Skip to content

Commit

Permalink
Update documentation
Browse files Browse the repository at this point in the history
  • Loading branch information
@chiaweh2
chiaweh2 committed Aug 27, 2024
1 parent bb3e49c commit 2b23418
Show file tree
Hide file tree
Showing 9 changed files with 110 additions and 98 deletions.
90 changes: 45 additions & 45 deletions _sources/content/data_access/access_erddap/python_get_gliders.ipynb
View file

Large diffs are not rendered by default.

42 changes: 21 additions & 21 deletions _sources/content/data_access/access_erddap/python_interface_griddap.ipynb
View file
Original file line number Diff line number Diff line change
Expand Up @@ -2,7 +2,7 @@
"cells": [
{
"cell_type": "markdown",
"id": "37c0bc85",
"id": "873125a0",
"metadata": {},
"source": [
"Accessing gridded data\n",
Expand All @@ -22,7 +22,7 @@
},
{
"cell_type": "markdown",
"id": "b79a104d",
"id": "5002680b",
"metadata": {},
"source": [
"In this page, we demonstrate how to extract/download data directly from a ERDDAP server and perform data processing, visualization, and export data in python environment. \n",
Expand All @@ -37,7 +37,7 @@
{
"cell_type": "code",
"execution_count": null,
"id": "c6ed36a4",
"id": "a59366d1",
"metadata": {},
"outputs": [],
"source": [
Expand All @@ -47,7 +47,7 @@
},
{
"cell_type": "markdown",
"id": "ea248093",
"id": "25294279",
"metadata": {},
"source": [
"- [**xarray**](https://docs.xarray.dev/en/stable/getting-started-guide/why-xarray.html) is used for data processing and netCDF file output. \n",
Expand All @@ -66,7 +66,7 @@
{
"cell_type": "code",
"execution_count": null,
"id": "d77da8dc",
"id": "10762a63",
"metadata": {},
"outputs": [],
"source": [
Expand All @@ -80,7 +80,7 @@
},
{
"cell_type": "markdown",
"id": "ebe84145",
"id": "9c752403",
"metadata": {},
"source": [
"```{note}\n",
Expand All @@ -99,7 +99,7 @@
{
"cell_type": "code",
"execution_count": null,
"id": "6af54936",
"id": "286501a8",
"metadata": {},
"outputs": [],
"source": [
Expand All @@ -111,7 +111,7 @@
},
{
"cell_type": "markdown",
"id": "f91ad01a",
"id": "1d9c99e8",
"metadata": {},
"source": [
"To have a quick view and setup of the download variables and range on different dimension, execute"
Expand All @@ -120,7 +120,7 @@
{
"cell_type": "code",
"execution_count": null,
"id": "a804b8dc",
"id": "66b5635c",
"metadata": {},
"outputs": [],
"source": [
Expand All @@ -135,7 +135,7 @@
},
{
"cell_type": "markdown",
"id": "ebfbbdd3",
"id": "33b52edd",
"metadata": {},
"source": [
"```{note}\n",
Expand All @@ -156,7 +156,7 @@
{
"cell_type": "code",
"execution_count": null,
"id": "6cfce669",
"id": "1f2d0736",
"metadata": {},
"outputs": [],
"source": [
Expand All @@ -167,7 +167,7 @@
},
{
"cell_type": "markdown",
"id": "5f3e8b47",
"id": "776a69e6",
"metadata": {},
"source": [
"## Download data \n",
Expand All @@ -180,7 +180,7 @@
{
"cell_type": "code",
"execution_count": null,
"id": "c2d506db",
"id": "d5b29e8b",
"metadata": {},
"outputs": [],
"source": [
Expand All @@ -189,7 +189,7 @@
},
{
"cell_type": "markdown",
"id": "2e2030ce",
"id": "812728ff",
"metadata": {},
"source": [
"The `ds` object constructed by Xarray is a great way to see the gridded data structure and perform quick visualization, preprocessing, and exporting to netCDF format."
Expand All @@ -198,7 +198,7 @@
{
"cell_type": "code",
"execution_count": null,
"id": "12d21a01",
"id": "bf2237f9",
"metadata": {},
"outputs": [],
"source": [
Expand All @@ -207,7 +207,7 @@
},
{
"cell_type": "markdown",
"id": "e070a4b8",
"id": "715957e0",
"metadata": {},
"source": [
"jupyter output cell above shows the coordinates, variables, and related attributes of the datasets and variables. \n",
Expand All @@ -220,7 +220,7 @@
{
"cell_type": "code",
"execution_count": null,
"id": "ab31d56c",
"id": "0bf0f47d",
"metadata": {},
"outputs": [],
"source": [
Expand All @@ -230,7 +230,7 @@
{
"cell_type": "code",
"execution_count": null,
"id": "dc38b3ee",
"id": "0dd6076e",
"metadata": {},
"outputs": [],
"source": [
Expand All @@ -239,7 +239,7 @@
},
{
"cell_type": "markdown",
"id": "9cec8235",
"id": "d3688713",
"metadata": {},
"source": [
"## Preprocess data\n",
Expand All @@ -249,7 +249,7 @@
{
"cell_type": "code",
"execution_count": null,
"id": "962a8f0a",
"id": "3a5a59fc",
"metadata": {},
"outputs": [],
"source": [
Expand All @@ -258,7 +258,7 @@
},
{
"cell_type": "markdown",
"id": "e580c271",
"id": "986890c4",
"metadata": {},
"source": [
"The `.mean(dim='longitude')` is the method Xarray provide for zonal averaging.\n",
Expand Down
40 changes: 20 additions & 20 deletions _sources/content/data_access/access_erddap/python_interface_tabledap.ipynb
View file
Original file line number Diff line number Diff line change
Expand Up @@ -2,7 +2,7 @@
"cells": [
{
"cell_type": "markdown",
"id": "56cd71cf",
"id": "66b0fad9",
"metadata": {},
"source": [
"Accessing table (spreadsheet) data\n",
Expand All @@ -23,7 +23,7 @@
},
{
"cell_type": "markdown",
"id": "cc4e2635",
"id": "c5857d96",
"metadata": {},
"source": [
"In this page, we demonstrate how to extract/download data directly from a ERDDAP server and perform data processing, visualization, and export data in python environment. \n",
Expand All @@ -38,7 +38,7 @@
{
"cell_type": "code",
"execution_count": null,
"id": "56a5de99",
"id": "3b0252c0",
"metadata": {},
"outputs": [],
"source": [
Expand All @@ -48,7 +48,7 @@
},
{
"cell_type": "markdown",
"id": "2778d5da",
"id": "f3fc502b",
"metadata": {},
"source": [
"- [**xarray**](https://docs.xarray.dev/en/stable/getting-started-guide/why-xarray.html) is used for data processing and netCDF file output. \n",
Expand All @@ -67,7 +67,7 @@
{
"cell_type": "code",
"execution_count": null,
"id": "e49cd721",
"id": "1212b7d4",
"metadata": {},
"outputs": [],
"source": [
Expand All @@ -81,7 +81,7 @@
},
{
"cell_type": "markdown",
"id": "fec6ce6f",
"id": "db83af4e",
"metadata": {},
"source": [
"```{note}\n",
Expand All @@ -100,7 +100,7 @@
{
"cell_type": "code",
"execution_count": null,
"id": "281c3722",
"id": "d0b4cd99",
"metadata": {},
"outputs": [],
"source": [
Expand All @@ -112,7 +112,7 @@
},
{
"cell_type": "markdown",
"id": "38f1d1d9",
"id": "ec52ddda",
"metadata": {},
"source": [
"## Download data \n",
Expand All @@ -125,7 +125,7 @@
{
"cell_type": "code",
"execution_count": null,
"id": "7812e388",
"id": "3115956a",
"metadata": {},
"outputs": [],
"source": [
Expand All @@ -135,7 +135,7 @@
},
{
"cell_type": "markdown",
"id": "beb41f0b",
"id": "dfb9375b",
"metadata": {},
"source": [
"The two steps approach to convert the Pandas dataframe to Xarray Dataset is to utilize the xarray method in the following.\n",
Expand All @@ -146,7 +146,7 @@
{
"cell_type": "code",
"execution_count": null,
"id": "b1643dd7",
"id": "3393f8c8",
"metadata": {},
"outputs": [],
"source": [
Expand All @@ -155,7 +155,7 @@
},
{
"cell_type": "markdown",
"id": "3f05fb9e",
"id": "d893927c",
"metadata": {},
"source": [
"jupyter output cell above shows the coordinates, variables, and related attributes of the datasets and variables. \n",
Expand All @@ -168,7 +168,7 @@
{
"cell_type": "code",
"execution_count": null,
"id": "87b2eeba",
"id": "c1fe0722",
"metadata": {},
"outputs": [],
"source": [
Expand All @@ -179,7 +179,7 @@
{
"cell_type": "code",
"execution_count": null,
"id": "aa38d06d",
"id": "4b103f9e",
"metadata": {},
"outputs": [],
"source": [
Expand All @@ -189,7 +189,7 @@
{
"cell_type": "code",
"execution_count": null,
"id": "2f9e704b",
"id": "37f8e5c9",
"metadata": {},
"outputs": [],
"source": [
Expand All @@ -205,7 +205,7 @@
{
"cell_type": "code",
"execution_count": null,
"id": "c01d2680",
"id": "5f7fb6e8",
"metadata": {},
"outputs": [],
"source": [
Expand All @@ -230,7 +230,7 @@
},
{
"cell_type": "markdown",
"id": "ba2e3791",
"id": "62d14b2b",
"metadata": {},
"source": [
"## Preprocess data\n",
Expand All @@ -240,7 +240,7 @@
{
"cell_type": "code",
"execution_count": null,
"id": "4ab45735",
"id": "9380e3fb",
"metadata": {},
"outputs": [],
"source": [
Expand All @@ -250,7 +250,7 @@
{
"cell_type": "code",
"execution_count": null,
"id": "1198308d",
"id": "4c9a9026",
"metadata": {},
"outputs": [],
"source": [
Expand All @@ -273,7 +273,7 @@
},
{
"cell_type": "markdown",
"id": "00cd8099",
"id": "1fe0d498",
"metadata": {},
"source": [
"## Export to netCDF\n",
Expand Down
12 changes: 8 additions & 4 deletions _sources/content/how_to_conda.md
View file
Original file line number Diff line number Diff line change
Expand Up @@ -13,14 +13,14 @@ Remember, each step is crucial to ensure the correct setup and functioning of yo
```{warning}
**Command Line Interface (CLI)** The following steps need to be performed in Command Line Interface. For Windows, search for `cmd` in the search bar for the Command Prompt app. For macOS and Linux, terminal app is the preinstalled app for CLI.
```
```{tips}
There are many useful setup intruction out there. The intruction here is unique in offering both R and Python setup for running the example shown in this entire cookbook. For other useful info related to setup other analyses ready material, we recommand [IOOS CodeLab](https://ioos.github.io/ioos_code_lab/content/ioos_installation_conda.html)
```{tip}
There are many useful setup instructions available, but this guide is unique in providing both R and Python setup for running the examples throughout this cookbook. For additional resources related to setting up other analysis-ready environments, we recommend [IOOS CodeLab](https://ioos.github.io/ioos_code_lab/content/ioos_installation_conda.html).
```


### Install and Initialize Conda
The following intruction will install "miniconda" which is the light-weight version of Conda system on a **Linux machine**.
For other system (macOS or Windows) please checkout the [official miniconda installation](https://docs.anaconda.com/miniconda/#quick-command-line-install)
The following intruction will install "miniconda" which is the light-weight version of Conda system.
For a more customizable setup, please check out the [official miniconda installation](https://docs.anaconda.com/miniconda/#quick-command-line-install).
`````{tab-set}
````{tab-item} Linux/MacOS
```
Expand Down Expand Up @@ -80,6 +80,10 @@ This way, you can maintain the integrity and functionality of each project indep
```````{dropdown} If you do not have wget (trouble shooting read)
``````{admonition} Quick fix/try
Change `wget` to `curl`
``````
``````{admonition} Method 1 (Manuel download)
- For Python, go to GitHub page which contain the Python version of [environment.yml file](https://github.com/NOAA-CEFI-Portal/cefi-cookbook/blob/main/environment.yml)
- For R, go to GitHub page which contain the R version of [environment.yml file](https://github.com/NOAA-CEFI-Portal/cefi-cookbook/blob/r-setup/environment.yml)
Expand Down
Loading

0 comments on commit 2b23418

Please sign in to comment.