def PMEL_extCoeff_get_plot_save(dist_LS, fname = False, as_time_series = False):
wavelength = [450., 525., 624.]
n = 1.455
# AOD_list_PMEL = []
AOD_dict_PMEL = {}
for w in wavelength:
AOD = dist_LS.calculate_optical_properties(wavelength=w, n = n)
# calculate_AOD(wavelength=w, n = n)
# opt= sizedistribution.OpticalProperties(AOD, dist_LS.bins)
# AOD_list.append({'wavelength':w, 'opt_inst': AOD})
AOD_dict_PMEL['%.1f'%w] = AOD
folder = '/Users/htelg/data/20150414_Svalbard/forPMEL/'
keys = list(AOD_dict_PMEL.keys())
keys.sort()
df = pd.DataFrame()
f,a = plt.subplots()
for e, key in enumerate(keys):
opt_tmp = AOD_dict_PMEL[key]
ext_coeff = opt_tmp.get_extinction_coeff_verticle_profile()
df[key] = ext_coeff.data['ext. coeff.']
ext_coeff.plot(ax = a, color = plt_tools.wavelength_to_rgb(key))
g = a.get_lines()[-1]
g.set_label(key)
# for k in keys:
a.legend()
if fname:
f.savefig(folder+fname + '_vp' + '.png', dpi = 300)
df.to_csv(folder+fname + '_vp' + '.csv')
vp = vertical_profile.VerticalProfile(df)
if type(as_time_series) == bool:
if not as_time_series:
ts = None
else:
ts = vp.convert2timeseries(as_time_series)
aa = ts.data.plot()
aa.set_ylabel('Extinction coefficient (m$^{-1}$)')
ff = aa.get_figure()
if fname:
ff.savefig(folder+fname + '_ts' + '.png', dpi = 300)
ts.data.to_csv(folder+fname + '_ts' + '.csv')
return a,aa,vp, ts
def PMEL_extCoeff_TS_get_plot_save(dist, fname = False):
# fname = False
wavelength = [450., 525., 624.]
n = 1.455
# AOD_list_PMEL = []
AOD_dict_PMEL = {}
for w in wavelength:
AOD = dist.calculate_optical_properties(wavelength=w, n = n)
# calculate_AOD(wavelength=w, n = n)
# opt= sizedistribution.OpticalProperties(AOD, dist.bins)
# AOD_list.append({'wavelength':w, 'opt_inst': AOD})
AOD_dict_PMEL['%.1f'%w] = AOD
folder = '/Users/htelg/data/20150414_Svalbard/forPMEL/'
keys = list(AOD_dict_PMEL.keys())
keys.sort()
df = pd.DataFrame()
f,a = plt.subplots()
for e, key in enumerate(keys):
opt_tmp = AOD_dict_PMEL[key]
ext_coeff = opt_tmp['extCoeff_perrow']
df[key] = ext_coeff.data['ext_coeff']
data = ext_coeff.data
a.plot(data.index,data.values*1e5, color = plt_tools.wavelength_to_rgb(key))
# ext_coeff.data.plot(ax = a, color = plt_tools.wavelength_to_rgb(key))
g = a.get_lines()[-1]
g.set_label(key)
a.set_ylabel('Extinction coefficient (10$^{-5}$ m$^{-1}$)')
a.set_xlabel('Time (UTC)')
# for k in keys:
f.autofmt_xdate()
a.legend()
df = df.sort_index()
df.index.name = 'TimeUTC'
if fname:
df.to_csv(folder+fname + '_ts' + '.csv')
f.savefig(folder+fname + '_ts' + '.png', dpi = 300)
return a,df
def plot(self,
offset=[0, 0, 0, 0],
airmassfct=True,
move_max=True,
legend=True,
all_on_one_axis=False,
additional_axes=False,
errors=False,
rayleigh=True):
"""plots ... sorry, but this is a messi function. Things should have been done different, e.g too much data
processing whith the data not put out ... need fixn
Arguments
---------
offset: list
airmassfct: bool.
If the airmass factor is included or not.
True: naturally the air-mass factor is included in the data, so this does nothing.
False: data is corrected to correct for the slant angle
rayleigh: bool or the aod part of the output of miniSASP.simulate_from_size_dist_LS.
make sure there is no airmassfkt included in this!!
all_on_one_axis: bool or axes instance
if True all is plotted in one axes. If axes instances this axis is used.
"""
m_size = 5
m_ewidht = 1.5
l_width = 2
gridspec_kw = {'wspace': 0.05}
no_axes = 4
if all_on_one_axis:
no_axes = 1
if additional_axes:
no_axes = no_axes + additional_axes
if type(all_on_one_axis).__name__ == 'AxesSubplot':
a = all_on_one_axis
f = a.get_figure()
else:
f, a = plt.subplots(1, no_axes, gridspec_kw=gridspec_kw)
columns = [
'460.3', '460.3 max', '550.4', '550.4 max', '671.2', '671.2 max',
'860.7', '860.7 max'
]
# peaks_max = [460.3, '460.3 max', 550.4, '550.4 max', 860.7, '860.7 max', 671.2,
# '671.2 max']
if not all_on_one_axis:
f.set_figwidth(15)
#################
for i in range(int(len(columns) / 2)):
col = plt_tools.wavelength_to_rgb(columns[i * 2]) * 0.8
intens = self.data[columns[i * 2]].dropna(
) # .plot(ax = a, style = 'o', label = '%s nm'%colums[i*2])
x = intens.index.get_level_values(1)
if type(rayleigh) == bool:
if rayleigh:
rayleigh_corr = 0
else:
# print('mach ick')
aodt = rayleigh[float(columns[i * 2])].loc[:, ['rayleigh']]
intenst = intens.copy()
intenst.index = intenst.index.droplevel(
['Time', 'Sunelevation'])
aodt_sit = pd.concat([aodt,
intenst]).sort_index().interpolate()
aodt_sit = aodt_sit.groupby(aodt_sit.index).mean().reindex(
intenst.index)
rayleigh_corr = aodt_sit.rayleigh.values / np.sin(
intens.index.get_level_values(2))
# return aodt
if not airmassfct:
amf_corr = np.sin(intens.index.get_level_values(2))
else:
amf_corr = 1
if not all_on_one_axis:
atmp = a[i]
else:
atmp = a
y = (offset[i] - np.log(intens) - rayleigh_corr) * amf_corr
g, = atmp.plot(y, x)
g.set_label('%s nm' % columns[i * 2])
g.set_linestyle('')
g.set_marker('o')
# g = a.get_lines()[-1]
g.set_markersize(m_size)
g.set_markeredgewidth(m_ewidht)
g.set_markerfacecolor('None')
g.set_markeredgecolor(col)
if move_max:
# sun_intensities.data.iloc[:,i*2+1].dropna().plot(ax = a)
intens = self.data[columns[i * 2 + 1]].dropna(
) # .plot(ax = a, style = 'o', label = '%s nm'%colums[i*2])
x = intens.index.values
g, = a[i].plot(offset[i] - np.log(intens), x)
# g = a.get_lines()[-1]
g.set_color(col)
# g.set_solid_joinstyle('round')
g.set_linewidth(l_width)
g.set_label(None)
if i != 0 and not all_on_one_axis:
atmp.set_yticklabels([])
if i == 4:
break
if all_on_one_axis:
a.legend()
else:
if legend:
for aa in a:
aa.legend()
if not airmassfct:
txt = 'OD'
else:
txt = 'OD * (air-mass factor)'
if all_on_one_axis:
atmp = a
else:
atmp = a[0]
atmp.set_xlabel(txt)
if not all_on_one_axis:
atmp.xaxis.set_label_coords(2.05, -0.07)
atmp.set_ylabel('Altitude (m)')
return a
def plot(self, offset=[0, 0, 0, 0], airmassfct=True, move_max=True, legend=True, all_on_one_axis = False,
additional_axes=False,
errors = False,
rayleigh=True):
"""plots ... sorry, but this is a messi function. Things should have been done different, e.g too much data
processing whith the data not put out ... need fixn
Arguments
---------
offset: list
airmassfct: bool.
If the airmass factor is included or not.
True: naturally the air-mass factor is included in the data, so this does nothing.
False: data is corrected to correct for the slant angle
rayleigh: bool or the aod part of the output of miniSASP.simulate_from_size_dist_LS.
make sure there is no airmassfkt included in this!!
all_on_one_axis: bool or axes instance
if True all is plotted in one axes. If axes instances this axis is used.
"""
m_size = 5
m_ewidht = 1.5
l_width = 2
gridspec_kw = {'wspace': 0.05}
no_axes = 4
if all_on_one_axis:
no_axes = 1
if additional_axes:
no_axes = no_axes + additional_axes
if type(all_on_one_axis).__name__ == 'AxesSubplot':
a = all_on_one_axis
f = a.get_figure()
else:
f, a = plt.subplots(1, no_axes, gridspec_kw=gridspec_kw)
columns = ['460.3', '460.3 max', '550.4', '550.4 max', '671.2', '671.2 max', '860.7', '860.7 max']
# peaks_max = [460.3, '460.3 max', 550.4, '550.4 max', 860.7, '860.7 max', 671.2,
# '671.2 max']
if not all_on_one_axis:
f.set_figwidth(15)
#################
for i in range(int(len(columns) / 2)):
col = plt_tools.wavelength_to_rgb(columns[i * 2]) * 0.8
intens = self.data[columns[i * 2]].dropna() # .plot(ax = a, style = 'o', label = '%s nm'%colums[i*2])
x = intens.index.get_level_values(1)
if type(rayleigh) == bool:
if rayleigh:
rayleigh_corr = 0
else:
# print('mach ick')
aodt = rayleigh[float(columns[i * 2])].loc[:, ['rayleigh']]
intenst = intens.copy()
intenst.index = intenst.index.droplevel(['Time', 'Sunelevation'])
aodt_sit = pd.concat([aodt, intenst]).sort_index().interpolate()
aodt_sit = aodt_sit.groupby(aodt_sit.index).mean().reindex(intenst.index)
rayleigh_corr = aodt_sit.rayleigh.values / np.sin(intens.index.get_level_values(2))
# return aodt
if not airmassfct:
amf_corr = np.sin(intens.index.get_level_values(2))
else:
amf_corr = 1
if not all_on_one_axis:
atmp = a[i]
else:
atmp = a
y = (offset[i] - np.log(intens) - rayleigh_corr) * amf_corr
g, = atmp.plot(y, x)
g.set_label('%s nm' % columns[i * 2])
g.set_linestyle('')
g.set_marker('o')
# g = a.get_lines()[-1]
g.set_markersize(m_size)
g.set_markeredgewidth(m_ewidht)
g.set_markerfacecolor('None')
g.set_markeredgecolor(col)
if move_max:
# sun_intensities.data.iloc[:,i*2+1].dropna().plot(ax = a)
intens = self.data[
columns[i * 2 + 1]].dropna() # .plot(ax = a, style = 'o', label = '%s nm'%colums[i*2])
x = intens.index.values
g, = a[i].plot(offset[i] - np.log(intens), x)
# g = a.get_lines()[-1]
g.set_color(col)
# g.set_solid_joinstyle('round')
g.set_linewidth(l_width)
g.set_label(None)
if i != 0 and not all_on_one_axis:
atmp.set_yticklabels([])
if i == 4:
break
if all_on_one_axis:
a.legend()
else:
if legend:
for aa in a:
aa.legend()
if not airmassfct:
txt = 'OD'
else:
txt = 'OD * (air-mass factor)'
if all_on_one_axis:
atmp = a
else:
atmp = a[0]
atmp.set_xlabel(txt)
if not all_on_one_axis:
atmp.xaxis.set_label_coords(2.05, -0.07)
atmp.set_ylabel('Altitude (m)')
return a
def plot_POPS_v_mSASP_OD_corr(sun_int_su,
aods_corr,
offset=[3.295,3.44,3.995,4.16],
additional_axes = False,
rayleigh = False,
error = False,
color = False):
"""plots the OD from miniSASP versus that which we simulate from the POPS results.
Data is corrected for airmass factor.
Arguments
---------
sun_int_su: sun_int_su instance
aod_corr: output of miniSASP.simulate_from_size_dist_LS
rayleigh: bool
if rayleigh is included or not.
color: bool, or matplotlib color
if bool, the color of the msasp channel is used"""
airmassfct = False
if not rayleigh:
rayleigh_corr = aods_corr
else:
rayleigh_corr = rayleigh
a = sun_int_su.plot(offset=offset, airmassfct = airmassfct, move_max = False, additional_axes = additional_axes, rayleigh = rayleigh_corr)
f = a[0].get_figure()
f.set_figwidth(15)
# f.set_figheight(10)
if rayleigh:
a[0].set_xlabel('Optical depth')
else:
a[0].set_xlabel('Arosol optical depth')
colors_fill = []
colors_l = []
for e,aa in enumerate(a):
# aa.set_xlim((-0.06,0.39))
if e > 3:
break
aa.set_xlim((-0.01,0.14))
aa.set_ylim((0,3200))
aa.grid()
g = aa.get_lines()[-1]
txt = g.get_label()
aa.set_title(txt)
rgb_l = plt_tools.wavelength_to_rgb(float(txt.strip(' nm'))) * 0.8
hls = list(colorsys.rgb_to_hls(*rgb_l))
hls_sum = np.array(hls).sum()
# hls[1] *=2. # lightness ... makes it brighter
hls[1] = 0.8 # lightness ... makes it brighter
# hls[2] *=0.4 #saturation ... makes it more gray
hls[2] = 0.5 #saturation ... makes it more gray
if hls_sum == 0:
hls[1] = 0.6
hls[2] = 0.0
rgb = colorsys.hls_to_rgb(*hls)
colors_fill.append(rgb)
colors_l.append(rgb_l)
# print('rgb', rgb_l)
g.set_label('miniSASP')
aa.locator_params(axis = 'x', nbins = 6)
# print(colors_l)
# print(colors_fill)
# aa.vlines(0,0,3200)
keys = list(aods_corr.keys())
keys.sort()
# ms1,ms2,ms3,ms4 = (a[0].get_lines()[-1],a[1].get_lines()[-1],a[2].get_lines()[-1],a[3].get_lines()[-1])
for e,k in enumerate(keys):
out = aods_corr[k]
if not np.any(color):
col_l = colors_l[e]
if rayleigh:
g, = a[e].plot(out['sum'].values, out['sum'].index.values)
g.set_linewidth(2)
g.set_color(col_l)
g.set_label('POPS')
x = out['aerosol'].values
y = out['aerosol'].index.values
if np.any(error):
col_fill = colors_fill[e]
a[e].fill_betweenx(y, x * (1 - (error[0] / 100)), x * (1 + (error[1]/100)), color = col_fill)
g2, = a[e].plot(x,y )
g2.set_linewidth(2)
g2.set_color(col_l)
if rayleigh:
g2.set_linestyle('--')
g2.set_label('AOD only')
else:
g2.set_label('POPS')
leg = a[e].legend(numpoints = 1,handlelength=1, prop={'size':17})
if e == 0:
leg.draw_frame(True)
txt = leg.get_texts()[0]
# txt.set_fontsize(10)
else:
leg.set_visible(False)
return a
