diff --git a/.idea/misc.xml b/.idea/misc.xml
index f8f8c44a92f31e0ec4149ad5fb04a57c4e1bf124..a32de7b6f3ee623ad5c65875b13b18844631a8d7 100644
--- a/.idea/misc.xml
+++ b/.idea/misc.xml
@@ -1,6 +1,6 @@
<?xml version="1.0" encoding="UTF-8"?>
<project version="4">
- <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.6" project-jdk-type="Python SDK" />
+ <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.5" project-jdk-type="Python SDK" />
<component name="PyCharmProfessionalAdvertiser">
<option name="shown" value="true" />
</component>
diff --git a/.idea/vcs.xml b/.idea/vcs.xml
index 830674470f8052bb64c64a5b513f5df2815a70c1..94a25f7f4cb416c083d265558da75d457237d671 100644
--- a/.idea/vcs.xml
+++ b/.idea/vcs.xml
@@ -1,7 +1,6 @@
<?xml version="1.0" encoding="UTF-8"?>
<project version="4">
<component name="VcsDirectoryMappings">
- <mapping directory="" vcs="Git" />
<mapping directory="$PROJECT_DIR$" vcs="Git" />
</component>
</project>
\ No newline at end of file
diff --git a/.idea/visu_trios.iml b/.idea/visu_trios.iml
index f3cf5399aa748f54217941acbf33f67672ac22f2..64028cfc53dd142c16f1ffc6572373bdfc6826b5 100644
--- a/.idea/visu_trios.iml
+++ b/.idea/visu_trios.iml
@@ -2,7 +2,7 @@
<module type="PYTHON_MODULE" version="4">
<component name="NewModuleRootManager">
<content url="file://$MODULE_DIR$" />
- <orderEntry type="jdk" jdkName="Python 3.6" jdkType="Python SDK" />
+ <orderEntry type="jdk" jdkName="Python 3.5" jdkType="Python SDK" />
<orderEntry type="sourceFolder" forTests="false" />
<orderEntry type="library" name="R User Library" level="project" />
<orderEntry type="library" name="R Skeletons" level="application" />
diff --git a/process/process.py b/process/process.py
index 4449d11613d2280006d1aca5a5a9d47277e146cb..3c25eb7f467ca22cfd64fa61f07d7a40a9e8fa8b 100644
--- a/process/process.py
+++ b/process/process.py
@@ -219,6 +219,11 @@ class iwr_process:
'''simple Edz model for homogeneous water column'''
return Ed0 * np.exp(-Kd*depth)
+ @staticmethod
+ def f_logEdz(depth, Kd, Ed0):
+ '''simple Edz model for homogeneous water column'''
+ return np.log(1 + iwr_process.f_Edz(depth, Kd, Ed0)) #Ed0) -Kd*depth
+
def Kd(self, depth, Edz):
Kd = np.diff(Edz) / np.diff(depth)
diff --git a/process/run_iwr.py b/process/run_iwr.py
index d0dfd488c263b9465582d4016f4ee50599c98fb0..e5d120e7bc4a5a042429b6e1dc28a6a91d34c5d9 100644
--- a/process/run_iwr.py
+++ b/process/run_iwr.py
@@ -5,6 +5,10 @@ import glob
import re
import matplotlib as mpl
+import matplotlib.pyplot as plt
+import cmocean
+
+
import plotly
import plotly.graph_objs as go
@@ -13,6 +17,14 @@ import utils.auxdata as ua
from process.process import *
+
+
+class fit:
+ def __init__(self,N=0,m=2):
+
+ self.popt = np.full([N,m],np.nan)
+ self.pcov = np.full([N,m,m],np.nan)
+
# import aeronet
# from config import *
@@ -36,7 +48,8 @@ iopw = ua.iopw()
iopw.load_iopw()
irr = ua.irradiance()
irr.load_F0()
-
+#TODO check noise values (e.g., NEI from Trios), should it be spectral?
+noise = 0.1
idpr = '178'
# loop over idpr
for idpr in idprs:#[-1:]:
@@ -68,15 +81,6 @@ for idpr in idprs:#[-1:]:
q25 = df.groupby('rounded_depth').quantile(0.25)
q75 = df.groupby('rounded_depth').quantile(0.75)
- import matplotlib.pyplot as plt
- import cmocean
-
-
- class fit:
- def __init__(self,N=0,m=2):
-
- self.popt = np.full([N,m],np.nan)
- self.pcov = np.full([N,m,m],np.nan)
N = len(wl_)
x = mean.prof_Edz #- 0.56
@@ -90,16 +94,18 @@ for idpr in idprs:#[-1:]:
y = mean.Edz.iloc[:, idx]
sigma = std.Edz.iloc[:, idx]
+ sigma[sigma<noise]=noise
sigma.fillna(np.inf,inplace=True)
res.popt[idx,:], res.pcov[idx,...] = curve_fit(iwr_process.f_Edz, x, y, [1.1 * aw, 100], bounds=([aw, 0], [np.inf, F0]))
- res_w.popt[idx,:], res_w.pcov[idx,...] = curve_fit(iwr_process.f_Edz, x, y, [1.1 * aw, 100], sigma=sigma, absolute_sigma=True, bounds=([aw, 0], [np.inf, F0]))
+ res_w.popt[idx,:], res_w.pcov[idx,...] = curve_fit(iwr_process.f_logEdz, x, np.log(1+y), [1.1 * aw, 100], bounds=([aw, 0], [np.inf, F0]))#, sigma=sigma, absolute_sigma=True
#res_rw.append( curve_fit(iwr_process.f_Edz, x, y, [1.1 * aw, 100], sigma=sigma, absolute_sigma=False, bounds=([aw, 0], [np.inf, F0])))
-
y = median.Edz.iloc[:, idx]
res_med.popt[idx,:], res_med.pcov[idx,...] = curve_fit(iwr_process.f_Edz, x, y, [1.1 * aw, 100], bounds=([aw, 0], [np.inf, F0]))
-
i=0
+
+ 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 (28, 37, 51, 71, 91, 105, 130, 140, 170):
@@ -114,21 +120,21 @@ for idpr in idprs:#[-1:]:
c=mean.Ed.iloc[:, idx],
alpha=0.6, cmap=cmocean.cm.thermal, label=None
)
- Ed_sim = iwr_process.f_Edz(depth_, *res[idx][0])
+ 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[idx][0])
- ax.plot(depth_, Ed_sim, linestyle=':', c='black', label='mean, weighted')
+ 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[idx][0])
+ Ed_sim = iwr_process.f_Edz(depth_, *res_med.popt[idx,:])
ax.plot(depth_, Ed_sim, linestyle='--', c='black', label='median')
- # ax.semilogy()
+ 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 = $' + str(round(wl_[idx], 1)) + ' nm, Kd = ' +
- str(round(res_w[idx][0][0], 3)) + '$m^{-1}$, Ed0 =' + str(round(res_w[idx][0][1], 1)))
+ str(round(res_w.popt[idx,0], 3)) + '$m^{-1}$, Ed0 =' + str(round(res_w.popt[idx,1], 1)), fontsize=12)
ax.legend(loc='best', frameon=False)
fig.suptitle('trios_iwr ' + name + ' idpr' + idpr, fontsize=16)
# fig.savefig(os.path.join(dirfig,'trios_iw_idpr' + idpr + '_'+name+'.png'),dpi=600)
@@ -142,28 +148,38 @@ for idpr in idprs:#[-1:]:
ax.fill_between(x,
mean - std,
mean + std, alpha=0.35, color=c)
- fig, axs = plt.subplots(nrows=1, ncols=2, figsize=(12, 7))
+
+ mpl.rcParams.update({'font.size': 18})
+ fig, axs = plt.subplots(nrows=1, ncols=2, figsize=(15, 6))
+ fig.subplots_adjust(left=0.1, right=0.9, hspace=.5, wspace=0.29)
iparam = 0
ax = axs.flat[iparam]
y, std = res.popt[:,iparam], res.pcov[:,iparam,iparam]
add_curve(ax, wl_, y, std, c='red', label='mean')
y, std = res_w.popt[:,iparam], res_w.pcov[:,iparam,iparam]
- add_curve(ax, wl_, y, std, c='black', label='mean, weighted')
+ add_curve(ax, wl_, y, std, c='orange', label='log-space')
y, std = res_med.popt[:,iparam], res_med.pcov[:,iparam,iparam]
- add_curve(ax, wl_, y, std, c='black', label='mean, weighted')
+ add_curve(ax, wl_, y, std, c='green', label='median')
+ ax.set_ylabel(r'$K_{d}\ ({m^{-1}})$')
+ ax.set_xlabel(r'Wavelength (nm)')
iparam = 1
ax = axs.flat[iparam]
- y, std = res.popt[:,iparam], res.pcov[:,iparam,iparam]
+ y, std = res.popt[:,iparam], np.sqrt(res.pcov[:,iparam,iparam])
add_curve(ax, wl_, y, std, c='red', label='mean')
- y, std = res_w.popt[:,iparam], res_w.pcov[:,iparam,iparam]
- add_curve(ax, wl_, y, std, c='black', label='mean, weighted')
+ y, std = res_w.popt[:,iparam], np.sqrt(res_w.pcov[:,iparam,iparam])
+ add_curve(ax, wl_, y, std, c='orange', label='log-space')
+ y, std = res_med.popt[:,iparam], np.sqrt(res_med.pcov[:,iparam,iparam])
+ add_curve(ax, wl_, y, std, c='green', label='median')
+ add_curve(ax, wl_, mean.Ed.mean(), mean.Ed.std(), c='black', label='Es')
- plt.plot(wl_,res.popt[:,iparam],)
- plt.fill_between(wl_,
- res.popt[:,iparam]- res.pcov[:,iparam,iparam],
- res.popt[:,iparam]+ res.pcov[:,iparam,iparam],alpha=0.35, color='grey')
+ ax.legend(loc='best', frameon=False)
+ ax.set_ylabel(r'$E_{d}(0^{-})\ ({mW\cdot m^{-2}\cdot nm^{-1}})$')
+ ax.set_xlabel(r'Wavelength (nm)')
+ fig.suptitle('trios_iwr ' + name + ' idpr' + idpr, fontsize=16)
+ fig.savefig(os.path.join(dirfig, 'trios_iwr_l2_idpr' + idpr + '_'+name+ '.pdf'))
+ plt.close()
# fig, ax = plt.subplots()
# N=df.Edz.shape[1]
diff --git a/utils/utils.py b/utils/utils.py
index 5ba29766260f2bf24f1b4c6c07d8abcc3828d837..53ecc6918784092d8b477577764b0d22d29b68a7 100644
--- a/utils/utils.py
+++ b/utils/utils.py
@@ -133,13 +133,13 @@ class iwr_data:
#''' interpolate Ed and Lsky data upon Lt wavelength'''
wl = wl_Luz
- Luz.columns = pd.MultiIndex.from_tuples(zip(['Luz'] * len(wl), wl), names=['param', 'wl'])
+ Luz.columns = pd.MultiIndex.from_tuples(list(zip(['Luz'] * len(wl), wl)), names=['param', 'wl'])
intEd = interp1d(wl_Ed, Ed.values, fill_value='extrapolate')(wl)
newEd = pd.DataFrame(index=Ed.index.get_level_values(0),
- columns=pd.MultiIndex.from_tuples(zip(['Ed'] * len(wl), wl), names=['param', 'wl']),
+ columns=pd.MultiIndex.from_tuples(list(zip(['Ed'] * len(wl), wl)), names=['param', 'wl']),
data=intEd)
intEdz = interp1d(wl_Edz, Edz.values, fill_value='extrapolate')(wl)
- newEdz = pd.DataFrame(index=Edz.index, columns=pd.MultiIndex.from_tuples(zip(['Edz'] * len(wl), wl),
+ newEdz = pd.DataFrame(index=Edz.index, columns=pd.MultiIndex.from_tuples(list(zip(['Edz'] * len(wl), wl)),
names=['param', 'wl']), data=intEdz)
# correct depth data for sensor to sensor distance