emmanuelle

from github import Github
from datetime import datetime

# Change start date here
start = datetime(2020, 4, 6)


def print_list(l, title=None):
    """From list of PullRequest of Issue objects,
    print their number and title.

emmanuelle

from github import Github
import plotly.express as px
from datetime import datetime

# First create a Github instance
# using an access token
g = Github("your own token here")

repo = g.get_repo("scikit-image/scikit-image")

emmanuelle

import plotly.express as px 
import pandas as pd

# Analytics file downloaded from matomo, each line is a visit
df = pd.read_csv('december10-skimage.csv', encoding='utf-16le')
df['unit'] = 1
df['hour'] = pd.to_datetime(df['serverTimePretty']).dt.hour
# For each country and hour, count number of visitors
dfg = df[['unit', 'country', 'hour']].groupby(['country', 'hour']).sum()
dfg.reset_index(inplace=True)

emmanuelle

import dash
from dash.dependencies import Input, Output
import dash_core_components as dcc
import dash_html_components as html
import plotly.graph_objects as go
import plotly.express as px

gapminder = px.data.gapminder().query("year == 2007").reset_index()

app = dash.Dash(__name__)

emmanuelle

emmanuelle

import dash
import dash_core_components as dcc
import dash_html_components as html
from dash.dependencies import Input, Output
import plotly.graph_objs as go

import numpy as np
from skimage import data

emmanuelle

import dash
import dash_core_components as dcc
import dash_html_components as html
from dash.dependencies import Input, Output
import plotly.graph_objs as go

import numpy as np
from skimage import data

emmanuelle

emmanuelle

import numpy as np
import inspect
from skimage import exposure, feature, filters, measure, morphology, \
                    restoration, segmentation, transform, util, data, color
from timeit import default_timer
import pandas as pd
import cv2
import time

###this python script returns a csv of function runtimes

emmanuelle

import numpy as np
from sklearn.externals.joblib import Parallel, delayed


def apply_parallel(func, data, *args, chunk=100, overlap=10, n_jobs=4, 
                                      **kwargs):
    """
    Apply a function in parallel to overlapping chunks of an array.
    
    joblib is used for parallel processing.