import math
import logging
import numpy as np
from sas.qtgui.Plotting.PlotterData import Data1D
PR_FIT_LABEL = r"$P_{fit}(r)$"
PR_LOADED_LABEL = r"$P_{loaded}(r)$"
IQ_DATA_LABEL = r"$I_{obs}(q)$"
IQ_FIT_LABEL = r"$I_{fit}(q)$"
IQ_SMEARED_LABEL = r"$I_{smeared}(q)$"
GROUP_ID_IQ_DATA = r"$I_{obs}(q)$"
GROUP_ID_PR_FIT = r"$P_{fit}(r)$"
PR_PLOT_PTS = 51
logger = logging.getLogger(__name__)
[docs]class InversionLogic(object):
"""
All the data-related logic. This class deals exclusively with Data1D/2D
No QStandardModelIndex here.
"""
def __init__(self, data=None):
self._data = data
self.data_is_loaded = False
if data is not None:
self.data_is_loaded = True
@property
def data(self):
return self._data
@data.setter
def data(self, value):
""" data setter """
self._data = value
self.data_is_loaded = (self._data is not None)
[docs] def isLoadedData(self):
""" accessor """
return self.data_is_loaded
[docs] def new1DPlot(self, out, pr, q=None):
"""
Create a new 1D data instance based on fitting results
"""
qtemp = pr.x
if q is not None:
qtemp = q
# Make a plot
maxq = max(qtemp)
minq = min(qtemp)
# Check for user min/max
if pr.q_min is not None and maxq >= pr.q_min >= minq:
minq = pr.q_min
if pr.q_max is not None and maxq >= pr.q_max >= minq:
maxq = pr.q_max
x = np.arange(minq, maxq, maxq / 301.0)
# Vectorised iq.
y = pr.iq(out, x)
err = np.sqrt(np.abs(y))
index = np.isnan(y)
if index.any():
y[index] = err[index] = 1.0
logger.info("Could not compute I(q) for q =", list((x[index])))
new_plot = Data1D(x, y)
new_plot.name = IQ_FIT_LABEL + f"[{self._data.name}]"
new_plot.xaxis("\\rm{Q}", 'A^{-1}')
new_plot.yaxis("\\rm{Intensity} ", "cm^{-1}")
title = "I(q)"
new_plot.title = title
# If we have a group ID, use it
if 'plot_group_id' in pr.info:
new_plot.group_id = pr.info["plot_group_id"]
new_plot.id = IQ_FIT_LABEL
# If we have used slit smearing, plot the smeared I(q) too
if pr.slit_width > 0 or pr.slit_height > 0:
x = np.arange(minq, maxq, maxq / 301.0)
# Vectorised iq_smeared.
y = pr.get_iq_smeared(out, x)
err = np.sqrt(np.abs(y))
index = np.isnan(y)
if index.any():
y[index] = err[index] = 1.0
logger.info("Could not compute smeared I(q) for q =", list((x[index])))
new_plot = Data1D(x, y)
new_plot.name = IQ_SMEARED_LABEL
new_plot.xaxis("\\rm{Q}", 'A^{-1}')
new_plot.yaxis("\\rm{Intensity} ", "cm^{-1}")
# If we have a group ID, use it
if 'plot_group_id' in pr.info:
new_plot.group_id = pr.info["plot_group_id"]
new_plot.id = IQ_SMEARED_LABEL
new_plot.title = title
new_plot.symbol = 'Line'
new_plot.hide_error = True
return new_plot
[docs] def newPRPlot(self, out, pr, cov=None):
"""
"""
# Show P(r)
x = np.arange(0.0, pr.d_max, pr.d_max / PR_PLOT_PTS)
if cov is None:
y = pr.pr(out, x)
new_plot = Data1D(x, y)
else:
(y, dy) = pr.pr_err(out, cov, x)
new_plot = Data1D(x, y, dy=dy)
new_plot.name = PR_FIT_LABEL + f"[{self._data.name}]"
new_plot.xaxis("\\rm{r}", 'A')
new_plot.yaxis("\\rm{P(r)} ", "cm^{-3}")
new_plot.title = "P(r) fit"
new_plot.id = PR_FIT_LABEL
new_plot.xtransform = "x"
new_plot.ytransform = "y"
new_plot.group_id = GROUP_ID_PR_FIT
return new_plot
[docs] def add_errors(self, sigma=0.05):
"""
Adds errors to data set is they are not available.
Uses $\Delta y = \sigma | y |$.
"""
self._data.dy = sigma * np.fabs(self._data.y)
[docs] def computeDataRange(self):
"""
Wrapper for calculating the data range based on local dataset
"""
return self.computeRangeFromData(self.data)
[docs] def computeRangeFromData(self, data):
"""
Compute the minimum and the maximum range of the data
return the npts contains in data
"""
qmin, qmax = None, None
if isinstance(data, Data1D):
try:
qmin = min(data.x)
qmax = max(data.x)
except (ValueError, TypeError):
msg = "Unable to find min/max/length of \n data named %s" % \
self.data.filename
raise ValueError(msg)
else:
qmin = 0
try:
x = max(np.fabs(data.xmin), np.fabs(data.xmax))
y = max(np.fabs(data.ymin), np.fabs(data.ymax))
except (ValueError, TypeError):
msg = "Unable to find min/max of \n data named %s" % \
self.data.filename
raise ValueError(msg)
qmax = np.sqrt(x * x + y * y)
return qmin, qmax