Hello guys,
I’m trying to plot price on log scale and i can’t get the minor ticks to show on the y-axis. Also, the exponents on the log scale tick labels overlap with the base. I have tried lots of different workarounds found in the internet but apparently you can’t manually define minor ticks in bokeh and I couldn’t find a single topic talking about the self overlap on the labels. Code with random data to exemplify (some of the solutions i tried are in the code comments):
import pandas as pd
import numpy as np
from bokeh.transform import cumsum
from bokeh.models import HoverTool, Span, Label, ColumnDataSource, Line, Grid, LabelSet
from bokeh.io import output_notebook, show, output_file, curdoc
from bokeh.plotting import figure
from bokeh.layouts import gridplot
import bokeh.palettes as bplt
curdoc().theme = 'dark_minimal'
output_notebook()
def plot_price_comparison():
# Data gathering and cleaning up
x = list(range(0, 5000))
df = pd.DataFrame({"days": x, "price": x * np.random.normal(loc=0, scale=90, size=5000)})
# Setting figure up
colors = bplt.all_palettes['Plasma'][4]
p = figure(title = f"Price comparison"
, x_axis_type = "linear", y_axis_type = "log"
, x_axis_label = 'Days since bottom', y_axis_label = "ROI"
, plot_width = 1200, plot_height = 800
, background_fill_color = "#000000", outline_line_color = "white")
# p.xaxis.ticker = list(range(0, 2200, 200))
p.xaxis.major_tick_line_color = "white"
p.xgrid.grid_line_color = 'white'
p.xgrid.grid_line_alpha = 0.5
# p.xaxis.ticker.num_minor_ticks = 10
# p.yaxis.ticker = np.logspace(0, 3, num=20, endpoint=True)
# p.yaxis.ticker = [1, 10, 100, 1000]
p.yaxis.axis_label_standoff = 20
# p.yaxis.major_tick_line_color = "white"
# p.yaxis.minor_tick_in = 10
p.ygrid.grid_line_color = 'gray'
p.ygrid.grid_line_alpha = 0.5
# p.yaxis.major_label_overrides = {1: '1', 10: '10', 100: '100', 1000: "1000"}
p.min_border_left = 120
p.border_fill_color = "black"
# Plotting data
source = ColumnDataSource(df)
line = p.line(x = "days", y = "price", legend_label=f"Price",
line_color=colors[0], line_width=4, source=source)
h = HoverTool(tooltips=[("return", "@return"),], mode = 'vline', renderers = [line])
p.add_tools(h)
show(p)
return df
plot_price_comparison()
Resulting figure:
