Fix more merge conflicts and add competent subset extraction

dpm_tools/io/_io_utils.py

@@ -187,10 +187,6 @@ def convert_filetype(filepath: pathlib.Path, convert_to: str, **kwargs) -> None:
                 image=original_image, filetype=convert_to)
 
 
-<< << << < HEAD
-== == == =
-
-
 def download_file(url: str, save_path: str = ".") -> None:
     """
     Download a file from the provided URL and save it to the specified save path.
@@ -207,6 +203,3 @@ def download_file(url: str, save_path: str = ".") -> None:
     wget.download(url, out=save_path)
 
     return
-
-
->>>>>> > 3dfbfc1db602ad1d542da0ed78e2787f6f89c60b

dpm_tools/io/_read_data.py

@@ -56,8 +56,8 @@ def _read_raw(filepath: pathlib.Path, **kwargs) -> np.ndarray:
     metadata = kwargs['meta']
 
     assert all(key in metadata for key in ['nz', 'ny', 'nx', 'bits', 'signed', 'byte_order']), \
-        f"Image metadata dictionary must " \
-        f"contain {{'nz', 'ny', 'nx', 'bits', 'signed', 'byte_order'} - metadata.keys()}"
+        f"Image metadata dictionary must contain {
+            ['nz', 'ny', 'nx', 'bits', 'signed', 'byte_order']} - found {list(metadata.keys())}"
 
     bits = metadata['bits']
     signed = metadata['signed']
@@ -93,6 +93,7 @@ def _read_raw(filepath: pathlib.Path, **kwargs) -> np.ndarray:
 
 # TODO: Review this function
 
+
 def _read_nc(filepath: pathlib.Path, **kwargs) -> np.ndarray:
     """
     A utility function to read in netcdf files
@@ -261,7 +262,8 @@ def __post_init__(self):
 
         # If vector is populated
         if self.vector is not None:
-            self.magnitude = np.sqrt(self.vector[0]**2 + self.vector[1]**2 + self.vector[2]**2)
+            self.magnitude = np.sqrt(
+                self.vector[0]**2 + self.vector[1]**2 + self.vector[2]**2)
 
         self.nz, self.nx, self.ny = self.scalar.shape
         self.shape = (self.nz, self.nx, self.ny)
@@ -285,9 +287,11 @@ def _read_data_from_files(self):
         A utility method to read images in from a list of filepaths
         """
 
-        images = [read_image(fp, meta=self.meta[i]) if fp else None for i, fp in enumerate(self.filepaths)]
+        images = [read_image(fp, meta=self.meta[i])
+                  if fp else None for i, fp in enumerate(self.filepaths)]
 
-        assert len(images) == 1 or len(images) == 3 or len(images) == 4, f"You must provide a scalar, a vector, or both"
+        assert len(images) == 1 or len(images) == 3 or len(
+            images) == 4, f"You must provide a scalar, a vector, or both"
 
         if len(images) == 1:
             self.scalar = images[0]

dpm_tools/metrics/_scalars.py

@@ -212,8 +212,10 @@ def histogram_statistics(*images: np.ndarray, plot_histogram: bool = False, nbin
     Get the statistics of multiple images.
 
     Parameters:
+        *images: A list of 2D or 3D images.
         plot_histograms: Plot the image histograms. Defaults to False.
-        bins: Number of histogram bins. Defaults to 256.
+        nbins: Number of histogram bins. Defaults to 256.
+        legend_elem: A list of strings to use as legend labels. Defaults to an empty list.
 
     Returns:
         dict: A dictionary of image metadata and histogram statistics.
@@ -226,25 +228,26 @@ def histogram_statistics(*images: np.ndarray, plot_histogram: bool = False, nbin
     for image in images:
         stats = _hist_stats(image, nbins)
 
-        print('-'*35)
+        print('-' * 35)
         print('Image statistics:')
         print(f'\tShape: {stats["shape"]}')
         print(f'\tData type: {stats["dtype"]}')
         print(f'\tMin: {stats["min"]}, Max: {
               stats["max"]}, Mean: {stats["mean"]}\n')
 
         img_stats.append(_hist_stats(image, nbins))
-        img_list.append(image.flatten())
 
     if plot_histogram:
         assert len(legend_elem) == len(
             images), 'Number of legend elements must match the number of images'
         fig, ax = plt.subplots()
-        ax.hist(img_list, bins=nbins, density=True,
-                histtype='step', fill=False)
+        for img, label in zip(images, legend_elem):
+            ax.hist(img.flatten(), bins=nbins, density=True,
+                    histtype='step', fill=False, label=label)
         ax.set_xlabel('Image Value', fontsize=16)
         ax.set_ylabel('Probability Density', fontsize=16)
         ax.grid(True)
-        ax.legend(legend_elem)
+        ax.legend()
+        # ax.legend(handles, labels)
 
     return img_stats

dpm_tools/visualization/_plot_2d.py

@@ -161,38 +161,19 @@ def plot_heterogeneity_curve(radii: np.ndarray, variances: np.ndarray, relative_
         relative_radii: If True, the plotted radii are relative to the first window size. Otherwise, the absolute radii are shown.
 
     Returns:
-<<<<<<< HEAD
-        fig, ax: Matplotlib figure and axes object.
-    """
-    if fig is None and ax is None:
-        fig1, ax1 = plt.subplots()
-    else:
-        assert fig is not None and ax is not None, "Both fig and ax must be provided."
-        fig1, ax1 = fig, ax
-    # plt.figure()
-    if relative_radii:
-        ax1.plot(variances, markersize=6, **kwargs)
-        ax1.set_xlabel("Relative Radius")
-    else:
-        ax1.plot(radii, variances, markersize=6,
-                 **kwargs)
-
-
-== == == =
         fig, ax: Matplotlib figure and axes objects.
     """
-    
+
     if fig is None or ax is None:
         fig1, ax1 = plt.subplots()
     else:
         fig1, ax1 = fig, ax
-        
+
     if relative_radii:
         ax1.plot(variances, **kwargs)
         ax1.set_xlabel("Relative Radius")
     else:
         ax1.plot(radii, variances, **kwargs)
->>>>>>> 3dfbfc1db602ad1d542da0ed78e2787f6f89c60b
         ax1.set_xlabel("Absolute Radius")
 
     x = np.linspace(-2, 17, len(variances))
@@ -210,9 +191,4 @@ def plot_heterogeneity_curve(radii: np.ndarray, variances: np.ndarray, relative_
 
     ax1.set_ylabel("Porosity Variance")
 
-<<<<<<< HEAD
-    # plt.legend()
-
-=======
->>>>>>> 3dfbfc1db602ad1d542da0ed78e2787f6f89c60b
     return fig1, ax1