def test_auto_correct_dispersion(f=750, phi=-6, gr=2400,
verbose=True, plot=True, close_plots=True, *args, **kwargs):
''' A test case to show the effect of wavelength dispersion (cf spectrometer
reciprocal function) on the slit function
Parameters
----------
f: focal length (mm)
default 750 (SpectraPro 2750i)
phi: angle in degrees (°)
default -6
gr: grooves spacing (gr/mm)
default 2400
'''
from radis.test.utils import getTestFile
from publib import set_style, fix_style
from radis.misc.warning import SlitDispersionWarning
if plot:
plt.ion() # dont get stuck with Matplotlib if executing through pytest
if close_plots:
plt.close('all')
w_slit_632, I_slit_632 = import_experimental_slit(getTestFile('slitfunction.txt'))
slit_measured_632nm = getTestFile('slitfunction.txt')
w, I = np.loadtxt(getTestFile('calc_N2C_spectrum_Trot1200_Tvib3000.txt')).T
s = calculated_spectrum(w, I, conditions={'Tvib': 3000, 'Trot': 1200},
Iunit='mW/cm2/sr/µm')
slit_dispersion = lambda w: linear_dispersion(w, f=f, phi=phi, m=1, gr=gr)
s.apply_slit(slit_measured_632nm)
if plot:
w_full_range = np.linspace(w.min(), w_slit_632.max())
set_style('origin')
plt.figure('Spectrometer Dispersion (f={0}mm, phi={1}°, gr={2}'.format(
f, phi, gr))
plt.plot(w_full_range, slit_dispersion(w_full_range))
plt.xlabel('Wavelength (nm)')
plt.ylabel('Reciprocal Linear Dispersion')
# Compare 2 spectra
s.plot(nfig='Linear dispersion effect', color='r', label='not corrected')
with pytest.warns(SlitDispersionWarning): # expect a "large slit dispersion" warning
s.apply_slit(slit_measured_632nm, slit_dispersion=slit_dispersion)
if plot:
s.plot(nfig='same', color='k', label='corrected')
plt.legend()
# Plot different slits:
s.plot_slit()
# plt.plot(w_slit_632, I_slit_632, color='r', label='Not corrected')
# plt.legend()
return True # nothing defined yet
def test_linear_dispersion_effect(verbose=True, plot=True, close_plots=True, *args, **kwargs):
''' A test case to show the effect of wavelength dispersion (cf spectrometer
reciprocal function) on the slit function
Test succeeds if a :py:data:`~radis.misc.warning.SlitDispersionWarning`
is correctly triggered
'''
from radis.test.utils import getTestFile
from publib import set_style, fix_style
if plot:
set_style('origin')
plt.ion() # dont get stuck with Matplotlib if executing through pytest
if close_plots:
plt.close('all')
w_slit, I_slit = import_experimental_slit(getTestFile('slitfunction.txt'))
if plot:
plt.figure(fig_prefix+'Linear dispersion effect')
plt.plot(w_slit, I_slit, '--k', label='Exp: FWHM @{0}nm: {1:.3f} nm'.format(632.8,
get_effective_FWHM(w_slit, I_slit)))
from radis.misc.warning import SlitDispersionWarning
with pytest.warns(SlitDispersionWarning): # expect a "large slit dispersion" warning
# Test how slit function FWHM scales with linear_dispersion
for w0, FWHM in zip([380, 1000, 4200, 5500],
[0.396, 0.388, 0.282, 0.188]):
w, I = dirac(w0)
wc, Ic = convolve_with_slit(w, I, w_slit, I_slit, norm_by='area',
slit_dispersion=linear_dispersion,
verbose=False)
assert np.isclose(FWHM, get_effective_FWHM(wc, Ic), atol=0.001)
if plot:
plt.plot(wc, Ic, label='FWHM @{0:.2f} nm: {1:.3f} nm'.format(w0,
get_effective_FWHM(wc, Ic)))
if plot:
plt.xlabel('Wavelength (nm)')
plt.ylabel('Dirac $x$ slit function')
plt.legend(loc='best', prop={'size': 15})
fix_style('article')
return True
result = np.empty(100)
# In[23]:
for i, n in enumerate(s):
int = Integrate(func, 2, n)
result[i]=int
# In[40]:
from publib import set_style, fix_style
set_style('article')
fig = plt.figure(figsize=(10,10))
ax = plt.subplot()
ax.scatter(ns, result, label="Logarithmic integral, $L_i(n)$")
plt.title("Primes")
plt.xlabel("$n$")
plt.legend()
fix_style('article')
plt.show()
# In[ ]:
def test_normalisation_mode(plot=True,
close_plots=True,
verbose=True,
*args,
**kwargs):
""" Test norm_by = 'area' vs norm_by = 'max' """
from radis.test.utils import getTestFile
if plot:
plt.ion() # dont get stuck with Matplotlib if executing through pytest
if close_plots:
plt.close("all")
from publib import set_style
set_style("origin")
# %% Compare spectra convolved with area=1 and max=1
# Slit in nm
# Spectrum in nm
# Specair units: mW/cm2/sr/µm
w, I = np.loadtxt(getTestFile("calc_N2C_spectrum_Trot1200_Tvib3000.txt")).T
s = calculated_spectrum(w,
I,
conditions={
"Tvib": 3000,
"Trot": 1200
},
Iunit="mW/cm2/sr/µm")
FWHM = 2
s.apply_slit(FWHM, norm_by="area") # spectrum convolved with area=1
w_area, I_area = s.get("radiance")
if plot:
fig = plt.figure(fig_prefix + "Spectrum in nm + slit in nm")
fig.clear()
ax = fig.gca()
s.plot(nfig=fig.number, wunit="nm", label="norm_by: area", lw=3)
s.apply_slit(FWHM, norm_by="max") # spectrum convolved with max=1
w_max, I_max = s.get("radiance", wunit="nm")
if plot:
ax.plot(w_max, I_max / FWHM, "r", label="(norm_by:max)/FWHM")
ax.legend(loc="best")
assert np.allclose(I_area, I_max / FWHM)
if verbose:
print("equivalence of normalisation mode for spectrum in 'nm': OK")
# %% Compare spectra convolved with area=1 and max=1
# Slit in nm
# Spectrum in cm-1
s = load_spec(getTestFile("CO_Tgas1500K_mole_fraction0.01.spec"),
binary=True)
s.update()
# spectrum convolved with area=1
s.apply_slit(FWHM, norm_by="area", plot_slit=plot)
w_area, I_area = s.get("radiance")
if plot:
fig = plt.figure(fig_prefix + "Spectrum in cm-1 + slit in nm")
fig.clear()
ax = fig.gca()
s.plot(nfig=fig.number, wunit="nm", label="norm_by: area", lw=3)
# spectrum convolved with max=1
s.apply_slit(FWHM, norm_by="max", plot_slit=plot)
w_max, I_max = s.get("radiance", wunit="nm")
if plot:
ax.plot(w_max, I_max / FWHM, "r", label="(norm_by:max)/FWHM")
ax.legend(loc="best")
assert np.allclose(I_area, I_max / FWHM)
if verbose:
print(
"equivalence of normalisation mode for spectrum in 'cm-1': {0}: OK"
)
assert is_homogeneous(s.units["radiance"], "mW/cm2/sr")
if verbose:
print(("radiance unit ({0}) is homogeneous to 'mW/cm2/sr': OK".format(
s.units["radiance"])))
return True
def plot_diff(
s1,
s2,
var=None,
wunit="default",
Iunit="default",
resample=True,
method="diff",
diff_window=0,
show_points=False,
label1=None,
label2=None,
figsize=None,
title=None,
nfig=None,
normalize=False,
verbose=True,
save=False,
show=True,
show_residual=False,
lw_multiplier=1,
diff_scale_multiplier=1,
discard_centile=0,
plot_medium="vacuum_only",
legendargs={"loc": "best"},
):
"""Plot two spectra, and the difference between them. ``method=`` allows
you to plot the absolute difference, ratio, or both.
If waveranges dont match, ``s2`` is interpolated over ``s1``.
Parameters
----------
s1, s2: Spectrum objects
var: str, or None
spectral quantity to plot (ex: ``'abscoeff'``). If None,
plot the first one in the Spectrum from ``'radiance'``,
``'radiance_noslit'``, ``'transmittance'``, etc.
wunit: ``'default'``, ``'nm'``, ``'cm-1'``, ``'nm_vac'``
wavespace unit: wavelength air, wavenumber, wavelength vacuum.
If ``'default'``, use first spectrum wunit
Iunit: str
if ``'default'``, use first spectrum unit
method: ``'distance'``, ``'diff'``, ``'ratio'``, or list of them.
If ``'diff'``, plot difference of the two spectra.
If ``'distance'``, plot Euclidian distance (note that units are meaningless then)
If ``'ratio'``, plot ratio of two spectra
Default ``'diff'``.
.. warning::
with ``'distance'``, calculation scales as ~N^2 with N the number
of points in a spectrum (against ~N with ``'diff'``). This can quickly
override all memory.
Can also be a list::
method=['diff', 'ratio']
normalize: bool
Normalize the spectra to be ploted
Other Parameters
----------------
diff_window: int
If non 0, calculates diff by offsetting s1 by ``diff_window`` number of
units on either side, and returns the minimum. Kinda compensates for experimental
errors on the w axis. Default 0. (look up code to understand...)
show_points: boolean
if ``True``, make all points appear with 'o'
label1, label2: str
curve names
figsize
figure size
nfig: int, str
figure number of name
title: str
title
verbose: boolean
if ``True``, plot stuff such as rescale ratio in normalize mode. Default ``True``
save: str
Default is ``False``. By default won't save anything, type the path of the
destination if you want to save it (format in the name).
show: Bool
Default is ``True``. Will show the plots : bad if there are more than 20.
show_residual: bool
if ``True``, calculates and shows on the graph the residual in L2 norm.
See :func:`~radis.spectrum.compare.get_residual`. ``diff_window`` is
used in the residual calculation too. ``normalize`` has no effect.
diff_scale_multiplier: float
dilate the diff plot scale. Default ``1``
discard_centile: int
if not ``0``, discard the firsts and lasts centile when setting the limits
of the diff window. Example::
discard_centile=1 # --> discards the smallest 1% and largest 1%
discard_centile=10 # --> discards the smallest 10% and largest 10%
Useful to remove spikes in a ratio, for instance.
Note that this does not change the values of the residual. It's just
a plot feature.
Default ``0``
plot_medium: bool, ``'vacuum_only'``
if ``True`` and ``wunit`` are wavelengths, plot the propagation medium
in the xaxis label (``[air]`` or ``[vacuum]``). If ``'vacuum_only'``,
plot only if ``wunit=='nm_vac'``. Default ``'vacuum_only'``
(prevents from inadvertently plotting spectra with different propagation
medium on the same graph).
legendargs: dict
format arguments forwarded to the legend
Returns
-------
fig: figure
fig
[ax0, ax1]: axes
spectra and difference axis
Examples
--------
Simple use::
from radis import plot_diff
plot_diff(s10, s50) # s10, s50 are two spectra
Advanced use, plotting the total power in the label, and getting the figure
and axes handle to edit them afterwards::
Punit = 'mW/cm2/sr'
fig, axes = plot_diff(s10, s50, 'radiance_noslit', figsize=(18,6),
label1='brd 10 cm-1, P={0:.2f} {1}'.format(s10.get_power(unit=Punit),Punit),
label2='brd 50 cm-1, P={0:.2f} {1}'.format(s50.get_power(unit=Punit),Punit)
)
# modify fig, axes..
See an example in :ref:`label_spectrum_howto_compare`, which produces the output below:
.. image:: https://radis.readthedocs.io/en/latest/_images/cdsd4000_vs_hitemp_3409K.svg
If you wish to plot in a logscale, it can be done in the following way::
fig, [ax0, ax1] = plot_diff(s0, s1, normalize=False, verbose=False)
ylim0 = ax0.get_ybound()
ax0.set_yscale("log")
ax0.set_ybound(ylim0)
See Also
--------
:func:`~radis.spectrum.compare.get_diff`,
:func:`~radis.spectrum.compare.get_ratio`,
:func:`~radis.spectrum.compare.get_distance`,
:func:`~radis.spectrum.compare.get_residual`,
:meth:`~radis.spectrum.spectrum.compare_with`
"""
if (not show) and (
not save
): # I added this line to avoid calculus in the case there is nothing to do (Minou)
if verbose:
print("plot_diff : Nothing to do")
return None, None
# Normal behaviour (Minou)
# Get defaults
# ---
if var is None: # if nothing is defined, try these first:
params = s1.get_vars()
if "radiance" in params:
var = "radiance"
elif "radiance_noslit" in params:
var = "radiance_noslit"
elif "transmittance" in params:
var = "transmittance"
elif "transmittance_noslit" in params:
var = "transmittance_noslit"
else:
# or plot the first variable we find
var = list(params)[0]
if var.replace("_noslit", "") in params:
var = var.replace("_noslit", "")
# ... check variable exist
if var not in s1.get_vars():
raise ValueError(
"{0} not defined in Spectrum {1}. Use one of : {2}".format(
var, s1.get_name(), s1.get_vars()))
if var not in s2.get_vars():
raise ValueError(
"{0} not defined in Spectrum {1}. Use one of : {2}".format(
var, s2.get_name(), s2.get_vars()))
if Iunit == "default":
try:
Iunit = s1.units[var]
except KeyError: # unit not defined in dictionary
raise KeyError(
"Iunit not defined in spectrum for variable {0}. ".format(var)
+ "Cant use default unit. Specify unit in s.units['{0}'].".
format(var))
if wunit == "default":
wunit = s1.get_waveunit()
if isinstance(method, list):
methods = method
else:
methods = [method]
for method in methods:
if diff_window != 0 and method != "diff":
raise NotImplementedError(
"diff_window with method {0}".format(method))
# Get data
# ----
if normalize:
# copy before modifying directly in spectrum
s1 = s1.copy()
s2 = s2.copy()
w1, I1 = s1.get(var, wunit=wunit, copy=False)
w2, I2 = s2.get(var, wunit=wunit, copy=False)
ratio = np.nanmax(I1) / np.nanmax(I2)
I1 /= np.nanmax(I1)
I2 /= np.nanmax(I2)
if verbose:
print(("Rescale factor: " + str(ratio)))
def get_wdiff_Idiff():
wdiffs, Idiffs = [], []
for method in methods:
if not normalize:
if method == "distance":
wdiff, Idiff = get_distance(s1,
s2,
var=var,
wunit=wunit,
Iunit=Iunit)
elif method == "diff":
wdiff, Idiff = get_diff(
s1,
s2,
var=var,
wunit=wunit,
Iunit=Iunit,
diff_window=diff_window,
)
elif method == "ratio":
wdiff, Idiff = get_ratio(s1,
s2,
var=var,
wunit=wunit,
Iunit=Iunit)
else:
raise ValueError(
"Unknown comparison method: {0}".format(method))
wdiffs.append(wdiff)
Idiffs.append(Idiff)
else:
if method == "distance":
raise ValueError(
"{0} was not implemented yet for normalized spectra".
format(method))
elif method == "diff":
wdiff, Idiff = curve_substract(w1, I1, w2, I2)
elif method == "ratio":
wdiff, Idiff = get_ratio(s1,
s2,
var=var,
wunit=wunit,
Iunit=Iunit)
else:
raise ValueError(
"Unknown comparison method: {0}".format(method))
wdiffs.append(wdiff)
Idiffs.append(Idiff)
return wdiffs, Idiffs
wdiffs, Idiffs = get_wdiff_Idiff()
# Plot
# ----
# Format units
xlabel = format_xlabel(wunit, plot_medium)
# Init figure
set_style("origin")
fig = plt.figure(num=nfig, figsize=figsize)
gs = gridspec.GridSpec(1 + len(methods),
1,
height_ratios=[3] + [1] * len(methods))
ax0 = plt.subplot(gs[0])
ax0.ticklabel_format(useOffset=False)
ax1 = []
for i in range(len(methods)):
ax1i = plt.subplot(gs[i + 1])
ax1i.get_shared_x_axes().join(ax0, ax1i)
ax1i.ticklabel_format(useOffset=False)
ax1.append(ax1i)
# Plotting style
if show_points:
style = "-o"
else:
style = "-"
# Get labels and names
if label1 is None:
label1 = s1.get_name()
if label2 is None:
label2 = s2.get_name()
# Max label length:
if len(label1) > 60:
label1 = label1[:58] + "..."
if len(label2) > 60:
label2 = label2[:58] + "..."
# Plot compared spectra
if normalize:
# TODO: add option to norm_on
ax0.plot(w1, I1, style, color="k", lw=3 * lw_multiplier, label=label1)
ax0.plot(w2, I2, style, color="r", lw=1 * lw_multiplier, label=label2)
else:
ax0.plot(*s1.get(var, wunit=wunit, Iunit=Iunit),
style,
color="k",
lw=3 * lw_multiplier,
label=label1)
ax0.plot(*s2.get(var, wunit=wunit, Iunit=Iunit),
style,
color="r",
lw=1 * lw_multiplier,
label=label2)
# cosmetic changes
Iunit = make_up_unit(Iunit, var)
ax0.tick_params(labelbottom=False)
if label1 is not None or label2 is not None:
ax0.legend(**legendargs)
# Start to 0
if var in ["radiance_noslit", "radiance", "abscoeff", "absorbance"]:
ax0.set_ylim(bottom=0)
# plot difference (sorted)
for ax1i, wdiff, Idiff in zip(ax1, wdiffs, Idiffs):
b = np.argsort(wdiff)
ax1i.plot(wdiff[b], Idiff[b], style, color="k", lw=1 * lw_multiplier)
# Write labels
ax1[-1].set_xlabel(make_up(xlabel))
if normalize:
fig.text(
0.02,
0.5,
("{0} (norm.)".format(make_up(var))),
va="center",
rotation="vertical",
)
else:
fig.text(
0.02,
0.5,
("{0} ({1})".format(make_up(var), Iunit)),
va="center",
rotation="vertical",
)
# Set limits of 'diff' window
for i, method in enumerate(methods):
if method == "diff":
# symmetrize error scale:
# auto-zoom on min, max, but discard first and last centile (case of spikes / divergences)
Idiff = Idiffs[i]
if discard_centile:
Idiff_sorted = np.sort(Idiff[~np.isnan(Idiff)])
ymax = max(
abs(Idiff_sorted[-int(discard_centile *
len(Idiff_sorted) // 100)]),
abs(Idiff_sorted[int(discard_centile * len(Idiff_sorted) //
100)]),
)
else:
ymax = np.nanmax(abs(Idiff))
ax1[i].set_ylim(-ymax * diff_scale_multiplier,
ymax * diff_scale_multiplier)
elif method == "distance":
if discard_centile:
raise NotImplementedError(
"discard_centile not implemented for method=distance")
_, ymax = ax1[i].get_ylim()
ax1[i].set_ylim(0, ymax * diff_scale_multiplier)
elif method == "ratio":
# auto-zoom on min, max, but discard first and last centile (case of spikes / divergences)
Idiff = Idiffs[i]
if discard_centile:
Idiff_sorted = np.sort(Idiff[~np.isnan(Idiff)])
ymin = Idiff_sorted[int(discard_centile * len(Idiff_sorted) //
100)]
ymax = Idiff_sorted[-int(discard_centile * len(Idiff_sorted) //
100)]
else:
ymin = np.nanmin(Idiff)
ymax = np.nanmax(Idiff)
ax1[i].set_ylim(
bottom=((ymin - 1) * diff_scale_multiplier + 1),
top=((ymax - 1) * diff_scale_multiplier + 1),
)
# ymax = max(abs(Idiff_sorted[len(Idiff_sorted)//100]-1),
# abs(Idiff_sorted[len(-Idiff_sorted)//100]-1))
# ax1[i].set_ylim(ymax*diff_scale_multiplier+1, -ymax*diff_scale_multiplier+1)
if title:
fig.suptitle(title)
# Fix format
fix_style("origin", ax=ax0)
for ax1i in ax1:
fix_style("origin", ax=ax1i)
plt.tight_layout()
if title:
plt.subplots_adjust(left=0.15, top=0.92)
else:
plt.subplots_adjust(left=0.15)
# Plot difference text
for i, method in enumerate(methods):
if method == "diff":
difftext = "diff"
ax1[i].axhline(y=0, color="grey", zorder=-1)
elif method == "distance":
difftext = "distance"
ax1[i].axhline(y=0, color="grey", zorder=-1)
elif method == "ratio":
difftext = "ratio"
ax1[i].axhline(y=1, color="grey", zorder=-1)
# Show residualget_residual
if show_residual:
difftext += " (residual={0:.2g})".format(
get_residual(s1,
s2,
var=var,
norm="L2",
ignore_nan=True,
diff_window=diff_window))
pos = ax1[i].get_position()
fig.text(0.09, pos.ymax + 0.02, difftext)
# Add cursors
axes = [ax0] + ax1
fig.cursors = MultiCursor(
fig.canvas,
axes,
color="r",
lw=1 * lw_multiplier,
alpha=0.2,
horizOn=False,
vertOn=True,
)
if show:
plt.show()
if save:
fig.savefig(save)
if not show:
plt.close(fig) # to avoid memory load
# Return graphs
return fig, axes
def Integrate(f, a, b):
I, _ = integrate.quad(f, a, b)
return I
ft = lambda t: 1 / log(t)
limits = list(linspace(10, 10**10, 100))
Li = []
n = []
for i in limits:
temp = Integrate(ft, 2, i)
#Append results i times
Li.append(temp)
n.append(i)
#Creating figure
set_style('article') #before the first plot
plt.figure()
ax = plt.subplot()
ax.plot(n, Li, 'o', label='Logarithmic integral')
plt.xlabel(r'$n \in [10,10^{10}]$')
plt.ylabel(r'$Li(n)=\int_{2}^n \frac{1}{\log \: t}dt$')
plt.title('A nice title')
plt.legend(loc='upper left')
fix_style('article')
plt.show()
def test_normalisation_mode(plot=True,
close_plots=True,
verbose=True,
*args,
**kwargs):
''' Test norm_by = 'area' vs norm_by = 'max' '''
from radis.test.utils import getTestFile
if plot:
plt.ion() # dont get stuck with Matplotlib if executing through pytest
if close_plots:
plt.close('all')
from publib import set_style
set_style('origin')
# %% Compare spectra convolved with area=1 and max=1
# Slit in nm
# Spectrum in nm
# Specair units: mW/cm2/sr/µm
w, I = np.loadtxt(getTestFile('calc_N2C_spectrum_Trot1200_Tvib3000.txt')).T
s = calculated_spectrum(w,
I,
conditions={
'Tvib': 3000,
'Trot': 1200
},
Iunit='mW/cm2/sr/µm')
FWHM = 2
s.apply_slit(FWHM, norm_by='area') # spectrum convolved with area=1
w_area, I_area = s.get('radiance')
if plot:
fig = plt.figure(fig_prefix + 'Spectrum in nm + slit in nm')
fig.clear()
ax = fig.gca()
s.plot(nfig=fig.number, wunit='nm', label='norm_by: area', lw=3)
s.apply_slit(FWHM, norm_by='max') # spectrum convolved with max=1
w_max, I_max = s.get('radiance', wunit='nm')
if plot:
ax.plot(w_max, I_max / FWHM, 'r', label='(norm_by:max)/FWHM')
ax.legend(loc='best')
assert np.allclose(I_area, I_max / FWHM)
if verbose:
print("equivalence of normalisation mode for spectrum in 'nm': OK")
# %% Compare spectra convolved with area=1 and max=1
# Slit in nm
# Spectrum in cm-1
s = load_spec(getTestFile('CO_Tgas1500K_mole_fraction0.01.spec'),
binary=True)
s.update()
# spectrum convolved with area=1
s.apply_slit(FWHM, norm_by='area', plot_slit=plot)
w_area, I_area = s.get('radiance')
if plot:
fig = plt.figure(fig_prefix + 'Spectrum in cm-1 + slit in nm')
fig.clear()
ax = fig.gca()
s.plot(nfig=fig.number, wunit='nm', label='norm_by: area', lw=3)
# spectrum convolved with max=1
s.apply_slit(FWHM, norm_by='max', plot_slit=plot)
w_max, I_max = s.get('radiance', wunit='nm')
if plot:
ax.plot(w_max, I_max / FWHM, 'r', label='(norm_by:max)/FWHM')
ax.legend(loc='best')
assert np.allclose(I_area, I_max / FWHM)
if verbose:
print(
"equivalence of normalisation mode for spectrum in 'cm-1': {0}: OK"
)
assert is_homogeneous(s.units['radiance'], 'mW/cm2/sr')
if verbose:
print(("radiance unit ({0}) is homogeneous to 'mW/cm2/sr': OK".format(
s.units['radiance'])))
return True
import numpy as np
from scipy import integrate
import matplotlib.pyplot as plt
import math
%matplotlib inline
from publib import set_style, fix_style
set_style(['origin','latex']) # the style sheet is from https://github.com/erwanp/publib
def Integrate(f, a, b):
I, _ = integrate.quad(f, a, b)
return I
f = lambda x : 1/(math.log(x))
fig, ax = plt.subplots(figsize=(16,6))
Ns = np.linspace(10,10**10,100)
Is = [Integrate(f,n,2) for n in Ns]
ax.plot(Ns,Is, label="Li(n)")
ax.legend(bbox_to_anchor=(1.2, 1))
fix_style(['origin', 'latex'])
from radis import cm2nm
from radis.misc import centered_diff
from publib import set_style, fix_style
#%% ===========================================================================
# Read Data and get Inputs
# =============================================================================
SAVE_OUTPUT = True
#%% Read atmosphere data
atm = pd.read_csv(r'data/atmosphere_standard_profile_1976.csv', comment='#')
atm['path_length'] = centered_diff(atm.z_km)
#%% Plot atmosphere data
set_style('origin')
fig, ax = plt.subplots()
ax.plot(atm.P_Pa * 1e-5, atm.z_km)
ax.set_xlabel('Pressure [bar]')
ax.set_ylabel('Altitude [km]')
ax2 = ax.twiny()
ax2.plot(atm.T_K, atm.z_km, 'r')
ax2.set_xlabel('Temperature [K]', color='r')
ax2.tick_params('x', colors='r')
plt.tight_layout()
fix_style('origin')
if SAVE_OUTPUT:
from radis.misc import make_folders
def plot_diff(s1,
s2,
var=None,
wunit='default',
Iunit='default',
medium='default',
resample=True,
method='diff',
show_points=False,
label1=None,
label2=None,
figsize=None,
title=None,
nfig=None,
normalize=False,
verbose=True):
''' Plot two spectra, and the difference between them
If waveranges dont match, `s2` is interpolated over `s1`.
Parameters
----------
s1, s2: Spectrum objects
var: str, or None
spectral quantity to plot (ex: 'abscoeff'). If None, plot the first one
in the Spectrum from 'radiance', 'radiance_noslit', 'transmittance', etc.
wunit: 'default', 'nm', 'cm-1'
if 'default', use first spectrum wunit
Iunit: str
if 'default', use first spectrum unit
medium: 'air', 'vacuum', 'default'
if 'default', use first spectrum propagating medium
method: 'distance', 'diff', 'ratio'
If 'diff', plot difference at same wavespace position.
If 'distance', plot Euclidian distance (note that units are meaningless then)
If 'ratio', plot ratio of two spectra
Default 'diff'.
Warning: with 'distance', calculation scales as ~N^2 with N the number
of points in a spectrum (against ~N with 'diff'). This can quickly
override all memory.
normalize: bool
Normalize the spectra to be ploted
Other Parameters
----------------
show_points: boolean
if True, make all points appear with 'o'
label1, label2: str
curve names
figsize
figure size
nfig: int, str
figure number of name
title: str
title
verbose: boolean
if True, plot stuff such as rescale ratio in normalize mode. Default True
Examples
--------
>>> Punit = 'mW/cm2/sr'
>>> fig, axes = plot_diff(s10, s50, figsize=(18,6),
>>> label1='brd 10 cm-1, P={0:.2f} {1}'.format(s10.get_power(unit=Punit),Punit),
>>> label2='brd 50 cm-1, P={0:.2f} {1}'.format(s50.get_power(unit=Punit),Punit)
>>> )
See Also
--------
:func:`~radis.spectrum.compare.get_diff`,
:func:`~radis.spectrum.compare.get_ratio`,
:func:`~radis.spectrum.compare.get_distance`,
:func:`~radis.spectrum.compare.get_residual`,
:meth:`~radis.spectrum.spectrum.compare_with`
'''
# Get defaults
# ---
if var is None: # if nothing is defined, try these first:
params = s1.get_vars()
if 'radiance' in params:
var = 'radiance'
elif 'radiance_noslit' in params:
var = 'radiance_noslit'
elif 'transmittance' in params:
var = 'transmittance'
elif 'transmittance_noslit' in params:
var = 'transmittance_noslit'
else:
# or plot the first variable we find
var = list(params)[0]
if var.replace('_noslit', '') in params:
var = var.replace('_noslit', '')
if Iunit == 'default':
try:
Iunit = s1.units[var]
except KeyError: # unit not defined in dictionary
raise KeyError('Iunit not defined in spectrum. Cant plot')
if wunit == 'default':
wunit = s1.get_waveunit()
if medium == 'default':
medium = s1.conditions.get('medium', None)
# Get data
# ----
if method == 'distance':
wdiff, Idiff = get_distance(s1,
s2,
var=var,
wunit=wunit,
Iunit=Iunit,
medium=medium)
elif method == 'diff':
wdiff, Idiff = get_diff(s1,
s2,
var=var,
wunit=wunit,
Iunit=Iunit,
medium=medium)
elif method == 'ratio':
wdiff, Idiff = get_ratio(s1,
s2,
var=var,
wunit=wunit,
Iunit=Iunit,
medium=medium)
else:
raise ValueError('Unknown comparison method: {0}'.format(method))
# Plot
# ----
# Format units
wunit = cast_waveunit(wunit)
if wunit == 'cm-1':
xlabel = 'Wavenumber (cm-1)'
elif wunit == 'nm':
xlabel = 'Wavelength (nm)'
# Init figure
set_style('origin')
fig = plt.figure(num=nfig, figsize=figsize)
gs = gridspec.GridSpec(2, 1, height_ratios=[3, 1])
ax0 = plt.subplot(gs[0])
ax1 = plt.subplot(gs[1])
ax1.get_shared_x_axes().join(ax0, ax1)
# Plotting style
if show_points:
style = '-o'
else:
style = '-'
# Get labels and names
if label1 is None:
label1 = s1.get_name()
if label2 is None:
label2 = s2.get_name()
# Plot compared spectra
if normalize:
w1, I1 = s1.get(var, wunit, Iunit, medium)
w2, I2 = s2.get(var, wunit, Iunit, medium)
if verbose: print(('Rescale factor: ' + str(np.max(I1) / np.max(I2))))
ax0.plot(w1, I1 / np.max(I1), ls=style, color='k', lw=3, label=label1)
ax0.plot(w2, I2 / np.max(I2), ls=style, color='r', lw=1, label=label2)
else:
ax0.plot(*s1.get(var, wunit, Iunit, medium),
ls=style,
color='k',
lw=3,
label=label1)
ax0.plot(*s2.get(var, wunit, Iunit, medium),
ls=style,
color='r',
lw=1,
label=label2)
Iunit = make_up(Iunit) # cosmetic changes
ax0.tick_params(labelbottom='off')
if label1 is not None or label2 is not None:
ax0.legend(loc='best')
# Start to 0
if var in ['radiance_noslit', 'radiance', 'abscoeff', 'absorbance']:
ax0.set_ylim(bottom=0)
# plot difference (sorted)
b = np.argsort(wdiff)
ax1.plot(wdiff[b], Idiff[b], style, color='k', lw=1)
if method == 'diff':
fig.text(0.09, 0.38, 'diff')
elif method == 'distance':
fig.text(0.09, 0.38, 'distance')
elif method == 'ratio':
fig.text(0.09, 0.38, 'ratio')
# Write labels
ax1.set_xlabel(make_up(xlabel))
if normalize:
fig.text(0.02, 0.5, 'Arb. Units', va='center', rotation='vertical')
else:
fig.text(0.02,
0.5, ('{0} ({1})'.format(make_up(var), Iunit)),
va='center',
rotation='vertical')
# Set limits
if method == 'diff':
# symmetrize error scale:
ymin, ymax = ax1.get_ylim()
ymax = max(abs(ymin), abs(ymax))
ax1.set_ylim(-ymax, ymax)
elif method == 'distance':
ax1.set_ylim(bottom=0)
elif method == 'ratio':
# auto-zoom on min, max, but discard first decile (case of spikes / divergences)
Idiff_s = np.sort(Idiff)
ax1.set_ylim(bottom=0,
top=Idiff_s[len(Idiff_s) // 10] +
Idiff_s[-len(Idiff_s) // 10])
if title:
fig.suptitle(title)
# Fix format
fix_style('origin', ax=ax0)
fix_style('origin', ax=ax1)
plt.tight_layout()
if title:
plt.subplots_adjust(left=0.15, top=0.92)
else:
plt.subplots_adjust(left=0.15)
# Add cursors
fig.cursors = MultiCursor(fig.canvas, (ax0, ax1),
color='r',
lw=1,
alpha=0.2,
horizOn=False,
vertOn=True)
return fig, [ax0, ax1]
def plot_diff(s1,
s2,
var=None,
wunit='default',
Iunit='default',
medium='default',
resample=True,
method='diff',
diff_window=0,
show_points=False,
label1=None,
label2=None,
figsize=None,
title=None,
nfig=None,
normalize=False,
verbose=True,
save=False,
show=True,
show_residual=False,
lw_multiplier=1,
diff_scale_multiplier=1,
discard_centile=0):
''' Plot two spectra, and the difference between them. ``method=`` allows
you to plot the absolute difference, ratio, or both.
If waveranges dont match, ``s2`` is interpolated over ``s1``.
Parameters
----------
s1, s2: Spectrum objects
var: str, or None
spectral quantity to plot (ex: ``'abscoeff'``). If None,
plot the first one in the Spectrum from ``'radiance'``,
``'radiance_noslit'``, ``'transmittance'``, etc.
wunit: ``'default'``, ``'nm'``, ``'cm-1'``
if ``'default'``, use first spectrum wunit
Iunit: str
if ``'default'``, use first spectrum unit
medium: ``'air'``, ``'vacuum'``, ``'default'``
if ``'default'``, use first spectrum propagating medium
method: ``'distance'``, ``'diff'``, ``'ratio'``, or list of them.
If ``'diff'``, plot difference of the two spectra.
If ``'distance'``, plot Euclidian distance (note that units are meaningless then)
If ``'ratio'``, plot ratio of two spectra
Default ``'diff'``.
.. warning::
with ``'distance'``, calculation scales as ~N^2 with N the number
of points in a spectrum (against ~N with ``'diff'``). This can quickly
override all memory.
Can also be a list::
method=['diff', 'ratio']
normalize: bool
Normalize the spectra to be ploted
Other Parameters
----------------
diff_window: int
If non 0, calculates diff by offsetting s1 by ``diff_window`` number of
units on either side, and returns the minimum. Kinda compensates for experimental
errors on the w axis. Default 0. (look up code to understand...)
show_points: boolean
if ``True``, make all points appear with 'o'
label1, label2: str
curve names
figsize
figure size
nfig: int, str
figure number of name
title: str
title
verbose: boolean
if ``True``, plot stuff such as rescale ratio in normalize mode. Default ``True``
save: str
Default is ``False``. By default won't save anything, type the path of the
destination if you want to save it (format in the name).
show: Bool
Default is ``True``. Will show the plots : bad if there are more than 20.
show_residual: bool
if ``True``, calculates and shows on the graph the residual in L2 norm.
See :func:`~radis.spectrum.compare.get_residual`. ``diff_window`` is
used in the residual calculation too. ``normalize`` has no effect.
diff_scale_multiplier: float
dilate the diff plot scale. Default ``1``
discard_centile: int
if not ``0``, discard the firsts and lasts centile when setting the limits
of the diff window. Example::
discard_centile=1 # --> discards the smallest 1% and largest 1%
discard_centile=10 # --> discards the smallest 10% and largest 10%
Useful to remove spikes in a ratio, for instance.
Note that this does not change the values of the residual. It's just
a plot feature.
Default ``0``
Returns
-------
fig: figure
fig
[ax0, ax1]: axes
spectra and difference axis
Examples
--------
Simple use::
from radis import plot_diff
plot_diff(s10, s50) # s10, s50 are two spectra
Advanced use, plotting the total power in the label, and getting the figure
and axes handle to edit them afterwards::
Punit = 'mW/cm2/sr'
fig, axes = plot_diff(s10, s50, 'radiance_noslit', figsize=(18,6),
label1='brd 10 cm-1, P={0:.2f} {1}'.format(s10.get_power(unit=Punit),Punit),
label2='brd 50 cm-1, P={0:.2f} {1}'.format(s50.get_power(unit=Punit),Punit)
)
# modify fig, axes..
See an example output in :ref:`Compare two Spectra <label_spectrum_howto_compare>`
See Also
--------
:func:`~radis.spectrum.compare.get_diff`,
:func:`~radis.spectrum.compare.get_ratio`,
:func:`~radis.spectrum.compare.get_distance`,
:func:`~radis.spectrum.compare.get_residual`,
:meth:`~radis.spectrum.spectrum.compare_with`
'''
if (not show) and (
not save
): #I added this line to avoid calculus in the case there is nothing to do (Minou)
if verbose: print('plot_diff : Nothing to do')
return None, None
#Normal behaviour (Minou)
# Get defaults
# ---
if var is None: # if nothing is defined, try these first:
params = s1.get_vars()
if 'radiance' in params:
var = 'radiance'
elif 'radiance_noslit' in params:
var = 'radiance_noslit'
elif 'transmittance' in params:
var = 'transmittance'
elif 'transmittance_noslit' in params:
var = 'transmittance_noslit'
else:
# or plot the first variable we find
var = list(params)[0]
if var.replace('_noslit', '') in params:
var = var.replace('_noslit', '')
# ... check variable exist
if var not in s1.get_vars():
raise ValueError(
'{0} not defined in Spectrum {1}. Use one of : {2}'.format(
var, s1.get_name(), s1.get_vars()))
if var not in s2.get_vars():
raise ValueError(
'{0} not defined in Spectrum {1}. Use one of : {2}'.format(
var, s2.get_name(), s2.get_vars()))
if Iunit == 'default':
try:
Iunit = s1.units[var]
except KeyError: # unit not defined in dictionary
raise KeyError('Iunit not defined in spectrum for variable {0}. '.format(var)+\
"Cant use default unit. Specify unit in s.units['{0}'].".format(var))
if wunit == 'default':
wunit = s1.get_waveunit()
if medium == 'default':
medium = s1.conditions.get('medium', None)
if isinstance(method, list):
methods = method
else:
methods = [method]
for method in methods:
if diff_window != 0 and method != 'diff':
raise NotImplementedError(
'diff_window with method {0}'.format(method))
# Get data
# ----
if normalize:
# copy before modifying directly in spectrum
s1 = s1.copy()
s2 = s2.copy()
w1, I1 = s1.get(var, copy=False)
w2, I2 = s2.get(var, copy=False)
I1 /= np.max(I1)
I2 /= np.max(I2)
if verbose:
print(('Rescale factor: ' + str(np.max(I1) / np.max(I2))))
def get_wdiff_Idiff():
wdiffs, Idiffs = [], []
for method in methods:
if not normalize:
if method == 'distance':
wdiff, Idiff = get_distance(s1,
s2,
var=var,
wunit=wunit,
Iunit=Iunit,
medium=medium)
elif method == 'diff':
wdiff, Idiff = get_diff(s1,
s2,
var=var,
wunit=wunit,
Iunit=Iunit,
medium=medium,
diff_window=diff_window)
elif method == 'ratio':
wdiff, Idiff = get_ratio(s1,
s2,
var=var,
wunit=wunit,
Iunit=Iunit,
medium=medium)
else:
raise ValueError(
'Unknown comparison method: {0}'.format(method))
wdiffs.append(wdiff)
Idiffs.append(Idiff)
else:
if method == 'distance':
raise ValueError(
'{0} was not implemented yet for normalized spectra'.
format(method))
elif method == 'diff':
wdiff, Idiff = curve_substract(w1, I1, w2, I2)
elif method == 'ratio':
wdiff, Idiff = get_ratio(s1,
s2,
var=var,
wunit=wunit,
Iunit=Iunit,
medium=medium)
else:
raise ValueError(
'Unknown comparison method: {0}'.format(method))
wdiffs.append(wdiff)
Idiffs.append(Idiff)
return wdiffs, Idiffs
wdiffs, Idiffs = get_wdiff_Idiff()
# Plot
# ----
# Format units
wunit = cast_waveunit(wunit)
if wunit == 'cm-1':
xlabel = 'Wavenumber (cm-1)'
elif wunit == 'nm':
xlabel = 'Wavelength (nm)'
# Init figure
set_style('origin')
fig = plt.figure(num=nfig, figsize=figsize)
gs = gridspec.GridSpec(1 + len(methods),
1,
height_ratios=[3] + [1] * len(methods))
ax0 = plt.subplot(gs[0])
ax0.ticklabel_format(useOffset=False)
ax1 = []
for i in range(len(methods)):
ax1i = plt.subplot(gs[i + 1])
ax1i.get_shared_x_axes().join(ax0, ax1i)
ax1i.ticklabel_format(useOffset=False)
ax1.append(ax1i)
# Plotting style
if show_points:
style = '-o'
else:
style = '-'
# Get labels and names
if label1 is None:
label1 = s1.get_name()
if label2 is None:
label2 = s2.get_name()
# Max label length:
if len(label1) > 60:
label1 = label1[:58] + '...'
if len(label2) > 60:
label2 = label2[:58] + '...'
# Plot compared spectra
if normalize:
# TODO: add option to norm_on
ax0.plot(w1,
I1,
ls=style,
color='k',
lw=3 * lw_multiplier,
label=label1)
ax0.plot(w2,
I2,
ls=style,
color='r',
lw=1 * lw_multiplier,
label=label2)
else:
ax0.plot(*s1.get(var, wunit, Iunit, medium),
ls=style,
color='k',
lw=3 * lw_multiplier,
label=label1)
ax0.plot(*s2.get(var, wunit, Iunit, medium),
ls=style,
color='r',
lw=1 * lw_multiplier,
label=label2)
Iunit = make_up(Iunit) # cosmetic changes
ax0.tick_params(labelbottom=False)
if label1 is not None or label2 is not None:
ax0.legend(loc='best')
# Start to 0
if var in ['radiance_noslit', 'radiance', 'abscoeff', 'absorbance']:
ax0.set_ylim(bottom=0)
# plot difference (sorted)
for ax1i, wdiff, Idiff in zip(ax1, wdiffs, Idiffs):
b = np.argsort(wdiff)
ax1i.plot(wdiff[b], Idiff[b], style, color='k', lw=1 * lw_multiplier)
# Write labels
ax1[-1].set_xlabel(make_up(xlabel))
if normalize:
fig.text(0.02, 0.5, 'Arb. Units', va='center', rotation='vertical')
else:
fig.text(0.02,
0.5, ('{0} ({1})'.format(make_up(var), Iunit)),
va='center',
rotation='vertical')
# Set limits of 'diff' window
for i, method in enumerate(methods):
if method == 'diff':
# symmetrize error scale:
# auto-zoom on min, max, but discard first and last centile (case of spikes / divergences)
Idiff = Idiffs[i]
if discard_centile:
Idiff_sorted = np.sort(Idiff[~np.isnan(Idiff)])
ymax = max(
abs(Idiff_sorted[-int(discard_centile *
len(Idiff_sorted) // 100)]),
abs(Idiff_sorted[int(discard_centile * len(Idiff_sorted) //
100)]))
else:
ymax = np.nanmax(abs(Idiff))
ax1[i].set_ylim(-ymax * diff_scale_multiplier,
ymax * diff_scale_multiplier)
elif method == 'distance':
if discard_centile:
raise NotImplementedError(
'discard_centile not implemented for method=distance')
_, ymax = ax1[i].get_ylim()
ax1[i].set_ylim(0, ymax * diff_scale_multiplier)
elif method == 'ratio':
# auto-zoom on min, max, but discard first and last centile (case of spikes / divergences)
Idiff = Idiffs[i]
if discard_centile:
Idiff_sorted = np.sort(Idiff[~np.isnan(Idiff)])
ymin = Idiff_sorted[int(discard_centile * len(Idiff_sorted) //
100)]
ymax = Idiff_sorted[-int(discard_centile * len(Idiff_sorted) //
100)]
else:
ymin = np.nanmin(Idiff)
ymax = np.nanmax(Idiff)
ax1[i].set_ylim(bottom=((ymin - 1) * diff_scale_multiplier + 1),
top=((ymax - 1) * diff_scale_multiplier + 1))
# ymax = max(abs(Idiff_sorted[len(Idiff_sorted)//100]-1),
# abs(Idiff_sorted[len(-Idiff_sorted)//100]-1))
# ax1[i].set_ylim(ymax*diff_scale_multiplier+1, -ymax*diff_scale_multiplier+1)
if title:
fig.suptitle(title)
# Fix format
fix_style('origin', ax=ax0)
for ax1i in ax1:
fix_style('origin', ax=ax1i)
plt.tight_layout()
if title:
plt.subplots_adjust(left=0.15, top=0.92)
else:
plt.subplots_adjust(left=0.15)
# Plot difference text
for i, method in enumerate(methods):
if method == 'diff':
difftext = 'diff'
ax1[i].axhline(y=0, color='grey', zorder=-1)
elif method == 'distance':
difftext = 'distance'
ax1[i].axhline(y=0, color='grey', zorder=-1)
elif method == 'ratio':
difftext = 'ratio'
ax1[i].axhline(y=1, color='grey', zorder=-1)
# Show residualget_residual
if show_residual:
difftext += ' (residual={0:.2g})'.format(
get_residual(s1,
s2,
var=var,
norm='L2',
ignore_nan=True,
diff_window=diff_window))
pos = ax1[i].get_position()
fig.text(0.09, pos.ymax + 0.02, difftext)
# Add cursors
axes = [ax0] + ax1
fig.cursors = MultiCursor(fig.canvas,
axes,
color='r',
lw=1 * lw_multiplier,
alpha=0.2,
horizOn=False,
vertOn=True)
if show:
plt.show()
if save:
fig.savefig(save)
if not show:
plt.close(fig) #to avoid memory load if
# Return graphs
return fig, axes
