prompt stringlengths 19 1.03M | completion stringlengths 4 2.12k | api stringlengths 8 90 |
|---|---|---|
# Copyright 2021 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, ... | pd.DataFrame() | pandas.DataFrame |
# Rutina que preprocesa y transforma los datos para series de tiempo
# <NAME>
# <NAME>
# ------------------------------------------------------------------
# Entrada: 2 o mas archivos .csv asincronos.
# Salida: Archivo binario hdf5 con chunks de datos sincronizados
#
# Cada archivo csv debe tener una columna temporal,... | pandas.Timedelta(seconds=1) | pandas.Timedelta |
import pandas as pd
from sklearn.model_selection import train_test_split
pd.options.mode.chained_assignment = None
def data_preprocessing():
df = pd.read_csv("data/SCADA_data.csv.gz")
status_data_wec = pd.read_csv("data/status_data_wec.csv")
df["Inverter avg. temp"] = df[
[
"CS101 : ... | pd.to_datetime(af_corr_time_wec_s) | pandas.to_datetime |
from neurospyke.cell import Cell
import glob
import os
import pandas as pd
import pickle
cache_dir = 'cached_data/'
cell_cache_dir = cache_dir + 'cells/'
query_cache_dir = cache_dir + 'queries/'
os.makedirs(query_cache_dir, exist_ok=True)
os.makedirs(cell_cache_dir, exist_ok=True)
def calc_pickle_cell_path(mat_cel... | pd.concat([df1, df2[cols_to_use]], axis=1, join_axes=[df1.index]) | pandas.concat |
# import cv2
import os,glob
import os
import sys
import tensorflow as tf
import matplotlib
import matplotlib.pyplot as plt
import matplotlib.patches as patches
import seaborn as sn
from mrcnn import utils
from mrcnn import visualize
from mrcnn.visualize import display_images
import mrcnn.model as modellib
from mrcnn.... | pd.DataFrame(listOfValues) | pandas.DataFrame |
# Steinbeck.py is a python program designed specifically for
# pulling time series data from the Johns Hopkins University
# COVID-19 github and turning them in to usable timeseries
# csv's for analysis.
# @author <NAME>, <EMAIL>
# This program written and produced for and by Cloud Brigade
import pandas as pd
import ... | pd.read_csv(datapath + case_ts) | pandas.read_csv |
# %%
import pandas as pd
from pandas.api.types import union_categoricals
import numpy as np
from utils.config import Config
#%%
c = Config()
c.validate_files()
# %%
df = pd.read_csv(c.predicted_300K)
# %%
df = df[["Monolayer 1", "Monolayer 2"]]
# %%
cats1 = pd.unique(df.to_numpy().ravel())
print(f"size of cats1: {c... | pd.Series(cats1, name='monolayer') | pandas.Series |
import spotipy
from spotipy.oauth2 import SpotifyClientCredentials
import wikipedia
import musicbrainzngs
import urllib.request
import urllib.request as urllib2
import urllib.parse
import json
import requests
from bs4 import BeautifulSoup
import re
import h5py
import time
import datetime
import pandas... | pd.DataFrame(columns=col) | pandas.DataFrame |
import argparse
import numpy as np
import pandas as pd
from tqdm import tqdm
from transformers import BertModel, BertTokenizer
def encode_query(text, tokenizer, model, device='cpu'):
max_length = 36 # hardcode for now
inputs = tokenizer(
'[CLS] [Q] ' + text + ' [MASK]' * max_length,
max_leng... | pd.DataFrame(embeddings) | pandas.DataFrame |
import os
import pandas as pd
import pytest
from pytest_mock import MockerFixture
from src import config
from src.selection import select_data
class TestFilter2017and2014and2011:
def test_data_frame(self):
code = select_data.COMPUTER_SCIENCE_CODE_2017_2014_2011
input_df = pd.DataFrame(columns=... | pd.Series([".ad..", "..d.."]) | pandas.Series |
import matplotlib.pyplot as plt
import datetime as datetime
import numpy as np
import pandas as pd
import talib
import seaborn as sns
from time import time
from sklearn import preprocessing
from pandas.plotting import register_matplotlib_converters
from .factorize import FactorManagement
import scipy.stats as stats
imp... | pd.to_datetime(close.index) | pandas.to_datetime |
from datetime import timedelta
import operator
from typing import Any, Callable, List, Optional, Sequence, Union
import numpy as np
from pandas._libs.tslibs import (
NaT,
NaTType,
frequencies as libfrequencies,
iNaT,
period as libperiod,
)
from pandas._libs.tslibs.fields import isleapyear_arr
from... | get_period_field_arr(alias, self.asi8, base) | pandas._libs.tslibs.period.get_period_field_arr |
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... | PeriodIndex([pd.NaT], freq='M') | pandas.PeriodIndex |
import pandas as pd
import numpy as np
from pandas.tseries.holiday import USFederalHolidayCalendar
from oolearning.transformers.TransformerBase import TransformerBase
class EncodeDateColumnsTransformer(TransformerBase):
"""
Replaces each date column with numeric/boolean columns that represent things such as:... | USFederalHolidayCalendar() | pandas.tseries.holiday.USFederalHolidayCalendar |
from .branches import MuType
import numpy as np
import pandas as pd
import os
from functools import reduce
from itertools import combinations as combn
from itertools import product
from operator import or_
from re import sub as gsub
from copy import deepcopy
from math import log10, floor
from sklearn.cluster impor... | pd.isnull(val) | pandas.isnull |
"""
Tests for zipline/utils/pandas_utils.py
"""
from unittest import skipIf
import pandas as pd
from zipline.testing import parameter_space, ZiplineTestCase
from zipline.testing.predicates import assert_equal
from zipline.utils.pandas_utils import (
categorical_df_concat,
nearest_unequal_elements,
new_pan... | pd.Series(['a', 'b', 'c'], dtype='category') | pandas.Series |
# -*- coding: utf-8 -*-
"""
Tests that quoting specifications are properly handled
during parsing for all of the parsers defined in parsers.py
"""
import csv
import pytest
from pandas.compat import PY3, StringIO, u
from pandas.errors import ParserError
from pandas import DataFrame
import pandas.util.testing as tm
... | StringIO(data) | pandas.compat.StringIO |
from functools import reduce
from config import PERIODO_INI, PERIODO_FIN
import pandas as pd
import numpy as np
pd.options.mode.chained_assignment = None
def check_periods(col):
print(pd.DataFrame(
{"Rango": [col.min(), col.max()]},
index=['MIN', 'MAX'])
)
# HELPER FUNCTIONS
d... | pd.DataFrame(df_polizas_pivoted.index) | pandas.DataFrame |
import os
import json
import pandas as pd
import numpy as np
from collections import namedtuple
import pytest
import sklearn.datasets as datasets
import sklearn.neighbors as knn
import mlflow.pyfunc.scoring_server as pyfunc_scoring_server
import mlflow.sklearn
from mlflow.protos.databricks_pb2 import ErrorCode, MALFO... | pd.DataFrame.from_dict(data) | pandas.DataFrame.from_dict |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu Aug 12 07:06:50 2021
@author: nmei
"""
import os
import numpy as np
import pandas as pd
import seaborn as sns
sns.set_style('white')
sns.set_context('paper',font_scale = 2)
from matplotlib import rc
from matplotlib import pyplot as plt
from matplotlib... | pd.concat(df_chance) | pandas.concat |
import json
from typing import Dict, List, Tuple
import numpy as np
import pandas as pd
from tqdm import tqdm
from utils.utils import normalize_www_prefix
NAN_VALUE = -1
def read_csv(path: str) -> pd.DataFrame:
"""Opens the csv dataset as DataFrame and cast types.
"""
date_parser = lambda c: | pd.to_datetime(c, format='%Y-%m-%dT%H:%M:%SZ', errors='coerce') | pandas.to_datetime |
# Copyright 1999-2020 Alibaba Group Holding 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 applicable law or a... | pd.Series([3, 4, 5, 3, 5, 4, 1, 2, 3], index=sindex2) | pandas.Series |
# importing all the required libraries
import numpy as np
import pandas as pd
from datetime import datetime
import time, datetime
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.preprocessing import StandardScaler, LabelEncoder, MinMaxScaler
from chart_studio.plotly import plotly
import plot... | pd.read_csv('date_info.csv') | pandas.read_csv |
import re
import pandas as pd
from google.oauth2 import service_account
from langdetect import detect_langs
from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer as SIA
import numpy as np
from numpy import mat, mean, sqrt, diag
import statsmodels.api as sm
import matplotlib.pyplot as plt
plt.style.use('... | pd.DataFrame(cs_X) | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""
Created on Fri Dec 21 18:49:14 2018
@author: kennedy
"""
import pandas as pd
import numpy as np
def process_time(df):
if 'timestamp' not in df.columns:
df.index = | pd.to_datetime(df.index) | pandas.to_datetime |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Implements the global import of all data
Created on Mon Dec 26 20:51:08 2016
@author: rwilson
"""
import numpy as np
import glob
import re
import os
import csv
from itertools import repeat
import pandas as pd
import h5py
from dateutil.parser import parse
import codec... | pd.read_csv(self.PVloc, sep=';', header=[1, 2]) | pandas.read_csv |
import pandas as pd
import xarray as xr
import re
import numpy as np
import datetime as dt
class AWS:
'''This class represents an Automatic Weather Station and its time series'''
def __init__(self, name, code, lat, lon, elev):
self.name = name
self.code = code
self.lat = lat
se... | pd.to_datetime(df['datetime'], format='%Y %m %d %H %M') | pandas.to_datetime |
# -*- coding: utf-8 -*-
# pylint: disable=E1101
# flake8: noqa
from datetime import datetime
import csv
import os
import sys
import re
import nose
import platform
from multiprocessing.pool import ThreadPool
from numpy import nan
import numpy as np
from pandas.io.common import DtypeWarning
from pandas import DataFr... | tm.assert_frame_equal(chunks[2], df[4:]) | pandas.util.testing.assert_frame_equal |
import warnings
import numpy as np
import pytest
import pandas.util._test_decorators as td
import pandas as pd
from pandas import (
DataFrame,
Series,
isna,
)
import pandas._testing as tm
class TestDataFrameCov:
def test_cov(self, float_frame, float_string_frame):
# min_periods no NAs (corn... | DataFrame({0: [1.0, -1.0], 1: [-1.0, 1.0]}) | pandas.DataFrame |
import abc
from unittest.mock import Mock
import numpy as np
import pandas as pd
import pytest
from rdt.transformers.base import BaseTransformer
class TestBaseTransformer:
def test_get_subclasses(self):
"""Test the ``get_subclasses`` method.
Validate that any subclass of the ``BaseTransformer`... | pd.testing.assert_frame_equal(data, expected) | pandas.testing.assert_frame_equal |
"""Data Updating Utility (:mod:`bucky.util.update_data_repos`).
A utility for fetching updated data for mobility and case data from public repositories.
This module pulls from public git repositories and preprocessed the
data if necessary. For case data, unallocated or unassigned cases are
distributed as necessary.
... | pd.read_csv(TERRITORY_DATA, index_col="fips") | pandas.read_csv |
#!/usr/bin/env python
# coding: utf-8
import pandas as pd
df = | pd.read_csv("Heart.csv") | pandas.read_csv |
import os
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib as mpl
import numpy as np
import matplotlib.dates as mdates
from datetime import date, timedelta, datetime
import seaborn as sns
import geopandas as gpd
from shapely.geometry import mapping, Point, Polygon
mpl.rcParams['pdf.fonttype'] = 42... | pd.read_csv(cleaned_line_list_fname) | pandas.read_csv |
"""
Collects data for the discovery cohort.
"""
from click import *
from logging import *
import janitor
import pandas as pd
import re
@command()
@option(
"--localization-input",
required=True,
help="the CSV file to load localizations from",
)
@option(
"--medication-input",
required=True,
he... | pd.read_feather(medication_input) | pandas.read_feather |
import os
import pandas as pd
import arff
import numpy as np
from functools import reduce
import sqlite3
import logging
from libs.planet_kaggle import to_multi_label_dict, get_file_count, enrich_with_feature_encoding, featurise_images, generate_validation_files
import tensorflow as tf
from keras.applications.resnet50 i... | pd.read_sql_query("SELECT * from Country", con) | pandas.read_sql_query |
from openff.toolkit.typing.engines.smirnoff import ForceField
from openff.toolkit.topology import Molecule, Topology
from biopandas.pdb import PandasPdb
import matplotlib.pyplot as plt
from operator import itemgetter
from mendeleev import element
from simtk.openmm import app
from scipy import optimize
import subprocess... | pd.DataFrame(data_tuples, columns=["Atom", "Charge"]) | pandas.DataFrame |
# -*- coding: utf-8 -*-
# pylint: disable-msg=E1101,W0612
from datetime import datetime, timedelta
import pytest
import re
from numpy import nan as NA
import numpy as np
from numpy.random import randint
from pandas.compat import range, u
import pandas.compat as compat
from pandas import Index, Series, DataFrame, isn... | tm.assert_series_equal(result, exp) | pandas.util.testing.assert_series_equal |
# -*- coding: utf-8 -*-
"""
analyze and plot results of experiments
"""
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sb
import yaml
#E2: How large can I make my output domain without loosing skill?
E2_results = pd.read_csv('param_optimization/E2_results_t2m_34_t2m.csv',sep... | pd.read_csv('param_optimization/E3_results_t2m_34_t2m_folds_2_5.csv',sep =';') | pandas.read_csv |
""" Exploratory data analysis EDA on insta-cart data-set.
"""
import pandas as pd
from pathlib import Path
import matplotlib.pyplot as plt
import seaborn as sns
def load_data():
""" Loads the data.
:return: dictionary of DataFrames with file names as keys.
"""
data_in_dict = dict()
data_path = Pa... | pd.read_csv(file) | pandas.read_csv |
import pytest
import pandas as pd
from pandas import compat
import pandas.util.testing as tm
import pandas.util._test_decorators as td
from pandas.util.testing import assert_frame_equal, assert_raises_regex
COMPRESSION_TYPES = [None, 'bz2', 'gzip',
pytest.param('xz', marks=td.skip_if_no_lzma)]
... | pd.read_json(uncompressed_path) | pandas.read_json |
#! /usr/bin/env python
# -*- coding: utf-8 -*-
import math
import numpy as np
import datetime
import time
import pandas as pd
from statsmodels.tsa.seasonal import seasonal_decompose
from pyramid.arima import auto_arima
from statsmodels.tsa.arima_model import ARIMA
from datetime import timedelta
import statsmodels.api ... | pd.to_datetime(self.startTs) | pandas.to_datetime |
import sys
import os
import seaborn as sns
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
plt.style.use("custom_standard")
# %%
def filter_mT_table(df, kd_up_lim, SE_upper_lim, kd_low_lim=0, drop_dup=True):
"""
filters existing masstitr table
filters out seqs containing * or X c... | pd.DataFrame(columns=p_fit_err_cutoffs, index=kd_cutoffs) | pandas.DataFrame |
#!/usr/bin/env python3
# -*- coding:utf-8 -*-
# ======================================================================================================================== #
# Project : Explainable Recommendation (XRec) #
# Version : 0.1.0 ... | pd.merge(df1, df2, how="left", left_on=col, right_on="value") | pandas.merge |
#!/usr/bin/env python
# coding: utf-8
# In[24]:
import numpy
import pandas as pd
import tensorflow as tf
from PyEMD import CEEMDAN
import warnings
warnings.filterwarnings("ignore")
### import the libraries
from tensorflow import keras
from tensorflow.keras import layers
from keras.models import Sequential
from ke... | pd.DataFrame(y) | pandas.DataFrame |
"""
Computational Cancer Analysis Library
Authors:
Huwate (Kwat) Yeerna (Medetgul-Ernar)
<EMAIL>
Computational Cancer Analysis Laboratory, UCSD Cancer Center
<NAME>
<EMAIL>
Computational Cancer Analysis Laboratory, UCSD Cancer Center
"""
from os.path import isfile
from matplo... | DataFrame(column_annotation) | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""
Created on Fri Nov 30 13:16:48 2018
@author: cenv0574
"""
import os
import pandas as pd
import numpy as np
import atra.utils
from ras_method import ras_method
import subprocess
import warnings
warnings.filterwarnings('ignore')
data_path= atra.utils.load_config()['paths']['data']
def cha... | pd.MultiIndex.from_arrays(
[region_col, sector_only+col_only], names=('region', 'col')) | pandas.MultiIndex.from_arrays |
import os
import pandas as pd
from scipy.spatial import distance
from tqdm import tqdm
import argparse
parser = argparse.ArgumentParser(description='To set W_PATH to the same directory that contains the coordinate data extracted from the pdb.cif files')
parser.add_argument('-o', '--output_directory', help='An output d... | pd.concat([df_2, df_1], axis=1, join='inner') | pandas.concat |
import seaborn as sns
import numpy as np
import pandas as pd
from scipy.stats import pearsonr
with open('./train.en') as f:
en = [s.strip().split(' ') for s in f]
en_len = np.array(list(map(len, en)))
with open('./train.jp') as f:
jp = [s.strip().split(' ') for s in f]
jp_len = np.array(list(map(len, ... | pd.DataFrame({'en_len':en_len, 'jp_len':jp_len}) | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""
Created on Mon Sep 17 13:52:10 2018
@author: i
"""
from tkinter import *
from tkinter import filedialog
from tkinter import messagebox
import datetime
from time import strftime
import os
import math
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
imp... | pd.DataFrame({mouse_name:my_sums}) | pandas.DataFrame |
import streamlit as st
import streamlit.components.v1 as stc
# Text Cleaning Pkgs
import neattext as nt
import neattext.functions as nfx
from collections import Counter
import pandas as pd
# Text Viz Pkgs
from wordcloud import WordCloud
from textblob import TextBlob
# Data Viz Pkgs
import matplotlib.pyplot as plt
... | pd.DataFrame({'tokens':x,'counts':y}) | pandas.DataFrame |
import logging
import copy
import yfinance as yf
import pandas as pd
import numpy as np
import pandas as pd
from pypfopt import black_litterman
from pypfopt.expected_returns import mean_historical_return
from pypfopt.black_litterman import BlackLittermanModel
from pypfopt.risk_models import CovarianceShrinkage
from s... | pd.DataFrame() | pandas.DataFrame |
import json
import os
import shutil
import jsonpickle
import pickle
import pandas as pd
from django.contrib.auth.mixins import LoginRequiredMixin
from django.shortcuts import redirect
# Create your views here.
from django.views import generic
from rest_framework.authentication import BasicAuthentication
from rest_fram... | pd.DataFrame(columns=sr.headers, data=sr.row_vals) | pandas.DataFrame |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Fri May 7 09:23:51 2021
@author: rayin
"""
import os, sys
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import warnings
import re
from pprint import pprint
from collections import Counter
from tableone import TableOne
from sdv.ev... | pd.concat([case_demographics, label], axis=1) | pandas.concat |
# -*- coding:utf-8 -*-
"""
Seamese architecture+abcnn
"""
from __future__ import division
import random
import os
import time
import datetime
import copy
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
from sklearn.metrics import accuracy_score, precision_recall_fscore_support, confusion_mat... | pd.value_counts(data['subject_senti']) | pandas.value_counts |
import numpy as np
import pandas as pd
from open_quant.labeling.multi_processing import mp_pandas
import sys
def test(a, b):
return a + b
def triple_barrier_method(close, events, pt_sl, molecule):
"""
Advances in Financial Machine Learning, Snippet 3.2, page 45.
Triple Barrier Labeling Method
Ap... | pd.Series(index=events.index) | pandas.Series |
import train_test_model
import pandas as pd
import numpy as np
#import math
import os, sys, time
from scipy.sparse import csr_matrix, save_npz, load_npz
import pickle
##########################################################################################
def usecases(predictions,item_vecs,model,movie_lis... | pd.read_pickle("./output/movies.pkl") | pandas.read_pickle |
import gzip
import math
import os
import time
from collections import OrderedDict, namedtuple
from datetime import datetime as dt
from datetime import timedelta
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from tqdm import tqdm_notebook
import ujson
LOGS = './logs/'
Window = namedtuple('Win... | pd.Timedelta('1 hour') | pandas.Timedelta |
import pandas as pd
import os
import json
import matplotlib.pyplot as plt
import seaborn as sns
with open("config.json") as file:
config = json.load(file)
config[
"intermediate_data"
] = "nextcloud-znes/KlimaSchiff/result_data/emissions"
#
# df_n = pd.read_csv(
# "/home/admin/klimaschiff/intermediate_da... | pd.to_datetime(x[0]) | pandas.to_datetime |
import nose
import warnings
import os
import datetime
import numpy as np
import sys
from distutils.version import LooseVersion
from pandas import compat
from pandas.compat import u, PY3
from pandas import (Series, DataFrame, Panel, MultiIndex, bdate_range,
date_range, period_range, Index, Categori... | assert_frame_equal(result, df) | pandas.util.testing.assert_frame_equal |
import ast
import json
import os
import sys
import uuid
import lxml
import networkx as nx
import pandas as pd
import geopandas as gpd
import pytest
from pandas.testing import assert_frame_equal, assert_series_equal
from shapely.geometry import LineString, Polygon, Point
from genet.core import Network
from genet.input... | assert_frame_equal(_network['links'][link_cols], network_1_geo_and_json['expected_geodataframe']['links'][link_cols], check_dtype=False) | pandas.testing.assert_frame_equal |
import pandas as pd
import numpy as np
from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer
from sklearn.decomposition import LatentDirichletAllocation
from sklearn.model_selection import GridSearchCV
PATH_HASHTAGS = 'tweet_tokens/training/hashtags/hashtags.csv'
PATH_MENTIONS = 'tweet_tokens/t... | pd.read_csv(PATH_MENTIONS, header=0, delimiter='\x01') | pandas.read_csv |
from Bio import SeqIO
import pandas as pd
import numpy as np
import subprocess
import os
import re
import time
import random
import itertools
import gzip
import json
import platform
import ast
import multiprocessing as mp
from multiprocessing import Manager
from os.path import expanduser
from importlib.machinery import... | pd.read_csv("./results/barcode_sort/mapped_%s_true_nucpos.csv"%(sampid)) | pandas.read_csv |
import keras
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from keras import metrics, losses
from keras.layers import Dense
from keras.layers import Dropout
from keras.layers import LSTM
from keras.models import Sequential
from sklearn.preprocessing import MinMaxScaler
chart_names = ["total-bi... | pd.read_csv('bitcoin_market_data.csv', sep=',') | pandas.read_csv |
import logging
import pandas as pd
import pytest
from split_schedule.errors import NoScheduleError
from split_schedule.schedule_builder import ScheduleBuilder, SchedulingError
from tests.helpers import init_classes_check, reduce_classes_check, total_classes_check
@pytest.mark.parametrize("max_tries", [1, 2])
@pytes... | pd.DataFrame(data_2) | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""
This script performs statistical analysis on instabilities and outputs figures
and a html with stats
"""
import os
import sys
import itertools
import glob
import numpy as np
import pandas as pd
import math
from scipy import stats
import pingouin as pg
import matplotlib.pyplot as plt
import... | pd.concat(data_long, axis=0, ignore_index=False) | pandas.concat |
import time
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras import regularizers
from tensorflow.keras import backend as K
import models as models
import helper as helper
import pandas as pd
import numpy as np
import matplotl... | pd.concat(dfs) | pandas.concat |
import pandas as pd
import numpy as np
import datetime
import calendar
from math import e
from brightwind.analyse import plot as plt
# noinspection PyProtectedMember
from brightwind.analyse.analyse import dist_by_dir_sector, dist_12x24, coverage, _convert_df_to_series
from ipywidgets import FloatProgress
from IPython.d... | pd.DataFrame([]) | pandas.DataFrame |
#!/usr/bin/env python
import pandas as pd
import numpy as np
import scipy, sklearn, os, sys, string, fileinput, glob, re, math, itertools, functools
import copy, multiprocessing, traceback, logging, pickle
import scipy.stats, sklearn.decomposition, sklearn.preprocessing, sklearn.covariance
from scipy.stats import des... | pd.DataFrame.from_dict({"gene": g, "rank": s}) | pandas.DataFrame.from_dict |
#!/usr/bin/env python
"""
Parsing GO Accession from a table file produced by InterProScan and mapping to GOSlim.
(c) <NAME> 2018 / MIT Licence
kinomoto[AT]sakura[DOT]idv[DOT]tw
"""
from __future__ import print_function
from os import path
import sys
import pandas as pd
from goatools.obo_parser import GODag
from goatoo... | pd.read_csv(interpro_file, sep='\t',skiprows=3,skipfooter=3,engine='python') | pandas.read_csv |
# PyLS-PM Library
# Author: <NAME>
# Creation: November 2016
# Description: Library based on <NAME>'s simplePLS,
# <NAME>'s plspm and <NAME>'s matrixpls made in R
import pandas as pd
import numpy as np
import scipy as sp
import scipy.stats
from .qpLRlib4 import otimiza, plotaIC
import scipy.linalg
from col... | pd.DataFrame.min(self.data, axis=0) | pandas.DataFrame.min |
from typing import List, Union, Dict, Any, Tuple
import os
import json
from glob import glob
from dataclasses import dataclass
import functools
import argparse
from sklearn import metrics
import torch
import pandas as pd
import numpy as np
from tqdm import tqdm
from sklearn.metrics import precision_recall_fscore_suppo... | pd.concat(all_report_df) | pandas.concat |
#!/usr/bin/env python
# -*- coding: utf-8; py-indent-offset:4 -*-
###############################################################################
# Copyright (C) 2020 <NAME>
# Use of this source code is governed by the MIT License
###############################################################################
from . im... | pd.Series(np.nan, index=self._series.index) | pandas.Series |
from src.evaluation.gnn_evaluation_module import eval_gnn
from src.models.gat_models import MonoGAT#, BiGAT, TriGAT
from src.models.rgcn_models import MonoRGCN, RGCN2
from src.models.appnp_model import MonoAPPNPModel
from src.models.multi_layered_model import MonoModel#, BiModel, TriModel
from torch_geometric.nn import... | pd.DataFrame() | pandas.DataFrame |
import functools
import numpy as np
import scipy
import scipy.linalg
import scipy
import scipy.sparse as sps
import scipy.sparse.linalg as spsl
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import warnings
import logging
import tables as tb
import os
import sandy
import py... | pd.DataFrame(*args, **kwargs) | pandas.DataFrame |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Th Jun 6 11:13:11 2019
@author: inesverissimo
Do SOMA contrasts and save outputs
"""
import os, json
import sys, glob
import re
import numpy as np
import pandas as pd
import nibabel as nb
from nilearn import surface
from nistats.design_matrix import m... | pd.DataFrame(new_events, columns=['onset','duration','trial_type']) | pandas.DataFrame |
#!/usr/bin/env python
# coding: utf-8
# In[1]:
import pandas as pd
import numpy as np
import xgboost as xgb
from sklearn.preprocessing import LabelEncoder
import lightgbm as lgb
from catboost import CatBoostClassifier
from sklearn.model_selection import train_test_split
#导入数据集
def read_data(base_info_path,
... | pd.read_csv(news_info_path) | pandas.read_csv |
import pandas as pd
from tqdm import tqdm
import os
genidlist=[]
LabGenID=[]
Labeldf = pd.read_csv('AMR_LAbel_EColi.csv', sep=",", dtype=str, low_memory=True)
selectedf = Labeldf[['genome_id']]
Antiboticslist=Labeldf.columns.values.tolist()
Antiboticslist.remove('Unnamed: 0')
Antiboticslist.remove( 'genome_id')
Antib... | pd.DataFrame({'genome_id':[''],'genome_name':[''],'taxon_id':[''],'ampicillin':[0], 'amoxicillin/clavulanic acid':[0], 'aztreonam':[0], 'cefepime':[0], 'cefotaxime':[0], 'cefoxitin':[0], 'ceftazidime':[0], 'ciprofloxacin':[0], 'gentamicin':[0], 'piperacillin/tazobactam':[0], 'sulfamethoxazole/trimethoprim':[0], 'tobram... | pandas.DataFrame |
from collections import defaultdict
from typing import DefaultDict
import pandas as pd
import pickle
from pathlib import Path
import numpy as np
import argparse
def process_dataframe(list_of_results):
results = pd.DataFrame(list_of_results,
columns=['user', 'model', 'image_type... | pd.concat(all_results_genuine) | pandas.concat |
import bs4
import requests
import lxml
import pandas as pd
import re
import os
total_page = 5
data_df = | pd.DataFrame(columns=["Reviews","Date","Rating"]) | pandas.DataFrame |
#!/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.to_datetime('today') | pandas.to_datetime |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu Apr 11 23:13:13 2019
@author: gaurav
"""
from sklearn.externals import joblib
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection i... | pd.read_csv('heart.csv') | pandas.read_csv |
#
# Authors: Security Intelligence Team within the Security Coordination Center
#
# Copyright (c) 2018 Adobe Systems Incorporated. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the Li... | pd.read_csv(filename) | pandas.read_csv |
# Copyright 1999-2021 Alibaba Group Holding 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 applicable law or a... | pd.testing.assert_frame_equal(expected, result) | pandas.testing.assert_frame_equal |
import pandas as pd
from SALib.analyze.radial_ee import analyze as ee_analyze
from SALib.analyze.sobol_jansen import analyze as jansen_analyze
from SALib.plotting.bar import plot as barplot
# results produced with
# python launch.py --specific_inputs oat_mc_10_samples.csv --num_cores 48
# python launch.py --specific_... | pd.read_csv(f'{data_dir}with_irrigation_extreme_results.csv', index_col=0) | pandas.read_csv |
# AUTOGENERATED! DO NOT EDIT! File to edit: nbs/04-model-suite.ipynb (unless otherwise specified).
__all__ = ['ScikitModel', 'create_train_test_indexes', 'calculate_error_metrics', 'calc_month_error_metrics',
'construct_prediction_df', 'ModelSuite', 'load_module_attr', 'run_parameterised_model', 'plot_obsv_... | pd.DataFrame(error_metrics) | pandas.DataFrame |
# This script is part of the supporting information to the manuscript entitled
# "Assessing the Calibration in Toxicological in Vitro Models with Conformal Prediction".
# The script was developed by <NAME> in the In Silico Toxicology and Structural Biology Group of
# Prof. Dr. <NAME> at the Charité Universitätsmedizin ... | pd.DataFrame(data=predictions[0][i]) | pandas.DataFrame |
# coding: utf-8
# In[1]:
import pandas as pd
import numpy as np
import gc
from utils import *
# In[3]:
train_active = pd.read_csv("../input/train_active.csv")
test_active = pd.read_csv("../input/test_active.csv")
train_periods = pd.read_csv("../input/periods_train.csv", parse_dates=['date_from', 'date_to'])
test... | pd.concat([train_active, test_active]) | pandas.concat |
import pandas as pd
from util import normalize_dates, conversion, normalize_numeric, normalize_text
from errors import ApportionSeriesCombinationError
import dateutil.relativedelta
from traffic import get_data, addIds
import json
def hasData(df, col):
if df[col].sum() > 0:
return True
else:
re... | pd.DataFrame() | pandas.DataFrame |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Load in ground-truth simulations together with fits from lavaan, HDDMnn and pyDDM. Visualize the results.
Created on Fri Mar 11 10:35:42 2022
@author: urai
"""
import pandas as pd
import scipy as sp
import numpy as np
import matplotlib.pyplot as plt
import seaborn a... | pd.merge(lavaan_df, sim_df, on='subj_idx') | pandas.merge |
#!/usr/bin/env python3
import os
import warnings
import numpy as np
import pandas as pd
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_distances
def read_explanations(path):
header = []
uid = None
df = pd.read_csv(path, sep='\t', dtype=str)
... | pd.DataFrame(explanations, columns=('uid', 'text')) | pandas.DataFrame |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# Copyright 1999-2017 Alibaba Group Holding 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-... | pd.DataFrame(None, columns=schema.names) | pandas.DataFrame |
"""
Functions for comparing and visualizing model performance. Most of these functions rely on ATOM's model tracker and
datastore services, which are not part of the standard AMPL installation, but a few functions will work on collections of
models saved as local files.
"""
import os
import sys
import pdb
import panda... | pd.Categorical(result_df['dset_key']) | pandas.Categorical |
import json
from itertools import product
from unittest.mock import ANY, MagicMock, patch
import numpy as np
import pandas as pd
import pytest
import woodwork as ww
from evalml.exceptions import PipelineScoreError
from evalml.model_understanding.prediction_explanations.explainers import (
ExplainPredictionsStage,... | pd.DataFrame() | pandas.DataFrame |
import pandas as pd
import pandas.testing as pdt
import pytest
import pytz
from werkzeug.exceptions import RequestEntityTooLarge
from sfa_api.conftest import (
VALID_FORECAST_JSON, VALID_CDF_FORECAST_JSON, demo_forecasts)
from sfa_api.utils import request_handling
from sfa_api.utils.errors import (
BadAPIRequ... | pd.Timestamp('2019-11-01T11:59Z') | pandas.Timestamp |
"""
Functions for calculating results from trained neural networks.
"""
# ---------------------------------- Imports ----------------------------------
# Python libraries
from datetime import datetime
import time
import numpy as np
import pandas as pd
from math import sqrt
from sklearn.metrics import conf... | pd.concat([self.df_results, df], axis=0, ignore_index=True) | pandas.concat |
#!/usr/bin/python
# -*- coding: utf-8 -*-
"""
Preprocess ieee-fraud-detection dataset.
(https://www.kaggle.com/c/ieee-fraud-detection).
Train shape:(590540,394),identity(144233,41)--isFraud 3.5%
Test shape:(506691,393),identity(141907,41)
############### TF Version: 1.13.1/Python Version: 3.7 ###############
"""
imp... | pd.concat(va_df) | pandas.concat |
import logging
import pandas as pd
import plotly.graph_objs as go
from plotly.subplots import make_subplots
from constants import (PLOTLY_TEMPLATE, PANDAS_TEMPLATE)
from sklearn.model_selection import train_test_split
from sklearn.metrics import confusion_matrix, accuracy_score, classification_report
from sklearn.metri... | pd.DataFrame(cols) | pandas.DataFrame |
from LIMBR import simulations
import pandas as pd
sims = {}
for i in range(1,21):
analysis = simulations.analyze('twenty_miss_5_NN_' + str(i) + '_true_classes.txt')
analysis.add_data('twenty_miss_5_NN_' + str(i) + '_LIMBR_processed__jtkout_GammaP.txt','LIMBR', include_missing=True)
analysis.calculate_auc(... | pd.concat([data, temp_data]) | pandas.concat |
import s3fs
import numpy as np
import pandas as pd
import xarray as xr
from glob import glob
from os.path import join, exists
from sklearn.preprocessing import StandardScaler, RobustScaler, MaxAbsScaler, MinMaxScaler
from operator import lt, le, eq, ne, ge, gt
scalers = {"MinMaxScaler": MinMaxScaler,
"MaxAb... | pd.read_csv(filename, index_col="Index") | pandas.read_csv |
# pylint: disable-msg=E1101,W0612
from datetime import datetime, time, timedelta, date
import sys
import os
import operator
from distutils.version import LooseVersion
import nose
import numpy as np
randn = np.random.randn
from pandas import (Index, Series, TimeSeries, DataFrame,
isnull, date_ran... | range(20) | pandas.compat.range |
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