def __init__(self):
# _members stores member regions in a dictionary keyed by RID
self._members = {}
# These IDGenerator instances generate unique ID numbers that are never
# reused, and always unique (once used an ID number cannot be
# reassigned to another agent). All instances of the Person class, for
# example, will have a unique ID number generated by the PIDGen
# IDGenerator instance.
self._PIDGen = IDGenerator()
self._RIDGen = IDGenerator()
class World(Agent_set):
"""
The world class generates new agents, while tracking ID numbers to ensure
that they are always unique across each agent type. It also contains a
dictionary with all the regions in the model.
"""
def __init__(self):
# _members stores member regions in a dictionary keyed by RID
self._members = {}
# These IDGenerator instances generate unique ID numbers that are never
# reused, and always unique (once used an ID number cannot be
# reassigned to another agent). All instances of the Person class, for
# example, will have a unique ID number generated by the PIDGen
# IDGenerator instance.
self._PIDGen = IDGenerator()
self._RIDGen = IDGenerator()
def set_DEM_data(DEM, gt, prj):
self._DEM_array = DEM
self._DEM_gt = gt
self._DEM_prj = prj
return 0
def get_DEM(self):
return self._DEM_array
def get_DEM_data(self):
return self._DEM_array, self._DEM_gt, self._DEM_prj
def set_world_mask_data(self, world_mask, gt, prj):
self._world_mask_array = world_mask
self._world_mask_gt = gt
self._world_mask_prj = prj
return 0
def get_world_mask(self):
return self._world_mask_array
def get_world_mask_data(self):
return self._world_mask_array, self._world_mask_gt, self._world_mask_prj
def set_lulc_data(self, lulc, gt, prj):
self._lulc_array = lulc
self._lulc_gt = gt
self._lulc_prj = prj
return 0
def get_lulc(self):
return self._lulc_array
def set_lulc(self, new_lulc):
self._lulc_array = new_lulc
def get_lulc_data(self):
return self._lulc_array, self._lulc_gt, self._lulc_prj
def set_lulc_markov_matrix(self, markov_file):
"""
Stores a markov transition matrix in dictionary form to be used for a
simple model of land cover change.
The transitions are stored in a dictionary keyed as:
class_t1:class[t_2]:probability
"""
file = open(markov_file, 'r')
lines = file.readlines()
file.close()
# Delete the first line since it is a header
if not (lines.pop(0) == '"first","second","lower","upper"\n'):
raise ValueError("Error loading transition matrix (%s doesn't look like a markov transition file)"%markov_file)
markov_dict = {}
for line in lines:
line = line.strip('\n')
t1, t2, lower, upper = line.split(',')
t1 = int(t1.strip('\'"'))
t2 = int(t2.strip('\'"'))
lower = float(lower.strip('\'"'))
upper = float(upper.strip('\'"'))
if not markov_dict.has_key(t1):
markov_dict[t1] = {}
if markov_dict[t1].has_key(t2):
raise ValueError("Error in transition matrix in %s"%markov_file)
markov_dict[t1][t2] = (lower, upper)
self._markov_dict = markov_dict
def lulc_markov_transition(self):
# TODO: Right now this only works if there are only two classes. Rewrite
# this to work for a wider range of scenarios.
current_lu = self.get_lulc()
new_lu = current_lu
random_values = np.random.random(np.shape(current_lu))
for t1 in self._markov_dict.keys():
for t2 in self._markov_dict[t1].keys():
lower_prob_bound, upper_prob_bound = self._markov_dict[t1][t2]
new_lu[(current_lu == t1) & (random_values >= lower_prob_bound)
& (random_values < upper_prob_bound)] = t2
self.set_lulc(new_lu)
def new_person(self, birthdate, PID=None, age=0, sex=None,
initial_agent=False, ethnicity=None, education=None, charcoal=None,
own_toilet=None, yrs_in_house_cat=None, x=None, y=None, health=None):
"Returns a new person agent."
if PID == None:
PID = self._PIDGen.next()
else:
# Update the generator so the PID will not be reused
self._PIDGen.use_ID(PID)
return Person(self, birthdate, PID, age, sex, initial_agent, ethnicity,
education, charcoal, own_toilet, yrs_in_house_cat, x, y, health)
def new_region(self, RID=None, initial_agent=False):
"Returns a new region agent, and adds it to the world member list."
if RID == None:
RID = self._RIDGen.next()
else:
# Update the generator so the RID will not be reused
self._RIDGen.use_ID(RID)
region = Region(self, RID, initial_agent)
self.add_agent(region)
return region
def get_regions(self):
return self._members.values()
def iter_regions(self):
"Convenience function for iteration over all regions in the world."
for region in self._members.values():
yield region
def iter_persons(self):
"Convenience function used for things like incrementing agent ages."
for region in self.iter_regions():
for person in region.iter_persons():
yield person
def num_persons(self):
"Convenience function used for getting total populationsize."
pop = 0
for region in self.iter_regions():
pop += region.num_persons()
return pop
def write_persons_to_csv(self, timestep, results_path):
"""
Writes a list of persons, with a header row, to CSV.
"""
psn_csv_file = os.path.join(results_path, "psns_time_%s.csv"%timestep)
out_file = open(psn_csv_file, "w")
csv_writer = csv.writer(out_file)
csv_writer.writerow(["pid", "x_utm30", "y_utm30", "rid", "gender", "age", "education", "charcoal", "own_toilet", "yrs_in_house_cat", "health", "ethnicity", "veg_fraction"])
for region in self.iter_regions():
for person in region.iter_persons():
new_row = []
new_row.append(person.get_ID())
new_row.append(person._x)
new_row.append(person._y)
new_row.append(person.get_parent_agent().get_ID())
new_row.append(person.get_sex())
new_row.append(person.get_age_years())
new_row.append(person._education)
new_row.append(person._charcoal)
new_row.append(person._own_toilet)
new_row.append(person._yrs_in_house_cat)
new_row.append(person._health)
new_row.append(person._ethnicity)
new_row.append(person._veg_fraction)
csv_writer.writerow(new_row)
out_file.close()
def extract_egocentric_neighborhoods(self, lulc_data, buffer):
"""
Extracts a buffer from the given dataset around each person in the
world.
Note that the 'lulc_data' parameter should be a tuple as is returned from
the get_**_data functions (get_lulc_data, get_DEM_data, etc.), and that
the 'buffer' parameter should be specified in meters.
"""
lulc_array, gt, prj = lulc_data
rows, cols = np.shape(lulc_array)
min_x = gt[0]
max_y = gt[3]
pixel_width = gt[1]
pixel_height = gt[5] # note pixel_height is negative
max_x = min_x + cols * pixel_width
min_y = max_y + rows * pixel_height
buffer_pixels_x = int(np.round(buffer / pixel_width, 0))
buffer_pixels_y = int(np.round(buffer / np.abs(pixel_height), 0))
def convert_to_img_coords(x, y):
img_x = int((x - min_x)/pixel_width)
img_y = int((y - max_y)/pixel_height)
return img_x, img_y
data = np.zeros((2*buffer_pixels_x, 2*buffer_pixels_y, self.num_persons()), dtype='int8')
person_IDs = []
n = -1
for person in self.iter_persons():
n += 1
person_IDs.append(person.get_ID())
x = person.get_x()
y = person.get_y()
assert ((x - buffer) > min_x) & ((x + buffer) < max_x), "Neighborhood must be within raster image"
assert ((y - buffer) > min_y) & ((y + buffer) < max_y), "Neighborhood must be within raster image"
# Round off coordinates to the nearest center of a cell
x = round((x - min_x) / pixel_width, 0)*pixel_width + min_x + pixel_width/2
y = round((y - min_y) / np.abs(pixel_height), 0)*np.abs(pixel_height) + min_y + np.abs(pixel_height)/2
center_x, center_y = convert_to_img_coords(x, y)
# In the below lines ul means "upper left", lr means "lower right"
ul_x, ul_y = center_x-buffer_pixels_x+1, center_y-buffer_pixels_y+1 # here buffer_pixels_y is positive but remember y increases going down in the image
lr_x, lr_y = center_x+buffer_pixels_x+1, center_y+buffer_pixels_y+1
box = lulc_array[ul_y:lr_y, ul_x:lr_x]
# TODO: The commented out line below should eventually replace the
# one following it for storing the box in the data array. The
# current line is used to ensure NANs get filled in areas where the
# neighborhood boundary for a person is outside the image.
#data[:,:,n] = box
assert np.shape(box) == np.shape(data)[0:2], "NBH box must always cover a full NBH"
data[0:np.shape(box)[0], 0:np.shape(box)[1], n] = box
return person_IDs, data
def calculate_veg_fractions(self):
# Vegetation is coded as:
# 0: NA
# 1: NONVEG
# 2: VEG
veg_value = rcParams['lulc.veg_value']
NA_value = rcParams['lulc.NA_value']
if rcParams['NBH_effects_type'] == "egocentric":
buffer = rcParams['lulc.buffer']
person_IDs, neighborhoods = self.extract_egocentric_neighborhoods(self.get_lulc_data(), buffer)
veg_fractions_dict = calculate_cover_fraction_NBH(person_IDs, neighborhoods, veg_value, NA_value)
veg_fractions = 0.
for person in self.iter_persons():
person._veg_fraction = veg_fractions_dict[person.get_ID()]
veg_fractions += person._veg_fraction
# Return mean veg fraction for use in progress tracking printout
# while running the model.
return veg_fractions / self.num_persons()
elif rcParams['NBH_effects_type'] == "vernacular":
# Otherwise use vernacular neighborhood (veg_fraction calculated
# over the entire world - this works since currently the model is
# only run with a single vernacular neighborhood at a time).
veg_fraction = calculate_cover_fraction_world(self.get_lulc(), veg_value, NA_value)
for person in self.iter_persons():
person._veg_fraction = veg_fraction
# Return FMV NBH veg fraction for use in progress tracking printout
# while running the model.
return veg_fraction
elif rcParams['NBH_effects_type'] == "none":
return 0
else:
raise ValueError('Invalid neighborhoods effects type "%s"'%rcParams['NBH_effects_type'])
