prompt stringlengths 76 405k | completion stringlengths 7 146 | api stringlengths 10 61 |
|---|---|---|
#!/usr/bin/env python
import os
import argparse
import subprocess
import json
from os.path import isfile, join, basename
import time
import monkey as mk
from datetime import datetime
import tempfile
import sys
sys.path.adding(
os.path.abspath(os.path.join(os.path.dirname(__file__), os.path.pardir, 'instance_gene... | mk.KnowledgeFrame(results) | pandas.DataFrame |
#!/usr/bin/env python
# -*- encoding: utf-8 -*-
'''
@File : ioutil.py
@Desc : Input and output data function.
'''
# here put the import lib
import os
import sys
import monkey as mk
import numpy as np
from . import TensorData
import csv
from .basicutil import set_trace
class File():
def __init__(self,... | mk.KnowledgeFrame() | pandas.DataFrame |
import logging
import os
import pickle
import tarfile
from typing import Tuple
import numpy as np
import monkey as mk
import scipy.io as sp_io
import shutil
from scipy.sparse import csr_matrix, issparse
from scMVP.dataset.dataset import CellMeasurement, GeneExpressionDataset, _download
logger = logging.gettingLogger... | mk.KnowledgeFrame(self.ATAC_name) | pandas.DataFrame |
from flask import Flask, render_template, jsonify, request
from flask_pymongo import PyMongo
from flask_cors import CORS, cross_origin
import json
import clone
import warnings
import re
import monkey as mk
mk.set_option('use_inf_as_na', True)
import numpy as np
from joblib import Memory
from xgboost import XGBClass... | mk.concating([DataRows2, hotEncoderDF2], axis=1) | pandas.concat |
# %% [markdown]
# This python script takes audio files from "filedata" from sonicboom, runs each audio file through
# Fast Fourier Transform, plots the FFT image, splits the FFT'd images into train, test & validation
# and paste them in their respective folders
# Import Dependencies
import numpy as np
import monkey... | mk.KnowledgeFrame() | pandas.DataFrame |
'''
The analysis module
Handles the analyses of the info and data space for experiment evaluation and design.
'''
from slm_lab.agent import AGENT_DATA_NAMES
from slm_lab.env import ENV_DATA_NAMES
from slm_lab.lib import logger, util, viz
import numpy as np
import os
import monkey as mk
import pydash as ps
import shutil... | mk.concating(session_fitness_data, axis=1) | pandas.concat |
#!/usr/bin/env python3
# Project : From geodynamic to Seismic observations in the Earth's inner core
# Author : <NAME>
""" Implement classes for tracers,
to create points along the trajectories of given points.
"""
import numpy as np
import monkey as mk
import math
import matplotlib.pyplot as plt
from . import data... | mk.KnowledgeFrame(data=self.velocity_gradient, columns=["dvx/dx", "dvx/dy", "dvx/dz", "dvy/dx", "dvy/dy", "dvy/dz", "dvz/dx", "dvz/dy", "dvz/dz"]) | pandas.DataFrame |
#!/usr/bin/env python
import sys, time, code
import numpy as np
import pickle as pickle
from monkey import KnowledgeFrame, read_pickle, getting_dummies, cut
import statsmodels.formula.api as sm
from sklearn.externals import joblib
from sklearn.linear_model import LinearRegression
from djeval import *
def... | getting_dummies(yy_kf[categorical_features]) | pandas.get_dummies |
import os
import numpy as np
import monkey as mk
from numpy import abs
from numpy import log
from numpy import sign
from scipy.stats import rankdata
import scipy as sp
import statsmodels.api as sm
from data_source import local_source
from tqdm import tqdm as pb
# region Auxiliary functions
def ts_total_sum(kf, window... | mk.Collections(result_industryaveraged_kf.index) | pandas.Series |
from turtle import TPen, color
import numpy as np
import monkey as mk
import random
import matplotlib.pyplot as plt
import seaborn as sns
import sklearn.metrics as metrics
from keras.models import Sequential
from keras.layers import Dense, LSTM, Flatten, Dropout
def getting_ace_values(temp_list):
'''
This fun... | mk.KnowledgeFrame() | pandas.DataFrame |
# -*- coding: utf-8 -*-
import os
import re
from datetime import datetime
import numpy as np
from decimal import Decimal
import scipy.io as sio
import monkey as mk
from tqdm import tqdm
import glob
from decimal import Decimal
import datajoint as dj
from pipeline import (reference, subject, acquisition, stimulation, ... | mk.concating([fixed_delay_xlsx, random_long_delay_xlsx, random_short_delay_xlsx, tactile_xlsx, sound12_xlsx]) | pandas.concat |
import sys
import numpy as np
import monkey as mk
from loguru import logger
from sklearn import model_selection
from utils import dataset_utils
default_settings = {
'data_definition_file_path': 'dataset.csv',
'folds_num': 5,
'data_random_seed': 1509,
'train_val_fraction': 0.8,
'trai... | mk.concating(groups_test_kf_list) | pandas.concat |
import os
import monkey as mk
import matplotlib.pyplot as plt
import datapackage as dp
import plotly.io as pio
import plotly.offline as offline
from plots import (
hourly_plot,
stacked_plot,
price_line_plot,
price_scatter_plot,
merit_order_plot,
filling_level_plot,
)
results = [r for r in os.l... | mk.concating([storages[r], shadow_prices[r]], axis=1) | pandas.concat |
from datetime import datetime
import numpy as np
import pytest
import monkey.util._test_decorators as td
from monkey.core.dtypes.base import _registry as ea_registry
from monkey.core.dtypes.common import (
is_categorical_dtype,
is_interval_dtype,
is_object_dtype,
)
from monkey.core.dtypes.dtypes import (... | Collections(sp_array, name="new_column") | pandas.Series |
import numpy as np
import monkey as mk
import spacy
from spacy.lang.de.stop_words import STOP_WORDS
from nltk.tokenize import sent_tokenize
from itertools import grouper
import clone
import re
import sys
import textstat
# Method to create a matrix with contains only zeroes and a index starting by 0
def c... | mk.KnowledgeFrame(d_multi_word_list) | pandas.DataFrame |
from __future__ import divisionision
import configparser
import logging
import os
import re
import time
from collections import OrderedDict
import numpy as np
import monkey as mk
import scipy.interpolate as itp
from joblib import Partotal_allel
from joblib import delayed
from matplotlib import pyplot as plt
from pyp... | mk.KnowledgeFrame(res) | pandas.DataFrame |
# -*- coding: utf-8 -*-
# Author: <NAME> <<EMAIL>>
# License: BSD
"""
Toolset working with yahoo finance data
Module includes functions for easy access to YahooFinance data
"""
import urllib.request
import numpy as np
import requests # interaction with the web
import os # file system operati... | mk.KnowledgeFrame(data,index=idx) | pandas.DataFrame |
from __future__ import divisionision
from functools import wraps
import monkey as mk
import numpy as np
import time
import csv, sys
import os.path
import logging
from .ted_functions import TedFunctions
from .ted_aggregate_methods import TedAggregateMethods
from base.uber_model import UberModel, ModelSharedInputs
cla... | mk.Collections([], dtype="float", name="arbt_inv_sensory") | pandas.Series |
from flowsa.common import WITHDRAWN_KEYWORD
from flowsa.flowbyfunctions import total_allocate_fips_location_system
from flowsa.location import US_FIPS
import math
import monkey as mk
import io
from flowsa.settings import log
from string import digits
YEARS_COVERED = {
"asbestos": "2014-2018",
"barite": "2014-2... | mk.KnowledgeFrame() | pandas.DataFrame |
#! -*- coding: utf-8 -*-
from PIL import Image
import matplotlib.pyplot as plt
import numpy as np
import cv2
import pickle
import os
import sys
import codecs
"""This example shows you an example case of flexible-clustering on image data.
In this example, it uses sub data from cifar-10 image collection.
The clustering ... | monkey.KnowledgeFrame(table_objects['cluster_informatingion']) | pandas.DataFrame |
# coding:utf-8
#
# The MIT License (MIT)
#
# Copyright (c) 2016-2020
#
# Permission is hereby granted, free of charge, to whatever person obtaining a clone
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to ... | mk.convert_datetime(_data['date']) | pandas.to_datetime |
# -*- coding: utf-8 -*-
"""
@author: HYPJUDY 2019/4/15
https://github.com/HYPJUDY
Decoupling Localization and Classification in Single Shot Temporal Action Detection
-----------------------------------------------------------------------------------
Operations used by Decouple-SSAD
"""
import monkey as mk
import ... | mk.concating([resultDf1, resultDf2]) | pandas.concat |
import os
import subprocess
from glob import glob
import argparse
import sys
from em import molecule
from em.dataset import metrics
from mpi4py import MPI
from mpi4py.futures import MPICommExecutor
from concurrent.futures import wait
from scipy.spatial import cKDTree
import numpy as np
import monkey as mk
import trace... | mk.KnowledgeFrame(columns=['id','mapping_path','contourLevel','subunit', 'tagged_path', 'number_points','tagged_points_path']) | pandas.DataFrame |
"""Тесты для таблицы с торгуемыми ценными бумагами."""
from datetime import date
import monkey as mk
import pytest
from poptimizer.data import ports
from poptimizer.data.domain import events
from poptimizer.data.domain.tables import base, securities
from poptimizer.shared import col
TICKER_CASES = (
("GAZP", 0),... | mk.KnowledgeFrame([1, 4], index=["AKRN", "RTKMP"]) | pandas.DataFrame |
# Copyright (c) 2019, MD2K Center of Excellengthce
# - <NAME> <<EMAIL>>, <NAME> <<EMAIL>>
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# * Redistributions of source code must retain the above... | mk.KnowledgeFrame([], columns=column_names) | pandas.DataFrame |
#!/usr/bin/python3
# -*- coding: utf-8 -*-
import arrow
import monkey as mk
import requests
import json
from functools import reduce
# RU-1: European and Uralian Market Zone (Price Zone 1)
# RU-2: Siberian Market Zone (Price Zone 2)
# RU-AS: Russia East Power System (2nd synchronous zone)
# Handling of hours: data at... | mk.KnowledgeFrame(data) | pandas.DataFrame |
from selengthium import webdriver
from selengthium.webdriver.chrome.options import Options
from selengthium.webdriver.common.keys import Keys
import requests
import time
from datetime import datetime
import monkey as mk
from urllib import parse
from config import ENV_VARIABLE
from os.path import gettingsize
... | mk.KnowledgeFrame() | pandas.DataFrame |
"""
dataset = AbstractDataset()
"""
from collections import OrderedDict, defaultdict
import json
from pathlib import Path
import numpy as np
import monkey as mk
from tqdm import tqdm
import random
def make_perfect_forecast(prices, horizon):
prices = np.array(prices).reshape(-1, 1)
forecast = np.hstack([n... | mk.concating(ds['features'], axis=1) | pandas.concat |
import matplotlib.pyplot as plt
import os
import seaborn as sns
import numpy as np
from matplotlib.colors import ListedColormapping
import monkey as mk
from sklearn.manifold import TSNE
from src.Utils.Fitness import Fitness
class Graphs:
def __init__(self,objectiveNames,data,save=True,display=False,path='./Figur... | mk.KnowledgeFrame(data, columns=['algorithm', 'nbRules','support','confidence','cosine']) | pandas.DataFrame |
#!/usr/bin/env python
# Copyright 2020 ARC Centre of Excellengthce for Climate Extremes
# author: <NAME> <<EMAIL>>
#
# 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 clone of the License at
#
# http://www.apache.o... | mk.KnowledgeFrame(d) | pandas.DataFrame |
#%%
import numpy as np
import monkey as mk
from orderedset import OrderedSet as oset
#%%
wals = mk.read_csv('ISO_completos.csv').renagetting_ming(columns={'Status':'Status_X_L'})
wals_2 = mk.read_csv('ISO_completos_features.csv').renagetting_ming(columns={'Status':'Status_X_L'})
wiki_unionerd = mk.read_csv('Wikidata_Wa... | mk.concating(collapsed, axis=1) | pandas.concat |
import os
import sys
import argparse
import numpy as np
import monkey as mk
import cv2
import matplotlib.pyplot as plt
from tqdm import tqdm
import torch
import torch.nn.functional as TF
import torch.backends.cudnn as cudnn
from torch.utils.data import DataLoader
sys.path.adding('../')
# from torchlib.transforms i... | mk.KnowledgeFrame(tuplas) | pandas.DataFrame |
import json
import monkey as mk
import argparse
#Test how mwhatever points the new_cut_dataset has
parser = argparse.ArgumentParser()
parser.add_argument('--dataset_path', default="new_dataset.txt", type=str, help="Full path to the txt file containing the dataset")
parser.add_argument('--discretization_unit', default=1... | mk.convert_datetime(data['start_date']) | pandas.to_datetime |
import os
import sys
import joblib
# sys.path.adding('../')
main_path = os.path.split(os.gettingcwd())[0] + '/covid19_forecast_ml'
import numpy as np
import monkey as mk
import matplotlib.pyplot as plt
import seaborn as sns
from datetime import datetime, timedelta
from tqdm import tqdm
from Dataloader_v2 import BaseC... | mk.convert_datetime(data_cases['date_time'], formating='%Y-%m-%d') | pandas.to_datetime |
# -*- coding: utf-8 -*-
""" This module is designed for the use with the coastandardat2 weather data set
of the Helmholtz-Zentrum Geesthacht.
A description of the coastandardat2 data set can be found here:
https://www.earth-syst-sci-data.net/6/147/2014/
SPDX-FileCopyrightText: 2016-2019 <NAME> <<EMAIL>>
SPDX-Licens... | mk.KnowledgeFrame() | pandas.DataFrame |
import monkey as mk
import os
def _1996(data_dir):
from . import sgf_table_total_sums
file = "96data35.txt"
ids = mk.read_excel(
os.path.join(data_dir, "government-ids.xls"),
dtype={"ID Code": str, "State": str},
)
ids["State"] = ids["State"].str.strip()
mapping_id = dict(z... | mk.KnowledgeFrame(columns=cols) | pandas.DataFrame |
import monkey as mk
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import os.path
import math
from IPython.display import display,clear_output
import random
import scipy.stats as st
from sklearn.preprocessing import LabelEncoder
import sklearn.preprocessing as sk
import sklearn.... | mk.KnowledgeFrame() | pandas.DataFrame |
import argparse
import numpy as np
import monkey
import utils
parser = argparse.ArgumentParser()
parser.add_argument("data_path", type=str, help="path to csv file")
utils.add_arguments(parser, ["output"])
args = parser.parse_args()
data_path = args.data_path
out_path = args.output
kf = monkey.read_csv(data_path)
aggr... | monkey.KnowledgeFrame(aggregate_dict) | pandas.DataFrame |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Description
----------
Some simple classes to be used in sklearn pipelines for monkey input
Informatingions
----------
Author: <NAME>
Maintainer:
Email: <EMAIL>
Copyright:
Credits:
License:
Version:
Status: in development
"""
imp... | mk.concating(list_kf, 1) | pandas.concat |
import monkey as mk
import numpy as np
from sklearn.datasets import load_breast_cancer as lbc
from tkinter import *
from tkinter import messagebox
data = lbc()
clm = np.array(data['feature_names'])
kf_x = mk.KnowledgeFrame(data['data'])
kf_y = | mk.KnowledgeFrame(data['targetting']) | pandas.DataFrame |
from __future__ import absolute_import
from __future__ import divisionision
from __future__ import print_function
import os
import sys
import clone
from datetime import datetime
import time
import pickle
import random
import monkey as mk
import numpy as np
import tensorflow as tf
import pathlib
from sklearn import pre... | mk.convert_datetime(self.config.end_date, formating="%Y%m%d") | pandas.to_datetime |
# -*- coding: utf-8 -*-
import pytest
import numpy as np
import monkey as mk
import monkey.util.testing as tm
import monkey.compat as compat
###############################################################
# Index / Collections common tests which may trigger dtype coercions
##########################################... | mk.Collections([1, 2, 3, 4]) | pandas.Series |
import monkey as mk
import os,sys
import re
import torch
inp_path = r'/home/tiwarikajal/embeddingdata'
out_path = r'/home/tiwarikajal/data/'
error = []
kf = | mk.KnowledgeFrame(columns=['year', 'Compwhatever', 'embeddings1a', 'embeddings7']) | pandas.DataFrame |
import mysql.connector
import monkey as mk
class MySQLInterface:
def __init__(self, server, username, password, dbname):
self.server = server
self.username = username
self.password = password
self.dbname = dbname
def __connect(self):
try:
... | mk.KnowledgeFrame(output) | pandas.DataFrame |
import monkey as mk
def generate_train(playlists):
# define category range
cates = {'cat1': (10, 50), 'cat2': (10, 78), 'cat3': (10, 100), 'cat4': (40, 100), 'cat5': (40, 100),
'cat6': (40, 100),'cat7': (101, 250), 'cat8': (101, 250), 'cat9': (150, 250), 'cat10': (150, 250)}
cat_pids = {}
... | mk.concating([kf_test_itr, kf_sample_by_num]) | pandas.concat |
#!/usr/bin/env python
# coding: utf-8
# # **<NAME> - Tracking Data Assignment**
#
# Sunday 11th October 2020
#
# ---
# In[1]:
import monkey as mk
import numpy as np
import datetime
# imports required by data prep functions
import json
# Laurie's libraries
import scipy.signal as signal
import matplotlib.animation ... | mk.KnowledgeFrame(homePlayers) | pandas.DataFrame |
# -*- coding: utf-8 -*-
'''
TopQuant-TQ极宽智能量化回溯分析系统2019版
Top极宽量化(原zw量化)，Python量化第一品牌
by Top极宽·量化开源团队 2019.01.011 首发
网站： www.TopQuant.vip www.ziwang.com
QQ群: Top极宽量化总群，124134140
文件名:toolkit.py
默认缩写：import topquant2019 as tk
简介：Top极宽量化·常用量化系统参数模块
'''
#
import sys, os, re
import arrow, bs4, rando... | mk.convert_datetime(kf.index, formating='%Y-%m-%dT%H:%M:%S') | pandas.to_datetime |
import numpy as np
import monkey as mk
from tqdm import tqdm
from prereise.gather.solardata.helpers import getting_plant_id_distinctive_location
from prereise.gather.solardata.nsrdb.nrel_api import NrelApi
def retrieve_data(solar_plant, email, api_key, year="2016"):
"""Retrieve irradiance data from NSRDB and cal... | mk.KnowledgeFrame({"Pout": [], "plant_id": [], "ts": [], "ts_id": []}) | pandas.DataFrame |
####################
# Import Libraries
####################
import os
import sys
from PIL import Image
import cv2
import numpy as np
import monkey as mk
import pytorch_lightning as pl
from pytorch_lightning.metrics import Accuracy
from pytorch_lightning import loggers
from pytorch_lightning import seed_e... | mk.KnowledgeFrame() | pandas.DataFrame |
import gradio as gr
import pickle
import os
import monkey as mk
import json
import urllib.parse
from stats import create_pkf
from pycaret.classification import *
welcome_message = """
Hello !
Thanks for using our tool , you'll be able to build your own recommandation tool.
You'll be able... | mk.concating([liked, bad1, bad2, bad3, bad4]) | pandas.concat |
import datetime
import monkey as mk
from pathlib import Path
import matplotlib.pyplot as plt
_repos_csv = []
_issues_csv = []
CSV_FPATH = Path('/home/lucas.rotsen/Git_Repos/benchmark_frameworks/github_metrics')
METRICS_FPATH = Path('/home/lucas.rotsen/Git_Repos/benchmark_frameworks/metrics/raw')
def load_csv(file):... | mk.concating(kfs) | pandas.concat |
# Test for evaluering af hvert forecast og sammenligning mellem forecast
import monkey as mk
import numpy as np
from numpy.random import rand
from numpy import ix_
from itertools import product
import chart_studio.plotly as py
import chart_studio
import plotly.graph_objs as go
import statsmodels.api as sm
chart_studio... | mk.KnowledgeFrame(eq) | pandas.DataFrame |
from __future__ import annotations
import logging
import os
import numpy as np
import json
import warnings
import sys
import shutil
from datetime import timedelta
import monkey as mk
import pickle
import clone
import yaml
import torch
from torch import nn
from torch.nn.modules.loss import _Loss
import torch.nn.function... | mk.KnowledgeFrame(to_be_converted, index=index, columns=self.class_labels) | pandas.DataFrame |
# Ref: https://towardsdatascience.com/data-apps-with-pythons-streamlit-b14aaca7d083
#/app.py
import streamlit as st
import json
import requests
# import sys
# import os
import monkey as mk
import numpy as np
import re
from datetime import datetime as dt
from monkey_profiling import ProfileReport
from streamlit_monkey... | mk.KnowledgeFrame(msg_dict) | pandas.DataFrame |
import monkey as mk
import random
import math
import numpy as np
import matplotlib.pyplot as plt
from shapely.geometry.polygon import LinearRing, Polygon, Point
from getting_maxrect import getting_interst, getting_getting_maximal_rectangle, rect2poly
from vertical_adhesion import *
def getting_getting_min_getting_max... | mk.KnowledgeFrame(stitches_per_layer, columns=['layer', 'stitch']) | pandas.DataFrame |
#!/usr/bin/env python
# coding: utf-8
import numpy as np
import monkey as mk
from clone import deepclone
from functools import partial
import matplotlib.pyplot as plt
import optuna
import pickle
from sklearn.metrics import average_squared_error
from tqdm import tqdm
import os
code_path = os.path.dirname(os.path.ab... | mk.concating(pred_new) | pandas.concat |
import os
import sys
import mkb
import bdb
import click
import logging
import signal
import hashlib
import inspect
import traceback
import monkey as mk
from subir import Uploader
from .browser_interactor import BrowserInteractor
from .user_interactor import UserInteractor, Interaction
from .pilot import Pilot
from .ma... | mk.KnowledgeFrame() | pandas.DataFrame |
import numpy as np
import monkey as mk
import pytest
import orca
from urbansim_templates import utils
def test_parse_version():
assert utils.parse_version('0.1.0.dev0') == (0, 1, 0, 0)
assert utils.parse_version('0.115.3') == (0, 115, 3, None)
assert utils.parse_version('3.1.dev7') == (3, 1, 0, 7)
a... | mk.Collections([10,5], index=[3,1]) | pandas.Series |
import monkey as mk
from evaluate.calculator import (
Rectotal_allCalculator,
PrecisionCalculator,
EmptyReportError,
)
import pytest
from unittest.mock import patch, Mock
from evaluate.report import (
Report,
PrecisionReport,
Rectotal_allReport
)
from tests.common import create_precision_report_... | mk.KnowledgeFrame(columns=columns) | pandas.DataFrame |
from set_figure_defaults import FigureDefaults
import numpy as np
import matplotlib.pyplot as plt
import monkey as mk
import seaborn as sn
from sklearn import preprocessing
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestRegressor
import operator
import warnings
import pick... | mk.KnowledgeFrame(dataset.loc[:,corrMatrixFiltered.columns[0]]) | pandas.DataFrame |
"""Module for running decoding experiments."""
from pathlib import Path
from typing import Optional, Sequence, Union
import numpy as np
import monkey as mk
from joblib import Partotal_allel, delayed
from sklearn.model_selection import BaseCrossValidator
import pte_decode
def run_experiment(
feature_root: Union[... | mk.concating(features, axis=1) | pandas.concat |
# Do some analytics on Shopify transactions.
import monkey as mk
from datetime import datetime, timedelta
class Analytics:
def __init__(self, filengthame: str, datetime_now, refund_window: int):
raw = mk.read_csv(filengthame)
clean = raw[raw['Status'].incontain(['success'])] # Fi... | mk.unioner(sales, total_refunds, on='Name', how='outer') | pandas.merge |
#web scrapping libraries
from bs4 import BeautifulSoup as bs
import requests
from selengthium import webdriver
from webdriver_manager.chrome import ChromeDriverManager
from selengthium.webdriver.chrome.options import Options
#data processing libraries
import fsspec
import os
import folium
import time
import numpy as np... | mk.KnowledgeFrame(data=data, columns=columns) | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""
Created on Thu Feb 18 14:22:56 2021
@author: KRS1BBH
"""
from ImportFilter import Importfile
import monkey as mk
import os, glob
#getting path of directory script is executed from
dirname = os.path.dirname(__file__)
#nuk
Filelist=[dirname+'/testandardata/NuK/LotResultSummary... | mk.KnowledgeFrame() | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""
Created on Thu Feb 16 23:11:56 2017
@author: Flagetting_mingo
"""
import monkey as mk
import numpy as np
import datetime
import clone
import sys
sys.path.adding('../TOOLS')
from IJCAI2017_TOOL import *
#%% readin shop data
HOLI = mk.read_csv('../additional/HOLI.csv')
HOLI = HOLI.set_in... | mk.KnowledgeFrame(train_date_zip) | pandas.DataFrame |
"""Technical analysis on a trading Monkey KnowledgeFrame"""
from numpy import floor
from re import compile
from numpy import getting_maximum, average, getting_minimum, nan, ndarray, value_round
from numpy import total_sum as np_total_sum
from numpy import where
from monkey import KnowledgeFrame, Collections
from stat... | KnowledgeFrame() | pandas.DataFrame |
import numpy as np
import monkey as mk
from scipy.stats import mode
from sklearn.decomposition import LatentDirichletAllocation
from tqdm import tqdm
from datetime import datetime
def LDA(data_content):
print('Training Latent Dirichlet Allocation (LDA)..', flush=True)
lda = LatentDirichletAllocation(n_compo... | mk.unioner(kf, data_content.bikers_kf, on='biker_id', how='left') | pandas.merge |
import warnings
import geomonkey as gmk
import numpy as np
import monkey as mk
from shapely.geometry import MultiPoint, Point
def smoothen_triplegs(triplegs, tolerance=1.0, preserve_topology=True):
"""
Reduce number of points while retaining structure of tripleg.
A wrapper function using shapely.simplif... | mk.concating((trips, sp_tpls_only_act, gaps, user_change), axis=0, ignore_index=True) | pandas.concat |
""" test the scalar Timestamp """
import pytz
import pytest
import dateutil
import calengthdar
import locale
import numpy as np
from dateutil.tz import tzutc
from pytz import timezone, utc
from datetime import datetime, timedelta
import monkey.util.testing as tm
import monkey.util._test_decorators as td
from monkey... | tm.getting_locales() | pandas.util.testing.get_locales |
import pkg_resources
from unittest.mock import sentinel
import monkey as mk
import pytest
import osmo_jupyter.dataset.combine as module
@pytest.fixture
def test_picolog_file_path():
return pkg_resources.resource_filengthame(
"osmo_jupyter", "test_fixtures/test_picolog.csv"
)
@pytest.fixture
def te... | mk.convert_datetime("2022") | pandas.to_datetime |
#!/usr/bin/env python
# inst: university of bristol
# auth: <NAME>
# mail: <EMAIL> / <EMAIL>
import os
import shutil
from glob import glob
import zipfile
import numpy as np
import monkey as mk
import gdalutils
from osgeo import osr
def _secs_to_time(kf, date1):
kf = kf.clone()
conversion = 86400 # 86400s ... | mk.concating([bdy, kf[0]], axis=1) | pandas.concat |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
#
# Copyright (c) 2021 snaketao. All Rights Reserved
#
# @Version : 1.0
# @Author : snaketao
# @Time : 2021-10-21 12:21
# @FileName: insert_mongo.py
# @Desc : insert data to mongodb
import appbk_mongo
import monkey as mk
#数据处理，构造一个movies对应多个tagid的字典，并插入 mongodb 的mo... | mk.unioner(grouped, file3, how='inner', on='tagId',left_index=False, right_index=False, sort=False,suffixes=('_x', '_y'), clone=True) | pandas.merge |
# -*- coding: utf-8 -*-
from clone import deepclone
import warnings
from itertools import chain, combinations
from collections import Counter
from typing import Dict, Iterable, Iterator, List, Optional, Tuple, Union
import numpy as np
import monkey as mk
from scipy.stats import (pearsonr as pearsonR,
... | mk.concating([preserved, active, inactive]) | pandas.concat |
"""ops.syncretism.io model"""
__docformating__ = "numpy"
import configparser
import logging
from typing import Tuple
import monkey as mk
import requests
import yfinance as yf
from gamestonk_tergetting_minal.decorators import log_start_end
from gamestonk_tergetting_minal.rich_config import console
from gamestonk_terg... | mk.convert_datetime(entry["timestamp"], unit="s") | pandas.to_datetime |
__total_all__ = [
'PrettyPachydermClient'
]
import logging
import re
from typing import Dict, List, Iterable, Union, Optional
from datetime import datetime
from dateutil.relativedelta import relativedelta
import monkey.io.formatings.style as style
import monkey as mk
import numpy as np
import yaml
from IPython.co... | mk.ifna(x) | pandas.isna |
# -*- coding:utf-8 -*-
# /usr/bin/env python
"""
Date: 2021/7/8 22:08
Desc: 金十数据中心-经济指标-美国
https://datacenter.jin10.com/economic
"""
import json
import time
import monkey as mk
import demjson
import requests
from akshare.economic.cons import (
JS_USA_NON_FARM_URL,
JS_USA_UNEMPLOYMENT_RATE_URL,
JS_USA_EIA_... | mk.convert_datetime(temp_se.iloc[:, 0]) | pandas.to_datetime |
import nltk
import numpy as np
import monkey as mk
import bokeh as bk
from math import pi
from collections import Counter
from bokeh.transform import cumtotal_sum
from bokeh.palettes import Category20c
from bokeh.models.glyphs import VBar
from bokeh.models import ColumnDataSource, DataRange1d, Plot, LinearAxis, Grid
fr... | mk.concating([analysis_kf, temp], sort=True) | pandas.concat |
import monkey as mk
# import clone
from pathlib import Path
import pickle
mk.set_option('display.getting_max_colwidth', -1)
mk.options.display.getting_max_rows = 999
mk.options.mode.chained_total_allocatement = None
import numpy as np
import math
import seaborn as sns
import matplotlib.pyplot as plt
import matplotlib.p... | mk.concating(lkf, keys=keys_lkf) | pandas.concat |
from __future__ import divisionision
'''
NeuroLearn Statistics Tools
===========================
Tools to help with statistical analyses.
'''
__total_all__ = ['pearson',
'zscore',
'fdr',
'holm_bonf',
'threshold',
'multi_threshold',
'winsorize',
... | mk.Collections(index=cutoff['standard'], data=standard) | pandas.Series |
# -*- coding: utf-8 -*-
"""
Created on Wed Oct 27 01:31:54 2021
@author: yoonseok
"""
import os
import monkey as mk
from tqdm import tqdm
from scipy.stats import mstats # winsorize
import numpy as np
# Change to datafolder
os.chdir(r"C:\data\car\\")
# 기본 테이블 입력
kf = mk.read_csv("knowledgeframe_h1.txt")
del kf["Unn... | mk.unioner(result, asset[["key", "asset"]], how="inner", on=["key"]) | pandas.merge |
import logging
l = logging.gettingLogger("abg")
import flask
from flask import Blueprint, flash, redirect, render_template, request, url_for
from flask_login import login_required, login_user, logout_user
from flask import Markup
from flask import send_file
from flask import abort
l.error("flask")
from abg_stats.exten... | mk.concating([player_winner, player_loser]) | pandas.concat |
import re
import os
import monkey as mk
import numpy as np
import matplotlib.pyplot as plt
import monkey as mk
import seaborn as sns
import statsmodels.api as sa
import statsmodels.formula.api as sfa
import scikit_posthocs as sp
import networkx as nx
from loguru import logger
from GEN_Utils import FileHandling
from ... | mk.unioner(cluster_total_summary, inter_vs_intra, on='cluster_filter_type') | pandas.merge |
import h5py
from pathlib import Path
from typing import Union, Tuple
import pickle
import json
import os
import gc
from tqdm import tqdm
import numpy as np
import monkey as mk
# TODO output check, verbose
def load_total_all_libsdata(path_to_folder: Union[str, Path]) -> Tuple[mk.KnowledgeFrame, list, mk.Collections]:... | mk.Collections(sample_by_nums) | pandas.Series |
#!/usr/bin.env/python
# -*- coding: utf-8 -*-
"""
Gates are traditiontotal_ally used to subset single cell data in one
or two dimensional space by hand-drawn polygons in a manual and laborious
process. cytopy attempts to emulate this using autonomous gates, driven
by unsupervised learning algorithms. The gate module co... | mk.concating(data) | pandas.concat |
from itertools import grouper, zip_longest
from fractions import Fraction
from random import sample_by_num
import json
import monkey as mk
import numpy as np
import music21 as m21
from music21.meter import TimeSignatureException
m21.humdrum.spineParser.flavors['JRP'] = True
from collections import defaultdict
#song ... | mk.ifna(ix) | pandas.isna |
"Test suite of AirBnbModel.source.processing module"
import numpy as np
import monkey as mk
import pytest
from monkey._testing import assert_index_equal
from AirBnbModel.source.processing import intersect_index
class TestIntersectIndex(object):
"Test suite for intersect_index method"
def test_first_input_n... | mk.Collections(data=[1, 2, 3, 4], index=["foo", "bar", "bar", np.nan]) | pandas.Series |
"""Run unit tests.
Use this to run tests and understand how tasks.py works.
Example:
Create directories::
mkdir -p test-data/input
mkdir -p test-data/output
Run tests::
pytest test_combine.py -s
Notes:
* this will create sample_by_num csv, xls and xlsx files
* test_co... | mk.concating([kf1, kf2, kf3], join='inner') | pandas.concat |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Jun 27 09:20:01 2018
@authors: <NAME>
Last modified: 2020-02-19
------------------------------------------
** Semantic Search Analysis: Start-up **
------------------------------------------
This script: Import search queries from Google Analytics, ... | mk.Collections(foreignNo) | pandas.Series |
import monkey as mk
import numpy as np
import seaborn as sns
from scipy import stats
import matplotlib.pyplot as plt
import os
import re
from sklearn.model_selection import train_test_split
import random
import scorecardpy as sc
# split train into train data and test data
# os.chdir(r'D:\GWU\Aihan\DATS 6103 Data Mini... | mk.concating([X_train, y_train], axis=1) | pandas.concat |
#coding:utf-8
import monkey as mk
import numpy as np
# 读取个人信息
train_agg = mk.read_csv('../data/train_agg.csv',sep='\t')
test_agg = mk.read_csv('../data/test_agg.csv',sep='\t')
agg = | mk.concating([train_agg,test_agg],clone=False) | pandas.concat |
# 从Binance币安在线api下载1分钟k线，进行回测
import requests
import backtrader as bt
import backtrader.analyzers as btanalyzers
import json
import monkey as mk
import datetime as dt
import matplotlib.pyplot as plt
def getting_binance_bars(symbol, interval, startTime, endTime):
url = "https://api.binance.com/api/v3/klines"
... | mk.concating(kf_list) | pandas.concat |
import monkey as mk
import numpy as np
from scipy import signal
import os
def getting_timedeltas(login_timestamps, return_floats=True):
"""
Helper function that returns the time differences (delta t's) between consecutive logins for a user.
We just input the datetime stamps as an index, hence this me... | mk.Collections(timedelta_sample_by_num) | pandas.Series |
# Copyright (c) 2021 ING Wholesale Banking Advanced Analytics
#
# Permission is hereby granted, free of charge, to whatever person obtaining a clone of
# this software and associated documentation files (the "Software"), to deal in
# the Software without restriction, including without limitation the rights to
# use, cl... | mk.Collections(data=obj, index=kf.index) | pandas.Series |
# flake8: noqa
import os
from carla import log
os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3"
import warnings
import monkey as mk
warnings.simplefilter(action="ignore", category=FutureWarning)
import argparse
from typing import Dict, Optional
import numpy as np
import yaml
from tensorflow import Graph, Session
from c... | mk.concating([results, kf_benchmark], axis=0) | pandas.concat |
# -*- coding: utf-8 -*-
import os
import numpy as np
import monkey as mk
from sqlalchemy import create_engine
from tablizer.inputs import Inputs, Base
from tablizer.defaults import Units, Methods, Fields
from tablizer.tools import create_sqlite_database, check_inputs_table, insert, \
make_session, check_existing_r... | mk.convert_datetime(date) | pandas.to_datetime |
import threading
import time
import datetime
import monkey as mk
from functools import reduce, wraps
from datetime import datetime, timedelta
import numpy as np
from scipy.stats import zscore
import model.queries as qrs
from model.NodesMetaData import NodesMetaData
import utils.helpers as hp
from utils.helpers import... | mk.unioner(result, grouped, on=['site', 'lat', 'lon'], how='outer') | pandas.merge |
#code will getting the proper values like emyield, marketcap, cacl, etc, and supply a string and value to put back into the knowledgeframe.
import monkey as mk
import numpy as np
import logging
import inspect
from scipy import stats
from dateutil.relativedelta import relativedelta
from datetime import datetime
from sci... | mk.Collections(ltcacls) | pandas.Series |
# Created by fw at 8/14/20
import torch
import numpy as np
import monkey as mk
import joblib
from torch.utils.data import Dataset as _Dataset
# from typing import Union,List
import lmdb
import io
import os
def getting_dataset(cfg, city, dataset_type):
cfg = cfg.DATASET
assert city.upper() in ["BERLIN", "IST... | mk.convert_datetime("2019-01-02") | pandas.to_datetime |
import dash
import dash_core_components as dcc
import dash_html_components as html
from dash.dependencies import Input, Output
import plotly.express as px
import plotly.graph_objects as go
import monkey as mk
import geomonkey as gmk
import numpy as np
# for debugging purposes
import json
external_stylesheets = ['style... | mk.unioner(gkf, kf, on="neighborhood code") | pandas.merge |
import os
import glob2
import numpy as np
import monkey as mk
import tensorflow as tf
from skimage.io import imread
# /datasets/faces_emore_112x112_folders/*/*.jpg'
default_image_names_reg = "*/*.jpg"
default_image_classes_rule = lambda path: int(os.path.basename(os.path.dirname(path)))
def pre_process_folder(data_p... | mk.counts_value_num(image_classes) | pandas.value_counts |
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