def line_fit(cx,cy,x_range,curve,style,reference,reference_plot,m_plot,axarr):
x_guide = np.linspace(x_range[0],x_range[1],100)
table,full_table = load_data.load(cx=cx,cy=cy,p_th=0.5,N_th=10,norm=False,p_values="d")
bins,table = load_data.assign(table=table,Nb=20,th=0.5,equal_samples=True,
redistribute=False,rd_th=0,ct_th=0,print_sizes=False)
if reference == "all galaxies":
reference_table = full_table
else:
reference_table = table
if curve == True:
def f(x,k,c1,c2):
return k**(-x + c1) + c2
else:
def f(x,k,c1,c2):
return k*x + c1
if reference_plot == True:
p_r,c_r = curve_fit(f,reference_table[:,-1],reference_table[:,-2],maxfev=10000)
for m in range(5):
if reference_plot == True:
axarr[m].plot(x_guide,f(x_guide,p_r[0],p_r[1],p_r[2]),color="k",linewidth=2,linestyle=style)
if m_plot == True:
t_m = table[bins[:,1] == m]
p,c = curve_fit(f,t_m[:,-1],t_m[:,-2],maxfev=10000)
axarr[m].plot(x_guide,f(x_guide,p[0],p[1],p[2]),color=colours[m],linewidth=2,linestyle=style)
return None
def histogram(cx,Nb,bin_extent,axarr,full_hist,style):
'''
Plot histograms-----------------------------------------------------------
--------------------------------------------------------------------------
Arguments:
cx: The data you want histogrammed (ie. a colour, mass etc.)
Nb: Number of bins.
bin_extent: List of form [lower bound,upper bound]
axarr: the plotting array as from make_grid or make_stack.
full_hist: set as "all" for all galaxies, "all spirals" for all spiral
galaxies, or "assigned spirals" for all spirals that meet the threshold to
be classified as having a particular arm number.
style: histogram line style eg. "solid"
--------------------------------------------------------------------------
'''
# Load all of the data and assign arms to each:
table,full_table = load_data.load(cx=cx,cy=["REDSHIFT_1"]
,p_th=0.5,N_th=5,norm=False,p_values="d")
bins,table = load_data.assign(table=table,Nb=20,th=0.5,
equal_samples=True,redistribute=False,rd_th=0,ct_th=0
,print_sizes=False)
# Define histogram bins:
bin_values=np.linspace(bin_extent[0],bin_extent[1],Nb+1)
for m in range(5):
t_select=table[bins[:,1] == m]
# Reference histograms:
if full_hist == "all":
axarr[m].hist(table[:,-1],bins=bin_values,normed=True
,histtype="step",linewidth=2, color="k",alpha=0.75)
elif full_hist == "all spirals":
axarr[m].hist(table[:,-1],bins=bin_values,normed=True
,histtype="step",linewidth=2, color="k",alpha=0.75)
else:
sel = table[(bins[:,1] != -999) & (bins[:,1] != 5)]
axarr[m].hist(sel[:,-1],bins=bin_values,normed=True
,histtype="step",linewidth=2, color="k",alpha=0.75)
if full_hist != "assigned spirals":
print("Invalid full_hist value; using 'assigned spirals'")
# Plot histograms.
axarr[m].hist(t_select[:,-1],bins=bin_values,normed=True
,histtype="step",linewidth=2,color=colours[m],linestyle=style)
return None
def plot_data(cx,axarr,Nb,equal_samples,style,errors,data_type):
'''
Plot data-----------------------------------------------------------------
--------------------------------------------------------------------------
Arguments:
cx: The data you want plotted (ie. a colour, mass etc.)
axarr: the plotting array as from make_grid or make_stack.
Nb: Number of bins.
equal samples: if True, bin into equally sized samples. If False, bin
into equally spaced bins.
style: linestyle eg. "dotted" or "solid"
errors: if True, plot errors from Cameron et al.
data_type: have either "d" for debiased, "r" for raw, or "w" for debiased
from Willet et al.
--------------------------------------------------------------------------
'''
table,full_table = load_data.load(cx=cx,cy=["REDSHIFT_1"],p_th=0.5,
N_th=10,norm=False,p_values=data_type)
bins,table = load_data.assign(table=table,Nb=Nb,th=0.5,
equal_samples=equal_samples,redistribute=False,rd_th=0,ct_th=0,
print_sizes=False)
for m in range(5):
fracs=load_data.get_fracs(table=table,bins=bins,m=m,c=0.683
,full_data="assigned spirals")
axarr[m].plot(fracs[:,0],fracs[:,1]/fracs[:,2],color=colours[m],
linestyle=style,linewidth=2)
if errors == True:
axarr[m].fill_between(fracs[:,0],fracs[:,3],fracs[:,4]
,color=colours[m],alpha=0.3,linestyle="dashed",hatch="/")
return None
def xy_plot(cx,cy,Nb,equal_samples,reference,style,axarr,standard_dev):
'''
Plot binned data ---------------------------------------------------------
--------------------------------------------------------------------------
Arguments:
cx,cy: x+y axis columns for contour (refer to the same cx+cy values in
load_data.load).
Nb: Number of bins to plot.
equal_samples: if set as True, all bins will have the same number of
galaxies.
reference: Can be set as 'all galaxies', 'all spirals' or None.
style: linestyle to plot eg. 'dashed' or 'solid'
reference_plot,m_plot: Plot style. Can have the following values:
axarr: plot array as from make_grid or make_stack.
standard_dev: if True, the standard deviation is plotted as a filled
contour.
--------------------------------------------------------------------------
'''
table,full_table = load_data.load(cx=cx,cy=cy,p_th=0.5,N_th=10,norm=False,p_values="d")
bins,table = load_data.assign(table=table,Nb=Nb,th=0.5,equal_samples=equal_samples,
redistribute=False,rd_th=0,ct_th=0,print_sizes=False)
full_bins,full_table = load_data.assign(table=full_table,Nb=Nb,th=0.5,equal_samples=equal_samples,
redistribute=False,rd_th=0,ct_th=0,print_sizes=False)
if reference == "all galaxies":
reference_table,reference_bins = full_table,full_bins
else:
reference_table,reference_bins = table,bins
if (reference != "all spirals") and (reference != None):
print("Invalid 'reference' value; using 'all spirals'")
xy_r = load_data.get_xy_binned(reference_table,reference_bins)
for m in range(5):
t_sel = table[bins[:,1] == m]
b_sel = bins[bins[:,1] == m]
xy = load_data.get_xy_binned(t_sel,b_sel)
if reference != None:
axarr[m].plot(xy_r[:,0],xy_r[:,2],color="k",linewidth=2,linestyle=style)
if standard_dev == True:
axarr[m].fill_between(xy_r[:,0],xy_r[:,2]+xy_r[:,3],xy_r[:,2]-xy_r[:,3],color="k",alpha=0.5)
axarr[m].plot(xy[:,0],xy[:,2],color=colours[m],linewidth=2,linestyle=style)
if standard_dev == True:
axarr[m].fill_between(xy[:,0],xy[:,2]+xy[:,3],xy[:,2]-xy[:,3],color=colours[m],alpha=0.5)
return None
def contour_plots(cx,cy,grid_spacing,axarr,xlims,ylims,reference_plot,
m_plot,reference,levels,alphas,sigma):
'''
Plot contours ------------------------------------------------------------
--------------------------------------------------------------------------
Arguments:
cx,cy: x+y axis columns for contour (refer to the same cx+cy values in
load_data.load).
grid_spacing: Number of cells to divide in to for the 2d histogram.
axarr: the plotting array as from make_grid or make_stack.
xlims,ylims: Lists of the form [lower bound, upper bound] for the
x+y histogram limits.
reference_plot,m_plot: Plot style. Can have the following values:
-------
"contour": pure line contour.
"contourf": pure filled contour.
"contourf+line": lines+filled contour.
"hist": 2D shaded histogram.
-------
reference: Can set as "all galaxies" to include all of the volume-limited
sample or "all spirals" to only show the spiral sample.
levels: Set the contour levels manually here.
alphas: 2 item list with [reference transparency, arm sample transparency]
values.
--------------------------------------------------------------------------
'''
table,full_table = load_data.load(cx=cx,cy=cy,p_th=0.5,N_th=10,norm=False,p_values="d")
#table[:,-2:] = np.random.randn(len(table),2)
#full_table[:,-2:] = np.random.randn(len(full_table),2)
bins,table = load_data.assign(table=table,Nb=20,th=0.5,equal_samples=True,
redistribute=False,rd_th=0,ct_th=0,print_sizes=False)
if reference == "all galaxies":
reference_table = full_table
elif reference == "all spirals":
reference_table = table
for m in range(5):
t_sel = table[bins[:,1] == m]
if (reference_plot is not None) & ((reference == "all galaxies") or (reference == "all spirals")):
contour(table=reference_table,xlims=xlims,ylims=ylims,grid_spacing=grid_spacing
,contour_type=reference_plot,ax=axarr[m],alpha=alphas[0],colour=["k","Greys"],levels=levels
,sigma=sigma)
if m_plot != None:
contour(table=t_sel,xlims=xlims,ylims=ylims,grid_spacing=grid_spacing
,contour_type=m_plot,ax=axarr[m],alpha=alphas[1],colour=[colours[m],cmaps[m]],levels=levels
,sigma=sigma)
axarr[0].set_xlim(xlims)
axarr[0].set_ylim(ylims)
return None
