source: sasmodels/doc/genmodel.py @ fac23b3

from __future__ import print_function

import sys, os, math, re
import numpy as np
import matplotlib.pyplot as plt
sys.path.insert(0, os.path.abspath('..'))
from sasmodels import generate, core
from sasmodels.direct_model import DirectModel, call_profile
from sasmodels.data import empty_data1D, empty_data2D

try:
    from typing import Dict, Any
except ImportError:
    pass
else:
    from matplotlib.axes import Axes
    from sasmodels.kernel import KernelModel
    from sasmodels.modelinfo import ModelInfo


def plot_1d(model, opts, ax):
    # type: (KernelModel, Dict[str, Any], Axes) -> None
    """
    Create a 1-D image.
    """
    q_min, q_max, nq = opts['q_min'], opts['q_max'], opts['nq']
    q_min = math.log10(q_min)
    q_max = math.log10(q_max)
    q = np.logspace(q_min, q_max, nq)
    data = empty_data1D(q)
    calculator = DirectModel(data, model)
    Iq1D = calculator()

    ax.plot(q, Iq1D, color='blue', lw=2, label=model.info.name)
    ax.set_xlabel(r'$Q \/(\AA^{-1})$')
    ax.set_ylabel(r'$I(Q) \/(\mathrm{cm}^{-1})$')
    ax.set_xscale(opts['xscale'])
    ax.set_yscale(opts['yscale'])
    #ax.legend(loc='best')

def plot_2d(model, opts, ax):
    # type: (KernelModel, Dict[str, Any], Axes) -> None
    """
    Create a 2-D image.
    """
    qx_max, nq2d = opts['qx_max'], opts['nq2d']
    q = np.linspace(-qx_max, qx_max, nq2d) # type: np.ndarray
    data2d = empty_data2D(q, resolution=0.0)
    calculator = DirectModel(data2d, model)
    Iq2D = calculator() #background=0)
    Iq2D = Iq2D.reshape(nq2d, nq2d)
    if opts['zscale'] == 'log':
        Iq2D = np.log(np.clip(Iq2D, opts['vmin'], np.inf))
    ax.imshow(Iq2D, interpolation='nearest', aspect=1, origin='lower',
              extent=[-qx_max, qx_max, -qx_max, qx_max], cmap=opts['colormap'])
    ax.set_xlabel(r'$Q_x \/(\AA^{-1})$')
    ax.set_ylabel(r'$Q_y \/(\AA^{-1})$')

def plot_profile_inset(model_info, ax):
    p = ax.get_position()
    width, height = 0.4*(p.x1-p.x0), 0.4*(p.y1-p.y0)
    left, bottom = p.x1-width, p.y1-height
    inset = plt.gcf().add_axes([left, bottom, width, height])
    x, y, labels = call_profile(model_info)
    inset.plot(x, y, '-')
    inset.locator_params(nbins=4)
    #inset.set_xlabel(labels[0])
    #inset.set_ylabel(labels[1])
    inset.text(0.99, 0.99, "profile",
               horizontalalignment="right",
               verticalalignment="top",
               transform=inset.transAxes)

def figfile(model_info):
    # type: (ModelInfo) -> str
    return model_info.id + '_autogenfig.png'

def make_figure(model_info, opts):
    # type: (ModelInfo, Dict[str, Any]) -> None
    """
    Generate the figure file to include in the docs.
    """
    model = core.build_model(model_info)

    fig_height = 3.0 # in
    fig_left = 0.6 # in
    fig_right = 0.5 # in
    fig_top = 0.6*0.25 # in
    fig_bottom = 0.6*0.75
    if model_info.parameters.has_2d:
        plot_height = fig_height - (fig_top+fig_bottom)
        plot_width = plot_height
        fig_width = 2*(plot_width + fig_left + fig_right)
        aspect = (fig_width, fig_height)
        ratio = aspect[0]/aspect[1]
        ax_left = fig_left/fig_width
        ax_bottom = fig_bottom/fig_height
        ax_height = plot_height/fig_height
        ax_width = ax_height/ratio # square axes
        fig = plt.figure(figsize=aspect)
        ax2d = fig.add_axes([0.5+ax_left, ax_bottom, ax_width, ax_height])
        plot_2d(model, opts, ax2d)
        ax1d = fig.add_axes([ax_left, ax_bottom, ax_width, ax_height])
        plot_1d(model, opts, ax1d)
        #ax.set_aspect('square')
    else:
        plot_height = fig_height - (fig_top+fig_bottom)
        plot_width = (1+np.sqrt(5))/2*fig_height
        fig_width = plot_width + fig_left + fig_right
        ax_left = fig_left/fig_width
        ax_bottom = fig_bottom/fig_height
        ax_width = plot_width/fig_width
        ax_height = plot_height/fig_height
        aspect = (fig_width, fig_height)
        fig = plt.figure(figsize=aspect)
        ax1d = fig.add_axes([ax_left, ax_bottom, ax_width, ax_height])
        plot_1d(model, opts, ax1d)

    if model_info.profile:
        plot_profile_inset(model_info, ax1d)

    # Save image in model/img
    path = os.path.join('model', 'img', figfile(model_info))
    plt.savefig(path, bbox_inches='tight')
    #print("figure saved in",path)

def gen_docs(model_info):
    # type: (ModelInfo) -> None
    """
    Generate the doc string with the figure inserted before the references.
    """

    # Load the doc string from the module definition file and store it in rst
    docstr = generate.make_doc(model_info)

    # Auto caption for figure
    captionstr = '\n'
    captionstr += '.. figure:: img/' + figfile(model_info) + '\n'
    captionstr += '\n'
    if model_info.parameters.has_2d:
        captionstr += '    1D and 2D plots corresponding to the default parameters of the model.\n'
    else:
        captionstr += '    1D plot corresponding to the default parameters of the model.\n'
    captionstr += '\n'

    # Add figure reference and caption to documentation (at end, before References)
    pattern = '\*\*REFERENCE'
    match = re.search(pattern, docstr.upper())

    if match:
        docstr1 = docstr[:match.start()]
        docstr2 = docstr[match.start():]
        docstr = docstr1 + captionstr + docstr2
    else:
        print('------------------------------------------------------------------')
        print('References NOT FOUND for model: ', model_info.id)
        print('------------------------------------------------------------------')
        docstr += captionstr

    open(sys.argv[2],'w').write(docstr)

def process_model(path):
    # type: (str) -> None
    """
    Generate doc file and image file for the given model definition file.
    """

    # Load the model file
    model_name = os.path.basename(path)[:-3]
    model_info = core.load_model_info(model_name)

    # Plotting ranges and options
    opts = {
        'xscale'    : 'log',
        'yscale'    : 'log' if not model_info.structure_factor else 'linear',
        'zscale'    : 'log' if not model_info.structure_factor else 'linear',
        'q_min'     : 0.001,
        'q_max'     : 1.0,
        'nq'        : 1000,
        'nq2d'      : 1000,
        'vmin'      : 1e-3,  # floor for the 2D data results
        'qx_max'    : 0.5,
        #'colormap'  : 'gist_ncar',
        'colormap'  : 'nipy_spectral',
        #'colormap'  : 'jet',
    }

    # Generate the RST file and the figure.  Order doesn't matter.
    gen_docs(model_info)
    make_figure(model_info, opts)

if __name__ == "__main__":
    process_model(sys.argv[1])