def plot_widths(width_data, canvassize=None, msize=None, \
xval='year', yunits='phase', \
xticks=True, yticks=True, xlabel=True, ylabel=True, \
sym='o', colour=None, mf_colour=None, me_colour=None, \
csize=3, xlim=None, ylim=None, \
figtext=None, gridlines=None,
ticklabelsize=18, axislabelsize=18):
possible_xval = ['mjd', 'mjd0', 'year', 'serial']
if(possible_xval.count(xval) == 0):
print "There is not value "+xval+ \
" in the data that we can plot on x axis. Exiting."
exit()
possible_yunits = ['phase', 'deg', 'rad', 'cos_phi']
if(possible_yunits.count(yunits) == 0):
print yunits+ " is not an option for y axis units. Exiting."
exit()
# If we have just the one set of residuals, cast it as a one-element
# list to make things nice and general
if(type(width_data) is dict):
width_data = [width_data]
# mjd0 will subtract the nearest 100 of the smallest mjd value from the
# mjd arrays
if(xval=='mjd0'):
min_mjd = np.array([])
for width in width_data:
min_mjd = np.append(min_mjd, np.amin(width['mjd']))
mjdint = np.floor(np.amin(min_mjd))
for width in width_data:
width['mjd0'] = width['mjd'] - mjdint
if(xval=='year'):
for width in width_data:
# date_out = [mjd.mjdtodate(m) for m in width['mjd']]
width['year'] = [mjd.mjdtoyear(m) for m in width['mjd']]
# width['year'] = [d.year + d.day/365. + \
# d.hour/(365.*24.) + \
# d.minute/(365.*24.*60.) + \
# d.second/(365.*24.*60.*60.) \
# for d in date_out]
# Set up plot limits now
xmin = np.amin(width_data[0][xval])-0.003
xmax = np.amax(width_data[0][xval])+0.003
ymin = np.amin(width_data[0]['width'] - width_data[0]['werr'])
ymax = np.amax(width_data[len(width_data)-1]['width'] + \
width_data[len(width_data)-1]['werr'])
xspan = abs(xmax - xmin)
yspan = abs(ymax - ymin)
# Set up the plot:
fig = plt.figure(figsize=canvassize)
ax = fig.add_axes([0.12, 0.14, 0.86, 0.83])
ax.xaxis.set_tick_params(labelsize=ticklabelsize, pad=8)
ax.yaxis.set_tick_params(labelsize=ticklabelsize, pad=8)
if(xlim==None):
ax.set_xlim(xmin - 0.01*xspan, xmax + 0.01*xspan)
else:
ax.set_xlim(xlim)
if(ylim==None):
ax.set_ylim(ymin - 0.01*yspan, ymax + 0.02*yspan)
else:
ax.set_ylim(ylim)
if (xlabel):
if(xval=='serial'):
ax.set_xlabel('Serial number', fontsize=axislabelsize, labelpad=12)
elif(xval=='mjd'):
ax.set_xlabel('MJD', fontsize=axislabelsize, labelpad=12)
elif(xval=='mjd0'):
ax.set_xlabel('MJD - {:d}'.format(int(mjdint)), fontsize=axislabelsize, labelpad=12)
elif(xval=='year'):
# Set formatting for years so that they have %d formatting:
xmajorFormatter = FormatStrFormatter('%d')
ax.set_xlabel('Year', fontsize=axislabelsize, labelpad=12)
ax.xaxis.set_major_formatter(xmajorFormatter)
if (ylabel):
ax.set_ylabel('Pulse width (degrees)', fontsize=axislabelsize)#, labelpad=8)
if(not xticks):
for tick in ax.xaxis.get_major_ticks():
tick.label1On = False
tick.label2On = False
if(not yticks):
for tick in ax.yaxis.get_major_ticks():
tick.label1On = False
tick.label2On = False
for i_width in range(len(width_data)):
width = width_data[i_width]
# Get colours
if(colour!=None):
if (type(colour) is list):
clr = colour[i_width]
else:
clr = colour
else:
# Set up automated colours
if (len(width_data)==1):
clr = 'black'
else:
clr = cm.gist_heat(float(i_width)/float(len(width_data)))
if (type(mf_colour) is list):
mf_clr = mf_colour[i_width]
else:
mf_clr = clr
if (type(me_colour) is list):
me_clr = me_colour[i_width]
else:
me_clr = clr
#ax.plot(res[xval], res['res'], 'o', markersize=msize, color=col)
if(gridlines!=None):
for ycoord in gridlines:
ax.axhline(ycoord, linestyle='--', color='black', \
linewidth=0.4)
# Change to appropriate units:
if (yunits=='deg'):
# Set formatting for degrees so that they have correct formatting:
ymajorFormatter = FormatStrFormatter('%5.1f')
ax.yaxis.set_major_formatter(ymajorFormatter)
y_plot = width['width']*360.
yerr_plot = width['werr']*360.
elif (yunits=='rad'):
y_plot = width['width']*2.*np.pi
yerr_plot = width['werr']*2.*np.pi
elif (yunits=='cos_phi'):
y_plot = cos(width['width']/2.)
yerr_plot = cos(width['werr']/2.)
else: # Just in units of phase as given in data file
y_plot = width['width']
yerr_plot = width['werr']
if(i_width==0):
xmin = np.amin(width[xval])
xmax = np.amax(width[xval])
ymin = np.amin(y_plot-yerr_plot)
ymax = np.amax(y_plot+yerr_plot)
else:
xmin = np.amin(np.append(width[xval], xmin))
xmax = np.amax(np.append(width[xval], xmax))
ymin = np.amin(np.append(y_plot-yerr_plot, ymin))
ymax = np.amax(np.append(y_plot+yerr_plot, ymax))
xspan = abs(xmax - xmin)
yspan = abs(ymax - ymin)
# Overplot error bars. Use fmt=None to tell it not to plot points:
ax.plot(width[xval], y_plot, sym, color=clr, mfc=mf_clr, mec=me_clr)
ax.errorbar(width[xval], y_plot, yerr=yerr_plot, \
capsize=csize, fmt=None, ecolor=clr, \
markersize=msize)
if(xlim==None):
ax.set_xlim(xmin - 0.025*xspan, xmax + 0.025*xspan)
else:
ax.set_xlim(xlim)
if(ylim==None):
ax.set_ylim(ymin - 0.1*yspan, ymax + 0.1*yspan)
else:
ax.set_ylim(ylim)
# Figure text must be a list of tuples: [(x, y, text), (x, y, text), ...]
if(figtext!=None):
for txt in figtext:
ax.text(txt[0], txt[1], txt[2], fontsize=10, \
horizontalalignment='center', \
verticalalignment='center')
# plt.savefig('test_widths.png')
return ax
def plot_dmx(dmx_data, canvassize=None, msize=None,
xval='year',
xticks=True, yticks=True, xlabel=True, ylabel=True,
sym='o', symsize=3.4,
colour='black', mf_colour=None, me_colour=None,
csize=2, xlim=None, ylim=None,
figtext=None, gridlines=None):
possible_xval = ['mjd', 'mjd0', 'year', 'serial']
if(possible_xval.count(xval) == 0):
print "There is not value "+xval+ \
" in the data that we can plot on x axis. Exiting."
exit()
# If we have just the one set of residuals, cast it as a one-element
# list to make things nice and general
if(type(dmx_data) is dict):
dmx_data = [dmx_data]
# mjd0 will subtract the nearest 100 of the smallest mjd value from the
# mjd arrays
if(xval=='mjd0'):
min_mjd = np.array([])
for dmx in dmx_data:
min_mjd = np.append(min_mjd, np.amin(dmx['mjd']))
mjdint = np.floor(np.amin(min_mjd))
for dmx in dmx_data:
dmx['mjd0'] = dmx['mjd'] - mjdint
if(xval=='year'):
for dmx in dmx_data:
# date_out = [mjd.mjdtodate(m) for m in width['mjd']]
dmx['year'] = [mjd.mjdtoyear(m) for m in dmx['mjd']]
# Set up plot limits now
## xmin = np.amin(dmx_data[0][xval])-0.003
## xmax = np.amax(dmx_data[0][xval])+0.003
## ymin = np.amin(dmx_data[0]['width'] - dmx_data[0]['werr'])
## ymax = np.amax(dmx_data[len(dmx_data)-1]['width'] + \
## dmx_data[len(dmx_data)-1]['werr'])
## xspan = abs(xmax - xmin)
## yspan = abs(ymax - ymin)
# Set up the plot:
fig = plt.figure(figsize=canvassize)
ax = fig.add_axes([0.15, 0.1, 0.8, 0.85])
ax.xaxis.set_tick_params(labelsize=16)
ax.yaxis.set_tick_params(labelsize=16)
## if(xlim==None):
## ax.set_xlim(xmin - 0.01*xspan, xmax + 0.01*xspan)
## else:
## ax.set_xlim(xlim)
## if(ylim==None):
## ax.set_ylim(ymin - 0.01*yspan, ymax + 0.02*yspan)
## else:
## ax.set_ylim(ylim)
if (xlabel):
if(xval=='serial'):
ax.set_xlabel('Serial number', fontsize=18)
elif(xval=='mjd'):
ax.set_xlabel('MJD', fontsize=18)
elif(xval=='mjd0'):
ax.set_xlabel('MJD - {:d}'.format(int(mjdint)), fontsize=18)
elif(xval=='year'):
# Set formatting for years so that they have %d formatting:
xmajorFormatter = FormatStrFormatter('%d')
ax.set_xlabel('Year', fontsize=18)
ax.xaxis.set_major_formatter(xmajorFormatter)
if (ylabel):
ax.set_ylabel('Delta DM (pc cm$^{-3}$)', fontsize=18)
if(not xticks):
for tick in ax.xaxis.get_major_ticks():
tick.label1On = False
tick.label2On = False
if(not yticks):
for tick in ax.yaxis.get_major_ticks():
tick.label1On = False
tick.label2On = False
for i_dmx in range(len(dmx_data)):
dmx = dmx_data[i_dmx]
# Get colours
if (type(colour) is list):
clr = colour[i_dmx]
else:
clr = colour
if (type(mf_colour) is list):
mf_clr = mf_colour[i_dmx]
else:
mf_clr = mf_colour
if (type(me_colour) is list):
me_clr = me_colour[i_dmx]
else:
me_clr = me_colour
#ax.plot(res[xval], res['res'], 'o', markersize=msize, color=col)
if(gridlines!=None):
for ycoord in gridlines:
ax.axhline(ycoord, linestyle='--', color='black', \
linewidth=0.4)
# Change to appropriate units:
# Set formatting for degrees so that they have correct
# formatting:
ymajorFormatter = FormatStrFormatter('%6.3f')
ax.yaxis.set_major_formatter(ymajorFormatter)
y_plot = dmx['delta_dm']
yerr_plot = dmx['delta_dm_err']
if(i_dmx==0):
xmin = np.amin(dmx[xval])
xmax = np.amax(dmx[xval])
ymin = np.amin(y_plot-yerr_plot)
ymax = np.amax(y_plot+yerr_plot)
else:
xmin = np.amin(np.append(dmx[xval], xmin))
xmax = np.amax(np.append(dmx[xval], xmax))
ymin = np.amin(np.append(y_plot-yerr_plot, ymin))
ymax = np.amax(np.append(y_plot+yerr_plot, ymax))
xspan = abs(xmax - xmin)
yspan = abs(ymax - ymin)
# Overplot error bars. Use fmt=None to tell it not to plot points:
ax.plot(dmx[xval], y_plot, marker=sym, markersize=symsize,
linestyle='None',
color=clr, mfc=mf_clr, mec=me_clr)
ax.errorbar(dmx[xval], y_plot, yerr=yerr_plot,
capsize=csize, fmt=None, ecolor=clr,
markersize=msize)
if(xlim==None):
ax.set_xlim(xmin - 0.025*xspan, xmax + 0.025*xspan)
else:
ax.set_xlim(xlim)
if(ylim==None):
ax.set_ylim(ymin - 0.1*yspan, ymax + 0.1*yspan)
else:
ax.set_ylim(ylim)
# Figure text must be a list of tuples: [(x, y, text), (x, y, text), ...]
if(figtext!=None):
for txt in figtext:
ax.text(txt[0], txt[1], txt[2], fontsize=10, \
horizontalalignment='center', \
verticalalignment='center')
return
def plot_resid(resid_data, info_plot=None, canvassize=None,
axis_limits=None, binsize=None, resoffset=0.,
preres=False, xunits='year', yunits='us',
xticks=True, yticks=True, xlabel=True, ylabel=True,
sym='o', symsize=1.8, colour=None, csize=1.3,
xlim=None, ylim=None, figtext=None, gridlines=None,
ticklabelsize=18, axislabelsize=18):
# mjdlim=None, yearlim=None, orbphaselim=None):
# First determine if certain keywords are one-dimensional. If so,
# will need to list-ify them
if(type(resid_data) is not list):
resid_data = [resid_data]
else:
print 'len(resid_data) = ', len(resid_data)
if(type(xunits) is list):
# Case where we want to plot multiple xunits for same
# residual data set
if(len(xunits) > 1 & len(resid_data) == 1):
resid_data = resid_data*len(xunits)
# Case where we want multiple residual data sets with same
# xunits.
elif(len(xunits) == 1 & len(resid_data) > 1):
xunits = xunits*len(resid_data)
# Otherwise, will need lengths of xunits and resid_data to
# be equal
elif(len(xunits) != len(resid_data)):
print 'plot_resid ERROR: xunits and resid_data must have same dimensions if they are both > 1 in length'
else:
# Case where we want one set of xunits, no matter the amount
# of residual data sets
xunits = [xunits]*len(resid_data)
# Deal with yunits the same as xunits:
if(type(yunits) is list):
# Case where we want to plot multiple yunits for same
# residual data set
if(len(yunits) > 1 & len(resid_data) == 1):
resid_data = resid_data*len(yunits)
# Case where we want multiple residual data sets with same
# yunits.
elif(len(yunits) == 1 & len(resid_data) > 1):
yunits = yunits*len(resid_data)
# Otherwise, will need lengths of yunits and resid_data to
# be equal
elif(len(yunits) != len(resid_data)):
print 'plot_resid ERROR: yunits and resid_data must have same dimensions if they are both > 1 in length'
else:
# Case where we want one set of yunits, no matter the amount
# of residual data sets
yunits = [yunits]*len(resid_data)
# For xlabels, xticks, ylabel, yticks:
# - if they are not passed as lists, then default will be to
# make one label/tick label for x, and one label for y axes.
# - if they are passed as lists, then it is up to the user to
# ensure that they correctly correspond to the residual
# data list
if(type(xlabel) is list):
if(len(xlabel) != len(resid_data)):
print 'plot_resid ERROR: xlabel list must have same dimensions as resid_data'
exit()
else:
xlabel = [xlabel]*len(resid_data)
if(type(ylabel) is list):
if(len(ylabel) != len(resid_data)):
print 'plot_resid ERROR: ylabel list must have same dimensions as resid_data'
exit()
else:
ylabel = [ylabel]*len(resid_data)
if (type(xticks) is list):
if(len(xticks) != len(resid_data)):
print 'plot_resid ERROR: xticks list must have same dimensions as resid_data'
exit()
else:
xticks = [xticks]*len(resid_data)
if (type(yticks) is list):
if(len(yticks) != len(resid_data)):
print 'plot_resid ERROR: yticks list must have same dimensions as resid_data'
exit()
else:
yticks = [yticks]*len(resid_data)
# Default is to plot same info IDs on each plot in the same way.
# However, may have different data sets with different info IDs; in
# such a case, ensure dimensions are the same as resid_data
if(info_plot != None):
if(type(info_plot[0]) is list):
if(len(info_plot) == 1):
# replicate this list to have the same dimensions
# as resid_data (need square brackets to have
# n *lists*; otherwise will continue on same list)
info_plot = [info_plot]*len(resid_data)
elif(len(info_plot) != len(resid_data)):
print 'plot_resid ERROR: info_plot keyword much has same dimensions as resid_data.'
exit()
else:
info_plot = [[info_plot]]*len(resid_data)
# Set axis limits. If they are not given, default behaviour is to
# divide evenly in vertical direction
# We assume that for default plotting, first data set is plotted
# on the bottome, and last is on the top opf the canvas
# Based on a full plot being [0.12, 0.1, 0.8, 0.85]
if(axis_limits is None):
axis_limits = []
for i_plot in np.arange(len(resid_data)):
x1 = 0.12
xwidth = 0.8
ywidth=0.85/len(resid_data)
y1 = 0.12 + i_plot*ywidth
axis_limits.append([x1, y1, xwidth, ywidth])
else:
if(type(axis_limits) is list):
if(len(axis_limits) == 1 & len(resid_data)>1):
axis_limits = [axis_limits]*len(resid_data)
elif(len(axis_limits) != len(resid_data)):
print 'plot_resid ERROR: If specifying multiple axis_limits to keyword, it must be a list of same dimensions as resid_data'
exit()
else:
axis_limits = [axis_limits]
# This doesn't change throughout, so set it up now:
possible_xunits = ['mjd', 'mjd0', 'year', 'orbphase', 'serial']
# Set up conversion factors for possible yunits
yconv = {'s':1e-6, 'ms':1e-3, 'us':1, 'ns':1e3}
# Set up the plot:
fig = plt.figure(figsize=canvassize)
ax = []
for i_plot in np.arange(len(resid_data)):
res_data = resid_data[i_plot]
ax.append(fig.add_axes(axis_limits[i_plot]))
ax[i_plot].xaxis.set_tick_params(labelsize=16)
ax[i_plot].yaxis.set_tick_params(labelsize=16)
if(possible_xunits.count(xunits[i_plot]) == 0):
print "There is no value "+xunits[i_plot]+ \
" in the data that we can plot on x axis. Exiting."
exit()
# mjd0 will subtract the nearest 100 of the smallest mjd value from the
# mjd arrays
if(xunits[i_plot]=='mjd0'):
min_mjd = np.amin(res_data['mjd'])
mjdint = np.floor(min_mjd)
res_data['mjd0'] = res_data['mjd'] - mjdint
if(xunits[i_plot]=='year'):
res_data['year'] = \
np.array([mjd.mjdtoyear(m) for m in res_data['mjd']])
# Convert units based on yunits keyword
res_data['res'] *= yconv[yunits[i_plot]]
res_data['reserr'] *= yconv[yunits[i_plot]]
# xmax = np.amax(res_data[xunits[i_plot]])+0.003
# ymin = np.amin(res_data['res'] - res_data['reserr'])
# ymax = np.amax(res_data['res'] + res_data['reserr'])
# xspan = abs(xmax - xmin)
# yspan = abs(ymax - ymin)
# if(xlim==None):
# xlim=(xmin, xmax)
# if(ylim==None):
# ylim=(ymin, ymax)
if (xlabel[i_plot]):
if(xunits[i_plot]=='serial'):
ax[i_plot].set_xlabel('Serial number', fontsize=axislabelsize, labelpad=12)
elif(xunits[i_plot]=='orbphase'):
ax[i_plot].set_xlabel('Orbital phase', fontsize=axislabelsize, labelpad=12)
elif(xunits[i_plot]=='mjd'):
ax[i_plot].set_xlabel('MJD', fontsize=axislabelsize, labelpad=12)
elif(xunits[i_plot]=='mjd0'):
ax[i_plot].set_xlabel('MJD - {:d}'.format(int(mjdint)),
fontsize=axislabelsize, labelpad=12)
elif(xunits[i_plot]=='year'):
ax[i_plot].set_xlabel('Year', fontsize=axislabelsize, labelpad=12)
xmajorFormatter = FormatStrFormatter('%4d')
ax[i_plot].xaxis.set_major_formatter(xmajorFormatter)
if (ylabel[i_plot]):
if(yunits[i_plot]=='s'):
ax[i_plot].set_ylabel('Residuals (s)', fontsize=axislabelsize, labelpad=6)
if(yunits[i_plot]=='ms'):
ax[i_plot].set_ylabel('Residuals (ms)', fontsize=axislabelsize, labelpad=6)
if(yunits[i_plot]=='us'):
ax[i_plot].set_ylabel('Residuals ($\mu$s)', fontsize=axislabelsize, labelpad=6)
if(yunits[i_plot]=='ns'):
ax[i_plot].set_ylabel('Residuals (ns)', fontsize=axislabelsize, labelpad=6)
ax[i_plot].tick_params(labelsize=ticklabelsize, pad=10)
if(xticks[i_plot]):
for tick in ax[i_plot].xaxis.get_major_ticks():
tick.label1On = True
tick.label2On = False #top ticks
else:
for tick in ax[i_plot].xaxis.get_major_ticks():
tick.label1On = False
tick.label2On = False
if(yticks[i_plot]):
for tick in ax[i_plot].yaxis.get_major_ticks():
tick.label1On = True
tick.label2On = False # right ticks
else:
for tick in ax[i_plot].yaxis.get_major_ticks():
tick.label1On = False
tick.label2On = False
if(gridlines!=None):
for ycoord in gridlines:
ax[i_plot].axhline(ycoord, linestyle='--', color='black', \
linewidth=0.4)
# To keep track of min and max x and y
x_min = []
x_max = []
y_min = []
y_max = []
# Set things up for plotting, especially if there are many infos
# and colours:
if (res_data['info'] != None):
# Find common info numbers between the command-line
# requested info numbers, and those in the info file itself,
# and only plot these:
# If no info array given, just plot all of them
if(info_plot==None):
info_common = res_data['info_val']
else:
# call this something else rather than info_plot since
# list may have different lengths in each element,
# depending on what user wants to plot
# first, cast as numpy array
info_this_plot=np.array(info_plot[i_plot])
# This will work but sorts the result... need way to
# preserve original order...
info_common = \
np.intersect1d(np.unique(info_this_plot),
res_data['info_val'])
print 'info_common = ', info_common
# Set up colours depending on number of info numbers
n_info = len(info_common)
for i_info in np.arange(n_info):
info_condition = \
res_data['info']==info_common[i_info]
info_ind = np.where(info_condition)
res_x = res_data[xunits[i_plot]][info_ind]
# np.where(res_data['info']==res_data['info_val'][i_info])
res_y = res_data['res'][info_ind] + \
float(i_info)*resoffset # zero by default
res_err = res_data['reserr'][info_ind]
if(binsize!=None):
res_x, res_y, res_err = bin_resids(res_x, res_y,
res_err,
binsize=binsize,
weigh_x=True)
# Keep track of min and max x and y values
x_min.append(np.amin(res_x))#[res_x > xlim[0]]))
x_max.append(np.amax(res_x))#[res_x < xlim[1]]))
y_min.append(np.amin(res_y - res_err))#[res_y > ylim[0]]))
y_max.append(np.amax(res_y + res_err))#[res_y < ylim[1]]))
if (n_info==1):
clr = 'black'
else:
if (colour==None):
clr = cm.jet(float(i_info)/float(n_info-1))
else:
if (len(colour) == n_info):
clr = colour[i_info]
print 'COLOUR = ', clr
else:
print 'Error: Must use same number of colours as info numbers. Exiting'
return
ax[i_plot].plot(res_x, res_y, sym,
markersize=symsize,
markeredgecolor=clr,
markerfacecolor=clr)
ax[i_plot].errorbar(res_x, res_y, yerr=res_err,
fmt=None, capsize=csize,
ecolor=clr)
else:
n_info = 1
if (colour==None):
clr = 'black'
else:
clr = colour
res_x = res_data[xunits[i_plot]]
res_y = res_data['res']
res_err = res_data['reserr']
if(binsize!=None):
res_x, res_y, res_err = bin_resids(res_x, res_y,
res_err,
binsize=binsize,
weigh_x=True)
ax[i_plot].plot(res_x, res_y, sym, markersize=symsize, \
markeredgecolor=clr, markerfacecolor=clr)
ax[i_plot].errorbar(res_x, res_y, yerr=res_err, \
fmt=None, capsize=csize, ecolor=clr)
x_min.append(np.amin(res_x))#[res_x > xlim[0]]))
x_max.append(np.amax(res_x))#[res_x < xlim[1]]))
y_min.append(np.amin(res_y - res_err))#[res_y > ylim[0]]))
y_max.append(np.amax(res_y + res_err))#[res_y < ylim[1]]))
# Now set limits based on min and max *plotted* data:
x_min = np.amin(np.array(x_min))
x_max = np.amax(np.array(x_max))
y_min = np.amin(np.array(y_min))
y_max = np.amax(np.array(y_max))
if(xlim==None):
ax[i_plot].set_xlim(x_min, x_max)
else:
ax[i_plot].set_xlim(xlim)
if(ylim==None):
ax[i_plot].set_ylim(y_min, y_max)
else:
ax[i_plot].set_ylim(ylim)
# Adjust limits to have about 5% breathing room of the plotted limits
# on either side:
#print 'GOT TO HERE'
x_lim = ax[i_plot].get_xlim()
x_buffer = 0.025*(x_lim[1]-x_lim[0])
ax[i_plot].set_xlim(x_lim[0]-x_buffer, x_lim[1]+x_buffer)
y_lim = ax[i_plot].get_ylim()
y_buffer = 0.05*(y_lim[1]-y_lim[0])
ax[i_plot].set_ylim(y_lim[0]-y_buffer, y_lim[1]+y_buffer)
#print 'ylim = ', y_lim
# Figure text must be a list of tuples: [(x, y, text), (x, y, text), ...]
if(figtext!=None):
for txt in figtext:
ax[i_plot].text(txt[0], txt[1], txt[2],
fontsize=10, \
horizontalalignment='center', \
verticalalignment='center')
def main():
input_files = argv[1:]
# Parse command line to get all width files we wish to plot
width_file = []
for file_name in input_files:
# For some reason this works and ".append()" doesn't:
width_file[len(width_file):] = glob.glob(file_name)
n_subplots = len(width_file)
print "N_SUBPLOTS = ", n_subplots
# Set up the plot:
fig = plt.figure()#figsize=canvassize)
fig_top = 0.95
fig_bottom = 0.13
fig_left = 0.13
fig_right = 0.95
for i_w in np.arange(n_subplots):
print "I_W = ", i_w
# width_data =[]
# for wfile in width_files:
width_data = read_widths(width_file[i_w])
# print res_data['mjd']
# Now plot width in degrees vs time in years for each subplot:
# for width in width_data:
# date_out = [mjd.mjdtodate(m) for m in width['mjd']]
width_data['year'] = [mjd.mjdtoyear(m) for m in width_data['mjd']]
width_data['width'] *= 360.
width_data['werr'] *= 360.
# width['year'] = [d.year + d.day/365. + \
# d.hour/(365.*24.) + \
# d.minute/(365.*24.*60.) + \
# d.second/(365.*24.*60.*60.) \
# for d in date_out]
# Set up plot limits now
xmin = np.amin(width_data['year'])-0.25
xmax = np.amax(width_data['year'])+0.25
ymin = np.amin(width_data['width'] - width_data['werr'])
ymax = np.amax(width_data['width'] + width_data['werr'])
xspan = abs(xmax - xmin)
yspan = abs(ymax - ymin)
# ax = fig.add_subplot(n_subplots, 1, i_w+1)
if(i_w == 0):
max_yspan = yspan
else:
# Keep track of max yspan to later set all plots to have same scale
if(yspan > max_yspan):
max_yspan = yspan
ax = fig.add_axes([fig_left, \
fig_bottom+(fig_top-fig_bottom)*(float(n_subplots-(i_w+1))/float(n_subplots)),\
fig_right-fig_left, \
(fig_top-fig_bottom)*(1./float(n_subplots))])
# ax.set_ylabel('Pulse width (degrees)')
ax.set_xlim(xmin, xmax)
# Set y limits so that all plots have same scale
ax.set_ylim(0.5*(ymin+ymax)-0.5*max_yspan, \
0.5*(ymin+ymax)+0.5*max_yspan)
if(i_w < n_subplots-1):
ax.xaxis.set_ticklabels([])
else:
xMajorFormatter = FormatStrFormatter('%d')
ax.xaxis.set_major_formatter(xMajorFormatter)
yMajorFormatter = FormatStrFormatter('%.1f')
ax.yaxis.set_major_formatter(yMajorFormatter)
ax.yaxis.set_major_locator(MaxNLocator(5, prune='both'))
# Now plot the widths
ax.plot(width_data['year'], width_data['width'], 'o')
ax.errorbar(width_data['year'], width_data['width'], \
width_data['werr'], fmt=None)
# plot_widths(width_data, yunits='deg')
# Finally, put axis labels for entire figure, and ensure that there aren't corner tick labels... maybe adjust y limits to avoid this? There was a way of setting it, but maybe too much waster of time to find it.
fig.text(0.5*(fig_left+fig_right), 0.06, 'Year', fontsize=16, ha='center', va='center')
fig.text(0.04, 0.5*(fig_top+fig_bottom), 'Pulse width (deg)', fontsize=16, ha='center', va='center', rotation='vertical')
#fig.text(0.1, 0.5, )
plot_file = 'widths_multi.png'
plt.savefig(plot_file)