def _find_test_times(
base_dir,
test_date
):
"""
Locates start and end times of tests in a larger dataframe containing
all tube data
Parameters
----------
base_dir : str
Base data directory, (e.g. `/d/Data/Raw/`)
test_date : str
ISO 8601 formatted date of test data
Returns
-------
pd.DataFrame
Dataframe containing start and end times of each test
"""
# end times
# The tube will only automatically go `Closed -> Vent` at the end of
# a completed test cycle
loc_state = os.path.join(base_dir, test_date, "tube state.tdms")
df_state = TdmsFile(loc_state).as_dataframe()
df_state.columns = ["time", "state", "mode"]
df_test_times = pd.DataFrame(columns=["shot", "start", "end"])
df_test_times["end"] = df_state[
(df_state["state"].shift(1) == "Tube Closed") &
(df_state["state"] == "Tube Vent") &
(df_state["mode"] == "auto")
]["time"]
df_test_times.reset_index(drop=True, inplace=True)
df_test_times["shot"] = df_test_times.index.values
# start times
# A test will be considered to have started at the last automatic mix
# section purge preceding its end time.
for i, time in enumerate(df_test_times["end"].values):
df_test_times.at[i, "start"] = df_state[
(df_state["time"].values < time) &
(df_state["state"] == "Mix Section Purge") &
(df_state["mode"] == "auto")
].iloc[-1].time
df_test_times["start"] = pd.to_datetime(df_test_times["start"])
return df_test_times
event_times = []
for time in events_df['Time']:
timestamp = time.split('-')[-1]
hh,mm,ss = timestamp.split(':')
timestamp = 3600*int(hh)+60*int(mm)+int(ss)-int(ignition)
event_times.append(timestamp)
events_df['Time Elapsed']=event_times
print(events_df)
events_df = events_df.set_index('Event')
for event in events_df:
event.split('-')
events_dict[test] = events_df
# exit()
#Read data dataframe
data_df.columns = [column[13:-1] for column in data_df.columns.values]
#Make a list of elapsed time
elapsed_time = []
for t in data_df['Time']:
timestamp = t.split(' ')[-1]
hh,mm,ss = timestamp.split(':')
timestamp = 3600*int(hh)+60*int(mm)+int(ss)-int(ignition)
elapsed_time.append(timestamp)
#Set index as elapsed time
data_df['Elapsed Time'] = elapsed_time
data_df = data_df.set_index('Elapsed Time')
event_times = []
for time in events_df['Time']:
timestamp = time.split('-')[-1]
hh, mm, ss = timestamp.split(':')
timestamp = 3600 * int(hh) + 60 * int(mm) + int(ss) - int(ignition)
event_times.append(timestamp)
events_df['Time Elapsed'] = event_times
print(events_df)
events_df = events_df.set_index('Event')
for event in events_df:
event.split('-')
events_dict[test] = events_df
# exit()
#Read data dataframe
data_df.columns = [column[13:-1] for column in data_df.columns.values]
#Make a list of elapsed time
elapsed_time = []
for t in data_df['Time']:
timestamp = t.split(' ')[-1]
hh, mm, ss = timestamp.split(':')
timestamp = 3600 * int(hh) + 60 * int(mm) + int(ss) - int(ignition)
elapsed_time.append(timestamp)
#Set index as elapsed time
data_df['Elapsed Time'] = elapsed_time
data_df = data_df.set_index('Elapsed Time')
#Divide data datafrane into
pre_exp_data = data_df.loc[:-1, :]
def __call__(
cls,
base_dir,
test_date,
sample_time=pd.Timedelta(seconds=70),
mech="gri30.cti",
diode_spacing=1.0668,
multiprocess=False
):
"""
Process data from a day of testing using the newer directory structure
Parameters
----------
base_dir : str
Base data directory, (e.g. `/d/Data/Raw/`)
test_date : str
ISO 8601 formatted date of test data
sample_time : None or pd.Timedelta
Length of hold period at the end of a fill state. If None is passed,
value will be read from nominal test conditions.
mech : str
Mechanism for cantera calculations
diode_spacing : float
Diode spacing, in meters
multiprocess : bool
Set true to parallelize data analysis
Returns
-------
Tuple[pd.DataFrame, Dict]
Tuple containing a dataframe of test results and a dictionary of
background subtracted schlieren images
"""
dir_data = os.path.join(base_dir, test_date)
df_tests = cls._find_test_times(base_dir, test_date)
n_found_tests = len(df_tests)
n_shot_dirs = len([d for d in os.listdir(dir_data) if "Shot" in d])
if n_found_tests == 0:
raise ValueError("No tests detected in sensor log.tdms")
elif n_found_tests != n_shot_dirs:
raise ValueError("Number of tests does not match number of shots")
df_nominal = cls._load_nominal_conditions(dir_data)
df_sensor = TdmsFile(os.path.join(
dir_data, "sensor log.tdms"
)).as_dataframe()
df_pressure = cls._extract_sensor_data(df_sensor, "pressure")
df_temperature = cls._extract_sensor_data(df_sensor, "temperature")
del df_sensor
df_schlieren = pd.DataFrame(columns=["shot", "schlieren"])
df_schlieren["schlieren"] = _collect_schlieren_dirs(
base_dir, test_date
)
df_schlieren["shot"] = df_schlieren["schlieren"]
df_schlieren["shot"] = [
int(os.path.split(d)[1].lower().replace("shot", "").strip())
for d in df_schlieren["schlieren"]
if "failed" not in d
]
df_tests = df_tests.merge(df_schlieren, on="shot", how="left")
df_diode_locs = pd.DataFrame(columns=["shot", "diodes"])
df_diode_locs["diodes"] = find_diode_data(dir_data)
df_diode_locs["shot"] = [
int(
os.path.split(
os.path.dirname(d))[1].lower().replace(
"shot", ""
).strip()
)
for d in df_diode_locs["diodes"]
]
df_tests = df_tests.merge(df_diode_locs, on="shot", how="left")
df_state = TdmsFile(os.path.join(
base_dir, test_date, "tube state.tdms"
)).as_dataframe()
df_state.columns = ["time", "state", "mode"]
images = dict()
if multiprocess:
pool = mp.Pool()
results = pool.starmap(
cls._process_single_test,
[[
idx,
df_nominal,
df_pressure,
df_temperature,
df_state,
sample_time,
test_time_row,
mech,
diode_spacing
] for idx, test_time_row in df_tests.iterrows()]
)
for idx, row_results in results:
if row_results["schlieren"] is not None:
images.update(row_results["schlieren"])
df_tests.at[idx, "t_0"] = row_results["t_0"]
df_tests.at[idx, "u_t_0"] = row_results["u_t_0"]
df_tests.at[idx, "p_0_nom"] = row_results["p_0_nom"]
df_tests.at[idx, "p_0"] = row_results["p_0"]
df_tests.at[idx, "u_p_0"] = row_results["u_p_0"]
df_tests.at[idx, "phi_nom"] = row_results["phi_nom"]
df_tests.at[idx, "phi"] = row_results["phi"]
df_tests.at[idx, "u_phi"] = row_results["u_phi"]
df_tests.at[idx, "fuel"] = row_results["fuel"]
df_tests.at[idx, "p_fuel"] = row_results["p_fuel"]
df_tests.at[idx, "u_p_fuel"] = row_results["u_p_fuel"]
df_tests.at[idx, "oxidizer"] = row_results["oxidizer"]
df_tests.at[idx, "p_oxidizer"] = row_results["p_oxidizer"]
df_tests.at[idx, "u_p_oxidizer"] = row_results["u_p_oxidizer"]
df_tests.at[idx, "diluent"] = row_results["diluent"]
df_tests.at[idx, "p_diluent"] = row_results["p_diluent"]
df_tests.at[idx, "u_p_diluent"] = row_results["u_p_diluent"]
df_tests.at[idx, "dil_mf_nom"] = row_results["dil_mf_nom"]
df_tests.at[idx, "dil_mf"] = row_results["dil_mf"]
df_tests.at[idx, "u_dil_mf"] = row_results["u_dil_mf"]
df_tests.at[idx, "wave_speed"] = row_results["wave_speed"]
df_tests.at[idx, "u_wave_speed"] = row_results["u_wave_speed"]
df_tests.at[idx, "cutoff_fuel"] = row_results["cutoff_fuel"]
df_tests.at[idx, "cutoff_vacuum"] = row_results["cutoff_vacuum"]
df_tests.at[idx, "cutoff_diluent"] = \
row_results["cutoff_diluent"]
df_tests.at[idx, "cutoff_oxidizer"] = \
row_results["cutoff_oxidizer"]
df_tests.at[idx, "u_cutoff_fuel"] = \
row_results["u_cutoff_fuel"]
df_tests.at[idx, "u_cutoff_vacuum"] = \
row_results["u_cutoff_vacuum"]
df_tests.at[idx, "u_cutoff_diluent"] = \
row_results["u_cutoff_diluent"]
df_tests.at[idx, "u_cutoff_oxidizer"] = \
row_results["u_cutoff_oxidizer"]
else:
for idx, test_time_row in df_tests.iterrows():
# noinspection PyTypeChecker
_, row_results = cls._process_single_test(
idx,
df_nominal,
df_pressure,
df_temperature,
df_state,
sample_time,
test_time_row,
mech,
diode_spacing
)
# output results
if row_results["schlieren"] is not None:
images.update(row_results["schlieren"])
df_tests.at[idx, "t_0"] = row_results["t_0"]
df_tests.at[idx, "u_t_0"] = row_results["u_t_0"]
df_tests.at[idx, "p_0_nom"] = row_results["p_0_nom"]
df_tests.at[idx, "p_0"] = row_results["p_0"]
df_tests.at[idx, "u_p_0"] = row_results["u_p_0"]
df_tests.at[idx, "phi_nom"] = row_results["phi_nom"]
df_tests.at[idx, "phi"] = row_results["phi"]
df_tests.at[idx, "u_phi"] = row_results["u_phi"]
df_tests.at[idx, "fuel"] = row_results["fuel"]
df_tests.at[idx, "p_fuel"] = row_results["p_fuel"]
df_tests.at[idx, "u_p_fuel"] = row_results["u_p_fuel"]
df_tests.at[idx, "oxidizer"] = row_results["oxidizer"]
df_tests.at[idx, "p_oxidizer"] = row_results["p_oxidizer"]
df_tests.at[idx, "u_p_oxidizer"] = row_results["u_p_oxidizer"]
df_tests.at[idx, "diluent"] = row_results["diluent"]
df_tests.at[idx, "p_diluent"] = row_results["p_diluent"]
df_tests.at[idx, "u_p_diluent"] = row_results["u_p_diluent"]
df_tests.at[idx, "dil_mf_nom"] = row_results["dil_mf_nom"]
df_tests.at[idx, "dil_mf"] = row_results["dil_mf"]
df_tests.at[idx, "u_dil_mf"] = row_results["u_dil_mf"]
df_tests.at[idx, "wave_speed"] = row_results["wave_speed"]
df_tests.at[idx, "u_wave_speed"] = row_results["u_wave_speed"]
df_tests.at[idx, "cutoff_fuel"] = row_results["cutoff_fuel"]
df_tests.at[idx, "cutoff_vacuum"] = row_results["cutoff_vacuum"]
df_tests.at[idx, "cutoff_diluent"] = \
row_results["cutoff_diluent"]
df_tests.at[idx, "cutoff_oxidizer"] = \
row_results["cutoff_oxidizer"]
df_tests.at[idx, "u_cutoff_fuel"] = \
row_results["u_cutoff_fuel"]
df_tests.at[idx, "u_cutoff_vacuum"] = \
row_results["u_cutoff_vacuum"]
df_tests.at[idx, "u_cutoff_diluent"] = \
row_results["u_cutoff_diluent"]
df_tests.at[idx, "u_cutoff_oxidizer"] = \
row_results["u_cutoff_oxidizer"]
df_tests["date"] = test_date
# keep diluent dtype consistent as object. Using notna because pd.where
# works backwards compared with np.where and also my brain.
df_tests["diluent"].where(
df_tests["diluent"].notna(),
"None",
inplace=True
)
return df_tests, images
