Deephaven Plugin for matplotlib
Project description
Deephaven Plugin for matplotlib
The Deephaven Plugin for matplotlib. Allows for opening matplotlib plots in a Deephaven environment. Any matplotlib plot should be viewable by default. For example:
import matplotlib.pyplot as plt
fig = plt.figure()
ax = fig.subplots() # Create a figure containing a single axes.
ax.plot([1, 2, 3, 4], [4, 2, 6, 7]) # Plot some data on the axes.
You can also use TableAnimation
, which allows updating a plot whenever a Deephaven Table is updated.
TableAnimation
Usage
TableAnimation
is a matplotlib Animation
that is driven by updates in a Deephaven Table. Every time the table that
is being listened to updates, the provided function will run again.
Line Plot
import matplotlib.pyplot as plt
from deephaven import time_table
from deephaven.plugin.matplotlib import TableAnimation
# Create a ticking table with the sin function
tt = time_table("00:00:01").update(["x=i", "y=Math.sin(x)"])
fig = plt.figure() # Create a new figure
ax = fig.subplots() # Add an axes to the figure
line, = ax.plot([],[]) # Plot a line. Start with empty data, will get updated with table updates.
# Define our update function. We only look at `data` here as the data is already stored in the format we want
def update_fig(data, update):
line.set_data([data['x'], data['y']])
# Resize and scale the axes. Our data may have expanded and we don't want it to appear off screen.
ax.relim()
ax.autoscale_view(True, True, True)
# Create our animation. It will listen for updates on `tt` and call `update_fig` whenever there is an update
ani = TableAnimation(fig, tt, update_fig)
Scatter Plot
Scatter plots require data in a different format that Line plots, so need to pass in the data differently.
import matplotlib.pyplot as plt
from deephaven import time_table
from deephaven.plugin.matplotlib import TableAnimation
tt = time_table("00:00:01").update(["x=Math.random()", "y=Math.random()", "z=Math.random()*50"])
fig = plt.figure()
ax = fig.subplots()
ax.set_xlim(0, 1)
ax.set_ylim(0, 1)
scat = ax.scatter([],[]) # Provide empty data initially
scatter_offsets = [] # Store separate arrays for offsets and sizes
scatter_sizes = []
def update_fig(data, update):
# This assumes that table is always increasing. Otherwise need to look at other
# properties in update for creates and removed items
added = update.added()
for i in range(0, len(added['x'])):
# Append new data to the sources
scatter_offsets.append([added['x'][i], added['y'][i]])
scatter_sizes.append(added['z'][i])
# Update the figure
scat.set_offsets(scatter_offsets)
scat.set_sizes(scatter_sizes)
ani = TableAnimation(fig, tt, update_fig)
Multiple Series
It's possible to have multiple kinds of series in the same figure. Here is an example driving a line and a scatter plot:
import matplotlib.pyplot as plt
from deephaven import time_table
from deephaven.plugin.matplotlib import TableAnimation
tt = time_table("00:00:01").update(["x=i", "y=Math.sin(x)", "z=Math.cos(x)", "r=Math.random()", "s=Math.random()*100"])
fig = plt.figure()
ax = fig.subplots()
line1, = ax.plot([],[])
line2, = ax.plot([],[])
scat = ax.scatter([], [])
scatter_offsets = []
scatter_sizes = []
def update_fig(data, update):
line1.set_data([data['x'], data['y']])
line2.set_data([data['x'], data['z']])
added = update.added()
for i in range(0, len(added['x'])):
scatter_offsets.append([added['x'][i], added['r'][i]])
scatter_sizes.append(added['s'][i])
scat.set_offsets(scatter_offsets)
scat.set_sizes(scatter_sizes)
ax.relim()
ax.autoscale_view(True, True, True)
ani = TableAnimation(fig, tt, update_fig)
Build
To create your build / development environment:
python3 -m venv .venv
source .venv/bin/activate
pip install --upgrade pip setuptools
pip install build deephaven-plugin matplotlib
To build:
python -m build --wheel
produces the wheel into dist/
.
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