molplotly is an add-on to plotly built on RDKit which allows 2D images of molecules to be shown in scatterplots when hovering over the datapoints.
Project description
molplotly
molplotly
is an add-on to plotly
built on RDKit which allows 2D images of molecules to be shown in plotly
figures when hovering over the datapoints.
Required packages:
➡️ A readable walkthrough of how to use the package together with some useful examples can be found in this blog post while a runnable notebook can be found in example.ipynb
:)
📜 Usage
import pandas as pd
import plotly.express as px
import molplotly
# load a DataFrame with smiles
df_esol = pd.read_csv('esol.csv')
df_esol['y_pred'] = df_esol['ESOL predicted log solubility in mols per litre']
df_esol['y_true'] = df_esol['measured log solubility in mols per litre']
# generate a scatter plot
fig = px.scatter(df_esol, x="y_true", y="y_pred")
# add molecules to the plotly graph - returns a Dash app
app = molplotly.add_molecules(fig=fig,
df=df_esol,
smiles_col='smiles',
title_col='Compound ID',
)
# run Dash app inline in notebook (or in an external server)
app.run_server(mode='inline', port=8011, height=1000)
Input parameters
fig
: plotly.graph_objects.Figure object
a plotly figure object containing datapoints plotted from dfdf
: pandas.DataFrame object
a pandas dataframe that contains the data plotted in figsmiles_col
: str, optional
name of the column in df containing the smiles plotted in fig (default 'SMILES')show_img
: bool, optional
whether or not to generate the molecule image in the dash app (default True)title_col
: str, optional
name of the column in df to be used as the title entry in the hover box (default None)show_coords
: bool, optional
whether or not to show the coordinates of the data point in the hover box (default True)caption_cols
: list, optional
list of column names in df to be included in the hover box (default None)caption_transform
: dict, optional
Functions applied to specific items in all cells. The dict must follow a key: function structure where the key must correspond to one of the columns in subset or tooltip. (default {})color_col
: str, optional
name of the column in df that is used to color the datapoints in df - necessary when there is discrete conditional coloring (default None)wrap
: bool, optional
whether or not to wrap the title text to multiple lines if the length of the text is too long (default True)wraplen
: int, optional
the threshold length of the title text before wrapping begins - adjust when changing the width of the hover box (default 20)width
: int, optional
the width in pixels of the hover box (default 150)fontfamily
: str, optional
the font family used in the hover box (default 'Arial')fontsize
: int, optional
the font size used in the hover box - the font of the title line is fontsize+2 (default 12)
Output parameters
by default a JupyterDash app
is returned which can be run inline in a jupyter notebook or deployed on a server via app.run_server()
- The recommended
height
of the app is50+(height of the plotly figure)
. - For the
port
of the app, make sure you don't pick the sameport
as anothermolplotly
plot otherwise the tooltips will clash with each other!
💻 Can I run this in colab?
JupyterDash is supposed to have support for Google Colab but at some point that seems to have broken... Keep an eye on the raised issue here!
💾 Can I save these plots?
moltplotly
works using a Dash app which is non-trivial to export because server side javascript is needed in addition to HTML/CSS styling (as detailed here)
Until I find a way to get around that, the best alternative is exporting the plotly figure without molecules showing :( as detailed in this page. If you want to use it in a presentation I'd suggest keeping the figure open in a browser and changing windows to it during your talk!
🛑 Warning about memory size
Just adding a warning here that memory usage in a notebook can increase significanly when using plotly (not molplotly
's fault!). If you notice your jupyter notebook slowing down, plotly itself is a likely culprit... In that case I'd consider either using plotly with static image rendering, or ... use seaborn :P
Acknowledgements
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