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# Export data from the structured database | ||
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## Export observations | ||
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### Export plotted data | ||
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The most convenient way to export observation records is to construct a plot of the data | ||
and export the underlying data. A click on "export" opens the export Window, where you can choose the data | ||
format, the output timesteps and evtl. interpolation methods | ||
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#### Data formats | ||
- Excel file | ||
- Textfile with comma seperated variables (CSV) | ||
- Textfile with tab seperated variables (TSV) | ||
- JSON Notation (best for web applications) | ||
- Apache parquet format (best for data science applications in eg. Python and R) | ||
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### Time steps | ||
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The next option chooses the scheme, which time steps should be exported. If you have regular time step | ||
data, eg. from a logger or you have created aggregated timesteps, the selection hardly matters. If | ||
the data you want to export has very irregular timesteps or different times, use the options below | ||
to get data at the time steps you are really interested. | ||
The options are: | ||
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- union: All timesteps from all datasets are exported as an own row. Records with the same time are | ||
put on the same line. If a plot line has no datapoint at the same time as another, a blank field is | ||
exported. To join nearly identical time steps, you can use a tolerance (see below) | ||
- intersection: Export only time steps where all plot lines have records. To get more exported lines, | ||
tolerances are used again | ||
- Regular time steps: Define a time grid, eg. every day (`D`) or every three hours (`3h`) for export. | ||
records with another time are only exported if they fit the output time tolerance | ||
- Timesteps from plot line: Use the time steps of a certain line and export data from other lines if their | ||
data points are within the tolerance | ||
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### Regular grid and tolerance | ||
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If regular grid is selected you can specify the grid using frequncy codes. ODMF uses a | ||
[library which defines many codes](https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#offset-aliases). The most important are: | ||
- `s` Second | ||
- `min` Minute | ||
- `h` Hour | ||
- `D` Day | ||
- `B` Business day | ||
- `W` Week | ||
- `M` Month | ||
- `Y` Year | ||
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These codes can be scaled, you can export biweekly with `2W` or every 3 hours as `3h`. A special code, | ||
that cannot be scaled is `decade`, here you get the data reported for three parts of a month, | ||
day 1-10, day 11-20 and day 21-28/29/30/31. | ||
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## Export metadata | ||
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Exporting metadata is currently under development. You can export **all** sites at the site list, but | ||
the export does not respect the filters. | ||
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Exporting datasets, logs and pictures is not developed yet. |
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