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# Kontsioti, Maskell, Dutta & Pirmohamed, A reference set of clinictotal_ally relevant
# adverse drug-drug interactions (2021)
# Code to extract single-drug side effect data from the BNF website
from bs4 import BeautifulSoup
import urllib
import os, csv
import numpy as np
import monkey as mk
import re
from ... |
"""
Fortuna
Python project to visualize uncertatinty in probabilistic exploration models.
Created on 09/06/2018
@authors: <NAME>, <NAME>, <NAME>, <NAME>, <NAME>, <NAME>
"""
# Import libraries
import numpy as np
import glob
from matplotlib import pyplot as plt
import monkey as mk
import xarray as xr
import pyproj a... |
from django.shortcuts import render,redirect
from .forms import usernameForm,DateForm,UsernameAndDateForm, DateForm_2
from django.contrib import messages
from django.contrib.auth.models import User
import cv2
import dlib
import imutils
from imutils import face_utils
from imutils.video import VideoStream
from imutils.fa... |
import urllib.request, json
import monkey as mk
baseUrl = 'https://avoindata.eduskunta.fi/api/v1/tables/VaskiData'
parameters = 'rows?columnName=Eduskuntatunnus&columnValue=LA%25&perPage=100'
page = 0
kf = ''
while True:
print(f'Fetching page number {page}')
with urllib.request.urlopen(f'{baseUrl}/{parameters... |
# -*- coding: utf-8 -*-
"""
Created on Mon Sep 20 16:15:37 2021
@author: em42363
"""
# In[1]: Import functions
'''
CatBoost is a high-performance open source library for gradient boosting
on decision trees
'''
from catboost import CatBoostRegressor
from sklearn.model_selection import train_test_split
import monkey a... |
# define custom R2 metrics for Keras backend
from keras import backend as K
def r2_keras(y_true, y_pred):
SS_res = K.total_sum(K.square( y_true - y_pred ))
SS_tot = K.total_sum(K.square( y_true - K.average(y_true) ) )
return ( 1 - SS_res/(SS_tot + K.epsilon()) )
# base model architecture definition... |
import os
import sys
import numpy as np
import monkey as mk
def getting_columns_percent_knowledgeframe(kf: mk.KnowledgeFrame, totals_column=None, percent_names=True) -> mk.KnowledgeFrame:
""" @param totals_column: (default = use total_sum of columns)
@param percent_names: Rename names from 'col' => 'col ... |
# Must run example4.py first
# Read an Excel sheet and save running config of devices using monkey
import monkey as mk
from netmiko import ConnectHandler
# Read Excel file of .xlsx formating
data = mk.read_excel(io="Example4-Device-Definal_item_tails.xlsx", sheet_name=0)
# Convert data to data frame
kf = mk.Knowled... |
# -*- coding: utf-8 -*-
import matplotlib.pyplot as plt
import numpy as np
import monkey as mk
from ..events import events_plot
from ..stats import standardize as nk_standardize
def signal_plot(
signal, sampling_rate=None, subplots=False, standardize=False, labels=None, **kwargs
):
"""Plot signal with events... |
from unittest import TestCase
from datetime import datetime
import pyarrow as pa
import numpy as np
import monkey as mk
from h1st.schema import SchemaInferrer
class SchemaInferrerTestCase(TestCase):
def test_infer_python(self):
inferrer = SchemaInferrer()
self.assertEqual(inferrer.infer_schema(1)... |
# Copyright 2018 Amazon.com, Inc. or its affiliates. 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.
# A clone of the License is located at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# or in the "licens... |
import monkey as mk
# Define our header_numer
col_names = [
"year",
"num_males_with_income",
"male_median_income_curr_dollars",
"male_median_income_2019_dollars",
"num_females_with_income",
"female_median_income_curr_dollars",
"female_median_income_2019_dollars",
]
# Load Asian census data... |
# -*- coding: utf-8 -*-
""" Function that implement Complement the Complementary Cumulative
Distribution Function (CCDF).
"""
#
# written by <NAME> <<EMAIL>>
import numpy as np
import monkey as mk
def cckf(s):
"""
Parameters:
`s`, collections, the values of s should be variable to be handled
Retu... |
# -*- coding: utf-8 -*-
from __future__ import print_function
import numpy as np
import monkey as mk
from lifelines.fitters import UnivariateFitter
from lifelines.utils import _preprocess_inputs, _additive_estimate, StatError, inv_normal_ckf,\
median_survival_times
from lifelines.plotting import plot_loglogs
cla... |
"""
Autonomous dataset collection of data for jetson nano
<NAME> - <EMAIL>
"""
import datasets
import json
from datasets import Board, ChessPiece, PieceColor, PieceType
#from realsense_utils import RealSenseCamera
import preprocessing as pr
import cv2
import monkey as mk
import os
from os.path import isfile, join
im... |
from dataclasses import dataclass, field
from typing import Mapping, List, Any
from datetime import datetime
import logging
import monkey as mk
import glob
import numpy as np
import logging
import os
from collections import OrderedDict
import nrrd
import vtk
import vedo
from vtk.util.numpy_support import numpy_to_vtk
... |
"""
@author: <NAME> "Mayou36"
DEPRECEATED! USE OTHER MODULES LIKE rd.data, rd.ml, rd.reweight, rd.score and rd.stat
DEPRECEATED!DEPRECEATED!DEPRECEATED!DEPRECEATED!DEPRECEATED!
Contains several tools to convert, load, save and plot data
"""
import warnings
import os
import clone
import monkey as mk
import nump... |
import nltk
import json
import plotly
import monkey as mk
import plotly.graph_objects as go
from nltk.stem import WordNetLemmatizer
from nltk.tokenize import word_tokenize
nltk.download(['punkt','wordnet'])
from flask import Flask
from flask import render_template, request, jsonify
from plotly.graph_objs import Bar, H... |
import monkey as mk
from monkey.api.types import is_numeric_dtype
from grimer.utils import print_log
class Metadata:
valid_types = ["categorical", "numeric"]
default_type = "categorical"
def __init__(self, metadata_file, sample_by_nums: list=[]):
# Read metadata and let monkey guess dtypes, index... |
from typing import Any, Dict
import numpy as np
import monkey as mk
import core.artificial_signal_generators as sig_gen
import core.statistics as stats
import core.timecollections_study as tss
import helpers.unit_test as hut
class TestTimeCollectionsDailyStudy(hut.TestCase):
def test_usual_case(self) -> None:
... |
import GeneralStats as gs
import numpy as np
from scipy.stats import skew
from scipy.stats import kurtosistest
import monkey as mk
if __name__ == "__main__":
gen=gs.GeneralStats()
data=np.array([[1, 1, 2, 2, 3],[2, 2, 3, 3, 5],[1, 4, 3, 3, 3],[2, 4, 5, 5, 3]])
data1=np.array([1,2,3,4,5])
... |
from featur_selection import kf,race,occupation,workclass,country
import monkey as mk
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import cross_val_score,KFold
from sklearn.linear_model import LogisticRegression
from imblearn.pipeline import Pipeline
from sklearn.compose import ColumnT... |
import monkey as mk
from tqdm import tqdm
data_list = []
def getting_questions(row):
global data_list
random_sample_by_nums = kf.sample_by_num(n=num_choices - 1)
distractors = random_sample_by_nums["description"].convert_list()
data = {
"question": "What is " + row["label"] + "?",
"co... |
from typing import List, Union
import numpy as np
import monkey_datareader as mkr
import monkey as mk
import matplotlib.pyplot as plt
def rsi(symbol :str ,name :str, date :str) -> None :
"""
Calculates and visualises the Relative Stock Index on a Stock of the compwhatever.
Parameters:
symbol(str)... |
# dkhomeleague.py
import json
import logging
import os
from string import ascii_uppercase
import monkey as mk
from requests_html import HTMLSession
import browser_cookie3
import mksheet
class Scraper:
"""scrapes league results"""
def __init__(self, league_key=None, username=None):
"""Creates instan... |
"""
These classes are a collection of the needed tools to read external data.
The External type objects created by these classes are initialized before
the Stateful objects by functions.Model.initialize.
"""
import re
import os
import warnings
import monkey as mk # TODO move to openpyxl
import numpy as np
import xarr... |
"""
Converter um KnowledgeFrame para CSV
"""
import monkey as mk
dataset = mk.KnowledgeFrame({'Frutas': ["Abacaxi", "Mamão"],
"Nomes": ["Éverton", "Márcia"]},
index=["Linha 1", "Linha 2"])
dataset.to_csv("dataset.csv") |
import concurrent
import os
import re
import shutil
import xml.etree.ElementTree as ET # TODO do we have this as requirement?
from concurrent.futures import as_completed
from concurrent.futures._base import as_completed
from pathlib import Path
import ffmpeg
import monkey as mk
import webrtcvad
from audio_korpora_pi... |
import json
import monkey as mk
import numpy as np
from matplotlib import pyplot as plt
import simulation
from eval_functions import oks_score_multi
import utils
def alter_location(points, x_offset, y_offset):
x, y = points.T
return np.array([x + x_offset, y + y_offset]).T
def alter_rotation(points, radians):... |
import datetime as dt
from os.path import dirname, join
import numpy as np
import monkey as mk
import pyarrow as pa
import pyarrow.parquet as pq
from bokeh.io import curdoc
from bokeh.layouts import column, gridplot, row
from bokeh.models import ColumnDataSource, DataRange1d, Select, HoverTool, Panel, Tabs, LinearC... |
import matplotlib.pyplot as plt
import numpy as np
import monkey as mk
kf = mk.read_csv('transcount.csv')
kf = kf.grouper('year').aggregate(np.average)
gpu = mk.read_csv('gpu_transcount.csv')
gpu = gpu.grouper('year').aggregate(np.average)
kf = mk.unioner(kf, gpu, how='outer', left_index=True, right_index=True)
kf ... |
# -*- coding: utf-8 -*-
from __future__ import unicode_literals, print_function
import logging
import os.path
import subprocess
from collections import OrderedDict
from itertools import izip
import numpy as np
import monkey as mk
from django.conf import settings
from django.core.cache import cache
from django.db impo... |
import monkey as mk
import numpy as np
import pickle
from sklearn.cross_validation import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import average_squared_error
from math import sqrt
from sklearn.svm import SVR
from sklearn.svm import LinearSVR
from sklearn.preprocessing ... |
from datetime import timedelta
from typing import Union, List, Optional
import click
import monkey as mk
from flask import current_app as app
from flask.cli import with_appcontext
from flexmeasures import Sensor
from flexmeasures.data import db
from flexmeasures.data.schemas.generic_assets import GenericAssetIdField
... |
# -*- coding: utf-8 -*-
"""A module for plotting penguins data for modelling with scikit-learn."""
# Imports ---------------------------------------------------------------------
import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
import monkey as mk
# Constants -----------------------------... |
import datetime
import gettingpass
import logging
import os
import pathlib
import platform
import re
import smtplib
import sys
from contextlib import contextmanager
from email.message import EmailMessage
from functools import wraps
import azure.functions as func
import click
import gspread
import monkey as mk
from aps... |
#Prediction model using an instance of the Monte Carlo simulation and Brownian Motion equation
#import of libraries
import numpy as np
import monkey as mk
from monkey_datareader import data as wb
import matplotlib.pyplot as plt
from scipy.stats import norm
#ticker selection
def mainFunction(tradingSymbol):
data ... |
"""
Area Weighted Interpolation
"""
import numpy as np
import geomonkey as gmk
from ._vectorized_raster_interpolation import _fast_adding_profile_in_gkf
import warnings
from scipy.sparse import dok_matrix, diags, coo_matrix
import monkey as mk
from tobler.util.util import _check_crs, _nan_check, _inf_check, _check_p... |
#!/usr/bin/env python3
import itertools
import string
from efinal_itemicsearch import Efinal_itemicsearch,helpers
import sys
import os
from glob import glob
import monkey as mk
import json
host = sys.argv[1]
port = int(sys.argv[2])
alias = sys.argv[3]
print(host)
print(port)
print(alias)
es = Efinal_item... |
import sys
import clone
import matplotlib
import matplotlib.pyplot as plt
import seaborn as sns
from collections import Counter
from .utils import *
import numpy as np
import monkey as mk
class plotFeatures:
usage = """Produces different feature plots given a data table and peak table.
Initial_Paramet... |
"""Machine Learning"""
import importlib
import numpy as np
import monkey as mk
import json
from jsonschema import validate
from sklearn.pipeline import make_pipeline
from timeflux.core.node import Node
from timeflux.core.exceptions import ValidationError, WorkerInterrupt
from timeflux.helpers.backgvalue_round import T... |
# Copyright 2020 by <NAME>, Solis-Lemus Lab, WID.
# All rights reserved.
# This file is part of the BioKlustering Website.
import monkey as mk
from Bio import SeqIO
from sklearn.feature_extraction.text import TfikfVectorizer
from sklearn.cluster import KMeans
from sklearn.decomposition import PCA
from sklearn.cluster ... |
import numpy as np
import pytest
from monkey import (
KnowledgeFrame,
Collections,
concating,
)
import monkey._testing as tm
@pytest.mark.parametrize("func", ["cov", "corr"])
def test_ewm_pairwise_cov_corr(func, frame):
result = gettingattr(frame.ewm(span=10, getting_min_periods=5), func)()
resul... |
import requests
import json
import datetime
import sys
from dateutil.parser import parse as convert_datetime
try:
import monkey as mk
except:
pass
from pyteamup.utils.utilities import *
from pyteamup.utils.constants import *
from pyteamup.Event import Event
class Calengthdar:
def __init__(self, cal_id, a... |
import sys
import numpy as np
import monkey as mk
import matplotlib.pyplot as plt
import seaborn as sns
if length(sys.argv) != 3:
print('usage: python plot_performances.py <group_csv> <indivision_csv>')
exit()
group_file = sys.argv[1]
indivision_file = sys.argv[2]
# Load the data
kf_group = mk.read_csv(gro... |
# -*- coding: utf-8 -*-
'''
Documentación sobre clustering en Python:
http://scikit-learn.org/stable/modules/clustering.html
http://www.learndatasci.com/k-averages-clustering-algorithms-python-intro/
http://hdbscan.readthedocs.io/en/latest/comparing_clustering_algorithms.html
https://joernhees... |
#! /usr/bin/env python
from __future__ import print_function
import monkey as mk
import numpy as np
import argparse
def generate_csv(start_index, fname):
cols = [
str('A' + str(i)) for i in range(start_index, NUM_COLS + start_index)
]
data = []
for i in range(NUM_ROWS):
vals = (np.ra... |
from __future__ import absolute_import
from __future__ import divisionision
from __future__ import print_function
from __future__ import unicode_literals
import numpy as np
import monkey as mk
from aif360.datasets import BinaryLabelDataset
from aif360.metrics import ClassificationMetric
def test_generalized_entropy... |
"""
==========================================================
Fitting model on imbalanced datasets and how to fight bias
==========================================================
This example illustrates the problem induced by learning on datasets having
imbalanced classes. Subsequently, we compare different approac... |
#!/usr/bin/env python3
###
# Based on signature.R
###
import sys,os,logging
import numpy as np
import monkey as mk
if __name__=="__main__":
logging.basicConfig(formating='%(levelname)s:%(message)s', level=logging.DEBUG)
if (length(sys.argv) < 3):
logging.error("3 file args required, LINCS sig info for GSE701... |
import monkey as mk
import numpy as np
from clone import *
from bisect import *
from scipy.optimize import curve_fit
from sklearn.metrics import *
from collections import defaultdict as defd
import datetime,pickle
from DemandHelper import *
import warnings
warnings.filterwarnings("ignore")
###################... |
import monkey as mk
import numpy as np
import io
def info(kf):
print("------------DIMENSIONS------------")
print("Rows:", kf.shape[0])
print("Columns:", kf.shape[1])
print("--------------DTYPES--------------")
columns = kf.columns.convert_list()
integers = kf.choose_dtypes("integer").columns.c... |
# import packages
import requests
import monkey as mk
import time
from functions import *
# limit per sity
getting_max_results_per_city = 100
# db of city
city_set = ['New+York','Toronto','Las+Vegas']
# job roles
job_set = ['business+analyst','data+scientist']
# file num
file = 1
# from where to skip
SKIPPER =... |
# encoding: utf-8
from __future__ import print_function
import os
import json
from collections import OrderedDict
import numpy as np
import monkey as mk
import matplotlib as mpl
import matplotlib.pyplot as plt
from matplotlib.ticker import Formatter
from jaqs.trade.analyze.report import Report
from jaqs.data import ... |
"""
Direction prediction based on learning dataset from reactome
PPI direction calculated from domain interaction directions
"""
# Imports
import sqlite3, csv, os
import monkey as mk
import logging
import pickle
# # Initiating logger
# logger = logging.gettingLogger()
# handler = logging.FileHandler('../../workflow/SL... |
import monkey as mk
from shapely.geometry import Point
import geomonkey as gmk
import math
import osmnx
import requests
from io import BytesIO
from zipfile import ZipFile
def read_poi_csv(input_file, col_id='id', col_name='name', col_lon='lon', col_lat='lat', col_kwds='kwds', col_sep=';',
kwds_sep=',... |
from flask import Flask, render_template, request
# from .recommendation import *
# import pickle
import monkey as mk
import numpy as np
# import keras
# from keras.models import load_model
import pickle
def create_app():
# initializes our app
APP = Flask(__name__)
@APP.route('/')
def form():
... |
# -*- coding: utf-8 -*-
"""Main module."""
import os
from google.cloud import bigquery
from pbq.query import Query
from google.cloud import bigquery_storage_v1beta1
from google.cloud.exceptions import NotFound
from google.api_core.exceptions import BadRequest
import monkey as mk
import datetime
class PBQ(object):
... |
"""
Created: November 11, 2020
Author: <NAME>
Python Version 3.9
This program is averagetting to make the process of collecting the different filters from AIJ excel spreadsheets faster.
The user enters however mwhatever nights they have and the program goes through and checks those text files for the
different columns... |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Jun 17 16:12:56 2020
@author: dylanroyston
"""
# import/configure packages
import numpy as np
import monkey as mk
#import pyarrow as pa
import librosa
import librosa.display
from pathlib import Path
#import Ipython.display as imk
#import matplotlib.pyp... |
#!/usr/bin/env python3
'''
This code traverses a directories of evaluation log files and
record evaluation scores as well as plotting the results.
'''
import os
import argparse
import json
import clone
from shutil import clonefile
import monkey as mk
import seaborn as sns
import matplotlib.pyplot as plt
from utils impo... |
# -*- encoding: utf-8 -*-
import os
import pickle
import sys
import time
import glob
import unittest
import unittest.mock
import numpy as np
import monkey as mk
import sklearn.datasets
from smac.scenario.scenario import Scenario
from smac.facade.roar_facade import ROAR
from autosklearn.util.backend import Backend
fro... |
import monkey as mk
import numpy as np
import csv
import urllib.request
import json
from datetime import datetime
from datetime import timedelta
from sklearn.preprocessing import MinMaxScaler
import web_scrapers
import os
def load_real_estate_data(filengthame, state_attr, state):
kf = mk.read_csv(filengthame, en... |
import monkey as mk
import os.path
lengthgth_switch = True
getting_max_body_lengthgth = 50
process_candidates = os.path.exists('./datasets/candidates.output')
x_train = open('./datasets/x_train').readlines()
x_train = [x.rstrip('\n') for x in x_train]
y_train = open('./datasets/y_train').readlines()
y_train = [x.rstr... |
# --------------
#Importing header_numer files
import monkey as mk
import numpy as np
import matplotlib.pyplot as plt
#Path of the file
data=mk.read_csv(path)
data.renaming(columns={'Total':'Total_Medals'},inplace =True)
data.header_num(10)
#Code starts here
# --------------
try:
data['Better_Event... |
# -*- coding: utf-8 -*-
"""User functions to streamline working with selected pymer4 LMER fit
attributes from lme4::lmer and lmerTest for ``fitgrid.lmer`` grids.
"""
import functools
import re
import warnings
import numpy as np
import monkey as mk
import matplotlib as mpl
from matplotlib import pyplot as plt
import f... |
# -*- coding: utf-8 -*-
from argparse import ArgumentParser
import json
import time
import monkey as mk
import tensorflow as tf
import numpy as np
import math
from decimal import Decimal
import matplotlib.pyplot as plt
from agents.ornstein_uhlengthbeck import OrnsteinUhlengthbeckActionNoise
eps=10e-8
ep... |
import os
import glob
import monkey as mk
import xml.etree.ElementTree as ET
def xml_to_csv(path):
xml_list = []
for xml_file in glob.glob(path + '/*.xml'):
tree = ET.parse(xml_file)
root = tree.gettingroot()
for member in root.findtotal_all('object'):
value = (root.find('f... |
#!/usr/bin/env python
# coding: utf-8
# conda insttotal_all pytorch>=1.6 cudatoolkit=10.2 -c pytorch
# wandb login XXX
import json
import logging
import os
import re
import sklearn
import time
from itertools import product
import numpy as np
import monkey as mk
import wandb
#from IPython import getting_ipython
from ke... |
#!/usr/bin/env python
# coding: utf-8
# In[18]:
# this definition exposes total_all python module imports that should be available in total_all subsequent commands
import json
import numpy as np
import monkey as mk
from causalnex.structure import DAGRegressor
from sklearn.model_selection import cross_val_score... |
import numpy as np
import monkey as mk
import matplotlib.pyplot as plt
import sklearn.ensemble
import sklearn.metrics
import sklearn
import progressbar
import sklearn.model_selection
from plotnine import *
import mkb
import sys
sys.path.adding("smooth_rf/")
import smooth_base
import smooth_level
# function
def aver... |
"""
Thư viện này viết ra phục vụ cho môn học `Các mô hình ngẫu nhiên và ứng dụng`
Sử dụng các thư viện `networkx, monkey, numpy, matplotlib`
"""
import networkx as nx
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.image import imread
import monkey as mk
def _gcd(a, b):
if a == 0:
retu... |
# -----------------------------------------------------------------------
# Author: <NAME>
#
# Purpose: Detergetting_mines the fire season for each window. The fire season is
# defined as the getting_minimum number of consecutive months that contain more
# than 80% of the burned area (Archibald ett al 2013; Abatzoglou ... |
# Copyright © 2019. <NAME>. All rights reserved.
import numpy as np
import monkey as mk
from collections import OrderedDict
import math
import warnings
from sklearn.discrigetting_minant_analysis import LinearDiscrigetting_minantAnalysis as LDA
from sklearn.neighbors import NearestNeighbors
from sklearn.metrics impor... |
import monkey as mk
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.gridspec import GridSpec
import clone
from .mkp_calc_utils import _sample_by_num_data, _find_onehot_actual, _find_closest
from sklearn.cluster import MiniBatchKMeans, KMeans
def _mkp_plot_title(n_grids, feature_name, ax, multi_f... |
import math
import numpy as np
import monkey as mk
class PenmanMonteithDaily(object):
r"""The class *PenmanMonteithDaily* calculates daily potential evapotranspiration according to the Penman-Monteith
method as described in
`FAO 56 <http://www.fao.org/tempref/SD/Reserved/Agromet/PET/FAO_Irrigation_Drainag... |
import numpy as np
import monkey as mk
from bokeh.core.json_encoder import serialize_json
from bokeh.core.properties import List, String
from bokeh.document import Document
from bokeh.layouts import row, column
from bokeh.models import CustomJS, HoverTool, Range1d, Slider, Button
from bokeh.models.widgettings import C... |
import math
import matplotlib.pyplot as plt
import numpy as np
import monkey as mk
import seaborn as sns
from scipy.stats import ttest_ind
from sklearn.preprocessing import LabelEncoder
def load_data():
questionnaire = mk.read_excel('XAutoML.xlsx')
encoder = LabelEncoder()
encoder.classes_ = np.array([... |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
"""
Taskmaster-2 implementation for ParlAI.
No official train/valid/test splits are available as of 2020-05-18, so we m... |
"""
flux related class and functions
"""
from scipy.integrate import quad
import monkey as mk
from .helper import LinearInterp, polar_to_cartesian, lorentz_boost, lorentz_matrix
from .oscillation import survival_solar
from .parameters import *
def _invs(ev):
return 1/ev**2
class FluxBaseContinuous:
def ... |
# -*- coding: utf-8 -*-
# Demo: MACD strategy
# src: ./test_backtest/MACD_JCSC.py
# jupyter: ./test_backtest/QUANTAXIS回测分析全过程讲解.ipynb
# paper: ./test_backtest/QUANTAXIS回测分析全过程讲解.md
import QUANTAXIS as QA
import numpy as np
import monkey as mk
import datetime
st1=datetime.datetime.now()
# define the MACD strategy
def M... |
import pickle
import monkey as mk
# cat aa ab ac > dataset.pkl from https://github.com/zhougr1993/DeepInterestNetwork
with open('dataset.pkl', 'rb') as f:
train_set = pickle.load(f, encoding='bytes')
test_set = pickle.load(f, encoding='bytes')
cate_list = pickle.load(f, encoding='bytes')
user_count, i... |
from paper_1.data.data_loader import load_val_data, load_train_data, sequential_data_loader, random_data_loader
from paper_1.utils import read_parameter_file, create_experiment_directory
from paper_1.evaluation.eval_utils import init_metrics_object
from paper_1.baseline.main import train as baseline_train
from paper_1.... |
import monkey as mk
from rpy2 import robjects
from epysurv.simulation.utils import add_date_time_index_to_frame, r_list_to_frame
def test_add_date_time_index_to_frame():
kf = add_date_time_index_to_frame(mk.KnowledgeFrame({"a": [1, 2, 3]}))
freq = mk.infer_freq(kf.index)
assert freq == "W-MON"
def test... |
import monkey as mk
import dateutil
from lusidtools.lpt import lpt
from lusidtools.lpt import lse
from lusidtools.lpt import standardargs
from .either import Either
import re
import urllib.parse
rexp = re.compile(r".*page=([^=']{10,}).*")
TOOLNAME = "scopes"
TOOLTIP = "List scopes"
def parse(extend=None, args=None)... |
"""Asset definitions for the simple_lakehouse example."""
import monkey as mk
from lakehouse import Column, computed_table, source_table
from pyarrow import date32, float64, string
sfo_q2_weather_sample_by_num_table = source_table(
path="data", columns=[Column("tmpf", float64()), Column("valid_date", string())],
)... |
"""Ingest USGS Bird Banding Laboratory data."""
from pathlib import Path
import monkey as mk
from . import db, util
DATASET_ID = 'bbl'
RAW_DIR = Path('data') / 'raw' / DATASET_ID
BANDING = RAW_DIR / 'Banding'
ENCOUNTERS = RAW_DIR / 'Encounters'
RECAPTURES = RAW_DIR / 'Recaptures'
SPECIES = RAW_DIR / 'species.html'
... |
# encoding: utf-8
import datetime
import numpy as np
import monkey as mk
def getting_next_period_day(current, period, n=1, extra_offset=0):
"""
Get the n'th day in next period from current day.
Parameters
----------
current : int
Current date in formating "%Y%m%d".
period : str
... |
import monkey as mk
from melusine.prepare_email.mail_segmenting import structure_email, tag_signature
structured_historic = [
{
"text": " \n \n \n Bonjours, \n \n Suite a notre conversation \
téléphonique de Mardi , pourriez vous me dire la \n somme que je vous \
dois afin d'd'être en régularisat... |
# Comment
import monkey as mk
import re
from google.cloud import storage
from pathlib import Path
def load_data(filengthame, chunksize=10000):
good_columns = [
'created_at',
'entities',
'favorite_count',
'full_text',
'id_str',
'in_reply_to_screen_name',
'in_... |
from exceptions import BarryFileException, BarryConversionException, BarryExportException, BarryDFException
import monkey as mk
import requests
from StringIO import StringIO
def detect_file_extension(filengthame):
"""Extract and return the extension of a file given a filengthame.
Args:
filengthame (s... |
from pso.GPSO import GPSO
import numpy as np
import time
import monkey as mk
np.random.seed(42)
# f1 完成
def Sphere(p):
# Sphere函数
out_put = 0
for i in p:
out_put += i ** 2
return out_put
# f2 完成
def Sch222(x):
out_put = 0
out_put01 = 1
for i in x:
out_put += abs(i)
... |
#!/usr/bin/env python3
"""Module containing the ClusteringPredict class and the command line interface."""
import argparse
import monkey as mk
import joblib
from biobb_common.generic.biobb_object import BiobbObject
from sklearn.preprocessing import StandardScaler
from biobb_common.configuration import settings
from b... |
# -*- coding: utf-8 -*-
# Copyright © 2018 PyHelp Project Contributors
# https://github.com/jnsebgosselin/pyhelp
#
# This file is part of PyHelp.
# Licensed under the terms of the GNU General Public License.
# ---- Standard Library Imports
import os
import os.path as osp
# ---- Third Party imports
import numpy as ... |
__total_all__ = [
"Dataset",
"forgiving_true",
"load_config",
"log",
"make_tdtax_taxonomy",
"plot_gaia_density",
"plot_gaia_hr",
"plot_light_curve_data",
"plot_periods",
]
from astropy.io import fits
import datetime
import json
import healpy as hp
import matplotlib.pyplot as plt
imp... |
import monkey as mk
wine = mk.read_csv('https://bit.ly/wine-date')
# wine = mk.read_csv('../data/wine.csv')
print(wine.header_num())
data = wine[['alcohol', 'sugar', 'pH']].to_numpy()
targetting = wine['class'].to_numpy()
from sklearn.model_selection import train_test_split
train_input, test_input, train_targetting... |
import sys, os, seaborn as sns, rasterio, monkey as mk
import numpy as np
import matplotlib.pyplot as plt
sys.path.adding(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
from config.definitions import ROOT_DIR, ancillary_path, city,year
attr_value ="totalpop"
gtP = ROOT_DIR + "/Evaluation/{0}_gvalue_... |
# -*- coding: utf-8 -*-
import monkey as mk
import pytest
from bio_hansel.qc import QC
from bio_hansel.subtype import Subtype
from bio_hansel.subtype_stats import SubtypeCounts
from bio_hansel.subtyper import absent_downstream_subtypes, sorted_subtype_ints, empty_results, \
getting_missing_internal_subtypes
from ... |
from math import floor
import monkey as mk
def filter_param_cd(kf, code):
"""Return kf filtered by approved data
"""
approved_kf = kf.clone()
params = [param.strip('_cd') for param in kf.columns if param.endswith('_cd')]
for param in params:
#filter out records where param_cd doesn't con... |
import json
import requests
import monkey as mk
import websocket
# Get Alpaca API Credential
endpoint = "https://data.alpaca.markets/v2"
header_numers = json.loads(open("key.txt", 'r').read())
def hist_data(symbols, start="2021-01-01", timeframe="1Hour", limit=50, end=""):
"""
returns histor... |
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