option to filter TimeSeries extractions by CloudMasks

sen2chain/time_series.py

@@ -80,6 +80,13 @@ class TimeSeries:
         field_names: str = None,
         multiproc: int = 0,
         getstat: bool = True,
+        cm_version: str = "CM001",
+        probability: int = 1,
+        iterations: int = 5,
+        cld_shad: bool = True,
+        cld_med_prob: bool = True,
+        cld_hi_prob: bool = True,
+        thin_cir: bool = True,
     ) -> None:
 
         self._vectors_file = Path(vectors_file)
@@ -90,6 +97,13 @@ class TimeSeries:
         self._field_names = field_names
         self._multiproc = multiproc
         self._out_path = Config().get("time_series_path")
+        self.cm_version = cm_version
+        self.probability = probability
+        self.iterations = iterations
+        self.cld_shad = cld_shad
+        self.cld_med_prob = cld_med_prob
+        self.cld_hi_prob = cld_hi_prob
+        self.thin_cir = thin_cir
 
         if indices is None:
             self._indices_list = IndicesCollection.list
@@ -150,6 +164,13 @@ class TimeSeries:
         date_max: str,
         cover_min: int,
         cover_max: int,
+        cm_version: str = "CM001",
+        probability: int = 1,
+        iterations: int = 5,
+        cld_shad: bool = True,
+        cld_med_prob: bool = True,
+        cld_hi_prob: bool = True,
+        thin_cir: bool = True,
     ) -> ProductsList:
         """Filters tile's indices products.
 
@@ -160,9 +181,17 @@ class TimeSeries:
         :param cover_min: minimum cloud coverage.
         :param cover_max: maximum cloud coverage.
         """
-        products = vars(tile)[
+        #test filtering with cm
+        products = getattr(vars(tile)[
             indice.lower()
-        ].masks.cm001  ##########"" Ajouter ici un truc pour choisir le CM !!!!!
+        ].masks,cm_version).params(
+            probability = probability,
+            iterations = iterations,
+            cld_shad = cld_shad,
+            cld_med_prob = cld_med_prob,
+            cld_hi_prob = cld_hi_prob,
+            thin_cir = thin_cir
+        )
         filt = products.filter_dates(date_min, date_max).filter_clouds(
             cover_min, cover_max
         )
@@ -283,6 +312,13 @@ class TimeSeries:
                             self._date_max,
                             self._cover_min,
                             self._cover_max,
+                            self.cm_version,
+                            self.probability,
+                            self.iterations,
+                            self.cld_shad,
+                            self.cld_med_prob,
+                            self.cld_hi_prob,
+                            self.thin_cir,
                         )
 
                         for index1, prod in enumerate(products):
@@ -387,7 +423,7 @@ class TimeSeries:
             result_dict[prop] = features[fid]["properties"][prop]
         shared_list.append(result_dict)
 
-    def _get_stats_multiproc(self) -> None:
+    def _get_stats_multiproc(self,) -> None:
         start = time.time()
         """Compute stats in polygons."""
         with fiona.open(str(self._vectors_file), "r") as vectors:
@@ -424,6 +460,13 @@ class TimeSeries:
                             self._date_max,
                             self._cover_min,
                             self._cover_max,
+                            self.cm_version,
+                            self.probability,
+                            self.iterations,
+                            self.cld_shad,
+                            self.cld_med_prob,
+                            self.cld_hi_prob,
+                            self.thin_cir,
                         )
 
                         proc_list = list(