Getter Reference: Time, Map Data, Summaries, and Results
Rendered from 11_all_get_functions.ipynb.
Time, curves, controls, and inflows
time_series
Kind: Object collection
| Getter | Source | Declared type | Declared size | Output note |
|---|---|---|---|---|
m.get.time_series.count(ids=None, format=None) | derived | integer | single | int scalar count |
m.get.time_series.datetime(ids=None, format=None) | user | datetime | sequence/list | One ID → scalar/structured value; many/all IDs → NumPy array or one-row DataFrame |
m.get.time_series.description(ids=None, format=None) | user | str | single | One ID → scalar/structured value; many/all IDs → NumPy array or one-row DataFrame |
m.get.time_series.filename(ids=None, format=None) | ref | path | single | One ID → scalar/structured value; many/all IDs → NumPy array or one-row DataFrame |
m.get.time_series.id(ids=None, format=None) | user | str | single | One ID → scalar/structured value; many/all IDs → NumPy array or one-row DataFrame |
m.get.time_series.values(ids=None, format=None) | user | float | sequence/list | One ID → scalar/structured value; many/all IDs → NumPy array or one-row DataFrame |
Copy-ready call list:
m.get.time_series.count(ids=None, format=None)
m.get.time_series.datetime(ids=None, format=None)
m.get.time_series.description(ids=None, format=None)
m.get.time_series.filename(ids=None, format=None)
m.get.time_series.id(ids=None, format=None)
m.get.time_series.values(ids=None, format=None)time_pattern
Kind: Object collection
| Getter | Source | Declared type | Declared size | Output note |
|---|---|---|---|---|
m.get.time_pattern.count(ids=None, format=None) | derived | integer | single | int scalar count |
m.get.time_pattern.id(ids=None, format=None) | user | str | single | One ID → scalar/structured value; many/all IDs → NumPy array or one-row DataFrame |
m.get.time_pattern.multipliers(ids=None, format=None) | user | float | sequence/list | One ID → scalar/structured value; many/all IDs → NumPy array or one-row DataFrame |
m.get.time_pattern.type(ids=None, format=None) | user | enum | series by ID | One ID → scalar/structured value; many/all IDs → NumPy array or one-row DataFrame |
Copy-ready call list:
m.get.time_pattern.count(ids=None, format=None)
m.get.time_pattern.id(ids=None, format=None)
m.get.time_pattern.multipliers(ids=None, format=None)
m.get.time_pattern.type(ids=None, format=None)curve
Kind: Object collection
| Getter | Source | Declared type | Declared size | Output note |
|---|---|---|---|---|
m.get.curve.count(ids=None, format=None) | derived | integer | single | int scalar count |
m.get.curve.id(ids=None, format=None) | user | str | single | One ID → scalar/structured value; many/all IDs → NumPy array or one-row DataFrame |
m.get.curve.points(ids=None, format=None) | user | tuple/list | sequence/list | One ID → scalar/structured value; many/all IDs → NumPy array or one-row DataFrame |
m.get.curve.type(ids=None, format=None) | user | enum | series by ID | One ID → scalar/structured value; many/all IDs → NumPy array or one-row DataFrame |
m.get.curve.x(ids=None, format=None) | user | float | sequence/list | One ID → scalar/structured value; many/all IDs → NumPy array or one-row DataFrame |
m.get.curve.y(ids=None, format=None) | user | float | sequence/list | One ID → scalar/structured value; many/all IDs → NumPy array or one-row DataFrame |
Copy-ready call list:
m.get.curve.count(ids=None, format=None)
m.get.curve.id(ids=None, format=None)
m.get.curve.points(ids=None, format=None)
m.get.curve.type(ids=None, format=None)
m.get.curve.x(ids=None, format=None)
m.get.curve.y(ids=None, format=None)control_rule
Kind: Object collection
| Getter | Source | Declared type | Declared size | Output note |
|---|---|---|---|---|
m.get.control_rule.action_log(ids=None, format=None) | result | table | event list | Time series; NumPy by default, or timestamp-indexed DataFrame with format='df' |
m.get.control_rule.actions(ids=None, format=None) | user/derived | expression/table | list/table | One ID → scalar/structured value; many/all IDs → NumPy array or one-row DataFrame |
m.get.control_rule.conditions(ids=None, format=None) | user/derived | expression/table | list/table | One ID → scalar/structured value; many/all IDs → NumPy array or one-row DataFrame |
m.get.control_rule.count(ids=None, format=None) | derived | integer | single | int scalar count |
m.get.control_rule.enabled(ids=None, format=None) | user | bool | single/series | One ID → scalar/structured value; many/all IDs → NumPy array or one-row DataFrame |
m.get.control_rule.id(ids=None, format=None) | user | str | single | One ID → scalar/structured value; many/all IDs → NumPy array or one-row DataFrame |
m.get.control_rule.priority(ids=None, format=None) | user | integer/float | single/series | One ID → scalar/structured value; many/all IDs → NumPy array or one-row DataFrame |
m.get.control_rule.text(ids=None, format=None) | user | str/list | list | One ID → scalar/structured value; many/all IDs → NumPy array or one-row DataFrame |
Copy-ready call list:
m.get.control_rule.action_log(ids=None, format=None)
m.get.control_rule.actions(ids=None, format=None)
m.get.control_rule.conditions(ids=None, format=None)
m.get.control_rule.count(ids=None, format=None)
m.get.control_rule.enabled(ids=None, format=None)
m.get.control_rule.id(ids=None, format=None)
m.get.control_rule.priority(ids=None, format=None)
m.get.control_rule.text(ids=None, format=None)external_inflow
Kind: Object collection
| Getter | Source | Declared type | Declared size | Output note |
|---|---|---|---|---|
m.get.external_inflow.baseline(ids=None, format=None) | user | float | table | One ID → scalar/structured value; many/all IDs → NumPy array or one-row DataFrame |
m.get.external_inflow.constituent(ids=None, format=None) | ref/user | str/enum | table | One ID → scalar/structured value; many/all IDs → NumPy array or one-row DataFrame |
m.get.external_inflow.node(ids=None, format=None) | ref | str | table | One ID → scalar/structured value; many/all IDs → NumPy array or one-row DataFrame |
m.get.external_inflow.pattern(ids=None, format=None) | ref | str | table | One ID → scalar/structured value; many/all IDs → NumPy array or one-row DataFrame |
m.get.external_inflow.scale_factor(ids=None, format=None) | user | float | table | One ID → scalar/structured value; many/all IDs → NumPy array or one-row DataFrame |
m.get.external_inflow.time_series(ids=None, format=None) | ref | str | table | One ID → scalar/structured value; many/all IDs → NumPy array or one-row DataFrame |
m.get.external_inflow.type(ids=None, format=None) | user | enum | table | One ID → scalar/structured value; many/all IDs → NumPy array or one-row DataFrame |
m.get.external_inflow.units_factor(ids=None, format=None) | user | float | table | One ID → scalar/structured value; many/all IDs → NumPy array or one-row DataFrame |
Copy-ready call list:
m.get.external_inflow.baseline(ids=None, format=None)
m.get.external_inflow.constituent(ids=None, format=None)
m.get.external_inflow.node(ids=None, format=None)
m.get.external_inflow.pattern(ids=None, format=None)
m.get.external_inflow.scale_factor(ids=None, format=None)
m.get.external_inflow.time_series(ids=None, format=None)
m.get.external_inflow.type(ids=None, format=None)
m.get.external_inflow.units_factor(ids=None, format=None)dry_weather_flow
Kind: Object collection
| Getter | Source | Declared type | Declared size | Output note |
|---|---|---|---|---|
m.get.dry_weather_flow.average_value(ids=None, format=None) | user | float | table | One ID → scalar/structured value; many/all IDs → NumPy array or one-row DataFrame |
m.get.dry_weather_flow.constituent(ids=None, format=None) | ref/user | str/enum | table | One ID → scalar/structured value; many/all IDs → NumPy array or one-row DataFrame |
m.get.dry_weather_flow.daily_pattern(ids=None, format=None) | ref | str | table | One ID → scalar/structured value; many/all IDs → NumPy array or one-row DataFrame |
m.get.dry_weather_flow.hourly_pattern(ids=None, format=None) | ref | str | table | One ID → scalar/structured value; many/all IDs → NumPy array or one-row DataFrame |
m.get.dry_weather_flow.monthly_pattern(ids=None, format=None) | ref | str | table | One ID → scalar/structured value; many/all IDs → NumPy array or one-row DataFrame |
m.get.dry_weather_flow.node(ids=None, format=None) | ref | str | table | One ID → scalar/structured value; many/all IDs → NumPy array or one-row DataFrame |
m.get.dry_weather_flow.weekend_pattern(ids=None, format=None) | ref | str | table | One ID → scalar/structured value; many/all IDs → NumPy array or one-row DataFrame |
Copy-ready call list:
m.get.dry_weather_flow.average_value(ids=None, format=None)
m.get.dry_weather_flow.constituent(ids=None, format=None)
m.get.dry_weather_flow.daily_pattern(ids=None, format=None)
m.get.dry_weather_flow.hourly_pattern(ids=None, format=None)
m.get.dry_weather_flow.monthly_pattern(ids=None, format=None)
m.get.dry_weather_flow.node(ids=None, format=None)
m.get.dry_weather_flow.weekend_pattern(ids=None, format=None)rdii
Kind: Object collection
| Getter | Source | Declared type | Declared size | Output note |
|---|---|---|---|---|
m.get.rdii.node(ids=None, format=None) | ref | str | table | One ID → scalar/structured value; many/all IDs → NumPy array or one-row DataFrame |
m.get.rdii.sewer_area(ids=None, format=None) | user | float | table | One ID → scalar/structured value; many/all IDs → NumPy array or one-row DataFrame |
m.get.rdii.unit_hydrograph(ids=None, format=None) | ref | str | table | One ID → scalar/structured value; many/all IDs → NumPy array or one-row DataFrame |
Copy-ready call list:
m.get.rdii.node(ids=None, format=None)
m.get.rdii.sewer_area(ids=None, format=None)
m.get.rdii.unit_hydrograph(ids=None, format=None)interface_file
Kind: Model-level category
| Getter | Source | Declared type | Declared size | Output note |
|---|---|---|---|---|
m.get.interface_file.hotstart(format=None) | ref | path | single | Model-level scalar, sequence, mapping, or table depending on the declared type |
m.get.interface_file.inflow(format=None) | ref | path | single | Model-level scalar, sequence, mapping, or table depending on the declared type |
m.get.interface_file.outflow(format=None) | ref | path | single | Model-level scalar, sequence, mapping, or table depending on the declared type |
m.get.interface_file.rainfall(format=None) | ref | path | single | Model-level scalar, sequence, mapping, or table depending on the declared type |
m.get.interface_file.rdii(format=None) | ref | path | single | Model-level scalar, sequence, mapping, or table depending on the declared type |
m.get.interface_file.runoff(format=None) | ref | path | single | Model-level scalar, sequence, mapping, or table depending on the declared type |
m.get.interface_file.save_file(format=None) | user | bool | single/table | Model-level scalar, sequence, mapping, or table depending on the declared type |
m.get.interface_file.use_file(format=None) | user | bool | single/table | Model-level scalar, sequence, mapping, or table depending on the declared type |
Copy-ready call list:
m.get.interface_file.hotstart(format=None)
m.get.interface_file.inflow(format=None)
m.get.interface_file.outflow(format=None)
m.get.interface_file.rainfall(format=None)
m.get.interface_file.rdii(format=None)
m.get.interface_file.runoff(format=None)
m.get.interface_file.save_file(format=None)
m.get.interface_file.use_file(format=None)Map data, summaries, and system results
coordinate
Kind: Model-level category
| Getter | Source | Declared type | Declared size | Output note |
|---|---|---|---|---|
m.get.coordinate.labels(format=None) | user | coordinates | table | Model-level scalar, sequence, mapping, or table depending on the declared type |
m.get.coordinate.link_vertices(format=None) | user | coordinates | sequence/list | Model-level scalar, sequence, mapping, or table depending on the declared type |
m.get.coordinate.map_dimensions(format=None) | user/derived | coordinates | single/list | Model-level scalar, sequence, mapping, or table depending on the declared type |
m.get.coordinate.map_units(format=None) | user | enum/str | single | Model-level scalar, sequence, mapping, or table depending on the declared type |
m.get.coordinate.node_coordinates(format=None) | user | coordinates | table | Model-level scalar, sequence, mapping, or table depending on the declared type |
m.get.coordinate.polygons(format=None) | user | coordinates | sequence/list | Model-level scalar, sequence, mapping, or table depending on the declared type |
m.get.coordinate.subcatchment_coordinates(format=None) | user | coordinates | table | Model-level scalar, sequence, mapping, or table depending on the declared type |
Copy-ready call list:
m.get.coordinate.labels(format=None)
m.get.coordinate.link_vertices(format=None)
m.get.coordinate.map_dimensions(format=None)
m.get.coordinate.map_units(format=None)
m.get.coordinate.node_coordinates(format=None)
m.get.coordinate.polygons(format=None)
m.get.coordinate.subcatchment_coordinates(format=None)summary
Kind: Model-level category
| Getter | Source | Declared type | Declared size | Output note |
|---|---|---|---|---|
m.get.summary.conduit_surcharge(format=None) | result | table | table | Time series; NumPy by default, or timestamp-indexed DataFrame with format='df' |
m.get.summary.counts(format=None) | derived | table/dict | table | Model-level scalar, sequence, mapping, or table depending on the declared type |
m.get.summary.flow_routing_continuity(format=None) | result | table/dict | single/table | Time series; NumPy by default, or timestamp-indexed DataFrame with format='df' |
m.get.summary.lid_performance(format=None) | result | table | table | Time series; NumPy by default, or timestamp-indexed DataFrame with format='df' |
m.get.summary.link_flow(format=None) | result | table | table | Time series; NumPy by default, or timestamp-indexed DataFrame with format='df' |
m.get.summary.link_velocity(format=None) | result | table | table | Time series; NumPy by default, or timestamp-indexed DataFrame with format='df' |
m.get.summary.model(format=None) | derived/result | table/dict | table | Model-level scalar, sequence, mapping, or table depending on the declared type |
m.get.summary.node_depth(format=None) | result | table | table | Time series; NumPy by default, or timestamp-indexed DataFrame with format='df' |
m.get.summary.node_flooding(format=None) | result | table | table | Time series; NumPy by default, or timestamp-indexed DataFrame with format='df' |
m.get.summary.node_inflow(format=None) | result | table | table | Time series; NumPy by default, or timestamp-indexed DataFrame with format='df' |
m.get.summary.node_surcharge(format=None) | result | table | table | Time series; NumPy by default, or timestamp-indexed DataFrame with format='df' |
m.get.summary.options(format=None) | derived/user | table/dict | table | Model-level scalar, sequence, mapping, or table depending on the declared type |
m.get.summary.outfall_loading(format=None) | result | table | table | Time series; NumPy by default, or timestamp-indexed DataFrame with format='df' |
m.get.summary.pump_operation(format=None) | result | table | table | Time series; NumPy by default, or timestamp-indexed DataFrame with format='df' |
m.get.summary.quality_routing_continuity(format=None) | result | table/dict | single/table | Time series; NumPy by default, or timestamp-indexed DataFrame with format='df' |
m.get.summary.runoff_continuity(format=None) | result | table/dict | single/table | Time series; NumPy by default, or timestamp-indexed DataFrame with format='df' |
m.get.summary.storage_volume(format=None) | result | table | table | Time series; NumPy by default, or timestamp-indexed DataFrame with format='df' |
m.get.summary.subcatchment_runoff(format=None) | result | table | table | Time series; NumPy by default, or timestamp-indexed DataFrame with format='df' |
m.get.summary.subcatchment_washoff(format=None) | result | table | table | Time series; NumPy by default, or timestamp-indexed DataFrame with format='df' |
m.get.summary.validation_issues(format=None) | derived | table | table | Model-level scalar, sequence, mapping, or table depending on the declared type |
Copy-ready call list:
m.get.summary.conduit_surcharge(format=None)
m.get.summary.counts(format=None)
m.get.summary.flow_routing_continuity(format=None)
m.get.summary.lid_performance(format=None)
m.get.summary.link_flow(format=None)
m.get.summary.link_velocity(format=None)
m.get.summary.model(format=None)
m.get.summary.node_depth(format=None)
m.get.summary.node_flooding(format=None)
m.get.summary.node_inflow(format=None)
m.get.summary.node_surcharge(format=None)
m.get.summary.options(format=None)
m.get.summary.outfall_loading(format=None)
m.get.summary.pump_operation(format=None)
m.get.summary.quality_routing_continuity(format=None)
m.get.summary.runoff_continuity(format=None)
m.get.summary.storage_volume(format=None)
m.get.summary.subcatchment_runoff(format=None)
m.get.summary.subcatchment_washoff(format=None)
m.get.summary.validation_issues(format=None)system_result
Kind: Model-level category
| Getter | Source | Declared type | Declared size | Output note |
|---|---|---|---|---|
m.get.system_result.air_temperature(format=None) | result | float | time series | Time series; NumPy by default, or timestamp-indexed DataFrame with format='df' |
m.get.system_result.direct_inflow(format=None) | result | float | time series | Time series; NumPy by default, or timestamp-indexed DataFrame with format='df' |
m.get.system_result.dry_weather_inflow(format=None) | result | float | time series | Time series; NumPy by default, or timestamp-indexed DataFrame with format='df' |
m.get.system_result.evaporation(format=None) | result | float | time series | Time series; NumPy by default, or timestamp-indexed DataFrame with format='df' |
m.get.system_result.flooding(format=None) | result | float | time series | Time series; NumPy by default, or timestamp-indexed DataFrame with format='df' |
m.get.system_result.groundwater_inflow(format=None) | result | float | time series | Time series; NumPy by default, or timestamp-indexed DataFrame with format='df' |
m.get.system_result.infiltration(format=None) | result | float | time series | Time series; NumPy by default, or timestamp-indexed DataFrame with format='df' |
m.get.system_result.outfall_flow(format=None) | result | float | time series | Time series; NumPy by default, or timestamp-indexed DataFrame with format='df' |
m.get.system_result.pollutant_loading(format=None) | result | float | time series × pollutant | Time series; NumPy by default, or timestamp-indexed DataFrame with format='df' |
m.get.system_result.rainfall(format=None) | result | float | time series | Time series; NumPy by default, or timestamp-indexed DataFrame with format='df' |
m.get.system_result.rdii_inflow(format=None) | result | float | time series | Time series; NumPy by default, or timestamp-indexed DataFrame with format='df' |
m.get.system_result.runoff(format=None) | result | float | time series | Time series; NumPy by default, or timestamp-indexed DataFrame with format='df' |
m.get.system_result.snow_depth(format=None) | result | float | time series | Time series; NumPy by default, or timestamp-indexed DataFrame with format='df' |
m.get.system_result.storage_volume(format=None) | result | float | time series | Time series; NumPy by default, or timestamp-indexed DataFrame with format='df' |
m.get.system_result.total_lateral_inflow(format=None) | result | float | time series | Time series; NumPy by default, or timestamp-indexed DataFrame with format='df' |
Copy-ready call list:
m.get.system_result.air_temperature(format=None)
m.get.system_result.direct_inflow(format=None)
m.get.system_result.dry_weather_inflow(format=None)
m.get.system_result.evaporation(format=None)
m.get.system_result.flooding(format=None)
m.get.system_result.groundwater_inflow(format=None)
m.get.system_result.infiltration(format=None)
m.get.system_result.outfall_flow(format=None)
m.get.system_result.pollutant_loading(format=None)
m.get.system_result.rainfall(format=None)
m.get.system_result.rdii_inflow(format=None)
m.get.system_result.runoff(format=None)
m.get.system_result.snow_depth(format=None)
m.get.system_result.storage_volume(format=None)
m.get.system_result.total_lateral_inflow(format=None)