U e{j5 @sdZddlZddlZddlmZddlmZmZmZm Z m Z ddl m Z m Z mZmZmZmZmZz$ddlZddlmZddlmZWn"ek rGd d d ZYnXd d d gZddgiZGdd d eZddd ZeeZddd ZdS)zK Normalization class for Matplotlib that can be used to produce colorbars. N)ma)AsymmetricPercentileInterval BaseIntervalManualIntervalMinMaxIntervalPercentileInterval) AsinhStretch BaseStretch LinearStretch LogStretch PowerStretch SinhStretch SqrtStretch)pyplot) Normalizec@seZdZddZdS)rcOs tddS)Nz2matplotlib is required in order to use this class.) ImportError)selfargskwargsrb/var/www/html/bwave/python/venv/lib/python3.8/site-packages/astropy/visualization/mpl_normalize.py__init__#szNormalize.__init__N)__name__ __module__ __qualname__rrrrrr"srImageNormalize simple_norm imshow_norm* matplotlibcsNeZdZdZddddeddffdd ZddZd d d Zdd d ZZ S)ra Normalization class to be used with Matplotlib. Parameters ---------- data : ndarray, optional The image array. This input is used only if ``interval`` is also input. ``data`` and ``interval`` are used to compute the vmin and/or vmax values only if ``vmin`` or ``vmax`` are not input. interval : `~astropy.visualization.BaseInterval` subclass instance, optional The interval object to apply to the input ``data`` to determine the ``vmin`` and ``vmax`` values. This input is used only if ``data`` is also input. ``data`` and ``interval`` are used to compute the vmin and/or vmax values only if ``vmin`` or ``vmax`` are not input. vmin, vmax : float, optional The minimum and maximum levels to show for the data. The ``vmin`` and ``vmax`` inputs override any calculated values from the ``interval`` and ``data`` inputs. stretch : `~astropy.visualization.BaseStretch` subclass instance The stretch object to apply to the data. The default is `~astropy.visualization.LinearStretch`. clip : bool, optional If `True`, data values outside the [0:1] range are clipped to the [0:1] range. invalid : None or float, optional Value to assign NaN values generated by this class. NaNs in the input ``data`` array are not changed. For matplotlib normalization, the ``invalid`` value should map to the matplotlib colormap "under" value (i.e., any finite value < 0). If `None`, then NaN values are not replaced. This keyword has no effect if ``clip=True``. NFcstj|||d||_||_|dkr.tdt|ts@td||_|dk r`t|t s`td||_ |j |_ ||_ ||_|dk r||dS)N)vminvmaxclipzstretch must be inputz5stretch must be an instance of a BaseStretch subclassz7interval must be an instance of a BaseInterval subclass)superrr"r# ValueError isinstancer TypeErrorstretchrintervalinverseinverse_stretchr$invalid _set_limits)rdatar*r"r#r)r$r- __class__rrrPs  zImageNormalize.__init__cCs|jdk r|jdk rdS|jdkrd|jdkrBt|t||_|jdkrt|t||_n0|j|\}}|jdkr||_|jdkr||_dS)N)r"r#r*npminisfinitemax get_limits)rr/Z_vminZ_vmaxrrrr.rs     zImageNormalize._set_limitscCs|dkr|j}|dkr|j}t|tjrF|r2d}n|j}||j}nd}t |rftj |gt d}ntj |dt d}| |tj ||j|dtj||j|j|d|rtj|dd|d}|jjr|j||d|d }n|j||dd }tj ||d S) ar Transform values using this normalization. Parameters ---------- values : array-like The input values. clip : bool, optional If `True`, values outside the [0:1] range are clipped to the [0:1] range. If `None` then the ``clip`` value from the `ImageNormalize` instance is used (the default of which is `False`). invalid : None or float, optional Value to assign NaN values generated by this class. NaNs in the input ``data`` array are not changed. For matplotlib normalization, the ``invalid`` value should map to the matplotlib colormap "under" value (i.e., any finite value < 0). If `None`, then the `ImageNormalize` instance value is used. This keyword has no effect if ``clip=True``. NF)dtypeT)copyr7)out?)r9r$r-)r9r$)mask)r$r-r'rZ MaskedArrayr<Zfilledr#r2Zisscalararrayfloatr.subtractr"Z true_divider)_supports_invalid_kw)rvaluesr$r-r<rrr__call__s,   zImageNormalize.__call__cCs>|jjr|j|d|d}n|j|dd}||j|j|jS)NF)r$r-)r$)r,r@r#r")rrAr-Z values_normrrrr+szImageNormalize.inverse)NN)N) rrr__doc__r rr.rBr+ __classcell__rrr0rr,s%" @linearr;皙?Fr!333333?c Cs|dk rt|} nF|dk s"|dk r6t|p*d|p0d} n"|dk sF|dk rRt||} nt} |dkrht}nh|dkrxt}nX|dkrt|}nF|dkrt| }n4|dkrt|}n"|d krt | }nt d |d | |\}}t |||| | d S) a Return a Normalization class that can be used for displaying images with Matplotlib. This function enables only a subset of image stretching functions available in `~astropy.visualization.mpl_normalize.ImageNormalize`. This function is used by the ``astropy.visualization.scripts.fits2bitmap`` script. Parameters ---------- data : ndarray The image array. stretch : {'linear', 'sqrt', 'power', log', 'asinh', 'sinh'}, optional The stretch function to apply to the image. The default is 'linear'. power : float, optional The power index for ``stretch='power'``. The default is 1.0. asinh_a : float, optional For ``stretch='asinh'``, the value where the asinh curve transitions from linear to logarithmic behavior, expressed as a fraction of the normalized image. Must be in the range between 0 and 1. The default is 0.1. min_cut : float, optional The pixel value of the minimum cut level. Data values less than ``min_cut`` will set to ``min_cut`` before stretching the image. The default is the image minimum. ``min_cut`` overrides ``min_percent``. max_cut : float, optional The pixel value of the maximum cut level. Data values greater than ``min_cut`` will set to ``min_cut`` before stretching the image. The default is the image maximum. ``max_cut`` overrides ``max_percent``. min_percent : float, optional The percentile value used to determine the pixel value of minimum cut level. The default is 0.0. ``min_percent`` overrides ``percent``. max_percent : float, optional The percentile value used to determine the pixel value of maximum cut level. The default is 100.0. ``max_percent`` overrides ``percent``. percent : float, optional The percentage of the image values used to determine the pixel values of the minimum and maximum cut levels. The lower cut level will set at the ``(100 - percent) / 2`` percentile, while the upper cut level will be set at the ``(100 + percent) / 2`` percentile. The default is 100.0. ``percent`` is ignored if either ``min_percent`` or ``max_percent`` is input. clip : bool, optional If `True`, data values outside the [0:1] range are clipped to the [0:1] range. log_a : float, optional The log index for ``stretch='log'``. The default is 1000. invalid : None or float, optional Value to assign NaN values generated by the normalization. NaNs in the input ``data`` array are not changed. For matplotlib normalization, the ``invalid`` value should map to the matplotlib colormap "under" value (i.e., any finite value < 0). If `None`, then NaN values are not replaced. This keyword has no effect if ``clip=True``. sinh_a : float, optional The scaling parameter for ``stretch='sinh'``. The default is 0.3. Returns ------- result : `ImageNormalize` instance An `ImageNormalize` instance that can be used for displaying images with Matplotlib. Nr:gY@rEsqrtpowerlogasinhsinhzUnknown stretch: .)r"r#r)r$r-) rrrrr rr r r rr&r6r)r/r)rJZasinh_aZmin_cutZmax_cutZ min_percentZ max_percentpercentr$Zlog_ar-Zsinh_ar*r"r#rrrrs>c      cKsd|krtdd|kr tdt|}d|i}tjD]}||kr6||||<q6tf||d<|dkrxtj|f|}n|j|f|}||dfS)a*A convenience function to call matplotlib's `matplotlib.pyplot.imshow` function, using an `ImageNormalize` object as the normalization. Parameters ---------- data : 2D or 3D array-like The data to show. Can be whatever `~matplotlib.pyplot.imshow` and `ImageNormalize` both accept. See `~matplotlib.pyplot.imshow`. ax : None or `~matplotlib.axes.Axes`, optional If None, use pyplot's imshow. Otherwise, calls ``imshow`` method of the supplied axes. **kwargs : dict, optional All other keyword arguments are parsed first by the `ImageNormalize` initializer, then to `~matplotlib.pyplot.imshow`. Returns ------- result : tuple A tuple containing the `~matplotlib.image.AxesImage` generated by `~matplotlib.pyplot.imshow` as well as the `ImageNormalize` instance. Notes ----- The ``norm`` matplotlib keyword is not supported. Examples -------- .. plot:: :include-source: import numpy as np import matplotlib.pyplot as plt from astropy.visualization import (imshow_norm, MinMaxInterval, SqrtStretch) # Generate and display a test image image = np.arange(65536).reshape((256, 256)) fig = plt.figure() ax = fig.add_subplot(1, 1, 1) im, norm = imshow_norm(image, ax, origin='lower', interval=MinMaxInterval(), stretch=SqrtStretch()) fig.colorbar(im) Xz#Cannot give both ``X`` and ``data``ZnormzoThere is no point in using imshow_norm if you give the ``norm`` keyword - use imshow directly if you want that.r/N)r&dict _norm_sig parameterspoprpltZimshow)r/ZaxrZ imshow_kwargsZ norm_kwargsZpnameZ imshow_resultrrrrVs / ) rEr;rFNNNNNFrGr!rH)N) rCinspectnumpyr2rr*rrrrrr)r r r r r rrr rrUZmatplotlib.colorsrr__all__Z__doctest_requires__rr signaturerRrrrrrs> $    %