def __init__(self, vessel):
self.name = vessel['Ship name']
self.lat = dms2dec(vessel['Latitude'])
self.lon = dms2dec(vessel['Longitude'])
# self.lat = vessel['Latitude']
# self.lon = vessel['Longitude']
self.velocity = 20
def check(long, lat):
dec_long = dms2dec(long)
dec_lat = dms2dec(lat)
for k, v in sea_area.items():
if dec_long < v['max_long'] and \
dec_long > v['min_long'] and \
dec_lat < v['max_lat'] and \
dec_lat > v['min_lat']:
return k
return None
def cleanup_and_get_location_data_for(news):
keys = news.keys()
if "content" in keys:
for i, content in enumerate(news['content']):
content = re.sub(r"\[\d\]", "", content)
content = unidecode(content)
news['content'][i] = content
if 'geo_dec' in keys:
coords = news['geo_dec'].split(' ')
for i, coord in enumerate(coords):
for s in coord:
if (s == "S" or s == "W"):
coords[i] = f"-{coord}"[:-2]
else:
coords[i] = f"{coord}"[:-2]
news['coords'] = {'lat': coords[0], 'lon': coords[1]}
elif 'geo_dms' in keys:
coords = news['geo_dms'].split(' ')
for i, coord in enumerate(coords):
coords[i] = dms2dec(coord.strip())
news['coords'] = {'lat': coords[0], 'lon': coords[1]}
elif 'location_string' in keys:
news['coords'] = location_help_from_here_api_with(
news['location_string'])
elif 'ptod' in keys:
news['coords'] = location_help_from_here_api_with(news['ptod'])
else:
news['coords'] = location_help_from_here_api_with(news['title'])
return news
def _get_XYZ(session, station_id):
"""
POSTs and parses XYZ coordinates
"""
# Request URL to get the data
url = 'http://meteosearch.meteo.gr/FormProc.asp'
# POST request body
req_body = {'stationID': station_id, 'SelectYear': 2020, 'SelectMonth': 12}
response = session.post(url, req_body)
# Check for code 2xx.
if response.ok:
station_data = response.text
xyz_line = station_data.splitlines()[3]
# Check if the 3rd line contains the data.
# Sometimes the 2nd one does.
if 'LAT:' in xyz_line:
z = xyz_line.split('ELEV:')[1].split('m')[0].strip()
x_dms = xyz_line.split('LONG:')[1].split('E')[0].strip()
y_dms = xyz_line.split('LAT:')[1].split('N')[0].strip()
# Check if line contains coordinates
if x_dms and y_dms:
# Convert DMS to DD
x = dms2dec(x_dms + "E")
y = dms2dec(y_dms + "N")
return x, y, z
else:
xyz_line = station_data.splitlines()[2]
if 'LAT:' in xyz_line:
z = xyz_line.split('ELEV:')[1].split('m')[0].strip()
x_dms = xyz_line.split('LONG:')[1].strip()
y_dms = xyz_line.split('LAT:')[1].split('LONG')[0].strip()
# Check if line contains coordinates
if x_dms and y_dms:
x = dms2dec(
x_dms.replace("deg", "°").replace("min", "'") +
" 00'\"E")
y = dms2dec(
y_dms.replace("deg", "°").replace("min", "'") +
"00'\"N")
return x, y, z
# If no data is found
return None, None, None
def find_horizon(sat_objs, sec, location):
"""
Get satellite altitude and azimuth list for specific observer and time
:param sat_objs: List of satellite objects
:param sec: Time in second
:param location: Observer location
:return: Azimuth list, altitude list, shell list (different shells have different colors)
"""
lat_dms = decimalDegrees2DMS(location[0])
long_dms = decimalDegrees2DMS(location[1])
observer = ephem.Observer()
observer.lon, observer.lat = long_dms, lat_dms
shifted_epoch = (pd.to_datetime(EPOCH) +
pd.to_timedelta(sec, unit='s')).strftime(
format='%Y/%m/%d %H:%M:%S')
observer.date = shifted_epoch
alt_list = []
azim_list = []
shell_list = []
for shell_cntr in range(0, NUM_SHELLS):
print(len(sat_objs[shell_cntr]))
for id in range(len(sat_objs[shell_cntr])):
sat = sat_objs[shell_cntr][id]
sat.compute(observer)
angle_alt = dms2dec(str(sat.alt))
if sat.alt < 0:
angle_alt = 0 - angle_alt
angle_az = dms2dec(str(sat.az))
if sat.az < 0:
angle_az = 0 - angle_az
print("%d %f %f" % (id, angle_alt, angle_az))
#if angle_alt > MIN_DEG_ELEVATION:
if angle_alt > 0.0:
#orb_id = math.floor(id/NUM_ORBS)
print("%d %f %f" % (id, angle_alt, angle_az))
alt_list.append(angle_alt)
azim_list.append(angle_az)
shell_list.append(shell_cntr)
X = np.array(azim_list)
Y = np.array(alt_list)
S = np.array(shell_list)
return X, Y, S
def plotly_plot(button, long_d, long_m, long_s, lat_d, lat_m, lat_s):
#convert dms to dd coordinate
dd_long = dms2dec(f'''{long_d}°{long_m}'{long_s}"N''')
dd_lat = dms2dec(f'''{lat_d}°{lat_m}'{lat_s}"E''')
#convert dd to lambert 72
x, y = transformer.transform(dd_long, dd_lat)
#create bounding box
x_left = x - 30
x_right = x + 30
y_top = y + 30
y_bottom = y - 30
#read in bounding box
rst = img.read(1, window=from_bounds(x_left, y_bottom, x_right, y_top, img.transform))
#create geopandas dataframe out of bounding box
rst_gdf = gpd.GeoDataFrame(rst)
rst_gdf = rst_gdf[::-1]
#make 3d plot out of dataframe
fig = go.Figure(data=[go.Surface(z=rst_gdf.values)])
fig.update_layout(autosize=True)
return fig
def getData(formdata):
LKP_time = float(formdata['LKP_time'])
LKP_Altitude = float(formdata['LKP_Altitude'])
Distress_time = float(formdata['Distress_time'])
Distress_Altitude = float(formdata['Distress_Altitude'])
brng = float(formdata['brng'])
GSpeed = float(formdata['GSpeed'])
VSpeed = float(formdata['VSpeed'])
Glide_TAS = float(formdata['Glide_TAS'])
Glide_Ratio = float(formdata['Glide_Ratio'])
Daylight_hours = 8
Search_Speed = 150
###input these as DD MM SS,DD MM SS
destination = formdata['destination']
if destination.find(','):
destination = destination.split(',')
for i in range(2):
a = destination[i].split()
if i == 0:
decVal = round(dms2dec(f'''{a[0]}°{a[1]}'{a[2]}N"'''), 5)
destination[i] = decVal
if i == 1:
decVal = round(dms2dec(f'''{a[0]}°{a[1]}'{a[2]}E"'''), 5)
destination[i] = decVal
else:
print("improper format")
Distress_position = formdata['Distress_position']
if Distress_position.find(','):
Distress_position = Distress_position.split(',')
for i in range(2):
a = Distress_position[i].split()
if i == 0:
decVal = round(dms2dec(f'''{a[0]}°{a[1]}'{a[2]}N"'''), 5)
Distress_position[i] = decVal
if i == 1:
decVal = round(dms2dec(f'''{a[0]}°{a[1]}'{a[2]}E"'''), 5)
Distress_position[i] = decVal
else:
print("improper format")
LKP_position = formdata['LKP_position']
if LKP_position.find(','):
LKP_position = LKP_position.split(',')
for i in range(2):
a = LKP_position[i].split()
if i == 0:
decVal = round(dms2dec(f'''{a[0]}°{a[1]}'{a[2]}N"'''), 5)
LKP_position[i] = decVal
if i == 1:
decVal = round(dms2dec(f'''{a[0]}°{a[1]}'{a[2]}E"'''), 5)
LKP_position[i] = decVal
else:
print("improper format")
Communication_Interval = .5
Terrain_Altitude = 0
Altitude_Loss = Distress_Altitude - Terrain_Altitude
Comm_Mode = 'GPS'
Craft_Type = 'DualEngine'
Search_Altitude = '600'
Search_obj = 'SmallAircraft'
visibility = '6'
vegetation = 'Moderate'
search_effort, Corr_Sweep_Width, Search_Endurance = availableSearchEffort.getSearchEffort(
Search_Speed, Daylight_hours, Search_Altitude, Search_obj, visibility,
vegetation)
Descent_Rate = Glide_TAS * 101 / Glide_Ratio
Datum_point = []
if (Distress_time and Distress_position):
E = positionError.getE(Comm_Mode, Craft_Type, GSpeed, VSpeed,
Distress_Altitude, Terrain_Altitude, LKP_time,
Distress_time)
Altitude_Loss = Distress_Altitude - Terrain_Altitude
Descent_Time = Altitude_Loss / Descent_Rate / 60
Glide_Distance = ((Glide_TAS * Descent_Time) / 60) * 1.852
Datum_point = Datum.Estimated_position(Distress_position,
Glide_Distance, brng)
Seach_area_width = effortAllocation.getSeachAreaWidth(
search_effort, E, Corr_Sweep_Width, None, Search_Speed,
Search_Endurance)
PMap_Type, Cell_Width, No_of_cells = Pmap.getPmapType(
E, Seach_area_width)
Grid = HeatmapPointData.getGrid(PMap_Type)
return Datum_point, Grid, Cell_Width
if (Distress_position == None and Distress_time):
E = positionError.getE(Comm_Mode, Craft_Type, GSpeed, VSpeed,
LKP_Altitude, Terrain_Altitude, LKP_time,
Distress_time)
Distance = ((Distress_time - LKP_time) * Gspeed) / 0.621371
Est_Distress_position = Datum.Estimated_position(
LKP_position, Distance, brng)
Altitude_Loss = LKP_Altitude - Terrain_Altitude
Descent_Time = Altitude_Loss / Descent_Rate / 60
Glide_Distance = ((Glide_TAS * Descent_Time) / 60) * 1.852
Datum_point = Datum.Estimated_position(Est_Distress_position,
Glide_Distance, brng)
Seach_area_width = effortAllocation.getSeachAreaWidth(
search_effort, E, Corr_Sweep_Width, None, Search_Speed,
Search_Endurance)
PMap_Type, Cell_Width, No_of_cells = Pmap.getPmapType(
E, Seach_area_width)
Grid = HeatmapPointData.getGrid(PMap_Type)
return Datum_point, Grid, Cell_Width
if (Distress_position == None and Distress_time == None):
Sample_len = 1 / 12
samples = []
i = 0
PMap_Type = []
Cell_Width = []
No_of_cells = []
Datum_point = []
Grid = []
while (i <= Communication_Interval):
samples.append(LKP_time + i)
i = i + Sample_len
for i in len(samples):
Distance = ((samples[i] - LKP_time) * speed) / 0.621371
E = positionError.getE(Comm_Mode, Craft_Type, GSpeed, VSpeed,
LKP_Altitude, Terrain_Altitude, LKP_time,
samples[i])
Est_Distress_position = Datum.Estimated_position(
LKP_position, Distance, brng)
Altitude_Loss = LKP_Altitude - Terrain_Altitude
Descent_Time = Altitude_Loss / Descent_Rate / 60
Glide_Distance = ((Glide_TAS * Descent_Time) / 60) * 1.852
Datum_point[i] = Datum.Estimated_position(Est_Distress_position,
Glide_Distance, brng)
Seach_area_width = effortAllocation.getSeachAreaWidth(
search_effort, E, Corr_Sweep_Width, None, Search_Speed,
Search_Endurance)
PMap_Type[i], Cell_Width[i], No_of_cells[i] = (Pmap.getPmapType(
E, Seach_area_width))
Grid[i] = HeatmapPointData.getGrid(typeMap[i](0))
Datum_line = [Est_Distress_position, destination]
Datum_length = Calc_distance(Est_Distress_position, destination)
E = positionError.getE(Comm_Mode, Craft_Type, GSpeed, VSpeed,
LKP_Altitude, Terrain_Altitude, LKP_time,
samples[len(samples)])
Seach_area_width = effortAllocation.getSeachAreaWidth(
search_effort, E, Corr_Sweep_Width, Datum_length, Search_Speed,
Search_Endurance)
PMap_Type[len(PMap_Type)], Cell_Width[len(Cell_Width)], No_of_cells[
len(No_of_cells)] = PMap_Line.getPmapType(E, Seach_area_width)
Grid.append(HeatmapLineData.getGrid(typeMap[len()](0)))
return Datum_point, Datum_line, Grid, Cell_Width
def coordinates_convert(loc_degree):
""" Convert degrees minutes seconds coordinate to decimal latitude or longitude """
loc_decimal = dms2dec(loc_degree)
if loc_degree[-1] == 'O':
loc_decimal = -loc_decimal
return loc_decimal
'Location {}: Latitude', 'Location {}: Longitude',
'Location {}: Facility name', 'Location {}: Target country',
'Location {}: Location description', 'Location {}: Comment on location'
]
deal_coords = {}
for i, row in deals_df.iterrows():
id = row['Deal ID']
deal_coords[id] = []
for i in range(1, 22):
lat = str(row['Location {}: Latitude'.format(i)]).replace(',', '.')
lng = str(row['Location {}: Longitude'.format(i)]).replace(',', '.')
try:
lat = float(lat)
except ValueError: # probably in degree minute seconds (DMS) format
lat = dms2dec(lat)
try:
lng = float(lng)
except ValueError: # probably in degree minute seconds (DMS) format
lng = dms2dec(lng)
country = row['Location {}: Target country'.format(i)]
accuracy = row['Location {}: Spatial accuracy level'.format(i)]
if np.isnan(lat):
break
deal_coords[id].append({
'coords': (lat, lng),
'country': country,
'accuracy': accuracy,
'agriculture': row['Agriculture'],
'size': row['Size']
def scrap_park_data():
"""
Extracts National Parks data from https://en.wikipedia.org/wiki/List_of_national_parks_of_the_United_States?oldformat=true
Saves a csv file containing:
- Park name
- State
- Latitude and Longitude
- Surface (acres km2)
- Number of visitors
- Description
"""
## load article, turn into soup and get the <table>s.
page = urllib.request.urlopen(NP_LINK)
## create soup
soup = BeautifulSoup(page, 'html.parser')
## find all tables
np_table = soup.find_all('table', class_='sortable')[0]
parks = []
data = {}
## find table header
## find table row (tr)
tbody = np_table.find_all("tbody")[0]
trs = tbody.find_all("tr")
## skip first tr since it contains the headers
for i in range(1, len(trs)):
## extract row content
tr = trs[i]
## find park name
np = tr.find_all("a")[0].get('title')
## clean park name
np = np.replace('ʻ', '')
np = np.replace('ā', 'a')
np = np.replace('–', '-')
parks.append(np)
data[np] = {}
## find park data
tds = tr.find_all("td")
## first td: image
## skip
## state + coordinates
## offset by one if first cell is td
if len(tds) == 6:
offset = 1
else:
offset = 2
## extract state, latitude, and longitude
data[np]["state"] = tds[offset].find("a").get('title')
lat = tds[offset].find("span", {"class": "latitude"}).text
long = tds[offset].find("span", {"class": "longitude"}).text
data[np]["latitude"] = dms2dec(lat)
data[np]["longitude"] = dms2dec(long)
## date
data[np]["date"] = tds[offset + 1].find("span").text
## area
surface = re.findall(r'(\d*\,?\d*\,?\d+\.?\d*)\s',
tds[offset + 2].text)
data[np]["surface_acres"] = float(surface[0].replace(',', ""))
data[np]["surface_km2"] = float(surface[1].replace(',', ""))
## visitors
data[np]["visitors"] = float(
re.search(r'(\d*\,?\d*\,?\d+\.?\d*)',
tds[offset + 3].text)[0].replace(",", ""))
## description
description = re.sub(r'\[?\(?\d+\)?\]?', '',
tds[offset + 4].text.strip())
data[np]["description"] = description
## create dataframe
parks = pd.DataFrame(data).T.reset_index()
## read park units
units = pd.read_csv("../scrapper/data/Parks.csv")
## merge
parks = pd.merge(left=parks,
right=units,
left_on='index',
right_on='parkname')
del parks['parkname']
## read park websites
websites = pd.read_csv('../scrapper/data/park_websites.csv')
## merge
parks = pd.merge(left=parks, right=websites, on='parkunit')
## count photos
parks['photo_count'] = parks['parkunit'].apply(get_photo_count)
## rename columns
parks = parks.rename(columns={"index": "parkname"})
## get topo
parks['boundaries'] = parks['parkunit'].apply(lambda x: get_geojson(x))
parks['bbox'] = parks['parkunit'].apply(lambda x: get_bbox(x))
return parks