Add 'cumulative percent' contribution analysis mode

activity_browser/docs/wiki/LCA-Results.md

@@ -119,9 +119,19 @@ in the next sections.
 ![contributions cutoff](./assets/contribution_manipulation.png)
 
 #### Cut-off
-You can manually change the `Cut-off type` of the results in two ways, `Relative` or `Top #`.
-- The `Relative` mode shows contributions _from_ entities of _x_% or higher.
-- The `Top #` mode shows contributions from the _x_ entities that contribute the most (as absolute).
+You can manually change the `Cut-off type` of the results in three ways:
+- The `Percent` mode shows contributions _from_ entities of at least _x_% or higher.
+  - For example: If the cut-off is set to 5% for process contribtions, then all contributions of at least 5% are shown.  
+- The `Cumulative percent` mode shows contributions that cumulatively contribute at least _x_%.
+  - For example: If the cut-off is set to 80% for process contributions, then the first _n_ processes (sorted highest 
+  to lowest) that count up to 80% are shown.
+- The `Number` mode shows contributions from the _x_ entities that contribute the most (as absolute).
+  - For example: If the cut-off is set to 5, then the first 5 processes (sorted highest 
+  to lowest) will be shown.
+
+The cut-off is applied per item (e.g. per reference flow or impact category, see [compare](#compare)) below).
+This means that if you want to see the top 5 contributors, you will only see the top 5 per item, even if a contributor would 
+also be present for another item. 
 
 You can adjust the `Cut-off level` to change how many results you see.
 
@@ -151,22 +161,22 @@ You can disable one of them if you want to focus on the other.
 
 #### Relative and Absolute
 You can choose between `Relative` and `Absolute` results.
-The `Relative` results will sum to 100% (the total `Range` or `Score`), 
+The `Relative` results will sum to 100% (the total `Score` or `Range`), 
 the `Absolute` results will sum to the impact score.
-For `Relative`, you can choose what you use as the 100% reference, the `Range` or the `Score`.
+For `Relative`, you can choose what you use as the 100% reference, the `Score` or the `Range`.
 
-#### Range and Score
-The `Range`/`Score` determines what you use as the _total_ to which the contributions are counted. 
-- For `Range`, this is the full _range_ of results
-  - For example, if all your negative results together have a score of -2 and all your positive results together have a 
-    score of 10, the _range_ is 12 (-2 * -1 + 10).
-  - An entity with a contribution of 4 would have a relative contribution of 4/12 = 33.3...%. 
+#### Score and Range
+The `Score`/`Range` determines what you use as the _total_ to which the contributions are counted. 
 - For `Score`, this is the total score (sum) of the results
   - For example, if all your negative results together have a score of -2 and all your positive results together have a 
     score of 10, the _score_ is 8 (-2 + 10).
   - An entity with a contribution of 4 would have a relative contribution of 4/8 = 50%.
+- For `Range`, this is the full _range_ of results
+  - For example, if all your negative results together have a score of -2 and all your positive results together have a 
+    score of 10, the _range_ is 12 (-2 * -1 + 10).
+  - An entity with a contribution of 4 would have a relative contribution of 4/12 = 33.3...%. 
 
-The `Range` or `Score` setting are only relevant when your results contain both positive and negative contributions.
+The `Score` or `Range` setting are only relevant when your results contain both positive and negative contributions.
 
 ### Positive and negative numbers in contribution results
 It can happen in LCA that you get both positive and negative numbers in your contribution results.

activity_browser/mod/bw2analyzer/contribution.py

@@ -12,19 +12,22 @@ def sort_array(self, data: np.array, limit: float = 25, limit_type: str = "numbe
         See PR above on why we overwrite this function.
         """
         if not total:
-            abs_total_flag = True
             total = np.abs(data).sum()
-        else:
-            abs_total_flag = False
 
         if total == 0 and limit_type == "cum_percent":
-            raise ValueError("Cumulative percentage cannot be calculated to a total of 0, use a different limit type or total")
+            raise ValueError(
+                "Cumulative percentage cannot be calculated to a total of 0, use a different limit type or total")
 
         if limit_type not in ("number", "percent", "cum_percent"):
             raise ValueError(f"limit_type must be either 'number', 'percent' or 'cum_percent' not '{limit_type}'.")
-        if limit_type  in ("percent", "cum_percent"):
+        if limit_type in ("percent", "cum_percent"):
             if not 0 < limit <= 1:
                 raise ValueError("Percentage limits > 0 and <= 1.")
+        if limit_type == "number":
+            if not int(limit) == limit:
+                raise ValueError("Number limit must a whole number.")
+            if not 0 < limit:
+                raise ValueError("Number limit must be < 0.")
 
         results = np.hstack(
             (data.reshape((-1, 1)), np.arange(data.shape[0]).reshape((-1, 1)))
@@ -38,30 +41,10 @@ def sort_array(self, data: np.array, limit: float = 25, limit_type: str = "numbe
             limit = (np.abs(data) >= (abs(total) * limit))
             results = results[limit, :]
             return results[np.argsort(np.abs(results[:, 0]))[::-1]]
-        elif limit_type == "cum_percent" and abs_total_flag:
+        elif limit_type == "cum_percent":
             # if we would apply this on the 'correct' order, this would stop just before the limit,
             # we want to be on or the first step over the limit.
             results = results[np.argsort(np.abs(data))]  # sort low to high impact
             cumsum = np.cumsum(np.abs(results[:, 0])) / abs(total)
             limit = (cumsum >= (1 - limit))  # find items under limit
             return results[limit, :][::-1]  # drop items under limit and set correct order
-        elif limit_type == "cum_percent" and not abs_total_flag:
-            # iterate over positive and negative values until limit is achieved or surpassed.
-            results = results[np.argsort(np.abs(data))][::-1]
-            pos_neg = [  # split into positive and negative sections
-                results[results[:, 0] > 0],
-                results[results[:, 0] < 0],
-            ]
-            # iterate over positive and negative sections
-            for i, arr in enumerate(pos_neg):
-                c = 0
-                # iterate over array until we have equalled or surpassed limit
-                for j, row in enumerate(arr):
-                    c += abs(row[0] / total)
-                    if c >= limit:
-                        break
-                arr = arr[:min(j + 1, len(arr)), :]
-                pos_neg[i] = arr
-
-            results = np.concatenate(pos_neg)  # rebuild into 1 array
-            return results[np.argsort(np.abs(results[:, 0]))][::-1]  # sort values