Version 1.0.0 is ready!

Author: dhjx1996

README.md

@@ -43,9 +43,11 @@ These notebooks double up as examples of how to use PythonicDISORT. The tests wh
 * Test Problem 2: Rayleigh Scattering, Beam Source
 * Test Problem 3: Henyey-Greenstein Scattering
 * Test Problem 4: Haze-L Scattering, Beam Source
-* Test Problem 5: Cloud C.1 Scattering, Beam Source (Test 5BDRF has Lambertian BDRF with albedo 1)
-* Test Problem 8:  Absorbing/Isotropic-Scattering Medium With Two Computational Layers 
-* Test Problem 9:  General Emitting/Absorbing/Scattering Medium with Every Computational Layer Different
+* Test Problem 5: Cloud C.1 Scattering, Beam Source
+* Test Problem 6: No Scattering, Increasingly Complex Sources (relevant for modeling longwave radiation)
+* Test Problem 7: Absorption + Scattering + All Possible Sources, Lambertian and Hapke Surface Reflectivities (one layer)
+* Test Problem 8: Absorbing / Isotropic-Scattering Medium With (two layers)
+* Test Problem 9: General Emitting / Absorbing / Scattering Medium with (many different layers)
 * Test Problem 11: Single-Layer vs. Multiple Layers (no corresponding Jupyter Notebook)
 
 # Installation

docs/Pythonic-DISORT.ipynb

@@ -9,7 +9,7 @@
 project = 'Pythonic DISORT'
 copyright = '2023, HO Jia Xu Dion'
 author = 'Dion HO Jia Xu'
-release = '0.9.5'
+release = '1.0.0'
 
 # -- General configuration ---------------------------------------------------
 # https://www.sphinx-doc.org/en/master/usage/configuration.html#general-configuration

docs/conf.py

@@ -223,7 +223,7 @@
     "do_pseudo_sphere = False\n",
     "dtauc = tau_arr\n",
     "ssalb = omega_arr\n",
-    "pmom = Leg_coeffs_all.T[: NQuad + 1]\n",
+    "pmom = Leg_coeffs_all.T\n",
     "temper = np.zeros(nlyr + 1)\n",
     "wvnmlo = 0\n",
     "wvnmhi = 0\n",
@@ -409,7 +409,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.legend.Legend at 0x2435d627910>"
+       "<matplotlib.legend.Legend at 0x219091a2c90>"
       ]
      },
      "execution_count": 12,
@@ -543,9 +543,7 @@
    "execution_count": 16,
    "id": "5c64fd85",
    "metadata": {
-    "code_folding": [
-     0
-    ]
+    "code_folding": []
    },
    "outputs": [],
    "source": [
@@ -567,7 +565,7 @@
     "do_pseudo_sphere = False\n",
     "dtauc = tau_arr\n",
     "ssalb = omega_arr\n",
-    "pmom = Leg_coeffs_all.T[: NQuad + 1]\n",
+    "pmom = Leg_coeffs_all.T\n",
     "temper = np.zeros(nlyr + 1)\n",
     "wvnmlo = 0\n",
     "wvnmhi = 0\n",
@@ -753,7 +751,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.legend.Legend at 0x2435dbda490>"
+       "<matplotlib.legend.Legend at 0x21909902c90>"
       ]
      },
      "execution_count": 21,
@@ -887,9 +885,7 @@
    "execution_count": 25,
    "id": "e528dff1",
    "metadata": {
-    "code_folding": [
-     0
-    ]
+    "code_folding": []
    },
    "outputs": [],
    "source": [
@@ -911,7 +907,7 @@
     "do_pseudo_sphere = False\n",
     "dtauc = tau_arr\n",
     "ssalb = omega_arr\n",
-    "pmom = Leg_coeffs_all.T[: NQuad + 1]\n",
+    "pmom = Leg_coeffs_all.T\n",
     "temper = np.zeros(nlyr + 1)\n",
     "wvnmlo = 0\n",
     "wvnmhi = 0\n",
@@ -1097,7 +1093,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.legend.Legend at 0x2435e138d50>"
+       "<matplotlib.legend.Legend at 0x21909de2dd0>"
       ]
      },
      "execution_count": 30,
@@ -1231,9 +1227,7 @@
    "execution_count": 34,
    "id": "4adda5fe",
    "metadata": {
-    "code_folding": [
-     0
-    ]
+    "code_folding": []
    },
    "outputs": [],
    "source": [
@@ -1255,7 +1249,7 @@
     "do_pseudo_sphere = False\n",
     "dtauc = tau_arr\n",
     "ssalb = omega_arr\n",
-    "pmom = Leg_coeffs_all.T[: NQuad + 1]\n",
+    "pmom = Leg_coeffs_all.T\n",
     "temper = np.zeros(nlyr + 1)\n",
     "wvnmlo = 0\n",
     "wvnmhi = 0\n",
@@ -1441,7 +1435,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.legend.Legend at 0x2435dbfc390>"
+       "<matplotlib.legend.Legend at 0x21909e45390>"
       ]
      },
      "execution_count": 39,
@@ -1575,9 +1569,7 @@
    "execution_count": 43,
    "id": "86b493bf",
    "metadata": {
-    "code_folding": [
-     0
-    ]
+    "code_folding": []
    },
    "outputs": [],
    "source": [
@@ -1599,7 +1591,7 @@
     "do_pseudo_sphere = False\n",
     "dtauc = tau_arr\n",
     "ssalb = omega_arr\n",
-    "pmom = Leg_coeffs_all.T[: NQuad + 1]\n",
+    "pmom = Leg_coeffs_all.T\n",
     "temper = np.zeros(nlyr + 1)\n",
     "wvnmlo = 0\n",
     "wvnmhi = 0\n",
@@ -1785,7 +1777,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.legend.Legend at 0x2435e3ec390>"
+       "<matplotlib.legend.Legend at 0x2190a3273d0>"
       ]
      },
      "execution_count": 48,
@@ -1919,9 +1911,7 @@
    "execution_count": 52,
    "id": "8c848bc7",
    "metadata": {
-    "code_folding": [
-     0
-    ]
+    "code_folding": []
    },
    "outputs": [],
    "source": [
@@ -1943,7 +1933,7 @@
     "do_pseudo_sphere = False\n",
     "dtauc = tau_arr\n",
     "ssalb = omega_arr\n",
-    "pmom = Leg_coeffs_all.T[: NQuad + 1]\n",
+    "pmom = Leg_coeffs_all.T\n",
     "temper = np.zeros(nlyr + 1)\n",
     "wvnmlo = 0\n",
     "wvnmhi = 0\n",
@@ -2129,7 +2119,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.legend.Legend at 0x2435e8d1310>"
+       "<matplotlib.legend.Legend at 0x2190a3275d0>"
       ]
      },
      "execution_count": 57,