bokeh#
fivecentplots v0.6 provides limited support for using bokeh in place of matplotlib as the plotting engine.
Setup#
First-time setup:
bokeh is not currently part of the default pacakge requirements for fcp so it is not automatically installed. For inline usage:
[ ]:
pip install bokeh
Switching engines:
Since matplotlib is the default plotting engine in fivecentplots, the user must manually invoke the bokeh engine. This can be done on a per-plot basis via the engine kwarg or by updating the global kwargs so the change persists within the users current interactive environment
Per-plot:
[ ]:
fcp.plot(df, x='x', y='y', engine='bokeh')
Global:
[ ]:
fcp.KWARGS['engine'] = 'bokeh'
Themes:
Due to differences between plotting libraries, it was found that the default fivecentplots theme files were not ideal in bokeh. It is also therefore recommended to update to a bokeh-specific theme when using this engine:
[ ]:
fcp.set_theme('gray_bokeh')
Maturity:
bokeh support for fivecentplots is currently limited to basic x-y scatter plots. This could be extended further in the future if a developer decides to take on the project.
Import packages
import fivecentplots as fcp
import pandas as pd
from pathlib import Path
Engine and themes#
fcp.KWARGS['inline'] = True
fcp.KWARGS['engine'] = 'bokeh'
fcp.set_theme('gray_bokeh')
Previous theme file found! Renaming to "defaults_old.py" and copying theme "gray_bokeh"...done!
Sample data#
df_xy = pd.read_csv(Path(fcp.__file__).parent / 'test_data/fake_data.csv')
df_xy.head(10)
| Substrate | Target Wavelength | Boost Level | Temperature [C] | Die | Voltage | I Set | I [A] | |
|---|---|---|---|---|---|---|---|---|
| 0 | Si | 450 | 0.2 | 25 | (1,1) | 0.0 | 0.0 | 0.0 |
| 1 | Si | 450 | 0.2 | 25 | (1,1) | 0.1 | 0.0 | 0.0 |
| 2 | Si | 450 | 0.2 | 25 | (1,1) | 0.2 | 0.0 | 0.0 |
| 3 | Si | 450 | 0.2 | 25 | (1,1) | 0.3 | 0.0 | 0.0 |
| 4 | Si | 450 | 0.2 | 25 | (1,1) | 0.4 | 0.0 | 0.0 |
| 5 | Si | 450 | 0.2 | 25 | (1,1) | 0.5 | 0.0 | 0.0 |
| 6 | Si | 450 | 0.2 | 25 | (1,1) | 0.6 | 0.0 | 0.0 |
| 7 | Si | 450 | 0.2 | 25 | (1,1) | 0.7 | 0.0 | 0.0 |
| 8 | Si | 450 | 0.2 | 25 | (1,1) | 0.8 | 0.0 | 0.0 |
| 9 | Si | 450 | 0.2 | 25 | (1,1) | 0.9 | 0.0 | 0.0 |
plot#
No legend#
fcp.plot(df_xy, x='Voltage', y='I [A]', lines=False, ax_size=[400, 400])
Filtered#
fcp.plot(df_xy, x='Voltage', y='I [A]', title='IV Data', lines=False,
filter='Substrate=="Si" & Target Wavelength==450 & Boost Level==0.2 & Temperature [C]==25')
Legend#
fcp.plot(df_xy, x='Voltage', y='I [A]', legend='Die',
filter='Substrate=="Si" & Target Wavelength==450 & Boost Level==0.2 & Temperature [C]==25')
Log axis#
Note
symlog and logit not yet supported
fcp.plot(df_xy, x='Voltage', y='I [A]', ax_scale='loglog', legend='Die', xmin=0.9, xmax=2.1, grid_minor=True,
filter='Substrate=="Si" & Target Wavelength==450 & Boost Level==0.2 & Temperature [C]==25')
Time series#
ts = pd.read_csv(Path(fcp.__file__).parent / 'test_data/fake_ts.csv')
fcp.plot(ts, x='Date', y='Happiness Quotient', markers=False, ax_size=[1000, 250])
Secondary axes#
fcp.plot(df_xy, x='Voltage', y=['Voltage', 'I [A]'], twin_x=True, legend='Die',
grid_major_y2=True, grid_major_y2_style='--', y2max=1.4,
filter='Substrate=="Si" & Target Wavelength==450 & Boost Level==0.2 & Temperature [C]==25 & Die=="(-1,2)"')
Multiple x & y values#
fcp.plot(df_xy, x=['Boost Level', 'I [A]'], y=['Voltage', 'Temperature [C]'], legend='Die',
filter='Substrate=="Si" & Target Wavelength==450 & Boost Level==0.2 & Temperature [C]==25')
Grouping#
Row plot#
fcp.plot(df_xy, x='Voltage', y='I [A]', legend='Die', row='Boost Level', ax_size=[225, 225],
filter='Substrate=="Si" & Target Wavelength==450 & Temperature [C]==25')
Column plot#
fcp.plot(df_xy, x='Voltage', y='I [A]', legend='Die', col='Boost Level', ax_size=[225, 225],
filter='Substrate=="Si" & Target Wavelength==450 & Temperature [C]==25')
Row x column grid#
fcp.plot(df_xy, x='Voltage', y='I [A]', legend='Die', col='Boost Level', row='Temperature [C]',
ax_size=[225, 225], filter='Substrate=="Si" & Target Wavelength==450')
That’s it for now…