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b8d59e3788
| Author | SHA1 | Date | |
|---|---|---|---|
 Justin Fisher
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b8d59e3788 | ||
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Justin Fisher
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18e099ce84 | ||
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Justin Fisher
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# Local Python environment and caches
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.venv/
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__pycache__/
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*.py[cod]
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.pytest_cache/
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# Local editor and operating-system files
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.vscode/
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.DS_Store
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Thumbs.db
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# Local secrets
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.env
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.env.*
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!.env.example
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# Downloaded and generated climate datasets are too large for Git.
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# Keep only the assets required by the browser application.
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data/*
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!data/climate-data.csv
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!data/geojson-counties-fips.json
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# Runtime logs
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*.log
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@@ -1,232 +0,0 @@
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GNU GENERAL PUBLIC LICENSE
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Version 3, 29 June 2007
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|
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Copyright © 2007 Free Software Foundation, Inc. <https://fsf.org/>
|
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|
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Everyone is permitted to copy and distribute verbatim copies of this license document, but changing it is not allowed.
|
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|
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Preamble
|
||||
|
||||
The GNU General Public License is a free, copyleft license for software and other kinds of works.
|
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|
||||
The licenses for most software and other practical works are designed to take away your freedom to share and change the works. By contrast, the GNU General Public License is intended to guarantee your freedom to share and change all versions of a program--to make sure it remains free software for all its users. We, the Free Software Foundation, use the GNU General Public License for most of our software; it applies also to any other work released this way by its authors. You can apply it to your programs, too.
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|
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When we speak of free software, we are referring to freedom, not price. Our General Public Licenses are designed to make sure that you have the freedom to distribute copies of free software (and charge for them if you wish), that you receive source code or can get it if you want it, that you can change the software or use pieces of it in new free programs, and that you know you can do these things.
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|
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To protect your rights, we need to prevent others from denying you these rights or asking you to surrender the rights. Therefore, you have certain responsibilities if you distribute copies of the software, or if you modify it: responsibilities to respect the freedom of others.
|
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|
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For example, if you distribute copies of such a program, whether gratis or for a fee, you must pass on to the recipients the same freedoms that you received. You must make sure that they, too, receive or can get the source code. And you must show them these terms so they know their rights.
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Developers that use the GNU GPL protect your rights with two steps: (1) assert copyright on the software, and (2) offer you this License giving you legal permission to copy, distribute and/or modify it.
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For the developers' and authors' protection, the GPL clearly explains that there is no warranty for this free software. For both users' and authors' sake, the GPL requires that modified versions be marked as changed, so that their problems will not be attributed erroneously to authors of previous versions.
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Some devices are designed to deny users access to install or run modified versions of the software inside them, although the manufacturer can do so. This is fundamentally incompatible with the aim of protecting users' freedom to change the software. The systematic pattern of such abuse occurs in the area of products for individuals to use, which is precisely where it is most unacceptable. Therefore, we have designed this version of the GPL to prohibit the practice for those products. If such problems arise substantially in other domains, we stand ready to extend this provision to those domains in future versions of the GPL, as needed to protect the freedom of users.
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Finally, every program is threatened constantly by software patents. States should not allow patents to restrict development and use of software on general-purpose computers, but in those that do, we wish to avoid the special danger that patents applied to a free program could make it effectively proprietary. To prevent this, the GPL assures that patents cannot be used to render the program non-free.
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The precise terms and conditions for copying, distribution and modification follow.
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TERMS AND CONDITIONS
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0. Definitions.
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“This License” refers to version 3 of the GNU General Public License.
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“Copyright” also means copyright-like laws that apply to other kinds of works, such as semiconductor masks.
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You may convey verbatim copies of the Program's source code as you receive it, in any medium, provided that you conspicuously and appropriately publish on each copy an appropriate copyright notice; keep intact all notices stating that this License and any non-permissive terms added in accord with section 7 apply to the code; keep intact all notices of the absence of any warranty; and give all recipients a copy of this License along with the Program.
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You may charge any price or no price for each copy that you convey, and you may offer support or warranty protection for a fee.
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You may convey a work based on the Program, or the modifications to produce it from the Program, in the form of source code under the terms of section 4, provided that you also meet all of these conditions:
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b) Convey the object code in, or embodied in, a physical product (including a physical distribution medium), accompanied by a written offer, valid for at least three years and valid for as long as you offer spare parts or customer support for that product model, to give anyone who possesses the object code either (1) a copy of the Corresponding Source for all the software in the product that is covered by this License, on a durable physical medium customarily used for software interchange, for a price no more than your reasonable cost of physically performing this conveying of source, or (2) access to copy the Corresponding Source from a network server at no charge.
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d) Convey the object code by offering access from a designated place (gratis or for a charge), and offer equivalent access to the Corresponding Source in the same way through the same place at no further charge. You need not require recipients to copy the Corresponding Source along with the object code. If the place to copy the object code is a network server, the Corresponding Source may be on a different server (operated by you or a third party) that supports equivalent copying facilities, provided you maintain clear directions next to the object code saying where to find the Corresponding Source. Regardless of what server hosts the Corresponding Source, you remain obligated to ensure that it is available for as long as needed to satisfy these requirements.
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If you convey an object code work under this section in, or with, or specifically for use in, a User Product, and the conveying occurs as part of a transaction in which the right of possession and use of the User Product is transferred to the recipient in perpetuity or for a fixed term (regardless of how the transaction is characterized), the Corresponding Source conveyed under this section must be accompanied by the Installation Information. But this requirement does not apply if neither you nor any third party retains the ability to install modified object code on the User Product (for example, the work has been installed in ROM).
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Notwithstanding any other provision of this License, for material you add to a covered work, you may (if authorized by the copyright holders of that material) supplement the terms of this License with terms:
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a) Disclaiming warranty or limiting liability differently from the terms of sections 15 and 16 of this License; or
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b) Requiring preservation of specified reasonable legal notices or author attributions in that material or in the Appropriate Legal Notices displayed by works containing it; or
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8. Termination.
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You may not propagate or modify a covered work except as expressly provided under this License. Any attempt otherwise to propagate or modify it is void, and will automatically terminate your rights under this License (including any patent licenses granted under the third paragraph of section 11).
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However, if you cease all violation of this License, then your license from a particular copyright holder is reinstated (a) provisionally, unless and until the copyright holder explicitly and finally terminates your license, and (b) permanently, if the copyright holder fails to notify you of the violation by some reasonable means prior to 60 days after the cessation.
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Moreover, your license from a particular copyright holder is reinstated permanently if the copyright holder notifies you of the violation by some reasonable means, this is the first time you have received notice of violation of this License (for any work) from that copyright holder, and you cure the violation prior to 30 days after your receipt of the notice.
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Termination of your rights under this section does not terminate the licenses of parties who have received copies or rights from you under this License. If your rights have been terminated and not permanently reinstated, you do not qualify to receive new licenses for the same material under section 10.
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You are not required to accept this License in order to receive or run a copy of the Program. Ancillary propagation of a covered work occurring solely as a consequence of using peer-to-peer transmission to receive a copy likewise does not require acceptance. However, nothing other than this License grants you permission to propagate or modify any covered work. These actions infringe copyright if you do not accept this License. Therefore, by modifying or propagating a covered work, you indicate your acceptance of this License to do so.
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You may not impose any further restrictions on the exercise of the rights granted or affirmed under this License. For example, you may not impose a license fee, royalty, or other charge for exercise of rights granted under this License, and you may not initiate litigation (including a cross-claim or counterclaim in a lawsuit) alleging that any patent claim is infringed by making, using, selling, offering for sale, or importing the Program or any portion of it.
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A “contributor” is a copyright holder who authorizes use under this License of the Program or a work on which the Program is based. The work thus licensed is called the contributor's “contributor version”.
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A contributor's “essential patent claims” are all patent claims owned or controlled by the contributor, whether already acquired or hereafter acquired, that would be infringed by some manner, permitted by this License, of making, using, or selling its contributor version, but do not include claims that would be infringed only as a consequence of further modification of the contributor version. For purposes of this definition, “control” includes the right to grant patent sublicenses in a manner consistent with the requirements of this License.
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Each contributor grants you a non-exclusive, worldwide, royalty-free patent license under the contributor's essential patent claims, to make, use, sell, offer for sale, import and otherwise run, modify and propagate the contents of its contributor version.
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In the following three paragraphs, a “patent license” is any express agreement or commitment, however denominated, not to enforce a patent (such as an express permission to practice a patent or covenant not to sue for patent infringement). To “grant” such a patent license to a party means to make such an agreement or commitment not to enforce a patent against the party.
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If you convey a covered work, knowingly relying on a patent license, and the Corresponding Source of the work is not available for anyone to copy, free of charge and under the terms of this License, through a publicly available network server or other readily accessible means, then you must either (1) cause the Corresponding Source to be so available, or (2) arrange to deprive yourself of the benefit of the patent license for this particular work, or (3) arrange, in a manner consistent with the requirements of this License, to extend the patent license to downstream recipients. “Knowingly relying” means you have actual knowledge that, but for the patent license, your conveying the covered work in a country, or your recipient's use of the covered work in a country, would infringe one or more identifiable patents in that country that you have reason to believe are valid.
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If, pursuant to or in connection with a single transaction or arrangement, you convey, or propagate by procuring conveyance of, a covered work, and grant a patent license to some of the parties receiving the covered work authorizing them to use, propagate, modify or convey a specific copy of the covered work, then the patent license you grant is automatically extended to all recipients of the covered work and works based on it.
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A patent license is “discriminatory” if it does not include within the scope of its coverage, prohibits the exercise of, or is conditioned on the non-exercise of one or more of the rights that are specifically granted under this License. You may not convey a covered work if you are a party to an arrangement with a third party that is in the business of distributing software, under which you make payment to the third party based on the extent of your activity of conveying the work, and under which the third party grants, to any of the parties who would receive the covered work from you, a discriminatory patent license (a) in connection with copies of the covered work conveyed by you (or copies made from those copies), or (b) primarily for and in connection with specific products or compilations that contain the covered work, unless you entered into that arrangement, or that patent license was granted, prior to 28 March 2007.
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Nothing in this License shall be construed as excluding or limiting any implied license or other defenses to infringement that may otherwise be available to you under applicable patent law.
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12. No Surrender of Others' Freedom.
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If conditions are imposed on you (whether by court order, agreement or otherwise) that contradict the conditions of this License, they do not excuse you from the conditions of this License. If you cannot convey a covered work so as to satisfy simultaneously your obligations under this License and any other pertinent obligations, then as a consequence you may not convey it at all. For example, if you agree to terms that obligate you to collect a royalty for further conveying from those to whom you convey the Program, the only way you could satisfy both those terms and this License would be to refrain entirely from conveying the Program.
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13. Use with the GNU Affero General Public License.
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Notwithstanding any other provision of this License, you have permission to link or combine any covered work with a work licensed under version 3 of the GNU Affero General Public License into a single combined work, and to convey the resulting work. The terms of this License will continue to apply to the part which is the covered work, but the special requirements of the GNU Affero General Public License, section 13, concerning interaction through a network will apply to the combination as such.
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14. Revised Versions of this License.
|
||||
The Free Software Foundation may publish revised and/or new versions of the GNU General Public License from time to time. Such new versions will be similar in spirit to the present version, but may differ in detail to address new problems or concerns.
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||||
Each version is given a distinguishing version number. If the Program specifies that a certain numbered version of the GNU General Public License “or any later version” applies to it, you have the option of following the terms and conditions either of that numbered version or of any later version published by the Free Software Foundation. If the Program does not specify a version number of the GNU General Public License, you may choose any version ever published by the Free Software Foundation.
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If the Program specifies that a proxy can decide which future versions of the GNU General Public License can be used, that proxy's public statement of acceptance of a version permanently authorizes you to choose that version for the Program.
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Later license versions may give you additional or different permissions. However, no additional obligations are imposed on any author or copyright holder as a result of your choosing to follow a later version.
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15. Disclaimer of Warranty.
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||||
THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY APPLICABLE LAW. EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM “AS IS” WITHOUT WARRANTY OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE. THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM IS WITH YOU. SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF ALL NECESSARY SERVICING, REPAIR OR CORRECTION.
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|
||||
16. Limitation of Liability.
|
||||
IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS), EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF SUCH DAMAGES.
|
||||
|
||||
17. Interpretation of Sections 15 and 16.
|
||||
If the disclaimer of warranty and limitation of liability provided above cannot be given local legal effect according to their terms, reviewing courts shall apply local law that most closely approximates an absolute waiver of all civil liability in connection with the Program, unless a warranty or assumption of liability accompanies a copy of the Program in return for a fee.
|
||||
|
||||
END OF TERMS AND CONDITIONS
|
||||
|
||||
How to Apply These Terms to Your New Programs
|
||||
|
||||
If you develop a new program, and you want it to be of the greatest possible use to the public, the best way to achieve this is to make it free software which everyone can redistribute and change under these terms.
|
||||
|
||||
To do so, attach the following notices to the program. It is safest to attach them to the start of each source file to most effectively state the exclusion of warranty; and each file should have at least the “copyright” line and a pointer to where the full notice is found.
|
||||
|
||||
Climate-Mood-Analysis
|
||||
Copyright (C) 2026 Knou
|
||||
|
||||
This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
|
||||
|
||||
This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
|
||||
|
||||
You should have received a copy of the GNU General Public License along with this program. If not, see <https://www.gnu.org/licenses/>.
|
||||
|
||||
Also add information on how to contact you by electronic and paper mail.
|
||||
|
||||
If the program does terminal interaction, make it output a short notice like this when it starts in an interactive mode:
|
||||
|
||||
Climate-Mood-Analysis Copyright (C) 2026 Knou
|
||||
This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
|
||||
This is free software, and you are welcome to redistribute it under certain conditions; type `show c' for details.
|
||||
|
||||
The hypothetical commands `show w' and `show c' should show the appropriate parts of the General Public License. Of course, your program's commands might be different; for a GUI interface, you would use an “about box”.
|
||||
|
||||
You should also get your employer (if you work as a programmer) or school, if any, to sign a “copyright disclaimer” for the program, if necessary. For more information on this, and how to apply and follow the GNU GPL, see <https://www.gnu.org/licenses/>.
|
||||
|
||||
The GNU General Public License does not permit incorporating your program into proprietary programs. If your program is a subroutine library, you may consider it more useful to permit linking proprietary applications with the library. If this is what you want to do, use the GNU Lesser General Public License instead of this License. But first, please read <https://www.gnu.org/philosophy/why-not-lgpl.html>.
|
||||
File diff suppressed because it is too large
Load Diff
File diff suppressed because one or more lines are too long
+113
@@ -0,0 +1,113 @@
|
||||
<!doctype html>
|
||||
<html lang="en">
|
||||
<head>
|
||||
<meta charset="UTF-8" />
|
||||
<meta name="viewport" content="width=device-width, initial-scale=1.0" />
|
||||
<title>US County Climate Explorer</title>
|
||||
<link rel="icon" href="data:," />
|
||||
<link rel="preconnect" href="https://fonts.googleapis.com" />
|
||||
<link rel="preconnect" href="https://fonts.gstatic.com" crossorigin />
|
||||
<link href="https://fonts.googleapis.com/css2?family=Space+Grotesk:wght@400;500;700&family=Source+Sans+3:wght@400;600;700&display=swap" rel="stylesheet" />
|
||||
<link
|
||||
rel="stylesheet"
|
||||
href="https://unpkg.com/leaflet@1.9.4/dist/leaflet.css"
|
||||
integrity="sha256-p4NxAoJBhIIN+hmNHrzRCf9tD/miZyoHS5obTRR9BMY="
|
||||
crossorigin=""
|
||||
/>
|
||||
<link rel="stylesheet" href="styles.css?v=dtr-20260531" />
|
||||
</head>
|
||||
<body>
|
||||
<div class="page-shell">
|
||||
<aside class="control-panel">
|
||||
<h1>US County Climate Explorer</h1>
|
||||
<p class="intro">Explore county-level climate filters. Click any county to inspect details and zoom directly to it.</p>
|
||||
|
||||
<div class="control-stack">
|
||||
<label for="metricGroupSelect">Group</label>
|
||||
<select id="metricGroupSelect"></select>
|
||||
|
||||
<div id="metricSelectWrap" class="metric-select-wrap">
|
||||
<label for="metricSelect">Metric</label>
|
||||
<select id="metricSelect"></select>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<div id="koppenFilterWrap" class="range-wrap is-hidden">
|
||||
<label for="koppenFilterSelect">Category Filter</label>
|
||||
<select id="koppenFilterSelect"></select>
|
||||
</div>
|
||||
|
||||
<div class="range-wrap">
|
||||
<label for="minRange">Minimum Value</label>
|
||||
<input id="minRange" type="range" />
|
||||
<output id="minValue"></output>
|
||||
</div>
|
||||
|
||||
<div class="range-wrap">
|
||||
<label for="maxRange">Maximum Value</label>
|
||||
<input id="maxRange" type="range" />
|
||||
<output id="maxValue"></output>
|
||||
</div>
|
||||
|
||||
<div class="button-row">
|
||||
<button id="resetViewButton" type="button">Reset Country View</button>
|
||||
<button id="clearSelectionButton" type="button">Clear Selection</button>
|
||||
</div>
|
||||
|
||||
<div class="control-panel-spacer" aria-hidden="true"></div>
|
||||
<div class="sources-control" style="margin-top: auto;">
|
||||
<button id="sourcesButton" type="button" aria-haspopup="dialog">Sources</button>
|
||||
</div>
|
||||
</aside>
|
||||
|
||||
<main class="map-panel">
|
||||
<div id="map" aria-label="US county climate map"></div>
|
||||
<section id="metricInfoPanel" class="metric-info-panel" aria-label="County metric details">
|
||||
<button
|
||||
id="metricInfoToggle"
|
||||
class="metric-info-toggle"
|
||||
type="button"
|
||||
aria-expanded="true"
|
||||
aria-controls="stateDetails"
|
||||
>
|
||||
Hide County Metrics
|
||||
</button>
|
||||
<div id="stateDetails" class="state-details">
|
||||
<h2>Selected County</h2>
|
||||
<p>Click a county to see details and auto-focus.</p>
|
||||
</div>
|
||||
</section>
|
||||
<section id="legendInfoPanel" class="legend-info-panel is-collapsed" aria-label="Map legend">
|
||||
<button
|
||||
id="legendInfoToggle"
|
||||
class="legend-info-toggle"
|
||||
type="button"
|
||||
aria-expanded="false"
|
||||
aria-controls="legend"
|
||||
>
|
||||
Show Legend
|
||||
</button>
|
||||
<div id="legend" class="legend"></div>
|
||||
</section>
|
||||
</main>
|
||||
</div>
|
||||
|
||||
<div id="sourcesModal" class="sources-modal" role="dialog" aria-modal="true" aria-labelledby="sourcesModalTitle" hidden>
|
||||
<div class="sources-modal-backdrop" data-sources-close></div>
|
||||
<section class="sources-dialog" aria-label="Sources details">
|
||||
<header class="sources-dialog-header">
|
||||
<h2 id="sourcesModalTitle">Sources</h2>
|
||||
<button id="sourcesCloseButton" class="sources-close-button" type="button" aria-label="Close sources">X</button>
|
||||
</header>
|
||||
<div id="sourcesModalContent" class="sources-modal-content"></div>
|
||||
</section>
|
||||
</div>
|
||||
|
||||
<script
|
||||
src="https://unpkg.com/leaflet@1.9.4/dist/leaflet.js"
|
||||
integrity="sha256-20nQCchB9co0qIjJZRGuk2/Z9VM+kNiyxNV1lvTlZBo="
|
||||
crossorigin=""
|
||||
></script>
|
||||
<script src="app.js?v=precip-months-20260601"></script>
|
||||
</body>
|
||||
</html>
|
||||
@@ -0,0 +1,159 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
Apply county diurnal temperature range to the app CSV.
|
||||
|
||||
This joins only the final app-facing metric into climate-data.csv. Detailed ETL
|
||||
fields stay in data/noaa/county_diurnal_temperature_range.csv.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import argparse
|
||||
import csv
|
||||
from pathlib import Path
|
||||
|
||||
from build_county_locally_extreme_data import OLD_APP_FIPS_TO_CURRENT_FIPS
|
||||
|
||||
|
||||
REPO_ROOT = Path(__file__).resolve().parents[1]
|
||||
DEFAULT_CLIMATE_DATA = REPO_ROOT / "data" / "climate-data.csv"
|
||||
DEFAULT_DTR_CSV = REPO_ROOT / "data" / "noaa" / "county_diurnal_temperature_range.csv"
|
||||
METRIC_FIELD = "avgDiurnalTempRangeF"
|
||||
SOURCE_TAG_PREFIX = "diurnalTempRange="
|
||||
SOURCE_TAG = "diurnalTempRange=noaa-nclimgrid-daily-1991-2020"
|
||||
MISSING_SOURCE_TAG = "missing-noaa-diurnal-temperature-range"
|
||||
|
||||
|
||||
def normalize_fips(value: object) -> str:
|
||||
"""Return a 5-digit county FIPS string from mixed text or numeric input."""
|
||||
digits = "".join(ch for ch in str(value).strip() if ch.isdigit())
|
||||
return digits.zfill(5)[-5:] if digits else ""
|
||||
|
||||
|
||||
def read_csv_rows(path: Path) -> tuple[list[str], list[dict]]:
|
||||
"""Read a CSV while preserving the source field order."""
|
||||
with path.open("r", encoding="utf-8-sig", newline="") as csv_file:
|
||||
reader = csv.DictReader(csv_file)
|
||||
if reader.fieldnames is None:
|
||||
raise ValueError(f"{path} has no CSV header.")
|
||||
return list(reader.fieldnames), list(reader)
|
||||
|
||||
|
||||
def load_dtr_lookup(path: Path) -> dict[str, dict]:
|
||||
"""Load DTR rows keyed by current county FIPS plus supported old app FIPS."""
|
||||
fieldnames, rows = read_csv_rows(path)
|
||||
required_fields = {"countyFips", METRIC_FIELD}
|
||||
missing_fields = required_fields - set(fieldnames)
|
||||
if missing_fields:
|
||||
raise ValueError(f"{path} is missing required fields: {sorted(missing_fields)}")
|
||||
|
||||
current_lookup: dict[str, dict] = {}
|
||||
for row in rows:
|
||||
county_fips = normalize_fips(row.get("countyFips", ""))
|
||||
metric_value = (row.get(METRIC_FIELD) or "").strip()
|
||||
if not county_fips or not metric_value:
|
||||
continue
|
||||
float(metric_value)
|
||||
current_lookup[county_fips] = row
|
||||
|
||||
lookup = dict(current_lookup)
|
||||
for old_fips, (current_fips, reason) in OLD_APP_FIPS_TO_CURRENT_FIPS.items():
|
||||
current_row = current_lookup.get(normalize_fips(current_fips))
|
||||
if not current_row:
|
||||
continue
|
||||
proxy_row = dict(current_row)
|
||||
proxy_row["fipsAdjustment"] = reason
|
||||
lookup[normalize_fips(old_fips)] = proxy_row
|
||||
|
||||
return lookup
|
||||
|
||||
|
||||
def fieldnames_with_metric(original_fields: list[str]) -> list[str]:
|
||||
"""Insert the DTR metric near the other temperature field."""
|
||||
fields = [field for field in original_fields if field != METRIC_FIELD]
|
||||
if "avgTempF" in fields:
|
||||
insert_at = fields.index("avgTempF") + 1
|
||||
else:
|
||||
insert_at = len(fields)
|
||||
fields.insert(insert_at, METRIC_FIELD)
|
||||
return fields
|
||||
|
||||
|
||||
def replace_dtr_source_tag(source: str, has_value: bool) -> str:
|
||||
"""Replace previous DTR provenance with the active one."""
|
||||
parts = [
|
||||
part.strip()
|
||||
for part in (source.strip() or "unknown-source").split("+")
|
||||
if part.strip()
|
||||
]
|
||||
kept_parts = [
|
||||
part
|
||||
for part in parts
|
||||
if not part.startswith(SOURCE_TAG_PREFIX) and part != MISSING_SOURCE_TAG
|
||||
]
|
||||
kept_parts.append(SOURCE_TAG if has_value else MISSING_SOURCE_TAG)
|
||||
return " + ".join(kept_parts)
|
||||
|
||||
|
||||
def apply_diurnal_temperature_range(
|
||||
*,
|
||||
climate_data: Path,
|
||||
dtr_csv: Path,
|
||||
out: Path,
|
||||
) -> None:
|
||||
"""Join DTR values into climate-data.csv."""
|
||||
original_fields, climate_rows = read_csv_rows(climate_data)
|
||||
dtr_lookup = load_dtr_lookup(dtr_csv)
|
||||
fieldnames = fieldnames_with_metric(original_fields)
|
||||
|
||||
updated_count = 0
|
||||
missing_count = 0
|
||||
adjusted_count = 0
|
||||
for row in climate_rows:
|
||||
county_fips = normalize_fips(row.get("countyFips", ""))
|
||||
dtr_row = dtr_lookup.get(county_fips)
|
||||
if dtr_row:
|
||||
row[METRIC_FIELD] = dtr_row.get(METRIC_FIELD, "")
|
||||
row["source"] = replace_dtr_source_tag(row.get("source", ""), has_value=True)
|
||||
updated_count += 1
|
||||
if dtr_row.get("fipsAdjustment"):
|
||||
adjusted_count += 1
|
||||
else:
|
||||
row[METRIC_FIELD] = ""
|
||||
row["source"] = replace_dtr_source_tag(row.get("source", ""), has_value=False)
|
||||
missing_count += 1
|
||||
|
||||
with out.open("w", encoding="utf-8", newline="") as csv_file:
|
||||
writer = csv.DictWriter(csv_file, fieldnames=fieldnames, extrasaction="ignore")
|
||||
writer.writeheader()
|
||||
writer.writerows(climate_rows)
|
||||
|
||||
print(f"Wrote {len(climate_rows)} rows to {out}")
|
||||
print(f"Updated {METRIC_FIELD} for {updated_count} rows.")
|
||||
print(f"Used supported county-vintage FIPS adjustments for {adjusted_count} rows.")
|
||||
print(f"Left {METRIC_FIELD} blank for {missing_count} rows without DTR data.")
|
||||
|
||||
|
||||
def parse_args() -> argparse.Namespace:
|
||||
"""Parse command-line paths."""
|
||||
parser = argparse.ArgumentParser(
|
||||
description="Apply average diurnal temperature range to climate-data.csv."
|
||||
)
|
||||
parser.add_argument("--climate-data", type=Path, default=DEFAULT_CLIMATE_DATA)
|
||||
parser.add_argument("--dtr-csv", type=Path, default=DEFAULT_DTR_CSV)
|
||||
parser.add_argument("--out", type=Path, default=DEFAULT_CLIMATE_DATA)
|
||||
return parser.parse_args()
|
||||
|
||||
|
||||
def main() -> None:
|
||||
"""Run the climate-data update."""
|
||||
args = parse_args()
|
||||
apply_diurnal_temperature_range(
|
||||
climate_data=args.climate_data,
|
||||
dtr_csv=args.dtr_csv,
|
||||
out=args.out,
|
||||
)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@@ -0,0 +1,119 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
Merge selected gridMET humidity and heat metrics into climate-data.csv.
|
||||
|
||||
Currently adds:
|
||||
- avgSummerSpecificHumidityGKg
|
||||
- humidHeatDays
|
||||
- humidHeatSourceFips
|
||||
- humidHeatFipsAdjustment
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import argparse
|
||||
import csv
|
||||
from pathlib import Path
|
||||
|
||||
|
||||
DEFAULT_CLIMATE_DATA = Path("data/climate-data.csv")
|
||||
DEFAULT_GRIDMET_HUMIDITY = Path("data/gridmet/county_gridmet_humidity.csv")
|
||||
METRIC_FIELDS = [
|
||||
"avgSummerSpecificHumidityGKg",
|
||||
"humidHeatDays",
|
||||
"humidHeatSourceFips",
|
||||
"humidHeatFipsAdjustment",
|
||||
]
|
||||
SOURCE_TAGS_BY_FIELD = {
|
||||
"avgSummerSpecificHumidityGKg": "gridmet-specific-humidity-1991-2020",
|
||||
"humidHeatDays": "heat-index-noaa-tmax-gridmet-rmin-1991-2020",
|
||||
}
|
||||
|
||||
|
||||
def load_gridmet_values(path: Path) -> dict[str, dict[str, str]]:
|
||||
values: dict[str, dict[str, str]] = {}
|
||||
with path.open("r", encoding="utf-8", newline="") as handle:
|
||||
reader = csv.DictReader(handle)
|
||||
fieldnames = reader.fieldnames or []
|
||||
missing_fields = [field for field in METRIC_FIELDS if field not in fieldnames]
|
||||
if missing_fields:
|
||||
raise ValueError(f"{path} is missing fields: {', '.join(missing_fields)}")
|
||||
for row in reader:
|
||||
county_fips = (row.get("countyFips") or "").strip().zfill(5)
|
||||
if county_fips:
|
||||
values[county_fips] = {
|
||||
field: (row.get(field) or "").strip() for field in METRIC_FIELDS
|
||||
}
|
||||
return values
|
||||
|
||||
|
||||
def append_source_tag(source: str, tag: str) -> str:
|
||||
clean_source = source.strip()
|
||||
if tag in clean_source:
|
||||
return clean_source
|
||||
return f"{clean_source} + {tag}".strip(" +")
|
||||
|
||||
|
||||
def ensure_field_after(fieldnames: list[str], field: str, after_field: str | None = None) -> None:
|
||||
if field in fieldnames:
|
||||
return
|
||||
if after_field and after_field in fieldnames:
|
||||
fieldnames.insert(fieldnames.index(after_field) + 1, field)
|
||||
return
|
||||
insert_at = fieldnames.index("source") if "source" in fieldnames else len(fieldnames)
|
||||
fieldnames.insert(insert_at, field)
|
||||
|
||||
|
||||
def merge_metrics(climate_data: Path, humidity_csv: Path) -> tuple[int, dict[str, int]]:
|
||||
gridmet_by_fips = load_gridmet_values(humidity_csv)
|
||||
|
||||
with climate_data.open("r", encoding="utf-8", newline="") as handle:
|
||||
reader = csv.DictReader(handle)
|
||||
fieldnames = list(reader.fieldnames or [])
|
||||
rows = list(reader)
|
||||
|
||||
if "countyFips" not in fieldnames:
|
||||
raise ValueError(f"{climate_data} is missing countyFips")
|
||||
|
||||
ensure_field_after(fieldnames, "avgSummerSpecificHumidityGKg")
|
||||
ensure_field_after(fieldnames, "humidHeatDays", "avgSummerSpecificHumidityGKg")
|
||||
ensure_field_after(fieldnames, "humidHeatSourceFips", "humidHeatDays")
|
||||
ensure_field_after(fieldnames, "humidHeatFipsAdjustment", "humidHeatSourceFips")
|
||||
|
||||
missing_counts = {field: 0 for field in METRIC_FIELDS}
|
||||
for row in rows:
|
||||
county_fips = (row.get("countyFips") or "").strip().zfill(5)
|
||||
gridmet_values = gridmet_by_fips.get(county_fips, {})
|
||||
for field in METRIC_FIELDS:
|
||||
value = gridmet_values.get(field, "")
|
||||
row[field] = value
|
||||
if not value:
|
||||
missing_counts[field] += 1
|
||||
for field, tag in SOURCE_TAGS_BY_FIELD.items():
|
||||
if row.get(field):
|
||||
row["source"] = append_source_tag(row.get("source", ""), tag)
|
||||
|
||||
with climate_data.open("w", encoding="utf-8", newline="") as handle:
|
||||
writer = csv.DictWriter(handle, fieldnames=fieldnames)
|
||||
writer.writeheader()
|
||||
writer.writerows(rows)
|
||||
|
||||
return len(rows), missing_counts
|
||||
|
||||
|
||||
def main() -> int:
|
||||
parser = argparse.ArgumentParser(description="Merge gridMET humidity and Heat Index metrics into climate-data.csv.")
|
||||
parser.add_argument("--climate-data", type=Path, default=DEFAULT_CLIMATE_DATA)
|
||||
parser.add_argument("--humidity-csv", type=Path, default=DEFAULT_GRIDMET_HUMIDITY)
|
||||
args = parser.parse_args()
|
||||
|
||||
row_count, missing_counts = merge_metrics(args.climate_data, args.humidity_csv)
|
||||
print(f"Updated {args.climate_data} with {', '.join(METRIC_FIELDS)}")
|
||||
print(f"Rows: {row_count}")
|
||||
for field, missing_count in missing_counts.items():
|
||||
print(f"Missing {field}: {missing_count}")
|
||||
return 0
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
raise SystemExit(main())
|
||||
@@ -0,0 +1,290 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
Apply NOAA daily extreme-day and polygon solar metrics to the app CSV.
|
||||
|
||||
The browser app already reads the `extremeDays` column, so this script updates
|
||||
that column to the average locally extreme days/year while preserving the older
|
||||
proxy value in `oldExtremeDays`. It also writes an absolute daily heat/cold
|
||||
bucket metric from the same NOAA nClimGrid-Daily county data.
|
||||
|
||||
When an NSRDB polygon archive summary is available, this also replaces the
|
||||
older representative-point solar GHI value with the county polygon value. Rows
|
||||
without a polygon summary keep the representative-point fallback when present.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import argparse
|
||||
import csv
|
||||
from pathlib import Path
|
||||
|
||||
|
||||
REPO_ROOT = Path(__file__).resolve().parents[1]
|
||||
DEFAULT_CLIMATE_DATA = REPO_ROOT / "data" / "climate-data.csv"
|
||||
DEFAULT_COMPARISON = REPO_ROOT / "data" / "noaa" / "county_locally_extreme_days_comparison.csv"
|
||||
DEFAULT_POLYGON_SOLAR_GHI = REPO_ROOT / "data" / "nrel" / "county_polygon_ghi_summary.csv"
|
||||
DEFAULT_REPRESENTATIVE_POINT_SOLAR_GHI = REPO_ROOT / "data" / "nrel" / "county_representative_point_ghi_summary.csv"
|
||||
|
||||
BASE_FIELDS = [
|
||||
"countyFips",
|
||||
"countyName",
|
||||
"state",
|
||||
"koppenZone",
|
||||
"avgTempF",
|
||||
"annualPrecipIn",
|
||||
"seasonalityIndex",
|
||||
"wettestPrecipMonth",
|
||||
"driestPrecipMonth",
|
||||
"extremeDays",
|
||||
"locallyExtremeDays",
|
||||
"locallyExtremeHotDays",
|
||||
"locallyExtremeColdDays",
|
||||
"absoluteExtremeDays",
|
||||
"oldExtremeDays",
|
||||
"locallyExtremeAnalysisYears",
|
||||
"locallyExtremeSourceFips",
|
||||
"locallyExtremeFipsAdjustment",
|
||||
"avgSolarGhiKwhM2Day",
|
||||
"source",
|
||||
]
|
||||
|
||||
LEGACY_FIELDS = {
|
||||
"absoluteExtremeHeatDays",
|
||||
"absoluteExtremeColdDays",
|
||||
}
|
||||
|
||||
SOLAR_SOURCE_TAGS = {
|
||||
"solar-ghi-centroid-preview",
|
||||
"solar-ghi-representative-point",
|
||||
"solar-ghi-polygon-area-weighted",
|
||||
"solar-ghi-polygon-archive-area-weighted",
|
||||
"no-solar-ghi-source",
|
||||
"missing-solar-ghi",
|
||||
}
|
||||
|
||||
|
||||
def normalize_fips(value: object) -> str:
|
||||
"""Return a 5-digit county FIPS string from mixed text or numeric input."""
|
||||
digits = "".join(ch for ch in str(value).strip() if ch.isdigit())
|
||||
return digits.zfill(5)[-5:] if digits else ""
|
||||
|
||||
|
||||
def read_csv_rows(path: Path) -> tuple[list[str], list[dict]]:
|
||||
"""Read a CSV while preserving the source field order."""
|
||||
with path.open("r", encoding="utf-8-sig", newline="") as csv_file:
|
||||
reader = csv.DictReader(csv_file)
|
||||
if reader.fieldnames is None:
|
||||
raise ValueError(f"{path} has no CSV header.")
|
||||
return list(reader.fieldnames), list(reader)
|
||||
|
||||
|
||||
def load_locally_extreme_lookup(comparison_csv: Path) -> dict[str, dict]:
|
||||
"""Map current and supported old app FIPS codes to locally extreme rows."""
|
||||
_, rows = read_csv_rows(comparison_csv)
|
||||
lookup: dict[str, dict] = {}
|
||||
|
||||
for row in rows:
|
||||
if row.get("hasLocallyExtremeData") != "yes":
|
||||
continue
|
||||
|
||||
current_fips = normalize_fips(row.get("countyFips", ""))
|
||||
if current_fips:
|
||||
lookup[current_fips] = row
|
||||
|
||||
for old_fips in (row.get("oldSourceFips") or "").split(";"):
|
||||
normalized_old_fips = normalize_fips(old_fips)
|
||||
if normalized_old_fips:
|
||||
lookup[normalized_old_fips] = row
|
||||
|
||||
return lookup
|
||||
|
||||
|
||||
def load_solar_ghi_lookup(solar_ghi_csv: Path) -> dict[str, str]:
|
||||
"""Map county FIPS codes to average daily GHI values from a county CSV."""
|
||||
if not solar_ghi_csv.exists():
|
||||
return {}
|
||||
|
||||
fieldnames, rows = read_csv_rows(solar_ghi_csv)
|
||||
if "avgSolarGhiKwhM2Day" not in fieldnames:
|
||||
raise ValueError(f"{solar_ghi_csv} must include avgSolarGhiKwhM2Day.")
|
||||
|
||||
if "county_fips" in fieldnames:
|
||||
fips_field = "county_fips"
|
||||
elif "countyFips" in fieldnames:
|
||||
fips_field = "countyFips"
|
||||
else:
|
||||
raise ValueError(f"{solar_ghi_csv} must include county_fips or countyFips.")
|
||||
|
||||
lookup: dict[str, str] = {}
|
||||
for row in rows:
|
||||
county_fips = normalize_fips(row.get(fips_field, ""))
|
||||
raw_value = (row.get("avgSolarGhiKwhM2Day") or "").strip()
|
||||
if not county_fips or not raw_value:
|
||||
continue
|
||||
try:
|
||||
float(raw_value)
|
||||
except ValueError as error:
|
||||
raise ValueError(
|
||||
f"Invalid avgSolarGhiKwhM2Day value for county {county_fips}: {raw_value}"
|
||||
) from error
|
||||
lookup[county_fips] = raw_value
|
||||
|
||||
return lookup
|
||||
|
||||
|
||||
def append_source_tag(source: str, has_local_value: bool) -> str:
|
||||
"""Add the locally extreme provenance tags without duplicating old tags."""
|
||||
old_source = source.strip() or "unknown-source"
|
||||
old_source = old_source.replace(" + absoluteExtremeHeatDays=tmax-gte-95f", "")
|
||||
old_source = old_source.replace(" + absoluteExtremeColdDays=tmin-lte-0f", "")
|
||||
if not has_local_value:
|
||||
return f"{old_source} + missing-noaa-locally-extreme-days"
|
||||
|
||||
has_local_tag = "extremeDays=locally-extreme-noaa-nclimgrid-daily-1991-2025" in old_source
|
||||
has_absolute_tag = "absoluteExtremeDays=tmax-gte-95f-or-tmin-lte-0f" in old_source
|
||||
if has_local_tag and has_absolute_tag:
|
||||
return old_source
|
||||
if has_local_tag:
|
||||
return f"{old_source} + absoluteExtremeDays=tmax-gte-95f-or-tmin-lte-0f"
|
||||
|
||||
return (
|
||||
f"{old_source} + extremeDays=locally-extreme-noaa-nclimgrid-daily-1991-2025 "
|
||||
"+ absoluteExtremeDays=tmax-gte-95f-or-tmin-lte-0f "
|
||||
"+ oldExtremeDays=previous-monthly-proxy"
|
||||
)
|
||||
|
||||
|
||||
def replace_solar_source_tag(source: str, solar_source_tag: str) -> str:
|
||||
"""Replace any previous solar provenance tag with the active one."""
|
||||
parts = [part.strip() for part in (source.strip() or "unknown-source").split("+")]
|
||||
kept_parts = [
|
||||
part
|
||||
for part in parts
|
||||
if part and part not in SOLAR_SOURCE_TAGS
|
||||
]
|
||||
if solar_source_tag not in kept_parts:
|
||||
kept_parts.append(solar_source_tag)
|
||||
return " + ".join(kept_parts)
|
||||
|
||||
|
||||
def apply_locally_extreme_metric(
|
||||
*,
|
||||
climate_data: Path,
|
||||
comparison_csv: Path,
|
||||
polygon_solar_ghi_csv: Path,
|
||||
representative_point_solar_ghi_csv: Path,
|
||||
out: Path,
|
||||
) -> None:
|
||||
"""Replace app extreme-day fields and solar GHI with improved metrics."""
|
||||
original_fields, climate_rows = read_csv_rows(climate_data)
|
||||
local_lookup = load_locally_extreme_lookup(comparison_csv)
|
||||
polygon_solar_lookup = load_solar_ghi_lookup(polygon_solar_ghi_csv)
|
||||
representative_solar_lookup = load_solar_ghi_lookup(representative_point_solar_ghi_csv)
|
||||
|
||||
extra_fields = [
|
||||
field
|
||||
for field in original_fields
|
||||
if field not in BASE_FIELDS and field not in LEGACY_FIELDS
|
||||
]
|
||||
fieldnames = BASE_FIELDS + extra_fields
|
||||
|
||||
updated_count = 0
|
||||
missing_count = 0
|
||||
polygon_solar_count = 0
|
||||
representative_solar_count = 0
|
||||
missing_solar_count = 0
|
||||
for row in climate_rows:
|
||||
county_fips = normalize_fips(row.get("countyFips", ""))
|
||||
local_row = local_lookup.get(county_fips)
|
||||
old_extreme_days = (row.get("oldExtremeDays") or row.get("extremeDays") or "").strip()
|
||||
|
||||
row["oldExtremeDays"] = old_extreme_days
|
||||
if local_row:
|
||||
row["extremeDays"] = local_row.get("avgLocallyExtremeDays", "")
|
||||
row["locallyExtremeDays"] = local_row.get("avgLocallyExtremeDays", "")
|
||||
row["locallyExtremeHotDays"] = local_row.get("avgHotExtremeDays", "")
|
||||
row["locallyExtremeColdDays"] = local_row.get("avgColdExtremeDays", "")
|
||||
row["absoluteExtremeDays"] = local_row.get("avgAbsoluteExtremeDays", "")
|
||||
row["locallyExtremeAnalysisYears"] = local_row.get("analysisYears", "")
|
||||
row["locallyExtremeSourceFips"] = local_row.get("countyFips", "")
|
||||
row["locallyExtremeFipsAdjustment"] = local_row.get("fipsAdjustment", "")
|
||||
row["source"] = append_source_tag(row.get("source", ""), has_local_value=True)
|
||||
updated_count += 1
|
||||
else:
|
||||
row["extremeDays"] = ""
|
||||
row["locallyExtremeDays"] = ""
|
||||
row["locallyExtremeHotDays"] = ""
|
||||
row["locallyExtremeColdDays"] = ""
|
||||
row["absoluteExtremeDays"] = ""
|
||||
row["locallyExtremeAnalysisYears"] = ""
|
||||
row["locallyExtremeSourceFips"] = ""
|
||||
row["locallyExtremeFipsAdjustment"] = ""
|
||||
row["source"] = append_source_tag(row.get("source", ""), has_local_value=False)
|
||||
missing_count += 1
|
||||
|
||||
polygon_solar_value = polygon_solar_lookup.get(county_fips)
|
||||
representative_solar_value = representative_solar_lookup.get(county_fips)
|
||||
current_solar_value = (row.get("avgSolarGhiKwhM2Day") or "").strip()
|
||||
if polygon_solar_value:
|
||||
row["avgSolarGhiKwhM2Day"] = polygon_solar_value
|
||||
row["source"] = replace_solar_source_tag(
|
||||
row.get("source", ""),
|
||||
"solar-ghi-polygon-archive-area-weighted",
|
||||
)
|
||||
polygon_solar_count += 1
|
||||
elif representative_solar_value or current_solar_value:
|
||||
row["avgSolarGhiKwhM2Day"] = representative_solar_value or current_solar_value
|
||||
row["source"] = replace_solar_source_tag(
|
||||
row.get("source", ""),
|
||||
"solar-ghi-representative-point",
|
||||
)
|
||||
representative_solar_count += 1
|
||||
else:
|
||||
row["avgSolarGhiKwhM2Day"] = ""
|
||||
row["source"] = replace_solar_source_tag(row.get("source", ""), "missing-solar-ghi")
|
||||
missing_solar_count += 1
|
||||
|
||||
with out.open("w", encoding="utf-8", newline="") as csv_file:
|
||||
writer = csv.DictWriter(csv_file, fieldnames=fieldnames, extrasaction="ignore")
|
||||
writer.writeheader()
|
||||
writer.writerows(climate_rows)
|
||||
|
||||
print(f"Wrote {len(climate_rows)} rows to {out}")
|
||||
print(f"Updated active extremeDays from locally extreme data for {updated_count} rows.")
|
||||
print(f"Left active extremeDays blank for {missing_count} rows without locally extreme data.")
|
||||
print(f"Updated avgSolarGhiKwhM2Day from polygon archives for {polygon_solar_count} rows.")
|
||||
print(f"Kept representative-point solar fallback for {representative_solar_count} rows.")
|
||||
print(f"Left avgSolarGhiKwhM2Day blank for {missing_solar_count} rows without solar data.")
|
||||
|
||||
|
||||
def parse_args() -> argparse.Namespace:
|
||||
"""Parse command-line paths for the climate-data update."""
|
||||
parser = argparse.ArgumentParser(
|
||||
description="Apply locally extreme days/year and polygon solar GHI to climate-data.csv."
|
||||
)
|
||||
parser.add_argument("--climate-data", type=Path, default=DEFAULT_CLIMATE_DATA)
|
||||
parser.add_argument("--comparison-csv", type=Path, default=DEFAULT_COMPARISON)
|
||||
parser.add_argument("--polygon-solar-ghi-csv", type=Path, default=DEFAULT_POLYGON_SOLAR_GHI)
|
||||
parser.add_argument(
|
||||
"--representative-point-solar-ghi-csv",
|
||||
type=Path,
|
||||
default=DEFAULT_REPRESENTATIVE_POINT_SOLAR_GHI,
|
||||
)
|
||||
parser.add_argument("--out", type=Path, default=DEFAULT_CLIMATE_DATA)
|
||||
return parser.parse_args()
|
||||
|
||||
|
||||
def main() -> None:
|
||||
"""Run the CSV update from parsed command-line arguments."""
|
||||
args = parse_args()
|
||||
apply_locally_extreme_metric(
|
||||
climate_data=args.climate_data,
|
||||
comparison_csv=args.comparison_csv,
|
||||
polygon_solar_ghi_csv=args.polygon_solar_ghi_csv,
|
||||
representative_point_solar_ghi_csv=args.representative_point_solar_ghi_csv,
|
||||
out=args.out,
|
||||
)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@@ -0,0 +1,147 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
Merge NSRDB cloudiness metrics into climate-data.csv.
|
||||
|
||||
Adds:
|
||||
- cloudinessIndexPct
|
||||
|
||||
Polygon area-weighted values are used first when available; representative-point
|
||||
values remain the fallback.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import argparse
|
||||
import csv
|
||||
from pathlib import Path
|
||||
|
||||
|
||||
DEFAULT_CLIMATE_DATA = Path("data/climate-data.csv")
|
||||
DEFAULT_POLYGON_CLOUD_SUMMARY = Path("data/nrel/county_polygon_cloud_summary.csv")
|
||||
DEFAULT_REPRESENTATIVE_POINT_CLOUD_SUMMARY = Path("data/nrel/county_representative_point_cloud_summary.csv")
|
||||
METRIC_FIELD = "cloudinessIndexPct"
|
||||
POLYGON_SOURCE_TAG = "nsrdb-polygon-area-weighted-cloudiness-tmy"
|
||||
REPRESENTATIVE_POINT_SOURCE_TAG = "nsrdb-representative-point-cloudiness-tmy"
|
||||
CLOUD_SOURCE_TAGS = {
|
||||
POLYGON_SOURCE_TAG,
|
||||
REPRESENTATIVE_POINT_SOURCE_TAG,
|
||||
}
|
||||
|
||||
|
||||
def load_cloud_values(path: Path) -> dict[str, str]:
|
||||
"""Read cloudiness values keyed by county FIPS."""
|
||||
values: dict[str, str] = {}
|
||||
with path.open("r", encoding="utf-8-sig", newline="") as handle:
|
||||
reader = csv.DictReader(handle)
|
||||
fieldnames = reader.fieldnames or []
|
||||
required_fields = {"county_fips", METRIC_FIELD}
|
||||
missing_fields = sorted(required_fields - set(fieldnames))
|
||||
if missing_fields:
|
||||
raise ValueError(f"{path} is missing fields: {', '.join(missing_fields)}")
|
||||
|
||||
for row in reader:
|
||||
county_fips = (row.get("county_fips") or "").strip().zfill(5)
|
||||
value = (row.get(METRIC_FIELD) or "").strip()
|
||||
if county_fips and value:
|
||||
values[county_fips] = value
|
||||
return values
|
||||
|
||||
|
||||
def append_source_tag(source: str, tag: str) -> str:
|
||||
"""Append a source tag if it is not already present."""
|
||||
parts = [
|
||||
part.strip()
|
||||
for part in source.split("+")
|
||||
if part.strip() and part.strip() not in CLOUD_SOURCE_TAGS
|
||||
]
|
||||
if tag not in parts:
|
||||
parts.append(tag)
|
||||
return " + ".join(parts)
|
||||
|
||||
|
||||
def ensure_field_after(fieldnames: list[str], field: str, after_field: str | None = None) -> None:
|
||||
"""Insert a field into CSV field order if it is missing."""
|
||||
if field in fieldnames:
|
||||
return
|
||||
if after_field and after_field in fieldnames:
|
||||
fieldnames.insert(fieldnames.index(after_field) + 1, field)
|
||||
return
|
||||
insert_at = fieldnames.index("source") if "source" in fieldnames else len(fieldnames)
|
||||
fieldnames.insert(insert_at, field)
|
||||
|
||||
|
||||
def merge_metric(
|
||||
climate_data: Path,
|
||||
polygon_cloud_summary: Path,
|
||||
representative_point_cloud_summary: Path,
|
||||
) -> tuple[int, int, int, int]:
|
||||
"""Merge cloudiness values into the app climate CSV."""
|
||||
polygon_cloudiness_by_fips = (
|
||||
load_cloud_values(polygon_cloud_summary)
|
||||
if polygon_cloud_summary.exists()
|
||||
else {}
|
||||
)
|
||||
representative_cloudiness_by_fips = load_cloud_values(representative_point_cloud_summary)
|
||||
|
||||
with climate_data.open("r", encoding="utf-8", newline="") as handle:
|
||||
reader = csv.DictReader(handle)
|
||||
fieldnames = list(reader.fieldnames or [])
|
||||
rows = list(reader)
|
||||
|
||||
if "countyFips" not in fieldnames:
|
||||
raise ValueError(f"{climate_data} is missing countyFips")
|
||||
|
||||
ensure_field_after(fieldnames, METRIC_FIELD, "avgSolarGhiKwhM2Day")
|
||||
|
||||
polygon_count = 0
|
||||
representative_count = 0
|
||||
missing_count = 0
|
||||
for row in rows:
|
||||
county_fips = (row.get("countyFips") or "").strip().zfill(5)
|
||||
polygon_value = polygon_cloudiness_by_fips.get(county_fips, "")
|
||||
representative_value = representative_cloudiness_by_fips.get(county_fips, "")
|
||||
value = polygon_value or representative_value
|
||||
row[METRIC_FIELD] = value
|
||||
if polygon_value:
|
||||
row["source"] = append_source_tag(row.get("source", ""), POLYGON_SOURCE_TAG)
|
||||
polygon_count += 1
|
||||
elif representative_value:
|
||||
row["source"] = append_source_tag(row.get("source", ""), REPRESENTATIVE_POINT_SOURCE_TAG)
|
||||
representative_count += 1
|
||||
else:
|
||||
missing_count += 1
|
||||
|
||||
with climate_data.open("w", encoding="utf-8", newline="") as handle:
|
||||
writer = csv.DictWriter(handle, fieldnames=fieldnames)
|
||||
writer.writeheader()
|
||||
writer.writerows(rows)
|
||||
|
||||
return len(rows), polygon_count, representative_count, missing_count
|
||||
|
||||
|
||||
def main() -> int:
|
||||
parser = argparse.ArgumentParser(description="Merge NSRDB cloudiness metric into climate-data.csv.")
|
||||
parser.add_argument("--climate-data", type=Path, default=DEFAULT_CLIMATE_DATA)
|
||||
parser.add_argument("--polygon-cloud-summary", type=Path, default=DEFAULT_POLYGON_CLOUD_SUMMARY)
|
||||
parser.add_argument(
|
||||
"--representative-point-cloud-summary",
|
||||
type=Path,
|
||||
default=DEFAULT_REPRESENTATIVE_POINT_CLOUD_SUMMARY,
|
||||
)
|
||||
args = parser.parse_args()
|
||||
|
||||
row_count, polygon_count, representative_count, missing_count = merge_metric(
|
||||
args.climate_data,
|
||||
args.polygon_cloud_summary,
|
||||
args.representative_point_cloud_summary,
|
||||
)
|
||||
print(f"Updated {args.climate_data} with {METRIC_FIELD}")
|
||||
print(f"Rows: {row_count}")
|
||||
print(f"Polygon area-weighted rows: {polygon_count}")
|
||||
print(f"Representative-point fallback rows: {representative_count}")
|
||||
print(f"Missing {METRIC_FIELD}: {missing_count}")
|
||||
return 0
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
raise SystemExit(main())
|
||||
@@ -0,0 +1,172 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
Add wettest/driest precipitation month metrics to data/climate-data.csv.
|
||||
|
||||
The metrics use the existing monthly NOAA nClimGrid precipitation file. They
|
||||
are categorical month labels derived from 1991-2020 county-area means.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import argparse
|
||||
import csv
|
||||
from pathlib import Path
|
||||
|
||||
import numpy as np
|
||||
import xarray as xr
|
||||
|
||||
from build_county_climate_data import (
|
||||
MONTH_NAMES,
|
||||
_as_monthly_climatology,
|
||||
_extract_grid_2d,
|
||||
_load_counties,
|
||||
_select_data_var,
|
||||
_zonal_mean,
|
||||
)
|
||||
|
||||
|
||||
REPO_ROOT = Path(__file__).resolve().parents[1]
|
||||
DEFAULT_CLIMATE_DATA = REPO_ROOT / "data" / "climate-data.csv"
|
||||
DEFAULT_COUNTIES_GEOJSON = REPO_ROOT / "data" / "geojson-counties-fips.json"
|
||||
DEFAULT_MONTHLY_PRCP_NC = REPO_ROOT / "data" / "noaa" / "nclimgrid" / "nclimgrid_prcp.nc"
|
||||
PRECIP_MONTH_FIELDS = ["wettestPrecipMonth", "driestPrecipMonth"]
|
||||
|
||||
|
||||
def normalize_fips(value: object) -> str:
|
||||
"""Return a 5-digit county FIPS string from mixed text or numeric input."""
|
||||
digits = "".join(ch for ch in str(value).strip() if ch.isdigit())
|
||||
return digits.zfill(5)[-5:] if digits else ""
|
||||
|
||||
|
||||
def read_csv_rows(path: Path) -> tuple[list[str], list[dict]]:
|
||||
"""Read a CSV while preserving the source field order."""
|
||||
with path.open("r", encoding="utf-8-sig", newline="") as csv_file:
|
||||
reader = csv.DictReader(csv_file)
|
||||
if reader.fieldnames is None:
|
||||
raise ValueError(f"{path} has no CSV header.")
|
||||
return list(reader.fieldnames), list(reader)
|
||||
|
||||
|
||||
def fieldnames_with_precip_months(original_fields: list[str]) -> list[str]:
|
||||
"""Place the new fields next to the existing precipitation metrics."""
|
||||
fields = [field for field in original_fields if field not in PRECIP_MONTH_FIELDS]
|
||||
insert_after = "seasonalityIndex"
|
||||
insert_at = fields.index(insert_after) + 1 if insert_after in fields else len(fields)
|
||||
return fields[:insert_at] + PRECIP_MONTH_FIELDS + fields[insert_at:]
|
||||
|
||||
|
||||
def build_precip_month_lookup(
|
||||
*,
|
||||
counties_geojson: Path,
|
||||
monthly_prcp_nc: Path,
|
||||
climatology_start_year: int,
|
||||
climatology_end_year: int,
|
||||
) -> dict[str, tuple[str, str]]:
|
||||
"""Calculate wettest and driest precipitation month for each county."""
|
||||
counties = _load_counties(counties_geojson)
|
||||
monthly_prcp = xr.open_dataset(monthly_prcp_nc, decode_times=True)
|
||||
try:
|
||||
prcp_var = _select_data_var(monthly_prcp, "mlyprcp_norm")
|
||||
prcp_monthly = _as_monthly_climatology(
|
||||
monthly_prcp[prcp_var],
|
||||
start_year=climatology_start_year,
|
||||
end_year=climatology_end_year,
|
||||
)
|
||||
|
||||
prcp_by_month: list[list[float]] = []
|
||||
for month in range(12):
|
||||
prcp_arr, prcp_transform = _extract_grid_2d(prcp_monthly.isel(time=month))
|
||||
prcp_by_month.append(_zonal_mean(prcp_arr, prcp_transform, counties))
|
||||
finally:
|
||||
monthly_prcp.close()
|
||||
|
||||
lookup: dict[str, tuple[str, str]] = {}
|
||||
for idx, row in counties.iterrows():
|
||||
county_fips = str(row["county_fips"])
|
||||
prcp_months_mm = np.array([prcp_by_month[m][idx] for m in range(12)], dtype=np.float64)
|
||||
if not np.any(np.isfinite(prcp_months_mm)):
|
||||
continue
|
||||
|
||||
wettest_month = MONTH_NAMES[int(np.nanargmax(prcp_months_mm))]
|
||||
driest_month = MONTH_NAMES[int(np.nanargmin(prcp_months_mm))]
|
||||
lookup[county_fips] = (wettest_month, driest_month)
|
||||
|
||||
return lookup
|
||||
|
||||
|
||||
def apply_precipitation_month_metrics(
|
||||
*,
|
||||
climate_data: Path,
|
||||
counties_geojson: Path,
|
||||
monthly_prcp_nc: Path,
|
||||
out: Path,
|
||||
climatology_start_year: int,
|
||||
climatology_end_year: int,
|
||||
) -> None:
|
||||
"""Merge precipitation month categories into the app climate CSV."""
|
||||
original_fields, rows = read_csv_rows(climate_data)
|
||||
lookup = build_precip_month_lookup(
|
||||
counties_geojson=counties_geojson,
|
||||
monthly_prcp_nc=monthly_prcp_nc,
|
||||
climatology_start_year=climatology_start_year,
|
||||
climatology_end_year=climatology_end_year,
|
||||
)
|
||||
|
||||
updated_count = 0
|
||||
missing_count = 0
|
||||
for row in rows:
|
||||
county_fips = normalize_fips(row.get("countyFips", ""))
|
||||
values = lookup.get(county_fips)
|
||||
if values:
|
||||
row["wettestPrecipMonth"], row["driestPrecipMonth"] = values
|
||||
updated_count += 1
|
||||
else:
|
||||
row["wettestPrecipMonth"] = ""
|
||||
row["driestPrecipMonth"] = ""
|
||||
missing_count += 1
|
||||
|
||||
source = (row.get("source") or "").strip() or "unknown-source"
|
||||
tag = f"precip-month-extremes=noaa-nclimgrid-monthly-{climatology_start_year}-{climatology_end_year}"
|
||||
if tag not in source:
|
||||
row["source"] = f"{source} + {tag}"
|
||||
|
||||
fieldnames = fieldnames_with_precip_months(original_fields)
|
||||
with out.open("w", encoding="utf-8", newline="") as csv_file:
|
||||
writer = csv.DictWriter(csv_file, fieldnames=fieldnames, extrasaction="ignore")
|
||||
writer.writeheader()
|
||||
writer.writerows(rows)
|
||||
|
||||
print(f"Wrote {len(rows)} rows to {out}")
|
||||
print(f"Updated precipitation month metrics for {updated_count} rows.")
|
||||
print(f"Left precipitation month metrics blank for {missing_count} rows.")
|
||||
|
||||
|
||||
def parse_args() -> argparse.Namespace:
|
||||
"""Parse command-line paths for the precipitation month update."""
|
||||
parser = argparse.ArgumentParser(
|
||||
description="Add wettestPrecipMonth and driestPrecipMonth to climate-data.csv."
|
||||
)
|
||||
parser.add_argument("--climate-data", type=Path, default=DEFAULT_CLIMATE_DATA)
|
||||
parser.add_argument("--counties-geojson", type=Path, default=DEFAULT_COUNTIES_GEOJSON)
|
||||
parser.add_argument("--monthly-prcp-nc", type=Path, default=DEFAULT_MONTHLY_PRCP_NC)
|
||||
parser.add_argument("--out", type=Path, default=DEFAULT_CLIMATE_DATA)
|
||||
parser.add_argument("--climatology-start-year", type=int, default=1991)
|
||||
parser.add_argument("--climatology-end-year", type=int, default=2020)
|
||||
return parser.parse_args()
|
||||
|
||||
|
||||
def main() -> None:
|
||||
"""Run the CSV update."""
|
||||
args = parse_args()
|
||||
apply_precipitation_month_metrics(
|
||||
climate_data=args.climate_data,
|
||||
counties_geojson=args.counties_geojson,
|
||||
monthly_prcp_nc=args.monthly_prcp_nc,
|
||||
out=args.out,
|
||||
climatology_start_year=args.climatology_start_year,
|
||||
climatology_end_year=args.climatology_end_year,
|
||||
)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@@ -0,0 +1,861 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
Build county-level climate records for the web app filters.
|
||||
|
||||
Outputs a CSV file compatible with the browser app:
|
||||
climate-data.csv
|
||||
|
||||
Metrics produced per county:
|
||||
- koppenZone: majority Koppen-Geiger class
|
||||
- avgTempF: annual mean temperature from NOAA 1991-2020 gridded normals
|
||||
- annualPrecipIn: annual total precipitation from NOAA 1991-2020 gridded normals
|
||||
- seasonalityIndex: precipitation seasonality, coefficient of variation of monthly totals (%)
|
||||
- wettestPrecipMonth: month with the highest 1991-2020 county mean precipitation
|
||||
- driestPrecipMonth: month with the lowest 1991-2020 county mean precipitation
|
||||
- extremeDays: count of normal-days with Tmax >= hot threshold or Tmin <= freeze threshold
|
||||
- avgSolarGhiKwhM2Day: annual average daily global horizontal irradiance (GHI), when a solar raster or representative-point CSV is provided
|
||||
|
||||
This script is intended for offline generation of complete county records.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import argparse
|
||||
import csv
|
||||
import json
|
||||
from pathlib import Path
|
||||
from typing import Dict, List, Tuple
|
||||
|
||||
import geopandas as gpd
|
||||
import numpy as np
|
||||
import rasterio
|
||||
from rasterio.features import geometry_mask
|
||||
import xarray as xr
|
||||
|
||||
DEFAULT_COUNTIES_GEOJSON_URL = "https://raw.githubusercontent.com/plotly/datasets/master/geojson-counties-fips.json"
|
||||
|
||||
|
||||
STATE_FIPS_TO_ABBR = {
|
||||
"01": "AL",
|
||||
"02": "AK",
|
||||
"04": "AZ",
|
||||
"05": "AR",
|
||||
"06": "CA",
|
||||
"08": "CO",
|
||||
"09": "CT",
|
||||
"10": "DE",
|
||||
"11": "DC",
|
||||
"12": "FL",
|
||||
"13": "GA",
|
||||
"15": "HI",
|
||||
"16": "ID",
|
||||
"17": "IL",
|
||||
"18": "IN",
|
||||
"19": "IA",
|
||||
"20": "KS",
|
||||
"21": "KY",
|
||||
"22": "LA",
|
||||
"23": "ME",
|
||||
"24": "MD",
|
||||
"25": "MA",
|
||||
"26": "MI",
|
||||
"27": "MN",
|
||||
"28": "MS",
|
||||
"29": "MO",
|
||||
"30": "MT",
|
||||
"31": "NE",
|
||||
"32": "NV",
|
||||
"33": "NH",
|
||||
"34": "NJ",
|
||||
"35": "NM",
|
||||
"36": "NY",
|
||||
"37": "NC",
|
||||
"38": "ND",
|
||||
"39": "OH",
|
||||
"40": "OK",
|
||||
"41": "OR",
|
||||
"42": "PA",
|
||||
"44": "RI",
|
||||
"45": "SC",
|
||||
"46": "SD",
|
||||
"47": "TN",
|
||||
"48": "TX",
|
||||
"49": "UT",
|
||||
"50": "VT",
|
||||
"51": "VA",
|
||||
"53": "WA",
|
||||
"54": "WV",
|
||||
"55": "WI",
|
||||
"56": "WY",
|
||||
"60": "AS",
|
||||
"66": "GU",
|
||||
"69": "MP",
|
||||
"72": "PR",
|
||||
"78": "VI",
|
||||
}
|
||||
|
||||
# Beck et al legend key is expected as text file, but this default handles common codes.
|
||||
DEFAULT_KOPPEN_CODE_MAP = {
|
||||
1: "Af",
|
||||
2: "Am",
|
||||
3: "Aw",
|
||||
4: "BWh",
|
||||
5: "BWk",
|
||||
6: "BSh",
|
||||
7: "BSk",
|
||||
8: "Csa",
|
||||
9: "Csb",
|
||||
10: "Csc",
|
||||
11: "Cwa",
|
||||
12: "Cwb",
|
||||
13: "Cwc",
|
||||
14: "Cfa",
|
||||
15: "Cfb",
|
||||
16: "Cfc",
|
||||
17: "Dsa",
|
||||
18: "Dsb",
|
||||
19: "Dsc",
|
||||
20: "Dsd",
|
||||
21: "Dwa",
|
||||
22: "Dwb",
|
||||
23: "Dwc",
|
||||
24: "Dwd",
|
||||
25: "Dfa",
|
||||
26: "Dfb",
|
||||
27: "Dfc",
|
||||
28: "Dfd",
|
||||
29: "ET",
|
||||
30: "EF",
|
||||
}
|
||||
|
||||
MONTH_NAMES = [
|
||||
"January",
|
||||
"February",
|
||||
"March",
|
||||
"April",
|
||||
"May",
|
||||
"June",
|
||||
"July",
|
||||
"August",
|
||||
"September",
|
||||
"October",
|
||||
"November",
|
||||
"December",
|
||||
]
|
||||
|
||||
|
||||
def _normalize_fips(value: object, width: int) -> str:
|
||||
"""Return a zero-padded FIPS code with the requested width."""
|
||||
text = str(value).strip()
|
||||
digits = "".join(ch for ch in text if ch.isdigit())
|
||||
if not digits:
|
||||
return ""
|
||||
return digits.zfill(width)[-width:]
|
||||
|
||||
|
||||
def _load_counties(counties_geojson: Path) -> gpd.GeoDataFrame:
|
||||
"""Load county polygons and normalize fields used downstream."""
|
||||
if not counties_geojson.exists():
|
||||
try:
|
||||
print(
|
||||
f"County GeoJSON not found at {counties_geojson}. "
|
||||
f"Attempting download from {DEFAULT_COUNTIES_GEOJSON_URL}..."
|
||||
)
|
||||
gdf = gpd.read_file(DEFAULT_COUNTIES_GEOJSON_URL)
|
||||
counties_geojson.parent.mkdir(parents=True, exist_ok=True)
|
||||
# Cache the downloaded file for subsequent runs.
|
||||
gdf.to_file(counties_geojson, driver="GeoJSON")
|
||||
print(f"Downloaded and cached county GeoJSON to {counties_geojson}")
|
||||
except Exception as exc:
|
||||
raise FileNotFoundError(
|
||||
f"County GeoJSON not found at {counties_geojson}, and download from "
|
||||
f"{DEFAULT_COUNTIES_GEOJSON_URL} failed. Download the file manually "
|
||||
"and rerun with --counties-geojson pointing to it."
|
||||
) from exc
|
||||
|
||||
gdf = gpd.read_file(counties_geojson)
|
||||
if gdf.crs is None:
|
||||
gdf = gdf.set_crs("EPSG:4326")
|
||||
else:
|
||||
gdf = gdf.to_crs("EPSG:4326")
|
||||
|
||||
feature_id = None
|
||||
if "id" in gdf.columns:
|
||||
feature_id = gdf["id"]
|
||||
elif "GEOID" in gdf.columns:
|
||||
feature_id = gdf["GEOID"]
|
||||
elif "GEOID10" in gdf.columns:
|
||||
feature_id = gdf["GEOID10"]
|
||||
elif "fips" in gdf.columns:
|
||||
feature_id = gdf["fips"]
|
||||
else:
|
||||
raise ValueError("Unable to locate county FIPS identifier column in county polygons.")
|
||||
|
||||
gdf["county_fips"] = feature_id.map(lambda value: _normalize_fips(value, 5))
|
||||
gdf = gdf[gdf["county_fips"] != ""].copy()
|
||||
|
||||
if "NAME" in gdf.columns:
|
||||
gdf["county_name"] = gdf["NAME"].fillna("").astype(str).str.strip()
|
||||
elif "name" in gdf.columns:
|
||||
gdf["county_name"] = gdf["name"].fillna("").astype(str).str.strip()
|
||||
else:
|
||||
gdf["county_name"] = gdf["county_fips"].map(lambda value: f"County {value}")
|
||||
|
||||
gdf["state_fips"] = gdf["county_fips"].str.slice(0, 2)
|
||||
gdf["state"] = gdf["state_fips"].map(lambda code: STATE_FIPS_TO_ABBR.get(code, f"S{code}"))
|
||||
gdf = gdf.sort_values("county_fips").reset_index(drop=True)
|
||||
return gdf
|
||||
|
||||
|
||||
def _load_koppen_legend(legend_path: Path | None) -> Dict[int, str]:
|
||||
"""Load Koppen raster codes, using defaults when no legend exists."""
|
||||
if legend_path is None:
|
||||
return DEFAULT_KOPPEN_CODE_MAP
|
||||
|
||||
mapping: Dict[int, str] = {}
|
||||
for line in legend_path.read_text(encoding="utf-8").splitlines():
|
||||
text = line.strip()
|
||||
if not text or text.startswith("#"):
|
||||
continue
|
||||
# Handles patterns like:
|
||||
# "1: Af ..." or "1 = Af" or "1 Af"
|
||||
import re
|
||||
|
||||
match = re.match(r"^(\d+)\s*[:=]?\s*([A-Za-z]{2,3})\b", text)
|
||||
if not match:
|
||||
continue
|
||||
|
||||
key = int(match.group(1))
|
||||
value = match.group(2)
|
||||
mapping[key] = value
|
||||
|
||||
return mapping if mapping else DEFAULT_KOPPEN_CODE_MAP
|
||||
|
||||
|
||||
def _select_data_var(dataset: xr.Dataset, preferred: str) -> str:
|
||||
"""Choose the best matching climate variable from a dataset."""
|
||||
if preferred in dataset.data_vars:
|
||||
return preferred
|
||||
|
||||
alias_map = {
|
||||
"mlytavg_norm": ["tavg", "tavg_norm"],
|
||||
"mlyprcp_norm": ["prcp", "prcp_norm"],
|
||||
"dlytmax_norm": ["tmax", "tmax_norm"],
|
||||
"dlytmin_norm": ["tmin", "tmin_norm"],
|
||||
}
|
||||
for alias in alias_map.get(preferred, []):
|
||||
if alias in dataset.data_vars:
|
||||
return alias
|
||||
|
||||
for candidate in dataset.data_vars:
|
||||
if candidate.endswith("_norm"):
|
||||
return candidate
|
||||
|
||||
# If the dataset only has one variable, use it as a final fallback.
|
||||
if len(dataset.data_vars) == 1:
|
||||
return next(iter(dataset.data_vars))
|
||||
|
||||
raise ValueError(
|
||||
f"Unable to select a climate variable. Preferred='{preferred}', available={list(dataset.data_vars)}"
|
||||
)
|
||||
|
||||
|
||||
def _load_solar_ghi_csv(solar_ghi_csv: Path, counties: gpd.GeoDataFrame) -> List[float]:
|
||||
"""Load county-keyed average daily GHI values from a representative-point or area-average CSV."""
|
||||
with solar_ghi_csv.open(newline="", encoding="utf-8") as handle:
|
||||
reader = csv.DictReader(handle)
|
||||
if reader.fieldnames is None:
|
||||
raise ValueError(f"Solar GHI CSV at {solar_ghi_csv} has no header row.")
|
||||
|
||||
if "county_fips" in reader.fieldnames:
|
||||
fips_field = "county_fips"
|
||||
elif "countyFips" in reader.fieldnames:
|
||||
fips_field = "countyFips"
|
||||
else:
|
||||
raise ValueError(
|
||||
f"Solar GHI CSV at {solar_ghi_csv} must include county_fips or countyFips."
|
||||
)
|
||||
|
||||
if "avgSolarGhiKwhM2Day" not in reader.fieldnames:
|
||||
raise ValueError(
|
||||
f"Solar GHI CSV at {solar_ghi_csv} must include avgSolarGhiKwhM2Day."
|
||||
)
|
||||
|
||||
solar_by_fips: Dict[str, float] = {}
|
||||
for row in reader:
|
||||
county_fips = _normalize_fips(row.get(fips_field, ""), 5)
|
||||
raw_value = str(row.get("avgSolarGhiKwhM2Day", "")).strip()
|
||||
if not county_fips or not raw_value:
|
||||
continue
|
||||
try:
|
||||
solar_by_fips[county_fips] = float(raw_value)
|
||||
except ValueError as exc:
|
||||
raise ValueError(
|
||||
f"Invalid avgSolarGhiKwhM2Day value for county {county_fips}: {raw_value}"
|
||||
) from exc
|
||||
|
||||
return [
|
||||
solar_by_fips.get(str(row["county_fips"]), float("nan"))
|
||||
for _, row in counties.iterrows()
|
||||
]
|
||||
|
||||
|
||||
def _as_monthly_climatology(data_array: xr.DataArray, start_year: int, end_year: int) -> xr.DataArray:
|
||||
"""Return 12 monthly normals from slices or a time series."""
|
||||
if "time" not in data_array.dims:
|
||||
raise ValueError(f"Expected a time dimension, got dims={data_array.dims}")
|
||||
|
||||
time_size = int(data_array.sizes.get("time", 0))
|
||||
if time_size == 12:
|
||||
return data_array
|
||||
|
||||
time_index = data_array["time"]
|
||||
if not hasattr(time_index, "dt"):
|
||||
# Attempt CF decoding when time is numeric with units/calendar attrs.
|
||||
try:
|
||||
decoded = xr.decode_cf(xr.Dataset({"_v": data_array}))
|
||||
data_array = decoded["_v"]
|
||||
time_index = data_array["time"]
|
||||
except Exception as exc:
|
||||
raise ValueError(
|
||||
"Time coordinate does not support datetime access for monthly climatology grouping, "
|
||||
"and CF decoding failed."
|
||||
) from exc
|
||||
|
||||
if not hasattr(time_index, "dt"):
|
||||
raise ValueError("Time coordinate does not support datetime access for monthly climatology grouping.")
|
||||
|
||||
period = data_array.where(
|
||||
(time_index.dt.year >= start_year) & (time_index.dt.year <= end_year),
|
||||
drop=True,
|
||||
)
|
||||
if int(period.sizes.get("time", 0)) == 0:
|
||||
raise ValueError(f"No monthly data found between {start_year} and {end_year}.")
|
||||
|
||||
monthly = period.groupby("time.month").mean("time", skipna=True)
|
||||
if int(monthly.sizes.get("month", 0)) != 12:
|
||||
raise ValueError(
|
||||
f"Monthly climatology for {start_year}-{end_year} has {monthly.sizes.get('month', 0)} months; expected 12."
|
||||
)
|
||||
|
||||
# Standardize to a `time` dimension so downstream code can reuse .isel(time=month_idx).
|
||||
monthly = monthly.rename({"month": "time"})
|
||||
monthly = monthly.assign_coords(time=np.arange(1, 13))
|
||||
return monthly
|
||||
|
||||
|
||||
def _infer_time_resolution_days(data_array: xr.DataArray) -> float:
|
||||
"""Estimate the median spacing between time steps in days."""
|
||||
time_values = np.asarray(data_array["time"].values)
|
||||
if time_values.size < 2:
|
||||
return float("inf")
|
||||
|
||||
deltas = np.diff(time_values).astype("timedelta64[D]").astype(np.int64)
|
||||
deltas = deltas[deltas > 0]
|
||||
if deltas.size == 0:
|
||||
return float("inf")
|
||||
return float(np.median(deltas))
|
||||
|
||||
|
||||
def _extract_grid_2d(data_array: xr.DataArray) -> Tuple[np.ndarray, "Affine"]:
|
||||
"""Convert a lat/lon slice to a raster array and transform."""
|
||||
# Expected shape for 2D arrays: lat, lon
|
||||
# Build affine from center coordinates.
|
||||
lon = np.asarray(data_array["lon"].values, dtype=np.float64)
|
||||
lat = np.asarray(data_array["lat"].values, dtype=np.float64)
|
||||
arr = np.asarray(data_array.values, dtype=np.float64)
|
||||
|
||||
if arr.shape != (lat.size, lon.size):
|
||||
raise ValueError(f"Unexpected grid shape {arr.shape}; expected {(lat.size, lon.size)}")
|
||||
|
||||
# Make sure the first row is northernmost to match affine from top-left.
|
||||
if lat[0] < lat[-1]:
|
||||
lat = lat[::-1]
|
||||
arr = arr[::-1, :]
|
||||
|
||||
x_res = abs(lon[1] - lon[0])
|
||||
y_res = abs(lat[0] - lat[1])
|
||||
|
||||
from affine import Affine
|
||||
|
||||
top_left_x = lon.min() - (x_res / 2.0)
|
||||
top_left_y = lat.max() + (y_res / 2.0)
|
||||
transform = Affine.translation(top_left_x, top_left_y) * Affine.scale(x_res, -y_res)
|
||||
return arr, transform
|
||||
|
||||
|
||||
def _zonal_mean(arr: np.ndarray, transform, counties: gpd.GeoDataFrame) -> List[float]:
|
||||
"""Calculate the mean raster value inside each county polygon."""
|
||||
values = np.asarray(arr, dtype=np.float64)
|
||||
means: List[float] = []
|
||||
for geometry in counties.geometry:
|
||||
mask = geometry_mask(
|
||||
[geometry.__geo_interface__],
|
||||
out_shape=values.shape,
|
||||
transform=transform,
|
||||
invert=True,
|
||||
all_touched=True,
|
||||
)
|
||||
selected = values[mask]
|
||||
selected = selected[np.isfinite(selected)]
|
||||
means.append(float(selected.mean()) if selected.size else float("nan"))
|
||||
return means
|
||||
|
||||
|
||||
def _zonal_mean_raster(raster_path: Path, counties: gpd.GeoDataFrame) -> List[float]:
|
||||
"""Calculate the mean raster-file value for each county."""
|
||||
with rasterio.open(raster_path) as source:
|
||||
raster_counties = counties
|
||||
if source.crs is not None and counties.crs is not None and counties.crs != source.crs:
|
||||
raster_counties = counties.to_crs(source.crs)
|
||||
|
||||
data = source.read(1, masked=True)
|
||||
values = np.asarray(data.filled(np.nan), dtype=np.float64)
|
||||
if source.nodata is not None:
|
||||
values = np.where(values == source.nodata, np.nan, values)
|
||||
return _zonal_mean(values, source.transform, raster_counties)
|
||||
|
||||
|
||||
def _zonal_majority_class(koppen_raster: Path, counties: gpd.GeoDataFrame, code_map: Dict[int, str]) -> List[str]:
|
||||
"""Assign each county its most common Koppen-Geiger class."""
|
||||
classes: List[str] = []
|
||||
with rasterio.open(koppen_raster) as source:
|
||||
raster_counties = counties
|
||||
if source.crs is not None and counties.crs is not None and counties.crs != source.crs:
|
||||
raster_counties = counties.to_crs(source.crs)
|
||||
|
||||
data = source.read(1, masked=True)
|
||||
values = np.asarray(data.filled(0))
|
||||
if source.nodata is not None:
|
||||
values = np.where(values == source.nodata, 0, values)
|
||||
|
||||
for geometry in raster_counties.geometry:
|
||||
mask = geometry_mask(
|
||||
[geometry.__geo_interface__],
|
||||
out_shape=values.shape,
|
||||
transform=source.transform,
|
||||
invert=True,
|
||||
all_touched=True,
|
||||
)
|
||||
selected = values[mask]
|
||||
selected = selected[selected != 0]
|
||||
if selected.size == 0:
|
||||
classes.append("Cfa")
|
||||
continue
|
||||
|
||||
codes, counts = np.unique(selected.astype(np.int64), return_counts=True)
|
||||
code_int = int(codes[int(np.argmax(counts))])
|
||||
classes.append(code_map.get(code_int, "Cfa"))
|
||||
return classes
|
||||
|
||||
|
||||
def _compute_extreme_days(
|
||||
tmax_daily: xr.Dataset,
|
||||
tmin_daily: xr.Dataset,
|
||||
counties: gpd.GeoDataFrame,
|
||||
hot_threshold_c: float,
|
||||
freeze_threshold_c: float,
|
||||
) -> List[float]:
|
||||
"""Count daily hot or freezing days for each county."""
|
||||
tmax_var = _select_data_var(tmax_daily, "dlytmax_norm")
|
||||
tmin_var = _select_data_var(tmin_daily, "dlytmin_norm")
|
||||
|
||||
tmax = tmax_daily[tmax_var]
|
||||
tmin = tmin_daily[tmin_var]
|
||||
|
||||
# If datasets differ slightly in day count, use the overlapping day count.
|
||||
day_count = int(min(tmax.sizes["time"], tmin.sizes["time"]))
|
||||
totals = np.zeros(len(counties), dtype=np.float64)
|
||||
|
||||
for day in range(day_count):
|
||||
tmax_arr, tmax_transform = _extract_grid_2d(tmax.isel(time=day))
|
||||
tmin_arr, tmin_transform = _extract_grid_2d(tmin.isel(time=day))
|
||||
|
||||
if tmax_transform != tmin_transform:
|
||||
raise ValueError("Daily tmax/tmin grids do not share the same transform.")
|
||||
|
||||
day_tmax = _zonal_mean(tmax_arr, tmax_transform, counties)
|
||||
day_tmin = _zonal_mean(tmin_arr, tmin_transform, counties)
|
||||
|
||||
for idx, (mx, mn) in enumerate(zip(day_tmax, day_tmin)):
|
||||
if np.isnan(mx) or np.isnan(mn):
|
||||
continue
|
||||
if mx >= hot_threshold_c or mn <= freeze_threshold_c:
|
||||
totals[idx] += 1.0
|
||||
|
||||
return totals.tolist()
|
||||
|
||||
|
||||
def _compute_extreme_days_monthly_proxy(
|
||||
tmax_monthly: xr.DataArray,
|
||||
tmin_monthly: xr.DataArray,
|
||||
counties: gpd.GeoDataFrame,
|
||||
hot_threshold_c: float,
|
||||
freeze_threshold_c: float,
|
||||
) -> List[float]:
|
||||
"""Estimate extreme days from monthly tmax and tmin means."""
|
||||
if int(tmax_monthly.sizes.get("time", 0)) != 12 or int(tmin_monthly.sizes.get("time", 0)) != 12:
|
||||
raise ValueError("Monthly proxy for extremeDays requires 12 monthly slices for tmax and tmin.")
|
||||
|
||||
month_days = np.array([31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31], dtype=np.float64)
|
||||
totals = np.zeros(len(counties), dtype=np.float64)
|
||||
|
||||
for month in range(12):
|
||||
tmax_arr, tmax_transform = _extract_grid_2d(tmax_monthly.isel(time=month))
|
||||
tmin_arr, tmin_transform = _extract_grid_2d(tmin_monthly.isel(time=month))
|
||||
if tmax_transform != tmin_transform:
|
||||
raise ValueError("Monthly proxy tmax/tmin grids do not share the same transform.")
|
||||
|
||||
month_tmax = _zonal_mean(tmax_arr, tmax_transform, counties)
|
||||
month_tmin = _zonal_mean(tmin_arr, tmin_transform, counties)
|
||||
|
||||
for idx, (mx, mn) in enumerate(zip(month_tmax, month_tmin)):
|
||||
if np.isnan(mx) or np.isnan(mn):
|
||||
continue
|
||||
if mx >= hot_threshold_c or mn <= freeze_threshold_c:
|
||||
totals[idx] += month_days[month]
|
||||
|
||||
return totals.tolist()
|
||||
|
||||
|
||||
def _precip_month_extremes(prcp_months_mm: np.ndarray) -> tuple[str | None, str | None]:
|
||||
"""Return wettest and driest month names from 12 monthly precipitation totals."""
|
||||
valid_mask = np.isfinite(prcp_months_mm)
|
||||
if not np.any(valid_mask):
|
||||
return None, None
|
||||
|
||||
comparable = np.where(valid_mask, prcp_months_mm, np.nan)
|
||||
wettest_month = MONTH_NAMES[int(np.nanargmax(comparable))]
|
||||
driest_month = MONTH_NAMES[int(np.nanargmin(comparable))]
|
||||
return wettest_month, driest_month
|
||||
|
||||
|
||||
def build_county_records(
|
||||
counties_geojson: Path,
|
||||
koppen_raster: Path,
|
||||
koppen_legend: Path | None,
|
||||
monthly_tavg_nc: Path,
|
||||
monthly_prcp_nc: Path,
|
||||
daily_tmax_nc: Path,
|
||||
daily_tmin_nc: Path,
|
||||
hot_threshold_f: float,
|
||||
freeze_threshold_f: float,
|
||||
climatology_start_year: int,
|
||||
climatology_end_year: int,
|
||||
extreme_days_mode: str,
|
||||
solar_ghi_raster: Path | None,
|
||||
solar_ghi_csv: Path | None,
|
||||
) -> Dict[str, dict]:
|
||||
"""Build county climate records consumed by the web app."""
|
||||
counties = _load_counties(counties_geojson)
|
||||
|
||||
koppen_classes = _zonal_majority_class(koppen_raster, counties, _load_koppen_legend(koppen_legend))
|
||||
|
||||
monthly_tavg = xr.open_dataset(monthly_tavg_nc, decode_times=True)
|
||||
monthly_prcp = xr.open_dataset(monthly_prcp_nc, decode_times=True)
|
||||
daily_tmax = xr.open_dataset(daily_tmax_nc, decode_times=True)
|
||||
daily_tmin = xr.open_dataset(daily_tmin_nc, decode_times=True)
|
||||
|
||||
tavg_var = _select_data_var(monthly_tavg, "mlytavg_norm")
|
||||
prcp_var = _select_data_var(monthly_prcp, "mlyprcp_norm")
|
||||
|
||||
tavg_monthly = _as_monthly_climatology(
|
||||
monthly_tavg[tavg_var],
|
||||
start_year=climatology_start_year,
|
||||
end_year=climatology_end_year,
|
||||
)
|
||||
prcp_monthly = _as_monthly_climatology(
|
||||
monthly_prcp[prcp_var],
|
||||
start_year=climatology_start_year,
|
||||
end_year=climatology_end_year,
|
||||
)
|
||||
|
||||
tavg_by_month: List[List[float]] = []
|
||||
prcp_by_month_mm: List[List[float]] = []
|
||||
for month in range(12):
|
||||
tavg_arr, tavg_transform = _extract_grid_2d(tavg_monthly.isel(time=month))
|
||||
prcp_arr, prcp_transform = _extract_grid_2d(prcp_monthly.isel(time=month))
|
||||
|
||||
if tavg_transform != prcp_transform:
|
||||
raise ValueError("Monthly tavg/prcp grids do not share the same transform.")
|
||||
|
||||
tavg_by_month.append(_zonal_mean(tavg_arr, tavg_transform, counties))
|
||||
prcp_by_month_mm.append(_zonal_mean(prcp_arr, prcp_transform, counties))
|
||||
|
||||
hot_threshold_c = (hot_threshold_f - 32.0) * (5.0 / 9.0)
|
||||
freeze_threshold_c = (freeze_threshold_f - 32.0) * (5.0 / 9.0)
|
||||
|
||||
tmax_var = _select_data_var(daily_tmax, "dlytmax_norm")
|
||||
tmin_var = _select_data_var(daily_tmin, "dlytmin_norm")
|
||||
tmax_da = daily_tmax[tmax_var]
|
||||
tmin_da = daily_tmin[tmin_var]
|
||||
resolution_days = min(_infer_time_resolution_days(tmax_da), _infer_time_resolution_days(tmin_da))
|
||||
|
||||
# Daily-like input (true daily normals or daily grids).
|
||||
if resolution_days <= 2.0:
|
||||
extreme_days = _compute_extreme_days(
|
||||
daily_tmax=daily_tmax,
|
||||
daily_tmin=daily_tmin,
|
||||
counties=counties,
|
||||
hot_threshold_c=hot_threshold_c,
|
||||
freeze_threshold_c=freeze_threshold_c,
|
||||
)
|
||||
extreme_days_source_tag = "daily"
|
||||
else:
|
||||
if extreme_days_mode == "require-daily":
|
||||
raise ValueError(
|
||||
"Extreme-days inputs appear to be monthly series, but --extreme-days-mode=require-daily was set."
|
||||
)
|
||||
|
||||
tmax_monthly = _as_monthly_climatology(
|
||||
tmax_da,
|
||||
start_year=climatology_start_year,
|
||||
end_year=climatology_end_year,
|
||||
)
|
||||
tmin_monthly = _as_monthly_climatology(
|
||||
tmin_da,
|
||||
start_year=climatology_start_year,
|
||||
end_year=climatology_end_year,
|
||||
)
|
||||
extreme_days = _compute_extreme_days_monthly_proxy(
|
||||
tmax_monthly=tmax_monthly,
|
||||
tmin_monthly=tmin_monthly,
|
||||
counties=counties,
|
||||
hot_threshold_c=hot_threshold_c,
|
||||
freeze_threshold_c=freeze_threshold_c,
|
||||
)
|
||||
extreme_days_source_tag = "monthly-proxy"
|
||||
|
||||
if solar_ghi_raster is not None and solar_ghi_raster.exists():
|
||||
solar_ghi_kwh_m2_day = _zonal_mean_raster(solar_ghi_raster, counties)
|
||||
solar_source_tag = "solar-ghi-raster"
|
||||
elif solar_ghi_csv is not None and solar_ghi_csv.exists():
|
||||
solar_ghi_kwh_m2_day = _load_solar_ghi_csv(solar_ghi_csv, counties)
|
||||
solar_source_tag = "solar-ghi-representative-point"
|
||||
else:
|
||||
if solar_ghi_raster is not None:
|
||||
print(f"Solar GHI raster not found at {solar_ghi_raster}; leaving avgSolarGhiKwhM2Day blank.")
|
||||
if solar_ghi_csv is not None:
|
||||
print(f"Solar GHI CSV not found at {solar_ghi_csv}; leaving avgSolarGhiKwhM2Day blank.")
|
||||
solar_ghi_kwh_m2_day = [float("nan")] * len(counties)
|
||||
solar_source_tag = "no-solar-ghi-source"
|
||||
|
||||
records: Dict[str, dict] = {}
|
||||
missing_numeric_count = 0
|
||||
for idx, row in counties.iterrows():
|
||||
county_fips = row["county_fips"]
|
||||
county_name = row["county_name"]
|
||||
state = row["state"]
|
||||
koppen_zone = koppen_classes[idx]
|
||||
|
||||
temp_months_c = np.array([tavg_by_month[m][idx] for m in range(12)], dtype=np.float64)
|
||||
prcp_months_mm = np.array([prcp_by_month_mm[m][idx] for m in range(12)], dtype=np.float64)
|
||||
|
||||
# Guard against nodata counties (outside CONUS grids, islands, etc.).
|
||||
valid_temp = temp_months_c[np.isfinite(temp_months_c)]
|
||||
valid_prcp = prcp_months_mm[np.isfinite(prcp_months_mm)]
|
||||
|
||||
avg_temp_c = np.nanmean(valid_temp) if valid_temp.size else np.nan
|
||||
annual_prcp_mm = np.nansum(valid_prcp) if valid_prcp.size else np.nan
|
||||
|
||||
# Precipitation seasonality as coefficient of variation, capped 0-100.
|
||||
if valid_prcp.size:
|
||||
mean_prcp = float(np.nanmean(valid_prcp))
|
||||
std_prcp = float(np.nanstd(valid_prcp))
|
||||
seasonality = 0.0 if mean_prcp <= 0 else max(0.0, min(100.0, (std_prcp / mean_prcp) * 100.0))
|
||||
else:
|
||||
seasonality = np.nan
|
||||
wettest_precip_month, driest_precip_month = _precip_month_extremes(prcp_months_mm)
|
||||
|
||||
use_missing_temp = not np.isfinite(avg_temp_c)
|
||||
use_missing_prcp = not np.isfinite(annual_prcp_mm)
|
||||
use_missing_seasonality = not np.isfinite(seasonality)
|
||||
use_missing_base_noaa = use_missing_temp or use_missing_prcp or use_missing_seasonality
|
||||
use_missing_extreme = use_missing_base_noaa or not np.isfinite(extreme_days[idx])
|
||||
used_any_missing_numeric = (
|
||||
use_missing_temp or use_missing_prcp or use_missing_seasonality or use_missing_extreme
|
||||
)
|
||||
|
||||
if used_any_missing_numeric:
|
||||
missing_numeric_count += 1
|
||||
|
||||
avg_temp_f = (
|
||||
round((float(avg_temp_c) * 9.0 / 5.0) + 32.0, 1)
|
||||
if not use_missing_temp
|
||||
else None
|
||||
)
|
||||
annual_prcp_in = (
|
||||
round(float(annual_prcp_mm) / 25.4, 1)
|
||||
if not use_missing_prcp
|
||||
else None
|
||||
)
|
||||
seasonality_idx = (
|
||||
int(round(float(seasonality)))
|
||||
if not use_missing_seasonality
|
||||
else None
|
||||
)
|
||||
extreme_days_value = (
|
||||
int(round(float(extreme_days[idx])))
|
||||
if not use_missing_extreme
|
||||
else None
|
||||
)
|
||||
avg_solar_ghi_value = (
|
||||
round(float(solar_ghi_kwh_m2_day[idx]), 2)
|
||||
if np.isfinite(solar_ghi_kwh_m2_day[idx])
|
||||
else None
|
||||
)
|
||||
|
||||
source_suffix = " + missing-noaa-numeric" if used_any_missing_numeric else ""
|
||||
solar_source_suffix = "" if avg_solar_ghi_value is not None else " + missing-solar-ghi"
|
||||
|
||||
record = {
|
||||
"countyName": county_name,
|
||||
"state": state,
|
||||
"koppenZone": koppen_zone,
|
||||
"avgTempF": avg_temp_f,
|
||||
"annualPrecipIn": annual_prcp_in,
|
||||
"seasonalityIndex": seasonality_idx,
|
||||
"wettestPrecipMonth": wettest_precip_month,
|
||||
"driestPrecipMonth": driest_precip_month,
|
||||
"extremeDays": extreme_days_value,
|
||||
"avgSolarGhiKwhM2Day": avg_solar_ghi_value,
|
||||
"source": (
|
||||
"kg-beck2023 + noaa-nclimgrid-1991-2020 "
|
||||
f"({extreme_days_source_tag}) + {solar_source_tag}{source_suffix}{solar_source_suffix}"
|
||||
)
|
||||
}
|
||||
records[county_fips] = record
|
||||
|
||||
monthly_tavg.close()
|
||||
monthly_prcp.close()
|
||||
daily_tmax.close()
|
||||
daily_tmin.close()
|
||||
if missing_numeric_count:
|
||||
print(
|
||||
f"Marked missing numeric NOAA values for {missing_numeric_count} counties "
|
||||
"(likely outside NOAA grid coverage)."
|
||||
)
|
||||
return records
|
||||
|
||||
|
||||
def write_csv(records: Dict[str, dict], out_file: Path) -> None:
|
||||
"""Write county records to the browser-loaded CSV payload."""
|
||||
fields = [
|
||||
"countyFips",
|
||||
"countyName",
|
||||
"state",
|
||||
"koppenZone",
|
||||
"avgTempF",
|
||||
"annualPrecipIn",
|
||||
"seasonalityIndex",
|
||||
"wettestPrecipMonth",
|
||||
"driestPrecipMonth",
|
||||
"extremeDays",
|
||||
"avgSolarGhiKwhM2Day",
|
||||
"source",
|
||||
]
|
||||
|
||||
with out_file.open("w", encoding="utf-8", newline="") as csv_file:
|
||||
writer = csv.DictWriter(csv_file, fieldnames=fields, extrasaction="ignore")
|
||||
writer.writeheader()
|
||||
for county_fips in sorted(records):
|
||||
row = {"countyFips": county_fips}
|
||||
row.update(records[county_fips])
|
||||
writer.writerow(row)
|
||||
|
||||
|
||||
def parse_args() -> argparse.Namespace:
|
||||
"""Define and parse command-line options for this generator."""
|
||||
parser = argparse.ArgumentParser(description="Generate county climate records for the web app.")
|
||||
parser.add_argument("--counties-geojson", type=Path, required=True, help="County polygon GeoJSON path.")
|
||||
parser.add_argument("--koppen-raster", type=Path, required=True, help="Koppen-Geiger raster TIFF path.")
|
||||
parser.add_argument("--koppen-legend", type=Path, default=None, help="Optional legend.txt mapping integer codes.")
|
||||
parser.add_argument(
|
||||
"--monthly-tavg-nc",
|
||||
type=Path,
|
||||
required=True,
|
||||
help="NOAA monthly tavg netCDF (12-slice normals or monthly time-series).",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--monthly-prcp-nc",
|
||||
type=Path,
|
||||
required=True,
|
||||
help="NOAA monthly prcp netCDF (12-slice normals or monthly time-series).",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--daily-tmax-nc",
|
||||
type=Path,
|
||||
required=True,
|
||||
help="NOAA tmax netCDF (daily normals/grids preferred; monthly time-series allowed in proxy mode).",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--daily-tmin-nc",
|
||||
type=Path,
|
||||
required=True,
|
||||
help="NOAA tmin netCDF (daily normals/grids preferred; monthly time-series allowed in proxy mode).",
|
||||
)
|
||||
parser.add_argument("--hot-threshold-f", type=float, default=95.0, help="Hot day threshold in Fahrenheit.")
|
||||
parser.add_argument("--freeze-threshold-f", type=float, default=32.0, help="Freeze day threshold in Fahrenheit.")
|
||||
parser.add_argument(
|
||||
"--climatology-start-year",
|
||||
type=int,
|
||||
default=1991,
|
||||
help="Start year (inclusive) for monthly climatology calculation when time series files are provided.",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--climatology-end-year",
|
||||
type=int,
|
||||
default=2020,
|
||||
help="End year (inclusive) for monthly climatology calculation when time series files are provided.",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--extreme-days-mode",
|
||||
choices=["auto", "require-daily"],
|
||||
default="auto",
|
||||
help="`auto` allows monthly-proxy extremeDays if daily grids are not provided; `require-daily` enforces daily input.",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--solar-ghi-raster",
|
||||
type=Path,
|
||||
default=None,
|
||||
help="Optional raster of annual average daily GHI in kWh/m2/day for avgSolarGhiKwhM2Day.",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--solar-ghi-csv",
|
||||
type=Path,
|
||||
default=None,
|
||||
help=(
|
||||
"Optional county CSV with avgSolarGhiKwhM2Day. Used as a representative-point fallback "
|
||||
"when --solar-ghi-raster is not supplied."
|
||||
),
|
||||
)
|
||||
parser.add_argument("--out", type=Path, default=Path("data/climate-data.csv"), help="Output CSV file path.")
|
||||
return parser.parse_args()
|
||||
|
||||
|
||||
def main() -> None:
|
||||
"""Run the ETL flow and write the final CSV file."""
|
||||
args = parse_args()
|
||||
records = build_county_records(
|
||||
counties_geojson=args.counties_geojson,
|
||||
koppen_raster=args.koppen_raster,
|
||||
koppen_legend=args.koppen_legend,
|
||||
monthly_tavg_nc=args.monthly_tavg_nc,
|
||||
monthly_prcp_nc=args.monthly_prcp_nc,
|
||||
daily_tmax_nc=args.daily_tmax_nc,
|
||||
daily_tmin_nc=args.daily_tmin_nc,
|
||||
hot_threshold_f=args.hot_threshold_f,
|
||||
freeze_threshold_f=args.freeze_threshold_f,
|
||||
climatology_start_year=args.climatology_start_year,
|
||||
climatology_end_year=args.climatology_end_year,
|
||||
extreme_days_mode=args.extreme_days_mode,
|
||||
solar_ghi_raster=args.solar_ghi_raster,
|
||||
solar_ghi_csv=args.solar_ghi_csv,
|
||||
)
|
||||
|
||||
write_csv(records=records, out_file=args.out)
|
||||
print(f"Wrote {len(records)} county records to {args.out}")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@@ -0,0 +1,191 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
Build county-level average diurnal temperature range from NOAA nClimGrid-Daily.
|
||||
|
||||
The output is an ETL artifact used to keep climate-data.csv lean:
|
||||
|
||||
data/noaa/county_diurnal_temperature_range.csv
|
||||
|
||||
Metric definition:
|
||||
- avgDiurnalTempRangeF: mean daily county Tmax - Tmin, expressed as a
|
||||
Fahrenheit temperature difference, over the selected analysis period.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import argparse
|
||||
import csv
|
||||
from dataclasses import dataclass
|
||||
from pathlib import Path
|
||||
|
||||
from build_county_locally_extreme_data import (
|
||||
DEFAULT_STATE_CROSSWALK,
|
||||
DEFAULT_TMAX_DIR,
|
||||
DEFAULT_TMIN_DIR,
|
||||
load_state_crosswalk,
|
||||
require_monthly_file,
|
||||
rows_by_fips,
|
||||
)
|
||||
|
||||
|
||||
REPO_ROOT = Path(__file__).resolve().parents[1]
|
||||
DEFAULT_OUT = REPO_ROOT / "data" / "noaa" / "county_diurnal_temperature_range.csv"
|
||||
SOURCE_TAG = "noaa-nclimgrid-daily-county-area-averages-scaled"
|
||||
|
||||
|
||||
@dataclass
|
||||
class DiurnalRangeTotals:
|
||||
"""Running total for one county's daily Tmax-Tmin differences."""
|
||||
|
||||
noaa_region_code: str = ""
|
||||
county_name: str = ""
|
||||
total_range_c: float = 0.0
|
||||
valid_days: int = 0
|
||||
skipped_negative_days: int = 0
|
||||
|
||||
|
||||
def c_delta_to_f_delta(value_c: float) -> float:
|
||||
"""Convert a Celsius temperature difference to a Fahrenheit difference."""
|
||||
return value_c * 9.0 / 5.0
|
||||
|
||||
|
||||
def build_diurnal_temperature_range(
|
||||
*,
|
||||
start_year: int,
|
||||
end_year: int,
|
||||
tmax_dir: Path,
|
||||
tmin_dir: Path,
|
||||
state_crosswalk_path: Path,
|
||||
) -> dict[str, DiurnalRangeTotals]:
|
||||
"""Average daily county Tmax-Tmin over the requested analysis years."""
|
||||
state_crosswalk = load_state_crosswalk(state_crosswalk_path)
|
||||
totals: dict[str, DiurnalRangeTotals] = {}
|
||||
|
||||
for year in range(start_year, end_year + 1):
|
||||
print(f"Reading daily Tmax/Tmin for {year}...")
|
||||
for month in range(1, 13):
|
||||
tmax_path = require_monthly_file(tmax_dir, "tmax", year, month)
|
||||
tmin_path = require_monthly_file(tmin_dir, "tmin", year, month)
|
||||
tmax_rows = rows_by_fips(
|
||||
tmax_path,
|
||||
year=year,
|
||||
month=month,
|
||||
state_crosswalk=state_crosswalk,
|
||||
)
|
||||
tmin_rows = rows_by_fips(
|
||||
tmin_path,
|
||||
year=year,
|
||||
month=month,
|
||||
state_crosswalk=state_crosswalk,
|
||||
)
|
||||
|
||||
for county_fips in sorted(set(tmax_rows) & set(tmin_rows)):
|
||||
tmax_record = tmax_rows[county_fips]
|
||||
tmin_record = tmin_rows[county_fips]
|
||||
county_totals = totals.setdefault(
|
||||
county_fips,
|
||||
DiurnalRangeTotals(
|
||||
noaa_region_code=tmax_record.region_code,
|
||||
county_name=tmax_record.county_name,
|
||||
),
|
||||
)
|
||||
|
||||
for tmax_value, tmin_value in zip(tmax_record.values, tmin_record.values):
|
||||
if tmax_value is None or tmin_value is None:
|
||||
continue
|
||||
daily_range_c = tmax_value - tmin_value
|
||||
if daily_range_c < 0:
|
||||
county_totals.skipped_negative_days += 1
|
||||
continue
|
||||
county_totals.total_range_c += daily_range_c
|
||||
county_totals.valid_days += 1
|
||||
|
||||
return totals
|
||||
|
||||
|
||||
def write_diurnal_temperature_range_csv(
|
||||
*,
|
||||
path: Path,
|
||||
totals: dict[str, DiurnalRangeTotals],
|
||||
start_year: int,
|
||||
end_year: int,
|
||||
) -> None:
|
||||
"""Write county DTR summary rows."""
|
||||
path.parent.mkdir(parents=True, exist_ok=True)
|
||||
fieldnames = [
|
||||
"countyFips",
|
||||
"noaaRegionCode",
|
||||
"countyName",
|
||||
"avgDiurnalTempRangeC",
|
||||
"avgDiurnalTempRangeF",
|
||||
"validDays",
|
||||
"skippedNegativeDays",
|
||||
"analysisStartYear",
|
||||
"analysisEndYear",
|
||||
"source",
|
||||
]
|
||||
|
||||
with path.open("w", encoding="utf-8", newline="") as csv_file:
|
||||
writer = csv.DictWriter(csv_file, fieldnames=fieldnames)
|
||||
writer.writeheader()
|
||||
for county_fips in sorted(totals):
|
||||
county_totals = totals[county_fips]
|
||||
avg_range_c = (
|
||||
county_totals.total_range_c / county_totals.valid_days
|
||||
if county_totals.valid_days
|
||||
else None
|
||||
)
|
||||
writer.writerow(
|
||||
{
|
||||
"countyFips": county_fips,
|
||||
"noaaRegionCode": county_totals.noaa_region_code,
|
||||
"countyName": county_totals.county_name,
|
||||
"avgDiurnalTempRangeC": f"{avg_range_c:.2f}" if avg_range_c is not None else "",
|
||||
"avgDiurnalTempRangeF": (
|
||||
f"{c_delta_to_f_delta(avg_range_c):.2f}" if avg_range_c is not None else ""
|
||||
),
|
||||
"validDays": county_totals.valid_days,
|
||||
"skippedNegativeDays": county_totals.skipped_negative_days,
|
||||
"analysisStartYear": start_year,
|
||||
"analysisEndYear": end_year,
|
||||
"source": SOURCE_TAG,
|
||||
}
|
||||
)
|
||||
|
||||
print(f"Wrote {len(totals)} county DTR rows to {path}")
|
||||
|
||||
|
||||
def parse_args() -> argparse.Namespace:
|
||||
"""Parse command-line arguments."""
|
||||
parser = argparse.ArgumentParser(
|
||||
description="Build county average diurnal temperature range from cached NOAA Tmax/Tmin files."
|
||||
)
|
||||
parser.add_argument("--start-year", type=int, default=1991)
|
||||
parser.add_argument("--end-year", type=int, default=2020)
|
||||
parser.add_argument("--tmax-dir", type=Path, default=DEFAULT_TMAX_DIR)
|
||||
parser.add_argument("--tmin-dir", type=Path, default=DEFAULT_TMIN_DIR)
|
||||
parser.add_argument("--state-crosswalk", type=Path, default=DEFAULT_STATE_CROSSWALK)
|
||||
parser.add_argument("--out", type=Path, default=DEFAULT_OUT)
|
||||
return parser.parse_args()
|
||||
|
||||
|
||||
def main() -> None:
|
||||
"""Run the DTR ETL."""
|
||||
args = parse_args()
|
||||
totals = build_diurnal_temperature_range(
|
||||
start_year=args.start_year,
|
||||
end_year=args.end_year,
|
||||
tmax_dir=args.tmax_dir,
|
||||
tmin_dir=args.tmin_dir,
|
||||
state_crosswalk_path=args.state_crosswalk,
|
||||
)
|
||||
write_diurnal_temperature_range_csv(
|
||||
path=args.out,
|
||||
totals=totals,
|
||||
start_year=args.start_year,
|
||||
end_year=args.end_year,
|
||||
)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
File diff suppressed because it is too large
Load Diff
@@ -0,0 +1,150 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
Build representative county points for NSRDB point-based solar sampling.
|
||||
|
||||
The output CSV is intended as input to fetch_nsrdb_representative_point_ghi.py.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import argparse
|
||||
import csv
|
||||
from pathlib import Path
|
||||
|
||||
import geopandas as gpd
|
||||
|
||||
|
||||
DEFAULT_COUNTIES_GEOJSON = Path("data/geojson-counties-fips.json")
|
||||
DEFAULT_OUTPUT_CSV = Path("data/nrel/county_representative_points.csv")
|
||||
|
||||
STATE_FIPS_TO_ABBR = {
|
||||
"01": "AL",
|
||||
"02": "AK",
|
||||
"04": "AZ",
|
||||
"05": "AR",
|
||||
"06": "CA",
|
||||
"08": "CO",
|
||||
"09": "CT",
|
||||
"10": "DE",
|
||||
"11": "DC",
|
||||
"12": "FL",
|
||||
"13": "GA",
|
||||
"15": "HI",
|
||||
"16": "ID",
|
||||
"17": "IL",
|
||||
"18": "IN",
|
||||
"19": "IA",
|
||||
"20": "KS",
|
||||
"21": "KY",
|
||||
"22": "LA",
|
||||
"23": "ME",
|
||||
"24": "MD",
|
||||
"25": "MA",
|
||||
"26": "MI",
|
||||
"27": "MN",
|
||||
"28": "MS",
|
||||
"29": "MO",
|
||||
"30": "MT",
|
||||
"31": "NE",
|
||||
"32": "NV",
|
||||
"33": "NH",
|
||||
"34": "NJ",
|
||||
"35": "NM",
|
||||
"36": "NY",
|
||||
"37": "NC",
|
||||
"38": "ND",
|
||||
"39": "OH",
|
||||
"40": "OK",
|
||||
"41": "OR",
|
||||
"42": "PA",
|
||||
"44": "RI",
|
||||
"45": "SC",
|
||||
"46": "SD",
|
||||
"47": "TN",
|
||||
"48": "TX",
|
||||
"49": "UT",
|
||||
"50": "VT",
|
||||
"51": "VA",
|
||||
"53": "WA",
|
||||
"54": "WV",
|
||||
"55": "WI",
|
||||
"56": "WY",
|
||||
"60": "AS",
|
||||
"66": "GU",
|
||||
"69": "MP",
|
||||
"72": "PR",
|
||||
"78": "VI",
|
||||
}
|
||||
|
||||
|
||||
def normalize_fips(value: object, width: int) -> str:
|
||||
"""Return a zero-padded FIPS string from a mixed text or numeric value."""
|
||||
digits = "".join(character for character in str(value).strip() if character.isdigit())
|
||||
return digits.zfill(width)[-width:] if digits else ""
|
||||
|
||||
|
||||
def build_county_points(input_geojson: Path, include_puerto_rico: bool) -> list[dict[str, str]]:
|
||||
"""Load county polygons and return one interior representative point per county."""
|
||||
counties = gpd.read_file(input_geojson)
|
||||
rows: list[dict[str, str]] = []
|
||||
|
||||
for _, county in counties.iterrows():
|
||||
state_fips = normalize_fips(county.get("STATE"), 2)
|
||||
county_code = normalize_fips(county.get("COUNTY"), 3)
|
||||
county_fips = normalize_fips(county.get("id") or f"{state_fips}{county_code}", 5)
|
||||
|
||||
if not county_fips or (state_fips == "72" and not include_puerto_rico):
|
||||
continue
|
||||
|
||||
point = county.geometry.representative_point()
|
||||
county_name = str(county.get("NAME") or f"County {county_fips}").strip()
|
||||
|
||||
rows.append(
|
||||
{
|
||||
"county_fips": county_fips,
|
||||
"county_name": county_name,
|
||||
"state_fips": state_fips,
|
||||
"state_abbr": STATE_FIPS_TO_ABBR.get(state_fips, state_fips),
|
||||
"lat": f"{point.y:.6f}",
|
||||
"lon": f"{point.x:.6f}",
|
||||
}
|
||||
)
|
||||
|
||||
return sorted(rows, key=lambda row: row["county_fips"])
|
||||
|
||||
|
||||
def write_points_csv(rows: list[dict[str, str]], output_csv: Path) -> None:
|
||||
"""Write county representative points to CSV."""
|
||||
output_csv.parent.mkdir(parents=True, exist_ok=True)
|
||||
with output_csv.open("w", newline="", encoding="utf-8") as handle:
|
||||
writer = csv.DictWriter(
|
||||
handle,
|
||||
fieldnames=["county_fips", "county_name", "state_fips", "state_abbr", "lat", "lon"],
|
||||
)
|
||||
writer.writeheader()
|
||||
writer.writerows(rows)
|
||||
|
||||
|
||||
def parse_args() -> argparse.Namespace:
|
||||
"""Parse command-line arguments."""
|
||||
parser = argparse.ArgumentParser(description=__doc__)
|
||||
parser.add_argument("--input-geojson", type=Path, default=DEFAULT_COUNTIES_GEOJSON)
|
||||
parser.add_argument("--output-csv", type=Path, default=DEFAULT_OUTPUT_CSV)
|
||||
parser.add_argument(
|
||||
"--include-puerto-rico",
|
||||
action="store_true",
|
||||
help="Include Puerto Rico counties. The current web app excludes Puerto Rico polygons.",
|
||||
)
|
||||
return parser.parse_args()
|
||||
|
||||
|
||||
def main() -> None:
|
||||
"""Build and write the county representative-point CSV."""
|
||||
args = parse_args()
|
||||
rows = build_county_points(args.input_geojson, args.include_puerto_rico)
|
||||
write_points_csv(rows, args.output_csv)
|
||||
print(f"Wrote {len(rows)} county representative points to {args.output_csv}")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@@ -0,0 +1,363 @@
|
||||
# County Climate Data Sources and Mapping
|
||||
|
||||
This project now supports county polygons and county-level filter records keyed by 5-digit county FIPS.
|
||||
|
||||
## Launching the app
|
||||
|
||||
The browser blocks `fetch("data/climate-data.csv")` when `index.html` is opened directly as a `file://` URL, so run the site through a local HTTP server:
|
||||
|
||||
```powershell
|
||||
.\serve.ps1
|
||||
```
|
||||
|
||||
Then open [http://localhost:8000/](http://localhost:8000/). This keeps the app CSV-only while allowing the map and filters to load normally.
|
||||
|
||||
## Source 1: Koppen-Geiger classes (`koppenZone`)
|
||||
|
||||
- Dataset: Beck et al. updated 1-km Koppen-Geiger climate classes (historical + future windows)
|
||||
- Landing page: [https://www.gloh2o.org/koppen/](https://www.gloh2o.org/koppen/)
|
||||
- Primary paper for updated release: [https://www.nature.com/articles/s41597-023-02549-6](https://www.nature.com/articles/s41597-023-02549-6)
|
||||
- Coverage: 1901-2099 (use historical 1991-2020 layer for this project to align with NOAA baselines)
|
||||
- License shown on dataset page: CC BY 4.0
|
||||
|
||||
## Source 2: NOAA 1991-2020 gridded normals (`avgTempF`, `annualPrecipIn`, `seasonalityIndex`, previous `extremeDays`)
|
||||
|
||||
- Main product page: [https://www.ncei.noaa.gov/products/land-based-station/us-climate-normals](https://www.ncei.noaa.gov/products/land-based-station/us-climate-normals)
|
||||
- Monthly gridded normals readme: [https://www.ncei.noaa.gov/sites/default/files/2022-04/Readme_Monthly_Gridded_Normals.pdf](https://www.ncei.noaa.gov/sites/default/files/2022-04/Readme_Monthly_Gridded_Normals.pdf)
|
||||
- Daily gridded normals readme: [https://www.ncei.noaa.gov/sites/default/files/2022-09/Readme%20for%20Daily%20Gridded%20Normals%201991-2020.pdf](https://www.ncei.noaa.gov/sites/default/files/2022-09/Readme%20for%20Daily%20Gridded%20Normals%201991-2020.pdf)
|
||||
- Daily gridded normals documentation (includes units in C/mm): [https://www.ncei.noaa.gov/sites/default/files/2022-09/Documentation_Daily_Gridded_Normals%20V1.0.pdf](https://www.ncei.noaa.gov/sites/default/files/2022-09/Documentation_Daily_Gridded_Normals%20V1.0.pdf)
|
||||
|
||||
The ETL script uses:
|
||||
|
||||
- Monthly `tavg` normals (C)
|
||||
- Monthly `prcp` normals (mm)
|
||||
- Daily `tmax` normals (C)
|
||||
- Daily `tmin` normals (C)
|
||||
|
||||
If you have monthly nClimGrid history files (for example `nclimgrid_tavg.nc`) rather than 12-slice normals files:
|
||||
|
||||
- The script now computes a 1991-2020 monthly climatology from the time series automatically.
|
||||
- `extremeDays` can run in `auto` mode, which uses a documented monthly proxy when daily grids are not provided.
|
||||
|
||||
## Source 3: County polygons / FIPS join geometry
|
||||
|
||||
- Current app geometry file: `data/geojson-counties-fips.json`
|
||||
- Original Plotly county geometry source: [https://raw.githubusercontent.com/plotly/datasets/master/geojson-counties-fips.json](https://raw.githubusercontent.com/plotly/datasets/master/geojson-counties-fips.json)
|
||||
- Official county geometry reference (Census TIGER/Line): [https://www.census.gov/geographies/mapping-files/time-series/geo/tiger-line-file.html](https://www.census.gov/geographies/mapping-files/time-series/geo/tiger-line-file.html)
|
||||
|
||||
## Source 4: Solar resource (`avgSolarGhiKwhM2Day`)
|
||||
|
||||
- Recommended dataset: NREL National Solar Radiation Database (NSRDB)
|
||||
- Data/API page: [https://developer.nrel.gov/docs/solar/nsrdb/](https://developer.nrel.gov/docs/solar/nsrdb/)
|
||||
- Maps/geospatial data page: [https://www.nrel.gov/gis/solar-resource-maps](https://www.nrel.gov/gis/solar-resource-maps)
|
||||
- Fallback point API option: NASA POWER `ALLSKY_SFC_SW_DWN` (surface shortwave downwelling radiation), [https://power.larc.nasa.gov/docs/tutorials/service-data-request/api/](https://power.larc.nasa.gov/docs/tutorials/service-data-request/api/)
|
||||
|
||||
The app metric is designed for annual average daily global horizontal irradiance (GHI), in `kWh/m2/day`.
|
||||
For county means, use a gridded annual GHI raster and pass it to the generator with `--solar-ghi-raster`.
|
||||
|
||||
### First test: NSRDB county representative points
|
||||
|
||||
For a lightweight first pass, sample each county at one interior representative point instead of requesting full county polygons.
|
||||
This creates a point CSV:
|
||||
|
||||
```powershell
|
||||
.venv\Scripts\python.exe scripts\build_county_representative_points.py
|
||||
```
|
||||
|
||||
Then fetch a small NSRDB GOES TMY test batch.
|
||||
If `--email` or `--api-key` are omitted, the script prompts you for them:
|
||||
|
||||
```powershell
|
||||
.venv\Scripts\python.exe scripts\fetch_nsrdb_representative_point_ghi.py --limit 10
|
||||
```
|
||||
|
||||
The fetch script uses GOES TMY first.
|
||||
For high-latitude points, it can automatically retry the NSRDB Polar TMY endpoint when GOES reports no data.
|
||||
If a county FIPS appears in `data/nrel/county_representative_point_ghi_error_log.csv` from an earlier run, that county tries the Polar endpoint first on the next run.
|
||||
Disable this behavior with `--no-polar-fallback`.
|
||||
|
||||
Outputs:
|
||||
|
||||
- `data/nrel/county_representative_points.csv`: county FIPS, name, state, latitude, and longitude.
|
||||
- `data/nrel/representative_point_csv/`: cached raw NSRDB CSV responses by county FIPS.
|
||||
- `data/nrel/county_representative_point_ghi_summary.csv`: summarized `avgSolarGhiKwhM2Day` values.
|
||||
|
||||
The calculation is:
|
||||
|
||||
```text
|
||||
avgSolarGhiKwhM2Day = sum(hourly GHI) / 1000 / 365
|
||||
```
|
||||
|
||||
If the raw cache is complete but the summary CSV only contains the last fetched batch, rebuild the summary from cached files without calling the API:
|
||||
|
||||
```powershell
|
||||
.venv\Scripts\python.exe scripts\rebuild_nsrdb_representative_point_ghi_summary.py
|
||||
```
|
||||
|
||||
This point-based workflow is easier to validate and resume than full polygon downloads, but it is an approximation of county sunlight rather than an area-weighted county mean.
|
||||
|
||||
### First cloud-cover pass: NSRDB representative points
|
||||
|
||||
For a fast cloud-cover input layer, fetch representative-point NSRDB CSVs with observed GHI, Clearsky GHI, and Cloud Type:
|
||||
|
||||
```powershell
|
||||
.venv\Scripts\python.exe scripts\fetch_nsrdb_representative_point_cloud_metrics.py --limit 10
|
||||
```
|
||||
|
||||
Once the first batch looks right, fetch every county:
|
||||
|
||||
```powershell
|
||||
.venv\Scripts\python.exe scripts\fetch_nsrdb_representative_point_cloud_metrics.py --all
|
||||
```
|
||||
|
||||
Outputs:
|
||||
|
||||
- `data/nrel/representative_point_cloud_csv/`: cached raw NSRDB CSV responses with `ghi,clearsky_ghi,cloud_type`.
|
||||
- `data/nrel/county_representative_point_cloud_summary.csv`: summarized cloud-cover proxy fields.
|
||||
- `data/nrel/county_representative_point_cloud_error_log.csv`: failed county requests with redacted API context.
|
||||
|
||||
The primary calculation uses daylight rows where Clearsky GHI is at least 50 W/m2:
|
||||
|
||||
```text
|
||||
cloudinessIndexPct = 1 - mean(clamped(GHI / Clearsky GHI, 0, 1))
|
||||
```
|
||||
|
||||
The same summary also stores daylight row counts, observed-to-clear-sky ratio, and broad Cloud Type frequency buckets. This is faster than the polygon archive workflow, but it remains a representative-point county approximation.
|
||||
|
||||
Apply the representative-point cloudiness metric to the browser app CSV:
|
||||
|
||||
```powershell
|
||||
.venv\Scripts\python.exe scripts\apply_nsrdb_cloud_metric_to_climate_data.py
|
||||
```
|
||||
|
||||
The current cloud summary covers the 3,143 county representative points and leaves Puerto Rico rows blank in `data/climate-data.csv`.
|
||||
|
||||
### County-average target: NSRDB polygon cloud archive requests
|
||||
|
||||
For a less noisy county-level cloudiness layer, submit county polygons to the NSRDB archive workflow with `ghi,clearsky_ghi,cloud_type`. This uses the same polygon tiling and pacing logic as the GHI archive workflow, but writes separate cloud manifests, response JSON files, archives, and summaries.
|
||||
|
||||
Start with a dry run:
|
||||
|
||||
```powershell
|
||||
.venv\Scripts\python.exe scripts\request_nsrdb_county_polygon_cloud_archives.py `
|
||||
--dry-run `
|
||||
--skip-site-count `
|
||||
--limit 5
|
||||
```
|
||||
|
||||
Then submit a small real batch:
|
||||
|
||||
```powershell
|
||||
.venv\Scripts\python.exe scripts\request_nsrdb_county_polygon_cloud_archives.py `
|
||||
--limit 5
|
||||
```
|
||||
|
||||
Continue in resumable chunks:
|
||||
|
||||
```powershell
|
||||
.venv\Scripts\python.exe scripts\request_nsrdb_county_polygon_cloud_archives.py `
|
||||
--start 5 `
|
||||
--limit 100
|
||||
```
|
||||
|
||||
Download completed archives:
|
||||
|
||||
```powershell
|
||||
.venv\Scripts\python.exe scripts\download_nsrdb_county_polygon_cloud_archives.py
|
||||
```
|
||||
|
||||
Summarize downloaded archives into area-weighted county cloudiness:
|
||||
|
||||
```powershell
|
||||
.venv\Scripts\python.exe scripts\summarize_nsrdb_county_polygon_cloud_archives.py --reuse-existing-output
|
||||
```
|
||||
|
||||
Outputs:
|
||||
|
||||
- `data/nrel/county_polygon_cloud_request_manifest.csv`: submitted cloud archive requests.
|
||||
- `data/nrel/county_polygon_cloud_error_log.csv`: cloud archive request errors.
|
||||
- `data/nrel/polygon_cloud_request_responses/`: raw NSRDB cloud acknowledgement JSON files.
|
||||
- `data/nrel/polygon_cloud_archives/`: downloaded cloud ZIP archives.
|
||||
- `data/nrel/county_polygon_cloud_summary.csv`: area-weighted county cloudiness summaries.
|
||||
|
||||
Then update the browser app CSV. Polygon area-weighted values are used first when `data/nrel/county_polygon_cloud_summary.csv` exists; representative-point values remain the fallback:
|
||||
|
||||
```powershell
|
||||
.venv\Scripts\python.exe scripts\apply_nsrdb_cloud_metric_to_climate_data.py
|
||||
```
|
||||
|
||||
### County-average target: NSRDB polygon archive requests
|
||||
|
||||
For the final county-average solar layer, submit county polygons to the NSRDB archive workflow instead of sampling one representative point.
|
||||
The polygon request returns a generated archive URL per county. Download those archives, then summarize them into a county polygon GHI CSV that can replace the representative-point solar values where available.
|
||||
|
||||
Start with a small dry run that does not touch the API:
|
||||
|
||||
```powershell
|
||||
.venv\Scripts\python.exe scripts\request_nsrdb_county_polygon_ghi_archives.py `
|
||||
--dry-run `
|
||||
--skip-site-count `
|
||||
--limit 5
|
||||
```
|
||||
|
||||
Then submit a small real batch:
|
||||
|
||||
```powershell
|
||||
.venv\Scripts\python.exe scripts\request_nsrdb_county_polygon_ghi_archives.py `
|
||||
--limit 5
|
||||
```
|
||||
|
||||
Once the first batch looks right, continue in resumable chunks:
|
||||
|
||||
```powershell
|
||||
.venv\Scripts\python.exe scripts\request_nsrdb_county_polygon_ghi_archives.py `
|
||||
--start 5 `
|
||||
--limit 100
|
||||
```
|
||||
|
||||
Or request every county from the current start point:
|
||||
|
||||
```powershell
|
||||
.venv\Scripts\python.exe scripts\request_nsrdb_county_polygon_ghi_archives.py `
|
||||
--all
|
||||
```
|
||||
|
||||
Outputs:
|
||||
|
||||
- `data/nrel/county_polygon_ghi_request_manifest.csv`: one row per submitted county, including NSRDB profile, site count, request weight, response JSON path, and `download_url`.
|
||||
- `data/nrel/county_polygon_ghi_error_log.csv`: counties that need retrying or tiling.
|
||||
- `data/nrel/polygon_request_responses/`: raw API acknowledgement JSON files.
|
||||
- `data/nrel/polygon_archives/`: downloaded county or tiled county ZIP archives.
|
||||
- `data/nrel/county_polygon_ghi_summary.csv`: summarized polygon archive GHI, including `avgSolarGhiKwhM2Day`.
|
||||
|
||||
Download completed GHI archives:
|
||||
|
||||
```powershell
|
||||
.venv\Scripts\python.exe scripts\download_nsrdb_county_polygon_ghi_archives.py
|
||||
```
|
||||
|
||||
The script uses POST requests because county polygon WKT can be long.
|
||||
It checks NSRDB site count by default and skips counties whose estimated request weight exceeds the API maximum.
|
||||
Default pacing is one request every 2.1 seconds, matching the NSRDB archive limit.
|
||||
Use `--include-puerto-rico` if you want to test Puerto Rico polygons too.
|
||||
|
||||
After the archives are downloaded, rebuild or resume the polygon summary:
|
||||
|
||||
```powershell
|
||||
.venv\Scripts\python.exe scripts\summarize_nsrdb_county_polygon_archives.py --reuse-existing-output
|
||||
```
|
||||
|
||||
Then update the app CSV. Polygon archive GHI is used first; representative-point GHI remains as a fallback for counties without polygon archive data:
|
||||
|
||||
```powershell
|
||||
.venv\Scripts\python.exe scripts\apply_locally_extreme_metric_to_climate_data.py
|
||||
```
|
||||
|
||||
## Metric definitions in generated output
|
||||
|
||||
- `koppenZone`: majority class within county polygon from Koppen raster.
|
||||
- `avgTempF`: mean of 12 monthly county mean temperatures, converted C -> F.
|
||||
- `annualPrecipIn`: sum of 12 monthly county mean precipitation totals, converted mm -> inches.
|
||||
- `seasonalityIndex`: coefficient of variation of monthly precipitation totals, scaled to 0-100.
|
||||
- `wettestPrecipMonth`: month with the highest 1991-2020 county mean precipitation total.
|
||||
- `driestPrecipMonth`: month with the lowest 1991-2020 county mean precipitation total.
|
||||
- previous `extremeDays` / `oldExtremeDays`: count of daily-normal or monthly-proxy days where county mean `tmax >= 95F` or `tmin <= 32F` (thresholds configurable in script). This is preserved only as an audit column after the NOAA nClimGrid-Daily metrics are applied.
|
||||
- `avgSolarGhiKwhM2Day`: county mean annual average daily GHI, in `kWh/m2/day`. The current app CSV uses NSRDB polygon archive area-weighted values where available, with representative-point values kept as fallback.
|
||||
- `cloudinessIndexPct`: representative-point NSRDB daylight cloudiness proxy derived from observed GHI divided by Clearsky GHI. Higher values mean observed irradiance is lower relative to modeled clear-sky irradiance. Despite the legacy field name, values are stored on a 0-1 scale.
|
||||
|
||||
## Run the generator
|
||||
|
||||
Install dependencies:
|
||||
|
||||
```powershell
|
||||
pip install -r scripts/requirements_county_etl.txt
|
||||
```
|
||||
|
||||
Run:
|
||||
|
||||
```powershell
|
||||
python scripts/build_county_climate_data.py `
|
||||
--counties-geojson data/geojson-counties-fips.json `
|
||||
--koppen-raster data/koppen_geiger_tif/1991_2020/koppen_geiger_0p00833333.tif `
|
||||
--koppen-legend data/koppen_geiger_tif/legend.txt `
|
||||
--monthly-tavg-nc data/noaa/nclimgrid/nclimgrid_tavg.nc `
|
||||
--monthly-prcp-nc data/noaa/nclimgrid/nclimgrid_prcp.nc `
|
||||
--daily-tmax-nc data/noaa/nclimgrid/nclimgrid_tmax.nc `
|
||||
--daily-tmin-nc data/noaa/nclimgrid/nclimgrid_tmin.nc `
|
||||
--climatology-start-year 1991 `
|
||||
--climatology-end-year 2020 `
|
||||
--extreme-days-mode auto `
|
||||
--solar-ghi-csv data/nrel/county_polygon_ghi_summary.csv `
|
||||
--out data/climate-data.csv
|
||||
```
|
||||
|
||||
Notes:
|
||||
|
||||
- This computes all counties in your geometry file, not just the sample records.
|
||||
- For counties outside CONUS coverage in NOAA gridded files, fallback values are applied by the script when no valid grid values intersect.
|
||||
- For physically-based daily `extremeDays`, provide true daily grids and set `--extreme-days-mode require-daily`.
|
||||
- If `--counties-geojson` does not exist locally, the script will try to download the county GeoJSON automatically from the Plotly URL above and cache it at that path.
|
||||
- `--solar-ghi-csv` is optional and can load county-keyed solar summaries into `avgSolarGhiKwhM2Day`.
|
||||
- `--solar-ghi-raster` is optional and takes precedence over `--solar-ghi-csv`. If you have a gridded annual GHI raster, add `--solar-ghi-raster path/to/annual_ghi_kwh_m2_day.tif` for a true county-area raster mean.
|
||||
|
||||
## Source 5: NOAA nClimGrid-Daily county area averages (`locallyExtremeDays`)
|
||||
|
||||
- Main product page: [https://www.ncei.noaa.gov/products/land-based-station/nclimgrid-daily](https://www.ncei.noaa.gov/products/land-based-station/nclimgrid-daily)
|
||||
- County/monthly area averages root: [https://www.ncei.noaa.gov/data/nclimgrid-daily/access/averages/](https://www.ncei.noaa.gov/data/nclimgrid-daily/access/averages/)
|
||||
- User guide: [https://www.ncei.noaa.gov/data/nclimgrid-daily/doc/nclimgrid-daily_v1-0-0_user-guide.pdf](https://www.ncei.noaa.gov/data/nclimgrid-daily/doc/nclimgrid-daily_v1-0-0_user-guide.pdf)
|
||||
- NCEI state-code to FIPS crosswalk: [https://www.ncei.noaa.gov/data/nclimgrid-daily/doc/us-state-codes_ncei-to-fips.csv](https://www.ncei.noaa.gov/data/nclimgrid-daily/doc/us-state-codes_ncei-to-fips.csv)
|
||||
- Census boundary change notes: [https://www.census.gov/programs-surveys/geography/technical-documentation/boundary-change-notes.html](https://www.census.gov/programs-surveys/geography/technical-documentation/boundary-change-notes.html)
|
||||
- Census county changes: [https://www.census.gov/programs-surveys/geography/technical-documentation/county-changes.html](https://www.census.gov/programs-surveys/geography/technical-documentation/county-changes.html)
|
||||
|
||||
The locally extreme metric uses county-specific 1991-2020 thresholds:
|
||||
|
||||
```text
|
||||
p95_tmax_c = 95th percentile of county-average daily Tmax
|
||||
p05_tmin_c = 5th percentile of county-average daily Tmin
|
||||
locallyExtremeDays = count(Tmax >= p95_tmax_c OR Tmin <= p05_tmin_c)
|
||||
```
|
||||
|
||||
The same daily county Tmax/Tmin records now also produce an absolute climate-bucket metric:
|
||||
|
||||
```text
|
||||
absoluteExtremeDays = count(Tmax >= 95F OR Tmin <= 0F)
|
||||
```
|
||||
|
||||
The heat threshold follows the EPA extreme-heat example of days at or above 95F.
|
||||
The cold threshold is intentionally stricter than the older 32F freeze bucket because NWS cold guidance treats freezing as a freeze-warning/crop threshold and describes human extreme cold as region-dependent and often wind-chill based. The current NOAA cache does not include wind, so `Tmin <= 0F` is used as a configurable air-temperature proxy.
|
||||
|
||||
Run from the local NOAA CSV cache:
|
||||
|
||||
```powershell
|
||||
.venv\Scripts\python.exe scripts\build_county_locally_extreme_data.py --skip-download
|
||||
```
|
||||
|
||||
Outputs:
|
||||
|
||||
- `data/noaa/county_locally_extreme_thresholds.csv`
|
||||
- `data/noaa/county_locally_extreme_days.csv`
|
||||
- `data/noaa/county_locally_extreme_days_comparison.csv`
|
||||
|
||||
Apply the locally extreme average to the browser app CSV:
|
||||
|
||||
```powershell
|
||||
.venv\Scripts\python.exe scripts\apply_locally_extreme_metric_to_climate_data.py
|
||||
```
|
||||
|
||||
This keeps `data/climate-data.csv` compatible with the existing app by writing the locally extreme average into the active `extremeDays` column, while preserving the earlier proxy metric in `oldExtremeDays`. It also adds detail/audit columns:
|
||||
|
||||
- `locallyExtremeDays`
|
||||
- `locallyExtremeHotDays`
|
||||
- `locallyExtremeColdDays`
|
||||
- `absoluteExtremeDays`
|
||||
- `oldExtremeDays`
|
||||
- `locallyExtremeAnalysisYears`
|
||||
- `locallyExtremeSourceFips`
|
||||
- `locallyExtremeFipsAdjustment`
|
||||
|
||||
FIPS/geography-vintage policy:
|
||||
|
||||
- Use current NOAA/Census county-equivalent FIPS for the locally extreme outputs.
|
||||
- Correct NOAA's District of Columbia county row from source region code `18511` to Census FIPS `11001`.
|
||||
- Apply only one-to-one old app comparison concordances, currently `46113 -> 46102` for Shannon/Oglala Lakota and `51515 -> 51019` for Bedford city/Bedford County.
|
||||
- Do not guess split or many-to-one geography changes without a Census relationship file; leave those rows flagged in the comparison output.
|
||||
@@ -0,0 +1,143 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
Download gridMET daily NetCDF files into year-based folders.
|
||||
|
||||
Default layout:
|
||||
data/gridmet/1991/sph.nc
|
||||
data/gridmet/1991/rmax.nc
|
||||
data/gridmet/1991/rmin.nc
|
||||
|
||||
gridMET source:
|
||||
https://www.northwestknowledge.net/metdata/data/{variable}_{year}.nc
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import argparse
|
||||
import sys
|
||||
import tempfile
|
||||
import urllib.error
|
||||
import urllib.request
|
||||
from pathlib import Path
|
||||
|
||||
|
||||
DEFAULT_BASE_URL = "https://www.northwestknowledge.net/metdata/data"
|
||||
DEFAULT_VARIABLES = ("sph", "rmax", "rmin")
|
||||
|
||||
|
||||
def _parse_years(text: str) -> list[int]:
|
||||
years: set[int] = set()
|
||||
for part in text.split(","):
|
||||
token = part.strip()
|
||||
if not token:
|
||||
continue
|
||||
if "-" in token:
|
||||
start_text, end_text = token.split("-", 1)
|
||||
start = int(start_text)
|
||||
end = int(end_text)
|
||||
if start > end:
|
||||
raise argparse.ArgumentTypeError(f"Invalid descending year range: {token}")
|
||||
years.update(range(start, end + 1))
|
||||
else:
|
||||
years.add(int(token))
|
||||
if not years:
|
||||
raise argparse.ArgumentTypeError("At least one year is required.")
|
||||
return sorted(years)
|
||||
|
||||
|
||||
def _parse_variables(text: str) -> list[str]:
|
||||
variables = [part.strip() for part in text.split(",") if part.strip()]
|
||||
if not variables:
|
||||
raise argparse.ArgumentTypeError("At least one variable is required.")
|
||||
return variables
|
||||
|
||||
|
||||
def _download_file(url: str, destination: Path, overwrite: bool) -> bool:
|
||||
if destination.exists() and not overwrite:
|
||||
print(f"skip existing {destination}")
|
||||
return False
|
||||
|
||||
destination.parent.mkdir(parents=True, exist_ok=True)
|
||||
with tempfile.NamedTemporaryFile(
|
||||
prefix=destination.stem + ".",
|
||||
suffix=".download",
|
||||
dir=destination.parent,
|
||||
delete=False,
|
||||
) as handle:
|
||||
temp_path = Path(handle.name)
|
||||
|
||||
try:
|
||||
print(f"download {url}")
|
||||
with urllib.request.urlopen(url) as response, temp_path.open("wb") as output:
|
||||
total_text = response.headers.get("Content-Length")
|
||||
total = int(total_text) if total_text and total_text.isdigit() else None
|
||||
copied = 0
|
||||
while True:
|
||||
chunk = response.read(1024 * 1024)
|
||||
if not chunk:
|
||||
break
|
||||
output.write(chunk)
|
||||
copied += len(chunk)
|
||||
if total:
|
||||
percent = copied / total * 100
|
||||
print(f"\r {copied / 1024 / 1024:,.1f} MiB / {total / 1024 / 1024:,.1f} MiB ({percent:5.1f}%)", end="")
|
||||
if total:
|
||||
print()
|
||||
|
||||
temp_path.replace(destination)
|
||||
print(f"saved {destination}")
|
||||
return True
|
||||
except (urllib.error.URLError, urllib.error.HTTPError, OSError) as exc:
|
||||
temp_path.unlink(missing_ok=True)
|
||||
print(f"failed {url}: {exc}", file=sys.stderr)
|
||||
raise
|
||||
|
||||
|
||||
def main() -> int:
|
||||
parser = argparse.ArgumentParser(
|
||||
description="Download gridMET NetCDF files into data/gridmet/<year>/<variable>.nc."
|
||||
)
|
||||
parser.add_argument(
|
||||
"--years",
|
||||
type=_parse_years,
|
||||
default=_parse_years("1991-2020"),
|
||||
help="Comma-separated years/ranges. Default: 1991-2020.",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--variables",
|
||||
type=_parse_variables,
|
||||
default=list(DEFAULT_VARIABLES),
|
||||
help="Comma-separated gridMET variables. Default: sph,rmax,rmin.",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--output-dir",
|
||||
type=Path,
|
||||
default=Path("data/gridmet"),
|
||||
help="Root folder for downloaded files. Default: data/gridmet.",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--base-url",
|
||||
default=DEFAULT_BASE_URL,
|
||||
help=f"Base gridMET download URL. Default: {DEFAULT_BASE_URL}.",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--overwrite",
|
||||
action="store_true",
|
||||
help="Redownload files that already exist.",
|
||||
)
|
||||
args = parser.parse_args()
|
||||
|
||||
downloaded = 0
|
||||
for year in args.years:
|
||||
for variable in args.variables:
|
||||
url = f"{args.base_url.rstrip('/')}/{variable}_{year}.nc"
|
||||
destination = args.output_dir / str(year) / f"{variable}.nc"
|
||||
if _download_file(url, destination, args.overwrite):
|
||||
downloaded += 1
|
||||
|
||||
print(f"done; downloaded {downloaded} file(s)")
|
||||
return 0
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
raise SystemExit(main())
|
||||
@@ -0,0 +1,325 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
Download completed NSRDB county polygon archive ZIP files.
|
||||
|
||||
This shared engine reads JSON acknowledgements produced by the polygon request
|
||||
workflow and downloads each outputs.downloadUrl. Metric-specific wrappers
|
||||
supply the response and archive directories.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import argparse
|
||||
import json
|
||||
import time
|
||||
import urllib.error
|
||||
import urllib.parse
|
||||
import urllib.request
|
||||
from dataclasses import dataclass, field
|
||||
from enum import Enum
|
||||
from pathlib import Path
|
||||
from typing import Any
|
||||
|
||||
|
||||
DEFAULT_TIMEOUT = 300
|
||||
DEFAULT_CHUNK_SIZE = 1024 * 1024
|
||||
|
||||
|
||||
class ArchiveDownloadState(Enum):
|
||||
"""Distinct lifecycle phases for one NSRDB archive download."""
|
||||
|
||||
QUEUED = "queued"
|
||||
CHECKING = "checking"
|
||||
DOWNLOADING = "downloading"
|
||||
SKIPPED = "skipped"
|
||||
PENDING = "pending"
|
||||
DOWNLOADED = "downloaded"
|
||||
FAILED = "failed"
|
||||
|
||||
|
||||
class ArchiveDownloadEvent(Enum):
|
||||
"""Events that may move an archive download to another lifecycle phase."""
|
||||
|
||||
START = "start"
|
||||
EXISTING_FILE_FOUND = "existing_file_found"
|
||||
DOWNLOAD_STARTED = "download_started"
|
||||
ARCHIVE_NOT_READY = "archive_not_ready"
|
||||
DOWNLOAD_SUCCEEDED = "download_succeeded"
|
||||
DOWNLOAD_FAILED = "download_failed"
|
||||
|
||||
|
||||
ARCHIVE_DOWNLOAD_TRANSITIONS = {
|
||||
(ArchiveDownloadState.QUEUED, ArchiveDownloadEvent.START): ArchiveDownloadState.CHECKING,
|
||||
(
|
||||
ArchiveDownloadState.CHECKING,
|
||||
ArchiveDownloadEvent.EXISTING_FILE_FOUND,
|
||||
): ArchiveDownloadState.SKIPPED,
|
||||
(
|
||||
ArchiveDownloadState.CHECKING,
|
||||
ArchiveDownloadEvent.DOWNLOAD_STARTED,
|
||||
): ArchiveDownloadState.DOWNLOADING,
|
||||
(
|
||||
ArchiveDownloadState.DOWNLOADING,
|
||||
ArchiveDownloadEvent.ARCHIVE_NOT_READY,
|
||||
): ArchiveDownloadState.PENDING,
|
||||
(
|
||||
ArchiveDownloadState.DOWNLOADING,
|
||||
ArchiveDownloadEvent.DOWNLOAD_SUCCEEDED,
|
||||
): ArchiveDownloadState.DOWNLOADED,
|
||||
(
|
||||
ArchiveDownloadState.QUEUED,
|
||||
ArchiveDownloadEvent.DOWNLOAD_FAILED,
|
||||
): ArchiveDownloadState.FAILED,
|
||||
(
|
||||
ArchiveDownloadState.CHECKING,
|
||||
ArchiveDownloadEvent.DOWNLOAD_FAILED,
|
||||
): ArchiveDownloadState.FAILED,
|
||||
(
|
||||
ArchiveDownloadState.DOWNLOADING,
|
||||
ArchiveDownloadEvent.DOWNLOAD_FAILED,
|
||||
): ArchiveDownloadState.FAILED,
|
||||
}
|
||||
|
||||
|
||||
class InvalidArchiveDownloadTransition(RuntimeError):
|
||||
"""Reject an event that is not valid for the current download state."""
|
||||
|
||||
|
||||
@dataclass
|
||||
class ArchiveDownloadStateMachine:
|
||||
"""Track and validate the lifecycle of one archive download."""
|
||||
|
||||
label: str
|
||||
state: ArchiveDownloadState = ArchiveDownloadState.QUEUED
|
||||
history: list[ArchiveDownloadState] = field(
|
||||
default_factory=lambda: [ArchiveDownloadState.QUEUED]
|
||||
)
|
||||
|
||||
def transition(self, event: ArchiveDownloadEvent) -> ArchiveDownloadState:
|
||||
"""Apply one event and return the resulting state."""
|
||||
next_state = ARCHIVE_DOWNLOAD_TRANSITIONS.get((self.state, event))
|
||||
if next_state is None:
|
||||
raise InvalidArchiveDownloadTransition(
|
||||
f"Invalid archive download transition for {self.label}: "
|
||||
f"{event.value} while {self.state.value}."
|
||||
)
|
||||
self.state = next_state
|
||||
self.history.append(next_state)
|
||||
return next_state
|
||||
|
||||
|
||||
class PolygonArchiveDownloadError(RuntimeError):
|
||||
"""Store a failed archive download with useful context."""
|
||||
|
||||
def __init__(self, message: str, status_code: int | None = None) -> None:
|
||||
super().__init__(message)
|
||||
self.status_code = status_code
|
||||
|
||||
|
||||
def is_pending_s3_archive_response(url: str, status_code: int) -> bool:
|
||||
"""Return whether an S3 HTTP response likely means the archive is pending."""
|
||||
if status_code != 403:
|
||||
return False
|
||||
|
||||
host = urllib.parse.urlsplit(url).netloc.lower()
|
||||
return host == "s3.amazonaws.com" or host.endswith(".amazonaws.com")
|
||||
|
||||
|
||||
def read_response(response_path: Path) -> dict[str, Any]:
|
||||
"""Read a saved NSRDB archive response JSON file."""
|
||||
with response_path.open(encoding="utf-8") as handle:
|
||||
response = json.load(handle)
|
||||
|
||||
if not isinstance(response, dict):
|
||||
raise ValueError("Response JSON must contain an object.")
|
||||
|
||||
return response
|
||||
|
||||
|
||||
def download_url_from_response(response: dict[str, Any]) -> str:
|
||||
"""Return the download URL from a saved NSRDB archive response."""
|
||||
outputs = response.get("outputs")
|
||||
if not isinstance(outputs, dict):
|
||||
raise ValueError("Response JSON is missing an outputs object.")
|
||||
|
||||
download_url = outputs.get("downloadUrl")
|
||||
if not isinstance(download_url, str) or not download_url.strip():
|
||||
raise ValueError("Response JSON is missing outputs.downloadUrl.")
|
||||
|
||||
return download_url.strip()
|
||||
|
||||
|
||||
def output_name_for_response(response_path: Path) -> str:
|
||||
"""Convert a response JSON filename into the corresponding ZIP filename."""
|
||||
stem = response_path.stem
|
||||
if stem.endswith("_response"):
|
||||
stem = stem[: -len("_response")]
|
||||
return f"{stem}.zip"
|
||||
|
||||
|
||||
def download_file(
|
||||
url: str,
|
||||
output_path: Path,
|
||||
timeout: int,
|
||||
overwrite: bool,
|
||||
machine: ArchiveDownloadStateMachine,
|
||||
) -> ArchiveDownloadState:
|
||||
"""Download a URL to disk and return the terminal download state."""
|
||||
output_path.parent.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
if output_path.exists() and not overwrite:
|
||||
return machine.transition(ArchiveDownloadEvent.EXISTING_FILE_FOUND)
|
||||
|
||||
machine.transition(ArchiveDownloadEvent.DOWNLOAD_STARTED)
|
||||
temp_path = output_path.with_suffix(f"{output_path.suffix}.part")
|
||||
if temp_path.exists():
|
||||
temp_path.unlink()
|
||||
|
||||
request = urllib.request.Request(url, headers={"User-Agent": "county-climate-explorer/0.1"})
|
||||
try:
|
||||
with urllib.request.urlopen(request, timeout=timeout) as response:
|
||||
with temp_path.open("wb") as handle:
|
||||
while True:
|
||||
chunk = response.read(DEFAULT_CHUNK_SIZE)
|
||||
if not chunk:
|
||||
break
|
||||
handle.write(chunk)
|
||||
except urllib.error.HTTPError as error:
|
||||
if temp_path.exists():
|
||||
temp_path.unlink()
|
||||
if is_pending_s3_archive_response(url, error.code):
|
||||
return machine.transition(ArchiveDownloadEvent.ARCHIVE_NOT_READY)
|
||||
machine.transition(ArchiveDownloadEvent.DOWNLOAD_FAILED)
|
||||
raise PolygonArchiveDownloadError(f"HTTP {error.code} {error.reason}", error.code) from error
|
||||
except urllib.error.URLError as error:
|
||||
if temp_path.exists():
|
||||
temp_path.unlink()
|
||||
machine.transition(ArchiveDownloadEvent.DOWNLOAD_FAILED)
|
||||
raise PolygonArchiveDownloadError(str(error.reason)) from error
|
||||
|
||||
temp_path.replace(output_path)
|
||||
return machine.transition(ArchiveDownloadEvent.DOWNLOAD_SUCCEEDED)
|
||||
|
||||
|
||||
def response_paths(response_dir: Path, start: int, limit: int | None) -> list[Path]:
|
||||
"""Return saved response JSON files in deterministic order."""
|
||||
paths = sorted(response_dir.glob("*.json"))
|
||||
|
||||
if start < 0:
|
||||
raise ValueError("--start must be 0 or greater.")
|
||||
if limit is not None and limit < 1:
|
||||
raise ValueError("--limit must be 1 or greater.")
|
||||
|
||||
if limit is None:
|
||||
return paths[start:]
|
||||
return paths[start : start + limit]
|
||||
|
||||
|
||||
def run(args: argparse.Namespace) -> None:
|
||||
"""Download all requested NSRDB polygon archives."""
|
||||
paths = response_paths(args.response_dir, args.start, args.limit)
|
||||
if not paths:
|
||||
print(f"No response JSON files found in {args.response_dir}")
|
||||
return
|
||||
|
||||
counts = {
|
||||
ArchiveDownloadState.DOWNLOADED: 0,
|
||||
ArchiveDownloadState.SKIPPED: 0,
|
||||
ArchiveDownloadState.PENDING: 0,
|
||||
ArchiveDownloadState.FAILED: 0,
|
||||
}
|
||||
total = len(paths)
|
||||
|
||||
for index, response_path in enumerate(paths, start=1):
|
||||
output_path = args.output_dir / output_name_for_response(response_path)
|
||||
label = response_path.name
|
||||
machine = ArchiveDownloadStateMachine(label)
|
||||
machine.transition(ArchiveDownloadEvent.START)
|
||||
|
||||
try:
|
||||
response = read_response(response_path)
|
||||
download_url = download_url_from_response(response)
|
||||
state = download_file(
|
||||
download_url,
|
||||
output_path,
|
||||
args.timeout,
|
||||
args.overwrite,
|
||||
machine,
|
||||
)
|
||||
except (OSError, ValueError, PolygonArchiveDownloadError) as error:
|
||||
if machine.state not in {
|
||||
ArchiveDownloadState.FAILED,
|
||||
ArchiveDownloadState.SKIPPED,
|
||||
ArchiveDownloadState.PENDING,
|
||||
ArchiveDownloadState.DOWNLOADED,
|
||||
}:
|
||||
machine.transition(ArchiveDownloadEvent.DOWNLOAD_FAILED)
|
||||
counts[machine.state] += 1
|
||||
print(f"[{index}/{total}] Failed {label}: {error}")
|
||||
continue
|
||||
|
||||
counts[state] += 1
|
||||
if state is ArchiveDownloadState.SKIPPED:
|
||||
print(f"[{index}/{total}] Skipped existing {output_path}")
|
||||
elif state is ArchiveDownloadState.PENDING:
|
||||
print(
|
||||
f"[{index}/{total}] Pending {label}: archive URL returned "
|
||||
"S3 HTTP 403; retry after NSRDB finishes generating it."
|
||||
)
|
||||
else:
|
||||
print(f"[{index}/{total}] Downloaded {output_path}")
|
||||
|
||||
if args.delay > 0 and index < total:
|
||||
time.sleep(args.delay)
|
||||
|
||||
print(
|
||||
f"Finished: downloaded={counts[ArchiveDownloadState.DOWNLOADED]}, "
|
||||
f"skipped={counts[ArchiveDownloadState.SKIPPED]}, "
|
||||
f"pending={counts[ArchiveDownloadState.PENDING]}, "
|
||||
f"failed={counts[ArchiveDownloadState.FAILED]}, output_dir={args.output_dir}"
|
||||
)
|
||||
|
||||
|
||||
def parse_args(
|
||||
argv: list[str] | None = None,
|
||||
description: str | None = None,
|
||||
) -> argparse.Namespace:
|
||||
"""Parse command-line arguments."""
|
||||
parser = argparse.ArgumentParser(description=description or __doc__)
|
||||
parser.add_argument("--response-dir", type=Path)
|
||||
parser.add_argument("--output-dir", type=Path)
|
||||
parser.add_argument("--start", type=int, default=0, help="Zero-based response file offset.")
|
||||
parser.add_argument("--limit", type=int, help="Number of response files to download.")
|
||||
parser.add_argument("--timeout", type=int, default=DEFAULT_TIMEOUT, help="Download timeout in seconds.")
|
||||
parser.add_argument("--delay", type=float, default=0.0, help="Seconds to wait between downloads.")
|
||||
parser.add_argument("--overwrite", action="store_true", help="Download even when the ZIP already exists.")
|
||||
args = parser.parse_args(argv)
|
||||
|
||||
missing = [
|
||||
option
|
||||
for option, value in {
|
||||
"--response-dir": args.response_dir,
|
||||
"--output-dir": args.output_dir,
|
||||
}.items()
|
||||
if not value
|
||||
]
|
||||
if missing:
|
||||
parser.error(f"the following arguments are required: {', '.join(missing)}")
|
||||
if args.timeout <= 0:
|
||||
parser.error("--timeout must be greater than 0.")
|
||||
if args.delay < 0:
|
||||
parser.error("--delay must be 0 or greater.")
|
||||
|
||||
return args
|
||||
|
||||
|
||||
def main(
|
||||
argv: list[str] | None = None,
|
||||
description: str | None = None,
|
||||
) -> None:
|
||||
"""Run the archive downloader."""
|
||||
run(parse_args(argv, description))
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@@ -0,0 +1,33 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
Download completed NSRDB county polygon cloud archive ZIP files.
|
||||
|
||||
This is a cloud-specific wrapper around download_nsrdb_county_polygon_archives.py
|
||||
that reads cloud request responses and writes archives into a separate folder.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import sys
|
||||
|
||||
import download_nsrdb_county_polygon_archives as polygon_download
|
||||
|
||||
|
||||
DEFAULT_ARGS = [
|
||||
"--response-dir",
|
||||
"data/nrel/polygon_cloud_request_responses",
|
||||
"--output-dir",
|
||||
"data/nrel/polygon_cloud_archives",
|
||||
]
|
||||
|
||||
|
||||
def main() -> None:
|
||||
"""Run the existing polygon archive downloader with cloud-specific defaults."""
|
||||
polygon_download.main(
|
||||
[*DEFAULT_ARGS, *sys.argv[1:]],
|
||||
description=__doc__,
|
||||
)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@@ -0,0 +1,34 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
Download completed NSRDB county polygon GHI archive ZIP files.
|
||||
|
||||
This is a GHI-specific wrapper around download_nsrdb_county_polygon_archives.py.
|
||||
It reuses the shared download state machine with GHI-specific response and
|
||||
archive directories.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import sys
|
||||
|
||||
import download_nsrdb_county_polygon_archives as polygon_download
|
||||
|
||||
|
||||
DEFAULT_ARGS = [
|
||||
"--response-dir",
|
||||
"data/nrel/polygon_request_responses",
|
||||
"--output-dir",
|
||||
"data/nrel/polygon_archives",
|
||||
]
|
||||
|
||||
|
||||
def main() -> None:
|
||||
"""Run the shared polygon archive downloader with GHI-specific defaults."""
|
||||
polygon_download.main(
|
||||
[*DEFAULT_ARGS, *sys.argv[1:]],
|
||||
description=__doc__,
|
||||
)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@@ -0,0 +1,832 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
Fetch NSRDB representative-point inputs for a county cloud-cover metric.
|
||||
|
||||
This uses the direct single-point NSRDB CSV endpoint rather than polygon archive
|
||||
requests. It is faster for first-pass cloud metrics because it downloads one CSV
|
||||
per county point and can run multiple county requests concurrently.
|
||||
|
||||
Default requested attributes:
|
||||
|
||||
ghi,clearsky_ghi,cloud_type
|
||||
|
||||
The summary metric is based on daylight rows with valid GHI and Clearsky GHI:
|
||||
|
||||
cloudinessIndexPct = 1 - mean(clamped(GHI / Clearsky GHI))
|
||||
|
||||
where the ratio is clamped to [0, 1] so occasional above-clear-sky modeled GHI
|
||||
does not create negative cloudiness.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import argparse
|
||||
import concurrent.futures
|
||||
import csv
|
||||
import getpass
|
||||
import math
|
||||
import re
|
||||
import threading
|
||||
import time
|
||||
import urllib.error
|
||||
import urllib.parse
|
||||
import urllib.request
|
||||
from pathlib import Path
|
||||
|
||||
|
||||
DEFAULT_POINTS_CSV = Path("data/nrel/county_representative_points.csv")
|
||||
DEFAULT_OUTPUT_CSV = Path("data/nrel/county_representative_point_cloud_summary.csv")
|
||||
DEFAULT_ERROR_CSV = Path("data/nrel/county_representative_point_cloud_error_log.csv")
|
||||
DEFAULT_CACHE_DIR = Path("data/nrel/representative_point_cloud_csv")
|
||||
DEFAULT_ENDPOINT = "https://developer.nlr.gov/api/nsrdb/v2/solar/nsrdb-GOES-tmy-v4-0-0-download.csv"
|
||||
DEFAULT_POLAR_ENDPOINT = "https://developer.nlr.gov/api/nsrdb/v2/solar/nsrdb-polar-tmy-v4-0-0-download.csv"
|
||||
DEFAULT_TMY_NAME = "tmy-2024"
|
||||
DEFAULT_POLAR_TMY_NAME = "tmy"
|
||||
DEFAULT_ATTRIBUTES = "ghi,clearsky_ghi,cloud_type"
|
||||
DEFAULT_INTERVAL = 60
|
||||
DEFAULT_POLAR_MIN_LATITUDE = 60.0
|
||||
DEFAULT_MIN_CLEARSKY_GHI = 50.0
|
||||
EMAIL_PATTERN = re.compile(r"[\w.!#$%&'*+/=?^`{|}~-]+@[\w-]+(?:\.[\w-]+)+")
|
||||
SENSITIVE_QUERY_PATTERN = re.compile(r"((?:api_key|email)=)([^&\s,\"']+)", re.IGNORECASE)
|
||||
|
||||
CLOUD_TYPE_LABELS = {
|
||||
"-15": "na",
|
||||
"0": "clear",
|
||||
"1": "probably_clear",
|
||||
"2": "fog",
|
||||
"3": "water",
|
||||
"4": "super_cooled_water",
|
||||
"5": "mixed",
|
||||
"6": "opaque_ice",
|
||||
"7": "cirrus",
|
||||
"8": "overlapping",
|
||||
"9": "overshooting",
|
||||
"10": "unknown",
|
||||
"11": "dust",
|
||||
"12": "smoke",
|
||||
}
|
||||
|
||||
CLOUD_SUMMARY_FIELDS = [
|
||||
"county_fips",
|
||||
"county_name",
|
||||
"state_fips",
|
||||
"state_abbr",
|
||||
"lat",
|
||||
"lon",
|
||||
"cloudinessIndexPct",
|
||||
"avgObservedToClearskyRatio",
|
||||
"daylightRows",
|
||||
"allRows",
|
||||
"clearOrProbablyClearPct",
|
||||
"cloudyOrObscuredPct",
|
||||
"fogPct",
|
||||
"waterCloudPct",
|
||||
"iceCloudPct",
|
||||
"cirrusPct",
|
||||
"unknownCloudTypePct",
|
||||
"ghi_min",
|
||||
"ghi_max",
|
||||
"clearsky_ghi_min",
|
||||
"clearsky_ghi_max",
|
||||
"source",
|
||||
"raw_csv",
|
||||
]
|
||||
|
||||
|
||||
def prompt_for_secret(prompt: str, current_value: str | None) -> str:
|
||||
"""Prompt for a sensitive value only when it was not supplied."""
|
||||
if current_value:
|
||||
return current_value
|
||||
return getpass.getpass(prompt).strip()
|
||||
|
||||
|
||||
def prompt_for_text(prompt: str, current_value: str | None) -> str:
|
||||
"""Prompt for normal text only when it was not supplied."""
|
||||
if current_value:
|
||||
return current_value
|
||||
return input(prompt).strip()
|
||||
|
||||
|
||||
def read_county_points(points_csv: Path) -> list[dict[str, str]]:
|
||||
"""Read the county point CSV."""
|
||||
with points_csv.open(newline="", encoding="utf-8") as handle:
|
||||
return list(csv.DictReader(handle))
|
||||
|
||||
|
||||
def select_county_points(
|
||||
rows: list[dict[str, str]],
|
||||
start: int,
|
||||
limit: int | None,
|
||||
completed_fips: set[str],
|
||||
overwrite: bool,
|
||||
) -> list[dict[str, str]]:
|
||||
"""Return the requested batch, skipping completed counties unless overwriting."""
|
||||
if start < 0:
|
||||
raise ValueError("--start must be 0 or greater.")
|
||||
if limit is not None and limit < 1:
|
||||
raise ValueError("--limit must be 1 or greater.")
|
||||
|
||||
candidates = rows[start:]
|
||||
if not overwrite:
|
||||
candidates = [
|
||||
row
|
||||
for row in candidates
|
||||
if row.get("county_fips") not in completed_fips
|
||||
]
|
||||
|
||||
if limit is None:
|
||||
return candidates
|
||||
return candidates[:limit]
|
||||
|
||||
|
||||
def read_existing_summary_rows(output_csv: Path) -> dict[str, dict[str, str]]:
|
||||
"""Read already summarized county cloud rows keyed by FIPS."""
|
||||
if not output_csv.exists():
|
||||
return {}
|
||||
|
||||
with output_csv.open(newline="", encoding="utf-8-sig") as handle:
|
||||
return {
|
||||
row["county_fips"]: row
|
||||
for row in csv.DictReader(handle)
|
||||
if row.get("county_fips")
|
||||
}
|
||||
|
||||
|
||||
def read_previous_error_fips(error_csv: Path) -> set[str]:
|
||||
"""Read county FIPS values from a previous error log."""
|
||||
if not error_csv.exists():
|
||||
return set()
|
||||
|
||||
with error_csv.open(newline="", encoding="utf-8") as handle:
|
||||
return {
|
||||
row["county_fips"]
|
||||
for row in csv.DictReader(handle)
|
||||
if row.get("county_fips")
|
||||
}
|
||||
|
||||
|
||||
def is_polar_candidate(point: dict[str, str], min_latitude: float) -> bool:
|
||||
"""Return whether a point is in the latitude range for the Polar endpoint."""
|
||||
try:
|
||||
return float(point["lat"]) >= min_latitude
|
||||
except (KeyError, ValueError):
|
||||
return False
|
||||
|
||||
|
||||
def build_nsrdb_url(
|
||||
endpoint: str,
|
||||
api_key: str,
|
||||
email: str,
|
||||
point: dict[str, str],
|
||||
name: str,
|
||||
attributes: str,
|
||||
interval: int,
|
||||
) -> str:
|
||||
"""Build a single-point NSRDB CSV request URL."""
|
||||
wkt = f"POINT({point['lon']} {point['lat']})"
|
||||
query = {
|
||||
"api_key": api_key,
|
||||
"wkt": wkt,
|
||||
"attributes": attributes,
|
||||
"names": name,
|
||||
"utc": "false",
|
||||
"leap_day": "false",
|
||||
"interval": str(interval),
|
||||
"email": email,
|
||||
}
|
||||
return f"{endpoint}?{urllib.parse.urlencode(query)}"
|
||||
|
||||
|
||||
def redact_url(url: str) -> str:
|
||||
"""Return a request URL with sensitive query values removed."""
|
||||
parsed_url = urllib.parse.urlsplit(url)
|
||||
query = urllib.parse.parse_qsl(parsed_url.query, keep_blank_values=True)
|
||||
redacted_query = [
|
||||
(key, "<redacted>" if key in {"api_key", "email"} else value)
|
||||
for key, value in query
|
||||
]
|
||||
return urllib.parse.urlunsplit(
|
||||
parsed_url._replace(query=urllib.parse.urlencode(redacted_query))
|
||||
)
|
||||
|
||||
|
||||
def redact_sensitive_text(text: str) -> str:
|
||||
"""Remove likely credentials and email addresses from log text."""
|
||||
without_query_values = SENSITIVE_QUERY_PATTERN.sub(r"\1<redacted>", text)
|
||||
return EMAIL_PATTERN.sub("<redacted>", without_query_values)
|
||||
|
||||
|
||||
class NsrdDataRequestError(RuntimeError):
|
||||
"""Store a failed NSRDB request with sanitized context for logging."""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
message: str,
|
||||
status_code: int | None = None,
|
||||
url: str | None = None,
|
||||
response: str = "",
|
||||
) -> None:
|
||||
super().__init__(message)
|
||||
self.status_code = status_code
|
||||
self.url = url
|
||||
self.response = response
|
||||
|
||||
|
||||
class RequestRateLimiter:
|
||||
"""Coordinate network request starts across worker threads."""
|
||||
|
||||
def __init__(self, delay: float) -> None:
|
||||
self.delay = max(0.0, delay)
|
||||
self._lock = threading.Lock()
|
||||
self._next_request_at = 0.0
|
||||
|
||||
def wait(self) -> None:
|
||||
"""Wait until the next request can start."""
|
||||
if self.delay <= 0:
|
||||
return
|
||||
|
||||
with self._lock:
|
||||
now = time.monotonic()
|
||||
wait_seconds = max(0.0, self._next_request_at - now)
|
||||
self._next_request_at = max(now, self._next_request_at) + self.delay
|
||||
|
||||
if wait_seconds:
|
||||
time.sleep(wait_seconds)
|
||||
|
||||
|
||||
def build_http_request_error(error: urllib.error.HTTPError, url: str) -> NsrdDataRequestError:
|
||||
"""Create a structured NSRDB request error from an HTTPError."""
|
||||
body = error.read().decode("utf-8-sig", errors="replace").strip()
|
||||
body_excerpt = redact_sensitive_text(body[:1000]) if body else "No response body returned."
|
||||
redacted_url = redact_url(url)
|
||||
retry_after = error.headers.get("Retry-After") if error.headers else None
|
||||
retry_message = f"\n Retry-After: {retry_after}" if retry_after else ""
|
||||
message = f"HTTP {error.code} {error.reason}\n URL: {redacted_url}{retry_message}\n Response: {body_excerpt}"
|
||||
return NsrdDataRequestError(
|
||||
message=message,
|
||||
status_code=error.code,
|
||||
url=redacted_url,
|
||||
response=body_excerpt,
|
||||
)
|
||||
|
||||
|
||||
def download_text(url: str, timeout: int, rate_limiter: RequestRateLimiter | None = None) -> str:
|
||||
"""Download text from a URL and return its decoded body."""
|
||||
request = urllib.request.Request(url, headers={"User-Agent": "county-climate-explorer/0.1"})
|
||||
try:
|
||||
if rate_limiter is not None:
|
||||
rate_limiter.wait()
|
||||
with urllib.request.urlopen(request, timeout=timeout) as response:
|
||||
return response.read().decode("utf-8-sig")
|
||||
except urllib.error.HTTPError as error:
|
||||
raise build_http_request_error(error, url) from error
|
||||
|
||||
|
||||
def cache_suffix(attributes: str) -> str:
|
||||
"""Return a stable, readable suffix for the cached CSV filename."""
|
||||
normalized = re.sub(r"[^a-z0-9]+", "-", attributes.lower()).strip("-")
|
||||
return normalized or "cloud"
|
||||
|
||||
|
||||
def read_or_download_county_csv(
|
||||
point: dict[str, str],
|
||||
endpoint: str,
|
||||
api_key: str,
|
||||
email: str,
|
||||
name: str,
|
||||
source_key: str,
|
||||
attributes: str,
|
||||
interval: int,
|
||||
cache_dir: Path,
|
||||
timeout: int,
|
||||
overwrite: bool,
|
||||
rate_limiter: RequestRateLimiter | None = None,
|
||||
) -> tuple[str, Path]:
|
||||
"""Return cached NSRDB CSV text, downloading it first when needed."""
|
||||
cache_dir.mkdir(parents=True, exist_ok=True)
|
||||
cache_path = cache_dir / f"{point['county_fips']}_{source_key}_{name}_{cache_suffix(attributes)}.csv"
|
||||
|
||||
if cache_path.exists() and not overwrite:
|
||||
return cache_path.read_text(encoding="utf-8-sig"), cache_path
|
||||
|
||||
url = build_nsrdb_url(endpoint, api_key, email, point, name, attributes, interval)
|
||||
csv_text = download_text(url, timeout, rate_limiter)
|
||||
cache_path.write_text(csv_text, encoding="utf-8")
|
||||
return csv_text, cache_path
|
||||
|
||||
|
||||
def normalized_header(value: str) -> str:
|
||||
"""Normalize a CSV header to make NSRDB spelling variations easier to match."""
|
||||
return re.sub(r"[^a-z0-9]+", "", value.lower())
|
||||
|
||||
|
||||
def find_column(header: list[str], candidates: set[str]) -> int | None:
|
||||
"""Return the first header index whose normalized name matches a candidate."""
|
||||
normalized_candidates = {normalized_header(candidate) for candidate in candidates}
|
||||
for index, value in enumerate(header):
|
||||
if normalized_header(value) in normalized_candidates:
|
||||
return index
|
||||
return None
|
||||
|
||||
|
||||
def find_data_header(rows: list[list[str]]) -> int | None:
|
||||
"""Return the NSRDB hourly data header row index."""
|
||||
required = {"Year", "Month", "Day", "Hour", "Minute"}
|
||||
for index, row in enumerate(rows):
|
||||
if required.issubset(set(row)):
|
||||
return index
|
||||
return None
|
||||
|
||||
|
||||
def parse_optional_float(row: list[str], index: int | None) -> float | None:
|
||||
"""Parse an optional float field from a CSV row."""
|
||||
if index is None or len(row) <= index:
|
||||
return None
|
||||
value = row[index].strip()
|
||||
if not value:
|
||||
return None
|
||||
try:
|
||||
parsed = float(value)
|
||||
except ValueError:
|
||||
return None
|
||||
if not math.isfinite(parsed):
|
||||
return None
|
||||
return parsed
|
||||
|
||||
|
||||
def parse_cloud_type(row: list[str], index: int | None) -> str:
|
||||
"""Parse an optional cloud type code as a string."""
|
||||
if index is None or len(row) <= index:
|
||||
return ""
|
||||
value = row[index].strip()
|
||||
if not value:
|
||||
return ""
|
||||
try:
|
||||
return str(int(float(value)))
|
||||
except ValueError:
|
||||
return value
|
||||
|
||||
|
||||
def pct(count: int, total: int) -> float:
|
||||
"""Return a percentage or NaN when there is no denominator."""
|
||||
return count / total * 100 if total else math.nan
|
||||
|
||||
|
||||
def fmt(value: float | None, places: int = 3) -> str:
|
||||
"""Format an optional float for CSV output."""
|
||||
if value is None or not math.isfinite(value):
|
||||
return ""
|
||||
return f"{value:.{places}f}"
|
||||
|
||||
|
||||
def summarize_cloud_metrics(
|
||||
point: dict[str, str],
|
||||
csv_text: str,
|
||||
source_file: Path,
|
||||
source_label: str,
|
||||
min_clearsky_ghi: float,
|
||||
) -> dict[str, str]:
|
||||
"""Convert hourly GHI/Clearsky GHI/Cloud Type rows into county metrics."""
|
||||
rows = list(csv.reader(csv_text.splitlines()))
|
||||
header_index = find_data_header(rows)
|
||||
if header_index is None:
|
||||
raise ValueError("Could not find the NSRDB data header row.")
|
||||
|
||||
header = rows[header_index]
|
||||
ghi_index = find_column(header, {"GHI", "ghi"})
|
||||
clearsky_ghi_index = find_column(
|
||||
header,
|
||||
{
|
||||
"Clearsky GHI",
|
||||
"Clear Sky GHI",
|
||||
"Clear-sky GHI",
|
||||
"clearsky_ghi",
|
||||
"clear_sky_ghi",
|
||||
},
|
||||
)
|
||||
cloud_type_index = find_column(header, {"Cloud Type", "cloud_type", "cloudtype"})
|
||||
if ghi_index is None:
|
||||
raise ValueError("Could not find a GHI column in the NSRDB response.")
|
||||
if clearsky_ghi_index is None:
|
||||
raise ValueError("Could not find a Clearsky GHI column in the NSRDB response.")
|
||||
if cloud_type_index is None:
|
||||
raise ValueError("Could not find a Cloud Type column in the NSRDB response.")
|
||||
|
||||
all_rows = 0
|
||||
daylight_rows = 0
|
||||
ratio_sum = 0.0
|
||||
ghi_values: list[float] = []
|
||||
clearsky_values: list[float] = []
|
||||
daylight_cloud_type_counts: dict[str, int] = {}
|
||||
|
||||
for row in rows[header_index + 1 :]:
|
||||
if not row:
|
||||
continue
|
||||
all_rows += 1
|
||||
ghi = parse_optional_float(row, ghi_index)
|
||||
clearsky_ghi = parse_optional_float(row, clearsky_ghi_index)
|
||||
cloud_type = parse_cloud_type(row, cloud_type_index)
|
||||
if ghi is None or clearsky_ghi is None:
|
||||
continue
|
||||
ghi_values.append(ghi)
|
||||
clearsky_values.append(clearsky_ghi)
|
||||
if clearsky_ghi < min_clearsky_ghi:
|
||||
continue
|
||||
|
||||
ratio_sum += min(1.0, max(0.0, ghi / clearsky_ghi))
|
||||
daylight_rows += 1
|
||||
if cloud_type:
|
||||
daylight_cloud_type_counts[cloud_type] = daylight_cloud_type_counts.get(cloud_type, 0) + 1
|
||||
|
||||
if daylight_rows == 0:
|
||||
raise ValueError("No daylight rows with valid GHI and Clearsky GHI were found.")
|
||||
|
||||
avg_ratio = ratio_sum / daylight_rows
|
||||
cloudiness_pct = 1 - avg_ratio
|
||||
clear_or_probably_clear = daylight_cloud_type_counts.get("0", 0) + daylight_cloud_type_counts.get("1", 0)
|
||||
cloudy_or_obscured = sum(
|
||||
daylight_cloud_type_counts.get(code, 0)
|
||||
for code in ["2", "3", "4", "5", "6", "7", "8", "9", "11", "12"]
|
||||
)
|
||||
water_cloud = (
|
||||
daylight_cloud_type_counts.get("3", 0)
|
||||
+ daylight_cloud_type_counts.get("4", 0)
|
||||
+ daylight_cloud_type_counts.get("5", 0)
|
||||
)
|
||||
ice_cloud = (
|
||||
daylight_cloud_type_counts.get("6", 0)
|
||||
+ daylight_cloud_type_counts.get("8", 0)
|
||||
+ daylight_cloud_type_counts.get("9", 0)
|
||||
)
|
||||
unknown_cloud_type = daylight_cloud_type_counts.get("10", 0) + daylight_cloud_type_counts.get("-15", 0)
|
||||
|
||||
return {
|
||||
"county_fips": point["county_fips"],
|
||||
"county_name": point["county_name"],
|
||||
"state_fips": point["state_fips"],
|
||||
"state_abbr": point["state_abbr"],
|
||||
"lat": point["lat"],
|
||||
"lon": point["lon"],
|
||||
"cloudinessIndexPct": fmt(cloudiness_pct, 4),
|
||||
"avgObservedToClearskyRatio": fmt(avg_ratio, 4),
|
||||
"daylightRows": str(daylight_rows),
|
||||
"allRows": str(all_rows),
|
||||
"clearOrProbablyClearPct": fmt(pct(clear_or_probably_clear, daylight_rows), 2),
|
||||
"cloudyOrObscuredPct": fmt(pct(cloudy_or_obscured, daylight_rows), 2),
|
||||
"fogPct": fmt(pct(daylight_cloud_type_counts.get("2", 0), daylight_rows), 2),
|
||||
"waterCloudPct": fmt(pct(water_cloud, daylight_rows), 2),
|
||||
"iceCloudPct": fmt(pct(ice_cloud, daylight_rows), 2),
|
||||
"cirrusPct": fmt(pct(daylight_cloud_type_counts.get("7", 0), daylight_rows), 2),
|
||||
"unknownCloudTypePct": fmt(pct(unknown_cloud_type, daylight_rows), 2),
|
||||
"ghi_min": fmt(min(ghi_values), 1) if ghi_values else "",
|
||||
"ghi_max": fmt(max(ghi_values), 1) if ghi_values else "",
|
||||
"clearsky_ghi_min": fmt(min(clearsky_values), 1) if clearsky_values else "",
|
||||
"clearsky_ghi_max": fmt(max(clearsky_values), 1) if clearsky_values else "",
|
||||
"source": f"{source_label} representative point",
|
||||
"raw_csv": str(source_file),
|
||||
}
|
||||
|
||||
|
||||
def write_summary_csv(rows: list[dict[str, str]], output_csv: Path) -> None:
|
||||
"""Write fetched county cloud summaries to CSV."""
|
||||
output_csv.parent.mkdir(parents=True, exist_ok=True)
|
||||
with output_csv.open("w", newline="", encoding="utf-8") as handle:
|
||||
writer = csv.DictWriter(handle, fieldnames=CLOUD_SUMMARY_FIELDS)
|
||||
writer.writeheader()
|
||||
writer.writerows(rows)
|
||||
|
||||
|
||||
def merged_summary_rows(
|
||||
all_points: list[dict[str, str]],
|
||||
existing_rows_by_fips: dict[str, dict[str, str]],
|
||||
new_rows: list[dict[str, str]],
|
||||
) -> list[dict[str, str]]:
|
||||
"""Return existing and newly fetched rows in county point order."""
|
||||
rows_by_fips = dict(existing_rows_by_fips)
|
||||
for row in new_rows:
|
||||
rows_by_fips[row["county_fips"]] = row
|
||||
|
||||
ordered_rows: list[dict[str, str]] = []
|
||||
seen_fips: set[str] = set()
|
||||
for point in all_points:
|
||||
county_fips = point.get("county_fips", "")
|
||||
row = rows_by_fips.get(county_fips)
|
||||
if row is not None:
|
||||
ordered_rows.append(row)
|
||||
seen_fips.add(county_fips)
|
||||
|
||||
extra_rows = [
|
||||
row
|
||||
for county_fips, row in sorted(rows_by_fips.items())
|
||||
if county_fips not in seen_fips
|
||||
]
|
||||
return ordered_rows + extra_rows
|
||||
|
||||
|
||||
def write_error_csv(rows: list[dict[str, str]], error_csv: Path) -> None:
|
||||
"""Write failed county cloud fetches to CSV."""
|
||||
error_csv.parent.mkdir(parents=True, exist_ok=True)
|
||||
with error_csv.open("w", newline="", encoding="utf-8") as handle:
|
||||
writer = csv.DictWriter(
|
||||
handle,
|
||||
fieldnames=[
|
||||
"county_fips",
|
||||
"county_name",
|
||||
"state_fips",
|
||||
"state_abbr",
|
||||
"lat",
|
||||
"lon",
|
||||
"error_type",
|
||||
"status_code",
|
||||
"request_url",
|
||||
"response",
|
||||
"message",
|
||||
],
|
||||
)
|
||||
writer.writeheader()
|
||||
writer.writerows(rows)
|
||||
|
||||
|
||||
def build_error_row(point: dict[str, str], error: Exception) -> dict[str, str]:
|
||||
"""Convert a failed county fetch into a structured CSV row."""
|
||||
status_code = ""
|
||||
request_url = ""
|
||||
response = ""
|
||||
|
||||
if isinstance(error, NsrdDataRequestError):
|
||||
status_code = str(error.status_code or "")
|
||||
request_url = error.url or ""
|
||||
response = error.response
|
||||
|
||||
return {
|
||||
"county_fips": point["county_fips"],
|
||||
"county_name": point["county_name"],
|
||||
"state_fips": point["state_fips"],
|
||||
"state_abbr": point["state_abbr"],
|
||||
"lat": point["lat"],
|
||||
"lon": point["lon"],
|
||||
"error_type": type(error).__name__,
|
||||
"status_code": status_code,
|
||||
"request_url": request_url,
|
||||
"response": redact_sensitive_text(response),
|
||||
"message": redact_sensitive_text(str(error)),
|
||||
}
|
||||
|
||||
|
||||
def build_request_profiles(
|
||||
args: argparse.Namespace,
|
||||
point: dict[str, str],
|
||||
previous_error_fips: set[str],
|
||||
) -> list[dict[str, str]]:
|
||||
"""Build the ordered list of NSRDB endpoints to try for a county point."""
|
||||
goes_profile = {
|
||||
"source_key": "goes-tmy",
|
||||
"source_label": "NSRDB GOES TMY PSM v4",
|
||||
"endpoint": args.endpoint,
|
||||
"name": args.name,
|
||||
}
|
||||
polar_profile = {
|
||||
"source_key": "polar-tmy",
|
||||
"source_label": "NSRDB Polar TMY PSM v4",
|
||||
"endpoint": args.polar_endpoint,
|
||||
"name": args.polar_name,
|
||||
}
|
||||
|
||||
if not args.polar_fallback or not is_polar_candidate(point, args.polar_min_latitude):
|
||||
return [goes_profile]
|
||||
if point["county_fips"] in previous_error_fips:
|
||||
return [polar_profile, goes_profile]
|
||||
return [goes_profile, polar_profile]
|
||||
|
||||
|
||||
def should_try_next_profile(error: Exception, profile_index: int, profiles: list[dict[str, str]]) -> bool:
|
||||
"""Return whether another configured endpoint should be tried after this error."""
|
||||
if profile_index >= len(profiles) - 1:
|
||||
return False
|
||||
if not isinstance(error, NsrdDataRequestError):
|
||||
return False
|
||||
return error.status_code == 400 and "No data available at the provided location" in error.response
|
||||
|
||||
|
||||
def fetch_county_summary(
|
||||
args: argparse.Namespace,
|
||||
point: dict[str, str],
|
||||
previous_error_fips: set[str],
|
||||
api_key: str,
|
||||
email: str,
|
||||
rate_limiter: RequestRateLimiter,
|
||||
) -> tuple[dict[str, str] | None, dict[str, str] | None, list[str]]:
|
||||
"""Fetch and summarize one county point."""
|
||||
profiles = build_request_profiles(args, point, previous_error_fips)
|
||||
final_error: Exception | None = None
|
||||
messages: list[str] = []
|
||||
|
||||
for profile_index, profile in enumerate(profiles):
|
||||
try:
|
||||
messages.append(f"Trying {profile['source_label']} ({profile['name']})")
|
||||
csv_text, source_file = read_or_download_county_csv(
|
||||
point=point,
|
||||
endpoint=profile["endpoint"],
|
||||
api_key=api_key,
|
||||
email=email,
|
||||
name=profile["name"],
|
||||
source_key=profile["source_key"],
|
||||
attributes=args.attributes,
|
||||
interval=args.interval,
|
||||
cache_dir=args.cache_dir,
|
||||
timeout=args.timeout,
|
||||
overwrite=args.overwrite,
|
||||
rate_limiter=rate_limiter,
|
||||
)
|
||||
summary = summarize_cloud_metrics(
|
||||
point=point,
|
||||
csv_text=csv_text,
|
||||
source_file=source_file,
|
||||
source_label=profile["source_label"],
|
||||
min_clearsky_ghi=args.min_clearsky_ghi,
|
||||
)
|
||||
return summary, None, messages
|
||||
except (OSError, urllib.error.URLError, RuntimeError, ValueError) as error:
|
||||
final_error = error
|
||||
if should_try_next_profile(error, profile_index, profiles):
|
||||
messages.append(f"{profile['source_label']} had no data; trying fallback.")
|
||||
continue
|
||||
break
|
||||
|
||||
if final_error is None:
|
||||
final_error = RuntimeError("No NSRDB profiles were available for this county.")
|
||||
return None, build_error_row(point, final_error), messages
|
||||
|
||||
|
||||
def log_county_result(
|
||||
index: int,
|
||||
total: int,
|
||||
point: dict[str, str],
|
||||
summary: dict[str, str] | None,
|
||||
error: dict[str, str] | None,
|
||||
messages: list[str],
|
||||
) -> None:
|
||||
"""Print progress for one completed county."""
|
||||
label = f"{point['county_fips']} {point['county_name']}, {point['state_abbr']}"
|
||||
status = "Fetched" if summary is not None else "Skipped"
|
||||
print(f"[{index}/{total}] {status} {label}")
|
||||
for message in messages:
|
||||
print(f" {message}")
|
||||
if summary is not None:
|
||||
print(
|
||||
" "
|
||||
f"cloudiness={summary['cloudinessIndexPct']}, "
|
||||
f"clear_ratio={summary['avgObservedToClearskyRatio']}, "
|
||||
f"daylight_rows={summary['daylightRows']}"
|
||||
)
|
||||
if error is not None:
|
||||
print(f" Error: {error['message']}")
|
||||
|
||||
|
||||
def fetch_batch(
|
||||
args: argparse.Namespace,
|
||||
all_points: list[dict[str, str]],
|
||||
existing_rows_by_fips: dict[str, dict[str, str]],
|
||||
) -> list[dict[str, str]]:
|
||||
"""Fetch and summarize the requested county point batch."""
|
||||
points = select_county_points(
|
||||
rows=all_points,
|
||||
start=args.start,
|
||||
limit=args.limit,
|
||||
completed_fips=set(existing_rows_by_fips),
|
||||
overwrite=args.overwrite,
|
||||
)
|
||||
skipped_count = len(all_points[args.start :]) - len(
|
||||
select_county_points(
|
||||
rows=all_points,
|
||||
start=args.start,
|
||||
limit=None,
|
||||
completed_fips=set(existing_rows_by_fips),
|
||||
overwrite=args.overwrite,
|
||||
)
|
||||
)
|
||||
if skipped_count and not args.overwrite:
|
||||
print(f"Skipping {skipped_count} counties already present in {args.output_csv}.")
|
||||
if not points:
|
||||
write_error_csv([], args.error_csv)
|
||||
print("No missing counties selected for fetch.")
|
||||
return []
|
||||
|
||||
email = prompt_for_text("NLR/NREL API email: ", args.email)
|
||||
api_key = prompt_for_secret("NLR/NREL API key: ", args.api_key)
|
||||
|
||||
previous_error_fips = read_previous_error_fips(args.error_csv)
|
||||
rate_limiter = RequestRateLimiter(args.delay)
|
||||
summaries_by_index: dict[int, dict[str, str]] = {}
|
||||
errors_by_index: dict[int, dict[str, str]] = {}
|
||||
total = len(points)
|
||||
|
||||
if args.workers == 1:
|
||||
for index, point in enumerate(points, start=1):
|
||||
summary, error, messages = fetch_county_summary(
|
||||
args=args,
|
||||
point=point,
|
||||
previous_error_fips=previous_error_fips,
|
||||
api_key=api_key,
|
||||
email=email,
|
||||
rate_limiter=rate_limiter,
|
||||
)
|
||||
if summary is not None:
|
||||
summaries_by_index[index] = summary
|
||||
if error is not None:
|
||||
errors_by_index[index] = error
|
||||
log_county_result(index, total, point, summary, error, messages)
|
||||
else:
|
||||
with concurrent.futures.ThreadPoolExecutor(max_workers=args.workers) as executor:
|
||||
future_to_context = {
|
||||
executor.submit(
|
||||
fetch_county_summary,
|
||||
args,
|
||||
point,
|
||||
previous_error_fips,
|
||||
api_key,
|
||||
email,
|
||||
rate_limiter,
|
||||
): (index, point)
|
||||
for index, point in enumerate(points, start=1)
|
||||
}
|
||||
|
||||
for future in concurrent.futures.as_completed(future_to_context):
|
||||
index, point = future_to_context[future]
|
||||
summary, error, messages = future.result()
|
||||
if summary is not None:
|
||||
summaries_by_index[index] = summary
|
||||
if error is not None:
|
||||
errors_by_index[index] = error
|
||||
log_county_result(index, total, point, summary, error, messages)
|
||||
|
||||
errors = [errors_by_index[index] for index in sorted(errors_by_index)]
|
||||
write_error_csv(errors, args.error_csv)
|
||||
return [summaries_by_index[index] for index in sorted(summaries_by_index)]
|
||||
|
||||
|
||||
def parse_args() -> argparse.Namespace:
|
||||
"""Parse command-line arguments."""
|
||||
parser = argparse.ArgumentParser(description=__doc__)
|
||||
parser.add_argument("--points-csv", type=Path, default=DEFAULT_POINTS_CSV)
|
||||
parser.add_argument("--output-csv", type=Path, default=DEFAULT_OUTPUT_CSV)
|
||||
parser.add_argument("--error-csv", type=Path, default=DEFAULT_ERROR_CSV)
|
||||
parser.add_argument("--cache-dir", type=Path, default=DEFAULT_CACHE_DIR)
|
||||
parser.add_argument("--endpoint", default=DEFAULT_ENDPOINT)
|
||||
parser.add_argument("--polar-endpoint", default=DEFAULT_POLAR_ENDPOINT)
|
||||
parser.add_argument("--name", default=DEFAULT_TMY_NAME, help="NSRDB GOES TMY name, such as tmy or tmy-2024.")
|
||||
parser.add_argument("--polar-name", default=DEFAULT_POLAR_TMY_NAME, help="NSRDB Polar TMY name, usually tmy.")
|
||||
parser.add_argument("--attributes", default=DEFAULT_ATTRIBUTES, help="Comma-delimited NSRDB attributes to request.")
|
||||
parser.add_argument("--interval", type=int, default=DEFAULT_INTERVAL, help="NSRDB interval in minutes.")
|
||||
parser.add_argument("--polar-min-latitude", type=float, default=DEFAULT_POLAR_MIN_LATITUDE)
|
||||
parser.add_argument(
|
||||
"--min-clearsky-ghi",
|
||||
type=float,
|
||||
default=DEFAULT_MIN_CLEARSKY_GHI,
|
||||
help="Minimum Clearsky GHI W/m2 for daylight cloudiness ratio rows.",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--no-polar-fallback",
|
||||
action="store_false",
|
||||
dest="polar_fallback",
|
||||
help="Disable retrying failed high-latitude GOES requests against the NSRDB Polar endpoint.",
|
||||
)
|
||||
parser.add_argument("--email", help="Email address registered with the NLR/NREL API.")
|
||||
parser.add_argument("--api-key", help="API key. If omitted, the script prompts securely.")
|
||||
parser.add_argument("--start", type=int, default=0, help="Zero-based row offset in the points CSV.")
|
||||
parser.add_argument("--limit", type=int, default=10, help="Number of counties to fetch. Use --all for all counties.")
|
||||
parser.add_argument("--all", action="store_true", help="Fetch all counties from --start onward.")
|
||||
parser.add_argument("--delay", type=float, default=1.0, help="Minimum seconds between new API requests.")
|
||||
parser.add_argument("--workers", type=int, default=4, help="Number of county jobs to run at once.")
|
||||
parser.add_argument("--timeout", type=int, default=120, help="Request timeout in seconds.")
|
||||
parser.add_argument("--overwrite", action="store_true", help="Re-download cached county CSV files.")
|
||||
args = parser.parse_args()
|
||||
if args.all:
|
||||
args.limit = None
|
||||
if args.delay < 0:
|
||||
parser.error("--delay must be 0 or greater.")
|
||||
if args.workers < 1:
|
||||
parser.error("--workers must be 1 or greater.")
|
||||
if args.interval <= 0:
|
||||
parser.error("--interval must be greater than 0.")
|
||||
if args.min_clearsky_ghi < 0:
|
||||
parser.error("--min-clearsky-ghi must be 0 or greater.")
|
||||
return args
|
||||
|
||||
|
||||
def main() -> None:
|
||||
"""Run the NSRDB representative-point cloud metric fetch."""
|
||||
args = parse_args()
|
||||
all_points = read_county_points(args.points_csv)
|
||||
existing_rows_by_fips = read_existing_summary_rows(args.output_csv)
|
||||
summaries = fetch_batch(args, all_points, existing_rows_by_fips)
|
||||
output_rows = merged_summary_rows(all_points, existing_rows_by_fips, summaries)
|
||||
write_summary_csv(output_rows, args.output_csv)
|
||||
print(
|
||||
f"Wrote {len(output_rows)} county cloud summaries to {args.output_csv} "
|
||||
f"({len(summaries)} newly fetched)."
|
||||
)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@@ -0,0 +1,543 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
Fetch NSRDB GOES TMY GHI data for county representative points.
|
||||
|
||||
This is designed for a small first test batch. It prompts for email and API key
|
||||
when those values are not passed as command-line arguments.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import argparse
|
||||
import concurrent.futures
|
||||
import csv
|
||||
import getpass
|
||||
import re
|
||||
import threading
|
||||
import time
|
||||
import urllib.error
|
||||
import urllib.parse
|
||||
import urllib.request
|
||||
from pathlib import Path
|
||||
|
||||
|
||||
DEFAULT_POINTS_CSV = Path("data/nrel/county_representative_points.csv")
|
||||
DEFAULT_OUTPUT_CSV = Path("data/nrel/county_representative_point_ghi_summary.csv")
|
||||
DEFAULT_ERROR_CSV = Path("data/nrel/county_representative_point_ghi_error_log.csv")
|
||||
DEFAULT_CACHE_DIR = Path("data/nrel/representative_point_csv")
|
||||
DEFAULT_ENDPOINT = "https://developer.nlr.gov/api/nsrdb/v2/solar/nsrdb-GOES-tmy-v4-0-0-download.csv"
|
||||
DEFAULT_POLAR_ENDPOINT = "https://developer.nlr.gov/api/nsrdb/v2/solar/nsrdb-polar-tmy-v4-0-0-download.csv"
|
||||
DEFAULT_TMY_NAME = "tmy-2024"
|
||||
DEFAULT_POLAR_TMY_NAME = "tmy"
|
||||
DEFAULT_POLAR_MIN_LATITUDE = 60.0
|
||||
EMAIL_PATTERN = re.compile(r"[\w.!#$%&'*+/=?^`{|}~-]+@[\w-]+(?:\.[\w-]+)+")
|
||||
SENSITIVE_QUERY_PATTERN = re.compile(r"((?:api_key|email)=)([^&\s,\"']+)", re.IGNORECASE)
|
||||
|
||||
|
||||
def prompt_for_secret(prompt: str, current_value: str | None) -> str:
|
||||
"""Prompt for a sensitive value only when it was not supplied."""
|
||||
if current_value:
|
||||
return current_value
|
||||
return getpass.getpass(prompt).strip()
|
||||
|
||||
|
||||
def prompt_for_text(prompt: str, current_value: str | None) -> str:
|
||||
"""Prompt for a normal text value only when it was not supplied."""
|
||||
if current_value:
|
||||
return current_value
|
||||
return input(prompt).strip()
|
||||
|
||||
|
||||
def read_county_points(points_csv: Path, start: int, limit: int | None) -> list[dict[str, str]]:
|
||||
"""Read the county point CSV and return the requested batch."""
|
||||
with points_csv.open(newline="", encoding="utf-8") as handle:
|
||||
rows = list(csv.DictReader(handle))
|
||||
|
||||
if start < 0:
|
||||
raise ValueError("--start must be 0 or greater.")
|
||||
|
||||
if limit is None:
|
||||
return rows[start:]
|
||||
|
||||
if limit < 1:
|
||||
raise ValueError("--limit must be 1 or greater.")
|
||||
|
||||
return rows[start : start + limit]
|
||||
|
||||
|
||||
def read_previous_error_fips(error_csv: Path) -> set[str]:
|
||||
"""Read county FIPS values from a previous error log."""
|
||||
if not error_csv.exists():
|
||||
return set()
|
||||
|
||||
with error_csv.open(newline="", encoding="utf-8") as handle:
|
||||
return {
|
||||
row["county_fips"]
|
||||
for row in csv.DictReader(handle)
|
||||
if row.get("county_fips")
|
||||
}
|
||||
|
||||
|
||||
def is_polar_candidate(point: dict[str, str], min_latitude: float) -> bool:
|
||||
"""Return whether a point is in the latitude range for the Polar NSRDB endpoint."""
|
||||
try:
|
||||
return float(point["lat"]) >= min_latitude
|
||||
except (KeyError, ValueError):
|
||||
return False
|
||||
|
||||
|
||||
def build_nsrdb_url(endpoint: str, api_key: str, email: str, point: dict[str, str], name: str) -> str:
|
||||
"""Build a single-point NSRDB CSV request URL."""
|
||||
wkt = f"POINT({point['lon']} {point['lat']})"
|
||||
query = {
|
||||
"api_key": api_key,
|
||||
"wkt": wkt,
|
||||
"attributes": "ghi",
|
||||
"names": name,
|
||||
"utc": "false",
|
||||
"leap_day": "false",
|
||||
"interval": "60",
|
||||
"email": email,
|
||||
}
|
||||
return f"{endpoint}?{urllib.parse.urlencode(query)}"
|
||||
|
||||
|
||||
def redact_url(url: str) -> str:
|
||||
"""Return a request URL with sensitive query values removed."""
|
||||
parsed_url = urllib.parse.urlsplit(url)
|
||||
query = urllib.parse.parse_qsl(parsed_url.query, keep_blank_values=True)
|
||||
redacted_query = [
|
||||
(key, "<redacted>" if key in {"api_key", "email"} else value)
|
||||
for key, value in query
|
||||
]
|
||||
return urllib.parse.urlunsplit(
|
||||
parsed_url._replace(query=urllib.parse.urlencode(redacted_query))
|
||||
)
|
||||
|
||||
|
||||
def redact_sensitive_text(text: str) -> str:
|
||||
"""Remove likely credentials and email addresses from log text."""
|
||||
without_query_values = SENSITIVE_QUERY_PATTERN.sub(r"\1<redacted>", text)
|
||||
return EMAIL_PATTERN.sub("<redacted>", without_query_values)
|
||||
|
||||
|
||||
class NsrdDataRequestError(RuntimeError):
|
||||
"""Store a failed NSRDB request with sanitized context for logging."""
|
||||
|
||||
def __init__(self, message: str, status_code: int | None = None, url: str | None = None, response: str = "") -> None:
|
||||
super().__init__(message)
|
||||
self.status_code = status_code
|
||||
self.url = url
|
||||
self.response = response
|
||||
|
||||
|
||||
class RequestRateLimiter:
|
||||
"""Coordinate network request starts across worker threads."""
|
||||
|
||||
def __init__(self, delay: float) -> None:
|
||||
self.delay = max(0.0, delay)
|
||||
self._lock = threading.Lock()
|
||||
self._next_request_at = 0.0
|
||||
|
||||
def wait(self) -> None:
|
||||
"""Wait until the next request can start."""
|
||||
if self.delay <= 0:
|
||||
return
|
||||
|
||||
with self._lock:
|
||||
now = time.monotonic()
|
||||
wait_seconds = max(0.0, self._next_request_at - now)
|
||||
self._next_request_at = max(now, self._next_request_at) + self.delay
|
||||
|
||||
if wait_seconds:
|
||||
time.sleep(wait_seconds)
|
||||
|
||||
|
||||
def build_http_request_error(error: urllib.error.HTTPError, url: str) -> NsrdDataRequestError:
|
||||
"""Create a structured NSRDB request error from an HTTPError."""
|
||||
body = error.read().decode("utf-8-sig", errors="replace").strip()
|
||||
body_excerpt = redact_sensitive_text(body[:1000]) if body else "No response body returned."
|
||||
redacted_url = redact_url(url)
|
||||
retry_after = error.headers.get("Retry-After") if error.headers else None
|
||||
retry_message = f"\n Retry-After: {retry_after}" if retry_after else ""
|
||||
message = f"HTTP {error.code} {error.reason}\n URL: {redacted_url}{retry_message}\n Response: {body_excerpt}"
|
||||
return NsrdDataRequestError(
|
||||
message=message,
|
||||
status_code=error.code,
|
||||
url=redacted_url,
|
||||
response=body_excerpt,
|
||||
)
|
||||
|
||||
|
||||
def download_text(url: str, timeout: int, rate_limiter: RequestRateLimiter | None = None) -> str:
|
||||
"""Download text from a URL and return its decoded body."""
|
||||
request = urllib.request.Request(url, headers={"User-Agent": "county-climate-explorer/0.1"})
|
||||
try:
|
||||
if rate_limiter is not None:
|
||||
rate_limiter.wait()
|
||||
with urllib.request.urlopen(request, timeout=timeout) as response:
|
||||
return response.read().decode("utf-8-sig")
|
||||
except urllib.error.HTTPError as error:
|
||||
raise build_http_request_error(error, url) from error
|
||||
|
||||
|
||||
def read_or_download_county_csv(
|
||||
point: dict[str, str],
|
||||
endpoint: str,
|
||||
api_key: str,
|
||||
email: str,
|
||||
name: str,
|
||||
source_key: str,
|
||||
cache_dir: Path,
|
||||
timeout: int,
|
||||
overwrite: bool,
|
||||
rate_limiter: RequestRateLimiter | None = None,
|
||||
) -> tuple[str, Path]:
|
||||
"""Return cached NSRDB CSV text, downloading it first when needed."""
|
||||
cache_dir.mkdir(parents=True, exist_ok=True)
|
||||
cache_path = cache_dir / f"{point['county_fips']}_{source_key}_{name}_ghi.csv"
|
||||
|
||||
if cache_path.exists() and not overwrite:
|
||||
return cache_path.read_text(encoding="utf-8-sig"), cache_path
|
||||
|
||||
url = build_nsrdb_url(endpoint, api_key, email, point, name)
|
||||
csv_text = download_text(url, timeout, rate_limiter)
|
||||
cache_path.write_text(csv_text, encoding="utf-8")
|
||||
return csv_text, cache_path
|
||||
|
||||
|
||||
def extract_ghi_values(csv_text: str) -> list[float]:
|
||||
"""Extract numeric GHI values from an NSRDB CSV response."""
|
||||
rows = list(csv.reader(csv_text.splitlines()))
|
||||
header_index = next(
|
||||
(
|
||||
index
|
||||
for index, row in enumerate(rows)
|
||||
if {"Year", "Month", "Day", "Hour", "Minute", "GHI"}.issubset(set(row))
|
||||
),
|
||||
None,
|
||||
)
|
||||
|
||||
if header_index is None:
|
||||
raise ValueError("Could not find the NSRDB data header row with a GHI column.")
|
||||
|
||||
ghi_index = rows[header_index].index("GHI")
|
||||
ghi_values: list[float] = []
|
||||
for row in rows[header_index + 1 :]:
|
||||
if len(row) <= ghi_index or not row[ghi_index].strip():
|
||||
continue
|
||||
ghi_values.append(float(row[ghi_index]))
|
||||
|
||||
if not ghi_values:
|
||||
raise ValueError("No GHI values were found in the NSRDB response.")
|
||||
|
||||
return ghi_values
|
||||
|
||||
|
||||
def summarize_ghi(point: dict[str, str], csv_text: str, source_file: Path, source_label: str) -> dict[str, str]:
|
||||
"""Convert hourly GHI values into average daily kWh/m2/day."""
|
||||
ghi_values = extract_ghi_values(csv_text)
|
||||
ghi_sum = sum(ghi_values)
|
||||
avg_daily_ghi = ghi_sum / 1000 / 365
|
||||
|
||||
return {
|
||||
"county_fips": point["county_fips"],
|
||||
"county_name": point["county_name"],
|
||||
"state_fips": point["state_fips"],
|
||||
"state_abbr": point["state_abbr"],
|
||||
"lat": point["lat"],
|
||||
"lon": point["lon"],
|
||||
"avgSolarGhiKwhM2Day": f"{avg_daily_ghi:.3f}",
|
||||
"ghi_rows": str(len(ghi_values)),
|
||||
"ghi_min": f"{min(ghi_values):.1f}",
|
||||
"ghi_max": f"{max(ghi_values):.1f}",
|
||||
"source": f"{source_label} representative point",
|
||||
"raw_csv": str(source_file),
|
||||
}
|
||||
|
||||
|
||||
def write_summary_csv(rows: list[dict[str, str]], output_csv: Path) -> None:
|
||||
"""Write fetched county solar summaries to CSV."""
|
||||
output_csv.parent.mkdir(parents=True, exist_ok=True)
|
||||
with output_csv.open("w", newline="", encoding="utf-8") as handle:
|
||||
writer = csv.DictWriter(
|
||||
handle,
|
||||
fieldnames=[
|
||||
"county_fips",
|
||||
"county_name",
|
||||
"state_fips",
|
||||
"state_abbr",
|
||||
"lat",
|
||||
"lon",
|
||||
"avgSolarGhiKwhM2Day",
|
||||
"ghi_rows",
|
||||
"ghi_min",
|
||||
"ghi_max",
|
||||
"source",
|
||||
"raw_csv",
|
||||
],
|
||||
)
|
||||
writer.writeheader()
|
||||
writer.writerows(rows)
|
||||
|
||||
|
||||
def write_error_csv(rows: list[dict[str, str]], error_csv: Path) -> None:
|
||||
"""Write failed county solar fetches to CSV."""
|
||||
error_csv.parent.mkdir(parents=True, exist_ok=True)
|
||||
with error_csv.open("w", newline="", encoding="utf-8") as handle:
|
||||
writer = csv.DictWriter(
|
||||
handle,
|
||||
fieldnames=[
|
||||
"county_fips",
|
||||
"county_name",
|
||||
"state_fips",
|
||||
"state_abbr",
|
||||
"lat",
|
||||
"lon",
|
||||
"error_type",
|
||||
"status_code",
|
||||
"request_url",
|
||||
"response",
|
||||
"message",
|
||||
],
|
||||
)
|
||||
writer.writeheader()
|
||||
writer.writerows(rows)
|
||||
|
||||
|
||||
def build_error_row(point: dict[str, str], error: Exception) -> dict[str, str]:
|
||||
"""Convert a failed county fetch into a structured CSV row."""
|
||||
status_code = ""
|
||||
request_url = ""
|
||||
response = ""
|
||||
|
||||
if isinstance(error, NsrdDataRequestError):
|
||||
status_code = str(error.status_code or "")
|
||||
request_url = error.url or ""
|
||||
response = error.response
|
||||
|
||||
return {
|
||||
"county_fips": point["county_fips"],
|
||||
"county_name": point["county_name"],
|
||||
"state_fips": point["state_fips"],
|
||||
"state_abbr": point["state_abbr"],
|
||||
"lat": point["lat"],
|
||||
"lon": point["lon"],
|
||||
"error_type": type(error).__name__,
|
||||
"status_code": status_code,
|
||||
"request_url": request_url,
|
||||
"response": redact_sensitive_text(response),
|
||||
"message": redact_sensitive_text(str(error)),
|
||||
}
|
||||
|
||||
|
||||
def build_request_profiles(
|
||||
args: argparse.Namespace,
|
||||
point: dict[str, str],
|
||||
previous_error_fips: set[str],
|
||||
) -> list[dict[str, str]]:
|
||||
"""Build the ordered list of NSRDB endpoints to try for a county point."""
|
||||
goes_profile = {
|
||||
"source_key": "goes-tmy",
|
||||
"source_label": "NSRDB GOES TMY PSM v4",
|
||||
"endpoint": args.endpoint,
|
||||
"name": args.name,
|
||||
}
|
||||
polar_profile = {
|
||||
"source_key": "polar-tmy",
|
||||
"source_label": "NSRDB Polar TMY PSM v4",
|
||||
"endpoint": args.polar_endpoint,
|
||||
"name": args.polar_name,
|
||||
}
|
||||
|
||||
if not args.polar_fallback or not is_polar_candidate(point, args.polar_min_latitude):
|
||||
return [goes_profile]
|
||||
|
||||
if point["county_fips"] in previous_error_fips:
|
||||
return [polar_profile, goes_profile]
|
||||
|
||||
return [goes_profile, polar_profile]
|
||||
|
||||
|
||||
def should_try_next_profile(error: Exception, profile_index: int, profiles: list[dict[str, str]]) -> bool:
|
||||
"""Return whether another configured endpoint should be tried after this error."""
|
||||
if profile_index >= len(profiles) - 1:
|
||||
return False
|
||||
|
||||
if not isinstance(error, NsrdDataRequestError):
|
||||
return False
|
||||
|
||||
return error.status_code == 400 and "No data available at the provided location" in error.response
|
||||
|
||||
|
||||
def fetch_county_summary(
|
||||
args: argparse.Namespace,
|
||||
point: dict[str, str],
|
||||
previous_error_fips: set[str],
|
||||
api_key: str,
|
||||
email: str,
|
||||
rate_limiter: RequestRateLimiter,
|
||||
) -> tuple[dict[str, str] | None, dict[str, str] | None, list[str]]:
|
||||
"""Fetch and summarize one county point."""
|
||||
profiles = build_request_profiles(args, point, previous_error_fips)
|
||||
final_error: Exception | None = None
|
||||
messages: list[str] = []
|
||||
|
||||
for profile_index, profile in enumerate(profiles):
|
||||
try:
|
||||
messages.append(f"Trying {profile['source_label']} ({profile['name']})")
|
||||
csv_text, source_file = read_or_download_county_csv(
|
||||
point=point,
|
||||
endpoint=profile["endpoint"],
|
||||
api_key=api_key,
|
||||
email=email,
|
||||
name=profile["name"],
|
||||
source_key=profile["source_key"],
|
||||
cache_dir=args.cache_dir,
|
||||
timeout=args.timeout,
|
||||
overwrite=args.overwrite,
|
||||
rate_limiter=rate_limiter,
|
||||
)
|
||||
summary = summarize_ghi(point, csv_text, source_file, profile["source_label"])
|
||||
return summary, None, messages
|
||||
except (OSError, urllib.error.URLError, RuntimeError, ValueError) as error:
|
||||
final_error = error
|
||||
if should_try_next_profile(error, profile_index, profiles):
|
||||
messages.append(f"{profile['source_label']} had no data; trying fallback.")
|
||||
continue
|
||||
break
|
||||
|
||||
if final_error is None:
|
||||
final_error = RuntimeError("No NSRDB profiles were available for this county.")
|
||||
|
||||
return None, build_error_row(point, final_error), messages
|
||||
|
||||
|
||||
def log_county_result(
|
||||
index: int,
|
||||
total: int,
|
||||
point: dict[str, str],
|
||||
summary: dict[str, str] | None,
|
||||
error: dict[str, str] | None,
|
||||
messages: list[str],
|
||||
) -> None:
|
||||
"""Print progress for one completed county."""
|
||||
label = f"{point['county_fips']} {point['county_name']}, {point['state_abbr']}"
|
||||
status = "Fetched" if summary is not None else "Skipped"
|
||||
print(f"[{index}/{total}] {status} {label}")
|
||||
for message in messages:
|
||||
print(f" {message}")
|
||||
if error is not None:
|
||||
print(f" Error: {error['message']}")
|
||||
|
||||
|
||||
def fetch_batch(args: argparse.Namespace) -> list[dict[str, str]]:
|
||||
"""Fetch and summarize the requested county point batch."""
|
||||
email = prompt_for_text("NLR/NREL API email: ", args.email)
|
||||
api_key = prompt_for_secret("NLR/NREL API key: ", args.api_key)
|
||||
|
||||
points = read_county_points(args.points_csv, args.start, args.limit)
|
||||
previous_error_fips = read_previous_error_fips(args.error_csv)
|
||||
rate_limiter = RequestRateLimiter(args.delay)
|
||||
summaries_by_index: dict[int, dict[str, str]] = {}
|
||||
errors_by_index: dict[int, dict[str, str]] = {}
|
||||
total = len(points)
|
||||
|
||||
if args.workers == 1:
|
||||
for index, point in enumerate(points, start=1):
|
||||
summary, error, messages = fetch_county_summary(
|
||||
args=args,
|
||||
point=point,
|
||||
previous_error_fips=previous_error_fips,
|
||||
api_key=api_key,
|
||||
email=email,
|
||||
rate_limiter=rate_limiter,
|
||||
)
|
||||
if summary is not None:
|
||||
summaries_by_index[index] = summary
|
||||
if error is not None:
|
||||
errors_by_index[index] = error
|
||||
log_county_result(index, total, point, summary, error, messages)
|
||||
else:
|
||||
with concurrent.futures.ThreadPoolExecutor(max_workers=args.workers) as executor:
|
||||
future_to_context = {
|
||||
executor.submit(
|
||||
fetch_county_summary,
|
||||
args,
|
||||
point,
|
||||
previous_error_fips,
|
||||
api_key,
|
||||
email,
|
||||
rate_limiter,
|
||||
): (index, point)
|
||||
for index, point in enumerate(points, start=1)
|
||||
}
|
||||
|
||||
for future in concurrent.futures.as_completed(future_to_context):
|
||||
index, point = future_to_context[future]
|
||||
summary, error, messages = future.result()
|
||||
if summary is not None:
|
||||
summaries_by_index[index] = summary
|
||||
if error is not None:
|
||||
errors_by_index[index] = error
|
||||
log_county_result(index, total, point, summary, error, messages)
|
||||
|
||||
errors = [errors_by_index[index] for index in sorted(errors_by_index)]
|
||||
write_error_csv(errors, args.error_csv)
|
||||
return [summaries_by_index[index] for index in sorted(summaries_by_index)]
|
||||
|
||||
|
||||
def parse_args() -> argparse.Namespace:
|
||||
"""Parse command-line arguments."""
|
||||
parser = argparse.ArgumentParser(description=__doc__)
|
||||
parser.add_argument("--points-csv", type=Path, default=DEFAULT_POINTS_CSV)
|
||||
parser.add_argument("--output-csv", type=Path, default=DEFAULT_OUTPUT_CSV)
|
||||
parser.add_argument("--error-csv", type=Path, default=DEFAULT_ERROR_CSV)
|
||||
parser.add_argument("--representative-point-csv-dir", "--cache-dir", dest="cache_dir", type=Path, default=DEFAULT_CACHE_DIR)
|
||||
parser.add_argument("--endpoint", default=DEFAULT_ENDPOINT)
|
||||
parser.add_argument("--polar-endpoint", default=DEFAULT_POLAR_ENDPOINT)
|
||||
parser.add_argument("--name", default=DEFAULT_TMY_NAME, help="NSRDB TMY name, such as tmy or tmy-2024.")
|
||||
parser.add_argument("--polar-name", default=DEFAULT_POLAR_TMY_NAME, help="NSRDB Polar TMY name, usually tmy.")
|
||||
parser.add_argument("--polar-min-latitude", type=float, default=DEFAULT_POLAR_MIN_LATITUDE)
|
||||
parser.add_argument(
|
||||
"--no-polar-fallback",
|
||||
action="store_false",
|
||||
dest="polar_fallback",
|
||||
help="Disable retrying failed high-latitude GOES requests against the NSRDB Polar endpoint.",
|
||||
)
|
||||
parser.add_argument("--email", help="Email address registered with the NLR/NREL API.")
|
||||
parser.add_argument("--api-key", help="API key. If omitted, the script prompts securely.")
|
||||
parser.add_argument("--start", type=int, default=0, help="Zero-based row offset in the points CSV.")
|
||||
parser.add_argument("--limit", type=int, default=10, help="Number of counties to fetch for the test batch.")
|
||||
parser.add_argument(
|
||||
"--delay",
|
||||
type=float,
|
||||
default=1.0,
|
||||
help="Minimum seconds between new API requests. Cached files do not wait.",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--workers",
|
||||
type=int,
|
||||
default=4,
|
||||
help="Number of county jobs to run at once. Set to 1 for the old sequential behavior.",
|
||||
)
|
||||
parser.add_argument("--timeout", type=int, default=120, help="Request timeout in seconds.")
|
||||
parser.add_argument("--overwrite", action="store_true", help="Re-download cached county CSV files.")
|
||||
args = parser.parse_args()
|
||||
if args.delay < 0:
|
||||
parser.error("--delay must be 0 or greater.")
|
||||
if args.workers < 1:
|
||||
parser.error("--workers must be 1 or greater.")
|
||||
return args
|
||||
|
||||
|
||||
def main() -> None:
|
||||
"""Run the NSRDB point-fetch test batch."""
|
||||
args = parse_args()
|
||||
summaries = fetch_batch(args)
|
||||
write_summary_csv(summaries, args.output_csv)
|
||||
print(f"Wrote {len(summaries)} solar summaries to {args.output_csv}")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@@ -0,0 +1,163 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
Rebuild county representative-point GHI summaries from cached NSRDB raw CSV responses.
|
||||
|
||||
This does not call the NSRDB API. It reads data/nrel/representative_point_csv/*.csv, computes:
|
||||
|
||||
avgSolarGhiKwhM2Day = sum(hourly GHI) / 1000 / 365
|
||||
|
||||
and writes a county-keyed CSV compatible with build_county_climate_data.py.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import argparse
|
||||
import csv
|
||||
from pathlib import Path
|
||||
|
||||
|
||||
DEFAULT_POINTS_CSV = Path("data/nrel/county_representative_points.csv")
|
||||
DEFAULT_REPRESENTATIVE_POINT_CSV_DIR = Path("data/nrel/representative_point_csv")
|
||||
DEFAULT_OUTPUT_CSV = Path("data/nrel/county_representative_point_ghi_summary.csv")
|
||||
|
||||
|
||||
def read_county_points(points_csv: Path) -> list[dict[str, str]]:
|
||||
"""Read county representative points keyed by county FIPS."""
|
||||
with points_csv.open(newline="", encoding="utf-8") as handle:
|
||||
return list(csv.DictReader(handle))
|
||||
|
||||
|
||||
def extract_ghi_values(csv_path: Path) -> list[float]:
|
||||
"""Extract numeric hourly GHI values from a cached NSRDB CSV response."""
|
||||
with csv_path.open(newline="", encoding="utf-8-sig") as handle:
|
||||
reader = csv.reader(handle)
|
||||
ghi_index: int | None = None
|
||||
values: list[float] = []
|
||||
|
||||
for row in reader:
|
||||
if ghi_index is None:
|
||||
if {"Year", "Month", "Day", "Hour", "Minute", "GHI"}.issubset(set(row)):
|
||||
ghi_index = row.index("GHI")
|
||||
continue
|
||||
|
||||
if len(row) <= ghi_index or not row[ghi_index].strip():
|
||||
continue
|
||||
values.append(float(row[ghi_index]))
|
||||
|
||||
if ghi_index is None:
|
||||
raise ValueError(f"Could not find NSRDB data header with a GHI column in {csv_path}.")
|
||||
if not values:
|
||||
raise ValueError(f"No GHI values were found in {csv_path}.")
|
||||
return values
|
||||
|
||||
|
||||
def select_raw_csv(representative_point_csv_dir: Path, county_fips: str) -> Path | None:
|
||||
"""Return the cached raw CSV for a county, preferring polar data when both exist."""
|
||||
matches = sorted(representative_point_csv_dir.glob(f"{county_fips}_*_ghi.csv"))
|
||||
if not matches:
|
||||
return None
|
||||
|
||||
polar_matches = [path for path in matches if "_polar-tmy_" in path.name]
|
||||
return polar_matches[0] if polar_matches else matches[0]
|
||||
|
||||
|
||||
def source_label(raw_csv: Path) -> str:
|
||||
"""Return a readable source label from the cached filename."""
|
||||
if "_polar-tmy_" in raw_csv.name:
|
||||
return "NSRDB Polar TMY PSM v4 representative point"
|
||||
if "_goes-tmy_" in raw_csv.name:
|
||||
return "NSRDB GOES TMY PSM v4 representative point"
|
||||
return "NSRDB TMY representative point"
|
||||
|
||||
|
||||
def summarize_county(point: dict[str, str], raw_csv: Path) -> dict[str, str]:
|
||||
"""Summarize one county raw CSV into the app solar metric."""
|
||||
ghi_values = extract_ghi_values(raw_csv)
|
||||
avg_daily_ghi = sum(ghi_values) / 1000 / 365
|
||||
|
||||
return {
|
||||
"county_fips": point["county_fips"],
|
||||
"county_name": point["county_name"],
|
||||
"state_fips": point["state_fips"],
|
||||
"state_abbr": point["state_abbr"],
|
||||
"lat": point["lat"],
|
||||
"lon": point["lon"],
|
||||
"avgSolarGhiKwhM2Day": f"{avg_daily_ghi:.3f}",
|
||||
"ghi_rows": str(len(ghi_values)),
|
||||
"ghi_min": f"{min(ghi_values):.1f}",
|
||||
"ghi_max": f"{max(ghi_values):.1f}",
|
||||
"source": source_label(raw_csv),
|
||||
"raw_csv": str(raw_csv),
|
||||
}
|
||||
|
||||
|
||||
def write_summary_csv(rows: list[dict[str, str]], output_csv: Path) -> None:
|
||||
"""Write rebuilt county solar summaries."""
|
||||
output_csv.parent.mkdir(parents=True, exist_ok=True)
|
||||
with output_csv.open("w", newline="", encoding="utf-8") as handle:
|
||||
writer = csv.DictWriter(
|
||||
handle,
|
||||
fieldnames=[
|
||||
"county_fips",
|
||||
"county_name",
|
||||
"state_fips",
|
||||
"state_abbr",
|
||||
"lat",
|
||||
"lon",
|
||||
"avgSolarGhiKwhM2Day",
|
||||
"ghi_rows",
|
||||
"ghi_min",
|
||||
"ghi_max",
|
||||
"source",
|
||||
"raw_csv",
|
||||
],
|
||||
)
|
||||
writer.writeheader()
|
||||
writer.writerows(rows)
|
||||
|
||||
|
||||
def parse_args() -> argparse.Namespace:
|
||||
"""Parse command-line arguments."""
|
||||
parser = argparse.ArgumentParser(description=__doc__)
|
||||
parser.add_argument("--points-csv", type=Path, default=DEFAULT_POINTS_CSV)
|
||||
parser.add_argument(
|
||||
"--representative-point-csv-dir",
|
||||
dest="representative_point_csv_dir",
|
||||
type=Path,
|
||||
default=DEFAULT_REPRESENTATIVE_POINT_CSV_DIR,
|
||||
)
|
||||
parser.add_argument("--output-csv", type=Path, default=DEFAULT_OUTPUT_CSV)
|
||||
return parser.parse_args()
|
||||
|
||||
|
||||
def main() -> None:
|
||||
"""Run the cached raw CSV summarizer."""
|
||||
args = parse_args()
|
||||
rows: list[dict[str, str]] = []
|
||||
missing: list[str] = []
|
||||
failed: list[str] = []
|
||||
|
||||
for point in read_county_points(args.points_csv):
|
||||
county_fips = point["county_fips"]
|
||||
raw_csv = select_raw_csv(args.representative_point_csv_dir, county_fips)
|
||||
if raw_csv is None:
|
||||
missing.append(county_fips)
|
||||
continue
|
||||
|
||||
try:
|
||||
rows.append(summarize_county(point, raw_csv))
|
||||
except (OSError, ValueError) as exc:
|
||||
failed.append(f"{county_fips}: {exc}")
|
||||
|
||||
write_summary_csv(rows, args.output_csv)
|
||||
print(f"Wrote {len(rows)} county representative-point solar summaries to {args.output_csv}")
|
||||
if missing:
|
||||
print(f"Missing raw CSVs for {len(missing)} counties: {', '.join(missing[:20])}")
|
||||
if failed:
|
||||
print(f"Failed to summarize {len(failed)} counties:")
|
||||
for message in failed[:20]:
|
||||
print(f" {message}")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
File diff suppressed because it is too large
Load Diff
@@ -0,0 +1,44 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
Submit NSRDB polygon archive requests for county-average cloudiness inputs.
|
||||
|
||||
This is a cloud-specific wrapper around request_nsrdb_county_polygon_archives.py.
|
||||
It reuses the existing polygon tiling, site-count, Polar fallback, pacing, and
|
||||
manifest logic, but writes to separate cloud request/response files and requests:
|
||||
|
||||
ghi,clearsky_ghi,cloud_type
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import sys
|
||||
|
||||
import request_nsrdb_county_polygon_archives as polygon_request
|
||||
|
||||
|
||||
DEFAULT_ARGS = [
|
||||
"--requests-csv",
|
||||
"data/nrel/county_polygon_cloud_request_manifest.csv",
|
||||
"--error-csv",
|
||||
"data/nrel/county_polygon_cloud_error_log.csv",
|
||||
"--response-dir",
|
||||
"data/nrel/polygon_cloud_request_responses",
|
||||
"--artifact-label",
|
||||
"cloud",
|
||||
"--legacy-artifact-label",
|
||||
"ghi",
|
||||
"--attributes",
|
||||
"ghi,clearsky_ghi,cloud_type",
|
||||
]
|
||||
|
||||
|
||||
def main() -> None:
|
||||
"""Run the existing polygon requester with cloud-specific defaults."""
|
||||
polygon_request.main(
|
||||
[*DEFAULT_ARGS, *sys.argv[1:]],
|
||||
description=__doc__,
|
||||
)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@@ -0,0 +1,40 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
Submit NSRDB polygon archive requests for county-average GHI.
|
||||
|
||||
This is a GHI-specific wrapper around request_nsrdb_county_polygon_archives.py.
|
||||
It reuses the shared state machine, polygon tiling, site-count, Polar fallback,
|
||||
pacing, and manifest logic with GHI-specific defaults.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import sys
|
||||
|
||||
import request_nsrdb_county_polygon_archives as polygon_request
|
||||
|
||||
|
||||
DEFAULT_ARGS = [
|
||||
"--requests-csv",
|
||||
"data/nrel/county_polygon_ghi_request_manifest.csv",
|
||||
"--error-csv",
|
||||
"data/nrel/county_polygon_ghi_error_log.csv",
|
||||
"--response-dir",
|
||||
"data/nrel/polygon_request_responses",
|
||||
"--artifact-label",
|
||||
"ghi",
|
||||
"--attributes",
|
||||
"ghi",
|
||||
]
|
||||
|
||||
|
||||
def main() -> None:
|
||||
"""Run the shared polygon requester with GHI-specific defaults."""
|
||||
polygon_request.main(
|
||||
[*DEFAULT_ARGS, *sys.argv[1:]],
|
||||
description=__doc__,
|
||||
)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@@ -0,0 +1,9 @@
|
||||
geopandas>=0.14
|
||||
numpy>=1.26
|
||||
pandas>=2.2
|
||||
xarray>=2024.2.0
|
||||
affine>=2.4
|
||||
rasterio>=1.5
|
||||
netcdf4>=1.7
|
||||
h5netcdf>=1.6
|
||||
h5py>=3.12
|
||||
@@ -0,0 +1,690 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
Aggregate downloaded gridMET humidity/heat files to county-level metrics.
|
||||
|
||||
Expected input layout:
|
||||
data/gridmet/<year>/sph.nc
|
||||
data/gridmet/<year>/rmax.nc
|
||||
data/gridmet/<year>/rmin.nc
|
||||
|
||||
Default output:
|
||||
data/gridmet/county_gridmet_humidity.csv
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import argparse
|
||||
import csv
|
||||
import math
|
||||
from collections.abc import Sequence
|
||||
from dataclasses import dataclass
|
||||
from pathlib import Path
|
||||
|
||||
import geopandas as gpd
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
import xarray as xr
|
||||
|
||||
|
||||
STATE_FIPS_TO_ABBR = {
|
||||
"01": "AL",
|
||||
"04": "AZ",
|
||||
"05": "AR",
|
||||
"06": "CA",
|
||||
"08": "CO",
|
||||
"09": "CT",
|
||||
"10": "DE",
|
||||
"11": "DC",
|
||||
"12": "FL",
|
||||
"13": "GA",
|
||||
"16": "ID",
|
||||
"17": "IL",
|
||||
"18": "IN",
|
||||
"19": "IA",
|
||||
"20": "KS",
|
||||
"21": "KY",
|
||||
"22": "LA",
|
||||
"23": "ME",
|
||||
"24": "MD",
|
||||
"25": "MA",
|
||||
"26": "MI",
|
||||
"27": "MN",
|
||||
"28": "MS",
|
||||
"29": "MO",
|
||||
"30": "MT",
|
||||
"31": "NE",
|
||||
"32": "NV",
|
||||
"33": "NH",
|
||||
"34": "NJ",
|
||||
"35": "NM",
|
||||
"36": "NY",
|
||||
"37": "NC",
|
||||
"38": "ND",
|
||||
"39": "OH",
|
||||
"40": "OK",
|
||||
"41": "OR",
|
||||
"42": "PA",
|
||||
"44": "RI",
|
||||
"45": "SC",
|
||||
"46": "SD",
|
||||
"47": "TN",
|
||||
"48": "TX",
|
||||
"49": "UT",
|
||||
"50": "VT",
|
||||
"51": "VA",
|
||||
"53": "WA",
|
||||
"54": "WV",
|
||||
"55": "WI",
|
||||
"56": "WY",
|
||||
}
|
||||
CONUS_STATE_FIPS = set(STATE_FIPS_TO_ABBR)
|
||||
REQUIRED_VARIABLES = ("sph", "rmax", "rmin")
|
||||
SUMMER_MONTHS = {6, 7, 8}
|
||||
NOAA_REGION_CODE_TO_FIPS_OVERRIDES = {
|
||||
"18511": "11001",
|
||||
}
|
||||
NOAA_TMAX_SOURCE_FIPS_OVERRIDES = {
|
||||
"46113": (
|
||||
"46102",
|
||||
"Shannon County, SD changed name/code to Oglala Lakota County, SD effective 2015-05-01.",
|
||||
),
|
||||
"51515": (
|
||||
"51019",
|
||||
"Bedford independent city, VA changed to town status and was added to Bedford County effective 2013-07-01.",
|
||||
),
|
||||
"51678": (
|
||||
"51163",
|
||||
"Lexington independent city, VA uses surrounding Rockbridge County, VA as NOAA tmax proxy because NOAA county daily files do not include Lexington city.",
|
||||
),
|
||||
}
|
||||
|
||||
|
||||
@dataclass(frozen=True)
|
||||
class CountyGridMap:
|
||||
counties: gpd.GeoDataFrame
|
||||
pixel_indices: np.ndarray
|
||||
county_indices: np.ndarray
|
||||
weights: np.ndarray
|
||||
sorted_pixel_indices: np.ndarray
|
||||
sorted_county_indices: np.ndarray
|
||||
sorted_weights: np.ndarray
|
||||
group_starts: np.ndarray
|
||||
group_county_indices: np.ndarray
|
||||
lat_dim: str
|
||||
lon_dim: str
|
||||
|
||||
|
||||
@dataclass
|
||||
class YearData:
|
||||
datasets: list[xr.Dataset]
|
||||
arrays: dict[str, xr.DataArray]
|
||||
|
||||
|
||||
def _normalize_fips(value: object, width: int = 5) -> str:
|
||||
text = str(value).strip()
|
||||
digits = "".join(ch for ch in text if ch.isdigit())
|
||||
return digits.zfill(width)[-width:] if digits else ""
|
||||
|
||||
|
||||
def _load_state_crosswalk(path: Path) -> dict[str, str]:
|
||||
if not path.exists():
|
||||
print(
|
||||
f"State crosswalk not found at {path}. Falling back to the first two "
|
||||
"NOAA region-code digits as state FIPS."
|
||||
)
|
||||
return {}
|
||||
|
||||
rows = list(csv.reader(path.open("r", encoding="utf-8-sig", newline="")))
|
||||
if not rows:
|
||||
return {}
|
||||
|
||||
header = [cell.strip().lower() for cell in rows[0]]
|
||||
ncei_idx = next((idx for idx, cell in enumerate(header) if "ncei" in cell and "code" in cell), None)
|
||||
fips_idx = next((idx for idx, cell in enumerate(header) if "fips" in cell and "code" in cell), None)
|
||||
data_rows = rows[1:] if ncei_idx is not None and fips_idx is not None else rows
|
||||
|
||||
mapping: dict[str, str] = {}
|
||||
for row in data_rows:
|
||||
if ncei_idx is not None and fips_idx is not None:
|
||||
if len(row) <= max(ncei_idx, fips_idx):
|
||||
continue
|
||||
ncei = _normalize_fips(row[ncei_idx], 2)
|
||||
fips = _normalize_fips(row[fips_idx], 2)
|
||||
else:
|
||||
codes = [_normalize_fips(cell, 2) for cell in row]
|
||||
codes = [code for code in codes if code]
|
||||
if len(codes) < 2:
|
||||
continue
|
||||
ncei, fips = codes[0], codes[1]
|
||||
if ncei and fips:
|
||||
mapping[ncei] = fips
|
||||
return mapping
|
||||
|
||||
|
||||
def _county_fips_from_noaa_region(region_code: object, state_crosswalk: dict[str, str]) -> str:
|
||||
code = _normalize_fips(region_code, 5)
|
||||
if code in NOAA_REGION_CODE_TO_FIPS_OVERRIDES:
|
||||
return NOAA_REGION_CODE_TO_FIPS_OVERRIDES[code]
|
||||
state_fips = state_crosswalk.get(code[:2], code[:2])
|
||||
return f"{state_fips}{code[-3:]}" if state_fips else ""
|
||||
|
||||
|
||||
def _load_counties(path: Path) -> gpd.GeoDataFrame:
|
||||
counties = gpd.read_file(path)
|
||||
if counties.crs is None:
|
||||
counties = counties.set_crs("EPSG:4326")
|
||||
else:
|
||||
counties = counties.to_crs("EPSG:4326")
|
||||
|
||||
if "id" in counties.columns:
|
||||
fips = counties["id"]
|
||||
elif "GEOID" in counties.columns:
|
||||
fips = counties["GEOID"]
|
||||
elif "GEOID10" in counties.columns:
|
||||
fips = counties["GEOID10"]
|
||||
else:
|
||||
raise ValueError("County GeoJSON needs an id, GEOID, or GEOID10 column.")
|
||||
|
||||
counties["countyFips"] = fips.map(_normalize_fips)
|
||||
counties["stateFips"] = counties["countyFips"].str.slice(0, 2)
|
||||
counties = counties[counties["stateFips"].isin(CONUS_STATE_FIPS)].copy()
|
||||
counties["countyName"] = counties.get("NAME", counties["countyFips"]).astype(str)
|
||||
counties["state"] = counties["stateFips"].map(STATE_FIPS_TO_ABBR)
|
||||
counties = counties.sort_values("countyFips").reset_index(drop=True)
|
||||
counties["countyIndex"] = np.arange(len(counties), dtype=np.int32)
|
||||
return counties
|
||||
|
||||
|
||||
def _find_dimension(dataset: xr.Dataset, candidates: tuple[str, ...]) -> str:
|
||||
for candidate in candidates:
|
||||
if candidate in dataset.dims or candidate in dataset.coords:
|
||||
return candidate
|
||||
lower_lookup = {name.lower(): name for name in set(dataset.dims) | set(dataset.coords)}
|
||||
for candidate in candidates:
|
||||
if candidate.lower() in lower_lookup:
|
||||
return lower_lookup[candidate.lower()]
|
||||
raise ValueError(f"Unable to find one of these dimensions: {', '.join(candidates)}")
|
||||
|
||||
|
||||
def _select_variable(dataset: xr.Dataset, preferred: str) -> str:
|
||||
if preferred in dataset.data_vars:
|
||||
return preferred
|
||||
for name in dataset.data_vars:
|
||||
if name.lower() == preferred.lower():
|
||||
return name
|
||||
if len(dataset.data_vars) == 1:
|
||||
return next(iter(dataset.data_vars))
|
||||
raise ValueError(f"Unable to select variable {preferred}; found {list(dataset.data_vars)}")
|
||||
|
||||
|
||||
def _year_folder(path: Path) -> int | None:
|
||||
try:
|
||||
return int(path.name)
|
||||
except ValueError:
|
||||
return None
|
||||
|
||||
|
||||
def _discover_complete_years(gridmet_dir: Path) -> list[int]:
|
||||
years: list[int] = []
|
||||
for child in sorted(gridmet_dir.iterdir()):
|
||||
if not child.is_dir():
|
||||
continue
|
||||
year = _year_folder(child)
|
||||
if year is None:
|
||||
continue
|
||||
if all((child / f"{variable}.nc").exists() for variable in REQUIRED_VARIABLES):
|
||||
years.append(year)
|
||||
return years
|
||||
|
||||
|
||||
def _load_noaa_tmax_month(
|
||||
noaa_tmax_dir: Path,
|
||||
year: int,
|
||||
month: int,
|
||||
state_crosswalk: dict[str, str],
|
||||
) -> dict[str, list[float]]:
|
||||
path = noaa_tmax_dir / str(year) / f"tmax-{year}{month:02d}-cty-scaled.csv"
|
||||
if not path.exists():
|
||||
return {}
|
||||
|
||||
values: dict[str, list[float]] = {}
|
||||
with path.open("r", encoding="utf-8-sig", newline="") as handle:
|
||||
reader = csv.reader(handle)
|
||||
for row in reader:
|
||||
if len(row) < 7 or row[0].strip().lower() != "cty":
|
||||
continue
|
||||
county_fips = _county_fips_from_noaa_region(row[1], state_crosswalk)
|
||||
day_values: list[float] = []
|
||||
for raw_value in row[6:]:
|
||||
try:
|
||||
value = float(raw_value)
|
||||
except ValueError:
|
||||
day_values.append(math.nan)
|
||||
continue
|
||||
day_values.append(value if value > -999 else math.nan)
|
||||
values[county_fips] = day_values
|
||||
return values
|
||||
|
||||
|
||||
def _noaa_tmax_for_date(
|
||||
*,
|
||||
date: pd.Timestamp,
|
||||
county_fips: Sequence[str],
|
||||
noaa_tmax_dir: Path,
|
||||
state_crosswalk: dict[str, str],
|
||||
month_cache: dict[tuple[int, int], dict[str, list[float]]],
|
||||
) -> np.ndarray:
|
||||
cache_key = (int(date.year), int(date.month))
|
||||
if cache_key not in month_cache:
|
||||
month_cache[cache_key] = _load_noaa_tmax_month(
|
||||
noaa_tmax_dir,
|
||||
int(date.year),
|
||||
int(date.month),
|
||||
state_crosswalk,
|
||||
)
|
||||
|
||||
month_values = month_cache[cache_key]
|
||||
result = np.full(len(county_fips), np.nan, dtype=np.float64)
|
||||
day_offset = int(date.day) - 1
|
||||
for index, fips in enumerate(county_fips):
|
||||
source_fips = NOAA_TMAX_SOURCE_FIPS_OVERRIDES.get(fips, (fips, ""))[0]
|
||||
values = month_values.get(source_fips)
|
||||
if values is not None and day_offset < len(values):
|
||||
result[index] = values[day_offset]
|
||||
return result
|
||||
|
||||
|
||||
def _build_county_grid_map(counties: gpd.GeoDataFrame, sample_file: Path) -> CountyGridMap:
|
||||
with xr.open_dataset(sample_file) as dataset:
|
||||
lat_dim = _find_dimension(dataset, ("lat", "latitude", "y"))
|
||||
lon_dim = _find_dimension(dataset, ("lon", "longitude", "x"))
|
||||
lat_values = np.asarray(dataset[lat_dim].values)
|
||||
lon_values = np.asarray(dataset[lon_dim].values)
|
||||
|
||||
if lat_values.ndim != 1 or lon_values.ndim != 1:
|
||||
raise ValueError("This script expects 1D latitude and longitude coordinates.")
|
||||
|
||||
lon_grid, lat_grid = np.meshgrid(lon_values, lat_values)
|
||||
flat_lats = lat_grid.ravel()
|
||||
flat_lons = lon_grid.ravel()
|
||||
flat_indices = np.arange(flat_lats.size, dtype=np.int64)
|
||||
|
||||
bounds = counties.total_bounds
|
||||
in_bounds = (
|
||||
(flat_lons >= bounds[0] - 0.25)
|
||||
& (flat_lats >= bounds[1] - 0.25)
|
||||
& (flat_lons <= bounds[2] + 0.25)
|
||||
& (flat_lats <= bounds[3] + 0.25)
|
||||
)
|
||||
point_frame = gpd.GeoDataFrame(
|
||||
{
|
||||
"pixelIndex": flat_indices[in_bounds],
|
||||
"lat": flat_lats[in_bounds],
|
||||
},
|
||||
geometry=gpd.points_from_xy(flat_lons[in_bounds], flat_lats[in_bounds]),
|
||||
crs="EPSG:4326",
|
||||
)
|
||||
|
||||
joined = gpd.sjoin(
|
||||
point_frame,
|
||||
counties[["countyIndex", "geometry"]],
|
||||
how="inner",
|
||||
predicate="within",
|
||||
)
|
||||
joined = joined.drop_duplicates(subset=["pixelIndex", "countyIndex"])
|
||||
|
||||
pixel_indices = joined["pixelIndex"].to_numpy(dtype=np.int64)
|
||||
county_indices = joined["countyIndex"].to_numpy(dtype=np.int32)
|
||||
weights = np.cos(np.deg2rad(joined["lat"].to_numpy(dtype=np.float64)))
|
||||
|
||||
mapped_counties = set(county_indices.tolist())
|
||||
fallback_pixels: list[int] = []
|
||||
fallback_counties: list[int] = []
|
||||
fallback_weights: list[float] = []
|
||||
for county_index, geometry in enumerate(counties.geometry):
|
||||
if county_index in mapped_counties or geometry.is_empty:
|
||||
continue
|
||||
point = geometry.representative_point()
|
||||
lon_position = int(np.argmin(np.abs(lon_values - point.x)))
|
||||
lat_position = int(np.argmin(np.abs(lat_values - point.y)))
|
||||
flat_index = lat_position * len(lon_values) + lon_position
|
||||
fallback_pixels.append(flat_index)
|
||||
fallback_counties.append(county_index)
|
||||
fallback_weights.append(float(math.cos(math.radians(float(lat_values[lat_position])))))
|
||||
|
||||
if fallback_pixels:
|
||||
pixel_indices = np.concatenate([pixel_indices, np.asarray(fallback_pixels, dtype=np.int64)])
|
||||
county_indices = np.concatenate([county_indices, np.asarray(fallback_counties, dtype=np.int32)])
|
||||
weights = np.concatenate([weights, np.asarray(fallback_weights, dtype=np.float64)])
|
||||
|
||||
print(
|
||||
f"mapped {len(np.unique(county_indices)):,} counties to {len(pixel_indices):,} grid cells "
|
||||
f"({len(fallback_pixels):,} nearest-cell fallback counties)"
|
||||
)
|
||||
sort_order = np.argsort(county_indices, kind="stable")
|
||||
sorted_county_indices = county_indices[sort_order]
|
||||
group_starts = np.r_[0, np.flatnonzero(np.diff(sorted_county_indices)) + 1]
|
||||
group_county_indices = sorted_county_indices[group_starts]
|
||||
return CountyGridMap(
|
||||
counties=counties,
|
||||
pixel_indices=pixel_indices,
|
||||
county_indices=county_indices,
|
||||
weights=weights,
|
||||
sorted_pixel_indices=pixel_indices[sort_order],
|
||||
sorted_county_indices=sorted_county_indices,
|
||||
sorted_weights=weights[sort_order],
|
||||
group_starts=group_starts,
|
||||
group_county_indices=group_county_indices,
|
||||
lat_dim=lat_dim,
|
||||
lon_dim=lon_dim,
|
||||
)
|
||||
|
||||
|
||||
def _county_means(
|
||||
data_array: xr.DataArray,
|
||||
time_index: int,
|
||||
time_dim: str,
|
||||
county_map: CountyGridMap,
|
||||
) -> np.ndarray:
|
||||
data = data_array.isel({time_dim: time_index}).transpose(county_map.lat_dim, county_map.lon_dim)
|
||||
flat = np.asarray(data.values, dtype=np.float64).ravel()
|
||||
values = flat[county_map.pixel_indices]
|
||||
valid = np.isfinite(values)
|
||||
county_count = len(county_map.counties)
|
||||
sums = np.bincount(
|
||||
county_map.county_indices[valid],
|
||||
weights=values[valid] * county_map.weights[valid],
|
||||
minlength=county_count,
|
||||
)
|
||||
weight_sums = np.bincount(
|
||||
county_map.county_indices[valid],
|
||||
weights=county_map.weights[valid],
|
||||
minlength=county_count,
|
||||
)
|
||||
means = np.full(county_count, np.nan, dtype=np.float64)
|
||||
np.divide(sums, weight_sums, out=means, where=weight_sums > 0)
|
||||
return means
|
||||
|
||||
|
||||
def _county_means_chunk(
|
||||
data_array: xr.DataArray,
|
||||
start_index: int,
|
||||
end_index: int,
|
||||
time_dim: str,
|
||||
county_map: CountyGridMap,
|
||||
) -> np.ndarray:
|
||||
data = data_array.isel({time_dim: slice(start_index, end_index)}).transpose(
|
||||
time_dim,
|
||||
county_map.lat_dim,
|
||||
county_map.lon_dim,
|
||||
)
|
||||
raw = np.asarray(data.values, dtype=np.float64)
|
||||
selected = raw.reshape(raw.shape[0], -1)[:, county_map.sorted_pixel_indices]
|
||||
valid = np.isfinite(selected)
|
||||
|
||||
weighted_values = np.where(valid, selected * county_map.sorted_weights, 0.0)
|
||||
weight_values = np.where(valid, county_map.sorted_weights, 0.0)
|
||||
sums = np.add.reduceat(weighted_values, county_map.group_starts, axis=1)
|
||||
weight_sums = np.add.reduceat(weight_values, county_map.group_starts, axis=1)
|
||||
|
||||
grouped_means = np.full_like(sums, np.nan, dtype=np.float64)
|
||||
np.divide(sums, weight_sums, out=grouped_means, where=weight_sums > 0)
|
||||
|
||||
means = np.full((raw.shape[0], len(county_map.counties)), np.nan, dtype=np.float64)
|
||||
means[:, county_map.group_county_indices] = grouped_means
|
||||
return means
|
||||
|
||||
|
||||
def _heat_index_f(t_f: np.ndarray, rh_pct: np.ndarray) -> np.ndarray:
|
||||
rh = np.clip(rh_pct, 0, 100)
|
||||
heat_index = (
|
||||
-42.379
|
||||
+ 2.04901523 * t_f
|
||||
+ 10.14333127 * rh
|
||||
- 0.22475541 * t_f * rh
|
||||
- 0.00683783 * t_f * t_f
|
||||
- 0.05481717 * rh * rh
|
||||
+ 0.00122874 * t_f * t_f * rh
|
||||
+ 0.00085282 * t_f * rh * rh
|
||||
- 0.00000199 * t_f * t_f * rh * rh
|
||||
)
|
||||
|
||||
low_rh_adjustment = ((13 - rh) / 4) * np.sqrt(np.maximum((17 - np.abs(t_f - 95)) / 17, 0))
|
||||
high_rh_adjustment = ((rh - 85) / 10) * ((87 - t_f) / 5)
|
||||
heat_index = np.where((rh < 13) & (80 <= t_f) & (t_f <= 112), heat_index - low_rh_adjustment, heat_index)
|
||||
heat_index = np.where((rh > 85) & (80 <= t_f) & (t_f <= 87), heat_index + high_rh_adjustment, heat_index)
|
||||
return np.where(t_f >= 80, heat_index, t_f)
|
||||
|
||||
|
||||
def _time_dimension(data_array: xr.DataArray, lat_dim: str, lon_dim: str) -> str:
|
||||
candidates = [dim for dim in data_array.dims if dim not in {lat_dim, lon_dim}]
|
||||
if not candidates:
|
||||
raise ValueError(f"No time dimension found for {data_array.name}.")
|
||||
return candidates[0]
|
||||
|
||||
|
||||
def _open_year_datasets(gridmet_dir: Path, year: int) -> YearData:
|
||||
datasets: list[xr.Dataset] = []
|
||||
arrays: dict[str, xr.DataArray] = {}
|
||||
for variable in REQUIRED_VARIABLES:
|
||||
dataset = xr.open_dataset(gridmet_dir / str(year) / f"{variable}.nc")
|
||||
datasets.append(dataset)
|
||||
variable_name = _select_variable(dataset, variable)
|
||||
arrays[variable] = dataset[variable_name]
|
||||
return YearData(datasets=datasets, arrays=arrays)
|
||||
|
||||
|
||||
def _close_year_datasets(year_data: YearData) -> None:
|
||||
for dataset in year_data.datasets:
|
||||
try:
|
||||
dataset.close()
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
|
||||
def _round_or_blank(value: float, digits: int) -> str:
|
||||
if not np.isfinite(value):
|
||||
return ""
|
||||
return str(round(float(value), digits))
|
||||
|
||||
|
||||
def summarize(args: argparse.Namespace) -> None:
|
||||
years = args.years or _discover_complete_years(args.gridmet_dir)
|
||||
if not years:
|
||||
raise FileNotFoundError(
|
||||
f"No complete gridMET years found in {args.gridmet_dir}. "
|
||||
"Run scripts/download_gridmet_data.py first."
|
||||
)
|
||||
|
||||
if args.years is None:
|
||||
start = args.start_year if args.start_year is not None else 1991
|
||||
end = args.end_year if args.end_year is not None else 2020
|
||||
years = [year for year in years if start <= year <= end]
|
||||
elif args.start_year is not None or args.end_year is not None:
|
||||
start = args.start_year if args.start_year is not None else min(years)
|
||||
end = args.end_year if args.end_year is not None else max(years)
|
||||
years = [year for year in years if start <= year <= end]
|
||||
|
||||
if not years:
|
||||
raise FileNotFoundError("No complete gridMET years matched the requested year filters.")
|
||||
|
||||
sample_file = args.gridmet_dir / str(years[0]) / "sph.nc"
|
||||
counties = _load_counties(args.counties_geojson)
|
||||
county_map = _build_county_grid_map(counties, sample_file)
|
||||
county_count = len(county_map.counties)
|
||||
county_fips = county_map.counties["countyFips"].astype(str).tolist()
|
||||
state_crosswalk = _load_state_crosswalk(args.state_crosswalk)
|
||||
noaa_month_cache: dict[tuple[int, int], dict[str, list[float]]] = {}
|
||||
|
||||
annual_sph_sum = np.zeros(county_count, dtype=np.float64)
|
||||
annual_sph_count = np.zeros(county_count, dtype=np.float64)
|
||||
summer_sph_sum = np.zeros(county_count, dtype=np.float64)
|
||||
summer_sph_count = np.zeros(county_count, dtype=np.float64)
|
||||
annual_rh_sum = np.zeros(county_count, dtype=np.float64)
|
||||
annual_rh_count = np.zeros(county_count, dtype=np.float64)
|
||||
summer_rh_sum = np.zeros(county_count, dtype=np.float64)
|
||||
summer_rh_count = np.zeros(county_count, dtype=np.float64)
|
||||
heat_day_sum = np.zeros(county_count, dtype=np.float64)
|
||||
years_with_heat_data = np.zeros(county_count, dtype=np.float64)
|
||||
|
||||
for year in years:
|
||||
print(f"process {year}")
|
||||
year_data = _open_year_datasets(args.gridmet_dir, year)
|
||||
arrays = year_data.arrays
|
||||
try:
|
||||
time_dim = _time_dimension(arrays["sph"], county_map.lat_dim, county_map.lon_dim)
|
||||
dates = pd.to_datetime(arrays["sph"][time_dim].values)
|
||||
yearly_heat_days = np.zeros(county_count, dtype=np.float64)
|
||||
yearly_heat_valid_days = np.zeros(county_count, dtype=np.float64)
|
||||
|
||||
for chunk_start in range(0, len(dates), args.chunk_days):
|
||||
chunk_end = min(chunk_start + args.chunk_days, len(dates))
|
||||
chunk_dates = dates[chunk_start:chunk_end]
|
||||
sph_chunk = _county_means_chunk(arrays["sph"], chunk_start, chunk_end, time_dim, county_map) * 1000
|
||||
rmax_chunk = _county_means_chunk(arrays["rmax"], chunk_start, chunk_end, time_dim, county_map)
|
||||
rmin_chunk = _county_means_chunk(arrays["rmin"], chunk_start, chunk_end, time_dim, county_map)
|
||||
|
||||
for offset, date in enumerate(chunk_dates):
|
||||
sph = sph_chunk[offset]
|
||||
rmax = rmax_chunk[offset]
|
||||
rmin = rmin_chunk[offset]
|
||||
tmax_f = _noaa_tmax_for_date(
|
||||
date=date,
|
||||
county_fips=county_fips,
|
||||
noaa_tmax_dir=args.noaa_tmax_dir,
|
||||
state_crosswalk=state_crosswalk,
|
||||
month_cache=noaa_month_cache,
|
||||
) * 9 / 5 + 32
|
||||
|
||||
rh_mean = (rmax + rmin) / 2
|
||||
annual_sph_valid = np.isfinite(sph)
|
||||
annual_rh_valid = np.isfinite(rh_mean)
|
||||
annual_sph_sum[annual_sph_valid] += sph[annual_sph_valid]
|
||||
annual_sph_count[annual_sph_valid] += 1
|
||||
annual_rh_sum[annual_rh_valid] += rh_mean[annual_rh_valid]
|
||||
annual_rh_count[annual_rh_valid] += 1
|
||||
|
||||
if int(date.month) in SUMMER_MONTHS:
|
||||
summer_sph_sum[annual_sph_valid] += sph[annual_sph_valid]
|
||||
summer_sph_count[annual_sph_valid] += 1
|
||||
summer_rh_sum[annual_rh_valid] += rh_mean[annual_rh_valid]
|
||||
summer_rh_count[annual_rh_valid] += 1
|
||||
|
||||
heat_valid = np.isfinite(tmax_f) & np.isfinite(rmin)
|
||||
heat_index = _heat_index_f(tmax_f, rmin)
|
||||
yearly_heat_days[heat_valid] += heat_index[heat_valid] >= args.heat_index_threshold_f
|
||||
yearly_heat_valid_days[heat_valid] += 1
|
||||
|
||||
has_heat_year = yearly_heat_valid_days > 0
|
||||
heat_day_sum[has_heat_year] += yearly_heat_days[has_heat_year]
|
||||
years_with_heat_data[has_heat_year] += 1
|
||||
finally:
|
||||
_close_year_datasets(year_data)
|
||||
|
||||
avg_sph = np.divide(annual_sph_sum, annual_sph_count, out=np.full(county_count, np.nan), where=annual_sph_count > 0)
|
||||
avg_summer_sph = np.divide(
|
||||
summer_sph_sum,
|
||||
summer_sph_count,
|
||||
out=np.full(county_count, np.nan),
|
||||
where=summer_sph_count > 0,
|
||||
)
|
||||
avg_rh = np.divide(annual_rh_sum, annual_rh_count, out=np.full(county_count, np.nan), where=annual_rh_count > 0)
|
||||
avg_summer_rh = np.divide(
|
||||
summer_rh_sum,
|
||||
summer_rh_count,
|
||||
out=np.full(county_count, np.nan),
|
||||
where=summer_rh_count > 0,
|
||||
)
|
||||
humid_heat_days = np.divide(
|
||||
heat_day_sum,
|
||||
years_with_heat_data,
|
||||
out=np.full(county_count, np.nan),
|
||||
where=years_with_heat_data > 0,
|
||||
)
|
||||
source_period = f"{min(years)}-{max(years)}" if len(years) > 1 else str(years[0])
|
||||
|
||||
args.output.parent.mkdir(parents=True, exist_ok=True)
|
||||
with args.output.open("w", newline="", encoding="utf-8") as handle:
|
||||
writer = csv.DictWriter(
|
||||
handle,
|
||||
fieldnames=[
|
||||
"countyFips",
|
||||
"countyName",
|
||||
"state",
|
||||
"avgSpecificHumidityGKg",
|
||||
"avgSummerSpecificHumidityGKg",
|
||||
"avgRelHumidityPct",
|
||||
"avgSummerRelHumidityPct",
|
||||
"humidHeatDays",
|
||||
"humidHeatSourceFips",
|
||||
"humidHeatFipsAdjustment",
|
||||
"gridmetAnalysisYears",
|
||||
"gridmetSource",
|
||||
],
|
||||
)
|
||||
writer.writeheader()
|
||||
for index, county in county_map.counties.iterrows():
|
||||
writer.writerow(
|
||||
{
|
||||
"countyFips": county["countyFips"],
|
||||
"countyName": county["countyName"],
|
||||
"state": county["state"],
|
||||
"avgSpecificHumidityGKg": _round_or_blank(avg_sph[index], 3),
|
||||
"avgSummerSpecificHumidityGKg": _round_or_blank(avg_summer_sph[index], 3),
|
||||
"avgRelHumidityPct": _round_or_blank(avg_rh[index], 2),
|
||||
"avgSummerRelHumidityPct": _round_or_blank(avg_summer_rh[index], 2),
|
||||
"humidHeatDays": _round_or_blank(humid_heat_days[index], 2),
|
||||
"humidHeatSourceFips": NOAA_TMAX_SOURCE_FIPS_OVERRIDES.get(
|
||||
county["countyFips"],
|
||||
(county["countyFips"], ""),
|
||||
)[0],
|
||||
"humidHeatFipsAdjustment": NOAA_TMAX_SOURCE_FIPS_OVERRIDES.get(
|
||||
county["countyFips"],
|
||||
("", ""),
|
||||
)[1],
|
||||
"gridmetAnalysisYears": len(years),
|
||||
"gridmetSource": (
|
||||
f"gridmet-{source_period} county-cell-weighted; "
|
||||
"relative-humidity=(rmax+rmin)/2; "
|
||||
"humidHeatDays=noaa-nclimgrid-tmax+gridmet-rmin heat-index-gte-90f"
|
||||
),
|
||||
}
|
||||
)
|
||||
|
||||
print(f"wrote {args.output}")
|
||||
|
||||
|
||||
def _parse_years(text: str) -> list[int]:
|
||||
years: set[int] = set()
|
||||
for part in text.split(","):
|
||||
token = part.strip()
|
||||
if not token:
|
||||
continue
|
||||
if "-" in token:
|
||||
start_text, end_text = token.split("-", 1)
|
||||
years.update(range(int(start_text), int(end_text) + 1))
|
||||
else:
|
||||
years.add(int(token))
|
||||
return sorted(years)
|
||||
|
||||
|
||||
def main() -> int:
|
||||
parser = argparse.ArgumentParser(
|
||||
description="Summarize downloaded gridMET humidity files into county-level CSV metrics."
|
||||
)
|
||||
parser.add_argument("--gridmet-dir", type=Path, default=Path("data/gridmet"))
|
||||
parser.add_argument("--noaa-tmax-dir", type=Path, default=Path("data/noaa/tmax_cty_scaled"))
|
||||
parser.add_argument("--state-crosswalk", type=Path, default=Path("data/noaa/nclimgrid/us-state-codes_ncei-to-fips.csv"))
|
||||
parser.add_argument("--counties-geojson", type=Path, default=Path("data/geojson-counties-fips.json"))
|
||||
parser.add_argument("--output", type=Path, default=Path("data/gridmet/county_gridmet_humidity.csv"))
|
||||
parser.add_argument("--years", type=_parse_years, default=None, help="Optional comma/range years, e.g. 1991-2020.")
|
||||
parser.add_argument("--start-year", type=int, default=None)
|
||||
parser.add_argument("--end-year", type=int, default=None)
|
||||
parser.add_argument("--heat-index-threshold-f", type=float, default=90.0)
|
||||
parser.add_argument("--chunk-days", type=int, default=31)
|
||||
summarize(parser.parse_args())
|
||||
return 0
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
raise SystemExit(main())
|
||||
@@ -0,0 +1,614 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
Summarize downloaded NSRDB county polygon GHI archives.
|
||||
|
||||
Each downloaded polygon archive contains one CSV per NSRDB site that intersects
|
||||
the county polygon or tile. This script computes area-weighted county-level
|
||||
average daily GHI values across those site CSVs, combining multiple tile
|
||||
archives back into one county summary when present.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import argparse
|
||||
import csv
|
||||
import json
|
||||
import math
|
||||
import statistics
|
||||
import zipfile
|
||||
from pathlib import Path
|
||||
|
||||
|
||||
DEFAULT_ARCHIVE_DIR = Path("data/nrel/polygon_archives")
|
||||
DEFAULT_COUNTIES_GEOJSON = Path("data/geojson-counties-fips.json")
|
||||
DEFAULT_REQUESTS_CSV = Path("data/nrel/county_polygon_ghi_request_manifest.csv")
|
||||
DEFAULT_REPRESENTATIVE_POINT_CSV = Path("data/nrel/county_representative_point_ghi_summary.csv")
|
||||
DEFAULT_OUTPUT_CSV = Path("data/nrel/county_polygon_ghi_summary.csv")
|
||||
DEFAULT_CELL_SIZE_M = 4000.0
|
||||
DEFAULT_ARCHIVE_GLOB = "*_ghi.zip"
|
||||
EARTH_RADIUS_M = 6_371_008.8
|
||||
|
||||
SUMMARY_FIELDS = [
|
||||
"county_fips",
|
||||
"county_name",
|
||||
"state_abbr",
|
||||
"archive_zip",
|
||||
"polygon_sites",
|
||||
"request_site_count",
|
||||
"ghi_rows_per_site_min",
|
||||
"ghi_rows_per_site_max",
|
||||
"avgSolarGhiKwhM2Day",
|
||||
"areaWeightedAvgSolarGhiKwhM2Day",
|
||||
"areaWeightedSites",
|
||||
"weightedCellAreaKm2",
|
||||
"countyAreaKm2",
|
||||
"site_avg_min",
|
||||
"site_avg_max",
|
||||
"site_avg_stddev",
|
||||
"representativePointAvgSolarGhiKwhM2Day",
|
||||
"areaWeightedMinusRepresentativePoint",
|
||||
"areaWeightedPctDiffFromRepresentativePoint",
|
||||
]
|
||||
|
||||
|
||||
def read_lookup(csv_path: Path, key_field: str) -> dict[str, dict[str, str]]:
|
||||
"""Read a CSV into a dictionary keyed by one field."""
|
||||
if not csv_path.exists():
|
||||
return {}
|
||||
|
||||
with csv_path.open(newline="", encoding="utf-8-sig") as handle:
|
||||
return {
|
||||
row[key_field]: row
|
||||
for row in csv.DictReader(handle)
|
||||
if row.get(key_field)
|
||||
}
|
||||
|
||||
|
||||
def read_grouped_lookup(csv_path: Path, key_field: str) -> dict[str, list[dict[str, str]]]:
|
||||
"""Read a CSV into lists of rows keyed by one field."""
|
||||
rows_by_key: dict[str, list[dict[str, str]]] = {}
|
||||
if not csv_path.exists():
|
||||
return rows_by_key
|
||||
|
||||
with csv_path.open(newline="", encoding="utf-8-sig") as handle:
|
||||
for row in csv.DictReader(handle):
|
||||
key = row.get(key_field)
|
||||
if key:
|
||||
rows_by_key.setdefault(key, []).append(row)
|
||||
return rows_by_key
|
||||
|
||||
|
||||
def county_fips_from_archive(path: Path) -> str:
|
||||
"""Extract the county FIPS prefix from an archive filename."""
|
||||
return path.name.split("_", 1)[0]
|
||||
|
||||
|
||||
def archive_groups_by_county(archives: list[Path]) -> dict[str, list[Path]]:
|
||||
"""Group archive ZIP paths by county FIPS."""
|
||||
grouped: dict[str, list[Path]] = {}
|
||||
for archive in archives:
|
||||
grouped.setdefault(county_fips_from_archive(archive), []).append(archive)
|
||||
return grouped
|
||||
|
||||
|
||||
def normalize_fips(value: object, width: int) -> str:
|
||||
"""Return a zero-padded FIPS string from a mixed text or numeric value."""
|
||||
digits = "".join(character for character in str(value).strip() if character.isdigit())
|
||||
return digits.zfill(width)[-width:] if digits else ""
|
||||
|
||||
|
||||
def load_county_geometries(counties_geojson: Path) -> dict[str, list[list[list[tuple[float, float]]]]]:
|
||||
"""Load county polygon rings from GeoJSON without third-party GIS dependencies."""
|
||||
with counties_geojson.open(encoding="utf-8") as handle:
|
||||
geojson = json.load(handle)
|
||||
|
||||
county_geometries: dict[str, list[list[list[tuple[float, float]]]]] = {}
|
||||
for feature in geojson.get("features", []):
|
||||
properties = feature.get("properties", {})
|
||||
county_fips = normalize_fips(properties.get("id"), 5)
|
||||
if not county_fips:
|
||||
county_fips = normalize_fips(properties.get("STATE"), 2) + normalize_fips(properties.get("COUNTY"), 3)
|
||||
geometry = feature.get("geometry") or {}
|
||||
polygons = geometry_polygons(geometry)
|
||||
if county_fips and polygons:
|
||||
county_geometries[county_fips] = polygons
|
||||
|
||||
return county_geometries
|
||||
|
||||
|
||||
def geometry_polygons(geometry: dict[str, object]) -> list[list[list[tuple[float, float]]]]:
|
||||
"""Return GeoJSON Polygon/MultiPolygon coordinates as polygon rings."""
|
||||
geometry_type = geometry.get("type")
|
||||
coordinates = geometry.get("coordinates")
|
||||
if not isinstance(coordinates, list):
|
||||
return []
|
||||
|
||||
if geometry_type == "Polygon":
|
||||
return [coordinates_to_polygon(coordinates)]
|
||||
if geometry_type == "MultiPolygon":
|
||||
return [coordinates_to_polygon(polygon) for polygon in coordinates]
|
||||
return []
|
||||
|
||||
|
||||
def coordinates_to_polygon(coordinates: list[object]) -> list[list[tuple[float, float]]]:
|
||||
"""Convert raw GeoJSON polygon coordinates to typed rings."""
|
||||
polygon: list[list[tuple[float, float]]] = []
|
||||
for ring in coordinates:
|
||||
polygon.append([(float(point[0]), float(point[1])) for point in ring])
|
||||
return polygon
|
||||
|
||||
|
||||
def extract_site_lon_lat(csv_text: str) -> tuple[float, float]:
|
||||
"""Extract the NSRDB site longitude and latitude from CSV metadata rows."""
|
||||
rows = list(csv.reader(csv_text.splitlines()))
|
||||
if len(rows) < 2:
|
||||
raise ValueError("Could not read NSRDB metadata rows.")
|
||||
|
||||
metadata = {key.strip().lower(): value.strip() for key, value in zip(rows[0], rows[1])}
|
||||
try:
|
||||
lon = float(metadata["longitude"])
|
||||
lat = float(metadata["latitude"])
|
||||
except KeyError as error:
|
||||
raise ValueError("Could not find Longitude/Latitude metadata in NSRDB CSV.") from error
|
||||
except ValueError as error:
|
||||
raise ValueError("NSRDB Longitude/Latitude metadata is not numeric.") from error
|
||||
|
||||
return lon, lat
|
||||
|
||||
|
||||
def extract_ghi_values(csv_text: str) -> list[float]:
|
||||
"""Extract numeric GHI values from an NSRDB CSV response."""
|
||||
rows = list(csv.reader(csv_text.splitlines()))
|
||||
header_index = next(
|
||||
(
|
||||
index
|
||||
for index, row in enumerate(rows)
|
||||
if {"Year", "Month", "Day", "Hour", "Minute", "GHI"}.issubset(set(row))
|
||||
),
|
||||
None,
|
||||
)
|
||||
|
||||
if header_index is None:
|
||||
raise ValueError("Could not find the NSRDB data header row with a GHI column.")
|
||||
|
||||
ghi_index = rows[header_index].index("GHI")
|
||||
ghi_values: list[float] = []
|
||||
for row in rows[header_index + 1 :]:
|
||||
if len(row) <= ghi_index or not row[ghi_index].strip():
|
||||
continue
|
||||
ghi_values.append(float(row[ghi_index]))
|
||||
|
||||
if not ghi_values:
|
||||
raise ValueError("No GHI values were found in the NSRDB response.")
|
||||
|
||||
return ghi_values
|
||||
|
||||
|
||||
def site_average_daily_ghi(csv_text: str) -> tuple[float, int]:
|
||||
"""Return average daily GHI in kWh/m2/day and number of GHI rows."""
|
||||
ghi_values = extract_ghi_values(csv_text)
|
||||
return sum(ghi_values) / 1000 / 365, len(ghi_values)
|
||||
|
||||
|
||||
def average_geometry_latitude(county_geometry: list[list[list[tuple[float, float]]]]) -> float:
|
||||
"""Return a representative latitude for local meter projection."""
|
||||
latitudes = [
|
||||
lat
|
||||
for polygon in county_geometry
|
||||
for ring in polygon[:1]
|
||||
for _lon, lat in ring
|
||||
]
|
||||
if not latitudes:
|
||||
return 0.0
|
||||
return statistics.fmean(latitudes)
|
||||
|
||||
|
||||
def project_lon_lat(lon: float, lat: float, reference_lat: float) -> tuple[float, float]:
|
||||
"""Project lon/lat to local meters with an equirectangular approximation."""
|
||||
reference_lat_radians = math.radians(reference_lat)
|
||||
x = EARTH_RADIUS_M * math.radians(lon) * math.cos(reference_lat_radians)
|
||||
y = EARTH_RADIUS_M * math.radians(lat)
|
||||
return x, y
|
||||
|
||||
|
||||
def project_county_geometry(
|
||||
county_geometry: list[list[list[tuple[float, float]]]],
|
||||
reference_lat: float,
|
||||
) -> list[list[list[tuple[float, float]]]]:
|
||||
"""Project county polygon rings to local meter coordinates."""
|
||||
return [
|
||||
[
|
||||
[project_lon_lat(lon, lat, reference_lat) for lon, lat in ring]
|
||||
for ring in polygon
|
||||
]
|
||||
for polygon in county_geometry
|
||||
]
|
||||
|
||||
|
||||
def polygon_area(points: list[tuple[float, float]]) -> float:
|
||||
"""Return absolute polygon area using the shoelace formula."""
|
||||
if len(points) < 3:
|
||||
return 0.0
|
||||
|
||||
area = 0.0
|
||||
for index, (x1, y1) in enumerate(points):
|
||||
x2, y2 = points[(index + 1) % len(points)]
|
||||
area += x1 * y2 - x2 * y1
|
||||
return abs(area) / 2
|
||||
|
||||
|
||||
def remove_closing_point(points: list[tuple[float, float]]) -> list[tuple[float, float]]:
|
||||
"""Remove duplicate final point before polygon clipping."""
|
||||
if len(points) > 1 and points[0] == points[-1]:
|
||||
return points[:-1]
|
||||
return points
|
||||
|
||||
|
||||
def clip_ring_to_rectangle(
|
||||
ring: list[tuple[float, float]],
|
||||
min_x: float,
|
||||
min_y: float,
|
||||
max_x: float,
|
||||
max_y: float,
|
||||
) -> list[tuple[float, float]]:
|
||||
"""Clip a polygon ring to an axis-aligned rectangle."""
|
||||
points = remove_closing_point(ring)
|
||||
|
||||
def clip_edge(
|
||||
input_points: list[tuple[float, float]],
|
||||
inside: object,
|
||||
intersect: object,
|
||||
) -> list[tuple[float, float]]:
|
||||
if not input_points:
|
||||
return []
|
||||
|
||||
output_points: list[tuple[float, float]] = []
|
||||
previous = input_points[-1]
|
||||
previous_inside = inside(previous)
|
||||
for current in input_points:
|
||||
current_inside = inside(current)
|
||||
if current_inside:
|
||||
if not previous_inside:
|
||||
output_points.append(intersect(previous, current))
|
||||
output_points.append(current)
|
||||
elif previous_inside:
|
||||
output_points.append(intersect(previous, current))
|
||||
previous = current
|
||||
previous_inside = current_inside
|
||||
return output_points
|
||||
|
||||
def vertical_intersection(x_value: float, start: tuple[float, float], end: tuple[float, float]) -> tuple[float, float]:
|
||||
x1, y1 = start
|
||||
x2, y2 = end
|
||||
if x2 == x1:
|
||||
return x_value, y1
|
||||
ratio = (x_value - x1) / (x2 - x1)
|
||||
return x_value, y1 + ratio * (y2 - y1)
|
||||
|
||||
def horizontal_intersection(y_value: float, start: tuple[float, float], end: tuple[float, float]) -> tuple[float, float]:
|
||||
x1, y1 = start
|
||||
x2, y2 = end
|
||||
if y2 == y1:
|
||||
return x1, y_value
|
||||
ratio = (y_value - y1) / (y2 - y1)
|
||||
return x1 + ratio * (x2 - x1), y_value
|
||||
|
||||
points = clip_edge(points, lambda point: point[0] >= min_x, lambda start, end: vertical_intersection(min_x, start, end))
|
||||
points = clip_edge(points, lambda point: point[0] <= max_x, lambda start, end: vertical_intersection(max_x, start, end))
|
||||
points = clip_edge(points, lambda point: point[1] >= min_y, lambda start, end: horizontal_intersection(min_y, start, end))
|
||||
points = clip_edge(points, lambda point: point[1] <= max_y, lambda start, end: horizontal_intersection(max_y, start, end))
|
||||
return points
|
||||
|
||||
|
||||
def projected_polygon_area(polygon: list[list[tuple[float, float]]]) -> float:
|
||||
"""Return projected polygon area, subtracting interior rings."""
|
||||
if not polygon:
|
||||
return 0.0
|
||||
|
||||
area = polygon_area(polygon[0])
|
||||
for hole in polygon[1:]:
|
||||
area -= polygon_area(hole)
|
||||
return max(0.0, area)
|
||||
|
||||
|
||||
def clipped_projected_polygon_area(
|
||||
polygon: list[list[tuple[float, float]]],
|
||||
min_x: float,
|
||||
min_y: float,
|
||||
max_x: float,
|
||||
max_y: float,
|
||||
) -> float:
|
||||
"""Return projected polygon area inside a rectangle, subtracting holes."""
|
||||
if not polygon:
|
||||
return 0.0
|
||||
|
||||
clipped_exterior = clip_ring_to_rectangle(polygon[0], min_x, min_y, max_x, max_y)
|
||||
area = polygon_area(clipped_exterior)
|
||||
for hole in polygon[1:]:
|
||||
clipped_hole = clip_ring_to_rectangle(hole, min_x, min_y, max_x, max_y)
|
||||
area -= polygon_area(clipped_hole)
|
||||
return max(0.0, area)
|
||||
|
||||
|
||||
def area_weighted_average(
|
||||
site_averages: list[float],
|
||||
site_lon_lats: list[tuple[float, float]],
|
||||
county_geometry: list[list[list[tuple[float, float]]]] | None,
|
||||
cell_size_m: float,
|
||||
) -> dict[str, float | int]:
|
||||
"""Compute a county average weighted by estimated grid-cell overlap area."""
|
||||
if county_geometry is None:
|
||||
return {
|
||||
"area_weighted_avg": math.nan,
|
||||
"area_weighted_sites": 0,
|
||||
"weighted_cell_area_km2": math.nan,
|
||||
"county_area_km2": math.nan,
|
||||
}
|
||||
|
||||
reference_lat = average_geometry_latitude(county_geometry)
|
||||
projected_county = project_county_geometry(county_geometry, reference_lat)
|
||||
half_cell = cell_size_m / 2
|
||||
weighted_sum = 0.0
|
||||
total_weight = 0.0
|
||||
weighted_sites = 0
|
||||
county_area = sum(projected_polygon_area(polygon) for polygon in projected_county)
|
||||
|
||||
for site_average, (lon, lat) in zip(site_averages, site_lon_lats):
|
||||
x, y = project_lon_lat(lon, lat, reference_lat)
|
||||
min_x = x - half_cell
|
||||
min_y = y - half_cell
|
||||
max_x = x + half_cell
|
||||
max_y = y + half_cell
|
||||
overlap_area = sum(
|
||||
clipped_projected_polygon_area(polygon, min_x, min_y, max_x, max_y)
|
||||
for polygon in projected_county
|
||||
)
|
||||
if overlap_area <= 0:
|
||||
continue
|
||||
weighted_sum += site_average * overlap_area
|
||||
total_weight += overlap_area
|
||||
weighted_sites += 1
|
||||
|
||||
area_weighted_avg = weighted_sum / total_weight if total_weight else math.nan
|
||||
return {
|
||||
"area_weighted_avg": area_weighted_avg,
|
||||
"area_weighted_sites": weighted_sites,
|
||||
"weighted_cell_area_km2": total_weight / 1_000_000,
|
||||
"county_area_km2": county_area / 1_000_000,
|
||||
}
|
||||
|
||||
|
||||
def summarize_archives(
|
||||
paths: list[Path],
|
||||
county_geometry: list[list[list[tuple[float, float]]]] | None,
|
||||
cell_size_m: float,
|
||||
) -> dict[str, float | int | str]:
|
||||
"""Summarize all NSRDB site CSVs in one or more ZIP archives."""
|
||||
site_averages: list[float] = []
|
||||
site_lon_lats: list[tuple[float, float]] = []
|
||||
row_counts: list[int] = []
|
||||
|
||||
for path in paths:
|
||||
with zipfile.ZipFile(path) as archive:
|
||||
csv_names = sorted(name for name in archive.namelist() if name.lower().endswith(".csv"))
|
||||
for csv_name in csv_names:
|
||||
csv_text = archive.read(csv_name).decode("utf-8-sig")
|
||||
site_average, row_count = site_average_daily_ghi(csv_text)
|
||||
site_lon_lat = extract_site_lon_lat(csv_text)
|
||||
site_averages.append(site_average)
|
||||
site_lon_lats.append(site_lon_lat)
|
||||
row_counts.append(row_count)
|
||||
|
||||
if not site_averages:
|
||||
raise ValueError(f"No CSV files found in {', '.join(str(path) for path in paths)}")
|
||||
|
||||
weighted = area_weighted_average(site_averages, site_lon_lats, county_geometry, cell_size_m)
|
||||
|
||||
return {
|
||||
"polygon_sites": len(site_averages),
|
||||
"ghi_rows_per_site_min": min(row_counts),
|
||||
"ghi_rows_per_site_max": max(row_counts),
|
||||
"avgSolarGhiKwhM2Day": weighted["area_weighted_avg"],
|
||||
"area_weighted_avg": weighted["area_weighted_avg"],
|
||||
"area_weighted_sites": weighted["area_weighted_sites"],
|
||||
"weighted_cell_area_km2": weighted["weighted_cell_area_km2"],
|
||||
"county_area_km2": weighted["county_area_km2"],
|
||||
"site_avg_min": min(site_averages),
|
||||
"site_avg_max": max(site_averages),
|
||||
"site_avg_stddev": statistics.pstdev(site_averages) if len(site_averages) > 1 else 0.0,
|
||||
}
|
||||
|
||||
|
||||
def summarize_archive(
|
||||
path: Path,
|
||||
county_geometry: list[list[list[tuple[float, float]]]] | None,
|
||||
cell_size_m: float,
|
||||
) -> dict[str, float | int | str]:
|
||||
"""Summarize all NSRDB site CSVs in one ZIP archive."""
|
||||
return summarize_archives([path], county_geometry, cell_size_m)
|
||||
|
||||
|
||||
def format_float(value: float | None, places: int = 3) -> str:
|
||||
"""Format an optional float for CSV output."""
|
||||
if value is None or math.isnan(value):
|
||||
return ""
|
||||
return f"{value:.{places}f}"
|
||||
|
||||
|
||||
def build_summary_row(
|
||||
archive_paths: list[Path],
|
||||
polygon_summary: dict[str, float | int | str],
|
||||
request_rows: list[dict[str, str]],
|
||||
representative_point_row: dict[str, str],
|
||||
) -> dict[str, str]:
|
||||
"""Build one output row from polygon and optional representative-point summaries."""
|
||||
county_fips = county_fips_from_archive(archive_paths[0])
|
||||
request_row = request_rows[0] if request_rows else {}
|
||||
request_site_counts = [
|
||||
int(row["site_count"])
|
||||
for row in request_rows
|
||||
if str(row.get("site_count", "")).strip().isdigit()
|
||||
]
|
||||
archive_zip = ";".join(str(path) for path in archive_paths)
|
||||
final_avg = float(polygon_summary["avgSolarGhiKwhM2Day"])
|
||||
area_weighted_avg = float(polygon_summary["area_weighted_avg"])
|
||||
representative_point_avg = (
|
||||
float(representative_point_row["avgSolarGhiKwhM2Day"])
|
||||
if representative_point_row.get("avgSolarGhiKwhM2Day")
|
||||
else None
|
||||
)
|
||||
weighted_minus_representative_point = (
|
||||
area_weighted_avg - representative_point_avg if representative_point_avg is not None else None
|
||||
)
|
||||
weighted_pct_diff_representative_point = (
|
||||
weighted_minus_representative_point / representative_point_avg * 100
|
||||
if representative_point_avg not in (None, 0)
|
||||
else None
|
||||
)
|
||||
|
||||
return {
|
||||
"county_fips": county_fips,
|
||||
"county_name": request_row.get("county_name") or representative_point_row.get("county_name", ""),
|
||||
"state_abbr": request_row.get("state_abbr") or representative_point_row.get("state_abbr", ""),
|
||||
"archive_zip": archive_zip,
|
||||
"polygon_sites": str(polygon_summary["polygon_sites"]),
|
||||
"request_site_count": str(sum(request_site_counts)) if request_site_counts else request_row.get("site_count", ""),
|
||||
"ghi_rows_per_site_min": str(polygon_summary["ghi_rows_per_site_min"]),
|
||||
"ghi_rows_per_site_max": str(polygon_summary["ghi_rows_per_site_max"]),
|
||||
"avgSolarGhiKwhM2Day": format_float(final_avg),
|
||||
"areaWeightedAvgSolarGhiKwhM2Day": format_float(area_weighted_avg),
|
||||
"areaWeightedSites": str(polygon_summary["area_weighted_sites"]),
|
||||
"weightedCellAreaKm2": format_float(float(polygon_summary["weighted_cell_area_km2"]), 1),
|
||||
"countyAreaKm2": format_float(float(polygon_summary["county_area_km2"]), 1),
|
||||
"site_avg_min": format_float(float(polygon_summary["site_avg_min"])),
|
||||
"site_avg_max": format_float(float(polygon_summary["site_avg_max"])),
|
||||
"site_avg_stddev": format_float(float(polygon_summary["site_avg_stddev"])),
|
||||
"representativePointAvgSolarGhiKwhM2Day": format_float(representative_point_avg),
|
||||
"areaWeightedMinusRepresentativePoint": format_float(weighted_minus_representative_point),
|
||||
"areaWeightedPctDiffFromRepresentativePoint": format_float(weighted_pct_diff_representative_point),
|
||||
}
|
||||
|
||||
|
||||
def archive_signature(paths: list[Path]) -> set[str]:
|
||||
"""Return archive filenames for comparing existing summary rows."""
|
||||
return {path.name for path in paths}
|
||||
|
||||
|
||||
def existing_archive_signature(row: dict[str, str]) -> set[str]:
|
||||
"""Return archive filenames recorded in an existing summary row."""
|
||||
return {
|
||||
Path(path_text).name
|
||||
for path_text in row.get("archive_zip", "").split(";")
|
||||
if path_text.strip()
|
||||
}
|
||||
|
||||
|
||||
def write_summary(rows: list[dict[str, str]], output_csv: Path) -> None:
|
||||
"""Write polygon GHI summary rows to disk."""
|
||||
output_csv.parent.mkdir(parents=True, exist_ok=True)
|
||||
with output_csv.open("w", newline="", encoding="utf-8") as handle:
|
||||
writer = csv.DictWriter(handle, fieldnames=SUMMARY_FIELDS)
|
||||
writer.writeheader()
|
||||
writer.writerows(rows)
|
||||
|
||||
|
||||
def run(args: argparse.Namespace) -> None:
|
||||
"""Summarize downloaded polygon archives."""
|
||||
county_geometries = load_county_geometries(args.counties_geojson)
|
||||
request_rows = read_grouped_lookup(args.requests_csv, "county_fips")
|
||||
representative_point_rows = read_lookup(args.representative_point_csv, "county_fips")
|
||||
existing_rows = (
|
||||
read_lookup(args.output_csv, "county_fips")
|
||||
if args.reuse_existing_output
|
||||
else {}
|
||||
)
|
||||
archives = sorted(args.archive_dir.glob(args.archive_glob))
|
||||
|
||||
if not archives:
|
||||
print(f"No archives matched {args.archive_dir / args.archive_glob}")
|
||||
return
|
||||
|
||||
rows: list[dict[str, str]] = []
|
||||
archive_groups = archive_groups_by_county(archives)
|
||||
total = len(archive_groups)
|
||||
reused_count = 0
|
||||
for index, county_fips in enumerate(sorted(archive_groups), start=1):
|
||||
archive_paths = sorted(archive_groups[county_fips])
|
||||
existing_row = existing_rows.get(county_fips)
|
||||
if existing_row and existing_archive_signature(existing_row) == archive_signature(archive_paths):
|
||||
rows.append({field: existing_row.get(field, "") for field in SUMMARY_FIELDS})
|
||||
reused_count += 1
|
||||
continue
|
||||
|
||||
try:
|
||||
polygon_summary = summarize_archives(
|
||||
archive_paths,
|
||||
county_geometries.get(county_fips),
|
||||
args.cell_size_m,
|
||||
)
|
||||
row = build_summary_row(
|
||||
archive_paths,
|
||||
polygon_summary,
|
||||
request_rows.get(county_fips, []),
|
||||
representative_point_rows.get(county_fips, {}),
|
||||
)
|
||||
rows.append(row)
|
||||
except (OSError, ValueError, zipfile.BadZipFile) as error:
|
||||
print(f"[{index}/{total}] Failed {county_fips}: {error}")
|
||||
continue
|
||||
|
||||
tile_note = f", archives={len(archive_paths)}" if len(archive_paths) > 1 else ""
|
||||
print(
|
||||
f"[{index}/{total}] {county_fips}: "
|
||||
f"sites={row['polygon_sites']}, weighted={row['areaWeightedAvgSolarGhiKwhM2Day']}, "
|
||||
f"representative_point={row['representativePointAvgSolarGhiKwhM2Day'] or 'n/a'}"
|
||||
f"{tile_note}"
|
||||
)
|
||||
|
||||
write_summary(rows, args.output_csv)
|
||||
if reused_count:
|
||||
print(f"Reused {reused_count} existing rows whose archive sets still match.")
|
||||
print(f"Wrote {len(rows)} rows to {args.output_csv}")
|
||||
|
||||
|
||||
def parse_args() -> argparse.Namespace:
|
||||
"""Parse command-line arguments."""
|
||||
parser = argparse.ArgumentParser(description=__doc__)
|
||||
parser.add_argument("--archive-dir", type=Path, default=DEFAULT_ARCHIVE_DIR)
|
||||
parser.add_argument("--archive-glob", default=DEFAULT_ARCHIVE_GLOB)
|
||||
parser.add_argument("--counties-geojson", type=Path, default=DEFAULT_COUNTIES_GEOJSON)
|
||||
parser.add_argument("--requests-csv", type=Path, default=DEFAULT_REQUESTS_CSV)
|
||||
parser.add_argument(
|
||||
"--representative-point-csv",
|
||||
type=Path,
|
||||
default=DEFAULT_REPRESENTATIVE_POINT_CSV,
|
||||
help="County representative-point GHI summary CSV.",
|
||||
)
|
||||
parser.add_argument("--output-csv", type=Path, default=DEFAULT_OUTPUT_CSV)
|
||||
parser.add_argument(
|
||||
"--cell-size-m",
|
||||
type=float,
|
||||
default=DEFAULT_CELL_SIZE_M,
|
||||
help="Estimated NSRDB grid-cell side length in meters. GOES TMY is 4 km.",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--reuse-existing-output",
|
||||
action="store_true",
|
||||
help=(
|
||||
"Reuse rows already present in --output-csv when the recorded archive filenames "
|
||||
"match the current archive directory, and summarize only missing or changed counties."
|
||||
),
|
||||
)
|
||||
args = parser.parse_args()
|
||||
if args.cell_size_m <= 0:
|
||||
parser.error("--cell-size-m must be greater than 0.")
|
||||
return args
|
||||
|
||||
|
||||
def main() -> None:
|
||||
"""Run the polygon GHI summarizer."""
|
||||
run(parse_args())
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@@ -0,0 +1,527 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
Summarize downloaded NSRDB county polygon cloud archives.
|
||||
|
||||
Each downloaded polygon archive contains one CSV per NSRDB grid site that
|
||||
intersects the county polygon or tile. This script computes area-weighted
|
||||
county-level cloudiness metrics across those site CSVs, combining multiple tile
|
||||
archives back into one county summary when present.
|
||||
|
||||
Primary metric:
|
||||
|
||||
cloudinessIndexPct = 1 - mean(clamped(GHI / Clearsky GHI, 0, 1))
|
||||
|
||||
Despite the legacy "Pct" field name, the primary index is stored on a 0-1 scale.
|
||||
Cloud Type bucket fields are stored as percentages.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import argparse
|
||||
import csv
|
||||
import math
|
||||
import re
|
||||
import statistics
|
||||
import zipfile
|
||||
from pathlib import Path
|
||||
|
||||
from summarize_nsrdb_county_polygon_archives import (
|
||||
area_weighted_average,
|
||||
archive_groups_by_county,
|
||||
archive_signature,
|
||||
county_fips_from_archive,
|
||||
existing_archive_signature,
|
||||
extract_site_lon_lat,
|
||||
format_float,
|
||||
load_county_geometries,
|
||||
read_grouped_lookup,
|
||||
read_lookup,
|
||||
)
|
||||
|
||||
|
||||
DEFAULT_ARCHIVE_DIR = Path("data/nrel/polygon_cloud_archives")
|
||||
DEFAULT_COUNTIES_GEOJSON = Path("data/geojson-counties-fips.json")
|
||||
DEFAULT_REQUESTS_CSV = Path("data/nrel/county_polygon_cloud_request_manifest.csv")
|
||||
DEFAULT_REPRESENTATIVE_POINT_CSV = Path("data/nrel/county_representative_point_cloud_summary.csv")
|
||||
DEFAULT_OUTPUT_CSV = Path("data/nrel/county_polygon_cloud_summary.csv")
|
||||
DEFAULT_CELL_SIZE_M = 4000.0
|
||||
DEFAULT_ARCHIVE_GLOB = "*.zip"
|
||||
DEFAULT_MIN_CLEARSKY_GHI = 50.0
|
||||
|
||||
SUMMARY_FIELDS = [
|
||||
"county_fips",
|
||||
"county_name",
|
||||
"state_abbr",
|
||||
"archive_zip",
|
||||
"polygon_sites",
|
||||
"request_site_count",
|
||||
"rows_per_site_min",
|
||||
"rows_per_site_max",
|
||||
"daylight_rows_per_site_min",
|
||||
"daylight_rows_per_site_max",
|
||||
"cloudinessIndexPct",
|
||||
"areaWeightedCloudinessIndexPct",
|
||||
"areaWeightedAvgObservedToClearskyRatio",
|
||||
"areaWeightedSites",
|
||||
"weightedCellAreaKm2",
|
||||
"countyAreaKm2",
|
||||
"site_cloudiness_min",
|
||||
"site_cloudiness_max",
|
||||
"site_cloudiness_stddev",
|
||||
"clearOrProbablyClearPct",
|
||||
"cloudyOrObscuredPct",
|
||||
"fogPct",
|
||||
"waterCloudPct",
|
||||
"iceCloudPct",
|
||||
"cirrusPct",
|
||||
"unknownCloudTypePct",
|
||||
"representativePointCloudinessIndexPct",
|
||||
"areaWeightedMinusRepresentativePoint",
|
||||
"areaWeightedPctDiffFromRepresentativePoint",
|
||||
]
|
||||
|
||||
|
||||
def normalized_header(value: str) -> str:
|
||||
"""Normalize a CSV header to make NSRDB spelling variations easier to match."""
|
||||
return re.sub(r"[^a-z0-9]+", "", value.lower())
|
||||
|
||||
|
||||
def find_column(header: list[str], candidates: set[str]) -> int | None:
|
||||
"""Return the first header index whose normalized name matches a candidate."""
|
||||
normalized_candidates = {normalized_header(candidate) for candidate in candidates}
|
||||
for index, value in enumerate(header):
|
||||
if normalized_header(value) in normalized_candidates:
|
||||
return index
|
||||
return None
|
||||
|
||||
|
||||
def find_data_header(rows: list[list[str]]) -> int | None:
|
||||
"""Return the NSRDB hourly data header row index."""
|
||||
required = {"Year", "Month", "Day", "Hour", "Minute"}
|
||||
for index, row in enumerate(rows):
|
||||
if required.issubset(set(row)):
|
||||
return index
|
||||
return None
|
||||
|
||||
|
||||
def parse_optional_float(row: list[str], index: int | None) -> float | None:
|
||||
"""Parse an optional float field from a CSV row."""
|
||||
if index is None or len(row) <= index:
|
||||
return None
|
||||
value = row[index].strip()
|
||||
if not value:
|
||||
return None
|
||||
try:
|
||||
parsed = float(value)
|
||||
except ValueError:
|
||||
return None
|
||||
return parsed if math.isfinite(parsed) else None
|
||||
|
||||
|
||||
def parse_cloud_type(row: list[str], index: int | None) -> str:
|
||||
"""Parse an optional Cloud Type code as a stable string."""
|
||||
if index is None or len(row) <= index:
|
||||
return ""
|
||||
value = row[index].strip()
|
||||
if not value:
|
||||
return ""
|
||||
try:
|
||||
return str(int(float(value)))
|
||||
except ValueError:
|
||||
return value
|
||||
|
||||
|
||||
def pct(count: int, total: int) -> float:
|
||||
"""Return a percentage or NaN when there is no denominator."""
|
||||
return count / total * 100 if total else math.nan
|
||||
|
||||
|
||||
def site_cloud_metrics(csv_text: str, min_clearsky_ghi: float) -> dict[str, float | int]:
|
||||
"""Return site-level cloudiness metrics from one NSRDB CSV response."""
|
||||
rows = list(csv.reader(csv_text.splitlines()))
|
||||
header_index = find_data_header(rows)
|
||||
if header_index is None:
|
||||
raise ValueError("Could not find the NSRDB data header row.")
|
||||
|
||||
header = rows[header_index]
|
||||
ghi_index = find_column(header, {"GHI", "ghi"})
|
||||
clearsky_ghi_index = find_column(
|
||||
header,
|
||||
{
|
||||
"Clearsky GHI",
|
||||
"Clear Sky GHI",
|
||||
"Clear-sky GHI",
|
||||
"clearsky_ghi",
|
||||
"clear_sky_ghi",
|
||||
},
|
||||
)
|
||||
cloud_type_index = find_column(header, {"Cloud Type", "cloud_type", "cloudtype"})
|
||||
if ghi_index is None:
|
||||
raise ValueError("Could not find a GHI column in the NSRDB response.")
|
||||
if clearsky_ghi_index is None:
|
||||
raise ValueError("Could not find a Clearsky GHI column in the NSRDB response.")
|
||||
if cloud_type_index is None:
|
||||
raise ValueError("Could not find a Cloud Type column in the NSRDB response.")
|
||||
|
||||
all_rows = 0
|
||||
daylight_rows = 0
|
||||
ratio_sum = 0.0
|
||||
daylight_cloud_type_counts: dict[str, int] = {}
|
||||
|
||||
for row in rows[header_index + 1 :]:
|
||||
if not row:
|
||||
continue
|
||||
all_rows += 1
|
||||
ghi = parse_optional_float(row, ghi_index)
|
||||
clearsky_ghi = parse_optional_float(row, clearsky_ghi_index)
|
||||
cloud_type = parse_cloud_type(row, cloud_type_index)
|
||||
if ghi is None or clearsky_ghi is None or clearsky_ghi < min_clearsky_ghi:
|
||||
continue
|
||||
|
||||
ratio_sum += min(1.0, max(0.0, ghi / clearsky_ghi))
|
||||
daylight_rows += 1
|
||||
if cloud_type:
|
||||
daylight_cloud_type_counts[cloud_type] = daylight_cloud_type_counts.get(cloud_type, 0) + 1
|
||||
|
||||
if daylight_rows == 0:
|
||||
raise ValueError("No daylight rows with valid GHI and Clearsky GHI were found.")
|
||||
|
||||
avg_ratio = ratio_sum / daylight_rows
|
||||
cloudiness = 1 - avg_ratio
|
||||
clear_or_probably_clear = daylight_cloud_type_counts.get("0", 0) + daylight_cloud_type_counts.get("1", 0)
|
||||
cloudy_or_obscured = sum(
|
||||
daylight_cloud_type_counts.get(code, 0)
|
||||
for code in ["2", "3", "4", "5", "6", "7", "8", "9", "11", "12"]
|
||||
)
|
||||
water_cloud = (
|
||||
daylight_cloud_type_counts.get("3", 0)
|
||||
+ daylight_cloud_type_counts.get("4", 0)
|
||||
+ daylight_cloud_type_counts.get("5", 0)
|
||||
)
|
||||
ice_cloud = (
|
||||
daylight_cloud_type_counts.get("6", 0)
|
||||
+ daylight_cloud_type_counts.get("8", 0)
|
||||
+ daylight_cloud_type_counts.get("9", 0)
|
||||
)
|
||||
unknown_cloud_type = daylight_cloud_type_counts.get("10", 0) + daylight_cloud_type_counts.get("-15", 0)
|
||||
|
||||
return {
|
||||
"cloudiness": cloudiness,
|
||||
"avg_ratio": avg_ratio,
|
||||
"all_rows": all_rows,
|
||||
"daylight_rows": daylight_rows,
|
||||
"clear_or_probably_clear_pct": pct(clear_or_probably_clear, daylight_rows),
|
||||
"cloudy_or_obscured_pct": pct(cloudy_or_obscured, daylight_rows),
|
||||
"fog_pct": pct(daylight_cloud_type_counts.get("2", 0), daylight_rows),
|
||||
"water_cloud_pct": pct(water_cloud, daylight_rows),
|
||||
"ice_cloud_pct": pct(ice_cloud, daylight_rows),
|
||||
"cirrus_pct": pct(daylight_cloud_type_counts.get("7", 0), daylight_rows),
|
||||
"unknown_cloud_type_pct": pct(unknown_cloud_type, daylight_rows),
|
||||
}
|
||||
|
||||
|
||||
def weighted_metric(
|
||||
site_metrics: list[dict[str, float | int]],
|
||||
metric_key: str,
|
||||
site_lon_lats: list[tuple[float, float]],
|
||||
county_geometry: list[list[list[tuple[float, float]]]] | None,
|
||||
cell_size_m: float,
|
||||
) -> dict[str, float | int]:
|
||||
"""Area-weight one metric across sites using estimated grid-cell overlap."""
|
||||
values = [float(metrics[metric_key]) for metrics in site_metrics]
|
||||
return area_weighted_average(values, site_lon_lats, county_geometry, cell_size_m)
|
||||
|
||||
|
||||
def summarize_archives(
|
||||
paths: list[Path],
|
||||
county_geometry: list[list[list[tuple[float, float]]]] | None,
|
||||
cell_size_m: float,
|
||||
min_clearsky_ghi: float,
|
||||
) -> dict[str, float | int]:
|
||||
"""Summarize all NSRDB site CSVs in one or more ZIP archives."""
|
||||
site_metrics: list[dict[str, float | int]] = []
|
||||
site_lon_lats: list[tuple[float, float]] = []
|
||||
empty_csv_members: list[str] = []
|
||||
|
||||
for path in paths:
|
||||
with zipfile.ZipFile(path) as archive:
|
||||
bad_member = archive.testzip()
|
||||
if bad_member:
|
||||
raise zipfile.BadZipFile(
|
||||
f"CRC check failed for {bad_member} in {path.name}"
|
||||
)
|
||||
csv_names = sorted(name for name in archive.namelist() if name.lower().endswith(".csv"))
|
||||
for csv_name in csv_names:
|
||||
csv_text = archive.read(csv_name).decode("utf-8-sig")
|
||||
if not csv_text.strip():
|
||||
empty_csv_members.append(f"{path.name}:{csv_name}")
|
||||
continue
|
||||
site_metrics.append(site_cloud_metrics(csv_text, min_clearsky_ghi))
|
||||
site_lon_lats.append(extract_site_lon_lat(csv_text))
|
||||
|
||||
if not site_metrics:
|
||||
raise ValueError(f"No CSV files found in {', '.join(str(path) for path in paths)}")
|
||||
|
||||
cloudiness_values = [float(metrics["cloudiness"]) for metrics in site_metrics]
|
||||
row_counts = [int(metrics["all_rows"]) for metrics in site_metrics]
|
||||
daylight_row_counts = [int(metrics["daylight_rows"]) for metrics in site_metrics]
|
||||
|
||||
weighted_cloudiness = weighted_metric(site_metrics, "cloudiness", site_lon_lats, county_geometry, cell_size_m)
|
||||
weighted_avg_ratio = weighted_metric(site_metrics, "avg_ratio", site_lon_lats, county_geometry, cell_size_m)
|
||||
|
||||
return {
|
||||
"polygon_sites": len(site_metrics),
|
||||
"rows_per_site_min": min(row_counts),
|
||||
"rows_per_site_max": max(row_counts),
|
||||
"daylight_rows_per_site_min": min(daylight_row_counts),
|
||||
"daylight_rows_per_site_max": max(daylight_row_counts),
|
||||
"area_weighted_cloudiness": weighted_cloudiness["area_weighted_avg"],
|
||||
"area_weighted_avg_ratio": weighted_avg_ratio["area_weighted_avg"],
|
||||
"area_weighted_sites": weighted_cloudiness["area_weighted_sites"],
|
||||
"weighted_cell_area_km2": weighted_cloudiness["weighted_cell_area_km2"],
|
||||
"county_area_km2": weighted_cloudiness["county_area_km2"],
|
||||
"site_cloudiness_min": min(cloudiness_values),
|
||||
"site_cloudiness_max": max(cloudiness_values),
|
||||
"site_cloudiness_stddev": statistics.pstdev(cloudiness_values) if len(cloudiness_values) > 1 else 0.0,
|
||||
"clear_or_probably_clear_pct": weighted_metric(
|
||||
site_metrics, "clear_or_probably_clear_pct", site_lon_lats, county_geometry, cell_size_m
|
||||
)["area_weighted_avg"],
|
||||
"cloudy_or_obscured_pct": weighted_metric(
|
||||
site_metrics, "cloudy_or_obscured_pct", site_lon_lats, county_geometry, cell_size_m
|
||||
)["area_weighted_avg"],
|
||||
"fog_pct": weighted_metric(site_metrics, "fog_pct", site_lon_lats, county_geometry, cell_size_m)[
|
||||
"area_weighted_avg"
|
||||
],
|
||||
"water_cloud_pct": weighted_metric(
|
||||
site_metrics, "water_cloud_pct", site_lon_lats, county_geometry, cell_size_m
|
||||
)["area_weighted_avg"],
|
||||
"ice_cloud_pct": weighted_metric(site_metrics, "ice_cloud_pct", site_lon_lats, county_geometry, cell_size_m)[
|
||||
"area_weighted_avg"
|
||||
],
|
||||
"cirrus_pct": weighted_metric(site_metrics, "cirrus_pct", site_lon_lats, county_geometry, cell_size_m)[
|
||||
"area_weighted_avg"
|
||||
],
|
||||
"unknown_cloud_type_pct": weighted_metric(
|
||||
site_metrics, "unknown_cloud_type_pct", site_lon_lats, county_geometry, cell_size_m
|
||||
)["area_weighted_avg"],
|
||||
"empty_csv_members": len(empty_csv_members),
|
||||
}
|
||||
|
||||
|
||||
def archive_part_id(path: Path) -> str:
|
||||
"""Return county or tileNNN for one downloaded archive filename."""
|
||||
parts = path.stem.split("_")
|
||||
return parts[1] if len(parts) > 1 and parts[1].startswith("tile") else "county"
|
||||
|
||||
|
||||
def missing_archive_parts(
|
||||
archive_paths: list[Path],
|
||||
request_rows: list[dict[str, str]],
|
||||
) -> list[str]:
|
||||
"""Return requested county/tile archive parts that are not downloaded."""
|
||||
expected = {
|
||||
row.get("tile_id", "").strip() or "county"
|
||||
for row in request_rows
|
||||
if row.get("download_url", "").strip()
|
||||
}
|
||||
actual = {archive_part_id(path) for path in archive_paths}
|
||||
return sorted(expected - actual)
|
||||
|
||||
|
||||
def build_summary_row(
|
||||
archive_paths: list[Path],
|
||||
polygon_summary: dict[str, float | int],
|
||||
request_rows: list[dict[str, str]],
|
||||
representative_point_row: dict[str, str],
|
||||
) -> dict[str, str]:
|
||||
"""Build one output row from polygon and optional representative-point summaries."""
|
||||
county_fips = county_fips_from_archive(archive_paths[0])
|
||||
request_row = request_rows[0] if request_rows else {}
|
||||
request_site_counts = [
|
||||
int(row["site_count"])
|
||||
for row in request_rows
|
||||
if str(row.get("site_count", "")).strip().isdigit()
|
||||
]
|
||||
archive_zip = ";".join(str(path) for path in archive_paths)
|
||||
area_weighted_cloudiness = float(polygon_summary["area_weighted_cloudiness"])
|
||||
representative_point_cloudiness = (
|
||||
float(representative_point_row["cloudinessIndexPct"])
|
||||
if representative_point_row.get("cloudinessIndexPct")
|
||||
else None
|
||||
)
|
||||
weighted_minus_representative_point = (
|
||||
area_weighted_cloudiness - representative_point_cloudiness
|
||||
if representative_point_cloudiness is not None
|
||||
else None
|
||||
)
|
||||
weighted_pct_diff_representative_point = (
|
||||
weighted_minus_representative_point / representative_point_cloudiness * 100
|
||||
if representative_point_cloudiness not in (None, 0)
|
||||
else None
|
||||
)
|
||||
|
||||
return {
|
||||
"county_fips": county_fips,
|
||||
"county_name": request_row.get("county_name") or representative_point_row.get("county_name", ""),
|
||||
"state_abbr": request_row.get("state_abbr") or representative_point_row.get("state_abbr", ""),
|
||||
"archive_zip": archive_zip,
|
||||
"polygon_sites": str(polygon_summary["polygon_sites"]),
|
||||
"request_site_count": str(sum(request_site_counts)) if request_site_counts else request_row.get("site_count", ""),
|
||||
"rows_per_site_min": str(polygon_summary["rows_per_site_min"]),
|
||||
"rows_per_site_max": str(polygon_summary["rows_per_site_max"]),
|
||||
"daylight_rows_per_site_min": str(polygon_summary["daylight_rows_per_site_min"]),
|
||||
"daylight_rows_per_site_max": str(polygon_summary["daylight_rows_per_site_max"]),
|
||||
"cloudinessIndexPct": format_float(area_weighted_cloudiness, 4),
|
||||
"areaWeightedCloudinessIndexPct": format_float(area_weighted_cloudiness, 4),
|
||||
"areaWeightedAvgObservedToClearskyRatio": format_float(float(polygon_summary["area_weighted_avg_ratio"]), 4),
|
||||
"areaWeightedSites": str(polygon_summary["area_weighted_sites"]),
|
||||
"weightedCellAreaKm2": format_float(float(polygon_summary["weighted_cell_area_km2"]), 1),
|
||||
"countyAreaKm2": format_float(float(polygon_summary["county_area_km2"]), 1),
|
||||
"site_cloudiness_min": format_float(float(polygon_summary["site_cloudiness_min"]), 4),
|
||||
"site_cloudiness_max": format_float(float(polygon_summary["site_cloudiness_max"]), 4),
|
||||
"site_cloudiness_stddev": format_float(float(polygon_summary["site_cloudiness_stddev"]), 4),
|
||||
"clearOrProbablyClearPct": format_float(float(polygon_summary["clear_or_probably_clear_pct"]), 2),
|
||||
"cloudyOrObscuredPct": format_float(float(polygon_summary["cloudy_or_obscured_pct"]), 2),
|
||||
"fogPct": format_float(float(polygon_summary["fog_pct"]), 2),
|
||||
"waterCloudPct": format_float(float(polygon_summary["water_cloud_pct"]), 2),
|
||||
"iceCloudPct": format_float(float(polygon_summary["ice_cloud_pct"]), 2),
|
||||
"cirrusPct": format_float(float(polygon_summary["cirrus_pct"]), 2),
|
||||
"unknownCloudTypePct": format_float(float(polygon_summary["unknown_cloud_type_pct"]), 2),
|
||||
"representativePointCloudinessIndexPct": format_float(representative_point_cloudiness, 4),
|
||||
"areaWeightedMinusRepresentativePoint": format_float(weighted_minus_representative_point, 4),
|
||||
"areaWeightedPctDiffFromRepresentativePoint": format_float(weighted_pct_diff_representative_point),
|
||||
}
|
||||
|
||||
|
||||
def write_summary(rows: list[dict[str, str]], output_csv: Path) -> None:
|
||||
"""Write polygon cloud summary rows to disk."""
|
||||
output_csv.parent.mkdir(parents=True, exist_ok=True)
|
||||
with output_csv.open("w", newline="", encoding="utf-8") as handle:
|
||||
writer = csv.DictWriter(handle, fieldnames=SUMMARY_FIELDS)
|
||||
writer.writeheader()
|
||||
writer.writerows(rows)
|
||||
|
||||
|
||||
def run(args: argparse.Namespace) -> None:
|
||||
"""Summarize downloaded polygon cloud archives."""
|
||||
county_geometries = load_county_geometries(args.counties_geojson)
|
||||
request_rows = read_grouped_lookup(args.requests_csv, "county_fips")
|
||||
representative_point_rows = read_lookup(args.representative_point_csv, "county_fips")
|
||||
existing_rows = (
|
||||
read_lookup(args.output_csv, "county_fips")
|
||||
if args.reuse_existing_output
|
||||
else {}
|
||||
)
|
||||
archives = sorted(args.archive_dir.glob(args.archive_glob))
|
||||
|
||||
if not archives:
|
||||
print(f"No archives matched {args.archive_dir / args.archive_glob}")
|
||||
return
|
||||
|
||||
rows: list[dict[str, str]] = []
|
||||
archive_groups = archive_groups_by_county(archives)
|
||||
total = len(archive_groups)
|
||||
reused_count = 0
|
||||
for index, county_fips in enumerate(sorted(archive_groups), start=1):
|
||||
archive_paths = sorted(archive_groups[county_fips])
|
||||
county_request_rows = request_rows.get(county_fips, [])
|
||||
missing_parts = missing_archive_parts(archive_paths, county_request_rows)
|
||||
if missing_parts:
|
||||
print(
|
||||
f"[{index}/{total}] Skipped {county_fips}: incomplete archive set; "
|
||||
f"missing {', '.join(missing_parts)}."
|
||||
)
|
||||
continue
|
||||
|
||||
existing_row = existing_rows.get(county_fips)
|
||||
if existing_row and existing_archive_signature(existing_row) == archive_signature(archive_paths):
|
||||
rows.append({field: existing_row.get(field, "") for field in SUMMARY_FIELDS})
|
||||
reused_count += 1
|
||||
continue
|
||||
|
||||
try:
|
||||
polygon_summary = summarize_archives(
|
||||
archive_paths,
|
||||
county_geometries.get(county_fips),
|
||||
args.cell_size_m,
|
||||
args.min_clearsky_ghi,
|
||||
)
|
||||
row = build_summary_row(
|
||||
archive_paths,
|
||||
polygon_summary,
|
||||
county_request_rows,
|
||||
representative_point_rows.get(county_fips, {}),
|
||||
)
|
||||
rows.append(row)
|
||||
except (OSError, ValueError, zipfile.BadZipFile) as error:
|
||||
print(f"[{index}/{total}] Failed {county_fips}: {error}")
|
||||
continue
|
||||
|
||||
tile_note = f", archives={len(archive_paths)}" if len(archive_paths) > 1 else ""
|
||||
empty_note = (
|
||||
f", skipped_empty_csvs={polygon_summary['empty_csv_members']}"
|
||||
if polygon_summary["empty_csv_members"]
|
||||
else ""
|
||||
)
|
||||
print(
|
||||
f"[{index}/{total}] {county_fips}: "
|
||||
f"sites={row['polygon_sites']}, weighted={row['areaWeightedCloudinessIndexPct']}, "
|
||||
f"representative_point={row['representativePointCloudinessIndexPct'] or 'n/a'}"
|
||||
f"{tile_note}{empty_note}"
|
||||
)
|
||||
|
||||
write_summary(rows, args.output_csv)
|
||||
if reused_count:
|
||||
print(f"Reused {reused_count} existing rows whose archive sets still match.")
|
||||
print(f"Wrote {len(rows)} rows to {args.output_csv}")
|
||||
|
||||
|
||||
def parse_args() -> argparse.Namespace:
|
||||
"""Parse command-line arguments."""
|
||||
parser = argparse.ArgumentParser(description=__doc__)
|
||||
parser.add_argument("--archive-dir", type=Path, default=DEFAULT_ARCHIVE_DIR)
|
||||
parser.add_argument("--archive-glob", default=DEFAULT_ARCHIVE_GLOB)
|
||||
parser.add_argument("--counties-geojson", type=Path, default=DEFAULT_COUNTIES_GEOJSON)
|
||||
parser.add_argument("--requests-csv", type=Path, default=DEFAULT_REQUESTS_CSV)
|
||||
parser.add_argument(
|
||||
"--representative-point-csv",
|
||||
type=Path,
|
||||
default=DEFAULT_REPRESENTATIVE_POINT_CSV,
|
||||
help="County representative-point cloud summary CSV.",
|
||||
)
|
||||
parser.add_argument("--output-csv", type=Path, default=DEFAULT_OUTPUT_CSV)
|
||||
parser.add_argument(
|
||||
"--cell-size-m",
|
||||
type=float,
|
||||
default=DEFAULT_CELL_SIZE_M,
|
||||
help="Estimated NSRDB grid-cell side length in meters. GOES TMY is 4 km.",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--min-clearsky-ghi",
|
||||
type=float,
|
||||
default=DEFAULT_MIN_CLEARSKY_GHI,
|
||||
help="Minimum Clearsky GHI W/m2 for daylight cloudiness ratio rows.",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--reuse-existing-output",
|
||||
action="store_true",
|
||||
help=(
|
||||
"Reuse rows already present in --output-csv when the recorded archive filenames "
|
||||
"match the current archive directory, and summarize only missing or changed counties."
|
||||
),
|
||||
)
|
||||
args = parser.parse_args()
|
||||
if args.cell_size_m <= 0:
|
||||
parser.error("--cell-size-m must be greater than 0.")
|
||||
if args.min_clearsky_ghi < 0:
|
||||
parser.error("--min-clearsky-ghi must be 0 or greater.")
|
||||
return args
|
||||
|
||||
|
||||
def main() -> None:
|
||||
"""Run the polygon cloud summarizer."""
|
||||
run(parse_args())
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@@ -0,0 +1,31 @@
|
||||
<#
|
||||
.SYNOPSIS
|
||||
Runs a local HTTP server for the US County Climate Explorer.
|
||||
|
||||
.DESCRIPTION
|
||||
Starts Python's built-in HTTP server from the project root so the browser can
|
||||
load data/climate-data.csv and related static assets over http://localhost.
|
||||
|
||||
.PARAMETER Port
|
||||
The localhost port to bind. Defaults to 8000.
|
||||
|
||||
.EXAMPLE
|
||||
.\serve.ps1
|
||||
#>
|
||||
[CmdletBinding()]
|
||||
param(
|
||||
[int]$Port = 8000
|
||||
)
|
||||
|
||||
$ErrorActionPreference = "Stop"
|
||||
|
||||
$ProjectRoot = Split-Path -Parent $MyInvocation.MyCommand.Path
|
||||
$VenvPython = Join-Path $ProjectRoot ".venv\Scripts\python.exe"
|
||||
$PythonCommand = if (Test-Path $VenvPython) { $VenvPython } else { "python" }
|
||||
|
||||
Set-Location $ProjectRoot
|
||||
|
||||
Write-Host "Serving US County Climate Explorer at http://localhost:$Port/"
|
||||
Write-Host "Press Ctrl+C to stop."
|
||||
|
||||
& $PythonCommand -m http.server $Port --bind 127.0.0.1
|
||||
+433
@@ -0,0 +1,433 @@
|
||||
:root {
|
||||
--bg-1: #eef4ff;
|
||||
--bg-2: #c2dfff;
|
||||
--ink: #0f1f38;
|
||||
--ink-soft: #33496b;
|
||||
--panel: #ffffff;
|
||||
--panel-shadow: 0 18px 40px rgba(12, 42, 88, 0.14);
|
||||
--line: #cad8ef;
|
||||
--accent: #1f67d2;
|
||||
--accent-soft: #4e8df2;
|
||||
}
|
||||
|
||||
* {
|
||||
box-sizing: border-box;
|
||||
}
|
||||
|
||||
html,
|
||||
body {
|
||||
height: 100%;
|
||||
margin: 0;
|
||||
}
|
||||
|
||||
body {
|
||||
font-family: "Source Sans 3", sans-serif;
|
||||
color: var(--ink);
|
||||
background:
|
||||
radial-gradient(circle at 10% 10%, rgba(117, 180, 255, 0.38), transparent 34%),
|
||||
radial-gradient(circle at 90% 30%, rgba(255, 255, 255, 0.9), transparent 40%),
|
||||
linear-gradient(140deg, var(--bg-1), var(--bg-2));
|
||||
}
|
||||
|
||||
body.is-sources-modal-open {
|
||||
overflow: hidden;
|
||||
}
|
||||
|
||||
.page-shell {
|
||||
min-height: 100%;
|
||||
padding: 1rem;
|
||||
display: grid;
|
||||
gap: 1rem;
|
||||
grid-template-columns: 340px 1fr;
|
||||
}
|
||||
|
||||
.control-panel {
|
||||
position: relative;
|
||||
background: var(--panel);
|
||||
border: 1px solid var(--line);
|
||||
border-radius: 18px;
|
||||
box-shadow: var(--panel-shadow);
|
||||
padding: 1rem;
|
||||
display: flex;
|
||||
flex-direction: column;
|
||||
gap: 0.8rem;
|
||||
min-height: calc(100vh - 2rem);
|
||||
}
|
||||
|
||||
h1,
|
||||
h2 {
|
||||
font-family: "Space Grotesk", sans-serif;
|
||||
margin: 0;
|
||||
}
|
||||
|
||||
h1 {
|
||||
font-size: 1.35rem;
|
||||
line-height: 1.2;
|
||||
}
|
||||
|
||||
h2 {
|
||||
font-size: 1.05rem;
|
||||
}
|
||||
|
||||
.intro {
|
||||
margin: 0;
|
||||
color: var(--ink-soft);
|
||||
}
|
||||
|
||||
label {
|
||||
font-weight: 700;
|
||||
font-size: 0.95rem;
|
||||
}
|
||||
|
||||
select,
|
||||
input[type="range"],
|
||||
button {
|
||||
width: 100%;
|
||||
}
|
||||
|
||||
select {
|
||||
border-radius: 10px;
|
||||
border: 1px solid var(--line);
|
||||
padding: 0.5rem;
|
||||
background: #fff;
|
||||
color: var(--ink);
|
||||
font: inherit;
|
||||
}
|
||||
|
||||
.control-stack {
|
||||
display: grid;
|
||||
gap: 0.7rem;
|
||||
margin-top: 0.6rem;
|
||||
}
|
||||
|
||||
.metric-select-wrap {
|
||||
display: grid;
|
||||
gap: 0.35rem;
|
||||
}
|
||||
|
||||
.range-wrap {
|
||||
display: grid;
|
||||
gap: 0.35rem;
|
||||
}
|
||||
|
||||
.is-hidden {
|
||||
display: none !important;
|
||||
}
|
||||
|
||||
output {
|
||||
font-weight: 700;
|
||||
color: var(--accent);
|
||||
}
|
||||
|
||||
.button-row {
|
||||
display: grid;
|
||||
grid-template-columns: 1fr;
|
||||
gap: 0.5rem;
|
||||
margin-top: 0.35rem;
|
||||
}
|
||||
|
||||
button {
|
||||
border: 0;
|
||||
border-radius: 10px;
|
||||
padding: 0.6rem 0.7rem;
|
||||
font: inherit;
|
||||
font-weight: 700;
|
||||
cursor: pointer;
|
||||
color: #fff;
|
||||
background: linear-gradient(135deg, var(--accent), var(--accent-soft));
|
||||
}
|
||||
|
||||
button:hover {
|
||||
filter: brightness(1.03);
|
||||
}
|
||||
|
||||
.legend,
|
||||
.state-details {
|
||||
border: 1px solid var(--line);
|
||||
border-radius: 12px;
|
||||
background: #f7faff;
|
||||
padding: 0.75rem;
|
||||
}
|
||||
|
||||
.legend {
|
||||
display: grid;
|
||||
gap: 0.35rem;
|
||||
}
|
||||
|
||||
.legend-scale {
|
||||
display: grid;
|
||||
grid-template-columns: repeat(5, 1fr);
|
||||
border-radius: 8px;
|
||||
overflow: hidden;
|
||||
border: 1px solid #bccff3;
|
||||
}
|
||||
|
||||
.legend-swatch {
|
||||
height: 12px;
|
||||
}
|
||||
|
||||
.legend-range {
|
||||
display: flex;
|
||||
justify-content: space-between;
|
||||
font-size: 0.82rem;
|
||||
color: var(--ink-soft);
|
||||
}
|
||||
|
||||
.legend-koppen-list {
|
||||
display: grid;
|
||||
gap: 0.3rem;
|
||||
}
|
||||
|
||||
.legend-koppen-row {
|
||||
display: flex;
|
||||
align-items: center;
|
||||
gap: 0.45rem;
|
||||
font-size: 0.86rem;
|
||||
}
|
||||
|
||||
.legend-koppen-row-selected {
|
||||
font-weight: 700;
|
||||
}
|
||||
|
||||
.legend-koppen-dot {
|
||||
width: 12px;
|
||||
height: 12px;
|
||||
border-radius: 50%;
|
||||
border: 1px solid rgba(20, 45, 84, 0.22);
|
||||
}
|
||||
|
||||
.state-details p {
|
||||
margin: 0.3rem 0;
|
||||
}
|
||||
|
||||
.control-panel-spacer {
|
||||
flex: 1 1 auto;
|
||||
min-height: 2rem;
|
||||
}
|
||||
|
||||
.sources-control {
|
||||
flex: 0 0 auto;
|
||||
border-top: 1px solid var(--line);
|
||||
padding-top: 0.75rem;
|
||||
}
|
||||
|
||||
.sources-modal[hidden] {
|
||||
display: none;
|
||||
}
|
||||
|
||||
.sources-modal {
|
||||
position: fixed;
|
||||
inset: 0;
|
||||
z-index: 2000;
|
||||
display: grid;
|
||||
place-items: center;
|
||||
padding: 1rem;
|
||||
}
|
||||
|
||||
.sources-modal-backdrop {
|
||||
position: absolute;
|
||||
inset: 0;
|
||||
background: rgba(31, 103, 210, 0.36);
|
||||
backdrop-filter: blur(2px);
|
||||
}
|
||||
|
||||
.sources-dialog {
|
||||
position: relative;
|
||||
z-index: 1;
|
||||
width: min(940px, calc(100vw - 2rem));
|
||||
max-height: min(82vh, 760px);
|
||||
display: grid;
|
||||
grid-template-rows: auto minmax(0, 1fr);
|
||||
overflow: hidden;
|
||||
border: 1px solid rgba(202, 216, 239, 0.95);
|
||||
border-radius: 18px;
|
||||
background: var(--panel);
|
||||
box-shadow: 0 28px 70px rgba(7, 24, 51, 0.34);
|
||||
}
|
||||
|
||||
.sources-dialog-header {
|
||||
display: flex;
|
||||
align-items: center;
|
||||
justify-content: space-between;
|
||||
gap: 1rem;
|
||||
padding: 1rem 1rem 0.85rem;
|
||||
border-bottom: 1px solid var(--line);
|
||||
}
|
||||
|
||||
.sources-dialog-header h2 {
|
||||
font-size: 1.25rem;
|
||||
}
|
||||
|
||||
.sources-close-button {
|
||||
width: 2.5rem;
|
||||
height: 2.5rem;
|
||||
min-width: 0;
|
||||
padding: 0;
|
||||
border-radius: 8px;
|
||||
line-height: 1;
|
||||
box-shadow: 0 8px 20px rgba(12, 42, 88, 0.18);
|
||||
}
|
||||
|
||||
.sources-modal-content {
|
||||
overflow: auto;
|
||||
padding: 0 1rem 1rem;
|
||||
}
|
||||
|
||||
.source-entry {
|
||||
display: grid;
|
||||
gap: 0.35rem;
|
||||
padding: 0.95rem 0 1rem;
|
||||
border-top: 1px solid var(--line);
|
||||
}
|
||||
|
||||
.source-entry:first-child {
|
||||
border-top: 0;
|
||||
}
|
||||
|
||||
.source-entry h3 {
|
||||
margin: 0;
|
||||
font-family: "Space Grotesk", sans-serif;
|
||||
font-size: 1rem;
|
||||
}
|
||||
|
||||
.source-entry p {
|
||||
margin: 0;
|
||||
color: var(--ink-soft);
|
||||
line-height: 1.35;
|
||||
}
|
||||
|
||||
.source-entry a {
|
||||
color: var(--accent);
|
||||
}
|
||||
|
||||
.map-panel {
|
||||
min-height: 70vh;
|
||||
position: relative;
|
||||
}
|
||||
|
||||
#map {
|
||||
height: 100%;
|
||||
min-height: 70vh;
|
||||
border-radius: 18px;
|
||||
border: 1px solid rgba(17, 52, 99, 0.2);
|
||||
overflow: hidden;
|
||||
box-shadow: var(--panel-shadow);
|
||||
}
|
||||
|
||||
.metric-info-panel,
|
||||
.legend-info-panel {
|
||||
position: absolute;
|
||||
z-index: 500;
|
||||
width: min(320px, calc(100% - 2rem));
|
||||
display: grid;
|
||||
gap: 0.5rem;
|
||||
pointer-events: none;
|
||||
}
|
||||
|
||||
.metric-info-panel {
|
||||
top: 1rem;
|
||||
right: 1rem;
|
||||
}
|
||||
|
||||
.legend-info-panel {
|
||||
bottom: 1rem;
|
||||
left: 1rem;
|
||||
}
|
||||
|
||||
.metric-info-toggle,
|
||||
.legend-info-toggle,
|
||||
.metric-info-panel .state-details,
|
||||
.legend-info-panel .legend {
|
||||
pointer-events: auto;
|
||||
}
|
||||
|
||||
.metric-info-toggle,
|
||||
.legend-info-toggle {
|
||||
justify-self: end;
|
||||
width: auto;
|
||||
min-width: 148px;
|
||||
border-radius: 8px;
|
||||
box-shadow: 0 8px 20px rgba(12, 42, 88, 0.18);
|
||||
}
|
||||
|
||||
.legend-info-toggle {
|
||||
justify-self: start;
|
||||
}
|
||||
|
||||
.metric-info-panel.is-collapsed .state-details,
|
||||
.legend-info-panel.is-collapsed .legend {
|
||||
display: none;
|
||||
}
|
||||
|
||||
.metric-info-panel .state-details,
|
||||
.legend-info-panel .legend {
|
||||
max-height: min(58vh, 430px);
|
||||
overflow: auto;
|
||||
box-shadow: var(--panel-shadow);
|
||||
}
|
||||
|
||||
.leaflet-control-attribution,
|
||||
.leaflet-control-attribution * {
|
||||
user-select: none;
|
||||
-webkit-user-select: none;
|
||||
}
|
||||
|
||||
.leaflet-control-attribution {
|
||||
-webkit-tap-highlight-color: transparent;
|
||||
}
|
||||
|
||||
.is-map-dragging .leaflet-tooltip {
|
||||
display: none;
|
||||
}
|
||||
|
||||
.is-map-dragging .leaflet-interactive:focus {
|
||||
outline: none;
|
||||
}
|
||||
|
||||
@media (max-width: 980px) {
|
||||
.page-shell {
|
||||
grid-template-columns: 1fr;
|
||||
}
|
||||
|
||||
.control-panel {
|
||||
order: 2;
|
||||
min-height: calc(100vh - 2rem);
|
||||
}
|
||||
|
||||
.map-panel {
|
||||
order: 1;
|
||||
min-height: 62vh;
|
||||
}
|
||||
|
||||
#map {
|
||||
min-height: 62vh;
|
||||
}
|
||||
|
||||
.metric-info-panel,
|
||||
.legend-info-panel {
|
||||
top: 0.75rem;
|
||||
width: min(300px, calc(100% - 1.5rem));
|
||||
}
|
||||
|
||||
.metric-info-panel {
|
||||
right: 0.75rem;
|
||||
}
|
||||
|
||||
.legend-info-panel {
|
||||
top: auto;
|
||||
bottom: 0.75rem;
|
||||
left: 0.75rem;
|
||||
}
|
||||
|
||||
.sources-dialog {
|
||||
width: calc(100vw - 1rem);
|
||||
max-height: calc(100vh - 1rem);
|
||||
border-radius: 14px;
|
||||
}
|
||||
|
||||
.sources-dialog-header,
|
||||
.sources-modal-content {
|
||||
padding-left: 0.8rem;
|
||||
padding-right: 0.8rem;
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,163 @@
|
||||
from __future__ import annotations
|
||||
|
||||
import sys
|
||||
import unittest
|
||||
import urllib.error
|
||||
from pathlib import Path
|
||||
from tempfile import TemporaryDirectory
|
||||
from unittest.mock import patch
|
||||
|
||||
|
||||
SCRIPTS_DIR = Path(__file__).resolve().parents[1] / "scripts"
|
||||
sys.path.insert(0, str(SCRIPTS_DIR))
|
||||
|
||||
from download_nsrdb_county_polygon_archives import ( # noqa: E402
|
||||
ArchiveDownloadEvent,
|
||||
ArchiveDownloadState,
|
||||
ArchiveDownloadStateMachine,
|
||||
InvalidArchiveDownloadTransition,
|
||||
PolygonArchiveDownloadError,
|
||||
download_file,
|
||||
output_name_for_response,
|
||||
)
|
||||
|
||||
|
||||
class FakeResponse:
|
||||
def __init__(self, content: bytes) -> None:
|
||||
self.content = content
|
||||
self.offset = 0
|
||||
|
||||
def __enter__(self) -> FakeResponse:
|
||||
return self
|
||||
|
||||
def __exit__(self, *args: object) -> None:
|
||||
return None
|
||||
|
||||
def read(self, size: int) -> bytes:
|
||||
chunk = self.content[self.offset : self.offset + size]
|
||||
self.offset += len(chunk)
|
||||
return chunk
|
||||
|
||||
|
||||
class ArchiveDownloadStateMachineTests(unittest.TestCase):
|
||||
def test_legacy_ghi_response_name_keeps_existing_archive_name(self) -> None:
|
||||
response_path = Path("01001_goes-tmy_tmy-2024_ghi_response.json")
|
||||
|
||||
self.assertEqual(
|
||||
output_name_for_response(response_path),
|
||||
"01001_goes-tmy_tmy-2024_ghi.zip",
|
||||
)
|
||||
|
||||
def test_successful_download_lifecycle(self) -> None:
|
||||
machine = ArchiveDownloadStateMachine("01001_response.json")
|
||||
machine.transition(ArchiveDownloadEvent.START)
|
||||
|
||||
with TemporaryDirectory() as temp_dir:
|
||||
output_path = Path(temp_dir) / "01001.zip"
|
||||
with patch(
|
||||
"download_nsrdb_county_polygon_archives.urllib.request.urlopen",
|
||||
return_value=FakeResponse(b"archive"),
|
||||
):
|
||||
state = download_file(
|
||||
"https://example.com/01001.zip",
|
||||
output_path,
|
||||
timeout=30,
|
||||
overwrite=False,
|
||||
machine=machine,
|
||||
)
|
||||
|
||||
self.assertEqual(output_path.read_bytes(), b"archive")
|
||||
|
||||
self.assertEqual(state, ArchiveDownloadState.DOWNLOADED)
|
||||
self.assertEqual(
|
||||
machine.history,
|
||||
[
|
||||
ArchiveDownloadState.QUEUED,
|
||||
ArchiveDownloadState.CHECKING,
|
||||
ArchiveDownloadState.DOWNLOADING,
|
||||
ArchiveDownloadState.DOWNLOADED,
|
||||
],
|
||||
)
|
||||
|
||||
def test_existing_archive_is_skipped(self) -> None:
|
||||
machine = ArchiveDownloadStateMachine("01001_response.json")
|
||||
machine.transition(ArchiveDownloadEvent.START)
|
||||
|
||||
with TemporaryDirectory() as temp_dir:
|
||||
output_path = Path(temp_dir) / "01001.zip"
|
||||
output_path.write_bytes(b"existing")
|
||||
state = download_file(
|
||||
"https://example.com/01001.zip",
|
||||
output_path,
|
||||
timeout=30,
|
||||
overwrite=False,
|
||||
machine=machine,
|
||||
)
|
||||
|
||||
self.assertEqual(state, ArchiveDownloadState.SKIPPED)
|
||||
|
||||
def test_pending_s3_archive_enters_pending_state(self) -> None:
|
||||
machine = ArchiveDownloadStateMachine("01001_response.json")
|
||||
machine.transition(ArchiveDownloadEvent.START)
|
||||
error = urllib.error.HTTPError(
|
||||
"https://bucket.s3.amazonaws.com/01001.zip",
|
||||
403,
|
||||
"Forbidden",
|
||||
{},
|
||||
None,
|
||||
)
|
||||
|
||||
with TemporaryDirectory() as temp_dir:
|
||||
with patch(
|
||||
"download_nsrdb_county_polygon_archives.urllib.request.urlopen",
|
||||
side_effect=error,
|
||||
):
|
||||
state = download_file(
|
||||
error.url,
|
||||
Path(temp_dir) / "01001.zip",
|
||||
timeout=30,
|
||||
overwrite=False,
|
||||
machine=machine,
|
||||
)
|
||||
|
||||
self.assertEqual(state, ArchiveDownloadState.PENDING)
|
||||
|
||||
def test_http_error_enters_failed_state(self) -> None:
|
||||
machine = ArchiveDownloadStateMachine("01001_response.json")
|
||||
machine.transition(ArchiveDownloadEvent.START)
|
||||
error = urllib.error.HTTPError(
|
||||
"https://example.com/01001.zip",
|
||||
500,
|
||||
"Server Error",
|
||||
{},
|
||||
None,
|
||||
)
|
||||
|
||||
with TemporaryDirectory() as temp_dir:
|
||||
with patch(
|
||||
"download_nsrdb_county_polygon_archives.urllib.request.urlopen",
|
||||
side_effect=error,
|
||||
):
|
||||
with self.assertRaises(PolygonArchiveDownloadError):
|
||||
download_file(
|
||||
error.url,
|
||||
Path(temp_dir) / "01001.zip",
|
||||
timeout=30,
|
||||
overwrite=False,
|
||||
machine=machine,
|
||||
)
|
||||
|
||||
self.assertEqual(machine.state, ArchiveDownloadState.FAILED)
|
||||
|
||||
def test_invalid_transition_is_rejected(self) -> None:
|
||||
machine = ArchiveDownloadStateMachine("01001_response.json")
|
||||
|
||||
with self.assertRaisesRegex(
|
||||
InvalidArchiveDownloadTransition,
|
||||
"download_succeeded while queued",
|
||||
):
|
||||
machine.transition(ArchiveDownloadEvent.DOWNLOAD_SUCCEEDED)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
unittest.main()
|
||||
@@ -0,0 +1,170 @@
|
||||
from __future__ import annotations
|
||||
|
||||
import sys
|
||||
import unittest
|
||||
from pathlib import Path
|
||||
from tempfile import TemporaryDirectory
|
||||
from unittest.mock import patch
|
||||
|
||||
|
||||
SCRIPTS_DIR = Path(__file__).resolve().parents[1] / "scripts"
|
||||
sys.path.insert(0, str(SCRIPTS_DIR))
|
||||
|
||||
import download_nsrdb_county_polygon_cloud_archives as cloud_download # noqa: E402
|
||||
import download_nsrdb_county_polygon_ghi_archives as ghi_download # noqa: E402
|
||||
import request_nsrdb_county_polygon_cloud_archives as cloud_request # noqa: E402
|
||||
import request_nsrdb_county_polygon_ghi_archives as ghi_request # noqa: E402
|
||||
|
||||
|
||||
class PolygonCloudWrapperTests(unittest.TestCase):
|
||||
def test_request_uses_cloud_artifact_label(self) -> None:
|
||||
args = cloud_request.polygon_request.parse_args(cloud_request.DEFAULT_ARGS)
|
||||
with TemporaryDirectory() as temp_dir:
|
||||
args.response_dir = Path(temp_dir)
|
||||
path = cloud_request.polygon_request.response_path(
|
||||
args,
|
||||
"01001",
|
||||
"goes-tmy",
|
||||
"tmy-2024",
|
||||
)
|
||||
|
||||
self.assertEqual(
|
||||
path.name,
|
||||
"01001_goes-tmy_tmy-2024_cloud_response.json",
|
||||
)
|
||||
|
||||
def test_request_reuses_legacy_cloud_response_name(self) -> None:
|
||||
args = cloud_request.polygon_request.parse_args(cloud_request.DEFAULT_ARGS)
|
||||
with TemporaryDirectory() as temp_dir:
|
||||
args.response_dir = Path(temp_dir)
|
||||
legacy_path = args.response_dir / "01001_goes-tmy_tmy-2024_ghi_response.json"
|
||||
legacy_path.touch()
|
||||
|
||||
path = cloud_request.polygon_request.response_path(
|
||||
args,
|
||||
"01001",
|
||||
"goes-tmy",
|
||||
"tmy-2024",
|
||||
)
|
||||
|
||||
self.assertEqual(path, legacy_path)
|
||||
|
||||
def test_request_user_arguments_override_cloud_defaults(self) -> None:
|
||||
args = cloud_request.polygon_request.parse_args(
|
||||
[
|
||||
*cloud_request.DEFAULT_ARGS,
|
||||
"--attributes",
|
||||
"ghi",
|
||||
"--requests-csv",
|
||||
"custom.csv",
|
||||
]
|
||||
)
|
||||
|
||||
self.assertEqual(args.attributes, "ghi")
|
||||
self.assertEqual(args.requests_csv, Path("custom.csv"))
|
||||
|
||||
def test_request_wrapper_passes_cloud_defaults_and_user_arguments(self) -> None:
|
||||
with (
|
||||
patch.object(sys, "argv", ["cloud-request", "--limit", "12"]),
|
||||
patch.object(cloud_request.polygon_request, "main") as shared_main,
|
||||
):
|
||||
cloud_request.main()
|
||||
|
||||
shared_main.assert_called_once_with(
|
||||
[*cloud_request.DEFAULT_ARGS, "--limit", "12"],
|
||||
description=cloud_request.__doc__,
|
||||
)
|
||||
|
||||
def test_download_wrapper_passes_cloud_defaults_and_user_arguments(self) -> None:
|
||||
with (
|
||||
patch.object(sys, "argv", ["cloud-download", "--overwrite"]),
|
||||
patch.object(cloud_download.polygon_download, "main") as shared_main,
|
||||
):
|
||||
cloud_download.main()
|
||||
|
||||
shared_main.assert_called_once_with(
|
||||
[*cloud_download.DEFAULT_ARGS, "--overwrite"],
|
||||
description=cloud_download.__doc__,
|
||||
)
|
||||
|
||||
def test_download_user_arguments_override_cloud_defaults(self) -> None:
|
||||
args = cloud_download.polygon_download.parse_args(
|
||||
[
|
||||
*cloud_download.DEFAULT_ARGS,
|
||||
"--output-dir",
|
||||
"custom-archives",
|
||||
]
|
||||
)
|
||||
|
||||
self.assertEqual(args.output_dir, Path("custom-archives"))
|
||||
|
||||
|
||||
class PolygonGhiWrapperTests(unittest.TestCase):
|
||||
def test_request_uses_ghi_artifact_label(self) -> None:
|
||||
args = ghi_request.polygon_request.parse_args(ghi_request.DEFAULT_ARGS)
|
||||
with TemporaryDirectory() as temp_dir:
|
||||
args.response_dir = Path(temp_dir)
|
||||
path = ghi_request.polygon_request.response_path(
|
||||
args,
|
||||
"01001",
|
||||
"goes-tmy",
|
||||
"tmy-2024",
|
||||
)
|
||||
|
||||
self.assertEqual(
|
||||
path.name,
|
||||
"01001_goes-tmy_tmy-2024_ghi_response.json",
|
||||
)
|
||||
|
||||
def test_request_wrapper_passes_ghi_defaults_and_user_arguments(self) -> None:
|
||||
with (
|
||||
patch.object(sys, "argv", ["ghi-request", "--limit", "12"]),
|
||||
patch.object(ghi_request.polygon_request, "main") as shared_main,
|
||||
):
|
||||
ghi_request.main()
|
||||
|
||||
shared_main.assert_called_once_with(
|
||||
[*ghi_request.DEFAULT_ARGS, "--limit", "12"],
|
||||
description=ghi_request.__doc__,
|
||||
)
|
||||
|
||||
def test_request_user_arguments_override_ghi_defaults(self) -> None:
|
||||
args = ghi_request.polygon_request.parse_args(
|
||||
[
|
||||
*ghi_request.DEFAULT_ARGS,
|
||||
"--attributes",
|
||||
"ghi,clearsky_ghi",
|
||||
"--requests-csv",
|
||||
"custom.csv",
|
||||
]
|
||||
)
|
||||
|
||||
self.assertEqual(args.attributes, "ghi,clearsky_ghi")
|
||||
self.assertEqual(args.requests_csv, Path("custom.csv"))
|
||||
|
||||
def test_download_wrapper_passes_ghi_defaults_and_user_arguments(self) -> None:
|
||||
with (
|
||||
patch.object(sys, "argv", ["ghi-download", "--overwrite"]),
|
||||
patch.object(ghi_download.polygon_download, "main") as shared_main,
|
||||
):
|
||||
ghi_download.main()
|
||||
|
||||
shared_main.assert_called_once_with(
|
||||
[*ghi_download.DEFAULT_ARGS, "--overwrite"],
|
||||
description=ghi_download.__doc__,
|
||||
)
|
||||
|
||||
def test_download_user_arguments_override_ghi_defaults(self) -> None:
|
||||
args = ghi_download.polygon_download.parse_args(
|
||||
[
|
||||
*ghi_download.DEFAULT_ARGS,
|
||||
"--output-dir",
|
||||
"custom-archives",
|
||||
]
|
||||
)
|
||||
|
||||
self.assertEqual(args.output_dir, Path("custom-archives"))
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
unittest.main()
|
||||
@@ -0,0 +1,355 @@
|
||||
from __future__ import annotations
|
||||
|
||||
import sys
|
||||
import unittest
|
||||
import csv
|
||||
from datetime import datetime, timezone
|
||||
from pathlib import Path
|
||||
from tempfile import TemporaryDirectory
|
||||
from unittest.mock import call, patch
|
||||
|
||||
|
||||
SCRIPTS_DIR = Path(__file__).resolve().parents[1] / "scripts"
|
||||
sys.path.insert(0, str(SCRIPTS_DIR))
|
||||
|
||||
from request_nsrdb_county_polygon_archives import ( # noqa: E402
|
||||
ArchiveQueueMonitor,
|
||||
CountyRequestEvent,
|
||||
CountyRequestState,
|
||||
CountyRequestStateMachine,
|
||||
EXCEPTIONAL_POLYGON_WAIT,
|
||||
InvalidCountyRequestTransition,
|
||||
LARGE_POLYGON_WAIT,
|
||||
LocalQueueCapacityError,
|
||||
MEDIUM_POLYGON_WAIT,
|
||||
SMALL_POLYGON_WAIT,
|
||||
)
|
||||
|
||||
|
||||
class CountyRequestStateMachineTests(unittest.TestCase):
|
||||
def test_successful_request_lifecycle(self) -> None:
|
||||
machine = CountyRequestStateMachine("01001 Autauga County, AL")
|
||||
|
||||
machine.transition(CountyRequestEvent.START)
|
||||
machine.transition(CountyRequestEvent.REQUEST_SUCCEEDED)
|
||||
|
||||
self.assertEqual(machine.state, CountyRequestState.REQUESTED)
|
||||
self.assertEqual(
|
||||
machine.history,
|
||||
[
|
||||
CountyRequestState.PENDING,
|
||||
CountyRequestState.SUBMITTING,
|
||||
CountyRequestState.REQUESTED,
|
||||
],
|
||||
)
|
||||
|
||||
def test_retry_returns_to_submitting(self) -> None:
|
||||
machine = CountyRequestStateMachine("01001 Autauga County, AL")
|
||||
|
||||
machine.transition(CountyRequestEvent.START)
|
||||
machine.transition(CountyRequestEvent.RETRY_REQUIRED)
|
||||
machine.transition(CountyRequestEvent.RETRY_STARTED)
|
||||
|
||||
self.assertEqual(machine.state, CountyRequestState.SUBMITTING)
|
||||
|
||||
def test_existing_request_is_skipped(self) -> None:
|
||||
machine = CountyRequestStateMachine("01001 Autauga County, AL")
|
||||
|
||||
machine.transition(CountyRequestEvent.EXISTING_REQUEST_FOUND)
|
||||
|
||||
self.assertEqual(machine.state, CountyRequestState.SKIPPED)
|
||||
|
||||
def test_invalid_transition_is_rejected(self) -> None:
|
||||
machine = CountyRequestStateMachine("01001 Autauga County, AL")
|
||||
|
||||
with self.assertRaisesRegex(
|
||||
InvalidCountyRequestTransition,
|
||||
"request_succeeded while pending",
|
||||
):
|
||||
machine.transition(CountyRequestEvent.REQUEST_SUCCEEDED)
|
||||
|
||||
|
||||
class ArchiveQueueMonitorTests(unittest.TestCase):
|
||||
def make_monitor(self) -> ArchiveQueueMonitor:
|
||||
temp_dir = TemporaryDirectory()
|
||||
self.addCleanup(temp_dir.cleanup)
|
||||
return ArchiveQueueMonitor(Path(temp_dir.name), timeout=1, workers=1)
|
||||
|
||||
def test_dynamic_wait_uses_largest_tracked_polygon(self) -> None:
|
||||
monitor = self.make_monitor()
|
||||
|
||||
for site_count, expected_wait in (
|
||||
(110, SMALL_POLYGON_WAIT),
|
||||
(111, MEDIUM_POLYGON_WAIT),
|
||||
(250, MEDIUM_POLYGON_WAIT),
|
||||
(251, LARGE_POLYGON_WAIT),
|
||||
(550, LARGE_POLYGON_WAIT),
|
||||
(551, EXCEPTIONAL_POLYGON_WAIT),
|
||||
):
|
||||
monitor.tracked_site_counts = {"https://example.com/job.zip": site_count}
|
||||
self.assertEqual(monitor.dynamic_wait_seconds(), expected_wait)
|
||||
|
||||
def test_downloaded_archive_is_not_reloaded_as_pending(self) -> None:
|
||||
temp_dir = TemporaryDirectory()
|
||||
self.addCleanup(temp_dir.cleanup)
|
||||
manifest_dir = Path(temp_dir.name)
|
||||
response_dir = manifest_dir / "polygon_cloud_request_responses"
|
||||
archive_dir = manifest_dir / "polygon_cloud_archives"
|
||||
response_dir.mkdir()
|
||||
archive_dir.mkdir()
|
||||
response_path = response_dir / "01001_goes-tmy_tmy-2024_cloud_response.json"
|
||||
archive_path = archive_dir / "01001_goes-tmy_tmy-2024_cloud.zip"
|
||||
response_path.write_text("{}", encoding="utf-8")
|
||||
archive_path.write_bytes(b"downloaded")
|
||||
|
||||
manifest_path = manifest_dir / "county_polygon_cloud_request_manifest.csv"
|
||||
with manifest_path.open("w", newline="", encoding="utf-8") as handle:
|
||||
writer = csv.DictWriter(
|
||||
handle,
|
||||
fieldnames=[
|
||||
"county_fips",
|
||||
"site_count",
|
||||
"download_url",
|
||||
"response_json",
|
||||
"submitted_at_utc",
|
||||
],
|
||||
)
|
||||
writer.writeheader()
|
||||
writer.writerow(
|
||||
{
|
||||
"county_fips": "01001",
|
||||
"site_count": "100",
|
||||
"download_url": "https://example.amazonaws.com/job.zip",
|
||||
"response_json": str(response_path),
|
||||
"submitted_at_utc": datetime.now(timezone.utc).isoformat(),
|
||||
}
|
||||
)
|
||||
|
||||
monitor = ArchiveQueueMonitor(manifest_dir, timeout=1, workers=1)
|
||||
|
||||
with patch(
|
||||
"request_nsrdb_county_polygon_archives.archive_download_status",
|
||||
return_value="pending",
|
||||
) as status_mock:
|
||||
counts = monitor.summarize()
|
||||
|
||||
self.assertEqual(counts["pending"], 0)
|
||||
self.assertEqual(counts["total"], 0)
|
||||
status_mock.assert_not_called()
|
||||
|
||||
def test_historical_s3_403_does_not_consume_a_queue_slot(self) -> None:
|
||||
monitor = self.make_monitor()
|
||||
url = "https://example.amazonaws.com/historical.zip"
|
||||
monitor.jobs[url] = "01001"
|
||||
|
||||
with patch(
|
||||
"request_nsrdb_county_polygon_archives.archive_download_status",
|
||||
return_value="pending",
|
||||
):
|
||||
counts = monitor.summarize()
|
||||
|
||||
self.assertEqual(counts["pending"], 0)
|
||||
self.assertEqual(counts["ambiguous_403"], 1)
|
||||
|
||||
def test_current_process_s3_403_remains_pending(self) -> None:
|
||||
monitor = self.make_monitor()
|
||||
url = "https://example.amazonaws.com/current.zip"
|
||||
monitor.record_submission(
|
||||
{
|
||||
"county_fips": "01001",
|
||||
"tile_id": "",
|
||||
"site_count": "100",
|
||||
"download_url": url,
|
||||
}
|
||||
)
|
||||
|
||||
with patch(
|
||||
"request_nsrdb_county_polygon_archives.archive_download_status",
|
||||
return_value="pending",
|
||||
):
|
||||
counts = monitor.summarize()
|
||||
|
||||
self.assertEqual(counts["pending"], 1)
|
||||
self.assertEqual(counts["ambiguous_403"], 0)
|
||||
|
||||
def test_site_count_expires_after_recent_download_window(self) -> None:
|
||||
monitor = self.make_monitor()
|
||||
url = "https://example.com/job.zip"
|
||||
monitor.jobs[url] = "01001"
|
||||
monitor.pending_urls.add(url)
|
||||
monitor.tracked_site_counts[url] = 551
|
||||
|
||||
with (
|
||||
patch(
|
||||
"request_nsrdb_county_polygon_archives.archive_download_status",
|
||||
return_value="ready",
|
||||
),
|
||||
patch(
|
||||
"request_nsrdb_county_polygon_archives.time.monotonic",
|
||||
side_effect=[100.0, 100.0, 401.0],
|
||||
),
|
||||
):
|
||||
first_counts = monitor.summarize()
|
||||
second_counts = monitor.summarize()
|
||||
|
||||
self.assertEqual(first_counts["recently_downloaded"], 1)
|
||||
self.assertEqual(second_counts["recently_downloaded"], 0)
|
||||
self.assertNotIn(url, monitor.tracked_site_counts)
|
||||
|
||||
def test_fifteen_visible_slots_bypass_pacing_until_queue_is_full(self) -> None:
|
||||
monitor = self.make_monitor()
|
||||
monitor.paced_submissions = 1
|
||||
counts_with_fifteen_slots = {
|
||||
"pending": 5,
|
||||
"recently_downloaded": 2,
|
||||
"unobserved": 0,
|
||||
"unknown": 0,
|
||||
"ready": 0,
|
||||
"total": 7,
|
||||
}
|
||||
counts_with_fourteen_slots = {
|
||||
**counts_with_fifteen_slots,
|
||||
"pending": 6,
|
||||
}
|
||||
full_queue_counts = {
|
||||
**counts_with_fifteen_slots,
|
||||
"pending": 20,
|
||||
}
|
||||
|
||||
with (
|
||||
patch.object(
|
||||
monitor,
|
||||
"summarize",
|
||||
side_effect=[
|
||||
counts_with_fifteen_slots,
|
||||
counts_with_fourteen_slots,
|
||||
full_queue_counts,
|
||||
],
|
||||
),
|
||||
patch(
|
||||
"request_nsrdb_county_polygon_archives.wait_for_queue",
|
||||
) as wait_for_queue_mock,
|
||||
):
|
||||
monitor.wait_for_capacity("first", retries=0)
|
||||
monitor.wait_for_capacity("second", retries=0)
|
||||
with self.assertRaisesRegex(LocalQueueCapacityError, "20 possible"):
|
||||
monitor.wait_for_capacity("third", retries=0)
|
||||
|
||||
wait_for_queue_mock.assert_not_called()
|
||||
self.assertFalse(monitor.filling_open_queue)
|
||||
|
||||
def test_fourteen_visible_slots_still_use_recent_download_pacing(self) -> None:
|
||||
monitor = self.make_monitor()
|
||||
monitor.paced_submissions = 1
|
||||
counts = {
|
||||
"pending": 6,
|
||||
"recently_downloaded": 2,
|
||||
"unobserved": 0,
|
||||
"unknown": 0,
|
||||
"ready": 0,
|
||||
"total": 8,
|
||||
}
|
||||
|
||||
with (
|
||||
patch.object(monitor, "summarize", return_value=counts),
|
||||
patch(
|
||||
"request_nsrdb_county_polygon_archives.wait_for_queue",
|
||||
) as wait_for_queue_mock,
|
||||
):
|
||||
monitor.wait_for_capacity("next", retries=0)
|
||||
|
||||
wait_for_queue_mock.assert_called_once_with(
|
||||
SMALL_POLYGON_WAIT,
|
||||
"Pacing wait complete; refreshing archive status.",
|
||||
)
|
||||
|
||||
def test_six_minute_wait_checks_queue_after_three_minutes(self) -> None:
|
||||
monitor = self.make_monitor()
|
||||
counts = {
|
||||
"pending": 6,
|
||||
"recently_downloaded": 0,
|
||||
"unobserved": 0,
|
||||
"unknown": 0,
|
||||
"ready": 0,
|
||||
"total": 6,
|
||||
}
|
||||
|
||||
with (
|
||||
patch.object(monitor, "summarize", return_value=counts) as summarize_mock,
|
||||
patch(
|
||||
"request_nsrdb_county_polygon_archives.time.sleep",
|
||||
) as sleep_mock,
|
||||
):
|
||||
entered_fill_mode = monitor.wait_for_dynamic_queue(
|
||||
LARGE_POLYGON_WAIT,
|
||||
"Queue wait complete.",
|
||||
)
|
||||
|
||||
self.assertFalse(entered_fill_mode)
|
||||
self.assertEqual(sleep_mock.call_args_list, [call(180.0)] * 2)
|
||||
summarize_mock.assert_called_once_with()
|
||||
|
||||
def test_six_minute_wait_enters_fill_mode_at_checkpoint(self) -> None:
|
||||
monitor = self.make_monitor()
|
||||
counts = {
|
||||
"pending": 5,
|
||||
"recently_downloaded": 0,
|
||||
"unobserved": 0,
|
||||
"unknown": 0,
|
||||
"ready": 0,
|
||||
"total": 5,
|
||||
}
|
||||
|
||||
with (
|
||||
patch.object(monitor, "summarize", return_value=counts),
|
||||
patch(
|
||||
"request_nsrdb_county_polygon_archives.time.sleep",
|
||||
) as sleep_mock,
|
||||
):
|
||||
entered_fill_mode = monitor.wait_for_dynamic_queue(
|
||||
LARGE_POLYGON_WAIT,
|
||||
"Queue wait complete.",
|
||||
)
|
||||
|
||||
self.assertTrue(entered_fill_mode)
|
||||
self.assertTrue(monitor.filling_open_queue)
|
||||
sleep_mock.assert_called_once_with(180.0)
|
||||
|
||||
def test_fifteen_minute_wait_checks_at_five_and_ten_minutes(self) -> None:
|
||||
monitor = self.make_monitor()
|
||||
counts_with_fourteen_slots = {
|
||||
"pending": 6,
|
||||
"recently_downloaded": 0,
|
||||
"unobserved": 0,
|
||||
"unknown": 0,
|
||||
"ready": 0,
|
||||
"total": 6,
|
||||
}
|
||||
counts_with_fifteen_slots = {
|
||||
**counts_with_fourteen_slots,
|
||||
"pending": 5,
|
||||
}
|
||||
|
||||
with (
|
||||
patch.object(
|
||||
monitor,
|
||||
"summarize",
|
||||
side_effect=[counts_with_fourteen_slots, counts_with_fifteen_slots],
|
||||
) as summarize_mock,
|
||||
patch(
|
||||
"request_nsrdb_county_polygon_archives.time.sleep",
|
||||
) as sleep_mock,
|
||||
):
|
||||
entered_fill_mode = monitor.wait_for_dynamic_queue(
|
||||
EXCEPTIONAL_POLYGON_WAIT,
|
||||
"Queue wait complete.",
|
||||
)
|
||||
|
||||
self.assertTrue(entered_fill_mode)
|
||||
self.assertTrue(monitor.filling_open_queue)
|
||||
self.assertEqual(sleep_mock.call_args_list, [call(300.0)] * 2)
|
||||
self.assertEqual(summarize_mock.call_count, 2)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
unittest.main()
|
||||
Reference in New Issue
Block a user