def hub_picture(data, dataset, method):
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(16, 10))
ax1.set_xticks([])
ax2.set_xticks([])
ax1.set_ylabel("Proportion", fontsize=16)
ax2.set_ylabel("Proportion", fontsize=16)
width = 0.8
startposition = 0.8
color_list = ["tomato", 'deepskyblue', "orange"]
label_list = ["more than 5 times", "1 to 5 times", "never appear"]
legend_list = list()
for i in range(len(method)):
bottom = 0
for j in range(3):
bar1, = ax1.bar(startposition + i * width,
height=data[j][i][0],
width=0.5,
bottom=bottom,
color=color_list[j],
align="edge",
edgecolor="black")
bottom += data[j][i][0]
ax1.text(startposition + i * width + 0.1,
y=-0.03,
s=method[i],
fontsize="14",
rotation=-90)
ax1.text(startposition + (len(method) / 2 - 2) * width,
y=1.08,
s=dataset[0],
fontsize="14")
for i in range(len(method)):
bottom = 0
for j in range(3):
bar2, = ax2.bar(startposition + i * width,
height=data[j][i + len(method)][0],
width=0.5,
bottom=bottom,
color=color_list[j],
align="edge",
edgecolor="black")
bottom += data[j][i + len(method)][0]
if i == 0:
legend_list.append(bar2)
ax2.text(startposition + i * width + 0.1,
y=-0.03,
s=method[i],
fontsize="14",
rotation=-90)
ax2.text(startposition + (len(method) / 2 - 2) * width,
y=1.08,
s=dataset[1],
fontsize="14")
plt.figlegend(legend_list, labels=label_list, loc="best")
# plt.legend()
plt.show()
def plot(dataset, grid, trafo):
assert os.path.exists('results.csv'), 'need to aggregate results first'
df = pandas.read_csv('results.csv')
df = df[(df.grid == grid) & (df.data == dataset) & (df.trafo == trafo)]
fig, ax = pylab.subplots(1, 1, dpi=200)
# fig.subplots_adjust(left=.1, bottom=.225, right=.98, top=.94, hspace=0.35, wspace=0.25)
models = [
NA, NB, NF, 'svm-rbf', 'svm-poly', 'svm-sigmoid', 'svm-primal',
'logisticregression', 'lda', 'rforest', 'etrees', 'xgb'
]
names = [name_dict[i] for i in models]
palette = [color_dict[i] for i in models]
palette = seaborn.xkcd_palette(palette)
legend = [mpatches.Patch(color=i, label=j) for i, j in zip(palette, names)]
seaborn.lineplot(x='sample_size',
y='test_score',
hue='model',
hue_order=models,
data=df,
ax=ax,
palette=palette,
legend=False,
err_style="bars",
ci=CI)
ax.set_title(f'Dataset: {dataset}', fontsize=8)
ax.set_xscale('log')
ax.set_xticks(df.sample_size.unique())
ax.get_xaxis().set_major_formatter(matplotlib.ticker.ScalarFormatter())
ax.get_xaxis().set_tick_params(which='minor', size=0)
ax.get_xaxis().set_tick_params(which='minor', width=0)
ax.set_ylabel('Accuracy')
ax.set_xlabel('Sample Size')
# ax.set_ylim(0.65, 1)
pylab.figlegend(handles=legend,
ncol=2,
fontsize=8,
loc='lower center',
frameon=False)
fig.savefig(f'plots/{dataset}.png')
def main():
sns.set_style("whitegrid")
sns.set_context("notebook")
n_species=['H', 'He','N','N2', 'O', 'O2']
Nn = sp.array([22340974.0, 1.170203e+08, 269365.12, 8.7995607e+12,5.6790339e+11,1.9389578e+12])*1e6
Tn = 179.29138
n_datablock = sp.zeros((len(Nn),2))
n_datablock[:,0] = Nn
n_datablock[:,1] = Tn
species = ['NO+','O2+','e-']
Ni = sp.array([1998.2988800000001, 57.700355999999999])*1e6
Ti = 175.1
Ne= 2055.9992320000001 *1e6
Te = 175.1
datablock = sp.zeros((len(Ni)+1,2))
datablock[:-1,0] = Ni
datablock[:-1,1] = Ti
datablock[-1,0] = Ne
datablock[-1,1] = Te
(nuparr,nuperp) = get_collisionfreqs(datablock,species,n_datablock, n_species)
#
ISS1 = ISSnew.ISRSpectrum(centerFrequency = 449e6, bMag = 0.4e-4, nspec=256, sampfreq=50e3,dFlag=True)
(omeg,spec_1)=ISS1.getspecsep(datablock,species,vel = 0.0,rcsflag=False)
(omeg,spec_2)=ISS1.getspecsep(datablock,species,vel = 0.0,rcsflag=False,col_calc = True)
(omeg,spec_3)=ISS1.getspecsep(datablock,species,vel = 0.0,rcsflag=False,col_calc = True,n_datablock=n_datablock,n_species=n_species)
(figmplf, axmat) = plt.subplots(1, 1,figsize=(20, 15), facecolor='w')
curax = axmat
lines = [None]*3
labels = ['Spectrum','Spectrum with Coulomb Collisions','Spectrum with Columb and Neutral Collisions']
lines[0] = curax.plot(omeg*1e-3,spec_1,label='Output',linewidth=5)[0]
lines[1] = curax.plot(omeg*1e-3,spec_2,label='Output',linewidth=5)[0]
lines[2] = curax.plot(omeg*1e-3,spec_3,label='Output',linewidth=5)[0]
figmplf.suptitle('Spectrums with and without collisions')
plt.figlegend( lines, labels, loc = 'lower center', ncol=5, labelspacing=0. )
curpath = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
imagepath = os.path.join(os.path.split(curpath)[0],'Doc','Figs')
plt.savefig(os.path.join(imagepath,'Collisions.png'))
def main():
sns.set_style("whitegrid")
sns.set_context("notebook")
n_species=['H', 'He','N','N2', 'O', 'O2']
Nn = sp.array([22340974.0, 1.170203e+08, 269365.12, 8.7995607e+12,5.6790339e+11,1.9389578e+12])*1e6
Tn = 179.29138
n_datablock = sp.zeros((len(Nn),2))
n_datablock[:,0] = Nn
n_datablock[:,1] = Tn
species = ['NO+','O2+','e-']
Ni = sp.array([1998.2988800000001, 57.700355999999999])*1e6
Ti = 175.1
Ne= 2055.9992320000001 *1e6
Te = 175.1
datablock = sp.zeros((len(Ni)+1,2))
datablock[:-1,0] = Ni
datablock[:-1,1] = Ti
datablock[-1,0] = Ne
datablock[-1,1] = Te
(nuparr,nuperp) = get_collisionfreqs(datablock,species,n_datablock, n_species)
#
ISS1 = ISSnew.ISRSpectrum(centerFrequency = 449e6, bMag = 0.4e-4, nspec=256, sampfreq=50e3,dFlag=True)
(omeg,spec_1)=ISS1.getspecsep(datablock,species,vel = 0.0,rcsflag=False)
(omeg,spec_2)=ISS1.getspecsep(datablock,species,vel = 0.0,rcsflag=False,col_calc = True)
(omeg,spec_3)=ISS1.getspecsep(datablock,species,vel = 0.0,rcsflag=False,col_calc = True,n_datablock=n_datablock,n_species=n_species)
(figmplf, axmat) = plt.subplots(1, 1,figsize=(20, 15), facecolor='w')
curax = axmat
lines = [None]*3
labels = ['Spectrum','Spectrum with Coulomb Collisions','Spectrum with Columb and Neutral Collisions']
lines[0] = curax.plot(omeg*1e-3,spec_1,label='Output',linewidth=5)[0]
lines[1] = curax.plot(omeg*1e-3,spec_2,label='Output',linewidth=5)[0]
lines[2] = curax.plot(omeg*1e-3,spec_3,label='Output',linewidth=5)[0]
figmplf.suptitle('Spectrums with and without collisions')
plt.figlegend( lines, labels, loc = 'lower center', ncol=5, labelspacing=0. )
plt.savefig('Collisions.png')
def main():
sns.set_style("whitegrid")
sns.set_context("notebook")
Ne = 1e11
Ti = 3e3
Te = 3e3
databloc = sp.array([[Ne,Ti],[Ne,Te]])
species = ['O+','e-']
Cia = sp.sqrt(v_Boltz*(Te+3.*Ti)/16./v_amu)
#make list of dictionaries
biglist = [{'name':'AMISR','Fo':449e6,'Fs':50e3,'alpha':70.},
{'name':'Sondrestrom','Fo':1290e6,'Fs':100e3,'alpha':80.},
{'name':'Haystack','Fo':440e6,'Fs':50e3,'alpha':65.},
{'name':'Arecibo','Fo':430e6,'Fs':50e3,'alpha':45.},
{'name':'Jicamarca','Fo':50e6,'Fs':10e3,'alpha':1.}]
(figmplf, axmat) = plt.subplots(3, 2,figsize=(20, 15), facecolor='w')
axvec = axmat.flatten()
lines = [None]*2
labels = ['Spectrum','Ion Acoustic Frequency']
for ima,idict in enumerate(biglist):
curax = axvec[ima]
k = 2.0*sp.pi*2*idict['Fo']/v_C_0
xloc = sp.array([-k*Cia,k*Cia])/2/sp.pi/2
ISS1 = ISSnew.ISRSpectrum(centerFrequency = idict['Fo'], bMag = 0.4e-4, nspec=256, sampfreq=idict['Fs'],dFlag=True)
(omeg,spec)=ISS1.getspecsep(databloc,species,vel = 0.0, alphadeg=idict['alpha'],rcsflag=False)
lines[0] = curax.plot(omeg*1e-3,spec,label='Output',linewidth=5)[0]
lines[1] = curax.stem(xloc*1e-3, sp.ones(2)*sp.amax(spec), linefmt='g--', markerfmt='go', basefmt=' ')[0]
curax.set_xlabel('f in kHz')
curax.set_ylabel('Amp')
curax.set_title(idict['name']+ ' Spectrtum')
figmplf.suptitle(r'Spectrums $N_e$ = {0:.1e}m$^{{-3}}$, $T_e$ = {1:.0f}$^o$K, $T_i$ = {2:.0f}$^o$K'.format(Ne,Te,Ti), fontsize=20)
plt.figlegend( lines, labels, loc = 'lower center', ncol=5, labelspacing=0. )
curpath = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
imagepath = os.path.join(os.path.split(curpath)[0],'Doc','Figs')
plt.savefig(os.path.join(imagepath,'DifferentSystems.png'))
def plotVarsGroupedBySex(varList, varNames, badStringList):
fig, axes = plt.subplots(1,2)
for i in range(2):
group = traindf.groupby([varList[i], 'Sex', 'Survived'])
group = group.size().unstack()
group = group.div(group.sum(1), axis=0)
cols = [[darkpink, darkblue],[lightpink, lightblue]]
group.plot(kind='barh', stacked=True, ax=axes[i],legend=False, color=cols)
labels = removeBadStringFromLabels(axes[i], badStringList)
axes[i].set_yticklabels(labels)
axes[i].get_xaxis().set_ticks([])
axes[i].set_ylabel('')
axes[i].set_title(varNames[i])
if i ==1:
axes[i].yaxis.tick_right()
axes[i].yaxis.set_label_position("right")
handles, labels = axes[0].get_legend_handles_labels()
plt.figlegend(handles[0], ['die', 'die'], loc='lower left')
plt.figlegend(handles[1], ['live','live'], loc='lower right')
fig.show()
def pretty_template_plot(template, size=(18.5, 10.5), save=False, title=False,
background=False, picks=False):
r"""Function to make a pretty plot of a single template, designed to work \
better than the default obspy plotting routine for short data lengths.
:type template: :class: obspy.Stream
:param template: Template stream to plot
:type size: tuple
:param size: tuple of plot size
:type save: bool
:param save: if False will plot to screen, if True will save
:type title: bool
:param title: String if set will be the plot title
:type background: :class: obspy.stream
:param background: Stream to plot the template within.
:type picks: list of obspy.core.event.pick
:param picks: List of obspy type picks.
"""
fig, axes = plt.subplots(len(template), 1, sharex=True, figsize=size)
if len(template) > 1:
axes = axes.ravel()
else:
return
if not background:
mintime = template.sort(['starttime'])[0].stats.starttime
else:
mintime = background.sort(['starttime'])[0].stats.starttime
template.sort(['network', 'station', 'starttime'])
lengths = []
lines = []
labels = []
for i, tr in enumerate(template):
delay = tr.stats.starttime - mintime
y = tr.data
x = np.linspace(0, (len(y)-1) * tr.stats.delta, len(y))
x += delay
if background:
btr = background.select(station=tr.stats.station,
channel=tr.stats.channel)[0]
bdelay = btr.stats.starttime - mintime
by = btr.data
bx = np.linspace(0, (len(by)-1) * btr.stats.delta, len(by))
bx += bdelay
axes[i].plot(bx, by, 'k', linewidth=1)
template_line, = axes[i].plot(x, y, 'r', linewidth=1.1,
label='Template')
if i == 0:
lines.append(template_line)
labels.append('Template')
lengths.append(max(bx[-1], x[-1]))
else:
template_line, = axes[i].plot(x, y, 'k', linewidth=1.1,
label='Template')
if i == 0:
lines.append(template_line)
labels.append('Template')
lengths.append(x[-1])
# print(' '.join([tr.stats.station, str(len(x)), str(len(y))]))
axes[i].set_ylabel('.'.join([tr.stats.station, tr.stats.channel]),
rotation=0, horizontalalignment='right')
axes[i].yaxis.set_ticks([])
# Plot the picks if they are given
if picks:
tr_picks = [pick for pick in picks if
pick.waveform_id.station_code == tr.stats.station and
pick.waveform_id.channel_code[0] +
pick.waveform_id.channel_code[-1] ==
tr.stats.channel[0] + tr.stats.channel[-1]]
for pick in tr_picks:
if 'P' in pick.phase_hint.upper():
pcolor = 'red'
label = 'P-pick'
elif 'S' in pick.phase_hint.upper():
pcolor = 'blue'
label = 'S-pick'
else:
pcolor = 'k'
label = 'Unknown pick'
pdelay = pick.time - mintime
# print(pdelay)
line = axes[i].axvline(x=pdelay, color=pcolor, linewidth=2,
linestyle='--', label=label)
if label not in labels:
lines.append(line)
labels.append(label)
# axes[i].plot([pdelay, pdelay], [])
axes[i].set_xlim([0, max(lengths)])
axes[len(template)-1].set_xlabel('Time (s) from start of template')
plt.figlegend(lines, labels, 'upper right')
if title:
axes[0].set_title(title)
else:
plt.subplots_adjust(top=0.98)
plt.tight_layout()
plt.subplots_adjust(hspace=0)
if not save:
plt.show()
plt.close()
else:
plt.savefig(save)
fig_width_mm=200,
fig_height_mm=155,
size_axe=[0.13, 0.127, 0.84, 0.835])
ax1.set_xscale('log')
ax1.set_yscale('log')
ax1.set_xlabel('$r$ (km)')
ax1.set_ylabel('$F_T$, $F_L$')
l_FT = ax1.plot(r, flatnessT, 'k', linewidth=2)
l_FL = ax1.plot(r, flatnessL, 'y', linewidth=2)
plt.rc('legend', numpoints=1)
leg1 = plt.figlegend([l_FT[0], l_FL[0]], ['$F_T$', '$F_L$'],
loc=(0.2, 0.2),
labelspacing=0.18)
cond = np.logical_and(r > 3, r < 2e2)
ax1.plot(r[cond], 6e2 * r[cond]**(-1), 'k', linewidth=1)
ax1.text(1e1, 1.8e1, '$r^{-1}$', fontsize=fontsize)
cond = np.logical_and(r > 3, r < 1e2)
ax1.plot(r[cond], 4e4 * r[cond]**(-3. / 2), 'k--', linewidth=1)
ax1.text(1e1, 1.8e3, '$r^{-3/2}$', fontsize=fontsize)
r1 = 1
r2 = 3e3
ax1.set_xlim([r1, r2])
ax1.set_ylim([1, 8e3])
ax1.set_yscale('log')
linewidth = 2.2
l_FT = ax1.plot(radim, flatnessT, 'k', linewidth=linewidth)
l_FL = ax1.plot(radim, flatnessL, 'y', linewidth=linewidth)
cond = np.logical_and(radim > 1.4e-2, radim < 2e-1)
ax1.plot(radim[cond], 4e0 * rxs[cond]**(-1), 'k', linewidth=1)
ax1.text(4e-2, 1e1, '$r^{-1}$', fontsize=fontsize)
ax1.set_xlim([r1, r2])
# ax1.set_ylim([1, 10])
plt.rc('legend', numpoints=1)
leg1 = plt.figlegend([l_FT[0], l_FL[0]], ['$F_T$', '$F_L$'],
loc=(0.2, 0.2),
labelspacing=0.18)
ax2 = fig.add_axes([0.62, 0.6, 0.32, 0.32])
ax2.set_xscale('log')
ax2.plot(radim, flatnessT / flatnessL, 'k', linewidth=linewidth)
value_shocks = 1.5
ax2.plot([r1, r2], [value_shocks, value_shocks], 'k:', linewidth=linewidth)
ax2.set_xlim([r1, r2])
ax2.set_ylim([0, 1.7])
ax2.set_yticks([0, 0.5, 1, 1.5])
ax2.set_xlabel('$r/L_f$')
ax2.set_ylabel('$F_T/F_L$')
def main():
Ne = 1e11
Ti = 3e3
Te = 3e3
databloc = sp.array([[Ne, Ti], [Ne, Te]])
species = ['O+', 'e-']
Cia = sp.sqrt(v_Boltz * (Te + 3. * Ti) / 16. / v_amu)
#make list of dictionaries
biglist = [{
'name': 'AMISR',
'Fo': 449e6,
'Fs': 50e3,
'alpha': 70.
}, {
'name': 'Sondrestrom',
'Fo': 1290e6,
'Fs': 100e3,
'alpha': 80.
}, {
'name': 'Haystack',
'Fo': 440e6,
'Fs': 50e3,
'alpha': 65.
}, {
'name': 'Arecibo',
'Fo': 430e6,
'Fs': 50e3,
'alpha': 45.
}, {
'name': 'Jicamarca',
'Fo': 50e6,
'Fs': 10e3,
'alpha': 1.
}]
(figmplf, axmat) = plt.subplots(3, 2, figsize=(20, 15), facecolor='w')
axvec = axmat.flatten()
lines = [None] * 2
labels = ['Spectrum', 'Ion Acoustic Frequency']
for ima, idict in enumerate(biglist):
curax = axvec[ima]
k = 2.0 * sp.pi * 2 * idict['Fo'] / v_C_0
xloc = sp.array([-k * Cia, k * Cia]) / 2 / sp.pi / 2
ISS1 = ISSnew.ISRSpectrum(centerFrequency=idict['Fo'],
bMag=0.4e-4,
nspec=256,
sampfreq=idict['Fs'],
dFlag=True)
(omeg, spec) = ISS1.getspecsep(databloc,
species,
vel=0.0,
alphadeg=idict['alpha'],
rcsflag=False)
lines[0] = curax.plot(omeg * 1e-3, spec, label='Output',
linewidth=5)[0]
lines[1] = curax.stem(xloc * 1e-3,
sp.ones(2) * sp.amax(spec),
linefmt='g--',
markerfmt='go',
basefmt=' ')[0]
curax.set_xlabel('f in kHz')
curax.set_ylabel('Amp')
curax.set_title(idict['name'] + ' Spectrtum')
figmplf.suptitle(
r'Spectrums $N_e$ = {0:.1e}m$^{{-3}}$, $T_e$ = {1:.0f}$^o$K, $T_i$ = {2:.0f}$^o$K'
.format(Ne, Te, Ti),
fontsize=20)
plt.figlegend(lines, labels, loc='lower center', ncol=5, labelspacing=0.)
plt.savefig('DifferentSystems.png')
def running_time(dir, method_list, dataset_list):
user_cmap = config_color_map()
color_map = cm.get_cmap("tab20c")
print()
cnorm = matplotlib.colors.Normalize(vmin=0, vmax=20)
sclarmap = cm.ScalarMappable(norm=cnorm, cmap=color_map)
data = xlrd.open_workbook(dir)
tabel = data.sheet_by_name("run_time")
# *****************行*******************
left_row_start = 3
left_row_end = 14
right_row_start = 3
right_row_end = 9
# ***********************列坐标*************
left_col_start = 3
left_col_end = 31
right_col_start = 34
right_col_end = 65
data_kind = [
"DBP_en_DBP_de", "DBP_en_DBP_fr", "DBP_en_WD_en", "DBP_en_YG_en"
]
left_data = []
right_data = []
plt.figure(figsize=(32, 6))
sub_i = 1
for dataset in dataset_list:
remove = data_kind.index(dataset)
left_col_start += 11 * remove
left_col_end += 11 * remove
right_col_start += 6 * remove
right_col_end += 6 * remove
for i in range(left_row_start, left_row_end):
line_all_row = tabel.row_slice(i, left_col_start, left_col_end)
left_data.append(line_all_row)
for i in range(right_row_start, right_row_end):
line_all_row = tabel.row_slice(i, right_col_start, right_col_end)
right_data.append(line_all_row)
print(left_data)
print(right_data)
all_data = left_data + right_data
# ****************************************开始画图相关工作*********************
x_label_list = [["15K_V1", "15K_V2"], ["100K_V1", "100K_V2"]]
color_list = [
"darkorange", "forestgreen", "lightsteelblue", "rosybrown", "gold",
"indigo", "red", "sienna", "skyblue", "deeppink", "slategray", "peru",
"grey", "olive", "cyan", "blue", "lightpink"
]
color_list = [sclarmap.to_rgba(i) for i in range(17)]
# ****************现在是17个方法******************
first_x_position = [
0.5,
12,
]
width = 0.5
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(16, 8))
ax1.set_xticks([])
ax2.set_xticks([])
ax1.set_ylabel("Time(s)", fontsize=16)
ax2.set_ylabel("Time(s)", fontsize=16)
for method_pos in range(len(first_x_position)):
for i in range(len(all_data)):
bar1, = ax1.bar(first_x_position[method_pos] + i * (width),
height=all_data[i][5 + method_pos * 7].value,
width=0.5,
bottom=0,
color=color_list[i],
edgecolor="black",
label=method_list[i])
ax1.text(x=3.5, y=-120, s=x_label_list[0][0], fontsize="16")
ax1.text(x=15.5, y=-120, s=x_label_list[0][1], fontsize="16")
legend_bar = list()
for method_pos in range(len(first_x_position)):
for i in range(len(all_data)):
bar2, = ax2.bar(first_x_position[method_pos] + i * (width),
height=all_data[i][14 + 5 + method_pos * 7].value,
width=0.5,
bottom=0,
color=color_list[i],
edgecolor="black",
label=method_list[i])
if i < len(first_x_position) / 2:
legend_bar.append(bar2)
ax2.text(x=3.5, y=-2400, s=x_label_list[1][0], fontsize="16")
ax2.text(x=15.5, y=-2400, s=x_label_list[1][1], fontsize="16")
plt.figlegend(legend_bar,
labels=method_list,
loc="upper center",
ncol=10,
bbox_to_anchor=(0.5, 0.95))
# plt.legend()
plt.show()
def plot_methods_recall_ratio(datasets, methods, method_labels):
n_datasets = len(datasets)
fig = plt.figure(figsize=(0.55 + 3.333 * len(datasets), 6.))
plt.rcParams.update({'font.size': 13})
plt.subplots_adjust(bottom=0.15, top=0.85, hspace=0.35)
for di, (dataset, dataset_label) in enumerate(zip(datasets,
dataset_labels)):
ax_recall = plt.subplot(2, n_datasets, di + 1)
plt.xlabel('Recall (%)')
plt.title(dataset_label)
if di == 0:
plt.ylabel('Query time (ms)')
plt.xlim(0, 100)
plt.title(dataset_label)
ax_ratio = plt.subplot(2, n_datasets, n_datasets + di + 1)
plt.xlabel('Ratio')
ax_ratio.set_xlim(1., 1.5)
ax_ratio.set_xticks([1., 1.1, 1.2, 1.3, 1.4, 1.5])
# plt.xlim(0, 100)
if di == 0:
plt.ylabel('Query time (ms)')
# plt.ylim(ymin=0)
for method, method_label, method_color, method_marker in zip(
methods, method_labels, method_colors, method_markers):
filename_prefix = get_file_prefix(dataset, method, distance)
filename = get_latest_res(filename_prefix)
if filename is None:
continue
print(filename)
time_recalls = []
time_ratios = []
settings = []
ress = []
for setting, res in parse_res(filename):
# print(setting, res)
time_recalls += [[gettime(res), getrecall(res)]]
time_ratios += [[gettime(res), getratio(res)]]
ress += [res]
settings += [setting]
time_recalls = np.array(time_recalls)
time_ratios = np.array(time_ratios)
# print(xys)
lower_x, lower_y = lower_bound_curve(time_recalls)
ax_recall.semilogy(lower_x,
lower_y,
'-',
color=method_color,
marker=method_marker,
label=method_label if di == 0 else "",
markevery=10,
markerfacecolor='none',
markersize=10)
# lower_x, lower_y = lower_bound_curve2(time_ratios[:, 0], time_ratios[:, 1])
qtime_ratio_lower = lower_bound_curve2(time_ratios[:, 0],
time_ratios[:, 1])
ax_ratio.semilogy(qtime_ratio_lower[:, 1],
qtime_ratio_lower[:, 0],
'-',
color=method_color,
marker=method_marker,
label="",
markevery=5,
markerfacecolor='none',
markersize=10)
plt.figlegend(fontsize=16,
bbox_to_anchor=(0.07, 0.85, 0.75, 0.2),
loc="center",
mode="expand",
borderaxespad=0,
ncol=len(methods))
plt.savefig('saved_figure/time_recall_ratio_%s.png' % distance,
bbox_inches='tight',
pad_inches=0.27,
dpi=fig.dpi)
plt.savefig('saved_figure/time_recall_ratio_%s.eps' % distance,
bbox_inches='tight',
pad_inches=0.27,
dpi=fig.dpi)
plt.show()
from Simulator import Simulator
from States import S_Start, S_DeadAge, S_DeadCancer
from numpy import average
from math import exp,log
import matplotlib.pylab as p
import HW2_model as Model
for cancer_mortality_rate in [0,.02,.05,.10]:
points = []
Model.TR[S_Start][S_DeadCancer]= lambda pt: \
1 - exp(-cancer_mortality_rate * pt.time_increment)
for start_age in range(25, 100):
s= Simulator(iters=5000, age=start_age, model=Model, time_increment = 1.0)
s.run()
le = average([pt.Age()-start_age for pt in s.patients])
points.append((start_age, le))
p.plot([x[0] for x in points],
[y[1] for y in points],
label="Gompertz plus cancer mortality = %s"%cancer_mortality_rate)
lines = p.gca().lines
p.figlegend(lines,
[l.get_label() for l in lines],
'upper right')
p.show()
for Pclass in classes:
if gender=='male':
colorscheme = [lightblue, darkblue] #blue for boys
row=0
else:
colorscheme = [lightpink, darkpink] #pink for a girl
row=1
group = train[(train.Sex==gender)&(train.Pclass==Pclass)]
group = group.groupby(['Embarked', 'Survived']).size().unstack()
group = group.div(group.sum(1), axis=0)
group.plot(kind='barh', ax=axes2[row, (int(Pclass)-1)], color=colorscheme, stacked=True, legend=False).set_title("{} {}".format("Class", Pclass)).axes.get_xaxis().set_ticks([])
plt.subplots_adjust(wspace=0.4, hspace=1.3)
fhandles, flabels = axes2[1,2].get_legend_handles_labels()
mhandles, mlabels = axes2[0,2].get_legend_handles_labels()
plt.figlegend(fhandles, ('die', 'live'), title='Female', loc='center', bbox_to_anchor=(0.06, 0.2, 1.1, .50))
plt.figlegend(mhandles, ('die', 'live'), 'center', title='Male',bbox_to_anchor=(-0.15, 0.2, 1.1, .50))
fig2.show()
bins = [0,5,14, 25, 40, 60, 100]
binNames =['Young Child', 'Child', 'Young Adult', 'Adult', 'Middle Aged', 'Older']
binAge = pd.cut(train.Age, bins, labels=binNames)
#cut uses the given bins, or if passed an integer, divides the range evenly
binFare = pd.qcut(train.Fare, 3, labels=['Cheap', 'Middle', 'Expensive'])
#qcut does quantiles
fig3, axes3 = plt.subplots(1,2)
binVars = [binAge, binFare]
varNames = ['Age', 'Fare']
badStringList=['(', ')', 'female', 'male', ',']
def removeBadStringFromString(string, badStringList):
def plot_data(data):
def proportionSurvived(discreteVar):
by_var = data.groupby([discreteVar, 'Survived'])
table = by_var.size()
table = table.unstack()
normedtable = table.div(table.sum(1), axis=0)
return normedtable
discreteVarList = ['Sex', 'Pclass', 'Embarked']
fig1, axes1 = plt.subplots(3, 1)
for i in range(3):
var = discreteVarList[i]
table = proportionSurvived(var)
table.plot(kind='barh', stacked=True, ax=axes1[i])
#fig1.show()
# Plot based on categories
fig2, axes2 = plt.subplots(2, 3)
genders = data.Sex.unique()
classes = data.Pclass.unique()
def normrgb(rgb):
rgb = [float(x)/255 for x in rgb]
return rgb
darkpink, lightpink = normrgb([255, 20, 147]), normrgb([255, 182, 193])
darkblue, lightblue = normrgb([0, 0, 128]), normrgb([135, 206, 250])
for gender in genders:
for pclass in classes:
if gender == 'male':
colorscheme = [lightblue, darkblue]
row = 0
else:
colorscheme = [lightpink, darkpink]
row = 1
group = data[(data.Sex==gender)&(data.Pclass==pclass)]
group = group.groupby(['Embarked', 'Survived']).size().unstack()
group = group.div(group.sum(1), axis=0)
group.plot(kind='barh', ax=axes2[row, (int(pclass)-1)], color=colorscheme, stacked=True, legend=False).set_title('Class '+str(pclass)).axes.get_xaxis().set_ticks([])
plt.subplots_adjust(wspace=0.4, hspace=1.3)
fhandles, flabels = axes2[1,2].get_legend_handles_labels()
mhandles, mlabels = axes2[0,2].get_legend_handles_labels()
plt.figlegend(fhandles, ('die', 'live'), title='Female', loc='center', bbox_to_anchor=(0.06, 0.45, 1.1, .102))
plt.figlegend(mhandles, ('die', 'live'), 'center', title='Male',bbox_to_anchor=(-0.15, 0.45, 1.1, .102))
#fig2.show()
# Plots using bins
bins = [0, 5, 14, 25, 40, 60, 100]
binNames = ['Young Child', 'Child', 'Young Adult', 'Adult', 'Middle Aged', 'Older']
binAge = pd.cut(data.Age, bins, labels=binNames)
binFare = pd.qcut(data.Fare, 3, labels=['Cheap', 'Middle', 'Expensive'])
fig3, axes3 = plt.subplots(1, 2)
binVars = [binAge, binFare]
varNames = ['Age', 'Fare']
badStringList = ['(', ')', 'female', 'male', ',']
def removeBadStringFromString(string):
for badString in ['(', ')', 'female', 'male']:
string = string.replace(badString, '')
return string
def removeBadStringFromLabels(ax, badStringList):
labels = [item.get_text() for item in ax.get_yticklabels()]
labels = [removeBadStringFromString(label) for label in labels]
return labels
for i in range(2):
group = data.groupby([binVars[i], 'Sex', 'Survived'])
group = group.size().unstack()
group = group.div(group.sum(1), axis=0)
cols = [[lightpink, lightblue],[darkpink, darkblue]]
group.plot(kind='barh', stacked=True, ax=axes3[i], legend=False, color=cols)
labels = removeBadStringFromLabels(axes3[i], badStringList)
axes3[i].set_yticklabels(labels)
axes3[i].get_xaxis().set_ticks([])
axes3[i].set_ylabel('')
axes3[i].set_title(varNames[i])
if i==1:
axes3[i].yaxis.tick_right()
axes3[i].yaxis.set_label_position("right")
handles, labels = axes3[0].get_legend_handles_labels()
plt.figlegend(handles[0], ['die', 'die'], loc='upper center')
plt.figlegend(handles[1], ['live', 'live'], loc='lower center')
fig3.show()
if gender=='male':
colorscheme = [lightblue, darkblue]
row=0
else:
colorscheme = [lightpink, darkpink]
row=1
group = traindf[(traindf.Sex==gender)&(traindf.Pclass==pclass)]
group = group.groupby(['Embarked', 'Survived']).size().unstack()
group = group.div(group.sum(1), axis=0)
group.plot(kind='barh', ax=axes2[row, (int(pclass)-1)], color=colorscheme, stacked=True, legend=False).set_title('Class '+str(pclass)).axes.get_xaxis().set_ticks([])
plt.subplots_adjust(wspace=0.4, hspace=1.3)
fhandles, flabels = axes2[1,2].get_legend_handles_labels()
mhandles, mlabels = axes2[0,2].get_legend_handles_labels()
plt.figlegend(fhandles, ('die', 'live'), title='Female', loc='center', bbox_to_anchor=(0.06, 0.45, 1.1, .102))
plt.figlegend(mhandles, ('die', 'live'), 'center', title='Male',bbox_to_anchor=(-0.15, 0.45, 1.1, .102))
fig2.show()
def removeBadStringFromString(string, badStringList):
for badString in badStringList:
string = string.replace(badString, '')
return string
def removeBadStringFromLabels(ax, badStringList):
labels = [item.get_text() for item in ax.get_yticklabels()]
labels = [removeBadStringFromString(label, badStringList) for label in labels]
return labels
def plotVarsGroupedBySex(varList, varNames, badStringList):
def plot_gene_coverage(ke, f, x_exon, gene
, chrom, sample_ids=None
, save_dir=None, **kwargs):
"""
Plot a gene's DegNorm-estimated and original coverage matrices. Option to save image.
:param ke: estimated coverage matrix
:param f: original coverage matrix
:param x_exon: numpy array with two columns, the first - start positions of exons comprising the gene,
the second - end positions of the exons started in the first column.
:param gene: str name of gene whose coverage is being plotted
:param chrom: str name of chromosome gene is on
:param sample_ids: list of str names of samples corresponding to coverage curves
:param save_dir: str path to output directory where gene coverage plot should be saved as
save_dir/<chromosome>/<gene>_coverage.png (optional, default None returns plt.Figure)
:param kwargs: keyword arguments to pass to matplotlib.pylab.figure (e.g. figsize)
:return: matplotlib.figure.Figure if save_dir is not specified, otherwise, a filepath to saved .png file
"""
# quality control.
if ke.shape != f.shape:
raise ValueError('ke and f arrays do not have the same shape.')
if sample_ids:
if not len(sample_ids) == ke.shape[0]:
raise ValueError('Number of supplied sample IDs does not match number'
'of samples in gene coverage matrix.')
else:
sample_ids = ['sample_{0}'.format(i + 1) for i in range(ke.shape[0])]
# get union of intersecting exons to prevent confusion when plotting exon junctions.
x_exon = get_exon_unions(x_exon)
# establish exon positioning on chromosome and junction break points.
rel_start = x_exon.min()
start = rel_start
if x_exon.shape[0] > 1:
diffs = x_exon[:, 1] - x_exon[:, 0]
rel_end = rel_start + np.sum(diffs)
end = x_exon.max()
else:
rel_end = x_exon.max()
end = rel_end
# before/after plot consists of 4 subplots, establish them.
fig = plt.figure(**kwargs)
fig.suptitle('Gene {0} coverage -- chromosome {1}'.format(gene, chrom))
gs = gridspec.GridSpec(2, 2,
width_ratios=[1, 1],
height_ratios=[20, 1])
with sns.axes_style('darkgrid'):
# upper-left subplot: original coverage curves
ax1 = plt.subplot(gs[0])
for i in range(f.shape[0]):
ax1.plot(f[i, :], label=sample_ids[i])
ax1.set_title('Original')
# upper-right subplot: DegNorm-estimated coverage curves
ax2 = plt.subplot(gs[1])
for i in range(ke.shape[0]):
ax2.plot(ke[i, :], label=sample_ids[i])
ax2.set_title('Normalized')
handles, labels = ax2.get_legend_handles_labels()
for ax in [ax1, ax2]:
ax.margins(x=0)
# lower-left subplot: exon positioning
ax3 = plt.subplot(gs[2])
ax3.set_xlim(start, end)
ax3.add_patch(Rectangle((start, 0)
, width=start + (rel_end - rel_start)
, height=1, fill=True, facecolor='red', lw=1))
# lower-right subplot: exon positioning
ax4 = plt.subplot(gs[3])
ax4.set_xlim(start, end)
ax4.add_patch(Rectangle((start, 0)
, width=start + (rel_end - rel_start)
, height=1, fill=True, facecolor='red', lw=1))
# exon splicing y-axis has no meaning, so remove it.
ax3.get_yaxis().set_visible(False)
ax4.get_yaxis().set_visible(False)
# only include start + end position of gene transcript in exon splicing map.
for ax in [ax3, ax4]:
ax.get_yaxis().set_visible(False)
ax.set_xticks([start, end])
ax.set_xticklabels([str(start), str(end)])
for i in range(x_exon.shape[0] - 1):
ax.axvline(x=x_exon[i, 1]
, ymin=0
, ymax=1
, color='w'
, lw=2)
# configure legend: determine if labels expand vertically or horizontally.
ncol = len(labels) if len(labels) < 6 else 1
loc = 'upper right' if ncol == 1 else 'lower center'
bbox_to_anchor = (1.1, 0.85) if ncol == 1 else None
plt.figlegend(handles
, labels
, loc=loc
, title='Sample'
, ncol=ncol
, bbox_to_anchor=bbox_to_anchor)
fig.tight_layout(rect=[0, 0.07, 1, 0.95])
# if save directory specified, save coverage plots to file save_dir/chrom/<gene>_coverage.png and close figure.
if not save_dir:
return fig
else:
if not os.path.isdir(os.path.join(save_dir, str(chrom))):
os.makedirs(os.path.join(save_dir, str(chrom)))
fig_path = os.path.abspath(os.path.join(save_dir, str(chrom), '{0}_coverage.png'.format(gene)))
fig.savefig(fig_path
, dpi=150
, bbox_inches='tight')
plt.close(fig)
return fig_path
def pretty_template_plot(template,
size=(10.5, 7.5),
save=False,
savefile=None,
title=False,
background=False,
picks=False):
r"""Function to make a pretty plot of a single template, designed to work \
better than the default obspy plotting routine for short data lengths.
:type template: :class: obspy.Stream
:param template: Template stream to plot
:type size: tuple
:param size: tuple of plot size
:type save: bool
:param save: if False will plot to screen, if True will save
:type savefile: str
:param savefile: String to save plot as, required if save=True.
:type title: bool
:param title: String if set will be the plot title
:type background: :class: obspy.stream
:param background: Stream to plot the template within.
:type picks: list of obspy.core.event.pick
:param picks: List of obspy type picks.
"""
_check_save_args(save, savefile)
fig, axes = plt.subplots(len(template), 1, sharex=True, figsize=size)
if len(template) > 1:
axes = axes.ravel()
if not background:
mintime = template.sort(['starttime'])[0].stats.starttime
else:
mintime = background.sort(['starttime'])[0].stats.starttime
template.sort(['network', 'station', 'starttime'])
lengths = []
lines = []
labels = []
for i, tr in enumerate(template):
# Cope with a singe channel template case.
if len(template) > 1:
axis = axes[i]
else:
axis = axes
delay = tr.stats.starttime - mintime
y = tr.data
x = np.linspace(0, (len(y) - 1) * tr.stats.delta, len(y))
x += delay
if background:
btr = background.select(station=tr.stats.station,
channel=tr.stats.channel)[0]
bdelay = btr.stats.starttime - mintime
by = btr.data
bx = np.linspace(0, (len(by) - 1) * btr.stats.delta, len(by))
bx += bdelay
axis.plot(bx, by, 'k', linewidth=1)
template_line, = axis.plot(x,
y,
'r',
linewidth=1.1,
label='Template')
if i == 0:
lines.append(template_line)
labels.append('Template')
lengths.append(max(bx[-1], x[-1]))
else:
template_line, = axis.plot(x,
y,
'k',
linewidth=1.1,
label='Template')
if i == 0:
lines.append(template_line)
labels.append('Template')
lengths.append(x[-1])
# print(' '.join([tr.stats.station, str(len(x)), str(len(y))]))
axis.set_ylabel('.'.join([tr.stats.station, tr.stats.channel]),
rotation=0,
horizontalalignment='right')
axis.yaxis.set_ticks([])
# Plot the picks if they are given
if picks:
tr_picks = [
pick for pick in picks
if pick.waveform_id.station_code == tr.stats.station
and pick.waveform_id.channel_code[0] +
pick.waveform_id.channel_code[-1] == tr.stats.channel[0] +
tr.stats.channel[-1]
]
for pick in tr_picks:
if 'P' in pick.phase_hint.upper():
pcolor = 'red'
label = 'P-pick'
elif 'S' in pick.phase_hint.upper():
pcolor = 'blue'
label = 'S-pick'
else:
pcolor = 'k'
label = 'Unknown pick'
pdelay = pick.time - mintime
# print(pdelay)
line = axis.axvline(x=pdelay,
color=pcolor,
linewidth=2,
linestyle='--',
label=label)
if label not in labels:
lines.append(line)
labels.append(label)
# axes[i].plot([pdelay, pdelay], [])
axis.set_xlim([0, max(lengths)])
if len(template) > 1:
axis = axes[len(template) - 1]
else:
axis = axes
axis.set_xlabel('Time (s) from start of template')
plt.figlegend(lines, labels, 'upper right')
if title:
if len(template) > 1:
axes[0].set_title(title)
else:
axes.set_title(title)
else:
plt.subplots_adjust(top=0.98)
plt.tight_layout()
plt.subplots_adjust(hspace=0)
if not save:
plt.show()
plt.close()
else:
plt.savefig(savefile)
