Tutorial 3 - Plotting¶
import matplotlib.pyplot as plt
from matplotlib.ticker import AutoMinorLocator, LogLocator
import simple
simple.set_logging_level('INFO')
models = simple.load_collection('data/CCSNe_FeNi.hdf5')
Loading existing file: data/CCSNe_FeNi.hdf5 Loading file: data/CCSNe_FeNi.hdf5 Loading model: Ri18_m15 (CCSNe) Loading model: iniab2.0E-02GN93 (IsoRef) Loading model: W17 (IsoRef) Loading model: Ri18_m20 (CCSNe) Loading model: Ri18_m25 (CCSNe) Loading model: Pi16_m15 (CCSNe) Loading model: iniab2.0E-02GN93_Lo03 (IsoRef) Loading model: Pi16_m20 (CCSNe) Loading model: Pi16_m25 (CCSNe) Loading model: La22_m15 (CCSNe) Loading model: La22_m20 (CCSNe) Loading model: La22_m25 (CCSNe) Loading model: Si18_m15 (CCSNe) Loading model: iniab1.3E-02Lo03_Lo03_merged (IsoRef) Loading model: Si18_m20 (CCSNe) Loading model: Si18_m25 (CCSNe) Loading model: Ra02_m15 (CCSNe) Loading model: rau_solar_ref (IsoRef) Loading model: Ra02_m20 (CCSNe) Loading model: Ra02_m25 (CCSNe) Loading model: LC18_m15 (CCSNe) Loading model: iniab1.3E-02As09_As09_merged (IsoRef) Loading model: LC18_m20 (CCSNe) Loading model: LC18_m25 (CCSNe) Time to load file: 0:00:00.218663
Adjusting Plots¶
Customising labels¶
Informative labels are autogenerated by the plotting functions. By default, information that is common to all data points is included in the axis labels to keep the data point labels short. Information that can be contained in the labels are: name of the attribute, or its label if one has been defined, the isotope or ratio key, the unit of the data, and finally the model name. The inclusion of these can be modified using the appropriate argument, see the get_data() function for more details.
By default, the attribute name is included in the label only if is different than the one defined by the default_attrname or the attribute name is defined in the key.
simple.plot(models, '.abundance[o16/c12]', 'o16', where='.dataset==Ra02',
ax_xscale='log', default_attrname='abundance');
To change the label behaviour you can give either a single argument that is used for all axis or you can supply a list containing one value per axis. It is also possible to specify the value for a single axis by prefixing the argument name with the name of the axis, e.g. xunit_in_label. This will override any argument passed to the generic argument for this axis.
simple.plot(models, '.abundance[o16/c12]', 'o16', where='.dataset==Ra02',
ax_xscale='log', attrname_in_label=False, xunit_in_label=False);
You can provide your own labels to the datapoints by giving a list to the label argument. Similarly, you can define the axis labels by prefixing the label argument by the axis name as in the example below.
simple.plot(models, '.abundance[o16/c12]', 'o16', where='.dataset==Ra02',
ax_xscale='log', label=['a', 'b', 'c'], xlabel='X-axis', ylabel='Y-axis');
Styling Plots¶
The SIMPLE plotting functions allow you to update the axes or figure object using keyword arguments. To update the axes object pass the name of the method, minus the set_ part, with the prefix ax_ equal to the desired value, e.g. ax_ylim=(1,2). You can do the same for axes.xaxis and axis.yaxis methods using the prefix xax_ and yax_. To update the figure object use the prefix fig_.
You can pass multiple arguments to the methods by passing a tuple or a dictionary which will be unpacked when the method is called. You can also define keyword arguments separately using the ax_kw_<method>_<keyword> prefix. Note however that the method is only called is the ax_<method> argument is also present. Setting the ax_<method> to False will not call the function while setting it to True will call the method with no arguments, unless keyword arguments are specified as described above. To pass a boolean value to a method simple place it within a tuple, e.g. (True, ).
If you want to reuse the same style arguments repeatedly you can place them in a dictionary and unpack it when needed.
abundance_kwargs = {'ax_tick_params': dict(left=True,right=True,top=True,labelleft=True,which='both'),
'xax_minor_locator': AutoMinorLocator(),
'yax_major_locator': LogLocator(subs=(1.0, ),numticks=999),
'yax_minor_locator': LogLocator(numticks=999, subs='auto'),
'ax_yscale': 'log'}
simple.plot_ccsne.abundance(models, 'o16/c12', where='.dataset==Ra02', unit='mol',
**abundance_kwargs);
Ra02_m15.abundance: Converting array from ``mass`` to ``mole`` unit by dividing the data by the mass number Ra02_m20.abundance: Converting array from ``mass`` to ``mole`` unit by dividing the data by the mass number Ra02_m25.abundance: Converting array from ``mass`` to ``mole`` unit by dividing the data by the mass number
slope_kwargs = {'ax_tick_params': dict(left=True,right=True,top=True,labelleft=True,which='both'),
'xax_minor_locator': AutoMinorLocator(),
'yax_minor_locator': AutoMinorLocator()}
Updating the Default Style¶
The default style of the plotting functions can be found through the default_kwargs attribute.
simple.plot.default_kwargs
{'linestyle': False,
'color': True,
'marker': True,
'fixed_model_linestyle': None,
'fixed_model_color': None,
'fixed_model_marker': None,
'ax_kw_xlabel_fontsize': 15,
'ax_kw_ylabel_fontsize': 15,
'markersize': 4,
'legend_outside': True,
'ax_tick_params': {'axis': 'both', 'left': True, 'right': True, 'top': True},
'fig_size': (7, 6.5)}
The ccsne plotting functions inherits the style of the generic plotting functions. Similarly, by default, shortcuts will inherit the style of the plotting function they are attached to. The default_kwargs attribute contains all the kwargs of the function, including inherited ones.
simple.plot_ccsne.default_kwargs
{'linestyle': True,
'color': True,
'marker': False,
'fixed_model_linestyle': None,
'fixed_model_color': None,
'fixed_model_marker': None,
'ax_kw_xlabel_fontsize': 15,
'ax_kw_ylabel_fontsize': 15,
'markersize': 4,
'legend_outside': True,
'ax_tick_params': {'axis': 'both', 'left': True, 'right': True, 'top': True},
'fig_size': (10, 5)}
simple.plot_ccsne.abundance.default_kwargs
{'linestyle': True,
'color': True,
'marker': False,
'fixed_model_linestyle': None,
'fixed_model_color': None,
'fixed_model_marker': None,
'ax_kw_xlabel_fontsize': 15,
'ax_kw_ylabel_fontsize': 15,
'markersize': 4,
'legend_outside': True,
'ax_tick_params': {'axis': 'both', 'left': True, 'right': True, 'top': True},
'fig_size': (10, 5),
'default_attrname': 'abundance',
'unit': 'mass'}
You can update the default style using the update_default_kwargs function. Optional arguments are clear_ and remove_. The former clears the dictionary associated with this function and the latter removes the specified item(s) in the dictionary. Note neither affects inherited items.
simple.plot.update_default_kwargs(ax_tick_params = dict(left=True,right=True,top=True,labelleft=True,which='both'),
ax_grid = (True,))
simple.plot_ccsne.abundance(models, 'o16/c12', where='.dataset==Ra02', unit='mol');
Ra02_m15.abundance: Converting array from ``mass`` to ``mole`` unit by dividing the data by the mass number Ra02_m20.abundance: Converting array from ``mass`` to ``mole`` unit by dividing the data by the mass number Ra02_m25.abundance: Converting array from ``mass`` to ``mole`` unit by dividing the data by the mass number
Shortcuts¶
Shortcuts are a convenient way to predefine a set of arguments for a plotting function. For example the intnorm and stdnorm shortcut sets the default_attrname to the path of the result from these normalisations. Using these shortcuts means you only have to specify the isotope or ratio you want to plot.
You can create your own shortcuts using the add_shortcut function attached to anything with a default_kwargs dictionary.
simple.plot_ccsne.add_shortcut('abulogy', ax_yscale='log', default_attrname='abundance')
simple.plot_ccsne.abulogy(models, 'o16/c12', where='.dataset==Ra02', unit='mol');
Ra02_m15.abundance: Converting array from ``mass`` to ``mole`` unit by dividing the data by the mass number Ra02_m20.abundance: Converting array from ``mass`` to ``mole`` unit by dividing the data by the mass number Ra02_m25.abundance: Converting array from ``mass`` to ``mole`` unit by dividing the data by the mass number
Subplots¶
You can specify the axes that is used for the plot using the ax argument. To create a series of subplots you can use the matplotlib plt.subplot_mosaic function and then use the ax argument to specify the subplot.
fig, subplots = plt.subplot_mosaic([['A'], ['B']], layout='constrained', figsize=(10,10))
simple.plot_ccsne.abundance(models, 'o16/c12', where='.dataset==Ra02', unit='mol', ax=subplots['A']);
simple.plot_ccsne.abulogy(models, 'o16/c12', where='.dataset==Ra02', unit='mol', ax=subplots['B'], fig_size=(10,6));
Ra02_m15.abundance: Converting array from ``mass`` to ``mole`` unit by dividing the data by the mass number Ra02_m20.abundance: Converting array from ``mass`` to ``mole`` unit by dividing the data by the mass number Ra02_m25.abundance: Converting array from ``mass`` to ``mole`` unit by dividing the data by the mass number Ra02_m15.abundance: Converting array from ``mass`` to ``mole`` unit by dividing the data by the mass number Ra02_m20.abundance: Converting array from ``mass`` to ``mole`` unit by dividing the data by the mass number Ra02_m25.abundance: Converting array from ``mass`` to ``mole`` unit by dividing the data by the mass number
Creating Custom Plots¶
The get_data function allows you to extract data to create your own plotting functions.
model_datapoints, axis_labels = simple.get_data(models, 'x, y', xkey='.masscoord', ykey='.abundance[o16/c12]',
where='.dataset==Ra02', yunit='mol')
plt.set_xlabel = axis_labels['x']
plt.set_ylabel = axis_labels['y']
# Iterate though the data and plot it
for model_name, datapoints in model_datapoints.items():
for dp in datapoints:
plt.plot(dp['x'], dp['y'], label=dp['label'])
plt.legend();
Ra02_m15.abundance: Converting array from ``mass`` to ``mole`` unit by dividing the data by the mass number Ra02_m20.abundance: Converting array from ``mass`` to ``mole`` unit by dividing the data by the mass number Ra02_m25.abundance: Converting array from ``mass`` to ``mole`` unit by dividing the data by the mass number
The update_axes function allows you to update the axes, axis and figure objects using the same arguments used for the simple plotting functions. If you want the plots to look similar you can copy the default_kwargs dictionary of the plotting function. The create_legend function can also be used to place the legend outside the plot.
You can also import the parse_lscm function from simple.plotting to get the default linestyles, colors and marker shapes.
from simple.plotting import parse_lscm
model_datapoints, axis_labels = simple.get_data(models, 'x, y', xkey='.masscoord', ykey='.abundance[o16/c12]',
where='.dataset==Ra02', yunit='mol')
# The account for the inheritance of default kwargs
default_kwargs = simple.plot_ccsne.default_kwargs
default_kwargs['ax_xlabel'] = axis_labels['x']
default_kwargs['ax_ylabel'] = axis_labels['y']
simple.update_axes(plt, default_kwargs)
# Get the default linestyles and colours, and disable markers
ls, c, m = parse_lscm(linestyle=default_kwargs.get('linestyle', True),
color=default_kwargs.get('color', True),
marker=default_kwargs.get('marker', False))
# Iterate though the data and plot it
for i, (model_name, datapoints) in enumerate(model_datapoints.items()):
for j, dp in enumerate(datapoints):
plt.plot(dp['x'], dp['y'], label=dp['label'], linestyle=ls[i], color=c[i], marker=m[i])
simple.create_legend(plt, outside=True)
Ra02_m15.abundance: Converting array from ``mass`` to ``mole`` unit by dividing the data by the mass number Ra02_m20.abundance: Converting array from ``mass`` to ``mole`` unit by dividing the data by the mass number Ra02_m25.abundance: Converting array from ``mass`` to ``mole`` unit by dividing the data by the mass number
