"""This module provides various helper functions."""
import logging
import sys
import os
import warnings
from functools import wraps
import urllib
import matplotlib.pyplot as plt
from matplotlib.colors import LogNorm
import numpy as np
from tqdm import tqdm
import astropy
from astropy.utils.data import download_file
from astropy.units.quantity import Quantity
import astropy.units as u
from astropy.visualization import (
PercentileInterval,
ImageNormalize,
SqrtStretch,
LinearStretch,
)
from astropy.time import Time
log = logging.getLogger(__name__)
__all__ = [
"LightkurveError",
"LightkurveWarning",
"KeplerQualityFlags",
"TessQualityFlags",
"bkjd_to_astropy_time",
"btjd_to_astropy_time",
"show_citation_instructions",
"finalize_notebook_url",
"remote_jupyter_proxy_url"
]
class QualityFlags(object):
"""Abstract class"""
STRINGS = {}
OPTIONS = {}
@classmethod
def decode(cls, quality):
"""Converts a QUALITY value into a list of human-readable strings.
This function takes the QUALITY bitstring that can be found for each
cadence in Kepler/K2/TESS' pixel and light curve files and converts into
a list of human-readable strings explaining the flags raised (if any).
Parameters
----------
quality : int
Value from the 'QUALITY' column of a Kepler/K2/TESS pixel or lightcurve file.
Returns
-------
flags : list of str
List of human-readable strings giving a short description of the
quality flags raised. Returns an empty list if no flags raised.
"""
# If passed an astropy quantity object, get the value
if isinstance(quality, Quantity):
quality = quality.value
result = []
for flag in cls.STRINGS.keys():
if quality & flag > 0:
result.append(cls.STRINGS[flag])
return result
@classmethod
def create_quality_mask(cls, quality_array, bitmask=None):
"""Returns a boolean array which flags good cadences given a bitmask.
This method is used by the readers of :class:`KeplerTargetPixelFile`
and :class:`KeplerLightCurve` to initialize their `quality_mask`
class attribute which is used to ignore bad-quality data.
Parameters
----------
quality_array : array of int
'QUALITY' column of a Kepler target pixel or lightcurve file.
bitmask : int or str
Bitmask (int) or one of 'none', 'default', 'hard', or 'hardest'.
Returns
-------
boolean_mask : array of bool
Boolean array in which `True` means the data is of good quality.
"""
# Return an array filled with `True` by default (i.e. ignore nothing)
if bitmask is None:
return np.ones(len(quality_array), dtype=bool)
if isinstance(quality_array, u.Quantity):
quality_array = quality_array.value
# A few pre-defined bitmasks can be specified as strings
if isinstance(bitmask, str):
try:
bitmask = cls.OPTIONS[bitmask]
except KeyError:
valid_options = tuple(cls.OPTIONS.keys())
raise ValueError(
"quality_bitmask='{}' is not supported, "
"expected one of {}"
"".format(bitmask, valid_options)
)
# The bitmask is applied using the bitwise AND operator
quality_mask = (quality_array & bitmask) == 0
# Log the quality masking as info or warning
n_cadences = len(quality_array)
n_cadences_masked = (~quality_mask).sum()
percent_masked = 100.0 * n_cadences_masked / n_cadences
logmsg = (
"{:.0f}% ({}/{}) of the cadences will be ignored due to the "
"quality mask (quality_bitmask={})."
"".format(percent_masked, n_cadences_masked, n_cadences, bitmask)
)
if percent_masked > 20:
log.warning("Warning: " + logmsg)
else:
log.info(logmsg)
return quality_mask
[docs]class KeplerQualityFlags(QualityFlags):
"""
This class encodes the meaning of the various Kepler QUALITY bitmask flags,
as documented in the Kepler Archive Manual (Ref. [1], Table 2.3).
References
----------
.. [1] Kepler: A Search for Terrestrial Planets. Kepler Archive Manual.
http://archive.stsci.edu/kepler/manuals/archive_manual.pdf
"""
AttitudeTweak = 1
SafeMode = 2
CoarsePoint = 4
EarthPoint = 8
ZeroCrossing = 16
Desat = 32
Argabrightening = 64
ApertureCosmic = 128
ManualExclude = 256
# Bit 2**10 = 512 is unused by Kepler
SensitivityDropout = 1024
ImpulsiveOutlier = 2048
ArgabrighteningOnCCD = 4096
CollateralCosmic = 8192
DetectorAnomaly = 16384
NoFinePoint = 32768
NoData = 65536
RollingBandInAperture = 131072
RollingBandInMask = 262144
PossibleThrusterFiring = 524288
ThrusterFiring = 1048576
#: DEFAULT bitmask identifies all cadences which are definitely useless.
DEFAULT_BITMASK = (
AttitudeTweak
| SafeMode
| CoarsePoint
| EarthPoint
| Desat
| ManualExclude
| DetectorAnomaly
| NoData
| ThrusterFiring
)
#: HARD bitmask is conservative and may identify cadences which are useful.
HARD_BITMASK = (
DEFAULT_BITMASK
| SensitivityDropout
| ApertureCosmic
| CollateralCosmic
| PossibleThrusterFiring
)
#: HARDEST bitmask identifies cadences with any flag set. Its use is not recommended.
HARDEST_BITMASK = 2096639
#: Dictionary which provides friendly names for the various bitmasks.
OPTIONS = {
"none": 0,
"default": DEFAULT_BITMASK,
"hard": HARD_BITMASK,
"hardest": HARDEST_BITMASK,
}
#: Pretty string descriptions for each flag
STRINGS = {
1: "Attitude tweak",
2: "Safe mode",
4: "Coarse point",
8: "Earth point",
16: "Zero crossing",
32: "Desaturation event",
64: "Argabrightening",
128: "Cosmic ray in optimal aperture",
256: "Manual exclude",
1024: "Sudden sensitivity dropout",
2048: "Impulsive outlier",
4096: "Argabrightening on CCD",
8192: "Cosmic ray in collateral data",
16384: "Detector anomaly",
32768: "No fine point",
65536: "No data",
131072: "Rolling band in optimal aperture",
262144: "Rolling band in full mask",
524288: "Possible thruster firing",
1048576: "Thruster firing",
}
[docs]class TessQualityFlags(QualityFlags):
"""
This class encodes the meaning of the various TESS QUALITY bitmask flags,
as documented in the TESS Data Products Description Document (Ref. [1], Table 28).
References
----------
.. [1] TESS Science Data Products Description Document (EXP-TESS-ARC-ICD-0014)
https://archive.stsci.edu/missions/tess/doc/EXP-TESS-ARC-ICD-TM-0014.pdf
"""
AttitudeTweak = 1
SafeMode = 2
CoarsePoint = 4
EarthPoint = 8
Argabrightening = 16
Desat = 32
ApertureCosmic = 64
ManualExclude = 128
Discontinuity = 256
ImpulsiveOutlier = 512
CollateralCosmic = 1024
#: The first stray light flag is set manually by MIT based on visual inspection.
Straylight = 2048
#: The second stray light flag is set automatically by Ames/SPOC based on background level thresholds.
Straylight2 = 4096
# See TESS Science Data Products Description Document
PlanetSearchExclude = 8192
BadCalibrationExclude = 16384
# Set in the sector 20 data release notes
InsufficientTargets = 32768
#: DEFAULT bitmask identifies all cadences which are definitely useless.
DEFAULT_BITMASK = (
AttitudeTweak | SafeMode | CoarsePoint | EarthPoint | Desat | ManualExclude
)
#: HARD bitmask is conservative and may identify cadences which are useful.
HARD_BITMASK = (
DEFAULT_BITMASK | ApertureCosmic | CollateralCosmic | Straylight | Straylight2
)
#: HARDEST bitmask identifies cadences with any flag set. Its use is not recommended.
HARDEST_BITMASK = 65535
#: Dictionary which provides friendly names for the various bitmasks.
OPTIONS = {
"none": 0,
"default": DEFAULT_BITMASK,
"hard": HARD_BITMASK,
"hardest": HARDEST_BITMASK,
}
#: Pretty string descriptions for each flag
STRINGS = {
1: "Attitude tweak",
2: "Safe mode",
4: "Coarse point",
8: "Earth point",
16: "Argabrightening",
32: "Desaturation event",
64: "Cosmic ray in optimal aperture",
128: "Manual exclude",
256: "Discontinuity corrected",
512: "Impulsive outlier",
1024: "Cosmic ray in collateral data",
2048: "Straylight",
4096: "Straylight2",
8192: "Planet Search Exclude",
16384: "Bad Calibration Exclude",
32768: "Insufficient Targets for Error Correction Exclude",
}
def channel_to_module_output(channel):
"""Returns a (module, output) pair given a CCD channel number.
Parameters
----------
channel : int
Channel number
Returns
-------
module, output : tuple of ints
Module and Output number
"""
if channel < 1 or channel > 88:
raise ValueError("Channel number must be in the range 1-88.")
lookup = _get_channel_lookup_array()
lookup[:, 0] = 0
modout = np.where(lookup == channel)
return modout[0][0], modout[1][0]
def module_output_to_channel(module, output):
"""Returns the CCD channel number for a given module and output pair.
Parameters
----------
module : int
Module number
output : int
Output number
Returns
-------
channel : int
Channel number
"""
if module < 1 or module > 26:
raise ValueError("Module number must be in range 1-26.")
if output < 1 or output > 4:
raise ValueError("Output number must be 1, 2, 3, or 4.")
return _get_channel_lookup_array()[module, output]
def _get_channel_lookup_array():
"""Returns a lookup table which maps (module, output) onto channel."""
# In the array below, channel == array[module][output]
# Note: modules 1, 5, 21, 25 are the FGS guide star CCDs.
return np.array(
[
[0, 0, 0, 0, 0],
[1, 85, 0, 0, 0],
[2, 1, 2, 3, 4],
[3, 5, 6, 7, 8],
[4, 9, 10, 11, 12],
[5, 86, 0, 0, 0],
[6, 13, 14, 15, 16],
[7, 17, 18, 19, 20],
[8, 21, 22, 23, 24],
[9, 25, 26, 27, 28],
[10, 29, 30, 31, 32],
[11, 33, 34, 35, 36],
[12, 37, 38, 39, 40],
[13, 41, 42, 43, 44],
[14, 45, 46, 47, 48],
[15, 49, 50, 51, 52],
[16, 53, 54, 55, 56],
[17, 57, 58, 59, 60],
[18, 61, 62, 63, 64],
[19, 65, 66, 67, 68],
[20, 69, 70, 71, 72],
[21, 87, 0, 0, 0],
[22, 73, 74, 75, 76],
[23, 77, 78, 79, 80],
[24, 81, 82, 83, 84],
[25, 88, 0, 0, 0],
]
)
def running_mean(data, window_size):
"""Returns the moving average of an array `data`.
Parameters
----------
data : array of numbers
The running mean will be computed on this data.
window_size : int
Window length used to compute the running mean.
"""
if window_size > len(data):
window_size = len(data)
cumsum = np.cumsum(np.insert(data, 0, 0))
return (cumsum[window_size:] - cumsum[:-window_size]) / float(window_size)
def bkjd_to_astropy_time(bkjd) -> Time:
"""Converts Kepler Barycentric Julian Day (BKJD) time values to an
`astropy.time.Time` object.
Kepler Barycentric Julian Day (BKJD) is a Julian day minus 2454833.0
(UTC=January 1, 2009 12:00:00) and corrected to the arrival times
at the barycenter of the Solar System.
BKJD is the format in which times are recorded in the Kepler data products.
The time is in the Barycentric Dynamical Time frame (TDB), which is a
time system that is not affected by leap seconds.
See Section 2.3.2 in the Kepler Archive Manual for details.
Parameters
----------
bkjd : float or array of floats
Barycentric Kepler Julian Day.
Returns
-------
time : `astropy.time.Time` object
Resulting time object.
"""
bkjd = np.atleast_1d(bkjd)
# Some data products have missing time values;
# we need to set these to zero or `Time` cannot be instantiated.
bkjd[~np.isfinite(bkjd)] = 0
return Time(bkjd, format="bkjd", scale="tdb")
def btjd_to_astropy_time(btjd) -> Time:
"""Converts TESS Barycentric Julian Day (BTJD) values to an
`astropy.time.Time` object.
TESS Barycentric Julian Day (BTJD) is a Julian day minus 2457000.0
and corrected to the arrival times at the barycenter of the Solar System.
BTJD is the format in which times are recorded in the TESS data products.
The time is in the Barycentric Dynamical Time frame (TDB), which is a
time system that is not affected by leap seconds.
Parameters
----------
btjd : float or array of floats
Barycentric TESS Julian Day
Returns
-------
time : `astropy.time.Time` object
Resulting time object.
"""
btjd = np.atleast_1d(btjd)
btjd[~np.isfinite(btjd)] = 0
return Time(btjd, format="btjd", scale="tdb")
def plot_image(
image,
ax=None,
scale="linear",
origin="lower",
xlabel="Pixel Column Number",
ylabel="Pixel Row Number",
clabel="Flux ($e^{-}s^{-1}$)",
title=None,
show_colorbar=True,
vmin=None,
vmax=None,
**kwargs
):
"""Utility function to plot a 2D image
Parameters
----------
image : 2d array
Image data.
ax : `~matplotlib.axes.Axes`
A matplotlib axes object to plot into. If no axes is provided,
a new one will be generated.
scale : str
Scale used to stretch the colormap.
Options: 'linear', 'sqrt', or 'log'.
origin : str
The origin of the coordinate system.
xlabel : str
Label for the x-axis.
ylabel : str
Label for the y-axis.
clabel : str
Label for the color bar.
title : str or None
Title for the plot.
show_colorbar : bool
Whether or not to show the colorbar
vmin : float
Minimum colorbar value. By default, the 2.5%-percentile is used.
vmax : float
Maximum colorbar value. By default, the 97.5%-percentile is used.
kwargs : dict
Keyword arguments to be passed to `matplotlib.pyplot.imshow`.
Returns
-------
ax : `~matplotlib.axes.Axes`
The matplotlib axes object.
"""
if isinstance(image, u.Quantity):
image = image.value
if ax is None:
_, ax = plt.subplots()
if vmin is None or vmax is None:
with warnings.catch_warnings():
warnings.simplefilter("ignore", RuntimeWarning) # ignore image NaN values
mask = np.nan_to_num(image) > 0
if mask.any() > 0:
vmin_default, vmax_default = PercentileInterval(95.0).get_limits(
image[mask]
)
else:
vmin_default, vmax_default = 0, 0
if vmin is None:
vmin = vmin_default
if vmax is None:
vmax = vmax_default
norm = None
if scale is not None:
if scale == "linear":
norm = ImageNormalize(
vmin=vmin, vmax=vmax, stretch=LinearStretch(), clip=False
)
elif scale == "sqrt":
norm = ImageNormalize(
vmin=vmin, vmax=vmax, stretch=SqrtStretch(), clip=False
)
elif scale == "log":
# To use log scale we need to guarantee that vmin > 0, so that
# we avoid division by zero and/or negative values.
norm = LogNorm(vmin=max(vmin, sys.float_info.epsilon), vmax=vmax, clip=True)
else:
raise ValueError("scale {} is not available.".format(scale))
cax = ax.imshow(image, origin=origin, norm=norm, **kwargs)
ax.set_xlabel(xlabel)
ax.set_ylabel(ylabel)
ax.set_title(title)
if show_colorbar:
cbar = plt.colorbar(cax, ax=ax, label=clabel)
cbar.ax.yaxis.set_tick_params(tick1On=False, tick2On=False)
cbar.ax.minorticks_off()
return ax
class LightkurveError(Exception):
"""Class for Lightkurve exceptions."""
pass
class LightkurveWarning(Warning):
"""Class for all Lightkurve warnings."""
pass
class LightkurveDeprecationWarning(LightkurveWarning):
"""Class for all Lightkurve deprecation warnings."""
pass
def suppress_stdout(f, *args, **kwargs):
"""A simple decorator to suppress function print outputs."""
@wraps(f)
def wrapper(*args, **kwargs):
# redirect output to `null`
with open(os.devnull, "w") as devnull:
old_out = sys.stdout
sys.stdout = devnull
try:
return f(*args, **kwargs)
# restore to default
finally:
sys.stdout = old_out
return wrapper
def validate_method(method, supported_methods):
"""Raises a `ValueError` if a method is not supported.
Parameters
----------
method : str
The method specified by the user.
supported_methods : list of str
The methods supported. All method names must be lowercase.
Returns
-------
method : str
Will return the method name if it is supported.
"""
method = method.lower()
if method in supported_methods:
return method
raise ValueError(
"method '{}' is not supported; "
"must be one of {}".format(method, supported_methods)
)
def centroid_quadratic(data, mask=None):
"""Computes the quadratic estimate of the centroid in a 2d-array.
This method will fit a simple 2D second-order polynomial
$P(x, y) = a + bx + cy + dx^2 + exy + fy^2$
to the 3x3 patch of pixels centered on the brightest pixel within
the image. This function approximates the core of the Point
Spread Function (PSF) using a bivariate quadratic function, and returns
the maximum (x, y) coordinate of the function using linear algebra.
For the motivation and the details around this technique, please refer
to Vakili, M., & Hogg, D. W. 2016, ArXiv, 1610.05873.
Caveat: if the brightest pixel falls on the edge of the data array, the fit
will tend to fail or be inaccurate.
Parameters
----------
data : 2D array
The 2D input array representing the pixel values of the image.
mask : array_like (bool), optional
A boolean mask, with the same shape as `data`, where a **True** value
indicates the corresponding element of data is masked.
Returns
-------
column, row : tuple
The coordinates of the centroid in column and row. If the fit failed,
then (NaN, NaN) will be returned.
"""
if isinstance(data, u.Quantity):
data = data.value
# Step 1: identify the patch of 3x3 pixels (z_)
# that is centered on the brightest pixel (xx, yy)
if mask is not None:
data = data * mask
arg_data_max = np.nanargmax(data)
yy, xx = np.unravel_index(arg_data_max, data.shape)
# Make sure the 3x3 patch does not leave the TPF bounds
if yy < 1:
yy = 1
if xx < 1:
xx = 1
if yy > (data.shape[0] - 2):
yy = data.shape[0] - 2
if xx > (data.shape[1] - 2):
xx = data.shape[1] - 2
z_ = data[yy - 1 : yy + 2, xx - 1 : xx + 2]
# Next, we will fit the coefficients of the bivariate quadratic with the
# help of a design matrix (A) as defined by Eqn 20 in Vakili & Hogg
# (arxiv:1610.05873). The design matrix contains a
# column of ones followed by pixel coordinates: x, y, x**2, xy, y**2.
A = np.array(
[
[1, -1, -1, 1, 1, 1],
[1, 0, -1, 0, 0, 1],
[1, 1, -1, 1, -1, 1],
[1, -1, 0, 1, 0, 0],
[1, 0, 0, 0, 0, 0],
[1, 1, 0, 1, 0, 0],
[1, -1, 1, 1, -1, 1],
[1, 0, 1, 0, 0, 1],
[1, 1, 1, 1, 1, 1],
]
)
# We also pre-compute $(A^t A)^-1 A^t$, cf. Eqn 21 in Vakili & Hogg.
At = A.transpose()
# In Python 3 this can become `Aprime = np.linalg.inv(At @ A) @ At`
Aprime = np.matmul(np.linalg.inv(np.matmul(At, A)), At)
# Step 2: fit the polynomial $P = a + bx + cy + dx^2 + exy + fy^2$
# following Equation 21 in Vakili & Hogg.
# In Python 3 this can become `Aprime @ z_.flatten()`
a, b, c, d, e, f = np.matmul(Aprime, z_.flatten())
# Step 3: analytically find the function maximum,
# following https://en.wikipedia.org/wiki/Quadratic_function
det = 4 * d * f - e ** 2
if abs(det) < 1e-6:
return np.nan, np.nan # No solution
xm = -(2 * f * b - c * e) / det
ym = -(2 * d * c - b * e) / det
return xx + xm, yy + ym
def _query_solar_system_objects(
ra, dec, times, radius=0.1, location="kepler", cache=True, show_progress=True
):
"""Returns a list of asteroids/comets given a position and time.
This function relies on The Virtual Observatory Sky Body Tracker (SkyBot)
service which can be found at http://vo.imcce.fr/webservices/skybot/
Parameters
----------
ra : float
Right Ascension in degrees.
dec : float
Declination in degrees.
times : array of float
Times in Julian Date.
radius : float
Search radius in degrees.
location : str
Spacecraft location. Options include `'kepler'` and `'tess'`.
cache : bool
Whether to cache the search result. Default is True.
show_progress : bool
Whether to display a progress bar during the download. Default is True.
Returns
-------
result : `pandas.DataFrame`
DataFrame containing the list of known solar system objects at the
requested time and location.
"""
# We import pandas locally, because it takes quite a bit of time to import,
# and it is only required for this specific feature.
import pandas as pd
if (location.lower() == "kepler") or (location.lower() == "k2"):
location = "C55"
elif location.lower() == "tess":
location = "C57"
url = "http://vo.imcce.fr/webservices/skybot/skybotconesearch_query.php?"
url += "-mime=text&"
url += "-ra={}&".format(ra)
url += "-dec={}&".format(dec)
url += "-bd={}&".format(radius)
url += "-loc={}&".format(location)
df = None
times = np.atleast_1d(times)
for time in tqdm(times, desc="Querying for SSOs", disable=~show_progress):
url_queried = url + "EPOCH={}".format(time)
response = download_file(url_queried, cache=cache, show_progress=show_progress)
if open(response).read(10) == "# Flag: -1": # error code detected?
raise IOError(
"SkyBot Solar System query failed.\n"
"URL used:\n" + url_queried + "\n"
"Response received:\n" + open(response).read()
)
res = pd.read_csv(response, delimiter="|", skiprows=2)
if len(res) > 0:
res["epoch"] = time
res.rename(
{"# Num ": "Num", " Name ": "Name", " Class ": "Class", " Mv ": "Mv"},
inplace=True,
axis="columns",
)
res = res[["Num", "Name", "Class", "Mv", "epoch"]].reset_index(drop=True)
if df is None:
df = res
else:
df = pd.concat([df, res])
if df is not None:
df.reset_index(drop=True)
return df
def show_citation_instructions():
"""Show citation instructions."""
from . import PACKAGEDIR, __citation__
# To make installing Lightkurve easier, ipython is an optional dependency,
# because we can assume it is installed when notebook-specific features are called
try:
from IPython.display import HTML
ipython_installed = True
except ModuleNotFoundError:
ipython_installed = False
if not is_notebook() or not ipython_installed:
print(__citation__)
else:
from pathlib import Path # local import to speed up `import lightkurve`
import astroquery # local import to speed up `import lightkurve`
templatefile = Path(PACKAGEDIR, "data", "show_citation_instructions.html")
template = open(templatefile, "r").read()
template = template.replace("LIGHTKURVE_CITATION", __citation__)
template = template.replace("ASTROPY_CITATION", astropy.__citation__)
template = template.replace("ASTROQUERY_CITATION", astroquery.__citation__)
return HTML(template)
def _get_notebook_environment():
"""Returns 'jupyter', 'colab', or 'terminal'.
One can detect whether or not a piece of Python is running by executing
`get_ipython().__class__`, which returns the following result:
* Jupyter notebook: `ipykernel.zmqshell.ZMQInteractiveShell`
* Google colab: `google.colab._shell.Shell`
* IPython terminal: `IPython.terminal.interactiveshell.TerminalInteractiveShell`
* Python terminal: `NameError: name 'get_ipython' is not defined`
"""
try:
ipy = str(type(get_ipython())).lower()
if "zmqshell" in ipy:
return "jupyter"
if "colab" in ipy:
return "colab"
except NameError:
pass # get_ipython() is not a builtin
return "terminal"
def is_notebook():
"""Returns `True` if we are running in a notebook."""
return _get_notebook_environment() in ["jupyter", "colab"]
def remote_jupyter_proxy_url(port):
"""
Callable to configure Bokeh's show method when a proxy must be
configured. If port is None we're asking about the URL
for the origin header.
"""
base_url = os.environ['LK_JUPYTERHUB_EXTERNAL_URL']
host = urllib.parse.urlparse(base_url).netloc
# If port is None we're asking for the URL origin
# so return the public hostname.
if port is None:
return host
service_url_path = os.environ['JUPYTERHUB_SERVICE_PREFIX']
proxy_url_path = 'proxy/%d' % port
user_url = urllib.parse.urljoin(base_url, service_url_path)
full_url = urllib.parse.urljoin(user_url, proxy_url_path)
return full_url
def finalize_notebook_url(notebook_url):
"""Based on `notebook_url` and the environment, compute a final value for
notebook_url to be passed on to bokeh enabling transparent operation on JupyterHub.
See Bokeh instructions here:
https://docs.bokeh.org/en/latest/docs/user_guide/output/jupyter.html
This handles two aspects of Bokeh made tricky by JupyterHub, firstly
accessing the random Bokeh server port while behind a proxy, and second not
triggering CORS restrictions while accessing a second server.
A key aspect of the computed URL is the externally visible DNS name of the
JupyterHub, so for the case of TIKE we might have:
export LK_JUPYTERHUB_EXTERNAL_URL="https://timeseries.science.stsci.edu"
If LK_JUPYTERHUB_EXTERNAL_URL is implicitly defined by the hub environment,
JupyterHub users can nominally ignore the notebook_url parameter and
Lightkurve should "just work" as if the local default URL localhost:8888
was sufficient.
For example remote_jupyter_proxy_url(25346) would return the URL
"https://test.timeseries.science.stsci.edu/user/homer@stsci.edu/proxy/24356"
which is essentially HUB + USER_SESSION + BOKEH_PORT_IN_SESSION
The function result should be identical to past behavior unless the definition
of LK_JUPYTERHUB_EXTERNAL_URL indicates JupyterHub is in use. In this case the
use of remote_jupyter_proxy_url is activated. This effectively makes it the
JupyterHub default instead of localhost:8888.
"""
if notebook_url is not None:
return notebook_url
elif os.environ.get("LK_JUPYTERHUB_EXTERNAL_URL"):
return remote_jupyter_proxy_url
else:
return "localhost:8888"