remove step 6 from 3.Tutorial; rename file; fix typo

hands-on/session II/1.Tutorial.ipynb

@@ -606,7 +606,7 @@
    "id": "db980ecf",
    "metadata": {},
    "source": [
-    "The followitng cell takes the loaded IDX files with Openvisus and statically visualizes it. "
+    "The following cell takes the loaded IDX files with Openvisus and statically visualizes it. "
    ]
   },
   {

hands-on/session III/3.Tutorial_PetascaleAnalysis.ipynb

@@ -375,83 +375,6 @@
     "print('Step 5 done.')"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "5e734534-d71a-4b65-b7b0-26028daea8cc",
-   "metadata": {},
-   "source": [
-    "## **Step 6: Calculate the percentage of world surface within the selected salinity range**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "ab0b2e45-03d5-45cb-b41c-c876ff712195",
-   "metadata": {
-    "scrolled": true,
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "----\n",
-      "874800.0 380479 494321 261747\n",
-      "----\n",
-      "----\n",
-      "350330.355378767 106714.95416864933 494321 141260.0510085231\n",
-      "----\n",
-      "percentage of land voxels 30.46%  (155,230,455 square km)\n",
-      "percentage of world voxels within the salinity range 40.32%  (205,480,686 square km)\n",
-      "percentage of water voxels within the salinity range 57.98%\n"
-     ]
-    }
-   ],
-   "source": [
-    "import math\n",
-    "total_voxels = np.ones((data.shape[0] , data.shape[1]))  \n",
-    "longitude_levels = data.shape[0] \n",
-    "latitude_levels  = data.shape[1] \n",
-    "#longitude_levels = 11\n",
-    "min_longitude    = -90\n",
-    "max_longitude    = 75\n",
-    "longitude_values = np.linspace(min_longitude, max_longitude, num=longitude_levels)\n",
-    "\n",
-    "world_chord_lengths = np.sin(((90- np.abs(longitude_values))/2) * np.pi / 180)\n",
-    "\n",
-    "world_lenghth_mask = np.transpose(np.atleast_2d(world_chord_lengths).repeat(repeats=latitude_levels, axis=0))\n",
-    "#print (world_lenghth_mask)\n",
-    "\n",
-    "total_surface = np.sum(total_voxels) \n",
-    "land_surface  = np.sum(data_land)\n",
-    "sea_surface   = np.sum( data_sea)\n",
-    "white_surface = np.sum(data_binary)\n",
-    "print(\"----\")\n",
-    "print(total_surface,land_surface,sea_surface, white_surface  )\n",
-    "print(\"----\")\n",
-    "\n",
-    "total_voxels = np.multiply(total_voxels, world_lenghth_mask)\n",
-    "white_voxels  = np.multiply(data_binary, world_lenghth_mask)\n",
-    "land_voxels  = np.multiply(data_land,  world_lenghth_mask)\n",
-    "sea_voxels   = np.multiply(data_sea   ,  world_lenghth_mask)\n",
-    "\n",
-    "world_surface_area =  509600000 #square km\n",
-    "\n",
-    "total_surface = np.sum(total_voxels) \n",
-    "land_surface  = np.sum(land_voxels)\n",
-    "sea_surface   = np.sum ( data_sea)\n",
-    "white_surface = np.sum(white_voxels)\n",
-    "print(\"----\")\n",
-    "print(total_surface,land_surface,sea_surface, white_surface  )\n",
-    "print(\"----\")\n",
-    "\n",
-    "print(\"percentage of land voxels\", '{:.2%}'.format(land_surface/total_surface),\" (\"  '{:,}'.format(int(land_surface/total_surface*world_surface_area)),\"square km)\")\n",
-    "print(\"percentage of world voxels within the salinity range\", '{:.2%}'.format(white_surface/(total_surface)) ,\" (\"  '{:,}'.format(int(white_surface/total_surface*world_surface_area)),\"square km)\")\n",
-    "print(\"percentage of water voxels within the salinity range\", '{:.2%}'.format(white_surface/(total_surface-land_surface)))\n",
-    "print('Step 6 done.')"
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "763806d3-ed89-44b1-8797-2299129c5ad9",
@@ -460,7 +383,7 @@
     "## **OBSERVE: Summary of test Results on desktop at different resolutions**\n",
     "## Note, you may want to look above and change the data resolution and see what results you get\n",
     "\n",
-    "## **Step 7: Exercise for the user**: How many errors are generated if you change the resolution? Note: the lower number means less data, which means more errors.\n",
+    "## **Step 6: Exercise for the user**: How many errors are generated if you change the resolution? Note: the lower number means less data, which means more errors.\n",
     "\n",
     "Test at data resolution  0 <br>\n",
     "55987200 24342221 16750511 <br>\n",
@@ -497,10 +420,7 @@
    "execution_count": null,
    "id": "5b2578187af7a10",
    "metadata": {
-    "collapsed": false,
-    "jupyter": {
-     "outputs_hidden": false
-    }
+    "collapsed": false
    },
    "outputs": [],
    "source": []
@@ -510,7 +430,7 @@
    "id": "25eabbf6-5e69-4cca-9b19-4bf4f39e2c9b",
    "metadata": {},
    "source": [
-    "## **Step 8: But, what if you want to see the full data for a certain region at a certain depth?**\n",
+    "## **Step 7: But, what if you want to see the full data for a certain region at a certain depth?**\n",
     "\n",
     "Set the proper x,y,z while reading the data. x and y are the bounding box, z is the depth/layer. \n"
    ]
@@ -521,9 +441,6 @@
    "id": "2504ff1ba9b17453",
    "metadata": {
     "collapsed": false,
-    "jupyter": {
-     "outputs_hidden": false
-    },
     "scrolled": true
    },
    "outputs": [
@@ -598,7 +515,7 @@
     }
    ],
    "source": [
-    "#Step 8b)\n",
+    "#Step 7b)\n",
     "# Use the x1, x2 y1 y2 region you set up above to read a subregion into the variable data_sub\n",
     "data_sub=db.read(time=0,z=[0,1],x=[x1,x2],y=[y1,y2])\n",
     "plt.imshow(data_sub[0,:,:], origin='lower',vmin=33,vmax=36,cmap='viridis')\n",
@@ -610,7 +527,7 @@
    "id": "5fb5dfb4-4cb5-4b1f-883d-dc51ab629ebe",
    "metadata": {},
    "source": [
-    "# **Step 9: Exercise for the user**: Change the region and try the calculations from above on the new region of interest \n",
+    "# **Step 8: Exercise for the user**: Change the region and try the calculations from above on the new region of interest \n",
     " \n",
     "# **What calculations do you want to run on that subregion?**"
    ]