repo_name stringlengths 6 67 | path stringlengths 5 185 | copies stringlengths 1 3 | size stringlengths 4 6 | content stringlengths 1.02k 962k | license stringclasses 15
values |
|---|---|---|---|---|---|
pederka/pythonBoids2D | flock.py | 1 | 1786 | '''Module containing the Flock class, meant for containing a group of Bird and
Predator objects
'''
import random
import matplotlib.pyplot as plt
from predator import Predator
from bird import Bird
class Flock(object):
''' Class for groups of birds and a possible predator
'''
def __init__(self, number, se... | mit |
OshynSong/scikit-learn | sklearn/metrics/cluster/bicluster.py | 359 | 2797 | from __future__ import division
import numpy as np
from sklearn.utils.linear_assignment_ import linear_assignment
from sklearn.utils.validation import check_consistent_length, check_array
__all__ = ["consensus_score"]
def _check_rows_and_columns(a, b):
"""Unpacks the row and column arrays and checks their shap... | bsd-3-clause |
eickenberg/scikit-learn | examples/text/document_clustering.py | 8 | 8032 | """
=======================================
Clustering text documents using k-means
=======================================
This is an example showing how the scikit-learn can be used to cluster
documents by topics using a bag-of-words approach. This example uses
a scipy.sparse matrix to store the features instead of ... | bsd-3-clause |
smblance/ggplot | ggplot/utils/utils.py | 12 | 2636 | """
Little functions used all over the codebase
"""
from __future__ import (absolute_import, division, print_function,
unicode_literals)
import numpy as np
import matplotlib.cbook as cbook
import six
def pop(dataframe, key, default):
"""
Pop element *key* from dataframe and return it.... | bsd-2-clause |
JoulesCESAR/domain_wall | polarization_three.py | 1 | 2447 | # Script for calculation of ferroelectric wall domain profile
from math import *
from numpy import *
import matplotlib.pyplot as plt
axes = plt.gca()
axes.set_xlim([-0.6,0.6])
axes.set_ylim([-0.8,0.8])
beta = -2.92e8
g = 0.54e-10
xi = 1.56e9
alpha0 = 7.6e5
q11 = 0.089
q12 = -0.026
s11 = -2.5
s12 = 9.0
e = 4.0/((8.8... | gpl-3.0 |
abhishekkrthakur/scikit-learn | sklearn/manifold/tests/test_locally_linear.py | 41 | 4827 | from itertools import product
from nose.tools import assert_true
import numpy as np
from numpy.testing import assert_almost_equal, assert_array_almost_equal
from scipy import linalg
from sklearn import neighbors, manifold
from sklearn.manifold.locally_linear import barycenter_kneighbors_graph
from sklearn.utils.testi... | bsd-3-clause |
lthurlow/Network-Grapher | proj/external/matplotlib-1.2.1/lib/mpl_examples/user_interfaces/embedding_in_wx3.py | 9 | 4849 | #!/usr/bin/env python
"""
Copyright (C) 2003-2004 Andrew Straw, Jeremy O'Donoghue and others
License: This work is licensed under the PSF. A copy should be included
with this source code, and is also available at
http://www.python.org/psf/license.html
This is yet another example of using matplotlib with wx. Hopeful... | mit |
bavardage/statsmodels | statsmodels/graphics/boxplots.py | 4 | 15866 | """Variations on boxplots."""
# Author: Ralf Gommers
# Based on code by Flavio Coelho and Teemu Ikonen.
import numpy as np
from scipy.stats import gaussian_kde
from . import utils
__all__ = ['violinplot', 'beanplot']
def violinplot(data, ax=None, labels=None, positions=None, side='both',
show_box... | bsd-3-clause |
openp2pdesign/PyMakerspaces | makerlabs/makeinitaly_foundation.py | 2 | 7684 | # -*- encoding: utf-8 -*-
#
# Access data from makeinitaly.foundation
#
# Author: Massimo Menichinelli
# Homepage: http://www.openp2pdesign.org
# License: LGPL v.3
#
#
from classes import Lab
import json
from simplemediawiki import MediaWiki
import pandas as pd
makeinitaly__foundation_api_url = "http://makeinitaly... | lgpl-3.0 |
depet/scikit-learn | sklearn/decomposition/tests/test_sparse_pca.py | 5 | 6098 | # Author: Vlad Niculae
# License: BSD 3 clause
import sys
import numpy as np
from sklearn.utils.testing import assert_array_almost_equal
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_array_equal
from sklearn.utils.testing import SkipTest
from sklearn.utils.testing import ass... | bsd-3-clause |
XiaoxiaoLiu/morphology_analysis | bigneuron/comparison_and_plots.py | 1 | 4676 | __author__ = 'xiaoxiaoliu'
import pandas as pd
import numpy as np
import os
from os import sys, path
import seaborn as sb
import matplotlib.pyplot as plt
WORK_PATH = "/Users/xiaoxiaoliu/work"
p = WORK_PATH + '/src/morphology_analysis'
sys.path.append(p)
sb.set_context("poster")
data_DIR = WORK_PATH+"/data/201510... | gpl-3.0 |
andaag/scikit-learn | examples/ensemble/plot_adaboost_hastie_10_2.py | 355 | 3576 | """
=============================
Discrete versus Real AdaBoost
=============================
This example is based on Figure 10.2 from Hastie et al 2009 [1] and illustrates
the difference in performance between the discrete SAMME [2] boosting
algorithm and real SAMME.R boosting algorithm. Both algorithms are evaluate... | bsd-3-clause |
fabioticconi/scikit-learn | sklearn/externals/joblib/parallel.py | 31 | 35665 | """
Helpers for embarrassingly parallel code.
"""
# Author: Gael Varoquaux < gael dot varoquaux at normalesup dot org >
# Copyright: 2010, Gael Varoquaux
# License: BSD 3 clause
from __future__ import division
import os
import sys
import gc
import warnings
from math import sqrt
import functools
import time
import thr... | bsd-3-clause |
ericdill/xray-vision | xray_vision/__init__.py | 3 | 3200 | # ######################################################################
# Copyright (c) 2014, Brookhaven Science Associates, Brookhaven #
# National Laboratory. All rights reserved. #
# #
# Redistribution and use in ... | bsd-3-clause |
RNAer/Calour | calour/tests/test_transforming.py | 1 | 5611 | # ----------------------------------------------------------------------------
# Copyright (c) 2016--, Calour development team.
#
# Distributed under the terms of the Modified BSD License.
#
# The full license is in the file COPYING.txt, distributed with this software.
# -----------------------------------------------... | bsd-3-clause |
guziy/basemap | examples/nsper_demo.py | 2 | 1909 | from __future__ import (absolute_import, division, print_function)
from mpl_toolkits.basemap import Basemap
import numpy as np
import matplotlib.pyplot as plt
import sys
def get_input(prompt):
if sys.hexversion > 0x03000000:
return input(prompt)
else:
return raw_input(prompt)
# create Basemap... | gpl-2.0 |
wheeler-microfluidics/pulse-counter-rpc | rename.py | 1 | 2569 | import sys
import pandas as pd
from path_helpers import path
def main(root, old_name, new_name):
names = pd.Series([old_name, new_name], index=['old', 'new'])
underscore_names = names.map(lambda v: v.replace('-', '_'))
camel_names = names.str.split('-').map(lambda x: ''.join([y.title()
... | gpl-3.0 |
prheenan/Research | Perkins/Projects/WetLab/Demos/Dilutions/2016-9-31-SolutionProtocols/2016-11-4-strept-standard-dilution/main_strept.py | 1 | 1815 | # force floating point division. Can still use integer with //
from __future__ import division
# This file is used for importing the common utilities classes.
import numpy as np
import matplotlib.pyplot as plt
import sys
sys.path.append("../../../../")
from Util import DilutionUtil
def run():
"""
For aliquot... | gpl-3.0 |
rishikksh20/scikit-learn | sklearn/datasets/tests/test_base.py | 16 | 9390 | import os
import shutil
import tempfile
import warnings
import numpy
from pickle import loads
from pickle import dumps
from sklearn.datasets import get_data_home
from sklearn.datasets import clear_data_home
from sklearn.datasets import load_files
from sklearn.datasets import load_sample_images
from sklearn.datasets im... | bsd-3-clause |
cbmoore/statsmodels | statsmodels/datasets/anes96/data.py | 25 | 4146 | """American National Election Survey 1996"""
__docformat__ = 'restructuredtext'
COPYRIGHT = """This is public domain."""
TITLE = __doc__
SOURCE = """
http://www.electionstudies.org/
The American National Election Studies.
"""
DESCRSHORT = """This data is a subset of the American National Election Stud... | bsd-3-clause |
Averroes/statsmodels | statsmodels/sandbox/examples/thirdparty/findow_1.py | 33 | 2548 | # -*- coding: utf-8 -*-
"""A quick look at volatility of stock returns for 2009
Just an exercise to find my way around the pandas methods.
Shows the daily rate of return, the square of it (volatility) and
a 5 day moving average of the volatility.
No guarantee for correctness.
Assumes no missing values.
colors of lines... | bsd-3-clause |
rcrowder/nupic | external/linux32/lib/python2.6/site-packages/matplotlib/contour.py | 69 | 42063 | """
These are classes to support contour plotting and
labelling for the axes class
"""
from __future__ import division
import warnings
import matplotlib as mpl
import numpy as np
from numpy import ma
import matplotlib._cntr as _cntr
import matplotlib.path as path
import matplotlib.ticker as ticker
import matplotlib.cm... | agpl-3.0 |
NUAAXXY/globOpt | evaluation/generatePolarPrimDirections.py | 2 | 9382 | from pylab import *
import argparse
import packages.primitive as primitive
import scipy.signal
parser = argparse.ArgumentParser(description='Generate polar view of the lines directions.')
parser.add_argument('primitivefile')
parser.add_argument('--shownormals', action="store_true", help="Shows the normal distribution ... | apache-2.0 |
devanshdalal/scikit-learn | examples/linear_model/plot_logistic_l1_l2_sparsity.py | 384 | 2601 | """
==============================================
L1 Penalty and Sparsity in Logistic Regression
==============================================
Comparison of the sparsity (percentage of zero coefficients) of solutions when
L1 and L2 penalty are used for different values of C. We can see that large
values of C give mo... | bsd-3-clause |
vybstat/scikit-learn | examples/model_selection/plot_train_error_vs_test_error.py | 349 | 2577 | """
=========================
Train error vs Test error
=========================
Illustration of how the performance of an estimator on unseen data (test data)
is not the same as the performance on training data. As the regularization
increases the performance on train decreases while the performance on test
is optim... | bsd-3-clause |
LingyuMa/kaggle_planet | src/models/predict_model.py | 1 | 4441 | import tensorflow as tf
import time
from datetime import datetime
import numpy as np
import math
import os
import src.models.network as network
import src.data.data_provider as data
import src.settings as settings
from sklearn.metrics import fbeta_score
def f2_score(y_true, y_pred):
y_true = tf.cast(y_true, "i... | mit |
gef756/statsmodels | statsmodels/tsa/filters/filtertools.py | 25 | 12438 | # -*- coding: utf-8 -*-
"""Linear Filters for time series analysis and testing
TODO:
* check common sequence in signature of filter functions (ar,ma,x) or (x,ar,ma)
Created on Sat Oct 23 17:18:03 2010
Author: Josef-pktd
"""
#not original copied from various experimental scripts
#version control history is there
fr... | bsd-3-clause |
gurgeh/data-preppy | cluster_csv.py | 1 | 2053 | import csv
import sys
import pandas as pd
from sklearn import cluster
from numpy.linalg import norm
"""
group = cc.mb.predict(cc.df)
cluster_csv.output_cluster(cc.mb, group, '1.csv', '2.csv')
"""
class ClusterCSV:
def __init__(self, fname='../data/filtered_fixed.csv', nr_clusters=5):
self.fname = fna... | apache-2.0 |
xmnlab/skdata | skdata/widgets.py | 2 | 9651 | from abc import ABCMeta, abstractmethod
from IPython.display import display, update_display
from ipywidgets import widgets, IntSlider
# locals from import
from .utils import plot2html
from .data import cross_fields
from .data import SkData
import numpy as np
import pandas as pd
class SkDataWidget:
"""
"""
... | mit |
pyspace/pyspace | pySPACE/missions/nodes/scikit_nodes.py | 1 | 33408 | # -*- coding:utf-8; -*-
""" Wrap the algorithms defined in `scikit.learn <http://scikit-learn.org/>`_ in pySPACE nodes
For details on parameter usage look at the
`scikit documentation <http://scikit-learn.org/>`_ or
the wrapped documentation of pySPACE: :ref:`scikit_nodes`.
The parameters given in the node specificati... | bsd-3-clause |
luo66/scikit-learn | examples/ensemble/plot_forest_iris.py | 335 | 6271 | """
====================================================================
Plot the decision surfaces of ensembles of trees on the iris dataset
====================================================================
Plot the decision surfaces of forests of randomized trees trained on pairs of
features of the iris dataset.
... | bsd-3-clause |
winklerand/pandas | pandas/tests/test_expressions.py | 3 | 18169 | # -*- coding: utf-8 -*-
from __future__ import print_function
# pylint: disable-msg=W0612,E1101
from warnings import catch_warnings
import re
import operator
import pytest
from numpy.random import randn
import numpy as np
from pandas.core.api import DataFrame, Panel
from pandas.core.computation import expressions a... | bsd-3-clause |
bdmckean/MachineLearning | fall_2017/hw2/feature_eng.py | 1 | 8400 | import os
import json
from csv import DictReader, DictWriter
import numpy as np
from numpy import array
from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer
from sklearn.linear_model import SGDClassifier
from sklearn.model_selection import train_test_split
from sklearn.base import BaseEstimato... | mit |
tkhirianov/kpk2016 | graphs/input_graph.py | 1 | 2588 | import networkx
import matplotlib.pyplot as plt
def input_edges_list():
"""считывает список рёбер в форме:
в первой строке N - число рёбер,
затем следует N строк из двух слов и одного числа
слова - названия вершин, концы ребра, а число - его вес
return граф в форме словаря рёбер и соответствую... | gpl-3.0 |
paulray/NICERsoft | scripts/cr_cut.py | 1 | 8209 | #!/usr/bin/env python
from __future__ import print_function, division
import os, sys
import matplotlib.pyplot as plt
import numpy as np
import argparse
from astropy import log
from os import path
from glob import glob
from subprocess import check_call
import shutil
from astropy.table import Table
from astropy.io import... | mit |
detrout/debian-statsmodels | statsmodels/graphics/functional.py | 31 | 14477 | """Module for functional boxplots."""
from statsmodels.compat.python import combinations, range
import numpy as np
from scipy import stats
from scipy.misc import factorial
from . import utils
__all__ = ['fboxplot', 'rainbowplot', 'banddepth']
def fboxplot(data, xdata=None, labels=None, depth=None, method='MBD',
... | bsd-3-clause |
TomAugspurger/pandas | pandas/tests/indexing/multiindex/test_chaining_and_caching.py | 1 | 1979 | import numpy as np
import pytest
from pandas import DataFrame, MultiIndex, Series
import pandas._testing as tm
import pandas.core.common as com
def test_detect_chained_assignment():
# Inplace ops, originally from:
# https://stackoverflow.com/questions/20508968/series-fillna-in-a-multiindex-dataframe-does-not... | bsd-3-clause |
tensorflow/model-analysis | tensorflow_model_analysis/api/model_eval_lib.py | 1 | 64480 | # Lint as: python3
# Copyright 2018 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
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agr... | apache-2.0 |
flowdy/sompyler | tests/pitch-reception-test.py | 1 | 1821 | from sine2wav import write_wavefile
from Sompyler.synthesizer import normalize_amplitude
# import matplotlib.pyplot as plt
from Sompyler.instrument import Variation
def write_file(sound, filename):
print "Writing", filename
normalize_amplitude(sound)
channels = ((x for x in sound),)
write_wavefile(fil... | gpl-3.0 |
hrjn/scikit-learn | sklearn/tests/test_cross_validation.py | 79 | 47914 | """Test the cross_validation module"""
from __future__ import division
import warnings
import numpy as np
from scipy.sparse import coo_matrix
from scipy.sparse import csr_matrix
from scipy import stats
from sklearn.exceptions import ConvergenceWarning
from sklearn.utils.testing import assert_true
from sklearn.utils.t... | bsd-3-clause |
msbeta/apollo | modules/tools/realtime_plot/stitem.py | 5 | 2611 | #!/usr/bin/env python
###############################################################################
# Copyright 2017 The Apollo 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 ... | apache-2.0 |
msyriac/alhazen | tests/varyBeam.py | 1 | 3943 | import matplotlib
matplotlib.use('Agg')
from orphics.tools.io import Plotter,dictFromSection,listFromConfig,getFileNameString
import flipper.liteMap as lm
from szlib.szcounts import ClusterCosmology
from alhazen.halos import NFWMatchedFilterSN
import numpy as np
from orphics.tools.cmb import loadTheorySpectraFromCAMB
f... | gpl-3.0 |
DonBeo/scikit-learn | sklearn/setup.py | 225 | 2856 | import os
from os.path import join
import warnings
def configuration(parent_package='', top_path=None):
from numpy.distutils.misc_util import Configuration
from numpy.distutils.system_info import get_info, BlasNotFoundError
import numpy
libraries = []
if os.name == 'posix':
libraries.appe... | bsd-3-clause |
DSLituiev/scikit-learn | sklearn/semi_supervised/label_propagation.py | 14 | 15965 | # coding=utf8
"""
Label propagation in the context of this module refers to a set of
semisupervised classification algorithms. In the high level, these algorithms
work by forming a fully-connected graph between all points given and solving
for the steady-state distribution of labels at each point.
These algorithms per... | bsd-3-clause |
white-lab/pyproteome | pyproteome/motifs/logo.py | 1 | 11772 |
from collections import Counter
import logging
import os
import re
from matplotlib import transforms
from matplotlib import pyplot as plt
import matplotlib.patches as patches
from matplotlib.text import TextPath
from matplotlib.patches import PathPatch
from matplotlib.font_manager import FontProperties
import numpy a... | bsd-2-clause |
dongjoon-hyun/tensorflow | tensorflow/contrib/learn/python/learn/grid_search_test.py | 137 | 2035 | # 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 |
mauriceleutenegger/windprofile | WindAbsorption/plotCD.py | 1 | 2145 | #!/usr/bin/env python2.5
import numpy as np
import matplotlib.pyplot as pl
# from scipy cookbook for publication quality figures:
fig_width_pt = 245.0 # Get this from LaTeX using \showthe\columnwidth
inches_per_pt = 1.0/72.27 # Convert pt to inches
fig_width = fig_width_pt*inches_per_pt # width in in... | gpl-2.0 |
AISpace2/AISpace2 | aipython/learnKMeans.py | 1 | 6029 | # learnKMeans.py - k-means learning
# AIFCA Python3 code Version 0.7.1 Documentation at http://aipython.org
# Artificial Intelligence: Foundations of Computational Agents
# http://artint.info
# Copyright David L Poole and Alan K Mackworth 2017.
# This work is licensed under a Creative Commons
# Attribution-NonCommerci... | gpl-3.0 |
grandtiger/trading-with-python | historicDataDownloader/historicDataDownloader.py | 77 | 4526 | '''
Created on 4 aug. 2012
Copyright: Jev Kuznetsov
License: BSD
a module for downloading historic data from IB
'''
import ib
import pandas
from ib.ext.Contract import Contract
from ib.opt import ibConnection, message
from time import sleep
import tradingWithPython.lib.logger as logger
from pandas impor... | bsd-3-clause |
scienceopen/dmcutils | dmcutils/neospool.py | 1 | 13811 | #!/usr/bin/env python
from pathlib import Path
from tempfile import mkstemp
from time import time, sleep
import logging
from configparser import ConfigParser
from datetime import datetime
from pytz import UTC
import numpy as np
import imageio
import h5py
import pandas
from typing import Dict, Any, Sequence, Tuple
try:... | gpl-3.0 |
stack-of-tasks/dynamic-graph-tutorial | src/dynamic_graph/tutorial/simu.py | 1 | 1574 | import dynamic_graph as dg
import dynamic_graph.tutorial as dgt
import matplotlib.pyplot as pl
import numpy as np
def build_graph():
# define inverted pendulum
a = dgt.InvertedPendulum("IP")
a.setCartMass(1.0)
a.setPendulumMass(1.0)
a.setPendulumLength(1.0)
b = dgt.FeedbackController("K")
... | bsd-2-clause |
thomaslima/PySpice | examples/operational-amplifier/astable.py | 1 | 2141 | ####################################################################################################
import matplotlib.pyplot as plt
####################################################################################################
import PySpice.Logging.Logging as Logging
logger = Logging.setup_logging()
#######... | gpl-3.0 |
Hiyorimi/scikit-image | skimage/viewer/utils/core.py | 19 | 6555 | import numpy as np
from ..qt import QtWidgets, has_qt, FigureManagerQT, FigureCanvasQTAgg
from ..._shared.utils import warn
import matplotlib as mpl
from matplotlib.figure import Figure
from matplotlib import _pylab_helpers
from matplotlib.colors import LinearSegmentedColormap
if has_qt and 'agg' not in mpl.get_backen... | bsd-3-clause |
ville-k/tensorflow | tensorflow/contrib/learn/python/learn/learn_io/io_test.py | 137 | 5063 | # 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 |
DentonW/Ps-H-Scattering | General Code/Python Scripts/Extrapolation.py | 1 | 15927 | #!/usr/bin/python
#TODO: Add checks for whether files are good
#TODO: Make relative difference function
import sys, scipy, pylab
import numpy as np
from math import *
import matplotlib.pyplot as plt
from xml.dom.minidom import parse, parseString
from xml.dom import minidom
def NumTermsOmega(omega): # Return the... | mit |
jblackburne/scikit-learn | examples/cluster/plot_kmeans_stability_low_dim_dense.py | 338 | 4324 | """
============================================================
Empirical evaluation of the impact of k-means initialization
============================================================
Evaluate the ability of k-means initializations strategies to make
the algorithm convergence robust as measured by the relative stan... | bsd-3-clause |
laurentperrinet/Khoei_2017_PLoSCB | scripts/default_param.py | 1 | 3196 | # -*- coding: utf-8 -*-
"""
Default parameters for all experiments
"""
from __future__ import division, print_function
import numpy as np
import MotionParticlesFLE as mp
N_X, N_Y, N_frame = mp.N_X, mp.N_Y, mp.N_frame
X_0 = -1.
V_X = 1.
PBP_D_x = mp.D_x*2.
PBP_D_V = np.inf #mp.D_V*1000.
PBP_prior = mp.v_prior #/1.e6
... | mit |
zhupengjia/beampackage | beampackage/bpmcalib.py | 2 | 17801 | #!/usr/bin/env python
import os,re,numpy
from harppos import *
from bpmfit import *
from signalfilter import decode,getrealpos
from runinfo import *
#class to calibrate bpm
class bpmcalib:
def __init__(self,keywords=False,treename="T",rootpath=os.getenv("REPLAY_OUT_PATH"), onlyb=False,forcefastbus=False,forceredec... | gpl-3.0 |
xya/sms-tools | lectures/08-Sound-transformations/plots-code/FFT-filtering.py | 21 | 1723 | import math
import matplotlib.pyplot as plt
import numpy as np
import time, os, sys
sys.path.append(os.path.join(os.path.dirname(os.path.realpath(__file__)), '../../../software/models/'))
import dftModel as DFT
import utilFunctions as UF
(fs, x) = UF.wavread('../../../sounds/orchestra.wav')
N = 2048
start = 1.0*fs
... | agpl-3.0 |
jorisvandenbossche/DS-python-data-analysis | _solved/spreaddiagram.py | 1 | 5658 | # -*- coding: utf-8 -*-
"""
@author: Stijnvh
"""
import sys
import datetime
import numpy as np
from scipy import stats
from scipy.stats import linregress
import pandas as pd
from pandas.tseries.offsets import DateOffset
import pylab as p
import matplotlib as mpl
mpl.rcParams['mathtext.default'] = 'regular'
import ... | bsd-3-clause |
elijah513/scikit-learn | sklearn/datasets/species_distributions.py | 198 | 7923 | """
=============================
Species distribution dataset
=============================
This dataset represents the geographic distribution of species.
The dataset is provided by Phillips et. al. (2006).
The two species are:
- `"Bradypus variegatus"
<http://www.iucnredlist.org/apps/redlist/details/3038/0>`_... | bsd-3-clause |
anurag313/scikit-learn | sklearn/linear_model/tests/test_bayes.py | 299 | 1770 | # Author: Alexandre Gramfort <alexandre.gramfort@inria.fr>
# Fabian Pedregosa <fabian.pedregosa@inria.fr>
#
# License: BSD 3 clause
import numpy as np
from sklearn.utils.testing import assert_array_equal
from sklearn.utils.testing import SkipTest
from sklearn.linear_model.bayes import BayesianRidge, ARDRegres... | bsd-3-clause |
mwaskom/PySurfer | examples/plot_label.py | 2 | 1518 | """
Display ROI Labels
==================
Using PySurfer you can plot Freesurfer cortical labels on the surface
with a large amount of control over the visual representation.
"""
import os
from surfer import Brain
print(__doc__)
subject_id = "fsaverage"
hemi = "lh"
surf = "smoothwm"
brain = Brain(subject_id, hemi, ... | bsd-3-clause |
thilbern/scikit-learn | examples/linear_model/plot_ard.py | 248 | 2622 | """
==================================================
Automatic Relevance Determination Regression (ARD)
==================================================
Fit regression model with Bayesian Ridge Regression.
See :ref:`bayesian_ridge_regression` for more information on the regressor.
Compared to the OLS (ordinary l... | bsd-3-clause |
gnuradio/gnuradio | gr-filter/examples/synth_filter.py | 6 | 1806 | #!/usr/bin/env python
#
# Copyright 2010,2012,2013 Free Software Foundation, Inc.
#
# This file is part of GNU Radio
#
# SPDX-License-Identifier: GPL-3.0-or-later
#
#
from gnuradio import gr
from gnuradio import filter
from gnuradio import blocks
import sys
import numpy
try:
from gnuradio import analog
except Imp... | gpl-3.0 |
LeeMendelowitz/basketball-reference | basketball_reference/boxscore.py | 1 | 3212 | """
Parse a box score page.
"""
from bs4 import BeautifulSoup
from itertools import izip
import pandas
import numpy as np
def parse_basic_team_stats(elem):
"""
Parse the basic team stats table from the box score page.
Both the visiting and home team have basic stats table, which
summarizes the stats for each ... | gpl-3.0 |
tradingcraig/trading-with-python | lib/qtpandas.py | 77 | 7937 | '''
Easy integration of DataFrame into pyqt framework
Copyright: Jev Kuznetsov
Licence: BSD
'''
from PyQt4.QtCore import (QAbstractTableModel,Qt,QVariant,QModelIndex,SIGNAL)
from PyQt4.QtGui import (QApplication,QDialog,QVBoxLayout, QHBoxLayout, QTableView, QPushButton,
QWidget,QTabl... | bsd-3-clause |
fspaolo/scikit-learn | benchmarks/bench_plot_fastkmeans.py | 294 | 4676 | from __future__ import print_function
from collections import defaultdict
from time import time
import numpy as np
from numpy import random as nr
from sklearn.cluster.k_means_ import KMeans, MiniBatchKMeans
def compute_bench(samples_range, features_range):
it = 0
results = defaultdict(lambda: [])
chun... | bsd-3-clause |
spikefairway/VOIAnalyzer | base.py | 1 | 2273 | #!/usr/bin/env python
# coding : utf-8
"""
Basis for VOI analyzer.
"""
import pandas as pd
import numpy as np
import VOIAnalyzer.utils as utils
def _analysis(img_mat, voi_mat, voi_no, eps=1e-12):
""" Extract VOI statistices for each VOI.
"""
vec = img_mat[voi_mat == voi_no]
vec2 = vec[~np.isnan(vec)... | mit |
d-mittal/pystruct | examples/plot_latent_crf.py | 4 | 2024 | """
===================
Latent Dynamics CRF
===================
Solving a 2d grid problem by introducing latent variable interactions. The
input data is the same as in plot_grid_crf, a cross pattern. But now, the
center is not given an extra state. That makes the problem much harder to solve
for a pairwise model.
We... | bsd-2-clause |
ionanrozenfeld/networkx | examples/drawing/unix_email.py | 26 | 2678 | #!/usr/bin/env python
"""
Create a directed graph, allowing multiple edges and self loops, from
a unix mailbox. The nodes are email addresses with links
that point from the sender to the recievers. The edge data
is a Python email.Message object which contains all of
the email message data.
This example shows the po... | bsd-3-clause |
asinghal17/graphAPI-tools | countLikes/countLikes.py | 1 | 2580 | #!/usr/bin/env python
#title : countLikes.py
#description : Take CSV with (Name,Facebook_ID,Type) and outputs a CSV with Total Like Counts using Graph API
#author : @asinghal
#=======================================================================================================================
imp... | mit |
vamsirajendra/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 |
jacobbieker/GCP-perpendicular-least-squares | pls.py | 1 | 35002 | __author__ = 'Jacob Bieker'
import os, sys, random
import numpy
import pandas
from astropy.table import Table, vstack
import copy
import scipy.odr as odr
from scipy.stats import linregress
from statsmodels.formula.api import ols
import statsmodels.api as sm
# Fit plane or line iteratively
# if more than one cluster,... | mit |
nextstrain/augur | scripts/identify_emerging_clades.py | 1 | 14036 | #!/usr/bin/env python3
# coding: utf-8
"""Identify emerging clades from previously defined clades based on a minimum
number of new mutations that have reached a minimum frequency in a given region.
Example use cases:
# Find subclades based on nucleotide mutations with defaults.
python3 scripts/identify_emerging_clades... | agpl-3.0 |
vivekmishra1991/scikit-learn | sklearn/linear_model/sag.py | 64 | 9815 | """Solvers for Ridge and LogisticRegression using SAG algorithm"""
# Authors: Tom Dupre la Tour <tom.dupre-la-tour@m4x.org>
#
# Licence: BSD 3 clause
import numpy as np
import warnings
from ..utils import ConvergenceWarning
from ..utils import check_array
from .base import make_dataset
from .sgd_fast import Log, Squ... | bsd-3-clause |
Habasari/sms-tools | lectures/04-STFT/plots-code/spectrogram.py | 19 | 1174 | import numpy as np
import time, os, sys
sys.path.append(os.path.join(os.path.dirname(os.path.realpath(__file__)), '../../../software/models/'))
import stft as STFT
import utilFunctions as UF
import matplotlib.pyplot as plt
from scipy.signal import hamming
from scipy.fftpack import fft
import math
(fs, x) = UF.wavrea... | agpl-3.0 |
chdecultot/erpnext | erpnext/selling/page/sales_funnel/sales_funnel.py | 13 | 4035 | # Copyright (c) 2018, Frappe Technologies Pvt. Ltd. and Contributors
# License: GNU General Public License v3. See license.txt
from __future__ import unicode_literals
import frappe
from frappe import _
from erpnext.accounts.report.utils import convert
import pandas as pd
@frappe.whitelist()
def get_funnel_data(from_... | gpl-3.0 |
joshrule/LOTlib | LOTlib/Legacy/MCMCSummary/VectorSummary.py | 3 | 8466 | import csv, math
import numpy as np
import pickle
from MCMCSummary import MCMCSummary
class VectorSummary(MCMCSummary):
"""
Summarize & plot data for MCMC with a VectorHypothesis (e.g. GrammarHypothesis).
"""
def __init__(self, skip=100, cap=100):
MCMCSummary.__init__(self, skip=skip, cap=cap... | gpl-3.0 |
clairetang6/bokeh | bokeh/charts/models.py | 9 | 8430 | from __future__ import absolute_import
from six import iteritems
import pandas as pd
from bokeh.models.renderers import GlyphRenderer
from bokeh.models.sources import ColumnDataSource
from bokeh.core.properties import (HasProps, String, Either, Float, Color, Instance, List,
Any, Dict)
fr... | bsd-3-clause |
barbagroup/PetIBM | examples/decoupledibpm/cylinder2dRe3000_GPU/scripts/plotDragCoefficient.py | 6 | 2037 | """
Plots the instantaneous drag coefficient between 0 and 3 time-units of flow
simulation and compares with numerical results from
Koumoutsakos and Leonard (1995).
_References:_
* Koumoutsakos, P., & Leonard, A. (1995).
High-resolution simulations of the flow around an impulsively started
cylinder using vortex me... | bsd-3-clause |
dsm054/pandas | pandas/tests/indexes/period/test_construction.py | 1 | 19613 | import numpy as np
import pytest
from pandas.compat import PY3, lmap, lrange, text_type
from pandas.core.dtypes.dtypes import PeriodDtype
import pandas as pd
from pandas import (
Index, Period, PeriodIndex, Series, date_range, offsets, period_range)
import pandas.core.indexes.period as period
import pandas.util.... | bsd-3-clause |
peterfpeterson/mantid | Framework/PythonInterface/mantid/simpleapi.py | 3 | 53905 | # Mantid Repository : https://github.com/mantidproject/mantid
#
# Copyright © 2018 ISIS Rutherford Appleton Laboratory UKRI,
# NScD Oak Ridge National Laboratory, European Spallation Source,
# Institut Laue - Langevin & CSNS, Institute of High Energy Physics, CAS
# SPDX - License - Identifier: GPL - 3.0 +
"""
... | gpl-3.0 |
cancan101/StarCluster | starcluster/balancers/sge/__init__.py | 1 | 37941 | # Copyright 2009-2014 Justin Riley
#
# This file is part of StarCluster.
#
# StarCluster 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) any
# later ... | lgpl-3.0 |
jakejhansen/minesweeper_solver | policy_gradients/train_full_6x6_CNN.py | 1 | 10221 | #Base code was written by Jonas Busk - Modified to suit project by Jacob Jon Hansen
import matplotlib.pyplot as plt
import numpy as np
import tensorflow as tf
from tensorflow.python.ops.nn import relu, softmax
import gym
import pickle
from sklearn.preprocessing import normalize
import sys
import os
sys.path.append('.... | mit |
timodonnell/genomisc | setup.py | 1 | 2502 | # Copyright (c) 2014. Mount Sinai School of Medicine
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law o... | apache-2.0 |
mulhod/reviewer_experience_prediction | util/cv_learn.py | 1 | 61443 | """
:author: Matt Mulholland (mulhodm@gmail.com)
:date: 10/14/2015
Command-line utility utilizing the RunCVExperiments class, which enables
one to run cross-validation experiments incrementally with a number of
different machine learning algorithms and parameter customizations, etc.
"""
import logging
from copy import... | mit |
henridwyer/scikit-learn | examples/svm/plot_separating_hyperplane.py | 62 | 1274 | """
=========================================
SVM: Maximum margin separating hyperplane
=========================================
Plot the maximum margin separating hyperplane within a two-class
separable dataset using a Support Vector Machines classifier with
linear kernel.
"""
print(__doc__)
import numpy as np
impo... | bsd-3-clause |
marcsans/cnn-physics-perception | phy/lib/python2.7/site-packages/matplotlib/testing/jpl_units/EpochConverter.py | 8 | 5505 | #===========================================================================
#
# EpochConverter
#
#===========================================================================
"""EpochConverter module containing class EpochConverter."""
#===========================================================================
# Pl... | mit |
elenita1221/BDA_py_demos | demos_ch3/demo3_6.py | 19 | 2810 | """Bayesian Data Analysis, 3rd ed
Chapter 3, demo 6
Illustrate posterior inference for Bioassay data (BDA3 p. 74-).
Instructions for exercise (3.11 in BDA3)
- Check that the range and spacing of A and B are sensible for the
alternative prior
- Compute the log-posterior in a grid
- Scale the log-posterior by subtra... | gpl-3.0 |
r-mart/scikit-learn | sklearn/neighbors/classification.py | 132 | 14388 | """Nearest Neighbor Classification"""
# Authors: Jake Vanderplas <vanderplas@astro.washington.edu>
# Fabian Pedregosa <fabian.pedregosa@inria.fr>
# Alexandre Gramfort <alexandre.gramfort@inria.fr>
# Sparseness support by Lars Buitinck <L.J.Buitinck@uva.nl>
# Multi-output support by ... | bsd-3-clause |
mavenlin/tensorflow | tensorflow/contrib/learn/python/learn/estimators/estimators_test.py | 21 | 6697 | # 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 |
schets/scikit-learn | examples/bicluster/plot_spectral_biclustering.py | 403 | 2011 | """
=============================================
A demo of the Spectral Biclustering algorithm
=============================================
This example demonstrates how to generate a checkerboard dataset and
bicluster it using the Spectral Biclustering algorithm.
The data is generated with the ``make_checkerboard`... | bsd-3-clause |
r-mart/scikit-learn | sklearn/datasets/tests/test_rcv1.py | 322 | 2414 | """Test the rcv1 loader.
Skipped if rcv1 is not already downloaded to data_home.
"""
import errno
import scipy.sparse as sp
import numpy as np
from sklearn.datasets import fetch_rcv1
from sklearn.utils.testing import assert_almost_equal
from sklearn.utils.testing import assert_array_equal
from sklearn.utils.testing i... | bsd-3-clause |
Mistobaan/tensorflow | tensorflow/python/estimator/canned/dnn_linear_combined_test.py | 46 | 26964 | # Copyright 2017 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 |
beepee14/scikit-learn | examples/tree/plot_tree_regression.py | 206 | 1476 | """
===================================================================
Decision Tree Regression
===================================================================
A 1D regression with decision tree.
The :ref:`decision trees <tree>` is
used to fit a sine curve with addition noisy observation. As a result, it
learns ... | bsd-3-clause |
gfyoung/pandas | pandas/tests/indexes/datetimelike_/test_equals.py | 2 | 6163 | """
Tests shared for DatetimeIndex/TimedeltaIndex/PeriodIndex
"""
from datetime import datetime, timedelta
import numpy as np
import pytest
import pandas as pd
from pandas import (
CategoricalIndex,
DatetimeIndex,
Index,
PeriodIndex,
TimedeltaIndex,
date_range,
period_range,
)
import panda... | bsd-3-clause |
ishanic/scikit-learn | sklearn/datasets/tests/test_mldata.py | 384 | 5221 | """Test functionality of mldata fetching utilities."""
import os
import shutil
import tempfile
import scipy as sp
from sklearn import datasets
from sklearn.datasets import mldata_filename, fetch_mldata
from sklearn.utils.testing import assert_in
from sklearn.utils.testing import assert_not_in
from sklearn.utils.test... | bsd-3-clause |
rgommers/statsmodels | statsmodels/stats/tests/test_weightstats.py | 30 | 21864 | '''tests for weightstats, compares with replication
no failures but needs cleanup
update 2012-09-09:
added test after fixing bug in covariance
TODOs:
- I don't remember what all the commented out code is doing
- should be refactored to use generator or inherited tests
- still gaps in test coverage... | bsd-3-clause |
appapantula/scikit-learn | sklearn/utils/tests/test_random.py | 230 | 7344 | 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 |
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