plot.py 19.9 KB
Newer Older
1 2 3 4 5 6 7 8 9 10 11 12 13
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program.  If not, see <http://www.gnu.org/licenses/>.
#
14
# Copyright(C) 2013-2019 Max-Planck-Society
15
#
16
# NIFTy is being developed at the Max-Planck-Institut fuer Astrophysik.
17

Martin Reinecke's avatar
Martin Reinecke committed
18 19
import os

20 21
import numpy as np

Martin Reinecke's avatar
fix  
Martin Reinecke committed
22 23 24
from . import dobj
from .domains.gl_space import GLSpace
from .domains.hp_space import HPSpace
Philipp Arras's avatar
Philipp Arras committed
25
from .domains.log_rg_space import LogRGSpace
Martin Reinecke's avatar
fix  
Martin Reinecke committed
26 27 28
from .domains.power_space import PowerSpace
from .domains.rg_space import RGSpace
from .field import Field
29

Martin Reinecke's avatar
Martin Reinecke committed
30 31 32 33 34 35 36 37
# relevant properties:
# - x/y size
# - x/y/z log
# - x/y/z min/max
# - colorbar/colormap
# - axis on/off
# - title
# - axis labels
Martin Reinecke's avatar
Martin Reinecke committed
38
# - labels
Martin Reinecke's avatar
Martin Reinecke committed
39

Martin Reinecke's avatar
PEP8  
Martin Reinecke committed
40

Martin Reinecke's avatar
Martin Reinecke committed
41 42 43
def _mollweide_helper(xsize):
    xsize = int(xsize)
    ysize = xsize//2
Martin Reinecke's avatar
Martin Reinecke committed
44
    res = np.full(shape=(ysize, xsize), fill_value=np.nan, dtype=np.float64)
Martin Reinecke's avatar
Martin Reinecke committed
45
    xc, yc = (xsize-1)*0.5, (ysize-1)*0.5
Martin Reinecke's avatar
Martin Reinecke committed
46
    u, v = np.meshgrid(np.arange(xsize), np.arange(ysize))
Martin Reinecke's avatar
Martin Reinecke committed
47
    u, v = 2*(u-xc)/(xc/1.02), (v-yc)/(yc/1.02)
Martin Reinecke's avatar
Martin Reinecke committed
48 49 50 51 52 53 54 55 56

    mask = np.where((u*u*0.25 + v*v) <= 1.)
    t1 = v[mask]
    theta = 0.5*np.pi-(
        np.arcsin(2/np.pi*(np.arcsin(t1) + t1*np.sqrt((1.-t1)*(1+t1)))))
    phi = -0.5*np.pi*u[mask]/np.maximum(np.sqrt((1-t1)*(1+t1)), 1e-6)
    phi = np.where(phi < 0, phi+2*np.pi, phi)
    return res, mask, theta, phi

Martin Reinecke's avatar
PEP8  
Martin Reinecke committed
57

58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183
def _rgb_data(spectral_cube):
    _xyz = np.array(
          [[0.000160, 0.000662, 0.002362, 0.007242, 0.019110,
            0.043400, 0.084736, 0.140638, 0.204492, 0.264737,
            0.314679, 0.357719, 0.383734, 0.386726, 0.370702,
            0.342957, 0.302273, 0.254085, 0.195618, 0.132349,
            0.080507, 0.041072, 0.016172, 0.005132, 0.003816,
            0.015444, 0.037465, 0.071358, 0.117749, 0.172953,
            0.236491, 0.304213, 0.376772, 0.451584, 0.529826,
            0.616053, 0.705224, 0.793832, 0.878655, 0.951162,
            1.014160, 1.074300, 1.118520, 1.134300, 1.123990,
            1.089100, 1.030480, 0.950740, 0.856297, 0.754930,
            0.647467, 0.535110, 0.431567, 0.343690, 0.268329,
            0.204300, 0.152568, 0.112210, 0.081261, 0.057930,
            0.040851, 0.028623, 0.019941, 0.013842, 0.009577,
            0.006605, 0.004553, 0.003145, 0.002175, 0.001506,
            0.001045, 0.000727, 0.000508, 0.000356, 0.000251,
            0.000178, 0.000126, 0.000090, 0.000065, 0.000046,
            0.000033],
           [0.000017, 0.000072, 0.000253, 0.000769, 0.002004,
            0.004509, 0.008756, 0.014456, 0.021391, 0.029497,
            0.038676, 0.049602, 0.062077, 0.074704, 0.089456,
            0.106256, 0.128201, 0.152761, 0.185190, 0.219940,
            0.253589, 0.297665, 0.339133, 0.395379, 0.460777,
            0.531360, 0.606741, 0.685660, 0.761757, 0.823330,
            0.875211, 0.923810, 0.961988, 0.982200, 0.991761,
            0.999110, 0.997340, 0.982380, 0.955552, 0.915175,
            0.868934, 0.825623, 0.777405, 0.720353, 0.658341,
            0.593878, 0.527963, 0.461834, 0.398057, 0.339554,
            0.283493, 0.228254, 0.179828, 0.140211, 0.107633,
            0.081187, 0.060281, 0.044096, 0.031800, 0.022602,
            0.015905, 0.011130, 0.007749, 0.005375, 0.003718,
            0.002565, 0.001768, 0.001222, 0.000846, 0.000586,
            0.000407, 0.000284, 0.000199, 0.000140, 0.000098,
            0.000070, 0.000050, 0.000036, 0.000025, 0.000018,
            0.000013],
           [0.000705, 0.002928, 0.010482, 0.032344, 0.086011,
            0.197120, 0.389366, 0.656760, 0.972542, 1.282500,
            1.553480, 1.798500, 1.967280, 2.027300, 1.994800,
            1.900700, 1.745370, 1.554900, 1.317560, 1.030200,
            0.772125, 0.570060, 0.415254, 0.302356, 0.218502,
            0.159249, 0.112044, 0.082248, 0.060709, 0.043050,
            0.030451, 0.020584, 0.013676, 0.007918, 0.003988,
            0.001091, 0.000000, 0.000000, 0.000000, 0.000000,
            0.000000, 0.000000, 0.000000, 0.000000, 0.000000,
            0.000000, 0.000000, 0.000000, 0.000000, 0.000000,
            0.000000, 0.000000, 0.000000, 0.000000, 0.000000,
            0.000000, 0.000000, 0.000000, 0.000000, 0.000000,
            0.000000, 0.000000, 0.000000, 0.000000, 0.000000,
            0.000000, 0.000000, 0.000000, 0.000000, 0.000000,
            0.000000, 0.000000, 0.000000, 0.000000, 0.000000,
            0.000000, 0.000000, 0.000000, 0.000000, 0.000000,
            0.000000]])

    MATRIX_SRGB_D65 = np.array(
            [[ 3.2404542, -1.5371385, -0.4985314],
             [-0.9692660,  1.8760108,  0.0415560],
             [ 0.0556434, -0.2040259,  1.0572252]])


    def _gammacorr(inp):
        mask = np.zeros(inp.shape, dtype=np.float64)
        mask[inp <= 0.0031308] = 1.
        r1 = 12.92*inp
        a = 0.055
        r2 = (1 + a) * (np.maximum(inp,0.0031308) ** (1/2.4)) - a
        return r1*mask + r2*(1.-mask)


    def lambda2rgb(lam):
        lammin=380.
        lammax=780.
        lam=np.asarray(lam, dtype=np.float64)
        lam = np.clip(lam, lammin, lammax)

        idx = (lam-lammin)/(lammax-lammin)*(_xyz.shape[1]-1)
        ii = np.maximum(0, np.minimum(79, int(idx)))
        w1 = 1.-(idx-ii)
        w2 = 1.-w1
        c = w1*_xyz[:,ii] + w2*_xyz[:,ii+1]
        c = _gammacorr(np.matmul(MATRIX_SRGB_D65, c))
        c = c.clip(0.,1.)
        return c

    def lambda2xyz(lam):
        lammin=380.
        lammax=780.
        lam=np.asarray(lam, dtype=np.float64)
        lam = np.clip(lam, lammin, lammax)

        idx = (lam-lammin)/(lammax-lammin)*(_xyz.shape[1]-1)
        ii = np.maximum(0, np.minimum(79, int(idx)))
        w1 = 1.-(idx-ii)
        w2 = 1.-w1
        c = w1*_xyz[:,ii] + w2*_xyz[:,ii+1]
        return c

    def getcol(n):
        E0, E1 = 1./700., 1./400.
        E = E0 + np.arange(n)*(E1-E0)/(n-1)
        res = np.zeros((3, n), dtype=np.float64)
        for i in range(n):
            res[:,i] = lambda2rgb(1./E[i])
        return res

    def getxyz(n):
        E0, E1 = 1./700., 1./400.
        E = E0 + np.arange(n)*(E1-E0)/(n-1)
        res = np.zeros((3, n), dtype=np.float64)
        for i in range(n):
            res[:,i] = lambda2xyz(1./E[i])
        return res

    xyz = getxyz(spectral_cube.shape[-1])
    xyz_data = np.tensordot(spectral_cube, xyz, axes=[-1, -1])
    xyz_data /= xyz_data.max()
    rgb_data = xyz_data.copy()
    it = np.nditer(xyz_data[:,:,0], flags=['multi_index'])
    while not it.finished:
        rgb_data[it.multi_index] = _gammacorr(
            np.matmul(MATRIX_SRGB_D65, xyz_data[it.multi_index]))
        it.iternext()
    rgb_data = rgb_data.clip(0.,1.)
    return rgb_data


Martin Reinecke's avatar
Martin Reinecke committed
184 185
def _find_closest(A, target):
    # A must be sorted
Martin Reinecke's avatar
Martin Reinecke committed
186 187
    idx = np.clip(A.searchsorted(target), 1, len(A)-1)
    idx -= target - A[idx-1] < A[idx] - target
Martin Reinecke's avatar
Martin Reinecke committed
188 189
    return idx

Martin Reinecke's avatar
PEP8  
Martin Reinecke committed
190

Martin Reinecke's avatar
Martin Reinecke committed
191
def _makeplot(name):
192
    import matplotlib.pyplot as plt
Martin Reinecke's avatar
Martin Reinecke committed
193
    if dobj.rank != 0:
194
        plt.close()
Martin Reinecke's avatar
tweaks  
Martin Reinecke committed
195
        return
Martin Reinecke's avatar
Martin Reinecke committed
196 197
    if name is None:
        plt.show()
198
        plt.close()
Martin Reinecke's avatar
Martin Reinecke committed
199 200
        return
    extension = os.path.splitext(name)[1]
201
    if extension in (".pdf", ".png", ".svg"):
Martin Reinecke's avatar
Martin Reinecke committed
202 203 204 205 206
        plt.savefig(name)
        plt.close()
    else:
        raise ValueError("file format not understood")

Martin Reinecke's avatar
PEP8  
Martin Reinecke committed
207

Martin Reinecke's avatar
Martin Reinecke committed
208
def _limit_xy(**kwargs):
Martin Reinecke's avatar
Martin Reinecke committed
209
    import matplotlib.pyplot as plt
Martin Reinecke's avatar
PEP8  
Martin Reinecke committed
210
    x1, x2, y1, y2 = plt.axis()
clienhar's avatar
clienhar committed
211 212 213 214
    x1 = kwargs.pop("xmin", x1)
    x2 = kwargs.pop("xmax", x2)
    y1 = kwargs.pop("ymin", y1)
    y2 = kwargs.pop("ymax", y2)
Martin Reinecke's avatar
PEP8  
Martin Reinecke committed
215 216
    plt.axis((x1, x2, y1, y2))

Martin Reinecke's avatar
Martin Reinecke committed
217

Martin Reinecke's avatar
Martin Reinecke committed
218 219 220 221 222 223 224 225 226
def _register_cmaps():
    try:
        if _register_cmaps._cmaps_registered:
            return
    except AttributeError:
        _register_cmaps._cmaps_registered = True

    from matplotlib.colors import LinearSegmentedColormap
    import matplotlib.pyplot as plt
Martin Reinecke's avatar
Martin Reinecke committed
227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272
    planckcmap = {'red':   ((0., 0., 0.), (.4, 0., 0.), (.5, 1., 1.),
                            (.7, 1., 1.), (.8, .83, .83), (.9, .67, .67),
                            (1., .5, .5)),
                  'green': ((0., 0., 0.), (.2, 0., 0.), (.3, .3, .3),
                            (.4, .7, .7), (.5, 1., 1.), (.6, .7, .7),
                            (.7, .3, .3), (.8, 0., 0.), (1., 0., 0.)),
                  'blue':  ((0., .5, .5), (.1, .67, .67), (.2, .83, .83),
                            (.3, 1., 1.), (.5, 1., 1.), (.6, 0., 0.),
                            (1., 0., 0.))}
    he_cmap = {'red':   ((0., 0., 0.), (.167, 0., 0.), (.333, .5, .5),
                         (.5, 1., 1.), (1., 1., 1.)),
               'green': ((0., 0., 0.), (.5, 0., 0.), (.667, .5, .5),
                         (.833, 1., 1.), (1., 1., 1.)),
               'blue':  ((0., 0., 0.), (.167, 1., 1.), (.333, .5, .5),
                         (.5, 0., 0.), (1., 1., 1.))}
    fd_cmap = {'red':   ((0., .35, .35), (.1, .4, .4), (.2, .25, .25),
                         (.41, .47, .47), (.5, .8, .8), (.56, .96, .96),
                         (.59, 1., 1.), (.74, .8, .8), (.8, .8, .8),
                         (.9, .5, .5), (1., .4, .4)),
               'green': ((0., 0., 0.), (.2, 0., 0.), (.362, .88, .88),
                         (.5, 1., 1.), (.638, .88, .88), (.8, .25, .25),
                         (.9, .3, .3), (1., .2, .2)),
               'blue':  ((0., .35, .35), (.1, .4, .4), (.2, .8, .8),
                         (.26, .8, .8), (.41, 1., 1.), (.44, .96, .96),
                         (.5, .8, .8), (.59, .47, .47), (.8, 0., 0.),
                         (1., 0., 0.))}
    fdu_cmap = {'red':   ((0., 1., 1.), (0.1, .8, .8), (.2, .65, .65),
                          (.41, .6, .6), (.5, .7, .7), (.56, .96, .96),
                          (.59, 1., 1.), (.74, .8, .8), (.8, .8, .8),
                          (.9, .5, .5), (1., .4, .4)),
                'green': ((0., .9, .9), (.362, .95, .95), (.5, 1., 1.),
                          (.638, .88, .88), (.8, .25, .25), (.9, .3, .3),
                          (1., .2, .2)),
                'blue':  ((0., 1., 1.), (.1, .8, .8), (.2, 1., 1.),
                          (.41, 1., 1.), (.44, .96, .96), (.5, .7, .7),
                          (.59, .42, .42), (.8, 0., 0.), (1., 0., 0.))}
    pm_cmap = {'red':   ((0., 1., 1.), (.1, .96, .96), (.2, .84, .84),
                         (.3, .64, .64), (.4, .36, .36), (.5, 0., 0.),
                         (1., 0., 0.)),
               'green': ((0., .5, .5), (.1, .32, .32), (.2, .18, .18),
                         (.3, .8, .8),  (.4, .2, .2), (.5, 0., 0.),
                         (.6, .2, .2), (.7, .8, .8), (.8, .18, .18),
                         (.9, .32, .32), (1., .5, .5)),
               'blue':  ((0., 0., 0.), (.5, 0., 0.), (.6, .36, .36),
                         (.7, .64, .64), (.8, .84, .84), (.9, .96, .96),
                         (1., 1., 1.))}
Martin Reinecke's avatar
Martin Reinecke committed
273 274 275

    plt.register_cmap(cmap=LinearSegmentedColormap("Planck-like", planckcmap))
    plt.register_cmap(cmap=LinearSegmentedColormap("High Energy", he_cmap))
Martin Reinecke's avatar
Martin Reinecke committed
276
    plt.register_cmap(cmap=LinearSegmentedColormap("Faraday Map", fd_cmap))
Martin Reinecke's avatar
Martin Reinecke committed
277 278
    plt.register_cmap(cmap=LinearSegmentedColormap("Faraday Uncertainty",
                                                   fdu_cmap))
Martin Reinecke's avatar
Martin Reinecke committed
279
    plt.register_cmap(cmap=LinearSegmentedColormap("Plus Minus", pm_cmap))
Martin Reinecke's avatar
Martin Reinecke committed
280

Martin Reinecke's avatar
Martin Reinecke committed
281

282
def _plot1D(f, ax, **kwargs):
283
    import matplotlib.pyplot as plt
284

285 286 287 288 289
    for i, fld in enumerate(f):
        if not isinstance(fld, Field):
            raise TypeError("incorrect data type")
        if i == 0:
            dom = fld.domain
290 291
            if (len(dom) != 1):
                raise ValueError("input field must have exactly one domain")
292 293 294
        else:
            if fld.domain != dom:
                raise ValueError("domain mismatch")
295
    dom = dom[0]
Martin Reinecke's avatar
Martin Reinecke committed
296

clienhar's avatar
clienhar committed
297
    label = kwargs.pop("label", None)
298
    if not isinstance(label, list):
Martin Reinecke's avatar
Martin Reinecke committed
299
        label = [label] * len(f)
Martin Reinecke's avatar
Martin Reinecke committed
300

Martin Reinecke's avatar
Martin Reinecke committed
301
    linewidth = kwargs.pop("linewidth", 1.)
Philipp Arras's avatar
Philipp Arras committed
302
    if not isinstance(linewidth, list):
Martin Reinecke's avatar
Martin Reinecke committed
303
        linewidth = [linewidth] * len(f)
Philipp Arras's avatar
Philipp Arras committed
304

clienhar's avatar
clienhar committed
305
    alpha = kwargs.pop("alpha", None)
Philipp Arras's avatar
Philipp Arras committed
306
    if not isinstance(alpha, list):
Martin Reinecke's avatar
Martin Reinecke committed
307
        alpha = [alpha] * len(f)
Philipp Arras's avatar
Philipp Arras committed
308

clienhar's avatar
clienhar committed
309 310 311
    ax.set_title(kwargs.pop("title", ""))
    ax.set_xlabel(kwargs.pop("xlabel", ""))
    ax.set_ylabel(kwargs.pop("ylabel", ""))
312

Martin Reinecke's avatar
Martin Reinecke committed
313
    if isinstance(dom, RGSpace):
314
        plt.yscale(kwargs.pop("yscale", "linear"))
315 316 317 318 319 320 321 322 323 324 325
        npoints = dom.shape[0]
        dist = dom.distances[0]
        xcoord = np.arange(npoints, dtype=np.float64)*dist
        for i, fld in enumerate(f):
            ycoord = fld.to_global_data()
            plt.plot(xcoord, ycoord, label=label[i],
                     linewidth=linewidth[i], alpha=alpha[i])
        _limit_xy(**kwargs)
        if label != ([None]*len(f)):
            plt.legend()
        return
326
    elif isinstance(dom, LogRGSpace):
Martin Reinecke's avatar
fixes  
Martin Reinecke committed
327
        plt.yscale(kwargs.pop("yscale", "log"))
328 329 330 331 332 333 334 335 336 337
        npoints = dom.shape[0]
        xcoord = dom.t_0 + np.arange(npoints-1)*dom.bindistances[0]
        for i, fld in enumerate(f):
            ycoord = fld.to_global_data()[1:]
            plt.plot(xcoord, ycoord, label=label[i],
                     linewidth=linewidth[i], alpha=alpha[i])
        _limit_xy(**kwargs)
        if label != ([None]*len(f)):
            plt.legend()
        return
Martin Reinecke's avatar
Martin Reinecke committed
338
    elif isinstance(dom, PowerSpace):
339 340
        plt.xscale(kwargs.pop("xscale", "log"))
        plt.yscale(kwargs.pop("yscale", "log"))
Philipp Arras's avatar
Philipp Arras committed
341
        xcoord = dom.k_lengths
Martin Reinecke's avatar
Martin Reinecke committed
342
        for i, fld in enumerate(f):
Martin Reinecke's avatar
tweaks  
Martin Reinecke committed
343
            ycoord = fld.to_global_data()
Martin Reinecke's avatar
Martin Reinecke committed
344 345
            plt.plot(xcoord, ycoord, label=label[i],
                     linewidth=linewidth[i], alpha=alpha[i])
Martin Reinecke's avatar
Martin Reinecke committed
346
        _limit_xy(**kwargs)
347 348
        if label != ([None]*len(f)):
            plt.legend()
Martin Reinecke's avatar
Martin Reinecke committed
349
        return
350 351 352 353 354 355 356 357
    raise ValueError("Field type not(yet) supported")


def _plot2D(f, ax, **kwargs):
    import matplotlib.pyplot as plt

    dom = f.domain

358 359 360 361 362 363 364 365 366 367
    if len(dom) > 2:
        raise ValueError("DomainTuple can have at most two entries.")

    # check for multifrequency plotting
    have_rgb = False
    if len(dom) == 2:
        if (not isinstance(dom[1], RGSpace)) or len(dom[1].shape) != 1:
            raise TypeError("need 1D RGSpace as second domain")
        rgb = _rgb_data(f.to_global_data())
        have_rgb = True
368 369 370 371 372

    label = kwargs.pop("label", None)

    foo = kwargs.pop("norm", None)
    norm = {} if foo is None else {'norm': foo}
373 374
    aspect = kwargs.pop("aspect", None)
    aspect = {} if foo is None else {'aspect': foo}
375 376 377 378 379

    ax.set_title(kwargs.pop("title", ""))
    ax.set_xlabel(kwargs.pop("xlabel", ""))
    ax.set_ylabel(kwargs.pop("ylabel", ""))
    dom = dom[0]
380 381
    if not have_rgb:
        cmap = kwargs.pop("colormap", plt.rcParams['image.cmap'])
382 383 384 385

    if isinstance(dom, RGSpace):
        nx, ny = dom.shape
        dx, dy = dom.distances
386 387 388 389 390 391 392 393 394 395
        if have_rgb:
            im = ax.imshow(
                rgb, extent=[0, nx*dx, 0, ny*dy], origin="lower", **norm,
                **aspect)
        else:
            im = ax.imshow(
                f.to_global_data().T, extent=[0, nx*dx, 0, ny*dy],
                vmin=kwargs.get("zmin"), vmax=kwargs.get("zmax"),
                cmap=cmap, origin="lower", **norm, **aspect)
            plt.colorbar(im)
396 397
        _limit_xy(**kwargs)
        return
Martin Reinecke's avatar
Martin Reinecke committed
398
    elif isinstance(dom, (HPSpace, GLSpace)):
Martin Reinecke's avatar
Martin Reinecke committed
399 400 401
        import pyHealpix
        xsize = 800
        res, mask, theta, phi = _mollweide_helper(xsize)
402 403 404
        if have_rgb:
            res = np.full(shape=res.shape+(3,), fill_value=1., dtype=np.float64)

Martin Reinecke's avatar
Martin Reinecke committed
405 406 407 408
        if isinstance(dom, HPSpace):
            ptg = np.empty((phi.size, 2), dtype=np.float64)
            ptg[:, 0] = theta
            ptg[:, 1] = phi
409
            base = pyHealpix.Healpix_Base(int(np.sqrt(dom.size//12)), "RING")
410 411 412 413
            if have_rgb:
                res[mask] = rgb[base.ang2pix(ptg)]
            else:
                res[mask] = f.to_global_data()[base.ang2pix(ptg)]
Martin Reinecke's avatar
Martin Reinecke committed
414 415 416 417 418 419
        else:
            ra = np.linspace(0, 2*np.pi, dom.nlon+1)
            dec = pyHealpix.GL_thetas(dom.nlat)
            ilat = _find_closest(dec, theta)
            ilon = _find_closest(ra, phi)
            ilon = np.where(ilon == dom.nlon, 0, ilon)
420 421 422 423
            if have_rgb:
                res[mask] = rgb[ilat*dom[0].nlon + ilon]
            else:
                res[mask] = f.to_global_data()[ilat*dom.nlon + ilon]
Martin Reinecke's avatar
Martin Reinecke committed
424
        plt.axis('off')
425 426 427 428 429 430
        if have_rgb:
            plt.imshow(res, origin="lower")
        else:
            plt.imshow(res, vmin=kwargs.get("zmin"), vmax=kwargs.get("zmax"),
                       cmap=cmap, origin="lower")
            plt.colorbar(orientation="horizontal")
431 432 433 434 435 436 437 438 439 440 441 442 443 444
        return
    raise ValueError("Field type not(yet) supported")


def _plot(f, ax, **kwargs):
    _register_cmaps()
    if isinstance(f, Field):
        f = [f]
    f = list(f)
    if len(f) == 0:
        raise ValueError("need something to plot")
    if not isinstance(f[0], Field):
            raise TypeError("incorrect data type")
    dom1 = f[0].domain
Martin Reinecke's avatar
Martin Reinecke committed
445 446
    if (len(dom1) == 1 and
        (isinstance(dom1[0], PowerSpace) or
447 448
            (isinstance(dom1[0], (RGSpace, LogRGSpace)) and
             len(dom1[0].shape) == 1))):
449 450 451 452 453 454
        _plot1D(f, ax, **kwargs)
        return
    else:
        if len(f) != 1:
            raise ValueError("need exactly one Field for 2D plot")
        _plot2D(f[0], ax, **kwargs)
Martin Reinecke's avatar
Martin Reinecke committed
455 456
        return
    raise ValueError("Field type not(yet) supported")
Martin Reinecke's avatar
Martin Reinecke committed
457

Martin Reinecke's avatar
tweaks  
Martin Reinecke committed
458

459 460 461 462 463 464 465 466 467 468
class Plot(object):
    def __init__(self):
        self._plots = []
        self._kwargs = []

    def add(self, f, **kwargs):
        """Add a figure to the current list of plots.

        Notes
        -----
Philipp Arras's avatar
Docs  
Philipp Arras committed
469 470
        After doing one or more calls `add()`, one needs to call `output()` to
        show or save the plot.
471 472 473

        Parameters
        ----------
Philipp Arras's avatar
Philipp Arras committed
474
        f: Field or list of Field
Philipp Arras's avatar
Philipp Arras committed
475
            If `f` is a single Field, it must be defined on a single `RGSpace`,
Martin Reinecke's avatar
typo  
Martin Reinecke committed
476
            `PowerSpace`, `HPSpace`, `GLSpace`.
Philipp Arras's avatar
Philipp Arras committed
477
            If it is a list, all list members must be Fields defined over the
478 479
            same one-dimensional `RGSpace` or `PowerSpace`.
        title: string
Philipp Arras's avatar
Docs  
Philipp Arras committed
480
            Title of the plot.
481
        xlabel: string
Philipp Arras's avatar
Philipp Arras committed
482
            Label for the x axis.
483
        ylabel: string
Philipp Arras's avatar
Philipp Arras committed
484
            Label for the y axis.
485
        [xyz]min, [xyz]max: float
Philipp Arras's avatar
Philipp Arras committed
486
            Limits for the values to plot.
487
        colormap: string
Philipp Arras's avatar
Philipp Arras committed
488
            Color map to use for the plot (if it is a 2D plot).
489
        linewidth: float or list of floats
Philipp Arras's avatar
Philipp Arras committed
490
            Line width.
491
        label: string of list of strings
Philipp Arras's avatar
Philipp Arras committed
492
            Annotation string.
493
        alpha: float or list of floats
Philipp Arras's avatar
Docs  
Philipp Arras committed
494
            Transparency value.
495 496 497 498 499 500 501 502 503 504
        """
        self._plots.append(f)
        self._kwargs.append(kwargs)

    def output(self, **kwargs):
        """Plot the accumulated list of figures.

        Parameters
        ----------
        title: string
Philipp Arras's avatar
Philipp Arras committed
505 506 507 508 509 510 511 512
            Title of the full plot.
        nx, ny: int
            Number of subplots to use in x- and y-direction.
            Default: square root of the numer of plots, rounded up.
        xsize, ysize: float
            Dimensions of the full plot in inches. Default: 6.
        name: string
            If left empty, the plot will be shown on the screen,
513
            otherwise it will be written to a file with the given name.
Philipp Arras's avatar
Philipp Arras committed
514
            Supported extensions: .png and .pdf. Default: None.
515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535
        """
        import matplotlib.pyplot as plt
        nplot = len(self._plots)
        fig = plt.figure()
        if "title" in kwargs:
            plt.suptitle(kwargs.pop("title"))
        nx = kwargs.pop("nx", int(np.ceil(np.sqrt(nplot))))
        ny = kwargs.pop("ny", int(np.ceil(np.sqrt(nplot))))
        if nx*ny < nplot:
            raise ValueError(
                'Figure dimensions not sufficient for number of plots. '
                'Available plot slots: {}, number of plots: {}'
                .format(nx*ny, nplot))
        xsize = kwargs.pop("xsize", 6)
        ysize = kwargs.pop("ysize", 6)
        fig.set_size_inches(xsize, ysize)
        for i in range(nplot):
            ax = fig.add_subplot(ny, nx, i+1)
            _plot(self._plots[i], ax, **self._kwargs[i])
        fig.tight_layout()
        _makeplot(kwargs.pop("name", None))