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Commit 76526650 authored by JOSSOUD Olivier's avatar JOSSOUD Olivier
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Flow Processor. Debubbler effect test data.

parent 7af61021
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Showing with 96 additions and 12 deletions
cfatools/processor/flow.py
View file @ 76526650
......@@ -43,34 +43,118 @@ def get_flow_timeseries(dataset: DatasetReader):
def get_debubbler_effect(dataset: DatasetReader):
pump_df = get_bubble_density_from_pump(dataset)
conduct_df = dataset.get_timeseries("CONDUCTI_periodic")
conduct_df = conduct_df[["C1"]]
# conduct_df = conduct_df[conduct_df["C1"] > 1.35]
conduct_df["datetime_C1"] = conduct_df.index
# conduct_df = dataset.get_timeseries("CONDUCTI_periodic")
# conduct_df = conduct_df[["C1"]]
# conduct_df["datetime_C1"] = conduct_df.index
conduct_df = pd.DataFrame({"datetime_C1": ["2021-02-12 15:57:48.90+00:00",
"2021-02-12 16:01:13.83+00:00",
"2021-02-12 16:06:12.04+00:00",
"2021-02-12 16:09:40.08+00:00",
"2021-02-12 16:11:22.75+00:00",
"2021-02-12 16:15:37.19+00:00",
"2021-02-12 16:18:09.62+00:00",
"2021-02-12 16:22:12.37+00:00",
"2021-02-12 16:24:41.02+00:00",
"2021-02-12 16:30:33.91+00:00",
"2021-02-12 16:32:25.83+00:00",
"2021-02-12 16:38:04.73+00:00",
"2021-02-12 16:42:14.41+00:00",
"2021-02-12 16:45:20.80+00:00",
"2021-02-12 16:48:19.08+00:00"],
"datetime_C2_reel": ["2021-02-12 15:57:57.64+00:00",
"2021-02-12 16:01:28.72+00:00",
"2021-02-12 16:06:24.72+00:00",
"2021-02-12 16:09:48.79+00:00",
"2021-02-12 16:11:30.73+00:00",
"2021-02-12 16:16:03.03+00:00",
"2021-02-12 16:18:32.37+00:00",
"2021-02-12 16:22:36.23+00:00",
"2021-02-12 16:25:02.58+00:00",
"2021-02-12 16:30:47.83+00:00",
"2021-02-12 16:32:38.51+00:00",
"2021-02-12 16:38:31.58+00:00",
"2021-02-12 16:42:38.04+00:00",
"2021-02-12 16:45:36.56+00:00",
"2021-02-12 16:48:33.43+00:00"],
"datetime_C4_reel": ["2021-02-12 15:58:09.00+00:00",
"2021-02-12 16:01:45.17+00:00",
"2021-02-12 16:06:39.78+00:00",
"2021-02-12 16:09:58.38+00:00",
"2021-02-12 16:11:39.23+00:00",
"2021-02-12 16:16:28.00+00:00",
"2021-02-12 16:18:55.56+00:00",
"2021-02-12 16:22:55.71+00:00",
"2021-02-12 16:25:20.49+00:00",
"2021-02-12 16:31:02.87+00:00",
"2021-02-12 16:32:51.78+00:00",
"2021-02-12 16:39:02.38+00:00",
"2021-02-12 16:43:06.65+00:00",
"2021-02-12 16:45:55.25+00:00",
"2021-02-12 16:48:49.91+00:00"],
"datetime_C5_reel": ["2021-02-12 15:58:21.479+00:00",
"2021-02-12 16:01:54.363+00:00",
"2021-02-12 16:06:46.968+00:00",
"2021-02-12 16:10:15.368+00:00",
"2021-02-12 16:11:52.210+00:00",
"2021-02-12 16:16:22.574+00:00",
"2021-02-12 16:18:49.811+00:00",
"",
"",
"",
"",
"2021-02-12 16:38:39.968+00:00",
"2021-02-12 16:42:45.176+00:00",
"2021-02-12 16:45:48.111+00:00",
"2021-02-12 16:48:42.705+00:00"]
})
conduct_df["datetime_C1"] = pd.to_datetime(conduct_df["datetime_C1"])
conduct_df["datetime_C2_reel"] = pd.to_datetime(conduct_df["datetime_C2_reel"])
conduct_df["datetime_C4_reel"] = pd.to_datetime(conduct_df["datetime_C4_reel"])
conduct_df["datetime_C5_reel"] = pd.to_datetime(conduct_df["datetime_C5_reel"])
conduct_df = conduct_df.set_index("datetime_C1", drop=False)
com_water_df = pump_df[["com_water_pump"]]
com_water_df = com_water_df.rename(columns={"com_water_pump": "mlmin"})
conduct_df["datetime_debubbler"] = get_datetime_out(conduct_df["datetime_C1"], com_water_df, 0.087, parallel=False)
vol_debub_C5 = 0.630
vol_debub_T1 = 0.432
vol_debub_C4 = 1.187
vol_T1_C4 = vol_debub_C4 - vol_debub_T1
vol_T1_C5 = vol_debub_C5 - vol_debub_T1
vol_T1_C2 = 0.108
vol_T1_C4 = 0.808
vol_T1_C5 = 0.198
colpic_df = pump_df[["pic", "col"]]
colpic_df = colpic_df.ewm(alpha=0.05).mean()
colpic_df["mlmin"] = colpic_df["pic"] + colpic_df["col"]
conduct_df["datetime_T1"] = get_datetime_out(conduct_df["datetime_debubbler"], colpic_df, vol_debub_T1, parallel=False)
col_df = pump_df[["col"]]
col_df = col_df.rename(columns={"col": "mlmin"})
conduct_df["datetime_C2"] = get_datetime_out(conduct_df["datetime_T1"], col_df, vol_T1_C2, parallel=False)
conduct_df["datetime_C4"] = get_datetime_out(conduct_df["datetime_T1"], col_df, vol_T1_C4, parallel=False)
pic_df = pump_df[["pic"]]
pic_df = pic_df.rename(columns={"pic": "mlmin"})
conduct_df["datetime_C5"] = get_datetime_out(conduct_df["datetime_T1"], pic_df, vol_T1_C5, parallel=False)
conduct_df["diff_C2_C1_reel"] = conduct_df["datetime_C2_reel"] - conduct_df["datetime_C1"]
conduct_df["diff_C2_C1"] = conduct_df["datetime_C2"] - conduct_df["datetime_C1"]
conduct_df["diff_diff_C2_C1"] = 100 * abs(conduct_df["diff_C2_C1"] - conduct_df["diff_C2_C1_reel"]) / conduct_df[
"diff_C2_C1_reel"]
conduct_df["diff_C4_C1_reel"] = conduct_df["datetime_C4_reel"] - conduct_df["datetime_C1"]
conduct_df["diff_C4_C1"] = conduct_df["datetime_C4"] - conduct_df["datetime_C1"]
conduct_df["diff_diff_C4_C1"] = 100 * abs(conduct_df["diff_C4_C1"] - conduct_df["diff_C4_C1_reel"]) / conduct_df[
"diff_C4_C1_reel"]
conduct_df["diff_C4_C2_reel"] = conduct_df["datetime_C4_reel"] - conduct_df["datetime_C2_reel"]
conduct_df["diff_C4_C2"] = conduct_df["datetime_C4"] - conduct_df["datetime_C2"]
conduct_df["diff_diff_C4_C2"] = 100 * abs(conduct_df["diff_C4_C2"] - conduct_df["diff_C4_C2_reel"]) / conduct_df["diff_C4_C2_reel"]
conduct_df["diff_C5_C1_reel"] = conduct_df["datetime_C5_reel"] - conduct_df["datetime_C1"]
conduct_df["diff_C5_C1"] = conduct_df["datetime_C5"] - conduct_df["datetime_C1"]
conduct_df["diff_diff_C5_C1"] = 100 * abs(conduct_df["diff_C5_C1"] - conduct_df["diff_C5_C1_reel"]) / conduct_df[
"diff_C5_C1_reel"]
pass
......@@ -148,7 +232,6 @@ def get_bubble_density_from_conduct(dataset: DatasetReader):
return compump_df
def get_datetime_out(datetime_in: pd.Series, mlmin_df: pd.DataFrame, tube_volume_ml: float, parallel: bool = True) -> pd.Series:
"""Get the date/time the fluid reaches the end of the tube.
......@@ -231,7 +314,7 @@ def __get_single_datetime_out__(datetime_in: pd.Timestamp, mlmin_df: pd.DataFram
# Compute the duration between the last data line and the moment when the tube's volume is reached
missing_volume = (tube_volume_ml + initial_volume) - object_mlmin_df['ml_cumul'].iloc[-1]
last_speed = object_mlmin_df['mlmin'].iloc[-1]
missing_duration_min = float(missing_volume * last_speed)
missing_duration_min = float(missing_volume / last_speed)
# Compute the datetime when the tube's output is reached
datetime_out = object_mlmin_df.index[-1] + datetime.timedelta(minutes=missing_duration_min)
......
cfatools/tests/test_processor/test_flow.py
View file @ 76526650
......@@ -59,7 +59,8 @@ class TestFlow(TestCase):
icecore_df.set_index("datetime_in", inplace=True)
icecore_df["datetime_in"] = icecore_df.index
icecore_df["datetime_out"] = flow.get_datetime_out(icecore_df["datetime_in"], mlmin_df, tube_volume_ml)
icecore_df["datetime_out"] = flow.get_datetime_out(icecore_df["datetime_in"], mlmin_df, tube_volume_ml,
parallel=False)
self.assertEqual(icecore_df.iloc[0]["datetime_out"], pd.to_datetime("2021-01-01 00:25:00"))
self.assertEqual(icecore_df.iloc[1]["datetime_out"], pd.to_datetime("2021-01-01 00:29:00"))
......
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