Feature/update py psa reader

PyPSA/README.md

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+# How to translate a PyPSA-Eur dataset to LEGO
+
+This guide explains how to convert an existing PyPSA-Eur network file (`.nc`) to LEGO Excel files.
+
+To build the PyPSA-Eur dataset first, follow
+[How to create a PyPSA-Eur dataset](https://github.com/IEE-TUGraz/pypsa-eur-multivoltage/blob/fix/corine-bool-and-offshore-keyerror/README_pypsa_dataset.md)
+in the `pypsa-eur-multivoltage` repository.
+
+Since the conversion can require a lot of memory and may also take some time, it is recommended to use a powerful
+machine.
+
+## 1. Set the mapping metadata
+
+In `pypsa_lego_mapping_config.yaml`, update the dataset metadata fields `dataPackage` and `dataSource`.
+
+Use the name of the PyPSA-Eur dataset for `dataSource`.
+
+Example:
+
+```yaml
+Metadata:
+    dataPackage: "PyPSA-Eur"
+    dataSource: "PyPSA-Eur_2025_CY2013"
+```
+
+## 2. Translate to LEGO
+
+From the `PyPSA/` folder, run the converter with:
+
+```
+python PypsaReader.py <input directory> <input file> <output directory> <output folder name>
+```
+
+Example:
+
+```
+python PypsaReader.py "../../pypsa-eur-multivoltage/resources/PyPSA-Eur_2025_CY2013/networks" "base_s_all_elec.nc" "../../" "LEGO_PyPSA-Eur_2025_CY2013"
+```
+
+This will write the LEGO Excel files to:
+
+```
+../../LEGO_PyPSA-Eur_2025_CY2013
+```
+
+## About the generated files
+
+The converter generates the following LEGO files:
+
+- `Power_BusInfo.xlsx`
+- `Power_Demand.xlsx`
+- `Power_Inflows.xlsx`
+- `Power_Network.xlsx`
+- `Power_Storage.xlsx`
+- `Power_ThermalGen.xlsx`
+- `Power_VRES.xlsx`
+- `Power_VRESProfiles.xlsx`
+
+The following files are not generated and have to be added and adapted manually:
+
+- `Data_Packages.xlsx`
+- `Data_Sources.xlsx`
+- `Global_Parameters.xlsx`
+- `Global_Scenarios.xlsx`
+- `Power_Hindex.xlsx`
+- `Power_Parameters.xlsx`
+- `Power_WeightsK.xlsx`
+- `Power_WeightsRP.xlsx`
+
+# Reader Documentation
+
+The `PypsaReader` module provides a specialized pipeline for converting PyPSA (Python for Power System Analysis)
+networks into the Excel-based data
+format required by the LEGO model.
+
+## Purpose & limitations
+
+- This reader only converts PyPSA networks to LEGO input files (EXCEL). It does not return a LEGO CaseStudy object.
+- For running the converted case study in LEGO, at least the files `Global_Parameters.xlsx`, `Power_Parameters.xlsx`,
+  `Power_Hindex.xlsx`,
+  `Power_WeightsK.xlsx` and `Power_WeightsRP.xlsx` must be defined.
+- Currently, only the power sector is supported by the converter.
+- For importing the generated Excel files into the LEGO database, the files `Global_Scenarios.xlsx`,
+  `Data_Packages.xlsx` and `Data_Sources.xlsx` must
+  be defined.
+
+## General Workflow
+
+The conversion process follows a structured workflow:
+
+1. **Network Loading**: A PyPSA network is loaded from a NetCDF (`.nc`) file.
+2. **Configuration Parsing**: The `NetworkDataExtractor` reads `pypsa_lego_mapping_config.yaml` to determine how PyPSA
+   components (buses, lines,
+   generators,
+   etc.) map to LEGO tables.
+3. **Data Extraction & Transformation**:
+    - **Source Retrieval**: Data is pulled either directly from PyPSA network attributes (e.g., `net.buses`) or via
+      complex processing in
+      `pypsa_helper.py`.
+    - **Filtering**: Technologies are filtered based on their `carrier` attribute using definitions in the `Metadata`
+      section of the config.
+    - **Column Mapping**: PyPSA attributes are mapped to LEGO column names.
+    - **Unit Conversion**: The `Conversions` class applies transformations (e.g., MW to kW, EUR to MEUR, or calculating
+      decommissioning years).
+4. **Normalization**: DataFrames are augmented with mandatory LEGO columns (`scenario`, `id`, `dataPackage`,
+   `dataSource`) and aligned with
+   definitions in `TableDefinitions.xml`.
+5. **Excel Export**: The `ExcelWriter` saves each processed DataFrame into individual `.xlsx` files. <br> **Warning:**
+   This can take up to several
+   hours for very
+   large networks!
+
+## Configuration File (`pypsa_lego_mapping_config.yaml`)
+
+The YAML configuration acts as the "translation map" between the two models.
+
+### Metadata Section
+
+* **Technology Filters**: Defines list of `carrier` strings that identify specific technologies (e.g.,
+  `solar: filter: ['solar', 'Solar']`).
+* **Fuel Costs**: Specifies fuel prices in EUR/MWh for thermal technologies.
+* **Categories**: Groups individual technologies into broader LEGO categories like `ThermalGen` or `VRES` for batch
+  processing.
+* **Global Settings**: Defines `dataPackage`, `dataSource`, and thresholds like `LDES_threshold`.
+
+### Table Mapping Section
+
+Each entry (e.g., `dPower_ThermalGen`) defines:
+
+* **source**: The data origin (`type: attribute` for direct PyPSA access or `type: helper` for function calls).
+* **category**: (Optional) References a metadata category to automatically filter the source data.
+* **index**: Defines the LEGO index columns (e.g., `i` for bus, `g` for generator).
+* **mapping**: A dictionary where keys are LEGO columns and values are either:
+    - A direct PyPSA attribute name.
+    - A static value (int/float).
+    - A dictionary specifying an `attr`, a `conversion` function, or a `factor`.
+
+## Helper Functions (`pypsa_helper.py`)
+
+When simple attribute mapping is insufficient, helper functions handle complex data aggregation and profile generation:
+
+* **Network Topology**:
+    - `prepare_ac_lines_and_dc_links`: Combines PyPSA `lines`, `links` (filtered for DC), and `transformers` into a
+      single LEGO `Power_Network` table.
+    - `prepare_ac_lines` / `prepare_transformers`: Calculate missing electrical parameters ($r, x, b$) based on standard
+      type definitions if they are
+      not explicitly set.
+* **Time-Series Profiles**:
+    - `prepare_renewable_profiles`: Extracts `p_max_pu` for VRES generators and reshapes it into a long-form LEGO
+      format.
+    - `prepare_inflow_profiles`: Aggregates inflow data from both `storage_units` (hydro) and `generators` (
+      Run-of-River) into a unified time-series.
+    - `prepare_demand_profiles`: Sums PyPSA load data per bus and formats it for the LEGO demand table.
+
+## Conversions & Logic
+
+The `Conversions` class in `PypsaReader.py` contains static methods for specialized logic:
+
+* **Unit Scaling**: `EUR_to_MEUR`, `MW_to_kW`, `V_to_kV`.
+* **Calculated Values**: `year_and_lifetime_to_year_decom` (derives decommissioning date) and
+  `total_capacity_to_number_of_units`.
+* **LEGO Specifics**: `is_ldes` (Long Duration Energy Storage detection) and `line_carrier_to_tec_repr` (mapping
+  carriers to LEGO technical
+  representations like `DC-OPF` or `TP`).