def _prettyplot(df, prep, prepi, out_file, title=None, size=None):
"""Plot using prettyplot wrapper around matplotlib.
"""
cats = ["concordant", "discordant-missing-total",
"discordant-extra-total", "discordant-shared-total"]
vtypes = df["variant.type"].unique()
fig, axs = ppl.subplots(len(vtypes), len(cats))
callers = sorted(df["caller"].unique())
width = 0.8
for i, vtype in enumerate(vtypes):
ax_row = axs[i] if len(vtypes) > 1 else axs
for j, cat in enumerate(cats):
ax = ax_row[j]
if i == 0:
ax.set_title(cat_labels[cat], size=14)
ax.get_yaxis().set_ticks([])
if j == 0:
ax.set_ylabel(vtype_labels[vtype], size=14)
vals, labels, maxval = _get_chart_info(df, vtype, cat, prep, callers)
ppl.bar(ax, np.arange(len(callers)), vals,
color=ppl.colors.set2[prepi], width=width)
ax.set_ylim(0, maxval)
if i == len(vtypes) - 1:
ax.set_xticks(np.arange(len(callers)) + width / 2.0)
ax.set_xticklabels([caller_labels.get(x, x).replace("__", "\n") if x else ""
for x in callers], size=8, rotation=45)
else:
ax.get_xaxis().set_ticks([])
_annotate(ax, labels, vals, np.arange(len(callers)), width)
fig.text(.5, .95, prep_labels.get(prep, "") if title is None else title, horizontalalignment='center', size=16)
fig.subplots_adjust(left=0.05, right=0.95, top=0.87, bottom=0.15, wspace=0.1, hspace=0.1)
#fig.tight_layout()
x, y = (10, 5) if size is None else size
fig.set_size_inches(x, y)
fig.savefig(out_file)
def PLOT_MATRIX_SAMPLING(matrix,idx=None,sampling_size=None,colorm='jet'):
from prettyplotlib import brewer2mpl
import pylab
red_purple = brewer2mpl.get_map('Blues', 'Sequential', 9).mpl_colormap
green_purple = brewer2mpl.get_map('PRGn', 'diverging', 11).mpl_colormap
if sampling_size == None:
sampling_size = [1e0,1e1,1e2,1e3,1e4,1e300]
fig,ax = ppl.subplots(nrows=2,ncols=3)
for ss in range(len(sampling_size)):
nn = sampling_size[ss]
pmatrix = 1 - np.exp(-nn*matrix)
aa = ax[ss/3,ss%3]
#plt.subplot(2,3,ss+1)
#GeneralPlotter.PLOT_MATRIX(pmatrix,axis=1)
#print ss/3,ss%3
pmatrix = GeneralPlotter.SORT_MATRIX(pmatrix,idx=idx,axis=1)
pmatrix = np.flipud(pmatrix)
#im = aa.matshow(pmatrix, aspect='auto', origin='lower', cmap=pylab.cm.YlGnBu)
#pp=aa.imshow(pmatrix,interpolation='nearest',cmap=colorm)
#fig.colorbar(pp,ax=aa)
pcolormesh(fig,aa,pmatrix,center_value=0.5, ax_colorbar=aa,cmap=red_purple)
aa.set_xticks([])
aa.set_yticks([])
aa.set_title(r'$n = %i$' % sampling_size[ss],fontsize=20)
if ss==5:
plt.title('$n = \infty$',fontsize=20)
def generate_plot(title, ylabel, x, y, width_in, height_in, plot_format):
fig, ax = ppl.subplots(1, 1, figsize=(width_in, height_in))
kwargs = dict(linewidth=1.0, alpha=0.7)
date_list = num2date(x,
units='seconds since 1900-01-01 00:00:00',
calendar='gregorian')
plot_time_series(fig,
ax,
date_list,
y,
title=title,
ylabel=ylabel,
title_font=title_font,
axis_font=axis_font,
**kwargs)
buf = io.BytesIO()
if plot_format not in ['svg', 'png']:
plot_format = 'svg'
plt.savefig(buf, format=plot_format)
buf.seek(0)
plt.clf()
plt.cla()
return buf
def _prettyplot(df, prep, prepi, out_file, title=None, size=None):
"""Plot using prettyplot wrapper around matplotlib.
"""
cats = ["concordant", "discordant-missing-total", "discordant-extra-total", "discordant-shared-total"]
vtypes = df["variant.type"].unique()
fig, axs = ppl.subplots(len(vtypes), len(cats))
callers = sorted(df["caller"].unique())
width = 0.8
for i, vtype in enumerate(vtypes):
ax_row = axs[i] if len(vtypes) > 1 else axs
for j, cat in enumerate(cats):
ax = ax_row[j]
if i == 0:
ax.set_title(cat_labels[cat], size=14)
ax.get_yaxis().set_ticks([])
if j == 0:
ax.set_ylabel(vtype_labels[vtype], size=14)
vals, labels, maxval = _get_chart_info(df, vtype, cat, prep, callers)
ppl.bar(ax, np.arange(len(callers)), vals, color=ppl.colors.set2[prepi], width=width)
ax.set_ylim(0, maxval)
if i == len(vtypes) - 1:
ax.set_xticks(np.arange(len(callers)) + width / 2.0)
ax.set_xticklabels(
[caller_labels.get(x, x).replace("__", "\n") if x else "" for x in callers], size=8, rotation=45
)
else:
ax.get_xaxis().set_ticks([])
_annotate(ax, labels, vals, np.arange(len(callers)), width)
fig.text(0.5, 0.95, prep_labels.get(prep, "") if title is None else title, horizontalalignment="center", size=16)
fig.subplots_adjust(left=0.05, right=0.95, top=0.87, bottom=0.15, wspace=0.1, hspace=0.1)
# fig.tight_layout()
x, y = (10, 5) if size is None else size
fig.set_size_inches(x, y)
fig.savefig(out_file)
def _subplots(self, *args, **kwargs):
if not kwargs:
kwargs = dict()
if 'figsize' not in kwargs:
kwargs['figsize'] = self.DEFAULT_SIZE
return ppl.subplots(*args, **kwargs)
def plot_hist_prettyplotlib(band, type, data):
dim = data.shape
print 'data dim ', dim
fig, ax = ppl.subplots(1)
ppl.pcolormesh(fig, ax, data, vmin=-0.0016, vmax=0.0016)
plt.title('%s: band %s' % (type, band))
plt.xlabel('buckets')
plt.ylabel('time')
fig.savefig("%s-histogram-%s.png" % (type, band))
def several_times(all_interval3, all_data3_3,
names3, p_title3, time_real_1,time_real_2,time_real_3,
delta_s,t_real, time_data, dycoms, fitting, s_point):
fig, ax = ppl.subplots()
color_set = brewer2mpl.get_map('paired', 'qualitative', 12).mpl_colors
color_set_2 = brewer2mpl.get_map('PuBu', 'sequential', 9).mpl_colors
#color_set_2 = brewer2mpl.get_map('Reds', 'sequential', 9).mpl_colors
color_set_3 = brewer2mpl.get_map('Dark2', 'qualitative', 8).mpl_colors
shape= np.shape(all_interval3)
time_length=shape[1]
print time_length
k=2
time_buffer=round(time_data[k]*t_real/60,2)
print 'real time t3',time_buffer
name_x='t='+str(time_buffer)
ax.plot(all_interval3[:,k],all_data3_3[:,k],color=color_set[1],linewidth=l_w_1, label=name_x)
k=6
time_buffer=round(time_data[k]*t_real/60,2)
print 'real time t3',time_buffer
name_x='t='+str(time_buffer)
ax.plot(all_interval3[:,k],all_data3_3[:,k],color=color_set[3],linewidth=l_w_1, label=name_x)
k=12
time_buffer=round(time_data[k]*t_real/60,2)
print 'real time t3',time_buffer
name_x='t='+str(time_buffer)
ax.plot(all_interval3[:,k],all_data3_3[:,k],color=color_set[5],linewidth=l_w_1, label=name_x)
k=15
time_buffer=round(time_data[k]*t_real/60,2)
print 'real time t3',time_buffer
name_x='t='+str(time_buffer)
ax.plot(all_interval3[:,k],all_data3_3[:,k],color=color_set[7],linewidth=l_w_1, label=name_x)
k=32
time_buffer=round(time_data[k]*t_real/60,2)
print 'real time t3',time_buffer
name_x='t='+str(time_buffer)
ax.plot(all_interval3[:,k],all_data3_3[:,k],color=color_set[9],linewidth=l_w_1, label=name_x)
ax.set_yscale('symlog')
plt.xlabel('Liquid', fontsize=size_axes)#,fontdict=font) #das r hier markiert, dass jetz latex code kommt
plt.ylabel('Amount of cloud droplets', fontsize=size_axes)#,fontdict=font)
plt.xlim(0,3)
plt.ylim([10,10**8])
plt.legend(loc=1, frameon=False)
plt.show()
def plot_data_compare(interval1,data1_1, data1_2, data1_3,
interval2, data2_1, data2_2, data2_3,
interval3, data3_1, data3_2, data3_3,
names1, names2, names3):
#COLORS FOR PLOTTING
fig, ax = ppl.subplots()
color_set = brewer2mpl.get_map('paired', 'qualitative', 12).mpl_colors
color_set_2 = brewer2mpl.get_map('PuBu', 'sequential', 9).mpl_colors
color_set_3 = brewer2mpl.get_map('Dark2', 'qualitative', 8).mpl_colors
########################################################################
#Scale data
########################################################################
data2_1=data2_1*30/18*1.3
data2_2=data2_2*30/18*1.3
data2_3=data2_3*30/18*1.3
data3_1=data3_1*50/18*1.4
data3_2=data3_2*50/18*1.4
data3_3=data3_3*50/18*1.4
########################################################################
#Plot data
########################################################################
ax.plot(interval1,data1_3, color=color_set[1],linewidth=l_w_1, label=names1[3])
ax.plot(interval2,data2_3, color=color_set[3],linewidth=l_w_1, label=names2[3])
ax.plot(interval3,data3_3, color=color_set[5],linewidth=l_w_1, label=names3[3])
########################################################################
#Plot properties
########################################################################
#GRID LINES
#axes = plt.gca()
#axes.grid(True)
ax.set_yscale('symlog') #use 'symlog'= for symmetrical log or 'log' for only positive values
plt.xlabel(r'$M/M_r$', fontsize=size_axes) #the argument r allows for latex code
plt.ylabel('Relative amount of droplets', fontsize=size_axes)
plt.xlim(0.1,2.2)
plt.ylim([10,10**8])
plt.legend(loc=1, frameon=False) # set legend and the location
#Hide the right and top spines
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
#Only show ticks on the left and bottom spines
ax.yaxis.set_ticks_position('left')
ax.xaxis.set_ticks_position('bottom')
plt.show()
def test_legend():
# Set the random seed for consistency
np.random.seed(12)
fig, ax = ppl.subplots(1)
# Show the whole color range
for i in range(8):
x = np.random.normal(loc=i, size=1000)
y = np.random.normal(loc=i, size=1000)
ppl.scatter(ax, x, y, label=str(i))
ppl.legend(ax)
def plot_meshless(args):
npoints = args.npoints
points = generate_2d_points(npoints, args.seed)
x = np.arange(0., 1., .01)
y = np.arange(0., 1., .01)
xx, yy = np.meshgrid(x, y)
import prettyplotlib as ppl
fig, ax = ppl.subplots()
for mypoint in points:
dens = compute_meshless_density(xx, yy, points, mypoint, args.sigma)
plt.contourf(xx, yy, dens, alpha=1. / npoints)
ppl.scatter(ax, points[:, 0], points[:, 1], color='white')
ax.autoscale(tight=True)
plt.title(args.subtitle)
def PLOT_CDF_SAMPLING(matrix, sampling_size = None):
if sampling_size == None:
sampling_size = [1e0,1e1,1e2,1e3,1e4,1e300]
fig,ax = ppl.subplots(nrows=2,ncols=3)
for ss in range(len(sampling_size)):
nn = sampling_size[ss]
pmatrix = 1 - np.exp(-nn*matrix)
#degrees = np.sum(pmatrix>0.8,axis=0) + np.sum(pmatrix>0.999,axis=1)
degrees = np.round(np.sum(pmatrix,axis=0) + np.sum(pmatrix,axis=1))
aa = ax[ss/3,ss%3]
values = np.array(degrees)
values.sort()
elements = np.unique(values)
elements = elements[::-1]
counting = np.zeros([len(elements),1])
for i in range(len(elements)):
counting[i] = len(values[values==elements[i]])
cdf = np.cumsum(1.0*counting)/np.sum(counting)
aa.semilogy(elements[::-1], cdf[::-1],'bo')
#plt.title('N = %i E= % i')
aa.set_xlabel('# of trophic links')
aa.set_ylabel('CDF value')
aa.set_title(r'$n = %i$' % sampling_size[ss],fontsize=20)
if ss==5:
plt.title('$n = \infty$',fontsize=20)
def _plot_evaluation(df_csv):
"""Provide plot of evaluation metrics, stratified by event size.
"""
if ppl is None:
return None
out_file = "%s.pdf" % os.path.splitext(df_csv)[0]
if not utils.file_uptodate(out_file, df_csv):
metrics = ["sensitivity", "precision"]
df = pd.read_csv(df_csv).fillna("0%")
fig, axs = ppl.subplots(len(_EVENT_SIZES), len(metrics))
callers = sorted(df["caller"].unique())
if "ensemble" in callers:
callers.remove("ensemble")
callers.append("ensemble")
for i, size in enumerate(_EVENT_SIZES):
size_label = "%s to %sbp" % size
size = "%s-%s" % size
for j, metric in enumerate(metrics):
ax = axs[i][j]
ax.get_xaxis().set_ticks([])
ax.spines['bottom'].set_visible(False)
ax.spines['left'].set_visible(False)
ax.set_xlim(0, 100.0)
if i == 0:
ax.set_title(metric, size=12, y=1.2)
vals, labels = _get_plot_val_labels(df, size, metric, callers)
ppl.barh(ax, np.arange(len(vals)), vals, yticklabels=callers)
if j == 0:
ax.tick_params(axis='y', which='major', labelsize=8)
ax.text(80, 4.2, size_label, fontsize=10)
else:
ax.get_yaxis().set_ticks([])
for ai, (val, label) in enumerate(zip(vals, labels)):
ax.annotate(label, (val + 1, ai + 0.35),
va='center',
size=7)
fig.set_size_inches(7, 6)
fig.savefig(out_file)
return out_file
def PLOT_MATRIX_SAMPLING(matrix,
idx=None,
sampling_size=None,
colorm='jet'):
from prettyplotlib import brewer2mpl
import pylab
red_purple = brewer2mpl.get_map('Blues', 'Sequential', 9).mpl_colormap
green_purple = brewer2mpl.get_map('PRGn', 'diverging', 11).mpl_colormap
if sampling_size == None:
sampling_size = [1e0, 1e1, 1e2, 1e3, 1e4, 1e300]
fig, ax = ppl.subplots(nrows=2, ncols=3)
for ss in range(len(sampling_size)):
nn = sampling_size[ss]
pmatrix = 1 - np.exp(-nn * matrix)
aa = ax[ss / 3, ss % 3]
#plt.subplot(2,3,ss+1)
#GeneralPlotter.PLOT_MATRIX(pmatrix,axis=1)
#print ss/3,ss%3
pmatrix = GeneralPlotter.SORT_MATRIX(pmatrix, idx=idx, axis=1)
pmatrix = np.flipud(pmatrix)
#im = aa.matshow(pmatrix, aspect='auto', origin='lower', cmap=pylab.cm.YlGnBu)
#pp=aa.imshow(pmatrix,interpolation='nearest',cmap=colorm)
#fig.colorbar(pp,ax=aa)
pcolormesh(fig,
aa,
pmatrix,
center_value=0.5,
ax_colorbar=aa,
cmap=red_purple)
aa.set_xticks([])
aa.set_yticks([])
aa.set_title(r'$n = %i$' % sampling_size[ss], fontsize=20)
if ss == 5:
plt.title('$n = \infty$', fontsize=20)
def _plot_evaluation(df_csv):
"""Provide plot of evaluation metrics, stratified by event size.
"""
if ppl is None:
return None
out_file = "%s.pdf" % os.path.splitext(df_csv)[0]
if not utils.file_uptodate(out_file, df_csv):
metrics = ["sensitivity", "precision"]
df = pd.read_csv(df_csv).fillna("0%")
fig, axs = ppl.subplots(len(_EVENT_SIZES), len(metrics))
callers = sorted(df["caller"].unique())
if "ensemble" in callers:
callers.remove("ensemble")
callers.append("ensemble")
for i, size in enumerate(_EVENT_SIZES):
size_label = "%s to %sbp" % size
size = "%s-%s" % size
for j, metric in enumerate(metrics):
ax = axs[i][j]
ax.get_xaxis().set_ticks([])
ax.spines['bottom'].set_visible(False)
ax.spines['left'].set_visible(False)
ax.set_xlim(0, 100.0)
if i == 0:
ax.set_title(metric, size=12, y=1.2)
vals, labels = _get_plot_val_labels(df, size, metric, callers)
ppl.barh(ax, np.arange(len(vals)), vals, yticklabels=callers)
if j == 0:
ax.tick_params(axis='y', which='major', labelsize=8)
ax.text(80, 4.2, size_label, fontsize=10)
else:
ax.get_yaxis().set_ticks([])
for ai, (val, label) in enumerate(zip(vals, labels)):
ax.annotate(label, (val + 1, ai + 0.35), va='center', size=7)
fig.set_size_inches(7, 6)
fig.savefig(out_file)
return out_file
def PLOT_CDF_SAMPLING(matrix, sampling_size=None):
if sampling_size == None:
sampling_size = [1e0, 1e1, 1e2, 1e3, 1e4, 1e300]
fig, ax = ppl.subplots(nrows=2, ncols=3)
for ss in range(len(sampling_size)):
nn = sampling_size[ss]
pmatrix = 1 - np.exp(-nn * matrix)
#degrees = np.sum(pmatrix>0.8,axis=0) + np.sum(pmatrix>0.999,axis=1)
degrees = np.round(
np.sum(pmatrix, axis=0) + np.sum(pmatrix, axis=1))
aa = ax[ss / 3, ss % 3]
values = np.array(degrees)
values.sort()
elements = np.unique(values)
elements = elements[::-1]
counting = np.zeros([len(elements), 1])
for i in range(len(elements)):
counting[i] = len(values[values == elements[i]])
cdf = np.cumsum(1.0 * counting) / np.sum(counting)
aa.semilogy(elements[::-1], cdf[::-1], 'bo')
#plt.title('N = %i E= % i')
aa.set_xlabel('# of trophic links')
aa.set_ylabel('CDF value')
aa.set_title(r'$n = %i$' % sampling_size[ss], fontsize=20)
if ss == 5:
plt.title('$n = \infty$', fontsize=20)
print 'no data, running again'
for nd,delta in enumerate(deltas):
y = eta**2/(2*gamma)
print nd, " of ",len(deltas)
for nphi,phi in enumerate(phis):
fun = lambda x: f(x,gamma,eta,phi,alpha,thetas,tau,delta)
y = opt.fsolve(fun, y)
epss[nd,nphi] = y/(eta**2/(2*gamma))
ls[nd,nphi] = lambdabar(thetas,gamma,eta,phi,alpha,tau,delta)
strings = [r'$\tau\delta$ = '+str(i*tau) for i in deltas]
#f = lambda i : plt.plot(alphas,epss[i,:],label=strings[i])
#map(f,range(len(deltas)))
fig, ax = ppl.subplots(1,figsize=(7,7))
ppl.plot(ls[0,:],epss[0,:],label = strings[0], ax=ax)
ppl.plot(ls[1,:],epss[1,:],label = strings[1], ax=ax)
ppl.plot(ls[2,:],epss[2,:],label = strings[2], ax=ax)
ppl.plot(ls[3,:],epss[3,:],label = strings[3], ax=ax)
ppl.plot(ls[4,:],epss[4,:],label = strings[4], ax=ax)
np.savez("../data/mean_field_ratedist.npz", eps = epss, ls=ls, phis = phis, deltas = deltas, tau = tau)
ax.set_xlim([0.0,25.0])
ax.set_xlabel(r'$\lambda$ in spikes/sec')
ax.set_ylabel(r'MMSE/$\epsilon_0$')
plt.title("Rate-Distortion Curve for Adaptive Neurons")
ppl.legend(ax)
plt.savefig("rate_distortion_mf_alpha.png")
plt.savefig("rate_distortion_mf_alpha.eps")
print "stringing"
string = [r'$\phi$ = %.2f' % float(i) for i in data[3]]
maxmmse = np.max(data[0])
def f(x):
line, = ppl.plot(data[2],data[0][:,x],label=string[x],axes=ax1)
return line.get_color()
def get_min(data):
minim = np.min(data)
indmin = np.argmin(data)
return minim,indmin
print "subplotting"
fig, ax1 = ppl.subplots(1)
ax1.set_title(r'Classification')
colors = map(f,range(data[3].size))
for i in range(data[3].size):
mi,ind = get_min(data[0][:,i])
ppl.plot(data[2][ind],mi,'o',color=colors[i],axes=ax1)
ppl.legend(ax1)
ax1.set_xlabel(r'$\alpha$')
ax1.set_ylabel(r'MMSE')
plt.savefig("mmse_classification.eps")
def generate_plot(data, plot_options):
# Define some fonts
title_font = {
'fontname': 'Calibri',
'size': '14',
'color': 'black',
'weight': 'bold'
}
axis_font = {'fontname': 'Calibri', 'size': '12', 'weight': 'bold'}
tick_font = {'axis': 'both', 'labelsize': 8, 'width': 1, 'color': 'k'}
# Get all the plot options
plot_format = plot_options['plot_format']
plot_layout = plot_options['plot_layout']
plot_profile_id = plot_options['profileid']
events = plot_options['events']
width_in = plot_options['width_in']
use_scatter = plot_options['use_scatter']
plot_qaqc = plot_options['use_qaqc']
# Generate the plot figure and axes
if isinstance(data, dict):
width = data['width']
height = data['height']
is_timeseries = False
if "time" == data['x_field'][0]:
data['x']['time'] = num2date(
data['x']['time'],
units='seconds since 1900-01-01 00:00:00',
calendar='gregorian')
is_timeseries = True
else:
width = data[0]['width']
height = data[0]['height']
for idx, dataset in enumerate(data):
if "time" == dataset['x_field'][0]:
data[idx]['x']['time'] = num2date(
data[idx]['x']['time'],
units='seconds since 1900-01-01 00:00:00',
calendar='gregorian')
fig, ax = ppl.subplots(1, 1, figsize=(width, height))
# Calculate the hypotenuse to determine appropriate font sizes
hypot = np.sqrt(width**2 + height**2) - 4
tick_font['labelsize'] = int(hypot)
axis_font['size'] = int(hypot) + 3
title_font['size'] = int(hypot) + 5
# Check the plot type and generate the plot!
if plot_layout == "timeseries":
'''
Plot time series data
'''
current_app.logger.debug('Plotting Time Series')
# Define some plot parameters
kwargs = dict(linewidth=1.5,
alpha=0.7,
linestyle='None',
marker=".",
markersize=10,
markeredgecolor='k')
# First check if we have a multiple stream data
if isinstance(data, list):
current_app.logger.debug('Plotting Multiple Streams')
ooi_plots.plot_multiple_streams(fig,
ax,
data,
colors,
title_font=title_font,
axis_font=axis_font,
tick_font=tick_font,
width_in=width_in,
plot_qaqc=plot_qaqc,
**kwargs)
# Check for a single time series plot
elif len(data['x_field']) == 1 and len(data['y_field']) == 1:
xlabel = data['x_field'][0]
ylabel = data['y_field'][0]
x = data['x'][xlabel]
y = data['y'][ylabel]
# QAQC logic
if plot_qaqc >= 10:
# Plot all of the qaqc flags results
qaqc_data = data['qaqc'][ylabel]
elif plot_qaqc >= 1:
# This is a case where the user wants to plot just one of the 9 QAQC tests
ind = np.where(data['qaqc'][ylabel] != plot_qaqc)
data['qaqc'][ylabel][ind] = 0
qaqc_data = data['qaqc'][ylabel]
else:
qaqc_data = []
ooi_plots.plot_time_series(fig,
is_timeseries,
ax,
x,
y,
title=data['title'],
xlabel=xlabel,
ylabel=ylabel + " (" +
data['y_units'][0] + ")",
title_font=title_font,
axis_font=axis_font,
tick_font=tick_font,
scatter=use_scatter,
events=events,
qaqc=qaqc_data,
**kwargs)
# Must be a multiple yaxes plot, single stream
else:
xdata = {}
xdata['time'] = data['x']['time']
ydata = data['y']
units = data['y_units']
for ind, key in enumerate(ydata):
xdata[key] = data['x']['time']
# QAQC logic
if plot_qaqc >= 10:
# Plot all of the qaqc flags results
pass
elif plot_qaqc >= 1:
# This is a case where the user wants to plot just one of the 9 QAQC tests
ind = np.where(data['qaqc'][key] != plot_qaqc)
data['qaqc'][key][ind] = 0
else:
data['qaqc'][key] = []
ooi_plots.plot_multiple_yaxes(fig,
ax,
xdata,
ydata,
colors,
title=data['title'],
units=units,
axis_font=axis_font,
title_font=title_font,
tick_font=tick_font,
scatter=use_scatter,
width_in=width_in,
qaqc=data['qaqc'],
**kwargs)
elif plot_layout == "depthprofile":
'''
Plot depth profiles (overlay)
'''
current_app.logger.debug('Plotting Depth Profile')
# Define some plot parameters
kwargs = dict(linewidth=1.5, alpha=0.7)
xlabel = data['x_field'] + " (" + request.args.get('x_units') + ")"
ylabel = data['y_field'] + " (" + request.args.get('y_units') + ")"
if plot_profile_id is None:
for profile_id in range(0, np.shape(data['x'])[0]):
# Remove the bad data
qaqc_data = data['qaqc'][data['x_field']]
ooi_plots.plot_profile(fig,
ax,
data['x'][profile_id],
data['y'][profile_id],
xlabel=xlabel,
ylabel=ylabel,
axis_font=axis_font,
tick_font=tick_font,
scatter=use_scatter,
**kwargs)
else:
if int(plot_profile_id) < int(np.shape(data['x'])[0]):
# get the profile selected
ooi_plots.plot_profile(fig,
ax,
data['x'][int(plot_profile_id)],
data['y'][int(plot_profile_id)],
xlabel=xlabel,
ylabel=ylabel,
axis_font=axis_font,
tick_font=tick_font,
scatter=use_scatter,
**kwargs)
else:
# return something
ooi_plots.plot_profile(fig,
ax,
data['x'][0],
data['y'][0],
xlabel=xlabel,
ylabel=ylabel,
axis_font=axis_font,
tick_font=tick_font,
scatter=use_scatter,
**kwargs)
plt.gca().invert_yaxis()
elif plot_layout == 'ts_diagram':
'''
Plot a Temperature-Salinity diagram
'''
current_app.logger.debug('Plotting T-S Diagram')
# Define some plot parameters
kwargs = dict(color='r', marker='o')
# This should be used with 'real' data only (NO COUNTS!!)
x = data['y'][data['y_field'][0]]
y = data['y'][data['y_field'][1]]
xlabel = data['y_field'][0] + " (" + data['y_units'][0] + ")"
ylabel = data['y_field'][1] + " (" + data['y_units'][1] + ")"
# # Mask the bad data
# qaqc_x = data['qaqc'][data['y_field'][0]] < 1
# qaqc_y = data['qaqc'][data['y_field'][1]] < 1
# mask = qaqc_x & qaqc_y
# x = x[mask]
# y = x[mask]
# if len(x) <= 0:
# raise(Exception('No good data avaliable!'))
ooi_plots.plot_ts_diagram(ax,
x,
y,
title=data['title'],
xlabel=xlabel,
ylabel=ylabel,
title_font=title_font,
axis_font=axis_font,
tick_font=tick_font,
**kwargs)
elif plot_layout == 'quiver':
'''
Plot magnitude and direction as a time series on a quiver plot
'''
current_app.logger.debug('Plotting Quiver')
# color='#0000FF',
# edgecolors='#000000',
kwargs = dict(units='y',
scale_units='y',
scale=1,
headlength=5,
headaxislength=5,
width=0.025,
alpha=0.5)
time = mdates.date2num(data['x']['time'])
start = plot_options['st_date']
end = plot_options['ed_date']
start_dt = mdates.date2num(
datetime.strptime(start.split('.')[0], '%Y-%m-%dT%H:%M:%S'))
end_dt = mdates.date2num(
datetime.strptime(end.split('.')[0], '%Y-%m-%dT%H:%M:%S'))
u = data['y'][data['y_field'][0]]
v = data['y'][data['y_field'][1]]
ylabel = data['y_field'][0] + " (" + data['y_units'][0] + ")"
# # Mask the bad data
# qaqc_u = data['qaqc'][data['y_field'][0]] < 1
# qaqc_v = data['qaqc'][data['y_field'][1]] < 1
# mask = qaqc_u & qaqc_v
# u = u[mask]
# v = v[mask]
# time = time[mask]
# if len(u) <= 0:
# raise(Exception('No good data avaliable!'))
ooi_plots.plot_1d_quiver(fig,
ax,
time,
u,
v,
title=data['title'] + '\n' + 'Quiver Plot',
ylabel=ylabel,
tick_font=tick_font,
title_font=title_font,
axis_font=axis_font,
start=start_dt,
end=end_dt,
**kwargs)
elif plot_layout == '3d_scatter':
'''
Plot 3d scatter plot
'''
current_app.logger.debug('Plotting 3D Scatter')
time = data['x']['time']
xlabel = data['y_field'][0]
ylabel = data['y_field'][1]
zlabel = data['y_field'][2]
x = data['y'][xlabel]
y = data['y'][ylabel]
z = data['y'][zlabel]
# # Mask the bad data
# qaqc_x = data['qaqc'][xlabel] < 1
# qaqc_y = data['qaqc'][ylabel] < 1
# qaqc_z = data['qaqc'][zlabel] < 1
# mask = qaqc_x & qaqc_y & qaqc_z
# x = x[mask]
# y = x[mask]
# if len(x) <= 0:
# raise(Exception('No good data avaliable!'))
ooi_plots.plot_3d_scatter(
fig,
ax,
x,
y,
z,
title=data['title'] + '\n' + '3D Scatter',
xlabel=xlabel + " (" + data['y_units'][0] + ")",
ylabel=ylabel + " (" + data['y_units'][1] + ")",
zlabel=zlabel + " (" + data['y_units'][2] + ")",
title_font=title_font,
tick_font=tick_font,
axis_font=axis_font)
elif plot_layout == 'rose':
'''
Plot rose
'''
plt.close(fig) # Need to create new fig and axes here
current_app.logger.debug('Plotting Rose')
xlabel = data['y_field'][0]
ylabel = data['y_field'][1]
magnitude = data['y'][xlabel]
direction = data['y'][ylabel]
# # Mask the bad data
# qaqc_mag = data['qaqc'][xlabel] < 1
# qaqc_dir = data['qaqc'][ylabel] < 1
# mask = qaqc_mag & qaqc_dir
# magnitude = magnitude[mask]
# direction = direction[mask]
# if len(magnitude) <= 0:
# raise(Exception('No good data avaliable!'))
legend_title = xlabel + " (" + data['y_units'][0] + ")"
size = height if height <= width else width
size = 6 if size < 6 else size
hypot = np.sqrt(size**2 + size**2) + 1
fig = ooi_plots.plot_rose(magnitude,
direction,
figsize=size,
bins=5,
title=data['title'],
title_font=title_font,
legend_title=legend_title,
fontsize=int(hypot) + 2)
buf = io.BytesIO()
# plt.tick_params(axis='both', which='major', labelsize=10)
if plot_format not in ['svg', 'png']:
plot_format = 'svg'
plt.savefig(buf, format=plot_format)
buf.seek(0)
plt.close(fig)
return buf
'''
Plot a counting Poisson process
'''
import prettyplotlib as ppl
from prettyplotlib import plt
import numpy
T = 10.
dt = 0.001
fig, ax, = ppl.subplots(1,figsize=(6,5));
Ns = numpy.zeros((int(T/dt),3))
la = [0.5,1.0,2.]
for i in range(3):
for n in range(Ns.shape[0]):
if numpy.random.rand()<la[i]*dt:
Ns[n,i] = Ns[n-1,i]+1
else:
Ns[n,i] = Ns[n-1,i]
legends=[]
for i in la:
legends.append(r'$\lambda = %.2lf$'%i)
for i in range(3):
ppl.plot( numpy.arange(0.0,dt*Ns.shape[0],dt), Ns[:,i],label=legends[i], linewidth=1.5, ax=ax )
ppl.legend(ax,loc=2)
plt.savefig('../figures/figure_2_1.png',dpi=300)
plt.savefig('../figures/figure_2_1.pdf')
nlevels = len((contour_levels)) if len((contour_levels)) < 7 else 12
cmap = cm.jet
h = plt.contourf(freq, deg, spectrum, nlevels, cmap=cmap)
# h = plt.pcolor(freq, deg, spectrum)
# fig.colorbar(h, aspect=5)
ax.set_ylabel('Direction (deg)', **axis_font)
ax.set_xlabel('Frequency (Hz)', **axis_font)
ax.set_title('Contour 2D Spectrum', **title_font)
# <codecell>
# Now plot the wave heights
fig, ax = ppl.subplots(3, 1, figsize=(12, 18))
hs = nc.variables['hs'][:]
h = ppl.plot(ax[0], noutcdates, hs, linewidth=2.0, color='b')
ax[0].set_ylabel('Significant Wave Height (m)')
ax[0].set_title('Wave Height', **title_font)
# Now plot the wave period
tp = 1 / nc.variables['fp'][:]
h = ppl.plot(ax[1], noutcdates, tp, linewidth=2.0, color='r')
ax[1].set_ylabel('Peak Period (s)')
ax[1].set_title('Wave Period', **title_font)
# Now plot the wave direction
def plot_data_compare_2(interval1,data1_1, data1_2, data1_3,
interval2, data2_1, data2_2, data2_3,
interval3, data3_1, data3_2, data3_3,
interval_flight, data_flight,
names1, style, p_title1, names2, p_title2, names3, p_title3,dycoms):
close='all'
if ( style == 0):
fig, ax = ppl.subplots()
color_set = brewer2mpl.get_map('paired', 'qualitative', 12).mpl_colors
color_set_2 = brewer2mpl.get_map('PuBu', 'sequential', 9).mpl_colors
# ax.plot(interval1,data1_1, color='black', label=names1[1])
# ax.plot(interval1,data1_2, color='red', label=names1[2])
# ax.plot(interval1,data1_3, color='blue',linestyle='-', label=names1[3])
ax.plot(interval1,data1_3, color=color_set_2[4],linewidth=2, label=names1[3])
data2_1=data2_1*30/18 # add the scaling factor
data2_2=data2_2*30/18
if (dycoms ==0):
data2_3=data2_3*30/18*1.9
else:
data2_3=data2_3*30/18*2
# ax.plot(interval2,data2_1, color='black',linestyle='--', label=names2[1])
# ax.plot(interval2,data2_2, color='red',linestyle='--', label=names2[2])
ax.plot(interval2,data2_3, color=color_set_2[6],linewidth=2, label=names2[3])
if (dycoms==0):
data3_1=data3_1*50/18 # add the scaling factor
data3_2=data3_2*50/18
data3_3=data3_3*50/18*0.92
# ax.plot(interval3,data3_1, color='black',linestyle=':',linewidth=2, label=names3[1])
# ax.plot(interval3,data3_2, color='red',linestyle=':',linewidth=2, label=names3[2])
ax.plot(interval3,data3_3,color=color_set_2[8],linewidth=2, label=names3[3])
########################################################################
#playground
########################################################################
# print interval1[0:34]
# print data1_3[0:34]
# size=1000000
# dist_names = ['alpha', 'beta', 'arcsine',
# 'weibull_min', 'weibull_max', 'rayleigh']
#
# for dist_name in dist_names:
# dist = getattr(scipy.stats, dist_name)
# param = dist.fit(data1_3[23:34])
# pdf_fitted = dist.pdf(interval1[23:34], *param[:-2], loc=param[-2], scale=param[-1]) * size
# ax.plot(pdf_fitted, label=dist_name)
# plt.legend(loc='upper left')
# beg=26
# end=beg+34
# beg_l=beg+1
# end_l=beg_l+15
# mirror=np.zeros(50)
# dummy=np.zeros(16)
# dummy[0:15]=data3_3[beg_l:end_l]
# dummy=dummy[::-1]
# mirror[0:16]=dummy[0:16]
# mirror[16:50]=data3_3[beg:end]
# integral_data= np.zeros(1)
# for i in np.arange(23,len(data3_3)):
# integral_data=integral_data+data3_3[i]*0.05
# approx_var_1= np.zeros(1)
# approx_var_2= np.zeros(1)
# j=0
# for i in np.arange(21,len(data3_3)):
# if (approx_var_2 < 1 ):
# approx_var_1=approx_var_1+ data3_3[i]*0.05
# #approx_var_2=approx_var_1 -integral_data*2*0.3413
# approx_var_2=approx_var_1 -np.std(mirror)
# j=j+1
if (dycoms ==0):
log_data= np.zeros(100)
log_interval= np.zeros(100)
for i in np.arange(len(data3_3)):
if (data3_3[i] != 0):
log_data[i] = np.log(data3_3[i])
elif (data3_3[i] == 0):
log_data[i]=data3_3[i]
j=1
max_val=max(log_data)
max_val_pos=len(log_data)+1
for i in np.arange(len(log_data)):
if ( log_data[i] == max_val):
max_val_pos=i
if (i > max_val_pos):
if (log_data[i] <= 0):
end_pos=i
break
z = np.polyfit(interval3[24:end_pos+1], log_data[24:end_pos+1], 2)
print 'polyfit',z
p = np.poly1d(z)
#ax.plot(interval3[24:end_pos+1], np.exp(log_data[24:end_pos+1]), '.', color=color_set[4], label='Fiting points' )
mean=z[1]/(-2*z[0])
sigma_square=(1/(-2*z[0]))
sigma_extrapolation=0.8**2
a=-1/(2*sigma_extrapolation)
mean_extrapolation=z[1]/(-2*a)
x = np.linspace(0,mean+6,1000)
print 'mean and sigma_square', mean, sigma_square
#y= (z[2]+z[1]**2/(4*-z[0]))-(((x-mean)**2)/(2*sigma_square))
y=np.exp( (z[2]+z[1]**2/(4*-z[0]))-(((x-mean)**2)/(2*sigma_square)) )
y_extrapolation=np.exp( (z[2]+z[1]**2/(4*-z[0]))-(((x-mean)**2)/(2*sigma_extrapolation)) )
#STATISTICS
skewness=0
for o in np.arange(len(x)):
skewness=skewness +((x[0]-mean)/np.sqrt(sigma_square))**3
skewness=skewness/len(x)
print 'skewness', skewness
ax.plot(x,y , color=color_set[2], label='Gaussian fit', linestyle='--', linewidth=2)
ax.plot(x,y_extrapolation , color=color_set[5], label='Gaussian extrapolation t=60', linestyle='--', linewidth=2)
data_flight=data_flight *5*10**7
ax.plot(interval_flight,data_flight, color=color_set[9],linestyle='-', linewidth=3, label='Flight measurements')
else:
log_data= np.zeros(100)
log_interval= np.zeros(100)
for i in np.arange(len(data2_3)):
if (data2_3[i] != 0):
log_data[i] = np.log(data2_3[i])
elif (data2_3[i] == 0):
log_data[i]=data2_3[i]
j=1
max_val=max(log_data)
max_val_pos=len(log_data)+1
for i in np.arange(len(log_data)):
if ( log_data[i] == max_val):
max_val_pos=i
if (i > max_val_pos):
if (log_data[i] <= 0):
end_pos=i
break
z = np.polyfit(interval2[24:end_pos+1], log_data[24:end_pos+1], 2)
print 'polyfit',z
p = np.poly1d(z)
#ax.plot(interval3[24:end_pos+1], np.exp(log_data[24:end_pos+1]), '.', color=color_set[4], label='Fiting points' )
mean=z[1]/(-2*z[0])
sigma_square=(1/(-2*z[0]))
sigma_extrapolation=0.8**2
a=-1/(2*sigma_extrapolation)
mean_extrapolation=z[1]/(-2*a)
x = np.linspace(0,mean+6,1000)
print 'mean and sigma_square', mean, sigma_square
#y= (z[2]+z[1]**2/(4*-z[0]))-(((x-mean)**2)/(2*sigma_square))
y=np.exp( (z[2]+z[1]**2/(4*-z[0]))-(((x-mean)**2)/(2*sigma_square)) )
y_extrapolation=np.exp( (z[2]+z[1]**2/(4*-z[0]))-(((x-mean)**2)/(2*sigma_extrapolation)) )
ax.plot(x,y , color=color_set[3], label='Gaussian fit', linestyle='--', linewidth=2)
# ax.plot(x,y_extrapolation , color=color_set[5], label='Gaussian extrapolation t=60', linestyle='--', linewidth=2)
# ax.plot(interval1[beg-16:beg+34],mirror, color='red')
# mean = 1.24
# variance = 0.009
# variance_arb = 0.001
# sigma = np.sqrt(variance)
# x = np.linspace(-3,5,1000)
# y = mlab.normpdf(x,mean,sigma)
# y = y*20000*(variance_arb/variance)
# ax.plot(x,y, color='magenta')
########################################################################
#playground end
########################################################################
#axes = plt.gca()
#axes.grid(True)
ax.set_yscale('symlog')
#ax.set_title(p_title3)#,fontdict=font)
# if (dycoms ==0):
# plt.title(p_title3, fontsize=size_title)#,fontdict=font)
# else:
# plt.title(p_title2, fontsize=size_title)#,fontdict=font)
plt.xlabel('Liquid', fontsize=size_axes)#,fontdict=font) #das r hier markiert, dass jetz latex code kommt
plt.ylabel('Amount of particles', fontsize=size_axes)#,fontdict=font)
plt.xlim(0,4)
plt.ylim([10,10**8])
plt.legend(loc=1, frameon=False)
plt.show()
transform = np.linalg.eig(solver.H)[1] # utils.sorted_eig(solver.H)[1] #
rearranged_basis_transform = np.zeros(transform.shape)
for basis_vec in transform.T:
if np.count_nonzero(basis_vec) == 1:
rearranged_basis_transform[:,2] = basis_vec
elif np.all(basis_vec >= 0) or np.all(basis_vec <= 0):
rearranged_basis_transform[:,1] = basis_vec
else:
rearranged_basis_transform[:,0] = basis_vec
site_exciton_transform[j,i] = rearranged_basis_transform
exciton_steady_states[j,i] = np.dot(rearranged_basis_transform.T, np.dot(ss, rearranged_basis_transform))
fig,ax = ppl.subplots(ncols=2, nrows=2, figsize=(8,7))
lw = 2
# Fano factor
plt.sca(ax[0,0])
ax[0,0].axhline(1, ls='--', color='grey')
ax[0,0].text(-10, 1.25, 'a.')
for i in range(T_c_values.size):
ppl.plot(bias_values, F2[i], linewidth=lw, label=r'$T_c = $' + str(T_c_values[i]), show_ticks=True)
ax[0,0].set_xlabel(r'$\epsilon / \Gamma_L$')
ax[0,0].set_ylabel('Fano factor')
#ax.set_ylim(0.3, 1.35)
ppl.legend(fontsize=10).draggable()
# coherence
plt.sca(ax[0,1])
def plot_fitting(interval3, data3_1, data3_2, data3_3):
#COLORS FOR FIGURES
fig, ax = ppl.subplots()
color_set = brewer2mpl.get_map('paired', 'qualitative', 12).mpl_colors
color_set_2 = brewer2mpl.get_map('PuBu', 'sequential', 9).mpl_colors
#SCALE DATA
data3_3=data3_3*50/18*0.92
#ax.plot(interval3,data3_3,color=color_set_2[8],linewidth=l_w_1, label=names3[3])
#!!!!!!!!!!!!!
#Fitting
#!!!!!!!!!!!!!
log_data= np.zeros(100)
log_interval= np.zeros(100)
for i in np.arange(len(data3_3)):
if (data3_3[i] != 0):
log_data[i] = np.log(data3_3[i])
elif (data3_3[i] == 0):
log_data[i]=data3_3[i]
j=1
max_val=max(log_data)
max_val_pos=len(log_data)+1
for i in np.arange(len(log_data)):
if ( log_data[i] == max_val):
max_val_pos=i
if (i > max_val_pos):
if (log_data[i] <= 0):
end_pos=i
break
begin_pos=s_point
print 'mean is set to', interval3[begin_pos]
z = np.polyfit(interval3[begin_pos:end_pos+1], log_data[begin_pos:end_pos+1], 2)
print 'polyfit',z
p = np.poly1d(z)
ax.plot(interval3[begin_pos:end_pos+1], log_data[begin_pos:end_pos+1], marker='D', color=color_set[5], label='Fitting points' )
ax.plot(interval3, log_data, '-', color=color_set_2[8],linewidth=l_w_1, label='Lagrange' )
mean=z[1]/(-2*z[0])
sigma_square=(1/(-2*z[0]))
#!!!!!!!!
#VERDI CAREFULL OLD VALUE FOR SIGAM EXTRAPOLATION
#!!!!!!!!
sigma_extrapolation=0.8**2
a=-1/(2*sigma_extrapolation)
mean_extrapolation=z[1]/(-2*a)
x = np.linspace(0,mean+6,1000)
print 'mean and sigma_square', mean, sigma_square
y_log=( (z[2]+z[1]**2/(4*-z[0]))-(((x-mean)**2)/(2*sigma_square)) )
y=np.exp( (z[2]+z[1]**2/(4*-z[0]))-(((x-mean)**2)/(2*sigma_square)) )
y_extrapolation=np.exp( (z[2]+z[1]**2/(4*-z[0]))-(((x-mean)**2)/(2*sigma_extrapolation)) )
ax.plot(x,y_log , color=color_set[2], label='Gaussian fit', linestyle='--', linewidth=l_w_2)
#PLOT PROPERTIES
plt.xlabel(r'$M/M_r$', fontsize=size_axes)#,fontdict=font) #das r hier markiert, dass jetz latex code kommt
plt.ylabel('Amount of cloud droplets', fontsize=size_axes)#,fontdict=font)
plt.legend(loc=1, frameon=False)
plt.xlim(0,4)
plt.ylim(0,20)
plt.show()
while dist > 1e-10:
matern0 = matern1
matern1 = IterateOnce(matern0, K_matern, dx,alpha =0.1)
dist = SquaredDistance(matern0,matern1)
print dist
OU0 = K_ou(xs)
OU1 = IterateOnce(OU0, K_ou, dx,alpha=0.1)
dist = SquaredDistance(OU0,OU1)
while dist > 1e-10:
OU0 = OU1
OU1 = IterateOnce(OU0, K_ou, dx,alpha =0.1)
dist = SquaredDistance(OU0,OU1)
print dist
fig, (ax1,ax2,ax3) = ppl.subplots(3,1)
#ax1 = plt.subplot(311)
#ax2 = plt.subplot(312)
#ax3 = plt.subplot(313)
plt.gcf().suptitle("Posterior kernels")
xs = xs[:6000]
OU1 = OU1[:6000]
matern1 = matern1[:6000]
rbf1 = rbf1[:6000]
ppl.plot(xs,K_ou(xs),label=r'OU prior kernel',ax=ax1)
ppl.plot(xs,OU1,label = r'OU posterior kernel',ax=ax1)
ppl.legend(ax1)
ppl.plot(xs,K_matern(xs),label=r'Matern prior kernel',ax=ax2)
ppl.plot(xs,matern1, label=r'Matern posterior kernel',ax=ax2)
avg3_time=avg3_time*50/60
avg5_time=avg5_time*50/60
avg6_time=avg6_time*50/60
else:
data_name='profile/dy_re800_general_avg_5s.dat'
avg5_x, avg5_y = np.loadtxt(data_name, unpack=True)
data_name='profile/dy_re800_general_avg_6s.dat'
avg6_x, avg6_y = np.loadtxt(data_name, unpack=True)
########################################################################
#PLOT
########################################################################
fig, ax = ppl.subplots()
color_set = brewer2mpl.get_map('paired', 'qualitative', 12).mpl_colors
color_set_2 = brewer2mpl.get_map('PuBu', 'sequential', 9).mpl_colors
if (diff == 1):
ax.plot(avg5_time,avg5_value , color=color_set[3], label='Parcel Liquid', linestyle='-',linewidth=l_w_1)
ax.plot(avg6_time,avg6_value , color=color_set[5], label='Droplet Liquid', linestyle='-',linewidth=l_w_1)
else:
avg5_x[:]=avg5_x[:]/np.max(avg5_x)
avg6_x[:]=avg6_x[:]/np.max(avg6_x)
ax.plot(avg5_y,avg5_x, color=color_set[3], label='Parcel Liquid', linestyle='-',linewidth=l_w_1)
ax.plot(avg6_y,avg6_x , color=color_set[5], label='Droplet Liquid', linestyle='-',linewidth=l_w_1)
#plt.xlim(0.9,2.6)
mean = data['mean']
from scipy.interpolate import interp1d
int_bias_values = np.linspace(-20,20,2000)
int_F2 = interp1d(bias_values, F2, kind='cubic')
data_mode = np.load('../../data/DQD_exact_mode_mean_F2_data.npz')
bias_values_mode = data_mode['bias_values']
F2_mode = data_mode['F2']
mean_mode = data_mode['mean']
# temp_data = np.load('../../data/DQD_HEOM_mean_F2_bias_strong_coupling_UBO_N7_K3_data_temp.npz')
# temp_F2 = temp_data['F2'][2]
# temp_bias_values = temp_data['bias_values']
fig,ax = ppl.subplots(1, 2, figsize=(8,3.5))
lw = 2
plt.sca(ax[0])
plt.text(-20, 0.0184, 'a.')
ppl.plot(bias_values, mean, linewidth=lw, label='non-perturbative', show_ticks=True)
ppl.plot(bias_values_mode, mean_mode[0,0], linewidth=lw, label='coherent mode', show_ticks=True)
ax[0].set_xlabel(r'$\epsilon / \Gamma_L$')
ax[0].set_ylabel(r'current / e')
ppl.legend(fontsize=10).draggable()
plt.sca(ax[1])
plt.text(-20, 0.98, 'b.')
ppl.plot(bias_values, F2, linewidth=lw, label='non-perturbative', show_ticks=True)
ppl.plot(bias_values_mode, F2_mode[0,0], linewidth=lw, label='coherent mode', show_ticks=True)
def plot_data_compare_2(interval1,data1_1, data1_2, data1_3,
interval2, data2_1, data2_2, data2_3,
interval3, data3_1, data3_2, data3_3,
interval_flight, data_flight,
names1, style, p_title1, names2, p_title2, names3, p_title3,
dycoms,extrapolation,extrapolation_2 , fitting,flight, only_last, s_point):
close='all'
if ( style == 0):
#COLORS FOR PLOTTING
fig, ax = ppl.subplots()
color_set = brewer2mpl.get_map('paired', 'qualitative', 12).mpl_colors
color_set_2 = brewer2mpl.get_map('PuBu', 'sequential', 9).mpl_colors
#color_set_2 = brewer2mpl.get_map('Reds', 'sequential', 9).mpl_colors
color_set_3 = brewer2mpl.get_map('Dark2', 'qualitative', 8).mpl_colors
#!!!!!!!!!!!!!
#General
#!!!!!!!!!!!!!
# ax.plot(interval1,data1_1, color='black', linewidth=l_w_1,label=names1[1])
# ax.plot(interval1,data1_2, color='red', linewidth=l_w_1,label=names1[2])
# ax.plot(interval1,data1_3, color='blue',linestyle='-', label=names1[3])
if (only_last !=1):
#ax.plot(interval1,data1_3, color=color_set_2[8],linewidth=l_w_1, label=names1[3])
ax.plot(interval1,data1_3, color=color_set[1],linewidth=l_w_1, label=names1[3])
if (dycoms ==0):
data2_1=data2_1*30/18*1.3
data2_2=data2_2*30/18*1.3
data2_3=data2_3*30/18*1.3
else:
data2_1=data2_1*30/18*1.75
data2_2=data2_2*30/18*1.75
data2_3=data2_3*30/18*1.75
# ax.plot(interval2,data2_1, color='red',linestyle='-', linewidth=l_w_1,label=names2[1])
# ax.plot(interval2,data2_2, color='black',linestyle='-', linewidth=l_w_1, label=names2[2])
if (dycoms==1):
if (only_last !=1):
#ax.plot(interval2,data2_3, color=color_set_2[6],linewidth=l_w_1, label=names2[3])
ax.plot(interval2,data2_3, color=color_set[3],linewidth=l_w_1, label=names2[3])
else:
ax.plot(interval2,data2_3, color=color_set[1],linewidth=l_w_1, label=names2[3])
else:
# ax.plot(interval2,data2_3, color=color_set_2[6],linewidth=l_w_1, label=names2[3])
if (only_last !=1):
ax.plot(interval2,data2_3, color=color_set[3],linewidth=l_w_1, label=names2[3])
if (dycoms==0):
data3_1=data3_1*50/18*1.4 # add the scaling factor
data3_2=data3_2*50/18*1.4
data3_3=data3_3*50/18*1.4
#ax.plot(interval3,data3_1, color='black',linestyle='-',linewidth=l_w_1, label=names3[1])
#ax.plot(interval3,data3_2, color='red',linestyle='-',linewidth=l_w_1, label=names3[2])
#ax.plot(interval3,data3_3,color=color_set_2[4],linewidth=l_w_1, label=names3[3])
if (only_last !=1):
ax.plot(interval3,data3_3,color=color_set[5],linewidth=l_w_1, label=names3[3])
else:
if( flight ==0):
ax.plot(interval3,data3_3,color=color_set[1],linewidth=l_w_1, label=names3[3])
#!!!!!!!!!!!!!
#Fitting
#!!!!!!!!!!!!!
if (dycoms ==0):
log_data= np.zeros(100)
log_interval= np.zeros(100)
for i in np.arange(len(data3_3)):
if (data3_3[i] != 0):
log_data[i] = np.log(data3_3[i])
elif (data3_3[i] == 0):
log_data[i]=data3_3[i]
j=1
max_val=max(log_data)
max_val_pos=len(log_data)+1
for i in np.arange(len(log_data)):
if ( log_data[i] == max_val):
max_val_pos=i
if (i > max_val_pos):
if (log_data[i] <= 0):
end_pos=i
break
begin_pos=s_point
print 'mean is set to', interval3[begin_pos]
print 'end_pos', end_pos
z = np.polyfit(interval3[begin_pos:end_pos+1], log_data[begin_pos:end_pos+1], 2)
print 'polyfit',z
p = np.poly1d(z)
#ax.plot(interval3[begin_pos:end_pos+1], np.exp(log_data[begin_pos:end_pos+1]), '.', color=color_set[5],linewidth=l_w_1, label='Fitting points' )
mean=z[1]/(-2*z[0])
sigma_square=(1/(-2*z[0]))
#VERDI
if( flight ==0):
sigma_extrapolation=0.545**2 #after 40 min
else:
#sigma_extrapolation=0.764**2 #after 50 min
sigma_extrapolation=0.74**2 #after 50 min
#sigma_extrapolation=0.91**2 #after 60 min
a=-1/(2*sigma_extrapolation)
mean_extrapolation=z[1]/(-2*a)
x = np.linspace(0,mean+6,1000)
print 'mean and sigma_square', mean, sigma_square
y_log=( (z[2]+z[1]**2/(4*-z[0]))-(((x-mean)**2)/(2*sigma_square)) )
y=np.exp( (z[2]+z[1]**2/(4*-z[0]))-(((x-mean)**2)/(2*sigma_square)) )
y_extrapolation=np.exp( (z[2]+z[1]**2/(4*-z[0]))-(((x-mean)**2)/(2*sigma_extrapolation)) )
if (flight != 1): #no fligh data
if (fitting == 1): #if we want fit
ax.plot(x,y , color=color_set_3[0], label='Gaussian fit', linestyle='--', linewidth=l_w_2)
if (extrapolation == 1): #if we want extrapolation
if( flight ==0): #if we want no fligh data
ax.plot(x,y_extrapolation , color=color_set[5], label='Extrapolation t=40', linestyle='--', linewidth=l_w_2)
if(extrapolation_2==1): #if we want second extrapolation
#extrapolation for all times
growing=[0.91, 1.80, 5.37, 10.7, 21.4]
for k in np.arange(len(growing)):
sigma_extrapolation=growing[k]**2
print sigma_extrapolation
x = np.linspace(0,mean+115,1000)
y_extrapolation=np.exp( (z[2]+z[1]**2/(4*-z[0]))-(((x-mean)**2)/(2*sigma_extrapolation)) )
ax.plot(x,y_extrapolation , color=color_set[k], linestyle='--',linewidth=l_w_2)
else: #if we want extrapolation with flight data together
ax.plot(x,y_extrapolation , color=color_set[5], label=r'Extrapolation t$\,\approx\,$50 [min]', linestyle='--', linewidth=l_w_2)
#SCALE FLIGHT DATA
data_flight=data_flight *5.5*10**7
if (flight == 1):
ax.plot(interval_flight,data_flight, color=color_set[9],marker='.',linestyle='None',markersize=m_s+9 , label='Flight measurements')
else: #if we want DYCOMS instead of verdi
log_data= np.zeros(100)
log_interval= np.zeros(100)
for i in np.arange(len(data2_3)):
if (data2_3[i] != 0):
log_data[i] = np.log(data2_3[i])
elif (data2_3[i] == 0):
log_data[i]=data2_3[i]
j=1
max_val=max(log_data)
max_val_pos=len(log_data)+1
for i in np.arange(len(log_data)):
if ( log_data[i] == max_val):
max_val_pos=i
if (i > max_val_pos):
if (log_data[i] <= 0):
end_pos=i
break
z = np.polyfit(interval2[s_point:end_pos+1], log_data[s_point:end_pos+1], 2)
print 'polyfit',z
p = np.poly1d(z)
#ax.plot(interval3[24:end_pos+1], np.exp(log_data[24:end_pos+1]), '.', color=color_set[4], label='Fiting points' )
mean=z[1]/(-2*z[0])
sigma_square=(1/(-2*z[0]))
#DYCOMS
sigma_extrapolation=0.525**2
a=-1/(2*sigma_extrapolation)
mean_extrapolation=z[1]/(-2*a)
print sigma_extrapolation
x = np.linspace(0,mean+6,1000)
print 'mean and sigma_square', mean, sigma_square
y=np.exp( (z[2]+z[1]**2/(4*-z[0]))-(((x-mean)**2)/(2*sigma_square)) )
y_extrapolation=np.exp( (z[2]+z[1]**2/(4*-z[0]))-(((x-mean)**2)/(2*sigma_extrapolation)) )
if (fitting == 1):
ax.plot(x,y , color=color_set_3[0], label='Gaussian fit', linestyle='--', linewidth=l_w_2)
if (extrapolation == 1):
ax.plot(x,y_extrapolation , color=color_set[5], label='Extrapolation t=40', linestyle='--',linewidth=l_w_2)
if(extrapolation_2==1):
#extrapolation for all times
growing=[0.76, 1.49, 4.4, 8.75, 17.4]
for k in np.arange(len(growing)):
sigma_extrapolation=growing[k]**2
print sigma_extrapolation
x = np.linspace(0,mean+100,1000)
y_extrapolation=np.exp( (z[2]+z[1]**2/(4*-z[0]))-(((x-mean)**2)/(2*sigma_extrapolation)) )
ax.plot(x,y_extrapolation , color=color_set[k], linestyle='--',linewidth=l_w_2)
#!!!!!!!!!!!!!
#Plotting
#!!!!!!!!!!!!!
#GRID LINES
#axes = plt.gca()
#axes.grid(True)
ax.set_yscale('symlog')
# if (dycoms ==0):
# plt.title(p_title3, fontsize=size_title)#,fontdict=font)
# else:
# plt.title(p_title2, fontsize=size_title)#,fontdict=font)
plt.xlabel(r'$M/M_r$', fontsize=size_axes)#,fontdict=font) #das r hier markiert, dass jetz latex code kommt
plt.ylabel('Relative amount of droplets', fontsize=size_axes)#,fontdict=font)
if (extrapolation == 1):
plt.xlim(0.9,5.2)
plt.xlim(0.8,4.5)
#plt.xlim(1.3,5.2)
if(extrapolation_2==1):
plt.xlim(0.9,115)
elif(flight == 1):
ax2 = ax.twiny()
ax2.set_xlabel(r"Radius [$\mu m$]", fontsize=size_axes)
tick_locs = [1,2,3,4,5]
tick_lbls = [10.3,13,14.9,16.4,17.7]
ax2.set_xticks(tick_locs)
ax2.set_xticklabels(tick_lbls)
#ax2.set_xlim(1.3,5.2)
ax2.set_xlim(0.8,4.5)
ax.legend(loc=0, frameon=False)
elif(fitting == 1):
plt.xlim(0.9,2.6)
else:
plt.xticks([0.1,0.5,1,1.5,2.0,2.5,3.0,3.5,4.0])
plt.xlim(0.1,2.2)
plt.ylim([10,10**8])
#plt.ylim([10**3,10**9])
#plt.xlim([0.9,3.0])
plt.legend(loc=1, frameon=False)
#Hide the right and top spines
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
#Only show ticks on the left and bottom spines
ax.yaxis.set_ticks_position('left')
ax.xaxis.set_ticks_position('bottom')
plt.show()
def several_fits(all_interval3, all_data3_3,
names3, p_title3, time_real_1,time_real_2,time_real_3,
delta_s,t_real, time_data, dycoms, fitting, s_point):
#SETUP COLORS FOR PLOTTING
fig, ax = ppl.subplots()
color_set = brewer2mpl.get_map('paired', 'qualitative', 12).mpl_colors
color_set_2 = brewer2mpl.get_map('PuBu', 'sequential', 9).mpl_colors
#color_set_2 = brewer2mpl.get_map('Reds', 'sequential', 9).mpl_colors
color_set_3 = brewer2mpl.get_map('Dark2', 'qualitative', 8).mpl_colors
#scale data
if (dycoms == 0):
all_data3_3=all_data3_3*50/18*0.92
else:
all_data3_3=all_data3_3*30/18*1.75
#DECIDE ABOUT PLOTTING STEPS
time_real_data=np.zeros(len(time_data))
for i in np.arange(len(time_data)):
time_real_data[i]=round(time_data[i]*t_real/60,2)
if (dycoms==0):
steps=[9, 14, 22, 35]
else:
steps=[10, 20, 28, 35]
c=1 #for color code
#CONSTRUCT DATA FROM WHERE TO FIT (TAIL) AND PLOT DIFFERENT STEPS
for k in steps:
label_name='t=' + str(time_real_data[k]) + ' [min]'
ax.plot(all_interval3[:,k],all_data3_3[:,k],color=color_set[c],linewidth=l_w_1, label=label_name)
log_data= np.zeros(100)
log_interval= np.zeros(100)
for i in np.arange(len(all_data3_3[:,k])):
if (all_data3_3[i,k] != 0):
log_data[i] = np.log(all_data3_3[i,k])
elif (all_data3_3[i,k] == 0):
log_data[i]=all_data3_3[i,k]
j=1
max_val=max(log_data)
max_val_pos=len(log_data)+1
for i in np.arange(len(log_data)):
if ( log_data[i] == max_val):
max_val_pos=i
if (i > max_val_pos):
if (log_data[i] <= 0):
end_pos=i
break
#CONSTRUCT FITS FOR SIMULATION STEPS
z = np.polyfit(all_interval3[s_point:end_pos+1,k], log_data[s_point:end_pos+1], 2)
print 'point where fitting starts', all_interval3[s_point,k], np.exp(log_data[s_point])
print 'polyfit',z
p = np.poly1d(z)
mean=z[1]/(-2*z[0])
sigma_square=(1/(-2*z[0]))
x = np.linspace(0,mean+6,1000)
print 'mean and sigma_square', mean, sigma_square
y=np.exp( (z[2]+z[1]**2/(4*-z[0]))-(((x-mean)**2)/(2*sigma_square)) )
print x[210]
ax.plot(x[0:len(x)],y[0:len(y)] , color=color_set[c], linestyle='--', linewidth=l_w_2)
c=c+2 #for color code
#PLOTTING PROPERTIES
ax.set_yscale('symlog')
plt.xlabel(r'$M/M_r$', fontsize=size_axes)#,fontdict=font) #das r hier markiert, dass jetz latex code kommt
plt.ylabel('Amount of cloud droplets', fontsize=size_axes)#,fontdict=font)
if(dycoms==0):
plt.xlim(1.4,2.7)
else:
plt.xlim(1.4,2.3)
plt.ylim([10,5*10**6])
plt.legend(loc=1, frameon=False)
plt.show()
transform = np.linalg.eig(H)[1]
rearranged_basis_transform = rearrange_transform(transform)
ss = site_steady_states[0,i]
ss.shape = 3,3
new_site_steady_states[0,i] = ss
exciton_steady_states[0,i] = np.dot(rearranged_basis_transform.T, np.dot(ss, rearranged_basis_transform))
for j,B in enumerate(beta_values):
rearranged_basis_transform = rearrange_transform(transform)
ss = site_steady_states[j+1,i]
ss.shape = 3,3
new_site_steady_states[j+1,i] = ss
exciton_steady_states[j+1,i] = np.dot(rearranged_basis_transform.T, np.dot(ss, rearranged_basis_transform))
fig,ax = ppl.subplots(2,2, figsize=(8,7))
lw = 2
plt.sca(ax[0,0])
plt.axhline(1, ls='--', color='grey')
ppl.plot(bias_values, F2[0], linewidth=lw, ls='--', color='k', show_ticks=True)
for i in range(1,4):
ppl.plot(bias_values, F2[i], linewidth=lw, label=r'$\beta = '+str(beta_values[i-1])+'$', show_ticks=True)
ppl.legend(fontsize=10).draggable()
ax[0,0].set_xlabel(r'$\epsilon / \Gamma_L$')
ax[0,0].set_ylabel(r'Fano factor')
plt.sca(ax[0,1])
ppl.plot(bias_values, np.abs(site_steady_states[0,:,5]), linewidth=lw, ls='--', color='k', show_ticks=True)
for i in range(1,4):
ppl.plot(bias_values, np.abs(site_steady_states[i,:,5]), linewidth=lw, label=r'$\beta = '+str(beta_values[i-1])+'$', show_ticks=True)
def generate_plot(data, plot_options):
# Define some fonts
title_font = {'fontname': 'Calibri',
'size': '14',
'color': 'black',
'weight': 'bold'}
axis_font = {'fontname': 'Calibri',
'size': '12',
'weight': 'bold'}
tick_font = {'axis': 'both',
'labelsize': 8,
'width': 1,
'color': 'k'}
# Get all the plot options
plot_format = plot_options['plot_format']
plot_layout = plot_options['plot_layout']
plot_profile_id = plot_options['profileid']
events = plot_options['events']
width_in = plot_options['width_in']
use_scatter = plot_options['use_scatter']
plot_qaqc = plot_options['use_qaqc']
# Generate the plot figure and axes
if isinstance(data, dict):
width = data['width']
height = data['height']
is_timeseries = False
if "time" == data['x_field'][0]:
data['x']['time'] = num2date(data['x']['time'], units='seconds since 1900-01-01 00:00:00', calendar='gregorian')
is_timeseries = True
else:
width = data[0]['width']
height = data[0]['height']
for idx, dataset in enumerate(data):
if "time" == dataset['x_field'][0]:
data[idx]['x']['time'] = num2date(data[idx]['x']['time'], units='seconds since 1900-01-01 00:00:00', calendar='gregorian')
fig, ax = ppl.subplots(1, 1, figsize=(width, height))
# Calculate the hypotenuse to determine appropriate font sizes
hypot = np.sqrt(width**2 + height**2) - 4
tick_font['labelsize'] = int(hypot)
axis_font['size'] = int(hypot) + 3
title_font['size'] = int(hypot) + 5
# Check the plot type and generate the plot!
if plot_layout == "timeseries":
'''
Plot time series data
'''
current_app.logger.debug('Plotting Time Series')
# Define some plot parameters
kwargs = dict(linewidth=1.5,
alpha=0.7,
linestyle='None',
marker=".",
markersize=10,
markeredgecolor='k')
# First check if we have a multiple stream data
if isinstance(data, list):
current_app.logger.debug('Plotting Multiple Streams')
ooi_plots.plot_multiple_streams(fig, ax, data, colors,
title_font=title_font,
axis_font=axis_font,
tick_font=tick_font,
width_in = width_in,
plot_qaqc=plot_qaqc,
**kwargs)
# Check for a single time series plot
elif len(data['x_field']) == 1 and len(data['y_field']) == 1:
xlabel = data['x_field'][0]
ylabel = data['y_field'][0]
x = data['x'][xlabel]
y = data['y'][ylabel]
# QAQC logic
if plot_qaqc >= 10:
# Plot all of the qaqc flags results
qaqc_data = data['qaqc'][ylabel]
elif plot_qaqc >= 1:
# This is a case where the user wants to plot just one of the 9 QAQC tests
ind = np.where(data['qaqc'][ylabel] != plot_qaqc)
data['qaqc'][ylabel][ind] = 0
qaqc_data = data['qaqc'][ylabel]
else:
qaqc_data = []
ooi_plots.plot_time_series(fig, is_timeseries, ax, x, y,
title=data['title'],
xlabel=xlabel,
ylabel=ylabel + " (" + data['y_units'][0] + ")",
title_font=title_font,
axis_font=axis_font,
tick_font=tick_font,
scatter=use_scatter,
events=events,
qaqc=qaqc_data,
**kwargs)
# Must be a multiple yaxes plot, single stream
else:
xdata = {}
xdata['time'] = data['x']['time']
ydata = data['y']
units = data['y_units']
for ind, key in enumerate(ydata):
xdata[key] = data['x']['time']
# QAQC logic
if plot_qaqc >= 10:
# Plot all of the qaqc flags results
pass
elif plot_qaqc >= 1:
# This is a case where the user wants to plot just one of the 9 QAQC tests
ind = np.where(data['qaqc'][key] != plot_qaqc)
data['qaqc'][key][ind] = 0
else:
data['qaqc'][key] = []
ooi_plots.plot_multiple_yaxes(fig, ax,
xdata,
ydata,
colors,
title=data['title'],
units=units,
axis_font=axis_font,
title_font=title_font,
tick_font=tick_font,
scatter = use_scatter,
width_in = width_in,
qaqc=data['qaqc'],
**kwargs)
elif plot_layout == "depthprofile":
'''
Plot depth profiles (overlay)
'''
current_app.logger.debug('Plotting Depth Profile')
# Define some plot parameters
kwargs = dict(linewidth=1.5, alpha=0.7)
xlabel = data['x_field'] + " (" + request.args.get('x_units') + ")"
ylabel = data['y_field'] + " (" + request.args.get('y_units') + ")"
if plot_profile_id is None:
for profile_id in range(0, np.shape(data['x'])[0]):
# Remove the bad data
qaqc_data = data['qaqc'][data['x_field']]
ooi_plots.plot_profile(fig,
ax,
data['x'][profile_id],
data['y'][profile_id],
xlabel=xlabel,
ylabel=ylabel,
axis_font=axis_font,
tick_font=tick_font,
scatter=use_scatter,
**kwargs)
else:
if int(plot_profile_id) < int(np.shape(data['x'])[0]):
# get the profile selected
ooi_plots.plot_profile(fig,
ax,
data['x'][int(plot_profile_id)],
data['y'][int(plot_profile_id)],
xlabel=xlabel,
ylabel=ylabel,
axis_font=axis_font,
tick_font=tick_font,
scatter=use_scatter,
**kwargs)
else:
# return something
ooi_plots.plot_profile(fig,
ax,
data['x'][0],
data['y'][0],
xlabel=xlabel,
ylabel=ylabel,
axis_font=axis_font,
tick_font=tick_font,
scatter=use_scatter,
**kwargs)
plt.gca().invert_yaxis()
elif plot_layout == 'ts_diagram':
'''
Plot a Temperature-Salinity diagram
'''
current_app.logger.debug('Plotting T-S Diagram')
# Define some plot parameters
kwargs = dict(color='r', marker='o')
# This should be used with 'real' data only (NO COUNTS!!)
x = data['y'][data['y_field'][0]]
y = data['y'][data['y_field'][1]]
xlabel = data['y_field'][0] + " (" + data['y_units'][0] + ")"
ylabel = data['y_field'][1] + " (" + data['y_units'][1] + ")"
# # Mask the bad data
# qaqc_x = data['qaqc'][data['y_field'][0]] < 1
# qaqc_y = data['qaqc'][data['y_field'][1]] < 1
# mask = qaqc_x & qaqc_y
# x = x[mask]
# y = x[mask]
# if len(x) <= 0:
# raise(Exception('No good data avaliable!'))
ooi_plots.plot_ts_diagram(ax, x, y,
title=data['title'],
xlabel=xlabel,
ylabel=ylabel,
title_font=title_font,
axis_font=axis_font,
tick_font=tick_font,
**kwargs)
elif plot_layout == 'quiver':
'''
Plot magnitude and direction as a time series on a quiver plot
'''
current_app.logger.debug('Plotting Quiver')
# color='#0000FF',
# edgecolors='#000000',
kwargs = dict(units='y',
scale_units='y',
scale=1,
headlength=1.0,
headaxislength=1.0,
width=0.002,
alpha=0.5)
time = mdates.date2num(data['x']['time'])
start = plot_options['st_date']
end = plot_options['ed_date']
start_dt = mdates.date2num(datetime.strptime(start.split('.')[0], '%Y-%m-%dT%H:%M:%S'))
end_dt = mdates.date2num(datetime.strptime(end.split('.')[0], '%Y-%m-%dT%H:%M:%S'))
u = np.array(data['y'][data['y_field'][0]])
v = np.array(data['y'][data['y_field'][1]])
ylabel = "Velocity" + " (" + data['y_units'][0] + ")"
# # Mask the bad data
# qaqc_u = data['qaqc'][data['y_field'][0]] < 1
# qaqc_v = data['qaqc'][data['y_field'][1]] < 1
# mask = qaqc_u & qaqc_v
# u = u[mask]
# v = v[mask]
# time = time[mask]
# if len(u) <= 0:
# raise(Exception('No good data avaliable!'))
ooi_plots.plot_1d_quiver(fig, ax, time, u, v,
title=data['title']+'\n'+'Quiver Plot',
ylabel=ylabel,
tick_font=tick_font,
title_font=title_font,
axis_font=axis_font,
start=start_dt,
end=end_dt,
key_units = data['y_units'][0],
**kwargs)
elif plot_layout == 'stacked':
'''
Plot colored stacked time series
'''
current_app.logger.debug('Plotting Stacked')
time = mdates.date2num(data['x']['time'])
z = np.array(data['y'][data['y_field'][0]])
label = data['y_field'][0] + " (" + data['y_units'][0] + ")"
ooi_plots.plot_stacked_time_series(fig, ax, time, np.arange(len(z[0]))[::-1], z.transpose(),
title=data['title']+'\n'+'Stacked Time Series Plot',
ylabel='bin',
cbar_title=label,
title_font=title_font,
axis_font=axis_font,
tick_font=tick_font)
elif plot_layout == '3d_scatter':
'''
Plot 3d scatter plot
'''
current_app.logger.debug('Plotting 3D Scatter')
time = data['x']['time']
xlabel = data['y_field'][0]
ylabel = data['y_field'][1]
zlabel = data['y_field'][2]
x = np.array(data['y'][xlabel])
y = np.array(data['y'][ylabel])
z = np.array(data['y'][zlabel])
# # Mask the bad data
# qaqc_x = data['qaqc'][xlabel] < 1
# qaqc_y = data['qaqc'][ylabel] < 1
# qaqc_z = data['qaqc'][zlabel] < 1
# mask = qaqc_x & qaqc_y & qaqc_z
# x = x[mask]
# y = x[mask]
# if len(x) <= 0:
# raise(Exception('No good data avaliable!'))
ooi_plots.plot_3d_scatter(fig, ax, x, y, z,
title=data['title']+'\n'+'3D Scatter',
xlabel=xlabel + " (" + data['y_units'][0] + ")",
ylabel=ylabel + " (" + data['y_units'][1] + ")",
zlabel=zlabel + " (" + data['y_units'][2] + ")",
title_font=title_font,
tick_font=tick_font,
axis_font=axis_font)
elif plot_layout == 'rose':
'''
Plot rose
'''
plt.close(fig) # Need to create new fig and axes here
current_app.logger.debug('Plotting Rose')
xlabel = data['y_field'][0]
ylabel = data['y_field'][1]
magnitude = np.array(data['y'][xlabel])
direction = np.array(data['y'][ylabel])
# # Mask the bad data
# qaqc_mag = data['qaqc'][xlabel] < 1
# qaqc_dir = data['qaqc'][ylabel] < 1
# mask = qaqc_mag & qaqc_dir
# magnitude = magnitude[mask]
# direction = direction[mask]
# if len(magnitude) <= 0:
# raise(Exception('No good data avaliable!'))
legend_title = xlabel + " (" + data['y_units'][0] + ")"
size = height if height <= width else width
size = 6 if size < 6 else size
hypot = np.sqrt(size**2 + size**2) + 1
fig = ooi_plots.plot_rose(magnitude, direction,
figsize=size,
bins=5,
title=data['title'],
title_font=title_font,
legend_title=legend_title,
fontsize=int(hypot)+2)
buf = io.BytesIO()
# plt.tick_params(axis='both', which='major', labelsize=10)
if plot_format not in ['svg', 'png']:
plot_format = 'svg'
plt.savefig(buf, format=plot_format)
buf.seek(0)
plt.close(fig)
return buf
def make_hists(self, num_bins=None, use_prettyplotlib=True):
if use_prettyplotlib:
try:
import prettyplotlib as plt
except ImportError:
import matplotlib.pyplot as plt
use_prettyplotlib = False
print "prettyplotlib not installed. Using matplotlib..."
else:
import matplotlib.pyplot as plt
# Setup subplots if plotting together
if self.subplot:
num = len(self.columns)
if num <= 3:
ncols = 1
nrows = num
elif num <= 8:
ncols = 2
nrows = num / 2
else: # Max columns right now is 12
ncols = 3
nrows = num / 3
# Check if we need an extra row.
if num % ncols != 0:
nrows += 1
# Make the objects
fig, axes = plt.subplots(nrows=nrows, ncols=ncols)
# This is a bit awkward to get the indices, but my matplotlib version
# doesn't use the same object type as prettyplotlib creates.
posns = np.indices(axes.shape)
x, y = posns[0].ravel(), posns[1].ravel()
# Keep the mean, median, std.
data_stats = {}
for i, column in enumerate(self.columns):
data = self.dataframe[column]
data = data[np.isfinite(data)]
if num_bins is None:
num_bins = np.sqrt(len(data))
data_stats[column] = [nanmean(data), nanstd(data), nanmedian(data)]
if self.subplot:
if use_prettyplotlib:
plt.hist(axes[x[i], y[i]],data, num_bins, grid="y")
else:
axes[x[i], y[i]].hist(data, num_bins)
axes[x[i], y[i]].set_xlabel(column) # ADD UNITS!
else:
fig, axes = plt.subplots(1)
axes.hist(data, num_bins)
axes.set_xlabel(column) # ADD UNITS!
if self.verbose and not self.subplot:
print column+" Stats: %s" % (data_stats[column])
p.show()
elif not self.subplot:
fig.savefig(self.save_name+"_"+column+"."+self.save_type)
p.close()
if self.subplot:
p.tight_layout()
if self.verbose:
for column in self.columns:
print column+" Stats: %s" % (data_stats[column])
p.show()
else:
fig.savefig(self.save_name+"_hists."+self.save_type)
def extrapolation_sigma(all_interval1, all_data1_3, all_interval2, all_data2_3,
all_interval3, all_data3_3,
names1,names2,names3, p_title3, time_real_1,time_real_2,time_real_3,
delta_s,t_real, dycoms, fitting, s_point):
sp=5 #part of determination of first timestep of sigma calculation
fig, ax = ppl.subplots()
color_set = brewer2mpl.get_map('paired', 'qualitative', 12).mpl_colors
#WE WILL ANALYZE THE DIFFERENT RE-NUMBER RUNS
#DATA RE800
if (dycoms == 1):
cf=5
else:
cf=3
shape= np.shape(all_interval1)
time_length=shape[1]
data1_3= np.zeros(100)
interval1= np.zeros(100)
sigma_vector_1= np.zeros(time_length)
for k in np.arange(sp+cf,time_length):
data1_3[:] = all_data1_3[:,k]
interval1[:] = all_interval1[:,k]
log_data= np.zeros(100)
for i in np.arange(len(data1_3)):
if (data1_3[i] != 0):
log_data[i] = np.log(data1_3[i])
elif (data1_3[i] == 0):
log_data[i]=data1_3[i]
j=1
max_val=max(log_data)
max_val_pos=len(log_data)+1
for i in np.arange(len(log_data)):
if ( log_data[i] == max_val):
max_val_pos=i
if (i > max_val_pos):
if (log_data[i] <= 0):
end_pos=i
break
z = np.polyfit(interval1[s_point:end_pos+1], log_data[s_point:end_pos+1], 2)
sigma_square=(1/(-2*z[0]))
sigma_vector_1[k]= np.sqrt(sigma_square)
sigma_fit_1 = np.polyfit(time_real_1[sp+cf:time_length], sigma_vector_1[sp+cf:time_length], 1)
ax.plot(time_real_1[sp+cf:time_length],sigma_vector_1[sp+cf:time_length] , color=color_set[1],marker='.',
linestyle='None',markersize=m_s , label=names1[3])
x_fit = np.linspace(0,60,1000)
p = sigma_fit_1[0]*x_fit + sigma_fit_1[1]
# if (dycoms != 0):
# ax.plot(x_fit,p ,linestyle='--', color=color_set[2], label='Fiting', linewidth=l_w_1)
#DATA RE400
cf=3
if (dycoms == 1):
cf=5
shape= np.shape(all_interval2)
time_length=shape[1]
time_length_re400=shape[1]
mean_fits400=np.zeros(50)
data2_3= np.zeros(100)
interval2= np.zeros(100)
sigma_vector_2= np.zeros(time_length)
for k in np.arange(sp+cf,time_length):
data2_3[:] = all_data2_3[:,k]
interval2[:] = all_interval2[:,k]
log_data= np.zeros(100)
for i in np.arange(len(data2_3)):
if (data2_3[i] != 0):
log_data[i] = np.log(data2_3[i])
elif (data2_3[i] == 0):
log_data[i]=data2_3[i]
j=1
max_val=max(log_data)
max_val_pos=len(log_data)+1
for i in np.arange(len(log_data)):
if ( log_data[i] == max_val):
max_val_pos=i
if (i > max_val_pos):
if (log_data[i] <= 0):
end_pos=i
break
mean_fits400[k]=log_data[s_point]
z = np.polyfit(interval2[s_point:end_pos+1], log_data[s_point:end_pos+1], 2)
sigma_square=(1/(-2*z[0]))
sigma_vector_2[k]= np.sqrt(sigma_square)
#print sigma_square, z[0]
sigma_fit_2 = np.polyfit(time_real_2[sp+cf:time_length], sigma_vector_2[sp+cf:time_length], 1)
ax.plot(time_real_2[sp+cf:time_length],sigma_vector_2[sp+cf:time_length] , color=color_set[3],marker='.',linestyle='None',markersize=m_s, label=names2[3])
if (dycoms != 0):
#LIQUID AFTER SPECIFIED TIME
#1 hour
x_fit = np.linspace(0,60,10000)
p = sigma_fit_2[0]*x_fit + sigma_fit_2[1]
print 'sigma at x=60: ', p[len(p)-1]
#2 hour
x_fit = np.linspace(0,120,10000)
p = sigma_fit_2[0]*x_fit + sigma_fit_2[1]
print 'sigma at x=120: ', p[len(p)-1]
#6 hour
x_fit = np.linspace(0,360,10000)
p = sigma_fit_2[0]*x_fit + sigma_fit_2[1]
print 'sigma at x=360: ', p[len(p)-1]
#12 hour
x_fit = np.linspace(0,720,10000)
p = sigma_fit_2[0]*x_fit + sigma_fit_2[1]
print 'sigma at x=720: ', p[len(p)-1]
#24 hour
x_fit = np.linspace(0,1440,10000)
p = sigma_fit_2[0]*x_fit + sigma_fit_2[1]
print 'sigma at x=1440: ', p[len(p)-1]
#48 hour
x_fit = np.linspace(0,2880,10000)
p = sigma_fit_2[0]*x_fit + sigma_fit_2[1]
print 'sigma at x=2880: ', p[len(p)-1]
x_fit = np.linspace(0,40,10000)
p = sigma_fit_2[0]*x_fit + sigma_fit_2[1]
print 'Verschiebung: ', sigma_fit_2[1], 'Steigung: ', sigma_fit_2[0]
if (dycoms != 0):
if (fitting == 1):
print 'sigma at x=40: ', p[len(p)-1]
ax.plot(x_fit,p ,linestyle='--', color=color_set[3], label='Fitting', linewidth=l_w_2)
if (dycoms == 0):
#DATA RE200
cf=0
shape= np.shape(all_interval3)
time_length=shape[1]
data3_3= np.zeros(100)
interval3= np.zeros(100)
sigma_vector= np.zeros(time_length)
#print sigma_vector[9]
c= np.zeros(time_length)
mean_fits200=np.zeros(50)
for k in np.arange(sp+cf,time_length):
data3_3[:] = all_data3_3[:,k]
interval3[:] = all_interval3[:,k]
log_data= np.zeros(100)
log_interval= np.zeros(100)
for i in np.arange(len(data3_3)):
if (data3_3[i] != 0):
log_data[i] = np.log(data3_3[i])
elif (data3_3[i] == 0):
log_data[i]=data3_3[i]
j=1
max_val=max(log_data)
max_val_pos=len(log_data)+1
for i in np.arange(len(log_data)):
if ( log_data[i] == max_val):
max_val_pos=i
if (i > max_val_pos):
if (log_data[i] <= 0):
end_pos=i
break
mean_fits200[k]=log_data[s_point]
z = np.polyfit(interval3[s_point:end_pos+1], log_data[s_point:end_pos+1], 2)
sigma_square=(1/(-2*z[0]))
sigma_vector[k]= np.sqrt(sigma_square)
sigma_fit = np.polyfit(time_real_3[sp+cf:time_length], sigma_vector[sp+cf:time_length], 1)
#print 'sigma_fit', sigma_fit
#LIQUID AFTER SPECIFIED TIME
#30 min
x_fit = np.linspace(0,30,10000)
p = sigma_fit_2[0]*x_fit + sigma_fit_2[1]
print 'sigma at x=30: ', p[len(p)-1]
#40 min
x_fit = np.linspace(0,40,10000)
p = sigma_fit_2[0]*x_fit + sigma_fit_2[1]
print 'sigma at x=40: ', p[len(p)-1]
#40 min
x_fit = np.linspace(0,41,10000)
p = sigma_fit_2[0]*x_fit + sigma_fit_2[1]
print 'sigma at x=41: ', p[len(p)-1]
#50 min
x_fit = np.linspace(0,50,10000)
p = sigma_fit_2[0]*x_fit + sigma_fit_2[1]
print 'sigma at x=50: ', p[len(p)-1]
#1 hour
x_fit = np.linspace(0,60,10000)
p = sigma_fit_2[0]*x_fit + sigma_fit_2[1]
print 'sigma at x=60: ', p[len(p)-1]
#2 hour
x_fit = np.linspace(0,120,10000)
p = sigma_fit_2[0]*x_fit + sigma_fit_2[1]
print 'sigma at x=120: ', p[len(p)-1]
#6 hour
x_fit = np.linspace(0,360,10000)
p = sigma_fit_2[0]*x_fit + sigma_fit_2[1]
print 'sigma at x=360: ', p[len(p)-1]
#12 hour
x_fit = np.linspace(0,720,10000)
p = sigma_fit_2[0]*x_fit + sigma_fit_2[1]
print 'sigma at x=720: ', p[len(p)-1]
#24 hour
x_fit = np.linspace(0,1440,10000)
p = sigma_fit_2[0]*x_fit + sigma_fit_2[1]
print 'sigma at x=1440: ', p[len(p)-1]
#48 hour
x_fit = np.linspace(0,2880,10000)
p = sigma_fit_2[0]*x_fit + sigma_fit_2[1]
print 'sigma at x=2880: ', p[len(p)-1]
x_fit = np.linspace(0,40,10000)
p = sigma_fit[0]*x_fit + sigma_fit[1]
print 'Verschiebung: ', sigma_fit[1], 'Steigung: ', sigma_fit[0]
ax.plot(time_real_3[sp+cf:time_length],sigma_vector[sp+cf:time_length] , color=color_set[5],marker='.',linestyle='None',markersize=m_s, label=names3[3])
if (fitting == 1):
print 'sigma at x=40: ', p[len(p)-1]
ax.plot(x_fit,p , color=color_set[5],linestyle='--', linewidth=l_w_2, label='Fitting')
#GENERAL PLOT SETTINGS
# if(dycoms == 0):
# plt.title(r'VERDI - Growth of $\sigma$ in time', fontsize=size_title)#,fontdict=font)
# else:
# plt.title(r'DYCOMS-II - Growth of $\sigma$ in time', fontsize=size_title)#,fontdict=font)
plt.xlabel('t [min]')#, fontsize=size_axes)#,fontdict=font) #das r hier markiert, dass jetz latex code kommt
plt.ylabel(r'\textbf{$\sigma$}', fontsize=90)#, fontsize=size_axes)#,fontdict=font)
#plt.xlim(0,40)
if (fitting == 1):
plt.ylim(0,0.35)
plt.xlim(0,25)
else:
plt.ylim(0,0.35)
plt.xlim(2,22)
plt.legend(loc=2, frameon=False)
plt.show()
plt.clf()
fig, ax = ppl.subplots()
if(dycoms == 0):
print 'after plot', time_real_3 , sp+cf, time_length
print 'first time step', time_real_3[sp+cf]
print mean_fits200
print 'first', mean_fits200[sp+cf], 'at point', sp+cf
if(dycoms == 0):
ax.plot(time_real_3[sp+cf:time_length],(mean_fits200[sp+cf:time_length]/mean_fits200[sp+cf]), color=color_set[5],linestyle='--',linewidth=l_w_2, label='Droplet number')
else:
ax.plot(time_real_2[sp+cf:time_length_re400],(mean_fits400[sp+cf:time_length_re400]/mean_fits400[sp+cf]), color=color_set[5],linestyle='--',linewidth=l_w_2, label='Droplet number')
plt.xlabel('t [min]')#, fontsize=size_axes)#,fontdict=font) #das r hier markiert, dass jetz latex code kommt
plt.ylabel(r'$N_{1.35}(t) / N_{1.35}(t_{0})$')#, fontsize=size_axes)#,fontdict=font)
#ax.set_yscale('symlog')
#plt.ylim(0.98,1.02)
#plt.legend(loc=1, frameon=False)
plt.show()
nlevels = len((contour_levels)) if len((contour_levels)) < 7 else 12
cmap = cm.jet
h = plt.contourf(freq, deg, spectrum, nlevels, cmap=cmap)
# h = plt.pcolor(freq, deg, spectrum)
# fig.colorbar(h, aspect=5)
ax.set_ylabel('Direction (deg)', **axis_font)
ax.set_xlabel('Frequency (Hz)', **axis_font)
ax.set_title('Contour 2D Spectrum', **title_font)
# <codecell>
# Now plot the wave heights
fig, ax = ppl.subplots(3, 1, figsize=(12, 18))
hs = nc.variables['hs'][:]
h = ppl.plot(ax[0], noutcdates, hs, linewidth=2.0, color='b')
ax[0].set_ylabel('Significant Wave Height (m)')
ax[0].set_title('Wave Height', **title_font)
# Now plot the wave period
tp = 1/nc.variables['fp'][:]
h = ppl.plot(ax[1], noutcdates, tp, linewidth=2.0, color='r')
ax[1].set_ylabel('Peak Period (s)')
ax[1].set_title('Wave Period', **title_font)
# Now plot the wave direction
def several_times_and_flight(all_interval3, all_data3_3,
names3, p_title3, time_real_1,time_real_2,time_real_3,
delta_s,t_real, time_data, dycoms, fitting, s_point,
interval_flight, data_flight,data3_3, interval3):
#setup colors
fig, ax1 = plt.subplots()
color_set = brewer2mpl.get_map('paired', 'qualitative', 12).mpl_colors
color_set_2 = brewer2mpl.get_map('PuBu', 'sequential', 9).mpl_colors
#color_set_2 = brewer2mpl.get_map('Reds', 'sequential', 9).mpl_colors
color_set_3 = brewer2mpl.get_map('Dark2', 'qualitative', 8).mpl_colors
shape= np.shape(all_interval3)
time_length=shape[1]
#scale data
all_data3_3=all_data3_3*50/18*0.92
########################################################################
#Plot the flight data
########################################################################
data_flight=data_flight*2.25*10**7#alberto
#data_flight=data_flight*5.0*10**7
#ax1.plot(interval_flight,data_flight, color=color_set[9],marker='.',linestyle='None',markersize=m_s+9 , label='Flight measurements')
#alberto
#ax1.plot(interval_flight,data_flight, color=color_set[9],marker='.',linestyle='None',markersize=m_s+9 )
ax1.plot(interval_flight,data_flight, color=color_set[9],marker='.',linestyle='None',markersize=m_s+9 , label='Flight measurements')
########################################################################
#Plot the data+fit
########################################################################
time_real_data=np.zeros(len(time_data))
for i in np.arange(len(time_data)):
time_real_data[i]=round(time_data[i]*t_real/60,2)
#print time_real_data
#steps=np.array([12,23, 35])
steps= np.arange(12,36)
print 'here steps', steps
c=1
l=0
b_new = np.zeros(len(steps))
a_new = np.zeros(len(steps))
sigma_square_new = np.zeros(len(steps))
x_max = np.zeros(len(steps))
for k in steps:
time_real_data[k]=round(time_real_data[k],0)
label_name='t=' + str(int(time_real_data[k])) + ' min'
#ax1.plot(all_interval3[:,k],all_data3_3[:,k],color=color_set[c],linewidth=l_w_1, label='Simulation ' + label_name)
#alberto
#ax1.plot(all_interval3[:,k],all_data3_3[:,k],color=color_set[c],linewidth=l_w_1)
if (k==12):
#ax1.plot(all_interval3[:,k],all_data3_3[:,k],color=color_set[1],linewidth=l_w_1)
ax1.plot(all_interval3[:,k],all_data3_3[:,k],color=color_set[1],linewidth=l_w_1, label='Simulation ' + label_name)
elif (k==35):
#ax1.plot(all_interval3[:,k],all_data3_3[:,k],color=color_set[1],linewidth=l_w_1)
ax1.plot(all_interval3[:,k],all_data3_3[:,k],color=color_set[1],linewidth=l_w_1, label='Simulation ' + label_name)
log_data= np.zeros(100)
log_interval= np.zeros(100)
for i in np.arange(len(all_data3_3[:,k])):
if (all_data3_3[i,k] != 0):
log_data[i] = np.log(all_data3_3[i,k])
elif (all_data3_3[i,k] == 0):
log_data[i]=all_data3_3[i,k]
j=1
max_val=max(log_data)
max_val_pos=len(log_data)+1
for i in np.arange(len(log_data)):
if ( log_data[i] == max_val):
max_val_pos=i
if (i > max_val_pos):
if (log_data[i] <= 0):
end_pos=i
break
#POLYNOMIAL FIT
z = np.polyfit(all_interval3[s_point:end_pos+1,k], log_data[s_point:end_pos+1], 2)
#print 'point where fitting starts', all_interval3[s_point,k], np.exp(log_data[s_point])
#print 'polyfit',z
p = np.poly1d(z)
#ax1.plot(interval3[24:end_pos+1], np.exp(log_data[24:end_pos+1]), '.', color=color_set[4], label='Fiting points' )
mean=z[1]/(-2*z[0])
sigma_square=(1/(-2*z[0]))
#x = np.linspace(0,mean+6,1000)
x = np.linspace(1.75,mean+6,1000) #alberto
#print 'FIT: mean and sigma_square', mean, sigma_square
#print 'k', k
y=np.exp( (z[2]+z[1]**2/(4*-z[0]))-(((x-mean)**2)/(2*sigma_square)) )
#print x[210]
#ax1.plot(x[0:len(x)],y[0:len(y)] , color=color_set[c], linestyle='--', linewidth=l_w_2)
if (k==12):
ax1.plot(x[0:len(x)],y[0:len(y)] , color=color_set[1], linestyle='--', linewidth=l_w_2)
elif (k==35):
ax1.plot(x[0:len(x)],y[0:len(y)] , color=color_set[1], linestyle='--', linewidth=l_w_2)
c=c+2
#CURVE FITTIN
x_zero=1.75
def pol_func(x,a,b,c):
return a*(x-x_zero)**2+b*(x-x_zero)+c
#return a*(x)**2+b*(x)+c
fitpars, covmat = so.curve_fit(pol_func,all_interval3[s_point:end_pos+1,k], log_data[s_point:end_pos+1])
mean_new=fitpars[1]/(-2*fitpars[0])
sigma_square_calc=(1/(-2*fitpars[0]))
#NEW Y WITH x=x-x_zero
c_fixed=13.2
y_new=np.exp( (c_fixed+fitpars[1]**2/(4*-fitpars[0]))-((((x-x_zero)-mean_new)**2)/(2*sigma_square_calc)) )
print 'CURVE FIT fitpars', fitpars
print 'CURVE FIT corresponding mean and sigma', mean_new, sigma_square_calc
#ax1.plot(x[0:len(x)],y_new[0:len(y)] , color=color_set[7], linestyle='--', linewidth=l_w_2-3)
a_new[l]=fitpars[0]
b_new[l]=fitpars[1]
x_max[l]=x_zero-b_new[l]/(2*a_new[l])
sigma_square_new[l]=-1/(2*fitpars[0])
l=l+1
#LINEAR FIT FOR a AND x_max FOR b
def lin_func(x,a,b):
return a*(x)+b
def pol2_func(x,a,b,c):
return a*(x)**2+b*x+c
def tanh_func(x,a,b,c):
return c+a*np.tanh(x+b)
def sqrt_func(x,a,b,c):
return c+a*np.sqrt(x+b)
def asympt_func(x,a,b,c):
return c+b/x+a/(x**2)
#fitpars_2, covmat_2 = so.curve_fit(lin_func,steps,a_new)
fitpars_2, covmat_2 = so.curve_fit(asympt_func,steps,a_new)
#fitpars_2, covmat_2 = so.curve_fit(tanh_func,steps,a_new)
#fitpars_2, covmat_2 = so.curve_fit(sqrt_func,steps,a_new)
#fitpars_2, covmat_2 = so.curve_fit(pol2_func,steps,a_new)
fitpars_3, covmat_3 = so.curve_fit(lin_func,steps,x_max)
#fitpars_3, covmat_3 = so.curve_fit(pol2_func,steps,x_max)
fitpars_4, covmat_3 = so.curve_fit(lin_func,steps,b_new)
fitpars_5, covmat_3 = so.curve_fit(lin_func,steps,sigma_square_new)
time_part=np.arange(0,len(time_real_3))
fitpars_time, covmat_3 = so.curve_fit(lin_func,time_part,time_real_3)
#USING ACTUAL FIT FOR time
x = np.linspace(5,90,100)
time_extrapol=fitpars_time[0]*x+fitpars_time[1]
steps_time=fitpars_time[0]*steps+fitpars_time[1]
#USING ACTUAL FIT FOR x_max
x_max_fit = fitpars_3[0]*x+fitpars_3[1]
#x_max_fit = fitpars_3[0]*x**2+fitpars_3[1]*x+fitpars_3[2]
#USING ACTUAL FIT FOR b
test_b = fitpars_4[0]*x+fitpars_4[1]
#USING ACTUAL FIT FOR a
#a_fit = fitpars_2[0]*x+fitpars_2[1]
a_fit = fitpars_2[0]*(1.0/x**2)+fitpars_2[1]*(1.0/x)+fitpars[2]
#a_fit = fitpars_2[2]+fitpars_2[0]*np.tanh(x+fitpars_2[1])
#a_fit = fitpars_2[2]+fitpars_2[0]*np.sqrt(x+fitpars_2[1])
#a_fit = fitpars_2[0]*x**2+fitpars_2[1]*x + fitpars_2[2]
#USING ACTUAL FIT FOR b
sigma_square_new_fit = fitpars_5[0]*x+fitpars_5[1]
#SEVERAL TIME STEPS
several_x = np.array([30, 35, 50 ,60 ,70 ,80,100,120, 250])
several_times=fitpars_time[0]*several_x+fitpars_time[1]
several_x_max = fitpars_3[0]*several_x+fitpars_3[1]
#several_x_max = fitpars_3[0]*several_x**2+fitpars_3[1]*several_x+fitpars_3[2]
several_test_b = fitpars_4[0]*several_x+fitpars_4[1]
#several_a = fitpars_2[0]*several_x+fitpars_2[1]
several_a = fitpars_2[0]*(1.0/several_x**2)+fitpars_2[1]*(1.0/several_x)+fitpars_2[2]
#several_a = fitpars_2[2]+fitpars_2[0]*np.tanh(several_x+fitpars_2[1])
#several_a = fitpars_2[2]+fitpars_2[0]*np.sqrt(several_x+fitpars_2[1])
#several_a = fitpars_2[0]*several_x**2+fitpars_2[1]*several_x +fitpars_2[2]
several_sigma_square_new = fitpars_5[0]*several_x+fitpars_5[1]
several_b = (several_x_max-x_zero)*(-2*several_a)
print 'several times', several_times
print 'corresponding x_max', several_x_max
print 'corresponding a', several_a
print 'calculated b', several_b
print 'test b', several_test_b
print 'several_sigma_new', several_sigma_square_new
#NEW EXTRAPOLATION
m=8
x_plot = np.linspace(1.75,6,100) #alberto
#new_mean=-several_test_b[m]/(2*several_a[m])
#new_mean=several_test_b[m]*sigma_square_new[m]
#new_mean=x_zero-several_b[m]/(2*several_a[m])
new_sigma=(1/(-2*several_a[m]))
#c_calc=np.log(1/(np.sqrt(several_sigma_square_new[m])*sqrt(2*3.14)))
#new_y_extrapolation=np.exp(13+several_test_b[m]**2/(4*several_a[m])+several_test_b[m]*(x_plot-x_zero)+several_a[m]*(x_plot-x_zero)**2)
#new_y_extrapolation=np.exp(13+several_test_b[0]*(x_plot-x_zero)+several_a[m]*(x_plot-x_zero)**2)
#with direct b
new_y_extrapolation=np.exp(13+several_test_b[0]*(x_plot-x_zero)-(1/(2*several_sigma_square_new[m])*(x_plot-x_zero)**2))
#indirect b
new_y_extrapolation_2=np.exp(13+(-2)*(x_plot-x_zero)-(1/(2*several_sigma_square_new[m])*(x_plot-x_zero)**2))
ax1.plot(x_plot,new_y_extrapolation , color=color_set[5], label=r'Extrapolation t$\,\approx\,$80 min ', linestyle='--', linewidth=l_w_2)
ax1.plot(x_plot,new_y_extrapolation_2 , color=color_set[7], label=r'Extrapolation t$\,\approx\,$80 min ', linestyle='--', linewidth=l_w_2)
########################################################################
#use one of the latest time-steps to extrapolate data
########################################################################
data3_3=data3_3*50/18*0.92
log_data= np.zeros(100)
log_interval= np.zeros(100)
for i in np.arange(len(data3_3)):
if (data3_3[i] != 0):
log_data[i] = np.log(data3_3[i])
elif (data3_3[i] == 0):
log_data[i]=data3_3[i]
j=1
max_val=max(log_data)
max_val_pos=len(log_data)+1
for i in np.arange(len(log_data)):
if ( log_data[i] == max_val):
max_val_pos=i
if (i > max_val_pos):
if (log_data[i] <= 0):
end_pos=i
break
begin_pos=s_point
print 'mean is set to', interval3[begin_pos]
#print 'end_pos', end_pos
z = np.polyfit(interval3[begin_pos:end_pos+1], log_data[begin_pos:end_pos+1], 2)
#print 'polyfit',z
p = np.poly1d(z)
#ax1.plot(interval3[begin_pos:end_pos+1], np.exp(log_data[begin_pos:end_pos+1]), '.', color=color_set[5],linewidth=l_w_1, label='Fitting points' )
mean=z[1]/(-2*z[0])
sigma_square=(1/(-2*z[0]))
#for 30min=0.434 40min=0.586 and 50min=0.74
#attetntion we will use growing**2
#growing=[0.2, 0.434, 0.586, 0.74]
#growing=[0.53]#alberto
#growing=[0.74]#50 min
growing=[0.21, 0.434, 0.74]
#growing=[0.21, 0.434, 0.74, 0.9, 1.2]
#l=0
#M_max_all= np.zeros(len(growing))
#test= np.zeros(len(growing))
sigma_extrapolation_all= np.zeros(len(growing))
for k in np.arange(len(growing)):
sigma_extrapolation=growing[k]**2 #after certain time
a=-1/(2*sigma_extrapolation) #only for testing
#mean_extrapolation=z[1]/(-2*a) #only for testing
#x = np.linspace(0,mean+6,1000)
x = np.linspace(1.75,mean+6,1000) #alberto
print 'EXTRAPOLATION: mean and sigma_square_extra', mean, sigma_extrapolation
#OLD extrapolation
#y_extrapolation=np.exp( (z[2]+z[1]**2/(4*-z[0]))-(((x-mean)**2)/(2*sigma_extrapolation)) )
y_extrapolation=np.exp( 1/(growing[k]*np.log(2*3.14))-(((x-mean)**2)/(2*sigma_extrapolation)) )
#M_max=-(z[1]/(2*a))
#M_max_all[l]=M_max
#sigma_extrapolation_all[l]=sigma_extrapolation
#print 'M_max verschiebung', M_max
##test[l]=np.exp( (z[2]+z[1]**2/(4*-z[0]))-(((M_max-mean)**2)/(2*sigma_extrapolation)) )
#test[l]=a*x**2+z[1]+z[2]
#print 'test', test[l]
#print 'a and b', a, z[1]
#
#l=l+1
###if (k==0):
###ax1.plot(x,y_extrapolation , color=color_set[5], label=r'Extrapolation t$\,\approx\,$30 \& 50 [min] ', linestyle='--', linewidth=l_w_2)
#alberto
#ax1.plot(x,y_extrapolation , color=color_set[5], label=r'Extrapolation t$\,\approx\,$60 min ', linestyle='--', linewidth=l_w_2)
###ax1.plot(x,y_extrapolation , color=color_set[5], linestyle='--', linewidth=l_w_2)
###elif (k==1):
###ax1.plot(x,y_extrapolation , color=color_set[5], linestyle='--', linewidth=l_w_2)
#ax1.plot(x,y_extrapolation , color=color_set[5], linestyle='--', linewidth=l_w_2)
#GRID LINES
axes = plt.gca()
axes.grid(True)
ax1.set_yscale('symlog')
#ax1.set_xlabel(r'$M/M_r$', fontsize=size_axes)#,fontdict=font) #das r hier markiert, dass jetz latex code kommt
#alberto
ax1.set_xlabel('Droplet mass', fontsize=size_axes)#,fontdict=font) #das r hier markiert, dass jetz latex code kommt
ax1.set_ylabel('Amount of cloud droplets', fontsize=size_axes)#,fontdict=font)
#ax1.set_xticks([0.1,0.5,1,1.5,2.0,2.5,3.0,3.5,4.0])
#ax1.set_xlim(1.5,5)
ax1.set_xlim(0.8,4)#alberto
ax1.set_ylim([10**2,10**8])
# ax12 = ax1.twiny()
# ax12.set_xlabel(r"Radius [$\mu m$]", fontsize=size_axes)
if (dycoms==0):
tick_locs = [0.5,1,1.5,2.0,2.5,3.0,3.5,4.0]
tick_lbls = [8.2,10.3,11.8,13,14,14.9,15.7,16.4]
else:
tick_locs = [0.5,1,1.5,2.0,2.5,3.0,3.5,4.0]
tick_lbls = [8.9,11,12.8,14,15.2,16.1,17,17.7]
# ax12.set_xticks(tick_locs)
# ax12.set_xticklabels(tick_lbls)
# ax12.set_xlim(0.1,3)
ax1.legend(loc=0, frameon=False)
plt.legend(loc=1, frameon=False)
########################################################################
#plot a with time
########################################################################
fig, ax2 = ppl.subplots()
ax2.plot(steps_time,a_new, color=color_set[5],marker='.',linestyle='None',markersize=m_s+9 )
ax2.plot(time_extrapol,a_fit , color=color_set[5],linestyle='--', linewidth=l_w_2)
########################################################################
#plot b with time
########################################################################
fig, ax3 = ppl.subplots()
ax3.plot(steps_time,b_new, color=color_set[3],marker='.',linestyle='None',markersize=m_s+9 )
ax3.plot(time_extrapol,test_b , color=color_set[3],linestyle='--', linewidth=l_w_2)
########################################################################
#plot sigma_square with time
########################################################################
fig, ax4 = ppl.subplots()
ax4.plot(steps_time,sigma_square_new, color=color_set[1],marker='.',linestyle='None',markersize=m_s+9 )
ax4.plot(time_extrapol,sigma_square_new_fit , color=color_set[7],linestyle='--', linewidth=l_w_2)
########################################################################
#plot m_max with time
########################################################################
#fig, ax5 = ppl.subplots()
#ax5.plot(steps_time,x_max, color=color_set[7],marker='.',linestyle='None',markersize=m_s+9 )
#ax5.plot(time_extrapol,x_max_fit , color=color_set[7],linestyle='--', linewidth=l_w_2)
plt.show()
def make_scatter(self, use_prettyplotlib=True, hists=True, num_bins=None):
'''
Plot two columns against each other. If self.subplot is enabled,
all comparisons returned in a triangle collection. Inspiration for
this form came from the package .
Small snippets to set the labels and figure size came from triangle.py.
'''
if use_prettyplotlib:
try:
import prettyplotlib as plt
except ImportError:
import matplotlib.pyplot as plt
use_prettyplotlib = False
print "prettyplotlib not installed. Using matplotlib..."
else:
import matplotlib.pyplot as plt
# Setup subplots if plotting together
if self.subplot:
# Make the objects
num = len(self.columns)
factor = 2.0 # size of one side of one panel
lbdim = 0.5 * factor # size of left/bottom margin
trdim = 0.3 * factor # size of top/right margin
whspace = 0.05 # w/hspace size
plotdim = factor * num + factor * (num - 1.) * whspace
dim = lbdim + plotdim + trdim
fig, axes = plt.subplots(nrows=num, ncols=num, figsize=(dim, dim))
lb = lbdim / dim
tr = (lbdim + plotdim) / dim
fig.subplots_adjust(left=lb, bottom=lb, right=tr, top=tr,
wspace=whspace, hspace=whspace)
for i, column1 in enumerate(self.columns):
for j, column2 in enumerate(self.columns):
data1 = self.dataframe[column1]
data2 = self.dataframe[column2]
# Get rid of nans
nans = np.isnan(data1) + np.isnan(data2)
data1 = data1[~nans]
data2 = data2[~nans]
if self.subplot:
ax = axes[i, j]
if j > i: # Don't bother plotting duplicates
ax.set_visible(False)
ax.set_frame_on(False)
else:
if j == i: # Plot histograms
# Set number of bins
if num_bins is None:
num_bins = np.sqrt(len(data1))
if hists == True:
if use_prettyplotlib:
plt.hist(ax, data1, num_bins, grid="y")
else:
ax.hist(data1, num_bins)
ax.grid(True)
else:
ax.set_visible(False)
ax.set_frame_on(False)
ax.set_xticklabels([])
ax.set_yticklabels([])
if j != i:
if use_prettyplotlib:
plt.scatter(ax, data2, data1)
else:
ax.scatter(data2, data1)
ax.grid(True)
ax.xaxis.set_major_locator(MaxNLocator(5))
ax.yaxis.set_major_locator(MaxNLocator(5))
if i < num - 1:
ax.set_xticklabels([])
else:
[l.set_rotation(45) for l in ax.get_xticklabels()]
ax.set_xlabel(column2)
ax.xaxis.set_label_coords(0.5, -0.3)
if j > 0:
ax.set_yticklabels([])
else:
[l.set_rotation(45) for l in ax.get_yticklabels()]
ax.set_ylabel(column1)
ax.yaxis.set_label_coords(-0.3, 0.5)
else:
if j < i:
fig, axes = plt.subplots(1)
if use_prettyplotlib:
plt.scatter(axes, data2, data1, grid="y")
else:
axes.scatter(data2, data1)
axes.grid(True)
axes.set_xlabel(column2) # ADD UNITS!
axes.set_ylabel(column1) # ADD UNITS!
if self.verbose:
p.show()
else:
fig.savefig(self.save_name+"_"+column1+"_"+column2+"."+self.save_type)
p.close()
if self.subplot:
# p.tight_layout()
if self.verbose:
p.show()
else:
fig.savefig(self.save_name+"_"+"scatter"+"."+self.save_type)
def extrapolation_sigma(all_interval1, all_data1_3, all_interval2, all_data2_3,
all_interval3, all_data3_3,
names3, p_title3, time_real_1,time_real_2,time_real_3,delta_s,t_real, dycoms):
close='all'
fig, ax = ppl.subplots()
color_set = brewer2mpl.get_map('paired', 'qualitative', 12).mpl_colors
#DATA RE800
shape= np.shape(all_interval1)
time_length=shape[1]
data1_3= np.zeros(100)
interval1= np.zeros(100)
sigma_vector_1= np.zeros(time_length)
for k in np.arange(time_length):
data1_3[:] = all_data1_3[:,k]
interval1[:] = all_interval1[:,k]
log_data= np.zeros(100)
for i in np.arange(len(data1_3)):
if (data1_3[i] != 0):
log_data[i] = np.log(data1_3[i])
elif (data1_3[i] == 0):
log_data[i]=data1_3[i]
j=1
max_val=max(log_data)
max_val_pos=len(log_data)+1
for i in np.arange(len(log_data)):
if ( log_data[i] == max_val):
max_val_pos=i
if (i > max_val_pos):
if (log_data[i] <= 0):
end_pos=i
break
z = np.polyfit(interval1[24:end_pos+1], log_data[24:end_pos+1], 2)
sigma_square=(1/(-2*z[0]))
sigma_vector_1[k]= np.sqrt(sigma_square)
sigma_fit_1 = np.polyfit(time_real_1, sigma_vector_1, 1)
ax.plot(time_real_1,sigma_vector_1 , color=color_set[3],linewidth=2, label='Data Re800')
x_fit = np.linspace(0,60,1000)
p = sigma_fit_1[0]*x_fit + sigma_fit_1[1]
#p = sigma_fit_2[0]*x_fit**2 + sigma_fit_2[1]*x_fit + sigma_fit_2[2]
# if (dycoms != 0):
# ax.plot(x_fit,p ,linestyle='--', color=color_set[2], label='Fiting', linewidth=2)
#DATA RE400
shape= np.shape(all_interval2)
time_length=shape[1]
data2_3= np.zeros(100)
interval2= np.zeros(100)
sigma_vector_2= np.zeros(time_length)
for k in np.arange(time_length):
data2_3[:] = all_data2_3[:,k]
interval2[:] = all_interval2[:,k]
log_data= np.zeros(100)
for i in np.arange(len(data2_3)):
if (data2_3[i] != 0):
log_data[i] = np.log(data2_3[i])
elif (data2_3[i] == 0):
log_data[i]=data2_3[i]
j=1
max_val=max(log_data)
max_val_pos=len(log_data)+1
for i in np.arange(len(log_data)):
if ( log_data[i] == max_val):
max_val_pos=i
if (i > max_val_pos):
if (log_data[i] <= 0):
end_pos=i
break
z = np.polyfit(interval2[24:end_pos+1], log_data[24:end_pos+1], 2)
sigma_square=(1/(-2*z[0]))
sigma_vector_2[k]= np.sqrt(sigma_square)
sigma_fit_2 = np.polyfit(time_real_2, sigma_vector_2, 1)
ax.plot(time_real_2,sigma_vector_2 , color=color_set[5],linewidth=2, label='Data Re400')
x_fit = np.linspace(0,60,1000)
p = sigma_fit_2[0]*x_fit + sigma_fit_2[1]
#p = sigma_fit_2[0]*x_fit**2 + sigma_fit_2[1]*x_fit + sigma_fit_2[2]
# if (dycoms != 0):
# ax.plot(x_fit,p ,linestyle='--', color=color_set[4], label='Fitting', linewidth=2)
if (dycoms == 0):
#DATA RE200
shape= np.shape(all_interval3)
time_length=shape[1]
data3_3= np.zeros(100)
interval3= np.zeros(100)
sigma_vector= np.zeros(time_length)
print sigma_vector[9]
c= np.zeros(time_length)
for k in np.arange(time_length):
data3_3[:] = all_data3_3[:,k]
interval3[:] = all_interval3[:,k]
log_data= np.zeros(100)
log_interval= np.zeros(100)
for i in np.arange(len(data3_3)):
if (data3_3[i] != 0):
log_data[i] = np.log(data3_3[i])
elif (data3_3[i] == 0):
log_data[i]=data3_3[i]
j=1
max_val=max(log_data)
max_val_pos=len(log_data)+1
for i in np.arange(len(log_data)):
if ( log_data[i] == max_val):
max_val_pos=i
if (i > max_val_pos):
if (log_data[i] <= 0):
end_pos=i
break
z = np.polyfit(interval3[24:end_pos+1], log_data[24:end_pos+1], 2)
sigma_square=(1/(-2*z[0]))
sigma_vector[k]= np.sqrt(sigma_square)
sigma_fit = np.polyfit(time_real_3, sigma_vector, 1)
print 'sigma_fit', sigma_fit
x_fit = np.linspace(0,60,1000)
#p = sigma_fit[0]*x_fit**2 + sigma_fit[1]*x_fit + sigma_fit[2]
p = sigma_fit[0]*x_fit + sigma_fit[1]
ax.plot(time_real_3,sigma_vector , color=color_set[1],linewidth=2, label='Data Re200')
ax.plot(x_fit,p , color=color_set[0],linestyle='--', linewidth=2, label='Fitting')
tau=t_real/delta_s
print len(time_real_3), len(sigma_vector)
for l in np.arange(len(time_real_3)):
c[l] = sigma_vector[l]*tau/time_real_3[l]
print np.mean(c), sigma_fit[0]
#p_test=np.mean(c)*x_fit
#ax.plot(x_fit,p_test ,color=color_set[9],linewidth=2, label='bla')
#GENERAL PLOT SETTINGS
# if(dycoms == 0):
# plt.title(r'VERDI - Growth of $\sigma$ in time', fontsize=size_title)#,fontdict=font)
# else:
# plt.title(r'DYCOMS-II - Growth of $\sigma$ in time', fontsize=size_title)#,fontdict=font)
plt.xlabel('t [min]')#, fontsize=size_axes)#,fontdict=font) #das r hier markiert, dass jetz latex code kommt
plt.ylabel(r'\textbf{$\sigma$}')#, fontsize=size_axes)#,fontdict=font)
#plt.xlim(0,4)
plt.ylim(0)
plt.legend(loc=2, frameon=False)
plt.show()
#!/usr/bin/python
# encoding=utf-8
import prettyplotlib as ppl
import numpy as np
# prettyplotlib imports
import matplotlib.pyplot as plt
import matplotlib as mpl
from prettyplotlib import brewer2mpl
# Set the random seed for consistency
np.random.seed(12)
fig, ax = ppl.subplots(1)
# Show the whole color range
for i in range(8):
y = np.random.normal(size=1000).cumsum()
x = np.arange(1000)
# For now, you need to specify both x and y :(
# Still figuring out how to specify just one
ppl.plot(ax, x, y, label=str(i), linewidth=0.75)
ppl.legend(ax)
fig.show()
ax1.plot(times,m-st,'gray',times,m+st,'gray')
#ax2 = plt.gcf().add_subplot(1,2,2)
#ax2.plot(times,s)
plt.xlabel('Time (in seconds)')
plt.ylabel('Space (in cm)')
plt.legend()
plt.title('State Estimation in a Diffusion System')
if plotting:
#matplotlib.rcParams['font.size']=10
if gaussian:
fig, (ax1,ax2) = ppl.subplots(1,2,figsize = (12,6))
else:
fig, ax2 = ppl.subplots(1)
times = np.arange(0.0,dt*timewindow,dt)
if gaussian:
if sum(sum(spsg)) !=0:
(ts,neurs) = np.where(spsg == 1)
spiketimes = times[ts]
thetas = [code.neurons[i].theta for i in neurs]
else:
spiketimes = []
thetas = []
l4, = ax1.plot(times,sg,label='True State')
l2, = ax1.plot(times,mg,label='Posterior Mean')
l1, = ax1.plot(spiketimes,thetas,'o',label='Observed Spikes')
def make_scatter(self, use_prettyplotlib=True, hists=True, num_bins=None):
'''
Plot two columns against each other. If self.subplot is enabled,
all comparisons returned in a triangle collection. Inspiration for
this form came from the package .
Small snippets to set the labels and figure size came from triangle.py.
'''
if use_prettyplotlib:
try:
import prettyplotlib as plt
except ImportError:
import matplotlib.pyplot as plt
use_prettyplotlib = False
print "prettyplotlib not installed. Using matplotlib..."
else:
import matplotlib.pyplot as plt
# Setup subplots if plotting together
if self.subplot:
# Make the objects
num = len(self.columns)
factor = 2.0 # size of one side of one panel
lbdim = 0.5 * factor # size of left/bottom margin
trdim = 0.3 * factor # size of top/right margin
whspace = 0.05 # w/hspace size
plotdim = factor * num + factor * (num - 1.) * whspace
dim = lbdim + plotdim + trdim
fig, axes = plt.subplots(nrows=num, ncols=num, figsize=(dim, dim))
lb = lbdim / dim
tr = (lbdim + plotdim) / dim
fig.subplots_adjust(left=lb,
bottom=lb,
right=tr,
top=tr,
wspace=whspace,
hspace=whspace)
for i, column1 in enumerate(self.columns):
for j, column2 in enumerate(self.columns):
data1 = self.dataframe[column1]
data2 = self.dataframe[column2]
# Get rid of nans
nans = np.isnan(data1) + np.isnan(data2)
data1 = data1[~nans]
data2 = data2[~nans]
if self.subplot:
ax = axes[i, j]
if j > i: # Don't bother plotting duplicates
ax.set_visible(False)
ax.set_frame_on(False)
else:
if j == i: # Plot histograms
# Set number of bins
if num_bins is None:
num_bins = np.sqrt(len(data1))
if hists == True:
if use_prettyplotlib:
plt.hist(ax, data1, num_bins, grid="y")
else:
ax.hist(data1, num_bins)
ax.grid(True)
else:
ax.set_visible(False)
ax.set_frame_on(False)
ax.set_xticklabels([])
ax.set_yticklabels([])
if j != i:
if use_prettyplotlib:
plt.scatter(ax, data2, data1)
else:
ax.scatter(data2, data1)
ax.grid(True)
ax.xaxis.set_major_locator(MaxNLocator(5))
ax.yaxis.set_major_locator(MaxNLocator(5))
if i < num - 1:
ax.set_xticklabels([])
else:
[l.set_rotation(45) for l in ax.get_xticklabels()]
ax.set_xlabel(column2)
ax.xaxis.set_label_coords(0.5, -0.3)
if j > 0:
ax.set_yticklabels([])
else:
[l.set_rotation(45) for l in ax.get_yticklabels()]
ax.set_ylabel(column1)
ax.yaxis.set_label_coords(-0.3, 0.5)
else:
if j < i:
fig, axes = plt.subplots(1)
if use_prettyplotlib:
plt.scatter(axes, data2, data1, grid="y")
else:
axes.scatter(data2, data1)
axes.grid(True)
axes.set_xlabel(column2) # ADD UNITS!
axes.set_ylabel(column1) # ADD UNITS!
if self.verbose:
p.show()
else:
fig.savefig(self.save_name + "_" + column1 + "_" +
column2 + "." + self.save_type)
p.close()
if self.subplot:
# p.tight_layout()
if self.verbose:
p.show()
else:
fig.savefig(self.save_name + "_" + "scatter" + "." +
self.save_type)
except:
p = mp.Pool(mp.cpu_count())
results = p.map(run_filters,zip(range(dthetas.size),dthetas))
for r in results:
i,dense,sparse,part = r
dense_eps[i] = dense
sparse_eps[i] = sparse
particle_eps[i] = part
with open(filename,"wb") as f:
pic.dump({'dense_eps':dense_eps,
'sparse_eps':sparse_eps,
'particle_eps':particle_eps,
'dthetas':dthetas},
f)
if plotting:
#matplotlib.rcParams['font.size']=10
fig, ax = ppl.subplots(1,figsize=(5,5))
ppl.plot(dthetas,sparse_eps,ax=ax,label='Full ADF')
ppl.plot(dthetas,dense_eps,ax=ax,label='Dense ADF')
ppl.plot(dthetas,particle_eps,ax=ax,label='Particle Filter')
ppl.legend(ax)
plt.show()
lamb = phi*np.sqrt(2*np.pi*alpha)
eps[n,m] = get_eq_eps( gamma, eta, alpha, lamb )
stoc_eps[n,m] = np.mean(full_stoc_sigma(0.01, dt, N, -gamma,
eta, alpha, lamb, 200,
discard=discard))
with open("figure_5_3.pik","w") as fi:
print "Writing pickle to figure_5_3.pik"
pic.dump({'alphas':alphas,'phis':phis,'mean_field':eps,'stochastic':stoc_eps},fi)
print "Plotting..."
font = {'size':12}
plt.rc('font',**font)
fig, (ax1,ax2) = ppl.subplots(1,2,figsize = (12,5))
alphas2,phis2 = np.meshgrid(np.arange(alphas.min(),alphas.max()+dalpha,dalpha)-dalpha/2,
np.arange(phis.min(),phis.max()+dphi,dphi)-dphi/2)
yellorred = brewer2mpl.get_map('YlOrRd','Sequential',9).mpl_colormap
p = ax1.pcolormesh(alphas2,phis2,eps.T,cmap=yellorred, rasterized=True)
ax1.axis([alphas2.min(),alphas2.max(),phis2.min(),phis2.max()])
#xticks = np.arange(alphas.min(),alphas.max(),0.5)
#xlabels = np.arange(alphas.min(),alphas.max(),0.5)-alphas.min()
#yticks = np.arange(phis.min(),phis.max(),0.5)
#ylabels = np.arange(phis.min(),phis.max(),0.5)-phis.min()
def make_hists(self, num_bins=None, use_prettyplotlib=True):
if use_prettyplotlib:
try:
import prettyplotlib as plt
except ImportError:
import matplotlib.pyplot as plt
use_prettyplotlib = False
print "prettyplotlib not installed. Using matplotlib..."
else:
import matplotlib.pyplot as plt
# Setup subplots if plotting together
if self.subplot:
num = len(self.columns)
if num <= 3:
ncols = 1
nrows = num
elif num <= 8:
ncols = 2
nrows = num / 2
else: # Max columns right now is 12
ncols = 3
nrows = num / 3
# Check if we need an extra row.
if num % ncols != 0:
nrows += 1
# Make the objects
fig, axes = plt.subplots(nrows=nrows, ncols=ncols)
# This is a bit awkward to get the indices, but my matplotlib version
# doesn't use the same object type as prettyplotlib creates.
posns = np.indices(axes.shape)
x, y = posns[0].ravel(), posns[1].ravel()
# Keep the mean, median, std.
data_stats = {}
for i, column in enumerate(self.columns):
data = self.dataframe[column]
data = data[np.isfinite(data)]
if num_bins is None:
num_bins = np.sqrt(len(data))
data_stats[column] = [
np.nanmean(data),
np.nanstd(data),
np.nanmedian(data)
]
if self.subplot:
if use_prettyplotlib:
plt.hist(axes[x[i], y[i]], data, num_bins, grid="y")
else:
axes[x[i], y[i]].hist(data, num_bins)
axes[x[i], y[i]].set_xlabel(column) # ADD UNITS!
else:
fig, axes = plt.subplots(1)
axes.hist(data, num_bins)
axes.set_xlabel(column) # ADD UNITS!
if self.verbose and not self.subplot:
print column + " Stats: %s" % (data_stats[column])
p.show()
elif not self.subplot:
fig.savefig(self.save_name + "_" + column + "." +
self.save_type)
p.close()
if self.subplot:
p.tight_layout()
if self.verbose:
for column in self.columns:
print column + " Stats: %s" % (data_stats[column])
p.show()
else:
fig.savefig(self.save_name + "_hists." + self.save_type)
