# -*- coding: utf-8 -*-
"""
MeasureRadius
=============
Measures intensity decays at spcified points in the data.
"""
__author__ = 'Christoph Kirst <christoph.kirst.ck@gmail.com>'
__license__ = 'GPLv3 - GNU General Pulic License v3 (see LICENSE)'
__copyright__ = 'Copyright © 2020 by Christoph Kirst'
__webpage__ = 'http://idisco.info'
__download__ = 'http://www.github.com/ChristophKirst/ClearMap2'
import numpy as np;
import ClearMap.IO.IO as io
import ClearMap.ParallelProcessing.DataProcessing.MeasurePointList as mpl
import ClearMap.Utils.Timer as tmr
###############################################################################
### Measure Radius
###############################################################################
[docs]def measure_expression(source, points, search_radius, method = 'max',
sink = None, processes = None, verbose = False):
"""Measures the expression around a list of points in a source.
Arguments
---------
source : array
Source for measurement.
points : array
List of indices to measure radis for.
search_radius : int or array
List of search radii to use around each point. If int use
this radius for all points. Array should be of length of points.
method : 'max' or 'min', 'mean'
Measurement type.
processes : int or None
Number of processes to use.
verbose : bool
If True, print progress info.
"""
source = io.as_source(source);
ndim = source.ndim;
if verbose:
timer = tmr.Timer();
print('Measuring expression of %d points in array of shape %r.' % (points.shape[0], source.shape));
if not hasattr(search_radius, '__len__'):
search_radius = search_radius * np.ones(points.shape[0]);
if len(search_radius) != len(points):
raise ValueError('The search_radius is not valid!');
indices, radii_indices = search_indices(search_radius, ndim);
if method == 'max':
expression = mpl.measure_max(source, points, indices, radii_indices, sink=sink, processes=processes, verbose=verbose);
elif method == 'min':
expression = mpl.measure_min(source, points, indices, radii_indices, sink=sink, processes=processes, verbose=verbose);
elif method == 'mean':
expression = mpl.measure_mean(source, points, indices, radii_indices, sink=sink, processes=processes, verbose=verbose);
elif method == 'sum':
expression = mpl.measure_sum(source, points, indices, radii_indices, sink=sink, processes=processes, verbose=verbose);
else:
raise ValueError("Method %r not in 'max', 'min', 'mean'" % method);
if verbose:
timer.print_elapsed_time('Measuring expression done');
return expression;
###############################################################################
### Search indices
###############################################################################
[docs]def search_indices(radii, ndim):
"""Creates all relative indices within a sphere of specified radius in an array with specified strides.
Arguments
---------
radius : tuple or float
Radius of the sphere of the search index list.
strides : tuple of ints
Srides of the array
scale : float, tuple or None
Spatial scale in each array dimension.
Returns
-------
indices : array
Array of ints of relative indices for the search area voxels.
"""
radius = int(np.ceil(np.max(radii)));
#create coordiante grid
grid = [np.arange(-radius,radius+1)] * ndim;
grid = np.array(np.meshgrid(*grid, indexing = 'ij'));
#sort indices by radius
dist = np.sum(grid*grid, axis = 0);
dist_shape = dist.shape;
dist = dist.reshape(-1);
dist_index = np.argsort(dist);
dist = np.sqrt(dist[dist_index]);
dist = np.hstack([dist, np.inf]);
#print(dist)
radii_indices = np.searchsorted(dist, radii, side='right');
# convert coordinates to full indices via strides
indices = np.array(np.unravel_index(dist_index, dist_shape)).T;
indices -= radius;
# remove center point
indices = indices[1:];
radii_indices[radii_indices > 0] -= 1;
return indices, radii_indices;
###############################################################################
### Tests
###############################################################################
[docs]def test():
import numpy as np
import ClearMap.Analysis.Measurements.MeasureExpression as mex;
reload(mex);
data = 10-np.abs(10-np.arange(0,21));
radii = [2,4]
search, indices = mex.search_indices(radii=radii, ndim=1)
points = np.array([10,10]);
d = mex.measure_expression(data, points, search_radius=radii, method='min', verbose = True, processes = None);
print(d)
d = mex.measure_expression(data, points, search_radius=radii, method='mean', verbose = True, processes = None);
print(d)
#3d data
data = np.zeros((30,40,50));
data[10:20,10:20,20:30] = 1;
data[13:16, 13:16, 23:26] = 2;
data[14,14,24] = 3;
points = np.array([[14,14,24], [3,3,3]]);
d = mex.measure_expression(data, points, search_radius=1, method='min', verbose = True, processes = None);
print(d)
Source
