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 |
|---|---|---|---|---|---|
xiwei-zhang/vigra | vigranumpy/lib/__init__.py | 4 | 80099 | #######################################################################
#
# Copyright 2009-2010 by Ullrich Koethe
#
# This file is part of the VIGRA computer vision library.
# The VIGRA Website is
# http://hci.iwr.uni-heidelberg.de/vigra/
# Please direct questions, bug reports, and contributions... | mit |
jorik041/scikit-learn | examples/svm/plot_iris.py | 225 | 3252 | """
==================================================
Plot different SVM classifiers in the iris dataset
==================================================
Comparison of different linear SVM classifiers on a 2D projection of the iris
dataset. We only consider the first 2 features of this dataset:
- Sepal length
- Se... | bsd-3-clause |
mmilutinovic1313/zipline-with-algorithms | tests/finance/test_slippage.py | 32 | 18400 | #
# Copyright 2013 Quantopian, Inc.
#
# 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 or agreed to in wr... | apache-2.0 |
hmendozap/auto-sklearn | test/test_pipeline/implementations/test_ProjLogit.py | 1 | 1277 | import unittest
import os
import numpy as np
#import scipy.io
from autosklearn.pipeline.implementations.ProjLogit import ProjLogit
class TestProjLogit(unittest.TestCase):
def test_sparse_filtering(self):
"""Test logistic regression implementation based on least squares"""
# simple test that shou... | bsd-3-clause |
drammock/mne-python | mne/io/base.py | 2 | 99893 | # -*- coding: utf-8 -*-
# Authors: Alexandre Gramfort <alexandre.gramfort@inria.fr>
# Matti Hämäläinen <msh@nmr.mgh.harvard.edu>
# Martin Luessi <mluessi@nmr.mgh.harvard.edu>
# Denis Engemann <denis.engemann@gmail.com>
# Teon Brooks <teon.brooks@gmail.com>
# Marijn van Vliet... | bsd-3-clause |
rajul/tvb-library | tvb/simulator/plot/tools.py | 2 | 37849 | # -*- coding: utf-8 -*-
#
#
# TheVirtualBrain-Scientific Package. This package holds all simulators, and
# analysers necessary to run brain-simulations. You can use it stand alone or
# in conjunction with TheVirtualBrain-Framework Package. See content of the
# documentation-folder for more details. See also http://ww... | gpl-2.0 |
djgagne/scikit-learn | examples/cluster/plot_lena_ward_segmentation.py | 271 | 1998 | """
===============================================================
A demo of structured Ward hierarchical clustering on Lena image
===============================================================
Compute the segmentation of a 2D image with Ward hierarchical
clustering. The clustering is spatially constrained in order
... | bsd-3-clause |
krez13/scikit-learn | examples/datasets/plot_random_multilabel_dataset.py | 278 | 3402 | """
==============================================
Plot randomly generated multilabel dataset
==============================================
This illustrates the `datasets.make_multilabel_classification` dataset
generator. Each sample consists of counts of two features (up to 50 in
total), which are differently distri... | bsd-3-clause |
elijah513/scikit-learn | examples/manifold/plot_lle_digits.py | 181 | 8510 | """
=============================================================================
Manifold learning on handwritten digits: Locally Linear Embedding, Isomap...
=============================================================================
An illustration of various embeddings on the digits dataset.
The RandomTreesEmbed... | bsd-3-clause |
bikong2/scikit-learn | sklearn/semi_supervised/tests/test_label_propagation.py | 307 | 1974 | """ test the label propagation module """
import nose
import numpy as np
from sklearn.semi_supervised import label_propagation
from numpy.testing import assert_array_almost_equal
from numpy.testing import assert_array_equal
ESTIMATORS = [
(label_propagation.LabelPropagation, {'kernel': 'rbf'}),
(label_propa... | bsd-3-clause |
SeanCameronConklin/aima-python | submissions/Blue/myNN.py | 10 | 3071 | from sklearn import datasets
from sklearn.neural_network import MLPClassifier
import traceback
from submissions.Blue import music
class DataFrame:
data = []
feature_names = []
target = []
target_names = []
musicATRB = DataFrame()
musicATRB.data = []
targetData = []
'''
Extract data from the CORGIS Mu... | mit |
az0/entity-metadata | code/combine_people_names.py | 1 | 6324 | #!/usr/bin/python3
#
# Copyright (C) 2019 by Compassion International. All rights reserved.
# License GPLv3+: GNU GPL version 3 or later <http://gnu.org/licenses/gpl.html>.
# This is free software: you are free to change and redistribute it.
# There is NO WARRANTY, to the extent permitted by law.
"""
This program g... | gpl-3.0 |
interactiveaudiolab/nussl | tests/ml/test_overfit.py | 1 | 3184 | from nussl import ml, datasets
import tempfile
from torch import optim
import numpy as np
import logging
import os
import torch
from matplotlib import pyplot as plt
logging.basicConfig(
format='%(asctime)s,%(msecs)d %(levelname)-8s [%(filename)s:%(lineno)d] %(message)s',
datefmt='%Y-%m-%d:%H:%M:%S',
level=... | mit |
Nyker510/scikit-learn | examples/manifold/plot_manifold_sphere.py | 258 | 5101 | #!/usr/bin/python
# -*- coding: utf-8 -*-
"""
=============================================
Manifold Learning methods on a severed sphere
=============================================
An application of the different :ref:`manifold` techniques
on a spherical data-set. Here one can see the use of
dimensionality reducti... | bsd-3-clause |
dh4gan/oberon | plot/plot_EBMlog_multiplefiles.py | 1 | 2302 | # Written 17/1/14 by dh4gan
# Code reads in log files from multiple EBMs in the same N Body simulation and plots them
import matplotlib.pyplot as plt
import numpy as np
import io_oberon.io_EBM
import filefinder.localfiles as ff
# Find log files in directory
inputfiles = ff.find_sorted_local_input_fileset('*.log')
... | gpl-3.0 |
spallavolu/scikit-learn | examples/cluster/plot_mini_batch_kmeans.py | 265 | 4081 | """
====================================================================
Comparison of the K-Means and MiniBatchKMeans clustering algorithms
====================================================================
We want to compare the performance of the MiniBatchKMeans and KMeans:
the MiniBatchKMeans is faster, but give... | bsd-3-clause |
breznak/nupic | external/linux32/lib/python2.6/site-packages/matplotlib/rcsetup.py | 69 | 23344 | """
The rcsetup module contains the default values and the validation code for
customization using matplotlib's rc settings.
Each rc setting is assigned a default value and a function used to validate any
attempted changes to that setting. The default values and validation functions
are defined in the rcsetup module, ... | agpl-3.0 |
malin1993ml/h-store | graphs/throughput-timeline.py | 4 | 7700 | #!/usr/bin/env python
# Example execution
# export b=tpcc ; export dataDir=/home/pavlo/Documents/H-Store/Papers/anticache/data/evictions
# ant compile hstore-benchmark -Dproject=$b -Dclient.interval=500 \
# -Dsite.anticache_enable=true -Dsite.anticache_profiling=true \
# -Dclient.output_memory=$dataDir/$b-memor... | gpl-3.0 |
benoitsteiner/tensorflow-opencl | tensorflow/contrib/distributions/python/ops/mixture.py | 13 | 18780 | # Copyright 2016 The TensorFlow Authors. All Rights Reserved.
#
# 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 applica... | apache-2.0 |
carrillo/scikit-learn | sklearn/feature_selection/tests/test_rfe.py | 209 | 11733 | """
Testing Recursive feature elimination
"""
import warnings
import numpy as np
from numpy.testing import assert_array_almost_equal, assert_array_equal
from nose.tools import assert_equal, assert_true
from scipy import sparse
from sklearn.feature_selection.rfe import RFE, RFECV
from sklearn.datasets import load_iris,... | bsd-3-clause |
kprestel/PyInvestment | pytech/fin/analysis/technical.py | 2 | 15398 | """
Contains functions to perform technical analysis on pandas OHLCV data frames
"""
import logging
from typing import Union
import pandas as pd
import pytech.utils.pandas_utils as pd_utils
logger = logging.getLogger(__name__)
def sma(df: pd.DataFrame,
period: int = 50,
col: str = pd_utils.CLOSE_CO... | mit |
aetilley/scikit-learn | examples/mixture/plot_gmm_selection.py | 248 | 3223 | """
=================================
Gaussian Mixture Model Selection
=================================
This example shows that model selection can be performed with
Gaussian Mixture Models using information-theoretic criteria (BIC).
Model selection concerns both the covariance type
and the number of components in th... | bsd-3-clause |
MichelJuillard/dlstats | dlstats/tests/fetchers/test_esri.py | 1 | 43458 | # -*- coding: utf-8 -*-
"""
Created on Thu Nov 26 10:20:04 2015
@author: salimeh
"""
"""
Created on Thu Nov 26 10:20:04 2015
@author: salimeh
"""
import io
import tempfile
import datetime
import os
import pandas
from pprint import pprint
from urllib.parse import urlparse, urljoin
from urllib.request import url2pathn... | agpl-3.0 |
YinongLong/scikit-learn | examples/semi_supervised/plot_label_propagation_digits_active_learning.py | 28 | 3417 | """
========================================
Label Propagation digits active learning
========================================
Demonstrates an active learning technique to learn handwritten digits
using label propagation.
We start by training a label propagation model with only 10 labeled points,
then we select the t... | bsd-3-clause |
xubenben/scikit-learn | sklearn/gaussian_process/gaussian_process.py | 83 | 34544 | # -*- coding: utf-8 -*-
# Author: Vincent Dubourg <vincent.dubourg@gmail.com>
# (mostly translation, see implementation details)
# Licence: BSD 3 clause
from __future__ import print_function
import numpy as np
from scipy import linalg, optimize
from ..base import BaseEstimator, RegressorMixin
from ..metrics... | bsd-3-clause |
qrsforever/workspace | python/test/fangzu/collect.py | 1 | 2931 | #!/usr/bin/python3
# -*- coding: utf-8 -*-
import re
from room import RoomInfo
from urllib.request import Request, urlopen
from urllib.error import URLError
import matplotlib.pyplot as plt
WEB_SITE = "http://bj.58.com"
AREA = "gaobeidianbj"
TYPE = "zufang"
SUFFIX = "pn"
MAX_PAGES = 20
RE_ROOM = r'<p class="room">(\d... | mit |
godrayz/trading-with-python | lib/functions.py | 76 | 11627 | # -*- coding: utf-8 -*-
"""
twp support functions
@author: Jev Kuznetsov
Licence: GPL v2
"""
from scipy import polyfit, polyval
import datetime as dt
#from datetime import datetime, date
from pandas import DataFrame, Index, Series
import csv
import matplotlib.pyplot as plt
import numpy as np
import p... | bsd-3-clause |
moble/sympy | sympy/physics/quantum/state.py | 58 | 29186 | """Dirac notation for states."""
from __future__ import print_function, division
from sympy import (cacheit, conjugate, Expr, Function, integrate, oo, sqrt,
Tuple)
from sympy.core.compatibility import u, range
from sympy.printing.pretty.stringpict import stringPict
from sympy.physics.quantum.qexpr ... | bsd-3-clause |
massmutual/scikit-learn | examples/neighbors/plot_nearest_centroid.py | 264 | 1804 | """
===============================
Nearest Centroid Classification
===============================
Sample usage of Nearest Centroid classification.
It will plot the decision boundaries for each class.
"""
print(__doc__)
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap
f... | bsd-3-clause |
liangz0707/scikit-learn | examples/linear_model/plot_ols.py | 220 | 1940 | #!/usr/bin/python
# -*- coding: utf-8 -*-
"""
=========================================================
Linear Regression Example
=========================================================
This example uses the only the first feature of the `diabetes` dataset, in
order to illustrate a two-dimensional plot of this regre... | bsd-3-clause |
jblackburne/scikit-learn | sklearn/feature_extraction/tests/test_image.py | 25 | 11187 | # Authors: Emmanuelle Gouillart <emmanuelle.gouillart@normalesup.org>
# Gael Varoquaux <gael.varoquaux@normalesup.org>
# License: BSD 3 clause
import numpy as np
import scipy as sp
from scipy import ndimage
from nose.tools import assert_equal, assert_true
from numpy.testing import assert_raises
from sklearn... | bsd-3-clause |
corburn/scikit-bio | skbio/io/format/genbank.py | 4 | 28763 | """
GenBank format (:mod:`skbio.io.format.genbank`)
===============================================
.. currentmodule:: skbio.io.format.genbank
GenBank format (GenBank Flat File Format) stores sequence and its annotation
together. The start of the annotation section is marked by a line beginning
with the word "LOCUS".... | bsd-3-clause |
Jimmy-Morzaria/scikit-learn | examples/decomposition/plot_faces_decomposition.py | 204 | 4452 | """
============================
Faces dataset decompositions
============================
This example applies to :ref:`olivetti_faces` different unsupervised
matrix decomposition (dimension reduction) methods from the module
:py:mod:`sklearn.decomposition` (see the documentation chapter
:ref:`decompositions`) .
"""... | bsd-3-clause |
lisa-lab/pylearn2 | pylearn2/utils/image.py | 39 | 18841 | """
Utility functions for working with images.
"""
import logging
import numpy as np
plt = None
axes = None
from theano.compat.six.moves import xrange
from theano.compat.six import string_types
import warnings
try:
import matplotlib.pyplot as plt
import matplotlib.axes
except (RuntimeError, ImportError, TypeErr... | bsd-3-clause |
lnls-fac/collective_effects | pycolleff/pycolleff/simulate_landau.py | 1 | 11039 | import numpy as np
from functools import partial as _partial
import scipy.integrate as scy_int
import mathphys as _mp
import resource
import matplotlib.pyplot as plt
c = _mp.constants.light_speed
def memory_limit():
soft, hard = resource.getrlimit(resource.RLIMIT_AS)
resource.setrlimit(resource.RLIMIT_AS, (g... | mit |
henrykironde/scikit-learn | examples/ensemble/plot_gradient_boosting_regularization.py | 355 | 2843 | """
================================
Gradient Boosting regularization
================================
Illustration of the effect of different regularization strategies
for Gradient Boosting. The example is taken from Hastie et al 2009.
The loss function used is binomial deviance. Regularization via
shrinkage (``lear... | bsd-3-clause |
Vimos/scikit-learn | examples/neighbors/plot_nearest_centroid.py | 58 | 1803 | """
===============================
Nearest Centroid Classification
===============================
Sample usage of Nearest Centroid classification.
It will plot the decision boundaries for each class.
"""
print(__doc__)
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap
f... | bsd-3-clause |
alephu5/Soundbyte | environment/lib/python3.3/site-packages/pandas/io/tests/test_ga.py | 1 | 7200 | import os
from datetime import datetime
import nose
import pandas as pd
from pandas import DataFrame
from pandas.util.testing import network, assert_frame_equal, with_connectivity_check
from numpy.testing.decorators import slow
import pandas.util.testing as tm
try:
import httplib2
import pandas.io.ga as ga
... | gpl-3.0 |
jomolinare/kobocat | onadata/apps/viewer/tests/test_export_list.py | 5 | 8203 | import os
from django.core.urlresolvers import reverse
from onadata.apps.main.tests.test_base import TestBase
from onadata.apps.viewer.models.export import Export
from onadata.apps.main.models.meta_data import MetaData
from onadata.apps.viewer.views import export_list
class TestExportList(TestBase):
def setUp(... | bsd-2-clause |
radiasoft/radtrack | radtrack/ui/matplotlibwidget.py | 1 | 1226 | #!/usr/bin/python
from PyQt4 import QtGui
from matplotlib.backends.backend_qt4agg import FigureCanvasQTAgg as FigureCanvas
from matplotlib.backends.backend_qt4agg import NavigationToolbar2QT as Navigationtoolbar
from matplotlib.figure import Figure
#Embeddable matplotlib figure/canvas
class MplCanvas(FigureCanvas):... | apache-2.0 |
valexandersaulys/airbnb_kaggle_contest | venv/lib/python3.4/site-packages/sklearn/cluster/spectral.py | 233 | 18153 | # -*- coding: utf-8 -*-
"""Algorithms for spectral clustering"""
# Author: Gael Varoquaux gael.varoquaux@normalesup.org
# Brian Cheung
# Wei LI <kuantkid@gmail.com>
# License: BSD 3 clause
import warnings
import numpy as np
from ..base import BaseEstimator, ClusterMixin
from ..utils import check_rand... | gpl-2.0 |
Matla/ForceTorqueSensor | code/bias_handler.py | 1 | 12326 | __author__ = "Mats Larsen"
__copyright__ = "SINTEF, NTNU 2013"
__credits__ = ["Morten Lind"]
__license__ = "GPL"
__maintainer__ = "Mats Larsen"
__email__ = "matsla@{ntnu.no}"
__status__ = "Development"
#--------------------------------------------------------------------
#File: bias_handler.py
#Module Description
"""
T... | gpl-3.0 |
fergalbyrne/nupic | external/linux32/lib/python2.6/site-packages/matplotlib/backends/backend_emf.py | 69 | 22336 | """
Enhanced Metafile backend. See http://pyemf.sourceforge.net for the EMF
driver library.
"""
from __future__ import division
try:
import pyemf
except ImportError:
raise ImportError('You must first install pyemf from http://pyemf.sf.net')
import os,sys,math,re
from matplotlib import verbose, __version__,... | agpl-3.0 |
swordsmaster/waf | testsuites/tmtests/tmcontext01/plot.py | 14 | 1341 | #
# Copyright (c) 2014 embedded brains GmbH. All rights reserved.
#
# The license and distribution terms for this file may be
# found in the file LICENSE in this distribution or at
# http://www.rtems.org/license/LICENSE.
#
import libxml2
from libxml2 import xmlNode
import matplotlib.pyplot as plt
doc = libxml2.parseF... | gpl-2.0 |
almarklein/scikit-image | doc/examples/plot_line_hough_transform.py | 2 | 4361 | r"""
=============================
Straight line Hough transform
=============================
The Hough transform in its simplest form is a `method to detect straight lines
<http://en.wikipedia.org/wiki/Hough_transform>`__.
In the following example, we construct an image with a line intersection. We
then use the Ho... | bsd-3-clause |
alddiaz/anasynth | lib/libsignalplot.py | 1 | 4480 | # Plot DWT magnitude spectrum
# Developed by Aldo Diaz
# University of Campinas, 2020
import numpy as np # numeric library
import matplotlib.pyplot as plt # graphics library
from lib.dftSpectrum import * # DFT magnitue spectrum
def plotSignal(x, y, fs=1):
# Input:
# x: Input signal
# y: Reconst... | gpl-2.0 |
zfrenchee/pandas | pandas/tests/indexes/common.py | 1 | 41457 | # -*- coding: utf-8 -*-
import pytest
from pandas import compat
from pandas.compat import PY3
import numpy as np
from pandas import (Series, Index, Float64Index, Int64Index, UInt64Index,
RangeIndex, MultiIndex, CategoricalIndex, DatetimeIndex,
TimedeltaIndex, PeriodIndex, Int... | bsd-3-clause |
idoerg/BOA | src/classify/mnbayes.py | 1 | 9467 | """
NOTE: OUT OF DATE
Multinomial Naive Bayes classifier
"""
import nltk
from nltk.classify.scikitlearn import SklearnClassifier
from sklearn.naive_bayes import MultinomialNB
import pylab as pl
from sklearn.utils import shuffle
from sklearn.metrics import roc_curve, auc
import os,sys
import itertools
import argparse
... | gpl-3.0 |
DeepRNN/image_captioning | utils/vocabulary.py | 1 | 2841 | import os
import numpy as np
import pandas as pd
from tqdm import tqdm
import string
from nltk.tokenize import word_tokenize
class Vocabulary(object):
def __init__(self, size, save_file=None):
self.words = []
self.word2idx = {}
self.word_frequencies = []
self.size = size
if ... | mit |
kc-lab/dms2dfe | dms2dfe/ana0_getfeats.py | 2 | 2594 | #!usr/bin/python
# Copyright 2016, Rohan Dandage <rraadd_8@hotmail.com,rohan@igib.in>
# This program is distributed under General Public License v. 3.
import sys
from os.path import exists,splitext
import logging
import numpy as np
import pandas as pd
from Bio.PDB import PDBParser
from Bio.PDB.Polypeptide import PP... | gpl-3.0 |
pligor/predicting-future-product-prices | 03_good_deal/relevant_deals.py | 1 | 3248 | # -*- coding: UTF-8 -*-
from __future__ import division
import numpy as np
import pandas as pd
import sys
import math
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
import re
import os
import csv
from sklearn.neighbors import NearestNeighbors
def printgr(obj):
print repr(obj).decode('unicode-escape... | agpl-3.0 |
mayanks43/auto-tag | msd_bof_knn_rf.py | 1 | 4469 | import os
import glob
import hdf5_getters
import pickle
import numpy as np
from sklearn.cluster import KMeans
import time
import resource
import random
import sqlite3
from sklearn.neighbors import KNeighborsClassifier
from sklearn.grid_search import GridSearchCV
from sklearn.pipeline import Pipeline
from sklearn.metric... | mit |
jcasner/nupic | external/linux32/lib/python2.6/site-packages/matplotlib/backends/backend_gtk.py | 69 | 43991 | from __future__ import division
import os, sys
def fn_name(): return sys._getframe(1).f_code.co_name
try:
import gobject
import gtk; gdk = gtk.gdk
import pango
except ImportError:
raise ImportError("Gtk* backend requires pygtk to be installed.")
pygtk_version_required = (2,2,0)
if gtk.pygtk_version <... | agpl-3.0 |
Djabbz/scikit-learn | examples/cluster/plot_affinity_propagation.py | 349 | 2304 | """
=================================================
Demo of affinity propagation clustering algorithm
=================================================
Reference:
Brendan J. Frey and Delbert Dueck, "Clustering by Passing Messages
Between Data Points", Science Feb. 2007
"""
print(__doc__)
from sklearn.cluster impor... | bsd-3-clause |
tectronics/scipy-cluster | old/cluster.py | 5 | 115147 | """
-----------------------------------------
Hierarchical Clustering Library for Scipy
Copyright (C) Damian Eads, 2007-2008.
New BSD License
-----------------------------------------
Flat cluster formation
fcluster forms flat clusters from hierarchical clusters.
fclusterdata forms fl... | bsd-3-clause |
metaml/nupic | external/linux32/lib/python2.6/site-packages/matplotlib/delaunay/testfuncs.py | 72 | 20890 | """Some test functions for bivariate interpolation.
Most of these have been yoinked from ACM TOMS 792.
http://netlib.org/toms/792
"""
import numpy as np
from triangulate import Triangulation
class TestData(dict):
def __init__(self, *args, **kwds):
dict.__init__(self, *args, **kwds)
self.__dict__ ... | agpl-3.0 |
anntzer/scikit-learn | benchmarks/bench_feature_expansions.py | 34 | 1761 | import matplotlib.pyplot as plt
import numpy as np
import scipy.sparse as sparse
from sklearn.preprocessing import PolynomialFeatures
from time import time
degree = 2
trials = 3
num_rows = 1000
dimensionalities = np.array([1, 2, 8, 16, 32, 64])
densities = np.array([0.01, 0.1, 1.0])
csr_times = {d: np.zeros(len(dimens... | bsd-3-clause |
jwdebelius/break_4w | break4w/tests/test_bool.py | 1 | 3129 | from unittest import TestCase, main
import pandas as pd
import numpy as np
# import numpy.testing as npt
import pandas.util.testing as pdt
from break4w.bool import Bool
class BoolTest(TestCase):
def setUp(self):
self.map_ = pd.DataFrame([['1', '2', '2', '4'],
['TBD', '... | bsd-2-clause |
htygithub/bokeh | bokeh/sampledata/periodic_table.py | 45 | 1542 | '''
This module provides the periodic table as a data set. It exposes an attribute 'elements'
which is a pandas dataframe with the following fields
elements['atomic Number'] (units: g/cm^3)
elements['symbol']
elements['name']
elements['atomic mass'] (units: amu)
elements['CPK'] ... | bsd-3-clause |
saketkc/ribo-seq-snakemake | configs/Oct_10_2016_HuR_Human.py | 1 | 2325 |
## Absolute location where all raw files are
RAWDATA_DIR = '/home/cmb-06/as/skchoudh/dna/Oct_10_2016_HuR_Human_Mouse_Liver/Ribo-Seq-Human'
## Output directory
OUT_DIR = '/staging/as/skchoudh/Oct_10_2016_HuR_Human_Mouse_Liver/Ribo-Seq_human'
## Absolute location to 're-ribo/scripts' directory
SRC_DIR = '/home/cmb-... | bsd-3-clause |
NSLS-II-SRX/ipython_ophyd | profile_bs/ipython_notebook_config.py | 14 | 24673 | # Configuration file for ipython-notebook.
c = get_config()
#------------------------------------------------------------------------------
# NotebookApp configuration
#------------------------------------------------------------------------------
# NotebookApp will inherit config from: BaseIPythonApplication, Appli... | bsd-2-clause |
djgroen/flee-release | plot-flee-output.py | 1 | 14013 | import pandas as pd
import matplotlib
matplotlib.use('Pdf')
import matplotlib.pyplot as plt
import numpy as np
import sys
import datamanager.handle_refugee_data as handle_refugee_data
import warnings
import outputanalysis.analysis as a
warnings.filterwarnings("ignore")
"""
This is a generic plotting program.
See an ... | bsd-3-clause |
ryfeus/lambda-packs | LightGBM_sklearn_scipy_numpy/source/sklearn/datasets/svmlight_format.py | 30 | 18814 | """This module implements a loader and dumper for the svmlight format
This format is a text-based format, with one sample per line. It does
not store zero valued features hence is suitable for sparse dataset.
The first element of each line can be used to store a target variable to
predict.
This format is used as the... | mit |
huangkuan/hack | lib/gcloud/monitoring/test__dataframe.py | 7 | 8218 | # Copyright 2016 Google Inc. All rights reserved.
#
# 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 or agree... | apache-2.0 |
saiwing-yeung/scikit-learn | examples/model_selection/plot_roc.py | 49 | 5041 | """
=======================================
Receiver Operating Characteristic (ROC)
=======================================
Example of Receiver Operating Characteristic (ROC) metric to evaluate
classifier output quality.
ROC curves typically feature true positive rate on the Y axis, and false
positive rate on the X a... | bsd-3-clause |
NicoliAraujo/rna-precipitacao-manaus | PrevisaoTempo/modules/util/PCA.py | 1 | 5185 | # -*- coding: utf-8 -*-
'''
Created on 28 de fev de 2017
@author: nicoli
'''
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
from sklearn.decomposition import PCA
def my_pca(filename, variable_list):
all_data = read_data_set(filename)
eval_data = np.array(all... | gpl-3.0 |
tbenthompson/tectosaur | examples/elastic_convergence.py | 1 | 1650 | import numpy as np
import scipy.sparse
import tectosaur.mesh as mesh
from tectosaur.constraints import constraints, build_constraint_matrix
from tectosaur.dense_integral_op import DenseIntegralOp
sm = 1.0
pr = 0.25
w = 4
corners = [[w, w, 0], [w, -w, 0], [-w, -w, 0], [-w, w, 0]]
m = mesh.make_rect(4, 4, corners)
cs = ... | mit |
toobaz/pandas | pandas/tests/reshape/test_melt.py | 2 | 33227 | import numpy as np
from numpy import nan
import pytest
import pandas as pd
from pandas import DataFrame, lreshape, melt, wide_to_long
import pandas.util.testing as tm
class TestMelt:
def setup_method(self, method):
self.df = tm.makeTimeDataFrame()[:10]
self.df["id1"] = (self.df["A"] > 0).astype(n... | bsd-3-clause |
arunsv/DTE | Python/DTEDriver.py | 1 | 7959 | import numpy as np
import math
import os
import sys
import time
import random
import matplotlib.pyplot as plt
#Setting parameters
def getTEParams(workDir,dMode,pMode) :
TEP = TEParams()
TEP.progMode = pMode
TEP.workPath = workDir
TEP.timingFileName = workDir + "/" + "timing.txt"
TEP.execPath =... | apache-2.0 |
lkishline/expyfun | expyfun/analyze/_viz.py | 1 | 19369 | """Analysis visualization functions
"""
import numpy as np
from itertools import chain
try:
import matplotlib.pyplot as plt
from matplotlib import rcParams
except ImportError:
plt = None
try:
from pandas.core.frame import DataFrame
except ImportError:
DataFrame = None
from .._utils import string_t... | bsd-3-clause |
nolanliou/tensorflow | tensorflow/contrib/metrics/python/ops/metric_ops_test.py | 4 | 262821 | # Copyright 2016 The TensorFlow Authors. All Rights Reserved.
#
# 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 applica... | apache-2.0 |
poryfly/scikit-learn | examples/feature_selection/plot_rfe_with_cross_validation.py | 226 | 1384 | """
===================================================
Recursive feature elimination with cross-validation
===================================================
A recursive feature elimination example with automatic tuning of the
number of features selected with cross-validation.
"""
print(__doc__)
import matplotlib.p... | bsd-3-clause |
RomainBrault/scikit-learn | examples/gaussian_process/plot_gpr_noisy_targets.py | 64 | 3706 | """
=========================================================
Gaussian Processes regression: basic introductory example
=========================================================
A simple one-dimensional regression example computed in two different ways:
1. A noise-free case
2. A noisy case with known noise-level per ... | bsd-3-clause |
nfsli926/tushare | tushare/util/dateu.py | 27 | 2184 | # -*- coding:utf-8 -*-
import datetime
import pandas as pd
def year_qua(date):
mon = date[5:7]
mon = int(mon)
return[date[0:4], _quar(mon)]
def _quar(mon):
if mon in [1, 2, 3]:
return '1'
elif mon in [4, 5, 6]:
return '2'
elif mon in [7, 8, 9]:
... | bsd-3-clause |
dpshelio/scikit-image | doc/examples/plot_multiblock_local_binary_pattern.py | 22 | 2498 | """
===========================================================
Multi-Block Local Binary Pattern for texture classification
===========================================================
This example shows how to compute multi-block local binary pattern (MB-LBP)
features as well as how to visualize them.
The features ar... | bsd-3-clause |
drewdru/AOI | controllers/segmentationController.py | 1 | 18817 | """
@package segmentationController
Controller for qml Segmentation
"""
import sys
import os
import numpy
import matplotlib.pyplot as plt
import random
import time
import math
sys.path.append(os.path.abspath(os.path.dirname(__file__) + '/' + '../..'))
from imageProcessor import colorModel, histogramService, im... | gpl-3.0 |
willgrass/pandas | pandas/lib/tests/test_tseries.py | 1 | 3175 | import unittest
import numpy as np
from pandas import Index
import pandas.util.testing as common
import pandas.lib.tseries as tseries
class TestTseriesUtil(unittest.TestCase):
def test_combineFunc(self):
pass
def test_reindex(self):
pass
def test_isnull(self):
pass
def test... | bsd-3-clause |
freephys/python_ase | ase/gui/bulk_modulus.py | 14 | 1044 | # -*- coding: utf-8 -*-
from math import sqrt
import numpy as np
from ase.units import kJ
from ase.utils.eos import EquationOfState
def BulkModulus(images):
v = np.array([abs(np.linalg.det(A)) for A in images.A])
#import matplotlib.pyplot as plt
import pylab as plt
plt.ion()
EquationOfState(v, i... | gpl-3.0 |
nismod/energy_demand | energy_demand/plotting/fig_total_demand_peak.py | 1 | 9915 | """Plot peak and total demand for
"""
import os
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from energy_demand.basic import conversions
from energy_demand.technologies import tech_related
from energy_demand.plotting import basic_plot_functions
from energy_demand.read_write import write_data... | mit |
rtogo/sap-cost-center-hierarchy | hierarquia.py | 1 | 2165 | # -*- coding: utf-8 -*-
import logging
log = logging.getLogger(__name__)
import pandas as pd
def transform_hierarquia(df_ks13, df_ksh3):
log.info('Convertendo hierarquia de centros de custo')
# Remove os centros de custo afim de manter somente as hierarquias
# no dataframe
df_ksh3 = df_ksh3.query('... | mit |
tomlof/scikit-learn | sklearn/ensemble/tests/test_voting_classifier.py | 19 | 10873 | """Testing for the VotingClassifier"""
import numpy as np
from sklearn.utils.testing import assert_almost_equal, assert_array_equal
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_raise_message
from sklearn.exceptions import NotFittedError
from sklearn.linear_model import Logist... | bsd-3-clause |
softwaremechanic/Miscellaneous | Python/causality_ex.py | 1 | 1299 | import numpy
import pandas as pd
from causality.inference.search import IC
from causality.inference.independence_tests import RobustRegressionTest
SIZE = 2000
x1 = numpy.random.normal(size=SIZE)
x2 = x1 + numpy.random.normal(size=SIZE)
x3 = x1 + numpy.random.normal(size=SIZE)
x4 = x2 + x3 + numpy.random.normal(size=S... | gpl-2.0 |
drufat/sympy | sympy/utilities/runtests.py | 7 | 82184 | """
This is our testing framework.
Goals:
* it should be compatible with py.test and operate very similarly
(or identically)
* doesn't require any external dependencies
* preferably all the functionality should be in this file only
* no magic, just import the test file and execute the test functions, that's it
* po... | bsd-3-clause |
mjabri/holoviews | holoviews/core/io.py | 1 | 33268 | """
Module defining input/output interfaces to HoloViews.
There are two components for input/output:
Exporters: Process (composite) HoloViews objects one at a time. For
instance, an exporter may render a HoloViews object as a
svg or perhaps pickle it.
Archives: A collection of HoloViews objects... | bsd-3-clause |
dgwakeman/mne-python | examples/visualization/plot_evoked_erf_erp.py | 12 | 1182 | """
=================================
Plotting ERF/ERP with evoked data
=================================
Load evoked data and plot.
"""
# Authors: Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
#
# License: BSD (3-clause)
import matplotlib.pyplot as plt
from mne.datasets import sample
from mne import ... | bsd-3-clause |
miaecle/deepchem | setup.py | 2 | 1915 | import sys
import time
from setuptools import setup, find_packages
if '--release' in sys.argv:
IS_RELEASE = True
sys.argv.remove('--release')
else:
# Build a nightly package by default.
IS_RELEASE = False
# get the version from deepchem/__init__.py
def _get_version():
with open('deepchem/__init__.py') as f... | mit |
quheng/scikit-learn | examples/missing_values.py | 233 | 3056 | """
======================================================
Imputing missing values before building an estimator
======================================================
This example shows that imputing the missing values can give better results
than discarding the samples containing any missing value.
Imputing does not ... | bsd-3-clause |
LohithBlaze/scikit-learn | sklearn/mixture/tests/test_gmm.py | 200 | 17427 | import unittest
import copy
import sys
from nose.tools import assert_true
import numpy as np
from numpy.testing import (assert_array_equal, assert_array_almost_equal,
assert_raises)
from scipy import stats
from sklearn import mixture
from sklearn.datasets.samples_generator import make_spd_ma... | bsd-3-clause |
jmenglund/pandas-validation | setup.py | 1 | 1045 | from setuptools import setup, find_packages
from os.path import join, dirname
setup(
name='pandas-validation',
version='0.5.0',
description=(
'A Python package for validating data with pandas'),
long_description=open(
join(dirname(__file__), 'README.rst'), encoding='utf-8').read(),
... | mit |
sachinpro/sachinpro.github.io | tensorflow/contrib/learn/python/learn/tests/dataframe/test_feeding_queue_runner.py | 1 | 3214 | # pylint: disable=g-bad-file-header
# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
#
# 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/LICENS... | apache-2.0 |
simonalford42/Better | Germain Python/Germain.py | 1 | 1134 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
import csv
import dateutil.parser
import matplotlib.pyplot as plt
import matplotlib.dates as dates
import numpy as np
f = open('First_Small.csv', 'r', encoding = 'latin1')
reader = csv.reader(f)
rows = [row for row in reader][1:]
times = [row[0].replace('.', ':', 2) for r... | mit |
michigraber/scikit-learn | examples/mixture/plot_gmm.py | 248 | 2817 | """
=================================
Gaussian Mixture Model Ellipsoids
=================================
Plot the confidence ellipsoids of a mixture of two Gaussians with EM
and variational Dirichlet process.
Both models have access to five components with which to fit the
data. Note that the EM model will necessari... | bsd-3-clause |
enixdark/Data-Practices | paratices/datawrangling/SQL_Exersice.py | 1 | 8073 | import pandas
import pandasql
def select_first_50(filename):
# Read in our aadhaar_data csv to a pandas dataframe. Afterwards, we rename the columns
# by replacing spaces with underscores and setting all characters to lowercase, so the
# column names more closely resemble columns names one might find in a... | lgpl-3.0 |
manashmndl/scikit-learn | examples/svm/plot_svm_scale_c.py | 223 | 5375 | """
==============================================
Scaling the regularization parameter for SVCs
==============================================
The following example illustrates the effect of scaling the
regularization parameter when using :ref:`svm` for
:ref:`classification <svm_classification>`.
For SVC classificati... | bsd-3-clause |
fyffyt/scikit-learn | sklearn/metrics/cluster/tests/test_supervised.py | 206 | 7643 | import numpy as np
from sklearn.metrics.cluster import adjusted_rand_score
from sklearn.metrics.cluster import homogeneity_score
from sklearn.metrics.cluster import completeness_score
from sklearn.metrics.cluster import v_measure_score
from sklearn.metrics.cluster import homogeneity_completeness_v_measure
from sklearn... | bsd-3-clause |
jjx02230808/project0223 | sklearn/metrics/scorer.py | 17 | 13148 | """
The :mod:`sklearn.metrics.scorer` submodule implements a flexible
interface for model selection and evaluation using
arbitrary score functions.
A scorer object is a callable that can be passed to
:class:`sklearn.model_selection.GridSearchCV` or
:func:`sklearn.model_selection.cross_val_score` as the ``scoring``
par... | bsd-3-clause |
XiaoTaoWang/HiC_pipeline | runHiC/quality.py | 1 | 9574 | # Created on Thu Sep 13 21:44:24 2018
# Author: XiaoTao Wang
import os, time
import numpy as np
import matplotlib, pickle, glob
matplotlib.use('Agg')
import matplotlib.pyplot as plt
## Plot Settings
import matplotlib.pyplot as plt
# Matplotlib Settings
matplotlib.rcParams['xtick.direction'] = 'out'
matplotlib.rcParam... | gpl-3.0 |
louispotok/pandas | pandas/tests/indexing/test_partial.py | 3 | 23230 | """
test setting *parts* of objects both positionally and label based
TOD: these should be split among the indexer tests
"""
import pytest
from warnings import catch_warnings
import numpy as np
import pandas as pd
from pandas import Series, DataFrame, Panel, Index, date_range
from pandas.util import testing as tm
... | bsd-3-clause |
srjoglekar246/sympy | examples/intermediate/mplot3d.py | 5 | 1252 | #!/usr/bin/env python
"""Matplotlib 3D plotting example
Demonstrates plotting with matplotlib.
"""
import sys
from sample import sample
from sympy import sin, Symbol
from sympy.external import import_module
def mplot3d(f, var1, var2, show=True):
"""
Plot a 3d function using matplotlib/Tk.
"""
im... | bsd-3-clause |
nkmk/python-snippets | notebook/pandas_to_csv_float.py | 1 | 1820 | import pandas as pd
df = pd.DataFrame({'col1': [0.123456789, 1000000000.0],
'col2': [123456789.0, 0.0],
'col3': [123456789, 0]})
print(df)
# col1 col2 col3
# 0 1.234568e-01 123456789.0 123456789
# 1 1.000000e+09 0.0 0
print(df.dtypes... | mit |
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