def _get_zoom(coordinate_array, width):
"""
If mapbox is being used for tranmission plotting, get the zoom based on the
bounding area of the input data and the width (in pixels) of the map
"""
# Keys are zoom levels, values are m/pixel at that zoom level
zoom_dict = {
0: 156412, 1: 78206, 2: 39103, 3: 19551, 4: 9776, 5: 4888,
6: 2444, 7: 1222, 8: 610.984, 9: 305.492, 10: 152.746,
11: 76.373, 12: 38.187, 13: 19.093, 14: 9.547, 15: 4.773,
16: 2.387, 17: 1.193, 18: 0.596, 19: 0.298}
bounds = [
coordinate_array.max(dim='locs').values,
coordinate_array.min(dim='locs').values]
max_distance = vincenty(*bounds)
metres_per_pixel = max_distance / width
for k, v in zoom_dict.items():
if v > metres_per_pixel:
continue
else:
zoom = k - 4
break
return zoom
def process_per_distance_constraints(tech_name, tech_settings, locations,
locations_comments, loc_from, loc_to):
# Process distance, if any per_distance constraints exist
if any('per_distance' in i
for i in tech_settings.keys_nested(subkeys_as='list')):
# If no distance was given, we calculate it from coordinates
if 'distance' not in tech_settings:
# Simple check - earlier sense-checking already ensures
# that all locations have either lat/lon or x/y coords
loc1 = locations[loc_from].coordinates
loc2 = locations[loc_to].coordinates
if 'lat' in locations[loc_from].coordinates:
distance = vincenty([loc1.lat, loc1.lon], [loc2.lat, loc2.lon])
else:
distance = math.sqrt((loc1.x - loc2.x)**2 +
(loc1.y - loc2.y)**2)
tech_settings.distance = distance
locations_comments.set_key(
'{}.links.{}.techs.{}.distance'.format(loc_from, loc_to,
tech_name),
'Distance automatically computed from coordinates')
# Add per-distance values to their not-per-distance cousins
# FIXME these are hardcoded for now
if 'energy_eff_per_distance' in tech_settings.constraints:
distance_energy_eff = (
tech_settings.constraints.energy_eff_per_distance**
tech_settings.distance)
tech_settings.constraints.energy_eff = (
tech_settings.constraints.get_key('energy_eff', 1.0) *
distance_energy_eff)
del tech_settings.constraints['energy_eff_per_distance']
locations_comments.set_key(
'{}.links.{}.techs.{}.constraints.energy_eff'.format(
loc_from, loc_to, tech_name),
'Includes value computed from energy_eff_per_distance')
for k in tech_settings.costs.keys_nested(subkeys_as='list'):
if 'energy_cap_per_distance' in k:
energy_cap_costs_per_distance = (
tech_settings.costs.get_key(k) * tech_settings.distance)
tech_settings.costs[k.split('.')[0]].energy_cap = (
tech_settings.costs[k.split('.')[0]].get_key(
'energy_cap', 0) + energy_cap_costs_per_distance)
tech_settings.costs.del_key(k)
locations_comments.set_key(
'{}.links.{}.techs.{}.costs.{}'.format(
loc_from, loc_to, tech_name, k),
'Includes value computed from energy_cap_per_distance')
elif 'purchase_per_distance' in k:
purchase_costs_per_distance = (tech_settings.costs.get_key(k) *
tech_settings.distance)
tech_settings.costs[k.split('.')[0]].purchase = (
tech_settings.costs[k.split('.')[0]].get_key(
'purchase', 0) + purchase_costs_per_distance)
tech_settings.costs.del_key(k)
locations_comments.set_key(
'{}.links.{}.techs.{}.costs.{}'.format(
loc_from, loc_to, tech_name, k),
'Includes value computed from purchase_per_distance')
return tech_settings
def process_per_distance_constraints(tech_name, tech_settings, locations, locations_comments, loc_from, loc_to):
# Process distance, if any per_distance constraints exist
if any('per_distance' in i
for i in tech_settings.keys_nested(subkeys_as='list')):
# If no distance was given, we calculate it from coordinates
if 'distance' not in tech_settings:
# Simple check - earlier sense-checking already ensures
# that all locations have either lat/lon or x/y coords
loc1 = locations[loc_from].coordinates
loc2 = locations[loc_to].coordinates
if 'lat' in locations[loc_from].coordinates:
distance = vincenty(
[loc1.lat, loc1.lon], [loc2.lat, loc2.lon]
)
else:
distance = math.sqrt(
(loc1.x - loc2.x)**2 + (loc1.y - loc2.y)**2
)
tech_settings.distance = distance
locations_comments.set_key(
'{}.links.{}.techs.{}.distance'.format(loc_from, loc_to, tech_name),
'Distance automatically computed from coordinates'
)
# Add per-distance values to their not-per-distance cousins
# FIXME these are hardcoded for now
if 'energy_eff_per_distance' in tech_settings.constraints:
distance_energy_eff = (
tech_settings.constraints.energy_eff_per_distance **
tech_settings.distance
)
tech_settings.constraints.energy_eff = (
tech_settings.constraints.get_key('energy_eff', 1.0) *
distance_energy_eff
)
del tech_settings.constraints['energy_eff_per_distance']
locations_comments.set_key(
'{}.links.{}.techs.{}.constraints.energy_eff'.format(loc_from, loc_to, tech_name),
'Includes value computed from energy_eff_per_distance'
)
for k in tech_settings.costs.keys_nested(subkeys_as='list'):
if 'energy_cap_per_distance' in k:
energy_cap_costs_per_distance = (
tech_settings.costs.get_key(k) *
tech_settings.distance
)
tech_settings.costs[k.split('.')[0]].energy_cap = (
tech_settings.costs[k.split('.')[0]].get_key('energy_cap', 0) +
energy_cap_costs_per_distance
)
tech_settings.costs.del_key(k)
locations_comments.set_key(
'{}.links.{}.techs.{}.costs.{}'.format(loc_from, loc_to, tech_name, k),
'Includes value computed from energy_cap_per_distance'
)
elif 'purchase_per_distance' in k:
purchase_costs_per_distance = (
tech_settings.costs.get_key(k) *
tech_settings.distance
)
tech_settings.costs[k.split('.')[0]].purchase = (
tech_settings.costs[k.split('.')[0]].get_key('purchase', 0) +
purchase_costs_per_distance
)
tech_settings.costs.del_key(k)
locations_comments.set_key(
'{}.links.{}.techs.{}.costs.{}'.format(loc_from, loc_to, tech_name, k),
'Includes value computed from purchase_per_distance'
)
return tech_settings