Want to understand matplotlib polar/radial plots-CodePudding

I am using

Now, I want to covert that into a radial or polar plot, like this: ( i used another tool to do that), where the angular axis is time and the radial is year month.

My question is, how do I calculate r and theta to plot that using matplotlib? In the how I mean the logic to convert cartesian to polar, not the actual code. I am looking to understand how it works in general.

c = ax.scatter(theta, r)

I have seen a few examples online, but none explains the logic behind? Thanks!

CodePudding user response：

I got it, most examples use radial coordinates while in this case I need to convert the cartesian coordinates to polar.

CodePudding user response：

Polar plotting in matplotlib can be challenging because of the coordinate conversion, as you mentioned, and more so when you add the date/time to the x/y axis like in your case. Here is a stab at it.

The first important step is to open the data and condition it. We can use the pandas library to open the csv with your data (

Okay now on to the polar part. For polar coordinates you need r, radial coordinate, and theta, an angle. For more info, you can check

As a sanity check, both graphs have a gap between the hours of 6 and 10, and a gap around September- October.

Hope this helps, matplotlib has a nice Polar plot example. I have to admit this was a challenging problem and I enjoyed finding an answer, thanks for the question. Cheers!

Here is a copy of the full script just in case:

from datetime import timezone, datetime
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.dates as mdates


df_csv = pd.read_csv('data-tweets.csv', encoding="ISO-8859-1", parse_dates=['created'])
# For some reason, the data is not in utf-8 so it needs encoding ISO-8859-1
print(f'Available columns: {df_csv.columns.values}')

# Filter for 2016 as an example
df = df_csv[(df_csv['created'] >= '2016-01-01 00:00:00') & (df_csv['created'] <= '2016-12-31 00:00:00')]
# Reset index with the 2016 data
df.reset_index(inplace=True)

# Extract year/month/day to plot as y-axis
date = pd.to_datetime(df['created']).dt.strftime('%Y/%m/%d').to_numpy()
# Extract hour:minute:second to plot as x-axis
time = pd.to_datetime(df['created']).dt.strftime('%H:%M:%S').to_numpy()

# Time and date are currently strings, we need them to be datetime (dt) objects so matplotlib can understand
date_dt = [datetime.strptime(x, '%Y/%m/%d').replace(tzinfo=timezone.utc) for x in date]
time_dt = [datetime.strptime(x, '%H:%M:%S').replace(tzinfo=timezone.utc) for x in time]

# Initiate figure
fig, ax = plt.subplots(figsize=(6, 6), layout='tight')
ax.plot(time_dt, date_dt, 'o')

# Format date/time for both x and y axes
x_fmt = mdates.DateFormatter('%H:%M:%S')
ax.xaxis.set_major_formatter(x_fmt)
y_fmt = mdates.DateFormatter('%Y/%m/%d')
ax.yaxis.set_major_formatter(y_fmt)
ax.tick_params(axis='x', rotation=45)

# Label axes
ax.set_xlabel('Time [HH:MM:SS]')
ax.set_ylabel('Date [Y/m/d]')
fig.suptitle('Tweet timeline')

plt.show()


# We need date and time to be timestamps (i.e. a number, not a datetime object) so we can operate with them
# Chose directly to do matplotlib timestamps as we can later format the axes like we did before
date_timestamp = mdates.date2num(date_dt)

# Now we need to make the time (hours/minutes/second) into matplotlib timestamps too
# However matplotlib timestamps only works with full datetime timestamps(not just hours)
# Get the full datetime timestamps
dates = pd.to_datetime(df['created']).dt.strftime('%Y/%m/%d %H:%M:%S').to_numpy()
# Make a datetime object like previously
date_and_time_dt = np.array([datetime.strptime(x, '%Y/%m/%d %H:%M:%S').replace(tzinfo=timezone.utc) for x in dates])
# Make them into matplotlib timestamps
date_and_time_timestamp = mdates.date2num(date_and_time_dt)

# Take out the 'year/month/day' part so we can keep the 'hour/minute/second' part
# Now we have the hour information in matplotlib timestamps
time_timestamp = date_and_time_timestamp - date_timestamp

# Convert the cartesian x and y coordinates into polar coordinates
r = np.sqrt(time_timestamp ** 2   date_timestamp ** 2)
# We need the percentages of 24 hours for theta
# Calculate what one day is in matplotlib timestamps
delta_one_day_plt = mdates.date2num(datetime(2016, 1, 2)) - mdates.date2num(datetime(2016, 1, 1))
# Divide each hour/minute/second by the max amount in a day, and transform it into radians with 2 * PI
theta = (time_timestamp / delta_one_day_plt) * 2 * np.pi

# Initiate polar figure
fig, ax = plt.subplots(figsize=(6, 6), subplot_kw={'projection': 'polar'}, layout='tight')
ax.scatter(theta, r, alpha=0.3)

# Make the labels go clockwise
ax.set_theta_direction(-1)
# Place Zero at Top
ax.set_theta_offset(np.pi/2)
# Set the circumference ticks
ax.set_xticks(np.linspace(0, 2 * np.pi, 24, endpoint=False))
# Set the label names
ticks = np.arange(0, 24, 1)
ax.set_xticklabels(ticks)
ax.set_xlabel('Date [Y/m/d] & Time [HH]')
# Set y lim so that it focuses on the dates in 2016
ax.set_ylim([mdates.date2num(datetime(2016, 1, 1)), mdates.date2num(datetime(2017, 1, 1))])
# Set y ticks so that it is in that 'gap' in the data and doesn't cover the points
ax.set_rlabel_position(140)
# Format y-axis for dates
y_fmt = mdates.DateFormatter('%Y/%m/%d')
ax.yaxis.set_major_formatter(y_fmt)

fig.suptitle('Tweet timeline')

plt.show()