def __init__(self, image=None, err_image=None,
qx_data=None, qy_data=None, q_data=None,
mask=None, dqx_data=None, dqy_data=None,
xmin=None, xmax=None, ymin=None, ymax=None,
zmin=None, zmax=None):
"""
"""
PlotData2D.__init__(self, image=image, err_image=err_image,
xmin=xmin, xmax=xmax, ymin=ymin, ymax=ymax,
zmin=zmin, zmax=zmax, qx_data=qx_data,
qy_data=qy_data)
LoadData2D.__init__(self, data=image, err_data=err_image,
qx_data=qx_data, qy_data=qy_data,
dqx_data=dqx_data, dqy_data=dqy_data,
q_data=q_data, mask=mask)
self.id = None
self.list_group_id = []
self.group_id = None
self.is_data = True
self.path = None
self.xtransform = None
self.ytransform = None
self.title = ""
self.scale = None
def __init__(self, image=None, err_image=None,
qx_data=None, qy_data=None, q_data=None,
mask=None, dqx_data=None, dqy_data=None,
xmin=None, xmax=None, ymin=None, ymax=None,
zmin=None, zmax=None):
"""
"""
PlotData2D.__init__(self, image=image, err_image=err_image,
xmin=xmin, xmax=xmax, ymin=ymin, ymax=ymax,
zmin=zmin, zmax=zmax, qx_data=qx_data,
qy_data=qy_data)
LoadData2D.__init__(self, data=image, err_data=err_image,
qx_data=qx_data, qy_data=qy_data,
dqx_data=dqx_data, dqy_data=dqy_data,
q_data=q_data, mask=mask)
self.id = None
self.list_group_id = []
self.group_id = None
self.is_data = True
self.path = None
self.xtransform = None
self.ytransform = None
self.title = ""
self.scale = None
def __init__(self, sas_data2d, data=None, err_data=None):
Data2D.__init__(self, data=data, err_data=err_data)
# Data can be initialized with a sas plottable or with vectors.
self.res_err_image = []
self.num_points = 0 # will be set by set_data
self.idx = []
self.qmin = None
self.qmax = None
self.smearer = None
self.radius = 0
self.res_err_data = []
self.sas_data = sas_data2d
self.set_data(sas_data2d)
def __init__(self, sas_data2d, data=None, err_data=None):
Data2D.__init__(self, data=data, err_data=err_data)
# Data can be initialized with a sas plottable or with vectors.
self.res_err_image = []
self.num_points = 0 # will be set by set_data
self.idx = []
self.qmin = None
self.qmax = None
self.smearer = None
self.radius = 0
self.res_err_data = []
self.sas_data = sas_data2d
self.set_data(sas_data2d)
def load_frames(self, frames):
frame_data = []
# Prepare axis values (arbitrary scale)
x = self.n_rasters * range(1, self.n_pixels + 1)
y = [self.n_pixels * [i] for i in range(1, self.n_rasters + 1)]
y = np.reshape(y, (1, self.n_pixels * self.n_rasters))[0]
x_bins = x[:self.n_pixels]
y_bins = y[0::self.n_pixels]
for frame in frames:
self.frame = frame
raw_frame_data = self.load_data()
data2d = Data2D(data=raw_frame_data, qx_data=x, qy_data=y)
data2d.x_bins = x_bins
data2d.y_bins = y_bins
data2d.Q_unit = '' # Using arbitrary units
frame_data.append(data2d)
return frame_data
def convert_image(self, rgb, xmin, xmax, ymin, ymax, zscale):
"""
Convert image to data2D
"""
x_len = len(rgb[0])
y_len = len(rgb)
x_vals = np.linspace(xmin, xmax, num=x_len)
y_vals = np.linspace(ymin, ymax, num=y_len)
# Instantiate data object
output = Data2D()
output.filename = os.path.basename(self.title)
output.id = output.filename
detector = Detector()
detector.pixel_size.x = None
detector.pixel_size.y = None
# Store the sample to detector distance
detector.distance = None
output.detector.append(detector)
# Initiazed the output data object
output.data = zscale * self.rgb2gray(rgb)
output.err_data = np.zeros([x_len, y_len])
output.mask = np.ones([x_len, y_len], dtype=bool)
output.xbins = x_len
output.ybins = y_len
output.x_bins = x_vals
output.y_bins = y_vals
output.qx_data = np.array(x_vals)
output.qy_data = np.array(y_vals)
output.xmin = xmin
output.xmax = xmax
output.ymin = ymin
output.ymax = ymax
output.xaxis('\\rm{Q_{x}}', '\AA^{-1}')
output.yaxis('\\rm{Q_{y}}', '\AA^{-1}')
# Store loading process information
output.meta_data['loader'] = self.title.split('.')[-1] + "Reader"
output.is_data = True
output = reader2D_converter(output)
if self.base is not None:
data = self.base.create_gui_data(output, self.title)
self.base.add_data({data.id: data})
def read(self, filename=None):
"""
Open and read the data in a file
@param file: path of the file
"""
read_it = False
for item in self.ext:
if filename.lower().find(item) >= 0:
read_it = True
if read_it:
try:
datafile = open(filename, 'r')
except:
raise RuntimeError, "danse_reader cannot open %s" % (filename)
# defaults
# wavelength in Angstrom
wavelength = 10.0
# Distance in meter
distance = 11.0
# Pixel number of center in x
center_x = 65
# Pixel number of center in y
center_y = 65
# Pixel size [mm]
pixel = 5.0
# Size in x, in pixels
size_x = 128
# Size in y, in pixels
size_y = 128
# Format version
fversion = 1.0
output = Data2D()
output.filename = os.path.basename(filename)
detector = Detector()
output.detector.append(detector)
output.data = numpy.zeros([size_x, size_y])
output.err_data = numpy.zeros([size_x, size_y])
data_conv_q = None
data_conv_i = None
if has_converter == True and output.Q_unit != '1/A':
data_conv_q = Converter('1/A')
# Test it
data_conv_q(1.0, output.Q_unit)
if has_converter == True and output.I_unit != '1/cm':
data_conv_i = Converter('1/cm')
# Test it
data_conv_i(1.0, output.I_unit)
read_on = True
while read_on:
line = datafile.readline()
if line.find("DATA:") >= 0:
read_on = False
break
toks = line.split(':')
if toks[0] == "FORMATVERSION":
fversion = float(toks[1])
if toks[0] == "WAVELENGTH":
wavelength = float(toks[1])
elif toks[0] == "DISTANCE":
distance = float(toks[1])
elif toks[0] == "CENTER_X":
center_x = float(toks[1])
elif toks[0] == "CENTER_Y":
center_y = float(toks[1])
elif toks[0] == "PIXELSIZE":
pixel = float(toks[1])
elif toks[0] == "SIZE_X":
size_x = int(toks[1])
elif toks[0] == "SIZE_Y":
size_y = int(toks[1])
# Read the data
data = []
error = []
if fversion == 1.0:
data_str = datafile.readline()
data = data_str.split(' ')
else:
read_on = True
while read_on:
data_str = datafile.readline()
if len(data_str) == 0:
read_on = False
else:
toks = data_str.split()
try:
val = float(toks[0])
err = float(toks[1])
if data_conv_i is not None:
val = data_conv_i(val, units=output._yunit)
err = data_conv_i(err, units=output._yunit)
data.append(val)
error.append(err)
except:
logging.info("Skipping line:%s,%s" %
(data_str, sys.exc_value))
# Initialize
x_vals = []
y_vals = []
ymin = None
ymax = None
xmin = None
xmax = None
# Qx and Qy vectors
theta = pixel / distance / 100.0
stepq = 4.0 * math.pi / wavelength * math.sin(theta / 2.0)
for i_x in range(size_x):
theta = (i_x - center_x + 1) * pixel / distance / 100.0
qx = 4.0 * math.pi / wavelength * math.sin(theta / 2.0)
if has_converter == True and output.Q_unit != '1/A':
qx = data_conv_q(qx, units=output.Q_unit)
x_vals.append(qx)
if xmin == None or qx < xmin:
xmin = qx
if xmax == None or qx > xmax:
xmax = qx
ymin = None
ymax = None
for i_y in range(size_y):
theta = (i_y - center_y + 1) * pixel / distance / 100.0
qy = 4.0 * math.pi / wavelength * math.sin(theta / 2.0)
if has_converter == True and output.Q_unit != '1/A':
qy = data_conv_q(qy, units=output.Q_unit)
y_vals.append(qy)
if ymin == None or qy < ymin:
ymin = qy
if ymax == None or qy > ymax:
ymax = qy
# Store the data in the 2D array
i_x = 0
i_y = -1
for i_pt in range(len(data)):
try:
value = float(data[i_pt])
except:
# For version 1.0, the data were still
# stored as strings at this point.
msg = "Skipping entry (v1.0):%s,%s" % (str(
data[i_pt]), sys.exc_value)
logging.info(msg)
# Get bin number
if math.fmod(i_pt, size_x) == 0:
i_x = 0
i_y += 1
else:
i_x += 1
output.data[i_y][i_x] = value
if fversion > 1.0:
output.err_data[i_y][i_x] = error[i_pt]
# Store all data
# Store wavelength
if has_converter == True and output.source.wavelength_unit != 'A':
conv = Converter('A')
wavelength = conv(wavelength,
units=output.source.wavelength_unit)
output.source.wavelength = wavelength
# Store distance
if has_converter == True and detector.distance_unit != 'm':
conv = Converter('m')
distance = conv(distance, units=detector.distance_unit)
detector.distance = distance
# Store pixel size
if has_converter == True and detector.pixel_size_unit != 'mm':
conv = Converter('mm')
pixel = conv(pixel, units=detector.pixel_size_unit)
detector.pixel_size.x = pixel
detector.pixel_size.y = pixel
# Store beam center in distance units
detector.beam_center.x = center_x * pixel
detector.beam_center.y = center_y * pixel
# Store limits of the image (2D array)
xmin = xmin - stepq / 2.0
xmax = xmax + stepq / 2.0
ymin = ymin - stepq / 2.0
ymax = ymax + stepq / 2.0
if has_converter == True and output.Q_unit != '1/A':
xmin = data_conv_q(xmin, units=output.Q_unit)
xmax = data_conv_q(xmax, units=output.Q_unit)
ymin = data_conv_q(ymin, units=output.Q_unit)
ymax = data_conv_q(ymax, units=output.Q_unit)
output.xmin = xmin
output.xmax = xmax
output.ymin = ymin
output.ymax = ymax
# Store x and y axis bin centers
output.x_bins = x_vals
output.y_bins = y_vals
# Units
if data_conv_q is not None:
output.xaxis("\\rm{Q_{x}}", output.Q_unit)
output.yaxis("\\rm{Q_{y}}", output.Q_unit)
else:
output.xaxis("\\rm{Q_{x}}", 'A^{-1}')
output.yaxis("\\rm{Q_{y}}", 'A^{-1}')
if data_conv_i is not None:
output.zaxis("\\rm{Intensity}", output.I_unit)
else:
output.zaxis("\\rm{Intensity}", "cm^{-1}")
if not fversion >= 1.0:
msg = "Danse_reader can't read this file %s" % filename
raise ValueError, msg
else:
logging.info("Danse_reader Reading %s \n" % filename)
# Store loading process information
output.meta_data['loader'] = self.type_name
output = reader2D_converter(output)
return output
return None
def read(self, filename=None):
"""
Open and read the data in a file
:param file: path of the file
"""
try:
import Image
import TiffImagePlugin
Image._initialized = 2
except:
msg = "tiff_reader: could not load file. Missing Image module."
raise RuntimeError(msg)
# Instantiate data object
output = Data2D()
output.filename = os.path.basename(filename)
# Read in the image
try:
im = Image.open(filename)
except:
raise RuntimeError("cannot open %s" % (filename))
data = im.getdata()
# Initiazed the output data object
output.data = np.zeros([im.size[0], im.size[1]])
output.err_data = np.zeros([im.size[0], im.size[1]])
output.mask = np.ones([im.size[0], im.size[1]], dtype=bool)
# Initialize
x_vals = []
y_vals = []
# x and y vectors
for i_x in range(im.size[0]):
x_vals.append(i_x)
itot = 0
for i_y in range(im.size[1]):
y_vals.append(i_y)
for val in data:
try:
value = float(val)
except:
logger.error("tiff_reader: had to skip a non-float point")
continue
# Get bin number
if math.fmod(itot, im.size[0]) == 0:
i_x = 0
i_y += 1
else:
i_x += 1
output.data[im.size[1] - 1 - i_y][i_x] = value
itot += 1
output.xbins = im.size[0]
output.ybins = im.size[1]
output.x_bins = x_vals
output.y_bins = y_vals
output.qx_data = np.array(x_vals)
output.qy_data = np.array(y_vals)
output.xmin = 0
output.xmax = im.size[0] - 1
output.ymin = 0
output.ymax = im.size[0] - 1
# Store loading process information
output.meta_data['loader'] = self.type_name
output = reader2D_converter(output)
return output
def _get_detector_qxqy_pixels(self):
"""
Get the pixel positions of the detector in the qx_value-qy_value space
"""
# update all param values
self.get_all_instrument_params()
# wavelength
wavelength = self.wave.wavelength
# Gavity correction
delta_y = self._get_beamcenter_drop() # in cm
# detector_pix size
detector_pix_size = self.detector_pix_size
# Square or circular pixel
if len(detector_pix_size) == 1:
pix_x_size = detector_pix_size[0]
pix_y_size = detector_pix_size[0]
# rectangular pixel pixel
elif len(detector_pix_size) == 2:
pix_x_size = detector_pix_size[0]
pix_y_size = detector_pix_size[1]
else:
raise ValueError(" Input value format error...")
# Sample to detector distance = sample slit to detector
# minus sample offset
sample2detector_distance = self.sample2detector_distance[0] - \
self.sample2sample_distance[0]
# detector offset in x-direction
detector_offset = 0
try:
detector_offset = self.sample2detector_distance[1]
except:
logger.error(sys.exc_value)
# detector size in [no of pix_x,no of pix_y]
detector_pix_nums_x = self.detector_size[0]
# get pix_y if it exists, otherwse take it from [0]
try:
detector_pix_nums_y = self.detector_size[1]
except:
detector_pix_nums_y = self.detector_size[0]
# detector offset in pix number
offset_x = detector_offset / pix_x_size
offset_y = delta_y / pix_y_size
# beam center position in pix number (start from 0)
center_x, center_y = self._get_beamcenter_position(detector_pix_nums_x,
detector_pix_nums_y,
offset_x, offset_y)
# distance [cm] from the beam center on detector plane
detector_ind_x = np.arange(detector_pix_nums_x)
detector_ind_y = np.arange(detector_pix_nums_y)
# shif 0.5 pixel so that pix position is at the center of the pixel
detector_ind_x = detector_ind_x + 0.5
detector_ind_y = detector_ind_y + 0.5
# the relative postion from the beam center
detector_ind_x = detector_ind_x - center_x
detector_ind_y = detector_ind_y - center_y
# unit correction in cm
detector_ind_x = detector_ind_x * pix_x_size
detector_ind_y = detector_ind_y * pix_y_size
qx_value = np.zeros(len(detector_ind_x))
qy_value = np.zeros(len(detector_ind_y))
i = 0
for indx in detector_ind_x:
qx_value[i] = self._get_qx(indx, sample2detector_distance, wavelength)
i += 1
i = 0
for indy in detector_ind_y:
qy_value[i] = self._get_qx(indy, sample2detector_distance, wavelength)
i += 1
# qx_value and qy_value values in array
qx_value = qx_value.repeat(detector_pix_nums_y)
qx_value = qx_value.reshape(detector_pix_nums_x, detector_pix_nums_y)
qy_value = qy_value.repeat(detector_pix_nums_x)
qy_value = qy_value.reshape(detector_pix_nums_y, detector_pix_nums_x)
qy_value = qy_value.transpose()
# p min and max values among the center of pixels
self.qx_min = np.min(qx_value)
self.qx_max = np.max(qx_value)
self.qy_min = np.min(qy_value)
self.qy_max = np.max(qy_value)
# Appr. min and max values of the detector display limits
# i.e., edges of the last pixels.
self.qy_min += self._get_qx(-0.5 * pix_y_size,
sample2detector_distance, wavelength)
self.qy_max += self._get_qx(0.5 * pix_y_size,
sample2detector_distance, wavelength)
#if self.qx_min == self.qx_max:
self.qx_min += self._get_qx(-0.5 * pix_x_size,
sample2detector_distance, wavelength)
self.qx_max += self._get_qx(0.5 * pix_x_size,
sample2detector_distance, wavelength)
# min and max values of detecter
self.detector_qx_min = self.qx_min
self.detector_qx_max = self.qx_max
self.detector_qy_min = self.qy_min
self.detector_qy_max = self.qy_max
# try to set it as a Data2D otherwise pass (not required for now)
try:
from sas.sascalc.dataloader.data_info import Data2D
output = Data2D()
inten = np.zeros_like(qx_value)
output.data = inten
output.qx_data = qx_value
output.qy_data = qy_value
except:
logger.error(sys.exc_value)
return output
def read(self, filename=None):
"""
Open and read the data in a file
:param filename: path of the file
"""
try:
import nxs
except:
msg = "Error reading Nexus file: Nexus package is missing.\n"
msg += " Get it from http://http://www.nexusformat.org/"
raise RuntimeError, msg
# Instantiate data object
output = Data2D()
output.filename = os.path.basename(filename)
fd = nxs.open(filename, 'rw')
# Read in the 2D data
fd.opengroup('mantid_workspace_1')
fd.opengroup('workspace')
fd.opendata('values')
output.data = fd.getdata().copy()
fd.closedata()
# Read in the errors
fd.opendata('errors')
output.err_data = fd.getdata().copy()
fd.closedata()
# Read in the values on each axis
fd.opendata('axis1')
output.x_bins = fd.getdata().copy()
fd.closedata()
fd.opendata('axis2')
output.y_bins = fd.getdata().copy()
fd.closedata()
fd.closegroup()
output.xmin = min(output.x_bins)
output.xmax = max(output.x_bins)
output.ymin = min(output.y_bins)
output.ymax = max(output.y_bins)
output.xaxis("\\rm{Q_{x}}", 'A^{-1}')
output.yaxis("\\rm{Q_{y}}", 'A^{-1}')
output.zaxis("\\rm{Intensity}", "cm^{-1}")
# Meta data
fd.opendata('title')
output.title = fd.getdata()
fd.closedata()
fd.opengroup('instrument')
fd.opendata('name')
output.instrument = fd.getdata()
fd.closedata()
fd.closegroup()
fd.opengroup('logs')
fd.opengroup('run_number')
fd.opendata('value')
output.run = fd.getdata()
fd.close()
# Store loading process information
output.meta_data['loader'] = self.type_name
return output
def load(self):
"""
Load the data from the file into a Data2D object
:return: A Data2D instance containing data from the file
:raises ValueError: Raises a ValueError if the file is incorrectly
formatted
"""
with open(self.data_path, 'r') as file_handle:
file_buffer = file_handle.read()
all_lines = file_buffer.splitlines()
# Load num_points line-by-line from lines into a numpy array,
# starting on line number start_line
def _load_points(lines, start_line, num_points):
qs = np.zeros(num_points)
n = start_line
filled = 0
while filled < num_points:
row = np.fromstring(lines[n], dtype=np.float32, sep=' ')
qs[filled:filled + len(row)] = row
filled += len(row)
n += 1
return n, qs
current_line = 4
try:
# Skip n_use_rec lines
n_use_rec = int(all_lines[current_line].strip()[0])
current_line += n_use_rec + 1
# Read qx data
num_qs = int(all_lines[current_line].strip())
current_line += 1
current_line, qx = _load_points(all_lines, current_line,
num_qs)
# Read qy data
num_qs = int(all_lines[current_line].strip())
current_line += 1
current_line, qy = _load_points(all_lines, current_line,
num_qs)
except ValueError as e:
err_msg = "File incorrectly formatted.\n"
if str(e).find('broadcast') != -1:
err_msg += "Incorrect number of q data points provided. "
err_msg += "Expected {}.".format(num_qs)
elif str(e).find('invalid literal') != -1:
err_msg += ("Expected integer on line {}. "
"Instead got '{}'").format(
current_line + 1, all_lines[current_line])
else:
err_msg += str(e)
raise ValueError(err_msg)
# dimensions: [width, height, scale]
try:
dimensions = np.fromstring(all_lines[current_line],
dtype=np.float32,
sep=' ')
if len(dimensions) != 3: raise ValueError()
width = int(dimensions[0])
height = int(dimensions[1])
except ValueError as e:
err_msg = "File incorrectly formatted.\n"
err_msg += ("Expected line {} to be of the form: <num_qx> "
"<num_qy> <scale>.").format(current_line + 1)
err_msg += " Instead got '{}'.".format(all_lines[current_line])
raise ValueError(err_msg)
if width > len(qx) or height > len(qy):
err_msg = "File incorrectly formatted.\n"
err_msg += ("Line {} says to use {}x{} points. "
"Only {}x{} provided.").format(
current_line + 1, width, height, len(qx),
len(qy))
raise ValueError(err_msg)
# More qx and/or qy points can be provided than are actually used
qx = qx[:width]
qy = qy[:height]
current_line += 1
# iflag = 1 => Only intensity data (not dealt with here)
# iflag = 2 => q axis and intensity data
# iflag = 3 => q axis, intensity and error data
try:
iflag = int(all_lines[current_line].strip()[0])
if iflag <= 0 or iflag > 3: raise ValueError()
except:
err_msg = "File incorrectly formatted.\n"
iflag = all_lines[current_line].strip()[0]
err_msg += ("Expected iflag on line {} to be 1, 2 or 3. "
"Instead got '{}'.").format(
current_line + 1, iflag)
raise ValueError(err_msg)
current_line += 1
try:
current_line, I = _load_points(all_lines, current_line,
width * height)
dI = np.zeros(width * height)
# Load error data if it's provided
if iflag == 3:
_, dI = _load_points(all_lines, current_line,
width * height)
except Exception as e:
err_msg = "File incorrectly formatted.\n"
if str(e).find("list index") != -1:
err_msg += ("Incorrect number of data points. Expected {}"
" intensity").format(width * height)
if iflag == 3:
err_msg += " and error"
err_msg += " points."
else:
err_msg += str(e)
raise ValueError(err_msg)
# Format data for use with Data2D
qx = list(qx) * height
qy = np.array([[y] * width for y in qy]).flatten()
data = Data2D(qx_data=qx, qy_data=qy, data=I, err_data=dI)
return data
def __str__(self):
"""
print data
"""
_str = "%s\n" % LoadData2D.__str__(self)
return _str
def __str__(self):
"""
print data
"""
_str = "%s\n" % LoadData2D.__str__(self)
return _str
def read(self, filename=None):
""" Read file """
if not os.path.isfile(filename):
raise ValueError("Specified file %s is not a regular "
"file" % filename)
output = Data2D()
output.filename = os.path.basename(filename)
detector = Detector()
if len(output.detector):
print(str(output.detector[0]))
output.detector.append(detector)
data_conv_q = data_conv_i = None
if has_converter and output.Q_unit != '1/A':
data_conv_q = Converter('1/A')
# Test it
data_conv_q(1.0, output.Q_unit)
if has_converter and output.I_unit != '1/cm':
data_conv_i = Converter('1/cm')
# Test it
data_conv_i(1.0, output.I_unit)
data_row = 0
wavelength = distance = center_x = center_y = None
dataStarted = isInfo = isCenter = False
with open(filename, 'r') as f:
for line in f:
data_row += 1
# Find setup info line
if isInfo:
isInfo = False
line_toks = line.split()
# Wavelength in Angstrom
try:
wavelength = float(line_toks[1])
except ValueError:
msg = "IgorReader: can't read this file, missing wavelength"
raise ValueError(msg)
# Distance in meters
try:
distance = float(line_toks[3])
except ValueError:
msg = "IgorReader: can't read this file, missing distance"
raise ValueError(msg)
# Distance in meters
try:
transmission = float(line_toks[4])
except:
msg = "IgorReader: can't read this file, "
msg += "missing transmission"
raise ValueError(msg)
if line.count("LAMBDA"):
isInfo = True
# Find center info line
if isCenter:
isCenter = False
line_toks = line.split()
# Center in bin number: Must subtract 1 because
# the index starts from 1
center_x = float(line_toks[0]) - 1
center_y = float(line_toks[1]) - 1
if line.count("BCENT"):
isCenter = True
# Find data start
if line.count("***"):
# now have to continue to blank line
dataStarted = True
# Check that we have all the info
if (wavelength is None or distance is None
or center_x is None or center_y is None):
msg = "IgorReader:Missing information in data file"
raise ValueError(msg)
if dataStarted:
if len(line.rstrip()):
continue
else:
break
# The data is loaded in row major order (last index changing most
# rapidly). However, the original data is in column major order (first
# index changing most rapidly). The swap to column major order is done
# in reader2D_converter at the end of this method.
data = np.loadtxt(filename, skiprows=data_row)
size_x = size_y = int(np.rint(np.sqrt(data.size)))
output.data = np.reshape(data, (size_x, size_y))
output.err_data = np.zeros_like(output.data)
# Det 640 x 640 mm
# Q = 4 * pi/lambda * sin(theta/2)
# Bin size is 0.5 cm
# Removed +1 from theta = (i_x - center_x + 1)*0.5 / distance
# / 100.0 and
# Removed +1 from theta = (i_y - center_y + 1)*0.5 /
# distance / 100.0
# ToDo: Need complete check if the following
# convert process is consistent with fitting.py.
# calculate qx, qy bin centers of each pixel in the image
theta = (np.arange(size_x) - center_x) * 0.5 / distance / 100.
qx = 4 * np.pi / wavelength * np.sin(theta / 2)
theta = (np.arange(size_y) - center_y) * 0.5 / distance / 100.
qy = 4 * np.pi / wavelength * np.sin(theta / 2)
if has_converter and output.Q_unit != '1/A':
qx = data_conv_q(qx, units=output.Q_unit)
qy = data_conv_q(qx, units=output.Q_unit)
xmax = np.max(qx)
xmin = np.min(qx)
ymax = np.max(qy)
ymin = np.min(qy)
# calculate edge offset in q.
theta = 0.25 / distance / 100.0
xstep = 4.0 * np.pi / wavelength * np.sin(theta / 2.0)
theta = 0.25 / distance / 100.0
ystep = 4.0 * np.pi / wavelength * np.sin(theta / 2.0)
# Store all data ######################################
# Store wavelength
if has_converter and output.source.wavelength_unit != 'A':
conv = Converter('A')
wavelength = conv(wavelength, units=output.source.wavelength_unit)
output.source.wavelength = wavelength
# Store distance
if has_converter and detector.distance_unit != 'm':
conv = Converter('m')
distance = conv(distance, units=detector.distance_unit)
detector.distance = distance
# Store transmission
output.sample.transmission = transmission
# Store pixel size (mm)
pixel = 5.0
if has_converter and detector.pixel_size_unit != 'mm':
conv = Converter('mm')
pixel = conv(pixel, units=detector.pixel_size_unit)
detector.pixel_size.x = pixel
detector.pixel_size.y = pixel
# Store beam center in distance units
detector.beam_center.x = center_x * pixel
detector.beam_center.y = center_y * pixel
# Store limits of the image (2D array)
xmin -= xstep / 2.0
xmax += xstep / 2.0
ymin -= ystep / 2.0
ymax += ystep / 2.0
if has_converter and output.Q_unit != '1/A':
xmin = data_conv_q(xmin, units=output.Q_unit)
xmax = data_conv_q(xmax, units=output.Q_unit)
ymin = data_conv_q(ymin, units=output.Q_unit)
ymax = data_conv_q(ymax, units=output.Q_unit)
output.xmin = xmin
output.xmax = xmax
output.ymin = ymin
output.ymax = ymax
# Store x and y axis bin centers
output.x_bins = qx.tolist()
output.y_bins = qy.tolist()
# Units
if data_conv_q is not None:
output.xaxis("\\rm{Q_{x}}", output.Q_unit)
output.yaxis("\\rm{Q_{y}}", output.Q_unit)
else:
output.xaxis("\\rm{Q_{x}}", 'A^{-1}')
output.yaxis("\\rm{Q_{y}}", 'A^{-1}')
if data_conv_i is not None:
output.zaxis("\\rm{Intensity}", output.I_unit)
else:
output.zaxis("\\rm{Intensity}", "cm^{-1}")
# Store loading process information
output.meta_data['loader'] = self.type_name
output = reader2D_converter(output)
return output
def read(self, filename=None):
""" Read file """
if not os.path.isfile(filename):
raise ValueError, \
"Specified file %s is not a regular file" % filename
# Read file
f = open(filename, 'r')
buf = f.read()
f.close()
# Instantiate data object
output = Data2D()
output.filename = os.path.basename(filename)
detector = Detector()
if len(output.detector) > 0:
print(str(output.detector[0]))
output.detector.append(detector)
# Get content
dataStarted = False
## Defaults
lines = buf.split('\n')
x = []
y = []
wavelength = None
distance = None
transmission = None
pixel_x = None
pixel_y = None
isInfo = False
isCenter = False
data_conv_q = None
data_conv_i = None
# Set units: This is the unit assumed for Q and I in the data file.
if has_converter == True and output.Q_unit != '1/A':
data_conv_q = Converter('1/A')
# Test it
data_conv_q(1.0, output.Q_unit)
if has_converter == True and output.I_unit != '1/cm':
data_conv_i = Converter('1/cm')
# Test it
data_conv_i(1.0, output.I_unit)
# Remove the last lines before the for loop if the lines are empty
# to calculate the exact number of data points
count = 0
while (len(lines[len(lines) - (count + 1)].lstrip().rstrip()) < 1):
del lines[len(lines) - (count + 1)]
count = count + 1
#Read Header and find the dimensions of 2D data
line_num = 0
# Old version NIST files: 0
ver = 0
for line in lines:
line_num += 1
## Reading the header applies only to IGOR/NIST 2D q_map data files
# Find setup info line
if isInfo:
isInfo = False
line_toks = line.split()
# Wavelength in Angstrom
try:
wavelength = float(line_toks[1])
# Units
if has_converter == True and \
output.source.wavelength_unit != 'A':
conv = Converter('A')
wavelength = conv(wavelength,
units=output.source.wavelength_unit)
except:
#Not required
pass
# Distance in mm
try:
distance = float(line_toks[3])
# Units
if has_converter == True and detector.distance_unit != 'm':
conv = Converter('m')
distance = conv(distance, units=detector.distance_unit)
except:
#Not required
pass
# Distance in meters
try:
transmission = float(line_toks[4])
except:
#Not required
pass
if line.count("LAMBDA") > 0:
isInfo = True
# Find center info line
if isCenter:
isCenter = False
line_toks = line.split()
# Center in bin number
center_x = float(line_toks[0])
center_y = float(line_toks[1])
if line.count("BCENT") > 0:
isCenter = True
# Check version
if line.count("Data columns") > 0:
if line.count("err(I)") > 0:
ver = 1
# Find data start
if line.count("ASCII data") > 0:
dataStarted = True
continue
## Read and get data.
if dataStarted == True:
line_toks = line.split()
if len(line_toks) == 0:
#empty line
continue
# the number of columns must be stayed same
col_num = len(line_toks)
break
# Make numpy array to remove header lines using index
lines_array = np.array(lines)
# index for lines_array
lines_index = np.arange(len(lines))
# get the data lines
data_lines = lines_array[lines_index >= (line_num - 1)]
# Now we get the total number of rows (i.e., # of data points)
row_num = len(data_lines)
# make it as list again to control the separators
data_list = " ".join(data_lines.tolist())
# split all data to one big list w/" "separator
data_list = data_list.split()
# Check if the size is consistent with data, otherwise
#try the tab(\t) separator
# (this may be removed once get the confidence
#the former working all cases).
if len(data_list) != (len(data_lines)) * col_num:
data_list = "\t".join(data_lines.tolist())
data_list = data_list.split()
# Change it(string) into float
#data_list = map(float,data_list)
data_list1 = map(check_point, data_list)
# numpy array form
data_array = np.array(data_list1)
# Redimesion based on the row_num and col_num,
#otherwise raise an error.
try:
data_point = data_array.reshape(row_num, col_num).transpose()
except:
msg = "red2d_reader: Can't read this file: Not a proper file format"
raise ValueError, msg
## Get the all data: Let's HARDcoding; Todo find better way
# Defaults
dqx_data = np.zeros(0)
dqy_data = np.zeros(0)
err_data = np.ones(row_num)
qz_data = np.zeros(row_num)
mask = np.ones(row_num, dtype=bool)
# Get from the array
qx_data = data_point[0]
qy_data = data_point[1]
data = data_point[2]
if ver == 1:
if col_num > (2 + ver):
err_data = data_point[(2 + ver)]
if col_num > (3 + ver):
qz_data = data_point[(3 + ver)]
if col_num > (4 + ver):
dqx_data = data_point[(4 + ver)]
if col_num > (5 + ver):
dqy_data = data_point[(5 + ver)]
#if col_num > (6 + ver): mask[data_point[(6 + ver)] < 1] = False
q_data = np.sqrt(qx_data*qx_data+qy_data*qy_data+qz_data*qz_data)
# Extra protection(it is needed for some data files):
# If all mask elements are False, put all True
if not mask.any():
mask[mask == False] = True
# Store limits of the image in q space
xmin = np.min(qx_data)
xmax = np.max(qx_data)
ymin = np.min(qy_data)
ymax = np.max(qy_data)
# units
if has_converter == True and output.Q_unit != '1/A':
xmin = data_conv_q(xmin, units=output.Q_unit)
xmax = data_conv_q(xmax, units=output.Q_unit)
ymin = data_conv_q(ymin, units=output.Q_unit)
ymax = data_conv_q(ymax, units=output.Q_unit)
## calculate the range of the qx and qy_data
x_size = math.fabs(xmax - xmin)
y_size = math.fabs(ymax - ymin)
# calculate the number of pixels in the each axes
npix_y = math.floor(math.sqrt(len(data)))
npix_x = math.floor(len(data) / npix_y)
# calculate the size of bins
xstep = x_size / (npix_x - 1)
ystep = y_size / (npix_y - 1)
# store x and y axis bin centers in q space
x_bins = np.arange(xmin, xmax + xstep, xstep)
y_bins = np.arange(ymin, ymax + ystep, ystep)
# get the limits of q values
xmin = xmin - xstep / 2
xmax = xmax + xstep / 2
ymin = ymin - ystep / 2
ymax = ymax + ystep / 2
#Store data in outputs
#TODO: Check the lengths
output.data = data
if (err_data == 1).all():
output.err_data = np.sqrt(np.abs(data))
output.err_data[output.err_data == 0.0] = 1.0
else:
output.err_data = err_data
output.qx_data = qx_data
output.qy_data = qy_data
output.q_data = q_data
output.mask = mask
output.x_bins = x_bins
output.y_bins = y_bins
output.xmin = xmin
output.xmax = xmax
output.ymin = ymin
output.ymax = ymax
output.source.wavelength = wavelength
# Store pixel size in mm
detector.pixel_size.x = pixel_x
detector.pixel_size.y = pixel_y
# Store the sample to detector distance
detector.distance = distance
# optional data: if all of dq data == 0, do not pass to output
if len(dqx_data) == len(qx_data) and dqx_data.any() != 0:
# if no dqx_data, do not pass dqy_data.
#(1 axis dq is not supported yet).
if len(dqy_data) == len(qy_data) and dqy_data.any() != 0:
# Currently we do not support dq parr, perp.
# tranfer the comp. to cartesian coord. for newer version.
if ver != 1:
diag = np.sqrt(qx_data * qx_data + qy_data * qy_data)
cos_th = qx_data / diag
sin_th = qy_data / diag
output.dqx_data = np.sqrt((dqx_data * cos_th) * \
(dqx_data * cos_th) \
+ (dqy_data * sin_th) * \
(dqy_data * sin_th))
output.dqy_data = np.sqrt((dqx_data * sin_th) * \
(dqx_data * sin_th) \
+ (dqy_data * cos_th) * \
(dqy_data * cos_th))
else:
output.dqx_data = dqx_data
output.dqy_data = dqy_data
# Units of axes
if data_conv_q is not None:
output.xaxis("\\rm{Q_{x}}", output.Q_unit)
output.yaxis("\\rm{Q_{y}}", output.Q_unit)
else:
output.xaxis("\\rm{Q_{x}}", 'A^{-1}')
output.yaxis("\\rm{Q_{y}}", 'A^{-1}')
if data_conv_i is not None:
output.zaxis("\\rm{Intensity}", output.I_unit)
else:
output.zaxis("\\rm{Intensity}", "cm^{-1}")
# Store loading process information
output.meta_data['loader'] = self.type_name
return output
def read(self, filename=None):
""" Read file """
if not os.path.isfile(filename):
raise ValueError, \
"Specified file %s is not a regular file" % filename
# Read file
f = open(filename, 'r')
buf = f.read()
# Instantiate data object
output = Data2D()
output.filename = os.path.basename(filename)
detector = Detector()
if len(output.detector) > 0:
print str(output.detector[0])
output.detector.append(detector)
# Get content
dataStarted = False
lines = buf.split('\n')
itot = 0
x = []
y = []
ncounts = 0
xmin = None
xmax = None
ymin = None
ymax = None
i_x = 0
i_y = -1
i_tot_row = 0
isInfo = False
isCenter = False
data_conv_q = None
data_conv_i = None
if has_converter == True and output.Q_unit != '1/A':
data_conv_q = Converter('1/A')
# Test it
data_conv_q(1.0, output.Q_unit)
if has_converter == True and output.I_unit != '1/cm':
data_conv_i = Converter('1/cm')
# Test it
data_conv_i(1.0, output.I_unit)
for line in lines:
# Find setup info line
if isInfo:
isInfo = False
line_toks = line.split()
# Wavelength in Angstrom
try:
wavelength = float(line_toks[1])
except:
msg = "IgorReader: can't read this file, missing wavelength"
raise ValueError, msg
#Find # of bins in a row assuming the detector is square.
if dataStarted == True:
try:
value = float(line)
except:
# Found a non-float entry, skip it
continue
# Get total bin number
i_tot_row += 1
i_tot_row = math.ceil(math.sqrt(i_tot_row)) - 1
#print "i_tot", i_tot_row
size_x = i_tot_row # 192#128
size_y = i_tot_row # 192#128
output.data = numpy.zeros([size_x, size_y])
output.err_data = numpy.zeros([size_x, size_y])
#Read Header and 2D data
for line in lines:
# Find setup info line
if isInfo:
isInfo = False
line_toks = line.split()
# Wavelength in Angstrom
try:
wavelength = float(line_toks[1])
except:
msg = "IgorReader: can't read this file, missing wavelength"
raise ValueError, msg
# Distance in meters
try:
distance = float(line_toks[3])
except:
msg = "IgorReader: can't read this file, missing distance"
raise ValueError, msg
# Distance in meters
try:
transmission = float(line_toks[4])
except:
msg = "IgorReader: can't read this file, "
msg += "missing transmission"
raise ValueError, msg
if line.count("LAMBDA") > 0:
isInfo = True
# Find center info line
if isCenter:
isCenter = False
line_toks = line.split()
# Center in bin number: Must substrate 1 because
#the index starts from 1
center_x = float(line_toks[0]) - 1
center_y = float(line_toks[1]) - 1
if line.count("BCENT") > 0:
isCenter = True
# Find data start
if line.count("***")>0:
dataStarted = True
# Check that we have all the info
if wavelength == None \
or distance == None \
or center_x == None \
or center_y == None:
msg = "IgorReader:Missing information in data file"
raise ValueError, msg
if dataStarted == True:
try:
value = float(line)
except:
# Found a non-float entry, skip it
continue
# Get bin number
if math.fmod(itot, i_tot_row) == 0:
i_x = 0
i_y += 1
else:
i_x += 1
output.data[i_y][i_x] = value
ncounts += 1
# Det 640 x 640 mm
# Q = 4pi/lambda sin(theta/2)
# Bin size is 0.5 cm
#REmoved +1 from theta = (i_x-center_x+1)*0.5 / distance
# / 100.0 and
#REmoved +1 from theta = (i_y-center_y+1)*0.5 /
# distance / 100.0
#ToDo: Need complete check if the following
# covert process is consistent with fitting.py.
theta = (i_x - center_x) * 0.5 / distance / 100.0
qx = 4.0 * math.pi / wavelength * math.sin(theta/2.0)
if has_converter == True and output.Q_unit != '1/A':
qx = data_conv_q(qx, units=output.Q_unit)
if xmin == None or qx < xmin:
xmin = qx
if xmax == None or qx > xmax:
xmax = qx
theta = (i_y - center_y) * 0.5 / distance / 100.0
qy = 4.0 * math.pi / wavelength * math.sin(theta / 2.0)
if has_converter == True and output.Q_unit != '1/A':
qy = data_conv_q(qy, units=output.Q_unit)
if ymin == None or qy < ymin:
ymin = qy
if ymax == None or qy > ymax:
ymax = qy
if not qx in x:
x.append(qx)
if not qy in y:
y.append(qy)
itot += 1
theta = 0.25 / distance / 100.0
xstep = 4.0 * math.pi / wavelength * math.sin(theta / 2.0)
theta = 0.25 / distance / 100.0
ystep = 4.0 * math.pi/ wavelength * math.sin(theta / 2.0)
# Store all data ######################################
# Store wavelength
if has_converter == True and output.source.wavelength_unit != 'A':
conv = Converter('A')
wavelength = conv(wavelength, units=output.source.wavelength_unit)
output.source.wavelength = wavelength
# Store distance
if has_converter == True and detector.distance_unit != 'm':
conv = Converter('m')
distance = conv(distance, units=detector.distance_unit)
detector.distance = distance
# Store transmission
output.sample.transmission = transmission
# Store pixel size
pixel = 5.0
if has_converter == True and detector.pixel_size_unit != 'mm':
conv = Converter('mm')
pixel = conv(pixel, units=detector.pixel_size_unit)
detector.pixel_size.x = pixel
detector.pixel_size.y = pixel
# Store beam center in distance units
detector.beam_center.x = center_x * pixel
detector.beam_center.y = center_y * pixel
# Store limits of the image (2D array)
xmin = xmin - xstep / 2.0
xmax = xmax + xstep / 2.0
ymin = ymin - ystep / 2.0
ymax = ymax + ystep / 2.0
if has_converter == True and output.Q_unit != '1/A':
xmin = data_conv_q(xmin, units=output.Q_unit)
xmax = data_conv_q(xmax, units=output.Q_unit)
ymin = data_conv_q(ymin, units=output.Q_unit)
ymax = data_conv_q(ymax, units=output.Q_unit)
output.xmin = xmin
output.xmax = xmax
output.ymin = ymin
output.ymax = ymax
# Store x and y axis bin centers
output.x_bins = x
output.y_bins = y
# Units
if data_conv_q is not None:
output.xaxis("\\rm{Q_{x}}", output.Q_unit)
output.yaxis("\\rm{Q_{y}}", output.Q_unit)
else:
output.xaxis("\\rm{Q_{x}}", 'A^{-1}')
output.yaxis("\\rm{Q_{y}}", 'A^{-1}')
if data_conv_i is not None:
output.zaxis("\\rm{Intensity}", output.I_unit)
else:
output.zaxis("\\rm{Intensity}", "cm^{-1}")
# Store loading process information
output.meta_data['loader'] = self.type_name
output = reader2D_converter(output)
return output
