small fixes

docs/source/datasets.rst

@@ -7,13 +7,15 @@ Currently, you can use several datasets and TAP service. Soon, we plan to add mo
 Available datasets
 ------------------
 In this section, we describe the datasets available in the terminal user interface and their characteristics.
+
 * Galaxies_400K (Galaxies_400K.csv)
 * Galaxies_1KK (Galaxies_1KK.csv)
 
 TAP service
 -----------
 The TAP service is a web service that allows you to query the available datasets. 
 In this section, we describe the TAP services available in the tterminal user interface and their characteristics.
+
 * RCSED ("http://rcsed-vo.sai.msu.ru/tap/")
 * Simbad ("http://simbad.u-strasbg.fr/simbad/sim-tap")
 * Ned ("http://ned.ipac.caltech.edu/tap")

docs/source/description.rst

@@ -52,11 +52,11 @@ You can read more about the simplex tree in the article:
 About clustering (ToMATo)
 -------------------------
 
-ToMATo is a clustering algorithm. ToMATo starts with `graph k nearest neighbors <https://en.wikipedia.org/wiki/Nearest_neighbor_graph>`_ , then using the density function defined on the point cloud, combines the pieces of the original graph and obtains the required clustering (which is determined by the number of clusters and/or the threshold of significance per density function). This algorithm is a composition of two ideas: `Union-find forest <https://en.wikipedia.org/wiki/Disjoint-set_data_structure>`_ and `Morse theory <https://en.wikipedia.org/wiki/Morse_theory>`_.
+ToMATo is a clustering algorithm. ToMATo starts with a graph :math:`G`, for example, `graph k nearest neighbors <https://en.wikipedia.org/wiki/Nearest_neighbor_graph>`_ , then using the density function defined on the point cloud, combines the pieces of the original graph and obtains the required clustering (which is determined by the number of clusters and/or the threshold of significance per density function). This algorithm is a composition of two ideas: `Union-find forest <https://en.wikipedia.org/wiki/Disjoint-set_data_structure>`_ and `Morse theory <https://en.wikipedia.org/wiki/Morse_theory>`_.
 Let :math:`f` be a density function. Let's describe the steps of the algorithm:
 
 * We construct a graph :math:`G` (:math:`k` nearest neighbors or a Rips graph with the parameter :math:`\delta`).
-* Passing through the vertices of the graph: connect the current vertex :math:`v` with the neighboring :math:`w` if :math`f(w)` is the largest value of :math:`f` among the vertices adjacent to the vertex :math:`v`. We get a spanning forest on the vertices of the original graph :math:`G`. Moreover, the root of each tree will be a vertex with a local maximum of the density function.
+* Passing through the vertices of the graph: connect the current vertex :math:`v` with the neighboring :math:`w` if :math:`f(w)` is the largest value of :math:`f` among the vertices adjacent to the vertex :math:`v`. We get a spanning forest on the vertices of the original graph :math:`G`. Moreover, the root of each tree will be a vertex with a local maximum of the density function.
 * We combine trees whose roots have a small value of the density function. Namely, we connect the roots of two trees from the root with a smaller value of the density function to the function with a larger value of the density function. We continue until the required clustering is obtained.
 
 You can read more about ToMATo in the article:

galaxywitness/__main__.py

@@ -199,7 +199,7 @@ def preconfiguration():
             print(f"\t\033[01;32m[{elem.index(name) + 1}] <-> {name}\033[0m")
     print("\t---------------------------\n")
 
-    table_num = int(input(f" > Enter number of your table [1-{len(elem)}]: "))
+    table_num = int(session.prompt(f" > Enter number of your table [1-{len(elem)}]: ", auto_suggest=AutoSuggestFromHistory()))
     path = os.path.abspath('.') + '/data/' + elem[table_num - 1]
 
     print(f"Loading data from \033[01;32m{path}\033[0m...")
@@ -226,8 +226,7 @@ def main():
     except ValueError as e:
         raise Exception("\033[01;31mFolder 'data' does not exist or empty!\033[0m") from e
 
-    # readline.set_auto_history(True)
-    n_gal = int(input(f" > Enter number of galaxies [10...{len(df)}]: "))
+    n_gal = int(session.prompt(f" > Enter number of galaxies [10...{len(df)}]: ", auto_suggest=AutoSuggestFromHistory()))
 
     print("Choose type of complex:")
     print("\n\t---------- complexes -----------")
@@ -244,7 +243,7 @@ def main():
         type_of_complex = "witness"
 
     if type_of_complex == "witness":
-        n_landmarks = int(input(f" > Enter number of landmarks [10...{n_gal}]: "))
+        n_landmarks = int(session.prompt(f" > Enter number of landmarks [10...{n_gal}]: ", auto_suggest=AutoSuggestFromHistory()))
     else:
         n_landmarks = n_gal
 
@@ -263,24 +262,24 @@ def main():
     # max_edge_length = np.inf
     sparse = None
 
-    key_plot_cloud = input(" > Do you want plot the point cloud? [y/n]: ")
-    key_anim = input(f" > Do you want watch the animation of {type_of_complex} filtration? [y/n]: ")
+    key_plot_cloud = session.prompt(' > Do you want plot the point cloud? [y/n]: ', auto_suggest=AutoSuggestFromHistory())
+    key_anim = session.prompt(f" > Do you want watch the animation of {type_of_complex} filtration? [y/n]: ", auto_suggest=AutoSuggestFromHistory())
     if key_anim == 'y':
-        key_fig = input(
-            " > What will we use for the animation of a filtered complex? [plotly(more slow, but more cool)/mpl(matplotlib)]: ")
-    key_save = input(" > Do you want save all plots to \033[01;32m./imgs\033[0m? [y/n]: ")
-    tomato_key = input(" > Do you want run\033[01;32m tomato\033[0m clustering? [y/n]: ")
-    key_adv = input(" > Advanced configuration? [y/n]: ")
+        key_fig = session.prompt(
+            " > What will we use for the animation of a filtered complex? [plotly(more slow, but more cool)/mpl(matplotlib)]: ", auto_suggest=AutoSuggestFromHistory())
+    key_save = session.prompt(" > Do you want save all plots to ./imgs? [y/n]: ", auto_suggest=AutoSuggestFromHistory())
+    tomato_key = session.prompt(" > Do you want run tomato clustering? [y/n]: ", auto_suggest=AutoSuggestFromHistory())
+    key_adv = session.prompt(" > Advanced configuration? [y/n]: ", auto_suggest=AutoSuggestFromHistory())
     if key_adv == 'y':
-        key_complex_type = input(" > What type of simplicial complex will we use? [gudhi/custom]: ")
-        r_max = float(input(
-            " > Enter max value of filtration[\033[01;32m usually \u2264 15\033[0m, the more the slower calculate]: "))
+        key_complex_type = session.prompt(" > What type of simplicial complex will we use? [gudhi/custom]: ", auto_suggest=AutoSuggestFromHistory())
+        r_max = float(session.prompt(
+            " > Enter max value of filtration [usually \u2264 15, the more the slower calculations]: ", auto_suggest=AutoSuggestFromHistory()))
         if type_of_complex == "witness":
-            first_witness = int(input(f" > Enter first witness [\033[01;32m usually 0\033[0m, range: 0...{len(df) - n_gal}]: "))
+            first_witness = int(session.prompt(f" > Enter first witness [range: 0...{len(df) - n_gal}]: ", auto_suggest=AutoSuggestFromHistory()))
         if r_max == -1:
             r_max = None
         if type_of_complex == "rips":
-            sparse = float(input(" > Enter 'sparse' parameter to build sparse Rips or -1: "))
+            sparse = float(session.prompt(" > Enter 'sparse' parameter to build sparse Rips or -1: ", auto_suggest=AutoSuggestFromHistory()))
             if sparse == -1:
                 sparse = None
 
@@ -319,7 +318,7 @@ def main():
 
     for name in ('ra', 'dec', 'z'):
         column_nums.append(
-            int(input(f" > Choose column for \033[01;32m{name}\033[0m, from list above (column names): ")))
+            int(session.prompt(f" > Choose column for {name}, from list above (column names): ", auto_suggest=AutoSuggestFromHistory())))
 
     column_names = [list(df)[column_nums[0]], list(df)[column_nums[1]], list(df)[column_nums[2]]]
 
@@ -422,16 +421,16 @@ def main():
             # clustering(complex_, complex_.witnesses, path_to_save, den_type, graph_type_)
             tomato = clustering(complex_, complex_.points, r_max, path_to_save)
 
-        print("Warning: results down below is additional experimental functionality")
-        cl_1, cl_2 = Clusterization(points), Clusterization(points)
-        cl_1.import_clustering(tomato.labels_)
-        cl_2.tomato(r_max)
-        print("Draw clustering...")
-        cl_2.draw_clustering()
-        print("Draw projections of clustering on random planes...")
-        cl_2.draw_projections(2)
-        diff = cl_1.compare_clusterization(cl_2)
-        print(f"Test Difference between clusterings is \033[01;32m {diff}\033[0m")
+            print("Warning: results down below is additional experimental functionality")
+            cl_1, cl_2 = Clusterization(points), Clusterization(points)
+            cl_1.import_clustering(tomato.labels_)
+            cl_2.tomato(r_max)
+            print("Draw clustering...")
+            cl_2.draw_clustering()
+            print("Draw projections of clustering on random planes...")
+            cl_2.draw_projections(2)
+            diff = cl_1.compare_clusterization(cl_2)
+            print(f"Test Difference between clusterings is \033[01;32m {diff}\033[0m")