1.5.1.2.5. polar2grid.compare module

Script for comparing writer output.

class polar2grid.compare.ArrayComparisonResult(almost_equal: 'bool', num_diff_pixels: 'int', total_pixels: 'int', different_shape: 'bool')

Bases: object

almost_equal: bool

different_shape: bool

property failed

num_diff_pixels: int

total_pixels: int

class polar2grid.compare.CompareHelper(atol: float = 0.0, rtol: float = 0.0, margin_of_error: float = 0.0, create_plot: bool = False)

Bases: object

Wrapper around various comparison operations.

compare(input1, input2, **kwargs) → list[polar2grid.compare.FileComparisonResults]

compare_dirs(dir1, dir2, **kwargs) → list[polar2grid.compare.FileComparisonResults]

compare_files(file1, file2, file_type=None, **kwargs)

class polar2grid.compare.FileComparisonResults(file1: 'str', file2: 'str', files_missing: 'bool', unknown_file_type: 'bool', sub_results: 'list[ArrayComparisonResult]' = <factory>)

Bases: object

property any_failed: bool

file1: str

file2: str

files_missing: bool

sub_results: list[polar2grid.compare.ArrayComparisonResult]

unknown_file_type: bool

class polar2grid.compare.FlatArrayComparisonResult(almost_equal: 'bool', num_diff_pixels: 'int', total_pixels: 'int', different_shape: 'bool', shape1: 'tuple', shape2: 'tuple', dtype1: 'np.dtype', dtype2: 'np.dtype')

Bases: ArrayComparisonResult

dtype1: dtype

dtype2: dtype

shape1: tuple

shape2: tuple

class polar2grid.compare.VariableComparisonResult(almost_equal: 'bool', num_diff_pixels: 'int', total_pixels: 'int', different_shape: 'bool', variable: 'str', variable_missing: 'bool')

Bases: ArrayComparisonResult

property failed

variable: str

variable_missing: bool

polar2grid.compare._compare_gtiff_colormaps(cmap1: dict, cmap2: dict, **kwargs) → VariableComparisonResult

polar2grid.compare._file_type(str_val)

polar2grid.compare._generate_html_summary(output_filename, file_comparison_results)

polar2grid.compare._generate_matplotlib_1d_thumbnail(input_arr, output_thumbnail_path, max_width) → bool

polar2grid.compare._generate_matplotlib_thumbnail(input_arr, output_thumbnail_path, max_width=512) → bool

polar2grid.compare._generate_pillow_thumbnail(input_arr, output_thumbnail_path, max_width=512) → bool

polar2grid.compare._generate_subresult_table_row(img_dst_dir: str, file_comparison_result: FileComparisonResults, sub_result: ArrayComparisonResult) → str

polar2grid.compare._generate_table_rows(output_filename: str, file_comparison_results: list[polar2grid.compare.FileComparisonResults]) → list[str]

polar2grid.compare._generate_thumbnail(input_arr, output_thumbnail_path, max_width=512) → bool

polar2grid.compare._generate_thumbnail_html(data_pathname: str, variable: Optional[str], img_dst_dir: str, tn_suffix: str, shape: Optional[tuple], dtype: Optional[dtype]) → str

polar2grid.compare._get_binary_array(input_filename: str, variable: str, shape: Optional[tuple], dtype: dtype) → Optional[ndarray]

polar2grid.compare._get_geotiff_array(gtiff_fn, dtype=None)

polar2grid.compare._get_geotiff_colormap(gtiff_fn, band_idx=1)

polar2grid.compare._get_hdf5_array(input_filename: str, variable: str, shape: Optional[tuple], dtype: Optional[dtype]) → Optional[ndarray]

polar2grid.compare._get_hdf5_variables(variables, name, obj)

polar2grid.compare._get_image_array(img_filename: str, variable: Optional[str] = None, shape: Optional[tuple] = None, dtype: Optional[dtype] = None) → Optional[ndarray]

polar2grid.compare._get_mpl_figsize(input_shape, max_width) → tuple[int, int]

polar2grid.compare._get_netcdf_array(input_filename: str, variable: str, shape: Optional[tuple], dtype: Optional[dtype]) → Optional[ndarray]

polar2grid.compare._get_thumbnail_array(input_data_path: str, variable: Optional[str], shape: Optional[tuple], dtype: Optional[dtype]) → Optional[ndarray]

polar2grid.compare._tranpose_for_thumbnail_if_multiband_array(arr: ndarray) → ndarray

polar2grid.compare.compare_array(array1, array2, plot=False, **kwargs) → ArrayComparisonResult

polar2grid.compare.compare_binary(fn1, fn2, shape, dtype, atol=0.0, margin_of_error=0.0, **kwargs) → list[polar2grid.compare.ArrayComparisonResult]

polar2grid.compare.compare_geotiff(gtiff_fn1, gtiff_fn2, atol=0.0, margin_of_error=0.0, **kwargs) → list[polar2grid.compare.ArrayComparisonResult]

Compare 2 single banded geotiff files.

Note

The binary arrays will be converted to 32-bit floats before comparison.

polar2grid.compare.compare_hdf5(h1_name, h2_name, variables, atol=0.0, margin_of_error=0.0, **kwargs) → list[polar2grid.compare.ArrayComparisonResult]

polar2grid.compare.compare_image(im1_name, im2_name, atol=0.0, margin_of_error=0.0, **kwargs) → list[polar2grid.compare.ArrayComparisonResult]

polar2grid.compare.compare_netcdf(nc1_name, nc2_name, variables, atol=0.0, margin_of_error=0.0, **kwargs) → list[polar2grid.compare.VariableComparisonResult]

polar2grid.compare.isclose_array(array1, array2, atol=0.0, rtol=0.0, margin_of_error=0.0, **kwargs) → ArrayComparisonResult

Compare 2 binary arrays per pixel.

Two pixels are considered different if the absolute value of their difference is greater than 1. This function assumes the arrays are in useful data types, which may cause erroneous results. For example, if both arrays are unsigned integers and the different results in a negative value overflow will occur and the threshold will likely not be met.

Parameters:

• array1 – numpy array for comparison

• array2 – numpy array for comparison

Returns:

1 if more than margin_of_error pixels are different, 0 otherwise.

polar2grid.compare.main(argv=['-b', 'html', '-d', 'build/doctrees', 'source', 'build/html'])

polar2grid.compare.num_failed_files(file_comparison_results: list[polar2grid.compare.FileComparisonResults]) → int

polar2grid.compare.plot_array(array1, array2, cmap='viridis', vmin=None, vmax=None, **kwargs)

Debug two arrays being different by visually comparing them.