def _load_csv(self, csv_file):
"""
Load simulation data from CSV file and return a numpy array.
"""
if csv_file is None:
return None
df = pd.read_csv(csv_file)
if df.isnull().values.any():
raise ValueError("CSV file contains missing values. Please check data consistency.")
data = df.to_numpy()
if data.ndim != 2:
raise ValueError("Data from CSV is not 2-dimensional (time versus variable)")
if data.shape[0] < 2:
logger.warning("CSV data does not contain more than one time step.")
if data.shape[1] < (self.system.dae.m + self.system.dae.n):
logger.warning("CSV data contains fewer variables than required.")
logger.warning("Check if the CSV data file is generated from the test case.")
# set start and end times from data
self.config.t0 = data[0, 0]
self.config.tf = data[-1, 0]
return data
def list2array(self):
"""
Set internal storage for timeseries data.
Open file and read data into internal storage.
"""
# TODO: timeseries file must exist for setup to pass. Consider moving
# the file reading to a later stage so that adding sheets to xlsx file can work
# without the file existing.
Model.list2array(self)
# read and store data
for ii in range(self.n):
idx = self.idx.v[ii]
path = self.path.v[ii]
sheet = self.sheet.v[ii]
if not os.path.isabs(path):
path = os.path.join(self.system.files.case_path, path)
if not os.path.exists(path):
raise FileNotFoundError('<%s idx=%s>: File not found: "%s"',
self.class_name, idx, path)
# --- read supported formats ---
if path.endswith("xlsx") or path.endswith("xls"):
df = self._read_excel(path, sheet, idx)
elif path.endswith("csv"):
df = pd.read_csv(path)
for field in self.fields.v[ii]:
if field not in df.columns:
raise ValueError(
'Field {} not found in timeseries data'.format(field))
self._data[idx] = df
logger.info('Read timeseries data from "%s"', path)