Source code for sas.qtgui.Plotting.PlotterData

"""
Adapters for fitting module
"""
import copy
import numpy
import math
from sas.sascalc.data_util.uncertainty import Uncertainty

from sas.qtgui.Plotting.Plottables import PlottableData1D
from sas.qtgui.Plotting.Plottables import PlottableData2D

from sas.sascalc.dataloader.data_info import Data1D as LoadData1D
from sas.sascalc.dataloader.data_info import Data2D as LoadData2D


[docs]class Data1D(PlottableData1D, LoadData1D): """ """ ROLE_DATA=0 ROLE_DEFAULT=1 ROLE_DELETABLE=2 ROLE_RESIDUAL=3
[docs] def __init__(self, x=None, y=None, dx=None, dy=None): """ """ if x is None: x = [] if y is None: y = [] PlottableData1D.__init__(self, x, y, dx, dy) LoadData1D.__init__(self, x, y, dx, dy) self.id = None self.list_group_id = [] self.group_id = None self.is_data = True self.path = None self.xtransform = None self.ytransform = None self.title = "" self.scale = None # plot_role: # 0: data - no reload on param change # 1: normal lifecycle (fit) # 2: deletable on model change (Q(I), S(I)...) # 3: separate chart on Show Plot (residuals) self.plot_role = Data1D.ROLE_DEFAULT # Q-range slider definitions self.show_q_range_sliders = False # Should sliders be shown? self.slider_update_on_move = True # Should the gui update during the move? self.slider_perspective_name = "" # Name of the perspective that this slider is associated with self.slider_tab_name = "" # Name of the tab where the data set is # The following q-range slider variables are optional but help tie # the slider to a GUI element for 2-way updates self.slider_low_q_input = [] # List of attributes that lead to a Qt input to tie a low Q input to the slider self.slider_high_q_input = [] # List of attributes that lead to a Qt input to tie a high Q input to the slider # Setters and getters are only needed for inputs that aren't Q values # e.g. Invariant perspective nPts self.slider_low_q_setter = [] # List of attributes that lead to a setter to tie a low Q method to the slider self.slider_low_q_getter = [] # List of attributes that lead to a getter to tie a low Q method to the slider self.slider_high_q_setter = [] # List of attributes that lead to a setter to tie a high Q method to the slider self.slider_high_q_getter = [] # List of attributes that lead to a getter to tie a high Q method to the slider
[docs] def copy_from_datainfo(self, data1d): """ copy values of Data1D of type DataLaoder.Data_info """ self.x = copy.deepcopy(data1d.x) self.y = copy.deepcopy(data1d.y) self.dy = copy.deepcopy(data1d.dy) if hasattr(data1d, "dx"): self.dx = copy.deepcopy(data1d.dx) if hasattr(data1d, "dxl"): self.dxl = copy.deepcopy(data1d.dxl) if hasattr(data1d, "dxw"): self.dxw = copy.deepcopy(data1d.dxw) self.xaxis(data1d._xaxis, data1d._xunit) self.yaxis(data1d._yaxis, data1d._yunit) self.title = data1d.title self.isSesans = data1d.isSesans
[docs] def __str__(self): """ print data """ _str = "%s\n" % LoadData1D.__str__(self) return _str
[docs] def _perform_operation(self, other, operation): """ """ # First, check the data compatibility dy, dy_other = self._validity_check(other) result = Data1D(x=[], y=[], dx=None, dy=None) result.clone_without_data(length=len(self.x), clone=self) result.copy_from_datainfo(data1d=self) if self.dxw is None: result.dxw = None else: result.dxw = numpy.zeros(len(self.x)) if self.dxl is None: result.dxl = None else: result.dxl = numpy.zeros(len(self.x)) for i in range(len(self.x)): result.x[i] = self.x[i] if self.dx is not None and len(self.x) == len(self.dx): result.dx[i] = self.dx[i] if self.dxw is not None and len(self.x) == len(self.dxw): result.dxw[i] = self.dxw[i] if self.dxl is not None and len(self.x) == len(self.dxl): result.dxl[i] = self.dxl[i] a = Uncertainty(self.y[i], dy[i]**2) if isinstance(other, Data1D): b = Uncertainty(other.y[i], dy_other[i]**2) if other.dx is not None: result.dx[i] *= self.dx[i] result.dx[i] += (other.dx[i]**2) result.dx[i] /= 2 result.dx[i] = math.sqrt(result.dx[i]) if result.dxl is not None and other.dxl is not None: result.dxl[i] *= self.dxl[i] result.dxl[i] += (other.dxl[i]**2) result.dxl[i] /= 2 result.dxl[i] = math.sqrt(result.dxl[i]) else: b = other output = operation(a, b) result.y[i] = output.x result.dy[i] = math.sqrt(math.fabs(output.variance)) return result
[docs] def _perform_union(self, other): """ """ # First, check the data compatibility self._validity_check_union(other) result = Data1D(x=[], y=[], dx=None, dy=None) tot_length = len(self.x) + len(other.x) result = self.clone_without_data(length=tot_length, clone=result) if self.dy is None or other.dy is None: result.dy = None else: result.dy = numpy.zeros(tot_length) if self.dx is None or other.dx is None: result.dx = None else: result.dx = numpy.zeros(tot_length) if self.dxw is None or other.dxw is None: result.dxw = None else: result.dxw = numpy.zeros(tot_length) if self.dxl is None or other.dxl is None: result.dxl = None else: result.dxl = numpy.zeros(tot_length) result.x = numpy.append(self.x, other.x) #argsorting ind = numpy.argsort(result.x) result.x = result.x[ind] result.y = numpy.append(self.y, other.y) result.y = result.y[ind] if result.dy is not None: result.dy = numpy.append(self.dy, other.dy) result.dy = result.dy[ind] if result.dx is not None: result.dx = numpy.append(self.dx, other.dx) result.dx = result.dx[ind] if result.dxw is not None: result.dxw = numpy.append(self.dxw, other.dxw) result.dxw = result.dxw[ind] if result.dxl is not None: result.dxl = numpy.append(self.dxl, other.dxl) result.dxl = result.dxl[ind] return result
[docs]class Data2D(PlottableData2D, LoadData2D): """ """
[docs] def __init__(self, image=None, err_image=None, qx_data=None, qy_data=None, q_data=None, mask=None, dqx_data=None, dqy_data=None, xmin=None, xmax=None, ymin=None, ymax=None, zmin=None, zmax=None): """ """ PlottableData2D.__init__(self, image=image, err_image=err_image, xmin=xmin, xmax=xmax, ymin=ymin, ymax=ymax, zmin=zmin, zmax=zmax, qx_data=qx_data, qy_data=qy_data) LoadData2D.__init__(self, data=image, err_data=err_image, qx_data=qx_data, qy_data=qy_data, dqx_data=dqx_data, dqy_data=dqy_data, q_data=q_data, mask=mask) self.id = None self.list_group_id = [] self.group_id = None self.is_data = True self.path = None self.xtransform = None self.ytransform = None self.title = "" self.scale = None # Always default self.plot_role = Data1D.ROLE_DEFAULT
[docs] def copy_from_datainfo(self, data2d): """ copy value of Data2D of type DataLoader.data_info """ self.data = copy.deepcopy(data2d.data) self.qx_data = copy.deepcopy(data2d.qx_data) self.qy_data = copy.deepcopy(data2d.qy_data) self.q_data = copy.deepcopy(data2d.q_data) self.mask = copy.deepcopy(data2d.mask) self.err_data = copy.deepcopy(data2d.err_data) self.x_bins = copy.deepcopy(data2d.x_bins) self.y_bins = copy.deepcopy(data2d.y_bins) if data2d.dqx_data is not None: self.dqx_data = copy.deepcopy(data2d.dqx_data) if data2d.dqy_data is not None: self.dqy_data = copy.deepcopy(data2d.dqy_data) self.xmin = data2d.xmin self.xmax = data2d.xmax self.ymin = data2d.ymin self.ymax = data2d.ymax if hasattr(data2d, "zmin"): self.zmin = data2d.zmin if hasattr(data2d, "zmax"): self.zmax = data2d.zmax self.xaxis(data2d._xaxis, data2d._xunit) self.yaxis(data2d._yaxis, data2d._yunit) self.title = data2d.title
[docs] def __str__(self): """ print data """ _str = "%s\n" % LoadData2D.__str__(self) return _str
[docs] def _perform_operation(self, other, operation): """ Perform 2D operations between data sets :param other: other data set :param operation: function defining the operation """ # First, check the data compatibility dy, dy_other = self._validity_check(other) result = Data2D(image=None, qx_data=None, qy_data=None, q_data=None, err_image=None, xmin=None, xmax=None, ymin=None, ymax=None, zmin=None, zmax=None) result.clone_without_data(len(self.data)) result.copy_from_datainfo(data2d=self) result.xmin = self.xmin result.xmax = self.xmax result.ymin = self.ymin result.ymax = self.ymax if self.dqx_data is None or self.dqy_data is None: result.dqx_data = None result.dqy_data = None else: result.dqx_data = numpy.zeros(len(self.data)) result.dqy_data = numpy.zeros(len(self.data)) for i in range(numpy.size(self.data)): result.data[i] = self.data[i] if self.err_data is not None and \ numpy.size(self.data) == numpy.size(self.err_data): result.err_data[i] = self.err_data[i] if self.dqx_data is not None: result.dqx_data[i] = self.dqx_data[i] if self.dqy_data is not None: result.dqy_data[i] = self.dqy_data[i] result.qx_data[i] = self.qx_data[i] result.qy_data[i] = self.qy_data[i] result.q_data[i] = self.q_data[i] result.mask[i] = self.mask[i] a = Uncertainty(self.data[i], dy[i]**2) if isinstance(other, Data2D): b = Uncertainty(other.data[i], dy_other[i]**2) if other.dqx_data is not None and \ result.dqx_data is not None: result.dqx_data[i] *= self.dqx_data[i] result.dqx_data[i] += (other.dqx_data[i]**2) result.dqx_data[i] /= 2 result.dqx_data[i] = math.sqrt(result.dqx_data[i]) if other.dqy_data is not None and \ result.dqy_data is not None: result.dqy_data[i] *= self.dqy_data[i] result.dqy_data[i] += (other.dqy_data[i]**2) result.dqy_data[i] /= 2 result.dqy_data[i] = math.sqrt(result.dqy_data[i]) else: b = other output = operation(a, b) result.data[i] = output.x result.err_data[i] = math.sqrt(math.fabs(output.variance)) return result
[docs] def _perform_union(self, other): """ Perform 2D operations between data sets :param other: other data set :param operation: function defining the operation """ # First, check the data compatibility self._validity_check_union(other) result = Data2D(image=None, qx_data=None, qy_data=None, q_data=None, err_image=None, xmin=None, xmax=None, ymin=None, ymax=None, zmin=None, zmax=None) length = len(self.data) tot_length = length + len(other.data) result.clone_without_data(tot_length) result.xmin = self.xmin result.xmax = self.xmax result.ymin = self.ymin result.ymax = self.ymax if self.dqx_data is None or self.dqy_data is None or \ other.dqx_data is None or other.dqy_data is None : result.dqx_data = None result.dqy_data = None else: result.dqx_data = numpy.zeros(len(self.data) + \ numpy.size(other.data)) result.dqy_data = numpy.zeros(len(self.data) + \ numpy.size(other.data)) result.data = numpy.append(self.data, other.data) result.qx_data = numpy.append(self.qx_data, other.qx_data) result.qy_data = numpy.append(self.qy_data, other.qy_data) result.q_data = numpy.append(self.q_data, other.q_data) result.mask = numpy.append(self.mask, other.mask) if result.err_data is not None: result.err_data = numpy.append(self.err_data, other.err_data) if self.dqx_data is not None: result.dqx_data = numpy.append(self.dqx_data, other.dqx_data) if self.dqy_data is not None: result.dqy_data = numpy.append(self.dqy_data, other.dqy_data) return result
[docs]def check_data_validity(data): """ Return True is data is valid enough to compute chisqr, else False """ flag = True if data is not None: if issubclass(data.__class__, Data2D): if (data.data is None) or (len(data.data) == 0)\ or (len(data.err_data) == 0): flag = False else: if (data.y is None) or (len(data.y) == 0): flag = False if not data.is_data: flag = False else: flag = False return flag