def test_Convert_binning():
subprocess.check_output(['MJOLNIRConvert', dataFiles[0]])
f = DataFile.DataFile(dataFiles[0].replace('hdf', 'nxs'))
assert (f.binning == 8)
subprocess.check_output(['MJOLNIRConvert', dataFiles[0], '-b 1'])
f = DataFile.DataFile(dataFiles[0].replace('hdf', 'nxs'))
assert (f.name == dataFiles[0].split('/')[1].replace('hdf', 'nxs'))
assert (f.binning == 1)
def test_Convert_SaveLocation():
if not os.path.isdir('Data/Data'):
os.makedirs('Data/Data')
subprocess.check_output(['MJOLNIRConvert', dataFiles[0], '-s Data/'])
fileName = dataFiles[0].split('/')[1].replace('hdf', 'nxs')
f = DataFile.DataFile('Data/' + fileName)
assert (f.binning == 8)
print(fileName)
print(f.name)
assert (f.name == fileName)
def test_Convert_SaveLocation():
if not os.path.isdir(os.path.join('Data','Data')):
os.makedirs(os.path.join('Data','Data'))
subprocess.check_output(['MJOLNIRConvert',dataFiles[0],'-s Data'+os.path.sep])
fileName = dataFiles[0].split(os.path.sep)[1].replace('hdf','nxs')
f = DataFile.DataFile('Data'+os.path.sep+fileName)
assert(f.binning == 8)
print(fileName)
print(f.name)
assert(f.name == fileName)
os.remove('Data'+os.path.sep+fileName.replace('hdf','nxs'))
import sys, os
sys.path.append('/home/lass/Dropbox/PhD/Software/MJOLNIR/')
from MJOLNIR.Data import DataFile
DataFile.assertFile('Data/camea2018n000137.nxs')
from MJOLNIR.Data import DataSet
from MJOLNIR import _tools
def test_cut2D(show=False):
import numpy as np
import matplotlib.pyplot as plt
file = 'Data/camea2018n000137.hdf'
DataObj = DataSet.DataSet(dataFiles=file)
DataObj.convertDataFile()
energy = DataObj.energy
EnergyBins = _tools.binEdges(energy, tolerance=0.125)
q1 = np.array([1.0, 0])
q2 = np.array([0, 1.0])
width = 0.1 # 1/A
minPixel = 0.01
ax, DataList, qBnLit, centerPos, binDIstance = DataObj.plotCutQE(
q1, q2, width, minPixel, EnergyBins, rlu=False)
plt.colorbar(ax.pmeshs[0])
ax.set_clim(0, 10)
plt.tight_layout()
## Cut and plot 1D
ax2, DataList, Bins, binCenter, binDistance = DataObj.plotCut1D(
_tools.updateSetting(settingsName, directory)
plot = args.plotList
binning = args.binning
argsIdx = []
booleanList = np.zeros((len(PlotType)), dtype=bool)
for arg in plot:
argsIdx.append(switch(arg))
argsIdx = np.unique([x for x in argsIdx if x is not None])
booleanList[np.array(argsIdx)] = True
File = DataFile.DataFile(file)
for id in argsIdx:
if PlotType[id] == 'A4':
File.plotA4(binning=binning)
if PlotType[id] == 'Normalization':
File.plotNormalization(binning=binning)
if PlotType[id] == 'Ef':
File.plotEf(binning=binning)
if PlotType[id] == 'EfOverview':
File.plotEfOverview(binning=binning)