def generate_chromatogram(n=5, twin=None):
"""
Generates a trace with n gaussian peaks distributed through it.
"""
if twin is None:
twin = (0, 60)
t = np.linspace(twin[0], twin[1], 300)
peak_locs = twin[1] * np.random.random(n)
peak_ws = 0.2 + 0.8 * np.random.random(n)
peak_hs = 0.2 + 0.8 * np.random.random(n)
y = np.zeros(len(t))
for peak_loc, peak_w, peak_h in zip(peak_locs, peak_ws, peak_hs):
y += gaussian(t, x=peak_loc, w=peak_w, h=peak_h)
y += np.random.normal(scale=0.01, size=len(t))
return Trace(y, t, ['X'])
def generate_chromatogram(n=5, twin=None):
"""
Generates a trace with n gaussian peaks distributed through it.
"""
if twin is None:
twin = (0, 60)
t = np.linspace(twin[0], twin[1], 300)
peak_locs = twin[1] * np.random.random(n)
peak_ws = 0.2 + 0.8 * np.random.random(n)
peak_hs = 0.2 + 0.8 * np.random.random(n)
y = np.zeros(len(t))
for peak_loc, peak_w, peak_h in zip(peak_locs, peak_ws, peak_hs):
y += gaussian(t, x=peak_loc, w=peak_w, h=peak_h)
y += np.random.normal(scale=0.01, size=len(t))
return Trace(y, t, ['X'])
def generate_gaussian():
t = np.linspace(0, 60, 300)
y = gaussian(t, x=30, w=2, h=1)
return Trace(y, t, ['X'])
def read_peaks(db, filename, ftype='isodat'):
if ftype is None:
with open(filename, 'r') as f:
header = f.readline()
if 'd 13C/12C[per mil]vs. VPDB' in header:
ftype = 'isodat'
else:
ftype = 'amdis'
if ftype == 'amdis':
delim = '\t'
cvtr = {'name': 'name',
'p-s-time': 'rt',
'p-s-area': 'area'}
elif ftype == 'isodat':
delim = ','
cvtr = {'name': 'peak nr.',
'p-s-time': 'rt[s]',
'p-s-area': 'area all[vs]',
'p-s-width': 'width[s]',
'p-s-d13c': 'd 13c/12c[per mil]vs. vpdb',
'p-s-d18o': 'd 18o/16o[per mil]vs. vsmow'}
headers = None
mapping = defaultdict(list)
ref_pk_info = {}
def get_val(line, cols, key):
return line.split(delim)[cols.index(key)]
with open(filename, 'r') as f:
for line in f:
if bool(re.match('filename' + delim,
line, re.I)) or headers is None:
headers = line.lower().split(',')
continue
fn = get_val(line, headers, 'filename')
if ftype == 'amdis':
# AMDIS has '.FIN' sufffixes and other stuff, so
# munge Filename to get it into right format
cmp_lvl = 2
fn = op.splitext('/'.join(fn.split('\\')[-cmp_lvl:]))[0]
# find if filtered filename overlaps with anything in the db
for dt in db.children_of_type('file'):
if fn in '/'.join(dt.rawdata.split(op.sep)):
break
else:
continue
info = {}
# load all the predefined fields
for k in cvtr:
info[k] = get_val(line, headers, cvtr[k])
# create peak shapes for plotting
if ftype == 'isodat':
rt = float(info['p-s-time']) / 60.
width = float(info['p-s-width']) / 60.
t = np.linspace(rt - width, rt + width)
data = []
for ion in ['44', '45', '46']:
area = float(get_val(line, headers,
'rarea ' + ion + '[mvs]')) / 60.
# bgd = float(get_val(line, headers, \
# 'bgd ' + ion + '[mv]'))
height = float(get_val(line, headers,
'ampl. ' + ion + '[mv]'))
# save the height at 44 into the info for linearity
if ion == '44':
info['p-s-ampl44'] = height
# 0.8 is a empirical number to make things look better
data.append(gaussian(t, x=rt, w=0.5 * area / height,
h=height))
# save info if this is the main ref gas peak
if info['name'].endswith('*'):
ref_pk_info[dt] = info
ts = Chromatogram(np.array(data).T, t, [44, 45, 46])
else:
ts = Chromatogram(np.array([np.nan]), np.array([np.nan]), [''])
mapping[dt] += [Peak(info, ts)]
# do drift correction
if ftype == 'isodat':
for dt in mapping:
ref_pks = []
hgt44 = ref_pk_info[dt]['p-s-ampl44']
d18o = float(ref_pk_info[dt]['p-s-d18o'])
d13c = float(ref_pk_info[dt]['p-s-d13c'])
for pk in mapping[dt]:
# if the d18o and height are similar, it's a ref peak
if abs(pk.info['p-s-ampl44'] - hgt44) < 10. and \
abs(float(pk.info['p-s-d18o']) - d18o) < 2.:
ref_pks.append(pk)
# get out the dd13C values and times for the ref gas peaks
d13cs = [float(pk.info['p-s-d13c']) for pk in ref_pks]
dd13cs = np.array(d13cs) - d13c
rts = [float(pk.info['p-s-time']) for pk in ref_pks]
# try to fit a linear model through all of them
p0 = [d13cs[0], 0]
def errfunc(p, x, y):
return p[0] + p[1] * x - y
try:
p, succ = leastsq(errfunc, p0, args=(np.array(rts), dd13cs))
except:
p = p0
# apply the linear model to get the dd13C linearity correction
# for a given time and add it to the value of this peak
for pk in mapping[dt]:
pk.info['p-s-d13c'] = str(-errfunc(p,
float(pk.info['p-s-time']),
float(pk.info['p-s-d13c'])))
# save everything
with db:
for dt in mapping:
dt.children += mapping[dt]
def read_peaks(db, filename, ftype='isodat'):
if ftype is None:
with open(filename, 'r') as f:
header = f.readline()
if 'd 13C/12C[per mil]vs. VPDB' in header:
ftype = 'isodat'
else:
ftype = 'amdis'
if ftype == 'amdis':
delim = '\t'
cvtr = {'name': 'name',
'p-s-time': 'rt',
'p-s-area': 'area'}
elif ftype == 'isodat':
delim = ','
cvtr = {'name': 'peak nr.',
'p-s-time': 'rt[s]',
'p-s-area': 'area all[vs]',
'p-s-width': 'width[s]',
'p-s-d13c': 'd 13c/12c[per mil]vs. vpdb',
'p-s-d18o': 'd 18o/16o[per mil]vs. vsmow'}
headers = None
mapping = defaultdict(list)
ref_pk_info = {}
def get_val(line, cols, key):
return line.split(delim)[cols.index(key)]
with open(filename, 'r') as f:
for line in f:
if bool(re.match('filename' + delim,
line, re.I)) or headers is None:
headers = line.lower().split(',')
continue
fn = get_val(line, headers, 'filename')
if ftype == 'amdis':
# AMDIS has '.FIN' sufffixes and other stuff, so
# munge Filename to get it into right format
cmp_lvl = 2
fn = op.splitext('/'.join(fn.split('\\')[-cmp_lvl:]))[0]
# find if filtered filename overlaps with anything in the db
for dt in db.children_of_type('file'):
if fn in '/'.join(dt.rawdata.split(op.sep)):
break
else:
continue
info = {}
# load all the predefined fields
for k in cvtr:
info[k] = get_val(line, headers, cvtr[k])
# create peak shapes for plotting
if ftype == 'isodat':
rt = float(info['p-s-time']) / 60.
width = float(info['p-s-width']) / 60.
t = np.linspace(rt - width, rt + width)
data = []
for ion in ['44', '45', '46']:
area = float(get_val(line, headers,
'rarea ' + ion + '[mvs]')) / 60.
# bgd = float(get_val(line, headers, \
# 'bgd ' + ion + '[mv]'))
height = float(get_val(line, headers,
'ampl. ' + ion + '[mv]'))
# save the height at 44 into the info for linearity
if ion == '44':
info['p-s-ampl44'] = height
# 0.8 is a empirical number to make things look better
data.append(gaussian(t, x=rt, w=0.5 * area / height,
h=height))
# save info if this is the main ref gas peak
if info['name'].endswith('*'):
ref_pk_info[dt] = info
ts = Chromatogram(np.array(data).T, t, [44, 45, 46])
else:
ts = Chromatogram(np.array([np.nan]), np.array([np.nan]), [''])
mapping[dt] += [Peak(info, ts)]
# do drift correction
if ftype == 'isodat':
for dt in mapping:
ref_pks = []
hgt44 = ref_pk_info[dt]['p-s-ampl44']
d18o = float(ref_pk_info[dt]['p-s-d18o'])
d13c = float(ref_pk_info[dt]['p-s-d13c'])
for pk in mapping[dt]:
# if the d18o and height are similar, it's a ref peak
if abs(pk.info['p-s-ampl44'] - hgt44) < 10. and \
abs(float(pk.info['p-s-d18o']) - d18o) < 2.:
ref_pks.append(pk)
# get out the dd13C values and times for the ref gas peaks
d13cs = [float(pk.info['p-s-d13c']) for pk in ref_pks]
dd13cs = np.array(d13cs) - d13c
rts = [float(pk.info['p-s-time']) for pk in ref_pks]
# try to fit a linear model through all of them
p0 = [d13cs[0], 0]
def errfunc(p, x, y):
return p[0] + p[1] * x - y
try:
p, succ = leastsq(errfunc, p0, args=(np.array(rts), dd13cs))
except Exception:
p = p0
# apply the linear model to get the dd13C linearity correction
# for a given time and add it to the value of this peak
for pk in mapping[dt]:
pk.info['p-s-d13c'] = str(-errfunc(p,
float(pk.info['p-s-time']),
float(pk.info['p-s-d13c'])))
# save everything
with db:
for dt in mapping:
dt.children += mapping[dt]
def plot(self, style='heatmap', legend=False, cmap=None, ax=None):
"""
Presents the AstonFrame using matplotlib.
Parameters
----------
style : {'heatmap', 'colors', ''}
legend : bool, optional
cmap: matplotlib.colors.Colormap, optional
ax : matplotlib.axes.Axes, optional
"""
# styles: 2d, colors, otherwise interpret as trace?
if ax is None:
import matplotlib.pyplot as plt
ax = plt.gca()
if style == 'heatmap':
ions = self.columns
ext = (self.index[0], self.index[-1], min(ions), max(ions))
grid = self.values[:, np.argsort(self.columns)].transpose()
if isinstance(self.values, scipy.sparse.spmatrix):
grid = grid.toarray()
img = ax.imshow(grid,
origin='lower',
aspect='auto',
extent=ext,
cmap=cmap)
if legend:
ax.figure.colorbar(img)
elif style == 'colors':
# TODO: importing gaussian at the top leads to a whole
# mess of dependency issues => fix somehow?
from aston.peak.peak_models import gaussian
from matplotlib.colors import ListedColormap
wvs = np.genfromtxt(np.array(self.columns).astype(bytes))
# wvs = self.columns.astype(float)
# http://www.ppsloan.org/publications/XYZJCGT.pdf
vis_filt = np.zeros((3, len(wvs)))
vis_filt[0] = 1.065 * gaussian(wvs, x=595.8, w=33.33) + \
0.366 * gaussian(wvs, x=446.8, w=19.44)
vis_filt[1] = 1.014 * gaussian(
np.log(wvs), x=np.log(556.3), w=0.075)
vis_filt[2] = 1.839 * gaussian(
np.log(wvs), x=np.log(449.8), w=0.051)
if isinstance(self.values, scipy.sparse.spmatrix):
xyz = np.dot(self.values.toarray(), vis_filt.T)
else:
xyz = np.dot(self.values.copy(), vis_filt.T)
# http://www.brucelindbloom.com/index.html?Eqn_RGB_XYZ_Matrix.html
xyz_rgb = [[3.2404542, -1.5371385, -0.4985314],
[-0.9692660, 1.8760108, 0.0415560],
[0.0556434, -0.2040259, 1.0572252]]
xyz_rgb = np.array(xyz_rgb)
rgb = np.dot(xyz_rgb, xyz.T).T
# normalize
rgb[rgb < 0] = 0
rgb /= np.max(rgb)
rgb = 1 - np.abs(rgb)
# plot
cmask = np.meshgrid(np.arange(rgb.shape[0]), 0)[0]
ax.imshow(cmask,
cmap=ListedColormap(rgb),
aspect='auto',
extent=(self.index[0], self.index[-1], 0, 1))
ax.yaxis.set_ticks([])
else:
if cmap is not None:
color = cmap(0, 1)
else:
color = 'k'
self.trace().plot(color=color, ax=ax)
def generate_gaussian():
t = np.linspace(0, 60, 300)
y = gaussian(t, x=30, w=2, h=1)
return Trace(y, t, ['X'])
def plot(self, style='heatmap', legend=False, cmap=None, ax=None):
"""
Presents the AstonFrame using matplotlib.
Parameters
----------
style : {'heatmap', 'colors', ''}
legend : bool, optional
cmap: matplotlib.colors.Colormap, optional
ax : matplotlib.axes.Axes, optional
"""
# styles: 2d, colors, otherwise interpret as trace?
if ax is None:
import matplotlib.pyplot as plt
ax = plt.gca()
if style == 'heatmap':
ions = self.columns
ext = (self.index[0], self.index[-1], min(ions), max(ions))
grid = self.values[:, np.argsort(self.columns)].transpose()
if isinstance(self.values, scipy.sparse.spmatrix):
grid = grid.toarray()
img = ax.imshow(grid, origin='lower', aspect='auto',
extent=ext, cmap=cmap)
if legend:
ax.figure.colorbar(img)
elif style == 'colors':
# TODO: importing gaussian at the top leads to a whole
# mess of dependency issues => fix somehow?
from aston.peak.peak_models import gaussian
from matplotlib.colors import ListedColormap
wvs = np.genfromtxt(np.array(self.columns).astype(bytes))
# wvs = self.columns.astype(float)
# http://www.ppsloan.org/publications/XYZJCGT.pdf
vis_filt = np.zeros((3, len(wvs)))
vis_filt[0] = 1.065 * gaussian(wvs, x=595.8, w=33.33) + \
0.366 * gaussian(wvs, x=446.8, w=19.44)
vis_filt[1] = 1.014 * gaussian(np.log(wvs), x=np.log(556.3),
w=0.075)
vis_filt[2] = 1.839 * gaussian(np.log(wvs), x=np.log(449.8),
w=0.051)
if isinstance(self.values, scipy.sparse.spmatrix):
xyz = np.dot(self.values.toarray(), vis_filt.T)
else:
xyz = np.dot(self.values.copy(), vis_filt.T)
# http://www.brucelindbloom.com/index.html?Eqn_RGB_XYZ_Matrix.html
xyz_rgb = [[3.2404542, -1.5371385, -0.4985314],
[-0.9692660, 1.8760108, 0.0415560],
[0.0556434, -0.2040259, 1.0572252]]
xyz_rgb = np.array(xyz_rgb)
rgb = np.dot(xyz_rgb, xyz.T).T
# normalize
rgb[rgb < 0] = 0
rgb /= np.max(rgb)
rgb = 1 - np.abs(rgb)
# plot
cmask = np.meshgrid(np.arange(rgb.shape[0]), 0)[0]
ax.imshow(cmask, cmap=ListedColormap(rgb), aspect='auto',
extent=(self.index[0], self.index[-1], 0, 1))
ax.yaxis.set_ticks([])
else:
if cmap is not None:
color = cmap(0, 1)
else:
color = 'k'
self.trace().plot(color=color, ax=ax)
