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processSaxs.py
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processSaxs.py
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import numpy as np
def is_number(s):
try:
float(s) # for int, long and float
except ValueError:
return False
return True
def read_standardiq_dat(file_name):
qis = 0
try:
file = open(file_name, 'r')
except Exception:
return 1, 1
q = []
i = []
s = []
comments = []
try:
content = file.readlines()
except UnicodeDecodeError:
return 2, 1
for line in content:
try:
if line[0] != "#":
word = line.strip().split()
if (len(word) == 3 or len(word) == 4) and is_number(word[0]):
qis = 3
if float(word[0]) == 0:
continue
q.append(float(word[0]))
i.append(float(word[1]))
s.append(float(word[2]))
elif len(word) == 2 and is_number(word[0]):
qis = 2
if float(word[0]) == 0:
continue
q.append(float(word[0]))
i.append(float(word[1]))
if len(q) == 0:
word = line.strip().split(',')
if (len(word) == 3 or len(word) == 4) and is_number(word[0]):
qis = 3
if float(word[0]) == 0:
continue
q.append(float(word[0]))
i.append(float(word[1]))
s.append(float(word[2]))
elif len(word) == 2 and is_number(word[0]):
qis = 2
if float(word[0]) == 0:
continue
q.append(float(word[0]))
i.append(float(word[1]))
except:
pass
q = np.array(q).reshape((-1, 1))
i = np.array(i).reshape((-1, 1))
s = np.array(s).reshape((-1, 1))
while q[-1] > 1:
q = q / 10
if qis == 3:
result = np.concatenate((q, i, s), axis=1)
elif qis == 2:
result = np.concatenate((q, i), axis=1)
return 0, result
def read_standardiq_out(file_name):
try:
file = open(file_name, 'r')
except Exception:
return 1, 1, 1
q = []
i = []
comments = []
try:
content = file.readlines()
except UnicodeDecodeError:
return 2, 1, 1
for line in content:
word = line.strip().split()
if 'Real space range' in line:
Rmax = float(word[-1]) / 2
print Rmax
all_digit = np.array(list(map(is_number, word)))
if len(word) == 5 and (all_digit == True).all():
q.append(float(word[0]))
i.append(float(word[3]))
q = np.array(q).reshape((-1, 1))
i = np.array(i).reshape((-1, 1))
while q[-1] > 1:
q = q / 10
Rmax = Rmax * 10
result = np.concatenate((q, i), axis=1)
return 0, result, Rmax
def read_iq(file_name):
separators = ['', ' ', ',', ';', '->', '&']
file = open(file_name, 'r')
q = []
i = []
s = []
comments = []
for line in file:
all_good = False
try:
if line[0] != "#":
keys = line.split("\n")[0].split()
new_keys = []
for key in keys:
if key not in separators:
new_keys.append(key)
keys = new_keys
q.append(float(keys[0]))
i.append(float(keys[1]))
else:
comments.append(line[0:len(line) - 1])
all_good = True
except:
pass
if not all_good:
print "WARNING TROUBLE READING THIS LINE:"
q = np.array(q).reshape((-1, 1))
i = np.array(i).reshape((-1, 1))
while q[-1] > 1:
q = q / 10
result = np.concatenate((q, i), axis=1)
return result
def average_filter(data, step, derivation=False):
size = data.shape[0]
seed = np.linspace(20, 1, 10)
weights = seed[:step] / (2 * sum(seed[:step]) - seed[0])
datacopy = np.zeros((size, 2))
if derivation == False:
for ii in range(step - 1, size - step + 1):
new_value = 0
for jj in range(step):
new_value = new_value + (data[ii - jj][1] + data[ii + jj][1]) * weights[jj]
new_value = new_value - data[ii][1] * weights[0]
datacopy[ii][1] = new_value
datacopy[step - 1:size - step + 1, 0] = data[step - 1:size - step + 1, 0]
return datacopy[step - 1:size - step + 1]
def process(file_path):
Rmax = ''
result = []
if file_path.split('.')[-1] == 'out':
stat, iq_curve, Rmax = read_standardiq_out(file_path)
if stat != 0:
return False
else:
# iq_curve = read_iq(file_path)
stat, iq_curve = read_standardiq_dat(file_path)
if stat != 0:
return False
fixiq = average_filter(iq_curve, 1).reshape((-1, 2))
# be careful numpy is ke bian bain liang, xiangdangyu yingyong
fitx = np.copy(fixiq[:, 0]).reshape(-1)
fity = np.copy(fixiq[:, 1]).reshape(-1)
if iq_curve[0, 0] // 0.005 == 0:
xstart = 0.005
elif iq_curve[0, 0] % 0.005 == 0:
xstart = (iq_curve[0, 0] // 0.005) * 0.005
else:
xstart = (iq_curve[0, 0] // 0.005 + 1) * 0.005
xend = 0.201 if 0.201 < iq_curve[-1, 0] else iq_curve[-1, 0]
x_scale = np.arange(xstart, xend, 0.005, dtype=float)
fitlen = len(x_scale)
y_value = np.zeros(shape=(fitlen), dtype=float)
position = 0
for ii in range(len(fitx)):
if fitx[ii] >= x_scale[position]:
if ii - 2 < 0:
y_fit = np.polyfit(fitx[:ii + 3], fity[:ii + 3], 1)
a, b = y_fit.tolist()
y_value[position] = a * x_scale[position] + b
continue
elif ii + 2 > len(fitx):
y_fit = np.polyfit(fitx[ii - 3:], fity[ii - 3:], 2)
else:
y_fit = np.polyfit(fitx[ii - 2:ii + 2], fity[ii - 2:ii + 2], 2)
a, b, c = y_fit.tolist()
y_value[position] = a * x_scale[position] ** 2 + b * x_scale[position] + c
position += 1
if position == fitlen:
break
preprocessediq = np.concatenate((x_scale.reshape(-1, 1), y_value.reshape(-1, 1)), axis=1)
result.append(preprocessediq)
if file_path.split('.')[-1] == 'out':
result.append(Rmax)
return result
def generatesaxsstr(file_path):
if file_path.split('.')[-1] == 'out':
stat, iq_curve, Rmax = read_standardiq_out(file_path)
if stat != 0:
return False
else:
stat, iq_curve = read_standardiq_dat(file_path)
if stat != 0:
return False
saxsstr = ''
for ii in range(len(iq_curve)):
saxsstr = saxsstr + '{x: %f, y: %f},' % (iq_curve[ii, 0], abs(iq_curve[ii, 1]))
print saxsstr[-70:]
return saxsstr