enhance pixel_plot

.gitignore

@@ -154,3 +154,7 @@ htmlcov/
 
 #stixpy
 stixpy/data/*.fits
+/Minergie Test Data/
+/Matplotlib Practice/
+.vscode/launch.json
+.vscode/settings.json

plottest.py

@@ -0,0 +1,37 @@
+from matplotlib import pyplot as plt
+from sunpy.net import Fido, attrs as a
+from sunpy.timeseries import TimeSeries
+from pathlib import Path
+
+from stixpy.net.client import STIXClient  # Need to import to register with Fido
+from stixpy import timeseries  # Need to import to register with timeseries
+from stixpy.science import ScienceData
+from stixcore.config.reader import read_subc_params
+from stixpy.data import test
+
+# Ok this look like an interesting event let look for some pixel data
+query = Fido.search(a.Time('2020-11-17T00:00', '2020-11-17T23:59'), a.Instrument.stix, a.stix.DataProduct.sci_xray_l1)
+query
+# Again the 2nd to last file seem to match the time range of interest
+pd_file = Fido.fetch(query[0])
+pd = ScienceData.from_fits(pd_file[0])
+print(pd_file[0])
+
+# jsut printg the object gives a textual overview, pixel data support the same plot methods as
+# pd
+
+#l1 = ScienceData.from_fits(test.STIX_SCI_XRAY_L1)
+#l2 = ScienceData.from_fits(test.STIX_SCI_XRAY_L2)
+#l2.plot_pixels()
+
+# plot the pixel data as default "kind=pixels"
+pd.plot_pixels(energy_indices=[[1, 5], [5, 20], [20, 30]])
+plt.show()
+
+# plot the pixel data as line chart with errorbars"
+pd.plot_pixels(kind='errorbar', energy_indices=[[1, 5], [5, 20], [20, 30]])
+plt.show()
+
+# plot the detector configuration (independent on the actual pixeldata)"
+pd.plot_pixels(kind='config')
+plt.show()