Plotting Notebook

[brendancol]

• [x] checkout Brendan's notebook branch (git fetch && git checkout brendan-notebook-changes)
• [x] fix transpose bug in pix funcs (scroll down and compare output images)
• [x] numba-ize pix functions (rectangle rasterization)
• [ ] add InteractiveImage (bokeh.output_notebook required) which queries RBush on each pan / zoom and updates datashader image. Ask Brendan for examples
• [ ] move rectangle rasterization inside core
• [ ] add the custom generate data cell inside of data library and call it gaussian_grid
• [ ] add centroids property
• [ ] add raster property which returns 2d array (datafor xarray / datashader)
• [ ] add partition(npartitions=9) method which break up rtree into N rtrees based on kmeans or some other clustering algo. on centroids. test using gaussian_grid data (future)
• [ ] add to_parquet method which write rtree to disk using pyarrow (future)

[brendancol]

import datashader as ds
from datashader.bokeh_ext import InteractiveImage
from functools import partial
from datashader.utils import export_image
from datashader.colors import colormap_select, Greys9, Hot, viridis, inferno
from IPython.core.display import HTML, display

background = "black"
export = partial(export_image, export_path="export", background=background)
cm = partial(colormap_select, reverse=(background=="black"))

def create_image(x_range, y_range, w=plot_width, h=plot_height):
    # query rbush for only rectangles inside of ranges
    cvs = ds.Canvas(plot_width=w, plot_height=h, x_range=x_range, y_range=y_range)
    agg = cvs.points(df, 'dropoff_x', 'dropoff_y',  ds.count('passenger_count'))
    img = tf.shade(agg, cmap=Hot, how='eq_hist')
    return tf.dynspread(img, threshold=0.5, max_px=4)

p = base_plot(background_fill_color=background)
export(create_image(*NYC),"NYCT_hot")
InteractiveImage(p, create_image)