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tutorials/00-key-concepts.ipynb

@@ -3,7 +3,7 @@
   {
    "cell_type": "code",
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-   "id": "0be3bfce",
+   "id": "0",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -19,7 +19,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "799b1bdc",
+   "id": "1",
    "metadata": {},
    "source": [
     "# Key Concepts\n",
@@ -30,7 +30,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "450d2db3",
+   "id": "2",
    "metadata": {
     "tags": [
      "parameters"
@@ -44,7 +44,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "fb35543a",
+   "id": "3",
    "metadata": {},
    "source": [
     "The time series file contains the following columns:\n",
@@ -57,7 +57,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "4e684437",
+   "id": "4",
    "metadata": {},
    "outputs": [
     {
@@ -200,7 +200,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e1043825",
+   "id": "5",
    "metadata": {},
    "source": [
     "## Time series\n",
@@ -211,7 +211,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "066d2f50",
+   "id": "6",
    "metadata": {},
    "outputs": [
     {
@@ -353,7 +353,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1d37b066",
+   "id": "7",
    "metadata": {},
    "source": [
     "In order to have a proper time series data structure, we can pivot the data to have each variable as a column and each observation as a row (see more details in the [data forms section below](#data-forms)):"
@@ -362,7 +362,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "2e22df91",
+   "id": "8",
    "metadata": {},
    "outputs": [
     {
@@ -503,7 +503,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9da5d2a4",
+   "id": "9",
    "metadata": {},
    "source": [
     "The time series above can be encapsulated in a `TS` object:"
@@ -512,7 +512,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "d1de0a6d",
+   "id": "10",
    "metadata": {},
    "outputs": [
     {
@@ -533,7 +533,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b3032d0c",
+   "id": "11",
    "metadata": {},
    "source": [
     "Why would we need such an encapsulation? Let us get the data for two stations and pivot it to have each variable *for each station* as a column and each observation as a row:"
@@ -542,7 +542,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "edee00ba",
+   "id": "12",
    "metadata": {},
    "outputs": [
     {
@@ -780,7 +780,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "de0a4298",
+   "id": "13",
    "metadata": {},
    "source": [
     "Note now that each station has several missing values, therefore aligning the time index would result in many nan values:"
@@ -789,7 +789,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "0ec08b34",
+   "id": "14",
    "metadata": {},
    "outputs": [
     {
@@ -817,7 +817,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3af368a9",
+   "id": "15",
    "metadata": {},
    "source": [
     "Instead, we can create a `TS` object for each station and put them in a pandas object. Let us first transform it into a long data frame:"
@@ -826,7 +826,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "612ab962",
+   "id": "16",
    "metadata": {},
    "outputs": [
     {
@@ -999,7 +999,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0b41b946",
+   "id": "17",
    "metadata": {},
    "source": [
     "We can now transform the above data frame into a series of `TS` objects:"
@@ -1008,7 +1008,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "e36afee0",
+   "id": "18",
    "metadata": {},
    "outputs": [
     {
@@ -1036,7 +1036,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6edf7385",
+   "id": "19",
    "metadata": {},
    "source": [
     "Note that a `TSArray` is [a pandas `ExtensionArray` which is used to support custom data types](https://pandas.pydata.org/docs/reference/api/pandas.api.extensions.ExtensionArray.html), in this case, `TS` objects.\n",
@@ -1049,7 +1049,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "26e70ec1",
+   "id": "20",
    "metadata": {
     "lines_to_next_cell": 2
    },
@@ -1312,7 +1312,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "73959a1e",
+   "id": "21",
    "metadata": {},
    "source": [
     "Note that we have many nan values. Finally, it is also possible to have a single `TS` object per module and station, in which case the `TS` objects of the first and third module would have multiple variables, all of which would be aligned in time:"
@@ -1321,7 +1321,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "b0a543a2",
+   "id": "22",
    "metadata": {},
    "outputs": [
     {
@@ -1502,7 +1502,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1a5ed90a",
+   "id": "23",
    "metadata": {},
    "source": [
     "TODO: geospatial example\n",
@@ -1520,7 +1520,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "8cdc44f1",
+   "id": "24",
    "metadata": {},
    "outputs": [
     {
@@ -1727,7 +1727,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9ccb3d70",
+   "id": "25",
    "metadata": {
     "lines_to_next_cell": 2
    },
@@ -1738,7 +1738,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "48a03544",
+   "id": "26",
    "metadata": {},
    "outputs": [
     {