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weather_metadata

Weather metadata module, implementing functionality for working with weather metadata files (.bin.json).

WeatherAttributes

Weather attributes containing metadata used to construct the "metadata" element and stored in .bin.json file. Used to initiate weather metadata object. If no metadata is specified, defaults are used. If metadata dict is specified, that dictionary is used as is, without setting any defaults.

Source code in emodpy_malaria/weather/weather_metadata.py 
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class WeatherAttributes:
    """
    Weather attributes containing metadata used to construct the "metadata" element and stored in .bin.json file.
    Used to initiate weather metadata object. If no metadata is specified, defaults are used. If metadata dict is
    specified, that dictionary is used as is, without setting any defaults.
    """

    def __init__(self,
                 attributes_dict: Dict[str, Union[str, int, float]] = None,
                 reference: str = None,
                 resolution: str = None,
                 provenance: str = None,
                 update_freq: str = None,
                 start_year: int = None,
                 end_year: int = None,
                 start_doy: int = None,
                 lat_min: float = None,
                 lat_max: float = None,
                 lon_min: float = None,
                 lon_max: float = None,
                 tool: str = None,
                 author: str = None,
                 schema_version: str = None):
        """
        Initialize the weather attributes object by either passing the metadata dictionary or setting.

        Args:
            attributes_dict:  (Optional) A dictionary containing user specified metadata attributes.
                            Used to initiate weather attributes object. A typical usage is to set metadata attributes
                            including those not required or explicitly implemented by this class. For example, when
                            reading existing metadata file (.bin.json), the dictionary read from the file allows
                            persisting all found attributes, including those explicitly implemented by this class.
                            This dictionary can be used in combination with other __init__ arguments.
            reference:      (Optional) Reference string expected by EMOD as a way to bind weather and demographic files.
            resolution:     (Optional) Spatial weather data resolution.
            provenance:     (Optional) Data provenance describing data source and how it was obtained.
            update_freq:    (Optional) The frequency.
            start_year:     (Optional) Weather data start year. Used to construct years range attribute.
            end_year:       (Optional) Weather data end year. Used to construct years range attribute.
            start_doy:      (Optional) The start day of year. By default, it is set to 1.
            lat_min:        (Optional) Minimal or top latitude of the weather spatial coverage.
            lat_max:        (Optional) Maximum or bottom latitude of the weather spatial coverage.
            lon_min:        (Optional) Minimal or top longitude of the weather spatial coverage.
            lon_max:        (Optional) Maximum or top longitude of the weather spatial coverage.
            tool:           (Optional) The tool used to prepare weather files.
            author:         (Optional) The author of weather files.
            schema_version: (Optional) The EMOD weather file schema version.

        """
        # Init the metadata dictionary to provided dictionary or defaults.
        self._attributes_dict: Dict[str, Union[str, int, float]] = attributes_dict or self.metadata_defaults_dict()

        # If needed, set date attributes based on today's date.
        date_years = f"{start_year}-{end_year}" if start_year is not None and end_year is not None else None

        # Init the metadata object and set arguments. Those that are None are ignored.
        metadata_args = {
            _META_ID_REFERENCE: reference,
            _META_SPATIAL_RESOLUTION: resolution,
            _META_PROVENANCE: provenance,
            _META_UPDATE_FREQUENCY: update_freq,
            _META_DATA_YEARS: date_years,
            _META_START_DOY: start_doy,
            _META_LAT_MIN: lat_min,
            _META_LAT_MAX: lat_max,
            _META_LON_MIN: lon_min,
            _META_LON_MAX: lon_max,
            _META_TOOL: tool,
            _META_AUTHOR: author,
            _META_WEATHER_SCHEMA_V: schema_version,
        }

        # Remove not specified attributes (Nones).
        metadata_args2 = {k: v for k, v in metadata_args.items() if v is not None}
        self._attributes_dict.update(metadata_args2)

        # Set missing required attributes (like IdReference)
        missing_required_defaults = self.required_metadata_defaults_dict(exclude_keys=list(self._attributes_dict.keys()))
        self._attributes_dict.update(missing_required_defaults)

    def __eq__(self, other: WeatherAttributes):
        """Equality operator for WeatherAttributes objects"""
        return self.attributes_dict == other.attributes_dict

    # Getter and setter methods for standard attributes. "snake_case" is for consistency with Python naming convention.

    @property
    def attributes_dict(self) -> Dict[str, Union[str, int, float]]:
        return self._attributes_dict

    @property
    def tool(self) -> str:
        """Tool name used to prepare weather files."""
        return self._attributes_dict.get(_META_TOOL, None)

    @tool.setter
    def tool(self, value: str) -> NoReturn:
        """Set tool name attribute."""
        validate_str_value(value)
        self._attributes_dict[_META_TOOL] = value

    @property
    def date_created(self) -> str:
        """Date weather files are created."""
        return self._attributes_dict.get(_META_DATE_CREATED, None)

    @date_created.setter
    def date_created(self, value: str) -> NoReturn:
        """Set creation date attribute."""
        value = str(value)
        validate_str_value(value)
        # TODO validate date
        self._attributes_dict[_META_DATE_CREATED] = value

    @property
    def author(self) -> str:
        """Author of weather files."""
        return self._attributes_dict.get(_META_AUTHOR, None)

    @author.setter
    def author(self, value: str):
        """Set author attribute."""
        validate_str_value(value)
        self._attributes_dict[_META_AUTHOR] = value

    @property
    def id_reference(self) -> str:
        """Id reference value used to semantically bind weather files."""
        return self._attributes_dict.get(_META_ID_REFERENCE, None)

    @id_reference.setter
    def id_reference(self, value: str):
        """Set id reference attribute."""
        validate_str_value(value)
        self._attributes_dict[_META_ID_REFERENCE] = value

    @property
    def update_resolution(self) -> str:
        """Data update frequency (e.g. monthly, yearly)."""
        return self._attributes_dict.get(_META_UPDATE_FREQUENCY, None)

    @update_resolution.setter
    def update_resolution(self, value: str):
        """Set data update frequency attribute."""
        validate_str_value(value)
        self._attributes_dict[_META_UPDATE_FREQUENCY] = value

    @property
    def data_years(self) -> str:
        """Data years range describing the temporal coverage."""
        return self._attributes_dict.get(_META_DATA_YEARS, None)

    @data_years.setter
    def data_years(self, value: str):
        """Set data years range attribute."""
        validate_str_value(value)
        import re
        assert re.match("20[0-3][0-9]-20[0-3][0-9]", value), "Years range format must be 20YY-20YY"
        self._attributes_dict[_META_DATA_YEARS] = value

    @property
    def provenance(self) -> str:
        """Data provenance, describing the data source and how the data was obtained."""
        return self._attributes_dict.get(_META_PROVENANCE, None)

    @provenance.setter
    def provenance(self, value: str):
        """Set provenance attribute."""
        validate_str_value(value)
        self._attributes_dict[_META_PROVENANCE] = value

    @property
    def spatial_resolution(self) -> str:
        """Weather data Spatial resolution (e.g., 30arcsec or 1km)"""
        return self._attributes_dict.get(_META_SPATIAL_RESOLUTION, None)

    @spatial_resolution.setter
    def spatial_resolution(self, value: str):
        """Set spatial resolution attribute."""
        validate_str_value(value)
        self._attributes_dict[_META_SPATIAL_RESOLUTION] = value

    @classmethod
    def format_create_date(cls, created: datetime) -> str:
        return created.strftime("%Y-%m-%d")

    @classmethod
    def metadata_defaults_dict(cls) -> Dict[str, Union[str, int, float]]:
        """Metadata defaults dictionary."""
        created = datetime.now()
        default_date_years = f"{created.year}-{created.year}"
        metadata = {
            _META_DATE_CREATED: cls.format_create_date(created),
            _META_ID_REFERENCE: _META_DEFAULT_ID_REFERENCE,
            _META_SPATIAL_RESOLUTION: _META_DEFAULT_UNSPECIFIED,
            _META_PROVENANCE: _META_DEFAULT_UNSPECIFIED,
            _META_UPDATE_FREQUENCY: _META_DEFAULT_UNSPECIFIED,
            _META_DATA_YEARS: default_date_years,
            _META_START_DOY: _META_DEFAULT_START_DOY,
            _META_TOOL: _META_DEFAULT_TOOL,
            _META_AUTHOR: _META_DEFAULT_AUTHOR,
            _META_WEATHER_SCHEMA_V: _META_DEFAULT_WEATHER_SCHEMA_V,
        }

        return metadata

    @classmethod
    def required_metadata_defaults_dict(cls, exclude_keys: List[str] = None) -> Dict[str, Union[str, int, float]]:
        """Dictionary of required metadata defaults."""
        exclude_keys = exclude_keys or []
        metadata = {
            k: v for k, v in cls.metadata_defaults_dict().items()
            if k in _META_REQUIRED_ARGS and k not in exclude_keys}
        return metadata

    def update(self, value: Dict[str, Union[int, str]]):
        """Update metadata dictionary."""
        assert isinstance(value, Dict), "Metadata must be a dictionary."
        self._attributes_dict.update(value)

    def validate(self) -> NoReturn:
        """Validate metadata contains requires argument."""
        for a in _META_REQUIRED_ARGS + _META_REQUIRED_CALC:
            assert self._attributes_dict[a] is not None and len(str(a).strip()) > 0, f"{a} metadata attribute is not set."

author property writable

Author of weather files.

data_years property writable

Data years range describing the temporal coverage.

date_created property writable

Date weather files are created.

id_reference property writable

Id reference value used to semantically bind weather files.

provenance property writable

Data provenance, describing the data source and how the data was obtained.

spatial_resolution property writable

Weather data Spatial resolution (e.g., 30arcsec or 1km)

tool property writable

Tool name used to prepare weather files.

update_resolution property writable

Data update frequency (e.g. monthly, yearly).

__eq__(other)

Equality operator for WeatherAttributes objects

Source code in emodpy_malaria/weather/weather_metadata.py 
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def __eq__(self, other: WeatherAttributes):
    """Equality operator for WeatherAttributes objects"""
    return self.attributes_dict == other.attributes_dict

__init__(attributes_dict=None, reference=None, resolution=None, provenance=None, update_freq=None, start_year=None, end_year=None, start_doy=None, lat_min=None, lat_max=None, lon_min=None, lon_max=None, tool=None, author=None, schema_version=None)

Initialize the weather attributes object by either passing the metadata dictionary or setting.

Parameters:

Name Type Description Default
attributes_dict Dict[str, Union[str, int, float]]

(Optional) A dictionary containing user specified metadata attributes. Used to initiate weather attributes object. A typical usage is to set metadata attributes including those not required or explicitly implemented by this class. For example, when reading existing metadata file (.bin.json), the dictionary read from the file allows persisting all found attributes, including those explicitly implemented by this class. This dictionary can be used in combination with other init arguments.

 None
reference str

(Optional) Reference string expected by EMOD as a way to bind weather and demographic files.

 None
resolution str

(Optional) Spatial weather data resolution.

 None
provenance str

(Optional) Data provenance describing data source and how it was obtained.

 None
update_freq str

(Optional) The frequency.

 None
start_year int

(Optional) Weather data start year. Used to construct years range attribute.

 None
end_year int

(Optional) Weather data end year. Used to construct years range attribute.

 None
start_doy int

(Optional) The start day of year. By default, it is set to 1.

 None
lat_min float

(Optional) Minimal or top latitude of the weather spatial coverage.

 None
lat_max float

(Optional) Maximum or bottom latitude of the weather spatial coverage.

 None
lon_min float

(Optional) Minimal or top longitude of the weather spatial coverage.

 None
lon_max float

(Optional) Maximum or top longitude of the weather spatial coverage.

 None
tool str

(Optional) The tool used to prepare weather files.

 None
author str

(Optional) The author of weather files.

 None
schema_version str

(Optional) The EMOD weather file schema version.

 None
Source code in emodpy_malaria/weather/weather_metadata.py 
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def __init__(self,
             attributes_dict: Dict[str, Union[str, int, float]] = None,
             reference: str = None,
             resolution: str = None,
             provenance: str = None,
             update_freq: str = None,
             start_year: int = None,
             end_year: int = None,
             start_doy: int = None,
             lat_min: float = None,
             lat_max: float = None,
             lon_min: float = None,
             lon_max: float = None,
             tool: str = None,
             author: str = None,
             schema_version: str = None):
    """
    Initialize the weather attributes object by either passing the metadata dictionary or setting.

    Args:
        attributes_dict:  (Optional) A dictionary containing user specified metadata attributes.
                        Used to initiate weather attributes object. A typical usage is to set metadata attributes
                        including those not required or explicitly implemented by this class. For example, when
                        reading existing metadata file (.bin.json), the dictionary read from the file allows
                        persisting all found attributes, including those explicitly implemented by this class.
                        This dictionary can be used in combination with other __init__ arguments.
        reference:      (Optional) Reference string expected by EMOD as a way to bind weather and demographic files.
        resolution:     (Optional) Spatial weather data resolution.
        provenance:     (Optional) Data provenance describing data source and how it was obtained.
        update_freq:    (Optional) The frequency.
        start_year:     (Optional) Weather data start year. Used to construct years range attribute.
        end_year:       (Optional) Weather data end year. Used to construct years range attribute.
        start_doy:      (Optional) The start day of year. By default, it is set to 1.
        lat_min:        (Optional) Minimal or top latitude of the weather spatial coverage.
        lat_max:        (Optional) Maximum or bottom latitude of the weather spatial coverage.
        lon_min:        (Optional) Minimal or top longitude of the weather spatial coverage.
        lon_max:        (Optional) Maximum or top longitude of the weather spatial coverage.
        tool:           (Optional) The tool used to prepare weather files.
        author:         (Optional) The author of weather files.
        schema_version: (Optional) The EMOD weather file schema version.

    """
    # Init the metadata dictionary to provided dictionary or defaults.
    self._attributes_dict: Dict[str, Union[str, int, float]] = attributes_dict or self.metadata_defaults_dict()

    # If needed, set date attributes based on today's date.
    date_years = f"{start_year}-{end_year}" if start_year is not None and end_year is not None else None

    # Init the metadata object and set arguments. Those that are None are ignored.
    metadata_args = {
        _META_ID_REFERENCE: reference,
        _META_SPATIAL_RESOLUTION: resolution,
        _META_PROVENANCE: provenance,
        _META_UPDATE_FREQUENCY: update_freq,
        _META_DATA_YEARS: date_years,
        _META_START_DOY: start_doy,
        _META_LAT_MIN: lat_min,
        _META_LAT_MAX: lat_max,
        _META_LON_MIN: lon_min,
        _META_LON_MAX: lon_max,
        _META_TOOL: tool,
        _META_AUTHOR: author,
        _META_WEATHER_SCHEMA_V: schema_version,
    }

    # Remove not specified attributes (Nones).
    metadata_args2 = {k: v for k, v in metadata_args.items() if v is not None}
    self._attributes_dict.update(metadata_args2)

    # Set missing required attributes (like IdReference)
    missing_required_defaults = self.required_metadata_defaults_dict(exclude_keys=list(self._attributes_dict.keys()))
    self._attributes_dict.update(missing_required_defaults)

metadata_defaults_dict() classmethod

Metadata defaults dictionary.

Source code in emodpy_malaria/weather/weather_metadata.py 
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@classmethod
def metadata_defaults_dict(cls) -> Dict[str, Union[str, int, float]]:
    """Metadata defaults dictionary."""
    created = datetime.now()
    default_date_years = f"{created.year}-{created.year}"
    metadata = {
        _META_DATE_CREATED: cls.format_create_date(created),
        _META_ID_REFERENCE: _META_DEFAULT_ID_REFERENCE,
        _META_SPATIAL_RESOLUTION: _META_DEFAULT_UNSPECIFIED,
        _META_PROVENANCE: _META_DEFAULT_UNSPECIFIED,
        _META_UPDATE_FREQUENCY: _META_DEFAULT_UNSPECIFIED,
        _META_DATA_YEARS: default_date_years,
        _META_START_DOY: _META_DEFAULT_START_DOY,
        _META_TOOL: _META_DEFAULT_TOOL,
        _META_AUTHOR: _META_DEFAULT_AUTHOR,
        _META_WEATHER_SCHEMA_V: _META_DEFAULT_WEATHER_SCHEMA_V,
    }

    return metadata

required_metadata_defaults_dict(exclude_keys=None) classmethod

Dictionary of required metadata defaults.

Source code in emodpy_malaria/weather/weather_metadata.py 
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@classmethod
def required_metadata_defaults_dict(cls, exclude_keys: List[str] = None) -> Dict[str, Union[str, int, float]]:
    """Dictionary of required metadata defaults."""
    exclude_keys = exclude_keys or []
    metadata = {
        k: v for k, v in cls.metadata_defaults_dict().items()
        if k in _META_REQUIRED_ARGS and k not in exclude_keys}
    return metadata

update(value)

Update metadata dictionary.

Source code in emodpy_malaria/weather/weather_metadata.py 
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def update(self, value: Dict[str, Union[int, str]]):
    """Update metadata dictionary."""
    assert isinstance(value, Dict), "Metadata must be a dictionary."
    self._attributes_dict.update(value)

validate()

Validate metadata contains requires argument.

Source code in emodpy_malaria/weather/weather_metadata.py 
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def validate(self) -> NoReturn:
    """Validate metadata contains requires argument."""
    for a in _META_REQUIRED_ARGS + _META_REQUIRED_CALC:
        assert self._attributes_dict[a] is not None and len(str(a).strip()) > 0, f"{a} metadata attribute is not set."

WeatherMetadata

Bases: WeatherAttributes

Weather metadata containing weather data attributes, counts and node offsets. Used to initiate WeatherData, expose metadata programmatically and create weather metadata files (.bin.json).

Source code in emodpy_malaria/weather/weather_metadata.py 
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class WeatherMetadata(WeatherAttributes):
    """
    Weather metadata containing weather data attributes, counts and node offsets.
    Used to initiate WeatherData, expose metadata programmatically and create weather metadata files (.bin.json).
    """

    # REQUIRED_ATTRIBUTES = ["Tool", ]
    def __init__(self,
                 node_ids: Union[List[int], Dict[int, int]],
                 series_len: int = None,
                 attributes: Union[WeatherMetadata, WeatherAttributes, Dict[str, Union[str, int, float]]] = None):
        """
        Initiate WeatherMetadata object.

        Args:
            node_ids: A dictionary with node ids as keys and offsets as values, or just a list of node ids.
                      If node-offset dictionary is provided, node offsets are set per that dictionary.
                      If a list of nodes ids is provided, offsets are calculated based on weather time series length.
            series_len: The length of a weather time series (aka "data value count").
            attributes: Weather attributes, either as an objects or a dictionary.

        """
        if isinstance(attributes, WeatherMetadata) or isinstance(attributes, WeatherAttributes):
            attributes_dict = attributes.attributes_dict
        else:
            attributes_dict = attributes

        super().__init__(attributes_dict=attributes_dict)

        # Set node offsets dictionary based on node_ids argument.
        if isinstance(node_ids, Dict):
            self._node_offsets = node_ids
            series_len = int(series_len or self._expected_series_len())
            self._validate_series_len(series_len)
        else:
            # If node id list is provided, offsets are calculated based on weather time series length.
            self._validate_series_len(series_len)   # if node_ids is a list a valid series_len must be provided.
            self._node_offsets = {
                node_id: series_len * node_ids.index(node_id) * SERIES_BYTE_VALUE_SIZE
                for node_id in sorted(node_ids)
            }

        self._series_len = series_len

        # init
        metadata_count_dict = self._metadata_count_dict

        self.update(metadata_count_dict)
        self.validate()

    def __eq__(self, other: WeatherMetadata):
        """Equality operator for WeatherMetadata objects"""
        attributes_eq = super().__eq__(other)
        nodes_eq = sorted(self.node_offsets) == sorted(other.node_offsets)
        offsets_eq = [self.node_offsets[k] == other.node_offsets[k] for k in self.node_offsets] if nodes_eq else [False]
        return attributes_eq and nodes_eq and offsets_eq

    @property
    def _metadata_count_dict(self):
        node_count = len(self.nodes)
        return {
            _META_OFFSET_COUNT: len(self._node_offsets),
            _META_DTK_NODES_COUNT: node_count,
            _META_NODE_COUNT: node_count,
            _META_DATA_VALUE_COUNT: self._series_len,
            _META_DATA_CELL_VALUE_COUNT: self._series_len,
        }

    @classmethod
    def _validate_series_len(cls, series_len: int):
        if not isinstance(series_len, int) or series_len <= 0:
            raise ValueError("Weather time series length must be an positive integer.")

    def _expected_series_len(self) -> int:
        """Returns expected node weather time series length."""
        if self._node_offsets and len(self._node_offsets) > 0:
            offsets2 = sorted(set(self._node_offsets.values()))[:2]
            expected = int(float(offsets2[1] - offsets2[0]) / 4) if len(offsets2) > 1 else -1
        else:
            expected = -1
        return expected

    def validate(self):
        """Validate metadata object node-related counts. Relies on inherited validation of metadata attributes."""
        super().validate()

        # Validate nodes and offsets
        assert isinstance(self.nodes, Iterable), "node_ids must be iterable"
        assert len(self.nodes) > 0, "node_ids must not be empty"
        assert all(map(lambda i: isinstance(i, int), self.nodes)), "node_ids must be integers"

        # Validate node id and offset range
        # https://github.com/InstituteforDiseaseModeling/DtkTrunk/blob/master/Eradication/Climate.h#L151-L154
        max_uint32 = int("FFFFFFFF", 16)   # max unsigned 32 bit value
        invalid_nodes, invalid_offsets = [], []
        for node_id, offset in self.node_offsets.items():
            if not 0 < node_id <= max_uint32:
                invalid_nodes.append(node_id)

            if not 0 <= offset <= max_uint32:
                invalid_offsets.append(offset)

        if len(invalid_nodes) > 0:
            print(f"Found {len(invalid_nodes)} invalid node ids: {invalid_nodes[:5]}")
            raise ValueError(f"Node values must be integers in (0, {str(max_uint32)}] interval.")

        if len(invalid_offsets) > 0:
            print(f"Found {len(invalid_offsets)} invalid offsets: {invalid_offsets[:5]}")
            raise ValueError(f"Node offset values must be integers in [0, {str(max_uint32)}] interval.")

        assert len(set(self.nodes)) == len(self.nodes), "node_ids must be unique"

        assert len(self.node_offset_str) == self.node_count * 16, "node_offset_str length doesn't match node count. "

        # Validate series_len
        self._validate_series_len(self._series_len)
        if 0 < self._expected_series_len() != self._series_len:
            raise ValueError("Weather time series length doesn't match provided offsets distance.")

    # Operational properties (read-only)
    @property
    def attributes(self) -> WeatherAttributes:
        """Cast back into WeatherAttributes."""
        meta_keys = list(self._metadata_count_dict)
        attributes_dict = {k: v for k, v in self._attributes_dict.items() if k not in meta_keys}
        wa = WeatherAttributes(attributes_dict=attributes_dict)
        return wa

    @property
    def datavalue_count(self) -> int:
        """Number of data values in each weather time series (must be > 0)."""
        return self._series_len

    @property
    def series_len(self) -> int:
        """Number of data values in each timeseries, should be > 0."""
        return self._series_len

    @property
    def series_count(self) -> int:
        """The number of weather time series (expected based on metadata), corresponding to the number of offsets."""
        return len(set(list(self._node_offsets.values())))

    @property
    def series_unique_count(self):
        """The number of unique weather time series (expected based on metadata), based on offsets."""
        return len(self.offset_nodes)

    @property
    def total_value_count(self) -> int:
        """The total count of all values, in all weather time series (expected based on metadata)."""
        return self.series_count * self.series_len

    @property
    def nodes(self) -> List[int]:
        """The list of nodes (node ids) in the node-offset dictionary."""
        return list(self._node_offsets)

    @property
    def node_count(self) -> int:
        """The number of node in the node-offset dictionary."""
        return len(self.nodes)

    @property
    def node_offset_str(self) -> str:
        """The node offset string, as it will appear in the weather metadata file (.bin.json)."""
        return self._convert_offset_dict_to_str(self._node_offsets)

    @property
    def node_offsets(self) -> Dict[int, int]:
        """Node-offset dictionary, mapping node ids (keys) to node offsets (values)."""
        return self._node_offsets

    @property
    def offset_nodes(self) -> Dict[int, List[int]]:
        """The offset-nodes dictionary, grouping nodes (values) by offset (key). Used to find unique series."""
        offset_nodes = invert_dict(self._node_offsets, sort=True)
        return offset_nodes

    # Import/Export members

    def to_file(self, file_path: Union[str, Path]) -> NoReturn:
        """
        Save weather metadata object as weather metadata file (.bin.json).

        Args:
            file_path: The path of the output weather metadata file.

        Returns:
            None
        """
        self.validate()
        # Ensure parent dir exists.
        make_path(Path(file_path).parent)
        # Construct the node offset string.
        offset_str = self._convert_offset_dict_to_str(self._node_offsets)
        # Prepare json object based on metadata and node offset string.
        content = dict(Metadata=self.attributes_dict, NodeOffsets=offset_str)
        # Save json object to a file.
        save_json(content=content, file_path=file_path)

        assert Path(file_path).is_file(), f"Failed to create weather metadata file {file_path}"

    @classmethod
    def from_file(cls, file_path: Union[str, Path]) -> WeatherMetadata:
        """
        Read weather metadata file into a weather metadata object.
        """
        # Load metadata json file into a json object.
        with open(str(file_path), "rb") as file:
            content = json.load(file)

        # Convert node offset string into a node-offset dictionary
        node_offsets = cls._convert_offset_str_to_dict(content["NodeOffsets"])
        if "Metadata" in content and _META_DATA_VALUE_COUNT in content["Metadata"]:
            series_len = content["Metadata"][_META_DATA_VALUE_COUNT]
        else:
            series_len = None
        # Instantiate the weather metadata object based on node-offset dictionary and metadata attribute dictionary.
        metadata = WeatherMetadata(node_ids=node_offsets, attributes=content["Metadata"], series_len=series_len)

        return metadata

    # Helpers

    @staticmethod
    def _convert_offset_str_to_dict(offset_str: str) -> Dict[int, int]:
        """
        Convert node offset string into a node-offset dictionary.

        Args:
            offset_str: The node offset string.

        Returns:
            The node-offset dictionary, having node ids as keys and offsets as values.
        """
        entry_count = len(offset_str) // 16
        node_offsets = {}
        for i in range(entry_count):
            idx = i * 16
            entry_str = offset_str[idx: idx + 16]
            node_id = int(entry_str[:8], 16)
            offset = int(entry_str[8:16], 16)
            node_offsets[node_id] = offset

        return node_offsets

    @staticmethod
    def _convert_offset_dict_to_str(node_offsets: Dict[int, int]) -> str:
        """
        Convert node-offset dictionary into a string.

        Args:
            node_offsets: The node offset string.

        Returns:
            The node offset string, as it appears in the weather metadata file.
        """
        offset_str = ""
        for node_id, offset in node_offsets.items():
            offset_str += f"{node_id:08x}{offset:08x}"

        return offset_str

attributes property

Cast back into WeatherAttributes.

datavalue_count property

Number of data values in each weather time series (must be > 0).

node_count property

The number of node in the node-offset dictionary.

node_offset_str property

The node offset string, as it will appear in the weather metadata file (.bin.json).

node_offsets property

Node-offset dictionary, mapping node ids (keys) to node offsets (values).

nodes property

The list of nodes (node ids) in the node-offset dictionary.

offset_nodes property

The offset-nodes dictionary, grouping nodes (values) by offset (key). Used to find unique series.

series_count property

The number of weather time series (expected based on metadata), corresponding to the number of offsets.

series_len property

Number of data values in each timeseries, should be > 0.

series_unique_count property

The number of unique weather time series (expected based on metadata), based on offsets.

total_value_count property

The total count of all values, in all weather time series (expected based on metadata).

__eq__(other)

Equality operator for WeatherMetadata objects

Source code in emodpy_malaria/weather/weather_metadata.py 
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def __eq__(self, other: WeatherMetadata):
    """Equality operator for WeatherMetadata objects"""
    attributes_eq = super().__eq__(other)
    nodes_eq = sorted(self.node_offsets) == sorted(other.node_offsets)
    offsets_eq = [self.node_offsets[k] == other.node_offsets[k] for k in self.node_offsets] if nodes_eq else [False]
    return attributes_eq and nodes_eq and offsets_eq

__init__(node_ids, series_len=None, attributes=None)

Initiate WeatherMetadata object.

Parameters:

Name Type Description Default
node_ids Union[List[int], Dict[int, int]]

A dictionary with node ids as keys and offsets as values, or just a list of node ids. If node-offset dictionary is provided, node offsets are set per that dictionary. If a list of nodes ids is provided, offsets are calculated based on weather time series length.

 required
series_len int

The length of a weather time series (aka "data value count").

 None
attributes Union[WeatherMetadata, WeatherAttributes, Dict[str, Union[str, int, float]]]

Weather attributes, either as an objects or a dictionary.

 None
Source code in emodpy_malaria/weather/weather_metadata.py 
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def __init__(self,
             node_ids: Union[List[int], Dict[int, int]],
             series_len: int = None,
             attributes: Union[WeatherMetadata, WeatherAttributes, Dict[str, Union[str, int, float]]] = None):
    """
    Initiate WeatherMetadata object.

    Args:
        node_ids: A dictionary with node ids as keys and offsets as values, or just a list of node ids.
                  If node-offset dictionary is provided, node offsets are set per that dictionary.
                  If a list of nodes ids is provided, offsets are calculated based on weather time series length.
        series_len: The length of a weather time series (aka "data value count").
        attributes: Weather attributes, either as an objects or a dictionary.

    """
    if isinstance(attributes, WeatherMetadata) or isinstance(attributes, WeatherAttributes):
        attributes_dict = attributes.attributes_dict
    else:
        attributes_dict = attributes

    super().__init__(attributes_dict=attributes_dict)

    # Set node offsets dictionary based on node_ids argument.
    if isinstance(node_ids, Dict):
        self._node_offsets = node_ids
        series_len = int(series_len or self._expected_series_len())
        self._validate_series_len(series_len)
    else:
        # If node id list is provided, offsets are calculated based on weather time series length.
        self._validate_series_len(series_len)   # if node_ids is a list a valid series_len must be provided.
        self._node_offsets = {
            node_id: series_len * node_ids.index(node_id) * SERIES_BYTE_VALUE_SIZE
            for node_id in sorted(node_ids)
        }

    self._series_len = series_len

    # init
    metadata_count_dict = self._metadata_count_dict

    self.update(metadata_count_dict)
    self.validate()

from_file(file_path) classmethod

Read weather metadata file into a weather metadata object.

Source code in emodpy_malaria/weather/weather_metadata.py 
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@classmethod
def from_file(cls, file_path: Union[str, Path]) -> WeatherMetadata:
    """
    Read weather metadata file into a weather metadata object.
    """
    # Load metadata json file into a json object.
    with open(str(file_path), "rb") as file:
        content = json.load(file)

    # Convert node offset string into a node-offset dictionary
    node_offsets = cls._convert_offset_str_to_dict(content["NodeOffsets"])
    if "Metadata" in content and _META_DATA_VALUE_COUNT in content["Metadata"]:
        series_len = content["Metadata"][_META_DATA_VALUE_COUNT]
    else:
        series_len = None
    # Instantiate the weather metadata object based on node-offset dictionary and metadata attribute dictionary.
    metadata = WeatherMetadata(node_ids=node_offsets, attributes=content["Metadata"], series_len=series_len)

    return metadata

to_file(file_path)

Save weather metadata object as weather metadata file (.bin.json).

Parameters:

Name Type Description Default
file_path Union[str, Path]

The path of the output weather metadata file.

 required

Returns:

Type Description
NoReturn

None
Source code in emodpy_malaria/weather/weather_metadata.py 
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def to_file(self, file_path: Union[str, Path]) -> NoReturn:
    """
    Save weather metadata object as weather metadata file (.bin.json).

    Args:
        file_path: The path of the output weather metadata file.

    Returns:
        None
    """
    self.validate()
    # Ensure parent dir exists.
    make_path(Path(file_path).parent)
    # Construct the node offset string.
    offset_str = self._convert_offset_dict_to_str(self._node_offsets)
    # Prepare json object based on metadata and node offset string.
    content = dict(Metadata=self.attributes_dict, NodeOffsets=offset_str)
    # Save json object to a file.
    save_json(content=content, file_path=file_path)

    assert Path(file_path).is_file(), f"Failed to create weather metadata file {file_path}"

validate()

Validate metadata object node-related counts. Relies on inherited validation of metadata attributes.

Source code in emodpy_malaria/weather/weather_metadata.py 
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def validate(self):
    """Validate metadata object node-related counts. Relies on inherited validation of metadata attributes."""
    super().validate()

    # Validate nodes and offsets
    assert isinstance(self.nodes, Iterable), "node_ids must be iterable"
    assert len(self.nodes) > 0, "node_ids must not be empty"
    assert all(map(lambda i: isinstance(i, int), self.nodes)), "node_ids must be integers"

    # Validate node id and offset range
    # https://github.com/InstituteforDiseaseModeling/DtkTrunk/blob/master/Eradication/Climate.h#L151-L154
    max_uint32 = int("FFFFFFFF", 16)   # max unsigned 32 bit value
    invalid_nodes, invalid_offsets = [], []
    for node_id, offset in self.node_offsets.items():
        if not 0 < node_id <= max_uint32:
            invalid_nodes.append(node_id)

        if not 0 <= offset <= max_uint32:
            invalid_offsets.append(offset)

    if len(invalid_nodes) > 0:
        print(f"Found {len(invalid_nodes)} invalid node ids: {invalid_nodes[:5]}")
        raise ValueError(f"Node values must be integers in (0, {str(max_uint32)}] interval.")

    if len(invalid_offsets) > 0:
        print(f"Found {len(invalid_offsets)} invalid offsets: {invalid_offsets[:5]}")
        raise ValueError(f"Node offset values must be integers in [0, {str(max_uint32)}] interval.")

    assert len(set(self.nodes)) == len(self.nodes), "node_ids must be unique"

    assert len(self.node_offset_str) == self.node_count * 16, "node_offset_str length doesn't match node count. "

    # Validate series_len
    self._validate_series_len(self._series_len)
    if 0 < self._expected_series_len() != self._series_len:
        raise ValueError("Weather time series length doesn't match provided offsets distance.")