iucm run¶

Run the model for the given experiment

usage: iucm run [-h] [-i str] [-f str] [-v str] [-t int]
                [-sm { 'consecutive' | 'random' }]
                [-um { 'categorical' | 'random' | 'forced' }] [-n int]
                [-c float1,float2,...] [-pctls] [-nr] [-ot int] [-seed int]
                [-stop-en-change float] [-agg-stop-steps int] [-agg-steps int]
                [-prob int] [-max float] [-ct float] [-cp str]

Named Arguments¶

`-i, --ifile`	The input file. If not specified, the input key in the experiment configuration is used
`-f, --forcing`	The forcing file (necessary if `update_method=='forced'`). If not specified, the forcing key in the experiment configuration is used
`-v, --vname`	The variable name to use. If not specified and only one variable exists in the dataset, this one is used. Otherwise, the `'run.vname'` key in the experiment configuration is used
`-t, --steps`	The number of time steps Default: 50
`-sm, --selection-method`
	The name of the method on how the data is selected. The default is consecutive. Possible selection methods are consecutive: Always the ncells consecutive cells are selected. random: ncells random cells in the field are updated.
`-um, --update-method`
	The name of the update method on how the selected cells (see selection_method are updated). The default is categorical. Possible update methods are categorical: The selected cells are updated to the lower level of the next category. random: The selected cells are updated to a randum number within the next category. forced: A forcing file is used (see the forcing parameter).
`-n, --ncells`	The number of cells that shall be changed during 1 step. The default value is 4
`-c, --categories`
	The values for the categories to use within the models
`-pctls, --use-pctls`
	If True, interprete categories as percentiles instead of real population density Default: False
`-nr, --no-restart`
	If True, and the run has already been conducted, restart it. Otherwise the previous run is continued Default: False
`-ot, --output-step`
	Make an output every output_step. If None, only the final result is written to the output file
`-seed`	The random seed for numpy to use. Specify this parameter for the experiment to guarantee reproducability
`-stop-en-change`
	The minimum of required relative energy consumption change. If the mean relative energy consumption change over the last agg_stop_steps steps is below this number, the run is stopped
`-agg-stop-steps`
	The number of steps to aggregate over when calculating the mean relative energy consumption change. Does not have an effect if stop_en_change is None Default: 100
`-agg-steps`	Use only every agg_steps energy consumption for the aggregation when checking the stop_en_change criteria Default: 1
`-prob, --probabilistic`
	The number of probabilistic scenarios. For each scenario the energy consumption is calculated and the final population is distributed to the cells with the ideal energy consumption. Set this to 0 to only use the weights by [LeNechet2012]. If this option is None, the value will be taken from the configuration with a default of 0 (i.e. no probabilistic run).
`-max, --max-pop`
	The maximum population for each cell. If None, the last value in categories will be used or what is stored in the experiment configuration
`-ct, --coord-transform`
	The transformation factor to transform the coordinate values into kilometres
`-cp, --copy-from`
	If not None, copy the run settings from the other given experiment