Skip to content

Latest commit

 

History

History
448 lines (301 loc) · 10.7 KB

plotting-tutorial.rst

File metadata and controls

448 lines (301 loc) · 10.7 KB
Raw

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()

Quick Spectrum Plot

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

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()

Quick Image Plot

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

BPT Plot

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

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])

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()

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])

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()

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)

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'})

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.]})

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')))

Plot Hα 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)

Plot [NII]/Hα 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')

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'])

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)