def get_2Dimage(detectid_obj, detects, fibers, width=100, height=50):
fiber_table = Table(
detects.hdfile.root.Fibers.read_where("detectid == detectid_obj"))
fsort = fiber_table.argsort(keys="weight", reverse=True)
fiber_table = fiber_table[fsort]
weight_total = np.sum(fiber_table["weight"])
# open blank image to store summed image and 4 brightest fibers
im_sum = np.zeros((height, width))
for fiber_i in fiber_table:
wave_obj = fiber_i["wavein"]
multiframe_obj = fiber_i["multiframe"]
expnum_obj = fiber_i["expnum"]
fibnum_obj = fiber_i["fibnum"]
weight = fiber_i["weight"] / weight_total
fiber_id_obj = fiber_i["fiber_id"]
im_fib = fibers.get_fib_image2D(
wave_obj=wave_obj,
fibnum_obj=fibnum_obj,
multiframe_obj=multiframe_obj,
expnum_obj=expnum_obj,
width=width,
height=height,
)
im_sum += weight * im_fib
return im_sum
def get_2Dimage_array(detectid_obj, detects, fibers, width=100, height=50):
fiber_table = Table(
detects.hdfile.root.Fibers.read_where("detectid == detectid_obj"))
fsort = fiber_table.argsort(keys="weight", reverse=True)
fiber_table = fiber_table[fsort]
weight_total = np.sum(fiber_table["weight"])
im_arr = np.zeros((4, height, width))
for i, fiber_i in enumerate(fiber_table[0:4]):
wave_obj = fiber_i["wavein"]
multiframe_obj = fiber_i["multiframe"]
expnum_obj = fiber_i["expnum"]
fibnum_obj = fiber_i["fibnum"]
weight = fiber_i["weight"] / weight_total
im_fib = fibers.get_fib_image2D(
wave_obj=wave_obj,
fibnum_obj=fibnum_obj,
multiframe_obj=multiframe_obj,
expnum_obj=expnum_obj,
width=width,
height=height,
)
im_arr[i] = im_fib
return im_arr, fiber_table
def get_part_function(molecule, interp=False, order=2):
# if fit=True the function will return
# a function fitted to the values
CATURL = SPECIES_PAGE_URL+'e{0}.cat'.format(molecule.split(' ')[0])
catpage = requests.get(CATURL)
catpage.close()
cathtml = bs4.BeautifulSoup(catpage.content, "lxml")
parttable = [i for i in cathtml.find_all('tr') if i.get_text()[1:3] == 'Q(']
parttable = [[j.get_text() for j in i.find_all('td')] for i in parttable]
# extract partition function value and Temperature
T, value = np.array(parttable).T
try:
# convert values into floats
value = value.astype('float')
except(ValueError):
# if its a value error, it could be
# that the table consists of two values
# like "2521.8958 (2141.5366, 380.3592)"
# e.g. for D2CO (as of 2016-11-14)
value = [str(i).split(" ")[0] for i in value]
value = np.array(value, dtype='float')
# convert temperature to floats
T = np.array([float(i[3:-1]) for i in T])
return_table = Table()
return_table['temp'] = T
return_table['part_value'] = value
return_table = return_table[return_table.argsort(keys='temp')]
return_table.meta['species'] = molecule.split(' ')[1]
return_table.meta['source'] = CATURL
return_table.meta['source_html'] = cathtml
# now if we want to fit a function to the values
if interp:
from scipy.interpolate import InterpolatedUnivariateSpline
qrot = InterpolatedUnivariateSpline(return_table['temp'], return_table['part_value'], k=order)
return return_table, qrot
return return_table