def add_distance_bearing(df_task):
"""
Add columns for bearing and distance (and cumsum)
"""
df_task['Bearing'] = np.nan
df_task['DistanceToGo'] = np.nan
for idx in df_task.index[:-1]:
(lat1, lon1) = (df_task.loc[idx, 'Lat'], df_task.loc[idx, 'Lon'])
(lat2, lon2) = (df_task.loc[idx + 1, 'Lat'], df_task.loc[idx + 1, 'Lon'])
df_task.loc[idx, 'Bearing'] = haversine_bearing(lat1, lon1, lat2, lon2)
df_task.loc[idx, 'DistanceToGo'] = haversine_distance(lat1, lon1, lat2, lon2)
df_task['DistanceToGoSum'] = df_task['DistanceToGo'].shift(1).fillna(0).cumsum()
df_task['DistanceToGoSumRev'] = df_task['DistanceToGo'].sum() - df_task['DistanceToGoSum']
return(df_task)
def calculate_data(df_points, m=1, g=9.81):
df_points['Lat2'] = df_points['Lat'].shift(1)
df_points['Lon2'] = df_points['Lon'].shift(1)
df_points['Speed'] = 1000 * df_points.apply(lambda pt: haversine_distance(pt['Lat'], pt['Lon'], pt['Lat2'], pt['Lon2']), axis=1) / df_points['Deltatime']
df_points['Speed'] = df_points['Speed'].fillna(0.0)
df_points.drop(['Lat2', 'Lon2'], axis=1, inplace=True)
df_points['Ec'] = 0.5 * m * df_points['Speed']**2 # kinetic energy
df_points['Ep'] = m * g * df_points['Altitude'] - m * g * df_points['Altitude'].iloc[0] # potential energy
df_points['Em'] = df_points['Ec'] + df_points['Ep']
df_points['Vz'] = (df_points['Altitude'] - df_points['Altitude'].shift(1)) / df_points['Deltatime']
df_points['Vz'] = df_points['Vz'].fillna(0.0)
df_points['Vz_comp'] = (df_points['Em'] - df_points['Em'].shift(1)) / (df_points['Deltatime'] * m *g)
df_points['Vz_comp'] = df_points['Vz_comp'].fillna(0.0)
return(df_points)
def add_distance_bearing(df_task):
"""
Add columns for bearing and distance (and cumsum)
"""
df_task['Bearing'] = np.nan
df_task['DistanceToGo'] = np.nan
for idx in df_task.index[:-1]:
(lat1, lon1) = (df_task.loc[idx, 'Lat'], df_task.loc[idx, 'Lon'])
(lat2, lon2) = (df_task.loc[idx + 1, 'Lat'], df_task.loc[idx + 1,
'Lon'])
df_task.loc[idx, 'Bearing'] = haversine_bearing(lat1, lon1, lat2, lon2)
df_task.loc[idx, 'DistanceToGo'] = haversine_distance(
lat1, lon1, lat2, lon2)
df_task['DistanceToGoSum'] = df_task['DistanceToGo'].shift(1).fillna(
0).cumsum()
df_task['DistanceToGoSumRev'] = df_task['DistanceToGo'].sum(
) - df_task['DistanceToGoSum']
return (df_task)
def calculate_data(df_points, m=1, g=9.81):
df_points['Lat2'] = df_points['Lat'].shift(1)
df_points['Lon2'] = df_points['Lon'].shift(1)
df_points['Speed'] = 1000 * df_points.apply(
lambda pt: haversine_distance(pt['Lat'], pt['Lon'], pt['Lat2'], pt[
'Lon2']),
axis=1) / df_points['Deltatime']
df_points['Speed'] = df_points['Speed'].fillna(0.0)
df_points.drop(['Lat2', 'Lon2'], axis=1, inplace=True)
df_points['Ec'] = 0.5 * m * df_points['Speed']**2 # kinetic energy
df_points['Ep'] = m * g * df_points['Altitude'] - m * g * df_points[
'Altitude'].iloc[0] # potential energy
df_points['Em'] = df_points['Ec'] + df_points['Ep']
df_points['Vz'] = (df_points['Altitude'] -
df_points['Altitude'].shift(1)) / df_points['Deltatime']
df_points['Vz'] = df_points['Vz'].fillna(0.0)
df_points['Vz_comp'] = (df_points['Em'] - df_points['Em'].shift(1)) / (
df_points['Deltatime'] * m * g)
df_points['Vz_comp'] = df_points['Vz_comp'].fillna(0.0)
return (df_points)
def main(igc_filename, z_mode, outdir, disp, use_c):
basepath = os.path.dirname(__file__)
if outdir == '':
outdir = os.path.join(basepath, 'out')
z_mode = z_mode.lower()
assert z_mode in ['baro', 'gps']
print("Reading '%s'" % igc_filename)
filename = igc_filename
filename_base, filename_ext = os.path.splitext(os.path.basename(filename))
df_all = pd.read_csv(filename, header=None, names=['Data'])
df_all['FirstChar'] = df_all['Data'].map(lambda s: s[0])
print(df_all['FirstChar'].value_counts())
df_task = df_all[df_all['FirstChar'] == 'C'].copy()
df_points = df_all[df_all['FirstChar'] == 'B'].copy()
Npts = len(df_points)
df_points.index = np.arange(Npts)
#s = 'B1005364607690N00610358EA0000001265'
#print(s)
#dat = igc_b_line_to_tuple(s)
#print(dat)
s_tuple = df_points['Data'].map(igc_b_line_to_tuple)
m = 100
g = 9.81
first_date = datetime.date.today() # ToFix
df_points['Time'] = s_tuple.map(lambda t: t[0])
df_points['TimeSup'] = (df_points['Time'] <
df_points['Time'].shift(1)).astype(int).cumsum()
df_points['Time'] = df_points['Time'].map(
lambda t: datetime.datetime.combine(first_date, t))
df_points['Time'] = df_points[
'Time'] + df_points['TimeSup'] * datetime.timedelta(days=1)
df_points['Deltatime'] = df_points['Time'] - df_points['Time'].shift(1)
#df_points['Deltatime'] = df_points['Deltatime'].fillna(np.timedelta64(0, 's'))
df_points['Deltatime'] = df_points['Deltatime'] / np.timedelta64(1, 's')
print(df_points['Deltatime'].value_counts())
#df_points['Deltatime'] = 10 # seconds
df_points['Lat'] = s_tuple.map(lambda t: t[1])
df_points['Lon'] = s_tuple.map(lambda t: t[2])
df_points['Lat2'] = df_points['Lat'].shift(1)
df_points['Lon2'] = df_points['Lon'].shift(1)
df_points['Speed'] = 1000 * df_points.apply(
lambda pt: haversine_distance(pt['Lat'], pt['Lon'], pt['Lat2'], pt[
'Lon2']),
axis=1) / df_points['Deltatime']
df_points['Speed'] = df_points['Speed'].fillna(0.0)
df_points['Speed_km/h'] = df_points['Speed'] * 3.6
df_points.drop(['Lat2', 'Lon2'], axis=1, inplace=True)
df_points['Ec'] = 0.5 * m * df_points['Speed']**2 # kinetic energy
df_points['Z_mode'] = s_tuple.map(lambda t: t[3][0])
df_points['Z_baro'] = s_tuple.map(lambda t: t[3][1])
df_points['Z_gps'] = s_tuple.map(lambda t: t[3][2])
df_points['Altitude'] = df_points["Z_%s" % z_mode]
df_points['Vz'] = (df_points['Altitude'] -
df_points['Altitude'].shift(1)) / df_points['Deltatime']
df_points['Vz'] = df_points['Vz'].fillna(0.0)
df_points['Ep'] = m * g * df_points['Altitude'] - m * g * df_points[
'Altitude'].loc[0] # potential energy
df_points['Em'] = df_points['Ec'] + df_points['Ep']
df_points['Vz_comp'] = (df_points['Em'] - df_points['Em'].shift(1)) / (
df_points['Deltatime'] * m * g)
df_points['Vz_comp'] = df_points['Vz_comp'].fillna(0.0)
"""
Ep = m g z
Ec = 1/2 m v^2
Em = Ep + Ec
dEm / dt = m g dz/dt + d/dt (1/2 m v^2)
VzComp = ( dEm / dt) * 1 / (m * g)
= dz/dt + 1/(2*g) * d/dt(v^2)
= Vz + VzK
with VzK = 1/(2*g) * d/dt(v^2)
"""
df_points['Name'] = ""
df_points.loc[0, "Name"] = "Start"
df_points.loc[Npts - 1, "Name"] = "Finish"
df_points = df_points.set_index('Time')
print("Points:\n%s" % df_points)
print("Max speed: %f km/h" % df_points['Speed_km/h'].max())
print("Max Vz: %f" % df_points['Vz'].max())
print("Min Vz: %f" % df_points['Vz'].min())
print("Max Vz_comp: %f" % df_points['Vz_comp'].max())
print("Min Vz_comp: %f" % df_points['Vz_comp'].min())
if len(df_task) == 0 or not use_c:
print("No C record - using first and last point")
df_task = df_points[df_points['Name'] != ""].copy()
df_task = df_task.reset_index()
else:
df_task['Data'] = df_task['Data'].map(igc_c_line_to_tuple)
df_task = df_task[df_task['Data'].notnull()].copy()
df_task.index = np.arange(len(df_task))
df_task['Lat'] = df_task['Data'].map(lambda t: t[0])
df_task['Lon'] = df_task['Data'].map(lambda t: t[1])
df_task['Name'] = df_task['Data'].map(lambda t: t[2])
df_task['Altitude'] = 0
print("Task:\n%s" % df_task)
points_to_kml_with_yattag(df_points, df_task, outdir, filename_base, disp)
def main(debug, waypoints_filename, output, outdir, disp, lat, lon, dist, dist_unit):
basepath = os.path.dirname(__file__)
if outdir=='':
outdir = os.path.join(basepath, 'out')
# Translate path like {Condor} using paths_default
d_name = paths_default.copy()
d_name['output'] = output
waypoints_filename = waypoints_filename.format(**d_name)
outdir = outdir.format(**d_name)
if not os.path.exists(outdir):
print("Create directory %s" % outdir)
os.makedirs(outdir)
lst_errors = []
filenames = glob.glob(waypoints_filename)
N_filenames = len(filenames)
print("")
print("="*20)
print("")
for i, filename in enumerate(filenames):
try:
logging.info("Read file %03d/%03d '%s'" % (i+1, N_filenames, filename))
filename_base, filename_ext = os.path.splitext(os.path.basename(filename))
if debug:
assert filename_ext in supported_input_extensions, \
"File extension of '%s' is '%s' but supported extension must be in %s" \
% (filename, filename_ext, supported_input_extensions)
cols = ['Name', 'Code', 'Country', 'Latitude', 'Longitude', 'Elevation',
'Waypoint style', 'Runway direction', 'Runway length',
'Airport Frequency', 'Description']
df_waypoints = pd.read_csv(filename, names=cols)
df_waypoints = df_waypoints.rename(columns={
'Latitude': 'Lat',
'Longitude': 'Lon',
'Elevation': 'Altitude',
})
#print(df_waypoints)
# ToDo: rename columns
# ToDo: map columns (at least Lat, Lon)
# ToDo: create kml file
df_waypoints['Waypoint style'] = df_waypoints['Waypoint style'].map(WaypointStyle)
df_waypoints['Lat'] = df_waypoints['Lat'].map(latlon2decimal)
df_waypoints['Lon'] = df_waypoints['Lon'].map(latlon2decimal)
df_waypoints['Altitude'] = df_waypoints['Altitude'].map(dist2quantity)
df_waypoints['Runway length'] = df_waypoints['Runway length'].map(dist2quantity)
df_waypoints['Distance'] = df_waypoints.apply(lambda wpt: haversine_distance(lat, lon, wpt['Lat'], wpt['Lon']), axis=1)
df_waypoints['Distance'] = df_waypoints['Distance'].map(lambda d: Q_(d, "km"))
df_waypoints = df_waypoints.sort(columns=['Distance'])
df_waypoints = df_waypoints[df_waypoints['Distance'] <= Q_(dist, dist_unit)]
print(df_waypoints)
print("Creating KML file (please wait)")
task_to_kml_with_yattag(df_waypoints, outdir, filename_base)
# Too many markers!!!
# Sol: filter with distance from a given point
# Sol: Use Google Maps instead
# see https://developers.google.com/maps/articles/toomanymarkers
except:
logging.error(traceback.format_exc())
lst_errors.append(filename)
print("")
print("="*20)
print("")
N_error = len(lst_errors)
print("Convert %d files - %d errors" % (N_filenames, N_error))
for i, filename_error in enumerate(lst_errors):
print(" * %03d / %03d: %s" % (i+1, N_error, filename_error))
def main(debug, waypoints_filename, output, outdir, disp, lat, lon, dist,
dist_unit):
basepath = os.path.dirname(__file__)
if outdir == '':
outdir = os.path.join(basepath, 'out')
# Translate path like {Condor} using paths_default
d_name = paths_default.copy()
d_name['output'] = output
waypoints_filename = waypoints_filename.format(**d_name)
outdir = outdir.format(**d_name)
if not os.path.exists(outdir):
print("Create directory %s" % outdir)
os.makedirs(outdir)
lst_errors = []
filenames = glob.glob(waypoints_filename)
N_filenames = len(filenames)
print("")
print("=" * 20)
print("")
for i, filename in enumerate(filenames):
try:
logging.info("Read file %03d/%03d '%s'" %
(i + 1, N_filenames, filename))
filename_base, filename_ext = os.path.splitext(
os.path.basename(filename))
if debug:
assert filename_ext in supported_input_extensions, \
"File extension of '%s' is '%s' but supported extension must be in %s" \
% (filename, filename_ext, supported_input_extensions)
cols = [
'Name', 'Code', 'Country', 'Latitude', 'Longitude',
'Elevation', 'Waypoint style', 'Runway direction',
'Runway length', 'Airport Frequency', 'Description'
]
df_waypoints = pd.read_csv(filename, names=cols)
df_waypoints = df_waypoints.rename(columns={
'Latitude': 'Lat',
'Longitude': 'Lon',
'Elevation': 'Altitude',
})
#print(df_waypoints)
# ToDo: rename columns
# ToDo: map columns (at least Lat, Lon)
# ToDo: create kml file
df_waypoints['Waypoint style'] = df_waypoints[
'Waypoint style'].map(WaypointStyle)
df_waypoints['Lat'] = df_waypoints['Lat'].map(latlon2decimal)
df_waypoints['Lon'] = df_waypoints['Lon'].map(latlon2decimal)
df_waypoints['Altitude'] = df_waypoints['Altitude'].map(
dist2quantity)
df_waypoints['Runway length'] = df_waypoints['Runway length'].map(
dist2quantity)
df_waypoints['Distance'] = df_waypoints.apply(
lambda wpt: haversine_distance(lat, lon, wpt['Lat'], wpt['Lon']
),
axis=1)
df_waypoints['Distance'] = df_waypoints['Distance'].map(
lambda d: Q_(d, "km"))
df_waypoints = df_waypoints.sort(columns=['Distance'])
df_waypoints = df_waypoints[
df_waypoints['Distance'] <= Q_(dist, dist_unit)]
print(df_waypoints)
print("Creating KML file (please wait)")
task_to_kml_with_yattag(df_waypoints, outdir, filename_base)
# Too many markers!!!
# Sol: filter with distance from a given point
# Sol: Use Google Maps instead
# see https://developers.google.com/maps/articles/toomanymarkers
except:
logging.error(traceback.format_exc())
lst_errors.append(filename)
print("")
print("=" * 20)
print("")
N_error = len(lst_errors)
print("Convert %d files - %d errors" % (N_filenames, N_error))
for i, filename_error in enumerate(lst_errors):
print(" * %03d / %03d: %s" % (i + 1, N_error, filename_error))
def main(igc_filename, z_mode, outdir, disp, use_c):
basepath = os.path.dirname(__file__)
if outdir=='':
outdir = os.path.join(basepath, 'out')
z_mode = z_mode.lower()
assert z_mode in ['baro', 'gps']
print("Reading '%s'" % igc_filename)
filename = igc_filename
filename_base, filename_ext = os.path.splitext(os.path.basename(filename))
df_all = pd.read_csv(filename, header=None, names=['Data'])
df_all['FirstChar'] = df_all['Data'].map(lambda s: s[0])
print(df_all['FirstChar'].value_counts())
df_task = df_all[df_all['FirstChar']=='C'].copy()
df_points = df_all[df_all['FirstChar']=='B'].copy()
Npts = len(df_points)
df_points.index = np.arange(Npts)
#s = 'B1005364607690N00610358EA0000001265'
#print(s)
#dat = igc_b_line_to_tuple(s)
#print(dat)
s_tuple = df_points['Data'].map(igc_b_line_to_tuple)
m = 100
g = 9.81
first_date = datetime.date.today() # ToFix
df_points['Time'] = s_tuple.map(lambda t: t[0])
df_points['TimeSup'] = (df_points['Time'] < df_points['Time'].shift(1)).astype(int).cumsum()
df_points['Time'] = df_points['Time'].map(lambda t: datetime.datetime.combine(first_date, t))
df_points['Time'] = df_points['Time'] + df_points['TimeSup'] * datetime.timedelta(days=1)
df_points['Deltatime'] = df_points['Time'] - df_points['Time'].shift(1)
#df_points['Deltatime'] = df_points['Deltatime'].fillna(np.timedelta64(0, 's'))
df_points['Deltatime'] = df_points['Deltatime'] / np.timedelta64(1, 's')
print(df_points['Deltatime'].value_counts())
#df_points['Deltatime'] = 10 # seconds
df_points['Lat'] = s_tuple.map(lambda t: t[1])
df_points['Lon'] = s_tuple.map(lambda t: t[2])
df_points['Lat2'] = df_points['Lat'].shift(1)
df_points['Lon2'] = df_points['Lon'].shift(1)
df_points['Speed'] = 1000 * df_points.apply(lambda pt: haversine_distance(pt['Lat'], pt['Lon'], pt['Lat2'], pt['Lon2']), axis=1) / df_points['Deltatime']
df_points['Speed'] = df_points['Speed'].fillna(0.0)
df_points['Speed_km/h'] = df_points['Speed'] * 3.6
df_points.drop(['Lat2', 'Lon2'], axis=1, inplace=True)
df_points['Ec'] = 0.5 * m * df_points['Speed']**2 # kinetic energy
df_points['Z_mode'] = s_tuple.map(lambda t: t[3][0])
df_points['Z_baro'] = s_tuple.map(lambda t: t[3][1])
df_points['Z_gps'] = s_tuple.map(lambda t: t[3][2])
df_points['Altitude'] = df_points["Z_%s" % z_mode]
df_points['Vz'] = (df_points['Altitude'] - df_points['Altitude'].shift(1)) / df_points['Deltatime']
df_points['Vz'] = df_points['Vz'].fillna(0.0)
df_points['Ep'] = m * g * df_points['Altitude'] - m * g * df_points['Altitude'].loc[0] # potential energy
df_points['Em'] = df_points['Ec'] + df_points['Ep']
df_points['Vz_comp'] = (df_points['Em'] - df_points['Em'].shift(1)) / (df_points['Deltatime'] * m *g)
df_points['Vz_comp'] = df_points['Vz_comp'].fillna(0.0)
"""
Ep = m g z
Ec = 1/2 m v^2
Em = Ep + Ec
dEm / dt = m g dz/dt + d/dt (1/2 m v^2)
VzComp = ( dEm / dt) * 1 / (m * g)
= dz/dt + 1/(2*g) * d/dt(v^2)
= Vz + VzK
with VzK = 1/(2*g) * d/dt(v^2)
"""
df_points['Name'] = ""
df_points.loc[0, "Name"] = "Start"
df_points.loc[Npts-1, "Name"] = "Finish"
df_points = df_points.set_index('Time')
print("Points:\n%s" % df_points)
print("Max speed: %f km/h" % df_points['Speed_km/h'].max())
print("Max Vz: %f" % df_points['Vz'].max())
print("Min Vz: %f" % df_points['Vz'].min())
print("Max Vz_comp: %f" % df_points['Vz_comp'].max())
print("Min Vz_comp: %f" % df_points['Vz_comp'].min())
if len(df_task)==0 or not use_c:
print("No C record - using first and last point")
df_task = df_points[df_points['Name'] != ""].copy()
df_task = df_task.reset_index()
else:
df_task['Data'] = df_task['Data'].map(igc_c_line_to_tuple)
df_task = df_task[df_task['Data'].notnull()].copy()
df_task.index = np.arange(len(df_task))
df_task['Lat'] = df_task['Data'].map(lambda t: t[0])
df_task['Lon'] = df_task['Data'].map(lambda t: t[1])
df_task['Name'] = df_task['Data'].map(lambda t: t[2])
df_task['Altitude'] = 0
print("Task:\n%s" % df_task)
points_to_kml_with_yattag(df_points, df_task, outdir, filename_base, disp)