---
jupytext:
  text_representation:
    extension: .md
    format_name: myst
    format_version: 0.13
    jupytext_version: 1.13.8
kernelspec:
  display_name: 'Python 3'
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---

(inflowSeries)=
# inflowSeries
Stochastic inflow data.

|   |   |
|---|---|
|Input connections||
|Output connections||
|License|PRODRISK_OPEN|
|Release version|9.6.1|

```{contents}
:local:
:depth: 1
```







## Attributes
```{code-cell} ipython3
:tags: ['remove-input', 'full-width']

import itables as itables
from itables import init_notebook_mode
init_notebook_mode(all_interactive=True, connected=True)
import pandas as pd
from IPython.core.display import HTML

table = pd.read_csv('../../../attributes.csv')
table.fillna(" ", inplace=True)
core_type_dict = {'int' : 'integer','double':'float','string':'string','int_array':'list-of-integer-values','double_array':'list-of-double-values','xy':'table-xy-curve','xy_array':'list-of-tables','txy':'time-series','txy_stochastic':'stochastic-time-series','txy_step':'stepwise-time-series'}
object_attributes = table[table["Object type"] == "inflowSeries"].reset_index().iloc[:, 1:]
for index, row in object_attributes.iterrows():
  object_attributes.at[index, "Attribute name"] = f"""<a href="{row['Object type']}.html#{row['Attribute name'].replace('_', '-').lower()}">{row['Attribute name']}</a>"""
  object_attributes.at[index, "Core data type"] = f"""<a href="../../datatypes.html#{core_type_dict[row['Core data type']].replace('_', '-')}">{row['Core data type']}</a>"""
  object_attributes.at[index, "Time series completion"] = f"""<a href="../../datatypes.html#time-series-completion">{row['Time series completion']}</a>"""
itables.show(object_attributes,
  dom='tlip',
  search={'regex': True, "caseInsensitive": True},
  column_filters='header',
  columns=[
    {
      'name': '',
      'className': 'dt-control',
      'orderable': False,
      'data': None,
      'defaultContent': '',
    },
    {
      'name': 'Attribute name',
      'className': 'dt-body-left'
    },
    {
      'name': 'Python data type',
      'className': 'dt-body-left'
    },
    {
      'name': 'Core data type',
      'className': 'dt-body-left'
    },
    {
      'name': 'Time series completion',
      'className': 'dt-body-left'
    },
    {
      'name': 'unit',
      'className': 'dt-body-left'
    },
    {
      'name': 'Default value',
      'className': 'dt-body-left'
    },
    {
      'name': 'I/O',
      'className': 'dt-body-left'
    },
    {
      'name': 'License',
      'className': 'dt-body-left'
    },
    {
      'name': 'Version added',
      'className': 'dt-body-left'
    },
    {
      'name': 'Description',
      'visible': False
    }
  ]
)
HTML('''<script>
$('tbody').on('click', 'td.dt-control', function () {
    var tr = $(this).closest('tr');
    var table = $(this).closest('table').DataTable();
    var row = table.row(tr);

    if (row.child.isShown()) {
        // This row is already open - close it
        row.child.hide();
        tr.removeClass('shown');
    } else {
        // Open this row
        row.child("<div align='left'>".concat(row.data()[6], "</div>")).show();
        tr.addClass('shown');
    }
});
</script>''')
```

(inflowSeries:autoCorrelation)=
### autoCorrelation
Autocorrelation coefficients in statistical model, represents the autocorrelation of the inflowSeries with all the other inflowSeries, including itself. (unit: none)


(inflowSeries:cutCoeffs)=
### cutCoeffs
Cut coefficients for inflow series. The inflow used in Prodrisk is normalized (normalized inflow = [inflow - mean] / stdev).The cut coefficients of the inflow is given as an array of XY tables. Each table in the array corresponds to a price level in ascending order. The x values are all 0, while the y values represent the coefficients. (unit: kEUR/normalized inflow)


(inflowSeries:histAverageInflow)=
### histAverageInflow
If set, this value is used in scaling of local inflow to each module. As default, the average over all scenarios for the last 52 weeks of the inflow scenarios is used. (unit: Mm3/year)


(inflowSeries:inflowScenarios)=
### inflowScenarios
Inflow scenarios. The number of scenarios must be the same as the number of scenarios in the price. Input should be given for at least 52 weeks (this input is used in scaling local inflow for each module connected to the inflow series). (unit: m3/s, time series completion: step curve)


(inflowSeries:lognormal_centers)=
### lognormal_centers
Center points from k-means clustering used in the lognormal inflow model (unit: none, time series completion: step curve)


(inflowSeries:name)=
### name
Inflow series name (unit: none)


(inflowSeries:outcomeProbability)=
### outcomeProbability
Probabilities for principal components in statistical model. (unit: none)


(inflowSeries:outcomeValue)=
### outcomeValue
Outcomes for principal components in statistical model. (unit: none)


(inflowSeries:relativeAverageInflow)=
### relativeAverageInflow
Relative average inflow in the statistical model. The relativeAverageInflow is a scaled version of the inflow input with weekly resolution. (unit: none, time series completion: step curve)


(inflowSeries:seriesId)=
### seriesId
Id used to connect module and inflowSeries (unit: none)


(inflowSeries:standardDeviation)=
### standardDeviation
Standard deviation in statistical model. The standard deviation in the scaled version of the inflow input (see relativeAverageInflow). (unit: none, time series completion: step curve)


(inflowSeries:standardized_weekly_inflows)=
### standardized_weekly_inflows
Standarized weekly inflows, used to generate the inflow model. (unit: Mm3/week, time series completion: step curve)