Jupyter notebook tests https://github.com/jupyter/notebook/blob/master/notebook/tests/selenium/utils.py
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May 15, 2019 21:08
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Jupyter OSBot-Commands
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#https://bokeh.pydata.org/ | |
#http://bokeh.pydata.org/en/latest/docs/installation.html | |
#https://nbviewer.jupyter.org/github/bokeh/bokeh-notebooks/blob/master/quickstart/quickstart.ipynb | |
!pip install bokeh | |
from bokeh.plotting import figure | |
from bokeh.io import output_notebook, show | |
output_notebook() | |
from numpy import cos, linspace | |
x = linspace(-6, 60, 1200) | |
y = cos(x) | |
p = figure(width=500, height=500) | |
p.circle(x, y, size=7, color="firebrick", alpha=0.5) | |
show(p) | |
# Reall cool example | |
from bokeh.sampledata.autompg import autompg | |
grouped = autompg.groupby("yr") | |
mpg = grouped.mpg | |
avg, std = mpg.mean(), mpg.std() | |
years = list(grouped.groups) | |
american = autompg[autompg["origin"]==1] | |
japanese = autompg[autompg["origin"]==3] | |
p = figure(title="MPG by Year (Japan and US)") | |
p.vbar(x=years, bottom=avg-std, top=avg+std, width=0.8, | |
fill_alpha=0.2, line_color=None, legend="MPG 1 stddev") | |
p.circle(x=japanese["yr"], y=japanese["mpg"], size=10, alpha=0.5, | |
color="red", legend="Japanese") | |
p.triangle(x=american["yr"], y=american["mpg"], size=10, alpha=0.3, | |
color="blue", legend="American") | |
p.legend.location = "top_right" | |
show(p) |
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#---- get issue | |
from osbot_jira.api.jira_server.API_Jira import API_Jira | |
api_jira = API_Jira() | |
#---- take screenshot | |
from osbot_aws.apis.Lambda import Lambda | |
lambda_name = 'osbot_browser.lambdas.lambda_browser' | |
lambda_browser = Lambda(lambda_name) | |
payload = {"params": ['screenshot','https://news.bbc.co.uk']} | |
png_data = lambda_browser.invoke(payload) | |
from IPython.display import display_html | |
html = '<img style="margin:0" align="left" src="data:image/png;base64,{}"/>'.format(png_data) | |
display_html(html, raw=True) | |
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# install dependencies | |
!pip install osbot_aws boto3 pbx_gs_python_utils osbot_jira jira | |
# list current servers | |
!jupyter notebook list | |
# stop server at 8888 | |
!jupyter notebook stop 8888 |
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#bash commands | |
apt-get install -y libnss3 libgconf-2-4 | |
pip install nest_asyncio | |
pip install syncer, pyppeteer | |
pip install osbot_browser osbot_aws pbx_gs_python_utils boto3 | |
# cell code (first version) | |
import nest_asyncio | |
nest_asyncio.apply() | |
import os | |
from osbot_browser.browser.Browser_Lamdba_Helper import Browser_Lamdba_Helper | |
url = 'https://www.google.com/asd' | |
#url = 'http://localhost:8888' | |
print(os.getenv('AWS_REGION')) | |
browser = Browser_Lamdba_Helper().setup().api_browser | |
browser.sync__open(url) | |
#print(browser.sync__url()) | |
png_data = browser.sync__screenshot_base64(url) | |
from IPython.display import display_html | |
html = '<img style="margin:0" align="left" src="data:image/png;base64,{}"/>'.format(png_data) | |
display_html(html, raw=True) | |
### Better version | |
import nest_asyncio | |
import os | |
from IPython.display import display_html | |
from osbot_browser.browser.Browser_Lamdba_Helper import Browser_Lamdba_Helper | |
nest_asyncio.apply() | |
def open_url(url): | |
browser = Browser_Lamdba_Helper().setup().api_browser | |
browser.sync__open(url) | |
png_data = browser.sync__screenshot_base64(url) | |
html = '<img style="margin:0" align="left" src="data:image/png;base64,{}"/>'.format(png_data) | |
display_html(html, raw=True) | |
open_url('https://www.google.com/asd') |
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%%javascript | |
document.onpaste = function(event) { | |
var items = event.clipboardData.items; | |
console.log(JSON.stringify(items)); // will give you the mime types | |
var blob = items[0].getAsFile(); | |
var reader = new FileReader(); | |
reader.onload = function(event){ | |
console.log(event.target.result) | |
Jupyter.notebook.cells_to_markdown() | |
Jupyter.notebook.get_selected_cell().set_text('<img style="border:1px solid black" align="left" src="' | |
+event.target.result + | |
'"/>') | |
Jupyter.notebook.get_selected_cell().execute() | |
}; | |
reader.readAsDataURL(blob); | |
} |
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//--- execute code in cell | |
code = Jupyter.notebook.get_selected_cell() | |
code.set_text('40+3') ; code.execute() ; | |
code.output_area.element.find('.output_result').text() // after execution, this is where the answer will be | |
//--- other commands | |
Jupyter.notebook.cells_to_markdown() // change in ui the current cell to markdown | |
Jupyter.notebook.cells_to_code() // change in ui the current cell to markdown | |
Jupyter.notebook.delete_cell() | |
Jupyter.notebook.get_cells() | |
Jupyter.notebook.insert_cell_below(); | |
Jupyter.notebook.select_next(true); | |
Jupyter.notebook.focus_cell(); | |
Jupyter.notebook.get_selected_cell().set_text('40+3') | |
Jupyter.notebook.get_selected_cell().execute(); | |
//--- UI changes | |
Jupyter.toolbar.add_buttons_group([ | |
{ | |
'label' : 'alert me', | |
'icon' : 'fa-terminal', // select your icon from | |
// http://fontawesome.io/icons/ | |
'callback': function(){alert(42)} | |
} | |
// add more button here if needed. | |
]); |
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#!pip install lightning-python | |
from lightning import Lightning | |
from numpy import random | |
lgn = Lightning(ipython=True, local=True) | |
#series = random.randn(10, 50) | |
#lgn.line(series) | |
# x = random.randn(1000) | |
# y = random.randn(1000) | |
# v = random.randn(1000) | |
# lgn.scatter(x, y, alpha=0.5, values=v, colormap='Reds') | |
mat = random.rand(100,100) | |
mat[mat<0.97] = 0 | |
lgn.force(mat) |
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#https://jupyter-notebook.readthedocs.io/en/stable/examples/Notebook/JavaScript%20Notebook%20Extensions.html | |
from jupyter_core.paths import jupyter_config_dir | |
jupyter_dir = jupyter_config_dir() | |
jupyter_dir | |
# ---- Create notebook from notebook | |
import nbformat as nbf | |
nb = nbf.v4.new_notebook() | |
text = """\ | |
# My first automatic Jupyter Notebook | |
This is an auto-generated notebook.""" | |
code = """\ | |
%pylab inline | |
hist(normal(size=2000), bins=50);""" | |
nb['cells'] = [nbf.v4.new_markdown_cell(text), | |
nbf.v4.new_code_cell(code) ] | |
nbf.write(nb, 'test.ipynb') |
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import numpy as np | |
import pandas as pd | |
import qgrid | |
randn = np.random.randn | |
df_types = pd.DataFrame({ | |
'A' : pd.Series(['2013-01-01', '2013-01-02', '2013-01-03', '2013-01-04', | |
'2013-01-05', '2013-01-06', '2013-01-07', '2013-01-08', '2013-01-09'],index=list(range(9)),dtype='datetime64[ns]'), | |
'B' : pd.Series(randn(9),index=list(range(9)),dtype='float32'), | |
'C' : pd.Categorical(["washington", "adams", "washington", "madison", "lincoln","jefferson", "hamilton", "roosevelt", "kennedy"]), | |
'D' : ["test 123", "bar", "buzz", "bippity","boppity", "foo", "foo", "bar", "zoo"] }) | |
df_types['E'] = df_types['D'] == 'foo' | |
qgrid_widget = qgrid.show_grid(df_types, show_toolbar=True) | |
qgrid_widget |
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