From 32164d0056173d5c28daeb50e07fa0bf85343ab8 Mon Sep 17 00:00:00 2001
From: Mikhail Karnevskiy <karnem@max-exfl001.desy.de>
Date: Tue, 7 Aug 2018 10:04:07 +0200
Subject: [PATCH] include more statisticcs information
---
notebooks/LPD/PlotFromCalDB.py | 81 +++++++++++++++++++++-------------
1 file changed, 51 insertions(+), 30 deletions(-)
diff --git a/notebooks/LPD/PlotFromCalDB.py b/notebooks/LPD/PlotFromCalDB.py
index 9845a140a..136b1334b 100644
--- a/notebooks/LPD/PlotFromCalDB.py
+++ b/notebooks/LPD/PlotFromCalDB.py
@@ -20,7 +20,7 @@ warnings.filterwarnings('ignore')
matplotlib.use("agg")
-start_date = "2018-07-25" # date to start investigation interval from
+start_date = "2018-06-25" # date to start investigation interval from
end_date = "now" # date to end investigation interval at, can be "now"
interval = 3 # interval for evaluation in days
detector = "LPD1M1" # detector to investigate
@@ -105,7 +105,7 @@ while dt < end:
if cdata is not None:
carr = np.zeros((5, max_cells, 3))
carr_glob = np.zeros((5, 3))
- carr_rad = np.zeros((cdata.shape[0], max_cells, 3))
+ carr_px = np.zeros((cdata.shape[0], max_cells, 3, 2))
for g in range(3):
td = np.nanmean(cdata[...,g], axis=(0,1))
print (td.shape)
@@ -138,9 +138,11 @@ while dt < end:
td = np.nanstd(cdata[...,g])
carr_glob[3, g] = td
- carr_rad[...,g] = np.nanmedian(cdata[...,g], axis=0)
+ carr_px[...,g, 0] = np.nanmedian(cdata[...,g], axis=0)
+ carr_px[...,g, 1] = np.nanmedian(cdata[...,g], axis=1)
- ret_constants[const][qm].append((creation_time,(carr, carr_glob, carr_rad)))
+ ret_constants[const][qm].append((creation_time,
+ (carr, carr_glob, carr_px)))
dt += step
@@ -167,41 +169,24 @@ for const, modules in ret_constants.items():
pmm, glob, _ = list(zip(*cd))
pma = np.array(pmm)
ga = np.array(glob)
-
d = pma[:,typ,:,:]
- print (pma.shape, ga.shape, d.shape)
if np.allclose(d, 0):
continue
- x = []
- y = []
- hue = []
-
- # loop over gain
- for g in range(3):
- dd = (pma[:,typ,:,g]-pma[0,typ,:,g])/pma[0,typ,:,g]
- print (pma[0,typ,:,g])
- dd[~np.isfinite(dd)] = 0
- x.append(np.repeat(np.array(ctimes)[:,None], dd.shape[1], axis=1).flatten())
- print (x)
- y.append(dd.flatten())
- hue.append(np.ones(dd.shape).flatten()*g)
- x = np.concatenate(x)
- y = np.concatenate(y)
- hue = np.concatenate(hue)
- print (x.shape)
+ dd = pma[:,typ,:,:]#-pma[0,typ,:,:])/pma[0,typ,:,:]
+ y = dd.flatten()
+ x = np.repeat(np.array(ctimes)[:,None],
+ dd[0,:,:].size, axis=1).flatten()
+ hue = np.repeat(np.array(['gain 0', 'gain 1', 'gain 2'])[:,None],
+ dd[:,:,0].size, axis=1).swapaxes(0,1).flatten()
seaborn.violinplot(x, y, hue, scale="width", dodge=False, saturation=0.7)
- #for i in range(max_cells):
- #ax.scatter(ctimes, (pma[:,typ,i,g]-pma[0,typ,i,g])/pma[0,typ,i,g], marker='.', color=colors[g], alpha=0.5)
- #ax.plot(ctimes, (ga[:,typ,g]-ga[0,typ,g])/ga[0,typ,g], color=colors[g], alpha=0.5)
-
- ax.set_ylim(-0.25, .25)
+
+ #ax.set_ylim(-0.25, .25)
if typ != len(types)-1:
ax.axes.get_xaxis().set_visible(False)
else:
def format_date(x, pos=None):
-
return ctimes[x].strftime('%d-%m')
ax.xaxis.set_major_formatter(ticker.FuncFormatter(format_date))
ax.set_xlabel("Date")
@@ -211,5 +196,41 @@ for const, modules in ret_constants.items():
plt.subplots_adjust(wspace=0.2, hspace=0.2)
if out_folder != "":
- fig.savefig("{}/{}_time_development.pdf".format(out_folder, const), bbox_inches='tight')
+ fig.savefig("{}/{}_time_development.pdf".format(out_folder, const),
+ bbox_inches='tight')
+ plt.show()
+
+
+
+
+ fig = plt.figure(figsize=(15,7))
+ ax = plt.subplot2grid((1, 1), (0, 0))
+
+ # loop over modules
+ for mod, data in modules.items():
+ ctimes, cd = list(zip(*data))
+ _, _, px = list(zip(*cd))
+ px = np.array(px)
+ print (px.shape)
+ y = px[:,:,5,0,:].flatten()
+ x = np.repeat(np.array(ctimes)[:,None],
+ px[0,:,5,0,:].size, axis=1).flatten()
+ hue = np.repeat(np.array(['px','py'])[:,None], px[:,:,5,0,0].size,
+ axis=1).swapaxes(0,1).flatten()
+
+ seaborn.violinplot(x, y, hue, palette="muted", split=True)
+
+ def format_date(x, pos=None):
+ return ctimes[x].strftime('%d-%m')
+ ax.xaxis.set_major_formatter(ticker.FuncFormatter(format_date))
+ ax.set_xlabel("Date")
+ ax.set_ylabel("Median over pixels")
+
+ plt.subplots_adjust(wspace=0.2, hspace=0.2)
+
+ if out_folder != "":
+ fig.savefig("{}/{}_pxtime_development.pdf".format(out_folder, const),
+ bbox_inches='tight')
+ plt.show()
+
--
GitLab