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Commit a3c2dd63 authored by Aleksandra Mnich's avatar Aleksandra Mnich
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QH notebooks adapted

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qh/HWC_IPD_QHDA.ipynb
View file @ a3c2dd63
%% Cell type:markdown id: tags:
<h1><center>Analysis of a QH discharge in an IPD Circuit</center></h1>
%% Cell type:markdown id: tags:
# 0. Initialise Working Environment
%% Cell type:code id: tags:
``` python
# External libraries
print('Loading (1/13)'); import sys; import pandas as pd
print('Loading (2/13)'); from multiprocessing import Pool
print('Loading (3/13)'); from IPython.display import display, HTML, Javascript, clear_output
# Internal libraries
print('Loading (4/13)'); import lhcsmapi
print('Loading (5/13)'); from lhcsmapi.Time import Time
print('Loading (6/13)'); from lhcsmapi.Timer import Timer
print('Loading (7/13)'); from lhcsmapi.analysis.IpdCircuitQuery import IpdCircuitQuery
print('Loading (8/13)'); from lhcsmapi.analysis.IpdCircuitAnalysis import IpdCircuitAnalysis
print('Loading (9/13)'); from lhcsmapi.analysis.expert_input import check_show_next, get_expert_decision
print('Loading (10/13)'); from lhcsmapi.analysis.report_template import apply_report_template
print('Loading (11/13)'); from lhcsmapi.gui.DateTimeBaseModule import DateTimeBaseModule
print('Loading (12/13)'); from lhcsmapi.gui.qh.QhPmSearchModuleMediator import QhPmSearchModuleMediator
print('Loading (13/13)'); from lhcsmapi.gui.hwc.HwcSearchModuleMediator import HwcSearchModuleMediator
clear_output()
lhcsmapi.get_lhcsmapi_version()
lhcsmapi.get_lhcsmhwc_version('../__init__.py')
print('Analysis performed by %s' % HwcSearchModuleMediator.get_user())
```
%% Cell type:markdown id: tags:
# 1. Find QH Post Mortem Entries
%% Cell type:code id: tags:
%% Cell type:code id: tags:parameters
``` python
circuit_type = 'IPD'
qh_pm_search = QhPmSearchModuleMediator(DateTimeBaseModule(start_date_time='2021-01-12 00:00:00+01:00', end_date_time=Time.to_string(Time.now())), circuit_type=circuit_type)
```
%% Cell type:markdown id: tags:
# 2. Query All Signals Prior to Analysis
%% Cell type:code id: tags:
``` python
ipd_query = IpdCircuitQuery(circuit_type, qh_pm_search.get_circuit_name())
ipd_analysis = IpdCircuitAnalysis(ipd_query.circuit_type)
with Timer():
circuit_name = qh_pm_search.get_circuit_name()
u_hds_dfss = ipd_query.query_qh_pm(qh_pm_search.source_timestamp_df, signal_names=['U_HDS'])
u_hds_ref_dfss = ipd_query.query_qh_pm(qh_pm_search.source_timestamp_df, signal_names=['U_HDS'], is_ref=True)
if 'qh_pm_search' in locals():
circuit_name = qh_pm_search.get_circuit_name()
discharge_level = qh_pm_search.get_discharge_level()
source_timestamp_df = qh_pm_search.source_timestamp_df
is_automatic = qh_pm_search.is_automatic_mode()
else:
import pandas as pd
source_timestamp_df = pd.DataFrame({'source': sources, 'timestamp': timestamps})
source_timestamp_df['datetime'] = pd.to_datetime(source_timestamp_df['timestamp'])
ipd_query = IpdCircuitQuery(circuit_type, circuit_name)
ipd_analysis = IpdCircuitAnalysis(circuit_type)
u_hds_dfss = ipd_query.query_qh_pm(source_timestamp_df, signal_names=['U_HDS'])
u_hds_ref_dfss = ipd_query.query_qh_pm(source_timestamp_df, signal_names=['U_HDS'], is_ref=True)
```
%% Cell type:markdown id: tags:
# 3. Quench Heaters
*CRITERIA*:
- all characteristic times of an exponential decay calculated with the 'charge' approach for voltage is +/- 5 ms from the reference ones
- the initial voltage should be between 810 V and 1000 V
- the final voltage should be between 0 V and 10 V
*GRAPHS*:
- t = 0 s corresponds to the start of the pseudo-exponential decay
Voltage view (linear and log)
- the queried and filtered quench heater voltage on the left axis (actual signal continuous, reference dashed), U_HDS
%% Cell type:code id: tags:
``` python
index = 0
for u_hds_dfs, u_hds_ref_dfs in zip(u_hds_dfss, u_hds_ref_dfss):
print(index, qh_pm_search.source_timestamp_df.loc[index, 'timestamp'])
print(index, source_timestamp_df.loc[index, 'timestamp'])
if u_hds_dfs:
ipd_analysis.analyze_single_qh_voltage_with_ref(qh_pm_search.get_circuit_name(), qh_pm_search.source_timestamp_df.loc[index, 'timestamp'], u_hds_dfs, u_hds_ref_dfs, nominal_voltage=qh_pm_search.get_discharge_level())
ipd_analysis.analyze_single_qh_voltage_with_ref(circuit_name, source_timestamp_df.loc[index, 'timestamp'], u_hds_dfs, u_hds_ref_dfs, nominal_voltage=discharge_level)
else:
print(f"No Quench Heater Discharges !")
index += 1
```
%% Cell type:markdown id: tags:
%% Cell type:markdown id: tags:ignore
# 4. Signature Decision
%% Cell type:code id: tags:
%% Cell type:code id: tags:ignore
``` python
signature = get_expert_decision('Expert Signature Decision: ', ['PASSED', 'FAILED'])
```
%% Cell type:markdown id: tags:
%% Cell type:markdown id: tags:ignore
# 5. Final Report
%% Cell type:code id: tags:
%% Cell type:code id: tags:ignore
``` python
if not qh_pm_search.source_timestamp_df.empty:
if not source_timestamp_df.empty:
analysis_start_time = Time.get_analysis_start_time()
date_time_qh = Time.to_datetime(qh_pm_search.source_timestamp_df.loc[0, 'timestamp']).strftime("%Y-%m-%d-%Hh%M")
date_time_qh = Time.to_datetime(source_timestamp_df.loc[0, 'timestamp']).strftime("%Y-%m-%d-%Hh%M")
file_name_html = "{}_QHDA-{}-{}_{}.html".format(circuit_name, date_time_qh, analysis_start_time, signature)
apply_report_template()
!mkdir -p /eos/project/m/mp3/IPD/$circuit_name/QHDA
full_path = '/eos/project/m/mp3/IPD/{}/QHDA/{}'.format(circuit_name, file_name_html)
print('Compact notebook report saved to (Windows): ' + '\\\\cernbox-smb' + full_path.replace('/', '\\'))
display(Javascript('IPython.notebook.save_notebook();'))
Time.sleep(5)
!{sys.executable} -m jupyter nbconvert --to html $'HWC_IPD_QHDA.ipynb' --output /eos/project/m/mp3/IPD/$circuit_name/QHDA/$file_name_html --TemplateExporter.exclude_input=True --TagRemovePreprocessor.remove_all_outputs_tags='["skip_output"]' --TagRemovePreprocessor.remove_cell_tags='["skip_cell"]'
```
%% Cell type:markdown id: tags:
%% Cell type:markdown id: tags:ignore
# 6. Save Timestamps in Reference Format (if update needed)
%% Cell type:code id: tags:
%% Cell type:code id: tags:ignore
``` python
if not qh_pm_search.source_timestamp_df.empty:
if not source_timestamp_df.empty:
!mkdir -p /eos/project/m/mp3/IPD/$circuit_name/QHDA
file_name = "{}_QHDA-{}-{}_Reference".format(circuit_name, date_time_qh, analysis_start_time)
csv_full_path = '/eos/project/m/mp3/IPD/{}/QHDA/{}.csv'.format(circuit_name, file_name)
cell_datetime_timestamp = pd.DataFrame(qh_pm_search.source_timestamp_df.rename(columns={'source': 'Cell'})['Cell'])
cell_datetime_timestamp['Timestamp String'] = qh_pm_search.source_timestamp_df['timestamp'].apply(lambda col: Time.to_string_short(col, n_dec_digits=9))
cell_datetime_timestamp['Timestamp'] = qh_pm_search.source_timestamp_df['timestamp']
cell_datetime_timestamp = pd.DataFrame(source_timestamp_df.rename(columns={'source': 'Cell'})['Cell'])
cell_datetime_timestamp['Timestamp String'] = source_timestamp_df['timestamp'].apply(lambda col: Time.to_string_short(col, n_dec_digits=9))
cell_datetime_timestamp['Timestamp'] = source_timestamp_df['timestamp']
cell_datetime_timestamp.to_csv(csv_full_path, index=False)
print('Reference timestamp table saved to (Windows): ' + '\\\\cernbox-smb' + csv_full_path.replace('/', '\\'))
```
......
qh/HWC_IPQ_QHDA.ipynb
View file @ a3c2dd63
%% Cell type:markdown id: tags:
<h1><center>Analysis of a QH discharge in an IPQ Circuit</center></h1>
%% Cell type:markdown id: tags:
# 0. Initialise Working Environment
%% Cell type:code id: tags:
``` python
# External libraries
print('Loading (1/13)'); import sys; import pandas as pd
print('Loading (2/13)'); from multiprocessing import Pool
print('Loading (3/13)'); from IPython.display import display, HTML, Javascript, clear_output
# Internal libraries
print('Loading (4/13)'); import lhcsmapi
print('Loading (5/13)'); from lhcsmapi.Time import Time
print('Loading (6/13)'); from lhcsmapi.Timer import Timer
print('Loading (7/13)'); from lhcsmapi.analysis.IpqCircuitQuery import IpqCircuitQuery
print('Loading (8/13)'); from lhcsmapi.analysis.IpqCircuitAnalysis import IpqCircuitAnalysis
print('Loading (9/13)'); from lhcsmapi.analysis.expert_input import check_show_next, get_expert_decision
print('Loading (10/13)'); from lhcsmapi.analysis.report_template import apply_report_template
print('Loading (11/13)'); from lhcsmapi.gui.DateTimeBaseModule import DateTimeBaseModule
print('Loading (12/13)'); from lhcsmapi.gui.qh.QhPmSearchModuleMediator import QhPmSearchModuleMediator
print('Loading (13/13)'); from lhcsmapi.gui.hwc.HwcSearchModuleMediator import HwcSearchModuleMediator
clear_output()
lhcsmapi.get_lhcsmapi_version()
lhcsmapi.get_lhcsmhwc_version('../__init__.py')
print('Analysis performed by %s' % HwcSearchModuleMediator.get_user())
```
%% Cell type:markdown id: tags:
# 1. Find QH Post Mortem Entries
%% Cell type:code id: tags:
%% Cell type:code id: tags:parameters
``` python
circuit_type = 'IPQ'
qh_pm_search = QhPmSearchModuleMediator(DateTimeBaseModule(start_date_time='2021-01-12 00:00:00+01:00', end_date_time=Time.to_string(Time.now())), circuit_type=circuit_type)
```
%% Cell type:markdown id: tags:
# 2. Query All Signals Prior to Analysis
%% Cell type:code id: tags:
``` python
ipq_query = IpqCircuitQuery(circuit_type, qh_pm_search.get_circuit_name())
ipq_analysis = IpqCircuitAnalysis(ipq_query.circuit_type, None, circuit_name=qh_pm_search.get_circuit_name())
with Timer():
circuit_name = qh_pm_search.get_circuit_name()
u_hds_dfss = ipq_query.query_qh_pm(qh_pm_search.source_timestamp_df, signal_names=['U_HDS'])
u_hds_ref_dfss = ipq_query.query_qh_pm(qh_pm_search.source_timestamp_df, signal_names=['U_HDS'], is_ref=True)
if 'qh_pm_search' in locals():
circuit_name = qh_pm_search.get_circuit_name()
discharge_level = qh_pm_search.get_discharge_level()
source_timestamp_df = qh_pm_search.source_timestamp_df
is_automatic = qh_pm_search.is_automatic_mode()
else:
import pandas as pd
source_timestamp_df = pd.DataFrame({'source': sources, 'timestamp': timestamps})
source_timestamp_df['datetime'] = pd.to_datetime(source_timestamp_df['timestamp'])
ipq_query = IpqCircuitQuery(circuit_type, circuit_name)
ipq_analysis = IpqCircuitAnalysis(circuit_type, None, circuit_name=circuit_name)
u_hds_dfss = ipq_query.query_qh_pm(source_timestamp_df, signal_names=['U_HDS'])
u_hds_ref_dfss = ipq_query.query_qh_pm(source_timestamp_df, signal_names=['U_HDS'], is_ref=True)
```
%% Cell type:markdown id: tags:
# 3. Quench Heaters
*CRITERIA*:
- all characteristic times of an exponential decay calculated with the 'charge' approach for voltage is +/- 5 ms from the reference ones
- the initial voltage should be between 810 V and 1000 V
- the final voltage should be between 0 V and 10 V
*GRAPHS*:
- t = 0 s corresponds to the start of the pseudo-exponential decay
Voltage view (linear and log)
- the queried and filtered quench heater voltage on the left axis (actual signal continuous, reference dashed), U_HDS
%% Cell type:code id: tags:
``` python
index = 0
for u_hds_dfs, u_hds_ref_dfs in zip(u_hds_dfss, u_hds_ref_dfss):
print(index, qh_pm_search.source_timestamp_df.loc[index, 'timestamp'])
print(index, source_timestamp_df.loc[index, 'timestamp'])
if u_hds_dfs:
ipq_analysis.analyze_single_qh_voltage_with_ref(qh_pm_search.get_circuit_name(), qh_pm_search.source_timestamp_df.loc[index, 'timestamp'], u_hds_dfs, u_hds_ref_dfs, nominal_voltage=qh_pm_search.get_discharge_level())
ipq_analysis.analyze_single_qh_voltage_with_ref(circuit_name, source_timestamp_df.loc[index, 'timestamp'], u_hds_dfs, u_hds_ref_dfs, nominal_voltage=qh_pm_search.get_discharge_level())
else:
print(f"No Quench Heater Discharges!")
index += 1
```
%% Cell type:markdown id: tags:
%% Cell type:markdown id: tags:ignore
# 4. Signature Decision
%% Cell type:code id: tags:
%% Cell type:code id: tags:ignore
``` python
signature = get_expert_decision('Expert Signature Decision: ', ['PASSED', 'FAILED'])
```
%% Cell type:markdown id: tags:
%% Cell type:markdown id: tags:ignore
# 5. Final Report
%% Cell type:code id: tags:
%% Cell type:code id: tags:ignore
``` python
if not qh_pm_search.source_timestamp_df.empty:
if not source_timestamp_df.empty:
analysis_start_time = Time.get_analysis_start_time()
prefix_circuit_name = circuit_name.split('.')[0]
date_time_qh = Time.to_datetime(qh_pm_search.source_timestamp_df.loc[0, 'timestamp']).strftime("%Y-%m-%d-%Hh%M")
date_time_qh = Time.to_datetime(source_timestamp_df.loc[0, 'timestamp']).strftime("%Y-%m-%d-%Hh%M")
apply_report_template()
!mkdir -p /eos/project/m/mp3/IPQ/$prefix_circuit_name/$circuit_name/QHDA
file_name_html = "{}_QHDA-{}-{}_{}.html".format(circuit_name, date_time_qh, analysis_start_time, signature)
full_path = '/eos/project/m/mp3/IPQ/{}/{}/QHDA/{}'.format(prefix_circuit_name, circuit_name, file_name_html)
print('Compact notebook report saved to (Windows): ' + '\\\\cernbox-smb' + full_path.replace('/', '\\'))
display(Javascript('IPython.notebook.save_notebook();'))
Time.sleep(5)
!{sys.executable} -m jupyter nbconvert --to html $'HWC_IPQ_QHDA.ipynb' --output /eos/project/m/mp3/IPQ/$prefix_circuit_name/$circuit_name/QHDA/$file_name_html --TemplateExporter.exclude_input=True --TagRemovePreprocessor.remove_all_outputs_tags='["skip_output"]' --TagRemovePreprocessor.remove_cell_tags='["skip_cell"]'
```
%% Cell type:markdown id: tags:
%% Cell type:markdown id: tags:ignore
# 6. Save Timestamps in Reference Format (if update needed)
%% Cell type:code id: tags:
%% Cell type:code id: tags:ignore
``` python
if not qh_pm_search.source_timestamp_df.empty:
if not source_timestamp_df.empty:
analysis_start_time = Time.get_analysis_start_time()
prefix_circuit_name = circuit_name.split('.')[0]
!mkdir -p /eos/project/m/mp3/IPQ/$prefix_circuit_name/$circuit_name/QHDA
file_name = "{}_QHDA-{}-{}_Reference".format(circuit_name, date_time_qh, analysis_start_time)
csv_full_path = '/eos/project/m/mp3/IPQ/{}/{}/QHDA/{}.csv'.format(prefix_circuit_name, circuit_name, file_name)
cell_datetime_timestamp = pd.DataFrame(qh_pm_search.source_timestamp_df.rename(columns={'source': 'Cell'})['Cell'])
cell_datetime_timestamp['Timestamp String'] = qh_pm_search.source_timestamp_df['timestamp'].apply(lambda col: Time.to_string_short(col, n_dec_digits=9))
cell_datetime_timestamp['Timestamp'] = qh_pm_search.source_timestamp_df['timestamp']
cell_datetime_timestamp = pd.DataFrame(source_timestamp_df.rename(columns={'source': 'Cell'})['Cell'])
cell_datetime_timestamp['Timestamp String'] = source_timestamp_df['timestamp'].apply(lambda col: Time.to_string_short(col, n_dec_digits=9))
cell_datetime_timestamp['Timestamp'] = source_timestamp_df['timestamp']
cell_datetime_timestamp.to_csv(csv_full_path, index=False)
print('Reference timestamp table saved to (Windows): ' + '\\\\cernbox-smb' + csv_full_path.replace('/', '\\'))
```
......
qh/HWC_IT_QHDA.ipynb
View file @ a3c2dd63
%% Cell type:markdown id: tags:
<h1><center>Analysis of a QH discharge in an IT Circuit</center></h1>
%% Cell type:markdown id: tags:
# 0. Initialise Working Environment
%% Cell type:code id: tags:
``` python
# External libraries
print('Loading (1/13)'); import sys; import pandas as pd
print('Loading (2/13)'); from multiprocessing import Pool
print('Loading (3/13)'); from IPython.display import display, HTML, Javascript, clear_output
# Internal libraries
print('Loading (4/13)'); import lhcsmapi
print('Loading (5/13)'); from lhcsmapi.Time import Time
print('Loading (6/13)'); from lhcsmapi.Timer import Timer
print('Loading (7/13)'); from lhcsmapi.analysis.ItCircuitQuery import ItCircuitQuery
print('Loading (8/13)'); from lhcsmapi.analysis.ItCircuitAnalysis import ItCircuitAnalysis
print('Loading (9/13)'); from lhcsmapi.analysis.expert_input import check_show_next, get_expert_decision
print('Loading (10/13)'); from lhcsmapi.analysis.report_template import apply_report_template
print('Loading (11/13)'); from lhcsmapi.gui.DateTimeBaseModule import DateTimeBaseModule
print('Loading (12/13)'); from lhcsmapi.gui.qh.QhPmSearchModuleMediator import QhPmSearchModuleMediator
print('Loading (13/13)'); from lhcsmapi.gui.hwc.HwcSearchModuleMediator import HwcSearchModuleMediator
clear_output()
lhcsmapi.get_lhcsmapi_version()
lhcsmapi.get_lhcsmhwc_version('../__init__.py')
print('Analysis performed by %s' % HwcSearchModuleMediator.get_user())
```
%% Cell type:markdown id: tags:
# 1. Find QH Post Mortem Entries
%% Cell type:code id: tags:
%% Cell type:code id: tags:parameters
``` python
circuit_type = 'IT'
qh_pm_search = QhPmSearchModuleMediator(DateTimeBaseModule(start_date_time='2021-01-12 00:00:00+01:00', end_date_time=Time.to_string(Time.now())), circuit_type=circuit_type)
```
%% Cell type:markdown id: tags:
# 2. Query All Signals Prior to Analysis
%% Cell type:code id: tags:
``` python
it_query = ItCircuitQuery(circuit_type, qh_pm_search.get_circuit_name())
it_analysis = ItCircuitAnalysis(it_query.circuit_type)
with Timer():
circuit_name = qh_pm_search.get_circuit_name()
u_hds_dfss = it_query.query_qh_pm(qh_pm_search.source_timestamp_df, signal_names=['U_HDS'])
u_hds_ref_dfss = it_query.query_qh_pm(qh_pm_search.source_timestamp_df, signal_names=['U_HDS'], is_ref=True)
if 'qh_pm_search' in locals():
circuit_name = qh_pm_search.get_circuit_name()
discharge_level = qh_pm_search.get_discharge_level()
source_timestamp_df = qh_pm_search.source_timestamp_df
is_automatic = qh_pm_search.is_automatic_mode()
else:
import pandas as pd
source_timestamp_df = pd.DataFrame({'source': sources, 'timestamp': timestamps})
source_timestamp_df['datetime'] = pd.to_datetime(source_timestamp_df['timestamp'])
it_query = ItCircuitQuery(circuit_type, circuit_name)
it_analysis = ItCircuitAnalysis(it_query.circuit_type)
u_hds_dfss = it_query.query_qh_pm(source_timestamp_df, signal_names=['U_HDS'])
u_hds_ref_dfss = it_query.query_qh_pm(source_timestamp_df, signal_names=['U_HDS'], is_ref=True)
```
%% Cell type:markdown id: tags:
# 3. Quench Heaters
*CRITERIA*:
- all characteristic times of an exponential decay calculated with the 'charge' approach for voltage is +/- 5 ms from the reference ones
- the initial voltage should be between 810 V and 1000 V
- the final voltage should be between 0 V and 10 V
*GRAPHS*:
t = 0 s corresponds to the start of the pseudo-exponential decay
Voltage view (linear and log)
- the queried and filtered quench heater voltage on the left axis (actual signal continuous, reference dashed), U_HDS
%% Cell type:code id: tags:
``` python
index = 0
for u_hds_dfs, u_hds_ref_dfs in zip(u_hds_dfss, u_hds_ref_dfss):
if u_hds_dfs:
print(index, qh_pm_search.source_timestamp_df.loc[index, 'timestamp'])
it_analysis.analyze_single_qh_voltage_with_ref(qh_pm_search.get_circuit_name(), qh_pm_search.source_timestamp_df.loc[index, 'timestamp'], u_hds_dfs, u_hds_ref_dfs, nominal_voltage=qh_pm_search.get_discharge_level())
print(index, source_timestamp_df.loc[index, 'timestamp'])
it_analysis.analyze_single_qh_voltage_with_ref(circuit_name, source_timestamp_df.loc[index, 'timestamp'], u_hds_dfs, u_hds_ref_dfs, nominal_voltage=discharge_level)
else:
print(f"No Quench Heater discharges for {qh_pm_search.get_circuit_name()} on time {qh_pm_search.source_timestamp_df.loc[index, 'timestamp']}!")
print(f"No Quench Heater discharges for {circuit_name} on time {source_timestamp_df.loc[index, 'timestamp']}!")
index += 1
```
%% Cell type:markdown id: tags:
%% Cell type:markdown id: tags:ignore
# 4. Signature Decision
%% Cell type:code id: tags:
%% Cell type:code id: tags:ignore
``` python
signature = get_expert_decision('Expert Signature Decision: ', ['PASSED', 'FAILED'])
```
%% Cell type:markdown id: tags:
%% Cell type:markdown id: tags:ignore
# 5. Final Report
%% Cell type:code id: tags:
%% Cell type:code id: tags:ignore
``` python
if not qh_pm_search.source_timestamp_df.empty:
if not source_timestamp_df.empty:
analysis_start_time = Time.get_analysis_start_time()
date_time_qh = Time.to_datetime(qh_pm_search.source_timestamp_df.loc[0, 'timestamp']).strftime("%Y-%m-%d-%Hh%M")
date_time_qh = Time.to_datetime(source_timestamp_df.loc[0, 'timestamp']).strftime("%Y-%m-%d-%Hh%M")
file_name_html = "{}_QHDA-{}-{}_{}.html".format(circuit_name, date_time_qh, analysis_start_time, signature)
apply_report_template()
!mkdir -p /eos/project/m/mp3/IT/$circuit_name/QHDA
full_path = '/eos/project/m/mp3/IT/{}/QHDA/{}'.format(circuit_name, file_name_html)
print('Compact notebook report saved to (Windows): ' + '\\\\cernbox-smb' + full_path.replace('/', '\\'))
display(Javascript('IPython.notebook.save_notebook();'))
Time.sleep(5)
!{sys.executable} -m jupyter nbconvert --to html $'HWC_IT_QHDA.ipynb' --output /eos/project/m/mp3/IT/$circuit_name/QHDA/$file_name_html --TemplateExporter.exclude_input=True --TagRemovePreprocessor.remove_all_outputs_tags='["skip_output"]' --TagRemovePreprocessor.remove_cell_tags='["skip_cell"]'
```
%% Cell type:markdown id: tags:
%% Cell type:markdown id: tags:ignore
# 6. Save Timestamps in Reference Format (if update needed)
%% Cell type:code id: tags:
%% Cell type:code id: tags:ignore
``` python
if not qh_pm_search.source_timestamp_df.empty:
if not source_timestamp_df.empty:
analysis_start_time = Time.get_analysis_start_time()
!mkdir -p /eos/project/m/mp3/IT/$circuit_name/QHDA
file_name = "{}_QHDA-{}-{}_Reference".format(circuit_name, date_time_qh, analysis_start_time)
csv_full_path = '/eos/project/m/mp3/IT/{}/QHDA/{}.csv'.format(circuit_name, file_name)
cell_datetime_timestamp = pd.DataFrame(qh_pm_search.source_timestamp_df.rename(columns={'source': 'Cell'})['Cell'])
cell_datetime_timestamp['Timestamp String'] = qh_pm_search.source_timestamp_df['timestamp'].apply(lambda col: Time.to_string_short(col, n_dec_digits=9))
cell_datetime_timestamp['Timestamp'] = qh_pm_search.source_timestamp_df['timestamp']
cell_datetime_timestamp = pd.DataFrame(source_timestamp_df.rename(columns={'source': 'Cell'})['Cell'])
cell_datetime_timestamp['Timestamp String'] = source_timestamp_df['timestamp'].apply(lambda col: Time.to_string_short(col, n_dec_digits=9))
cell_datetime_timestamp['Timestamp'] = source_timestamp_df['timestamp']
cell_datetime_timestamp.to_csv(csv_full_path, index=False)
print('Reference timestamp table saved to (Windows): ' + '\\\\cernbox-smb' + csv_full_path.replace('/', '\\'))
```
%% Cell type:code id: tags:
``` python
```
......
qh/HWC_QHD_PM_LIST.ipynb
View file @ a3c2dd63
......@@ -125,7 +125,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.5"
"version": "3.8.6"
},
"toc": {
"base_numbering": 1,
......
qh/HWC_RB_QHDA.ipynb
View file @ a3c2dd63
%% Cell type:markdown id: tags:
<h1><center>Analysis of a QH discharge in an RB Circuit</center></h1>
%% Cell type:markdown id: tags:
# 0. Initialise Working Environment
%% Cell type:code id: tags:
``` python
# External libraries
print('Loading (1/13)'); import sys; import pandas as pd; import warnings
print('Loading (2/12)'); from multiprocessing import Pool
print('Loading (3/12)'); from IPython.display import display, HTML, Javascript, clear_output
# Internal libraries
print('Loading (4/12)'); import lhcsmapi
print('Loading (5/12)'); from lhcsmapi.Time import Time
print('Loading (6/12)'); from lhcsmapi.Timer import Timer
print('Loading (7/12)'); from lhcsmapi.analysis.RbCircuitQuery import RbCircuitQuery
print('Loading (8/12)'); from lhcsmapi.analysis.RbCircuitAnalysis import RbCircuitAnalysis
print('Loading (9/12)'); from lhcsmapi.analysis.expert_input import check_show_next, get_expert_decision
print('Loading (10/12)'); from lhcsmapi.analysis.report_template import apply_report_template
print('Loading (11/12)'); from lhcsmapi.gui.DateTimeBaseModule import DateTimeBaseModule
print('Loading (12/12)'); from lhcsmapi.gui.qh.QhPmSearchModuleMediator import QhPmSearchModuleMediator
print('Loading (13/13)'); from lhcsmapi.gui.hwc.HwcSearchModuleMediator import HwcSearchModuleMediator
clear_output()
lhcsmapi.get_lhcsmapi_version()
lhcsmapi.get_lhcsmhwc_version('../__init__.py')
print('Analysis performed by %s' % HwcSearchModuleMediator.get_user())
```
%% Cell type:markdown id: tags:
# 1. Find QH Post Mortem Entries
%% Cell type:code id: tags:
%% Cell type:code id: tags:parameters
``` python
circuit_type = 'RB'
qh_pm_search = QhPmSearchModuleMediator(DateTimeBaseModule(start_date_time='2021-05-19 11:00:00+01:00', end_date_time='2021-05-19 12:09:00+01:00'), circuit_type=circuit_type)
```
%% Cell type:markdown id: tags:
# 2. Query All Signals Prior to Analysis
%% Cell type:code id: tags:skip_output
%% Cell type:code id: tags:
``` python
from lhcsmapi.pyedsl.QueryBuilder import QueryBuilder
from lhcsmapi.reference.Reference import Reference
def query_qh_parallel(input_param):
source, timestamp, is_ref = input_param
if is_ref:
timestamp = Reference.get_quench_heater_reference_discharge('RB', source, timestamp)
u_hds_dfs = QueryBuilder().with_pm() \
.with_timestamp(timestamp) \
.with_circuit_type('RB') \
.with_metadata(circuit_name=qh_pm_search.get_circuit_name(), system='QH', signal=['U_HDS', 'I_HDS'], source=source,
.with_metadata(circuit_name=circuit_name, system='QH', signal=['U_HDS', 'I_HDS'], source=source,
wildcard={'CELL': source}) \
.signal_query() \
.synchronize_time(timestamp) \
.convert_index_to_sec() \
.drop_first_npoints(5) \
.drop_last_npoints(5).dfs
print('Done: %s: %d' % (source, timestamp))
return source, timestamp, u_hds_dfs
```
%% Cell type:code id: tags:skip_output