#
# Functions to process parameter data (for Interpolants)
#
import os
import pandas as pd
import json
import numpy as np
def _process_name(name, path, ext):
if not name.endswith(ext):
name = name + ext
# Set the path
if path is not None:
# first look in the specified path
filename = os.path.join(path, name)
if not os.path.exists(filename):
# then look in the "data" subfolder
filename = os.path.join(path, "data", name)
if not os.path.exists(filename):
raise FileNotFoundError(
"Could not find file '{}' in '{}' or '{}'".format(
name, path, os.path.join(path, "data")
)
)
else:
filename = name
# Split the name (in case original name was a path)
_, name = os.path.split(filename)
# Remove the extension from the name
return (filename, name.split(".")[0])
[docs]def process_1D_data(name, path=None):
"""
Process 1D data from a csv file
"""
filename, name = _process_name(name, path, ".csv")
data = pd.read_csv(
filename, comment="#", skip_blank_lines=True, header=None
).to_numpy()
# Save name and data
return (name, ([data[:, 0]], data[:, 1]))
[docs]def process_2D_data(name, path=None):
"""
Process 2D data from a JSON file
"""
filename, name = _process_name(name, path, ".json")
with open(filename, "r") as jsonfile:
json_data = json.load(jsonfile)
data = json_data["data"]
data[0] = [np.array(el) for el in data[0]]
data[1] = np.array(data[1])
return (name, tuple(data))
[docs]def process_2D_data_csv(name, path=None):
"""
Process 2D data from a csv file. Assumes
data is in the form of a three columns
and that all data points lie on a regular
grid. The first column is assumed to
be the 'slowest' changing variable and
the second column the 'fastest' changing
variable, which is the C convention for
indexing multidimensional arrays (as opposed
to the Fortran convention where the 'fastest'
changing variable comes first).
Parameters
----------
name : str
The name to be given to the function
path : str
The path to the file where the three
dimensional data is stored.
Returns
-------
formatted_data: tuple
A tuple containing the name of the function
and the data formatted correctly for use
within three-dimensional interpolants.
"""
filename, name = _process_name(name, path, ".csv")
df = pd.read_csv(filename)
x1 = np.array(list(set(df.iloc[:, 0])))
x2 = np.array(list(set(df.iloc[:, 1])))
value = df.iloc[:, 2].to_numpy()
x1.sort()
x2.sort()
x = (x1, x2)
value_data = np.reshape(
value,
(len(x1), len(x2)),
order="C", # use the C convention
)
formatted_data = (name, (x, value_data))
return formatted_data
[docs]def process_3D_data_csv(name, path=None):
"""
Process 3D data from a csv file. Assumes
data is in the form of four columns and
that all data points lie on a
regular grid. The first column is assumed to
be the 'slowest' changing variable and
the third column the 'fastest' changing
variable, which is the C convention for
indexing multidimensional arrays (as opposed
to the Fortran convention where the 'fastest'
changing variable comes first).
Parameters
----------
name : str
The name to be given to the function
path : str
The path to the file where the three
dimensional data is stored.
Returns
-------
formatted_data: tuple
A tuple containing the name of the function
and the data formatted correctly for use
within three-dimensional interpolants.
"""
filename, name = _process_name(name, path, ".csv")
df = pd.read_csv(filename)
x1 = np.array(list(set(df.iloc[:, 0])))
x2 = np.array(list(set(df.iloc[:, 1])))
x3 = np.array(list(set(df.iloc[:, 2])))
value = df.iloc[:, 3].to_numpy()
x1.sort()
x2.sort()
x3.sort()
x = (x1, x2, x3)
value_data = np.reshape(
value,
(len(x1), len(x2), len(x3)),
order="C",
)
formatted_data = (name, (x, value_data))
return formatted_data