From 8759b8911ba3b9d990dc092e17101703a8b32f84 Mon Sep 17 00:00:00 2001 From: Bhanu Kamapantula <bhanu.kamapantula@gramener.com> Date: Tue, 24 Oct 2017 00:51:30 +0530 Subject: [PATCH 1/6] ENH: clustering HELP docs --- gramex.yaml | 2 + help/README.md | 203 +++++++++++++++++++ help/gramex.yaml | 62 ++++++ help/guide.py | 40 ++++ help/help/README.md | 19 ++ help/help/algorithms/README.md | 6 + help/help/datasets/README.md | 66 +++++++ help/help/images/features.png | Bin 0 -> 138133 bytes help/help/images/map.png | Bin 0 -> 151793 bytes help/help/images/matrix.png | Bin 0 -> 126099 bytes help/help/images/similar-comparison.png | Bin 0 -> 54438 bytes help/help/images/similar.png | Bin 0 -> 107459 bytes help/help/images/table.png | Bin 0 -> 112775 bytes help/help/images/tree.png | Bin 0 -> 13149 bytes help/help/install/README.md | 10 + help/help/visuals/README.md | 103 ++++++++++ help/images/features.png | Bin 0 -> 138133 bytes help/images/map.png | Bin 0 -> 151793 bytes help/images/matrix.png | Bin 0 -> 126099 bytes help/images/similar-comparison.png | Bin 0 -> 54438 bytes help/images/similar.png | Bin 0 -> 107459 bytes help/images/table.png | Bin 0 -> 112775 bytes help/images/tree.png | Bin 0 -> 13149 bytes help/markdown.template | 31 +++ help/style.css | 246 ++++++++++++++++++++++++ 25 files changed, 788 insertions(+) create mode 100644 help/README.md create mode 100644 help/gramex.yaml create mode 100644 help/guide.py create mode 100644 help/help/README.md create mode 100644 help/help/algorithms/README.md create mode 100644 help/help/datasets/README.md create mode 100644 help/help/images/features.png create mode 100644 help/help/images/map.png create mode 100644 help/help/images/matrix.png create mode 100644 help/help/images/similar-comparison.png create mode 100644 help/help/images/similar.png create mode 100644 help/help/images/table.png create mode 100644 help/help/images/tree.png create mode 100644 help/help/install/README.md create mode 100644 help/help/visuals/README.md create mode 100644 help/images/features.png create mode 100644 help/images/map.png create mode 100644 help/images/matrix.png create mode 100644 help/images/similar-comparison.png create mode 100644 help/images/similar.png create mode 100644 help/images/table.png create mode 100644 help/images/tree.png create mode 100644 help/markdown.template create mode 100644 help/style.css diff --git a/gramex.yaml b/gramex.yaml index 644efc5..340a6a9 100644 --- a/gramex.yaml +++ b/gramex.yaml @@ -1,6 +1,8 @@ # The `app:` section defines the settings for the Tornado application. # http://tornado.readthedocs.org/en/stable/web.html#tornado.web.Application.settings +import: '*/gramex.yaml' + url: # The home page is a static HTML page that introduces the app. # Users can click on a login button to login and visit the cluster page. diff --git a/help/README.md b/help/README.md new file mode 100644 index 0000000..84bdc54 --- /dev/null +++ b/help/README.md @@ -0,0 +1,203 @@ +# Help + +For any questions regarding clustering, please reach out to cto@gramener.com. Anand, Bhanu, Sowmya are the most familiar with the functioning of the Clustering product. + +Visit any of the below sections to understand the Clustering feature functionality. + +1. [Install](#install) +1. [Datasets](#datasets) + 1. [Data formats](#data-formats) + 1. [Creating config.yaml](#creating-configyaml) +1. [Algorithms](#algorithms) +1. [Visuals](#visuals) + 1. [Map](#visuals-map) + 1. [Features](#visuals-features) + 1. [Matrix](#visuals-matrix) + 1. [Table](#visuals-table) + 1. [Entities](#visuals-entities) + +### Install + +1) Clone or download the [geocluster repository](https://code.gramener.com/s.anand/geocluster/) to your local machine (/some/path/to/cluster_directory). + +2) Create a subdirectory titled `your-app-name` under `app/` directory within the cloned `geocluster` directory. + +3) Move your data file to `your-app-name` and create a new `config.yaml` ([specific section](#creating-configyaml) for instructions) file where the data rules will be defined. For example, in the below example, `barc` is an application name. + +![example config YAML](images/tree.png) + +### Datasets + +#### Data formats + +Supports `CSV`, `XLS (XLSX)` file formats. Data is structured in a tabular format. Each row indicates a record. Columns can consist of any type of data. Clustering algorithms consider only numerical columns in to account. + +#### Creating config.yaml + +The `config.yaml` requires some mandatory and the rest sections are optional. + +##### Mandatory sections + +Mandatory sections: `name`, `csv`, `columns`, `keys`, `visuals`. + +```yaml +name: Your analysis/dataset name +csv: path/to/csv-file.csv +columns: + col_1: {name: '', value: ''} + ... +keys: [list of keys] +visuals: at least one +``` + +If there are no unique keys, mention an empty list. + +Here's a minimal working `config.yaml`. + +```yaml +name: India census 2011 +csv: india-districts-2011.csv +columns: + literacy: {name: 'Literacy in district', value: 'Literates in district'} +keys: [District code] +visuals: + table: {} +``` + +##### Optional sections + +Optional sections: `access`, `description`, `summary`, `sample`, `segments`, `selection`, `clustering`. + +For all the supported sections, refer to the [app/README.md](https://code.gramener.com/s.anand/geocluster/blob/master/app/README.md) in the [geocluster](https://code.gramener.com/s.anand/geocluster/) repository. + +Note that the default clustering algorithm is `k-means`. + +#### Preparing columns + +If your dataset has columns with percentages (10, 20, 12 etc.) to begin with, the default rendering might look something like this (1000%, 2000%, 1200% etc) which is incorrect, of course. + +#### Approach 1 (Recommended) + +Data preparation: change your values to percentage points (0.1, 0.2, .12 etc.) + +#### Approach 2 (Tedious) + +You can use format option for each column to specify how you would like to render the values from a specific column. Format supports two types of displays: d3.format ('.1%') and python (',0.f') + +If a column has normalized values like below +0.73, 0.27 -- use {format: '.2%'} -- this will display as a percentage value and with 2 decimals + +#### Other column parameters + +- scale: if some of the columns have comparably large values (ex: population in census data), use a log scale. +- formula: use existing columns to derive new columns + +### Algorithms + +Clustering supports a range of algorithms: k-means, mini-batch k-means, mean-shift, affinity propagation, agglomerative, birch, spectral, gaussian mixture, cluster tree, DBSCAN and HDBSCAN. + +Visit [scikit-learn clustering](http://scikit-learn.org/stable/modules/classes.html#module-sklearn.cluster) website for the exact methodology of these algorithms. + +### Visuals + +Clustering supports the below visual renderings: + +a) [Map](#visual-map) + +b) [Features](#visual-features) + +c) [Matrix](#visual-matrix) + +d) [Cluster table](#visual-table) + +e) [Similar and dissimilar entities](#visual-entities) + +#### Visual map + +Clustering currently supports the following geographic maps: + +a) India districts +b) USA states +c) New York state + +![Example](images/map.png) + +The colors indicate cluster numbers assigned by the clustering algorithm (k-means in this case with 6 clusters). + +##### How to add a new one? + +You can easily add a new map as described here. + +Maps in clustering use `topojson` file. Consider you want to create a map of `New York` state. Follow the below steps: + +Step 1) Get the shape file of the target map + +Clustering internally uses `Leaflet`, a JavaScript visualization library, to create geographical maps. + +Step 2) Install `topojson` library + +Use `npm`, a `node.js` package manager to install `topojson` package as below: + +```bash +npm install -g topojson +``` + +Step 3) Convert shapefile to TopoJSON + +`topojson --properties STATEFP10 --properties GEOID10 --properties CLASSFP10 --properties ZCTA5CE10 -o new-york-2010-topo.json tl_2010_36_zcta510.shp` + +STATEFP10, GEOID10, CLASSFP10 and ZCTA5CE10 (state, geography ID, class, zip code respectively) are the properties in shape file. You can see these using qgis desktop software. + +Step 4) YAML configuration + +```yaml +map: + topojson: app/static/new-york-2010-topo.json + datakey: Zip_Code + mapkey: ZCTA5CE10 + width: 800px + height: 800px + latitude: 43 + longitude: -75 + zoom: 7 +``` + +##### Notes +- mapkey refers to the zip code attribute in the created TopoJSON file (new-york-2010-topo.json from above). +- `Zip_Code` refers to the zip code column in your data file. This will map the data on to the map. +- lat/long details: search New York on Google maps, identify the top right, top left, bottom right, bottom left arbitrarily (so it covers the entire New York map in the view); collected the latitude/longitude coordinates; identified the boundaries of lat/long and calculated the mid point which is used in the YAML. + +```js +// bottom left: 40, -80 +// top left: 45, -80 +// top right: 45, -71 +// bottom right: 40, -71 +// boundaries: [[40, 45], [-80, -71]] +// midpoint: 43, -75 +``` + +Make sure to calculate the coordinates carefully as the map needs to be centered accordingly otherwise the map isn't rendered accurately. + +#### Visual features + +![Features view](images/features.png) + +#### Visual matrix + +![Matrix view](images/matrix.png) + +#### Visual table + +![Table view](images/table.png) + +#### Visual entities + +Clustering supports the comparison of similar and dissimilar entities. Each entity relates to a record in the dataset. + +You can control the number of entities to be compared (defaults to 50). + +![Similar entities](images/similar.png) + +**On clicking a entity combination, a view comparing the features (columns) for the pair of entities will be shown.** + +![Entity comparison](images/similar-comparison.png) diff --git a/help/gramex.yaml b/help/gramex.yaml new file mode 100644 index 0000000..682b002 --- /dev/null +++ b/help/gramex.yaml @@ -0,0 +1,62 @@ +variables: + GUIDE_ROOT: $YAMLURL # Used by guide.py to pass to markdown.template +import: '*/gramex.yaml' # Import gramex.yaml from immediate subdirs +handlers: + FileHandler: + ignore: + - ".*" # Hide only dotfiles, allow gramex.yaml +url: + # Default FileHandler. Serve all .md files as Markdown + markdown: + pattern: /$YAMLURL/(.*) + handler: FileHandler + kwargs: + path: $YAMLPATH + default_filename: README.md + index: true + transform: + "*.md": + function: guide.markdown_template(content, handler) + encoding: utf-8 + headers: + Content-Type: text/html + headers: + Cache-Control: public, max-age=300 + # The final *computed* gramex.yaml configuration + final-config: + pattern: /$YAMLURL/final-config + handler: FunctionHandler + kwargs: + function: guide.config + headers: + Content-Type: text/yaml + # Just serve the root gramex.yaml file + root-config: + pattern: /$YAMLURL/root.gramex.yaml + handler: FileHandler + kwargs: + path: $GRAMEXPATH/gramex.yaml + headers: + Cache-Control: public, max-age=300 + + # Static files are cached on the browser + static: + pattern: /$YAMLURL/(.*.css|.*.js) + priority: 1 # Override markdown: handler + handler: FileHandler + cache: {duration: 300} + kwargs: + path: $YAMLPATH/* + headers: + Cache-Control: public, max-age=300 + + # /path.source shows the raw file as plain text + source: + priority: +1 + pattern: /$YAMLURL/(.*)\.source + handler: FileHandler + kwargs: + path: $YAMLPATH + headers: + Content-Type: text/plain + Cache-Control: public, max-age=300 diff --git a/help/guide.py b/help/guide.py new file mode 100644 index 0000000..1859731 --- /dev/null +++ b/help/guide.py @@ -0,0 +1,40 @@ +"""Utility functions for gramex-guide.""" + +import os +import yaml +import string +import markdown +from orderedattrdict import DefaultAttrDict +import gramex + + +folder = os.path.dirname(os.path.abspath(__file__)) +template_path = os.path.join(folder, 'markdown.template') +md = markdown.Markdown(extensions=[ + 'markdown.extensions.extra', + 'markdown.extensions.meta', + 'markdown.extensions.codehilite', + 'markdown.extensions.smarty', + 'markdown.extensions.sane_lists', + 'markdown.extensions.toc', +], output_format='html5') +stringtemplate = gramex.cache.opener(string.Template, read=True) + + +def markdown_template(content, handler): + """Markdown template rendering.""" + # Template has optional parameters like $title. DefaultAttrDict prevents errors. + kwargs = DefaultAttrDict(str) + # GUIDE_ROOT has the absolute URL of the Gramex guide + kwargs.GUIDE_ROOT = gramex.config.variables.GUIDE_ROOT + kwargs.body = md.convert(content) + for key, val in md.Meta.items(): + kwargs[key] = val[0] + if 'xsrf' in content: + handler.xsrf_token + return gramex.cache.open(template_path, stringtemplate).substitute(kwargs) + + +def config(handler): + """Dump the final resolved config.""" + return yaml.dump(gramex.conf, default_flow_style=False) diff --git a/help/help/README.md b/help/help/README.md new file mode 100644 index 0000000..ea62b13 --- /dev/null +++ b/help/help/README.md @@ -0,0 +1,19 @@ +# Help + +For any questions regarding clustering, please reach out to cto@gramener.com. Anand, Bhanu, and Sowmya are the most familiar with the functioning of the Clustering product. + +Visit below sections to understand the specifics of Clustering features. + +1. [Install](install/) +1. [Datasets](datasets/) + - [Data formats](datasets/#data-formats) + - [Creating config.yaml](datasets/#creating-configyaml) + +1. [Algorithms](algorithms/) +1. [Visuals](visuals/) + - [Map](visuals/#visuals-map) + - [Features](visuals/#visuals-features) + - [Matrix](visuals/#visuals-matrix) + - [Table](visuals/#visuals-table) + - [Entities](visuals/#visuals-entities) + diff --git a/help/help/algorithms/README.md b/help/help/algorithms/README.md new file mode 100644 index 0000000..1194ee8 --- /dev/null +++ b/help/help/algorithms/README.md @@ -0,0 +1,6 @@ +### Algorithms + +Clustering supports a range of algorithms: k-means, mini-batch k-means, mean-shift, affinity propagation, agglomerative, birch, spectral, gaussian mixture, cluster tree, DBSCAN and HDBSCAN. + +Visit [scikit-learn clustering](http://scikit-learn.org/stable/modules/classes.html#module-sklearn.cluster) website for the exact methodology of these algorithms. + diff --git a/help/help/datasets/README.md b/help/help/datasets/README.md new file mode 100644 index 0000000..12cc90b --- /dev/null +++ b/help/help/datasets/README.md @@ -0,0 +1,66 @@ +### Datasets + +#### Data formats + +Supports `CSV`, `XLS (XLSX)` file formats. Data is structured in a tabular format. Each row indicates a record. Columns can consist of any type of data. Clustering algorithms consider only numerical columns in to account. + +#### Creating config.yaml + +The `config.yaml` requires some mandatory and the rest sections are optional. + +##### Mandatory sections + +Mandatory sections: `name`, `csv`, `columns`, `keys`, `visuals`. + +```yaml +name: Your analysis/dataset name +csv: path/to/csv-file.csv +columns: + col_1: {name: '', value: ''} + ... +keys: [list of keys] +visuals: at least one +``` + +If there are no unique keys, mention an empty list. + +Here's a minimal working `config.yaml`. + +```yaml +name: India census 2011 +csv: india-districts-2011.csv +columns: + literacy: {name: 'Literacy in district', value: 'Literates in district'} +keys: [District code] +visuals: + table: {} +``` + +##### Optional sections + +Optional sections: `access`, `description`, `summary`, `sample`, `segments`, `selection`, `clustering`. + +For all the supported sections, refer to the [app/README.md](https://code.gramener.com/s.anand/geocluster/blob/master/app/README.md) in the [geocluster](https://code.gramener.com/s.anand/geocluster/) repository. + +Note that the default clustering algorithm is `k-means`. + +#### Preparing columns + +If your dataset has columns with percentages (10, 20, 12 etc.) to begin with, the default rendering might look something like this (1000%, 2000%, 1200% etc) which is incorrect, of course. + +#### Approach 1 (Recommended) + +Data preparation: change your values to percentage points (0.1, 0.2, .12 etc.) + +#### Approach 2 (Tedious) + +You can use format option for each column to specify how you would like to render the values from a specific column. Format supports two types of displays: d3.format ('.1%') and python (',0.f') + +If a column has normalized values like below +0.73, 0.27 -- use {format: '.2%'} -- this will display as a percentage value and with 2 decimals + +#### Other column parameters + +- scale: if some of the columns have comparably large values (ex: population in census data), use a log scale. +- formula: use existing columns to derive new columns + diff --git a/help/help/images/features.png b/help/help/images/features.png new file mode 100644 index 0000000000000000000000000000000000000000..5b0397d9eb7094e1bace1086b975d70b2d104d66 GIT binary patch literal 138133 zcmbsRRajj?*S3jH9D+k|4-(v+pb2il-QC^Y-Q6v?dvJ%~!QI{6tykXn|Nid1yZ6-x zd!DdZS5Y-eW{pwzGm5aEGNOo|a6bV6fG95ZLmmJi>;M4V9Tpt)4P23?CFlo)gOIo) zEG+EGmh2|zR~$!SRYwIIV@DS~dn3Ta+Q!O=&cVRm$jI8k)W-1wqKgjzhyn2*f{L!0 zryX933M#FSAFAU=y7J2cqjr|lwT8D8+=H$0%6)Uj$535<w6q9fFgi7t3e1v5WvaBw zSIV}KJiaXc58>K*47zNTgDdf+C_4FK6%>_$RECK^_6*f~=?tOKs18)0-r}&(#9UQQ 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file to `your-app-name` and create a new `config.yaml` ([specific section](../datasets/#creating-configyaml) for instructions) file where the data rules will be defined. For example, in the below example, `barc` is an application name. + +![example config YAML](../images/tree.png) + diff --git a/help/help/visuals/README.md b/help/help/visuals/README.md new file mode 100644 index 0000000..784b5dd --- /dev/null +++ b/help/help/visuals/README.md @@ -0,0 +1,103 @@ +### Visuals + +Clustering supports the below visual renderings: + +a) [Map](#visual-map) + +b) [Features](#visual-features) + +c) [Matrix](#visual-matrix) + +d) [Cluster table](#visual-table) + +e) [Similar and dissimilar entities](#visual-entities) + +#### Visual map + +Clustering currently supports the following geographic maps: + +a) India districts +b) USA states +c) New York state + +![Example](../images/map.png) + +The colors indicate cluster numbers assigned by the clustering algorithm (k-means in this case with 6 clusters). + +##### How to add a new one? + +You can easily add a new map as described here. + +Maps in clustering use `topojson` file. Consider you want to create a map of `New York` state. Follow the below steps: + +Step 1) Get the shape file of the target map + +Clustering internally uses `Leaflet`, a JavaScript visualization library, to create geographical maps. + +Step 2) Install `topojson` library + +Use `npm`, a `node.js` package manager to install `topojson` package as below: + +```bash +npm install -g topojson +``` + +Step 3) Convert shapefile to TopoJSON + +`topojson --properties STATEFP10 --properties GEOID10 --properties CLASSFP10 --properties ZCTA5CE10 -o new-york-2010-topo.json tl_2010_36_zcta510.shp` + +STATEFP10, GEOID10, CLASSFP10 and ZCTA5CE10 (state, geography ID, class, zip code respectively) are the properties in shape file. You can see these using qgis desktop software. + +Step 4) YAML configuration + +```yaml +map: + topojson: app/static/new-york-2010-topo.json + datakey: Zip_Code + mapkey: ZCTA5CE10 + width: 800px + height: 800px + latitude: 43 + longitude: -75 + zoom: 7 +``` + +##### Notes +- mapkey refers to the zip code attribute in the created TopoJSON file (new-york-2010-topo.json from above). +- `Zip_Code` refers to the zip code column in your data file. This will map the data on to the map. +- lat/long details: search New York on Google maps, identify the top right, top left, bottom right, bottom left arbitrarily (so it covers the entire New York map in the view); collected the latitude/longitude coordinates; identified the boundaries of lat/long and calculated the mid point which is used in the YAML. + +```js +// bottom left: 40, -80 +// top left: 45, -80 +// top right: 45, -71 +// bottom right: 40, -71 +// boundaries: [[40, 45], [-80, -71]] +// midpoint: 43, -75 +``` + +Make sure to calculate the coordinates carefully as the map needs to be centered accordingly otherwise the map isn't rendered accurately. + +#### Visual features + +![Features view](../images/features.png) + +#### Visual matrix + +![Matrix view](../images/matrix.png) + +#### Visual table + +![Table view](../images/table.png) + +#### Visual entities + +Clustering supports the comparison of similar and dissimilar entities. Each entity relates to a record in the dataset. + +You can control the number of entities to be compared (defaults to 50). + +![Similar entities](../images/similar.png) + +**On clicking a entity combination, a view comparing the features (columns) for the pair of entities will be shown.** + +![Entity comparison](../images/similar-comparison.png) diff --git a/help/images/features.png b/help/images/features.png new file mode 100644 index 0000000000000000000000000000000000000000..5b0397d9eb7094e1bace1086b975d70b2d104d66 GIT binary patch literal 138133 zcmbsRRajj?*S3jH9D+k|4-(v+pb2il-QC^Y-Q6v?dvJ%~!QI{6tykXn|Nid1yZ6-x zd!DdZS5Y-eW{pwzGm5aEGNOo|a6bV6fG95ZLmmJi>;M4V9Tpt)4P23?CFlo)gOIo) zEG+EGmh2|zR~$!SRYwIIV@DS~dn3Ta+Q!O=&cVRm$jI8k)W-1wqKgjzhyn2*f{L!0 zryX933M#FSAFAU=y7J2cqjr|lwT8D8+=H$0%6)Uj$535<w6q9fFgi7t3e1v5WvaBw zSIV}KJiaXc58>K*47zNTgDdf+C_4FK6%>_$RECK^_6*f~=?tOKs18)0-r}&(#9UQQ z9DR~4hPg~~I&$8=O|HKTufJrnlvY<GqrgIf`w0-EgxX#97hnSaK7zivTD(DzqJn!0 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For example, in the below example, `barc` is an application name. + +![example config YAML](../images/tree.png) + -- GitLab From 0ce7ada61f0e173b189632c9a0baec6d5e94939d Mon Sep 17 00:00:00 2001 From: Bhanu Kamapantula <bhanu.kamapantula@gramener.com> Date: Tue, 24 Oct 2017 00:54:46 +0530 Subject: [PATCH 3/6] Updated HELP README, remove recursive help directory --- help/README.md | 217 ++---------------------- help/help/README.md | 19 --- help/help/algorithms/README.md | 6 - help/help/datasets/README.md | 66 ------- help/help/images/features.png | Bin 138133 -> 0 bytes help/help/images/map.png | Bin 151793 -> 0 bytes help/help/images/matrix.png | Bin 126099 -> 0 bytes help/help/images/similar-comparison.png | Bin 54438 -> 0 bytes help/help/images/similar.png | Bin 107459 -> 0 bytes help/help/images/table.png | Bin 112775 -> 0 bytes help/help/images/tree.png | Bin 13149 -> 0 bytes help/help/install/README.md | 10 -- help/help/visuals/README.md | 103 ----------- help/markdown.template | 8 +- 14 files changed, 20 insertions(+), 409 deletions(-) delete mode 100644 help/help/README.md delete mode 100644 help/help/algorithms/README.md delete mode 100644 help/help/datasets/README.md delete mode 100644 help/help/images/features.png delete mode 100644 help/help/images/map.png delete mode 100644 help/help/images/matrix.png delete mode 100644 help/help/images/similar-comparison.png delete mode 100644 help/help/images/similar.png delete mode 100644 help/help/images/table.png delete mode 100644 help/help/images/tree.png delete mode 100644 help/help/install/README.md delete mode 100644 help/help/visuals/README.md diff --git a/help/README.md b/help/README.md index 84bdc54..2cd8f50 100644 --- a/help/README.md +++ b/help/README.md @@ -1,203 +1,18 @@ # Help -For any questions regarding clustering, please reach out to cto@gramener.com. Anand, Bhanu, Sowmya are the most familiar with the functioning of the Clustering product. - -Visit any of the below sections to understand the Clustering feature functionality. - -1. [Install](#install) -1. [Datasets](#datasets) - 1. [Data formats](#data-formats) - 1. [Creating config.yaml](#creating-configyaml) -1. [Algorithms](#algorithms) -1. [Visuals](#visuals) - 1. [Map](#visuals-map) - 1. [Features](#visuals-features) - 1. [Matrix](#visuals-matrix) - 1. [Table](#visuals-table) - 1. [Entities](#visuals-entities) - -### Install - -1) Clone or download the [geocluster repository](https://code.gramener.com/s.anand/geocluster/) to your local machine (/some/path/to/cluster_directory). - -2) Create a subdirectory titled `your-app-name` under `app/` directory within the cloned `geocluster` directory. - -3) Move your data file to `your-app-name` and create a new `config.yaml` ([specific section](#creating-configyaml) for instructions) file where the data rules will be defined. For example, in the below example, `barc` is an application name. - -![example config YAML](images/tree.png) - -### Datasets - -#### Data formats - -Supports `CSV`, `XLS (XLSX)` file formats. Data is structured in a tabular format. Each row indicates a record. Columns can consist of any type of data. Clustering algorithms consider only numerical columns in to account. - -#### Creating config.yaml - -The `config.yaml` requires some mandatory and the rest sections are optional. - -##### Mandatory sections - -Mandatory sections: `name`, `csv`, `columns`, `keys`, `visuals`. - -```yaml -name: Your analysis/dataset name -csv: path/to/csv-file.csv -columns: - col_1: {name: '', value: ''} - ... -keys: [list of keys] -visuals: at least one -``` - -If there are no unique keys, mention an empty list. - -Here's a minimal working `config.yaml`. - -```yaml -name: India census 2011 -csv: india-districts-2011.csv -columns: - literacy: {name: 'Literacy in district', value: 'Literates in district'} -keys: [District code] -visuals: - table: {} -``` - -##### Optional sections - -Optional sections: `access`, `description`, `summary`, `sample`, `segments`, `selection`, `clustering`. - -For all the supported sections, refer to the [app/README.md](https://code.gramener.com/s.anand/geocluster/blob/master/app/README.md) in the [geocluster](https://code.gramener.com/s.anand/geocluster/) repository. - -Note that the default clustering algorithm is `k-means`. - -#### Preparing columns - -If your dataset has columns with percentages (10, 20, 12 etc.) to begin with, the default rendering might look something like this (1000%, 2000%, 1200% etc) which is incorrect, of course. - -#### Approach 1 (Recommended) - -Data preparation: change your values to percentage points (0.1, 0.2, .12 etc.) - -#### Approach 2 (Tedious) - -You can use format option for each column to specify how you would like to render the values from a specific column. Format supports two types of displays: d3.format ('.1%') and python (',0.f') - -If a column has normalized values like below -0.73, 0.27 -- use {format: '.2%'} -- this will display as a percentage value and with 2 decimals - -#### Other column parameters - -- scale: if some of the columns have comparably large values (ex: population in census data), use a log scale. -- formula: use existing columns to derive new columns - -### Algorithms - -Clustering supports a range of algorithms: k-means, mini-batch k-means, mean-shift, affinity propagation, agglomerative, birch, spectral, gaussian mixture, cluster tree, DBSCAN and HDBSCAN. - -Visit [scikit-learn clustering](http://scikit-learn.org/stable/modules/classes.html#module-sklearn.cluster) website for the exact methodology of these algorithms. - -### Visuals - -Clustering supports the below visual renderings: - -a) [Map](#visual-map) - -b) [Features](#visual-features) - -c) [Matrix](#visual-matrix) - -d) [Cluster table](#visual-table) - -e) [Similar and dissimilar entities](#visual-entities) - -#### Visual map - -Clustering currently supports the following geographic maps: - -a) India districts -b) USA states -c) New York state - -![Example](images/map.png) - -The colors indicate cluster numbers assigned by the clustering algorithm (k-means in this case with 6 clusters). - -##### How to add a new one? - -You can easily add a new map as described here. - -Maps in clustering use `topojson` file. Consider you want to create a map of `New York` state. Follow the below steps: - -Step 1) Get the shape file of the target map - -Clustering internally uses `Leaflet`, a JavaScript visualization library, to create geographical maps. - -Step 2) Install `topojson` library - -Use `npm`, a `node.js` package manager to install `topojson` package as below: - -```bash -npm install -g topojson -``` - -Step 3) Convert shapefile to TopoJSON - -`topojson --properties STATEFP10 --properties GEOID10 --properties CLASSFP10 --properties ZCTA5CE10 -o new-york-2010-topo.json tl_2010_36_zcta510.shp` - -STATEFP10, GEOID10, CLASSFP10 and ZCTA5CE10 (state, geography ID, class, zip code respectively) are the properties in shape file. You can see these using qgis desktop software. - -Step 4) YAML configuration - -```yaml -map: - topojson: app/static/new-york-2010-topo.json - datakey: Zip_Code - mapkey: ZCTA5CE10 - width: 800px - height: 800px - latitude: 43 - longitude: -75 - zoom: 7 -``` - -##### Notes -- mapkey refers to the zip code attribute in the created TopoJSON file (new-york-2010-topo.json from above). -- `Zip_Code` refers to the zip code column in your data file. This will map the data on to the map. -- lat/long details: search New York on Google maps, identify the top right, top left, bottom right, bottom left arbitrarily (so it covers the entire New York map in the view); collected the latitude/longitude coordinates; identified the boundaries of lat/long and calculated the mid point which is used in the YAML. - -```js -// bottom left: 40, -80 -// top left: 45, -80 -// top right: 45, -71 -// bottom right: 40, -71 -// boundaries: [[40, 45], [-80, -71]] -// midpoint: 43, -75 -``` - -Make sure to calculate the coordinates carefully as the map needs to be centered accordingly otherwise the map isn't rendered accurately. - -#### Visual features - -![Features view](images/features.png) - -#### Visual matrix - -![Matrix view](images/matrix.png) - -#### Visual table - -![Table view](images/table.png) - -#### Visual entities - -Clustering supports the comparison of similar and dissimilar entities. Each entity relates to a record in the dataset. - -You can control the number of entities to be compared (defaults to 50). - -![Similar entities](images/similar.png) - -**On clicking a entity combination, a view comparing the features (columns) for the pair of entities will be shown.** - -![Entity comparison](images/similar-comparison.png) +For any questions regarding clustering, please reach out to cto@gramener.com. Anand, Bhanu, and Sowmya are the most familiar with the functioning of the Clustering product. + +Visit below sections to understand the specifics of Clustering features. + +1. [Install](installation/) +1. [Datasets](datasets/) + - [Data formats](datasets/#data-formats) + - [Creating config.yaml](datasets/#creating-configyaml) + +1. [Algorithms](algorithms/) +1. [Visuals](visuals/) + - [Map](visuals/#visuals-map) + - [Features](visuals/#visuals-features) + - [Matrix](visuals/#visuals-matrix) + - [Table](visuals/#visuals-table) + - [Entities](visuals/#visuals-entities) diff --git a/help/help/README.md b/help/help/README.md deleted file mode 100644 index ea62b13..0000000 --- a/help/help/README.md +++ /dev/null @@ -1,19 +0,0 @@ -# Help - -For any questions regarding clustering, please reach out to cto@gramener.com. Anand, Bhanu, and Sowmya are the most familiar with the functioning of the Clustering product. - -Visit below sections to understand the specifics of Clustering features. - -1. [Install](install/) -1. [Datasets](datasets/) - - [Data formats](datasets/#data-formats) - - [Creating config.yaml](datasets/#creating-configyaml) - -1. [Algorithms](algorithms/) -1. [Visuals](visuals/) - - [Map](visuals/#visuals-map) - - [Features](visuals/#visuals-features) - - [Matrix](visuals/#visuals-matrix) - - [Table](visuals/#visuals-table) - - [Entities](visuals/#visuals-entities) - diff --git a/help/help/algorithms/README.md b/help/help/algorithms/README.md deleted file mode 100644 index 1194ee8..0000000 --- a/help/help/algorithms/README.md +++ /dev/null @@ -1,6 +0,0 @@ -### Algorithms - -Clustering supports a range of algorithms: k-means, mini-batch k-means, mean-shift, affinity propagation, agglomerative, birch, spectral, gaussian mixture, cluster tree, DBSCAN and HDBSCAN. - -Visit [scikit-learn clustering](http://scikit-learn.org/stable/modules/classes.html#module-sklearn.cluster) website for the exact methodology of these algorithms. - diff --git a/help/help/datasets/README.md b/help/help/datasets/README.md deleted file mode 100644 index 12cc90b..0000000 --- a/help/help/datasets/README.md +++ /dev/null @@ -1,66 +0,0 @@ -### Datasets - -#### Data formats - -Supports `CSV`, `XLS (XLSX)` file formats. Data is structured in a tabular format. Each row indicates a record. Columns can consist of any type of data. Clustering algorithms consider only numerical columns in to account. - -#### Creating config.yaml - -The `config.yaml` requires some mandatory and the rest sections are optional. - -##### Mandatory sections - -Mandatory sections: `name`, `csv`, `columns`, `keys`, `visuals`. - -```yaml -name: Your analysis/dataset name -csv: path/to/csv-file.csv -columns: - col_1: {name: '', value: ''} - ... -keys: [list of keys] -visuals: at least one -``` - -If there are no unique keys, mention an empty list. - -Here's a minimal working `config.yaml`. - -```yaml -name: India census 2011 -csv: india-districts-2011.csv -columns: - literacy: {name: 'Literacy in district', value: 'Literates in district'} -keys: [District code] -visuals: - table: {} -``` - -##### Optional sections - -Optional sections: `access`, `description`, `summary`, `sample`, `segments`, `selection`, `clustering`. - -For all the supported sections, refer to the [app/README.md](https://code.gramener.com/s.anand/geocluster/blob/master/app/README.md) in the [geocluster](https://code.gramener.com/s.anand/geocluster/) repository. - -Note that the default clustering algorithm is `k-means`. - -#### Preparing columns - -If your dataset has columns with percentages (10, 20, 12 etc.) to begin with, the default rendering might look something like this (1000%, 2000%, 1200% etc) which is incorrect, of course. - -#### Approach 1 (Recommended) - -Data preparation: change your values to percentage points (0.1, 0.2, .12 etc.) - -#### Approach 2 (Tedious) - -You can use format option for each column to specify how you would like to render the values from a specific column. 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subdirectory titled `your-app-name` under `app/` directory within the cloned `geocluster` directory. - -3) Move your data file to `your-app-name` and create a new `config.yaml` ([specific section](../datasets/#creating-configyaml) for instructions) file where the data rules will be defined. For example, in the below example, `barc` is an application name. - -![example config YAML](../images/tree.png) - diff --git a/help/help/visuals/README.md b/help/help/visuals/README.md deleted file mode 100644 index 784b5dd..0000000 --- a/help/help/visuals/README.md +++ /dev/null @@ -1,103 +0,0 @@ -### Visuals - -Clustering supports the below visual renderings: - -a) [Map](#visual-map) - -b) [Features](#visual-features) - -c) [Matrix](#visual-matrix) - -d) [Cluster table](#visual-table) - -e) [Similar and dissimilar entities](#visual-entities) - -#### Visual map - -Clustering currently supports the following geographic maps: - -a) India districts -b) USA states -c) New York state - -![Example](../images/map.png) - -The colors indicate cluster numbers assigned by the clustering algorithm (k-means in this case with 6 clusters). - -##### How to add a new one? - -You can easily add a new map as described here. - -Maps in clustering use `topojson` file. Consider you want to create a map of `New York` state. Follow the below steps: - -Step 1) Get the shape file of the target map - -Clustering internally uses `Leaflet`, a JavaScript visualization library, to create geographical maps. - -Step 2) Install `topojson` library - -Use `npm`, a `node.js` package manager to install `topojson` package as below: - -```bash -npm install -g topojson -``` - -Step 3) Convert shapefile to TopoJSON - -`topojson --properties STATEFP10 --properties GEOID10 --properties CLASSFP10 --properties ZCTA5CE10 -o new-york-2010-topo.json tl_2010_36_zcta510.shp` - -STATEFP10, GEOID10, CLASSFP10 and ZCTA5CE10 (state, geography ID, class, zip code respectively) are the properties in shape file. You can see these using qgis desktop software. - -Step 4) YAML configuration - -```yaml -map: - topojson: app/static/new-york-2010-topo.json - datakey: Zip_Code - mapkey: ZCTA5CE10 - width: 800px - height: 800px - latitude: 43 - longitude: -75 - zoom: 7 -``` - -##### Notes -- mapkey refers to the zip code attribute in the created TopoJSON file (new-york-2010-topo.json from above). -- `Zip_Code` refers to the zip code column in your data file. This will map the data on to the map. -- lat/long details: search New York on Google maps, identify the top right, top left, bottom right, bottom left arbitrarily (so it covers the entire New York map in the view); collected the latitude/longitude coordinates; identified the boundaries of lat/long and calculated the mid point which is used in the YAML. - -```js -// bottom left: 40, -80 -// top left: 45, -80 -// top right: 45, -71 -// bottom right: 40, -71 -// boundaries: [[40, 45], [-80, -71]] -// midpoint: 43, -75 -``` - -Make sure to calculate the coordinates carefully as the map needs to be centered accordingly otherwise the map isn't rendered accurately. - -#### Visual features - -![Features view](../images/features.png) - -#### Visual matrix - -![Matrix view](../images/matrix.png) - -#### Visual table - -![Table view](../images/table.png) - -#### Visual entities - -Clustering supports the comparison of similar and dissimilar entities. Each entity relates to a record in the dataset. - -You can control the number of entities to be compared (defaults to 50). - -![Similar entities](../images/similar.png) - -**On clicking a entity combination, a view comparing the features (columns) for the pair of entities will be shown.** - -![Entity comparison](../images/similar-comparison.png) diff --git a/help/markdown.template b/help/markdown.template index a9dcb37..6ec9e8e 100644 --- a/help/markdown.template +++ b/help/markdown.template @@ -11,10 +11,10 @@ <nav class="menu"> <h1><a href="/$GUIDE_ROOT/">Gramex Guide</a></h1> <ul> - <li><a href="/$GUIDE_ROOT/help/install/">Install Clustering product</a></li> - <li><a href="/$GUIDE_ROOT/help/datasets/">Configure datasets</a></li> - <li><a href="/$GUIDE_ROOT/help/algorithms/">Supported algorithms</a></li> - <li><a href="/$GUIDE_ROOT/help/visuals/">Visualizing output</a></li> + <li><a href="/$GUIDE_ROOT/installation/">Install Clustering product</a></li> + <li><a href="/$GUIDE_ROOT/datasets/">Configure datasets</a></li> + <li><a href="/$GUIDE_ROOT/algorithms/">Supported algorithms</a></li> + <li><a href="/$GUIDE_ROOT/visuals/">Visualizing output</a></li> </ul> </nav> -- GitLab From cbd22779b120490fc6666e7a5c8e81245acc7299 Mon Sep 17 00:00:00 2001 From: Bhanu Kamapantula <bhanu.kamapantula@gramener.com> Date: Tue, 24 Oct 2017 00:56:14 +0530 Subject: [PATCH 4/6] HELP docs for algorithms, visuals, datasets --- help/algorithms/README.md | 6 +++ help/datasets/README.md | 66 ++++++++++++++++++++++++ help/visuals/README.md | 103 ++++++++++++++++++++++++++++++++++++++ 3 files changed, 175 insertions(+) create mode 100644 help/algorithms/README.md create mode 100644 help/datasets/README.md create mode 100644 help/visuals/README.md diff --git a/help/algorithms/README.md b/help/algorithms/README.md new file mode 100644 index 0000000..1194ee8 --- /dev/null +++ b/help/algorithms/README.md @@ -0,0 +1,6 @@ +### Algorithms + +Clustering supports a range of algorithms: k-means, mini-batch k-means, mean-shift, affinity propagation, agglomerative, birch, spectral, gaussian mixture, cluster tree, DBSCAN and HDBSCAN. + +Visit [scikit-learn clustering](http://scikit-learn.org/stable/modules/classes.html#module-sklearn.cluster) website for the exact methodology of these algorithms. + diff --git a/help/datasets/README.md b/help/datasets/README.md new file mode 100644 index 0000000..12cc90b --- /dev/null +++ b/help/datasets/README.md @@ -0,0 +1,66 @@ +### Datasets + +#### Data formats + +Supports `CSV`, `XLS (XLSX)` file formats. Data is structured in a tabular format. Each row indicates a record. Columns can consist of any type of data. Clustering algorithms consider only numerical columns in to account. + +#### Creating config.yaml + +The `config.yaml` requires some mandatory and the rest sections are optional. + +##### Mandatory sections + +Mandatory sections: `name`, `csv`, `columns`, `keys`, `visuals`. + +```yaml +name: Your analysis/dataset name +csv: path/to/csv-file.csv +columns: + col_1: {name: '', value: ''} + ... +keys: [list of keys] +visuals: at least one +``` + +If there are no unique keys, mention an empty list. + +Here's a minimal working `config.yaml`. + +```yaml +name: India census 2011 +csv: india-districts-2011.csv +columns: + literacy: {name: 'Literacy in district', value: 'Literates in district'} +keys: [District code] +visuals: + table: {} +``` + +##### Optional sections + +Optional sections: `access`, `description`, `summary`, `sample`, `segments`, `selection`, `clustering`. + +For all the supported sections, refer to the [app/README.md](https://code.gramener.com/s.anand/geocluster/blob/master/app/README.md) in the [geocluster](https://code.gramener.com/s.anand/geocluster/) repository. + +Note that the default clustering algorithm is `k-means`. + +#### Preparing columns + +If your dataset has columns with percentages (10, 20, 12 etc.) to begin with, the default rendering might look something like this (1000%, 2000%, 1200% etc) which is incorrect, of course. + +#### Approach 1 (Recommended) + +Data preparation: change your values to percentage points (0.1, 0.2, .12 etc.) + +#### Approach 2 (Tedious) + +You can use format option for each column to specify how you would like to render the values from a specific column. Format supports two types of displays: d3.format ('.1%') and python (',0.f') + +If a column has normalized values like below +0.73, 0.27 -- use {format: '.2%'} -- this will display as a percentage value and with 2 decimals + +#### Other column parameters + +- scale: if some of the columns have comparably large values (ex: population in census data), use a log scale. +- formula: use existing columns to derive new columns + diff --git a/help/visuals/README.md b/help/visuals/README.md new file mode 100644 index 0000000..784b5dd --- /dev/null +++ b/help/visuals/README.md @@ -0,0 +1,103 @@ +### Visuals + +Clustering supports the below visual renderings: + +a) [Map](#visual-map) + +b) [Features](#visual-features) + +c) [Matrix](#visual-matrix) + +d) [Cluster table](#visual-table) + +e) [Similar and dissimilar entities](#visual-entities) + +#### Visual map + +Clustering currently supports the following geographic maps: + +a) India districts +b) USA states +c) New York state + +![Example](../images/map.png) + +The colors indicate cluster numbers assigned by the clustering algorithm (k-means in this case with 6 clusters). + +##### How to add a new one? + +You can easily add a new map as described here. + +Maps in clustering use `topojson` file. Consider you want to create a map of `New York` state. Follow the below steps: + +Step 1) Get the shape file of the target map + +Clustering internally uses `Leaflet`, a JavaScript visualization library, to create geographical maps. + +Step 2) Install `topojson` library + +Use `npm`, a `node.js` package manager to install `topojson` package as below: + +```bash +npm install -g topojson +``` + +Step 3) Convert shapefile to TopoJSON + +`topojson --properties STATEFP10 --properties GEOID10 --properties CLASSFP10 --properties ZCTA5CE10 -o new-york-2010-topo.json tl_2010_36_zcta510.shp` + +STATEFP10, GEOID10, CLASSFP10 and ZCTA5CE10 (state, geography ID, class, zip code respectively) are the properties in shape file. You can see these using qgis desktop software. + +Step 4) YAML configuration + +```yaml +map: + topojson: app/static/new-york-2010-topo.json + datakey: Zip_Code + mapkey: ZCTA5CE10 + width: 800px + height: 800px + latitude: 43 + longitude: -75 + zoom: 7 +``` + +##### Notes +- mapkey refers to the zip code attribute in the created TopoJSON file (new-york-2010-topo.json from above). +- `Zip_Code` refers to the zip code column in your data file. This will map the data on to the map. +- lat/long details: search New York on Google maps, identify the top right, top left, bottom right, bottom left arbitrarily (so it covers the entire New York map in the view); collected the latitude/longitude coordinates; identified the boundaries of lat/long and calculated the mid point which is used in the YAML. + +```js +// bottom left: 40, -80 +// top left: 45, -80 +// top right: 45, -71 +// bottom right: 40, -71 +// boundaries: [[40, 45], [-80, -71]] +// midpoint: 43, -75 +``` + +Make sure to calculate the coordinates carefully as the map needs to be centered accordingly otherwise the map isn't rendered accurately. + +#### Visual features + +![Features view](../images/features.png) + +#### Visual matrix + +![Matrix view](../images/matrix.png) + +#### Visual table + +![Table view](../images/table.png) + +#### Visual entities + +Clustering supports the comparison of similar and dissimilar entities. Each entity relates to a record in the dataset. + +You can control the number of entities to be compared (defaults to 50). + +![Similar entities](../images/similar.png) + +**On clicking a entity combination, a view comparing the features (columns) for the pair of entities will be shown.** + +![Entity comparison](../images/similar-comparison.png) -- GitLab From 45b9ec1bebc8ffbd2e15e43b6476a78474021c17 Mon Sep 17 00:00:00 2001 From: "karmanya.aggarwal" <karmanya.aggarwal@gramener.com> Date: Sat, 25 Aug 2018 05:59:10 +0530 Subject: [PATCH 5/6] updated docs for settings component --- help/README.md | 1 + help/datasets/README.md | 6 +++--- help/images/adding-sub-keys.png | Bin 0 -> 27378 bytes help/images/cog.png | Bin 0 -> 9272 bytes help/installation/README.md | 2 ++ help/settings/README.md | 18 ++++++++++++++++++ 6 files changed, 24 insertions(+), 3 deletions(-) create mode 100644 help/images/adding-sub-keys.png create mode 100644 help/images/cog.png create mode 100644 help/settings/README.md diff --git a/help/README.md b/help/README.md index 2cd8f50..ef055ef 100644 --- a/help/README.md +++ b/help/README.md @@ -16,3 +16,4 @@ Visit below sections to understand the specifics of Clustering features. - [Matrix](visuals/#visuals-matrix) - [Table](visuals/#visuals-table) - [Entities](visuals/#visuals-entities) +1. [Settings Component](settings/) diff --git a/help/datasets/README.md b/help/datasets/README.md index 12cc90b..d396526 100644 --- a/help/datasets/README.md +++ b/help/datasets/README.md @@ -2,7 +2,7 @@ #### Data formats -Supports `CSV`, `XLS (XLSX)` file formats. Data is structured in a tabular format. Each row indicates a record. Columns can consist of any type of data. Clustering algorithms consider only numerical columns in to account. +Supports any file format that formhandler does. Can connect to any sqlalchemy database as well. Data is structured in a tabular format. Each row indicates a record. Columns can consist of any type of data. Clustering algorithms consider only numerical columns in to account. #### Creating config.yaml @@ -10,11 +10,11 @@ The `config.yaml` requires some mandatory and the rest sections are optional. ##### Mandatory sections -Mandatory sections: `name`, `csv`, `columns`, `keys`, `visuals`. +Mandatory sections: `name`, `csv`/`url`, `columns`, `keys`, `visuals`. ```yaml name: Your analysis/dataset name -csv: path/to/csv-file.csv +csv: path/to/csv-file.csv #Links to files/databases can be populated in the url field instead columns: col_1: {name: '', value: ''} ... diff --git a/help/images/adding-sub-keys.png b/help/images/adding-sub-keys.png new file mode 100644 index 0000000000000000000000000000000000000000..9cb1c29fc77fdc2ef3b8615a0a5c50877440f7cd GIT binary patch literal 27378 zcmc$GbyOT*w<Ylek`UYiB#pbfC8Ti++BgJvcMl2BcnI#nolfK4xJz(%cX#P2^83E^ z-kSB^oB3zf)asRPs&3tT>&iL%?0qVMpJl~fp%S8^prE{x5C<!upgeX&L3t4K{2_40 z6`%SL_<Ce7ETQ!L`Sa;Txdq@)0tXRQ2SsZm2j?$#hA75X)|Q40_WE{)hF10_)(-oR 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zNn0#vyO7j&#~Pc{ZXn(7=Y%p;WXP|=a@Y?IJlIOquCFn4v~}1#MMjTUCv2YFzb_Wz zQ5#-D0Xq*;>OR+5<y1)X;bkx8kZQsCXI7y)=*y9j_P<`s@aBCWTzN_%T14P!M4pOP z$C&(Yn6o=#4fkmq6T@WUUvrk>WmT51WWanrc(Et!GG%)rj-w6Vk>m-A2v7b=*7337 z&K9Cbh(G4p{-20C{{3Y4pE4t@^fk7}2$2`~BH26?4^2-J5=*@QQ7!U+3H)y~DF3fe c@4VkGiGO4G$oJz(`vbg}R+1`_`277p0BvD98vp<R literal 0 HcmV?d00001 diff --git a/help/installation/README.md b/help/installation/README.md index db15168..ac3501e 100644 --- a/help/installation/README.md +++ b/help/installation/README.md @@ -6,5 +6,7 @@ 3) Move your data file to `your-app-name` and create a new `config.yaml` ([specific section](../datasets/#creating-configyaml) for instructions) file where the data rules will be defined. For example, in the below example, `barc` is an application name. +4) Alternatively, you can use the Settings component to add datasets by clicking on the `add dataset` button. + ![example config YAML](../images/tree.png) diff --git a/help/settings/README.md b/help/settings/README.md new file mode 100644 index 0000000..8ad4caa --- /dev/null +++ b/help/settings/README.md @@ -0,0 +1,18 @@ +### Settings component + +#### Adding a dataset +The settings page can be used to add a dataset by clicking on the `Add dataset` button on the list of all apps page eg, [cluster page](https://gramener.com/cluster/cluster) + +The settings page is a hierarchical component, the highest level contains each of the sub structures of the `config.yaml` file. + +On loading the settings page to edit a blank dataset, 4 fields are immediately visible, +`name`, `csv`, `url` and `query` as well as other sub-keys such as `access`, `authors`, `columns`, `clustering` and `visuals` + +To add fields or sub keys, simply click on the properties button and either select a pre-filled option, or, add a new key. + +![Adding New Keys](../images/adding-sub-keys.png) + +#### Editing Datasets +Anyone with view access to a dataset can also edit it using the settings component, by clicking on the cog icon in the top left corner of the dataset page. + +![cog](../images/cog.png) -- GitLab From 06abce76e1d8c905440afaef43c818189f070752 Mon Sep 17 00:00:00 2001 From: "karmanya.aggarwal" <karmanya.aggarwal@gramener.com> Date: Mon, 27 Aug 2018 08:02:49 +0530 Subject: [PATCH 6/6] doc change --- help/installation/README.md | 12 +++++++++--- 1 file changed, 9 insertions(+), 3 deletions(-) diff --git a/help/installation/README.md b/help/installation/README.md index ac3501e..3bf5fbc 100644 --- a/help/installation/README.md +++ b/help/installation/README.md @@ -2,11 +2,17 @@ 1) Clone or download the [geocluster repository](https://code.gramener.com/s.anand/geocluster/) to your local machine (/some/path/to/cluster_directory). -2) Create a subdirectory titled `your-app-name` under `app/` directory within the cloned `geocluster` directory. +2) Inside the directory, run `gramex setup .` -3) Move your data file to `your-app-name` and create a new `config.yaml` ([specific section](../datasets/#creating-configyaml) for instructions) file where the data rules will be defined. For example, in the below example, `barc` is an application name. +3) Run gramex and navigate to the appropriate url in your browser. (The default is localhost:9988) +#### Adding an app -4) Alternatively, you can use the Settings component to add datasets by clicking on the `add dataset` button. +1) Create a subdirectory titled `your-app-name` under `app/` directory within the cloned `geocluster` directory. + +2) Move your data file to `your-app-name` and create a new `config.yaml` ([specific section](../datasets/#creating-configyaml) for instructions) file where the data rules will be defined. For example, in the below example, `barc` is an application name. ![example config YAML](../images/tree.png) +Alternatively, you can use the Settings component to add datasets by clicking on the `add dataset` button. + + -- GitLab