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# %% {"nbsphinx": "hidden"}
import os
import carmapy as _cm
if os.environ.get("RUN_CARMA") != "True":
    _orig = _cm.Carma.run
    def _rtd_run(self, *a, suppress_output=False, **kw):
        if suppress_output:
            return
        return _orig(self, *a, suppress_output=True, **kw)
    _cm.Carma.run = _rtd_run

# %% [markdown]
# # Creating Spectra with Picaso
#
# carmapy has a few built-in functions that make it easier to collate the results
# of a carma simulation into a form usable by picaso.  Picaso
# (https://github.com/natashabatalha/picaso) is a python package that, among
# other things, has tools for creating exoplanet and brown dwarf emission spectra.
# This tutorial assumes that you have already installed picaso and configured your
# environment variables to point to the references folder (see
# https://natashabatalha.github.io/picaso/installation.html).  Alternatively you
# can uncomment the lines below which set the environment variables and adjust the
# path if you don't have them already configured. To check that picaso is
# installed and configured properly run the following code:

# %%
import os
import picaso

# This notebook expects `picaso_refdata` and `PYSYN_CDBS` to already be set in
# your environment (e.g. in your shell rc). If you'd rather set them inline,
# uncomment and edit:
# path = '/path/to/picaso/reference'
# os.environ['picaso_refdata'] = path
# os.environ['PYSYN_CDBS'] = path


# %% [markdown]
# Now let's import picaso submodules and carmapy, and load our data again

# %%
import numpy as np
from matplotlib import pyplot as plt

from picaso import justdoit as jdi
from picaso import justplotit as jpi

import carmapy

jpi.output_notebook()

carma = carmapy.load_carma("my_first_carma")
carma.read_results()

# %% [markdown]
# Next, let us set up picaso to generate the spectra of free-floating brown
# dwarfs in thermal emission.  If you wish to instead look at a planet orbiting a
# star, look at the picaso tutorials for that case and modify the below code
# appropriately.

# %%
opacity = jdi.opannection(wave_range=[.5,15])
example_case = jdi.inputs(calculation='browndwarf')
example_case.phase_angle(0)

# %% [markdown]
# We now tell carma to calculate the atmospheric and cloud optical properties and
# pass those numbers to picaso:

# %%
λs = np.linspace(5e-4, 2e-3, 1000)

carma.results.gen_picaso_atm_file()
carma.results.gen_picaso_cloud_file(λs)

example_case.gravity(gravity=carma.surface_grav,
                     gravity_unit=jdi.u.Unit('cm/(s**2)'))

example_case.atmosphere(filename=f'{carma.name}/fastchem.atm', sep='\\s+')
example_case.clouds(filename=f"{carma.name}/clouds.atm", sep='\\s+')


# %% [markdown]
# We can now calculate and then bin the spectrum as follows

# %%
df = example_case.spectrum(opacity, full_output=True,calculation='thermal') #note the new last key

R = 1000
wno, fp = df['wavenumber'] , df['thermal']
wno_bin, fp_bin = jdi.mean_regrid(wno, fp, R=R)

# %% [markdown]
# And here is an example of how to plot the spectrum

# %%
fig, ax = plt.subplots(figsize=(12, 5))

λ = 1e4/wno_bin
plt.plot(λ, fp_bin/np.max(fp_bin))

plt.xlabel("Wavelength [μm]")
plt.ylabel("Normalized Flux (Fν)")
plt.xlim(.5, 15)
plt.show()

# %% [markdown]
# Similarly, here is the spectrum instead plotted as brightness temperature

# %%
fig, ax = plt.subplots(figsize=(10, 5))


λ = 1e4/wno_bin
T = jpi.brightness_temperature({"wavenumber": wno,
                                "thermal": fp},
                                R=R,
                                plot=False)

plt.plot(λ, T)

plt.xlabel("Wavelength [μm]")
plt.ylabel("Brightness Temperature [K]")
plt.xlim(.5, 15)
plt.show()