How to zoom in the CIE 1931 Chromaticity Diagram?

[goofy2k]

Hi, I create a CIE1931 chromaticity diagram with colour.plotting.temperature.plot_planckian_locus_in_chromaticity_diagram (or alike).

How can I plot a limited x,y range? I tried plt.xlim etc. But without succes. Thanks, Fred

[KelSolaar]

Hi @goofy2k,

If using colour native plotting features:

>>> import colour
>>> colour.plotting.temperature.plot_planckian_locus_in_chromaticity_diagram(['D65'], bounding_box=[0.4, 0.6, 0, 1])

Or if you want to do it via Matplotlib, you will need to use the standalone keyword as we are rendering/showing the figure by default:

>>> import colour
>>> import matplotlib.pyplot as plt
>>> colour.plotting.temperature.plot_planckian_locus_in_chromaticity_diagram(['D65'], standalone=False)
>>> plt.xlim([0.4, 0.6])
>>> plt.show()

[goofy2k]

Thanks Thomas, I will give this a try! I notice that I have to learn the difference between colours-native plotting and the other way. I use a headless system (server) to run the python code. So, I don't use plt.show but save the plot in a file that I access via a browser. Thanks, Fred Op vr 22 jan. 2021 20:57 schreef Thomas Mansencal <[email protected]

…

: Hi @goofy2k <https://github.com/goofy2k>, If using colour native plotting features: >>> import colour>>> colour.plotting.temperature.plot_planckian_locus_in_chromaticity_diagram(['D65'], bounding_box=[0.4, 0.6, 0, 1]) Or if you want to do it via Matplotlib, you will need to use the standalone keyword as we are rendering/showing the figure by default: >>> import colour>>> import matplotlib.pyplot as plt>>> colour.plotting.temperature.plot_planckian_locus_in_chromaticity_diagram(['D65'], standalone=False) (<Figure size 640x640 with 1 Axes>, <AxesSubplot:title={'center':'D65 Illuminants - Planckian Locus\nCIE 1931 Chromaticity Diagram - CIE 1931 2 Degree Standard Observer'}, xlabel='CIE x', ylabel='CIE y'>)>>> plt.xlim([0.4, 0.6]) (0.4, 0.6)>>> plt.show() — You are receiving this because you were mentioned. Reply to this email directly, view it on GitHub <#761 (comment)>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/AGII3TQ5CLBYYNYYHDPQ3ODS3HKBPANCNFSM4WNUOJNA> .

[KelSolaar]

Ah sweet, so all our plotting definitions can do that directly by passing a keyword argument like that:filename=my_figure.png, most of them are implemented in the colour.plotting.render definition that we call at the end of all our definitions: https://github.com/colour-science/colour/blob/develop/colour/plotting/common.py#L470

    Other Parameters
    ----------------
    figure : Figure, optional
        Figure to apply the render elements onto.
    axes : Axes, optional
        Axes to apply the render elements onto.
    filename : unicode, optional
        Figure will be saved using given ``filename`` argument.
    standalone : bool, optional
        Whether to show the figure and call :func:`plt.show` definition.
    aspect : unicode, optional
        Matplotlib axes aspect.
    axes_visible : bool, optional
        Whether the axes are visible. Default is *True*.
    bounding_box : array_like, optional
        Array defining current axes limits such
        `bounding_box = (x min, x max, y min, y max)`.
    tight_layout : bool, optional
        Whether to invoke the :func:`plt.tight_layout` definition.
    legend : bool, optional
        Whether to display the legend. Default is *False*.
    legend_columns : int, optional
        Number of columns in the legend. Default is *1*.
    transparent_background : bool, optional
        Whether to turn off the background patch. Default is *True*.
    title : unicode, optional
        Figure title.
    wrap_title : unicode, optional
        Whether to wrap the figure title. Default is *True*.
    x_label : unicode, optional
        *X* axis label.
    y_label : unicode, optional
        *Y* axis label.
    x_ticker : bool, optional
        Whether to display the *X* axis ticker. Default is *True*.
    y_ticker : bool, optional
        Whether to display the *Y* axis ticker. Default is *True*.

    Returns
    -------
    tuple
        Current figure and axes.

[KelSolaar]

Oh and worth noting that you can writeout SVG files!

[goofy2k]

OK, thanks. I succeeded to plot part of the xy space by adding the bounding_box parameter ! I definitely have to learn how to pass parameters, e.g. via kWargs etc. I kind of miss the experience with pyplot / Mathplotlib, which is evidently a requirement for using colour-science. I have to learn this from it's docs, which is not very explanatory for a starting user of it. This is probably als why I have trouble removing the data points triggered by the ['A ', 'B', 'C'] parameter in your example. I'll try to remove that entry completely instead of replacing it by an empty list [ ]. My current code now looks like this: annotate_kwargs = [ {'xytext': (-25, 15), 'arrowprops':{'arrowstyle':'-'}}, {'arrowprops':{'arrowstyle':'-['}}, {}, ] plot_kwargs = [ { 'markersize' : 15, }, { 'color': 'r'}, {}, ] colour.plotting.temperature.plot_planckian_locus_in_chromaticity_diagram( ['A', 'B', 'C'], bounding_box=[0.4, 0.6, 0, 1], annotate_kwargs=annotate_kwargs, plot_kwargs=plot_kwargs, standalone = False ) I assume that I can add the parameters of render, that you showed in your other post, to this call just like the bounding_box parameter? Fred <http://www.avg.com/email-signature?utm_medium=email&utm_source=link&utm_campaign=sig-email&utm_content=webmail> Virus-free. www.avg.com <http://www.avg.com/email-signature?utm_medium=email&utm_source=link&utm_campaign=sig-email&utm_content=webmail> <#DAB4FAD8-2DD7-40BB-A1B8-4E2AA1F9FDF2> Op vr 22 jan. 2021 om 22:01 schreef Thomas Mansencal < [email protected]>:

…

Ah sweet, so all our plotting definitions can do that directly by passing a keyword argument like that:filename=my_figure.png, most of them are implemented in the colour.plotting.render definition that we call at the end of all our definitions: https://github.com/colour-science/colour/blob/develop/colour/plotting/common.py#L470 Other Parameters ---------------- figure : Figure, optional Figure to apply the render elements onto. axes : Axes, optional Axes to apply the render elements onto. filename : unicode, optional Figure will be saved using given ``filename`` argument. standalone : bool, optional Whether to show the figure and call :func:`plt.show` definition. aspect : unicode, optional Matplotlib axes aspect. axes_visible : bool, optional Whether the axes are visible. Default is *True*. bounding_box : array_like, optional Array defining current axes limits such `bounding_box = (x min, x max, y min, y max)`. tight_layout : bool, optional Whether to invoke the :func:`plt.tight_layout` definition. legend : bool, optional Whether to display the legend. Default is *False*. legend_columns : int, optional Number of columns in the legend. Default is *1*. transparent_background : bool, optional Whether to turn off the background patch. Default is *True*. title : unicode, optional Figure title. wrap_title : unicode, optional Whether to wrap the figure title. Default is *True*. x_label : unicode, optional *X* axis label. y_label : unicode, optional *Y* axis label. x_ticker : bool, optional Whether to display the *X* axis ticker. Default is *True*. y_ticker : bool, optional Whether to display the *Y* axis ticker. Default is *True*. Returns ------- tuple Current figure and axes. — You are receiving this because you were mentioned. Reply to this email directly, view it on GitHub <#761 (reply in thread)>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/AGII3TW2U5FLOD7DY3TD75LS3HRTZANCNFSM4WNUOJNA> .

[KelSolaar]

This is probably als why I have trouble removing the data points triggered by the ['A ', 'B', 'C'] parameter in your example. I'll try to remove that entry completely instead of replacing it by an empty list [ ].

Not really, the definition is currently expecting some illuminants, admittedly, it is not super clear from the definition name.

It is, however, possible to compose a figure with all the building blocks and draw on top. It really depends on what you are trying to achieve, for example:

>>> import colour
>>> figure, axes = colour.plotting.diagrams.plot_chromaticity_diagram_CIE1931(standalone=False)
>>> colour.plotting.temperature.plot_planckian_locus_CIE1931(axes=axes, standalone=False)
>>> axes.plot([0.4, 0.6], [0.4, 0.6], 'o-')
>>> colour.plotting.render(axes=axes, bounding_box=[0.2, 0.8, 0.2, 0.8])

[goofy2k]

When I remove ['A', 'B', 'C'] from the function call I get an error: TypeError: plot_planckian_locus_in_chromaticity_diagram() missing 1 required positional argument: 'illuminants'

What if I don't want to show any of the data points? Should I put my sd_data over there? I now use:

XYZ = colour.sd_to_XYZ(sd_power_1nm, cmfs, illuminant)
xy = colour.XYZ_to_xy(XYZ)

to calculate colour coordinates, add them in a list observations and add them to the plot with:

 for  ii in range(len(observations)) :
    xy = observations[ii] #OK
    x, y = xy
    plt.plot(x, y, 'o-', color='black') #'white'    

I also don not understand why the call to colour.sd_to_XYZ needs an illuminant as input, as my emission data are in sd_power_1nm

(Noticed incoming messages from you, so perhaps answers and questions are crossing now :-) )

Fred

[KelSolaar]

Right, so I think I understand better what you are trying to achieve here! To display a series of custom illuminants you would do something like that:

>>> import colour
>>> FL2 = colour.XYZ_to_xy(colour.sd_to_XYZ(colour.SDS_ILLUMINANTS['FL2']) / 100)
>>> FL3 = colour.XYZ_to_xy(colour.sd_to_XYZ(colour.SDS_ILLUMINANTS['FL3']) / 100)
>>> colour.plotting.plot_planckian_locus_in_chromaticity_diagram_CIE1931({'FL2': FL2, 'FL3': FL3})

A few notes now on what is happening:

• The integration from spectral to CIE XYZ tristimulus values uses a default Equal Energy illuminant, so if you want to integrate radiant power, you don't need to pass anything besides you radiant power spectral distribution.
• There is some magic occurring when the illuminants argument is used: it is filtered and expanded with the colour.plotting.common.filter_passthrough definition. The intent is to basically be able to use the shipped dataset elements by simply using their key but also passing arbitrary but compatible data, in this case, a mapping of xy chromaticity coordinates. The above code could be modified like that:

>>> colour.plotting.plot_planckian_locus_in_chromaticity_diagram_CIE1931(['A', {'FL2': FL2, 'FL3': FL3}])

A here would be directly pulled from colour.CCS_ILLUMINANTS['CIE 1931 2 Degree Standard Observer'].

[goofy2k]

OK. This is about it, I think. Two questions: 1) I do not understand where my data array is. Or is this referenced by the FL2? Where should I make that connection with my data that is eg. in sd_power? 2) What is the background of the /100 ? I also understand that there is trickery on board, that I probably have to learn by doing, accompanied by reading docs Fred Op vr 22 jan. 2021 23:15 schreef Thomas Mansencal <[email protected]

…

: Right, so I think I understand better what you are trying to achieve here! To display a series of custom illuminants you would do something like that: >>> import colour>>> FL2 = colour.XYZ_to_xy(colour.sd_to_XYZ(colour.SDS_ILLUMINANTS['FL2']) / 100)>>> FL3 = colour.XYZ_to_xy(colour.sd_to_XYZ(colour.SDS_ILLUMINANTS['FL3']) / 100)>>> colour.plotting.plot_planckian_locus_in_chromaticity_diagram_CIE1931({'FL2': FL2, 'FL3': FL3}) [image: image] <https://user-images.githubusercontent.com/99779/105553889-2810b700-5d6b-11eb-959e-9a65f18c13d3.png> A few notes now on what is happening: - The integration from spectral to CIE XYZ tristimulus values uses a default Equal Energy illuminant, so if you want to integrate radiant power, you don't need to pass anything besides you radiant power spectral distribution. - There is some magic occurring when the illuminants argument is used: it is filtered and expanded with the colour.plotting.common.filter_passthrough definition. The intent is to basically be able to use the shipped dataset elements by simply using their key but also passing arbitrary but compatible data, in this case, a mapping of xy chromaticity coordinates. The above code could be modified like that: >>> colour.plotting.plot_planckian_locus_in_chromaticity_diagram_CIE1931(['A', {'FL2': FL2, 'FL3': FL3}]) A here would be directly pulled from colour.CCS_ILLUMINANTS['CIE 1931 2 Degree Standard Observer']. [image: image] <https://user-images.githubusercontent.com/99779/105554370-14198500-5d6c-11eb-9679-31c05850afad.png> — You are receiving this because you were mentioned. Reply to this email directly, view it on GitHub <#761 (reply in thread)>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/AGII3TSQDGB2KENEXLXEOHDS3H2JFANCNFSM4WNUOJNA> .

[KelSolaar]

What is sd_power here? Is it an instance of colour.SpectralDistribution?

The tutorial should give answer, to both of your questions: https://colab.research.google.com/notebook#fileId=1Im9J7or9qyClQCv5sPHmKdyiQbG4898K&offline=true&sandboxMode=true

[goofy2k]

Yes, it is a 1nm sd_copy of the power distribution. Going to read the linked part of your docs! Fred Op za 23 jan. 2021 00:23 schreef Thomas Mansencal <[email protected]

…

: What is sd_power here? Is it an instance of colour.SpectralDistribution? The tutorial should give answer, to both of your questions: https://colab.research.google.com/notebook#fileId=1Im9J7or9qyClQCv5sPHmKdyiQbG4898K&offline=true&sandboxMode=true — You are receiving this because you were mentioned. Reply to this email directly, view it on GitHub <#761 (reply in thread)>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/AGII3TQD446TCREYEN4ODJTS3ICIJANCNFSM4WNUOJNA> .

[goofy2k]

How can I write out SVG files? JUst change the file extension? It is not in the docs.... Thanks <http://www.avg.com/email-signature?utm_medium=email&utm_source=link&utm_campaign=sig-email&utm_content=webmail> Virus-free. www.avg.com <http://www.avg.com/email-signature?utm_medium=email&utm_source=link&utm_campaign=sig-email&utm_content=webmail> <#DAB4FAD8-2DD7-40BB-A1B8-4E2AA1F9FDF2> Op vr 22 jan. 2021 om 22:02 schreef Thomas Mansencal < [email protected]>:

…

Oh and worth noting that you can writeout SVG files! — You are receiving this because you were mentioned. Reply to this email directly, view it on GitHub <#761 (reply in thread)>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/AGII3TSC5XDLGXU7IB77IDLS3HRYBANCNFSM4WNUOJNA> .

[KelSolaar]

Not everything is, and especially when it is Matplotlib feature, but yes, changing extension is the way! :)

[goofy2k]

OK, thanks! I have no experience with it. I probably have to adapt my UI, where I want to use this output... Fred Op di 26 jan. 2021 12:24 schreef Thomas Mansencal <[email protected]

…

: Not everything is, and especially when it is Matplotlib feature, but yes, changing extension is the way! :) — You are receiving this because you were mentioned. Reply to this email directly, view it on GitHub <#761 (reply in thread)>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/AGII3TSZLRKYP4WVIEKGIP3S32RANANCNFSM4WNUOJNA> .