small language corrections

ghislainp · Apr 29, 2022 · c1a802f · c1a802f
1 parent c41f702
commit c1a802f
Showing 1 changed file with 11 additions and 11 deletions.
diff --git a/examples/Shrubs-cool-down-permafrost-NatureGeoscience2022/salix_vs_tundra.ipynb b/examples/Shrubs-cool-down-permafrost-NatureGeoscience2022/salix_vs_tundra.ipynb
@@ -24,7 +24,7 @@
     "# install mfm if needed\n",
     "# !pip install git+https://github.com/ghislainp/mfm --log tmp.log\n",
     "\n",
-    "# other option is to point to mfm:\n",
+    "# the other option is to point to mfm:\n",
     "import sys\n",
     "sys.path.append(\"../..\")"
    ]
@@ -59,6 +59,8 @@
    },
    "outputs": [],
    "source": [
+    "# set this parameter to choose the site\n",
+    "\n",
     "location = \"salix\"\n",
     "\n",
     "#location = \"tundra\""
@@ -79,7 +81,7 @@
     "    met = pd.read_csv(\"data/Ttundra.csv\", parse_dates=True, index_col=0)\n",
     "    met += 273.15\n",
     "    \n",
-    "    met = met.loc['2018-09-15':]  # start the simulation 1 dec 2018\n",
+    "    met = met.loc['2018-09-15':]  # start the simulation 15 Sep 2018\n",
     "    \n",
     "    nlyr_snow = 38  # 38 cm\n",
     "\n",
@@ -98,7 +100,7 @@
     "    ks_snow_spring = np.zeros(nlyr_snow)\n",
     "    ks_snow_melt = np.zeros(nlyr_snow)\n",
     "    \n",
-    "    # density. Each layer is 1cm. 0 is the ground, 38 is the top\n",
+    "    # density. Each layer is 1 cm thick. 0 is the ground, 38 is the top\n",
     "    rho_snow[0:10] = 220\n",
     "    rho_snow[10:20] = 325\n",
     "    rho_snow[20:38] = 350\n",
@@ -124,7 +126,7 @@
     "    met = pd.read_csv(\"data/Tsalix.csv\", parse_dates=True, index_col=0)\n",
     "    met += 273.15\n",
     "\n",
-    "    met = met.loc['2018-09-15':]  # start the simulation 1 dec 2018\n",
+    "    met = met.loc['2018-09-15':]  # start the simulation 15 Sep 2018\n",
     "    \n",
     "    nlyr_snow = 40  # 40 cm\n",
     "\n",
@@ -144,7 +146,7 @@
     "    ks_snow_spring = np.zeros(nlyr_snow)\n",
     "    ks_snow_melt = np.zeros(nlyr_snow)\n",
     "    \n",
-    "    # density. Each layer is 1cm. 0 is the ground, 38 is the top\n",
+    "    # density. Each layer is 1cm thick. 0 is the ground, 38 is the top\n",
     "    rho_snow[0:10] = 210\n",
     "    rho_snow[10:20] = 240\n",
     "    rho_snow[20:40] = 330\n",
@@ -192,7 +194,6 @@
     "\n",
     "soil_layers = 2\n",
     "\n",
-    "\n",
     "# 2 LAYER SOIL\n",
     "if soil_layers == 2:\n",
     "  # summer (unfrozen) soil\n",
@@ -297,7 +298,7 @@
     "\n",
     "# initial temperature profile\n",
     "#\n",
-    "# Ajuster ici z0 et temp0 pour decrire le profil de temperature initial\n",
+    "# Here, ajust the z0 and temp0 parameters to prescribe a realistic initial profile of temperature\n",
     "#\n",
     "z0    = [30, 5, 0, -5, -10, -30, -100, -200, -500]   # depths in centimeter  (positive = snow, negative = soil, 0 = interface soil/snow)\n",
     "temp0 = [0, 0, 0, 0, 0, 0, -4, -7,-11] # temperature at each depth\n",
@@ -359,10 +360,9 @@
     "             temp[0:itop] = temp[itop]  # copy the surface temperature up\n",
     "           itop = nlyr_snow - int(np.round(height_timeseries_copy.pop(0)[1]))\n",
     "           if itop < 0:\n",
-    "             print(\"Erreur, la hauteur dans height_date est trop elevée par rapport à nlyr_snow\")\n",
+    "             print(\"Error: the height in 'height_date' is too large compared to 'nlyr_snow'\")\n",
     "\n",
     "        for j in range(0, 3600, params.dt):  # sub-hourly loop\n",
-    "            #temp = model_dirichlet(ts, temp, params)\n",
     "            otemp = np.full_like(temp, np.nan)\n",
     "            otemp[itop:] = cranknicolson_dirichlet(temp[itop:], ts, params.dt, params.dx[itop:], params.ks[itop:],\n",
     "                               params.rho[itop:], cpice=params.cp[itop:], tbase=params.tbase) # run the model for 1 timestep\n",
@@ -440,8 +440,8 @@
     "\n",
     "  df = pd.DataFrame(np.transpose(out), columns=moddepths, index=o_time)\n",
     "\n",
-    "  #df.to_excel(f'results-{location}.xls')\n",
-    "  df.to_csv(f'results-{location}.csv')\n",
+    "  #df.to_excel(f'results-{location}.xls')  # save as an excel file\n",
+    "  df.to_csv(f'results-{location}.csv')     # save as a csv file\n",
     "\n",
     "plot()"
    ]