def plotDif(outputname, outDir, title, x, y, colour):
"""Plot modelled data.
To use: plotDif(outputname,outDir, title, x, y, colour)"""
#
matplotlib.rcParams["axes.grid"] = True
matplotlib.rcParams["legend.fancybox"] = True
matplotlib.rcParams["figure.figsize"] = 11.69, 8.27 # A4
matplotlib.rcParams["savefig.dpi"] = 300
plotName = outputname + ".pdf"
pp1 = PdfPages(os.path.join(outDir, plotName))
fig1 = plt.figure(1)
ax1 = fig1.add_subplot(111)
xmax = max(x)
xmin = min(x)
ymax = max(y)
ymin = min(y)
labelString = title
ax1.plot(x, y, color=colour, marker="o", linestyle="None", label=labelString)
matplotlib.pyplot.axes().set_position([0.04, 0.065, 0.8, 0.9])
ax1.legend(bbox_to_anchor=(0.0, 1), loc=2, borderaxespad=0.1, ncol=3, title="Julian Day")
ax1.plot([xmin, xmax], [xmin, xmax], "-k")
plt.axis([xmin + 0.1, xmax + 0.1, ymin + 0.1, ymax + 0.1])
plt.xlabel("Measured Melt (m.w.e.)")
plt.ylabel("Modelled Melt (m.w.e.)")
plt.title("Modelled against Measured")
# plt.show()
pp1.savefig(bbox_inches="tight")
pp1.close()
plt.close()
return 0
def savefig_pdf(self, fn, *args, **kwargs):
# Get the git commit information.
git_info = get_git_info()
# See if we have any extra information to save
extra_info = kwargs.get("extra_info", None)
if extra_info is not None:
git_info = update_git_info_with_extra(git_info, extra_info)
# If there is no information, just call the mpl savefig.
if git_info is None:
return mpl_savefig(self, fn, *args, **kwargs)
# Build the PDF object that will take the metadata.
fn = os.path.splitext(fn)[0] + ".pdf"
kwargs["format"] = "pdf"
fig = PdfPages(fn)
# Save the figure.
ret = mpl_savefig(self, fig, *args, **kwargs)
# Add the metadata.
metadata = fig.infodict()
metadata["Keywords"] = json.dumps(git_info, sort_keys=True)
# Commit the changes.
fig.close()
return ret
def profile_batch(radius,output):
pp = PDF(output)
for sim in glob('sim*.fits'):
print sim
header = pyfits.open(sim)[0].header
w = header['W']
N = header['N']
pitch = header['PITCH']
ang = header['VIEWANG']
pitch = int(np.pi*2/pitch)
ang = int(np.pi*2/ang)
v, line, _ = salty.line_profile(sim,radius,pxbin=4.,plot=False)
ax = plt.figure().add_subplot(111)
ax.set_xlabel('Velocity [km/s]')
ax.set_ylabel('Normalized power')
ax.set_title(sim)
ax.text(300,0.005,
'$w={}$\n$N={}$\n$p=\\tau/{}$\n$\\theta_{{view}}=\\tau/{}$'.\
format(w,N,pitch,ang))
ax.plot(v,line)
pp.savefig(ax.figure)
pp.close()
def show_or_save(plt, fig, use_x11, filename): if use_x11: plt.show() else: pp = PdfPages(filename) pp.savefig(fig) pp.close()
def make_lick_individual(targetSN, w1, w2):
""" Make maps for the kinematics. """
filename = "lick_corr_sn{0}.tsv".format(targetSN)
binimg = pf.getdata("voronoi_sn{0}_w{1}_{2}.fits".format(targetSN, w1, w2))
intens = "collapsed_w{0}_{1}.fits".format(w1, w2)
extent = calc_extent(intens)
bins = np.loadtxt(filename, usecols=(0,), dtype=str).tolist()
bins = np.array([x.split("bin")[1] for x in bins]).astype(int)
data = np.loadtxt(filename, usecols=np.arange(25)+1).T
labels = [r'Hd$_A$', r'Hd$_F$', r'CN$_1$', r'CN$_2$', r'Ca4227', r'G4300',
r'Hg$_A$', r'Hg$_F$', r'Fe4383', r'Ca4455', r'Fe4531', r'C4668',
r'H$_\beta$', r'Fe5015', r'Mg$_1$', r'Mg$_2$', r'Mg$_b$', r'Fe5270',
r'Fe5335', r'Fe5406', r'Fe5709', r'Fe5782', r'Na$_D$', r'TiO$_1$',
r'TiO$_2$']
mag = "[mag]"
ang = "[\AA]"
units = [ang, ang, mag, mag, ang, ang,
ang, ang, ang, ang, ang, ang,
ang, ang, mag, mag, ang, ang,
ang, ang, ang, ang, ang, mag,
mag]
lims = [[None, None], [None, None], [None, None], [None, None],
[None, None], [None, None], [None, None], [None, None],
[None, None], [None, None], [None, None], [None, None],
[None, None], [None, None], [None, None], [None, None],
[None, None], [None, None], [None, None], [None, None],
[None, None], [None, None], [None, None], [None, None],
[None, None], [None, None], [None, None], [None, None]]
pdf = PdfPages("figs/lick_sn{0}.pdf".format(targetSN))
fig = plt.figure(1, figsize=(6.25,5))
plt.subplots_adjust(bottom=0.12, right=0.97, left=0.09, top=0.96)
plt.minorticks_on()
ax = plt.subplot(111)
ax.minorticks_on()
plot_indices = np.arange(12,22)
for i, vector in enumerate(data):
if i not in plot_indices:
continue
print "Making plot for {0}...".format(labels[i])
kmap = np.zeros_like(binimg)
kmap[:] = np.nan
for bin,v in zip(bins, vector):
idx = np.where(binimg == bin)
kmap[idx] = v
vmin = lims[i][0] if lims[i][0] else np.median(vector) - 2 * vector.std()
vmax = lims[i][1] if lims[i][1] else np.median(vector) + 2 * vector.std()
m = plt.imshow(kmap, cmap="inferno", origin="bottom", vmin=vmin,
vmax=vmax, extent=extent, aspect="equal")
make_contours()
plt.minorticks_on()
plt.xlabel("X [kpc]")
plt.ylabel("Y [kpc]")
plt.xlim(extent[0], extent[1])
plt.ylim(extent[2], extent[3])
cbar = plt.colorbar(m)
cbar.set_label("{0} {1}".format(labels[i], units[i]))
pdf.savefig()
plt.clf()
pdf.close()
return
def plot_psi_weights(output,
modelfile='/d/monk/eigenbrot/WIYN/14B-0456/anal/models/allZ2_vardisp/allz2_vardisp_batch_interp.fits'):
#Like the last page of all the fit plots, but for all pointings at once
#cribbed from plot_bc_vardisp.py
m = pyfits.open(modelfile)[1].data[0]
numZ = np.unique(m['Z'][:,0]).size
numAge = np.unique(m['AGE'][:,0]).size
big_W = np.zeros((numZ,numAge))
for p in range(6):
coeffile = 'NGC_891_P{}_bin30_allz2.coef.fits'.format(p+1)
print coeffile
coef_arr = pyfits.open(coeffile)[1].data
numap = coef_arr['VSYS'].size
for i in range(numap):
wdata = coef_arr[i]['LIGHT_FRAC'].reshape(numZ,numAge)
big_W += wdata/np.max(wdata)
bwax = plt.figure().add_subplot(111)
bwax.imshow(big_W,origin='lower',cmap='Blues',interpolation='none')
bwax.set_xlabel('SSP Age [Gyr]')
bwax.set_xticks(range(numAge))
bwax.set_xticklabels(m['AGE'][:numAge,0]/1e9)
bwax.set_ylabel(r'$Z/Z_{\odot}$')
bwax.set_yticks(range(numZ))
bwax.set_yticklabels(m['Z'][::numAge,0])
pp = PDF(output)
pp.savefig(bwax.figure)
pp.close()
plt.close(bwax.figure)
return
def main():
data = scipy.io.loadmat('data.mat')
x1 = data['x1'][0]
x2 = data['x2'][0]
n = len(x1)
kl = [1, 7, 14, 28] # k = 14 may be an optimal
x = np.arange(-6, 6.05, 0.05)
fig = plt.figure()
plt.rcParams['font.size'] = 10
for i in range(len(kl)):
k = kl[i]
p1 = np.zeros(len(x))
p2 = np.zeros(len(x))
for j in range(len(x)):
r1 = sorted(abs(x1 - x[j]))
r2 = sorted(abs(x2 - x[j]))
p1[j] = float(k) / (n * 2 * r1[k-1])
p2[j] = float(k) / (n * 2 * r2[k-1])
plt.subplot(2, 2, i+1)
plt.plot(x, p1, label=r'$p(\mathbf{x} \mid c_1)$')
plt.plot(x, p2, label=r'$p(\mathbf{x} \mid c_2)$')
plt.legend(framealpha=0, fontsize=7)
plt.title(r'$k = %d$' % k)
plt.xlabel(r'$x$')
plt.ylabel(r'$p(\mathbf{x} \mid c_i)$')
plt.tight_layout()
pp = PdfPages('knn.pdf')
pp.savefig(fig)
pp.close()
plt.clf()
def makeStackedBarGraph(coords_list, bar_labels_list, stack_label_list, title, axis_labels, file_name):
bar_width = 0.2
step = 2000
inds = np.arange(len(bar_labels_list))
fig = mpl.figure.Figure(figsize=(15,10))
canvas = FigureCanvas(fig)
fig.suptitle(title)
fig.subplots_adjust(wspace = 0.5, hspace = 0.5)
# for color bar
# colorRange, colorTable = orr.generateColorRange(stack_label_list, step=step)
# norm = Normalize(vmax = max(stack_label_list), vmin = min(stack_label_list), clip = True)
# cmap_xvalues = norm(xrange(min(stack_label_list), max(stack_label_list), step))
# cmap_xvalues[0] = 0.
# cmap_xvalues[-1] = 1
# cmap_list = [(val, c) for val, c in zip(cmap_xvalues, orr.generateColorRange(stack_label_list, raw=True, step=step)[1])]
# cmap = LinearSegmentedColormap.from_list('mycmap', cmap_list)
# sm = ScalarMappable(norm=norm, cmap = cmap)
# sm.set_array(stack_label_list)
colorRange, colorTable = orr.generateColors(stack_label_list, [0], step=step)
ax = fig.add_subplot(2,2,1)
ax.set_title(title, fontsize=28)
ax.set_xlabel(axis_labels[0], fontsize=24)
ax.set_ylabel(axis_labels[1], fontsize=24)
pre = coords_list[0][1]
bars = []
bottom = np.zeros((len(coords_list[0][1]),))
for i, (coords, stack_label) in enumerate(zip(coords_list, stack_label_list)):
# if i == 0:
# bar = ax.bar(inds, coords[1], color=colorTable[colorRange.index(coords[0])], align='center', linewidth=0)
# else:
# bar = ax.bar(inds, coords[1], color=colorTable[colorRange.index(coords[0])], align='center', bottom = bottom, linewidth=0)
if i == 0:
bar = ax.bar(inds, coords[1], color=colorTable[i], align='center', linewidth=0)
else:
bar = ax.bar(inds, coords[1], color=colorTable[i], align='center', bottom = bottom, linewidth=0)
bars.append(bar)
bottom = bottom + np.array(coords[1])
ax.set_xticks(inds)
ax.set_xticklabels(bar_labels_list, rotation=45, ha='right')
ax.tick_params(axis='both', which='both', labelsize=18)
ax.legend(bars, stack_label_list, loc=1, bbox_to_anchor=(1.2, 1), ncol=1, markerscale=0.2, fontsize=6)
# fig.colorbar(sm)
pdf = PdfPages(file_name)
pdf.savefig(fig)
pdf.close()
print('Graphing Resource usage: {}kb'.format(getattr(resource.getrusage(resource.RUSAGE_SELF), 'ru_maxrss') / 1000))
def print_pdf_graph(file_f, regulon, conn):
pdf = PdfPages(file_f)
edgesLimits = [50, 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000]
#CRP = regulon_set['LexA']
for lim in edgesLimits:
print lim
g = buildSimilarityGraph_top_10_v2(conn, lim)
# Here the node is motif, eg 87878787_1, the first 8 digits represent gi
node_color = [ 1 if node[0:8] in regulon else 0 for node in g ]
pos = nx.graphviz_layout(g, prog="neato")
plt.figure(figsize=(10.0, 10.0))
plt.axis("off")
nx.draw(g,
pos,
node_color = node_color,
node_size = 20,
alpha=0.8,
with_labels=False,
cmap=plt.cm.jet,
vmax=1.0,
vmin=0.0
)
pdf.savefig()
plt.close()
pdf.close()
def plot_data_dict_1D(self, results_path, file_n, data, timepoints):
print 'plotting now...'
pp = PdfPages(results_path+'/'+file_n)
#nBins = 50
cc = 0
xmin, xmax = -1, 7
x_grid = linspace(xmin, xmax, 1000)
for tp in timepoints:
dat = log10(1+data[tp][:,0])
dat[isneginf(dat)] = 0
print dat
kde = st.gaussian_kde(dat, bw_method=0.2)
pdf = kde.evaluate(x_grid)
ax = plt.subplot(4, 5, cc + 1)
ax.plot(x_grid, pdf, color='blue', alpha=0.5, lw=3)
ax.set_xlim([-1, 7])
#plt.hist( data[tp], nBins )
#plt.xscale('log')
cc += 1
pp.savefig()
plt.close()
pp.close()
def plot_data_comb_2D(self, results_path, file_n, data, fit, timepoints):
pp = PdfPages(results_path+'/'+file_n)
cc = 0
for tp in timepoints:
xmin, xmax = -3, 3
ymin, ymax = -3, 3
xx, yy = mgrid[xmin:xmax:100j, ymin:ymax:100j]
positions = vstack([xx.ravel(), yy.ravel()])
values = vstack([ log10(1+data[tp][:, 0]), log10(1+data[tp][:, 1])])
kernel = st.gaussian_kde(values)
f = reshape(kernel(positions).T, xx.shape)
xxf, yyf = mgrid[xmin:xmax:100j, ymin:ymax:100j]
positions_f = vstack([xxf.ravel(), yyf.ravel()])
values_f = vstack([log10(1+fit[tp][:, 0]), log10(1+fit[tp][:, 1])])
kernel_f = st.gaussian_kde(values_f)
ff = reshape(kernel_f(positions_f).T, xxf.shape)
ax = plt.subplot(4, 5, cc+1)
ax.contourf(xx, yy, f, cmap='Blues')
ax.contourf(xxf, yyf, ff, cmap='Reds')
ax.set_xlim([-1, 3])
ax.set_ylim([-1, 3])
cc += 1
pp.savefig()
plt.close()
pp.close()
def plot_distributions(distributions, bucket_pct, axes, out_file):
x_max, y_max = axes
pp = PdfPages(out_file)
variances = sorted(distributions)
subsamples = sorted(distributions[variances[-1]])
if x_max == 0.:
x_max = distributions[0][-1]
bucket_width = x_max * bucket_pct
bucket_boundaries = np.arange(0, x_max + bucket_width / 2., bucket_width)
x_axis_points = np.arange(bucket_width / 2., x_max, bucket_width)
for i,v in enumerate(variances):
plt.figure(i)
plt.xlabel("regret distribution")
if v == 0:
plt.title("true game")
cum_dist = np.array([bisect(distributions[0], b) for b in \
bucket_boundaries])
plt.plot(x_axis_points, (cum_dist[1:] - cum_dist[:-1]) / \
float(cum_dist[-1]), label="true game")
else:
plt.title("$\sigma \\approx$" +str(v))
for s in subsamples:
cum_dist = np.array([bisect(distributions[v][s], b) for b in \
bucket_boundaries])
plt.plot(x_axis_points, (cum_dist[1:] - cum_dist[:-1]) / \
float(cum_dist[-1]), label=str(s)+" samples")
plt.legend(loc="upper right", prop={'size':6})
if y_max != 0.:
plt.axis([0, x_max, 0, y_max])
pp.savefig()
pp.close()
def make_comp_plot_1D(self, results_path, file_n, data, sims, timepoints, ind=0):
pp = PdfPages(results_path+'/'+file_n)
cc = 0
xmin, xmax = -1, 7
x_grid = linspace(xmin, xmax, 1000)
def kernel_est(d, ind, x_grid):
dl = log10(1+d[:, ind])
#dl[isneginf(dl)] = 0
dl = dl[isfinite(dl)]
kde = st.gaussian_kde(dl, bw_method=0.2)
pdf = kde.evaluate(x_grid)
return pdf
for tp in timepoints:
pdf_data = kernel_est(data[tp], ind, x_grid)
pdf_sim = kernel_est(sims[tp], ind, x_grid)
ax = plt.subplot(4, 5, cc + 1)
ax.plot(x_grid, pdf_data, color='blue', alpha=0.5, lw=3)
ax.plot(x_grid, pdf_sim, color='red', alpha=0.5, lw=3)
cc += 1
pp.savefig()
plt.close()
pp.close()
def err_histogram(output, basedir='.',bins=10, field='MLWA', err='dMLWA', suffix='coef',
label=r'$\delta\tau_{L,\mathrm{fit}}/\tau_L$',exclude=exclude, ymax=90):
ratio_list = []
for p in range(6):
coef = '{}/NGC_891_P{}_bin30_allz2.{}.fits'.format(basedir,p+1,suffix)
print coef
c = pyfits.open(coef)[1].data
tmp = c[err]
if field == 'TAUV':
tmp *= 1.086
else:
tmp /= c[field]
tmp = np.delete(tmp,np.array(exclude[p]) - 1)
ratio_list.append(tmp)
ratio = np.hstack(ratio_list)
ratio = ratio[ratio == ratio]
ratio = ratio[np.where(ratio < 0.8)[0]]
ax = plt.figure().add_subplot(111)
ax.set_xlabel(label)
ax.set_ylabel(r'$N$')
ax.hist(ratio, bins=bins, histtype='step', color='k')
ax.set_xlim(0,0.52)
ax.set_xticks([0,0.1,0.2,0.3,0.4,0.5])
ax.set_ylim(0,ymax)
ax.set_yticks(range(0,int(ymax/10)*10+10,int(int(ymax/10)/4)*10))
pp = PDF(output)
pp.savefig(ax.figure)
pp.close()
plt.close(ax.figure)
return
def pdfdiagnostics(self,what='specs',n_subplot = 5):
print 'creating a diagnostic pdf of '+what
from matplotlib.backends.backend_pdf import PdfPages
exec('data = self.%s'%what)
data.sort_index(axis=1,inplace=True)# arrange alphabetically
pp = PdfPages('%s.pdf'%self.group)
for i in xrange(0, len(data.columns), n_subplot+1):
Axes = data[data.columns[i:i+n_subplot]].plot(subplots=True)
tick_params(labelsize=6)
#y ticklabels
[setp(item.yaxis.get_majorticklabels(), 'size', 7) for item in Axes.ravel()]
#x ticklabels
[setp(item.xaxis.get_majorticklabels(), 'size', 5) for item in Axes.ravel()]
#y labels
[setp(item.yaxis.get_label(), 'size', 10) for item in Axes.ravel()]
#x labels
[setp(item.xaxis.get_label(), 'size', 10) for item in Axes.ravel()]
tight_layout()
ylabel('mix ratio')
#plt.locator_params(axis='y',nbins=2)
print '%.03f'%(float(i) / float(len(data.columns)) ) , '% done'
savefig(pp, format='pdf')
close('all')
pp.close()
print 'PDF out'
close('all')
class PlotDocument(object):
def __init__(
self,
pages,
statistics_manager,
page_size=(
17,
11),
pdf_filename=None):
if pdf_filename:
self.plot_pdf = PdfPages(pdf_filename)
else:
self.plot_pdf = None
self.pages = pages
self.page_size = page_size
self.statistics_manager = statistics_manager
def make_pages(self):
for page in self.pages:
if page:
if page.type == 'GridPlot':
page.add_plots(
self.statistics_manager,
self.page_size,
self.plot_pdf)
else:
page.add_text(self.page_size, self.plot_pdf)
if self.plot_pdf:
plt.close()
self.plot_pdf.close()
def err_plot(output,basedir='.', field='MLWA', err='dMLWA', suffix='syserr',
label=r'$\tau_L$',err_label=r'$\delta\tau_{L,\mathrm{sys}}$',exclude=exclude):
fig = plt.figure()
ax = fig.add_subplot(111)
ax.set_xlabel(label)
ax.set_ylabel(err_label)
for p in range(6):
coef = '{}/NGC_891_P{}_bin30_allz2.{}.fits'.format(basedir,p+1,suffix)
print coef
c = pyfits.open(coef)[1].data
exarr = np.array(exclude[p]) - 1
d = np.delete(c[field], exarr)
e = np.delete(c[err], exarr)
ax.scatter(d,e/d, c='k', alpha=0.7, linewidth=0)
ax.set_yticks([0.1,0.2,0.3,0.4,0.5])
pp = PDF(output)
pp.savefig(fig)
pp.close()
plt.close(fig)
return
def plot_tica_and_clusters(component_j, transformed_data, clusterer, lag_time, component_i, label = "dot", active_cluster_ids = [], intermediate_cluster_ids = [], inactive_cluster_ids = [], tica_dir = ""):
trajs = np.concatenate(transformed_data)
plt.hexbin(trajs[:,component_i], trajs[:,component_j], bins='log', mincnt=1)
plt.xlabel("tIC %d" %(component_i + 1))
plt.ylabel('tIC %d' %(component_j+1))
centers = clusterer.cluster_centers_
indices = [j for j in range(0,len(active_cluster_ids),1)]
for i in [active_cluster_ids[j] for j in indices]:
center = centers[i,:]
if label == "dot":
plt.scatter([center[component_i]],[center[component_j]], marker='v', c='k', s=10)
else:
plt.annotate('%d' %i, xy=(center[component_i],center[component_j]), xytext=(center[component_i], center[component_j]),size=6)
indices = [j for j in range(0,len(intermediate_cluster_ids),5)]
for i in [intermediate_cluster_ids[j] for j in indices]:
center = centers[i,:]
if label == "dot":
plt.scatter([center[component_i]],[center[component_j]], marker='8', c='m', s=10)
else:
plt.annotate('%d' %i, xy=(center[component_i],center[component_j]), xytext=(center[component_i], center[component_j]),size=6)
indices = [j for j in range(0,len(inactive_cluster_ids),5)]
for i in [inactive_cluster_ids[j] for j in indices]:
center = centers[i,:]
if label == "dot":
plt.scatter([center[component_i]],[center[component_j]], marker='s', c='w', s=10)
else:
plt.annotate('%d' %i, xy=(center[component_i],center[component_j]), xytext=(center[component_i], center[component_j]),size=6)
pp = PdfPages("%s/c%d_c%d_clusters%d.pdf" %(tica_dir, component_i, component_j, np.shape(centers)[0]))
pp.savefig()
pp.close()
plt.clf()
def plot(results):
xs = np.array(results.keys())
xs.sort()
ys = np.array([results[k] for k in xs])
maxy = max(results.values())
# plt.plot(xs, ys, 'bo', alpha=0.5)
plt.ylim([0, maxy * 1.1])
# plt.plot(xs, ys, 'ro--', label="Time")
plt.bar(range(SAMPLES), ys, color="r", align="center")
plt.xticks(range(SAMPLES), xs)
# Styling
plt.xlabel("seed")
plt.ylabel("seconds")
plt.title("Genetic Algorithm - Seeds")
plt.grid(True, which="both", linestyle="dotted")
plt.legend()
from matplotlib.backends.backend_pdf import PdfPages
pp = PdfPages("plots/{}.pdf".format(NAME))
plt.savefig(pp, format="pdf")
pp.close()
plt.show()
def plot_pnas_vs_tics(pnas_dir, tic_dir, pnas_names, directory, scale = 7.14, refcoords_file = None):
pnas = np.concatenate(load_file(pnas_dir))
pnas[:,0] *= scale
print(np.shape(pnas))
print(len(pnas_names))
if("ktICA" in tic_dir):
tics = load_dataset(tic_dir)
else:
tics = verboseload(tic_dir)
print(np.shape(tics))
tics = np.concatenate(tics)
print(np.shape(tics))
if len(pnas_names) != np.shape(pnas)[1]:
print("Invalid pnas names")
return
for i in range(0,np.shape(pnas)[1]):
for j in range(0,np.shape(tics)[1]):
tic = tics[:,j]
pnas_coord = pnas[:,i]
plt.hexbin(tic, pnas_coord, bins = 'log', mincnt=1)
coord_name = pnas_names[i]
tic_name = "tIC.%d" %(j+1)
plt.xlabel(tic_name)
plt.ylabel(coord_name)
pp = PdfPages("%s/%s_%s_hexbin.pdf" %(directory, tic_name, coord_name))
pp.savefig()
pp.close()
plt.clf()
return
def plot_tica(transformed_data_dir, lag_time):
transformed_data = verboseload(transformed_data_dir)
trajs = np.concatenate(transformed_data)
plt.hexbin(trajs[:,0], trajs[:,1], bins='log', mincnt=1)
pp = PdfPages("/scratch/users/enf/b2ar_analysis/tica_phi_psi_chi2_t%d.pdf" %lag_time)
pp.savefig()
pp.close()
def anal2pdf():
pp = PdfPages('../../datafiles/jul14/analplots.pdf')
fnames = [
'../../datafiles/jul14/vsweep_10_1.h5',
'../../datafiles/jul14/vsweep_10_1b.h5',
'../../datafiles/jul14/vsweep_10_2.h5',
'../../datafiles/jul14/vsweep_10_3.h5',
'../../datafiles/jul14/vsweep_10_4.h5',
'../../datafiles/jul14/vsweep_10_5.h5',
'../../datafiles/jul14/vsweep_10_6.h5']
for fn in fnames:
anal_vsweep(fn)
figure(1)
suptitle('Voltage sweep, 5096MHz, raw phase(Y), time_samples(X)')
f=gcf()
f.savefig(pp,format='pdf')
figure(3)
suptitle('Voltage sweep, 5096MHz, radians(Y) vs mV(X)')
f=gcf()
f.savefig(pp,format='pdf')
pp.close()
def write_pressure_graph(self):
P.xlabel("Temperature")
P.ylabel("Pressure")
P.title("Pressure per Temperature")
P.axis([0.0,self.temp_points[-1]+10., 0.0,self.pressure_points[-1]+1])
ax = P.gca()
ax.set_autoscale_on(False)
popt,pcov = curve_fit(fit,self.temp_points,self.pressure_points)
y_fit = [popt[0]*x+popt[1] for x in self.temp_points]
y_fit.insert(0, popt[1])
fit_x_points = self.temp_points[:]
fit_x_points.insert(0,0.)
pp = PdfPages(str(SCREEN_SIZE)+"_"+str(N)+".pdf")
P.plot(self.temp_points, self.pressure_points, "o", fit_x_points, y_fit, "--")
#P.savefig()
#P.plot(
#pp.savefig()
#print(self.pressure_points)
#print(y_fit)
#print(fit_x_points)
#print(y_fit)
pp.savefig()
pp.close()
def readCurvesFromFileCallback():
# Ask for input file
_filename = askForInputFile(fileFilter="*.cur")
if _filename == "":
return
# Load data from file
[_readCurves, _readVoltages] = loadDataFromFile(_filename)
for i in range(0, len(_readVoltages)):
_voltages = []
_currents = []
for j in range(0, len(_readCurves[i])):
_voltages.extend([_readCurves[i][j][0]])
_currents.extend([_readCurves[i][j][1]])
# Plot read data
onlyFilename = _filename.split("/")[-1]
plotCurves(_readCurves, _readVoltages, onlyFilename, interpolate=True)
# Ask if wants to save plot to PDF file
d = YesNoDialog(rootWindowHandler, 'Save plot to PDF file?', 'Yes', 'No')
rootWindowHandler.wait_window(d.top)
if not yesNoReturnedValue:
return
_fileTypes = '*.*'
_filename = askForOutputFilename(_fileTypes)
if _filename == "":
return
_filename = fixExtensionOfFilename(_filename, 'pdf')
pp = PdfPages(_filename)
plot.savefig(pp, format='pdf')
pp.close()
print 'Saved curves to PDF file: "%s"' % _filename
def complexAll(self, f1=0., f2=0., amax=.16, nrows=1, ncols=1, antList=allAnts ) :
pyplot.ioff()
pp = PdfPages( 'ComplexLeaks.pdf' )
scale = 10./math.sqrt(ncols*nrows)
if f1 == 0. :
[f1, f2] = LkSet.xlimits( self ) # default is to find freq limits in the data
print "frequency limits: %.3f - %.3f GHz" % (f1,f2)
ymin = -1.*amax
ymax = amax
npanel = 0
for ant in antList :
npanel = npanel + 1
if npanel > nrows * ncols :
npanel = 1
pyplot.clf()
p = pyplot.subplot(nrows, ncols, npanel, aspect='equal') # DL,DR in one panel
p.tick_params( axis='both', which='major', labelsize=scale )
p.axis( [ymin, ymax, ymin, ymax] )
p.grid(True)
for Leak in self.LeakList :
if Leak.ant == ant :
print "plotting DR and DL for antenna %d" % ant
Leak.plotComplex( p, f1, f2 )
#pyplot.title("C%d DR (circles, solid) and DL (diamonds, dashed)" % ant, fontdict={'fontsize': scale})
if (npanel == nrows*ncols) or (ant == antList[-1] ) :
pyplot.savefig( pp, format='pdf' )
pp.close()
def multipage(filename, figs=None, dpi=200):
pp = PdfPages(filename)
if figs is None:
figs = [plt.figure(n) for n in plt.get_fignums()]
for fig in figs:
fig.savefig(pp, format='pdf')
pp.close()
def plot_miri_comparison():
inst = webbpsf.MIRI()
filtlist_W = [f for f in inst.filter_list if f[-1] == 'W']
filtlist_C = [f for f in inst.filter_list if f[-1] != 'W']
from matplotlib.backends.backend_pdf import PdfPages
pdf=PdfPages('weights_miri_comparison.pdf')
for filts in [filtlist_W, filtlist_C]:
try:
os.unlink('/Users/mperrin/software/webbpsf/data/MIRI/filters')
except:
pass
os.symlink('/Users/mperrin/software/webbpsf/data/MIRI/real_filters', '/Users/mperrin/software/webbpsf/data/MIRI/filters')
plotweights('miri', filtlist=filts)
os.unlink('/Users/mperrin/software/webbpsf/data/MIRI/filters')
os.symlink('/Users/mperrin/software/webbpsf/data/MIRI/fake_filters', '/Users/mperrin/software/webbpsf/data/MIRI/filters')
plotweights('miri', filtlist=filts, overplot=True, ls='--')
P.draw()
pdf.savefig()
pdf.close()
def compare_board_estimations(esti_extrinsics, board, board_dim, \ actual_boards, save_name=None): """ Plots true and estimated boards on the same figure Args: esti_extrinsics: dictionary, keyed by image number, values are Extrinsics board: board_dim: (board_width, board_height) actual_boards: list of dictionaries save_name: filename, string """ if save_name: pp = PdfPages(save_name) plt.clf() for i in xrange(len(actual_boards)): fig = plt.figure() ax = fig.add_subplot(111, projection='3d') act_board = actual_boards[i] aX, aY, aZ = util.board_dict2array(act_board, board_dim) ax.plot_wireframe(aX, aY, aZ, color='b') if i in esti_extrinsics: esti_loc = esti_extrinsics[i].trans_vec esti_board = util.move_board(board, esti_loc) eX, eY, eZ = util.board_dict2array(esti_board, board_dim) ax.plot_wireframe(eX, eY, eZ, color='r') if pp: pp.savefig() else: plt.show() if pp: pp.close()
def heat_map_single(data, file = "heat_map_plate.pdf", *args, **kwargs):
""" Create a heat_map for a single readout
Create a heat_map for a single readout
..todo:: Share code between heat_map_single and heat_map_multiple
"""
np_data = data.data
pp = PdfPages(os.path.join(PATH, file))
fig, ax = plt.subplots()
im = ax.pcolormesh(np_data, vmin=np_data.min(), vmax=np_data.max()) # cmap='RdBu'
fig.colorbar(im)
# put the major ticks at the middle of each cell
ax.set_xticks(np.arange(np_data.shape[1]) + 0.5, minor=False)
ax.set_yticks(np.arange(np_data.shape[0]) + 0.5, minor=False)
# Invert the y-axis such that the data is displayed as it appears on the plate.
ax.invert_yaxis()
ax.xaxis.tick_top()
ax.set_xticklabels(data.axes['x'], minor=False)
ax.set_yticklabels(data.axes['y'], minor=False)
pp.savefig(fig)
pp.close()
fig.clear()
return ax
def plotLoad(dataFolder, srv, TS, trace_type, tsInterval):
srv_file = srv + "_" + TS + "_" + trace_type + ".json"
srv_load = json.load(open(dataFolder + srv_file))
fig, ax = plt.subplots()
print "Ploting QoE evaluation for server :", srv
ts = [int(x) for x in srv_load.keys() if int(x) > tsInterval[0] and int(x) < tsInterval[1]]
sorted_ts = sorted(ts)
tr_vals = [srv_load[str(cur_ts)] for cur_ts in sorted_ts]
srvName = '$S_{' + str(int(srv.split('-')[1])) + '}$'
plt.plot(sorted_ts, tr_vals, 'b-', label=srvName, linewidth=2.0, markersize=8)
## Change the time stamp ticks
num_intvs = int((tsInterval[1] - tsInterval[0])/900) + 1
ts_labels = [tsInterval[0] + x*900 for x in range(num_intvs)]
str_ts = [datetime.datetime.fromtimestamp(x*900 + tsInterval[0]).strftime('%H:%M') for x in range(num_intvs)]
plt.xticks(ts_labels, str_ts, fontsize=15)
box = ax.get_position()
#ax.legend(loc='lower center', bbox_to_anchor=(0.5, 0),
# fancybox=True, shadow=True, ncol=4, prop={'size':20})
#params = {'legend.fontsize': 20,
# 'legend.linewidth': 2}
#plt.rcParams.update(params)
# ax.set_title('Server QoE Score Observed on $S_{10}$', fontsize=20)
# ax.set_xlabel("Time in a day", fontsize=20)
ax.set_ylabel("Server Load", fontsize=20)
# ax.set_ylim([0,5])
plt.show()
pdf = PdfPages(dataFolder + '/imgs/' + cache_agent + '_' + 'load.pdf')
pdf.savefig(fig)
pdf.close()
def plot(sideband,
nominal,
x_range,
fit,
fit_err,
outputname,
titel='',
signal=None,
sideband_sigma=None,
nominal_sigma=None):
pp = PdfPages(outputname)
plt.clf()
#plt.semilogy()
plt.yscale('log', nonposy='clip')
ax = plt.gca()
ax.set_ylim([.01, 10])
ax.set_xlim([650, 1850])
plt.title(
titel, fontsize=10
) # , loc='right')"CMS $\it{Preliminary}$ 35.9 fb$^{-1}$ (13 TeV)"
plt.plot(x_range,
nominal,
label="Exp $95\%$ CL nominal",
color='red',
linestyle='dotted') #,color = exp_LH.bands[0][2])
plt.plot(x_range,
sideband,
label="Exp $95\%$ CL sideband",
color='blue',
linestyle='dotted') #,color = exp_LH.bands[0][2])
if signal:
plt.plot(x_range,
signal,
label="signal cross section",
color='black',
linestyle='dotted') #,color = exp_LH.bands[0][2])
if sideband_sigma:
plt.fill_between(x_range,
sideband_sigma[0],
sideband_sigma[1],
alpha=0.3,
facecolor='yellow',
edgecolor='yellow',
linewidth=0,
label="$\pm$ 1 std. deviation sideband")
if nominal_sigma:
plt.fill_between(x_range,
nominal_sigma[0],
nominal_sigma[1],
alpha=0.6,
facecolor='yellow',
edgecolor='yellow',
linewidth=0,
label="$\pm$ 1 std. deviation nominal")
ax.errorbar(x_range,
fit,
yerr=fit_err,
label="fitted cross section",
color='green',
linestyle='dotted') #,color = exp_LH.bands[0][2])
#ax.set_yscale('symlog')
plt.xlabel('VLQ mass (GeV)')
plt.ylabel(
r'$\mathbf{\sigma \times}$ BR(VLQ$\mathbf{\rightarrow}$tW) (pb)')
plt.legend(loc="upper center", prop={'size': 12}, frameon=False)
plt.savefig(pp, format='pdf')
pp.close()
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.backends.backend_pdf import PdfPages
whats = ("Q2", "Tk", "ang")
inpath = "qelma"
with PdfPages("qel_delta_s_ratio.pdf") as pdf:
for what in whats:
plt.figure()
plt.title("numu, nc, E = 1000 MeV")
x1, y1 = np.loadtxt("{}/numu_s+0.2_{}.txt".format(inpath, what), delimiter=' ', unpack=True)
x2, y2 = np.loadtxt("{}/numu_s-0.2_{}.txt".format(inpath, what), delimiter=' ', unpack=True)
x1r, y1r = np.loadtxt("{}/numu_s+0.2r_{}.txt".format(inpath, what), delimiter=' ', unpack=True)
x2r, y2r = np.loadtxt("{}/numu_s-0.2r_{}.txt".format(inpath, what), delimiter=' ', unpack=True)
plt.xlabel(what)
plt.ylabel("#events")
plt.plot(x1, (y1r - y1) / (y1r + y1), 'r-', label='$\Delta s = 0.2$')
plt.plot(x1, (y2r - y2) / (y2r + y2), 'b-', label='$\Delta s = -0.2$')
plt.legend()
pdf.savefig()
# define the radii to be used for aperture photometry
radii = np.arange(40)+1
#make an array for the calculation of the area of each bagel (annulus)
area = [0 for x in range(len(radii))]
#calculate area of each bagel
for i in range(0, len(area)):
if i == 0:
area[i] = math.pi*math.pow(radii[0],2)
else:
area[i] = math.pi*(math.pow(radii[i],2)-math.pow(radii[i-1],2))
# create a PDF file for the plots
with PdfPages('jr_compilation_J0905.pdf') as pdf:
fig = plt.figure()
collection = ['F475W','F814W','F160W']
flux = np.zeros([len(collection),len(radii)])
subflux = np.zeros([len(collection),len(radii)])
for i in range (0, len(collection)):
# read in the images
file = glob.glob(dir+'J0905_final_'+collection[i]+'*sci.fits')
hdu = fits.open(file[0])
data[i], header[i] = hdu[0].data, hdu[0].header
fnu[i] = header[i]['PHOTFNU']
from piecewise import piecewise_plot
from piecewise import piecewise
from matplotlib.backends.backend_pdf import PdfPages
# chrom start sample svclass size_bin IMD
# 7 7365075 NSLC-0057-T01 inversion 1-10Kb 0.0
# 7 7369129 NSLC-0057-T01 inversion 1-10Kb 0.0
#input_dir = "/Users/khandekara2/iCloud/Sherlock-Lung/segments/"
input_dir = "/Users/azhark/iCloud/dev/Pediatric-Tumors/results/IMD-Plots/"
#input_dir = "/Users/khandekara2/iCloud/Alexandrov_Lab/data/SV/data/Mutographs_ESCC_Train9/segments/"
os.chdir(input_dir)
project = 'KiCS'
#project = 'Mutographs-ESCC'
output_path = "/Users/azhark/iCloud/dev/Pediatric-Tumors/results/IMD-Plots/"
pp = PdfPages(output_path + project + '_IMD_plots' + '.pdf')
#pp = PdfPages(project + '_IMD_plots' + '.pdf')
sizes = {
'>10Mb': 50,
'1Mb-10Mb': 40,
'1-10Kb': 10,
'100kb-1Mb': 30,
'10-100kb': 20
}
size_order = ['1-10Kb', '10-100kb', '100kb-1Mb', '1Mb-10Mb', '>10Mb']
count = 0
for file in os.listdir('.'):
if file.endswith(".IMD.tsv"):
sample = file.split(".")[0].split("_")[0]
j += 1
diff = abs(
(X.created_date_time[i] - X.created_date_time[curr_index])
/ (60000))
if diff <= 1000 and factor == n:
time_difference.append(diff)
y.append(hh)
i += 1
if len(y) > 0:
# plot (time_difference, y)
print(time_difference)
print(y)
path = ''
pp = PdfPages('./Plot' + str(n) + '.pdf')
for file_name in file_names:
try:
path = file_name
dataset = pd.read_csv((path))
except:
continue
X = dataset.iloc[:, :]
time_to_change_temperature(X)
k += 1
pp.close()
def main() :
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.backends.backend_pdf import PdfPages
import math
import argparse #A parser for command line options.
import sys
import csv
import glob
#Since python cannot figure out my path I append my function directory to the path.
sys.path.append('/home/jdw/UM2020Spring/M567/Functions/')
#Now import StockFunctions.py.
import StockFunctions as sf
#Create an argparse object.
# parser = argparse.ArgumentParser()
#Add some optional arguments.
#Get the name of the company for which the stock prices were obtained.
# stockNameHelp = 'Input the name of a company whose stock prices you want to visualize.'
# parser.add_argument('-s','--stockName', help = stockNameHelp, default = 'Google')
#Get the year the stock prices were obtained.
# yearHelp = 'Input the year the stock price was obtained. Value is integer.'
# parser.add_argument('-y', '--year', help = yearHelp, default = 2020, type = int)
#Get the month the stock prices were obtained.
# monthHelp = 'Input the month the stock price was obtained. Value is integer.'
# parser.add_argument('-m', '--month', help = monthHelp, default = 3, type = int)
#Get the day the stock prices were obtained.
# dayHelp = 'Input the day the stock price was obtained. Value is integer.'
# parser.add_argument('-d', '--day', help = dayHelp, default = 2, type = int)
#Get the input directory for the plot.
# inputHelp = 'Input the full path of the directory in which the data is found.'
# defaultInputDir = '/home/jdw/UM2020Spring/M567/Data/Google/'
# parser.add_argument('-D','--Directory', help = inputHelp, default = defaultInputDir)
#Get the output directory for the plot.
# outputHelp = 'Input the full path of the directory in which to place the output file.'
# defaultOutputDir = '/home/jdw/UM2020Spring/M567/Plots/'
# parser.add_argument('-P','--Plots', help = outputHelp, default = defaultOutputDir)
#Set the input arguments to the args object.
# args = parser.parse_args()
#Make up the input and output file names.
# stockAbbrev = sf.GetStockAbbrev(args.stockName)
# datetime = str(args.year) + str('{0:02d}'.format(args.month)) + str('{0:02d}'.format(args.day))
#Choose a company whose stock price you want to visualize.
stockName = 'Verizon'
stockAbbrev = sf.GetStockAbbrev(stockName)
directory = ('/home/jdw/UM2020Spring/M567/Data/' + stockName +
'/StockPrice_' + stockAbbrev + '*.txt')
infiles = glob.glob(directory)
for filename in infiles :
if(filename[-4:] == '.txt') :
datetime = filename[-16 : -4]
month = filename[-12 : -10]
month = month.lstrip('0')
day = filename[-10 : -8]
day = day.lstrip('0')
year = filename[-16 : -12]
#Create a output file name to where the plot will be saved.
outfilepath = '/home/jdw/UM2020Spring/M567/Data/' + stockName + '/'
outfilename = stockName + 'DailyStockPrice' + datetime + '.pdf'
outfile = outfilepath + outfilename
#Open and read in the saved data. We need to use infile[0] because glob returns a list.
with open(filename) as csv_file :
csv_reader = csv.reader(csv_file, delimiter = ',')
line_count = 0
dateTime = []
price = []
for row in csv_reader :
dateTime.append(float(row[0]))
price.append(float(row[1]))
#End of the for loop - for row in csv_reader:
#End of the with/open loop.
numHalfHours = 13
dpperhalfhour = math.ceil(len(price)/numHalfHours)
tickval = np.zeros(8)
for i in range(1, 8) :
tickval[i] = dpperhalfhour*(2*i - 1)
#End of the for loop - for i in range(1, 8) :
#Create a time vector.
t = np.arange(1, len(price) + 1)
#Get the date for the data.
monthNameStr = sf.convertMonthNumToMonthName(int(month))
dateStr = (monthNameStr + ' ' + day + ' ' + year)
#Create a title string.
titlestr = ('Stock Price vs Time for ' + dateStr + ' - ' + stockName)
#Lets make sure we have a clean canvas.
plt.close('all')
plt.figure()
plt.plot(t, price, 'b')
plt.grid('on')
plt.title(titlestr, fontsize = 9)
plt.ylabel('Stock Price(Dollars)')
plt.xlabel('Time')
plt.xticks(tickval, ('7:30 ',
'8:00AM','9:00AM','10:00AM','11:00AM','12:00PM','1:00PM','2:00PM'),
fontsize = 7)
#Save the plot to a file.
pp = PdfPages(outfile)
pp.savefig()
pp.close()
plt.cla()
plt.clf()
nsupp, ny_2p5, nx2p5 = np.shape(xlocation)
ncfile.close()
print 'min, max rlon = ', np.min(rlon), np.max(rlon)
# ---- read in the fraction of samples with climatological zero precipitation
infile = data_directory + \
'climatology_gamma_parameters_ndfd2p5_'+cleadb+'_to_'+cleade+'_'+cmonth+'.nc'
print 'reading from ', infile
nc = Dataset(infile)
fraction_zero = nc.variables['fraction_zero'][:]
nc.close()
# ---- get nearest NDFD 2.5-km gridpoint to input lon, lat. Then same for CCPA
with PdfPages('supp_locations_' + cleadb + '_to_' + cleade + '_' + cmonth +
'.pdf') as pdf:
for city in cities:
rlonin, rlatin = cities[city]
print 'rlonin, rlatin = ', rlonin, rlatin
istat, iloc, jloc = find_nearest_latlon(rlonin, rlatin, rlon, rlat)
print city, ' jloc, iloc = ', jloc, iloc
print 'i, iloc, jloc, lon(2.5), lat(2.5) '
for i in range(50):
ix = xlocation[i, jloc, iloc]
jy = ylocation[i, jloc, iloc]
print i, ix, jy, rlon[jy, ix], rlat[jy, ix]
colorst = ['White','#ECFFFF','#D9F7FF','#C4E8FF','#E8FBE8','#C7F4C7','#92F592','Yellow','Gold',\
'Orange','#FFB2B2','#EC5B71','Red','Magenta','DarkOrchid','White']
colorstblack=['White','Black','Black','Black','Black', 'Black','Black','Black',\
plt.annotate("mean: " + str(airpact_3d[sp][t,:,:].mean()) + " "+ unit_list[i], xy=(0, 1.02), xycoords='axes fraction')
plt.savefig(outpng)
plt.show()
# This requires ffmpeg program, which is not easy to install in aeolus/kamiak
# To make a video, download all the pngs in your computer and execute the command below
# "ffmpeg -framerate 3 -i WRFChem_hourly_basemap_T2_%05d.png T2_output.mp4"
#
# check_call(["ffmpeg", "-framerate", "3", "-i", "outputs/WRFChem_hourly_basemap_"+sp+ "_%05d.png", "outputs/"+sp + "_output.mp4"])
'''
############################################
# averaged domain basemaps
############################################
#save maps into the pdf file (two maps in single page)
with PdfPages(base_dir + 'maps/airpact_avg_basemap_' + '_' +
start.strftime("%Y%m%d") + '-' + end.strftime("%Y%m%d") +
'.pdf') as pdf:
for i, sp in enumerate(var_list):
fig = plt.figure(figsize=(14, 10))
plt.title(sp)
# compute auto color-scale using maximum concentrations
#down_scale = np.percentile(airpact_3d[sp], 5)
#up_scale = np.percentile(airpact_3d[sp], 95)
down_scale = 28
up_scale = 46
clevs = np.round(
np.arange(down_scale, up_scale, (up_scale - down_scale) / 10), 3)
print("debug clevs", clevs, sp)
def main():
selected_cols_ids = [
'1.0', '2.0', '3.0', '4.0', '5.0', '6.0', '7.0', '8.0', '9.0', '10.0',
'11.0', '12.0', '13.0', '14.0', '15.0', '15.5', '16.0', '16.5', '17.0'
]
remove_questions = [
"pokken / kunt u aangeven welke van de onderstaande (kinder)ziektes u gehad hebt?",
"op hoeveel momenten van de dag eet u iets?"
]
input_df_path = sys.argv[1] # input questionnaire data
input_question_overview = sys.argv[2] # input of the questionnaire items
input_recode_info = sys.argv[
3] # input file with the recoding and model information
input_question_pgs_combinations = sys.argv[
4] # selection file of the question x prs combinations
input_question_pgs_combinations_pvalues = sys.argv[
5] # pvalues from the meta analysis
input_pgs_path_ugli = sys.argv[
6] # path to the directory with the Global Screening Array input data
input_pgs_path_cyto = sys.argv[
7] # path to the directory with the HumanCytoSNP-12 input data
input_analysis_output_ugli_dir = sys.argv[
8] # path to the directory with the Global Screening Array output results
input_analysis_output_cyto_dir = sys.argv[
9] # path to the directory with the HumanCytoSNP-12 output results
translation_questions_path = sys.argv[
10] # input path with the question translations
translation_prs_path = sys.argv[11] # input path wit the prs translations
output_dir = sys.argv[12] # output dit
# create output dit
create_dir(output_dir)
# read the input data
df = pd.read_pickle(input_df_path)
df_question_ids_total = pd.read_csv(input_question_overview,
sep="\t",
index_col=0,
dtype="str")
df_recode_info = pd.read_pickle(input_recode_info)
df_recode_info = df_recode_info.set_index("question_id")
df_question_pgs = pd.read_pickle(input_question_pgs_combinations)
df_question_pgs_pvalues = pd.read_pickle(
input_question_pgs_combinations_pvalues)
#process ugli
df_pgs_ugli = pd.read_pickle(input_pgs_path_ugli)
df_pgs_ugli.columns = df_pgs_ugli.columns.str.replace(
"/", ".").str.replace(" ", ".").str.replace("-", ".").str.replace(
"(", ".").str.replace(")", ".")
df_pgs_ugli = df_pgs_ugli.loc[df_pgs_ugli.index.intersection(df.index), :]
#process cyto
df_pgs_cyto = pd.read_pickle(input_pgs_path_cyto)
df_pgs_cyto.columns = df_pgs_cyto.columns.str.replace(
"/", ".").str.replace(" ", ".").str.replace("-", ".").str.replace(
"(", ".").str.replace(")", ".")
df_pgs_cyto = df_pgs_cyto.loc[df_pgs_cyto.index.intersection(df.index), :]
# process the translations
df_translation_info = None
if translation_questions_path is not None:
df_translation_info = pd.read_csv(translation_questions_path,
sep="\t",
index_col="Question")
df_translation_prs_info = None
if translation_prs_path is not None:
df_translation_prs_info = pd.read_csv(translation_prs_path,
sep="\t",
index_col="PRS's")
# create output lists
plot_data = []
# process the questions per question
for index, row in df_question_pgs.iterrows():
if index not in remove_questions:
single_question_ids = df_question_ids_total.loc[index,
selected_cols_ids]
# find the first question id (except for the ever positive tested, we there for use the last question id)
if index == "Positive tested cumsum":
single_question_ids = single_question_ids.iloc[::-1]
single_question_first_id = func_first_value(single_question_ids)
# get model type to create the correct plot
model_type = df_recode_info.loc[single_question_first_id,
"model_type"]
answer_options_str = None
if index in df_translation_info.index:
answer_options_str = df_translation_info.loc[
index, "Answers_options_plots"]
if isinstance(model_type, pd.core.series.Series):
model_type = model_type.iloc[0]
answer_options = dict()
if answer_options_str is not None and pd.isna(
answer_options_str) == False:
if isinstance(answer_options_str, pd.core.series.Series):
answer_options_str = answer_options_str.iloc[0]
answer_options = json.loads(answer_options_str)
# get the right plot type
graph_type = "boxplots"
if model_type == "gaussian":
graph_type = "scatter"
row_filtered = row.dropna()
# loop through the PRS to create the plot per PRS
for pgs_id, single_z_score in row_filtered.iteritems():
single_pvalue = float(df_question_pgs_pvalues.loc[index,
pgs_id])
y_ugli = df_pgs_ugli.loc[:, pgs_id]
x_ugli = df.loc[y_ugli.index, single_question_first_id]
x_ugli = x_ugli.dropna()
# fix plotting issue
if single_question_first_id == "covt16_christmas_adu_q_1_b":
x_ugli = x_ugli.replace({3.0: 2.0})
y_ugli = y_ugli.loc[x_ugli.index]
y_cyto = df_pgs_cyto.loc[:, pgs_id]
x_cyto = df.loc[y_cyto.index, single_question_first_id]
x_cyto = x_cyto.dropna()
# fix plotting issue
if single_question_first_id == "covt16_christmas_adu_q_1_b":
x_cyto = x_cyto.replace({3.0: 2.0})
y_cyto = y_cyto.loc[x_cyto.index]
# calculate z scores and p values for the
# individual plots of ugli and cyto
z_score_ugli, pvalue_ugli = caalculate_z_scrore_from_models(
input_analysis_output_ugli_dir, pgs_id, index,
single_question_first_id)
z_score_cyto, pvalue_cyto = caalculate_z_scrore_from_models(
input_analysis_output_cyto_dir, pgs_id, index,
single_question_first_id)
# Find the correct translations of question and prs
label_en = index
if index in df_translation_info.index:
label_en = df_translation_info.loc[index, "label_en"]
label_prs_en = pgs_id
if pgs_id in df_translation_prs_info.index:
label_prs_en = df_translation_prs_info.loc[pgs_id,
"English Label"]
# Save the plotting information
single_plot_data = {
"x_ugli": x_ugli,
"x_cyto": x_cyto,
"y_ugli": y_ugli,
"y_cyto": y_cyto,
"question_title": label_en,
"pgs_title": label_prs_en,
"graph_type": graph_type,
"answers": answer_options,
"z_score": single_z_score,
"pvalue": single_pvalue,
"z_score_ugli": z_score_ugli,
"pvalue_ugli": pvalue_ugli,
"z_score_cyto": z_score_cyto,
"pvalue_cyto": pvalue_cyto,
}
plot_data.append(single_plot_data)
###
### create plots
###
# The PDF document
plot_file_name = "question_pgs_plots_{date}.pdf".format(
date=datetime.now().strftime("%d-%m-%Y"))
pdf_pages = PdfPages(os.path.join(output_dir, plot_file_name))
for index, data in enumerate(plot_data):
fig = plt.figure(figsize=(11.69, 8.27), dpi=100)
figure_title = "{question}\nPGS: {pgs}\nMeta analysis Z-score: {zscore:0.02f}, p-value: {pvalue:0.2E}".format(
question=data["question_title"],
pgs=data["pgs_title"],
zscore=data["z_score"],
pvalue=data["pvalue"])
fig.suptitle(figure_title, y=0.96, fontsize=12)
##
## UGLI
##
plt.subplot2grid((1, 2), (0, 0), fig=fig)
ax_ugli = plt.gca()
if data["graph_type"] == "scatter":
sns.regplot(data["x_ugli"],
data["y_ugli"],
ax=ax_ugli,
scatter_kws={
"color": "black",
"s": 20,
'alpha': 0.3,
"rasterized": True,
"clip_on": False
})
else:
sns.boxplot(data["x_ugli"],
data["y_ugli"],
ax=ax_ugli,
color="w",
showfliers=False)
# add n-values above the boxes
unique_labels = data["x_ugli"].unique()
n_value_labels = []
hivalues = []
lovalues = []
for unique_label in unique_labels:
single_data = data["y_ugli"].where(
data["x_ugli"] == unique_label).dropna()
q1, med, q3 = np.percentile(single_data, [25, 50, 75])
iqr = q3 - q1
hival = q3 + 1.5 * iqr
loval = q1 - 1.5 * iqr
wiskhi = single_data[single_data <= hival]
if len(wiskhi) == 0 or np.max(wiskhi) < q3:
hival = q3
else:
hival = np.max(wiskhi)
hivalues.append(hival)
wisklo = single_data[single_data >= loval]
if len(wisklo) == 0 or np.min(wisklo) > q1:
loval = q1
else:
loval = np.min(wisklo)
lovalues.append(loval)
if data["question_title"] == "Average time spend sitting per weekend day" or data[
"question_title"] == "Average time spend sitting per working day":
unique_label = unique_label - 1
n_value_labels.append({
"x_pos":
unique_label -
1 if min(unique_labels) != 0 else unique_label,
"y_pos":
hival,
"label":
"n={}".format(single_data.shape[0])
})
text_distance = (max(hivalues) - min(lovalues)) * 0.005
for label in n_value_labels:
if unique_labels.shape[0] < 6:
ax_ugli.text(label["x_pos"],
label["y_pos"] + text_distance,
label["label"],
ha='center',
va='bottom',
color="gray")
else:
ax_ugli.text(label["x_pos"],
label["y_pos"] + text_distance,
label["label"],
ha='center',
va='bottom',
color="gray",
fontsize=6)
# Add title
title_ugli = "Global Screening Array\nZ-score: {zscore:0.02f}, p-value: {pvalue:0.2E}".format(
zscore=data["z_score_ugli"], pvalue=data["pvalue_ugli"])
ax_ugli.set_title(title_ugli, fontsize=12)
# set the labels
ax_ugli.set_xlabel(short_str(data["question_title"], 50), fontsize=10)
ax_ugli.set_ylabel("PRS: {}".format(short_str(data["pgs_title"], 50)),
fontsize=10)
# edit the design of the plot
ax_ugli.grid(False)
ax_ugli.spines['right'].set_color('none')
ax_ugli.spines['top'].set_color('none')
ax_ugli.spines['bottom'].set_position(('axes', -0.05))
ax_ugli.yaxis.set_ticks_position('left')
ax_ugli.spines['left'].set_position(('axes', -0.05))
ax_ugli.spines['left'].set_color("dimgray")
ax_ugli.spines['bottom'].set_color("dimgray")
##
## CYTO
##
plt.subplot2grid((1, 2), (0, 1), fig=fig)
ax_cyto = plt.gca()
if data["graph_type"] == "scatter":
sns.regplot(data["x_cyto"],
data["y_cyto"],
ax=ax_cyto,
scatter_kws={
"color": "black",
"s": 20,
'alpha': 0.3,
"rasterized": True,
"clip_on": False
})
else:
sns.boxplot(data["x_cyto"],
data["y_cyto"],
ax=ax_cyto,
color="w",
showfliers=False)
# Add the n-values above the boxes
unique_labels = data["x_cyto"].unique()
if True:
n_value_labels = []
hivalues = []
lovalues = []
for unique_label in unique_labels:
single_data = data["y_cyto"].where(
data["x_cyto"] == unique_label).dropna()
q1, med, q3 = np.percentile(single_data, [25, 50, 75])
iqr = q3 - q1
hival = q3 + 1.5 * iqr
loval = q1 - 1.5 * iqr
wiskhi = single_data[single_data <= hival]
if len(wiskhi) == 0 or np.max(wiskhi) < q3:
hival = q3
else:
hival = np.max(wiskhi)
hivalues.append(hival)
wisklo = single_data[single_data >= loval]
if len(wisklo) == 0 or np.min(wisklo) > q1:
loval = q1
else:
loval = np.min(wisklo)
lovalues.append(loval)
if data["question_title"] == "Average time spend sitting per weekend day" or data[
"question_title"] == "Average time spend sitting per working day":
unique_label = unique_label - 1
n_value_labels.append({
"x_pos":
unique_label -
1 if min(unique_labels) != 0 else unique_label,
"y_pos":
hival,
"label":
"n={}".format(single_data.shape[0])
})
text_distance = (max(hivalues) - min(lovalues)) * 0.005
for label in n_value_labels:
if unique_labels.shape[0] < 6:
ax_cyto.text(label["x_pos"],
label["y_pos"] + text_distance,
label["label"],
ha='center',
va='bottom',
color="gray")
else:
ax_cyto.text(label["x_pos"],
label["y_pos"] + text_distance,
label["label"],
ha='center',
va='bottom',
color="gray",
fontsize=6)
# Add the title
title_ugli = "HumanCytoSNP-12\nZ-score: {zscore:0.02f}, p-value: {pvalue:0.2E}".format(
zscore=data["z_score_cyto"], pvalue=data["pvalue_cyto"])
ax_cyto.set_title(title_ugli, fontsize=12)
# Set the labels
ax_cyto.set_xlabel(short_str(data["question_title"], 50), fontsize=10)
ax_cyto.set_ylabel("PRS: {}".format(short_str(data["pgs_title"], 50)),
fontsize=10)
# edit the design of the plot
ax_cyto.grid(False)
ax_cyto.spines['right'].set_color('none')
ax_cyto.spines['top'].set_color('none')
ax_cyto.spines['bottom'].set_position(('axes', -0.05))
ax_cyto.yaxis.set_ticks_position('left')
ax_cyto.spines['left'].set_position(('axes', -0.05))
ax_cyto.spines['left'].set_color("dimgray")
ax_cyto.spines['bottom'].set_color("dimgray")
#change tick labels
if len(data["answers"]) > 0:
answers_df = pd.DataFrame.from_dict(data["answers"],
orient="index",
columns=["answer_str"])
answers_df.index = answers_df.index.astype("float")
if np.min(answers_df.index) > 0:
if data["graph_type"] != "scatter":
answers_df.index = answers_df.index - 1
answers_df = answers_df.sort_index()
answers_df["answer_str"] = answers_df["answer_str"].replace(
"NaN", np.nan)
answers_df["answer_str"] = answers_df["answer_str"].apply(
short_str)
answers_df = answers_df.dropna()
ax_ugli.set_xticks(list(answers_df.index))
ax_ugli.set_xticklabels(list(answers_df["answer_str"]),
rotation=45,
ha='right')
ax_cyto.set_xticks(list(answers_df.index))
ax_cyto.set_xticklabels(list(answers_df["answer_str"]),
rotation=45,
ha='right')
# save the plots
plt.tight_layout()
plt.subplots_adjust(top=0.80,
bottom=0.27,
left=0.13,
right=0.94,
wspace=0.3)
pdf_pages.savefig()
plt.clf()
pdf_pages.close()
print("end script")
def renormalization(model, settings, sol, options, temp, dLfrac, anh_order):
"""
:param model: The LD model
:param sol : The optimal solution vector from original fit
:temp : the temperature at which the renormalization is performed
:anh_order : the order of anharmonicity for renormalization (only 4th for now)
:return:
"""
print('\n')
print('!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!')
print('!!!!!! STARTING ANHARMONIC RENORMALIZATION @ ', temp, 'K !!!!!!')
print('!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!')
print('\n')
if anh_order < 4:
raise ValueError('Max anharmonic order must at least be 4!')
path = str(temp) + 'K/'
primitive = settings['structure']['prim']
nconfig = int(settings['renormalization']['nconfig'])
nprocess = int(settings['renormalization']['nprocess'])
mix_old = float(settings['renormalization']['mix_old'])
conv_thresh = float(settings['renormalization']['conv_thresh'])
nac = NA_correction.from_dict(settings['phonon'])
etafac = settings['phonon'].getfloat('etafac', 8.0)
pdfout = 'plots-' + temp + 'K.pdf'
pdfout = PdfPages(pdfout.strip())
atexit.register(upon_exit, pdfout)
try:
os.mkdir(path)
except:
pass
if os.path.isfile('SPOSCAR'):
pass
else:
raise ValueError('SPOSCAR not found!')
with open(primitive, 'r') as f:
lines = f.readlines()
#lines[1] = str(float(lines[1])*(1+dLfrac))+' \n'
with open(primitive + str(temp) + 'K', 'w') as ff:
ff.writelines(lines)
with open('SPOSCAR', 'r') as f:
lines = f.readlines()
#lines[1] = str(float(lines[1])*(1+dLfrac))+' \n'
with open(path + 'SPOSCAR', 'w') as ff:
ff.writelines(lines)
fcfile = 'FORCE_CONSTANTS_2ND'
shutil.copy(fcfile, fcfile + '_ORG')
shutil.copy(fcfile + '_ORG', path + fcfile)
shutil.copy(path + fcfile, path + fcfile + '_OLD')
prim = SymmetrizedStructure.init_structure(settings['structure'],
primitive + str(temp) + 'K',
options.symm_step,
options.symm_prim,
options.log_level)
model = init_ld_model(
prim, settings['model'],
settings['LDFF'] if 'LDFF' in settings.sections() else {},
options.clus_step, options.symC_step, options.ldff_step)
print(prim.lattice.a)
print(prim.lattice)
print(settings['training']['traindat1'])
# Set-up initial sensing matrix with structures used for FC fitting
Amat_TD, fval_TD = init_training(model, settings['training'], step=2)
nVal, nCorr = Amat_TD.shape
settings_TD = copy.deepcopy(settings)
settings_TD['training'][
'traindat1'] = path + 'SPOSCAR ' + path + 'disp*' # change path to TD path going forward
sol = np.ones(nCorr) * sol
sol_renorm = np.copy(sol[:])
param = model.get_params()
print('params : ', param)
start2 = 0
for order in range(2):
start2 += param[order]
start4 = start2
for order in range(2, 4):
start4 += param[order]
print('start2 : ', start2, ', start4 : ', start4)
sol2orig = np.copy(sol[start2:start2 + param[2]])
sol2renorm_old = np.zeros(len(sol2orig))
sol4 = np.copy(sol[start4:start4 + param[4]])
if anh_order >= 6:
start6 = start4
for order in range(4, 6):
start6 += param[order]
sol6 = np.copy(sol[start6:start6 + param[6]])
# Calculate free energy and T-dependent QCV matrix
free_energy_old, Lmatcov, poscar = get_qcv(prim.atomic_masses, temp,
path) # initial free energy
count = 0
while True:
count += 1
print('##############')
print('ITERATION ', count)
print('##############')
if count > 1:
# Generate T-dependent atomic displacements using QCV
qcv_displace(Lmatcov, poscar, nconfig, nprocess, path)
# Set-up T-dependent sensing matrix
Amat_TD, fval_TD = init_training(model,
settings_TD['training'],
step=2)
nVal, nCorr = Amat_TD.shape
# collect displacements for each order
A2 = Amat_TD[:, start2:start2 + param[2]].toarray()
A4 = Amat_TD[:, start4:start4 + param[4]].toarray()
if anh_order >= 6:
A6 = Amat_TD[:, start6:start6 + param[6]].toarray()
##### RENORMALIZE FC2 #####
A2inv = np.linalg.pinv(
A2
) # Moore-Penrose pseudo-inverse...essentially a least-squares solver
sol2renorm = A2inv.dot(A4.dot(sol4)) # least-squares solution
if anh_order >= 6:
sol2renorm += A2inv.dot(A6.dot(sol6))
sol_renorm[
start2:start2 +
param[2]] = sol2orig + sol2renorm_old * mix_old + sol2renorm * (
1 - mix_old)
print('Renormalized sol2 : \n', sol_renorm[start2:start2 + param[2]])
# Save renormalized FORCE_CONSTANTS_2ND
phonon = Phonon(prim,
model,
sol_renorm,
pdfout,
NAC=nac,
etafac=etafac)
save_pot(model, sol_renorm, settings['export_potential'], 2, phonon)
shutil.copy(path + fcfile, path + fcfile + '_OLD')
shutil.copy(fcfile, path + fcfile)
free_energy, Lmatcov, poscar = get_qcv(prim.atomic_masses, temp, path)
# Check relative difference in sol2renorm
if count > 1:
cosine_sim = np.dot(sol2renorm, sol2renorm_old) / np.linalg.norm(
sol2renorm) / np.linalg.norm(sol2renorm_old)
else:
cosine_sim = 0
d_free_energy = (free_energy - free_energy_old) / free_energy
rel_diff = np.sum(abs(sol2renorm) / abs(sol2orig)) / len(sol2renorm)
print('Cosine similiarty to the previous sol2renorm is ', cosine_sim)
print('Relative difference from original sol2 is ', rel_diff)
print('Relative change in free energy (meV/atom) is ', d_free_energy)
sol2renorm_old = np.copy(sol2renorm[:])
free_energy_old = free_energy
# BREAK if relative difference in Free Energy is small
# if abs(d_free_energy) < conv_thresh and count > 1:
if cosine_sim > conv_thresh and count > 1:
print('!!!!! Convergence Reached - Renormalization Done for ',
str(temp), ' K !!!!!')
break
sol_renorm = np.asarray(sol_renorm).reshape(1, nCorr)
np.savetxt('solution_all_' + temp + 'K', sol_renorm)
phonon_step(model, prim, sol_renorm, settings['phonon'], temp,
options.phonon_step, pdfout) # Perform final phonon analysis
shutil.move(fcfile + '_ORG', fcfile)
for i in range(nconfig):
shutil.rmtree(path + 'disp-' + str(i + 1))
#plt.savefig('cond_%s_%s.pdf' % (parameters[i], parameters[j]), bbox_tight=True)
# plt.close()
plt.subplots_adjust(wspace=0,
hspace=0,
bottom=0.2,
top=0.8,
left=0.2,
right=0.8)
plt.savefig(prefix + 'marg.pdf')
plt.savefig(prefix + 'marg.png')
plt.close()
else:
from matplotlib.backends.backend_pdf import PdfPages
sys.stderr.write('1dimensional only. Set the D environment variable \n')
sys.stderr.write('to D=2 to force 2d marginal plots.\n')
pp = PdfPages(prefix + 'marg1d.pdf')
for i in range(n_params):
plt.figure(figsize=(3, 3))
plt.xlabel(parameters[i])
plt.locator_params(nbins=5)
m = s['marginals'][i]
iqr = m['q99%'] - m['q01%']
xlim = m['q01%'] - 0.3 * iqr, m['q99%'] + 0.3 * iqr
#xlim = m['5sigma']
plt.xlim(xlim)
oldax = plt.gca()
x, w, patches = oldax.hist(values[:, i],
bins=numpy.linspace(xlim[0], xlim[1], 20),
def plot_best_latent(exp_results,
out_filenames):
sample_d = pickle.load(open(exp_results))
chains = sample_d['chains']
exp = sample_d['exp']
data_filename = exp['data_filename']
data_dict = pickle.load(open(data_filename, 'r'))
meta_filename = data_filename[:-4] + "meta"
m = pickle.load(open(meta_filename, 'r'))
meta_infile = m['infile']
meta = pickle.load(open(meta_infile, 'r'))
conn_matrix = meta['conn']
chains = [c for c in chains if type(c['scores']) != int]
CHAINN = len(chains)
chains_sorted_order = np.argsort([d['scores'][-1] for d in chains])[::-1]
from matplotlib.backends.backend_pdf import PdfPages
# get data
for chain_pos, (latent_fname, latent_pickle) in enumerate(out_filenames):
best_chain_i = chains_sorted_order[chain_pos]
best_chain = chains[best_chain_i]
sample_latent = best_chain['state']
jobs_assignment = np.array(sample_latent['domains']['jobs']['assignment'])
users_assignment = np.array(sample_latent['domains']['users']['assignment'])
ji = np.argsort(jobs_assignment).flatten()
ja = jobs_assignment[ji]
j_pos = np.argwhere(np.diff(ja) != 0).flatten()
ui = np.argsort(users_assignment).flatten()
ua = users_assignment[ui]
u_pos = np.argwhere(np.diff(ua) != 0).flatten()
pp = PdfPages(latent_fname)
f = pylab.figure()
ax = f.add_subplot(1, 1, 1)
cm = conn_matrix['link']
cm = cm[ui, :]
cm = cm[:, ji]
ax.imshow(cm > 0, interpolation='nearest', cmap=pylab.cm.Greys)
for i in u_pos:
ax.axhline(i)
for i in j_pos:
ax.axvline(i)
f.savefig(pp, format='pdf')
f = pylab.figure()
plot_t1t2_params(f, conn_matrix, users_assignment, jobs_assignment,
sample_latent['relations']['R1']['ss'],
sample_latent['relations']['R1']['hps'],
model = data_dict['relations']['R1']['model'],
MAX_DIST = 30, MAX_CLASSES=10)
f.savefig(pp, format='pdf')
pp.close()
pickle.dump(sample_latent, open(latent_pickle, 'w'))
import datetime
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.backends.backend_pdf import PdfPages
# Create the PdfPages object to which we will save the pages:
# The with statement makes sure that the PdfPages object is closed properly at
# the end of the block, even if an Exception occurs.
with PdfPages("multipage_pdf.pdf") as pdf:
plt.figure(figsize=(3, 3))
plt.plot(range(7), [3, 1, 4, 1, 5, 9, 2], "r-o")
plt.title("Page One")
pdf.savefig() # saves the current figure into a pdf page
plt.close()
# # if LaTeX is not installed or error caught, change to `usetex=False`
# plt.rc('text', usetex=False)
# plt.figure(figsize=(8, 6))
# x = np.arange(0, 5, 0.1)
# plt.plot(x, np.sin(x), 'b-')
# plt.title('Page Two')
# pdf.attach_note("plot of sin(x)") # you can add a pdf note to
# # attach metadata to a page
pdf.savefig()
plt.close()
# plt.rc('text', usetex=False)
# fig = plt.figure(figsize=(4, 5))
# plt.plot(x, x ** 2, 'ko')
# plt.title('Page Three')
import matplotlib
x = np.arange(0.01, 10, 0.05)
y = np.arange(0.01, 10, 0.05)
X, Y = np.meshgrid(x, y)
m = np.array([1.0, 2.0])
Z = X + Y - m[0] - m[1] - X * np.log(X/m[0]) - Y * np.log(Y/m[1])
fig = plt.figure()
ax = plt.axes()
plt.pcolormesh(X, Y, Z, cmap='magma')
pp=plt.colorbar (orientation="vertical")
plt.rcParams['font.size'] = 16
plt.rcParams['xtick.labelsize'] = 16
plt.rcParams['ytick.labelsize'] = 16
matplotlib.rcParams['ps.useafm'] = True
matplotlib.rcParams['pdf.use14corefonts'] = True
cont=plt.contour(X,Y,Z,8,vmin=-1,vmax=1, colors=['black'])
cont.clabel(fmt='%1.1f', fontsize=16)
plt.plot(1.0,2.0,marker='.',markersize=16)
plt.xlabel(r'$x_1$', fontsize=16)
plt.ylabel(r'$x_2$', fontsize=16)
plt.tick_params(which='major', labelsize=16)
plt.gca().set_aspect('equal')
ppdf = PdfPages('information_entropy.pdf')
ppdf.savefig(fig,bbox_inches="tight", pad_inches=0.0)
ppdf.close()
def plot_popular_industry(industry_data, number, indicator):
occupation_rank = industry_data.newdf.sort_values(
by=indicator, ascending=False).ix[:number]
occupation_rank["unapproved_case"] = occupation_rank[
"application_pool"] - occupation_rank["approved_case"]
with PdfPages('popular occupation groups ranked by ' + indicator +
'.pdf') as pdf:
plt.figure(figsize=(30, 20))
ax1 = occupation_rank["approved_case"].plot(
kind="barh",
alpha=0.7,
color=["skyblue", "pink", "green"],
figsize=(20, 15))
ax1.set_title('Application pool for top ' + str(number) + ' ' +
indicator + ' occupation groups')
plt.subplots_adjust(bottom=0.1, left=0.3)
pdf.savefig()
plt.close()
plt.figure(figsize=(30, 20))
ax2 = occupation_rank[["approved_case", "unapproved_case"
]].plot(kind="bar",
alpha=0.7,
stacked=True,
color=["skyblue", "pink"],
figsize=(20, 15))
ax3 = occupation_rank["approval_rate"].plot(kind="line",
secondary_y=True,
style='ko--')
ax2.set_xticklabels(occupation_rank.index, rotation=45)
ax3.set_xticklabels(occupation_rank.index, rotation=45)
ax2.set_title('Approval rate for for top ' + str(number) + ' ' +
indicator + ' occupation groups')
plt.subplots_adjust(bottom=0.3, left=0.1)
pdf.savefig()
plt.close()
plt.figure(figsize=(30, 20))
ax4 = occupation_rank["average_wage"].plot(
kind="barh",
ylim=[40000, 120000],
color="skyblue",
figsize=(20, 15),
title="Average Wage for Top 10 Largest Application Pool Companies")
ax4.set_title('Average wage for top for top ' + str(number) + ' ' +
indicator + ' occupation groups')
plt.subplots_adjust(bottom=0.1, left=0.3)
pdf.savefig()
plt.close()
print(
"Further analysis has been saved in your local folder as PDF, please open your folder to check"
)
print(
"please return to the previous dictionary to explore other functions\n"
)
return
well = (row, col)
time_list = []
value_list = []
for time, value in sorted(MES[reading_label][plate_id][well].iteritems()):
time_list.append(time)
value_list.append(value)
time_array = array(time_list)
if len(time_list):
time_array = (time_array - time_list[0]) / 3600
return time_array, array(value_list)
MES = CollectData("../data/tecan/PL6-96.tar.gz", number_of_plates=4)
_mkdir('../res/tecan')
pp = PdfPages('../res/tecan/2011-02-06_PL6-96.pdf')
#rcParams['text.usetex'] = True
rcParams['legend.fontsize'] = 12
#rcParams['font.family'] = 'sans-serif'
#rcParams['font.size'] = 8
#rcParams['lines.linewidth'] = 0.3
#rcParams['lines.markersize'] = 2
#rcParams['figure.figsize'] = [5, 10]
#rcParams['figure.subplot.hspace'] = 0.3
#figure()
plot_growth_rate = False
fit_window_size = 1.5 # hours
fit_start_threshold = 0.01
simperc = numpy.array(simperc)
simmedfrac = numpy.array(simmedfrac)
simmedabs = numpy.array(simmedabs)
f = open('temp25.pkl', 'rb')
(fit_input, _) = pickle.load(f)
dataperc = numpy.percentile(numpy.sum(fit_input['rho1']**2, axis=1),
(50, 50 - 34, 50 + 34))
datamedfrac, datamedabs = meanfrac(fit_input)
asort = numpy.argsort(numpy.append(simperc[:, 0], dataperc[0]))
resort = numpy.argsort(asort)
pp = PdfPages('output25_sim' + tag + '/pvalues_delta2.pdf')
plt.errorbar(
resort[:-1],
simperc[:, 0],
yerr=[simperc[:, 0] - simperc[:, 1], simperc[:, 2] - simperc[:, 0]],
fmt='.',
color='blue')
plt.errorbar(resort[-1],
dataperc[0],
yerr=[[dataperc[0] - dataperc[1]], [dataperc[2] - dataperc[0]]],
fmt='.',
color='red')
plt.xlim([-1, len(resort)])
plt.xlabel('Sorted index')
plt.ylabel(r'$\delta^2$')
plt.savefig(pp, format='pdf')
output.write(
"The first row for each Items(plants) Exported Value in Saudi Arabia with their Year Value"
)
output.write("\n")
#show the first row for each group
print(grouped.first(), file=output)
#Code to draw bar plot for all the Items(plants)
items = []
itemslist = []
#save available data to array based on thier group
for key, item in grouped:
itemslist.append(grouped.get_group(key))
items.append(key)
with PdfPages(
r'/Users/abeer/downloads/Cropsandlivestockproducts_ExportValueCharts.pdf'
) as export_pdf:
for i in range(len(itemslist)):
Y = []
y_values = []
x_values = []
output.write("The name of the item(value) Exported Value: ")
print(items[i], file=output)
output.write("\n")
output.write(
"The Table that shows the Year and Value of the item(value) Exported Value"
)
output.write("\n")
print(itemslist[i], file=output)
output.write("\n")
Y = itemslist[i].to_numpy()
def evaluate(corpus):
N = 100 # must be large to ensure convergence
w_a_tmp = 0
w_b_tmp = 0
# counters that save the number of label predictions given by predict() function
correct_predictions_a = 0
correct_predictions_b = 0
# numpy arrays that store accuracy scores over a number of iterations
accuracy_a = np.array([])
accuracy_b = np.array([])
# numpy arrays that store number of words used for training over a num of iterations
w_a = np.array([])
w_b = np.array([])
# create test set by selecting 10% of all sentences randomly
np.random.shuffle(corpus)
corpus_length = len(corpus)
test_set_size = int(round(corpus_length / 10, 0))
training_set_size = int(corpus_length - test_set_size)
training_set, test_set = corpus[:training_set_size], corpus[test_set_size:]
num_predictions_so_far = 0
# create instance A of MaxEntModel to be used with train()
A = MaxEntModel()
A.initialize(training_set)
# create instance B of MaxEntModel to be used with train_batch()
B = MaxEntModel()
B.initialize(training_set)
# train A and B
for i in range(N):
print(Colors.WARNING + "Iteration: " + Colors.ENDC, i)
print(Colors.WARNING + "Training A..." + Colors.ENDC)
A.train(1)
w_a_tmp += 1
print(Colors.WARNING + "Training B..." + Colors.ENDC)
B.train_batch(1, 1)
w_b_tmp += B.get_num_training_words()
# execute predict on the test_set
for sentence in test_set:
for j in range(len(test_set)):
word = sentence[j][0]
label = sentence[j][1]
if j == 0:
prev_label = 'start'
else:
prev_label = sentence[j - 1][1]
print(Colors.WARNING + "Predicting label for A..." +
Colors.ENDC)
prediction_a = A.predict(word, prev_label)
print(Colors.WARNING + "Predicting label for B..." +
Colors.ENDC)
prediction_b = B.predict(word, prev_label)
num_predictions_so_far += 1
if prediction_a == label:
correct_predictions_a += 1
if prediction_b == label:
correct_predictions_b += 1
# compute accuracy for model A and B
it_accuracy_a = correct_predictions_a / num_predictions_so_far
it_accuracy_b = correct_predictions_b / num_predictions_so_far
accuracy_a = np.append(accuracy_a, it_accuracy_a)
accuracy_b = np.append(accuracy_b, it_accuracy_b)
w_a = np.append(w_a, w_a_tmp)
w_b = np.append(w_b, w_b_tmp)
# plot the data (accuracy against number of words)
print(Colors.WARNING + "Plotting data..." + Colors.ENDC)
chart_a = plt.plot(w_a, accuracy_a)
chart_b = plt.plot(w_b, accuracy_b)
plt.setp(chart_a, color='r', linewidth=2.0)
plt.setp(chart_b, color='b', linewidth=2.0)
# save the plot on file
pp = PdfPages('plot.pdf')
pp.savefig()
pp.close()
lge1, eff1, eff_err1 = get_eff_area(sys.argv[2])
print(lge0)
print(lge1)
diff = list()
rel_err0, rel_err1 = list(), list()
for i in range(len(lge0)):
d = (eff0[i] - eff1[i]) / math.sqrt(eff_err0[i] * eff_err0[i] +
eff_err1[i] * eff_err1[i])
diff.append(d)
rel_err0.append(eff_err0[i] / eff0[i])
rel_err1.append(eff_err1[i] / eff1[i])
figName = 'figures/EffArea_testingSplit.pdf'
with PdfPages(figName) as pdf:
fig = plt.figure(figsize=(8, 6), tight_layout=True)
ax = plt.gca()
ax.set_yscale('log')
ax.set_xlabel(r'log$_{10}$($E$/TeV)')
ax.set_ylabel(r'$A_\mathrm{eff}$ (cm$^2$)')
ax.errorbar(lge0, eff0, yerr=eff_err0, marker='o', linestyle='none')
ax.errorbar(lge1,
eff1,
yerr=eff_err1,
marker='s',
linestyle='none',
markersize=15,
fillstyle='none')
def energy_spectrum(self,
Directory=False,
PdfPages=False,
sources=False,
real_time=False,
color=False):
# energy calib E=a*x + b, x is the channel number
a = 0.04258
b = 0.09599
for i in np.arange(len(sources)):
fig = plt.figure()
ax = fig.add_subplot(111)
data = loadtxt("Source_spectra/" + sources[i] + "/" + sources[i] +
"_spectrum_calibrated.txt")
x1 = np.arange(0, len(data), 1)
x_calib = a * x1 + b
y1 = data
y_rate = y1 / real_time[i]
y_norm = y1 / np.max(y1)
plt.step(x_calib,
y_norm,
where='mid',
label=sources[i],
linewidth=0.6,
color='#B40431',
zorder=1.3) # default farben
plt.bar(x_calib,
y_norm,
width=0.0425,
linewidth=0.6,
color='#F5A9BC',
zorder=1.2,
label='_nolegend_')
ax = plt.gca()
if sources[i] == "Am":
plt.xlim(0, 70)
ax.annotate(r'$Am\ {\gamma}_{2,0}$',
xy=(60, 0.95),
xytext=(-5, 5),
ha='right',
textcoords='offset points',
fontsize=8)
if sources[i] == "Fe":
plt.xlim(0, 15)
ax.annotate(r'${K}_{\alpha}^{Fe}$',
xy=(7.1, 0.95),
xytext=(-5, 5),
ha='right',
textcoords='offset points',
fontsize=8)
ax.annotate(r'${K}_{\beta}^{Fe}$',
xy=(7.5, 0.15),
xytext=(-5, 5),
ha='right',
textcoords='offset points',
fontsize=8)
if sources[i] == "Cd":
plt.xlim(0, 60)
ax.annotate(r'${K}_{\alpha}^{Cd}$',
xy=(25.0, 0.95),
xytext=(-5, 5),
ha='right',
textcoords='offset points',
fontsize=8)
ax.annotate(r'${K}_{\beta}^{Cd}$',
xy=(28.0, 0.15),
xytext=(-5, 5),
ha='right',
textcoords='offset points',
fontsize=8)
if sources[i] == "Background":
point_label = [
r'${K}_{\alpha}^{Fe}$', r'${K}_{\alpha}^{Ni}$',
r'${K}_{\alpha}^{Cu}$', r'${K}_{\alpha}^{Zn}$',
r'${K}_{\alpha}^{Ga}$', r'${K}_{\alpha}^{Ag}$',
r'${K}_{\alpha}^{ln}$', r'${K}_{\beta}^{Ag}$'
]
y = [0.19, 0.12, 0.1, 0.08, 0.05, 0.95, 0.58, 0.55]
x = [8.2, 9.1, 10.2, 11.0, 11.5, 24.5, 25.8, 27.5]
for j, txt in enumerate(point_label):
ax.annotate(txt,
xy=(x[j], y[j]),
xytext=(-5, 5),
ha='right',
textcoords='offset points',
fontsize=8)
plt.xlim(0, 45)
ax.set_ylim(bottom=0)
plt.xlabel('Energy [keV]')
plt.ylabel('Counts (normalized)')
#ax.set_xscale('log')
#ax.set_yscale('log')
ax.legend(loc='upper right')
plt.tight_layout()
plt.savefig("Source_spectra/" + sources[i] + "/" + sources[i] +
"_spectrum_calibrated.png",
bbox_inches='tight')
PdfPages.savefig()
ax.grid(True)
fontP = FontProperties()
fontP.set_size('small')
handles, labels = ax.get_legend_handles_labels()
ax.legend(handles, labels, prop=fontP, loc="upper right")
print '\n'
return fig
if __name__ == '__main__':
####################################################################
## create pdf generator
####################################################################
pdf_pages = PdfPages(sys.argv[1])
####################################################################
## loop through tests
####################################################################
for bm in BENCHMARKS:
fig = do_a_benchmark(bm)
pdf_pages.savefig(fig)
####################################################################
## done
####################################################################
print 'make pdf...'
pdf_pages.close()
print 'done!'
if __name__ == '__main__':
global PdfPages
Directory = "Amptek_Si_PIN_Detector/"
sources = ["Background", "Cd", "Am", "Fe"]
color = [
'#F5A9BC', '#d62728', '#1f77b4', '#7f7f7f', '#7e0044', "magenta",
'red', '#33D1FF', "maroon", "yellow", 'lightblue', '#006381', 'grey'
]
point_label = [
r'${K}_{\alpha}^{Fe}$', r'${K}_{\beta}^{Fe}$', r'${K}_{\alpha}^{Mo}$',
r'${K}_{\alpha}^{Cd}$', r'${K}_{\beta}^{Cd}$', r'$Am\ {\gamma}_{2,0}$'
]
real_time = [661.16, 60.715000, 2148.976000, 1.011000,
6272.115000] # Number is accumulation time
PdfPages = PdfPages('Amptel_Spectrum.pdf')
scan = Amptel_Spectrum()
a, a_error, b, b_error = scan.channel_energy_calibration(
PdfPages=PdfPages,
file=
'Energy_channel_calibration/channel_energy_calibration_full_range.txt',
point_label=point_label)
scan.energy_spectrum(PdfPages=PdfPages,
Directory=Directory,
sources=sources,
real_time=real_time,
color=color)
scan.plot_calibration_charge(PdfPages=PdfPages,
Directory=Directory,
point_label=point_label)
scan.close()
def generateReport(cself, filename="report.pdf", showOnScreen=True):
figs = list()
plotter = PlotCollection.PlotCollection("Calibration report")
offset = 3010
#Output calibration results in text form.
sstream = StringIO()
printResultTxt(cself, sstream)
text = [line for line in StringIO(sstream.getvalue())]
linesPerPage = 40
while True:
fig = pl.figure(offset)
offset += 1
left, width = .05, 1.
bottom, height = -.05, 1.
right = left + width
top = bottom + height
ax = fig.add_axes([.0, .0, 1., 1.])
# axes coordinates are 0,0 is bottom left and 1,1 is upper right
p = patches.Rectangle((left, bottom), width, height, fill=False, transform=ax.transAxes, \
clip_on=False, edgecolor="none")
ax.add_patch(p)
pl.axis('off')
printText = lambda t: ax.text(left, top, t, fontsize=8, \
horizontalalignment='left', verticalalignment='top',\
transform=ax.transAxes)
if len(text) > linesPerPage:
printText("".join(text[0:linesPerPage]))
figs.append(fig)
text = text[linesPerPage:]
else:
printText("".join(text[0:]))
figs.append(fig)
break
#plot imu stuff (if we have imus)
for iidx, imu in enumerate(cself.ImuList):
f = pl.figure(offset + iidx)
plots.plotAccelerations(cself, iidx, fno=f.number, noShow=True)
plotter.add_figure("imu{0}: accelerations".format(iidx), f)
figs.append(f)
offset += len(cself.ImuList)
f = pl.figure(offset + iidx)
plots.plotAccelErrorPerAxis(cself, iidx, fno=f.number, noShow=True)
plotter.add_figure("imu{0}: acceleration error".format(iidx), f)
figs.append(f)
offset += len(cself.ImuList)
f = pl.figure(offset + iidx)
plots.plotAccelBias(cself, iidx, fno=f.number, noShow=True)
plotter.add_figure("imu{0}: accelerometer bias".format(iidx), f)
figs.append(f)
offset += len(cself.ImuList)
f = pl.figure(offset + iidx)
plots.plotAngularVelocities(cself, iidx, fno=f.number, noShow=True)
plotter.add_figure("imu{0}: angular velocities".format(iidx), f)
figs.append(f)
offset += len(cself.ImuList)
f = pl.figure(offset + iidx)
plots.plotGyroErrorPerAxis(cself, iidx, fno=f.number, noShow=True)
plotter.add_figure("imu{0}: angular velocity error".format(iidx), f)
figs.append(f)
offset += len(cself.ImuList)
f = pl.figure(offset + iidx)
plots.plotAngularVelocityBias(cself, iidx, fno=f.number, noShow=True)
plotter.add_figure("imu{0}: gyroscope bias".format(iidx), f)
figs.append(f)
offset += len(cself.ImuList)
#plot cam stuff
if cself.CameraChain:
for cidx, cam in enumerate(cself.CameraChain.camList):
f = pl.figure(offset + cidx)
title = "cam{0}: reprojection errors".format(cidx)
plots.plotReprojectionScatter(cself,
cidx,
fno=f.number,
noShow=True,
title=title)
plotter.add_figure(title, f)
figs.append(f)
offset += len(cself.CameraChain.camList)
#write to pdf
pdf = PdfPages(filename)
for fig in figs:
pdf.savefig(fig)
pdf.close()
if showOnScreen:
plotter.show()
def imgsum(im,
obs,
obs_uncal,
outname,
outdir='.',
title='imgsum',
commentstr="",
fontsize=FONTSIZE,
cfun='afmhot',
snrcut=0.,
gainplots=True,
ampplots=True,
cphaseplots=True,
campplots=True,
ebar=True,
debias=True,
cp_uv_min=False,
sysnoise=0,
syscnoise=0):
"""Produce an image summary plot for an image and uvfits file.
Args:
im (Image): an Image object
obs (Obsdata): the self-calibrated Obsdata object
obs_uncal (Obsdata): the original Obsdata object
outname (str): output pdf file name
outdir (str): directory for output file
title (str): the pdf file title
commentstr (str): a comment for the top line of the pdf
fontsize (float): the font size for text in the sheet
cfun (float): matplotlib color function
gainplots (bool): include gain plots or not
ampplots (bool): include amplitude consistency plots or not
cphaseplots (bool): include closure phase consistency plots or not
campplots (bool): include closure amplitude consistency plots or not
ebar (bool): include error bars or not
debias (bool): debias visibility amplitudes before computing chisq or not
cp_uv_min (bool): minimum uv-distance cutoff for including a baseline in closure phase
sysnoise (float): percent systematic noise added in quadrature
syscnoise (float): closure phase systematic noise in degrees added in quadrature
snrcut (dict): a dictionary of snrcut values for each quantity
Returns:
"""
plt.rc('font', family='serif')
plt.rc('text', usetex=True)
plt.rc('font', size=FONTSIZE)
plt.rc('axes', titlesize=FONTSIZE)
plt.rc('axes', labelsize=FONTSIZE)
plt.rc('xtick', labelsize=FONTSIZE)
plt.rc('ytick', labelsize=FONTSIZE)
plt.rc('legend', fontsize=FONTSIZE)
plt.rc('figure', titlesize=FONTSIZE)
if fontsize == 0: fontsize = FONTSIZE
snrcut_dict = {
key: 0.
for key in ['vis', 'amp', 'cphase', 'logcamp', 'camp']
}
if type(snrcut) is dict:
for key in snrcut.keys():
snrcut_dict[key] = snrcut[key]
else:
for key in snrcut_dict.keys():
snrcut_dict[key] = snrcut
with PdfPages(outname) as pdf:
titlestr = 'Summary Sheet for %s on MJD %s' % (im.source, im.mjd)
#pdf metadata
d = pdf.infodict()
d['Title'] = title
d['Author'] = u'EHT Team 1'
d['Subject'] = titlestr
d['CreationDate'] = datetime.datetime.today()
d['ModDate'] = datetime.datetime.today()
#define the figure
fig = plt.figure(1, figsize=(18, 28), dpi=200)
gs = gridspec.GridSpec(6, 4, wspace=WSPACE, hspace=HSPACE)
#user comments
if len(commentstr) > 1:
titlestr = titlestr + '\n' + str(commentstr)
else:
titlestr = titlestr
plt.suptitle(titlestr, y=.9, va='center', fontsize=int(1.2 * fontsize))
################################################################################
print("===========================================")
print("displaying the image")
ax = plt.subplot(gs[0:2, 0:2])
ax.set_title('Submitted Image')
ax = display_img(im,
axis=ax,
show=False,
has_title=False,
cfun=cfun,
fontsize=fontsize)
print("===========================================")
print("displaying the blurred image")
ax = plt.subplot(gs[0:2, 2:5])
ax.set_title('Image blurred to nominal resolution')
#beamparams=obs_uncal.fit_gauss()
#fwhm = np.min((np.abs(beamparams[0]),np.abs(beamparams[1])))
fwhm = obs.res()
print("blur_FWHM: ", fwhm / RADPERUAS)
beamparams = [fwhm, fwhm, 0]
res = obs.res()
imblur = im.blur_gauss(beamparams, frac=1.0)
#imblur = im.blur_circ(res)
ax = display_img(imblur,
beamparams=beamparams,
axis=ax,
show=False,
has_title=False,
cfun=cfun,
fontsize=fontsize)
################################################################################
print("===========================================")
print("calculating statistics")
#display the overall chi2
ax = plt.subplot(gs[2, 0:2])
ax.set_title('Image statistics')
#ax.axis('off')
ax.set_yticks([])
ax.set_xticks([])
flux = im.total_flux()
# SNR ordering
#obs.reorder_tarr_snr()
#obs_uncal.reorder_tarr_snr()
maxset = False
# compute chi^2
chi2vis = obs.chisq(im,
dtype='vis',
ttype='nfft',
systematic_noise=sysnoise,
maxset=maxset,
snrcut=snrcut_dict['vis'])
chi2amp = obs.chisq(im,
dtype='amp',
ttype='nfft',
systematic_noise=sysnoise,
maxset=maxset,
snrcut=snrcut_dict['amp'])
chi2cphase = obs.chisq(im,
dtype='cphase',
ttype='nfft',
systematic_noise=sysnoise,
systematic_cphase_noise=syscnoise,
maxset=maxset,
cp_uv_min=cp_uv_min,
snrcut=snrcut_dict['cphase'])
chi2logcamp = obs.chisq(im,
dtype='logcamp',
ttype='nfft',
systematic_noise=sysnoise,
maxset=maxset,
snrcut=snrcut_dict['logcamp'])
chi2camp = obs.chisq(im,
dtype='camp',
ttype='nfft',
systematic_noise=sysnoise,
maxset=maxset,
snrcut=snrcut_dict['camp'])
chi2vis_uncal = obs.chisq(im,
dtype='vis',
ttype='nfft',
systematic_noise=0,
maxset=maxset,
snrcut=snrcut_dict['vis'])
chi2amp_uncal = obs.chisq(im,
dtype='amp',
ttype='nfft',
systematic_noise=0,
maxset=maxset,
snrcut=snrcut_dict['amp'])
chi2cphase_uncal = obs.chisq(im,
dtype='cphase',
ttype='nfft',
systematic_noise=0,
systematic_cphase_noise=0,
maxset=maxset,
cp_uv_min=cp_uv_min,
snrcut=snrcut_dict['cphase'])
chi2logcamp_uncal = obs.chisq(im,
dtype='logcamp',
ttype='nfft',
systematic_noise=0,
maxset=maxset,
snrcut=snrcut_dict['logcamp'])
chi2camp_uncal = obs.chisq(im,
dtype='camp',
ttype='nfft',
systematic_noise=0,
maxset=maxset,
snrcut=snrcut_dict['camp'])
print("chi^2 vis: %0.2f %0.2f" % (chi2vis, chi2vis_uncal))
print("chi^2 amp: %0.2f %0.2f" % (chi2amp, chi2amp_uncal))
print("chi^2 cphase: %0.2f %0.2f" % (chi2cphase, chi2cphase_uncal))
print("chi^2 logcamp: %0.2f %0.2f" % (chi2logcamp, chi2logcamp_uncal))
print("chi^2 camp: %0.2f %0.2f" % (chi2logcamp, chi2logcamp_uncal))
fs = int(1 * fontsize)
fs2 = int(.8 * fontsize)
ax.text(.05,
.9,
"Source:",
fontsize=fs,
ha='left',
va='center',
transform=ax.transAxes)
ax.text(.05,
.7,
"MJD:",
fontsize=fs,
ha='left',
va='center',
transform=ax.transAxes)
ax.text(.05,
.5,
"FREQ:",
fontsize=fs,
ha='left',
va='center',
transform=ax.transAxes)
ax.text(.05,
.3,
"FOV:",
fontsize=fs,
ha='left',
va='center',
transform=ax.transAxes)
ax.text(.05,
.1,
"FLUX:",
fontsize=fs,
ha='left',
va='center',
transform=ax.transAxes)
ax.text(.23,
.9,
"%s" % im.source,
fontsize=fs,
ha='left',
va='center',
transform=ax.transAxes)
ax.text(.23,
.7,
"%i" % im.mjd,
fontsize=fs,
ha='left',
va='center',
transform=ax.transAxes)
ax.text(.23,
.5,
"%0.0f GHz" % (im.rf / 1.e9),
fontsize=fs,
ha='left',
va='center',
transform=ax.transAxes)
ax.text(.23,
.3,
"%0.1f $\mu$as" % (im.fovx() / RADPERUAS),
fontsize=fs,
ha='left',
va='center',
transform=ax.transAxes)
ax.text(.23,
.1,
"%0.2f Jy" % flux,
fontsize=fs,
ha='left',
va='center',
transform=ax.transAxes)
ax.text(.5,
.9,
"$\chi^2_{vis}$",
fontsize=fs,
ha='left',
va='center',
transform=ax.transAxes)
ax.text(.5,
.7,
"$\chi^2_{amp}$",
fontsize=fs,
ha='left',
va='center',
transform=ax.transAxes)
ax.text(.5,
.5,
"$\chi^2_{cphase}$",
fontsize=fs,
ha='left',
va='center',
transform=ax.transAxes)
ax.text(.5,
.3,
"$\chi^2_{log camp}$",
fontsize=fs,
ha='left',
va='center',
transform=ax.transAxes)
ax.text(.5,
.1,
"$\chi^2_{camp}$",
fontsize=fs,
ha='left',
va='center',
transform=ax.transAxes)
ax.text(.72,
.9,
"%0.2f" % chi2vis,
fontsize=fs,
ha='left',
va='center',
transform=ax.transAxes)
ax.text(.72,
.7,
"%0.2f" % chi2amp,
fontsize=fs,
ha='left',
va='center',
transform=ax.transAxes)
ax.text(.72,
.5,
"%0.2f" % chi2cphase,
fontsize=fs,
ha='left',
va='center',
transform=ax.transAxes)
ax.text(.72,
.3,
"%0.2f" % chi2logcamp,
fontsize=fs,
ha='left',
va='center',
transform=ax.transAxes)
ax.text(.72,
.1,
"%0.2f" % chi2camp,
fontsize=fs,
ha='left',
va='center',
transform=ax.transAxes)
ax.text(.85,
.9,
"(%0.2f)" % chi2vis_uncal,
fontsize=fs2,
ha='left',
va='center',
transform=ax.transAxes)
ax.text(.85,
.7,
"(%0.2f)" % chi2amp_uncal,
fontsize=fs2,
ha='left',
va='center',
transform=ax.transAxes)
ax.text(.85,
.5,
"(%0.2f)" % chi2cphase_uncal,
fontsize=fs2,
ha='left',
va='center',
transform=ax.transAxes)
ax.text(.85,
.3,
"(%0.2f)" % chi2logcamp,
fontsize=fs2,
ha='left',
va='center',
transform=ax.transAxes)
ax.text(.85,
.1,
"(%0.2f)" % chi2camp_uncal,
fontsize=fs2,
ha='left',
va='center',
transform=ax.transAxes)
################################################################################
print("===========================================")
print("calculating cphase statistics")
#display the closure phase chi2
ax = plt.subplot(gs[3:6, 0:2])
ax.set_title('Closure phase statistics')
ax.set_yticks([])
ax.set_xticks([])
# get closure triangle combinations
# ANDREW -- hacky, fix this!
cp = obs.c_phases(mode="all",
count="min",
uv_min=cp_uv_min,
snrcut=snrcut_dict['cphase'])
n_cphase = len(cp)
alltris = [(str(cpp['t1']), str(cpp['t2']), str(cpp['t3']))
for cpp in cp]
uniqueclosure_tri = []
for tri in alltris:
if tri not in uniqueclosure_tri: uniqueclosure_tri.append(tri)
# generate data
obs_model = im.observe_same(obs, add_th_noise=False, ttype='nfft')
# TODO: check SNR cut
cphases_obs = obs.c_phases(mode='all',
count='max',
vtype='vis',
uv_min=cp_uv_min,
snrcut=snrcut_dict['cphase'])
if snrcut_dict['cphase'] > 0:
cphases_obs_all = obs.c_phases(mode='all',
count='max',
vtype='vis',
uv_min=cp_uv_min,
snrcut=0.)
cphases_model_all = obs_model.c_phases(mode='all',
count='max',
vtype='vis',
uv_min=cp_uv_min,
snrcut=0.)
mask = [cphase in cphases_obs for cphase in cphases_obs_all]
cphases_model = cphases_model_all[mask]
print('cphase snr cut', snrcut_dict['cphase'], ' : kept',
len(cphases_obs), '/', len(cphases_obs_all))
else:
cphases_model = obs_model.c_phases(mode='all',
count='max',
vtype='vis',
uv_min=cp_uv_min,
snrcut=0.)
#generate chi^2 -- NO SYSTEMATIC NOISES
ncphase = 0
cphase_chisq_data = []
for c in range(0, len(uniqueclosure_tri)):
cphases_obs_tri = obs.cphase_tri(uniqueclosure_tri[c][0],
uniqueclosure_tri[c][1],
uniqueclosure_tri[c][2],
vtype='vis',
ang_unit='deg',
cphases=cphases_obs)
if len(cphases_obs_tri) > 0:
cphases_model_tri = obs_model.cphase_tri(
uniqueclosure_tri[c][0],
uniqueclosure_tri[c][1],
uniqueclosure_tri[c][2],
vtype='vis',
ang_unit='deg',
cphases=cphases_model)
chisq_tri = 2 * np.sum(
(1.0 - np.cos(cphases_obs_tri['cphase'] * DEGREE -
cphases_model_tri['cphase'] * DEGREE)) /
((cphases_obs_tri['sigmacp'] * DEGREE)**2))
npts = len(cphases_obs_tri)
data = [
uniqueclosure_tri[c][0], uniqueclosure_tri[c][1],
uniqueclosure_tri[c][2], npts, chisq_tri
]
cphase_chisq_data.append(data)
#sort by decreasing chi^2
idx = np.argsort([data[-1] for data in cphase_chisq_data])
idx = list(reversed(idx))
chisqtab = r"\begin{tabular}{ l|l|l|l } \hline Triangle & $N_{tri}$ & $\chi^2_{tri}/N_{tri}$ & $\chi^2_{tri}/N_{tot}$\\ \hline \hline"
first = True
for i in range(len(cphase_chisq_data)):
if i > 30: break
data = cphase_chisq_data[idx[i]]
tristr = r"%s-%s-%s" % (data[0], data[1], data[2])
nstr = r"%i" % data[3]
chisqstr = r"%0.1f" % data[4]
rchisqstr = r"%0.1f" % (float(data[4]) / float(data[3]))
rrchisqstr = r"%0.3f" % (float(data[4]) / float(n_cphase))
if first:
chisqtab += r" " + tristr + " & " + nstr + " & " + rchisqstr + " & " + rrchisqstr
first = False
else:
chisqtab += r" \\" + tristr + " & " + nstr + " & " + rchisqstr + " & " + rrchisqstr
chisqtab += r" \end{tabular}"
ax.text(0.5,
.975,
chisqtab,
ha="center",
va="top",
transform=ax.transAxes,
size=fontsize)
################################################################################
print("===========================================")
print("calculating camp statistics")
#display the log closure amplitude chi2
ax = plt.subplot(gs[2:6, 2::])
ax.set_title('Log Closure amplitude statistics')
#ax.axis('off')
ax.set_yticks([])
ax.set_xticks([])
# get closure amplitude combinations
# TODO -- hacky, fix this!
cp = obs.c_amplitudes(mode="all",
count="min",
ctype='logcamp',
debias=debias)
n_camps = len(cp)
allquads = [(str(cpp['t1']), str(cpp['t2']), str(cpp['t3']),
str(cpp['t4'])) for cpp in cp]
uniqueclosure_quad = []
for quad in allquads:
if quad not in uniqueclosure_quad:
uniqueclosure_quad.append(quad)
# generate data
# TODO: check SNR cut
camps_obs = obs.c_amplitudes(mode='all',
count='max',
ctype='logcamp',
debias=debias,
snrcut=snrcut_dict['logcamp'])
if snrcut_dict['logcamp'] > 0:
camps_obs_all = obs.c_amplitudes(mode='all',
count='max',
ctype='logcamp',
debias=debias,
snrcut=0.)
camps_model_all = obs_model.c_amplitudes(mode='all',
count='max',
ctype='logcamp',
debias=False,
snrcut=0.)
mask = [
camp['camp'] in camps_obs['camp'] for camp in camps_obs_all
]
camps_model = camps_model_all[mask]
print('closure amp snrcut', snrcut_dict['logcamp'], ': kept',
len(camps_obs), '/', len(camps_obs_all))
else:
camps_model = obs_model.c_amplitudes(mode='all',
count='max',
ctype='logcamp',
debias=False,
snrcut=0.)
#generate chi2 -- NO SYSTEMATIC NOISES
ncamp = 0
camp_chisq_data = []
for c in range(0, len(uniqueclosure_quad)):
camps_obs_quad = obs.camp_quad(uniqueclosure_quad[c][0],
uniqueclosure_quad[c][1],
uniqueclosure_quad[c][2],
uniqueclosure_quad[c][3],
vtype='vis',
camps=camps_obs,
ctype='logcamp')
if len(camps_obs_quad) > 0:
camps_model_quad = obs.camp_quad(uniqueclosure_quad[c][0],
uniqueclosure_quad[c][1],
uniqueclosure_quad[c][2],
uniqueclosure_quad[c][3],
vtype='vis',
camps=camps_model,
ctype='logcamp')
chisq_quad = np.sum(
np.abs(
(camps_obs_quad['camp'] - camps_model_quad['camp']) /
camps_obs_quad['sigmaca'])**2)
npts = len(camps_obs_quad)
data = (uniqueclosure_quad[c][0], uniqueclosure_quad[c][1],
uniqueclosure_quad[c][2], uniqueclosure_quad[c][3],
npts, chisq_quad)
camp_chisq_data.append(data)
#sort by decreasing chi^2
idx = np.argsort([data[-1] for data in camp_chisq_data])
idx = list(reversed(idx))
chisqtab = r"\begin{tabular}{ l|l|l|l } \hline Quadrangle & $N_{quad}$ & $\chi^2_{quad}/N_{quad}$ & $\chi^2_{quad}/N_{tot}$ \\ \hline \hline"
for i in range(len(camp_chisq_data)):
if i > 45: break
data = camp_chisq_data[idx[i]]
tristr = r"%s-%s-%s-%s" % (data[0], data[1], data[2], data[3])
nstr = r"%i" % data[4]
chisqstr = r"%0.1f" % data[5]
rchisqstr = r"%0.1f" % (data[5] / float(data[4]))
rrchisqstr = r"%0.3f" % (data[5] / float(n_camps))
if i == 0:
chisqtab += r" " + tristr + " & " + nstr + " & " + rchisqstr + " & " + rrchisqstr
else:
chisqtab += r" \\" + tristr + " & " + nstr + " & " + rchisqstr + " & " + rrchisqstr
chisqtab += r" \end{tabular}"
ax.text(0.5,
.975,
chisqtab,
ha="center",
va="top",
transform=ax.transAxes,
size=fontsize)
#save the first page of the plot
print('saving pdf page 1')
pdf.savefig(pad_inches=MARGINS, bbox_inches='tight')
plt.close()
################################################################################
#plot the vis amps
fig = plt.figure(2, figsize=(18, 28), dpi=200)
gs = gridspec.GridSpec(6, 4, wspace=WSPACE, hspace=HSPACE)
print("===========================================")
print("plotting vis amps")
ax = plt.subplot(gs[0:2, 0:2])
obs_tmp = obs_model.copy()
obs_tmp.data['sigma'] *= 0.
ax = plotall_obs_compare([obs, obs_tmp],
'uvdist',
'amp',
axis=ax,
legend=False,
clist=['k', SCOLORS[1]],
ttype='nfft',
show=False,
debias=debias,
snrcut=snrcut_dict['amp'],
ebar=ebar,
markersize=MARKERSIZE)
#modify the labels
ax.set_title('Calibrated Visiblity Amplitudes')
ax.set_xlabel('u-v distance (G$\lambda$)')
ax.set_xlim([0, 1.e10])
ax.set_xticks([0, 2.e9, 4.e9, 6.e9, 8.e9, 10.e9])
ax.set_xticklabels(["0", "2", "4", "6", "8", "10"])
ax.set_xticks([1.e9, 3.e9, 5.e9, 7.e9, 9.e9], minor=True)
ax.set_xticklabels([], minor=True)
ax.set_ylabel('Amplitude (Jy)')
ax.set_ylim([0, 1.2 * flux])
yticks_maj = np.array([0, .2, .4, .6, .8, 1]) * flux
ax.set_yticks(yticks_maj)
ax.set_yticklabels(["%0.2f" % fl for fl in yticks_maj])
yticks_min = np.array([.1, .3, .5, .7, .9]) * flux
ax.set_yticks(yticks_min, minor=True)
ax.set_yticklabels([], minor=True)
################################################################################3
#plot the caltable gains
if gainplots:
print("===========================================")
print("plotting gains")
ax2 = plt.subplot(gs[0:2, 2:6])
obs_tmp = obs_uncal.copy()
for i in range(1):
ct = selfcal(obs_tmp,
im,
method='amp',
ttype='nfft',
caltable=True,
gain_tol=.2,
processes=PROCESSES)
ct = ct.pad_scans()
obs_tmp = ct.applycal(obs_tmp,
interp='nearest',
extrapolate=True) #apply caltable
if np.any(np.isnan(obs_tmp.data['vis'])):
print("Warning: NaN in applycal vis table!")
break
if i > 0:
ct_out = ct_out.merge([ct])
else:
ct_out = ct
ax2 = ct_out.plot_gains('all',
rangey=[.1, 10],
yscale='log',
axis=ax2,
legend=True,
show=False)
#median gains
ax = plt.subplot(gs[3:6, 2:5])
ax.set_title('Station gain statistics')
ax.set_yticks([])
ax.set_xticks([])
gain_data = []
for station in ct_out.tarr['site']:
try:
gain = np.median(np.abs(ct_out.data[station]['lscale']))
except:
continue
pdiff = np.abs(gain - 1) * 100
data = (station, gain, pdiff)
gain_data.append(data)
#sort by decreasing chi^2
idx = np.argsort([data[-1] for data in gain_data])
idx = list(reversed(idx))
chisqtab = r"\begin{tabular}{ l|l|l } \hline Site & Median Gain & Percent diff. \\ \hline \hline"
for i in range(len(gain_data)):
if i > 45: break
data = gain_data[idx[i]]
sitestr = r"%s" % (data[0])
gstr = r"%0.2f" % data[1]
pstr = r"%0.0f" % data[2]
if i == 0:
chisqtab += r" " + sitestr + " & " + gstr + " & " + pstr
else:
chisqtab += r" \\" + sitestr + " & " + gstr + " & " + pstr
chisqtab += r" \end{tabular}"
ax.text(0.5,
.975,
chisqtab,
ha="center",
va="top",
transform=ax.transAxes,
size=fontsize)
################################################################################3
#baseline amplitude chi2
print("===========================================")
print("baseline vis amps chisq")
ax = plt.subplot(gs[3:6, 0:2])
ax.set_title('Visibility amplitude statistics')
ax.set_yticks([])
ax.set_xticks([])
bl_unpk = obs.unpack(['t1', 't2'], debias=debias)
n_bl = len(bl_unpk)
allbl = [(str(bl['t1']), str(bl['t2'])) for bl in bl_unpk]
uniquebl = []
for bl in allbl:
if bl not in uniquebl:
uniquebl.append(bl)
#generate chi2 -- NO SYSTEMATIC NOISES
ncamp = 0
bl_chisq_data = []
for ii in range(0, len(uniquebl)):
bl = uniquebl[ii]
amps_bl = obs.unpack_bl(bl[0],
bl[1], ['amp', 'sigma'],
debias=debias)
if len(amps_bl) > 0:
amps_bl_model = obs_model.unpack_bl(bl[0],
bl[1], ['amp', 'sigma'],
debias=False)
if snrcut_dict['amp'] > 0:
amask = amps_bl['amp'] / amps_bl['sigma'] > snrcut_dict[
'amp']
amps_bl = amps_bl[amask]
amps_bl_model = amps_bl_model[amask]
chisq_bl = np.sum(
np.abs((amps_bl['amp'] - amps_bl_model['amp']) /
amps_bl['sigma'])**2)
npts = len(amps_bl_model)
data = (bl[0], bl[1], npts, chisq_bl)
bl_chisq_data.append(data)
#sort by decreasing chi^2
idx = np.argsort([data[-1] for data in bl_chisq_data])
idx = list(reversed(idx))
chisqtab = r"\begin{tabular}{ l|l|l|l } \hline Baseline & $N_{amp}$ & $\chi^2_{amp}/N_{amp}$ & $\chi^2_{amp}/N_{total}$ \\ \hline \hline"
for i in range(len(bl_chisq_data)):
if i > 45: break
data = bl_chisq_data[idx[i]]
tristr = r"%s-%s" % (data[0], data[1])
nstr = r"%i" % data[2]
chisqstr = r"%0.1f" % data[3]
rchisqstr = r"%0.1f" % (data[3] / float(data[2]))
rrchisqstr = r"%0.3f" % (data[3] / float(n_bl))
if i == 0:
chisqtab += r" " + tristr + " & " + nstr + " & " + rchisqstr + " & " + rrchisqstr
else:
chisqtab += r" \\" + tristr + " & " + nstr + " & " + rchisqstr + " & " + rrchisqstr
chisqtab += r" \end{tabular}"
ax.text(0.5,
.975,
chisqtab,
ha="center",
va="top",
transform=ax.transAxes,
size=fontsize)
#save the first page of the plot
print('saving pdf page 2')
#plt.tight_layout()
#plt.subplots_adjust(wspace=1,hspace=1)
#plt.savefig(outname, pad_inches=MARGINS,bbox_inches='tight')
pdf.savefig(pad_inches=MARGINS, bbox_inches='tight')
plt.close()
################################################################################
#plot the visibility amplitudes
page = 3
if ampplots:
print("===========================================")
print("plotting amplitudes")
fig = plt.figure(3, figsize=(18, 28), dpi=200)
plt.suptitle("Amplitude Plots",
y=.9,
va='center',
fontsize=int(1.2 * fontsize))
gs = gridspec.GridSpec(6, 4, wspace=WSPACE, hspace=HSPACE)
i = 0
j = 0
switch = False
obs_model.data['sigma'] *= 0
amax = 1.1 * np.max(np.abs(np.abs(obs_model.data['vis'])))
obs_all = [obs, obs_model]
for bl in uniquebl:
ax = plt.subplot(gs[2 * i:2 * (i + 1), 2 * j:2 * (j + 1)])
ax = plot_bl_obs_compare(obs_all,
bl[0],
bl[1],
'amp',
rangey=[0, amax],
markersize=MARKERSIZE,
debias=debias,
snrcut=snrcut_dict['amp'],
axis=ax,
legend=False,
clist=['k', SCOLORS[1]],
ttype='nfft',
show=False,
ebar=ebar)
if ax is None: continue
if switch:
i += 1
j = 0
switch = False
else:
j = 1
switch = True
ax.set_xlabel('')
if i == 3:
print('saving pdf page %i' % page)
page += 1
pdf.savefig(pad_inches=MARGINS, bbox_inches='tight')
plt.close()
fig = plt.figure(3, figsize=(18, 28), dpi=200)
gs = gridspec.GridSpec(6, 4, wspace=WSPACE, hspace=HSPACE)
i = 0
j = 0
switch = False
print('saving pdf page %i' % page)
page += 1
pdf.savefig(pad_inches=MARGINS, bbox_inches='tight')
plt.close()
################################################################################
#plot the closure phases
if cphaseplots:
print("===========================================")
print("plotting closure phases")
fig = plt.figure(3, figsize=(18, 28), dpi=200)
plt.suptitle("Closure Phase Plots",
y=.9,
va='center',
fontsize=int(1.2 * fontsize))
gs = gridspec.GridSpec(6, 4, wspace=WSPACE, hspace=HSPACE)
i = 0
j = 0
switch = False
obs_all = [obs, obs_model]
cphases_model['sigmacp'] *= 0
cphases_all = [cphases_obs, cphases_model]
for tri in uniqueclosure_tri:
ax = plt.subplot(gs[2 * i:2 * (i + 1), 2 * j:2 * (j + 1)])
ax = plot_cphase_obs_compare(obs_all,
tri[0],
tri[1],
tri[2],
rangey=[-185, 185],
cphases=cphases_all,
markersize=MARKERSIZE,
axis=ax,
legend=False,
clist=['k', SCOLORS[1]],
ttype='nfft',
show=False,
ebar=ebar)
if ax is None: continue
if switch:
i += 1
j = 0
switch = False
else:
j = 1
switch = True
ax.set_xlabel('')
if i == 3:
print('saving pdf page %i' % page)
page += 1
pdf.savefig(pad_inches=MARGINS, bbox_inches='tight')
plt.close()
fig = plt.figure(3, figsize=(18, 28), dpi=200)
gs = gridspec.GridSpec(6, 4, wspace=WSPACE, hspace=HSPACE)
i = 0
j = 0
switch = False
print('saving pdf page %i' % page)
page += 1
pdf.savefig(pad_inches=MARGINS, bbox_inches='tight')
plt.close()
################################################################################
#plot the log closure amps
if campplots:
print("===========================================")
print("plotting closure amplitudes")
fig = plt.figure(3, figsize=(18, 28), dpi=200)
plt.suptitle("Closure Amplitude Plots",
y=.9,
va='center',
fontsize=int(1.2 * fontsize))
gs = gridspec.GridSpec(6, 4, wspace=WSPACE, hspace=HSPACE)
i = 0
j = 0
switch = False
obs_all = [obs, obs_model]
camps_model['sigmaca'] *= 0
camps_all = [camps_obs, camps_model]
cmax = 1.1 * np.max(np.abs(camps_obs['camp']))
for quad in uniqueclosure_quad:
ax = plt.subplot(gs[2 * i:2 * (i + 1), 2 * j:2 * (j + 1)])
ax = plot_camp_obs_compare(obs_all,
quad[0],
quad[1],
quad[2],
quad[3],
markersize=MARKERSIZE,
ctype='logcamp',
rangey=[-cmax, cmax],
camps=camps_all,
axis=ax,
legend=False,
clist=['k', SCOLORS[1]],
ttype='nfft',
show=False,
ebar=ebar)
if ax is None: continue
if switch:
i += 1
j = 0
switch = False
else:
j = 1
switch = True
ax.set_xlabel('')
if i == 3:
print('saving pdf page %i' % page)
page += 1
pdf.savefig(pad_inches=MARGINS, bbox_inches='tight')
plt.close()
fig = plt.figure(3, figsize=(18, 28), dpi=200)
gs = gridspec.GridSpec(6, 4, wspace=WSPACE, hspace=HSPACE)
i = 0
j = 0
switch = False
print('saving pdf page %i' % page)
page += 1
pdf.savefig(pad_inches=MARGINS, bbox_inches='tight')
plt.close()
def close(self):
PdfPages.close()
import sys
from numpy import *
from numpy.random import *
import matplotlib.pyplot as plt
import scipy.stats as st
from matplotlib.backends.backend_pdf import PdfPages
pp = PdfPages("output-histogram.pdf")
import matplotlib
#font = {'family' : 'normal',
# 'weight' : 'bold',
# 'size' : 10}
#matplotlib.rc('font', **font)
matplotlib.rc('xtick', labelsize=2)
matplotlib.rc('ytick', labelsize=2)
sys.path.append("/home/cbarnes/dev/cuda-sim-area/cuda-sim/trunk/")
import cudasim
import cudasim.EulerMaruyama as EulerMaruyama
import cudasim.Gillespie as Gillespie
import cudasim.Lsoda as Lsoda
def print_results(result, timepoints, outfile, sx=-1, model=0):
out = open(outfile, 'w')
print >> out, 0, 0, 0, 0,
for i in range(len(timepoints)):
print >> out, timepoints[i],
print >> out, ""
# loop over threads
model_name = [sys.argv[2]]
for s in model_name:
print('using model ' + version + '_' + s)
network_xml = '/Users/leahanderson/Code/Lanksershim_Network/Lshim_' + version + '_' + model_name[
0] + '.xml'
output_prefix = {}
for mt in model_name:
output_prefix[
mt] = '/Users/leahanderson/Code/Lanksershim_Network/output/' + version + '_' + mt
dataset = '/Users/leahanderson/Code/datasets_external/lankershim'
time_aggregation = 5
sys.path.append(dataset)
pp = PdfPages('densities_' + version + '.pdf')
import network_properties as netprops
intersections = netprops.intersection_ids
links = netprops.link_ids
initial_time = 0
final_time = (netprops.time_range[1] - netprops.time_range[0]) / 1000
time_bounds = range(initial_time, final_time, time_aggregation)
network = load_network(network_xml)
link_densities = {}
with open(dataset + '/densities_links.csv', 'rb') as csvfile:
reader = csv.reader(csvfile, delimiter=',', quotechar='|')
for row in reader:
link = row[0] + row[1]
movement = row[2]
denlist = [int(d) for d in row[3::]]
df["year"] = np.floor(1960 + df.SALE_DATE / 365.25)
df = df.loc[df.SALE_AMOUNT >= 50000, :]
df = df.loc[df.SALE_AMOUNT <= 1000000, :]
df["log_SALE_AMOUNT"] = np.log2(df.SALE_AMOUNT)
df = df.loc[df.SALE_DATE >= 365.25 * 20]
df["age"] = df.year - df.YEAR_BUILT
fml = "log_SALE_AMOUNT ~ bs(year, 6) * bs(age, 6) + bs(year, 6) * (bs(LAND_SQUARE_FOOTAGE, 6) + bs(LIVING_SQUARE_FEET, 6) + bs(age, 6))"
model = sm.OLS.from_formula(fml, df)
result = model.fit()
pdf = PdfPages("salesprice_lm.pdf")
plt.clf()
for age in 0, 10, 20, 40:
pred, cb, fvals = predict_functional(result,
"year",
values={"age": age},
summaries={
"LAND_SQUARE_FOOTAGE": np.median,
"LIVING_SQUARE_FEET": np.median
})
plt.plot(fvals, pred, '-', label=str(age))
plt.grid(True)
ha, lb = plt.gca().get_legend_handles_labels()
leg = plt.figlegend(ha, lb, "center right")
ax.set_xlabel('PC1', fontsize=12, fontweight='bold')
ax.set_ylabel('PC2', fontsize=12, fontweight='bold')
fontsize = 12
ax = gca()
for tick in ax.xaxis.get_major_ticks():
tick.label1.set_fontsize(fontsize)
tick.label1.set_fontweight('bold')
for tick in ax.yaxis.get_major_ticks():
tick.label1.set_fontsize(fontsize)
tick.label1.set_fontweight('bold')
labels = df.Linage.values
for label, x, y in zip(labels, X_r[:, 0], X_r[:, 1]):
plt.annotate(label, xy=(x, y), textcoords='data', fontsize=7)
pp = PdfPages('ancestral_pca.pdf')
plt.savefig(pp, format='pdf')
pp.close()
########################### PCA Variance ##################################
# Select all 64 principal components
pca = PCA(92) # project from 64 to 2 dimensions
X_r = pca.fit_transform(scale(X, with_std=False))
# Obtain the explained variance for each principal component
varianceExp = pca.explained_variance_ratio_
# Compute the total sum of variance
totVarExp = np.cumsum(
np.round(pca.explained_variance_ratio_, decimals=4) * 100)