Changeset b2b3009d in sasview for src/sas/sasgui/perspectives
- Timestamp:
- Apr 4, 2017 12:00:30 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:
- f68d503
- Parents:
- 571bf4b (diff), ed2276f (diff)
Note: this is a merge changeset, the changes displayed below correspond to the merge itself.
Use the (diff) links above to see all the changes relative to each parent. - git-author:
- Andrew Jackson <andrew.jackson@…> (04/04/17 12:00:30)
- git-committer:
- GitHub <noreply@…> (04/04/17 12:00:30)
- Location:
- src/sas/sasgui/perspectives
- Files:
-
- 11 edited
-
calculator/data_operator.py (modified) (2 diffs)
-
calculator/gen_scatter_panel.py (modified) (19 diffs)
-
fitting/basepage.py (modified) (13 diffs)
-
fitting/fitpage.py (modified) (15 diffs)
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fitting/fitting.py (modified) (15 diffs)
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fitting/model_thread.py (modified) (8 diffs)
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fitting/pagestate.py (modified) (4 diffs)
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fitting/utils.py (modified) (2 diffs)
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pr/explore_dialog.py (modified) (2 diffs)
-
pr/pr.py (modified) (15 diffs)
-
simulation/simulation.py (modified) (4 diffs)
Legend:
- Unmodified
- Added
- Removed
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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
r5156918 red2276f 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 … … 2107 2107 for data in self.data_list: 2108 2108 # q value from qx and qy 2109 radius = n umpy.sqrt(data.qx_data * data.qx_data +2109 radius = np.sqrt(data.qx_data * data.qx_data + 2110 2110 data.qy_data * data.qy_data) 2111 2111 # get unmasked index … … 2113 2113 (radius <= float(self.qmax.GetValue())) 2114 2114 index_data = (index_data) & (data.mask) 2115 index_data = (index_data) & (n umpy.isfinite(data.data))2115 index_data = (index_data) & (np.isfinite(data.data)) 2116 2116 2117 2117 if len(index_data[index_data]) < 10: … … 2148 2148 index_data = (float(self.qmin.GetValue()) <= radius) & \ 2149 2149 (radius <= float(self.qmax.GetValue())) 2150 index_data = (index_data) & (n umpy.isfinite(data.y))2150 index_data = (index_data) & (np.isfinite(data.y)) 2151 2151 2152 2152 if len(index_data[index_data]) < 5: … … 2220 2220 2221 2221 # Check that min is less than max 2222 low = -n umpy.inf if min_str == "" else float(min_str)2223 high = n umpy.inf if max_str == "" else float(max_str)2222 low = -np.inf if min_str == "" else float(min_str) 2223 high = np.inf if max_str == "" else float(max_str) 2224 2224 if high < low: 2225 2225 min_ctrl.SetBackgroundColour("pink") … … 2631 2631 self.qmin_x = data_min 2632 2632 self.qmax_x = math.sqrt(x * x + y * y) 2633 # self.data.mask = n umpy.ones(len(self.data.data),dtype=bool)2633 # self.data.mask = np.ones(len(self.data.data),dtype=bool) 2634 2634 # check smearing 2635 2635 if not self.disable_smearer.GetValue(): … … 3343 3343 3344 3344 if value[1] == 'array': 3345 pd_vals = n umpy.array(value[2])3346 pd_weights = n umpy.array(value[3])3345 pd_vals = np.array(value[2]) 3346 pd_weights = np.array(value[3]) 3347 3347 if len(pd_vals) == 0 or len(pd_vals) != len(pd_weights): 3348 3348 msg = ("bad array distribution parameters for %s" -
src/sas/sasgui/perspectives/fitting/fitpage.py
r5156918 red2276f 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 … … 1914 1914 self.default_mask = copy.deepcopy(self.data.mask) 1915 1915 if self.data.err_data is not None \ 1916 and n umpy.any(self.data.err_data):1916 and np.any(self.data.err_data): 1917 1917 di_flag = True 1918 1918 if self.data.dqx_data is not None \ 1919 and n umpy.any(self.data.dqx_data):1919 and np.any(self.data.dqx_data): 1920 1920 dq_flag = True 1921 1921 else: 1922 1922 self.slit_smearer.Enable(True) 1923 1923 self.pinhole_smearer.Enable(True) 1924 if self.data.dy is not None and n umpy.any(self.data.dy):1924 if self.data.dy is not None and np.any(self.data.dy): 1925 1925 di_flag = True 1926 if self.data.dx is not None and n umpy.any(self.data.dx):1926 if self.data.dx is not None and np.any(self.data.dx): 1927 1927 dq_flag = True 1928 elif self.data.dxl is not None and n umpy.any(self.data.dxl):1928 elif self.data.dxl is not None and np.any(self.data.dxl): 1929 1929 dq_flag = True 1930 1930 … … 2060 2060 if self.data.__class__.__name__ == "Data2D" or \ 2061 2061 self.enable2D: 2062 radius = n umpy.sqrt(self.data.qx_data * self.data.qx_data +2062 radius = np.sqrt(self.data.qx_data * self.data.qx_data + 2063 2063 self.data.qy_data * self.data.qy_data) 2064 2064 index_data = (self.qmin_x <= radius) & (radius <= self.qmax_x) 2065 2065 index_data = (index_data) & (self.data.mask) 2066 index_data = (index_data) & (n umpy.isfinite(self.data.data))2066 index_data = (index_data) & (np.isfinite(self.data.data)) 2067 2067 npts2fit = len(self.data.data[index_data]) 2068 2068 else: … … 2097 2097 # make sure stop button to fit button all the time 2098 2098 self._on_fit_complete() 2099 if out is None or not n umpy.isfinite(chisqr):2099 if out is None or not np.isfinite(chisqr): 2100 2100 raise ValueError, "Fit error occured..." 2101 2101 … … 2108 2108 2109 2109 # Check if chi2 is finite 2110 if chisqr is not None and n umpy.isfinite(chisqr):2110 if chisqr is not None and np.isfinite(chisqr): 2111 2111 # format chi2 2112 2112 chi2 = format_number(chisqr, True) … … 2160 2160 2161 2161 if cov[ind] is not None: 2162 if n umpy.isfinite(float(cov[ind])):2162 if np.isfinite(float(cov[ind])): 2163 2163 val_err = format_number(cov[ind], True) 2164 2164 item[4].SetForegroundColour(wx.BLACK) … … 2280 2280 self.smear_type = 'Pinhole2d' 2281 2281 len_data = len(data.data) 2282 data.dqx_data = n umpy.zeros(len_data)2283 data.dqy_data = n umpy.zeros(len_data)2282 data.dqx_data = np.zeros(len_data) 2283 data.dqy_data = np.zeros(len_data) 2284 2284 else: 2285 2285 self.smear_type = 'Pinhole' 2286 2286 len_data = len(data.x) 2287 data.dx = n umpy.zeros(len_data)2287 data.dx = np.zeros(len_data) 2288 2288 data.dxl = None 2289 2289 data.dxw = None … … 2458 2458 try: 2459 2459 self.dxl = float(self.smear_slit_height.GetValue()) 2460 data.dxl = self.dxl * n umpy.ones(data_len)2460 data.dxl = self.dxl * np.ones(data_len) 2461 2461 self.smear_slit_height.SetBackgroundColour(wx.WHITE) 2462 2462 except: 2463 2463 self.dxl = None 2464 data.dxl = n umpy.zeros(data_len)2464 data.dxl = np.zeros(data_len) 2465 2465 if self.smear_slit_height.GetValue().lstrip().rstrip() != "": 2466 2466 self.smear_slit_height.SetBackgroundColour("pink") … … 2471 2471 self.dxw = float(self.smear_slit_width.GetValue()) 2472 2472 self.smear_slit_width.SetBackgroundColour(wx.WHITE) 2473 data.dxw = self.dxw * n umpy.ones(data_len)2473 data.dxw = self.dxw * np.ones(data_len) 2474 2474 except: 2475 2475 self.dxw = None 2476 data.dxw = n umpy.zeros(data_len)2476 data.dxw = np.zeros(data_len) 2477 2477 if self.smear_slit_width.GetValue().lstrip().rstrip() != "": 2478 2478 self.smear_slit_width.SetBackgroundColour("pink") … … 2601 2601 if event is None: 2602 2602 output = "-" 2603 elif not n umpy.isfinite(event.output):2603 elif not np.isfinite(event.output): 2604 2604 output = "-" 2605 2605 else: -
src/sas/sasgui/perspectives/fitting/fitting.py
r5156918 red2276f 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 … … 1323 1323 new_theory = copy_data.data 1324 1324 new_theory[res.index] = res.theory 1325 new_theory[res.index == False] = n umpy.nan1325 new_theory[res.index == False] = np.nan 1326 1326 correct_result = True 1327 1327 #get all fittable parameters of the current model … … 1332 1332 param_list.remove(param) 1333 1333 if not correct_result or res.fitness is None or \ 1334 not n umpy.isfinite(res.fitness) or \1335 numpy.any(res.pvec == None) or not \1336 numpy.all(numpy.isfinite(res.pvec)):1334 not np.isfinite(res.fitness) or \ 1335 np.any(res.pvec == None) or not \ 1336 np.all(np.isfinite(res.pvec)): 1337 1337 data_name = str(None) 1338 1338 if data is not None: … … 1343 1343 msg += "Data %s and Model %s did not fit.\n" % (data_name, 1344 1344 model_name) 1345 ERROR = n umpy.NAN1345 ERROR = np.NAN 1346 1346 cell = BatchCell() 1347 1347 cell.label = res.fitness … … 1357 1357 batch_inputs["error on %s" % str(param)].append(ERROR) 1358 1358 else: 1359 # TODO: Why sometimes res.pvec comes with n umpy.float64?1359 # TODO: Why sometimes res.pvec comes with np.float64? 1360 1360 # probably from scipy lmfit 1361 if res.pvec.__class__ == n umpy.float64:1361 if res.pvec.__class__ == np.float64: 1362 1362 res.pvec = [res.pvec] 1363 1363 … … 1523 1523 fit_msg = res.mesg 1524 1524 if res.fitness is None or \ 1525 not n umpy.isfinite(res.fitness) or \1526 numpy.any(res.pvec == None) or \1527 not n umpy.all(numpy.isfinite(res.pvec)):1525 not np.isfinite(res.fitness) or \ 1526 np.any(res.pvec == None) or \ 1527 not np.all(np.isfinite(res.pvec)): 1528 1528 fit_msg += "\nFitting did not converge!!!" 1529 1529 wx.CallAfter(self._update_fit_button, page_id) 1530 1530 else: 1531 1531 #set the panel when fit result are float not list 1532 if res.pvec.__class__ == n umpy.float64:1532 if res.pvec.__class__ == np.float64: 1533 1533 pvec = [res.pvec] 1534 1534 else: 1535 1535 pvec = res.pvec 1536 if res.stderr.__class__ == n umpy.float64:1536 if res.stderr.__class__ == np.float64: 1537 1537 stderr = [res.stderr] 1538 1538 else: … … 1682 1682 if dy is None: 1683 1683 new_plot.is_data = False 1684 new_plot.dy = n umpy.zeros(len(y))1684 new_plot.dy = np.zeros(len(y)) 1685 1685 # If this is a theory curve, pick the proper symbol to make it a curve 1686 1686 new_plot.symbol = GUIFRAME_ID.CURVE_SYMBOL_NUM … … 1731 1731 """ 1732 1732 try: 1733 n umpy.nan_to_num(y)1733 np.nan_to_num(y) 1734 1734 new_plot = self.create_theory_1D(x, y, page_id, model, data, state, 1735 1735 data_description=model.name, … … 1815 1815 that can be plot. 1816 1816 """ 1817 n umpy.nan_to_num(image)1817 np.nan_to_num(image) 1818 1818 new_plot = Data2D(image=image, err_image=data.err_data) 1819 1819 new_plot.name = model.name + '2d' … … 2007 2007 if data_copy.__class__.__name__ == "Data2D": 2008 2008 if index == None: 2009 index = n umpy.ones(len(data_copy.data), dtype=bool)2009 index = np.ones(len(data_copy.data), dtype=bool) 2010 2010 if weight != None: 2011 2011 data_copy.err_data = weight 2012 2012 # get rid of zero error points 2013 2013 index = index & (data_copy.err_data != 0) 2014 index = index & (n umpy.isfinite(data_copy.data))2014 index = index & (np.isfinite(data_copy.data)) 2015 2015 fn = data_copy.data[index] 2016 2016 theory_data = self.page_finder[page_id].get_theory_data(fid=data_copy.id) … … 2022 2022 # 1 d theory from model_thread is only in the range of index 2023 2023 if index == None: 2024 index = n umpy.ones(len(data_copy.y), dtype=bool)2024 index = np.ones(len(data_copy.y), dtype=bool) 2025 2025 if weight != None: 2026 2026 data_copy.dy = weight 2027 2027 if data_copy.dy == None or data_copy.dy == []: 2028 dy = n umpy.ones(len(data_copy.y))2028 dy = np.ones(len(data_copy.y)) 2029 2029 else: 2030 2030 ## Set consistently w/AbstractFitengine: … … 2047 2047 return 2048 2048 2049 residuals = res[n umpy.isfinite(res)]2049 residuals = res[np.isfinite(res)] 2050 2050 # get chisqr only w/finite 2051 chisqr = n umpy.average(residuals * residuals)2051 chisqr = np.average(residuals * residuals) 2052 2052 2053 2053 self._plot_residuals(page_id=page_id, data=data_copy, … … 2086 2086 residuals.qy_data = data_copy.qy_data 2087 2087 residuals.q_data = data_copy.q_data 2088 residuals.err_data = n umpy.ones(len(residuals.data))2088 residuals.err_data = np.ones(len(residuals.data)) 2089 2089 residuals.xmin = min(residuals.qx_data) 2090 2090 residuals.xmax = max(residuals.qx_data) … … 2100 2100 # 1 d theory from model_thread is only in the range of index 2101 2101 if data_copy.dy == None or data_copy.dy == []: 2102 dy = n umpy.ones(len(data_copy.y))2102 dy = np.ones(len(data_copy.y)) 2103 2103 else: 2104 2104 if weight == None: 2105 dy = n umpy.ones(len(data_copy.y))2105 dy = np.ones(len(data_copy.y)) 2106 2106 ## Set consitently w/AbstractFitengine: 2107 2107 ## But this should be corrected later. … … 2122 2122 residuals.y = (fn - gn[index]) / en 2123 2123 residuals.x = data_copy.x[index] 2124 residuals.dy = n umpy.ones(len(residuals.y))2124 residuals.dy = np.ones(len(residuals.y)) 2125 2125 residuals.dx = None 2126 2126 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
r27109e5 red2276f 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)
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