Source code for colour.io.tabular

# -*- coding: utf-8 -*-
"""
CSV Tabular Data Input / Output
===============================

Defines various input / output objects for *CSV* tabular data files:

-   :func:`colour.read_spectral_data_from_csv_file`
-   :func:`colour.read_sds_from_csv_file`
-   :func:`colour.write_sds_to_csv_file`
"""

from __future__ import division, unicode_literals

from collections import OrderedDict
import csv

from colour.colorimetry import SpectralDistribution
from colour.constants import DEFAULT_FLOAT_DTYPE

__author__ = 'Colour Developers'
__copyright__ = 'Copyright (C) 2013-2020 - Colour Developers'
__license__ = 'New BSD License - https://opensource.org/licenses/BSD-3-Clause'
__maintainer__ = 'Colour Developers'
__email__ = 'colour-developers@colour-science.org'
__status__ = 'Production'

__all__ = [
    'read_spectral_data_from_csv_file', 'read_sds_from_csv_file',
    'write_sds_to_csv_file'
]


[docs]def read_spectral_data_from_csv_file(path, delimiter=',', fields=None, default=0): """ Reads the spectral data from given *CSV* file in the following form: 390, 4.15003E-04, 3.68349E-04, 9.54729E-03 395, 1.05192E-03, 9.58658E-04, 2.38250E-02 400, 2.40836E-03, 2.26991E-03, 5.66498E-02 ... 830, 9.74306E-07, 9.53411E-08, 0.00000 and returns it as an *OrderedDict* of *dict* as follows: OrderedDict([ ('field', {'wavelength': 'value', ..., 'wavelength': 'value'}), ..., ('field', {'wavelength': 'value', ..., 'wavelength': 'value'})]) Parameters ---------- path : unicode Absolute *CSV* file path. delimiter : unicode, optional *CSV* file content delimiter. fields : array_like, optional *CSV* file spectral data fields names. If no value is provided the first line of the file will be used as spectral data fields names. default : numeric, optional Default value for fields row with missing value. Returns ------- OrderedDict *CSV* file content. Raises ------ RuntimeError If the *CSV* spectral data file doesn't define the appropriate fields. Notes ----- - A *CSV* spectral data file should define at least define two fields: one for the wavelengths and one for the associated values of one spectral distribution. - If no value is provided for the fields names, the first line of the file will be used as spectral data fields names. Examples -------- >>> import os >>> from pprint import pprint >>> csv_file = os.path.join(os.path.dirname(__file__), 'tests', ... 'resources', 'colorchecker_n_ohta.csv') >>> sds_data = read_spectral_data_from_csv_file(csv_file) >>> pprint(list(sds_data.keys())) ['1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12', '13', '14', '15', '16', '17', '18', '19', '20', '21', '22', '23', '24'] """ with open(path, 'rU') as csv_file: reader = csv.DictReader( csv_file, delimiter=str(delimiter), fieldnames=fields) if len(reader.fieldnames) == 1: raise RuntimeError(('A "CSV" spectral data file should define ' 'the following fields: ' '("wavelength", "field 1", ..., "field n")!')) wavelength = reader.fieldnames[0] fields = reader.fieldnames[1:] data = OrderedDict(zip(fields, ({} for _ in range(len(fields))))) for line in reader: for field in fields: try: value = DEFAULT_FLOAT_DTYPE(line[field]) except ValueError: value = default data[field][DEFAULT_FLOAT_DTYPE(line[wavelength])] = value return data
[docs]def read_sds_from_csv_file(path, delimiter=',', fields=None, default=0): """ Reads the spectral data from given *CSV* file and return its content as an *OrderedDict* of :class:`colour.SpectralDistribution` classes. Parameters ---------- path : unicode Absolute *CSV* file path. delimiter : unicode, optional *CSV* file content delimiter. fields : array_like, optional *CSV* file spectral data fields names. If no value is provided the first line of the file will be used for as spectral data fields names. default : numeric Default value for fields row with missing value. Returns ------- OrderedDict :class:`colour.SpectralDistribution` classes of given *CSV* file. Examples -------- >>> from colour.utilities import numpy_print_options >>> import os >>> csv_file = os.path.join(os.path.dirname(__file__), 'tests', ... 'resources', 'colorchecker_n_ohta.csv') >>> sds = read_sds_from_csv_file(csv_file) >>> print(tuple(sds.keys())) ('1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12', '13', \ '14', '15', '16', '17', '18', '19', '20', '21', '22', '23', '24') >>> with numpy_print_options(suppress=True): ... sds['1'] # doctest: +ELLIPSIS SpectralDistribution([[ 380. , 0.048], [ 385. , 0.051], [ 390. , 0.055], [ 395. , 0.06 ], [ 400. , 0.065], [ 405. , 0.068], [ 410. , 0.068], [ 415. , 0.067], [ 420. , 0.064], [ 425. , 0.062], [ 430. , 0.059], [ 435. , 0.057], [ 440. , 0.055], [ 445. , 0.054], [ 450. , 0.053], [ 455. , 0.053], [ 460. , 0.052], [ 465. , 0.052], [ 470. , 0.052], [ 475. , 0.053], [ 480. , 0.054], [ 485. , 0.055], [ 490. , 0.057], [ 495. , 0.059], [ 500. , 0.061], [ 505. , 0.062], [ 510. , 0.065], [ 515. , 0.067], [ 520. , 0.07 ], [ 525. , 0.072], [ 530. , 0.074], [ 535. , 0.075], [ 540. , 0.076], [ 545. , 0.078], [ 550. , 0.079], [ 555. , 0.082], [ 560. , 0.087], [ 565. , 0.092], [ 570. , 0.1 ], [ 575. , 0.107], [ 580. , 0.115], [ 585. , 0.122], [ 590. , 0.129], [ 595. , 0.134], [ 600. , 0.138], [ 605. , 0.142], [ 610. , 0.146], [ 615. , 0.15 ], [ 620. , 0.154], [ 625. , 0.158], [ 630. , 0.163], [ 635. , 0.167], [ 640. , 0.173], [ 645. , 0.18 ], [ 650. , 0.188], [ 655. , 0.196], [ 660. , 0.204], [ 665. , 0.213], [ 670. , 0.222], [ 675. , 0.231], [ 680. , 0.242], [ 685. , 0.251], [ 690. , 0.261], [ 695. , 0.271], [ 700. , 0.282], [ 705. , 0.294], [ 710. , 0.305], [ 715. , 0.318], [ 720. , 0.334], [ 725. , 0.354], [ 730. , 0.372], [ 735. , 0.392], [ 740. , 0.409], [ 745. , 0.42 ], [ 750. , 0.436], [ 755. , 0.45 ], [ 760. , 0.462], [ 765. , 0.465], [ 770. , 0.448], [ 775. , 0.432], [ 780. , 0.421]], interpolator=SpragueInterpolator, interpolator_kwargs={}, extrapolator=Extrapolator, extrapolator_kwargs={...}) """ data = read_spectral_data_from_csv_file(path, delimiter, fields, default) sds = OrderedDict(((key, SpectralDistribution(value, name=key)) for key, value in data.items())) return sds
[docs]def write_sds_to_csv_file(sds, path, delimiter=',', fields=None): """ Writes the given spectral distributions to given *CSV* file. Parameters ---------- sds : dict Spectral distributions to write. path : unicode Absolute *CSV* file path. delimiter : unicode, optional *CSV* file content delimiter. fields : array_like, optional *CSV* file spectral data fields names. If no value is provided the order of fields will be the one defined by the sorted spectral distributions *dict*. Returns ------- bool Definition success. Raises ------ RuntimeError If the given spectral distributions have different shapes. """ if len(sds) != 1: shapes = [sd.shape for sd in sds.values()] if not all(shape == shapes[0] for shape in shapes): raise RuntimeError(('Cannot write spectral distributions ' 'with different shapes to "CSV" file!')) wavelengths = tuple(sds.values())[0].wavelengths with open(path, 'w') as csv_file: fields = list(fields) if fields is not None else sorted(sds.keys()) writer = csv.DictWriter( csv_file, delimiter=str(delimiter), fieldnames=['wavelength'] + fields, lineterminator='\n') writer.writeheader() for wavelength in wavelengths: row = {'wavelength': wavelength} row.update( dict((field, sds[field][wavelength]) for field in fields)) writer.writerow(row) return True