import arcpy

raster_path = 'C:/blablabla/raster_bla.tif'
raster = arcpy.Raster(raster_path)
raster_data = arcpy.RasterToNumPyArray(raster)
# import rasterio
# raster = rasterio.open(raster_path)
# raster_data = raster.read()

def leaf_area_index(land_use, ndvi):
    result = ndvi.copy()
    for lu in np.unique(land_use):
        results[land_use == lu] = _calculate_lai(ndvi[land_use == lu], lu)
    return result

def np_to_raster(array, raster_ref):
    """Utility to convert a numpy array to a Raster object using another Raster as reference

    Arguments:
    -----------
    array : np.ndarray
        data to convert
    raster_ref : arcpy.Raster, or str
        reference raster or path to reference raster

    Returns:
    -----------
    out : arcpy.Raster
        converted Raster object
    """
    if not isinstance(array, np.ndarray):
        raise TypeError('The input must be a numpy array')
    meta = arcpy.Describe(raster_ref)
    cell_size_x = meta.meanCellWidth
    cell_size_y = meta.meanCellHeight
    lowerleftcorner = meta.extent.lowerLeft

    out_raster = arcpy.NumPyArrayToRaster(array, lowerleftcorner, cell_size_x, cell_size_y, 0)
    return out_raster

def write_img(array, raster_ref, outpath):
    newprofile = {
        'count': 1,
        'crs': raster_ref.crs,
        'dtype': 'uint8',
        'driver': 'GTiff',
        'transform': raster_ref.transform,
        'height': data.shape[0],
        'width': data.shape[1],
        'blockxsize': 256,
        'tiled': True,
        'blockysize': 256,
        'nodata': None
    }

    with rasterio.open(outpath, 'w', **newprofile) as dst:
        dst.write(data)

def run(land_use_path, ndvi_path, output_path):

    #here you load your stuff
    land_use = arcpy.Raster(land_use_path)
    lu_meta = arcpy.Describe(land_use)

    #here you test the rasters to see if they are aligned
    assert lu_meta.height == ndvi_meta.height

    #here you get the data out of the raster
    lu_data = arcpy.RasterToNumPyArray(land_use)

    #here you do your calculation
    lai_data = leaf_area_index(lu_data, ndvi_data)

    #here you convert the result to raster
    lai_raster = np_to_raster(lai_data, land_use)
    lai_raster.save(output_path)
    # or write_img(lai_data, land_use, output_path) if you are working with rasterio