Commit ad27df60 authored by Michal Maciejewski's avatar Michal Maciejewski
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Added time-dependent metadata for RQ

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%% Cell type:markdown id: tags:
<h1><center>Analysis of an FPA in an 600A Circuit - RCD-RCO Family</center></h1>
Figure below shows a generic circuit diagram, equipped with parallel resistance, as well as lead resistances and a quench resistance.
<img src="https://gitlab.cern.ch/LHCData/lhc-sm-hwc/-/raw/master/figures/600A/600A_without_EE.png" width=75%>
source: Test Procedure and Acceptance Criteria for the 600 A Circuits, MP3 Procedure, <a href="https://edms.cern.ch/document/874716/5.3">https://edms.cern.ch/document/874716/5.3</a>
%% Cell type:markdown id: tags:
# Analysis Assumptions
- We consider standard analysis scenarios, i.e., all signals can be queried. If a signal is missing, an analysis can raise a warning and continue or an error and abort the analysis.
- It is recommended to execute each cell one after another. However, since the signals are queried prior to analysis, any order of execution is allowed. In case an analysis cell is aborted, the following ones may not be executed (e.g. I\_MEAS not present).
# Plot Convention
- Scales are labeled with signal name followed by a comma and a unit in square brackets, e.g., I_MEAS, [A].
- If a reference signal is present, it is represented with a dashed line.
- If the main current is present, its axis is on the left. Remaining signals are attached to the axis on the right. The legend of these signals is located on the lower left and upper right, respectively.
- The grid comes from the left axis.
- The title contains timestamp, circuit name, and signal name allowing to re-access the signal.
- The plots assigned to the left scale have colors: blue (C0) and orange (C1). Plots presented on the right have colors red (C2) and green (C3).
- Each plot has an individual time-synchronization mentioned explicitly in the description.
- If an axis has a single signal, then the color of the label matches the signal's color. Otherwise, the label color is black.
%% Cell type:markdown id: tags:
# 0. Initialise Working Environment
%% Cell type:code id: tags:
``` python
# External libraries
print('Loading (1/15)'); import pandas as pd
print('Loading (2/15)'); import numpy as np
print('Loading (3/15)'); import sys
print('Loading (4/15)'); from IPython.display import display, Javascript, clear_output, HTML
# Internal libraries
print('Loading (5/15)'); import lhcsmapi
print('Loading (6/15)'); from lhcsmapi.Time import Time
print('Loading (7/15)'); from lhcsmapi.Timer import Timer
print('Loading (8/15)'); from lhcsmapi.analysis.R600ACircuitQuery import R600ACircuitQuery
print('Loading (9/15)'); from lhcsmapi.analysis.R600ACircuitAnalysis import R600ACircuitAnalysis
print('Loading (10/15)'); from lhcsmapi.analysis.expert_input import get_expert_decision
print('Loading (11/15)'); from lhcsmapi.analysis.report_template import apply_report_template
print('Loading (12/15)'); from lhcsmapi.gui.qh.DateTimeBaseModule import DateTimeBaseModule
print('Loading (13/15)'); from lhcsmapi.gui.pc.R600AFgcPmSearchBaseModule import R600ARcdoFgcPmSearchBaseModule
print('Loading (14/15)'); from lhcsmapi.gui.pc.FgcPmSearchModuleMediator import FgcPmSearchModuleMediator
print('Loading (15/15)'); from lhcsmapi.metadata.SignalMetadata import SignalMetadata
analysis_start_time = Time.get_analysis_start_time()
clear_output()
lhcsmapi.get_lhcsmapi_version()
lhcsmapi.get_lhcsmhwc_version('../__init__.py')
```
%% Cell type:markdown id: tags:
# 1. Select FGC Post Mortem Entry
%% Cell type:markdown id: tags:skip_cell
In order to perform the analysis of a FPA in an RCD/O 600A circuit please:
1. Select circuit name (e.g., RC.A12B1)
2. Choose start and end time
3. Choose analysis mode (Automatic by default)
Once these inputs are provided, click 'Find FGC PM entries' button. This will trigger a search of the PM database in order to provide a list of timestamps of FGC events associated with the selected circuit name for the provided period of time. Select one timestamp from the 'FGC PM Entries' list to be processed by the following cells.
**Note that 24 hours is the maximum duration of a single PM query for an event. To avoid delays in querying events, please restrict your query duration as much as possible.**
%% Cell type:code id: tags:
``` python
fgc_pm_search = FgcPmSearchModuleMediator(DateTimeBaseModule(start_date_time='2018-03-19 00:00:00+01:00',
end_date_time='2018-03-20 00:00:00+01:00'), R600ARcdoFgcPmSearchBaseModule(), circuit_type='600A_RCDO')
```
%% Cell type:markdown id: tags:
# 2. Query All Signals Prior to Analysis
%% Cell type:code id: tags:skip_output
``` python
with Timer():
author = fgc_pm_search.get_author()
is_automatic = fgc_pm_search.is_automatic_mode()
circuit_type = '600A'
circuit_names = fgc_pm_search.get_fgc_circuit()
timestamp_fgc_rcd, timestamp_fgc_rco = fgc_pm_search.get_fgc_timestamp()
query_rcd = R600ACircuitQuery(circuit_type, circuit_names[0], max_executions=10)
query_rco = R600ACircuitQuery(circuit_type, circuit_names[1], max_executions=10)
# RCD
i_meas_rcd_df, i_a_rcd_df, i_ref_rcd_df, i_earth_rcd_df = query_rcd.query_pc_pm(timestamp_fgc_rcd, timestamp_fgc_rcd,
signal_names=['I_MEAS', 'I_A', 'I_REF', 'I_EARTH'])
i_meas_rcd_df, i_a_rcd_df, i_ref_rcd_df, i_earth_rcd_df = query_rcd.query_pc_pm(timestamp_fgc_rcd, timestamp_fgc_rcd, signal_names=['I_MEAS', 'I_A', 'I_REF', 'I_EARTH'])
events_action_rcd_df, events_symbol_rcd_df = query_rcd.query_pc_pm_events(timestamp_fgc_rcd, signal_names=['ACTION', 'SYMBOL'])
# RCO
i_meas_rco_df, i_a_rco_df, i_ref_rco_df, i_earth_rco_df = query_rco.query_pc_pm(timestamp_fgc_rco, timestamp_fgc_rco,
signal_names=['I_MEAS', 'I_A', 'I_REF', 'I_EARTH'])
i_meas_rco_df, i_a_rco_df, i_ref_rco_df, i_earth_rco_df = query_rco.query_pc_pm(timestamp_fgc_rco, timestamp_fgc_rco, signal_names=['I_MEAS', 'I_A', 'I_REF', 'I_EARTH'])
events_action_rco_df, events_symbol_rco_df = query_rco.query_pc_pm_events(timestamp_fgc_rco, signal_names=['ACTION', 'SYMBOL'])
# PIC
# # RCD
timestamp_pic_rcd = query_rcd.find_timestamp_pic(timestamp_fgc_rcd, spark=spark)
# # RCO
timestamp_pic_rco = query_rco.find_timestamp_pic(timestamp_fgc_rco, spark=spark)
# QDS NXCALS - To check if there was any drift of QDS cards prior to the trigger
# # RCD
i_meas_nxcals_rcd_df = query_rcd.query_pc_nxcals(timestamp_fgc_rcd, signal_names=['I_MEAS'], spark=spark)[0]
u_res_nxcals_rcd_df = query_rcd.query_iqps_nxcals(timestamp_fgc_rcd, signal_names=['U_RES'], spark=spark)[0]
# # RCO
i_meas_nxcals_rco_df = query_rco.query_pc_nxcals(timestamp_fgc_rco, signal_names=['I_MEAS'], spark=spark)[0]
u_res_nxcals_rco_df = query_rco.query_iqps_nxcals(timestamp_fgc_rco, signal_names=['U_RES'], spark=spark)[0]
# EE
# # RCD
source_timestamp_ee_rcd_df = query_rcd.find_source_timestamp_ee(timestamp_fgc_rcd)
if not source_timestamp_ee_rcd_df.empty:
timestamp_ee_rcd = source_timestamp_ee_rcd_df.loc[0, 'timestamp']
u_dump_res_rcd_df = query_rcd.query_ee_u_dump_res_pm(timestamp_ee_rcd, timestamp_fgc_rcd, system='EE', signal_names=['U_DUMP_RES'])[0]
else:
timestamp_ee_rcd = float('nan')
# QDS PM
# # RCD
source_timestamp_qds_rcd_df = query_rcd.find_source_timestamp_qds(timestamp_fgc_rcd, duration=[(2, 's'), (2, 's')])
timestamp_qds_rcd = np.nan if source_timestamp_qds_rcd_df.empty else source_timestamp_qds_rcd_df.loc[0, 'timestamp']
i_dcct_rcd_df, i_didt_rcd_df, u_res_rcd_df, u_diff_rcd_df = query_rcd.query_qds_pm(timestamp_qds_rcd, timestamp_qds_rcd,
signal_names=['I_DCCT', 'I_DIDT', 'U_RES', 'U_DIFF'])
i_dcct_rcd_df, i_didt_rcd_df, u_res_rcd_df, u_diff_rcd_df = query_rcd.query_qds_pm(timestamp_qds_rcd, timestamp_qds_rcd, signal_names=['I_DCCT', 'I_DIDT', 'U_RES', 'U_DIFF'])
# # RCO
source_timestamp_qds_rco_df = query_rco.find_source_timestamp_qds(timestamp_fgc_rco, duration=[(2, 's'), (2, 's')])
timestamp_qds_rco = np.nan if source_timestamp_qds_rco_df.empty else source_timestamp_qds_rco_df.loc[0, 'timestamp']
i_dcct_rco_df, i_didt_rco_df, u_res_rco_df, u_diff_rco_df = query_rco.query_qds_pm(timestamp_qds_rco, timestamp_qds_rco,
signal_names=['I_DCCT', 'I_DIDT', 'U_RES', 'U_DIFF'])
i_dcct_rco_df, i_didt_rco_df, u_res_rco_df, u_diff_rco_df = query_rco.query_qds_pm(timestamp_qds_rco, timestamp_qds_rco, signal_names=['I_DCCT', 'I_DIDT', 'U_RES', 'U_DIFF'])
# LEADS
# # RCD
leads_name = [x for x in SignalMetadata.get_system_types_per_circuit_name(circuit_type, circuit_names[0]) if 'LEADS' in x][0]
source_timestamp_leads_rcd_df = query_rcd.find_timestamp_leads(timestamp_fgc_rcd, leads_name)
u_hts_leads_rcd_dfs = query_rcd.query_leads(timestamp_fgc_rcd, source_timestamp_leads_rcd_df, system=leads_name, signal_names=['U_HTS'], spark=spark, duration=[(300, 's'), (900, 's')])
u_res_leads_rcd_dfs = query_rcd.query_leads(timestamp_fgc_rcd, source_timestamp_leads_rcd_df, system=leads_name, signal_names=['U_RES'], spark=spark, duration=[(300, 's'), (900, 's')])
# # RCO
leads_name = [x for x in SignalMetadata.get_system_types_per_circuit_name(circuit_type, circuit_names[1]) if 'LEADS' in x][0]
source_timestamp_leads_rco_df = query_rco.find_timestamp_leads(timestamp_fgc_rco, leads_name)
u_hts_leads_rco_dfs = query_rco.query_leads(timestamp_fgc_rco, source_timestamp_leads_rco_df, system=leads_name, signal_names=['U_HTS'], spark=spark, duration=[(300, 's'), (900, 's')])
u_res_leads_rco_dfs = query_rco.query_leads(timestamp_fgc_rco, source_timestamp_leads_rco_df, system=leads_name, signal_names=['U_RES'], spark=spark, duration=[(300, 's'), (900, 's')])
# Create results table - RCD/O
results_table = query_rcd.create_report_analysis_template_rcd(timestamp_fgc_rcd, timestamp_fgc_rco, circuit_names, author=author)
results_table = query_rcd.create_report_analysis_template_rcd(timestamp_fgc_rcd, timestamp_fgc_rco, '../__init__.py', circuit_names, author=author)
analysis_rcd = R600ACircuitAnalysis(circuit_type, results_table, is_automatic=is_automatic)
analysis_rco = R600ACircuitAnalysis(circuit_type, results_table, is_automatic=is_automatic)
timestamp_dct = {'FGC_RCD': timestamp_fgc_rcd, 'FGC_RCO': timestamp_fgc_rco,
'PIC_RCD': timestamp_pic_rcd, 'PIC_RCO': timestamp_pic_rco,
'EE_RCD': timestamp_ee_rcd,
'QDS_A_RCD':source_timestamp_qds_rcd_df.loc[0, 'timestamp'] if len(source_timestamp_qds_rcd_df) > 0 else np.nan,
'QDS_B_RCD':source_timestamp_qds_rcd_df.loc[1, 'timestamp'] if len(source_timestamp_qds_rcd_df) > 1 else np.nan,
'QDS_A_RCO':source_timestamp_qds_rco_df.loc[0, 'timestamp'] if len(source_timestamp_qds_rco_df) > 0 else np.nan,
'QDS_B_RCO':source_timestamp_qds_rco_df.loc[1, 'timestamp'] if len(source_timestamp_qds_rco_df) > 1 else np.nan}
```
%% Cell type:markdown id: tags:
# 3. Timestamps
The analysis for MP3 consists of checking the existence of PM file and of consistency of the PM timestamps (PC, QPS). The criterion of passing this test described in detail in 600APIC2.
In short the following criteria should be checked:
- 2 PM DQAMGNA (A+B) files and 1 PM EE file should be generated for 600 A circuits with EE
- Difference between QPS board A and B timestamp = 1 ms
- PC timestamp is QPS timestamp +/- 20 ms
If one or more of these conditions are not fulfilled, then an in-depth analysis has to be performed by the QPS team.
%% Cell type:code id: tags:
``` python
timestamp_dct = {'FGC_RCD': timestamp_fgc_rcd, 'FGC_RCO': timestamp_fgc_rco,
'PIC_RCD': timestamp_pic_rcd, 'PIC_RCO': timestamp_pic_rco,
'QDS_A_RCD':source_timestamp_qds_rcd_df.loc[0, 'timestamp'] if len(source_timestamp_qds_rcd_df) > 0 else np.nan,
'QDS_B_RCD':source_timestamp_qds_rcd_df.loc[1, 'timestamp'] if len(source_timestamp_qds_rcd_df) > 1 else np.nan,
'QDS_A_RCO':source_timestamp_qds_rco_df.loc[0, 'timestamp'] if len(source_timestamp_qds_rco_df) > 0 else np.nan,
'QDS_B_RCO':source_timestamp_qds_rco_df.loc[1, 'timestamp'] if len(source_timestamp_qds_rco_df) > 1 else np.nan}
analysis_rcd.create_timestamp_table(timestamp_dct, circuit_names[0])
```
%% Cell type:markdown id: tags:
# 4. PC
## 4.1. Main Current
*ANALYSIS*:
- determination of the source of an FPA trigger based on EVENTS.SYMBOL and EVENTS.ACTION signals
- detection of the start of a quench as a deviation of I_A and I_REF
- calculation of the MIITs
- calculation of the quench current
- calculation of the duration of a plateau / the ramp rate before a quench (based on PM event buffer)
*GRAPHS*:
- dashed blue line denotes the start of a quench (I_A starts to deviate from I_REF)
- t = 0 s corresponds to the FGC timestamp
%% Cell type:code id: tags:
``` python
import matplotlib as mpl
mpl.rcParams['savefig.dpi'] = 80
mpl.rcParams['figure.dpi'] = 80
%matplotlib notebook
t_quench = analysis_rcd.find_time_of_quench(i_ref_rcd_df, i_a_rcd_df)
t_quench = 0 if t_quench is None else t_quench
analysis_rcd.plot_i_meas_pc(circuit_names[0], timestamp_fgc_rcd, [i_meas_rcd_df, i_a_rcd_df, i_ref_rcd_df], xlim=(t_quench-1, t_quench+1))
```
%% Cell type:code id: tags:
``` python
analysis_rcd.plot_i_meas_pc_zoom(circuit_names[0], timestamp_fgc_rcd, t_quench, [i_meas_rcd_df, i_a_rcd_df, i_ref_rcd_df], xlim=(t_quench-0.1, t_quench+0.1))
analysis_rcd.analyze_i_meas_pc_trigger(timestamp_fgc_rcd, events_action_rcd_df, events_symbol_rcd_df)
analysis_rcd.calculate_current_miits(i_meas_rcd_df, t_quench, col_name='I_MEAS MIITs RCD')
analysis_rcd.calculate_current_miits(i_meas_rcd_df, t_quench, col_name='MIITS_RCD')
analysis_rcd.calculate_quench_current(i_meas_rcd_df, t_quench, col_name='I_Q_RCD')
analysis_rcd.calculate_current_slope(i_meas_rcd_df, col_name=['Ramp Rate RCD', 'Plateau Duration RCD'])
analysis_rcd.calculate_current_slope(i_meas_rcd_df, col_name=['Ramp rate RCD', 'Plateau duration RCD'])
```
%% Cell type:code id: tags:
``` python
t_quench = analysis_rco.find_time_of_quench(i_ref_rco_df, i_a_rco_df)
t_quench = 0 if t_quench is None else t_quench
analysis_rco.plot_i_meas_pc(circuit_names[1], timestamp_fgc_rco, [i_meas_rco_df, i_a_rco_df, i_ref_rco_df], xlim=(t_quench-1, t_quench+1))
```
%% Cell type:code id: tags:
``` python
analysis_rco.plot_i_meas_pc_zoom(circuit_names[1], timestamp_fgc_rco, t_quench, [i_meas_rco_df, i_a_rco_df, i_ref_rco_df], xlim=(t_quench-0.1, t_quench+0.1))
analysis_rco.analyze_i_meas_pc_trigger(timestamp_fgc_rco, events_action_rco_df, events_symbol_rco_df)
analysis_rco.calculate_current_miits(i_meas_rco_df, t_quench, col_name='I_MEAS MIITs RCO')
analysis_rco.calculate_current_miits(i_meas_rco_df, t_quench, col_name='MIITS_RCO')
analysis_rco.calculate_quench_current(i_meas_rco_df, t_quench, col_name='I_Q_RCO')
analysis_rco.calculate_current_slope(i_meas_rco_df, col_name=['Ramp Rate RCO', 'Plateau Duration RCO'])
analysis_rco.calculate_current_slope(i_meas_rco_df, col_name=['Ramp rate RCO', 'Plateau duration RCO'])
```
%% Cell type:code id: tags:
``` python
analysis_rcd.results_table[['Circuit Name', 'Date', 'Time', 'I_Q_RCD', 'I_Q_RCO', 'I_MEAS MIITs RCD', 'I_MEAS MIITs RCO', 'Ramp Rate RCD', 'Ramp Rate RCO', 'Plateau Duration RCD', 'Plateau Duration RCO']]
analysis_rcd.results_table[['Circuit Name', 'Date (FGC)', 'Time (FGC)', 'I_Q_RCD', 'I_Q_RCO', 'MIITS_RCD', 'MIITS_RCO', 'Ramp rate RCD', 'Ramp rate RCO', 'Plateau duration RCD', 'Plateau duration RCO']]
```
%% Cell type:markdown id: tags:
## 4.2. Earth Current
*ANALYSIS*:
- calculation of the maximum absolute earth current (maintaining the sign)
*GRAPHS*:
- t = 0 s corresponds to the FGC timestamp
%% Cell type:code id: tags:
``` python
analysis_rcd.plot_i_earth_pc(circuit_names[0], timestamp_fgc_rcd, i_earth_rcd_df)
analysis_rcd.calculate_max_i_earth_pc(i_earth_rcd_df, col_name='Earth Current RCD')
analysis_rcd.calculate_max_i_earth_pc(i_earth_rcd_df, col_name='I_Earth_max_RCD')
```
%% Cell type:code id: tags:
``` python
analysis_rco.plot_i_earth_pc(circuit_names[0], timestamp_fgc_rco, i_earth_rco_df)
analysis_rco.calculate_max_i_earth_pc(i_earth_rco_df, col_name='Earth Current RCO')
analysis_rco.calculate_max_i_earth_pc(i_earth_rco_df, col_name='I_Earth_max_RCO')
```
%% Cell type:markdown id: tags:
# 5. EE
The analysis is only performed for circuits with an EE system.
*ANALYSIS*:
- calculation of the maximum voltage over the energy extraction system
*GRAPHS*:
- t = 0 s corresponds to the FGC timestamp
%% Cell type:code id: tags:
``` python
analysis_rcd.analyze_u_dump_res_ee(circuit_names[0], timestamp_fgc_rcd, i_meas_rcd_df, u_dump_res_rcd_df, col_name='U_EE_RCD_max')
```
%% Cell type:code id: tags:
``` python
analysis_rcd.results_table['EE_RCD analysis'] = input('EE_RCD analysis comment: ')
```
%% Cell type:markdown id: tags:
# 5. QDS
The quench voltage U_RES is calculated according to the following formula:
\begin{equation}
U_{\text{RES}} = U_{\text{DIFF}} + L d/dt (I+U_{\text{DIFF}}/R).
\end{equation}
Note that I_DCCT is the QPS signal name, even though the current is actually measured not with a DCCT, but with a LEM detector, hence the poorer quality w.r.t. to the FGC I_A/B/MEAS signals that are measured with a DCCT.
It can be seen from the sign convention in the figure below that a resistive voltage always has opposite sign to the measured current.
<img src="https://gitlab.cern.ch/LHCData/lhc-sm-hwc/-/raw/master/figures/600A/600A_without_EE.png" width=75%>
As U_DIFF contributes directly to U_RES, the resolution of U_RES is, at least partially, limited by that of U_DIFF. Moreover, U_RES is affected by the noisy time derivative of the current signal.
The QPS signals that are communicated to the post-mortem system have only 12 bit resolution.
%% Cell type:markdown id: tags:
## 5.1. Resistive Voltage
*ANALYSIS*:
- Check if the U_RES signal before a quench is increasing for at least one board, which would indicate a QPS trip
- Calculate the initial voltage slope of U_RES signal. The slope is calculated as a ratio of the voltage change from 50 to 200 mV and the corresponding time change.
*GRAPHS*:
First plot (U_RES and I_MEAS prior to a quench)
- t = 0 s corresponds to the FGC timestamp
Second plot (U_RES and the initial slope of U_RES)
- t = 0 s corresponds to the QPS timestamp
%% Cell type:code id: tags:
``` python
analysis_rcd.plot_u_res(circuit_names[0], timestamp_qds_rcd, u_res_nxcals_rcd_df, i_meas_nxcals_rcd_df)
u_res_rcd_slope_df = analysis_rcd.calculate_u_res_slope(u_res_rcd_df, col_name='dUres/dt RCD')
u_res_rcd_slope_df = analysis_rcd.calculate_u_res_slope(u_res_rcd_df, col_name='dU_QPS/dt_RCD')
analysis_rcd.plot_u_res_slope(circuit_names[0], timestamp_qds_rcd, u_res_rcd_df, u_res_rcd_slope_df)
```
%% Cell type:code id: tags:
``` python
analysis_rco.plot_u_res(circuit_names[1], timestamp_qds_rco, u_res_nxcals_rco_df, i_meas_nxcals_rco_df)
u_res_rco_slope_df = analysis_rco.calculate_u_res_slope(u_res_rco_df, col_name='dUres/dt RCO')
u_res_rco_slope_df = analysis_rco.calculate_u_res_slope(u_res_rco_df, col_name='dU_QPS/dt_RCO')
analysis_rco.plot_u_res_slope(circuit_names[1], timestamp_qds_rco, u_res_rco_df, u_res_rco_slope_df)
```
%% Cell type:code id: tags:
``` python
analysis_rcd.results_table[['Circuit Name', 'Date', 'Time', 'dUres/dt RCD', 'dUres/dt RCO']]
analysis_rcd.results_table[['Circuit Name', 'Date (FGC)', 'Time (FGC)', 'dU_QPS/dt_RCD', 'dU_QPS/dt_RCO']]
```
%% Cell type:markdown id: tags:
## 5.2. I_DCCT, I_DIDT Currents; U_RES, U_DIFF Voltages
*ANALYSIS*
- Check the integrity of all four signals (U_DIFF, I_DCCT, I_DIDT and U_RES). If one of the signals (especially U_DIFF or I_DCCT) stays at zero or shows wrong values the cabling of this quench detector could have issues. Compare U_DIFF (measured signal) to U_REF (signal compensated for inductive voltage).
*CRITERIA*
- **U_RES < 0.7*100 mV and noise of U_RES on the plateaus < 20mV**
*GRAPHS*:
- t = 0 s corresponds to the QPS timestamp
%% Cell type:code id: tags:
``` python
analysis_rcd.plot_qds(circuit_names[0], timestamp_qds_rcd, i_dcct_rcd_df, i_didt_rcd_df, u_diff_rcd_df, u_res_rcd_df)
```
%% Cell type:code id: tags:
``` python
analysis_rco.plot_qds(circuit_names[1], timestamp_qds_rco, i_dcct_rco_df, i_didt_rco_df, u_diff_rco_df, u_res_rco_df)
```
%% Cell type:markdown id: tags:
## 5.3. LEADS
*ANALYSIS*:
- check if U_HTS for 2 consecutive datapoints is above the threshold 3 mV
- check if U_RES for 2 consecutive datapoints is above the threshold 100 mV
*GRAPHS*:
- t = 0 s corresponds to the FGC timestamp
%% Cell type:code id: tags:
``` python
analysis_rcd.analyze_leads_voltage(u_hts_leads_rcd_dfs, circuit_names[0], timestamp_fgc_rcd, signal='U_HTS', value_min=-0.003, value_max=0.003)
```
%% Cell type:code id: tags:
``` python
analysis_rcd.analyze_leads_voltage(u_res_leads_rcd_dfs, circuit_names[0], timestamp_fgc_rcd, signal='U_RES', value_min=-0.1, value_max=0.1)
```
%% Cell type:code id: tags:
``` python
analysis_rco.analyze_leads_voltage(u_hts_leads_rco_dfs, circuit_names[1], timestamp_fgc_rco, signal='U_HTS', value_min=-0.003, value_max=0.003)
```
%% Cell type:code id: tags:
``` python
analysis_rco.analyze_leads_voltage(u_res_leads_rco_dfs, circuit_names[1], timestamp_fgc_rco, signal='U_RES', value_min=-0.1, value_max=0.1)
```
%% Cell type:code id: tags:
``` python
# Get expert input on reason
analysis_rco.results_table['Reason'] = get_expert_decision('Circuit Reason for FPA: ', ['RCD', 'RCO', 'other'])
# Get expert input on quench origin
analysis_rco.results_table['Quench Origin'] = get_expert_decision('Origin of a quench', ['magnet', 'busbar', 'other'])
analysis_rco.results_table['FPA Reason'] = get_expert_decision('Reason for FPA: ', ['QPS trip', 'Converter trip', 'EE spurious opening', 'Spurious heater firing', 'Busbar quench', 'Magnet quench', 'HTS current lead quench' ,'RES current lead overvoltage', 'Unknown'])
analysis_rco.results_table['Type of Quench'] = get_expert_decision('Type of Quench: ', ['Training', 'Heater-provoked', 'Beam-induced', 'GHe propagation', 'QPS crate reset', 'Single Event Upset' ,'Short-to-ground', 'EM disturbance' ,'Unknown'])
analysis_rco.results_table['QDS trigger origin'] = get_expert_decision('QDS trigger origin: ', ['QPS', 'HTS current lead', 'RES current lead','Busbar'])
```
%% Cell type:markdown id: tags:
# 6. Analysis Comment
%% Cell type:code id: tags:
``` python
analysis_rco.results_table['Comment'] = input('Comment: ')
```
%% Cell type:markdown id: tags:
# 6. Final Report
# 7. Final Report
%% Cell type:code id: tags:
``` python
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
circuit_name = circuit_names[0]
timestamp_fgc = timestamp_fgc_rcd if not np.isnan(timestamp_fgc_rcd) else timestamp_fgc_rco
date_time_fgc = Time.to_datetime(timestamp_fgc).strftime("%Y-%m-%d-%Hh%M")
prefix_circuit_name = SignalMetadata.parse_600A_circuit_name_to_family_name(circuit_name)
!mkdir -p /eos/project/m/mp3/600A/$prefix_circuit_name/$circuit_name/FPA
file_name = "{}_FPA-{}-{}".format(circuit_name, date_time_fgc, analysis_start_time)
full_path = '/eos/project/m/mp3/600A/{}/{}/FPA/{}.csv'.format(prefix_circuit_name, circuit_name, file_name)
mp3_results_table = analysis_rco.create_mp3_results_table_rcdo()
display(HTML(mp3_results_table.T.to_html()))
mp3_results_table.to_csv(full_path)
mp3_results_table.to_csv(full_path, index=False, sep='\t')
print('MP3 results table saved to (Windows): ' + '\\\\cernbox-smb' + full_path.replace('/', '\\'))
apply_report_template()
full_path = '/eos/project/m/mp3/600A/{}/{}/FPA/{}.html'.format(prefix_circuit_name, circuit_name, file_name)
print('Compact notebook report saved to (Windows): ' + '\\\\cernbox-smb' + full_path.replace('/', '\\'))
display(Javascript('IPython.notebook.save_notebook();'))
Time.sleep(5)
file_name_html = file_name + '.html'
!{sys.executable} -m jupyter nbconvert --to html $'AN_600A_RCDO_FPA.ipynb' --output /eos/project/m/mp3/600A/$prefix_circuit_name/$circuit_name/FPA/$file_name_html --TemplateExporter.exclude_input=True --TagRemovePreprocessor.remove_all_outputs_tags='["skip_output"]' --TagRemovePreprocessor.remove_cell_tags='["skip_cell"]'
```
%% Cell type:code id: tags:
``` python
```
......