Plotting Tutorial¶

General Tips¶

Matplotlib Style Sheets¶

Set Style Sheet¶

import matplotlib.pyplot as plt
plt.style.use('seaborn-darkgrid')

Restore Default Style¶

import matplotlib
matplotlib.rcdefaults()

Quick Map Plot¶

from marvin.tools import Maps
maps = Maps('8485-1901')
ha = maps.emline_gflux_ha_6564
ha.plot()

(Source code)

Quick Spectrum Plot¶

from marvin.tools import Cube
cube = Cube('8485-1901')
spax = cube[17, 17]
spax.flux.plot()

(Source code)

Quick Model Fit Plot¶

from marvin.tools import Maps
maps = Maps('8485-1901')

# must use Maps.getSpaxel() to get cube and modelcube
spax = maps.getSpaxel(x=17, y=17, xyorig='lower', cube=True, modelcube=True)

# mask out pixels lacking model fit
no_fit = ~spax.full_fit.masked.mask

# extra arguments to plot are passed to the matplotlib routine
ax = spax.flux.plot(label='observed')
ax.plot(spax.full_fit.wavelength[no_fit], spax.full_fit.value[no_fit], label='model')
ax.legend()

(Source code)

Quick Image Plot¶

import matplotlib.pyplot as plt
from marvin.tools.image import Image
image = Image(plateifu='8553-12702')
image.plot()

(Source code)

BPT Plot¶

from marvin.tools import Maps
maps = Maps('8485-1901')
masks, fig, axes = maps.get_bpt()

(Source code)

Multi-panel Map Plot (Single Galaxy)¶

This code produces the right panel of Figure 1 from the Marvin paper.

import matplotlib.pyplot as plt
import numpy as np
from marvin.tools import Maps

maps = Maps('7977-12705')

halpha = maps.emline_gflux_ha_6564
nii_ha = np.log10(maps.emline_gflux_nii_6585 / halpha)
stvel = maps.stellar_vel
stsig = maps.stellar_sigma
stsig_corr = stsig.inst_sigma_correction()

with plt.style.context('seaborn-darkgrid'):
    fig, axes = plt.subplots(nrows=2, ncols=2, figsize=(12, 11))
    halpha.plot(fig=fig, ax=axes[0, 0])
    nii_ha.plot(fig=fig, ax=axes[0, 1], title="log([NII]6585 / H-alpha)", snr_min=None)
    stvel.plot(fig=fig, ax=axes[1, 0])
    stsig_corr.plot(fig=fig, ax=axes[1, 1])

(Source code)

Multi-panel Map Plot (Multiple Galaxies)¶

import matplotlib.pyplot as plt
from marvin.tools import Maps
import marvin.utils.plot.map as mapplot

plateifus = ['8485-1901', '7443-12701']
mapnames = ['stellar_vel', 'stellar_sigma']

with plt.style.context('seaborn-darkgrid'):
    rows = len(plateifus)
    cols = len(mapnames)
    fig, axes = plt.subplots(rows, cols, figsize=(8, 6))
    for row, plateifu in zip(axes, plateifus):
        maps = Maps(plateifu=plateifu)
        for ax, mapname in zip(row, mapnames):
            mapplot.plot(dapmap=maps[mapname], fig=fig, ax=ax, title=' '.join((plateifu, mapname)))

    fig.tight_layout()

(Source code)

Zoom-in Map Plot¶

from marvin.tools import Maps
maps = Maps('8485-1901')
ha = maps.emline_gflux_ha_6564

fig, ax = ha.plot()
ax.axis([13, 21, 13, 21])

(Source code)

Custom Map Colorbar Range Options¶

:align: center
:include-source: True

from marvin.tools import Maps
maps = Maps('8485-1901')
ha = maps.emline_gflux_ha_6564

fig, ax = ha.plot(percentile_clip=(1, 99))
fig, ax = ha.plot(sigma_clip=2)
fig, ax = ha.plot(cbrange=[2, 10])
fig, ax = ha.plot(symmetric=True)
fig, ax = ha.plot(log_cb=True)

Multi-panel Map Plot with Matching Colorbar Ranges¶

import numpy as np
import matplotlib.pyplot as plt
from marvin.tools import Maps
import marvin.utils.plot.map as mapplot

maps = Maps('8485-1901')
havel = maps.emline_gvel_ha_6564
stvel = maps.stellar_vel
vel_maps = [havel, stvel]

cbranges = [vel_map.plot(return_cbrange=True) for vel_map in vel_maps]
cb_max = np.max(np.abs(cbranges))
cbrange = (-cb_max, cb_max)

fig, axes = plt.subplots(ncols=2, figsize=(10, 4))
for ax, vel_map in zip(axes, vel_maps):
    vel_map.plot(fig=fig, ax=ax, cbrange=cbrange)

fig.tight_layout()

(Source code)

Custom Minimum Signal-to-Noise Ratio¶

from marvin.tools import Maps
maps = Maps('8485-1901')
ha = maps.emline_gflux_ha_6564

# Default is 1 except for velocities, which default to 0.
fig, ax = ha.plot(snr_min=10)

(Source code)

Custom No Usable IFU Data Region¶

from marvin.tools import Maps
maps = Maps('8485-1901')
ha = maps.emline_gflux_ha_6564

# Defaults:
# gray background (facecolor=''#A8A8A8'),
# white lines (edgecolor='w'),
# dense hatching: (hatch= 'xxxx')

# Custom: black background, cyan lines, less dense hatching
fig, ax = ha.plot(patch_kws={'facecolor': 'k',
                             'edgecolor': 'c',
                             'hatch': 'xx'})

(Source code)

Custom Axis and Colorbar Locations for Map Plot¶

import matplotlib.pyplot as plt
from marvin.tools import Maps

maps = Maps('8485-1901')
ha = maps.emline_gflux_ha_6564

fig = plt.figure()
ax = fig.add_axes([0.12, 0.1, 2 / 3., 5 / 6.])
fig, ax = ha.plot(fig=fig, ax=ax, cb_kws={'axloc': [0.8, 0.1, 0.03, 5 / 6.]})

(Source code)

Custom Spectrum and Model Fit¶

import matplotlib.pyplot as plt
from marvin.tools import Maps
plt.style.use('seaborn-darkgrid')

maps = Maps('1-209232')
spax = maps.getSpaxel(x=0, y=0, xyorig='center', cube=True, modelcube=True)

fig, ax = plt.subplots()

pObs = ax.plot(spax.flux.wavelength, spax.flux.value)
pModel = ax.plot(spax.full_fit.wavelength, spax.full_fit.value)
pEmline = ax.plot(spax.emline_fit.wavelength, spax.emline_fit.value)
plt.legend(pObs + pEmline + pModel, ['observed', 'emline model', 'model'])

ax.axis([6700, 7100, -0.1, 3])
ax.set_xlabel('observed wavelength [{}]'.format(spax.flux.wavelength.unit.to_string('latex')))
ax.set_ylabel('flux [{}]'.format(spax.flux.unit.to_string('latex')))

(Source code)

Plot H\(\alpha\) Map of Star-forming Spaxels¶

import numpy as np
from marvin.tools import Maps
maps = Maps('8485-1901')
ha = maps.emline_gflux_ha_6564
masks = maps.get_bpt(show_plot=False, return_figure=False)

# Create a bitmask for non-star-forming spaxels by taking the
# complement (`~`) of the BPT global star-forming mask (where True == star-forming)
# and set bit 30 (DONOTUSE) for those spaxels.
mask_non_sf = ~masks['sf']['global'] * ha.pixmask.labels_to_value('DONOTUSE')

# Do a bitwise OR between DAP mask and non-star-forming mask.
mask = ha.mask | mask_non_sf

ha.plot(mask=mask)

(Source code)

Plot [NII]/H\(\alpha\) Flux Ratio Map of Star-forming Spaxels¶

from marvin.tools import Maps
maps = Maps('8485-1901')
ha = maps.emline_gflux_ha_6564
nii = maps.emline_gflux_nii_6585
nii_ha = nii / ha

# Mask out non-star-forming spaxels
masks, __, __ = maps.get_bpt(show_plot=False)

# Create a bitmask for non-star-forming spaxels by taking the
# complement (`~`) of the BPT global star-forming mask (where True == star-forming)
# and set bit 30 (DONOTUSE) for those spaxels.
mask_non_sf = ~masks['sf']['global'] * ha.pixmask.labels_to_value('DONOTUSE')

# Do a bitwise OR between DAP mask and non-star-forming mask.
mask = nii_ha.mask | mask_non_sf

nii_ha.plot(mask=mask, cblabel='[NII]6585 / Halpha flux ratio')

(Source code)

Qualitative Colorbar¶

import numpy as np
from matplotlib import pyplot as plt
from matplotlib.colors import ListedColormap
from marvin.tools import Maps
import marvin.utils.plot.map as mapplot

maps = Maps('8485-1901')
ha = maps.emline_gflux_ha_6564

# divide data into classes
ha_class = np.ones(ha.shape, dtype=int)
ha_class[np.where(ha.value > 5)] = 2
ha_class[np.where(ha.value > 20)] = 3

cmap = ListedColormap(['#104e8b', '#5783ad', '#9fb8d0'])
fig, ax, cb = mapplot.plot(dapmap=ha, value=ha_class, cmap=cmap, cbrange=(0.5, 3.5),
                           title='', cblabel='Class', return_cb=True)
cb.set_ticks([1, 2, 3])
cb.set_ticklabels(['I', 'II', 'III'])

(Source code)

Custom Values and Custom Mask¶

from marvin.tools import Maps
import marvin.utils.plot.map as mapplot

maps = Maps('8485-1901')
ha = maps.emline_gflux_ha_6564

# Mask spaxels without IFU coverage
# nocov = ha.mask & 2**0
nocov = ha.pixmask.get_mask('NOCOV')

# Mask spaxels with low Halpha flux
low_ha = (ha.value < 6) * ha.pixmask.labels_to_value('DONOTUSE')

# Combine masks using bitwise OR (`|`)
mask = nocov | low_ha

fig, ax = mapplot.plot(dapmap=ha, value=ha.value, mask=mask)

(Source code)