def mixing_ratio(full_output, **kwargs):
"""Returns plot of mixing ratios
Parameters
----------
full_output : class
picaso.atmsetup.ATMSETUP
**kwargs : dict
Any key word argument for bokeh.figure()
"""
#set plot defaults
molecules = full_output['weights'].keys()
pressure = full_output['layer']['pressure']
kwargs['plot_height'] = kwargs.get('plot_height', 300)
kwargs['plot_width'] = kwargs.get('plot_width', 400)
kwargs['title'] = kwargs.get('title', 'Mixing Ratios')
kwargs['y_axis_label'] = kwargs.get('y_axis_label', 'Pressure(Bars)')
kwargs['x_axis_label'] = kwargs.get('x_axis_label', 'Mixing Ratio(v/v)')
kwargs['y_axis_type'] = kwargs.get('y_axis_type', 'log')
kwargs['x_axis_type'] = kwargs.get('x_axis_type', 'log')
kwargs['y_range'] = kwargs.get(
'y_range', [np.max(pressure), np.min(pressure)])
kwargs['x_range'] = kwargs.get('x_range', [1e-20, 1e2])
fig = figure(**kwargs)
if len(molecules) < 3: ncol = 5
else: ncol = len(molecules)
cols = colfun1(ncol)
legend_it = []
for mol, c in zip(molecules, cols):
ind = np.where(mol == np.array(molecules))[0][0]
f = fig.line(full_output['layer']['mixingratios'][mol],
pressure,
color=c,
line_width=3,
muted_color=c,
muted_alpha=0.2)
legend_it.append((mol, [f]))
legend = Legend(items=legend_it, location=(0, -20))
legend.click_policy = "mute"
fig.add_layout(legend, 'left')
return fig
def flux_at_top(full_output,
plot_bb=True,
R=None,
pressures=[1e-1, 1e-2, 1e-3],
ng=None,
nt=None,
**kwargs):
"""
Routine to plot the OLR with overlaying black bodies.
Flux units are CGS = erg/s/cm^3
Parameters
----------
full_output : dict
full dictionary output with {'wavenumber','thermal','full_output'}
plot_bb : bool , optional
Default is to plot black bodies for three pressures specified by `pressures`
R : float
New constant R to bin to
pressures : list, optional
Default is a list of three pressures (in bars) = [1e-1,1e-2,1e-3]
ng : int
Used for 3D calculations to select point on the sphere (equivalent to longitude point)
nt : int
Used for 3D calculations to select point on the sphere (equivalent to latitude point)
**kwargs : dict
Any key word argument for bokeh.figure()
"""
if ((ng == None) & (nt == None)):
pressure_all = full_output['full_output']['layer']['pressure']
temperature_all = full_output['full_output']['layer']['temperature']
else:
pressure_all = full_output['full_output']['layer']['pressure'][:, ng,
nt]
temperature_all = full_output['full_output']['layer'][
'temperature'][:, ng, nt]
if not isinstance(pressures, (np.ndarray, list)):
raise Exception(
'check pressure input. It must be list or array. You can still input a single value as `pressures = [1e-3]`'
)
kwargs['plot_height'] = kwargs.get('plot_height', 300)
kwargs['plot_width'] = kwargs.get('plot_width', 400)
kwargs['title'] = kwargs.get('title', 'Outgoing Thermal Radiation')
kwargs['y_axis_label'] = kwargs.get('y_axis_label', 'Flux (erg/s/cm^3)')
kwargs['x_axis_label'] = kwargs.get('x_axis_label', 'Wavelength [μm]')
kwargs['y_axis_type'] = kwargs.get('y_axis_type', 'log')
kwargs['x_axis_type'] = kwargs.get('x_axis_type', 'log')
fig = figure(**kwargs)
if len(pressures) < 3: ncol = 5
else: ncol = len(pressures)
cols = colfun1(ncol)
wno = full_output['wavenumber']
if isinstance(R, (int, float)):
wno, thermal = mean_regrid(wno, full_output['thermal'], R=R)
else:
thermal = full_output['thermal']
fig.line(1e4 / wno, thermal, color='black', line_width=4)
for p, c in zip(pressures, cols):
ip = find_nearest_1d(pressure_all, p)
t = temperature_all[ip]
intensity = blackbody(t, 1 / wno)[0]
flux = np.pi * intensity
fig.line(1e4 / wno,
flux,
color=c,
alpha=0.5,
legend_label=str(int(t)) + ' K at ' + str(p) + ' bars',
line_width=4)
return fig
def cloud(full_output):
"""
Plotting the cloud input from ``picaso``.
The plot itselfs creates maps of the wavelength dependent single scattering albedo
and cloud opacity as a function of altitude.
Parameters
----------
full_output
Returns
-------
A row of two bokeh plots with the single scattering and optical depth map
"""
cols = colfun1(200)
color_mapper = LinearColorMapper(palette=cols, low=0, high=1)
dat01 = full_output['layer']['cloud']
#PLOT W0
scat01 = np.flip(dat01['w0'], 0) #[0:10,:]
xr, yr = scat01.shape
f01a = figure(x_range=[0, yr],
y_range=[0, xr],
x_axis_label='Wavelength (micron)',
y_axis_label='Pressure (bar)',
title="Single Scattering Albedo",
plot_width=300,
plot_height=300)
f01a.image(image=[scat01],
color_mapper=color_mapper,
x=0,
y=0,
dh=xr,
dw=yr)
color_bar = ColorBar(
color_mapper=color_mapper, #ticker=LogTicker(),
label_standoff=12,
border_line_color=None,
location=(0, 0))
f01a.add_layout(color_bar, 'left')
#PLOT OPD
scat01 = np.flip(dat01['opd'] + 1e-60, 0)
xr, yr = scat01.shape
cols = colfun2(200)[::-1]
color_mapper = LogColorMapper(palette=cols, low=1e-3, high=10)
f01 = figure(x_range=[0, yr],
y_range=[0, xr],
x_axis_label='Wavelength (micron)',
y_axis_label='Pressure (bar)',
title="Cloud Optical Depth Per Layer",
plot_width=300,
plot_height=300)
f01.image(image=[scat01],
color_mapper=color_mapper,
x=0,
y=0,
dh=xr,
dw=yr)
color_bar = ColorBar(color_mapper=color_mapper,
ticker=LogTicker(),
label_standoff=12,
border_line_color=None,
location=(0, 0))
f01.add_layout(color_bar, 'left')
#PLOT G0
scat01 = np.flip(dat01['g0'] + 1e-60, 0)
xr, yr = scat01.shape
cols = colfun3(200)[::-1]
color_mapper = LinearColorMapper(palette=cols, low=0, high=1)
f01b = figure(x_range=[0, yr],
y_range=[0, xr],
x_axis_label='Wavelength (micron)',
y_axis_label='Pressure (bar)',
title="Assymetry Parameter",
plot_width=300,
plot_height=300)
f01b.image(image=[scat01],
color_mapper=color_mapper,
x=0,
y=0,
dh=xr,
dw=yr)
color_bar = ColorBar(color_mapper=color_mapper,
ticker=BasicTicker(),
label_standoff=12,
border_line_color=None,
location=(0, 0))
f01b.add_layout(color_bar, 'left')
#CHANGE X AND Y AXIS TO BE PHYSICAL UNITS
#indexes for pressure plot
pressure = [
"{:.1E}".format(i) for i in full_output['layer']['pressure'][::-1]
] #flip since we are also flipping matrices
wave = ["{:.2F}".format(i) for i in 1e4 / full_output['wavenumber']]
nwave = len(wave)
npres = len(pressure)
iwave = np.array(range(nwave))
ipres = np.array(range(npres))
#set how many we actually want to put on the figure
#hard code ten on each..
iwave = iwave[::int(nwave / 10)]
ipres = ipres[::int(npres / 10)]
pressure = pressure[::int(npres / 10)]
wave = wave[::int(nwave / 10)]
#create dictionary for tick marks
ptick = {int(i): j for i, j in zip(ipres, pressure)}
wtick = {int(i): j for i, j in zip(iwave, wave)}
for i in [f01a, f01, f01b]:
i.xaxis.ticker = iwave
i.yaxis.ticker = ipres
i.xaxis.major_label_overrides = wtick
i.yaxis.major_label_overrides = ptick
return row(f01a, f01, f01b)
def plot_cld_input(nwno,
nlayer,
filename=None,
df=None,
pressure=None,
wavelength=None,
**pd_kwargs):
"""
This function was created to investigate CLD input file for PICASO.
The plot itselfs creates maps of the wavelength dependent single scattering albedo
and cloud opacity and assymetry parameter as a function of altitude.
Parameters
----------
nwno : int
Number of wavenumber points. For runs from Ackerman & Marley, this will always be 196.
nlayer : int
Should be one less than the number of levels in your pressure temperature grid. Cloud
opacity is assigned for slabs.
file : str , optional
(Optional)Path to cloud input file
df : str
(Optional)Dataframe of cloud input file
wavelength : array , optional
(Optional) this allows you to reset the tick marks to wavelengths instead of indicies
pressure : array, optional
(Optional) this allows you to reset the tick marks to pressure instead of indicies
pd_kwargs : kwargs
Pandas key word arguments for `pandas.read_csv`
Returns
-------
Three bokeh plots with the single scattering, optical depth, and assymetry maps
"""
if (pressure is not None):
pressure_label = 'Pressure (units by user)'
else:
pressure_label = 'Pressure Grid, TOA ->'
if (wavelength is not None):
wavelength_label = 'Wavelength (units by user)'
else:
wavelength_label = 'Wavenumber Grid'
cols = colfun1(200)
color_mapper = LinearColorMapper(palette=cols, low=0, high=1)
if not isinstance(filename, type(None)):
dat01 = pd.read_csv(filename, **pd_kwargs)
elif not isinstance(df, type(None)):
dat01 = df
#PLOT W0
scat01 = np.flip(np.reshape(dat01['w0'].values, (nlayer, nwno)), 0)
xr, yr = scat01.shape
f01a = figure(x_range=[0, yr],
y_range=[0, xr],
x_axis_label=wavelength_label,
y_axis_label=pressure_label,
title="Single Scattering Albedo",
plot_width=300,
plot_height=300)
f01a.image(image=[scat01],
color_mapper=color_mapper,
x=0,
y=0,
dh=xr,
dw=yr)
color_bar = ColorBar(
color_mapper=color_mapper, #ticker=LogTicker(),
label_standoff=12,
border_line_color=None,
location=(0, 0))
f01a.add_layout(color_bar, 'left')
#PLOT OPD
scat01 = np.flip(np.reshape(dat01['opd'].values, (nlayer, nwno)), 0)
xr, yr = scat01.shape
cols = colfun2(200)[::-1]
color_mapper = LogColorMapper(palette=cols, low=1e-3, high=10)
f01 = figure(x_range=[0, yr],
y_range=[0, xr],
x_axis_label=wavelength_label,
y_axis_label=pressure_label,
title="Cloud Optical Depth Per Layer",
plot_width=300,
plot_height=300)
f01.image(image=[scat01],
color_mapper=color_mapper,
x=0,
y=0,
dh=xr,
dw=yr)
color_bar = ColorBar(color_mapper=color_mapper,
ticker=LogTicker(),
label_standoff=12,
border_line_color=None,
location=(0, 0))
f01.add_layout(color_bar, 'left')
#PLOT G0
scat01 = np.flip(np.reshape(dat01['g0'].values, (nlayer, nwno)), 0)
xr, yr = scat01.shape
cols = colfun3(200)[::-1]
color_mapper = LinearColorMapper(palette=cols, low=0, high=1)
f01b = figure(x_range=[0, yr],
y_range=[0, xr],
x_axis_label=wavelength_label,
y_axis_label=pressure_label,
title="Assymetry Parameter",
plot_width=300,
plot_height=300)
f01b.image(image=[scat01],
color_mapper=color_mapper,
x=0,
y=0,
dh=xr,
dw=yr)
color_bar = ColorBar(color_mapper=color_mapper,
ticker=BasicTicker(),
label_standoff=12,
border_line_color=None,
location=(0, 0))
f01b.add_layout(color_bar, 'left')
#CHANGE X AND Y AXIS TO BE PHYSICAL UNITS
#indexes for pressure plot
if (pressure is not None):
pressure = ["{:.1E}".format(i) for i in pressure[::-1]
] #flip since we are also flipping matrices
npres = len(pressure)
ipres = np.array(range(npres))
#set how many we actually want to put on the figure
#hard code ten on each..
ipres = ipres[::int(npres / 10)]
pressure = pressure[::int(npres / 10)]
#create dictionary for tick marks
ptick = {int(i): j for i, j in zip(ipres, pressure)}
for i in [f01a, f01, f01b]:
i.yaxis.ticker = ipres
i.yaxis.major_label_overrides = ptick
if (wavelength is not None):
wave = ["{:.2F}".format(i) for i in wavelength]
nwave = len(wave)
iwave = np.array(range(nwave))
iwave = iwave[::int(nwave / 10)]
wave = wave[::int(nwave / 10)]
wtick = {int(i): j for i, j in zip(iwave, wave)}
for i in [f01a, f01, f01b]:
i.xaxis.ticker = iwave
i.xaxis.major_label_overrides = wtick
return row(f01a, f01, f01b)
def all_optics(out):
"""
Maps of the wavelength dependent single scattering albedo
and cloud opacity and asymmetry parameter as a function of altitude.
Parameters
----------
out : dict
Dictionary output from pyeddy run
Returns
-------
Three bokeh plots with the single scattering, optical depth, and assymetry maps
"""
#get into DataFrame format
dat01 = pyeddy.picaso_format(out['opd_per_layer'],
out['asymmetry'],out['single_scattering'])
nwno=len(out['wave'])
nlayer=len(out['pressure'])
pressure=out['pressure']
pressure_label = 'Pressure (Bars)'
wavelength_label = 'Wavelength (um)'
wavelength = out['wave']
cols = colfun1(200)
color_mapper = LinearColorMapper(palette=cols, low=0, high=1)
#PLOT W0
scat01 = np.flip(np.reshape(dat01['w0'].values,(nlayer,nwno)),0)
xr, yr = scat01.shape
f01a = figure(x_range=[0, yr], y_range=[0,xr],
x_axis_label=wavelength_label, y_axis_label=pressure_label,
title="Single Scattering Albedo",
plot_width=300, plot_height=300)
f01a.image(image=[scat01], color_mapper=color_mapper, x=0,y=0,dh=xr,dw =yr )
color_bar = ColorBar(color_mapper=color_mapper, #ticker=LogTicker(),
label_standoff=12, border_line_color=None, location=(0,0))
f01a.add_layout(color_bar, 'left')
#PLOT OPD
scat01 = np.flip(np.reshape(dat01['opd'].values,(nlayer,nwno)),0)
xr, yr = scat01.shape
cols = colfun2(200)[::-1]
color_mapper = LogColorMapper(palette=cols, low=1e-3, high=10)
f01 = figure(x_range=[0, yr], y_range=[0,xr],
x_axis_label=wavelength_label, y_axis_label=pressure_label,
title="Cloud Optical Depth Per Layer",
plot_width=320, plot_height=300)
f01.image(image=[scat01], color_mapper=color_mapper, x=0,y=0,dh=xr,dw =yr )
color_bar = ColorBar(color_mapper=color_mapper, ticker=LogTicker(),
label_standoff=12, border_line_color=None, location=(0,0))
f01.add_layout(color_bar, 'left')
#PLOT G0
scat01 = np.flip(np.reshape(dat01['g0'].values,(nlayer,nwno)),0)
xr, yr = scat01.shape
cols = colfun3(200)[::-1]
color_mapper = LinearColorMapper(palette=cols, low=0, high=1)
f01b = figure(x_range=[0, yr], y_range=[0,xr],
x_axis_label=wavelength_label, y_axis_label=pressure_label,
title="Assymetry Parameter",
plot_width=300, plot_height=300)
f01b.image(image=[scat01], color_mapper=color_mapper, x=0,y=0,dh=xr,dw =yr )
color_bar = ColorBar(color_mapper=color_mapper, ticker=BasicTicker(),
label_standoff=12, border_line_color=None, location=(0,0))
f01b.add_layout(color_bar, 'left')
#CHANGE X AND Y AXIS TO BE PHYSICAL UNITS
#indexes for pressure plot
if (pressure is not None):
pressure = ["{:.1E}".format(i) for i in pressure[::-1]] #flip since we are also flipping matrices
npres = len(pressure)
ipres = np.array(range(npres))
#set how many we actually want to put on the figure
#hard code ten on each..
ipres = ipres[::int(npres/10)]
pressure = pressure[::int(npres/10)]
#create dictionary for tick marks
ptick = {int(i):j for i,j in zip(ipres,pressure)}
for i in [f01a, f01, f01b]:
i.yaxis.ticker = ipres
i.yaxis.major_label_overrides = ptick
if (wavelength is not None):
wave = ["{:.2F}".format(i) for i in wavelength]
nwave = len(wave)
iwave = np.array(range(nwave))
iwave = iwave[::int(nwave/10)]
wave = wave[::int(nwave/10)]
wtick = {int(i):j for i,j in zip(iwave,wave)}
for i in [f01a, f01, f01b]:
i.xaxis.ticker = iwave
i.xaxis.major_label_overrides = wtick
return row(f01a, f01,f01b)
def cloud(full_output):
"""
Plotting the cloud input from ``picaso``.
The plot itselfs creates maps of the wavelength dependent single scattering albedo
and cloud opacity as a function of altitude.
Parameters
----------
full_output
Returns
-------
A row of two bokeh plots with the single scattering and optical depth map
"""
cols = colfun1(200)
color_mapper = LinearColorMapper(palette=cols, low=0, high=1)
dat01 = full_output['layer']['cloud']
#PLOT W0
scat01 = np.flip(dat01['w0'], 0) #[0:10,:]
xr, yr = scat01.shape
f01a = figure(x_range=[0, yr],
y_range=[0, xr],
x_axis_label='Wavenumber Grid',
y_axis_label='Pressure Grid, TOA ->',
title="Single Scattering Albedo",
plot_width=300,
plot_height=300)
f01a.image(image=[scat01],
color_mapper=color_mapper,
x=0,
y=0,
dh=xr,
dw=yr)
color_bar = ColorBar(
color_mapper=color_mapper, #ticker=LogTicker(),
label_standoff=12,
border_line_color=None,
location=(0, 0))
f01a.add_layout(color_bar, 'left')
#PLOT OPD
scat01 = np.flip(dat01['opd'] + 1e-60, 0)
xr, yr = scat01.shape
cols = colfun2(200)[::-1]
color_mapper = LogColorMapper(palette=cols, low=1e-3, high=10)
f01 = figure(x_range=[0, yr],
y_range=[0, xr],
x_axis_label='Wavenumber Grid',
y_axis_label='Pressure Grid, TOA ->',
title="Cloud Optical Depth Per Layer",
plot_width=300,
plot_height=300)
f01.image(image=[scat01],
color_mapper=color_mapper,
x=0,
y=0,
dh=xr,
dw=yr)
color_bar = ColorBar(color_mapper=color_mapper,
ticker=LogTicker(),
label_standoff=12,
border_line_color=None,
location=(0, 0))
f01.add_layout(color_bar, 'left')
#PLOT G0
scat01 = np.flip(dat01['g0'] + 1e-60, 0)
xr, yr = scat01.shape
cols = colfun3(200)[::-1]
color_mapper = LinearColorMapper(palette=cols, low=0, high=1)
f01b = figure(x_range=[0, yr],
y_range=[0, xr],
x_axis_label='Wavenumber Grid',
y_axis_label='Pressure Grid, TOA ->',
title="Assymetry Parameter",
plot_width=300,
plot_height=300)
f01b.image(image=[scat01],
color_mapper=color_mapper,
x=0,
y=0,
dh=xr,
dw=yr)
color_bar = ColorBar(color_mapper=color_mapper,
ticker=BasicTicker(),
label_standoff=12,
border_line_color=None,
location=(0, 0))
f01b.add_layout(color_bar, 'left')
return column(row(f01a, f01, row(f01b)))
def plot_cld_input(nwno, nlayer, filename=None, df=None, **pd_kwargs):
"""
This function was created to investigate CLD input file for PICASO.
The plot itselfs creates maps of the wavelength dependent single scattering albedo
and cloud opacity and assymetry parameter as a function of altitude.
Parameters
----------
nwno : int
Number of wavenumber points. For runs from Ackerman & Marley, this will always be 196.
nlayer : int
Should be one less than the number of levels in your pressure temperature grid. Cloud
opacity is assigned for slabs.
file : str
(Optional)Path to cloud input file
df : str
(Optional)Dataframe of cloud input file
pd_kwargs : kwargs
Pandas key word arguments for `pandas.read_csv`
Returns
-------
Three bokeh plots with the single scattering, optical depth, and assymetry maps
"""
cols = colfun1(200)
color_mapper = LinearColorMapper(palette=cols, low=0, high=1)
if not isinstance(filename, type(None)):
dat01 = pd.read_csv(filename, **pd_kwargs)
elif not isinstance(df, type(None)):
dat01 = df
#PLOT W0
scat01 = np.flip(np.reshape(dat01['w0'].values, (nlayer, nwno)), 0)
xr, yr = scat01.shape
f01a = figure(x_range=[0, yr],
y_range=[0, xr],
x_axis_label='Wavenumber Grid',
y_axis_label='Pressure Grid, TOA ->',
title="Single Scattering Albedo",
plot_width=300,
plot_height=300)
f01a.image(image=[scat01],
color_mapper=color_mapper,
x=0,
y=0,
dh=xr,
dw=yr)
color_bar = ColorBar(
color_mapper=color_mapper, #ticker=LogTicker(),
label_standoff=12,
border_line_color=None,
location=(0, 0))
f01a.add_layout(color_bar, 'left')
#PLOT OPD
scat01 = np.flip(np.reshape(dat01['opd'].values, (nlayer, nwno)), 0)
xr, yr = scat01.shape
cols = colfun2(200)[::-1]
color_mapper = LogColorMapper(palette=cols, low=1e-3, high=10)
f01 = figure(x_range=[0, yr],
y_range=[0, xr],
x_axis_label='Wavenumber Grid',
y_axis_label='Pressure Grid, TOA ->',
title="Cloud Optical Depth Per Layer",
plot_width=300,
plot_height=300)
f01.image(image=[scat01],
color_mapper=color_mapper,
x=0,
y=0,
dh=xr,
dw=yr)
color_bar = ColorBar(color_mapper=color_mapper,
ticker=LogTicker(),
label_standoff=12,
border_line_color=None,
location=(0, 0))
f01.add_layout(color_bar, 'left')
#PLOT G0
scat01 = np.flip(np.reshape(dat01['g0'].values, (nlayer, nwno)), 0)
xr, yr = scat01.shape
cols = colfun3(200)[::-1]
color_mapper = LinearColorMapper(palette=cols, low=0, high=1)
f01b = figure(x_range=[0, yr],
y_range=[0, xr],
x_axis_label='Wavenumber Grid',
y_axis_label='Pressure Grid, TOA ->',
title="Assymetry Parameter",
plot_width=300,
plot_height=300)
f01b.image(image=[scat01],
color_mapper=color_mapper,
x=0,
y=0,
dh=xr,
dw=yr)
color_bar = ColorBar(color_mapper=color_mapper,
ticker=BasicTicker(),
label_standoff=12,
border_line_color=None,
location=(0, 0))
f01b.add_layout(color_bar, 'left')
return column(row(f01a, f01, row(f01b)))
def investigate_fsed(directory, metal, distance, fsed, plot_file='plot.html'):
"""
This functionw as created to investigate CLD output from the grid created in Batalha+2018.
The CLD files are not included in the SQLite database so these file names refer to the
specific naming function and directory. These files are available upon request.
Email [email protected]
The plot itselfs plots maps of the wavelength dependent single scattering albedo
and cloud opacity for up to three different fseds at a single metallicty and distance.
It was created becaues there are several times when fsed does funky numerical things
in the upper layers of the atmosphere.
Parameters
----------
directory : str
Directory which points to the fortney grid of cloud output from Ackerman Marley code
metal : str
Metallicity: Options are m0.0 m0.5 m1.0 m1.5 m1.7 m2.0
distance : str
Distance to G-type star. Options are d0.5 d0.6 d0.7 d0.85 d1.0 d1.5 d2.0 d3.0 d4.0 d5.0
fsed : list of str
Up to three sedimentation efficiencies. Options are f0.01 f0.03 f0.3 f1 f3 f6
plot_file : str
(Optional)This is the html plotting file Default = 'plot.html'
"""
cols = colfun1(200)
color_mapper = LinearColorMapper(palette=cols, low=0, high=1)
dat01 = pd.read_csv(os.path.join(
directory, metal, distance,
metal + 'x_rfacv0.5-nc_tint150-' + fsed[0] + '-' + distance + '.cld'),
header=None,
delim_whitespace=True)
dat1 = pd.read_csv(os.path.join(
directory, metal, distance,
metal + 'x_rfacv0.5-nc_tint150-' + fsed[1] + '-' + distance + '.cld'),
header=None,
delim_whitespace=True)
dat6 = pd.read_csv(os.path.join(
directory, metal, distance,
metal + 'x_rfacv0.5-nc_tint150-' + fsed[2] + '-' + distance + '.cld'),
header=None,
delim_whitespace=True)
scat01 = np.flip(np.reshape(dat01[4], (60, 196)), 0) #[0:10,:]
scat1 = np.flip(np.reshape(dat1[4], (60, 196)), 0) #[0:10,:]
scat6 = np.flip(np.reshape(dat6[4], (60, 196)), 0) #[0:10,:]
xr, yr = scat01.shape
f01a = figure(x_range=[150, yr],
y_range=[0, xr],
x_axis_label='Wavelength Grid',
y_axis_label='Pressure Grid, TOA ->',
title="Scattering Fsed = 0.01",
plot_width=300,
plot_height=300)
f1a = figure(x_range=[150, yr],
y_range=[0, xr],
x_axis_label='Wavelength Grid',
y_axis_label='Pressure Grid, TOA ->',
title="Scattering Fsed = 0.3",
plot_width=300,
plot_height=300)
f6a = figure(x_range=[150, yr],
y_range=[0, xr],
x_axis_label='Wavelength Grid',
y_axis_label='Pressure Grid, TOA ->',
title="Scattering Fsed = 1",
plot_width=300,
plot_height=300)
f01a.image(image=[scat01],
color_mapper=color_mapper,
x=0,
y=0,
dh=xr,
dw=yr)
f1a.image(image=[scat1], color_mapper=color_mapper, x=0, y=0, dh=xr, dw=yr)
f6a.image(image=[scat6], color_mapper=color_mapper, x=0, y=0, dh=xr, dw=yr)
color_bar = ColorBar(
color_mapper=color_mapper, #ticker=LogTicker(),
label_standoff=12,
border_line_color=None,
location=(0, 0))
f01a.add_layout(color_bar, 'right')
#PLOT OPD
scat01 = np.flip(np.reshape(dat01[2] + 1e-60, (60, 196)), 0)
scat1 = np.flip(np.reshape(dat1[2] + 1e-60, (60, 196)), 0)
scat6 = np.flip(np.reshape(dat6[2] + 1e-60, (60, 196)), 0)
xr, yr = scat01.shape
cols = colfun2(200)[::-1]
color_mapper = LogColorMapper(palette=cols, low=1e-3, high=10)
f01 = figure(x_range=[150, yr],
y_range=[0, xr],
x_axis_label='Wavelength Grid',
y_axis_label='Pressure Grid, TOA ->',
title="Optical Depth Fsed = 0.01",
plot_width=300,
plot_height=300)
f1 = figure(x_range=[150, yr],
y_range=[0, xr],
x_axis_label='Wavelength Grid',
y_axis_label='Pressure Grid, TOA ->',
title="Optical Depth Fsed = 0.3",
plot_width=300,
plot_height=300)
f6 = figure(x_range=[150, yr],
y_range=[0, xr],
x_axis_label='Wavelength Grid',
y_axis_label='Pressure Grid, TOA ->',
title="Optical Depth Fsed = 1",
plot_width=300,
plot_height=300)
f01.image(image=[scat01],
color_mapper=color_mapper,
x=0,
y=0,
dh=xr,
dw=yr)
f1.image(image=[scat1], color_mapper=color_mapper, x=0, y=0, dh=xr, dw=yr)
f6.image(image=[scat6], color_mapper=color_mapper, x=0, y=0, dh=xr, dw=yr)
color_bar = ColorBar(color_mapper=color_mapper,
ticker=LogTicker(),
label_standoff=12,
border_line_color=None,
location=(0, 0))
f01.add_layout(color_bar, 'right')
output_file(plot_file)
show(row(column(f01a, f1a, f6a), column(f01, f1, f6)))