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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
 Advanced
Step1:  This first line is only necessary for ipython noteboooks - it allows the plots to be shown on this page instead of in interactive mode.  Depending on your version of Jupyter, Python, and matplotlib - you may or may not need this line in order to see plots in the notebook.
Step2:  First we're going to create some fake observations so that we can show how to plot observational data.  In real life, we would use something like np.loadtxt to get arrays from a data file instead.
Step3:  Now we'll create a new Bundle and attach an orbit dataset (without observations) and a light curve dataset (with our "fake" observations - see Datasets for more details)
Step4:  And run several forward models.  See Computing Observables for more details.
Step5:  Time (highlight and uncover)
The built-in plot method also provides convenience options to either highlight the interpolated point for a given time, or only show the dataset up to a given time.
Highlight
The higlight option is enabled by default so long as a time (or times) is passed to plot.  It simply adds an extra marker at the sent time - interpolating in the synthetic model if necessary.
Step6:  To change the style of the "highlighted" points, you can pass matplotlib recognized markers, colors, and markersizes to the highlight_marker, highlight_color, and highlight_ms keywords, respectively.
Step7:  To disable highlighting, simply send highlight=False
Step8:  Uncover
Uncover shows the observations or synthetic model up to the provided time and is disabled by default, even when a time is provided, but is enabled simply by providing uncover=True.  There are no additional options available for uncover.
Step9:  Units
Likewise, each array that is plotted is automatically plotted in its default units. To override these defaults, simply provide the unit (as a string or as a astropy units object) for a given axis.
Step10:  WARNING
Step11:  Axes Limits
Axes limits are determined by the data automatically.  To set custom axes limits, either use matplotlib methods on the returned axes objects, or pass limits as a list or tuple.
Step12:  Errorbars
In the cases of observational data, errorbars can be added by passing the name of the column.
Step13:  To disable the errorbars, simply set yerror=None.
Step14:  Colors
Colors of points and lines, by default, cycle according to matplotlib's color policy.  To manually set the color, simply pass a matplotlib recognized color to the 'c' keyword.
Step15:  In addition, you can point to an array in the dataset to use as color.
Step16:  Choosing colors works slightly differently for meshes (ie you can set fc for facecolor and ec for edgecolor).  For more details, see the tutorial on the MESH dataset.
Colormaps
The colormaps is determined automatically based on the parameter used for coloring (ie RVs will be a red-blue colormap).  To override this, pass a matplotlib recognized colormap to the cmap keyword.
Step17:  Adding a Colorbar
To add a colorbar (or sizebar, etc), send draw_sidebars=True to the plot call.
Step18:  Labels and Legends
To add a legend, include legend=True.
For details on placement and formatting of the legend see matplotlib's documentation.
Step19:  The legend labels are generated automatically, but can be overriden by passing a string to the label keyword.
Step20:  To override the position or styling of the legend, you can pass valid options to legend_kwargs which will be passed on to plt.legend
Step21:  Other Plotting Options
Valid plotting options that are directly passed to matplotlib include
Step22:  3D Axes
To plot a in 3d, simply pass projection='3d' to the plot call.  To override the defaults for the z-direction, pass a twig or array just as you would for x or y.
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Python Code:
#!pip install -I "phoebe>=2.4,<2.5"
Explanation: Advanced: Plotting Options
For basic plotting usage, see the plotting tutorial
PHOEBE 2.4 uses autofig 1.1 as an intermediate layer for highend functionality to matplotlib.
Setup
Let's first make sure we have the latest version of PHOEBE 2.4 installed (uncomment this line if running in an online notebook session such as colab).
End of explanation
%matplotlib inline
import phoebe
from phoebe import u # units
import numpy as np
logger = phoebe.logger()
Explanation: This first line is only necessary for ipython noteboooks - it allows the plots to be shown on this page instead of in interactive mode.  Depending on your version of Jupyter, Python, and matplotlib - you may or may not need this line in order to see plots in the notebook.
End of explanation
b = phoebe.default_binary()
b.add_dataset('lc', compute_phases=phoebe.linspace(0,1,101))
b.run_compute(irrad_method='none')
times = b.get_value('times', context='model')
fluxes = b.get_value('fluxes', context='model') + np.random.normal(size=times.shape) * 0.01
sigmas = np.ones_like(times) * 0.05
Explanation: First we're going to create some fake observations so that we can show how to plot observational data.  In real life, we would use something like np.loadtxt to get arrays from a data file instead.
End of explanation
b = phoebe.default_binary()
b.set_value('q', 0.8)
b.set_value('ecc', 0.1)
b.set_value('irrad_method', 'none')
b.add_dataset('orb', compute_times=np.linspace(0,4,1000), dataset='orb01', component=['primary', 'secondary'])
b.add_dataset('lc', times=times, fluxes=fluxes, sigmas=sigmas, dataset='lc01')
Explanation: Now we'll create a new Bundle and attach an orbit dataset (without observations) and a light curve dataset (with our "fake" observations - see Datasets for more details):
End of explanation
b.set_value(qualifier='incl', kind='orbit', value=90)
b.run_compute(model='run_with_incl_90') 
b.set_value(qualifier='incl', kind='orbit', value=85)
b.run_compute(model='run_with_incl_85')
b.set_value(qualifier='incl', kind='orbit', value=80)
b.run_compute(model='run_with_incl_80')
Explanation: And run several forward models.  See Computing Observables for more details.
End of explanation
afig, mplfig = b['orb@run_with_incl_80'].plot(time=1.0, show=True)
Explanation: Time (highlight and uncover)
The built-in plot method also provides convenience options to either highlight the interpolated point for a given time, or only show the dataset up to a given time.
Highlight
The higlight option is enabled by default so long as a time (or times) is passed to plot.  It simply adds an extra marker at the sent time - interpolating in the synthetic model if necessary.
End of explanation
afig, mplfig = b['orb@run_with_incl_80'].plot(time=1.0, 
                                              highlight_marker='s', 
                                              highlight_color='g', 
                                              highlight_ms=20, 
                                              show=True)
Explanation: To change the style of the "highlighted" points, you can pass matplotlib recognized markers, colors, and markersizes to the highlight_marker, highlight_color, and highlight_ms keywords, respectively.
End of explanation
afig, mplfig = b['orb@run_with_incl_80'].plot(time=1.0, 
                                              highlight=False, 
                                              show=True)
Explanation: To disable highlighting, simply send highlight=False
End of explanation
afig, mplfig = b['orb@run_with_incl_80'].plot(time=0.5, 
                                              uncover=True, 
                                              show=True)
Explanation: Uncover
Uncover shows the observations or synthetic model up to the provided time and is disabled by default, even when a time is provided, but is enabled simply by providing uncover=True.  There are no additional options available for uncover.
End of explanation
afig, mplfig = b['orb01@primary@run_with_incl_80'].plot(xunit='AU', yunit='AU', show=True)
Explanation: Units
Likewise, each array that is plotted is automatically plotted in its default units. To override these defaults, simply provide the unit (as a string or as a astropy units object) for a given axis.
End of explanation
afig, mplfig = b['orb01@primary@run_with_incl_80'].plot(xlabel='X POS', ylabel='Z POS', show=True)
Explanation: WARNING: when plotting two arrays with the same dimensions, PHOEBE attempts to set the aspect ratio to equal, but overriding to use two different units will result in undesired results.  This may be fixed in the future, but for now can be avoided by using consistent units for the x and y axes when they have the same dimensions.
Axes Labels
Axes labels are automatically generated from the qualifier of the array and the plotted units.  To override these defaults, simply pass a string for the label of a given axis.
End of explanation
afig, mplfig = b['orb01@primary@run_with_incl_80'].plot(xlim=(-2,2), show=True)
Explanation: Axes Limits
Axes limits are determined by the data automatically.  To set custom axes limits, either use matplotlib methods on the returned axes objects, or pass limits as a list or tuple.
End of explanation
afig, mplfig = b['lc01@dataset'].plot(yerror='sigmas', show=True)
Explanation: Errorbars
In the cases of observational data, errorbars can be added by passing the name of the column.
End of explanation
afig, mplfig = b['lc01@dataset'].plot(yerror=None, show=True)
Explanation: To disable the errorbars, simply set yerror=None.
End of explanation
afig, mplfig = b['orb01@primary@run_with_incl_80'].plot(c='r', show=True)
Explanation: Colors
Colors of points and lines, by default, cycle according to matplotlib's color policy.  To manually set the color, simply pass a matplotlib recognized color to the 'c' keyword.
End of explanation
afig, mplfig = b['orb01@primary@run_with_incl_80'].plot(x='times', c='vws', show=True)
Explanation: In addition, you can point to an array in the dataset to use as color.
End of explanation
afig, mplfig = b['orb01@primary@run_with_incl_80'].plot(x='times', c='vws', cmap='spring', show=True)
Explanation: Choosing colors works slightly differently for meshes (ie you can set fc for facecolor and ec for edgecolor).  For more details, see the tutorial on the MESH dataset.
Colormaps
The colormaps is determined automatically based on the parameter used for coloring (ie RVs will be a red-blue colormap).  To override this, pass a matplotlib recognized colormap to the cmap keyword.
End of explanation
afig, mplfig = b['orb01@primary@run_with_incl_80'].plot(x='times', c='vws', draw_sidebars=True, show=True)
Explanation: Adding a Colorbar
To add a colorbar (or sizebar, etc), send draw_sidebars=True to the plot call.
End of explanation
afig, mplfig = b['orb@run_with_incl_80'].plot(show=True, legend=True)
Explanation: Labels and Legends
To add a legend, include legend=True.
For details on placement and formatting of the legend see matplotlib's documentation.
End of explanation
afig, mplfig = b['primary@orb@run_with_incl_80'].plot(label='primary')
afig, mplfig = b['secondary@orb@run_with_incl_80'].plot(label='secondary', legend=True, show=True)
Explanation: The legend labels are generated automatically, but can be overriden by passing a string to the label keyword.
End of explanation
afig, mplfig = b['orb@run_with_incl_80'].plot(show=True, legend=True, legend_kwargs={'loc': 'center', 'facecolor': 'r'})
Explanation: To override the position or styling of the legend, you can pass valid options to legend_kwargs which will be passed on to plt.legend
End of explanation
afig, mplfig = b['orb01@primary@run_with_incl_80'].plot(linestyle=':', s=0.1, show=True)
Explanation: Other Plotting Options
Valid plotting options that are directly passed to matplotlib include:
- linestyle
- marker
Note that sizes (markersize, linewidth) should be handled by passing the size to 's' and attempting to set markersize or linewidth directly will raise an error.  See also the autofig documention on size scales.
End of explanation
afig, mplfig = b['orb@run_with_incl_80'].plot(time=0, projection='3d', show=True)
Explanation: 3D Axes
To plot a in 3d, simply pass projection='3d' to the plot call.  To override the defaults for the z-direction, pass a twig or array just as you would for x or y.
End of explanation
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