# PV data comparison

![png](/img/pv_analysis.png)

## Overview

These functions automate the creation of PV performance plots, including:
- Capacity factor time-series comparisons (``generate_PV_timeseries_plots()``)
- Scatter plots between measured and simulated data (``generate_PV_timeseries_plots()``)
- Error metrics (mean difference, MAE, RMSE) (``generate_PV_timeseries_plots()``)
- High-resolution plots for clear and cloudy sky conditions (``generate_high_res_PV_plots()``)

All results are automatically saved in:

```
results/{location_name}/Time series analysis results/
```
The functions automatically create missing output directories.  
If data for the specified year is unavailable, the functions skip plotting.

All plot aesthetics (colors, linestyles, widths) are managed and can be changed in the [style_config](https://github.com/giumonros/Measured-vs-simulated-PV/blob/main/src/simeasren/plotting/plot_style_config.py) module.   

---

## Timeseries analysis plots

The function ``generate_PV_timeseries_plots()`` generates standard PV time-series comparison plots between measured and simulated datasets:

- Capacity factor time-series plots  
- Scatter comparison plots  
- Error metrics bar charts (mean difference, MAE, RMSE)

**Parameters:**

| Name | Type | Description |
|------|------|--------------|
| data_sim_meas | pandas.DataFrame | Contains measured (`"PV-MEAS"`) and simulated PV data. |
| location_name | str | Location name (e.g., "Turin"). |
| year | str | Year of the analyzed dataset. |
| output_root | str, optional | Root directory for results (default: "results"). |

**Example:**

```python
from simeasren import generate_PV_timeseries_plots, prepare_pv_data_for_plots
df, _, _ = prepare_pv_data_for_plots("Turin", "2019")
generate_PV_timeseries_plots(df, "Turin", "2019")
```

**Output:**
- `*_Capacity_Factors.png`
- `*_scatterplot.png`
- `*_Errors_Analysis.png`

![png](/img/timeseries_plot.png)

---

## Plot high resolution PV data

The function ``generate_high_res_PV_plots()`` reates high-resolution PV plots for **clear-sky** and **cloudy-sky** conditions.

**Parameters:**

| Name | Type | Description |
|------|------|--------------|
| clear_sky_df | pandas.DataFrame | High-frequency PV data for clear-sky days. |
| cloudy_sky_df | pandas.DataFrame | High-frequency PV data for cloudy-sky days. |
| location_name | str | Location name (e.g., "Almeria"). |
| year | str | Year of data. |
| output_root | str, optional | Root directory for output (default: "results"). |

**Example:**

```python
from simeasren import generate_high_res_PV_plots, prepare_pv_data_for_plots
_, clear_df, cloudy_df = prepare_pv_data_for_plots("Utrecht", "2017")
generate_high_res_PV_plots(clear_df, cloudy_df, "Utrecht", "2017")
```

**Output:**
- `*_highres_clear_vs_cloudy.png`

![png](/img/Highres.png)

---

## Generate all plots

``generate_PV_plots()``calls both `generate_PV_timeseries_plots()` and `generate_high_res_PV_plots()` for a complete analysis in a single step and creates:

- All time-series comparison plots (capacity factor, scatter, error metrics)  
- High-resolution plots for clear and cloudy conditions  

**Parameters:**

| Name | Type | Description |
|------|------|--------------|
| data_sim_meas | pandas.DataFrame | Measured and simulated PV time-series data. |
| clear_sky_df | pandas.DataFrame | High-resolution clear-sky PV data. |
| cloudy_sky_df | pandas.DataFrame | High-resolution cloudy-sky PV data. |
| location_name | str | Location name (e.g., "Utrecht"). |
| year | str | Year of analysis. |
| output_root | str, optional | Output directory (default: "results"). |

**Example:**

```python
from simeasren import generate_PV_plots, prepare_pv_data_for_plots
data_sim_meas, clear_df, cloudy_df = prepare_pv_data_for_plots("Almeria", "2023")
generate_PV_plots(data_sim_meas, clear_df, cloudy_df, "Almeria", "2023")
```

**Generated files:**
- `Almeria2023_Capacity_Factors.png`
- `Almeria2023_scatterplot.png`
- `Almeria2023_Errors_Analysis.png`
- `Almeria_highres_clear_vs_cloudy.png`