From cfcf8a995cc17dbf9d6d0a2106977627b9623d4b Mon Sep 17 00:00:00 2001
From: David Schaefer <david.schaefer@ufz.de>
Date: Wed, 11 Dec 2019 21:00:13 +0100
Subject: [PATCH] some cleanups
---
saqc/lib/plotting.py | 43 +++++++++++++++++++------------------------
1 file changed, 19 insertions(+), 24 deletions(-)
diff --git a/saqc/lib/plotting.py b/saqc/lib/plotting.py
index 4d3f85bae..72ae15a72 100644
--- a/saqc/lib/plotting.py
+++ b/saqc/lib/plotting.py
@@ -41,6 +41,10 @@ def _plot(
# todo: try catch warn (once) return
# only import if plotting is requested by the user
import matplotlib as mpl
+ import matplotlib.pyplot as plt
+ from pandas.plotting import register_matplotlib_converters
+ # needed for datetime conversion
+ register_matplotlib_converters()
if not interactive_backend:
# Import plot libs without interactivity, if not needed. This ensures that this can
@@ -49,13 +53,8 @@ def _plot(
mpl.use("Agg")
else:
mpl.use("TkAgg")
- from matplotlib import pyplot as plt
- # needed for datetime conversion
- from pandas.plotting import register_matplotlib_converters
- register_matplotlib_converters()
-
- if not isinstance(varname, (list, set)):
+ if np.isscalar(varname):
varname = [varname]
varname = set(varname)
@@ -68,18 +67,18 @@ def _plot(
logging.warning(f"Cannot plot column '{var}', because it is not present in data.")
if not tmp:
return
- varname = tmp
+ varnames = tmp
- plots = len(varname)
+ plots = len(varnames)
if plots > 1:
fig, axes = plt.subplots(plots, 1, sharex=True)
axes[0].set_title(title)
- for i, v in enumerate(varname):
- _plotByQualtyFlag(data, v, flagger, flagmask, axes[i], plot_nans)
+ for i, v in enumerate(varnames):
+ _plotByQualityFlag(data, v, flagger, flagmask, axes[i], plot_nans)
else:
fig, ax = plt.subplots()
plt.title(title)
- _plotByQualtyFlag(data, varname.pop(), flagger, flagmask, ax, plot_nans)
+ _plotByQualityFlag(data, varnames.pop(), flagger, flagmask, ax, plot_nans)
# dummy plot for the label `missing` see plot_vline for more info
plt.plot([], [], ":", color="silver", label="missing data")
@@ -91,7 +90,7 @@ def _plot(
plt.show()
-def _plotByQualtyFlag(data, varname, flagger, flagmask, ax, plot_nans):
+def _plotByQualityFlag(data, varname, flagger, flagmask, ax, plot_nans):
ax.set_ylabel(varname)
x = data.index
@@ -106,7 +105,7 @@ def _plotByQualtyFlag(data, varname, flagger, flagmask, ax, plot_nans):
oldflags = flagged & ~flagmask
ax.plot(x[oldflags], y[oldflags], ".", color="black", label="flagged by other test")
if plot_nans:
- _plot_nans(y[oldflags], 'black', ax)
+ _plotNans(y[oldflags], 'black', ax)
# now we just want to show data that was flagged
if flagmask is not True:
@@ -117,29 +116,26 @@ def _plotByQualtyFlag(data, varname, flagger, flagmask, ax, plot_nans):
if x.empty:
return
- suspicious = pd.Series(data=np.ones(len(y), dtype=bool), index=y.index)
- # flag by categories
-
# plot UNFLAGGED (only nans are needed)
flag, color = flagger.UNFLAGGED, _colors['unflagged']
flagged = flagger.isFlagged(varname, flag=flag, comparator='==')
ax.plot(x[flagged], y[flagged], '.', color=color, label=f"flag: {flag}")
if plot_nans:
- _plot_nans(y[flagged], color, ax)
+ _plotNans(y[flagged], color, ax)
# plot GOOD
flag, color = flagger.GOOD, _colors['good']
flagged = flagger.isFlagged(varname, flag=flag, comparator='==')
ax.plot(x[flagged], y[flagged], '.', color=color, label=f"flag: {flag}")
if plot_nans:
- _plot_nans(y[flagged], color, ax)
+ _plotNans(y[flagged], color, ax)
# plot BAD
flag, color = flagger.BAD, _colors['bad']
flagged = flagger.isFlagged(varname, flag=flag, comparator='==')
ax.plot(x[flagged], y[flagged], '.', color=color, label=f"flag: {flag}")
if plot_nans:
- _plot_nans(y[flagged], color, ax)
+ _plotNans(y[flagged], color, ax)
# plot SUSPICIOS
color = _colors['suspicious']
@@ -147,18 +143,17 @@ def _plotByQualtyFlag(data, varname, flagger, flagmask, ax, plot_nans):
flagged &= flagger.isFlagged(varname, flag=flagger.BAD, comparator='<')
ax.plot(x[flagged], y[flagged], '.', color=color, label=f"{flagger.GOOD} < flag < {flagger.BAD}")
if plot_nans:
- _plot_nans(y[flagged], color, ax)
+ _plotNans(y[flagged], color, ax)
-def _plot_nans(y, color, ax):
+def _plotNans(y, color, ax):
nans = y.isna()
_plotVline(ax, y[nans].index, color=color)
-def _plotVline(plt, points, color="blue"):
+def _plotVline(ax, points, color="blue"):
# workaround for ax.vlines() as this work unexpected
# normally this should work like so:
# ax.vlines(idx, *ylim, linestyles=':', color='silver', label="missing")
for point in points:
- plt.axvline(point, color=color, linestyle=":")
-
+ ax.axvline(point, color=color, linestyle=":")
--
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