plotly#
As of v0.6, fivecentplots plotly can be used as the plotting engine instead of matplotlib.
Setup#
First-time setup:
plotly is not currently part of the default package requirements for fcp so it is not automatically installed. For inline usage:
[ ]:
pip install plotly
To save and/or show plotly images as .png files automatically with keyword save or show, an additional package install is required:
[ ]:
pip install kaleido
Switching engines:
Since matplotlib is the default plotting engine in fivecentplots, the user must manually invoke the plotly 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='plotly')
Global (i.e., all plots):
[ ]:
fcp.KWARGS['engine'] = 'plotly'
Themes:
Due to differences between plotting libraries, it was found that the default fivecentplots theme files were not ideal in plotly. It is also therefore recommended to update to a plotly-specific theme when using this engine:
[ ]:
fcp.set_theme('gray_plotly')
Maturity:
All plot types except gantt have been ported to plotly with some base level of support. However, feature parity with matplotlib has not yet been achieved. Please open a new support issue if a needed feature is missing.
Some plot examples are demonstrated below, but the demonstration is not exhaustive and many other plot configurations are still available.
Imports#
import fivecentplots as fcp
import pandas as pd
import imageio.v3 as imageio
import cv2
from pathlib import Path
import plotly
plotly.offline.init_notebook_mode()
Engine and themes#
fcp.KWARGS['inline'] = True
fcp.KWARGS['engine'] = 'plotly'
fcp.set_theme('gray_plotly')
Previous theme file found! Renaming to "defaults_old.py" and copying theme "gray_plotly"...done!
Sample data#
Several data sets are used in this tutorial:
x-y 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 |
Time series data#
ts = pd.read_csv(Path(fcp.__file__).parent / 'test_data/fake_ts.csv')
ts.head()
| Date | Happiness Quotient | |
|---|---|---|
| 0 | 1/1/2015 | 16.088954 |
| 1 | 1/2/2015 | 18.186724 |
| 2 | 1/3/2015 | 35.744313 |
| 3 | 1/4/2015 | 38.134045 |
| 4 | 1/5/2015 | 46.147279 |
Bar plot data#
df_bar = pd.read_csv(Path(fcp.__file__).parent / 'test_data/fake_data_bar.csv')
df_bar.head()
| Liquid | pH | Measurement | T [C] | |
|---|---|---|---|---|
| 0 | Lemon juice | 2.4 | A | 25 |
| 1 | Orange juice | 3.5 | A | 25 |
| 2 | Battery acid | 1.0 | A | 25 |
| 3 | Bottled water | 6.7 | A | 25 |
| 4 | Coke | 3.0 | A | 25 |
Box plot data#
df_box = pd.read_csv(Path(fcp.__file__).parent / 'test_data/fake_data_box.csv')
df_box.head()
| Batch | Sample | Region | Value | ID | |
|---|---|---|---|---|---|
| 0 | 101 | 1 | Alpha123 | 3.5 | ID701223A |
| 1 | 101 | 1 | Alpha123 | 0.0 | ID7700-1222B |
| 2 | 101 | 1 | Alpha123 | 3.3 | ID701223A |
| 3 | 101 | 1 | Alpha123 | 3.2 | ID7700-1222B |
| 4 | 101 | 1 | Alpha123 | 4.0 | ID701223A |
Contour data#
df_contour = pd.read_csv(Path(fcp.__file__).parent / 'test_data/fake_data_contour.csv')
df_contour.head()
| Experiment | Batch | X | Y | Value | |
|---|---|---|---|---|---|
| 0 | Control | 101 | 1 | -4 | 3.5 |
| 1 | Control | 101 | 1 | -2 | 2.1 |
| 2 | Control | 101 | 1 | 0 | 3.3 |
| 3 | Control | 101 | 1 | 2 | 3.2 |
| 4 | Control | 101 | 1 | 4 | 4.0 |
Heatmap data#
df_heatmap = pd.read_csv(Path(fcp.__file__).parent / 'test_data/fake_data_heatmap.csv')
df_heatmap.head()
| Player | Category | Average | |
|---|---|---|---|
| 0 | Lebron James | Points | 27.5 |
| 1 | Lebron James | Assists | 9.1 |
| 2 | Lebron James | Rebounds | 8.6 |
| 3 | Lebron James | Blocks | 0.9 |
| 4 | James Harden | Points | 30.4 |
Histogram data#
df_hist = pd.read_csv(Path(fcp.__file__).parent / 'test_data/fake_data_box.csv')
img_hist = cv2.imread(str(Path(fcp.__file__).parent / 'test_data/imshow_cat_pirate.png'))
Image data#
img_bgr = cv2.imread(str(Path(fcp.__file__).parent / 'test_data/imshow_cat_pirate.png'))
img_rgb = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2RGB)
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')
Categorical ticks#
fcp.plot(df_xy, x='Die', y='I [A]',
filter='Substrate=="Si" & Target Wavelength==450 & Boost Level==0.2 & Temperature [C]==25 & Voltage==1.5')
Time series#
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], legend_edge_color='#000000',
filter='Substrate=="Si" & Target Wavelength==450 & Temperature [C]==25', label_row_fill_color='#000000', ax_edge_width=4,
label_row_font_color='#ff0000', label_row_edge_color='#0000ff', label_row_edge_width=2, legend_edge_width=3)
Column plot#
fcp.plot(df_xy, x='Voltage', y='I [A]', legend='Die', col='Boost Level', ax_size=[225, 225], legend_edge_color='#000000',
filter='Substrate=="Si" & Target Wavelength==450 & Temperature [C]==25', label_col_fill_color='#000000', label_col_font_color='#ff0000')
Row x column grid#
fcp.plot(df_xy, x='Voltage', y='I [A]', legend='Die', col='Boost Level', row='Temperature [C]',
modebar_visible=True, modebar_fill_color='#000000', ax_edge_width=1, ymin=0,
ax_size=[225, 225], filter='Substrate=="Si" & Target Wavelength==450', label_rc_font_size=13)
Wrap plot#
fcp.plot(df_xy, x='Voltage', y='I [A]', legend='Die', wrap=['Temperature [C]', 'Boost Level'],
ax_size=[225, 225], filter='Substrate=="Si" & Target Wavelength==450', label_rc_font_size=13)
Horizontal & vertical lines#
fcp.plot(df_xy, x='Voltage', y='I [A]', title='IV Data', lines=False, legend=True,
filter='Substrate=="Si" & Target Wavelength==450 & Boost Level==0.2 & Temperature [C]==25',
ax_hlines=[(0, '#FF0000', '--', 3, 1, 'Open', '#555555', 0.25), 1.2],
ax_vlines=[(0.6, '#0000ff', ':'), (1, '#00FF00')])
Curve fitting#
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',
fit=1, fit_eqn=True, fit_rsq=True, fit_range_x=[1.3, 2])
Stat Lines#
fcp.plot(df_xy, x='Voltage', y=['Boost Level', 'I [A]'], legend=True, stat='median',
filter='Substrate=="Si" & Target Wavelength==450 & Boost Level==0.2 & Temperature [C]==25')
Confidence intervals#
To demonstrate this feature we will use a special dataset:
df_interval = pd.read_csv(Path(fcp.__file__).parent / 'test_data/fake_data_interval.csv')
df_interval.head()
| x | y | |
|---|---|---|
| 0 | -1.0 | -10.715459 |
| 1 | -1.0 | -9.972410 |
| 2 | -1.0 | -30.740532 |
| 3 | -1.0 | -31.368963 |
| 4 | -1.0 | -29.058633 |
fcp.plot(df_interval, x='x', y='y', lines=False, conf_int=0.95)
Control limits#
fcp.plot(df_interval, x='x', y='y', lines=False, ucl=50, lcl=-50, ucl_fill_color='#FF0000', legend=True)
Reference line#
fcp.plot(df_xy, x='Voltage', y='I [A]', title='IV Data', legend='Die',
filter='Substrate=="Si" & Target Wavelength==450 & Boost Level==0.2 & Temperature [C]==25',
ref_line=df_xy['Voltage'], ref_line_legend_text='y=x', xmin=0, ymin=0, xmax=1.6, ymax=1.6)
bar#
Simple#
fcp.bar(df_bar, x='Liquid', y='pH', filter='Measurement=="A" & T [C]==25', horizontal=True)
Legend#
fcp.bar(df_bar, x='Liquid', y='pH', tick_labels_major_x_rotation=90, legend='Measurement')
Stacked#
fcp.bar(df_bar, x='Liquid', y='pH', tick_labels_major_x_rotation=90, stacked=True, legend='Measurement')
Grouping#
fcp.bar(df_bar, x='Liquid', y='pH', tick_labels_major_x_rotation=90, col='Measurement', row='T [C]', ax_hlines=0, ax_size=[300, 300])
boxplot#
Basic#
fcp.boxplot(df_box, y='Value', groups=['Batch', 'Sample'], group_means=True)
Legend#
df_box['Row'] = [int(f) for f in df_box.index / 4]
fcp.boxplot(df_box, y='Value', groups=['Batch', 'Sample'], mean_diamonds=True, conf_coeff=0.95, legend='Row')
Violins#
fcp.boxplot(df_box, y='Value', groups=['Batch', 'Sample'], violin=True)
Grouping#
fcp.boxplot(df_box, y='Value', groups=['Batch', 'Sample'], col='Region', ax_size=[300, 300])
Note
auto-sizing for boxplot is not yet available with plotly
contour#
No fill#
fcp.contour(df_contour, x='X', y='Y', z='Value', filled=False, cbar=False)
Points#
fcp.contour(df_contour, x='X', y='Y', z='Value', filled=False, levels=40, contour_width=2,
xmin=-4, xmax=5, ymin=-4, ymax=6, cbar=True,
show_points=True, marker_size=26, marker_fill_color='#00FF00')
Grid with fill#
fcp.contour(df_contour, x='X', y='Y', z='Value', row='Batch', col='Experiment', filled=True,
cbar=False, xmin=-3, xmax=3, ymin=-3, ymax=3, ax_size=[250,250],
label_rc_font_size=12, levels=40)
gantt#
Better luck next time…
heatmap#
fcp.heatmap(df_heatmap, x='Category', y='Player', z='Average')
hist#
Basic#
fcp.hist(df_hist, x='Value', horizontal=True, bins=20)
kde#
fcp.hist(df_hist, x='Value', legend='Region', kde=True, kde_width=2)
Image#
fcp.hist(img_rgb, legend='Channel', markers=False, ax_size=[600, 400], line_width=2, colors=fcp.RGB)
imshow#
RGB#
fcp.imshow(img_rgb, ax_size=[600, 300])
Raw / Grayscale#
img_raw = fcp.utilities.img_grayscale(img_rgb)
fcp.imshow(img_raw, ax_size=[600, 600], title='Fake RAW, Fake Pirate', cbar=True, title_edge_width=1, title_edge_color='#ff0000')