repo_name stringlengths 7 90 | path stringlengths 5 191 | copies stringlengths 1 3 | size stringlengths 4 6 | content stringlengths 976 581k | license stringclasses 15
values |
|---|---|---|---|---|---|
virneo/nupic | external/linux32/lib/python2.6/site-packages/matplotlib/backends/backend_gtkcairo.py | 69 | 2207 | """
GTK+ Matplotlib interface using cairo (not GDK) drawing operations.
Author: Steve Chaplin
"""
import gtk
if gtk.pygtk_version < (2,7,0):
import cairo.gtk
from matplotlib.backends import backend_cairo
from matplotlib.backends.backend_gtk import *
backend_version = 'PyGTK(%d.%d.%d) ' % gtk.pygtk_version + \
... | agpl-3.0 |
rohanp/scikit-learn | benchmarks/bench_isotonic.py | 268 | 3046 | """
Benchmarks of isotonic regression performance.
We generate a synthetic dataset of size 10^n, for n in [min, max], and
examine the time taken to run isotonic regression over the dataset.
The timings are then output to stdout, or visualized on a log-log scale
with matplotlib.
This alows the scaling of the algorith... | bsd-3-clause |
zhangsaithu/rose_demo | script/demo_test.py | 1 | 3023 | ##################################################
# A demo to predict ribosome stalling using ROSE #
##################################################
import sys, os
import re, fileinput, math
import numpy as np
import random
import caffe
import h5py
from gensim.models import word2vec
import ribo_convnet
from sklearn... | mit |
Maccimo/intellij-community | python/helpers/pydev/pydevd.py | 9 | 90108 | '''
Entry point module (keep at root):
This module starts the debugger.
'''
import os
import sys
from contextlib import contextmanager
import weakref
# allow the debugger to work in isolated mode Python
here = os.path.dirname(os.path.abspath(__file__))
if here not in sys.path:
sys.path.insert(0, here)
from _pyde... | apache-2.0 |
hagne/atm-py | atmPy/data_archives/arm/_tools.py | 1 | 6697 | import pandas as _pd
import os as _os
from pathlib import Path
import numpy as _np
def path2info(path, verbose = False):
path = Path(path)
if path.is_dir():
path = list(path.iterdir())[0]
#suffix
suffix = path.suffix
suffixlist = ['.nc', '.cdf']
if suffix not in suffixlist:
raise V... | mit |
olimastro/DeepMonster | tools/plot.py | 1 | 5634 | import argparse
import time
import sys, os
import numpy as np
import matplotlib.pylab as plt
import PIL.Image as Image
from subprocess import call
def animate(y, ndim, cmap) :
plt.ion()
if ndim == 5:
plt.figure()
plt.show()
for i in range(y.shape[1]) :
print "Showing batch... | mit |
zihua/scikit-learn | examples/decomposition/plot_pca_iris.py | 65 | 1485 | #!/usr/bin/python
# -*- coding: utf-8 -*-
"""
=========================================================
PCA example with Iris Data-set
=========================================================
Principal Component Analysis applied to the Iris dataset.
See `here <https://en.wikipedia.org/wiki/Iris_flower_data_set>`_ f... | bsd-3-clause |
jorik041/scikit-learn | sklearn/ensemble/tests/test_voting_classifier.py | 37 | 7136 | """Testing for the boost module (sklearn.ensemble.boost)."""
import numpy as np
from sklearn.utils.testing import assert_almost_equal
from sklearn.utils.testing import assert_equal
from sklearn.linear_model import LogisticRegression
from sklearn.naive_bayes import GaussianNB
from sklearn.ensemble import RandomForestCl... | bsd-3-clause |
evanthebouncy/nnhmm | graph1/saved_graph.py | 2 | 2630 | import networkx as nx
import matplotlib.pyplot as plt
from graph import *
N = 20
G_V = [(0.91653633515404, 0.4932070258979898), (0.09295461450752995, 0.9645329007473591), (0.24451556906631566, 0.4652259375620821), (0.7653140324185863, 0.8614988863794735), (0.21015262875012264, 0.3194260792001117), (0.3041107966578056... | mit |
auDeep/auDeep | audeep/cli/predict.py | 1 | 7714 | # Copyright (C) 2017-2018 Michael Freitag, Shahin Amiriparian, Sergey Pugachevskiy, Nicholas Cummins, Björn Schuller
#
# This file is part of auDeep.
#
# auDeep is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation,... | gpl-3.0 |
vlukes/sfepy | sfepy/mesh/bspline.py | 5 | 24238 | from __future__ import print_function
from __future__ import absolute_import
import sys
from six.moves import range
sys.path.append('.')
import numpy as nm
from sfepy.base.base import Struct
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from mpl_toolkits.mplot3d.art3d import Poly3DCollection... | bsd-3-clause |
yanlend/scikit-learn | sklearn/ensemble/tests/test_bagging.py | 13 | 25689 | """
Testing for the bagging ensemble module (sklearn.ensemble.bagging).
"""
# Author: Gilles Louppe
# License: BSD 3 clause
import numpy as np
from sklearn.base import BaseEstimator
from sklearn.utils.testing import assert_array_equal
from sklearn.utils.testing import assert_array_almost_equal
from sklearn.utils.te... | bsd-3-clause |
wernwa/lwfa-las-chicane-gui | gui/TabStripChartVolt.py | 1 | 3055 | # -*- coding: utf-8 -*-
#
# Strip chart for displaying the voltage over time
# This class is derived from TabStripChart.py
#
# author: Watler Werner
# email: wernwa@gmail.com
#
import os
import pprint
import random
import sys
import wx
import time
import thread
import traceback
# The recommended way to use... | gpl-3.0 |
larsoner/mne-python | examples/visualization/plot_3d_to_2d.py | 15 | 4941 | """
.. _ex-electrode-pos-2d:
====================================================
How to convert 3D electrode positions to a 2D image.
====================================================
Sometimes we want to convert a 3D representation of electrodes into a 2D
image. For example, if we are using electrocorticography ... | bsd-3-clause |
yanqd0/csft | tests/test_csft.py | 1 | 3293 | from collections import Iterable, OrderedDict
from os.path import dirname, isfile, join
from pandas import DataFrame, Series
from pytest import fixture
from csft import _csft
from csft._csft import column
def test_file_type():
assert '.py' == _csft.type_of_file(__file__)
assert '' == _csft.type_of_file('no_... | mit |
elkingtoncode/People-Networks | tests/consensus/runtests.py | 4 | 20659 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""Augur consensus tests.
To run consensus, call the Serpent functions in this order:
interpolate
center
tokenize
covariance
loop max_components:
blank
loop max_iterations:
loadings
latent
deflate
score
reputation_delta
weighted_delta
select_sc... | gpl-3.0 |
bayespy/bayespy | bayespy/demos/pattern_search.py | 5 | 3944 | ################################################################################
# Copyright (C) 2015 Jaakko Luttinen
#
# This file is licensed under the MIT License.
################################################################################
"""
Demonstration of the pattern search method for PCA.
The pattern s... | mit |
khkaminska/scikit-learn | examples/linear_model/plot_sgd_iris.py | 286 | 2202 | """
========================================
Plot multi-class SGD on the iris dataset
========================================
Plot decision surface of multi-class SGD on iris dataset.
The hyperplanes corresponding to the three one-versus-all (OVA) classifiers
are represented by the dashed lines.
"""
print(__doc__)
... | bsd-3-clause |
Weihonghao/ECM | Vpy34/lib/python3.5/site-packages/pandas/tests/series/test_timeseries.py | 6 | 31551 | # coding=utf-8
# pylint: disable-msg=E1101,W0612
import pytest
import numpy as np
from datetime import datetime, timedelta, time
import pandas as pd
import pandas.util.testing as tm
from pandas._libs.tslib import iNaT
from pandas.compat import lrange, StringIO, product
from pandas.core.indexes.timedeltas import Time... | agpl-3.0 |
ssaeger/scikit-learn | sklearn/manifold/locally_linear.py | 37 | 25852 | """Locally Linear Embedding"""
# Author: Fabian Pedregosa -- <fabian.pedregosa@inria.fr>
# Jake Vanderplas -- <vanderplas@astro.washington.edu>
# License: BSD 3 clause (C) INRIA 2011
import numpy as np
from scipy.linalg import eigh, svd, qr, solve
from scipy.sparse import eye, csr_matrix
from ..base import B... | bsd-3-clause |
NonWhite/IA_EP3 | code/classifier_utils.py | 1 | 1093 | import math
from copy import copy
from sklearn.cross_validation import cross_val_score
def import_csv( filepath , has_header = True ) :
csv_data = []
with open( filepath , 'r' ) as f :
print "Reading %s" % filepath
p = lambda x : int( x ) if float( x ) == math.trunc( float( x ) ) else float( x )
for line in f ... | gpl-2.0 |
ternaus/kaggle_digit_recognizer | src/double_layer.py | 1 | 3615 | from __future__ import division
from lasagne import layers
from lasagne.updates import nesterov_momentum
from nolearn.lasagne import NeuralNet
from lasagne.nonlinearities import softmax
from sklearn.preprocessing import StandardScaler
import numpy as np
from sklearn.preprocessing import LabelEncoder
__author__ = 'Vladi... | mit |
adam-rabinowitz/ngs_analysis | scripts/Variants/plotChromVariantFrequency.py | 2 | 4545 | '''plotChromVariantFrequency.py
Usage:
plotChromVariantFrequency.py <snpfile> <mincov> <bamfile> <outfile>
'''
import collections
import os
import re
import pysam
import matplotlib as mpl
mpl.use('Agg')
import matplotlib.pyplot as plt
from general_python import docopt
# Extract and process arguments
args = do... | gpl-2.0 |
wronk/mne-python | mne/stats/regression.py | 4 | 17595 | # Authors: Tal Linzen <linzen@nyu.edu>
# Teon Brooks <teon.brooks@gmail.com>
# Denis A. Engemann <denis.engemann@gmail.com>
# Jona Sassenhagen <jona.sassenhagen@gmail.com>
# Marijn van Vliet <w.m.vanvliet@gmail.com>
#
# License: BSD (3-clause)
from inspect import isgenerator
from co... | bsd-3-clause |
eramirem/astroML | book_figures/chapter8/fig_cross_val_B.py | 3 | 2641 | """
Cross Validation Examples: part 2
---------------------------------
Figure 8.13
Three models of increasing complexity applied to our toy dataset (eq. 8.75).
The d = 2 model, like the linear model in figure 8.12, suffers from high bias,
and underfits the data. The d = 19 model suffers from high variance, and
overfi... | bsd-2-clause |
cschenck/blender_sim | cutil/video_creator.py | 1 | 18026 | #!/usr/bin/env python
import os
import cv2
import numpy as np
import subprocess
import tempfile
import connor_util as cutil
def draw_arrow(image, p, q, color, arrow_magnitude=9, thickness=1, line_type=8, shift=0):
# adapted from http://mlikihazar.blogspot.com.au/2013/02/draw-arrow-opencv.html
# draw arrow t... | gpl-3.0 |
EJFielding/ISCE_utils | ISCE2ROI.py | 1 | 9027 | #! /usr/bin/env python
# Create ROI_pac format Inputs for Paul's version of Rowena's resamptool script
# modified from Pietro's script "PreparePaul.py" EJF 2014/12/11-29
# uses some GIAnT functions so must run under Python2
# does not yet convert the LOS angles to the ROI_pac convention
import numpy as np
import matp... | apache-2.0 |
dmnfarrell/epitopemap | modules/pepdata/hpv.py | 1 | 2977 | # Copyright (c) 2014. Mount Sinai School of Medicine
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law o... | apache-2.0 |
jorge2703/scikit-learn | examples/ensemble/plot_adaboost_regression.py | 311 | 1529 | """
======================================
Decision Tree Regression with AdaBoost
======================================
A decision tree is boosted using the AdaBoost.R2 [1] algorithm on a 1D
sinusoidal dataset with a small amount of Gaussian noise.
299 boosts (300 decision trees) is compared with a single decision tr... | bsd-3-clause |
andrewcbennett/iris | docs/iris/example_code/General/rotated_pole_mapping.py | 7 | 1662 | """
Rotated pole mapping
=====================
This example uses several visualisation methods to achieve an array of
differing images, including:
* Visualisation of point based data
* Contouring of point based data
* Block plot of contiguous bounded data
* Non native projection and a Natural Earth shaded relief ... | gpl-3.0 |
Akshay0724/scikit-learn | examples/manifold/plot_swissroll.py | 330 | 1446 | """
===================================
Swiss Roll reduction with LLE
===================================
An illustration of Swiss Roll reduction
with locally linear embedding
"""
# Author: Fabian Pedregosa -- <fabian.pedregosa@inria.fr>
# License: BSD 3 clause (C) INRIA 2011
print(__doc__)
import matplotlib.pyplot... | bsd-3-clause |
nmayorov/scikit-learn | sklearn/model_selection/_validation.py | 14 | 35585 | """
The :mod:`sklearn.model_selection._validation` module includes classes and
functions to validate the model.
"""
# Author: Alexandre Gramfort <alexandre.gramfort@inria.fr>,
# Gael Varoquaux <gael.varoquaux@normalesup.org>,
# Olivier Grisel <olivier.grisel@ensta.org>
# License: BSD 3 clause
from __... | bsd-3-clause |
wjlei1990/EarlyWarning | nn.linear/data/crop_data.py | 1 | 5218 | """
1) use the arrival time to generate windows
2) use the window and seismogram to generate measurements(inside windows)
"""
from __future__ import print_function, division
import os
import sys # NOQA
import numpy as np
import h5py
import pandas as pd
import obspy
import json
from obspy import UTCDateTime
import matp... | gpl-3.0 |
dwhswenson/contact_map | contact_map/contact_count.py | 1 | 14734 | import collections
import scipy
import numpy as np
import pandas as pd
import warnings
from .plot_utils import ranged_colorbar, make_x_y_ranges, is_cmap_diverging
# matplotlib is technically optional, but required for plotting
try:
import matplotlib
import matplotlib.pyplot as plt
except ImportError:
HAS_M... | lgpl-2.1 |
ArvinPan/opencog | opencog/python/spatiotemporal/temporal_events/__init__.py | 33 | 9273 | from scipy.stats.distributions import rv_frozen
from spatiotemporal.temporal_events.relation_formulas import FormulaCreator, RelationFormulaGeometricMean, BaseRelationFormula, RelationFormulaConvolution
from spatiotemporal.temporal_events.util import calculate_bounds_of_probability_distribution
from spatiotemporal.time... | agpl-3.0 |
gimli-org/gimli | pygimli/solver/solver.py | 1 | 89934 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""TODO DOCUMENT ME"""
from copy import deepcopy
import numpy as np
import numpy.matlib
import pygimli as pg
def parseDictKey_(key, markers):
return parseMarkersDictKey(key, markers)
def parseMarkersDictKey(key, markers):
""" Parse dictionary key of type str to ... | apache-2.0 |
dandanvidi/capacity-usage | scripts/correlation_E_to_CU.py | 3 | 3073 | # -*- coding: utf-8 -*-
"""
Created on Mon Jun 20 13:39:20 2016
@author: dan
"""
import pandas as pd
from capacity_usage import CAPACITY_USAGE
import matplotlib.pyplot as plt
#import seaborn as sns
import sys, os
import numpy as np
from scipy.stats import ranksums, wilcoxon, pearsonr, spearmanr
cmap = plt.cm.Blues
... | mit |
FireCARES/fire-risk | fire_risk/models/DIST/providers/ahs.py | 1 | 11306 | import random
from pandas import DataFrame, melt
FDID_TO_AHS = {
'WP801-TX': 'Austin-Round Rock, TX AHS Area',
'24001-NY': 'New York, NY AHS Area',
'07212-FL': 'Orlando, FL AHS Area',
'28008-NY': 'Rochester, NY AHS Area',
'23035-PA': 'Philadelphia, PA-NJ AHS Area',
# 'Northern New Jersey, NJ A... | mit |
lotrus28/TaboCom | linear_model/model_test/old_par_cross_test_patients.py | 1 | 9420 | import pandas as pd
import itertools
import sys
import time
import multiprocessing
import numpy as np
global num_compl
num_compl = 0
def acc_for_multiplier(RMSEs, mult):
heal_RMSE = RMSEs[0]
ibd_RMSE = RMSEs[1]
delta = heal_RMSE - (ibd_RMSE * mult)
delta = ['Healthy' if i < 0 else 'IBD' for i in delta... | apache-2.0 |
decebel/librosa | tests/test_time_frequency.py | 3 | 9433 | #!/usr/bin/env python
# -*- encoding: utf-8 -*-
# CREATED:2015-02-14 19:13:49 by Brian McFee <brian.mcfee@nyu.edu>
'''Unit tests for time and frequency conversion'''
import os
try:
os.environ.pop('LIBROSA_CACHE_DIR')
except KeyError:
pass
import matplotlib
matplotlib.use('Agg')
import librosa
import numpy as... | isc |
blaisb/cfdemUtilities | phillips/compareMonitorTorque.py | 2 | 3652 | # This programs compares two log file of two different openfoam cases being run (or that have finished, etc.)
# The variable compared is the torque in the Z direction
# Author : Bruno Blais
# Last modified : 23-01-2014
#Python imports
#----------------------------------------
import os
import sys
import numpy
import ... | lgpl-3.0 |
alphacsc/alphacsc | alphacsc/tests/test_learn_d_z_multi.py | 1 | 4601 | import pytest
import numpy as np
from alphacsc.utils import check_random_state
from alphacsc.learn_d_z_multi import learn_d_z_multi
from alphacsc.convolutional_dictionary_learning import BatchCDL, GreedyCDL
from alphacsc.online_dictionary_learning import OnlineCDL
from alphacsc.init_dict import init_dictionary
@pyte... | bsd-3-clause |
kenshay/ImageScript | ProgramData/SystemFiles/Python/Lib/site-packages/scipy/interpolate/interpolate.py | 4 | 103293 | """ Classes for interpolating values.
"""
from __future__ import division, print_function, absolute_import
__all__ = ['interp1d', 'interp2d', 'spline', 'spleval', 'splmake', 'spltopp',
'lagrange', 'PPoly', 'BPoly', 'NdPPoly',
'RegularGridInterpolator', 'interpn']
import itertools
import warnin... | gpl-3.0 |
sauloal/cnidaria | scripts/venv/lib/python2.7/site-packages/numpy/lib/function_base.py | 30 | 124613 | from __future__ import division, absolute_import, print_function
import warnings
import sys
import collections
import operator
import numpy as np
import numpy.core.numeric as _nx
from numpy.core import linspace, atleast_1d, atleast_2d
from numpy.core.numeric import (
ones, zeros, arange, concatenate, array, asarr... | mit |
albertbup/DeepBeliefNet | examples/save_demo.py | 3 | 1274 | import numpy as np
np.random.seed(1337) # for reproducibility
from sklearn.datasets import load_digits
from sklearn.model_selection import train_test_split
from sklearn.metrics.classification import accuracy_score
from dbn.tensorflow import SupervisedDBNClassification
# Loading dataset
digits = load_digits()
X, Y =... | mit |
gef756/statsmodels | statsmodels/tsa/statespace/mlemodel.py | 2 | 88741 | """
State Space Model
Author: Chad Fulton
License: Simplified-BSD
"""
from __future__ import division, absolute_import, print_function
import numpy as np
import pandas as pd
from scipy.stats import norm
from .kalman_smoother import KalmanSmoother, SmootherResults
from .kalman_filter import (
KalmanFilter, Filter... | bsd-3-clause |
toobaz/pandas | pandas/io/formats/csvs.py | 2 | 11062 | """
Module for formatting output data into CSV files.
"""
import csv as csvlib
from io import StringIO
import os
import warnings
from zipfile import ZipFile
import numpy as np
from pandas._libs import writers as libwriters
from pandas.core.dtypes.generic import (
ABCDatetimeIndex,
ABCIndexClass,
ABCMult... | bsd-3-clause |
sperka/shogun | examples/undocumented/python_modular/graphical/inverse_covariance_estimation_demo.py | 26 | 2520 | #!/usr/bin/env python
import numpy as np
import matplotlib.pyplot as plt
from pylab import show, imshow
def simulate_data (n,p):
from modshogun import SparseInverseCovariance
import numpy as np
#create a random pxp covariance matrix
cov = np.random.normal(size=(p,p))
#generate data set with multivariate Gaussi... | gpl-3.0 |
clemkoa/scikit-learn | examples/linear_model/plot_sparse_logistic_regression_mnist.py | 31 | 2702 | """
=====================================================
MNIST classfification using multinomial logistic + L1
=====================================================
Here we fit a multinomial logistic regression with L1 penalty on a subset of
the MNIST digits classification task. We use the SAGA algorithm for this
pur... | bsd-3-clause |
lancezlin/ml_template_py | lib/python2.7/site-packages/pandas/tests/plotting/test_datetimelike.py | 7 | 47670 | from datetime import datetime, timedelta, date, time
import nose
from pandas.compat import lrange, zip
import numpy as np
from pandas import Index, Series, DataFrame
from pandas.tseries.index import date_range, bdate_range
from pandas.tseries.offsets import DateOffset
from pandas.tseries.period import period_range, ... | mit |
CivicKnowledge/metatab-packages | healthpolicy.ucla.edu-chis/pylib/__init__.py | 1 | 1680 | """ Example pylib functions"""
def convert(resource, doc, env, *args, **kwargs):
""" Read a stata file for CHIS, convert to codes, and yield it back out
"""
from metapack.rowgenerator import PandasDataframeSource
from publicdata.chis.prepare import to_codes
import pandas as pd
fspath = doc.... | mit |
chris-ch/omarket | python-lab/src/pricetools.py | 1 | 1689 | import logging
import os
import csv
import pandas
from datetime import date
def load_prices(prices_path, exchange, security_code):
letter = security_code[0]
dir_path = os.sep.join([prices_path, exchange, letter, security_code])
logging.info('accessing prices from: %s' % str(os.path.abspath(dir_path)))
... | apache-2.0 |
ruohoruotsi/librosa | librosa/decompose.py | 1 | 17664 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Spectrogram decomposition
=========================
.. autosummary::
:toctree: generated/
decompose
hpss
nn_filter
"""
import numpy as np
import scipy.sparse
from scipy.ndimage import median_filter
import sklearn.decomposition
from . import core
fro... | isc |
calico/basenji | basenji/emerald.py | 1 | 2997 | # Copyright 2017 Calico LLC
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# https://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, sof... | apache-2.0 |
fzalkow/scikit-learn | examples/linear_model/lasso_dense_vs_sparse_data.py | 348 | 1862 | """
==============================
Lasso on dense and sparse data
==============================
We show that linear_model.Lasso provides the same results for dense and sparse
data and that in the case of sparse data the speed is improved.
"""
print(__doc__)
from time import time
from scipy import sparse
from scipy ... | bsd-3-clause |
rmhyman/DataScience | Lesson2/get_hourly_entries_mta_data.py | 1 | 2504 | import pandas
def get_hourly_entries(df):
'''
The data in the MTA Subway Turnstile data reports on the cumulative
number of entries and exits per row. Assume that you have a dataframe
called df that contains only the rows for a particular turnstile machine
(i.e., unique SCP, C/A, and UNIT).... | mit |
procoder317/scikit-learn | sklearn/decomposition/tests/test_nmf.py | 47 | 8566 | import numpy as np
from scipy import linalg
from sklearn.decomposition import nmf
from scipy.sparse import csc_matrix
from sklearn.utils.testing import assert_true
from sklearn.utils.testing import assert_false
from sklearn.utils.testing import assert_raise_message
from sklearn.utils.testing import assert_array_almost... | bsd-3-clause |
xubenben/scikit-learn | examples/plot_kernel_ridge_regression.py | 230 | 6222 | """
=============================================
Comparison of kernel ridge regression and SVR
=============================================
Both kernel ridge regression (KRR) and SVR learn a non-linear function by
employing the kernel trick, i.e., they learn a linear function in the space
induced by the respective k... | bsd-3-clause |
BleekerLab/Solanum_sRNAs | scripts/get_mirnas_from_shortstack_res.py | 1 | 1417 | #!/usr/bin/env python
"""
Take one Shortstack result file and creates a fasta file containing all miRNAs
Usage:
python get_mirnas_from_shortstack_res.py -i [shortstack result file] -o [path/to/outfile]
Example: python get_mirnas_from_shortstack_res.py -i shortstack/C32/Results.txt -o C32_miRNAs.fasta
"""
#########... | mit |
procoder317/scikit-learn | sklearn/preprocessing/tests/test_imputation.py | 213 | 11911 | import numpy as np
from scipy import sparse
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_array_equal
from sklearn.utils.testing import assert_raises
from sklearn.utils.testing import assert_false
from sklearn.utils.testing import assert_true
from sklearn.preprocessing.imputa... | bsd-3-clause |
shyamalschandra/scikit-learn | sklearn/cluster/tests/test_k_means.py | 41 | 27789 | """Testing for K-means"""
import sys
import numpy as np
from scipy import sparse as sp
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_array_equal
from sklearn.utils.testing import assert_array_almost_equal
from sklearn.utils.testing import SkipTest
from sklearn.utils.testing i... | bsd-3-clause |
cybernet14/scikit-learn | sklearn/cross_decomposition/tests/test_pls.py | 215 | 11427 | import numpy as np
from sklearn.utils.testing import (assert_array_almost_equal,
assert_array_equal, assert_true, assert_raise_message)
from sklearn.datasets import load_linnerud
from sklearn.cross_decomposition import pls_
from nose.tools import assert_equal
def test_pls():
d =... | bsd-3-clause |
mjbommar/cscs-530-w2016 | notebooks/basic-stats/hiv_model.py | 2 | 14808 | #m.space Standard imports
import copy
import itertools
# Scientific computing imports
import numpy
import matplotlib.pyplot as plt
import networkx
import pandas
import seaborn
class Person(object):
"""
Person class, which encapsulates the entire behavior of a person.
"""
def __init__(self, model,... | bsd-2-clause |
reuk/parallel-reverb-raytracer | filter_test/linkwitzriley.py | 1 | 2168 | import numpy as np
import scipy.signal as signal
import matplotlib.pyplot as plt
from scikits.audiolab import Format, Sndfile
from os.path import splitext
def getC(co, sr):
wcT = np.pi * co / sr
return np.cos(wcT) / np.sin(wcT)
def lopass1ord(c):
a0 = c + 1
b = [1 / a0, 1 / a0]
a = [1.0, (1 - c) /... | gpl-2.0 |
vanpact/scipy | scipy/interpolate/fitpack2.py | 39 | 61117 | """
fitpack --- curve and surface fitting with splines
fitpack is based on a collection of Fortran routines DIERCKX
by P. Dierckx (see http://www.netlib.org/dierckx/) transformed
to double routines by Pearu Peterson.
"""
# Created by Pearu Peterson, June,August 2003
from __future__ import division, print_function, abs... | bsd-3-clause |
DrXyzzy/smc | src/smc_sagews/smc_sagews/graphics.py | 2 | 27549 | ###############################################################################
#
# CoCalc: Collaborative Calculation in the Cloud
#
# Copyright (C) 2016, Sagemath Inc.
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as publishe... | agpl-3.0 |
lmallin/coverage_test | python_venv/lib/python2.7/site-packages/pandas/tests/plotting/test_series.py | 6 | 30296 | # coding: utf-8
""" Test cases for Series.plot """
import itertools
import pytest
from datetime import datetime
import pandas as pd
from pandas import Series, DataFrame, date_range
from pandas.compat import range, lrange
import pandas.util.testing as tm
from pandas.util.testing import slow
import numpy as np
from... | mit |
OSHI7/Learning1 | test1.py | 1 | 1085 | list=['happy', 'sad', 'quick', 'slow']
# for item in list:
# print(item)
import numpy as np
import Utils
#%% cell
list=['happy', 'sad', 'quick', 'slow']
for item in list:
print(item)
i=iter(list)
#print(i())
print(i)
print('hello dead')
print('eh mon')
import matplotlib.pyplot as plt
#%% Add and giv... | mit |
madsbk/bohrium | doc/source/conf.py | 3 | 6213 | # -*- coding: utf-8 -*-
#
# Bohrium documentation build configuration file, created by
# sphinx-quickstart on Tue Nov 14 14:03:06 2017.
#
# This file is execfile()d with the current directory set to its
# containing dir.
#
# Note that not all possible configuration values are present in this
# autogenerated file.
#
# A... | apache-2.0 |
jstraub/rtmf | python/evalNYU.py | 1 | 3426 | # Copyright (c) 2015, Julian Straub <jstraub@csail.mit.edu> Licensed
# under the MIT license. See the license file LICENSE.
#import matplotlib.pyplot as plt
#import matplotlib.cm as cm
import numpy as np
#import cv2
import scipy.io
import subprocess as subp
import os, re, time, random
import argparse
#from vpCluster... | mit |
mbraeunlein/CurrentVoltage | old/get_peaks.py | 1 | 11343 | import numpy as np
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
import matplotlib.mathtext
import sys, glob
import datetime
import pdb
plt.ion()
########################################################################
def smooth(x,window_len=11,window='hanning'):
if x.ndim != 1:
raise Val... | mit |
nrhine1/scikit-learn | examples/feature_selection/plot_permutation_test_for_classification.py | 250 | 2233 | """
=================================================================
Test with permutations the significance of a classification score
=================================================================
In order to test if a classification score is significative a technique
in repeating the classification procedure aft... | bsd-3-clause |
depet/scikit-learn | examples/gaussian_process/plot_gp_regression.py | 253 | 4054 | #!/usr/bin/python
# -*- coding: utf-8 -*-
r"""
=========================================================
Gaussian Processes regression: basic introductory example
=========================================================
A simple one-dimensional regression exercise computed in two different ways:
1. A noise-free cas... | bsd-3-clause |
ClaudioNahmad/Servicio-Social | Parametros/CosmoMC/prerrequisitos/plc-2.0/src/python/clik/smicahlp.py | 2 | 37204 | import parobject as php
import numpy as nm
import re
def base_smica(root_grp,hascl,lmin,lmax,nT,nP,wq,rqhat,Acmb,rq0=None,bins=None):
if bins==None:
nbins = 0
else:
bins.shape=(-1,(lmax+1-lmin)*nm.sum(hascl))
nbins = bins.shape[0]
bins=bins.flat[:]
lkl_grp = php.add_lkl_generic(root_grp,"smica",1... | gpl-3.0 |
UiL-OTS-labs/iSpector | utils/arguments.py | 1 | 3674 | #!/usr/bin/env python
##
# \file arguments.py
#
# In this file handeling of commandline arguments is handled.
import argparse
import matplotlib
from gui.ispectorgui import MainGuiModel
import gui.ispectorgui
PARSER = None
ARGS = None
LOGO = "iSpectorLogo.svg"
class TestActionOption(argparse.Action):
## mess... | gpl-2.0 |
Vvucinic/Wander | venv_2_7/lib/python2.7/site-packages/pandas/tools/plotting.py | 9 | 132091 | # being a bit too dynamic
# pylint: disable=E1101
import datetime
import warnings
import re
from math import ceil
from collections import namedtuple
from contextlib import contextmanager
from distutils.version import LooseVersion
import numpy as np
from pandas.util.decorators import cache_readonly, deprecate_kwarg
fr... | artistic-2.0 |
pratapvardhan/scikit-learn | examples/cluster/plot_agglomerative_clustering_metrics.py | 402 | 4492 | """
Agglomerative clustering with different metrics
===============================================
Demonstrates the effect of different metrics on the hierarchical clustering.
The example is engineered to show the effect of the choice of different
metrics. It is applied to waveforms, which can be seen as
high-dimens... | bsd-3-clause |
charanpald/wallhack | wallhack/modelselect/GenerateToyData2.py | 1 | 2467 | """
We generate a toy regression dataset
"""
import numpy
import logging
import sys
import scipy.stats
import matplotlib.pyplot as plt
from sandbox.util.PathDefaults import PathDefaults
logging.basicConfig(stream=sys.stdout, level=logging.DEBUG)
numpy.random.seed(21)
numFeatures = 2
numCentres = 10
numPositives = ... | gpl-3.0 |
scholi/pySPM | pySPM/utils/elts.py | 1 | 13517 | # -- coding: utf-8 --
# Copyright 2018 Olivier Scholder <o.scholder@gmail.com>
"""
Handle elements to calculate mass, abundance, etc.
"""
from __future__ import absolute_import
import sqlite3
import os
import re
from .constants import me
from .misc import deprecated
def formulafy(x):
"""
Convert the input ... | apache-2.0 |
Nyker510/scikit-learn | examples/ensemble/plot_random_forest_embedding.py | 286 | 3531 | """
=========================================================
Hashing feature transformation using Totally Random Trees
=========================================================
RandomTreesEmbedding provides a way to map data to a
very high-dimensional, sparse representation, which might
be beneficial for classificati... | bsd-3-clause |
slifty/audfprint | audfprint_match.py | 3 | 18994 | """
audfprint_match.py
Fingerprint matching code for audfprint
2014-05-26 Dan Ellis dpwe@ee.columbia.edu
"""
import librosa
import numpy as np
import scipy.signal
import time
# for checking phys mem size
import resource
# for localtest and illustrate
import audfprint_analyze
import matplotlib.pyplot as plt
import a... | mit |
sebastianhaas/PyLaTeX | examples/basic.py | 3 | 1310 | #!/usr/bin/python
"""
This example shows matplotlib functionality.
.. :copyright: (c) 2014 by Jelte Fennema.
:license: MIT, see License for more details.
"""
# begin-doc-include
from pylatex import Document, Section, Subsection
from pylatex.utils import italic, escape_latex
def fill_document(doc):
"""Add a... | mit |
willgrass/pandas | bench/serialize.py | 1 | 2061 | import time, os
import numpy as np
import la
import pandas
def timeit(f, iterations):
start = time.clock()
for i in xrange(iterations):
f()
return time.clock() - start
def roundtrip_archive(N, iterations=10):
# Create data
arr = np.random.randn(N, N)
lar = la.larry(arr)
dma = p... | bsd-3-clause |
foxsi/foxsi-smex | pyfoxsi/doc/source/conf.py | 6 | 10204 | # -*- coding: utf-8 -*-
#
# PyFOXSI documentation build configuration file, created by
# sphinx-quickstart on Tue Sep 1 09:46:00 2015.
#
# This file is execfile()d with the current directory set to its
# containing dir.
#
# Note that not all possible configuration values are present in this
# autogenerated file.
#
# A... | mit |
bgris/ODL_bgris | lib/python3.5/site-packages/skimage/feature/tests/test_util.py | 35 | 2818 | import numpy as np
try:
import matplotlib.pyplot as plt
except ImportError:
plt = None
from numpy.testing import assert_equal, assert_raises
from skimage.feature.util import (FeatureDetector, DescriptorExtractor,
_prepare_grayscale_input_2D,
_mask... | gpl-3.0 |
DGrady/pandas | asv_bench/benchmarks/series_methods.py | 6 | 3587 | from .pandas_vb_common import *
class series_constructor_no_data_datetime_index(object):
goal_time = 0.2
def setup(self):
self.dr = pd.date_range(
start=datetime(2015,10,26),
end=datetime(2016,1,1),
freq='50s'
) # ~100k long
def time_series_constructo... | bsd-3-clause |
lthurlow/Boolean-Constrained-Routing | networkx-1.8.1/doc/make_gallery.py | 12 | 2477 | #!/usr/bin/env python
# generate a thumbnail gallery of examples
template = """\
{%% extends "layout.html" %%}
{%% set title = "Gallery" %%}
{%% block body %%}
<h3>Click on any image to see source code</h3>
<br/>
%s
{%% endblock %%}
"""
link_template = """\
<a href="%s"><img src="%s" border="0" alt="%s"/></a>
"""
... | mit |
andersbll/deeppy-website | _downloads/convnet_mnist.py | 5 | 3024 | #!/usr/bin/env python
"""
Convnets for image classification (1)
=====================================
"""
import numpy as np
import deeppy as dp
import matplotlib
import matplotlib.pyplot as plt
# Fetch MNIST data
dataset = dp.dataset.MNIST()
x_train, y_train, x_test, y_test = dataset.data(dp_dtypes=True)
# Bring... | mit |
kaslusimoes/SummerSchool2016 | python/almostnewsimulation.py | 1 | 7049 | #! /bin/env python2
# coding: utf-8
import numpy as np
import networkx as nx
import matplotlib.pyplot as plt
import random as rd
import os
from pickle import dump, load
class Data:
def __init__(self):
self.m_list1 = []
self.m_list2 = []
N = 100
M = 100
MAX = N + M + 1
MAX_EDGE = 380
MAX_DEG = 450... | apache-2.0 |
cainiaocome/scikit-learn | sklearn/tests/test_grid_search.py | 68 | 28778 | """
Testing for grid search module (sklearn.grid_search)
"""
from collections import Iterable, Sized
from sklearn.externals.six.moves import cStringIO as StringIO
from sklearn.externals.six.moves import xrange
from itertools import chain, product
import pickle
import sys
import numpy as np
import scipy.sparse as sp
... | bsd-3-clause |
DSLituiev/scikit-learn | sklearn/svm/tests/test_sparse.py | 35 | 13182 | from nose.tools import assert_raises, assert_true, assert_false
import numpy as np
from scipy import sparse
from numpy.testing import (assert_array_almost_equal, assert_array_equal,
assert_equal)
from sklearn import datasets, svm, linear_model, base
from sklearn.datasets import make_classif... | bsd-3-clause |
mayblue9/scikit-learn | benchmarks/bench_plot_lasso_path.py | 301 | 4003 | """Benchmarks of Lasso regularization path computation using Lars and CD
The input data is mostly low rank but is a fat infinite tail.
"""
from __future__ import print_function
from collections import defaultdict
import gc
import sys
from time import time
import numpy as np
from sklearn.linear_model import lars_pat... | bsd-3-clause |
dingocuster/scikit-learn | sklearn/feature_selection/__init__.py | 244 | 1088 | """
The :mod:`sklearn.feature_selection` module implements feature selection
algorithms. It currently includes univariate filter selection methods and the
recursive feature elimination algorithm.
"""
from .univariate_selection import chi2
from .univariate_selection import f_classif
from .univariate_selection import f_... | bsd-3-clause |
epfl-mobots/thymio-ground-localisation | code/create_plots.py | 1 | 21615 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
# cython: profile=False
# kate: replace-tabs off; indent-width 4; indent-mode normal; remove-trailing-spaces all;
# vim: ts=4:sw=4:noexpandtab
import os
import numpy as np
import matplotlib
matplotlib.use("PDF") # do this before pylab so you don't get the default back end.... | lgpl-3.0 |
uglyboxer/linear_neuron | net-p3/lib/python3.5/site-packages/matplotlib/widgets.py | 10 | 56160 | """
GUI Neutral widgets
===================
Widgets that are designed to work for any of the GUI backends.
All of these widgets require you to predefine an :class:`matplotlib.axes.Axes`
instance and pass that as the first arg. matplotlib doesn't try to
be too smart with respect to layout -- you will have to figure ou... | mit |
tejasckulkarni/hydrology | ch_599/ch_599_daily_wb_ver_2.py | 2 | 31458 | __author__ = 'kiruba'
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import itertools
from spread import spread
from scipy.optimize import curve_fit
import math
from matplotlib import rc
from datetime import timedelta
import scipy as sp
import meteolib as met
from bisect import bisect_left
imp... | gpl-3.0 |
anirudhjayaraman/scikit-learn | sklearn/cluster/tests/test_birch.py | 342 | 5603 | """
Tests for the birch clustering algorithm.
"""
from scipy import sparse
import numpy as np
from sklearn.cluster.tests.common import generate_clustered_data
from sklearn.cluster.birch import Birch
from sklearn.cluster.hierarchical import AgglomerativeClustering
from sklearn.datasets import make_blobs
from sklearn.l... | bsd-3-clause |
aleksandr-bakanov/astropy | examples/coordinates/plot_obs-planning.py | 3 | 6298 | # -*- coding: utf-8 -*-
"""
===================================================================
Determining and plotting the altitude/azimuth of a celestial object
===================================================================
This example demonstrates coordinate transformations and the creation of
visibility cur... | bsd-3-clause |
5agado/conversation-analyzer | src/util/plotting.py | 1 | 5870 | import os
from datetime import datetime
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import util.io as mio
from util import statsUtil
from model.message import Message
SAVE_PLOT = False
def plotBasicLengthStatsByYearAndMonth(data, yearsToShow=None, targetStats=None,
... | apache-2.0 |
Clyde-fare/scikit-learn | sklearn/decomposition/tests/test_fastica.py | 272 | 7798 | """
Test the fastica algorithm.
"""
import itertools
import warnings
import numpy as np
from scipy import stats
from nose.tools import assert_raises
from sklearn.utils.testing import assert_almost_equal
from sklearn.utils.testing import assert_array_almost_equal
from sklearn.utils.testing import assert_true
from skl... | bsd-3-clause |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.