Metadata-Version: 2.1
Name: pyedautils
Version: 0.0.1
Summary: Python Energy Data Analysis Utilities
Home-page: https://github.com/retomarek/pyedautils
Author: Reto Marek
License: BSD 2-clause
Project-URL: Homepage, https://github.com/retomarek/pyedautils/
Project-URL: Issues, https://github.com/retomarek/pyedautils/issues
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.9
Description-Content-Type: text/markdown
License-File: LICENSE.txt

# pyedautils
Python Energy Data Analysis Utilities

The Python-package ‘pyedautils’ provides frequently used utility functions for
the analysis and visualization of comfort and energy data in python. These
functions reduce the complexity of the analysis task and allow a fast
visualization of the data.

## Installation

You can install the package from [GitHub](https://github.com/) with:

``` python
pip install git+https://github.com/retomarek/pyedautils.git
```

<!---
## Functions

### getSeason()

Get the season name out of a date for filter and grouping purposes.

``` r
library(redutils)
x <- as.Date("2019-04-01")
getSeason(x)
#> [1] "Spring"
```

Default language is English. You can change that by passing the argument
`seasonlab`:

``` r
library(redutils)
x <- as.Date("2019-04-01")
getSeason(x, seasonlab = c("Winter","Frühling","Sommer","Herbst"))
#> [1] "Frühling"
```

### getTypEleConsHousehold()

Get a typical electricity consumption of a Swiss household in kWh/year.
This is useful to compare a real dataset with a typical consumption
value.

``` r
# single family house
library(redutils)
getTypEleConsHousehold(occupants=3,
                       rooms=5.5,
                       bldgType="single",
                       waterHeater="heatPump",
                       eleCommon="included")
#> [1] 5370
```

``` r
# flat in a multi family house
library(redutils)
getTypEleConsHousehold(occupants=3,
                       bldgType="multi",
                       freezer="none")
#> [1] 2900
```

Hint: varoius settings can get changed via function arguments.

## Plots

### plotEnergyConsBeforeAfter()

Plot a Graph with Energy Consumption per Month before/after an
Optimization.

``` r
library(redutils)
data <- readRDS(system.file("sampleData/flatHeatingEnergy.rds", package = "redutils"))
plotSeasonalXYBeforeAfter(data, dateOptimization = "2017-09-01")
```

<img src="man/figures/README-plotSeasonalXYBeforeAfter-1.png" width="100%" />

### plotEnergyConsDailyProfileOverview()

Plot a Graph with Daily Energy Consumption Profiles by Weekday and
Season.

``` r
library(redutils)
data <- readRDS(system.file("sampleData/eboBookEleMeter.rds", package = "redutils"))
plotDailyProfilesOverview(data, locTimeZone = "Europe/Zurich")
```

<img src="man/figures/README-plotDailyProfilesOverview-1.png" width="100%" />

### plotDailyProfilesDecomposed()

Plot a Graph with Decomposed Daily Energy Consumption Profiles by
Weekday. Decomposed means that the trend component (average of 2 week
per default) is removed and only the seasonal component is showed. This
allows an easier comparison.

``` r
library(redutils)
data <- readRDS(system.file("sampleData/eboBookEleMeter.rds", package = "redutils"))
plotDailyProfilesDecomposed(data, locTimeZone = "Europe/Zurich")
```

<img src="man/figures/README-plotDailyProfilesdecomposition-1.png" width="100%" />

### plotHeatmapMedianWeeks()

Plot Heatmap of Median Energy Consumption by Hour, Weekdays and Seasons
of Year.

``` r
library(redutils)
data <- readRDS(system.file("sampleData/eboBookEleMeter.rds", package = "redutils"))
plotHeatmapMedianWeeks(data, locTimeZone = "Europe/Zurich")
```

<img src="man/figures/README-plotHeatmapMedianWeeks-1.png" width="100%" />

### plotMollierHxDiagram()

Plot a D3 Mollier hx Diagram with scatter plot and comfort zone.

<img src="inst/mollierHxDiagram/example.png" class="illustration" width=600/>

Hint: varoius settings can get changed via function arguments.

-->
<hr>

**Disclaimer**<br> The author decline any liability or responsibility
in connection with the published documentation
