repo_name stringlengths 6 96 | path stringlengths 4 191 | copies stringclasses 322
values | size stringlengths 4 6 | content stringlengths 762 753k | license stringclasses 15
values |
|---|---|---|---|---|---|
pascalgutjahr/Praktikum-1 | Schwingung/phaselinear.py | 1 | 1829 | import numpy as np
import uncertainties.unumpy as unp
from uncertainties.unumpy import (nominal_values as noms, std_devs as stds)
import matplotlib.pyplot as plt
import matplotlib as mpl
from scipy.optimize import curve_fit
plt.rcParams['figure.figsize'] = (12, 8)
plt.rcParams['font.size'] = 13
plt.rcParams['lines.line... | mit |
kenshay/ImageScripter | ProgramData/SystemFiles/Python/Lib/site-packages/pandas/tseries/tdi.py | 7 | 32620 | """ implement the TimedeltaIndex """
from datetime import timedelta
import numpy as np
from pandas.types.common import (_TD_DTYPE,
is_integer, is_float,
is_bool_dtype,
is_list_like,
is_sc... | gpl-3.0 |
vinhqdang/my_mooc | coursera/advanced_machine_learning_spec/4_nlp/natural-language-processing-master/project/dialogue_manager.py | 1 | 3010 | import os
from sklearn.metrics.pairwise import pairwise_distances_argmin
from chatterbot import ChatBot
from utils import *
class ThreadRanker(object):
def __init__(self, paths):
self.word_embeddings, self.embeddings_dim = load_embeddings(paths['WORD_EMBEDDINGS'])
self.thread_embeddings_folder = ... | mit |
czhengsci/veidt | veidt/metrics.py | 1 | 1250 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import six
from sklearn.metrics import mean_squared_error, mean_absolute_error
from veidt.utils.general_utils import deserialize_veidt_object
from veidt.utils.general_utils import serialize_... | bsd-3-clause |
pepper-johnson/Erudition | Thesis/Processing/Pipeline/reddit_slim_comments.py | 1 | 1815 | import json
import datetime
import pandas as pd
# ***********
# Methods:
# ***********
def get_config(config_file):
assert type(config_file) == str
with open(config_file) as f:
config = json.load(f)
return config
# ********
# Main:
# - purpose: take all reddit comment files that were produc... | apache-2.0 |
henrykironde/scikit-learn | examples/svm/plot_svm_kernels.py | 329 | 1971 | #!/usr/bin/python
# -*- coding: utf-8 -*-
"""
=========================================================
SVM-Kernels
=========================================================
Three different types of SVM-Kernels are displayed below.
The polynomial and RBF are especially useful when the
data-points are not linearly sep... | bsd-3-clause |
Myasuka/scikit-learn | sklearn/feature_extraction/tests/test_dict_vectorizer.py | 276 | 3790 | # Authors: Lars Buitinck <L.J.Buitinck@uva.nl>
# Dan Blanchard <dblanchard@ets.org>
# License: BSD 3 clause
from random import Random
import numpy as np
import scipy.sparse as sp
from numpy.testing import assert_array_equal
from sklearn.utils.testing import (assert_equal, assert_in,
... | bsd-3-clause |
yavalvas/yav_com | build/matplotlib/doc/mpl_examples/pylab_examples/fill_between_demo.py | 6 | 2116 | #!/usr/bin/env python
import matplotlib.pyplot as plt
import numpy as np
x = np.arange(0.0, 2, 0.01)
y1 = np.sin(2*np.pi*x)
y2 = 1.2*np.sin(4*np.pi*x)
fig, (ax1, ax2, ax3) = plt.subplots(3,1, sharex=True)
ax1.fill_between(x, 0, y1)
ax1.set_ylabel('between y1 and 0')
ax2.fill_between(x, y1, 1)
ax2.set_ylabel('betwee... | mit |
equialgo/scikit-learn | examples/ensemble/plot_adaboost_multiclass.py | 354 | 4124 | """
=====================================
Multi-class AdaBoosted Decision Trees
=====================================
This example reproduces Figure 1 of Zhu et al [1] and shows how boosting can
improve prediction accuracy on a multi-class problem. The classification
dataset is constructed by taking a ten-dimensional ... | bsd-3-clause |
jjx02230808/project0223 | examples/manifold/plot_mds.py | 45 | 2731 | """
=========================
Multi-dimensional scaling
=========================
An illustration of the metric and non-metric MDS on generated noisy data.
The reconstructed points using the metric MDS and non metric MDS are slightly
shifted to avoid overlapping.
"""
# Author: Nelle Varoquaux <nelle.varoquaux@gmail.... | bsd-3-clause |
muneebalam/scrapenhl2 | scrapenhl2/scrape/schedules.py | 1 | 15634 | """
This module contains methods related to season schedules.
"""
import arrow
import datetime
import functools
import json
import os.path
import urllib.request
import feather
import pandas as pd
import scrapenhl2.scrape.general_helpers as helpers
import scrapenhl2.scrape.organization as organization
import scrapenh... | mit |
Odingod/mne-python | mne/io/fiff/tests/test_raw.py | 1 | 38869 | from __future__ import print_function
# Author: Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
# Denis Engemann <denis.engemann@gmail.com>
#
# License: BSD (3-clause)
import os
import os.path as op
import glob
from copy import deepcopy
import warnings
import itertools as itt
import numpy as np
... | bsd-3-clause |
HolgerPeters/scikit-learn | sklearn/__check_build/__init__.py | 345 | 1671 | """ Module to give helpful messages to the user that did not
compile the scikit properly.
"""
import os
INPLACE_MSG = """
It appears that you are importing a local scikit-learn source tree. For
this, you need to have an inplace install. Maybe you are in the source
directory and you need to try from another location.""... | bsd-3-clause |
apdavison/elephant | elephant/current_source_density_src/icsd.py | 9 | 35175 | # -*- coding: utf-8 -*-
'''
py-iCSD toolbox!
Translation of the core functionality of the CSDplotter MATLAB package
to python.
The methods were originally developed by Klas H. Pettersen, as described in:
Klas H. Pettersen, Anna Devor, Istvan Ulbert, Anders M. Dale, Gaute T. Einevoll,
Current-source density estimation ... | bsd-3-clause |
lthurlow/Network-Grapher | proj/external/matplotlib-1.2.1/doc/make.py | 1 | 7453 | #!/usr/bin/env python
from __future__ import print_function
import fileinput
import glob
import os
import shutil
import sys
### Begin compatibility block for pre-v2.6: ###
#
# ignore_patterns and copytree funtions are copies of what is included
# in shutil.copytree of python v2.6 and later.
#
### When compatibility i... | mit |
jasonleaster/Machine_Learning | SAMME/tester.py | 1 | 1351 | """
Programmer : EOF
Date : 2015.11.22
File : tester.py
File Description:
This file is used to test the adaboost which is a classical
automatic classifier.
"""
import numpy
import matplotlib.pyplot as pyplot
from samme import SAMME
Original_Data = numpy.array([
['teenager', 'no',... | gpl-2.0 |
rolando/theusual-kaggle-seeclickfix-ensemble | Bryan/ensembles.py | 2 | 19613 | """
Classes and functions for working with base models and ensembles.
"""
__author__ = 'bgregory'
__email__ = 'bryan.gregory1@gmail.com'
__date__ = '11-23-2013'
#Internal modules
import utils
#Start logger to record all info, warnings, and errors to Logs/logfile.log
log = utils.start_logging(__name__)
impor... | bsd-3-clause |
dandanvidi/effective-capacity | scripts/gauge.py | 3 | 3523 | # -*- coding: utf-8 -*-
"""
Created on Wed Jun 22 13:58:55 2016
@author: dan
"""
import os, sys
import matplotlib
from matplotlib import cm
from matplotlib import pyplot as plt
import numpy as np
from matplotlib.patches import Circle, Wedge, Rectangle
def degree_range(n):
start = np.linspace(0,180,n+1, endpoint... | mit |
ezekielsilverstein/JPL | Sloan_List_Script.py | 1 | 15678 | #Numerical Python
import numpy as np
#Pylab Plotting
import pylab
import matplotlib.pyplot as plt
#INTERNET
#Selenium Internet Browsing
from selenium import webdriver
from selenium.webdriver.common.keys import Keys
import os
from decimal import *
import time
import csv
#Internet
import urllib2
print "Start up ... | mit |
rhuelga/sms-tools | lectures/08-Sound-transformations/plots-code/stftFiltering-orchestra.py | 2 | 1670 | import numpy as np
import time, os, sys
import matplotlib.pyplot as plt
sys.path.append(os.path.join(os.path.dirname(os.path.realpath(__file__)), '../../../software/models/'))
sys.path.append(os.path.join(os.path.dirname(os.path.realpath(__file__)), '../../../software/transformations/'))
import utilFunctions as UF
impo... | agpl-3.0 |
12yujim/pymtl | pymtl/tools/simulation/SimulationMetrics.py | 8 | 8999 | #=========================================================================
# SimulationMetrics.py
#=========================================================================
from __future__ import print_function
import pickle
#-------------------------------------------------------------------------
# SimulationMetri... | bsd-3-clause |
numenta-archive/htmresearch | projects/dp1/dp_experiment1.py | 3 | 12622 | # Numenta Platform for Intelligent Computing (NuPIC)
# Copyright (C) 2016, Numenta, Inc. Unless you have an agreement
# with Numenta, Inc., for a separate license for this software code, the
# following terms and conditions apply:
#
# This program is free software: you can redistribute it and/or modify
# it under the ... | agpl-3.0 |
vigilv/scikit-learn | sklearn/manifold/tests/test_t_sne.py | 53 | 21055 | 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_equal
from sklearn.utils.testing import assert_almost_equal
from sklearn.utils.testing import assert_array_almost_equal
fr... | bsd-3-clause |
Twangist/log_calls | tests/test_with_sklearn/test_decorate_sklearn_KMeans_functions.py | 1 | 6482 | __author__ = 'brianoneill'
###############################################################################
def test_deco_sklearn_cluster_kmeans_function():
"""
Dunno how to decorate `sklearn.cluster.kmeans` so that the decorated funciton
is called via `sklearn.cluster.kmeans(...)`. What gets decorated is... | mit |
fspaolo/scikit-learn | examples/linear_model/plot_ols.py | 8 | 1966 | #!/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 |
jmontoyam/mne-python | mne/preprocessing/tests/test_infomax.py | 6 | 5969 | # Authors: Denis A. Engemann <denis.engemann@gmail.com>
#
# License: BSD (3-clause)
"""
Test the infomax algorithm.
Parts of this code are taken from scikit-learn
"""
import numpy as np
from numpy.testing import assert_almost_equal
from scipy import stats
from scipy import linalg
from mne.preprocessing.infomax_ imp... | bsd-3-clause |
MTgeophysics/mtpy | tests/SmartMT/test_exportDialog.py | 1 | 12640 | import os
from unittest import TestCase
import matplotlib.pyplot as plt
import numpy as np
from qtpy import QtCore
from qtpy.QtWidgets import QFileDialog, QMessageBox, QDialog
from qtpy.QtTest import QTest
from mtpy.gui.SmartMT.gui.export_dialog import ExportDialog, IMAGE_FORMATS
from tests import make_temp_dir
from ... | gpl-3.0 |
cmcantalupo/geopm | integration/experiment/power_sweep/gen_power_sweep_summary.py | 1 | 3310 | #!/usr/bin/env python
#
# Copyright (c) 2015 - 2021, Intel Corporation
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions
# are met:
#
# * Redistributions of source code must retain the above copyright
# notice, thi... | bsd-3-clause |
theroncarmichael/GC-CaT-Metallicitiy | interp.py | 1 | 9342 | #! /usr/bin/env python
'''
Created on Mar 17, 2011
@author: Chris Usher
'''
import numpy as np
#import matplotlib.pyplot as plt
import scipy.interpolate as interpolate
def redisperse(inputwavelengths, inputfluxes, firstWavelength=None, lastWavelength=None, dispersion=None, nPixels=None, outside=None, function='splin... | bsd-3-clause |
mikechan0731/tunnel_calculation | forTR_ver4.py | 1 | 12948 | # C:\Python27\Scripts
# -*- coding: utf-8 -*-
# Author : MikeChan
# Email : m7807031@gmail.com
import pandas as pd
import numpy as np
from scipy import optimize
import xlrd, os
from time import sleep, time
import matplotlib.pyplot as plt
import FileDialog
#===== helper func. =====
def draw_parsley_ver4(t=0.05):
... | apache-2.0 |
jmsolano/picongpu | examples/ThermalTest/tools/dispersion.py | 11 | 2689 | #!/usr/bin/env python
#
# Copyright 2013 Heiko Burau, Axel Huebl
#
# This file is part of PIConGPU.
#
# PIConGPU 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 o... | gpl-3.0 |
chenyyx/scikit-learn-doc-zh | examples/en/feature_selection/plot_select_from_model_boston.py | 146 | 1527 | """
===================================================
Feature selection using SelectFromModel and LassoCV
===================================================
Use SelectFromModel meta-transformer along with Lasso to select the best
couple of features from the Boston dataset.
"""
# Author: Manoj Kumar <mks542@nyu.edu>... | gpl-3.0 |
themrmax/scikit-learn | examples/feature_selection/plot_feature_selection_pipeline.py | 58 | 1049 | """
==================
Pipeline Anova SVM
==================
Simple usage of Pipeline that runs successively a univariate
feature selection with anova and then a C-SVM of the selected features.
"""
from sklearn import svm
from sklearn.datasets import samples_generator
from sklearn.feature_selection import SelectKBest,... | bsd-3-clause |
rileyrustad/pdxapartmentfinder | pipeline/crawler.py | 1 | 2738 | # -*- coding: utf-8 -*-
"""
Created on Thu Jan 7 10:59:34 2016
@author: Riley Rustad <rileyrustad@gmail.com>
This Script is designed to scrape data from Multnomah County apartment ads
from Craigslist.
"""
# =============================================================================
# Imports
import numpy as np
... | mit |
logpai/logparser | logparser/Drain/Drain.py | 1 | 12453 | """
Description : This file implements the Drain algorithm for log parsing
Author : LogPAI team
License : MIT
"""
import re
import os
import numpy as np
import pandas as pd
import hashlib
from datetime import datetime
class Logcluster:
def __init__(self, logTemplate='', logIDL=None):
... | mit |
phobson/statsmodels | statsmodels/datasets/co2/data.py | 3 | 3045 | #! /usr/bin/env python
"""Mauna Loa Weekly Atmospheric CO2 Data"""
__docformat__ = 'restructuredtext'
COPYRIGHT = """This is public domain."""
TITLE = """Mauna Loa Weekly Atmospheric CO2 Data"""
SOURCE = """
Data obtained from http://cdiac.ornl.gov/trends/co2/sio-keel-flask/sio-keel-flaskmlo_c.html
Obt... | bsd-3-clause |
abhisg/scikit-learn | examples/cross_decomposition/plot_compare_cross_decomposition.py | 128 | 4761 | """
===================================
Compare cross decomposition methods
===================================
Simple usage of various cross decomposition algorithms:
- PLSCanonical
- PLSRegression, with multivariate response, a.k.a. PLS2
- PLSRegression, with univariate response, a.k.a. PLS1
- CCA
Given 2 multivari... | bsd-3-clause |
openmichigan/metrics_tools | openmichigan-metrics-pdf/ga_api_timeseries.py | 1 | 20456 | import sys
import infofile
import requests, json
import get_material_links
from pylab import * #?
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
from datetime import date, timedelta
from apiclient.errors import HttpError
from oauth2client.client import AccessTokenRefreshError
imp... | mit |
briney/abstar | abstar/utils/pandaseq.py | 1 | 7169 | #!/usr/bin/python
# filename: pandaseq.py
#
# Copyright (c) 2015 Bryan Briney
# License: The MIT license (http://opensource.org/licenses/MIT)
#
# Permission is hereby granted, free of charge, to any person obtaining a copy of this software
# and associated documentation files (the "Software"), to deal in the Software ... | mit |
shirtsgroup/pygo | analysis/QRE_scripts/MBAR_foldingcurve.py | 1 | 6622 | # Ellen Zhong
# ellen.zhong@virginia.edu
# 03/08/2014
import sys
import numpy
import pymbar # for MBAR analysis
import timeseries # for timeseries analysis
import os
import os.path
import pdb
import wham
from optparse import OptionParser
def parse_args():
parser=OptionParser()
parser.add_option("-r", "--repl... | gpl-2.0 |
sinhrks/expandas | pandas_ml/skaccessors/test/test_multioutput.py | 2 | 1807 | #!/usr/bin/env python
try:
import sklearn.multioutput as multioutput
except ImportError:
pass
import numpy as np
import pandas as pd
import pandas_ml as pdml
import pandas_ml.util.testing as tm
class TestMultiOutput(tm.TestCase):
def test_objectmapper(self):
df = pdml.ModelFra... | bsd-3-clause |
crichardson17/starburst_atlas | Low_resolution_sims/DustFree_LowRes/Padova_cont/padova_cont_5/Rest.py | 33 | 7215 | import csv
import matplotlib.pyplot as plt
from numpy import *
import scipy.interpolate
import math
from pylab import *
from matplotlib.ticker import MultipleLocator, FormatStrFormatter
import matplotlib.patches as patches
from matplotlib.path import Path
import os
# --------------------------------------------------... | gpl-2.0 |
hainm/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 |
msultan/msmbuilder | msmbuilder/cluster/agglomerative.py | 6 | 11834 | # Author: Robert McGibbon <rmcgibbo@gmail.com>
# Contributors: Brooke Husic <brookehusic@gmail.com>
# Copyright (c) 2017, Stanford University
# All rights reserved.
#-----------------------------------------------------------------------------
# Imports
#----------------------------------------------------------------... | lgpl-2.1 |
mattilyra/scikit-learn | examples/datasets/plot_iris_dataset.py | 35 | 1929 | #!/usr/bin/python
# -*- coding: utf-8 -*-
"""
=========================================================
The Iris Dataset
=========================================================
This data sets consists of 3 different types of irises'
(Setosa, Versicolour, and Virginica) petal and sepal
length, stored in a 150x4 numpy... | bsd-3-clause |
cython-testbed/pandas | pandas/core/config_init.py | 8 | 17165 | """
This module is imported from the pandas package __init__.py file
in order to ensure that the core.config options registered here will
be available as soon as the user loads the package. if register_option
is invoked inside specific modules, they will not be registered until that
module is imported, which may or may... | bsd-3-clause |
rl-institut/reegis-hp | reegis_hp/de21/test.py | 3 | 1906 | import pandas as pd
from matplotlib import pyplot as plt
import logging
from oemof.tools import logger
logger.define_logging()
exit(0)
df = pd.read_csv('/home/uwe/geo.csv', index_col='zip_code')
del df['Unnamed: 0']
del df['gid']
df.to_csv('/home/uwe/git_local/reegis-hp/reegis_hp/de21/geometries/postcode.csv')
exit(0... | gpl-3.0 |
yvesalexandre/privacy-tools | within_voronoi_translation/within_voronoi_translation.py | 1 | 7700 | #!/usr/bin/env python
"""
within_voronoi_translation.py: Move antennas uniformly within their voronoi cell.
Noise is often added to the GPS coordinates of antennas to hinter's an attacker
ability to link outside information to the released database. This code takes as
input a list of antennas location and moves them ... | mit |
toobaz/pandas | pandas/core/tools/timedeltas.py | 2 | 6506 | """
timedelta support tools
"""
import warnings
import numpy as np
from pandas._libs.tslibs import NaT
from pandas._libs.tslibs.timedeltas import Timedelta, parse_timedelta_unit
from pandas.util._decorators import deprecate_kwarg
from pandas.core.dtypes.common import is_list_like
from pandas.core.dtypes.generic imp... | bsd-3-clause |
ManuelMBaumann/opt_tau | num_exper/mekrylov.py | 1 | 10316 | import scipy.sparse as sparse
import matplotlib.pyplot as plt
#import scipy.io as io
import numpy as np
import scipy.sparse.linalg as spla
import pyamg
from math import sqrt, atan, cos, sin, pi, atan2
from numpy.linalg import norm
#from scipy.io import mmwrite
from nutils import *
from numpy.linalg import solve
from s... | mit |
cxxgtxy/tensorflow | tensorflow/contrib/learn/python/learn/estimators/estimator_input_test.py | 72 | 12865 | # 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 |
behzadnouri/scipy | scipy/optimize/nonlin.py | 34 | 46681 | r"""
Nonlinear solvers
-----------------
.. currentmodule:: scipy.optimize
This is a collection of general-purpose nonlinear multidimensional
solvers. These solvers find *x* for which *F(x) = 0*. Both *x*
and *F* can be multidimensional.
Routines
~~~~~~~~
Large-scale nonlinear solvers:
.. autosummary::
newto... | bsd-3-clause |
jakobj/UP-Tasks | NEST/single_neuron_task/single_neuron.py | 3 | 1344 | import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import nest # import NEST module
def single_neuron(spike_times, sim_duration):
nest.set_verbosity('M_WARNING') # reduce NEST output
nest.ResetKernel() # reset simulation kernel
# create LIF neuron with exponential synaptic currents... | gpl-2.0 |
flightgong/scikit-learn | benchmarks/bench_plot_omp_lars.py | 31 | 4457 | """Benchmarks of orthogonal matching pursuit (:ref:`OMP`) versus least angle
regression (:ref:`least_angle_regression`)
The input data is mostly low rank but is a fat infinite tail.
"""
from __future__ import print_function
import gc
import sys
from time import time
import numpy as np
from sklearn.linear_model impo... | bsd-3-clause |
harisbal/pandas | pandas/tests/io/test_sql.py | 3 | 96428 | """SQL io tests
The SQL tests are broken down in different classes:
- `PandasSQLTest`: base class with common methods for all test classes
- Tests for the public API (only tests with sqlite3)
- `_TestSQLApi` base class
- `TestSQLApi`: test the public API with sqlalchemy engine
- `TestSQLiteFallbackApi`: t... | bsd-3-clause |
RayMick/scikit-learn | examples/linear_model/plot_sgd_penalties.py | 249 | 1563 | """
==============
SGD: Penalties
==============
Plot the contours of the three penalties.
All of the above are supported by
:class:`sklearn.linear_model.stochastic_gradient`.
"""
from __future__ import division
print(__doc__)
import numpy as np
import matplotlib.pyplot as plt
def l1(xs):
return np.array([np.... | bsd-3-clause |
rvraghav93/scikit-learn | sklearn/neural_network/rbm.py | 26 | 12280 | """Restricted Boltzmann Machine
"""
# Authors: Yann N. Dauphin <dauphiya@iro.umontreal.ca>
# Vlad Niculae
# Gabriel Synnaeve
# Lars Buitinck
# License: BSD 3 clause
import time
import numpy as np
import scipy.sparse as sp
from scipy.special import expit # logistic function
from ..base im... | bsd-3-clause |
JackKelly/neuralnilm_prototype | scripts/e328.py | 2 | 6737 | from __future__ import print_function, division
import matplotlib
import logging
from sys import stdout
matplotlib.use('Agg') # Must be before importing matplotlib.pyplot or pylab!
from neuralnilm import (Net, RealApplianceSource,
BLSTMLayer, DimshuffleLayer,
Bidirectio... | mit |
kdebrab/pandas | pandas/tests/extension/base/groupby.py | 3 | 2747 | import pytest
import pandas.util.testing as tm
import pandas as pd
from .base import BaseExtensionTests
class BaseGroupbyTests(BaseExtensionTests):
"""Groupby-specific tests."""
def test_grouping_grouper(self, data_for_grouping):
df = pd.DataFrame({
"A": ["B", "B", None, None, "A", "A", ... | bsd-3-clause |
ArtezGDA/MappingTheCity-Maps | Kimberley ter Heerdt/Poster/Visual-3/wiki-birthsvisualmetdatatekst.py | 1 | 1087 | import json
import matplotlib.pyplot as plt
def visual_file(file_name, line_color):
fig = plt.figure(1)
with open(file_name, 'r') as f:
data = json.load(f)
for d in data:
cur_births = d['birth']
for cur_birth in cur_births:
year = cur_birth['year']
... | mit |
skrueger111/zazzie | src/scripts/convergence_test.py | 3 | 31326 | # from __future__ import absolute_import
# from __future__ import division
# from __future__ import print_function
# # from __future__ import unicode_literals
"""SASSIE: Copyright (C) 2011-2015 Joseph E. Curtis, Ph.D.
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU G... | gpl-3.0 |
Simclass/EDXD_Analysis | bin/data_creation.py | 3 | 3978 | import numpy as np
import matplotlib.pyplot as plt
import time
from pyxe.williams import sigma_xx, sigma_yy, sigma_xy, cart2pol
from pyxe.fitting_functions import strain_transformation, shear_transformation
def plane_strain_s2e(sigma_xx, sigma_yy, sigma_xy, E, v, G=None):
if G is None:
G = E / (2 * (1 - v... | mit |
jwlockhart/concept-networks | examples/draw_tripartite.py | 1 | 3581 | # @author Jeff Lockhart <jwlock@umich.edu>
# Script for drawing the tripartite network underlying analysis.
# version 1.0
import pandas as pd
import networkx as nx
import matplotlib.pyplot as plt
import sys
#add the parent directory to the current session's path
sys.path.insert(0, '../')
from network_utils import *
#... | gpl-3.0 |
caperren/Archives | OSU Coursework/ROB 456 - Intelligent Robotics/Homework 4 - A Star Pathfinding/hw4.py | 1 | 7720 | import csv
from matplotlib import pyplot, patches
from math import sqrt
from heapq import *
CSV_PATH = "world.csv"
VAL_TO_COLOR = {
0: "green",
1: "red",
-1: "blue"
}
EDGE_COST = 1
START_POSITION = (0, 0)
END_POSITION = (19, 19)
def import_csv_as_array(csv_path):
csv_file = open(csv_path, "rU") ... | gpl-3.0 |
thirdwing/mxnet | python/mxnet/model.py | 4 | 39905 | # 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 |
LaDO-IOUSP/Curious | Python/pizzaplot.py | 1 | 1687 | # -*- coding: UTF-8 -*-
import matplotlib.pyplot as plt
from matplotlib.patches import Wedge
def pizzaplot(center, radius, angle=0,nb=2, ax=None, colors=[],**kwargs):
''' Plots circle with inputed number of divisions with different colors(multicolor scatter).
==================================================... | mit |
belteshassar/cartopy | lib/cartopy/tests/test_polygon.py | 3 | 17387 | # (C) British Crown Copyright 2011 - 2016, Met Office
#
# This file is part of cartopy.
#
# cartopy 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 version 3 of the License, or
# (at your option)... | gpl-3.0 |
bundgus/python-playground | matplotlib-playground/examples/pylab_examples/boxplot_demo.py | 2 | 1288 | import matplotlib.pyplot as plt
import numpy as np
# fake up some data
spread = np.random.rand(50) * 100
center = np.ones(25) * 50
flier_high = np.random.rand(10) * 100 + 100
flier_low = np.random.rand(10) * -100
data = np.concatenate((spread, center, flier_high, flier_low), 0)
# basic plot
plt.boxplot(data)
# notch... | mit |
RayMick/scikit-learn | benchmarks/bench_plot_parallel_pairwise.py | 297 | 1247 | # Author: Mathieu Blondel <mathieu@mblondel.org>
# License: BSD 3 clause
import time
import pylab as pl
from sklearn.utils import check_random_state
from sklearn.metrics.pairwise import pairwise_distances
from sklearn.metrics.pairwise import pairwise_kernels
def plot(func):
random_state = check_random_state(0)
... | bsd-3-clause |
smartscheduling/scikit-learn-categorical-tree | examples/covariance/plot_lw_vs_oas.py | 248 | 2903 | """
=============================
Ledoit-Wolf vs OAS estimation
=============================
The usual covariance maximum likelihood estimate can be regularized
using shrinkage. Ledoit and Wolf proposed a close formula to compute
the asymptotically optimal shrinkage parameter (minimizing a MSE
criterion), yielding th... | bsd-3-clause |
Jimmy-Morzaria/scikit-learn | benchmarks/bench_plot_ward.py | 290 | 1260 | """
Benchmark scikit-learn's Ward implement compared to SciPy's
"""
import time
import numpy as np
from scipy.cluster import hierarchy
import pylab as pl
from sklearn.cluster import AgglomerativeClustering
ward = AgglomerativeClustering(n_clusters=3, linkage='ward')
n_samples = np.logspace(.5, 3, 9)
n_features = n... | bsd-3-clause |
larsmans/seqlearn | seqlearn/datasets.py | 4 | 3045 | # Copyright 2013 Lars Buitinck
from contextlib import closing
from itertools import chain, groupby
import numpy as np
from sklearn.feature_extraction import FeatureHasher
from sklearn.externals import six
def load_conll(f, features, n_features=(2 ** 16), split=False):
"""Load CoNLL file, extract features on the... | mit |
smenon8/AnimalWildlifeEstimator | script/RegressionCapsuleClass.py | 1 | 1640 | # python-3
# Regression Capsule Class
# In the same lines as ClassifierCapsuleClass
from sklearn.metrics import mean_absolute_error, mean_squared_error
from BaseCapsuleClass import BaseCapsule
from collections import OrderedDict
import pandas as pd
class RegressionCapsule(BaseCapsule):
def __init__(self,clfObj,method... | bsd-3-clause |
zingale/hydro_examples | compressible/riemann-slow-shock.py | 1 | 1642 | # plot the Hugoniot loci for a compressible Riemann problem
from __future__ import print_function
import matplotlib.pyplot as plt
import numpy as np
import riemann
import matplotlib as mpl
# Use LaTeX for rendering
mpl.rcParams['mathtext.fontset'] = 'cm'
mpl.rcParams['mathtext.rm'] = 'serif'
mpl.rcParams['font.size... | bsd-3-clause |
cybernet14/scikit-learn | sklearn/tree/tree.py | 59 | 34839 | """
This module gathers tree-based methods, including decision, regression and
randomized trees. Single and multi-output problems are both handled.
"""
# Authors: Gilles Louppe <g.louppe@gmail.com>
# Peter Prettenhofer <peter.prettenhofer@gmail.com>
# Brian Holt <bdholt1@gmail.com>
# Noel Da... | bsd-3-clause |
qifeigit/scikit-learn | sklearn/linear_model/tests/test_logistic.py | 105 | 26588 | import numpy as np
import scipy.sparse as sp
from scipy import linalg, optimize, sparse
from sklearn.utils.testing import assert_almost_equal
from sklearn.utils.testing import assert_array_equal
from sklearn.utils.testing import assert_array_almost_equal
from sklearn.utils.testing import assert_equal
from sklearn.util... | bsd-3-clause |
chrisburr/scikit-learn | examples/covariance/plot_covariance_estimation.py | 99 | 5074 | """
=======================================================================
Shrinkage covariance estimation: LedoitWolf vs OAS and max-likelihood
=======================================================================
When working with covariance estimation, the usual approach is to use
a maximum likelihood estimator,... | bsd-3-clause |
wogsland/QSTK | build/lib.linux-x86_64-2.7/Bin/converter.py | 5 | 2926 | '''
(c) 2011, 2012 Georgia Tech Research Corporation
This source code is released under the New BSD license. Please see
http://wiki.quantsoftware.org/index.php?title=QSTK_License
for license details.
Created on Jan 1, 2011
@author:Drew Bratcher
@contact: dbratcher@gatech.edu
@summary: Contains tutorial for... | bsd-3-clause |
rahuldhote/scikit-learn | sklearn/utils/tests/test_fixes.py | 281 | 1829 | # Authors: Gael Varoquaux <gael.varoquaux@normalesup.org>
# Justin Vincent
# Lars Buitinck
# License: BSD 3 clause
import numpy as np
from nose.tools import assert_equal
from nose.tools import assert_false
from nose.tools import assert_true
from numpy.testing import (assert_almost_equal,
... | bsd-3-clause |
toobaz/pandas | pandas/tests/arithmetic/test_timedelta64.py | 2 | 76159 | # Arithmetic tests for DataFrame/Series/Index/Array classes that should
# behave identically.
from datetime import datetime, timedelta
import numpy as np
import pytest
from pandas.errors import NullFrequencyError, OutOfBoundsDatetime, PerformanceWarning
import pandas as pd
from pandas import (
DataFrame,
Dat... | bsd-3-clause |
hansomesong/TracesAnalyzer | Plot/Plot_variable_time/Pie_Chart.py | 1 | 3974 | # -* coding:UTF-8 -*
# __author__ = 'yueli'
import numpy as np
import matplotlib.pyplot as plt
# All the codes in this python file can be referenced to
# http://matplotlib.org/1.2.1/examples/pylab_examples/pie_demo.html
# 由于此文件的input文件已不存在，所以此文件已被Pie_chart_v4.py替代
# Import the targeted raw CSV file
rawCSV_file1 = "/... | gpl-2.0 |
ellisonbg/altair | altair/vegalite/v2/examples/stem_and_leaf.py | 1 | 1273 | """
Stem and Leaf Plot
------------------
This example shows how to make a stem and leaf plot.
"""
# category: other charts
import altair as alt
import pandas as pd
import numpy as np
np.random.seed(42)
# Generating random data
original_data = pd.DataFrame({'samples': np.array(np.random.normal(50, 15, 100), dtype=np.i... | bsd-3-clause |
potash/scikit-learn | sklearn/grid_search.py | 6 | 38777 | """
The :mod:`sklearn.grid_search` includes utilities to fine-tune the parameters
of an estimator.
"""
from __future__ import print_function
# Author: Alexandre Gramfort <alexandre.gramfort@inria.fr>,
# Gael Varoquaux <gael.varoquaux@normalesup.org>
# Andreas Mueller <amueller@ais.uni-bonn.de>
# ... | bsd-3-clause |
blbarker/spark-tk | regression-tests/sparktkregtests/testcases/graph/betweenness_centrality_test.py | 4 | 5737 | # vim: set encoding=utf-8
# Copyright (c) 2016 Intel Corporation
#
# 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 require... | apache-2.0 |
sightmachine/SimpleCV2 | SimpleCV/examples/machine-learning/machine-learning_nuts-vs-bolts.py | 12 | 2782 | '''
This Example uses scikits-learn to do a binary classfication of images
of nuts vs. bolts. Only the area, height, and width are used to classify
the actual images but data is extracted from the images using blobs.
This is a very crude example and could easily be built upon, but is just
meant to give an introductor... | bsd-3-clause |
Gezerj/Data-Analysis | Task-Problems/TASK 6.py | 1 | 1263 | # -*- coding: utf-8 -*-
"""
Created on Wed Oct 04 10:57:34 2017
@author: Gerwyn
"""
from __future__ import division
import numpy as np
import scipy.stats as sc
import matplotlib.pyplot as plt
P = np.array([79, 82, 85, 88, 90])
T = np.array([8, 17, 30, 37, 52])
n = len(T)
N = 5000
Tmin = -500
Tmax = 0
sigma_... | gpl-3.0 |
CVML/scikit-learn | sklearn/cluster/mean_shift_.py | 106 | 14056 | """Mean shift clustering algorithm.
Mean shift clustering aims to discover *blobs* in a smooth density of
samples. It is a centroid based algorithm, which works by updating candidates
for centroids to be the mean of the points within a given region. These
candidates are then filtered in a post-processing stage to elim... | bsd-3-clause |
andim/evolimmune | figSIaltphases/figure-SIaltphases.py | 1 | 6607 |
# coding: utf-8
# # Influence of parameter choice on the phase diagram
# To study to what extend the phase diagram depends on the cost of infection $c_{\rm inf}$, and on the trade-off shapes $c_{\rm def}(c_{\rm con}), c_{\rm uptake}(p_{\rm uptake})$ we plot the phase diagram for a number of different choices in the ... | mit |
Clyde-fare/scikit-learn | sklearn/calibration.py | 137 | 18876 | """Calibration of predicted probabilities."""
# Author: Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
# Balazs Kegl <balazs.kegl@gmail.com>
# Jan Hendrik Metzen <jhm@informatik.uni-bremen.de>
# Mathieu Blondel <mathieu@mblondel.org>
#
# License: BSD 3 clause
from __future__ impo... | bsd-3-clause |
ottermegazord/ottermegazord.github.io | sortify-master/seaborn/rcmod.py | 3 | 16173 | """Functions that alter the matplotlib rc dictionary on the fly."""
from distutils.version import LooseVersion
import functools
import numpy as np
import matplotlib as mpl
from . import palettes, _orig_rc_params
mpl_ge_150 = LooseVersion(mpl.__version__) >= '1.5.0'
__all__ = ["set", "reset_defaults", "reset_orig"... | mit |
wdurhamh/statsmodels | statsmodels/examples/tsa/ex_var.py | 33 | 1280 |
from __future__ import print_function
import numpy as np
import statsmodels.api as sm
from statsmodels.tsa.api import VAR
# some example data
mdata = sm.datasets.macrodata.load().data
mdata = mdata[['realgdp','realcons','realinv']]
names = mdata.dtype.names
data = mdata.view((float,3))
use_growthrate = False #True #... | bsd-3-clause |
QInfer/python-qinfer | doc/source/conf.py | 3 | 12884 | # -*- coding: utf-8 -*-
#
# QInfer documentation build configuration file, created by
# sphinx-quickstart on Tue Aug 14 21:12:57 2012.
#
# 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.
#
# All ... | bsd-3-clause |
soundcloud/essentia | src/examples/tutorial/essentia_tutorial.py | 10 | 6577 | # Copyright (C) 2006-2013 Music Technology Group - Universitat Pompeu Fabra
#
# This file is part of Essentia
#
# Essentia is free software: you can redistribute it and/or modify it under
# the terms of the GNU Affero General Public License as published by the Free
# Software Foundation (FSF), either version 3 of the ... | agpl-3.0 |
zaxtax/scikit-learn | sklearn/preprocessing/tests/test_label.py | 12 | 17807 | import numpy as np
from scipy.sparse import issparse
from scipy.sparse import coo_matrix
from scipy.sparse import csc_matrix
from scipy.sparse import csr_matrix
from scipy.sparse import dok_matrix
from scipy.sparse import lil_matrix
from sklearn.utils.multiclass import type_of_target
from sklearn.utils.testing impor... | bsd-3-clause |
kernc/scikit-learn | benchmarks/bench_plot_ward.py | 290 | 1260 | """
Benchmark scikit-learn's Ward implement compared to SciPy's
"""
import time
import numpy as np
from scipy.cluster import hierarchy
import pylab as pl
from sklearn.cluster import AgglomerativeClustering
ward = AgglomerativeClustering(n_clusters=3, linkage='ward')
n_samples = np.logspace(.5, 3, 9)
n_features = n... | bsd-3-clause |
laurentgo/arrow | dev/archery/archery/lang/python.py | 3 | 7570 | # 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 |
chetan51/nupic | external/linux32/lib/python2.6/site-packages/matplotlib/bezier.py | 70 | 14387 | """
A module providing some utility functions regarding bezier path manipulation.
"""
import numpy as np
from math import sqrt
from matplotlib.path import Path
from operator import xor
# some functions
def get_intersection(cx1, cy1, cos_t1, sin_t1,
cx2, cy2, cos_t2, sin_t2):
""" return a... | gpl-3.0 |
gclenaghan/scikit-learn | examples/cluster/plot_digits_linkage.py | 369 | 2959 | """
=============================================================================
Various Agglomerative Clustering on a 2D embedding of digits
=============================================================================
An illustration of various linkage option for agglomerative clustering on
a 2D embedding of the di... | bsd-3-clause |
nikitasingh981/scikit-learn | sklearn/ensemble/tests/test_forest.py | 9 | 43013 | """
Testing for the forest module (sklearn.ensemble.forest).
"""
# Authors: Gilles Louppe,
# Brian Holt,
# Andreas Mueller,
# Arnaud Joly
# License: BSD 3 clause
import pickle
from collections import defaultdict
from itertools import combinations
from itertools import product
import numpy ... | bsd-3-clause |
alphacsc/alphacsc | examples/csc/plot_simulate_randomstate.py | 1 | 3040 | """
==============================
Selecting random state for CSC
==============================
The CSC problem is non-convex. Therefore, the solution depends
on the initialization. Here, we show how to select the
best atoms amongst different initializations.
"""
# Authors: Mainak Jas <mainak.jas@telecom-paristech.... | bsd-3-clause |
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