saqc/lib/plotting.py

@@ -6,7 +6,7 @@
 
 # -*- coding: utf-8 -*-
 
-from typing import Optional
+from typing import Optional, Union
 from typing_extensions import Literal
 from saqc.lib.tools import toSequence
 import pandas as pd
@@ -46,7 +46,7 @@ def makeFig(
     flags: Flags,
     level: float,
     max_gap: Optional[str] = None,
-    history: Optional[Literal["valid", "complete"]] = "valid",
+    history: Union[Optional[Literal["valid", "complete"]], list] = "valid",
     xscope: Optional[slice] = None,
     phaseplot: Optional[str] = None,
     ax_kwargs: Optional[dict] = None,
@@ -75,13 +75,16 @@ def makeFig(
         below `max_gap` get connected via the plotting line.
 
 
-    history : {"valid", "complete", None}, default "valid"
+     history : {"valid", "complete", None, list of strings}, 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.
+        * list of strings - for any string ``s`` in the list, plot the flags set by test labeled, ``s`` - if ``s`` is
+          not present in the history labels, plot any flags, set by a test labeled ``s``
 
     xscope : slice or Offset, default None
         Parameter, that determines a chunk of the data to be plotted /
@@ -97,6 +100,8 @@ def makeFig(
 
     """
 
+    if ax_kwargs is None:
+        ax_kwargs = {}
     # data retrieval
     d = data[field]
     # data slicing:
@@ -110,6 +115,9 @@ def makeFig(
     default = plt.rcParams["font.size"]
     plt.rcParams["font.size"] = ax_kwargs.pop("fontsize", None) or default
 
+    # set shapecycle start:
+    cyclestart = ax_kwargs.pop("cycleskip", 0)
+
     na_mask = d.isna()
     d = d[~na_mask]
     if phaseplot:
@@ -147,6 +155,7 @@ def makeFig(
         plot_kwargs,
         ax_kwargs,
         SCATTER_KWARGS,
+        cyclestart,
     )
 
     plt.rcParams["font.size"] = default
@@ -165,6 +174,7 @@ def _plotVarWithFlags(
     plot_kwargs,
     ax_kwargs,
     scatter_kwargs,
+    cyclestart,
 ):
     scatter_kwargs = scatter_kwargs.copy()
     ax.set_title(datser.name)
@@ -176,8 +186,25 @@ def _plotVarWithFlags(
         "color", plt.rcParams["axes.prop_cycle"].by_key()["color"]
     )
     color_cycle = itertools.cycle(toSequence(color_cycle))
+    for k in range(0, cyclestart):
+        next(color_cycle)
+        next(shape_cycle)
+
     if history:
         for i in flags_hist.columns:
+            if isinstance(history, list):
+                meta_field = "label" if "label" in flags_meta[i].keys() else "func"
+                to_plot = (
+                    flags_meta[i][meta_field]
+                    if flags_meta[i][meta_field] in history
+                    else None
+                )
+                if not to_plot:
+                    continue
+                else:
+                    hist_key = "valid"
+            else:
+                hist_key = history
             # catch empty but existing history case (flags_meta={})
             if len(flags_meta[i]) == 0:
                 continue
@@ -187,12 +214,12 @@ def _plotVarWithFlags(
             )
             scatter_kwargs.update({"label": label})
             flags_i = flags_hist[i].astype(float)
-            if history == "complete":
+            if hist_key == "complete":
                 scatter_kwargs.update(
                     {"color": next(color_cycle), "marker": next(shape_cycle)}
                 )
                 _plotFlags(ax, datser, flags_i, na_mask, level, scatter_kwargs)
-            if history == "valid":
+            if hist_key == "valid":
                 # only plot those flags, that do not get altered later on:
                 mask = flags_i.eq(flags_vals)
                 flags_i[~mask] = np.nan
@@ -219,6 +246,7 @@ def _plotVarWithFlags(
                     level,
                     scatter_kwargs,
                 )
+
         ax.legend()
     else:
         scatter_kwargs.update({"color": next(color_cycle), "marker": next(shape_cycle)})

saqc/lib/tools.py

@@ -430,11 +430,11 @@ def estimateFrequency(
     min_energy = delta_f[0] * min_energy
     # calc/assign low/high freq cut offs (makes life easier):
     min_rate_i = int(
-        len_f / (pd.Timedelta(min_rate).total_seconds() * (10 ** delta_precision))
+        len_f / (pd.Timedelta(min_rate).total_seconds() * (10**delta_precision))
     )
     delta_f[:min_rate_i] = 0
     max_rate_i = int(
-        len_f / (pd.Timedelta(max_rate).total_seconds() * (10 ** delta_precision))
+        len_f / (pd.Timedelta(max_rate).total_seconds() * (10**delta_precision))
     )
     hf_cutoff = min(max_rate_i, len_f // 2)
     delta_f[hf_cutoff:] = 0

saqc/lib/ts_operators.py

@@ -415,7 +415,7 @@ def _coeffMat(x, deg):
     mat_[:, 1] = x
     if deg > 1:
         for n in range(2, deg + 1):
-            mat_[:, n] = x ** n
+            mat_[:, n] = x**n
     return mat_
 
 

sphinxdoc/conf.py

@@ -98,7 +98,6 @@ import numpy as np
 from saqc.constants import *
 """
 # -- Other options -----------------------------------------------------------
-
 # Add any paths that contain templates here, relative to this directory.
 templates_path = ["_templates"]
 

tests/funcs/test_generic_config_functions.py

@@ -107,7 +107,7 @@ def test_arithmeticOperators(data):
         ("var1 * 100 > 200", data * 100 > 200),
         ("var1 / 100 > .1", data / 100 > 0.1),
         ("var1 % 2 == 1", data % 2 == 1),
-        ("var1 ** 2 == 0", data ** 2 == 0),
+        ("var1 ** 2 == 0", data**2 == 0),
     ]
 
     for test, expected in tests:

tests/funcs/test_modelling.py

@@ -32,7 +32,7 @@ def test_modelling_polyFit_forRegular(dat):
     flags = initFlagsLike(data)
     result1, _ = calculatePolynomialResidues(data, "data", flags, 11, 2, numba=False)
     result2, _ = calculatePolynomialResidues(data, "data", flags, 11, 2, numba=True)
-    assert (result1["data"] - result2["data"]).abs().max() < 10 ** -10
+    assert (result1["data"] - result2["data"]).abs().max() < 10**-10
     result3, _ = calculatePolynomialResidues(
         data, "data", flags, "110min", 2, numba=False
     )
@@ -40,7 +40,7 @@ def test_modelling_polyFit_forRegular(dat):
     result4, _ = calculatePolynomialResidues(
         data, "data", flags, 11, 2, numba=True, min_periods=11
     )
-    assert (result4["data"] - result2["data"]).abs().max() < 10 ** -10
+    assert (result4["data"] - result2["data"]).abs().max() < 10**-10
     data.iloc[13:16] = np.nan
     result5, _ = calculatePolynomialResidues(
         data, "data", flags, 11, 2, numba=True, min_periods=9