Changeset 14d9c7b in sasview

Timestamp:

Nov 23, 2016 4:21:03 AM (7 years ago)

Author:

Piotr Rozyczko <rozyczko@…>

Branches:

ESS_GUI, ESS_GUI_Docs, ESS_GUI_batch_fitting, ESS_GUI_bumps_abstraction, ESS_GUI_iss1116, ESS_GUI_iss879, ESS_GUI_iss959, ESS_GUI_opencl, ESS_GUI_ordering, ESS_GUI_sync_sascalc

Children:

31c5b58

Parents:

49e124c

Message:

2D plotter cleanup

Location:

src/sas/qtgui

Files:

• DataExplorer.py (modified) (2 diffs)
• PlotUtilities.py (added)
• Plotter2D.py (modified) (4 diffs)

src/sas/qtgui/DataExplorer.py

@@ -388 +388 @@
         """
         Create a new matplotlib chart from selected data
-
-        TODO: Add 2D-functionality
         """
         plots = GuiUtils.plotsFromCheckedItems(self.model)
@@ -437 +435 @@
         old_plot = PlotHelper.plotById(plot_id)
 
-        # Add new data to the old plot
+        # Add new data to the old plot, if data type is the same.
         for plot_set in new_plots:
-            old_plot.data(plot_set)
-            old_plot.plot()
+            if type(plot_set) is type(old_plot._data):
+                old_plot.data(plot_set)
+                old_plot.plot()
 
     def chooseFiles(self):

src/sas/qtgui/Plotter2D.py

@@ -16 +16 @@
 DEFAULT_CMAP = pylab.cm.jet
 
+import PlotUtilities
 import PlotHelper
 
@@ -60 +61 @@
     def data(self, data=None):
         """ data setter """
-        self._data = data.data
+        self._data = data
         self._qx_data=data.qx_data
         self._qy_data=data.qy_data
@@ -116 +117 @@
         # self.figure.subplots_adjust(left=0.2, right=.8, bottom=.2)
 
-        output = self._build_matrix()
+        output = PlotUtilities.build_matrix(self._data.data, self._qx_data, self._qy_data)
 
         im = ax.imshow(output,
@@ -144 +145 @@
         event.accept()
 
-    def _build_matrix(self):
-        """
-        Build a matrix for 2d plot from a vector
-        Returns a matrix (image) with ~ square binning
-        Requirement: need 1d array formats of
-        self.data, self._qx_data, and self._qy_data
-        where each one corresponds to z, x, or y axis values
-
-        """
-        # No qx or qy given in a vector format
-        if self._qx_data == None or self._qy_data == None \
-                or self._qx_data.ndim != 1 or self._qy_data.ndim != 1:
-            # do we need deepcopy here?
-            return self._data
-
-        # maximum # of loops to fillup_pixels
-        # otherwise, loop could never stop depending on data
-        max_loop = 1
-        # get the x and y_bin arrays.
-        self._get_bins()
-        # set zero to None
-
-        #Note: Can not use scipy.interpolate.Rbf:
-        # 'cause too many data points (>10000)<=JHC.
-        # 1d array to use for weighting the data point averaging
-        #when they fall into a same bin.
-        weights_data = numpy.ones([self._data.size])
-        # get histogram of ones w/len(data); this will provide
-        #the weights of data on each bins
-        weights, xedges, yedges = numpy.histogram2d(x=self._qy_data,
-                                                    y=self._qx_data,
-                                                    bins=[self.y_bins, self.x_bins],
-                                                    weights=weights_data)
-        # get histogram of data, all points into a bin in a way of summing
-        image, xedges, yedges = numpy.histogram2d(x=self._qy_data,
-                                                  y=self._qx_data,
-                                                  bins=[self.y_bins, self.x_bins],
-                                                  weights=self._data)
-        # Now, normalize the image by weights only for weights>1:
-        # If weight == 1, there is only one data point in the bin so
-        # that no normalization is required.
-        image[weights > 1] = image[weights > 1] / weights[weights > 1]
-        # Set image bins w/o a data point (weight==0) as None (was set to zero
-        # by histogram2d.)
-        image[weights == 0] = None
-
-        # Fill empty bins with 8 nearest neighbors only when at least
-        #one None point exists
-        loop = 0
-
-        # do while loop until all vacant bins are filled up up
-        #to loop = max_loop
-        while not(numpy.isfinite(image[weights == 0])).all():
-            if loop >= max_loop:  # this protects never-ending loop
-                break
-            image = self._fillup_pixels(image=image, weights=weights)
-            loop += 1
-
-        return image
-
-    def _get_bins(self):
-        """
-        get bins
-        set x_bins and y_bins into self, 1d arrays of the index with
-        ~ square binning
-        Requirement: need 1d array formats of
-        self._qx_data, and self._qy_data
-        where each one corresponds to  x, or y axis values
-        """
-        # No qx or qy given in a vector format
-        if self._qx_data == None or self._qy_data == None \
-                or self._qx_data.ndim != 1 or self._qy_data.ndim != 1:
-            return self._data
-
-        # find max and min values of qx and qy
-        xmax = self._qx_data.max()
-        xmin = self._qx_data.min()
-        ymax = self._qy_data.max()
-        ymin = self._qy_data.min()
-
-        # calculate the range of qx and qy: this way, it is a little
-        # more independent
-        x_size = xmax - xmin
-        y_size = ymax - ymin
-
-        # estimate the # of pixels on each axes
-        npix_y = int(numpy.floor(numpy.sqrt(len(self._qy_data))))
-        npix_x = int(numpy.floor(len(self._qy_data) / npix_y))
-
-        # bin size: x- & y-directions
-        xstep = x_size / (npix_x - 1)
-        ystep = y_size / (npix_y - 1)
-
-        # max and min taking account of the bin sizes
-        xmax = xmax + xstep / 2.0
-        xmin = xmin - xstep / 2.0
-        ymax = ymax + ystep / 2.0
-        ymin = ymin - ystep / 2.0
-
-        # store x and y bin centers in q space
-        x_bins = numpy.linspace(xmin, xmax, npix_x)
-        y_bins = numpy.linspace(ymin, ymax, npix_y)
-
-        #set x_bins and y_bins
-        self.x_bins = x_bins
-        self.y_bins = y_bins
-
-    def _fillup_pixels(self, image=None, weights=None):
-        """
-        Fill z values of the empty cells of 2d image matrix
-        with the average over up-to next nearest neighbor points
-
-        :param image: (2d matrix with some zi = None)
-
-        :return: image (2d array )
-
-        :TODO: Find better way to do for-loop below
-
-        """
-        # No image matrix given
-        if image == None or numpy.ndim(image) != 2 \
-                or numpy.isfinite(image).all() \
-                or weights == None:
-            return image
-        # Get bin size in y and x directions
-        len_y = len(image)
-        len_x = len(image[1])
-        temp_image = numpy.zeros([len_y, len_x])
-        weit = numpy.zeros([len_y, len_x])
-        # do for-loop for all pixels
-        for n_y in range(len(image)):
-            for n_x in range(len(image[1])):
-                # find only null pixels
-                if weights[n_y][n_x] > 0 or numpy.isfinite(image[n_y][n_x]):
-                    continue
-                else:
-                    # find 4 nearest neighbors
-                    # check where or not it is at the corner
-                    if n_y != 0 and numpy.isfinite(image[n_y - 1][n_x]):
-                        temp_image[n_y][n_x] += image[n_y - 1][n_x]
-                        weit[n_y][n_x] += 1
-                    if n_x != 0 and numpy.isfinite(image[n_y][n_x - 1]):
-                        temp_image[n_y][n_x] += image[n_y][n_x - 1]
-                        weit[n_y][n_x] += 1
-                    if n_y != len_y - 1 and numpy.isfinite(image[n_y + 1][n_x]):
-                        temp_image[n_y][n_x] += image[n_y + 1][n_x]
-                        weit[n_y][n_x] += 1
-                    if n_x != len_x - 1 and numpy.isfinite(image[n_y][n_x + 1]):
-                        temp_image[n_y][n_x] += image[n_y][n_x + 1]
-                        weit[n_y][n_x] += 1
-                    # go 4 next nearest neighbors when no non-zero
-                    # neighbor exists
-                    if n_y != 0 and n_x != 0 and\
-                         numpy.isfinite(image[n_y - 1][n_x - 1]):
-                        temp_image[n_y][n_x] += image[n_y - 1][n_x - 1]
-                        weit[n_y][n_x] += 1
-                    if n_y != len_y - 1 and n_x != 0 and \
-                        numpy.isfinite(image[n_y + 1][n_x - 1]):
-                        temp_image[n_y][n_x] += image[n_y + 1][n_x - 1]
-                        weit[n_y][n_x] += 1
-                    if n_y != len_y and n_x != len_x - 1 and \
-                        numpy.isfinite(image[n_y - 1][n_x + 1]):
-                        temp_image[n_y][n_x] += image[n_y - 1][n_x + 1]
-                        weit[n_y][n_x] += 1
-                    if n_y != len_y - 1 and n_x != len_x - 1 and \
-                        numpy.isfinite(image[n_y + 1][n_x + 1]):
-                        temp_image[n_y][n_x] += image[n_y + 1][n_x + 1]
-                        weit[n_y][n_x] += 1
-
-        # get it normalized
-        ind = (weit > 0)
-        image[ind] = temp_image[ind] / weit[ind]
-
-        return image