Merge branch 'plotInHistory' into cookBux

saqc/core/modules/tools.py

@@ -6,6 +6,7 @@ from typing import Optional
 from typing_extensions import Literal
 
 import saqc
+import numpy as np
 from saqc.lib.types import FreqString
 from saqc.core.modules.base import ModuleBase
 
@@ -42,6 +43,7 @@ class Tools(ModuleBase):
         xscope: Optional[slice] = None,
         stats_dict: Optional[dict] = None,
         store_kwargs: Optional[dict] = None,
+        to_mask: Optional[float] = np.inf,
         **kwargs,
     ) -> saqc.SaQC:
         return self.defer("plot", locals())

saqc/funcs/tools.py

@@ -16,6 +16,7 @@ from saqc.lib.types import FreqString
 from saqc.core import processing, Flags
 from saqc.lib.tools import periodicMask
 from saqc.lib.plotting import makeFig
+from saqc.core.register import _maskData
 
 _MPL_DEFAULT_BACKEND = mpl.get_backend()
 
@@ -252,18 +253,19 @@ def plot(
     path: Optional[str] = None,
     max_gap: Optional[FreqString] = None,
     stats: bool = False,
-    history: Optional[Literal["valid", "complete"]] = "valid",
+    history: Optional[Literal["valid", "complete", "clear"]] = "valid",
     xscope: Optional[slice] = None,
     stats_dict: Optional[dict] = None,
     store_kwargs: Optional[dict] = None,
+    to_mask: Optional[float] = np.inf,
     **kwargs,
 ):
     """
     Stores or shows a figure object, containing data graph with flag marks for field.
 
-    There are two modes, 'interactive' and 'store' mode, wich is determind via the
+    There are two modes, 'interactive' and 'store', which are determind through the
     ``save_path`` keyword. In interactive mode (default) the plot is shown at runtime
-    and the execution stops until the plot window is closed manually by a user. In
+    and the program execution stops until the plot window is closed manually. In
     store mode the generated plot is stored to disk and no manually interaction is
     needed.
 
@@ -287,7 +289,7 @@ def plot(
     max_gap : str, default None
         If None, all the points in the data will be connected, resulting in long linear
         lines, where continous chunks of data is missing. Nans in the data get dropped
-        before plotting. If an Offset string is passed, only points that have a distance
+        before plotting. If an offset string is passed, only points that have a distance
         below `max_gap` get connected via the plotting line.
 
     stats : bool, default False
@@ -298,34 +300,13 @@ def plot(
         * "valid" - Only plot those flags, that do not get altered or "unflagged" by subsequent tests. Only list tests
           in the legend, that actually contributed flags to the overall resault.
         * "complete" - plot all the flags set and list all the tests ran on a variable. Suitable for debugging/tracking.
+        * "clear" - clear plot from all the flagged values
         * None - just plot the resulting flags for one variable, without any historical meta information.
 
-    s : slice or Offset, default None
+    xscope : slice or Offset, default None
         Parameter, that determines a chunk of the data to be plotted /
         processed. `s` can be anything, that is a valid argument to the ``pandas.Series.__getitem__`` method.
 
-    ax_kwargs : dict, default None
-        ax_kwargs : dict, default None
-        Keyword arguments controlling plot generation. Will be passed on to the
-        ``Matplotlib.axes.Axes.set()`` property batch setter for the axes showing the
-        data plot. The most relevant of those properties might be "ylabel",
-        "title" and in addition: "ylim".
-        The "xlim" keyword can be passed a slice object with date offset entries to controll figure
-        scope.
-
-
-    fig_kwargs : dict, default None
-        Keyword arguments controlling figure generation. In interactive mode,
-        ``None`` defaults to ``{"figsize": (16, 9)}`` to ensure a proper figure size
-        in store-mode.
-
-    scatter_kwargs : dict, default None
-        Keyword arguments controlling the appearance of the dots, marking flagged values.
-        Dict just gets passed on to the matplotlib.pyplot.scatter method. Keywords of interest may be:
-        ``"alpha"`` (transparancy), ``"marker"`` (marker appearance) and ``"s"`` (dot size).
-        The ``"marker"`` and ``"color"`` keywords can also be passed lists, that the plotting routine will then cycle
-        through.
-
     store_kwargs : dict, default {}
         Keywords to be passed on to the ``matplotlib.pyplot.savefig`` method, handling
         the figure storing. To store an pickle object of the figure, use the option
@@ -363,6 +344,13 @@ def plot(
     >>> func = lambda x, y, z: round((x.isna().sum()) / len(x), 2)
     """
     interactive = path is None
+    level = kwargs.get("flag", BAD)
+
+    if to_mask < np.inf:
+        data = data.copy()
+        data.loc[flags[field] >= to_mask, field] = np.nan
+        if level <= to_mask:
+            history = None
 
     if store_kwargs is None:
         store_kwargs = {}
@@ -377,7 +365,7 @@ def plot(
         data=data,
         field=field,
         flags=flags,
-        level=kwargs.get("flag", BAD),
+        level=level,
         max_gap=max_gap,
         stats=stats,
         history=history,

saqc/lib/plotting.py

@@ -23,8 +23,7 @@ STATSDICT = {
     "flagged percentage": lambda x, y, z: round(((y >= z).sum()) / len(x), 2),
 }
 
-PLOT_KWARGS = {"alpha": 0.8,
-               "linewidth": 1}
+PLOT_KWARGS = {"alpha": 0.8, "linewidth": 1}
 AX_KWARGS = {}
 FIG_KWARGS = {"figsize": (16, 9)}
 SCATTER_KWARGS = {
@@ -36,6 +35,7 @@ SCATTER_KWARGS = {
     "s": 70,
 }
 
+
 def makeFig(
     data: DiosLikeT,
     field: str,
@@ -73,7 +73,18 @@ def makeFig(
     stats : bool, default False
         Whether to include statistics table in plot.
 
-    xscope :
+    history : {"valid", "complete", None}, default "valid"
+        Discriminate the plotted flags with respect to the tests they originate from.
+        * "valid" - Only plot those flags, that do not get altered or "unflagged" by subsequent tests. Only list tests
+          in the legend, that actually contributed flags to the overall resault.
+        * "complete" - plot all the flags set and list all the tests ran on a variable. Suitable for debugging/tracking.
+        * "clear" - clear plot from all the flagged values
+        * None - just plot the resulting flags for one variable, without any historical meta information.
+
+    xscope : slice or Offset, default None
+        Parameter, that determines a chunk of the data to be plotted /
+        processed. `s` can be anything, that is a valid argument to the ``pandas.Series.__getitem__`` method.
+
 
     stats_dict: dict, default None
         (Only relevant if `stats`=True).