LinearResidualsResultCSVModel

class LinearResidualsResultCSVModel

Bases: TimeSeriesResultCSVModel

LinearResidualsResultCSVModel

Attributes

remote_file_name: str

Default:

'linear_residual_v2.csv'

local_file_name: str

Default:

None

do_download: bool, optional

Default:

None

local_storage: str, optional

Default:

None

temp_file: str

Default:

'/tmp/tmpx48idwvm/055f86b1-368b-47da-9fa3-a4e9ef4abed4.csv'

Properties

x_columns

Get x column

raw_values

Get the raw CSV data.

Returns:

Dictionary containing the raw CSV data.

Return type:

dict

values

Get the current data.

Returns:

Dictionary containing the current data.

Return type:

dict

averages

Get average data over last 10%

Returns:

Dictionary containing CL, CD, CFx/y/z, CMx/y/z and other columns available in data

Return type:

dict

Additional Constructors

classmethod from_dict(data)

Load from data dictionary

Parameters:

data (dict)

Methods

load_from_local(filename)

Load CSV data from a local file.

Parameters:

filename (str) – Path to the local CSV file.

load_from_remote(**kwargs_download)

Load CSV data from a remote source.

reload_data(filter_physical_steps_only=False, include_time=False)

Change default behavior of data loader, reload

Parameters:

• filter_physical_steps_only (bool)

• include_time (bool)

download(to_file=None, to_folder='.', overwrite=False, **kwargs)

Download the CSV file.

Parameters:

• to_file (str, optional) – The name of the file after downloading.

• to_folder (str, optional) – The folder where the file will be downloaded.

• overwrite (bool, optional) – Flag indicating whether to overwrite existing files.

update(df)

Update containing value to the given DataFrame

Parameters:

df (DataFrame)

to_file(filename=None)

Save the data to a CSV file.

Parameters:

filename (str, optional) – The name of the file to save the CSV data.

as_dict()

Convert the data to a dictionary.

Returns:

Dictionary containing the data.

Return type:

dict

as_numpy()

Convert the data to a NumPy array.

Returns:

NumPy array containing the data.

Return type:

numpy.ndarray

as_dataframe()

Convert the data to a Pandas DataFrame.

Returns:

DataFrame containing the data.

Return type:

pandas.DataFrame

wait(timeout_minutes=60)

Wait until the Case finishes processing, refresh periodically. Useful for postprocessing, eg sectional data

include_time()

Set the option to include time in the data

filter_physical_steps_only()

filters data to contain only last pseudo step data for every physical step

get_averages(average_fraction)

Computes the average of data