# 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:
```python
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
```python
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("PT00:00:01").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.
```python
import matplotlib.pyplot as plt
from deephaven import time_table
from deephaven.plugin.matplotlib import TableAnimation
tt = time_table("PT00:00:01").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:
```python
import matplotlib.pyplot as plt
from deephaven import time_table
from deephaven.plugin.matplotlib import TableAnimation
tt = time_table("PT00:00:01").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 (skip the first two lines if you already have a venv):
```sh
python -m venv .venv
source .venv/bin/activate
pip install --upgrade pip setuptools
pip install build deephaven-plugin matplotlib
```
To build:
```sh
python -m build --wheel
```
The wheel is stored in `dist/`.
To test within [deephaven-core](https://github.com/deephaven/deephaven-core), note where this wheel is stored (using `pwd`, for example).
Then, follow the directions in the top-level README.md to install the wheel into your Deephaven environment.
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"description": "# Deephaven Plugin for Matplotlib\n\nThe Deephaven Plugin for Matplotlib. Allows for opening Matplotlib plots in a Deephaven environment. Any Matplotlib plot\nshould be viewable by default. For example:\n\n```python\nimport matplotlib.pyplot as plt\n\nfig = plt.figure()\nax = fig.subplots() # Create a figure containing a single axes.\nax.plot([1, 2, 3, 4], [4, 2, 6, 7]) # Plot some data on the axes.\n```\n\nYou can also use `TableAnimation`, which allows updating a plot whenever a Deephaven Table is updated.\n\n## `TableAnimation` Usage\n\n`TableAnimation` is a Matplotlib `Animation` that is driven by updates in a Deephaven Table. Every time the table that\nis being listened to updates, the provided function will run again.\n\n### Line Plot\n\n```python\nimport matplotlib.pyplot as plt\nfrom deephaven import time_table\nfrom deephaven.plugin.matplotlib import TableAnimation\n\n# Create a ticking table with the sin function\ntt = time_table(\"PT00:00:01\").update([\"x=i\", \"y=Math.sin(x)\"])\n\nfig = plt.figure() # Create a new figure\nax = fig.subplots() # Add an axes to the figure\n(line,) = ax.plot(\n [], []\n) # Plot a line. Start with empty data, will get updated with table updates.\n\n# Define our update function. We only look at `data` here as the data is already stored in the format we want\ndef update_fig(data, update):\n line.set_data([data[\"x\"], data[\"y\"]])\n\n # Resize and scale the axes. Our data may have expanded and we don't want it to appear off screen.\n ax.relim()\n ax.autoscale_view(True, True, True)\n\n\n# Create our animation. It will listen for updates on `tt` and call `update_fig` whenever there is an update\nani = TableAnimation(fig, tt, update_fig)\n```\n\n### Scatter Plot\n\nScatter plots require data in a different format that Line plots, so need to pass in the data differently.\n\n```python\nimport matplotlib.pyplot as plt\nfrom deephaven import time_table\nfrom deephaven.plugin.matplotlib import TableAnimation\n\ntt = time_table(\"PT00:00:01\").update(\n [\"x=Math.random()\", \"y=Math.random()\", \"z=Math.random()*50\"]\n)\n\nfig = plt.figure()\nax = fig.subplots()\nax.set_xlim(0, 1)\nax.set_ylim(0, 1)\nscat = ax.scatter([], []) # Provide empty data initially\nscatter_offsets = [] # Store separate arrays for offsets and sizes\nscatter_sizes = []\n\n\ndef update_fig(data, update):\n # This assumes that table is always increasing. Otherwise need to look at other\n # properties in update for creates and removed items\n added = update.added()\n for i in range(0, len(added[\"x\"])):\n # Append new data to the sources\n scatter_offsets.append([added[\"x\"][i], added[\"y\"][i]])\n scatter_sizes.append(added[\"z\"][i])\n\n # Update the figure\n scat.set_offsets(scatter_offsets)\n scat.set_sizes(scatter_sizes)\n\n\nani = TableAnimation(fig, tt, update_fig)\n```\n\n### Multiple Series\n\nIt's possible to have multiple kinds of series in the same figure. Here is an example driving a line and a scatter plot:\n\n```python\nimport matplotlib.pyplot as plt\nfrom deephaven import time_table\nfrom deephaven.plugin.matplotlib import TableAnimation\n\ntt = time_table(\"PT00:00:01\").update(\n [\"x=i\", \"y=Math.sin(x)\", \"z=Math.cos(x)\", \"r=Math.random()\", \"s=Math.random()*100\"]\n)\n\nfig = plt.figure()\nax = fig.subplots()\n(line1,) = ax.plot([], [])\n(line2,) = ax.plot([], [])\nscat = ax.scatter([], [])\nscatter_offsets = []\nscatter_sizes = []\n\n\ndef update_fig(data, update):\n line1.set_data([data[\"x\"], data[\"y\"]])\n line2.set_data([data[\"x\"], data[\"z\"]])\n added = update.added()\n for i in range(0, len(added[\"x\"])):\n scatter_offsets.append([added[\"x\"][i], added[\"r\"][i]])\n scatter_sizes.append(added[\"s\"][i])\n scat.set_offsets(scatter_offsets)\n scat.set_sizes(scatter_sizes)\n ax.relim()\n ax.autoscale_view(True, True, True)\n\n\nani = TableAnimation(fig, tt, update_fig)\n```\n\n## Build\n\nTo create your build / development environment (skip the first two lines if you already have a venv):\n\n```sh\npython -m venv .venv\nsource .venv/bin/activate\npip install --upgrade pip setuptools\npip install build deephaven-plugin matplotlib\n```\n\nTo build:\n\n```sh\npython -m build --wheel\n```\n\nThe wheel is stored in `dist/`.\n\nTo test within [deephaven-core](https://github.com/deephaven/deephaven-core), note where this wheel is stored (using `pwd`, for example).\nThen, follow the directions in the top-level README.md to install the wheel into your Deephaven environment.\n",
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