Fix pl plotting

.readthedocs.yml

@@ -1,7 +1,11 @@
 version: 2
 
+build:
+  os: "ubuntu-22.04"
+  tools:
+    python: "3.11"
+
 python:
-   version: 3.7
    install:
       - requirements: docs/requirements.txt
       - method: pip

RELEASE.txt

@@ -1,3 +1,7 @@
+0.3.3
+    - Fix plotting methods of Persistence Landscapes, add doc strings.
+    - Update to notebooks.
+
 0.3.2
     - Update codebase to support python 3.7 - 3.12.
     - Change `PersistenceLandscaper` API for sklearn compatibility.

docs/notebooks/Classification with persistence images.ipynb

@@ -361,9 +361,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "persimenv",
+   "display_name": "Python 3",
    "language": "python",
-   "name": "persimenv"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
@@ -375,7 +375,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.9"
+   "version": "3.8.5"
   }
  },
  "nbformat": 4,

persim/_version.py

@@ -1 +1 @@
-__version__ = "0.3.2"
+__version__ = "0.3.3"

persim/landscapes/approximate.py

@@ -152,6 +152,7 @@ def __init__(
         self.start = start
         self.stop = stop
         self.values = values
+        self.max_depth = len(self.values)
         self.num_steps = num_steps
         if compute:
             self.compute_landscape()
@@ -178,7 +179,6 @@ def compute_landscape(self, verbose: bool = False) -> list:
         if self.values.size:
             verboseprint("values was stored, exiting")
             return
-
         verboseprint("values was empty, computing values")
         # make grid
         grid_values, step = np.linspace(
@@ -214,17 +214,14 @@ def compute_landscape(self, verbose: bool = False) -> list:
                 j += 1
                 # j*step: adding points from a line with slope 1
                 W[ind_in_Wb + j].append(j * step)
-
             j = 0
             # j in (b+d/2, d)
             for _ in range(mid_pt + 1, ind_in_Wd):
                 j += 1
                 W[ind_in_Wd - j].append(j * step)
-
         # sort each list in W
         for i in range(len(W)):
             W[i] = sorted(W[i], reverse=True)
-
         # calculate k: max length of lists in W
         K = max([len(_) for _ in W])
 
@@ -235,13 +232,12 @@ def compute_landscape(self, verbose: bool = False) -> list:
         for i in range(self.num_steps):
             for k in range(len(W[i])):
                 L[k][i] = W[i][k]
-
         # check if L is empty
         if not L.size:
             L = np.array(["empty"])
             print("Bad choice of grid, values is empty")
-
         self.values = L
+        self.max_depth = len(L)
         return
 
     def values_to_pairs(self):