def extract_locations(self):
"""Preprocess the location data"""
default_pos_columns = common_cfg.coord_col_names
if set(default_pos_columns).issubset(set(self._raw_data.columns)):
print('Location data found')
# check and drop units outside provided city boundary
geometry = [
shapely.geometry.Point(xy) for xy in zip(
self._raw_data[default_pos_columns[0]], # Long
self._raw_data[default_pos_columns[1]])
] # Lat
b_within_boundary = np.array(
list(
map(lambda p: p.within(self.model_city.convhull),
geometry)))
if not all(b_within_boundary):
print(
'%s -- dropping %i units outside city.' %
(self.servicetype, sum(np.bitwise_not(b_within_boundary))))
self._raw_data = self._raw_data.iloc[
b_within_boundary, :].reset_index()
# store geolocations as geopy Point
locations = [
geopy.Point(yx) for yx in zip(
self._raw_data[default_pos_columns[1]], # Lat
self._raw_data[default_pos_columns[0]])
] # Long
propert_data = self._raw_data.drop(default_pos_columns, axis=1)
else:
raise NotImplementedError('Locations not found - not implemented!')
return propert_data, locations
def addTriangle(gwlat, gwlon, bearing, distance, features):
if (distance < 1):
return
origin = geopy.Point(gwlat, gwlon)
destination = geopy.distance.distance(
kilometers=distance / 1000.0).destination(
origin, ((360 + bearing - (0.5 / 2.0)) % 360))
lat, lon = destination.latitude, destination.longitude
destination = geopy.distance.distance(
kilometers=distance / 1000.0).destination(
origin, ((360 + bearing + (0.5 / 2.0)) % 360))
lat2, lon2 = destination.latitude, destination.longitude
feature = {}
feature["type"] = "Feature"
feature["geometry"] = {}
feature["geometry"]["type"] = "Polygon"
feature["geometry"]["coordinates"] = [[[gwlon, gwlat],
[round(lon, 6),
round(lat, 6)],
[round(lon2, 6),
round(lat2, 6)], [gwlon, gwlat]]]
features.append(copy.deepcopy(feature))
import fiona from itertools import chain import geopy.distance distance = geopy.distance.vincenty baseFile = 'data/alameda-tracts-2000/alameda-tracts-2000' baseFile = 'data/Oakland_parcels/parcels' baseFile = 'data/Oakland_parcels_subset2/parcels' # radius of 1e-3 baseFile = 'data/Oakland_parcels_subset/parcels' # radius of 1e-2 # compute map bounds center = (37.8058428, -122.2399758) # (lat, long), Armenian Church radius = 1e-3 # should be 0.001 #--- origin = geopy.Point(lat1, lon1) destination = VincentyDistance(kilometers=d).destination(origin, b) lat2, lon2 = destination.latitude, destination.longitude #--- ll = (center[1] - radius * 10, center[0] - radius) ur = (center[1] + radius * 10, center[0] + radius) #shp = fiona.open(baseFile + '.shp') #bds = shp.bounds #shp.close() extra = 0.001 #ll = (bds[0], bds[1]) #ur = (bds[2], bds[3]) coords = list(chain(ll, ur)) w, h = coords[2] - coords[0], coords[3] - coords[1]
import time
from datetime import datetime
from pymongo import MongoClient
import geopy
from geopy.distance import VincentyDistance
client = MongoClient()
db = client.testDB
recordsFutian = []
futianCenter = geopy.Point(22.543, 114.055)
northLat = VincentyDistance(kilometers=2).destination(futianCenter, 0).latitude
southLat = VincentyDistance(kilometers=2).destination(futianCenter,
180).latitude
eastLon = VincentyDistance(kilometers=2).destination(futianCenter,
90).longitude
westLon = VincentyDistance(kilometers=2).destination(futianCenter,
270).longitude
print(northLat, southLat, eastLon, westLon)
with open('./confidentialData/GPS_2016_01_02', 'rb') as file:
while True:
entry = file.readline().decode('utf-8')
if entry == "":
break
else:
infoArray = entry.split(',')
# 01 . license plate
# TODO: write regular expressions to check the data validity
try:
licensePlate = infoArray[0]
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import geopy
import geopy.distance
import sys
pt1 = geopy.Point(sys.argv[1].replace(",", "."), sys.argv[2].replace(",", "."))
pt2 = geopy.Point(sys.argv[3].replace(",", "."), sys.argv[4].replace(",", "."))
dist = geopy.distance.distance(pt1, pt2).km
print dist
def test_centroid(igroup):
assert igroup.centroid('abc') == geopy.Point(1, 1)
vehicle.simple_goto(targetLocation, groundspeed=1)
while vehicle.mode.name == "GUIDED": #Stop action if we are no longer in guided mode.
currentPoint = geopy.Point(vehicle.location.global_relative_frame.lat,
vehicle.location.global_relative_frame.lon)
remainingDistance = VincentyDistance(currentPoint, targetPoint).meters
print "Distance to target: ", remainingDistance
if remainingDistance <= 1.0: # Near enough
print "Reached target"
break
time.sleep(2)
print "home heading: ", vehicle.heading
homeHeading = vehicle.heading
homePoint = geopy.Point(vehicle.location.global_relative_frame.lat,
vehicle.location.global_relative_frame.lon)
# determine the marker locations
frontRightMarker = VincentyDistance(
meters=int(5 / sin(radians(45)))).destination(homePoint,
(homeHeading + 45) % 360)
frontLeftMarker = VincentyDistance(meters=10).destination(
frontRightMarker, (homeHeading - 90) % 360)
backLeftMarker = VincentyDistance(meters=10).destination(
frontLeftMarker, homeHeading)
backRightMarker = VincentyDistance(meters=10).destination(
backLeftMarker, (homeHeading + 90) % 360)
targetAltitude = 3
arm_and_takeoff(targetAltitude)
car_distance = 0.5 # km
gpx_tag_attribute = {}
directory_path = "/Users/TommyChang/Desktop/So Simple Mobility Simulation/src/examples/SSMS/exp/trajectories/" # will be appended with _0.gpx, _1.gpx, ...
first_car_id = 10000 # Will be used as the file name. The second car will be first_car_id + 1, etc.
# User input end -------------------------------------------------
gpx_list = []
bearing = calculate_bearing(
(direction_coord["lat"], direction_coord["lng"]),
(first_car_start_coord["lat"], first_car_start_coord["lng"]))
for i in range(0, car_count):
dist = geopy.distance.distance(kilometers=car_distance * i)
coord = dist.destination(point=geopy.Point(first_car_start_coord["lat"],
first_car_start_coord["lng"]),
bearing=bearing)
print(coord)
start_coord = {"lat": coord[0], "lng": coord[1]}
t = Track(
start_time=start_time,
end_time=end_time,
start_coord=start_coord,
direction_coord=direction_coord,
trkpt_time_interval=trkpt_time_interval, # seconds
moving_speed=moving_speed, # km/h
bearing=None)
gpx_list.append(t.create_gpx(gpx_tag_attribute=gpx_tag_attribute))
seis = read(stalist[s], format='PICKLE')
if clean:
for tr in seis.select(channel='BX*'):
seis.remove(tr)
print('delete channel ' + tr.stats['channel'])
else:
for tr in seis.select(channel='BX90*'):
seis.remove(tr)
print('delete channel ' + tr.stats['channel'])
#While we are at it there is a mistake in data_processing_2 where the stats of seis[0] (vertical component) get overwritten by those of a horizontal component... fixing this here.
seis[0].stats['channel'] = 'BHZ'
# print(seis[0].stats['channel'])
#Calculate receiver at 90 distance
origin = geopy.Point(srcdict['latitude'], srcdict['longitude'])
destination = VincentyDistance(kilometers=90 *
(6371 * np.pi / 180.)).destination(
origin, seis[0].stats['az'])
receiver = instaseis.Receiver(latitude=destination.latitude,
longitude=destination.longitude,
network=seis[0].stats['network'],
station=seis[0].stats['station'])
eventtime = seis[0].stats['eventtime']
starttime = seis[0].stats['starttime']
endtime = seis[0].stats['endtime']
st = db.get_seismograms(source=source,
receiver=receiver,
kind='displacement',
dt=0.1)
def index(self, document):
"""
The method that actually performs the indexing.
:param document: The document as a memory file.
"""
from django.contrib.gis.geos import (
Point, LineString, MultiLineString)
from obspy import read_events
# Collect all indices in a list. Each index has to be a dictionary.
indices = []
inv = read_events(document, format="quakeml")
for event in inv:
if event.origins:
org = event.preferred_origin() or event.origins[0]
else:
org = None
if event.magnitudes:
mag = event.preferred_magnitude() or event.magnitudes[0]
else:
mag = None
has_focal_mechanism = False
has_moment_tensor = False
if event.focal_mechanisms:
has_focal_mechanism = True
if any(mt for mt in event.focal_mechanisms):
has_moment_tensor = True
# Parse attributes in the baynet namespace.
# The public attribute defaults to None, it can only be set to
# True by utilizing the baynet namespace as of now.
extra = event.get("extra", {})
if "public" in extra:
public = extra["public"]["value"]
if public.lower() in ["false", "f"]:
public = False
elif public.lower() in ["true", "t"]:
public = True
else:
public = None
else:
public = None
if "evaluationMode" in extra:
evaluation_mode = extra["evaluationMode"]["value"]
else:
evaluation_mode = None
# parse horizontal uncertainties
if org and org.origin_uncertainty:
org_unc = org.origin_uncertainty
if org_unc.preferred_description == 'horizontal uncertainty':
horizontal_uncertainty_max = org_unc.horizontal_uncertainty
horizontal_uncertainty_min = org_unc.horizontal_uncertainty
horizontal_uncertainty_max_azimuth = 0
elif org_unc.preferred_description == 'uncertainty ellipse':
horizontal_uncertainty_max = \
org_unc.max_horizontal_uncertainty
horizontal_uncertainty_min = \
org_unc.min_horizontal_uncertainty
horizontal_uncertainty_max_azimuth = \
org_unc.azimuth_max_horizontal_uncertainty
else:
horizontal_uncertainty_max = None
horizontal_uncertainty_min = None
horizontal_uncertainty_max_azimuth = None
else:
horizontal_uncertainty_max = None
horizontal_uncertainty_min = None
horizontal_uncertainty_max_azimuth = None
geometry = None
if org:
geometry = [Point(org.longitude, org.latitude)]
if all(value is not None for value in (
horizontal_uncertainty_max, horizontal_uncertainty_min,
horizontal_uncertainty_max_azimuth)):
import geopy
import geopy.distance
start = geopy.Point(latitude=org.latitude,
longitude=org.longitude)
lines = []
for distance, azimuth in (
(horizontal_uncertainty_max,
horizontal_uncertainty_max_azimuth),
(horizontal_uncertainty_min,
horizontal_uncertainty_max_azimuth + 90)):
azimuth = azimuth % 180
distance = geopy.distance.VincentyDistance(
kilometers=distance / 1e3)
end1 = distance.destination(
point=start, bearing=azimuth)
end2 = distance.destination(
point=start, bearing=azimuth + 180)
line = LineString((end1.longitude, end1.latitude),
(org.longitude, org.latitude),
(end2.longitude, end2.latitude))
lines.append(line)
geometry.append(MultiLineString(lines))
else:
geometry.append(MultiLineString([]))
# phase counts
used_phase_count = None
used_p = None
used_s = None
if org:
if org.quality:
used_phase_count = org.quality.used_phase_count
if org.quality and org.quality.get('extra'):
extra = org.quality.get('extra', {})
used_p = extra.get(
'usedPhaseCountP', {}).get('value', None)
used_s = extra.get(
'usedPhaseCountS', {}).get('value', None)
if used_p is not None:
used_p = int(used_p)
if used_s is not None:
used_s = int(used_s)
# set first/last pick times
first_pick_time = None
last_pick_time = None
if event.picks:
pick_times = [
pick.time for pick in event.picks if pick.time is not None]
if pick_times:
first_pick_time = str(min(pick_times))
last_pick_time = str(max(pick_times))
indices.append({
"quakeml_id": str(event.resource_id),
"latitude": org.latitude if org else None,
"longitude": org.longitude if org else None,
"depth_in_m": org.depth if org else None,
"origin_time": str(org.time) if org else None,
"first_pick_time": first_pick_time,
"last_pick_time": last_pick_time,
"used_phase_count": used_phase_count,
"used_p": used_p,
"used_s": used_s,
"magnitude": mag.mag if mag else None,
"magnitude_type": mag.magnitude_type if mag else None,
"agency":
event.creation_info and event.creation_info.agency_id or None,
"author":
event.creation_info and event.creation_info.author or None,
"public": public,
"evaluation_mode": evaluation_mode,
"event_type": event.event_type,
"has_focal_mechanism": has_focal_mechanism,
"has_moment_tensor": has_moment_tensor,
# The special key geometry can be used to store geographic
# information about the indexes geometry. Useful for very
# fast queries using PostGIS.
"geometry": geometry,
"horizontal_uncertainty_max": horizontal_uncertainty_max,
"horizontal_uncertainty_min": horizontal_uncertainty_min,
"horizontal_uncertainty_max_azimuth":
horizontal_uncertainty_max_azimuth,
})
return indices
#import utm
import math
import geopy
import geopy.distance
from geographiclib.geodesic import Geodesic
#FRASCA_WSG86_500POINT_LAT=64.93166666666667
#FRASCA_WSG86_500POINT_LON=25.359166666666667
FRASCA_WSG86_500POINT_LAT = 64.931388
FRASCA_WSG86_500POINT_LON = 25.375800
global point0
point0 = geopy.Point(FRASCA_WSG86_500POINT_LAT, FRASCA_WSG86_500POINT_LON)
def from_frasca(x, y, force_point0=False):
global point0
dx = x - 500.0
dy = y - 500.0
dc = math.sqrt(pow(dy, 2) + pow(dx, 2))
alpha = 0 if dy == 0.0 else math.atan(abs(dx) / abs(dy))
heading = (alpha if dy > 0 and dx >= 0 else
(math.pi - alpha if dy <= 0 and dx > 0 else
(alpha + math.pi if dy < 0 and dx <= 0 else
(2 * math.pi - alpha if dy >= 0 and dx < 0 else -1))))
dist = geopy.distance.geodesic(nautical=dc)
point1 = dist.destination(point=(force_point0 if force_point0 else point0),
bearing=heading * (180.0 / math.pi))
return point1
def create_heatmap(list_of_lat_longs):
'''
This function takes a list of tuples as a paremeter, I think thats what its called
we don't particularly care here about the points themselves, that is handled by the placemarks
as long as we keep them in pairs, we don't need to preserve any other information about them
'''
#1. Create the square
lats = []
longs = []
for p in list_of_lat_longs:
lats.append(p[0])
longs.append(p[1])
lats.sort()
longs.sort()
max_lat = lats[-1]
min_lat = lats[0]
max_long = longs[-1]
min_long = longs[0]
#Start in SW corner (-,-)
STEP_AMOUNT = 1#length of each square
steps_right = 0
current_lat = float(min_lat)
current_long = float(min_long)
dist_xy = geopy.distance.geodesic(miles = STEP_AMOUNT)
dist_z = geopy.distance.geodesic(miles = (math.sqrt(2)*STEP_AMOUNT))
dist_c = geopy.distance.geodesic(miles = (math.sqrt(2)*STEP_AMOUNT)/2)#sqrt(2n)/2
grids = []
# Find all the grid points
while current_long < max_long and current_long >= min_long: #Since we always go to min_long I need = here
if current_lat < max_lat:
t_grid = [0,0,0,0]
start = geopy.Point(current_lat, current_long) #Starting point at current_lat and lon
dest_up = dist_xy.destination(start,0) # move upwards
dest_right = dist_xy.destination(start,90) #move right
dest_corner = dist_z.destination(start,45) #move diagonally
t_grid[0] = (current_lat, current_long)
t_grid[1] = (dest_up.latitude,dest_up.longitude)
t_grid[2] = (dest_corner.latitude,dest_corner.longitude)
t_grid[3] = (dest_right.latitude,dest_right.longitude)
current_lat = dest_up.latitude
grids.append(t_grid)
elif current_lat > max_lat:
steps_right +=1
current_lat = min_lat
origin = geopy.Point(min_lat, min_long)
dist_x = geopy.distance.geodesic(miles = STEP_AMOUNT*steps_right)
dest_right = dist_x.destination(origin,90)
current_long = dest_right.longitude
center_points = []
#find all centers
for grid in grids:
corner = geopy.Point(grid[0][0], grid[0][1])
center = dist_c.destination(corner,45)
center_points.append((center.latitude,center.longitude))
#print(center_points)
#calculate a_values for each center
R_FACTOR = 6
a_vals = []
for center in center_points:
a_val = 0
for point in list_of_lat_longs:
distance = geodesic(center,point).miles
a_point_val = math.exp(-1*(distance**2/(R_FACTOR*STEP_AMOUNT)))
a_val += a_point_val
a_vals.append(a_val)
print(a_val)
#a_vals_ordered = a_vals
#a_vals_ordered.sort()
#max_a_val = a_vals_ordered[-1]
#for x in range(len(a_vals)):
# a_vals[x] = a_vals[x]/max_a_val #get a value between 0 and 1
export_file = open('test.kml','w')
export_file.write('<?xml version="1.0" encoding="UTF-8"?>\n')
export_file.write('<kml xmlns="http://earth.google.com/kml/2.0">\n')
export_file.write(' <Document>\n')
export_file.write(' <Style id="red">\n')
export_file.write(' <PolyStyle>\n')
export_file.write(' <color>6f0000ff</color>\n')
export_file.write(' <outline>0</outline>\n')
export_file.write(' </PolyStyle>\n')
export_file.write(' </Style>\n')
export_file.write(' <Style id="yellow">\n')
export_file.write(' <PolyStyle>\n')
export_file.write(' <color>6f00ffff</color>\n')
export_file.write(' <outline>0</outline>\n')
export_file.write(' </PolyStyle>\n')
export_file.write(' </Style>\n')
export_file.write(' <Style id="green">\n')
export_file.write(' <PolyStyle>\n')
export_file.write(' <color>6f00ff00</color>\n')
export_file.write(' <outline>0</outline>\n')
export_file.write(' </PolyStyle>\n')
export_file.write(' </Style>\n')
export_file.write(' <Style id="blue">\n')
export_file.write(' <PolyStyle>\n')
export_file.write(' <color>6fff0000</color>\n')
export_file.write(' <outline>0</outline>\n')
export_file.write(' </PolyStyle>\n')
export_file.write(' </Style>\n')
export_file.write(' <Style id="black">\n')
export_file.write(' <PolyStyle>\n')
export_file.write(' <color>00000000</color>\n')
export_file.write(' <outline>0</outline>\n')
export_file.write(' </PolyStyle>\n')
export_file.write(' </Style>\n')
counter = 0
for g in grids:
#print(g)
corners = g
bot_left_s = ''
top_left_s = ''
top_right_s = ''
bot_right_s = ''
for i in reversed(corners[0]):
bot_left_s = bot_left_s+str(i)+','
for i in reversed(corners[1]):
top_left_s = top_left_s+str(i)+','
for i in reversed(corners[2]):
top_right_s = top_right_s+str(i)+','
for i in reversed(corners[3]):
bot_right_s = bot_right_s+str(i)+','
bot_left = ' '+bot_left_s+'0\n'
top_left = ' '+top_left_s+'0\n'
top_right = ' '+top_right_s+'0\n'
bot_right = ' '+bot_right_s+'0\n'
i = random.randint(1,5)
export_file.write(' <Placemark>\n')
if a_vals[counter] <= .1:
export_file.write(' <styleUrl>#black</styleUrl>\n')
elif a_vals[counter] <= .8:
export_file.write(' <styleUrl>#blue</styleUrl>\n')
elif a_vals[counter] <= 1.2:
export_file.write(' <styleUrl>#green</styleUrl>\n')
elif a_vals[counter] <= 1.4:
export_file.write(' <styleUrl>#yellow</styleUrl>\n')
else:
export_file.write(' <styleUrl>#red</styleUrl>\n')
export_file.write(' <Polygon> <outerBoundaryIs> <LinearRing> \n')
export_file.write(' <coordinates>\n')
export_file.write(bot_left)
export_file.write(bot_right)
export_file.write(top_right)
export_file.write(top_left)
export_file.write(bot_left)
export_file.write(' </coordinates>\n')
export_file.write(' </LinearRing> </outerBoundaryIs> </Polygon>\n')
export_file.write(' </Placemark>\n')
counter+=1
export_file.write(' </Document>\n')
export_file.write('</kml>')
export_file.close()
def subset(self, query):
# Ensure we have an output folder that will be cleaned by tmpreaper
if not os.path.isdir("/tmp/subset"):
os.makedirs("/tmp/subset")
working_dir = "/tmp/subset/"
entire_globe = True # subset the globe?
if 'min_range' in query:
# Area explicitly specified
entire_globe = False
# Bounding box extents
bottom_left = [float(x) for x in query.get('min_range').split(',')]
top_right = [float(x) for x in query.get('max_range').split(',')]
# Time range
try:
# Time is an index into timestamps array
time_range = [int(x) for x in query.get('time').split(',')]
except ValueError:
# Time is in ISO 8601 format and we need the dataset quantum
quantum = query.get('quantum')
if quantum == 'day' or quantum == 'hour':
def find_time_index(isoDate: datetime.datetime):
for idx, date in enumerate(self.timestamps):
# Only compare year, month, day.
# Some daily/hourly average datasets have an
# hour and minute offset that messes up
# the index search.
if date.date() == isoDate.date():
return idx
else:
def find_time_index(isoDate: datetime.datetime):
for idx, date in enumerate(self.timestamps):
# Only compare year and month
if date.date().year == isoDate.date().year and \
date.date().month == isoDate.date().month:
return idx
time_range = [
dateutil.parser.parse(x) for x in query.get('time').split(',')
]
time_range = [find_time_index(x) for x in time_range]
apply_time_range = False
if time_range[0] != time_range[1]:
apply_time_range = True
# Finds a variable in a dictionary given a substring containing common characters.
# Don't use regex here since compiling a new pattern every call WILL add huge overhead.
# This is guaranteed to be the fastest method.
def find_variable(substring: str, variables: list):
for key in variables:
if substring in key:
return key
return None
variable_list = list(self._dataset.variables.keys())
# Get lat/lon variable names from dataset (since they all differ >.>)
lat_var = find_variable("lat", variable_list)
lon_var = find_variable("lon", variable_list)
depth_var = find_variable("depth", variable_list)
if not entire_globe:
# Find closest indices in dataset corresponding to each calculated point
ymin_index, xmin_index, _ = find_nearest_grid_point(
bottom_left[0], bottom_left[1], self._dataset,
self._dataset.variables[lat_var],
self._dataset.variables[lon_var])
ymax_index, xmax_index, _ = find_nearest_grid_point(
top_right[0], top_right[1], self._dataset,
self._dataset.variables[lat_var],
self._dataset.variables[lon_var])
# Compute min/max for each slice in case the values are flipped
# the netCDF4 module does not support unordered slices
y_slice = slice(min(ymin_index, ymax_index),
max(ymin_index, ymax_index))
x_slice = slice(min(xmin_index, xmax_index),
max(xmin_index, xmax_index))
# Get nicely formatted bearings
p0 = geopy.Point(bottom_left)
p1 = geopy.Point(top_right)
else:
y_slice = slice(self._dataset.variables[lat_var].size)
x_slice = slice(self._dataset.variables[lon_var].size)
p0 = geopy.Point([-85.0, -180.0])
p1 = geopy.Point([85.0, 180.0])
# Get timestamp
time_var = find_variable("time", variable_list)
timestamp = str(
format_date(
pandas.to_datetime(
np.float64(self._dataset[time_var][time_range[0]].values)),
"yyyyMMdd"))
endtimestamp = ""
if apply_time_range:
endtimestamp = "-" + str(
format_date(
pandas.to_datetime(
np.float64(
self._dataset[time_var][time_range[1]].values)),
"yyyyMMdd"))
dataset_name = query.get('dataset_name')
# Figure out coordinate dimension names
if "riops" in dataset_name:
lon_coord = "xc"
lat_coord = "yc"
elif dataset_name == "giops_forecast":
lon_coord = "longitude"
lat_coord = "latitude"
else:
lon_coord = "x"
lat_coord = "y"
# Do subset along coordinates
subset = self._dataset.isel(**{lat_coord: y_slice, lon_coord: x_slice})
# Select requested time (time range if applicable)
if apply_time_range:
subset = subset.isel(**{
time_var:
slice(int(time_range[0]),
int(time_range[1]) + 1)
}) # slice doesn't include the last element
else:
subset = subset.isel(
**
{time_var: slice(int(time_range[0]),
int(time_range[0]) + 1)})
# Filter out unwanted variables
output_vars = query.get('variables').split(',')
output_vars.extend([depth_var, time_var, lat_var,
lon_var]) # Keep the coordinate variables
for variable in subset.data_vars:
if variable not in output_vars:
subset = subset.drop(variable)
output_format = query.get('output_format')
filename = dataset_name + "_" + "%dN%dW-%dN%dW" % (p0.latitude, p0.longitude, p1.latitude, p1.longitude) \
+ "_" + timestamp + endtimestamp + "_" + output_format
# "Special" output
if output_format == "NETCDF3_NC":
# Regrids an input data array according to it's input grid definition
# to the output definition
def regrid(data: np.ndarray,
input_def: pyresample.geometry.SwathDefinition,
output_def: pyresample.geometry.SwathDefinition):
data = np.rollaxis(data, 0, 4) # Roll time axis backward
data = np.rollaxis(data, 0, 4) # Roll depth axis backward
data = data.reshape([data.shape[0], data.shape[1],
-1]) # Merge time + depth axis together
# Perform regridding using nearest neighbour weighting
regridded = pyresample.kd_tree.resample_nearest(
input_def,
data,
output_def,
50000,
fill_value=None,
nprocs=8)
return np.moveaxis(regridded, -1,
0) # Move merged axis back to front
GRID_RESOLUTION = 50
# Check lat/lon wrapping
lon_vals, lat_vals = pyresample.utils.check_and_wrap(
lons=subset[lon_var].values, lats=subset[lat_var].values)
# Generate our lat/lon grid of 50x50 resolution
min_lon, max_lon = np.amin(lon_vals), np.amax(lon_vals)
min_lat, max_lat = np.amin(lat_vals), np.amax(lat_vals)
XI = np.linspace(min_lon,
max_lon,
num=GRID_RESOLUTION,
dtype=lon_vals.dtype)
YI = np.linspace(min_lat,
max_lat,
num=GRID_RESOLUTION,
dtype=lat_vals.dtype)
XI_mg, YI_mg = np.meshgrid(XI, YI)
# Define input/output grid definitions
input_def = pyresample.geometry.SwathDefinition(lons=lon_vals,
lats=lat_vals)
output_def = pyresample.geometry.SwathDefinition(lons=XI_mg,
lats=YI_mg)
# Find correct variable names in subset
temp_var = find_variable('temp', subset.variables)
saline_var = find_variable('salin', subset.variables)
x_vel_var = find_variable('crtx', subset.variables)
y_vel_var = find_variable('crty', subset.variables)
# Create file
time_range = len(subset[time_var][:]) - 1
filename = dataset_name.upper() + "_" + \
datetime.date.today().strftime("%Y%m%d") +"_d0" + \
(("-"+str(time_range)) if time_range > 0 else "") + "_" + \
str(np.round(top_right[0]).astype(int)) + "N" + str(np.abs(np.round(bottom_left[1]).astype(int))).zfill(3) + "W" + \
str(np.round(bottom_left[0]).astype(int)) + "N" + str(np.abs(np.round(top_right[1])).astype(int)).zfill(3) + "W" + \
"_" + output_format
ds = netCDF4.Dataset(working_dir + filename + ".nc",
'w',
format="NETCDF3_CLASSIC")
ds.description = "Converted " + dataset_name
ds.history = "Created: " + str(datetime.datetime.now())
ds.source = "www.navigator.oceansdata.ca"
# Create the netcdf dimensions
ds.createDimension('lat', GRID_RESOLUTION)
ds.createDimension('lon', GRID_RESOLUTION)
ds.createDimension('time', len(subset[time_var][:]))
# Create the netcdf variables and assign the values
latitudes = ds.createVariable('lat', 'd', ('lat', ))
longitudes = ds.createVariable('lon', 'd', ('lon', ))
latitudes[:] = YI
longitudes[:] = XI
# Variable Attributes
latitudes.long_name = "Latitude"
latitudes.units = "degrees_north"
latitudes.NAVO_code = 1
longitudes.long_name = "Longitude"
longitudes.units = "degrees_east"
longitudes.NAVO_code = 2
# LOL I had CreateDimension vs createDimension here >.< Stumped Clyde too hehe :P
ds.createDimension('depth', len(subset[depth_var][:]))
levels = ds.createVariable('depth', 'i', ('depth', ))
levels[:] = subset[depth_var][:]
levels.long_name = "Depth"
levels.units = "meter"
levels.positive = "down"
levels.NAVO_code = 5
if temp_var is not None:
origshape = subset[temp_var].shape
temp_data = regrid(subset[temp_var].values, input_def,
output_def)
temp_data = np.reshape(temp_data,
(origshape[0], origshape[1],
GRID_RESOLUTION, GRID_RESOLUTION))
temp = ds.createVariable('water_temp',
'd', ('time', 'depth', 'lat', 'lon'),
fill_value=-30000.0)
# Convert from Kelvin to Celcius
for i in range(0, len(subset[depth_var][:])):
temp[:, i, :, :] = temp_data[:, i, :, :] - 273.15
temp.valid_min = -100.0
temp.valid_max = 100.0
temp.long_name = "Water Temperature"
temp.units = "degC"
temp.NAVO_code = 15
if saline_var is not None:
salinity = ds.createVariable('salinity',
'd',
('time', 'depth', 'lat', 'lon'),
fill_value=-30000.0)
salinity[:] = regrid(
subset[saline_var].values, input_def, output_def
)[:] # Note the automatic reshaping by numpy here ^.^
salinity.long_name = "Salinity"
salinity.units = "psu"
salinity.valid_min = 0.0
salinity.valid_max = 45.0
salinity.NAVO_code = 16
if x_vel_var is not None:
x_velo = ds.createVariable('water_u',
'd',
('time', 'depth', 'lat', 'lon'),
fill_value=-30000.0)
x_velo[:] = regrid(subset[x_vel_var].values, input_def,
output_def)[:]
x_velo.long_name = "Eastward Water Velocity"
x_velo.units = "meter/sec"
x_velo.NAVO_code = 17
if y_vel_var is not None:
y_velo = ds.createVariable('water_v',
'd',
('time', 'depth', 'lat', 'lon'),
fill_value=-30000.0)
y_velo[:] = regrid(subset[y_vel_var].values, input_def,
output_def)[:]
y_velo.long_name = "Northward Water Velocity"
y_velo.units = "meter/sec"
y_velo.NAVO_code = 18
temp_file_name = working_dir + str(uuid.uuid4()) + ".nc"
subset.to_netcdf(temp_file_name)
subset.close()
# Reopen using netCDF4 to get non-encoded time values
subset = netCDF4.Dataset(temp_file_name, 'r')
times = ds.createVariable('time', 'i', ('time', ))
# Convert time from seconds to hours
for i in range(0, len(subset[time_var])):
times[i] = subset[time_var][i] / 3600
times.long_name = "Validity time"
times.units = "hours since 1950-01-01 00:00:00"
times.time_origin = "1950-01-01 00:00:00"
ds.close()
subset.close()
else:
# Save subset normally
subset.to_netcdf(working_dir + filename + ".nc",
format=output_format)
if int(query.get('should_zip')) == 1:
myzip = zipfile.ZipFile('%s%s.zip' % (working_dir, filename),
mode='w')
myzip.write('%s%s.nc' % (working_dir, filename),
os.path.basename('%s%s.nc' % (working_dir, filename)))
myzip.comment = b"Generated from www.navigator.oceansdata.ca"
myzip.close() # Must be called to actually create zip
return working_dir, filename + ".zip"
return working_dir, filename + ".nc"
def run():
content =""
with open('tmp/op.json', 'r') as content_file:
content = content_file.read()
content = content.replace("var somepoints = ","")
json_data = json.loads(content)
for way in json_data:
curr=None
prev = None
for latlongs in way['latlongs']:
if not prev:
prev = geopy.Point(latlongs["lat"],latlongs["lng"])
continue
curr = geopy.Point(latlongs["lat"],latlongs["lng"])
distance = calculateDistance(prev,curr)
bearing = bearing_tuple(prev,curr)
prev = curr
print("++++++++++++++++++++++++")
new_json_data = []
for way in json_data:
payload ={}
payload['id'] = way['id']
payload['latlongs'] = []
curr = None
prev = None
for latlongs in way['latlongs']:
if not prev:
prev = geopy.Point(latlongs["lat"],latlongs["lng"])
payload['latlongs'].append(latlongs)
continue
curr = geopy.Point(latlongs["lat"],latlongs["lng"])
bearing = bearing_tuple(prev,curr)
distance = calculateDistance(prev,curr)
distance = int(distance)
if distance > min_distance_in_m:
np = getNextPoint(prev,bearing)
payload['latlongs'].append({
"lat":np.latitude,
"lng": np.longitude
})
for i in range(int(distance/min_distance_in_m -1) ):
np = getNextPoint(np,bearing)\
payload['latlongs'].append({
"lat":np.latitude,
"lng": np.longitude
})
payload['latlongs'].append({
"lat":curr.latitude,
"lng":curr.longitude
})
prev = curr
new_json_data.append(payload)
f = open("tmp/all_road_10m_min_distance.json", "w")
f.write("var all_road_10m_min_distance = " + json.dumps(new_json_data))
f.close()
country = 'malawi'
clustering = np.load(os.path.join(out_dir, country, 'clustering.npy'))
counting = np.load(os.path.join(out_dir, country, 'counting.npy'))
nlats = np.load("../data/output/LSMS/malawi/nlats.npy")
nlons = np.load("../data/output/LSMS/malawi/nlons.npy")
nlats_min = np.min(nlats)
nlats_max = np.max(nlats)
nlons_min = np.min(nlons)
nlons_max = np.max(nlons)
lats_dif = nlats_max - nlats_min
lons_dif = nlons_max - nlons_min
mid_point = geopy.Point(((nlats_max + nlats_min) / 2),
((nlons_max + nlons_min) / 2))
d = geopy.distance.great_circle(kilometers=1)
onek_lats = mid_point.latitude - d.destination(point=mid_point,
bearing=180).latitude
onek_lons = mid_point.longitude - d.destination(point=mid_point,
bearing=270).longitude
size_y = math.ceil(lats_dif / onek_lats)
size_x = math.ceil(lons_dif / onek_lons)
df = pd.read_csv(os.path.join(out_dir, country, 'data.csv'), index_col='id')
cdl = pd.read_csv(os.path.join(out_dir, country,
'candidate_download_locs.csv'),
index_col='name')
startTime = datetime.now()
# Enter the lat long of location that is the center of the area. We choose an area of 20KmX20Km. lat_S and long_S
# are the latitude and longitude of the chosen city
Side_secon_net = 70
Side_of_cell = 10
diag_secon_net = ((Side_secon_net**2 + Side_secon_net**2)**0.5) / 2
lat_S = 39.9612
long_S = -82.9988
d = [diag_secon_net] * 4
dis = [400] * 4
num_of_cells = (Side_secon_net**2) / (Side_of_cell**2)
# given: lat1, lon1, b = bearing in degrees, d = distance in kilometers
origin = geopy.Point(lat_S, long_S)
# With the north line across the city we find the bearing of the diagonal corners(lower left and upper right) of the square around
# the city. Convert bearing from degrees to radians
bearing_deg = [45, 135, 225, 315]
des = []
des_big = []
for index in range(len(bearing_deg)):
destination = VincentyDistance(kilometers=d[index]).destination(
origin, bearing_deg[index])
destination_big = VincentyDistance(kilometers=dis[index]).destination(
origin, bearing_deg[index])
lat2 = destination.latitude
lon2 = destination.longitude
latBig = destination_big.latitude
lonBig = destination_big.longitude
# radial interpolation of atmpospheric variables
# repeated for other variables, and different radius distances (not all shown here)
# this example is for Geopotential Heights (z500) within 1,500 km of location
z500_1500km = np.empty([14880, 1081])
for k in range(8):
lon = lons_8[k]
lat = lats_8[k]
latvec = []
lonvec = []
step = steps[k]
# calculate interpolation grid
for a in np.arange(50, 1550, 50): # 50 km radial increments (rho's)
for b in np.arange(10, 370,
10): # 10 degree azimuth increments (theta's)
start = geopy.Point(lat, lon) # origin
circ = geopy.distance.distance(
kilometers=a) # great circle distance
dest = circ.destination(point=start,
bearing=b) # transect along great circle
latvec.append(dest[0])
lonvec.append(dest[1])
latvec = np.hstack([lat, latvec]) # add origin lat
lonvec = np.hstack([lon, lonvec]) # add origin lon
# interpolate underlying values using radial grid
interp_vals = scipy.interpolate.interpn((merra_lons, merra_lats), z500_ja,
(lonvec, latvec))
del latvec, lonvec
interp_vals = np.squeeze(interp_vals)
z500_1500km[
step, :] = interp_vals.T # interpolated values from r = 1,500 km grid
if len(close_clusters_indices) == 0:
# create a new cluster
new_cluster = Cluster(cid=len(clusters),
nb_points=1,
last_seen=point.timestamp,
lat=point.lat,
lon=point.lon,
angle=point.angle)
clusters.append(new_cluster)
roadnet.add_node(new_cluster.cid)
current_cluster = new_cluster.cid
# recompute the cluster index
update_cluster_index = True
else:
# add the point to the cluster
pt = geopy.Point(point.get_coordinates())
close_clusters_distances = [
geopy.distance.distance(
pt, geopy.Point(
clusters[clu_index].get_coordinates())).meters
for clu_index in close_clusters_indices
]
closest_cluster_indx = close_clusters_indices[
close_clusters_distances.index(
min(close_clusters_distances))]
clusters[closest_cluster_indx].add(point)
current_cluster = closest_cluster_indx
# Adding the edge:
if prev_cluster == -1:
prev_cluster = current_cluster
continue
lon1 = radians(coord1[1])
lat2 = asin(sin(lat1) * cos(distance / R) + cos(lat1) * sin(distance / R) * cos(bearing))
lon2 = lon1 + atan2(
sin(bearing) * sin(distance / R) * cos(lat1),
cos(distance / R) - sin(lat1) * sin(lat2))
lat2 = degrees(lat2)
lon2 = degrees(lon2)
return lat2, lon2
import geopy
import geopy.distance
# Define starting point.
start = geopy.Point(48.853, 2.349)
# Define a general distance object, initialized with a distance of 1 km.
d = geopy.distance.VincentyDistance(meters = 1)
# Use the `destination` method with a bearing of 0 degrees (which is north)
# in order to go from point `start` 1 km to north.
print(d.destination(point=start, bearing=0).)
print(compute_offset(1, 0, (48.853, 2.349)))
chi = [41.850033,-87.650055]
nyc = [40.714268,-74.005974]
print(haversine(chi,nyc))
# https://www.movable-type.co.uk/scripts/latlong.html
plot_point_ex = plt.gcf()
plot_point_ex.savefig("plot_point_ex.png")
#Clear current matplotlib plot
plt.clf()
#plt.figure(figsize=(40,20))
#plt.show()
def get_grid_cell_indices(given_lat,given_lon,lat_grid,lon_grid):
distances = np.sqrt((given_lon - lon_grid)**2 + (given_lat - lat_grid)**2)
#return lat_grid[np.where(distances == np.amin(distances))][0], lon_grid[np.where(distances == np.amin(distances))][0]
return np.where(distances == np.amin(distances))[0][0], np.where(distances == np.amin(distances))[1][0]
# Define starting point.
start = geopy.Point(storm_lat, storm_lon)
# Define a general distance object, initialized with a distance of 400 km.
radius = 800
d = geopy.distance.VincentyDistance(kilometers = radius)
# Use the `destination` method with a bearing of 0 degrees (which is north)
# in order to go from point `start` 1 km to north.
north_point = d.destination(point=start, bearing=0)
south_point = d.destination(point=start, bearing=180)
east_point = d.destination(point=start, bearing=90)
west_point = d.destination(point=start, bearing=270)
urcrnrlat = north_point.latitude
urcrnrlon = east_point.longitude
def main():
"""Runs the program."""
#ask the user if a test is being run
test = is_test()
#if it is a test, get any extra constraints from the user
if test:
type_of_test = decide_test()
#will store extra constraints if a test is being run
extra_con = []
#read in the databases (each database contains the city name and its
#longitude/latitude coordinate).
canada = read_files("canada", "Canada Cities.csv")
america = read_files("america", "US Cities.csv")
# create a list for canadian and american cities
canada_cities = []
america_cities = []
for entry in canada:
canada_cities.append(entry["city"].lower())
for entry in america:
america_cities.append(entry["city"].lower())
#get the raw location from the user and clarify any duplicates to get the
#starting and ending city (the countries will of course remain the same)
raw_location = raw_location_input(canada_cities, america_cities)
start_city, end_city = clarify_duplicates(canada, america, raw_location)
start_country = raw_location["starting country"]
end_country = raw_location["ending country"]
is_urgent = get_urgency()
#calculate the total distance between the starting and ending city
start_coord = (start_city["latitude"], start_city["longitude"])
end_coord = (end_city["latitude"], end_city["longitude"])
total_dist = calc_distance(start_coord, end_coord)
print(str(start_coord) + " " + str(end_coord))
#tell the user the total number of km
print("A trip from " + start_city["city"] + ", " +
start_city["province/state"] + " to " + end_city["city"] + ", " +
end_city["province/state"] + " is " + str(total_dist) + " km long.")
#calculate 1/tenth of the distance from the start to the end
#the user will be given 10 choices of evenly spaced cities to stop at along the way
#they can stop at 0, 1, or multiple; their choice
next_dist = total_dist / 10
geodesic = pyproj.Geod(ellps='WGS84')
#calculates the initial bearing (fwd_azimuth) and the final bearing
fwd_azimuth, back_azimuth, distance = geodesic.inv(start_city["longitude"],
start_city["latitude"],
end_city["longitude"],
end_city["latitude"])
final_bearing = back_azimuth - 180
#Define the starting and ending points.
temp_start = geopy.Point(start_city["latitude"], start_city["longitude"])
end = geopy.Point(end_city["latitude"], end_city["longitude"])
start = temp_start
#Define a general distance object, initialized with a distance of the stop distance (in km).
d = geopy.distance.distance(kilometers=next_dist)
#lists that will hold all the stops and the stops that the user chooses, respectively
all_stops = []
chosen_stops = []
#define the geolocator
geolocator = Nominatim(user_agent="Bing")
#loop 10 times (for 10 stops)
for i in range(10):
# Use the destination method with our starting point and initial bearing
# in order to go from our starting point to the next city in the line of stops.
#finds the next point from the starting point given the bearing
#if we are closer to the start, use our initial bearing; otherwise, use the final bearing
if (i < 5):
final = d.destination(point=temp_start, bearing=fwd_azimuth)
else:
final = d.destination(point=temp_start, bearing=final_bearing)
#finds the location
location = geolocator.reverse(str(final))
print(str(i) + ": " + str(location))
#add it to the list of all stops
all_stops.append({"location": str(location), "coord": final})
#reset the next starting point
temp_start = final
#add the starting location to the chosen stops
chosen_stops.append({"location": start_city["city"], "coord": start})
user_input = -2 #initizalize
#get the user input for the stops they would like and store it in chosen_stops
print(
"Please enter which stops you would like to take along the way." +
"If you are done entering stops, please enter '-1'. If you don't want to take any stops,"
+ " enter -1 right away.")
while (user_input != -1):
user_input = int(input("Enter your next stop: "))
if (user_input < -1 or user_input > 9):
print("Wrong input! Please try again!")
else:
if (user_input != -1):
chosen_stops.append(all_stops[user_input])
#add the ending location to the chosen stops
#chosen_stops is now a list of all stops including the start and end
chosen_stops.append({"location": end_city["city"], "coord": end})
for i in range(len(chosen_stops) - 1):
#calculate the distance between each stop
distance = calc_distance(chosen_stops[i]["coord"],
chosen_stops[i + 1]["coord"])
print("The distance between " + str(chosen_stops[i]["location"]) +
" and " + str(chosen_stops[i + 1]["location"]) + " is " +
str(distance) + " km. ")
dict_string = str(chosen_stops[i]["location"]) + " to " + str(
chosen_stops[i + 1]["location"])
#set up the dictionary and append it to the list
entry = {"location": dict_string, "distance": distance}
stop_info.append(entry)
#loop through every stop
for i in range(len(stop_info)):
#now that we know the distance, we can calculate the time needed to travel
#between each stop with each mode of transportation
distance = stop_info[i]["distance"]
drive_time = calc_time(distance, "drive")
transit_time = calc_time(distance, "transit")
plane_time = calc_time(distance, "plane")
travel = determine_travel_modes(drive_time, transit_time, plane_time)
for mode in travel:
print(mode + " from " + stop_info[i]["location"] + ":" +
str(travel[mode]) + " hours.")
all_modes = []
urgent = {}
#determine the FASTEST mode of travel
if travel != {}:
if "drive" in travel.keys():
all_modes.append(travel["drive"])
if "transit" in travel.keys():
all_modes.append(travel["transit"])
if "plane" in travel.keys():
all_modes.append(travel["plane"])
fastest = min(all_modes)
for mode in travel:
if travel[mode] <= fastest:
urgent[mode] = travel[mode]
#add a new key, the dictionary of available travel modes, to the list
stop_info[i]["travel"] = travel
#do the same with the urgent travel mode
stop_info[i]["urgent"] = urgent
#reset the travel modes
travel = {}
urgent = {}
#determine if the travel is international or not and set the appropriate constraint
border = get_international(start_country, end_country)
#add constraints for the appropriate test, if it is a test
if test:
if type_of_test == "w":
extra_con = test_weather(stop_info)
elif type_of_test == "a":
extra_con = test_affordability()
elif type_of_test == "t":
extra_con = test_travel()
#solve!
solve(border, is_urgent, test, extra_con)
def google_earth_button():
'''
When map button is pressed
'''
database_wb = openpyxl.load_workbook(DATABASE_FILE)
database_sheet = database_wb[DATABASE_SHEET_NAME]
projects = get_project_types()
years = get_years() #list of years to select
clients = get_client_types() #list of client types
#check if location is checked
if loc_spec_var.get():
#if it is get the location to select by
coords = get_location()
radius = int(loc_radius_var.get())
export_file = open('RACEGIS.kml',
'w') #open a text file of the date + rest
export_file.write(kml_header_1)
export_file.write(kml_header)
if loc_spec_var.get(): #write radius
long_r = radius / 52.28
lat_r = radius / 69.01
export_file.write(kml_circle)
dist = geopy.distance.distance(miles=radius)
for theta in range(361):
pt = dist.destination(point=geopy.Point(coords), bearing=theta)
point = str(pt[1]) + ',' + str(pt[0]) + ',0\n'
export_file.write('\t\t' + point)
export_file.write(kml_circle_end)
for i in range(2, database_sheet.max_row):
#FILTER
proj_id = database_sheet["A" + str(i)].value
proj_year = str(proj_id)[:4]
if int(proj_year) in years: #check if meets year criteria
if database_sheet["D" + str(
i
)].value in projects: #check if meets project type criteria
if database_sheet["F" + str(
i
)].value in clients: #check if meets client type criteria
if not loc_spec_var.get(
): #if we do NOT need to sort by location
name = str(database_sheet["B" + str(i)].value)
if "&" in name:
name = name.replace("&", "/")
proj_id = str(database_sheet["A" + str(i)].value)
if "&" in proj_id:
proj_id = proj_id.replace("&", "/")
location = str(database_sheet["C" + str(i)].value)
if "&" in location:
location = location.replace("&", "/")
proj_type = str(database_sheet["D" + str(i)].value)
if "&" in proj_type:
proj_type = proj_type.replace("&", "/")
client = str(database_sheet["E" + str(i)].value)
if "&" in client:
client = client.replace("&", "/")
client_type = str(database_sheet["F" + str(i)].value)
if "&" in client_type:
client_type = client_type.replace("&", "/")
long_lat_str = str(
database_sheet["H" + str(i)].value) + "," + str(
database_sheet["G" + str(i)].value)
export_file.write(' <Placemark>\n')
export_file.write(' <name>' + name + '</name>\n')
export_file.write(
' <styleUrl>#project-style</styleUrl>\n')
export_file.write(' <ExtendedData>\n')
export_file.write(' <Data name="ID">\n')
export_file.write(' <value>' + proj_id + '</value>\n')
export_file.write(' </Data>\n')
export_file.write(' <Data name="Address">\n')
export_file.write(' <value>' + location +
'</value>\n')
export_file.write(' </Data>\n')
export_file.write(' <Data name="projectType">\n')
export_file.write(' <value>' + proj_type +
'</value>\n')
export_file.write(' </Data>\n')
export_file.write(' <Data name="clientType">\n')
export_file.write(' <value>' + client_type +
'</value>\n')
export_file.write(' </Data>\n')
export_file.write(' <Data name="clientName">\n')
export_file.write(' <value>' + client +
'</value>\n')
export_file.write(' </Data>\n')
export_file.write(' </ExtendedData>\n')
export_file.write(' <Point>\n')
export_file.write(' <coordinates>' +
long_lat_str + ',0</coordinates>\n')
export_file.write(' </Point>\n')
export_file.write(' </Placemark>\n')
else:
if database_sheet["G" + str(i)].value != None:
t_coords = (float(database_sheet["G" +
str(i)].value),
float(database_sheet["H" +
str(i)].value))
distance = geodesic(coords, t_coords).miles
statusbar_text.set(distance)
if distance < radius:
name = str(database_sheet["B" + str(i)].value)
if "&" in name:
name = name.replace("&", "/")
proj_id = str(database_sheet["A" +
str(i)].value)
if "&" in proj_id:
proj_id = proj_id.replace("&", "/")
location = str(database_sheet["C" +
str(i)].value)
if "&" in location:
location = location.replace("&", "/")
proj_type = str(database_sheet["D" +
str(i)].value)
if "&" in proj_type:
proj_type = proj_type.replace("&", "/")
client = str(database_sheet["E" +
str(i)].value)
if "&" in client:
client = client.replace("&", "/")
client_type = str(database_sheet["F" +
str(i)].value)
if "&" in client_type:
client_type = client_type.replace("&", "/")
long_lat_str = str(database_sheet[
"H" + str(i)].value) + "," + str(
database_sheet["G" + str(i)].value)
export_file.write(' <Placemark>\n')
export_file.write(' <name>' + name +
'</name>\n')
export_file.write(
' <styleUrl>#project-style</styleUrl>\n'
)
export_file.write(' <ExtendedData>\n')
export_file.write(' <Data name="ID">\n')
export_file.write(' <value>' + proj_id +
'</value>\n')
export_file.write(' </Data>\n')
export_file.write(
' <Data name="Address">\n')
export_file.write(' <value>' +
location + '</value>\n')
export_file.write(' </Data>\n')
export_file.write(
' <Data name="projectType">\n')
export_file.write(' <value>' +
proj_type + '</value>\n')
export_file.write(' </Data>\n')
export_file.write(
' <Data name="clientType">\n')
export_file.write(' <value>' +
client_type + '</value>\n')
export_file.write(' </Data>\n')
export_file.write(
' <Data name="clientName">\n')
export_file.write(' <value>' + client +
'</value>\n')
export_file.write(' </Data>\n')
export_file.write(' </ExtendedData>\n')
export_file.write(' <Point>\n')
export_file.write(' <coordinates>' +
long_lat_str +
',0</coordinates>\n')
export_file.write(' </Point>\n')
export_file.write(' </Placemark>\n')
export_file.write(kml_footer)
export_file.close()
os.startfile("RACEGIS.kml")
def jitterLocation(location=None, maxMeters=10):
origin = geopy.Point(location[0], location[1])
b = random.randint(0, 360)
d = math.sqrt(random.random()) * (float(maxMeters) / 1000)
destination = geopy.distance.distance(kilometers=d).destination(origin, b)
return (destination.latitude, destination.longitude, location[2])
def getPath(pointToReturn):
global px, py
return geopy.Point(px[pointToReturn], py[pointToReturn])
poly_overall = fr.values.reshape((len(dfa), 180, 2))
center = []
Cell_coord = []
TV_Tower_Dist = []
TV_Tower_Loc = []
TV_HAAT_Data = []
TV_ERP_Data = []
TV_Chan = []
TV_Receiver_Dist = []
TV_Receiver_Loc = []
bear_degree = [45, 135, 225, 315]
out_channels = {}
for cell in range(0, len(Data)):
CENTER = (Data[str(cell)]['location'][1], Data[str(cell)]['location'][0])
dis = ((2 * (Side_of_cell**2))**0.5) / 2
orig = geopy.Point(CENTER[0], CENTER[1])
val = []
for b in range(0, len(bear_degree)):
destination = VincentyDistance(kilometers=dis).destination(
orig, bear_degree[b])
lat = destination.latitude
lon = destination.longitude
ext = (lat, lon)
val.extend([ext])
Cell_coord.append(val)
chan_avl = (Data[str(cell)]['Available_channel'])
TV_chan = []
TV_TX_Dist = []
TV_TX_loc = []
TV_haat = []
TV_erp = []
def getGPSLocation():
global locationIndex
currenctLocation = gps.getPositionData()
return geopy.Point(currenctLocation.fLatitude, currenctLocation.fLongitude)
# Upper left corner lat_a = max(ys) lon_a = min(xs) # Lower left corner lat_b = min(ys) lon_b = min(xs) # Upper ritght corner lat_c = max(ys) lon_c = max(xs) coords_a = (lat_a, lon_a) coords_b = (lat_b, lon_b) coords_c = (lat_c, lon_c) # Rectangle profile start = geopy.Point(lat_a, lon_a) h = geopy.distance.vincenty(coords_a, coords_b).m w = geopy.distance.vincenty(coords_a, coords_c).m print(str(h) + ' Meters (Height)') print(str(w) + ' Meters (Width)') # The number of crop units placed horizontally and vertically units_v = h / crop_y units_h = w / crop_x # Distance move EAST = 90 SOUTH = 180 d = geopy.distance.VincentyDistance(meters=crop_center[1])
#!/usr/bin/python
import json
import math
import geopy
import random
from geopy.distance import VincentyDistance
# given: lat1, lon1, b = bearing in degrees, d = distance in kilometers
# FIDI Manhattan
origin = geopy.Point(40.705150, -74.0085300)
orig_bearing = 139.3
# Crown Heights
#origin = geopy.Point(40.673512, -73.958879)
#orig_bearing = 195
with open('/home2/greenim9/data/data.json') as data_file:
data_loaded = json.load(data_file)
stroke_weight = float(data_loaded['DATA']['PWR'])
destination = VincentyDistance(kilometers=15).destination(
origin, orig_bearing + float(data_loaded['DATA']['DOA']))
lat2, lon2 = destination.latitude, destination.longitude
print "Content-type: application/json"
print
def __init__(self, data: dict) -> None:
self.code = data["LocationCode"]
self.point = geopy.Point(data["Latitude"], data["Longitude"])
self.facility_owned = bool(data["FacilityOwnedByCarvana"])
self.trips = []
self.visited = False
return requests.post(url, data=json.dumps(param),
headers=headers).json()
@classmethod
def request(cls, urls):
' 以20个一轮,进行批量处理 '
out_list = []
obj = cls()
for chunck in chunks(urls, 20):
out_list.extend(obj.__batch_by_amap(chunck))
return out_list
if __name__ == "__main__":
# 单点绑路
print "单点绑路: ", BindRoad.one(geopy.Point(39.9849500000, 116.3077580000))
# 批量绑路服务测试
SIZE = 30
origins = [
x for x in repeat(geopy.Point(39.9849500000, 116.3077580000), SIZE)
]
result = [x for x in BindRoad.batch(origins)]
print "批量绑路: ", SIZE
assert len(result) == SIZE
# 附近搜索命名
result = Around.one(geopy.Point(39.9849500000, 116.3077580000))
print "单点命名: ", result[0]['name'], result[0]['type'], result[0]['distance']
# 批量调用高德接口
SIZE = 30
