repo_name stringlengths 7 60 | path stringlengths 6 134 | copies stringlengths 1 3 | size stringlengths 4 6 | content stringlengths 1.04k 149k | license stringclasses 12
values |
|---|---|---|---|---|---|
cemarchi/biosphere | Src/BioAnalyzer/Analysis/GenePrioritization/Steps/DataIntegration/IntermediateRepresentation/Transformers/MicroRnaToGeneTransformer.py | 1 | 4546 | import math
import statistics
from itertools import groupby
from random import randint
from typing import Dict, Tuple, Counter
import pandas as pd
from Src.BioAnalyzer.Analysis.GenePrioritization.Steps.DataIntegration.IntermediateRepresentation.Generators import \
IntermediateRepresentationGeneratorBase
from Src.... | bsd-3-clause |
winklerand/pandas | pandas/tests/test_errors.py | 9 | 1147 | # -*- coding: utf-8 -*-
import pytest
from warnings import catch_warnings
import pandas # noqa
import pandas as pd
@pytest.mark.parametrize(
"exc", ['UnsupportedFunctionCall', 'UnsortedIndexError',
'OutOfBoundsDatetime',
'ParserError', 'PerformanceWarning', 'DtypeWarning',
'E... | bsd-3-clause |
ammarkhann/FinalSeniorCode | lib/python2.7/site-packages/pandas/tests/frame/test_query_eval.py | 11 | 42389 | # -*- coding: utf-8 -*-
from __future__ import print_function
import operator
import pytest
from pandas.compat import (zip, range, lrange, StringIO)
from pandas import DataFrame, Series, Index, MultiIndex, date_range
import pandas as pd
import numpy as np
from numpy.random import randn
from pandas.util.testing imp... | mit |
JT5D/scikit-learn | examples/plot_multilabel.py | 9 | 4299 | # Authors: Vlad Niculae, Mathieu Blondel
# License: BSD 3 clause
"""
=========================
Multilabel classification
=========================
This example simulates a multi-label document classification problem. The
dataset is generated randomly based on the following process:
- pick the number of labels: n ... | bsd-3-clause |
bgris/ODL_bgris | lib/python3.5/site-packages/odl/util/graphics.py | 1 | 15419 | # Copyright 2014-2016 The ODL development group
#
# This file is part of ODL.
#
# ODL 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, either version 3 of the License, or
# (at your option) any later version.
#... | gpl-3.0 |
DistrictDataLabs/django-data-product | irisfinder/views.py | 1 | 1948 | from django.shortcuts import render
import datetime
from models import Iris, SVMModels
from forms import UserIrisData
import sklearn
from sklearn import svm
from sklearn.cross_validation import train_test_split
import numpy as np
from django.conf import settings
import cPickle
import scipy
from pytz import timezone
imp... | apache-2.0 |
nvoron23/statsmodels | statsmodels/sandbox/examples/try_multiols.py | 33 | 1243 | # -*- coding: utf-8 -*-
"""
Created on Sun May 26 13:23:40 2013
Author: Josef Perktold, based on Enrico Giampieri's multiOLS
"""
#import numpy as np
import pandas as pd
import statsmodels.api as sm
from statsmodels.sandbox.multilinear import multiOLS, multigroup
data = sm.datasets.longley.load_pandas()
df = data.e... | bsd-3-clause |
ChanderG/scikit-learn | sklearn/manifold/tests/test_isomap.py | 226 | 3941 | from itertools import product
import numpy as np
from numpy.testing import assert_almost_equal, assert_array_almost_equal
from sklearn import datasets
from sklearn import manifold
from sklearn import neighbors
from sklearn import pipeline
from sklearn import preprocessing
from sklearn.utils.testing import assert_less
... | bsd-3-clause |
nmayorov/scikit-learn | sklearn/linear_model/logistic.py | 9 | 67760 |
"""
Logistic Regression
"""
# Author: Gael Varoquaux <gael.varoquaux@normalesup.org>
# Fabian Pedregosa <f@bianp.net>
# Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
# Manoj Kumar <manojkumarsivaraj334@gmail.com>
# Lars Buitinck
# Simon Wu <s8wu@uwaterloo.ca>
im... | bsd-3-clause |
google-research/google-research | smu/parser/smu_utils_lib_test.py | 1 | 35529 | # coding=utf-8
# Copyright 2021 The Google Research Authors.
#
# 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 applicab... | apache-2.0 |
fyffyt/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 |
cyliustack/sofa | bin/sofa_analyze.py | 1 | 50661 | import argparse
import matplotlib
matplotlib.use('agg')
import csv
import json
import multiprocessing as mp
import os
import random
import re
import sys
from functools import partial
from operator import attrgetter, itemgetter
import networkx as nx
import numpy as np
import pandas as pd
import time
from sofa_aisi impor... | apache-2.0 |
zrhans/pythonanywhere | .virtualenvs/django19/lib/python3.4/site-packages/pandas/tseries/tests/test_frequencies.py | 9 | 25284 | from datetime import datetime, time, timedelta
from pandas.compat import range
import sys
import os
import nose
import numpy as np
from pandas import Index, DatetimeIndex, Timestamp, Series, date_range, period_range
import pandas.tseries.frequencies as frequencies
from pandas.tseries.tools import to_datetime
impor... | apache-2.0 |
hanteng/babel | scripts/geoname_cldr.py | 1 | 2479 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
#歧視無邊，回頭是岸。鍵起鍵落，情真情幻。
# url_target="https://raw.githubusercontent.com/datasets/country-codes/master/data/country-codes.csv"
import csv
import pandas as pd
import codecs
def export_to_csv(df, ex_filename, sep=','):
if sep==',':
df.to_csv(ex_filename, sep=sep, q... | bsd-3-clause |
tuanvu216/udacity-course | intro_to_machine_learning/lesson/lesson_4_choose_your_own_algorithm/your_algorithm.py | 1 | 2628 | #!/usr/bin/python
import matplotlib.pyplot as plt
from prep_terrain_data import makeTerrainData
from class_vis import prettyPicture
from time import time
features_train, labels_train, features_test, labels_test = makeTerrainData()
### the training data (features_train, labels_train) have both "fast" and "slow" point... | mit |
NZRS/content-analysis | netflix.py | 2 | 3126 | from bs4 import BeautifulSoup
from urllib2 import quote
import unicodedata
import requests
import json
import glob
import pandas as pd
movie_list = []
for page in glob.glob('*.html'):
with open(page, 'r+') as f:
my_page = f.read()
my_soup = BeautifulSoup(my_page)
for div in my_soup.find_al... | agpl-3.0 |
einarhuseby/arctic | tests/integration/test_arctic.py | 4 | 6898 | from datetime import datetime as dt, timedelta as dtd
from mock import patch
from pandas import DataFrame
from pandas.util.testing import assert_frame_equal
import pytest
import time
import numpy as np
from arctic.arctic import Arctic, VERSION_STORE
from arctic.exceptions import LibraryNotFoundException, QuotaExceeded... | lgpl-2.1 |
BlueBrain/NEST | testsuite/manualtests/cross_check_test_mip_corrdet.py | 13 | 2594 | # -*- coding: utf-8 -*-
#
# cross_check_test_mip_corrdet.py
#
# This file is part of NEST.
#
# Copyright (C) 2004 The NEST Initiative
#
# NEST 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, either version 2 o... | gpl-2.0 |
ammarkhann/FinalSeniorCode | lib/python2.7/site-packages/jupyter_core/tests/dotipython/profile_default/ipython_console_config.py | 24 | 21691 | # Configuration file for ipython-console.
c = get_config()
#------------------------------------------------------------------------------
# ZMQTerminalIPythonApp configuration
#------------------------------------------------------------------------------
# ZMQTerminalIPythonApp will inherit config from: TerminalIP... | mit |
ofgulban/scikit-image | doc/examples/filters/plot_rank_mean.py | 7 | 1525 | """
============
Mean filters
============
This example compares the following mean filters of the rank filter package:
* **local mean**: all pixels belonging to the structuring element to compute
average gray level.
* **percentile mean**: only use values between percentiles p0 and p1
(here 10% and 90%).
* **bila... | bsd-3-clause |
tienjunhsu/trading-with-python | lib/widgets.py | 78 | 3012 | # -*- coding: utf-8 -*-
"""
A collection of widgets for gui building
Copyright: Jev Kuznetsov
License: BSD
"""
from __future__ import division
import sys
from PyQt4.QtCore import *
from PyQt4.QtGui import *
import numpy as np
from matplotlib.backends.backend_qt4agg import FigureCanvasQTAgg as Figur... | bsd-3-clause |
igabriel85/dmon-adp | adpformater/adpformater.py | 1 | 1615 | import pandas as pd
class DataFormatter():
def __init__(self, dataloc):
self.dataloc = dataloc
def aggJsonToCsv(self):
return "CSV file"
def expTimestamp(self):
return "Expand metric timestamp"
def window(self):
return "Window metrics"
def pivot(self):
r... | apache-2.0 |
Weihonghao/ECM | Vpy34/lib/python3.5/site-packages/pandas/io/sql.py | 7 | 58343 | # -*- coding: utf-8 -*-
"""
Collection of query wrappers / abstractions to both facilitate data
retrieval and to reduce dependency on DB-specific API.
"""
from __future__ import print_function, division
from datetime import datetime, date, time
import warnings
import re
import numpy as np
import pandas._libs.lib as ... | agpl-3.0 |
rboyes/KerasScripts | CSVTrainer.py | 1 | 5321 | import os
import datetime
import sys
import time
import string
import random
import pandas as pd
import numpy as np
import gc
if(len(sys.argv) < 2):
print('Usage: CSVTrainer.py train.csv validation.csv model.h5 log.txt')
sys.exit(1)
trainingName = sys.argv[1]
validationName = sys.argv[2]
modelName = sys.... | apache-2.0 |
saimn/astropy | astropy/visualization/wcsaxes/frame.py | 8 | 10649 | # Licensed under a 3-clause BSD style license - see LICENSE.rst
import abc
from collections import OrderedDict
import numpy as np
from matplotlib import rcParams
from matplotlib.lines import Line2D, Path
from matplotlib.patches import PathPatch
__all__ = ['RectangularFrame1D', 'Spine', 'BaseFrame', 'RectangularFr... | bsd-3-clause |
mbayon/TFG-MachineLearning | vbig/lib/python2.7/site-packages/pandas/io/json/table_schema.py | 12 | 5184 | """
Table Schema builders
http://specs.frictionlessdata.io/json-table-schema/
"""
from pandas.core.dtypes.common import (
is_integer_dtype, is_timedelta64_dtype, is_numeric_dtype,
is_bool_dtype, is_datetime64_dtype, is_datetime64tz_dtype,
is_categorical_dtype, is_period_dtype, is_string_dtype
)
def as_js... | mit |
ianatpn/nupictest | external/linux32/lib/python2.6/site-packages/matplotlib/pylab.py | 70 | 10245 | """
This is a procedural interface to the matplotlib object-oriented
plotting library.
The following plotting commands are provided; the majority have
Matlab(TM) analogs and similar argument.
_Plotting commands
acorr - plot the autocorrelation function
annotate - annotate something in the figure
arrow ... | gpl-3.0 |
googleinterns/cabby | cabby/model/datasets.py | 1 | 4391 | # coding=utf-8
# Copyright 2020 Google 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
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to ... | apache-2.0 |
georgid/sms-tools | lectures/7-Sinusoidal-plus-residual-model/plots-code/stochasticSynthesisFrame.py | 2 | 2997 | import numpy as np
import matplotlib.pyplot as plt
from scipy.signal import hamming, hanning, triang, blackmanharris, resample
import math
import sys, os, time
from scipy.fftpack import fft, ifft
sys.path.append(os.path.join(os.path.dirname(os.path.realpath(__file__)), '../../../software/models/'))
import utilFunction... | agpl-3.0 |
sriki18/scipy | scipy/signal/_max_len_seq.py | 41 | 4942 | # Author: Eric Larson
# 2014
"""Tools for MLS generation"""
import numpy as np
from ._max_len_seq_inner import _max_len_seq_inner
__all__ = ['max_len_seq']
# These are definitions of linear shift register taps for use in max_len_seq()
_mls_taps = {2: [1], 3: [2], 4: [3], 5: [3], 6: [5], 7: [6], 8: [7, 6, 1],
... | bsd-3-clause |
zseder/hunmisc | hunmisc/utils/plotting/matplotlib_simple_xy.py | 1 | 1535 | """
Copyright 2011-13 Attila Zseder
Email: zseder@gmail.com
This file is part of hunmisc project
url: https://github.com/zseder/hunmisc
hunmisc is free software; you can redistribute it and/or
modify it under the terms of the GNU Lesser General Public
License as published by the Free Software Foundation; either
versi... | gpl-3.0 |
pap/nupic | external/linux32/lib/python2.6/site-packages/matplotlib/fontconfig_pattern.py | 72 | 6429 | """
A module for parsing and generating fontconfig patterns.
See the `fontconfig pattern specification
<http://www.fontconfig.org/fontconfig-user.html>`_ for more
information.
"""
# Author : Michael Droettboom <mdroe@stsci.edu>
# License : matplotlib license (PSF compatible)
# This class is defined here because it m... | agpl-3.0 |
appapantula/scikit-learn | examples/neural_networks/plot_rbm_logistic_classification.py | 258 | 4609 | """
==============================================================
Restricted Boltzmann Machine features for digit classification
==============================================================
For greyscale image data where pixel values can be interpreted as degrees of
blackness on a white background, like handwritten... | bsd-3-clause |
idlead/scikit-learn | examples/linear_model/plot_sgd_comparison.py | 112 | 1819 | """
==================================
Comparing various online solvers
==================================
An example showing how different online solvers perform
on the hand-written digits dataset.
"""
# Author: Rob Zinkov <rob at zinkov dot com>
# License: BSD 3 clause
import numpy as np
import matplotlib.pyplot a... | bsd-3-clause |
abhishekkrthakur/scikit-learn | examples/svm/plot_oneclass.py | 249 | 2302 | """
==========================================
One-class SVM with non-linear kernel (RBF)
==========================================
An example using a one-class SVM for novelty detection.
:ref:`One-class SVM <svm_outlier_detection>` is an unsupervised
algorithm that learns a decision function for novelty detection:
... | bsd-3-clause |
ati-ozgur/KDD99ReviewArticle | HelperCodes/create_table_JournalAndArticleCounts.py | 1 | 1930 | import ReviewHelper
import pandas as pd
df = ReviewHelper.get_pandas_data_frame_created_from_bibtex_file()
#df_journal = df.groupby('journal')["ID"]
dfJournalList = df.groupby(['journal'])['ID'].count().order(ascending=False)
isOdd = (dfJournalList.size % 2 == 1)
if (isOdd):
table_row_length = dfJournalList.si... | mit |
arthurmensch/modl | benchmarks/log.py | 1 | 2179 | import time
import numpy as np
from lightning.impl.primal_cd import CDClassifier
from lightning.impl.sag import SAGAClassifier
from sklearn.datasets import fetch_20newsgroups_vectorized
from lightning.classification import SAGClassifier
from sklearn.linear_model import LogisticRegression
bunch = fetch_20newsgroups_v... | bsd-2-clause |
hansonrobotics/chatbot | src/chatbot/stats.py | 1 | 3618 | import os
import logging
import pandas as pd
import glob
import re
import datetime as dt
from collections import Counter
logger = logging.getLogger('hr.chatbot.stats')
trace_pattern = re.compile(
r'../(?P<fname>.*), (?P<tloc>\(.*\)), (?P<pname>.*), (?P<ploc>\(.*\))')
def collect_history_data(history_dir, days):
... | mit |
hdmetor/scikit-learn | sklearn/linear_model/ridge.py | 89 | 39360 | """
Ridge regression
"""
# Author: Mathieu Blondel <mathieu@mblondel.org>
# Reuben Fletcher-Costin <reuben.fletchercostin@gmail.com>
# Fabian Pedregosa <fabian@fseoane.net>
# Michael Eickenberg <michael.eickenberg@nsup.org>
# License: BSD 3 clause
from abc import ABCMeta, abstractmethod
impor... | bsd-3-clause |
apaloczy/ap_tools | utils.py | 1 | 54151 | # Description: General-purpose functions for personal use.
# Author: André Palóczy
# E-mail: paloczy@gmail.com
__all__ = ['seasonal_avg',
'seasonal_std',
'deseason',
'blkavg',
'blkavgdir',
'blkavgt',
'blkapply',
'stripmsk',
... | mit |
camallen/aggregation | experimental/condor/animal_EM.py | 2 | 7334 | #!/usr/bin/env python
__author__ = 'greghines'
import numpy as np
import os
import pymongo
import sys
import cPickle as pickle
import bisect
import csv
import matplotlib.pyplot as plt
import random
import math
import urllib
import matplotlib.cbook as cbook
def index(a, x):
'Locate the leftmost value exactly equal... | apache-2.0 |
ominux/scikit-learn | examples/linear_model/plot_sgd_iris.py | 4 | 2171 | """
========================================
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__
i... | bsd-3-clause |
HyperloopTeam/FullOpenMDAO | lib/python2.7/site-packages/mpl_toolkits/axisartist/grid_helper_curvelinear.py | 18 | 26105 | """
An experimental support for curvilinear grid.
"""
from __future__ import (absolute_import, division, print_function,
unicode_literals)
import six
from six.moves import zip
from itertools import chain
from .grid_finder import GridFinder
from .axislines import AxisArtistHelper, GridHelperB... | gpl-2.0 |
vshtanko/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 |
soleneulmer/atmos | indicators_molec.py | 1 | 4324 | # ===================================
# CALCULATES Ioff and Ires
# Indicators described in Molecfit II
#
# Solene 20.09.2016
# ===================================
#
import numpy as np
from astropy.io import fits
import matplotlib.pyplot as plt
# from PyAstronomy import pyasl
from scipy.interpolate import interp1d
from ... | mit |
allenlavoie/tensorflow | tensorflow/contrib/learn/python/learn/learn_io/pandas_io.py | 28 | 5024 | # 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 |
sysid/kg | quora/Ensemble_CNN_TD_Quora.py | 1 | 12948 | # coding: utf-8
# In[1]:
import pandas as pd
import numpy as np
import nltk
from nltk.corpus import stopwords
from nltk.stem import SnowballStemmer
import re
from sklearn.metrics import accuracy_score
import matplotlib.pyplot as plt
# In[2]:
train = pd.read_csv("../input/train.csv")
test = pd.read_csv("../input/te... | mit |
lovexiaov/SandwichApp | venv/lib/python2.7/site-packages/py2app/build_app.py | 9 | 77527 | """
Mac OS X .app build command for distutils
Originally (loosely) based on code from py2exe's build_exe.py by Thomas Heller.
"""
from __future__ import print_function
import imp
import sys
import os
import zipfile
import plistlib
import shlex
import shutil
import textwrap
import pkg_resources
import collections
from... | apache-2.0 |
ashhher3/scikit-learn | examples/cluster/plot_dict_face_patches.py | 337 | 2747 | """
Online learning of a dictionary of parts of faces
==================================================
This example uses a large dataset of faces to learn a set of 20 x 20
images patches that constitute faces.
From the programming standpoint, it is interesting because it shows how
to use the online API of the sciki... | bsd-3-clause |
metaml/nupic | external/linux32/lib/python2.6/site-packages/matplotlib/lines.py | 69 | 48233 | """
This module contains all the 2D line class which can draw with a
variety of line styles, markers and colors.
"""
# TODO: expose cap and join style attrs
from __future__ import division
import numpy as np
from numpy import ma
from matplotlib import verbose
import artist
from artist import Artist
from cbook import ... | agpl-3.0 |
imaculate/scikit-learn | sklearn/linear_model/randomized_l1.py | 11 | 24849 | """
Randomized Lasso/Logistic: feature selection based on Lasso and
sparse Logistic Regression
"""
# Author: Gael Varoquaux, Alexandre Gramfort
#
# License: BSD 3 clause
import itertools
from abc import ABCMeta, abstractmethod
import warnings
import numpy as np
from scipy.sparse import issparse
from scipy import spar... | bsd-3-clause |
SophieIPP/ipp-macro-series-parser | ipp_macro_series_parser/demographie/parser.py | 1 | 3235 | # -*- coding: utf-8 -*-
# TAXIPP -- A French microsimulation model
# By: IPP <taxipp@ipp.eu>
#
# Copyright (C) 2012, 2013, 2014, 2015 IPP
# https://github.com/taxipp
#
# This file is part of TAXIPP.
#
# TAXIPP is free software; you can redistribute it and/or modify
# it under the terms of the GNU Affero General Publi... | gpl-3.0 |
gef756/scipy | scipy/interpolate/interpolate.py | 25 | 80287 | """ Classes for interpolating values.
"""
from __future__ import division, print_function, absolute_import
__all__ = ['interp1d', 'interp2d', 'spline', 'spleval', 'splmake', 'spltopp',
'ppform', 'lagrange', 'PPoly', 'BPoly', 'RegularGridInterpolator',
'interpn']
import itertools
from numpy impo... | bsd-3-clause |
tsherwen/AC_tools | Scripts/2D_GEOSChem_slice_subregion_plotter_example.py | 1 | 2934 | #!/usr/bin/python
# -*- coding: utf-8 -*-
"""
Plotter for 2D slices of GEOS-Chem output NetCDFs files.
NOTES
---
- This is setup for Cly, but many other options (plot/species) are availible
by just updating passed variables/plotting function called.
"""
import AC_tools as AC
import numpy as np
import matplotli... | mit |
LaboratoireMecaniqueLille/crappy | crappy/blocks/grapher.py | 1 | 5641 | # coding: utf-8
import numpy as np
from .block import Block
from .._global import OptionalModule
try:
import matplotlib.pyplot as plt
from matplotlib.widgets import Button
except (ModuleNotFoundError, ImportError):
plt = OptionalModule("matplotlib")
Button = OptionalModule("matplotlib")
class Grapher(Block... | gpl-2.0 |
zorojean/scikit-learn | sklearn/ensemble/tests/test_gradient_boosting_loss_functions.py | 221 | 5517 | """
Testing for the gradient boosting loss functions and initial estimators.
"""
import numpy as np
from numpy.testing import assert_array_equal
from numpy.testing import assert_almost_equal
from numpy.testing import assert_equal
from nose.tools import assert_raises
from sklearn.utils import check_random_state
from ... | bsd-3-clause |
debsankha/bedtime-programming | ls222/visual-lotka.py | 1 | 5120 | #!/usr/bin/env python
from math import *
import thread
import random
import time
import pygtk
pygtk.require("2.0")
import gtk
import gtk.glade
import commands
import matplotlib.pyplot
class rodent:
def __init__(self):
self.time_from_last_childbirth=0
class felix:
def __init__(self):
self.size=0
self.is_virgin... | gpl-3.0 |
karvenka/sp17-i524 | project/S17-IR-P014/code/delay.py | 15 | 5276 | import sys
import csv
import sip
#import org.apache.log4j.{Level, Logger}
import matplotlib
#matplotlib.user('agg')
import matplotlib.pyplot as plt
plt.switch_backend('agg')
from matplotlib import rcParams
rcParams.update({'figure.autolayout': True})
from pyspark import SparkContext, SparkConf
from datetime import date... | apache-2.0 |
garrettkatz/directional-fibers | dfibers/experiments/levy_opt/levy_opt.py | 1 | 6952 | """
Measure global optimization performance of Levy function
"""
import sys, time
import numpy as np
import matplotlib.pyplot as pt
import multiprocessing as mp
import dfibers.traversal as tv
import dfibers.numerical_utilities as nu
import dfibers.logging_utilities as lu
import dfibers.fixed_points as fx
import dfiber... | mit |
jreback/pandas | pandas/io/formats/latex.py | 2 | 25201 | """
Module for formatting output data in Latex.
"""
from abc import ABC, abstractmethod
from typing import Iterator, List, Optional, Sequence, Tuple, Type, Union
import numpy as np
from pandas.core.dtypes.generic import ABCMultiIndex
from pandas.io.formats.format import DataFrameFormatter
def _split_into_full_shor... | bsd-3-clause |
junbochen/pylearn2 | pylearn2/scripts/papers/jia_huang_wkshp_11/evaluate.py | 44 | 3208 | from __future__ import print_function
from optparse import OptionParser
import warnings
try:
from sklearn.metrics import classification_report
except ImportError:
classification_report = None
warnings.warn("couldn't find sklearn.metrics.classification_report")
try:
from sklearn.metrics import confusion... | bsd-3-clause |
versae/DH2304 | data/arts1.py | 1 | 1038 | import numpy as np
import pandas as pd
arts = pd.DataFrame()
# Clean the dates so you only see numbers.
def clean_years(value):
result = value
chars_to_replace = ["c.", "©", ", CARCC", "no date", "n.d.", " SODRAC", ", CA", " CARCC", ""]
chars_to_split = ["-", "/"]
if isinstance(result, str):
... | mit |
nakul02/systemml | src/main/python/systemml/classloader.py | 4 | 7952 | #-------------------------------------------------------------
#
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under... | apache-2.0 |
sauloal/cnidaria | scripts/venv/lib/python2.7/site-packages/mpl_toolkits/axisartist/axisline_style.py | 8 | 5277 | from __future__ import (absolute_import, division, print_function,
unicode_literals)
import six
from matplotlib.patches import _Style, FancyArrowPatch
from matplotlib.transforms import IdentityTransform
from matplotlib.path import Path
import numpy as np
class _FancyAxislineStyle:
class S... | mit |
yunfeilu/scikit-learn | examples/semi_supervised/plot_label_propagation_versus_svm_iris.py | 286 | 2378 | """
=====================================================================
Decision boundary of label propagation versus SVM on the Iris dataset
=====================================================================
Comparison for decision boundary generated on iris dataset
between Label Propagation and SVM.
This demon... | bsd-3-clause |
pianomania/scikit-learn | sklearn/utils/tests/test_random.py | 85 | 7349 | from __future__ import division
import numpy as np
import scipy.sparse as sp
from scipy.misc import comb as combinations
from numpy.testing import assert_array_almost_equal
from sklearn.utils.random import sample_without_replacement
from sklearn.utils.random import random_choice_csc
from sklearn.utils.testing import ... | bsd-3-clause |
dingocuster/scikit-learn | sklearn/metrics/regression.py | 175 | 16953 | """Metrics to assess performance on regression task
Functions named as ``*_score`` return a scalar value to maximize: the higher
the better
Function named as ``*_error`` or ``*_loss`` return a scalar value to minimize:
the lower the better
"""
# Authors: Alexandre Gramfort <alexandre.gramfort@inria.fr>
# Ma... | bsd-3-clause |
timqian/sms-tools | lectures/5-Sinusoidal-model/plots-code/sineModelAnal-flute.py | 24 | 1179 | import numpy as np
import matplotlib.pyplot as plt
import sys, os, time
sys.path.append(os.path.join(os.path.dirname(os.path.realpath(__file__)), '../../../software/models/'))
import stft as STFT
import sineModel as SM
import utilFunctions as UF
(fs, x) = UF.wavread(os.path.join(os.path.dirname(os.path.realpath(__fi... | agpl-3.0 |
jcrist/blaze | blaze/compute/tests/test_bcolz_compute.py | 9 | 5874 | from __future__ import absolute_import, division, print_function
import pytest
bcolz = pytest.importorskip('bcolz')
from datashape import discover, dshape
import numpy as np
import pandas.util.testing as tm
from odo import into
from blaze import by
from blaze.expr import symbol
from blaze.compute.core import compu... | bsd-3-clause |
themrmax/scikit-learn | sklearn/manifold/tests/test_t_sne.py | 11 | 25443 | import sys
from sklearn.externals.six.moves import cStringIO as StringIO
import numpy as np
import scipy.sparse as sp
from sklearn.neighbors import BallTree
from sklearn.utils.testing import assert_less_equal
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_almost_equal
from skle... | bsd-3-clause |
xhochy/arrow | python/pyarrow/tests/test_hdfs.py | 1 | 13325 | # Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not u... | apache-2.0 |
sumitsourabh/opencog | opencog/python/utility/functions.py | 34 | 11056 | from math import fabs, isnan
from datetime import datetime
from spatiotemporal.unix_time import UnixTime
from utility.generic import convert_dict_to_sorted_lists
from utility.numeric.globals import EPSILON
from numpy import NINF as NEGATIVE_INFINITY, PINF as POSITIVE_INFINITY
from scipy.integrate import quad
__author_... | agpl-3.0 |
jeffery-do/Vizdoombot | doom/lib/python3.5/site-packages/scipy/stats/_stats_mstats_common.py | 12 | 8157 | from collections import namedtuple
import numpy as np
from . import distributions
__all__ = ['_find_repeats', 'linregress', 'theilslopes']
def linregress(x, y=None):
"""
Calculate a linear least-squares regression for two sets of measurements.
Parameters
----------
x, y : array_like
T... | mit |
nikitasingh981/scikit-learn | examples/semi_supervised/plot_label_propagation_versus_svm_iris.py | 50 | 2378 | """
=====================================================================
Decision boundary of label propagation versus SVM on the Iris dataset
=====================================================================
Comparison for decision boundary generated on iris dataset
between Label Propagation and SVM.
This demon... | bsd-3-clause |
kylerbrown/scikit-learn | sklearn/covariance/tests/test_robust_covariance.py | 213 | 3359 | # Author: Alexandre Gramfort <alexandre.gramfort@inria.fr>
# Gael Varoquaux <gael.varoquaux@normalesup.org>
# Virgile Fritsch <virgile.fritsch@inria.fr>
#
# License: BSD 3 clause
import numpy as np
from sklearn.utils.testing import assert_almost_equal
from sklearn.utils.testing import assert_array_alm... | bsd-3-clause |
Mako-kun/mangaki | mangaki/mangaki/utils/svd.py | 2 | 5410 | from django.contrib.auth.models import User
from mangaki.models import Rating, Work, Recommendation
from mangaki.utils.chrono import Chrono
from mangaki.utils.values import rating_values
from scipy.sparse import lil_matrix
from sklearn.utils.extmath import randomized_svd
import numpy as np
from django.db import connect... | agpl-3.0 |
IndraVikas/scikit-learn | examples/hetero_feature_union.py | 288 | 6236 | """
=============================================
Feature Union with Heterogeneous Data Sources
=============================================
Datasets can often contain components of that require different feature
extraction and processing pipelines. This scenario might occur when:
1. Your dataset consists of hetero... | bsd-3-clause |
samuelstjean/dipy | scratch/very_scratch/diffusion_sphere_stats.py | 20 | 18082 | import nibabel
import os
import numpy as np
import dipy as dp
#import dipy.core.generalized_q_sampling as dgqs
import dipy.reconst.gqi as dgqs
import dipy.reconst.dti as ddti
import dipy.reconst.recspeed as rp
import dipy.io.pickles as pkl
import scipy as sp
from matplotlib.mlab import find
#import dipy.core.sphere_pl... | bsd-3-clause |
Vimos/scikit-learn | sklearn/kernel_approximation.py | 7 | 18505 | """
The :mod:`sklearn.kernel_approximation` module implements several
approximate kernel feature maps base on Fourier transforms.
"""
# Author: Andreas Mueller <amueller@ais.uni-bonn.de>
#
# License: BSD 3 clause
import warnings
import numpy as np
import scipy.sparse as sp
from scipy.linalg import svd
from .base im... | bsd-3-clause |
xebitstudios/Kayak | examples/poisson_glm.py | 3 | 1224 | import numpy as np
import numpy.random as npr
import matplotlib.pyplot as plt
import sys
sys.path.append('..')
import kayak
N = 10000
D = 5
P = 1
learn = 0.00001
batch_size = 500
# Random inputs.
X = npr.randn(N,D)
true_W = npr.randn(D,P)
lam = np.exp(np.dot(X, true_W))
Y = npr.poisson(lam)
kyk_batcher = k... | mit |
jbloom/mutpath | src/plot.py | 1 | 10257 | """Module for performing plotting for ``mutpath`` package.
This module uses ``pylab`` and ``matplotlib`` to make plots. These plots will
fail if ``pylab`` and ``matplotlib`` are not available for importation. Before
running any function in this module, you can run the *PylabAvailable*
function to determine if ``pylab`... | gpl-3.0 |
edxnercel/edx-platform | .pycharm_helpers/pydev/pydev_ipython/inputhook.py | 52 | 18411 | # coding: utf-8
"""
Inputhook management for GUI event loop integration.
"""
#-----------------------------------------------------------------------------
# Copyright (C) 2008-2011 The IPython Development Team
#
# Distributed under the terms of the BSD License. The full license is in
# the file COPYING, distribu... | agpl-3.0 |
mmottahedi/neuralnilm_prototype | scripts/e249.py | 2 | 3897 | from __future__ import print_function, division
import matplotlib
matplotlib.use('Agg') # Must be before importing matplotlib.pyplot or pylab!
from neuralnilm import Net, RealApplianceSource, BLSTMLayer, DimshuffleLayer
from lasagne.nonlinearities import sigmoid, rectify
from lasagne.objectives import crossentropy, mse... | mit |
carlvlewis/bokeh | bokeh/charts/builder/tests/test_line_builder.py | 33 | 2376 | """ This is the Bokeh charts testing interface.
"""
#-----------------------------------------------------------------------------
# Copyright (c) 2012 - 2014, Continuum Analytics, Inc. All rights reserved.
#
# Powered by the Bokeh Development Team.
#
# The full license is in the file LICENSE.txt, distributed with thi... | bsd-3-clause |
kevin-intel/scikit-learn | sklearn/datasets/_openml.py | 2 | 34451 | import gzip
import json
import os
import shutil
import hashlib
from os.path import join
from warnings import warn
from contextlib import closing
from functools import wraps
from typing import Callable, Optional, Dict, Tuple, List, Any, Union
import itertools
from collections.abc import Generator
from collections import... | bsd-3-clause |
zrhans/pythonanywhere | pyscripts/ply_wrose.py | 1 | 1678 | """
DATA,Chuva,Chuva_min,Chuva_max,VVE,VVE_min,VVE_max,DVE,DVE_min,DVE_max,Temp.,Temp._min,Temp._max,Umidade,Umidade_min,Umidade_max,Rad.,Rad._min,Rad._max,Pres.Atm.,Pres.Atm._min,Pres.Atm._max,Temp.Int.,Temp.Int._min,Temp.Int._max,CH4,CH4_min,CH4_max,HCnM,HCnM_min,HCnM_max,HCT,HCT_min,HCT_max,SO2,SO2_min,SO2_max,O3,O3... | apache-2.0 |
dudulianangang/vps | EneConsTest.py | 1 | 5969 | import sdf
import matplotlib.pyplot as plt
import numpy as np
import matplotlib as mpl
plt.style.use('seaborn-white')
# plt.rcParams['font.family'] = 'sans-serif'
# plt.rcParams['font.sans-serif'] = 'Tahoma'
# # plt.rcParams['font.monospace'] = 'Ubuntu Mono'
plt.rcParams['font.size'] = 16
# plt.rcParams['axes.labelsiz... | apache-2.0 |
planetarymike/IDL-Colorbars | IDL_py_test/027_Eos_B.py | 1 | 5942 | from matplotlib.colors import LinearSegmentedColormap
from numpy import nan, inf
cm_data = [[1., 1., 1.],
[1., 1., 1.],
[0.498039, 0.498039, 0.498039],
[0., 0., 0.513725],
[0., 0., 0.533333],
[0., 0., 0.54902],
[0., 0., 0.564706],
[0., 0., 0.580392],
[0., 0., 0.6],
[0., 0., 0.615686],
[0., 0., 0.568627],
[0., 0., 0.584... | gpl-2.0 |
ManuSchmi88/landlab | landlab/plot/imshow.py | 3 | 21050 | #! /usr/bin/env python
"""
Methods to plot data defined on Landlab grids.
Plotting functions
++++++++++++++++++
.. autosummary::
:toctree: generated/
~landlab.plot.imshow.imshow_grid
~landlab.plot.imshow.imshow_grid_at_cell
~landlab.plot.imshow.imshow_grid_at_node
"""
import numpy as np
import insp... | mit |
hrjn/scikit-learn | examples/feature_selection/plot_f_test_vs_mi.py | 75 | 1647 | """
===========================================
Comparison of F-test and mutual information
===========================================
This example illustrates the differences between univariate F-test statistics
and mutual information.
We consider 3 features x_1, x_2, x_3 distributed uniformly over [0, 1], the
targ... | bsd-3-clause |
waddell/urbansim | urbansim/urbanchoice/mnl.py | 4 | 9002 | """
Number crunching code for multinomial logit.
``mnl_estimate`` and ``mnl_simulate`` especially are used by
``urbansim.models.lcm``.
"""
from __future__ import print_function
import logging
import numpy as np
import pandas as pd
import scipy.optimize
import pmat
from pmat import PMAT
from ..utils.logutil import ... | bsd-3-clause |
sinhrks/scikit-learn | sklearn/tree/tests/test_tree.py | 32 | 52369 | """
Testing for the tree module (sklearn.tree).
"""
import pickle
from functools import partial
from itertools import product
import platform
import numpy as np
from scipy.sparse import csc_matrix
from scipy.sparse import csr_matrix
from scipy.sparse import coo_matrix
from sklearn.random_projection import sparse_rand... | bsd-3-clause |
dmitriz/zipline | zipline/utils/tradingcalendar.py | 6 | 11182 | #
# 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 |
barbagroup/PetIBM | examples/ibpm/cylinder2dRe40/scripts/plotVorticity.py | 4 | 1401 | """
Computes, plots, and saves the 2D vorticity field from a PetIBM simulation
after 2000 time steps (20 non-dimensional time-units).
"""
import pathlib
import h5py
import numpy
from matplotlib import pyplot
simu_dir = pathlib.Path(__file__).absolute().parents[1]
data_dir = simu_dir / 'output'
# Read vorticity fiel... | bsd-3-clause |
rhoscanner-team/pcd-plotter | delaunay_example.py | 1 | 1435 | import numpy as np
from scipy.spatial import Delaunay
points = np.random.rand(30, 2) # 30 points in 2-d
tri = Delaunay(points)
# Make a list of line segments:
# edge_points = [ ((x1_1, y1_1), (x2_1, y2_1)),
# ((x1_2, y1_2), (x2_2, y2_2)),
# ... ]
edge_points = []
edges = set()
def ad... | gpl-2.0 |
rahul-c1/scikit-learn | sklearn/cluster/tests/test_mean_shift.py | 19 | 2844 | """
Testing for mean shift clustering methods
"""
import numpy as np
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_false
from sklearn.utils.testing import assert_true
from sklearn.utils.testing import assert_array_equal
from sklearn.cluster import MeanShift
from sklearn.clu... | bsd-3-clause |
hainm/scikit-learn | examples/cluster/plot_kmeans_assumptions.py | 270 | 2040 | """
====================================
Demonstration of k-means assumptions
====================================
This example is meant to illustrate situations where k-means will produce
unintuitive and possibly unexpected clusters. In the first three plots, the
input data does not conform to some implicit assumptio... | bsd-3-clause |
wdurhamh/statsmodels | statsmodels/sandbox/examples/ex_cusum.py | 33 | 3219 | # -*- coding: utf-8 -*-
"""
Created on Fri Apr 02 11:41:25 2010
Author: josef-pktd
"""
import numpy as np
from scipy import stats
from numpy.testing import assert_almost_equal
import statsmodels.api as sm
from statsmodels.sandbox.regression.onewaygls import OneWayLS
from statsmodels.stats.diagnostic import recursive... | bsd-3-clause |
nmartensen/pandas | pandas/io/sql.py | 3 | 58612 | # -*- coding: utf-8 -*-
"""
Collection of query wrappers / abstractions to both facilitate data
retrieval and to reduce dependency on DB-specific API.
"""
from __future__ import print_function, division
from datetime import datetime, date, time
import warnings
import re
import numpy as np
import pandas._libs.lib as ... | bsd-3-clause |
jenshnielsen/basemap | examples/maskoceans.py | 4 | 1922 | from mpl_toolkits.basemap import Basemap, shiftgrid, maskoceans, interp
import numpy as np
import matplotlib.pyplot as plt
# example showing how to mask out 'wet' areas on a contour or pcolor plot.
topodatin = np.loadtxt('etopo20data.gz')
lonsin = np.loadtxt('etopo20lons.gz')
latsin = np.loadtxt('etopo20lats.gz')
#... | gpl-2.0 |
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