inclusion of iwr process in main, fix for first guess x0 in iwr.process

README.md

@@ -75,10 +75,26 @@ Usage:
 
 ## Running the tests
 
+- For `awr` data:
+
 ```
 trios_processing ./test/data/ 150 awr --lat 42.30351823 --lon 9.462897398 --odir ./test/results --no_clobber
 trios_processing ./test/data/ 150 awr --lat 42.30351823 --lon 9.462897398 --odir ./test/results --method M99 --name _M99 --plot --figdir ./test/fig
-trios_processing ./test/data/ 150 awr --lat 42.30351823 --lon 9.462897398 --odir ./test/results --method osoaa --name _osoaa --plot
+trios_processing ./test/data/ 150 awr --lat 42.30351823 --lon 9.462897398 --odir ./test/results --method osoaa --name _osoaa --plot --figdir ./test/fig
+trios_processing ./test/data/ 150 awr --lat 42.30351823 --lon 9.462897398 --odir ./test/results --method osoaa --ws 5 --aot=0.2 --name _osoaaws5_aot0.1 --plot --figdir ./test/fig
+trios_processing ./test/data/ 150 awr --lat 42.30351823 --lon 9.462897398 --odir ./test/results --method temp_opt --name _optimization --plot --figdir ./test/fig
+```
+
+- For `iwr` data:
+
+```
+trios_processing ./test/data/ 150 iwr --lat 42.30351823 --lon 9.462897398 --odir ./test/results --no_clobber --plot --figdir ./test/fig
+```
+
+- For `swr` data:
+
+```
+trios_processing ./test/data/ 150 swr --lat 42.30351823 --lon 9.462897398 --odir ./test/results --no_clobber --plot --figdir ./test/fig
 ```
 
 

test/results/Rrs_awr_2018-05-30_idpr150_M99.csv

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+881,44.0,0.0002589662954916847,0.0003597838905517663,4.381741105278289e-05,8.888011249962233e-05,0.00016991991974290557,0.00026599073399766193,0.0022945444054811013
+884,44.0,0.0002588564083563562,0.00036154672120262206,4.452439135128094e-05,8.754584210388927e-05,0.00016730128899622603,0.000267530325180921,0.0023047758532725375
+887,44.0,0.00025870510993656937,0.00036468064281827835,4.0239621253348914e-05,8.938244856800217e-05,0.00016471423892382427,0.0002590798864750392,0.0023249306611182793
+890,44.0,0.0002588074766274756,0.00036638350107149703,3.93190665342487e-05,8.194938381014366e-05,0.00016396050702607596,0.0002583482678431032,0.00233339507532099
+893,44.0,0.0002577866373790621,0.00036609187514193367,3.048462125876405e-05,8.59783695030096e-05,0.00016906423618328302,0.0002600769858381514,0.002326733173663478
+896,44.0,0.00025065080213853685,0.00036437302782838665,3.258789151097865e-05,7.647431677054389e-05,0.00016942872532557044,0.0002508079500243706,0.002309325769951368
+899,44.0,0.00024252348091716982,0.000360618229545191,2.5275292326826063e-05,7.193215584702661e-05,0.00014962077213943257,0.0002483730272918759,0.0022874101064551716
+902,44.0,0.00024075812550267104,0.0003650996558287996,1.4271191765074983e-05,6.509795828739303e-05,0.00014372470064425493,0.0002454202586248108,0.0022994915374901135
+905,44.0,0.00024368390465442455,0.00036857987176591635,9.432239647463089e-06,6.128370841780783e-05,0.00015692130615746914,0.00023789903107276697,0.0023228635366177672
+908,44.0,0.00023750035507067986,0.00036718051340406963,1.935599801242437e-05,6.584638490904339e-05,0.0001525781272425978,0.0002337433517398536,0.002319015769697332
+911,44.0,0.0002355218530510906,0.0003676611159792037,1.8214652478805986e-05,7.309755752293935e-05,0.0001480383841207827,0.0002294178390212984,0.002314062447323905
+914,44.0,0.00023274506696059332,0.0003695566532492817,7.625248544254246e-06,6.194646413013971e-05,0.00013599330479403246,0.00021879448753657114,0.002331376686314012
+917,44.0,0.00022980518880380118,0.00036846005597169015,3.611710880683133e-06,6.173963040405457e-05,0.00014256477842520632,0.00021867548038531181,0.002309274670920128
+920,44.0,0.00022815668986946375,0.00036937631680671913,-7.65734523368126e-06,5.172292891798523e-05,0.00013993265433663718,0.0002196881680544213,0.0023048057095939765
+923,44.0,0.0002267072889854349,0.00037280133732842043,-9.679578100192372e-06,4.4161913536475206e-05,0.00013373509472468345,0.00022128377102000304,0.0023262770850329324
+926,44.0,0.00022286106262842865,0.00037440146935614657,-8.655602014959408e-06,4.60854862498112e-05,0.00012894708254501436,0.00021994131387570215,0.0023285248919756854
+929,44.0,0.00021901697559667288,0.00036741776645760613,-1.1222157661405543e-05,5.464904999299802e-05,0.00012318240177889932,0.00022582908924146882,0.002283811435074845
+932,44.0,0.00022348897331086285,0.0003470822577481269,-2.7496236478048154e-05,5.7794463930036175e-05,0.00013877135015374198,0.0002436889748838176,0.0021821656137652077
+935,44.0,0.00020706093761315838,0.00033050456968099006,-5.212073943891432e-05,4.3075524775231574e-05,0.00012949328901583655,0.00023455209242338358,0.002045103425763617
+938,44.0,0.00019422370542927003,0.0003452582173228475,-8.832344916884375e-05,3.710879884694506e-05,0.00011151073116388312,0.00022728554911997242,0.0021089301792086546
+941,44.0,0.00016661450970693458,0.0003393585619666861,-0.00014210420206273625,1.068613318467681e-05,6.807456274119284e-05,0.00020415136122342527,0.001986823842689277
+944,44.0,0.0001938481733745334,0.0003309040067866306,-6.200549836366426e-05,3.3907144144390135e-05,0.00012531466335633467,0.00019978832554865556,0.00199645345070853
+947,44.0,0.00019165916627664586,0.0003392960223368115,-7.581018845197376e-05,4.212261800389266e-05,0.00010903529218851003,0.00019699826393773822,0.002077498164062764
+950,44.0,0.00019184010570890455,0.000328313367157994,-6.9138059028104e-05,4.5891981809802985e-05,0.00010589945049722131,0.00018663621171748388,0.001973942536061817

trios/config.py

@@ -16,3 +16,8 @@ F0_file =  os.path.join(root, '../aux/Thuillier_2003_0.3nm.dat')
 wl_step = 3
 wl_common = np.arange(320,950+wl_step,wl_step)
 
+# wavelength indexes for iwr plotting
+# for close red/IR
+# idx_list_for_plot=(165,170,175,180,185,190,195,200,205)
+# through visible/IR
+idx_list_for_plot=(28, 37, 51, 71, 91, 105, 130, 140, 170)
\ No newline at end of file

trios/main.py

@@ -3,7 +3,8 @@
 Usage:
   trios_processing <input_dir> <IDpr> <measurement_type> --lat <lat> --lon <lon> \
    [--altitude=alt] [--ofile <ofile>] [--odir <odir>] [--plot] [--figdir <figdir>] \
-   [--name <name>] [--method <method>] [--no_clobber]
+   [--name <name>] [--method <method>] [--no_clobber] \
+   [--vza <vza>] [--azi <azi>] [--ws <ws>] [--aot <aot>]
   trios_processing -h | --help
   trios_processing -v | --version
 
@@ -26,6 +27,14 @@ Options:
   --name name  Keyword to append to file name and figures [default: ]
   --method method  Keyword for the method to apply for data processing.
                    For awr: M99, M15, osoaa, temp_opt [default: M99]
+  --vza vza  Viewing zenith angle (in deg) of the awr radiometer (applicable for all awr methods)
+            [default: 40]
+  --azi azi  Relative azimuth angle (in deg) of the awr radiometer (applicable for all awr methods)
+            [default: 135]
+  --ws ws  Wind speed (in m/s) (applicable for awr methods: M99, M15, osoaa)
+           [default: 2]
+  --aot aot  Aerosol optical thickness at 550 nm (applicable for awr methods: osoaa)
+             [default: 0.1]
   --no_clobber     Do not process  <input_dir> <IDpr> files if <output_file> already exists.
 '''
 
@@ -65,6 +74,10 @@ def main():
     plot = args['--plot']
     figdir = os.path.abspath(args['--figdir'])
     noclobber = args['--no_clobber']
+    azi = float(args['--azi'])
+    vza = float(args['--vza'])
+    ws = float(args['--ws'])
+    aot = float(args['--aot'])
 
     try:
         type_ = type_list[meas_type]
@@ -103,17 +116,15 @@ def main():
                 ax.set_ylabel(r'$R_{rs}\  (sr^{-1})$')
                 ax.set_xlabel(r'Wavelength (nm)')
                 ax.set_title('ID: ' + idpr + ', ' + date + ', sza=' + str(round(df.sza.mean(), 2)))
-                fig.savefig(os.path.join(figdir, 'trios_swr_' + date + '_idpr' + idpr + name+'.png'), bbox_inches='tight')
+                fig.savefig(os.path.join(figdir, 'trios_swr_' + date + '_idpr' + idpr + name + '.png'),
+                            bbox_inches='tight')
                 plt.close()
 
     elif meas_type == 'awr':
         # -----------------------------------------------
         #   AWR processing
         # -----------------------------------------------
-        azi = 135
-        vza = 40
-        ws = 2
-        method = 'M99'
+
         uawr = u.awr_data(idpr, files)
         if uawr.Edf:
             df, wl = uawr.reader(lat, lon, alt)
@@ -127,17 +138,39 @@ def main():
                 print('Skip processing: data already processed with "--no_clobber" set')
                 return
 
-            awr = awr_process(df, wl, name, idpr)
-
             if plot:
-                figfile = os.path.join(figdir, 'trios_awr_' + date + '_idpr' + idpr + name +'.png')
+                figfile = os.path.join(figdir, 'trios_awr_' + date + '_idpr' + idpr + name + '.png')
             else:
                 figfile = ""
 
-            Rrs = awr.call_process(method, ofile, vza=vza, azi=azi, plot_file=figfile)
+            awr = awr_process(df, wl, name, idpr)
+            Rrs = awr.call_process(method, ofile, vza=vza, azi=azi, ws=ws, aot=aot, plot_file=figfile)
 
     elif meas_type == 'iwr':
-        pass
+        # -----------------------------------------------
+        #   IWR processing
+        # -----------------------------------------------
+
+        uiwr = u.iwr_data(idpr, files)
+        if uiwr.file:
+            df, wl = uiwr.reader(lat, lon, alt)
+            date = df.index[0].date().__str__()
+            if ofile:
+                ofile = os.path.join(odir, ofile)
+            else:
+                ofile = os.path.join(odir, 'Rrs_iwr_' + date + '_idpr' + idpr + name + '.csv')
+
+            if noclobber and os.path.exists(ofile):
+                print('Skip processing: data already processed with "--no_clobber" set')
+                return
+
+            if plot:
+                figfile = os.path.join(figdir, 'trios_iwr_' + date + '_idpr' + idpr + name + '.pdf')
+            else:
+                figfile = ""
+
+            iwr = iwr_process(df, wl, name, idpr)
+            results = iwr.call_process(ofile, plot_file=figfile)
 
 
 if __name__ == "__main__":

trios/process.py

+# standard lib
 import pandas as pd
 from scipy import interpolate, integrate
 import scipy.optimize as so
 
+# for plotting pruposes
+import cmocean
 import plotly.graph_objs as go
-
+from matplotlib.backends.backend_pdf import PdfPages
 import matplotlib as mpl
 import matplotlib.pyplot as plt
 
 plt.rcParams.update({'font.size': 18})
 
+# package
 from trios.utils.utils import plot as up
 import trios.utils.utils as uu
 from trios.utils.utils import reshape as r
@@ -92,7 +96,7 @@ class awr_process:
     def call_process(self, method='M99', ofile="", vza=40, azi=135, ws=2, aot=0.1, plot_file=""):
 
         wl = self.wl
-        vza, azi, ws = [vza], [azi], [ws]  # formatting for interpolation functions
+        vza, azi, ws, aot = [vza], [azi], [ws], [aot]  # formatting for interpolation functions
 
         # ------------------
         # filtering
@@ -111,7 +115,7 @@ class awr_process:
         elif method == 'osoaa':
             Rrs, rho = self.process_wrapper(wl, clean, clean.sza, vza=vza, azi=azi, ws=ws, aot=aot, method=method)
         elif method == 'temp_opt':
-            Rrs, Rrs_opt_std = self.process_optimization(wl, Lt, Lsky, Ed, sza, vza=vza, azi=azi)
+            Rrs, rho, Rrs_opt, Rrs_opt_std = self.process_optimization(wl, Lt, Lsky, Ed, sza, vza=vza, azi=azi)
 
         self.Rrs = Rrs
 
@@ -121,7 +125,6 @@ class awr_process:
             Rrs_stat = Rrs_stat.T
             Rrs_stat.to_csv(ofile)
 
-
         if plot_file:
             # ------------------
             # plotting
@@ -155,21 +158,21 @@ class awr_process:
             up.add_curve(ax, wl, Lt.mean(axis=0), Lt.std(axis=0),
                          label=r'$L_t$', c='black')
             up.add_curve(ax, wl, Lsky.mean(axis=0) * rho, Lsky.std(axis=0) * rho,
-                         label=method+' (' + str(round(rho, 4)) + ')', c='violet')
+                         label=method + ' (' + str(round(rho, 4)) + ')', c='violet')
             ax.set_ylabel(r'$L_t\ or L_{surf}$')
             ax.set_xlabel(r'Wavelength (nm)')
 
             # ---- Proportion o(Lt - Lsurf ) /Lt
             ax = axs[0, 2]
             up.add_curve(ax, wl, Lsky.mean(axis=0) * rho / Ltm, Lsky.std(axis=0) * rho,
-                         label=method+' (' + str(round(rho, 4)) + ')', c='violet')
+                         label=method + ' (' + str(round(rho, 4)) + ')', c='violet')
             ax.set_ylabel(r'$L_{surf}/L_t$')
             ax.set_xlabel(r'Wavelength (nm)')
 
             # ---- Lw
             ax = axs[1, 0]
             up.add_curve(ax, wl, Rrs.mean(axis=0) * Edm, Rrs.std(axis=0) * Edm,
-                         label=method+' (' + str(round(rho, 4)) + ')', c='violet')
+                         label=method + ' (' + str(round(rho, 4)) + ')', c='violet')
 
             ax.set_ylabel(r'$L_{w}\  (sr^{-1})$')
             ax.set_xlabel(r'Wavelength (nm)')
@@ -177,7 +180,7 @@ class awr_process:
             # ---- Rrs
             ax = axs[1, 1]
             up.add_curve(ax, wl, Rrs.transpose().mean(axis=1), Rrs.transpose().std(axis=1),
-                         label=method+' (' + str(round(rho, 4)) + ')', c='violet')
+                         label=method + ' (' + str(round(rho, 4)) + ')', c='violet')
 
             ax.set_ylabel(r'$R_{rs}\  (sr^{-1})$')
             ax.set_xlabel(r'Wavelength (nm)')
@@ -263,7 +266,7 @@ class awr_process:
         # initialization of mean/median values
         # ------------------------------
         Rrs, rho = self.process(wl, Lt, Lsky, Ed, sza, ws=ws, azi=azi)
-        Rrs_bar = Rrs.mean(axis=0)
+        Rrs_bar = np.nanmedian(Rrs, axis=0)
 
         # -----------------------------
         # non-linear optimization
@@ -288,7 +291,9 @@ class awr_process:
                 print(res_lsq.x, res_lsq.cost)
             Rrs_bar = np.mean(Rrs_est, axis=0)
             Rrs_std = np.std(Rrs_est, axis=0)
-        return Rrs_bar, Rrs_std
+            Rrs_est = pd.DataFrame(Rrs_est)
+            Rrs_est.columns = pd.MultiIndex.from_product([['Rrs'], wl], names=['param', 'wl'])
+        return Rrs_est, np.mean(rho_est, axis=0), Rrs_bar, Rrs_std
 
     @classmethod
     def filtering(cls, Lt, Lsky, Ed, **kargs):
@@ -400,9 +405,256 @@ class swr_process:
 
 
 class iwr_process:
-    def __init__(self, df=None, wl=None, ):
+    def __init__(self, df=None, wl=None, name="", idpr=""):
         self.df = df
         self.wl = wl
+        self.name = name
+        self.idpr = idpr
+
+    def call_process(self, ofile="", plot_file=""):
+
+        # ---------------------------
+        # Data formatting
+        # ---------------------------
+        df = self.df
+        wl = self.wl
+
+        # set pandas dataframes with data and parameters
+        df['rounded_depth', ''] = df.prof_Edz.round(1)
+
+        # data filtering
+        df[df.rounded_depth < -0.05] = np.nan
+        # df[df == 0] = np.nan
+
+        mean = df.groupby('rounded_depth').mean()
+        median = df.groupby('rounded_depth').median()
+        std = df.groupby('rounded_depth').std()
+        q25 = df.groupby('rounded_depth').quantile(0.25)
+        q75 = df.groupby('rounded_depth').quantile(0.75)
+
+        # ---------------------------
+        # Data processing
+        # ---------------------------
+        res = self.process(mean, std)
+        res_w = self.process(mean, std, mode='log')
+        res_med = self.process(median, std)
+        res_lsq = self.process(mean, std, mode='lsq')
+
+        Kd = res_lsq.popt[:, 0]
+        Edm = res_lsq.popt[:, 1]
+        Es = mean.Ed.mean()
+        Lwm = res_lsq.popt[:, 3]
+        rrs = Lwm / Edm
+        Es_from_Edm = 0.96 * Edm
+        Lwp = 0.541 * Lwm
+        Rrs_Edp = Lwp / Es
+        Rrs_Edm = Lwp / Es_from_Edm
+
+        Rrs_df = pd.DataFrame(Rrs_Edp)
+        Rrs_df.columns = ['Rrs']
+        Rrs_df['Kd'] = Kd
+
+        if ofile:
+            # -----------------------------------------------
+            #   write result data
+            # -----------------------------------------------
+            Rrs_df.to_csv(ofile)
+
+        if plot_file:
+            # ---------------------------
+            # Plotting section
+            # ---------------------------
+
+            pdf = PdfPages(plot_file)
+
+            # -------------------------------------
+            # Profile plotting
+            # -------------------------------------
+            # Ed profile
+            i = 0
+            x = mean.prof_Edz
+            depth_ = np.linspace(0, x.max(), 200)
+
+            mpl.rcParams.update({'font.size': 12})
+
+            fig, axs = plt.subplots(nrows=3, ncols=3, figsize=(16, 10))
+            fig.subplots_adjust(left=0.1, right=0.9, hspace=.5, wspace=0.25)
+
+            for idx in idx_list_for_plot:
+                ax = axs.flat[i]
+                i += 1
+                y = mean.Edz.iloc[:, idx]
+                sigma = std.Edz.iloc[:, idx]
+                yerr = [median.Edz.iloc[:, idx] - q25.Edz.iloc[:, idx], q75.Edz.iloc[:, idx] - median.Edz.iloc[:, idx]]
+                ax.errorbar(x, y, yerr=yerr, ecolor='lightgray', alpha=0.43, fmt='none', elinewidth=3, capsize=0,
+                            label='std')
+                ax.scatter(x, y,
+                           # s = std.Edz.iloc[:,50]/std.Edz.iloc[:,50].mean()+100,
+                           c=mean.Ed.iloc[:, idx],
+                           alpha=0.6, cmap=cmocean.cm.thermal, label=None
+                           )
+                Ed_sim = iwr_process().f_Edz(depth_, *res.popt[idx, :])
+                ax.plot(depth_, Ed_sim, linestyle='-', c='black', label='mean')
+                Ed_sim = iwr_process().f_Edz(depth_, *res_w.popt[idx, :])
+                ax.plot(depth_, Ed_sim, linestyle=':', c='black', label='log-space')
+                # Ed_sim = iwr_process.f_Edz(depth_, *res_rw[idx][0])
+                # ax.plot(depth_, Ed_sim, linestyle=':', c='red', label='mean, relativ weighted')
+                Ed_sim = iwr_process().f_Edz(depth_, *res_med.popt[idx, :])
+                ax.plot(depth_, Ed_sim, linestyle='--', c='black', label='median')
+
+                ax.semilogy()
+                # ax.colorbar()
+                ax.set_ylabel(r'$E_d\ ({mW\cdot m^{-2}\cdot nm^{-1}})$')
+                ax.set_xlabel('Depth (m)')
+                ax.set_title(r'$\lambda = $' + str(round(wl[idx], 1)) + ' nm, Kd = ' +
+                             str(round(res_w.popt[idx, 0], 3)) + '$m^{-1}$, Ed0 =' + str(round(res_w.popt[idx, 1], 1)),
+                             fontsize=12)
+            ax.legend(loc='best', frameon=False)
+            fig.suptitle('trios_iwr ' + self.name + ' idpr' + self.idpr, fontsize=16)
+            # fig.savefig(os.path.join(dirfig,'trios_iw_idpr' + idpr + '_'+name+'.png'),dpi=600)
+            # fig.savefig(os.path.join(dirfig, 'trios_iwr_Ed_profile_idpr' + idpr + '_' + name + '.pdf'))
+            pdf.savefig()
+            plt.close()
+
+            # Lu profile
+            i = 0
+            x = mean.prof_Luz
+            depth_ = np.linspace(0, x.max(), 200)
+            mpl.rcParams.update({'font.size': 12})
+
+            fig, axs = plt.subplots(nrows=3, ncols=3, figsize=(16, 10))
+            fig.subplots_adjust(left=0.1, right=0.9, hspace=.5, wspace=0.25)
+
+            for idx in idx_list_for_plot:
+                ax = axs.flat[i]
+                i += 1
+                y = mean.Luz.iloc[:, idx]
+                sigma = std.Luz.iloc[:, idx]
+                yerr = [y - q25.Luz.iloc[:, idx], q75.Luz.iloc[:, idx] - y]
+                yerr = sigma
+                ax.errorbar(x, y, yerr=yerr, ecolor='lightgray', alpha=0.43, fmt='none', elinewidth=3, capsize=0,
+                            label='std')
+                ax.scatter(x, y,
+                           # s = std.Luz.iloc[:,50]/std.Luz.iloc[:,50].mean()+100,
+                           c=mean.Ed.iloc[:, idx],
+                           alpha=0.6, cmap=cmocean.cm.thermal, label=None
+                           )
+                Lu_sim = iwr_process().f_Lu(depth_, *res_lsq.popt[idx, -2:])
+                ax.plot(depth_, Lu_sim, linestyle='-', c='black', label='mean')
+                # Lu_sim = iwr_process().f_Luz(depth_, *res_w.popt[idx, :])
+                # ax.plot(depth_, Lu_sim, linestyle=':', c='black', label='log-space')
+                # # Lu_sim = iwr_process.f_Luz(depth_, *res_rw[idx][0])
+                # # ax.plot(depth_, Lu_sim, linestyle=':', c='red', label='mean, relativ weighted')
+                # Lu_sim = iwr_process().f_Luz(depth_, *res_med.popt[idx, :])
+                # ax.plot(depth_, Lu_sim, linestyle='--', c='black', label='median')
+
+                ax.semilogy()
+                # ax.colorbar()
+                ax.set_ylabel(r'$L_u\ ({mW\cdot m^{-2}\cdot sr^{-1}\cdot nm^{-1}})$')
+                ax.set_xlabel('Depth (m)')
+                ax.set_title(r'$\lambda = $' + str(round(wl[idx], 1)) + r'$ nm, K_{Lu} = $' +
+                             str(round(res_lsq.popt[idx, 2], 3)) + '$m^{-1}$, Lu0 =' + str(
+                    round(res_lsq.popt[idx, 3], 1)),
+                             fontsize=12)
+            ax.legend(loc='best', frameon=False)
+            fig.suptitle('trios_iwr ' + self.name + ' idpr' + self.idpr, fontsize=16)
+            # fig.savefig(os.path.join(dirfig,'trios_iw_idpr' + idpr + '_'+name+'.png'),dpi=600)
+            # fig.savefig(os.path.join(dirfig, 'trios_iwr_Lu_profile_idpr' + idpr + '_' + name + '.pdf'))
+            pdf.savefig()
+
+            plt.close()
+
+            # -------------------------------------
+            # Spectra plotting
+            # -------------------------------------
+            mpl.rcParams.update({'font.size': 18})
+            fig, axs = plt.subplots(nrows=2, ncols=2, figsize=(15, 12))
+            fig.subplots_adjust(left=0.1, right=0.9, hspace=.5, wspace=0.29)
+
+            iparam = 0
+            ax = axs.flat[iparam]
+            y, sigma = res.popt[:, iparam], res.pcov[:, iparam, iparam]
+            up.add_curve(ax, wl, y, sigma, c='red', label='mean')
+            y, sigma = res_w.popt[:, iparam], res_w.pcov[:, iparam, iparam]
+            up.add_curve(ax, wl, y, sigma, c='orange', label='log-space')
+            y, sigma = res_med.popt[:, iparam], res_med.pcov[:, iparam, iparam]
+            up.add_curve(ax, wl, y, sigma, c='green', label='median')
+            y, sigma = res_lsq.popt[:, iparam], np.sqrt(res_lsq.pcov[:, iparam, iparam])
+            up.add_curve(ax, wl, y, sigma, c='blue', label='lsq')
+            ax.set_ylabel(r'$K_{d}\ ({m^{-1}})$')
+            ax.set_xlabel(r'Wavelength (nm)')
+
+            iparam = 1
+            ax = axs.flat[iparam]
+            y, sigma = res.popt[:, iparam], np.sqrt(res.pcov[:, iparam, iparam])
+            up.add_curve(ax, wl, y, sigma, c='red', label='mean')
+            y, sigma = res_w.popt[:, iparam], np.sqrt(res_w.pcov[:, iparam, iparam])
+            up.add_curve(ax, wl, y, sigma, c='orange', label='log-space')
+            y, sigma = res_med.popt[:, iparam], np.sqrt(res_med.pcov[:, iparam, iparam])
+            up.add_curve(ax, wl, y, sigma, c='green', label='median')
+            y, sigma = res_lsq.popt[:, iparam], np.sqrt(res_lsq.pcov[:, iparam, iparam])
+            up.add_curve(ax, wl, y, sigma, c='blue', label='lsq')
+            up.add_curve(ax, wl, mean.Ed.mean(), mean.Ed.std(), c='black', label='Es')
+            ax.legend(loc='best', frameon=False)
+            ax.set_ylabel(r'$E_{d}(0^{-})\ ({mW\cdot m^{-2}\cdot nm^{-1}})$')
+            ax.set_xlabel(r'Wavelength (nm)')
+
+            iparam = 2
+            ax = axs.flat[iparam]
+            y, sigma = res_lsq.popt[:, iparam], np.sqrt(res_lsq.pcov[:, iparam, iparam]) * 0
+            sigma[sigma > 10 * np.nanmedian(sigma)] = np.nan
+            up.add_curve(ax, wl, y, sigma, c='blue', label='lsq')
+            ax.set_ylabel(r'$K_{Lu}\ ({m^{-1}})$')
+            ax.set_xlabel(r'Wavelength (nm)')
+
+            iparam = 3
+            ax = axs.flat[iparam]
+
+            y, sigma = res_lsq.popt[:, iparam], np.sqrt(res_lsq.pcov[:, iparam, iparam]) * 0
+            sigma[sigma > 10 * np.nanmedian(sigma)] = np.nan
+            up.add_curve(ax, wl, y, sigma, c='blue', label='lsq')
+            ax.set_ylabel(r'$L_{w}(0^{-})\ ({mW\cdot m^{-2}\cdot sr^{-1}\cdot nm^{-1})$')
+            ax.set_xlabel(r'Wavelength (nm)')
+
+            fig.suptitle('trios_iwr ' + self.name + ' idpr' + self.idpr, fontsize=16)
+            # fig.savefig(os.path.join(dirfig, 'trios_iwr_l2_idpr' + idpr + '_' + name + '.pdf'))
+            pdf.savefig()
+
+            plt.close()
+
+            # Edm = res_lsq.popt[:,1]
+            # Es = mean.Ed.mean()
+            # Lwm = res_lsq.popt[:,3]
+            # rrs = Lwm / Edm
+            # Es_from_Edm = 0.96 * Edm
+            # Lwp = 0.541 * Lwm
+            # Rrs_Edp = Lwp / Es
+            # Rrs_Edm = Lwp / Es_from_Edm
+
+            mpl.rcParams.update({'font.size': 18})
+            fig, axs = plt.subplots(nrows=2, ncols=2, figsize=(15, 12))
+            fig.subplots_adjust(left=0.1, right=0.9, hspace=.5, wspace=0.29)
+
+            ax = axs.flat[0]
+            up.add_curve(ax, wl, Es / Edm, c='red', label='Es over Ed0-')
+            ax.set_xlabel(r'Wavelength (nm)')
+            ax.legend(loc='best', frameon=False)
+            ax = axs.flat[1]
+            up.add_curve(ax, wl, rrs, c='red', label='rrs')
+            ax.set_xlabel(r'Wavelength (nm)')
+            ax.legend(loc='best', frameon=False)
+            ax = axs.flat[2]
+            up.add_curve(ax, wl, Rrs_Edm, c='red', label='Rrs (from Ed0-)')
+            up.add_curve(ax, wl, Rrs_Edp, c='blue', label='Rrs (from Ed0+)')
+
+            ax.set_xlabel(r'Wavelength (nm)')
+            ax.legend(loc='best', frameon=False)
+            ax = axs.flat[3]
+
+            fig.suptitle('trios_iwr ' + self.name + ' idpr' + self.idpr, fontsize=16)
+            pdf.savefig()
+            plt.close()
+            pdf.close()
 
     def process(self, meas, std, mode='linear'):
         wl_ = self.wl
@@ -446,8 +698,8 @@ class iwr_process:
 
                 sigma = (sig_Edz, sig_Luz)
                 sigma = (1, 1)
-                x0 = [1.1 * aw, meas.Ed.iloc[:, idx].mean(), 1.1 * aw, meas.Luz.iloc[0, idx]]
-
+                x0 = [1.1 * aw, min(F0,meas.Ed.iloc[:, idx].mean()), 1.1 * aw, meas.Luz.iloc[0, idx]]
+                print('x0',wl,F0,aw, x0)
                 lsq = so.least_squares(self.cost_func, x0, args=(z, y, sigma),
                                        bounds=([aw, 0, aw / 2, 0], [np.inf, F0, np.inf, np.inf]))
                 cost = 2 * lsq.cost  # res.cost is half sum of squares!