# Licensed under a 3-clause BSD style license - see LICENSE.rst
from __future__ import (absolute_import, division, print_function,
unicode_literals)
import copy
import numpy as np
import operator
import astropy.units as u
from astropy.time import Time
from collections.abc import Sequence
import warnings
import pytz
from ..exceptions import PlotWarning
from ..utils import _set_mpl_style_sheet
__all__ = ['plot_airmass', 'plot_schedule_airmass', 'plot_parallactic',
'plot_altitude']
def _secz_to_altitude(secant_z):
"""
Convert airmass (approximated as the secant of the zenith angle) to
an altitude (aka elevation) in degrees.
Parameters
----------
secant_z : float
Secant of the zenith angle
Returns
-------
altitude : float
Altitude [degrees]
"""
return np.degrees(np.pi/2 - np.arccos(1./secant_z))
def _has_twin(ax):
"""
Solution for detecting twin axes built on `ax`. Courtesy of
Jake Vanderplas http://stackoverflow.com/a/36209590/1340208
"""
for other_ax in ax.figure.axes:
if other_ax is ax:
continue
if other_ax.bbox.bounds == ax.bbox.bounds:
return True
return False
[docs]
def plot_airmass(targets, observer, time, ax=None, style_kwargs=None,
style_sheet=None, brightness_shading=False,
altitude_yaxis=False, min_airmass=1.0, min_region=None,
max_airmass=3.0, max_region=None, use_local_tz=False):
r"""
Plots airmass as a function of time for a given target.
If a `~matplotlib.axes.Axes` object already exists, an additional
airmass plot will be "stacked" on it. Otherwise, creates a new
`~matplotlib.axes.Axes` object and plots airmass on top of that.
When a scalar `~astropy.time.Time` object is passed in (e.g.,
``Time('2000-1-1')``), the resulting plot will use a 24-hour window
centered on the time indicated, with airmass sampled at regular
intervals throughout.
However, the user can control the exact number and frequency of airmass
calculations used by passing in a non-scalar `~astropy.time.Time`
object. For instance, ``Time(['2000-1-1 23:00:00', '2000-1-1
23:30:00'])`` will result in a plot with only two airmass measurements.
For examples with plots, visit the documentation of
:ref:`plots_time_dependent`.
Parameters
----------
targets : list of `~astroplan.FixedTarget` objects
The celestial bodies of interest.
If a single object is passed it will be converted to a list.
observer : `~astroplan.Observer`
The person, telescope, observatory, etc. doing the observing.
time : `~astropy.time.Time`
If scalar (e.g., ``Time('2000-1-1')``), will result in plotting target
airmasses once an hour over a 24-hour window.
If non-scalar (e.g., ``Time(['2000-1-1'])``, ``[Time('2000-1-1')]``,
``Time(['2000-1-1', '2000-1-2'])``),
will result in plotting data at the exact times specified.
ax : `~matplotlib.axes.Axes` or None, optional.
The `~matplotlib.axes.Axes` object to be drawn on.
If None, uses the current ``Axes``.
style_kwargs : dict or None, optional.
A dictionary of keywords passed into `~matplotlib.pyplot.plot_date`
to set plotting styles.
style_sheet : dict or `None` (optional)
matplotlib style sheet to use. To see available style sheets in
astroplan, print *astroplan.plots.available_style_sheets*. Defaults
to the light theme.
brightness_shading : bool
Shade background of plot to scale roughly with sky brightness. Dark
shading signifies times when the sun is below the horizon. Default
is `False`.
altitude_yaxis : bool
Add alternative y-axis on the right side of the figure with target
altitude. Default is `False`.
min_airmass : float
Lower limit of y-axis airmass range in the plot. Default is ``1.0``.
max_airmass : float
Upper limit of y-axis airmass range in the plot. Default is ``3.0``.
min_region : float
If set, defines an interval between ``min_airmass`` and ``min_region``
that will be shaded. Default is `None`.
max_region : float
If set, defines an interval between ``max_airmass`` and ``max_region``
that will be shaded. Default is `None`.
use_local_tz : bool
If the time is specified in a local timezone, the time will be plotted
in that timezone.
Returns
-------
ax : `~matplotlib.axes.Axes`
An ``Axes`` object with added airmass vs. time plot.
Notes
-----
y-axis is inverted and shows airmasses between 1.0 and 3.0 by default.
If user wishes to change these, use ``ax.<set attribute>`` before drawing
or saving plot:
"""
# Import matplotlib, set style sheet
if style_sheet is not None:
_set_mpl_style_sheet(style_sheet)
import matplotlib.pyplot as plt
from matplotlib import dates
# Set up plot axes and style if needed.
if ax is None:
ax = plt.gca()
if style_kwargs is None:
style_kwargs = {}
style_kwargs = dict(style_kwargs)
if 'lw' not in style_kwargs:
style_kwargs.setdefault('linewidth', 1.5)
if 'ls' not in style_kwargs and 'linestyle' not in style_kwargs:
style_kwargs.setdefault('fmt', '-')
if hasattr(time, 'utcoffset') and use_local_tz:
tzoffset = time.utcoffset()
tzname = time.tzname()
tzinfo = time.tzinfo
else:
tzoffset = 0
tzname = 'UTC'
tzinfo = None
# Populate time window if needed.
# (plot against local time if that's requested)
time_ut = Time(time)
if time_ut.isscalar:
time_ut = time_ut + np.linspace(-12, 12, 100)*u.hour
elif len(time_ut) == 1:
warnings.warn('You used a Time array of length 1. You probably meant '
'to use a scalar. (Or maybe a list with length > 1?).',
PlotWarning)
timetoplot = time_ut + tzoffset
if not isinstance(targets, Sequence):
targets = [targets]
for target in targets:
# Calculate airmass
airmass = observer.altaz(time_ut, target).secz
# Mask out nonsense airmasses
masked_airmass = np.ma.array(airmass, mask=airmass < 1)
# Some checks & info for labels.
try:
target_name = target.name
except AttributeError:
target_name = ''
# Plot data (against timezone-offset time)
ax.plot_date(timetoplot.plot_date, masked_airmass, label=target_name, **style_kwargs)
# Format the time axis
xlo, xhi = (timetoplot[0]), (timetoplot[-1])
ax.set_xlim([xlo.plot_date, xhi.plot_date])
date_formatter = dates.DateFormatter('%H:%M')
ax.xaxis.set_major_formatter(date_formatter)
plt.setp(ax.get_xticklabels(), rotation=30, ha='right')
# Shade background during night time
if brightness_shading:
start = time_ut[0]
# Calculate and order twilights and set plotting alpha for each
twilights = [
(observer.sun_set_time(start, which='next'), 0.0),
(observer.twilight_evening_civil(start, which='next'), 0.1),
(observer.twilight_evening_nautical(start, which='next'), 0.2),
(observer.twilight_evening_astronomical(start, which='next'), 0.3),
(observer.twilight_morning_astronomical(start, which='next'), 0.4),
(observer.twilight_morning_nautical(start, which='next'), 0.3),
(observer.twilight_morning_civil(start, which='next'), 0.2),
(observer.sun_rise_time(start, which='next'), 0.1),
]
# add 'UTC' to each datetime object created above
twilights = [(t[0].datetime.replace(tzinfo=pytz.utc), t[1])
for t in twilights]
twilights.sort(key=operator.itemgetter(0))
# add in left & right edges, so that if the airmass plot is requested
# during the day, night is properly shaded
left_edges = [(xlo.datetime.replace(tzinfo=tzinfo), twilights[0][1])] + twilights
right_edges = twilights + [(xhi.datetime.replace(tzinfo=tzinfo), twilights[0][1])]
for tw_left, tw_right in zip(left_edges, right_edges):
left = tw_left[0]
right = tw_right[0]
if tzinfo is not None:
# convert to local time zone (which is plotted), then hack away the tzinfo
# so that matplotlib doesn't try to double down on the conversion
left = left.astimezone(tzinfo).replace(tzinfo=None)
right = right.astimezone(tzinfo).replace(tzinfo=None)
ax.axvspan(left, right,
ymin=0, ymax=1, color='grey', alpha=tw_right[1])
# Invert y-axis and set limits.
y_lim = ax.get_ylim()
if y_lim[1] > y_lim[0]:
ax.invert_yaxis()
ax.set_ylim([max_airmass, min_airmass])
# Draw lo/hi limit regions, if present
ymax, ymin = ax.get_ylim() # should be (hi_limit, lo_limit)
if max_region is not None:
ax.axhspan(ymax, max_region, facecolor='#F9EB4E', alpha=0.10)
if min_region is not None:
ax.axhspan(min_region, ymin, facecolor='#F9EB4E', alpha=0.10)
# Set labels.
ax.set_ylabel("Airmass")
ax.set_xlabel("Time from {0} [{1}]".format(min(timetoplot).datetime.date(), tzname))
if altitude_yaxis and not _has_twin(ax):
altitude_ticks = np.array([90, 60, 50, 40, 30, 20])
airmass_ticks = 1./np.cos(np.radians(90 - altitude_ticks))
ax2 = ax.twinx()
ax2.invert_yaxis()
ax2.set_yticks(airmass_ticks)
ax2.set_yticklabels(altitude_ticks)
ax2.set_ylim(ax.get_ylim())
ax2.set_ylabel('Altitude [degrees]')
# Redraw figure for interactive sessions.
ax.figure.canvas.draw()
# Output.
return ax
[docs]
def plot_altitude(targets, observer, time, ax=None, style_kwargs=None,
style_sheet=None, brightness_shading=False,
airmass_yaxis=False, min_altitude=0, min_region=None,
max_altitude=90, max_region=None):
r"""
Plots altitude as a function of time for a given target.
If a `~matplotlib.axes.Axes` object already exists, an additional
altitude plot will be "stacked" on it. Otherwise, creates a new
`~matplotlib.axes.Axes` object and plots altitude on top of that.
When a scalar `~astropy.time.Time` object is passed in (e.g.,
``Time('2000-1-1')``), the resulting plot will use a 24-hour window
centered on the time indicated, with altitude sampled at regular
intervals throughout.
However, the user can control the exact number and frequency of altitude
calculations used by passing in a non-scalar `~astropy.time.Time`
object. For instance, ``Time(['2000-1-1 23:00:00', '2000-1-1
23:30:00'])`` will result in a plot with only two altitude measurements.
For examples with plots, visit the documentation of
:ref:`plots_time_dependent`.
Parameters
----------
targets : list of `~astroplan.FixedTarget` objects
The celestial bodies of interest.
If a single object is passed it will be converted to a list.
observer : `~astroplan.Observer`
The person, telescope, observatory, etc. doing the observing.
time : `~astropy.time.Time`
If scalar (e.g., ``Time('2000-1-1')``), will result in plotting target
altitudes once an hour over a 24-hour window.
If non-scalar (e.g., ``Time(['2000-1-1'])``, ``[Time('2000-1-1')]``,
``Time(['2000-1-1', '2000-1-2'])``),
will result in plotting data at the exact times specified.
ax : `~matplotlib.axes.Axes` or None, optional.
The `~matplotlib.axes.Axes` object to be drawn on.
If None, uses the current ``Axes``.
style_kwargs : dict or None, optional.
A dictionary of keywords passed into `~matplotlib.pyplot.plot_date`
to set plotting styles.
style_sheet : dict or `None` (optional)
matplotlib style sheet to use. To see available style sheets in
astroplan, print *astroplan.plots.available_style_sheets*. Defaults
to the light theme.
brightness_shading : bool
Shade background of plot to scale roughly with sky brightness. Dark
shading signifies times when the sun is below the horizon. Default
is `False`.
altitude_yaxis : bool
Add alternative y-axis on the right side of the figure with target
altitude. Default is `False`.
min_altitude : float
Lower limit of y-axis altitude range in the plot. Default is ``1.0``.
max_altitude : float
Upper limit of y-axis altitude range in the plot. Default is ``3.0``.
min_region : float
If set, defines an interval between ``min_altitude`` and ``min_region``
that will be shaded. Default is `None`.
max_region : float
If set, defines an interval between ``max_altitude`` and ``max_region``
that will be shaded. Default is `None`.
Returns
-------
ax : `~matplotlib.axes.Axes`
An ``Axes`` object with added altitude vs. time plot.
"""
# Import matplotlib, set style sheet
if style_sheet is not None:
_set_mpl_style_sheet(style_sheet)
import matplotlib.pyplot as plt
from matplotlib import dates
# Set up plot axes and style if needed.
if ax is None:
ax = plt.gca()
if style_kwargs is None:
style_kwargs = {}
style_kwargs = dict(style_kwargs)
if 'ls' not in style_kwargs and 'fmt' not in style_kwargs:
style_kwargs.setdefault('linestyle', '-')
if 'lw' not in style_kwargs:
style_kwargs.setdefault('linewidth', 1.5)
# Populate time window if needed.
time = Time(time)
if time.isscalar:
time = time + np.linspace(-12, 12, 100)*u.hour
elif len(time) == 1:
warnings.warn('You used a Time array of length 1. You probably meant '
'to use a scalar. (Or maybe a list with length > 1?).',
PlotWarning)
if not isinstance(targets, Sequence):
targets = [targets]
for target in targets:
# Calculate airmass
altitude = observer.altaz(time, target).alt
# Mask out nonsense airmasses
masked_altitude = np.ma.array(altitude, mask=altitude < 0)
# Some checks & info for labels.
try:
target_name = target.name
except AttributeError:
target_name = ''
# Plot data
ax.plot_date(time.plot_date, masked_altitude, label=target_name, **style_kwargs)
# Format the time axis
ax.set_xlim([time[0].plot_date, time[-1].plot_date])
date_formatter = dates.DateFormatter('%H:%M')
ax.xaxis.set_major_formatter(date_formatter)
plt.setp(ax.get_xticklabels(), rotation=30, ha='right')
# Shade background during night time
if brightness_shading:
start = time[0].datetime
# Calculate and order twilights and set plotting alpha for each
twilights = [
(observer.sun_set_time(Time(start), which='next').datetime, 0.0),
(observer.twilight_evening_civil(Time(start), which='next').datetime, 0.1),
(observer.twilight_evening_nautical(Time(start), which='next').datetime, 0.2),
(observer.twilight_evening_astronomical(Time(start), which='next').datetime, 0.3),
(observer.twilight_morning_astronomical(Time(start), which='next').datetime, 0.4),
(observer.twilight_morning_nautical(Time(start), which='next').datetime, 0.3),
(observer.twilight_morning_civil(Time(start), which='next').datetime, 0.2),
(observer.sun_rise_time(Time(start), which='next').datetime, 0.1),
]
twilights.sort(key=operator.itemgetter(0))
for i, twi in enumerate(twilights[1:], 1):
ax.axvspan(twilights[i - 1][0], twilights[i][0],
ymin=0, ymax=1, color='grey', alpha=twi[1])
# Invert y-axis and set limits.
# y_lim = ax.get_ylim()
# if y_lim[1] > y_lim[0]:
# ax.invert_yaxis()
ax.set_ylim([min_altitude, max_altitude])
# Draw lo/hi limit regions, if present
ymax, ymin = ax.get_ylim() # should be (hi_limit, lo_limit)
if max_region is not None:
ax.axhspan(ymax, max_region, facecolor='#F9EB4E', alpha=0.10)
if min_region is not None:
ax.axhspan(min_region, ymin, facecolor='#F9EB4E', alpha=0.10)
# Set labels.
ax.set_ylabel("Altitude")
ax.set_xlabel("Time from {0} [UTC]".format(min(time).datetime.date()))
if airmass_yaxis and not _has_twin(ax):
# altitude_ticks = np.array([90, 60, 50, 40, 30, 20])
# airmass_ticks = 1./np.cos(np.radians(90 - altitude_ticks))
airmass_ticks = np.array([1, 2, 3])
altitude_ticks = 90 - np.degrees(np.arccos(1/airmass_ticks))
ax2 = ax.twinx()
# ax2.invert_yaxis()
ax2.set_yticks(altitude_ticks)
ax2.set_yticklabels(airmass_ticks)
ax2.set_ylim(ax.get_ylim())
ax2.set_ylabel('Airmass')
# Redraw figure for interactive sessions.
ax.figure.canvas.draw()
# Output.
return ax
[docs]
def plot_schedule_airmass(schedule, show_night=False):
"""
Plots when observations of targets are scheduled to occur superimposed
upon plots of the airmasses of the targets.
Parameters
----------
schedule : `~astroplan.Schedule`
a schedule object output by a scheduler
show_night : bool
Shades the night-time on the plot
Returns
-------
ax : `~matplotlib.axes.Axes`
An ``Axes`` object with added airmass and schedule vs. time plot.
"""
import matplotlib.pyplot as plt
blocks = copy.copy(schedule.scheduled_blocks)
sorted_blocks = sorted(schedule.observing_blocks, key=lambda x: x.priority)
targets = [block.target for block in sorted_blocks]
ts = (schedule.start_time +
np.linspace(0, (schedule.end_time - schedule.start_time).value, 100) * u.day)
targ_to_color = {}
color_idx = np.linspace(0, 1, len(targets))
# lighter, bluer colors indicate higher priority
for target, ci in zip(set(targets), color_idx):
plot_airmass(target, schedule.observer, ts, style_kwargs=dict(color=plt.cm.cool(ci)))
targ_to_color[target.name] = plt.cm.cool(ci)
if show_night:
# I'm pretty sure this overlaps a lot, creating darker bands
for test_time in ts:
midnight = schedule.observer.midnight(test_time)
previous_sunset = schedule.observer.sun_set_time(
midnight, which='previous')
next_sunrise = schedule.observer.sun_rise_time(
midnight, which='next')
previous_twilight = schedule.observer.twilight_evening_astronomical(
midnight, which='previous')
next_twilight = schedule.observer.twilight_morning_astronomical(
midnight, which='next')
plt.axvspan(previous_sunset.plot_date, next_sunrise.plot_date,
facecolor='lightgrey', alpha=0.05)
plt.axvspan(previous_twilight.plot_date, next_twilight.plot_date,
facecolor='lightgrey', alpha=0.05)
for block in blocks:
if hasattr(block, 'target'):
plt.axvspan(block.start_time.plot_date, block.end_time.plot_date,
fc=targ_to_color[block.target.name], lw=0, alpha=.6)
else:
plt.axvspan(block.start_time.plot_date, block.end_time.plot_date,
color='k')
plt.axhline(3, color='k', label='Transitions')
# TODO: make this output a `axes` object
[docs]
def plot_parallactic(target, observer, time, ax=None, style_kwargs=None,
style_sheet=None):
"""
Plots parallactic angle as a function of time for a given target.
If a `~matplotlib.axes.Axes` object already exists, an additional
parallactic angle plot will be "stacked" on it. Otherwise, creates a
new `~matplotlib.axes.Axes` object and plots on top of that.
When a scalar `~astropy.time.Time` object is passed in (e.g.,
``Time('2000-1-1')``), the resulting plot will use a 24-hour window
centered on the time indicated, with parallactic angle sampled at
regular intervals throughout.
However, the user can control the exact number and frequency of parallactic
angle calculations used by passing in a non-scalar `~astropy.time.Time`
object. For instance, ``Time(['2000-1-1 23:00:00', '2000-1-1 23:30:00'])``
will result in a plot with only two parallactic angle measurements.
For examples with plots, visit the documentation of
:ref:`plots_time_dependent`.
Parameters
----------
target : `~astroplan.FixedTarget`
The celestial body of interest.
observer : `~astroplan.Observer`
The person, telescope, observatory, etc. doing the observing.
time : `~astropy.time.Time`
If scalar (e.g., ``Time('2000-1-1')``), will result in plotting target
parallactic angle once an hour over a 24-hour window.
If non-scalar (e.g., ``Time(['2000-1-1'])``, ``[Time('2000-1-1')]``,
``Time(['2000-1-1', '2000-1-2'])``),
will result in plotting data at the exact times specified.
ax : `~matplotlib.axes.Axes` or None, optional.
The ``Axes`` object to be drawn on.
If None, uses the current ``Axes``.
style_kwargs : dict or None, optional.
A dictionary of keywords passed into `~matplotlib.pyplot.plot_date`
to set plotting styles.
style_sheet : dict or `None` (optional)
matplotlib style sheet to use. To see available style sheets in
astroplan, print *astroplan.plots.available_style_sheets*. Defaults
to the light theme.
Returns
-------
ax : `~matplotlib.axes.Axes`
An ``Axes`` object with added parallactic angle vs. time plot.
"""
# Import matplotlib, set style sheet
if style_sheet is not None:
_set_mpl_style_sheet(style_sheet)
import matplotlib.pyplot as plt
from matplotlib import dates
# Set up plot axes and style if needed.
if ax is None:
ax = plt.gca()
if style_kwargs is None:
style_kwargs = {}
style_kwargs = dict(style_kwargs)
style_kwargs.setdefault('fmt', '-')
if 'ls' not in style_kwargs and 'fmt' not in style_kwargs:
style_kwargs.setdefault('linestyle', '-')
# Populate time window if needed.
time = Time(time)
if time.isscalar:
time = time + np.linspace(-12, 12, 100)*u.hour
elif len(time) == 1:
warnings.warn('You used a Time array of length 1. You probably meant '
'to use a scalar. (Or maybe a list with length > 1?).',
PlotWarning)
# Calculate parallactic angle.
p_angle = observer.parallactic_angle(time, target)
# Some checks & info for labels.
assert len(time) == len(p_angle)
if not hasattr(target, 'name'):
target_name = ''
else:
target_name = target.name
style_kwargs.setdefault('label', target_name)
# Plot data.
ax.plot_date(time.plot_date, p_angle, **style_kwargs)
# Format the time axis
date_formatter = dates.DateFormatter('%H:%M')
ax.xaxis.set_major_formatter(date_formatter)
plt.setp(ax.get_xticklabels(), rotation=30, ha='right')
# Set labels.
ax.set_ylabel("Parallactic Angle - Radians")
ax.set_xlabel("Time from {0} [UTC]".format(min(time).datetime.date()))
# Redraw figure for interactive sessions.
ax.figure.canvas.draw()
return ax