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ProjectPythia · Jun 15, 2024 · e1c05b5 · e1c05b5
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diff --git a/notebooks/ocn-carbonfluxes.ipynb b/notebooks/ocn-carbonfluxes.ipynb
@@ -247,21 +247,21 @@
    "id": "9f3a04c6-f810-4fcb-bcb7-3786e06e1d80",
    "metadata": {},
    "source": [
-    "## Processing - global means in time and space"
+    "## Processing - means in time and space"
    ]
   },
   {
    "cell_type": "markdown",
    "id": "e6cb6cfa-758a-4eca-9249-ce550c2d732e",
    "metadata": {},
    "source": [
-    "Pull in the functions we defined in the nutrients notebook..."
+    "Pull in the function we defined in the nutrients notebook..."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "f6b93b4b-3e8d-4f20-a560-26e6d2e8a576",
+   "id": "1bdf13bd-b1f9-42cc-aac1-ff4d0cc5248f",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -274,17 +274,33 @@
     "    # Make a DataArray with the number of days in each month, size = len(time)\n",
     "    month_length = ds.time.dt.days_in_month\n",
     "\n",
-    "    # Calculate the weights by grouping by 'time.season'\n",
+    "    # Calculate the weights by grouping by 'time.year'\n",
     "    weights = (\n",
     "        month_length.groupby(\"time.year\") / month_length.groupby(\"time.year\").sum()\n",
     "    )\n",
     "\n",
-    "    # Test that the sum of the weights for each season is 1.0\n",
+    "    # Test that the sum of the year for each season is 1.0\n",
     "    np.testing.assert_allclose(weights.groupby(\"time.year\").sum().values, np.ones((len(ds.groupby(\"time.year\")), )))\n",
     "\n",
     "    # Calculate the weighted average\n",
-    "    return (ds * weights).groupby(\"time.year\").sum(dim=\"time\")\n",
-    "    \n",
+    "    return (ds * weights).groupby(\"time.year\").sum(dim=\"time\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "79d7db02-ed13-4f8e-b75f-349378c3bc3c",
+   "metadata": {},
+   "source": [
+    "We also define a new function to take global mean in space."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "90bb975d-36fc-43ab-b829-88a151161c62",
+   "metadata": {},
+   "outputs": [],
+   "source": [
     "def global_mean(ds, ds_grid, compute_vars, normalize=True, include_ms=False):\n",
     "    \"\"\"\n",
     "    Compute the global mean on a POP dataset. \n",

diff --git a/notebooks/ocn-iron.ipynb b/notebooks/ocn-iron.ipynb
@@ -24,9 +24,9 @@
     "## Overview\n",
     "Iron is one of the most important micronutrients in the ocean, limiting production in some areas. In this notebook, we make a map of iron concentration and compare to observational data.\n",
     "\n",
-    "1. Gemeral setup\n",
+    "1. General setup\n",
     "2. Subsetting\n",
-    "3. Processing data\n",
+    "3. Processing - long-term mean\n",
     "4. Comparing to observational data\n",
     "   "
    ]
@@ -248,7 +248,7 @@
    "id": "5e3e1e23-3443-419f-b724-2f0ef79a9087",
    "metadata": {},
    "source": [
-    "## Transforming from monthly to annual data"
+    "## Processing - long-term mean"
    ]
   },
   {
@@ -275,12 +275,12 @@
     "    # Make a DataArray with the number of days in each month, size = len(time)\n",
     "    month_length = ds.time.dt.days_in_month\n",
     "\n",
-    "    # Calculate the weights by grouping by 'time.season'\n",
+    "    # Calculate the weights by grouping by 'time.year'\n",
     "    weights = (\n",
     "        month_length.groupby(\"time.year\") / month_length.groupby(\"time.year\").sum()\n",
     "    )\n",
     "\n",
-    "    # Test that the sum of the weights for each season is 1.0\n",
+    "    # Test that the sum of the year for each season is 1.0\n",
     "    np.testing.assert_allclose(weights.groupby(\"time.year\").sum().values, np.ones((len(ds.groupby(\"time.year\")), )))\n",
     "\n",
     "    # Calculate the weighted average\n",
@@ -371,7 +371,7 @@
     "### CESM\n",
     "ax = fig.add_subplot(1,2,1, projection=ccrs.Robinson(central_longitude=305.0))\n",
     "lon, lat, field = adjust_pop_grid(lons, lats,  ds.Fe.isel(z_t=0))\n",
-    "pc=ax.pcolormesh(lon, lat, field, cmap='jet',vmin=0.05,vmax=1.6,transform=ccrs.PlateCarree())\n",
+    "pc=ax.pcolormesh(lon, lat, field, cmap='plasma',vmin=0.05,vmax=1.6,transform=ccrs.PlateCarree())\n",
     "land = cartopy.feature.NaturalEarthFeature('physical', 'land', scale='110m', edgecolor='k', facecolor='white', linewidth=0.5)\n",
     "ax.add_feature(land)\n",
     "ax.set_title('CESM Fe at surface', fontsize=10)\n",
@@ -385,8 +385,8 @@
     "\n",
     "df_sub = df.loc[(df.depth <= 2.5)]\n",
     "sc = ax.scatter(df_sub.lon, df_sub.lat, c=df_sub.dFe_obs.values,\n",
-    "                cmap='jet',\n",
-    "                vmin=0.05, vmax=0.6, \n",
+    "                cmap='plasma',\n",
+    "                vmin=0.05, vmax=1.6, \n",
     "                transform=ccrs.PlateCarree())\n",
     "\n"
    ]

diff --git a/notebooks/ocn-macronuts.ipynb b/notebooks/ocn-macronuts.ipynb
@@ -32,7 +32,7 @@
     "\n",
     "1. General setup\n",
     "2. Subsetting\n",
-    "3. Transforming from monthly to annual data\n",
+    "3. Processing - means in time and space\n",
     "4. Compare to World Ocean Atlas data\n",
     "5. Make depth profiles\n"
    ]
@@ -300,12 +300,12 @@
     "    # Make a DataArray with the number of days in each month, size = len(time)\n",
     "    month_length = ds.time.dt.days_in_month\n",
     "\n",
-    "    # Calculate the weights by grouping by 'time.season'\n",
+    "    # Calculate the weights by grouping by 'time.year'\n",
     "    weights = (\n",
     "        month_length.groupby(\"time.year\") / month_length.groupby(\"time.year\").sum()\n",
     "    )\n",
     "\n",
-    "    # Test that the sum of the weights for each season is 1.0\n",
+    "    # Test that the sum of the year for each season is 1.0\n",
     "    np.testing.assert_allclose(weights.groupby(\"time.year\").sum().values, np.ones((len(ds.groupby(\"time.year\")), )))\n",
     "\n",
     "    # Calculate the weighted average\n",

diff --git a/notebooks/ocn-phyto-biomass.ipynb b/notebooks/ocn-phyto-biomass.ipynb
@@ -38,7 +38,7 @@
     "1. General setup\n",
     "2. Subsetting\n",
     "3. Taking a quick look\n",
-    "4. Processing data\n",
+    "4. Processing - long-term mean\n",
     "5. Mapping biomass at different depths\n",
     "6. Mapping productivity\n",
     "7. Compare NPP to satellite observations\n",
@@ -276,15 +276,15 @@
    "id": "674bbf53-101d-4867-9858-f13740b36da6",
    "metadata": {},
    "source": [
-    "## Processing - global mean in time"
+    "## Processing - long-term mean"
    ]
   },
   {
    "cell_type": "markdown",
    "id": "b78f8deb-5853-4fd8-9d5f-b3e7b382f164",
    "metadata": {},
    "source": [
-    "Pull in the functions we defined in the nutrients notebook..."
+    "Pull in the function we defined in the nutrients notebook..."
    ]
   },
   {
@@ -303,12 +303,12 @@
     "    # Make a DataArray with the number of days in each month, size = len(time)\n",
     "    month_length = ds.time.dt.days_in_month\n",
     "\n",
-    "    # Calculate the weights by grouping by 'time.season'\n",
+    "    # Calculate the weights by grouping by 'time.year'\n",
     "    weights = (\n",
     "        month_length.groupby(\"time.year\") / month_length.groupby(\"time.year\").sum()\n",
     "    )\n",
     "\n",
-    "    # Test that the sum of the weights for each season is 1.0\n",
+    "    # Test that the sum of the year for each season is 1.0\n",
     "    np.testing.assert_allclose(weights.groupby(\"time.year\").sum().values, np.ones((len(ds.groupby(\"time.year\")), )))\n",
     "\n",
     "    # Calculate the weighted average\n",

diff --git a/notebooks/ocn-phyto-lims.ipynb b/notebooks/ocn-phyto-lims.ipynb
@@ -37,10 +37,10 @@
     "\n",
     "1. General setup\n",
     "2. Subsetting\n",
-    "4. Processing data\n",
-    "5. Mapping nutrient limitation at the surface\n",
-    "6. Mapping biomass-weighted nutrient limitation in the top 100m\n",
-    "7. Making monthly climatology maps\n",
+    "3. Processing - long-term mean\n",
+    "4. Mapping nutrient limitation at the surface\n",
+    "5. Mapping biomass-weighted nutrient limitation in the top 100m\n",
+    "6. Making monthly climatology maps\n",
     "   "
    ]
   },
@@ -249,23 +249,15 @@
    "id": "1ab5692f-1739-4093-ba41-fd13334bd07f",
    "metadata": {},
    "source": [
-    "## Processing"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d0839bf0-81e4-4ad4-84e4-dcbd7357ffb8",
-   "metadata": {},
-   "source": [
-    "### Global mean in time"
+    "## Processing - long-term mean"
    ]
   },
   {
    "cell_type": "markdown",
    "id": "0a6077af-aa1b-4a2a-b800-efd718206ab3",
    "metadata": {},
    "source": [
-    "Pull in the functions we defined in the nutrients notebook..."
+    "Pull in the function we defined in the nutrients notebook..."
    ]
   },
   {

diff --git a/notebooks/ocn-zoo.ipynb b/notebooks/ocn-zoo.ipynb
@@ -37,10 +37,10 @@
     "\n",
     "1. General setup\n",
     "2. Subsetting\n",
-    "4. Processing data\n",
-    "5. Mapping zooplankton biomass at the surface\n",
-    "6. Comparing mesozooplankton biomass to observations\n",
-    "7. Making monthly climatology maps to compare to observations\n",
+    "3. Processing - long-term mean\n",
+    "4. Mapping zooplankton biomass at the surface\n",
+    "5. Comparing mesozooplankton biomass to observations\n",
+    "6. Making monthly climatology maps to compare to observations\n",
     "   "
    ]
   },
@@ -250,15 +250,15 @@
    "id": "febdf9c0-a98c-49fa-b224-bbe085895384",
    "metadata": {},
    "source": [
-    "## Processing - global mean in time"
+    "## Processing - long-term mean"
    ]
   },
   {
    "cell_type": "markdown",
    "id": "65b8fc08-c579-4353-bacf-af1b4f11d33d",
    "metadata": {},
    "source": [
-    "Pull in the functions we defined in the nutrients notebook..."
+    "Pull in the function we defined in the nutrients notebook..."
    ]
   },
   {
@@ -270,19 +270,18 @@
    "source": [
     "def year_mean(ds):\n",
     "    \"\"\"\n",
-    "    Add a docstring\n",
     "    Source: https://ncar.github.io/esds/posts/2021/yearly-averages-xarray/\n",
     "    \"\"\"\n",
     "    \n",
     "    # Make a DataArray with the number of days in each month, size = len(time)\n",
     "    month_length = ds.time.dt.days_in_month\n",
     "\n",
-    "    # Calculate the weights by grouping by 'time.season'\n",
+    "    # Calculate the weights by grouping by 'time.year'\n",
     "    weights = (\n",
     "        month_length.groupby(\"time.year\") / month_length.groupby(\"time.year\").sum()\n",
     "    )\n",
     "\n",
-    "    # Test that the sum of the weights for each season is 1.0\n",
+    "    # Test that the sum of the weights for each year is 1.0\n",
     "    np.testing.assert_allclose(weights.groupby(\"time.year\").sum().values, np.ones((len(ds.groupby(\"time.year\")), )))\n",
     "\n",
     "    # Calculate the weighted average\n",
@@ -296,7 +295,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Take the long-term mean of our data set, processing years and months separately (improve this comment)\n",
+    "# Take the long-term mean of our data set, processing years and months separately\n",
     "\n",
     "ds_annual = year_mean(ds).mean(\"year\")"
    ]
@@ -321,7 +320,7 @@
     "ax = fig.add_subplot(2,1,1, projection=ccrs.Robinson(central_longitude=305.0))\n",
     "ax.set_title('microzooC at surface', fontsize=12)\n",
     "lon, lat, field = adjust_pop_grid(lons, lats,  ds_annual.microzooC.isel(z_t_150m=0))\n",
-    "pc=ax.pcolormesh(lon, lat, field, cmap='jet',vmin=0,vmax=2,transform=ccrs.PlateCarree())\n",
+    "pc=ax.pcolormesh(lon, lat, field, cmap='Blues',vmin=0,vmax=2,transform=ccrs.PlateCarree())\n",
     "cbar1 = fig.colorbar(pc, ax=ax,extend='max',label='microzooC (mmol m$^{-3}$)')\n",
     "land = cartopy.feature.NaturalEarthFeature('physical', 'land', scale='110m', edgecolor='k', facecolor='white', linewidth=0.5)\n",
     "ax.add_feature(land)\n",
@@ -330,7 +329,7 @@
     "ax = fig.add_subplot(2,1,2, projection=ccrs.Robinson(central_longitude=305.0))\n",
     "ax.set_title('mesozooC at surface', fontsize=12)\n",
     "lon, lat, field = adjust_pop_grid(lons, lats,  ds_annual.mesozooC.isel(z_t_150m=0))\n",
-    "pc=ax.pcolormesh(lon, lat, field, cmap='jet',vmin=0,vmax=4,transform=ccrs.PlateCarree())\n",
+    "pc=ax.pcolormesh(lon, lat, field, cmap='Oranges',vmin=0,vmax=4,transform=ccrs.PlateCarree())\n",
     "cbar1 = fig.colorbar(pc, ax=ax,extend='max',label='mesozooC (mmol m$^{-3}$)')\n",
     "land = cartopy.feature.NaturalEarthFeature('physical', 'land', scale='110m', edgecolor='k', facecolor='white', linewidth=0.5)\n",
     "ax.add_feature(land)"