diff --git a/.github/workflows/team-assignment.yml b/.github/workflows/team-assignment.yml new file mode 100644 index 00000000..3b0d0343 --- /dev/null +++ b/.github/workflows/team-assignment.yml @@ -0,0 +1,266 @@ +name: Team Assignment + +on: + issues: + types: [labeled] + +jobs: + assign-to-team: + runs-on: ubuntu-latest + steps: + - name: Process team label + uses: actions/github-script@v7 + with: + github-token: ${{ secrets.PROJECTS_PAT }} + script: | + const label = context.payload.label.name; + const issueNumber = context.payload.issue.number; + + // Check if label matches team: pattern + const teamMatch = label.match(/^team:(.+)$/); + if (!teamMatch) { + console.log(`Label "${label}" does not match team: pattern, skipping`); + return; + } + + const teamName = teamMatch[1]; + console.log(`Processing team assignment for team: ${teamName}`); + + const podaacProjectNumber = 75; + const org = 'podaac'; + const repo = context.repo.repo; + + // Define reusable GraphQL mutation for updating project status + const updateStatusMutation = ` + mutation($projectId: ID!, $itemId: ID!, $fieldId: ID!, $value: String!) { + updateProjectV2ItemFieldValue( + input: { + projectId: $projectId + itemId: $itemId + fieldId: $fieldId + value: { singleSelectOptionId: $value } + } + ) { + projectV2Item { + id + } + } + } + `; + + // ======================================== + // Step 1: Update status to "triaged" in podaac project + // ======================================== + + console.log('Step 1: Updating podaac project status to "triaged"...'); + + // Get the podaac project information + const podaacProjectQuery = ` + query($org: String!, $number: Int!) { + organization(login: $org) { + projectV2(number: $number) { + id + fields(first: 20) { + nodes { + ... on ProjectV2SingleSelectField { + id + name + options { + id + name + } + } + } + } + } + } + } + `; + + const podaacProjectData = await github.graphql(podaacProjectQuery, { + org: org, + number: podaacProjectNumber + }); + + const podaacProject = podaacProjectData.organization.projectV2; + const podaacStatusField = podaacProject.fields.nodes.find(field => field.name === 'Status'); + const triagedOption = podaacStatusField?.options.find(option => option.name === 'triaged'); + + let podaacProjectItem = null; + let podaacStatusUpdated = false; + + if (!triagedOption) { + core.warning('Could not find "triaged" status option in podaac project'); + } else { + // Get the issue's project item in podaac project + const issueProjectItemQuery = ` + query($org: String!, $repo: String!, $number: Int!) { + repository(owner: $org, name: $repo) { + issue(number: $number) { + projectItems(first: 10) { + nodes { + id + project { + id + number + } + } + } + } + } + } + `; + + const issueData = await github.graphql(issueProjectItemQuery, { + org: org, + repo: repo, + number: issueNumber + }); + + const projectItems = issueData.repository.issue.projectItems.nodes; + podaacProjectItem = projectItems.find(item => item.project.id === podaacProject.id); + + if (!podaacProjectItem) { + core.warning('Issue is not in podaac project, cannot update status'); + } else { + // Update the status to "triaged" + await github.graphql(updateStatusMutation, { + projectId: podaacProject.id, + itemId: podaacProjectItem.id, + fieldId: podaacStatusField.id, + value: triagedOption.id + }); + + podaacStatusUpdated = true; + console.log('✅ Successfully updated status to "triaged" in podaac project'); + } + } + + // ======================================== + // Step 2: Add to team project if it exists + // ======================================== + + console.log(`Step 2: Looking for team project named "${teamName}"...`); + + // Search for team project by name + const teamProjectSearchQuery = ` + query($org: String!, $searchQuery: String!) { + organization(login: $org) { + projectsV2(first: 20, query: $searchQuery) { + nodes { + id + number + title + fields(first: 20) { + nodes { + ... on ProjectV2SingleSelectField { + id + name + options { + id + name + } + } + } + } + } + } + } + } + `; + + const teamProjectSearchData = await github.graphql(teamProjectSearchQuery, { + org: org, + searchQuery: teamName + }); + + const teamProjects = teamProjectSearchData.organization.projectsV2.nodes; + const teamProject = teamProjects.find(p => p.title.toLowerCase() === teamName.toLowerCase()); + + if (!teamProject) { + console.log(`⚠️ No project found with name "${teamName}", skipping team project assignment`); + core.notice(`No project found for team "${teamName}". If you want the issue added to a team project, create a project named "${teamName}".`); + return; + } + + console.log(`Found team project: ${teamProject.title} (number: ${teamProject.number})`); + + // Check if issue is already in the team project + const issueWithProjectsQuery = ` + query($org: String!, $repo: String!, $number: Int!) { + repository(owner: $org, name: $repo) { + issue(number: $number) { + id + projectItems(first: 20) { + nodes { + id + project { + id + } + } + } + } + } + } + `; + + const issueWithProjects = await github.graphql(issueWithProjectsQuery, { + org: org, + repo: repo, + number: issueNumber + }); + + const issueNodeId = issueWithProjects.repository.issue.id; + const existingTeamProjectItem = issueWithProjects.repository.issue.projectItems.nodes.find( + item => item.project.id === teamProject.id + ); + + let teamProjectItemId; + let wasAlreadyInProject = false; + + if (existingTeamProjectItem) { + // Issue is already in team project + teamProjectItemId = existingTeamProjectItem.id; + wasAlreadyInProject = true; + console.log(`ℹ️ Issue is already in team project "${teamName}"`); + } else { + // Add issue to team project + const addToProjectMutation = ` + mutation($projectId: ID!, $contentId: ID!) { + addProjectV2ItemById(input: { + projectId: $projectId + contentId: $contentId + }) { + item { + id + } + } + } + `; + + const addResult = await github.graphql(addToProjectMutation, { + projectId: teamProject.id, + contentId: issueNodeId + }); + + teamProjectItemId = addResult.addProjectV2ItemById.item.id; + console.log(`✅ Successfully added issue to team project "${teamName}"`); + } + + // Summary + const summaryItems = [`Processed team label: ${teamName}`]; + + if (podaacStatusUpdated) { + summaryItems.push(`Updated podaac project status to "triaged"`); + } + + if (wasAlreadyInProject) { + summaryItems.push(`Issue was already in team project "${teamName}"`); + } else { + summaryItems.push(`Added issue to team project "${teamName}"`); + } + + core.summary + .addHeading(`Team Assignment Complete: ${teamName}`) + .addList(summaryItems) + .write(); diff --git a/CHANGELOG.md b/CHANGELOG.md index a7805b13..74a5af49 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -13,6 +13,22 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 ### Security +## [3.3.1] +### Added +- Updated readme documentation +### Changed +- Changed way Cygnss collection finds time variable +### Deprecated +### Removed +### Fixed +- [issue/394] (https://github.com/podaac/l2ss-py/issues/394) Fixes HDF handling with datatree +- [issue/394] (https://github.com/podaac/l2ss-py/issues/394) Update ODL parser to handle multi swath/grid collections +- [issue/396] (https://github.com/podaac/l2ss-py/issues/396) Handle new GPM v08 collections with netcdf format, but still containing wonky ScanTime swath group +- [issue/400] (https://github.com/podaac/l2ss-py/issues/400) Fixed temporal subsetting for MOPITT collections +- [issue/394] (https://github.com/podaac/l2ss-py/issues/394) Update ODL parser to apply direct mapping from DataField and GeoField objects +### Security + + ## [3.3.0] ### Added - [issue/364] (https://github.com/podaac/l2ss-py/issues/364) Implement vertical subsetting via dimensions. diff --git a/README.md b/README.md index dd236c7e..a13a2cc1 100644 --- a/README.md +++ b/README.md @@ -1,4 +1,3 @@ - # l2ss-py [![Coverage](https://sonarcloud.io/api/project_badges/measure?project=podaac_l2ss-py&metric=coverage)](https://sonarcloud.io/dashboard?id=podaac_l2ss-py) @@ -13,6 +12,7 @@ Harmony service for subsetting L2 data. l2ss-py supports: - GeoJSON subsetting - Temporal subsetting - Variable subsetting +- Vertical subsetting If you would like to contribute to l2ss-py, refer to the [contribution document](CONTRIBUTING.md). @@ -57,11 +57,9 @@ to run this, the l2ss-py package must be installed in your current Python interpreter ``` -$ l2ss-py --help -usage: run_subsetter.py [-h] [--bbox BBOX BBOX BBOX BBOX] - [--variables VARIABLES [VARIABLES ...]] - [--min-time MIN_TIME] [--max-time MAX_TIME] [--cut] - input_file output_file +$ l2ss-py --help +usage: l2ss-py [-h] [--bbox BBOX BBOX BBOX BBOX] [--variables VARIABLES [VARIABLES ...]] [--min-time MIN_TIME] [--max-time MAX_TIME] [--cut] [--shapefile SHAPEFILE] [--pixel_subset] [--vertical-var VERTICAL_VAR] [--vertical-min VERTICAL_MIN] [--vertical-max VERTICAL_MAX] + input_file output_file Run l2ss-py @@ -69,28 +67,40 @@ positional arguments: input_file File to subset output_file Output file -optional arguments: +options: -h, --help show this help message and exit --bbox BBOX BBOX BBOX BBOX - Bounding box in the form min_lon min_lat max_lon - max_lat + Bounding box in the form min_lon min_lat max_lon max_lat --variables VARIABLES [VARIABLES ...] - Variables, only include if variable subset is desired. - Should be a space separated list of variable names - e.g. sst wind_dir sst_error ... - --min-time MIN_TIME Min time. Should be ISO-8601 format. Only include if - temporal subset is desired. - --max-time MAX_TIME Max time. Should be ISO-8601 format. Only include if - temporal subset is desired. + Variables, only include if variable subset is desired. Should be a space separated list of variable names e.g. sst wind_dir sst_error ... + --min-time MIN_TIME Min time. Should be ISO-8601 format. Only include if temporal subset is desired. + --max-time MAX_TIME Max time. Should be ISO-8601 format. Only include if temporal subset is desired. --cut If provided, scanline will be cut --shapefile SHAPEFILE Path to either shapefile or geojson file used to subset the provided input granule + --pixel_subset To pixel cut based the lon lat on the bounding box + --vertical-var VERTICAL_VAR + Name of the vertical variable to subset by (e.g., depth, altitude) + --vertical-min VERTICAL_MIN + Minimum value for vertical subsetting (inclusive) + --vertical-max VERTICAL_MAX + Maximum value for vertical subsetting (inclusive) ``` +#### Additional subsetting options + +The following options are also recorded in the output file's metadata history for provenance and reproducibility: +- `--min-time` and `--max-time` (temporal subsetting) +- `--variables` (variable subsetting) +- `--vertical-var`, `--vertical-min`, `--vertical-max` (vertical subsetting) +- `--cut`, `--pixel_subset`, `--shapefile`, and `--bbox` (spatial subsetting) + +All subsetting parameters used in a run are included in the output file's `history` and `history_json` attributes. + For example: ``` -l2ss-py /path/to/input.nc /path/to/output.nc --bbox -50 -10 50 10 --variables wind_speed wind_dir ice_age time --min-time '2015-07-02T09:00:00' --max-time '2015-07-02T10:00:00' --cut +l2ss-py /path/to/input.nc /path/to/output.nc --bbox -50 -10 50 10 --variables wind_speed wind_dir ice_age time --min-time '2015-07-02T09:00:00' --max-time '2015-07-02T10:00:00' --cut --vertical-var depth --vertical-min 0 --vertical-max 100 ``` An addition to providing a bounding box, spatial subsetting can be achieved by passing in a shapefile or a geojson file. diff --git a/podaac/subsetter/datatree_subset.py b/podaac/subsetter/datatree_subset.py index 89f373cb..4b0f6e78 100755 --- a/podaac/subsetter/datatree_subset.py +++ b/podaac/subsetter/datatree_subset.py @@ -690,7 +690,9 @@ def method_6(path, ds): if result: # Normalize /sample_time to /solar_time for this unique case if result == "/sample_time": - return "/solar_time" + # Check if '/solar_time' exists in the dataset + if "solar_time" in ds.variables: + return "/solar_time" return result return None @@ -998,8 +1000,7 @@ def get_ancestor_coords(node: DataTree) -> Set[str]: def update_dataset_with_time(og_ds, time_name="timeMidScan", group_path=None): """ - Update dataset dimensions based on 'DimensionNames' attributes and compute a time variable - if not present, using values from the dataset. + Compute a time variable if not present, using values from the dataset. """ ds = og_ds.copy() diff --git a/podaac/subsetter/subset.py b/podaac/subsetter/subset.py index cc4b7179..1bbfdc1c 100755 --- a/podaac/subsetter/subset.py +++ b/podaac/subsetter/subset.py @@ -40,9 +40,12 @@ from podaac.subsetter.utils import file_utils from podaac.subsetter.utils import variables_utils from podaac.subsetter.vertical_subset import vertical_subset +from podaac.subsetter.utils import hdf_utils SERVICE_NAME = 'l2ss-py' +_HDF_EXTENSIONS: list[str] = ['.hdf5', '.he5', '.h5', '.hdf'] + def subset_with_shapefile_multi(dataset: xr.Dataset, lat_var_names: List[str], @@ -192,10 +195,21 @@ def subset_with_bbox(dataset: xr.Dataset, # pylint: disable=too-many-branches time_path = None if time_var_name: time_path = file_utils.get_path(time_var_name) - time_data = dataset[time_var_name] - if time_data.ndim == 1 and lon_data.ndim == 2 and temporal_cond is not True: - temporal_cond = mask_utils.align_time_to_lon_dim(time_data, lon_data, temporal_cond) + try: + time_data = dataset[time_var_name] + except KeyError: + time_data = None + + if ( + time_data is not None + and time_data.ndim == 1 + and lon_data.ndim == 2 + and temporal_cond is not True + ): + temporal_cond = mask_utils.align_time_to_lon_dim( + time_data, lon_data, temporal_cond + ) operation = ( oper((lon_data >= lon_bounds[0]), (lon_data <= lon_bounds[1])) & @@ -309,7 +323,8 @@ def subset(file_to_subset: str, bbox: np.ndarray, output_file: str, file_extension = os.path.splitext(file_to_subset)[1] file_utils.override_decode_cf_datetime() - hdf_type = False + hdf_type = "" + scantime_present = False args = { 'decode_coords': False, @@ -318,10 +333,11 @@ def subset(file_to_subset: str, bbox: np.ndarray, output_file: str, } with xr.open_datatree(file_to_subset, **args) as dataset: - if '.HDF5' == file_extension: - for group in dataset.groups: - if "ScanTime" in group: - hdf_type = 'GPM' + if file_extension.lower() in _HDF_EXTENSIONS: + hdf_type = file_utils.get_hdf_type(dataset) + scantime_present = hdf_type == "GPM" + else: + scantime_present = file_utils.has_scantime(dataset) if min_time or max_time: fill_value_f8 = nc.default_fillvals.get('f8') @@ -341,7 +357,8 @@ def subset(file_to_subset: str, bbox: np.ndarray, output_file: str, except Exception: # pylint: disable=broad-exception-caught pass - if hdf_type == 'GPM': + # set decode_times to false if scantime present (e.g. GPM) + if scantime_present: args['decode_times'] = False time_encoding = {} @@ -375,7 +392,7 @@ def subset(file_to_subset: str, bbox: np.ndarray, output_file: str, if calendar: time_encoding[group_path][var_name]['calendar'] = calendar if units: - if time != '/solar_time': + if time not in ['/solar_time', '/HDFEOS/SWATHS/MOP02/Geolocation Fields/Time']: time_encoding[group_path][var_name]['units'] = time_utils.check_time_units(units) if dtype and units: time_encoding[group_path][var_name]['dtype'] = dtype @@ -387,7 +404,8 @@ def subset(file_to_subset: str, bbox: np.ndarray, output_file: str, with xr.open_datatree(file_to_subset, **args) as dataset: - hdf_type = file_utils.get_hdf_type(dataset) + if hdf_type: + dataset = hdf_utils.rename_phony_dims(dataset) lat_var_names, lon_var_names, time_var_names = coordinate_utils.get_coordinate_variable_names( dataset=dataset, @@ -396,7 +414,9 @@ def subset(file_to_subset: str, bbox: np.ndarray, output_file: str, time_var_names=time_var_names ) - if '.HDF5' == file_extension: + # assumption is that GPM HDFEOS or GPM V08+ NetCDF4 will have + # scantime. + if scantime_present: new_time_var_names = [] for group in dataset.groups: if "ScanTime" in group: diff --git a/podaac/subsetter/utils/file_utils.py b/podaac/subsetter/utils/file_utils.py index 6808989a..ef904b18 100644 --- a/podaac/subsetter/utils/file_utils.py +++ b/podaac/subsetter/utils/file_utils.py @@ -74,14 +74,19 @@ def test_access_sst_dtime_values(nc_dataset): def get_hdf_type(tree: DataTree) -> Optional[str]: """ - Determine the HDF type (OMI or MLS) from a DataTree object. + Determine the HDF type (OMI, MLS, or GPM) from a DataTree object. """ try: + if has_scantime(tree): + return 'GPM' + + # try to get types from instrument name additional_attrs = tree['/HDFEOS/ADDITIONAL/FILE_ATTRIBUTES'] if additional_attrs is not None and 'InstrumentName' in additional_attrs.attrs: instrument = additional_attrs.attrs['InstrumentName'] if isinstance(instrument, bytes): instrument = instrument.decode("utf-8") + else: return None if 'OMI' in instrument: @@ -97,3 +102,8 @@ def get_path(s): """Extracts the path by removing the last part after the final '/'.""" path = s.rsplit('/', 1)[0] if '/' in s else s return path if path.startswith('/') else f'/{path}' + + +def has_scantime(dataset: DataTree) -> bool: + """Check if "ScanTime" group present in dataset""" + return any("ScanTime" in group for group in dataset.groups) diff --git a/podaac/subsetter/utils/hdf_utils.py b/podaac/subsetter/utils/hdf_utils.py new file mode 100644 index 00000000..90c012cd --- /dev/null +++ b/podaac/subsetter/utils/hdf_utils.py @@ -0,0 +1,267 @@ +""" +=============== +hdf_utils.py +=============== + +Utility functions for handling HDF files, and dimension recovery +""" + +import re +import warnings + +import numpy as np +import xarray as xr +from xarray import DataTree + +_PHONY_RE = re.compile(r"^phony_dim_(\d+)$") + + +def _is_phony(dim: str) -> bool: + return _PHONY_RE.match(dim) is not None + + +def rename_phony_dims(dt: DataTree) -> DataTree: + """ + Return a new DataTree with all phony_dim_N dimensions renamed to + meaningful names. First check if `DimensionNames` is present as + attributes for each variable otherwise check for `StructMeta.0` + and assuming an HDFEOS structure. the first one that produces a + non-empty mapping is used. + + Parameters + ---------- + dt : DataTree + Tree loaded from an HDF/HDF-EOS5 file + + Returns + ------- + DataTree + A new tree with renamed dimensions. + """ + mapping = _mapping_from_dimension_names(dt) + if mapping: + dt = _apply_mapping(dt, mapping) + + struct_text = _find_struct_metadata(dt) + if struct_text: + scope_field_dimlists = _parse_odl_field_dimlists(struct_text) + if scope_field_dimlists: + dt = _apply_from_field_dimlists(dt, scope_field_dimlists) + + # null condition + return dt + + +def _mapping_from_dimension_names(dt: DataTree) -> dict[str, str]: + """ + Iterate every variable in every group. If a variable carries a + DimensionNames attribute (comma-separated list of real dim names), + zip those names against the variable's current (phony) dim names to + build the global mapping. + + A variable with dims (phony_dim_0, phony_dim_1) and attribute + DimensionNames = "nTimes,nXtrack" results in {phony_dim_0: nTimes, + phony_dim_1: nXtrack} + """ + mapping: dict[str, str] = {} + + for node in dt.subtree: + ds = node.ds + if ds is None: + continue + for _, da in ds.items(): + dim_names_attr = da.attrs.get("DimensionNames") + if dim_names_attr is None: + continue + + real_names = [n.strip() for n in dim_names_attr.split(",")] + + # if attribute shapes mismatch, then continue + if len(real_names) != len(da.dims): + continue + + for phony, real in zip(da.dims, real_names): + # guard for incorrectly specified HDFEOS data + # which might not have phony_dims being populated + if not _is_phony(phony): + continue + existing = mapping.get(phony) + if existing is not None and existing != real: + # if two variables disagree on what this phony dim + # should be called then keep the first assignment and warn. + warnings.warn( + f"Conflicting DimensionNames for {phony!r}: " + f"{existing!r} vs {real!r}. Keeping {existing!r}." + ) + else: + mapping[phony] = real + + return mapping + + +def _find_struct_metadata(dt: DataTree) -> str | None: + """Return the decoded text of StructMetadata.0 if present anywhere + in the tree. + """ + for node in dt.subtree: + ds = node.ds + if ds is None: + continue + for var_name in ds.data_vars: + if "StructMetadata" in var_name: + raw = ds[var_name].values + if isinstance(raw, (bytes, np.bytes_)): + return raw.decode("utf-8", errors="replace") + if hasattr(raw, "item"): + val = raw.item() + if isinstance(val, (bytes, np.bytes_)): + return val.decode("utf-8", errors="replace") + return str(val) + return str(raw) + return None + + +def _find_scope_roots(dt: DataTree) -> list[DataTree]: + """ + Return the immediate children of /HDFEOS/SWATHS or /HDFEOS/GRIDS. + These represent individual swaths / grids and are the correct scopes + within which phony dims should be unified. + """ + candidates = [] + for node in dt.subtree: + path = node.path.rstrip("/").casefold() + if path in ( + "/hdfeos/swaths", + "/hdfeos/grids", + ): + candidates.extend(node.children.values()) + return candidates + + +def _parse_odl_field_dimlists(odl_text: str) -> dict[str, dict[str, list[str]]]: + """ + Parse the ODL block and return a per-scope, per-field dim list: + {scope_name: {field_name: [dim_name, ...]}} + + Handles both SwathStructure (SWATH_N / SwathName) and GridStructure + (GRID_N / GridName) blocks. The DimList order matches the axis + order of the variable as stored in the file, so it can be zipped + directly against the variable's actual (phony) dims. + """ + result: dict[str, dict[str, list[str]]] = {} + + scope_blocks = re.split(r"END_GROUP\s*=\s*(?:SWATH|GRID)_\d+", odl_text) + + for block in scope_blocks: + name_match = re.search(r'(?:SwathName|GridName)\s*=\s*"([^"]+)"', block) + if not name_match: + continue + scope_name = name_match.group(1).strip() + field_dimlists: dict[str, list[str]] = {} + + # GeoField and DataField blocks both use the same OBJECT structure + for field_block in re.split(r"END_OBJECT\s*=\s*(?:Geo|Data)Field_\d+", block): + field_name_match = re.search( + r'(?:GeoFieldName|DataFieldName)\s*=\s*"([^"]+)"', field_block + ) + dimlist_match = re.search(r"DimList\s*=\s*\(([^)]+)\)", field_block) + if not field_name_match or not dimlist_match: + continue + + field_name = field_name_match.group(1).strip() + dim_names = [ + d.strip().strip('"') for d in dimlist_match.group(1).split(",") + ] + field_dimlists[field_name] = dim_names + + if field_dimlists: + result[scope_name] = field_dimlists + + return result + + +def _apply_mapping(dt: DataTree, mapping: dict[str, str]) -> DataTree: + """ + Walk the tree, apply rename_dims to every group dataset that contains + any of the phony dims in mapping, then reconstruct the entire tree + from a flat path -> dataset dict using DataTree.from_dict. + + This avoids any direct node copying and is the idiomatic way to rebuild + a modified DataTree. + """ + path_to_ds: dict[str, xr.Dataset] = {} + + for node in dt.subtree: + ds = node.ds if node.ds is not None else xr.Dataset() + + local_mapping = {k: v for k, v in mapping.items() if k in ds.dims} + if local_mapping: + ds = ds.rename_dims(local_mapping) + + path_to_ds[node.path] = ds + + return DataTree.from_dict(path_to_ds) + + +def _find_scope_for_node(node: DataTree, scope_roots: list[DataTree]) -> str | None: + """ + Return the scope name (last path component of the scope root) that this + node lives under, or None if the node is not under any known scope root. + """ + for scope_root in scope_roots: + scope_path = scope_root.path.rstrip("/") + if node.path == scope_path or node.path.startswith(scope_path + "/"): + return scope_path.split("/")[-1] + return None + + +def _apply_from_field_dimlists( + dt: DataTree, + scope_field_dimlists: dict[str, dict[str, list[str]]], +) -> DataTree: + """ + Walk every node in the tree. For each variable in the node, look up its + real dim names by scope name + variable name directly from the ODL field + dimlists, then rename only that variable's dims within that node. + + This never builds a global phony to real mapping, so two variables with the + same name in different scopes are always resolved against their own scope's + DimList entries independently. + """ + scope_roots = _find_scope_roots(dt) + path_to_ds: dict[str, xr.Dataset] = {} + + for node in dt.subtree: + ds = node.ds if node.ds is not None else xr.Dataset() + + scope_name = _find_scope_for_node(node, scope_roots) + field_dimlists = scope_field_dimlists.get(scope_name, {}) if scope_name else {} + + if field_dimlists: + local_mapping: dict[str, str] = {} + + for var_name, da in ds.items(): + odl_dims = field_dimlists.get(var_name) + if odl_dims is None or len(odl_dims) != len(da.dims): + continue + for current_dim, real_name in zip(da.dims, odl_dims): + # guard for incorrectly specified HDFEOS data + # which might not have phony_dims being populated + if not _is_phony(current_dim): + continue + existing = local_mapping.get(current_dim) + if existing is not None and existing != real_name: + warnings.warn( + f"Conflicting ODL DimList for {current_dim!r} in " + f"{node.path!r}: {existing!r} vs {real_name!r}. " + f"Keeping {existing!r}." + ) + else: + local_mapping[current_dim] = real_name + + if local_mapping: + ds = ds.rename_dims(local_mapping) + + path_to_ds[node.path] = ds + + return DataTree.from_dict(path_to_ds) diff --git a/podaac/subsetter/utils/mask_utils.py b/podaac/subsetter/utils/mask_utils.py index e1f32ee4..a8a9ddcf 100644 --- a/podaac/subsetter/utils/mask_utils.py +++ b/podaac/subsetter/utils/mask_utils.py @@ -51,7 +51,7 @@ def align_dims_cond_only(dataset: xr.Dataset, cond: xr.Dataset) -> xr.Dataset: # Helper to get dim sizes def get_sizes(obj): if isinstance(obj, xr.Dataset): - return dict(obj.dims) + return dict(obj.sizes) return dict(obj.sizes) dataset_sizes = get_sizes(dataset) diff --git a/podaac/subsetter/utils/time_utils.py b/podaac/subsetter/utils/time_utils.py index 8dee19ce..c8dcfa31 100644 --- a/podaac/subsetter/utils/time_utils.py +++ b/podaac/subsetter/utils/time_utils.py @@ -85,6 +85,68 @@ def _convert_time_from_description(seconds_since, description: str): return result +def _get_time_data(dataset: xr.Dataset, time_var_name: str): + """ + Return the time data array, handling special solar_time encoding. + """ + if time_var_name != '/solar_time': + return dataset[time_var_name] + + reference_date = dataset.attrs.get('time_coverage_start') + ref_ts = pd.to_datetime(reference_date) + if hasattr(ref_ts, 'tzinfo') and ref_ts.tzinfo is not None: + ref_ts = ref_ts.tz_convert(None) + seconds = dataset['solar_time'].values + return ref_ts + pd.to_timedelta(seconds, unit='s') + + +def _coerce_timestamp_for_timedelta(dataset: xr.Dataset, time_var_name: str, timestamp): + """ + Adjust a timestamp when the dataset time is a timedelta from an epoch. + """ + if _is_time_mjd(dataset, time_var_name): + mjd_datetime = _datetime_from_mjd(dataset, time_var_name) + if mjd_datetime is None: + raise ValueError('Unable to get datetime from dataset to calculate time delta') + return timestamp - np.datetime64(mjd_datetime) + epoch_var = _get_time_epoch_var(dataset, time_var_name) + epoch_datetime = dataset[epoch_var].values[0] + return timestamp - epoch_datetime + + +def _convert_time_data_for_floating(dataset: xr.Dataset, time_var_name: str, time_data): + """ + Convert floating time data to datetime64 when possible. + """ + description = time_data.attrs.get('description') or time_data.attrs.get('Units') + long_name = time_data.attrs.get('long_name') + if description: + return _convert_time_from_description(time_data, description) + if long_name == "Approximate observation time for each row": + start_time = dataset.attrs.get('REV_START_TIME') + date_str = start_time.split("T")[0] + start_date = pd.to_datetime(date_str, format="%Y-%j") + return np.datetime64(start_date) + time_data.values.astype('timedelta64[s]') + if time_var_name == '/HDFEOS/SWATHS/MOP02/Geolocation Fields/Time': + start_time = dataset['/HDFEOS/ADDITIONAL/FILE_ATTRIBUTES'].attrs.get('StartDateTime') + date = datetime.datetime.fromisoformat(start_time.replace("Z", "+00:00")).date() + seconds_in_day = dataset['/HDFEOS/SWATHS/MOP02/Geolocation Fields/SecondsinDay'] + start_date = pd.to_datetime(date, format="%Y-%j") + return np.datetime64(start_date) + seconds_in_day.values.astype('timedelta64[s]') + return time_data + + +def _maybe_apply_reference_time_offset(dataset: xr.Dataset, time_data): + """ + Apply the sst reference time offset if present. + """ + if getattr(time_data, 'long_name', None) == "reference time of sst file": + base_time = dataset['time'].astype('datetime64[s]') + offset = dataset['sst_dtime'].astype('timedelta64[s]') + return base_time + offset + return time_data + + def build_temporal_cond(min_time: str, max_time: str, dataset: xr.Dataset, time_var_name: str): """ Build the temporal condition used in the xarray 'where' call which @@ -93,45 +155,16 @@ def build_temporal_cond(min_time: str, max_time: str, dataset: xr.Dataset, time_ def build_cond(str_timestamp, compare): timestamp = pd.to_datetime(_translate_timestamp(str_timestamp)).to_datetime64() - if time_var_name == '/solar_time': - reference_date = dataset.attrs.get('time_coverage_start') # Replace with the correct date - ref_ts = pd.to_datetime(reference_date) - # Remove timezone if present - if hasattr(ref_ts, 'tzinfo') and ref_ts.tzinfo is not None: - ref_ts = ref_ts.tz_convert(None) - seconds = dataset['solar_time'].values - time_data = ref_ts + pd.to_timedelta(seconds, unit='s') - else: - time_data = dataset[time_var_name] + time_data = _get_time_data(dataset, time_var_name) dtype = time_data.dtype if np.issubdtype(dtype, np.datetime64): pass elif np.issubdtype(dtype, np.timedelta64): - if _is_time_mjd(dataset, time_var_name): - mjd_datetime = _datetime_from_mjd(dataset, time_var_name) - if mjd_datetime is None: - raise ValueError('Unable to get datetime from dataset to calculate time delta') - timestamp -= np.datetime64(mjd_datetime) - else: - epoch_var = _get_time_epoch_var(dataset, time_var_name) - epoch_datetime = dataset[epoch_var].values[0] - timestamp -= epoch_datetime + timestamp = _coerce_timestamp_for_timedelta(dataset, time_var_name, timestamp) elif np.issubdtype(dtype, np.floating): - description = time_data.attrs.get('description') or time_data.attrs.get('Units') - long_name = time_data.attrs.get('long_name') - if description: - epoch_datetime = _extract_epoch(description) - time_data = _convert_time_from_description(time_data, description) - elif long_name == "Approximate observation time for each row": - start_time = dataset.attrs.get('REV_START_TIME') - date_str = start_time.split("T")[0] - start_date = pd.to_datetime(date_str, format="%Y-%j") - time_data = np.datetime64(start_date) + time_data.values.astype('timedelta64[s]') - if getattr(time_data, 'long_name', None) == "reference time of sst file": - base_time = dataset['time'].astype('datetime64[s]') - offset = dataset['sst_dtime'].astype('timedelta64[s]') - time_data = base_time + offset + time_data = _convert_time_data_for_floating(dataset, time_var_name, time_data) + time_data = _maybe_apply_reference_time_offset(dataset, time_data) return compare(time_data, timestamp) temporal_conds = [] if min_time: diff --git a/poetry.lock b/poetry.lock index 2f5d7eec..ed9596c3 100644 --- a/poetry.lock +++ b/poetry.lock @@ -14,14 +14,14 @@ files = [ [[package]] name = "anyio" -version = "4.12.1" +version = "4.13.0" description = "High-level concurrency and networking framework on top of asyncio or Trio" optional = false -python-versions = ">=3.9" +python-versions = ">=3.10" groups = ["main"] files = [ - 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b/tests/test_hdf_phony_dims.py @@ -0,0 +1,273 @@ +import xarray as xr +from podaac.subsetter.utils.hdf_utils import rename_phony_dims +from xarray import DataTree + + +def make_tree(groups: dict[str, xr.Dataset]) -> DataTree: + return DataTree.from_dict(groups) + + +def phony(n: int) -> str: + return f"phony_dim_{n}" + + +def test_dimension_names_single_group(): + da = xr.DataArray( + data=[[1, 2], [3, 4], [5, 6]], + dims=[phony(0), phony(1)], + attrs={"DimensionNames": "nTimes,nXtrack"}, + ) + dt = make_tree({"/": xr.Dataset({"var": da})}) + result = rename_phony_dims(dt) + assert "nTimes" in result.ds.dims + assert "nXtrack" in result.ds.dims + + +def test_dimension_names_match_across_groups(): + da_data = xr.DataArray( + [[1.0, 2.0]], + dims=[phony(0), phony(1)], + attrs={"DimensionNames": "nTimes,nXtrack"}, + ) + da_geo = xr.DataArray( + [[3.0, 4.0]], + dims=[phony(2), phony(3)], + attrs={"DimensionNames": "nTimes,nXtrack"}, + ) + dt = make_tree( + { + "/Data Fields": xr.Dataset({"ColumnAmount": da_data}), + "/Geolocation Fields": xr.Dataset({"Latitude": da_geo}), + } + ) + result = rename_phony_dims(dt) + data_dims = result["/Data Fields"].ds["ColumnAmount"].dims + geo_dims = result["/Geolocation Fields"].ds["Latitude"].dims + assert data_dims == geo_dims == ("nTimes", "nXtrack") + + +def test_dimension_names_no_phony_dims_unchanged(): + da = xr.DataArray([[1.0]], dims=["nTimes", "nXtrack"]) + dt = make_tree({"/": xr.Dataset({"var": da})}) + result = rename_phony_dims(dt) + assert list(result.ds.dims) == ["nTimes", "nXtrack"] + + +def test_size_matching_different_sizes_get_different_names(): + da = xr.DataArray( + [[[1.0] * 4] * 60] * 1496, + dims=[phony(0), phony(1), phony(2)], + ) + dt = make_tree({"/HDFEOS/SWATHS/S/Data Fields": xr.Dataset({"a": da})}) + result = rename_phony_dims(dt) + dims = result["/HDFEOS/SWATHS/S/Data Fields"].ds["a"].dims + assert len(set(dims)) == 3 + + +def test_dimension_names_independent_swaths_do_not_share_dims(): + da1 = xr.DataArray( + [[1.0, 2.0]], + dims=[phony(0), phony(1)], + attrs={"DimensionNames": "nTimesA,nXtrackA"}, + ) + da2 = xr.DataArray( + [[3.0, 4.0]], + dims=[phony(2), phony(3)], + attrs={"DimensionNames": "nTimesB,nXtrackB"}, + ) + dt = make_tree( + { + "/HDFEOS/SWATHS/SwathA/Data Fields": xr.Dataset({"a": da1}), + "/HDFEOS/SWATHS/SwathB/Data Fields": xr.Dataset({"b": da2}), + } + ) + result = rename_phony_dims(dt) + dims_a = result["/HDFEOS/SWATHS/SwathA/Data Fields"].ds["a"].dims + dims_b = result["/HDFEOS/SWATHS/SwathB/Data Fields"].ds["b"].dims + assert dims_a == ("nTimesA", "nXtrackA") + assert dims_b == ("nTimesB", "nXtrackB") + assert set(dims_a).isdisjoint(set(dims_b)) + + +def test_struct_metadata_renames_dims(): + odl = ( + "GROUP=SwathStructure\n" + "GROUP=SWATH_1\n" + 'SwathName="MySWATH\n"' + "GROUP=Dimension\n" + "OBJECT=Dimension_1\n" + 'DimensionName="nTimes"\n' + "Size=1496\n" + "END_OBJECT=Dimension_1\n" + "OBJECT=Dimension_2\n" + 'DimensionName="nXtrack"\n' + "Size=60\n" + "END_OBJECT=Dimension_2\n" + "END_GROUP=Dimension\n" + "GROUP=DimensionMap\n" + "END_GROUP=DimensionMap\n" + "GROUP=DataField\n" + "OBJECT=DataField_1\n" + 'DataFieldName="ColumnAmount"\n' + 'DimList=("nTimes","nXtrack")\n' + "END_OBJECT=DataField_1\n" + "END_GROUP=DataField\n" + "END_GROUP=SWATH_1\n" + "END_GROUP=SwathStructure\n" + ) + + struct_da = xr.DataArray(odl) + da = xr.DataArray( + [[1.0] * 60] * 1496, + dims=[phony(0), phony(1)], + ) + dt = make_tree( + { + "/HDFEOS INFORMATION": xr.Dataset({"StructMetadata.0": struct_da}), + "/HDFEOS/SWATHS/MySWATH/Data Fields": xr.Dataset({"ColumnAmount": da}), + } + ) + result = rename_phony_dims(dt) + dims = result["/HDFEOS/SWATHS/MySWATH/Data Fields"].ds["ColumnAmount"].dims + assert dims == ("nTimes", "nXtrack") + + +def test_struct_metadata_renames_dims_multi_swath(): + # note(ocs): this type of multi swatch structure occurs in collections like OMPIXCOR_003 + odl = ( + "GROUP=SwathStructure\n" + "GROUP=SWATH_1\n" + 'SwathName="MySWATH\n"' + "GROUP=Dimension\n" + "OBJECT=Dimension_1\n" + 'DimensionName="nTimes"\n' + "Size=1496\n" + "END_OBJECT=Dimension_1\n" + "OBJECT=Dimension_2\n" + 'DimensionName="nXtrack"\n' + "Size=60\n" + "END_OBJECT=Dimension_2\n" + "END_GROUP=Dimension\n" + "GROUP=DimensionMap\n" + "END_GROUP=DimensionMap\n" + "GROUP=DataField\n" + "OBJECT=DataField_1\n" + 'DataFieldName="ColumnAmount"\n' + 'DimList=("nTimes","nXtrack")\n' + "END_OBJECT=DataField_1\n" + "END_GROUP=DataField\n" + "END_GROUP=SWATH_1\n" + # start of swath 2 + "GROUP=SWATH_2\n" + 'SwathName="MySWATH2\n"' + "GROUP=Dimension\n" + "OBJECT=Dimension_1\n" + 'DimensionName="nTimes"\n' + "Size=1496\n" + "END_OBJECT=Dimension_1\n" + "OBJECT=Dimension_2\n" + 'DimensionName="nXtrack"\n' + # this nXtrack size is different in swath 2 + "Size=30\n" + "END_OBJECT=Dimension_2\n" + "END_GROUP=Dimension\n" + "GROUP=DataField\n" + "OBJECT=DataField_1\n" + 'DataFieldName="ColumnAmount2"\n' + 'DimList=("nTimes","nXtrack")\n' + "END_OBJECT=DataField_1\n" + "END_GROUP=DataField\n" + "END_GROUP=SWATH_2\n" + "END_GROUP=SwathStructure\n" + ) + struct_da = xr.DataArray(odl) + da = xr.DataArray( + [[1.0] * 60] * 1496, + dims=[phony(0), phony(1)], + ) + + da2 = xr.DataArray( + [[1.0] * 30] * 1496, + dims=[phony(2), phony(3)], + ) + dt = make_tree( + { + "/HDFEOS INFORMATION": xr.Dataset({"StructMetadata.0": struct_da}), + "/HDFEOS/SWATHS/MySWATH/Data Fields": xr.Dataset({"ColumnAmount": da}), + "/HDFEOS/SWATHS/MySWATH2/Data Fields": xr.Dataset({"ColumnAmount2": da2}), + } + ) + result = rename_phony_dims(dt) + swath1_dims = result["/HDFEOS/SWATHS/MySWATH/Data Fields"].ds["ColumnAmount"].dims + swath2_dims = result["/HDFEOS/SWATHS/MySWATH2/Data Fields"].ds["ColumnAmount2"].dims + + # have the names been recovered? + assert swath1_dims == ("nTimes", "nXtrack") + assert swath2_dims == ("nTimes", "nXtrack") + + swath1_dim_shape = ( + result["/HDFEOS/SWATHS/MySWATH/Data Fields"].ds["ColumnAmount"].shape + ) + swath2_dim_shape = ( + result["/HDFEOS/SWATHS/MySWATH2/Data Fields"].ds["ColumnAmount2"].shape + ) + + # are dimensions in differnet swaths, with different sizes but + # same name matched correctly? + assert swath1_dim_shape == (1496, 60) + assert swath2_dim_shape == (1496, 30) + + +def test_struct_grid_metadata_renames_dims(): + odl = ( + "GROUP=GridStructure\n" + "GROUP=GRID_1\n" + 'SwathName="MyGRID\n"' + "GROUP=Dimension\n" + "OBJECT=Dimension_1\n" + 'DimensionName="nTimes"\n' + "Size=1496\n" + "END_OBJECT=Dimension_1\n" + "OBJECT=Dimension_2\n" + 'DimensionName="nXtrack"\n' + "Size=60\n" + "END_OBJECT=Dimension_2\n" + "END_GROUP=Dimension\n" + "GROUP=DimensionMap\n" + "END_GROUP=DimensionMap\n" + "GROUP=DataField\n" + "OBJECT=DataField_1\n" + 'DataFieldName="ColumnAmount"\n' + 'DimList=("nTimes","nXtrack")\n' + "END_OBJECT=DataField_1\n" + "END_GROUP=DataField\n" + "END_GROUP=GRID_1\n" + "END_GROUP=GridStructure\n" + ) + struct_da = xr.DataArray(odl) + da = xr.DataArray( + [[1.0] * 60] * 1496, + dims=[phony(0), phony(1)], + ) + dt = make_tree( + { + "/HDFEOS INFORMATION": xr.Dataset({"StructMetadata.0": struct_da}), + "/HDFEOS/GRIDS/MyGRID/Data Fields": xr.Dataset({"ColumnAmount": da}), + } + ) + result = rename_phony_dims(dt) + dims = result["/HDFEOS/GRIDS/MyGRID/Data Fields"].ds["ColumnAmount"].dims + assert dims == ("nTimes", "nXtrack") + + +def test_no_phony_dims_returns_unchanged(): + da = xr.DataArray([[1.0]], dims=["x", "y"]) + dt = make_tree({"/": xr.Dataset({"var": da})}) + result = rename_phony_dims(dt) + assert list(result.ds["var"].dims) == ["x", "y"] + + +def test_empty_tree_does_not_raise(): + dt = make_tree({"/": xr.Dataset()}) + result = rename_phony_dims(dt) + assert result is not None diff --git a/tests/test_subset.py b/tests/test_subset.py index 332c7402..71d17c70 100755 --- a/tests/test_subset.py +++ b/tests/test_subset.py @@ -1646,23 +1646,148 @@ def test_get_unique_groups(): assert expected_groups_single == unique_groups_single assert expected_diff_counts_single == diff_counts_single -def test_subset_gpm_compute_new_var_data(data_dir, subset_output_dir, request): +@pytest.fixture(scope="session") +def fake_gpm_file(tmp_path_factory): + """ + Create fake gpm data at run time which mirrors structure and behavior of data pulled from CMR + + Will create data which mirrors how libnetcdf resolves dimension + names with when reading data from CMR. Dimension names are given + phony between groups even if dimension is shared. E.g. all data + variables will share the same dimensions "nTimes,nXtrack", but + will be given phony_dim names as seen below + + Group: + /HDFEOS/SWATHS/GPM_DPR/Data Fields + Dimensions: (phony_dim_0: 10, phony_dim_1: 5) + Dimensions without coordinates: phony_dim_0, phony_dim_1 + Data variables: + precipRateNearSurface (phony_dim_0, phony_dim_1) + Group: /HDFEOS/SWATHS/GPM_DPR/Geolocation Fields + Dimensions: (phony_dim_2: 10, phony_dim_3: 5) + Dimensions without coordinates: phony_dim_2, phony_dim_3 + Data variables: + Latitude (phony_dim_2, phony_dim_3) + Longitude (phony_dim_2, phony_dim_3) + + + """ + filepath = tmp_path_factory.mktemp("data") / "fake_gpm.HDF5" + + with h5py.File(filepath, "w") as f: + + # mirror the real gpm HDFEOS group structure + data_fields = f.require_group("HDFEOS/SWATHS/GPM_DPR/Data Fields") + geo_fields = f.require_group("HDFEOS/SWATHS/GPM_DPR/Geolocation Fields") + scan_time = f.require_group("HDFEOS/SWATHS/GPM_DPR/ScanTime") + + info = f.require_group("HDFEOS INFORMATION") + + n_times = 10 + n_xtrack = 5 + + # write data fields variable with dimensionnames attr as gpm does + precip = data_fields.create_dataset( + "precipRateNearSurface", + data=np.random.rand(n_times, n_xtrack).astype(np.float32), + ) + precip.attrs["DimensionNames"] = "nTimes,nXtrack" + precip.attrs["Units"] = "mm/hr" + precip.attrs["MissingValue"] = -9999.9 + + # geolocation fields variables + lat = geo_fields.create_dataset( + "Latitude", + data=np.linspace(-90, 90, n_times * n_xtrack) + .reshape(n_times, n_xtrack) + .astype(np.float32), + ) + lat.attrs["DimensionNames"] = "nTimes,nXtrack" + lat.attrs["Units"] = "degrees_north" + + lon = geo_fields.create_dataset( + "Longitude", + data=np.linspace(-180, 180, n_times * n_xtrack) + .reshape(n_times, n_xtrack) + .astype(np.float32), + ) + lon.attrs["DimensionNames"] = "nTimes,nXtrack" + lon.attrs["Units"] = "degrees_east" + + # scantime variables + year_data = np.full(n_times, 2021, dtype=np.float32) + month_data = np.full(n_times, 5, dtype=np.float32) + day_data = np.full(n_times, 30, dtype=np.float32) + hour_data = np.full(n_times, 9, dtype=np.float32) + minute_data = np.full(n_times, 57, dtype=np.float32) + second_data = np.full(n_times, 0.0, dtype=np.float32) + milli_data = np.full(n_times, 0.0, dtype=np.float32) + doy_data = np.full(n_times, 150, dtype=np.float32) + sod_data = np.full(n_times, 35820.0, dtype=np.float64) + + for name, data in [ + ("Year", year_data), + ("Month", month_data), + ("DayOfMonth", day_data), + ("Hour", hour_data), + ("Minute", minute_data), + ("Second", second_data), + ("MilliSecond", milli_data), + ("DayOfYear", doy_data), + ("SecondOfDay", sod_data), + ]: + ds = scan_time.create_dataset(name, data=data) + ds.attrs["DimensionNames"] = "nTimes" + + # structmetadata + odl = ( + "GROUP=SwathStructure\n" + "GROUP=SWATH_1\n" + "SwathName=\"GPM_DPR\"\n" + "GROUP=Dimension\n" + "OBJECT=Dimension_1\n" + "DimensionName=\"nTimes\"\n" + f"Size={n_times}\n" + "END_OBJECT=Dimension_1\n" + "OBJECT=Dimension_2\n" + "DimensionName=\"nXtrack\"\n" + f"Size={n_xtrack}\n" + "END_OBJECT=Dimension_2\n" + "END_GROUP=Dimension\n" + "END_GROUP=SWATH_1\n" + "END_GROUP=SwathStructure\n" + ) + info.create_dataset("StructMetadata.0", data=odl) + + return filepath + + +def test_subset_gpm_compute_new_var_data(fake_gpm_file, data_dir, subset_output_dir, request): """Test GPM files that have scantime variable to compute the time for seconds since 1980-01-06""" - - gpm_dir = join(data_dir, 'GPM') - gpm_file = 'GPM_test_file_2.HDF5' # Keep the correct file name + + with xr.open_datatree(fake_gpm_file) as dtree: + for node in dtree.subtree: + ds = node.ds + if isinstance(ds, xr.Dataset): + dims = ds.dims.keys() + + # ensure that test data is being read by netcdf backend with phony dim names + # like we expect from GPM data from CMR + for dim in dims: + assert 'phony' in dim, f"Unexpected non 'phony' dimension found: {dim}" + # Correct bbox with (min_lon, min_lat) and (max_lon, max_lat) bbox = np.array(((-180, 90), (-90, 90))) - output_file = f"gpm_test_{gpm_file}" # Keep the extension + output_file = f"gpm_test_subset.HDF5" # Keep the extension subset_output_file = join(subset_output_dir, output_file) subset.subset( - file_to_subset=join(gpm_dir, gpm_file), + file_to_subset=fake_gpm_file, bbox=bbox, output_file=subset_output_file - ) + ) # Open subsetted file using xarray-datatree dtree = xr.open_datatree(subset_output_file) @@ -1681,6 +1806,261 @@ def test_subset_gpm_compute_new_var_data(data_dir, subset_output_dir, request): if "ScanTime" in group: assert int(dtree[group].ds.variables["timeMidScan"][:][0]) == 1306403820 + +@pytest.fixture(scope="session") +def fake_gpm_mhs_file(tmp_path_factory): + """ + Creates fake data following structure of GPM_2AGPROFMETOPBMHS_08 + which has a netcdf4 format as opposed to HDF5EOS like previous + iterations of GPM products. + + Specifically based off 2A.METOPB.MHS.GPROFNNv1.20260228-S233156-E011316.069788.V08A.nc + """ + filepath = tmp_path_factory.mktemp("data") / "fake_mhs.nc" + + nscan = 10 + npixel = 5 + nlyrs = 5 + + # root + global attributes + root_dict = { + "attrs": { + "FileHeader": "DOI=10.5067/GPM/MHS/METOPB/GPROF/2A/08;\nAlgorithmID=2AGPROFMHS;", + "InputRecord": "InputFileNames=1C.METOPB.MHS.XCAL2016.nc;", + "NavigationRecord": "LongitudeOnEquator=-44.554371;", + "FileInfo": "DataFormatVersion=8b;", + "GprofInfo": "Satellite=METOPB;Sensor=MHS;", + }, + "dims": {}, + "coords": {}, + "data_vars": {}, + } + xr.Dataset.from_dict(root_dict).to_netcdf(filepath, mode="w", engine="netcdf4") + + # GprofDHeadr group containing the layer heights + headr_dict = { + "attrs": {}, + "dims": {"nlyrs": nlyrs}, + "coords": {}, + "data_vars": { + "hgtTopLayer": { + "dims": ("nlyrs",), + "data": np.arange(nlyrs, dtype=np.float32), + "attrs": {"DimensionNames": "nlyrs", "Units": "km"}, + } + }, + } + xr.Dataset.from_dict(headr_dict).to_netcdf( + filepath, mode="a", group="GprofDHeadr", engine="netcdf4" + ) + + # primary swath variables (but only a few) + s1_dict = { + "attrs": {"SwathHeader": "NumberScansInSet=1;\nNumberPixels=90;"}, + "dims": {"nscan": nscan, "npixel": npixel, "nlyrs": nlyrs}, + "coords": {}, + "data_vars": { + "Latitude": { + "dims": ("nscan", "npixel"), + "data": np.linspace(-90, 90, nscan * npixel) + .reshape(nscan, npixel) + .astype(np.float32), + "attrs": {"DimensionNames": "nscan,npixel", "Units": "degrees"}, + }, + "Longitude": { + "dims": ("nscan", "npixel"), + "data": np.linspace(-180, 180, nscan * npixel) + .reshape(nscan, npixel) + .astype(np.float32), + "attrs": {"DimensionNames": "nscan,npixel", "Units": "degrees"}, + }, + "surfacePrecipitation": { + "dims": ("nscan", "npixel"), + "data": np.linspace(0, 50, nscan * npixel) + .reshape(nscan, npixel) + .astype(np.float32), + "attrs": {"DimensionNames": "nscan,npixel", "Units": "mm/hr"}, + }, + "sunGlintAngle": { + "dims": ("nscan", "npixel"), + "data": np.linspace(0, 180, nscan * npixel) + .reshape(nscan, npixel) + .astype(np.float32), + "attrs": {"DimensionNames": "nscan,npixel", "Units": "degrees"}, + }, + "rainWaterContent": { + "dims": ("nscan", "npixel", "nlyrs"), + "data": np.linspace(0, 10, nscan * npixel * nlyrs) + .reshape(nscan, npixel, nlyrs) + .astype(np.float32), + "attrs": {"DimensionNames": "nscan,npixel,nlyrs", "Units": "g/m3"}, + }, + }, + } + xr.Dataset.from_dict(s1_dict).to_netcdf( + filepath, mode="a", group="S1", engine="netcdf4" + ) + + # scantime subgroup is nested under S1 containing temporal variables + scantime_dict = { + "attrs": {}, + "dims": {"nscan": nscan}, + "coords": {}, + "data_vars": { + "Year": { + "dims": ("nscan",), + "data": np.full(nscan, 2026, dtype=np.int16), + "attrs": { + "DimensionNames": "nscan", + "Units": "years", + "_FillValue": -9999, + "CodeMissingValue": "-9999", + }, + }, + "Month": { + "dims": ("nscan",), + "data": np.full(nscan, 2, dtype=np.int8), + "attrs": { + "DimensionNames": "nscan", + "Units": "months", + "_FillValue": -99, + "CodeMissingValue": "-99", + }, + }, + "DayOfMonth": { + "dims": ("nscan",), + "data": np.full(nscan, 28, dtype=np.int8), + "attrs": { + "DimensionNames": "nscan", + "Units": "days", + "_FillValue": -99, + "CodeMissingValue": "-99", + }, + }, + "Hour": { + "dims": ("nscan",), + "data": np.full(nscan, 23, dtype=np.int8), + "attrs": { + "DimensionNames": "nscan", + "Units": "hours", + "_FillValue": -99, + "CodeMissingValue": "-99", + }, + }, + "Minute": { + "dims": ("nscan",), + "data": np.full(nscan, 31, dtype=np.int8), + "attrs": { + "DimensionNames": "nscan", + "Units": "minutes", + "_FillValue": -99, + "CodeMissingValue": "-99", + }, + }, + "Second": { + "dims": ("nscan",), + "data": np.full(nscan, 56, dtype=np.int8), + "attrs": { + "DimensionNames": "nscan", + "Units": "s", + "_FillValue": -99, + "CodeMissingValue": "-99", + }, + }, + "MilliSecond": { + "dims": ("nscan",), + "data": np.full(nscan, 0, dtype=np.int16), + "attrs": { + "DimensionNames": "nscan", + "Units": "ms", + "_FillValue": -9999, + "CodeMissingValue": "-9999", + }, + }, + "DayOfYear": { + "dims": ("nscan",), + "data": np.full(nscan, 59, dtype=np.int16), + "attrs": { + "DimensionNames": "nscan", + "Units": "days", + "_FillValue": -9999, + "CodeMissingValue": "-9999", + }, + }, + "SecondOfDay": { + "dims": ("nscan",), + "data": np.linspace(84716.0, 84726.0, nscan).astype(np.float64), + "attrs": { + "DimensionNames": "nscan", + "Units": "s", + "_FillValue": -9999.9, + "CodeMissingValue": "-9999.9", + }, + }, + }, + } + xr.Dataset.from_dict(scantime_dict).to_netcdf( + filepath, mode="a", group="S1/ScanTime", engine="netcdf4" + ) + + # SCstatus subgroup nested under S1 + scstatus_dict = { + "attrs": {}, + "dims": {"nscan": nscan}, + "coords": {}, + "data_vars": { + "SCaltitude": { + "dims": ("nscan",), + "data": np.full(nscan, 400.0, dtype=np.float32), + "attrs": {"DimensionNames": "nscan", "Units": "km"}, + } + }, + } + xr.Dataset.from_dict(scstatus_dict).to_netcdf( + filepath, mode="a", group="S1/SCstatus", engine="netcdf4" + ) + + return filepath + + +def test_subset_gpm_mhs_compute_new_var_data(fake_gpm_mhs_file, subset_output_dir): + """ + Tests that a GPM v08 file with NetCDF4 format can run through subset and produce correct variables + """ + + subset_output_file = Path(subset_output_dir) / "mhs_test_subset.nc" + + # perform the subsetting operation on the fake data, make sure to + # keep created timeMidScan var created by l2ss to compare + subset.subset( + file_to_subset=fake_gpm_mhs_file, + bbox=np.array(((-90.1, 90.1), (-45.1, 45.1))), + variables=[ + "S1/surfacePrecipitation", + "S1/ScanTime/timeMidScan", + ], + output_file=subset_output_file, + cut=True, + ) + dtree = xr.open_datatree(subset_output_file) + + # are /only/ the requested variables + neccisary location data present? + assert set(dtree["S1"].variables) == { + "surfacePrecipitation", + "Latitude", + "Longitude", + } + assert "timeMidScan" in dtree["S1/ScanTime"].variables + + # are the requested variables dims the correct size? + assert dtree["S1/Latitude"].shape == (6, 5) + assert dtree["S1/Longitude"].shape == (6, 5) + assert dtree["S1/surfacePrecipitation"].shape == (6, 5) + + # timeMidScan created, and correct? + assert int(dtree["S1/ScanTime"].variables["timeMidScan"][0]) == 1456356716 + + def test_temporal_subset_tempo(data_dir, subset_output_dir, request): tempo_file = "TEMPO_HCHO_L2_V01_20240110T170237Z_S005G08.nc"