Python distance.distance方法代码示例

# 需要导入模块: from geopy import distance [as 别名]
# 或者: from geopy.distance import distance [as 别名]
def nearby_now(self) -> List[Tuple[str, Pos, float]]:
        now = datetime.utcnow()

        t1 = time()
        self.last_query_t = t1

        lons, lats, alts, errors = self.orbs.get_lonlatalt(now)
        t2 = time()
        rough_near = np.logical_and(np.abs(lats - self.loc.lat) < 3, np.abs(lons - self.loc.long) < 3)
        valid_satpos = list(
            zip(self.satnames[~errors][rough_near], lats[rough_near], lons[rough_near], alts[rough_near]))
        nearby = [(name, Pos(lat=lat, long=lon), alt) for name, lat, lon, alt in valid_satpos if
                  distance.distance(self.loc, (lat, lon)).km < 200]
        t3 = time()
        print("loc:{:.2f}s dist: {:.2f}s tot: {:.2f}s, sats: {:02d}".format(t2 - t1, t3 - t2, t3 - t1, len(nearby)))

        if not self.filtered_errors:
            print("filtering errors")
            self.satnames = self.satnames[~errors]
            self.tles = itertools.compress(self.tles, ~errors)
            self.create_orbitals()
            self.filtered_errors = True
        return nearby 

# 需要导入模块: from geopy import distance [as 别名]
# 或者: from geopy.distance import distance [as 别名]
def mk_dx_dy_from_geotif_layer(geotif):
    """
    Extracts the change in x and y coordinates from the geotiff file. Presently
    only supports WGS-84 files.
    """
    ELLIPSOID_MAP = {'WGS84': 'WGS-84'}
    ellipsoid = ELLIPSOID_MAP[geotif.grid_coordinates.wkt]
    d = distance(ellipsoid=ellipsoid)
    dx = geotif.grid_coordinates.x_axis
    dy = geotif.grid_coordinates.y_axis
    dX = np.zeros((dy.shape[0]-1))
    for j in xrange(len(dX)):
        dX[j] = d.measure((dy[j+1], dx[1]), (dy[j+1], dx[0])) * 1000  # km2m
    dY = np.zeros((dy.shape[0]-1))
    for i in xrange(len(dY)):
        dY[i] = d.measure((dy[i], 0), (dy[i+1], 0)) * 1000  # km2m
    return dX, dY 

# 需要导入模块: from geopy import distance [as 别名]
# 或者: from geopy.distance import distance [as 别名]
def _approximate_bbox_area(self):
        """
        Calculate the approximate area of the 
        bounding box in square kilometers.
        Returns: numeric area of the bounding box
            in km squared.
        """
        lat_min, lon_min, lat_max, lon_max = self.bbox
        lower_right_point = (lat_min, lon_max)
        lower_left_point = (lat_min, lon_min)
        upper_left_point = (lat_max, lon_min)
        lower_edge = distance.distance(lower_left_point, lower_right_point).km
        left_edge = distance.distance(lower_left_point, upper_left_point).km
        area = lower_edge * left_edge
        if self.logger:
            self.logger.info('Approx area of bounding box: {:,.2f} sq. km'.format(area))
        return area 

# 需要导入模块: from geopy import distance [as 别名]
# 或者: from geopy.distance import distance [as 别名]
def set_google_maps_fields(self, latlng=None):
        """
        Uses the Google Maps API to set:
          - geocoded latlng
          - nearest school name + distance
          - nearest train station name + distance
        """
        client = Client(key=settings.GOOGLE_MAPS_API_SERVER_KEY)
        if not latlng:
            data = client.geocode(self.address)
            if not data:
                raise Exception("Unable to resolve the address: '%s'" % address)
            latlng = data[0]["geometry"]["location"]
        self.point = GEOSGeometry("POINT(%(lng)s %(lat)s)" % latlng)

        error = ""
        for field in ("school", "train_station"):
            try:
                place = client.places_nearby(location=latlng, rank_by="distance", type=field)["results"][0]
            except IndexError:
                continue
            except Exception as e:
                error = e
                continue
            setattr(self, "nearest_%s" % field, place["name"])
            place_latlng = place["geometry"]["location"]
            d = distance((latlng["lat"], latlng["lng"]), (place_latlng["lat"], place_latlng["lng"])).km
            setattr(self, "nearest_%s_distance" % field, round(d, 2))
        if error:
            raise Exception(error) 

# 需要导入模块: from geopy import distance [as 别名]
# 或者: from geopy.distance import distance [as 别名]
def closest_led(self, pos: Pos, alt: float) -> Tuple[Pos, int, float]:
        level = self._level_from_alt(alt)
        closest_led_index = None
        closest_led_distance = (2 * self.eq_radius) ** 2
        for i, ledpos in enumerate(self.levels_led_poss[level]):
            # naive distance calculation, accurate enough for the inter-led distances
            d = ((ledpos.long - pos.long) * self.longfactor) ** 2 + (ledpos.lat - pos.lat) ** 2
            if d < closest_led_distance:
                closest_led_distance = d
                closest_led_index = i

        closest_led_pos = self.levels_led_poss[level][closest_led_index]
        closest_led_index += level * self.level_ledn
        return closest_led_pos, closest_led_index, closest_led_distance 

# 需要导入模块: from geopy import distance [as 别名]
# 或者: from geopy.distance import distance [as 别名]
def get_latest_measures(self, lat, lon, max_distance, size=50):
        """
        If cache is used, latest measures will be fetched and cached with a larger radius.
        Arbitrary values are used in that case:
            max_distance: 10 km
            lat and lon: rounded to 1 decimal
        This ensures that all measure points are covered by at least 1 cache key, with a tolerance of 2km.
        At the equator: distance((0,0),(0.05,0.05)) is 7.9 km
        """
        if not self.use_cache:
            return self._fetch_latest_measures(lat, lon, max_distance=max_distance, size=size)

        assert max_distance <= 2000, "Cached recycling data cannot be retrieved for radius > 2km"
        rounded_lat, rounded_lon = f"{lat:.1f}", f"{lon:.1f}"
        key = f"recycling_latest_measures_{rounded_lat}_{rounded_lon}"
        results = RedisWrapper.cache_it(key, self._fetch_latest_measures, expire=self.cache_expire)(
            rounded_lat, rounded_lon, max_distance=10_000, size=10_000
        )

        if not results:
            return []

        def get_distance_of_result(r):
            measure_geoloc = r["_source"]["metadata"]["location"]["geolocation"]
            return distance((lat, lon), (measure_geoloc["lat"], measure_geoloc["lon"])).meters

        return list(islice((r for r in results if get_distance_of_result(r) < max_distance), size)) 

# 需要导入模块: from geopy import distance [as 别名]
# 或者: from geopy.distance import distance [as 别名]
def can_find_home_match(cur_locs, locations, next_index, n):
    """
    Searches the list of locations to see if some combination of locations
    starting at next_index can be added to the locations currently in
    cur_locs that satisfy the constraint that all locations in cur_locs
    must be at most 15km from each other.  If 5 such points are found,
    return success
    """

    # The next location to test
    loc2 = locations[next_index]

    # Check that the next point that could be added (at next_index) would
    # satisfy the distance requirement with the current location group
    for loc1 in cur_locs:
        if get_distance(loc1, loc2) > 15:
            return False

    # Push on the next location, to see if we can meet the requirements
    # while it is a member of the group
    cur_locs.append(locations[next_index])

    # If we have 5 locations that are all within 15km, return success!
    if len(cur_locs) == 5:
        return True

    # Search the remaining locations to see if some combination can satisfy
    # the requirements when this new location is added to the group
    for j in range(next_index+1, n):
        if can_find_home_match(cur_locs, locations, j, n):
            return True

    # Remove the last item added since no match could be found when it is a
    # member of the current location group
    cur_locs.pop()        
    return False 

# 需要导入模块: from geopy import distance [as 别名]
# 或者: from geopy.distance import distance [as 别名]
def get_distance(p1, p2):
    """
    Computes the distance between the two latitude-longitude Points using
    Vincenty's Formula
    """
    return distance.distance(p1, p2).kilometers 

# 需要导入模块: from geopy import distance [as 别名]
# 或者: from geopy.distance import distance [as 别名]
def parse_sample_extensions(sample, track_point):
    if hasattr(track_point, 'extensions'):
        for extension in track_point.extensions.iterchildren():
            if extension.tag == GPX_NAMESPACE_TRACKPOINTEXTENSION_V1 + GPX_EXTENSION_TRACKPOINTEXTENSION:
                if hasattr(extension, GPX_EXTENSION_TRACKPOINTEXTENSION_HEARTRATE):
                    sample.hr = float(extension.hr) / 60.0  # BPM

            for namespace in GPX_NAMESPACES.values():
                if extension.tag.startswith(namespace):
                    tag = extension.tag.replace(namespace, '')
                    text = extension.text
                    if tag == GPX_EXTENSION_GPX_V1_TEMP:
                        sample.temperature = float(text) + 273.15  # Kelvin
                    elif tag == GPX_EXTENSION_GPX_V1_DISTANCE:
                        sample.distance = float(text)
                    elif tag == GPX_EXTENSION_GPX_V1_ALTITUDE:
                        sample.gps_altitude = float(text)
                        sample.altitude = int(round(sample.gps_altitude))
                    elif tag == GPX_EXTENSION_GPX_V1_ENERGY:
                        sample.energy_consumption = float(text)
                    elif tag == GPX_EXTENSION_GPX_V1_SEALEVELPRESSURE:
                        sample.sea_level_pressure = float(text)
                    elif tag == GPX_EXTENSION_GPX_V1_SPEED:
                        sample.speed = float(text)
                    elif tag == GPX_EXTENSION_GPX_V1_VSPEED:
                        sample.vertical_speed = float(text)
                    break 

# 需要导入模块: from geopy import distance [as 别名]
# 或者: from geopy.distance import distance [as 别名]
def insert_pause(samples, insert_pause_idx, move, pause_type):
    if (insert_pause_idx <= 0):
        return

    stop_sample = samples[insert_pause_idx - 1]
    start_sample = samples[insert_pause_idx]

    pause_duration = start_sample.time - stop_sample.time
    pause_distance = distance((radian_to_degree(stop_sample.latitude), radian_to_degree(stop_sample.longitude)),
                              (radian_to_degree(start_sample.latitude), radian_to_degree(start_sample.longitude))).meters

    # Introduce start of pause sample
    pause_sample = Sample()
    pause_sample.move = move
    pause_sample.utc = stop_sample.utc
    pause_sample.time = stop_sample.time
    stop_sample.utc -= timedelta(microseconds=1)  # Cut off 1ms from last recorded sample in order to introduce the new pause sample and keep time order
    stop_sample.time -= timedelta(microseconds=1)

    pause_sample.events = {"pause": {"state": "True",
                                     "type": str(pause_type),
                                     "duration": str(pause_duration),
                                     "distance": str(pause_distance),
                                    }}
    samples.insert(insert_pause_idx, pause_sample)  # Duplicate last element

    # Introduce end of pause sample
    pause_sample = Sample()
    pause_sample.move = move
    pause_sample.utc = start_sample.utc
    pause_sample.time = start_sample.time
    start_sample.utc += timedelta(microseconds=1)  # Add 1ms to the first recorded sample in order to introduce the new pause sample and keep time order
    start_sample.time += timedelta(microseconds=1)
    pause_sample.events = {"pause": {"state": "False",
                                     "duration": "0",
                                     "distance": "0",
                                     "type": str(pause_type)
                                    }}
    samples.insert(insert_pause_idx + 1, pause_sample) 

# 需要导入模块: from geopy import distance [as 别名]
# 或者: from geopy.distance import distance [as 别名]
def get_distance(region, src):
    """
    Compute within-region distances from the src pixels.

    Parameters
    ----------
    region : np.ndarray(shape=(m, n), dtype=bool)
        mask of the region
    src : np.ndarray(shape=(m, n), dtype=bool)
        mask of the source pixels to compute distances from.

    Returns
    -------
    d : np.ndarray(shape=(m, n), dtype=float)
        approximate within-region distance from the nearest src pixel;
        (distances outside of the region are arbitrary).
    """

    dmax = float(region.size)
    d = np.full(region.shape, dmax)
    d[src] = 0
    for n in range(region.size):
        d_orth = minimum_filter(d, footprint=_ORTH2) + 1
        d_diag = minimum_filter(d, (3, 3)) + _SQRT2
        d_adj = np.minimum(d_orth[region], d_diag[region])
        d[region] = np.minimum(d_adj, d[region])
        if (d[region] < dmax).all():
            break
    return d 

# 需要导入模块: from geopy import distance [as 别名]
# 或者: from geopy.distance import distance [as 别名]
def analyze_location_2():
    aux = defaultdict(lambda: defaultdict(lambda: defaultdict(str)))

    locations_from_db = defaultdict(list)
    with db.cursor() as cur:
        cur.execute('select '
                    'c1.id, '
                    'c1.name, '
                    'c1.latitude, '
                    'c1.longitude, '
                    'r.name, '
                    'c2.name, '
                    'c2.iso_code '
                    'from geography.city c1 '
                    'inner join geography.region r on c1.region_id = r.id '
                    'inner join geography.country c2 on r.country_id = c2.id')
        for c1_id, c1, latitude, longitude, r, c2_name, c2_code in cur:
            aux[c2_name][r][c1] = c1_id
            locations_from_db[c2_code].append((c1, latitude, longitude))

    for location_uuid, location in locations.items():
        coordinate = coordinates[location_uuid]
        city = cities[location['city']]
        city_name = city['name']
        region = regions[city['region']]
        region_name = region['name']
        country = countries[region['country']]
        country_code = country['code']
        country_name = country['name']

        res = aux.get(country_name)
        if res is not None:
            res = res.get(region_name)
            if res is not None:
                res = res.get(city_name)
                if res is not None:
                    print('Match: {}'.format(city_name))
                    continue
        min_dist = None
        result_name = None
        for c1_name, latitude, longitude in locations_from_db[country_code]:
            point1 = (float(latitude), float(longitude))
            point2 = (float(coordinate['latitude']), float(coordinate['longitude']))
            dist = distance(point1, point2).km
            if min_dist is None or dist < min_dist:
                min_dist = dist
                result_name = c1_name
        print('Actual: {}, calculated: {}'.format(city_name, result_name)) 

# 需要导入模块: from geopy import distance [as 别名]
# 或者: from geopy.distance import distance [as 别名]
def _fetch_latest_measures(self, lat, lon, max_distance, size):
        self.refresh_token()
        query = {
            "bool": {
                "filter": [
                    {"range": {"hour": {"gte": f"now-{self.measures_max_age_in_hours}h"}}},
                    {
                        "nested": {
                            "path": "metadata.location",
                            "query": {
                                "bool": {
                                    "filter": [
                                        {
                                            "geo_distance": {
                                                "distance": f"{max_distance}m",
                                                "metadata.location.geolocation": {
                                                    "lon": lon,
                                                    "lat": lat,
                                                },
                                            }
                                        }
                                    ]
                                }
                            },
                        }
                    },
                ]
            }
        }

        response = self.session.post(
            f"{self.base_url}/{self.data_index}/{self.data_collection}/_search",
            json={
                "query": query,
                # keep the latest document for each measuring point
                "collapse": {"field": "measuringPointId"},
                "sort": {"hour": "desc"},
            },
            params={"size": size},
            timeout=self.request_timeout,
        )

        response.raise_for_status()
        return response.json().get("result", {}).get("hits", []) 

# 需要导入模块: from geopy import distance [as 别名]
# 或者: from geopy.distance import distance [as 别名]
def get_geometric_median(coordinates):
    """
    Returns the geometric median of the list of locations.
    """

    n = len(coordinates)
    
    # The geometric median is only defined for n > 3 points, so just return
    # an arbitrary point if we have fewer
    if n == 1:
        return coordinates[0]
    elif n == 2:
        return coordinates[random.randint(0, 1)]
    
    min_distance_sum = 10000000
    median = None # Point type
    
    # Loop through all the points, finding the point that minimizes the
    # geodetic distance to all other points.  By construction median will
    # always be assigned to some non-None value by the end of the loop.
    for i in range(0, n):
        p1 = coordinates[i]
        dist_sum = 0
        for j in range(0, n):

            # Skip self-comparison
            if i == j:
                continue

            p2 = coordinates[j]
            dist = get_distance(p1, p2)
            dist_sum += dist

            # Abort early if we already know this isn't the median
            if dist_sum > min_distance_sum:
                    break

        if dist_sum < min_distance_sum:
            min_distance_sum = dist_sum
            median = p1

    return median 

# 需要导入模块: from geopy import distance [as 别名]
# 或者: from geopy.distance import distance [as 别名]
def calculate_distances(model, samples):
    total_distance_horizontal = 0.0
    total_distance_real = 0.0
    total_distance_descent = 0.0
    total_distance_ascent = 0.0
    total_distance_flat = 0.0
    previous_gps_sample = None
    current_altitude_sample = None
    previous_altitude_sample = None

    for sample in samples:
        if sample.altitude:
            current_altitude_sample = sample
        if sample.latitude:
            if previous_gps_sample:
                distance_horizontal = distance(_sample_to_point(previous_gps_sample), _sample_to_point(sample)).meters
                if previous_altitude_sample:
                    if current_altitude_sample != previous_altitude_sample and distance_horizontal > 0:
                        total_distance_horizontal += distance_horizontal
                        hm = current_altitude_sample.altitude - previous_altitude_sample.altitude
                        distance_real = math.sqrt(distance_horizontal ** 2 + hm ** 2)

                        if hm > 0:
                            total_distance_ascent += distance_real
                        elif hm < 0:
                            total_distance_descent += distance_real
                        else:
                            total_distance_flat += distance_real

                        total_distance_real += distance_real
                        previous_gps_sample = sample
                        previous_altitude_sample = current_altitude_sample
                else:
                    previous_altitude_sample = current_altitude_sample
            else:
                previous_gps_sample = sample

    model['total_distance_horizontal'] = total_distance_horizontal
    model['total_distance_ascent'] = total_distance_ascent
    model['total_distance_descent'] = total_distance_descent
    model['total_distance_flat'] = total_distance_flat
    model['total_distance_real'] = total_distance_real