def thetaTwothetaToQxQz(data, output_grid, wavelength=5.0, qxmin=-0.003, qxmax=0.003, qxbins=101, qzmin=0.0001, qzmax=0.1, qzbins=101):
""" Figures out the Qx, Qz values of each datapoint
and throws them in the correct bin. If no grid is specified,
one is created that covers the whole range of Q in the dataset
If autofill_gaps is True, does reverse lookup to plug holes
in the output (but pixel count is still zero for these bins)
**Inputs**
data (ospec2d): input data
output_grid (ospec2d): empty data object with axes defined (optional)
wavelength (float): override wavelength in data file
qxmin (float): lower bound of Qx range in rebinning
qxmax (float): upper bound of Qx range in rebinning
qxbins (int): number of bins to subdivide the range between qxmin and qxmax
qzmin (float): lower bound of Qz range in rebinning
qzmax (float): upper bound of Qz range in rebinning
qzbins (int): number of bins to subdivide the range between qzmin and qzmax
**Returns**
output (ospec2d): output data rebinned into Qx, Qz
2016-04-01 Brian Maranville
"""
print "output grid: ", output_grid
if output_grid == None:
info = [{"name": "qx", "units": "inv. Angstroms", "values": linspace(qxmin, qxmax, qxbins) },
{"name": "qz", "units": "inv. Angstroms", "values": linspace(qzmin, qzmax, qzbins) },]
old_info = data.infoCopy()
info.append(old_info[2]) # column information!
info.append(old_info[3]) # creation story!
output_grid = MetaArray(zeros((qxbins, qzbins, data.shape[-1])), info=info)
else:
outgrid_info = deepcopy(output_grid._info) # take axes and creation story from emptyqxqz...
outgrid_info[2] = deepcopy(data._info[2]) # take column number and names from dataset
output_grid = MetaArray(zeros((output_grid.shape[0], output_grid.shape[1], data.shape[2])), info=outgrid_info)
theta_axis = data._getAxis('theta')
twotheta_axis = data._getAxis('twotheta')
qLength = 2.0 * pi / wavelength
th_array = data.axisValues('theta').copy()
twotheta_array = data.axisValues('twotheta').copy()
if theta_axis < twotheta_axis: # then theta is first: add a dimension at the end
th_array.shape = th_array.shape + (1,)
twotheta_array.shape = (1,) + twotheta_array.shape
else:
twotheta_array.shape = twotheta_array.shape + (1,)
th_array.shape = (1,) + th_array.shape
tilt_array = th_array - (twotheta_array / 2.0)
qxOut = 2.0 * qLength * sin((pi / 180.0) * (twotheta_array / 2.0)) * sin(pi * tilt_array / 180.0)
qzOut = 2.0 * qLength * sin((pi / 180.0) * (twotheta_array / 2.0)) * cos(pi * tilt_array / 180.0)
# getting values from output grid:
outgrid_info = output_grid.infoCopy()
numcols = len(outgrid_info[2]['cols'])
qx_array = output_grid.axisValues('qx')
dqx = qx_array[1] - qx_array[0]
qz_array = output_grid.axisValues('qz')
dqz = qz_array[1] - qz_array[0]
#framed_array = zeros((qz_array.shape[0] + 2, qx_array.shape[0] + 2, numcols))
target_qx = ((qxOut - qx_array[0]) / dqx).astype(int)
#return target_qx, qxOut
target_qz = ((qzOut - qz_array[0]) / dqz).astype(int)
target_mask = (target_qx >= 0) * (target_qx < qx_array.shape[0])
target_mask *= (target_qz >= 0) * (target_qz < qz_array.shape[0])
target_qx_list = target_qx[target_mask]
target_qz_list = target_qz[target_mask]
for i, col in enumerate(outgrid_info[2]['cols']):
values_to_bin = data[:,:,col['name']].view(ndarray)[target_mask]
outshape = (output_grid.shape[0], output_grid.shape[1])
hist2d, xedges, yedges = histogram2d(target_qx_list,target_qz_list, \
bins = (outshape[0],outshape[1]), range=((0,outshape[0]),(0,outshape[1])), weights=values_to_bin)
output_grid[:,:,col['name']] += hist2d
#framed_array[target_qz_list, target_qx_list, i] = data[:,:,col['name']][target_mask]
cols = outgrid_info[2]['cols']
data_cols = [col['name'] for col in cols if col['name'].startswith('counts')]
monitor_cols = [col['name'] for col in cols if col['name'].startswith('monitor')]
# just take the first one...
if len(monitor_cols) > 0:
monitor_col = monitor_cols[0]
data_missing_mask = (output_grid[:,:,monitor_col] == 0)
for dc in data_cols:
output_grid[:,:,dc].view(ndarray)[data_missing_mask] = NaN;
#extra_info
output_grid._info[-1] = data._info[-1].copy()
print "output shape:", output_grid.shape
return output_grid
def apply(self, data, theta=None, qxmin=None, qxmax=None, qxbins=None, qzmin=None, qzmax=None, qzbins=None):
info = [{"name": "qx", "units": "inv. Angstroms", "values": linspace(qxmin, qxmax, qxbins) },
{"name": "qz", "units": "inv. Angstroms", "values": linspace(qzmin, qzmax, qzbins) },]
old_info = data.infoCopy()
info.append(old_info[2]) # column information!
info.append(old_info[3]) # creation story!
output_grid = MetaArray(zeros((qxbins, qzbins, data.shape[-1])), info=info)
#if output_grid == None:
# output_grid = EmptyQxQzGrid(*self.default_qxqz_gridvals)
#else:
# output_grid = deepcopy(output_grid)
if (theta == "") or (theta == None):
if 'state' in data._info[-1]:
theta = float(data._info[-1]['state']['A[1]'])
print 'theta:', theta
else:
print "can't run without theta!"
return
wl_array = data.axisValues('wavelength').copy()
wl_array.shape = wl_array.shape + (1,)
twotheta_array = data.axisValues('twotheta').copy()
twotheta_array.shape = (1,) + twotheta_array.shape
qxOut, qzOut = self.getQxQz(theta, twotheta_array, wl_array)
# getting values from output grid:
outgrid_info = output_grid.infoCopy()
numcols = len(outgrid_info[2]['cols'])
qx_array = output_grid.axisValues('qx')
dqx = qx_array[1] - qx_array[0]
qz_array = output_grid.axisValues('qz')
dqz = qz_array[1] - qz_array[0]
framed_array = zeros((qz_array.shape[0]+2, qx_array.shape[0]+2, numcols))
target_qx = ((qxOut - qx_array[0])/dqx + 1).astype(int)
#return target_qx, qxOut
target_qz = ((qzOut - qz_array[0])/dqz + 1).astype(int)
target_mask = (target_qx >= 0) * (target_qx < qx_array.shape[0])
target_mask *= (target_qz >= 0) * (target_qz < qz_array.shape[0])
target_qx_list = target_qx[target_mask]
target_qz_list = target_qz[target_mask]
#target_qx = target_qx.clip(0, qx_array.shape[0]+1)
#target_qz = target_qz.clip(0, qz_array.shape[0]+1)
for i, col in enumerate(outgrid_info[2]['cols']):
values_to_bin = data[:,:,col['name']][target_mask]
outshape = (output_grid.shape[0], output_grid.shape[1])
hist2d, xedges, yedges = histogram2d(target_qx_list,target_qz_list, bins = (outshape[0],outshape[1]), range=((0,outshape[0]),(0,outshape[1])), weights=values_to_bin)
output_grid[:,:,col['name']] += hist2d
#framed_array[target_qz_list, target_qx_list, i] = data[:,:,col['name']][target_mask]
#trimmed_array = framed_array[1:-1, 1:-1]
#output_grid[:,:] = trimmed_array
creation_story = data._info[-1]['CreationStory']
new_creation_story = creation_story + ".filter('{0}', {1})".format(self.__class__.__name__, output_grid._info[-1]['CreationStory'])
#print new_creation_story
output_grid._info[-1] = data._info[-1].copy()
output_grid._info[-1]['CreationStory'] = new_creation_story
return output_grid
