reporting

measurement.reporting

Classes

DataImport

Bases: PermissionsBase

Model to store the data imports.

This model stores the data imports, which are files with data that are uploaded to the system. The data is then processed asynchronously and stored in the database.

Attributes:

Name	Type	Description
station	ForeignKey	Station to which the data belongs.
format	ForeignKey	Format of the data.
rawfile	FileField	File with the data to be imported.
date	DateTimeField	Date of submission of the data.
start_date	DateTimeField	Start date of the data.
end_date	DateTimeField	End date of the data.
records	IntegerField	Number of records in the data.
observations	TextField	Notes or observations about the data.
status	TextField	Status of the import.
log	TextField	Log of the data ingestion, indicating any errors.
reprocess	BooleanField	If checked, the data will be reprocessed.

Functions

clean()

Validate information and uploads the measurement data.

Source code in importing\models.py

def clean(self) -> None:
    """Validate information and uploads the measurement data."""
    tz = self.station.timezone
    if not tz:
        raise ValidationError("Station must have a timezone set.")

    # If the file has changed, we reprocess the data
    if self.pk and self.rawfile != self.__class__.objects.get(pk=self.pk).rawfile:
        self.reprocess = True

    if self.reprocess:
        self.status = "N"
        self.reprocess = False

Measurement

Bases: MeasurementBase

Class to store the measurements and their validation status.

This class holds the value of a given variable and station at a specific time, as well as auxiliary information such as maximum and minimum values, depth and direction, for vector quantities. All of these have a raw version where a backup of the original data is kept, should this change at any point.

Flags to monitor its validation status, if the data is active (and therefore can be used for reporting) and if it has actually been used for that is also included.

Attributes:

Name	Type	Description
depth	int	Depth of the measurement.
direction	Decimal	Direction of the measurement, useful for vector quantities.
raw_value	Decimal	Original value of the measurement.
raw_maximum	Decimal	Original maximum value of the measurement.
raw_minimum	Decimal	Original minimum value of the measurement.
raw_direction	Decimal	Original direction of the measurement.
raw_depth	int	Original depth of the measurement.
is_validated	bool	Flag to indicate if the measurement has been validated.
is_active	bool	Flag to indicate if the measurement is active. An inactive measurement is not used for reporting

Attributes

overwritten: bool property

Indicates if any of the values associated to the entry have been overwritten.

Returns:

Name	Type	Description
bool	bool	True if any raw field is different to the corresponding standard field.

raws: tuple[str, ...] property

Return the raw fields of the measurement.

Returns:

Type	Description
tuple[str, ...]	tuple[str]: Tuple with the names of the raw fields of the measurement.

Functions

clean()

Check consistency of validation, reporting and backs-up values.

Source code in measurement\models.py

def clean(self) -> None:
    """Check consistency of validation, reporting and backs-up values."""
    # Check consistency of validation
    if not self.is_validated and not self.is_active:
        raise ValidationError("Only validated entries can be declared as inactive.")

    # Backup values to raws, if needed
    for r in self.raws:
        value = getattr(self, r.removeprefix("raw_"))
        if value and not getattr(self, r):
            setattr(self, r, value)

Report

Bases: MeasurementBase

Holds the different reporting data.

It also keeps track of which data has already been used when creating the reports.

Attributes:

Name	Type	Description
report_type	str	Type of report. It can be hourly, daily or monthly.
completeness	Decimal	Completeness of the report. Eg. a daily report with 24 hourly measurements would have a completeness of 100%.

Functions

clean()

Validate that the report type and use of the data is consistent.

Source code in measurement\models.py

def clean(self) -> None:
    """Validate that the report type and use of the data is consistent."""
    if self.report_type == ReportType.HOURLY:
        self.time = self.time.replace(minute=0, second=0, microsecond=0)
    elif self.report_type == ReportType.DAILY:
        self.time = self.time.replace(hour=0, minute=0, second=0, microsecond=0)
    elif self.report_type == ReportType.MONTLY:
        self.time = self.time.replace(
            day=1, hour=0, minute=0, second=0, microsecond=0
        )

Station

Bases: PermissionsBase

Main representation of a station, including several metadata.

Attributes:

Name	Type	Description
visibility	str	Visibility level of the object, including an "internal" option.
station_id	int	Primary key.
station_code	str	Unique code for the station.
station_name	str	Brief description of the station.
station_type	StationType	Type of the station.
country	Country	Country where the station is located.
region	Region	Region within the Country where the station is located.
ecosystem	Ecosystem	Ecosystem associated with the station.
institution	Institution	Institutional partner responsible for the station.
place_basin	PlaceBasin	Place-Basin association.
station_state	bool	Is the station operational?
timezone	str	Timezone of the station.
station_latitude	Decimal	Latitude of the station, in degrees [-90 to 90].
station_longitude	Decimal	Longitude of the station, in degrees [-180 to 180].
station_altitude	int	Altitude of the station.
influence_km	Decimal	Area of influence in km2.
station_file	ImageField	Photography of the station.
station_external	bool	Is the station external?
variables	str	Comma-separated list of variables measured by the station.

Attributes

variables_list: list[str] property

Return the list of variables measured by the station.

Only variables with data in the database are returned.

Returns:

Type	Description
list[str]	list[str]: List of variables measured by the station.

Functions

__str__()

Return the station code.

Source code in station\models.py

def __str__(self) -> str:
    """Return the station code."""
    return str(self.station_code)

get_absolute_url()

Return the absolute url of the station.

Source code in station\models.py

def get_absolute_url(self) -> str:
    """Return the absolute url of the station."""
    return reverse("station:station_detail", kwargs={"pk": self.pk})

set_object_permissions()

Set object-level permissions.

This method is called by the save method of the model to set the object-level permissions based on the visibility level of the object. In addition to the standard permissions for the station, the view_measurements permission is set which controls who can view the measurements associated to the station.

Source code in station\models.py

def set_object_permissions(self) -> None:
    """Set object-level permissions.

    This method is called by the save method of the model to set the object-level
    permissions based on the visibility level of the object. In addition to the
    standard permissions for the station, the view_measurements permission is set
    which controls who can view the measurements associated to the station.
    """
    super().set_object_permissions()

    standard_group = Group.objects.get(name="Standard")
    anonymous_user = get_anonymous_user()

    # Assign view_measurements permission based on permissions level
    if self.visibility == "public":
        assign_perm("view_measurements", standard_group, self)
        assign_perm("view_measurements", anonymous_user, self)
        if self.owner:
            remove_perm("view_measurements", self.owner, self)
    elif self.visibility == "internal":
        assign_perm("view_measurements", standard_group, self)
        remove_perm("view_measurements", anonymous_user, self)
        if self.owner:
            remove_perm("view_measurements", self.owner, self)
    elif self.visibility == "private":
        remove_perm("view_measurements", standard_group, self)
        remove_perm("view_measurements", anonymous_user, self)
        if self.owner:
            assign_perm("view_measurements", self.owner, self)

Variable

Bases: PermissionsBase

A variable with a physical meaning.

Such as precipitation, wind speed, wind direction, soil moisture, including the associated unit. It also includes metadata to help identify what is a reasonable value for the data, to flag outliers and to help with the validation process.

The nature of the variable can be one of the following:

• sum: Cumulative value over a period of time.
• average: Average value over a period of time.
• value: One-off value.

Attributes:

Name	Type	Description
variable_id	AutoField	Primary key.
variable_code	CharField	Code of the variable, eg. airtemperature.
name	CharField	Human-readable name of the variable, eg. Air temperature.
unit	ForeignKey	Unit of the variable.
maximum	DecimalField	Maximum value allowed for the variable.
minimum	DecimalField	Minimum value allowed for the variable.
diff_error	DecimalField	If two sequential values in the time-series data of this variable differ by more than this value, the validation process can mark this with an error flag.
outlier_limit	DecimalField	The statistical deviation for defining outliers, in times the standard deviation (sigma).
null_limit	DecimalField	The max % of null values (missing, caused by e.g. equipment malfunction) allowed for hourly, daily, monthly data. Cumulative values are not deemed trustworthy if the number of missing values in a given period is greater than the null_limit.
nature	CharField	Nature of the variable, eg. if it represents a one-off value, the average over a period of time or the cumulative value over a period

Attributes

is_cumulative: bool property

Return True if the nature of the variable is sum.

Functions

__str__()

Return the string representation of the object.

Source code in variable\models.py

def __str__(self) -> str:
    """Return the string representation of the object."""
    return str(self.name)

clean()

Validate the model fields.

Source code in variable\models.py

def clean(self) -> None:
    """Validate the model fields."""
    if self.maximum < self.minimum:
        raise ValidationError(
            {
                "maximum": "The maximum value must be greater than the minimum "
                "value."
            }
        )
    if not self.variable_code.isidentifier():
        raise ValidationError(
            {
                "variable_code": "The variable code must be a valid Python "
                "identifier. Only letters, numbers and underscores are allowed, and"
                " it cannot start with a number."
            }
        )
    return super().clean()

get_absolute_url()

Get the absolute URL of the object.

Source code in variable\models.py

def get_absolute_url(self) -> str:
    """Get the absolute URL of the object."""
    return reverse("variable:variable_detail", kwargs={"pk": self.pk})

Functions

calculate_reports(data, station, variable, operation)

Calculates the report for the chosen days.

Parameters:

Name	Type	Description	Default
data	DataFrame	The dataframe with the data.	required
station	str	The name of the station.	required
variable	str	The name of the variable.	required
operation	str	Aggregation operation to perform on the data when calculating the report.	required

Returns:

Type	Description
DataFrame	A dataframe with the hourly, daily and monthly reports.

Source code in measurement\reporting.py

def calculate_reports(
    data: pd.DataFrame, station: str, variable: str, operation: str
) -> pd.DataFrame:
    """Calculates the report for the chosen days.

    Args:
        data: The dataframe with the data.
        station: The name of the station.
        variable: The name of the variable.
        operation: Aggregation operation to perform on the data when calculating the
            report.

    Returns:
        A dataframe with the hourly, daily and monthly reports.
    """
    cols = ["time", "value"]
    if "maximum" in data.columns:
        cols.append("maximum")
    if "minimum" in data.columns:
        cols.append("minimum")

    # Calculate the reports
    hourly = data[cols].resample("h", on="time").agg(operation)
    daily = hourly.resample("D").agg(operation)
    monthly = daily.resample("MS").agg(operation)

    # Get the right data_import for each period. We use the mode to get the most common
    # data_import value in the period.
    def mode(x: pd.Series) -> str | None:
        modes = x.mode()
        return modes[0] if not modes.empty else None

    cols2 = ["time", "data_import_id"]
    hourly["data_import_id"] = data[cols2].resample("h", on="time").agg(mode)
    daily["data_import_id"] = data[cols2].resample("D", on="time").agg(mode)
    monthly["data_import_id"] = data[cols2].resample("MS", on="time").agg(mode)

    # Put everything together
    hourly["report_type"] = "hourly"
    daily["report_type"] = "daily"
    monthly["report_type"] = "monthly"

    report = pd.concat([hourly, daily, monthly])
    report["station"] = station
    report["variable"] = variable

    return report

get_data_to_report(station, variable, start_time, end_time)

Retrieves data to be reported about.

It enforces to retrieve only active measurements and to use the station timezone.

Parameters:

Name	Type	Description	Default
station	str	Station of interest.	required
variable	str	Variable of interest.	required
start_time	datetime	Start time.	required
end_time	datetime	End time.	required

Returns:

Type	Description
DataFrame	A dataframe with the data to report about.

Source code in measurement\reporting.py

def get_data_to_report(
    station: str,
    variable: str,
    start_time: datetime,
    end_time: datetime,
) -> pd.DataFrame:
    """Retrieves data to be reported about.

    It enforces to retrieve only active measurements and to use the station timezone.

    Args:
        station: Station of interest.
        variable: Variable of interest.
        start_time: Start time.
        end_time: End time.

    Returns:
        A dataframe with the data to report about.
    """

    return pd.DataFrame.from_records(
        Measurement.objects.filter(
            station__station_code=station,
            variable__variable_code=variable,
            time__date__range=(start_time.date(), end_time.date()),
            is_active=True,
        ).values()
    )

get_report_data_from_db(station, variable, start_time, end_time, report_type, whole_months=True) cached

Retrieves the report data from the database.

Time is set to the station timezone and the time range is inclusive of both start and end times.

Parameters:

Name	Type	Description	Default
station	str	Station of interest.	required
variable	str	Variable of interest.	required
start_time	str	Start time.	required
end_time	str	End time.	required
report_type	str	Type of report to retrieve.	required
whole_months	bool	Whether to cover whole months or not.	True

Returns:

Type	Description
DataFrame	A dataframe with the report data.

Source code in measurement\reporting.py

@lru_cache(1)
def get_report_data_from_db(
    station: str,
    variable: str,
    start_time: str,
    end_time: str,
    report_type: str,
    whole_months: bool = True,
) -> pd.DataFrame:
    """Retrieves the report data from the database.

    Time is set to the station timezone and the time range is inclusive of both
    start and end times.

    Args:
        station: Station of interest.
        variable: Variable of interest.
        start_time: Start time.
        end_time: End time.
        report_type: Type of report to retrieve.
        whole_months: Whether to cover whole months or not.

    Returns:
        A dataframe with the report data.
    """
    start_time_, end_time_ = reformat_dates(start_time, end_time, whole_months)

    if report_type == "measurement":
        data = pd.DataFrame.from_records(
            Measurement.objects.filter(
                station__station_code=station,
                variable__variable_code=variable,
                time__date__range=(start_time_.date(), end_time_.date()),
            ).values()
        )
        raw_cols = [col for col in data.columns if col.startswith("raw_")]
        normal = [col.strip("raw_") for col in raw_cols]
        data = data.drop(columns=normal).rename(columns=dict(zip(raw_cols, normal)))

    elif report_type == "validated":
        data = pd.DataFrame.from_records(
            Measurement.objects.filter(
                station__station_code=station,
                variable__variable_code=variable,
                time__date__range=(start_time_.date(), end_time_.date()),
                is_validated=True,
                is_active=True,
            ).values()
        )
        raw_cols = [col for col in data.columns if col.startswith("raw_")]
        data = data.drop(columns=raw_cols)

    else:
        data = pd.DataFrame.from_records(
            Report.objects.filter(
                station__station_code=station,
                variable__variable_code=variable,
                time__date__range=(start_time_.date(), end_time_.date()),
                report_type=report_type,
            ).values()
        )

    data = data.rename(columns={"station_id": "station", "variable_id": "variable"})

    if data.empty:
        return data

    tz = timezone.get_current_timezone()
    data["time"] = data["time"].dt.tz_convert(tz)
    return data.sort_values("time")

launch_reports_calculation(station, variable, start_time, end_time)

Launches the calculation of the reports.

Time is set to the station timezone and the time range is inclusive of both start and end times.

Parameters:

Name	Type	Description	Default
station	str	Station of interest.	required
variable	str	Variable of interest.	required
start_time	str	Start time.	required
end_time	str	End time.	required

Source code in measurement\reporting.py

def launch_reports_calculation(
    station: str,
    variable: str,
    start_time: str,
    end_time: str,
) -> None:
    """Launches the calculation of the reports.

    Time is set to the station timezone and the time range is inclusive of both
    start and end times.

    Args:
        station: Station of interest.
        variable: Variable of interest.
        start_time: Start time.
        end_time: End time.
    """
    operation = (
        "sum" if Variable.objects.get(variable_code=variable).is_cumulative else "mean"
    )

    start_time_, end_time_ = reformat_dates(start_time, end_time)
    data = get_data_to_report(station, variable, start_time_, end_time_)
    report = calculate_reports(data, station, variable, operation)
    remove_report_data_in_range(station, variable, start_time_, end_time_)
    save_report_data(report)

reformat_dates(start_time, end_time, whole_months=True)

Reformat dates so they have the right timezone and cover full days.

The start date is always the first day of the first month and the end date is the last day of the last month. Times are set to 00:00:00 and 23:59:59, respectively, and the timezone is set to the station timezone.

Parameters:

Name	Type	Description	Default
start_time	str	Start time.	required
end_time	str	End time.	required
whole_months	bool	Whether to cover whole months or not.	True

Returns:

Type	Description
tuple[datetime, datetime]	A series with the dates to be validated.

Source code in measurement\reporting.py

def reformat_dates(
    start_time: str,
    end_time: str,
    whole_months: bool = True,
) -> tuple[datetime, datetime]:
    """Reformat dates so they have the right timezone and cover full days.

    The start date is always the first day of the first month and the end date is the
    last day of the last month. Times are set to 00:00:00 and 23:59:59, respectively,
    and the timezone is set to the station timezone.

    Args:
        start_time: Start time.
        end_time: End time.
        whole_months: Whether to cover whole months or not.

    Returns:
        A series with the dates to be validated.
    """
    tz = timezone.get_current_timezone()

    if whole_months:
        start_time_ = datetime.strptime(start_time, "%Y-%m-%d").replace(
            day=1, tzinfo=tz
        )
        end_time_ = (
            datetime.strptime(end_time, "%Y-%m-%d").replace(day=1)
            + pd.DateOffset(months=1)
            - pd.DateOffset(seconds=1)
        )
        end_time_ = datetime.fromtimestamp(end_time_.timestamp()).astimezone(tz)
    else:
        start_time_ = datetime.strptime(start_time, "%Y-%m-%d").replace(tzinfo=tz)
        end_time_ = (
            datetime.strptime(end_time, "%Y-%m-%d")
            + pd.DateOffset(days=1)
            - pd.DateOffset(seconds=1)
        )
        end_time_ = datetime.fromtimestamp(end_time_.timestamp()).astimezone(tz)
    return start_time_, end_time_

remove_report_data_in_range(station, variable, start_time, end_time)

Removes data in the range from the database.

It enforces to use the station timezone.

Parameters:

Name	Type	Description	Default
station	str	Station of interest.	required
variable	str	Variable of interest.	required
start_time	datetime	Start time.	required
end_time	datetime	End time.	required

Source code in measurement\reporting.py

def remove_report_data_in_range(
    station: str,
    variable: str,
    start_time: datetime,
    end_time: datetime,
) -> None:
    """Removes data in the range from the database.

    It enforces to use the station timezone.

    Args:
        station: Station of interest.
        variable: Variable of interest.
        start_time: Start time.
        end_time: End time.
    """

    Report.objects.filter(
        station__station_code=station,
        variable__variable_code=variable,
        time__date__range=(start_time.date(), end_time.date()),
    ).delete()

save_report_data(data)

Saves the report data into the database.

Before saving, the function removes maximum and minimum columns if they have all NaN and removes rows with NaN in the value column.

Parameters:

Name	Type	Description	Default
data	DataFrame	The dataframe with the report data.	required

Source code in measurement\reporting.py

def save_report_data(data: pd.DataFrame) -> None:
    """Saves the report data into the database.

    Before saving, the function removes maximum and minimum columns if they have all NaN
    and removes rows with NaN in the value column.

    Args:
        data: The dataframe with the report data.
    """
    data_ = data.dropna(axis=1, how="all").dropna(axis=0, subset=["value"])
    data_import_avail = "data_import_id" in data_.columns
    Report.objects.bulk_create(
        [
            Report(
                data_import=DataImport.objects.get(pk=row["data_import_id"])
                if data_import_avail and not pd.isna(row["data_import_id"])
                else None,
                station=Station.objects.get(station_code=row["station"]),
                variable=Variable.objects.get(variable_code=row["variable"]),
                time=time,
                value=row["value"],
                maximum=row.get("maximum", None),
                minimum=row.get("minimum", None),
                report_type=row["report_type"],
            )
            for time, row in data_.iterrows()
        ]
    )