LECA.prep.interactive_data_visualize

LECA.prep.interactive_data_visualize(df: DataFrame, fig: Figure, features: List[str], show_axes: List[str], highlight_data: DataFrame | None = None) → None

Visualize dataset with a 2d or 3d plot. Special formulation to support integration with notebook interactive widgets. Plots the distribution of measurement points within the min-max range of each selected feature.

Parameters:

• data (DataFrame) – Dataframe of experimental measurements.

• fig (plt.Figure) – Figure object passed to contain plots.

• features (List[str]) – List of all features.

• show_axes (List[str]) – List of features to use as axes for the data distribution plot.

• highlight_data (Optional[DataFrame]) –

  Optional DataFrame of measurements to highlight within the plot (shown as red triangles).

  Default value None.

Examples

from ipywidgets import interact, widgets
import matplotlib.pyplot as plt
from IPython.display import display
%matplotlib notebook

# Define which DataFrames to use for included/excluded data
df = sliced_df
cut_df = cutoff_df

# Initialize plt.Figure object
fig = plt.figure(figsize=(4,4))
ax = fig.add_subplot()

# Define and show interactive widgets
X_slider=widgets.Dropdown(options=features, description='X')
Y_slider=widgets.Dropdown(options=features, description='Y')
Z_slider=widgets.Dropdown(options=[None]+features, description='Z (optional)')
display(X_slider)
display(Y_slider)
display(Z_slider)

# Function call to update plot
def data_vis(change=None):
    ax.clear()
    X_feature = X_slider.value
    Y_feature = Y_slider.value
    Z_feature = Z_slider.value
    if Z_feature:
        prep.interactive_data_visualize(df, fig, features, show_axes=[X_feature, Y_feature, Z_feature], highlight_data=cut_df)
    else:
        prep.interactive_data_visualize(df, fig, features, show_axes=[X_feature, Y_feature], highlight_data=cut_df)

# Make button to plot data
btn=widgets.Button(description="Run")
display(btn)
btn.on_click(data_vis)

Return type:

None