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Verified Commit 4c50e449 authored by David Beniamine's avatar David Beniamine
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dashboads/bokeh.ipynb 0 → 100644
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%% Cell type:code id: tags:
``` python
# Imported from seaborn tutorial https://nbviewer.jupyter.org/github/bokeh/bokeh-notebooks/blob/master/tutorial/06%20-%20Linking%20and%20Interactions.ipynb
from bokeh.io import output_notebook, show
from bokeh.plotting import figure
output_notebook()
from bokeh.layouts import column
from bokeh.models import CustomJS, ColumnDataSource, Slider
x = [x*0.005 for x in range(0, 201)]
source = ColumnDataSource(data=dict(x=x, y=x))
plot = figure(plot_width=400, plot_height=400)
plot.line('x', 'y', source=source, line_width=3, line_alpha=0.6)
slider = Slider(start=0.1, end=6, value=1, step=.1, title="power")
update_curve = CustomJS(args=dict(source=source, slider=slider), code="""
var data = source.data;
var f = slider.value;
x = data['x']
y = data['y']
for (i = 0; i < x.length; i++) {
y[i] = Math.pow(x[i], f)
}
// necessary becasue we mutated source.data in-place
source.change.emit();
""")
slider.js_on_change('value', update_curve)
show(column(slider, plot))
```
%% Output
%% Cell type:code id: tags:
``` python
#Imported from Interact_seaborn
from ipywidgets import interact, interactive
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
tips = sns.load_dataset("tips")
from bokeh.plotting import figure
from bokeh.models import ColumnDataSource
from bokeh.io import output_notebook, show
from bokeh.palettes import Category10_10 as palette
from bokeh.transform import factor_cmap
output_notebook()
@interact(color=["smoker","sex","day","time","size"])
def f(color):
cds_df = ColumnDataSource(tips)
p = figure(title="Tips repartition",
x_axis_label="Total bill",
y_axis_label="Tip")
categories=[str(x) for x in tips[color].unique()]
# Hack to fix bug with size being integer values
tips['size']=[str(x) for x in tips['size']]
colors = factor_cmap(color, palette=palette, factors=categories)
p.circle(x='total_bill', y='tip', color=colors, legend=colors, source=cds_df)
show(p)
```
%% Output
%% Cell type:code id: tags:
``` python
# Interactive plot with only matplotlib and jupyter interactive widget
from ipywidgets import interact, interactive
import matplotlib.pyplot as plt
import numpy as np
def ma_fonction(a,b,c):
plt.figure(2) # ???
x = np.linspace(-10,10, num=1000)
plt.plot(x, a*x**2+b*x+c)
plt.ylim(-10,10)
plt.show()
interactive_plot = interactive(ma_fonction, a=(-10,10), b=(-20,20), c=(-5,5))
interactive_plot
```
%% Output
%% Cell type:code id: tags:
``` python
# And again bokeh code
# Imported from seaborn tutorial https://nbviewer.jupyter.org/github/bokeh/bokeh-notebooks/blob/master/tutorial/06%20-%20Linking%20and%20Interactions.ipynb
from bokeh.io import output_notebook, show
from bokeh.plotting import figure
output_notebook()
from bokeh.layouts import column
from bokeh.models import CustomJS, ColumnDataSource, Slider
x = [x*0.005 for x in range(0, 201)]
source = ColumnDataSource(data=dict(x=x, y=x))
plot = figure(plot_width=400, plot_height=400)
plot.line('x', 'y', source=source, line_width=3, line_alpha=0.6)
slider = Slider(start=0.1, end=6, value=1, step=.1, title="power")
update_curve = CustomJS(args=dict(source=source, slider=slider), code="""
var data = source.data;
var f = slider.value;
x = data['x']
y = data['y']
for (i = 0; i < x.length; i++) {
y[i] = Math.pow(x[i], f)
}
// necessary becasue we mutated source.data in-place
source.change.emit();
""")
slider.js_on_change('value', update_curve)
show(column(slider, plot))
```
%% Output
%% Cell type:code id: tags:
``` python
```
dashboads/csv.ipynb 0 → 100644
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%% Cell type:code id: tags:
``` python
import csv
lines = []
with open('test.csv') as csv_file:
dialect = csv.Sniffer().sniff(csv_file.read(1024))
csv_file.seek(0)
reader = csv.reader(csv_file, dialect)
for row in reader:
lines.append(row)
print(lines)
```
%% Output
[['nom', 'prenom', 'surnom'], ['toto', 'd', 'toto'], ['titi', 'a', 'tutu']]
%% Cell type:code id: tags:
``` python
```
%% Cell type:code id: tags:
``` python
```
data/Readme.md 0 → 100644
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# example dashboard
This repository is an example dashboard for the [django voila-stack](https://gitlab.tetras-libre.fr/tetras-libre/jupyter/voila-stack)
It works as follow
```
data/
mydata.py # Python script exposing three methods
# extractLastData() -> scraps the last data and return the downloaded file path
# Upload(path) -> upload data contained in path
# getDataSample() -> return a sample data for debugging pupose
dashboard/
myfirstdashboard.ipynb # Ipynotebook dashboard that will be rendered by voila
myfirstdashboard_housekeeping.py # Python script that generates data aggregates for myfirstdashbord
myseconddashboard.ipynb #Ipynotebook dashboard that will be rendered by voila
myseconddashboard_housekeeping.py # Python script that generates data aggregates for myseconddashbord
```
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