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 |
|---|---|---|---|---|---|
nkmk/python-snippets | notebook/pandas_ohlc_candlestick_chart.py | 1 | 5049 | import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
import mpl_finance
df_org = pd.read_csv('data/src/aapl_2015_2019.csv', index_col=0, parse_dates=True)['2017']
print(df_org)
# open high low close volume
# 2017-01-03 115.80 116.3300 114.760 116.15 28... | mit |
rsivapr/scikit-learn | sklearn/tree/tests/test_export.py | 3 | 2897 | """
Testing for export functions of decision trees (sklearn.tree.export).
"""
from numpy.testing import assert_equal
from nose.tools import assert_raises
from sklearn.tree import DecisionTreeClassifier
from sklearn.tree import export_graphviz
from sklearn.externals.six import StringIO
# toy sample
X = [[-2, -1], [-1... | bsd-3-clause |
eickenberg/scikit-learn | sklearn/feature_selection/tests/test_from_model.py | 244 | 1593 | import numpy as np
import scipy.sparse as sp
from nose.tools import assert_raises, assert_true
from sklearn.utils.testing import assert_less
from sklearn.utils.testing import assert_greater
from sklearn.datasets import load_iris
from sklearn.linear_model import LogisticRegression
from sklearn.linear_model import SGD... | bsd-3-clause |
posborne/Anvil | anvil/examples/commit_histogram.py | 2 | 1787 | # Copyright (c) 2012 Paul Osborne <osbpau@gmail.com>
#
# 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 without restriction, including without limitation the
# rights to use, copy, modify, mer... | mit |
natasasdj/OpenWPM | analysis_parallel/08_third-party_images.py | 1 | 2769 | import sqlite3
import os
import pandas as pd
# on how many pages appear third-party/one-pixel/zero-size images
# on how many homesites appear third party/one-pixel/zero-size images
# on how many first links appear third-party/one-pixel/zero-size images
# on how many domains appear third party/one-pixel/zero-size image... | gpl-3.0 |
tcrossland/time_series_prediction | ann/scenario.py | 1 | 2422 | import time
import matplotlib.pyplot as plt
import numpy as np
class Config:
def __init__(self, time_series, look_back=6, batch_size=1, topology=None, validation_split=0.3,
include_index=False, activation='tanh', optimizer='adam'):
self.time_series = time_series
self.look_back = ... | mit |
manjunaths/tensorflow | tensorflow/contrib/learn/python/learn/learn_io/pandas_io.py | 18 | 6444 | # 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 |
Sibada/VIC_Hime | Hime/calibrater.py | 1 | 13255 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
import copy
from collections import OrderedDict
from datetime import datetime
import numpy as np
import pandas as pd
from Hime import log
from Hime.uh_creater import load_rout_data
from Hime.routing import confluence, gather_to_month, gather_to_year
from Hime.statistic i... | gpl-3.0 |
shadowleaves/deep_learning | theano/rnn_minibatch.py | 1 | 30441 | """ Vanilla RNN
Parallelizes scan over sequences by using mini-batches.
@author Graham Taylor
"""
import numpy as np
import theano
import theano.tensor as T
# from sklearn.base import BaseEstimator
import logging
import time
import os
import datetime
import cPickle as pickle
from collections import OrderedDict
logger ... | mit |
naoyak/Agile_Data_Code_2 | ch07/train_sklearn_model.py | 1 | 5282 | import sys, os, re
sys.path.append("lib")
import utils
import numpy as np
import sklearn
import iso8601
import datetime
print("Imports loaded...")
# Load and check the size of our training data. May take a minute.
print("Original JSON file size: {:,} Bytes".format(os.path.getsize("data/simple_flight_delay_features.js... | mit |
daniel-vainsencher/regularized_weighting | src/simpleInterface.py | 1 | 6881 | from numpy import array, inf, ones, zeros
import matplotlib.pyplot as plt
from sklearn.svm import SVC
#from sklearn.metrics import zero_one_score
import time
import alternatingAlgorithms as aa
import weightedModelTypes as wmt
from minL2PenalizedLossOverSimplex import penalizedMultipleWeightedLoss2, weightsForLosses, w... | bsd-2-clause |
endangeredoxen/pywebify | setup.py | 1 | 4007 | """A setuptools based setup module.
See:
https://packaging.python.org/en/latest/distributing.html
https://github.com/pypa/sampleproject
"""
# Always prefer setuptools over distutils
from setuptools import setup, find_packages
# To use a consistent encoding
from codecs import open
from os import path
here = path.abspa... | gpl-2.0 |
berkeley-stat222/mousestyles | mousestyles/path_diversity/path_index.py | 3 | 2301 | from __future__ import (absolute_import, division,
print_function, unicode_literals)
import numpy as np
def path_index(movement, stop_threshold, min_path_length):
r"""
Return a list object containing start and end indices
for a specific movement. Each element in the list is
a ... | bsd-2-clause |
glouppe/scikit-learn | examples/applications/topics_extraction_with_nmf_lda.py | 4 | 3761 | """
=======================================================================================
Topic extraction with Non-negative Matrix Factorization and Latent Dirichlet Allocation
=======================================================================================
This is an example of applying Non-negative Matrix ... | bsd-3-clause |
reychil/project-alpha-1 | code/utils/scripts/multi_regression_script.py | 1 | 7291 | # multi_regression_script.py
# In this file we will be creating multiple regressions using the the glm
# function. Moreover, the features added with be: seperating the conditions
# from each other (i.e. x_1 = cond1 HRF, x_2 = cond2 HRF, and x_3 = cond3 HRF.
# I will be running it with np.convolve and convolution_spe... | bsd-3-clause |
rhyolight/nupic.research | projects/wavelet_dataAggregation/runDataAggregationExperiment.py | 11 | 21206 | from os.path import isfile, join, exists
import pandas as pd
import numpy as np
from scipy import signal
import numpy.matlib
import csv
import os
import time
os.environ['TZ'] = 'GMT'
time.tzset()
display = True
if display:
import matplotlib.pyplot as plt
plt.close('all')
plt.ion()
def plotWaveletPower(sig, cw... | gpl-3.0 |
crscardellino/dnnwsd | dnnwsd/pipeline/semisupervised.py | 1 | 5597 | # -*- coding: utf-8 -*-
import logging
import os
from copy import deepcopy
from sklearn import linear_model, tree
from ..corpus import sensem, semeval, unannotated
from ..experiment import results, semisupervised
from ..model import mlp
from ..processor import bowprocessor, vecprocessor
from ..utils.setup_logging im... | bsd-3-clause |
tskisner/pytoast | src/python/tests/ops_dipole.py | 1 | 7665 | # Copyright (c) 2015-2018 by the parties listed in the AUTHORS file.
# All rights reserved. Use of this source code is governed by
# a BSD-style license that can be found in the LICENSE file.
from ..mpi import MPI
from .mpi import MPITestCase
import sys
import os
import numpy as np
import numpy.testing as nt
impor... | bsd-2-clause |
cybernet14/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 |
mtb0/flightmodel | src/download/get_files.py | 1 | 2224 | #!/usr/bin/env python
"""Download Latitude/Longitude information and Flight time information from
the Bureau of Transportation Statistics website, using wget."""
import os
import pandas as pd
import tempfile
URL='http://tsdata.bts.gov/'
LATLONG='187806114_T_MASTER_CORD'
FLIGHT='On_Time_On_Time_Performance'
def get_... | mit |
LiaoPan/blaze | blaze/compute/tests/test_numpy_compute.py | 3 | 16537 | from __future__ import absolute_import, division, print_function
import pytest
import numpy as np
import pandas as pd
from datetime import datetime, date
from blaze.compute.core import compute, compute_up
from blaze.expr import symbol, by, exp, summary, Broadcast, join, concat
from blaze import sin
from odo import i... | bsd-3-clause |
jorge2703/scikit-learn | sklearn/covariance/graph_lasso_.py | 127 | 25626 | """GraphLasso: sparse inverse covariance estimation with an l1-penalized
estimator.
"""
# Author: Gael Varoquaux <gael.varoquaux@normalesup.org>
# License: BSD 3 clause
# Copyright: INRIA
import warnings
import operator
import sys
import time
import numpy as np
from scipy import linalg
from .empirical_covariance_ im... | bsd-3-clause |
joshloyal/scikit-learn | examples/cluster/plot_ward_structured_vs_unstructured.py | 320 | 3369 | """
===========================================================
Hierarchical clustering: structured vs unstructured ward
===========================================================
Example builds a swiss roll dataset and runs
hierarchical clustering on their position.
For more information, see :ref:`hierarchical_clus... | bsd-3-clause |
Lab603/PicEncyclopedias | jni-build/jni-build/jni/include/tensorflow/contrib/learn/python/learn/tests/dataframe/feeding_functions_test.py | 30 | 4777 | # 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... | mit |
whitews/dpconverge | test_dp_3params.py | 1 | 1245 | from dpconverge.data_set import DataSet
import numpy as np
from sklearn.datasets.samples_generator import make_blobs
n_features = 3
points_per_feature = 100
centers = [[2, 2, 1], [2, 4, 2], [4, 2, 3], [4, 4, 4]]
ds = DataSet(parameter_count=n_features)
rnd_state = np.random.RandomState()
rnd_state.seed(3)
for i, ce... | bsd-3-clause |
blbradley/subset-selector | subset_selector/selector.py | 1 | 3200 | import numpy as np
import matplotlib
import matplotlib.pyplot as plt
from matplotlib.backends.backend_nbagg import NavigationIPy, FigureManagerNbAgg
BUTTONS = ('Home', 'Back', 'Forward', 'Download')
default_facecolor = matplotlib.rcParams['axes.facecolor']
def on_click(event):
if event.inaxes and event.button ==... | mit |
Averroes/statsmodels | statsmodels/sandbox/survival2.py | 35 | 17924 | #Kaplan-Meier Estimator
import numpy as np
import numpy.linalg as la
import matplotlib.pyplot as plt
from scipy import stats
from statsmodels.iolib.table import SimpleTable
class KaplanMeier(object):
"""
KaplanMeier(...)
KaplanMeier(data, endog, exog=None, censoring=None)
Create an object of... | bsd-3-clause |
snnn/tensorflow | tensorflow/contrib/distributions/python/ops/mixture.py | 22 | 21121 | # 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 |
zhoukekestar/drafts | 2020-10~12/2020-12-13-python/k-means.py | 1 | 2941 | import matplotlib.pyplot as plt
import numpy as np
import random
pointsA = np.random.normal(loc=[40, 60], scale=15, size=[200, 2])
plt.scatter(pointsA.T[0], pointsA.T[1], marker='o', label="A")
pointsB = np.random.normal(loc=[80, 100], scale=12, size=[100, 2])
plt.scatter(pointsB.T[0], pointsB.T[1], marker='^', labe... | mit |
pombredanne/metamorphosys-desktop | metamorphosys/META/models/DynamicsTeam/RISoT/post_processing/common/post_processing_class.py | 18 | 28308 | # Copyright (C) 2013-2015 MetaMorph Software, Inc
# Permission is hereby granted, free of charge, to any person obtaining a
# copy of this data, including any software or models in source or binary
# form, as well as any drawings, specifications, and documentation
# (collectively "the Data"), to deal in the Data ... | mit |
with-git/tensorflow | tensorflow/python/estimator/inputs/queues/feeding_queue_runner_test.py | 116 | 5164 | # 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 |
luminescence/PolyLibScan | helpers/msms.py | 1 | 13918 | import pathlib2 as pl
import os
import subprocess as sp
import tempfile
import numpy as np
import pandas as pd
import collections as col
import Bio.PDB as PDB
import PolyLibScan.Database.db as DB
class HydrophobicParameterisation(object):
def __init__(self, pdb_path,
pdb_to_xyzrn=None, atmty... | mit |
chrisburr/scikit-learn | sklearn/feature_selection/tests/test_feature_select.py | 103 | 22297 | """
Todo: cross-check the F-value with stats model
"""
from __future__ import division
import itertools
import warnings
import numpy as np
from scipy import stats, sparse
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_almost_equal
from sklearn.utils.testing import assert_raises... | bsd-3-clause |
yyjiang/scikit-learn | sklearn/tree/tree.py | 113 | 34767 | """
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 |
idealabasu/code_pynamics | python/pynamics_examples/in_development/pendulum_script_mode.py | 1 | 2823 | # -*- coding: utf-8 -*-
"""
Written by Daniel M. Aukes
Email: danaukes<at>gmail.com
Please see LICENSE for full license.
"""
import pynamics
from pynamics.frame import Frame
from pynamics.variable_types import Differentiable,Constant
from pynamics.system import System
from pynamics.body import Body
from pynamics.dyadi... | mit |
RMKD/networkx | networkx/convert.py | 22 | 13215 | """Functions to convert NetworkX graphs to and from other formats.
The preferred way of converting data to a NetworkX graph is through the
graph constuctor. The constructor calls the to_networkx_graph() function
which attempts to guess the input type and convert it automatically.
Examples
--------
Create a graph wit... | bsd-3-clause |
lancezlin/ml_template_py | lib/python2.7/site-packages/pandas/util/doctools.py | 9 | 6779 | import numpy as np
import pandas as pd
import pandas.compat as compat
class TablePlotter(object):
"""
Layout some DataFrames in vertical/horizontal layout for explanation.
Used in merging.rst
"""
def __init__(self, cell_width=0.37, cell_height=0.25, font_size=7.5):
self.cell_width = cell_... | mit |
robmarano/nyu-python | course-2/session-7/pandas/df_basics.py | 1 | 2677 | #!/usr/bin/env python3
try:
# for Python 2.x
import StringIO
except:
# for Python 3.x
import io
import csv
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import re
# define data
csv_input = """timestamp,title,reqid
2016-07-23 11:05:08,SVP,2356556-AS
2016-12-12 01:23:33,VP,556... | mit |
nmayorov/scikit-learn | examples/svm/plot_svm_nonlinear.py | 268 | 1091 | """
==============
Non-linear SVM
==============
Perform binary classification using non-linear SVC
with RBF kernel. The target to predict is a XOR of the
inputs.
The color map illustrates the decision function learned by the SVC.
"""
print(__doc__)
import numpy as np
import matplotlib.pyplot as plt
from sklearn imp... | bsd-3-clause |
Nyker510/scikit-learn | examples/text/hashing_vs_dict_vectorizer.py | 284 | 3265 | """
===========================================
FeatureHasher and DictVectorizer Comparison
===========================================
Compares FeatureHasher and DictVectorizer by using both to vectorize
text documents.
The example demonstrates syntax and speed only; it doesn't actually do
anything useful with the e... | bsd-3-clause |
neale/CS-program | 434-MachineLearning/final_project/linearClassifier/sklearn/datasets/__init__.py | 72 | 3807 | """
The :mod:`sklearn.datasets` module includes utilities to load datasets,
including methods to load and fetch popular reference datasets. It also
features some artificial data generators.
"""
from .base import load_diabetes
from .base import load_digits
from .base import load_files
from .base import load_iris
from .... | unlicense |
ma-compbio/SPEID | pairwise/read_FIMO_results.py | 2 | 5408 | import numpy as np
import csv
from sklearn.metrics import average_precision_score
from keras.optimizers import Adam # needed to compile prediction model
import h5py
import load_data_pairs as ld # my own scripts for loading data
import build_small_model as bm
import util
fimo_root = '/home/sss1/Desktop/projects/DeepInt... | gpl-3.0 |
neale/CS-program | 434-MachineLearning/final_project/linearClassifier/sklearn/semi_supervised/label_propagation.py | 9 | 15941 | # 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... | unlicense |
vitaly-krugl/nupic | external/linux32/lib/python2.6/site-packages/matplotlib/backends/backend_gtkagg.py | 70 | 4184 | """
Render to gtk from agg
"""
from __future__ import division
import os
import matplotlib
from matplotlib.figure import Figure
from matplotlib.backends.backend_agg import FigureCanvasAgg
from matplotlib.backends.backend_gtk import gtk, FigureManagerGTK, FigureCanvasGTK,\
show, draw_if_interactive,\
error_ms... | agpl-3.0 |
BhallaLab/moose-examples | tutorials/ExcInhNet/ExcInhNet_Ostojic2014_Brunel2000_brian2.py | 2 | 8226 | '''
The LIF network is based on:
Ostojic, S. (2014).
Two types of asynchronous activity in networks of
excitatory and inhibitory spiking neurons.
Nat Neurosci 17, 594-600.
Key parameter to change is synaptic coupling J (mV).
Tested with Brian 1.4.1
Written by Aditya Gilra, CAMP 2014, Bangalore, 20 June, 2014.
Upd... | gpl-2.0 |
BlueFern/DBiharMesher | util/PlotColumn2D.py | 1 | 2787 | # -*- coding: utf-8 -*-
"""
Read SMC Ca2+ values from a line of cells in temporal series and produce a 2D plot
"""
import re
import os
import vtk
import numpy
import matplotlib.pyplot as plt
print 'Importing ', __file__
def tryInt(s):
try:
return int(s)
except:
return s
def alphaNumKey(s):
... | gpl-2.0 |
mohittahiliani/PIE-ns3 | src/flow-monitor/examples/wifi-olsr-flowmon.py | 108 | 7439 | # -*- Mode: Python; -*-
# Copyright (c) 2009 INESC Porto
#
# This program is free software; you can redistribute it and/or modify
# it under the terms of the GNU General Public License version 2 as
# published by the Free Software Foundation;
#
# This program is distributed in the hope that it will be useful,
#... | gpl-2.0 |
vicky2135/lucious | oscar/lib/python2.7/site-packages/IPython/core/tests/test_pylabtools.py | 12 | 7550 | """Tests for pylab tools module.
"""
# Copyright (c) IPython Development Team.
# Distributed under the terms of the Modified BSD License.
from __future__ import print_function
from io import UnsupportedOperation, BytesIO
import matplotlib
matplotlib.use('Agg')
from matplotlib.figure import Figure
from nose import ... | bsd-3-clause |
anhaidgroup/py_entitymatching | py_entitymatching/matcher/logregmatcher.py | 1 | 1284 | """
This module contains the functions for Logistic Regression classifier.
"""
from py_entitymatching.matcher.mlmatcher import MLMatcher
from sklearn.linear_model import LogisticRegression
from py_entitymatching.matcher.matcherutils import get_ts
class LogRegMatcher(MLMatcher):
"""
Logistic Regression matcher.... | bsd-3-clause |
zihua/scikit-learn | examples/manifold/plot_compare_methods.py | 31 | 4051 | """
=========================================
Comparison of Manifold Learning methods
=========================================
An illustration of dimensionality reduction on the S-curve dataset
with various manifold learning methods.
For a discussion and comparison of these algorithms, see the
:ref:`manifold module... | bsd-3-clause |
Lawrence-Liu/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 |
esquishesque/musical-potato | analysis_bulk.py | 1 | 14383 | import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np
import sys
import math
#full data frame with all data
df=pd.read_excel('first survey - English final (Responses).xlsx',sheetname=5,skiprows=1,keep_default_na=False)
#df=pd.read_excel('survey_test.xls',sheetname=1)
#get the c... | gpl-3.0 |
dcolombo/FilFinder | examples/paper_figures/match_resolution.py | 3 | 7990 | # Licensed under an MIT open source license - see LICENSE
'''
Check resolution effects of masking process.
Degrade an image to match the resolution of a more distant one,
then compare the outputs.
'''
from fil_finder import fil_finder_2D
import numpy as np
from astropy.io.fits import getdata
from astropy import convo... | mit |
ua-snap/downscale | old/bin/old/cld_cru_ts31_downscaling.py | 2 | 11324 | # # #
# Current implementation of the cru ts31 (ts32) delta downscaling procedure
#
# Author: Michael Lindgren (malindgren@alaska.edu)
# # #
import numpy as np
def write_gtiff( output_arr, template_meta, output_filename, compress=True ):
'''
DESCRIPTION:
------------
output a GeoTiff given a numpy ndarray, rasterio... | mit |
crystalrood/piggie | public/test_scripts/updating_status_mongo.py | 1 | 1334 | # this test file takes an encoded emai, decodes it, parese out order information and saves it to the database
# also changes the status of the messages db from "need to scrape" to "scraped"
#
#
#
#required encoding for scraping, otherwise defaults to unicode and screws things up
from bs4 import BeautifulSoup
import req... | mit |
great-expectations/great_expectations | tests/core/test_expectation_suite.py | 1 | 17469 | import datetime
from copy import copy, deepcopy
from typing import Any, Dict, List
import pytest
from ruamel.yaml import YAML
from great_expectations.core.expectation_configuration import ExpectationConfiguration
from great_expectations.core.expectation_suite import ExpectationSuite
from great_expectations.util impor... | apache-2.0 |
carefree0910/MachineLearning | b_NaiveBayes/Original/GaussianNB.py | 1 | 4093 | import os
import sys
root_path = os.path.abspath("../../")
if root_path not in sys.path:
sys.path.append(root_path)
import matplotlib.pyplot as plt
from b_NaiveBayes.Original.Basic import *
from b_NaiveBayes.Original.MultinomialNB import MultinomialNB
from Util.Util import DataUtil
class Gaussian... | mit |
mindw/shapely | docs/code/polygon2.py | 6 | 1798 | from matplotlib import pyplot
from matplotlib.patches import Circle
from shapely.geometry import Polygon
from descartes.patch import PolygonPatch
from figures import SIZE
COLOR = {
True: '#6699cc',
False: '#ff3333'
}
def v_color(ob):
return COLOR[ob.is_valid]
def plot_coords(ax, ob):
x, y = ob.... | bsd-3-clause |
FireElementalNE/RetroColorAnalysis | scatterplots/scatter_plot.py | 1 | 1813 | import os
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import globals.global_values as global_values
class ScatterPlot:
def __init__(self, cl, _dirs, _is_agg):
'''
init a scatter plot
:param cl: the list of colors
:param _dirs: the dirs structure
... | gpl-2.0 |
trichter/sito | bin/noise/noise_s_final_autocorr1.py | 1 | 4932 | #!/usr/bin/env python
# by TR
from obspy.core import UTCDateTime as UTC
from sito.data import IPOC
from sito.noisexcorr import (prepare, get_correlations,
plotXcorrs, noisexcorrf, stack)
from sito import util
import matplotlib.pyplot as plt
from sito.stream import read
from multiprocessing ... | mit |
mjgrav2001/scikit-learn | examples/tree/plot_tree_regression_multioutput.py | 206 | 1800 | """
===================================================================
Multi-output Decision Tree Regression
===================================================================
An example to illustrate multi-output regression with decision tree.
The :ref:`decision trees <tree>`
is used to predict simultaneously the ... | bsd-3-clause |
NixaSoftware/CVis | venv/lib/python2.7/site-packages/pandas/tests/series/test_asof.py | 11 | 5289 | # coding=utf-8
import pytest
import numpy as np
from pandas import (offsets, Series, notna,
isna, date_range, Timestamp)
import pandas.util.testing as tm
from .common import TestData
class TestSeriesAsof(TestData):
def test_basic(self):
# array or list or dates
N = 50
... | apache-2.0 |
jkarnows/scikit-learn | sklearn/cluster/tests/test_spectral.py | 262 | 7954 | """Testing for Spectral Clustering methods"""
from sklearn.externals.six.moves import cPickle
dumps, loads = cPickle.dumps, cPickle.loads
import numpy as np
from scipy import sparse
from sklearn.utils import check_random_state
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_a... | bsd-3-clause |
manifoldai/merf | merf/merf.py | 1 | 15249 | """
Mixed Effects Random Forest model.
"""
import logging
import numpy as np
import pandas as pd
from sklearn.ensemble import RandomForestRegressor
from sklearn.exceptions import NotFittedError
logger = logging.getLogger(__name__)
class MERF(object):
"""
This is the core class to instantiate, train, and pre... | mit |
heli522/scikit-learn | sklearn/linear_model/tests/test_least_angle.py | 98 | 20870 | from nose.tools import assert_equal
import numpy as np
from scipy import linalg
from sklearn.cross_validation import train_test_split
from sklearn.utils.testing import assert_array_almost_equal
from sklearn.utils.testing import assert_true
from sklearn.utils.testing import assert_less
from sklearn.utils.testing impor... | bsd-3-clause |
etkirsch/scikit-learn | examples/gaussian_process/plot_gp_regression.py | 253 | 4054 | #!/usr/bin/python
# -*- coding: utf-8 -*-
r"""
=========================================================
Gaussian Processes regression: basic introductory example
=========================================================
A simple one-dimensional regression exercise computed in two different ways:
1. A noise-free cas... | bsd-3-clause |
wesm/statsmodels | scikits/statsmodels/sandbox/examples/thirdparty/ex_ratereturn.py | 1 | 4385 | # -*- coding: utf-8 -*-
"""Playing with correlation of DJ-30 stock returns
this uses pickled data that needs to be created with findow.py
to see graphs, uncomment plt.show()
Created on Sat Jan 30 16:30:18 2010
Author: josef-pktd
"""
import numpy as np
import matplotlib.finance as fin
import matplotlib.pyplot as plt... | bsd-3-clause |
johnmgregoire/PythonCompositionPlots | quaternary_FOM_stackedtern5.py | 1 | 2984 | import matplotlib.cm as cm
import numpy
import pylab
import operator, copy, os
#pylab.rc('font',**{'family':'serif''serif':['Times New Roman']})
#pylab.rcParams['font.family']='serif'
#pylab.rcParams['font.serif']='Times New Roman'
pylab.rc('font', family='serif', serif='Times New Roman')
#os.chdir('C:/Users/Gregoire... | bsd-3-clause |
ywcui1990/nupic | external/linux32/lib/python2.6/site-packages/matplotlib/backends/backend_gtkcairo.py | 69 | 2207 | """
GTK+ Matplotlib interface using cairo (not GDK) drawing operations.
Author: Steve Chaplin
"""
import gtk
if gtk.pygtk_version < (2,7,0):
import cairo.gtk
from matplotlib.backends import backend_cairo
from matplotlib.backends.backend_gtk import *
backend_version = 'PyGTK(%d.%d.%d) ' % gtk.pygtk_version + \
... | agpl-3.0 |
jshiv/turntable | test/lib/python2.7/site-packages/scipy/spatial/tests/test__plotutils.py | 71 | 1463 | from __future__ import division, print_function, absolute_import
from numpy.testing import dec, assert_, assert_array_equal
try:
import matplotlib
matplotlib.rcParams['backend'] = 'Agg'
import matplotlib.pyplot as plt
has_matplotlib = True
except:
has_matplotlib = False
from scipy.spatial import ... | mit |
mrshu/scikit-learn | examples/linear_model/plot_logistic.py | 5 | 1389 | #!/usr/bin/python
# -*- coding: utf-8 -*-
"""
=========================================================
Logit function
=========================================================
Show in the plot is how the logistic regression would, in this
synthetic dataset, classify values as either 0 or 1,
i.e. class one or two, us... | bsd-3-clause |
trmznt/fatools | fatools/scripts/facmd.py | 2 | 17741 |
import sys, argparse, yaml, csv, transaction
from fatools.lib.utils import cout, cerr, get_dbhandler, set_verbosity
from fatools.lib import params
from fatools.lib.const import assaystatus, peaktype
from fatools.lib.fautil import algo
def init_argparser(parser=None):
if parser:
p = parser
else:
... | lgpl-3.0 |
vmAggies/omniture-master | build/lib/omniture/query.py | 2 | 16229 | # encoding: utf-8
from __future__ import absolute_import
import time
from copy import copy, deepcopy
import functools
from dateutil.relativedelta import relativedelta
import json
import logging
import sys
import pandas as pd
import io
import requests
from .elements import Value
from . import reports
from . import uti... | mit |
borismarin/genesis2.4gamma | Scripts/gpython-tools/plotVm.py | 1 | 2028 | #!/usr/bin/env python
# plotVm ver 0.5 - a command line utility to plot a wildcarded argument
# list of files containing membrane potential data, and plots them in
# different colors on the same axes
import sys, os
import matplotlib.pyplot as plt
import numpy as np
def plot_file(file,format):
print 'Plotting %s'... | gpl-2.0 |
scienceopen/CVutils | DemoMedianFilter.py | 1 | 1893 | #!/usr/bin/env python
import cv2
import numpy as np
from skimage.util import random_noise
from matplotlib.pyplot import figure, show
from typing import Tuple
def gen_patterns(
x: int, y: int, dtype=np.uint8, noise: float = 0.0
) -> Tuple[np.ndarray, np.ndarray]:
if dtype == np.uint8:
V = 255
elif... | mit |
koobonil/Boss2D | Boss2D/addon/tensorflow-1.2.1_for_boss/tensorflow/contrib/learn/python/learn/estimators/kmeans_test.py | 44 | 19373 | # 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... | mit |
shikhardb/scikit-learn | examples/tree/plot_tree_regression.py | 40 | 1470 | """
===================================================================
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/arrays/boolean/test_construction.py | 6 | 12857 | import numpy as np
import pytest
import pandas as pd
import pandas._testing as tm
from pandas.arrays import BooleanArray
from pandas.core.arrays.boolean import coerce_to_array
def test_boolean_array_constructor():
values = np.array([True, False, True, False], dtype="bool")
mask = np.array([False, False, Fals... | bsd-3-clause |
sandeepdsouza93/TensorFlow-15712 | tensorflow/contrib/learn/python/learn/experiment.py | 5 | 16349 | # 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 |
aflaxman/scikit-learn | sklearn/feature_extraction/dict_vectorizer.py | 16 | 12486 | # Authors: Lars Buitinck
# Dan Blanchard <dblanchard@ets.org>
# License: BSD 3 clause
from array import array
from collections import Mapping
from operator import itemgetter
import numpy as np
import scipy.sparse as sp
from ..base import BaseEstimator, TransformerMixin
from ..externals import six
from ..ext... | bsd-3-clause |
kyleabeauchamp/HMCNotes | code/correctness/june11/test_various_hmc.py | 1 | 2454 | import lb_loader
import pandas as pd
import simtk.openmm.app as app
import numpy as np
import simtk.openmm as mm
from simtk import unit as u
from openmmtools import hmc_integrators, testsystems
precision = "mixed"
sysname = "chargedswitchedaccurateljbox"
system, positions, groups, temperature, timestep, langevin_tim... | gpl-2.0 |
leesavide/pythonista-docs | Documentation/matplotlib/examples/event_handling/lasso_demo.py | 9 | 2365 | """
Show how to use a lasso to select a set of points and get the indices
of the selected points. A callback is used to change the color of the
selected points
This is currently a proof-of-concept implementation (though it is
usable as is). There will be some refinement of the API.
"""
from matplotlib.widgets import... | apache-2.0 |
Mohitsharma44/citibike-challenge | citibike-challenge-aio.py | 2 | 3386 | import pylab as plt
import pandas as pd
import numpy as np
import datetime as dt
def datestr_as_datetime(dstr):
#2014-01-27 12:28:45
dstr=dstr.split()
y,mo,day=dstr[0].split('-')
hh,mm,ss=dstr[1].split(':')
return dt.datetime(int(y),int(mo),int(day),int(hh),int(mm),int(ss))
cbs=pd.read_csv("./citi... | mit |
gaoce/TimeVis | setup.py | 1 | 1191 | from __future__ import print_function
import os
from setuptools import setup
# Utility function to read the README file.
def read(fname):
return open(os.path.join(os.path.dirname(__file__), fname)).read()
# Setup
# 1. zip_safe needs to be False since we need access to templates
setup(
name="TimeVis",
v... | mit |
larsoner/mne-python | mne/decoding/tests/test_transformer.py | 7 | 9311 | # Author: Mainak Jas <mainak@neuro.hut.fi>
# Romain Trachel <trachelr@gmail.com>
#
# License: BSD (3-clause)
import os.path as op
import numpy as np
import pytest
from numpy.testing import (assert_array_equal, assert_array_almost_equal,
assert_allclose, assert_equal)
from mne impor... | bsd-3-clause |
DR08/mxnet | example/svm_mnist/svm_mnist.py | 44 | 4094 | # 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 |
mikehankey/fireball_camera | analyze-stacks.py | 1 | 55222 | #!/usr/bin/python3
# next steps. save off a cache of the diffs,so we can save time on multiple re-runs.
# do a crop cnt confirm.
# script to make master stacks per night and hour from the 1 minute stacks
from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas
from matplotlib.figure import Figur... | gpl-3.0 |
unnikrishnankgs/va | venv/lib/python3.5/site-packages/matplotlib/patheffects.py | 10 | 14296 | """
Defines classes for path effects. The path effects are supported in
:class:`~matplotlib.text.Text`, :class:`~matplotlib.lines.Line2D`
and :class:`~matplotlib.patches.Patch`.
"""
from __future__ import (absolute_import, division, print_function,
unicode_literals)
import six
from matplotlib... | bsd-2-clause |
bundgus/python-playground | matplotlib-playground/examples/pylab_examples/plotfile_demo.py | 1 | 1195 | import matplotlib.pyplot as plt
import numpy as np
import matplotlib.cbook as cbook
fname = cbook.get_sample_data('msft.csv', asfileobj=False)
fname2 = cbook.get_sample_data('data_x_x2_x3.csv', asfileobj=False)
# test 1; use ints
plt.plotfile(fname, (0, 5, 6))
# test 2; use names
plt.plotfile(fname, ('date', 'volum... | mit |
jungla/ICOM-fluidity-toolbox | Detectors/plot_FSLE_v.py | 1 | 2388 | #!~/python
import fluidity_tools
import matplotlib as mpl
mpl.use('ps')
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import myfun
import numpy as np
import pyvtk
import vtktools
import copy
import os
exp = 'r_3k_B_1F0_r'
filename = './ring_checkpoint.detectors'
filename2 = '/tamay2/mensa/f... | gpl-2.0 |
abhisg/scikit-learn | benchmarks/bench_isotonic.py | 268 | 3046 | """
Benchmarks of isotonic regression performance.
We generate a synthetic dataset of size 10^n, for n in [min, max], and
examine the time taken to run isotonic regression over the dataset.
The timings are then output to stdout, or visualized on a log-log scale
with matplotlib.
This alows the scaling of the algorith... | bsd-3-clause |
dssg/wikienergy | disaggregator/build/pandas/doc/sphinxext/numpydoc/tests/test_docscrape.py | 39 | 18326 | # -*- encoding:utf-8 -*-
from __future__ import division, absolute_import, print_function
import sys, textwrap
from numpydoc.docscrape import NumpyDocString, FunctionDoc, ClassDoc
from numpydoc.docscrape_sphinx import SphinxDocString, SphinxClassDoc
from nose.tools import *
if sys.version_info[0] >= 3:
sixu = la... | mit |
andrewnc/scikit-learn | sklearn/linear_model/tests/test_logistic.py | 59 | 35368 | import numpy as np
import scipy.sparse as sp
from scipy import linalg, optimize, sparse
import scipy
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... | bsd-3-clause |
glouppe/scikit-learn | sklearn/linear_model/tests/test_ridge.py | 19 | 26553 | import numpy as np
import scipy.sparse as sp
from scipy import linalg
from itertools import product
from sklearn.utils.testing import assert_true
from sklearn.utils.testing import assert_almost_equal
from sklearn.utils.testing import assert_array_almost_equal
from sklearn.utils.testing import assert_equal
from sklearn... | bsd-3-clause |
lucidfrontier45/scikit-learn | examples/linear_model/plot_logistic_path.py | 7 | 1170 | #!/usr/bin/env python
"""
=================================
Path with L1- Logistic Regression
=================================
Computes path on IRIS dataset.
"""
print __doc__
# Author: Alexandre Gramfort <alexandre.gramfort@inria.fr>
# License: BSD Style.
from datetime import datetime
import numpy as np
import py... | bsd-3-clause |
tauzero7/Motion4D | python/schwarzschildLightPulse.py | 1 | 1198 | """
Light pulse in Schwarzschild spacetime
"""
import numpy as np
import matplotlib.pyplot as plt
import m4d
obj = m4d.Object()
obj.setMetric("SchwarzschildCart")
obj.setSolver("GSL_RK4")
obj.setSolverParam("eps_a", 1e-8)
obj.setSolverParam("stepctrl", False)
boxSize = 20.0
obj.setSolverParam("lower_bb", -1e12, -... | gpl-3.0 |
brianholland/tiler | tiler.py | 1 | 7835 | """Run like python tiler.py myimage.jpg.
Tiler produces myimage.txt and myimage.txt.png."""
import sys, getopt
import matplotlib as mpl
#http://stackoverflow.com/questions/25561009/how-do-you-i-use-mandarin-charecters-in-matplotlib
#mpl.use("pgf") #I'm not there with Chinese yet.
import matplotlib.pyplot as plt, cStr... | mit |
ZENGXH/scikit-learn | examples/linear_model/plot_sgd_comparison.py | 167 | 1659 | """
==================================
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 |
cxxgtxy/tensorflow | tensorflow/contrib/learn/python/learn/learn_io/pandas_io_test.py | 111 | 7865 | # Copyright 2015 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 |
elingg/tensorflow | tensorflow/contrib/learn/python/learn/estimators/__init__.py | 6 | 11427 | # 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 |
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