Welcome back to AWIPS Tips! Today we're diving into more capabilities of python-awips! Previously, we've discussed how to access model data and how to access and write out specific GOES imagery. Today, we're switching gears slightly from meteorological data, to focus on how to access and use map data.
While meteorological data are great, they usually need additional information for full context and interpretation. That is where map data becomes so important. Because python-awips is just one of many Python libraries available, there are numerous ways to access useful map data (think of cartopy for example). However, to keep things simpler, most relevant data can simply be accessed from an EDEX server. One additional benefit of using python-awips for this task is that a user can easily query and limit by County Warning Areas (CWAs), which is not a common functionality of other python libraries.
In our example Notebook titled Map Resources and Topography, we walk through how to add additional map data, layer by layer, to create a robust contextual plot. A summarized graphic of the process is as follows:
Accessing map data is done in the same manner as accessing other data in EDEX. The first step is to create a data request object and use the 'maps' data type:
request = DataAccessLayer.newDataRequest('maps')
From this point, we set many different attributes on the request object. Most notably we change the 'table' identifier several times throughout the Notebook:
A full list of all available tables can be found as an appendix on our website.
It is also important to note, in order to be efficient, we only pull back data that are relevant to the geographical location we're interested in. This is accomplished by creating all subsequent data requests with a filter known as an 'envelope':
request = DataAccessLayer.newDataRequest('maps', envelope=envelope)
Geographic filters are useful for creating fast and efficient notebooks and can be done when accessing any data type on EDEX (not just map resources).
By adding one data set after another, we can see how much more context the map information offers to our overall plot. In this particular example, we don't actually have any meteorological data, but our resulting output is still a valuable product on its own. All of the map sources used in this Notebook can be used to help add context to products from models, satellites, radars and more!
We hope you found this week's entry useful, please check back in two weeks for the next blog post, where we dive into viewing soundings in CAVE.
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Karibu tena kwenye AWIPS Tips! Leo sisi ni kupiga mbizi katika uwezo zaidi wa python-awips! Kwa mfano, tunajifunza jinsi ya kupata data ya mfano na jinsi ya kupata na kuandika picha maalum za GOES. Leo, tunabadilisha gia kidogo kutoka kwa data ya hali ya hewa, ili kuzingatia jinsi ya kupata na kutumia data ya ramani. Ingawa data za hali ya hewa ni nzuri, kwa kawaida zinahitaji habari za ziada kwa muktadha kamili na tafsiri. Hapo ndipo data ya ramani inapoanza kuwa muhimu sana. Kwa sababu python-awips ni moja tu ya maktaba nyingi za Python zinazopatikana, kuna njia nyingi za kupata data muhimu ya ramani (fikiria cartopy kwa mfano). Hata hivyo, ili kuweka mambo rahisi, data muhimu zaidi inaweza tu kupatikana kutoka kwa seva ya EDEX. Faida moja ya ziada ya kutumia Python-awips kwa kazi hii ni kwamba mtumiaji anaweza kwa urahisi kuuliza na kikomo na Kaunti Warning Areas (CWA), ambayo si kazi ya kawaida ya maktaba nyingine Python. Katika mfano wetu Notebook yenye jina Map Resources na Topography, sisi kutembea kupitia jinsi ya kuongeza data ya ramani ya ziada, safu kwa safu, kuunda plot imara contextual. Ukurasa wa jumla wa mchakato ni kama ifuatavyo: Upatikanaji wa data ya ramani hufanywa kwa njia ile ile kama kupata data nyingine katika EDEX. Hatua ya kwanza ni kuunda data ombi kitu na kutumia 'ramani' aina ya data: ombi = DataAccessLayer.newDataRequest('ramani') Kutoka hatua hii, sisi kuweka sifa nyingi tofauti juu ya ombi kitu. Hasa sisi kubadilisha'meza' kitambulisho mara kadhaa katika Notebook: orodha kamili ya meza zote inapatikana kama nyongeza kwenye tovuti yetu. Ni muhimu pia kutambua kwamba, ili kuwa na ufanisi, sisi tu kuvuta nyuma data kwamba ni muhimu kwa eneo la kijiografia tunapendezwa. Hii ni kufanyika kwa kuunda maombi yote ya data ya baadaye na kichujio inayojulikana kama 'envelope': request = DataAccessLayer.newDataRequest (map), envelope = envelope. filters kijiografia ni muhimu kwa ajili ya kujenga haraka na ufanisi notebooks na inaweza kufanyika wakati wa kupata aina yoyote ya data juu ya EDEX (si map rasilimali tu). Kwa kuongeza seti moja ya data baada ya nyingine, tunaweza kuona ni nini zaidi mazingira ya habari ramani inatoa kwa njama yetu ya jumla. Katika mfano huu maalum, hatuna data yoyote ya hali ya hewa, lakini pato letu linalotokana bado ni bidhaa yenye thamani yenyewe. Vyanzo vyote vya ramani kutumika katika kitabu hiki cha kumbukumbu inaweza kutumika kusaidia kuongeza muktadha kwa bidhaa kutoka mifano, satelaiti, radars na zaidi! Tunatarajia kuwa utapata maelezo haya ya wiki hii muhimu, tafadhali angalia tena katika wiki mbili kwa chapisho la blogi ijayo, ambapo tunaingia kwenye kutazama sauti katika CAVE. Kuangalia blogi zilizorekodiwa, tembelea AWIPS Tips blog tag, na upate taarifa za sasisho za hivi karibuni kutoka kwa timu ya AWIPS kwa kujiandikisha kwa orodha ya barua ya AWIPS. Maswali au mapendekezo kwa ajili ya timu juu ya mada ya baadaye? Tuambie katika email@example.com