{"action":"created","comment":{"url":"https://api.github.com/repos/Nikea/scikit-xray/pulls/comments/34731051","id":34731051,"diff_hunk":"@@ -0,0 +1,311 @@\n+# ######################################################################\n+# Copyright (c) 2014, Brookhaven Science Associates, Brookhaven        #\n+# National Laboratory. All rights reserved.                            #\n+#                                                                      #\n+# Developed at the NSLS-II, Brookhaven National Laboratory             #\n+# Developed by Sameera K. Abeykoon, May 2015                           #\n+#                                                                      #\n+# Redistribution and use in source and binary forms, with or without   #\n+# modification, are permitted provided that the following conditions   #\n+# are met:                                                             #\n+#                                                                      #\n+# * Redistributions of source code must retain the above copyright     #\n+#   notice, this list of conditions and the following disclaimer.      #\n+#                                                                      #\n+# * Redistributions in binary form must reproduce the above copyright  #\n+#   notice this list of conditions and the following disclaimer in     #\n+#   the documentation and/or other materials provided with the         #\n+#   distribution.                                                      #\n+#                                                                      #\n+# * Neither the name of the Brookhaven Science Associates, Brookhaven  #\n+#   National Laboratory nor the names of its contributors may be used  #\n+#   to endorse or promote products derived from this software without  #\n+#   specific prior written permission.                                 #\n+#                                                                      #\n+# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS  #\n+# \"AS IS\" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT    #\n+# LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS    #\n+# FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE       #\n+# COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT,           #\n+# INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES   #\n+# (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR   #\n+# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION)   #\n+# HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,  #\n+# STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OTHERWISE) ARISING   #\n+# IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE   #\n+# POSSIBILITY OF SUCH DAMAGE.                                          #\n+########################################################################\n+\n+\"\"\"\n+    This module will provide statistical analysis for the speckle patterns\n+\"\"\"\n+\n+\n+from __future__ import (absolute_import, division, print_function,\n+                        unicode_literals)\n+import six\n+import numpy as np\n+from six.moves import zip\n+from six import string_types\n+\n+import skxray.correlation as corr\n+import skxray.roi as roi\n+import skxray.core as core\n+from scipy.ndimage.measurements import maximum, mean\n+\n+try:\n+    iteritems = dict.iteritems\n+except AttributeError:\n+    iteritems = dict.items  # python 3\n+\n+import logging\n+logger = logging.getLogger(__name__)\n+\n+\n+def roi_max_counts(images_sets, label_array):\n+    \"\"\"\n+    Return the brightest pixel in any ROI in any image in the image set.\n+\n+    Parameters\n+    ----------\n+    images_sets : array\n+        iterable of 4D arrays\n+        shapes is: (len(images_sets), )\n+\n+    label_array : array\n+        labeled array; 0 is background.\n+        Each ROI is represented by a distinct label (i.e., integer).\n+\n+    Returns\n+    -------\n+    max_counts : int\n+        maximum pixel counts\n+    \"\"\"\n+    max_cts = 0\n+    for img_set in images_sets:\n+        for img in img_set:\n+            max_cts = max(max_cts, maximum(img, label_array))\n+    return max_cts\n+\n+\n+def roi_pixel_values(image, labels, index=None):\n+    \"\"\"\n+    This will provide intensities of the ROI's of the labeled array\n+    according to the pixel list\n+    eg: intensities of the rings of the labeled array\n+\n+    Parameters\n+    ----------\n+    image : array\n+        image data dimensions are: (rr, cc)\n+\n+    labels : array\n+        labeled array; 0 is background.\n+        Each ROI is represented by a distinct label (i.e., integer).\n+\n+    index_list : list, optional\n+        labels list\n+        eg: 5 ROI's\n+        index = [1, 2, 3, 4, 5]\n+\n+    Returns\n+    -------\n+    roi_pix : dict\n+        intensities of the ROI's of the labeled array according\n+        to the pixel list\n+        {ROI 1 : intensities of the pixels of ROI 1, ROI 2 : intensities of\n+         the pixels of ROI 2}\n+    \"\"\"\n+\n+    if labels.shape != image.shape:\n+        raise ValueError(\"Shape of the image data should be equal to\"\n+                         \" shape of the labeled array\")\n+    if index is None:\n+        index = np.arange(1, np.max(labels) + 1)\n+\n+    roi_pix = {n: image[labels == n] for n in index}\n+    return roi_pix, index\n+\n+\n+def mean_intensity_sets(images_set, labels):\n+    \"\"\"\n+    Mean intensities for ROIS' of the labeled array for different image sets\n+\n+    Parameters\n+    ----------\n+    images_set : array\n+        images sets\n+        shapes is: (len(images_sets), )\n+        one images_set is iterable of 2D arrays dimensions are: (rr, cc)\n+\n+    labels : array\n+        labeled array; 0 is background.\n+        Each ROI is represented by a distinct label (i.e., integer).\n+\n+    Returns\n+    -------\n+    mean_intensity_dict : dict\n+        average intensity of each ROI as a dictionary\n+        shape len(images_sets)\n+        eg: 2 image sets,\n+        {image set 1 : len(images in image set 1),\n+        image set 2 : len(images in image set 2)}\n+\n+    index_list : list\n+        labels list for each image set\n+\n+    \"\"\"\n+    mean_intensity_dict = {}\n+    index_list = []\n+    for n in range(len(images_set)):\n+        mean_int, index = mean_intensity(images_set[n], labels)\n+        mean_intensity_dict[n] = mean_int\n+        index_list.append(index)\n+\n+    return mean_intensity_dict, index_list\n+\n+\n+def mean_intensity(images, labels, index=None):\n+    \"\"\"\n+    Mean intensities for ROIS' of the labeled array for set of images\n+\n+    Parameters\n+    ----------\n+    images : array\n+        iterable of 2D arrays\n+        dimensions are: (rr, cc)\n+\n+    labels : array\n+        labeled array; 0 is background.\n+        Each ROI is represented by a distinct label (i.e., integer).\n+\n+    index : list\n+        labels list\n+        eg: 5 ROI's\n+        index = [1, 2, 3, 4, 5]\n+\n+    Returns\n+    -------\n+    mean_intensity : array\n+        mean intensity of each ROI for the set of images as an array\n+        shape (len(images), number of labels)\n+\n+    \"\"\"\n+    if labels.shape != images[0].shape[0:]:\n+        raise ValueError(\"Shape of the images should be equal to\"\n+                         \" shape of the label array\")\n+    if index is None:\n+        index = np.arange(1, np.max(labels) + 1)\n+\n+    mean_intensity = np.zeros((images.shape[0], index.shape[0]))\n+    for n, img in enumerate(images):\n+        mean_intensity[n] = mean(img, labels, index=index)\n+\n+    return mean_intensity, index\n+\n+\n+def combine_mean_intensity(mean_int_dict, index_list):\n+    \"\"\"\n+    Combine mean intensities of the images(all images sets) for each ROI\n+    if the labels list of all the images are same\n+\n+    Parameters\n+    ----------\n+    mean_int_dict : dict\n+        mean intensity of each ROI as a dictionary\n+        eg: 2 image sets,\n+        {image set 1 : (len(images in image set 1),\n+        image set 2 : (len(images in image set 2)}\n+\n+    index_list : list\n+        labels list for each image sets\n+\n+    Returns\n+    -------\n+    combine_mean_int : array\n+        combine mean intensities of image sets for each ROI of labeled array\n+        shape (len(images in all image sets), number of labels)\n+\n+    \"\"\"\n+    if np.all(map(lambda x: x == index_list[0], index_list)):\n+        combine_mean_intensity = np.vstack(list(mean_int_dict.values()))\n+    else:\n+        raise ValueError(\"Labels list for the image sets are different\")\n+\n+    return np.vstack(list(mean_int_dict.values()))","path":"skxray/speckle_analysis.py","position":235,"original_position":235,"commit_id":"4d0baf561787fd781d271a34fc7bc698054546be","original_commit_id":"4d0baf561787fd781d271a34fc7bc698054546be","user":{"login":"tacaswell","id":199813,"avatar_url":"https://avatars.githubusercontent.com/u/199813?v=3","gravatar_id":"","url":"https://api.github.com/users/tacaswell","html_url":"https://github.com/tacaswell","followers_url":"https://api.github.com/users/tacaswell/followers","following_url":"https://api.github.com/users/tacaswell/following{/other_user}","gists_url":"https://api.github.com/users/tacaswell/gists{/gist_id}","starred_url":"https://api.github.com/users/tacaswell/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/tacaswell/subscriptions","organizations_url":"https://api.github.com/users/tacaswell/orgs","repos_url":"https://api.github.com/users/tacaswell/repos","events_url":"https://api.github.com/users/tacaswell/events{/privacy}","received_events_url":"https://api.github.com/users/tacaswell/received_events","type":"User","site_admin":false},"body":"and then do it again to return ?\r\n\r\nThe order of this will be arbitrary.  In python 2 it will be the same from run-to-run, but in python 3.4 this order will be random between python processes.","created_at":"2015-07-15T21:21:47Z","updated_at":"2015-07-15T21:21:47Z","html_url":"https://github.com/Nikea/scikit-xray/pull/283#discussion_r34731051","pull_request_url":"https://api.github.com/repos/Nikea/scikit-xray/pulls/283","_links":{"self":{"href":"https://api.github.com/repos/Nikea/scikit-xray/pulls/comments/34731051"},"html":{"href":"https://github.com/Nikea/scikit-xray/pull/283#discussion_r34731051"},"pull_request":{"href":"https://api.github.com/repos/Nikea/scikit-xray/pulls/283"}}},"pull_request":{"url":"https://api.github.com/repos/Nikea/scikit-xray/pulls/283","id":36659478,"html_url":"https://github.com/Nikea/scikit-xray/pull/283","diff_url":"https://github.com/Nikea/scikit-xray/pull/283.diff","patch_url":"https://github.com/Nikea/scikit-xray/pull/283.patch","issue_url":"https://api.github.com/repos/Nikea/scikit-xray/issues/283","number":283,"state":"open","locked":false,"title":"XSVS1","user":{"login":"sameera2004","id":5701652,"avatar_url":"https://avatars.githubusercontent.com/u/5701652?v=3","gravatar_id":"","url":"https://api.github.com/users/sameera2004","html_url":"https://github.com/sameera2004","followers_url":"https://api.github.com/users/sameera2004/followers","following_url":"https://api.github.com/users/sameera2004/following{/other_user}","gists_url":"https://api.github.com/users/sameera2004/gists{/gist_id}","starred_url":"https://api.github.com/users/sameera2004/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/sameera2004/subscriptions","organizations_url":"https://api.github.com/users/sameera2004/orgs","repos_url":"https://api.github.com/users/sameera2004/repos","events_url":"https://api.github.com/users/sameera2004/events{/privacy}","received_events_url":"https://api.github.com/users/sameera2004/received_events","type":"User","site_admin":false},"body":"This will provide most of the static tests need to do before - X-ray speckle visibility spectroscopy(XSVS)","created_at":"2015-06-01T19:01:42Z","updated_at":"2015-07-15T21:21:47Z","closed_at":null,"merged_at":null,"merge_commit_sha":"225618314cfe4eda5c1389fc5f4a1de1c3c2bf8d","assignee":null,"milestone":null,"commits_url":"https://api.github.com/repos/Nikea/scikit-xray/pulls/283/commits","review_comments_url":"https://api.github.com/repos/Nikea/scikit-xray/pulls/283/comments","review_comment_url":"https://api.github.com/repos/Nikea/scikit-xray/pulls/comments{/number}","comments_url":"https://api.github.com/repos/Nikea/scikit-xray/issues/283/comments","statuses_url":"https://api.github.com/repos/Nikea/scikit-xray/statuses/4d0baf561787fd781d271a34fc7bc698054546be","head":{"label":"sameera2004:xsvs1","ref":"xsvs1","sha":"4d0baf561787fd781d271a34fc7bc698054546be","user":{"login":"sameera2004","id":5701652,"avatar_url":"https://avatars.githubusercontent.com/u/5701652?v=3","gravatar_id":"","url":"https://api.github.com/users/sameera2004","html_url":"https://github.com/sameera2004","followers_url":"https://api.github.com/users/sameera2004/followers","following_url":"https://api.github.com/users/sameera2004/following{/other_user}","gists_url":"https://api.github.com/users/sameera2004/gists{/gist_id}","starred_url":"https://api.github.com/users/sameera2004/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/sameera2004/subscriptions","organizations_url":"https://api.github.com/users/sameera2004/orgs","repos_url":"https://api.github.com/users/sameera2004/repos","events_url":"https://api.github.com/users/sameera2004/events{/privacy}","received_events_url":"https://api.github.com/users/sameera2004/received_events","type":"User","site_admin":false},"repo":{"id":21700905,"name":"scikit-xray","full_name":"sameera2004/scikit-xray","owner":{"login":"sameera2004","id":5701652,"avatar_url":"https://avatars.githubusercontent.com/u/5701652?v=3","gravatar_id":"","url":"https://api.github.com/users/sameera2004","html_url":"https://github.com/sameera2004","followers_url":"https://api.github.com/users/sameera2004/followers","following_url":"https://api.github.com/users/sameera2004/following{/other_user}","gists_url":"https://api.github.com/users/sameera2004/gists{/gist_id}","starred_url":"https://api.github.com/users/sameera2004/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/sameera2004/subscriptions","organizations_url":"https://api.github.com/users/sameera2004/orgs","repos_url":"https://api.github.com/users/sameera2004/repos","events_url":"https://api.github.com/users/sameera2004/events{/privacy}","received_events_url":"https://api.github.com/users/sameera2004/received_events","type":"User","site_admin":false},"private":false,"html_url":"https://github.com/sameera2004/scikit-xray","description":"Core 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