Tips for arranging the angles in a polar plot in a clockwise direction with the starting angle set at 0 degrees on top

Question

Tips for arranging the angles in a polar plot in a clockwise direction with the starting angle set at 0 degrees on top

I am currently using matplotlib and numpy to generate a polar plot within my project. Below is a snippet of the code I've been working with:

import numpy as np
import matplotlib.pyplot as plt

angle = np.arange(0, 360, 10, dtype=float) * np.pi / 180.0
arbitrary_values = np.abs(np.sin(angle)) + 0.1 * (np.random.random(size=angle.shape) - 0.5)

plt.clf()
plt.polar(angle, arbitrary_values)
plt.show()

In this plot, you'll observe that the 0-degree mark is positioned at 3 o'clock while the angles progress in a counterclockwise direction. To enhance the visualization of my data, I'm interested in having the 0-degree point located at 12 o'clock and the angles progressing in a clockwise manner. Are there any approaches available for achieving this adjustment without resorting to manually rotating the data or modifying the axis labels?

python numpy matplotlib plot

Answer 1

Answer №1

In the latest version of Matplotlib (1.1), there are now two new methods available in the PolarAxes for setting the theta direction (CW/CCW) and defining the location for theta=0.

To learn more about these methods, visit

Make sure to take a look at set_theta_direction() and set_theta_offset().

It appears that many individuals are interested in creating compass-like plots using these features.

Answer 2

In the latest version of Matplotlib (1.1), there are now two new methods available in the PolarAxes for setting the theta direction (CW/CCW) and defining the location for theta=0.

To learn more about these methods, visit

Make sure to take a look at set_theta_direction() and set_theta_offset().

It appears that many individuals are interested in creating compass-like plots using these features.

Answer 3

Answer №2

To further elaborate on klimaat's response, here is an example:

from math import radians
import matplotlib.pyplot as plt

angle=[0.,5.,10.,15.,20.,25.,30.,35.,40.,45.,50.,55.,60.,65.,70.,75.,\
       80.,85.,90.,95.,100.,105.,110.,115.,120.,125.]

angle = [radians(a) for a in angle]

lux=[12.67,12.97,12.49,14.58,12.46,12.59,11.26,10.71,17.74,25.95,\
     15.07,7.43,6.30,6.39,7.70,9.19,11.30,13.30,14.07,15.92,14.70,\
     10.70,6.27,2.69,1.29,0.81]

plt.clf()
sp = plt.subplot(1, 1, 1, projection='polar')
sp.set_theta_zero_location('N')
sp.set_theta_direction(-1)
plt.plot(angle, lux)
plt.show()

https://i.stack.imgur.com/ml38s.png

matplotlib: Guide for creating custom scales and transformations

Answer 4

To further elaborate on klimaat's response, here is an example:

from math import radians
import matplotlib.pyplot as plt

angle=[0.,5.,10.,15.,20.,25.,30.,35.,40.,45.,50.,55.,60.,65.,70.,75.,\
       80.,85.,90.,95.,100.,105.,110.,115.,120.,125.]

angle = [radians(a) for a in angle]

lux=[12.67,12.97,12.49,14.58,12.46,12.59,11.26,10.71,17.74,25.95,\
     15.07,7.43,6.30,6.39,7.70,9.19,11.30,13.30,14.07,15.92,14.70,\
     10.70,6.27,2.69,1.29,0.81]

plt.clf()
sp = plt.subplot(1, 1, 1, projection='polar')
sp.set_theta_zero_location('N')
sp.set_theta_direction(-1)
plt.plot(angle, lux)
plt.show()

https://i.stack.imgur.com/ml38s.png

matplotlib: Guide for creating custom scales and transformations

Answer 5

Answer №3

After some exploration, I discovered that matplotlib offers the flexibility to create custom projections. In my case, I designed one that inherits from the PolarAxes class.

import numpy as np
import matplotlib.pyplot as plt

from matplotlib.projections import PolarAxes, register_projection
from matplotlib.transforms import Affine2D, Bbox, IdentityTransform

class NorthPolarAxes(PolarAxes):
    '''
    A modified version of PolarAxes in which theta originates from the north and progresses clockwise.
    '''
    name = 'northpolar'

    class NorthPolarTransform(PolarAxes.PolarTransform):
        def transform(self, tr):
            xy   = np.zeros(tr.shape, np.float_)
            t    = tr[:, 0:1]
            r    = tr[:, 1:2]
            x    = xy[:, 0:1]
            y    = xy[:, 1:2]
            x[:] = r * np.sin(t)
            y[:] = r * np.cos(t)
            return xy

        transform_non_affine = transform

        def inverted(self):
            return NorthPolarAxes.InvertedNorthPolarTransform()

    class InvertedNorthPolarTransform(PolarAxes.InvertedPolarTransform):
        def transform(self, xy):
            x = xy[:, 0:1]
            y = xy[:, 1:]
            r = np.sqrt(x*x + y*y)
            theta = np.arctan2(y, x)
            return np.concatenate((theta, r), 1)

        def inverted(self):
            return NorthPolarAxes.NorthPolarTransform()

        def _set_lim_and_transforms(self):
            PolarAxes._set_lim_and_transforms(self)
            self.transProjection = self.NorthPolarTransform()
            self.transData = (self.transScale + self.transProjection + (self.transProjectionAffine + self.transAxes))
            self._xaxis_transform = (self.transProjection + self.PolarAffine(IdentityTransform(), Bbox.unit()) + self.transAxes)
            self._xaxis_text1_transform = (self._theta_label1_position + self._xaxis_transform)
            self._yaxis_transform = (Affine2D().scale(np.pi * 2.0, 1.0) + self.transData)
            self._yaxis_text1_transform = (self._r_label1_position + Affine2D().scale(1.0 / 360.0, 1.0) + self._yaxis_transform)

register_projection(NorthPolarAxes)

angle = np.arange(0, 360, 10, dtype=float) * np.pi / 180.0
arbitrary_data = (np.abs(np.sin(angle)) + 0.1 * 
    (np.random.random_sample(size=angle.shape) - 0.5))

plt.clf()
plt.subplot(1, 1, 1, projection='northpolar')
plt.plot(angle, arbitrary_data)
plt.show()

https://i.stack.imgur.com/ds6ow.png

Answer 6

After some exploration, I discovered that matplotlib offers the flexibility to create custom projections. In my case, I designed one that inherits from the PolarAxes class.

import numpy as np
import matplotlib.pyplot as plt

from matplotlib.projections import PolarAxes, register_projection
from matplotlib.transforms import Affine2D, Bbox, IdentityTransform

class NorthPolarAxes(PolarAxes):
    '''
    A modified version of PolarAxes in which theta originates from the north and progresses clockwise.
    '''
    name = 'northpolar'

    class NorthPolarTransform(PolarAxes.PolarTransform):
        def transform(self, tr):
            xy   = np.zeros(tr.shape, np.float_)
            t    = tr[:, 0:1]
            r    = tr[:, 1:2]
            x    = xy[:, 0:1]
            y    = xy[:, 1:2]
            x[:] = r * np.sin(t)
            y[:] = r * np.cos(t)
            return xy

        transform_non_affine = transform

        def inverted(self):
            return NorthPolarAxes.InvertedNorthPolarTransform()

    class InvertedNorthPolarTransform(PolarAxes.InvertedPolarTransform):
        def transform(self, xy):
            x = xy[:, 0:1]
            y = xy[:, 1:]
            r = np.sqrt(x*x + y*y)
            theta = np.arctan2(y, x)
            return np.concatenate((theta, r), 1)

        def inverted(self):
            return NorthPolarAxes.NorthPolarTransform()

        def _set_lim_and_transforms(self):
            PolarAxes._set_lim_and_transforms(self)
            self.transProjection = self.NorthPolarTransform()
            self.transData = (self.transScale + self.transProjection + (self.transProjectionAffine + self.transAxes))
            self._xaxis_transform = (self.transProjection + self.PolarAffine(IdentityTransform(), Bbox.unit()) + self.transAxes)
            self._xaxis_text1_transform = (self._theta_label1_position + self._xaxis_transform)
            self._yaxis_transform = (Affine2D().scale(np.pi * 2.0, 1.0) + self.transData)
            self._yaxis_text1_transform = (self._r_label1_position + Affine2D().scale(1.0 / 360.0, 1.0) + self._yaxis_transform)

register_projection(NorthPolarAxes)

angle = np.arange(0, 360, 10, dtype=float) * np.pi / 180.0
arbitrary_data = (np.abs(np.sin(angle)) + 0.1 * 
    (np.random.random_sample(size=angle.shape) - 0.5))

plt.clf()
plt.subplot(1, 1, 1, projection='northpolar')
plt.plot(angle, arbitrary_data)
plt.show()

https://i.stack.imgur.com/ds6ow.png

Answer 7

Answer №4

To make adjustments to the matplotlib/projections/polar.py file, follow these steps:

Locate the following section in the file:

def transform(self, tr):
        xy   = npy.zeros(tr.shape, npy.float_)
        t    = tr[:, 0:1]
        r    = tr[:, 1:2]
        x    = xy[:, 0:1]
        y    = xy[:, 1:2]
        x[:] = r * npy.cos(t)
        y[:] = r * npy.sin(t)
        return xy

Replace it with the following code snippet:

def transform(self, tr):
        xy   = npy.zeros(tr.shape, npy.float_)
        t    = tr[:, 0:1]
        r    = tr[:, 1:2]
        x    = xy[:, 0:1]
        y    = xy[:, 1:2]
        x[:] = - r * npy.sin(t)
        y[:] = r * npy.cos(t)
        return xy

Keep in mind that you may need to adjust the x[:] and y[:] assignments based on your requirements. This modification will impact all applications utilizing the matplotlib polar plot feature.

Answer 8

To make adjustments to the matplotlib/projections/polar.py file, follow these steps:

Locate the following section in the file:

def transform(self, tr):
        xy   = npy.zeros(tr.shape, npy.float_)
        t    = tr[:, 0:1]
        r    = tr[:, 1:2]
        x    = xy[:, 0:1]
        y    = xy[:, 1:2]
        x[:] = r * npy.cos(t)
        y[:] = r * npy.sin(t)
        return xy

Replace it with the following code snippet:

def transform(self, tr):
        xy   = npy.zeros(tr.shape, npy.float_)
        t    = tr[:, 0:1]
        r    = tr[:, 1:2]
        x    = xy[:, 0:1]
        y    = xy[:, 1:2]
        x[:] = - r * npy.sin(t)
        y[:] = r * npy.cos(t)
        return xy

Keep in mind that you may need to adjust the x[:] and y[:] assignments based on your requirements. This modification will impact all applications utilizing the matplotlib polar plot feature.

Answer 9

Answer №5

Ensure that both inversion routines reference the complete path to the transformation modules:

return FullPathNorthPolarAxes.InvertedNorthPolarTransform()

as well as

return FullPathNorthPolarAxes.NorthPolarTransform()

This will allow automatically generated subclasses of NorthPolarAxes like NorthPolarAxesSubplot to utilize these transform functions seamlessly.

Trust this explanation clarifies any confusion.

Answer 10

Ensure that both inversion routines reference the complete path to the transformation modules:

return FullPathNorthPolarAxes.InvertedNorthPolarTransform()

as well as

return FullPathNorthPolarAxes.NorthPolarTransform()

This will allow automatically generated subclasses of NorthPolarAxes like NorthPolarAxesSubplot to utilize these transform functions seamlessly.

Trust this explanation clarifies any confusion.

Tips for arranging the angles in a polar plot in a clockwise direction with the starting angle set at 0 degrees on top

Answer №1

Answer №2

Answer №3

Answer №4

Answer №5

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