diff --git a/.gitignore b/.gitignore index 66bd3a4..3b42279 100644 --- a/.gitignore +++ b/.gitignore @@ -68,3 +68,6 @@ target/ *Theta45 *checkpoint.ipynb *.png + +# ignores added by Lia +*.hdf5 \ No newline at end of file diff --git a/calculate_atmosphere_opacities.py b/calculate_atmosphere_opacities.py new file mode 100644 index 0000000..a2cf0ae --- /dev/null +++ b/calculate_atmosphere_opacities.py @@ -0,0 +1,229 @@ +#! /usr/bin/env python +## +## calculate_atmosphere_opacities.py -- Calculate the opacity, +## dtau/dz, for gas-phase molecules provided by the opacity database +## (from MacDonald et al., in prep). Functions in this file were +## tested in test_gas_opac.ipynb +## +## 2019.01.11 - liac@umich.edu +## +## 2019.04.06 - upgraded to include continuum opacity from CIA and H- +## - r.macdonald@ast.cam.ac.uk +##------------------------------------------------------------------- + +import os +import numpy as np +import scipy.constants as sc +from astropy.io import fits + +from maplib import load_out3, cumulative_integral +import opacity_NEW as opac + +HOME_DIR = os.environ['HOME'] + '/Dropbox/Science/cloud-academy/Les_Houches_Cloud_Activity/' +DATA_DIR = HOME_DIR + 'static_weather_results/HATP_7b' +OUT_DIR = HOME_DIR + 'Gas_Ext/' +DB_DIR = os.environ['HOME'] + '/dev/cloudacademyMap/gas_opac/' + +LONS = np.arange(-180., 180.01, 15) # deg +LATS = np.arange(0., 67.51, 22.5) # deg + +#--------- +# Set up for MacDonald's database + +# Grabbed from Readme.txt +RMcD_gas = np.array(['H3+', 'Na', 'K', 'Li', 'Rb', 'Cs', 'H2O', 'CH4', 'NH3', 'HCN', 'CO', \ + 'CO2', 'C2H2', 'H2S', 'N2', 'O2', 'O3', 'OH', 'NO', 'SO2', 'PH3', 'TiO', 'VO', \ + 'AlO', 'SiO', 'CaO', 'TiH', 'CrH', 'FeH', 'ScH', 'AlH', 'SiH', 'BeH', 'CaH', \ + 'MgH', 'LiH', 'SiH', 'CH', 'SH', 'NH', 'Fe', 'Fe+', 'Ti', 'Ti+']) + +RMcD_cia = np.array(['H2-H2', 'H2-He']) # Collisionally-induced absorption pairs + +RMcD_gas_upper = np.array([g.upper() for g in RMcD_gas]) + +OPACITY_TREATMENT = 'Log-avg' + +#-------- +# Supporting functions + +def wavel_grid_constant_R(wl_min, wl_max, R): + """ + Calculate a wavelength grid ranging from wl_min to wl_max at a + constant spectral resolution R = wl/dwl. + """ + + delta_log_wl = 1.0/R + N_wl = (np.log(wl_max) - np.log(wl_min)) / delta_log_wl + N_wl = np.around(N_wl).astype(np.int64) + log_wl = np.linspace(np.log(wl_min), np.log(wl_max), N_wl) + wl = np.exp(log_wl) + + return wl + +def calc_dtau_dz_gas(gas, opac_dict, ch_dict): + """ + Calculate dtau/dz for all available gases in the atmosphere. + """ + n_z = ch_dict[gas.upper()] # Number density for this gas (cm^-3) + sigma = opac_dict[gas] # Cross section (m^2) + + NP = len(n_z) # Number of vertical data points + NWL = len(sigma[0,:]) # Number of wavelengths + + result = np.zeros(shape=(NP, NWL)) + + # For each layer, compute extinction coefficient + for i in range(NP): + result[i,:] = n_z[i] * (sigma[i,:] * 1.0e4) # cm^-1 + + return result + +def calc_dtau_dz_CIA(pair, cia_dict, ch_dict): + """ + Calculate dtau/dz for CIA pairs. + + Only two pairs matter here, so no need for a fancy loop. + """ + + if (pair == 'H2-H2'): + + n_1 = ch_dict['H2'] # Number density of first gas in pair (cm^-3) + n_2 = ch_dict['H2'] # Number density of second gas in pair (cm^-3) + + elif (pair == 'H2-He'): + + n_1 = ch_dict['H2'] # Number density of first gas in pair (cm^-3) + n_2 = ch_dict['HE'] # Number density of second gas in pair (cm^-3) + + sigma = cia_dict[pair] # Binary cross section (m^5) + + NP = len(n_1) # Number of vertical data points + NWL = len(sigma[0,:]) # Number of wavelengths + + result = np.zeros(shape=(NP, NWL)) + + # For each layer, compute extinction coefficient + for i in range(NP): + result[i,:] = (n_1[i] * n_2[i]) * (sigma[i,:] * 1.0e10) # cm^-1 + + return result + +def calc_dtau_dz_H_minus(H_minus_bf, H_minus_ff, n_e, ch_dict): + """ + Calculate dtau/dz for both sources of H- opacity. + """ + + # Load necessary number densities (n_e- through function argument) + n_H_m = ch_dict['H-'] # Number density of H- (cm^-3) + n_H = ch_dict['H'] # Number density of H (cm^-3) + + sigma_bf = H_minus_bf # Cross section (m^2) + sigma_ff = H_minus_ff # Binary cross section (m^5) + + NP = len(n_H_m) # Number of vertical data points + NWL = len(sigma_bf) # Number of wavelengths + + result = np.zeros(shape=(NP, NWL)) + result_bf = np.zeros(shape=(NP, NWL)) + result_ff = np.zeros(shape=(NP, NWL)) + + # For each layer, compute extinction coefficient + for i in range(NP): + result_bf[i,:] = n_H_m[i] * (sigma_bf[:] * 1.0e4) # cm^-1 + result_ff[i,:] = (n_H[i] * n_e[i]) * (sigma_ff[i,:] * 1.0e10) # cm^-1 + + result[i,:] = result_bf[i,:] + result_ff[i,:] + + return result + +def write_opac_fits(dtau, filename): + """ + A function for writing dtau_dz (or any other opacity dictionary) to a FITS file. + """ + hdr = fits.Header() + hdr['COMMENT'] = "dtau/dz for gas phase elements of HATP-7b" + hdr['COMMENT'] = "Made from opacity tables of R. MacDonald" + hdr['COMMENT'] = "See out3_*.dat files for p, T, and z profiles" + + primary_hdu = fits.PrimaryHDU(header=hdr) + hdus_to_write = [primary_hdu] + for k in dtau.keys(): + hdus_to_write.append(fits.ImageHDU(dtau[k], name=k)) + + hdu_list = fits.HDUList(hdus=hdus_to_write) + hdu_list.writeto(filename, overwrite=True) + return + +def run_calculation(lon, lat): + """ + Runs the dtau_dz calculation for all availabe gases in MacDonald's + database and saves them to a file. + """ + # Open the out3_thermo.dat file for a given sight line and grab all of the gases listed + #thermo = load_out3('thermo', lon, lat, root=DATA_DIR) + c1 = load_out3('chem1', lon, lat, root=DATA_DIR) + c2 = load_out3('chem2', lon, lat, root=DATA_DIR) + c3 = load_out3('chem3', lon, lat, root=DATA_DIR) + Ch_gas = [] + Ch_dens = {} + for chem in [c1, c2, c3]: + for k in chem.keys(): + if k not in ['z','p','T','n']: + Ch_gas.append(k) + Ch_dens[k.upper()] = chem[k] # cm^-3 + + gases, gases_missing = [], [] + for g in Ch_gas: + g_up = g.upper() + if g_up in RMcD_gas_upper: + ig = np.where(RMcD_gas_upper == g_up)[0] + gases.append(RMcD_gas[ig][0]) + else: + gases_missing.append(g) + + print("Gases missing: ", gases_missing) + print("Gases found: ", gases) + + # Grab the relevant wavelengths + wavel = load_out3('wavel', lon, lat, root=DATA_DIR) + thermo = load_out3('thermo', lon, lat, root=DATA_DIR) + + # Convert electron pressure (dyn cm^-2) into electron number density (cm^-3) + n_e = thermo['pel']/(1.38066e-16 * thermo['T']) # Hard coded value is Boltzmann constant in cgs + P = thermo['p']*1.0e-6 # Convert pressure to bar (expected in opacitiy functions) + T = thermo['T'] + + # Array listing CIA pair names + cia_pairs = RMcD_cia + + # Get the opacities for each gas (loaded into a dictionary) + cross_sections, CIA, H_minus_bf, H_minus_ff = opac.Extract_opacity_NEW(np.array(gases), cia_pairs, P, T, + wavel, OPACITY_TREATMENT, root=DB_DIR) + #opacities = demo.Extract_opacity_PTpairs(np.array(gases), thermo['p'], thermo['T'], wavel, + # OPACITY_TREATMENT, root=DB_DIR) + + dtau_dz = dict() + + # First add normal gas cross sections + for g in gases: + dtau_dz[g] = calc_dtau_dz_gas(g, cross_sections, Ch_dens) + + # Secondly, add CIA opacities + for pair in cia_pairs: + dtau_dz[pair] = calc_dtau_dz_CIA(pair, CIA, Ch_dens) + + # Finally, add contributions from both sources of H- opacity + dtau_dz['H-'] = calc_dtau_dz_H_minus(H_minus_bf, H_minus_ff, n_e, Ch_dens) + + # Save the files for this calculation + fname = OUT_DIR + 'Phi{:.1f}Theta{:.1f}_dtau_dz.fits'.format(lon, lat) + write_opac_fits(dtau_dz, fname) + + return + +#---------------- + +# Run the calculations +if __name__ == '__main__': + for i in LONS: + for j in LATS: + run_calculation(i, j) diff --git a/calculate_hires_opacities.py b/calculate_hires_opacities.py new file mode 100644 index 0000000..18951c7 --- /dev/null +++ b/calculate_hires_opacities.py @@ -0,0 +1,113 @@ +#! /usr/bin/env python +## +## calculate_hires_opacities.py -- Calculate the opacity, dtau/dz, for +## gas-phase molecules provided by the opacity database (from +## MacDonald et al., in prep) with a high resolution grid of wavelengths. +## +## Depends on functions in calculate_atmosphere_opacities.py +## +## 2019.02.11 - liac@umich.edu +##------------------------------------------------------------------------ + +import os +import numpy as np +from astropy.io import fits + +from maplib import load_out3, cumulative_integral +import opacity_NEW as opac + +import calculate_atmosphere_opacities as cao + +# Set up paths +HOME_DIR = os.environ['HOME'] + '/Dropbox/Science/cloud-academy/Les_Houches_Cloud_Activity/' +DATA_DIR = HOME_DIR + 'static_weather_results/HATP_7b' +OUT_DIR = HOME_DIR + 'Gas_Ext/hires_' +DB_DIR = os.environ['HOME'] + '/dev/cloudacademyMap/gas_opac/' + +# Make some pointers for easy reference +RMcD_gas = cao.RMcD_gas +RMcD_cia = cao.RMcD_cia +RMcD_gas_upper = cao.RMcD_gas_upper +OPACITY_TREATMENT = cao.OPACITY_TREATMENT + +# Longitude, latitude pairs for calculation +LLS = [(-180.,0.), (-90.,0.), (0.,0.), (90.,0.)] # deg + +# Wavelength grid to use +WGRID = cao.wavel_grid_constant_R(0.4, 50.0, 1000.0) # um +#WGRID = np.logspace(np.log10(0.3), 2.0, 1000) # um + +def run_hires_calculation(lon, lat, wavel=WGRID): + """ + Runs the dtau/dz calculation for all available gases in the + MacDonald databes and saves them to a file. Uses the specified + wavelength grid instead of the one provided by the + static_weather_results files. + """ + # Open the out3_thermo.dat file for a given sight line and grab all of the gases listed + #thermo = load_out3('thermo', lon, lat, root=DATA_DIR) + c1 = load_out3('chem1', lon, lat, root=DATA_DIR) + c2 = load_out3('chem2', lon, lat, root=DATA_DIR) + c3 = load_out3('chem3', lon, lat, root=DATA_DIR) + Ch_gas = [] + Ch_dens = {} + for chem in [c1, c2, c3]: + for k in chem.keys(): + if k not in ['z','p','T','n']: + Ch_gas.append(k) + Ch_dens[k.upper()] = chem[k] # cm^-3 + + # Find gases that are also in the MacDonald database + gases, gases_missing = [], [] + for g in Ch_gas: + g_up = g.upper() + if g_up in RMcD_gas_upper: + ig = np.where(RMcD_gas_upper == g_up)[0] + gases.append(RMcD_gas[ig][0]) + else: + gases_missing.append(g) + + print("Gases missing: ", gases_missing) + print("Gases found: ", gases) + + thermo = load_out3('thermo', lon, lat, root=DATA_DIR) + + # Convert electron pressure (dyn cm^-2) into electron number density (cm^-3) + n_e = thermo['pel']/(1.38066e-16 * thermo['T']) # Hard coded value is Boltzmann constant in cgs + P = thermo['p']*1.0e-6 # Convert pressure to bar (expected in opacitiy functions) + T = thermo['T'] + + # Array listing CIA pair names + cia_pairs = RMcD_cia + + # Get the opacities for each gas (loaded into a dictionary) + cross_sections, CIA, H_minus_bf, H_minus_ff = opac.Extract_opacity_NEW(np.array(gases), cia_pairs, P, T, + wavel, OPACITY_TREATMENT, root=DB_DIR) + #opacities = demo.Extract_opacity_PTpairs(np.array(gases), thermo['p'], thermo['T'], wavel, + # OPACITY_TREATMENT, root=DB_DIR) + + dtau_dz = dict() + + # First add normal gas cross sections + for g in gases: + dtau_dz[g] = cao.calc_dtau_dz_gas(g, cross_sections, Ch_dens) + + # Secondly, add CIA opacities + for pair in cia_pairs: + dtau_dz[pair] = cao.calc_dtau_dz_CIA(pair, CIA, Ch_dens) + + # Finally, add contributions from both sources of H- opacity + dtau_dz['H-'] = cao.calc_dtau_dz_H_minus(H_minus_bf, H_minus_ff, n_e, Ch_dens) + + # Save the files for this calculation + fname = OUT_DIR + 'Phi{:.1f}Theta{:.1f}_dtau_dz.fits'.format(lon, lat) + cao.write_opac_fits(dtau_dz, fname) + + return + +##---------------- +## Do the things! + +if __name__ == '__main__': + for lon, lat in LLS: + run_hires_calculation(lon, lat) diff --git a/gas_opac/Readme.txt b/gas_opac/Readme.txt new file mode 100644 index 0000000..d13be7c --- /dev/null +++ b/gas_opac/Readme.txt @@ -0,0 +1,41 @@ +*****DISCLAIMER**** + +The cross section database enclosed is a work in progress. The method used to compute these cross sections is currently unpublished (MacDonald, in prep), but the resulting cross sections have been benchmarked against other groups (e.g. EXOMOL, NASA Ames). They should be considered experimental at this stage, and I would appreciate if you do not distribute them beyond this collaboration without prior approval until further testing has been completed. If you would like to use these cross sections for other projects before the database is published, please drop me an email at r.macdonald@ast.cam.ac.uk and I'll be happy to provide you with updates. + +*****CONTENTS***** + +The full details underlying the cross sections will be laid out elsewhere, but the main characteristics are as follows: + +*Ions included: H3+, Fe+, Ti+ +*Atoms included: Na, K, Li, Rb, Cs, Fe, Ti +*Molecules included: H2, H2O, CH4, NH3, HCN, CO, CO2, C2H2, H2S, N2, O2, O3, OH, NO, SO2, PH3, TiO, VO, ALO, + SiO, CaO, TiH, CrH, FeH, ScH, AlH, SiH, BeH, CaH, MgH, LiH, SiH, CH, SH, NH + +*All species are broadened by H2 and He in an 85% / 15% ratio (accounting for angular momentum quantum number J dependance where available). + +*Cross sections are calculated on a uniform wavenumber grid from 200 to 25000 cm^-1 (i.e. 50 um -> 0.4 um) with a spacing of 0.01 cm^-1 (R~10^6). + +*Each cross section calculated at 162 pressure-temperaure points: +log(P/bar) = -6.0 -> 2.0 (1 dex spacing) +T/K = [100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 1200, 1400, 1600, 1800, 2000, 2500, 3000, 3500] + +*All linelists are the latest available in the literature (mostly ExoMol). + +*For cross sections, the stored values are log10(cross section / m^2) (species, nu, P, T). + +***NEW*** + +*Collisionally-induced absorption (CIA) is only a function of T and nu, and is tabulated from HITRAN. + +*For CIA, the stored values are log10(binary cross section / m^5) (cia_pair, nu, T). + +*****USAGE***** + +See the python script 'opacity_demo.py' in this folder for an example of how to open the database, select a given species, and plot it at a given temperature and pressure. + +Note that the pre-computed cross section arrays are extremely large (9x18x2480001 elements for each species), so running on a computer with >8 GB RAM is generally advised. + +*****QUESTIONS/FEEDBACK***** + +Any questions, comments, or feedback? New atoms, molecules, or ions you would like to see included? +Please drop a message to Ryan MacDonald @ r.macdonald@ast.cam.ac.uk diff --git a/gas_opac/opacity_NEW.py b/gas_opac/opacity_NEW.py new file mode 100644 index 0000000..7e4b946 --- /dev/null +++ b/gas_opac/opacity_NEW.py @@ -0,0 +1,1127 @@ +# Demostrates how to open the opacity database and interpolate to a given P, T, and wavelength grid +# Author: Ryan J. MacDonald (w/ improvements from Lia Corrales) + +# V1.0: Evaluates cross sections at a single P,T point (8th November 2018) +# V2.0: Evaluates cross sections on a grid of (P,T) points (6th December 2018) +# V3.0: Evaluates collisionally-induced opacity on a grid of (T) points (17th February 2019) +# V4.0: Inclusion of H- bound-free and free-free opacities (26th February 2019) +# V5.0: Optimised for speed wby interpolating directly to layer conditions (5th April 2019) + +import numpy as np +import h5py +from scipy.ndimage import gaussian_filter1d as gauss_conv +from numba.decorators import jit +import matplotlib.pyplot as plt +from matplotlib.ticker import MultipleLocator, FormatStrFormatter + +#***** Begin function declarations *****# + +@jit(nopython = True) +def prior_index(vec, value, start): + + '''Finds the index of a grid closest to a specified value''' + + value_tmp = value + + if (value_tmp > vec[-1]): + return (len(vec) - 1) + + # Check if value out of bounds, if so set to edge value + if (value_tmp < vec[0]): value_tmp = vec[0] + if (value_tmp > vec[-2]): value_tmp = vec[-2] + + index = start + + for i in range(len(vec)-start): + if (vec[i+start] > value_tmp): + index = (i+start) - 1 + break + + return index + +@jit(nopython=True) +def prior_index_V2(val, grid_start, grid_end, N_grid): + + if (val < grid_start): + return 0 + + elif (val > grid_end): + return N_grid-1 + + else: + i = (N_grid-1) * ((val - grid_start) / (grid_end - grid_start)) + return int(i) + +@jit(nopython=True) +def closest_index(val, grid_start, grid_end, N_grid): + + '''Finds the index of a UNIFORM grid closest to a specified value. + + Assuming a uniform grid dramatically speeds calculation''' + + if (val < grid_start): + return 0 + + elif (val > grid_end): + return N_grid-1 + + else: + i = (N_grid-1) * ((val - grid_start) / (grid_end - grid_start)) + if ((i%1)<=0.5): + return int(i) + else: + return int(i)+1 + +@jit(nopython = True) +def T_interpolation_init(N_T_out, T_grid, T_out, y): + + ''' Precomputes the T interpolation weight factors, so this does not + need to be done multiple times across all species. + ''' + + w_T = np.zeros(N_T_out) # Temperature interpolation weight factors + + # Find T index in cross secion arrays prior to fine temperature value + for j in xrange(N_T_out): + + if (T_out[j] < T_grid[0]): # If fine temperature point falls off LHS of temperaure grid + y[j] = -1 # Special value (-1) stored, interpreted in interpolator + w_T[j] = 0.0 # Weight not used in this case + + elif (T_out[j] >= T_grid[-1]): # If fine temperature point falls off RHS of temperaure grid + y[j] = -2 # Special value (-2) stored, interpreted in interpolator + w_T[j] = 0.0 # Weight not used in this case + + else: + + # Have to use prior_index (V1) here as T_grid is not uniformly spaced + y[j] = prior_index(T_grid, T_out[j], 0) # Index in cross secion arrays prior to desired temperature value + + # Pre-computed temperature values to left and right of desired temperature value + T1 = T_grid[y[j]] + T2 = T_grid[y[j]+1] + + # Precompute temperature interpolation weight factor + w_T[j] = (1.0/((1.0/T2) - (1.0/T1))) + + return w_T + +@jit(nopython=True) +def interpolate_cia(log_cia, nu_l, nu_out, nu_r, nu_cia, T, T_grid_cia, N_T_cia, N_D, N_wl_out, N_nu_out, y, w_T, mode): + + ''' Interpolates a collisionally-induced absorption (CIA) binary cross + section onto the T value in each layer of the model atmosphere. + + The input is in wavenumnber to take advantage of fast prior index + location on a uniform grid, which wouldn't work for the (non-uniform) + wavelength grid. Array reversal to output in increasing wavelength is + handled by indexing by a factor of (N_wl-1)-k throughout. + + Wavelength initialisation is handled via either opacity sampling + (choosing nearest pre-computed wavelength point) or via averaging + the logarithm of the cross section over the wavelength bin range + surrounding each wavelength on the output model wavelength grid. + + ''' + + cia_inp = np.zeros(shape=(N_D, N_wl_out)) # Initialise output CIA cross section + + N_nu_cia = len(nu_cia) # Number of wavenumber points in CIA array + + for i in xrange(N_D): # Loop over layer temperatures + + T_i = T[i] # Temperature to interpolate to + T1 = T_grid_cia[y[i]] # Closest lower temperaure on CIA opacity grid + T2 = T_grid_cia[y[i]+1] # Closest higher temperaure on CIA opacity grid + + for k in xrange(N_nu_out): # Loop over wavenumbers + + # Find closest index on CIA wavenumber grid to each model wavenumber + z_l = closest_index(nu_l[k], nu_cia[0], nu_cia[-1], N_nu_cia) + z = closest_index(nu_out[k], nu_cia[0], nu_cia[-1], N_nu_cia) + z_r = closest_index(nu_r[k], nu_cia[0], nu_cia[-1], N_nu_cia) + + # If this wavenumber falls out of range of opacity grid, set opacity to zero + if ((z_l == 0) or (z_r == (N_nu_cia-1))): + cia_inp[i, ((N_wl_out-1)-k)] = 0.0 + + else: # Otherwise, proceed with interpolation + + # Opacity sampling + if (mode == 1): + + # If layer T below min value on CIA grid (200 K), set CIA to value at min T on opac grid + if (y[i] == -1): + cia_inp[i, ((N_wl_out-1)-k)] = 10 ** (log_cia[0, z]) + + # If layer T above max value (3500 K), set CIA to value at max T on opac grid + elif (y[i] == -2): + cia_inp[i, ((N_wl_out-1)-k)] = 10 ** (log_cia[(N_T_cia-1), z]) + + # Interpolate CIA to temperature in this layer + else: + cia_reduced = 10 ** (log_cia[y[i]:y[i]+2, z]) + cia_T1, cia_T2 = cia_reduced[0], cia_reduced[1] # CIA(T1)[j,z], CIA(T2)[j,z] + + cia_inp[i, ((N_wl_out-1)-k)] = (np.power(cia_T1, (w_T[i]*((1.0/T2) - (1.0/T_i)))) * + np.power(cia_T2, (w_T[i]*((1.0/T_i) - (1.0/T1))))) + + # Log averaging + elif (mode == 2): + + # If layer T below min value on CIA grid (200 K), set CIA to value at min T on opac grid + if (y[i] == -1): + cia_in_bin = np.mean(log_cia[0, z_l:z_r+1]) + cia_inp[i, ((N_wl_out-1)-k)] = 10 ** (cia_in_bin) + + # If layer T above max value (3500 K), set CIA to value at max T on opac grid + elif (y[i] == -2): + cia_in_bin = np.mean(log_cia[(N_T_cia-1), z_l:z_r+1]) + cia_inp[i, ((N_wl_out-1)-k)] = 10 ** (cia_in_bin) + + # Interpolate CIA to temperature in this layer + else: + cia_in_bin_T1 = 10 ** np.mean(log_cia[y[i], z_l:z_r+1]) # CIA in bin at temperaure T1 + cia_in_bin_T2 = 10 ** np.mean(log_cia[y[i]+1, z_l:z_r+1]) # CIA in bin at temperaure T2 + + cia_inp[i, ((N_wl_out-1)-k)] = (np.power(cia_in_bin_T1, (w_T[i]*((1.0/T2) - (1.0/T_i)))) * + np.power(cia_in_bin_T2, (w_T[i]*((1.0/T_i) - (1.0/T1))))) + + return cia_inp + +@jit(nopython=True) +def interpolate_sigma(log_sigma, nu_l, nu_out, nu_r, nu_opac, P, T, N_D, log_P_grid, T_grid, N_P_opac, N_T_opac, N_wl_out, N_nu_out, y, w_T, mode): + + ''' Interpolates a cross section onto the (P,T) values in each layer of + the model atmosphere. + + The input is in wavenumnber to take advantage of fast prior index location + on a uniform grid, which wouldn't work for the (non-uniform) wavelength grid + Array reversal to output in increasing wavelength is handled by indexing + by a factor of (N_wl-1)-k throughout. + + ''' + + sigma_inp = np.zeros(shape=(N_D, N_wl_out)) # Initialise output cross section + + N_nu_opac = len(nu_opac) # Number of wavenumber points in opacity array + + #***** Firstly, find pressure interpolation weighting factors *****# + log_P = np.log10(P) # Log of model pressure grid + + # Array of indicies on opacity pressure opacity grid prior to model atmosphere layer pressures + x = np.zeros(N_D).astype(np.int64) + + w_P = np.zeros(N_D) # Pressure weights + + # Useful functions of weights for interpolation + b1 = np.zeros(shape=(N_D)) + b2 = np.zeros(shape=(N_D)) + + # Find closest P indicies in opacity grid corresponding to model layer pressures + for i in xrange(N_D): + + # If pressure below minimum, do not interpolate + if (log_P[i] < log_P_grid[0]): + x[i] = -1 # Special value (1) used in opacity initialiser + w_P[i] = 0.0 + + # If pressure above maximum, do not interpolate + elif (log_P[i] >= log_P_grid[-1]): + x[i] = -2 # Special value (2) used in opacity initialiser + w_P[i] = 0.0 + + else: + x[i] = prior_index_V2(log_P[i], log_P_grid[0], log_P_grid[-1], N_P_opac) + + # Weights - fractional distance along pressure axis of sigma array + w_P[i] = (log_P[i]-log_P_grid[x[i]])/(log_P_grid[x[i]+1]-log_P_grid[x[i]]) + + # Precalculate interpolation pre-factors to reduce computation overhead + b1[i] = (1.0-w_P[i]) + b2[i] = w_P[i] + + # Note: temperaure interpolation indicies and weights passed through function arguments + + # Begin interpolation procedure + for i in xrange(N_D): # Loop over model layers + + T_i = T[i] # Layer temperature to interpolate to + T1 = T_grid[y[i]] # Closest lower temperaure on opacity grid + T2 = T_grid[y[i]+1] # Closest higher temperaure on opacity grid + + for k in xrange(N_nu_out): # Loop over model wavenumbers + + # Calculate closest indicies to output wavenumber bin left, centre, and right edge + z_l = closest_index(nu_l[k], nu_opac[0], nu_opac[-1], N_nu_opac) + z = closest_index(nu_out[k], nu_opac[0], nu_opac[-1], N_nu_opac) + z_r = closest_index(nu_r[k], nu_opac[0], nu_opac[-1], N_nu_opac) + + # If this wavenumber falls out of range of opacity grid, set opacity to zero + if ((z_l == 0) or (z_r == (N_nu_opac-1))): + sigma_inp[i, ((N_wl_out-1)-k)] = 0.0 + + else: # Otherwise, proceed with interpolation + + # Opacity sampling + if (mode == 1): + + # Find rectangle of stored opacity points located at [log_P1, log_P2, T1, T2, z] + log_sigma_PT_rectangle = log_sigma[x[i]:x[i]+2, y[i]:y[i]+2, z] + + # Pressure interpolation is handled first, followed by temperaure interpolation + # First, check for off-grid special cases + + # If layer P below minimum on opacity grid (1.0e-6 bar), set value to edge case + if (x[i] == -1): + + # If layer T also below minimum on opacity grid (100 K), set value to edge case + if (y[i] == -1): + sigma_inp[i, ((N_wl_out-1)-k)] = 10 ** (log_sigma[0, 0, z]) # No interpolation needed + + # If layer T above maximum on opacity grid (3500 K), set value to edge case + elif (y[i] == -2): + sigma_inp[i, ((N_wl_out-1)-k)] = 10 ** (log_sigma[0, (N_T_opac-1), z]) # No interpolation needed + + # If desired temperaure is on opacity grid, set T1 and T2 values to those at min P on grid + else: + sig_T1 = 10 ** (log_sigma[0, y[i], z]) + sig_T2 = 10 ** (log_sigma[0, y[i]+1, z]) + + # Only need to interpolate over temperaure interpolate cross section to layer temperature + sigma_inp[i, ((N_wl_out-1)-k)] = (np.power(sig_T1, (w_T[i]*((1.0/T2) - (1.0/T_i)))) * + np.power(sig_T2, (w_T[i]*((1.0/T_i) - (1.0/T1))))) + + # If layer P above maximum on opacity grid (100 bar), set value to edge case + elif (x[i] == -2): + + # If layer T below minimum on opacity grid (100 K), set value to edge case + if (y[i] == -1): + sigma_inp[i, ((N_wl_out-1)-k)] = 10 ** (log_sigma[(N_P_opac-1), 0, z]) # No interpolation needed + + # If layer T also above maximum on opacity grid (3500 K), set value to edge case + elif (y[i] == -2): + sigma_inp[i, ((N_wl_out-1)-k)] = 10 ** (log_sigma[(N_P_opac-1), (N_T_opac-1), z]) # No interpolation needed + + # If desired temperaure is on opacity grid, set T1 and T2 values to those at maximum P on grid + else: + sig_T1 = 10 ** (log_sigma[(N_P_opac-1), y[i], z]) + sig_T2 = 10 ** (log_sigma[(N_P_opac-1), y[i]+1, z]) + + # Now interpolate cross section to layer temperature + sigma_inp[i, ((N_wl_out-1)-k)] = (np.power(sig_T1, (w_T[i]*((1.0/T2) - (1.0/T_i)))) * + np.power(sig_T2, (w_T[i]*((1.0/T_i) - (1.0/T1))))) + + # If both desired P and T are on opacity grid (should be true in most cases!) + else: + + # Interpolate log(cross section) in log(P), then power to get interpolated values at T1 and T2 + sig_T1 = 10 ** (b1[i]*(log_sigma_PT_rectangle[0,0]) + # Cross section at T1 + b2[i]*(log_sigma_PT_rectangle[1,0])) + sig_T2 = 10 ** (b1[i]*(log_sigma_PT_rectangle[0,1]) + # Cross section at T2 + b2[i]*(log_sigma_PT_rectangle[1,1])) + + # Now interpolate cross section to layer temperature + sigma_inp[i, ((N_wl_out-1)-k)] = (np.power(sig_T1, (w_T[i]*((1.0/T2) - (1.0/T_i)))) * + np.power(sig_T2, (w_T[i]*((1.0/T_i) - (1.0/T1))))) + + # Log averaging + elif (mode == 2): + + # If layer P below minimum on opacity grid (1.0e-6 bar), set value to edge case + if (x[i] == -1): + + # If layer T also below minimum on opacity grid (100 K), set value to edge case + if (y[i] == -1): + sigma_in_bin = np.mean(log_sigma[0, 0, z_l:z_r+1]) + sigma_inp[i, ((N_wl_out-1)-k)] = 10 ** (sigma_in_bin) # No interpolation needed + + # If layer T above maximum on opacity grid (3500 K), set value to edge case + elif (y[i] == -2): + sigma_in_bin = np.mean(log_sigma[0, (N_T_opac-1), z_l:z_r+1]) + sigma_inp[i, ((N_wl_out-1)-k)] = 10 ** (sigma_in_bin) # No interpolation needed + + # If desired temperaure is on opacity grid, set T1 and T2 values to those at min P on grid + else: + sig_T1 = 10 ** (np.mean(log_sigma[0, y[i], z_l:z_r+1])) + sig_T2 = 10 ** (np.mean(log_sigma[0, y[i]+1, z_l:z_r+1])) + + # Only need to interpolate over temperaure interpolate cross section to layer temperature + sigma_inp[i, ((N_wl_out-1)-k)] = (np.power(sig_T1, (w_T[i]*((1.0/T2) - (1.0/T_i)))) * + np.power(sig_T2, (w_T[i]*((1.0/T_i) - (1.0/T1))))) + + # If layer P above maximum on opacity grid (100 bar), set value to edge case + elif (x[i] == -2): + + # If layer T below minimum on opacity grid (100 K), set value to edge case + if (y[i] == -1): + sigma_in_bin = np.mean(log_sigma[(N_P_opac-1), 0, z_l:z_r+1]) + sigma_inp[i, ((N_wl_out-1)-k)] = 10 ** (sigma_in_bin) # No interpolation needed + + # If layer T also above maximum on opacity grid (3500 K), set value to edge case + elif (y[i] == -2): + sigma_in_bin = np.mean(log_sigma[(N_P_opac-1), (N_T_opac-1), z_l:z_r+1]) + sigma_inp[i, ((N_wl_out-1)-k)] = 10 ** (sigma_in_bin) # No interpolation needed + + # If desired temperaure is on opacity grid, set T1 and T2 values to those at maximum P on grid + else: + sig_T1 = 10 ** (np.mean(log_sigma[(N_P_opac-1), y[i], z_l:z_r+1])) + sig_T2 = 10 ** (np.mean(log_sigma[(N_P_opac-1), y[i]+1, z_l:z_r+1])) + + # Now interpolate cross section to layer temperature + sigma_inp[i, ((N_wl_out-1)-k)] = (np.power(sig_T1, (w_T[i]*((1.0/T2) - (1.0/T_i)))) * + np.power(sig_T2, (w_T[i]*((1.0/T_i) - (1.0/T1))))) + + # If both desired P and T are on opacity grid (should be true in most cases!) + else: + + sig_in_bin_P1_T1 = np.mean(log_sigma[x[i], y[i], z_l:z_r+1]) # Cross section in bin at P1 and T1 + sig_in_bin_P2_T1 = np.mean(log_sigma[x[i]+1, y[i], z_l:z_r+1]) # Cross section in bin at P2 and T1 + sig_in_bin_P1_T2 = np.mean(log_sigma[x[i], y[i]+1, z_l:z_r+1]) # Cross section in bin at P1 and T2 + sig_in_bin_P2_T2 = np.mean(log_sigma[x[i]+1, y[i]+1, z_l:z_r+1]) # Cross section in bin at P2 and T2 + + # Interpolate log(cross section) in log(P), then power to get interpolated values at T1 and T2 + sig_T1 = 10 ** (b1[i]*(sig_in_bin_P1_T1) + # Cross section at T1 + b2[i]*(sig_in_bin_P2_T1)) + sig_T2 = 10 ** (b1[i]*(sig_in_bin_P1_T2) + # Cross section at T2 + b2[i]*(sig_in_bin_P2_T2)) + + # Now interpolate cross section to layer temperature + sigma_inp[i, ((N_wl_out-1)-k)] = (np.power(sig_T1, (w_T[i]*((1.0/T2) - (1.0/T_i)))) * + np.power(sig_T2, (w_T[i]*((1.0/T_i) - (1.0/T1))))) + + return sigma_inp + +@jit +def H_minus_bound_free(wl_um): + + ''' Computes the bound-free cross section (alpha_bf) of the H- ion as a + function of wavelength. The fitting function is taken from "The + Observation and Analysis of Stellar Photospheres" (Gray, 2005). + + The extinction coefficient (in m^-1) can then be calculated via: + alpha_bf * n_(H-) [i.e. multiply by the H- number density (in m^-3) ]. + + Inputs: + + wl_um => array of wavelength values (um) + + Outputs: + + alpha_bf => bound-free H- cross section (m^2 / n_(H-) ) at each input + wavelength + + ''' + + # Initialise array to store absorption coefficients + alpha_bf = np.zeros(shape=(len(wl_um))) + + # Convert micron to A (as used in bound-free fit) + wl_A = wl_um * 1.0e4 + + # Fitting function constants (Gray, 2005, p.156) + a0 = 1.99654 + a1 = -1.18267e-5 + a2 = 2.64243e-6 + a3 = -4.40524e-10 + a4 = 3.23992e-14 + a5 = -1.39568e-18 + a6 = 2.78701e-23 + + for k, wl in enumerate(wl_A): + + # Photodissociation only possible for photons with wl < 1.6421 micron + if (wl <= 16421.0): + + # Compute bound-free absorption coefficient at this wavelength + alpha_bf_wl = (a0 + a1*wl + a2*(wl**2) + a3*(wl**3) + a4*(wl**4) + a5*(wl**5) + a6*(wl**6)) * 1.0e-18 + alpha_bf[k] = alpha_bf_wl * 1.0e-4 # Convert from cm^2 / H- ion to m^2 / H- ion + + else: + + alpha_bf[k] = 1.0e-250 # Small value (proxy for zero, but avoids log(0) issues) + + return alpha_bf + +@jit +def H_minus_free_free(T_arr, wl_um): + + ''' Computes the free-free cross section (alpha_ff) of the H- ion as a + function of wavelength. The fitting function is taken from "Continuous + absorption by the negative hydrogen ion reconsidered" (John, 1987). + + The extinction coefficient (in m^-1) can then be calculated via: + alpha_ff * n_H * n_(e-) [i.e. multiply by the H and e- number densities + (both in in m^-3) ]. + + Inputs: + + wl_um => array of wavelength values (um) + T_arr => array of temperatures (K) + + Outputs: + + alpha_ff => free-free H- cross section (m^5 / n_H / n_e-) for each + input wavelength and temperature + + ''' + + # Initialise array to store absorption coefficients + alpha_ff = np.zeros(shape=(len(T_arr), len(wl_um))) + + for i, T in enumerate(T_arr): + + theta = 5040.0/T # Reciprocal temperature commonly used in these fits + kT = 1.38066e-16 * T # Boltzmann constant * temperature (erg) + + for k, wl in enumerate(wl_um): + + # Range of validity of fit (wl > 0.182 um) + if (wl < 0.182): + + alpha_ff[i,k] = 1.0e-250 # Small value (proxy for zero, but avoids log(0) issues) + + # Short wavelength fit + elif ((wl >= 0.182) and (wl < 0.3645)): + + A = np.array([518.1021, 473.2636, -482.2089, 115.5291, 0.0, 0.0]) + B = np.array([-734.8666, 1443.4137, -737.1616, 169.6374, 0.0, 0.0]) + C = np.array([1021.1775, -1977.3395, 1096.8827, -245.6490, 0.0, 0.0]) + D = np.array([-479.0721, 922.3575, -521.1341, 114.2430, 0.0, 0.0]) + E = np.array([93.1373, -178.9275, 101.7963, -21.9972, 0.0, 0.0]) + F = np.array([-6.4285, 12.3600, -7.0571, 1.5097, 0.0, 0.0]) + + # For each set of fit coefficients + for n in range(1,7): + + # Compute free-free absorption coefficient at this wavelength and temperature + alpha_ff[i,k] += 1.0e-29 * (np.power(theta, ((n + 1.0)/2.0)) * + (A[n-1]*(wl**2) + B[n-1] + C[n-1]/wl + + D[n-1]/(wl**2) + E[n-1]/(wl**3) + + F[n-1]/(wl**4))) * kT # cm^5 / H / e- + + alpha_ff[i,k] *= 1.0e-10 # Convert cm^5 to m^5 + + # Long wavelength fit + elif (wl >= 0.3645): + + A = np.array([0.0, 2483.3460, -3449.8890, 2200.0400, -696.2710, 88.2830]) + B = np.array([0.0, 285.8270, -1158.3820, 2427.7190, -1841.4000, 444.5170]) + C = np.array([0.0, -2054.2910, 8746.5230, -13651.1050, 8624.9700, -1863.8640]) + D = np.array([0.0, 2827.7760, -11485.6320, 16755.5240, -10051.5300, 2095.2880]) + E = np.array([0.0, -1341.5370, 5303.6090, -7510.4940, 4400.0670, -901.7880]) + F = np.array([0.0, 208.9520, -812.9390, 1132.7380, -655.0200, 132.9850]) + + # For each set of fit coefficients + for n in range(1,7): + + # Compute free-free absorption coefficient at this wavelength and temperature + alpha_ff[i,k] += 1.0e-29 * (np.power(theta, ((n + 1.0)/2.0)) * + (A[n-1]*(wl**2) + B[n-1] + C[n-1]/wl + + D[n-1]/(wl**2) + E[n-1]/(wl**3) + + F[n-1]/(wl**4))) * kT # cm^5 / H / e- + alpha_ff[i,k] *= 1.0e-10 # Convert cm^5 to m^5 + + return alpha_ff + +def Extract_opacity_NEW(chemical_species, cia_pairs, P, T, wl_out, opacity_treatment, root='../'): + + ''' Read in all opacities and interpolate onto the pressure and + temperature in each layer for desired wavelength grid + + ''' + + print("Now reading in cross sections") + + # First, check from config.py which opacity calculation mode is specified + if (opacity_treatment == 'Opacity-sample'): calculation_mode = 1 + elif (opacity_treatment == 'Log-avg'): calculation_mode = 2 + + #***** Firstly, we initialise the various quantities needed *****# + + # Open HDF5 files containing molecular + atomic opacities and CIA + opac_file = h5py.File(root + 'Opacity_database_0.01cm-1.hdf5', 'r') + cia_file = h5py.File(root + 'Opacity_database_cia.hdf5', 'r') + + # Convert model wavelength grid to wavenumber grid + nu_out = 1.0e4/wl_out # Model wavenumber grid (cm^-1) + nu_out = nu_out[::-1] # Reverse direction to increase with wavenumber + + N_nu_out = len(nu_out) # Number of wavenumbers on output grid + N_wl_out = len(wl_out) # Number of wavelengths on output grid + + N_D = len(P) # No. of depth points (layers) in atmosphere + + # Find wavenumbers at left and right edge of each bin on outpur grid + # Vectorized by Lia + nu_edges = np.append(nu_out[0] - (nu_out[1] - nu_out[0]), nu_out) + nu_edges = np.append(nu_edges, nu_out[-1] + (nu_out[-1] - nu_out[-2])) + nu_l = 0.5 * (nu_edges[:-2] + nu_edges[1:-1]) + nu_r = 0.5 * (nu_edges[1:-1] + nu_edges[2:]) + + #***** Process collisionally Induced Absorption (CIA) *****# + + # Initialise cia opacity array, interpolated to temperature in each model layer + cia_stored = dict() + + for q in cia_pairs: + + #***** Read in T grid used in this CIA file*****# + T_grid_cia_q = np.array(cia_file[q + '/T']) + N_T_cia_q = len(T_grid_cia_q) # Number of temperatures in this grid + + # Read in wavenumber array used in this CIA file + nu_cia_q = np.array(cia_file[q + '/nu']) + + # Find T index in CIA arrays prior to each layer temperature + y_cia_q = np.zeros(N_D, dtype=np.int) + w_T_cia_q = T_interpolation_init(N_D, T_grid_cia_q, T, y_cia_q) + + # Read in log10(binary cross section) for specified CIA pair + log_cia_q = np.array(cia_file[q + '/log(cia)']).astype(np.float64) + + # Interpolate CIA to temperature in each atmospheric layer + cia_stored[q] = interpolate_cia(log_cia_q, nu_l, nu_out, nu_r, nu_cia_q, T, T_grid_cia_q, N_T_cia_q, N_D, N_wl_out, N_nu_out, y_cia_q, w_T_cia_q, calculation_mode) + + del log_cia_q, nu_cia_q, w_T_cia_q, y_cia_q # Clear raw cross section to free up memory + + print(q + " done") + + cia_file.close() + + #***** Process molecular and atomic opacities *****# + + # Initialise molecular and atomic opacity dictionary, interpolated to temperatures and pressures in each model layer + sigma_stored = dict() + + # Load molecular and atomic absorption cross sections + for q in chemical_species: + + #***** Read in grids used in this opacity file *****# + T_grid_q = np.array(opac_file[q + '/T']) + log_P_grid_q = np.array(opac_file[q + '/log(P)']) # Units: log10(P/bar)! + + # Read in wavenumber array used in this opacity file + nu_q = np.array(opac_file[q + '/nu']) + + N_T_q = len(T_grid_q) # Number of temperatures in this grid + N_P_q = len(log_P_grid_q) # Number of pressures in this grid + + # Find T index in cross secion arrays prior to each layer temperature + y_q = np.zeros(N_D, dtype=np.int) + w_T_q = T_interpolation_init(N_D, T_grid_q, T, y_q) + + # Read in log10(cross section) of specified molecule + log_sigma_q = np.array(opac_file[q + '/log(sigma)']).astype(np.float64) + + # Interpolate cross section to (P,T) in each atmospheric layer + sigma_stored[q] = interpolate_sigma(log_sigma_q, nu_l, nu_out, nu_r, nu_q, P, T, N_D, log_P_grid_q, T_grid_q, N_P_q, N_T_q, N_wl_out, N_nu_out, y_q, w_T_q, calculation_mode) + + del log_sigma_q, nu_q, w_T_q, y_q # Clear raw cross section to free up memory + + print(q + " done") + + opac_file.close() + + #***** Compute H- bound-free and free-free opacities *****# + + alpha_bf = H_minus_bound_free(wl_out) + alpha_ff = H_minus_free_free(T, wl_out) + + print("H- done") + + return sigma_stored, cia_stored, alpha_bf, alpha_ff + + +#***** Functions below not currently used, preserved for reference ****** + +@jit(nopython = True) +def T_interpolate(N_T, N_wl, sigma_pre_inp, T_grid, T, y, w_T): + + sigma_inp = np.zeros(shape=(N_wl)) + + T1 = T_grid[y] + T2 = T_grid[y+1] + + for k in range(N_wl): # Loop over wavelengths + + # If T_fine below min value (100 K), set sigma to value at min T + if (y == -1): + sigma_inp[k] = sigma_pre_inp[0, k] + + # If T_fine above max value (3500 K), set sigma to value at max T + elif (y == -2): + sigma_inp[k] = sigma_pre_inp[(N_T-1), k] + + # Interpolate sigma to fine temperature grid value + else: + sig_reduced = sigma_pre_inp[y:y+2, k] + sig_1, sig_2 = sig_reduced[0], sig_reduced[1] # sigma(T1)[i,k], sigma(T2)[i,k] + + sigma_inp[k] = (np.power(sig_1, (w_T*((1.0/T2) - (1.0/T)))) * + np.power(sig_2, (w_T*((1.0/T) - (1.0/T1))))) + + return sigma_inp + +@jit(nopython = True) +def P_interpolate_wl_initialise(N_T, N_P, N_wl, log_sigma, + nu_l, nu_model, nu_r, nu_opac, N_nu, + x, b1, b2, mode): + + '''Interpolates raw cross sections onto the model P and wl grid. + + Note: input sigma has format log10(cross_sec)[log(P)_grid, T_grid, nu_grid], + whilst output has format cross_sec[T, wl_model] + + The input is in wavenumnber to take advantage of fast prior index location + on a uniform grid, which wouldn't work for the (non-uniform) wavelength grid + Array reversal to output in increasing wavelength is handled by indexing + by a factor of (N_wl-1)-k throughout + ''' + + sigma_pre_inp = np.zeros(shape=(N_T, N_wl)) + + N_nu_opac = len(nu_opac) # Number of wavenumber points in sigma array + + for k in range(N_nu): # Note that the k here is looping over wavenumber + + # Indicies in pre-computed wavenumber array of LHS, centre, and RHS of desired wavenumber grid + z_l = closest_index(nu_l[k], nu_opac[0], nu_opac[-1], N_nu_opac) + z = closest_index(nu_model[k], nu_opac[0], nu_opac[-1], N_nu_opac) + z_r = closest_index(nu_r[k], nu_opac[0], nu_opac[-1], N_nu_opac) + + for j in range(N_T): + + # If nu (wl) point out of range of opacity grid, set opacity to zero + if ((z == 0) or (z == (N_nu_opac-1))): + sigma_pre_inp[j, ((N_wl-1)-k)] = 0.0 + + else: + + # Opacity sampling + if (mode == 1): + + # If pressure below minimum, set to value at min pressure + if (x == -1): + sigma_pre_inp[j, ((N_wl-1)-k)] = 10 ** (log_sigma[0, j, z]) + + # If pressure above maximum, set to value at max pressure + elif (x == -2): + sigma_pre_inp[j, ((N_wl-1)-k)] = 10 ** (log_sigma[(N_P-1), j, z]) + + # Interpolate sigma in logsace, then power to get interp array + else: + reduced_sigma = log_sigma[x:x+2, j, z] + + sigma_pre_inp[j, ((N_wl-1)-k)] = 10 ** (b1*(reduced_sigma[0]) + + b2*(reduced_sigma[1])) + + # Log averaging + elif (mode == 2): + + # If pressure below minimum, set to value at min pressure + if (x == -1): + sigma_in_bin = np.mean(log_sigma[0, j, z_l:z_r+1]) + sigma_pre_inp[j, ((N_wl-1)-k)] = 10 ** (sigma_in_bin) + + # If pressure above maximum, set to value at max pressure + elif (x == -2): + sigma_in_bin = np.mean(log_sigma[(N_P-1), j, z_l:z_r+1]) + sigma_pre_inp[j, ((N_wl-1)-k)] = 10 ** (sigma_in_bin) + + # Interpolate sigma in logsace, then power to get interp array + else: + sigma_in_bin_P1 = np.mean(log_sigma[x, j, z_l:z_r+1]) + sigma_in_bin_P2 = np.mean(log_sigma[x+1, j, z_l:z_r+1]) + + sigma_pre_inp[j, ((N_wl-1)-k)] = 10 ** (b1*(sigma_in_bin_P1) + + b2*(sigma_in_bin_P2)) + + return sigma_pre_inp + +## Written by Lia +## To separate out reading step +def load_db(filename='Opacity_database_0.01cm-1.hdf5', root='../'): + + open_filename = root + filename + print("Reading opacity database file") + opac_file = h5py.File(open_filename, 'r') + + #***** Read in T and P grids used in opacity files*****# + T_grid = np.array(opac_file['H2O/T']) # H2O here simply used as dummy (same grid for all molecules) + log_P_grid = np.array(opac_file['H2O/log(P)']) # Units: log10(P/bar)! + + #***** Read in wavenumber arrays used in opacity files*****# + nu_opac = np.array(opac_file['H2O/nu']) # H2O here simply used as dummy (same grid for all molecules) + + return T_grid, log_P_grid, nu_opac, opac_file + + +# Algorithm for loading cross-sections from one pressure and temperature +# from cross-section table of a single species (log_sigma) +def _get_one_PT(log_sigma, T_grid, log_P_grid, nu_opac, + new_P, new_T, wl_out, opacity_treatment): + + if (opacity_treatment == 'Opacity-sample'): calculation_mode = 1 + elif (opacity_treatment == 'Log-avg'): calculation_mode = 2 + + N_P = len(log_P_grid) # No. of pressures in opacity files + N_T = len(T_grid) # No. of temperatures in opacity files + + # Convert model wavelength grid to wavenumber grid + nu_out = 1.0e4/wl_out # Model wavenumber grid (cm^-1) + nu_out = nu_out[::-1] # Reverse direction, such that increases with wavenumber + + N_nu = len(nu_out) # Number of wavenumbers on model grid + N_wl = len(wl_out) # Number of wavelengths on model grid + + # Initialise arrays of wavenumber locations of left and right bin edges + #nu_l = np.zeros(N_nu) # Left edge + #nu_r = np.zeros(N_nu) # Right edge + # Look below for definitions of nu_l, nu_r + + # Find logarithm of desired pressure + log_P = np.log10(new_P) + + # If pressure below minimum, do not interpolate + if (log_P < log_P_grid[0]): + x = -1 # Special value (1) used in opacity inialiser + w_P = 0.0 + # If pressure above maximum, do not interpolate + elif (log_P >= log_P_grid[-1]): + x = -2 # Special value (2) used in opacity inialiser + w_P = 0.0 + else: + # Closest P indicies in opacity grid corresponding to model pressure + x = prior_index_V2(log_P, log_P_grid[0], log_P_grid[-1], N_P) + # Weights - fractional distance along pressure axis of sigma array + w_P = (log_P-log_P_grid[x])/(log_P_grid[x+1]-log_P_grid[x]) + + # Precalculate interpolation pre-factors to reduce computation overhead + b1 = (1.0-w_P) + b2 = w_P + + # Find wavenumber indicies in arrays of model grid + # Vectorized by Lia + nu_edges = np.append(nu_out[0] - (nu_out[1] - nu_out[0]), + nu_out) + nu_edges = np.append(nu_edges, nu_out[-1] + (nu_out[-1] - nu_out[-2])) + nu_l = 0.5 * (nu_edges[:-2] + nu_edges[1:-1]) + nu_r = 0.5 * (nu_edges[1:-1] + nu_edges[2:]) + + '''for k in range(N_nu): + + if (k != 0) and (k != (N_nu-1)): + nu_l[k] = 0.5*(nu_out[k-1] + nu_out[k]) + nu_r[k] = 0.5*(nu_out[k] + nu_out[k+1]) + + # Special case for boundary values + elif (k == 0): + nu_l[k] = nu_out[k] - 0.5*(nu_out[k+1] - nu_out[k]) + nu_r[k] = 0.5*(nu_out[k] + nu_out[k+1]) + elif (k == (N_nu-1)): + nu_l[k] = 0.5*(nu_out[k-1] + nu_out[k]) + nu_r[k] = nu_out[k] + 0.5*(nu_out[k] - nu_out[k-1])''' + + # Evaluate temperature interpolation weighting factor + y, w_T = T_interpolation_init(T_grid, new_T) + + sigma_pre_T_inp = P_interpolate_wl_initialise(N_T, N_P, N_wl, + log_sigma, nu_l, nu_out, nu_r, + nu_opac, N_nu, x, b1, b2, calculation_mode) + + sigma_result = T_interpolate(N_T, N_wl, sigma_pre_T_inp, T_grid, new_T, y, w_T) + + return sigma_result + +def Extract_opacity_Lia(chemical_species, P, T, wl_out, opacity_treatment): + + '''Convienient function to read in all opacities and pre-interpolate + them onto the desired pressure, temperature, and wavelength grid''' + + T_grid, log_P_grid, nu_opac, opac_file = load_db() + + # Initialise molecular and atomic opacity array, interpolated to model wavelength grid + #sigma_stored = np.zeros(shape=(N_species, N_wl)) + # Lia -- Making this a dictionary instead of a numpy array. It's easier to use for plotting. + # Later, a 2D numpy array will be faster for summing across a large number of species + # But for now, two dozen is not a lot. + sigma_stored = dict() + + #***** Process molecular and atomic opacities *****# + + # Load molecular and atomic absorption cross sections + for q in chemical_species: + + log_sigma = np.array(opac_file[q + '/log(sigma)']).astype(np.float64) + + sigma_stored[q] = _get_one_PT(log_sigma, T_grid, log_P_grid, nu_opac, + P, T, wl_out, opacity_treatment) + + del log_sigma # Clear raw cross section to free up memory + + print(q + " done") + + opac_file.close() + + return sigma_stored + +# Written by Lia for a set of (p,T) values +def Extract_opacity_PTpairs(chemical_species, P, T, wl_out, opacity_treatment, root='../'): + + '''Convienient function to read in all opacities and pre-interpolate + them onto the desired pressure, temperature, and wavelength grid''' + + assert len(P) == len(T) + + T_grid, log_P_grid, nu_opac, opac_file = load_db(root=root) + + # Initialise molecular and atomic opacity array, interpolated to model wavelength grid + #sigma_stored = np.zeros(shape=(N_species, N_wl)) + # Lia -- Making this a dictionary instead of a numpy array. It's easier to use for plotting. + # Later, a 2D numpy array will be faster for summing across a large number of species + # But for now, two dozen is not a lot. + sigma_stored = dict() + + #***** Process molecular and atomic opacities *****# + + # Load molecular and atomic absorption cross sections + for q in chemical_species: + + log_sigma = np.array(opac_file[q + '/log(sigma)']).astype(np.float64) + + result = np.zeros(shape=(len(P), len(wl_out))) + for i in range(len(P)): + result[i,:] = _get_one_PT(log_sigma, T_grid, log_P_grid, nu_opac, + P[i], T[i], wl_out, opacity_treatment) + + sigma_stored[q] = result + + del log_sigma # Clear raw cross section to free up memory + + print(q + " done") + + opac_file.close() + + return sigma_stored + +# ***** Functions from here on used for diagnostic plotting of cross sections *****# + +def plot_opacity(chemical_species, cia_pairs, sigma_stored, cia_stored, alpha_bf, alpha_ff, + P, T, wl_grid, savefigs=False, **kwargs): + + # Max number of species this can plot is 9 (clustered beyond that!) + + # Optional smoothing of cross sections (can improve clarity) + smooth = False + smooth_factor = 5 + + # Specify cross sections to plot, along with colours for each + #colours_plot = np.array(['royalblue', 'purple', 'crimson', 'orange', 'black', 'grey', 'green', 'magenta', 'chocolate']) + + # Initialise plot + #ax = plt.gca() + #ax.set_xscale("log") + + ax = plt.subplot(111) + #xmajorLocator = MultipleLocator(1.0) + #xmajorFormatter = FormatStrFormatter('%.1f') + #xminorLocator = MultipleLocator(0.2) + + #ax.xaxis.set_major_locator(xmajorLocator) + #ax.xaxis.set_major_formatter(xmajorFormatter) + #ax.xaxis.set_minor_locator(xminorLocator) + + # Plot each cross section + for species in chemical_species: + #species_idx = np.where(chemical_species == species)[0][0] + sigma_plt = sigma_stored[species][0,:] # Cross section of this species at given (P,T) pair (m^2) + + if (smooth == True): + sigma_plt = gauss_conv(sigma_plt, sigma=smooth_factor, mode='nearest') + + # Plot cross section + plt.semilogy(wl_grid, sigma_plt, label=species, **kwargs) + + plt.ylim([1.0e-31, 1.0e-21]) + plt.xlim([min(wl_grid), max(wl_grid)]) + plt.ylabel(r'$\mathrm{Cross \, \, Section \, \, (m^{2} \, mol^{-1})}$', fontsize = 15) + plt.xlabel(r'$\mathrm{Wavelength} \; \mathrm{(\mu m)}$', fontsize = 15) + + ax.text(min(wl_grid)*1.20, 2.0e-22, (r'$\mathrm{T = }$' + str(T) + \ + r'$\mathrm{K \, \, P = }$' + \ + str(P*1000) + r'$\mathrm{mbar}$'), fontsize = 14) + + legend = plt.legend(loc='upper right', frameon=False, prop={'size':6}, ncol=2) + + '''for legline in legend.legendHandles: + legline.set_linewidth(1.0)''' + + if savefigs: + plt.savefig('./cross_sections_' + str(T) + 'K_' + str(P*1000) + 'mbar_TEST.pdf', bbox_inches='tight', fmt='pdf', dpi=1000) + + plt.show() + + plt.clf() + + #***** Now plot CIA ***** + + #colours_cia = np.array(['royalblue', 'crimson']) + + #ax = plt.gca() + #ax.set_xscale("log") + + ax = plt.subplot(111) + #xmajorLocator = MultipleLocator(1.0) + #xmajorFormatter = FormatStrFormatter('%.1f') + #xminorLocator = MultipleLocator(0.2) + + #ax.xaxis.set_major_locator(xmajorLocator) + #ax.xaxis.set_major_formatter(xmajorFormatter) + #ax.xaxis.set_minor_locator(xminorLocator) + + # Plot each cross section + for pair in cia_pairs: + + cia_plt = cia_stored[pair][0,:] # Cross section of species q at given (P,T) pair (m^5) + + # Plot cross section + plt.semilogy(wl_grid, cia_plt, label=pair, **kwargs) + + plt.ylim([1.0e-60, 1.0e-52]) + plt.xlim([min(wl_grid), max(wl_grid)]) + plt.ylabel(r'$\mathrm{Binary \, \, Cross \, \, Section \, (CIA) \, \, (m^{5} mol^{-2})}$', fontsize = 15) + plt.xlabel(r'$\mathrm{Wavelength} \; \mathrm{(\mu m)}$', fontsize = 15) + + ax.text(min(wl_grid)*1.20, 3.0e-53, (r'$\mathrm{T = }$' + str(T) + r'$\mathrm{K}$'), fontsize = 14) + + legend = plt.legend(loc='upper right', shadow=False, frameon=False, prop={'size':8}) + + '''for legline in legend.legendHandles: + legline.set_linewidth(1.0)''' + + if savefigs: + plt.savefig('./CIA_cross_sections_' + str(T) + 'K.pdf', bbox_inches='tight', fmt='pdf', dpi=1000) + + plt.show() + + plt.clf() + + #***** Now plot H- bound-free opacity ***** + + # Initialise plot + #ax = plt.gca() + + ax = plt.subplot(111) + #xmajorLocator = MultipleLocator(0.5) + #xmajorFormatter = FormatStrFormatter('%.1f') + #xminorLocator = MultipleLocator(0.1) + #xminorFormatter = NullFormatter() + + #ax.xaxis.set_major_locator(xmajorLocator) + #ax.xaxis.set_major_formatter(xmajorFormatter) + #ax.xaxis.set_minor_locator(xminorLocator) + #ax.xaxis.set_minor_formatter(xminorFormatter) + + # Plot cross section + plt.semilogy(wl, alpha_bf, label = r'H- bound-free', **kwargs) + + plt.ylim([1.0e-23, 1.0e-20]) + plt.xlim([min(wl), 1.8]) + plt.ylabel(r'$\mathrm{Cross \, \, Section \, \, (m^{2} \, / n_{H-})}$', fontsize = 15) + plt.xlabel(r'$\mathrm{Wavelength} \; \mathrm{(\mu m)}$', fontsize = 15) + + #ax.set_xticks([0.4, 0.6, 0.8, 1.0, 1.2, 1.4, 1.6, 1.8]) + + legend = plt.legend(loc='upper right', shadow=False, frameon=False, prop={'size':8}, ncol=2) + + if savefigs: + plt.savefig('./H-_bound_free.pdf', bbox_inches='tight', fmt='pdf', dpi=1000) + + plt.show() + + plt.clf() + + #***** Now plot H- free-free opacity ***** + + # Initialise plot + #ax = plt.gca() + #ax.set_xscale("log") + + ax = plt.subplot(111) + #xmajorLocator = MultipleLocator(10.0) + #xmajorFormatter = FormatStrFormatter('%.1f') + #xminorLocator = MultipleLocator(0.2) + #xminorFormatter = NullFormatter() + + #ax.xaxis.set_major_locator(xmajorLocator) + #ax.xaxis.set_major_formatter(xmajorFormatter) + #ax.xaxis.set_minor_locator(xminorLocator) + #ax.xaxis.set_minor_formatter(xminorFormatter) + + # Plot cross section + plt.semilogy(wl, alpha_ff, label = r'H- free-free', **kwargs) + + plt.ylim([8.0e-50, 2.0e-47]) + plt.xlim([min(wl), max(wl)]) + plt.ylabel(r'$\mathrm{Binary \, \, Cross \, \, Section \, \, (m^{5} \, / n_{H} / n_{e-})}$', fontsize = 15) + plt.xlabel(r'$\mathrm{Wavelength} \; \mathrm{(\mu m)}$', fontsize = 15) + + #ax.set_xticks([0.4, 1.0, 2.0, 3.0, 4.0, 5.0]) + + ax.text(min(wl_grid)*1.20, 1.5e-47, (r'$\mathrm{T = }$' + str(T) + r'$\mathrm{K}$'), fontsize = 14) + + legend = plt.legend(loc='upper right', shadow=False, frameon=False, prop={'size':8}, ncol=1) + + if savefigs: + plt.savefig('./H-_free_free_' + str(T) + 'K.pdf', bbox_inches='tight', fmt='pdf', dpi=1000) + + plt.show() + + plt.clf() + + return + +#***** Begin main program ***** + +# This code will execute if you run the script from the terminal +# Otherwise, no +if __name__ == '__main__': + # Specify which molecules you want to extract from the database (full list available in the readme) + chemical_species = np.array(['H2O', 'CH4', 'NH3', 'HCN', 'CO', 'CO2']) + + # Specify collisionally-induced absorption (CIA) pairs + cia_pairs = np.array(['H2-H2', 'H2-He']) + + # Specify layer pressures and temperatures in each layer + # For this plotting test, take same P and T throughout atmosphere + P = np.ones(100) * 1.0e-3 # Pressures in each layer (bar) + #P = np.logspace(-6.0, 2.0, 100.0) # Pressures in each layer (bar) + T = np.ones(100) * 2000.0 # Temperature in each layer (K) + + # Specify wavelength grid to extract cross section onto + wl_min = 0.4 # Minimum wavelength of grid (micron) + wl_max = 5.0 # Maximum wavelength of grid (micron) + N_wl = 1000 # Number of wavelength points + + wl = np.linspace(wl_min, wl_max, N_wl) # Uniform grid used here for demonstration purposes + + # Either sample the nearest wavelength points from the high resolution (R~10^6) cross section database or use an averaging prescription + opacity_treatment = 'Log-avg' # Options: Opacity-sample / Log-avg + #opacity_treatment = 'Opacity-sample' # Opacity sampling is faster, but for low-resolution wavelength grids log averaging is recommended + + # Extract desired cross sections from the database + cross_sections, cia_absorption, alpha_bf, alpha_ff = Extract_opacity_NEW(chemical_species, cia_pairs, P, T, wl, opacity_treatment) # Format: np array(N_species, N_wl) / Units: (m^2 / species) + + # Evaluate H- bound-free and free-free opacities + #alpha_bf = H_minus_bound_free(wl) + #alpha_ff = H_minus_free_free(T, wl) + + # Example: seperate H2O cross section, and print to terminal + #H2O_cross_section = cross_sections['H2O'] # Format: np array(N_wl) / Units: (m^2 / molecule) + #print (H2O_cross_section) + + # Plot cross sections + plot_opacity(chemical_species, cia_pairs, cross_sections, cia_absorption, + alpha_bf, alpha_ff[0,:], P[0], T[0], wl, savefigs=True, alpha=0.8, lw=0.5) + diff --git a/gas_opac/opacity_demo.py b/gas_opac/opacity_demo.py new file mode 100644 index 0000000..b2e67c1 --- /dev/null +++ b/gas_opac/opacity_demo.py @@ -0,0 +1,459 @@ +# Demostrates how to open the opacity database and interpolate to a given P, T, and wavelength grid +# Author: Ryan J. MacDonald - 8th November, 2018 + +import numpy as np +import h5py +from scipy.ndimage import gaussian_filter1d as gauss_conv +from numba.decorators import jit +import matplotlib.pyplot as plt +from matplotlib.ticker import MultipleLocator, FormatStrFormatter + +#***** Begin function declarations *****# + +@jit(nopython = True) +def prior_index(vec, value, start): + + '''Finds the index of a grid closest to a specified value''' + + value_tmp = value + + if (value_tmp > vec[-1]): + return (len(vec) - 1) + + # Check if value out of bounds, if so set to edge value + if (value_tmp < vec[0]): value_tmp = vec[0] + if (value_tmp > vec[-2]): value_tmp = vec[-2] + + index = start + + for i in range(len(vec)-start): + if (vec[i+start] > value_tmp): + index = (i+start) - 1 + break + + return index + +@jit(nopython=True) +def prior_index_V2(val, grid_start, grid_end, N_grid): + + if (val < grid_start): + return 0 + + elif (val > grid_end): + return N_grid-1 + + else: + i = (N_grid-1) * ((val - grid_start) / (grid_end - grid_start)) + return int(i) + +@jit(nopython=True) +def closest_index(val, grid_start, grid_end, N_grid): + + '''Finds the index of a UNIFORM grid closest to a specified value. + + Assuming a uniform grid dramatically speeds calculation''' + + if (val < grid_start): + return 0 + + elif (val > grid_end): + return N_grid-1 + + else: + i = (N_grid-1) * ((val - grid_start) / (grid_end - grid_start)) + if ((i%1)<=0.5): + return int(i) + else: + return int(i)+1 + +@jit(nopython = True) +def P_interpolate_wl_initialise(N_T, N_P, N_wl, log_sigma, + nu_l, nu_model, nu_r, nu_opac, N_nu, + x, b1, b2, mode): + + '''Interpolates raw cross sections onto the model P and wl grid. + + Note: input sigma has format log10(cross_sec)[log(P)_grid, T_grid, nu_grid], + whilst output has format cross_sec[T, wl_model] + + The input is in wavenumnber to take advantage of fast prior index location + on a uniform grid, which wouldn't work for the (non-uniform) wavelength grid + Array reversal to output in increasing wavelength is handled by indexing + by a factor of (N_wl-1)-k throughout + ''' + + sigma_pre_inp = np.zeros(shape=(N_T, N_wl)) + + N_nu_opac = len(nu_opac) # Number of wavenumber points in sigma array + + for k in range(N_nu): # Note that the k here is looping over wavenumber + + # Indicies in pre-computed wavenumber array of LHS, centre, and RHS of desired wavenumber grid + z_l = closest_index(nu_l[k], nu_opac[0], nu_opac[-1], N_nu_opac) + z = closest_index(nu_model[k], nu_opac[0], nu_opac[-1], N_nu_opac) + z_r = closest_index(nu_r[k], nu_opac[0], nu_opac[-1], N_nu_opac) + + for j in range(N_T): + + # If nu (wl) point out of range of opacity grid, set opacity to zero + if ((z == 0) or (z == (N_nu_opac-1))): + sigma_pre_inp[j, ((N_wl-1)-k)] = 0.0 + + else: + + # Opacity sampling + if (mode == 1): + + # If pressure below minimum, set to value at min pressure + if (x == -1): + sigma_pre_inp[j, ((N_wl-1)-k)] = 10 ** (log_sigma[0, j, z]) + + # If pressure above maximum, set to value at max pressure + elif (x == -2): + sigma_pre_inp[j, ((N_wl-1)-k)] = 10 ** (log_sigma[(N_P-1), j, z]) + + # Interpolate sigma in logsace, then power to get interp array + else: + reduced_sigma = log_sigma[x:x+2, j, z] + + sigma_pre_inp[j, ((N_wl-1)-k)] = 10 ** (b1*(reduced_sigma[0]) + + b2*(reduced_sigma[1])) + + # Log averaging + elif (mode == 2): + + # If pressure below minimum, set to value at min pressure + if (x == -1): + sigma_in_bin = np.mean(log_sigma[0, j, z_l:z_r+1]) + sigma_pre_inp[j, ((N_wl-1)-k)] = 10 ** (sigma_in_bin) + + # If pressure above maximum, set to value at max pressure + elif (x == -2): + sigma_in_bin = np.mean(log_sigma[(N_P-1), j, z_l:z_r+1]) + sigma_pre_inp[j, ((N_wl-1)-k)] = 10 ** (sigma_in_bin) + + # Interpolate sigma in logsace, then power to get interp array + else: + sigma_in_bin_P1 = np.mean(log_sigma[x, j, z_l:z_r+1]) + sigma_in_bin_P2 = np.mean(log_sigma[x+1, j, z_l:z_r+1]) + + sigma_pre_inp[j, ((N_wl-1)-k)] = 10 ** (b1*(sigma_in_bin_P1) + + b2*(sigma_in_bin_P2)) + + return sigma_pre_inp + +@jit(nopython = True) +def T_interpolation_init(T_grid, T): + + y = 0 # Index in cross secion arrays prior to fine temperature value + + if (T < T_grid[0]): # If temperature falls off LHS of temperaure grid + y = -1 # Special value (-1) stored, interpreted in interpolator + w_T = 0.0 # Weight not used in this case + + elif (T >= T_grid[-1]): # If temperature falls off RHS of temperaure grid + y = -2 # Special value (-2) stored, interpreted in interpolator + w_T = 0.0 # Weight not used in this case + + else: + + # Have to use prior_index (V1) here as T_grid is not uniformly spaced + y = prior_index(T_grid, T, 0) # Index in cross secion arrays prior to desired temperature value + + # Pre-computed temperature values to left and right of desired temperature value + T1 = T_grid[y] + T2 = T_grid[y+1] + + # Precompute temperature interpolation weight factor + w_T = (1.0/((1.0/T2) - (1.0/T1))) + + return y, w_T + +@jit(nopython = True) +def T_interpolate(N_T, N_wl, sigma_pre_inp, T_grid, T, y, w_T): + + sigma_inp = np.zeros(shape=(N_wl)) + + T1 = T_grid[y] + T2 = T_grid[y+1] + + for k in range(N_wl): # Loop over wavelengths + + # If T_fine below min value (100 K), set sigma to value at min T + if (y == -1): + sigma_inp[k] = sigma_pre_inp[0, k] + + # If T_fine above max value (3500 K), set sigma to value at max T + elif (y == -2): + sigma_inp[k] = sigma_pre_inp[(N_T-1), k] + + # Interpolate sigma to fine temperature grid value + else: + sig_reduced = sigma_pre_inp[y:y+2, k] + sig_1, sig_2 = sig_reduced[0], sig_reduced[1] # sigma(T1)[i,k], sigma(T2)[i,k] + + sigma_inp[k] = (np.power(sig_1, (w_T*((1.0/T2) - (1.0/T)))) * + np.power(sig_2, (w_T*((1.0/T) - (1.0/T1))))) + + return sigma_inp + + +## Written by Lia +## To separate out reading step +def load_db(filename='Opacity_database_0.01cm-1.hdf5', root='./'): + + open_filename = root + filename + print("Reading opacity database file") + opac_file = h5py.File(open_filename, 'r') + + #***** Read in T and P grids used in opacity files*****# + T_grid = np.array(opac_file['H2O/T']) # H2O here simply used as dummy (same grid for all molecules) + log_P_grid = np.array(opac_file['H2O/log(P)']) # Units: log10(P/bar)! + + #***** Read in wavenumber arrays used in opacity files*****# + nu_opac = np.array(opac_file['H2O/nu']) # H2O here simply used as dummy (same grid for all molecules) + + return T_grid, log_P_grid, nu_opac, opac_file + + +# Algorithm for loading cross-sections from one pressure and temperature +# from cross-section table of a single species (log_sigma) +def _get_one_PT(log_sigma, T_grid, log_P_grid, nu_opac, + new_P, new_T, wl_out, opacity_treatment): + + if (opacity_treatment == 'Opacity-sample'): calculation_mode = 1 + elif (opacity_treatment == 'Log-avg'): calculation_mode = 2 + + N_P = len(log_P_grid) # No. of pressures in opacity files + N_T = len(T_grid) # No. of temperatures in opacity files + + # Convert model wavelength grid to wavenumber grid + nu_out = 1.0e4/wl_out # Model wavenumber grid (cm^-1) + nu_out = nu_out[::-1] # Reverse direction, such that increases with wavenumber + + N_nu = len(nu_out) # Number of wavenumbers on model grid + N_wl = len(wl_out) # Number of wavelengths on model grid + + # Initialise arrays of wavenumber locations of left and right bin edges + #nu_l = np.zeros(N_nu) # Left edge + #nu_r = np.zeros(N_nu) # Right edge + # Look below for definitions of nu_l, nu_r + + # Find logarithm of desired pressure + log_P = np.log10(new_P) + + # If pressure below minimum, do not interpolate + if (log_P < log_P_grid[0]): + x = -1 # Special value (1) used in opacity inialiser + w_P = 0.0 + # If pressure above maximum, do not interpolate + elif (log_P >= log_P_grid[-1]): + x = -2 # Special value (2) used in opacity inialiser + w_P = 0.0 + else: + # Closest P indicies in opacity grid corresponding to model pressure + x = prior_index_V2(log_P, log_P_grid[0], log_P_grid[-1], N_P) + # Weights - fractional distance along pressure axis of sigma array + w_P = (log_P-log_P_grid[x])/(log_P_grid[x+1]-log_P_grid[x]) + + # Precalculate interpolation pre-factors to reduce computation overhead + b1 = (1.0-w_P) + b2 = w_P + + # Find wavenumber indicies in arrays of model grid + # Vectorized by Lia + nu_edges = np.append(nu_out[0] - (nu_out[1] - nu_out[0]), + nu_out) + nu_edges = np.append(nu_edges, nu_out[-1] + (nu_out[-1] - nu_out[-2])) + nu_l = 0.5 * (nu_edges[:-2] + nu_edges[1:-1]) + nu_r = 0.5 * (nu_edges[1:-1] + nu_edges[2:]) + + '''for k in range(N_nu): + + if (k != 0) and (k != (N_nu-1)): + nu_l[k] = 0.5*(nu_out[k-1] + nu_out[k]) + nu_r[k] = 0.5*(nu_out[k] + nu_out[k+1]) + + # Special case for boundary values + elif (k == 0): + nu_l[k] = nu_out[k] - 0.5*(nu_out[k+1] - nu_out[k]) + nu_r[k] = 0.5*(nu_out[k] + nu_out[k+1]) + elif (k == (N_nu-1)): + nu_l[k] = 0.5*(nu_out[k-1] + nu_out[k]) + nu_r[k] = nu_out[k] + 0.5*(nu_out[k] - nu_out[k-1])''' + + # Evaluate temperature interpolation weighting factor + y, w_T = T_interpolation_init(T_grid, new_T) + + sigma_pre_T_inp = P_interpolate_wl_initialise(N_T, N_P, N_wl, + log_sigma, nu_l, nu_out, nu_r, + nu_opac, N_nu, x, b1, b2, calculation_mode) + + sigma_result = T_interpolate(N_T, N_wl, sigma_pre_T_inp, T_grid, new_T, y, w_T) + + return sigma_result + + +def Extract_opacity(chemical_species, P, T, wl_out, opacity_treatment): + + '''Convienient function to read in all opacities and pre-interpolate + them onto the desired pressure, temperature, and wavelength grid''' + + T_grid, log_P_grid, nu_opac, opac_file = load_db() + + # Initialise molecular and atomic opacity array, interpolated to model wavelength grid + #sigma_stored = np.zeros(shape=(N_species, N_wl)) + # Lia -- Making this a dictionary instead of a numpy array. It's easier to use for plotting. + # Later, a 2D numpy array will be faster for summing across a large number of species + # But for now, two dozen is not a lot. + sigma_stored = dict() + + #***** Process molecular and atomic opacities *****# + + # Load molecular and atomic absorption cross sections + for q in chemical_species: + + log_sigma = np.array(opac_file[q + '/log(sigma)']).astype(np.float64) + + sigma_stored[q] = _get_one_PT(log_sigma, T_grid, log_P_grid, nu_opac, + P, T, wl_out, opacity_treatment) + + del log_sigma # Clear raw cross section to free up memory + + print(q + " done") + + opac_file.close() + + return sigma_stored + +# Written by Lia for a set of (p,T) values +def Extract_opacity_PTpairs(chemical_species, P, T, wl_out, opacity_treatment, root='./'): + + '''Convienient function to read in all opacities and pre-interpolate + them onto the desired pressure, temperature, and wavelength grid''' + + assert len(P) == len(T) + + T_grid, log_P_grid, nu_opac, opac_file = load_db(root=root) + + # Initialise molecular and atomic opacity array, interpolated to model wavelength grid + #sigma_stored = np.zeros(shape=(N_species, N_wl)) + # Lia -- Making this a dictionary instead of a numpy array. It's easier to use for plotting. + # Later, a 2D numpy array will be faster for summing across a large number of species + # But for now, two dozen is not a lot. + sigma_stored = dict() + + #***** Process molecular and atomic opacities *****# + + # Load molecular and atomic absorption cross sections + for q in chemical_species: + + log_sigma = np.array(opac_file[q + '/log(sigma)']).astype(np.float64) + + result = np.zeros(shape=(len(P), len(wl_out))) + for i in range(len(P)): + result[i,:] = _get_one_PT(log_sigma, T_grid, log_P_grid, nu_opac, + P[i], T[i], wl_out, opacity_treatment) + + sigma_stored[q] = result + + del log_sigma # Clear raw cross section to free up memory + + print(q + " done") + + opac_file.close() + + return sigma_stored + + +def plot_opacity(chemical_species, sigma_stored, P, T, wl_grid, savefig=False, **kwargs): + + # Max number of species this can plot is 9 (clustered beyond that!) + + # Optional smoothing of cross sections (can improve clarity) + smooth = False + smooth_factor = 5 + + # Specify cross sections to plot, along with colours for each + #colours_plot = np.array(['royalblue', 'purple', 'crimson', 'orange', 'black', 'grey', 'green', 'magenta', 'chocolate']) + + # Initialise plot + #ax = plt.gca() + #ax.set_xscale("log") + + ax = plt.subplot(111) + #xmajorLocator = MultipleLocator(1.0) + #xmajorFormatter = FormatStrFormatter('%.1f') + #xminorLocator = MultipleLocator(0.2) + + #ax.xaxis.set_major_locator(xmajorLocator) + #ax.xaxis.set_major_formatter(xmajorFormatter) + #ax.xaxis.set_minor_locator(xminorLocator) + + # Plot each cross section + for species in chemical_species: + #species_idx = np.where(chemical_species == species)[0][0] + sigma_plt = sigma_stored[species]*1.0e4 # Cross section of species q at given (P,T) pair (cm^2) + + if (smooth == True): + sigma_plt = gauss_conv(sigma_plt, sigma=smooth_factor, mode='nearest') + + # Plot cross section + plt.semilogy(wl_grid, sigma_plt, label=species, **kwargs) + + plt.ylim([1.0e-28, 2.0e-18]) + plt.xlim([min(wl_grid), max(wl_grid)]) + plt.ylabel(r'$\mathrm{Cross \, \, Section \, \, (cm^{2})}$', fontsize = 15) + plt.xlabel(r'$\mathrm{Wavelength} \; \mathrm{(\mu m)}$', fontsize = 15) + + ax.text(min(wl_grid)*1.05, 6.0e-19, (r'$\mathrm{T = }$' + str(T) + \ + r'$\mathrm{K \, \, P = }$' + \ + str(P*1000) + r'$\mathrm{mbar}$'), fontsize = 14) + + legend = plt.legend(loc='upper right', frameon=False, prop={'size':6}, ncol=2) + + '''for legline in legend.legendHandles: + legline.set_linewidth(1.0)''' + + plt.show() + + if savefig: + plt.savefig('./cross_sections_' + str(T) + 'K_' + str(P*1000) + 'mbar.pdf', bbox_inches='tight', fmt='pdf', dpi=1000) + + plt.close() + + return + +#***** Begin main program ***** + +# This code will execute if you run the script from the terminal +# Otherwise, no +if __name__ == '__main__': + # Specify which molecules you want to extract from the database (full list available in the readme) + chemical_species = np.array(['H2O', 'CH4', 'NH3', 'HCN', 'CO', 'CO2']) + + # At what temperature and pressure do you desire the cross sections? + P = 1.0e-3 # Pressure (bar) + T = 1000.0 # Temperature (K) + + # Specify wavelength grid to extract cross section onto + wl_min = 0.4 # Minimum wavelength of grid (micron) + wl_max = 5.0 # Maximum wavelength of grid (micron) + N_wl = 1000 # Number of wavelength points + + wl = np.linspace(wl_min, wl_max, N_wl) # Uniform grid used here for demonstration purposes + + # Either sample the nearest wavelength points from the high resolution (R~10^6) cross section database or use an averaging prescription + opacity_treatment = 'Log-avg' # Options: Opacity-sample / Log-avg + #opacity_treatment = 'Opacity-sample' # Opacity sampling is faster, but for low-resolution wavelength grids log averaging is recommended + + # Extract desired cross sections from the database + cross_sections = Extract_opacity(chemical_species, P, T, wl, opacity_treatment) # Format: np array(N_species, N_wl) / Units: (m^2 / species) + + # Example: seperate H2O cross section, and print to terminal + H2O_cross_section = cross_sections['H2O'] # Format: np array(N_wl) / Units: (m^2 / molecule) + #print (H2O_cross_section) + + # Plot cross sections + plot_opacity(chemical_species, cross_sections, P, T, wl, savefig=True, alpha=0.8, lw=0.5) + diff --git a/gas_opac/opacity_demo_V2.py b/gas_opac/opacity_demo_V2.py new file mode 100644 index 0000000..0435eb8 --- /dev/null +++ b/gas_opac/opacity_demo_V2.py @@ -0,0 +1,461 @@ +# Demostrates how to open the opacity database and interpolate to given (P,T) and wavelength grid +# Author: Ryan J. MacDonald +# V1.0: Evaluates cross sections at a single P,T point (8th November 2018) +# V2.0: Evaluates cross sections on a grid of (P,T) points (6th december 2018) + +# 2018.12.06 - Modified slightly by liac@umich.edu + +import numpy as np +import h5py +from scipy.ndimage import gaussian_filter1d as gauss_conv +from numba.decorators import jit +import matplotlib.pyplot as plt +from matplotlib.ticker import MultipleLocator, FormatStrFormatter + +#***** Begin function declarations *****# + +@jit(nopython = True) +def prior_index(vec, value, start): + + '''Finds the previous index of a grid closest to a specified value + ''' + + value_tmp = value + + if (value_tmp > vec[-1]): + return (len(vec) - 1) + + # Check if value out of bounds, if so set to edge value + if (value_tmp < vec[0]): value_tmp = vec[0] + if (value_tmp > vec[-2]): value_tmp = vec[-2] + + index = start + + for i in range(len(vec)-start): + if (vec[i+start] > value_tmp): + index = (i+start) - 1 + break + + return index + +@jit(nopython=True) +def prior_index_V2(val, grid_start, grid_end, N_grid): + + '''Finds the previous index of a UNIFORM grid closest to a specified value. + + A uniform grid dramatically speeds calculation over a non-uniform grid. + ''' + + if (val < grid_start): + return 0 + + elif (val > grid_end): + return N_grid-1 + + else: + i = (N_grid-1) * ((val - grid_start) / (grid_end - grid_start)) + return int(i) + +@jit(nopython=True) +def closest_index(val, grid_start, grid_end, N_grid): + + '''Finds the index of a UNIFORM grid closest to a specified value. + + A uniform grid dramatically speeds calculation over a non-uniform grid. + ''' + + if (val < grid_start): + return 0 + + elif (val > grid_end): + return N_grid-1 + + else: + i = (N_grid-1) * ((val - grid_start) / (grid_end - grid_start)) + if ((i%1)<=0.5): + return int(i) + else: + return int(i)+1 + +@jit(nopython = True) +def P_interpolate_wl_initialise(nu_grid, N_P_out, N_T, N_P, N_wl_out, N_nu_out, + log_sigma, x, z_l, z, z_r, b1, b2, mode): + + '''Interpolates raw cross sections onto the output P and wl grids. + + Note: input sigma has format log10(cross_sec)[log(P)_grid, T_grid, nu_grid], + whilst output has format cross_sec[log(P)_out, T_grid, wl_out] + + The input is in wavenumnber to take advantage of fast prior index location + on a uniform grid, which wouldn't work for the (non-uniform) wavelength grid + Array reversal to output in increasing wavelength is handled by indexing + by a factor of (N_wl-1)-k throughout. + ''' + + sigma_pre_inp = np.zeros(shape=(N_P_out, N_T, N_wl_out)) + + N_nu = len(nu_grid) # Number of wavenumber points in sigma array + + for i in xrange(N_P_out): # For each pressure in output array + for j in xrange(N_T): # For each temperature in input array + for k in xrange(N_nu_out): # Note that the k here is looping over wavenumber + + # If nu (wl) point out of range of opacity grid, set opacity to zero + if ((z[k] == 0) or (z[k] == (N_nu-1))): + sigma_pre_inp[i, j, ((N_wl_out-1)-k)] = 0.0 + + else: + + # Opacity sampling + if (mode == 1): + + # If pressure below minimum, set to value at min pressure + if (x[i] == -1): + sigma_pre_inp[i, j, ((N_wl-1)-k)] = 10 ** (log_sigma[0, j, z[k]]) + + # If pressure above maximum, set to value at max pressure + elif (x[i] == -2): + sigma_pre_inp[i, j, ((N_wl-1)-k)] = 10 ** (log_sigma[(N_P-1), j, z[k]]) + + # Interpolate sigma in logsace, then power to get interp array + else: + reduced_sigma = log_sigma[x[i]:x[i]+2, j, z[k]] + + sigma_pre_inp[i, j, ((N_wl-1)-k)] = 10 ** (b1[i]*(reduced_sigma[0]) + + b2[i]*(reduced_sigma[1])) + + # Log averaging + elif (mode == 2): + + # If pressure below minimum, set to value at min pressure + if (x[i] == -1): + sigma_in_bin = np.mean(log_sigma[0, j, z_l[k]:z_r[k]+1]) + sigma_pre_inp[i, j, ((N_wl-1)-k)] = 10 ** (sigma_in_bin) + + # If pressure above maximum, set to value at max pressure + elif (x[i] == -2): + sigma_in_bin = np.mean(log_sigma[(N_P-1), j, z_l[k]:z_r[k]+1]) + sigma_pre_inp[i, j, ((N_wl-1)-k)] = 10 ** (sigma_in_bin) + + # Interpolate sigma in logsace, then power to get interp array + else: + sigma_in_bin_P1 = np.mean(log_sigma[x[i], j, z_l[k]:z_r[k]+1]) + sigma_in_bin_P2 = np.mean(log_sigma[x[i]+1, j, z_l[k]:z_r[k]+1]) + + sigma_pre_inp[i, j, ((N_wl-1)-k)] = 10 ** (b1[i]*(sigma_in_bin_P1) + + b2[i]*(sigma_in_bin_P2)) + + return sigma_pre_inp + +@jit(nopython = True) +def T_interpolation_init(N_T_out, T_grid, T_out, y): + + ''' Precomputes the T interpolation weight factors, so this does not + need to be done multiple times across all species. + ''' + + w_T = np.zeros(N_T_out) # Temperature interpolation weight factors + + # Find T index in cross secion arrays prior to fine temperature value + for j in xrange(N_T_out): + + if (T_out[j] < T_grid[0]): # If fine temperature point falls off LHS of temperaure grid + y[j] = -1 # Special value (-1) stored, interpreted in interpolator + w_T[j] = 0.0 # Weight not used in this case + + elif (T_out[j] >= T_grid[-1]): # If fine temperature point falls off RHS of temperaure grid + y[j] = -2 # Special value (-2) stored, interpreted in interpolator + w_T[j] = 0.0 # Weight not used in this case + + else: + + # Have to use prior_index (V1) here as T_grid is not uniformly spaced + y[j] = prior_index(T_grid, T_out[j], 0) # Index in cross secion arrays prior to desired temperature value + + # Pre-computed temperature values to left and right of desired temperature value + T1 = T_grid[y[j]] + T2 = T_grid[y[j]+1] + + # Precompute temperature interpolation weight factor + w_T[j] = (1.0/((1.0/T2) - (1.0/T1))) + + return w_T + +@jit(nopython = True) +def T_interpolate(N_P_out, N_T_out, N_T, N_wl_out, sigma_pre_inp, T_grid, T_out, y, w_T): + + '''Interpolates pre-processed cross section onto the output T grid. + + Note: input sigma has format log10(cross_sec)[log(P)_out, T_grid, wl_out], + whilst output has format cross_sec[log(P)_out, T_out, wl_out]. + + Output is the final interpolated cross section as a 3D array on the + desired P_out, T_out, wl_out grids. + ''' + + sigma_inp = np.zeros(shape=(N_P_out, N_T_out, N_wl_out)) + + for i in xrange(N_P_out): # Loop over output pressure array + for j in xrange(N_T_out): # Loop over input temperature array + + T = T_out[j] # Temperature we wish to interpolate to + T1 = T_grid[y[j]] + T2 = T_grid[y[j]+1] + + for k in xrange(N_wl_out): # Loop over wavelengths + + # If T_out below min pre-computed value (100 K), set sigma to value at min T + if (y[j] == -1): + sigma_inp[i, j, k] = sigma_pre_inp[i, 0, k] + + # If T_out above max pre-computed value (3500 K), set sigma to value at max T + elif (y[j] == -2): + sigma_inp[i, j, k] = sigma_pre_inp[i, (N_T-1), k] + + # Interpolate sigma to output temperature grid value + else: + sig_reduced = sigma_pre_inp[i, y[j]:y[j]+2, k] + sig_1, sig_2 = sig_reduced[0], sig_reduced[1] # sigma(T1)[i,k], sigma(T2)[i,k] + + sigma_inp[i, j, k] = (np.power(sig_1, (w_T[j]*((1.0/T2) - (1.0/T)))) * + np.power(sig_2, (w_T[j]*((1.0/T) - (1.0/T1))))) + + return sigma_inp + +def Extract_opacity(chemical_species, P_out, T_out, wl_out, opacity_treatment): + + '''Main function to read in all opacities and interpolate them onto the + desired pressure, temperature, and wavelength grids. + ''' + + print("Now reading in cross sections") + + # First, check from config.py which opacity calculation mode is specified + if (opacity_treatment == 'Opacity-sample'): calculation_mode = 1 + elif (opacity_treatment == 'Log-avg'): calculation_mode = 2 + + #***** Firstly, we initialise the various quantities needed for pre-interpolation*****# + + # Open HDF5 files containing molecular + atomic opacities and CIA + opac_file = h5py.File('./Opacity_database_0.01cm-1.hdf5', 'r') + + #***** Read in T and P grids used in opacity files*****# + T_grid = np.array(opac_file['H2O/T']) # H2O here simply used as dummy (same grid for all molecules) + log_P_grid = np.array(opac_file['H2O/log(P)']) # Units: log10(P/bar) + log_P_out = np.log10(P_out) # Units: log10(P/bar) + + #***** Read in wavenumber arrays used in opacity files*****# + nu_grid = np.array(opac_file['H2O/nu']) # H2O here simply used as dummy (same grid for all molecules) + + # Convert model wavelength grid to wavenumber grid + nu_out = 1.0e4/wl_out # Model wavenumber grid (cm^-1) + nu_out = nu_out[::-1] # Reverse direction, such that increases with wavenumber + + N_nu = len(nu_grid) # No. of wavenumbers in each opacity file (2480001) + N_P = len(log_P_grid) # No. of pressures in opacity files + N_T = len(T_grid) # No. of temperatures in opacity files + N_species = len(chemical_species) # No. of chemical species user wishes to store + + N_P_out = len(P_out) # No. of pressures on output pressure grid + N_T_out = len(T_out) # No. of temperatures on output temperature grid + N_nu_out = len(nu_out) # No. of wavenumbers on output grid + N_wl_out = len(wl_out) # No. of wavelengths on output grid + + # Initialise arrays of wavenumber locations of left and right bin edges + nu_l = np.zeros(N_nu_out) # Left edge + nu_r = np.zeros(N_nu_out) # Right edge + + # Initialise arrays of indicies on wavenumber opacity grid corresponding to nu_l, nu_out, and nu_r + z_l = np.zeros(N_wl_out, dtype=np.int) # Bin left edge + z = np.zeros(N_wl_out, dtype=np.int) # Bin centre + z_r = np.zeros(N_wl_out, dtype=np.int) # Bin right edge + + # Initialise array of indicies on pre-calculated pressure opacity grid prior to defined atmosphere layer pressures + x = np.zeros(N_P_out, dtype=np.int) + + # Weights + w_P = np.zeros(N_P_out) + + # Useful functions of weights for interpolation + b1 = np.zeros(shape=(N_P_out)) + b2 = np.zeros(shape=(N_P_out)) + + # Now find closest P indicies in opacity grid corresponding to output pressures + for i in xrange(N_P_out): + + # If pressure below minimum, do not interpolate + if (log_P_out[i] < log_P_grid[0]): + x[i] = -1 # Special value (1) used in opacity initialiser + w_P[i] = 0.0 + + # If pressure above maximum, do not interpolate + elif (log_P_out[i] >= log_P_grid[-1]): + x[i] = -2 # Special value (2) used in opacity initialiser + w_P[i] = 0.0 + + else: + x[i] = prior_index_V2(log_P_out[i], log_P_grid[0], log_P_grid[-1], N_P) + + # Weights - fractional distance along pressure axis of cross section array + w_P[i] = (log_P_out[i]-log_P_grid[x[i]])/(log_P_grid[x[i]+1]-log_P_grid[x[i]]) + + # Precalculate interpolation pre-factors to reduce computation overhead + b1[i] = (1.0-w_P[i]) + b2[i] = w_P[i] + + # Find wavenumber indicies on input grid closest to values on output grid + for k in xrange(N_nu_out): + + if (k != 0) and (k != (N_nu_out-1)): + nu_l[k] = 0.5*(nu_out[k-1] + nu_out[k]) + nu_r[k] = 0.5*(nu_out[k] + nu_out[k+1]) + + # Boundary values of each output wavenumber point + elif (k == 0): + nu_l[k] = nu_out[k] - 0.5*(nu_out[k+1] - nu_out[k]) + nu_r[k] = 0.5*(nu_out[k] + nu_out[k+1]) + elif (k == (N_nu-1)): + nu_l[k] = 0.5*(nu_out[k-1] + nu_out[k]) + nu_r[k] = nu_out[k] + 0.5*(nu_out[k] - nu_out[k-1]) + + # Calculate closest indicies to output wavenumber bin left, centre, and right edge + z_l[k] = closest_index(nu_l[k], nu_grid[0], nu_grid[-1], N_nu) + z[k] = closest_index(nu_out[k], nu_grid[0], nu_grid[-1], N_nu) + z_r[k] = closest_index(nu_r[k], nu_grid[0], nu_grid[-1], N_nu) + + # Initialise molecular and atomic opacity array, interpolated to output (P,T,wl) grid + # Lia - modified to use a dictionary instead (easier for reference in her codes) + #sigma_stored = np.zeros(shape=(N_species, N_P_out, N_T_out, N_wl_out)) + sigma_stored = dict() + + # Evaluate temperature interpolation weighting factor + y = np.zeros(N_T_out, dtype=np.int) + w_T = T_interpolation_init(N_T_out, T_grid, T_out, y) + + #***** Process molecular and atomic opacities *****# + + # Load molecular and atomic absorption cross sections + for q in xrange(N_species): + + species_q = chemical_species[q] # Species name specified by user + + # Read in log10(cross section) of specified species + log_sigma = np.array(opac_file[species_q + '/log(sigma)']).astype(np.float64) + + # Pre-interpolate cross section to desired P and wl grid + sigma_pre_inp = P_interpolate_wl_initialise(nu_grid, N_P_out, N_T, N_P, N_wl_out, N_nu_out, log_sigma, x, z_l, z, z_r, b1, b2, calculation_mode) + + # Interplate to desired temperature + #sigma_stored[q,:,:,:] = T_interpolate(N_P_out, N_T_out, N_T, N_wl_out, sigma_pre_inp, T_grid, T_out, y, w_T) + sigma_stored[q] = T_interpolate(N_P_out, N_T_out, N_T, N_wl_out, sigma_pre_inp, T_grid, T_out, y, w_T) + + del log_sigma, sigma_pre_inp # Clear raw cross section to free up memory + + print(species_q + " done") + + # Clear up storage + del z, z_l, z_r, nu_grid, nu_l, nu_r, nu_out, y, w_T + + opac_file.close() + + return sigma_stored + +def plot_opacity(chemical_species, sigma_stored, P_desired, T_desired, wl_grid): + + # Max number of species this can plot is 9 (clustered beyond that!) + + # Optional smoothing of cross sections (can improve clarity) + smooth = False + smooth_factor = 5 + + # Specify cross sections to plot, along with colours for each + colours_plot = np.array(['royalblue', 'purple', 'crimson', 'orange', 'black', 'grey', 'green', 'magenta', 'chocolate']) + + # Initialise plot + ax = plt.gca() + #ax.set_xscale("log") + + xmajorLocator = MultipleLocator(1.0) + xmajorFormatter = FormatStrFormatter('%.1f') + xminorLocator = MultipleLocator(0.2) + + ax.xaxis.set_major_locator(xmajorLocator) + ax.xaxis.set_major_formatter(xmajorFormatter) + ax.xaxis.set_minor_locator(xminorLocator) + + # Plot each cross section + for q in range(len(chemical_species)): + + species = chemical_species[q] # Species to plot cross section of + colour = colours_plot[q] # Colour of cross section for plot + + #print(species) + + species_idx = np.where(chemical_species == species)[0][0] + + sigma_plt = sigma_stored[species_idx,:]*1.0e4 # Cross section of species q at given (P,T) pair (cm^2) + + if (smooth == True): + sigma_plt = gauss_conv(sigma_plt, sigma=smooth_factor, mode='nearest') + + # Plot cross section + plt.semilogy(wl_grid, sigma_plt, lw=0.5, alpha = 1.0, color= colour, label = species) + + plt.ylim([1.0e-28, 2.0e-18]) + plt.xlim([min(wl_grid), max(wl_grid)]) + plt.ylabel(r'$\mathrm{Cross \, \, Section \, \, (cm^{2})}$', fontsize = 15) + plt.xlabel(r'$\mathrm{Wavelength} \; \mathrm{(\mu m)}$', fontsize = 15) + + ax.text(min(wl_grid)*1.05, 6.0e-19, (r'$\mathrm{T = }$' + str(T_desired) + r'$\mathrm{K \, \, P = }$' + str(P_desired*1000) + r'$\mathrm{mbar}$'), fontsize = 14) + + legend = plt.legend(loc='upper right', shadow=False, frameon=False, prop={'size':6}, ncol=2) + + for legline in legend.legendHandles: + legline.set_linewidth(1.0) + + #plt.close() + #plt.show() + + #plt.savefig('./cross_sections_' + str(T_desired) + 'K_' + str(P_desired*1000) + 'mbar.pdf', bbox_inches='tight', fmt='pdf', dpi=1000) + + +""" +#***** Begin main program ***** + +# Specify which molecules you want to extract from the database (full list available in the readme) +chemical_species = np.array(['H2O', 'CH4', 'NH3', 'HCN', 'CO', 'CO2']) + +# Specify temperature and pressure grids on which to evaluate cross sections. Example: +P = np.logspace(-6, 2.0, 10) # 20 pressures logarithmically spaced from 10^-6 -> 10^2 bar. +T = np.linspace(1000.0, 3000.0, 100) # 100 temperatures linearly spaced from 1000K -> 3000 K. + +# Specify wavelength grid to extract cross section onto +wl_min = 0.4 # Minimum wavelength of grid (micron) +wl_max = 5.0 # Maximum wavelength of grid (micron) +N_wl = 1000 # Number of wavelength points + +wl = np.linspace(wl_min, wl_max, N_wl) # Uniform grid used here for demonstration purposes + +# Can use any P,T,wl grids, doesn't have to be uniform, but MUST be a numpy array. +# Note: if you use a v. large number of (P,T,wl) points, the memory usage could be quite high! +# This example has 10x100x1000 = 10^6 cross section values stored for each species. + +# Either sample the nearest wavelength points from the high resolution (R~10^6) cross section database or use an averaging prescription +opacity_treatment = 'Log-avg' # Options: Opacity-sample / Log-avg +#opacity_treatment = 'Opacity-sample' # Opacity sampling is faster, but for low-resolution wavelength grids log averaging is recommended + +# Extract desired cross sections from the database +cross_sections = Extract_opacity(chemical_species, P, T, wl, opacity_treatment) # Format: np array(N_species, N_P, N_T, N_wl) / Units: (m^2 / species) + +# Example: seperate H2O cross section at 1 bar and 2000K, and print to terminal +P_desired = 1.0 +T_desired = 2000.0 +P_idx = np.argmin(np.abs(P - P_desired)) # Closest index in the P array to desired value +T_idx = np.argmin(np.abs(T - T_desired)) # Closest index in the T array to desired value + +H2O_cross_section = cross_sections[(np.where(chemical_species=='H2O')[0][0]),P_idx,T_idx,:] # Format: np array(N_wl) / Units: (m^2 / molecule) +#print (H2O_cross_section) + +# Plot cross sections at the desired P and T +plot_opacity(chemical_species, cross_sections[:,P_idx,T_idx,:], P_desired, T_desired, wl) +""" diff --git a/gas_opac/opacity_demo_V3.py b/gas_opac/opacity_demo_V3.py new file mode 100644 index 0000000..263c1dd --- /dev/null +++ b/gas_opac/opacity_demo_V3.py @@ -0,0 +1,645 @@ +# Demostrates how to open the opacity database and interpolate to given (P,T) and wavelength grid +# Author: Ryan J. MacDonald +# V1.0: Evaluates cross sections at a single P,T point (8th November 2018) +# V2.0: Evaluates cross sections on a grid of (P,T) points (6th December 2018) +# V3.0: Evaluates collisionally-induced opacity on a grid of (T) points (17th February 2019) + +import numpy as np +import h5py +from scipy.ndimage import gaussian_filter1d as gauss_conv +from numba.decorators import jit +import matplotlib.pyplot as plt +from matplotlib.ticker import MultipleLocator, FormatStrFormatter + +from pylab import rcParams +plt.style.use('classic') +plt.rc('font', family='serif') + +#***** Begin function declarations *****# + +@jit(nopython = True) +def prior_index(vec, value, start): + + '''Finds the previous index of a grid closest to a specified value + ''' + + value_tmp = value + + if (value_tmp > vec[-1]): + return (len(vec) - 1) + + # Check if value out of bounds, if so set to edge value + if (value_tmp < vec[0]): value_tmp = vec[0] + if (value_tmp > vec[-2]): value_tmp = vec[-2] + + index = start + + for i in range(len(vec)-start): + if (vec[i+start] > value_tmp): + index = (i+start) - 1 + break + + return index + +@jit(nopython=True) +def prior_index_V2(val, grid_start, grid_end, N_grid): + + '''Finds the previous index of a UNIFORM grid closest to a specified value. + + A uniform grid dramatically speeds calculation over a non-uniform grid. + ''' + + if (val < grid_start): + return 0 + + elif (val > grid_end): + return N_grid-1 + + else: + i = (N_grid-1) * ((val - grid_start) / (grid_end - grid_start)) + return int(i) + +@jit(nopython=True) +def closest_index(val, grid_start, grid_end, N_grid): + + '''Finds the index of a UNIFORM grid closest to a specified value. + + A uniform grid dramatically speeds calculation over a non-uniform grid. + ''' + + if (val < grid_start): + return 0 + + elif (val > grid_end): + return N_grid-1 + + else: + i = (N_grid-1) * ((val - grid_start) / (grid_end - grid_start)) + if ((i%1)<=0.5): + return int(i) + else: + return int(i)+1 + +@jit(nopython = True) +def P_interpolate_wl_initialise(nu_grid, N_P_out, N_T, N_P, N_wl_out, N_nu_out, + log_sigma, x, z_l, z, z_r, b1, b2, mode): + + '''Interpolates raw cross sections onto the output P and wl grids. + + Note: input sigma has format log10(cross_sec)[log(P)_grid, T_grid, nu_grid], + whilst output has format cross_sec[log(P)_out, T_grid, wl_out] + + The input is in wavenumnber to take advantage of fast prior index location + on a uniform grid, which wouldn't work for the (non-uniform) wavelength grid + Array reversal to output in increasing wavelength is handled by indexing + by a factor of (N_wl-1)-k throughout. + ''' + + sigma_pre_inp = np.zeros(shape=(N_P_out, N_T, N_wl_out)) + + N_nu = len(nu_grid) # Number of wavenumber points in sigma array + + for i in xrange(N_P_out): # For each pressure in output array + for j in xrange(N_T): # For each temperature in input array + for k in xrange(N_nu_out): # Note that the k here is looping over wavenumber + + # If nu (wl) point out of range of opacity grid, set opacity to zero + if ((z[k] == 0) or (z[k] == (N_nu-1))): + sigma_pre_inp[i, j, ((N_wl_out-1)-k)] = 0.0 + + else: + + # Opacity sampling + if (mode == 1): + + # If pressure below minimum, set to value at min pressure + if (x[i] == -1): + sigma_pre_inp[i, j, ((N_wl_out-1)-k)] = 10 ** (log_sigma[0, j, z[k]]) + + # If pressure above maximum, set to value at max pressure + elif (x[i] == -2): + sigma_pre_inp[i, j, ((N_wl_out-1)-k)] = 10 ** (log_sigma[(N_P-1), j, z[k]]) + + # Interpolate sigma in logsace, then power to get interp array + else: + reduced_sigma = log_sigma[x[i]:x[i]+2, j, z[k]] + + sigma_pre_inp[i, j, ((N_wl_out-1)-k)] = 10 ** (b1[i]*(reduced_sigma[0]) + + b2[i]*(reduced_sigma[1])) + + # Log averaging + elif (mode == 2): + + # If pressure below minimum, set to value at min pressure + if (x[i] == -1): + sigma_in_bin = np.mean(log_sigma[0, j, z_l[k]:z_r[k]+1]) + sigma_pre_inp[i, j, ((N_wl_out-1)-k)] = 10 ** (sigma_in_bin) + + # If pressure above maximum, set to value at max pressure + elif (x[i] == -2): + sigma_in_bin = np.mean(log_sigma[(N_P-1), j, z_l[k]:z_r[k]+1]) + sigma_pre_inp[i, j, ((N_wl_out-1)-k)] = 10 ** (sigma_in_bin) + + # Interpolate sigma in logsace, then power to get interp array + else: + sigma_in_bin_P1 = np.mean(log_sigma[x[i], j, z_l[k]:z_r[k]+1]) + sigma_in_bin_P2 = np.mean(log_sigma[x[i]+1, j, z_l[k]:z_r[k]+1]) + + sigma_pre_inp[i, j, ((N_wl_out-1)-k)] = 10 ** (b1[i]*(sigma_in_bin_P1) + + b2[i]*(sigma_in_bin_P2)) + + return sigma_pre_inp + +@jit(nopython = True) +def wl_initialise_cia(nu_cia, N_T_cia, N_wl_out, N_nu_out, log_cia, nu_l, nu_out, nu_r, mode): + + ''' Interpolates raw collisionally-induced absorption (CIA) binary cross + section onto the desired model wl grid. + + Input cia has format log10(alpha)[T_grid, nu_grid], + whilst output has format alpha[T_grid, wl_model]. + + The input is in wavenumnber to take advantage of fast prior index + location on a uniform grid, which wouldn't work for the (non-uniform) + wavelength grid. Array reversal to output in increasing wavelength is + handled by indexing by a factor of (N_wl-1)-k throughout . + + Wavelength initialisation is handled via either opacity sampling + (choosing nearest pre-computed wavelength point) or via averaging + the logarithm of the cross section over the wavelength bin range + surrounding each wavelength on the output model wavelength grid. + + ''' + + cia_pre_inp = np.zeros(shape=(N_T_cia, N_wl_out)) + + N_nu_cia = len(nu_cia) # Number of wavenumber points in CIA array + + for i in xrange(N_T_cia): + for k in xrange(N_nu_out): + + z_l = closest_index(nu_l[k], nu_cia[0], nu_cia[-1], N_nu_cia) + z = closest_index(nu_out[k], nu_cia[0], nu_cia[-1], N_nu_cia) + z_r = closest_index(nu_r[k], nu_cia[0], nu_cia[-1], N_nu_cia) + + # If wl out of range of opacity, set opacity to zero + if ((z_l == 0) or (z_r == (N_nu_cia-1))): + cia_pre_inp[i, ((N_wl_out-1)-k)] = 0.0 + + else: + + # Opacity sampling + if (mode == 1): + + cia_pre_inp[i, ((N_wl_out-1)-k)] = 10 ** (log_cia[i, z]) + + # Log averaging + elif (mode == 2): + + cia_in_bin = np.mean(log_cia[i, z_l:z_r+1]) + cia_pre_inp[i, ((N_wl_out-1)-k)] = 10 ** (cia_in_bin) + + return cia_pre_inp + +@jit(nopython = True) +def T_interpolation_init(N_T_out, T_grid, T_out, y): + + ''' Precomputes the T interpolation weight factors, so this does not + need to be done multiple times across all species. + ''' + + w_T = np.zeros(N_T_out) # Temperature interpolation weight factors + + # Find T index in cross secion arrays prior to fine temperature value + for j in xrange(N_T_out): + + if (T_out[j] < T_grid[0]): # If fine temperature point falls off LHS of temperaure grid + y[j] = -1 # Special value (-1) stored, interpreted in interpolator + w_T[j] = 0.0 # Weight not used in this case + + elif (T_out[j] >= T_grid[-1]): # If fine temperature point falls off RHS of temperaure grid + y[j] = -2 # Special value (-2) stored, interpreted in interpolator + w_T[j] = 0.0 # Weight not used in this case + + else: + + # Have to use prior_index (V1) here as T_grid is not uniformly spaced + y[j] = prior_index(T_grid, T_out[j], 0) # Index in cross secion arrays prior to desired temperature value + + # Pre-computed temperature values to left and right of desired temperature value + T1 = T_grid[y[j]] + T2 = T_grid[y[j]+1] + + # Precompute temperature interpolation weight factor + w_T[j] = (1.0/((1.0/T2) - (1.0/T1))) + + return w_T + +@jit(nopython = True) +def T_interpolate_sigma(N_P_out, N_T_out, N_T, N_wl_out, sigma_pre_inp, T_grid, T_out, y, w_T): + + '''Interpolates pre-processed cross section onto the output T grid. + + Note: input sigma has format log10(cross_sec)[log(P)_out, T_grid, wl_out], + whilst output has format cross_sec[log(P)_out, T_out, wl_out]. + + Output is the final interpolated cross section as a 3D array on the + desired P_out, T_out, wl_out grids. + ''' + + sigma_inp = np.zeros(shape=(N_P_out, N_T_out, N_wl_out)) + + for i in xrange(N_P_out): # Loop over output pressure array + for j in xrange(N_T_out): # Loop over output temperature array + + T = T_out[j] # Temperature we wish to interpolate to + T1 = T_grid[y[j]] + T2 = T_grid[y[j]+1] + + for k in xrange(N_wl_out): # Loop over wavelengths + + # If T_out below min pre-computed value (100 K), set sigma to value at min T + if (y[j] == -1): + sigma_inp[i, j, k] = sigma_pre_inp[i, 0, k] + + # If T_out above max pre-computed value (3500 K), set sigma to value at max T + elif (y[j] == -2): + sigma_inp[i, j, k] = sigma_pre_inp[i, (N_T-1), k] + + # Interpolate sigma to output temperature grid value + else: + sig_reduced = sigma_pre_inp[i, y[j]:y[j]+2, k] + sig_1, sig_2 = sig_reduced[0], sig_reduced[1] # sigma(T1)[i,k], sigma(T2)[i,k] + + sigma_inp[i, j, k] = (np.power(sig_1, (w_T[j]*((1.0/T2) - (1.0/T)))) * + np.power(sig_2, (w_T[j]*((1.0/T) - (1.0/T1))))) + + return sigma_inp + +@jit(nopython = True) +def T_interpolate_cia(N_T_out, N_T_cia, N_wl_out, cia_pre_inp, T_grid_cia, T_out, y, w_T): + + ''' Interpolates pre-processed collisionally-induced absorption (CIA) + binary cross section onto the output T grid. + + Note: input sigma has format alpha[T_grid, wl_model], + whilst output has format alpha[T_fine, wl_model]. + + Output is the interpolated cia cross section as a 2D array on the + desired T_out, wl_out grids. + + ''' + + cia_inp = np.zeros(shape=(N_T_out, N_wl_out)) + + for j in xrange(N_T_out): # Loop over output temperature array + + T = T_out[j] # Temperature we wish to interpolate to + T1 = T_grid_cia[y[j]] + T2 = T_grid_cia[y[j]+1] + + for k in xrange(N_wl): # Loop over wavelengths + + # If T_fine below min value (200 K), set cia to value at min T + if (y[j] == -1): + cia_inp[j, k] = cia_pre_inp[0, k] + + # If T_fine above max value (3500 K), set cia to value at max T + elif (y[j] == -2): + cia_inp[j, k] = cia_pre_inp[(N_T_cia-1), k] + + # Interpolate sigma to fine temperature grid value + else: + cia_reduced = cia_pre_inp[y[j]:y[j]+2, k] + cia_1, cia_2 = cia_reduced[0], cia_reduced[1] # sigma(T1)[i,k], sigma(T2)[i,k] + + cia_inp[j, k] = (np.power(cia_1, (w_T[j]*((1.0/T2) - (1.0/T)))) * + np.power(cia_2, (w_T[j]*((1.0/T) - (1.0/T1))))) + + return cia_inp + +def Extract_opacity(chemical_species, cia_pairs, P_out, T_out, wl_out, opacity_treatment): + + '''Main function to read in all opacities and interpolate them onto the + desired pressure, temperature, and wavelength grids. + ''' + + print("Now reading in cross sections") + + # First, check from config.py which opacity calculation mode is specified + if (opacity_treatment == 'Opacity-sample'): calculation_mode = 1 + elif (opacity_treatment == 'Log-avg'): calculation_mode = 2 + + #***** Firstly, we initialise the various quantities needed for pre-interpolation*****# + + # Open HDF5 files containing molecular + atomic opacities and CIA + opac_file = h5py.File('./Opacity_database_0.01cm-1.hdf5', 'r') + cia_file = h5py.File('./Opacity_database_cia.hdf5', 'r') + + #***** Read in T and P grids used in opacity files*****# + T_grid = np.array(opac_file['H2O/T']) # H2O here simply used as dummy (same grid for all molecules) + log_P_grid = np.array(opac_file['H2O/log(P)']) # Units: log10(P/bar) + log_P_out = np.log10(P_out) # Units: log10(P/bar) + + #***** Read in wavenumber arrays used in opacity files*****# + nu_grid = np.array(opac_file['H2O/nu']) # H2O here simply used as dummy (same grid for all molecules) + + # Convert model wavelength grid to wavenumber grid + nu_out = 1.0e4/wl_out # Model wavenumber grid (cm^-1) + nu_out = nu_out[::-1] # Reverse direction, such that increases with wavenumber + + N_nu = len(nu_grid) # No. of wavenumbers in each opacity file (2480001) + N_P = len(log_P_grid) # No. of pressures in opacity files + N_T = len(T_grid) # No. of temperatures in opacity files + N_species = len(chemical_species) # No. of chemical species user wishes to store + N_cia_pairs = len(cia_pairs) # Number of CIA pairs user wishes to store (= 2 for H2-H2 & H2-He) + + N_P_out = len(P_out) # No. of pressures on output pressure grid + N_T_out = len(T_out) # No. of temperatures on output temperature grid + N_nu_out = len(nu_out) # No. of wavenumbers on output grid + N_wl_out = len(wl_out) # No. of wavelengths on output grid + + # Initialise arrays of wavenumber locations of left and right bin edges + nu_l = np.zeros(N_nu_out) # Left edge + nu_r = np.zeros(N_nu_out) # Right edge + + # Initialise arrays of indicies on wavenumber opacity grid corresponding to nu_l, nu_out, and nu_r + z_l = np.zeros(N_wl_out, dtype=np.int) # Bin left edge + z = np.zeros(N_wl_out, dtype=np.int) # Bin centre + z_r = np.zeros(N_wl_out, dtype=np.int) # Bin right edge + + # Initialise array of indicies on pre-calculated pressure opacity grid prior to defined atmosphere layer pressures + x = np.zeros(N_P_out, dtype=np.int) + + # Weights + w_P = np.zeros(N_P_out) + + # Useful functions of weights for interpolation + b1 = np.zeros(shape=(N_P_out)) + b2 = np.zeros(shape=(N_P_out)) + + # Now find closest P indicies in opacity grid corresponding to output pressures + for i in xrange(N_P_out): + + # If pressure below minimum, do not interpolate + if (log_P_out[i] < log_P_grid[0]): + x[i] = -1 # Special value (1) used in opacity initialiser + w_P[i] = 0.0 + + # If pressure above maximum, do not interpolate + elif (log_P_out[i] >= log_P_grid[-1]): + x[i] = -2 # Special value (2) used in opacity initialiser + w_P[i] = 0.0 + + else: + x[i] = prior_index_V2(log_P_out[i], log_P_grid[0], log_P_grid[-1], N_P) + + # Weights - fractional distance along pressure axis of cross section array + w_P[i] = (log_P_out[i]-log_P_grid[x[i]])/(log_P_grid[x[i]+1]-log_P_grid[x[i]]) + + # Precalculate interpolation pre-factors to reduce computation overhead + b1[i] = (1.0-w_P[i]) + b2[i] = w_P[i] + + # Find wavenumber indicies on input grid closest to values on output grid + for k in xrange(N_nu_out): + + if (k != 0) and (k != (N_nu_out-1)): + nu_l[k] = 0.5*(nu_out[k-1] + nu_out[k]) + nu_r[k] = 0.5*(nu_out[k] + nu_out[k+1]) + + # Boundary values of each output wavenumber point + elif (k == 0): + nu_l[k] = nu_out[k] - 0.5*(nu_out[k+1] - nu_out[k]) + nu_r[k] = 0.5*(nu_out[k] + nu_out[k+1]) + elif (k == (N_nu-1)): + nu_l[k] = 0.5*(nu_out[k-1] + nu_out[k]) + nu_r[k] = nu_out[k] + 0.5*(nu_out[k] - nu_out[k-1]) + + # Calculate closest indicies to output wavenumber bin left, centre, and right edge + z_l[k] = closest_index(nu_l[k], nu_grid[0], nu_grid[-1], N_nu) + z[k] = closest_index(nu_out[k], nu_grid[0], nu_grid[-1], N_nu) + z_r[k] = closest_index(nu_r[k], nu_grid[0], nu_grid[-1], N_nu) + + # Initialise molecular and atomic opacity array, interpolated to output (P,T,wl) grid + sigma_stored = np.zeros(shape=(N_species, N_P_out, N_T_out, N_wl_out)) + + # Initialise CIA opacity array, interpolated to to output (T,wl) grid + cia_stored = np.zeros(shape=(N_cia_pairs, N_T_out, N_wl)) + + #***** Process collisionally Induced Absorption (CIA) *****# + + for q in xrange(N_cia_pairs): + + cia_pair_q = cia_pairs[q] # CIA pair name + + #***** Read in T grid used in this CIA file *****# + T_grid_cia_q = np.array(cia_file[cia_pair_q + '/T']) + N_T_cia_q = len(T_grid_cia_q) # Number of temperatures on this grid + + # Read in wavenumber array used in this CIA file + nu_cia_q = np.array(cia_file[cia_pair_q + '/nu']) + + # Evaluate temperature interpolation weighting factor + y_cia = np.zeros(N_T_out, dtype=np.int) + w_T_cia = T_interpolation_init(N_T_out, T_grid_cia_q, T_out, y_cia) + + # Read in log10(binary cross section) for specified cia pair + log_cia = np.array(cia_file[cia_pair_q + '/log(cia)']) + + # Pre-interpolate cross section to desired wl grid + cia_pre_inp = wl_initialise_cia(nu_cia_q, N_T_cia_q, N_wl_out, N_nu_out, log_cia, nu_l, nu_out, nu_r, calculation_mode) + + # Interplate to desired temperature + cia_stored[q,:,:] = T_interpolate_cia(N_T_out, N_T_cia_q, N_wl_out, cia_pre_inp, T_grid_cia_q, T_out, y_cia, w_T_cia) + + # Clear raw cross section to free up memory + del log_cia, cia_pre_inp, nu_cia_q, w_T_cia, y_cia + + print(cia_pair_q + " done") + + #***** Process molecular and atomic opacities *****# + + # Evaluate temperature interpolation weighting factor + y_sigma = np.zeros(N_T_out, dtype=np.int) + w_T_sigma = T_interpolation_init(N_T_out, T_grid, T_out, y_sigma) + + # Load molecular and atomic absorption cross sections + for q in xrange(N_species): + + species_q = chemical_species[q] # Species name specified by user + + # Read in log10(cross section) of specified species + log_sigma = np.array(opac_file[species_q + '/log(sigma)']).astype(np.float64) + + # Pre-interpolate cross section to desired P and wl grid + sigma_pre_inp = P_interpolate_wl_initialise(nu_grid, N_P_out, N_T, N_P, N_wl_out, N_nu_out, log_sigma, x, z_l, z, z_r, b1, b2, calculation_mode) + + # Interplate to desired temperature + sigma_stored[q,:,:,:] = T_interpolate_sigma(N_P_out, N_T_out, N_T, N_wl_out, sigma_pre_inp, T_grid, T_out, y_sigma, w_T_sigma) + + # Clear raw cross section to free up memory + del log_sigma, sigma_pre_inp + + print(species_q + " done") + + # Clear up storage + del z, z_l, z_r, nu_grid, nu_l, nu_r, nu_out, y_sigma, w_T_sigma + + opac_file.close() + + return sigma_stored, cia_stored + +def plot_opacity(chemical_species, cia_pairs, sigma_stored, cia_stored, P_desired, T_desired, wl_grid): + + # Max number of species this can plot is 9 (cluttered beyond that!) + + # Optional smoothing of cross sections (can improve clarity) + smooth = False + smooth_factor = 5 + + # Specify cross sections to plot, along with colours for each + colours_sigma = np.array(['royalblue', 'purple', 'crimson', 'orange', 'black', 'grey', 'green', 'magenta', 'chocolate']) + + # Initialise plot + ax = plt.gca() + #ax.set_xscale("log") + + xmajorLocator = MultipleLocator(1.0) + xmajorFormatter = FormatStrFormatter('%.1f') + xminorLocator = MultipleLocator(0.2) + + ax.xaxis.set_major_locator(xmajorLocator) + ax.xaxis.set_major_formatter(xmajorFormatter) + ax.xaxis.set_minor_locator(xminorLocator) + + # Plot each cross section + for q in range(len(chemical_species)): + + species = chemical_species[q] # Species to plot cross section of + colour = colours_sigma[q] # Colour of cross section for plot + + #print(species) + + species_idx = np.where(chemical_species == species)[0][0] + + sigma_plt = sigma_stored[species_idx,:] # Cross section of species q at given (P,T) pair (m^2) + + if (smooth == True): + sigma_plt = gauss_conv(sigma_plt, sigma=smooth_factor, mode='nearest') + + # Plot cross section + plt.semilogy(wl_grid, sigma_plt, lw=0.5, alpha = 1.0, color= colour, label = species) + + plt.ylim([1.0e-32, 2.0e-22]) + plt.xlim([min(wl_grid), max(wl_grid)]) + plt.ylabel(r'$\mathrm{Cross \, \, Section \, \, (m^{2} \, mol^{-1})}$', fontsize = 15) + plt.xlabel(r'$\mathrm{Wavelength} \; \mathrm{(\mu m)}$', fontsize = 15) + + ax.text(min(wl_grid)*1.20, 6.0e-23, (r'$\mathrm{T = }$' + str(T_desired) + r'$\mathrm{K \, \, P = }$' + str(P_desired*1000) + r'$\mathrm{mbar}$'), fontsize = 14) + + legend = plt.legend(loc='upper right', shadow=False, frameon=False, prop={'size':6}, ncol=2) + + for legline in legend.legendHandles: + legline.set_linewidth(1.0) + + #plt.close() + #plt.show() + + plt.savefig('./cross_sections_' + str(T_desired) + 'K_' + str(P_desired*1000) + 'mbar.pdf', bbox_inches='tight', fmt='pdf', dpi=1000) + + plt.clf() + + #plt.close() + + #***** Now plot CIA ***** + + colours_cia = np.array(['royalblue', 'crimson']) + + ax = plt.gca() + #ax.set_xscale("log") + + xmajorLocator = MultipleLocator(1.0) + xmajorFormatter = FormatStrFormatter('%.1f') + xminorLocator = MultipleLocator(0.2) + + ax.xaxis.set_major_locator(xmajorLocator) + ax.xaxis.set_major_formatter(xmajorFormatter) + ax.xaxis.set_minor_locator(xminorLocator) + + # Plot each cross section + for q in range(len(cia_pairs)): + + cia_pair = cia_pairs[q] # Species to plot cross section of + colour = colours_cia[q] # Colour of cross section for plot + + #print(species) + + cia_idx = np.where(cia_pairs == cia_pair)[0][0] + + cia_plt = cia_stored[cia_idx,:] # Cross section of species q at given (P,T) pair (m^5) + + # Plot cross section + plt.semilogy(wl_grid, cia_plt, lw=0.5, alpha = 1.0, color = colour, label = cia_pair) + + plt.ylim([1.0e-60, 1.0e-52]) + plt.xlim([min(wl_grid), max(wl_grid)]) + plt.ylabel(r'$\mathrm{Binary \, \, Cross \, \, Section \, (CIA) \, \, (m^{5} mol^{-2})}$', fontsize = 15) + plt.xlabel(r'$\mathrm{Wavelength} \; \mathrm{(\mu m)}$', fontsize = 15) + + ax.text(min(wl_grid)*1.20, 3.0e-53, (r'$\mathrm{T = }$' + str(T_desired) + r'$\mathrm{K}$'), fontsize = 14) + + legend = plt.legend(loc='upper right', shadow=False, frameon=False, prop={'size':8}) + + for legline in legend.legendHandles: + legline.set_linewidth(1.0) + + #plt.close() + #plt.show() + + plt.savefig('./CIA_cross_sections_' + str(T_desired) + 'K.pdf', bbox_inches='tight', fmt='pdf', dpi=1000) + + +#***** Begin main program ***** + +# Specify which molecules you want to extract from the database (full list available in the readme) +chemical_species = np.array(['H2O', 'CH4', 'NH3', 'HCN', 'CO', 'CO2']) + +# Specify collisionally-induced absorption (CIA) pairs +cia_pairs = np.array(['H2-H2', 'H2-He']) + +# Specify temperature and pressure grids on which to evaluate cross sections. Example: +P = np.logspace(-6, 2.0, 10) # 20 pressures logarithmically spaced from 10^-6 -> 10^2 bar. +T = np.linspace(800.0, 3000.0, 100) # 100 temperatures linearly spaced from 800K -> 3000 K. + +# Specify wavelength grid to extract cross section onto +wl_min = 0.4 # Minimum wavelength of grid (micron) +wl_max = 5.0 # Maximum wavelength of grid (micron) +N_wl = 1000 # Number of wavelength points + +wl = np.linspace(wl_min, wl_max, N_wl) # Uniform grid used here for demonstration purposes + +# Can use any P,T,wl grids, doesn't have to be uniform, but MUST be a numpy array. +# Note: if you use a v. large number of (P,T,wl) points, the memory usage could be quite high! +# This example has 10x100x1000 = 10^6 cross section values stored for each species. + +# Either sample the nearest wavelength points from the high resolution (R~10^6) cross section database or use an averaging prescription +opacity_treatment = 'Log-avg' # Options: Opacity-sample / Log-avg +#opacity_treatment = 'Opacity-sample' # Opacity sampling is faster, but for low-resolution wavelength grids log averaging is recommended + +# Extract desired cross sections from the database +cross_sections, cia_absorption = Extract_opacity(chemical_species, cia_pairs, P, T, wl, opacity_treatment) # Format: np array(N_species, N_P, N_T, N_wl) / Units: (m^2 / species) + +# Example: seperate H2O cross section at 1 mbar and 1000K, and print to terminal +P_desired = 1.0e-3 +T_desired = 1000.0 +P_idx = np.argmin(np.abs(P - P_desired)) # Closest index in the P array to desired value +T_idx = np.argmin(np.abs(T - T_desired)) # Closest index in the T array to desired value + +H2O_cross_section = cross_sections[(np.where(chemical_species=='H2O')[0][0]),P_idx,T_idx,:] # Format: np array(N_wl) / Units: (m^2 / molecule) +#print (H2O_cross_section) + +# Plot cross sections at the desired P and T +plot_opacity(chemical_species, cia_pairs, cross_sections[:,P_idx,T_idx,:], + cia_absorption[:,T_idx,:], P_desired, T_desired, wl) + diff --git a/gas_opac/opacity_demo_V4.py b/gas_opac/opacity_demo_V4.py new file mode 100644 index 0000000..c13f5cf --- /dev/null +++ b/gas_opac/opacity_demo_V4.py @@ -0,0 +1,849 @@ +# Demostrates how to open the opacity database and interpolate to given (P,T) and wavelength grid +# Author: Ryan J. MacDonald +# V1.0: Evaluates cross sections at a single P,T point (8th November 2018) +# V2.0: Evaluates cross sections on a grid of (P,T) points (6th December 2018) +# V3.0: Evaluates collisionally-induced opacity on a grid of (T) points (17th February 2019) +# V4.0: Inclusion of H- bound-free and free-free opacities (26th February 2019) + +import numpy as np +import h5py +from scipy.ndimage import gaussian_filter1d as gauss_conv +from numba.decorators import jit +import matplotlib.pyplot as plt +from matplotlib.ticker import MultipleLocator, FormatStrFormatter, NullFormatter + +from pylab import rcParams +plt.style.use('classic') +plt.rc('font', family='serif') + +#***** Begin function declarations *****# + +@jit(nopython = True) +def prior_index(vec, value, start): + + '''Finds the previous index of a grid closest to a specified value + ''' + + value_tmp = value + + if (value_tmp > vec[-1]): + return (len(vec) - 1) + + # Check if value out of bounds, if so set to edge value + if (value_tmp < vec[0]): value_tmp = vec[0] + if (value_tmp > vec[-2]): value_tmp = vec[-2] + + index = start + + for i in range(len(vec)-start): + if (vec[i+start] > value_tmp): + index = (i+start) - 1 + break + + return index + +@jit(nopython=True) +def prior_index_V2(val, grid_start, grid_end, N_grid): + + '''Finds the previous index of a UNIFORM grid closest to a specified value. + + A uniform grid dramatically speeds calculation over a non-uniform grid. + ''' + + if (val < grid_start): + return 0 + + elif (val > grid_end): + return N_grid-1 + + else: + i = (N_grid-1) * ((val - grid_start) / (grid_end - grid_start)) + return int(i) + +@jit(nopython=True) +def closest_index(val, grid_start, grid_end, N_grid): + + '''Finds the index of a UNIFORM grid closest to a specified value. + + A uniform grid dramatically speeds calculation over a non-uniform grid. + ''' + + if (val < grid_start): + return 0 + + elif (val > grid_end): + return N_grid-1 + + else: + i = (N_grid-1) * ((val - grid_start) / (grid_end - grid_start)) + if ((i%1)<=0.5): + return int(i) + else: + return int(i)+1 + +@jit(nopython = True) +def P_interpolate_wl_initialise(nu_grid, N_P_out, N_T, N_P, N_wl_out, N_nu_out, + log_sigma, x, z_l, z, z_r, b1, b2, mode): + + '''Interpolates raw cross sections onto the output P and wl grids. + + Note: input sigma has format log10(cross_sec)[log(P)_grid, T_grid, nu_grid], + whilst output has format cross_sec[log(P)_out, T_grid, wl_out] + + The input is in wavenumnber to take advantage of fast prior index location + on a uniform grid, which wouldn't work for the (non-uniform) wavelength grid + Array reversal to output in increasing wavelength is handled by indexing + by a factor of (N_wl-1)-k throughout. + ''' + + sigma_pre_inp = np.zeros(shape=(N_P_out, N_T, N_wl_out)) + + N_nu = len(nu_grid) # Number of wavenumber points in sigma array + + for i in xrange(N_P_out): # For each pressure in output array + for j in xrange(N_T): # For each temperature in input array + for k in xrange(N_nu_out): # Note that the k here is looping over wavenumber + + # If nu (wl) point out of range of opacity grid, set opacity to zero + if ((z[k] == 0) or (z[k] == (N_nu-1))): + sigma_pre_inp[i, j, ((N_wl_out-1)-k)] = 0.0 + + else: + + # Opacity sampling + if (mode == 1): + + # If pressure below minimum, set to value at min pressure + if (x[i] == -1): + sigma_pre_inp[i, j, ((N_wl_out-1)-k)] = 10 ** (log_sigma[0, j, z[k]]) + + # If pressure above maximum, set to value at max pressure + elif (x[i] == -2): + sigma_pre_inp[i, j, ((N_wl_out-1)-k)] = 10 ** (log_sigma[(N_P-1), j, z[k]]) + + # Interpolate sigma in logsace, then power to get interp array + else: + reduced_sigma = log_sigma[x[i]:x[i]+2, j, z[k]] + + sigma_pre_inp[i, j, ((N_wl_out-1)-k)] = 10 ** (b1[i]*(reduced_sigma[0]) + + b2[i]*(reduced_sigma[1])) + + # Log averaging + elif (mode == 2): + + # If pressure below minimum, set to value at min pressure + if (x[i] == -1): + sigma_in_bin = np.mean(log_sigma[0, j, z_l[k]:z_r[k]+1]) + sigma_pre_inp[i, j, ((N_wl_out-1)-k)] = 10 ** (sigma_in_bin) + + # If pressure above maximum, set to value at max pressure + elif (x[i] == -2): + sigma_in_bin = np.mean(log_sigma[(N_P-1), j, z_l[k]:z_r[k]+1]) + sigma_pre_inp[i, j, ((N_wl_out-1)-k)] = 10 ** (sigma_in_bin) + + # Interpolate sigma in logsace, then power to get interp array + else: + sigma_in_bin_P1 = np.mean(log_sigma[x[i], j, z_l[k]:z_r[k]+1]) + sigma_in_bin_P2 = np.mean(log_sigma[x[i]+1, j, z_l[k]:z_r[k]+1]) + + sigma_pre_inp[i, j, ((N_wl_out-1)-k)] = 10 ** (b1[i]*(sigma_in_bin_P1) + + b2[i]*(sigma_in_bin_P2)) + + return sigma_pre_inp + +@jit(nopython = True) +def wl_initialise_cia(nu_cia, N_T_cia, N_wl_out, N_nu_out, log_cia, nu_l, nu_out, nu_r, mode): + + ''' Interpolates raw collisionally-induced absorption (CIA) binary cross + section onto the desired model wl grid. + + Input cia has format log10(alpha)[T_grid, nu_grid], + whilst output has format alpha[T_grid, wl_model]. + + The input is in wavenumnber to take advantage of fast prior index + location on a uniform grid, which wouldn't work for the (non-uniform) + wavelength grid. Array reversal to output in increasing wavelength is + handled by indexing by a factor of (N_wl-1)-k throughout . + + Wavelength initialisation is handled via either opacity sampling + (choosing nearest pre-computed wavelength point) or via averaging + the logarithm of the cross section over the wavelength bin range + surrounding each wavelength on the output model wavelength grid. + + ''' + + cia_pre_inp = np.zeros(shape=(N_T_cia, N_wl_out)) + + N_nu_cia = len(nu_cia) # Number of wavenumber points in CIA array + + for i in xrange(N_T_cia): + for k in xrange(N_nu_out): + + z_l = closest_index(nu_l[k], nu_cia[0], nu_cia[-1], N_nu_cia) + z = closest_index(nu_out[k], nu_cia[0], nu_cia[-1], N_nu_cia) + z_r = closest_index(nu_r[k], nu_cia[0], nu_cia[-1], N_nu_cia) + + # If wl out of range of opacity, set opacity to zero + if ((z_l == 0) or (z_r == (N_nu_cia-1))): + cia_pre_inp[i, ((N_wl_out-1)-k)] = 0.0 + + else: + + # Opacity sampling + if (mode == 1): + + cia_pre_inp[i, ((N_wl_out-1)-k)] = 10 ** (log_cia[i, z]) + + # Log averaging + elif (mode == 2): + + cia_in_bin = np.mean(log_cia[i, z_l:z_r+1]) + cia_pre_inp[i, ((N_wl_out-1)-k)] = 10 ** (cia_in_bin) + + return cia_pre_inp + +@jit(nopython = True) +def T_interpolation_init(N_T_out, T_grid, T_out, y): + + ''' Precomputes the T interpolation weight factors, so this does not + need to be done multiple times across all species. + ''' + + w_T = np.zeros(N_T_out) # Temperature interpolation weight factors + + # Find T index in cross secion arrays prior to fine temperature value + for j in xrange(N_T_out): + + if (T_out[j] < T_grid[0]): # If fine temperature point falls off LHS of temperaure grid + y[j] = -1 # Special value (-1) stored, interpreted in interpolator + w_T[j] = 0.0 # Weight not used in this case + + elif (T_out[j] >= T_grid[-1]): # If fine temperature point falls off RHS of temperaure grid + y[j] = -2 # Special value (-2) stored, interpreted in interpolator + w_T[j] = 0.0 # Weight not used in this case + + else: + + # Have to use prior_index (V1) here as T_grid is not uniformly spaced + y[j] = prior_index(T_grid, T_out[j], 0) # Index in cross secion arrays prior to desired temperature value + + # Pre-computed temperature values to left and right of desired temperature value + T1 = T_grid[y[j]] + T2 = T_grid[y[j]+1] + + # Precompute temperature interpolation weight factor + w_T[j] = (1.0/((1.0/T2) - (1.0/T1))) + + return w_T + +@jit(nopython = True) +def T_interpolate_sigma(N_P_out, N_T_out, N_T, N_wl_out, sigma_pre_inp, T_grid, T_out, y, w_T): + + '''Interpolates pre-processed cross section onto the output T grid. + + Note: input sigma has format log10(cross_sec)[log(P)_out, T_grid, wl_out], + whilst output has format cross_sec[log(P)_out, T_out, wl_out]. + + Output is the final interpolated cross section as a 3D array on the + desired P_out, T_out, wl_out grids. + ''' + + sigma_inp = np.zeros(shape=(N_P_out, N_T_out, N_wl_out)) + + for i in xrange(N_P_out): # Loop over output pressure array + for j in xrange(N_T_out): # Loop over output temperature array + + T = T_out[j] # Temperature we wish to interpolate to + T1 = T_grid[y[j]] + T2 = T_grid[y[j]+1] + + for k in xrange(N_wl_out): # Loop over wavelengths + + # If T_out below min pre-computed value (100 K), set sigma to value at min T + if (y[j] == -1): + sigma_inp[i, j, k] = sigma_pre_inp[i, 0, k] + + # If T_out above max pre-computed value (3500 K), set sigma to value at max T + elif (y[j] == -2): + sigma_inp[i, j, k] = sigma_pre_inp[i, (N_T-1), k] + + # Interpolate sigma to output temperature grid value + else: + sig_reduced = sigma_pre_inp[i, y[j]:y[j]+2, k] + sig_1, sig_2 = sig_reduced[0], sig_reduced[1] # sigma(T1)[i,k], sigma(T2)[i,k] + + sigma_inp[i, j, k] = (np.power(sig_1, (w_T[j]*((1.0/T2) - (1.0/T)))) * + np.power(sig_2, (w_T[j]*((1.0/T) - (1.0/T1))))) + + return sigma_inp + +@jit(nopython = True) +def T_interpolate_cia(N_T_out, N_T_cia, N_wl_out, cia_pre_inp, T_grid_cia, T_out, y, w_T): + + ''' Interpolates pre-processed collisionally-induced absorption (CIA) + binary cross section onto the output T grid. + + Note: input sigma has format alpha[T_grid, wl_model], + whilst output has format alpha[T_fine, wl_model]. + + Output is the interpolated cia cross section as a 2D array on the + desired T_out, wl_out grids. + + ''' + + cia_inp = np.zeros(shape=(N_T_out, N_wl_out)) + + for j in xrange(N_T_out): # Loop over output temperature array + + T = T_out[j] # Temperature we wish to interpolate to + T1 = T_grid_cia[y[j]] + T2 = T_grid_cia[y[j]+1] + + for k in xrange(N_wl): # Loop over wavelengths + + # If T_fine below min value (200 K), set cia to value at min T + if (y[j] == -1): + cia_inp[j, k] = cia_pre_inp[0, k] + + # If T_fine above max value (3500 K), set cia to value at max T + elif (y[j] == -2): + cia_inp[j, k] = cia_pre_inp[(N_T_cia-1), k] + + # Interpolate sigma to fine temperature grid value + else: + cia_reduced = cia_pre_inp[y[j]:y[j]+2, k] + cia_1, cia_2 = cia_reduced[0], cia_reduced[1] # sigma(T1)[i,k], sigma(T2)[i,k] + + cia_inp[j, k] = (np.power(cia_1, (w_T[j]*((1.0/T2) - (1.0/T)))) * + np.power(cia_2, (w_T[j]*((1.0/T) - (1.0/T1))))) + + return cia_inp + +def Extract_opacity(chemical_species, cia_pairs, P_out, T_out, wl_out, opacity_treatment): + + '''Main function to read in all opacities and interpolate them onto the + desired pressure, temperature, and wavelength grids. + ''' + + print("Now reading in cross sections") + + # First, check from config.py which opacity calculation mode is specified + if (opacity_treatment == 'Opacity-sample'): calculation_mode = 1 + elif (opacity_treatment == 'Log-avg'): calculation_mode = 2 + + #***** Firstly, we initialise the various quantities needed for pre-interpolation*****# + + # Open HDF5 files containing molecular + atomic opacities and CIA + opac_file = h5py.File('./Opacity_database_0.01cm-1.hdf5', 'r') + cia_file = h5py.File('./Opacity_database_cia.hdf5', 'r') + + #***** Read in T and P grids used in opacity files*****# + T_grid = np.array(opac_file['H2O/T']) # H2O here simply used as dummy (same grid for all molecules) + log_P_grid = np.array(opac_file['H2O/log(P)']) # Units: log10(P/bar) + log_P_out = np.log10(P_out) # Units: log10(P/bar) + + #***** Read in wavenumber arrays used in opacity files*****# + nu_grid = np.array(opac_file['H2O/nu']) # H2O here simply used as dummy (same grid for all molecules) + + # Convert model wavelength grid to wavenumber grid + nu_out = 1.0e4/wl_out # Model wavenumber grid (cm^-1) + nu_out = nu_out[::-1] # Reverse direction, such that increases with wavenumber + + N_nu = len(nu_grid) # No. of wavenumbers in each opacity file (2480001) + N_P = len(log_P_grid) # No. of pressures in opacity files + N_T = len(T_grid) # No. of temperatures in opacity files + N_species = len(chemical_species) # No. of chemical species user wishes to store + N_cia_pairs = len(cia_pairs) # Number of CIA pairs user wishes to store (= 2 for H2-H2 & H2-He) + + N_P_out = len(P_out) # No. of pressures on output pressure grid + N_T_out = len(T_out) # No. of temperatures on output temperature grid + N_nu_out = len(nu_out) # No. of wavenumbers on output grid + N_wl_out = len(wl_out) # No. of wavelengths on output grid + + # Initialise arrays of wavenumber locations of left and right bin edges + nu_l = np.zeros(N_nu_out) # Left edge + nu_r = np.zeros(N_nu_out) # Right edge + + # Initialise arrays of indicies on wavenumber opacity grid corresponding to nu_l, nu_out, and nu_r + z_l = np.zeros(N_wl_out, dtype=np.int) # Bin left edge + z = np.zeros(N_wl_out, dtype=np.int) # Bin centre + z_r = np.zeros(N_wl_out, dtype=np.int) # Bin right edge + + # Initialise array of indicies on pre-calculated pressure opacity grid prior to defined atmosphere layer pressures + x = np.zeros(N_P_out, dtype=np.int) + + # Weights + w_P = np.zeros(N_P_out) + + # Useful functions of weights for interpolation + b1 = np.zeros(shape=(N_P_out)) + b2 = np.zeros(shape=(N_P_out)) + + # Now find closest P indicies in opacity grid corresponding to output pressures + for i in xrange(N_P_out): + + # If pressure below minimum, do not interpolate + if (log_P_out[i] < log_P_grid[0]): + x[i] = -1 # Special value (1) used in opacity initialiser + w_P[i] = 0.0 + + # If pressure above maximum, do not interpolate + elif (log_P_out[i] >= log_P_grid[-1]): + x[i] = -2 # Special value (2) used in opacity initialiser + w_P[i] = 0.0 + + else: + x[i] = prior_index_V2(log_P_out[i], log_P_grid[0], log_P_grid[-1], N_P) + + # Weights - fractional distance along pressure axis of cross section array + w_P[i] = (log_P_out[i]-log_P_grid[x[i]])/(log_P_grid[x[i]+1]-log_P_grid[x[i]]) + + # Precalculate interpolation pre-factors to reduce computation overhead + b1[i] = (1.0-w_P[i]) + b2[i] = w_P[i] + + # Find wavenumber indicies on input grid closest to values on output grid + for k in xrange(N_nu_out): + + if (k != 0) and (k != (N_nu_out-1)): + nu_l[k] = 0.5*(nu_out[k-1] + nu_out[k]) + nu_r[k] = 0.5*(nu_out[k] + nu_out[k+1]) + + # Boundary values of each output wavenumber point + elif (k == 0): + nu_l[k] = nu_out[k] - 0.5*(nu_out[k+1] - nu_out[k]) + nu_r[k] = 0.5*(nu_out[k] + nu_out[k+1]) + elif (k == (N_nu-1)): + nu_l[k] = 0.5*(nu_out[k-1] + nu_out[k]) + nu_r[k] = nu_out[k] + 0.5*(nu_out[k] - nu_out[k-1]) + + # Calculate closest indicies to output wavenumber bin left, centre, and right edge + z_l[k] = closest_index(nu_l[k], nu_grid[0], nu_grid[-1], N_nu) + z[k] = closest_index(nu_out[k], nu_grid[0], nu_grid[-1], N_nu) + z_r[k] = closest_index(nu_r[k], nu_grid[0], nu_grid[-1], N_nu) + + # Initialise molecular and atomic opacity array, interpolated to output (P,T,wl) grid + sigma_stored = np.zeros(shape=(N_species, N_P_out, N_T_out, N_wl_out)) + + # Initialise CIA opacity array, interpolated to to output (T,wl) grid + cia_stored = np.zeros(shape=(N_cia_pairs, N_T_out, N_wl)) + + #***** Process collisionally Induced Absorption (CIA) *****# + + for q in xrange(N_cia_pairs): + + cia_pair_q = cia_pairs[q] # CIA pair name + + #***** Read in T grid used in this CIA file *****# + T_grid_cia_q = np.array(cia_file[cia_pair_q + '/T']) + N_T_cia_q = len(T_grid_cia_q) # Number of temperatures on this grid + + # Read in wavenumber array used in this CIA file + nu_cia_q = np.array(cia_file[cia_pair_q + '/nu']) + + # Evaluate temperature interpolation weighting factor + y_cia = np.zeros(N_T_out, dtype=np.int) + w_T_cia = T_interpolation_init(N_T_out, T_grid_cia_q, T_out, y_cia) + + # Read in log10(binary cross section) for specified cia pair + log_cia = np.array(cia_file[cia_pair_q + '/log(cia)']) + + # Pre-interpolate cross section to desired wl grid + cia_pre_inp = wl_initialise_cia(nu_cia_q, N_T_cia_q, N_wl_out, N_nu_out, log_cia, nu_l, nu_out, nu_r, calculation_mode) + + # Interplate to desired temperature + cia_stored[q,:,:] = T_interpolate_cia(N_T_out, N_T_cia_q, N_wl_out, cia_pre_inp, T_grid_cia_q, T_out, y_cia, w_T_cia) + + # Clear raw cross section to free up memory + del log_cia, cia_pre_inp, nu_cia_q, w_T_cia, y_cia + + print(cia_pair_q + " done") + + #***** Process molecular and atomic opacities *****# + + # Evaluate temperature interpolation weighting factor + y_sigma = np.zeros(N_T_out, dtype=np.int) + w_T_sigma = T_interpolation_init(N_T_out, T_grid, T_out, y_sigma) + + # Load molecular and atomic absorption cross sections + for q in xrange(N_species): + + species_q = chemical_species[q] # Species name specified by user + + # Read in log10(cross section) of specified species + log_sigma = np.array(opac_file[species_q + '/log(sigma)']).astype(np.float64) + + # Pre-interpolate cross section to desired P and wl grid + sigma_pre_inp = P_interpolate_wl_initialise(nu_grid, N_P_out, N_T, N_P, N_wl_out, N_nu_out, log_sigma, x, z_l, z, z_r, b1, b2, calculation_mode) + + # Interplate to desired temperature + sigma_stored[q,:,:,:] = T_interpolate_sigma(N_P_out, N_T_out, N_T, N_wl_out, sigma_pre_inp, T_grid, T_out, y_sigma, w_T_sigma) + + # Clear raw cross section to free up memory + del log_sigma, sigma_pre_inp + + print(species_q + " done") + + # Clear up storage + del z, z_l, z_r, nu_grid, nu_l, nu_r, nu_out, y_sigma, w_T_sigma + + opac_file.close() + + return sigma_stored, cia_stored + +@jit +def H_minus_bound_free(wl_um): + + ''' Computes the bound-free cross section (alpha_bf) of the H- ion as a + function of wavelength. The fitting function is taken from "The + Observation and Analysis of Stellar Photospheres" (Gray, 2005). + + The extinction coefficient (in m^-1) can then be calculated via: + alpha_bf * n_(H-) [i.e. multiply by the H- number density (in m^-3) ]. + + Inputs: + + wl_um => array of wavelength values (um) + + Outputs: + + alpha_bf => bound-free H- cross section (m^2 / n_(H-) ) at each input + wavelength + + ''' + + # Initialise array to store absorption coefficients + alpha_bf = np.zeros(shape=(len(wl_um))) + + # Convert micron to A (as used in bound-free fit) + wl_A = wl_um * 1.0e4 + + # Fitting function constants (Gray, 2005, p.156) + a0 = 1.99654 + a1 = -1.18267e-5 + a2 = 2.64243e-6 + a3 = -4.40524e-10 + a4 = 3.23992e-14 + a5 = -1.39568e-18 + a6 = 2.78701e-23 + + for k, wl in enumerate(wl_A): + + # Photodissociation only possible for photons with wl < 1.6421 micron + if (wl <= 16421.0): + + # Compute bound-free absorption coefficient at this wavelength + alpha_bf_wl = (a0 + a1*wl + a2*(wl**2) + a3*(wl**3) + a4*(wl**4) + a5*(wl**5) + a6*(wl**6)) * 1.0e-18 + alpha_bf[k] = alpha_bf_wl * 1.0e-4 # Convert from cm^2 / H- ion to m^2 / H- ion + + else: + + alpha_bf[k] = 1.0e-250 # Small value (proxy for zero, but avoids log(0) issues) + + return alpha_bf + +@jit +def H_minus_free_free(wl_um, T_arr): + + ''' Computes the free-free cross section (alpha_ff) of the H- ion as a + function of wavelength. The fitting function is taken from "Continuous + absorption by the negative hydrogen ion reconsidered" (John, 1987). + + The extinction coefficient (in m^-1) can then be calculated via: + alpha_ff * n_H * n_(e-) [i.e. multiply by the H and e- number densities + (both in in m^-3) ]. + + Inputs: + + wl_um => array of wavelength values (um) + T_arr => array of temperatures (K) + + Outputs: + + alpha_ff => free-free H- cross section (m^5 / n_H / n_e-) for each + input wavelength and temperature + + ''' + + # Initialise array to store absorption coefficients + alpha_ff = np.zeros(shape=(len(wl_um), len(T_arr))) + + for i, T in enumerate(T_arr): + + theta = 5040.0/T # Reciprocal temperature commonly used in these fits + kT = 1.38066e-16 * T # Boltzmann constant * temperature (erg) + + for k, wl in enumerate(wl_um): + + # Range of validity of fit (wl > 0.182 um) + if (wl < 0.182): + + alpha_ff[k,i] = 1.0e-250 # Small value (proxy for zero, but avoids log(0) issues) + + # Short wavelength fit + elif ((wl >= 0.182) and (wl < 0.3645)): + + A = np.array([518.1021, 473.2636, -482.2089, 115.5291, 0.0, 0.0]) + B = np.array([-734.8666, 1443.4137, -737.1616, 169.6374, 0.0, 0.0]) + C = np.array([1021.1775, -1977.3395, 1096.8827, -245.6490, 0.0, 0.0]) + D = np.array([-479.0721, 922.3575, -521.1341, 114.2430, 0.0, 0.0]) + E = np.array([93.1373, -178.9275, 101.7963, -21.9972, 0.0, 0.0]) + F = np.array([-6.4285, 12.3600, -7.0571, 1.5097, 0.0, 0.0]) + + # For each set of fit coefficients + for n in range(1,7): + + # Compute free-free absorption coefficient at this wavelength and temperature + alpha_ff[k,i] += 1.0e-29 * (np.power(theta, ((n + 1.0)/2.0)) * + (A[n-1]*(wl**2) + B[n-1] + C[n-1]/wl + + D[n-1]/(wl**2) + E[n-1]/(wl**3) + + F[n-1]/(wl**4))) * kT + + alpha_ff[k,i] *= 1.0e-10 # Convert from cm^5 / H- ion to m^5 / H- ion + + # Long wavelength fit + elif (wl >= 0.3645): + + A = np.array([0.0, 2483.3460, -3449.8890, 2200.0400, -696.2710, 88.2830]) + B = np.array([0.0, 285.8270, -1158.3820, 2427.7190, -1841.4000, 444.5170]) + C = np.array([0.0, -2054.2910, 8746.5230, -13651.1050, 8624.9700, -1863.8640]) + D = np.array([0.0, 2827.7760, -11485.6320, 16755.5240, -10051.5300, 2095.2880]) + E = np.array([0.0, -1341.5370, 5303.6090, -7510.4940, 4400.0670, -901.7880]) + F = np.array([0.0, 208.9520, -812.9390, 1132.7380, -655.0200, 132.9850]) + + # For each set of fit coefficients + for n in range(1,7): + + # Compute free-free absorption coefficient at this wavelength and temperature + alpha_ff[k,i] += 1.0e-29 * (np.power(theta, ((n + 1.0)/2.0)) * + (A[n-1]*(wl**2) + B[n-1] + C[n-1]/wl + + D[n-1]/(wl**2) + E[n-1]/(wl**3) + + F[n-1]/(wl**4))) * kT # cm^5 / H / e- + alpha_ff[k,i] *= 1.0e-10 # Convert from cm^5 / H- ion to m^5 / H- ion + + return alpha_ff + +def plot_opacity(chemical_species, cia_pairs, sigma_stored, cia_stored, + alpha_bf, alpha_ff, P_desired, T_desired, wl_grid): + + # Max number of species this can plot is 9 (cluttered beyond that!) + + # Optional smoothing of cross sections (can improve clarity) + smooth = False + smooth_factor = 5 + + # Specify cross sections to plot, along with colours for each + colours_sigma = np.array(['royalblue', 'purple', 'crimson', 'orange', 'black', 'grey', 'green', 'magenta', 'chocolate']) + + # Initialise plot + ax = plt.gca() + #ax.set_xscale("log") + + xmajorLocator = MultipleLocator(1.0) + xmajorFormatter = FormatStrFormatter('%.1f') + xminorLocator = MultipleLocator(0.2) + xminorFormatter = NullFormatter() + + ax.xaxis.set_major_locator(xmajorLocator) + ax.xaxis.set_major_formatter(xmajorFormatter) + ax.xaxis.set_minor_locator(xminorLocator) + ax.xaxis.set_minor_formatter(xminorFormatter) + + # Plot each cross section + for q in range(len(chemical_species)): + + species = chemical_species[q] # Species to plot cross section of + colour = colours_sigma[q] # Colour of cross section for plot + + species_idx = np.where(chemical_species == species)[0][0] + + sigma_plt = sigma_stored[species_idx,:] # Cross section of species q at given (P,T) pair (m^2) + + if (smooth == True): + sigma_plt = gauss_conv(sigma_plt, sigma=smooth_factor, mode='nearest') + + # Plot cross section + plt.semilogy(wl_grid, sigma_plt, lw=0.5, alpha = 1.0, color= colour, label = species) + + plt.ylim([1.0e-31, 1.0e-21]) + plt.xlim([min(wl_grid), max(wl_grid)]) + plt.ylabel(r'$\mathrm{Cross \, \, Section \, \, (m^{2} \, mol^{-1})}$', fontsize = 15) + plt.xlabel(r'$\mathrm{Wavelength} \; \mathrm{(\mu m)}$', fontsize = 15) + + ax.set_xticks([1, 2, 3, 4, 5]) + + ax.text(min(wl_grid)*1.20, 2.0e-22, (r'$\mathrm{T = }$' + str(T_desired) + r'$\mathrm{K \, \, P = }$' + str(P_desired*1000) + r'$\mathrm{mbar}$'), fontsize = 14) + + legend = plt.legend(loc='upper right', shadow=False, frameon=False, prop={'size':6}, ncol=2) + + for legline in legend.legendHandles: + legline.set_linewidth(1.0) + + plt.savefig('./cross_sections_' + str(T_desired) + 'K_' + str(P_desired*1000) + 'mbar.pdf', bbox_inches='tight', fmt='pdf', dpi=1000) + + plt.show() + + plt.clf() + + #***** Now plot CIA ***** + + colours_cia = np.array(['royalblue', 'crimson']) + + ax = plt.gca() + #ax.set_xscale("log") + + xmajorLocator = MultipleLocator(1.0) + xmajorFormatter = FormatStrFormatter('%.1f') + xminorLocator = MultipleLocator(0.2) + + ax.xaxis.set_major_locator(xmajorLocator) + ax.xaxis.set_major_formatter(xmajorFormatter) + ax.xaxis.set_minor_locator(xminorLocator) + + # Plot each cross section + for q in range(len(cia_pairs)): + + cia_pair = cia_pairs[q] # Species to plot cross section of + colour = colours_cia[q] # Colour of cross section for plot + + cia_idx = np.where(cia_pairs == cia_pair)[0][0] + + cia_plt = cia_stored[cia_idx,:] # Cross section of species q at given (P,T) pair (m^5) + + # Plot cross section + plt.semilogy(wl_grid, cia_plt, lw=0.5, alpha = 1.0, color = colour, label = cia_pair) + + plt.ylim([1.0e-60, 1.0e-52]) + plt.xlim([min(wl_grid), max(wl_grid)]) + plt.ylabel(r'$\mathrm{Binary \, \, Cross \, \, Section \, (CIA) \, \, (m^{5} mol^{-2})}$', fontsize = 15) + plt.xlabel(r'$\mathrm{Wavelength} \; \mathrm{(\mu m)}$', fontsize = 15) + + ax.text(min(wl_grid)*1.20, 3.0e-53, (r'$\mathrm{T = }$' + str(T_desired) + r'$\mathrm{K}$'), fontsize = 14) + + legend = plt.legend(loc='upper right', shadow=False, frameon=False, prop={'size':8}) + + for legline in legend.legendHandles: + legline.set_linewidth(1.0) + + plt.savefig('./CIA_cross_sections_' + str(T_desired) + 'K.pdf', bbox_inches='tight', fmt='pdf', dpi=1000) + + plt.show() + + plt.clf() + + #***** Now plot H- bound-free opacity ***** + + # Initialise plot + ax = plt.gca() + + xmajorLocator = MultipleLocator(0.5) + xmajorFormatter = FormatStrFormatter('%.1f') + xminorLocator = MultipleLocator(0.1) + xminorFormatter = NullFormatter() + + ax.xaxis.set_major_locator(xmajorLocator) + ax.xaxis.set_major_formatter(xmajorFormatter) + ax.xaxis.set_minor_locator(xminorLocator) + ax.xaxis.set_minor_formatter(xminorFormatter) + + # Plot cross section + plt.semilogy(wl, alpha_bf, lw=1.0, alpha = 0.5, color= 'crimson', label = r'H- bound-free') + + plt.ylim([1.0e-23, 1.0e-20]) + plt.xlim([min(wl), 1.8]) + plt.ylabel(r'$\mathrm{Cross \, \, Section \, \, (m^{2} \, / n_{H-})}$', fontsize = 15) + plt.xlabel(r'$\mathrm{Wavelength} \; \mathrm{(\mu m)}$', fontsize = 15) + + ax.set_xticks([0.4, 0.6, 0.8, 1.0, 1.2, 1.4, 1.6, 1.8]) + + legend = plt.legend(loc='upper right', shadow=False, frameon=False, prop={'size':8}, ncol=2) + + plt.savefig('./H-_bound_free.pdf', bbox_inches='tight', fmt='pdf', dpi=1000) + + plt.show() + + plt.clf() + + #***** Now plot H- free-free opacity ***** + + # Initialise plot + ax = plt.gca() + #ax.set_xscale("log") + + xmajorLocator = MultipleLocator(10.0) + xmajorFormatter = FormatStrFormatter('%.1f') + xminorLocator = MultipleLocator(0.2) + xminorFormatter = NullFormatter() + + ax.xaxis.set_major_locator(xmajorLocator) + ax.xaxis.set_major_formatter(xmajorFormatter) + ax.xaxis.set_minor_locator(xminorLocator) + ax.xaxis.set_minor_formatter(xminorFormatter) + + # Plot cross section + plt.semilogy(wl, alpha_ff, lw=1.0, alpha = 0.5, color= 'royalblue', label = r'H- free-free') + + plt.ylim([8.0e-50, 2.0e-47]) + plt.xlim([min(wl), max(wl)]) + plt.ylabel(r'$\mathrm{Binary \, \, Cross \, \, Section \, \, (m^{5} \, / n_{H} / n_{e-})}$', fontsize = 15) + plt.xlabel(r'$\mathrm{Wavelength} \; \mathrm{(\mu m)}$', fontsize = 15) + + ax.set_xticks([0.4, 1.0, 2.0, 3.0, 4.0, 5.0]) + + ax.text(min(wl_grid)*1.20, 1.5e-47, (r'$\mathrm{T = }$' + str(T_desired) + r'$\mathrm{K}$'), fontsize = 14) + + legend = plt.legend(loc='upper right', shadow=False, frameon=False, prop={'size':8}, ncol=1) + + plt.savefig('./H-_free_free_' + str(T_desired) + 'K.pdf', bbox_inches='tight', fmt='pdf', dpi=1000) + + +#***** Begin main program ***** + +# Specify which molecules you want to extract from the database (full list available in the readme) +chemical_species = np.array(['H2O', 'CH4', 'NH3', 'HCN', 'CO', 'CO2']) +#chemical_species = np.array(['HCN']) + +# Specify collisionally-induced absorption (CIA) pairs +cia_pairs = np.array(['H2-H2', 'H2-He']) + +# Specify temperature and pressure grids on which to evaluate cross sections. Example: +P = np.logspace(-6, 2.0, 10) # 20 pressures logarithmically spaced from 10^-6 -> 10^2 bar. +T = np.linspace(1000.0, 3000.0, 100) # 100 temperatures linearly spaced from 800K -> 3000 K. + +# Specify wavelength grid to extract cross section onto +wl_min = 0.4 # Minimum wavelength of grid (micron) +wl_max = 5.0 # Maximum wavelength of grid (micron) +N_wl = 1000 # Number of wavelength points + +wl = np.linspace(wl_min, wl_max, N_wl) # Uniform grid used here for demonstration purposes + +# Can use any P,T,wl grids, doesn't have to be uniform, but MUST be a numpy array. +# Note: if you use a v. large number of (P,T,wl) points, the memory usage could be quite high! +# This example has 10x100x1000 = 10^6 cross section values stored for each species. + +# Either sample the nearest wavelength points from the high resolution (R~10^6) cross section database or use an averaging prescription +opacity_treatment = 'Log-avg' # Options: Opacity-sample / Log-avg +#opacity_treatment = 'Opacity-sample' # Opacity sampling is faster, but for low-resolution wavelength grids log averaging is recommended + +# Extract desired cross sections from the database +cross_sections, cia_absorption = Extract_opacity(chemical_species, cia_pairs, P, T, wl, opacity_treatment) # Format: np array(N_species, N_P, N_T, N_wl) / Units: (m^2 / species) + +# Evaluate H- bound-free and free-free opacities +alpha_bf = H_minus_bound_free(wl) +alpha_ff = H_minus_free_free(wl, T) + +# Specify pressure and temperature where cross section plot is desired +P_desired = 1.0e-3 +T_desired = 2000.0 +P_idx = np.argmin(np.abs(P - P_desired)) # Closest index in the P array to desired value +T_idx = np.argmin(np.abs(T - T_desired)) # Closest index in the T array to desired value + +# Example: seperate H2O cross section at 1 mbar and 1000K, and print to terminal +#H2O_cross_section = cross_sections[(np.where(chemical_species=='H2O')[0][0]),P_idx,T_idx,:] # Format: np array(N_wl) / Units: (m^2 / molecule) +#print (H2O_cross_section) + +# Plot cross sections at the desired P and T +plot_opacity(chemical_species, cia_pairs, cross_sections[:,P_idx,T_idx,:], + cia_absorption[:,T_idx,:], alpha_bf, alpha_ff[:,T_idx], + P_desired, T_desired, wl) + diff --git a/hires_taugas_eq1.py b/hires_taugas_eq1.py new file mode 100644 index 0000000..570d237 --- /dev/null +++ b/hires_taugas_eq1.py @@ -0,0 +1,214 @@ +#! /usr/bin/env python +## +## hires_taugas_eq1.py -- Make high (wavelength) resolution plots of +## the atmospheric pressure at which tau(gas) = 1. +## +## 2019.02.11 - liac@umich.edu +##----------------------------------------------------------------------- + +import os +import numpy as np +import matplotlib.pyplot as plt +from matplotlib.gridspec import GridSpec +from matplotlib.ticker import NullFormatter, MultipleLocator, FormatStrFormatter + +# Plotting settings +from matplotlib import rcParams +rcParams['font.family'] = 'sans-serif' +rcParams['xtick.labelsize'] = 10 +rcParams['ytick.labelsize'] = 10 +rcParams['axes.labelsize'] = 14 + +from astropy.io import fits +import astropy.units as u +from maplib import load_out3, cumulative_integral, cloud_depth +from calculate_hires_opacities import WGRID, LLS + + +# Set up paths +HOME_DIR = os.environ['HOME'] + '/Dropbox/Science/cloud-academy/Les_Houches_Cloud_Activity/' +INP_DIR = HOME_DIR + 'Gas_Ext/hires_' + +# Set up colors +'''colors = {'CO':'green', 'CH4':'gray', 'SIO':'xkcd:sky', + 'H':'red', 'H2S':'xkcd:sun yellow', 'FE':'brown', + 'TIO':'blue', 'K':'orange', # up to here: from NI in "conventions" doc; from here: using color schemes inspired by Fig 8 (cloud type groupings) + 'VO':'#2c7fb8', # Metals: Ocean blue to light blue/green + 'ALO':'#41b6c4', + 'CO2':'#2ca25f', + 'H2O':'#2c7fb8', # dusty blue + 'NA':'#756bb1', # grey-purple + 'OH':'#980043', # deep magenta, + 'MGH':'#fcae91', # peach + 'ALH':'#e34a33', # burnt orange + 'CS':'#cccccc', # light grey + 'SIH':'#54278f', # deep purple + 'LI':'#b3de69' # grass green + } +''' +# Choose a color scheme inspired by Fig 8 +colors = {'TIO':'#91003f', # Metals: Dark magenta to pink + #'VO':'#ce1256', + 'VO':'magenta', + 'ALO':'#e7298a', + 'ALH':'#df65b0', + 'SIO':'#a63603', # Silicate related stuff (Si, Mg, O): burnt orange to yellow + 'SIH':'#e6550d', + 'MGH':'#fd8d3c', + 'H2O':'#0c2c84', # Volatiles: Ocean blue to light blue/green + 'CO2':'#225ea8', + 'CO':'#1d91c0', + 'CH4':'#41b6c4', + 'FEH': 'limegreen', + 'OH':'#7fcdbb', + 'H2S':'#c7e9b4', + 'CS':'xkcd:sun yellow', + 'H':'#252525', # Individual atoms: black/grey/purple + 'FE':'crimson', + 'TI':'royalblue', + 'K':'#54278f', + 'NA':'#756bb1', + 'LI':'#cbc9e2', + 'H2-H2': 'black', + 'H2-HE': 'dimgrey', + 'H-': 'darkgreen' + } + +labels = {'H2O':'H$_2$O', 'CO2':'CO$_2$', 'CH4':'CH$_4$', + 'C2H2':'C$_2$H$_2$', 'NH3':'NH$_3$', 'SIO':'SiO', + 'ALH':'AlH', 'ALO':'AlO', 'MGH':'MgH', 'FEH':'FeH', + 'H2S':'H$_2$S', 'TIO':'TiO', 'CAH':'CaH', 'NA':'Na', + 'LI':'Li', 'SIH':'SiH', 'H2-H2':'H$_2$-H$_2$', + 'H2-HE':'H$_2$-He', 'H-':'H-', 'FE':'Fe'} + +def read_opac_file(filename): + """ + Read the FITS files output from calculate_atmosphere_opacities.py + or calculate_hires_opacities.py + """ + hdulist = fits.open(filename) + opac_dict = dict() + for h in hdulist: + if h.name == 'PRIMARY': + pass + else: + opac_dict[h.name] = h.data + hdulist.close() + return opac_dict + +def sum_ext(opac_dict, keys=None): + """Sum the extinction opacities from a dictionary of dtau/dz + + Parameters + ---------- + opac_dict : dict + Contains key-value pairs of chemical species with dtau/dz + + keys : list (Default: None) + Key values for opac_dict to include in calculation. If None, + all of the available opacities will be summed. + """ + if keys is None: + keys = list(opac_dict.keys()) + dtau_dz = np.zeros_like(opac_dict[keys[0]]) + for k in keys: + dtau_dz += opac_dict[k] + return dtau_dz + +def atmosphere_depth(lon, lat, tau=1.0, keys=None, wavel=WGRID): + infile = INP_DIR + 'Phi{:.1f}Theta{:.1f}_dtau_dz.fits'.format(lon, lat) + dtau_dz_g = read_opac_file(infile) # dtau/dz for each gas + dtau_dz = sum_ext(dtau_dz_g, keys=keys) + + thermo = load_out3('thermo', lon, lat) + z = thermo['z'] # cm + p_unit = u.dyne / u.cm**2 + p = thermo['p'] * p_unit.to(u.bar) + + ci = cumulative_integral(z, dtau_dz) + result = [] + for i in range(len(wavel)): + result.append(np.interp(tau, ci[:,i], p)) + return np.array(result) + + +def plot_depth(ax, ll, keys, wavel, ylim): + # Plot the gas depth + for k in keys: + print(k) + atm_depth = atmosphere_depth(ll[0], ll[1], keys=[k]) + test = True + #test = np.min(atm_depth) < ylim[0] + if test: + lbl = k + if k in list(labels.keys()): lbl = labels[k] + if k in list(colors.keys()): + ax.plot(wavel, atm_depth, alpha=0.8, color=colors[k], label=lbl) + else: + ax.plot(wavel, atm_depth, alpha=0.8, label=lbl) + + # Plot the cloud depth + p_unit = u.dyne / u.cm**2 + w, cld_depth = cloud_depth('{:.1f}'.format(ll[0]), '{:.1f}'.format(ll[1]), p_val=True) + ax.plot(w, cld_depth * p_unit.to(u.bar), color='k', ls='--', lw=3, label='Clouds') + + xmajorLocator = MultipleLocator(2.0) + xmajorFormatter = FormatStrFormatter('%.1f') + #xminorLocator = MultipleLocator(0.2) + xminorFormatter = NullFormatter() + + ax.set_yscale('log') + ax.set_xscale('log') + + ax.xaxis.set_major_locator(xmajorLocator) + ax.xaxis.set_major_formatter(xmajorFormatter) + #ax.xaxis.set_minor_locator(xminorLocator) + ax.xaxis.set_minor_formatter(xminorFormatter) + + ax.set_xlabel(r'$\lambda$ [$\mu$m]') + ax.set_ylabel(r'$p_{\rm gas}(\tau_{x}(\lambda) = 1)$ [bar]') + ax.set_ylim(ylim) + ax.set_xlim(0.4, 50.0) + ax.set_xticks([0.4, 1, 2, 4, 6, 10, 20, 40]) + return + +##-------------------- +## Do the things! + +infile0 = INP_DIR + 'Phi{:.1f}Theta{:.1f}_dtau_dz.fits'.format(LLS[0][0], LLS[0][1]) +dtau_dz_g_0 = read_opac_file(infile0) # dtau/dz for each gas + + + +YLIM = [10.0, 1.0e-5] + +fig = plt.figure(figsize=(12.8, 9.6)) +gs = GridSpec(2, 2, wspace=0.03) + +titles = ['Anti-stellar point', + r'Morning terminator ($\theta = 0^{\circ}$)', + 'Sub-stellar point', + r'Evening terminator ($\theta = 0^{\circ}$)'] + +#KEYS_TO_PLOT = list(dtau_dz_g_0.keys()) +KEYS_TO_PLOT = ['H2O','CO','CO2','CS','TIO','VO','SIO','OH','FEH','ALH','MGH','H2S','NA','K','FE','TI','H2-H2', 'H2-HE','H-'] + +for i in range(len(titles)): + print("Running {}".format(titles[i])) + ax = plt.subplot(gs[i]) + plot_depth(ax, LLS[i], KEYS_TO_PLOT, WGRID, YLIM) + ax.set_title(titles[i]) + if i in [1,3]: + ax.set_ylabel('') + ax.yaxis.set_ticklabels([]) + if i in [0,1]: + ax.set_xlabel('') + if i in [2,3]: + ax.set_ylim(10.0, 3.e-6) + if i == 2: # the only one with all the elements listed + ax.legend(loc='upper right', frameon=False, ncol=5) + + +#plt.tight_layout() +plt.savefig('./gas_opacity_wavel.png', format='png', dpi=500) +#plt.show() diff --git a/map_atm_depth.py b/map_atm_depth.py new file mode 100644 index 0000000..7f05f53 --- /dev/null +++ b/map_atm_depth.py @@ -0,0 +1,209 @@ +#! /usr/bin/env python +## +## map_atm_depth.py -- Map the pressure at which the gas + cloud +## opacity from the atmosphere, tau = 1 +## +## 2019.01.22 - liac@umich.edu +##------------------------------------------------------------------- + +import numpy as np +import matplotlib.pylab as plt + +import matplotlib.cm as cm +from mpl_toolkits.basemap import Basemap +from matplotlib.colors import LogNorm + +from astropy.io import fits +import astropy.units as u +from maplib import load_out3, get_ext_data, cumulative_integral + +file_root = 'static_weather_results/HATP_7b' # root name for profile folders +gas_root = 'dtau-dz/' # root name for gas opacity (dtau/dz) files +out_root = 'Cloud_Opt_Depth/' # root name for output files + +# Function for reading in the gas opacity file +# (results of calculate_atmosphere_opacities.py) +def read_opac_file(filename): + hdulist = fits.open(filename) + opac_dict = dict() + for h in hdulist: + if h.name == 'PRIMARY': + pass + else: + opac_dict[h.name] = h.data + hdulist.close() + return opac_dict + + +def sum_ext(opac_dict, keys=None): + """ + Function for summing each gas contribution to the opacity. + + Parameters + ---------- + opac_dict : dict + The dtau_dz values as a function of wavelength and pressure + layer, for each gas phase element or molecule + + keys : list of strings + The names of the gas or molecules to be incorporated into the + sum. If None, all available gases in opac_dict will be summed. + + Returns + ------- + Dictionary of the total dtau_dz as a function of wavelength and + pressure layer + """ + if keys is None: + keys = list(opac_dict.keys()) + + dtau_dz = np.zeros_like(opac_dict[keys[0]]) + for k in keys: + dtau_dz += opac_dict[k] + + return dtau_dz + + +def atmosphere_depth(lon, lat, tau=1.0, keys=None): + """ + Find the atmosphere depth (pressure) at which the cumulative gas + plus cloud opacity (tau) reaches a chosen value. + + Parameters + ---------- + lon : float + + lat : float + + tau : float (Default: 1.0) + Chosen tau value for which the atmosphere depth will be returned + + keys : list of strings + The names of the gas or molecules to be incorporated into the + gas opacity calculation. If None, all available gases will be + summed. + + Returns + ------- + List of atmospheric pressures (bar) for which tau reaches a threshold + value, as a function of wavelength. + """ + # Gas portion + infile = gas_root + 'Phi{:.1f}Theta{:.1f}_dtau_dz.fits'.format(lon, lat) + dtau_dz_g = read_opac_file(infile) # dtau/dz for each gas + dtau_dz_gsum = sum_ext(dtau_dz_g, keys=keys) + + # Cloud portion + zfile = file_root + '_Phi{:.1f}Theta{:.1f}/out3_thermo.dat'.format(lon, lat) + extfile = file_root + '_Phi{:.1f}Theta{:.1f}/out3_extinction.dat'.format(lon, lat) + z, dtau_dz_cl = get_ext_data(zfile, extfile) + + dtau_dz = dtau_dz_cl + dtau_dz_gsum + + thermo = load_out3('thermo', lon, lat) + z = thermo['z'] # cm + p_unit = u.dyne / u.cm**2 + p = thermo['p'] * p_unit.to(u.bar) + wavel = load_out3('wavel', lon, lat) + + ci = cumulative_integral(z, dtau_dz) + result = [] + for i in range(len(wavel)): + result.append(np.interp(tau, ci[:,i], p)) + return result + + +##-------------- + +## Load the data +lons = np.arange(-180., 180.01, 15) # deg +lats = np.arange(0., 67.51, 22.5) # deg +nprof = len(lons) * len(lats) # number of (lon, lat) profiles + +wavel = load_out3('wavel', lons[0], lats[0], root=file_root) + +NLO, NLA, NWA = len(lons), len(lats), len(wavel) +Z = np.zeros((NLO, NLA, NWA)) +for i in np.arange(NLO)[:-1]: + for j in np.arange(NLA): + d = atmosphere_depth(lons[i], lats[j], tau=1.0) # bar + Z[i,j,:] = d + +# Make +180 slice equal to -180 +Z[-1,:,:] = np.copy(Z[0,:,:]) + +# Mesh grid to use for the map +X, Y = np.meshgrid(lons,lats) + +# Default contour levels to use +nlev = 21 +log_pmin, log_pmax = -6, 3 +lev = np.linspace(log_pmin, log_pmax, nlev) + +def map_atm_depth(i, levels=lev, cmap=plt.cm.RdYlBu_r): + """ + Map the gas properties provided in matrix Z for a wavelength value (index i) + + Parameters + ---------- + i : int + Index for the wavelength value to be mapped + + levels : numpy.ndarray + Levels values for the contour map (in logspace) + + cmap : matplotlib colormap + + Returns + ------- + Plots the maps and + """ + m = Basemap(projection='kav7', lon_0=0, resolution=None) + CS_north = m.contourf(X, Y, np.log10(Z[:,:,i].T), + levels=levels, extend='both', cmap=cmap, latlon=True) + CS_south = m.contourf(X, -Y, np.log10(Z[:,:,i].T), + levels=levels, extend='both', cmap=cmap, latlon=True) + + # grey out areas that hit an upper limit + logZmax = 2.2 # max pressure to cut off + cmap2 = plt.cm.binary + levels2 = np.array([-3, 3]) + Z2 = np.ma.masked_where(np.log10(Z[:,:,i]) < logZmax, np.log10(Z[:,:,i])) + CS2 = m.contourf(X, Y, Z2.T, + levels=levels2, extend='both', cmap=cmap2, latlon=True) + CS2 = m.contourf(X, -Y, Z2.T, + levels=levels2, extend='both', cmap=cmap2, latlon=True) + + for c in CS_north.collections: + c.set_edgecolor("face") + + CB = plt.colorbar(CS_north, label=r'log $p_{\rm gas}(\tau_{\rm cloud}(\lambda) = 1)$ [bar]', + ticks=np.arange(log_pmin+1, log_pmax+1)[::2], + orientation='horizontal') + + plt.title('{:.2f} $\mu$m'.format(wavel[i])) + + ## -- Plot lat-lon lines on map + # String formatting function + def fmtfunc(inlon): + string = r'{:g}'.format(inlon)+r'$^{\circ}$' + #print string + return string + + meridians = np.arange(0., 360., 90) + m.drawmeridians(meridians, labels=[False,False,False,True], + labelstyle=None, fmt=fmtfunc, dashes=[3,3], color='k', fontsize=14) + parallels = np.arange(-90., 90, 30) + m.drawparallels(parallels, labels=[True,False,False,False], + labelstyle='+/-', dashes=[3,3], color='k',fontsize=14) + + # and we're done! + return CS_north + +## Plot the things!! + +for i in range(len(wavel)): + map_atm_depth(i) + plt.savefig(out_root + 'atm_depth_{:.2f}um.pdf'.format(wavel[i])) + plt.clf() + diff --git a/map_cloud_depth.py b/map_cloud_depth.py index 6af4ce2..1c1b711 100755 --- a/map_cloud_depth.py +++ b/map_cloud_depth.py @@ -7,6 +7,7 @@ # 2018.09.26 - Modified by L. R. Corrales (liac@umich.edu) # To map the cloud depth (distance from top, where dust tau=1) # 2018.11.09 - Modified to use maplib +# 2018.12.18 - Show pressure instead of cloud depth # Requires: # --------- @@ -35,7 +36,7 @@ from mpl_toolkits.basemap import Basemap from matplotlib.colors import LogNorm -from maplib import get_wavel, cloud_depth, stringy +from maplib import get_wavel, cloud_depth, stringy, load_out3 file_root = 'static_weather_results/HATP_7b' # root name for profile folders nlay = 53 # number of vertical layers @@ -43,7 +44,7 @@ OUTPUT_DIR = 'Cloud_Opt_Depth/' -MAX_DEPTH = 10000 # km +p_convert = 1.e-6 # bar / (dyne/cm^2) ## ---- Load the data @@ -63,13 +64,13 @@ # Make a 3D array with dimensions (lon, lat, wavel) NLO, NLA, NWA = len(lons), len(lats), len(wavel) Z = np.zeros((NLO, NLA, NWA)) -for i in range(NLO): - for j in range(NLA): - w, d = cloud_depth(stringy(lons[i]), stringy(lats[j])) - Z[i,j,:] = d +for i in np.arange(NLO)[:-1]: + for j in np.arange(NLA): + w, d = cloud_depth(stringy(lons[i]), stringy(lats[j]), p_val=True) + Z[i,j,:] = d * p_convert -# The 180 longitude is same as -180 -#Z[0,:,:] = Z[-1,:,:] +# Force +180 slice to equal -180 +Z[-1,:,:] = np.copy(Z[0,:,:]) ## ---- Mapping and plotting part @@ -78,9 +79,14 @@ # Default contour levels to use nlev = 21 -lev = np.linspace(0, MAX_DEPTH, nlev) +log_pmin, log_pmax = -6, 3.0 +lev = np.linspace(log_pmin, log_pmax, nlev) -def map_cloud_depth(i, levels=lev, cmap=plt.cm.RdYlBu_r, log=False): +# Get pressure values for reference +pdata = load_out3('thermo', lons[0], lats[0]) +pres = pdata['p'] * p_convert # pressure (bar) + +def map_cloud_depth(i, levels=lev, cmap=plt.cm.RdYlBu_r): """ Makes a plot of HAT-P-7b cloud depth at some wavelength @@ -95,9 +101,6 @@ def map_cloud_depth(i, levels=lev, cmap=plt.cm.RdYlBu_r, log=False): cmap : matplotlib.pyplot.colormap object Color map to use - log : bool --> Not implemented as of now!! - If True, uses logarithmically spaced colorbar - Returns ------- Results of Basemap.contourf for the northern hemisphere of the planet @@ -105,12 +108,30 @@ def map_cloud_depth(i, levels=lev, cmap=plt.cm.RdYlBu_r, log=False): Also produces a plot. """ m = Basemap(projection='kav7', lon_0=0, resolution=None) - CS_north = m.contourf(X, Y, Z[:,:,i].T, + #print(np.log10(Z[:,:,i])) + CS_north = m.contourf(X, Y, np.log10(Z[:,:,i].T), levels=levels, extend='both', cmap=cmap, latlon=True) - CS_south = m.contourf(X, -Y, Z[:,:,i].T, + CS_south = m.contourf(X, -Y, np.log10(Z[:,:,i].T), levels=levels, extend='both', cmap=cmap, latlon=True) - plt.colorbar(label='Cloud Depth (km)') - plt.title('{:.1f} $\mu$m'.format(wavel[i])) + + # grey out areas that hit an upper limit + logZmax = np.max(np.log10(pres)) # max pressure to cut off + cmap2 = plt.cm.binary + levels2 = np.array([3.,5.]) # not reaching tau=1 results in p(bar) = 10^4 + Z2 = np.ma.masked_where(np.log10(Z[:,:,i]) < logZmax, np.log10(Z[:,:,i])) + CS2 = m.contourf(X, Y, Z2.T, + levels=levels2, extend='both', cmap=cmap2, latlon=True) + CS2 = m.contourf(X, -Y, Z2.T, + levels=levels2, extend='both', cmap=cmap2, latlon=True) + + for c in CS_north.collections: + c.set_edgecolor("face") + + CB = plt.colorbar(CS_north, label=r'log $p_{\rm gas}(\tau_{\rm cloud}(\lambda) = 1)$ [bar]', + ticks=np.arange(log_pmin+1, log_pmax+1)[::2], + orientation='horizontal') + + plt.title('{:.2f} $\mu$m'.format(wavel[i])) ## -- Plot lat-lon lines on map # String formatting function @@ -133,7 +154,7 @@ def fmtfunc(inlon): ## Plot everything and save it, if running this file as a script def save_plot(i, root_string=OUTPUT_DIR+'cloud_depth_', verbose=True): - filename = root_string + '{:.1f}um.pdf'.format(wavel[i]) + filename = root_string + '{:.2f}um.pdf'.format(wavel[i]) if verbose: print("Saving map to file: {}".format(filename)) diff --git a/map_cloud_depth_km.py b/map_cloud_depth_km.py new file mode 100755 index 0000000..6af4ce2 --- /dev/null +++ b/map_cloud_depth_km.py @@ -0,0 +1,147 @@ +#! /usr/bin/env python + +# G.K.H. Lee : Basic plotting routine to map a variable at a chosen pressure level +# requires the basemap plotting package +# install with anaconda using: conda install -c conda-forge basemap + +# 2018.09.26 - Modified by L. R. Corrales (liac@umich.edu) +# To map the cloud depth (distance from top, where dust tau=1) +# 2018.11.09 - Modified to use maplib + +# Requires: +# --------- +# numpy, matplotlib, basemap +# Custom library of python functions: 'maplib' + +## -------- +## TO USE: +## +## In an interactive python session, one can import the main functions: +## +## >>> from map_cloud_depth import map_cloud_depth +## >>> map_cloud_depth[0] +## +## Or you can run the file as a script, and it will save all of the maps to a pdf +## +## $ ./map_cloud_depth.py +## +## Be sure to modify the `OUTPUT_DIR` variable to point to your desired output folder +## ------- + + +import numpy as np +import matplotlib.pylab as plt +import matplotlib.cm as cm +from mpl_toolkits.basemap import Basemap +from matplotlib.colors import LogNorm + +from maplib import get_wavel, cloud_depth, stringy + +file_root = 'static_weather_results/HATP_7b' # root name for profile folders +nlay = 53 # number of vertical layers +Teff = 2700.0 # eff/eq temperature + +OUTPUT_DIR = 'Cloud_Opt_Depth/' + +MAX_DEPTH = 10000 # km + +## ---- Load the data + +lons = np.arange(-180., 180.01, 15) # deg +lats = np.arange(0., 67.51, 22.5) # deg +nprof = len(lons) * len(lats) # number of (lon, lat) profiles + +#lons = np.arange(-180., 180.01, 45) # deg +#lats = np.arange(0., 67.51, 22.5) # deg +#nprof = len(lons) * len(lats) # number of (lon, lat) profiles + +# Get the wavelengths of interest. Assume all files are the same +wavel_file = file_root + '_Phi{}Theta{}/out3_extinction.dat'.format(stringy(lons[0]), stringy(lats[0])) +wavel = get_wavel(wavel_file) # um + +# Read in the cloud depth values for each file. +# Make a 3D array with dimensions (lon, lat, wavel) +NLO, NLA, NWA = len(lons), len(lats), len(wavel) +Z = np.zeros((NLO, NLA, NWA)) +for i in range(NLO): + for j in range(NLA): + w, d = cloud_depth(stringy(lons[i]), stringy(lats[j])) + Z[i,j,:] = d + +# The 180 longitude is same as -180 +#Z[0,:,:] = Z[-1,:,:] + +## ---- Mapping and plotting part + +# Mesh grid to use for the map +X, Y = np.meshgrid(lons,lats) + +# Default contour levels to use +nlev = 21 +lev = np.linspace(0, MAX_DEPTH, nlev) + +def map_cloud_depth(i, levels=lev, cmap=plt.cm.RdYlBu_r, log=False): + """ + Makes a plot of HAT-P-7b cloud depth at some wavelength + + Parameters + --------- + i : int + Index to use from wavelength array + + levels : numpy.ndarray + Contour levels to use for the map + + cmap : matplotlib.pyplot.colormap object + Color map to use + + log : bool --> Not implemented as of now!! + If True, uses logarithmically spaced colorbar + + Returns + ------- + Results of Basemap.contourf for the northern hemisphere of the planet + + Also produces a plot. + """ + m = Basemap(projection='kav7', lon_0=0, resolution=None) + CS_north = m.contourf(X, Y, Z[:,:,i].T, + levels=levels, extend='both', cmap=cmap, latlon=True) + CS_south = m.contourf(X, -Y, Z[:,:,i].T, + levels=levels, extend='both', cmap=cmap, latlon=True) + plt.colorbar(label='Cloud Depth (km)') + plt.title('{:.1f} $\mu$m'.format(wavel[i])) + + ## -- Plot lat-lon lines on map + # String formatting function + def fmtfunc(inlon): + string = r'{:g}'.format(inlon)+r'$^{\circ}$' + #print string + return string + + meridians = np.arange(0., 360., 90) + m.drawmeridians(meridians, labels=[False,False,False,True], + labelstyle=None, fmt=fmtfunc, dashes=[3,3], color='k', fontsize=14) + parallels = np.arange(-90., 90, 30) + m.drawparallels(parallels, labels=[True,False,False,False], + labelstyle='+/-', dashes=[3,3], color='k',fontsize=14) + + # and we're done! + return CS_north + +## ------ +## Plot everything and save it, if running this file as a script + +def save_plot(i, root_string=OUTPUT_DIR+'cloud_depth_', verbose=True): + filename = root_string + '{:.1f}um.pdf'.format(wavel[i]) + if verbose: + print("Saving map to file: {}".format(filename)) + + map_cloud_depth(i) + plt.savefig(filename) + plt.clf() + return + +if __name__ == '__main__': + for i in range(len(wavel)): + save_plot(i) diff --git a/map_gas_depth.py b/map_gas_depth.py new file mode 100644 index 0000000..d0dd0c6 --- /dev/null +++ b/map_gas_depth.py @@ -0,0 +1,200 @@ +#! /usr/bin/env python +## +## map_gas_depth.py -- Map the pressure at which the gas opacity from +## the atmosphere, tau = 1 +## +## 2019.01.21 - liac@umich.edu +##------------------------------------------------------------------- + +import numpy as np +import matplotlib.pylab as plt + +import matplotlib.cm as cm +from mpl_toolkits.basemap import Basemap +from matplotlib.colors import LogNorm + +from astropy.io import fits +import astropy.units as u +from maplib import load_out3, cumulative_integral + +file_root = 'static_weather_results/HATP_7b' # root name for profile folders +gas_root = 'dtau-dz/' # root name for gas opacity (dtau/dz) files +out_root = 'Cloud_Opt_Depth/' # root name for output files + +# Function for reading in the gas opacity file +# (results of calculate_atmosphere_opacities.py) +def read_opac_file(filename): + hdulist = fits.open(filename) + opac_dict = dict() + for h in hdulist: + if h.name == 'PRIMARY': + pass + else: + opac_dict[h.name] = h.data + hdulist.close() + return opac_dict + + +def sum_ext(opac_dict, keys=None): + """ + Function for summing each gas contribution to the opacity. + + Parameters + ---------- + opac_dict : dict + The dtau_dz values as a function of wavelength and pressure + layer, for each gas phase element or molecule + + keys : list of strings + The names of the gas or molecules to be incorporated into the + sum. If None, all available gases in opac_dict will be summed. + + Returns + ------- + Dictionary of the total dtau_dz as a function of wavelength and + pressure layer + """ + if keys is None: + keys = list(opac_dict.keys()) + + dtau_dz = np.zeros_like(opac_dict[keys[0]]) + for k in keys: + dtau_dz += opac_dict[k] + + return dtau_dz + + +def gas_depth(lon, lat, tau=1.0, keys=None): + """ + Find the atmosphere depth (pressure) at which the cumulative gas + opacity (tau) reaches a chosen value. + + Parameters + ---------- + lon : float + + lat : float + + tau : float (Default: 1.0) + Chosen tau value for which the atmosphere depth will be returned + + keys : list of strings + The names of the gas or molecules to be incorporated into the + gas opacity calculation. If None, all available gases will be + summed. + + Returns + ------- + List of atmospheric pressures (bar) for which tau reaches a threshold + value, as a function of wavelength. + """ + infile = gas_root + 'Phi{:.1f}Theta{:.1f}_dtau_dz.fits'.format(lon, lat) + dtau_dz_g = read_opac_file(infile) # dtau/dz for each gas + dtau_dz = sum_ext(dtau_dz_g, keys=keys) + + thermo = load_out3('thermo', lon, lat, root=file_root) + z = thermo['z'] # cm + p_unit = u.dyne / u.cm**2 + p = thermo['p'] * p_unit.to(u.bar) + wavel = load_out3('wavel', lon, lat) + + ci = cumulative_integral(z, dtau_dz) + result = [] + for i in range(len(wavel)): + result.append(np.interp(tau, ci[:,i], p)) + return result + +##-------------- + +## Load the data +lons = np.arange(-180., 180.01, 15) # deg +lats = np.arange(0., 67.51, 22.5) # deg +nprof = len(lons) * len(lats) # number of (lon, lat) profiles + +wavel = load_out3('wavel', lons[0], lats[0], root=file_root) + +NLO, NLA, NWA = len(lons), len(lats), len(wavel) +Z = np.zeros((NLO, NLA, NWA)) +for i in np.arange(NLO)[:-1]: + for j in np.arange(NLA): + d = gas_depth(lons[i], lats[j], tau=1.0) # bar + Z[i,j,:] = d + +# Make +180 slice equal -180 +Z[-1,:,:] = np.copy(Z[0,:,:]) + +# Mesh grid to use for the map +X, Y = np.meshgrid(lons,lats) + +# Default contour levels to use +nlev = 21 +log_pmin, log_pmax = -6, 3 +lev = np.linspace(log_pmin, log_pmax, nlev) + +def map_gas_depth(i, levels=lev, cmap=plt.cm.RdYlBu_r): + """ + Map the gas properties provided in matrix Z for a wavelength value (index i) + + Parameters + ---------- + i : int + Index for the wavelength value to be mapped + + levels : numpy.ndarray + Levels values for the contour map (in logspace) + + cmap : matplotlib colormap + + Returns + ------- + Plots the maps and + """ + m = Basemap(projection='kav7', lon_0=0, resolution=None) + CS_north = m.contourf(X, Y, np.log10(Z[:,:,i].T), + levels=levels, extend='both', cmap=cmap, latlon=True) + CS_south = m.contourf(X, -Y, np.log10(Z[:,:,i].T), + levels=levels, extend='both', cmap=cmap, latlon=True) + + # grey out areas that hit an upper limit + logZmax = 2.2 # max pressure to cut off + cmap2 = plt.cm.binary + levels2 = np.array([-3, 3]) + Z2 = np.ma.masked_where(np.log10(Z[:,:,i]) < logZmax, np.log10(Z[:,:,i])) + CS2 = m.contourf(X, Y, Z2.T, + levels=levels2, extend='both', cmap=cmap2, latlon=True) + CS2 = m.contourf(X, -Y, Z2.T, + levels=levels2, extend='both', cmap=cmap2, latlon=True) + + for c in CS_north.collections: + c.set_edgecolor("face") + + CB = plt.colorbar(CS_north, label=r'log $p_{\rm gas}(\tau_{\rm cloud}(\lambda) = 1)$ [bar]', + ticks=np.arange(log_pmin+1, log_pmax+1)[::2], + orientation='horizontal') + + plt.title('{:.2f} $\mu$m'.format(wavel[i])) + + ## -- Plot lat-lon lines on map + # String formatting function + def fmtfunc(inlon): + string = r'{:g}'.format(inlon)+r'$^{\circ}$' + #print string + return string + + meridians = np.arange(0., 360., 90) + m.drawmeridians(meridians, labels=[False,False,False,True], + labelstyle=None, fmt=fmtfunc, dashes=[3,3], color='k', fontsize=14) + parallels = np.arange(-90., 90, 30) + m.drawparallels(parallels, labels=[True,False,False,False], + labelstyle='+/-', dashes=[3,3], color='k',fontsize=14) + + # and we're done! + return CS_north + +## Plot the things!! + +for i in range(len(wavel)): + map_gas_depth(i) + plt.savefig(out_root + 'gas_depth_{:.2f}um.pdf'.format(wavel[i])) + plt.clf() + diff --git a/map_growth.py b/map_growth.py new file mode 100755 index 0000000..ec61f95 --- /dev/null +++ b/map_growth.py @@ -0,0 +1,275 @@ +#! /usr/bin/env python + +# G.K.H. Lee : Basic plotting routine to map a variable at a chosen pressure level +# requires the basemap plotting package +# install with anaconda using: conda install -c conda-forge basemap + +# 2018.12.20 - Modified by L. R. Corrales (liac@umich.edu) +# To map the cloud growth properties as outlined in Overleaf document (Section) +# 2018.11.09 - Modified to use maplib +# 2018.12.18 - Show pressure instead of cloud depth + +# Requires: +# --------- +# numpy, matplotlib, basemap +# Custom library of python functions: 'maplib' + +## -------- +## TO USE: +## +## In an interactive python session, one can import the main functions: +## +## >>> from map_cloud_depth import map_cloud_depth +## >>> map_cloud_depth[0] +## +## Or you can run the file as a script, and it will save all of the maps to a pdf +## +## $ ./map_cloud_depth.py +## +## Be sure to modify the `OUTPUT_DIR` variable to point to your desired output folder +## ------- + + +import numpy as np +import matplotlib.pylab as plt +import matplotlib.cm as cm +from mpl_toolkits.basemap import Basemap +from matplotlib.colors import LogNorm, SymLogNorm + +import astropy.units as u + +from maplib import get_wavel, stringy, load_out3 + +file_root = 'static_weather_results/HATP_7b' # root name for profile folders +nlay = 53 # number of vertical layers +Teff = 2700.0 # eff/eq temperature + +OUTPUT_DIR = 'Grain_Growth/' + +## ---- Load the data + +lons = np.arange(-180., 180.01, 15) # deg +lats = np.arange(0., 67.51, 22.5) # deg +nprof = len(lons) * len(lats) # number of (lon, lat) profiles + + +def interpolate_pressure_map(dtype, values, p_want): + """ + Calculate the value (or sum of values) at some pressure level in the atmosphere. + + Parameters + ---------- + dtype : str + Data file type to use for load_out3 (must contain 'p' values) + + values : list + List of dictionary keywords to sum. (One value is acceptable, but must be a provided as a list) + + p_want : float + Pressure value desired, must be in the same units as the 'p' column in the data file. + Data will be interpolated to this pressure value. + + Returns + ------- + 2D map for the (summed) value of interest interpolated to the pressure value at p_want. + """ + # Read in the cloud depth values for each file. + # Make a 2D array with dimensions (lon, lat) + NLO, NLA = len(lons), len(lats) + result = np.zeros((NLO, NLA)) + for i in range(NLO): + for j in range(NLA): + + data = load_out3(dtype, lons[i], lats[j]) + p = data['p'] + + # Add up all the values + total = np.zeros_like(p) + for v in values: + total += data[v] + + # interpolate to get the value at the pressure scale we want + result[i,j] = np.interp(p_want, p, total) + return result + +## ---- Mapping and plotting part + +# Mesh grid to use for the map +X, Y = np.meshgrid(lons,lats) + +def map_result(Z, levels, cmap=plt.cm.RdYlBu_r, logplot=True, **kwargs): + """ + Makes a plot of HAT-P-7b at some pressure level + + Parameters + --------- + levels : numpy.ndarray + Contour levels to use for the map + + cmap : matplotlib.pyplot.colormap object + Color map to use + + **kwargs : keyword arguments for plt.colorbar + + Returns + ------- + Results of Basemap.contourf for the northern hemisphere of the planet + + Also produces a plot. + """ + + if logplot: + zmap = np.ones_like(Z) * -99.0 + zmap[Z != 0.0] = np.log10(Z[Z != 0.0]) + else: + zmap = Z + + m = Basemap(projection='kav7', lon_0=0, resolution=None) + CS_north = m.contourf(X, Y, zmap.T, + levels=levels, extend='both', cmap=cmap, latlon=True) + CS_south = m.contourf(X, -Y, zmap.T, + levels=levels, extend='both', cmap=cmap, latlon=True) + + plt.colorbar(orientation='horizontal', **kwargs) + + ## -- Plot lat-lon lines on map + # String formatting function + def fmtfunc(inlon): + string = r'{:g}'.format(inlon)+r'$^{\circ}$' + #print string + return string + + meridians = np.arange(0., 360., 90) + m.drawmeridians(meridians, labels=[False,False,False,True], + labelstyle=None, fmt=fmtfunc, dashes=[3,3], color='k', fontsize=14) + parallels = np.arange(-90., 90, 30) + m.drawparallels(parallels, labels=[True,False,False,False], + labelstyle='+/-', dashes=[3,3], color='k',fontsize=14) + + # and we're done! + return CS_north + +def map_symlognorm(Z, linthresh=0.03, lmin=-3.0, lmax=3.0, cmap=plt.cm.RdBu, **kwargs): + """ + Makes a plot of HAT-P-7b at some pressure level + + Parameters + --------- + levels : numpy.ndarray + Contour levels to use for the map + + cmap : matplotlib.pyplot.colormap object + Color map to use + + **kwargs : keyword arguments for plt.colorbar + + Returns + ------- + Results of Basemap.contourf for the northern hemisphere of the planet + + Also produces a plot. + """ + + zmap = Z + levels = np.append(np.append(-np.logspace(np.log10(np.abs(lmin)), np.log10(linthresh), 10)[:-1], + np.linspace(-linthresh, linthresh, 3)), + np.logspace(np.log10(linthresh), np.log10(lmax), 10)[1:]) + symlog = SymLogNorm(linthresh=linthresh, vmin=lmin, vmax=lmax) + + m = Basemap(projection='kav7', lon_0=0, resolution=None) + CS_north = m.contourf(X, Y, Z.T, norm=symlog, + levels=levels, extend='both', cmap=cmap, latlon=True) + CS_south = m.contourf(X, -Y, Z.T, norm=symlog, + levels=levels, extend='both', cmap=cmap, latlon=True) + + plt.colorbar(orientation='horizontal', **kwargs) + + ## -- Plot lat-lon lines on map + # String formatting function + def fmtfunc(inlon): + string = r'{:g}'.format(inlon)+r'$^{\circ}$' + #print string + return string + + meridians = np.arange(0., 360., 90) + m.drawmeridians(meridians, labels=[False,False,False,True], + labelstyle=None, fmt=fmtfunc, dashes=[3,3], color='k', fontsize=14) + parallels = np.arange(-90., 90, 30) + m.drawparallels(parallels, labels=[True,False,False,False], + labelstyle='+/-', dashes=[3,3], color='k',fontsize=14) + + # and we're done! + return CS_north + + + +## ------ +## Plot everything and save it, if running this file as a script + +if __name__ == '__main__': + nlev = 21 + plevels = [1.e-4, 1.e-3, 1.e-2, 1.0] * u.bar # 0.1, 1, and 10 mbar; then 1 bar (personal interest) + plabels = dict(zip(plevels, + ['10$^{-4}$ bar','1 mbar','10$^{-2}$ bar','1 bar'])) + + ## ---- Plot J* + valnames = ['J*_C', 'J*_SiO', 'J*_TiO2'] + log_Jmin, log_Jmax = -30, 5 + for pw in plevels: + Jlev = np.linspace(log_Jmin, log_Jmax, nlev) + Jstar = interpolate_pressure_map('nuclea', valnames, pw.to('dyne/cm^2').value) + _J = map_result(Jstar, Jlev, logplot=True, + label=r'$J^{*}$ [cm$^{-3}$ s$^{-1}$]', + ticks=np.arange(log_Jmin, log_Jmax+1)[::5]) + plt.title(plabels[pw]) + + if pw < 1.0*u.bar: + filename = OUTPUT_DIR + 'jstar_map_{:.1f}mbar.pdf'.format(pw.to(u.bar).value * 1.e3) + else: + plt.title('{:.1f} bar'.format(pw.to(u.bar).value)) + filename = OUTPUT_DIR + 'jstar_map_{:.1f}bar.pdf'.format(pw.to(u.bar).value) + + print('Saving {}'.format(filename)) + plt.savefig(filename, format='pdf') + plt.clf() + + ## ---- Plot number density of cloud particles + valnames = ['N'] + log_nmin, log_nmax = -3, 6 + for pw in plevels: + ndlev = np.linspace(log_nmin, log_nmax, nlev) + ndens = interpolate_pressure_map('dist', valnames, pw.to('bar').value) + _N = map_result(ndens, ndlev, logplot=True, + label=r'$N$ [cm$^{-3}$]', + ticks=np.arange(log_nmin, log_nmax+1)) + plt.title(plabels[pw]) + + if pw < 1.0*u.bar: + filename = OUTPUT_DIR + 'ndens_map_{:.1f}mbar.pdf'.format(pw.to(u.bar).value * 1.e3) + else: + filename = OUTPUT_DIR + 'ndens_map_{:.1f}bar.pdf'.format(pw.to(u.bar).value) + + print('Saving {}'.format(filename)) + plt.savefig(filename, format='pdf') + plt.clf() + + ## ---- Plot chinet + valnames = ['chinet'] + myticks = np.array([-3.0, -1.0, -1.e-3, -1.e-6, -1.e-9, 1.e-9, 1.e-6, 1.e-3, 1.0, 3.0]) + + for pw in plevels: + chimap = interpolate_pressure_map('dust', valnames, pw.to('dyne/cm^2').value) + _ch = map_symlognorm(chimap, lmin=-1.0, lmax=1.0, linthresh=1.e-9, + label=r'$\chi_{\rm net}$ [cm/s]', + ticks=myticks) + plt.title(plabels[pw]) + + if pw < 1.0*u.bar: + filename = OUTPUT_DIR + 'chinet_map_{:.1f}mbar.pdf'.format(pw.to(u.bar).value * 1.e3) + else: + filename = OUTPUT_DIR + 'chinet_map_{:.1f}bar.pdf'.format(pw.to(u.bar).value) + + print('Saving {}'.format(filename)) + plt.savefig(filename, format='pdf') + plt.clf() + diff --git a/maplib.py b/maplib.py index 94ea9e6..a760d1e 100644 --- a/maplib.py +++ b/maplib.py @@ -6,7 +6,7 @@ ## Created 2018.11.08 - liac@umich.edu ## ## Requirements: -## numpy, scipy, glob, re +## numpy, scipy, glob, re, astropy ## ## All packages can be installed using conda ##--------------------------------------------------------------------- @@ -18,6 +18,8 @@ from scipy.integrate import trapz from scipy.interpolate import interp1d +from astropy.table import Table + # Change this string to match your directory structure FOLDER_ROOT = 'static_weather_results/HATP_7b' @@ -87,7 +89,7 @@ def get_foldernames(root=FOLDER_ROOT): # ---- Contents of read_file.py (munazza.alam@cfa.harvard.edu) -def read_file(fname): +def read_file(fname, skip_lines=3): ''' Read in static_weather output files & write data values to a dictionary @@ -103,15 +105,23 @@ def read_file(fname): data columns from input file ''' - #read in static_weather output file - names = np.genfromtxt(fname,delimiter='',skip_header=3,comments='#',dtype=None,encoding=None) - data = np.genfromtxt(fname,delimiter='',skip_header=4,comments='#',dtype=None,encoding=None) + #read in static_weather output file + hstart = skip_lines + dstart = skip_lines + 1 + # I switched to using astropy because of version issues + ''' + names = np.genfromtxt(fname,delimiter='',skip_header=hstart,comments='#',dtype=None,encoding=None) + data = np.genfromtxt(fname,delimiter='',skip_header=dstart,comments='#',dtype=None,encoding=None) + + keys = names[0] + values = dict() + for k, i in zip(keys, range(len(keys))): + values[k] = np.array([data[j][i] for j in range(len(data))]) - keys = names[0] #column names - values = [data[:,i] for i in range(len(keys))] - data_dict = dict(zip(keys,values)) - - return data_dict + return values + ''' + result = Table.read(fname, format='ascii', header_start=hstart-1) + return result # ---- Contents of column.py (liac@umich.edu) @@ -198,7 +208,7 @@ def cumulative_integral(z, ext): result[i,:] = trapz(ext[:i+1,:], z2d[:i+1,:], axis=0) return result -def cloud_depth(lon, lat, tau=1.0): +def cloud_depth(lon, lat, tau=1.0, p_val=True): """ Calculates the depth (distance from top of atmosphere) for which the cloud opacity becomes optically thick (tau=1) or hits some @@ -215,6 +225,10 @@ def cloud_depth(lon, lat, tau=1.0): tau : float (Default: 1.0) Target tau value + p_val : bool + if True, returns the pressure value at the desired tau value + if False, returns the vertical distance at the desired tau value + Returns ------- wavel : numpy.ndarray @@ -232,18 +246,24 @@ def cloud_depth(lon, lat, tau=1.0): wavel = get_wavel(extfile) tau_z = cumulative_integral(z, dtau_dz) - #depth_km = calc_cloud_depth(z, tau_z, tau=1.0) * 1.e-5 # km + # Interpolate to the appropriate 'z' or 'p' value depending on the p_val flag + x = z + if p_val: + thermo = read_file(zfile) + x = thermo['p'] - # interpolate over the cumulative integral to get the depth at - # which tau reaches the desired threshold + # Interpolate over the cumulative integral to get the depth at + # which tau reaches the desired threshold. In some cases, the + # integrated optical depth never reaches the target value. This + # will return z[-1] (the deepest part of the atmosphere). result = [] for i in range(len(tau_z[0])): - tau_interp = interp1d(tau_z[:,i], z, - fill_value=(z[0], z[-1]), bounds_error=False) #^(1) + tau_interp = interp1d(tau_z[:,i], x, + fill_value=(1.e-10, 1.e10), bounds_error=False) + #fill_value=(x[0], x[-1]), bounds_error=False) result.append(tau_interp(tau)) - # (1) In some cases, the integrated optical depth never reaches the target value - # Return z[-1] (the deepest part of the atmosphere) in that case - return wavel, np.array(result) * 1.e-5 # micron, km + + return wavel, np.array(result) # micron, cm (or dyne/cm^2) ## ---- Convenience function for formatting latitudes and longitudes @@ -303,7 +323,10 @@ def load_out3(type, lon, lat, root=FOLDER_ROOT): # Returns the adjusted rho_d column, so that # rho_d = 0 when there are no dust grains (a = 0) def get_rhod(lon, lat): - dust = load_out3('dust', lon, lat) + dust = load_out3('dust', lon, lat) thermo = load_out3('thermo', lon, lat) - return dust['rhod/rho'] * thermo['rho'] + result = dust['rhod/rho'] * thermo['rho'] + izero = (np.log10(result) < -99.) + result[izero] = 0.0 + return result diff --git a/notebooks/grain_growth.ipynb b/notebooks/grain_growth.ipynb new file mode 100644 index 0000000..8a3ee75 --- /dev/null +++ b/notebooks/grain_growth.ipynb @@ -0,0 +1,973 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "%matplotlib inline\n", + "\n", + "from matplotlib import rcParams\n", + "rcParams['font.family'] = 'sans-serif'\n", + "rcParams['xtick.labelsize'] = 10\n", + "rcParams['ytick.labelsize'] = 10\n", + "rcParams['axes.labelsize'] = 14\n", + "#rcParams['figure.figsize'] = [6.4, 4.8]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Examine grain growth in HATP 7b clouds" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import astropy.units as u\n", + "from maplib import read_file, load_out3" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "from matplotlib import gridspec" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "import ipywidgets as widgets\n", + "from ipywidgets import interact" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "lons = np.arange(-165., 180.01, 15) # deg\n", + "lats = [45.0]" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "night_dust = load_out3('dust', lons[0], lats[0])\n", + "day_dust = load_out3('dust', lons[12], lats[0])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Where are the clouds?" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "ZLIM = (8000., 0.0)\n", + "PLIM = (3, -6)\n", + "def plot_clouds(ax, lon, lat, key, ylim=ZLIM, **kwargs):\n", + " dust = load_out3('dust', lon, lat)\n", + " z = dust['z'] * u.cm\n", + " ax.plot(dust[key], z.to(u.km).value, **kwargs)\n", + " ax.set_ylabel('Distance from top (km)')\n", + " ax.set_ylim(ylim)\n", + " return" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ALIM = (1.e-8, 10.0)\n", + "ax = plt.subplot(111)\n", + "\n", + "for i in range(len(lons)):\n", + " plot_clouds(ax, lons[i], lats[0], '', lw=1, color='k', alpha=0.5, label='')\n", + "\n", + "plot_clouds(ax, lons[0], lats[0], '', lw=2, color='b', label='night side')\n", + "plot_clouds(ax, lons[12], lats[0], '', lw=2, color='r', label='day side')\n", + "\n", + "ax.set_xlim(ALIM)\n", + "ax.set_xscale('log')\n", + "ax.set_xlabel(' (cm)')\n", + "ax.set_title(r'$\\theta$ = {:.1f} deg'.format(lats[0]))\n", + "ax.legend(loc='upper right', frameon=False)\n", + "\n", + "plt.savefig(\"vertical_grain_sizes_Theta45.0.pdf\", format='pdf')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### What are the clouds doing? (Drifting, evaporating, growing?)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, '$\\\\theta$ = 45.0 deg')" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ax = plt.subplot(111)\n", + "for i in range(len(lons)):\n", + " plot_clouds(ax, lons[i], lats[0], '', \n", + " lw=1, color='k', alpha=0.5, label='')\n", + "plot_clouds(ax, lons[0], lats[0], '',\n", + " lw=2, color='b', label='night side')\n", + "plot_clouds(ax, lons[12], lats[0], '',\n", + " lw=2, color='r', label='day side')\n", + "\n", + "ax.set_xlabel(' (cm/s)')\n", + "ax.legend(loc='upper right', frameon=False)\n", + "ax.set_title(r'$\\theta$ = {:.1f} deg'.format(lats[0]))" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, '$\\\\theta$ = 45.0 deg')" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ax = plt.subplot(111)\n", + "for i in range(len(lons)):\n", + " plot_clouds(ax, lons[i], lats[0], 'chinet', \n", + " lw=1, color='k', alpha=0.5, label='')\n", + "plot_clouds(ax, lons[0], lats[0], 'chinet',\n", + " lw=2, color='b', label='night side')\n", + "plot_clouds(ax, lons[12], lats[0], 'chinet',\n", + " lw=2, color='r', label='day side')\n", + "\n", + "ax.set_xlabel('chinet (cm/s)')\n", + "ax.legend(loc='lower left', frameon=False)\n", + "ax.set_title(r'$\\theta$ = {:.1f} deg'.format(lats[0]))" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ax = plt.subplot(111)\n", + "plot_clouds(ax, lons[0], lats[0], 'chinet',\n", + " lw=2, color='b', label='night side')" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "VLIM = (0.1, 3.e4)\n", + "RHOLIM = (1.e-10, 1.e-7)\n", + "NLIM = (1.e-5, 1.e6)\n", + "def plot_drift(ax, lon, lat, vs_p=False):\n", + " dust = load_out3('dust', lon, lat)\n", + " z = dust['z'] * u.cm\n", + " p = dust['p'] * u.dyne / u.cm**2\n", + " \n", + " if vs_p:\n", + " y = np.log10(p.to(u.bar).value)\n", + " ylabel = 'log P (bar)'\n", + " ylim = PLIM\n", + " else:\n", + " y = z.to(u.km).value\n", + " ylabel = 'Distance from top (km)'\n", + " ylim = ZLIM\n", + " \n", + " #thermo = load_out3('thermo', lon, lat)\n", + " #ax.plot(dust['rhod/rho'] * thermo['rho'], y, \n", + " # lw=2, color='k', label='')\n", + " \n", + " dist = load_out3('dist', lon, lat)\n", + " ax.plot(dist['N'], y, lw=2, ls='-', color='k', label='')\n", + "\n", + " dat2 = load_out3('imp', lon, lat)\n", + " ax2 = ax.twiny()\n", + " vdr = dust['']\n", + " #vdr = dat2['drift']\n", + " ax2.set_xlabel(r'<$v_{\\rm dr}$> (cm/s)', color='g', size=14)\n", + " #vdr = dat2['vdr[m/s]']\n", + " ax2.plot(vdr, y, color='g', alpha=0.8)\n", + " ax2.set_xlim(VLIM)\n", + " #ax2.set_xlim(0, 100)\n", + " \n", + " ax.set_ylabel(ylabel)\n", + " ax.set_xlabel(r'$n_d$ (cm$^{-3}$)')\n", + " ax.set_xscale('log')\n", + " ax2.set_xscale('log')\n", + " ax.set_xlim(NLIM)\n", + " ax.set_ylim(ylim)\n", + " ax2.tick_params('x', colors='g')\n", + " return" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ax = plt.subplot(111)\n", + "plot_drift(ax, lons[6], lats[0], vs_p=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "CHILIM = (-11.3, 11.3)\n", + "\n", + "def plot_growth(ax, lon, lat, vs_p=False):\n", + " dust = load_out3('dust', lon, lat)\n", + " z = dust['z'] * u.cm\n", + " p = dust['p'] * u.dyne / u.cm**2\n", + " \n", + " if vs_p:\n", + " y = np.log10(p.to(u.bar).value)\n", + " ylabel = 'log P (bar)'\n", + " ylim = PLIM\n", + " else:\n", + " y = z.to(u.km).value\n", + " ylabel = 'Distance from top (km)'\n", + " ylim = ZLIM\n", + " \n", + " ax.plot(dust[''], y, lw=2, color='k', label='')\n", + " \n", + " chinet = dust['chinet']\n", + " ax2 = ax.twiny()\n", + " # plot growth\n", + " ig = chinet >= 0.0\n", + " ax2.plot(np.log10(chinet[ig]), y[ig], \n", + " lw=2, color='b', ls='', marker='o', alpha=0.5)\n", + " # plot destruction\n", + " ide = chinet < 0.0\n", + " ax2.plot(np.log10(np.abs(chinet[ide])), y[ide], \n", + " lw=2, ls='', color='r', marker='o', alpha=0.5)\n", + " \n", + " ax.set_ylabel(ylabel)\n", + " ax.set_xlabel('Grain size (cm)')\n", + " ax.set_xscale('log')\n", + " ax.set_xlim(ALIM)\n", + " ax.set_ylim(ylim)\n", + " ax2.set_xlabel(r'log $\\chi_{net}$ (cm/s)', color='b')\n", + " ax2.set_xlim(CHILIM)\n", + " #ax2.tick_params('x', colors='b')\n", + " return" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ax = plt.subplot(111)\n", + "plot_growth(ax, lons[6], lats[0], vs_p=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "test = load_out3('nuclea', -180, 45)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['z',\n", + " 'p',\n", + " 'S_TiO2[s]',\n", + " 'S_Mg2SiO4[s]',\n", + " 'S_SiO[s]',\n", + " 'S_SiO2[s]',\n", + " 'S_Fe[s]',\n", + " 'S_Al2O3[s]',\n", + " 'S_CaTiO3[s]',\n", + " 'S_FeO[s]',\n", + " 'S_FeS[s]',\n", + " 'S_Fe2O3[s]',\n", + " 'S_MgO[s]',\n", + " 'S_MgSiO3[s]',\n", + " 'S_CaSiO3[s]',\n", + " 'S_Fe2SiO4[s]',\n", + " 'S_C[s]',\n", + " 'N*_TiO2',\n", + " 'J*_TiO2',\n", + " 'N*_C',\n", + " 'J*_C',\n", + " 'N*_SiO',\n", + " 'J*_SiO']" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "test.keys()" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "S_TiO2[s]\n", + "1.3168241338384653e+24\n", + "S_Mg2SiO4[s]\n", + "1.654204959893145e+28\n", + "S_SiO[s]\n", + "1599366777.689682\n", + "S_SiO2[s]\n", + "1.233313280605789e+18\n", + "S_Fe[s]\n", + "572220886482543.8\n", + "S_Al2O3[s]\n", + "1.207998161384327e+24\n", + "S_CaTiO3[s]\n", + "1.6659211889529396e+29\n", + "S_FeO[s]\n", + "567009843270.6959\n", + "S_FeS[s]\n", + "186612425170773.72\n", + "S_Fe2O3[s]\n", + "3708112790.4008718\n", + "S_MgO[s]\n", + "3.1563329236643076e+16\n", + "S_MgSiO3[s]\n", + "6.651227262755179e+28\n", + "S_CaSiO3[s]\n", + "1.1793931297756697e+29\n", + "S_Fe2SiO4[s]\n", + "9.990918303012977e+22\n", + "S_C[s]\n", + "298.6676373300888\n" + ] + } + ], + "source": [ + "for k in test.keys():\n", + " ss = k.split('S_')\n", + " if len(ss) > 1:\n", + " print(k)\n", + " print(np.mean(test[k]))" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "NUCLIM = (-50, 6)\n", + "ATMLIM = (1.e-10, 0.1)\n", + "\n", + "def plot_nuclea(ax, lon, lat, vs_p=False):\n", + " \n", + " gas = load_out3('thermo', lon, lat)\n", + " z = gas['z'] * u.cm\n", + " p = gas['p'] * u.dyne / u.cm**2\n", + "\n", + " nuclea = load_out3('nuclea', lon, lat)\n", + "\n", + " if vs_p:\n", + " y = np.log10(p.to(u.bar).value)\n", + " ylabel = 'log P (bar)'\n", + " ylim = PLIM\n", + " else:\n", + " y = z.to(u.km).value\n", + " ylabel = 'Distance from top (km)'\n", + " ylim = ZLIM\n", + " \n", + " for nuc in ['TiO2', 'C', 'SiO', 'Fe', 'Mg2SiO4']:\n", + " ax.plot(nuclea['S_{}[s]'.format(nuc)], y, \n", + " lw=2, alpha=0.8, label='S_{}'.format(nuc))\n", + "\n", + " #for k in nuclea.keys():\n", + " # ss = k.split('S_')\n", + " # if len(ss) > 1:\n", + " # nuc = ss[1].rstrip('[s]')\n", + " # if np.any(nuclea['S_{}[s]'.format(nuc)] > 1.0):\n", + " # ax.plot(nuclea['S_{}[s]'.format(nuc)], y, \n", + " # lw=2, alpha=0.8, label='S_{}'.format(nuc))\n", + " \n", + " plt.legend(loc='lower right', ncol=2, frameon=False)\n", + " \n", + " ax2 = ax.twiny()\n", + " for nuc in ['TiO2', 'C', 'SiO']:\n", + " jstar = nuclea['J*_' + nuc]\n", + " # plot growth\n", + " ig = np.log10(jstar) > -90\n", + " ax2.plot(np.log10(jstar[ig]), y[ig], \n", + " lw=2, ls='', marker='o', alpha=0.5, label=nuc)\n", + " #print(np.any(jstar < 0))\n", + " if np.all(np.log10(jstar) < -90):\n", + " print(\"No {} nucleation\".format(nuc))\n", + " \n", + " ax.set_ylabel(ylabel)\n", + " ax.set_xlabel('S')\n", + " ax.set_xscale('log')\n", + " ax.set_xlim(0.8, 300)\n", + " ax.set_ylim(ylim)\n", + " ax2.set_xlabel(r'log J* (cm$^{-3}$ s$^{-1}$)')\n", + " #plt.legend(loc='lower right', frameon=False)\n", + " ax2.set_xlim(NUCLIM)\n", + " #ax2.tick_params('x', colors='b')\n", + " return" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "No C nucleation\n", + "No SiO nucleation\n" + ] + }, + { + "data": { + "image/png": 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hGWOMySc50ZqrMcaY3JNya64iMhk4CxiN81R1jKpemOa4jDHGZFlKCUJEPg88CrwNHA68CUwACoCXMhadMcaYrEn1FNPPgJ+q6pFAM/A1nIb7ngNeyEhkxhhjsirVBDEFeCj6OQgUq2oTTuL4z0wEZowxJrtSTRC1QGH0cyUwMfrZAxyQ7qCMMcZkX6oXqV8HjgHWAU8CvxCRQ4HTgRUZis0YY0wWpZogvgeURD9fB5QCZwIfRccZY4zpZXKiy1ER+S/gC0AAWA98XVX3djSdPUltjDGd1+3mvpPM9AQRuTz6OqHr4e3nH8A0VT0E56jkyjTO2xhjTBek+hzEOJwWXacDFdHBw0XkXeBMVd3QnSBU9dm4r6/hPJBnjDEmi1I9grgbqAHGq+poVR0NjAf2AnelOaYLgafSPE9jjDGdlOpF6iOBuaq6uWWAqm4Wke+S4l1MIvIcMCzBqKtU9fFomauAEPBAO/NZBCwC8Pl8yYoZY4zpps70B1GUYHgh8FkqM1DVk9obLyLnA/OBE7WdK+equhRYCs5F6lSWbYwxpvNSPcX0feDXIjJXRNzR11zgl9Fx3SIipwI/BBaoakN352eMMab7kt7mKiK1tO6HuhBwA5HodxcQBppUtV+3ghD5BKfhv13RQa+p6iUdTWe3uRpjTOelo8Ogy9MYT7tUdWLHpYwxxvSknHhQrqvsCMIYYzqv2w/KiUhpJxfYqfLGGGNyW3sXqT8WkatFZGSyAiLiEpF/F5F/AJelPzxjjDHZ0t41iGOBG4ANIvIOsBKnqe8mnCa+pwJzgUbg58DvMxuqMcaYntThNQgRGQX8B07CGIPzPMROnO5HnwH+T1UjyeeQOYUFBfqv396Py+XC7XLhcgselwu324XLJbjdznB3y2e3C5fLhSf63eUSRFwgAgIiEv0sLWsPLnGGw75xIs64RNPEzc+ZRDjw/o37xX7OlH6cO/UADhxYCC5XbH4t0yASHU5sec5btJDLtS+OaKjSdppo2VYxtlk/EfafRsSZa6JpJG57GGPyUqrXIPL6InWh26WPHzMNACGFSiu2qnFlpdW3/ebj1IXSdiqn3L66ufXUsu/beYddBW53XKXcEovi1jAloQYGBOsYEKxlbGMVs/d8AMAbBxzITl8ZgwPVzNn7AWOad+63hp8WDuH1/lPY6S1jULCaOXs/ZGzTjmjcibZHm7WLBrupcBCvl01hQ+EQGtwF+MPNjGvazujGHWwuGswObz8GB2s4suZjxjbt3Lc1RNhYMIjXyiaxw9ePwcFa5tZ+wvjArn1JMpZMhY0FA1lRMoEdnlKGhGo5sn4j48J7Yxs6tq1d+5KtJEh0Gz0H8GrhaLa7ShgSqeeo4BYmRGr2Ja/oMje4+/GqZyRVrmKG0sgx4UrGUwfARlcZL7mHsV2KGUoTx1DFRKl3tpFEdwxwgcD6SAkvyRCqKGSoNPE51x4meRrjkmn03eWK7XBINIZPIkW8ECyjKuJjmCvI8YX1TC4IxtYvYfIViY37OODh+YYiPmz2URcRSt0wpTDECaUBAJ6vK6Ay5MIXnT6AUO6NcEJZEER4vtrLtpCLYV5lYmGET5rcbAsKwwqUkw5QJvsVED5qcLF8r4vKAAwvgBMHwOTS+B2Q6A5R3M7ER7XKc7ugsknxuhQRIaAuhhcKE/ywvh4qmmF4kXDSUA9Tyjytfg/p2ylr2V4tX9vu2LSZDpLvlLWZrls7ZfHT5NhOWZ9IED6vV2/9+qXOF1VA91WAceslAOoC11RECvfNIMmqS3gTrua/46IJiZZpeXeHYWRV6tvs4jk3758cYssPIwi+SIB+wVrG1n1Gk6sQERjctJuicCON7iLqPUWcXPkvRjVsi036WfEwni0/Dn+ocb9yo+PKdaRlPmGErf5hiIKK0L95D1VFQxlf9ylDmnbR6C6ioU0cLdMWx8XQtkzb5aRSNpV4O5pPe+WAHom7u+uc7G8zoqGSJlchRH8nAZebj/tNAGByzXq8kTA7CgbExheFG9leMJANpWMS/j07sz0SrVvb5Te5CpIu6+mhx/DG4JmouAGlMNTI7N3vcHzVik79DrrCAwyJCF61I+DJr6zt9nMQOU9wM2jgifsN17h/237aEqzrcL7KRDaPnM2OQe/iCtS1yiMjK8KM+ke460G3sm+vIujyUe3rT1DcgDAmvBWA4nAjAG8PmN7qP9DbA6bjDzXGxicr15G3BkynONTIZ/5yfOEQXg0RdHmoLC6nONTIXl9/hjbtis3/rQHTGRmdf8u0bWOIL9N2OamUTSXejubTXjmgR+Lu7jon+9vsbfM7+cw/nqJQEwhsKxrKlJr11Hr9xP+O9haUURhuTvj37Mz2SLRubZcPmnBZS8cvpNI/3JlBdE+8yevn5YEz2eMt4/QtT2c0SQSBPSgDsnJCPD/ldYJweVwcceYcUNCWarzlLb5WV2XjO7uo2dnIkGAt2xt27DevfgWluMXZHL4RISYceSKughNh27vw0dOxcjJY8UwK0O/jYHuBgacw+fiWeQEuFERQl5uAt9iJW6DIXRArV0CEXUWDGeDbdydxrX8IB0g9EV8/Ij43Ya8bL7CdoTSOORBFUXW2imr0Fd0W+4YpFTKa/lJHLWUUSJAgXgAatYh+BU3UahmNpQNiy63Qwewud55rbJm2sU0zXfFlYsM6UbY9qc6nvXJAj8Td3rS7yp097lY/0zY7NVtkFGVSRw1lFEiAAB4UqNFi528I1Jb0p4YyfNIMQIMWUVNaRgNOmRp/PxSoln74CFBNKXtLSmPL2IFTYfejhgAlsQgU2E45G4YObROb82mjexil1NBIMXukBB8BBKijBFTxSjN78LPTXxSLtdJVHjutGS/i8bG+/3CeLpvFkeGXnN9um23ZMmTfe6KxtImy9Wc/Xo5kKE6112YJrSsM9osy4ZmW1ktNeFyi8ePamwfRcppgVPyw2H/kfaNUEW1TJvomxJXXuPEpdhSd16eYOvOgXOUne3np4Y8ZMrYfx54zkZVVK3lq41O8v/t9ANzi5ugRR3PmpDMZ5k/U6GwcVQg2QP1OaNgJ9buc940vwfZ14HLDeU+gniLGXfV00tm4AA+KRxS/RBjpDhAIKxoJMzeyGw0G0UCAmhCURgJcsHt1bNp7B8yg1uWjNBKIzsxFfXE/yvw+vjUygmfYMHwjRuAdMQLP0KGI250whv/5x0dUNwZZV1lDczBModdNUzDMzrpmSgu9lBV5mTt+IADVjUHKirx8998mt5q2rMgbm1/bMm2Xk0rZ9qQ6n/bKtf2cqbi7u87J/jYt7wDzpgxhxYZdNEe/F3rdzB0/kBc+3B4bD7Biwy5qostu+/fszPZItG5tl6+QcFn3vrqp3fWdM/YAHrrkqA63i+m+dDS10auUDSkGYO+2ely4mFM+hznlc1i/dz1/X/93Xq98nRe3vMirFa8yf/x8Fk5ZiEuSPCYiAj6/8yoZCjs+gNoKqI8emYhzUVrcbjbd9HnG/ujJ/WbhcwtelxAIK+IR/CUFjB7Vn5qmEC4RIgOKKS30UNsUQuubOGv6AIbJ6YR37yG0ayf/XlHDfdvcuAP1FDXUUtccpq4pxKnb1lD7/p7W4Xo8eMrL8Y0cgW/sWAqmTKFg4kRcRUWcOm0oS1/cyLDSAj6qqqM5FEFVmTTYz/qdDUwZWkJEldqmENWNQRYese+xmJZpgVisbct0pWx7Up1PR+V6Iu7urnOyv83YgcWx30l1Y5Dxg4p5c+MeFJg6vJTqxiAD/L7Y+NJCD8NKC6jc25j079nZOOPXre3y65vCCZflcUGondM7q7dUc9Vj7/DVI8dwUHlZStvIZFa7RxAi4sJprfVLgBd4DviZqjb1THjt68wRhKqy7Dfv0Fgb4JSLD6ZscHGr8VX1VTz68aP8a4tz0e74UcdzyaFt2gtsroM9G2H3Bue182PY8SFEQvvKDBgHcy+DUUckjOP9ymr+uOJTXl2/k7rmMAVuYYC/gGH9C5laXsap05xD+qffq2Lr3kZG9Hcq8UT/Yd6vrI6VG17q5aRhHiZG6ghVVRGsqCS4dSuBrVsI79y137S4XPjGjsU/ZzZbDz2KZzfVsbaimpqmEGVFHqaWlzF5qJ+PqurbjSM+hvZi7WzZ9qQ6n/bK9VTc3V3nlunb/m3a/k58bucumOawxpYTP35E/6J2/55diTN+mrbLT7Ss373wMY+vqUo6P58L/IVehpYWsvjECXz+kBEpbyfTOWm5i0lErgSWAMtxHog7BbhfVRelK9Du6GxbTCv+tp7P1u3m8FPHMGHmkIRl1u5ay42v/ZxgsJ6lky+grLZqX0Ko277/BCIwYAKUHwKj5sDI2ftuYcsRkcZGghUVBLdsofmT9TR9+AGBjZsg4uzOiddLv9NO44CvfmXfbYzGZMCZt7/Eqs9q9hvudoHHBS5x4RKIKBw6qj9zxg3s8o6ESS5dCeJD4Feqekf0+6nA34Ci9jr16SmdTRCfrNrOW898yuiDBzD3ixP2jQg0wLZ3oGI1WvE2l1avZDdhbg6WMFbjzt27fXDAGBgwPvqaAEMOgoKSNK5Vz4g0NdG07n1qnnqKxrfeAmDoVT+meObMLEdmers7//Uxv3zuE0LhCOo8bUIExSWCSxQRF6FwhNJCL8dOGoTL5WLR58ZZkkijdF2DGAMsi/v+DM4F+eHA1q6Hlx0Dyp3tUbOzydl73vQifPQsbHkTws7F3jUSZLc3zABPMaOHHwcDJ+xLCP1GOBegewFXYSHFMw+jeOZhVN14Iw0rV9G8fr0lCJNx3zxuEiMPKOa259ezrbqJpmAYF85NNi6XC1Uo8LoJhiOs/HQPRV4PP3liHT9dMNWSRA/rKEH4cE4tAaCqKiIBnM598o6/v9OHdX3lNvShnyM1W5wRIjBkKsHyQ7hv92sQbuCUg76Ca+KXshhtZqkqwc2bqX7ySRpWrgKg8MADsxyV6Ss+f8gIxg8u4Y8rPmXZO5U0hyKUFripaw5D9GgiGFZqGoOoKlUVTXz3wTV2baKHpXIX040iEt8NqA/4iYhUtwxQ1W93JwgRORu4DjgImK2qK7szv2R8BS48jdsI1lUTGlCFt385TD8Lxh9PpPgAfrf6dioiTYwoHcXnx38+EyFklUYiNL3zDg0rV9KwchWhHdFmObxeBlxwAUXTp2c5QtOXHFRexs/POISjJw7k2sfXEQhH8ETbU2sORXALhCJKIKSUFrpB4Lbn1zN+cIkdSfSQjhLEi8CENsNeBUbHfU/HtYj3gDOAO9Mwr6TkgydwNe4CKSI8dzHeGfNjp4xe2Pw8L299mUJ3IZfPuByvy9vB3PKHRiLUr1jB3kceIbj5s9hwd1kZRYfPpGz+fHxjxmQxQtOXtRwR3Pb8euqagjQGI7hdSigciT1XMbCkgCKvm4q9jfzkiXWMHlDcrTvhTGraTRCqOq8nglDV94G0N0i1n4//gUtmQMlQdNLnYskhGAny6MePAnDR9IsY3398ZuPoIRoOU7/iNScxfOYkBvfAAZSecALFs2bhGz/e7loyOaHllFPLLb3rKmvY2xCk2OdmYEkBxT4Pu+ubaQxGqNjTAKqs3ryXZ97bZqedMijvHpQTkUXAIgCfz5f6hA27oWotLtdhUFBKJLLvwGdFxQp2Nu5kZMlIjhlxTLpD7nGRpibq/vlPqp/4O6Htzq25nkGDKDvzDErnzUM6s92M6SEHlZe1ei7juw+ucZqdiT45vrs+SIFHqGkKEdzVQFgVVfivpz+0004Z0mMJQkSeAxK1YXGVqj6e6nxUdSmwFJzbXFMOoLbSicNXCOJC4xLE85ufB+C08aclf3o6TzS89RY7fvVrInVOo4SeYcMo+9IXncTg7T2nzUzvdlB5GYtPnMBtz69nV32AgX4fxT431Y0BUGhsDtIYihBR2FMPS/6+jgcWHZntsHudHksQqnpSTy0rofqdALg8TiUZCe9LEJtqNgFw+JDDezysdIoEAuy8804idXX4xo6l/9lnUTx7tp1GMnkp/rTT1r1O/xvVjUFUI9QHneTQ4pUNu/nOn1fyq3NnZS/gXijvTjF1Wchp7TL2HEP0ckdEIzRHx5XGtZaaj6off5zwzl14R49i+H/dYonB5L22p53OufOsaN5/AAAdVklEQVS1/ZJDiyfWVDF1+Md887hJPRxl75UTNYiInC4iW4AjgSdF5Jm0L0Sd1iZbuhRquSBeG6glQoRSbynuPH4Irumjj6h+7K8ADPzGNyw5mF7noPIyjp44kHCSE8sK3Ldic4/G1NuldAQhIqOTjFKgSVX372ChE1T1r8BfuzOPjhcSiS6rdYKoaXbahelX0C+ji8+kYNV2Kq+5BkJh/EcfTdHBB2c7JGMyYvGJk3h2XVXSVmGrG9vpp8V0WqqnmDbRzvMOIlID/AG4QlVDycplVaTlCMLZs265Fu1xOZsgGMnjH1Y4BCFn/QZd+q0sB2NM5hxUXkZpgYc9jftXMwKt+rQw3ZdqgjgXuAX4HfB6dNgcnNtNrwP6A1cDtcBP0htimrScYmpzBFHsdZr9brkOkY88g51e0nC57BZW0yu1NC2+rrKahmAED5BoT/T8I5Od7DBdkWqC+BbwXVV9LG7Y89HWXr+jqseJyHbgp+Rqgoi0uQYRPYJwi3PdIazp6me650UaGxGPBw2FiNTX4y7N74vtxsR7v7KapS9upKzIS3VDkCKPi/qIUuSC5pASwTl6OHRUP7tAnWapJog5wLsJhr8HtPSMswLoXPdgWdD2CKLlwnQokptnxlKx54EH0FAI37hxuEryr+lxY1ok6rjo6feqKCtyur+tbQ4xckARn+5qIKLKAX4PbhHcbhc3nnFItsPvdVK91eVTok8vt3Ex0HLbwGBgdzqCyqRwxEkMLrfz3nINIp8ThGeI0/mRu6ws882VGJMhLUcK1Y1ByssKqW4MsvTFjaytqKa00Pl/2q/Qi9vlYszAYop8Hob0K6S8fxEnTBlsT1JnQKpHEN8HHhWR04A3cS5YH4HTkN+Z0TJHAA+nPcI0UoVwOJoYvE5ubAo5vadG1OnvN58q2HB1NbX//Ce1y50nwRtXr0aDQXti2gDp6+I1U/NrK/5IAfZdcN66t5HaphBlRV4mDvGz6tO9AIwf5Gfq8DKqG4N89UhrbDITUkoQqvqkiEwCLgWm4JzyewL4napujpa5I2NRpkljyI+qUFjixeV2EsQrFa8AMH3w9JxPDhoIEKysJFhRQf1rr1P/2orY3UstbS1Zcsi8TFeU6RB/3j5+b7yrPbOle36JbN3bSHlZYathpYUeyoo8sdtXB/gLmDK0hA+r6ugXTSYLjxiZc9u/t0j5SWpV/Qy4MoOxZFx90Ll46+/v9HdUUVfBYx85192PG3lc1uKKp6qEd+8muHWr04/01grnvaLC6b8hvqdXEYpnHU7pySdTdNhhGXk4Lh8qw57UExVlOiTbG3/6vaouxZnu+SUyon8R1Y3BVreq1jaFmFpeFrsWsXVvI2MHlXDJvAk5tb17q5QThIgMBS4DpuKcYloL/FZVqzIUW9rVBA4AoOSAArY3bOf6166nOlDN9EHTmVM+p0djiTQ2xir+WBLYupVgZSXanOSWW5cLb/kwPOXlFEyYSMnx8/BGrz9kQr5Uhj2pJyrKdEi2N751b2OSKXp2fomcOm0oS1/cGJt3bVOI6sZg7Aghl7ZvX5Hqk9RHA08DVTh3KwF8FfieiJyiqiuSTpxD9jQNAqBgoHDDazewu2k3Bw04iB/M+kHsYnU6RRoaCO3YQWj7doLbqvYlhIoKwruTX893l/XDU16Od/hwvCNGOO/DR+AdOqRHTyHlS2XYk3qiokyHZHvjI/oX5cT8EjmovIxFnxvX6ojVTh9lV6q14q3An4FLVJ02K0TEhfPg3C+AozITXnrtaRqEivL4rofZJtsY228sVxxxBYWewo4nTkCDQYLbthGsqCS0fTuhnTud9x07CO3YEWtyOxHxevEMG4Z3xPBoAogmgRHDcefIrar5Uhn2pJ6oKNOhvb3xXJhfMnakkFtSTRAzgAtakgOAqkZE5L+BtzMSWZqFw1DdPIDd3kY+iLzDQP9Afjj7h7EnqdujoRDBLVto3riR4JatBLdsIVixleC2KogkaRSGaBIYMgTP4MF4hg7dlwhGjMAzeFDON6iXL5VhT+qpirK70r03bnv3fVOqCaIaGAd82Gb4OGBvWiPKkJpqoVmF7d7t4I7w/VnfZ0DhgIRlw7W1NK17n6Z162j+4H0Cn25GgwnaanK5nKOA4cPxDh2Ce9AgvC0JYfBgXHn+XEK+VIY9KZ8qynTvjdvefd+TaoJ4ELhbRK4AXsW5SH0McBPOqaec19joolaU5oIajh15LBP6T4iNC+/dS9O6dTStW0fj2rUEN3+23/SeYcMoGD8O78hReEeOwDdyJN7y8l7d9lE+VYY9ySpK01ekmiCuwHn24Z64aYLAb4EfZSCutAsEoA4l5Akwb9Q8wjU11D7/PHX/fIHgli2tyorXS8HkyRROnUrh1IPwjZ+Au8SfpcizyypD05epKuGIEm55j75CkdbfW4ZFVAmFo+8RJRyJEArvP32iaWLzVSUcjhBWCEcihCOt30OJpo8okUjrd2d83PSx+aXeU3OqD8oFgO+IyJU4T08L8ImqNnRpqychIqcCvwLcwF2qelO65r2zqZmQgM8Vpvxf7/PZw9fHThtJYSGFUyZTePDBFE6dSsGECb36yMCYdFBVVGlVGUUiOJVSOxViRxVq0oow6TQaVwnvX6G2xNJSCYd1/4p0/zidirQTdWmv1Kl7O6MJIVGjfd0mIm7gduDfgC3AmyLyhKquS8f8a0JNoMLkDc3sfcM5K1Y0cyb9Tj2FokMPRTx9p/dV0zMibfY896vkIko4HK1YNXllFV+xtiqTpEJst2Jtbw+zzZ5m8kp436svcAm4XYLH5cLtkv1fIrjdzrsnOszjFlzR7y5X63e3CG6XC487bvok07Qsw/nswu3CeZf94/DEvceWKc5825bv9+3U1j1prSgiT6S6AVV1Qapl2zEb56hkQ3T5DwJfBNKSIOrDAYqafPTbGcRVVMTg73+P4sMOS8esTZpEIkogHCEYjhAMa/Q9QiDkVHoJD90T7BG23dNM16F7oj3MZHvCkT6y9+kS9quMXNK2cnMlrVDbrdzi5+tyWmztXCUcrRzbqVBdSZYdX3Hn840m3dXebvOuHovCMQKIvzq8BaeZ8bQIhUIUN/hwCQxavNiSQztUnYq6KRihORimKRihKRSmqeVzMPo55HwOhiMEo5V4IBwhGNpXuQfDSiAcJhR2xoXCGq3wIwTCSjAUiZXtjRVq24qxZa8xUYW6/15jgoq1nb3GVnuYCfY0O6yE4yptj8u1XyXedj5ucaY3vVfSBKGqX+/JQIBEv7T9qgwRWUS06XFfJ64TyJ56XFpM0O/BP2d2l4PMNlWlPhBmT32AhoBTOYci+yrkUNyed2x4SAlGIrHKvTmucm8MhGlu+RwM0xyM0BwKZ6WyFgGPS/C6Xfg8LrxuF1634HG78LldePLg0L2lUve4BBH69N6nyX+5dOJ9CzAq7vtIoKJtIVVdCiwF8Pv9KVdj2uRckI4UursVZCLhiFLXHKI++mqpcJuilW2sYg4535tDkX17zhFts/ftnLoIxFX4gTYVf09U3l63UOBxU+h1Ueh1U+R1U+B1RYfFf99XmfvcTuXc8jm+gi/w7BvndTnTtC5rh/PG5JpcShBvApNEZBywFTgH+HK6Zi5Bp0Mg9XacICIRZVd9gMrqRir2NrG3IRBNAGHqmoNxn0PUNYVoDPZsd6VFXjcH+L34fR6nwvU4e67eaIXrcbvwugSvxxXbI/e6XbHKvtDrotDjpsDrjhvmptCz77PbTh0Y0+flTIJQ1ZCIXA48g3Ob6z2qujZtC4i2iBGJq/hUlY076/moqpaKvU2xhFBZ3UgwnPpuuggU+9yUFHgo9nkoiqt4CzyuaEUcXzG78Lhc+KIVu8ct0b1vp5KP/+yNnlrxepw975Y9cmOMybScSRAAqvp/wP9lejm76wM89tYWXv5kJ7vqAgnL9C/2MqxfIcP7FzGwxIff56Gk0ENpgYfiAg8lBR5KCz34CzwUe912sc4Y0+vkVILIqGhHO83BMIvuX0lzyDmkOMDvY8bIMkYcUER5WRHD+xdSXlaEv6DvbBpjjEmkz9SCEVUUnDt1QhHmjh/A2bNGMXFwie39G2NMAn0mQeyud04liQhLvjSNQ0f1z3JExhiT2/rE1c4dtc3UNzt3MZUWeiw5GGNMCvpEgvh0V33sGoTY6SRjjElJn0gQBw8vi90aGurE7avGGNOX9YkEUeRzU+JzLreE2uki1BhjzD59IkEA+DzOqaW+0kSxMcZ0V59JEC1nllzW1o8xxqSkzyQItSMHY4zplD6TIIwxxnSOJQhjjDEJ9b0EYZcgjDEmJX0vQRhjjEmJJQhjjDEJ5VSCEJF7RGS7iLyX7ViMMaavy7XWXO8FfgPcn0rhUCDI3RdfA6RwaSFQ1p24jMkJN9xwA//7v/+L2+3G5XJx5513MmfOnFZlTj/9dDZu3EhdXR07duxg3LhxANxxxx3cc889fO9732Pq1KlUV1ezePFiXnnlFQCOPvpobrvtNsrKyli9ejXf+ta3qKmpwe12c9VVV7Fw4cIeX99MSmVbAsybN4/KykqKiooAuPrqqznrrLN6OtysyKkEoaovisjY1CcQNDDB+ZjiJO6CTodlTE5YsWIFy5Yt46233qKgoICdO3cSCOzfI+Jf//pXAF544QVuvfVWli1bFht31FFHxT5fdNFFTJs2jfvvd/bHfvKTn/CNb3yDv/zlLxQXF3P//fczadIkKioqOPzwwznllFPo3793tISc6rZs8cADDzBr1qwejDA35FSC6DSX4h+7qYNC+44tPAVeTrjwsoyGZEymVFZWMmjQIAoKnL2cQYMGdXoe8+bN49Zbb6V///6sWrWKhx56KDbu2muvZeLEiaxfv57JkyfHhg8fPpwhQ4awY8eOXpMg0rEt//SnP/HrX/+aQCDAnDlzuOOOO3C73ekONavyLkGIyCJgEYDP5+Pcn16X3YBMn/SF217OyHz/vviYpONOPvlkfvaznzF58mROOukkFi5cyHHHHdel5axbt44ZM2a0qtDcbjczZsxg7dq1TJgwITb8jTfeIBAItBqWLhvPzMypmnGPPtLu+M5uy6985SuxU0zLly9n+/btPPTQQ7zyyit4vV4uvfRSHnjgAc4777y0rke25dRF6lSo6lJVnaWqszyevMtvxnRZSUkJq1atYunSpQwePJiFCxdy7733dmleqookaJes7fDKykq+9rWv8Yc//AGXK++qi6Q6uy0feOABVq9ezerVqxk4cCDLly9n1apVHHHEEcyYMYPly5ezYcOGnluBHmI1rDFd0N6efia53W7mzZvHvHnzmD59Ovfddx8XXHBBp+dz8MEH8/bbbxOJRGIVfyQSYc2aNRx00EEA1NTU8PnPf54lS5Ywd+7cdK5GTEd7+pnUnW2pqpx//vnceOONmQ0yy3Jql0BE/gysAKaIyBYRuSjbMRmTKz788EM+/vjj2PfVq1czZsyYLs1r4sSJHHbYYSxZsiQ2bMmSJcycOZOJEycSCAQ4/fTTOe+88zj77LO7HXuu6e62PPHEE3nkkUfYvn07ALt37+bTTz9Ne5zZllNHEKp6brZjMCZX1dXVsXjxYvbu3YvH42HixIksXbq0y/O7++67Wbx4MRMnTkRVOfLII7n77rsBePjhh3nxxRfZtWtX7NTLvffey4wZM9KxKlnX3W05depUlixZwsknn0wkEsHr9XL77bd3OWHnKlHN32aw/X6/1tfXZzsMY4zJKyLSoKr+jsrl1CkmY4wxuSOnTjEZYzqn5anpeDfffDOnnHJKliLKX7Yt92enmIwxpo+xU0zGGGO6xRKEMcaYhCxBGGOMScgShDHGmIQsQRiTR2644QYOPvhgDjnkEGbMmMHrr7+esFwwGORHP/oRkyZNYtq0acyePZunnnqqh6PNbaluy3nz5jF69Gjib+j50pe+RElJSYfL+MpXvsKUKVOYNm0aF154IcFgEICqqirmz5/PoYceytSpUznttNMAqKioaNXXxMsvv8zs2bM58MADOfDAAxM+zPfII48gIqxcubJT658SVc3bV3FxsRrTV7z66qs6d+5cbWpqUlXVHTt26NatWxOW/eEPf6jnnXderOy2bdv0oYce6rFYc11ntuVxxx2n06dP15deeklVVffs2aOzZ89Wv9/f4XKefPJJjUQiGolE9JxzztE77rhDVVUXLVqkv/zlL2Pl1qxZs9+0lZWVOmrUKF21alUsxpkzZ+qyZctiZWpqavTYY4/VOXPm6Jtvvpni2qsC9ZpCHWtHEMbkiUR9GAwfPny/cg0NDfz+97/ntttui5UdOnQo//Ef/9Gj8eayVLdli3POOYcHH3wQgMcee4wzzjgjNi4SiXDppZdy8MEHM3/+fE477TQeecRphPC0005DRBARZs+ezZYtW2LLHzlyZGwehxxyCACbNm1i2rRpANx+++1ccMEFzJw5MxbjLbfcwk033RSb7pprruGKK66gsLCw29skEXtQzpiuuLNr/TB06Jv/Sjoq1T4MPvnkE0aPHk2/fv0yE2MaPfzzNzMy3//48RHtju9sfxAnnngiF198MeFwmAcffJClS5dy/fXXA07C2LRpE++++y7bt2/noIMO4sILL2w1fTAY5I9//CO/+tWvALjssstYuHAhv/nNbzjppJP4+te/vl+CWrt2Leeff36rYbNmzWLt2rUAvP3223z22WfMnz+fW2+9NbUN00l2BGFMnkhnfxB9XWe3pdvt5phjjuGhhx6isbGRsWPHxsa9/PLLnH322bhcLoYNG8bxxx+/3/SXXnopn/vc5zj22GMBOOWUU9iwYQMXX3wxH3zwAYcddhg7duxoNY0m6bNDRIhEInz3u9/lF7/4Rdc2QIrsCMKYrmhnTz+TUunDYOLEiWzevJna2lpKS0uzEmeqOtrTz6TO9gdxzjnncPrpp3Pddde1Gq4dtEbx05/+lB07dnDnnXe2Gj5gwAC+/OUv8+Uvf5n58+fz4osvcvjhh8fGH3zwwaxcuZIFCxbEhq1atYqpU6dSW1vLe++9x7x58wDYtm0bCxYs4Iknnkhr39l2BGFMnki1D4Pi4mIuuugivv3tbxMIBADnnPef/vSnHos113WlP4hjjz2WK6+8knPPbd0rwTHHHMOjjz5KJBKhqqqKF154ITburrvu4plnnuHPf/5zqx75nn/+eRoaGgCora1l/fr1jB49utV8L7vsMu69915Wr14NwK5du/jhD3/IFVdcQVlZGTt37mTTpk1s2rSJuXPnpj05QA4dQYjIKOB+YBgQAZaq6q+yG5UxuaMzfRgsWbKEq6++mqlTp1JYWIjf7+dnP/tZD0ecu7rSH4SI8IMf/GC/4WeeeSbLly9n2rRpTJ48mTlz5lBWVgbAJZdcwpgxYzjyyCMBOOOMM7j22mtZtWoVl19+OR6Ph0gkwje+8Q2OOOIINm3aFJtveXk5f/rTn7j44oupra1FVfnP//xPvvCFL6RvQ3QgZxrrE5FyoFxV3xKRUmAV8CVVXZdsGmuszxiTC+rq6igpKWHXrl3Mnj2bV155hWHDhmU7rKRSbawvZ44gVLUSqIx+rhWR94ERQNIEYYwxuWD+/Pns3buXQCDANddck9PJoTNy5gginoiMBV4EpqlqTbJydgRh+jrrwyB9+tK2TPUIIucShIiUAP8CblDVxxKMXwQsAvD5fIc3Nzf3cITGGJPf8jJBiIgXWAY8o6r/3VF5O4IwxpjOy7sOg8R5IuRu4P1UkoMxxpjMypkEARwNfA04QURWR1+nZTsoY4zpq3LpLqaXgf2fKzfGGJMVuXQEYYzpQKp9GCxbtozDDjss1t9ASzMPv/vd77j//vsBp4mIJUuWMGnSJCZPnszxxx8fawjOGMihIwhjTPtWrFjBsmXLeOuttygoKGDnzp2xpjTiBYNBFi1axBtvvMHIkSNpbm6OPaF7ySWXxMrdfvvtvPrqq6xZs4bi4mKeffZZFixYwNq1azPWfLTJL3YEYUyeSLUPg9raWkKhEAMHDgSgoKCAKVOmAHDdddfFmoa++eabue222yguLgacJrCPOuooHnjggZ5YHZMH7AjCmC5YuGxhRub70PyHko5LtQ+DAQMGsGDBAsaMGcOJJ57I/PnzOffcc1s1FldTU0N9fT0TJkxoNW18fwPG2BGEMXmiM30Y3HXXXSxfvpzZs2dz66237teBTTLJ+iAwfZMdQRjTBe3t6WdSZ/owmD59OtOnT+drX/sa48aNa5VM+vXrh9/vZ8OGDYwfPz42/K233mq3ZzXTt9gRhDF5ItU+DOrq6lr1SZCs3P/7f/+Pb3/72zQ2NgLw3HPP8fLLL/PlL385/cGbvGRHEMbkiVT7MFBVbrnlFr75zW9SVFSE3+9PeCpq8eLF7Nmzh+nTp+N2uxk2bBiPP/44RUVFPbA2Jh/kVFtMnWVtMRljTOflXVtMxhhjcoudYjImj/WlPgxMz7NTTMYY08fYKSZjjDHdYgnCGGNMQpYgjDHGJJQzCUJECkXkDRFZIyJrReSn2Y7JGGP6sly6i6kZOEFV66J9U78sIk+p6mvZDswYY/qinEkQ6txOVRf96o2+8vcWK2OMyXM5c4oJQETcIrIa2A78Q1UTd5dljDEm43LmCAJAVcPADBHpD/xVRKap6nvxZURkEbAo7ntDktm5gXCC4R4glKaQ0y1ZzLkw785O35nyHZXtznj7HaR33l2ZPtVpUinXlb91e+P66u8gtQa3VDUnX8BPgB90UGZpZ8cBK7O9bl1Zn2zPu7PTd6Z8R2W7M95+B9n9HXRmmlTKdeVvbb+Drs87Z04xicjg6JEDIlIEnAR80MFkf+/iuFyVyZi7O+/OTt+Z8h2V7c54+x2kd95dmT7VaVIp19W/tf0OujDvnGlqQ0QOAe7DOaxyAQ+r6s8ysJyVqjor3fM1+cV+Bwbsd9CRnLkGoarvAIf1wKL2b0Df9EX2OzBgv4N25cwRhDHGmNySM9cgjDHG5BZLEMYYYxKyBGGMMSahPp0gRMQvIveJyO9F5CvZjsdkh4iMF5G7ReSRbMdiskdEvhStCx4XkZOzHU8u6HUJQkTuEZHtItL2CexTReRDEflERH4UHXwG8IiqXgws6PFgTcZ05negqhtU9aLsRGoyqZO/g79F64ILgIVZCDfn9LoEAdwLnBo/QETcwO3AvwNTgXNFZCowEvgsWixTj7Sb7LiX1H8Hpve6l87/Dq6Oju/zel2CUNUXgd1tBs8GPonuKQaAB4EvAltwkgT0wm3Rl3Xyd2B6qc78DsRxM/CUqr7V07Hmor5SKY5g35ECOIlhBPAYcKaI/Jb8fBTfdE7C34GIDBSR3wGHiciV2QnN9KBk9cFinCZ+zhKRS7IRWK7JmSepM0wSDFNVrQe+3tPBmKxJ9jvYBViF0Hck+x38Gvh1TweTy/rKEcQWYFTc95FARZZiMdljvwMD9jtIWV9JEG8Ck0RknIj4gHOAJ7Ick+l59jswYL+DlPW6BCEifwZWAFNEZIuIXKSqIeBy4BngfZyWYtdmM06TWfY7MGC/g+6yxvqMMcYk1OuOIIwxxqSHJQhjjDEJWYIwxhiTkCUIY4wxCVmCMMYYk5AlCGOMMQlZgjDGGJOQJQhj0khEBovIHSKySUSaRaRKRJaLyL9lOzZjOquvNNZnTE95FCgGLgI+AYYAxwEDsxmUMV1hT1IbkyYi0h/YA/ybqj6X7XiM6S47xWRM+tRFXwtEpDDbwRjTXZYgjEmTaCNwFwBfBfaKyAoRuVVE5mQ3MmO6xhKEMWmkqo8Cw4EvAE8BRwGviciPsxqYMV1g1yCMyTARuQs4DyiJ9oFsTF6wIwhjMm8dzh2Ddl3C5BW7zdWYNBGRgcBfgHuAd4BaYBZwBbBcVWuyGJ4xnWYJwpj0qQNeA74DTAQKgK3A/wJLshiXMV1i1yCMMcYkZNcgjDHGJGQJwhhjTEKWIIwxxiRkCcIYY0xCliCMMcYkZAnCGGNMQpYgjDHGJGQJwhhjTEKWIIwxxiT0/wFKUAQYBXIf9gAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ax = plt.subplot(111)\n", + "plot_nuclea(ax, lons[6], lats[0], vs_p=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "def plot_growth_widget(i):\n", + " vs_p = True\n", + " fig = plt.figure(figsize=(18,4))\n", + " gs = gridspec.GridSpec(1, 3)\n", + " \n", + " ax0 = plt.subplot(gs[0])\n", + " plot_growth(ax0, lons[i], lats[0], vs_p=vs_p)\n", + " \n", + " ax1 = plt.subplot(gs[1])\n", + " plot_drift(ax1, lons[i], lats[0], vs_p=vs_p)\n", + " \n", + " ax2 = plt.subplot(gs[2])\n", + " plot_nuclea(ax2, lons[i], lats[0], vs_p=vs_p)\n", + " #ax2.legend(loc='lower right', frameon=False)\n", + " \n", + " print('Phi = {:.1f} Theta = {:.1f}'.format(lons[i], lats[0]))\n", + " return" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "72d674ec95754a6691b2188ed80be50f", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "interactive(children=(IntSlider(value=12, continuous_update=False, description='i', max=23), Output()), _dom_c…" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "w = widgets.IntSlider(12, min=0, max=len(lons)-1, continuous_update=False)\n", + "interact(plot_growth_widget, i=w)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "No C nucleation\n", + "Phi = -165.0 Theta = 45.0\n", + "No C nucleation\n", + "No SiO nucleation\n", + "Phi = -75.0 Theta = 45.0\n", + "No C nucleation\n", + "No SiO nucleation\n", + "Phi = 15.0 Theta = 45.0\n" + ] + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "for i in [0, 6, 12]:\n", + " plot_growth_widget(i)\n", + " plt.savefig('Grain_Growth/HATP_7b_Phi{:.1f}Theta{:.1f}.pdf'.format(lons[i], lats[0]), \n", + " format='pdf')\n", + " plt.clf()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Make a plot with all the J* values" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "import astropy.units as u" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "lons = np.arange(-165., 180.01, 15) # deg\n", + "lats = np.arange(0., 67.51, 22.5) # deg" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "def total_jstar(ax, lon, lat, **kwargs):\n", + " nuclea = load_out3('nuclea', lon, lat)\n", + " p_unit = u.dyne / u.cm**2\n", + " p = nuclea['p'] * p_unit.to(u.bar)\n", + " js = nuclea['J*_TiO2'] + nuclea['J*_SiO']\n", + " ax.plot(p, js, **kwargs)\n", + " return" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "nightside = np.append(lons[:5], lons[18:])\n", + "dayside = lons[6:17]\n", + "morning = lons[5]\n", + "evening = lons[17]\n", + "\n", + "COLORS = {'dayside':np.array([200, 82, 0])/255., # burnt orange\n", + " 'nightside':np.array([0, 107, 164])/255., # dark blue\n", + " 'evening':np.array([255,128,14])/255., # orange\n", + " 'morning':np.array([108, 180, 239])/255.} # light blue" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ax = plt.subplot(111)\n", + "\n", + "for j in range(len(lats)):\n", + " for lon in nightside:\n", + " total_jstar(ax, lon, lats[j], color=COLORS['nightside'], lw=2, label='')\n", + "\n", + "for j in range(len(lats)):\n", + " for lon in dayside:\n", + " total_jstar(ax, lon, lats[j], color=COLORS['dayside'], lw=2, label='')\n", + "\n", + "for j in range(len(lats)): \n", + " total_jstar(ax, morning, lats[j], color=COLORS['morning'], lw=2, label='')\n", + " total_jstar(ax, evening, lats[j], color=COLORS['evening'], lw=2, label='')\n", + "\n", + "total_jstar(ax, 0.0, 0.0, color='k', lw=3, ls='--', label='substellar point') # nothing to see here\n", + "total_jstar(ax, -180.0, 0.0, color='k', lw=3, ls='dashdot', label='antistellar')\n", + "\n", + "ax.set_yscale('log')\n", + "ax.set_ylim(1.e-20, 1.e8)\n", + "ax.set_xscale('log')\n", + "ax.set_xlabel('P (bar)')\n", + "ax.set_ylabel('J$_*$ [cm$^{-3}$ s$^{-1}$]')\n", + "\n", + "#ax.legend(loc='upper right', fontsize=12)\n", + "\n", + "plt.tight_layout()\n", + "plt.savefig(\"Grain_Growth/jstar_vertical_all.png\", format='png')" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [], + "source": [ + "def plot_Jstar(ax, lon, lat, jval, **kwargs):\n", + " nuclea = load_out3('nuclea', lon, lat)\n", + " p = nuclea['p'] * u.dyne/u.cm**2\n", + " ax.plot(p.to(u.bar).value, nuclea['J*_'+jval], **kwargs)" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "LLONG = [0.0, 22.5, 45.0, 67.5]\n", + "\n", + "ax = plt.subplot(111)\n", + "\n", + "'''for i in range(len(lons)):\n", + " plot_Jstar(ax, lons[i], lats[j], 'TiO2', color='b', alpha=0.2, lw=1, label='')\n", + " plot_Jstar(ax, lons[i], lats[j], 'SiO', color='g', alpha=0.2, lw=1, label='')\n", + " plot_Jstar(ax, lons[i], lats[j], 'C', color='orange', alpha=0.2, lw=1, label='')\n", + "'''\n", + "\n", + "'''plot_Jstar(ax, lons[0], lats[0], 'TiO2', color='b', lw=3, label='TiO2 (night)')\n", + "plot_Jstar(ax, lons[6], lats[0], 'TiO2', color='b', lw=2, ls='--', label='TiO2 (-90 deg)')\n", + "#plot_Jstar(ax, lons[18], lats[0], 'TiO2', color='b', lw=2, ls='--', label='') # nothing showed up\n", + "\n", + "plot_Jstar(ax, lons[0], lats[0], 'SiO', color='g', lw=3, label='SiO (night)')\n", + "#plot_Jstar(ax, lons[6], lats[0], 'SiO', color='g', lw=2, ls='--', label='') # nothing showed up\n", + "#plot_Jstar(ax, lons[18], lats[0], 'SiO', color='g', lw=2, ls='--', label='') # nothing showed up\n", + "plt.legend(loc='upper right', frameon=False)'''\n", + "\n", + "total_jstar(ax, -180., 0.0, color='k', lw=3, ls='dashdot', label='')\n", + "\n", + "plot_Jstar(ax, -180., 0.0, 'SiO', color=COLORS['nightside'], lw=2, ls='--', label='SiO')\n", + "plot_Jstar(ax, -180., 0.0, 'TiO2', color=COLORS['nightside'], lw=2, ls=':', label='TiO2')\n", + "\n", + "for ll in LLONG[1:]:\n", + " plot_Jstar(ax, -180., ll, 'SiO', color=COLORS['nightside'], lw=2, ls='--', label='')\n", + " plot_Jstar(ax, -180., ll, 'TiO2', color=COLORS['nightside'], lw=2, ls=':', label='')\n", + "for ll in LLONG[1:]:\n", + " plot_Jstar(ax, 0., ll, 'SiO', color=COLORS['dayside'], lw=2, ls='--', label='')\n", + " plot_Jstar(ax, 0., ll, 'TiO2', color=COLORS['dayside'], lw=2, ls=':', label='')\n", + "for ll in LLONG[1:]:\n", + " plot_Jstar(ax, morning, ll, 'SiO', color=COLORS['morning'], lw=2, ls='--', label='')\n", + " plot_Jstar(ax, morning, ll, 'TiO2', color=COLORS['morning'], lw=2, ls=':', label='')\n", + "for ll in LLONG[1:]:\n", + " plot_Jstar(ax, evening, ll, 'SiO', color=COLORS['evening'], lw=2, ls='--', label='')\n", + " plot_Jstar(ax, evening, ll, 'TiO2', color=COLORS['evening'], lw=2, ls=':', label='')\n", + "\n", + "ax.set_xscale('log')\n", + "ax.set_yscale('log')\n", + "ax.set_ylim(1.e-20, 1.e8)\n", + "ax.set_xlim(3.e-6, 3.0)\n", + "\n", + "ax.set_xlabel('P (bar)', size=14)\n", + "ax.set_ylabel('J$_*$ [cm$^{-3}$ s$^{-1}$]')\n", + "ax.legend(loc='upper right', frameon=False)\n", + "\n", + "plt.tight_layout()\n", + "plt.savefig(\"Grain_Growth/jstar_vertical_some.png\", format='png')" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'3.0.0'" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "matplotlib.__version__" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'/anaconda3/envs/cloud-academy/lib/python3.6/site-packages/matplotlib/__init__.py'" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "matplotlib.__file__" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.6" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/notebooks/test_cloud_depth.ipynb b/notebooks/test_cloud_depth.ipynb index a5bcef3..ee24049 100644 --- a/notebooks/test_cloud_depth.ipynb +++ b/notebooks/test_cloud_depth.ipynb @@ -3,9 +3,7 @@ { "cell_type": "code", "execution_count": 1, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", @@ -25,9 +23,7 @@ { "cell_type": "code", "execution_count": 2, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "from scipy.integrate import trapz\n", @@ -37,9 +33,7 @@ { "cell_type": "code", "execution_count": 3, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "from astropy.table import Table" @@ -47,39 +41,19 @@ }, { "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'/Users/cbaxter/PhD/code/cloudacademyMap'" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "%pwd" - ] - }, - { - "cell_type": "code", - "execution_count": 9, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ - "lat, lon = 105, 67.5\n", - "zfile = 'Les_Houches_Cloud_Activity/HATP_7b_Phi-{}Theta{}/out3_chem1.dat'.format(lat,lon)\n", - "extfile = 'Les_Houches_Cloud_Activity/HATP_7b_Phi-{}Theta{}/out3_extinction.dat'.format(lat,lon)\n", - "thermofile = 'Les_Houches_Cloud_Activity/HATP_7b_Phi-{}Theta{}/out3_thermo.dat'.format(lat,lon)" + "lat, lon = 0, 0\n", + "zfile = 'static_weather_results/HATP_7b_Phi-180.0Theta0.0/out3_chem1.dat'\n", + "extfile = 'static_weather_results/HATP_7b_Phi-180.0Theta0.0/out3_extinction.dat'\n", + "thermofile = 'static_weather_results/HATP_7b_Phi-180.0Theta0.0/out3_thermo.dat'" ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -89,7 +63,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -98,18 +72,20 @@ "[]" ] }, - "execution_count": 11, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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+ "image/png": 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\n", "text/plain": [ "
" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -120,10 +96,8 @@ }, { "cell_type": "code", - "execution_count": 12, - "metadata": { - "collapsed": true - }, + "execution_count": 7, + "metadata": {}, "outputs": [], "source": [ "junk = np.loadtxt(extfile, skiprows=4)\n", @@ -136,10 +110,8 @@ }, { "cell_type": "code", - "execution_count": 13, - "metadata": { - "collapsed": true - }, + "execution_count": 8, + "metadata": {}, "outputs": [], "source": [ "# get the wavelengths\n", @@ -153,10 +125,8 @@ }, { "cell_type": "code", - "execution_count": 14, - "metadata": { - "collapsed": true - }, + "execution_count": 9, + "metadata": {}, "outputs": [], "source": [ "wavel = get_wavel(extfile)" @@ -171,37 +141,41 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "Text(0,0.5,'$d\\\\tau/dz$ (dust)')" + "(1e-10, 10.0)" ] }, - "execution_count": 15, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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5SXlcUTwMdnY3xqT36NGD8ePHk5SUVKaS+YryqCrUG5i0NJx1EVf5V2d/Jj7p\nd+NHU8p5WDCAsznxjPcLpiAzk7m76mIRdhD7Z56h+tT30VhYIKUkcs8V9i0/i0EvaftsHUI6ed4I\npiLSYODc4TD2r1hKwvlobBydaDt4OCFPPvVAT8DfTEpJflQqmXviyI9OQ5ibYNelFjZtPdCYP/pf\nyY/+GTwm4uPjqVbNOH91WFgYBoMBZ2dnHBwc7lkyX1EeRTq9gUlLwvnz+FWmdK/LSx1q3/jwylFY\nMICDpvBaTU880+CD5TaIhCiqT30fh8GDEUKQnpjL9gWniDuTRo0ARzoNq3tLR7zBoCdq/14OrPyd\npJiL2FerTpexrxLc4QlMtH9vit/bMeQVkhORSNaeKxRey0Fja4bdU97YtKiO5jZP5z+qVKhUEc89\n9xw7duwovvqYNm0aOp0OgHHjxrFs2TJ+/PFHtFotlpaWLFmyBCHEHUvmK8qjTKc3MHHxUTaciOfd\nHoGMae9748OYA7BwANtsHXjb3pQ2iQ6MX5yORqPHc+4crBo3BiD6YALbF55GAB2HBhDU1qPUrWEp\nJVH797B36QJSr8bhVKMm3Se8Qd3W7dGU01TchgI9eadTyAlPJO9MCuglpu7WOD7rj1UDV4T20b3N\ndSfidvfk/8maNm0qDx06VNnNUBTlDgoKDby6+AibTibwfz0DGd2uRKCc3wmLn2OVkxtTrSQDrtRg\nwOI4zNzdqfnrL5h5eVFYoGfPH9Gc3H2F6r52dB1dD1sni1LHiDkRwa6Fv5FwPhqXmrVo9ewQ6jRr\nhdA8+Jd8YXo+eWdSyDudSn50KlJnQGNrhlV9Fyzru2LmZVuh/Z5bxn9DoF1jLCf64ezh/rf3I4Q4\nLKW85+x/6kpFUZQqo6DQwIRFR9gcmcDU3kGMbONz48PoLbB0GHPdPJluXsDY8950/v0clg0b4vnD\n92gdHUlLyGHjrydIjs2iUVcvWjzti0mJTu9rF8+ze/FcLoYfxtbZlW7j/0Vgu45oNH//ykTqJQWX\nM8g7bQwSXXw2ACYO5lg1qYZliAvmPvZVas6Th0mFiqIoVYJOb+CVRUfYEpnAtD7BvNDa+8aHkWtg\n2Sh+9vDhO20ub5+sTdM1Udh06ECN/32NxtKSqIPx7FhwBhOthp6v1Mc75EZ9rKzUFHYv/I3IPTuw\nsLKmw7BRNHyq198eFqzPKiDvTKrxiiQqDZlXCBqBubcd9t19sKjriNbN6rEcialCRVGUSqc3SCYt\nDb99oET8DivH8VPNAL7XZDL1qC/Bm6Kw69Mbj48/BhMt+1ef4/CGS7jXtqfr6GBsHItKsBj0HNu8\nnj1L5qMv1NGsT3+aPz3gvqc0A1HcAAAgAElEQVTylVJSmJBD7okkcs+koovNBAkaW1Msg52xqOuE\nRR0HNHd43uVxov4FFEWpVAaD5O1lEfwZcZV3ewSWDpTDc2Hta/zoXY8fSOODo74EbYrGcchzVPu/\n/0Ovl2ybfZKzh64R1Mad9kMCim93pcZfYcOML7l69gy16jfiyVHjcHSvUeZ2SSnRxWYZg+RkMoVJ\nuSDArKYtdp1rYVHXCVN368fmtlZZqVBRFKXSSCl5b/UJlh+J5V+d/UuP8jr0G6ybxI++jfjBkMQH\nR3wJ2hyN45AhVHvv/8jL0rH+xwjiz2fQ6pnaNOrqhRACKSUndmxh+2+/YKLV0nPiWwS0bl/mW1GF\nyblkh8WTE34NfXqB8bZWbXts2tbAMtgZE9uqNzFWVaJCRVGUSiGl5OM/T7HwQAzjOtRm4pMlnla/\nHii1G/ODPvFGoAwdSrX/e5f0a7msnRFOdnoBT42ph18TNwByMzPY8ut3RB/YR83g+nR/5XVsne89\n94jUS/JOJZN14Cr50WmgAQt/J+y6umAZ6PSPeo7kYVOhUkVcvnyZ4cOHEx8fj0ajYezYsbz22mul\n1lHl75V/kq+3RDFzzwVGtPZmcrcSc60fngPrJvGDb2N+vE2gJMVmsfbbcAD6vt6I6j7Gp90vHQ9n\n4/dfkZORQfuhI42TZd1jiLDUS3KOJpARehl9Sh4m9mbYdfbCull1TOzNH+bp/2OpUKkitFotX375\nJY0bNyYzM5MmTZrQpUuXW6oNq/L3yj/BjzvO8W3oWQY29eT9XkElAsXYh/KDbyN+NCQy7YgvgSUC\nJf58Buu+O4aZhQl9XmuIY3VrpJQcWb+anfNn4+juQd/JU6nmU/uux5d6SU74NTJCY9An52FawwaH\nYYFYBDojTFQfyYNQoVJFuLu74+5ufDDJ1taWwMBA4uLiyhQMqvy98iiZs/cCn288TZ8GHnzar/6N\nGlxH5sHaiUWBknQjUIYNo9q7/yb2VCrrf4rA2sGcpyc1wtbJAn2hjm2zf+L4tk3Uad6a7q+8jqmF\nxR2PLaUk71QK6esvUJiUi6m7NQ7PB2ER5PRYDv99GFSo3GT371EkXc4q13261LSh3UD/Mq9/8eJF\njh49SosWLW75TJW/Vx5lSw/G8MHaSLoGVePLgQ0wKREocs2r/ODbkJ8MSXx42Je6W24EyvnwRDbP\nOoljdWv6TGyIlZ0ZuZkZrP3qUy5HHqfFM4NoM3DoXW936RKySVt3nvzoNLSuljgPC8Qi2FmFSTlT\noVLFZGVl0b9/f/73v/+VqkwMqvy98mhbHR7HOyuO08HflRlDGmF6/Un3I/ORayYWBUryjUB5/nmq\n/XsK0QcT2PpbJNV87Oj5SgMsrE1JuRLHys8+IDMliR4T3iCwXac7HlfqDGSExpC5MxZhZoJ9b19s\nWj7a5eWrskcqVIQQvsC7gL2UckDRso7AR8BJYImUcseDHON+rijKm06no3///gwdOpR+/frd8rkq\nf688qnZFJfLG78do5u3Ez883wVxbVBbl6ALkmlf53qcBP98mUKLCEtg2JxIPfwd6jm+AqbkJCRfO\nsfwT4wCVge9/god/4B2Pm38+ndQV0RQm5WLV2A37nr6YWKuRXA9ThUW1EGK2EOKaEOLETcu7CSHO\nCCHOCiHuOmRJSnleSvnizYuBLMACiC3fVlccKSUvvvgigYGBvP7667ddJz4+vviqpGT5+2bNmhWX\nvy8oKGDJkiX06dOnIpuvKHd07HIa4xYcpk41W2a+0PTGFMBHFyJXT+A7n/r8LJP56HqgDC8KlAPx\nbJ0TiYe/Iz1fMQZK7KkT/D5tClpTMwZP++KOgSL1BtI3XSTx1wikQeLyYj2cBgaoQKkAFXmlMgf4\nDph3fYEQwgT4HuiCMRAOCiHWACbApzdtP0pKee02+90tpdwphKgGfAUMfQhtf+j27t3L/PnzCQkJ\noWHDhgB88sknxMTEAKr8vfJoOp+Yxcg5B3G2MWPuyGbYWRR9qYcvQq5+hRk+IfwqU/jocG0CtkQZ\nA2XKFM4ciGfb3FN4BjjSY3x9TM1MOH/kIGu/+hQ7Vzf6v/sRdi6utz1mYUoeKUtOUxCTiVWTajj0\nqY3G/NGa5/1RVmGhIqXcJYTwvmlxc+CslPI8gBBiCfC0lPJToFcZ92soepkKPLIDy9u2bXvbvpGS\nJkyYwIQJE277WY8ePejRo8fDaJqi/C0JGXk8PysMAcwb1QI3u6JRWeGLkKvGFwVK6q2Bsj+ebfNK\nB8qpPTvY+MPXuNbyod+UaXeciTEnIpHUFdEgwWlwAFYN3SruhBWg8vtUagCXS7yPBW4d8lRECOEM\nfAw0EkJMkVJ+KoToBzwFOGC8ErrddmOBsQBeXl7l1HRFUe4kPVfHC7PDSMspYMnYVvi4WBs/CF9c\nKlD+c8gX/61ROL0wHLd33uH0X/GEzjcGSs/x9dGamXA8dDObf5mBZ2Awfd96H3Mrq1uOJ3UG0tae\nIzssHjMvW5wG10XrdOehxcrDc9+hIoSwBvKklPpyOP7thijd8ee6lDIZGHfTshXAirsdREr5C/AL\nGCfpuv9mKopSVnk6PWPmHuJcYhazRzQjxLPoquLYEuSql/nWpx4zZSr/OVz7pkC5Suj809Ss60iP\nl42BErFtE1t+mYF3g8b0efNdTM1uvRmhz8gnef4pCi5nYtvRE7sutdTIrkp0z1ARQmiAwRj7KpoB\n+YC5ECIRWA/8IqWM/pvHjwVqlnjvCVz5m/tSFKWS6Q2SiYuPcvBSCt8MbkS7OkX9HseWIleOKwqU\nNGOgbInC6YUXcHtnMqf2XWX7gtPUDHSix7iQokDZyJZfvsO7YROefuPd2859UhCbSdK8SGReIc7D\nArGsd+86X8rDVZY43w7UBqYA1aWUNaWUbkA7YD/wmRBi2N88/kGgjhDCRwhhhjG81vzNfSmKUomk\nlPzfquPGWRt7BdGnQdGw9ojfkavG8U1RoHx86PaB4hXoRI+XiwJlqzFQfO4SKLmnU0j8OQKhEbi+\n3FAFShVRlttfnaWUupsXSilTgOXAciHEPcfpCSEWAx0BFyFELDBVSjlLCDEB2IRxxNdsKeXJ+zkB\nRVGqhq+3RLE47DKvdKrNiOvTAEf8gVz5Ev+rFcTsokCps/WmQJl/Gq9gJ7qPC0FrasKxLevZOvMH\nfBo1pc/r/75toGQfjCd1ZTSm1a1xGVlPlaOvQu4ZKtcDRQjxuZRycsnPri+7XejcZj/P3WH5eoy3\n0RRFeUTN++si34aeZVDTmrzZNcC4MOIP5MqxxkAhnU8O++G39QxOI0bgNvltIvdcYcfCM3gFO9N9\nXD20piaEb17Ptlk/4Nu4Gb1f/zda09K/V6WUZG6LIWNrDOZ1HHAeFojGvLLHGykl3U9vVpfbLOte\nXg1RwNvbu/g5laZNm97y+cKFC6lfvz7169endevWHDt2DDCWze/UqROBgYEEBwfzzTffVHTTlcfY\nnxFXmbrmJJ0Dq/HxM/WMJYKOL0OuHMvX1wPlkB9+W24NlFr1SgTKpj/vHih6SdrKs2RsjcGqsRsu\nI4JVoFRBZemofxkYD9QWQkSU+MgW2PewGva42r59Oy4ut7837OPjw86dO3F0dGTDhg2MHTuWAwcO\nlLlsvqKUt31nk/jX0nCa1nLkuyGN0JpojIGyYgxf1writ+uBsvUMTiNH4vb2W5zcfYWdi4oC5aUQ\nTEw1HN20jtDZP+HbpDm9/zXllkAxFOhJWXSavNMp2HaqiV3XWqq+XRVVlphfBGzA+IR7yTIqmUX9\nKkoFad26dfHrli1bEhtrrErzIGXzFeXvOhGXztj5h/FxsWbm8GbG8isnlhcFSqAxUA7Wxm/bbQIl\nxJnuY8sWKPqsApLnRlIQm4lDXz9sWrpX0hkrZVGWPpV0IF0IsQJIkVJmCiH+D2gshPhISnn0obey\nAm2f8wvXLp0v13261fKl04ix91xPCEHXrl0RQvDSSy8xduydt5k1axbdu9969/FuZfMVpbxcSs5m\nxG9h2FuaMndUc+ytTOHECuTyMXxVK5A5ZBQFShROo0bh9tabxYHiXd+FbmPqGQNl41pCf/uZ2k1b\n0Ptf72CiLR0ohcm5JM0+QWF6Ac7DgrAMdq6kM1bK6n5uSL4npfxDCNEW4xPs04GfuMsT8Mr92bt3\nLx4eHly7do0uXbpQt25d2rdvf8t627dvZ9asWezZs6fU8ruVzVeU8hKXlsuQXw+gN0jmjmpOdXsL\nOLkSuXw0X3oFMJcMPj3oR+1tZ+4aKEc2rGX7nJ+p3bQlvf81+ZZAKbicSdLck2CQuI4JwbyW+m/6\nUXA/oXL9CfqewI9SytVCiA/Kv0mVqyxXFA/L9XL1bm5uPPPMM4SFhd0SKhEREYwePZoNGzbg7Hzj\nV9u9yuYrSnlIyMhjyK/7ycjTsXhMS/zcbIyBsuxFpnsFME9kljFQ1rB9zi/4NWtJr0m3BkruiSRS\nlp5BY2uGy8hgTF1vLc2iVE33M/orTgjxMzAQWC+EML/P7ZW7yM7OJjMzs/j15s2bqVevXql1YmJi\n6NevH/Pnz8ff/8a8L2Upm68oDyopK5+hMw+QlJnP3FHNqVfDHk6uKhUon10PlBdvCpQQ5xuBsn51\nUaC0uiVQpJRk7o4leeEpTKtb4za+gQqUR8z9XKkMBLoB06WUaUIId+Cth9Osx09CQgLPPPMMAIWF\nhQwZMoRu3brx008/AcbS9x9++CHJycmMHz8eAK1Wy6FDh+5YNl9VLVbKS1pOAcNmHiA2NYe5I5vT\n2MsRIlcjl43iv17+zC8KFN/rgfLmm0TuudEp362oU/7wn6vZMe/XEoFy4ytIn1VA6rJo8k6nYBns\njOOgADRmqmT9o0bcq9z6P03Tpk3loUOHKrsZivLISM/RMWzWAc4kZDLrhabGel6Ra5DLRvJFzTos\nEFl8ftAPn21ncB79Iq5vvHHjOZQSo7wO/7mKHfNmUqd5a3q+9napQMmLSiXl9zMY8gqx7+6DTWsP\nNWS4nGwZ/w2Bdo2xnOiHs8ffHzknhDgspbz1AbqblPlKRQjx/u2WSyk/vJ+GKYry6EjLKWDYrANE\nxWfx0/ONjYFS1IfyhZd/UaDUwWfb6bsGysG1K9i1YDZ1WrSm58QbgSILDaRvuEDW3itoq1nhOjoE\n0+rWlXzWyoO4n9tf2SVeW2CcROtU+TZHUZSqIjW7gKEzD3A2MYufhzehU4Bb0YONY/nCK4AFIpPP\nw/zwCT2N85jRuL7+eukn5YsCJWz1MnYvmoN/y7b0ePVNTLRaDPl6ciOTydoZiy4+G+tW7jj08EGY\nqttdj7oyh4qU8suS74UQ01EVhRXlHymlKFDOJWbx6/CmdPB3hWNLMawaxye16rKU64FypjhQTu29\neiNQip6UP7Dyd/YsmUdAq/Z0G/0auvOZpB9JIPdkMlJnwMTJAucRwVjWdarsU1bKyYMUzrECfMur\nIYqiVA3XMvMYPiuMC0nZN/pQwhdhWDWej3wCWS4zmX7QH6/QU6UCZdeC09St40CzJq5kbrhA8vEL\n2KWY07/262gTTIn/TxgAwkKLVWM3rBq5YeZlh9CovpN/kvvpUznOjVkZTQBX4KOH0ShFUSpHXFou\nw2YeID49j9kjmtHGzwWOzEe/5lWm+QSxSmbw5QF/PLefwnnMGJxffpWzS8+QezCBng6miMRsMlaf\nw6AxkJmThNbRArv6NTGxNkVjaYrW2QILf0eEVj2N8E91P1cqvUq8LgQSpJSF5dweRVEqyfnELIbN\nPEBmfiELRjenSS0nODwH/drXeM8nmHWGDP633x/3nadxHDUZjUszrvxnP5YG0FiYYNPKHXNvO06E\nh7J77XyCOzxJ13ET0WhUP8nj5J4/F4QQrwshXgf6l/gzCJhYtFwpB3l5eTRv3pwGDRoQHBzM1KlT\nARg6dCgBAQHUq1ePUaNGodOVnrrm4MGDmJiYsGzZstvut6CggLFjx+Lv70/dunVZvnw5YHyQslOn\nTjRq1Ij69euzfr2a0uZxdupqBgN//ov8QgNLxrY0BsrBWRSufY0pvvVYZ8hgxr5AvGLcsBvwLYUp\ntck5n865XD1nXK3wmtoSh56+HD250RgoHTurQHlMleVKxbbo7wCMc9Rf75zvDex6GI16HJmbmxMa\nGoqNjQ06nY62bdvSvXt3hg4dyoIFCwAYMmQIM2fO5OWXXwZAr9czefJknnrqqTvu9+OPP8bNzY2o\nqCgMBgMpKcbC0v/5z38YOHAgL7/8MpGRkfTo0YOLFy8+9PNUqp6wCymMnnsQa3Mt819sYSy9cuAX\ndBve4h3feuwv0LHw2As42IYgGlijrW5Dups1W7fHUjPQie4vh2Ci1bB36XwOrPydkCe60mXMBIRG\n3eJ6HJWlSvE0ACHEZqCxlDKz6P0HwB8PtXWPESEENjY2gLGOl06nQwhR6qn45s2bF5e7B5gxYwb9\n+/fn4MGDd9zv7NmzOX36NAAajaZ4rhYhBBkZGQCkp6cX1x1THi8bjl/ltaXh1HS0ZO6o5ng6WsFf\nP6DbNIX3ajWlWnwzFid1RmNtjsY6E+fn63P6XDp7/jhbPAWwiYlg+9xfOLphLfWf7Ebn0eNVoDzG\n7qdPxQsoKPG+APAu19bcgxDCF3gXsJdSDihaFgR8ACQD26SUt78PVEZpa89RcCX73iveBzMPaxx6\n177nenq9niZNmnD27FleeeWVUuXrdTod8+fPL57VMS4ujpUrVxIaGnrHUElLSwPgvffeY8eOHdSu\nXZvvvvuOatWq8cEHH9C1a1dmzJhBdnY2W7duLYczVR4lc/dd5IO1J2ns5cjM4U1xtDaDfTPI2/QJ\nq+xeY0R0G2wMVuiuHsayjRuu44ZweONF9q86j29DV7q+GAzCwMYf/kfk7u007vE0HZ9/UQXKY+5+\n/tefD4QJIT4QQkwFDgBzy7qxEGK2EOKaEOLETcu7CSHOCCHOCiHeudP2AFLK81LKF29a3B2YIaV8\nGRhe1vZURSYmJoSHhxMbG0tYWBgnTtz4pxo/fjzt27enXbt2AEyaNInPP/8cE5M737MuLCwkNjaW\nNm3acOTIEVq1asWbb74JwOLFixkxYgSxsbGsX7+e559/HoPB8HBPUKkS9AbJp+tPFU8BvHB0Cxyt\nzTBs/5aU9Se4rJtL28QumGTHkh36IfZdnHF56Tn2rzrH/lXn8W9RjafGBGOQOtZ89QmRu7fTZuAw\nOg4frQJFua+HHz8WQmwA2hUtGnmfE3TNAb4D5l1fIIQwAb4HugCxwEEhxBqMQ5Y/vWn7UVLKa7fZ\n73xgqhCiD/DAM/iU5YriYXNwcKBjx45s3LiRevXqMW3aNBITE/n555+L1zl06BCDBw8GICkpifXr\n16PVaunbt2/xOs7OzlhZWRUXqnz22WeZNWsWYJzka+PGjQC0atWKvLw8kpKScHNzq6jTVCpBdn4h\nk5aGsyUygWEtvfigdzAanYGM+YvIjPJD0ohj1uH4nj+G5V97qfb+ezg+9xx7/ogmIjSWoHYedHwu\ngIK8HFZ98RGxp0/y5IvjadhVFS9VjMoyR72QRVUnpZRHgCN3W+dOpJS7hBDeNy1uDpyVUp4v2s8S\n4Gkp5aeUHsJ8t/1eA14pCqgVZdmmKkpMTMTU1BQHBwdyc3PZunUrkydPZubMmWzatIlt27ahKfEr\n8MKFC8WvR4wYQa9evUoFChj7TXr37s2OHTt44okn2LZtW/EUw15eXmzbto0RI0Zw6tQp8vLycHV1\nrZiTVSrFlbRcRs89xOn4DD7oHcTzTTzJ3hVLVuhZDLqanLGN5Fe71by5W2B5/ALVP5yG/YBn2bHg\nNJF7r9LgiZq0edaPzKREVn4+jZQrsfR49U0C23So7FNTqpCyXKlsF0IsB1ZLKWOuLxRCmAFtgReA\n7RivRO5XDeByifex3GUmSSGEM/Ax0EgIMUVK+WlRUP0bsAb+e4ftxgJjwfhlWhVdvXqVF154Ab1e\nj8FgYODAgfTq1QutVkutWrVo1aoVAP369eP9929b27NYw4YNCQ8PB+Dzzz/n+eefZ9KkSbi6uvLb\nb78B8OWXXzJmzBi+/vprhBDMmTNHVYX9B9t/PpkJi46QpzPw29AmNErSkfDFIQw5hZhqwvmh9gFC\nDaf4fr07Fmfj8Pjic2x79GTrb5FEH0ygaQ9vmvf2IeFcNCu/+BC9Tscz73yAd/1GlX1qShVzz9L3\nQggLYBQwFPAB0gBLjP0xm4HvpZThZTqYMQDWSSnrFb1/FnhKSjm66P3zQHMp5at/52TKQpW+Vx4n\nUkp+23uRj9efItDBihkBNTALT0LmFmLheBVNzn951d+C2PQMvlnrjEVsEjW+/gqrDk+wedZJzocn\n0rKvL026eRN9YB/rv/sSK3sH+r0zFWfPqvkDTSmtypW+l1LmAT8APwghTAEXIFdKmfa3W3dDLFCz\nxHtP4Eo57FdRHnvpuTreWR7BnhPxTHNxomOmhL/iMa/riJ1mARkXf2aMfzDpaVl8t9IB0+Q0PH/6\nEbMmLVj/YwQxkSm0G1SHkI6eHFj1B3uWzMPdz5+n3/w/rB0cK/v0lCrqvgpKSil1wNVyPP5BoI4Q\nwgeIAwYDQ8px/4ryWDoak8q/Fx6lQ7qBt03sMU0qxLKeM7Yd3DHb9yrxUesZ4xeETM7jf8ttMM3K\noebMXxH+Iaz++iiJMZk8MbwuPg3sWD39Y84d2k9A6/Y89fJrmJqZV/bpKVXYg1Qpvi9CiMVAR8BF\nCBELTJVSzhJCTAA2YRzxNVtKebKi2qQo/zSFegNzt0aTuj2WGZhijsAq2Bm7J7wwddbA0ue5fHE7\nY3zrYp2o46PfTdHqC6k5Zw4F1XxZ+8VhstPy6f5yfaztMlg4ZSoZSdfo9MIYGnXvo/rdlHu6nyrF\n/YEV9xrldSdSyufusHw9oApPKcoDij6Xwl+LT9IhS2KGGWYhLjh3qYWpmxXkZcDCQZyL288Ynzq4\nJ0jeXQqmZqZ4zZlFhnl11v73MAa9gT6TGpJ48QBrvvgZC1tbBk79jBoBgZV9esoj4n6uVBYAq4QQ\nw6SUegAhxEgp5W8Pp2mKopRFfnIuYUsjqRGTTQcgzceOWv38MXW1Mq6QlQgL+xOZGsVLtXwJuWTC\nq8tyMXVwxGvOb8SlWbH5uyNYWGvpNsaPAyu+49yh/XjVa0DPiW9hZe9QqeenPFruJ1ROAzuB5UKI\nZ4v6V14FVKgoSiUoTMkjdsN5OJ6MJ5LjjlpaPhdMLS/7GyulxcC8vhzIT+Q1T0+ePGXGsFVpmNep\ng+dPPxF5Ip+9yyNw87IlsGUuq754i4KcbDoMG0WTnn3VE/LKfbufUJFSyp+EEDnAGiFEP0DdYFWU\nClaYnEvKtkvkHU3EICVbtHq8e/rSu6VX6T6Pa6dgfj82mhQwxc2J4Yet6LYhEauWLXH/3//Y++dV\nIndfoVawFULuZNNPO3Dzrk339z/BpWatyjtB5ZF2P6GSCiClnFcULH9inFJYUZQKoEvMISM0hpzw\nRHRSspoCUus5MbFvMC42N43IunwQFg5goY0VX1hb8eYeR5ruTsCuZ0/sp3zAn7OiiItKpaZ/PJfC\n11GQm0PLfoNo2X8wJlrTyjlB5R/hfmp/PVni9TIhRB5/7yl6RVHugy4hm4zQy+QcS0QnYLnM56Cr\nGW/2a0xzH6dbNzi9Hrn8RWa4uDLHxMDHm52pfTQBp5EjMQwYw7L/hpOTcRVr632cDYumRt0gOo9+\nRV2dKOWiLLW/ugPHpJSlHkqUUq7D+CCkoigPQcHVbDJDY8g9nkSBBn4nnw0WBkZ0rsOilrXQmtym\nv2P/j+g2TuEjLz+25+bx7Wp7nM8n4Pr2ZK74dGbP9L3IwgPkZ4YjC2zo+tJE6nXsrPpOlHJTliuV\n/sBHQohqGDvrw4FjRX+fuj4STFGU8lEQl0XGthjyIpMpMBH8rilgGQX0a+/Nmo5+2Fvd5vaUQQ8b\np5B26Ffe8K1L4tUsvl1tgWVeLk7TZ7DvggMX5i7AUHAQKKRRt960HPAclja2t+5LUR5AWcq0XK/L\nNQmoA1wAOgG/ACkYS6soivKA8mMyyAy9TN7pFHSmgj/MdMwryKVjfXdWdqtLTac7dGHmZ8Hy0Zy/\nsIUJvv54n8zh0/UazJzs4L3prFh3iJyUHUhDBrWbtqD90FE4edSo2JNTHhv301E/UkrZ4PobIcQP\nwFvl3yRFebzkX0wnI/Qy+VGp6Mw0/GFeyJz8HBr5Of9/e3ceHXWZ53v8/dSaVBJCEgh7gBBA9i1G\nIAohYItAK7a7tmO3Oup0u03fuXec5YxzzxnFbsXd1lZka0TcFTdcWATZFxEEFBElCVtCUklVZan1\ne/9IvJNmCFBQlUrw+zon55AfVU8+Tyjyya/qV8/DX38xilE5J1lny3sEFl/Duuq93N+9N9d/EqRo\nUxDbyHx2j5nB9399GglXkNmjD5Nv+Wdyho5oeSylYiCaUvEYY8aIyFYAEdlqjBkQp1xKnfP8+6vx\nLC/B/30NQaeF15LDzK/3MCinI3MuGcb4fqd4yfLgNuTVm1hsqWeBszP/sUjocbCBIzNuZEd5KaEt\nf8WZksXEX9/HkKJJWCwt7xKqVKxEUyq3An81xuwGtgLDgGBcUil1jhIR/PsayyTwo4dgkpXXUyK8\nVOshr3sH/nzt+RQN7HzqNba+XETg/T/wcHY2JT8m86ePwJuewarCgdSVbsBiS+P8y2+l8JpfYrW1\n2hJ/Sp3W1V/jgA0istcYMx74FTAc2EPj5lhKqVMQEfx73Y1lUuIlkGzl9TThJa+b3tmpPDFzNFOH\ndj11mYQCsOx+SrfP5/5ufRizsoFb9jjZfN5gPFIDtZXkjrmSS39/HUkpya0zOaWaOZ1fYW4GnjXG\n7AWWActE5PX4xlLq3CAiNOypwrOihGCZD7/LxpK0CPO9Hnp1SuGP00Zw2YgeWC2nsThF1Q/w5q18\n5t7NAksvblgklKf1Y90VHecAACAASURBVH1/ARrI7HUxl/7uRrrm6pX+KnFO5+qvOwGMMecBlwLz\njTHpNG4hvAxYq5cVK/W3JCI07K7Es7yE4OFaGlJsLEqNsMhXRd/sVGbPGMX0Yd1Or0wAdr5B3Qf/\nyBOuZCw7uzK5qgvfdLEDdlyZBRT/3fUMOH6ZFqWAM1pW/ixE8476b2h8n8rjxphkGi8rvhp4DDjl\nFpNK/RxIRKj/+hie5SWEjtZRn2pjfmqYJT4P/bum8dTlo5k6pCuW0y2Thhr46H6++uYNXqnMIXdv\nBt7kJCrTnDhSxlAw8wrypw7Eatc3L6q2IZr9VNaJyHgAEamncQ8U3QdFKUDCQv2OCjwrSghV1FOb\nZmduSojXfR4G9+jAc1eMYcqgLqdfJgDfr6Th3bt4qQLC342gszUJrysJm3MMQ4ouZfyVg3F1cMRv\nUkqdgWguC0k6/oAx5iIRWRPDPEq1KxKOULe9Au/KUkLH6vF1sPOCK8jbXg/De3VkztWDmTQwO7qn\npXzlyLJ/5bOtK9lxoC+EkzDWJGzJ55M7qohxVw6hU8/U+E1KqbMQTakMNMa8DewCvgaOAnOAfvEI\nplRbJqEIddvK8awqJVzVgKeDneeSg7zv8TC6dwYLJg/jov6doiuTSATZOo8d78xmxYGuRIKDMCRj\nTS6g54CxFF43nK656aceR6kEiqZUfgAeAoYCY4DuwP+NRyil2ioJRajdcgTvqjLC1X6q0+08kxRk\nmcfDBX0zeXnycMb1y4r6BXM5vJN9C/7Ax7vC+AO5GJKxJY8lu9tQLrz5fHqdd4LViJVqg6IplYCI\nbAY2xyvMqRhjZgLTgWzgWRH5xBgzCLiXxhWTl4vIc4nKp85dEgxTu+kI3s/LCHsCVHW086QzwPIa\nDxfmdeLV4hFckJsV/bi+Y3y/6N/5dN1e6vwuLKRgc40lvWN/Jtx0Pn1HRfnUmVIJdtpL3wMTzuYL\nGWPmAjOAchEZ2uz4VOBJwArMEZGHWxpDRN4B3jHGZACPAp+IyB7gTmOMBXjxbDIqdbxIIEztxsN4\nV5cR8QY51tHO404/n1d7KBrYmTeLRzOm90nW5mqBBOrZt+RBVn62Aa8/CYvJxuYaR1paTyb+eiy5\n+d0x0byor1QbcdpL3wNdjDFns/T9fOAZYOFPB4wxVuBZ4GKgDNhsjFlKY8HMOu7+t4hIedOf/73p\nfj+Ncxlwf9P4Sp21iD+Eb/1hfGsOEqkNcjTDwWxHA+uqPUwZlM27xf0Z0atj9OOGgux94wnWfPQZ\nngY7FpON3TWelKROTPj1+eSNz9UzE9WutdrS9yKy2hjT57jDBcA+Ednf9DWWAJeLyCwaz2r+hmn8\n3/Yw8JGIbGs29lJgqTHmA2Dx6eRR6kQiDSF86w7h++IgkboQhzLtPBJoYLPbwyVDuvB+cX+G9oj+\nxfJIKMS3bz3Nmvc/weu3YjGdsaeMI9mRTuH1oxg0YbCWiTonJHrp+x5AabPPy4ALTnL7u4EpQLox\nJk9EnjfGFNG4HpmTFt43Y4y5HbgdICcn5ywjq3NRpC6Id+0hfGsPIQ0hyjLt/DFQz3a3h2nDurGs\nOI/zunaIetxwMMiet59n7fsf4fNbsJhO2FPG47InU3DtcIZNGqVlos4piV76/kT/m1pcVUBEngKe\nOu7YKmDVyb6IiLxA45kV+fn5rb1qgWrDwrVBfF8cxLfuEOIPcyDLwaxQHbvdIX45ojt/mpRH/y7R\n744YDgXZ9fYc1r33PrV+g8XSubFMrHbyrxvKyMkFcZiNUokXTancAiyK8dL3ZUCvZp/3BA6d5ZhK\nnVLYF8C75iC16w8hgQj7s+w8FPLyndvLzJE9eGpSP3I7R/8Gw1AwyNfvzGX9e+9R5weLJRt7SiEp\nFmH0tYMZdXFhHGajVNsRzdpf38Vh6fvNQH9jTF/gIHAdcMNZjqlUi8KeAN7VZdRuPIyEIuzNtDPL\n7eUHd4SrxvTkhaI8crJa2Lb3JIIBPzvffImNH35EXUCwWLpgTx2PywQZeW1/8i+eGIfZKNX2RLV7\nT9OVXq83fUTFGPMKUAR0MsaUAQ+IyEvGmLuAj2m84muuiOyKdmylTiVU48f3eRm+TYeRsPBNpp2H\nqjyUVQvXnN+LeUX96JlxBmXS0MBXb7zAxmWf0BAEi7Ub9tRCki21jLiuHwWTi2I/GaXasFbbEk5E\nrm/huC5MqeIm5G7A+3kZtZuPICJ8nWHnoSo3R2vg+rG9uGNiP7p3jH4zq0B9HdtffY5Nn67EHwKL\ntSeO1LEkW2sZdkNvLiiaFIfZKNX26T6j6pwUqqzHu6qM2q1HEeCrDCsPVbqp8hhuLOzNHRNyye7w\nP9ZIPSV/rY8vlzzL5uVrCITBYs1pLBN7DSNv6kd+4UWxn4xS7YiWijqnBCvq8K4spW57OWIMWzOs\nzKp04/VauGlCX267KJfOac6ox23wetm2+Em2fL6BYBgstj44UsficrgZc/MARo4dG4fZKNX+aKmo\nc0KwvA7vihLqvqpArIaNGVZmVVbj91m5eVIut16YS2ZK9HuP1Htq2LroCbau2UQoYrDYcnGmFpDs\nrKLgtvMYNlr3p1OqOS0V1a4Fj9TiWVFC/c5jiNXCugwrD1dVE6q18tvJedxS2IeOrujLpK7azZaF\nj7Ft3TbCYrDa++N0FZCcXMH424cyaPjIOMxGqfZPS0W1S4GDPjwrSmjYVUnEbmF1hpVHqqqh3sZt\nF/fn5sI+dEiyRz2ur/IYWxbO5suNO4iIwWofiNOZj8tVwYW/G86AQUNPPYhSP2NaKqpdCZR6G8tk\nTxURh4UVGRZmu6ux+x3cOXUgN43rTaoz+oe1t+Iomxc8yldbdhMRg8U+GKdzNCmp5Uz8/WhyB5wX\nh9kode7RUlHtgv+AB8/yEvx73YSdFj7paOGJ6mqSHU7umTaIG8fm4HJE/3D2HDnEpgWPsmPbtwgW\nrI6hJDlG4upQTvHd59M7Ny8Os1Hq3KWloto0//4aPCtK8O+rJpxk5cOOhqeqq+ngdPK/Zgzm+oIc\nkh3WqMetPlTKxnmPsGvH901lMgK7YzgpGUeZfM9YeuX0jcNslDr3aamoNkdE8H9fg2d5CYEfaggl\nWVmabvhzjZsMZxL/evkQrs7vRZI9+jKpKv2RjfMeYfeuHwErVudI7LbhpHY6wsX3FtK9R69TDaGU\nOgktFdVmiAj+76oby+SAh2CylbfS4S81brKTk/mPK4Zx5ZgeOG3Rl0nlge/ZMO8RvtlTSmOZjMFh\nG0Jql6NMvW8C2V26xX5CSv0MaamohBMRGr5141leQrDUS8Bl47UOwlyPm24uF/911XCuGNUDu9US\n9dgV+79hw9zZ7P3uMI1lko/DNpi0buVM/cMkOmdlx35CSv2MaamohJGI0LCnEs+KUoIHffhTbCxO\ni7DAW0VO5xT+eO0Ifjm8O7YzKJPy73axfu5s9u0vB6xYkwpwWgeQ1vMY0+6dQmZWVuwnpJTSUlGt\nTyJC/dfH8K4oJXikloZUGwvTIrzsrSI3O5XHZoxi+rBuWC3R74h4ZM921s97nP0HKjHYsCaNxWnN\no0PvSqbfewkdO2bEYUZKqZ9oqahWIxGhfkcFnhWlhMrrqEuzMz81zKs+D/27pvH0ZaOZOqQrljMo\nk0Nfb2H9/Cf5sdSNwY4taTxOa1/Sc6uZfu80OqRFvxWwUip6Wioq7iQs1G0vx7uylNCxemrT7MxJ\nCfGm18PgHh147ooxTBnU5YzKpGz7BtYveJqSQzUYHNiSL8RpySFjgIcZ91xGiislDjNSSrVES0XF\njYQj1G0rx7OylHBVA94Odv7iCvKu18OIXh2Zc/VgJg3MxpjoykREKN22lvULn6HsiA+DE1vyBJym\nB5mDa5lx9xW4kqLfI0Updfa0VFTMSShC7dajeFeWEq72U5Nu58/JQT7weMjvncGCycO4qH+nMyqT\nAxtXsW7RcxyuqMOQhC25CKfpRqfh9cz4/ZUkOaLfI0UpFTtaKipmJBihdvMRvJ+XEq4J4O5o5+mk\nIJ/UeBibm8niycMZl5t1RmXyw7pPWffyixytrMfgwpZcTJLJpvPoINPvuBqnI/qViJVSsdeuSsUY\nMxOYDmQDz4rIJ8aYi4AbaZzLYBEZn8iMP0eRQJjaTUfwfl5GxBugMsPOk0kBVlR7uDCvE68Wj+CC\n3Ogv4RURvl/9IesWz6Wi2o8xKdhcU0giky4XCNNuvRaHPfqViJVS8dNqpWKMmQvMAMpFZGiz41OB\nJwErMEdEHm5pDBF5B3jHGJMBPAp8IiJrgDVNhbM5nnNQfyviD1O74TDeNWVEfEHKMxw87vSzxu2h\naGBn3iwezZje0V/CK5EI3618j3WvLqCyJoDFpGFzTSSJdLqNtzD9lhuwWqN/V71SKv5a80xlPvAM\nsPCnA8YYK/AscDFQBmw2xiylsWBmHXf/W0SkvOnP/950v+ZuAG6LfWx1vEhDCN/6w/jWlBGpC3Ek\n08GjjgY2uD1MGZTNu8X9GdGrY/TjRsJ8+9nbrH9tEW5vCIvpgN1VjNOk0mOCk0v/7hqsFi0Tpdqy\nVisVEVltjOlz3OECYJ+I7AcwxiwBLheRWTSe1fwN0/hk/MPARyKyrdnxHKBGRDxxiq+ASH0I39qD\neNceQupDHMx08EiwgS1VHqYO6cr7xXkM7ZEe/bjhMN98/Drr3niFmtowFtMRe8p4nCSTMzmFS66/\nCosl+nfVK6VaX6JfU+kBlDb7vAy44CS3vxuYAqQbY/JE5Pmm47cC81q6kzHmduB2gJycnLMK/HMU\nqQvi/eIgvrWHEH+Y0iwHDwfr2eH2MG1YN5YV53Fe1+jfXBgOhdjz4RLWvf0a3roIFksG9pRCnNjp\ne0kGU66eqWWiVDuT6FI50WVA0tKNReQp4KkTHH/gZF9ERF4AXgDIz89vcXz1t8K+AL4vDuJbdxgJ\nhPkhy8HDYS97qjxcNqI7s4vzyMtOi37cUJBdS//K+nffwdcQwWLJwp5SSBKGvBldKJo5Q8tEqXYq\n0aVSBjTfwKIncChBWVSTsDeAd3UZtRsOI8EI+7LsPFTt5Xu3l5kje/D0pH7kdk6NetxQIMDOd+ax\n4f33qfMLFktn7KmFJBFmwOU9KLpsehxmo5RqTYkulc1Af2NMX+AgcB2NL7irBAh7/Hg/L8O38QgS\njrA3085Dbi8H3BGuGtOTOUV55GS5oh436G9g51svseHDZdQHBIulK/bU8SQZP4Ou7MVFl14Sh9ko\npRKhNS8pfgUoAjoZY8qAB0TkJWPMXcDHNF7xNVdEdrVWJtUoVN2Ad1UZtVuOIGFhT6adB6s8HKoW\nrjm/FwuK+tEz4wzKpKGB7a//hU0ff0pDECzW7thTx5NsqWPotX0YN6U4DrNRSiVSa179dX0Lxz8E\nPmytHOq/haoa8K4qpXbrUURgZ6aNByvdVNTADeNyuGNiLt3So19DK1Bfx5evPsfmT1fiD4HF2hNH\n6niSbT6GX9+XgqKJcZiNUqotSPTTXyoBQsfq8awspe7Lo4gxbM+w8eCxKqprDDcW9uaOCblkd4h+\nDa0Gn5cvlzzLlhVfEAiDxZaDI3UsyXYvo27qx5jCwjjMRinVlmip/IwEy+vwriylbns5YjFsybDx\nUKWbWo+Fmyb25e8vyqVTqjPqceu9HrYtfootqzYQioDV1gdH6lhcjmryfzuQEQUnu0pcKXUu0VL5\nGQgercWzopT6HRWI1bAh08asSjcBn5WbJ+Vy64W5ZKZEvyBjXU01Wxc9wbYvNhOKGKy2fjhcBbiS\nqhh722CGjBodh9kopdoyLZVzWOCQD++KEuq/riRit7A2w8ofq6oJ11q5ZXIevy3sQ0dX9GVS665i\ny8LH+HL9l4TFYLUPwOkqwOU6xrjbhzJo2Ig4zEYp1R5oqZyDAmVePCtKadhdScRh4fNMK49UVWPq\nbdx2cX9uLuxDh6ToV/f1Hatg88LZbN+0k4gYrPbzcDrzcaUc46J/GEn/QYPiMBulVHuipXIO8Zd4\n8C4voeFbNxGHhc8yLTxWVY3D7+Afpg7kpnG9SXVG/0/uqTjC5vmPsmPrnqYyGYzTOYaUtHIm/m4M\nuQMGxGE2Sqn2SEvlHOD/sQbP8hL831UTdlpZlmHhSXc1roCTe6cN4saxObgc0f9T1xw5yKb5j7Lz\ny70IFqyOYTgdo0hNL6f47gJy+ubGYTZKqfZMS6WdEhH8+2vwLi/Bv7+GUJKVDzoanql208Hp5H//\ncjDXF+SQZI9+qfjqQyVsmPsIu3fubyqTEdgdI0jJOMqUe8bSM6d3HGaklDoXaKm0MyKCf181nuUl\nBH70EEq28k5Hw3PVbrKcSfzr5UO4Or/XGZVJVckPbJz/KLt3/QhYsTpHYbcPI7VTOb+490K6de8R\n8/kopc4tWirthIjQ8K0b74oSAiVegi4bb6bDCzVuspOS+c9fDePK0T1x2KJf3bfyx32sn/cI335T\nRmOZ5OOwDSa1SwWX3DeBLl26xX5CSqlzkpZKGyciNOypwrO8hOBBH4EUG0s6CPM8VXTPcvFfVw3n\nilE9sFujL5OK779hw7zZ7P3uEGDD6jwfp20wqd3LmXbfFLI6Rb+vvFLq501LpY2SiFC/qxLvihKC\nh2vxp9h4OS3CAm8VvTun8KdrR/DL4d2xnUGZHN27k/XzHuf7/eUYbFiTxuK0DiCt1zGm3zeFjIzM\nOMxIKfVzoKXSxkhEqN9ZgWdFKaGjdTSk2liQGmGxr4rc7FQenzGK6cO6YbWcaH+zkzu8ezvr5z/O\nDwcqm5VJf9L7VDLt3kvomB79vvJKKdWclkobIWGhbkcF3hUlhCrqqUuzMzc1zGs+DwO6pvHM5aO5\nZEhXLGdQJgd3bmH9/Cc5UObGYMeWVIjD2peO/aqZce800lKj371RKaVOREslwSQcoe7LCrwrSwhV\nNuDrYOfFlBBveT0M6dGB568Yw5RBXc6oTEq/XMf6hc9QesiDwYEt+UKclj5kDPQw4+7LSHFFv0eK\nUkqdjJZKgkgoQu22o3hXlhJ2+/Gm23nOFeQ9j4cRvToy9+ohFA3sjDHRlYmIULL1C9YtfJZDR30Y\nnNiSJ+K09CRrSC0zfjeT5OTol7VXSqnToaXSyiQYoXbLEbyrygjX+KnuaOfZ5CAf1XjI753BgsnD\nuKh/pzMqkx83rGTdouc5cqwOQzK25CKclm50Gu5nxu+uIMmhZaKUii8tlVYiwTC+TUfwfl5GxBOg\nKsPOU0kBPqv2MDY3k8WThzMuN+uMymT/2k9Z9/ILlFc1YHBhc03GabLJHh1kxu3X4HBEv3ikUkqd\nCS2VOIsEwtRuOIx3dRkRX5BjGQ6eSAqwyu3hwrxOvFo8kgtyo38/iEQi7Fv9Eetemcuxaj8Wk4LN\ndTFOk0nXAph+27XYbfrPq5RqXe3qp44xZhBwL9AJWC4izxljcoF/A9JF5KqEBmwm4g/hW38Y35oy\nIrUhyjMdzHb4Wev2UDSwM28Wj2ZM74zox42E+W7Fe6x7dQFVniAWk4bNVUSSSad7oY1pv7kWqzX6\nJVqUUioWWq1UjDFzgRlAuYgMbXZ8KvAkYAXmiMjDLY0hInuAO40xFuDFpmP7gVuNMW/EM//pijSE\n8K09hG/tQSJ1IQ5nOXgk2MCmKg9TBnXh3eI8RvSK/v0gkUiYbz99i3WvvUy1L4TFpGN3jcdpUug5\n0cWlv74Kyxm8EVIppWKpNc9U5gPPAAt/OmCMsQLPAhcDZcBmY8xSGgtm1nH3v0VEyo0xlwH3N43V\nZkTqgnibykQawpRlOfhTsIFtlR6mDunK+8V5DO2RHv244TB7lr3G+jeXUFMbxmI6Yk8pxEkyvaek\n8YvrrsBi0TJRSrUNrVYqIrLaGNPnuMMFwL6msw2MMUuAy0VkFo1nNScaZymw1BjzAbA4folPT7g2\niO+Lg/jWHUL8YQ5kOfhjyMfOKg/ThnVjWXEe53XtEP24oSC7P3iF9W+/gbc+gsVkNpaJsZM7NZPJ\nV16mZaKUanMS/ZpKD6C02edlwAUt3dgYUwT8CnACHzYdywIeBEYZY/6lqZCOv9/twO0AOTk5MQke\n9gbwrjlI7YZDSCDC/iwHs8Jevq3ycNmI7jxWnEdedvTvVA8Fg+xaupD1S9+htkGwWDo1lYkhb0YX\nimfOiPoKMaWUai2JLpUT/XSUlm4sIquAVccdqwTuPNkXEZEXgBcA8vPzWxz/dIQ9Abyry6jdeBgJ\nRfguy8FDbjf73V5mjuzBs5P6kds5NepxQ4EAO9+Zx4b33qcuIFgs2dhTC0kyEQbO7MnEGZeeTWyl\nlGoViS6VMqBXs897AocSlOWkwt4AnhUl1G4+goSFb7PsPFjlobRKuGpMT14qyiMnK/plT4L+Bna8\nOYeNH31MfUCwWLo2loklwOCrenPhJVPiMBullIqPRJfKZqC/MaYvcBC4DrghsZFOLNIQwrfpCLsz\nbDxY6eaIG64p6MmdE/vRMyP6Mgk01PPVa39h4yef4Q+Cxdq9sUys9Qy/tg9jJ0+KwyyUUiq+WvOS\n4leAIqCTMaYMeEBEXjLG3AV8TOMVX3NFZFdrZYrGV7UN/H3EQ2214YZxOdwxMZdu6clRj+Ovq2P7\nq39m02erCITAau2FI208ybZaht+QS8GEi+KQXimlWkdrXv11fQvHP6TpRfe2bGSvjvxmcn+uL+hF\ndofo19Bq8Hn58pVn2bzyC4JhsNh640gdS7LDx5i/68+ocWPjkFoppVpXop/+ajdsVgv3Tukf9f3q\nPTVsW/w0Wz5fTyhisNr64kgdi8tZQ/5vBjGiID8OaZVSKjG0VOKkrqaarYueYOsXmwlHDFZbHg7X\nBbiS3Yy7dQiDR41MdESllIo5LZUYq3VXsXnhbLav305YDFb7AJyuApJdlRTeMZzzhg499SBKKdVO\naanEiPdYBZsXzOarzTuJiMFqH4TTmY8r9RgT7hxF3qDzEh1RKaXiTkvlLHnKj7BpwaPs2LIHwWB1\nDMHpGENKhwqKfp9P37y8REdUSqlWo6VyhmoOH2Tj/Ef4evt3CBasjmHYnKNJSS+n+K4Cevftm+iI\nSinV6rRUouQ+WMLGeY+wa+d+wILVMQK7YyQpmeVcfM94evTqmeiISimVMFoqp8lz5CBf/OVB9uw+\nAFixOkdjtw8jtXMFl9xzIV27d090RKWUSjgtldP0zSdv883uQ1id+TjsQ0ntUsHU+yaSnd0l0dGU\nUqrN0FI5TVn5xSR/3pXU7uVMv6+YzKzo95VXSqlznZbKaeo3eDC3PJ+H0+5IdBSllGqzdOvAKGih\nKKXUyWmpKKWUihktFaWUUjGjpaKUUipmtFSUUkrFjJaKUkqpmNFSUUopFTNaKkoppWLGiEiiM7Qq\nY0wFcCDROZp0Ao4lOkQU2lPe9pQV2ldezRo/bTlvbxHpfKob/exKpS0xxmwRkXazSX17ytueskL7\nyqtZ46e95T0RffpLKaVUzGipKKWUihktlcR6IdEBotSe8ranrNC+8mrW+Glvef8HfU1FKaVUzOiZ\nilJKqZjRUlFKKRUzWipKKaViRkuljTHG5BpjXjLGvHGyY22JMSbHGLPUGDPXGHN/ovOcijGmyBiz\nxhjzvDGmKNF5TsYYM9MY86Ix5l1jzC8SnedE2vrj83jGmBRjzFZjzIxEZzkZY8xFTY/ROcaYdYnO\nc7q0VGKo6YdquTHm6+OOTzXGfGuM2XeqH7oisl9Ebj3VsbaUGRgAfCAitwCD45GzWa5Y5BXAByQB\nZW05q4i8IyJ/D/wGuDZeWY8XTfZ4Pj5Pxxl8n/8ZeK11U/7/TNF8X9eIyJ3A+8CCROQ9IyKiHzH6\nACYAo4Gvmx2zAt8DuYAD+IrGH7zDaHywNP/Ibna/N04w/v841hYyA1nASmAF8Nu2/j0GLE336wK8\n3JazNrvfbGB0W3wsx/PxGYfv8xTgOhpLekZbztrs718DOiTie3smHzZUzIjIamNMn+MOFwD7RGQ/\ngDFmCXC5iMwCEn76HYvMxph/Ah5oGusNYF5bztuMG3DGIyfE7HtrgIeBj0RkW7yyHi+a7MDu1sp1\nIlFmTQVSaCyYemPMhyISaaNZdxtjcoAaEfG0VsazpU9/xV8PoLTZ52VNx07IGJNljHkeGGWM+ZeW\njsVZVJmBZcA9TRl/jGOulkT7Pf6VMeYvwF+BZ+Kc7XjRfm/vpvG366uMMXfGM9hpOGH2BDw+T8cJ\ns4rIv4nIfcBi4MXWLJSTONlj4lbi+EtaPOiZSvyZExxr8R2nIlIJ3HmqY3EWbeavgaviF+eUos37\nFvBW/OKcVLRZnwKeil+cqJwwewIen6fjpN9nEZnfelFOqcWsIvJAK2c5a3qmEn9lQK9mn/cEDiUo\ny+lqb5nbU972lPV47Sm7Zk0QLZX42wz0N8b0NcY4aHyRcGmCM51Ke8vcnvK2p6zHa0/ZNWuiJPpK\ngXPpA3gFOAwEafzt49am49OAvTRe4fFvic7ZnjO3p7ztKWt7zq5Z29aHLiiplFIqZvTpL6WUUjGj\npaKUUipmtFSUUkrFjJaKUkqpmNFSUUopFTNaKkoppWJGS0WpBDDGvGGMyY3BOEuMMf1jkUmpWNBS\nUaqVGWOGAFZpWpX2LD0H/J8YjKNUTOiCkkrFUNNKwj8trpgO/Cgik4672Y3Au83uMxV4iMZ9NY6J\nyGRjzH8CfYFuNG6C9gdgLHApcBD4pYgEgTXAfGOMTURCcZuYUqdJz1SUiiEReV5ERgLn07gMx2Mn\nuFkhsBXAGNMZeBG4UkRGAFc3u10/YDqNe2ssAlaKyDCgvuk40rh0+z5gRFwmpFSUtFSUio8ngRUi\n8t4J/q4bUNH057HAahH5AUBEqprd7qOms5GdNJ7FLGs6vhPo0+x25UD32EVX6szp019KxZgx5jdA\nb+CuFm5SDyT9dHNa3k/FD41nI8aYoPz3Qn0R/vb/blLTmEolnJ6pKBVDxpgxwD8Bv5aWdxXcA+Q1\n/Xk9MNEY07fpXp+KVgAAAKhJREFU/pln8GUHALvO4H5KxZyWilKxdReQCaw0xmw3xsw5wW0+AIoA\nRKQCuB14yxjzFfBqNF/MGNMFqBeRw2eVWqkY0aXvlWplxphkYCVQKCLhsxzrHwGPiLwUk3BKnSU9\nU1GqlYlIPfAA0CMGw1UDC2IwjlIxoWcqSimlYkbPVJRSSsWMlopSSqmY0VJRSikVM1oqSimlYkZL\nRSmlVMxoqSillIqZ/wdWdNhKR3JHDQAAAABJRU5ErkJggg==\n", 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fnkopfBlZ+DKyKDvnAgB62ts4uH0rB7Zuovy1V9j+0l9xxnkoXLSEWSvOoGTZClyesf/ydnudFMxLpWBeKgDa0LQf7qWusoPaynYq32lg54Y6bDZFZnGSWbcshfRjBKYtPh7v8uV4lx/9dxXp6KB/61Z6N26ib+NGWn78Y/O9eb2kLl3KRaeu4OoV1xJ3ahkRpdnfuZ/ytnLKW8vZ2bqTd3a8g2ENMshNyDXDpGgJ80+5mXkpc/H2tsDhndC4Aw7vgOq3YOezRxvlKxgeJNmLzM76Cf6+MpPiuHFFATeuKCAYjvKn7fU88kYVX3lmO7/fXMuD1y4kP2XyznjEyWOkP85H+zx56qmn2LJlC6+//vqw8sbGRm6++WaeeOKJmIXGRJx0ZxxjMfoj9O9up39HC8G9HRDV2P1miHgXpuPMSzhuly/CoQFqy3dwYOtGDmzdRG9HOw6ni6Ily5lzxtkULzkFZ1zcxzp2NGzQWNVFbUU7tZXttNSYf9l7Ep3kz0uxQicFT+LEhrRG2tvp27yFvo0b6d20cdhkR8/yZcSvWIF3xQri5s1DORz0hfuoaKtgV9sudrbupLy1nPqeevM5ykaJv4T5qUfPTGYmz8TZ3wWNH5hBciRQ2g4wuGCyN214mGQtMjvkJ/g/nGFontlcy3f+an5f2Dcum8eNp4x/NQIhYPyXql555RW+/OUv8/rrr5ORkTFY3t3dzbnnnss999zD9ddfH9O2yqWqjxkcQxn9Efor2ujf2Upwnxkijgwv8adk4V2aEdPO9Q/ThkH93kr2vPMm+za+TW9nBw63m5KlK5h3zvkULlyKzf7xZ4n3dYeorWynZlcbNRXtBHvCoCCjIJECa8mVzMLEMReA/LBIayt9W7bQt2kTvZs2jRkkAO3Bdspbj56VlLeW0zlgjupy292UppSyIG3B4KWugsQCVKgHmnZZQfKBGSzNu8EwByPgSoDM+cMDJX0uOMYOxtr2Pv7tuR28W9XGuXPS+dlnlhIvQ3rFOEUiEWbPns2rr75Kbm4up5xyCr/73e8oKzt6iWvbtm1cd911rFu3blj/RSgU4pJLLuHyyy/nrrvuinlbJTgmITiGMvoj9O9spXfzYUK1AbArPPPTSDwnD1dOwqS+1phtMaLUV+5iz7tvsee9twgGuon3J1N61rmUrTyf9BlF/9DxtaFpqQ1Qs6uNQ+XtNB3sQmtzTkleaQoFZSnMKEsl3j/xwQSjBkl8PJ6lS/EsWYx38WLiFi7EnmD+XLXW1PfUDwuSyvZK+iPmENskVxJlqWXMSZnD7OTZzEmZQ5GvCKehoaXy6FlJ4w5oKoeQuVIxNidkzD16VpK90OyUdyd+pN2GofnNu9Xc/5cKzpyZxhOfWyH9HmLcXnzxRe666y6i0Si33norX//61/nmN7/J8uXLueKKK7jwwgvZuXMn2dnZABQUFPDCCy/w1FNP8bnPfW5YyDz++OMsXrw4Ju2U4Jjk4BgqfLiX3k2H6d3ahB6IEleaQuJ5+bhnTN7qteMVjYSp2raFitdfper9zRjRKOmFxZStvIC5Z52D1/ePD9sL9oap291hno3sOroAZGpuAgVl5irC2SU+7B9jnszQIOnbvMUcuaU1KIV71iw8ixcP3lxFR0eaRYwIBzoPDIZJRVsF+zv3E7bOMBw2ByW+ksEwORIoKS4/tFdZZyVDAqXv6HwWkosga4F5ZpI139xPygWleOq9Q3zjf8v590tLWbvyH1v2XojpRoIjhsFxhNEXpufdRnrersfoi+Ce5SfpwhlTEiAAfd1d7H77DSreeJWmqv3Y7HYKFy+j7JwLKF66YsIjs0aitaa9oZdDVog07u/CiGocLhtZxT5yZ/vJmZVMZmESdufEgyQaCNC/Ywf927fTv/0D+j/4AKPbHAlj8/nwLFqIZ9EiPPPnEzd/Po7U1MHnho0wh7oOsadjD3s79prb9r209LcM1knzpDEneQ6zU6wwSZ5DYdIMnL2t1hnJTrMz/nA5tB842jBPMmTOR2ct4IkDify+zs9tV6/muhXFH/+HKcQ0I8FxHILjCCMUpfe9RgKv12H0hombk0zShTNw5X/0ksfx0lp7iF2vv0rlWxvo7WgnLj6BOWeeQ+npZ5M9ew52x+T0z4SCEer3dFBb2UHDvg7a6nsBsDttZMxIJKvIR0ZhEqm58fjSPRPuI9GGQejgQStIzNvA/gODkxMd2dl45pcRV1ZG3Lx5uEtKcGRnD5tM2R5sN4Ok3QyUvR17OdB5YPDsxGlzUuwrpthfTImvhBJ/CcX+YgpcyTha9g4Jk53mMGHrEllI2wn45pBaejYUnAozL4Q432T8WIWYEhIcxzE4jjAGovS820DPG3UYfRHiSlNIuqBgSgPEiEY5tHM7u15/lQOb3yMSDuFwu8mdM4+C+YvIL1tAZtHMf6hjfahgb5iGfZ007OvkcFUXLbWBwWVS7A4b/kwvKTnxg0ulpGTHk5TumVB/QbSnl4HKCvrLdxEsLydYXj74xVkAyuPBXVSEq6QEd0mxtS3BlZ+Pss66wkaY6q7qwbOS/Z37qeqqGhzRBeblrsKkQkr8JZT4SijyF1GUUEBBJIq9sZK/v/YKKR07We6swmUEwe6GOathyWdh1oWT8vMU4niS4JiC4DjCCEboeaeBwBv16GAEZ34iiSvz8JSlxnxS4bEM9PVSU/4BNeU7qN21g7Y685sGXR4vuaXzyJlVSvasUrJmzsbtnZw5C9GwQVtDj7lMypClUgLtRxdftDts+LO8pGR58Wd68Wd5Sc6Mx5/pxekeX6BFAwEG9uxh4EAVoaoDDOw/wEBVFZHGxqOVnE5cBQW4i4txlRSb2+IS3EWF2OLjAegL93Gw+yBVnVVmmHRWcaDrAHWBOjRH/1/Jjs+mKKmI1o4Udla5+X+S3dyX3Yxvzwtmf8mKL8DF3wH78Rt5J8Q/SoJjCoPjCCMYoe/9ZnrerifSFsSR7sG3uhBPWdpUNw2A3s4OanftoGbXDhr2VA4GCUqRlldA9qw5ZM8qJWd2KSk5eWOupTURoWCEjsY+M0isMOls6qW7LciQz2cSkt34M70kZ5lBkmwFS0Kye1zzKYzeXgaqDg4Lk1BVFaGaGohGB+s5srOtICk2z1KKzWCxp6ailCIYCVLdXW3eusxtVWcVBzoPEDKs5fi1Iic+j7JohDPqyzk/ZSEpVz5sLvAoxAlgyoNDKfUYcBnQrLWeb5WlAL8HCoFq4AatdYcyPwEeAi4F+oBbtNbvW89ZAxyZe/9trfUTY732dAmOI7Sh6S9vpfvVGiJNfXgWpJF8zSxsx1h8cCoEe3s4vH8vjfv20LhvN4379hDsNYeuujxeK0jmkDOrlKxZc/AkTP4luEg4SldzPx2H++hs6qOjqZdOaz8UHPJB77INCZJ4UnPjSc9PJDE1blyBokMhQjU1g0EyUFVlLvB48CC67+iijTafzwqUItwlM4mbN5e4uXOx+8y+jIgRoSZQw7o92/mf997GcDSQktJC+0ATNq05ZSDElbnnctklP0dN0krAQsTKdAiOlUAP8JshwfF/gHat9QNKqbuBZK3115RSlwJfxgyOU4GHtNanWkGzBViO+XfoVmCZ1rrjWK893YLjCB01CLxRR/crNThzEki/fT62aTyRTGtNR2P9YJA07N1Na82hwe8hSc7OJWd2KTmz55I7t8w8K4nRrGqtNX3dIToP99HR1Hd0+6GzFJfHQVpeAmn5CaTnJ5KWn0Bydjz2cXbKa8Mg0tR09JLXkECJth4dsuvMzSVu3jwzSObNwz13LocdCdz+xBb2twT459Xx2Fzv8ve9f6BaB7nSnsy9n3oJ5yR/T7w4MYy1rDrAs88+y3333YdSikWLFvG73/0OgH/7t3/jr3/9K4ZhsGrVKh566KGY/X825cFhNaIQ+MuQ4NgDnKu1blRKZQMbtNZzlFL/Y+0/PbTekZvW+gtW+bB6o5muwXFE/6422p6qIH5FFslXx2aVy1gJBftpOrCPhn17BgOlr8uc1e31+ckrLSN37nzy580nLX/GpF7eGk0kFKWtoZfW2gAttT201gZoq+shYn37os2hSMmOHwyStPxE0nITjrnc/Iiv09FBsKKCYEUFA5WVBHdVDOuUt6en4SydyyvRFNbpdK77fy/mU+fP4xd/voWHO7ZxefIC7r/sNzhs0/ePBTH5xrOs+r59+7jhhhtYv349ycnJNDc3k5GRwTvvvMO//uu/8sYbbwBw1lln8b3vfY9zzz03Jm2droscZmqtGwGs8DiyIEsuUDukXp1VNlr5Cc1TlkrCGTn0vN1Awlm5ONNPnMXzXHEe8ssWkl+2EDDPBDoPN5gr/FbspG73LvZuNJeDjotPICElFWWzoZQNZbNhs5nbD+8Pu6+O8diH6n24zGm3kVNsI6dYMdAfpa87TF9XhL7uELvfDhMeMAAFyoYn0UViipeMwgIKF5eRUeA7Zt+JIzmZhDPPJOHMMwfLoj09DOzebQbKLjNUzjrwNmcZBrz7KB/k5HPVimXkBt7nF0U7uOVva/j2Wd+h0FcY61+VmCY2bdrEzJkzKS425/zceOON/OlPfxoWHL/85S+54447SE42V3s+slaVUopgMEgoFDK/pTQcJjMz8/i/iQ+ZLn/6jPR/qj5G+UcPoNRaYC2Y0/Wnu8Rz8+l5r5HejYfxX3biTiJTSpGcnUtydi4Lzr8IgO6WZuoqy6nbvWvwK3a1Yd4M40P7kYh5X4/w2JHbBB9jnGfRgT4INEFDJWxf58bmyMMVX0T6jDKyZ80gIz+RtPxE/FneUS912RMSPrJicLSnl8AHH/D043/Du6+C5etfY3Z3lB8Cbb5t/LnkcrKv/zTXXf3v//DPX0zA3+425+JMpqwFcMkDx6xSX19Pfn7+4P28vDw2btw4rM7evXsBOPPMM4lGo9x3332sXr2a008/nfPOO4/s7Gy01tx5553MnTt3ct/Dx3C8g6NJKZU95FLVka+DqwPyh9TLAxqs8nM/VL5hpANrrR8BHgHzUtXkNnvy2RNduEv8DBzoHLvyCSYpPYN56eczb+X5U/L6WusxA8cwomhDY0Qi1O/dy76NW6jfs4Ng5yvUdr5C3c5EbI4Z2JwzcLgK8GWmkpxlju5KzrKGDGfF4x7hcpc9IR7/mWdw07JTuPbn7/DjQJD11+XA96/E1ZlFYnkXrvefpHJ3P3Pv+c8p+AmJ42k8y6pHIhH27dvHhg0bqKur4+yzz6a8vJzW1lYqKyupq6sDYNWqVbzxxhusXLnyuLR9NMc7OF4A1gAPWNs/DSm/Uyn1DGbneJcVLi8B31VKHfm2nouAkb826wRkT3ASaeobu6KYEKUUym4H7IxnFog/K5uyleeYl92aGqnZuZ3qD7ZRU76DUG854V4wwll0N+VxYGsmypaDspkjyjxJLnxpHpLS40hK85j71i3R5+K/rl/EVT97mz91xHNLYSf+U2/ky5FmTv+f91j62/9F332/LNN+vIxxZhAreXl51NYeveJeV1dHTk7OR+qcdtppOJ1OioqKmDNnzmCQnHbaaSRYC35ecsklvPfee5/c4FBKPY15tpCmlKoD7sUMjGeVUrcBNcCRxeVfxBxRtR9zOO7nALTW7Uqp/wQ2W/Xu11q3x6rNx5vN48Doj0x1M4RFKUVyVg7JWTksWnUphhGl6cB+Du3YRt3uXTTsKSc8YA668PrTSUwtwuXNJxLOomFvIvs2NQ27SmZ32EhMjePGfjdtrxzkvZ6reWe/jX1GG5esvBpj/x9pf/wJUj93y9S8YXFcnHLKKezbt4+DBw+Sm5vLM888Mzhi6oirrrqKp59+mltuuYXW1lb27t1LcXExVVVV/PKXv+See+5Ba83rr79+XJZXH0vMgkNr/elRHrpghLoauGOU4zwGPDaJTZs27P44dChKqL4HV+7xXZpdjM1msw/OXQFz+ZaWQwetr/ndRf2eCvq6NgHgjPOQVTKLlJxiElJn4PTkMdDr5HBDD47WHuLbwmwN34S7HK7gfKqB6pXnYHsnQkm0nDOvm/WxlqkX05/D4eCnP/0pF1988eCy6mVlZcOWVb/44ov5+9//zrx587Db7fzXf/0XqampXHfddaxfv54FCxboP+BuAAAgAElEQVSglGL16tVcfvnlU/2WZOb4VDKCEQ7/n8040jyk3b4Am2ty1osSx8eREWWN+/cOTphsOXQQw5qRnpieyT6dgkP10JPzFu7Ddq5tnIdR20dPQg77Z143eKyLbitj1ilTP1pGnNymxTyOqXKiBAdA344W2p/ejTMvkbQ187AnTOyrWsX0Eh4I0nTwANWVlfz1lfdI6dyHLWIGSVJfkDLlx7fySlpT51O+0ZzHuvLG2cw/J1f6OsSUG29wTP23np/kvAvTSb15HpHDvTT//APCzdJZfkJzuNjYk8hTlRX40t/E5UojZ6AMhYtubxyb/PPYsD93MDQSkt3klSZLaIgTipxxTBMDNd20PVGBDhskXzcL78L0qW6SGCetNXsOd/PWu+to3PYk2bWHcPTfSI83nXB4F0a4CtAk5yxiwYWfIbskm+TseLxJLgkMMa1M15njYhTugiQy/nkJ7b+tpP13uwlVd+O7tAj1Mb6OVcReV3+YjTu2svft36D2NuHp9mEP+Ui3zyHgyMdgC/SFcXsSWLD6GhZeeDHJWTljH1iIE4AExzTi8LlJX7uQrr8dpOftBkJ1AVJumovDJ6NtporWmubOANs3/Z36LZsINXSiAjYIuzC0jajNSZQkgtEWsJvzWT2eLLJmnU3ZOSsoWXbqpHxlrxDTiQTHNKMcNvyXl+AqTKLj/+6j6cfvk3BqNvZkN0YgBEqZC7FYWzVkf/gKLXrE3WH0yHdGvXo5Sv1h5SM8VzltJJyRM63Pnvr6+9m+6Q0Obd5CT20bke4oOmw3w4EoBv0YRhcYgWHPs+MlOT2bGYtXUrRkITmzS3F746foXQhxfEhwTFPeBek4M+PpWldN4PXa0T/8TxDGQBTfqhnH9zUNTUtbMwd3baN531666xsZ6Owh0hsmGjLQEdDagYENA42he9BGFzB8UqbChcPhIsGXRGbRYgqXLiNrVjHJ2Tk43XHH9T2JE9NYy6ofOnSIW2+9lZaWFlJSUnjqqafIy8sDoKamhttvv53a2lqUUrz44osUFhZOwbs4SjrHTwDRnhC9mw/T/dKhsStPQ0kXzSDx7DyU8+OfcUQjEZoaq6jdtZPmg9V0N7US7Ooh0hfGCBkYEYW21jfUWmMQRRPG0AOgg0B0lCPbUDixKTt2h5O4JC8ZhTOYveJMMmbOJCkjA6dLLhWKj288y6pff/31XHbZZaxZs4b169fz61//mieffBKAc889l69//eusWrWKnp4ebDYb3kn6aucPk87xTxB7gouk8wpIOm/sVX+jXQM0PrBpSs9QHGkeHGke8xsObYpwWz8NT2+nN9BJsDdAsL+P8ECQaChMNBLBiBpEo1FrUUKNoQ0UNmzKhl3ZzMtxRNHa/PCPAzykEjLi2dO1CQPDemWFwoVSDhR27DY7Dnscdmc8jjgXcUmJ+DLTySqeReGCRfiyMnF7vTKyScTUeJZVr6io4Ec/+hEA5513HlddddVgeSQSYdWqVQCDa1ZNNQmOTxi7z03e984+Zh1taHTEQA9E0aEohrXVIQMdjqLDhnkLRc16YQMjbEDYILi/k8gYc00irf1EWvs/Uu7FiZcUIMUscPAP/wvMXbQQ/8oZZBaX4PbGSwiIUT246UF2t++e1GOWppTytRVfO2ad8SyrvmjRIp5//nm+8pWv8Mc//pFAIEBbWxt79+7F7/dzzTXXcPDgQS688EIeeOAB7PapXWVCguMkpGwK5bLDBJc46d/dTs87DRN6TmeoDY2BgYFGo5UGGyiHwuZ0YI9zEef14klKMj/47TaUQ6GcdpTDZl7esn80DJQCFecg95QslE3CQkxf41lW/fvf/z533nknjz/+OCtXriQ3NxeHw0EkEuHNN99k27ZtFBQU8KlPfYrHH3+c22677Xg1f0QSHGJU4VCYV59+jEPvbCfbmMHS1DPHftIQydfPJm+ZrL8kpoexzgxiZTzLqufk5PCHP/wBgJ6eHp5//nl8Ph95eXksWbJk8DLXVVddxXvvvSfBIaaPYLCflx9/hPotFYR7o8TZXaTHJbPUezrZ3qIRn+NdlokrPxFnTjz2JDf2BOe0HnYrxPE2nmXVW1tbSUlJwWaz8b3vfY9bb7118LkdHR20tLSQnp7O+vXrWb58zL7rmJPgOEl1dbTx+m9/Q0dFLa6gB68tHq/TQ5ojkaKki0hIScZhMyeuhewh7MlxxC/PwpWfaF4asiucWfGyoq8QYxjPsuobNmzgnnvuQSnFypUr+dnPfgaA3W7n+9//PhdccAFaa5YtW8bnP//5KX5HMhz3pFBffYAtTz8PdWEStJ94eyLxjgQSnD6ctqOr8Ro6Sp/RAz47mQtn4cyKx13kw5HmkU5nIU4CMhz3JLZz45vsenod/v50Ut3pJLvTWGY/HRLB0Aa9kQC9kQCd4YM4s+KZs+osEmekYffHoUboiBZCiKEkOD4Berq7eOlnP8N+UJPmzCLDk81ZcashDrpDHTQF6+lxdJMwL4tln7qcuCRZEkMI8fFJcJxgOlqaef23T9C/t52EiB+/M5VkdyqnOM/ElmIjZAzQMtBAXVwNi2+5gryZsiKrEGJyxSw4lFL5wG+ALMAAHtFaP6SUSgF+DxQC1cANWusOZV5Efwi4FOgDbtFav28daw3wDevQ39ZaPxGrdk8XWmu2v/EKO9e9TLR5gCRS8btSSHalssi9FGeS2TcRigbpCLWy39hF+tmzKbvyfIplVJMQIoZiecYRAf4/rfX7SqlEYKtS6mXgFuBVrfUDSqm7gbuBrwGXALOs26nAw8CpVtDcCyzHXEhjq1LqBa11Rwzbflw1N9Tx1rNP0br7EPZ+B15bEj6XjxRXKqe7z8OTbi4zENVRukJt1IcPYs/zUHbNKhIL0ymWjmshxHEUs+DQWjcCjdZ+QClVCeQCVwLnWtWeADZgBseVwG+0OczrPaWUXymVbdV9WWvdDmCFz2rg6Vi1PRbamhrZ+tKfaazYi9EexhWOw23zEu9IINEZz0xnMUuSTsHpPzrKKRDpotPRgVHqovD8U3DlJMgcCSHElDsufRxKqUJgCbARyLRCBa11o1Iqw6qWC9QOeVqdVTZa+YdfYy2wFqCgYOzFAP8Rne3t1FTsoKnqAN2Hmwl39KL7DGwhhcNwYNcuHDhxKhdOmxO3PQ6vw0OBPZs5jtnYU4bPfeiL9NJHgC5/F/mnLiShKM2cI+GVLwAS4pPg1ltv5S9/+QsZGRmUl5d/5PENGzZw5ZVXUlRkTrS95ppr+OY3vwmMvST7VIh5cCilEoDngbu01t3HmA8w0gP6GOXDC7R+BHgEYGZmrv7jP38bZSiUVti0QmEz97Fj3rNZ/7VhU3ZrNVaFUnarzIZd2Qe35r4du3JgV3bilZ1ZqgiHbbbZAI91+5CIESEY7aPf6KVTd9DuaCc+L43CUxfjyfThSIvD5pYxCkJ8kt1yyy3ceeedfPaznx21ztlnn81f/vKXYWXRaJQ77rhj2JLsV1xxxbCVdadCTD+xlFJOzND4rdb6D1Zxk1Iq2zrbyAaarfI6IH/I0/OABqv83A+VbzjW63ptiZziPWdcbTR0FEMb5hbD2jdvUR3B0FGiRAkZIaI6atWLYigDw27eiFM4kuLwpvlJKc4nq6QEl9+LzeOQS0tCCFauXEl1dfWEnzeeJdmnQixHVSngV0Cl1vqHQx56AVgDPGBt/zSk/E6l1DOYneNdVri8BHxXKZVs1bsIuOdYrx2097MvZx9Orxu314srMR5vUhLeFD8JqanEJ/uwO83vipCVVYU4ORz+7ncZqJzcZdXdc0vJ+vd/n5RjvfvuuyxatIicnBy+//3vU1ZWNq4l2adCLM84zgRuBnYqpbZbZf+OGRjPKqVuA2qA663HXsQcirsfczju5wC01u1Kqf8ENlv17j/SUT6apKx0zvvnWyfzvQghRMwsXbqUQ4cOkZCQwIsvvshVV13Fvn37xrUk+1SI5aiqtxi5fwLgghHqa+COUY71GPDY5LVOCHGymawzg1hISkoa3L/00kv50pe+RGtr67iWZJ8KcgFeCCGm2OHDhwfPLjZt2oRhGKSmpg5bkj0UCvHMM89wxRVXTHFrZckRIYSIuU9/+tNs2LBh8CziW9/6FuFwGIB/+qd/4rnnnuPhhx/G4XDg8Xh45plnUEqNuiT7VJNl1YUQQgDjX1ZdLlUJIYSYEAkOIYQQEyLBIYQQYkIkOIQQQkyIBIcQQogJkeAQQggxIRIcQggRQ7W1tZx33nnMnTuXsrIyHnrooY/U2bBhAz6fj8WLF7N48WLuv//+wcfWrVvHnDlzmDlzJg888MDxbPqoZAKgEELEkMPh4Ac/+AFLly4lEAiwbNkyVq1a9ZEVbk+kZdXljEMIIWIoOzubpUuXApCYmMjcuXOpr68f13OHLqvucrkGl1WfanLGIYQ4Kbz57F5aa3sm9Zhp+QmcfcPscdevrq5m27ZtnHrqqR95TJZVF0IIMUxPTw/XXnstP/7xj4ethguyrLoQQkxLEzkzmGzhcJhrr72Wm266iWuuueYjj8uy6kIIIQZprbntttuYO3cu//Iv/zJiHVlWXQghxKC3336bJ598kgULFrB48WIAvvvd71JTUwPIsurThiyrLoQQEzfly6orpeKUUpuUUh8opXYppb5llRcppTYqpfYppX6vlHJZ5W7r/n7r8cIhx7rHKt+jlLo4Vm0WQggxtlj2cQwA52utFwGLgdVKqdOAB4Efaa1nAR3AbVb924AOrfVM4EdWPZRS84AbgTJgNfBzpZQ9hu0WQghxDDELDm06Mmjaad00cD7wnFX+BHCVtX+ldR/r8QuUOe7sSuAZrfWA1vogsB9YEat2CyGEOLaYjqpSStmVUtuBZuBl4ADQqbWOWFXqgFxrPxeoBbAe7wJSh5aP8Jyhr7VWKbVFKbWlpaUlFm9HCCEEMQ4OrXVUa70YyMM8S5g7UjVrO9KsFn2M8g+/1iNa6+Va6+Xp6ekft8lCCCHGcFzmcWitO4ENwGmAXyl1ZBhwHtBg7dcB+QDW4z6gfWj5CM8RQghxnMVyVFW6Uspv7XuAC4FK4DXgOqvaGuDIil0vWPexHl+vzbHCLwA3WqOuioBZwKZYtVsIISZbYWHh4DyO5cs/Otr1t7/9LQsXLmThwoWcccYZfPDBB8D4lmSfCrGcAJgNPGGNgLIBz2qt/6KUqgCeUUp9G9gG/Mqq/yvgSaXUfswzjRsBtNa7lFLPAhVABLhDax2NYbuFEGLSvfbaa6SlpY34WFFREa+//jrJycn87W9/Y+3atWzcuHHcS7IfbzELDq31DmDJCOVVjDAqSmsdBK4f5VjfAb4z2W0UQojp4IwzzhjcP+2006irqwPMJdmzs7OB4Uuyf2KDQwghppPXHn+E5kNVk3rMjBnFnHfL2jHrKaW46KKLUErxhS98gbVrR3/Or371Ky655JKPlB9rSfbjTYJDCCFi7O233yYnJ4fm5mZWrVpFaWkpK1eu/Ei91157jV/96le89dZbw8qPtST7VJDgEEKcFMZzZhArR5ZCz8jI4Oqrr2bTpk0fCY4dO3Zw++2387e//Y3U1NTB8rGWZJ8Ksqy6EELEUG9vL4FAYHD/73//O/Pnzx9Wp6amhmuuuYYnn3yS2bOPfm/IeJZknwpyxiGEEDHU1NTE1VdfDUAkEuEzn/kMq1ev5he/+AVgLqt+//3309bWxpe+9CUAHA4HW7ZsGXVJ9ksvvXRq3oxFllUXQggBTINl1YUQQnwySXAIIYSYkHH1cSillgNnAzlAP1AOvKK1bo9h24QQQkxDxzzjUErdopR6H7gH8AB7MJdIPwt4WSn1hFKqIPbNFEIIMV2MdcYRD5ypte4f6UGl1GLMRQdrJrthQgghpqdjBofW+mdjPL59cpsjhBBiuhtX57hSqkgp9UOl1B+UUi8cucW6cUIIcaILBoOsWLGCRYsWUVZWxr333gvATTfdxJw5c5g/fz633nor4XB42PM2b96M3W7nueeeG+mwhEIh1q5dy+zZsyktLeX5558HzMmE5513HkuWLGHhwoW8+OKLk/6exjsB8H8xlz3/M2BMeiuEEOITyu12s379ehISEgiHw5x11llccskl3HTTTTz11FMAfOYzn+HRRx/li1/8IgDRaJSvfe1rXHzxxaMe9zvf+Q4ZGRns3bsXwzBobzfHKn3729/mhhtu4Itf/CIVFRVceumlVFdXT+p7Gm9wBLXW/z2pryyEECcBpRQJCQmAue5UOBxGKTVs9veKFSsGl1IH+MlPfsK1117L5s2bRz3uY489xu7duwGw2WyD3/WhlKK7uxuArq6uwXWyJtN4g+MhpdS9wN+BgSOFWuv3J71FQggRA51/PkCooXdSj+nKicd/ecmY9aLRKMuWLWP//v3ccccdw5ZGD4fDPPnkk4Pf7ldfX88f//hH1q9fP2pwdHZ2AvAf//EfbNiwgZKSEn7605+SmZnJfffdx0UXXcRPfvITent7eeWVVybhnQ433gmAC4DPAw8AP7Bu35/01gghxCeQ3W5n+/bt1NXVsWnTJsrLywcf+9KXvsTKlSs5++yzAbjrrrt48MEHsdvtox4vEolQV1fHmWeeyfvvv8/pp5/OV7/6VQCefvppbrnlFurq6njxxRe5+eabMYzJ7WEY11pVSqndwEKtdWhSXz1GZK0qIcR09a1vfYv4+Hi++tWv8q1vfYtt27bxhz/8AZvN/Du+qKiII5/Lra2teL1eHnnkEa666qrBY2itSUhIIBAIYLPZqK2tZfXq1ezatYuysjLWrVtHfn4+AMXFxbz33ntkZGSM2bbJXqvqA8A/zrofbohdKbVNKfUX636RUmqjUmqfUur3SimXVe627u+3Hi8ccox7rPI9SqnRe4uEEGKaaWlpGby01N/fzyuvvEJpaSmPPvooL730Ek8//fRgaAAcPHiQ6upqqqurue666/j5z38+LDTA7Me4/PLL2bBhAwCvvvrq4NfJFhQU8OqrrwJQWVlJMBgkPT19Ut/TePs4MoHdSqnNDO/juGIcz/0KUAkc+dqqB4Efaa2fUUr9ArgNeNjadmitZyqlbrTqfUopNQ+4ESjDXPLkFaXUbK11dJxtF0KIKdPY2MiaNWuIRqMYhsENN9zAZZddhsPhYMaMGZx++ukAXHPNNXzzm9885rEWL17M9u3m9LkHH3yQm2++mbvuuov09HR+/etfA/CDH/yAz3/+8/zoRz9CKcXjjz+OUmpS39N4L1WdM1K51vr1MZ6XBzwBfAf4F+ByoAXI0lpHlFKnA/dprS9WSr1k7b+rlHIAh4F04G7rtb5nHXOw3mivK5eqhBBi4ib7UpVXa/360BswdxzP+zHwbxyd+5EKdGqtI9b9OiDX2s8FagGsx7us+oPlIzxnkFJqrVJqi1JqS0tLyzjflhBCiIkab3D8h1Lq/CN3lFJfA6481hOUUpcBzVrrrUOLR6iqx3jsWM85WqD1I1rr5Vrr5ZN9PU8IIcRR4+3juAL4i1LqX4HVQKlVdixnAlcopS4F4jD7OH4M+JVSDuusIg9osOrXAflAnXWpyge0Dyk/YuhzhBBCHGfjOuPQWrdiBsXPMDuor9Nah8d4zj1a6zytdSFm5/Z6rfVNwGvAdVa1NcCfrP0XrPtYj6/XZgfMC8CN1qirIszVeDeN8/0JIYSYZMc841BKBRh+WcgFFAPXKaW01jpp5Gce09eAZ5RS3wa2Ya6BhbV9Uim1H/NM40YArfUupdSzQAUQAe6QEVVCCDF1xjWq6kQjo6qEEGLiJmVU1dBJeKM8rqwht0IIIU4SY3WO/5dSyobZD7EVcw5GHDATOA+4ALgXswNbCCHESWCsbwC83pq5fRNwK5AN9GHOBH8R+I7WOhjzVgohhJg2xhyOq7WuAL5+HNoihBDiBDDeCYBCCCEEIMEhhBBigiQ4hBBCTMi4gkMp9aq1dMjQskdi0yQhhBDT2XjPOIqAr1nfO37EmJNEhBBCfPKMNzg6MedsZCql/qyU8sWwTUIIIaax8QaH0lpHtNZfAp4H3gLG/gJbIYQQnzjjXVb9F0d2tNaPK6V2AnfEpklCCCGms3EFh9b6fz50fyvmTHIhhBAnGRmOK4QQYkIkOIQQQkyIBIcQQogJkeAQQggxIRIcQgghJiSmwaGUqlZK7VRKbVdKbbHKUpRSLyul9lnbZKtcKaX+Wym1Xym1Qym1dMhx1lj19yml1sSyzUIIIY7teJxxnKe1Xjzke2zvBl7VWs8CXrXuA1wCzLJua4GHwQwazG8ZPBVYAdx7JGyEEEIcf1NxqepK4Alr/wngqiHlv9Gm9wC/UiobuBh4WWvdrrXuAF4GVh/vRgshhDDFOjg08Hel1Fal1FqrLFNr3QhgbY8sXZIL1A55bp1VNlr5MEqptUqpLUqpLS0tLZP8NoQQQhwx3iVHPq4ztdYNSqkM4GWl1O5j1FUjlOljlA8v0PoR4BGA5cuXf+RxIYQQkyOmZxxa6wZr2wz8EbOPosm6BIW1bbaq1wH5Q56eBzQco1wIIcQUiFlwKKXilVKJR/aBi4By4AXgyMioNcCfrP0XgM9ao6tOA7qsS1kvARcppZKtTvGLrDIhhBBTIJaXqjKBPyqljrzO77TW65RSm4FnlVK3ATXA9Vb9F4FLgf1AH/A5AK11u1LqP4HNVr37tdbtMWy3EEKIY1Baf/K6A5YvX663bNky1c0QQogTilJq65CpE6OSmeNCCCEmRIJDCCHEhEhwCCGEmBAJDiGEEBMiwSGEEGJCJDiEEEJMiASHEEKICZHgEEIIMSESHEIIISZEgkMIIcSESHAIIYSYEAkOIYQQEyLBIYQQYkIkOIQQQkyIBIcQQogJkeAQQggxIRIcQgghJkSCQwghxITENDiUUn6l1HNKqd1KqUql1OlKqRSl1MtKqX3WNtmqq5RS/62U2q+U2qGUWjrkOGus+vuUUmti2WYhhBDHFuszjoeAdVrrUmARUAncDbyqtZ4FvGrdB7gEmGXd1gIPAyilUoB7gVOBFcC9R8JGCCHE8Rez4FBKJQErgV8BaK1DWutO4ErgCavaE8BV1v6VwG+06T3Ar5TKBi4GXtZat2utO4CXgdWxarcQQohji+UZRzHQAvxaKbVNKfWoUioeyNRaNwJY2wyrfi5QO+T5dVbZaOVCCCGmQCyDwwEsBR7WWi8Bejl6WWokaoQyfYzy4U9Waq1SaotSaktLS8vHaa8QQohxiGVw1AF1WuuN1v3nMIOkyboEhbVtHlI/f8jz84CGY5QPo7V+RGu9XGu9PD09fVLfiBBCiKNiFhxa68NArVJqjlV0AVABvAAcGRm1BviTtf8C8FlrdNVpQJd1Kesl4CKlVLLVKX6RVSaEEGIKOGJ8/C8Dv1VKuYAq4HOYYfWsUuo2oAa43qr7InApsB/os+qitW5XSv0nsNmqd7/Wuj3G7RZCCDEKpfVHugtOeMuXL9dbtmyZ6mYIIcQJRSm1VWu9fKx6MnNcCCHEhEhwCCGEmBAJDiGEEBMiwSGEEGJCJDiEEEJMiASHEEKICZHgEEIIMSESHEIIISZEgkMIIcSESHAIIYSYEAkOIYQQEyLBIYQQYkIkOIQQQkyIBIcQQogJkeAQQggxIRIcQgghJkSCQwghxIRIcAghhJiQmAWHUmqOUmr7kFu3UuoupVSKUuplpdQ+a5ts1VdKqf9WSu1XSu1QSi0dcqw1Vv19Sqk1sWqzEEKIscUsOLTWe7TWi7XWi4FlQB/wR+Bu4FWt9Sz+//buPcaOsozj+PdnmwIlci8iLdoaC7FUA7IpRExMKVKqhDblYhsiCNUKgkQaoxJMmqhETIwkEECL1S1EC6Vp2grIJYXQiBToVboisoKWlVtLCxopl8LjH/NuOSy7e3Z2Z85lz++TbPbMO/POeZ5uzzxn5px5X1iTlgFmABPTz3zgJgBJhwALgROBKcDC7mJjZma1V6tLVdOAf0TEv4CZwJLUvgSYlR7PBG6JzDrgIEkfBaYD90fEzojYBdwPnF6juM3MrIdaFY45wNL0+CMR8QJA+n14ah8LPFfRpyu19dVuZmZ1UHrhkDQKOBO4o9qmvbRFP+09n2e+pPWS1m/fvj1/oGZmNiC1OOOYAWyMiJfS8kvpEhTp98upvQs4qqLfOOD5ftrfJyIWRURbRLSNGTOm4BTMzKxbLQrHXN67TAWwGuj+ZtQFwKqK9vPTt6tOAl5Ll7LuBU6TdHD6UPy01GZmZnUwssydSxoNfBH4ZkXzNcAySfOAbcA5qf1u4EtAJ9k3sC4EiIidkn4MPJ62+1FE7CwzbjMz65siPvBxQdNra2uL9evX1zsMM7OmImlDRLRV2853jpuZWS4uHGZmlosLh5mZ5eLCYWZmubhwmJlZLi4cZmaWiwuHmZnl4sJhZma5uHCYmVkuLhxmZpaLC4eZmeXiwmFmZrm4cJiZWS4uHGZmlosLh5mZ5eLCYWZmubhwmJlZLi4cZmaWiwuHmZnlUmrhkHSFpA5JWyUtlbSvpAmSHpX0tKTbJY1K2+6TljvT+vEV+7kytT8laXqZMZuZWf9KKxySxgKXA20RMRkYAcwBfgZcGxETgV3AvNRlHrArIj4JXJu2Q9Kk1O9Y4HTgRkkjyorbzMz6V/alqpHAfpJGAqOBF4BTgOVp/RJgVno8My2T1k+TpNR+W0S8GRHPAp3AlJLjNjOzPowsa8cR8W9JPwe2AbuB+4ANwKsRsSdt1gWMTY/HAs+lvnskvQYcmtrXVey6ss9ekuYD89Pim5K2FptRwzsM2FHvIGrMObcG51w7Hx/IRqUVDkkHk50tTABeBe4AZvSyaXR36WNdX+3vb4hYBCxKz70+ItoGEXbTcs6twTm3hkbPucxLVacCz0bE9oh4G1gBfA44KF26AhgHPJ8edwFHAaT1BwI7K9t76WNmZjVWZuHYBpwkaUztjoYAAAZMSURBVHT6rGIa8FfgQeDstM0FwKr0eHVaJq1/ICIitc9J37qaAEwEHisxbjMz60eZn3E8Kmk5sBHYA2wiu5R0F3CbpJ+ktsWpy2LgVkmdZGcac9J+OiQtIys6e4BLI+KdKk+/qOh8moBzbg3OuTU0dM7K3tSbmZkNjO8cNzOzXFw4zMwsFxcOMzPLpWUKh6RPSFqcPrDvbttf0hJJN0s6r57xlUnSxyStlvQbST+odzy1IOlDkq6WdL2kC6r3GB7S/+kNks6odyy1IGlWev2uknRaveMpQyMep5qicKQD3ss97waXdHoa+LCz2gExIp6JiHk9mmcDyyPiG8CZBYddiCJyB44G7oqIi4BJpQVbkIJynkk2wsDbZPcCNbSCcgb4PrCsnCiLVdDremV6/X4N+EqJ4RYqZ+4Nd5xqisIBtJMNcLhXGujwBrK70ScBcyVNkvRpSXf2+Dm8j/2OIw1zAlT7im+9tDP03DeR3QvzANl9NI2unaHnfAzwSEQsAC6pcfyD0c4Qc5Z0KtnX1l+qdfCD1E5xr+sfpn7Nop0B5k4DHqdKu4+jSBGxVhXDrCdTgM6IeAZA0m3AzIj4KTDQ0/Qusj/KZhq0iBaRu6TvAgvTvpYDvy036qEpKOcu4K202BAvtv4UlPNUYH+yg85uSXdHxLulBj4EBeUs4BrgjxGxsdyIi5MndxrwONUQQQzS3kERk14HP+wm6VBJvwSOl3Rlal4BnCXpJuAPpUVavFy5A/cAl6f8/1liXGXKm/MKYLqk64G1ZQZWolw5R8RVEfEd4PfAzY1cNPqR9+/8bbLhjc6WdHGZgdVAX7k33HGqKc44+jCgwQ/3roh4Bbi4R9v/gAsLjqsW8ua+lfeGeWlWeXN+nffmemlWuXLeu0FEe/Gh1Ezev/N1wHXlhVNTvebeiMepZj7jaOXBD1sxd+fsnIe7psm9mQvH48BEZVPRjiIb22p1nWOqlVbM3Tk75+GuaXJvisIhaSnwCHCMpC5J89JkUJcB9wJPAssioqOecZahFXN3zs6ZYZpzt2bP3YMcmplZLk1xxmFmZo3DhcPMzHJx4TAzs1xcOMzMLBcXDjMzy8WFw8zMcnHhMDOzXFw4zMwsFxcOa0nKZoP8cpVtfiXp5MH0rdh2P0kPpbkWhkzSSEl3SdohaXKVbfuaLGiUpLWSmnmQU6sjFw5rVccBW6pscyKwbpB9u10ErIiIouYEuQl4imyehtsljetn23Z6TBYEEBFvAWtoohnzrLG4cFhLkHS0pD9JekLSFcARwH8kbZLUIel1SZslrVM2X/mngL9HxDu99Y2IrrTfB1K/zZLekHROj6c+D1hVEcf5kv4iaYukWyWNl/Q3Sb+WtFXS7ySdKulhSU9LmlLRdyHwWkQsiIiHga8DSyUd2FvOEbEW2NnHP8nKFJtZbh6ryoa9dEnmz8BlEfGYpBuBYyJiWlo/BbgqImZW9FkAvArc0l/fiu0vAaYCc7vPLtIIp9si4oi0fCzZpDwnR8QOSYcABwCdwPFAB9kIqVvI5hI5E7gwImYNIffxwJ0RMblH+wjgxYgYM9h9W+vyNU5rBbOBJyPisbTcAeyuWD85tVWaTjZ5TrW+SDqfbJ7os3pckjqMrPh0OwVYHhE7ACJip6QDgGcj4om0rw5gTUSEpCeA8YPIt6p0JvWWpA9HxH/LeA4bvnypylrBZ4ANFcsn8P7PKCYBez9AljQaOCginq/WN12aOg84NyLe7vG8u4F9K5ZF77PZvVnx+N2K5XfJ8eZO0qUVl82OHECXfYA3Brp/s24uHNYKXiE7q0DSCcBcYHPF+iOBFyuWpwIPVusr6QzgW8DsiPjAATgidgEjJHUXjzXAuZIOTf0PKSK5iue7ISKOSz/9zhyXYtjeS7Ezq8qFw1rBrcBxkjYD3yO7fPRkxfp7gcWSvpCWZwD3DKDvErLpPR9O7/J7m+P8PuDzAGlSnquBhyRtAX5RUH696m2yoIrVU4G7y3x+G7784bhZD5I2AicW8W5c0vHAgoj46tAjK46kFcCVEfFUvWOx5uMzDrMeIuKzRV3CiYhNwINF3QBYhPRtr5UuGjZYPuMwM7NcfMZhZma5uHCYmVkuLhxmZpaLC4eZmeXiwmFmZrm4cJiZWS4uHGZmlsv/Ad2phknFWH1UAAAAAElFTkSuQmCC\n", "text/plain": [ "
" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], "source": [ "for i in range(len(wavel))[::5]:\n", - " plt.plot(z, dtau_dz[:,i], label='{:.2f}'.format(wavel[i]))\n", - "plt.legend(loc='upper left', frameon=False)\n", - "plt.loglog()\n", - "plt.xlabel('z (cm)')\n", - "plt.ylabel(r'$d\\tau/dz$ (dust)')" + " plt.plot(dtau_dz[:,i], z * 1.e-5, label='{:.2f}'.format(wavel[i]))\n", + "plt.legend(loc='upper right', frameon=False)\n", + "plt.semilogx()\n", + "plt.ylabel('z (km)')\n", + "plt.xlabel(r'$d\\tau/dz$ (cm^-1)')\n", + "plt.ylim(8000, 0)\n", + "plt.xlim(1.e-10, 10.0)" ] }, { @@ -213,10 +187,8 @@ }, { "cell_type": "code", - "execution_count": 16, - "metadata": { - "collapsed": true - }, + "execution_count": 11, + "metadata": {}, "outputs": [], "source": [ "def cumulative_integral(z, ext):\n", @@ -229,10 +201,8 @@ }, { "cell_type": "code", - "execution_count": 17, - "metadata": { - "collapsed": true - }, + "execution_count": 12, + "metadata": {}, "outputs": [], "source": [ "tau = cumulative_integral(z, dtau_dz)" @@ -240,76 +210,82 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "(0.001, 10000.0)" ] }, - "execution_count": 18, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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v8gLZO8+RdywVAOdwH7xHNcO5pS/KWPNvDf6zamKo7AaaK6WaAHHABOAu+5YkhKhtopKy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\nnDIDDcBP4hnfCjFXOrke42lmtgqoJ00T0hDlemx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\n", 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" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], "source": [ "for i in range(len(wavel))[::5]:\n", - " plt.plot(z, tau[:,i], label='{:.2f}'.format(wavel[i]))\n", - "plt.legend(loc='lower right', frameon=False)\n", - "plt.loglog()\n", - "plt.xlabel('z (cm)')\n", - "plt.ylabel(r'$\\tau$ (dust)')\n", - "plt.axhline(1.0, color='k', lw=1)" + " plt.plot(tau[:,i], z*1.e-5, label='{:.2f}'.format(wavel[i]))\n", + "plt.legend(loc='lower left', frameon=False)\n", + "plt.semilogx()\n", + "plt.ylabel('z (km)')\n", + "plt.xlabel(r'$\\tau$ (dust)')\n", + "plt.ylim(8000, 0)\n", + "plt.axvline(1.0, color='k', lw=1)\n", + "plt.xlim(1.e-3, 1.e4)" ] }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "(1000.0, 1e-06)" + "(0.01, 100)" ] }, - "execution_count": 19, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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Hj+YPf/hDvEM3xsRL6DkOwuxxBDe5UE5VV8UspPrCSRyiqs+pqi/08TuaqStl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\nRaRMRNY3aJ8mIltEZLuI3Au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+ "image/png": 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\n", "text/plain": [ "
" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], "source": [ "for i in range(len(wavel))[::5]:\n", - " plt.plot(tau[:,i], p * 1.e-6, label='{:.2f}'.format(wavel[i]))\n", + " plt.plot(tau[:,i], p * 1.e-6, label='{:.2f} $\\mu$m'.format(wavel[i]))\n", "plt.legend(loc='lower left', frameon=False)\n", "plt.loglog()\n", "plt.xlabel(r'$\\tau$ (cloud)')\n", - "plt.ylabel(r'p (bar)')\n", + "plt.ylabel(r'p (bar?)')\n", "plt.ylim(1.e3, 1.e-6)\n", - "#plt.axhline(1.0, color='k', lw=1, ls='--')\n", - "#plt.xlim(7.e7, z_out[-1])" + "plt.axvline(1.0, color='k', lw=1, ls='--')\n", + "plt.xlim(0.01, 100)" ] }, { @@ -321,54 +297,66 @@ }, { "cell_type": "code", - "execution_count": 20, - "metadata": { - "collapsed": true - }, + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "from maplib import read_file" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, "outputs": [], "source": [ - "def calc_cloud_depth(z, integrand, tau=1.0):\n", + "def calc_cloud_depth(z, integrand, tau=1.0, p_val=True):\n", + " x = z\n", + " if p_val:\n", + " thermo = read_file(thermofile)\n", + " x = thermo['p'] * 1.e-6 # bar\n", + " \n", " result = []\n", " for i in range(len(integrand[0])):\n", - " tau_interp = interp1d(integrand[:,i], z)\n", + " tau_interp = interp1d(integrand[:,i], x, fill_value=(x[0], x[-1]), bounds_error=False)\n", " result.append(tau_interp(tau))\n", " return np.array(result) # cm" ] }, { "cell_type": "code", - "execution_count": 21, - "metadata": { - "collapsed": true - }, + "execution_count": 17, + "metadata": {}, "outputs": [], "source": [ - "dep = calc_cloud_depth(z, tau) * 1.e-5 # km" + "dep = calc_cloud_depth(z, tau, p_val=False) * 1.e-5 # km" ] }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "[]" + "(2000, 0)" ] }, - "execution_count": 22, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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OaB/X8+XOlLtg/U66tCjZynvPfraaV2av55cnHM7FCUVPsChS0UTTx5F37Y0s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\nYII=\n", 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\n", 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" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -376,7 +364,45 @@ "plt.plot(wavel, dep)\n", "plt.xlabel('Wavelength ($\\mu$m)')\n", "plt.ylabel('Cloud Depth (km)')\n", - "plt.semilogx()" + "plt.semilogx()\n", + "plt.ylim(2000, 0)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(1000.0, 1e-06)" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "dep2 = calc_cloud_depth(z, tau, p_val=True) # bar\n", + "plt.plot(wavel, dep2)\n", + "plt.xlabel('Wavelength ($\\mu$m)')\n", + "plt.ylabel('Atmosphere depth (bar)')\n", + "plt.loglog()\n", + "plt.ylim(1.e3, 1.e-6)" ] }, { @@ -388,77 +414,116 @@ }, { "cell_type": "code", - "execution_count": 23, - "metadata": { - "collapsed": true - }, + "execution_count": 20, + "metadata": {}, "outputs": [], "source": [ - "from cloud_depth import cloud_depth" + "from maplib import cloud_depth" ] }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "w, d = cloud_depth('-180.0', '0.0', p_val=False) # returns wavelength (um), depth (cm)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, "metadata": {}, "outputs": [ { - "ename": "KeyError", - "evalue": "('-105', '67.5')", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mw\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0md\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcloud_depth\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'-105'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'67.5'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# returns wavelength (um), depth (km)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;32m~/PhD/code/cloudacademyMap/cloud_depth.py\u001b[0m in \u001b[0;36mcloud_depth\u001b[0;34m(lon, lat)\u001b[0m\n\u001b[1;32m 90\u001b[0m \u001b[0mDistance\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mtop\u001b[0m \u001b[0mof\u001b[0m \u001b[0matmosphere\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mkm\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mwhich\u001b[0m \u001b[0mtau\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 91\u001b[0m \"\"\"\n\u001b[0;32m---> 92\u001b[0;31m \u001b[0mfname\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mFNAMES\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlon\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlat\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 93\u001b[0m \u001b[0mzfile\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfname\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m'out3_thermo.dat'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 94\u001b[0m \u001b[0mextfile\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfname\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m'out3_extinction.dat'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mKeyError\u001b[0m: ('-105', '67.5')" - ] + "data": { + "text/plain": [ + "(2000, 0)" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" } ], "source": [ - "w, d = cloud_depth('-105', '67.5') # returns wavelength (um), depth (km)" + "plt.plot(w, d*1.e-5)\n", + "plt.xlabel('Wavelength ($\\mu$m)')\n", + "plt.ylabel('Cloud Depth (km)')\n", + "plt.semilogx()\n", + "plt.ylim(2000, 0)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "w, p = cloud_depth('-180.0', '0.0', p_val=True) # returns wavelength (um), depth (dyne/cm^2)" ] }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 24, "metadata": {}, "outputs": [ { - "ename": "NameError", - "evalue": "name 'w' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mw\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0md\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mxlabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Wavelength ($\\mu$m)'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mylabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Cloud Depth (km)'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msemilogx\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maxvline\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mw\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m25\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mNameError\u001b[0m: name 'w' is not defined" - ] + "data": { + "text/plain": [ + "(1000.0, 1e-06)" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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v38uE+96msGdbHpk0Mi1WoX19VTE3P7qQznlN+eOkkXRv2yzZIUkCJCJxfItgr4yDVXw+DGjn7q/VKdIamNkg4DtAe+A1d7+/mnOVODLYym17+J9ZK3nro510atWEm887iatP657UvvgDZUeZ8Lt3KDlQxqzvnJ1We7Iv2LibSTPm0zgni0cmjWRQ51bJDknqKCGLHJpZNnCru/+mlkFMBy4FdhzbATBSPg64m2Ajp4fd/c4Y6soCHnL3yTWdq66qzPbuup38dvZHfLBhN53zmnDzeX25qrBbUhLI/31yMU8sKOJPk0ZxVr/29X7/sK0p3svXpn3A/rKjTLv+NEb21u6ODVlCJgC6ezkwoQ5xzADGVQosG7gPuBgoACaaWYGZnWxmL1T66hi55jLgbaDa1o0GxwVg9Ent+es3TufPk0fRpXVTfvzMMs775Zs8+v5GDh0pr7c4nlm0hcfnF/GtMX3TMmkA9M9vyVM3j6Zjy8ZcN+19Xlm+PdkhST2IZXD850Ae8Ffg+Ksr7r4wphv8857jZwA/dfcvRI5/EKnvjhjqetHdv1jN5+qqks/xyGZFv3l1DYs2fUqHlo25YXQvrhvVk7xm4c0BWf/JPsbf+zYFXVox88bT036Tqt37y5g0Yx5Lij7ljstP5urTeiQ7JKmFRM7jGB3582dRZQ6cX5vAgK7A5qjjImBUVSeb2RjgcqAxMKua86YAUwDKyjJjSW6pmZlxTv8OnN2vPe+u28WDc9bzy5dX8/s31nLNyB5MOqs3XVs3Teg9Dx0p55a/LCI3J4t7Jg5P+6QB0LZ5Ln+5cRTf/PNCvv/UUnbuK+PmMSc1iPk1Er8aE4e7n5fge57oJ6nKZo+7vwm8WVOl7j4VmArBGEctY5M0ZWac2bc9Z/Ztz4qte5g6Zx0z3t3AI+9uYPzQLtwwuhdDu7dOyL3+Z9ZKVmzbw/QbCumcl9iklMqa5ebw8PWFfO+Jxfzy5dXs3HeYH3+xoMFMdJTYxTRz3My+CAwmeB0XAHf/WdVXVKsI6B513A3YWsu6ROJW0KUVv71mON8bN5Dpb3/MYx9s4m+LtnBKtzyuG9WT8UO70DS3dgPpLy3dxh/nbuTGs3tz/sD8BEee+hplZ3HXVcNo16Ix097+mF37yvjVlUPJzUn/VlcmiWWM4wGgGXAe8DBwBfBBLG83Ra7vxefHOHKANcAFwBZgHvAVd19eu2/hn+mtKonH3kNH+NuiLfz5vY2sKd5HqyY5XDGiO9ed3iOupdw37z7AJfe8RZ8OLXjiG2dk9C9Ld+eBf6znF39fxdn92vPAdSNonqTdDSV2Cdtz3MyWuPspUX+2AJ5297ExBDETGEMwB6MYuN3dp5nZJcBvCV7Hne7uP6/5W4qdEofUhrvzwce7+dN7G3l5+XaOlDtn9m3H5cO7cdHg/GoXVCw7WsGVD85l/Sf7mHXr2ZoQF/H4vM3c9vQSTu7Wmj/ccFqDW2ol0yQycbzv7qPM7D2CQepdwDJ3T9l1E5Q4pK527D3E4/M2M/ODzWz59CC52Vmc0789XzylMxcOyqdlpSTy8xdX8NBbH3P/tady8cna9Cja7BXF3PKXhXRtE8wy79ZGSTVVJTJx/Bi4l6Br6T6CgeyH3P0niQg0DEockijuzqLNnzJryTZeXLqNbaWHyM3J4tz+Hbj0lM5cMCif99fvYvIj8/naGT352YQhNVeageZt2M3kGfNolpvDI5NGMqBTy2SHJCeQsMRRqdLGQBN3T8kZdprHIWGqqAiSyItLtjFr6Ta27wmSSE6W0atdc56+eXTKrI+VilZt38P10z/gYFk50284jcJemmWeahLZ4mgC3AycRdDaeBu4390PJSLQMKjFIWGrqHAWbirhhSXbWLqllF9ecUpa7Ykels27D3D99A/Y8ulBfn/tqVwwKPPePEtliUwcjwN7gT9HiiYCbdz9yjpHGRIlDpHUtWvfYb4+Yx7Lt+7hzstP5srC7jVfJPUikTPHB7j70KjjN8xsce1DE5FM1q5FY2beeDo3/XkB33tyCbv2l/GNc/polnkDEsuL5ovM7PRjB2Y2CngnvJBEJN01b5zDtOtPY/zQLtz50ip+/uJKKiq04ENDUWWLw8yWEoxpNAK+ZmabIsc9gRX1E158ogbHkx2KiNQgNyeLu68eRrvmuTz89sds2HWAr4zqzuiT2uslgxRX3UZOPau70N03hhJRAmiMQ6ThcHcenLOe372+ln2Hj9IsN5tz+3fgooJ8zh/YMSW3Ak5XidgBsIW776vhJjWekwxKHCINz+Gj5cxdt4vZK4p5dWUxxXsOk51ljOzVlosK8rmoIF8z8kOWiMTxGvAh8CywwN33R8r7EKxbdRXBRMAnExZ1HWkeh0h6qKhwlmwpZfaK7cxeUcya4uD/p4M6t+KignzGFuQzuEsrDagnWKK2jr0EuBY4E2gDHAVWAy8C09w9Jbf7UotDJL1s2Lmf2SuKmb2imPkbd1Ph0LV10+MtkZG929IoA/Y9CVsoM8dTnVocIulv177DvLZqB7NXFPPWR59w6EgFrZrkcP7AjlxU0IlzB3SghVbirZWMTBzHqMUhkhkOlpXz1kef8MqKYl5ftYPd+8vIzc5idN92QWtkUD4dWzWpuSIBMjRxqMUhkrnKK5wFG0t4Zfl2Zq8sZuOuAwAM696aiwry+cLgfE7q0ELjItVQ4lDiEMlY7s5HO/YFSWRFMYuLgnVZe7dvfnxwfXiPNmRrW9vPSWjiMLNsIJ+oCYPuvqlOEcbBzL4EfBHoCNzn7q9Ud766qkQk2vbSQ8xeGQyuz123kyPlTrvmuVwwqCNjCzpxVj9NOoTELnL4beB2gh38KiLF7u6nxBjIdOBSYMex7WMj5eOAuwl2AXzY3e+Moa42wK+q2rZWLQ4RqcneQ0d4c/UnzF5RzBurd7D30FGaNsrm7H7tGTu4E+cP7JixOxUmcpHD7xAsdLirlrHMAH4H/DEquGyCTaEuAoqAeWb2HEESuaPS9ZPcfUfk7z+KXCciUistmzRi/NAujB/ahbKjFbz/8a7jr/q+sqKYLIPCXm0ZW5DP2IJO9GinSYeVxdLieAO4yN2P1vomZr2AF461OMzsDOCn7v6FyPEPANy9ctI4dr0BdwKz3f3VKs6ZAkwB6NGjx4iNG1N2RRQRSUHuzrIte5i9YjuvrChm1fa9AAzIb8nYwcF8kZO75qX14HoiZo7/e+Svg4EBBJP+Dh/73N3viiOYXnw+cVwBjHP3f40cfxUY5e63VHH9rcD1wDzgQ3d/oLr7aYxDROpq8+4DvLKimNkrtvPBx8Gkw06tmhyfdHh6n3bk5qTXpMNEdFUd2xR4U+QrN/IFwSq5dXGilF1lne5+D3BPHe8pIhKz7m2bMfms3kw+qzcl+8t4PTLp8MkFRfzpvY20bJzDmIEduaggnzEDOtCqSaNkh1xvqkwc7v6fAGZ2pbs/Ef2ZmdV1978iIHrbr27A1jrWKSISijbNc/nyiG58eUQ3Dh0p5521O48vxvj84q00yjZO79OOsQX5XFiQT+e8pskOOVSxjHEsdPdTayqroY5efL6rKgdYA1wAbCHogvqKuy+PK/oqqKtKROpDeYXz4eaSoEtreTHrd+4H4JRueVw0KJ+xgzvRP7/hTDpMxBjHxcAlBKvg/jXqo1ZAgbuPjDGQmcAYoD3BK723u/u0yAKKvyV4k2q6u/88lvpiocQhIsmwdse+yNtZ21m06VMAerRtdnzS4YiebchJ4cUYE5E4hgLDgJ8BP4n6aC/whruXJCLQMChxiEiy7dh7iNdW7uCV5dt5Z90uyo5W0KZZI84fmM/Ywfmc068DTXNTa9JhIicANiIYzB5IMIC92t3LEhJlgmkCoIikon2HjzJnTTDp8PVVOyg9eITGOVmc3a8DYwvyOX9QR9q3aJzsMBOaOC4BHgTWESSQ3sA33P2lRAQaBrU4RCRVHSmvYN7HuyOv+haz5dODmEFhzzaRV3070bt986TElsjEsQq41N3XRo5PAl5094EJiTQEShwi0hC4Oyu27QnGRZYXs2LbHgD6dWxxfL7I0G6tyaqnxRgTmTjmuPs5UccG/CO6LNUocYhIQ1RUcoBXI0ufvP/xbsornI4tG3NhJImMPqkdjXPCGxdJZOK4H+gJPE4wxnElwfax7wC4+9N1jjbBlDhEpKErPXCEN1bv4JUV2/nH6k/YX1ZO89xsxgzoyNjB+YwZ0JG8pomddJjIxPGHaj52d58Ub3Bh0eC4iKSjQ0fKmbt+F68sDyYdfrL3MDlZxqg+bRlb0IkLC/Lp2rrukw4zciOnY9TiEJF0VVHhLC769Pjg+tod+wAY3KUVFxXkc1Vhd7rUMokkbFl1M+sP3A/ku/sQMzsFuMzd/7tWkYmISK1lZRnDe7RheI82fH/cQNZ/su/4svB3v/YRYwZ0rHXiiFUsXVX/AL4HPOjuwyNly6I3ZUoV6qoSkUy2c99h2jbLrfVbWLG2OGKZ+97M3T+oVFbrvTnC5O7Pu/uUvLy8ZIciIlLv2rdoXC+v7saSOHZG5m44HN9LY1uoUdWSmY03s6mlpaXJDkVEJG3Fkji+RTBzfKCZbQH+Dbgp1KhqSS0OEZHw1Tg47u7rgQvNrDmQ5e57ww+rdqLGOJIdiohI2qq2xWFmA8zs12b2IsEEwNsjb1mlJLU4RETCV2XiMLMzgDcJllGfCjwE7AfeNLPT6yW6II4+ZjbNzJ6M4VyNcYiIhKy6FsdPgInu/lN3f9bdn3H324GJwO2xVG5m081sh5ktq1Q+zsxWm9laM7utujrcfb27T47lfiIiEr7qEsdJ7v5m5UJ3/wfQJ8b6ZwDjogvMLBu4D7gYKAAmmlmBmZ1sZi9U+uoY431ERKSeVDc4Xt0g+P5YKnf3OZH9xqONBNZGBt0xs8eACe5+B3BpLPWeiJlNAaYAlJWl5D5TIiJpobrE0d3M7jlBuQFd63DPrsDmqOMiYFRVJ5tZO+DnwHAz+0EkwfwTd59KMBZDYWFh+i3AJSKSIqpLHN+r5rO6rCB4ommNVf6id/ddpOi8ERGRTFRl4nD3R0K6ZxHQPeq4G7A1pHuJiEiCxTJzPNHmAf3MrLeZ5QLXAM8lIQ4REamFUBOHmc0E5gIDzKzIzCa7+1HgFuBlYCXwuLsvDzMOERFJnBqXHKkLd59YRfksYFaY9xYRkXBUmTjM7F6qH7S+NZSIREQkpVXXVTUfWAA0AU4FPop8DQPKww8tflpyREQkfLHsAPgGMNbdj0SOGwGvuPt59RBfrWjPcRGR+CVyB8AuQMuo4xaRMhERyUCxDI7fCSyKtDwAzgV+GlpEIiKS0mLZyOkPZvYSny0Lcpu7bw83rNrRRk4iIuGrsavKzM4B+gMlka/+kbKUo42cRETCF0tXVfSaVU0IVrddAJwfSkQiIpLSYumqGh99bGbdgf8NLaI6UFeViEj4arPkSBEwJNGBJIK6qkREwldji6PSDPIsggmAi8MMqrbU4hARCV8sEwCvjzo8Cmxw93dCjaqONAFQRCR+sU4AjGWM45HI8uf9I0Wr6xpcWNTiEBEJXyyv444hWKPqPuD3wBq9jisikrlieR331wRrVa0GMLP+wExgRJiBHWNmzQkSVhnwprs/Ws25anGIiIQslreqGh1LGgDuvgZoVJebmtl0M9thZssqlY8zs9VmttbMbosUXw486e43ApfV5b4iIlJ3sSSO+WY2zczGRL4eIpgAWBczgHHRBWaWTdAddjFQAEw0swKCPck3R05LyeXcRUQySSyJ45vAcuBW4DvACuCmutzU3ecAuysVjwTWuvt6dy8DHgMmEMwb6RZLvBrjEBEJXyxvVR0G7op8hakrn7UsIEgYo4B7gN+Z2ReB56u62MymAFMAevToEWKYIiKZrbqtY5dS/daxpyQ4FjvxbXw/8PWaLnb3qcBUCOZxJDg2ERGJqK7FcWm9RREoArpHHXcDttZzDCIiUoPqxgwaAd3cfWP0F9CD2F7jjdc8oJ+Z9Y5MOLwGeC6E+4iISB1Ulzh+C+w9QfnByGe1ZmYzgbnAADMrMrPJ7n4UuAV4GVgJPO7uy+tyHxERSbzqWg693H1J5UJ3n29mvepyU3efWEX5LGCwbFKMAAALBklEQVRWXeoWEZFwVdfiaFLNZ00THYiIiDQM1SWOeWZ2Y+VCM5tM3ScAhsLMxpvZ1NLS0mSHIiKStqpcVt3M8oG/EawRdSxRFAK5wL+4+/Z6ibAWtKy6iEj86rysursXA6PN7Dw+2/HvRXd/PUExiohIAxTLzPE3gDfqIRYREWkAarPneMrSGIeISPjSKnFokUMRkfClVeIQEZHwpVXiUFeViEj40ipxqKtKRCR8aZU41OIQEQlfWiUOtThERMKXVolDLQ4RkfClVeJQi0NEJHwpnzjMrI+ZTTOzJ2M4Vy0OEZGQJSVxmNl0M9thZssqlY8zs9VmttbMbgNw9/XuPjkZcYqIyD9LVotjBjAuusDMsoH7gIuBAmCimRXEU6m6qkREwpeUxOHuc4DdlYpHAmsjLYwy4DFgQjz1qqtKRCR8qTTG0RXYHHVcBHQ1s3Zm9gAw3Mx+UNXFZjYFuB04taysLNxIRUQyWI3LqtcjO0GZu/su4KaaLnb3qcBUCDZySnBsIiISkUotjiKge9RxN2BrkmIREZEqpFLimAf0M7PeZpYLXAM8l+SYRESkkmS9jjsTmAsMMLMiM5vs7keBW4CXgZXA4+6+PBnxiYhI1ZIyxuHuE6sonwXMqudwREQkDqnUVSUiIg1AWiUOzeMQEQlfWiUOzRwXEQlfWiUOEREJnxKHiIjEJa0Sh8Y4RETCl1aJQ2McIiLhS6vEISIi4UurxKGuKhGR8KVV4lBXlYhI+NIqcYiISPjSKnGoq0pEJHxplThERCR8aZU4NMYhIhK+lE8cZvYlM3vIzJ41s7E1nKuuKhGRkCVrI6fpZrbDzJZVKh9nZqvNbK2Z3Qbg7s+4+43ADcDVSQhXRESiJKvFMQMYF11gZtnAfcDFQAEw0cwKok75UeTzKqmrSkQkfElJHO4+B9hdqXgksNbd17t7GfAYMMECvwBecveF1dWrrioRkfAlZevYKnQFNkcdFwGjgG8DFwJ5ZtbX3R840cVmNgWYAlBWVhZyqCIimSuVEoedoMzd/R7gnpoudvepwFSAwsJCT3BsIiISkUpvVRUB3aOOuwFbkxSLiIhUIZUSxzygn5n1NrNc4BrguSTHJCIilSTrddyZwFxggJkVmdlkdz8K3AK8DKwEHnf35cmIT0REqpaUMQ53n1hF+SxgVj2HIyIicUilrioREWkA0ipxaB6HiEj40ipxaOa4iEj40ipxiIhI+JQ4REQkLmmVODTGISISvrRKHBrjEBEJX1olDhERCV9aJQ51VYmIhC+tEoe6qkREwpdWiUNERMKXVolDXVUiIuFLq8ShrioRkfClVeJQi0NEJHwpnzjMbJCZPWBmT5rZN5Mdj4hIpkvWRk7TzWyHmS2rVD7OzFab2Vozuw3A3Ve6+03AVUBhdfWqq0pEJHzJanHMAMZFF5hZNnAfcDFQAEw0s4LIZ5cBbwOvVVepuqpERMKXlMTh7nOA3ZWKRwJr3X29u5cBjwETIuc/5+6jgWvrN1IREaksKVvHVqErsDnquAgYZWZjgMuBxlSzrayZTQGmAKxdu3afma2O4955QF2bKfHWEcv5NZ1T1efxlFcuaw/srCGuRGuIzz/ez2Ita4jPP4xnX9N5YTz/hvjsa1NHdef3jKkGd0/KF9ALWBZ1fCXwcNTxV4F76ymWqfVdRyzn13ROVZ/HU165DJifhJ+FBvf84/0sjrIG9/zDePbJeP4N8dmH+fyr+0qlt6qKgO5Rx92ArfV07+eTUEcs59d0TlWfx1OeiO+9rhri84/3s1R99lD3OMJ49jWdly7PP1V/9qtlkQxU78ysF/CCuw+JHOcAa4ALgC3APOAr7r48KQFmKDOb7+7Vvr0m4dHzTx49+9gl63XcmcBcYICZFZnZZHc/CtwCvAysBB5X0kiKqckOIMPp+SePnn2MktbiEBGRhimVxjhERKQBUOIQEZG4KHGIiEhclDikSmbW3MweMbOHzEyz9uuZmfUxs2lm9mSyY8lEZvalyM/+s2Y2NtnxpBIljgwTzwKTBDP2n3T3G4HL6j3YNBTnAp/r3X1yciJNT3E+/2ciP/s3AFcnIdyUpcSReWYQ+wKT3fhsGZjyeowxnc0gjgU+JeFmEP/z/1Hkc4lQ4sgwHt8Ck0UEyQP0s5IQcT5/SbB4nr8FfgG85O4L6zvWVKZfBgInXmCyK/A08GUzu5/UWJ4hXZ3w+ZtZOzN7ABhuZj9ITmgZoaqf/28DFwJXmNlNyQgsVaXS6riSPHaCMnf3/cDX6zuYDFTV898F6BdW+Kp6/vcA99R3MA2BWhwCyV1gUvT8k03PP05KHALBgpL9zKy3meUC1wDPJTmmTKLnn1x6/nFS4sgwWmAyufT8k0vPPzG0yKGIiMRFLQ4REYmLEoeIiMRFiUNEROKixCEiInFR4hARkbgocYiISFyUOEREJC5KHCIiEhclDmnwzOw3ZvZvUccvm9nDUce/NrN/T/A99yW4vtZmdnPUca/Kmw1Vc21TM/tHZF+JusaRa2ZzzEwLoEqVlDgkHbwLjAYwsyygPTA46vPRwDtJiCserYGbazzrxCYBT7t7nTfbiuxH8Rra8U6qocQh6eAdIomDIGEsA/aaWRszawwMAhaZ2TNmtsDMlpvZlGMXm9kvKv1v/6dm9h9mdp2ZfWBmH5rZgyf6H31V50RaDCsje1YvN7NXzKxp5LMfm9kqM5ttZjPN7LvAncBJkXp+Gak++0TXn8C1wLNRMb1pZgMif29nZssi8awys4cjx4+a2YVm9o6ZfWRmI6PqeyZSp8gJKXFIg+fuW4GjZtaDIIHMBd4HzgAKgSWR/0lPcvcRkbJbzaxdpIrH+Pz/sK8C5kfKznT3YQRb537ul6mZDarhnH7Afe4+GPiUYFOsQuDLwHCCPd0LI+feBqxz92Hu/r2qrq/8vUdWc+3j7huiivsCH0X+fgqwNKr87kjZQOArwFnAd4EfRl2/DDit8r1EjlE/pqSLY62O0cBdBDu4jQZKCbqyIEgW/xL5e3eCX8y73H2RmXU0sy5AB6AEOBkYAcwzM4CmwI5K97yghnM+dvcPI39fAPQi6EZ71t0PAphZdTsrnuj6ytoTJBUi9fUEtrh7RaToFGBJVH1LI+ctB15zdzezpdF1u3u5mZWZWUt331tNfJKhlDgkXRwb5ziZ4H/Mm4H/APYA081sDME2oGe4+wEzexNoEnX9k8AVQCeCFogBj7h7dVu21nTO4ai/lxMklhPtNleVE11f2UE+/30M47NEAUFi++sJ6quIOq7gn38XNAYOxRGrZBB1VUm6eAe4FNjt7uXuvptgwPkMgq6rPKAkkjQGAqdXuv4xgg18riBIIq8R7DXdEcDM2kb+Nx8tlnMqexsYb2ZNzKwF8MVI+V6gZbzftLuXEIyFHEseQ4kkEjPrB0zgs66qmES68D5x9yPxxiOZQYlD0sVSgm6b9yqVlbr7TuDvQI6ZLQH+q9J5RDbuaUnQzbPN3VcAPwJeiVwzG+hc6Zoaz6nM3ecR7C63GHiaYCylNLK/+DuRgetfVlfHCbxCMFYBQYsjy8wWAz8h2Jjo+jjrOw+YFec1kkG0kZNIPTOzFu6+z8yaAXOAKe6+sA71DQf+3d2/amZrgeF1GZsws6eBH7j76trWIelNYxwi9W+qmRUQdCk9UpekARAZ3H/DzPKAijomjVzgGSUNqY5aHCIiEheNcYiISFyUOEREJC5KHCIiEhclDhERiYsSh4iIxEWJQ0RE4qLEISIicVHiEBGRuPx/wX13Feo+2LAAAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" } ], "source": [ - "plt.plot(w, d)\n", + "plt.plot(w, p*1.e-6)\n", "plt.xlabel('Wavelength ($\\mu$m)')\n", - "plt.ylabel('Cloud Depth (km)')\n", - "plt.semilogx()\n", - "plt.axvline(w[25])" + "plt.ylabel('Cloud Depth (bar)')\n", + "plt.loglog()\n", + "plt.ylim(1.e3, 1.e-6)" ] }, { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "cloud-academy", "language": "python", - "name": "python3" + "name": "cloud-academy" }, "language_info": { "codemirror_mode": { @@ -470,7 +535,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.4" + "version": "3.6.6" } }, "nbformat": 4, diff --git a/notebooks/test_gas_opac.ipynb b/notebooks/test_gas_opac.ipynb new file mode 100644 index 0000000..d688348 --- /dev/null +++ b/notebooks/test_gas_opac.ipynb @@ -0,0 +1,794 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import h5py\n", + "from scipy.ndimage import gaussian_filter1d as gauss_conv\n", + "from numba.decorators import jit\n", + "import matplotlib.pyplot as plt\n", + "from matplotlib.ticker import MultipleLocator, FormatStrFormatter\n", + "\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/Users/lia/dev/cloudacademyMap/gas_opac\n" + ] + } + ], + "source": [ + "cd ~/dev/cloudacademyMap/gas_opac/" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "import astropy.units as u" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "import opacity_demo as demo" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Reading opacity database file\n", + "H2O done\n", + "CH4 done\n", + "NH3 done\n", + "HCN done\n", + "CO done\n", + "CO2 done\n" + ] + } + ], + "source": [ + "# Specify which molecules you want to extract from the database (full list available in the readme)\n", + "chemical_species = np.array(['H2O', 'CH4', 'NH3', 'HCN', 'CO', 'CO2'])\n", + "\n", + "# At what temperature and pressure do you desire the cross sections?\n", + "P = 1.0e-3 # Pressure (bar)\n", + "T = 1000.0 # Temperature (K) \n", + "\n", + "# Specify wavelength grid to extract cross section onto\n", + "wl_min = 0.4 # Minimum wavelength of grid (micron)\n", + "wl_max = 5.0 # Maximum wavelength of grid (micron)\n", + "N_wl = 1000 # Number of wavelength points\n", + "\n", + "wl = np.linspace(wl_min, wl_max, N_wl) # Uniform grid used here for demonstration purposes \n", + "\n", + "# Either sample the nearest wavelength points from the high resolution (R~10^6) cross section database or use an averaging prescription \n", + "opacity_treatment = 'Log-avg' # Options: Opacity-sample / Log-avg\n", + "#opacity_treatment = 'Opacity-sample' # Opacity sampling is faster, but for low-resolution wavelength grids log averaging is recommended\n", + " \n", + "# Extract desired cross sections from the database\n", + "cross_sections = demo.Extract_opacity(chemical_species, P, T, wl, opacity_treatment) # Format: np array(N_species, N_wl) / Units: (m^2 / species)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "# Example: seperate H2O cross section, and print to terminal\n", + "H2O_cross_section = cross_sections['H2O'] # Format: np array(N_wl) / Units: (m^2 / molecule)\n", + "#print (H2O_cross_section)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "# Copy/paste plot function from opacity_demo.py because I'm getting some weird errors\n", + "\n", + "def plot_opacity(chemical_species, sigma_stored, P, T, wl_grid, **kwargs):\n", + "\n", + " # Max number of species this can plot is 9 (clustered beyond that!) \n", + "\n", + " # Optional smoothing of cross sections (can improve clarity) \n", + " smooth = False\n", + " smooth_factor = 5\n", + "\n", + " # Specify cross sections to plot, along with colours for each \n", + " #colours_plot = np.array(['royalblue', 'purple', 'crimson', 'orange', 'black', 'grey', 'green', 'magenta', 'chocolate']) \n", + "\n", + " # Initialise plot \n", + " #ax = plt.gca()\n", + " #ax.set_xscale(\"log\") \n", + "\n", + " ax = plt.subplot(111)\n", + " #xmajorLocator = MultipleLocator(1.0)\n", + " #xmajorFormatter = FormatStrFormatter('%.1f')\n", + " #xminorLocator = MultipleLocator(0.2)\n", + "\n", + " #ax.xaxis.set_major_locator(xmajorLocator)\n", + " #ax.xaxis.set_major_formatter(xmajorFormatter)\n", + " #ax.xaxis.set_minor_locator(xminorLocator)\n", + "\n", + " # Plot each cross section \n", + " for species in chemical_species:\n", + " #species_idx = np.where(chemical_species == species)[0][0]\n", + " sigma_plt = sigma_stored[species]*1.0e4 # Cross section of species q at given (P,T) pair (cm^2) \n", + "\n", + " if (smooth == True):\n", + " sigma_plt = gauss_conv(sigma_plt, sigma=smooth_factor, mode='nearest')\n", + "\n", + " # Plot cross section \n", + " plt.semilogy(wl_grid, sigma_plt, label = species, **kwargs)\n", + "\n", + " plt.ylim([1.0e-28, 2.0e-18])\n", + " plt.xlim([min(wl_grid), max(wl_grid)])\n", + " plt.ylabel(r'$\\mathrm{Cross \\, \\, Section \\, \\, (cm^{2})}$', fontsize = 15)\n", + " plt.xlabel(r'$\\mathrm{Wavelength} \\; \\mathrm{(\\mu m)}$', fontsize = 15)\n", + "\n", + " ax.text(min(wl_grid)*1.05, 6.0e-19, (r'$\\mathrm{T = }$' + str(T) + r'$\\mathrm{K \\, \\, P = }$' + str(P*1000) + r'$\\mathrm{mbar}$'), fontsize = 14)\n", + "\n", + " legend = plt.legend(loc='upper right', frameon=False, prop={'size':6}, ncol=2)\n", + "\n", + " '''for legline in legend.legendHandles:\n", + " legline.set_linewidth(1.0)'''\n", + " return" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Plot cross sections\n", + "plot_opacity(chemical_species, cross_sections, P, T, wl, lw=0.5, alpha=0.8)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Grab all the opacities possible\n", + "\n", + "Based on gases present both in Ryan's library and in the static weather output." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "# Grabbed from Readme.txt\n", + "RMcD_gas = np.array(['H3+', 'Na', 'K', 'Li', 'Rb', 'Cs', 'H2', 'H2O', 'CH4', 'NH3', 'HCN', 'CO', \\\n", + " 'CO2', 'C2H2', 'H2S', 'N2', 'O2', 'O3', 'OH', 'NO', 'SO2', 'PH3', 'TiO', 'VO', \\\n", + " 'ALO', 'SiO', 'CaO', 'TiH', 'CrH', 'FeH', 'ScH', 'AlH', 'SiH', 'BeH', 'CaH', \\\n", + " 'MgH', 'LiH', 'SiH', 'CH', 'SH', 'NH'])\n", + "\n", + "RMcD_gas_upper = np.array([g.upper() for g in RMcD_gas])" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([False, False, False, False, False, False, False, False, False,\n", + " False, False, False, False, False, False, False, False, False,\n", + " False, False, False, False, True, False, False, False, False,\n", + " False, False, False, False, False, False, False, False, False,\n", + " False, False, False, False, False])" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.array(RMcD_gas_upper) == 'TIO'" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/Users/lia/dev/cloudacademyMap\n" + ] + } + ], + "source": [ + "cd .." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "from maplib import load_out3" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/Users/lia/Dropbox/Science/cloud-academy/Les_Houches_Cloud_Activity\n" + ] + } + ], + "source": [ + "cd ~/Dropbox/Science/cloud-academy/Les_Houches_Cloud_Activity/" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "# An example file to work from. I hope all of the reported columns are the same\n", + "PHI, THETA = -180, 0\n", + "\n", + "# Get gas information\n", + "thermo = load_out3('thermo', PHI, THETA)\n", + "\n", + "# Get wavelengths for later\n", + "wavel = load_out3('wavel', PHI, THETA)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['H', 'Na', 'Mg', 'Fe', 'Ca', 'Al', 'Ti', 'C', 'TIO', 'TIO2', 'TIS', 'SIO', 'SIS', 'SIH', 'H2O', 'H2', 'H2S', 'MGH', 'MGOH', 'MG(OH)2', 'FEO', 'FES', 'FEH', 'FE(OH)2', 'ALOH', 'ALH', 'AL2O', 'ALO2H', 'CAO', 'CA(OH)2', 'CAS', 'CAOH', 'CAH', 'C2', 'C3', 'C2H', 'C2H2', 'CH4']\n" + ] + } + ], + "source": [ + "thermo_gas = []\n", + "for k in thermo.keys():\n", + " x = k.split('n_')\n", + " if len(x) == 2:\n", + " thermo_gas.append(x[1])\n", + "print(thermo_gas)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "# Gases in static_weather model that are also in Ryan's opacity library\n", + "gases, gases_missing = [], []\n", + "for g in thermo_gas:\n", + " if g in RMcD_gas_upper:\n", + " ig = np.where(RMcD_gas_upper == g)[0]\n", + " gases.append(RMcD_gas[ig][0])\n", + " else:\n", + " gases_missing.append(g)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Gases in both static_weather and Ryan's opacity library: (13 total)\n", + "['TiO', 'SiO', 'SiH', 'H2O', 'H2', 'H2S', 'MgH', 'FeH', 'AlH', 'CaO', 'CaH', 'C2H2', 'CH4']\n", + "\n", + "Gases missing opacities: (25 total)\n", + "['H', 'Na', 'Mg', 'Fe', 'Ca', 'Al', 'Ti', 'C', 'TIO2', 'TIS', 'SIS', 'MGOH', 'MG(OH)2', 'FEO', 'FES', 'FE(OH)2', 'ALOH', 'AL2O', 'ALO2H', 'CA(OH)2', 'CAS', 'CAOH', 'C2', 'C3', 'C2H']\n" + ] + } + ], + "source": [ + "print(\"Gases in both static_weather and Ryan's opacity library: ({} total)\".format(len(gases)))\n", + "print(gases)\n", + "print(\"\\nGases missing opacities: ({} total)\".format(len(gases_missing)))\n", + "print(gases_missing)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/Users/lia/dev/cloudacademyMap/gas_opac\n" + ] + } + ], + "source": [ + "cd ~/dev/cloudacademyMap/gas_opac" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Takes a few minutes to read in**" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Reading opacity database file\n", + "TiO done\n", + "SiO done\n", + "SiH done\n", + "H2O done\n", + "H2 done\n", + "H2S done\n", + "MgH done\n", + "FeH done\n", + "AlH done\n", + "CaO done\n", + "CaH done\n", + "C2H2 done\n", + "CH4 done\n", + "CPU times: user 2min, sys: 54.3 s, total: 2min 54s\n", + "Wall time: 3min 17s\n" + ] + } + ], + "source": [ + "%%time\n", + "sw_xsects = demo.Extract_opacity(np.array(gases), P, T, wavel, opacity_treatment)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(0.2, 330)" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plot_opacity(np.array(gases), sw_xsects, P, T, wavel, marker='o', ls=':', alpha=0.5)\n", + "plt.semilogx()\n", + "plt.xlim(0.2, 330)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "304" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(thermo['p'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## To do next\n", + "\n", + "[x] Load opacities for each (p, T) value\n", + "\n", + "[x] Calculate dtau_dz for each element and vertical profile.\n", + "\n", + "[x] Save the dtau_dz profiles to a file.\n", + "\n", + "[ ] Write a function for reading in file.\n", + "\n", + "[ ] Write a function for summing the gas contributions to dtau/dz.\n", + "\n", + "[ ] Add those dtau_dz values to the atmosphere depth calculation." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Look at densities as a function of temperature" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "for g in gases:\n", + " plt.plot(thermo['T'], thermo['n_' + g.upper()], label=g)\n", + "plt.semilogy()\n", + "plt.xlim(500, 3000)\n", + "plt.xlabel('Temperature (K)')\n", + "plt.ylabel('$n_Z$')\n", + "plt.legend(loc='lower right', frameon=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "# Sanity check for pressure, temperature interpolation\n", + "# Suggested by Ryan\n", + "#T0 = [100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 1200, 1400, 1600, 1800, 2000, 2500, 3000, 3500]\n", + "#T1 = np.arange(100, 3000, 50)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Reading opacity database file\n", + "TiO done\n", + "SiO done\n", + "SiH done\n", + "H2O done\n", + "H2 done\n", + "H2S done\n", + "MgH done\n", + "FeH done\n", + "AlH done\n", + "CaO done\n", + "CaH done\n", + "C2H2 done\n", + "CH4 done\n", + "CPU times: user 6min 11s, sys: 51 s, total: 7min 2s\n", + "Wall time: 7min 19s\n" + ] + } + ], + "source": [ + "%%time\n", + "opacity_treatment = 'Log-avg'\n", + "test = demo.Extract_opacity_PTpairs(np.array(gases), thermo['p'], thermo['T'], wavel, opacity_treatment)" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0.00000000e+00, 0.00000000e+00, 2.14347212e-22, 4.47281423e-22,\n", + " 1.29905700e-21, 3.53249151e-22, 1.04683496e-22, 2.52623100e-23,\n", + " 8.56768717e-24, 1.58196485e-24, 9.39862143e-26, 3.68280847e-26,\n", + " 2.96488882e-27, 5.33163439e-28, 3.14810018e-29, 3.65333659e-30,\n", + " 3.32698108e-30, 1.56147535e-30, 2.14301779e-31, 1.40997078e-29,\n", + " 6.98666068e-31, 2.51045302e-27, 2.40541312e-30, 2.66165606e-31,\n", + " 5.96403292e-30, 4.35892019e-29, 7.09350429e-33, 8.54948670e-28,\n", + " 1.86176468e-34, 1.76090418e-32, 0.00000000e+00])" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "test['TiO'][0]" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "def calc_dtau_dz(gas, opac_dict):\n", + " n_z = thermo['n_' + gas.upper()]\n", + " sigma = opac_dict[gas]\n", + " \n", + " NP = len(n_z) # number of vertical data points\n", + " NWL = len(sigma[0,:]) # number of wavelengths\n", + " \n", + " result = np.zeros(shape=(NP, NWL))\n", + " for i in range(NP):\n", + " result[i,:] = n_z[i] * sigma[i,:] # cm^-1\n", + " return result" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "dtau_dz = dict()\n", + "for g in gases:\n", + " dtau_dz[g] = calc_dtau_dz(g, test)" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "iw = 10\n", + "p = thermo['p'] * u.dyne / u.cm**2\n", + "for g in gases:\n", + " plt.plot(dtau_dz[g][:,iw], p.to(u.bar), label=g)\n", + "plt.loglog()\n", + "plt.ylim(1.e3, 1.e-6)\n", + "plt.xlim(1.e-35, 1.e-8)\n", + "plt.xlabel(r'$d\\tau/dz$ (cm$^{-1}$)')\n", + "plt.ylabel('p (bar)')\n", + "plt.legend(loc='lower left', frameon=False, ncol=3)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Make a 3D array and save it\n", + "\n", + "I'm going to use a FITS file for now. Seems like the simplest thing??" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [], + "source": [ + "from astropy.io import fits" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [], + "source": [ + "def write_opac_fits(opac_dict, filename):\n", + " hdr = fits.Header()\n", + " hdr['COMMENT'] = \"dtau/dz for gas phase elements of HATP-7b\"\n", + " hdr['COMMENT'] = \"Made from opacity tables of R. MacDonald\"\n", + " hdr['COMMENT'] = \"See out3_*.dat files for p, T, and z profiles\"\n", + " \n", + " primary_hdu = fits.PrimaryHDU(header=hdr)\n", + " hdus_to_write = [primary_hdu]\n", + " for k in opac_dict.keys():\n", + " hdus_to_write.append(fits.ImageHDU(opac_dict[k], name=k))\n", + " \n", + " hdu_list = fits.HDUList(hdus=hdus_to_write)\n", + " hdu_list.writeto(filename, overwrite=True)\n", + " return" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [], + "source": [ + "write_opac_fits(test, 'test.fits')" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [], + "source": [ + "tt = fits.open('test.fits')" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Filename: test.fits\n", + "No. Name Ver Type Cards Dimensions Format\n", + " 0 PRIMARY 1 PrimaryHDU 7 () \n", + " 1 TIO 1 ImageHDU 8 (31, 304) float64 \n", + " 2 SIO 1 ImageHDU 8 (31, 304) float64 \n", + " 3 SIH 1 ImageHDU 8 (31, 304) float64 \n", + " 4 H2O 1 ImageHDU 8 (31, 304) float64 \n", + " 5 H2 1 ImageHDU 8 (31, 304) float64 \n", + " 6 H2S 1 ImageHDU 8 (31, 304) float64 \n", + " 7 MGH 1 ImageHDU 8 (31, 304) float64 \n", + " 8 FEH 1 ImageHDU 8 (31, 304) float64 \n", + " 9 ALH 1 ImageHDU 8 (31, 304) float64 \n", + " 10 CAO 1 ImageHDU 8 (31, 304) float64 \n", + " 11 CAH 1 ImageHDU 8 (31, 304) float64 \n", + " 12 C2H2 1 ImageHDU 8 (31, 304) float64 \n", + " 13 CH4 1 ImageHDU 8 (31, 304) float64 \n" + ] + } + ], + "source": [ + "tt.info()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "cloud-academy", + "language": "python", + "name": "cloud-academy" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.6" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/notebooks/test_map_cloud.ipynb b/notebooks/test_map_cloud.ipynb index 56b95a2..2c8b200 100644 --- a/notebooks/test_map_cloud.ipynb +++ b/notebooks/test_map_cloud.ipynb @@ -27,7 +27,9 @@ "outputs": [], "source": [ "import matplotlib.cm as cm\n", - "from mpl_toolkits.basemap import Basemap" + "from matplotlib.colors import LogNorm\n", + "from mpl_toolkits.basemap import Basemap\n", + "import astropy.units as u" ] }, { @@ -81,6 +83,28 @@ "cell_type": "code", "execution_count": 7, "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1e-06\n" + ] + } + ], + "source": [ + "P_UNIT = u.dyne / (u.cm**2)\n", + "P_CONVERT = P_UNIT.to(u.bar)\n", + "\n", + "pmin, pmax = 1.e-6, 1.e3\n", + "\n", + "print(P_CONVERT)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, "outputs": [], "source": [ "# Read in the cloud depth values for each file.\n", @@ -89,8 +113,8 @@ "Z = np.zeros((NLO, NLA, NWA))\n", "for i in range(NLO):\n", " for j in range(NLA):\n", - " w, d = cloud_depth(stringy(lons[i]), stringy(lats[j]))\n", - " Z[i,j,:] = d\n", + " w, d = cloud_depth(stringy(lons[i]), stringy(lats[j]), p_val=True)\n", + " Z[i,j,:] = d * P_CONVERT\n", "\n", "# The 180 longitude is same as -180\n", "#Z[0,:,:] = Z[-1,:,:]" @@ -98,22 +122,22 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 8, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", + "image/png": 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\n", "text/plain": [ "
" ] @@ -127,22 +151,22 @@ "source": [ "# rough sketch of where we are using imshow\n", "im = Z[:,:,20]\n", - "plt.imshow(im)\n", - "plt.colorbar(label='Cloud Depth (km)')" + "plt.imshow(im, norm=LogNorm(), vmin=pmin, vmax=pmax)\n", + "plt.colorbar(label='Cloud Depth (bar)')" ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "931.2627714150648" + "12.7180111267731" ] }, - "execution_count": 9, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -160,7 +184,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -169,7 +193,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -178,13 +202,13 @@ "Text(0.5, 1.0, 'HAT-P-7b, 1.0 $\\\\mu$m')" ] }, - "execution_count": 11, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -201,16 +225,18 @@ "m = Basemap(projection='kav7',lon_0=0,resolution=None)\n", "\n", "nlev = 21\n", - "#levels = np.linspace(np.amin(Z),np.amax(Z),nlev)\n", - "levels = np.linspace(0, 10000, nlev)\n", + "#levels = np.logspace(-6, 3, nlev)\n", + "levels = np.linspace(-6, 3, nlev)\n", "\n", "cmap = plt.cm.RdYlBu_r\n", "\n", "iw = 10\n", - "CS = m.contourf(X,Y,Z[:,:,iw].T,levels=levels,extend='both',cmap=cmap,latlon=True)\n", + "CS = m.contourf(X, Y, np.log10(Z[:,:,iw].T), levels=levels, \n", + " extend='both', cmap=cmap, latlon=True)\n", "\n", "# mirror southern-hemisphere\n", - "CS = m.contourf(X, -Y, Z[:,:,iw].T, levels=levels, extend='both', cmap=cmap, latlon=True)\n", + "CS = m.contourf(X, -Y, np.log10(Z[:,:,iw].T), levels=levels, \n", + " extend='both', cmap=cmap, latlon=True)\n", "\n", "def fmtfunc(inlon):\n", " string = r'{:g}'.format(inlon)+r'$^{\\circ}$'\n", @@ -224,22 +250,23 @@ "m.drawparallels(parallels, labels=[True,False,False,False],\n", " labelstyle='+/-', dashes=[3,3], color='k',fontsize=16)\n", "\n", - "plt.colorbar(label='Cloud Depth (km)')\n", + "labels = ['{:.1f}'.format(x) for x in levels[::3]]\n", + "\n", + "plt.colorbar(label='log Depth (bar)', ticks=np.arange(-5, 4)[::2], orientation='horizontal')\n", "plt.title('HAT-P-7b, {:.1f} $\\mu$m'.format(wavel[iw]))" ] }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "[ 0. 500. 1000. 1500. 2000. 2500. 3000. 3500. 4000. 4500.\n", - " 5000. 5500. 6000. 6500. 7000. 7500. 8000. 8500. 9000. 9500.\n", - " 10000.]\n" + "[-6. -5.55 -5.1 -4.65 -4.2 -3.75 -3.3 -2.85 -2.4 -1.95 -1.5 -1.05\n", + " -0.6 -0.15 0.3 0.75 1.2 1.65 2.1 2.55 3. ]\n" ] } ], @@ -258,7 +285,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ @@ -267,12 +294,12 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 16, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -289,7 +316,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 17, "metadata": {}, "outputs": [ { @@ -298,7 +325,7 @@ "(25, 4, 31)" ] }, - "execution_count": 15, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } @@ -307,26 +334,6 @@ "mcd.Z.shape" ] }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(25, 4, 31)" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Z.shape" - ] - }, { "cell_type": "code", "execution_count": 18, @@ -335,37 +342,21 @@ { "data": { "text/plain": [ - "" + "(25, 4, 31)" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" } ], "source": [ - "for i in range(len(lons))[::6]:\n", - " plt.plot(wavel, mcd.Z[i,0,:], label='$\\phi = {:.1f}$'.format(lons[i]))\n", - "\n", - "plt.semilogx()\n", - "plt.legend(loc='upper left', frameon=False)" + "Z.shape" ] }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 19, "metadata": {}, "outputs": [ { @@ -374,7 +365,7 @@ "0.0" ] }, - "execution_count": 20, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } @@ -398,7 +389,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 20, "metadata": {}, "outputs": [], "source": [ @@ -408,7 +399,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 21, "metadata": {}, "outputs": [], "source": [ @@ -419,13 +410,13 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "1f3f9b32b38d46f4a5e31e611c5ea737", + "model_id": "c23181e37e1c4767bb5f108b88101586", "version_major": 2, "version_minor": 0 }, @@ -442,7 +433,7 @@ "" ] }, - "execution_count": 23, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } @@ -461,7 +452,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 23, "metadata": {}, "outputs": [], "source": [ @@ -470,7 +461,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 24, "metadata": {}, "outputs": [], "source": [ @@ -484,16 +475,16 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 32, + "execution_count": 25, "metadata": {}, "output_type": "execute_result" }, diff --git a/notebooks/test_map_gas_opac.ipynb b/notebooks/test_map_gas_opac.ipynb new file mode 100644 index 0000000..469f3dd --- /dev/null +++ b/notebooks/test_map_gas_opac.ipynb @@ -0,0 +1,649 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "%matplotlib inline\n", + "\n", + "from matplotlib import rcParams\n", + "rcParams['font.family'] = 'sans-serif'\n", + "rcParams['xtick.labelsize'] = 10\n", + "rcParams['ytick.labelsize'] = 10\n", + "rcParams['axes.labelsize'] = 14" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from astropy.io import fits\n", + "import astropy.units as u\n", + "from maplib import load_out3, cumulative_integral, cloud_depth\n", + "\n", + "import matplotlib.cm as cm\n", + "from mpl_toolkits.basemap import Basemap\n", + "from matplotlib.colors import LogNorm\n", + "from matplotlib.gridspec import GridSpec" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# Test values\n", + "LON = -180.\n", + "LAT = 0.\n", + "infile = 'Gas_Ext/Phi{:.1f}Theta{:.1f}_dtau_dz.fits'.format(LON, LAT)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Read in the relevant file and calculate the extinction along the sight line" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "def read_opac_file(filename):\n", + " hdulist = fits.open(filename)\n", + " opac_dict = dict()\n", + " for h in hdulist:\n", + " if h.name == 'PRIMARY':\n", + " pass\n", + " else:\n", + " opac_dict[h.name] = h.data\n", + " hdulist.close()\n", + " return opac_dict" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "def sum_ext(opac_dict, keys=None):\n", + " if keys is None:\n", + " keys = list(opac_dict.keys())\n", + " \n", + " dtau_dz = np.zeros_like(opac_dict[keys[0]])\n", + " for k in keys:\n", + " dtau_dz += opac_dict[k]\n", + " \n", + " return dtau_dz" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "dtau_dz_g = read_opac_file(infile) # dtau/dz for each gas\n", + "dtau_dz = sum_ext(dtau_dz_g)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "thermo = load_out3('thermo', LON, LAT)\n", + "z = thermo['z'] # cm\n", + "\n", + "p_unit = u.dyne / u.cm**2\n", + "p = thermo['p'] * p_unit.to(u.bar)\n", + "\n", + "wavel = load_out3('wavel', LON, LAT)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, '$d\\\\tau/dz$ (cm$^{-1}$)')" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "iw = 2\n", + "ylim = (1.e-15, 1.e-5) \n", + "plt.plot(p, dtau_dz[:,iw], color='k', lw=3, alpha=0.5, label='Total')\n", + "\n", + "for k in dtau_dz_g.keys():\n", + " if max(dtau_dz_g[k][:,iw]) > ylim[0]: ll = k\n", + " else: ll = ''\n", + " plt.plot(p, dtau_dz_g[k][:,iw], lw=1, label=ll)\n", + "\n", + "plt.loglog()\n", + "plt.ylim(ylim)\n", + "plt.legend(loc='upper left', frameon=False, ncol=2)\n", + "\n", + "plt.title('{:.1f} $\\mu$m'.format(wavel[iw]))\n", + "plt.xlabel('p (bar)')\n", + "plt.ylabel(r'$d\\tau/dz$ (cm$^{-1}$)')" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "# Important gases, based on above plot\n", + "IMPGAS = ['TIO', 'SIO', 'SIH', 'H2O', 'H2', 'H2S', 'MGH', 'FEH', 'ALH', 'CAO', 'CAH', 'C2H2', 'CH4']" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "ci = cumulative_integral(z, dtau_dz)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "for i in range(len(wavel))[::3]:\n", + " if max(ci[:,i]) > 1.e-2: ll = '{:.1f} $\\mu$m'.format(wavel[i])\n", + " else: ll = ''\n", + " _ = plt.plot(p, ci[:,i], alpha=0.5, label=ll)\n", + "\n", + "_ = plt.axhline(1.0, color='k', lw=1)\n", + "\n", + "plt.loglog()\n", + "plt.ylim(1.e-2, 1.e3)\n", + "\n", + "plt.xlabel('p (bar)')\n", + "plt.ylabel(r'$\\tau_{gas}$', size=16)\n", + "plt.legend(loc='upper left', frameon=False, ncol=2)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "def atmosphere_depth(lon, lat, tau=1.0, keys=None):\n", + " infile = 'Gas_Ext/Phi{:.1f}Theta{:.1f}_dtau_dz.fits'.format(lon, lat)\n", + " dtau_dz_g = read_opac_file(infile) # dtau/dz for each gas\n", + " dtau_dz = sum_ext(dtau_dz_g, keys=keys)\n", + " \n", + " thermo = load_out3('thermo', lon, lat)\n", + " z = thermo['z'] # cm\n", + " p_unit = u.dyne / u.cm**2\n", + " p = thermo['p'] * p_unit.to(u.bar)\n", + " wavel = load_out3('wavel', lon, lat)\n", + " \n", + " ci = cumulative_integral(z, dtau_dz)\n", + " result = []\n", + " for i in range(len(wavel)):\n", + " result.append(np.interp(tau, ci[:,i], p))\n", + " return result" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "YLIM = [0.3, 3.e-6]\n", + "\n", + "def smooth_curve(w, t, nx=100): \n", + " new_x = np.logspace(np.log10(w[0]), np.log10(w[-1]), nx)\n", + " #cs = CubicSpline(w, t)\n", + " #return new_x, cs(new_x)\n", + " new_y = spline(w, t, new_x, order=1)\n", + " return new_x, new_y\n", + "\n", + "fig = plt.figure(figsize=(19.2, 4.8))\n", + "gs = GridSpec(1, 3, wspace=0.03)\n", + "\n", + "ax0 = plt.subplot(gs[0])\n", + "ax1 = plt.subplot(gs[1])\n", + "ax2 = plt.subplot(gs[2])\n", + "\n", + "lon, lat = -180., 0.\n", + "for k in dtau_dz_g.keys():\n", + " atm_depth = atmosphere_depth(lon, lat, keys=[k])\n", + " if np.min(atm_depth) < YLIM[0]:\n", + " ax0.plot(wavel, atm_depth, alpha=0.8, marker='.', label=k)\n", + " \n", + "w, cld_depth = cloud_depth('180.0', '0.0', p_val=True)\n", + "ax0.plot(w, cld_depth * p_unit.to(u.bar), alpha=0.5, color='k', lw=3, label='Clouds')\n", + "\n", + "ax0.set_yscale('log')\n", + "ax0.set_xscale('log')\n", + "ax0.set_xlabel(r'$\\lambda$ [$\\mu$m]')\n", + "ax0.set_ylabel(r'$p_{\\rm gas}(\\tau_{v,x}(\\lambda) = 1)$ [bar]')\n", + "ax0.set_ylim(YLIM)\n", + "ax0.set_xlim(0.3, 100.0)\n", + "ax0.legend(loc='upper right',frameon=False, ncol=3)\n", + "ax0.set_title('Anti-stellar point', size=14)\n", + "\n", + "lon, lat = -90., 0.\n", + "for k in dtau_dz_g.keys():\n", + " atm_depth = atmosphere_depth(lon, lat, keys=[k])\n", + " if np.min(atm_depth) < YLIM[0]:\n", + " ax1.plot(wavel, atm_depth, alpha=0.8, marker='.', label=k)\n", + " \n", + "w, cld_depth = cloud_depth('-90.0', '0.0', p_val=True)\n", + "ax1.plot(w, cld_depth * p_unit.to(u.bar), alpha=0.5, color='k', lw=3, label='Clouds')\n", + "\n", + "ax1.set_yscale('log')\n", + "ax1.set_xscale('log')\n", + "ax1.set_xlabel(r'$\\lambda$ [$\\mu$m]')\n", + "ax1.set_ylim(YLIM)\n", + "ax1.set_xlim(0.3, 100.0)\n", + "ax1.yaxis.set_ticklabels('')\n", + "ax1.legend(loc='upper right',frameon=False, ncol=3)\n", + "ax1.set_title(r'Morning Terminator ($\\theta=0^{\\circ}$)', size=14)\n", + "\n", + "lon, lat = 0., 0.\n", + "for k in dtau_dz_g.keys():\n", + " atm_depth = atmosphere_depth(lon, lat, keys=[k])\n", + " if np.min(atm_depth) < YLIM[0]:\n", + " ax2.plot(wavel, atm_depth, alpha=0.8, marker='.', label=k)\n", + " \n", + "w, cld_depth = cloud_depth('0.0', '0.0', p_val=True)\n", + "ax2.plot(w, cld_depth * p_unit.to(u.bar), alpha=0.5, color='k', lw=3, label='Clouds')\n", + "\n", + "ax2.set_yscale('log')\n", + "ax2.set_xscale('log')\n", + "ax2.set_xlabel(r'$\\lambda$ [$\\mu$m]')\n", + "ax2.set_ylim(YLIM)\n", + "ax2.set_xlim(0.3, 100.0)\n", + "ax2.yaxis.set_ticklabels('')\n", + "ax2.legend(loc='upper right',frameon=False, ncol=3)\n", + "ax2.set_title(r'Sub-stellar point', size=14)\n", + "\n", + "plt.tight_layout()\n", + "plt.savefig('gas_opacity_wavel.png', format='png')" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0.00048102, 0.00047853, 0.00047752, 0.00047679, 0.00047643,\n", + " 0.0004759 , 0.00047535, 0.00047506, 0.00047454, 0.00047421,\n", + " 0.00047389, 0.0004736 , 0.00047299, 0.00047241, 0.00047169,\n", + " 0.00047102, 0.00047069, 0.00047039, 0.00046982, 0.0004695 ,\n", + " 0.00046867, 0.0004644 , 0.00046173, 0.00046114, 0.00046172,\n", + " 0.00046309, 0.00046457, 0.00051869, 0.00058623, 0.00375563,\n", + " 0.04670139])" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cld_depth * p_unit.to(u.bar)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Make a function for producing the map" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [], + "source": [ + "lons = np.arange(-180., 180.01, 15) # deg\n", + "lats = np.arange(0., 67.51, 22.5) # deg\n", + "nprof = len(lons) * len(lats) # number of (lon, lat) profiles\n", + "\n", + "NLO, NLA, NWA = len(lons), len(lats), len(wavel)\n", + "Z = np.zeros((NLO, NLA, NWA))\n", + "for i in np.arange(NLO)[:-1]:\n", + " for j in np.arange(NLA):\n", + " d = atmosphere_depth(lons[i], lats[j], tau=1.0) # bar\n", + " Z[i,j,:] = d\n", + " \n", + "Z[-1,:,:] = np.copy(Z[0,:,:])" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(0.3, 1e-06)" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Check how much the curves change across the regions\n", + "for i in lons:\n", + " for j in lats:\n", + " ad = atmosphere_depth(i, j, keys=['H2O'])\n", + " plt.plot(wavel, ad)\n", + "\n", + "plt.loglog()\n", + "plt.xlabel(r'$\\lambda$ ($\\mu$m)', size=14)\n", + "plt.ylabel(r'$\\tau=1$ (p, bar)', size=14)\n", + "plt.ylim(0.3, 1.e-6)" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [], + "source": [ + "# Mesh grid to use for the map\n", + "X, Y = np.meshgrid(lons,lats)\n", + "\n", + "# Default contour levels to use\n", + "nlev = 21\n", + "log_pmin, log_pmax = -5.0, 3.0\n", + "lev = np.linspace(log_pmin, log_pmax, nlev)" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "((4, 25), (4, 25), (4, 25))" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Z[:,:,0].T.shape, X.shape, Y.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [], + "source": [ + "def map_gas_depth(i, levels=lev, cmap=plt.cm.RdYlBu_r):\n", + " m = Basemap(projection='kav7', lon_0=0, resolution=None)\n", + " CS_north = m.contourf(X, Y, np.log10(Z[:,:,i].T),\n", + " levels=levels, extend='both', cmap=cmap, latlon=True)\n", + " CS_south = m.contourf(X, -Y, np.log10(Z[:,:,i].T),\n", + " levels=levels, extend='both', cmap=cmap, latlon=True)\n", + " \n", + " plt.colorbar(label=r'$\\tau(\\lambda) = 1$ [log bar]',\n", + " #ticks=np.arange(log_pmin+1, log_pmax+1)[::2],\n", + " orientation='horizontal')\n", + " plt.title('{:.2f} $\\mu$m'.format(wavel[i]))\n", + " \n", + " ## -- Plot lat-lon lines on map\n", + " # String formatting function\n", + " def fmtfunc(inlon):\n", + " string = r'{:g}'.format(inlon)+r'$^{\\circ}$'\n", + " #print string\n", + " return string\n", + "\n", + " meridians = np.arange(0., 360., 90)\n", + " m.drawmeridians(meridians, labels=[False,False,False,True],\n", + " labelstyle=None, fmt=fmtfunc, dashes=[3,3], color='k', fontsize=14)\n", + " parallels = np.arange(-90., 90, 30)\n", + " m.drawparallels(parallels, labels=[True,False,False,False],\n", + " labelstyle='+/-', dashes=[3,3], color='k',fontsize=14)\n", + "\n", + " # and we're done!\n", + " return CS_north" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "map_gas_depth(5)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Make a widget to view different wavelength maps" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [], + "source": [ + "import ipywidgets as widgets\n", + "from ipywidgets import interact" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [], + "source": [ + "def widget_map(i):\n", + " result = map_gas_depth(i)\n", + " return" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "ed42a673c9704eeea968cbc97d13d176", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "interactive(children=(IntSlider(value=10, continuous_update=False, description='i', max=30), Output()), _dom_c…" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "w = widgets.IntSlider(10, min=0, max=len(wavel)-1, continuous_update=False)\n", + "interact(widget_map, i=w)" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "dict_keys(['CH', 'CO', 'H2O', 'C2H2', 'CH4', 'CO2', 'OH', 'HCN', 'NH', 'NO', 'NH3', 'O3', 'SIH', 'SIO', 'ALH', 'ALO', 'MGH', 'CS', 'FEH', 'H2S', 'SO2', 'H2', 'N2', 'O2', 'TIO', 'CAO', 'CAH', 'TIH', 'LIH', 'VO', 'NA', 'K', 'LI'])\n" + ] + } + ], + "source": [ + "print(dtau_dz_g.keys())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "cloud-academy", + "language": "python", + "name": "cloud-academy" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.6" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/notebooks/test_map_growth.ipynb b/notebooks/test_map_growth.ipynb new file mode 100644 index 0000000..b0473cb --- /dev/null +++ b/notebooks/test_map_growth.ipynb @@ -0,0 +1,266 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Map grain growth in clouds at several pressure levels" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import astropy.units as u\n", + "from matplotlib.colors import LogNorm, SymLogNorm\n", + "\n", + "from maplib import load_out3\n", + "import map_growth as mg" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "OUTPUT_DIR = 'Cloud_Growth/'\n", + "\n", + "lons = np.arange(-180., 180.01, 15) # deg\n", + "lats = np.arange(0., 67.51, 22.5) # deg\n", + "nprof = len(lons) * len(lats) # number of (lon, lat) profiles\n", + "\n", + "p_convert = 1.e-6 # bar / (dyne/cm^2)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "X, Y = np.meshgrid(lons,lats)\n", + "\n", + "# Default contour levels to use\n", + "nlev = 21\n", + "log_zmin, log_zmax = -30, 5\n", + "lev = np.linspace(log_zmin, log_zmax, nlev)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "valnames = ['J*_C', 'J*_SiO', 'J*_TiO2']\n", + "P_WANT = 0.01 * u.bar # 10 mbar\n", + "\n", + "Jstar = mg.interpolate_pressure_map('nuclea', valnames, P_WANT.to('dyne/cm^2').value)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, '10 bar mbar')" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "_ = mg.map_result(Jstar, lev,\n", + " label=r'$J^{*}$ [cm$^{-3}$ s$^{-1}$]',\n", + " ticks=np.arange(log_zmin, log_zmax+1)[::5])\n", + "plt.title('{:.0f} mbar'.format(P_WANT * 1.e3))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Map number density of particles" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "valnames = ['N']\n", + "ndens = mg.interpolate_pressure_map('dist', valnames, P_WANT.to('bar').value)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ndlev = np.linspace(-3, 6, nlev)\n", + "_ = mg.map_result(ndens, ndlev,\n", + " label=r'$N$ [cm$^{-3}$]',\n", + " ticks=np.arange(-3, 7))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Do chinet" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "valnames = ['chinet']\n", + "pw = 0.1 *u.bar\n", + "chimap = mg.interpolate_pressure_map('dust', valnames, pw.to('dyne/cm^2').value)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.imshow(chimap, norm=SymLogNorm(linthresh=1.e-10, vmin=-1.0, vmax=1.0), cmap=plt.cm.RdBu)\n", + "plt.colorbar()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "myticks = np.array([-3.0, -1.0, -1.e-3, -1.e-6, -1.e-9, 0.0, 1.e-9, 1.e-6, 1.e-3, 1.0, 3.0])\n", + "mg.map_symlognorm(chimap, lmin=-1.0, lmax=1.0, linthresh=1.e-9, ticks=myticks)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "cloud-academy", + "language": "python", + "name": "cloud-academy" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.6" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +}