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Functions

Maths calculations used in the PyPSA-China workflow.

HVAC_cost_curve(distance)

Calculate the cost of HVAC lines based on distance. Args: distance (float): distance in km Returns: float: cost in currency

Source code in workflow/scripts/functions.py 
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def HVAC_cost_curve(distance):
    """Calculate the cost of HVAC lines based on distance.
    Args:
        distance (float): distance in km
    Returns:
        float: cost in currency
    """
    d = np.array([608, 656, 730, 780, 903, 920, 1300])
    c = 1000 / 7.5 * np.array([5.5, 4.71, 5.5, 5.57, 5.5, 5.5, 5.51])

    c_func = interpolate.interp1d(d, c, fill_value="extrapolate")
    c_results = c_func(distance)

    return c_results

area_from_lon_lat_poly(geometry)

For shapely geometry in lon-lat coordinates, returns area in km^2.

Source code in workflow/scripts/functions.py 
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def area_from_lon_lat_poly(geometry):
    """For shapely geometry in lon-lat coordinates,
    returns area in km^2."""

    project = partial(
        pyproj.transform,
        pyproj.Proj(init="epsg:4326"),
        pyproj.Proj(proj="aea"),  # Source: Lon-Lat
    )  # Target: Albers Equal Area Conical https://en.wikipedia.org/wiki/Albers_projection
    # TODO fix
    new_geometry = transform(project, geometry)

    # default area is in m^2
    return new_geometry.area / 1e6

cartesian(s1, s2)

Compute the Cartesian product of two pandas Series.

Parameters:

Name Type Description Default
s1 Series

first series

 required
s2 Series

second series

 required

Returns: pd.DataFrame: A DataFrame representing the Cartesian product of s1 and s2.

Examples:

>>> s1 = pd.Series([1, 2, 3], index=["a", "b", "c"])
>>> s2 = pd.Series([4, 5, 6], index=["d", "e", "f"])
>>> cartesian(s1, s2)
d  e   f
a  4  5   6
b  8 10  12
c 12 15  18
Source code in workflow/scripts/functions.py 
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def cartesian(s1: pd.Series, s2: pd.Series) -> pd.DataFrame:
    """
    Compute the Cartesian product of two pandas Series.

    Args:
        s1 (pd.Series): first series
        s2 (pd.Series): second series
    Returns:
        pd.DataFrame: A DataFrame representing the Cartesian product of s1 and s2.

    Examples:
        >>> s1 = pd.Series([1, 2, 3], index=["a", "b", "c"])
        >>> s2 = pd.Series([4, 5, 6], index=["d", "e", "f"])
        >>> cartesian(s1, s2)
        d  e   f
        a  4  5   6
        b  8 10  12
        c 12 15  18
    """
    return pd.DataFrame(np.outer(s1, s2), index=s1.index, columns=s2.index)

haversine(p1, p2)

Calculate the great circle distance in km between two points on the earth (specified in decimal degrees)

Parameters:

Name Type Description Default
p1 Point

location 1 in decimal deg

 required
p2 Point

location 2 in decimal deg

 required

Returns:

Name Type Description
float float

great circle distance in [km]
Source code in workflow/scripts/functions.py 
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def haversine(p1, p2) -> float:
    """Calculate the great circle distance in km between two points on
    the earth (specified in decimal degrees)

    Args:
        p1 (shapely.Point): location 1 in decimal deg
        p2 (shapely.Point): location 2 in decimal deg

    Returns:
        float: great circle distance in [km]
    """

    # convert decimal degrees to radians
    lon1, lat1, lon2, lat2 = map(radians, [p1[0], p1[1], p2[0], p2[1]])

    # haversine formula
    dlon = lon2 - lon1
    dlat = lat2 - lat1
    a = sin(dlat / 2) ** 2 + cos(lat1) * cos(lat2) * sin(dlon / 2) ** 2
    c = 2 * asin(sqrt(a))
    r = 6371  # Radius of earth in kilometers. Use 3956 for miles
    return c * r