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])