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import csv
import os
import numpy as np
import pytest
from sasdata.dataloader.data_info import Data1D
from sasdata.dataloader.loader import Loader
from sas.sascalc.size_distribution.SizeDistribution import sizeDistribution
def find(filename):
return os.path.join(os.path.dirname(__file__), "data", filename)
@pytest.fixture(autouse=True)
def data1():
data = Loader().load(find("Alumina_usaxs_irena_input.csv"))[0]
size_distribution = sizeDistribution(data)
size_distribution.aspectRatio = 1.0
size_distribution.diamMin = 25
size_distribution.diamMax = 10000
size_distribution.nbins = 100
size_distribution.logbin = True
size_distribution.skyBackground = 1.94434e-5
size_distribution.weightType = 'dI'
size_distribution.weightFactor = 1.9033
size_distribution.iterMax = 100
size_distribution.ndx_qmin = 13
size_distribution.ndx_qmax = 94
return size_distribution
def fetch_answer():
bins = np.array([])
mags = np.array([])
with open(find("AluminaUSAXS_IrenaTenShotsResults.csv")) as fp:
spamreader = csv.reader(fp, delimiter=",")
for row in spamreader:
try:
bins = np.append(bins, float(row[0]))
mags = np.append(mags, float(row[4]))
except:
pass
return bins, mags
def test_noRes(data1):
# Subtract a (low Q) power law background. Power = 4 and scale =2.5508e-7
y_power_back = 2.5508e-7 * data1.data.x ** -4
# Subtract falt background (high Q - e.g. incoherent scattering) background = 0.121176
y_flat = np.ones(len(data1.data.x))*.121176
# Now compute full background to subtract from the data
y_back = y_power_back + y_flat
data_to_subtract = Data1D(data1.data.x, y_back, dy=0)
trim_data, intensities, init_binsBack, sigma = data1.prep_maxEnt(data_to_subtract, full_fit=True, rngseed=0)
# Now run the fit.
# The return is a list over the number of runs (only one for quick fit and 10 for a full fit
# each element of the list is a list containing the convergence truth and the number of
# iterations it took to converge.
convergence = data1.run_maxEnt(trim_data, intensities, init_binsBack, sigma)
# Now retrieve the "ground truth" to compare to.
answerBins, answerMags = fetch_answer()
# TODO: Need to understand how IRENA is reporting bins. It seems to be using bin 1 and last as the
# values input from the user while we are starting from the midpoint of what should be the
# the first bin if the min diameter is the bottom edge of the bin.
assert convergence[0][0] is True
assert data1.MaxEnt_statistics['volume'] == pytest.approx(4.84, abs=0.02)
assert data1.BinMagnitude_maxEnt == pytest.approx(answerMags, abs=1e-3)
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