Skip to content
GitLab
Commit 05c1d4db authored by Agata Chadaj's avatar Agata Chadaj
Browse files

wip

parent 06da42d8
Pipeline #3176858 passed with stage
in 30 seconds
Hide whitespace changes
Inline Side-by-side
CI/send_results.py
View file @ 05c1d4db
......@@ -24,8 +24,8 @@ def get_result():
result_nb = nbformat.read(result_path, as_version=4)
nb = outputs.NotebookOutput(result_nb)
try:
summary = nb.get_cells_by_tag('result')[0].get_cell_output(outputs.CellOutputType.STDOUT)[0]
result_flag = nb.get_cells_by_tag('result')[1].get_cell_output(outputs.CellOutputType.STDOUT)[0]
summary = nb.get_cells_by_tag('result')[1].get_cell_output(outputs.CellOutputType.STDOUT)[0]
result_flag = nb.get_cells_by_tag('result')[0].get_cell_output(outputs.CellOutputType.STDOUT)[0]
except IndexError:
return 'FAILED', ''
return result_flag, summary
......@@ -35,7 +35,7 @@ def save_data_to_file(data: dict):
file_path = '/eos/home-m/mpesoft/test.csv'
df = pd.DataFrame([data])
with open(file_path, 'a') as f:
df.to_csv(f, mode='a', header=f.tell() == 0)
df.to_csv(f, mode='a', header=f.tell() == 0, index=False)
if __name__ == '__main__':
......
papermill_nxcals/src/papermill_nxcals/engine.py
View file @ 05c1d4db
......@@ -21,7 +21,9 @@ class PapermillNxcalsEngine(NBClientEngine):
to the notebook. Then calls :func: `NBClientEngine.execute_managed_notebook` to execute the parameterized
notebook locally.
"""
print('papermill engine')
print(**kwargs)
print(kwargs)
# make all cells tagged with 'ignore' raw
for cell in nb_man.nb.cells:
if _IGNORE_TAG in cell.metadata.tags:
......@@ -34,6 +36,7 @@ class PapermillNxcalsEngine(NBClientEngine):
# call the papermill execution engine:
return super().execute_managed_notebook(nb_man, kernel_name, **kwargs)
# save_analysis_result()
def _replace_matplotlib_backend(nb_man, new_backend='inline'):
......@@ -58,3 +61,7 @@ def _get_nxcals_config_cell():
def _get_nxcals_config():
return pkgutil.get_data('resources', _MANUAL_SPARK_CONFIG_FILE).decode("utf-8")
def save_analysis_result():
pass
qh/HWC_IPQ_QHDA.ipynb
View file @ 05c1d4db
%% 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 (1/16)'); import sys; import pandas as pd
print('Loading (2/16)'); from multiprocessing import Pool
print('Loading (3/16)'); from IPython.display import display, HTML, Javascript, clear_output
# Internal libraries
print('Loading (4/16)'); import lhcsmapi
print('Loading (5/16)'); from lhcsmapi.Time import Time
print('Loading (6/16)'); from lhcsmapi.Timer import Timer
print('Loading (7/16)'); from lhcsmapi.analysis.IpqCircuitQuery import IpqCircuitQuery
print('Loading (8/16)'); from lhcsmapi.analysis.IpqCircuitAnalysis import IpqCircuitAnalysis
print('Loading (9/16)'); from lhcsmapi.analysis.expert_input import check_show_next, get_expert_decision
print('Loading (10/16)'); from lhcsmapi.analysis.qh.QuenchHeaterQuery import QuenchHeaterQuery
print('Loading (11/16)'); from lhcsmapi.analysis.report_template import apply_report_template
print('Loading (12/16)'); from lhcsmapi.gui.DateTimeBaseModule import DateTimeBaseModule
print('Loading (13/16)'); from lhcsmapi.gui.hwc.HwcSearchModuleMediator import HwcSearchModuleMediator
print('Loading (14/16)'); from lhcsmapi.gui.qh.QhPmSearchModuleMediator import QhPmSearchModuleMediator
print('Loading (15/16)'); from lhcsmapi.metadata.SignalMetadata import SignalMetadata
print('Loading (16/16)'); from lhcsmnb.parameters import are_all_parameters_injected, get_source_timestamp_df, NbType
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:parameters
``` python
circuit_type = 'IPQ'
```
%% Cell type:code id: tags:
``` python
with Timer():
if not are_all_parameters_injected(NbType.QH, locals()):
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)
else:
circuit_sub_type = SignalMetadata.get_circuit_type_for_circuit_name(circuit_name)
source_timestamp_df = QuenchHeaterQuery(circuit_sub_type, circuit_name).find_source_timestamp_qh_with_pattern('*', start_time, end_time)
display(HTML(source_timestamp_df.to_html()))
```
%% Cell type:markdown id: tags:
# 2. Query All Signals Prior to Analysis
%% Cell type:code id: tags:
``` python
with Timer():
if not are_all_parameters_injected(NbType.QH, 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()
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, source_timestamp_df.loc[index, 'timestamp'])
if u_hds_dfs:
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=discharge_level)
else:
print(f"No Quench Heater Discharges!")
index += 1
```
%% Cell type:markdown id: tags:ignore
# 4. Signature Decision
%% Cell type:markdown id: tags:ignore
# 5. Final Report
%% Cell type:code id: tags:ignore
``` python
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(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:ignore
# 6. Save Timestamps in Reference Format (if update needed)
%% Cell type:code id: tags:ignore
``` python
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(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:result
``` python
ipq_analysis.get_result()
ipq_analysis.get_status()
```
%% Cell type:code id: tags:result
``` python
ipq_analysis.get_analysis_result()
```
......
Supports Markdown
0% or .
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment