source: ddcv/README @ 1286

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adding ddcvis

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1===========================================================
2IPCC Data Distribution Centre visualisation web-application
3===========================================================
4
5This package provides a web-application which visualises the
6IPCC-DDC's data as a interactive map and produces publication quality
7plots.
8
9Dependencies
10============
11
12#. Paste - A Python web-application framework.
13
14#. Matplotlib - A scientific plotting package.
15
16#. Basemap - An extension for matplotlib to draw maps.
17
18#. Python Imaging Library - Image manipulation utilities
19
20#. CDAT-5 or cdat_lite - A climate science toolkit to read CF-NetCDF.
21
22#. OpenLayers - An AJAX WMS client.
23
24Installation
25============
26
27!TODO
28
29Package Overview
30================
31
32The application is divided into 3 types of components:
33
341. pywms - A WMS server that reads CF-NetCDF and supports figure generation.
35
362. pywmsc - An WMS client web-application using OpenLayers.
37
383. ddc-vis - The Paste application that creates the DDC-specific
39configurations of pywms and pywmsc.
40
41
42ddc-vis can be thought of as a series of pywms and pywmsc components
43glued together by ddc-vis.  Each DDC dataset is served by one pywms
44component (CRU observations, SAR, TAR and AR4).  There is one pywmsc
45component that displays the WMS client for all the pywms components.
46The best place to start understanding how the components are connected
47is the Paste initiation file "server.ini".
48
49
50Configuration files
51-------------------
52
53#. server.ini: The Paste initiation file that connects together all components
54
55#. *.conf: Configuration file for each pywms component.
56
57paster *.ini
58----------
59
60The paster initiation file (server.ini or ddc-vis.ini) contains a series of sections, each configuring a component.  Each section begins with a header of the form [type:name] where type:
61
62- server: the web-server.  hostname and port are set here.
63
64- app: An application component.
65
66- filter: filters the input/output of a component
67
68- pipeline: connects app and filter components together in a pipeline
69
70- composite: maps maps other components to URLs
71
72
73pywms components point to an external configuration file like this:
74
75        [app:obs]
76        use: egg:pywms#wms
77        wms_config: %(here)s/obs.conf
78
79
80pywms configuration
81-------------------
82
83[cdms_wms]
84
85- title: The title of the WMS.  This will be displayed in the pywmsc
86layer browser as a tree branch label.
87
88- abstract: A description of the WMS.  This will be displayed in the
89right-hand column of the pywmsc browser.
90
91- cdms_files: A glob file pattern pointing to files readable by CDMS
92containing the data (CDML or NetCDF files).
93
94- folder_keys: A space separated list of keys that partition the
95dataset into a tree hierarchy.  For instance the observation dataset
96is divided into 2 climatologies using the key "clim".  !TODO: more explanation
97
98- key_pattern: A Python regular expression to match against each
99pathname in cdms_files.  The expression should contain key groups
100of the form "(?P<key>...)" to map parts of the path to folder_keys.
101
102- exclude: A space separated list of variable names to ignore.
103
104- reverse_time: !TODO
105
106- url: A url linking to more information about this WMS
107
108Pywms will use this section to map each pathname onto a set of
109key/value pairs that are then used to drive the rest of the
110configuration.
111
112[key:<key>=<value>]
113
114- title: The label for this tree item.
115
116Other items in this section are displayed in the left-hand panel of
117the layer browser and used to draw figure captions.
118
119- caption_style: Used by the pywms figure plotter to decide how to
120draw the figure caption.
121
122- url: A url linking to more information about all nodes in the tree with this key.
123
124[variable:<var>]
125
126- title: The label for this variable.  If absent the variable name
127will be used.
128
129- min_value: The minimum value of the variable, used to render the
130colourbar.  If absent the dataset will be sampled on initialisation to
131decide a sensible value.
132
133- max_value: The maximum value of the data, used to render the
134colourbar.  See min_value.
135
136- cmap: The colourmap to use.  !TODO: Explain colourmaps.
137
138Other items in this section are displayed in the left-hand panel of
139the layer browser and used to draw figure captions.
140
Note: See TracBrowser for help on using the repository browser.