Changeset 9a5097c in sasview for src/sas/sasgui
- Timestamp:
- Mar 26, 2017 11:33:16 PM (8 years ago)
- Branches:
- master, ESS_GUI, ESS_GUI_Docs, ESS_GUI_batch_fitting, ESS_GUI_bumps_abstraction, ESS_GUI_iss1116, ESS_GUI_iss879, ESS_GUI_iss959, ESS_GUI_opencl, ESS_GUI_ordering, ESS_GUI_sync_sascalc, costrafo411, magnetic_scatt, release-4.2.2, ticket-1009, ticket-1094-headless, ticket-1242-2d-resolution, ticket-1243, ticket-1249, ticket885, unittest-saveload
- Children:
- ed2276f
- Parents:
- 9146ed9
- Location:
- src/sas/sasgui
- Files:
-
- 20 edited
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guiframe/dataFitting.py (modified) (7 diffs)
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guiframe/data_processor.py (modified) (2 diffs)
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guiframe/local_perspectives/plotting/Plotter1D.py (modified) (2 diffs)
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guiframe/local_perspectives/plotting/Plotter2D.py (modified) (2 diffs)
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guiframe/local_perspectives/plotting/boxSlicer.py (modified) (3 diffs)
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guiframe/local_perspectives/plotting/masking.py (modified) (3 diffs)
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perspectives/calculator/data_operator.py (modified) (2 diffs)
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perspectives/calculator/gen_scatter_panel.py (modified) (19 diffs)
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perspectives/fitting/basepage.py (modified) (13 diffs)
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perspectives/fitting/fitpage.py (modified) (15 diffs)
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perspectives/fitting/fitting.py (modified) (15 diffs)
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perspectives/fitting/model_thread.py (modified) (8 diffs)
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perspectives/fitting/pagestate.py (modified) (4 diffs)
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perspectives/fitting/utils.py (modified) (2 diffs)
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perspectives/pr/explore_dialog.py (modified) (2 diffs)
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perspectives/pr/pr.py (modified) (15 diffs)
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perspectives/simulation/simulation.py (modified) (4 diffs)
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plottools/PlotPanel.py (modified) (8 diffs)
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plottools/fitDialog.py (modified) (5 diffs)
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plottools/plottables.py (modified) (6 diffs)
Legend:
- Unmodified
- Added
- Removed
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src/sas/sasgui/guiframe/dataFitting.py
r68adf86 r9a5097c 3 3 """ 4 4 import copy 5 import numpy 5 import numpy as np 6 6 import math 7 7 from sas.sascalc.data_util.uncertainty import Uncertainty … … 81 81 result.dxw = None 82 82 else: 83 result.dxw = n umpy.zeros(len(self.x))83 result.dxw = np.zeros(len(self.x)) 84 84 if self.dxl == None: 85 85 result.dxl = None 86 86 else: 87 result.dxl = n umpy.zeros(len(self.x))87 result.dxl = np.zeros(len(self.x)) 88 88 89 89 for i in range(len(self.x)): … … 128 128 result.dlam = None 129 129 else: 130 result.dlam = n umpy.zeros(tot_length)130 result.dlam = np.zeros(tot_length) 131 131 if self.dy == None or other.dy is None: 132 132 result.dy = None 133 133 else: 134 result.dy = n umpy.zeros(tot_length)134 result.dy = np.zeros(tot_length) 135 135 if self.dx == None or other.dx is None: 136 136 result.dx = None 137 137 else: 138 result.dx = n umpy.zeros(tot_length)138 result.dx = np.zeros(tot_length) 139 139 if self.dxw == None or other.dxw is None: 140 140 result.dxw = None 141 141 else: 142 result.dxw = n umpy.zeros(tot_length)142 result.dxw = np.zeros(tot_length) 143 143 if self.dxl == None or other.dxl is None: 144 144 result.dxl = None 145 145 else: 146 result.dxl = n umpy.zeros(tot_length)147 148 result.x = n umpy.append(self.x, other.x)146 result.dxl = np.zeros(tot_length) 147 148 result.x = np.append(self.x, other.x) 149 149 #argsorting 150 ind = n umpy.argsort(result.x)150 ind = np.argsort(result.x) 151 151 result.x = result.x[ind] 152 result.y = n umpy.append(self.y, other.y)152 result.y = np.append(self.y, other.y) 153 153 result.y = result.y[ind] 154 result.lam = n umpy.append(self.lam, other.lam)154 result.lam = np.append(self.lam, other.lam) 155 155 result.lam = result.lam[ind] 156 156 if result.dlam != None: 157 result.dlam = n umpy.append(self.dlam, other.dlam)157 result.dlam = np.append(self.dlam, other.dlam) 158 158 result.dlam = result.dlam[ind] 159 159 if result.dy != None: 160 result.dy = n umpy.append(self.dy, other.dy)160 result.dy = np.append(self.dy, other.dy) 161 161 result.dy = result.dy[ind] 162 162 if result.dx is not None: 163 result.dx = n umpy.append(self.dx, other.dx)163 result.dx = np.append(self.dx, other.dx) 164 164 result.dx = result.dx[ind] 165 165 if result.dxw is not None: 166 result.dxw = n umpy.append(self.dxw, other.dxw)166 result.dxw = np.append(self.dxw, other.dxw) 167 167 result.dxw = result.dxw[ind] 168 168 if result.dxl is not None: 169 result.dxl = n umpy.append(self.dxl, other.dxl)169 result.dxl = np.append(self.dxl, other.dxl) 170 170 result.dxl = result.dxl[ind] 171 171 return result … … 230 230 result.dxw = None 231 231 else: 232 result.dxw = n umpy.zeros(len(self.x))232 result.dxw = np.zeros(len(self.x)) 233 233 if self.dxl == None: 234 234 result.dxl = None 235 235 else: 236 result.dxl = n umpy.zeros(len(self.x))237 238 for i in range(n umpy.size(self.x)):236 result.dxl = np.zeros(len(self.x)) 237 238 for i in range(np.size(self.x)): 239 239 result.x[i] = self.x[i] 240 240 if self.dx is not None and len(self.x) == len(self.dx): … … 282 282 result.dlam = None 283 283 else: 284 result.dlam = n umpy.zeros(tot_length)284 result.dlam = np.zeros(tot_length) 285 285 if self.dy == None or other.dy is None: 286 286 result.dy = None 287 287 else: 288 result.dy = n umpy.zeros(tot_length)288 result.dy = np.zeros(tot_length) 289 289 if self.dx == None or other.dx is None: 290 290 result.dx = None 291 291 else: 292 result.dx = n umpy.zeros(tot_length)292 result.dx = np.zeros(tot_length) 293 293 if self.dxw == None or other.dxw is None: 294 294 result.dxw = None 295 295 else: 296 result.dxw = n umpy.zeros(tot_length)296 result.dxw = np.zeros(tot_length) 297 297 if self.dxl == None or other.dxl is None: 298 298 result.dxl = None 299 299 else: 300 result.dxl = n umpy.zeros(tot_length)301 result.x = n umpy.append(self.x, other.x)300 result.dxl = np.zeros(tot_length) 301 result.x = np.append(self.x, other.x) 302 302 #argsorting 303 ind = n umpy.argsort(result.x)303 ind = np.argsort(result.x) 304 304 result.x = result.x[ind] 305 result.y = n umpy.append(self.y, other.y)305 result.y = np.append(self.y, other.y) 306 306 result.y = result.y[ind] 307 result.lam = n umpy.append(self.lam, other.lam)307 result.lam = np.append(self.lam, other.lam) 308 308 result.lam = result.lam[ind] 309 309 if result.dy != None: 310 result.dy = n umpy.append(self.dy, other.dy)310 result.dy = np.append(self.dy, other.dy) 311 311 result.dy = result.dy[ind] 312 312 if result.dx is not None: 313 result.dx = n umpy.append(self.dx, other.dx)313 result.dx = np.append(self.dx, other.dx) 314 314 result.dx = result.dx[ind] 315 315 if result.dxw is not None: 316 result.dxw = n umpy.append(self.dxw, other.dxw)316 result.dxw = np.append(self.dxw, other.dxw) 317 317 result.dxw = result.dxw[ind] 318 318 if result.dxl is not None: 319 result.dxl = n umpy.append(self.dxl, other.dxl)319 result.dxl = np.append(self.dxl, other.dxl) 320 320 result.dxl = result.dxl[ind] 321 321 return result … … 409 409 result.dqy_data = None 410 410 else: 411 result.dqx_data = n umpy.zeros(len(self.data))412 result.dqy_data = n umpy.zeros(len(self.data))413 for i in range(n umpy.size(self.data)):411 result.dqx_data = np.zeros(len(self.data)) 412 result.dqy_data = np.zeros(len(self.data)) 413 for i in range(np.size(self.data)): 414 414 result.data[i] = self.data[i] 415 415 if self.err_data is not None and \ 416 numpy.size(self.data) == numpy.size(self.err_data):416 np.size(self.data) == np.size(self.err_data): 417 417 result.err_data[i] = self.err_data[i] 418 418 if self.dqx_data is not None: … … 473 473 result.dqy_data = None 474 474 else: 475 result.dqx_data = n umpy.zeros(len(self.data) + \476 numpy.size(other.data))477 result.dqy_data = n umpy.zeros(len(self.data) + \478 numpy.size(other.data))479 480 result.data = n umpy.append(self.data, other.data)481 result.qx_data = n umpy.append(self.qx_data, other.qx_data)482 result.qy_data = n umpy.append(self.qy_data, other.qy_data)483 result.q_data = n umpy.append(self.q_data, other.q_data)484 result.mask = n umpy.append(self.mask, other.mask)475 result.dqx_data = np.zeros(len(self.data) + \ 476 np.size(other.data)) 477 result.dqy_data = np.zeros(len(self.data) + \ 478 np.size(other.data)) 479 480 result.data = np.append(self.data, other.data) 481 result.qx_data = np.append(self.qx_data, other.qx_data) 482 result.qy_data = np.append(self.qy_data, other.qy_data) 483 result.q_data = np.append(self.q_data, other.q_data) 484 result.mask = np.append(self.mask, other.mask) 485 485 if result.err_data is not None: 486 result.err_data = n umpy.append(self.err_data, other.err_data)486 result.err_data = np.append(self.err_data, other.err_data) 487 487 if self.dqx_data is not None: 488 result.dqx_data = n umpy.append(self.dqx_data, other.dqx_data)488 result.dqx_data = np.append(self.dqx_data, other.dqx_data) 489 489 if self.dqy_data is not None: 490 result.dqy_data = n umpy.append(self.dqy_data, other.dqy_data)490 result.dqy_data = np.append(self.dqy_data, other.dqy_data) 491 491 492 492 return result -
src/sas/sasgui/guiframe/data_processor.py
r468c253 r9a5097c 1091 1091 # When inputs are from an external file 1092 1092 return inputs, outputs 1093 inds = n umpy.lexsort((to_be_sort, to_be_sort))1093 inds = np.lexsort((to_be_sort, to_be_sort)) 1094 1094 for key in outputs.keys(): 1095 1095 key_list = outputs[key] … … 1379 1379 return 1380 1380 if dy == None: 1381 dy = n umpy.zeros(len(y))1381 dy = np.zeros(len(y)) 1382 1382 #plotting 1383 1383 new_plot = Data1D(x=x, y=y, dy=dy) -
src/sas/sasgui/guiframe/local_perspectives/plotting/Plotter1D.py
r29e872e r9a5097c 14 14 import sys 15 15 import math 16 import numpy 16 import numpy as np 17 17 import logging 18 18 from sas.sasgui.plottools.PlotPanel import PlotPanel … … 288 288 :Param value: float 289 289 """ 290 idx = (n umpy.abs(array - value)).argmin()290 idx = (np.abs(array - value)).argmin() 291 291 return int(idx) # array.flat[idx] 292 292 -
src/sas/sasgui/guiframe/local_perspectives/plotting/Plotter2D.py
rb2b36932 r9a5097c 14 14 import sys 15 15 import math 16 import numpy 16 import numpy as np 17 17 import logging 18 18 from sas.sasgui.plottools.PlotPanel import PlotPanel … … 567 567 """ 568 568 # Find the best number of bins 569 npt = math.sqrt(len(self.data2D.data[n umpy.isfinite(self.data2D.data)]))569 npt = math.sqrt(len(self.data2D.data[np.isfinite(self.data2D.data)])) 570 570 npt = math.floor(npt) 571 571 from sas.sascalc.dataloader.manipulations import CircularAverage -
src/sas/sasgui/guiframe/local_perspectives/plotting/boxSlicer.py
rd85c194 r9a5097c 1 1 import wx 2 2 import math 3 import numpy 3 import numpy as np 4 4 from sas.sasgui.guiframe.events import NewPlotEvent 5 5 from sas.sasgui.guiframe.events import StatusEvent … … 358 358 # # Reset x, y- coordinates if send as parameters 359 359 if x != None: 360 self.x = n umpy.sign(self.x) * math.fabs(x)360 self.x = np.sign(self.x) * math.fabs(x) 361 361 if y != None: 362 self.y = n umpy.sign(self.y) * math.fabs(y)362 self.y = np.sign(self.y) * math.fabs(y) 363 363 # # Draw lines and markers 364 364 self.inner_marker.set(xdata=[0], ydata=[self.y]) … … 465 465 # # reset x, y -coordinates if given as parameters 466 466 if x != None: 467 self.x = n umpy.sign(self.x) * math.fabs(x)467 self.x = np.sign(self.x) * math.fabs(x) 468 468 if y != None: 469 self.y = n umpy.sign(self.y) * math.fabs(y)469 self.y = np.sign(self.y) * math.fabs(y) 470 470 # # draw lines and markers 471 471 self.inner_marker.set(xdata=[self.x], ydata=[0]) -
src/sas/sasgui/guiframe/local_perspectives/plotting/masking.py
rd85c194 r9a5097c 24 24 import math 25 25 import copy 26 import numpy 26 import numpy as np 27 27 from sas.sasgui.plottools.PlotPanel import PlotPanel 28 28 from sas.sasgui.plottools.plottables import Graph … … 298 298 self.subplot.set_ylim(self.data.ymin, self.data.ymax) 299 299 self.subplot.set_xlim(self.data.xmin, self.data.xmax) 300 mask = n umpy.ones(len(self.data.mask), dtype=bool)300 mask = np.ones(len(self.data.mask), dtype=bool) 301 301 self.data.mask = mask 302 302 # update mask plot … … 343 343 self.mask = mask 344 344 # make temperary data to plot 345 temp_mask = n umpy.zeros(len(mask))345 temp_mask = np.zeros(len(mask)) 346 346 temp_data = copy.deepcopy(self.data) 347 347 # temp_data default is None -
src/sas/sasgui/perspectives/calculator/data_operator.py
r61780e3 r9a5097c 5 5 import sys 6 6 import time 7 import numpy 7 import numpy as np 8 8 from sas.sascalc.dataloader.data_info import Data1D 9 9 from sas.sasgui.plottools.PlotPanel import PlotPanel … … 541 541 theory, _ = theory_list.values()[0] 542 542 dnames.append(theory.name) 543 ind = n umpy.argsort(dnames)543 ind = np.argsort(dnames) 544 544 if len(ind) > 0: 545 val_list = n umpy.array(self._data.values())[ind]545 val_list = np.array(self._data.values())[ind] 546 546 for datastate in val_list: 547 547 data = datastate.data -
src/sas/sasgui/perspectives/calculator/gen_scatter_panel.py
r0f7c930 r9a5097c 7 7 import sys 8 8 import os 9 import numpy 9 import numpy as np 10 10 #import math 11 11 import wx.aui as aui … … 741 741 marker = 'o' 742 742 m_size = 3.5 743 sld_tot = (n umpy.fabs(sld_mx) + numpy.fabs(sld_my) + \744 n umpy.fabs(sld_mz) + numpy.fabs(output.sld_n))743 sld_tot = (np.fabs(sld_mx) + np.fabs(sld_my) + \ 744 np.fabs(sld_mz) + np.fabs(output.sld_n)) 745 745 is_nonzero = sld_tot > 0.0 746 746 is_zero = sld_tot == 0.0 … … 757 757 pix_symbol = output.pix_symbol[is_nonzero] 758 758 # II. Plot selective points in color 759 other_color = n umpy.ones(len(pix_symbol), dtype='bool')759 other_color = np.ones(len(pix_symbol), dtype='bool') 760 760 for key in color_dic.keys(): 761 761 chosen_color = pix_symbol == key 762 if n umpy.any(chosen_color):762 if np.any(chosen_color): 763 763 other_color = other_color & (chosen_color != True) 764 764 color = color_dic[key] … … 767 767 markeredgecolor=color, markersize=m_size, label=key) 768 768 # III. Plot All others 769 if n umpy.any(other_color):769 if np.any(other_color): 770 770 a_name = '' 771 771 if output.pix_type == 'atom': … … 795 795 draw magnetic vectors w/arrow 796 796 """ 797 max_mx = max(n umpy.fabs(sld_mx))798 max_my = max(n umpy.fabs(sld_my))799 max_mz = max(n umpy.fabs(sld_mz))797 max_mx = max(np.fabs(sld_mx)) 798 max_my = max(np.fabs(sld_my)) 799 max_mz = max(np.fabs(sld_mz)) 800 800 max_m = max(max_mx, max_my, max_mz) 801 801 try: … … 812 812 unit_z2 = sld_mz / max_m 813 813 # 0.8 is for avoiding the color becomes white=(1,1,1)) 814 color_x = n umpy.fabs(unit_x2 * 0.8)815 color_y = n umpy.fabs(unit_y2 * 0.8)816 color_z = n umpy.fabs(unit_z2 * 0.8)814 color_x = np.fabs(unit_x2 * 0.8) 815 color_y = np.fabs(unit_y2 * 0.8) 816 color_z = np.fabs(unit_z2 * 0.8) 817 817 x2 = pos_x + unit_x2 * max_step 818 818 y2 = pos_y + unit_y2 * max_step 819 819 z2 = pos_z + unit_z2 * max_step 820 x_arrow = n umpy.column_stack((pos_x, x2))821 y_arrow = n umpy.column_stack((pos_y, y2))822 z_arrow = n umpy.column_stack((pos_z, z2))823 colors = n umpy.column_stack((color_x, color_y, color_z))820 x_arrow = np.column_stack((pos_x, x2)) 821 y_arrow = np.column_stack((pos_y, y2)) 822 z_arrow = np.column_stack((pos_z, z2)) 823 colors = np.column_stack((color_x, color_y, color_z)) 824 824 arrows = Arrow3D(panel, x_arrow, z_arrow, y_arrow, 825 825 colors, mutation_scale=10, lw=1, … … 880 880 if self.is_avg or self.is_avg == None: 881 881 self._create_default_1d_data() 882 i_out = n umpy.zeros(len(self.data.y))882 i_out = np.zeros(len(self.data.y)) 883 883 inputs = [self.data.x, [], i_out] 884 884 else: 885 885 self._create_default_2d_data() 886 i_out = n umpy.zeros(len(self.data.data))886 i_out = np.zeros(len(self.data.data)) 887 887 inputs = [self.data.qx_data, self.data.qy_data, i_out] 888 888 … … 989 989 :Param input: input list [qx_data, qy_data, i_out] 990 990 """ 991 out = n umpy.empty(0)991 out = np.empty(0) 992 992 #s = time.time() 993 993 for ind in range(len(input[0])): … … 998 998 inputi = [input[0][ind:ind + 1], [], input[2][ind:ind + 1]] 999 999 outi = self.model.run(inputi) 1000 out = n umpy.append(out, outi)1000 out = np.append(out, outi) 1001 1001 else: 1002 1002 if ind % 50 == 0 and update != None: … … 1006 1006 input[2][ind:ind + 1]] 1007 1007 outi = self.model.runXY(inputi) 1008 out = n umpy.append(out, outi)1008 out = np.append(out, outi) 1009 1009 #print time.time() - s 1010 1010 if self.is_avg or self.is_avg == None: … … 1027 1027 self.npts_x = int(float(self.npt_ctl.GetValue())) 1028 1028 self.data = Data2D() 1029 qmax = self.qmax_x #/ n umpy.sqrt(2)1029 qmax = self.qmax_x #/ np.sqrt(2) 1030 1030 self.data.xaxis('\\rm{Q_{x}}', '\AA^{-1}') 1031 1031 self.data.yaxis('\\rm{Q_{y}}', '\AA^{-1}') … … 1048 1048 qstep = self.npts_x 1049 1049 1050 x = n umpy.linspace(start=xmin, stop=xmax, num=qstep, endpoint=True)1051 y = n umpy.linspace(start=ymin, stop=ymax, num=qstep, endpoint=True)1050 x = np.linspace(start=xmin, stop=xmax, num=qstep, endpoint=True) 1051 y = np.linspace(start=ymin, stop=ymax, num=qstep, endpoint=True) 1052 1052 ## use data info instead 1053 new_x = n umpy.tile(x, (len(y), 1))1054 new_y = n umpy.tile(y, (len(x), 1))1053 new_x = np.tile(x, (len(y), 1)) 1054 new_y = np.tile(y, (len(x), 1)) 1055 1055 new_y = new_y.swapaxes(0, 1) 1056 1056 # all data reuire now in 1d array 1057 1057 qx_data = new_x.flatten() 1058 1058 qy_data = new_y.flatten() 1059 q_data = n umpy.sqrt(qx_data * qx_data + qy_data * qy_data)1059 q_data = np.sqrt(qx_data * qx_data + qy_data * qy_data) 1060 1060 # set all True (standing for unmasked) as default 1061 mask = n umpy.ones(len(qx_data), dtype=bool)1061 mask = np.ones(len(qx_data), dtype=bool) 1062 1062 # store x and y bin centers in q space 1063 1063 x_bins = x 1064 1064 y_bins = y 1065 1065 self.data.source = Source() 1066 self.data.data = n umpy.ones(len(mask))1067 self.data.err_data = n umpy.ones(len(mask))1066 self.data.data = np.ones(len(mask)) 1067 self.data.err_data = np.ones(len(mask)) 1068 1068 self.data.qx_data = qx_data 1069 1069 self.data.qy_data = qy_data … … 1084 1084 :warning: This data is never plotted. 1085 1085 residuals.x = data_copy.x[index] 1086 residuals.dy = n umpy.ones(len(residuals.y))1086 residuals.dy = np.ones(len(residuals.y)) 1087 1087 residuals.dx = None 1088 1088 residuals.dxl = None … … 1091 1091 self.qmax_x = float(self.qmax_ctl.GetValue()) 1092 1092 self.npts_x = int(float(self.npt_ctl.GetValue())) 1093 qmax = self.qmax_x #/ n umpy.sqrt(2)1093 qmax = self.qmax_x #/ np.sqrt(2) 1094 1094 ## Default values 1095 1095 xmax = qmax 1096 1096 xmin = qmax * _Q1D_MIN 1097 1097 qstep = self.npts_x 1098 x = n umpy.linspace(start=xmin, stop=xmax, num=qstep, endpoint=True)1098 x = np.linspace(start=xmin, stop=xmax, num=qstep, endpoint=True) 1099 1099 # store x and y bin centers in q space 1100 1100 #self.data.source = Source() 1101 y = n umpy.ones(len(x))1102 dy = n umpy.zeros(len(x))1103 dx = n umpy.zeros(len(x))1101 y = np.ones(len(x)) 1102 dy = np.zeros(len(x)) 1103 dx = np.zeros(len(x)) 1104 1104 self.data = Data1D(x=x, y=y) 1105 1105 self.data.dx = dx … … 1171 1171 state = None 1172 1172 1173 n umpy.nan_to_num(image)1173 np.nan_to_num(image) 1174 1174 new_plot = Data2D(image=image, err_image=data.err_data) 1175 1175 new_plot.name = model.name + '2d' … … 1640 1640 for key in sld_list.keys(): 1641 1641 if ctr_list[0] == key: 1642 min_val = n umpy.min(sld_list[key])1643 max_val = n umpy.max(sld_list[key])1644 mean_val = n umpy.mean(sld_list[key])1642 min_val = np.min(sld_list[key]) 1643 max_val = np.max(sld_list[key]) 1644 mean_val = np.mean(sld_list[key]) 1645 1645 enable = (min_val == max_val) and \ 1646 1646 sld_data.pix_type == 'pixel' … … 1733 1733 npts = -1 1734 1734 break 1735 if n umpy.isfinite(n_val):1735 if np.isfinite(n_val): 1736 1736 npts *= int(n_val) 1737 1737 if npts > 0: … … 1770 1770 ctl.Refresh() 1771 1771 return 1772 if n umpy.isfinite(s_val):1772 if np.isfinite(s_val): 1773 1773 s_size *= s_val 1774 1774 self.sld_data.set_pixel_volumes(s_size) … … 1787 1787 try: 1788 1788 sld_data = self.parent.get_sld_from_omf() 1789 #nop = (nop * n umpy.pi) / 61789 #nop = (nop * np.pi) / 6 1790 1790 nop = len(sld_data.sld_n) 1791 1791 except: -
src/sas/sasgui/perspectives/fitting/basepage.py
rb301db9 r9a5097c 5 5 import os 6 6 import wx 7 import numpy 7 import numpy as np 8 8 import time 9 9 import copy … … 100 100 self.graph_id = None 101 101 # Q range for data set 102 self.qmin_data_set = n umpy.inf102 self.qmin_data_set = np.inf 103 103 self.qmax_data_set = None 104 104 self.npts_data_set = 0 … … 278 278 279 279 """ 280 x = n umpy.linspace(start=self.qmin_x, stop=self.qmax_x,280 x = np.linspace(start=self.qmin_x, stop=self.qmax_x, 281 281 num=self.npts_x, endpoint=True) 282 282 self.data = Data1D(x=x) … … 295 295 """ 296 296 if self.qmin_x >= 1.e-10: 297 qmin = n umpy.log10(self.qmin_x)297 qmin = np.log10(self.qmin_x) 298 298 else: 299 299 qmin = -10. 300 300 301 301 if self.qmax_x <= 1.e10: 302 qmax = n umpy.log10(self.qmax_x)302 qmax = np.log10(self.qmax_x) 303 303 else: 304 304 qmax = 10. 305 305 306 x = n umpy.logspace(start=qmin, stop=qmax,306 x = np.logspace(start=qmin, stop=qmax, 307 307 num=self.npts_x, endpoint=True, base=10.0) 308 308 self.data = Data1D(x=x) … … 341 341 qstep = self.npts_x 342 342 343 x = n umpy.linspace(start=xmin, stop=xmax, num=qstep, endpoint=True)344 y = n umpy.linspace(start=ymin, stop=ymax, num=qstep, endpoint=True)343 x = np.linspace(start=xmin, stop=xmax, num=qstep, endpoint=True) 344 y = np.linspace(start=ymin, stop=ymax, num=qstep, endpoint=True) 345 345 # use data info instead 346 new_x = n umpy.tile(x, (len(y), 1))347 new_y = n umpy.tile(y, (len(x), 1))346 new_x = np.tile(x, (len(y), 1)) 347 new_y = np.tile(y, (len(x), 1)) 348 348 new_y = new_y.swapaxes(0, 1) 349 349 # all data reuire now in 1d array 350 350 qx_data = new_x.flatten() 351 351 qy_data = new_y.flatten() 352 q_data = n umpy.sqrt(qx_data * qx_data + qy_data * qy_data)352 q_data = np.sqrt(qx_data * qx_data + qy_data * qy_data) 353 353 # set all True (standing for unmasked) as default 354 mask = n umpy.ones(len(qx_data), dtype=bool)354 mask = np.ones(len(qx_data), dtype=bool) 355 355 # store x and y bin centers in q space 356 356 x_bins = x … … 358 358 359 359 self.data.source = Source() 360 self.data.data = n umpy.ones(len(mask))361 self.data.err_data = n umpy.ones(len(mask))360 self.data.data = np.ones(len(mask)) 361 self.data.err_data = np.ones(len(mask)) 362 362 self.data.qx_data = qx_data 363 363 self.data.qy_data = qy_data … … 783 783 # Skip non-data lines 784 784 logging.error(traceback.format_exc()) 785 return n umpy.array(angles), numpy.array(weights)785 return np.array(angles), np.array(weights) 786 786 except: 787 787 raise … … 2120 2120 for data in self.data_list: 2121 2121 # q value from qx and qy 2122 radius = n umpy.sqrt(data.qx_data * data.qx_data +2122 radius = np.sqrt(data.qx_data * data.qx_data + 2123 2123 data.qy_data * data.qy_data) 2124 2124 # get unmasked index … … 2126 2126 (radius <= float(self.qmax.GetValue())) 2127 2127 index_data = (index_data) & (data.mask) 2128 index_data = (index_data) & (n umpy.isfinite(data.data))2128 index_data = (index_data) & (np.isfinite(data.data)) 2129 2129 2130 2130 if len(index_data[index_data]) < 10: … … 2161 2161 index_data = (float(self.qmin.GetValue()) <= radius) & \ 2162 2162 (radius <= float(self.qmax.GetValue())) 2163 index_data = (index_data) & (n umpy.isfinite(data.y))2163 index_data = (index_data) & (np.isfinite(data.y)) 2164 2164 2165 2165 if len(index_data[index_data]) < 5: … … 2233 2233 2234 2234 # Check that min is less than max 2235 low = -n umpy.inf if min_str == "" else float(min_str)2236 high = n umpy.inf if max_str == "" else float(max_str)2235 low = -np.inf if min_str == "" else float(min_str) 2236 high = np.inf if max_str == "" else float(max_str) 2237 2237 if high < low: 2238 2238 min_ctrl.SetBackgroundColour("pink") … … 2654 2654 self.qmin_x = data_min 2655 2655 self.qmax_x = math.sqrt(x * x + y * y) 2656 # self.data.mask = n umpy.ones(len(self.data.data),dtype=bool)2656 # self.data.mask = np.ones(len(self.data.data),dtype=bool) 2657 2657 # check smearing 2658 2658 if not self.disable_smearer.GetValue(): … … 3366 3366 3367 3367 if value[1] == 'array': 3368 pd_vals = n umpy.array(value[2])3369 pd_weights = n umpy.array(value[3])3368 pd_vals = np.array(value[2]) 3369 pd_weights = np.array(value[3]) 3370 3370 if len(pd_vals) == 0 or len(pd_vals) != len(pd_weights): 3371 3371 msg = ("bad array distribution parameters for %s" -
src/sas/sasgui/perspectives/fitting/fitpage.py
rd85f1d8a r9a5097c 6 6 import wx 7 7 import wx.lib.newevent 8 import numpy 8 import numpy as np 9 9 import copy 10 10 import math … … 1115 1115 if item.GetValue(): 1116 1116 if button_list.index(item) == 0: 1117 flag = 0 # dy = n umpy.ones_like(dy_data)1117 flag = 0 # dy = np.ones_like(dy_data) 1118 1118 elif button_list.index(item) == 1: 1119 1119 flag = 1 # dy = dy_data 1120 1120 elif button_list.index(item) == 2: 1121 flag = 2 # dy = n umpy.sqrt(numpy.abs(data))1121 flag = 2 # dy = np.sqrt(np.abs(data)) 1122 1122 elif button_list.index(item) == 3: 1123 flag = 3 # dy = n umpy.abs(data)1123 flag = 3 # dy = np.abs(data) 1124 1124 break 1125 1125 return flag … … 1422 1422 key = event.GetKeyCode() 1423 1423 length = len(self.data.x) 1424 indx = (n umpy.abs(self.data.x - x_data)).argmin()1424 indx = (np.abs(self.data.x - x_data)).argmin() 1425 1425 # return array.flat[idx] 1426 1426 if key == wx.WXK_PAGEUP or key == wx.WXK_NUMPAD_PAGEUP: … … 1477 1477 self.enable2D: 1478 1478 # set mask 1479 radius = n umpy.sqrt(self.data.qx_data * self.data.qx_data +1479 radius = np.sqrt(self.data.qx_data * self.data.qx_data + 1480 1480 self.data.qy_data * self.data.qy_data) 1481 1481 index_data = ((self.qmin_x <= radius) & (radius <= self.qmax_x)) 1482 1482 index_data = (index_data) & (self.data.mask) 1483 index_data = (index_data) & (n umpy.isfinite(self.data.data))1483 index_data = (index_data) & (np.isfinite(self.data.data)) 1484 1484 if len(index_data[index_data]) < 10: 1485 1485 msg = "Cannot Plot :No or too little npts in" … … 1598 1598 and data.dqx_data.any() != 0: 1599 1599 self.smear_type = "Pinhole2d" 1600 self.dq_l = format_number(n umpy.average(data.dqx_data))1601 self.dq_r = format_number(n umpy.average(data.dqy_data))1600 self.dq_l = format_number(np.average(data.dqx_data)) 1601 self.dq_r = format_number(np.average(data.dqy_data)) 1602 1602 return 1603 1603 else: 1604 1604 return 1605 1605 # check if it is pinhole smear and get min max if it is. 1606 if data.dx is not None and n umpy.any(data.dx):1606 if data.dx is not None and np.any(data.dx): 1607 1607 self.smear_type = "Pinhole" 1608 1608 self.dq_l = data.dx[0] … … 1612 1612 elif data.dxl is not None or data.dxw is not None: 1613 1613 self.smear_type = "Slit" 1614 if data.dxl is not None and n umpy.all(data.dxl, 0):1614 if data.dxl is not None and np.all(data.dxl, 0): 1615 1615 self.dq_l = data.dxl[0] 1616 if data.dxw is not None and n umpy.all(data.dxw, 0):1616 if data.dxw is not None and np.all(data.dxw, 0): 1617 1617 self.dq_r = data.dxw[0] 1618 1618 # return self.smear_type,self.dq_l,self.dq_r … … 1921 1921 self.default_mask = copy.deepcopy(self.data.mask) 1922 1922 if self.data.err_data is not None \ 1923 and n umpy.any(self.data.err_data):1923 and np.any(self.data.err_data): 1924 1924 di_flag = True 1925 1925 if self.data.dqx_data is not None \ 1926 and n umpy.any(self.data.dqx_data):1926 and np.any(self.data.dqx_data): 1927 1927 dq_flag = True 1928 1928 else: 1929 1929 self.slit_smearer.Enable(True) 1930 1930 self.pinhole_smearer.Enable(True) 1931 if self.data.dy is not None and n umpy.any(self.data.dy):1931 if self.data.dy is not None and np.any(self.data.dy): 1932 1932 di_flag = True 1933 if self.data.dx is not None and n umpy.any(self.data.dx):1933 if self.data.dx is not None and np.any(self.data.dx): 1934 1934 dq_flag = True 1935 elif self.data.dxl is not None and n umpy.any(self.data.dxl):1935 elif self.data.dxl is not None and np.any(self.data.dxl): 1936 1936 dq_flag = True 1937 1937 … … 2067 2067 if self.data.__class__.__name__ == "Data2D" or \ 2068 2068 self.enable2D: 2069 radius = n umpy.sqrt(self.data.qx_data * self.data.qx_data +2069 radius = np.sqrt(self.data.qx_data * self.data.qx_data + 2070 2070 self.data.qy_data * self.data.qy_data) 2071 2071 index_data = (self.qmin_x <= radius) & (radius <= self.qmax_x) 2072 2072 index_data = (index_data) & (self.data.mask) 2073 index_data = (index_data) & (n umpy.isfinite(self.data.data))2073 index_data = (index_data) & (np.isfinite(self.data.data)) 2074 2074 npts2fit = len(self.data.data[index_data]) 2075 2075 else: … … 2104 2104 # make sure stop button to fit button all the time 2105 2105 self._on_fit_complete() 2106 if out is None or not n umpy.isfinite(chisqr):2106 if out is None or not np.isfinite(chisqr): 2107 2107 raise ValueError, "Fit error occured..." 2108 2108 … … 2115 2115 2116 2116 # Check if chi2 is finite 2117 if chisqr is not None and n umpy.isfinite(chisqr):2117 if chisqr is not None and np.isfinite(chisqr): 2118 2118 # format chi2 2119 2119 chi2 = format_number(chisqr, True) … … 2167 2167 2168 2168 if cov[ind] is not None: 2169 if n umpy.isfinite(float(cov[ind])):2169 if np.isfinite(float(cov[ind])): 2170 2170 val_err = format_number(cov[ind], True) 2171 2171 item[4].SetForegroundColour(wx.BLACK) … … 2291 2291 self.smear_type = 'Pinhole2d' 2292 2292 len_data = len(data.data) 2293 data.dqx_data = n umpy.zeros(len_data)2294 data.dqy_data = n umpy.zeros(len_data)2293 data.dqx_data = np.zeros(len_data) 2294 data.dqy_data = np.zeros(len_data) 2295 2295 else: 2296 2296 self.smear_type = 'Pinhole' 2297 2297 len_data = len(data.x) 2298 data.dx = n umpy.zeros(len_data)2298 data.dx = np.zeros(len_data) 2299 2299 data.dxl = None 2300 2300 data.dxw = None … … 2469 2469 try: 2470 2470 self.dxl = float(self.smear_slit_height.GetValue()) 2471 data.dxl = self.dxl * n umpy.ones(data_len)2471 data.dxl = self.dxl * np.ones(data_len) 2472 2472 self.smear_slit_height.SetBackgroundColour(wx.WHITE) 2473 2473 except: 2474 2474 self.dxl = None 2475 data.dxl = n umpy.zeros(data_len)2475 data.dxl = np.zeros(data_len) 2476 2476 if self.smear_slit_height.GetValue().lstrip().rstrip() != "": 2477 2477 self.smear_slit_height.SetBackgroundColour("pink") … … 2482 2482 self.dxw = float(self.smear_slit_width.GetValue()) 2483 2483 self.smear_slit_width.SetBackgroundColour(wx.WHITE) 2484 data.dxw = self.dxw * n umpy.ones(data_len)2484 data.dxw = self.dxw * np.ones(data_len) 2485 2485 except: 2486 2486 self.dxw = None 2487 data.dxw = n umpy.zeros(data_len)2487 data.dxw = np.zeros(data_len) 2488 2488 if self.smear_slit_width.GetValue().lstrip().rstrip() != "": 2489 2489 self.smear_slit_width.SetBackgroundColour("pink") … … 2612 2612 if event is None: 2613 2613 output = "-" 2614 elif not n umpy.isfinite(event.output):2614 elif not np.isfinite(event.output): 2615 2615 output = "-" 2616 2616 else: -
src/sas/sasgui/perspectives/fitting/fitting.py
rddbac66 r9a5097c 16 16 import wx 17 17 import logging 18 import numpy 18 import numpy as np 19 19 import time 20 20 from copy import deepcopy … … 1332 1332 new_theory = copy_data.data 1333 1333 new_theory[res.index] = res.theory 1334 new_theory[res.index == False] = n umpy.nan1334 new_theory[res.index == False] = np.nan 1335 1335 correct_result = True 1336 1336 #get all fittable parameters of the current model … … 1341 1341 param_list.remove(param) 1342 1342 if not correct_result or res.fitness is None or \ 1343 not n umpy.isfinite(res.fitness) or \1344 numpy.any(res.pvec == None) or not \1345 numpy.all(numpy.isfinite(res.pvec)):1343 not np.isfinite(res.fitness) or \ 1344 np.any(res.pvec == None) or not \ 1345 np.all(np.isfinite(res.pvec)): 1346 1346 data_name = str(None) 1347 1347 if data is not None: … … 1352 1352 msg += "Data %s and Model %s did not fit.\n" % (data_name, 1353 1353 model_name) 1354 ERROR = n umpy.NAN1354 ERROR = np.NAN 1355 1355 cell = BatchCell() 1356 1356 cell.label = res.fitness … … 1366 1366 batch_inputs["error on %s" % str(param)].append(ERROR) 1367 1367 else: 1368 # TODO: Why sometimes res.pvec comes with n umpy.float64?1368 # TODO: Why sometimes res.pvec comes with np.float64? 1369 1369 # probably from scipy lmfit 1370 if res.pvec.__class__ == n umpy.float64:1370 if res.pvec.__class__ == np.float64: 1371 1371 res.pvec = [res.pvec] 1372 1372 … … 1533 1533 fit_msg = res.mesg 1534 1534 if res.fitness is None or \ 1535 not n umpy.isfinite(res.fitness) or \1536 numpy.any(res.pvec == None) or \1537 not n umpy.all(numpy.isfinite(res.pvec)):1535 not np.isfinite(res.fitness) or \ 1536 np.any(res.pvec == None) or \ 1537 not np.all(np.isfinite(res.pvec)): 1538 1538 fit_msg += "\nFitting did not converge!!!" 1539 1539 wx.CallAfter(self._update_fit_button, page_id) 1540 1540 else: 1541 1541 #set the panel when fit result are float not list 1542 if res.pvec.__class__ == n umpy.float64:1542 if res.pvec.__class__ == np.float64: 1543 1543 pvec = [res.pvec] 1544 1544 else: 1545 1545 pvec = res.pvec 1546 if res.stderr.__class__ == n umpy.float64:1546 if res.stderr.__class__ == np.float64: 1547 1547 stderr = [res.stderr] 1548 1548 else: … … 1692 1692 if dy is None: 1693 1693 new_plot.is_data = False 1694 new_plot.dy = n umpy.zeros(len(y))1694 new_plot.dy = np.zeros(len(y)) 1695 1695 # If this is a theory curve, pick the proper symbol to make it a curve 1696 1696 new_plot.symbol = GUIFRAME_ID.CURVE_SYMBOL_NUM … … 1741 1741 """ 1742 1742 try: 1743 n umpy.nan_to_num(y)1743 np.nan_to_num(y) 1744 1744 new_plot = self.create_theory_1D(x, y, page_id, model, data, state, 1745 1745 data_description=model.name, … … 1826 1826 that can be plot. 1827 1827 """ 1828 n umpy.nan_to_num(image)1828 np.nan_to_num(image) 1829 1829 new_plot = Data2D(image=image, err_image=data.err_data) 1830 1830 new_plot.name = model.name + '2d' … … 2018 2018 if data_copy.__class__.__name__ == "Data2D": 2019 2019 if index == None: 2020 index = n umpy.ones(len(data_copy.data), dtype=bool)2020 index = np.ones(len(data_copy.data), dtype=bool) 2021 2021 if weight != None: 2022 2022 data_copy.err_data = weight 2023 2023 # get rid of zero error points 2024 2024 index = index & (data_copy.err_data != 0) 2025 index = index & (n umpy.isfinite(data_copy.data))2025 index = index & (np.isfinite(data_copy.data)) 2026 2026 fn = data_copy.data[index] 2027 2027 theory_data = self.page_finder[page_id].get_theory_data(fid=data_copy.id) … … 2033 2033 # 1 d theory from model_thread is only in the range of index 2034 2034 if index == None: 2035 index = n umpy.ones(len(data_copy.y), dtype=bool)2035 index = np.ones(len(data_copy.y), dtype=bool) 2036 2036 if weight != None: 2037 2037 data_copy.dy = weight 2038 2038 if data_copy.dy == None or data_copy.dy == []: 2039 dy = n umpy.ones(len(data_copy.y))2039 dy = np.ones(len(data_copy.y)) 2040 2040 else: 2041 2041 ## Set consistently w/AbstractFitengine: … … 2058 2058 return 2059 2059 2060 residuals = res[n umpy.isfinite(res)]2060 residuals = res[np.isfinite(res)] 2061 2061 # get chisqr only w/finite 2062 chisqr = n umpy.average(residuals * residuals)2062 chisqr = np.average(residuals * residuals) 2063 2063 2064 2064 self._plot_residuals(page_id=page_id, data=data_copy, … … 2097 2097 residuals.qy_data = data_copy.qy_data 2098 2098 residuals.q_data = data_copy.q_data 2099 residuals.err_data = n umpy.ones(len(residuals.data))2099 residuals.err_data = np.ones(len(residuals.data)) 2100 2100 residuals.xmin = min(residuals.qx_data) 2101 2101 residuals.xmax = max(residuals.qx_data) … … 2111 2111 # 1 d theory from model_thread is only in the range of index 2112 2112 if data_copy.dy == None or data_copy.dy == []: 2113 dy = n umpy.ones(len(data_copy.y))2113 dy = np.ones(len(data_copy.y)) 2114 2114 else: 2115 2115 if weight == None: 2116 dy = n umpy.ones(len(data_copy.y))2116 dy = np.ones(len(data_copy.y)) 2117 2117 ## Set consitently w/AbstractFitengine: 2118 2118 ## But this should be corrected later. … … 2133 2133 residuals.y = (fn - gn[index]) / en 2134 2134 residuals.x = data_copy.x[index] 2135 residuals.dy = n umpy.ones(len(residuals.y))2135 residuals.dy = np.ones(len(residuals.y)) 2136 2136 residuals.dx = None 2137 2137 residuals.dxl = None -
src/sas/sasgui/perspectives/fitting/model_thread.py
rc1c9929 r9a5097c 4 4 5 5 import time 6 import numpy 6 import numpy as np 7 7 import math 8 8 from sas.sascalc.data_util.calcthread import CalcThread … … 68 68 69 69 # Define matrix where data will be plotted 70 radius = n umpy.sqrt((self.data.qx_data * self.data.qx_data) + \70 radius = np.sqrt((self.data.qx_data * self.data.qx_data) + \ 71 71 (self.data.qy_data * self.data.qy_data)) 72 72 … … 75 75 index_model = (self.qmin <= radius) & (radius <= self.qmax) 76 76 index_model = index_model & self.data.mask 77 index_model = index_model & n umpy.isfinite(self.data.data)77 index_model = index_model & np.isfinite(self.data.data) 78 78 79 79 if self.smearer is not None: … … 91 91 self.data.qy_data[index_model] 92 92 ]) 93 output = n umpy.zeros(len(self.data.qx_data))93 output = np.zeros(len(self.data.qx_data)) 94 94 # output default is None 95 95 # This method is to distinguish between masked … … 163 163 """ 164 164 self.starttime = time.time() 165 output = n umpy.zeros((len(self.data.x)))165 output = np.zeros((len(self.data.x))) 166 166 index = (self.qmin <= self.data.x) & (self.data.x <= self.qmax) 167 167 … … 175 175 self.qmax) 176 176 mask = self.data.x[first_bin:last_bin+1] 177 unsmeared_output = n umpy.zeros((len(self.data.x)))177 unsmeared_output = np.zeros((len(self.data.x))) 178 178 unsmeared_output[first_bin:last_bin+1] = self.model.evalDistribution(mask) 179 179 self.smearer.model = self.model … … 183 183 # Check that the arrays are compatible. If we only have a model but no data, 184 184 # the length of data.y will be zero. 185 if isinstance(self.data.y, n umpy.ndarray) and output.shape == self.data.y.shape:186 unsmeared_data = n umpy.zeros((len(self.data.x)))187 unsmeared_error = n umpy.zeros((len(self.data.x)))185 if isinstance(self.data.y, np.ndarray) and output.shape == self.data.y.shape: 186 unsmeared_data = np.zeros((len(self.data.x))) 187 unsmeared_error = np.zeros((len(self.data.x))) 188 188 unsmeared_data[first_bin:last_bin+1] = self.data.y[first_bin:last_bin+1]\ 189 189 * unsmeared_output[first_bin:last_bin+1]\ … … 209 209 210 210 if p_model is not None and s_model is not None: 211 sq_values = n umpy.zeros((len(self.data.x)))212 pq_values = n umpy.zeros((len(self.data.x)))211 sq_values = np.zeros((len(self.data.x))) 212 pq_values = np.zeros((len(self.data.x))) 213 213 sq_values[index] = s_model.evalDistribution(self.data.x[index]) 214 214 pq_values[index] = p_model.evalDistribution(self.data.x[index]) -
src/sas/sasgui/perspectives/fitting/pagestate.py
rd5aff7f r9a5097c 18 18 import copy 19 19 import logging 20 import numpy 20 import numpy as np 21 21 import traceback 22 22 … … 410 410 for fittable, name, value, _, uncert, lower, upper, units in params: 411 411 if not value: 412 value = n umpy.nan412 value = np.nan 413 413 if not uncert or uncert[1] == '' or uncert[1] == 'None': 414 414 uncert[0] = False 415 uncert[1] = n umpy.nan415 uncert[1] = np.nan 416 416 if not upper or upper[1] == '' or upper[1] == 'None': 417 417 upper[0] = False 418 upper[1] = n umpy.nan418 upper[1] = np.nan 419 419 if not lower or lower[1] == '' or lower[1] == 'None': 420 420 lower[0] = False 421 lower[1] = n umpy.nan421 lower[1] = np.nan 422 422 if is_string: 423 423 p[name] = str(value) … … 449 449 lower = params.get(name + ".lower", '-inf') 450 450 units = params.get(name + ".units") 451 if std is not None and std is not n umpy.nan:451 if std is not None and std is not np.nan: 452 452 std = [True, str(std)] 453 453 else: 454 454 std = [False, ''] 455 if lower is not None and lower is not n umpy.nan:455 if lower is not None and lower is not np.nan: 456 456 lower = [True, str(lower)] 457 457 else: 458 458 lower = [True, '-inf'] 459 if upper is not None and upper is not n umpy.nan:459 if upper is not None and upper is not np.nan: 460 460 upper = [True, str(upper)] 461 461 else: … … 1100 1100 % (line, tagname, name)) 1101 1101 logging.error(msg + traceback.format_exc()) 1102 dic[name] = n umpy.array(value_list)1102 dic[name] = np.array(value_list) 1103 1103 setattr(self, varname, dic) 1104 1104 -
src/sas/sasgui/perspectives/fitting/utils.py
rd85c194 r9a5097c 2 2 Module contains functions frequently used in this package 3 3 """ 4 import numpy 4 import numpy as np 5 5 6 6 … … 19 19 data = data.y 20 20 if flag == 0: 21 weight = n umpy.ones_like(data)21 weight = np.ones_like(data) 22 22 elif flag == 1: 23 23 weight = dy_data 24 24 elif flag == 2: 25 weight = n umpy.sqrt(numpy.abs(data))25 weight = np.sqrt(np.abs(data)) 26 26 elif flag == 3: 27 weight = n umpy.abs(data)27 weight = np.abs(data) 28 28 return weight -
src/sas/sasgui/perspectives/pr/explore_dialog.py
rd85c194 r9a5097c 19 19 20 20 import wx 21 import numpy 21 import numpy as np 22 22 import logging 23 23 import sys … … 65 65 66 66 step = (self.max - self.min) / (self.npts - 1) 67 self.x = n umpy.arange(self.min, self.max + step * 0.01, step)68 dx = n umpy.zeros(len(self.x))69 y = n umpy.ones(len(self.x))70 dy = n umpy.zeros(len(self.x))67 self.x = np.arange(self.min, self.max + step * 0.01, step) 68 dx = np.zeros(len(self.x)) 69 y = np.ones(len(self.x)) 70 dy = np.zeros(len(self.x)) 71 71 72 72 # Plot area -
src/sas/sasgui/perspectives/pr/pr.py
ra69a967 r9a5097c 21 21 import time 22 22 import math 23 import numpy 23 import numpy as np 24 24 import pylab 25 25 from sas.sasgui.guiframe.gui_manager import MDIFrame … … 207 207 r = pylab.arange(0.01, d_max, d_max / 51.0) 208 208 M = len(r) 209 y = n umpy.zeros(M)210 pr_err = n umpy.zeros(M)209 y = np.zeros(M) 210 pr_err = np.zeros(M) 211 211 212 212 total = 0.0 … … 253 253 """ 254 254 # Show P(r) 255 y_true = n umpy.zeros(len(x))255 y_true = np.zeros(len(x)) 256 256 257 257 sum_true = 0.0 … … 307 307 308 308 x = pylab.arange(minq, maxq, maxq / 301.0) 309 y = n umpy.zeros(len(x))310 err = n umpy.zeros(len(x))309 y = np.zeros(len(x)) 310 err = np.zeros(len(x)) 311 311 for i in range(len(x)): 312 312 value = pr.iq(out, x[i]) … … 337 337 if pr.slit_width > 0 or pr.slit_height > 0: 338 338 x = pylab.arange(minq, maxq, maxq / 301.0) 339 y = n umpy.zeros(len(x))340 err = n umpy.zeros(len(x))339 y = np.zeros(len(x)) 340 err = np.zeros(len(x)) 341 341 for i in range(len(x)): 342 342 value = pr.iq_smeared(out, x[i]) … … 382 382 x = pylab.arange(0.0, pr.d_max, pr.d_max / self._pr_npts) 383 383 384 y = n umpy.zeros(len(x))385 dy = n umpy.zeros(len(x))386 y_true = n umpy.zeros(len(x))384 y = np.zeros(len(x)) 385 dy = np.zeros(len(x)) 386 y_true = np.zeros(len(x)) 387 387 388 388 total = 0.0 389 389 pmax = 0.0 390 cov2 = n umpy.ascontiguousarray(cov)390 cov2 = np.ascontiguousarray(cov) 391 391 392 392 for i in range(len(x)): … … 480 480 """ 481 481 # Read the data from the data file 482 data_x = n umpy.zeros(0)483 data_y = n umpy.zeros(0)484 data_err = n umpy.zeros(0)482 data_x = np.zeros(0) 483 data_y = np.zeros(0) 484 data_err = np.zeros(0) 485 485 scale = None 486 486 min_err = 0.0 … … 504 504 #err = 0 505 505 506 data_x = n umpy.append(data_x, x)507 data_y = n umpy.append(data_y, y)508 data_err = n umpy.append(data_err, err)506 data_x = np.append(data_x, x) 507 data_y = np.append(data_y, y) 508 data_err = np.append(data_err, err) 509 509 except: 510 510 logging.error(sys.exc_value) … … 528 528 """ 529 529 # Read the data from the data file 530 data_x = n umpy.zeros(0)531 data_y = n umpy.zeros(0)532 data_err = n umpy.zeros(0)530 data_x = np.zeros(0) 531 data_y = np.zeros(0) 532 data_err = np.zeros(0) 533 533 scale = None 534 534 min_err = 0.0 … … 555 555 #err = 0 556 556 557 data_x = n umpy.append(data_x, x)558 data_y = n umpy.append(data_y, y)559 data_err = n umpy.append(data_err, err)557 data_x = np.append(data_x, x) 558 data_y = np.append(data_y, y) 559 data_err = np.append(data_err, err) 560 560 except: 561 561 logging.error(sys.exc_value) … … 640 640 # Now replot the original added data 641 641 for plot in self._added_plots: 642 self._added_plots[plot].y = n umpy.copy(self._default_Iq[plot])642 self._added_plots[plot].y = np.copy(self._default_Iq[plot]) 643 643 wx.PostEvent(self.parent, 644 644 NewPlotEvent(plot=self._added_plots[plot], … … 664 664 # Now scale the added plots too 665 665 for plot in self._added_plots: 666 total = n umpy.sum(self._added_plots[plot].y)666 total = np.sum(self._added_plots[plot].y) 667 667 npts = len(self._added_plots[plot].x) 668 668 total *= self._added_plots[plot].x[npts - 1] / npts … … 814 814 # Save Pr invertor 815 815 self.pr = pr 816 cov = n umpy.ascontiguousarray(cov)816 cov = np.ascontiguousarray(cov) 817 817 818 818 # Show result on control panel … … 982 982 all_zeros = True 983 983 if err == None: 984 err = n umpy.zeros(len(pr.y))984 err = np.zeros(len(pr.y)) 985 985 else: 986 986 for i in range(len(err)): … … 1088 1088 # If we have not errors, add statistical errors 1089 1089 if y is not None: 1090 if err == None or n umpy.all(err) == 0:1091 err = n umpy.zeros(len(y))1090 if err == None or np.all(err) == 0: 1091 err = np.zeros(len(y)) 1092 1092 scale = None 1093 1093 min_err = 0.0 -
src/sas/sasgui/perspectives/simulation/simulation.py
rd85c194 r9a5097c 10 10 import wx 11 11 import os 12 import numpy 12 import numpy as np 13 13 import time 14 14 import logging … … 46 46 def compute(self): 47 47 x = self.x 48 output = n umpy.zeros(len(x))49 error = n umpy.zeros(len(x))48 output = np.zeros(len(x)) 49 error = np.zeros(len(x)) 50 50 51 51 self.starttime = time.time() … … 123 123 # Q-values for plotting simulated I(Q) 124 124 step = (self.q_max-self.q_min)/(self.q_npts-1) 125 self.x = n umpy.arange(self.q_min, self.q_max+step*0.01, step)125 self.x = np.arange(self.q_min, self.q_max+step*0.01, step) 126 126 127 127 # Set the list of panels that are part of the simulation perspective … … 187 187 # Q-values for plotting simulated I(Q) 188 188 step = (self.q_max-self.q_min)/(self.q_npts-1) 189 self.x = n umpy.arange(self.q_min, self.q_max+step*0.01, step)189 self.x = np.arange(self.q_min, self.q_max+step*0.01, step) 190 190 191 191 # Compute the simulated I(Q) -
src/sas/sasgui/plottools/PlotPanel.py
r198fa76 r9a5097c 29 29 DEFAULT_CMAP = pylab.cm.jet 30 30 import copy 31 import numpy 31 import numpy as np 32 32 33 33 from sas.sasgui.guiframe.events import StatusEvent … … 1452 1452 if self.zmin_2D <= 0 and len(output[output > 0]) > 0: 1453 1453 zmin_temp = self.zmin_2D 1454 output[output > 0] = n umpy.log10(output[output > 0])1454 output[output > 0] = np.log10(output[output > 0]) 1455 1455 #In log scale Negative values are not correct in general 1456 #output[output<=0] = math.log(n umpy.min(output[output>0]))1456 #output[output<=0] = math.log(np.min(output[output>0])) 1457 1457 elif self.zmin_2D <= 0: 1458 1458 zmin_temp = self.zmin_2D 1459 output[output > 0] = n umpy.zeros(len(output))1459 output[output > 0] = np.zeros(len(output)) 1460 1460 output[output <= 0] = -32 1461 1461 else: 1462 1462 zmin_temp = self.zmin_2D 1463 output[output > 0] = n umpy.log10(output[output > 0])1463 output[output > 0] = np.log10(output[output > 0]) 1464 1464 #In log scale Negative values are not correct in general 1465 #output[output<=0] = math.log(n umpy.min(output[output>0]))1465 #output[output<=0] = math.log(np.min(output[output>0])) 1466 1466 except: 1467 1467 #Too many problems in 2D plot with scale … … 1492 1492 X = self.x_bins[0:-1] 1493 1493 Y = self.y_bins[0:-1] 1494 X, Y = n umpy.meshgrid(X, Y)1494 X, Y = np.meshgrid(X, Y) 1495 1495 1496 1496 try: … … 1555 1555 # 1d array to use for weighting the data point averaging 1556 1556 #when they fall into a same bin. 1557 weights_data = n umpy.ones([self.data.size])1557 weights_data = np.ones([self.data.size]) 1558 1558 # get histogram of ones w/len(data); this will provide 1559 1559 #the weights of data on each bins 1560 weights, xedges, yedges = n umpy.histogram2d(x=self.qy_data,1560 weights, xedges, yedges = np.histogram2d(x=self.qy_data, 1561 1561 y=self.qx_data, 1562 1562 bins=[self.y_bins, self.x_bins], 1563 1563 weights=weights_data) 1564 1564 # get histogram of data, all points into a bin in a way of summing 1565 image, xedges, yedges = n umpy.histogram2d(x=self.qy_data,1565 image, xedges, yedges = np.histogram2d(x=self.qy_data, 1566 1566 y=self.qx_data, 1567 1567 bins=[self.y_bins, self.x_bins], … … 1581 1581 # do while loop until all vacant bins are filled up up 1582 1582 #to loop = max_loop 1583 while not(n umpy.isfinite(image[weights == 0])).all():1583 while not(np.isfinite(image[weights == 0])).all(): 1584 1584 if loop >= max_loop: # this protects never-ending loop 1585 1585 break … … 1630 1630 1631 1631 # store x and y bin centers in q space 1632 x_bins = n umpy.linspace(xmin, xmax, npix_x)1633 y_bins = n umpy.linspace(ymin, ymax, npix_y)1632 x_bins = np.linspace(xmin, xmax, npix_x) 1633 y_bins = np.linspace(ymin, ymax, npix_y) 1634 1634 1635 1635 #set x_bins and y_bins … … 1650 1650 """ 1651 1651 # No image matrix given 1652 if image == None or n umpy.ndim(image) != 2 \1653 or n umpy.isfinite(image).all() \1652 if image == None or np.ndim(image) != 2 \ 1653 or np.isfinite(image).all() \ 1654 1654 or weights == None: 1655 1655 return image … … 1657 1657 len_y = len(image) 1658 1658 len_x = len(image[1]) 1659 temp_image = n umpy.zeros([len_y, len_x])1660 weit = n umpy.zeros([len_y, len_x])1659 temp_image = np.zeros([len_y, len_x]) 1660 weit = np.zeros([len_y, len_x]) 1661 1661 # do for-loop for all pixels 1662 1662 for n_y in range(len(image)): 1663 1663 for n_x in range(len(image[1])): 1664 1664 # find only null pixels 1665 if weights[n_y][n_x] > 0 or n umpy.isfinite(image[n_y][n_x]):1665 if weights[n_y][n_x] > 0 or np.isfinite(image[n_y][n_x]): 1666 1666 continue 1667 1667 else: 1668 1668 # find 4 nearest neighbors 1669 1669 # check where or not it is at the corner 1670 if n_y != 0 and n umpy.isfinite(image[n_y - 1][n_x]):1670 if n_y != 0 and np.isfinite(image[n_y - 1][n_x]): 1671 1671 temp_image[n_y][n_x] += image[n_y - 1][n_x] 1672 1672 weit[n_y][n_x] += 1 1673 if n_x != 0 and n umpy.isfinite(image[n_y][n_x - 1]):1673 if n_x != 0 and np.isfinite(image[n_y][n_x - 1]): 1674 1674 temp_image[n_y][n_x] += image[n_y][n_x - 1] 1675 1675 weit[n_y][n_x] += 1 1676 if n_y != len_y - 1 and n umpy.isfinite(image[n_y + 1][n_x]):1676 if n_y != len_y - 1 and np.isfinite(image[n_y + 1][n_x]): 1677 1677 temp_image[n_y][n_x] += image[n_y + 1][n_x] 1678 1678 weit[n_y][n_x] += 1 1679 if n_x != len_x - 1 and n umpy.isfinite(image[n_y][n_x + 1]):1679 if n_x != len_x - 1 and np.isfinite(image[n_y][n_x + 1]): 1680 1680 temp_image[n_y][n_x] += image[n_y][n_x + 1] 1681 1681 weit[n_y][n_x] += 1 1682 1682 # go 4 next nearest neighbors when no non-zero 1683 1683 # neighbor exists 1684 if n_y != 0 and n_x != 0 and \1685 numpy.isfinite(image[n_y - 1][n_x - 1]):1684 if n_y != 0 and n_x != 0 and \ 1685 np.isfinite(image[n_y - 1][n_x - 1]): 1686 1686 temp_image[n_y][n_x] += image[n_y - 1][n_x - 1] 1687 1687 weit[n_y][n_x] += 1 1688 1688 if n_y != len_y - 1 and n_x != 0 and \ 1689 numpy.isfinite(image[n_y + 1][n_x - 1]):1689 np.isfinite(image[n_y + 1][n_x - 1]): 1690 1690 temp_image[n_y][n_x] += image[n_y + 1][n_x - 1] 1691 1691 weit[n_y][n_x] += 1 1692 1692 if n_y != len_y and n_x != len_x - 1 and \ 1693 numpy.isfinite(image[n_y - 1][n_x + 1]):1693 np.isfinite(image[n_y - 1][n_x + 1]): 1694 1694 temp_image[n_y][n_x] += image[n_y - 1][n_x + 1] 1695 1695 weit[n_y][n_x] += 1 1696 1696 if n_y != len_y - 1 and n_x != len_x - 1 and \ 1697 numpy.isfinite(image[n_y + 1][n_x + 1]):1697 np.isfinite(image[n_y + 1][n_x + 1]): 1698 1698 temp_image[n_y][n_x] += image[n_y + 1][n_x + 1] 1699 1699 weit[n_y][n_x] += 1 -
src/sas/sasgui/plottools/fitDialog.py
rdd5bf63 r9a5097c 2 2 from plottables import Theory1D 3 3 import math 4 import numpy 4 import numpy as np 5 5 import fittings 6 6 import transform … … 482 482 483 483 if self.xLabel.lower() == "log10(x)": 484 tempdy = n umpy.asarray(tempdy)484 tempdy = np.asarray(tempdy) 485 485 tempdy[tempdy == 0] = 1 486 486 chisqr, out, cov = fittings.sasfit(self.model, … … 491 491 math.log10(xmax)) 492 492 else: 493 tempdy = n umpy.asarray(tempdy)493 tempdy = np.asarray(tempdy) 494 494 tempdy[tempdy == 0] = 1 495 495 chisqr, out, cov = fittings.sasfit(self.model, … … 572 572 if self.rg_on: 573 573 if self.Rg_tctr.IsShown(): 574 rg = n umpy.sqrt(-3 * float(cstA))574 rg = np.sqrt(-3 * float(cstA)) 575 575 value = format_number(rg) 576 576 self.Rg_tctr.SetValue(value) 577 577 if self.I0_tctr.IsShown(): 578 val = n umpy.exp(cstB)578 val = np.exp(cstB) 579 579 self.I0_tctr.SetValue(format_number(val)) 580 580 if self.Rgerr_tctr.IsShown(): … … 585 585 self.Rgerr_tctr.SetValue(value) 586 586 if self.I0err_tctr.IsShown(): 587 val = n umpy.abs(numpy.exp(cstB) * errB)587 val = np.abs(np.exp(cstB) * errB) 588 588 self.I0err_tctr.SetValue(format_number(val)) 589 589 if self.Diameter_tctr.IsShown(): 590 rg = n umpy.sqrt(-2 * float(cstA))591 _diam = 4 * n umpy.sqrt(-float(cstA))590 rg = np.sqrt(-2 * float(cstA)) 591 _diam = 4 * np.sqrt(-float(cstA)) 592 592 value = format_number(_diam) 593 593 self.Diameter_tctr.SetValue(value) -
src/sas/sasgui/plottools/plottables.py
ra9f579c r9a5097c 43 43 # Support for ancient python versions 44 44 import copy 45 import numpy 45 import numpy as np 46 46 import sys 47 47 import logging … … 706 706 self.dy = None 707 707 if not has_err_x: 708 dx = n umpy.zeros(len(x))708 dx = np.zeros(len(x)) 709 709 if not has_err_y: 710 dy = n umpy.zeros(len(y))710 dy = np.zeros(len(y)) 711 711 for i in range(len(x)): 712 712 try: … … 796 796 tempdy = [] 797 797 if self.dx == None: 798 self.dx = n umpy.zeros(len(self.x))798 self.dx = np.zeros(len(self.x)) 799 799 if self.dy == None: 800 self.dy = n umpy.zeros(len(self.y))800 self.dy = np.zeros(len(self.y)) 801 801 if self.xLabel == "log10(x)": 802 802 for i in range(len(self.x)): … … 826 826 tempdy = [] 827 827 if self.dx == None: 828 self.dx = n umpy.zeros(len(self.x))828 self.dx = np.zeros(len(self.x)) 829 829 if self.dy == None: 830 self.dy = n umpy.zeros(len(self.y))830 self.dy = np.zeros(len(self.y)) 831 831 if self.yLabel == "log10(y)": 832 832 for i in range(len(self.x)): … … 859 859 tempdy = [] 860 860 if self.dx == None: 861 self.dx = n umpy.zeros(len(self.x))861 self.dx = np.zeros(len(self.x)) 862 862 if self.dy == None: 863 self.dy = n umpy.zeros(len(self.y))863 self.dy = np.zeros(len(self.y)) 864 864 if xmin != None and xmax != None: 865 865 for i in range(len(self.x)): … … 1228 1228 1229 1229 def sample_graph(): 1230 import numpy as n x1230 import numpy as np 1231 1231 1232 1232 # Construct a simple graph 1233 1233 if False: 1234 x = n x.array([1, 2, 3, 4, 5, 6], 'd')1235 y = n x.array([4, 5, 6, 5, 4, 5], 'd')1236 dy = n x.array([0.2, 0.3, 0.1, 0.2, 0.9, 0.3])1234 x = np.array([1, 2, 3, 4, 5, 6], 'd') 1235 y = np.array([4, 5, 6, 5, 4, 5], 'd') 1236 dy = np.array([0.2, 0.3, 0.1, 0.2, 0.9, 0.3]) 1237 1237 else: 1238 x = n x.linspace(0, 1., 10000)1239 y = n x.sin(2 * nx.pi * x * 2.8)1240 dy = n x.sqrt(100 * nx.abs(y)) / 1001238 x = np.linspace(0, 1., 10000) 1239 y = np.sin(2 * np.pi * x * 2.8) 1240 dy = np.sqrt(100 * np.abs(y)) / 100 1241 1241 data = Data1D(x, y, dy=dy) 1242 1242 data.xaxis('distance', 'm')
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