored in memory.
Parameters:
name : str
Name of the tplot variable
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time_begin : float
The beginning of the time series
time_end : float
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MAVENSDC/PyTplot | pytplot/timebar.py | timebar | def timebar(t, varname = None, databar = False, delete = False, color = 'black', thick = 1, dash = False):
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Name of the tplot variable
option : str
The name of the option. See section below
value : str/int/floa... | python | def options(name, option, value):
"""
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Name of the tplot variable
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MAVENSDC/PyTplot | pytplot/tplot_restore.py | tplot_restore | def tplot_restore(filename):
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MAVENSDC/PyTplot | pytplot/tplot.py | tplot | def tplot(name,
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gui=False,
qt=False,
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MAVENSDC/PyTplot | pytplot/staticplot_tavg.py | static2dplot_timeaveraged | def static2dplot_timeaveraged(var, time):
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MAVENSDC/PyTplot | pytplot/tplot_save.py | tplot_save | def tplot_save(names, filename=None):
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MAVENSDC/PyTplot | pytplot/get_ylimits.py | get_ylimits | def get_ylimits(name, trg=None):
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name : str
Name of the tplot variable
trg : list, optional
The time range that you would like to look in
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MAVENSDC/PyTplot | pytplot/timestamp.py | timestamp | def timestamp(val):
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A string that can either be 'on' or 'off'.
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i... | python | def timestamp(val):
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MAVENSDC/PyTplot | pytplot/QtPlotter/CustomImage/UpdatingImage.py | makeARGBwithNaNs | def makeARGBwithNaNs(data, lut=None, levels=None, scale=None, useRGBA=False):
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MAVENSDC/PyTplot | pytplot/QtPlotter/PyTPlot_Exporter.py | PytplotExporter.getPaintItems | def getPaintItems(self, root=None):
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ismms-himc/clustergrammer2 | clustergrammer2/clustergrammer_fun/normalize_fun.py | run_norm | def run_norm(net, df=None, norm_type='zscore', axis='row', keep_orig=False):
'''
A dataframe (more accurately a dictionary of dataframes, e.g. mat,
mat_up...) can be passed to run_norm and a normalization will be run (
e.g. zscore) on either the rows or columns
'''
# df here is actually a dictionary of sev... | python | def run_norm(net, df=None, norm_type='zscore', axis='row', keep_orig=False):
'''
A dataframe (more accurately a dictionary of dataframes, e.g. mat,
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ismms-himc/clustergrammer2 | clustergrammer2/clustergrammer_fun/normalize_fun.py | qn_df | def qn_df(df, axis='row', keep_orig=False):
'''
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'''
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inst_df = df[mat_type]
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'''
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'''
df_qn = {}
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inst_df = df[mat_type]
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ismms-himc/clustergrammer2 | clustergrammer2/clustergrammer_fun/normalize_fun.py | calc_common_dist | def calc_common_dist(df):
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'''
# axis is col
tmp_arr = np.array([])
col_names = df.columns.tolist()
for inst_col in col_names:
# sort column
tmp_vect = df[inst_col].sort_values(ascending=False).values
# stacking ... | python | def calc_common_dist(df):
'''
calculate a common distribution (for col qn only) that will be used to qn
'''
# axis is col
tmp_arr = np.array([])
col_names = df.columns.tolist()
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# sort column
tmp_vect = df[inst_col].sort_values(ascending=False).values
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ismms-himc/clustergrammer2 | clustergrammer2/clustergrammer_fun/normalize_fun.py | zscore_df | def zscore_df(df, axis='row', keep_orig=False):
'''
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'''
df_z = {}
for mat_type in df:
if keep_orig and mat_type == 'mat':
mat_orig = deepcopy(df[mat_type])
inst_df = df[mat_type]
if axis == 'row':
inst_df = inst... | python | def zscore_df(df, axis='row', keep_orig=False):
'''
take the zscore of a dataframe dictionary, does not write to net (self)
'''
df_z = {}
for mat_type in df:
if keep_orig and mat_type == 'mat':
mat_orig = deepcopy(df[mat_type])
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ismms-himc/clustergrammer2 | clustergrammer2/clustergrammer_fun/cat_pval.py | main | def main(net):
'''
calculate pvalue of category closeness
'''
# calculate the distance between the data points within the same category and
# compare to null distribution
for inst_rc in ['row', 'col']:
inst_nodes = deepcopy(net.dat['nodes'][inst_rc])
inst_index = deepcopy(net.dat['node_info'][inst... | python | def main(net):
'''
calculate pvalue of category closeness
'''
# calculate the distance between the data points within the same category and
# compare to null distribution
for inst_rc in ['row', 'col']:
inst_nodes = deepcopy(net.dat['nodes'][inst_rc])
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ismms-himc/clustergrammer2 | clustergrammer2/clustergrammer_fun/proc_df_labels.py | main | def main(df):
'''
1) check that rows are strings (in case of numerical names)
2) check for tuples, and in that case load tuples to categories
'''
import numpy as np
from ast import literal_eval as make_tuple
test = {}
test['row'] = df['mat'].index.tolist()
test['col'] = df['mat'].columns.tolist()
... | python | def main(df):
'''
1) check that rows are strings (in case of numerical names)
2) check for tuples, and in that case load tuples to categories
'''
import numpy as np
from ast import literal_eval as make_tuple
test = {}
test['row'] = df['mat'].index.tolist()
test['col'] = df['mat'].columns.tolist()
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ismms-himc/clustergrammer2 | clustergrammer2/clustergrammer_fun/data_formats.py | df_to_dat | def df_to_dat(net, df, define_cat_colors=False):
'''
This is always run when data is loaded.
'''
from . import categories
# check if df has unique values
df['mat'] = make_unique_labels.main(net, df['mat'])
net.dat['mat'] = df['mat'].values
net.dat['nodes']['row'] = df['mat'].index.tolist()
net.dat['... | python | def df_to_dat(net, df, define_cat_colors=False):
'''
This is always run when data is loaded.
'''
from . import categories
# check if df has unique values
df['mat'] = make_unique_labels.main(net, df['mat'])
net.dat['mat'] = df['mat'].values
net.dat['nodes']['row'] = df['mat'].index.tolist()
net.dat['... | [
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ismms-himc/clustergrammer2 | clustergrammer2/clustergrammer_fun/data_formats.py | mat_to_numpy_arr | def mat_to_numpy_arr(self):
''' convert list to numpy array - numpy arrays can not be saved as json '''
import numpy as np
self.dat['mat'] = np.asarray(self.dat['mat']) | python | def mat_to_numpy_arr(self):
''' convert list to numpy array - numpy arrays can not be saved as json '''
import numpy as np
self.dat['mat'] = np.asarray(self.dat['mat']) | [
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ismms-himc/clustergrammer2 | clustergrammer2/clustergrammer_fun/calc_clust.py | cluster_row_and_col | def cluster_row_and_col(net, dist_type='cosine', linkage_type='average',
dendro=True, run_clustering=True, run_rank=True,
ignore_cat=False, calc_cat_pval=False, links=False):
''' cluster net.dat and make visualization json, net.viz.
optionally leave out dendrogram col... | python | def cluster_row_and_col(net, dist_type='cosine', linkage_type='average',
dendro=True, run_clustering=True, run_rank=True,
ignore_cat=False, calc_cat_pval=False, links=False):
''' cluster net.dat and make visualization json, net.viz.
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ismms-himc/clustergrammer2 | clustergrammer2/clustergrammer_fun/categories.py | check_categories | def check_categories(lines):
'''
find out how many row and col categories are available
'''
# count the number of row categories
rcat_line = lines[0].split('\t')
# calc the number of row names and categories
num_rc = 0
found_end = False
# skip first tab
for inst_string in rcat_line[1:]:
if ins... | python | def check_categories(lines):
'''
find out how many row and col categories are available
'''
# count the number of row categories
rcat_line = lines[0].split('\t')
# calc the number of row names and categories
num_rc = 0
found_end = False
# skip first tab
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ismms-himc/clustergrammer2 | clustergrammer2/clustergrammer_fun/categories.py | dict_cat | def dict_cat(net, define_cat_colors=False):
'''
make a dictionary of node-category associations
'''
# print('---------------------------------')
# print('---- dict_cat: before setting cat colors')
# print('---------------------------------\n')
# print(define_cat_colors)
# print(net.viz['cat_colors'])
... | python | def dict_cat(net, define_cat_colors=False):
'''
make a dictionary of node-category associations
'''
# print('---------------------------------')
# print('---- dict_cat: before setting cat colors')
# print('---------------------------------\n')
# print(define_cat_colors)
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ismms-himc/clustergrammer2 | clustergrammer2/clustergrammer_fun/categories.py | calc_cat_clust_order | def calc_cat_clust_order(net, inst_rc):
'''
cluster category subset of data
'''
from .__init__ import Network
from copy import deepcopy
from . import calc_clust, run_filter
inst_keys = list(net.dat['node_info'][inst_rc].keys())
all_cats = [x for x in inst_keys if 'cat-' in x]
if len(all_cats) > 0:
... | python | def calc_cat_clust_order(net, inst_rc):
'''
cluster category subset of data
'''
from .__init__ import Network
from copy import deepcopy
from . import calc_clust, run_filter
inst_keys = list(net.dat['node_info'][inst_rc].keys())
all_cats = [x for x in inst_keys if 'cat-' in x]
if len(all_cats) > 0:
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ismms-himc/clustergrammer2 | clustergrammer2/clustergrammer_fun/categories.py | order_categories | def order_categories(unordered_cats):
'''
If categories are strings, then simple ordering is fine.
If categories are values then I'll need to order based on their values.
The final ordering is given as the original categories (including titles) in a
ordered list.
'''
no_titles = remove_titles(unordered_c... | python | def order_categories(unordered_cats):
'''
If categories are strings, then simple ordering is fine.
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ismms-himc/clustergrammer2 | clustergrammer2/clustergrammer_fun/__init__.py | Network.load_file_as_string | def load_file_as_string(self, file_string, filename=''):
'''
Load file as a string.
'''
load_data.load_file_as_string(self, file_string, filename=filename) | python | def load_file_as_string(self, file_string, filename=''):
'''
Load file as a string.
'''
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ismms-himc/clustergrammer2 | clustergrammer2/clustergrammer_fun/__init__.py | Network.load_data_file_to_net | def load_data_file_to_net(self, filename):
'''
Load Clustergrammer's dat format (saved as JSON).
'''
inst_dat = self.load_json_to_dict(filename)
load_data.load_data_to_net(self, inst_dat) | python | def load_data_file_to_net(self, filename):
'''
Load Clustergrammer's dat format (saved as JSON).
'''
inst_dat = self.load_json_to_dict(filename)
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ismms-himc/clustergrammer2 | clustergrammer2/clustergrammer_fun/__init__.py | Network.cluster | def cluster(self, dist_type='cosine', run_clustering=True,
dendro=True, views=['N_row_sum', 'N_row_var'],
linkage_type='average', sim_mat=False, filter_sim=0.1,
calc_cat_pval=False, run_enrichr=None, enrichrgram=None):
'''
The main function performs hierarchica... | python | def cluster(self, dist_type='cosine', run_clustering=True,
dendro=True, views=['N_row_sum', 'N_row_var'],
linkage_type='average', sim_mat=False, filter_sim=0.1,
calc_cat_pval=False, run_enrichr=None, enrichrgram=None):
'''
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ismms-himc/clustergrammer2 | clustergrammer2/clustergrammer_fun/__init__.py | Network.load_df | def load_df(self, df):
'''
Load Pandas DataFrame.
'''
# self.__init__()
self.reset()
df_dict = {}
df_dict['mat'] = deepcopy(df)
# always define category colors if applicable when loading a df
data_formats.df_to_dat(self, df_dict, define_cat_colors=True) | python | def load_df(self, df):
'''
Load Pandas DataFrame.
'''
# self.__init__()
self.reset()
df_dict = {}
df_dict['mat'] = deepcopy(df)
# always define category colors if applicable when loading a df
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ismms-himc/clustergrammer2 | clustergrammer2/clustergrammer_fun/__init__.py | Network.df_to_dat | def df_to_dat(self, df, define_cat_colors=False):
'''
Load Pandas DataFrame (will be deprecated).
'''
data_formats.df_to_dat(self, df, define_cat_colors) | python | def df_to_dat(self, df, define_cat_colors=False):
'''
Load Pandas DataFrame (will be deprecated).
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ismms-himc/clustergrammer2 | clustergrammer2/clustergrammer_fun/__init__.py | Network.widget | def widget(self, which_viz='viz'):
'''
Generate a widget visualization using the widget. The export_viz_to_widget
method passes the visualization JSON to the instantiated widget, which is
returned and visualized on the front-end.
'''
if hasattr(self, 'widget_class') == True:
# run cluster... | python | def widget(self, which_viz='viz'):
'''
Generate a widget visualization using the widget. The export_viz_to_widget
method passes the visualization JSON to the instantiated widget, which is
returned and visualized on the front-end.
'''
if hasattr(self, 'widget_class') == True:
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ismms-himc/clustergrammer2 | clustergrammer2/clustergrammer_fun/__init__.py | Network.widget_df | def widget_df(self):
'''
Export a DataFrame from the front-end visualization. For instance, a user
can filter to show only a single cluster using the dendrogram and then
get a dataframe of this cluster using the widget_df method.
'''
if hasattr(self, 'widget_instance') == True:
if self.w... | python | def widget_df(self):
'''
Export a DataFrame from the front-end visualization. For instance, a user
can filter to show only a single cluster using the dendrogram and then
get a dataframe of this cluster using the widget_df method.
'''
if hasattr(self, 'widget_instance') == True:
if self.w... | [
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ismms-himc/clustergrammer2 | clustergrammer2/clustergrammer_fun/__init__.py | Network.write_json_to_file | def write_json_to_file(self, net_type, filename, indent='no-indent'):
'''
Save dat or viz as a JSON to file.
'''
export_data.write_json_to_file(self, net_type, filename, indent) | python | def write_json_to_file(self, net_type, filename, indent='no-indent'):
'''
Save dat or viz as a JSON to file.
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ismms-himc/clustergrammer2 | clustergrammer2/clustergrammer_fun/__init__.py | Network.filter_sum | def filter_sum(self, inst_rc, threshold, take_abs=True):
'''
Filter a network's rows or columns based on the sum across rows or columns.
'''
inst_df = self.dat_to_df()
if inst_rc == 'row':
inst_df = run_filter.df_filter_row_sum(inst_df, threshold, take_abs)
elif inst_rc == 'col':
ins... | python | def filter_sum(self, inst_rc, threshold, take_abs=True):
'''
Filter a network's rows or columns based on the sum across rows or columns.
'''
inst_df = self.dat_to_df()
if inst_rc == 'row':
inst_df = run_filter.df_filter_row_sum(inst_df, threshold, take_abs)
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ismms-himc/clustergrammer2 | clustergrammer2/clustergrammer_fun/__init__.py | Network.filter_N_top | def filter_N_top(self, inst_rc, N_top, rank_type='sum'):
'''
Filter the matrix rows or columns based on sum/variance, and only keep the top
N.
'''
inst_df = self.dat_to_df()
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self.df_to_dat(inst_df) | python | def filter_N_top(self, inst_rc, N_top, rank_type='sum'):
'''
Filter the matrix rows or columns based on sum/variance, and only keep the top
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'''
inst_df = self.dat_to_df()
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ismms-himc/clustergrammer2 | clustergrammer2/clustergrammer_fun/__init__.py | Network.filter_threshold | def filter_threshold(self, inst_rc, threshold, num_occur=1):
'''
Filter the matrix rows or columns based on num_occur values being above a
threshold (in absolute value).
'''
inst_df = self.dat_to_df()
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self.... | python | def filter_threshold(self, inst_rc, threshold, num_occur=1):
'''
Filter the matrix rows or columns based on num_occur values being above a
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'''
inst_df = self.dat_to_df()
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ismms-himc/clustergrammer2 | clustergrammer2/clustergrammer_fun/__init__.py | Network.filter_cat | def filter_cat(self, axis, cat_index, cat_name):
'''
Filter the matrix based on their category. cat_index is the index of the category, the first category has index=1.
'''
run_filter.filter_cat(self, axis, cat_index, cat_name) | python | def filter_cat(self, axis, cat_index, cat_name):
'''
Filter the matrix based on their category. cat_index is the index of the category, the first category has index=1.
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ismms-himc/clustergrammer2 | clustergrammer2/clustergrammer_fun/__init__.py | Network.clip | def clip(self, lower=None, upper=None):
'''
Trim values at input thresholds using pandas function
'''
df = self.export_df()
df = df.clip(lower=lower, upper=upper)
self.load_df(df) | python | def clip(self, lower=None, upper=None):
'''
Trim values at input thresholds using pandas function
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df = self.export_df()
df = df.clip(lower=lower, upper=upper)
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ismms-himc/clustergrammer2 | clustergrammer2/clustergrammer_fun/__init__.py | Network.normalize | def normalize(self, df=None, norm_type='zscore', axis='row', keep_orig=False):
'''
Normalize the matrix rows or columns using Z-score (zscore) or Quantile Normalization (qn). Users can optionally pass in a DataFrame to be normalized (and this will be incorporated into the Network object).
'''
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'''
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ismms-himc/clustergrammer2 | clustergrammer2/clustergrammer_fun/__init__.py | Network.downsample | def downsample(self, df=None, ds_type='kmeans', axis='row', num_samples=100, random_state=1000):
'''
Downsample the matrix rows or columns (currently supporting kmeans only). Users can optionally pass in a DataFrame to be downsampled (and this will be incorporated into the network object).
'''
return d... | python | def downsample(self, df=None, ds_type='kmeans', axis='row', num_samples=100, random_state=1000):
'''
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ismms-himc/clustergrammer2 | clustergrammer2/clustergrammer_fun/__init__.py | Network.random_sample | def random_sample(self, num_samples, df=None, replace=False, weights=None, random_state=100, axis='row'):
'''
Return random sample of matrix.
'''
if df is None:
df = self.dat_to_df()
if axis == 'row':
axis = 0
if axis == 'col':
axis = 1
df = self.export_df()
df = df.... | python | def random_sample(self, num_samples, df=None, replace=False, weights=None, random_state=100, axis='row'):
'''
Return random sample of matrix.
'''
if df is None:
df = self.dat_to_df()
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if axis == 'col':
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ismms-himc/clustergrammer2 | clustergrammer2/clustergrammer_fun/__init__.py | Network.add_cats | def add_cats(self, axis, cat_data):
'''
Add categories to rows or columns using cat_data array of objects. Each object in cat_data is a dictionary with one key (category title) and value (rows/column names) that have this category. Categories will be added onto the existing categories and will be added in the o... | python | def add_cats(self, axis, cat_data):
'''
Add categories to rows or columns using cat_data array of objects. Each object in cat_data is a dictionary with one key (category title) and value (rows/column names) that have this category. Categories will be added onto the existing categories and will be added in the o... | [
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ismms-himc/clustergrammer2 | clustergrammer2/clustergrammer_fun/__init__.py | Network.enrichrgram | def enrichrgram(self, lib, axis='row'):
'''
Add Enrichr gene enrichment results to your visualization (where your rows
are genes). Run enrichrgram before clustering to incldue enrichment results
as row categories. Enrichrgram can also be run on the front-end using the
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... | python | def enrichrgram(self, lib, axis='row'):
'''
Add Enrichr gene enrichment results to your visualization (where your rows
are genes). Run enrichrgram before clustering to incldue enrichment results
as row categories. Enrichrgram can also be run on the front-end using the
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ismms-himc/clustergrammer2 | clustergrammer2/clustergrammer_fun/__init__.py | Network.load_gene_exp_to_df | def load_gene_exp_to_df(inst_path):
'''
Loads gene expression data from 10x in sparse matrix format and returns a
Pandas dataframe
'''
import pandas as pd
from scipy import io
from scipy import sparse
from ast import literal_eval as make_tuple
# matrix
Matrix = io.mmread( inst_... | python | def load_gene_exp_to_df(inst_path):
'''
Loads gene expression data from 10x in sparse matrix format and returns a
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'''
import pandas as pd
from scipy import io
from scipy import sparse
from ast import literal_eval as make_tuple
# matrix
Matrix = io.mmread( inst_... | [
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ismms-himc/clustergrammer2 | clustergrammer2/clustergrammer_fun/__init__.py | Network.sim_same_and_diff_category_samples | def sim_same_and_diff_category_samples(self, df, cat_index=1, dist_type='cosine',
equal_var=False, plot_roc=True,
precalc_dist=False, calc_roc=True):
'''
Calculate the similarity of samples from the same and different categori... | python | def sim_same_and_diff_category_samples(self, df, cat_index=1, dist_type='cosine',
equal_var=False, plot_roc=True,
precalc_dist=False, calc_roc=True):
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ismms-himc/clustergrammer2 | clustergrammer2/clustergrammer_fun/__init__.py | Network.generate_signatures | def generate_signatures(self, df_ini, category_level, pval_cutoff=0.05,
num_top_dims=False, verbose=True, equal_var=False):
''' Generate signatures for column categories '''
df_t = df_ini.transpose()
# remove columns with constant values
df_t = df_t.loc[:, (df_t != d... | python | def generate_signatures(self, df_ini, category_level, pval_cutoff=0.05,
num_top_dims=False, verbose=True, equal_var=False):
''' Generate signatures for column categories '''
df_t = df_ini.transpose()
# remove columns with constant values
df_t = df_t.loc[:, (df_t != d... | [
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ismms-himc/clustergrammer2 | clustergrammer2/clustergrammer_fun/__init__.py | Network.predict_cats_from_sigs | def predict_cats_from_sigs(self, df_data_ini, df_sig_ini, dist_type='cosine', predict_level='Predict Category',
truth_level=1, unknown_thresh=-1):
''' Predict category using signature '''
keep_rows = df_sig_ini.index.tolist()
data_rows = df_data_ini.index.tolist()
... | python | def predict_cats_from_sigs(self, df_data_ini, df_sig_ini, dist_type='cosine', predict_level='Predict Category',
truth_level=1, unknown_thresh=-1):
''' Predict category using signature '''
keep_rows = df_sig_ini.index.tolist()
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ismms-himc/clustergrammer2 | clustergrammer2/clustergrammer_fun/__init__.py | Network.confusion_matrix_and_correct_series | def confusion_matrix_and_correct_series(self, y_info):
''' Generate confusion matrix from y_info '''
a = deepcopy(y_info['true'])
true_count = dict((i, a.count(i)) for i in set(a))
a = deepcopy(y_info['pred'])
pred_count = dict((i, a.count(i)) for i in set(a))
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ismms-himc/clustergrammer2 | clustergrammer2/clustergrammer_fun/load_data.py | load_data_to_net | def load_data_to_net(net, inst_net):
''' load data into nodes and mat, also convert mat to numpy array'''
net.dat['nodes'] = inst_net['nodes']
net.dat['mat'] = inst_net['mat']
data_formats.mat_to_numpy_arr(net) | python | def load_data_to_net(net, inst_net):
''' load data into nodes and mat, also convert mat to numpy array'''
net.dat['nodes'] = inst_net['nodes']
net.dat['mat'] = inst_net['mat']
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ismms-himc/clustergrammer2 | clustergrammer2/clustergrammer_fun/export_data.py | export_net_json | def export_net_json(net, net_type, indent='no-indent'):
''' export json string of dat '''
import json
from copy import deepcopy
if net_type == 'dat':
exp_dict = deepcopy(net.dat)
if type(exp_dict['mat']) is not list:
exp_dict['mat'] = exp_dict['mat'].tolist()
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... | python | def export_net_json(net, net_type, indent='no-indent'):
''' export json string of dat '''
import json
from copy import deepcopy
if net_type == 'dat':
exp_dict = deepcopy(net.dat)
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exp_dict['mat'] = exp_dict['mat'].tolist()
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ismms-himc/clustergrammer2 | clustergrammer2/clustergrammer_fun/export_data.py | write_matrix_to_tsv | def write_matrix_to_tsv(net, filename=None, df=None):
'''
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'''
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ismms-himc/clustergrammer2 | clustergrammer2/clustergrammer_fun/make_unique_labels.py | main | def main(net, df=None):
'''
Run in load_data module (which runs when file is loaded or dataframe is loaded),
check for duplicate row/col names, and add index to names if necesary
'''
if df is None:
df = net.export_df()
# rows
#############
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if... | python | def main(net, df=None):
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Run in load_data module (which runs when file is loaded or dataframe is loaded),
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ismms-himc/clustergrammer2 | clustergrammer2/clustergrammer_fun/make_viz.py | viz_json | def viz_json(net, dendro=True, links=False):
''' make the dictionary for the clustergram.js visualization '''
from . import calc_clust
import numpy as np
all_dist = calc_clust.group_cutoffs()
for inst_rc in net.dat['nodes']:
inst_keys = net.dat['node_info'][inst_rc]
all_cats = [x for x in inst_keys... | python | def viz_json(net, dendro=True, links=False):
''' make the dictionary for the clustergram.js visualization '''
from . import calc_clust
import numpy as np
all_dist = calc_clust.group_cutoffs()
for inst_rc in net.dat['nodes']:
inst_keys = net.dat['node_info'][inst_rc]
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ismms-himc/clustergrammer2 | setupbase.py | install_npm | def install_npm(path=None, build_dir=None, source_dir=None, build_cmd='build', force=False, npm=None):
"""Return a Command for managing an npm installation.
Note: The command is skipped if the `--skip-npm` flag is used.
Parameters
----------
path: str, optional
The base path of the node pa... | python | def install_npm(path=None, build_dir=None, source_dir=None, build_cmd='build', force=False, npm=None):
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ismms-himc/clustergrammer2 | setupbase.py | _glob_pjoin | def _glob_pjoin(*parts):
"""Join paths for glob processing"""
if parts[0] in ('.', ''):
parts = parts[1:]
return pjoin(*parts).replace(os.sep, '/') | python | def _glob_pjoin(*parts):
"""Join paths for glob processing"""
if parts[0] in ('.', ''):
parts = parts[1:]
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ismms-himc/clustergrammer2 | setupbase.py | _get_data_files | def _get_data_files(data_specs, existing, top=HERE):
"""Expand data file specs into valid data files metadata.
Parameters
----------
data_specs: list of tuples
See [create_cmdclass] for description.
existing: list of tuples
The existing distrubution data_files metadata.
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"""Expand data file specs into valid data files metadata.
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A list of glob patterns for the data file locations.
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ismms-himc/clustergrammer2 | setupbase.py | _get_package_data | def _get_package_data(root, file_patterns=None):
"""Expand file patterns to a list of `package_data` paths.
Parameters
-----------
root: str
The relative path to the package root from `HERE`.
file_patterns: list or str, optional
A list of glob patterns for the data file locations.
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"""Expand file patterns to a list of `package_data` paths.
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The relative path to the package root from `HERE`.
file_patterns: list or str, optional
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ismms-himc/clustergrammer2 | clustergrammer2/clustergrammer_fun/run_filter.py | df_filter_row_sum | def df_filter_row_sum(df, threshold, take_abs=True):
''' filter rows in matrix at some threshold
and remove columns that have a sum below this threshold '''
from copy import deepcopy
from .__init__ import Network
net = Network()
if take_abs is True:
df_copy = deepcopy(df['mat'].abs())
else:
df_c... | python | def df_filter_row_sum(df, threshold, take_abs=True):
''' filter rows in matrix at some threshold
and remove columns that have a sum below this threshold '''
from copy import deepcopy
from .__init__ import Network
net = Network()
if take_abs is True:
df_copy = deepcopy(df['mat'].abs())
else:
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ismms-himc/clustergrammer2 | clustergrammer2/clustergrammer_fun/run_filter.py | df_filter_col_sum | def df_filter_col_sum(df, threshold, take_abs=True):
''' filter columns in matrix at some threshold
and remove rows that have all zero values '''
from copy import deepcopy
from .__init__ import Network
net = Network()
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''' filter columns in matrix at some threshold
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ismms-himc/clustergrammer2 | clustergrammer2/clustergrammer_fun/run_filter.py | filter_threshold | def filter_threshold(df, inst_rc, threshold, num_occur=1):
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'''
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ismms-himc/clustergrammer2 | clustergrammer2/clustergrammer_fun/make_clust_fun.py | make_clust | def make_clust(net, dist_type='cosine', run_clustering=True, dendro=True,
requested_views=['pct_row_sum', 'N_row_sum'],
linkage_type='average', sim_mat=False, filter_sim=0.1,
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requested_views=['pct_row_sum', 'N_row_sum'],
linkage_type='average', sim_mat=False, filter_sim=0.1,
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vstinner/bytecode | bytecode/flags.py | infer_flags | def infer_flags(bytecode, is_async=False):
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riga/tfdeploy | tfdeploy.py | setup | def setup(tf, order=None):
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Sets up global variables (currently only the tensorflow version) to adapt to peculiarities of
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creation, not for evaluation. Therefore, the tensorflow module *tf* must be passed:
... | python | def setup(tf, order=None):
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Sets up global variables (currently only the tensorflow version) to adapt to peculiarities of
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Prints the dependency graph of a :py:class:`Operation` *td_op*, where each new level is indented
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riga/tfdeploy | tfdeploy.py | print_tf_tensor | def print_tf_tensor(tf_tensor, indent="| ", max_depth=-1, depth=0):
""" print_tf_tensor(tf_tensor, indent=" ", max_depth=-1)
Prints the dependency graph of a tensorflow tensor *tf_tensor*, where each new level is indented
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""" print_tf_tensor(tf_tensor, indent=" ", max_depth=-1)
Prints the dependency graph of a tensorflow tensor *tf_tensor*, where each new level is indented
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riga/tfdeploy | tfdeploy.py | print_tf_op | def print_tf_op(tf_op, indent="| ", max_depth=-1, depth=0):
""" print_tf_op(tf_tensor, indent=" ", max_depth=-1)
Prints the dependency graph of a tensorflow operation *tf_op*, where each new level is indented
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... | python | def print_tf_op(tf_op, indent="| ", max_depth=-1, depth=0):
""" print_tf_op(tf_tensor, indent=" ", max_depth=-1)
Prints the dependency graph of a tensorflow operation *tf_op*, where each new level is indented
by *indent*. When *max_depth* is positive, the graph is truncated at that depth, where each
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riga/tfdeploy | tfdeploy.py | LinSpace | def LinSpace(start, stop, num):
"""
Linspace op.
"""
return np.linspace(start, stop, num=num, dtype=np.float32), | python | def LinSpace(start, stop, num):
"""
Linspace op.
"""
return np.linspace(start, stop, num=num, dtype=np.float32), | [
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riga/tfdeploy | tfdeploy.py | Range | def Range(start, limit, delta):
"""
Range op.
"""
return np.arange(start, limit, delta, dtype=np.int32), | python | def Range(start, limit, delta):
"""
Range op.
"""
return np.arange(start, limit, delta, dtype=np.int32), | [
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riga/tfdeploy | tfdeploy.py | RandomStandardNormal | def RandomStandardNormal(shape, dtype, seed):
"""
Standard (mu=0, sigma=1) gaussian op.
"""
if seed:
np.random.seed(seed)
return np.random.normal(size=reduce(mul, shape)).reshape(shape).astype(dtype_map[dtype]), | python | def RandomStandardNormal(shape, dtype, seed):
"""
Standard (mu=0, sigma=1) gaussian op.
"""
if seed:
np.random.seed(seed)
return np.random.normal(size=reduce(mul, shape)).reshape(shape).astype(dtype_map[dtype]), | [
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riga/tfdeploy | tfdeploy.py | TruncatedNormal | def TruncatedNormal(shape, dtype, seed):
"""
Standard (mu=0, sigma=1) gaussian op with truncation above 2 sigma.
"""
if seed:
np.random.seed(seed)
n = reduce(mul, shape)
r = np.empty(n, dtype=dtype_map[dtype])
idxs = np.ones(n, dtype=np.bool)
while n:
r[idxs] = np.random.... | python | def TruncatedNormal(shape, dtype, seed):
"""
Standard (mu=0, sigma=1) gaussian op with truncation above 2 sigma.
"""
if seed:
np.random.seed(seed)
n = reduce(mul, shape)
r = np.empty(n, dtype=dtype_map[dtype])
idxs = np.ones(n, dtype=np.bool)
while n:
r[idxs] = np.random.... | [
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riga/tfdeploy | tfdeploy.py | RandomUniform | def RandomUniform(shape, dtype, seed):
"""
Random uniform op.
"""
if seed:
np.random.seed(seed)
return np.random.uniform(size=shape).astype(dtype_map[dtype]), | python | def RandomUniform(shape, dtype, seed):
"""
Random uniform op.
"""
if seed:
np.random.seed(seed)
return np.random.uniform(size=shape).astype(dtype_map[dtype]), | [
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riga/tfdeploy | tfdeploy.py | RandomUniformInt | def RandomUniformInt(shape, minval, maxval, seed):
"""
Random uniform int op.
"""
if seed:
np.random.seed(seed)
return np.random.randint(minval, maxval, size=shape), | python | def RandomUniformInt(shape, minval, maxval, seed):
"""
Random uniform int op.
"""
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np.random.seed(seed)
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riga/tfdeploy | tfdeploy.py | RandomShuffle | def RandomShuffle(a, seed):
"""
Random uniform op.
"""
if seed:
np.random.seed(seed)
r = a.copy()
np.random.shuffle(r)
return r, | python | def RandomShuffle(a, seed):
"""
Random uniform op.
"""
if seed:
np.random.seed(seed)
r = a.copy()
np.random.shuffle(r)
return r, | [
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riga/tfdeploy | tfdeploy.py | Rank | def Rank(a):
"""
Rank op.
"""
return np.array([len(a.shape)], dtype=np.int32), | python | def Rank(a):
"""
Rank op.
"""
return np.array([len(a.shape)], dtype=np.int32), | [
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riga/tfdeploy | tfdeploy.py | Squeeze | def Squeeze(a, squeeze_dims):
"""
Squeeze op, i.e. removes singular axes.
"""
if not squeeze_dims:
squeeze_dims = list(range(len(a.shape)))
slices = [(0 if (dim == 1 and i in squeeze_dims) else slice(None)) \
for i, dim in enumerate(a.shape)]
return np.copy(a)[slices], | python | def Squeeze(a, squeeze_dims):
"""
Squeeze op, i.e. removes singular axes.
"""
if not squeeze_dims:
squeeze_dims = list(range(len(a.shape)))
slices = [(0 if (dim == 1 and i in squeeze_dims) else slice(None)) \
for i, dim in enumerate(a.shape)]
return np.copy(a)[slices], | [
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riga/tfdeploy | tfdeploy.py | ExpandDims | def ExpandDims(a, dim):
"""
Expand dim op, i.e. add singular axis at dim.
"""
shape = list(a.shape)
if dim >= 0:
shape.insert(dim, 1)
else:
shape.insert(len(shape) + dim + 1, 1)
return np.copy(a).reshape(*shape), | python | def ExpandDims(a, dim):
"""
Expand dim op, i.e. add singular axis at dim.
"""
shape = list(a.shape)
if dim >= 0:
shape.insert(dim, 1)
else:
shape.insert(len(shape) + dim + 1, 1)
return np.copy(a).reshape(*shape), | [
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riga/tfdeploy | tfdeploy.py | Slice | def Slice(a, begin, size):
"""
Slicing op.
"""
return np.copy(a)[[slice(*tpl) for tpl in zip(begin, begin+size)]], | python | def Slice(a, begin, size):
"""
Slicing op.
"""
return np.copy(a)[[slice(*tpl) for tpl in zip(begin, begin+size)]], | [
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riga/tfdeploy | tfdeploy.py | Split | def Split(axis, a, n):
"""
Split op with n splits.
"""
return tuple(np.split(np.copy(a), n, axis=axis)) | python | def Split(axis, a, n):
"""
Split op with n splits.
"""
return tuple(np.split(np.copy(a), n, axis=axis)) | [
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riga/tfdeploy | tfdeploy.py | SplitV | def SplitV(a, splits, axis):
"""
Split op with multiple split sizes.
"""
return tuple(np.split(np.copy(a), np.cumsum(splits), axis=axis)) | python | def SplitV(a, splits, axis):
"""
Split op with multiple split sizes.
"""
return tuple(np.split(np.copy(a), np.cumsum(splits), axis=axis)) | [
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riga/tfdeploy | tfdeploy.py | ConcatV2 | def ConcatV2(inputs):
"""
Concat op.
"""
axis = inputs.pop()
return np.concatenate(inputs, axis=axis), | python | def ConcatV2(inputs):
"""
Concat op.
"""
axis = inputs.pop()
return np.concatenate(inputs, axis=axis), | [
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