prompt stringlengths 19 1.03M | completion stringlengths 4 2.12k | api stringlengths 8 90 |
|---|---|---|
import pytest
import numpy as np
import pandas as pd
from dask.array import from_array, Array
from dask.delayed import Delayed
from dask.dataframe import from_pandas, Series, to_numeric
@pytest.mark.parametrize("arg", ["5", 5, "5 "])
def test_to_numeric_on_scalars(arg):
output = to_numeric(arg)
assert isinst... | pd.Series(["1.0", "2", -3, -5.1]) | pandas.Series |
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import itertools
from sklearn.metrics import accuracy_score
from scipy.optimize import curve_fit
from sklearn.metrics import r2_score
from matplotlib.patches import Rectangle
def objective(x, a, b, c):
return a * np.exp(... | pd.read_pickle(dataset_pkl) | pandas.read_pickle |
import matplotlib.pyplot as plt
import cartopy.crs as ccrs
import cartopy.io.shapereader as shpreader
from matplotlib import cm
from matplotlib import colors
import monet as m
import numpy as np
import pandas as pd
import wrf as wrfpy
import xarray as xr
def get_proj(ds):
"""
Extracts information about the CM... | pd.Timestamp(f'{date} 00') | pandas.Timestamp |
"""
generic
"""
from __future__ import (absolute_import, division,
print_function, unicode_literals)
import sys
import numpy as np
import pandas as pd
from sklearn.ensemble import ExtraTreesRegressor, GradientBoostingRegressor
from scipy import stats
from ..util import timeout, TimeoutError
... | pd.Series(index=X.columns, name=y.name) | pandas.Series |
# coding=utf-8
# pylint: disable-msg=E1101,W0612
from datetime import datetime, timedelta
import operator
from itertools import product, starmap
from numpy import nan, inf
import numpy as np
import pandas as pd
from pandas import (Index, Series, DataFrame, isnull, bdate_range,
NaT, date_range, ti... | pd.offsets.Second(5) | pandas.offsets.Second |
import re
from datetime import datetime, timedelta
import numpy as np
import pandas.compat as compat
import pandas as pd
from pandas.compat import u, StringIO
from pandas.core.base import FrozenList, FrozenNDArray, DatetimeIndexOpsMixin
from pandas.util.testing import assertRaisesRegexp, assert_isinstance
from pandas i... | tm.assert_series_equal(result, expected_s) | pandas.util.testing.assert_series_equal |
#!/usr/bin/env python
# coding: utf-8
# # Experiments @Fischer in Montebelluna 28.02.20
# We had the oppurtunity to use the Flexometer for ski boots of Fischer with their help at Montebelluna. The idea is to validate our system acquiring simultaneously data by our sensor setup and the one from their machine. With the... | pd.DataFrame(f[0],columns=['force [N]']) | pandas.DataFrame |
# Place this file in src/ of the DIPS folder preprocessed as in
# https://github.com/amorehead/DIPS-Plus up to and including the step prune_pairs.py (but not beyond that step).
import logging
import os
import random
from pathlib import Path
import atom3.pair as pa
import click
import pandas as pd
from atom3 import da... | pd.read_csv(pairs_postprocessed_txt, header=None) | pandas.read_csv |
import pandas as pd
import numpy as np
import json
import pycountry_convert as pc
from ai4netmon.Analysis.aggregate_data import data_collectors as dc
from ai4netmon.Analysis.aggregate_data import graph_methods as gm
FILES_LOCATION = 'https://raw.githubusercontent.com/sermpezis/ai4netmon/main/data/misc/'
PATH_AS_RANK ... | pd.isna(cc) | pandas.isna |
# -*- coding: utf-8 -*-
"""
Created on Sun Apr 1 00:49:21 2018
@author: teo
"""
# -*- coding: utf-8 -*-
"""
Created on Thu Mar 8 10:32:18 2018
@author: teo
"""
import pandas as pd
from plotly import tools
import numpy as np
import matplotlib.pyplot as plt
import plotly.plotly as py
import... | pd.DataFrame({'email':df_plot_helper_department2.index, 'list':df_plot_helper_department2.values}) | pandas.DataFrame |
'''
Copyright 2022 Airbus SAS
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 writing, software
dis... | pd.DataFrame({'years':years, 'share_investment': share_invest}) | pandas.DataFrame |
#===============================================================================#
# PyGrouper - <NAME>
from __future__ import print_function
import re, os, sys, time
import itertools
import json
import logging
from time import sleep
from collections import defaultdict
from functools import partial
from math import cei... | pd.merge(genes_df, gpgs, on='GeneID', how='left') | pandas.merge |
# -*- coding: utf-8 -*-
# Loading libraries
import os
import sys
import time
from networkx.algorithms.centrality import group
import pandas as pd
import re
import csv
from swmmtoolbox import swmmtoolbox as swmm
from datetime import datetime
from os import listdir
from concurrent import futures
from sqlalchemy import cr... | pd.DataFrame() | pandas.DataFrame |
import pandas as pd
import numpy as np
import os
import datetime
import requests
from tqdm import tqdm
from collections import Counter
import joblib
import os
# TODO: re-implement sucking data from the internet by checking for all days
# and sucking only what it needs and put that in the load_data module
# so it a... | pd.DatetimeIndex(deaths_series.index) | pandas.DatetimeIndex |
import pandas as pd
import numpy as np
import pandas
import csv
import ast
from sklearn.tree import DecisionTreeClassifier
from sklearn.neural_network import MLPClassifier
from sklearn.linear_model import LogisticRegression
from sklearn import svm
from sklearn.metrics import f1_score,confusion_matrix
from sklearn.metri... | pd.merge(df_prob_synt,df_train,on=["ProblemID"]) | pandas.merge |
import sys
import os
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import scipy as sp
import pylab
from matplotlib import colors, colorbar
from scipy import cluster
#import rpy2
#import rpy2.robjects as robjects
#from rpy2.robjects.packages import importr
from tqdm import tqdm
#from rpy2.robjec... | pd.pivot_table(at, index="cellBC", columns="intBC", values="UMI", aggfunc="count") | pandas.pivot_table |
import inspect
import os
import datetime
from collections import OrderedDict
import numpy as np
from numpy import nan, array
import pandas as pd
import pytest
from pandas.util.testing import assert_series_equal, assert_frame_equal
from numpy.testing import assert_allclose
from pvlib import tmy
from pvlib import pvsy... | pd.Series([10]) | pandas.Series |
import datetime
from collections import OrderedDict
import warnings
import numpy as np
from numpy import array, nan
import pandas as pd
import pytest
from numpy.testing import assert_almost_equal, assert_allclose
from conftest import assert_frame_equal, assert_series_equal
from pvlib import irradiance
from conftes... | pd.Series([0, .5], index=times) | pandas.Series |
# Import 311 CARE/CARE+ Requests and clean
import numpy as np
import pandas as pd
import geopandas as gpd
import intake
from shapely.geometry import Point
import boto3
catalog = intake.open_catalog('./catalogs/*.yml')
bucket_name = 's3://public-health-dashboard/'
s3 = boto3.client('s3')
df = catalog.car... | pd.to_datetime(df[col]) | pandas.to_datetime |
"""
A module for parsing information from various files.
"""
import os
import re
from typing import Dict, List, Match, Optional, Tuple, Union
import numpy as np
import pandas as pd
import qcelemental as qcel
from arkane.exceptions import LogError
from arkane.ess import ess_factory, GaussianLog, MolproLog, OrcaLog, Q... | pd.DataFrame.from_dict(ic_dict) | pandas.DataFrame.from_dict |
"""Module to run a basic decision tree model
Author(s):
<NAME> (<EMAIL>)
"""
import pandas as pd
import numpy as np
import logging
from sklearn import preprocessing
from primrose.base.transformer import AbstractTransformer
class ExplicitCategoricalTransform(AbstractTransformer):
DEFAULT_NUMERIC = -9999
... | pd.to_numeric(data[name]) | pandas.to_numeric |
#!/usr/bin/env python3
"""Pastrami - Population scale haplotype copying script"""
__author__ = "<NAME>, <NAME>"
__copyright__ = "Copyright 2021, <NAME>, <NAME>"
__credits__ = ["<NAME>", "<NAME>", "<NAME>", "<NAME>"]
__license__ = "GPL"
__version__ = "0.3"
__maintainer__ = "<NAME>, <NAME>"
__email__ = "<EMAIL>; <EMAIL>... | pd.read_table(self.reference_tfam_file, index_col=None, header=None, sep=' ') | pandas.read_table |
import os
from functools import reduce
import pandas as pd
import numpy as np
from . import settings
def get_data(cryptocurrency, fillna=0):
crypto_path = os.path.join(settings.RESOURCES_DIR, cryptocurrency)
# Currency related data frames
price_df = _read_csv(os.path.join(crypto_path, 'price.csv'))
... | pd.to_datetime(topic_df['date']) | pandas.to_datetime |
import pandas as pd
c1 = pd.read_csv('machine/Calling/Sensors_1.csv')
c2 = pd.read_csv('machine/Calling/Sensors_2.csv')
c3 = pd.read_csv('machine/Calling/Sensors_3.csv')
c4 = pd.read_csv('machine/Calling/Sensors_4.csv')
c5 = pd.read_csv('machine/Calling/Sensors_5.csv')
c6 = pd.read_csv('machine/Calling/Sensors_6.csv')... | pd.read_csv('machine/Texting/Sensors_8.csv') | pandas.read_csv |
import os
import minerva as mine
import pandas as pd
import random
import re
def sentence_to_conll_string(
sentence: mine.Sentence, entity_name: str, conflate: bool = False
) -> str:
words = [t.text for t in sentence]
annos = sentence.get_annotation(entity_name)
labels = ["O"] * len(words)
if ann... | pd.read_excel(XLSX_PATH, sheet_name=sheet + "_Identified") | pandas.read_excel |
import numpy as np
import pandas as pd
def set_order(df, row):
if pd.isnull(row['order']):
if pd.notnull(row['family']):
row['order'] = df[(pd.notnull(df['order']) &
df['family']== row['family'])]['order'].head(1)
elif pd.notnull(row['genus']):
... | pd.notnull(df['order']) | pandas.notnull |
# Copyright (c) 2017, Intel Research and Development Ireland Ltd.
#
# 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 app... | pandas.DataFrame() | pandas.DataFrame |
import numpy as np
import pandas as pd
import os, errno
import datetime
import uuid
import itertools
import yaml
import subprocess
import scipy.sparse as sp
from scipy.spatial.distance import squareform
from sklearn.decomposition.nmf import non_negative_factorization
from sklearn.cluster import KMeans
from sklearn.me... | pd.Series(gene_counts_var/gene_counts_mean) | pandas.Series |
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import warnings
import calendar
import seaborn as sns
sns.set(style='white', palette='deep')
plt.style.use('grayscale')
warnings.filterwarnings('ignore')
width = 0.35
# Funções
def autolabel(rects,ax, df): #autolabel
for rect in rects:
... | pd.cut(df_compra['hora'], bins) | pandas.cut |
from strategy.rebalance import get_relative_to_expiry_rebalance_dates, \
get_fixed_frequency_rebalance_dates, \
get_relative_to_expiry_instrument_weights
from strategy.calendar import get_mtm_dates
import pandas as pd
import pytest
from pandas.util.testing import assert_index_equal, assert_frame_equal
def ass... | pd.Timestamp("2015-03-17") | pandas.Timestamp |
####################################################################################################
# EXPERIMENT TRACKING ROUTINES
####################################################################################################
import numpy as np
import imageio
from matplotlib import pyplot as plt
from ma... | pd.read_json(path + '\\' + df_name + '.json') | pandas.read_json |
#!/usr/bin/env python3
import ccxt
from configparser import ConfigParser
import json
import os
import pickle
import redis
import socket
import tempfile
import time
import threading
import zlib
import numpy as np
import talib.abstract as ta
from pandas import DataFrame, Series
from requests_futures.sessions import Futu... | Series(index=series1.index, data=series2) | pandas.Series |
# AUTOGENERATED! DO NOT EDIT! File to edit: nbs/03_key_driver_analysis.ipynb (unless otherwise specified).
__all__ = ['KeyDriverAnalysis']
# Cell
import numpy as np
import pandas as pd
pd.set_option('display.max_columns', 500)
import time
from sklearn.model_selection import train_test_split
from sklearn.ensemble impo... | pd.Series(rf.feature_importances_, index=driverNames) | pandas.Series |
# coding=utf-8
from hielen2.source import CloudSource, ActionSchema, GeoInfoSchema
from hielen2.utils import LocalFile, ColorMap, Style, FTPPath
from hielen2.ext.source_rawsource import Source as RawSource
import hielen2.api.features as featman
from hielen2.mapmanager import Multiraster
from hielen2.cloudmanager impo... | read_csv(points_file,sep=";",index_col=0,header=None) | pandas.read_csv |
import numpy as np
from pandas import Categorical, Series
import pandas._testing as tm
class TestUnique:
def test_unique_data_ownership(self):
# it works! GH#1807
Series(Series(["a", "c", "b"]).unique()).sort_values()
def test_unique(self):
# GH#714 also, dtype=float
ser = Se... | tm.assert_categorical_equal(result, cat) | pandas._testing.assert_categorical_equal |
import numpy as np
import pandas as pd
MONTHS_IN_QUARTER = 3
MONTHS_IN_YEAR = 12
CONVERSION_YIELD = 0.06
CONVERSION_FV = 1.03
GLOBEX_CODES = ("ZN", "ZB", "UB", "ZT", "TN", "Z3N", "ZF")
def _n_and_v(globex_code, year_fraction):
mask = np.in1d(globex_code, ("ZN", "ZB", "UB", "TN"))
n = mask * np.nan
v = m... | pd.concat(deliverables) | pandas.concat |
import os
from matplotlib import use
use('Agg')
from matplotlib import pyplot as plt
from matplotlib.cm import get_cmap
plt.switch_backend('agg')
import cartopy.crs as ccrs
import numpy as np
from blackswan.utils import get_time
from blackswan import templating
from blackswan.utils import prepare_static_folder
i... | pd.DataFrame() | pandas.DataFrame |
import re
import pandas as pd
# Dataframe cleaning
from qutil.format.number import fmtl, fmtn, fmtpx
def clean_column_names(df, inplace=True):
clean_cols = df.columns.str.lower().str.replace(' ', '_')
clean_cols = [re.sub(r'\W+', '', x) for x in clean_cols]
clean_cols = [re.sub('__', '_', x) for x in cle... | pd.to_datetime(df[key]) | pandas.to_datetime |
import datetime
from collections import OrderedDict
import numpy as np
import pandas as pd
import pytest
from numpy.testing import assert_almost_equal, assert_allclose
from pandas.util.testing import assert_frame_equal, assert_series_equal
from pvlib.location import Location
from pvlib import clearsky
from pvlib im... | pd.Series([80, 100, 85, 70, 85]) | pandas.Series |
import os
import pandas as pd
import json
from cloud_pricing.data.interface import FixedInstance
class AWSProcessor(FixedInstance):
aws_gpu_ram = {
'p3': ('V100', 16),
'p2': ('K80', 12),
'g4': ('T4', 16),
'g3': ('M60', 8)
}
aws_pricing_index_ohio_url = "https://pricing.us... | pd.DataFrame(pricing_data) | pandas.DataFrame |
import json
import pandas as pd
def get_char_lines(script):
dialog_json = script["dialog"]
dialog = json_normalize(dialog_json)
return dialog
def get_char_names(script):
char_names = script["characters"]
return char_names
def get_char_lines(char_names, dialog):
char_lines = list(... | pd.concat(char_lines, axis=0) | pandas.concat |
from __future__ import division
import numpy as np
import os.path
import sys
import pandas as pd
from base.uber_model import UberModel, ModelSharedInputs
from .therps_functions import TherpsFunctions
import time
from functools import wraps
def timefn(fn):
@wraps(fn)
def measure_time(*args, **kwargs):
... | pd.Series([], dtype='float', name="out_eec_arq_herp_hm") | pandas.Series |
import re
from copy import copy
from typing import Iterable, Optional, Union
import pandas as pd
import requests
from bs4 import BeautifulSoup
from pvoutput.consts import (
MAP_URL,
PV_OUTPUT_COUNTRY_CODES,
PV_OUTPUT_MAP_COLUMN_NAMES,
REGIONS_URL,
)
_MAX_NUM_PAGES = 1024
def get_pv_systems_for_coun... | pd.DataFrame(metadata) | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""
Created on Sun Mar 29 11:21:50 2020
@author: kaisa
"""
import os
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib import cm
import matplotlib.dates as mdates
import seaborn as sns
from datetime import datetime, timedelta
import numpy as np
from bokeh.plotting import Col... | pd.Series(covid_conf[c], name='Cases') | pandas.Series |
# Mar21, 2022
##
#---------------------------------------------------------------------
# SERVER only input all files (.bam and .fa) output MeH matrix in .csv
# August 3, 2021 clean
# FINAL github
#---------------------------------------------------------------------
import random
import math
import pysam
import csv
... | pd.read_csv(tomerge_dir) | pandas.read_csv |
from distutils.version import LooseVersion
from warnings import catch_warnings
import numpy as np
import pytest
from pandas._libs.tslibs import Timestamp
import pandas as pd
from pandas import (
DataFrame,
HDFStore,
Index,
MultiIndex,
Series,
_testing as tm,
bdate_range,
concat,
d... | tm.assert_frame_equal(expected, result) | pandas._testing.assert_frame_equal |
# -*- coding: utf-8 -*-
# This file as well as the whole tsfresh package are licenced under the MIT licence (see the LICENCE.txt)
# <NAME> (<EMAIL>), Blue Yonder Gmbh, 2016
import pandas as pd
import numpy as np
from tests.fixtures import DataTestCase
import mock
from tsfresh.transformers.relevant_feature_augmenter im... | pd.DataFrame(index=[10, 500]) | pandas.DataFrame |
import pandas as pd
import numpy as np
import datetime
import pickle
from mlxtend.preprocessing import TransactionEncoder
from mlxtend.frequent_patterns import apriori
from mlxtend.frequent_patterns import association_rules
with open("top_n-20m.pickle", "rb") as fp:
top_n = pickle.load(fp)
top_n_items = [ [x[0] fo... | pd.DataFrame.sparse.from_spmatrix(te_ary, columns=te.columns_) | pandas.DataFrame.sparse.from_spmatrix |
import numpy as np
import os
import pandas as pd
######## feature template ########
def get_bs_cat(df_policy, idx_df, col):
'''
In:
DataFrame(df_policy),
Any(idx_df),
str(col),
Out:
Series(cat_),
Description:
get category directly from df_policy
'''
df = ... | pd.isnull(real_mc_mean) | pandas.isnull |
"""Plotting Utils."""
import altair as alt
import numpy as np
import pandas as pd
def similarity_heatmaps(sim_of_sim, labels_dict, axis_title='', width=300, columns=2, min_step=1):
plot_data = pd.DataFrame()
for key in sim_of_sim:
# Compute x^2 + y^2 across a 2D grid
labels = labels_dict[key] or rang... | pd.DataFrame() | pandas.DataFrame |
import re
import numpy as np
import pandas as pd
import itertools
from collections import OrderedDict
from tqdm.auto import tqdm
import datetime
from sklearn.model_selection import KFold, StratifiedKFold
from sklearn.feature_extraction.text import CountVectorizer
from logging import getLogger
logger = getLogger("splt... | pd.read_csv("../data/merge_A1-uid.csv") | pandas.read_csv |
from IMLearn.utils import split_train_test
from IMLearn.learners.regressors import LinearRegression
from typing import NoReturn
import numpy as np
import pandas as pd
import plotly.graph_objects as go
import plotly.express as px
import plotly.io as pio
pio.templates.default = "simple_white"
def load_data(filename: ... | pd.concat([floors, data[features], zipcodes], axis=1) | pandas.concat |
#-----------------------------------------------------------------
#-- Master Thesis - Model-Based Predictive Maintenance on FPGA
#--
#-- File : prediction.py
#-- Description : Model analysis module on test data
#--
#-- Author : <NAME>
#-- Master : MSE Mechatronics
#-- Date : 14.01.2022
#-------------------------------... | DataFrame() | pandas.DataFrame |
"""
Tests for simulation of time series
Author: <NAME>
License: Simplified-BSD
"""
import numpy as np
import pandas as pd
from numpy.testing import assert_, assert_allclose, assert_equal
import pytest
from scipy.signal import lfilter
from .test_impulse_responses import TVSS
from statsmodels.tools.sm_exceptions impor... | pd.date_range(start='2000', periods=2, freq='M') | pandas.date_range |
import os
import pandas as pd
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from typing import Union, Optional, List, Dict
from tqdm import tqdm
from .basic_predictor import BasicPredictor
from .utils import inverse_preprocess_data
from common_utils_dev import to_parquet, to_abs_... | pd.DataFrame(abs_bins) | pandas.DataFrame |
"""
oil price data source: https://www.ppac.gov.in/WriteReadData/userfiles/file/PP_9_a_DailyPriceMSHSD_Metro.pdf
"""
import pandas as pd
import numpy as np
import tabula
import requests
import plotly.express as px
import plotly.graph_objects as go
import time
from pandas.tseries.offsets import MonthEnd
import re
impor... | pd.melt(consumption_df, id_vars = 'products',var_name='month',value_name='average_cons') | pandas.melt |
#!/usr/bin/env python
import click
import numpy as np
import os
import pandas as pd
import re
import torch
from tqdm import tqdm
bar_format = "{percentage:3.0f}%|{bar:20}{r_bar}"
# Local imports
from architectures import PWM, get_metrics
from train import _get_seqs_labels_ids, _get_data_loader
from utils import get_f... | pd.DataFrame(aucs, columns=["PWM"]+[m for m in metrics]) | pandas.DataFrame |
#! python3
#import os
#os.environ["R_HOME"] = r""
#os.environ["path"] = r"C:\Users\localadmin\Anaconda3;C:\Users\localadmin\Anaconda3\Scripts;C:\Users\localadmin\Anaconda3\Library\bin;C:\Users\localadmin\Anaconda3\Library\mingw-w64\lib;C:\Users\localadmin\Anaconda3\Library\mingw-w64\bin;" + os.environ["path"]
import o... | pd.read_csv(r["ion"]) | pandas.read_csv |
# -*- coding: utf-8 -*-
import pytest
import numpy as np
from pandas.compat import range
import pandas as pd
import pandas.util.testing as tm
# -------------------------------------------------------------------
# Comparisons
class TestFrameComparisons(object):
def test_df_boolean_comparison_error(self):
... | pd.DataFrame(np.nan, index=df.index, columns=df.columns) | pandas.DataFrame |
#!/usr/bin/env python
# coding: utf-8
# -------------------------------------------------------------------
# **TD DSA 2021 de <NAME> - rapport de <NAME>**
# ------------------------- -------------------------------------
# # Analyse descriptive
# ## Setup
# In[5]:
get_ipython().system('pip install textbl... | pd.Series(positive_text_prepro) | pandas.Series |
#from subprocess import Popen, check_call
#import os
import pandas as pd
import numpy as np
import math
import PySimpleGUI as sg
import webbrowser
# Read Data
csv_path1 = "output/final_data.csv"
prop_df = pd.read_csv(csv_path1)
n = prop_df.shape[0]
prop_df.sort_values(by=["PRICE"],ascending=True,inplace=True)
prop... | pd.isnull(prop_df["ZESTIMATE"][i]) | pandas.isnull |
import os
import zipfile as zp
import pandas as pd
import numpy as np
import core
import requests
class Labels:
init_cols = [
'station_id', 'station_name', 'riv_or_lake', 'hydroy', 'hydrom', 'day',
'lvl', 'flow', 'temp', 'month']
trans_cols = [
'date', 'year', 'month', 'day', 'hydroy', 'hydrom', 'station_id'... | pd.to_datetime(trans_df[['year', 'month', 'day']]) | pandas.to_datetime |
# -*- coding: utf-8 -*-
# e_bb_retriever
# <NAME>
version = 'e_bb_retriever.v.9.0.0'
# Python modules
import os
import pickle as pic
import argparse
# External modules
import pandas as pd
# Local modules
from classes.libdesign import LibDesign
from classes.logger import Logger
if __name__ == '__mai... | pd.concat(int_dfs) | pandas.concat |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Sat Jan 9 13:55:53 2021
@author: Clement
"""
import pandas
import geopandas as gpd
import numpy
import os
import sys
import datetime
sys.path.append(os.path.dirname(os.path.dirname(os.path.realpath(__file__))))
from gen_fct import file_fct
from gen_fct im... | pandas.DataFrame(index=db_daily.index, columns=['date', 'delta_day']) | pandas.DataFrame |
from tracemalloc import Statistic
from turtle import color
from unittest import result
import pandas as pd
import numpy as np
import scipy
from scipy.stats import norm
from scipy.optimize import minimize
import ipywidgets as widgets
from IPython.display import display
def drawdown(return_series: pd.Series, amount: fl... | pd.DataFrame() | pandas.DataFrame |
# Arithmetic tests for DataFrame/Series/Index/Array classes that should
# behave identically.
from datetime import datetime, timedelta
import numpy as np
import pytest
from pandas.errors import (
NullFrequencyError, OutOfBoundsDatetime, PerformanceWarning)
import pandas as pd
from pandas import (
DataFrame, ... | pd.DataFrame([1, 2, 3], index=tdi) | pandas.DataFrame |
#!/usr/bin/env python3
# SPDX-License-Identifier: BSD-3-Clause-Clear
# Copyright (c) 2019, The Numerical Algorithms Group, Ltd. All rights reserved.
"""Shared routines for different Metric Sets
"""
from warnings import warn
import numpy
import pandas
from ..trace import Trace
from ..traceset import TraceSet
from ..... | pandas.Series(data=[0.0], index=[idxkey]) | pandas.Series |
from IMLearn.utils import split_train_test
from IMLearn.learners.regressors import LinearRegression
from typing import NoReturn
import numpy as np
import pandas as pd
import plotly.graph_objects as go
import plotly.express as px
import plotly.io as pio
pio.templates.default = "simple_white"
def load_data(filename: ... | pd.DatetimeIndex(df['date']) | pandas.DatetimeIndex |
import matplotlib.pyplot as plt
import os
import numpy as np
import pandas as pd
from matplotlib import cm
# import matplotlib
from adjustText import adjust_text
import re
import matplotlib.patheffects as pe
import scipy.stats as st
# deprecated
def plot_hist_exp_1(results, household_size, pool_size, prevalence):
... | pd.read_csv(filename) | pandas.read_csv |
import os
import sys
import xarray as xr
import numpy as np
import pandas as pd
from datetime import datetime
from dateutil.relativedelta import relativedelta
pkg_dir = os.path.join(os.path.dirname(__file__),'..')
sys.path.append(pkg_dir)
from silverpieces.functions import *
def fill_time_index(nd_array):
td = ... | pd.to_datetime('2009-12-31') | pandas.to_datetime |
# coding: utf-8
# In[2]:
get_ipython().magic('matplotlib inline')
import matplotlib.pyplot as plt
from keras.layers import Bidirectional, Input, LSTM, Dense, Activation, Conv1D, Flatten, Embedding, MaxPooling1D, Dropout
#from keras.layers.embeddings import Embedding
from keras.preprocessing.sequence import pad_seq... | pd.concat([X_train_toxic, X_train_severe_toxic, X_train_obscene, X_train_threat, X_train_insult, X_train_identity_hate, X_train_rest]) | pandas.concat |
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not u... | pd.Series.unique(series) | pandas.Series.unique |
import math
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import os
from pandas.core.frame import DataFrame
from torch.utils.data import Dataset, DataLoader
import torch
import pickle
import datetime
class data_loader(Dataset):
def __init__(self, df_feature, df_label, df_label_reg, t=No... | pd.to_datetime(end_date) | pandas.to_datetime |
import pandas as pd
from src.utility.file_utility import get_directory_files, create_directory, copy_file
from src.utility.system_utility import progress_bar
from src.utility.image_utility import load_image, crop_roi, save_image
from sklearn.model_selection import train_test_split
def get_labels(n_labels, as_string=T... | pd.read_csv(data_frame_path, sep=sep) | pandas.read_csv |
from __future__ import print_function
from datetime import datetime, timedelta
import numpy as np
import pandas as pd
from pandas import (Series, Index, Int64Index, Timestamp, Period,
DatetimeIndex, PeriodIndex, TimedeltaIndex,
Timedelta, timedelta_range, date_range, Float64Index... | tm.assert_index_equal(res, exp) | pandas.util.testing.assert_index_equal |
# Copyright 2021 VicEdTools 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 by applicable law or agreed to in writi... | pd.read_csv(student_details_file, dtype=np.str) | pandas.read_csv |
# -*- coding: utf-8 -*-
import torch
from pytorch_fid import fid_score
import pandas as pd
from glob import glob
import os, argparse
import numpy as np
# %%
device = torch.device('cuda' if (torch.cuda.is_available()) else 'cpu')
batch_size = 50
dim = 2048
#path1 = './Datasets/Zurich_patches/fold2/patch... | pd.DataFrame(columns=header) | pandas.DataFrame |
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
plt.style.use('ggplot')
target = 'scale'
# IP
plot_mode = 'all_in_one'
obj = 'occ'
# Port
flow_dir = 'all'
port_dir = 'sys'
user_plot_pr = ['TCP']
user_plot_pr = ['UDP']
port_hist = pd.DataFrame({'A' : []})
user_port_hist = pd.DataFrame({'A' : []... | pd.read_csv("./postprocessed_data/%s/day2_90user.csv" % files[data_idx]) | pandas.read_csv |
#!/usr/bin/env python
"""
DataExplore Application based on pandastable.
Created January 2014
Copyright (C) <NAME>
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 versio... | pd.read_excel(filename,sheetname=None) | pandas.read_excel |
import glob
import pandas as pd
import numpy as np
import config
from lcoc import afdc
import warnings
warnings.simplefilter(action='ignore', category=FutureWarning)
##### Functions #####
###################
### Residential ###
###################
def res_rates_to_utils(scenario = 'baseline',
... | pd.read_csv(urdb_rates_files[prof], low_memory=False) | pandas.read_csv |
# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #
# Distance Measurement Calculation.py: This file uses the Mahalanobis Distance distance-based #
# matching technique to match donors and recipients #
# ... | pd.DataFrame() | pandas.DataFrame |
from package import dataHandler as dh
from package import featureHandler as fh
from sklearn.model_selection import KFold
from sklearn.linear_model import LogisticRegression
from sklearn.svm import SVC
from sklearn.naive_bayes import GaussianNB
from sklearn.neighbors import KNeighborsClassifier
from sklearn.tree import ... | pd.DataFrame(columns=['Possible PD matches','How many']) | pandas.DataFrame |
# coding: utf-8
import pandas as pd
from pandas import Series,DataFrame
import numpy as np
import itertools
import matplotlib.pyplot as plt
get_ipython().magic('matplotlib inline')
from collections import Counter
import re
import datetime as dt
from datetime import date
from datetime import datetime
i... | pd.to_datetime(tweets.date) | pandas.to_datetime |
import pytest
import os
from mapping import util
from pandas.util.testing import assert_frame_equal, assert_series_equal
import pandas as pd
from pandas import Timestamp as TS
import numpy as np
@pytest.fixture
def price_files():
cdir = os.path.dirname(__file__)
path = os.path.join(cdir, 'data/')
files = ... | TS('2015-01-03') | pandas.Timestamp |
import numpy as np
import pandas as pd
from datetime import date
### Settings
all_persons = [
101, 102, 103, 104, 105, 106, 107, 211, 212, 213, 214, 215, 216, 217
]
persons_no_task = [0, 0, 0, 0, 0]
nr_of_tasks = 10
nr_of_weeks_new = 54
nr_of_monthly_tasks = 4
hallways = 5 #task number of hallways... | pd.DataFrame({'Day': datevector_str}) | pandas.DataFrame |
import subprocess
import numpy as np
import pandas as pd
from nicenumber import __version__, getlog
from nicenumber import nicenumber as nn
from pytest import raises
def test_init():
"""Test main package __init__.py"""
# test getlog function works to create logger
log = getlog(__name__)
assert log.n... | pd.isnull(expected_result) | pandas.isnull |
import nltk
from nltk.corpus import stopwords
import pandas as pd
import string
from collections import Counter
from keras.preprocessing.text import Tokenizer
from keras.models import Sequential
from keras.layers import Dense, Dropout
import random
from numpy import array
from pandas import DataFrame
from matplotlib im... | pd.get_dummies(data['season']) | pandas.get_dummies |
""" I/O functions of the aecg package: tools for annotated ECG HL7 XML files
This module implements helper functions to parse and read annotated
electrocardiogram (ECG) stored in XML files following HL7
specification.
See authors, license and disclaimer at the top level directory of this project.
"""
# Imports ====... | pd.DataFrame([valrow2], columns=VALICOLS) | pandas.DataFrame |
import os
import sqlite3
import numpy as np
import scipy.special as ss
import pylab as pl
import pandas as pd
from astropy.io import fits
import om10_lensing_equations as ole
__all__ = ['LensedHostGenerator', 'generate_lensed_host',
'lensed_sersic_2d', 'random_location']
def boundary_max(data):
ny, nx... | pd.merge(host_df, lens_df, on='lens_cat_sys_id', how='inner') | pandas.merge |
# -*- coding: utf-8 -*-
import numpy as np
import pandas as pd
import datetime
from reda.importers.eit_version_2010 import _average_swapped_current_injections
def _extract_adc_data(mat, **kwargs):
"""Extract adc-channel related data (i.e., data that is captured for all 48
channels of the 40-channel medusa sys... | pd.to_datetime(df['datetime']) | pandas.to_datetime |
import requests
import pandas as pd
_MACHINE_SCHEDULE_RDB_URL='http://rdb.pri.diamond.ac.uk/php/opr/cs_oprgetjsonyearcal.php'
class MachineScheduleItem:
def __init__(self, item: dict):
self._item = item
@staticmethod
def _str_to_datetime(dt_str: str):
return pd.to_datetime(dt_str, utc=Tr... | pd.Series([r.duration for r in run]) | pandas.Series |
import os
import sys
import math
from neuralprophet.df_utils import join_dataframes
import numpy as np
import pandas as pd
import torch
from collections import OrderedDict
from neuralprophet import hdays as hdays_part2
import holidays as pyholidays
import warnings
import logging
log = logging.getLogger("NP.utils")
d... | pd.DataFrame() | pandas.DataFrame |
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import json
import warnings
import sys
import absl.logging
absl.logging.set_verbosity(absl.logging.ERROR)
import argparse
import logging
import os
"""Silence every warning of notice from tensorflow."""
logging.getLogger('tensorflow').setLevel(logg... | pd.read_csv(self.test_path) | pandas.read_csv |
import numpy as np
import pandas as pd
import json
from mplsoccer.pitch import Pitch, VerticalPitch
path = "C:/Users/brand/desktop/events/events_England.json"
with open(path) as f:
data = json.load(f)
train = pd.DataFrame(data)
path2 = "C:/Users/brand/desktop/players.json"
with open(path... | pd.DataFrame(columns=["Goal","X","Y"], dtype=object) | pandas.DataFrame |
import unittest
import platform
import random
import string
import platform
import pandas as pd
import numpy as np
import numba
import hpat
from hpat.tests.test_utils import (count_array_REPs, count_parfor_REPs, count_parfor_OneDs,
count_array_OneDs, dist_IR_contains, get_start_end)
... | pd.DataFrame({'A': ['aa', 'b', None, 'ccc']}) | pandas.DataFrame |
from collections import OrderedDict
from datetime import timedelta
import numpy as np
import pytest
from pandas.core.dtypes.dtypes import CategoricalDtype, DatetimeTZDtype
import pandas as pd
from pandas import (
Categorical,
DataFrame,
Series,
Timedelta,
Timestamp,
_np_version_under1p14,
... | Timestamp("2013-01-01 00:00:00-0500", tz="US/Eastern") | pandas.Timestamp |
from datetime import datetime, timedelta
from typing import Any
import weakref
import numpy as np
from pandas._libs import index as libindex
from pandas._libs.lib import no_default
from pandas._libs.tslibs import frequencies as libfrequencies, resolution
from pandas._libs.tslibs.parsing import parse_time_string
from ... | PeriodArray(rawarr, freq=self.freq) | pandas.core.arrays.period.PeriodArray |
"""
This library contains a set of functions that help you detect similar
images in your archive. It will detect the 'best' image per group using a
high-pass filter and copy these to another folder.
Thus, you do not have to pre-select images for your photo show yourself
(content itself is not considered a qua... | pd.Series(image) | pandas.Series |
import datetime
import logging
from pathlib import Path
import numpy as np
import pandas as pd
import plotly.graph_objs as go
from numpy.linalg import inv
from scipy.linalg import sqrtm
from sklearn import covariance
from sklearn.base import BaseEstimator
from sklearn.covariance import EmpiricalCovariance
from sklearn... | pd.Series(self.global_sharpe, index=sigma.index) | pandas.Series |
# -*- coding: utf-8 -*-
"""
Created on Tue Sep 14 10:59:05 2021
@author: franc
"""
import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from pathlib import Path
import json
from collections import Counter, OrderedDict
import math
import torchtext
from torchtext.data import get_tokenizer
... | pd.DataFrame({'spanish': ["bonito"], 'english': ["atlantic_bonito"]}) | pandas.DataFrame |
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