From 3d64fd689e611a70e9d1f1432a3db8cfa8cdd7da Mon Sep 17 00:00:00 2001
From: Olivier Jossoud <olivier.jossoud@lsce.ipsl.fr>
Date: Tue, 8 Oct 2019 15:45:55 +0200
Subject: [PATCH] Instant_config_df: fill NAs with previous values, to match
new CFA conductcalib setup.
---
src/dataprovider/conductcalibprovider.py | 16 ++++++++++++++++
1 file changed, 16 insertions(+)
diff --git a/src/dataprovider/conductcalibprovider.py b/src/dataprovider/conductcalibprovider.py
index bc8e245..730309e 100644
--- a/src/dataprovider/conductcalibprovider.py
+++ b/src/dataprovider/conductcalibprovider.py
@@ -61,18 +61,34 @@ class CalibRun:
def update_steps_df(self):
config_df = self.instant_config_df.copy()
+
+ # From long to wide format
config_df = config_df.pivot(index='datetime', columns='name', values='value')
+
+ # Forward fill NAs. Values not changing are not recorded in instant_config_df, so fill with previous value.
+ config_df = config_df.fillna(method='ffill')
+
+ # Compute step number for each line
config_df["step"] = np.arange(len(config_df)) + 1
+
+ # Set start/end datetime for each step
config_df["start_datetime"] = config_df.index
config_df["end_datetime"] = config_df["start_datetime"].shift(-1)
+
+ # For the last step, compute the end_datetime from expected length written in User-defined config file.
config_df.loc[config_df.index[-1], 'end_datetime'] = \
config_df.loc[config_df.index[-1], 'start_datetime'] \
+ datetime.timedelta(seconds=int(self.user_config_df.loc[self.user_config_df.index[-1], 'length_sec']))
config_df.reset_index(drop=True, inplace=True)
+
+ # Reshape: separate column for excite and headgain
config_df = pd.wide_to_long(config_df, ["excite", "headgain"], i="step", j="channel", sep="_").reset_index()
+
+ # Add "stable" timeframe limit for each step, i.e. remove transition phases at the beginning/end of each step.
config_df["stable_start_datetime"] = config_df["start_datetime"] + datetime.timedelta(seconds=2)
config_df["stable_end_datetime"] = config_df["end_datetime"] - datetime.timedelta(seconds=2)
+ # Compute mean, standard deviation and validity for each step/channelComment
for channel in range(1, 6):
channel_config_df = config_df[config_df["channel"] == channel].copy()
for index, row in channel_config_df.iterrows():
--
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