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The absolute bare minimum to get datashader working in matplotlib
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import matplotlib.pyplot as plt | |
import pandas as pd | |
import datashader as ds | |
import datashader.transfer_functions as tf | |
from datashader.colors import Hot | |
time_period = 60 | |
def bin_data(): | |
global time_period, grouped, group_count, counter, times, groups | |
grouped = df.groupby([times.hour, times.minute // time_period]) | |
groups = sorted(grouped.groups.keys(), key=lambda r: (r[0], r[1])) | |
group_count = len(groups) | |
counter = 0 | |
class LiveImageDisplay(object): | |
def __init__(self, h=500, w=500, niter=50, radius=2., power=2): | |
self.height = h | |
self.width = w | |
def __call__(self, xstart, xend, ystart, yend): | |
canvas = ds.Canvas(plot_width=self.width, | |
plot_height=self.height, | |
x_range=(xstart, xend), | |
y_range=(ystart, yend)) | |
agg = canvas.points(df, 'dropoff_x', 'dropoff_y', | |
ds.count('trip_distance')) | |
img = tf.interpolate(agg, cmap=Hot, how='log') | |
print(img.data.shape, img.data.dtype) | |
return img.data | |
def ax_update(self, ax): | |
ax.set_autoscale_on(False) # Otherwise, infinite loop | |
# Get the number of points from the number of pixels in the window | |
dims = ax.axesPatch.get_window_extent().bounds | |
self.width = int(dims[2] + 0.5) | |
self.height = int(dims[2] + 0.5) | |
# Get the range for the new area | |
xstart, ystart, xdelta, ydelta = ax.viewLim.bounds | |
xend = xstart + xdelta | |
yend = ystart + ydelta | |
# Update the image object with our new data and extent | |
im = ax.images[-1] | |
im.set_data(self.__call__(xstart, xend, ystart, yend)) | |
im.set_extent((xstart, xend, ystart, yend)) | |
ax.figure.canvas.draw_idle() | |
path = './nyc_taxi.csv' | |
datetime_field = 'tpep_dropoff_datetime' | |
cols = ['dropoff_x', 'dropoff_y', 'trip_distance', datetime_field] | |
df = pd.read_csv(path, usecols=cols, parse_dates=[datetime_field]).dropna(axis=0) | |
times = pd.DatetimeIndex(df[datetime_field]) | |
group_count = grouped = groups = None | |
bin_data() | |
xmin = -8240227.037 | |
ymin = 4974203.152 | |
xmax = -8231283.905 | |
ymax = 4979238.441 | |
img = LiveImageDisplay() | |
Z = img(xmin, xmax, ymin, ymax) | |
fig1, ax2 = plt.subplots(1, 1) | |
ax2.imshow(Z, origin='lower', extent=(xmin, xmax, ymin, ymax)) | |
# Connect for changing the view limits | |
ax2.callbacks.connect('xlim_changed', img.ax_update) | |
ax2.callbacks.connect('ylim_changed', img.ax_update) | |
plt.show() |
This worked for me:
img_rgba = img.data.view(np.uint8).reshape(img.shape + (4,))
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The colors of the output look totally broken. It looks like you are passing int32 rgba data into matplotlib which is interpreting it as straight colormap data. I tried to convert it with some code like this, but I'm still having trouble getting it to display right. Something is weird.
I'm also wondering how this example compares to something like this: holoviz/datashader#200