remove utils.hfov_from_table

divtel/utils.py

@@ -1,9 +1,6 @@
 import numpy as np
 import matplotlib.pyplot as plt
 
-from shapely.ops import unary_union, polygonize
-from shapely.geometry import LineString, Point
-
 import astropy.units as u
 
 def calc_mean(table, columns):
@@ -95,54 +92,3 @@ def convert_radius(radius, focal, toDeg=False):
         temp = radius.to(u.rad)
         radius= np.tan(temp.value)*focal
     return radius
-
-def hfov_from_table(table, m_cut=0, return_multiplicity=False, full_output=False):
-
-        if table['alt'].unit == 'rad':
-            table['alt']=table['alt']*180/np.pi
-            table['az']=table['az']*180/np.pi
-
-
-
-        if max(table["az"])-min(table["az"]) > 180:
-            polygons = []
-
-            for az, alt, r in zip(table['az'], table['alt'], table["radius"]):
-                if az < 180:
-                    polygons.append(Point(az, alt).buffer(r))
-                else:
-                    polygons.append(Point(az-360, alt).buffer(r))
-        else:
-            polygons = [Point(az, alt).buffer(r) for az, alt, r in zip(table['az'], table['alt'], table["radius"])]
-
-        union = unary_union([LineString(list(pol.exterior.coords)) for pol in polygons])
-        geoms = [geom for geom in polygonize(union)]
-        hfov = [geom.area for geom in geoms]
-
-        count_overlaps = np.zeros(len(geoms))
-        for i, geom in enumerate(geoms):
-            count_overlaps[i] = sum([1 for pol in polygons if abs(geom.difference(pol).area)<1e-5])
-
-        hfov = np.array(hfov)
-
-        # multiplicity associated with each patch
-        overlaps = np.array(count_overlaps)
-        multiplicity = np.array([[i, hfov[overlaps==i].sum()] for i in set(overlaps)])
-        eff_overlaps=[]
-        eff_geoms=[]
-        for i in range(len(overlaps)):
-            if overlaps[i]>m_cut:
-                eff_overlaps.append(overlaps[i])
-                eff_geoms.append(geoms[i])
-
-        fov = sum(multiplicity[:,1][multiplicity[:,0]>=m_cut])*u.deg**2
-
-        if full_output:
-            return multiplicity, eff_overlaps, eff_geoms
-        elif return_multiplicity:
-            m_ave = np.average(multiplicity[:,0], weights=multiplicity[:,1])
-            m_var = np.average((multiplicity[:,0]-m_ave)**2, weights=multiplicity[:,1])
-            return fov, m_ave, m_var
-        else:
-            return fov
-