#! /usr/bin/env python
"""
Module for image statistics.
"""
__author__ = 'Carlos Alberto Gomez Gonzalez'
__all__ = ['frame_histo_stats',
'frame_average_radprofile']
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
from ..var import frame_center
from ..conf.utils_conf import check_array, vip_figsize
def frame_average_radprofile(frame, sep=1, init_rad=None, plot=True):
""" Calculates the average radial profile of an image.
Parameters
----------
frame : numpy ndarray
Input image or 2d array.
sep : int, optional
The average radial profile is recorded every ``sep`` pixels.
plot : bool, optional
If True the profile is plotted.
Returns
-------
df : dataframe
Pandas dataframe with the radial profile and distances.
Notes
-----
https://stackoverflow.com/questions/21242011/most-efficient-way-to-calculate-radial-profile
https://stackoverflow.com/questions/48842320/what-is-the-best-way-to-calculate-radial-average-of-the-image-with-python
https://github.com/keflavich/image_tools/blob/master/image_tools/radialprofile.py
"""
check_array(frame, dim=2)
cy, cx = frame_center(frame)
if init_rad is None:
init_rad = 1
x, y = np.indices((frame.shape))
r = np.sqrt((x - cx) ** 2 + (y - cy) ** 2)
r = r.astype(int)
tbin = np.bincount(r.ravel(), frame.ravel())
nr = np.bincount(r.ravel())
radprofile = tbin / nr
radists = np.arange(init_rad + 1, int(cy), sep) - 1
radprofile_radists = radprofile[radists]
nr_radists = nr[radists]
df = pd.DataFrame({'rad': radists, 'radprof': radprofile_radists,
'npx': nr_radists})
if plot:
plt.figure(figsize=vip_figsize)
plt.plot(radists, radprofile_radists, '.-', alpha=0.6)
plt.grid(which='both', alpha=0.4)
plt.xlabel('Pixels')
plt.ylabel('Counts')
plt.minorticks_on()
plt.xlim(0)
return df
def frame_histo_stats(image_array, plot=True):
"""Plots a frame with a colorbar, its histogram and some statistics: mean,
median, maximum, minimum and standard deviation values.
Parameters
----------
image_array : numpy ndarray
The input frame.
plot : bool, optional
If True plots the frame and the histogram with the values.
Return
------
mean : float
Mean value of array.
median : float
Median value of array.
std : float
Standard deviation of array.
maxim : int or float
Maximum value.
minim : int or float
Minimum value.
"""
vector = image_array.flatten()
mean = vector.mean()
median = np.median(vector)
maxim = vector.max()
minim = vector.min()
std = vector.std()
if plot:
fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(10, 4))
ax0, ax1 = axes.flat
bins = int(np.sqrt(vector.shape[0]))
txt = 'Mean = {:.3f}\n'.format(mean) + \
'Median = {:.3f}\n'.format(median) +\
'Stddev = {:.3f}\n'.format(std) +\
'Max = {:.3f}\n'.format(maxim) +\
'Min = {:.3f}\n\n'.format(minim)
ax0.imshow(image_array, interpolation="nearest", origin ="lower",
cmap='viridis')
ax0.set_title('Frame')
ax0.grid('off')
ax1.hist(vector, bins=bins, label=txt, alpha=0.5, histtype='stepfilled')
ax1.set_yscale('log')
ax1.set_title('Histogram')
ax1.text(0.98, 0.98, txt, transform=ax1.transAxes, fontsize=12,
verticalalignment='top', horizontalalignment='right')
plt.show()
return mean, median, std, maxim, minim
