| 1 | #!/usr/bin/env python |
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| 2 | # -*- coding: utf-8 -*- |
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| 3 | |
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| 4 | import sys, os |
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| 5 | import datetime |
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| 6 | |
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| 7 | import numpy as np |
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| 8 | |
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| 9 | def load_model(modelname): |
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| 10 | from os.path import abspath, join as joinpath, dirname |
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| 11 | from sasmodels import gen |
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| 12 | modelpath = abspath(joinpath(dirname(gen.__file__), 'models', |
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| 13 | modelname+'.c')) |
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| 14 | return gen.make(modelpath) |
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| 15 | |
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| 16 | def opencl_model(modelname, dtype="single"): |
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| 17 | from sasmodels import gpu |
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| 18 | |
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| 19 | source, meta, _ = load_model(modelname) |
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| 20 | # for debugging, save source to a .cl file, edit it, and reload as model |
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| 21 | #open(modelname+'.cl','w').write(source) |
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| 22 | #source = open(modelname+'.cl','r').read() |
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| 23 | return gpu.GpuModel(source, meta, dtype) |
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| 24 | |
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| 25 | |
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| 26 | if sys.platform == 'darwin': |
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| 27 | COMPILE = "gcc-mp-4.7 -shared -fPIC -std=c99 -fopenmp -O2 -Wall %s -o %s -lm -lgomp" |
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| 28 | elif os.name == 'nt': |
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| 29 | COMPILE = "gcc -shared -fPIC -std=c99 -fopenmp -O2 -Wall %s -o %s -lm" |
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| 30 | else: |
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| 31 | COMPILE = "cc -shared -fPIC -std=c99 -fopenmp -O2 -Wall %s -o %s -lm" |
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| 32 | DLL_PATH = "/tmp" |
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| 33 | def dll_path(meta): |
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| 34 | from os.path import join as joinpath, split as splitpath, splitext |
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| 35 | basename = splitext(splitpath(meta['filename'])[1])[0] |
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| 36 | return joinpath(DLL_PATH, basename+'.so') |
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| 37 | |
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| 38 | def dll_model(modelname): |
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| 39 | import os |
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| 40 | from sasmodels import dll |
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| 41 | |
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| 42 | source, meta, _ = load_model(modelname) |
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| 43 | dllpath = dll_path(meta) |
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| 44 | if not os.path.exists(dllpath) \ |
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| 45 | or (os.path.getmtime(dllpath) < os.path.getmtime(meta['filename'])): |
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| 46 | # Replace with a proper temp file |
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| 47 | srcfile = '/tmp/%s.c'%modelname |
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| 48 | open(srcfile, 'w').write(source) |
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| 49 | os.system(COMPILE%(srcfile, dllpath)) |
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| 50 | return dll.DllModel(dllpath, meta) |
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| 51 | |
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| 52 | TIC = None |
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| 53 | def tic(): |
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| 54 | global TIC |
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| 55 | then = datetime.datetime.now() |
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| 56 | TIC = lambda: (datetime.datetime.now()-then).total_seconds() |
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| 57 | return TIC |
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| 58 | |
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| 59 | def toc(): |
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| 60 | return TIC() |
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| 61 | |
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| 62 | def load_data(filename): |
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| 63 | from sans.dataloader.loader import Loader |
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| 64 | loader = Loader() |
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| 65 | data = loader.load(filename) |
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| 66 | if data is None: |
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| 67 | raise IOError("Data %r could not be loaded"%filename) |
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| 68 | return data |
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| 69 | |
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| 70 | def fake_data2D(qx, qy=None): |
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| 71 | from sans.dataloader.data_info import Data2D, Detector |
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| 72 | |
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| 73 | |
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| 74 | if qy is None: |
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| 75 | qy = qx |
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| 76 | Qx,Qy = np.meshgrid(qx,qy) |
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| 77 | Qx,Qy = Qx.flatten(), Qy.flatten() |
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| 78 | Iq = 100*np.ones_like(Qx) |
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| 79 | dIq = np.sqrt(Iq) |
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| 80 | mask = np.ones(len(Iq), dtype='bool') |
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| 81 | |
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| 82 | data = Data2D() |
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| 83 | data.filename = "fake data" |
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| 84 | data.qx_data = Qx |
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| 85 | data.qy_data = Qy |
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| 86 | data.data = Iq |
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| 87 | data.err_data = dIq |
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| 88 | data.mask = mask |
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| 89 | |
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| 90 | # 5% dQ/Q resolution |
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| 91 | data.dqx_data = 0.05*Qx |
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| 92 | data.dqy_data = 0.05*Qy |
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| 93 | |
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| 94 | detector = Detector() |
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| 95 | detector.pixel_size.x = 5 # mm |
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| 96 | detector.pixel_size.y = 5 # mm |
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| 97 | detector.distance = 4 # m |
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| 98 | data.detector.append(detector) |
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| 99 | data.xbins = qx |
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| 100 | data.ybins = qy |
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| 101 | data.source.wavelength = 5 # angstroms |
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| 102 | data.source.wavelength_unit = "A" |
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| 103 | data.Q_unit = "1/A" |
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| 104 | data.I_unit = "1/cm" |
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| 105 | data.q_data = np.sqrt(Qx**2 + Qy**2) |
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| 106 | data.xaxis("Q_x", "A^{-1}") |
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| 107 | data.yaxis("Q_y", "A^{-1}") |
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| 108 | data.zaxis("Intensity", r"\text{cm}^{-1}") |
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| 109 | return data |
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| 110 | |
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| 111 | |
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| 112 | def set_beam_stop(data, radius, outer=None): |
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| 113 | from sans.dataloader.manipulations import Ringcut |
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| 114 | if hasattr(data, 'qx_data'): |
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| 115 | data.mask = Ringcut(0, radius)(data) |
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| 116 | if outer is not None: |
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| 117 | data.mask += Ringcut(outer,np.inf)(data) |
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| 118 | else: |
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| 119 | data.mask = (data.x>=radius) |
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| 120 | if outer is not None: |
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| 121 | data.mask &= (data.x<outer) |
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| 122 | |
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| 123 | def set_half(data, half): |
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| 124 | from sans.dataloader.manipulations import Boxcut |
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| 125 | if half == 'right': |
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| 126 | data.mask += Boxcut(x_min=-np.inf, x_max=0.0, y_min=-np.inf, y_max=np.inf)(data) |
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| 127 | if half == 'left': |
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| 128 | data.mask += Boxcut(x_min=0.0, x_max=np.inf, y_min=-np.inf, y_max=np.inf)(data) |
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| 129 | |
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| 130 | def set_top(data, max): |
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| 131 | from sans.dataloader.manipulations import Boxcut |
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| 132 | data.mask += Boxcut(x_min=-np.inf, x_max=np.inf, y_min=-np.inf, y_max=max)(data) |
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| 133 | |
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| 134 | def plot_data(data, iq, vmin=None, vmax=None): |
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| 135 | from numpy.ma import masked_array |
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| 136 | import matplotlib.pyplot as plt |
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| 137 | img = masked_array(iq, data.mask) |
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| 138 | xmin, xmax = min(data.qx_data), max(data.qx_data) |
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| 139 | ymin, ymax = min(data.qy_data), max(data.qy_data) |
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| 140 | plt.imshow(img.reshape(128,128), |
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| 141 | interpolation='nearest', aspect=1, origin='upper', |
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| 142 | extent=[xmin, xmax, ymin, ymax], vmin=vmin, vmax=vmax) |
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| 143 | |
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| 144 | def plot_result2D(data, theory, view='linear'): |
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| 145 | import matplotlib.pyplot as plt |
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| 146 | from numpy.ma import masked_array, masked |
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| 147 | #print "not a number",sum(np.isnan(data.data)) |
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| 148 | #data.data[data.data<0.05] = 0.5 |
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| 149 | mdata = masked_array(data.data, data.mask) |
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| 150 | mdata[np.isnan(mdata)] = masked |
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| 151 | if view is 'log': |
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| 152 | mdata[mdata <= 0] = masked |
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| 153 | mdata = np.log10(mdata) |
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| 154 | mtheory = masked_array(np.log10(theory), mdata.mask) |
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| 155 | else: |
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| 156 | mtheory = masked_array(theory, mdata.mask) |
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| 157 | mresid = masked_array((theory-data.data)/data.err_data, data.mask) |
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| 158 | vmin = min(mdata.min(), mtheory.min()) |
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| 159 | vmax = max(mdata.max(), mtheory.max()) |
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| 160 | print np.exp(np.mean(mtheory)), np.std(mtheory),np.max(mtheory),np.min(mtheory) |
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| 161 | |
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| 162 | plt.subplot(1, 3, 1) |
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| 163 | plot_data(data, mdata, vmin=vmin, vmax=vmax) |
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| 164 | plt.colorbar() |
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| 165 | plt.subplot(1, 3, 2) |
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| 166 | plot_data(data, mtheory, vmin=vmin, vmax=vmax) |
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| 167 | plt.colorbar() |
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| 168 | plt.subplot(1, 3, 3) |
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| 169 | print abs(mresid).max() |
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| 170 | plot_data(data, mresid) |
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| 171 | plt.colorbar() |
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| 172 | |
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| 173 | |
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| 174 | def plot_result1D(data, theory, view='linear'): |
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| 175 | import matplotlib.pyplot as plt |
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| 176 | from numpy.ma import masked_array, masked |
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| 177 | #print "not a number",sum(np.isnan(data.y)) |
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| 178 | #data.y[data.y<0.05] = 0.5 |
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| 179 | mdata = masked_array(data.y, data.mask) |
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| 180 | mdata[np.isnan(mdata)] = masked |
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| 181 | if view is 'log': |
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| 182 | mdata[mdata <= 0] = masked |
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| 183 | mtheory = masked_array(theory, mdata.mask) |
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| 184 | mresid = masked_array((theory-data.y)/data.dy, mdata.mask) |
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| 185 | |
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| 186 | plt.subplot(1,2,1) |
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| 187 | plt.errorbar(data.x, mdata, yerr=data.dy) |
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| 188 | plt.plot(data.x, mtheory, '-', hold=True) |
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| 189 | plt.yscale(view) |
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| 190 | plt.subplot(1, 2, 2) |
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| 191 | plt.plot(data.x, mresid, 'x') |
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| 192 | #plt.axhline(1, color='black', ls='--',lw=1, hold=True) |
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| 193 | #plt.axhline(0, color='black', lw=1, hold=True) |
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| 194 | #plt.axhline(-1, color='black', ls='--',lw=1, hold=True) |
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| 195 | |
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| 196 | |
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| 197 | class BumpsModel(object): |
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| 198 | def __init__(self, data, model, cutoff=1e-5, **kw): |
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| 199 | from bumps.names import Parameter |
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| 200 | from . import gpu |
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| 201 | |
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| 202 | # interpret data |
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| 203 | self.is2D = hasattr(data,'qx_data') |
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| 204 | self.data = data |
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| 205 | if self.is2D: |
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| 206 | self.index = (data.mask==0) & (~np.isnan(data.data)) |
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| 207 | self.iq = data.data[self.index] |
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| 208 | self.diq = data.err_data[self.index] |
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| 209 | self._theory = np.zeros_like(data.data) |
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| 210 | q_vectors = [data.qx_data, data.qy_data] |
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| 211 | else: |
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| 212 | self.index = (data.mask==0) & (~np.isnan(data.y)) |
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| 213 | self.iq = data.y[self.index] |
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| 214 | self.diq = data.dy[self.index] |
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| 215 | self._theory = np.zeros_like(data.y) |
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| 216 | q_vectors = [data.x] |
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| 217 | #input = model.make_input(q_vectors) |
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| 218 | input = model.make_input([v[self.index] for v in q_vectors]) |
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| 219 | |
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| 220 | # create model |
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| 221 | self.fn = model(input, cutoff=cutoff) |
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| 222 | |
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| 223 | # define bumps parameters |
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| 224 | pars = [] |
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| 225 | for p in model.meta['parameters']: |
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| 226 | name, default, limits, ptype = p[0], p[2], p[3], p[4] |
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| 227 | value = kw.pop(name, default) |
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| 228 | setattr(self, name, Parameter.default(value, name=name, limits=limits)) |
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| 229 | pars.append(name) |
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| 230 | if ptype != "": |
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| 231 | for xpart,xdefault,xlimits in [ |
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| 232 | ('_pd', 0, limits), |
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| 233 | ('_pd_n', 35, (0,1000)), |
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| 234 | ('_pd_nsigma', 3, (0,10)), |
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| 235 | ]: |
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| 236 | xname = name+xpart |
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| 237 | xvalue = kw.pop(xname, xdefault) |
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| 238 | setattr(self, xname, Parameter.default(xvalue, name=xname)) |
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| 239 | pars.append(xname) |
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| 240 | if kw: |
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| 241 | raise TypeError("unexpected parameters: %s"%(", ".join(sorted(kw.keys())))) |
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| 242 | self._parameter_names = pars |
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| 243 | self.update() |
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| 244 | |
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| 245 | def update(self): |
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| 246 | self._cache = {} |
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| 247 | |
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| 248 | def numpoints(self): |
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| 249 | return len(self.iq) |
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| 250 | |
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| 251 | def parameters(self): |
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| 252 | return dict((k,getattr(self,k)) for k in self._parameter_names) |
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| 253 | |
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| 254 | def theory(self): |
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| 255 | if 'theory' not in self._cache: |
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| 256 | pars = dict((k,getattr(self,k).value) for k in self._parameter_names) |
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| 257 | #print pars |
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| 258 | self._theory[self.index] = self.fn.eval(pars) |
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| 259 | #self._theory[:] = self.fn.eval(pars) |
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| 260 | self._cache['theory'] = self._theory |
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| 261 | return self._cache['theory'] |
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| 262 | |
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| 263 | def residuals(self): |
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| 264 | #if np.any(self.err ==0): print "zeros in err" |
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| 265 | return (self.theory()[self.index]-self.iq)/self.diq |
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| 266 | |
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| 267 | def nllf(self): |
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| 268 | R = self.residuals() |
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| 269 | #if np.any(np.isnan(R)): print "NaN in residuals" |
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| 270 | return 0.5*np.sum(R**2) |
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| 271 | |
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| 272 | def __call__(self): |
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| 273 | return 2*self.nllf()/self.dof |
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| 274 | |
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| 275 | def plot(self, view='log'): |
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| 276 | if self.is2D: |
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| 277 | plot_result2D(self.data, self.theory(), view=view) |
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| 278 | else: |
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| 279 | plot_result1D(self.data, self.theory(), view=view) |
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| 280 | |
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| 281 | def save(self, basename): |
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| 282 | pass |
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| 283 | |
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| 284 | def demo(): |
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| 285 | data = load_data('DEC07086.DAT') |
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| 286 | set_beam_stop(data, 0.004) |
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| 287 | plot_data(data) |
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| 288 | import matplotlib.pyplot as plt; plt.show() |
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| 289 | |
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| 290 | if __name__ == "__main__": |
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| 291 | demo() |
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