text stringlengths 0 1.25M | meta stringlengths 47 1.89k |
|---|---|
def analysis():
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from scipy import stats
file = input('Enter the file name ')
df = pd.read_csv(file, index_col=0)
print('\n')
print('\033[1m' + 'Summary Statistic' + '\033[0m')
print('\n')
print('There are tot... | {"hexsha": "6bcca5b1ec4e9d40957ddcfb60033a7489020361", "size": 2099, "ext": "py", "lang": "Python", "max_stars_repo_path": "build/lib/analytica/__init__.py", "max_stars_repo_name": "d0r1h/Analytica", "max_stars_repo_head_hexsha": "36afee1e2574bd1d3451ebe539b0c7283c3a27cf", "max_stars_repo_licenses": ["MIT"], "max_stars... |
from kernel_exp_family.examples.tools import pdf_grid, visualise_array
from kernel_hmc.tools.mcmc_convergence import autocorr
import matplotlib.pyplot as plt
import numpy as np
def visualise_trajectory(Qs, acc_probs, log_pdf_q, D, log_pdf=None):
assert Qs.ndim == 2
plot_density = log_pdf is not None and D... | {"hexsha": "971ce94b3676385f3eac9cd769b4cfdba5470a92", "size": 3334, "ext": "py", "lang": "Python", "max_stars_repo_path": "kernel_hmc/examples/plotting.py", "max_stars_repo_name": "karlnapf/kernel_hmc", "max_stars_repo_head_hexsha": "8ab93ae0470cc5916d5349b40bae7f91075bc385", "max_stars_repo_licenses": ["BSD-3-Clause"... |
\newcommand{\symb}[2]{\makebox[6em][l]{#1} #2}% used to generate the list of symbols
\chapter{List of Symbols}
\symb{$i$}{Unit imaginary number; or, an index of numbers}\\
\symb{$j$, $ j' $}{Fine-structure angular momentum quantum number of individual atoms in a ground state or excited state (with prime), respectivel... | {"hexsha": "dc453eed5c728b9c809977a8f02812b8a484845a", "size": 5311, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "inputs/listofsymbols.tex", "max_stars_repo_name": "i2000s/PhD_Thesis", "max_stars_repo_head_hexsha": "a9bc6bc4213896c70c90cbb3d9b533782d428761", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
import js
from RobotRaconteur.Client import *
import importlib_resources
import traceback
import numpy as np
import base64
from pyri.webui_browser import util
class NewCameraIntrinsicCalibrationDialog:
def __init__(self, new_name, core, device_manager):
self.vue = None
self.core = core
sel... | {"hexsha": "b6b312416ef61bbe7db92b1e7a13a1a89f615c01", "size": 6386, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/pyri/vision_browser/dialogs/new_calibrate_intrinsic_dialog.py", "max_stars_repo_name": "pyri-project/pyri-vision-browser", "max_stars_repo_head_hexsha": "7cd501e4ec0633be16f5f6c62146a6e006163d... |
# ---------------------------------------------------------------------------- #
#
# hdgSolveElas.jl
#
# Solve convection-diffusion equations (n-dimensional)
#
# λυτέος
# Fall 2017
#
# Max Opgenoord
#
# ---------------------------------------------------------------------------- #
"""
hdgSolve( master::M... | {"hexsha": "517340716ad46db21d74112a0755711e4667029c", "size": 8975, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/solve/hdgSolveCDR.jl", "max_stars_repo_name": "mopg/luteos.jl", "max_stars_repo_head_hexsha": "05b72e7ab6c905f55a768ae943ac3173dcf980ae", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
import numpy as np
import pandas as pd
from pathlib import Path
import os
from lightgbm import LGBMRegressor
from sklearn.metrics import mean_squared_error
os.makedirs('../output/ensemble')
pref = '10'
versions = ['051', '052', '074', '076', '078', '079', '080', '081', '082']
for i, version in enumerate(versions):
... | {"hexsha": "3c6e9957df40e6716bdbe64d7ffaa2dd12ac2cb8", "size": 3002, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/lgbm_stacking.py", "max_stars_repo_name": "ishikei14k/atma11_1st_solution", "max_stars_repo_head_hexsha": "91d29eb83f3e5470f82470f0434ad0fc75a90c61", "max_stars_repo_licenses": ["MIT"], "max_s... |
"""Training a face recognizer with TensorFlow based on the FaceNet paper
FaceNet: A Unified Embedding for Face Recognition and Clustering: http://arxiv.org/abs/1503.03832
"""
# MIT License
#
# Copyright (c) 2016 David Sandberg
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this ... | {"hexsha": "02a33d354ac1d6d168840d21af415e88d486e414", "size": 18072, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/facenet_train_classifier.py", "max_stars_repo_name": "KittenCN/pyFaceNet", "max_stars_repo_head_hexsha": "0804d06a3533a83ff865a3c4343cfca2a5cbe063", "max_stars_repo_licenses": ["MIT"], "max_s... |
[STATEMENT]
lemma monad_fail_alt_writerT [locale_witness]:
assumes "monad_fail_alt return bind fail alt"
shows "monad_fail_alt return_writer bind_writer fail_writer alt_writer"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. monad_fail_alt local.return_writer local.bind_writer local.fail_writer local.alt_writer
[... | {"llama_tokens": 771, "file": "Monomorphic_Monad_Monomorphic_Monad", "length": 11} |
##########################################
# Some uncertain datasets
##########################################
UV = UncertainValueDataset(example_uvals)
##########################################
# Apply functions to datasets `n` times
##########################################
n = 3
@test resample(median, UV, n) i... | {"hexsha": "d7b0af7064262780cf70e8628828913cb3172929", "size": 411, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/resampling/uncertain_datasets/test_resampling_datasets_uncertainvaluedataset_apply_funcs.jl", "max_stars_repo_name": "JuliaTagBot/UncertainData.jl", "max_stars_repo_head_hexsha": "4d9dc513b97f0... |
struct CountingFunction{F} <: AbstractFunction
counter::Base.RefValue{Int}
f::F
end
getdim(f::CountingFunction) = getdim(f.f)
CountingFunction(f::Function) = CountingFunction(Ref(0), f)
function (f::CountingFunction)(x)
f.counter[] += 1
return f.f(x)
end
| {"hexsha": "e0dba427bf5999c5acc322fb674b12e8fb27c3ea", "size": 271, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/functions/counting_function.jl", "max_stars_repo_name": "tmigot/Nonconvex.jl", "max_stars_repo_head_hexsha": "688f699ada98844427b9b701422638d533aed313", "max_stars_repo_licenses": ["MIT"], "max_... |
import numpy as np
import shapely #may need submodules
from shapely.geometry import Point, Polygon
class Item:
"""
Parent class for all items
"""
def __init__(self, polygon):
self.polygon = polygon #May need only dimensions
self.type = None
self.subtype = None
self.pos = self.get_position()
def get_posi... | {"hexsha": "bb03a9903edd9f0a0e03bcb26931c9b042d56938", "size": 3318, "ext": "py", "lang": "Python", "max_stars_repo_path": "swarm_tasks/utils/item.py", "max_stars_repo_name": "rmvanarse/swarm_tasks", "max_stars_repo_head_hexsha": "3335297ba8fcdbff756ae519002bcce919d54a84", "max_stars_repo_licenses": ["MIT"], "max_stars... |
\section{201403-4}
\input{problem/1/201403-4-p.tex} | {"hexsha": "2da76ac9a25ebc32d45cc653aa8b234a13adf0a4", "size": 52, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "problem/1/201403-4.tex", "max_stars_repo_name": "xqy2003/CSP-Project", "max_stars_repo_head_hexsha": "26ef348463c1f948c7c7fb565edf900f7c041560", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_c... |
from stock.marketdata import *
import logging
import logging.config
from stock.globalvar import *
import numpy as np
logging.config.fileConfig(LOGCONF)
logger = logging.getLogger(__name__)
class CoVar:
def __init__(self, marketdata):
self.marketdata = marketdata
def check(self, exsymbols):
ba... | {"hexsha": "1325cbb68b662e431180f8c15ad093feffc7f0cd", "size": 2530, "ext": "py", "lang": "Python", "max_stars_repo_path": "stock/quant/covar.py", "max_stars_repo_name": "shenzhongqiang/cnstock_py", "max_stars_repo_head_hexsha": "2bb557657a646acb9d20d3ce78e15cf68390f8ea", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
import argparse
import logging
logger = logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)
logger.setLevel(logging.INFO)
import os
import time
import chainer
import chainercv
import chainer.functions as F
import cv2
import numpy as np
from predict import prepare_setting, restore_args
from food101_da... | {"hexsha": "b6be3a3db79dfcdfe6c40f00846b92427bf5ffeb", "size": 3366, "ext": "py", "lang": "Python", "max_stars_repo_path": "demo.py", "max_stars_repo_name": "terasakisatoshi/chainer-food-101-revised", "max_stars_repo_head_hexsha": "3d84f596f22b6a95e33fe196ed5ebba3d3c573a2", "max_stars_repo_licenses": ["MIT"], "max_star... |
from EQTransformer.core.EqT_utils import f1, SeqSelfAttention, FeedForward, LayerNormalization
from EQTransformer.core.mseed_predictor import (
mseed_predictor,
_mseed2nparry,
PreLoadGeneratorTest,
_picker,
_get_snr,
_output_writter_prediction,
_plotter_prediction,
_resampling,
)
impor... | {"hexsha": "552b73840d960daa9168491530323b78f5ca37da", "size": 18226, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/compare.py", "max_stars_repo_name": "Cuda-Chen/EQTransformer-onnx-convertor", "max_stars_repo_head_hexsha": "fe5a72c785da69f8282325c96c01ea79a89f508a", "max_stars_repo_licenses": ["MIT"], "... |
# -*- coding: utf-8 -*-
"""
Created on Sat May 25 23:47:20 2019
@author: YQ
"""
import tensorflow as tf
import tensorflow_probability as tfp
import numpy as np
from rnn import HyperLSTMCell
from rnn import LayerNormLSTMCell as LSTMCell
ohc = tfp.distributions.OneHotCategorical
seq2seq = tf.contrib.seq2seq
w_init = ... | {"hexsha": "cf365419b44616ad0741670856698e8ff0a797c9", "size": 18366, "ext": "py", "lang": "Python", "max_stars_repo_path": "model.py", "max_stars_repo_name": "y33-j3T/DeepMusicvStyle", "max_stars_repo_head_hexsha": "f1a6b149d8412ad480952e6820708b2b6eaf4b96", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_coun... |
!
! CalculiX - A 3-dimensional finite element program
! Copyright (C) 1998-2019 Guido Dhondt
!
! 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(version 2);
!
!
! ... | {"hexsha": "25e9665c4a206f07157d9ec6500c3cb1a28baec4", "size": 1516, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "ccx_prool/CalculiX/ccx_2.16/src/hcrit.f", "max_stars_repo_name": "alleindrach/calculix-desktop", "max_stars_repo_head_hexsha": "2cb2c434b536eb668ff88bdf82538d22f4f0f711", "max_stars_repo_licenses"... |
subroutine onepath(phpad, index, nleg, deg, iorder,
& cxc, rs, vint, xmu, edge, xkf, rnrmav, gamach,
& versn, ipot, rat, iz,
& ipol, evec, elpty, xivec,
& innnn, ijson, ivrbse, ri, beta, eta,
& ne1,col1,col2,col3,col4,col5,col6,col7)
implicit double precision (a... | {"hexsha": "3f412b0dcfd4bdb8758c98e746770c6ffdc497f8", "size": 24222, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "src/GENFMT/onepath.f", "max_stars_repo_name": "bruceravel/feff85exafs", "max_stars_repo_head_hexsha": "9698ce3703a73def4c1a965f276708d689ea5acb", "max_stars_repo_licenses": ["BSD-2-Clause"], "max... |
# Function to pull python functions
# included in spotify.ipynb
import time
import os
import dotenv
import requests
import pandas as pd
import numpy as np
def pull_albums(artist_id):
global requests
global url
album_names_dates = {}
# for duplicates
albs_added = []
to_remove = []
track_info = []
repeat_de... | {"hexsha": "706a92bd3f865d0a0df49c7087496d194f33fc17", "size": 1700, "ext": "py", "lang": "Python", "max_stars_repo_path": "spotify_proj/pull_albums.py", "max_stars_repo_name": "DrakeWagner/projects", "max_stars_repo_head_hexsha": "998ef5ef0320db5167fb1bfcf46085b3a18abc42", "max_stars_repo_licenses": ["MIT"], "max_star... |
#include <boost/callable_traits.hpp>
#include <functional>
#include <iostream>
#include <type_traits>
template < //
typename Derived, //
bool IsConst, //
bool IsNoexcept, //
typename Return, //
typename... Args //
>
class function_ref_impl
{
private:
using erased_fn_type =... | {"hexsha": "62a714acb4aa6888253bfff24e957763e5468529", "size": 10744, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "function_ref.cpp", "max_stars_repo_name": "SuperV1234/Experiments", "max_stars_repo_head_hexsha": "572c94d1afb367c241645b479019f6cb3883f98f", "max_stars_repo_licenses": ["AFL-3.0"], "max_stars_coun... |
import tensorflow as tf
import numpy as np
from RelationNetwork import RN
from prepare import SClevrDataset,ClevrDataset
from utils import Config, Config_SClevr
import argparse
import sys
def str2bool(s):
if s == 'true':
return True
else:
return False
class Trainer(object):
def __init__(self, config):
"""
... | {"hexsha": "b86fed276e3f02d8212cc16bc2fe889a6fbab68c", "size": 2093, "ext": "py", "lang": "Python", "max_stars_repo_path": "Trainer.py", "max_stars_repo_name": "obitto/relation-network", "max_stars_repo_head_hexsha": "2cbea587c9d43d6e02dba8ddd79e9ae18eca5356", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null... |
import os
import numpy as np
from astropy.table import Table
# sky_subd_sciences[ap] = [waves,diff,bool_mask]
from zestipy.data_structures import waveform
from zestipy.plotting_tools import summary_plot
from zestipy.sncalc import sncalc
from zestipy.z_est import z_est
def fit_redshifts_wrapper(input_dict):
retu... | {"hexsha": "ce434e76b2f8a52f0ea13bc56228a83cd1bd8e09", "size": 4361, "ext": "py", "lang": "Python", "max_stars_repo_path": "pyM2FS/fit_redshifts.py", "max_stars_repo_name": "akremin/M2FSreduce", "max_stars_repo_head_hexsha": "42092f18aa1e5d7ad6f6528a395ee93e89165b30", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_s... |
#!/usr/bin/env python
import os
import csv
import linecache
import numpy as np
from CP2K_kit.tools import log_info
from CP2K_kit.tools import traj_info
from CP2K_kit.tools import data_op
from CP2K_kit.analyze import check_analyze
from CP2K_kit.lib import rmsd_mod
from CP2K_kit.lib import statistic_mod
def rmsd(atoms_... | {"hexsha": "5feaf4e2377da098ac39b3f0dcb206932ae6eaa2", "size": 5210, "ext": "py", "lang": "Python", "max_stars_repo_path": "analyze/rmsd.py", "max_stars_repo_name": "JunboLu/CP2K_kit", "max_stars_repo_head_hexsha": "0950f37f253c3f90d6a0539c57f1be1045e7317d", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count":... |
import numpy as np
from OSIM.Modeling.AbstractComponents.SingleComponent import SingleComponent
from OSIM.Modeling.CircuitSystemEquations import CircuitSystemEquations
class Impedance(SingleComponent):
def __init__(self, nodes, name, value, superComponent, **kwargs):
if complex(value) == 0:
p... | {"hexsha": "2879adb759859814e05f2c32651e178746b73d21", "size": 862, "ext": "py", "lang": "Python", "max_stars_repo_path": "OSIM/Modeling/Components/Impedance.py", "max_stars_repo_name": "tmaiwald/OSIM", "max_stars_repo_head_hexsha": "11127aaee61d93bb6f26ca5147a300af05db14ec", "max_stars_repo_licenses": ["BSD-2-Clause"]... |
import json
import numpy as np
"""
Format of ecosystem is:
{
'last_generation': int,
'times': [float,...],
'improvements': [float,...],
'average_total_improve': [float,...],
'runtime_running_avg': float,
'total_runtime': float,
'need_drift': [False,...],
'drifted_last_generation': [False,...],
'best_... | {"hexsha": "12cca18c9be9ffdb1a103d42395d879d645d949d", "size": 1335, "ext": "py", "lang": "Python", "max_stars_repo_path": "stats.py", "max_stars_repo_name": "ZackYovel/using_genetic_algorithm_for_hyper_parameter_tuning", "max_stars_repo_head_hexsha": "10530c5c3802d65f8a5cb651a5e0245d049d2702", "max_stars_repo_licenses... |
\section{Fit}%
\label{period.detailed}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\subsection{Fit folder}%
\label{period.folder}
\begin{figure}[h]
$$\image{0cm;0cm}{PFolder.eps}$$%
\caption{The frequency folder}%
\label{period.folder.dialog}
\end{figure}
This folder shows almost ... | {"hexsha": "6eefc4ae05b7ba54f166f2bc8d98ff35a2b86518", "size": 21801, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "doc/src/d_period.tex", "max_stars_repo_name": "msperl/Period", "max_stars_repo_head_hexsha": "da4b4364e8228852cc2b82639470dab0b3579055", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "m... |
import matplotlib
from sigpipes.sigcontainer import SigContainer
from sigpipes.sigoperator import (
Print, UfuncOnSignals, Convolution, FeatureExtraction,
SampleSplitter)
from sigpipes.joiner import JoinChannels
from sigpipes.plotting import Plot, GraphOpts
import numpy as np
from glob import glob
for filenam... | {"hexsha": "5d14161a08aec04e536077ed12bb20707c2283a9", "size": 655, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/ikon.py", "max_stars_repo_name": "Poselsky/signal-pipes", "max_stars_repo_head_hexsha": "ded180cfce4f25931554e0099330b962c2af4550", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count... |
# coding=utf-8
import logging
from typing import List
import numpy as np
import openeye.oechem as oechem
import openeye.oeomega as oeomega
import openeye.oeshape as oeshape
import utils
from .slurmmanager import slurmmanager
class rocs_similarity_base(object):
def __init__(self, ligand: utils.FilePath, max_tan... | {"hexsha": "abe5240f070653944afaac2399e62d9c049b3ff3", "size": 4169, "ext": "py", "lang": "Python", "max_stars_repo_path": "scoring/rocs_similarity.py", "max_stars_repo_name": "MauriceKarrenbrock/reinvent-memory", "max_stars_repo_head_hexsha": "57860dabb6534daf14fe2ab81d57589a90760442", "max_stars_repo_licenses": ["MIT... |
"""
Module for specifying output variables as part of the data file.
"""
import numpy as np
from andes.core.model import ModelData, Model
from andes.core.param import DataParam
class OutputData(ModelData):
"""
Data for outputs.
"""
def __init__(self):
ModelData.__init__(self, three_params=Fa... | {"hexsha": "23082220a72e9c3c5f7cdccece64b5ec3ee1a4db", "size": 1555, "ext": "py", "lang": "Python", "max_stars_repo_path": "andes/models/misc/output.py", "max_stars_repo_name": "cuihantao/Andes", "max_stars_repo_head_hexsha": "6cdc057986c4a8382194ef440b6e92b8dfb77e25", "max_stars_repo_licenses": ["Apache-2.0"], "max_st... |
import pickle
import re
import string
import pkg_resources
from gensim.models import KeyedVectors
import numpy as np
class Preprocessor(object):
char_search = re.compile(r"[^\u0020\u0027\u002b-\u002e\u0030-\u0039\u0041-\u005a\u0061-\u007a]")
strip_multi_ws = re.compile(r"( {2,})")
word_re = re.compile(r... | {"hexsha": "75f7ab4bbf5d8cf47ec3fbf3a2c2a5049a17141c", "size": 4703, "ext": "py", "lang": "Python", "max_stars_repo_path": "title_graph/title_graph.py", "max_stars_repo_name": "estasney/TitleGraph", "max_stars_repo_head_hexsha": "ad44215849dae7069cad7729c30249a6b87a7dc0", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
# Copyright 2018 Amazon.com, Inc. or its affiliates. All Rights Reserved.
# SPDX-License-Identifier: Apache-2.0
import numpy as np
from emukit.core import ParameterSpace, ContinuousParameter, InformationSourceParameter
from emukit.core.loop.user_function import MultiSourceFunctionWrapper
def multi_fidelity_forrest... | {"hexsha": "5813147889717c28661ecc036ff12c226c567f44", "size": 2753, "ext": "py", "lang": "Python", "max_stars_repo_path": "emukit/test_functions/forrester.py", "max_stars_repo_name": "ndalchau/emukit", "max_stars_repo_head_hexsha": "eb6754ea016a7cd82b275bb4075676b5ed662634", "max_stars_repo_licenses": ["Apache-2.0"], ... |
"""
Responsible for providing detiled views about a single stock and closely related views
"""
from collections import defaultdict
from datetime import datetime
import pandas as pd
import numpy as np
from django.contrib.auth.decorators import login_required
from django.shortcuts import render
from app.models import (va... | {"hexsha": "c41b941f5c5201336e6973a35a378411b096bc50", "size": 8479, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/viewer/app/views/stock.py", "max_stars_repo_name": "mappin/asxtrade", "max_stars_repo_head_hexsha": "2b97ffcdefae642a49ce5bfcc131db17796f1691", "max_stars_repo_licenses": ["Apache-2.0"], "max_... |
import os
import shutil
import pandas as pd
import numpy as np
import tensorflow as tf
from sklearn.model_selection import train_test_split
from urllib.request import urlopen
def clean_run(model_dir='', source_data=''):
"""Remove model and data files for a clean run"""
if model_dir:
if os.path.exists... | {"hexsha": "75a275e62bbf72e499a766dfa022865f8681b359", "size": 5998, "ext": "py", "lang": "Python", "max_stars_repo_path": "Project 8 -- Deep Learning for Cancer Classification/dnn_data_classifier/main.py", "max_stars_repo_name": "Vauke/Deep-Neural-Networks-HealthCare", "max_stars_repo_head_hexsha": "a6e0cc9d44e06ab3b3... |
# Energy spectrum of oscillations at a fixed point.
using FFTW, JLD2, CurveFit, PyPlot
using Vlasiator: RE
file = "satellites_uniform_sampled.jld2"
data = JLD2.load(file)
nSatellite = length(data["t"])
nI, nJ = size(data["rho"])[2:3]
t = data["t"]
# Select spatial point
i, j = 5, 5
var = data["rho"][:,i,j]
dt =... | {"hexsha": "e505ca97dacc490c4e67fcc99e1a965fc83ac58f", "size": 1194, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "examples/demo_energy_spectrum.jl", "max_stars_repo_name": "alhom/Vlasiator.jl", "max_stars_repo_head_hexsha": "615333705b5346522479ab72398f059cb94ab026", "max_stars_repo_licenses": ["MIT"], "max_st... |
%% EVENT OBJECT (event.m) %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% This class is designed to define a generic agent and import this variables
% into the simulation space for the purpose of multi-vehicle control simulation.
% Author: James A. Douthwaite
classdef eventDefinition
%%% EVENT BASE CLASS %%%%%%%... | {"author": "douthwja01", "repo": "OpenMAS", "sha": "962f321f82167db78066b2c88c783423ecc3b73a", "save_path": "github-repos/MATLAB/douthwja01-OpenMAS", "path": "github-repos/MATLAB/douthwja01-OpenMAS/OpenMAS-962f321f82167db78066b2c88c783423ecc3b73a/environment/events/eventDefinition.m"} |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Evaluate submissions on kbpo server.
"""
import pdb
import sys
import csv
import logging
from collections import Counter, defaultdict
import numpy as np
from tqdm import tqdm
from kbpo import evaluation_api
logger = logging.getLogger(__name__)
logger.setLevel(loggi... | {"hexsha": "0fa164c3cae6c5f5a4884d9be20a0ccb1cd7de90", "size": 2089, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/evaluate.py", "max_stars_repo_name": "arunchaganty/kbp-online", "max_stars_repo_head_hexsha": "9f8763d8f4bfb1fb8a01f1f4f506f56625dd38d8", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
# -*- coding: utf-8 -*-
from __future__ import absolute_import
from PyQt5 import QtCore, QtWidgets
import gr
from qtgr import GRWidget
import csv
from util.logger import Logger
import sys
from statistics.pdf import PDF, Kernels
import numpy as np
import os
logger = Logger("gui.pdf_widget")
logger.setstream("defaul... | {"hexsha": "b51b1ae0a53562e3645ef1d8837b1a551501df46", "size": 10235, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/gui/pdf_widget.py", "max_stars_repo_name": "sciapp/pyMolDyn", "max_stars_repo_head_hexsha": "fba6ea91cb185f916b930cd25b4b1d28a22fb4c5", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
import torch
import numpy as np
EPS = 1e-8
class TrajStorage(object):
def __init__(self, rollouts, aug_fn=None):
trajs = []
num_processes = rollouts.obs.shape[1]
for env_index in range(num_processes):
env_masks = rollouts.masks[:, env_index]
env_obs = rollouts.obs[:, env_index]
... | {"hexsha": "a26c79e74dd7d77408a9f371fe4f0ea119d2073a", "size": 4952, "ext": "py", "lang": "Python", "max_stars_repo_path": "ucb_rl2_meta/algo/contrastive_helpers.py", "max_stars_repo_name": "agarwl/auto-drac", "max_stars_repo_head_hexsha": "d86c480b51929e6e4ec0ae1adba84d9f78e91705", "max_stars_repo_licenses": ["MIT"], ... |
// Boost string_algo library std_containers_traits.hpp header file ---------------------------//
// Copyright Pavol Droba 2002-2003.
//
// Distributed under the Boost Software License, Version 1.0.
// (See accompanying file LICENSE_1_0.txt or copy at
// http://www.boost.org/LICENSE_1_0.txt)
// See htt... | {"hexsha": "5053380e7652502ad8272193b0eb7e1a53ad1d86", "size": 1027, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "src/third_party/boost/boost/algorithm/string/std_containers_traits.hpp", "max_stars_repo_name": "benety/mongo", "max_stars_repo_head_hexsha": "203430ac9559f82ca01e3cbb3b0e09149fec0835", "max_stars_r... |
"""
Helper infrastructure to compile and sample models using `cmdstan`.
[`StanModel`](@ref) wraps a model definition (source code), while [`stan_sample`](@ref) can
be used to sample from it.
[`stan_compile`](@ref) can be used to pre-compile a model without sampling. A
[`StanModelError`](@ref) is thrown if this fails,... | {"hexsha": "a166adea21177c19bdac1632c07f0b89ae5d146c", "size": 871, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/StanSample.jl", "max_stars_repo_name": "UnofficialJuliaMirror/StanSample.jl-c1514b29-d3a0-5178-b312-660c88baa699", "max_stars_repo_head_hexsha": "768894f98284a1840f01fd9c6c51c5247bae2ad5", "max_... |
# Experiment 2
# Shift in fire regime from small-frequent to large-infrequent
# Shift occurs at different times in the past, and between widely divergent regimes and closer regimes
## Launch model
library(doParallel)
library(foreach)
source("cat_face_mortality_pfire_split.r")
registerDoParallel(cores=16)
#########... | {"hexsha": "27be05c146badeb378b5155648ed2e628e060be7", "size": 4973, "ext": "r", "lang": "R", "max_stars_repo_path": "experiment_2.r", "max_stars_repo_name": "ozjimbob/FireScar", "max_stars_repo_head_hexsha": "da4b1a8c5ef13427e01c057e80c7c09cb3d6882f", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "max_star... |
import numpy as np
import pytest
from brainio.assemblies import BehavioralAssembly
from brainscore.benchmarks.objectnet import Objectnet
from brainscore.model_interface import BrainModel
@pytest.mark.private_access
class TestObjectnet:
def test_groundtruth(self):
benchmark = Objectnet()
source = b... | {"hexsha": "a1bce2802a86168bc10e21a52cecbc127ae9bfea", "size": 1417, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_benchmarks/test_objectnet.py", "max_stars_repo_name": "BonnerLab/brain-score", "max_stars_repo_head_hexsha": "8edbbfcdb8efc5112768bfa2b57746f250f3abd4", "max_stars_repo_licenses": ["MIT... |
struct LocalVar
is_mutable :: Ref{Bool} # mutability
is_shared :: Ref{Bool} # shared between different physical scopes/actual functions.
sym :: Symbol
end
GlobalVar = Symbol
readable_var(sym::Symbol) = LocalVar(Ref(false), Ref(false), sym)
global_var(sym::Symbol) = sym
| {"hexsha": "c7f9fe443b7688e6a3b1c12348048d8d3aa1c571", "size": 295, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/Variable.jl", "max_stars_repo_name": "devmotion/NameResolution.jl", "max_stars_repo_head_hexsha": "df4997900ea492dfb5bac52278dae4e5e53968ed", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
(* Title: HOL/Imperative_HOL/ex/Linked_Lists.thy
Author: Lukas Bulwahn, TU Muenchen
*)
section {* Linked Lists by ML references *}
theory Linked_Lists
imports "../Imperative_HOL" "~~/src/HOL/Library/Code_Target_Int"
begin
section {* Definition of Linked Lists *}
setup {* Sign.add_const_constraint (@{c... | {"author": "Josh-Tilles", "repo": "isabelle", "sha": "990accf749b8a6e037d25012258ecae20d59ca62", "save_path": "github-repos/isabelle/Josh-Tilles-isabelle", "path": "github-repos/isabelle/Josh-Tilles-isabelle/isabelle-990accf749b8a6e037d25012258ecae20d59ca62/src/HOL/Imperative_HOL/ex/Linked_Lists.thy"} |
__author__ = "Laurence Elliott - 16600748"
from capstone import *
import pefile, os
import numpy as np
from matplotlib import pyplot as plt
benignPaths = ["../bin-utf8-vec/benignSamples/" + sample for sample in os.listdir("../bin-utf8-vec/benignSamples")]
malwarePaths = ["../bin-utf8-vec/malwareSamples/" + sample for... | {"hexsha": "e87545d3326a550777fb0f0d104b93477628966e", "size": 5618, "ext": "py", "lang": "Python", "max_stars_repo_path": "bin-opcodes-vec/bin-opcodes-vec.py", "max_stars_repo_name": "laurencejbelliott/Ensemble_DL_Ransomware_Detector", "max_stars_repo_head_hexsha": "0cae02c2425e787a810513537a47897f3a42e5b5", "max_star... |
-- Local Variables:
-- idris-load-packages: ("prelude" "effects" "contrib" "base")
-- End:
import Data.Vect
import Data.Fin
-- https://en.wikipedia.org/wiki/Netpbm_format
-- A little file for writing PPM format. Why? Because I want to do some little image things
-- in Idris like the ones in the class I helped with us... | {"hexsha": "6fb974b053b095710c0bcecde3782c74e30ea558", "size": 1682, "ext": "idr", "lang": "Idris", "max_stars_repo_path": "PPM.idr", "max_stars_repo_name": "clarissalittler/idris-practice", "max_stars_repo_head_hexsha": "e307a93fa4ab7bce9f6cf7fef9973c398b3d65ea", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
from pathlib import Path
from typing import List
import pandas as pd
from pandas import DataFrame
import matplotlib.pyplot as plt
import os
import zipfile
from src_homework.config import COMMON_COLUMN
# data pre processing
base_data_folder_path = 'data/HMOG'
file_name_to_colume_names = {
'Accelerometer.csv': ['S... | {"hexsha": "e8803d10eb2ac453b4b92d1fabaa284905e2a33d", "size": 8330, "ext": "py", "lang": "Python", "max_stars_repo_path": "src_homework/HW2_starter_files.py", "max_stars_repo_name": "jjkindergarten/Nonlinear-Data-Analysis", "max_stars_repo_head_hexsha": "4ee31f0e9ef231fb0087307b1235558c27586a5a", "max_stars_repo_licen... |
"""
Dynamic Edge-Conditioned Filters in Convolutional Neural Networks on Graphs
https://github.com/mys007/ecc
https://arxiv.org/abs/1704.02901
2017 Martin Simonovsky
"""
from __future__ import division
from __future__ import print_function
from builtins import range
import unittest
import nu... | {"hexsha": "a7307d2f4ba6fe8dd921013ed1a47942d97da45f", "size": 3023, "ext": "py", "lang": "Python", "max_stars_repo_path": "learning/ecc/test_GraphConvModule.py", "max_stars_repo_name": "davijo/superpoint_graph", "max_stars_repo_head_hexsha": "0d60fb364bfa37fb70570784899ce46c0296ee22", "max_stars_repo_licenses": ["MIT"... |
import numpy as np
import pandas as pd
import keras.backend as K
from keras.layers import multiply
from keras.layers.core import Dense, Reshape, Lambda, RepeatVector, Permute, Flatten
from keras.layers.recurrent import LSTM
from keras.models import Model, Input
# plot part.
import matplotlib.pyplot as plt
# ## Helpe... | {"hexsha": "f21675789278a6dcaa0b38f16e225d0a458b1718", "size": 4225, "ext": "py", "lang": "Python", "max_stars_repo_path": "attention_function.py", "max_stars_repo_name": "deepak-kaji/mimic-lstm", "max_stars_repo_head_hexsha": "4900bb6fa3b4828000a18e35c534bb1b3f23dd05", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
// Copyright Carl Philipp Reh 2009 - 2016.
// Distributed under the Boost Software License, Version 1.0.
// (See accompanying file LICENSE_1_0.txt or copy at
// http://www.boost.org/LICENSE_1_0.txt)
#include <fcppt/make_int_range_count.hpp>
#include <fcppt/tag_type.hpp>
#include <fcppt/use.hpp>
#... | {"hexsha": "58c95c677fea2c14535fce1b62b52caaa2de8e91", "size": 1568, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "test/algorithm/loop.cpp", "max_stars_repo_name": "vinzenz/fcppt", "max_stars_repo_head_hexsha": "3f8cc5babdee178a9bbd06ca3ce7ad405d19aa6a", "max_stars_repo_licenses": ["BSL-1.0"], "max_stars_count":... |
C @(#)getyeq.f 20.3 2/13/96
subroutine getyeq(k1, k2, id, ksect, yeq, y1, yxy, y2)
C compute the following 2-port Y-matrices:
C YEQ - Equivalent parallel 2-port
C Y1 - 2-port left of section KSECT
C YXY - 2-port for section KSECT
C Y2 - 2-port right of section KSECT
include 'ipfi... | {"hexsha": "5d45a9ed0f150ba5374462137df56eb17960721c", "size": 5246, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "ipf/getyeq.f", "max_stars_repo_name": "mbheinen/bpa-ipf-tsp", "max_stars_repo_head_hexsha": "bf07dd456bb7d40046c37f06bcd36b7207fa6d90", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 14, "... |
[STATEMENT]
lemma redT_updLns_iff [simp]:
"\<And>ln. redT_updLns ls t ln las $ l = upd_threadRs (ln $ l) (ls $ l) t (las $ l)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<And>ln. redT_updLns ls t ln las $ l = upd_threadRs (ln $ l) (ls $ l) t (las $ l)
[PROOF STEP]
by(simp add: redT_updLns_def) | {"llama_tokens": 150, "file": "JinjaThreads_Framework_FWLockingThread", "length": 1} |
#!conda install -c anaconda seaborn -y
#conda install -c anaconda nltk
import re
import string
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
import seaborn as sns
from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize
# importing data set into dataframes ... | {"hexsha": "ccb4c96312e9d701743669f23a16682c6f2e7454", "size": 4030, "ext": "py", "lang": "Python", "max_stars_repo_path": "NLP Model Code.py", "max_stars_repo_name": "keenanbernard/Data", "max_stars_repo_head_hexsha": "ae3460f02ac913e5482c7b3e63d8760d5d41dbfc", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_cou... |
from amfe.io import AmfeMeshConverter, GidJsonMeshReader
from amfe.tools import amfe_dir
from amfe.material import KirchhoffMaterial
from amfe.component import StructuralComponent
from amfe.neumann import FixedDirectionNeumann
import logging
import numpy as np
from amfe.mesh import Mesh
# Units:
# Length: mm
# M... | {"hexsha": "71bdf5847ae2da927b7801f8a81f4d9cce6f30bb", "size": 1305, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples/simple_beam/simple_beam.py", "max_stars_repo_name": "ma-kast/AMfe", "max_stars_repo_head_hexsha": "99686cc313fb8904a093fb42e6cf0b38f8cfd791", "max_stars_repo_licenses": ["BSD-3-Clause"], ... |
import time
import numpy as np
import pandas as pd
import pickle
import more_itertools as mit
import matplotlib.pyplot as plt
plt.switch_backend('agg')
import pandas as pd
from itertools import combinations
from scipy import stats
# from sklearn.linear_model import LinearRegression
from sklearn.metrics import r2_score
... | {"hexsha": "98c723b074c1c0a3c08bc10e1b39b09d2ef5564d", "size": 18669, "ext": "py", "lang": "Python", "max_stars_repo_path": "scripts_for_public/FigS8-morphoLateralization-activitySymmetry.py", "max_stars_repo_name": "NeLy-EPFL/Ascending_neuron_screen_analysis_pipeline", "max_stars_repo_head_hexsha": "438b9db15765bf2658... |
import numpy as np
def np_sma(data):
return np.sum(data) / len(data)
| {"hexsha": "ed8d69fe4ea110682181005edf32caa06186b1bf", "size": 74, "ext": "py", "lang": "Python", "max_stars_repo_path": "indicators/sma.py", "max_stars_repo_name": "Tiqur/live-crypto-alerts", "max_stars_repo_head_hexsha": "860fe73c9f2a960b398d132c2eb2c3ee333aa968", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
module MLib.Prelude.RelProps where
open import MLib.Prelude.FromStdlib
import Relation.Binary.Indexed as I
open FE using (cong)
import Data.Product.Relation.SigmaPropositional as OverΣ
Σ-bij : ∀ {a b c} {A : Set a} {B : A → Set b} {C : A → Set c} → (∀ x → B x ↔ C x) → Σ A B ↔ Σ A C
Σ-bij pw = record
{ to = ≡.→-to-... | {"hexsha": "6209fecb1c83bcdc3ebcf0eeac237ade0ae7417b", "size": 1975, "ext": "agda", "lang": "Agda", "max_stars_repo_path": "src/MLib/Prelude/RelProps.agda", "max_stars_repo_name": "bch29/agda-matrices", "max_stars_repo_head_hexsha": "e26ae2e0aa7721cb89865aae78625a2f3fd2b574", "max_stars_repo_licenses": ["MIT"], "max_st... |
@testset "Born-Mayer Unit Tests" begin
A = 1.0u"eV"
ρ = 0.25u"bohr"
σ = 0.25u"bohr"
C = 1.0u"eV*Å"
D = 1.0u"eV*Å"
rcutoff = 2.0u"Å"
species = [:Ar, :H]
p = BornMayer(A, ρ, σ, C, D, rcutoff, species)
@test p isa EmpiricalPotential{NamedTuple{(:A, :ρ, :σ, :C, :D)},NamedTuple{(:rcutoff... | {"hexsha": "4e02d78d8d8dea68f69132c8523a329253069af0", "size": 1430, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/unit/empirical/bm.jl", "max_stars_repo_name": "cesmix-mit/InteratomicPotentials.jl", "max_stars_repo_head_hexsha": "100af9067e69d4e3fa2f4697b4915c93cb08f419", "max_stars_repo_licenses": ["MIT"... |
\documentclass[modern]{aastex63}
\usepackage{amsmath}
\newcommand{\dd}{\ensuremath{\mathrm{d}}}
\newcommand{\diff}[2]{\frac{\dd #1}{\dd #2}}
% Affiliations
\newcommand{\flatironCCA}{Center for Computational Astrophysics, Flatiron Institute, 162 5th Ave, New York NY 10010, United States}
\newcommand{\stonybrook}{Depa... | {"hexsha": "af9e584338d8bee27dc064f481317475e0cb11d3", "size": 12760, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "note/reweighting.tex", "max_stars_repo_name": "farr/Reweighting", "max_stars_repo_head_hexsha": "3762a0849c98799bb97d0748d803d05406645518", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1,... |
type PiecewiseYieldCurve{B <: BootstrapHelper, DC <: DayCount, P <: Interpolation, T <: BootstrapTrait, BT <: Bootstrap} <: InterpolatedCurve{P, T}
lazyMixin::LazyMixin
settlementDays::Int
referenceDate::Date
instruments::Vector{B}
dc::DC
interp::P
trait::T
accuracy::Float64
boot::BT
times::Vector{F... | {"hexsha": "52e9ab11071fb20327bc14c0aaf7438e0108434a", "size": 1469, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/termstructures/yield/piecewise_yield_curve.jl", "max_stars_repo_name": "JuliaQuant/QuantLib.jl", "max_stars_repo_head_hexsha": "b1a806daa3b15b1f3705e36f716e66cc24c1dd5f", "max_stars_repo_licens... |
"""
Implement Twin-Delayed DDPG in Addressing Function Approximation Error in Actor-Critic Methods, Fujimoto et al, 2018
The key difference with DDPG lies in
1. Add noise to target policy served as regularization to prevent overfitting to current best policy
2. Use clipped double Q function to avoid overestimation in Q... | {"hexsha": "72d6ad51220ae5a127d0b660303edd1b59c480e4", "size": 7854, "ext": "py", "lang": "Python", "max_stars_repo_path": "torchlib/deep_rl/algorithm/td3/agent.py", "max_stars_repo_name": "vermouth1992/torchlib", "max_stars_repo_head_hexsha": "63b2bedb40f670b2d9fbfc0daeab4a8d44623095", "max_stars_repo_licenses": ["MIT... |
#! -*- coding:utf-8 -*-
# CLUE评测
# iflytek文本分类
# 思路：取[CLS]然后接Dense+Softmax分类
import json
import numpy as np
from snippets import *
from bert4keras.backend import keras
from bert4keras.snippets import sequence_padding, DataGenerator
from bert4keras.snippets import open
from tqdm import tqdm
# 基本参数
num_classes = 119
ma... | {"hexsha": "13f1dbada436e122cfd0376d9089ce4a0980010f", "size": 3982, "ext": "py", "lang": "Python", "max_stars_repo_path": "clue/iflytek.py", "max_stars_repo_name": "dumpmemory/roformer-v2", "max_stars_repo_head_hexsha": "95b71ae03b8bb910998285e194d7752b1e4104c0", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_c... |
from tkinter import *
from tkinter import messagebox
from tkinter import filedialog
from tkinter import ttk
from tkinter.scrolledtext import ScrolledText
import xmltodict
import uuid
import os
import shutil
import json
import copy
from cyclus_gui.gui.sim_window import SimulationWindow
from cyclus_gui.gui.arche_window i... | {"hexsha": "55b49b7a0add29859508d234d0201ddae7ce6592", "size": 25758, "ext": "py", "lang": "Python", "max_stars_repo_path": "cyclus_gui/gui/gui.py", "max_stars_repo_name": "gonuke/cyclus_gui", "max_stars_repo_head_hexsha": "ef67df351585ab8a476b1577380ec6034bf0753f", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_sta... |
\documentclass[]{article}
\usepackage{lmodern}
\usepackage{amssymb,amsmath}
\usepackage{ifxetex,ifluatex}
\usepackage{fixltx2e} % provides \textsubscript
\ifnum 0\ifxetex 1\fi\ifluatex 1\fi=0 % if pdftex
\usepackage[T1]{fontenc}
\usepackage[utf8]{inputenc}
\else % if luatex or xelatex
\ifxetex
\usepackage{mat... | {"hexsha": "aa7f4f9a630d3481d9283f43eb215dcf91912746", "size": 21019, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "notebooks/rev5_exploration-2.tex", "max_stars_repo_name": "bjsmith/reversallearning", "max_stars_repo_head_hexsha": "023304731d41c3109bacbfd49d4c850a92353978", "max_stars_repo_licenses": ["Apache-2... |
# coding=utf-8
# Copyright 2020 The Google AI Perception Team Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required b... | {"hexsha": "31a250ac726221166b46775a62514c34bca1a94a", "size": 1783, "ext": "py", "lang": "Python", "max_stars_repo_path": "aist_plusplus/visualizer.py", "max_stars_repo_name": "google/aistplusplus_ap", "max_stars_repo_head_hexsha": "83d78d8cbc9b417616cd2200b9afdf37228509e1", "max_stars_repo_licenses": ["Apache-2.0"], ... |
"""Identity matrix."""
from scipy import sparse
import numpy as np
def iden(dim: int, is_sparse: bool = False) -> np.ndarray:
r"""
Calculate the :code:`dim`-by-:code:`dim` identity matrix [WIKID]_.
Returns the :code:`dim`-by-:code:`dim` identity matrix. If :code:`is_sparse
= False` then the matrix w... | {"hexsha": "bd68070b41d4e02ad4840cfc88ac6d7927ba3541", "size": 2119, "ext": "py", "lang": "Python", "max_stars_repo_path": "toqito/matrices/iden.py", "max_stars_repo_name": "paniash/toqito", "max_stars_repo_head_hexsha": "ab67c2a3fca77b3827be11d1e79531042ea62b82", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
//
// $Id: Exception.hpp 2008 2010-05-29 02:46:49Z brendanx $
//
//
// Original author: Matt Chambers <matt.chambers .@. vanderbilt.edu>
//
// Copyright 2010 Vanderbilt University - Nashville, TN 37232
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in complianc... | {"hexsha": "c8680129b10c9780deede757811072a94451c50f", "size": 1840, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "cpp/src/pwiz/utility/misc/Std.hpp", "max_stars_repo_name": "toppic-suite/topmsv", "max_stars_repo_head_hexsha": "fef5d1f1f1c00ffdad2c258401d319f1e227c7cb", "max_stars_repo_licenses": ["Apache-2.0"],... |
[STATEMENT]
lemma nxtActive_lactive:
assumes "\<exists>i\<ge>n. \<parallel>c\<parallel>\<^bsub>t i\<^esub>"
and "\<not> (\<exists>i>\<langle>c \<rightarrow> t\<rangle>\<^bsub>n\<^esub>. \<parallel>c\<parallel>\<^bsub>t i\<^esub>)"
shows "\<langle>c \<rightarrow> t\<rangle>\<^bsub>n\<^esub>=\<langle>c \<and> t\<... | {"llama_tokens": 2710, "file": "DynamicArchitectures_Configuration_Traces", "length": 18} |
using Replace
using Test
using MacroTools
module Sine
sine(x) = sin(x)
end
@testset "Replace" begin
@testset "Basic" begin
@test 1.0 == @replace sin cos sin(0.0)
@test 0.0 == @replace cos sin cos(0.0)
# make sure that we haven't clobbered the definition of sin and cos
@assert cos(0.0) == 1.0 && sin(0.0) == ... | {"hexsha": "919f624e20417f1aacb35baefb48315eefc6cc92", "size": 1960, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/runtests.jl", "max_stars_repo_name": "ScottishCovidResponse/Replace.jl", "max_stars_repo_head_hexsha": "610c1df9007ee0b1328acf3cd4500b63d4478a93", "max_stars_repo_licenses": ["MIT"], "max_star... |
from sklearn.tree import DecisionTreeClassifier
from sklearn import preprocessing
import numpy as np
le = preprocessing.LabelEncoder()
clf = DecisionTreeClassifier()
training = np.array([
[3, "yes", 62, "accept"],
[4, "yes", 70, "accept"],
[2, "yes", 71, "reject"],
[5, "yes", 58, "reject"],
[1, "... | {"hexsha": "6ff00d76af086eae544bfe2f1f7d72eba520be05", "size": 727, "ext": "py", "lang": "Python", "max_stars_repo_path": "script.py", "max_stars_repo_name": "ashwinvaidya17/DecisionTree-ScikitLearn", "max_stars_repo_head_hexsha": "a1e9e382a6b3ed96352a96bbe600420139923dc0", "max_stars_repo_licenses": ["MIT"], "max_star... |
import argparse
import sys
sys.path.append('/home/lyan/Documents/tf-pose-estimation/tf_pose/')
sys.path.append('/home/lyan/Documents/tf-pose-estimation/')
import json
import numpy as np
import cv2
from tf_pose.estimator import TfPoseEstimator
parser = argparse.ArgumentParser(description='inference speed tester')
... | {"hexsha": "56906dd47cf67f76a12d01932ea22d3c56c564e9", "size": 3685, "ext": "py", "lang": "Python", "max_stars_repo_path": "utilities/evaluate_scores.py", "max_stars_repo_name": "dodler/tf-pose-estimation", "max_stars_repo_head_hexsha": "539d4a1d351ca32d67c1418f5a796a1e69e9075b", "max_stars_repo_licenses": ["Apache-2.0... |
import numpy
import chainer
from chainer import Variable
import chainer.functions as F
def func_y(w, x, dim):
pred_y = sum([w[d] * (x ** d) for d in range(dim + 1)])
return pred_y.reshape(pred_y.shape[0])
def func_J(y, pred_y):
return 0.5 * F.sqrt(F.mean_squared_error(y, pred_y))
class LSM():
"""
... | {"hexsha": "618afdfbb1cdaa88fc9a5291ba86d5dccc9f7c8b", "size": 3299, "ext": "py", "lang": "Python", "max_stars_repo_path": "Python/likely_chainer/models.py", "max_stars_repo_name": "Atsuto0519/LSMuseSGD", "max_stars_repo_head_hexsha": "ec40572b59f9a4ac2d85e5a690d590434e36fa7c", "max_stars_repo_licenses": ["MIT"], "max_... |
[STATEMENT]
lemma protocol_inverse:
assumes "m0 \<in> carrier \<G>" "m1 \<in> carrier \<G>"
shows" ((\<^bold>g [^] ((a*b) mod (order \<G>))) [^] (s1 :: nat)) \<otimes> ((\<^bold>g [^] b) [^] (r1::nat)) \<otimes> (if v then m0 else m1) \<otimes> inv (((\<^bold>g [^] a) [^] s1 \<otimes> \<^bold>g [^] r1) [^] b)
... | {"llama_tokens": 6401, "file": "Multi_Party_Computation_Noar_Pinkas_OT", "length": 24} |
import os
import os.path as osp
from tqdm import tqdm
import numpy as np
import torch
import torch.backends.cudnn as cudnn
from torch.utils.data import DataLoader
from torchnet import meter
from model.resnet_deconv import get_deconv_net
from model.hourglass import PoseNet
from model.loss import My_SmoothL1Loss
from d... | {"hexsha": "492ef1964c4ccffbc0b0340ad2f39a5e0548372c", "size": 4941, "ext": "py", "lang": "Python", "max_stars_repo_path": "test.py", "max_stars_repo_name": "Jvictor97/AWR-Adaptive-Weighting-Regression", "max_stars_repo_head_hexsha": "2c29f8ac3d824edfff07465232ffed8e4d837ebf", "max_stars_repo_licenses": ["MIT"], "max_s... |
function evol(fitness, lb, ub, numParticles, maxiter, verbose)
sr = [(lb[i], ub[i]) for i=1:length(lb)]
fopt = 10000
xopt = []
for i=1:15
println(i)
result = BlackBoxOptim.bboptimize(fitness; SearchRange = sr, NumDimensions = length(lb),
Method = :adaptive_de_ra... | {"hexsha": "ec8ecca634b48dc9e517df694b4da92e47de7e12", "size": 1050, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/evol.jl", "max_stars_repo_name": "rjvial/LotMassing.jl", "max_stars_repo_head_hexsha": "e69d823bc80720b59b3a13c7609cb9dfea0baff6", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "... |
//
// Created by mbodych on 11.05.18.
//
#include <v4r/features/types.h>
#include <boost/algorithm/string.hpp>
namespace v4r {
std::istream &operator>>(std::istream &in, FeatureType &t) {
std::string token;
in >> token;
boost::to_upper(token);
if (token == "FPFH")
t = FeatureType::FPFH;
else if (token ... | {"hexsha": "8c6a8aaee2e94e4db6c9db048092710cb36f3218", "size": 2169, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "modules/features/src/types.cpp", "max_stars_repo_name": "v4r-tuwien/v4r", "max_stars_repo_head_hexsha": "ff3fbd6d2b298b83268ba4737868bab258262a40", "max_stars_repo_licenses": ["BSD-1-Clause", "BSD-2... |
import lang
import normalization
import data.set
-- V⟦−⟧ : type → PowerSet(ClosedVal)
-- V⟦−⟧ : type → (ClosedVal → 2)
-- interp_val : type → val → Prop
--
open exp typ
notation e ` ↦* `:90 e' := is_many_step e e'
def irred (e:exp) := ¬(∃ e', e ↦str e')
-- Approach to defining inductive relation inspired from Modu... | {"author": "upamanyus", "repo": "pl-experiments", "sha": "ff4434ae9df0c00f50520eac64b87d5ae42991c1", "save_path": "github-repos/lean/upamanyus-pl-experiments", "path": "github-repos/lean/upamanyus-pl-experiments/pl-experiments-ff4434ae9df0c00f50520eac64b87d5ae42991c1/stlc/src/typesafety.lean"} |
#!/usr/bin/env python2
# -*- coding: utf-8 -*-
"""
Created on Sat Jun 16 17:03:00 2018
@author: jumtsai
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
'''Import this part for using Tensor Board to visualizing each nodes in CNN.
'''
#DCNN's TensorFlow(G... | {"hexsha": "9570eb931026eab404061bf45a31776ebba279d8", "size": 10959, "ext": "py", "lang": "Python", "max_stars_repo_path": "DCNN.py", "max_stars_repo_name": "caibojun/DeconvolutionalNeuralNetwork", "max_stars_repo_head_hexsha": "7ddf459fcec5b3fb01f2f6f4a074e7a16a9bfca2", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
from __future__ import print_function
import numpy as np
from astropy.table import Table
def split_asts(ast_file, sd_map_file, bin_width=1.):
"""
Split the ASTs into sub-files for each source density bin.
Parameters
----------
ast_file : string
Name of the file that contains the AST resul... | {"hexsha": "81d9a3c8e22c95da399e92592be16762ebb19a61", "size": 2247, "ext": "py", "lang": "Python", "max_stars_repo_path": "beast/tools/split_asts_by_source_density.py", "max_stars_repo_name": "marthaboyer/beast", "max_stars_repo_head_hexsha": "1ca71fb64ab60827e4e4e1937b64f319a98166c3", "max_stars_repo_licenses": ["BSD... |
#include <bitset>
#include <sstream>
#include <string>
#include <thread>
#include <boost/beast/core.hpp>
#include <boost/beast/websocket.hpp>
#include <boost/asio/ip/tcp.hpp>
#include <math_nerd/hill_cipher.h>
#include "file_handler.h"
namespace beast = boost::beast;
namespace http = beast::http;
namespace websocket ... | {"hexsha": "def46877309b8d2471fae2a6bf473936da9696c5", "size": 5030, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "hill.cpp", "max_stars_repo_name": "JacobSzepsy/Hill-Cipher-Webpage", "max_stars_repo_head_hexsha": "7fb9c7af9fd90af993de992fb0d5f556ecdc6b71", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
[STATEMENT]
lemma lower_higher_commute: "higher (lower p s) t = lower (higher p t) s"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. higher (lower p s) t = lower (higher p t) s
[PROOF STEP]
by (rule poly_mapping_eqI, simp add: lookup_higher lookup_lower) | {"llama_tokens": 103, "file": "Polynomials_MPoly_Type_Class_Ordered", "length": 1} |
import math
import os
from pathlib import Path
from typing import Dict, Optional, Tuple, Union
import librosa
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
# import pandas as pd
import pytorch_lightning as pl
import segmentation_models_pytorch as smp
import torch
import torch.nn.functional as... | {"hexsha": "61a2f15513307e995cdbf833cd56a2f12a619ade", "size": 24307, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/modeling/pl_model.py", "max_stars_repo_name": "caoyizhi-filter/kaggle_Google_Smartphone_Decimeter_Challenge-10th-", "max_stars_repo_head_hexsha": "10e6bd6cb3b7222dc141e820daea4fc1db5cabe2", "... |
from PIL import Image
import numpy as np
from ImageProcessing import mediumGaussian
def readStack(dir, filted = None):
"""
dir --- directory of image
This method takes input directory of image, and read the image as a numpy stack
"""
img = Image.open(dir)
maxiter = 1000 # large defau... | {"hexsha": "a6a3206e62bf439abc38ca122b8167bb44759a70", "size": 1068, "ext": "py", "lang": "Python", "max_stars_repo_path": "InputOutput/ReadTiffStack.py", "max_stars_repo_name": "jzw0025/Kyber", "max_stars_repo_head_hexsha": "ce2069da469095e6a086f7bbf9cd980f10563b22", "max_stars_repo_licenses": ["Unlicense"], "max_star... |
"""Test that the following CLI command returns the expected outputs
label-maker package -d integration-od -c test/fixtures/integration/config.integration.object_detection.json"""
import unittest
from os import makedirs
from shutil import copyfile, copytree, rmtree
import subprocess
import numpy as np
class TestObject... | {"hexsha": "acf3e5bccd40f0b64f13f30e105d6ac2afe2d5e0", "size": 1494, "ext": "py", "lang": "Python", "max_stars_repo_path": "test/integration/test_object_package.py", "max_stars_repo_name": "jonaslalin/label-maker", "max_stars_repo_head_hexsha": "c271189fbfd0f0c198184ef45032e16546e25243", "max_stars_repo_licenses": ["MI... |
import os
import tempfile
import unittest
import numpy as np
from PIL import Image
import colortrans
np.random.seed(0)
class TestColorTrans(unittest.TestCase):
"""colortrans tests"""
def test_colortrans(self):
content = np.random.randint(256, size=(20, 30, 3), dtype=np.uint8)
reference = n... | {"hexsha": "43cfef2d67c5d19eba972f8f5468dd24b1106b33", "size": 1605, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_colortrans.py", "max_stars_repo_name": "dstein64/colortrans", "max_stars_repo_head_hexsha": "bda872f85733a91c375f138c694d9692f719b7fa", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
##############################################################################
# Institute for the Design of Advanced Energy Systems Process Systems
# Engineering Framework (IDAES PSE Framework) Copyright (c) 2018-2020, by the
# software owners: The Regents of the University of California, through
# Lawrence Berkeley N... | {"hexsha": "e248e9f538e22f3ac7ebef1238fbde96b42966e5", "size": 18183, "ext": "py", "lang": "Python", "max_stars_repo_path": "idaes/apps/rankine/simple_rankine_cycle.py", "max_stars_repo_name": "shermanjasonaf/idaes-pse", "max_stars_repo_head_hexsha": "b3c69a9c2a31cfe79683a95161a98112b9059912", "max_stars_repo_licenses"... |
import numpy as np
from torch.utils.data._utils.collate import default_collate
from torch.utils.data.distributed import DistributedSampler
from .datasets import get_dataset
from .dataloader import FastDataloader
from augment import (get_transforms, get_center_crop_transforms,
get_simple_transfor... | {"hexsha": "d3195679916e2d34d420d8cc2d307459ea0a617f", "size": 4502, "ext": "py", "lang": "Python", "max_stars_repo_path": "data/getters.py", "max_stars_repo_name": "merlinarer/scrl", "max_stars_repo_head_hexsha": "f5bc426ed6eef130d44dd3a5609dc0772da59613", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": ... |
!
! :::::::::::::: BOUND :::::::::::::::::::::::::::::::::::::::::::
! This routine sets the boundary values for a given grid
! at level level.
! We are setting the values for a strip ng zones wide on
! both borders.
!
! Outputs from this routine:
! The values around the border of the grid are... | {"hexsha": "caa87d2e0e102acbdd4419d62d79af1e7d6a08ca", "size": 2819, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "src/1d/bound.f90", "max_stars_repo_name": "navravi/amrclaw", "max_stars_repo_head_hexsha": "727d98d243c521267c927f6fe107ba6f1155597b", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_cou... |
from __future__ import print_function
import os
import scipy
from py2gcode import gcode_cmd
from py2gcode import cnc_pocket
from py2gcode import cnc_boundary
from params import params
alignTest = False
# Cutting parameters
safeZ = 0.5
startZ = 0.0
overlap = 0.4
overlapFinish = 0.6
maxCutDepth = 0.05
if alignTest:
... | {"hexsha": "416a1e2f40ad47e1bf41172b3461a5f2080396d1", "size": 3205, "ext": "py", "lang": "Python", "max_stars_repo_path": "cnc/motor_hub/motor_hub/magnet_and_boundary/mill.py", "max_stars_repo_name": "iorodeo/stir_plate_mechanics", "max_stars_repo_head_hexsha": "ad721e708d962afcb14dd69456df4231c83ffed8", "max_stars_re... |
'''
MIT License
Copyright 2019 Oak Ridge National Laboratory
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... | {"hexsha": "4d493e189904a7264c7723bd85003cea3588e9f1", "size": 14863, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/faro/FaceGallery.py", "max_stars_repo_name": "reidej/faro", "max_stars_repo_head_hexsha": "b85b1c6ba7cb69fac6cfd62ba64558676de24fd0", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 9,... |
%% brute_force_tune
% Code to test the performance of various tuning parameters
% Works sorta like RANSAC I guess?
% Adam Werries 2016, see Apache 2.0 license.
k_max = 50;
% Specify ranges
accel_bias_PSD = logspace(-10,-4,100);
gyro_bias_PSD = logspace(-10,-4,100);
% Repeat arrays
accel_bias_PSD = repmat(accel_bias_PS... | {"author": "awerries", "repo": "kalman-localization", "sha": "558ca7fae1779aa71da61ec4829299bbbdbf62ff", "save_path": "github-repos/MATLAB/awerries-kalman-localization", "path": "github-repos/MATLAB/awerries-kalman-localization/kalman-localization-558ca7fae1779aa71da61ec4829299bbbdbf62ff/MATLAB/Tuning/tune_bias_psd.m"} |
# Copyright (c) 2021 Graphcore Ltd. All rights reserved.
# Copyright (c) 2020 jeonsworld
#
# 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
#
... | {"hexsha": "c651432f332c335b93039868e4a3990ce79b3efd", "size": 2700, "ext": "py", "lang": "Python", "max_stars_repo_path": "applications/pytorch/vit/validation.py", "max_stars_repo_name": "payoto/graphcore_examples", "max_stars_repo_head_hexsha": "46d2b7687b829778369fc6328170a7b14761e5c6", "max_stars_repo_licenses": ["... |
import unittest
import pycqed as pq
import numpy as np
import matplotlib.pyplot as plt
import os
from pycqed.analysis_v2 import measurement_analysis as ma
class Test_flipping_analysis(unittest.TestCase):
@classmethod
def tearDownClass(self):
plt.close("all")
@classmethod
def setUpClass(self):... | {"hexsha": "0744383b09a279190056db6deaf34c6754c68525", "size": 6985, "ext": "py", "lang": "Python", "max_stars_repo_path": "pycqed/tests/analysis_v2/test_timedomain_analysis_v2.py", "max_stars_repo_name": "nuttamas/PycQED_py3", "max_stars_repo_head_hexsha": "1ee35c7428d36ed42ba4afb5d4bda98140b2283e", "max_stars_repo_li... |
"""Here special plot scripts are defined, which can be accessed from the config"""
from forge.tools import (
customize_plot,
config_layout,
relabelPlot,
reject_outliers,
text_box,
)
import holoviews as hv
from holoviews import opts
from holoviews.operation import histogram
import logging
import pand... | {"hexsha": "e2ddc2b569b0b4fbbfcbccf4661d2705b3b2a213", "size": 15771, "ext": "py", "lang": "Python", "max_stars_repo_path": "COMET/misc_plugins/PlotScripts/forge/specialPlots.py", "max_stars_repo_name": "dallaval5u/COMET", "max_stars_repo_head_hexsha": "8c5793faafe2797dd4100507aa0fe1e71cf9f6c0", "max_stars_repo_license... |
import os
# hacky, but whatever
import sys
my_path = os.path.dirname(os.path.abspath(__file__))
sys.path.append(os.path.join(my_path, '..'))
import mdpsim # noqa: #402
import pytest # noqa: E402
import tensorflow as tf # noqa: E402
import numpy as np # noqa: E402
pytest.register_assert_rewrite('models')
from mode... | {"hexsha": "49c085c1e0faa3b8f92412f5e2bc2c1413bdc226", "size": 1608, "ext": "py", "lang": "Python", "max_stars_repo_path": "asnets/tests/tests.py", "max_stars_repo_name": "xf1590281/ASNets", "max_stars_repo_head_hexsha": "5f4b29fb62a5e72004b813228442d06246c9ec33", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
import numpy as np
import pandas as pd
import sklearn
import matplotlib.pyplot as plt
import seaborn as sns
import warnings
warnings.simplefilter(action='ignore', category=FutureWarning)
ratings = pd.read_csv("https://s3-us-west-2.amazonaws.com/recommender-tutorial/ratings.csv")
# a = ratings.head()
# print(a) # 1
... | {"hexsha": "620f5c722e340285a5cc275bd99f47ed35f878e4", "size": 5364, "ext": "py", "lang": "Python", "max_stars_repo_path": "first_test.py", "max_stars_repo_name": "VictorBenoiston/algorithms_testing", "max_stars_repo_head_hexsha": "78ea075d4c49515d2a4ae96901e1c7d66ed7b9f3", "max_stars_repo_licenses": ["MIT"], "max_star... |
MODULE params_model
USE common, ONLY: r_size
IMPLICIT NONE
PUBLIC
! Now definable via namelist at runtime:
! MOM4 ncep2012 tripolar converted to spherical
#ifdef DYNAMIC
INTEGER :: nlon=720
INTEGER :: nlat=410
INTEGER :: nlev=5
#else
INTEGER,PARAMETER :: nlon=720
INTEGER,PARAMETER :: nlat=410
INTEG... | {"hexsha": "e43f4eecd057fbbf6968ac267c6e9dd5d812feaa", "size": 4212, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "src/model_specific/sis/params_model.f90", "max_stars_repo_name": "GEOS-ESM/Ocean-LETKF", "max_stars_repo_head_hexsha": "a7c4bbf86cdbff078212914dcc059d0b1450accf", "max_stars_repo_licenses": ["Ap... |
# Invertible network based on Glow (Kingma and Dhariwal, 2018)
# Includes 1x1 convolution and residual block
# Author: Philipp Witte, pwitte3@gatech.edu
# Date: February 2020
export NetworkGlow, NetworkGlow3D
"""
G = NetworkGlow(n_in, n_hidden, L, K; k1=3, k2=1, p1=1, p2=0, s1=1, s2=1)
G = NetworkGlow3D(n_in... | {"hexsha": "8b130ec3ed00254c2b31ba70d87b6dde2b86903e", "size": 7300, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/networks/invertible_network_glow.jl", "max_stars_repo_name": "PetersBas/InvertibleNetworks.jl", "max_stars_repo_head_hexsha": "c53dacf426ecd1381f79f297f6954e6695c515b3", "max_stars_repo_license... |
import cv2
import numpy as np
import matplotlib.pyplot as plt
# Read image
img = cv2.imread("imori.jpg").astype(np.float32)
H, W, C = img.shape
img2 = cv2.imread("thorino.jpg").astype(np.float32)
a = 0.6
out = img * a + img2 * (1 - a)
out = out.astype(np.uint8)
# Save result
cv2.imwrite("out.jpg", out)
cv2.imsh... | {"hexsha": "7003d5902ab62a4f060edc115b6aa7ccb1358e5d", "size": 377, "ext": "py", "lang": "Python", "max_stars_repo_path": "Question_51_60/answers/answer_60.py", "max_stars_repo_name": "Zpadger/ImageProcessing100Wen", "max_stars_repo_head_hexsha": "993ebc6c16c43b1fc664382833ef7724b439e1ec", "max_stars_repo_licenses": ["... |
# Script 2/2 to ensure that corr did the same thing between R and Python (June 2016)
import numpy as np
import pandas as pd
METHOD='spearman'
# my_df = pd.DataFrame([[3,2,np.nan], [5,9,3], [1,np.nan],[2,8,2], [4,1,8]])
my_df = pd.DataFrame.from_dict({"a":[3,5,1,2,4], "b":[2,9,np.nan,8,1], "c":[np.nan,3,5,2,8]})
print... | {"hexsha": "148a5e2e9927f2c1ab6e88f0e36d9db56e3f7a7a", "size": 1142, "ext": "py", "lang": "Python", "max_stars_repo_path": "broadinstitute_psp/utils/corr_verification.py", "max_stars_repo_name": "cmap/psp", "max_stars_repo_head_hexsha": "9389e9d86424e460e577dd1d9027f4a1d1f8227a", "max_stars_repo_licenses": ["BSD-3-Clau... |
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