def Tester():
import numpy as np
import matplotlib.pyplot as plt
from MJOLNIR import _tools
from MJOLNIR.Data import DataSet
# To generate a list of data files from nubers
numbers = '457-458,460,461'
files = _tools.fileListGenerator(numbers,'/home/lass/Dropbox/PhD/CAMEAData/',year=2018)
## Creating a non-linear distribution of points
points = np.exp(np.linspace(-1,np.log(10),21))
minimumBinSize = 1
Bins = _tools.binEdges(points,minimumBinSize)
fig,ax = plt.subplots()
ax.scatter(points,np.ones_like(points),c='r',label='Data Points',zorder=2)
[ax.plot([Bins[i],Bins[i]],[0.5,1.5],'k',zorder=1) for i in range(len(Bins))]
ax.plot(np.concatenate([Bins,np.flip(Bins)]),np.concatenate([np.ones_like(Bins)*0.5,np.ones_like(Bins)*1.5]),c='k',label='Bins',zorder=1)
ax.set_xticks(np.linspace(0,10,11))
ax.set_yticks([])
ax.set_ylim(-1,3)
ax.grid(True,c='k',zorder=0)
fig.legend()
fig.savefig('/home/lass/Dropbox/PhD/Software/MJOLNIR/docs/Tutorials/Tools/Binning.png',format='png',dpi=300)
def test_Powder(show=False):
import matplotlib.pyplot as plt
file = 'Data/camea2018n000137.hdf'
DataObj = DataSet.DataSet(dataFiles=file)
DataObj.convertDataFile()
I = DataObj.I
qx = DataObj.qx
qy = DataObj.qy
energy = DataObj.energy
Norm = DataObj.Norm
Monitor = DataObj.Monitor
EBinEdges = _tools.binEdges(energy, tolerance=0.125)
ax, Data, qbins = DataObj.plotCutPowder(EBinEdges, qMinBin=0.05)
plt.colorbar(ax.pmeshs[0])
ax2, Data2, qbins2 = DataSet.plotCutPowder([qx, qy, energy],
I,
Norm,
Monitor,
EBinEdges,
qMinBin=0.05)
plt.colorbar(ax2.pmeshs[0])
Data3, qbins3 = DataObj.cutPowder(EBinEdges)
ax2.set_clim(0, 0.01)
if show:
plt.show()
else:
if os.path.exists('Data/camea2018n000137.nxs'):
os.remove('Data/camea2018n000137.nxs')
def test_DataSet_cutPowder():
Tolerance = 0.01
plt.ioff()
import matplotlib
matplotlib.use('Agg')
convertFiles = [os.path.join(dataPath,'camea2018n000136.hdf')]
Datset = DataSet(dataFiles = convertFiles)
Datset.convertDataFile(saveFile=True)
mask = np.ones_like(Datset.I.data)
Datset.mask = mask
Datset.mask = np.logical_not(mask)
eBins = _tools.binEdges(Datset.energy,0.25)
ax,D,q = Datset.plotCutPowder(eBins,Tolerance)# Remove to improve test ,vmin=0,vmax=1e-6)
D2,q2 = Datset.cutPowder(eBins,Tolerance)
assert(np.all(D.equals(D2)))
for i in range(len(q)):
for j in range(len(q[i])):
assert(np.all(q[i][j]==q2[i][j]))
def test_DataSet_binEdges():
X = np.random.rand(100)*3 # array between 0 and 3 -ish
X.sort()
tolerance = 0.01
Bins = _tools.binEdges(X,tolerance=tolerance)
assert(Bins[0]==X[0]-0.1*tolerance)
assert(np.isclose(Bins[-1],X[-1],atol=5) or Bins[-1]>X[-1])
assert(len(Bins)<=3.0/tolerance)
assert(np.all(np.diff(Bins[:-1])>tolerance*0.99))
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(
q1,
q2,
width,
minPixel,
rlu=False,
Emin=0.2,
Emax=1.7,
plotCoverage=True)
if show:
plt.show()
else:
if os.path.exists('Data/camea2018n000137.nxs'):
os.remove('Data/camea2018n000137.nxs')
def test_binEdges():
values = np.exp(np.linspace(-0.1, 1, 101)) # Generate non-linear points
#values[0]-tolerance/2.0 and ends at values[-1]+tolerance/2.0.
minBin = 0.1
bins = binEdges(values, minBin)
assert (np.isclose(bins[0], values[0] - 0.1 * minBin)
) # First bin starts at values[0]-tolerance*0.1
assert (bins[-1] >= values[-1] + 0.1 * minBin
) # Last bin ends at values[-1]+tolerance*0.1
assert (np.all(np.diff(bins) >= minBin * 0.99)
) # Assert that all bins are at least of size minBin
binsCut = binEdges(values, 0.01, startPoint=0.0)
assert (binsCut[0] >= 0.0)
binsCut = binEdges(values + np.min(values) + 0.0099,
0.01,
startPoint=0.0,
endPoint=0.5)
assert (binsCut[0] >= 0.0)
assert (binsCut[-1] <= 0.5)
binsCut = binEdges(values + np.min(values) + 0.0101,
0.01,
startPoint=0.0,
endPoint=0.5)
assert (binsCut[0] > 0.01)
binsCut = binEdges(np.linspace(0, 0.95, 101),
0.01,
startPoint=0.0,
endPoint=1.0101)
assert (binsCut[-1] <= 1.0)
binsCut = binEdges(values - np.max(values) + 1.0,
0.01,
startPoint=0.0,
endPoint=1.01)
assert (binsCut[-1] <= 1.01)
def Tester():
from MJOLNIR.Data import DataSet
from MJOLNIR import _tools
import numpy as np
import matplotlib.pyplot as plt
numbers = '483-489,494-500'
fileList = _tools.fileListGenerator(numbers,
'/home/lass/Dropbox/PhD/CAMEAData/',
2018)
ds = DataSet.DataSet(fileList)
ds.convertDataFile(saveFile=False)
# Define the positions to be cut through
Q1 = np.array([0, 0, 0])
Q2 = np.array([0, 0, 1])
Q3 = np.array([-1, 0, 1])
Q4 = np.array([-1, 0, -1])
# Collect them into one array
QPoints = np.array([Q1, Q2, Q3, Q4])
# Define orthogonal width and minimum pixel size along Q-cut
width = np.array([0.05, 0.03, 0.05]) # 1/AA
minPixel = np.array([0.03, 0.01, 0.01]) # 1/AA
# Define energy bins through the binEdges methods found in _tools
Energies = np.concatenate(ds.energy, axis=0)
EnergyBins = np.array([
_tools.binEdges(Energies, 0.04),
_tools.binEdges(Energies, 0.1),
_tools.binEdges(Energies, 0.07)
])
# Create figure into which the plot is made
fig = plt.figure(figsize=(14, 6))
ax = fig.gca()
ax,DataLists,BinListTotal,centerPositionTotal,binDistanceTotal = \
ds.plotCutQELine(QPoints=QPoints, width=width, minPixel=minPixel, \
ax=ax, EnergyBins=EnergyBins, ticks = 12,\
vmin=1e-8, vmax=1e-5, tickRound = 4, plotSeperator = True,
seperatorWidth=0.5,zorder=10)
ax.grid(True, zorder=0, c='k')
fig.savefig(
'/home/lass/Dropbox/PhD/Software/MJOLNIR/docs/Tutorials/Advanced/plotCutQELineMnF2.png',
format='png',
dpi=300)
# Plot a single cut through the data set
fig2, ax2 = plt.subplots()
segID = 2
# Find all energies in segment
E = np.array([x[0][-1] for x in centerPositionTotal[segID]])
# Find index of energies corresponding to the list
EnergyIndexes = [E.searchsorted(x) for x in np.linspace(1.5, 6.5, 5)]
plot = 0
for energyID in EnergyIndexes:
# Extract data for segment 2 and current energy
_dataList = DataLists[np.logical_and(
DataLists['qCut'] == segID,
DataLists['energyCut'] == energyID)].astype(float)
Intensity = _dataList['Int'] * 1e5
Intensity_err = np.divide(
np.sqrt(_dataList['Intensity']) * _dataList['BinCount'],
_dataList['Monitor'] * _dataList['Normalization'])
position = _dataList['L'] # Plotting along L
Energy = np.mean(_dataList['Energy'])
ax2.errorbar(position,
Intensity + len(EnergyIndexes) - plot,
yerr=Intensity_err,
fmt='-',
label='E = {:.1f} meV'.format(Energy))
plot += 1
ax2.set_title('Cut through data along $L$')
ax2.set_xlabel('L [rlu]')
ax2.set_ylabel('Int [arb]')
ax2.legend()
ax2.grid(True)
fig2.tight_layout()
fig2.savefig(
'/home/lass/Dropbox/PhD/Software/MJOLNIR/docs/Tutorials/Advanced/plotCutQELineMnF21D.png',
format='png',
dpi=300)
def Tester():
from MJOLNIR.Data import DataSet
from MJOLNIR import _tools
import numpy as np
import matplotlib.pyplot as plt
numbers = '483-489,494-500'
fileList = _tools.fileListGenerator(numbers,
'/home/lass/Dropbox/PhD/CAMEAData/',
2018)
ds = DataSet.DataSet(fileList)
ds.convertDataFile(saveFile=False)
# Define the positions to be cut through
Q1 = np.array([0, 0, 0])
Q2 = np.array([0, 0, 1])
Q3 = np.array([-1, 0, 1])
Q4 = np.array([-1, 0, -1])
# Collect them into one array
QPoints = np.array([Q1, Q2, Q3, Q4])
# Define orthogonal width and minimum pixel size along Q-cut
width = np.array([0.05, 0.03, 0.05]) # 1/AA
minPixel = np.array([0.03, 0.01, 0.01]) # 1/AA
# Define energy bins through the binEdges methods found in _tools
Energies = np.concatenate(ds.energy, axis=0)
EnergyBins = np.array([
_tools.binEdges(Energies, 0.04),
_tools.binEdges(Energies, 0.1),
_tools.binEdges(Energies, 0.07)
])
ax,DataLists,BinListTotal = \
ds.plotCutQELine(QPoints=QPoints, width=width, minPixel=minPixel, \
EnergyBins=EnergyBins, ticks = 12,\
vmin=1e-8, vmax=1e-5, plotSeperator = True,
seperatorWidth=0.5,zorder=10)
ax.grid(True, zorder=0, c='k')
ax.get_figure().savefig(
'/home/lass/Dropbox/PhD/Software/MJOLNIR/docs/Tutorials/Advanced/plotCutQELineMnF2.png',
format='png',
dpi=300)
# Plot a single cut through the data set
fig2, ax2 = plt.subplots()
segID = 2
# Extract the data from SegmentId
df = DataLists[segID]
for energy, EnergyDF in df.groupby('Energy'):
Intensity = EnergyDF['Int'] * 1e5
Intensity_err = EnergyDF['Int_err']
position = EnergyDF['L'] # Plotting along L
ax2.errorbar(position,
Intensity,
yerr=Intensity_err,
fmt='-',
label='E = {:.1f} meV'.format(energy))
ax2.set_title('Cut through data along $L$')
ax2.set_xlabel('L [rlu]')
ax2.set_ylabel('Int [arb]')
ax2.legend()
ax2.grid(True)
fig2.tight_layout()
fig2.savefig(
'/home/lass/Dropbox/PhD/Software/MJOLNIR/docs/Tutorials/Advanced/plotCutQELineMnF21D.png',
format='png',
dpi=300)