def daynight_terminator(date, lons):
""" Return the coordinates of day/night terminator for RTI plotting.
Parameters
----------
date : datetime.datetime
a datetime.datetime object (assumed UTC)
lons : list
a numpy array of lons
Returns
-------
lat
the latitude of the day night terminator
tau
grenwich hour angle
dec
solar declination
"""
import mpl_toolkits.basemap.solar as solar
dg2rad = np.pi / 180.
# compute greenwich hour angle and solar declination
# from datetime object (assumed UTC).
tau, dec = solar.epem(date)
# compute day/night terminator from hour angle, declination.
longitude = lons + tau
lats = np.arctan(-np.cos(longitude * dg2rad) /
np.tan(dec * dg2rad)) / dg2rad
return lats, tau, dec
def daynight_terminator(date, lons):
""" Return the coordinates of day/night terminator for RTI plotting.
Parameters
----------
date : datetime.datetime
a datetime.datetime object (assumed UTC)
lons : list
a numpy array of lons
Returns
-------
lat
the latitude of the day night terminator
tau
grenwich hour angle
dec
solar declination
"""
import mpl_toolkits.basemap.solar as solar
import numpy as np
dg2rad = np.pi / 180.
# compute greenwich hour angle and solar declination
# from datetime object (assumed UTC).
tau, dec = solar.epem(date)
# compute day/night terminator from hour angle, declination.
longitude = lons + tau
lats = np.arctan(
-np.cos(longitude * dg2rad) / np.tan(dec * dg2rad)) / dg2rad
return lats, tau, dec
def daynight_terminator(date, lons):
"""
date is datetime object (assumed UTC).
"""
import mpl_toolkits.basemap.solar as solar
dg2rad = np.pi/180.
# compute greenwich hour angle and solar declination
# from datetime object (assumed UTC).
tau, dec = solar.epem(date)
# compute day/night terminator from hour angle, declination.
longitude = lons + tau
lats = np.arctan(-np.cos(longitude*dg2rad)/np.tan(dec*dg2rad))/dg2rad
return lats,tau,dec
def daynight_terminator(date, lons):
"""
date is datetime object (assumed UTC).
"""
import mpl_toolkits.basemap.solar as solar
dg2rad = np.pi / 180.
# compute greenwich hour angle and solar declination
# from datetime object (assumed UTC).
tau, dec = solar.epem(date)
# compute day/night terminator from hour angle, declination.
longitude = lons + tau
lats = np.arctan(-np.cos(longitude * dg2rad) /
np.tan(dec * dg2rad)) / dg2rad
return lats, tau, dec
def daynight_terminator(date, lons):
"""Calculates the latitude, Greenwich Hour Angle, and solar declination from a given latitude and longitude.
This routine is used by musicRTI for terminator calculations.
**Args**:
* **date** (datetime.datetime): UT date and time of terminator calculation.
* **lons** (np.array): Longitudes of which to calculate the terminator.
**Returns**:
* **lats** (np.array): Latitudes of solar terminator.
* **tau** (np.array): Greenwhich Hour Angle.
* **dec** (np.array): Solar declination.
Adapted from mpl_toolkits.basemap.solar by Nathaniel A. Frissell, Fall 2013
"""
import mpl_toolkits.basemap.solar as solar
dg2rad = np.pi/180.
# compute greenwich hour angle and solar declination
# from datetime object (assumed UTC).
tau, dec = solar.epem(date)
# compute day/night terminator from hour angle, declination.
longitude = lons + tau
lats = np.arctan(-np.cos(longitude*dg2rad)/np.tan(dec*dg2rad))/dg2rad
return lats,tau,dec
def daynight_terminator(date, lons):
""" Return the coordinates of day/night terminator for RTI plotting.
**Args**:
* **date**: a datetime.datetime object (assumed UTC)
* **lons**: a numpy array of lons
**Returns**:
*lat, the latitude of the day night terminator
*tau, grenwich hour angle
*dec, solar declination
"""
import mpl_toolkits.basemap.solar as solar
dg2rad = np.pi / 180.
# compute greenwich hour angle and solar declination
# from datetime object (assumed UTC).
tau, dec = solar.epem(date)
# compute day/night terminator from hour angle, declination.
longitude = lons + tau
lats = np.arctan(-np.cos(longitude * dg2rad) /
np.tan(dec * dg2rad)) / dg2rad
return lats, tau, dec
def __init__(self,
sTime,
eTime,
gme_param,
oversamp_T=datetime.timedelta(minutes=1),
plot_info=None,
comment=None,
parent=None):
# Save the input plot_info to override default data later.
_plot_info = plot_info
plot_info = {}
plot_info['sTime'] = sTime
plot_info['eTime'] = eTime
if 'omni' in gme_param:
ind_class = gme.ind.readOmni(sTime, eTime, res=1)
omni_list = []
omni_time = []
for xx in ind_class:
tmp = {}
# tmp['res'] = xx.res
# tmp['timeshift'] = xx.timeshift
# tmp['al'] = xx.al
# tmp['au'] = xx.au
# tmp['asyd'] = xx.asyd
# tmp['asyh'] = xx.asyh
# tmp['symd'] = xx.symd
# tmp['beta'] = xx.beta
# tmp['bye'] = xx.bye
# tmp['bze'] = xx.bze
# tmp['e'] = xx.e
# tmp['flowSpeed'] = xx.flowSpeed
# tmp['vxe'] = xx.vxe
# tmp['vye'] = xx.vye
# tmp['vzy'] = xx.vzy
# tmp['machNum'] = xx.machNum
# tmp['np'] = xx.np
# tmp['temp'] = xx.temp
# tmp['time'] = xx.time
tmp['ae'] = xx.ae
tmp['bMagAvg'] = xx.bMagAvg
tmp['bx'] = xx.bx
tmp['bym'] = xx.bym
tmp['bzm'] = xx.bzm
tmp['pDyn'] = xx.pDyn
tmp['symh'] = xx.symh
tmp['flowSpeed'] = xx.flowSpeed
tmp['np'] = xx.np
tmp['temp'] = xx.temp
omni_time.append(xx.time)
omni_list.append(tmp)
omni_df_raw = pd.DataFrame(omni_list, index=omni_time)
del omni_time
del omni_list
self.omni_df_raw = omni_df_raw
self.omni_df = omni_df_raw.resample('T')
self.omni_df = self.omni_df.interpolate()
plot_info['x_label'] = 'Date [UT]'
if gme_param == 'ae':
# Read data with DavitPy routine and place into numpy arrays.
ind_class = gme.ind.readAe(sTime, eTime, res=1)
ind_data = [(x.time, x.ae) for x in ind_class]
df_raw = pd.DataFrame(ind_data, columns=['time', 'raw'])
df_raw = df_raw.set_index('time')
plot_info['title'] = 'Auroral Electrojet (AE) Index'
plot_info['symbol'] = 'Auroral Electrojet (AE) Index'
plot_info['gme_label'] = 'AE Index [nT]'
elif (gme_param == 'omni_by'):
df_raw = pd.DataFrame(omni_df_raw['bym'])
plot_info['symbol'] = 'OMNI By GSM'
plot_info['gme_label'] = 'OMNI By GSM [nT]'
elif gme_param == 'omni_bz':
df_raw = pd.DataFrame(omni_df_raw['bzm'])
plot_info['symbol'] = 'OMNI Bz GSM'
plot_info['gme_label'] = 'OMNI Bz GSM [nT]'
elif gme_param == 'omni_flowSpeed':
df_raw = pd.DataFrame(omni_df_raw['flowSpeed'])
plot_info['symbol'] = 'OMNI v'
plot_info['gme_label'] = 'OMNI v [km/s]'
elif gme_param == 'omni_np':
df_raw = pd.DataFrame(omni_df_raw['np'])
plot_info['symbol'] = 'OMNI Np'
plot_info['gme_label'] = 'OMNI Np [N/cm^3]'
elif gme_param == 'omni_pdyn':
df_raw = pd.DataFrame(omni_df_raw['pDyn'])
plot_info['symbol'] = 'OMNI pDyn'
plot_info['gme_label'] = 'OMNI pDyn [nPa]'
elif gme_param == 'omni_symh':
df_raw = pd.DataFrame(omni_df_raw['symh'])
plot_info['title'] = 'OMNI Sym-H'
plot_info['symbol'] = 'OMNI Sym-H'
plot_info['gme_label'] = 'OMNI Sym-H\n[nT]'
elif gme_param == 'omni_bmagavg':
df_raw = pd.DataFrame(omni_df_raw['bMagAvg'])
plot_info['symbol'] = 'OMNI |B|'
plot_info['gme_label'] = 'OMNI |B| [nT]'
elif gme_param == 'solar_dec':
times = []
taus = []
decs = []
curr_time = sTime
while curr_time < eTime:
tau, dec = solar.epem(curr_time)
times.append(curr_time)
taus.append(tau)
decs.append(dec)
curr_time += datetime.timedelta(days=1)
df_raw = pd.DataFrame(decs, index=times)
plot_info['symbol'] = 'Solar Dec.'
plot_info['gme_label'] = 'Solar Dec.'
if plot_info.get('title') is None:
plot_info['title'] = '{}'.format(gme_param.upper())
if _plot_info is not None:
plot_info.update(_plot_info)
# Enforce sTime, eTime
tf = np.logical_and(df_raw.index >= sTime, df_raw.index < eTime)
df_raw = df_raw[tf].copy()
df_raw.columns = ['raw']
if parent is None:
# This section is for compatibility with code that only uses
# the single level GmeDataSet.
# Resample data.
df_rsmp = df_raw.resample(oversamp_T)
df_rsmp = df_rsmp.interpolate()
df_rsmp.columns = ['processed']
self.ind_df_raw = df_raw
self.sTime = sTime
self.eTime = eTime
self.ind_df = df_rsmp
self.ind_times = df_rsmp.index.to_pydatetime()
self.ind_vals = df_rsmp['processed']
else:
# This section is for attributes of the new container-style
# GmeObject class.
self.data = df_raw['raw']
self.data.name = gme_param
self.parent = parent
self.gme_param = gme_param
self.history = {datetime.datetime.now(): comment}
self.plot_info = plot_info
self.metadata = plot_info #Create alias for compatibility with other code.