QGIS

API Access

The RenewMap API allows you to integrate up-to-date project data with data analysis platforms such as QGIS. By calling the API directly (rather than using a CSV export), you are guaranteed to always have the most up-to-date data in your GIS tools.

Setup

In QGIS, you can run a Python script to fetch RenewMap API data into your project.

1. Select **Plugins **> Python Console and then select 📝 Show editor
2. Create a new blank script using the ➕ icon.
3. Paste the code below into the blank script, replacing 'YOUR_API_KEY' with your actual API key.
4. Run the script with the ▶️ icon.
5. The latest RenewMap data will appear in your project:
6. A new Point layer called RenewMap with energy project locations and attributes.

** Note: ** the layer is stored in memory and will be lost when you close the project. Re-run the script whenever you want to get the latest data.

If you want to persist the layer to your next session, right click the layer in the Layers pane and select Make permanent. However, this permanent layer will not update with new data.

Projects

import pandas as pd
import requests
import json
from typing import List, Dict

API_KEY = "YOUR_API_KEY" # Replace with your actual API key
BASE_URL =  "https://api.renewmap.com.au/api/v1/projects"
DATA_KEY = "projects"
ADD_FIELDS = False # Your account needs to have the fields APdI enabled

headers = {
    "accept": "application/json",
    "Authorization": f"Bearer {API_KEY}"
}

class APIConfig:
    """Configuration class for API parameters.

    Attributes:
        base_url: Base URL for the API endpoint
        limit: Number of records per request
        headers: HTTP headers for API requests
        data_key: Key in API response containing the data array
    """
    base_url: str = BASE_URL
    limit: int = 10000
    headers: Dict = headers
    add_fields: bool = ADD_FIELDS
    data_key: str = DATA_KEY

    def get_url(self, offset=0):
        url = f"{self.base_url}?limit={self.limit}&offset={offset}"
        if self.add_fields:
            url = f"{url}&fields=all"
        return url

def fetch_data(config: APIConfig) -> pd.DataFrame:
    """
    Fetch data from API using pagination.

    Args:
        config: APIConfig object containing API configuration

    Returns:
        DataFrame containing all fetched data, with fields flattened if ADD_FIELDS
    """
    projects_data = []
    offset=0

    # Fetch data in chunks using pagination
    while True:
        url = config.get_url(offset)
        response = requests.get(url, headers=config.headers)
        response.raise_for_status()
        data_dict = response.json()
        batch = data_dict['projects']

        if len(batch) == 0:
            break # No more data available

        projects_data.extend(batch)
        offset += config.limit # Increment offset for next request

    # Flatten the fields data if applicable
    if config.add_fields:
        projects_data = [flatten_fields(p) for p in projects_data]
        print("Flattened fields")

    print(f"Fetched {len(projects_data)} projects")
    return pd.DataFrame(projects_data)

def flatten_fields(project_json):
    """
    Unpacks and formats the fields API extension.

    Args:
        project_json: the API json response for a project.

    Returns:
        project_json: the flattened json response

    """
    try:
        if 'fields' not in project_json:
            return project_json
        fields = project_json.pop('fields')
        for field in fields:
            field_name = field['field_name']
            field_value = field['value']

            # Convert list values to CSV for QGIS
            if type(field_value) == list:
                field_value = ', '.join([str(i) for i in field_value])
            project_json.update({field_name: field_value})

        return project_json

    except Exception as e:
        print(f"⚠️ There was an error flattening fields for {project_json.get('project_name', 'unknown')}: {e}")
        return project_json

def create_qgis_fields(df: pd.DataFrame) -> List[QgsField]:
    """
    Create QGIS fields based on DataFrame columns.

    Args:
        df: Input DataFrame with project data

    Returns:
        List of QgsField objects
    """
    # Create longitude and latitude fields
    fields = [
    QgsField('Longitude', QVariant.Double),
    QgsField('Latitude', QVariant.Double)
]
    # Create fields for remaining columns
    fields.extend([
        QgsField(column, QVariant.String if df[column].dtype == 'O' else QVariant.Double)
        for column in df.columns if column != 'point'
    ])
    return fields

def create_feature(row: pd.Series) -> QgsFeature:
    """Create QGIS feature from DataFrame row.

    Args:
        row: Series containing single project data row

    Returns:
        QgsFeature object with geometry and attributes
    """
    feature = QgsFeature()
    longitude, latitude = row['point']
    # Set point geometry
    feature.setGeometry(QgsGeometry.fromPointXY(QgsPointXY(longitude, latitude)))
    # Set attributes including coordinates and other fields
    feature.setAttributes([longitude, latitude] + [
        row[column] for column in row.index if column != 'point'
    ])
    return feature

def create_vector_layer(df: pd.DataFrame) -> QgsVectorLayer:
    """Create QGIS vector layer from DataFrame.

    Args:
        df: DataFrame containing project data

    Returns:
        QgsVectorLayer object with all features
    """
    # Create memory layer
    layer = QgsVectorLayer("Point?crs=epsg:4326", "RenewMap", "memory")
    provider = layer.dataProvider()

    # Add fields to layer
    fields = create_qgis_fields(df)
    provider.addAttributes(fields)
    layer.updateFields()

    # Add features to layer
    features = []
    for _, row in df.iterrows():
        feature = create_feature(row)
        if feature is not None:
            features.append(feature)

    provider.addFeatures(features)
    layer.updateExtents()
    print(f"Created layer with {len(features)} features")
    return layer

config = APIConfig()
df = fetch_data(config)
layer = create_vector_layer(df)
QgsProject.instance().addMapLayer(layer)

Adding the fields extension

N.B. This data is returned in json format and needs to be unpacked in the script. The logic to unpack and reshape the fields data can be found in flatten_fields().

Network

import pandas as pd
import requests

from shapely.geometry import shape
from typing import List, Dict

API_KEY = "YOUR_API_KEY"  # Replace with your actual API key
BASE_URL = "https://api.renewmap.com.au/api/v1/network"
DATA_KEY = "network"

headers = {
    "accept": "application/json",
    "Authorization": f"Bearer {API_KEY}"
}

class APIConfig:
    """Configuration class for API parameters.

    Attributes:
        base_url: Base URL for the API endpoint
        limit: Number of records per request
        headers: HTTP headers for API requests
        data_key: Key in API response containing the data array
    """
    base_url: str = BASE_URL
    limit: int = 1000
    headers: Dict = headers
    add_fields: bool = False  # Network API doesn't support fields
    data_key: str = DATA_KEY

    def get_url(self, offset=0):
        url = f"{self.base_url}?limit={self.limit}&offset={offset}"
        if self.add_fields:
            url = f"{url}&fields=all"
        return url

def fetch_data(config: APIConfig) -> pd.DataFrame:
    """Fetch data from API using pagination.

    Args:
        config: APIConfig object containing API configuration

    Returns:
        DataFrame containing all fetched data
    """
    data_list = []
    offset = 0

    # Fetch data in chunks using pagination
    while True:
        url = config.get_url(offset)
        response = requests.get(url, headers=config.headers)
        response.raise_for_status()
        data_dict = response.json()
        batch = data_dict[config.data_key]

        if len(batch) == 0:
            break  # No more data available

        data_list.extend(batch)
        offset += config.limit  # Increment offset for next request

    print(f"Fetched {len(data_list)} items")
    return pd.DataFrame(data_list)


def create_qgis_fields(gdf: pd.DataFrame) -> List[QgsField]:
    """Create QGIS fields based on GeoDataFrame columns.

    Args:
        gdf: Input GeoDataFrame with project data

    Returns:
        List of QgsField objects
    """
    # Loop through columns to dynamically create QgsField objects
    fields = []
    for column in gdf.columns:
        field_type = QVariant.String  # Adjust the type based on your data
        fields.append(QgsField(column, field_type))

    return fields

def create_feature(row: pd.Series) -> QgsFeature:
    """Create QGIS feature from GeoDataFrame row.

    Args:
        row: Series containing single project data row with geometry

    Returns:
        QgsFeature object with geometry and attributes
    """
    feature = QgsFeature()

    # Convert shapely geometry to QgsGeometry
    shapely_geom = shape(row['geometry'])
    qgs_geometry = QgsGeometry.fromWkt(shapely_geom.wkt)
    # Set geometry
    feature.setGeometry(qgs_geometry)

    # Set attributes for all columns except geometry
    attributes = [str(row[column]) for column in row.index if column != 'geometry']
    feature.setAttributes(attributes)
    return feature

def create_multilinestring_layer(gdf: pd.DataFrame) -> QgsVectorLayer:
    """Create QGIS vector layer from DataFrame.

    Args:
        df: DataFrame containing project data

    Returns:
        QgsVectorLayer object with all features
    """
    # Create a memory layer for MultiLineStrings
    layer = QgsVectorLayer("LineString?crs=epsg:4326", "Network", "memory")

    # Add fields to the layer
    fields = create_qgis_fields(gdf)
    layer.dataProvider().addAttributes(fields)
    layer.updateFields()

    # Add features to the multilinestring layer
    multiline_data = gdf[gdf['geometry'].apply(lambda x: shape(x).geom_type == 'MultiLineString')]
    for _, row in multiline_data.iterrows():
        feature = create_feature(row)
        if not feature.geometry().isNull():
            layer.dataProvider().addFeature(feature)

    layer.updateExtents()
    return layer

def create_multipolygon_layer(gdf: pd.DataFrame) -> QgsVectorLayer:
    # Create a memory layer for MultiPolygons
    layer = QgsVectorLayer("Polygon?crs=epsg:4326", "Network - Development", "memory")

    # Add fields to the layer
    fields = create_qgis_fields(gdf)
    layer.dataProvider().addAttributes(fields)
    layer.updateFields()

    # Add features to the multipolygon layer
    polygon_data = gdf[gdf['geometry'].apply(lambda x: shape(x).geom_type == 'MultiPolygon')]
    for _, row in polygon_data.iterrows():
        feature = create_feature(row)
        if not feature.geometry().isNull():
            layer.dataProvider().addFeature(feature)

    layer.updateExtents()
    return layer

config = APIConfig()
gdf = fetch_data(config)

multilinestring_layer = create_multilinestring_layer(gdf)
multipolygon_layer = create_multipolygon_layer(gdf)
QgsProject.instance().addMapLayer(multilinestring_layer)
QgsProject.instance().addMapLayer(multipolygon_layer)

Turbines

import pandas as pd
import json
import requests
from typing import Dict, List

API_KEY = "YOUR_API_KEY" # Replace with your actual API key
BASE_URL = "https://api.renewmap.com.au/api/v1/turbines"
DATA_KEY = "turbines"

headers = {
    "accept": "application/json",
    "Authorization": f"Bearer {API_KEY}"
}

class APIConfig:
    """Configuration class for API parameters.

    Attributes:
        base_url: Base URL for the API endpoint
        limit: Number of records per request
        headers: HTTP headers for API requests
        data_key: Key in API response containing the data array
    """
    base_url: str = BASE_URL
    limit: int = 10000
    headers: Dict = headers
    add_fields: bool = False  # Turbine API doesn't support fields
    data_key: str = DATA_KEY

    def get_url(self, offset=0):
        url = f"{self.base_url}?limit={self.limit}&offset={offset}"
        if self.add_fields:
            url = f"{url}&fields=all"
        return url

def fetch_data(config: APIConfig) -> pd.DataFrame:
    """Fetch data from API using pagination.

    Args:
        config: APIConfig object containing API configuration

    Returns:
        DataFrame containing all fetched data
    """
    data_list = []
    offset = 0

    # Fetch data in chunks using pagination
    while True:
        url = config.get_url(offset)
        response = requests.get(url, headers=config.headers)
        response.raise_for_status()
        data_dict = response.json()
        batch = data_dict[config.data_key]

        if len(batch) == 0:
            break  # No more data available

        data_list.extend(batch)
        offset += config.limit  # Increment offset for next request

    print(f"Fetched {len(data_list)} items")
    return pd.DataFrame(data_list)

def create_qgis_fields(df: pd.DataFrame) -> List[QgsField]:
    """Create QGIS fields based on DataFrame columns.

    Args:
        df: Input DataFrame with project data

    Returns:
        List of QgsField objects
    """
    # Create longitude and latitude fields
    fields = [
        QgsField('Longitude', QVariant.Double),
        QgsField('Latitude', QVariant.Double)
    ]
    # Add other fields from the DataFrame
    for column in df.columns:
        if column != 'point':
            field_type = QVariant.String if df[column].dtype == 'O' else QVariant.Double
            fields.append(QgsField(column, field_type))

    return fields

def create_feature(row: pd.Series) -> QgsFeature:
    """Create QGIS feature from DataFrame row.

    Args:
        row: Series containing single turbine data row

    Returns:
        QgsFeature object with geometry and attributes
    """
    feature = QgsFeature()

    # Extract longitude and latitude from the 'point' column
    longitude, latitude = row['point']

    # Set geometry and attributes
    point = QgsPointXY(longitude, latitude)
    feature.setGeometry(QgsGeometry.fromPointXY(point))

    # Prepare attribute values
    attributes = [longitude, latitude]
    for column in df.columns:
        if column != 'point':
            value = row[column]
            if isinstance(value, list):  # Handle arrays of numbers
                value = ','.join(map(str, value))  # Convert to comma-separated string
            attributes.append(value)

    feature.setAttributes(attributes)

    return feature

def create_vector_layer(df: pd.DataFrame) -> QgsVectorLayer:
    """Create a QgsVectorLayer from a DataFrame.

    Args:
        df: Input DataFrame with turbine data

    Returns:
        QgsVectorLayer object for QGIS map display
    """
    # Create qgis layer
    layer = QgsVectorLayer("Point?crs=epsg:4326", "Wind Turbine Database", "memory")
    provider = layer.dataProvider()

    # Add fields to layer
    fields = create_qgis_fields(df)
    provider.addAttributes(fields)
    layer.updateFields()

    # Add features to layer
    features = [create_feature(row) for _, row in df.iterrows()]
    provider.addFeatures(features)
    layer.updateExtents()

    print(f"Created layer with {len(features)} features")
    return layer

config = APIConfig()
df = fetch_data(config)
layer = create_vector_layer(df)

# Add the layer to the map
QgsProject.instance().addMapLayer(layer)

Troubleshooting