Merge branch 'main' into bugfix/1689-forest-carbon-geom
This commit is contained in:
Megan Nissel
commit
f9dfd99b7f
|
|
@ -0,0 +1,39 @@
|
|||
---
|
||||
name: Bug report
|
||||
about: Did you find a problem in InVEST? Let us know!
|
||||
title: ''
|
||||
labels: ''
|
||||
assignees: ''
|
||||
type: 'Bug'
|
||||
|
||||
---
|
||||
|
||||
## Description
|
||||
Briefly describe the problem.
|
||||
|
||||
## To replicate
|
||||
Provide detailed steps to replicate the behavior.
|
||||
|
||||
1.
|
||||
|
||||
## Expected behavior
|
||||
Describe what you expected to happen.
|
||||
|
||||
## Observed behavior
|
||||
Describe what actually happened.
|
||||
|
||||
## Logfile(s)
|
||||
If the problem occurred when running an InVEST model, please upload the logfile. (You can find this file in your workspace directory.)
|
||||
|
||||
If you ran the model from the Workbench, please also upload the Workbench logfile. (To find this file, navigate to the Workbench's `About` menu, select `Report a problem`, then select `Find my logs`).
|
||||
|
||||
## Screenshots
|
||||
If applicable, add screenshots to help illustrate the problem.
|
||||
|
||||
## Environment
|
||||
- Operating System:
|
||||
- InVEST version number:
|
||||
- InVEST interface (Workbench, CLI, or Python API):
|
||||
|
||||
## Additional context
|
||||
Please provide any additional details that might help us understand and debug the issue, including what you think the cause might be, and any solutions or workarounds you have tried.
|
||||
|
|
@ -0,0 +1,27 @@
|
|||
---
|
||||
name: Feature request
|
||||
about: Have an idea for a new InVEST feature? Let us know!
|
||||
title: ''
|
||||
labels: ''
|
||||
assignees: ''
|
||||
type: 'Feature'
|
||||
|
||||
---
|
||||
|
||||
## Background/Motivation
|
||||
Provide any background information relevant to this request, such as how or why you came up with the idea.
|
||||
|
||||
## Description
|
||||
Describe the desired end result.
|
||||
|
||||
## Benefits
|
||||
Explain how the proposed addition or modification would be valuable to InVEST users.
|
||||
|
||||
## Mitigation of side effects
|
||||
Are there any potential side effects of your proposed feature? How do you propose preventing or mitigating them?
|
||||
|
||||
## Alternatives
|
||||
Have you considered any alternative solutions or features? Please describe them.
|
||||
|
||||
## Additional context
|
||||
Please provide any additional details that might help us understand and evaluate your proposal.
|
||||
|
|
@ -0,0 +1,5 @@
|
|||
blank_issues_enabled: false
|
||||
contact_links:
|
||||
- name: Question or troubleshooting support
|
||||
url: https://community.naturalcapitalproject.org/
|
||||
about: If you have a question related to using InVEST or need help troubleshooting a specific issue, please visit the NatCap Community Forum.
|
||||
|
|
@ -0,0 +1,65 @@
|
|||
# Contributing to InVEST
|
||||
|
||||
## Asking Questions
|
||||
Do you have a question about using InVEST or need help troubleshooting a specific issue? Please visit the [NatCap Community Forum](https://community.naturalcapitalproject.org/).
|
||||
|
||||
## Reporting Bugs
|
||||
Did you find a bug in the InVEST software?
|
||||
1. Search the [InVEST Open Issues on GitHub](https://github.com/natcap/invest/issues) to see if it has already been reported.
|
||||
2. If you don’t find an existing issue, create a new issue. Select “Bug report”, and provide as much detail as you can.
|
||||
3. Do you know how to fix the bug? Learn how we handle code contributions in the [Contributing Code](#contributing-code) section of this guide.
|
||||
|
||||
## Requesting Features
|
||||
A feature is any addition or modification that affects users of the InVEST software but does not directly address a specific bug. A feature may be very small (e.g., minor styling updates in a small component of the Workbench), very large (e.g., a new InVEST model), or somewhere in between.
|
||||
|
||||
If you want to suggest an InVEST feature:
|
||||
1. Search the [InVEST Open Issues on GitHub](https://github.com/natcap/invest/issues) to see if it has already been suggested.
|
||||
2. If you don’t find an existing issue, create a new issue. Select “Feature request”, and provide as much detail as you can.
|
||||
|
||||
Every proposed feature, large or small, will be weighed in terms of its potential impact on InVEST users and the capacity of the NatCap team to implement and maintain the feature. Please note that major features, including new InVEST models, must go through a peer-review process with our Platform Steering Committee. In addition, requests for science-related changes to existing InVEST models, such as the modification of equations, or other changes to the way the model functions, will require a NatCap science review.
|
||||
|
||||
## Contributing Code
|
||||
|
||||
### Contributor License Agreement
|
||||
Any code you contribute to InVEST will fall under the same license as InVEST. Please read the [InVEST Contributor License Agreement](https://natcap.github.io/invest-cla) and, if you agree to the terms, sign it. If you do not sign the Contributor License Agreement, you will not be able to contribute code to InVEST.
|
||||
|
||||
### Finding Open Issues
|
||||
1. Browse the [InVEST Open Issues on GitHub](https://github.com/natcap/invest/issues). You may wish to search by keyword and/or filter by label, such as the `good first issue` label.
|
||||
2. When you find something you want to work on, comment on the issue to let us know you’re interested in working on it.
|
||||
3. Wait for a member of the NatCap team to respond. One of the following will happen:
|
||||
1. If it’s OK for you to work on the issue, a member of the NatCap team will let you know (in a comment on the issue) and assign the issue to you. Proceed to step 4.
|
||||
2. If there’s some reason you shouldn’t work on the issue (because the issue is invalid, or we are intentionally delaying work on it, or—oops—someone else is already working on it), a member of the NatCap team will let you know by leaving a comment on the issue. Return to step 1!
|
||||
4. Review the [InVEST Contributor License Agreement](https://natcap.github.io/invest-cla), if you haven’t already. If you do not sign the Contributor License Agreement, you will not be able to contribute code to InVEST.
|
||||
|
||||
### Forking the Repository and Creating a Branch
|
||||
1. Fork the `natcap/invest` repository. Check [GitHub’s “Fork a repository” guide](https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/working-with-forks/fork-a-repo) for details, if needed.
|
||||
2. On your fork, create a new branch off `main`. Give your branch a descriptive name with the form: `<category>/<issue-number>-<short-description>`. For example:
|
||||
- `bugfix/12345-remove-moth-from-mainframe`,
|
||||
- `feature/23456-new-ui-theme`, or
|
||||
- `docs/34567-update-readme`.
|
||||
|
||||
### Working on Code Changes
|
||||
1. Make sure you have followed the instructions in [Forking the Repository and Creating a Branch](#forking-the-repository-and-creating-a-branch).
|
||||
2. Refer to the [InVEST README](./README.rst) for guidance in setting up your local development environment.
|
||||
3. As you work, use commit messages that are descriptive and include the issue number (e.g., “Validate input LULC raster against biophysical table (#1234)”) rather than generic (e.g., “update code”). This will make it easier to distinguish between commits in the commit history, and it will make it easier for you to write a PR description.
|
||||
4. Update automated tests as needed. If you are adding anything new, such as a new input parameter, a new output, more robust validation, or a larger feature, you should add tests to ensure the new behavior is covered.
|
||||
5. Manually test your code changes to catch any issues the automated tests might have missed. Manual tests may include interacting with the Workbench UI to verify visual appearance as well as behavior, running a model (via the CLI or the Workbench) and verifying the outputs in a GIS, and/or other related activities.
|
||||
6. Consistent code style keeps our codebase readable and helps us focus on the content of code changes. We follow the [PEP 8 Style Guide for Python Code](https://peps.python.org/pep-0008/) and [PEP 257 Python Docstring Conventions](https://peps.python.org/pep-0257/). When in doubt about how to format your code, please refer to existing InVEST code for examples.
|
||||
|
||||
### Submitting a Pull Request
|
||||
1. Make sure you have followed the instructions in [Working on Code Changes](#working-on-code-changes).
|
||||
2. Once your code is ready to be reviewed, open a draft PR.
|
||||
1. Select the appropriate target branch on the `natcap` fork. This should be the same branch you branched from when starting work on your PR. (For example: if you branched from `main`, the target branch should be `main`.) If you’re unsure what the target branch should be, ask us in a comment on the relevant issue.
|
||||
2. Double-check that your PR includes all the changes you want (and none that you don’t).
|
||||
3. Add a description of the changes you made. Be sure to reference the issue your PR is addressing.
|
||||
4. If you have made any UI changes, include relevant screenshots and/or screen recordings.
|
||||
5. Follow the checklist in the PR template. If you’ve completed an item, check its box.
|
||||
6. Select “Create draft pull request”.
|
||||
3. As soon as your (draft) PR is open, a series of automated checks (test runners, build processes, etc.) will begin. Wait for these checks to complete.
|
||||
4. After you’ve completed all relevant items on the checklist, and all the automated checks are passing, mark your PR “Ready for review”.
|
||||
5. Wait for someone to review your PR. One or more members of the NatCap team will review your PR. They may request some additional changes and/or ask clarifying questions about the changes you’ve made. You may browse our closed PRs for examples of our PR review process, including conversations about PR details.
|
||||
6. After you receive a review on your PR, you may need to do one or more of the following:
|
||||
1. Respond to reviewers’ questions or comments.
|
||||
2. Make additional changes, then request a “re-review” from your reviewer(s).
|
||||
3. Repeat steps 5 and 6 as needed.
|
||||
7. Once the NatCap team has determined your PR is ready to be merged, they will approve and merge your PR. Thanks for contributing to InVEST! 🎉
|
||||
17
HISTORY.rst
17
HISTORY.rst
|
|
@ -65,6 +65,23 @@
|
|||
Unreleased Changes
|
||||
------------------
|
||||
|
||||
Workbench
|
||||
=========
|
||||
* Fixed a bug that did not allow users to select a folder as the location
|
||||
to extract a datastack archive.
|
||||
(`#1879 <https://github.com/natcap/invest/issues/1879>`_).
|
||||
|
||||
Crop Production
|
||||
===============
|
||||
* Both the Percentile and Regression models now issue a warning if any LULC
|
||||
code in the LULC raster is not present in the landcover to crop table, or if
|
||||
any LULC code in the landcover to crop table is not present in the LULC
|
||||
raster (`#925 <https://github.com/natcap/invest/issues/925>`_).
|
||||
* The Regression model now correctly validates crop names against the existence
|
||||
of a corresponding regression yield table
|
||||
(`#1723 <https://github.com/natcap/invest/issues/1723>`_).
|
||||
|
||||
|
||||
3.15.0 (2025-04-03)
|
||||
-------------------
|
||||
Highlights
|
||||
|
|
|
|||
|
|
@ -9,6 +9,6 @@ Project to develop this policy and any trademark licensing. Visit
|
|||
naturalcapitalproject.stanford.edu/invest-trademark-and-logo-use-policy for the
|
||||
complete trademark and logo policy.
|
||||
|
||||
Copyright (c) 2023, Natural Capital Project
|
||||
Copyright (c) 2025, Natural Capital Project
|
||||
|
||||
------------------------------------------------------------------------------
|
||||
|
|
|
|||
|
|
@ -611,6 +611,33 @@ def execute(args):
|
|||
crop_to_landcover_df = validation.get_validated_dataframe(
|
||||
args['landcover_to_crop_table_path'],
|
||||
**MODEL_SPEC['args']['landcover_to_crop_table_path'])
|
||||
|
||||
lucodes_in_table = set(list(
|
||||
crop_to_landcover_df[_EXPECTED_LUCODE_TABLE_HEADER]))
|
||||
|
||||
def update_unique_lucodes_in_raster(unique_codes, block):
|
||||
unique_codes.update(numpy.unique(block))
|
||||
return unique_codes
|
||||
|
||||
unique_lucodes_in_raster = pygeoprocessing.raster_reduce(
|
||||
update_unique_lucodes_in_raster,
|
||||
(args['landcover_raster_path'], 1),
|
||||
set())
|
||||
|
||||
lucodes_missing_from_raster = lucodes_in_table.difference(
|
||||
unique_lucodes_in_raster)
|
||||
if lucodes_missing_from_raster:
|
||||
LOGGER.warning(
|
||||
"The following lucodes are in the landcover to crop table but "
|
||||
f"aren't in the landcover raster: {lucodes_missing_from_raster}")
|
||||
|
||||
lucodes_missing_from_table = unique_lucodes_in_raster.difference(
|
||||
lucodes_in_table)
|
||||
if lucodes_missing_from_table:
|
||||
LOGGER.warning(
|
||||
"The following lucodes are in the landcover raster but aren't "
|
||||
f"in the landcover to crop table: {lucodes_missing_from_table}")
|
||||
|
||||
bad_crop_name_list = []
|
||||
for crop_name in crop_to_landcover_df.index:
|
||||
crop_climate_bin_raster_path = os.path.join(
|
||||
|
|
|
|||
|
|
@ -505,32 +505,42 @@ def execute(args):
|
|||
args['fertilization_rate_table_path'],
|
||||
**MODEL_SPEC['args']['fertilization_rate_table_path'])
|
||||
|
||||
crop_lucodes = list(crop_to_landcover_df[_EXPECTED_LUCODE_TABLE_HEADER])
|
||||
lucodes_in_table = set(list(
|
||||
crop_to_landcover_df[_EXPECTED_LUCODE_TABLE_HEADER]))
|
||||
|
||||
unique_lucodes = numpy.array([])
|
||||
for _, lu_band_data in pygeoprocessing.iterblocks(
|
||||
(args['landcover_raster_path'], 1)):
|
||||
unique_block = numpy.unique(lu_band_data)
|
||||
unique_lucodes = numpy.unique(numpy.concatenate(
|
||||
(unique_lucodes, unique_block)))
|
||||
def update_unique_lucodes_in_raster(unique_codes, block):
|
||||
unique_codes.update(numpy.unique(block))
|
||||
return unique_codes
|
||||
|
||||
missing_lucodes = set(crop_lucodes).difference(
|
||||
set(unique_lucodes))
|
||||
if len(missing_lucodes) > 0:
|
||||
unique_lucodes_in_raster = pygeoprocessing.raster_reduce(
|
||||
update_unique_lucodes_in_raster,
|
||||
(args['landcover_raster_path'], 1),
|
||||
set())
|
||||
|
||||
lucodes_missing_from_raster = lucodes_in_table.difference(
|
||||
unique_lucodes_in_raster)
|
||||
if lucodes_missing_from_raster:
|
||||
LOGGER.warning(
|
||||
"The following lucodes are in the landcover to crop table but "
|
||||
"aren't in the landcover raster: %s", missing_lucodes)
|
||||
f"aren't in the landcover raster: {lucodes_missing_from_raster}")
|
||||
|
||||
lucodes_missing_from_table = unique_lucodes_in_raster.difference(
|
||||
lucodes_in_table)
|
||||
if lucodes_missing_from_table:
|
||||
LOGGER.warning(
|
||||
"The following lucodes are in the landcover raster but aren't "
|
||||
f"in the landcover to crop table: {lucodes_missing_from_table}")
|
||||
|
||||
LOGGER.info("Checking that crops correspond to known types.")
|
||||
for crop_name in crop_to_landcover_df.index:
|
||||
crop_climate_bin_raster_path = os.path.join(
|
||||
crop_regression_yield_table_path = os.path.join(
|
||||
args['model_data_path'],
|
||||
_EXTENDED_CLIMATE_BIN_FILE_PATTERN % crop_name)
|
||||
if not os.path.exists(crop_climate_bin_raster_path):
|
||||
_REGRESSION_TABLE_PATTERN % crop_name)
|
||||
if not os.path.exists(crop_regression_yield_table_path):
|
||||
raise ValueError(
|
||||
"Expected climate bin map called %s for crop %s "
|
||||
"specified in %s", crop_climate_bin_raster_path, crop_name,
|
||||
args['landcover_to_crop_table_path'])
|
||||
f"Expected regression yield table called "
|
||||
f"{crop_regression_yield_table_path} for crop {crop_name} "
|
||||
f"specified in {args['landcover_to_crop_table_path']}")
|
||||
|
||||
landcover_raster_info = pygeoprocessing.get_raster_info(
|
||||
args['landcover_raster_path'])
|
||||
|
|
|
|||
|
|
@ -0,0 +1,395 @@
|
|||
"""Helper module for generating test data related to Crop Production tests."""
|
||||
import pandas
|
||||
|
||||
|
||||
def sample_nutrient_df():
|
||||
"""Generate a sample nutrient DataFrame for crops.
|
||||
|
||||
This function creates a small DataFrame containing example nutrient
|
||||
and production data for crops such as corn and soybean. It can be
|
||||
used for testing nutrient calculations and validating model outputs.
|
||||
|
||||
Returns:
|
||||
pandas.DataFrame: Nutrient and production data indexed by crop name.
|
||||
|
||||
"""
|
||||
|
||||
return pandas.DataFrame([
|
||||
{'crop': 'corn', 'area (ha)': 21.0, 'production_observed': 0.2,
|
||||
'percentrefuse': 7, 'protein': 42., 'lipid': 8, 'energy': 476.,
|
||||
'ca': 27.0, 'fe': 15.7, 'mg': 280.0, 'ph': 704.0, 'k': 1727.0,
|
||||
'na': 2.0, 'zn': 4.9, 'cu': 1.9, 'fl': 8, 'mn': 2.9, 'se': 0.1,
|
||||
'vita': 3.0, 'betac': 16.0, 'alphac': 2.30, 'vite': 0.8,
|
||||
'crypto': 1.6, 'lycopene': 0.36, 'lutein': 63.0, 'betat': 0.5,
|
||||
'gammat': 2.1, 'deltat': 1.9, 'vitc': 6.8, 'thiamin': 0.4,
|
||||
'riboflavin': 1.8, 'niacin': 8.2, 'pantothenic': 0.9,
|
||||
'vitb6': 1.4, 'folate': 385.0, 'vitb12': 2.0, 'vitk': 41.0},
|
||||
|
||||
{'crop': 'soybean', 'area (ha)': 5., 'production_observed': 4.,
|
||||
'percentrefuse': 9, 'protein': 33., 'lipid': 2., 'energy': 99.,
|
||||
'ca': 257., 'fe': 15.7, 'mg': 280., 'ph': 704.0, 'k': 197.0,
|
||||
'na': 2., 'zn': 4.9, 'cu': 1.6, 'fl': 3., 'mn': 5.2, 'se': 0.3,
|
||||
'vita': 3.0, 'betac': 16.0, 'alphac': 1.0, 'vite': 0.8,
|
||||
'crypto': 0.6, 'lycopene': 0.3, 'lutein': 61.0, 'betat': 0.5,
|
||||
'gammat': 2.3, 'deltat': 1.2, 'vitc': 3.0, 'thiamin': 0.42,
|
||||
'riboflavin': 0.82, 'niacin': 12.2, 'pantothenic': 0.92,
|
||||
'vitb6': 5.4, 'folate': 305., 'vitb12': 3., 'vitk': 42.},
|
||||
]).set_index('crop')
|
||||
|
||||
|
||||
def tabulate_regr_results_table():
|
||||
"""Generate expected output for unit tests of tabulated regression results.
|
||||
|
||||
This function returns a DataFrame that represents the expected output
|
||||
of the `tabulate_regression_results` function, with the parameters
|
||||
defined in the unit test `test_tabulate_regression_results`.
|
||||
|
||||
Returns:
|
||||
pandas.DataFrame: Expected tabulated results for regression output.
|
||||
|
||||
"""
|
||||
|
||||
return pandas.DataFrame([
|
||||
{'crop': 'corn', 'area (ha)': 20.0,
|
||||
'production_observed': 80.0, 'production_modeled': 40.0,
|
||||
'protein_modeled': 15624000.0, 'protein_observed': 31248000.0,
|
||||
'lipid_modeled': 2976000.0, 'lipid_observed': 5952000.0,
|
||||
'energy_modeled': 177072000.0, 'energy_observed': 354144000.0,
|
||||
'ca_modeled': 10044000.0, 'ca_observed': 20088000.0,
|
||||
'fe_modeled': 5840400.0, 'fe_observed': 11680800.0,
|
||||
'mg_modeled': 104160000.0, 'mg_observed': 208320000.0,
|
||||
'ph_modeled': 261888000.0, 'ph_observed': 523776000.0,
|
||||
'k_modeled': 642444000.0, 'k_observed': 1284888000.0,
|
||||
'na_modeled': 744000.0, 'na_observed': 1488000.0,
|
||||
'zn_modeled': 1822800.0, 'zn_observed': 3645600.0,
|
||||
'cu_modeled': 706800.0, 'cu_observed': 1413600.0,
|
||||
'fl_modeled': 2976000.0, 'fl_observed': 5952000.0,
|
||||
'mn_modeled': 1078800.0, 'mn_observed': 2157600.0,
|
||||
'se_modeled': 37200.0, 'se_observed': 74400.0,
|
||||
'vita_modeled': 1116000.0, 'vita_observed': 2232000.0,
|
||||
'betac_modeled': 5952000.0, 'betac_observed': 11904000.0,
|
||||
'alphac_modeled': 855600.0, 'alphac_observed': 1711200.0,
|
||||
'vite_modeled': 297600.0, 'vite_observed': 595200.0,
|
||||
'crypto_modeled': 595200.0, 'crypto_observed': 1190400.0,
|
||||
'lycopene_modeled': 133920.0, 'lycopene_observed': 267840.0,
|
||||
'lutein_modeled': 23436000.0, 'lutein_observed': 46872000.0,
|
||||
'betat_modeled': 186000.0, 'betat_observed': 372000.0,
|
||||
'gammat_modeled': 781200.0, 'gammat_observed': 1562400.0,
|
||||
'deltat_modeled': 706800.0, 'deltat_observed': 1413600.0,
|
||||
'vitc_modeled': 2529600.0, 'vitc_observed': 5059200.0,
|
||||
'thiamin_modeled': 148800.0, 'thiamin_observed': 297600.0,
|
||||
'riboflavin_modeled': 669600.0, 'riboflavin_observed': 1339200.0,
|
||||
'niacin_modeled': 3050400.0, 'niacin_observed': 6100800.0,
|
||||
'pantothenic_modeled': 334800.0, 'pantothenic_observed': 669600.0,
|
||||
'vitb6_modeled': 520800.0, 'vitb6_observed': 1041600.0,
|
||||
'folate_modeled': 143220000.0, 'folate_observed': 286440000.0,
|
||||
'vitb12_modeled': 744000.0, 'vitb12_observed': 1488000.0,
|
||||
'vitk_modeled': 15252000.0, 'vitk_observed': 30504000.0},
|
||||
{'crop': 'soybean', 'area (ha)': 40.0,
|
||||
'production_observed': 120.0, 'production_modeled': 70.0,
|
||||
'protein_modeled': 21021000.0, 'protein_observed': 36036000.0,
|
||||
'lipid_modeled': 1274000.0, 'lipid_observed': 2184000.0,
|
||||
'energy_modeled': 63063000.0, 'energy_observed': 108108000.0,
|
||||
'ca_modeled': 163709000.0, 'ca_observed': 280644000.0,
|
||||
'fe_modeled': 10000900.0, 'fe_observed': 17144400.0,
|
||||
'mg_modeled': 178360000.0, 'mg_observed': 305760000.0,
|
||||
'ph_modeled': 448448000.0, 'ph_observed': 768768000.0,
|
||||
'k_modeled': 125489000.0, 'k_observed': 215124000.0,
|
||||
'na_modeled': 1274000.0, 'na_observed': 2184000.0,
|
||||
'zn_modeled': 3121300.0, 'zn_observed': 5350800.0,
|
||||
'cu_modeled': 1019200.0, 'cu_observed': 1747200.0,
|
||||
'fl_modeled': 1911000.0, 'fl_observed': 3276000.0,
|
||||
'mn_modeled': 3312400.0, 'mn_observed': 5678400.0,
|
||||
'se_modeled': 191100.0, 'se_observed': 327600.0,
|
||||
'vita_modeled': 1911000.0, 'vita_observed': 3276000.0,
|
||||
'betac_modeled': 10192000.0, 'betac_observed': 17472000.0,
|
||||
'alphac_modeled': 637000.0, 'alphac_observed': 1092000.0,
|
||||
'vite_modeled': 509600.0, 'vite_observed': 873600.0,
|
||||
'crypto_modeled': 382200.0, 'crypto_observed': 655200.0,
|
||||
'lycopene_modeled': 191100.0, 'lycopene_observed': 327600.0,
|
||||
'lutein_modeled': 38857000.0, 'lutein_observed': 66612000.0,
|
||||
'betat_modeled': 318500.0, 'betat_observed': 546000.0,
|
||||
'gammat_modeled': 1465100.0, 'gammat_observed': 2511600.0,
|
||||
'deltat_modeled': 764400.0, 'deltat_observed': 1310400.0,
|
||||
'vitc_modeled': 1911000.0, 'vitc_observed': 3276000.0,
|
||||
'thiamin_modeled': 267540.0, 'thiamin_observed': 458640.0,
|
||||
'riboflavin_modeled': 522340.0, 'riboflavin_observed': 895440.0,
|
||||
'niacin_modeled': 7771400.0, 'niacin_observed': 13322400.0,
|
||||
'pantothenic_modeled': 586040.0, 'pantothenic_observed': 1004640.0,
|
||||
'vitb6_modeled': 3439800.0, 'vitb6_observed': 5896800.0,
|
||||
'folate_modeled': 194285000.0, 'folate_observed': 333060000.0,
|
||||
'vitb12_modeled': 1911000.0, 'vitb12_observed': 3276000.0,
|
||||
'vitk_modeled': 26754000.0, 'vitk_observed': 45864000.0}])
|
||||
|
||||
|
||||
def tabulate_pctl_results_table():
|
||||
"""Generate expected output for unit tests of tabulated percentile results.
|
||||
|
||||
This function returns the expected DataFrame output of the
|
||||
`tabulate_percentile_results` function, with the parameters
|
||||
defined in the unit test `test_tabulate_percentile_results`.
|
||||
|
||||
Returns:
|
||||
pandas.DataFrame: Expected tabulated results for percentile output.
|
||||
|
||||
"""
|
||||
|
||||
return pandas.DataFrame({
|
||||
"crop": ["corn", "soybean"], "area (ha)": [2, 4],
|
||||
"production_observed": [4, 7], "production_25": [1.25, 2.25],
|
||||
"production_50": [2.5, 4.5], "production_75": [3.75, 6.75],
|
||||
"protein_25": [488250, 675675], "protein_50": [976500, 1351350],
|
||||
"protein_75": [1464750, 2027025],
|
||||
"protein_observed": [1562400, 2102100],
|
||||
"lipid_25": [93000, 40950], "lipid_50": [186000, 81900],
|
||||
"lipid_75": [279000, 122850], "lipid_observed": [297600, 127400],
|
||||
"energy_25": [5533500, 2027025], "energy_50": [11067000, 4054050],
|
||||
"energy_75": [16600500, 6081075],
|
||||
"energy_observed": [17707200, 6306300],
|
||||
"ca_25": [313875, 5262075], "ca_50": [627750, 10524150],
|
||||
"ca_75": [941625, 15786225], "ca_observed": [1004400, 16370900],
|
||||
"fe_25": [182512.5, 321457.5], "fe_50": [365025, 642915],
|
||||
"fe_75": [547537.5, 964372.5], "fe_observed": [584040, 1000090],
|
||||
"mg_25": [3255000, 5733000], "mg_50": [6510000, 11466000],
|
||||
"mg_75": [9765000, 17199000], "mg_observed": [10416000, 17836000],
|
||||
"ph_25": [8184000, 14414400], "ph_50": [16368000, 28828800],
|
||||
"ph_75": [24552000, 43243200], "ph_observed": [26188800, 44844800],
|
||||
"k_25": [20076375, 4033575], "k_50": [40152750, 8067150],
|
||||
"k_75": [60229125, 12100725], "k_observed": [64244400, 12548900],
|
||||
"na_25": [23250, 40950], "na_50": [46500, 81900],
|
||||
"na_75": [69750, 122850], "na_observed": [74400, 127400],
|
||||
"zn_25": [56962.5, 100327.5], "zn_50": [113925, 200655],
|
||||
"zn_75": [170887.5, 300982.5], "zn_observed": [182280, 312130],
|
||||
"cu_25": [22087.5, 32760], "cu_50": [44175, 65520],
|
||||
"cu_75": [66262.5, 98280], "cu_observed": [70680, 101920],
|
||||
"fl_25": [93000, 61425], "fl_50": [186000, 122850],
|
||||
"fl_75": [279000, 184275], "fl_observed": [297600, 191100],
|
||||
"mn_25": [33712.5, 106470], "mn_50": [67425, 212940],
|
||||
"mn_75": [101137.5, 319410], "mn_observed": [107880, 331240],
|
||||
"se_25": [1162.5, 6142.5], "se_50": [2325, 12285],
|
||||
"se_75": [3487.5, 18427.5], "se_observed": [3720, 19110],
|
||||
"vita_25": [34875, 61425], "vita_50": [69750, 122850],
|
||||
"vita_75": [104625, 184275], "vita_observed": [111600, 191100],
|
||||
"betac_25": [186000, 327600], "betac_50": [372000, 655200],
|
||||
"betac_75": [558000, 982800], "betac_observed": [595200, 1019200],
|
||||
"alphac_25": [26737.5, 20475], "alphac_50": [53475, 40950],
|
||||
"alphac_75": [80212.5, 61425], "alphac_observed": [85560, 63700],
|
||||
"vite_25": [9300, 16380], "vite_50": [18600, 32760],
|
||||
"vite_75": [27900, 49140], "vite_observed": [29760, 50960],
|
||||
"crypto_25": [18600, 12285], "crypto_50": [37200, 24570],
|
||||
"crypto_75": [55800, 36855], "crypto_observed": [59520, 38220],
|
||||
"lycopene_25": [4185, 6142.5], "lycopene_50": [8370, 12285],
|
||||
"lycopene_75": [12555, 18427.5], "lycopene_observed": [13392, 19110],
|
||||
"lutein_25": [732375, 1248975], "lutein_50": [1464750, 2497950],
|
||||
"lutein_75": [2197125, 3746925], "lutein_observed": [2343600, 3885700],
|
||||
"betat_25": [5812.5, 10237.5], "betat_50": [11625, 20475],
|
||||
"betat_75": [17437.5, 30712.5], "betat_observed": [18600, 31850],
|
||||
"gammat_25": [24412.5, 47092.5], "gammat_50": [48825, 94185],
|
||||
"gammat_75": [73237.5, 141277.5], "gammat_observed": [78120, 146510],
|
||||
"deltat_25": [22087.5, 24570], "deltat_50": [44175, 49140],
|
||||
"deltat_75": [66262.5, 73710], "deltat_observed": [70680, 76440],
|
||||
"vitc_25": [79050, 61425], "vitc_50": [158100, 122850],
|
||||
"vitc_75": [237150, 184275], "vitc_observed": [252960, 191100],
|
||||
"thiamin_25": [4650, 8599.5], "thiamin_50": [9300, 17199],
|
||||
"thiamin_75": [13950, 25798.5], "thiamin_observed": [14880, 26754],
|
||||
"riboflavin_25": [20925, 16789.5], "riboflavin_50": [41850, 33579],
|
||||
"riboflavin_75": [62775, 50368.5],
|
||||
"riboflavin_observed": [66960, 52234], "niacin_25": [95325, 249795],
|
||||
"niacin_50": [190650, 499590], "niacin_75": [285975, 749385],
|
||||
"niacin_observed": [305040, 777140],
|
||||
"pantothenic_25": [10462.5, 18837], "pantothenic_50": [20925, 37674],
|
||||
"pantothenic_75": [31387.5, 56511],
|
||||
"pantothenic_observed": [33480, 58604],
|
||||
"vitb6_25": [16275, 110565], "vitb6_50": [32550, 221130],
|
||||
"vitb6_75": [48825, 331695], "vitb6_observed": [52080, 343980],
|
||||
"folate_25": [4475625, 6244875], "folate_50": [8951250, 12489750],
|
||||
"folate_75": [13426875, 18734625],
|
||||
"folate_observed": [14322000, 19428500],
|
||||
"vitb12_25": [23250, 61425], "vitb12_50": [46500, 122850],
|
||||
"vitb12_75": [69750, 184275], "vitb12_observed": [74400, 191100],
|
||||
"vitk_25": [476625, 859950], "vitk_50": [953250, 1719900],
|
||||
"vitk_75": [1429875, 2579850], "vitk_observed": [1525200, 2675400]
|
||||
})
|
||||
|
||||
|
||||
def aggregate_regr_polygons_table():
|
||||
"""Generate expected aggregated nutrient results by polygon (e.g., FID).
|
||||
|
||||
This function returns the expected DataFrame output of the
|
||||
`aggregate_regression_results_to_polygons` function for use in the
|
||||
`test_aggregate_regression_results_to_polygons` unit test. It
|
||||
summarizes nutrient production values for multiple crops grouped by
|
||||
polygon identifiers (FIDs).
|
||||
|
||||
Returns:
|
||||
pandas.DataFrame: Aggregated modeled and observed values by FID.
|
||||
|
||||
"""
|
||||
|
||||
return pandas.DataFrame([
|
||||
{"FID": 0, "corn_modeled": 10, "corn_observed": 40,
|
||||
"soybean_modeled": 20, "soybean_observed": 50,
|
||||
"protein_modeled": 9912000, "protein_observed": 30639000,
|
||||
"lipid_modeled": 1108000, "lipid_observed": 3886000,
|
||||
"energy_modeled": 62286000, "energy_observed": 222117000,
|
||||
"ca_modeled": 49285000, "ca_observed": 126979000,
|
||||
"fe_modeled": 4317500, "fe_observed": 12983900,
|
||||
"mg_modeled": 77000000, "mg_observed": 231560000,
|
||||
"ph_modeled": 193600000, "ph_observed": 582208000,
|
||||
"k_modeled": 196465000, "k_observed": 732079000,
|
||||
"na_modeled": 550000, "na_observed": 1654000,
|
||||
"zn_modeled": 1347500, "zn_observed": 4052300,
|
||||
"cu_modeled": 467900, "cu_observed": 1434800,
|
||||
"fl_modeled": 1290000, "fl_observed": 4341000,
|
||||
"mn_modeled": 1216100, "mn_observed": 3444800,
|
||||
"se_modeled": 63900, "se_observed": 173700,
|
||||
"vita_modeled": 825000, "vita_observed": 2481000,
|
||||
"betac_modeled": 4400000, "betac_observed": 13232000,
|
||||
"alphac_modeled": 395900, "alphac_observed": 1310600,
|
||||
"vite_modeled": 220000, "vite_observed": 661600,
|
||||
"crypto_modeled": 258000, "crypto_observed": 868200,
|
||||
"lycopene_modeled": 88080, "lycopene_observed": 270420,
|
||||
"lutein_modeled": 16961000, "lutein_observed": 51191000,
|
||||
"betat_modeled": 137500, "betat_observed": 413500,
|
||||
"gammat_modeled": 613900, "gammat_observed": 1827700,
|
||||
"deltat_modeled": 395100, "deltat_observed": 1252800,
|
||||
"vitc_modeled": 1178400, "vitc_observed": 3894600,
|
||||
"thiamin_modeled": 113640, "thiamin_observed": 339900,
|
||||
"riboflavin_modeled": 316640, "riboflavin_observed": 1042700,
|
||||
"niacin_modeled": 2983000, "niacin_observed": 8601400,
|
||||
"pantothenic_modeled": 251140, "pantothenic_observed": 753400,
|
||||
"vitb6_modeled": 1113000, "vitb6_observed": 2977800,
|
||||
"folate_modeled": 91315000, "folate_observed": 281995000,
|
||||
"vitb12_modeled": 732000, "vitb12_observed": 2109000,
|
||||
"vitk_modeled": 11457000, "vitk_observed": 34362000},
|
||||
{"FID": 1, "corn_modeled": 40, "corn_observed": 80,
|
||||
"soybean_modeled": 70, "soybean_observed": 120,
|
||||
"protein_modeled": 36645000, "protein_observed": 67284000,
|
||||
"lipid_modeled": 4250000, "lipid_observed": 8136000,
|
||||
"energy_modeled": 240135000, "energy_observed": 462252000,
|
||||
"ca_modeled": 173753000, "ca_observed": 300732000,
|
||||
"fe_modeled": 15841300, "fe_observed": 28825200,
|
||||
"mg_modeled": 282520000, "mg_observed": 514080000,
|
||||
"ph_modeled": 710336000, "ph_observed": 1292544000,
|
||||
"k_modeled": 767933000, "k_observed": 1500012000,
|
||||
"na_modeled": 2018000, "na_observed": 3672000,
|
||||
"zn_modeled": 4944100, "zn_observed": 8996400,
|
||||
"cu_modeled": 1726000, "cu_observed": 3160800,
|
||||
"fl_modeled": 4887000, "fl_observed": 9228000,
|
||||
"mn_modeled": 4391200, "mn_observed": 7836000,
|
||||
"se_modeled": 228300, "se_observed": 402000,
|
||||
"vita_modeled": 3027000, "vita_observed": 5508000,
|
||||
"betac_modeled": 16144000, "betac_observed": 29376000,
|
||||
"alphac_modeled": 1492600, "alphac_observed": 2803200,
|
||||
"vite_modeled": 807200, "vite_observed": 1468800,
|
||||
"crypto_modeled": 977400, "crypto_observed": 1845600,
|
||||
"lycopene_modeled": 325020, "lycopene_observed": 595440,
|
||||
"lutein_modeled": 62293000, "lutein_observed": 113484000,
|
||||
"betat_modeled": 504500, "betat_observed": 918000,
|
||||
"gammat_modeled": 2246300, "gammat_observed": 4074000,
|
||||
"deltat_modeled": 1471200, "deltat_observed": 2724000,
|
||||
"vitc_modeled": 4440600, "vitc_observed": 8335200,
|
||||
"thiamin_modeled": 416340, "thiamin_observed": 756240,
|
||||
"riboflavin_modeled": 1191940, "riboflavin_observed": 2234640,
|
||||
"niacin_modeled": 10821800, "niacin_observed": 19423200,
|
||||
"pantothenic_modeled": 920840, "pantothenic_observed": 1674240,
|
||||
"vitb6_modeled": 3960600, "vitb6_observed": 6938400,
|
||||
"folate_modeled": 337505000, "folate_observed": 619500000,
|
||||
"vitb12_modeled": 2655000, "vitb12_observed": 4764000,
|
||||
"vitk_modeled": 42006000, "vitk_observed": 76368000}
|
||||
], dtype=float)
|
||||
|
||||
|
||||
def aggregate_pctl_polygons_table():
|
||||
"""Generate expected aggregated nutrient results by polygon (e.g., FID).
|
||||
|
||||
This function returns the expected DataFrame output of the
|
||||
`aggregate_to_polygons` function for use in the
|
||||
`test_aggregate_to_polygons` unit test. The DataFrame contains observed
|
||||
and modeled (at different percentiles) crop yields and nutrient totals,
|
||||
aggregated by polygon (FID).
|
||||
|
||||
Returns:
|
||||
pandas.DataFrame: A table with observed and percentile-based modeled
|
||||
values for crop yield and nutrient totals, grouped by FID.
|
||||
|
||||
"""
|
||||
|
||||
data = [
|
||||
[0, 0.25, 0.5, 0.75, 1, 0.5, 1, 1.5, 2, 247800, 495600, 743400,
|
||||
991200, 27700, 55400, 83100, 110800, 1557150, 3114300, 4671450,
|
||||
6228600, 1232125, 2464250, 3696375, 4928500, 107937.5, 215875,
|
||||
323812.5, 431750, 1925000, 3850000, 5775000, 7700000, 4840000,
|
||||
9680000, 14520000, 19360000, 4911625, 9823250, 14734875,
|
||||
19646500, 13750, 27500, 41250, 55000, 33687.5, 67375,
|
||||
101062.5, 134750, 11697.5, 23395, 35092.5, 46790, 32250,
|
||||
64500, 96750, 129000, 30402.5, 60805, 91207.5, 121610, 1597.5,
|
||||
3195, 4792.5, 6390, 20625, 41250, 61875, 82500, 110000, 220000,
|
||||
330000, 440000, 9897.5, 19795, 29692.5, 39590, 5500, 11000,
|
||||
16500, 22000, 6450, 12900, 19350, 25800, 2202, 4404, 6606,
|
||||
8808, 424025, 848050, 1272075, 1696100, 3437.5, 6875, 10312.5,
|
||||
13750, 15347.5, 30695, 46042.5, 61390, 9877.5, 19755, 29632.5,
|
||||
39510, 29460, 58920, 88380, 117840, 2841, 5682, 8523, 11364,
|
||||
7916, 15832, 23748, 31664, 74575, 149150, 223725, 298300,
|
||||
6278.5, 12557, 18835.5, 25114, 27825, 55650, 83475, 111300,
|
||||
2282875, 4565750, 6848625, 9131500, 18300, 36600, 54900,
|
||||
73200, 286425, 572850, 859275, 1145700],
|
||||
[1, 1.25, 2.5, 3.75, 4, 2.25, 4.5, 6.75, 7, 1163925, 2327850,
|
||||
3491775, 3664500, 133950, 267900, 401850, 425000, 7560525,
|
||||
15121050, 22681575, 24013500, 5575950, 11151900, 16727850,
|
||||
17375300, 503970, 1007940, 1511910, 1584130, 8988000,
|
||||
17976000, 26964000, 28252000, 22598400, 45196800, 67795200,
|
||||
71033600, 24109950, 48219900, 72329850, 76793300, 64200,
|
||||
128400, 192600, 201800, 157290, 314580, 471870, 494410,
|
||||
54847.5, 109695, 164542.5, 172600, 154425, 308850, 463275,
|
||||
488700, 140182.5, 280365, 420547.5, 439120, 7305, 14610,
|
||||
21915, 22830, 96300, 192600, 288900, 302700, 513600, 1027200,
|
||||
1540800, 1614400, 47212.5, 94425, 141637.5, 149260, 25680,
|
||||
51360, 77040, 80720, 30885, 61770, 92655, 97740, 10327.5,
|
||||
20655, 30982.5, 32502, 1981350, 3962700, 5944050, 6229300,
|
||||
16050, 32100, 48150, 50450, 71505, 143010, 214515, 224630,
|
||||
46657.5, 93315, 139972.5, 147120, 140475, 280950, 421425,
|
||||
444060, 13249.5, 26499, 39748.5, 41634, 37714.5, 75429,
|
||||
113143.5, 119194, 345120, 690240, 1035360, 1082180, 29299.5,
|
||||
58599, 87898.5, 92084, 126840, 253680, 380520, 396060,
|
||||
10720500, 21441000, 32161500, 33750500, 84675, 169350, 254025,
|
||||
265500, 1336575, 2673150, 4009725, 4200600]
|
||||
]
|
||||
|
||||
columns = [
|
||||
"FID", "corn_25", "corn_50", "corn_75", "corn_observed",
|
||||
"soybean_25", "soybean_50", "soybean_75", "soybean_observed",
|
||||
"protein_25", "protein_50", "protein_75", "protein_observed",
|
||||
"lipid_25", "lipid_50", "lipid_75", "lipid_observed",
|
||||
"energy_25", "energy_50", "energy_75", "energy_observed",
|
||||
"ca_25", "ca_50", "ca_75", "ca_observed", "fe_25", "fe_50",
|
||||
"fe_75", "fe_observed", "mg_25", "mg_50", "mg_75",
|
||||
"mg_observed", "ph_25", "ph_50", "ph_75", "ph_observed",
|
||||
"k_25", "k_50", "k_75", "k_observed", "na_25", "na_50",
|
||||
"na_75", "na_observed", "zn_25", "zn_50", "zn_75",
|
||||
"zn_observed", "cu_25", "cu_50", "cu_75", "cu_observed",
|
||||
"fl_25", "fl_50", "fl_75", "fl_observed", "mn_25", "mn_50",
|
||||
"mn_75", "mn_observed", "se_25", "se_50", "se_75",
|
||||
"se_observed", "vita_25", "vita_50", "vita_75",
|
||||
"vita_observed", "betac_25", "betac_50", "betac_75",
|
||||
"betac_observed", "alphac_25", "alphac_50", "alphac_75",
|
||||
"alphac_observed", "vite_25", "vite_50", "vite_75",
|
||||
"vite_observed", "crypto_25", "crypto_50", "crypto_75",
|
||||
"crypto_observed", "lycopene_25", "lycopene_50", "lycopene_75",
|
||||
"lycopene_observed", "lutein_25", "lutein_50", "lutein_75",
|
||||
"lutein_observed", "betat_25", "betat_50", "betat_75",
|
||||
"betat_observed", "gammat_25", "gammat_50", "gammat_75",
|
||||
"gammat_observed", "deltat_25", "deltat_50", "deltat_75",
|
||||
"deltat_observed", "vitc_25", "vitc_50", "vitc_75",
|
||||
"vitc_observed", "thiamin_25", "thiamin_50", "thiamin_75",
|
||||
"thiamin_observed", "riboflavin_25", "riboflavin_50",
|
||||
"riboflavin_75", "riboflavin_observed", "niacin_25",
|
||||
"niacin_50", "niacin_75", "niacin_observed", "pantothenic_25",
|
||||
"pantothenic_50", "pantothenic_75", "pantothenic_observed",
|
||||
"vitb6_25", "vitb6_50", "vitb6_75", "vitb6_observed",
|
||||
"folate_25", "folate_50", "folate_75", "folate_observed",
|
||||
"vitb12_25", "vitb12_50", "vitb12_75", "vitb12_observed",
|
||||
"vitk_25", "vitk_50", "vitk_75", "vitk_observed"
|
||||
]
|
||||
|
||||
return pandas.DataFrame(data, columns=columns, dtype=float)
|
||||
|
|
@ -18,7 +18,7 @@ gdal.UseExceptions()
|
|||
|
||||
|
||||
def make_simple_raster(base_raster_path, fill_val, nodata_val):
|
||||
"""Create a 10x10 raster on designated path with fill value.
|
||||
"""Create a 10x10 int32 raster on designated path with fill value.
|
||||
|
||||
Args:
|
||||
base_raster_path (str): the raster path for making the new raster.
|
||||
|
|
@ -26,32 +26,25 @@ def make_simple_raster(base_raster_path, fill_val, nodata_val):
|
|||
nodata_val (int or None): for defining a band's nodata value.
|
||||
|
||||
Returns:
|
||||
lulc_path (str): the path of the raster file.
|
||||
None.
|
||||
|
||||
"""
|
||||
|
||||
srs = osr.SpatialReference()
|
||||
srs.ImportFromEPSG(26910) # UTM Zone 10N
|
||||
projection_wkt = srs.ExportToWkt()
|
||||
# origin hand-picked for this epsg:
|
||||
geotransform = [461261, 1.0, 0.0, 4923265, 0.0, -1.0]
|
||||
origin = (461261, 4923265)
|
||||
|
||||
n = 10
|
||||
gtiff_driver = gdal.GetDriverByName('GTiff')
|
||||
new_raster = gtiff_driver.Create(
|
||||
base_raster_path, n, n, 1, gdal.GDT_Int32, options=[
|
||||
'TILED=YES', 'BIGTIFF=YES', 'COMPRESS=LZW',
|
||||
'BLOCKXSIZE=16', 'BLOCKYSIZE=16'])
|
||||
new_raster.SetProjection(projection_wkt)
|
||||
new_raster.SetGeoTransform(geotransform)
|
||||
new_band = new_raster.GetRasterBand(1)
|
||||
array = numpy.empty((n, n))
|
||||
array = numpy.empty((n, n), dtype=numpy.int32)
|
||||
array.fill(fill_val)
|
||||
new_band.WriteArray(array)
|
||||
if nodata_val is not None:
|
||||
new_band.SetNoDataValue(nodata_val)
|
||||
new_raster.FlushCache()
|
||||
new_band = None
|
||||
new_raster = None
|
||||
|
||||
pixel_size = (1, -1)
|
||||
|
||||
pygeoprocessing.numpy_array_to_raster(
|
||||
array, nodata_val, pixel_size, origin, projection_wkt,
|
||||
base_raster_path)
|
||||
|
||||
|
||||
def assert_raster_equal_value(base_raster_path, val_to_compare):
|
||||
|
|
|
|||
|
|
@ -30,6 +30,7 @@ def _get_pixels_per_hectare(raster_path):
|
|||
|
||||
Returns:
|
||||
A float representing the number of pixels per hectare.
|
||||
|
||||
"""
|
||||
raster_info = pygeoprocessing.get_raster_info(raster_path)
|
||||
pixel_area = abs(numpy.prod(raster_info['pixel_size']))
|
||||
|
|
@ -37,12 +38,14 @@ def _get_pixels_per_hectare(raster_path):
|
|||
|
||||
|
||||
def make_aggregate_vector(path_to_shp):
|
||||
"""
|
||||
Generate shapefile with two overlapping polygons
|
||||
"""Generate shapefile with two overlapping polygons.
|
||||
|
||||
Args:
|
||||
path_to_shp (str): path to store watershed results vector
|
||||
Outputs:
|
||||
path_to_shp (str): path to store results vector
|
||||
|
||||
Returns:
|
||||
None
|
||||
|
||||
"""
|
||||
# (xmin, ymin), (xmax, ymin), (xmax, ymax), (xmin, ymax)
|
||||
shapely_geometry_list = [
|
||||
|
|
@ -71,10 +74,13 @@ def make_aggregate_vector(path_to_shp):
|
|||
|
||||
def make_simple_raster(base_raster_path, array):
|
||||
"""Create a raster on designated path with arbitrary values.
|
||||
|
||||
Args:
|
||||
base_raster_path (str): the raster path for making the new raster.
|
||||
|
||||
Returns:
|
||||
None.
|
||||
None
|
||||
|
||||
"""
|
||||
# UTM Zone 10N
|
||||
srs = osr.SpatialReference()
|
||||
|
|
@ -90,31 +96,6 @@ def make_simple_raster(base_raster_path, array):
|
|||
base_raster_path)
|
||||
|
||||
|
||||
def create_nutrient_df():
|
||||
"""Creates a nutrient DataFrame for testing."""
|
||||
return pandas.DataFrame([
|
||||
{'crop': 'corn', 'area (ha)': 21.0, 'production_observed': 0.2,
|
||||
'percentrefuse': 7, 'protein': 42., 'lipid': 8, 'energy': 476.,
|
||||
'ca': 27.0, 'fe': 15.7, 'mg': 280.0, 'ph': 704.0, 'k': 1727.0,
|
||||
'na': 2.0, 'zn': 4.9, 'cu': 1.9, 'fl': 8, 'mn': 2.9, 'se': 0.1,
|
||||
'vita': 3.0, 'betac': 16.0, 'alphac': 2.30, 'vite': 0.8,
|
||||
'crypto': 1.6, 'lycopene': 0.36, 'lutein': 63.0, 'betat': 0.5,
|
||||
'gammat': 2.1, 'deltat': 1.9, 'vitc': 6.8, 'thiamin': 0.4,
|
||||
'riboflavin': 1.8, 'niacin': 8.2, 'pantothenic': 0.9,
|
||||
'vitb6': 1.4, 'folate': 385.0, 'vitb12': 2.0, 'vitk': 41.0},
|
||||
|
||||
{'crop': 'soybean', 'area (ha)': 5., 'production_observed': 4.,
|
||||
'percentrefuse': 9, 'protein': 33., 'lipid': 2., 'energy': 99.,
|
||||
'ca': 257., 'fe': 15.7, 'mg': 280., 'ph': 704.0, 'k': 197.0,
|
||||
'na': 2., 'zn': 4.9, 'cu': 1.6, 'fl': 3., 'mn': 5.2, 'se': 0.3,
|
||||
'vita': 3.0, 'betac': 16.0, 'alphac': 1.0, 'vite': 0.8,
|
||||
'crypto': 0.6, 'lycopene': 0.3, 'lutein': 61.0, 'betat': 0.5,
|
||||
'gammat': 2.3, 'deltat': 1.2, 'vitc': 3.0, 'thiamin': 0.42,
|
||||
'riboflavin': 0.82, 'niacin': 12.2, 'pantothenic': 0.92,
|
||||
'vitb6': 5.4, 'folate': 305., 'vitb12': 3., 'vitk': 42.},
|
||||
]).set_index('crop')
|
||||
|
||||
|
||||
def _create_crop_rasters(output_dir, crop_names, file_suffix):
|
||||
"""Creates raster files for test setup."""
|
||||
_OBSERVED_PRODUCTION_FILE_PATTERN = os.path.join(
|
||||
|
|
@ -138,6 +119,33 @@ def _create_crop_rasters(output_dir, crop_names, file_suffix):
|
|||
make_simple_raster(crop_production_raster_path, crop_array)
|
||||
|
||||
|
||||
def _create_crop_pctl_rasters(output_dir, crop_names, file_suffix, pctls):
|
||||
"""Creates crop percentile raster files for test setup."""
|
||||
_OBSERVED_PRODUCTION_FILE_PATTERN = os.path.join(
|
||||
'.', '%s_observed_production%s.tif')
|
||||
_CROP_PRODUCTION_FILE_PATTERN = os.path.join(
|
||||
'.', '%s_%s_production%s.tif')
|
||||
|
||||
for i, crop in enumerate(crop_names):
|
||||
observed_yield_path = os.path.join(
|
||||
output_dir,
|
||||
_OBSERVED_PRODUCTION_FILE_PATTERN % (crop, file_suffix))
|
||||
# Create arbitrary raster arrays
|
||||
observed_array = numpy.array(
|
||||
[[i, 1], [i*2, 3]], dtype=numpy.int16)
|
||||
make_simple_raster(observed_yield_path, observed_array)
|
||||
|
||||
for pctl in pctls:
|
||||
crop_production_raster_path = os.path.join(
|
||||
output_dir,
|
||||
_CROP_PRODUCTION_FILE_PATTERN % (crop, pctl, file_suffix))
|
||||
|
||||
crop_array = numpy.array(
|
||||
[[i, 1], [i*3, 4]], dtype=numpy.int16) * float(pctl[-2:])/100
|
||||
|
||||
make_simple_raster(crop_production_raster_path, crop_array)
|
||||
|
||||
|
||||
class CropProductionTests(unittest.TestCase):
|
||||
"""Tests for the Crop Production model."""
|
||||
|
||||
|
|
@ -626,82 +634,10 @@ class CropProductionTests(unittest.TestCase):
|
|||
"""Test `tabulate_regression_results`"""
|
||||
from natcap.invest.crop_production_regression import \
|
||||
tabulate_regression_results
|
||||
from crop_production.data_helpers import sample_nutrient_df
|
||||
from crop_production.data_helpers import tabulate_regr_results_table
|
||||
|
||||
def _create_expected_results():
|
||||
"""Creates the expected results DataFrame."""
|
||||
return pandas.DataFrame([
|
||||
{'crop': 'corn', 'area (ha)': 20.0,
|
||||
'production_observed': 80.0, 'production_modeled': 40.0,
|
||||
'protein_modeled': 15624000.0, 'protein_observed': 31248000.0,
|
||||
'lipid_modeled': 2976000.0, 'lipid_observed': 5952000.0,
|
||||
'energy_modeled': 177072000.0, 'energy_observed': 354144000.0,
|
||||
'ca_modeled': 10044000.0, 'ca_observed': 20088000.0,
|
||||
'fe_modeled': 5840400.0, 'fe_observed': 11680800.0,
|
||||
'mg_modeled': 104160000.0, 'mg_observed': 208320000.0,
|
||||
'ph_modeled': 261888000.0, 'ph_observed': 523776000.0,
|
||||
'k_modeled': 642444000.0, 'k_observed': 1284888000.0,
|
||||
'na_modeled': 744000.0, 'na_observed': 1488000.0,
|
||||
'zn_modeled': 1822800.0, 'zn_observed': 3645600.0,
|
||||
'cu_modeled': 706800.0, 'cu_observed': 1413600.0,
|
||||
'fl_modeled': 2976000.0, 'fl_observed': 5952000.0,
|
||||
'mn_modeled': 1078800.0, 'mn_observed': 2157600.0,
|
||||
'se_modeled': 37200.0, 'se_observed': 74400.0,
|
||||
'vita_modeled': 1116000.0, 'vita_observed': 2232000.0,
|
||||
'betac_modeled': 5952000.0, 'betac_observed': 11904000.0,
|
||||
'alphac_modeled': 855600.0, 'alphac_observed': 1711200.0,
|
||||
'vite_modeled': 297600.0, 'vite_observed': 595200.0,
|
||||
'crypto_modeled': 595200.0, 'crypto_observed': 1190400.0,
|
||||
'lycopene_modeled': 133920.0, 'lycopene_observed': 267840.0,
|
||||
'lutein_modeled': 23436000.0, 'lutein_observed': 46872000.0,
|
||||
'betat_modeled': 186000.0, 'betat_observed': 372000.0,
|
||||
'gammat_modeled': 781200.0, 'gammat_observed': 1562400.0,
|
||||
'deltat_modeled': 706800.0, 'deltat_observed': 1413600.0,
|
||||
'vitc_modeled': 2529600.0, 'vitc_observed': 5059200.0,
|
||||
'thiamin_modeled': 148800.0, 'thiamin_observed': 297600.0,
|
||||
'riboflavin_modeled': 669600.0, 'riboflavin_observed': 1339200.0,
|
||||
'niacin_modeled': 3050400.0, 'niacin_observed': 6100800.0,
|
||||
'pantothenic_modeled': 334800.0, 'pantothenic_observed': 669600.0,
|
||||
'vitb6_modeled': 520800.0, 'vitb6_observed': 1041600.0,
|
||||
'folate_modeled': 143220000.0, 'folate_observed': 286440000.0,
|
||||
'vitb12_modeled': 744000.0, 'vitb12_observed': 1488000.0,
|
||||
'vitk_modeled': 15252000.0, 'vitk_observed': 30504000.0},
|
||||
{'crop': 'soybean', 'area (ha)': 40.0,
|
||||
'production_observed': 120.0, 'production_modeled': 70.0,
|
||||
'protein_modeled': 21021000.0, 'protein_observed': 36036000.0,
|
||||
'lipid_modeled': 1274000.0, 'lipid_observed': 2184000.0,
|
||||
'energy_modeled': 63063000.0, 'energy_observed': 108108000.0,
|
||||
'ca_modeled': 163709000.0, 'ca_observed': 280644000.0,
|
||||
'fe_modeled': 10000900.0, 'fe_observed': 17144400.0,
|
||||
'mg_modeled': 178360000.0, 'mg_observed': 305760000.0,
|
||||
'ph_modeled': 448448000.0, 'ph_observed': 768768000.0,
|
||||
'k_modeled': 125489000.0, 'k_observed': 215124000.0,
|
||||
'na_modeled': 1274000.0, 'na_observed': 2184000.0,
|
||||
'zn_modeled': 3121300.0, 'zn_observed': 5350800.0,
|
||||
'cu_modeled': 1019200.0, 'cu_observed': 1747200.0,
|
||||
'fl_modeled': 1911000.0, 'fl_observed': 3276000.0,
|
||||
'mn_modeled': 3312400.0, 'mn_observed': 5678400.0,
|
||||
'se_modeled': 191100.0, 'se_observed': 327600.0,
|
||||
'vita_modeled': 1911000.0, 'vita_observed': 3276000.0,
|
||||
'betac_modeled': 10192000.0, 'betac_observed': 17472000.0,
|
||||
'alphac_modeled': 637000.0, 'alphac_observed': 1092000.0,
|
||||
'vite_modeled': 509600.0, 'vite_observed': 873600.0,
|
||||
'crypto_modeled': 382200.0, 'crypto_observed': 655200.0,
|
||||
'lycopene_modeled': 191100.0, 'lycopene_observed': 327600.0,
|
||||
'lutein_modeled': 38857000.0, 'lutein_observed': 66612000.0,
|
||||
'betat_modeled': 318500.0, 'betat_observed': 546000.0,
|
||||
'gammat_modeled': 1465100.0, 'gammat_observed': 2511600.0,
|
||||
'deltat_modeled': 764400.0, 'deltat_observed': 1310400.0,
|
||||
'vitc_modeled': 1911000.0, 'vitc_observed': 3276000.0,
|
||||
'thiamin_modeled': 267540.0, 'thiamin_observed': 458640.0,
|
||||
'riboflavin_modeled': 522340.0, 'riboflavin_observed': 895440.0,
|
||||
'niacin_modeled': 7771400.0, 'niacin_observed': 13322400.0,
|
||||
'pantothenic_modeled': 586040.0, 'pantothenic_observed': 1004640.0,
|
||||
'vitb6_modeled': 3439800.0, 'vitb6_observed': 5896800.0,
|
||||
'folate_modeled': 194285000.0, 'folate_observed': 333060000.0,
|
||||
'vitb12_modeled': 1911000.0, 'vitb12_observed': 3276000.0,
|
||||
'vitk_modeled': 26754000.0, 'vitk_observed': 45864000.0}])
|
||||
|
||||
nutrient_df = create_nutrient_df()
|
||||
nutrient_df = sample_nutrient_df()
|
||||
|
||||
pixel_area_ha = 10
|
||||
workspace_dir = self.workspace_dir
|
||||
|
|
@ -728,7 +664,7 @@ class CropProductionTests(unittest.TestCase):
|
|||
# Read only the first 2 crop's data (skipping total area)
|
||||
actual_result_table = pandas.read_csv(target_table_path, nrows=2,
|
||||
header=0)
|
||||
expected_result_table = _create_expected_results()
|
||||
expected_result_table = tabulate_regr_results_table()
|
||||
|
||||
# Compare expected vs actual
|
||||
pandas.testing.assert_frame_equal(actual_result_table,
|
||||
|
|
@ -738,82 +674,9 @@ class CropProductionTests(unittest.TestCase):
|
|||
"""Test `aggregate_regression_results_to_polygons`"""
|
||||
from natcap.invest.crop_production_regression import \
|
||||
aggregate_regression_results_to_polygons
|
||||
|
||||
def _create_expected_agg_table():
|
||||
"""Create expected output results"""
|
||||
# Define the new values manually
|
||||
return pandas.DataFrame([
|
||||
{"FID": 0, "corn_modeled": 10, "corn_observed": 40,
|
||||
"soybean_modeled": 20, "soybean_observed": 50,
|
||||
"protein_modeled": 9912000, "protein_observed": 30639000,
|
||||
"lipid_modeled": 1108000, "lipid_observed": 3886000,
|
||||
"energy_modeled": 62286000, "energy_observed": 222117000,
|
||||
"ca_modeled": 49285000, "ca_observed": 126979000,
|
||||
"fe_modeled": 4317500, "fe_observed": 12983900,
|
||||
"mg_modeled": 77000000, "mg_observed": 231560000,
|
||||
"ph_modeled": 193600000, "ph_observed": 582208000,
|
||||
"k_modeled": 196465000, "k_observed": 732079000,
|
||||
"na_modeled": 550000, "na_observed": 1654000,
|
||||
"zn_modeled": 1347500, "zn_observed": 4052300,
|
||||
"cu_modeled": 467900, "cu_observed": 1434800,
|
||||
"fl_modeled": 1290000, "fl_observed": 4341000,
|
||||
"mn_modeled": 1216100, "mn_observed": 3444800,
|
||||
"se_modeled": 63900, "se_observed": 173700,
|
||||
"vita_modeled": 825000, "vita_observed": 2481000,
|
||||
"betac_modeled": 4400000, "betac_observed": 13232000,
|
||||
"alphac_modeled": 395900, "alphac_observed": 1310600,
|
||||
"vite_modeled": 220000, "vite_observed": 661600,
|
||||
"crypto_modeled": 258000, "crypto_observed": 868200,
|
||||
"lycopene_modeled": 88080, "lycopene_observed": 270420,
|
||||
"lutein_modeled": 16961000, "lutein_observed": 51191000,
|
||||
"betat_modeled": 137500, "betat_observed": 413500,
|
||||
"gammat_modeled": 613900, "gammat_observed": 1827700,
|
||||
"deltat_modeled": 395100, "deltat_observed": 1252800,
|
||||
"vitc_modeled": 1178400, "vitc_observed": 3894600,
|
||||
"thiamin_modeled": 113640, "thiamin_observed": 339900,
|
||||
"riboflavin_modeled": 316640, "riboflavin_observed": 1042700,
|
||||
"niacin_modeled": 2983000, "niacin_observed": 8601400,
|
||||
"pantothenic_modeled": 251140, "pantothenic_observed": 753400,
|
||||
"vitb6_modeled": 1113000, "vitb6_observed": 2977800,
|
||||
"folate_modeled": 91315000, "folate_observed": 281995000,
|
||||
"vitb12_modeled": 732000, "vitb12_observed": 2109000,
|
||||
"vitk_modeled": 11457000, "vitk_observed": 34362000},
|
||||
{"FID": 1, "corn_modeled": 40, "corn_observed": 80,
|
||||
"soybean_modeled": 70, "soybean_observed": 120,
|
||||
"protein_modeled": 36645000, "protein_observed": 67284000,
|
||||
"lipid_modeled": 4250000, "lipid_observed": 8136000,
|
||||
"energy_modeled": 240135000, "energy_observed": 462252000,
|
||||
"ca_modeled": 173753000, "ca_observed": 300732000,
|
||||
"fe_modeled": 15841300, "fe_observed": 28825200,
|
||||
"mg_modeled": 282520000, "mg_observed": 514080000,
|
||||
"ph_modeled": 710336000, "ph_observed": 1292544000,
|
||||
"k_modeled": 767933000, "k_observed": 1500012000,
|
||||
"na_modeled": 2018000, "na_observed": 3672000,
|
||||
"zn_modeled": 4944100, "zn_observed": 8996400,
|
||||
"cu_modeled": 1726000, "cu_observed": 3160800,
|
||||
"fl_modeled": 4887000, "fl_observed": 9228000,
|
||||
"mn_modeled": 4391200, "mn_observed": 7836000,
|
||||
"se_modeled": 228300, "se_observed": 402000,
|
||||
"vita_modeled": 3027000, "vita_observed": 5508000,
|
||||
"betac_modeled": 16144000, "betac_observed": 29376000,
|
||||
"alphac_modeled": 1492600, "alphac_observed": 2803200,
|
||||
"vite_modeled": 807200, "vite_observed": 1468800,
|
||||
"crypto_modeled": 977400, "crypto_observed": 1845600,
|
||||
"lycopene_modeled": 325020, "lycopene_observed": 595440,
|
||||
"lutein_modeled": 62293000, "lutein_observed": 113484000,
|
||||
"betat_modeled": 504500, "betat_observed": 918000,
|
||||
"gammat_modeled": 2246300, "gammat_observed": 4074000,
|
||||
"deltat_modeled": 1471200, "deltat_observed": 2724000,
|
||||
"vitc_modeled": 4440600, "vitc_observed": 8335200,
|
||||
"thiamin_modeled": 416340, "thiamin_observed": 756240,
|
||||
"riboflavin_modeled": 1191940, "riboflavin_observed": 2234640,
|
||||
"niacin_modeled": 10821800, "niacin_observed": 19423200,
|
||||
"pantothenic_modeled": 920840, "pantothenic_observed": 1674240,
|
||||
"vitb6_modeled": 3960600, "vitb6_observed": 6938400,
|
||||
"folate_modeled": 337505000, "folate_observed": 619500000,
|
||||
"vitb12_modeled": 2655000, "vitb12_observed": 4764000,
|
||||
"vitk_modeled": 42006000, "vitk_observed": 76368000}
|
||||
], dtype=float)
|
||||
from crop_production.data_helpers import sample_nutrient_df
|
||||
from crop_production.data_helpers import \
|
||||
aggregate_regr_polygons_table
|
||||
|
||||
workspace = self.workspace_dir
|
||||
|
||||
|
|
@ -829,7 +692,7 @@ class CropProductionTests(unittest.TestCase):
|
|||
landcover_raster_projection = spatial_ref.ExportToWkt()
|
||||
|
||||
crop_names = ['corn', 'soybean']
|
||||
nutrient_df = create_nutrient_df()
|
||||
nutrient_df = sample_nutrient_df()
|
||||
output_dir = os.path.join(workspace, "OUTPUT")
|
||||
os.makedirs(output_dir, exist_ok=True)
|
||||
file_suffix = 'test'
|
||||
|
|
@ -852,11 +715,91 @@ class CropProductionTests(unittest.TestCase):
|
|||
actual_aggregate_table = pandas.read_csv(aggregate_table_path,
|
||||
dtype=float)
|
||||
|
||||
expected_aggregate_table = _create_expected_agg_table()
|
||||
expected_aggregate_table = aggregate_regr_polygons_table()
|
||||
|
||||
pandas.testing.assert_frame_equal(
|
||||
actual_aggregate_table, expected_aggregate_table)
|
||||
|
||||
def test_aggregate_to_polygons(self):
|
||||
"""Test `aggregate_to_polygons`"""
|
||||
from natcap.invest.crop_production_percentile import \
|
||||
aggregate_to_polygons
|
||||
from crop_production.data_helpers import sample_nutrient_df
|
||||
from crop_production.data_helpers import aggregate_pctl_polygons_table
|
||||
|
||||
workspace = self.workspace_dir
|
||||
|
||||
base_aggregate_vector_path = os.path.join(workspace,
|
||||
"agg_vector.shp")
|
||||
make_aggregate_vector(base_aggregate_vector_path)
|
||||
|
||||
target_aggregate_vector_path = os.path.join(workspace,
|
||||
"agg_vector_prj.shp")
|
||||
|
||||
spatial_ref = osr.SpatialReference()
|
||||
spatial_ref.ImportFromEPSG(26910)
|
||||
landcover_raster_projection = spatial_ref.ExportToWkt()
|
||||
|
||||
crop_names = ['corn', 'soybean']
|
||||
nutrient_df = sample_nutrient_df()
|
||||
yield_percentile_headers = ['25', '50', '75']
|
||||
pixel_area_ha = 1
|
||||
output_dir = os.path.join(workspace, "OUTPUT")
|
||||
os.makedirs(output_dir, exist_ok=True)
|
||||
file_suffix = 'v1'
|
||||
target_aggregate_table_path = os.path.join(output_dir,
|
||||
"results.csv")
|
||||
|
||||
_create_crop_pctl_rasters(output_dir, crop_names, file_suffix,
|
||||
yield_percentile_headers)
|
||||
|
||||
aggregate_to_polygons(
|
||||
base_aggregate_vector_path, target_aggregate_vector_path,
|
||||
landcover_raster_projection, crop_names, nutrient_df,
|
||||
yield_percentile_headers, pixel_area_ha, output_dir,
|
||||
file_suffix, target_aggregate_table_path)
|
||||
|
||||
actual_aggregate_pctl_table = pandas.read_csv(
|
||||
target_aggregate_table_path, dtype=float)
|
||||
expected_aggregate_pctl_table = aggregate_pctl_polygons_table()
|
||||
|
||||
pandas.testing.assert_frame_equal(
|
||||
actual_aggregate_pctl_table, expected_aggregate_pctl_table)
|
||||
|
||||
def test_tabulate_percentile_results(self):
|
||||
"""Test `tabulate_results"""
|
||||
from natcap.invest.crop_production_percentile import \
|
||||
tabulate_results
|
||||
from crop_production.data_helpers import sample_nutrient_df
|
||||
from crop_production.data_helpers import tabulate_pctl_results_table
|
||||
|
||||
nutrient_df = sample_nutrient_df()
|
||||
output_dir = os.path.join(self.workspace_dir, "output")
|
||||
os.makedirs(output_dir, exist_ok=True)
|
||||
yield_percentile_headers = ['yield_25', 'yield_50', 'yield_75']
|
||||
crop_names = ['corn', 'soybean']
|
||||
pixel_area_ha = 1
|
||||
landcover_raster_path = os.path.join(self.workspace_dir,
|
||||
"landcover.tif")
|
||||
landcover_nodata = -1
|
||||
|
||||
make_simple_raster(landcover_raster_path,
|
||||
numpy.array([[1, 4], [2, 2]], dtype=numpy.int16))
|
||||
file_suffix = 'test'
|
||||
target_table_path = os.path.join(output_dir, "result_table.csv")
|
||||
_create_crop_pctl_rasters(output_dir, crop_names, file_suffix,
|
||||
yield_percentile_headers)
|
||||
tabulate_results(nutrient_df, yield_percentile_headers,
|
||||
crop_names, pixel_area_ha,
|
||||
landcover_raster_path, landcover_nodata,
|
||||
output_dir, file_suffix, target_table_path)
|
||||
|
||||
actual_table = pandas.read_csv(target_table_path, nrows=2)
|
||||
expected_table = tabulate_pctl_results_table()
|
||||
|
||||
pandas.testing.assert_frame_equal(actual_table, expected_table,
|
||||
check_dtype=False)
|
||||
|
||||
|
||||
class CropValidationTests(unittest.TestCase):
|
||||
"""Tests for the Crop Productions' MODEL_SPEC and validation."""
|
||||
|
|
|
|||
|
|
@ -1,10 +1,12 @@
|
|||
"""Module for Regression Testing the InVEST Forest Carbon Edge model."""
|
||||
import os
|
||||
import pickle
|
||||
import shutil
|
||||
import tempfile
|
||||
import unittest
|
||||
|
||||
import numpy
|
||||
import pandas
|
||||
import pygeoprocessing
|
||||
import shapely
|
||||
from osgeo import gdal
|
||||
|
|
@ -18,13 +20,68 @@ REGRESSION_DATA = os.path.join(
|
|||
'forest_carbon_edge_effect')
|
||||
|
||||
|
||||
def make_simple_vector(path_to_shp):
|
||||
"""
|
||||
Generate shapefile with one rectangular polygon
|
||||
|
||||
Args:
|
||||
path_to_shp (str): path to target shapefile
|
||||
|
||||
Returns:
|
||||
None
|
||||
"""
|
||||
# (xmin, ymin), (xmax, ymin), (xmax, ymax), (xmin, ymax), (xmin, ymin)
|
||||
shapely_geometry_list = [
|
||||
Polygon([(461251, 4923195), (461501, 4923195),
|
||||
(461501, 4923445), (461251, 4923445),
|
||||
(461251, 4923195)])
|
||||
]
|
||||
|
||||
srs = osr.SpatialReference()
|
||||
srs.ImportFromEPSG(26910)
|
||||
projection_wkt = srs.ExportToWkt()
|
||||
vector_format = "ESRI Shapefile"
|
||||
fields = {"id": ogr.OFTReal}
|
||||
attribute_list = [{"id": 0}]
|
||||
pygeoprocessing.shapely_geometry_to_vector(shapely_geometry_list,
|
||||
path_to_shp, projection_wkt,
|
||||
vector_format, fields,
|
||||
attribute_list)
|
||||
|
||||
|
||||
def make_simple_raster(base_raster_path, array, nodata_val=-1):
|
||||
"""Create a raster on designated path.
|
||||
|
||||
Args:
|
||||
base_raster_path (str): the raster path for the new raster.
|
||||
array (array): numpy array to convert to tif.
|
||||
nodata_val (int or None): for defining a raster's nodata value.
|
||||
|
||||
Returns:
|
||||
None
|
||||
|
||||
"""
|
||||
|
||||
srs = osr.SpatialReference()
|
||||
srs.ImportFromEPSG(26910) # UTM Zone 10N
|
||||
projection_wkt = srs.ExportToWkt()
|
||||
# origin hand-picked for this epsg:
|
||||
origin = (461261, 4923265)
|
||||
|
||||
pixel_size = (1, -1)
|
||||
|
||||
pygeoprocessing.numpy_array_to_raster(
|
||||
array, nodata_val, pixel_size, origin, projection_wkt,
|
||||
base_raster_path)
|
||||
|
||||
|
||||
class ForestCarbonEdgeTests(unittest.TestCase):
|
||||
"""Tests for the Forest Carbon Edge Model."""
|
||||
|
||||
def setUp(self):
|
||||
"""Overriding setUp function to create temp workspace directory."""
|
||||
# this lets us delete the workspace after its done no matter the
|
||||
# the rest result
|
||||
# test result
|
||||
self.workspace_dir = tempfile.mkdtemp()
|
||||
|
||||
def tearDown(self):
|
||||
|
|
@ -246,6 +303,234 @@ class ForestCarbonEdgeTests(unittest.TestCase):
|
|||
actual_message = str(cm.exception)
|
||||
self.assertTrue(expected_message in actual_message, actual_message)
|
||||
|
||||
def test_combine_carbon_maps(self):
|
||||
"""Test `combine_carbon_maps`"""
|
||||
from natcap.invest.forest_carbon_edge_effect import combine_carbon_maps
|
||||
|
||||
# note that NODATA_VALUE = -1
|
||||
carbon_arr1 = numpy.array([[7, 2, -1], [0, -2, -1]])
|
||||
carbon_arr2 = numpy.array([[-1, 900, -1], [1, 20, 0]])
|
||||
|
||||
expected_output = numpy.array([[7, 902, -1], [1, 18, 0]])
|
||||
|
||||
actual_output = combine_carbon_maps(carbon_arr1, carbon_arr2)
|
||||
|
||||
numpy.testing.assert_allclose(actual_output, expected_output)
|
||||
|
||||
def test_aggregate_carbon_map(self):
|
||||
"""Test `_aggregate_carbon_map`"""
|
||||
from natcap.invest.forest_carbon_edge_effect import \
|
||||
_aggregate_carbon_map
|
||||
|
||||
aoi_vector_path = os.path.join(self.workspace_dir, "aoi.shp")
|
||||
carbon_map_path = os.path.join(self.workspace_dir, "carbon.tif")
|
||||
target_vector_path = os.path.join(self.workspace_dir,
|
||||
"agg_carbon.shp")
|
||||
|
||||
# make data
|
||||
make_simple_vector(aoi_vector_path)
|
||||
carbon_array = numpy.array(([1, 2, 3], [4, 5, 6]))
|
||||
make_simple_raster(carbon_map_path, carbon_array)
|
||||
|
||||
_aggregate_carbon_map(aoi_vector_path, carbon_map_path,
|
||||
target_vector_path)
|
||||
|
||||
# Validate fields in the agg carbon results vector
|
||||
with gdal.OpenEx(target_vector_path,
|
||||
gdal.OF_VECTOR | gdal.GA_Update) as ws_ds:
|
||||
ws_layer = ws_ds.GetLayer()
|
||||
for field_name, expected_value in zip(['c_sum', 'c_ha_mean'],
|
||||
[0.0021, 0.000336]):
|
||||
actual_values = [ws_feat.GetField(field_name)
|
||||
for ws_feat in ws_layer][0]
|
||||
error_msg = f"Error with {field_name} in agg_carbon.shp"
|
||||
self.assertEqual(actual_values, expected_value, msg=error_msg)
|
||||
|
||||
def test_calculate_lulc_carbon_map(self):
|
||||
"""Test `_calculate_lulc_carbon_map`"""
|
||||
from natcap.invest.forest_carbon_edge_effect import \
|
||||
_calculate_lulc_carbon_map
|
||||
|
||||
# Make synthetic data
|
||||
lulc_raster_path = os.path.join(self.workspace_dir, "lulc.tif")
|
||||
lulc_array = numpy.array([[1, 2, 3], [3, 2, 1]], dtype=numpy.int16)
|
||||
make_simple_raster(lulc_raster_path, lulc_array)
|
||||
|
||||
biophysical_table_path = os.path.join(self.workspace_dir,
|
||||
"biophysical_table.csv")
|
||||
|
||||
data = {"lucode": [1, 2, 3]}
|
||||
df = pandas.DataFrame(data).set_index("lucode")
|
||||
df["is_tropical_forest"] = [0, 1, 0]
|
||||
df["c_above"] = [100, 500, 200]
|
||||
df.to_csv(biophysical_table_path)
|
||||
|
||||
carbon_pool_type = 'c_above'
|
||||
ignore_tropical_type = False
|
||||
compute_forest_edge_effects = True
|
||||
carbon_map_path = os.path.join(self.workspace_dir, "output_carbon.tif")
|
||||
|
||||
_calculate_lulc_carbon_map(
|
||||
lulc_raster_path, biophysical_table_path, carbon_pool_type,
|
||||
ignore_tropical_type, compute_forest_edge_effects,
|
||||
carbon_map_path)
|
||||
|
||||
actual_output = pygeoprocessing.raster_to_numpy_array(carbon_map_path)
|
||||
expected_output = numpy.array([[100, 500, 200], [200, 500, 100]])
|
||||
|
||||
numpy.testing.assert_allclose(actual_output, expected_output)
|
||||
|
||||
def test_map_distance_from_tropical_forest_edge(self):
|
||||
"""Test `_map_distance_from_tropical_forest_edge`"""
|
||||
from natcap.invest.forest_carbon_edge_effect import \
|
||||
_map_distance_from_tropical_forest_edge
|
||||
|
||||
# Make synthetic data
|
||||
base_lulc_raster_path = os.path.join(self.workspace_dir, "lulc.tif")
|
||||
lulc_array = numpy.array([
|
||||
[2, 2, 3, 3, 3, 2, 2],
|
||||
[2, 1, 1, 1, 1, 1, 2],
|
||||
[3, 1, 1, 1, 1, 1, 3],
|
||||
[2, 1, 1, 1, 1, 1, 2],
|
||||
[2, 2, 3, 3, 3, 2, 2]
|
||||
], dtype=numpy.int16)
|
||||
make_simple_raster(base_lulc_raster_path, lulc_array)
|
||||
|
||||
biophysical_table_path = os.path.join(self.workspace_dir,
|
||||
"biophysical_table.csv")
|
||||
|
||||
data = {"lucode": [1, 2, 3]}
|
||||
df = pandas.DataFrame(data).set_index("lucode")
|
||||
df["is_tropical_forest"] = [1, 0, 0]
|
||||
df["c_above"] = [100, 500, 200]
|
||||
df.to_csv(biophysical_table_path)
|
||||
|
||||
target_edge_distance_path = os.path.join(self.workspace_dir,
|
||||
"edge_distance.tif")
|
||||
target_mask_path = os.path.join(self.workspace_dir,
|
||||
"non_forest_mask.tif")
|
||||
|
||||
_map_distance_from_tropical_forest_edge(
|
||||
base_lulc_raster_path, biophysical_table_path,
|
||||
target_edge_distance_path, target_mask_path)
|
||||
|
||||
# check forest mask
|
||||
actual_output = pygeoprocessing.raster_to_numpy_array(target_mask_path)
|
||||
expected_output = numpy.array([
|
||||
[1, 1, 1, 1, 1, 1, 1],
|
||||
[1, 0, 0, 0, 0, 0, 1],
|
||||
[1, 0, 0, 0, 0, 0, 1],
|
||||
[1, 0, 0, 0, 0, 0, 1],
|
||||
[1, 1, 1, 1, 1, 1, 1]
|
||||
], dtype=numpy.int16)
|
||||
numpy.testing.assert_allclose(actual_output, expected_output)
|
||||
|
||||
# check edge distance map
|
||||
actual_output = pygeoprocessing.raster_to_numpy_array(
|
||||
target_edge_distance_path)
|
||||
expected_output = numpy.array([
|
||||
[0, 0, 0, 0, 0, 0, 0],
|
||||
[0, 1, 1, 1, 1, 1, 0],
|
||||
[0, 1, 2, 2, 2, 1, 0],
|
||||
[0, 1, 1, 1, 1, 1, 0],
|
||||
[0, 0, 0, 0, 0, 0, 0]
|
||||
], dtype=numpy.int16)
|
||||
numpy.testing.assert_allclose(actual_output, expected_output)
|
||||
|
||||
def test_calculate_tropical_forest_edge_carbon_map(self):
|
||||
"""Test `_calculate_tropical_forest_edge_carbon_map`"""
|
||||
from natcap.invest.forest_carbon_edge_effect import \
|
||||
_calculate_tropical_forest_edge_carbon_map
|
||||
from scipy.spatial import cKDTree
|
||||
|
||||
edge_dist_array = numpy.array([
|
||||
[0, 0, 0, 0, 0, 0, 0],
|
||||
[0, 1, 1, 1, 1, 1, 0],
|
||||
[0, 1, 2, 2, 2, 1, 0],
|
||||
[0, 1, 1, 1, 1, 1, 0],
|
||||
[0, 0, 0, 0, 0, 0, 0]
|
||||
], dtype=numpy.int16)
|
||||
edge_distance_path = os.path.join(self.workspace_dir, "edge_dist.tif")
|
||||
make_simple_raster(edge_distance_path, edge_dist_array)
|
||||
spatial_index_pickle_path = os.path.join(self.workspace_dir,
|
||||
"spatial_index.pkl")
|
||||
|
||||
def _create_spatial_index_pickle(spatial_index_pickle_path,
|
||||
raster_path):
|
||||
"""
|
||||
Create and save a KD-tree.
|
||||
|
||||
This function reads the spatial extent and resolution from a raster
|
||||
file, then generates a grid of sample points in geographic space
|
||||
(one point every other row, all columns). It builds a KD-tree from
|
||||
these points for fast spatial lookup, along with synthetic theta
|
||||
and method model parameters, and saves the result as a pickle file.
|
||||
|
||||
Args:
|
||||
spatial_index_pickle_path (str): Path to save the pickle file.
|
||||
raster_path (string): Path to the raster used to extract
|
||||
spatial metadata.
|
||||
|
||||
Return:
|
||||
None
|
||||
|
||||
"""
|
||||
# Get origin and pixel_size
|
||||
raster_info = pygeoprocessing.get_raster_info(raster_path)
|
||||
gt = raster_info['geotransform']#461261, 4923265
|
||||
origin_x, origin_y = gt[0], gt[3]
|
||||
pixel_size_x, pixel_size_y = raster_info['pixel_size']
|
||||
cols, rows = raster_info['raster_size']
|
||||
|
||||
# only create a point every other row and every col (in raster)
|
||||
row_col_pairs = [(r, c) for r in range(0, rows, 2) for c in range(cols)]
|
||||
# Get spatial coordinates
|
||||
points = []
|
||||
for row, col in row_col_pairs:
|
||||
x = origin_x + col * pixel_size_x
|
||||
y = origin_y + row * pixel_size_y
|
||||
points.append((y, x))
|
||||
# note: row → y, col → x (so KD-tree works with (lat, lon))
|
||||
|
||||
theta_model_parameters = numpy.linspace(
|
||||
100, 200, len(points)*3).reshape(len(points), 3) # Nx3
|
||||
|
||||
method_model_parameter = numpy.ones((len(points),))
|
||||
# change method for one area
|
||||
method_model_parameter[0] = 3
|
||||
|
||||
kd_tree = cKDTree(points)
|
||||
|
||||
# Save the data as a tuple in a pickle file
|
||||
with open(spatial_index_pickle_path, 'wb') as f:
|
||||
pickle.dump((kd_tree, theta_model_parameters,
|
||||
method_model_parameter), f)
|
||||
|
||||
_create_spatial_index_pickle(spatial_index_pickle_path,
|
||||
edge_distance_path)
|
||||
|
||||
n_nearest_model_points = 6
|
||||
biomass_to_carbon_conversion_factor = 10
|
||||
tropical_forest_edge_carbon_map_path = os.path.join(self.workspace_dir,
|
||||
"output.tif")
|
||||
|
||||
_calculate_tropical_forest_edge_carbon_map(
|
||||
edge_distance_path, spatial_index_pickle_path,
|
||||
n_nearest_model_points, biomass_to_carbon_conversion_factor,
|
||||
tropical_forest_edge_carbon_map_path)
|
||||
|
||||
actual_output = pygeoprocessing.raster_to_numpy_array(
|
||||
tropical_forest_edge_carbon_map_path)
|
||||
actual_output_sum = actual_output.sum()
|
||||
|
||||
# Comparing sums as 1 value in the output arrays is ~8% different
|
||||
# on windows vs. mac, which seemed too high a relative tolerance to set
|
||||
numpy.testing.assert_allclose(actual_output_sum, 3659.7915, rtol=0.009)
|
||||
# Spot checking several values too
|
||||
self.assertAlmostEqual(actual_output[1, 2], 142.47273)
|
||||
self.assertAlmostEqual(actual_output[3, 5], 274.47815)
|
||||
self.assertAlmostEqual(actual_output[0, 1], -1)
|
||||
|
||||
def test_invalid_geometries(self):
|
||||
"""Forest Carbon: Check handling of invalid vector geometries."""
|
||||
from natcap.invest import forest_carbon_edge_effect
|
||||
|
|
|
|||
|
|
@ -6,8 +6,9 @@ import tempfile
|
|||
import unittest
|
||||
|
||||
import numpy
|
||||
import pandas
|
||||
import pygeoprocessing
|
||||
from osgeo import gdal
|
||||
from osgeo import gdal, gdal_array
|
||||
from osgeo import ogr
|
||||
from osgeo import osr
|
||||
from shapely.geometry import Polygon
|
||||
|
|
@ -16,18 +17,26 @@ gdal.UseExceptions()
|
|||
|
||||
|
||||
def make_raster_from_array(
|
||||
base_array, base_raster_path, nodata_val=-1, gdal_type=gdal.GDT_Int32):
|
||||
base_array, base_raster_path, nodata_val=-1, gdal_type=None,
|
||||
pixel_size=1):
|
||||
"""Make a raster from an array on a designated path.
|
||||
|
||||
Args:
|
||||
base_array (numpy.ndarray): the 2D array for making the raster.
|
||||
base_raster_path (str): the path for the raster to be created.
|
||||
nodata_val (int; float): nodata value for the raster.
|
||||
gdal_type (gdal datatype; int): gdal datatype for the raster.
|
||||
base_raster_path (str): the path for the raster to be created.
|
||||
pixel_size (int): pixel size for the raster.
|
||||
|
||||
Returns:
|
||||
None.
|
||||
"""
|
||||
if gdal_type is None:
|
||||
numpy_dtype = base_array.dtype
|
||||
else:
|
||||
numpy_dtype = gdal_array.GDALTypeCodeToNumericTypeCode(gdal_type)
|
||||
base_array = base_array.astype(numpy_dtype)
|
||||
|
||||
# Projection to user for generated sample data UTM Zone 10N
|
||||
srs = osr.SpatialReference()
|
||||
srs.ImportFromEPSG(26910)
|
||||
|
|
@ -35,7 +44,8 @@ def make_raster_from_array(
|
|||
origin = (1180000, 690000)
|
||||
|
||||
pygeoprocessing.numpy_array_to_raster(
|
||||
base_array, nodata_val, (1, -1), origin, project_wkt, base_raster_path)
|
||||
base_array, nodata_val, (pixel_size, -pixel_size),
|
||||
origin, project_wkt, base_raster_path)
|
||||
|
||||
|
||||
def make_access_shp(access_shp_path):
|
||||
|
|
@ -2332,3 +2342,63 @@ class HabitatQualityTests(unittest.TestCase):
|
|||
|
||||
with self.assertRaises(TypeError):
|
||||
habitat_quality.execute(args)
|
||||
|
||||
def test_compute_rarity_operation(self):
|
||||
"""Test `_compute_rarity_operation`"""
|
||||
from natcap.invest.habitat_quality import _compute_rarity_operation
|
||||
|
||||
base_lulc_path_band = (os.path.join(self.workspace_dir, "base_lulc.tif"), 1)
|
||||
lulc_path_band = (os.path.join(self.workspace_dir, "fut_lulc.tif"), 1)
|
||||
new_cover_path = (os.path.join(self.workspace_dir, "new_cover.tif"), 1)
|
||||
rarity_raster_path = os.path.join(self.workspace_dir, "out_rarity.tif")
|
||||
rarity_csv_path = os.path.join(self.workspace_dir, "out_rarity.csv")
|
||||
|
||||
lulc_array = numpy.array([[1, 3, 1, 4], [1, 3, 3, 3]])
|
||||
fut_array = numpy.array([[2, 1, 3, 3], [1, 3, 4, 4]])
|
||||
make_raster_from_array(lulc_array, base_lulc_path_band[0])
|
||||
make_raster_from_array(fut_array, lulc_path_band[0], pixel_size=2)
|
||||
|
||||
_compute_rarity_operation(
|
||||
base_lulc_path_band, lulc_path_band, new_cover_path,
|
||||
rarity_raster_path, rarity_csv_path)
|
||||
|
||||
actual_rarity = pandas.read_csv(rarity_csv_path)['rarity_value']
|
||||
expected_rarity = [0.27272727, 0, 0.25, 0.1111111111]
|
||||
numpy.testing.assert_allclose(actual_rarity, expected_rarity)
|
||||
|
||||
def test_raster_values_in_bounds(self):
|
||||
"""Test `_raster_values_in_bounds`"""
|
||||
from natcap.invest.habitat_quality import _raster_values_in_bounds
|
||||
|
||||
raster_path_band = (os.path.join(self.workspace_dir, "ras.tif"), 1)
|
||||
lower_bound = 1
|
||||
upper_bound = 50
|
||||
|
||||
# create array with a value (0) outside of range(lower, upper)
|
||||
array = numpy.array([[1, 0], [50, 4]])
|
||||
|
||||
make_raster_from_array(array, raster_path_band[0])
|
||||
|
||||
values_valid = _raster_values_in_bounds(
|
||||
raster_path_band, lower_bound, upper_bound)
|
||||
|
||||
self.assertFalse(values_valid)
|
||||
|
||||
def test_decay_distance(self):
|
||||
"""Test `_decay_distance`"""
|
||||
from natcap.invest.habitat_quality import _decay_distance
|
||||
|
||||
dist_raster_path = os.path.join(self.workspace_dir, "dist.tif")
|
||||
max_dist = 20
|
||||
decay_type = 'linear'
|
||||
target_path = os.path.join(self.workspace_dir, "output.tif")
|
||||
|
||||
dist_array = numpy.array([[0, 21, 1], [2, 50, 600]], dtype=float)
|
||||
make_raster_from_array(dist_array, dist_raster_path)
|
||||
|
||||
_decay_distance(dist_raster_path, max_dist, decay_type, target_path)
|
||||
|
||||
actual_output = pygeoprocessing.raster_to_numpy_array(target_path)
|
||||
expected_output = numpy.array([[1.0, 0.0, 0.95],
|
||||
[0.9, 0.0, 0.0]])
|
||||
numpy.testing.assert_allclose(actual_output, expected_output)
|
||||
|
|
|
|||
|
|
@ -5,6 +5,7 @@ import tempfile
|
|||
import unittest
|
||||
|
||||
import numpy
|
||||
import pandas
|
||||
import pygeoprocessing
|
||||
import shapely.geometry
|
||||
from osgeo import gdal
|
||||
|
|
@ -46,6 +47,27 @@ EXPECTED_FILE_LIST = [
|
|||
'intermediate_outputs/reprojected_farm_vector.shx']
|
||||
|
||||
|
||||
def make_simple_raster(base_raster_path, array):
|
||||
"""Create a raster on designated path with arbitrary values.
|
||||
Args:
|
||||
base_raster_path (str): the raster path for making the new raster.
|
||||
Returns:
|
||||
None.
|
||||
"""
|
||||
# UTM Zone 10N
|
||||
srs = osr.SpatialReference()
|
||||
srs.ImportFromEPSG(26910)
|
||||
projection_wkt = srs.ExportToWkt()
|
||||
|
||||
origin = (461251, 4923245)
|
||||
pixel_size = (30, -30)
|
||||
no_data = -1
|
||||
|
||||
pygeoprocessing.numpy_array_to_raster(
|
||||
array, no_data, pixel_size, origin, projection_wkt,
|
||||
base_raster_path)
|
||||
|
||||
|
||||
class PollinationTests(unittest.TestCase):
|
||||
"""Tests for the Pollination model."""
|
||||
|
||||
|
|
@ -479,6 +501,143 @@ class PollinationTests(unittest.TestCase):
|
|||
"The following files were expected but not found: " +
|
||||
'\n'.join(missing_files))
|
||||
|
||||
def test_parse_scenario_variables(self):
|
||||
"""Test `_parse_scenario_variables`"""
|
||||
from natcap.invest.pollination import _parse_scenario_variables
|
||||
|
||||
def _create_guild_table_csv(output_path):
|
||||
data = {"species": ["Bee_A", "Bee_B", "Butterfly_C"]}
|
||||
df = pandas.DataFrame(data).set_index("species")
|
||||
|
||||
df["nesting_suitability_soil_index"] = [0.8, 0.5, 0.2]
|
||||
df["nesting_suitability_wood_index"] = [0.3, 0.2, 0.4]
|
||||
df["foraging_activity_spring_index"] = [1.0, 0.8, 0.6]
|
||||
df["foraging_activity_summer_index"] = [.9, 0.6, 0.3]
|
||||
df["alpha"] = [150, 200, 120]
|
||||
df["relative_abundance"] = [0.4, 0.3, 0.2]
|
||||
|
||||
df.to_csv(output_path)
|
||||
|
||||
def _create_biophysical_table(output_path):
|
||||
data = {"lucode": [100, 200, 300]}
|
||||
df = pandas.DataFrame(data).set_index("lucode")
|
||||
|
||||
df["nesting_soil_availability_index"] = [0.7, 0.4, 0.6]
|
||||
df["nesting_wood_availability_index"] = [0.1, 0.2, 0.6]
|
||||
df["floral_resources_spring_index"] = [0.3, 0.3, 0.8]
|
||||
df["floral_resources_summer_index"] = [0.9, 0.6, 0.3]
|
||||
|
||||
df.to_csv(output_path)
|
||||
|
||||
def _create_farm_vector(output_path):
|
||||
from shapely import Polygon
|
||||
|
||||
shapely_geometry_list = [
|
||||
Polygon([(0, 0), (1, 0), (1, 1), (0, 1), (0, 0)]),
|
||||
Polygon([(0, 0), (1, 0), (1, 1), (0, 1), (0, 0)])
|
||||
]
|
||||
|
||||
srs = osr.SpatialReference()
|
||||
srs.ImportFromEPSG(26910)
|
||||
projection_wkt = srs.ExportToWkt()
|
||||
|
||||
fields = {"crop_type": ogr.OFTString, "half_sat": ogr.OFTReal,
|
||||
"season": ogr.OFTString, "fr_spring": ogr.OFTReal,
|
||||
"fr_summer": ogr.OFTReal,
|
||||
"n_soil": ogr.OFTReal, "n_wood": ogr.OFTReal,
|
||||
"p_dep": ogr.OFTReal,
|
||||
"p_managed": ogr.OFTReal}
|
||||
|
||||
attribute_list = [{
|
||||
"crop_type": "barley", "half_sat": 0.5, "season": "spring",
|
||||
"fr_spring": 0.8, "fr_summer": 0.3, "n_soil": 0.7,
|
||||
"n_wood": 0.5, "p_dep": 0.9, "p_managed": 0.4},
|
||||
{"crop_type": "almonds", "half_sat": 0.7, "season": "summer",
|
||||
"fr_spring": 0.4, "fr_summer": 0.9, "n_soil": 0.5,
|
||||
"n_wood": 0.6, "p_dep": 0.8, "p_managed": 0.6}]
|
||||
|
||||
pygeoprocessing.shapely_geometry_to_vector(
|
||||
shapely_geometry_list, output_path, projection_wkt,
|
||||
"ESRI Shapefile", fields, attribute_list,
|
||||
ogr_geom_type=ogr.wkbPolygon)
|
||||
|
||||
def _generate_output_dict():
|
||||
return {'season_list': ['spring', 'summer'],
|
||||
'substrate_list': ['soil', 'wood'],
|
||||
'species_list': ['bee_a', 'bee_b', 'butterfly_c'],
|
||||
'alpha_value': {'bee_a': 150., 'bee_b': 200., 'butterfly_c': 120.},
|
||||
'species_abundance': {'bee_a': 0.44444444444444453,
|
||||
'bee_b': 0.33333333333333337,
|
||||
'butterfly_c': 0.22222222222222227},
|
||||
'species_foraging_activity': {
|
||||
('bee_a', 'spring'): 0.5263157894736842,
|
||||
('bee_a', 'summer'): 0.4736842105263158,
|
||||
('bee_b', 'spring'): 0.5714285714285715,
|
||||
('bee_b', 'summer'): 0.4285714285714286,
|
||||
('butterfly_c', 'spring'): 0.6666666666666667,
|
||||
('butterfly_c', 'summer'): 0.33333333333333337},
|
||||
'landcover_substrate_index': {
|
||||
'soil': {100: .7, 200: .4, 300: 0.6},
|
||||
'wood': {100: 0.1, 200: 0.2, 300: .6}},
|
||||
'landcover_floral_resources': {
|
||||
'spring': {100: 0.3, 200: 0.3, 300: 0.8},
|
||||
'summer': {100: 0.9, 200: 0.6, 300: 0.3}},
|
||||
'species_substrate_index': {
|
||||
'bee_a': {'soil': 0.8, 'wood': 0.3},
|
||||
'bee_b': {'soil': 0.5, 'wood': 0.2},
|
||||
'butterfly_c': {'soil': 0.2, 'wood': 0.4}},
|
||||
'foraging_activity_index': {
|
||||
('bee_a', 'spring'): 1.0, ('bee_a', 'summer'): 0.9,
|
||||
('bee_b', 'spring'): 0.8, ('bee_b', 'summer'): 0.6,
|
||||
('butterfly_c', 'spring'): 0.6,
|
||||
('butterfly_c', 'summer'): 0.3}}
|
||||
|
||||
args = {'guild_table_path':
|
||||
os.path.join(self.workspace_dir, "guild_table.csv"),
|
||||
'landcover_biophysical_table_path':
|
||||
os.path.join(self.workspace_dir, "biophysical_table.csv"),
|
||||
'farm_vector_path': os.path.join(self.workspace_dir, "farm.shp")}
|
||||
|
||||
_create_guild_table_csv(args['guild_table_path'])
|
||||
_create_biophysical_table(args['landcover_biophysical_table_path'])
|
||||
_create_farm_vector(args['farm_vector_path'])
|
||||
|
||||
actual_dict = _parse_scenario_variables(args)
|
||||
expected_dict = _generate_output_dict()
|
||||
|
||||
self.assertDictEqual(actual_dict, expected_dict)
|
||||
|
||||
def test_calculate_habitat_nesting_index(self):
|
||||
"""Test `_calculate_habitat_nesting_index`"""
|
||||
from natcap.invest.pollination import _calculate_habitat_nesting_index
|
||||
|
||||
substrate_path_map = {
|
||||
"wood": os.path.join(self.workspace_dir, "wood.tif"),
|
||||
"soil": os.path.join(self.workspace_dir, "soil.tif")
|
||||
}
|
||||
|
||||
make_simple_raster(substrate_path_map["wood"],
|
||||
numpy.array([[5, 6], [3, 2]]))
|
||||
make_simple_raster(substrate_path_map["soil"],
|
||||
numpy.array([[2, 12], [1, 0]]))
|
||||
|
||||
species_substrate_index_map = {"wood": 0.8, "soil": 0.5}
|
||||
|
||||
target_habitat_nesting_index_path = os.path.join(
|
||||
self.workspace_dir, "habitat_nesting.tif")
|
||||
|
||||
_calculate_habitat_nesting_index(
|
||||
substrate_path_map, species_substrate_index_map,
|
||||
target_habitat_nesting_index_path)
|
||||
|
||||
# read habitat nesting tif
|
||||
habitat_raster = gdal.Open(target_habitat_nesting_index_path)
|
||||
band = habitat_raster.GetRasterBand(1)
|
||||
actual_array = band.ReadAsArray()
|
||||
expected_array = numpy.array([[4, 6], [2.4, 1.6]])
|
||||
|
||||
numpy.testing.assert_allclose(actual_array, expected_array)
|
||||
|
||||
|
||||
class PollinationValidationTests(unittest.TestCase):
|
||||
"""Tests for the Pollination Model MODEL_SPEC and validation."""
|
||||
|
|
|
|||
|
|
@ -299,18 +299,29 @@ class SetupTab extends React.Component {
|
|||
const { pyModuleName, switchTabs, t } = this.props;
|
||||
let datastack;
|
||||
try {
|
||||
if (filepath.endsWith('gz')) { // .tar.gz, .tgz
|
||||
if (filepath.endsWith('gz')) { // .tar.gz, .tgz
|
||||
const extractLocation = await ipcRenderer.invoke(
|
||||
ipcMainChannels.SHOW_SAVE_DIALOG,
|
||||
{ title: t('Choose location to extract archive') }
|
||||
ipcMainChannels.SHOW_OPEN_DIALOG,
|
||||
{
|
||||
title: t('Choose location to extract archive'),
|
||||
properties: ['openDirectory'],
|
||||
}
|
||||
);
|
||||
if (extractLocation.filePath) {
|
||||
datastack = await fetchDatastackFromFile({
|
||||
filepath: filepath,
|
||||
extractPath: extractLocation.filePath});
|
||||
} else {
|
||||
return;
|
||||
}
|
||||
if (extractLocation.filePaths.length) {
|
||||
const directoryPath = extractLocation.filePaths[0];
|
||||
const writable = await ipcRenderer.invoke(
|
||||
ipcMainChannels.CHECK_FILE_PERMISSIONS, directoryPath);
|
||||
if (writable) {
|
||||
datastack = await fetchDatastackFromFile({
|
||||
filepath: filepath,
|
||||
extractPath: directoryPath,
|
||||
});
|
||||
} else {
|
||||
alert( // eslint-disable-line no-alert
|
||||
`${t('Permission denied extracting files to:')}\n${directoryPath}`
|
||||
);
|
||||
}
|
||||
} else { return; } // dialog closed without selection
|
||||
} else {
|
||||
datastack = await fetchDatastackFromFile({ filepath: filepath });
|
||||
}
|
||||
|
|
|
|||
|
|
@ -48,7 +48,6 @@ function renderInvestTab(job = DEFAULT_JOB) {
|
|||
<InvestTab
|
||||
job={job}
|
||||
tabID={tabID}
|
||||
investSettings={{ nWorkers: '-1', loggingLevel: 'INFO', taskgraphLoggingLevel: 'ERROR' }}
|
||||
saveJob={() => {}}
|
||||
updateJobProperties={() => {}}
|
||||
/>
|
||||
|
|
@ -125,6 +124,9 @@ describe('Run status Alert renders with status from a recent run', () => {
|
|||
|
||||
describe('Open Workspace button', () => {
|
||||
const spec = {
|
||||
pyname: 'natcap.invest.foo',
|
||||
model_name: 'Foo Model',
|
||||
userguide: 'foo.html',
|
||||
args: {},
|
||||
};
|
||||
|
||||
|
|
@ -481,6 +483,45 @@ describe('Sidebar Buttons', () => {
|
|||
expect(input2).toHaveValue(mockDatastack.args.port);
|
||||
});
|
||||
|
||||
test('Load parameters from datastack: tgz asks for extract location', async () => {
|
||||
const mockDatastack = {
|
||||
module_name: spec.pyname,
|
||||
args: {
|
||||
workspace: 'myworkspace',
|
||||
port: '9999',
|
||||
},
|
||||
};
|
||||
fetchDatastackFromFile.mockResolvedValue(mockDatastack);
|
||||
const mockDialogData = {
|
||||
canceled: false,
|
||||
filePaths: ['foo.tgz'],
|
||||
};
|
||||
ipcRenderer.invoke.mockImplementation((channel, options) => {
|
||||
if (channel === ipcMainChannels.SHOW_OPEN_DIALOG) {
|
||||
return Promise.resolve(mockDialogData);
|
||||
}
|
||||
if (channel === ipcMainChannels.CHECK_FILE_PERMISSIONS) {
|
||||
return Promise.resolve(true);
|
||||
}
|
||||
return Promise.resolve(undefined);
|
||||
});
|
||||
|
||||
const job = new InvestJob({
|
||||
modelRunName: 'carbon',
|
||||
modelHumanName: 'Carbon Model',
|
||||
argsValues: {},
|
||||
});
|
||||
const { findByText, findByLabelText } = renderInvestTab(job);
|
||||
|
||||
const loadButton = await findByText('Load parameters from file');
|
||||
await userEvent.click(loadButton);
|
||||
|
||||
const input1 = await findByLabelText((content) => content.startsWith(spec.args.workspace.name));
|
||||
expect(input1).toHaveValue(mockDatastack.args.workspace);
|
||||
const input2 = await findByLabelText((content) => content.startsWith(spec.args.port.name));
|
||||
expect(input2).toHaveValue(mockDatastack.args.port);
|
||||
});
|
||||
|
||||
test('Load parameters from file does nothing when canceled', async () => {
|
||||
// callback data if the OS dialog was canceled
|
||||
const mockDialogData = {
|
||||
|
|
|
|||
Loading…
Reference in New Issue