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Commit e0482224 authored by Jon Mease's avatar Jon Mease
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Use go.FigureWidget

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notebooks/DataShaderExample.ipynb
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%% Cell type:markdown id: tags:
 
# Overview
This notebook demonstrates how to use DataShader to display large datasets inside a plotly `FigureWidget`. Change callbacks are used to recompute the datashader image whenever the axis range or figure size changes
 
%% Cell type:markdown id: tags:
 
## Imports
 
%% Cell type:code id: tags:
 
``` python
from plotly.graph_objs import FigureWidget
# core
import io
import base64
import time
 
# pandas
import pandas as pd
 
# numpy
import numpy as np
 
# scikit learn
from sklearn import datasets
 
# datashader
import datashader as ds
import datashader.transfer_functions as tf
from datashader.colors import inferno
```
 
%% Cell type:markdown id: tags:
 
## Generate dataset
We will create a large dataset by duplicating the Iris dataset many times with random noise
 
%% Cell type:code id: tags:
 
``` python
num_copies = 7000 # 1,050,000 rows
 
iris_data = datasets.load_iris()
feature_names = [name.replace(' (cm)', '').replace(' ', '_') for name in iris_data.feature_names]
iris_df_orig = pd.DataFrame(iris_data.data, columns=feature_names)
target_orig = iris_data.target + 1
 
# frame of features
iris_df = pd.concat(
np.random.normal(scale=0.2, size=iris_df_orig.shape) + iris_df_orig for i in range(num_copies)
).reset_index(drop=True)
 
# array of targets
target = [t for i in range(num_copies) for t in target_orig]
 
# dataframe that includes target as categorical
iris_target_df = pd.concat([iris_df, pd.Series(target, name='target', dtype='category')], axis=1)
 
iris_df.describe()
```
 
%% Cell type:markdown id: tags:
 
## Define DataShader image generation function
Define a function that inputs an x/y ranges and the plot width/height and generates a DataShader image of the dataset. The image will be returned as a PIL image object
 
%% Cell type:code id: tags:
 
``` python
def gen_ds_image(x_range, y_range, plot_width, plot_height):
if x_range is None or y_range is None or plot_width is None or plot_height is None:
return None
 
cvs = ds.Canvas(x_range=x_range, y_range=y_range, plot_height=plot_height, plot_width=plot_width)
agg_scatter = cvs.points(iris_target_df,
'sepal_length', 'sepal_width',
ds.count_cat('target'))
img = tf.shade(agg_scatter)
img = tf.dynspread(img, threshold=0.95, max_px=5, shape='circle')
 
return img.to_pil()
```
 
%% Cell type:markdown id: tags:
 
## Define initial ranges and plot size
 
%% Cell type:code id: tags:
 
``` python
x_range=[3, 10]
y_range=[0, 6]
plot_height=500
plot_width=700
```
 
%% Cell type:code id: tags:
 
``` python
# Test image generation function and display the PIL image
initial_img = gen_ds_image(x_range, y_range, plot_width, plot_height)
```
 
%% Cell type:code id: tags:
 
``` python
initial_img
```
 
%% Cell type:markdown id: tags:
 
# Create FigureWidget with background image
 
%% Cell type:code id: tags:
 
``` python
f = FigureWidget(data=[{'x': x_range,
'y': y_range,
'mode': 'markers',
'marker': {'opacity': 0}}], # invisible trace to init axes and to support autoresize
layout={'width': plot_width, 'height': plot_height})
import plotly.graph_objs as go
```
%% Cell type:code id: tags:
``` python
f = go.FigureWidget(data=[{'x': x_range,
'y': y_range,
'mode': 'markers',
'marker': {'opacity': 0}}], # invisible trace to init axes and to support autoresize
layout={'width': plot_width, 'height': plot_height})
f
```
 
%% Cell type:code id: tags:
 
``` python
# Set background image
f.layout.images = [dict(
f.layout.images = [go.layout.Image(
source = initial_img, # plotly now performs auto conversion of PIL image to png data URI
xref = "x",
yref = "y",
x = x_range[0],
y = y_range[1],
sizex = x_range[1] - x_range[0],
sizey = y_range[1] - y_range[0],
sizing = "stretch",
layer = "below")]
```
 
%% Cell type:markdown id: tags:
 
## Install change callback to update image on zoom/resize
 
%% Cell type:code id: tags:
 
``` python
def update_ds_image(layout, x_range, y_range, plot_width, plot_height):
img = f.layout.images[0]
 
# Update with batch_update so all updates happen simultaneously
with f.batch_update():
img.x = x_range[0]
img.y = y_range[1]
img.sizex = x_range[1] - x_range[0]
img.sizey = y_range[1] - y_range[0]
img.source = gen_ds_image(x_range, y_range, plot_width, plot_height)
 
# Install callback to run exactly once if one or more of the following properties changes
# - xaxis range
# - yaxis range
# - figure width
# - figure height
f.layout.on_change(update_ds_image, 'xaxis.range', 'yaxis.range', 'width', 'height')
```
 
%% Cell type:markdown id: tags:
 
## Image updates on drag zoom
 
%% Cell type:code id: tags:
 
``` python
f.layout.dragmode = 'zoom'
f
```
 
%% Cell type:code id: tags:
 
``` python
```
......
notebooks/nyc_taxi_selection.ipynb
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......@@ -262,7 +262,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.5"
"version": "3.6.6"
}
},
"nbformat": 4,
......
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