Code to make a histogram plot with X axes in the logarithmic scale

histogram_logx_scale.py

import numpy as np
import plotly.graph_objects as go

def log_histogram(data_list, names=None, bins=None):
    """ Plot histogram in log scale bins
    
        It constructs the numpy histogram with logarithm of data
        and plots a bar graph with customized ticks and hover.
        
        The plotly library does not have the option of ploting logscale
        histograms.
        
        Params
        ------
        data_list: list[np.array]
            Array to construct histogram
        names: list[str]
            Names for legend of data. Optional
        bins: array
            Custom array of bin edges to construct the histogram count, 
            by default it uses 'doane' automatic method from numpy.
            Should consider the log of data.
        
        Returns
        -------
        fig: plotly.graph_objs._figure.Figure
            Figure objetct with the histogram plot. To visualize use: fig.show()
        bins: array 
            Return the bin edges.            
    """
    
    fig = go.Figure()
    if names is None:
        names = [None]*len(data_list)
    print(names)
    
    fig_data = []
    histograms = {}
    for i, data in enumerate(data_list):
        log_data = np.log1p(data)
        # log_data = data
    
        if bins is None and i == 0:
            counts, bins = np.histogram(log_data, bins='doane', density=False)
        else:
            counts, bins = np.histogram(log_data, bins=bins, density=False)
        
        center_bins = 0.5 * (bins[:-1] + bins[1:])
        
        # Percentage 
        counts = counts/np.sum(counts)
        
        histograms[names[i]] = counts
        #Custom data should be of shape [len(x), cols]
        real_bins = np.exp(bins)-1
        ranges = np.stack((real_bins[:-1], real_bins[1:]), axis=-1)
        
        fig.add_trace(go.Bar(x=center_bins, y=counts, name=names[i]))
        
        fig.update_traces(customdata=ranges,
                          hovertemplate=
                          "bin:%{customdata[0]:.3f} - %{customdata[1]:.3f}<br><b>Count(%):%{y:.3f}</b>")
        #fig_data.append(go.Bar(x=center_bins, y=counts, name=names[i])
        

    #fig = go.Figure(data=fig_data)
    fig.update_layout(
        xaxis = dict(
            tickmode = 'array',
            tickvals = bins[::2], 
            ticktext = np.round(np.exp(bins[::2]),0)
        )
    )
    # fig.update_layout(barmode='stack')
    return fig, bins, histograms