def meijaard_figure_four(time, rollRate, steerRate, speed):
width = 4.0 # inches
golden_ratio = (np.sqrt(5.0) - 1.0) / 2.0
height = width * golden_ratio
fig = figure()
fig.set_size_inches([width, height])
params = {'backend': 'ps',
'axes.labelsize': 10,
'text.fontsize': 10,
'legend.fontsize': 10,
'xtick.labelsize': 8,
'ytick.labelsize': 8,
'text.usetex': True}
rcParams.update(params)
fig.subplots_adjust(right=0.85, left=0.15, bottom=0.15)
rateAxis = fig.add_subplot(111)
speedAxis = rateAxis.twinx()
p1, = rateAxis.plot(time, rollRate, "k--",label="Roll Rate")
p2, = rateAxis.plot(time, steerRate, "k:", label="Steer Rate")
p3, = speedAxis.plot(time, speed, "k-", label="Speed")
rateAxis.set_ylim(-0.5, 1.0)
rateAxis.set_yticks([-0.5, 0.0, 0.5, 1.0])
rateAxis.set_xticks([0., 1., 2., 3., 4., 5.])
rateAxis.set_xlabel('Time [sec]')
rateAxis.set_ylabel('Angular Rate [rad/sec]')
lines = [p1, p2, p3]
rateAxis.legend(lines, [l.get_label() for l in lines])
speedAxis.set_ylim(4.55, 4.7)
speedAxis.set_yticks([4.55, 4.60, 4.65, 4.70])
speedAxis.set_ylabel('Speed [m/s]')
return fig
def meijaard_figure_four(time, rollRate, steerRate, speed):
width = 4.0 # inches
golden_ratio = (np.sqrt(5.0) - 1.0) / 2.0
height = width * golden_ratio
fig = figure()
fig.set_size_inches([width, height])
params = {'backend': 'ps',
'axes.labelsize': 10,
'text.fontsize': 10,
'legend.fontsize': 10,
'xtick.labelsize': 8,
'ytick.labelsize': 8,
'text.usetex': True}
rcParams.update(params)
fig.subplots_adjust(right=0.85, left=0.15, bottom=0.15)
rateAxis = fig.add_subplot(111)
speedAxis = rateAxis.twinx()
p1, = rateAxis.plot(time, rollRate, "k--",label="Roll Rate")
p2, = rateAxis.plot(time, steerRate, "k:", label="Steer Rate")
p3, = speedAxis.plot(time, speed, "k-", label="Speed")
rateAxis.set_ylim(-0.5, 1.0)
rateAxis.set_yticks([-0.5, 0.0, 0.5, 1.0])
rateAxis.set_xticks([0., 1., 2., 3., 4., 5.])
rateAxis.set_xlabel('Time [sec]')
rateAxis.set_ylabel('Angular Rate [rad/sec]')
lines = [p1, p2, p3]
rateAxis.legend(lines, [l.get_label() for l in lines])
speedAxis.set_ylim(4.55, 4.7)
speedAxis.set_yticks([4.55, 4.60, 4.65, 4.70])
speedAxis.set_ylabel('Speed [m/s]')
return fig
def plot_infection(trial_exposures, blocking=True, multiple=False, leg=None, title=None, file_name=None, size=None, font_size=None, leg_loc=None):
size = fig_size if size is None else size
font_size = plot_font_size if font_size is None else font_size
steps = len(trial_exposures) if not multiple else len(trial_exposures[0])
fig = figure(title, figsize=size)
rcParams.update({'font.size': font_size})
ax = fig.gca()
if multiple:
for data in trial_exposures:
ax.plot(data, linewidth=plot_line_width)
else:
ax.plot(trial_exposures, linewidth=plot_line_width)
ax.set_ylim(.2, .8)
ax.set_xlim(0, steps)
xlabel('Time Steps, n')
ylabel('Network Infection')
if title is not None: ax.set_title(title)
if leg is not None and leg_loc is None: legend(leg)
elif leg_loc is not None: legend(leg, bbox_to_anchor=leg_loc, loc='upper left')
if file_name is not None:
try: savefig(file_name, bbox_inches='tight', pad_inches=0)
except FileExistsError: pass
if blocking:
show()
def plot_timestep_convergence(self):
from matplotlib.pyplot import figure,subplot,xlabel,ylabel,plot,errorbar,title,text,xticks,rcParams,savefig,xlim
params = {'legend.fontsize':14,'figure.facecolor':'white','figure.subplot.hspace':0.,
'axes.labelsize':16,'xtick.labelsize':14,'ytick.labelsize':14}
rcParams.update(params)
timesteps = self.timesteps
energies = convert(self.energies.copy(),'Ha','eV')
errors = convert(self.errors.copy(),'Ha','eV')
Esmall = energies[0]
figure()
tsrange = [0,1.1*timesteps[-1]]
plot(tsrange,[0,0],'k-')
errorbar(timesteps,energies-Esmall,errors,fmt='k.')
text(array(tsrange).mean(),0,'{0:6.4f} eV'.format(Esmall))
xticks(timesteps)
xlim(tsrange)
xlabel('Timestep (Ha)')
ylabel('Total Energy (eV)')
title('DMC Timestep Convergence')
savefig('TimestepConvergence.png',format='png',bbox_inches ='tight',pad_inches=1)
def main():
assert len(argv) == 3
with open(argv[1], "r") as file:
line = file.readline()
if "." in line:
x = loadtxt(argv[1], dtype="float32", delimiter="\n")
kwargs = {"discrete": False}
else:
x = loadtxt(argv[1], dtype="int32", delimiter="\n")
kwargs = {"discrete": True}
rcParams.update({"font.family": "monospace"})
(_, ax) = subplots(figsize=(9, 6))
histplot(x, kde=False, color="darkgray", ec="w", ax=ax, **kwargs)
ax.set_ylabel(None)
kwargs = {
"lw": 3,
"ls": "--",
"alpha": 0.625,
}
m = mean(x)
ax.axvline(m, label=f"mean => {m:.2f}", c="blue", **kwargs)
m = median(x)
ax.axvline(m, label=f"median => {m:.2f}", c="red", **kwargs)
ax.legend()
tight_layout()
savefig(argv[2])
close()
def make_skewt_axes(self,pmax=1050.,pmin=100.):
self.fig = figure(figsize=(7,8))
self.fig.clf()
rcParams.update({\
'font.size':10,\
})
self.skewxaxis=self.fig.add_axes([.085,.1,.83,.8], projection='skewx')
self.skewxaxis.set_yscale('log')
xticklocs=arange(-80,45,10)
T0 = xticklocs
P=linspace(pmax,pmin,37)
w = array([0.0001,0.0004,0.001, 0.002, 0.004, 0.007, 0.01, 0.016, 0.024, 0.032])
self.skewxaxis.add_mixratio_isopleths(w,P[P>=550],color='purple',ls='--',alpha=1.,lw=0.5)
self.skewxaxis.add_dry_adiabats(linspace(250,440,20)-273.15,P,color='red',ls='--',alpha=1.,lw=0.5)
self.skewxaxis.add_moist_adiabats(linspace(8,32,7),P[P>=200],color='g',ls='--',alpha=1.,lw=0.5)
self.skewxaxis.other_housekeeping()
self.wbax=self.fig.add_axes([0.815,0.1,0.1,0.8],sharey=self.skewxaxis,frameon=False)
self.wbax.xaxis.set_ticks([],[])
self.wbax.yaxis.grid(True,ls='-',color='y',lw=0.5)
for tick in self.wbax.yaxis.get_major_ticks():
# tick.label1On = False
pass
self.wbax.get_yaxis().set_tick_params(size=0,color='y')
self.wbax.set_xlim(-1.5,1.5)
self.wbax.get_yaxis().set_visible(False)
# Set up standard atmosphere height scale on
# LHS of plot. It's jus
majorLocatorKM = MultipleLocator(2)
majorLocatorKFT = MultipleLocator(5)
minorLocator = MultipleLocator(1)
self.kmhax=self.fig.add_axes([0.915,0.1,1e-6,0.8],frameon=True)
self.kmhax.xaxis.set_ticks([],[])
self.kmhax.spines['left'].set_color('k')
self.kmhax.spines['right'].set_visible(False)
self.kmhax.tick_params(axis='y', colors='k',labelsize=10)
self.kmhax.set_ylim(0,16.18)
self.kmhax.set_title("km/kft",fontsize=12)
self.kmhax.get_yaxis().set_tick_params(which="both",direction='out')
self.kmhax.yaxis.set_major_locator(majorLocatorKM)
self.kmhax.yaxis.set_minor_locator(minorLocator)
self.fthax=self.kmhax.twinx()
self.fthax.xaxis.set_ticks([],[])
self.fthax.tick_params(axis='y', colors='k',labelsize=10)
self.fthax.set_ylim(0,53.084)
self.fthax.get_yaxis().set_tick_params(which="both",direction='out')
self.fthax.yaxis.set_major_locator(majorLocatorKFT)
self.fthax.yaxis.set_minor_locator(minorLocator)
def ploter(plot1, cv1, plot2, cv2, plot3, cv3, args):
rcParams['figure.figsize'] = args['size'] # 16, 8
floor = 1
factor = 0.9
gap = 0.4
intensity = 0.9
if len(cv1):
points = len(plot1)
limit_cv = float(max(cv1))
for n, c in zip(xrange(points - 1), cv1):
# if c != 0:
# intensity = factor * (floor + ((c * (1 - floor)) / limit_cv))
# else:
# intensity = gap
plot([plot1[n][0], plot1[n + 1][0]],
[plot1[n][1], plot1[n + 1][1]], color=(0, intensity, intensity))
if len(cv2):
points = len(plot2)
limit_cv = float(max(cv2))
for n, c in zip(xrange(points - 1), cv2):
# if c != 0:
# intensity = factor * (floor + ((c * (1 - floor)) / limit_cv))
# else:
# intensity = gap
plot([plot2[n][0], plot2[n + 1][0]],
[plot2[n][1], plot2[n + 1][1]], color=(intensity, 0, intensity))
if len(cv3):
points = len(plot3)
limit_cv = float(max(cv3))
for n, c in zip(xrange(points - 1), cv3):
# if c != 0:
# intensity = factor * (floor + ((c * (1 - floor)) / limit_cv))
# else:
# intensity = gap
plot([plot3[n][0], plot3[n + 1][0]],
[plot3[n][1], plot3[n + 1][1]], color=(intensity, intensity, 0))
# plot(x1, y1, '#009999', x2, y2, '#990099', x3, y3, '#999900', linewidth=
# 1.3)
title(args['title'])
xlimit = args['xlim']
ylimit = args['ylim']
if (xlimit[0] != 0 or xlimit[1] != 0) and (type(xlimit[0]) == type(xlimit[1]) == float):
xlim(xlimit)
else:
xlimit = (1, max([len(plot1), len(plot2), len(plot3)]))
xlim(xlimit)
if (ylimit[0] != 0 or ylimit[1] != 0) and (type(ylimit[0]) == type(ylimit[1]) == float):
ylim(ylimit)
# else:
# ny = [y for y in y1 + y2 + y3 if y is not None]
# ylimit = (min(ny) - 1, max(ny) + 1)
# ylim(ylimit)
rcParams.update({'font.size': args['font_size']})
savefig(args['outfile'], bbox_inches=0)
show()
clf()
def draw_png(filename, saveDir):
dict = readXDC(filename)
name = filename.split('/')[-1].split('.', 1)[0]
if not os.path.exists(saveDir):
os.makedirs(saveDir)
keys = list(dict.keys()) # keys are integer, which is block index
width = 560 + gap * (len(vu11p) - 1)
height = 5414.4 if len(keys) > 80 else 1000
fontsize = 80 if len(keys) > 80 else 40
rcParams.update({'font.size': fontsize})
# draw transparent polygons on a new image
fig, ax = plt.subplots(figsize=(256,100))
fig.set_size_inches(width / 100, height / 100)
ax.set_xlim(0, width + 20)
ax.set_ylim(0, height + 20)
rect = patches.Rectangle((10, 10), width, height, linewidth=1, facecolor='#f6f6f6')
ax.add_patch(rect)
drawBackGround(ax, width, height, gap)
for j in range(len(keys)):
key = keys[j]
entries = dict.get(key)
sites = np.zeros(shape=(len(entries) * 4, 2))
for pair in entries:
site = pair[0]
x = 0
y = 0
if site.startswith('DSP'):
x, y = drawDSP(ax, site, gap, dsp_block)
elif site.startswith('RAMB'):
x, y = drawBRAM(ax, site, gap, bram_block)
elif site.startswith('URAM'):
x, y = drawURAM(ax, site, gap, uram_block)
for index in range(4):
sites[entries.index(pair) * 4 + index, 0] = x[index]
sites[entries.index(pair) * 4 + index, 1] = y[index]
drawTiles(ax, sites, '#000000')
# add axis
legend_elements = [
Patch(facecolor=dsp_block, label='DSP48'),
Patch(facecolor=bram_block, label='BRAM'),
Patch(facecolor=uram_block, label='URAM')
]
# ax.legend(handles=legend_elements, prop={'size':13})
# plt.savefig(saveDir + name + '.png', dpi=50)
# plt.close()
plt.savefig(saveDir + name + '.pdf')
plt.close()
print(saveDir + name + '.pdf')
return saveDir + name + '.pdf'
def meijaard_figure_four(time, rollRate, steerRate, speed):
"""Returns a figure that matches Figure #4 in [Meijaard2007]_.
References
----------
.. [Meijaard2007]_ J. P. Meijaard, Jim M. Papadopoulos, Andy Ruina, and A.
L. Schwab. Linearized dynamics equations for the balance and steer of a
bicycle: A benchmark and review. Proceedings of the Royal Society A:
Mathematical, Physical and Engineering Sciences, 463(2084):1955–1982,
August 2007.
"""
width = 4.0 # inches
golden_ratio = (np.sqrt(5.0) - 1.0) / 2.0
height = width * golden_ratio
fig = figure()
fig.set_size_inches([width, height])
params = {
'backend': 'ps',
'axes.labelsize': 10,
'text.fontsize': 10,
'legend.fontsize': 10,
'xtick.labelsize': 8,
'ytick.labelsize': 8,
'text.usetex': True
}
rcParams.update(params)
fig.subplots_adjust(right=0.85, left=0.15, bottom=0.15)
rateAxis = fig.add_subplot(111)
speedAxis = rateAxis.twinx()
p1, = rateAxis.plot(time, rollRate, "k--", label="Roll Rate")
p2, = rateAxis.plot(time, steerRate, "k:", label="Steer Rate")
p3, = speedAxis.plot(time, speed, "k-", label="Speed")
rateAxis.set_ylim(-0.5, 1.0)
rateAxis.set_yticks([-0.5, 0.0, 0.5, 1.0])
rateAxis.set_xticks([0., 1., 2., 3., 4., 5.])
rateAxis.set_xlabel('Time [sec]')
rateAxis.set_ylabel('Angular Rate [rad/sec]')
lines = [p1, p2, p3]
rateAxis.legend(lines, [l.get_label() for l in lines])
speedAxis.set_ylim(4.55, 4.7)
speedAxis.set_yticks([4.55, 4.60, 4.65, 4.70])
speedAxis.set_ylabel('Speed [m/s]')
return fig
def everything(datfile,
volSample,
pSal,
totalCarbonate,
totalPhosphate,
totalSilicate,
concAcid,
WhichKs=10,
WhoseKSO4=1,
WhoseKF=1,
WhoseTB=2,
totalAmmonia=0,
totalH2Sulfide=0):
"""Plot everything for a single titration from a VINDTA-style .dat file."""
(massAcid, emf, massSample, f1Guess, LGuess, alkGuess, emf0Guess, granEmf0,
alk_emf0, alkSim, alk0Sim, RMS, Npts,
rgb) = prep(datfile,
volSample,
pSal,
totalCarbonate,
totalPhosphate,
totalSilicate,
concAcid,
WhichKs=WhichKs,
WhoseKSO4=WhoseKSO4,
WhoseKF=WhoseKF,
WhoseTB=WhoseTB,
totalAmmonia=totalAmmonia,
totalH2Sulfide=totalH2Sulfide)
fig = figure(figsize=[17, 10])
rcParams.update({'font.size': 10})
gs = fig.add_gridspec(4, 2)
subplots_adjust(wspace=0.3, hspace=0.8)
ax1 = fig.add_subplot(gs[0, 0])
emfTitration(ax1, massAcid, emf, massSample, concAcid, alk_emf0, alkGuess,
rgb, '(a)')
ax2 = fig.add_subplot(gs[1, 0])
f1Gran(ax2, massAcid, massSample, concAcid, f1Guess, alkGuess, rgb, '(b)')
ax3 = fig.add_subplot(gs[2, 0])
emf0Estimates(ax3, massAcid, massSample, concAcid, granEmf0, emf0Guess,
alkGuess, LGuess, rgb, '(c)')
ax4 = fig.add_subplot(gs[3, 0])
alkTitration(ax4, massAcid, alkSim, rgb, '(d)')
ax5 = fig.add_subplot(gs[0, 1])
alkEstimates(ax5, massAcid, alk0Sim, rgb, alk_emf0, RMS, Npts, '(e)')
ax6 = fig.add_subplot(gs[1:4, 1])
components(ax6, massAcid, alk0Sim, alkSim, '(f)')
return fig
def meijaard_figure_four(time, rollRate, steerRate, speed):
"""Returns a figure that matches Figure #4 in [Meijaard2007]_.
References
----------
.. [Meijaard2007]_ J. P. Meijaard, Jim M. Papadopoulos, Andy Ruina, and A.
L. Schwab. Linearized dynamics equations for the balance and steer of a
bicycle: A benchmark and review. Proceedings of the Royal Society A:
Mathematical, Physical and Engineering Sciences, 463(2084):1955–1982,
August 2007.
"""
width = 4.0 # inches
golden_ratio = (np.sqrt(5.0) - 1.0) / 2.0
height = width * golden_ratio
fig = figure()
fig.set_size_inches([width, height])
params = {'backend': 'ps',
'axes.labelsize': 10,
'text.fontsize': 10,
'legend.fontsize': 10,
'xtick.labelsize': 8,
'ytick.labelsize': 8,
'text.usetex': True}
rcParams.update(params)
fig.subplots_adjust(right=0.85, left=0.15, bottom=0.15)
rateAxis = fig.add_subplot(111)
speedAxis = rateAxis.twinx()
p1, = rateAxis.plot(time, rollRate, "k--", label="Roll Rate")
p2, = rateAxis.plot(time, steerRate, "k:", label="Steer Rate")
p3, = speedAxis.plot(time, speed, "k-", label="Speed")
rateAxis.set_ylim(-0.5, 1.0)
rateAxis.set_yticks([-0.5, 0.0, 0.5, 1.0])
rateAxis.set_xticks([0., 1., 2., 3., 4., 5.])
rateAxis.set_xlabel('Time [sec]')
rateAxis.set_ylabel('Angular Rate [rad/sec]')
lines = [p1, p2, p3]
rateAxis.legend(lines, [l.get_label() for l in lines])
speedAxis.set_ylim(4.55, 4.7)
speedAxis.set_yticks([4.55, 4.60, 4.65, 4.70])
speedAxis.set_ylabel('Speed [m/s]')
return fig
def plot(data, samples, sims):
rcParams.update({"font.family": "monospace"})
fig = figure(figsize=(14, 13.5), dpi=75)
gs = GridSpec(2, 3, figure=fig, height_ratios=[1, 4])
ax0 = fig.add_subplot(gs[0, :])
ax1 = fig.add_subplot(gs[1, 0])
ax2 = fig.add_subplot(gs[1, 1])
ax3 = fig.add_subplot(gs[1, 2])
histplot(
samples.goals_pred,
kde=True,
discrete=True,
color="darkgray",
ec="w",
ax=ax0,
)
ax0.set_ylabel("sim goals")
ax0.axvline(data.goal.sum(), ls="--", color="r", lw=2, label="obs goals")
ax0.legend()
kwargs = {
"cmap": "bone",
}
ax1.set_title("obs shot clusters, density")
kdeplot(x=data.y, y=data.x, fill=True, ax=ax1, **kwargs)
scatterplot(
x=data.y,
y=data.x,
hue=data.cluster,
palette="Dark2",
ax=ax1,
)
ax2.set_title("pred shot probability")
ax2.tricontourf(sims["y"], sims["x"], sims["shot_prob"], **kwargs)
scatterplot(x=data.y, y=data.x, ax=ax2)
ax3.set_title("pred goal probability")
ax3.tricontourf(sims["y"], sims["x"], sims["goal_prob"], **kwargs)
ax3.set_yticks([])
scatterplot(x=data.y, y=data.x, hue=data.goal.astype("int32"), ax=ax3)
for ax in [ax1, ax2, ax3]:
ax.set_aspect("equal")
ax.set_xlim([-45.0, 45.0])
ax.set_ylim([200.0, 0.0])
ax.set_xlabel("")
ax.set_ylabel("")
tight_layout()
savefig(FILENAME["results"])
close()
def plot_network(network,
blocking=True,
netx_plot=False,
size=fig_size,
weights=None,
file_name=None,
plot_edges=False,
alph=.05):
fig = figure(figsize=size)
rcParams.update({
'font.size': plot_font_size,
'mathtext.default': 'regular'
})
ax = fig.gca()
ax.axis('off') # Disable axis
graph = network if netx_plot else network.network_plot
plot_layout = kamada_kawai_layout(graph)
cmap = cm.get_cmap('coolwarm')
sizes, edge_colors, node_colors = 80, 'k', 'w'
if weights is not None:
sizes = [50 if weight == 0 else 80 for weight in weights]
node_colors = weights
min_val, max_val = min(weights), max(weights)
if plot_edges: draw_networkx_edges(graph, plot_layout, alpha=alph)
draw_networkx_nodes(graph,
plot_layout,
node_size=sizes,
linewidths=.5,
edgecolors='k',
node_color=node_colors,
cmap=cmap)
draw()
if weights is not None:
plt = cm.ScalarMappable(cmap=cmap, norm=Normalize(vmin=0, vmax=1))
plt._A = []
colorbar(plt)
if file_name is not None:
savefig(file_name, bbox_inches='tight', pad_inches=0)
show(block=blocking) # Open matplotlib window
def plot_timestep_convergence(self):
from matplotlib.pyplot import (
figure,
subplot,
xlabel,
ylabel,
plot,
errorbar,
title,
text,
xticks,
rcParams,
savefig,
xlim,
)
params = {
"legend.fontsize": 14,
"figure.facecolor": "white",
"figure.subplot.hspace": 0.0,
"axes.labelsize": 16,
"xtick.labelsize": 14,
"ytick.labelsize": 14,
}
rcParams.update(params)
timesteps = self.timesteps
energies = convert(self.energies.copy(), "Ha", "eV")
errors = convert(self.errors.copy(), "Ha", "eV")
Esmall = energies[0]
figure()
tsrange = [0, 1.1 * timesteps[-1]]
plot(tsrange, [0, 0], "k-")
errorbar(timesteps, energies - Esmall, errors, fmt="k.")
text(array(tsrange).mean(), 0, "{0:6.4f} eV".format(Esmall))
xticks(timesteps)
xlim(tsrange)
xlabel("Timestep (Ha)")
ylabel("Total Energy (eV)")
title("DMC Timestep Convergence")
savefig("TimestepConvergence.png", format="png", bbox_inches="tight", pad_inches=1)
def plot(self,i, praefix=''):
# plot i-th component
h = self.h
t = self.tout
x = self.xout[:,i]
#ordnet den jeweiligen Stellen im Vektor die richtige Bezeichnung zu, die vom Plot aufgerufen werden kann
label = ['Anzahl der Gesunden', 'Anzahl der Kranken', 'Anzahl der Resistenten']
p0 = plot(t, x, linewidth = 2, label = label[i])
#das Folgende ist auskommentiert, da wir keine exakte Lösung haben
#try:
#self.exactsol()
#p1=plot(t,self.xexact, 'r',label='exakte Loesung')
#except:
#print("no exact solution given")
title(self.example+' mit '+self.method+', h='+str(h)+', beta = '+str(self.beta)+', gamma = '+str(self.gamma))
rcParams.update({'font.size': 12})
legend(loc='center right', shadow=True)
xlabel('t')
ylabel('x(t)')
#speichert die Plotts, sobald alle 3 Graphen enthalten sind
if i == 2:
savefig('sol_'+str(praefix)+'_'+self.example+'_'+self.method+'_s'+str(self.snum)+'_i'+str(i)+str(self.beta)+str(self.gamma)+'.png')
def plot_bandstructure(self,
filename=None,
filepath=None,
max_min_e=None,
show=False,
save=True,
show_vbm_cbm=True):
if 'bands' in self:
from structure import get_kpath
if filename == None:
filename = 'band_structure.pdf'
if filepath == None:
filepath = os.path.join(self.abspath, filename)
else:
filepath = os.path.join(filepath, filename)
#end if
try:
import matplotlib
gui_envs = ['GTKAgg', 'TKAgg', 'agg', 'Qt4Agg', 'WXAgg']
for gui in gui_envs:
try:
matplotlib.use(gui, warn=False, force=True)
from matplotlib import pyplot
success = True
break
except:
continue
#end try
#end for
from matplotlib.pyplot import figure, plot, xlabel, ylabel, title, show, ylim, legend, xlim, rcParams, rc, savefig, gca, xticks, axvline, scatter
params = {
'legend.fontsize': 14,
'figure.facecolor': 'white',
'figure.subplot.hspace': 0.,
'axes.labelsize': 16,
'xtick.labelsize': 14,
'ytick.labelsize': 14
}
rcParams.update(params)
except (ImportError, RuntimeError):
success = False
if not success:
figure, plot, xlabel, ylabel, title, show, ylim, legend, xlim, rcParams, savefig, bar, xticks, subplot, grid, setp, errorbar, loglog, semilogx, semilogy, text = unavailable(
'matplotlib.pyplot', 'figure', 'plot', 'xlabel', 'ylabel',
'title', 'show', 'ylim', 'legend', 'xlim', 'rcParams',
'savefig', 'bar', 'xticks', 'subplot', 'grid', 'setp',
'errorbar', 'loglog', 'semilogx', 'semilogy', 'text')
#end if
fig = figure()
ax = gca()
kpath = get_kpath(structure=self.input_structure,
check_standard=False)
x = kpath['explicit_path_linearcoords']
labels = kpath['explicit_kpoints_labels']
nbands = self.input.system.nbnd
for nb in range(nbands):
y = []
for bi in self.bands.up:
y.append(bi['eigs'][nb])
#end for
y = array(y) - self.bands.vbm.energy
plot(x, y, 'k')
if len(self.bands.down) > 0:
y = []
for bi in self.bands.down:
y.append(bi['eigs'][nb])
#end for
y = array(y) - self.bands.vbm.energy
plot(x, y, 'r')
#end if
#end for
for ln, li in enumerate(labels):
if li is not '':
axvline(x[ln], ymin=-100, ymax=100, linewidth=3, color='k')
if li == 'GAMMA':
labels[ln] = r'$\Gamma$'
elif li is not '':
labels[ln] = '${0}$'.format(li)
#end if
if labels[ln - 1] is not '' and ln > 0:
labels[ln] = labels[ln - 1] + '|' + labels[ln]
labels[ln - 1] = ''
#end if
#end if
#end for
xlim([min(x), max(x)])
if max_min_e is None:
ylim(-5, +5)
else:
ylim(max_min_e[0], max_min_e[1])
#end if
ylabel('Energy (eV)')
xticks(x, labels)
ax.tick_params(axis='x', which='both', length=0)
ax.tick_params(axis='x', which='both', pad=10)
#end if
if show_vbm_cbm:
vbm = self.bands.vbm
cbm = self.bands.cbm
for kn, ki in enumerate(self.bands.up):
if (vbm.kpoint_rel == ki['kpoint_rel']).all():
scatter(x[kn], 0, c='green', s=100)
#end if
if (cbm.kpoint_rel == ki['kpoint_rel']).all():
scatter(x[kn], cbm.energy - vbm.energy, c='r', s=100)
#end if
#end for
#end if
if save:
savefig(filename, format='pdf', bbox_inches='tight')
#end if
if show:
show()
def make_skewt_axes(self,pmax=1050.,pmin=100.,tmin=-40.,tmax=30.):
"""Set up the skew-t axis the way I like to see it"""
self.fig = figure(figsize=(8,8))
self.fig.clf()
rcParams.update({\
'font.size':10,\
})
self.skewxaxis=self.fig.add_axes([.065,.1,.71,.8], projection='skewx')
self.skewxaxis.set_yscale('log')
self.skewxaxis.pmax=pmax
self.skewxaxis.pmin=pmin
self.skewxaxis.tmax=tmax
self.skewxaxis.tmin=tmin
xticklocs=arange(-80,45,10)
T0 = xticklocs
# P=linspace(pmax,pmin,101)
P=logspace(log10(pmax),log10(pmin),101)
w = array([0.00001,0.0001,0.0004,0.001, 0.002, 0.004, 0.007, 0.01, 0.016, 0.024, 0.032])
self.skewxaxis.add_mixratio_isopleths(w,P[P>=700],color='g',ls='--',alpha=1.,lw=0.5)
self.skewxaxis.add_dry_adiabats(linspace(210,550,18)-degCtoK,P,color='g',ls='--',alpha=1.,lw=0.5)
self.skewxaxis.add_moist_adiabats(linspace(0,44,12),pmax,color='g',ls='--',alpha=1.,lw=0.5)
#self.skewxaxis.add_moist_adiabats(linspace(0,38,12),pmax,color='g',ls='--',alpha=1.,lw=0.5)
self.skewxaxis.set_title("%s %s"%(self['StationNumber'],self['SoundingDate']))
self.skewxaxis.other_housekeeping()
self.wbax=self.fig.add_axes([0.685,0.1,0.1,0.8],sharey=self.skewxaxis,frameon=False)
self.wbax.xaxis.set_ticks([],[])
self.wbax.yaxis.grid(True,ls='-',color='y',lw=0.5)
for tick in self.wbax.yaxis.get_major_ticks():
# tick.label1On = False
pass
self.wbax.get_yaxis().set_tick_params(size=0,color='y')
self.wbax.set_xlim(-1.5,1.5)
self.wbax.get_yaxis().set_visible(False)
self.wbax.set_title('kn',fontsize=10,color='k',ha='right')
# Set up standard atmosphere height scale on
# LHS of plot.
majorLocatorKM = MultipleLocator(2)
majorLocatorKFT = MultipleLocator(5)
minorLocator = MultipleLocator(1)
# determine base height from base pressure (nominally 1050 hPa)
# via hydrostatic equilibrium for standard atmosphere
# model atmospheric conditions with constant lapse rate and
# NIST (1013.25hPa and 20C)
zmin=barometric_equation_inv(0,293.15,101325.,pmax*100.)
zmax=barometric_equation_inv(0,293.15,101325.,pmin*100.)
zminf=zmin*3.2808
zmaxf=zmax*3.2808
self.kmhax=self.fig.add_axes([0.775,0.1,1e-6,0.8],frameon=True)
self.kmhax.xaxis.set_ticks([],[])
self.kmhax.spines['left'].set_color('k')
self.kmhax.spines['right'].set_visible(False)
self.kmhax.tick_params(axis='y', colors='k',labelsize=8)
self.kmhax.set_ylim(zmin*1e-3,zmax*1e-3)
self.kmhax.set_title("km/kft",fontsize=10)
self.kmhax.get_yaxis().set_tick_params(which="both",direction='out')
self.kmhax.yaxis.set_major_locator(majorLocatorKM)
self.kmhax.yaxis.set_minor_locator(minorLocator)
self.fthax=self.kmhax.twinx()
self.fthax.xaxis.set_ticks([],[])
self.fthax.tick_params(axis='y', colors='k',labelsize=8)
self.fthax.set_ylim(zminf*1e-3,zmaxf*1e-3)
self.fthax.get_yaxis().set_tick_params(which="both",direction='out')
self.fthax.yaxis.set_major_locator(majorLocatorKFT)
self.fthax.yaxis.set_minor_locator(minorLocator)
from periodic_table import pt
from unit_converter import convert
from generic import obj
from developer import DevBase, unavailable
try:
from matplotlib.pyplot import figure, plot, xlabel, ylabel, title, show, ylim, legend, xlim, rcParams, savefig, bar, xticks, subplot, grid, setp, errorbar, loglog, semilogx, semilogy
params = {
'legend.fontsize': 14,
'figure.facecolor': 'white',
'figure.subplot.hspace': 0.,
'axes.labelsize': 16,
'xtick.labelsize': 14,
'ytick.labelsize': 14
}
rcParams.update(params)
except (ImportError, RuntimeError):
figure, plot, xlabel, ylabel, title, show, ylim, legend, xlim, rcParams, savefig, bar, xticks, subplot, grid, setp, errorbar, loglog, semilogx, semilogy = unavailable(
'matplotlib.pyplot', 'figure', 'plot', 'xlabel', 'ylabel', 'title',
'show', 'ylim', 'legend', 'xlim', 'rcParams', 'savefig', 'bar',
'xticks', 'subplot', 'grid', 'setp', 'errorbar', 'loglog', 'semilogx',
'semilogy')
#end try
class Pseudopotential(DevBase):
colors = dict(s='k', p='r', d='b', f='m')
lcolors = ['k', 'r', 'b', 'm']
ldict = {0: 's', 1: 'p', 2: 'd', 3: 'f'}
format = 'generic'
def make_skewt_axes(self, pmax=1100., pmin=50.):
self.fig = figure(figsize=(9, 8))
self.fig.clf()
rcParams.update({\
'font.size':10,})
P = linspace(pmax, pmin, 37)
pres_levels = np.arange(1000, 0, -100)
self.skewxaxis = self.fig.add_axes([.085, .1, .73, .8],
projection='skewx')
self.skewxaxis.set_yscale('log')
xticklocs = arange(-80, 45, 10)
T0 = xticklocs
w = array([
0.0001, 0.0004, 0.001, 0.002, 0.004, 0.007, 0.01, 0.016, 0.024,
0.032
])
self.skewxaxis.add_mixratio_isopleths(w,
P[P >= 550],
color='purple',
ls='--',
alpha=1.,
lw=0.5)
self.skewxaxis.add_dry_adiabats(linspace(250, 440, 20) - 273.15,
P,
color='red',
ls='--',
alpha=1.,
lw=0.5)
self.skewxaxis.add_moist_adiabats(linspace(8, 32, 7),
P[P >= 200],
color='g',
ls='--',
alpha=1.,
lw=0.5)
self.skewxaxis.other_housekeeping()
self.wbax = self.fig.add_axes([0.75, 0.1, 0.1, 0.8],
sharey=self.skewxaxis,
frameon=False) # wind barb axis
self.wbax.xaxis.set_ticks([], [])
self.wbax.yaxis.grid(True, ls='-', color='y', lw=0.5)
for tick in self.wbax.yaxis.get_major_ticks():
# tick.label1On = False
pass
self.wbax.get_yaxis().set_tick_params(size=0, color='y')
self.wbax.set_xlim(-1.5, 1.5)
self.wbax.get_yaxis().set_visible(False)
# Set up standard atmosphere height scale on
# LHS of plot. It's jus
majorLocatorKM = MultipleLocator(2)
majorLocatorKFT = MultipleLocator(5)
minorLocator = MultipleLocator(1)
self.htax = self.fig.add_axes([0.1, 0.1, 1e-6, 0.8],
sharey=self.skewxaxis,
frameon=False)
self.htax.xaxis.set_ticks([], [])
self.htax.spines['left'].set_color('k')
self.htax.spines['right'].set_visible(False)
self.htax.get_yaxis().set_tick_params(size=0, color='y')
self.htax.get_yaxis().set_visible(False)
pres_heights = np.array([
self.data['hght'][np.argmin(np.abs(_ - self.data['pres']))]
for _ in pres_levels
])
for ph in range(pres_levels.shape[0]):
self.htax.text(0,
pres_levels[ph],
'%d m' % pres_heights[ph],
fontsize=8)
#print pres_heights
self.fig.text(0.83, 0.9, 'Sounding Params')
#$\underline{Sounding Params}$')
#h0 = interp(0, self.data['temp'], self.data['hght'])
h0 = self.data['hght'][np.argmin(abs(self.data['temp'] - 0))]
h20 = self.data['hght'][np.argmin(abs(self.data['temp'] + 20))]
h40 = self.data['hght'][np.argmin(abs(self.data['temp'] + 40))]
lcl_height = 216 * (self.data['temp'][0] - self.data['dwpt'][0])
mr = MixRatio(SatVap(self.data['dwpt']), self.data['pres'] * 100)
pdiff = -1.0 * np.diff(self.data['pres'])
#print mr.shape, pdiff.shape
# precipitable water
pw = np.sum(
np.array([
mr[_] * 100.0 * pdiff[_] / 9.8 for _ in range(pdiff.shape[0])
]))
# crude estimate of wet bulb temp
tw = self.data['temp'][0] - (self.data['temp'][0] -
self.data['dwpt'][0]) / 3.
# now some shear calculations
wspd6km = self.data['sknt'][np.argmin(abs(self.data['hght'] - 6000))]
wdir6km = self.data['drct'][np.argmin(abs(self.data['hght'] - 6000))]
udiff = wspd6km * np.cos(
np.radians(270 - wdir6km)) - self.data['sknt'][3] * np.cos(
np.radians(270 - self.data['drct'][3]))
vdiff = wspd6km * np.sin(
np.radians(270 - wdir6km)) - self.data['sknt'][3] * np.sin(
np.radians(270 - self.data['drct'][3]))
#print udiff, vdiff
shear6km = np.sqrt(udiff**2 + vdiff**2)
#trop_index = tropopause_index(self.data['temp'])
#if trop_index == -999: tropopause_pressure = -999.
#else: tropopause_pressure = self.data['pres'][trop_index]
# New way to do tropopause pressure
# first calculate the parameters that go in
smooth_temp = running_mean(self.data['temp'], 3, ntimes=30)
dp = np.gradient(self.data['pres'])
#lapse = 1000.0*np.diff(smooth)/np.diff(S.data['hght'])
deriv = np.gradient(smooth_temp, dp)
deriv_smooth = running_mean(deriv, 3, ntimes=30)
theta = running_mean(Theta(273.15 + self.data['temp'],
self.data['pres'] * 100.0),
3,
ntimes=50)
dtheta = -1.0 * running_mean(np.gradient(theta, dp), 3, ntimes=10)
rh = running_mean(RH(self.data['temp'], self.data['dwpt']),
3,
ntimes=30)
drh = -1.0 * np.gradient(rh, dp)
weights = {
'theta': 1.0,
'dtdp': 1.0,
'T': 1.0,
'dthetadp': 1.0,
'drhdp': 0.6,
'pres': 1.0
}
input_data = {
'theta': theta,
'dtdp': deriv_smooth,
'T': smooth_temp,
'dthetadp': dtheta,
'drhdp': drh,
'pres': self.data['pres']
}
tropopause_pressure = fuzzy_tropopause(input_data, weights)
self.fig.text(0.83, 0.88, '0%s: %d m' % (degC, h0))
self.fig.text(0.83, 0.86, '-20%s: %d m' % (degC, h20))
self.fig.text(0.83, 0.84, '-40%s: %d m' % (degC, h40))
self.fig.text(0.83, 0.82, 'LCL: %d m' % lcl_height)
self.fig.text(0.83, 0.80, 'PW: %.1f mm' % pw)
self.fig.text(0.83, 0.78, '6 km shear: %d kts' % shear6km)
self.fig.text(0.83, 0.76, 'Trop: %d hPa' % (tropopause_pressure))
self.fig.text(0.83, 0.70, 'Surface')
self.fig.text(0.83, 0.68, 'P: %.1f hPa' % self.data['pres'][0])
self.fig.text(0.83, 0.66, 'Ht: %d m' % (self.data['hght'][0]))
self.fig.text(0.83, 0.64, 'T: %.1f %s' % (self.data['temp'][0], degC))
self.fig.text(0.83, 0.62,
'T$_D$: %.1f %s' % (self.data['dwpt'][0], degC))
self.fig.text(0.83, 0.60, 'T$_W$: %.1f %s' % (tw, degC))
# now do the hodograph?
self.hodoax = self.fig.add_axes([0.08, 0.69, 0.20, 0.20],
frameon=True,
polar=True)
self.hodoax.xaxis.set_ticks([], [])
self.hodoax.yaxis.set_ticks([], [])
speed_mask = np.ma.masked_where(self.data['sknt'] > 999,
self.data['sknt'])
angle = 270 - self.data['drct']
rad_angle = np.radians(angle)
pres_mask = np.bitwise_and(
self.data['pres'] > 200.,
self.data['drct'] < 361.) # only look at valid winds below 200 mb
if self.fmt == 'EDT' or self.fmt == 'EC':
self.hodoax.scatter(rad_angle[pres_mask], self.data['sknt'][pres_mask], c = self.data['hght'][pres_mask], \
edgecolors = 'none', s = 5, cmap = plt.cm.jet_r)
elif self.fmt == 'UWYO':
self.hodoax.plot(rad_angle,
self.data['sknt'],
c='red',
linewidth=3)
#self.hodoax.plot(rad_angle, speed_mask, c = 'red', linewidth = 3)
#self.hodoax.set_rmax(100)
self.hodoax.set_yticks(np.arange(0, 150, 30))
self.hodoax.tick_params(labelsize=5)
self.hodoax.set_xticks(np.arange(0, 2 * np.pi, np.pi / 2))
self.hodoax.set_xticklabels([])
self.hodoax.grid(True)
try:
self.hodoax.set_rlabel_position(180)
except AttributeError:
pass
df_tot['Sample_ID'] = files
df_tot.set_index('Sample_ID', inplace=True)
df_tot = df_tot[dfs[longest].columns]
df_tot.to_csv(save_path + "Merged_%s-%s_%s_TO_%s.csv" %
(sol.model, sol.model_type_str, files[0], files[-1]))
print("Batch file successfully saved in:\n", save_path)
def print_diagn(M, q, r, s):
return raftery_lewis(M, q, r, s, verbose=0)
def plot_par():
rc = {
u'figure.dpi': 72.0,
u'figure.edgecolor': 'white',
u'figure.facecolor': 'white',
u'savefig.bbox': u'tight',
u'savefig.directory': u'~',
u'savefig.dpi': 200.0,
u'savefig.edgecolor': u'white',
u'savefig.facecolor': u'white',
u'axes.formatter.use_mathtext': True,
u'xtick.direction': 'in',
u'ytick.direction': 'in',
}
return rc
rcParams.update(plot_par())
# %autoreload 2
#%reload_ext autoreload
import numpy
import matplotlib
from matplotlib import pylab, mlab, pyplot
np = numpy
plt = pyplot
from IPython.display import display
from IPython.core.pylabtools import figsize, getfigs
from pylab import *
from numpy import *
from matplotlib.pyplot import rcParams
rcParams.update({"font.size": 15})
#%%%% cd working directory
import os
os.chdir("/Users/cham/projects/sb2")
print(os.getcwd())
#%%%% code
"""
https://phoenix.astro.physik.uni-goettingen.de/
"""
from astropy.io import fits
flux_hi = fits.getdata(
"/Users/cham/Documents/slides/stellar_parameters/demo_ccf/lte05800-4.50-0.0.PHOENIX-ACES-AGSS-COND-2011-HiRes.fits"
)
def plot_scatter_data(data,
multiple=False,
file_name=None,
leg=None,
blocking=True,
x_label=None,
y_label=None,
x_log=False,
size=None,
font_size=None,
connect=False,
y_format=False,
dot_size=80,
leg_loc=None,
y_bnd=None):
size = data_fig_size if size is None else size
font_size = plot_font_size if font_size is None else font_size
fig = figure(figsize=size)
rcParams.update({'font.size': font_size, 'mathtext.default': 'regular'})
ax = fig.gca()
min_x, max_x, min_y, max_y = 1, 0, 1, 0
if multiple:
for run in data:
tmp_min_x, tmp_max_x = min(run[0, :]), max(run[0, :])
tmp_min_y, tmp_max_y = min(run[1, :]), max(run[1, :])
min_x = tmp_min_x if min_x > tmp_min_x else min_x
max_x = tmp_max_x if max_x < tmp_max_x else max_x
min_y = tmp_min_y if min_y > tmp_min_y else min_y
max_y = tmp_max_y if max_y < tmp_max_y else max_y
ax.scatter(run[0, :], run[1, :], s=dot_size)
if connect: ax.plot(run[0, :], run[1, :])
else:
ax.scatter(data[0, :], data[1, :], s=dot_size)
if connect: ax.plot(data[0, :], data[1, :])
min_x, max_x = min(data[0, :]), max(data[0, :])
min_y, max_y = min(data[1, :]), max(data[1, :])
ax.set_xlim(min_x, max_x)
y_delta = (max_y - min_y) * .05
if y_bnd is None:
ax.set_ylim(min_y - y_delta, max_y + y_delta)
else:
ax.set_ylim(y_bnd[0], y_bnd[1])
if y_format: ticklabel_format(style='sci', axis='y', scilimits=(-5, 1))
if x_label is not None: ax.set_xlabel(x_label)
if y_label is not None: ax.set_ylabel(y_label)
if x_log: ax.set_xscale('log')
if leg is not None and leg_loc is None: legend(leg)
elif leg_loc is not None:
legend(leg, bbox_to_anchor=leg_loc, loc='upper left')
if file_name is not None:
try:
savefig(file_name, bbox_inches='tight', pad_inches=0)
except FileExistsError:
pass
if blocking: show()
@author: naus010
The file in which I can combine experiments, saved from OH_GTB.
I should run OH_GTB_read_data_helper, then OH_GBT, then this file
vPP: This is the setup that produces powerpoint images. Mostly larger text.
"""
import os
import sys
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.pyplot import rcParams
rcParams.update({'font.size': 16})
rcParams.update({'lines.markersize': 9})
rcParams.update({'lines.linewidth': 1.5})
def read_full(exp_name, dataset):
'''Reads the 'full' array (opt C and E and OH)'''
filename = exp_name +'_'+ dataset + '_full'
filepointer = os.path.join('Data output','arrays',filename)
full_array = np.loadtxt(filepointer)
return unpack_full(full_array)
def read_x(exp_name, dataset):
'''Reads the xopt array'''
filename = exp_name +'_'+ dataset + '_xopt'
filepointer = os.path.join('Data output','arrays',filename)
xopt = np.loadtxt(filepointer)
dfs = [pd.read_csv(working_path+"/Results/%s/INV_%s-%s_%s.csv" %(f,sol.model,sol.model_type_str,f)) for f in files]
listed_dfs = [list(d) for d in dfs]
df_tot = pd.concat(dfs, axis=0)
longest = max(enumerate(listed_dfs), key = lambda tup: len(tup[1]))[0]
df_tot['Sample_ID'] = files
df_tot.set_index('Sample_ID', inplace=True)
df_tot = df_tot[dfs[longest].columns]
df_tot.to_csv(save_path+"Merged_%s-%s_%s_TO_%s.csv" %(sol.model,sol.model_type_str,files[0],files[-1]))
print("Batch file successfully saved in:\n", save_path)
def print_diagn(M, q, r, s):
return raftery_lewis(M, q, r, s, verbose=0)
def plot_par():
rc = {
u'figure.edgecolor': 'white',
u'figure.facecolor': 'white',
u'savefig.bbox': u'tight',
u'savefig.directory': u'~',
u'savefig.edgecolor': u'white',
u'savefig.facecolor': u'white',
u'axes.formatter.use_mathtext': True,
u'xtick.direction' : 'in',
u'ytick.direction' : 'in',
}
return rc
rcParams.update(plot_par())
def make_skewt_axes(self, pmax=1050., pmin=100., tmin=-40., tmax=30.,
fig=None):
"""Set up the skew-t axis the way I like to see it"""
if fig is None:
self.fig = figure(figsize=(8, 8))
# self.fig.clf()
else:
self.fig = fig
rcParams.update({'font.size': 10, })
self.skewxaxis = self.fig.add_axes([.065, .1, .71, .8],
projection='skewx')
self.skewxaxis.set_yscale('log')
self.skewxaxis.pmax = pmax
self.skewxaxis.pmin = pmin
self.skewxaxis.tmax = tmax
self.skewxaxis.tmin = tmin
xticklocs = arange(-80, 45, 10)
T0 = xticklocs
# P=linspace(pmax,pmin,101)
P = logspace(log10(pmax), log10(pmin), 101)
w = array([0.00001, 0.0001, 0.0004, 0.001, 0.002, 0.004,
0.007, 0.01, 0.016, 0.024, 0.032])
self.skewxaxis.add_mixratio_isopleths(
w, P[P >= 700], color='g', ls='--', alpha=1., lw=0.5)
self.skewxaxis.add_dry_adiabats(
linspace(210, 550, 18)-degCtoK, P, color='g', ls='--', alpha=1.,
lw=0.5)
self.skewxaxis.add_moist_adiabats(
linspace(0, 44, 12), pmax, color='g', ls='--', alpha=1., lw=0.5)
self.skewxaxis.set_title("%s %s" % (self['StationNumber'],
self['SoundingDate']))
self.skewxaxis.other_housekeeping()
self.wbax = self.fig.add_axes([0.685, 0.1, 0.1, 0.8],
sharey=self.skewxaxis, frameon=False)
self.wbax.xaxis.set_ticks([], [])
self.wbax.yaxis.grid(True, ls='-', color='y', lw=0.5)
for tick in self.wbax.yaxis.get_major_ticks():
# tick.label1On = False
pass
self.wbax.get_yaxis().set_tick_params(size=0, color='y')
self.wbax.set_xlim(-1.5, 1.5)
self.wbax.get_yaxis().set_visible(False)
self.wbax.set_title('kn', fontsize=10, color='k', ha='right')
# Set up standard atmosphere height scale on
# LHS of plot.
majorLocatorKM = MultipleLocator(2)
majorLocatorKFT = MultipleLocator(5)
minorLocator = MultipleLocator(1)
# determine base height from base pressure (nominally 1050 hPa)
# via hydrostatic equilibrium for standard atmosphere
# model atmospheric conditions with constant lapse rate and
# NIST (1013.25hPa and 20C)
zmin = barometric_equation_inv(0, 293.15, 101325., pmax*100.)
zmax = barometric_equation_inv(0, 293.15, 101325., pmin*100.)
zminf = zmin * 3.2808
zmaxf = zmax * 3.2808
self.kmhax = self.fig.add_axes([0.775, 0.1, 1e-6, 0.8], frameon=True)
self.kmhax.xaxis.set_ticks([], [])
self.kmhax.spines['left'].set_color('k')
self.kmhax.spines['right'].set_visible(False)
self.kmhax.tick_params(axis='y', colors='k', labelsize=8)
self.kmhax.set_ylim(zmin*1e-3, zmax*1e-3)
self.kmhax.set_title("km/kft", fontsize=10)
self.kmhax.get_yaxis().set_tick_params(which="both", direction='out')
self.kmhax.yaxis.set_major_locator(majorLocatorKM)
self.kmhax.yaxis.set_minor_locator(minorLocator)
self.fthax = self.kmhax.twinx()
self.fthax.xaxis.set_ticks([], [])
self.fthax.tick_params(axis='y', colors='k', labelsize=8)
self.fthax.set_ylim(zminf*1e-3, zmaxf*1e-3)
self.fthax.get_yaxis().set_tick_params(which="both", direction='out')
self.fthax.yaxis.set_major_locator(majorLocatorKFT)
self.fthax.yaxis.set_minor_locator(minorLocator)
def show_plot(Tair, Tfree, at, Q, days, fileName):
fig = figure(fileName)
energy = fig.add_subplot(211, axisbg='black')
temperature = fig.add_subplot(212, axisbg='black')
energy.set_axisbelow(True)
temperature.set_axisbelow(True)
#minor grids
energy.xaxis.grid(True, 'minor', color='w', linestyle='-', linewidth=.2)
energy.yaxis.grid(True, 'minor', color='w', linestyle='-', linewidth=.2)
#ticks
energy.xaxis.set_ticks(np.arange(0, 24 * noOfDays + 1, 24))
energy.minorticks_on()
energy.set_xticklabels(days)
#major grids
energy.xaxis.grid(True, 'major', linewidth=.5, linestyle='-', color='w')
energy.yaxis.grid(True, 'major', linewidth=.5, linestyle='-', color='w')
#labels
energy.set_ylabel("Required Power (W)")
#minor grids
temperature.xaxis.grid(True,
'minor',
color='w',
linestyle='-',
linewidth=.2)
temperature.yaxis.grid(True,
'minor',
color='w',
linestyle='-',
linewidth=.2)
#ticks
temperature.xaxis.set_ticks(np.arange(0, 24 * noOfDays + 1, 24))
temperature.minorticks_on()
temperature.set_xticklabels(days)
#major grids
temperature.xaxis.grid(True,
'major',
linewidth=.5,
linestyle='-',
color='w')
temperature.yaxis.grid(True,
'major',
linewidth=.5,
linestyle='-',
color='w')
#labels
temperature.set_ylabel("Temperature ($^{o}C$)")
temperature.set_xlabel("day")
#for mathematical fonts
params = {'mathtext.default': 'regular'}
rcParams.update(params)
energy.plot(hour, Q, linewidth='1.5', label='Energy requirements')
temperature.plot(hour, Tair - 273.15, linewidth='1.5', label='$T_{wAC}$')
temperature.plot(hour,
Tfree - 273.15,
linewidth='1.5',
label='$T_{free}$',
linestyle='--')
temperature.plot(hour, at - 273.15, linewidth='1.5', label='$T_{outside}$')
temperature.legend(bbox_to_anchor=(1, 1),
loc=2,
borderaxespad=0.,
fontsize='medium',
fancybox=True,
shadow=True)
show()
# snarfed from https://stackoverflow.com/questions/22263807/how-is-order-of-items-in-matplotlib-legend-determined
handles, labels = ax.get_legend_handles_labels()
labels, handles = zip(*sorted(zip(labels, handles), key=lambda t: t[0]))
ax.legend(handles, labels)
def plot_Likelihoods(Likelihoods, ax=None):
ax.set_title('Progress')
ax.plot(Likelihoods)
ax.set_xticks(range(1, len(Likelihoods)))
ax.set_ylabel('log Likelihood')
ax.set_xlabel('Iteration')
if __name__ == '__main__':
rcParams.update({"text.usetex": True})
parser = ArgumentParser('CAVI for single Gaussian')
parser.add_argument('--N', type=int, default=5000, help='Dataset size')
parser.add_argument('--mean',
type=float,
default=0.5,
help='Mean for dataset')
parser.add_argument('--sigma',
type=float,
default=0.5,
help='Standard deviation')
parser.add_argument('--seed',
type=int,
default=None,
help='Seed for random number generator')
from numpy import linspace, array, log
from matplotlib.pyplot import figure, show, rcParams, savefig
from utilities import fig_size
x = linspace(0, 1, 101)
y = array([
-a * log(a) + -(1 - a) * log(1 - a) if a != 0 and a != 1 else 0 for a in x
])
fig = figure(figsize=fig_size)
rcParams.update({'font.size': 16, 'mathtext.default': 'regular'})
ax = fig.gca()
ax.plot(x, y, linewidth=3)
ax.set_xlim(0, 1)
ax.set_ylim(0, .8)
ax.set_xlabel('$\\bar{B}_i$')
ax.set_ylabel('$I_0$ = $\\frac{ \\bar{R}_i }{ \\bar{R}_i + \\bar{B}_i }$')
path = '../../results/binary_entropy.png'
savefig(path)
show()
# csv files with data need to be in:
# workdir/data/id.csv
# where id is the identifiacion of the metro zone to be analyzed.
# The first column is daily deaths and the second daily confirmed cases
# The rest of the information is contained in a doctionary:
# General information for the metro zone or region to be analyzed:
# The actual instance of the covid_mcmc object is store in the last item of the list, once it has been instantiated by AnalyzeZM
# id Name num_relax_days Population init date intervention and relax dates
ZMs = $Zone
# The (optional) hospital occupancy data are stored in:
# workdir/data/hosp/
rcParams.update({'font.size': 14})
if __name__=='__main__':
q = $plottingQuantiles # [10, 25, 50, 75, 90]
workdir = "$workdir"
os.makedirs(workdir, exist_ok=True)
os.makedirs(workdir+"output", exist_ok=True)
os.makedirs(workdir+"figs", exist_ok=True)
os.makedirs(workdir+"data", exist_ok=True)
#os.makedirs(workdir+"data/hospitales_datos", exist_ok=True)
os.makedirs(workdir+"csv", exist_ok=True)
#os.makedirs(workdir+"logs", exist_ok=True)
import os
from subprocess import Popen
from numpy import linspace,array,zeros,append,mgrid,empty,exp
from xmlreader import readxml
from superstring import string2val,split_delims
from periodic_table import pt
from unit_converter import convert
from generic import obj
from developer import DevBase,unavailable
try:
from matplotlib.pyplot import figure,plot,xlabel,ylabel,title,show,ylim,legend,xlim,rcParams,savefig,bar,xticks,subplot,grid,setp,errorbar,loglog,semilogx,semilogy
params = {'legend.fontsize':14,'figure.facecolor':'white','figure.subplot.hspace':0.,
'axes.labelsize':16,'xtick.labelsize':14,'ytick.labelsize':14}
rcParams.update(params)
except (ImportError,RuntimeError):
figure,plot,xlabel,ylabel,title,show,ylim,legend,xlim,rcParams,savefig,bar,xticks,subplot,grid,setp,errorbar,loglog,semilogx,semilogy = unavailable('matplotlib.pyplot','figure','plot','xlabel','ylabel','title','show','ylim','legend','xlim','rcParams','savefig','bar','xticks','subplot','grid','setp','errorbar','loglog','semilogx','semilogy')
#end try
class Pseudopotential(DevBase):
colors = dict(s='k',p='r',d='b',f='m')
lcolors= ['k','r','b','m']
ldict = {0:'s',1:'p',2:'d',3:'f'}
format = 'generic'
conv_table = {('upf','fsatom'):('--upf_pot','--xml')}
extensions = dict(upf='upf',fsatom='xml',gamess='gamess',casino='data')
def thermalLoad(fileName):
global alpha, N, air_temp, dew_point, epsi, cloudCover, relHum, noOfDays, N, hour
global sol_airn, sol_aire, sol_airs, sol_airw
for sett in session.query(Settings).filter(Settings.name == fileName):
length = sett.length
width = sett.width
height = sett.height
#material properties
thickness = sett.thickness
h_c = sett.conv_coeff
rho = sett.density
c = sett.spec_heat
k = sett.therm_cond
Ti = sett.initTemp
Tcomf = sett.comfTemp
alpha = sett.swAbs
epsi = sett.lwEWall
sigma = 5.67 * (10)**(-8)
power = sett.altitude
noOfDays = int(sett.numDays)
N = noOfDays * 500
hour = np.linspace(0, noOfDays * 24, N)
sol_airn = np.empty(N)
sol_aire = np.empty(N)
sol_airs = np.empty(N)
sol_airw = np.empty(N)
air_temp, dew_point, relHum, cloudCover, days = fit(fileName)
R = thickness / k
Cc = rho * c * thickness / 2
#call function of air and vapor pressure
excel = "Data/Radiation/ShortwaveRadiation-" + fileName + ".xlsx"
try:
rad = pd.read_excel(excel)
except FileNotFoundError as error:
sp = sunpath.calculateSunPath(fileName)
r = radiation.calculateRadiation(fileName)
rad = pd.read_excel(excel)
I_sn = rad['Northern']
I_se = rad['Eastern']
I_ss = rad['Southern']
I_sw = rad['Western']
sol_airn = np.empty(N)
sol_aire = np.empty(N)
sol_airs = np.empty(N)
sol_airw = np.empty(N)
at = np.empty(N)
sky = []
for i in range(N):
sol_airn[i] = T_sa(i, I_sn)
sol_aire[i] = T_sa(i, I_se)
sol_airs[i] = T_sa(i, I_ss)
sol_airw[i] = T_sa(i, I_sw)
at[i] = air_temp(hour[i])
# plot(sol_airn)
# plot(sol_aire)
# plot(sol_airw)
# plot(sol_airs)
# show()
# #heat
# T1n = np.empty(N)
# T2n = np.empty(N)
# T1e = np.empty(N)
# T2e = np.empty(N)
# T1s = np.empty(N)
# T2s = np.empty(N)
# T1w = np.empty(N)
# T2w = np.empty(N)
# T1n[0] = 273.15 + Ti
# T2n[0] = 273.15 + Ti+.001
# T1e[0] = 273.15 + Ti
# T2e[0] = 273.15 + Ti+.001
# T1s[0] = 273.15 + Ti
# T2s[0] = 273.15 + Ti+.001
# T1w[0] = 273.15 + Ti
# T2w[0] = 273.15 + Ti+.001
# T1nf = np.empty(N)
# T2nf = np.empty(N)
# T1ef = np.empty(N)
# T2ef = np.empty(N)
# T1sf = np.empty(N)
# T2sf = np.empty(N)
# T1wf = np.empty(N)
# T2wf = np.empty(N)
# T1nf[0] = 273.15 + Ti
# T2nf[0] = 273.15 + Ti+.001
# T1ef[0] = 273.15 + Ti
# T2ef[0] = 273.15 + Ti+.001
# T1sf[0] = 273.15 + Ti
# T2sf[0] = 273.15 + Ti+.001
# T1wf[0] = 273.15 + Ti
# T2wf[0] = 273.15 + Ti+.001
# Tair = np.empty(N)
# Tair[0] = 273.15 + Ti
# Tfree = np.empty(N)
# Tfree[0] = 273.15 + Ti
# s = (noOfDays*24*3600-0)/N
# an = width*height
# ae = length*height
# aS = width*height
# aw = length*width
# A_f = (length-2*thickness)*(width-2*thickness)
# V = A_f*(height)
# dens = 1.225 #density of air
# c_a = 0.718*1000 #specific heat of air
# C_air = V*dens*c_a
# Q = np.zeros(N)
# a = thickness*height
# Tc = np.empty(N)
# Tc[0] = Tair[0]
Tcomfmin = Tcomf - 3
Tcomfmax = Tcomf + 1
dc = []
c = []
pow = np.linspace(0, 2000, 2001)
for k in pow:
T1n = np.empty(N)
T2n = np.empty(N)
T1e = np.empty(N)
T2e = np.empty(N)
T1s = np.empty(N)
T2s = np.empty(N)
T1w = np.empty(N)
T2w = np.empty(N)
T1n[0] = 273.15 + Ti
T2n[0] = 273.15 + Ti + .001
T1e[0] = 273.15 + Ti
T2e[0] = 273.15 + Ti + .001
T1s[0] = 273.15 + Ti
T2s[0] = 273.15 + Ti + .001
T1w[0] = 273.15 + Ti
T2w[0] = 273.15 + Ti + .001
T1nf = np.empty(N)
T2nf = np.empty(N)
T1ef = np.empty(N)
T2ef = np.empty(N)
T1sf = np.empty(N)
T2sf = np.empty(N)
T1wf = np.empty(N)
T2wf = np.empty(N)
T1nf[0] = 273.15 + Ti
T2nf[0] = 273.15 + Ti + .001
T1ef[0] = 273.15 + Ti
T2ef[0] = 273.15 + Ti + .001
T1sf[0] = 273.15 + Ti
T2sf[0] = 273.15 + Ti + .001
T1wf[0] = 273.15 + Ti
T2wf[0] = 273.15 + Ti + .001
Tair = np.empty(N)
Tair[0] = 273.15 + Ti
Tfree = np.empty(N)
Tfree[0] = 273.15 + Ti
s = (noOfDays * 24 * 3600 - 0) / N
an = width * height
ae = length * height
aS = width * height
aw = length * width
A_f = (length - 2 * thickness) * (width - 2 * thickness)
V = A_f * (height)
dens = 1.225 #density of air
c_a = 0.718 * 1000 #specific heat of air
C_air = V * dens * c_a
Q = np.zeros(N)
a = thickness * height
Tc = np.empty(N)
Tc[0] = Tair[0]
for i in range(N - 1):
T1n[i + 1] = T1n[i] + s * (h_rc *
((sol_airn[i] + 273.15) - T1n[i]) / Cc +
(T2n[i] - T1n[i]) / (R * Cc))
T1nf[i +
1] = T1nf[i] + s * (h_rc *
((sol_airn[i] + 273.15) - T1nf[i]) / Cc +
(T2nf[i] - T1nf[i]) / (R * Cc))
T2n[i + 1] = T2n[i] + s * (h_c * (Tfree[i] - T2n[i]) / Cc -
(T2n[i] - T1n[i]) / (R * Cc))
T2nf[i + 1] = T2nf[i] + s * (h_c * (Tair[i] - T2nf[i]) / Cc -
(T2nf[i] - T1nf[i]) / (R * Cc))
T1e[i + 1] = T1e[i] + s * (h_rc *
((sol_aire[i] + 273.15) - T1e[i]) / Cc +
(T2e[i] - T1e[i]) / (R * Cc))
T1ef[i +
1] = T1ef[i] + s * (h_rc *
((sol_aire[i] + 273.15) - T1ef[i]) / Cc +
(T2ef[i] - T1ef[i]) / (R * Cc))
T2e[i + 1] = T2e[i] + s * (h_c * (Tfree[i] - T2e[i]) / Cc -
(T2e[i] - T1e[i]) / (R * Cc))
T2ef[i + 1] = T2ef[i] + s * (h_c * (Tair[i] - T2ef[i]) / Cc -
(T2ef[i] - T1ef[i]) / (R * Cc))
T1s[i + 1] = T1s[i] + s * (h_rc *
((sol_airs[i] + 273.15) - T1s[i]) / Cc +
(T2s[i] - T1s[i]) / (R * Cc))
T1sf[i +
1] = T1sf[i] + s * (h_rc *
((sol_airs[i] + 273.15) - T1sf[i]) / Cc +
(T2sf[i] - T1sf[i]) / (R * Cc))
T2s[i + 1] = T2s[i] + s * (h_c * (Tfree[i] - T2s[i]) / Cc -
(T2s[i] - T1s[i]) / (R * Cc))
T2sf[i + 1] = T2sf[i] + s * (h_c * (Tair[i] - T2sf[i]) / Cc -
(T2sf[i] - T1sf[i]) / (R * Cc))
T1w[i + 1] = T1w[i] + s * (h_rc *
((sol_airw[i] + 273.15) - T1w[i]) / Cc +
(T2w[i] - T1w[i]) / (R * Cc))
T1wf[i +
1] = T1wf[i] + s * (h_rc *
((sol_airw[i] + 273.15) - T1wf[i]) / Cc +
(T2wf[i] - T1wf[i]) / (R * Cc))
T2w[i + 1] = T2w[i] + s * (h_c * (Tfree[i] - T2w[i]) / Cc -
(T2w[i] - T1w[i]) / (R * Cc))
T2wf[i + 1] = T2wf[i] + s * (h_c * (Tair[i] - T2wf[i]) / Cc -
(T2wf[i] - T1wf[i]) / (R * Cc))
Q[i] = k #h_c*((an-a)*(T2nf[i]-273.15-Tcomf)+(ae-a)*(T2ef[i]-273.15-Tcomf)+(aS-a)*(T2sf[i]-273.15-Tcomf)+(aw-a)*(T2wf[i]-273.15-Tcomf))
if Q[i] < 0:
Q[i] = 0
Tfree[i + 1] = Tfree[i] + s * (h_c *
((an - a) * (T2n[i] - Tfree[i]) +
(ae - a) * (T2e[i] - Tfree[i]) +
(aS - a) * (T2s[i] - Tfree[i]) +
(aw - a) *
(T2w[i] - Tfree[i]))) / (C_air)
Tair[i + 1] = Tair[i] + s * (h_c * (
(an - a) * (T2nf[i] - Tair[i]) + (ae - a) *
(T2ef[i] - Tair[i]) + (aS - a) * (T2sf[i] - Tair[i]) +
(aw - a) * (T2wf[i] - Tair[i]))) / (C_air) - s * Q[i] / (C_air)
Q[N - 1] = Q[N - 2]
#
secs = np.linspace(0, noOfDays * 24 * 3600, N)
energy = simps(y=Q, x=secs, even='avg')
#show_plot(Tair,Tfree,at,Q, days,fileName)
Tairp = np.empty(N)
np.copyto(Tairp, Tair)
discomfort = measureD(Tair, secs, Tcomfmin, Tcomfmax)
comfort = measureC(Tairp, secs, Tcomfmin, Tcomfmax)
print(fileName)
print('Required energy {} (days):{} kJ'.format(
noOfDays, round((energy / 1000), 2)))
print('Average power required: {} W'.format(round(Q.mean(), 2)))
print('Average temperature in free mode: {} C'.format(Tfree.mean() -
273.15))
print('Discomfort (D): {}'.format(
round(discomfort / (comfort + discomfort), 3)))
print('Comfort (C): {}'.format(
round(comfort / (comfort + discomfort), 3)))
dc.append(round(discomfort / (comfort + discomfort), 3))
c.append(round(comfort / (comfort + discomfort), 3))
days = dayNames(days)
fig = figure('Thermal Comfort vs Power')
energy = fig.add_subplot(111, axisbg='black')
energy.set_axisbelow(True)
energy.xaxis.set_ticks(np.arange(0, 2000, 100))
params = {'mathtext.default': 'regular'}
rcParams.update(params)
energy.xaxis.grid(True, 'major', linewidth=.5, linestyle='-', color='w')
energy.yaxis.grid(True, 'major', linewidth=.5, linestyle='-', color='w')
energy.set_xlabel("Power ($W$)")
energy.set_ylabel("Thermal Comfort")
energy.plot(pow, dc, label='Discomfort ($Q$)', linewidth='1.5')
energy.plot(pow, c, label='Comfort ($P$)', linewidth='1.5')
energy.legend(loc='best', fontsize='medium', fancybox=True, shadow=True)
show()
c = np.array(c)
index_comf_power = np.where(c == 1)
min_opt_power = index_comf_power[0][0]
max_opt_power = index_comf_power[0][len(index_comf_power[0]) - 1]
pow = [min_opt_power, max_opt_power]
count = 0
for k in pow:
T1n = np.empty(N)
T2n = np.empty(N)
T1e = np.empty(N)
T2e = np.empty(N)
T1s = np.empty(N)
T2s = np.empty(N)
T1w = np.empty(N)
T2w = np.empty(N)
T1n[0] = 273.15 + Ti
T2n[0] = 273.15 + Ti + .001
T1e[0] = 273.15 + Ti
T2e[0] = 273.15 + Ti + .001
T1s[0] = 273.15 + Ti
T2s[0] = 273.15 + Ti + .001
T1w[0] = 273.15 + Ti
T2w[0] = 273.15 + Ti + .001
T1nf = np.empty(N)
T2nf = np.empty(N)
T1ef = np.empty(N)
T2ef = np.empty(N)
T1sf = np.empty(N)
T2sf = np.empty(N)
T1wf = np.empty(N)
T2wf = np.empty(N)
T1nf[0] = 273.15 + Ti
T2nf[0] = 273.15 + Ti + .001
T1ef[0] = 273.15 + Ti
T2ef[0] = 273.15 + Ti + .001
T1sf[0] = 273.15 + Ti
T2sf[0] = 273.15 + Ti + .001
T1wf[0] = 273.15 + Ti
T2wf[0] = 273.15 + Ti + .001
Tair = np.empty(N)
Tair[0] = 273.15 + Ti
Tfree = np.empty(N)
Tfree[0] = 273.15 + Ti
s = (noOfDays * 24 * 3600 - 0) / N
an = width * height
ae = length * height
aS = width * height
aw = length * width
A_f = (length - 2 * thickness) * (width - 2 * thickness)
V = A_f * (height)
dens = 1.225 #density of air
c_a = 0.718 * 1000 #specific heat of air
C_air = V * dens * c_a
Q = np.zeros(N)
a = thickness * height
Tc = np.empty(N)
Tc[0] = Tair[0]
for i in range(N - 1):
T1n[i + 1] = T1n[i] + s * (h_rc *
((sol_airn[i] + 273.15) - T1n[i]) / Cc +
(T2n[i] - T1n[i]) / (R * Cc))
T1nf[i +
1] = T1nf[i] + s * (h_rc *
((sol_airn[i] + 273.15) - T1nf[i]) / Cc +
(T2nf[i] - T1nf[i]) / (R * Cc))
T2n[i + 1] = T2n[i] + s * (h_c * (Tfree[i] - T2n[i]) / Cc -
(T2n[i] - T1n[i]) / (R * Cc))
T2nf[i + 1] = T2nf[i] + s * (h_c * (Tair[i] - T2nf[i]) / Cc -
(T2nf[i] - T1nf[i]) / (R * Cc))
T1e[i + 1] = T1e[i] + s * (h_rc *
((sol_aire[i] + 273.15) - T1e[i]) / Cc +
(T2e[i] - T1e[i]) / (R * Cc))
T1ef[i +
1] = T1ef[i] + s * (h_rc *
((sol_aire[i] + 273.15) - T1ef[i]) / Cc +
(T2ef[i] - T1ef[i]) / (R * Cc))
T2e[i + 1] = T2e[i] + s * (h_c * (Tfree[i] - T2e[i]) / Cc -
(T2e[i] - T1e[i]) / (R * Cc))
T2ef[i + 1] = T2ef[i] + s * (h_c * (Tair[i] - T2ef[i]) / Cc -
(T2ef[i] - T1ef[i]) / (R * Cc))
T1s[i + 1] = T1s[i] + s * (h_rc *
((sol_airs[i] + 273.15) - T1s[i]) / Cc +
(T2s[i] - T1s[i]) / (R * Cc))
T1sf[i +
1] = T1sf[i] + s * (h_rc *
((sol_airs[i] + 273.15) - T1sf[i]) / Cc +
(T2sf[i] - T1sf[i]) / (R * Cc))
T2s[i + 1] = T2s[i] + s * (h_c * (Tfree[i] - T2s[i]) / Cc -
(T2s[i] - T1s[i]) / (R * Cc))
T2sf[i + 1] = T2sf[i] + s * (h_c * (Tair[i] - T2sf[i]) / Cc -
(T2sf[i] - T1sf[i]) / (R * Cc))
T1w[i + 1] = T1w[i] + s * (h_rc *
((sol_airw[i] + 273.15) - T1w[i]) / Cc +
(T2w[i] - T1w[i]) / (R * Cc))
T1wf[i +
1] = T1wf[i] + s * (h_rc *
((sol_airw[i] + 273.15) - T1wf[i]) / Cc +
(T2wf[i] - T1wf[i]) / (R * Cc))
T2w[i + 1] = T2w[i] + s * (h_c * (Tfree[i] - T2w[i]) / Cc -
(T2w[i] - T1w[i]) / (R * Cc))
T2wf[i + 1] = T2wf[i] + s * (h_c * (Tair[i] - T2wf[i]) / Cc -
(T2wf[i] - T1wf[i]) / (R * Cc))
Q[i] = k #h_c*((an-a)*(T2nf[i]-273.15-Tcomf)+(ae-a)*(T2ef[i]-273.15-Tcomf)+(aS-a)*(T2sf[i]-273.15-Tcomf)+(aw-a)*(T2wf[i]-273.15-Tcomf))
if Q[i] < 0:
Q[i] = 0
Tfree[i + 1] = Tfree[i] + s * (h_c *
((an - a) * (T2n[i] - Tfree[i]) +
(ae - a) * (T2e[i] - Tfree[i]) +
(aS - a) * (T2s[i] - Tfree[i]) +
(aw - a) *
(T2w[i] - Tfree[i]))) / (C_air)
Tair[i + 1] = Tair[i] + s * (h_c * (
(an - a) * (T2nf[i] - Tair[i]) + (ae - a) *
(T2ef[i] - Tair[i]) + (aS - a) * (T2sf[i] - Tair[i]) +
(aw - a) * (T2wf[i] - Tair[i]))) / (C_air) - s * Q[i] / (C_air)
Q[N - 1] = Q[N - 2]
#
secs = np.linspace(0, noOfDays * 24 * 3600, N)
energy = simps(y=Q, x=secs, even='avg')
if count == 0:
title = '(minimum comfortable power: {} W)'.format(k)
else:
title = '(maximum comfortable power: {} W)'.format(k)
show_plot(Tair, Tfree, at, Q, days, fileName + ' ' + title)
show()
count += 1
secs = np.linspace(0, noOfDays * 24 * 3600, N)
energy = simps(y=Q, x=secs, even='avg')
#show_plot(Tair,Tfree,at,Q, days,fileName)
Tairp = np.empty(N)
np.copyto(Tairp, Tair)
discomfort = measureD(Tair, secs, Tcomfmin, Tcomfmax)
comfort = measureC(Tairp, secs, Tcomfmin, Tcomfmax)
print('Required energy {} (days):{} kJ'.format(
noOfDays, round((energy / 1000), 2)))
print('Average power required: {} W'.format(round(Q.mean(), 2)))
print('Average temperature in free mode: {} C'.format(Tfree.mean() -
273.15))
print('Discomfort (D): {}'.format(
round(discomfort / (comfort + discomfort), 3)))
print('Comfort (C): {}'.format(
round(comfort / (comfort + discomfort), 3)))
from pylearn2.utils import serial
import sys
from matplotlib import pyplot
from pylearn2.config import yaml_parse
import theano.tensor as T
from theano import function
import numpy as np
from matplotlib.pyplot import figure, axes
from matplotlib.pyplot import rcParams
figure(figsize=(4.5, 2.0))
axes([0.1, 0.2, 0.88, 0.6])
#figure(figsize=(4.5, 3.))
rcParams.update({'xtick.labelsize' : 8, 'ytick.labelsize' : 8})
rcParams['ps.useafm'] = True
rcParams['pdf.use14corefonts'] = True
rcParams['text.usetex'] = True
ignore, model_path = sys.argv
model = serial.load(model_path)
dataset_yaml_src = model.dataset_yaml_src
dataset = yaml_parse.load(dataset_yaml_src)
input_space = model.get_input_space()
X = input_space.make_theano_batch()
if X.ndim > 2:
assert False # doesn't support topo yet
