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A python module for reading *.eso files generated by EnergyPlus

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esoreader
=========

A python module for reading *.eso files generated by EnergyPlus

# Introduction

The building simulation engine
[EnergyPlus](http://apps1.eere.energy.gov/buildings/energyplus/) stores its
main output in a file with the ending '.eso'. This format makes it easy to log
variable values during simulation, but is hard to use for post-processing. EnergyPlus
offers a sqlite version of this data, but using it requires understanding the eso file
format itself. EnergyPlus also can output a csv file, but that is limited in the number of
columns.

The `esoreader` module makes it very easy to explore the output of EnergyPlus, say, in an 
[IPython notebook](http://ipython.org/notebook.html) interactive environment.

# Features

- parse *.eso files to pandas `DataFrame`s
- search for output variables by substring

# Installation

esoreader requires **Python 3.10+**.

Install with:

```
$ pip install esoreader
```

# Using esoreader with Pandas

```python
In [1]: import esoreader

In [2]: eso = esoreader.read_from_path(r"C:\...\experiment01.eso")

In [3]: eso.find_variable('heating')
Out[3]:
[('TimeStep', None, 'Heating:EnergyTransfer'),
 ('TimeStep',
  'DEFAULT_ZONEZONEHVAC:IDEALLOADSAIRSYSTEM',
  'Zone Ideal Loads Zone Total Heating Energy')]

In [4]: df = eso.to_frame('heating energy')

In [5]: df[:10]
Out[5]:
   DEFAULT_ZONEZONEHVAC:IDEALLOADSAIRSYSTEM
0                            8596050.719384
1                            8672511.667988
2                            8737544.119096
3                            8799182.506582
4                            8862116.803218
5                            8928593.537248
6                            5296266.226576
7                                  0.000000
8                                  0.000002
9                                  0.000000

In [6]: df.plot()
Out[6]: <matplotlib.axes._subplots.AxesSubplot at 0x7854090>

In [7]: %matplotlib tk

In [8]: df.plot()
Out[8]: <matplotlib.axes._subplots.AxesSubplot at 0x7b66670>
```

![Zone Ideal Loads Zone Total Heating Energy](https://raw.githubusercontent.com/architecture-building-systems/esoreader/master/doc/ZoneIdealLoadsZoneTotalHeatingEnergy.png)

Notice in the above example how the variable is matched by substring - you
don't have to specify the whole variable name. Each matching variable will show
up in the resulting `DataFrame` with the key used as the column name - in this
case 'DEFAULT_ZONEZONEHVAC:IDEALLOADSAIRSYSTEM'.

Also, as this is an IPython session, I used the magic variable incantation `%matplotlib tk` to switch on the GUI loop that allowes plotting. You can choose another backend if you like, but I am pretty sure that `tk` should be available with your Python distribution.

An example with multiple columns:

```python
In [1]: eso.find_variable('net thermal radiation heat gain energy')
Out[1]:
[('TimeStep',
  'DPVROOF:1157058.3',
  'Surface Outside Face Net Thermal Radiation Heat Gain Energy'),
 ('TimeStep',
  'DPVWALL:1157028',
  'Surface Outside Face Net Thermal Radiation Heat Gain Energy'),
 ('TimeStep',
  'DPVWALL:1157027',
  'Surface Outside Face Net Thermal Radiation Heat Gain Energy'),
 ('TimeStep',
  'DPVROOF:1157058.2',
  'Surface Outside Face Net Thermal Radiation Heat Gain Energy'),
 ('TimeStep',
  'DPVFLOOR:1157042',
  'Surface Outside Face Net Thermal Radiation Heat Gain Energy'),
 ('TimeStep',
  'DPVWALL:1157029',
  'Surface Outside Face Net Thermal Radiation Heat Gain Energy'),
 ('TimeStep',
  'DPVROOF:1157058.1',
  'Surface Outside Face Net Thermal Radiation Heat Gain Energy'),
 ('TimeStep',
  'DPVROOF:1157058.0',
  'Surface Outside Face Net Thermal Radiation Heat Gain Energy'),
 ('TimeStep',
  'DPVWALL:1157026',
  'Surface Outside Face Net Thermal Radiation Heat Gain Energy')]

In [2]: df = eso.to_frame('net thermal radiation heat gain energy')

In [3]: df.plot()
Out[3]: <matplotlib.axes._subplots.AxesSubplot at 0xbd11150>
```

![Net Thermal Radiation Heat Gain Energy](https://raw.githubusercontent.com/architecture-building-systems/esoreader/master/doc/NetThermalRadiationHeatGainEnergy.png)

## The `key` parameter to `to_frame`

You can use the `key` parameter to select a single column:

```python
In [1]: df = eso.to_frame('net thermal radiation', key='DPVROOF:1157058.3')

In [2]: df[:10]
Out[2]:
   DPVROOF:1157058.3
0    -8985934.016604
1    -8453530.628023
2    -7611418.498363
3    -6936246.291753
4    -6206109.857522
5    -5879653.262523
6    -5676601.453020
7    -5606988.050900
8    -5844912.195173
9    -4712551.701917
```

## The `index` parameter to `to_frame`

You can use the `index` parameter to specify an index for the DataFrame. Since this is time-series data, a common pattern could be:

```python
In [1]: hours_in_year = pd.date_range('2013-01-01', '2013-12-31 T23:00', freq='H')

In [2]: df = eso.to_frame('heating energy', index=hours_in_year)

In [3]: df[:10]
Out[4]:
                     DEFAULT_ZONEZONEHVAC:IDEALLOADSAIRSYSTEM
2013-01-01 00:00:00                            8596050.719384
2013-01-01 01:00:00                            8672511.667988
2013-01-01 02:00:00                            8737544.119096
2013-01-01 03:00:00                            8799182.506582
2013-01-01 04:00:00                            8862116.803218
2013-01-01 05:00:00                            8928593.537248
2013-01-01 06:00:00                            5296266.226576
2013-01-01 07:00:00                                  0.000000
2013-01-01 08:00:00                                  0.000002
2013-01-01 09:00:00                                  0.000000
```

# Using esoreader without pandas

**NOTE:** This is an older API that requires a better understanding about the
eso file structure than using the pandas interface explained above. It is
cumbersome but might be of use to users not using pandas. If you come up with a
nicer interface, send me a pull request.

The eso files generated by EnergyPlus contains a data dictionary, which
describes the values reported by EnergyPlus. The list of values reported
depends on the simulation input file, specifically the `Output:Variable` and
`Output:Meter` objects.  EnergyPlus can output the same variable at different
frequencies and for different "keys", which are for instance surfaces or
equipment names.

Following the data dictionary is a list of output variable values for each of the configured
variable coordinates.

The output of the esoreader module is therefore a data dictionary object that contains
a mapping of variable "coordinates" (grouping of reporting frequency, key and variable name)
to the index used by EnergyPlus and a data object, which essentially just maps that index to
the timeseries data.

## Example

```python
import esoreader

PATH_TO_ESO = r'/Path/To/EnergyPlus/Output/eplusout.eso'
dd, data = esoreader.read(PATH_TO_ESO)
frequency, key, variable = dd.find_variable('Zone Ventilation Total Heat Loss Energy')[0]
idx = dd.index[frequency, key, variable]
time_series = data[idx]
```

# Contribute

- Issue Tracker: https://github.com/architecture-building-systems/esoreader/issues
- Source Code: https://github.com/architecture-building-systems/esoreader

# Support

If you are having issues, please let us know. 
We have a mailing list located at: http://groups.google.com/group/esoreader.

# License & Credit

This project is licensed under the terms of the MIT license. See the file
"LICENSE" in the project root for more information.

This module was developed by Daren Thomas at the chair for
[Architecture and Building Systems (A/S)](http://systems.arch.ethz.ch) at the [Institute of Technology in
Architecture](http://ita.arch.ethz.ch), ETH Zürich, Switzerland.

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