rework fit/com sample code

xsocs/examples/id01_peak.py

-import numpy as np
-import os
+#!/usr/bin/python
+# coding: utf-8
+"""
+This scripts illustrates the API of the gaussian/center-of-mass processing
+"""
 
-from xsocs.process.fit import peak_fit
+import sys
+import numpy
+from xsocs.process.fit import PeakFitter, FitTypes, BackgroundTypes
 
-# output directory (some temporary files will also be written there)
-workdir = '/path/to/workdir/'
-
-# path to the hdf5 file written by the img_to_qspace function
+# path to the hdf5 file written by the kmap_2_qspace function
 qspace_f = '/path/to/qspace.h5'
 
-# result file
-result_file = os.path.join(workdir, 'results.txt')
+# Name of the text file where to store the resuls
+result_file = 'results.txt'
 
-# positions (on the sample) to convert to qspace
-# indices = array with indices (of the sample positions array)
+# List of QSpace indices to process
+# This selects sample positions for which QSpace are processed.
+# If None, all QSpace are processed
 indices = None
 
-# number of processes to use
-# If None, will use the number of availble core (see multiprocessing.cpu_count)
+# Number of processors to use
+# If None, all available cores are used.
 n_proc = None
 
-results, success = peak_fit.peak_fit(qspace_f,
-                                     indices=indice,
-                                     fit_type=peak_fit.FitTypes.GAUSSIAN,
-                                     n_proc=n_proc)
-
-with open(result_file, 'w+') as res_f:
-    res_f.write('# X Y qx qy qz q I valid\n')
-    for i, s in enumerate(success):
-        x_pos = results[i, 0]
-        y_pos = results[i, 1]
-        xpeak = results[i, 3]
-        ypeak = results[i, 6]
-        zpeak = results[i, 9]
-        xpeak_max = results[i, 2]
-        q = np.sqrt(xpeak**2 + ypeak**2 + zpeak**2)
-        r = (x_pos, y_pos, xpeak, ypeak, zpeak, q, xpeak_max, s)
-        res_str = '{0} {1} {2} {3} {4} {5} {6} {7} ({8})\n'.format(i, *r)
-        res_f.write(res_str)
+# Set-up the processing for a gaussian fit without background subtraction
+fitter = PeakFitter(qspace_f,
+                    fit_type=FitTypes.GAUSSIAN,
+                    indices=indices,
+                    n_proc=n_proc,
+                    roi_indices=None,  # QSpace ROI
+                    background=BackgroundTypes.NONE)
+
+# Run the processing and get the results
+results = fitter.peak_fit()
+
+# Check for errors
+if fitter.status != fitter.DONE:
+    print("Fit process failed")
+    sys.exit()
+
+# Prepare columns header and data
+# Start with position (x, y) on the sample
+headers = ['sample_x', 'sample_y']
+values = [results.sample_x, results.sample_y]
+
+# Add one column for each parameter of the fit/COM result
+# For each axis of the QSpace
+for dimension, axis_name in enumerate(results.qspace_dimension_names):
+    # For each fitted/computed parameter of the result
+    for parameter_name in results.available_result_names:
+        # Add its name to the header and its data to the values
+        headers.append(axis_name + '_' + parameter_name)
+        values.append(results.get_results(dimension, parameter_name))
+
+# Transpose values from (parameters x points) to (points x parameters)
+values = numpy.transpose(numpy.array(values))
+
+# Write results to text file
+with open(result_file, 'w') as res_f:
+    res_f.write('\t'.join(headers) + '\n')
+    for row_values in values:
+        res_f.write('\t'.join(str(v) for v in row_values) + '\n')

xsocs/process/fit/peak_fit.py

@@ -119,7 +119,7 @@ class FitResult(object):
         self._fit_results = numpy.transpose(fit_results)
 
     @property
-    def available_results(self, dimension=None):
+    def available_result_names(self, dimension=None):
         """Returns the available result names
 
         :param Union[int,None] dimension:
@@ -173,7 +173,7 @@ class FitResult(object):
                     self._fit_results,
                     (fitH5.set_qx_result, fitH5.set_qy_result, fitH5.set_qz_result),
                     (0, 1, 2)):
-                for name in self.available_results:
+                for name in self.available_result_names:
                     results = self.get_results(axis, name, copy=False)
                     if name == 'Status':
                         fitH5.set_status(fit_name, axis, results)