Update dependencies (#98)

* Refactoring `dssdata.reductions.get_taps_changes` to avoid Pandas Warnings.

The usage of `dssdata.reductions.get_taps_changes` was displaying Warning.
Thus, this commit refactor this functions to be more conscise and avoid
unecessary allocations in the memory.

Also, this commit provides a better documentation for this function.

* Fixing code highlight in docs

* Updating dependencies and bump version

* Removing poetry.lock from the repository

.gitignore

@@ -112,4 +112,5 @@ dmypy.json
 Sistema*/
 .vscode
 Meuteste*.py
-.venv/
+.venv/
+poetry.lock

dssdata/reductions/regs/__init__.py

@@ -1,42 +1,40 @@
 import pandas
-from typing import List
-from operator import add
 
 
 def get_taps_changes(tapDataFrame: pandas.DataFrame) -> pandas.DataFrame:
     """
     Count the taps changes. That function is a reduction function.
 
+    E.g.:
+    If a trafo starts in tap number 4 and goes to tap number -3.
+    The result is 7. 
+    
+    `(4 -> 3 -> 2 -> 1 -> 0 -> -1 -> -2 -> -3)`
+
     Args:
         tapDataFrame: The return of [get_all_taps_number][dssdata.tools.regs.get_all_taps_number] or [get_tap_number][dssdata.tools.regs.get_tap_number].
 
     Returns:
         The number of taps changes after time series power flow.
     """  # noqa
 
-    def calc_one_step_chgs(first: list, second: list):
-        return map(lambda data1, data2: abs(data1 - data2), first, second)
-
-    def sum_changes_taps(list_df: List[list]):
-        first = list_df[0]
-        second = list_df[1]
-        if len(list_df) == 2:
-            return calc_one_step_chgs(first, second)
-        else:
-            return map(
-                add,
-                calc_one_step_chgs(first, second),
-                sum_changes_taps(list_df[1:]),
-            )
-
-    iter_by_step = tapDataFrame.groupby(["step"])
-
-    list_df = tuple(map(lambda data: data[1]["tap"].to_list(), iter_by_step))
-    reg_names = tuple(
-        map(lambda data: data[1]["reg_name"].to_list(), iter_by_step)
-    )[0]
-
-    return pandas.DataFrame(
-        data=tuple(zip(reg_names, sum_changes_taps(list_df))),
-        columns=["reg_name", "number_changes_tap"],
-    )
+    mapper = {"tap": "number_changes_tap"}
+
+    def prepair_df(df, step):
+        return df.loc[step].reset_index(drop=True).rename(columns=mapper)
+
+    df = tapDataFrame.set_index(["step"])
+    steps = df.index.unique()
+
+    df_new = prepair_df(df, steps[0])
+    df_new["number_changes_tap"] = 0
+
+    df_prev = prepair_df(df, steps[0])
+
+    for step in steps:
+        df_step = prepair_df(df, step)
+        diff = abs(df_prev["number_changes_tap"] - df_step["number_changes_tap"])
+        df_new["number_changes_tap"] += diff
+        df_prev = df_step
+
+    return df_new

mkdocs.yml

@@ -16,7 +16,10 @@ theme:
     - navigation.instant
 
 markdown_extensions:
-  - codehilite
+  - pymdownx.highlight:
+      use_pygments: true
+  - pymdownx.superfences
+
 
 plugins:
   - search