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 thetaTwothetaToAlphaIAlphaF(data):
""" Figures out the angle in, angle out 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 the dataset
**Inputs**
data (ospec2d): input data
**Returns**
output (ospec2d): output data rebinned into alpha_i, alpha_f
2016-04-01 Brian Maranville
"""
theta_axis = data._getAxis('theta')
twotheta_axis = data._getAxis('twotheta')
th_array = data.axisValues('theta').copy()
twotheta_array = data.axisValues('twotheta').copy()
two_theta_step = twotheta_array[1] - twotheta_array[0]
theta_step = th_array[1] - th_array[0]
af_max = (twotheta_array.max() - th_array.min())
af_min = (twotheta_array.min() - th_array.max())
alpha_i = th_array.copy()
alpha_f = arange(af_min, af_max, two_theta_step)
info = [{"name": "alpha_i", "units": "degrees", "values": th_array.copy() },
{"name": "alpha_f", "units": "degrees", "values": alpha_f.copy() },]
old_info = data.infoCopy()
info.append(old_info[2]) # column information!
info.append(old_info[3]) # creation story!
output_grid = MetaArray(zeros((th_array.shape[0], alpha_f.shape[0], data.shape[-1])), info=info)
if theta_axis < twotheta_axis: # then theta is first: add a dimension at the end
alpha_i.shape = alpha_i.shape + (1,)
ai_out = indices((th_array.shape[0], twotheta_array.shape[0]))[0]
twotheta_array.shape = (1,) + twotheta_array.shape
else:
alpha_i.shape = (1,) + alpha_i.shape
ai_out = indices((twotheta_array.shape[0], th_array.shape[0]))[1]
twotheta_array.shape = twotheta_array.shape + (1,)
af_out = twotheta_array - alpha_i
# getting values from output grid:
outgrid_info = output_grid.infoCopy()
numcols = len(outgrid_info[2]['cols'])
#target_ai = ((ai_out - th_array[0]) / theta_step).flatten().astype(int).tolist()
target_ai = ai_out.flatten().astype(int).tolist()
#return target_qx, qxOut
target_af = ((af_out - af_min) / two_theta_step).flatten().astype(int).tolist()
for i, col in enumerate(outgrid_info[2]['cols']):
values_to_bin = data[:,:,col['name']].view(ndarray).flatten().tolist()
print len(target_ai), len(target_af), len(values_to_bin)
outshape = (output_grid.shape[0], output_grid.shape[1])
hist2d, xedges, yedges = histogram2d(target_ai, target_af, bins = (outshape[0],outshape[1]), range=((0,outshape[0]),(0,outshape[1])), weights=values_to_bin)
output_grid[:,:,col['name']] += hist2d
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 changed
output_grid._info[-1] = data._info[-1].copy()
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
