repo_name stringlengths 7 90 | path stringlengths 5 191 | copies stringlengths 1 3 | size stringlengths 4 6 | content stringlengths 976 581k | license stringclasses 15
values |
|---|---|---|---|---|---|
mmadsen/sklearn-mmadsen | setup.py | 1 | 4354 | #!/usr/bin/env python
from ez_setup import use_setuptools
use_setuptools()
from setuptools import setup, find_packages, Command
from setuptools.command.develop import develop
from setuptools.command.install import install
import subprocess
import os
import re
# create a decorator that wraps the normal develop and
#... | apache-2.0 |
nomadcube/scikit-learn | examples/cluster/plot_segmentation_toy.py | 258 | 3336 | """
===========================================
Spectral clustering for image segmentation
===========================================
In this example, an image with connected circles is generated and
spectral clustering is used to separate the circles.
In these settings, the :ref:`spectral_clustering` approach solve... | bsd-3-clause |
ClimbsRocks/scikit-learn | examples/applications/face_recognition.py | 48 | 5691 | """
===================================================
Faces recognition example using eigenfaces and SVMs
===================================================
The dataset used in this example is a preprocessed excerpt of the
"Labeled Faces in the Wild", aka LFW_:
http://vis-www.cs.umass.edu/lfw/lfw-funneled.tgz (2... | bsd-3-clause |
hugobowne/scikit-learn | sklearn/linear_model/tests/test_randomized_l1.py | 57 | 4736 | # Authors: Alexandre Gramfort <alexandre.gramfort@inria.fr>
# License: BSD 3 clause
import numpy as np
from scipy import sparse
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_array_equal
from sklearn.utils.testing import assert_raises
from sklearn.linear_model.randomized_l1 i... | bsd-3-clause |
AIML/scikit-learn | examples/applications/plot_prediction_latency.py | 234 | 11277 | """
==================
Prediction Latency
==================
This is an example showing the prediction latency of various scikit-learn
estimators.
The goal is to measure the latency one can expect when doing predictions
either in bulk or atomic (i.e. one by one) mode.
The plots represent the distribution of the pred... | bsd-3-clause |
kenshay/ImageScripter | ProgramData/SystemFiles/Python/Lib/site-packages/scipy/signal/fir_filter_design.py | 3 | 41878 | # -*- coding: utf-8 -*-
"""Functions for FIR filter design."""
from __future__ import division, print_function, absolute_import
from math import ceil, log
import warnings
import numpy as np
from numpy.fft import irfft, fft, ifft
from scipy.special import sinc
from scipy.linalg import toeplitz, hankel, pinv
from scipy... | gpl-3.0 |
X-martin/robot_quant | test/factor_method_test.py | 1 | 1270 | import db_stocks_test as dbst
from datetime import datetime
from datetime import timedelta
import pandas as pd
def base(base_factor_name, stock_list, date, args):
time_list = [date]
dbst.get_base_factor_val(base_factor_name, time_list, stock_list)
def ma(base_factor_name, stock_list, date, args):
dt = t... | mit |
jmontoyam/mne-python | mne/parallel.py | 8 | 4977 | """Parallel util function
"""
# Author: Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
#
# License: Simplified BSD
from .externals.six import string_types
import logging
import os
from . import get_config
from .utils import logger, verbose, warn
from .fixes import _get_args
if 'MNE_FORCE_SERIAL' in os... | bsd-3-clause |
fzalkow/scikit-learn | sklearn/utils/validation.py | 67 | 24013 | """Utilities for input validation"""
# Authors: Olivier Grisel
# Gael Varoquaux
# Andreas Mueller
# Lars Buitinck
# Alexandre Gramfort
# Nicolas Tresegnie
# License: BSD 3 clause
import warnings
import numbers
import numpy as np
import scipy.sparse as sp
from ..externals i... | bsd-3-clause |
redmeros/Lean | Algorithm.Framework/Portfolio/MeanVarianceOptimizationPortfolioConstructionModel.py | 1 | 8847 | # QUANTCONNECT.COM - Democratizing Finance, Empowering Individuals.
# Lean Algorithmic Trading Engine v2.0. Copyright 2014 QuantConnect 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 Lice... | apache-2.0 |
scikit-learn-contrib/categorical-encoding | category_encoders/james_stein.py | 1 | 25065 | """James-Stein"""
import numpy as np
import pandas as pd
import scipy
from scipy import optimize
from sklearn.base import BaseEstimator
from category_encoders.ordinal import OrdinalEncoder
import category_encoders.utils as util
from sklearn.utils.random import check_random_state
__author__ = 'Jan Motl'
class JamesSt... | bsd-3-clause |
aminert/scikit-learn | examples/linear_model/plot_logistic.py | 312 | 1426 | #!/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, u... | bsd-3-clause |
kernc/scikit-learn | sklearn/cluster/tests/test_mean_shift.py | 150 | 3651 | """
Testing for mean shift clustering methods
"""
import numpy as np
import warnings
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_false
from sklearn.utils.testing import assert_true
from sklearn.utils.testing import assert_array_equal
from sklearn.utils.testing import asser... | bsd-3-clause |
RobertABT/heightmap | build/matplotlib/lib/matplotlib/backends/backend_gdk.py | 4 | 16652 | from __future__ import division, print_function
import math
import os
import sys
import warnings
def fn_name(): return sys._getframe(1).f_code.co_name
import gobject
import gtk; gdk = gtk.gdk
import pango
pygtk_version_required = (2,2,0)
if gtk.pygtk_version < pygtk_version_required:
raise ImportError ("PyGTK %d.... | mit |
russel1237/scikit-learn | examples/text/document_clustering.py | 230 | 8356 | """
=======================================
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 |
vermouthmjl/scikit-learn | examples/datasets/plot_random_dataset.py | 348 | 2254 | """
==============================================
Plot randomly generated classification dataset
==============================================
Plot several randomly generated 2D classification datasets.
This example illustrates the :func:`datasets.make_classification`
:func:`datasets.make_blobs` and :func:`datasets.... | bsd-3-clause |
sangwook236/sangwook-library | python/test/signal_processing/fft_util.py | 2 | 2939 | #!/usr/bin/env python
# REF [site] >> https://docs.scipy.org/doc/scipy/reference/fftpack.html
from scipy import fftpack
import numpy as np
import matplotlib.pyplot as plt
import math
# REF [site] >> https://kr.mathworks.com/help/matlab/ref/fft.html
def generate_toy_signal_1(time, noise=True, DC=True):
sig_amp, sig_... | gpl-2.0 |
ClimbsRocks/scikit-learn | sklearn/linear_model/least_angle.py | 15 | 57254 | """
Least Angle Regression algorithm. See the documentation on the
Generalized Linear Model for a complete discussion.
"""
from __future__ import print_function
# Author: Fabian Pedregosa <fabian.pedregosa@inria.fr>
# Alexandre Gramfort <alexandre.gramfort@inria.fr>
# Gael Varoquaux
#
# License: BSD 3 ... | bsd-3-clause |
cellular-nanoscience/pyotic | pyotc/plotting.py | 1 | 8016 | # -*- coding: utf-8 -*-
# """
# - Author: steve simmert
# - E-mail: steve.simmert@uni-tuebingen.de
# - Copyright: 2015
# """
import matplotlib.gridspec as gridspec
import matplotlib.pyplot as plt
col_dict = {'x': 'blue',
'y': 'green',
'z': 'orange',
'psdX': 'blue',
'psd... | apache-2.0 |
jiangwen84/libmesh | doc/statistics/libmesh_mailinglists.py | 1 | 8892 | #!/usr/bin/env python
import matplotlib.pyplot as plt
import numpy as np
from operator import add
# Import stuff for working with dates
from datetime import datetime
from matplotlib.dates import date2num, num2date
# Number of messages to libmesh-devel and libmesh-users over the life
# of the project. I cut and paste... | lgpl-2.1 |
joernhees/scikit-learn | examples/covariance/plot_outlier_detection.py | 36 | 5023 | """
==========================================
Outlier detection with several methods.
==========================================
When the amount of contamination is known, this example illustrates three
different ways of performing :ref:`outlier_detection`:
- based on a robust estimator of covariance, which is assum... | bsd-3-clause |
philrosenfield/padova_tracks | eep/define_eep.py | 1 | 11156 |
import os
from scipy.signal import argrelextrema
import matplotlib.pylab as plt
import numpy as np
from scipy.interpolate import splev, splprep
from .critical_point import CriticalPoint, Eep
from .. import utils
from ..config import *
from ..graphics.graphics import annotate_plot, hrd
def check_for_monotonic_incr... | mit |
openconnectome/ndreg | ndreg-old/ndreg-old.py | 1 | 63081 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
from __future__ import print_function
import numpy as np
import SimpleITK as sitk
import os
import math
import sys
import subprocess
import tempfile
import shutil
import requests
import matplotlib.pyplot as plt
from matplotlib.ticker import ScalarFormatter
from itertools im... | apache-2.0 |
beiko-lab/gengis | bin/Lib/site-packages/matplotlib/backends/qt4_editor/formlayout.py | 3 | 21226 | # -*- coding: utf-8 -*-
"""
formlayout
==========
Module creating Qt form dialogs/layouts to edit various type of parameters
formlayout License Agreement (MIT License)
------------------------------------------
Copyright (c) 2009 Pierre Raybaut
Permission is hereby granted, free of charge, to any person
obtaining ... | gpl-3.0 |
ContinuumIO/xdata-feat | feat/metrics.py | 1 | 2681 | import pandas as pd
def compute_quotient_metrics(filename,
index_col=0,
resample_period='1M',
shift=0,
quotient_metrics=[('Volume', 'max', 'median'),
('Clos... | mit |
cligs/pyzeta | scripts/preprocess.py | 1 | 2921 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
# file: preprocess.py
# author: #cf
# version: 0.3.0
"""
The "preprocess" module is the first step in the pyzeta pipeline.
This module deals with linguistic annotation of the texts.
Subsequent modules are: prepare, calculate and visualize.
"""
# ========================... | gpl-3.0 |
altairpearl/scikit-learn | examples/exercises/plot_iris_exercise.py | 323 | 1602 | """
================================
SVM Exercise
================================
A tutorial exercise for using different SVM kernels.
This exercise is used in the :ref:`using_kernels_tut` part of the
:ref:`supervised_learning_tut` section of the :ref:`stat_learn_tut_index`.
"""
print(__doc__)
import numpy as np
i... | bsd-3-clause |
plotly/plotly.py | packages/python/plotly/plotly/graph_objs/histogram2d/_colorbar.py | 1 | 73359 | from plotly.basedatatypes import BaseTraceHierarchyType as _BaseTraceHierarchyType
import copy as _copy
class ColorBar(_BaseTraceHierarchyType):
# class properties
# --------------------
_parent_path_str = "histogram2d"
_path_str = "histogram2d.colorbar"
_valid_props = {
"bgcolor",
... | mit |
deffi/zofoplot | src/tests/matplotlib_tests.py | 1 | 1291 | import os
from matplotlib import pyplot as plt
print("moo")
x1=[2,3,4,5]
y1=[4,9,16,25]
x2=[2.5, 3.4, 4.3]
y2=[9, 11, 11.5]
left, width = 0.1, 0.8
rect1 = [left, 0.7, width, 0.2]
rect2 = [left, 0.3, width, 0.4]
rect3 = [left, 0.1, width, 0.2]
fig = plt.figure(facecolor='white')
ax1 = fig.add_axes(rect1)
ax2 = ... | agpl-3.0 |
rmst/chi | examples/experimental/dqn_car.py | 1 | 5365 | """
"""
import chi
import tensorflow as tf
from chi import experiment, Experiment
from chi.rl.async_dqn import DQN
from chi.rl.util import print_env, Plotter, draw
from chi.rl.wrappers import DiscretizeActions
from chi.util import log_top, log_nvidia_smi
from matplotlib import pyplot as plt
import numpy as np
@exper... | mit |
zhenv5/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 |
statwonk/lifetimes | lifetimes/estimation.py | 1 | 14905 | from __future__ import print_function
from collections import OrderedDict
import numpy as np
from numpy import log, exp, logaddexp, asarray, any as npany, c_ as vconcat,\
isinf, isnan, ones_like
from pandas import DataFrame
from scipy import special
from scipy import misc
from lifetimes.utils impor... | mit |
ldirer/scikit-learn | sklearn/neural_network/tests/test_mlp.py | 20 | 22194 | """
Testing for Multi-layer Perceptron module (sklearn.neural_network)
"""
# Author: Issam H. Laradji
# License: BSD 3 clause
import sys
import warnings
import numpy as np
from numpy.testing import assert_almost_equal, assert_array_equal
from sklearn.datasets import load_digits, load_boston, load_iris
from sklearn... | bsd-3-clause |
hunter-cameron/Bioinformatics | python/checkm_select_bins.py | 1 | 1283 |
import argparse
import pandas
parser = argparse.ArgumentParser(description="Subsets a checkm tab-separated outfile to include only entries that have the specified completeness/contamination level")
parser.add_argument("-checkm", help="the checkm out file", required=True)
parser.add_argument("-completeness", help="com... | mit |
abhishekkrthakur/scikit-learn | sklearn/tree/export.py | 30 | 4529 | """
This module defines export functions for decision trees.
"""
# Authors: Gilles Louppe <g.louppe@gmail.com>
# Peter Prettenhofer <peter.prettenhofer@gmail.com>
# Brian Holt <bdholt1@gmail.com>
# Noel Dawe <noel@dawe.me>
# Satrajit Gosh <satrajit.ghosh@gmail.com>
# Licence: BSD 3 ... | bsd-3-clause |
chen0510566/MissionPlanner | Lib/site-packages/numpy/lib/recfunctions.py | 58 | 34495 | """
Collection of utilities to manipulate structured arrays.
Most of these functions were initially implemented by John Hunter for matplotlib.
They have been rewritten and extended for convenience.
"""
import sys
import itertools
import numpy as np
import numpy.ma as ma
from numpy import ndarray, recarray
from nump... | gpl-3.0 |
ranjinidas/Axelrod | docs/conf.py | 2 | 8630 | # -*- coding: utf-8 -*-
#
# Axelrod documentation build configuration file, created by
# sphinx-quickstart on Sat Mar 7 07:05:57 2015.
#
# 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.
#
# A... | mit |
jbogaardt/chainladder-python | chainladder/utils/utility_functions.py | 1 | 13539 | # This Source Code Form is subject to the terms of the Mozilla Public
# License, v. 2.0. If a copy of the MPL was not distributed with this
# file, You can obtain one at https://mozilla.org/MPL/2.0/.
import pandas as pd
import numpy as np
from chainladder.utils.cupy import cp
from chainladder.utils.sparse import sp
fro... | mit |
chanceraine/nupic | external/linux32/lib/python2.6/site-packages/matplotlib/pyplot.py | 69 | 77521 | import sys
import matplotlib
from matplotlib import _pylab_helpers, interactive
from matplotlib.cbook import dedent, silent_list, is_string_like, is_numlike
from matplotlib.figure import Figure, figaspect
from matplotlib.backend_bases import FigureCanvasBase
from matplotlib.image import imread as _imread
from matplotl... | agpl-3.0 |
yanlend/scikit-learn | sklearn/neighbors/tests/test_nearest_centroid.py | 305 | 4121 | """
Testing for the nearest centroid module.
"""
import numpy as np
from scipy import sparse as sp
from numpy.testing import assert_array_equal
from numpy.testing import assert_equal
from sklearn.neighbors import NearestCentroid
from sklearn import datasets
from sklearn.metrics.pairwise import pairwise_distances
# t... | bsd-3-clause |
Shathra/EloPredicting | random_forest.py | 1 | 1443 | import numpy as np
from sklearn.ensemble import RandomForestRegressor
validation_path = "validation/features/"
features_path = "training/features/"
features = []
features.append( "checkmate_move_done")
features.append( "is_draw")
features.append( "last_scores")
features.append( "match_len")
features.append( "mean")
f... | mit |
nhejazi/scikit-learn | sklearn/cluster/dbscan_.py | 9 | 12816 | # -*- coding: utf-8 -*-
"""
DBSCAN: Density-Based Spatial Clustering of Applications with Noise
"""
# Author: Robert Layton <robertlayton@gmail.com>
# Joel Nothman <joel.nothman@gmail.com>
# Lars Buitinck
#
# License: BSD 3 clause
import numpy as np
from scipy import sparse
from ..base import BaseEst... | bsd-3-clause |
syagev/kaggle_dsb | luna16/src/candidate_merging.py | 1 | 9496 | import csv
import glob
import os
import numpy as np
from collections import defaultdict
import candidates as ca
import image_read_write
from pandas import DataFrame as df
import pandas as pd
import candidates
import make_candidatelist_with_unet_candidates as mcwuc
import pipeline_candidates as pica
import evaluate_cand... | apache-2.0 |
ishank08/scikit-learn | examples/linear_model/plot_sgd_loss_functions.py | 86 | 1234 | """
==========================
SGD: convex loss functions
==========================
A plot that compares the various convex loss functions supported by
:class:`sklearn.linear_model.SGDClassifier` .
"""
print(__doc__)
import numpy as np
import matplotlib.pyplot as plt
def modified_huber_loss(y_true, y_pred):
z ... | bsd-3-clause |
kklmn/xrt | examples/withRaycing/01_SynchrotronSources/U32TaperedScan.py | 1 | 2904 | # -*- coding: utf-8 -*-
__author__ = "Roman Chernikov"
__date__ = "08 Mar 2016"
#import pickle
import numpy as np
#import matplotlib.pyplot as plt
import os, sys; sys.path.append(os.path.join('..', '..', '..')) # analysis:ignore
import xrt.backends.raycing as raycing
import xrt.backends.raycing.sources as rs
import ... | mit |
richardwolny/sms-tools | software/models_interface/sprModel_function.py | 18 | 3422 | # function to call the main analysis/synthesis functions in software/models/sprModel.py
import numpy as np
import matplotlib.pyplot as plt
import os, sys
from scipy.signal import get_window
sys.path.append(os.path.join(os.path.dirname(os.path.realpath(__file__)), '../models/'))
import utilFunctions as UF
import sprMod... | agpl-3.0 |
zinderud/ysa | sklearn/7irisproblem.py | 1 | 1648 | """
Quick Question:
If we want to design an algorithm to recognize iris species, what might the data be?
Remember: we need a 2D array of size [n_samples x n_features].
What would the n_samples refer to?
What might the n_features refer to?
Remember that there must be a fixed number of features for each samp... | apache-2.0 |
ryfeus/lambda-packs | LightGBM_sklearn_scipy_numpy/source/sklearn/gaussian_process/tests/test_kernels.py | 51 | 12799 | """Testing for kernels for Gaussian processes."""
# Author: Jan Hendrik Metzen <jhm@informatik.uni-bremen.de>
# License: BSD 3 clause
from sklearn.externals.funcsigs import signature
import numpy as np
from sklearn.gaussian_process.kernels import _approx_fprime
from sklearn.metrics.pairwise \
import PAIRWISE_K... | mit |
koverholt/bayes-fire | Example_Cases/Correlation_Fire_Size/Scripts/pymc_heat_flux_5.py | 1 | 1660 | #!/usr/bin/env python
"""
PyMC Radiation Heat Flux Example Series
Example 5: PyMC simulation using maximum a posteriori estimate.
In this example, we use the point source radiation model
along with the maximum a posteriori (MAP) method to start
with better initial values. Also, information is calculated
for the AIC, ... | bsd-3-clause |
thientu/scikit-learn | examples/exercises/plot_cv_diabetes.py | 231 | 2527 | """
===============================================
Cross-validation on diabetes Dataset Exercise
===============================================
A tutorial exercise which uses cross-validation with linear models.
This exercise is used in the :ref:`cv_estimators_tut` part of the
:ref:`model_selection_tut` section of ... | bsd-3-clause |
kjung/scikit-learn | sklearn/linear_model/tests/test_sgd.py | 8 | 44274 | import pickle
import unittest
import numpy as np
import scipy.sparse as sp
from sklearn.utils.testing import assert_array_equal
from sklearn.utils.testing import assert_almost_equal
from sklearn.utils.testing import assert_array_almost_equal
from sklearn.utils.testing import assert_greater
from sklearn.utils.testing ... | bsd-3-clause |
NumCosmo/NumCosmo.github.io | examples/example_fit_snia.py | 1 | 2225 | #!/usr/bin/python2
try:
import gi
gi.require_version('NumCosmo', '1.0')
gi.require_version('NumCosmoMath', '1.0')
except:
pass
from math import *
import matplotlib.pyplot as plt
from gi.repository import GObject
from gi.repository import NumCosmo as Nc
from gi.repository import NumCosmoMath as Ncm
#
# Initi... | apache-2.0 |
yongfuyang/vnpy | vn.trader/ctaAlgo/ctaBacktesting.py | 1 | 38363 | # encoding: UTF-8
'''
本文件中包含的是CTA模块的回测引擎，回测引擎的API和CTA引擎一致，
可以使用和实盘相同的代码进行回测。
'''
from __future__ import division
from datetime import datetime, timedelta
from collections import OrderedDict
from itertools import product
import multiprocessing
import pymongo
from ctaBase import *
from ctaSetting import *
import csv
... | mit |
pravsripad/jumeg | jumeg/epocher/jumeg_epocher_plot.py | 2 | 8315 | # -*- coding: utf-8 -*-
"""
Created on 08.06.2018
@author: fboers
"""
import os,os.path,logging
import numpy as np
import matplotlib.pyplot as pl
from matplotlib.backends.backend_pdf import PdfPages
import mne
from jumeg.base.jumeg_base import JuMEG_Base_IO
logger = logging.getLogger('jumeg')
__version__="2019.05... | bsd-3-clause |
tspus/python-matchingPursuit | src/dictionary.py | 1 | 9861 | #!/usr/bin/env python
#-*- coding: utf-8 -*-
'''
# This file is part of Matching Pursuit Python program (python-MP).
#
# python-MP 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 ... | gpl-3.0 |
gnieboer/tensorflow | tensorflow/contrib/learn/python/learn/tests/dataframe/tensorflow_dataframe_test.py | 51 | 12969 | # 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 |
fspaolo/scikit-learn | examples/decomposition/plot_pca_3d.py | 8 | 2410 | #!/usr/bin/python
# -*- coding: utf-8 -*-
"""
=========================================================
Principal components analysis (PCA)
=========================================================
These figures aid in illustrating how a point cloud
can be very flat in one direction--which is where PCA
comes in to ch... | bsd-3-clause |
zitouni/gnuradio-3.6.1 | gnuradio-core/src/examples/pfb/synth_filter.py | 17 | 2270 | #!/usr/bin/env python
#
# Copyright 2010 Free Software Foundation, Inc.
#
# This file is part of GNU Radio
#
# GNU Radio 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, or (at your option)
# ... | gpl-3.0 |
nwillemse/nctrader | nctrader/price_handler/yahoo_daily_csv_bar.py | 1 | 4524 | import os
import pandas as pd
from ..price_parser import PriceParser
from .base import AbstractBarPriceHandler
from ..event import BarEvent
class YahooDailyCsvBarPriceHandler(AbstractBarPriceHandler):
"""
YahooDailyBarPriceHandler is designed to read CSV files of
Yahoo Finance daily Open-High-Low-Close-... | mit |
PyAbel/PyAbel | doc/transform_methods/comparison/fig_gaussian/gaussian.py | 1 | 2409 | import numpy as np
import matplotlib.pyplot as plt
import abel
transforms = [
("basex", abel.basex.basex_transform),
("direct", abel.direct.direct_transform),
("hansenlaw", abel.hansenlaw.hansenlaw_transform),
("onion_bordas", abel.onion_bordas.onion_bordas_transform),
("onion_peeling", a... | mit |
mhoffman/catmap | docs/source/conf.py | 6 | 11807 | # -*- coding: utf-8 -*-
#
# CatMAP documentation build configuration file, created by
# sphinx-quickstart on Tue Nov 25 08:51:50 2014.
#
# 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.
#
# Al... | gpl-3.0 |
joyeshmishra/spark-tk | regression-tests/sparktkregtests/testcases/frames/bin_col_test.py | 12 | 6666 | # 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 |
rhyolight/nupic | external/linux32/lib/python2.6/site-packages/matplotlib/backends/backend_mixed.py | 70 | 3776 | from matplotlib._image import frombuffer
from matplotlib.backends.backend_agg import RendererAgg
class MixedModeRenderer(object):
"""
A helper class to implement a renderer that switches between
vector and raster drawing. An example may be a PDF writer, where
most things are drawn with PDF vector comm... | agpl-3.0 |
GuessWhoSamFoo/pandas | pandas/util/_decorators.py | 1 | 12597 | from functools import wraps
import inspect
from textwrap import dedent
import warnings
from pandas._libs.properties import cache_readonly # noqa
from pandas.compat import PY2, callable, signature
def deprecate(name, alternative, version, alt_name=None,
klass=None, stacklevel=2, msg=None):
"""Retur... | bsd-3-clause |
mne-tools/mne-tools.github.io | 0.16/_downloads/plot_mixed_source_space_inverse.py | 5 | 5418 | """
=======================================================================
Compute MNE inverse solution on evoked data in a mixed source space
=======================================================================
Create a mixed source space and compute MNE inverse solution on evoked dataset.
"""
# Author: Annalisa ... | bsd-3-clause |
andrewnc/scikit-learn | sklearn/cross_decomposition/cca_.py | 209 | 3150 | from .pls_ import _PLS
__all__ = ['CCA']
class CCA(_PLS):
"""CCA Canonical Correlation Analysis.
CCA inherits from PLS with mode="B" and deflation_mode="canonical".
Read more in the :ref:`User Guide <cross_decomposition>`.
Parameters
----------
n_components : int, (default 2).
numb... | bsd-3-clause |
alimanfoo/anhima | anhima/util.py | 1 | 12073 | # -*- coding: utf-8 -*-
"""
Miscellaneous utilities.
"""
from __future__ import division, print_function, absolute_import
from anhima.compat import range
# third party dependencies
import numpy as np
import pandas
def block_take2d(dataset, row_indices, col_indices=None, block_size=None):
"""Select rows and o... | mit |
webmasterraj/FogOrNot | flask/lib/python2.7/site-packages/pandas/computation/scope.py | 24 | 9002 | """Module for scope operations
"""
import sys
import struct
import inspect
import datetime
import itertools
import pprint
import numpy as np
import pandas as pd
from pandas.compat import DeepChainMap, map, StringIO
from pandas.core.base import StringMixin
import pandas.computation as compu
def _ensure_scope(level,... | gpl-2.0 |
mpharrigan/mixtape | msmbuilder/hmm/discrete_approx.py | 12 | 6593 | """Discrete approximations to continuous distributions"""
# Author: Robert McGibbon <rmcgibbo@gmail.com>
# Contributors:
# Copyright (c) 2014, Stanford University
# All rights reserved.
#-----------------------------------------------------------------------------
# Imports
#-------------------------------------------... | lgpl-2.1 |
wesley1001/formhub | utils/export_tools.py | 4 | 30828 | import os
import re
import csv
import json
from openpyxl.workbook import Workbook
from openpyxl.shared.date_time import SharedDate
from bson import json_util
from datetime import datetime
from django.conf import settings
from pyxform.section import Section, RepeatingSection
from pyxform.question import Question
from d... | bsd-2-clause |
liangz0707/scikit-learn | examples/linear_model/plot_sgd_weighted_samples.py | 344 | 1458 | """
=====================
SGD: Weighted samples
=====================
Plot decision function of a weighted dataset, where the size of points
is proportional to its weight.
"""
print(__doc__)
import numpy as np
import matplotlib.pyplot as plt
from sklearn import linear_model
# we create 20 points
np.random.seed(0)
X ... | bsd-3-clause |
bdo311/chirpseq-analysis | runRetroviral.py | 1 | 6613 | # runRetroviral.py
# 3/27/2016
# Makes histograms and count tables for retroviral data; requires either a
# trimmed FASTQ file or a STAR-generated BAM file as input
import sys
import os
import argparse
import pandas as pd
import numpy as np
import collections
import csv
csv.register_dialect("textdialect", ... | apache-2.0 |
akrherz/idep | scripts/tillage_timing/dynamic_tillage_mod_rot.py | 2 | 5319 | """Yikes, inspect WB file, do dynamic tillage dates for 2018."""
import sys
import datetime
import pandas as pd
from pandas.io.sql import read_sql
from pyiem.util import get_dbconn
from pyiem.dep import read_wb
from tqdm import tqdm
APR15 = pd.Timestamp(year=2018, month=4, day=15)
MAY30 = pd.Timestamp(year=2018, mont... | mit |
tbenthompson/tectosaur | tectosaur/qd/phase_space_parallel.py | 1 | 1876 | import numpy as np
import subprocess
import os
import uuid
import cloudpickle
import multiprocessing
import matplotlib.pyplot as plt
from sklearn.preprocessing import MinMaxScaler
LINE_WIDTH = 0.25
SIAY = 60 * 60 * 24 * 365.25
USE_N_CORES = 30
# Read Ben's data for Cascadia
pts, _tris, t, slip_all, state_all = np.lo... | mit |
VenkateshBejjenki/Machine_Learning_Specialization | smartcab/visuals.py | 17 | 7709 | ###########################################
# Suppress matplotlib user warnings
# Necessary for newer version of matplotlib
import warnings
warnings.filterwarnings("ignore", category = UserWarning, module = "matplotlib")
###########################################
#
# Display inline matplotlib plots with IPython
from I... | gpl-3.0 |
pompiduskus/scikit-learn | sklearn/manifold/tests/test_spectral_embedding.py | 216 | 8091 | from nose.tools import assert_true
from nose.tools import assert_equal
from scipy.sparse import csr_matrix
from scipy.sparse import csc_matrix
import numpy as np
from numpy.testing import assert_array_almost_equal, assert_array_equal
from nose.tools import assert_raises
from nose.plugins.skip import SkipTest
from sk... | bsd-3-clause |
harmslab/pytc-gui | pytc_gui/widgets/plot_box.py | 2 | 2624 | __description__ = \
"""
Class for generating main plots.
"""
__author__ = "Hiranmayi Duvvurii"
__date__ = "2017-06-01"
from PyQt5 import QtWidgets as QW
from PyQt5 import QtCore as QC
from matplotlib.backends.backend_qt5agg import FigureCanvasQTAgg as FigureCanvas
from matplotlib.figure import Figure
import matplo... | unlicense |
hlin117/statsmodels | statsmodels/sandbox/examples/example_gam.py | 33 | 2343 | '''original example for checking how far GAM works
Note: uncomment plt.show() to display graphs
'''
example = 2 # 1,2 or 3
import numpy as np
import numpy.random as R
import matplotlib.pyplot as plt
from statsmodels.sandbox.gam import AdditiveModel
from statsmodels.sandbox.gam import Model as GAM #?
from statsmode... | bsd-3-clause |
bala4901/odoo | addons/resource/faces/timescale.py | 170 | 3902 | ############################################################################
# Copyright (C) 2005 by Reithinger GmbH
# mreithinger@web.de
#
# This file is part of faces.
#
# faces is free software; you can redistribute it and/or modify
# ... | agpl-3.0 |
Minhua722/NMF | egs/ar/local/ar_nmf_face_recog.py | 1 | 3323 | #!/usr/bin/env python
import cv2
import numpy as np
import argparse
import math
import pickle
from sklearn.decomposition import PCA
from nmf_support import *
import sys, os
if __name__ == '__main__':
#------------------------------------------------------
# Args parser
#--------------------------------------... | apache-2.0 |
blab/stability | augur/src/analyze_validation.py | 2 | 2524 | import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import cPickle
plt.ion()
val_data = []
res='12y'
grid = [0.1, 0.3, 1.0,3.0, 10.0]
for flu in ['H3N2', 'H1N1pdm', 'Vic', 'Yam']:
for minaa in [0]: #,1,'epi']:
for hi, lam_HI in enumerate(grid):
for training in ['measuremen... | agpl-3.0 |
anderspitman/scikit-bio | skbio/stats/distance/tests/test_permanova.py | 8 | 4865 | # ----------------------------------------------------------------------------
# Copyright (c) 2013--, scikit-bio 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 |
initNirvana/Easyphotos | env/lib/python3.4/site-packages/IPython/extensions/sympyprinting.py | 12 | 5609 | """
A print function that pretty prints sympy Basic objects.
:moduleauthor: Brian Granger
Usage
=====
Once the extension is loaded, Sympy Basic objects are automatically
pretty-printed.
As of SymPy 0.7.2, maintenance of this extension has moved to SymPy under
sympy.interactive.ipythonprinting, any modifications to ... | mit |
mvdroest/RTLSDR-Scanner | src/file.py | 1 | 20503 | #
# rtlsdr_scan
#
# http://eartoearoak.com/software/rtlsdr-scanner
#
# Copyright 2012 - 2015 Al Brown
#
# A frequency scanning GUI for the OsmoSDR rtl-sdr library at
# http://sdr.osmocom.org/trac/wiki/rtl-sdr
#
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU Gene... | gpl-3.0 |
JaviMerino/trappy | trappy/plotter/StaticPlot.py | 1 | 9823 | # Copyright 2016-2016 ARM Limited
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in w... | apache-2.0 |
wlamond/scikit-learn | examples/covariance/plot_mahalanobis_distances.py | 348 | 6232 | r"""
================================================================
Robust covariance estimation and Mahalanobis distances relevance
================================================================
An example to show covariance estimation with the Mahalanobis
distances on Gaussian distributed data.
For Gaussian dis... | bsd-3-clause |
theislab/scanpy | scanpy/plotting/_preprocessing.py | 1 | 4222 | from typing import Optional, Union
import numpy as np
import pandas as pd
from matplotlib import pyplot as pl
from matplotlib import rcParams
from anndata import AnnData
from . import _utils
# --------------------------------------------------------------------------------
# Plot result of preprocessing functions
# -... | bsd-3-clause |
qrqiuren/sms-tools | lectures/03-Fourier-properties/plots-code/anal-synth.py | 24 | 1154 | 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
from scipy.io.wavfile import read
from scipy.fftpack import fft, ifft
import math
(fs, x) =... | agpl-3.0 |
gpersistence/tstop | scripts/plots/plot_multi_persistence.py | 1 | 5663 | #TSTOP
#
#This program is free software: you can redistribute it and/or modify
#it under the terms of the GNU General Public License as published by
#the Free Software Foundation, either version 3 of the License, or
#(at your option) any later version.
#
#This program is distributed in the hope that it will be useful,
... | gpl-3.0 |
JoeJimFlood/RugbyPredictifier | 2017SuperRugby/round.py | 1 | 6839 | import os
os.chdir(os.path.dirname(__file__))
import pandas as pd
import matchup
import xlsxwriter
import xlrd
import sys
import time
import collections
import matplotlib.pyplot as plt
def rgb2hex(r, g, b):
r_hex = hex(r)[-2:].replace('x', '0')
g_hex = hex(g)[-2:].replace('x', '0')
b_hex = hex(b)[-2:].rep... | mit |
mugizico/scikit-learn | sklearn/tests/test_grid_search.py | 68 | 28778 | """
Testing for grid search module (sklearn.grid_search)
"""
from collections import Iterable, Sized
from sklearn.externals.six.moves import cStringIO as StringIO
from sklearn.externals.six.moves import xrange
from itertools import chain, product
import pickle
import sys
import numpy as np
import scipy.sparse as sp
... | bsd-3-clause |
plin1112/pysimm | Examples/10_mof_swelling/prepare_mof.py | 3 | 2742 | import requests
import re
from StringIO import StringIO
from pysimm import system, lmps, forcefield
try:
import pandas as pd
except ImportError:
pd = None
# Check whether the pandas installed or not
if not pd:
print('The script requires pandas to be installed. Exiting...')
exit(1)
# Requesting the XY... | mit |
alistairlow/tensorflow | tensorflow/examples/get_started/regression/test.py | 41 | 4037 | # 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 |
jwiggins/scikit-image | doc/examples/edges/plot_circular_elliptical_hough_transform.py | 6 | 4826 | """
========================================
Circular and Elliptical Hough Transforms
========================================
The Hough transform in its simplest form is a `method to detect
straight lines <http://en.wikipedia.org/wiki/Hough_transform>`__
but it can also be used to detect circles or ellipses.
The algo... | bsd-3-clause |
probml/pyprobml | scripts/kmeansYeastDemo.py | 1 | 1963 | from scipy.io import loadmat
from sklearn.cluster import KMeans
import matplotlib.pyplot as plt
import pyprobml_utils as pml
from matplotlib import cm
from matplotlib.colors import ListedColormap,LinearSegmentedColormap
data = loadmat('/pyprobml/data/yeastData310.mat') # dictionary containing 'X', 'genes', 'times'
X ... | mit |
AWI-Paleodyn/Python_Helpers | plot_tools/seasonal_amplitude.py | 2 | 7150 | import numpy
import matplotlib.pyplot
import scipy.io.netcdf
from . import _find_nearest_idx
from mpl_toolkits.basemap import shiftgrid, addcyclic
def _decorate_x_axes_for_ymonmean(ax):
# Some decoration stuff
ax.set_xlabel("Month")
ax.set_xlim(-1, 12)
ax.xaxis.set_ticks(numpy.arange(12))
ax.xaxis... | gpl-2.0 |
crichardson17/starburst_atlas | Low_resolution_sims/DustFree_LowRes/Padova_cont/padova_cont_2/UV2.py | 33 | 7365 | 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 |
vybstat/scikit-learn | examples/neighbors/plot_digits_kde_sampling.py | 251 | 2022 | """
=========================
Kernel Density Estimation
=========================
This example shows how kernel density estimation (KDE), a powerful
non-parametric density estimation technique, can be used to learn
a generative model for a dataset. With this generative model in place,
new samples can be drawn. These... | bsd-3-clause |
jimsrc/seatos | etc/n_CR/for.paper/src/vmc_lo.py | 1 | 3460 | #!/usr/bin/env ipython
from pylab import *
#from load_data import sh, mc, cr
import func_data as fd
import share.funcs as ff
import matplotlib.patches as patches
import matplotlib.transforms as transforms
#++++++++++++++++++++++++++++++++++++++++++++++++++++
dir_inp_sh = '../../../../sheaths/ascii/MCflag2/wShiftC... | mit |
henridwyer/scikit-learn | sklearn/tree/tree.py | 12 | 34690 | """
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 |
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