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Commit 861be23e authored by Thijs vanEeden's avatar Thijs vanEeden
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make plot for maarten

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.ipynb_checkpoints/pmt_gain-checkpoint.ipynb
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%% Cell type:code id:6b9d9ddb-8cf4-4c9c-bdc0-0de78f0ec35d tags:
``` python
#pip install km3dia
```
%% Cell type:code id:4d699873-c366-4870-a262-56b4c1312855 tags:
``` python
#pip install xmltodict
```
%% Cell type:code id:9034b755-3525-4a06-bbd5-59fe2a1ce3d3 tags:
``` python
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import xmltodict
import km3db
import km3dia
```
%% Cell type:code id:d9bf120b-39cc-4adc-8c2f-ecb425d7cd18 tags:
``` python
dia = km3dia.DBManager(container='pd')
DOMInt = km3dia.DOMIntegrationSummary()
```
%% Cell type:code id:1959e023-8415-465c-90a2-7da4b9c3a93b tags:
``` python
#Get measurement results
df_tests = DOMInt.test_results
df_tests = df_tests.reset_index().set_index('UPI')
```
%% Cell type:code id:8ba41596-7e7a-4925-80f9-98c697e705f4 tags:
``` python
# Generate the list of relevant keys
gain_keys = [key for key in df_tests.columns if key.lower().find('gain') != -1]
dc_keys = [key for key in df_tests.columns if key.lower().find('dark') != -1]
pmt_keys = gain_keys + dc_keys
DOMid_keys = ['SERIAL', 'SiteID']
```
%% Cell type:code id:7f681bc2-bfb5-4232-8983-de877bf2f1d4 tags:
``` python
# Cut the dataframe to no NaN
df_pmt = df_tests.dropna(subset=pmt_keys)
df_pmt = df_pmt[pmt_keys + DOMid_keys]
```
%% Cell type:code id:5d602ade-f9d6-4303-ab4c-94938eb0660a tags:
``` python
# Convert pmt results result branches to float
df_pmt = df_pmt.astype(dict(zip(pmt_keys, [float]*len(pmt_keys))))
```
%% Cell type:code id:7c303c75-fa66-48f2-a09f-a5bf146538bc tags:
``` python
def count_chan_above_threshold( gain_threshold = 1.7, ncr_pmt_threshold = 3, siteID = "999" ):
"""
Count number of channels above a gain threshold for each DOM
siteID = "999" means for all sites, "1" is Amsterdam
"""
doms = []
bad_channels = [] # number of bad channels
for index, row in df_pmt.iterrows():
bad = 0
if siteID != "999" and row["SiteID"] != siteID: continue # only amsterdam
for gain_key in gain_keys:
if row[gain_key] > gain_threshold: bad +=1
if bad == 31: continue # there is a group of (older?) DOMS with all gains around 29
doms.append( row["SERIAL"] )
bad_channels.append( bad )
bad_doms = [i for i,v in enumerate(bad_channels) if v > ncr_pmt_threshold]
return doms, bad_channels, len(bad_doms)
```
%% Cell type:code id:4a9a5b4b-4d41-450d-bc2e-fdb10a870ed8 tags:
``` python
import matplotlib.pyplot as plt
gain_threshold = 1.7
ncr_pmt_threshold = 3
location = {"Amsterdam":"1", "All":"999"}
# amsterdam
doms, bad_channels, n_bad_doms = count_chan_above_threshold( gain_threshold, ncr_pmt_threshold, location["Amsterdam"] )
fig, axes = plt.subplots(1,2, figsize=[20,5] )
axes[0].hist(bad_channels, [-0.5, 0.5, 1.5, 2.5, 3.5, 4.5, 5.5, 6.5, 7.5, 8.5, 9.5, 10.5])
axes[0].plot([bad_pmt_threshold,bad_pmt_threshold], [0,30], color='red')
axes[0].set_xlabel("Number of channels with gain > " + str(gain_threshold))
axes[0].set_ylabel("Number of DOMs")
axes[0].set_title("Location: Amsterdam, NCR doms: " + str(n_bad_doms) )
print("failed doms in Amsterdam", n_bad_doms)
# all locations
doms, bad_channels, n_bad_doms = count_chan_above_threshold( gain_threshold, ncr_pmt_threshold, location["All"] )
axes[1].hist(bad_channels, [-0.5, 0.5, 1.5, 2.5, 3.5, 4.5, 5.5, 6.5, 7.5, 8.5, 9.5, 10.5])
axes[1].plot([bad_pmt_threshold,bad_pmt_threshold], [0,60], color='red')
axes[1].set_xlabel("Number of channels with gain > " + str(gain_threshold))
axes[1].set_ylabel("Number of DOMs")
axes[1].set_title("Location: All, NCR doms: " + str(n_bad_doms) )
print("failed doms in All", n_bad_doms)
```
%% Output
failed doms in Amsterdam 20
failed doms in All 30
%% Cell type:code id:4eaed37c-3cae-44ef-88db-fac0ce64acbf tags:
``` python
import numpy as np
def ncr_doms_vs_gain_threshold( gain_min, gain_max, gain_step, ncr_pmt_threshold = 3, siteID = "999" ):
"""
Plot number of bad doms (doms with ncr) versus max gain threshold
"""
gain_thresholds = np.arange( gain_min, gain_max, gain_step )
number_bad_doms = []
for gain_tmp in gain_thresholds:
doms, bad_channels, n_bad_doms = count_chan_above_threshold( gain_tmp, ncr_pmt_threshold, siteID )
number_bad_doms.append( n_bad_doms )
return gain_thresholds, number_bad_doms
# amsterdam
gain_thresholds, number_bad_doms = ncr_doms_vs_gain_threshold(1.7, 2.0, 0.05, 3, location["Amsterdam"] )
fig, axes = plt.subplots(1,2, figsize=[20,5] )
axes[0].plot(gain_thresholds, number_bad_doms)
axes[0].set_xlabel("Max gain in acceptance test")
axes[0].set_ylabel("Number of DOMs with NCR")
axes[0].set_title("Location: Amsterdam" )
# all locations
gain_thresholds, number_bad_doms = ncr_doms_vs_gain_threshold(1.7, 2.0, 0.05, 3, location["All"] )
axes[1].plot(gain_thresholds, number_bad_doms)
axes[1].set_xlabel("Max gain in acceptance test")
axes[1].set_ylabel("Number of DOMs with NCR")
axes[1].set_title("Location: Amsterdam" )
```
%% Output
Text(0.5, 1.0, 'Location: Amsterdam')
%% Cell type:code id:ca1f27e5-fecb-42d5-8e17-dd58da923f08 tags:
``` python
###
### Maarten asked in the group meeting (27-01-2022) what the distribution of all PMTs is
###
bins = np.arange( -0.1, 2.1, 0.1 )
df_gains = df_pmt[gain_keys].stack().reset_index()
df_gains = df_gains[ df_gains[0] < 2.5 ]
df_gains.hist(bins=bins)
plt.plot([0.3,0.3], [0,1500], color='red')
plt.plot([1.7,1.7], [0,1500], color='red')
plt.xlabel("Fitted gain in acceptance test")
plt.ylabel("Counts")
plt.title("Mean: {}, std_dev: {}".format( round(df_gains[0].mean(),3), round(df_gains[0].std(),3) ) )
```
%% Output
Text(0.5, 1.0, 'Mean: 1.299, std_dev: 0.247')
......
pmt_gain.ipynb
+ 43
− 5
View file @ 861be23e
%% Cell type:code id:6b9d9ddb-8cf4-4c9c-bdc0-0de78f0ec35d tags:
``` python
#pip install km3dia
```
%% Cell type:code id:4d699873-c366-4870-a262-56b4c1312855 tags:
``` python
#pip install xmltodict
```
%% Cell type:code id:9034b755-3525-4a06-bbd5-59fe2a1ce3d3 tags:
``` python
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import xmltodict
import km3db
import km3dia
```
%% Cell type:code id:d9bf120b-39cc-4adc-8c2f-ecb425d7cd18 tags:
``` python
dia = km3dia.DBManager(container='pd')
DOMInt = km3dia.DOMIntegrationSummary()
```
%% Cell type:code id:1959e023-8415-465c-90a2-7da4b9c3a93b tags:
``` python
#Get measurement results
df_tests = DOMInt.test_results
df_tests = df_tests.reset_index().set_index('UPI')
```
%% Cell type:code id:8ba41596-7e7a-4925-80f9-98c697e705f4 tags:
``` python
# Generate the list of relevant keys
gain_keys = [key for key in df_tests.columns if key.lower().find('gain') != -1]
dc_keys = [key for key in df_tests.columns if key.lower().find('dark') != -1]
pmt_keys = gain_keys + dc_keys
DOMid_keys = ['SERIAL', 'SiteID']
```
%% Cell type:code id:7f681bc2-bfb5-4232-8983-de877bf2f1d4 tags:
``` python
# Cut the dataframe to no NaN
df_pmt = df_tests.dropna(subset=pmt_keys)
df_pmt = df_pmt[pmt_keys + DOMid_keys]
```
%% Cell type:code id:5d602ade-f9d6-4303-ab4c-94938eb0660a tags:
``` python
# Convert pmt results result branches to float
df_pmt = df_pmt.astype(dict(zip(pmt_keys, [float]*len(pmt_keys))))
```
%% Cell type:code id:7c303c75-fa66-48f2-a09f-a5bf146538bc tags:
``` python
def count_chan_above_threshold( gain_threshold = 1.7, ncr_pmt_threshold = 3, siteID = "999" ):
"""
Count number of channels above a gain threshold for each DOM
siteID = "999" means for all sites, "1" is Amsterdam
"""
doms = []
bad_channels = [] # number of bad channels
for index, row in df_pmt.iterrows():
bad = 0
if siteID != "999" and row["SiteID"] != siteID: continue # only amsterdam
for gain_key in gain_keys:
if row[gain_key] > gain_threshold: bad +=1
if bad == 31: continue # there is a group of (older?) DOMS with all gains around 29
doms.append( row["SERIAL"] )
bad_channels.append( bad )
bad_doms = [i for i,v in enumerate(bad_channels) if v > ncr_pmt_threshold]
return doms, bad_channels, len(bad_doms)
```
%% Cell type:code id:4a9a5b4b-4d41-450d-bc2e-fdb10a870ed8 tags:
``` python
import matplotlib.pyplot as plt
gain_threshold = 1.7
ncr_pmt_threshold = 3
location = {"Amsterdam":"1", "All":"999"}
# amsterdam
doms, bad_channels, n_bad_doms = count_chan_above_threshold( gain_threshold, ncr_pmt_threshold, location["Amsterdam"] )
fig, axes = plt.subplots(1,2, figsize=[20,5] )
axes[0].hist(bad_channels, [-0.5, 0.5, 1.5, 2.5, 3.5, 4.5, 5.5, 6.5, 7.5, 8.5, 9.5, 10.5])
axes[0].plot([bad_pmt_threshold,bad_pmt_threshold], [0,30], color='red')
axes[0].set_xlabel("Number of channels with gain > " + str(gain_threshold))
axes[0].set_ylabel("Number of DOMs")
axes[0].set_title("Location: Amsterdam, NCR doms: " + str(n_bad_doms) )
print("failed doms in Amsterdam", n_bad_doms)
# all locations
doms, bad_channels, n_bad_doms = count_chan_above_threshold( gain_threshold, ncr_pmt_threshold, location["All"] )
axes[1].hist(bad_channels, [-0.5, 0.5, 1.5, 2.5, 3.5, 4.5, 5.5, 6.5, 7.5, 8.5, 9.5, 10.5])
axes[1].plot([bad_pmt_threshold,bad_pmt_threshold], [0,60], color='red')
axes[1].set_xlabel("Number of channels with gain > " + str(gain_threshold))
axes[1].set_ylabel("Number of DOMs")
axes[1].set_title("Location: All, NCR doms: " + str(n_bad_doms) )
print("failed doms in All", n_bad_doms)
```
%% Output
failed doms in Amsterdam 20
failed doms in All 30
%% Cell type:code id:4eaed37c-3cae-44ef-88db-fac0ce64acbf tags:
``` python
import numpy as np
def ncr_doms_vs_gain_threshold( gain_min, gain_max, gain_step, ncr_pmt_threshold = 3, siteID = "999" ):
"""
Plot number of bad doms (doms with ncr) versus max gain threshold
"""
gain_thresholds = np.arange( gain_min, gain_max, gain_step )
number_bad_doms = []
for gain_tmp in gain_thresholds:
doms, bad_channels, n_bad_doms = count_chan_above_threshold( gain_tmp, ncr_pmt_threshold, siteID )
number_bad_doms.append( n_bad_doms )
return gain_thresholds, number_bad_doms
# amsterdam
gain_thresholds, number_bad_doms = ncr_doms_vs_gain_threshold(1.7, 2.0, 0.05, 3, location["Amsterdam"] )
fig, axes = plt.subplots(1,2, figsize=[20,5] )
axes[0].plot(gain_thresholds, number_bad_doms)
axes[0].set_xlabel("Max gain in acceptance test")
axes[0].set_ylabel("Number of DOMs with NCR")
axes[0].set_title("Location: Amsterdam" )
# all locations
gain_thresholds, number_bad_doms = ncr_doms_vs_gain_threshold(1.7, 2.0, 0.05, 3, location["All"] )
axes[1].plot(gain_thresholds, number_bad_doms)
axes[1].set_xlabel("Max gain in acceptance test")
axes[1].set_ylabel("Number of DOMs with NCR")
axes[1].set_title("Location: Amsterdam" )
```
%% Output
Text(0.5, 1.0, 'Location: Amsterdam')
%% Cell type:code id:ca1f27e5-fecb-42d5-8e17-dd58da923f08 tags:
``` python
###
### Maarten asked in the group meeting (27-01-2022) what the distribution of all PMTs is
###
bins = np.arange( -0.1, 2.1, 0.1 )
df_gains = df_pmt[gain_keys].stack().reset_index()
df_gains = df_gains[ df_gains[0] < 2.5 ]
df_gains.hist(bins=bins)
plt.plot([0.3,0.3], [0,1500], color='red')
plt.plot([1.7,1.7], [0,1500], color='red')
plt.xlabel("Fitted gain in acceptance test")
plt.ylabel("Counts")
plt.title("Mean: {}, std_dev: {}".format( round(df_gains[0].mean(),3), round(df_gains[0].std(),3) ) )
```
%% Output
Text(0.5, 1.0, 'Mean: 1.299, std_dev: 0.247')
......
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