zospy.analyses.mtf.fft_through_focus_mtf.FFTThroughFocusMTFResult

zospy.analyses.mtf.fft_through_focus_mtf.FFTThroughFocusMTFResult#

class zospy.analyses.mtf.fft_through_focus_mtf.FFTThroughFocusMTFResult#

Bases: RootModel[list[FFTThroughFocusMTFData]]

Attributes:
model_extra

Get extra fields set during validation.

model_fields_set

Returns the set of fields that have been explicitly set on this model instance.

Methods

copy(*[, include, exclude, update, deep])

Returns a copy of the model.

model_construct(root[, _fields_set])

Create a new model using the provided root object and update fields set.

model_copy(*[, update, deep])

!!! abstract "Usage Documentation"

model_dump(*[, mode, include, exclude, ...])

!!! abstract "Usage Documentation"

model_dump_json(*[, indent, ensure_ascii, ...])

!!! abstract "Usage Documentation"

model_json_schema(by_alias, ref_template, ...)

Generates a JSON schema for a model class.

model_parametrized_name(params)

Compute the class name for parametrizations of generic classes.

model_post_init(context, /)

Override this method to perform additional initialization after __init__ and model_construct.

model_rebuild(*[, force, raise_errors, ...])

Try to rebuild the pydantic-core schema for the model.

model_validate(obj, *[, strict, extra, ...])

Validate a pydantic model instance.

model_validate_json(json_data, *[, strict, ...])

!!! abstract "Usage Documentation"

model_validate_strings(obj, *[, strict, ...])

Validate the given object with string data against the Pydantic model.

to_dataframe()

Convert the data to a Pandas DataFrame.

construct

dict

from_orm

json

parse_file

parse_obj

parse_raw

schema

schema_json

update_forward_refs

validate

model_config = {}#

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

to_dataframe() DataFrame#

Convert the data to a Pandas DataFrame.

The separate dataframes for each field are combined in a DataFrame in long format. In addition to the columns for each wavelength, the returned DataFrame has the following columns:

  • Direction: The direction of the fan, either ‘Tangential’ or ‘Sagittal’.

  • Field Number: The field number.

  • Field: The field coordinate.

Returns:
DataFrame

The data in long format.