[fmt] setup.py and visualization

package/MDAnalysis/visualization/__init__.py

@@ -24,4 +24,4 @@
 from . import streamlines
 from . import streamlines_3D
 
-__all__ = ['streamlines', 'streamlines_3D']
+__all__ = ["streamlines", "streamlines_3D"]

package/MDAnalysis/visualization/streamlines.py

@@ -52,16 +52,15 @@
     import matplotlib.path
 except ImportError:
     raise ImportError(
-            '2d streamplot module requires: matplotlib.path for its '
-            'path.Path.contains_points method. The installation '
-            'instructions for the matplotlib module can be found here: '
-            'http://matplotlib.org/faq/installing_faq.html?highlight=install'
-            ) from None
+        "2d streamplot module requires: matplotlib.path for its "
+        "path.Path.contains_points method. The installation "
+        "instructions for the matplotlib module can be found here: "
+        "http://matplotlib.org/faq/installing_faq.html?highlight=install"
+    ) from None
 
 import MDAnalysis
 
 
-
 def produce_grid(tuple_of_limits, grid_spacing):
     """Produce a 2D grid for the simulation system.
 
@@ -120,12 +119,16 @@
     # produce an array containing the cartesian coordinates of all vertices in the grid:
     x_array, y_array = grid
     grid_vertex_cartesian_array = np.dstack((x_array, y_array))
-    #the grid_vertex_cartesian_array has N_rows, with each row corresponding to a column of coordinates in the grid (
+    # the grid_vertex_cartesian_array has N_rows, with each row corresponding to a column of coordinates in the grid (
     # so a given row has shape N_rows, 2); overall shape (N_columns_in_grid, N_rows_in_a_column, 2)
-    #although I'll eventually want a pure numpy/scipy/vector-based solution, for now I'll allow loops to simplify the
+    # although I'll eventually want a pure numpy/scipy/vector-based solution, for now I'll allow loops to simplify the
     #  division of the cartesian coordinates into a list of the squares in the grid
-    list_all_squares_in_grid = []  # should eventually be a nested list of all the square vertices in the grid/system
-    list_parent_index_values = []  # want an ordered list of assignment indices for reconstructing the grid positions
+    list_all_squares_in_grid = (
+        []
+    )  # should eventually be a nested list of all the square vertices in the grid/system
+    list_parent_index_values = (
+        []
+    )  # want an ordered list of assignment indices for reconstructing the grid positions
     # in the parent process
     current_column = 0
     while current_column < grid_vertex_cartesian_array.shape[0] - 1:
@@ -134,100 +137,182 @@
         current_row = 0
         while current_row < grid_vertex_cartesian_array.shape[1] - 1:
             # all rows except the top row, which doesn't have a row above it for forming squares
-            bottom_left_vertex_current_square = grid_vertex_cartesian_array[current_column, current_row]
-            bottom_right_vertex_current_square = grid_vertex_cartesian_array[current_column + 1, current_row]
-            top_right_vertex_current_square = grid_vertex_cartesian_array[current_column + 1, current_row + 1]
-            top_left_vertex_current_square = grid_vertex_cartesian_array[current_column, current_row + 1]
-            #append the vertices of this square to the overall list of square vertices:
+            bottom_left_vertex_current_square = grid_vertex_cartesian_array[
+                current_column, current_row
+            ]
+            bottom_right_vertex_current_square = grid_vertex_cartesian_array[
+                current_column + 1, current_row
+            ]
+            top_right_vertex_current_square = grid_vertex_cartesian_array[
+                current_column + 1, current_row + 1
+            ]
+            top_left_vertex_current_square = grid_vertex_cartesian_array[
+                current_column, current_row + 1
+            ]
+            # append the vertices of this square to the overall list of square vertices:
             list_all_squares_in_grid.append(
-                [bottom_left_vertex_current_square, bottom_right_vertex_current_square, top_right_vertex_current_square,
-                    top_left_vertex_current_square])
+                [
+                    bottom_left_vertex_current_square,
+                    bottom_right_vertex_current_square,
+                    top_right_vertex_current_square,
+                    top_left_vertex_current_square,
+                ]
+            )
             list_parent_index_values.append([current_row, current_column])
             current_row += 1
         current_column += 1
-    #split the list of square vertices [[v1,v2,v3,v4],[v1,v2,v3,v4],...,...] into roughly equally-sized sublists to
+    # split the list of square vertices [[v1,v2,v3,v4],[v1,v2,v3,v4],...,...] into roughly equally-sized sublists to
     # be distributed over the available cores on the system:
-    list_square_vertex_arrays_per_core = np.array_split(list_all_squares_in_grid, num_cores)
-    list_parent_index_values = np.array_split(list_parent_index_values, num_cores)
-    return [list_square_vertex_arrays_per_core, list_parent_index_values, current_row, current_column]
-
-
-def per_core_work(topology_file_path, trajectory_file_path, list_square_vertex_arrays_this_core, MDA_selection,
-                  start_frame, end_frame, reconstruction_index_list, maximum_delta_magnitude):
+    list_square_vertex_arrays_per_core = np.array_split(
+        list_all_squares_in_grid, num_cores
+    )
+    list_parent_index_values = np.array_split(
+        list_parent_index_values, num_cores
+    )
+    return [
+        list_square_vertex_arrays_per_core,
+        list_parent_index_values,
+        current_row,
+        current_column,
+    ]
+
+
+def per_core_work(
+    topology_file_path,
+    trajectory_file_path,
+    list_square_vertex_arrays_this_core,
+    MDA_selection,
+    start_frame,
+    end_frame,
+    reconstruction_index_list,
+    maximum_delta_magnitude,
+):
     """Run the analysis on one core.
 
     The code to perform on a given core given the list of square vertices assigned to it.
     """
     # obtain the relevant coordinates for particles of interest
-    universe_object = MDAnalysis.Universe(topology_file_path, trajectory_file_path)
+    universe_object = MDAnalysis.Universe(
+        topology_file_path, trajectory_file_path
+    )
     list_previous_frame_centroids = []
     list_previous_frame_indices = []
-    #define some utility functions for trajectory iteration:
+    # define some utility functions for trajectory iteration:
 
     def produce_list_indices_point_in_polygon_this_frame(vertex_coord_list):
         list_indices_point_in_polygon = []
         for square_vertices in vertex_coord_list:
             path_object = matplotlib.path.Path(square_vertices)
-            index_list_in_polygon = np.where(path_object.contains_points(relevant_particle_coordinate_array_xy))
+            index_list_in_polygon = np.where(
+                path_object.contains_points(
+                    relevant_particle_coordinate_array_xy
+                )
+            )
             list_indices_point_in_polygon.append(index_list_in_polygon)
         return list_indices_point_in_polygon
 
     def produce_list_centroids_this_frame(list_indices_in_polygon):
         list_centroids_this_frame = []
         for indices in list_indices_in_polygon:
-            if not indices[0].size > 0:  # if there are no particles of interest in this particular square
+            if (
+                not indices[0].size > 0
+            ):  # if there are no particles of interest in this particular square
                 list_centroids_this_frame.append(None)
             else:
-                current_coordinate_array_in_square = relevant_particle_coordinate_array_xy[indices]
-                current_square_indices_centroid = np.average(current_coordinate_array_in_square, axis=0)
-                list_centroids_this_frame.append(current_square_indices_centroid)
+                current_coordinate_array_in_square = (
+                    relevant_particle_coordinate_array_xy[indices]
+                )
+                current_square_indices_centroid = np.average(
+                    current_coordinate_array_in_square, axis=0
+                )
+                list_centroids_this_frame.append(
+                    current_square_indices_centroid
+                )
         return list_centroids_this_frame  # a list of numpy xy centroid arrays for this frame
 
     for ts in universe_object.trajectory:
         if ts.frame < start_frame:  # don't start until first specified frame
             continue
-        relevant_particle_coordinate_array_xy = universe_object.select_atoms(MDA_selection).positions[..., :-1]
+        relevant_particle_coordinate_array_xy = universe_object.select_atoms(
+            MDA_selection
+        ).positions[..., :-1]
         # only 2D / xy coords for now
-        #I will need a list of indices for relevant particles falling within each square in THIS frame:
-        list_indices_in_squares_this_frame = produce_list_indices_point_in_polygon_this_frame(
-            list_square_vertex_arrays_this_core)
-        #likewise, I will need a list of centroids of particles in each square (same order as above list):
-        list_centroids_in_squares_this_frame = produce_list_centroids_this_frame(list_indices_in_squares_this_frame)
-        if list_previous_frame_indices:  # if the previous frame had indices in at least one square I will need to use
+        # I will need a list of indices for relevant particles falling within each square in THIS frame:
+        list_indices_in_squares_this_frame = (
+            produce_list_indices_point_in_polygon_this_frame(
+                list_square_vertex_arrays_this_core
+            )
+        )
+        # likewise, I will need a list of centroids of particles in each square (same order as above list):
+        list_centroids_in_squares_this_frame = (
+            produce_list_centroids_this_frame(
+                list_indices_in_squares_this_frame
+            )
+        )
+        if (
+            list_previous_frame_indices
+        ):  # if the previous frame had indices in at least one square I will need to use
             #  those indices to generate the updates to the corresponding centroids in this frame:
-            list_centroids_this_frame_using_indices_from_last_frame = produce_list_centroids_this_frame(
-                list_previous_frame_indices)
-            #I need to write a velocity of zero if there are any 'empty' squares in either frame:
+            list_centroids_this_frame_using_indices_from_last_frame = (
+                produce_list_centroids_this_frame(list_previous_frame_indices)
+            )
+            # I need to write a velocity of zero if there are any 'empty' squares in either frame:
             xy_deltas_to_write = []
-            for square_1_centroid, square_2_centroid in zip(list_centroids_this_frame_using_indices_from_last_frame,
-                                                            list_previous_frame_centroids):
+            for square_1_centroid, square_2_centroid in zip(
+                list_centroids_this_frame_using_indices_from_last_frame,
+                list_previous_frame_centroids,
+            ):
                 if square_1_centroid is None or square_2_centroid is None:
                     xy_deltas_to_write.append([0, 0])
                 else:
-                    xy_deltas_to_write.append(np.subtract(square_1_centroid, square_2_centroid).tolist())
+                    xy_deltas_to_write.append(
+                        np.subtract(
+                            square_1_centroid, square_2_centroid
+                        ).tolist()
+                    )
 
-            #xy_deltas_to_write = np.subtract(np.array(
+            # xy_deltas_to_write = np.subtract(np.array(
             # list_centroids_this_frame_using_indices_from_last_frame),np.array(list_previous_frame_centroids))
             xy_deltas_to_write = np.array(xy_deltas_to_write)
-            #now filter the array to only contain distances in the range [-8,8] as a placeholder for dealing with PBC
+            # now filter the array to only contain distances in the range [-8,8] as a placeholder for dealing with PBC
             #  issues (Matthieu seemed to use a limit of 8 as well);
-            xy_deltas_to_write = np.clip(xy_deltas_to_write, -maximum_delta_magnitude, maximum_delta_magnitude)
+            xy_deltas_to_write = np.clip(
+                xy_deltas_to_write,
+                -maximum_delta_magnitude,
+                maximum_delta_magnitude,
+            )
 
-            #with the xy and dx,dy values calculated I need to set the values from this frame to previous frame
+            # with the xy and dx,dy values calculated I need to set the values from this frame to previous frame
             # values in anticipation of the next frame:
-            list_previous_frame_centroids = list_centroids_in_squares_this_frame[:]
+            list_previous_frame_centroids = (
+                list_centroids_in_squares_this_frame[:]
+            )
             list_previous_frame_indices = list_indices_in_squares_this_frame[:]
         else:  # either no points in squares or after the first frame I'll just reset the 'previous' values so they
             # can be used when consecutive frames have proper values
-            list_previous_frame_centroids = list_centroids_in_squares_this_frame[:]
+            list_previous_frame_centroids = (
+                list_centroids_in_squares_this_frame[:]
+            )
             list_previous_frame_indices = list_indices_in_squares_this_frame[:]
         if ts.frame > end_frame:
             break  # stop here
     return list(zip(reconstruction_index_list, xy_deltas_to_write.tolist()))
 
 
-def generate_streamlines(topology_file_path, trajectory_file_path, grid_spacing, MDA_selection, start_frame,
-                         end_frame, xmin, xmax, ymin, ymax, maximum_delta_magnitude, num_cores='maximum'):
+def generate_streamlines(
+    topology_file_path,
+    trajectory_file_path,
+    grid_spacing,
+    MDA_selection,
+    start_frame,
+    end_frame,
+    xmin,
+    xmax,
+    ymin,
+    ymax,
+    maximum_delta_magnitude,
+    num_cores="maximum",
+):
     r"""Produce the x and y components of a 2D streamplot data set.
 
     Parameters
@@ -311,35 +396,58 @@
 
     """
     # work out the number of cores to use:
-    if num_cores == 'maximum':
+    if num_cores == "maximum":
         num_cores = multiprocessing.cpu_count()  # use all available cores
     else:
         num_cores = num_cores  # use the value specified by the user
-        #assert isinstance(num_cores,(int,long)), "The number of specified cores must (of course) be an integer."
-    np.seterr(all='warn', over='raise')
+        # assert isinstance(num_cores,(int,long)), "The number of specified cores must (of course) be an integer."
+    np.seterr(all="warn", over="raise")
     parent_list_deltas = []  # collect all data from child processes here
 
     def log_result_to_parent(delta_array):
         parent_list_deltas.extend(delta_array)
 
     tuple_of_limits = (xmin, xmax, ymin, ymax)
-    grid = produce_grid(tuple_of_limits=tuple_of_limits, grid_spacing=grid_spacing)
-    list_square_vertex_arrays_per_core, list_parent_index_values, total_rows, total_columns = \
-        split_grid(grid=grid,
-                   num_cores=num_cores)
+    grid = produce_grid(
+        tuple_of_limits=tuple_of_limits, grid_spacing=grid_spacing
+    )
+    (
+        list_square_vertex_arrays_per_core,
+        list_parent_index_values,
+        total_rows,
+        total_columns,
+    ) = split_grid(grid=grid, num_cores=num_cores)
     pool = multiprocessing.Pool(num_cores)
-    for vertex_sublist, index_sublist in zip(list_square_vertex_arrays_per_core, list_parent_index_values):
-        pool.apply_async(per_core_work, args=(
-            topology_file_path, trajectory_file_path, vertex_sublist, MDA_selection, start_frame, end_frame,
-            index_sublist, maximum_delta_magnitude), callback=log_result_to_parent)
+    for vertex_sublist, index_sublist in zip(
+        list_square_vertex_arrays_per_core, list_parent_index_values
+    ):
+        pool.apply_async(
+            per_core_work,
+            args=(
+                topology_file_path,
+                trajectory_file_path,
+                vertex_sublist,
+                MDA_selection,
+                start_frame,
+                end_frame,
+                index_sublist,
+                maximum_delta_magnitude,
+            ),
+            callback=log_result_to_parent,
+        )
     pool.close()
     pool.join()
     dx_array = np.zeros((total_rows, total_columns))
     dy_array = np.zeros((total_rows, total_columns))
-    #the parent_list_deltas is shaped like this: [ ([row_index,column_index],[dx,dy]), ... (...),...,]
-    for index_array, delta_array in parent_list_deltas:  # go through the list in the parent process and assign to the
+    # the parent_list_deltas is shaped like this: [ ([row_index,column_index],[dx,dy]), ... (...),...,]
+    for (
+        index_array,
+        delta_array,
+    ) in (
+        parent_list_deltas
+    ):  # go through the list in the parent process and assign to the
         #  appropriate positions in the dx and dy matrices:
-        #build in a filter to replace all values at the cap (currently between -8,8) with 0 to match Matthieu's code
+        # build in a filter to replace all values at the cap (currently between -8,8) with 0 to match Matthieu's code
         # (I think eventually we'll reduce the cap to a narrower boundary though)
         index_1 = index_array.tolist()[0]
         index_2 = index_array.tolist()[1]
@@ -352,9 +460,14 @@
         else:
             dy_array[index_1, index_2] = delta_array[1]
 
-    #at Matthieu's request, we now want to calculate the average and standard deviation of the displacement values:
-    displacement_array = np.sqrt(dx_array ** 2 + dy_array ** 2)
+    # at Matthieu's request, we now want to calculate the average and standard deviation of the displacement values:
+    displacement_array = np.sqrt(dx_array**2 + dy_array**2)
     average_displacement = np.average(displacement_array)
     standard_deviation_of_displacement = np.std(displacement_array)
 
-    return (dx_array, dy_array, average_displacement, standard_deviation_of_displacement)
+    return (
+        dx_array,
+        dy_array,
+        average_displacement,
+        standard_deviation_of_displacement,
+    )