def get_abs(g_agg, gl_ab, e_opts1, e_land, Sg, lon, lat, DatLists, map_lons,
map_lats, s_o_lons, s_o_lats, typeof):
# FIND GEOGRAPHIC MATCH
dist_from_o = []
for si, val in enumerate(s_o_lons):
d = fxns.haversine(val, s_o_lats[si], lon, lat)
dist_from_o.append(d)
dist_from_o = np.array(dist_from_o)
geo_match = (g_agg / (g_agg + dist_from_o))
p_or_a_geo = np.random.binomial(1, geo_match, Sg)
lon = min(map_lons, key=lambda x: abs(x - lon))
lat = min(map_lats, key=lambda x: abs(x - lat))
i1 = map_lons.tolist().index(lon)
i2 = map_lats.tolist().index(lat)
# FIND ENVIRONMENTAL MATCH
matches = []
for ii, ls in enumerate(DatLists):
e_val = ls[i2][i1]
diff = np.abs(e_opts1[ii] - e_val)
match = (0.1 / (0.1 + diff))
matches.append(match)
avg_match = np.mean(matches, axis=0)
p_or_a_env = np.random.binomial(1, avg_match, Sg)
sad = []
if typeof in ['model1', 'model2']:
sad = geo_match * p_or_a_geo
if typeof in ['model3', 'model4', 'model5', 'model6']:
sad = geo_match * p_or_a_geo * gl_ab
if typeof in ['model7', 'model8']:
sad = avg_match * p_or_a_env
elif typeof in ['model9', 'model10']:
sad = avg_match * p_or_a_geo * gl_ab
elif typeof in ['model11', 'model12']:
sad = avg_match * p_or_a_geo * gl_ab * e_land
return sad
print(len(lons), len(lats))
#sys.exit()
Is = range(len(lons))
for i in range(100000):
i1, i2 = np.random.choice(Is, size=2, replace=False)
lon1 = lons[i1]
lat1 = lats[i1]
lon2 = lons[i2]
lat2 = lats[i2]
if lon1 < -180 or lon1 > 180: continue
if lon2 < -180 or lon2 > 180: continue
if lat1 < -90 or lat1 > 90: continue
if lat2 < -90 or lat2 > 90: continue
D = fxns.haversine(lon1, lat1, lon2, lat2)
outlist = [name, D]
outlist = str(outlist).strip('[]')
outlist = outlist.replace("'", "")
outlist = outlist.replace(" ", "")
OUT.write(outlist + '\n')
print(i, name)
OUT.close()
def run_model(j, typeof, ldg):
r = int(1000) # number of sites
Sg = int(1000) # number of species
num_iter = int(10000)
lat = np.random.uniform(-90, 90) # starting latitude
lon = np.random.uniform(-180, 180) # starting longitude
loc1 = [lat, lon] # starting location
DatLists = [
PopDat, TopoDat, LandDat, VegDat, SeaDat, NppDat, ChlDat, RainDat,
IceDat, PermDat, SicsDat, LaiDat
]
geoDs = [] # geo distances
maxDs = [] # max geo distances
Slopes = [] # DDR slopes
s_o_lons = []
s_o_lats = []
if ldg == 0:
ed = float(pi * 6371.0087714150598)
method = 'great_circle'
lons_lats = fxns.get_pts(Sg, loc1, ed, method)
for lon_lat in lons_lats:
s_o_lons.append(lon_lat[0])
s_o_lats.append(lon_lat[1])
elif ldg == 1:
s_o_lats = np.random.normal(0, 25, Sg).tolist()
for indx, val in enumerate(s_o_lats):
if val < -90:
s_o_lats[indx] = -90
elif val > 90:
s_o_lats[indx] = 90
s_o_lons = np.linspace(-180, 180, Sg)
terrestrial = np.random.binomial(1, 0.5, Sg)
# whether species is terrestrial or not
# 1 == terrestrial
# 0 == aquatic
e_opts1 = [] # list to species environmental optimums
for ie in range(12): # each species gets 12 env optimums
opts = np.array(np.random.uniform(0, 1, Sg))
e_opts1.append(opts)
if typeof in ['model5', 'model6']:
g_agg = 10**np.random.uniform(1, 3, Sg)
else:
g_agg = np.ones(Sg) * 10**3
gl_ab = 10**np.random.uniform(1, 4, Sg)
Ds = [
0, 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 1500, 2000, 2500,
3000, 3500, 4000, 4500, 5000, 5500, 6000, 6500, 7000, 7500, 8000, 8500,
9000, 9500, 10000, 11000, 12000, 13000, 14000, 15000, 16000, 17000,
18000, 19000, 20015.1
]
######################### RUN SIMULATIONS #################################
for i in Ds:
pts = []
if i < 600:
lat1, lon1 = loc1
lat_o, lon_o = loc1
ed = float(pi * 6371)
for ii in range(r):
b = np.random.uniform(-180, 180)
di = (np.random.uniform(i) + np.random.uniform(i)) / 2
origin = geopy.Point(lat_o, lon_o)
destination = great_circle(kilometers=di).destination(
origin, b)
lat1, lon1 = destination.latitude, destination.longitude
pts.append([lon1, lat1])
else:
method = 'great_circle'
pts = fxns.get_pts(r, loc1, i, method) # get locations for samples
geoDs2 = [] # mean geo distances
comDs2 = [] # community distances
s_by_s = [] # site-by-species matrix
for pt in pts:
lon, lat = pt
is_lnd = is_land(lon, lat)
if is_lnd is False:
terrestrial = 1 - terrestrial
sad = get_abs(g_agg, gl_ab, e_opts1, terrestrial, Sg, lon, lat,
DatLists, map_lons, map_lats, s_o_lons, s_o_lats,
typeof)
if max(sad) != 0:
s_by_s.append(sad)
else:
s_by_s.append([0] * Sg)
s_by_s = np.asarray(s_by_s) # Site by species matrix
#for ind1 in range(len(pts)):
# for ind2 in range(len(pts)):
for ii in range(num_iter):
ind1, ind2 = np.random.choice(range(len(pts)), 2, replace=False)
if ind1 >= ind2: continue
lon1, lat1 = pts[ind1]
sad1 = s_by_s[ind1]
lon2, lat2 = pts[ind2]
sad2 = s_by_s[ind2]
if max(sad1) == 0 or max(sad2) == 0: continue
dx = fxns.haversine(lon1, lat1, lon2, lat2)
sadr = np.asarray([sad1, sad2])
sadr = np.delete(sadr, np.where(~sadr.any(axis=0))[0], axis=1)
sad1 = sadr[0]
sad2 = sadr[1]
pair = np.asarray([sad1, sad2])
dy = 1 - spatial.distance.pdist(pair, metric='braycurtis')[0]
if np.isnan(dy) == True or dy < 0 or dy > 1: continue
comDs2.append(dy)
geoDs2.append(dx)
g = np.array(geoDs2) / max(geoDs2)
c = np.array(comDs2) / max(comDs2)
slope, intercept, r_value, p_value, std_err = stats.linregress(g, c)
avgD = np.mean(geoDs2)
pstr = str(j) + ' : ' + str(np.round(i, 1)) + ' ' + str(
np.round(avgD, 1))
pstr += ' | BC: ' + str(np.round(np.mean(comDs2), 3)) + ' : slope:'
pstr += str(np.round(slope, 3)) + ' | model:' + str(typeof)
print(pstr)
geoDs.append(np.mean(geoDs2))
Slopes.append(slope)
maxDs.append(max(geoDs2))
geoDs, maxDs, Slopes = zip(*sorted(zip(geoDs, maxDs, Slopes)))
clr = randcolor()
return [list(geoDs), list(maxDs), list(Slopes), clr]
