def ecdf(data, show=False,savename=None):
ecdf = sm.distributions.ECDF(data)
x = np.linspace(data.min(),data.max())
y = ecdf(x)
cutoff = x[y>0.85][0]
fig = plt.figure()
ax = fig.add_subplot(111)
ax.plot(x,y,'k',linewidth=3)
artist.adjust_spines(ax)
ax.annotate(r'\Large \textbf{Cutoff:} $%.03f$'%cutoff, xy=(.3, .2), xycoords='axes fraction',
horizontalalignment='center', verticalalignment='center')
ax.set_xlabel(artist.format('Absolute Correlation'))
ax.set_ylabel(artist.format('Percentile'))
ax.axhline(y=0.85,color='r',linestyle='--',linewidth=2)
ax.axvline(x=cutoff,color='r',linestyle='--',linewidth=2)
ax.set_xlim((0,1))
plt.tight_layout()
if savename:
plt.savefig('%s.png'%savename,dpi=200)
if show:
plt.show()
return cutoff
def snapshots(data, indices,basepath=None, data_label='data'):
indices = zip(indices,indices[1:])
for start_idx,stop_idx in indices:
initial_distribution = data[:,start_idx]
final_distribution = data[:,stop_idx]
fig = plt.figure()
ax = fig.add_subplot(111)
ax.hist(initial_distribution,color='r',alpha=0.5,bins=20,label='Initial', range=(-1,1))
ax.hist(final_distribution,color='k',alpha=0.5,bins=20,label='Final',range=(-1,1))
artist.adjust_spines(ax)
ax.set_xlabel(artist.format(data_label))
ax.set_ylabel(artist.format('Prevalence'))
H,p =kruskal(initial_distribution,final_distribution)
effect_size = np.linalg.norm(final_distribution-initial_distribution)
ax.annotate('\Large $d=%.02f, \; p=%.04f$'%(effect_size,p), xy=(.3, .9),
xycoords='axes fraction', horizontalalignment='right', verticalalignment='top')
plt.tight_layout()
plt.legend(frameon=False)
filename = os.path.join(basepath,'%s-compare-%d-%d.png'%(data_label,start_idx,stop_idx))
plt.savefig(filename,dpi=300)
plt.close()
def coefficients(model,labels,show=False,savename=None,title=None):
fig = plt.figure(figsize=(8,10))
ax = fig.add_subplot(111)
x = -model.coef_.transpose()
x /= np.absolute(x).max()
y = np.arange(len(x))+0.5
cutoff = scoreatpercentile(np.absolute(x),85)
ax.barh(y,x,color=['r' if datum < 0 else 'g' for datum in x])
ax.axvline(cutoff,linewidth=2,linestyle='--',color='r')
ax.axvline(-cutoff,linewidth=2,linestyle='--',color='r')
artist.adjust_spines(ax)
ax.grid(True)
ax.set_ylim(ymax=62)
ax.set_xlim(xmin=-1.1,xmax=1.1)
ax.set_yticks(y)
ax.set_yticklabels(map(format,labels),y)
ax.set_xlabel(format('Regression coefficient'))
if title:
ax.set_title(r'\Large \textbf{%s}'%title)
plt.tight_layout()
if show:
plt.show()
def frequencies(self,str, ax=None, cutoff=30):
words = nltk.word_tokenize(''.join(ch for ch in str
if ch not in exclude
and ord(ch)<128
and not ch.isdigit()).lower())
words = [word for word in words if word not in stopwords
and word not in emoticons
and word not in ['rt','amp']]
fdist = nltk.FreqDist(words)
freqs = fdist.items()[:cutoff]
word,f =zip(*freqs)
f = np.array(f).astype(float)
print f,'kkkkkkk'
f /= float(f.sum())
print f,'jjjjjjjjjjjj'
if not ax:
fig = plt.figure()
ax = fig.add_subplot(111)
ax.plot(-f*np.log(f),'k',linewidth=2)
artist.adjust_spines(ax)
ax.yaxis.grid()
ax.xaxis.grid()
ax.set_xticks(range(len(word)))
ax.set_xticklabels(map(format,word),range(len(word)), rotation=45)
ax.set_ylabel(r'\Large $\log \left(\mathbf{Frequency}\right)$')
plt.tight_layout()
plt.show()
def covariance(heatmap,labels,show=False,savename=None,ml=False):
#Covariance matrix
fig = plt.figure(figsize=(13,13))
ax = fig.add_subplot(111)
cax = ax.imshow(heatmap,interpolation='nearest',aspect='equal')
artist.adjust_spines(ax)
ax.set_xticks(range(len(labels)))
ax.set_xticklabels(map(artist.format,labels),range(len(labels)),rotation=90)
ax.set_yticks(range(len(labels)))
ax.set_yticklabels(map(artist.format,labels))
if ml:
ax.annotate(r'\LARGE \textbf{Training}', xy=(.2, .2), xycoords='axes fraction',
horizontalalignment='center', verticalalignment='center')
ax.annotate(r'\LARGE \textbf{Testing}', xy=(.7, .7), xycoords='axes fraction',
horizontalalignment='center', verticalalignment='center')
plt.colorbar(cax, fraction=0.10, shrink=0.8)
plt.tight_layout()
if savename:
plt.savefig('%s.png'%savename,dpi=200)
if show:
plt.show()
def draw(self):
GraphicsCard.enable('blend')
GraphicsCard.setBlendFunction('src_alpha', 'one_minus_src_alpha')
GraphicsCard.disable('lighting', 'texture_2d')
alpha = max( float(self.timeleft / 2.0), 0 )
GraphicsCard.setColorRGBA( alpha/2.0, 0, 0, alpha )
Graphics.drawSphere( self.location, 4, 8, 4 )
GraphicsCard.enable('texture_2d')
def plot(aList,ax, cutoff=20): tokens,frequencies = zip(*sorted(aList,key=lambda item:item[1],reverse=True)) tokens = tokens[:cutoff][::-1] frequencies = frequencies[:cutoff][::-1] ax.plot(frequencies,xrange(cutoff),'k--', linewidth=2) artist.adjust_spines(ax) ax.set_yticks(xrange(cutoff)) ax.set_yticklabels(map(artist.format,tokens),rotation='horizontal')
def main():
# Loop until the user clicks the close button.
done = False
loopCount = 0
while not done:
loopCount += 1
Input.update()
if(Input.Trigger(Input.Close)):
done = True
map.update()
Graphics.update()
def draw(self): #find camera's transform matrix m = MathUtil.buildTransformMatrix(self.ent.headLocation, self.ent.yaw + math.pi, self.ent.pitch, self.ent.roll) glPushMatrix() glMultMatrixf( m.toList() ) Graphics.drawPyramid(30, 10, 100.0) Graphics.drawWirePyramid(30, 10, 100.0) glPopMatrix()
def network_stability(energy_trace,savename):
fig = plt.figure()
ax = fig.add_subplot(111)
ax.plot(energy_trace,'k',linewidth=2)
artist.adjust_spines(ax)
ax.set_xlabel(artist.format('Time'))
ax.set_ylabel(artist.format('Stability (energy)'))
plt.savefig('%s.png'%savename,dpi=200)
plt.close()
def SystemShutdown(self):
import Graphics
Graphics._ViewManager().Shutdown()
self.exitTaskletManager = True
# tell all services to shut down!
for service in self._running_services.values():
try:
print "shutting down - ", service.__ID__
service.state = SERVICE_STATES.STOPPED
service.OnSystemShutdown()
except Exception, e:
print "Exception in Service", service.__ID__, e
def plot_and_save(frequencies, words, ylabel, savefile):
fig = plt.figure()
ax = fig.add_subplot(111)
ax.semilogy(frequencies,'k--',linewidth=3)
artist.adjust_spines(ax)
ax.set_xticks(xrange(len(words)))
ax.set_xticklabels([r'\textbf{\textsc{%s}'%word for word in words],rotation='vertical')
ax.set_ylabel(artist.format(ylabel))
plt.tight_layout()
plt.show()
plt.savefig(savefile, bbox_inches="tight")
def memory_stability(mat,savename):
fig = plt.figure()
ax = fig.add_subplot(111)
cax = ax.imshow(mat,interpolation='nearest',aspect='auto')
artist.adjust_spines(ax)
ax.set_ylabel(artist.format('Memory'))
ax.set_xlabel(artist.format('Time'))
cbar = plt.colorbar(cax)
cbar.set_label(artist.format('Energy'))
plt.savefig('%s.png'%savename,dpi=200)
plt.close()
def runWin(): Label(window, image=photo).grid(row=0, column=1) Label(window, text="BigRep Configuration UI", font=Graphics.getBigRepFont(window, 40)).grid(row=0, column = 2)
def _processRenderSurface(logDir, attributes):
def attr(name):
return attributes[name]
w, h = attr("render_surface_width"), attr("render_surface_height")
redMask = attr("red_mask")
greenMask = attr("green_mask")
blueMask = attr("blue_mask")
alphaMask = attr("alpha_mask")
depthMask = attr("depth_mask")
stencilMask = attr("stencil_mask")
isLinear = attr("is_linear")
isPremultiplied = attr("is_premultiplied")
# Convert the color buffer
if "color_buffer" in attributes:
fileName = attr("color_buffer")
if not os.path.exists(fileName):
fileName = os.path.join(logDir, fileName)
fileNameOut = fileName.rsplit(".", 1)[0] + ".png"
# Only do the conversion if the image doesn't already exist
# or if the source file is newer.
if fileName.endswith(".dat") and \
(not os.path.exists(fileNameOut) or \
(os.path.exists(fileName) and os.path.getmtime(fileName) > os.path.getmtime(fileNameOut))
):
stride = attr("color_stride")
f = open(fileName, "rb")
data = f.read(stride * h)
f.close()
if len(data) != h * stride or not data:
Log.error("Invalid color buffer data size: %d" % len(data))
return
colorBuffer = Graphics.decodeImageData(data, (w, h), stride, redMask, greenMask, blueMask, alphaMask, isLinear, isPremultiplied)
colorBuffer = colorBuffer.convert("RGBA")
colorBuffer.save(fileNameOut)
# We can remove the original file now
os.unlink(fileName)
# Replace the original file name with the decoded file
attributes["color_buffer"] = fileNameOut
# Eat the render surface attributes since they are of little use further down the road
#for attrName in ["red_mask", "green_mask", "blue_mask", "alpha_mask",
# "depth_mask", "stencil_mask", "color_stride",
# "is_linear", "is_premultiplied", "color_data_type",
# "depth_data_type", "stencil_data_type"]:
# if attrName in attributes:
# del attributes[attrName]
for bufferName in ["depth_buffer", "stencil_buffer"]:
if bufferName in attributes and not os.path.exists(attributes[bufferName]):
# Fill in the full buffer file name
attributes[bufferName] = os.path.join(logDir, attr(bufferName))
def sensitivity(x,y, show=False, savename=None):
fig = plt.figure()
ax = fig.add_subplot(111)
ax.plot(x,y,'ko--',linewidth=2,clip_on=False)
artist.adjust_spines(ax)
ax.set_xlabel(r'\Large \textbf{Mixing fraction,} $\; \alpha$')
ax.set_ylabel(r'\Large \textbf{Maximum accuracy,}$\; q_{\max}$')
ax.set_ylim((-1.1))
plt.tight_layout()
if savename:
plt.savefig('%s.png'%savename,dpi=300)
if show:
plt.show()
plt.close()
return pearsonr(x,y)
def minConflicts(csp, variables):
"""
Min-conflicts approach to solving the Sudoku puzzle
"""
assignment, domains = csp # Domains not applicable for this problem, but included for readability
# Iterate through list, and randomly assign numbers
for var in variables:
# Randomly assign number
num = random.randint(1,9)
# Update the stack to include the new number, whether it works or not
assignment[var[0]][var[1]] = num
# Loop while the problem is not solved
while variables != []:
Graphics.showPuzzle(assignment)
print variables
# Randomly choose a variable from the list
var = random.choice(variables)
# Create value for least-conflict value
bestValue, leastConstraints = None, sys.maxsize
# Loop over possible domain values, and update best value if applicable
for value in range(1,10):
conflicts = countConflicts(assignment, var, value)
if conflicts < leastConstraints:
bestValue, leastConstraints = value, conflicts
# Update the state with the new value
assignment[var[0]][var[1]] = bestValue
# If the variable does not violate any constraints, remove it from conflicted
if leastConstraints == 0:
variables.remove(var)
print "Solution Found!"
return assignment
def grayscale(imageName):
win = g.GraphWin()
image = g.Image(point(0, 0), imageName)
# for each row in the image:
for rown in image:
for column in image:
r, g, b = # get pixel information for current row and column
brightness = int(round((0.299 * r) + (0.587 * g) + (0.114 * b)))
pixel_color = color_rgb(brightness, brightness, brightness)
# update the image # to see progress row by row
for row in image:
for column in image:
r, g, b = g.getPixel(row, column)
brightness = int(round(0.299 * r + 0.587 * g + 0.114 * b))
g.color_rgb(brightness, brightness, brightness)
g.draw(win)
def mvr_coefficients(model,labels,show=False,savename=None):
fig = plt.figure()
ax = fig.add_subplot(111)
cax = ax.imshow(model.coef_.transpose(),interpolation='nearest',aspect='auto',
vmin=-0.5,vmax=0.5)
artist.adjust_spines(ax)
ax.set_yticks(range(len(labels)))
ax.set_yticklabels(map(artist.format,[name for name in labels if 'EVD' not in name]))
ax.set_xticks(range(3))
ax.set_xticklabels(map(artist.format,range(1,4)))
ax.set_xlabel(artist.format('Placement grade'))
plt.colorbar(cax)
plt.tight_layout()
if savename:
plt.savefig('%s.png'%savename,dpi=200)
if show:
plt.show()
def correlation_visualization(data, show=False,savename=None):
correlations = ['Quu','Qru','Qvu']
#Analyze correlations
fig,axs = plt.subplots(nrows=3,ncols=1,sharex=True)
for ax,data,label in zip(axs,map(dq,[data[correlation] for correlation in correlations]),correlations):
ax.plot(data,'k',linewidth=2,label=artist.format(label))
artist.adjust_spines(ax)
ax.set_ylabel(r'\Large $\mathbf{\partial \left(\det %s_{%s}\right)}$'%(label[0],label[1:]),rotation='horizontal')
ax.set_xlabel(artist.format('Time'))
plt.tight_layout()
if savename:
plt.savefig('%s.png'%savename,dpi=200)
if show:
plt.show()
plt.close()
def setUpPlayer (self, pos, clrBase = None, clrTurret = None):
if self.side == 'player':
self.maze.addPlayer (self)
self.pos = pos
self.vel = 0
self.cooldownTimer = 0
self.viewTheta = math.pi / 8
self.base = Graphics.cube (clrBase)
self.base.transform (Matrix.scale (7.5, 7.5, 3.75))
self.base.translate (self.pos.x (), self.pos.y(), 10)
self.turretp1 = Graphics.icosahedron (clrTurret)
self.turretp1.transform (Matrix.scale (5, 5, 5))
self.turretp2 = Graphics.cube (clrTurret)
self.turretp2.transform (Matrix.scale (1, 7.5, 1))
self.turretp2.translate (0, 3.5, 0)
self.turret = self.turretp1.union (self.turretp2)
self.turret.translate (self.pos.x (), self.pos.y(), 0)
self.theta = 0
self.turretTheta = 0
self.changeAngle (math.pi / 4)
self.alive = True
def compare_distributions(variable_source_name,idxs,rng=(0,1)):
#Assume idxs is dictionary structured as {name:[corresponding indices]}
fig = plt.figure()
ax = fig.add_subplot(111)
data = np.loadtxt(make_filename('%s.txt'%(variable_source_name)),delimiter=TAB)
fig = plt.figure()
ax = fig.add_subplot(111)
plt.hold(True)
for subpopulation,idx,color in idxs:
weights = np.ones_like(data[idx])/len(data[idx])
ax.hist(data[idx],color=color,label=artist.format(subpopulation),histtype='step',range=rng,weights=weights)
fig.canvas.mpl_connect('draw_event', artist.on_draw)
artist.adjust_spines(ax)
ax.set_ylabel(artist.format('Prevalance'))
ax.set_xlabel(artist.format(variable_source_name))
plt.legend(frameon=False,ncol=2,loc='upper center',bbox_to_anchor=(.5,1.05))
plt.tight_layout()
plt.savefig(make_filename('%s-%s.png'%(variable_source_name,'-'.join([idx[0] for idx in idxs]))),dpi=300)
del fig,ax
def dashboard(data,numpc=3):
coeff,projections,latent = tech.princomp(data,numpc=numpc)
panels = {'projection':plt.subplot2grid((2,3),(0,0),colspan=2, rowspan=2),
'spectrum':plt.subplot2grid((2,3),(0,2)),
'silhouette':plt.subplot2grid((2,3),(1,2))}
panels['projection'].scatter(projections[0],projections[1],s=30)
artist.adjust_spines(panels['projection'])
panels['projection'].set_xlabel(r'\Large \textbf{Principal Component 1}')
panels['projection'].set_ylabel(r'\Large \textbf{Principal Component 2}')
cutoff=10
panels['spectrum'].stem(range(1,cutoff+1),latent[:cutoff]/np.sum(latent))
panels['spectrum'].set_xlim(0,cutoff+1)
panels['spectrum'].set_ylim((0,1))
artist.adjust_spines(panels['spectrum'])
panels['spectrum'].set_xlabel(r'\Large \textbf{Eigenvector}')
panels['spectrum'].set_ylabel(r'\Large \textbf{Eigenvalue} $\left(\lambda\right)$')
silhouettes = tech.silhouette(projections, k=8)
idx = range(2,len(silhouettes)+2)
panels['silhouette'].stem(idx,[silhouettes[x]['data'] for x in idx])
#Get confidence intervals
CIs = np.array([scoreatpercentile(silhouettes[x]['distribution'], 95) for x in idx])
plt.hold(True)
panels['silhouette'].plot(idx,CIs,linewidth=2,color='r',linestyle='--')
artist.adjust_spines(panels['silhouette'])
panels['silhouette'].set_xlim((0,len(idx)+3))
panels['silhouette'].set_ylim((0,1))
panels['silhouette'].set_xlabel(r'\Large \textbf{Number of clusters}')
panels['silhouette'].set_ylabel(r'\Large \textbf{Silhouette coefficient}')
plt.tight_layout()
rot = plt.figure()
panel = rot.add_subplot(111)
dt = coeff*(latent[:3]/np.sum(latent))
dt_args = np.argsort(dt,axis=0)
cax = panel.imshow(np.sort(coeff*(latent[:3]/np.sum(latent)),axis=0)[::-1], aspect='auto',interpolation='nearest', vmin=-.15,vmax=0.15)
artist.adjust_spines(panel)
panel.set_yticks(np.arange(len(useful_keys)))
panel.set_yticklabels(map(lambda word: word.strip().capitalize(),[useful_keys[x[0]] for x in dt_args[::-1]]))
panel.set_xticks(np.arange(3))
panel.set_xticklabels(np.arange(3)+1)
panel.set_xlabel(r'\Large \textbf{Principal Component}')
rot.colorbar(cax)
plt.tight_layout()
plt.grid(True)
plt.show()
def plot_variable(data,basepath=None,dataname='',criterion=None, criterionname=[]):
fig = plt.figure()
ax = fig.add_subplot(111)
x = range(data.shape[1])
ap('Plotting %s'%dataname)
if criterion != None:
if type(criterion) != list:
median, lq, uq = perc(data[criterion,:])
ax.plot(x,median,linewidth=2, color='#B22400')
ax.fill_between(x, lq, uq, alpha=0.25, linewidth=0, color='#B22400')
else:
bmap = brewer2mpl.get_map('Set2', 'qualitative', 7)
colors = bmap.mpl_colors
for i,(x_criterion,x_label) in enumerate(itertools.izip_longest(criterion,criterionname,fillvalue='Group')):
median, lq, uq = perc(data[x_criterion,:])
ax.plot(x,median,linewidth=2, color=colors[i], label=artist.format(x_label))
ax.fill_between(x, lq, uq, alpha=0.25, linewidth=0, color=colors[i])
median, lq, uq = perc(data)
ax.plot(x,median,linewidth=2, color='#B22400',label=artist.format('Full population'))
ax.fill_between(x, lq, uq, alpha=0.25, linewidth=0, color='#B22400')
artist.adjust_spines(ax)
ax.set_ylabel(artist.format(dataname))
ax.set_xlabel(artist.format('Time'))
ax.axvline(data.shape[1]/3,color='r',linewidth=2,linestyle='--')
ax.axvline(2*data.shape[1]/3,color='r',linewidth=2,linestyle='--')
plt.legend(frameon=False,loc='lower left')
plt.tight_layout()
plt.savefig(os.path.join(basepath,'%s.png'%dataname))
def betterSimulatedAnnealing(csp, variables):
"""
Better Min-conflicts with a factor of randomness approach. Schedule is a mapping of time to temperature
"""
# Unpack the CSP
assignment, domains = csp
# Create Dictionary of conflicted variables and number of conflicts
conflicted = {}
# Iterate through list, randomly assign numbers, and add conflicted variables to array
for var in variables:
# Randomly assign number
num = random.choice(domains[var])
# Update the stack to include the new number, whether it works or not
assignment[var[0]][var[1]] = num
for var in variables:
conflicted[var] = countConflicts(assignment, var, assignment[var[0]][var[1]])
t = 1
while assessValue(assignment) != 0 and t < 1000000:
print "Completion =", 100-assessValue(assignment),"%"
changingVar = random.choice(conflicted.keys())
totalConflicts = assessValue(assignment)
originalVal = assignment[changingVar[0]][changingVar[1]]
newVal = random.choice(domains[changingVar])
assignment[changingVar[0]][changingVar[1]] = newVal
newTotalConflicts = assessValue(assignment)
deltaE = newTotalConflicts - totalConflicts
if deltaE > 0:
if random.random() > P(deltaE, T(t)):
#reset to original
assignment[changingVar[0]][changingVar[1]] = originalVal
Graphics.showPuzzle(assignment)
t+=1
for var in variables:
conflicted[var] = countConflicts(assignment, var, assignment[var[0]][var[1]])
if t != 1000000:
Graphics.showPuzzle(assignment)
print "Solution Found!"
return assignment
else:
Graphics.showPuzzle(assignment)
print "Failed with Completion =", 100-assessValue(assignment),"%"
return -1
def getImageLoaders(project, trace):
"""
Return a list of (event, func) pairs, where event is a image upload event and
func is a function that returns an Image containing the image data when called.
"""
library = project.targets["code"].library
constants = Collections.DictProxy(library.constants)
loaders = []
formats = {
constants.VG_sRGBX_8888: ("b", 4, False, False, 0x00ff0000, 0x0000ff00, 0x000000ff, 0xff000000),
constants.VG_sRGBA_8888: ("b", 4, False, False, 0x00ff0000, 0x0000ff00, 0x000000ff, 0xff000000),
constants.VG_sRGBA_8888_PRE: ("b", 4, False, True, 0x00ff0000, 0x0000ff00, 0x000000ff, 0xff000000),
constants.VG_sRGB_565: ("h", 3, False, False, 0x001f, 0x07e0, 0xf800, 0x0),
constants.VG_sRGBA_5551: ("h", 4, False, False, 0x001f, 0x03e0, 0x7c00, 0x8000),
constants.VG_sRGBA_4444: ("h", 4, False, False, 0x000f, 0x00f0, 0x0f00, 0xf000),
constants.VG_sL_8: ("b", 1, False, False, 0xff, 0x0, 0x0, 0x0),
constants.VG_lRGBX_8888: ("b", 4, True, False, 0x00ff0000, 0x0000ff00, 0x000000ff, 0xff000000),
constants.VG_lRGBA_8888: ("b", 4, True, False, 0x00ff0000, 0x0000ff00, 0x000000ff, 0xff000000),
constants.VG_lRGBA_8888_PRE: ("b", 4, True, True, 0x00ff0000, 0x0000ff00, 0x000000ff, 0xff000000),
constants.VG_lL_8: ("b", 1, True, False, 0xff, 0x0, 0x0, 0x0),
constants.VG_A_8: ("b", 1, True, False, 0xff, 0x0, 0x0, 0x0),
constants.VG_BW_1: ("b", 1, True, False, 0x1, 0x0, 0x0, 0x0),
}
task = Task.startTask("prepare-images", "Looking for images", len(trace.events))
for event in trace.events:
task.step()
if event.name == "vgImageSubData" and event.values.get("data"):
width = event.values["width"]
height = event.values["height"]
stride = event.values["dataStride"]
format = event.values["dataFormat"]
if format in formats:
unit, components, isLinear, isPremultiplied, redMask, greenMask, blueMask, alphaMask = formats[format]
else:
continue
data = event.values["data"]
data = struct.pack("<%d%s" % (len(data), unit), *data)
size = (width, height)
# Construct copies of the passed variables to make sure the proper data goes into the lambda when called
func = lambda d=data, s=size, st=stride, rb=redMask, gb=greenMask, bb=blueMask, ab=alphaMask, il=isLinear, ip=isPremultiplied: \
Graphics.decodeImageData(d, s, st, rb, gb, bb, ab, isLinear = il, isPremultiplied = ip)
loaders.append((event, func))
return loaders
def handleInput(hand,selection,state,play_made): # temporary value should throw error if unchanged card = 52 #wait for keypress keypress = wait() # check for QUIT events for event in pygame.event.get(QUIT): #end the game state = False if keypress == K_ESCAPE: #end the game state = False if keypress == K_RETURN and len(hand)>0: #display player's played card card = hand.pop(selection) graphics.displayCardPlay(card,0) play_made = True #if we select the last card, we must decrement selection, #since the hand is now one card fewer if selection == len(hand): selection -= 1 if keypress == K_LEFT and selection > 0: #move the selection left selection -= 1 if keypress == K_RIGHT and selection < len(hand)-1: #move the selection right selection += 1 return (hand,selection,state,play_made,card)
def track_matrices(mat,savename):
mat = mat.astype(np.float32)
base = mat[:,:,0]
base_values,base_vectors = np.linalg.eig(base)
idx = np.argsort(base_values)[::-1] #Want eigenvectors in descending order
base_vectors = base_vectors[idx][:10] #Now eigvectors are in descending order, display top 10
base_norm = np.linalg.norm(base_vectors)
DURATION = mat.shape[2]
#Awful magic constant of 10 eigenvectors
data = np.zeros((10,DURATION))
for t in range(1,DURATION):
these_values,these_vectors = np.linalg.eig(mat[:,:,t])
this_idx = np.argsort(these_values)[::-1]
these_vectors = these_vectors[this_idx][:10]
data[:,t] = [a.dot(b.T)/(np.linalg.norm(a)*np.linalg.norm(b)) for a,b in zip(base_vectors,these_vectors)]
#Scale by eigenvalues?
fig = plt.figure()
ax = fig.add_subplot(111)
cax= ax.imshow(data,interpolation='nearest',aspect='auto')
artist.adjust_spines(ax)
ax.set_xlabel(artist.format('Time'))
ax.set_yticks(range(10))
ax.set_yticklabels([r'\Large $\mathbf{e_%d\left(0\right) \cdot e_%d}$'%(x,x) for x in range(10)])
ax.set_xticks(range(mat.shape[2])[::10])
ax.set_xticklabels([10*x for x in range(mat.shape[2])[::10]])
plt.colorbar(cax)
fig.tight_layout()
plt.savefig('%s.png'%savename,dpi=200)
plt.close()
def time_series(basepath=None, criterion=None, criterionname=''):
filename = os.path.join(basepath,'attitudes.txt')
attitudes = np.loadtxt(filename,delimiter=TAB)
fig = plt.figure()
ax = fig.add_subplot(111)
#ax.fill_between(xrange(attitudes.shape[1]), attitudes.mean(axis=0)-attitudes.std(axis=0),
# attitudes.mean(axis=0) + attitudes.std(axis=0), color='k', alpha=0.4,
# label=artist.format('Full population'))
ax.errorbar(xrange(attitudes.shape[1]),attitudes.mean(axis=0),yerr=(attitudes.std(axis=0)/attitudes.shape[0]))
# ax.plot(xrange(attitudes.shape[1]),attitudes.mean(axis=0),color='k',linewidth=2)
if criterion:
data = attitudes[criterion]
ax.fill_between(xrange(data.shape[1]), data.mean(axis=0)-data.std(axis=0),
data.mean(axis=0) + data.std(axis=0), color='r', alpha=0.4,
label=artist.format('criterionname'))
ax.plot(xrange(data.shape[1]),data.mean(axis=0),color='r',linewidth=2)
artist.adjust_spines(ax)
ax.axvline(attitudes.shape[1]/3.,color='r',linewidth=2,linestyle='--') #This is a hack
ax.axvline(2*attitudes.shape[1]/3.,color='r',linewidth=2,linestyle='--') #This is a hack
ax.set_ylabel(artist.format('Intent to drink'))
ax.set_xlabel(artist.format('Time'))
ax.set_ylim(ymin=0)
filename = os.path.join(os.getcwd(),basepath,'timecourse.png' if criterionname == '' else 'timecourse-%s.png'%criterionname)
plt.savefig(filename,dpi=300)
def sensitivities(x,im,show=False, savename=None):
fig = plt.figure()
ax = fig.add_subplot(111)
cax = ax.imshow(im,interpolation='nearest',aspect='auto', vmin=-1,vmax=1)
cbar = plt.colorbar(cax)
artist.adjust_spines(ax)
ax.set_xticks(range(len(x)))
xlabs = [r'\Large $\mathbf{%.02f}$'%alpha for alpha in map(sn,x)]
xlabs[0] = r'\Large $\mathrm{All \; signal}$'
xlabs[-1] = r'\Large $\mathrm{All \; noise}$'
ax.set_xticklabels(xlabs)
ax.set_xlabel(r'\Large $\mathrm{\frac{Signal}{Noise}}$')
ax.set_ylabel(r'\Large $\mathrm{Pattern} $', rotation='horizontal')
cbar.set_label(r'\Large $\mathrm{Accuracy,} \; q_{\max}$')
plt.tight_layout()
if savename:
plt.savefig('%s.png'%savename,dpi=200)
if show:
plt.show()
plt.close()
return [pearsonr(row,x) for row in im]
out_file.write('fitness, generation, highest round\n')
# load starting graphics that don't need to be reloaded
# Graphics.load_static_gfx()
# loops the whole simulation
done = 0
Stats.generation = 0
while done < 30:
game = Game(birds_for_generation)
done_generation = False
# loops each generation
while not done_generation:
screen.fill(Constants.WHITE)
game.update_obstacles()
game.draw_game(screen)
Graphics.draw_stats(screen)
Graphics.draw_key(screen)
# game.check_actions returns True if all the birds are dead
if game.check_actions(game.birds):
done_generation = True
# pygame specific functions
pygame.display.update()
pygame.display.flip()
# check to quit
for event in pygame.event.get():
if event.type == pygame.QUIT:
print("Reached quit in check_actions function, quitting now...")
exit(0)
def getTextureLoaders(project, trace):
"""
Return a list of (event, func) pairs, where event is a texture upload event and
func is a function that returns an Image containing the texture data when called.
"""
library = project.targets["code"].library
constants = Collections.DictProxy(library.constants)
loaders = []
componentCount = {
constants.GL_ALPHA: 1,
constants.GL_RGB: 3,
constants.GL_RGBA: 4,
constants.GL_LUMINANCE: 1,
constants.GL_LUMINANCE_ALPHA: 2,
}
task = Task.startTask("prepare-textures", "Looking for textures", len(trace.events))
for event in trace.events:
task.step()
# We don't handle compressed texture formats
if event.name in ["glTexImage2D", "glTexSubImage2D"] and event.values.get("pixels"):
width = event.values["width"]
height = event.values["height"]
format = event.values["format"]
type = event.values["type"]
if format in componentCount:
components = componentCount[format]
else:
continue
if type == constants.GL_UNSIGNED_BYTE:
bytesPerPixel = components
format = "b"
redMask = 0x00ff0000
greenMask = 0x0000ff00
blueMask = 0x000000ff
alphaMask = 0xff000000
elif type == constants.GL_UNSIGNED_SHORT_5_6_5:
bytesPerPixel = 2
format = "h"
redMask = 0x001f
greenMask = 0x07e0
blueMask = 0xf800
alphaMask = 0x0000
elif type == constants.GL_UNSIGNED_SHORT_5_5_5_1:
bytesPerPixel = 2
format = "h"
redMask = 0x001f
greenMask = 0x03e0
blueMask = 0x7c00
alphaMask = 0x8000
elif type == constants.GL_UNSIGNED_SHORT_4_4_4_4:
bytesPerPixel = 2
format = "h"
redMask = 0x000f
greenMask = 0x00f0
blueMask = 0x0f00
alphaMask = 0xf000
else:
continue
pixels = event.values["pixels"]
data = struct.pack("<%d%s" % (len(pixels), format), *pixels)
if components < 4:
alphaMask = 0
if components < 3:
blueMask = 0
if components < 2:
greenMask = 0
size = (width, height)
stride = width * bytesPerPixel
# Construct copies of the passed variables to make sure the proper data goes into the lambda when called
func = lambda d=data, s=size, st=stride, rb=redMask, gb=greenMask, bb=blueMask, ab=alphaMask: \
Graphics.decodeImageData(d, s, st, rb, gb, bb, ab)
loaders.append((event, func))
return loaders
class Container():
def __init__(self, msgbrowser):
self.unitOp = []
self.thermoPackage = None
self.compounds = None
self.flowsheet = None
self.conn = defaultdict(list)
self.op = defaultdict(list)
self.ip = defaultdict(list)
self.msg = msgbrowser
self.msg.setText("")
self.opl = []
self.result = []
self.graphics = Graphics(self.unitOp)
def currentTime(self):
now = datetime.datetime.now()
time = str(now.hour) + ":" + str(now.minute) + ":" + str(now.second)
return time
def updateConn(self, key, value):
self.conn[key].append(value)
self.msg.append("<span style=\"color:blue\">[" +
str(self.currentTime()) + "]<b> " + key.name +
" </b> output is connected to input of<b> " +
value.name + " </b></span>")
def connection(self):
try:
self.op.clear()
self.ip.clear()
self.opl.clear()
stm = ['MaterialStream', 'EngStm']
for i in self.conn:
if i.type not in stm:
self.op[i] = self.conn[i]
for j in range(len(self.conn[i])):
if self.conn[i][j].type not in stm:
self.ip[self.conn[i][j]].append(i)
for i in self.op:
i.connect(InputStms=self.ip[i], OutputStms=self.op[i])
self.opl.append([self.op[i] for i in self.op])
self.opl = flatlist(flatlist(self.opl))
except Exception as e:
print(e)
@staticmethod
def addUnitOpObj(obj):
self.unitOp.append(obj)
def addUnitOp(self, obj):
box = None
self.obj = obj
self.scene = self.graphics.getScene()
box = self.graphics.createNodeItem(self.obj)
self.scene.addItem(box)
box.setPos(2500 - 30, 2500 - 30)
if (obj in self.unitOp):
pass
else:
self.unitOp.append(obj)
self.msg.append("<span style=\"color:blue\">[" +
str(self.currentTime()) + "]<b> " + obj.name +
" </b>is instantiated ."
"</span>")
def fetchObject(self, name):
for i in self.unitOp:
if (i.name == name):
return i
def addCompounds(self, comp):
self.compounds = comp
def add_thermoPackage(self, thermo):
self.thermoPackage = thermo
def msgBrowser(self):
std = self.flowsheet.stdout.decode("utf-8")
if (std):
stdout = str(std)
stdout = stdout.replace("\n", "<br/>")
self.msg.append("<span style=\"color:green\">" + stdout +
"</span>")
stde = self.flowsheet.stderr.decode("utf-8")
if (stde):
stdout = str(stde)
stdout = stdout.replace("\n", "<br/>")
self.msg.append("<span style=\"color:red\">" + stdout + "</span>")
def simulate(self, mode):
print("SIMULATE")
print(mode)
self.compounds = compound_selected
self.flowsheet = Flowsheet()
self.flowsheet.add_comp_list(self.compounds)
print("######## connection master#########\n", self.conn)
for i in self.unitOp:
print("here", i)
self.flowsheet.add_UnitOpn(i)
if mode == 'SM':
self.msg.append(
"<span>[" + str(self.currentTime()) +
"] Simulating in <b>Sequential</b> mode ... </span>")
self.flowsheet.simulateSM(self.ip, self.op)
self.msgBrowser()
self.result = self.flowsheet.resdata
print("under SEQ mode simulation")
elif mode == 'EQN':
self.msg.append("<span>[" + str(self.currentTime()) +
"] Simulating in <b>equation</b> mode ... </span>")
self.flowsheet.simulateEQN()
self.msgBrowser()
self.result = self.flowsheet.resdata
print("under Eqn mode simulation")
map_type = SimulationParams.MAP_TYPE_TREN_MADRID
else:
print('Error: Unknown Simulation Type')
if DisplayParams.DRAW_GRAPHS:
vmax = 0.11 # TODO: minor, clean this up. it's color map encoding
if SimulationParams.SIMULATION_TYPE == SimulationParams.SUBWAY_SIM:
vmax = 0.0011
elif SimulationParams.SIMULATION_TYPE == SimulationParams.TRAIN_SIM:
vmax = 0.0007
#Draws the graph. figure and model required if you want to draw the map as well
Graphics.draw_graph(nx_graph,
DisplayParams.GRAPH_BY_FEATURE,
timestamp=str(i),
map_type=map_type,
figure=f,
model=model,
vmax=vmax)
f.savefig("Visualizations/time" + f'{i:03}')
if DisplayParams.ALWAYS_SHOW_GRAPH or (math.log2(i).is_integer() and
DisplayParams.SHOW_EVERY_2X):
plt.show()
plt.close(f)
# Save final SEIR Results
f = plt.figure()
Graphics.draw_SEIR_curve(SEIR_Statistics,
f,
benchmark_SEIR=benchmark_statistics)
def Game(self):
turn = 0
turnCounter = 15
turnCondition = True
key = True
while turnCondition:
stddraw.show(0)
Graphics.drawTurnCounter(turnCounter)
Graphics.drawWinThing()
if turn == 0:
if stddraw.mousePressed():
mx = stddraw.mouseX()
my = stddraw.mouseY()
if mx < 4.69 and my >= 1 and my < 10:
Graphics.clickHelp()
for i in range(self.x):
if mx > i / 1.5 and mx < i / 1.5 + (2 / 3):
mDrawX1 = i / 1.5
mEmojiX1 = i + 1
for i in range(self.y + 1):
if my > i and my < i + 1:
mDrawY1 = i
mEmojiY1 = i + 1 / 2
for i in range(self.y):
for j in range(self.x):
if self.board[i][
j].x == mEmojiX1 and self.board[i][
j].y == mEmojiY1:
e1emoji = self.board[i][j].emoji
e1i, e1j = i, j
Graphics.tileSelected(mDrawX1, mDrawY1)
turn = 1
if turn == 1:
if stddraw.mousePressed():
mx = stddraw.mouseX()
my = stddraw.mouseY()
if mx < 4.69 and my >= 1 and my < 10:
for i in range(self.x):
if mx > i / 1.5 and mx < i / 1.5 + (2 / 3):
mEmojiX2 = i + 1
for i in range(self.y + 1):
if my > i and my < i + 1:
mEmojiY2 = i + 1 / 2
for i in range(self.y):
for j in range(self.x):
if self.board[i][
j].x == mEmojiX2 and self.board[i][
j].y == mEmojiY2:
e2emoji = self.board[i][j].emoji
e2i, e2j = i, j
#Swapping the emojis
"""
NOTE THAT THE X POSITIONS ETC HAVE STILL NOT BEEN FIXED
11/18/2018 12:14 AM
Conversion system new: when x == 1, emoji true position is 1/3, x == 2, 1,
x == 3, 5/3
"""
if mEmojiX2 == mEmojiX1 + 1 and mEmojiY2 == mEmojiY1 or mEmojiX2 == mEmojiX1 - 1 and mEmojiY2 == mEmojiY1 or mEmojiY2 == mEmojiY1 + 1 and mEmojiX2 == mEmojiX1 or mEmojiY2 == mEmojiY1 - 1 and mEmojiX2 == mEmojiX1:
Graphics.clickHelpClear()
self.board[e1i][e1j].emoji = e2emoji
self.board[e2i][e2j].emoji = e1emoji
counter = 0
while test.check(self.board) != 0:
self.checkDraw(mDrawX1, mDrawY1)
#Erase Vertical
if test.getD() == 0:
counter = 0
marker = self.board[test.getY()][
test.getX()].emoji
while marker != 0:
if test.getY() + counter != 9:
if marker == self.board[test.getY(
) + counter][test.getX()].emoji:
self.board[test.getY() +
counter][test.getX(
)].emoji = 0
Score.score += 1
counter += 1
else:
marker = 0
else:
marker = 0
self.checkDraw(mDrawX1, mDrawY1)
#Vertical Drop
count = 0
for j in range(9):
if test.getY() + count + counter < 9:
self.board[
test.getY() + count][test.getX(
)].emoji = self.board[
test.getY() + count +
counter][test.getX()].emoji
self.board[
test.getY() + count +
counter][test.getX()].emoji = 0
count += 1
self.checkDraw(mDrawX1, mDrawY1)
temp = 9 - counter
for i in range(counter):
self.board[temp][
test.getX()].emoji = self.ranNum()
temp += 1
self.checkDraw(mDrawX1, mDrawY1)
#Erase Horizontal
else:
counter = 0
marker = self.board[test.getY()][
test.getX()].emoji
while marker != 0:
if test.getX() + counter != 7:
if marker == self.board[test.getY(
)][test.getX() + counter].emoji:
self.board[test.getY()][
test.getX() +
counter].emoji = 0
Score.score += 1
counter += 1
else:
marker = 0
else:
marker = 0
self.checkDraw(mDrawX1, mDrawY1)
#Horizontal Drop
count = 0
for i in range(counter):
count = 0
for j in range(9):
if test.getY() + count < 8:
temp = count + 1
self.board[
test.getY() + count][
test.getX() +
i].emoji = self.board[
test.getY() +
temp][test.getX() +
i].emoji
self.board[test.getY() +
temp][test.getX() +
i].emoji = 0
count += 1
self.checkDraw(mDrawX1, mDrawY1)
for i in range(counter):
self.board[8][test.getX() +
i].emoji = self.ranNum()
self.checkDraw(mDrawX1, mDrawY1)
turn = 0
#For when it is not a match 3
if counter == 0:
self.board[e1i][e1j].emoji = e1emoji
self.board[e2i][e2j].emoji = e2emoji
Graphics.clickHelpClear()
Graphics.drawInvalidMatch3()
Graphics.clearInvalidSwitch()
Graphics.tileSelectedClear(mDrawX1, mDrawY1)
turn = 0
else:
turnCounter -= 1
#For when you want to select the tile again
elif mEmojiX1 == mEmojiX2 and mEmojiY1 == mEmojiY2:
Graphics.clickHelpClear()
Graphics.tileSelectedClear(mDrawX1, mDrawY1)
turn = 0
#For when there is an invaid switch
else:
Graphics.clickHelpClear()
Graphics.tileSelectedClear(mDrawX1, mDrawY1)
Graphics.drawInvalidSwitch()
Graphics.clearInvalidSwitch()
turn = 0
else:
turn = 1
#Note, mouse does not need to be pressed in order for this CheckWin
if turnCounter == 0 and Score.score >= 75:
Graphics.drawTurnCounter(turnCounter)
turnCondition = False
while key:
Graphics.drawWinCondition()
if stddraw.mousePressed():
key = False
elif turnCounter == 0 and Score.score <= 75:
Graphics.drawTurnCounter(turnCounter)
turnCondition = False
while key:
Graphics.drawLossCondition()
if stddraw.mousePressed():
key = False
return 800 * x + 100
T_a = analyticSol(x)
error = fvm.calcError(T, T_a)
datos = {'x(m)': x,
'T(x)': T,
'Analytic': T_a,
'Error': error}
fvm.printFrame(datos)
print('||Error|| = ', np.linalg.norm(error))
print('.'+ '-'*70 + '.')
# -------------------------------------------------------------------
# Calculamos la solución exacta en una malla más fina para graficar
# -------------------------------------------------------------------
x1 = np.linspace(0,L,100)
T_a = analyticSol(x1)
# -----------------------------------------------------
# Se grafica la solución
# -----------------------------------------------------
plt.close('all')
title_graf = 'Solución de $ \partial (k \partial T/\partial x)/\partial x = 0$ con FVM'
plt2.plotG(x, T, kind = '--o', xlabel = '$x$ [m]', ylabel = 'T [°C]',
label = 'Sol. FVM', title_graf = title_graf)
plt2.plotG(x1, T_a, kind = "-", xlabel = '$x$ [m]', ylabel = 'T [°C]',
label = 'Sol. analítica', lw=2, title_graf = title_graf)
plt.show()
# Guarda la grafica
#plt.savefig('Tarea1.svg')
friction=0.3,
categoryBits=0x0004,
maskBits=0x0002)
box.filter.groupIndex = -2
body = world.CreateDynamicBody(position=(25, 20), fixtures=box)
timeStep = 1.0 / 60
vel_iters, pos_iters = 10, 10
while True:
for event in pygame.event.get():
if event.type == pygame.QUIT:
pygame.quit()
sys.exit()
Graphics.BackDrop(dif, land, window)
x1, y1 = joint1.GetReactionForce(1 / timeStep)
x2, y2 = joint2.GetReactionForce(1 / timeStep)
x3, y3 = joint3.GetReactionForce(1 / timeStep)
x4, y4 = joint4.GetReactionForce(1 / timeStep)
x5, y5 = joint5.GetReactionForce(1 / timeStep)
x6, y6 = joint6.GetReactionForce(1 / timeStep)
x7, y7 = joint7.GetReactionForce(1 / timeStep)
x8, y8 = joint8.GetReactionForce(1 / timeStep)
x9, y9 = joint9.GetReactionForce(1 / timeStep)
x10, y10 = joint10.GetReactionForce(1 / timeStep)
x11, y11 = joint11.GetReactionForce(1 / timeStep)
x12, y12 = joint12.GetReactionForce(1 / timeStep)
x13, y13 = joint13.GetReactionForce(1 / timeStep)
x14, y14 = joint14.GetReactionForce(1 / timeStep)
class Container():
def __init__(self, msgbrowser):
self.unitOp = []
self.thermoPackage = None
self.compounds = None
self.flowsheet = None
self.conn = defaultdict(list)
self.op = defaultdict(list)
self.ip = defaultdict(list)
self.msg = msgbrowser
self.msg.setText("")
self.opl = []
self.result = []
self.graphics = Graphics(self.unitOp)
self.scene = self.graphics.getScene()
def currentTime(self):
now = datetime.datetime.now()
time = str(now.hour) + ":" + str(now.minute) + ":" + str(now.second)
return time
# def updateConn(self,key,value):
# self.conn[key].append(value)
# self.msg.append("<span style=\"color:blue\">["+str(self.currentTime())+"]<b> "+key.name+" </b> output is connected to input of<b> "+value.name +" </b></span>")
#
# def connection(self):
# try:
# self.op.clear()
# self.ip.clear()
# self.opl.clear()
# stm = ['MaterialStream','EngStm']
# for i in self.conn:
# if i.type not in stm:
# self.op[i]=self.conn[i]
#
# for j in range(len(self.conn[i])):
# if self.conn[i][j].type not in stm:
# self.ip[self.conn[i][j]].append(i)
#
# for i in self.op:
# i.connect(InputStms=self.ip[i],OutputStms=self.op[i])
#
# self.opl.append([self.op[i] for i in self.op])
# self.opl=flatlist(flatlist(self.opl))
# except Exception as e:
# print(e)
# @staticmethod
# def addUnitOpObj(obj):
# self.unitOp.append(obj)
def addUnitOp(self, obj):
box = None
self.obj = obj
self.scene = self.graphics.getScene()
box = self.graphics.createNodeItem(self.obj, self)
self.scene.addItem(box)
box.setPos(2500 - 30, 2500 - 30)
if (obj in self.unitOp):
pass
else:
self.unitOp.append(obj)
data = self.unitOp[:]
data.append(compound_selected)
PUSH('Undo', data)
self.msg.append("<span style=\"color:blue\">[" +
str(self.currentTime()) + "]<b> " + obj.name +
" </b>is instantiated ."
"</span>")
'''
Deletes the selected item from the canvas and also the objects created for that type.
'''
def delete(self, l):
for item in l:
self.scene.removeItem(item)
if hasattr(item, 'Input'):
for x in item.Input:
if x.newLine:
self.scene.removeItem(x.newLine)
del x.newLine
if x.otherLine:
self.scene.removeItem(x.otherLine)
del x.otherLine
if hasattr(item, 'Output'):
for x in item.Output:
if x.newLine:
self.scene.removeItem(x.newLine)
del x.newLine
if x.otherLine:
self.scene.removeItem(x.otherLine)
del x.otherLine
if hasattr(item, 'obj'):
self.unitOp.remove(item.obj)
for k in list(self.conn):
if item.obj == k:
del self.conn[k]
elif item.obj in self.conn[k]:
self.conn[k].remove(item.obj)
self.msg.append("<span style=\"color:blue\">[" +
str(self.currentTime()) + "]<b> " +
item.obj.name + " </b>is deleted ."
"</span>")
del item.obj
del item
CLEAN_FILE('Redo')
data = self.unitOp[:]
data.append(compound_selected)
PUSH('Undo', data)
def fetchObject(self, name):
for i in self.unitOp:
if (i.name == name):
return i
def addCompounds(self, comp):
self.compounds = comp
def add_thermoPackage(self, thermo):
self.thermoPackage = thermo
def msgBrowser(self):
std = self.flowsheet.stdout.decode("utf-8")
if (std):
stdout = str(std)
stdout = stdout.replace("\n", "<br/>")
self.msg.append("<span style=\"color:green\">" + stdout +
"</span>")
stde = self.flowsheet.stderr.decode("utf-8")
if (stde):
stdout = str(stde)
stdout = stdout.replace("\n", "<br/>")
self.msg.append("<span style=\"color:red\">" + stdout + "</span>")
def simulate(self, mode):
print("SIMULATE")
print(mode)
self.compounds = compound_selected
self.flowsheet = Flowsheet()
self.flowsheet.add_comp_list(self.compounds)
print("######## connection master#########\n", self.conn)
for i in self.unitOp:
print("here", i)
self.flowsheet.add_UnitOpn(i)
if mode == 'SM':
self.msg.append(
"<span>[" + str(self.currentTime()) +
"] Simulating in <b>Sequential</b> mode ... </span>")
self.flowsheet.simulateSM(self.ip, self.op)
self.msgBrowser()
self.result = self.flowsheet.resdata
print("under SEQ mode simulation")
elif mode == 'EQN':
self.msg.append("<span>[" + str(self.currentTime()) +
"] Simulating in <b>equation</b> mode ... </span>")
self.flowsheet.simulateEQN()
self.msgBrowser()
self.result = self.flowsheet.resdata
print("under Eqn mode simulation")
DockWidget.showResult(NodeItem.getDockWidget())
import numpy as np
import matplotlib.pyplot as plt
import Graphics as artist
from matplotlib import rcParams
rcParams['text.usetex'] = True
m = np.loadtxt('correlation-matrix.tsv', delimiter='\t')
fig = plt.figure()
ax = fig.add_subplot(111)
cax = ax.imshow(m,
interpolation='nearest',
aspect='auto',
cmap=plt.cm.bone_r,
vmin=-1,
vmax=1)
artist.adjust_spines(ax)
plt.colorbar(cax)
plt.show()
def main(n_epochs=1):
if n_epochs == 1:
# Single trial
n_training_trials = 40 # Training trials
n_navigation_trials = 20 # Navigation trials
elif n_epochs == 3:
# For reasonable data
n_training_trials = 100 # Training trials
n_navigation_trials = 20 # Navigation trials
else:
# For quick trials
n_training_trials = 20 # Training trials
n_navigation_trials = 20 # Navigation trials
training_steps = np.zeros((n_training_trials, n_epochs), dtype=float)
navigation_steps = np.zeros((n_navigation_trials, n_epochs), dtype=float)
"""
# This version uses threads, which are quite slow in python because of GIL
threads = [None] * n_epochs
for epoch in range(n_epochs):
threads[epoch] = MazeThread(epoch, n_training_trials, n_navigation_trials)
threads[epoch].start()
# Wait for all threads to complete
for t in threads:
t.join()
for epoch in range(n_epochs):
training_steps[:, epoch] = threads[epoch].training_steps
navigation_steps[:, epoch] = threads[epoch].navigation_steps
"""
threads = multiprocessing.Pool(n_epochs)
training_results = threads.map(testMaze,
[(n_training_trials, n_navigation_trials)
for x in range(n_epochs)])
for epoch in range(n_epochs):
training_steps[:, epoch] = training_results[epoch][0] * MOVE_DISTACE
navigation_steps[:, epoch] = training_results[epoch][1] * MOVE_DISTACE
mean_training_steps = np.reshape(np.mean(training_steps, axis=1),
(n_training_trials, 1))
mean_navigation_steps = np.reshape(np.mean(navigation_steps, axis=1),
(n_navigation_trials, 1))
# Use this for plotting absolute data deviation
"""
min_dev_training_steps = np.reshape(np.min(training_steps-mean_training_steps, axis=1), (1, n_training_trials))
max_dev_training_steps = np.reshape(np.max(training_steps-mean_training_steps, axis=1), (1, n_training_trials))
err_training_steps = np.abs(np.append(min_dev_training_steps, max_dev_training_steps, axis=0))
min_dev_navigation_steps = np.reshape(np.min(navigation_steps-mean_navigation_steps, axis=1), (1, n_navigation_trials))
max_dev_navigation_steps = np.reshape(np.max(navigation_steps-mean_navigation_steps, axis=1), (1, n_navigation_trials))
err_navigation_steps = np.abs(np.append(min_dev_navigation_steps, max_dev_navigation_steps, axis=0))
"""
# For plotting the SEM measure, use this!
err_training_steps = stats.sem(training_steps, axis=1)
err_navigation_steps = stats.sem(navigation_steps, axis=1)
# Print all the data before plotting
# print('%d Training trials'%n_training_trials)
# pprint(mean_training_steps)
training_fig = pl.figure()
training_ax = training_fig.add_subplot(111)
training_ax.errorbar(range(n_training_trials),
mean_training_steps,
yerr=err_training_steps,
marker='d',
linewidth=2.0,
elinewidth=2.0,
alpha=0.7,
ecolor='black',
capsize=2.0)
training_ax.set_xlabel('Trials')
training_ax.set_ylabel('Distance Moved')
training_ax.set_xlim((-1, 20))
training_ax.set_xticks((0, 5, 10, 15, 20))
training_ax.set_ylim((0, 100))
training_ax.set_yticks((0, 50, 100))
Graphics.cleanAxes(training_ax)
pl.show()
# print('%d Navigation trials'%n_navigation_trials)
# pprint(mean_navigation_steps)
navigation_fig = pl.figure()
navigation_ax = navigation_fig.add_subplot(111)
navigation_ax.errorbar(range(n_navigation_trials),
mean_navigation_steps,
yerr=err_navigation_steps,
marker='o',
linewidth=2.0,
elinewidth=2.0,
ecolor='black',
alpha=0.7,
capsize=2.0)
navigation_ax.set_xlabel('Trials')
navigation_ax.set_ylabel('Distance Moved')
navigation_ax.set_ylim((0, 100))
navigation_ax.set_xlim((-1, 20))
navigation_ax.set_xticks((0, 5, 10, 15, 20))
navigation_ax.set_yticks((0, 50, 100))
Graphics.cleanAxes(navigation_ax)
pl.show()
print('Execution complete. Exiting!')
import AudioLoader #enables loading and unloading of music and sound import Graphics #system to handle loading unloading and rendering of graphics import Entities import Events #!!!! Priority write assignments !!!! #Connect map to render #add entities #####INITIALIZATION##### pygame.init() screenRez = ((400, 300)) Plot = Events.Plot() #//screen = pygame.display.set_mode((800,600)) #background = load image here Graphics.init(screenRez) run = True #player = Entities.Player() #player.x = (40 * 4) #player.y = (40 * 2) - 15 #Graphics.add(player) #//player sprite #king = Entities.King() #king.x = (40 * 9) #king.y = (40 * 2) - 15 #Graphics.add(king) #king = Entities.King() #Graphics.add(king) #vizzi = Entities.Vizzi() #Graphics.add(vizzi)
exec(f'from {player1name} import *')
if player1name != 'HumanPlayer':
exec(f'player1 = {player1name}({timeLimit})')
else:
exec(f'player1 = {player1name}({1e6})')
exec(f'from {player2name} import *')
if player2name != 'HumanPlayer':
exec(f'player2 = {player2name}({timeLimit})')
else:
exec(f'player2 = {player2name}({1e6})')
size = int(input('Enter the width of the graphics window (0 for text mode): '))
if size > 0:
from Graphics import *
g = Graphics(size)
else:
g = None
state = Pente()
moveSequence = []
while not state.gameOver():
print(state)
if state.getTurn() % 2 == 0:
player1._startTime = time.time()
player1.findMove(state)
move = player1.getMove()
def _processRenderSurface(logDir, attributes):
def attr(name):
return attributes[name]
w, h = attr("render_surface_width"), attr("render_surface_height")
redMask = attr("red_mask")
greenMask = attr("green_mask")
blueMask = attr("blue_mask")
alphaMask = attr("alpha_mask")
depthMask = attr("depth_mask")
stencilMask = attr("stencil_mask")
isLinear = attr("is_linear")
isPremultiplied = attr("is_premultiplied")
# Convert the color buffer
if "color_buffer" in attributes:
fileName = attr("color_buffer")
if not os.path.exists(fileName):
fileName = os.path.join(logDir, fileName)
fileNameOut = fileName.rsplit(".", 1)[0] + ".png"
# Only do the conversion if the image doesn't already exist
# or if the source file is newer.
if fileName.endswith(".dat") and \
(not os.path.exists(fileNameOut) or \
(os.path.exists(fileName) and os.path.getmtime(fileName) > os.path.getmtime(fileNameOut))
):
stride = attr("color_stride")
f = open(fileName, "rb")
data = f.read(stride * h)
f.close()
if len(data) != h * stride or not data:
Log.error("Invalid color buffer data size: %d" % len(data))
return
colorBuffer = Graphics.decodeImageData(data, (w, h), stride,
redMask, greenMask,
blueMask, alphaMask,
isLinear, isPremultiplied)
colorBuffer = colorBuffer.convert("RGBA")
colorBuffer.save(fileNameOut)
# We can remove the original file now
os.unlink(fileName)
# Replace the original file name with the decoded file
attributes["color_buffer"] = fileNameOut
# Eat the render surface attributes since they are of little use further down the road
#for attrName in ["red_mask", "green_mask", "blue_mask", "alpha_mask",
# "depth_mask", "stencil_mask", "color_stride",
# "is_linear", "is_premultiplied", "color_data_type",
# "depth_data_type", "stencil_data_type"]:
# if attrName in attributes:
# del attributes[attrName]
for bufferName in ["depth_buffer", "stencil_buffer"]:
if bufferName in attributes and not os.path.exists(
attributes[bufferName]):
# Fill in the full buffer file name
attributes[bufferName] = os.path.join(logDir, attr(bufferName))
def testMaze(n_trials, dbg_lvl=1):
ValueLearning.DBG_LVL = dbg_lvl
move_distance = 0.29
# Open field - Rather boring
# maze = Environment.RandomGoalOpenField(nx, ny)
# Maze with partition - 6 x 6 environment
# ----------------- (6,6)
# | |
# | (2,3) (4,3) |
# |----- -----| (6,3)
# | |
# | |
# (0,0) -----------------
"""
nx = 6
ny = 6
maze = Environment.RandomGoalOpenField(nx, ny, move_distance)
use_limits = True
"""
# Adding walls and constructing the environment
"""
nx = 6
ny = 6
lp_wall = Environment.Wall((0,3), (2,3))
rp_wall = Environment.Wall((4,3), (6,3))
maze = Environment.MazeWithWalls(nx, ny, [lp_wall, rp_wall], move_distance)
use_limits = False
"""
# Maze with walls - 10 x 10 environment
# (2,10) (8,10)
# --------------------- (10,10)
# | | |
# | | (4, 6) | | (10, 8)
# | | ------| |
# | | (6,4) | |
# (0,4) | |------ | |
# | | | |
# | (2,2) | |
# (0,0) ---------------------
nx = 10
ny = 10
# Adding walls and constructing the environment
lh_wall = Environment.Wall((2,4), (6,4))
lv_wall = Environment.Wall((2,2), (2,10))
rh_wall = Environment.Wall((4,6), (8,6))
rv_wall = Environment.Wall((8,0), (8,8))
maze = Environment.MazeWithWalls(nx, ny, [lh_wall, lv_wall, rh_wall, rv_wall])
use_limits = False
n_fields = round(1.0 * (nx+3) * (ny+3))
Hippocampus.N_CELLS_PER_FIELD = 1
n_cells = n_fields * Hippocampus.N_CELLS_PER_FIELD
place_fields = Hippocampus.setupPlaceFields(maze, n_fields)
place_cells = Hippocampus.assignPlaceCells(n_cells, place_fields)
# Learn the value function
amateur_critic = None
n_episodes = 25
canvas = Graphics.MazeCanvas(maze)
weights = np.empty((n_cells, n_episodes), dtype=float)
for episode in range(n_episodes):
(_, amateur_critic, _) = ValueLearning.learnValueFunction(n_trials, maze, place_cells, critic=amateur_critic, max_steps=1000)
weights[:, episode] = amateur_critic.getWeights()
print('Ended Episode %d'% episode)
# canvas.plotValueFunction(place_cells, amateur_critic, continuous=True)
# input()
# Draw the final value funciton
canvas.plotValueFunction(place_cells, amateur_critic, continuous=True, limits=use_limits)
# canvas.plotValueFunction(place_cells, amateur_critic)
""" DEBUG
print(components.explained_variance_ratio_)
print(components.singular_values_)
"""
# Graphics.showDecomposition(weights)
# Evaluate the theoritical value function for a random policy
ideal_critic = Agents.IdealValueAgent(maze, place_cells)
optimal_value_function = ideal_critic.getValueFunction()
scaling_factor = 1.0/(1 - amateur_critic.getDiscountFactor())
# Graphics.showImage(optimal_value_function, xticks=range(1,nx), yticks=range(1,ny), range=(maze.NON_GOAL_STATE_REWARD, scaling_factor * maze.GOAL_STATE_REWARD))
Graphics.showImage(optimal_value_function, xticks=range(1,nx), yticks=range(1,ny), range=(maze.NON_GOAL_STATE_REWARD, scaling_factor * maze.GOAL_STATE_REWARD))
input('Press any key to Exit!')
class MainApp(QMainWindow,ui):
flag = True
def __init__(self):
'''
Initializing the application
'''
QMainWindow.__init__(self)
self.setupUi(self)
self.zoomcount = 0
self.graphics = Graphics()
self.scene = self.graphics.getScene()
Graphics.flag = MainApp.flag
self.previewBtn.setChecked(True)
self.editingBtn.toggled.connect(lambda:self.btnState(self.previewBtn))
self.previewBtn.toggled.connect(lambda:self.btnState(self.editingBtn))
self.graphicsView.setScene(self.scene)
self.graphicsView.setMouseTracking(True)
self.graphicsView.keyPressEvent=self.deleteCall
self.menuBar()
self.unitOperationListInit()
def unitOperationListInit(self):
self.gl1.addWidget(self.createCellWidget("Air-blownCooler"), 1, 0)
self.gl1.addWidget(self.createCellWidget("Bag"), 1, 1)
self.gl1.addWidget(self.createCellWidget("Boiler"), 1, 2)
self.gl1.addWidget(self.createCellWidget("Tank"), 2, 0)
self.gl1.addWidget(self.createCellWidget("Breaker"), 2, 1)
self.gl1.addWidget(self.createCellWidget("BriquettingMachine"), 2, 2)
self.gl1.addWidget(self.createCellWidget("Centrifugal"), 3, 0)
self.gl1.addWidget(self.createCellWidget("CentrifugalCompressor"), 3, 1)
self.gl1.addWidget(self.createCellWidget("CentrifugalPump"), 3, 2)
self.gl1.addWidget(self.createCellWidget("CentrifugalPump2"), 4, 0)
self.gl1.addWidget(self.createCellWidget("CentrifugalPump3"), 4, 1)
self.gl1.addWidget(self.createCellWidget("Column"), 4, 2)
self.gl1.addWidget(self.createCellWidget("Compressor"), 5, 0)
self.gl1.addWidget(self.createCellWidget("CompressorSilencers"), 5, 1)
self.gl1.addWidget(self.createCellWidget("Condenser"), 5, 2)
self.gl1.addWidget(self.createCellWidget("Cooler"), 6, 0)
self.gl1.addWidget(self.createCellWidget("CoolingTower2"), 6, 1)
self.gl1.addWidget(self.createCellWidget("CoolingTower3"), 6, 2)
self.gl1.addWidget(self.createCellWidget("Crusher"), 7, 0)
self.gl1.addWidget(self.createCellWidget("DoublePipeHeat"), 7, 1)
self.gl1.addWidget(self.createCellWidget("ExtractorHood"), 7, 2)
self.gl1.addWidget(self.createCellWidget("FiredHeater"), 8, 0)
self.gl1.addWidget(self.createCellWidget("Forced-draftCooling"), 8, 1)
self.gl1.addWidget(self.createCellWidget("Forced-draftCoolingTower"), 8, 2)
self.gl1.addWidget(self.createCellWidget("Furnance"), 9, 0)
self.gl1.addWidget(self.createCellWidget("GasBottle"), 9, 1)
self.gl1.addWidget(self.createCellWidget("HalfPipeMixingVessel"), 9, 2)
self.gl1.addWidget(self.createCellWidget("Heater"), 10, 0)
self.gl1.addWidget(self.createCellWidget("HeatExchanger"), 10, 1)
self.gl1.addWidget(self.createCellWidget("HeatExchanger2"), 10, 2)
self.gl1.addWidget(self.createCellWidget("HorizontalVessel"), 11, 0)
self.gl1.addWidget(self.createCellWidget("JacketedMixingVessel"), 11, 1)
self.gl1.addWidget(self.createCellWidget("LiquidRingCompressor"), 11, 2)
self.gl1.addWidget(self.createCellWidget("Mixing"),12 , 0)
self.gl1.addWidget(self.createCellWidget("MixingReactor"), 12, 1)
self.gl1.addWidget(self.createCellWidget("OilBurner"), 12, 2)
self.gl1.addWidget(self.createCellWidget("OpenTank"), 13, 0)
self.gl1.addWidget(self.createCellWidget("ProportioningPump"), 13, 1)
self.gl1.addWidget(self.createCellWidget("Pump"), 13, 2)
self.gl1.addWidget(self.createCellWidget("Pump2"), 14, 0)
self.gl1.addWidget(self.createCellWidget("ReboilerHeatExchanger"), 14, 1)
self.gl1.addWidget(self.createCellWidget("ReciprocativeCompressor"), 14, 2)
self.gl1.addWidget(self.createCellWidget("RotaryCompressor"), 15, 0)
self.gl1.addWidget(self.createCellWidget("RotaryGearPump"), 15, 1)
self.gl1.addWidget(self.createCellWidget("ScrewPump"),15 , 2)
self.gl1.addWidget(self.createCellWidget("SelectableCompressor"), 16, 0)
self.gl1.addWidget(self.createCellWidget("SelectableFan"), 16, 1)
self.gl1.addWidget(self.createCellWidget("SinglePassHeat"), 16, 2)
self.gl1.addWidget(self.createCellWidget("SpiralHeatExchanger"), 17, 0)
self.gl1.addWidget(self.createCellWidget("StraightTubesHeat"), 17, 1)
self.gl1.addWidget(self.createCellWidget("Tank"), 17, 2)
self.gl1.addWidget(self.createCellWidget("TurbinePump"), 18, 0)
self.gl1.addWidget(self.createCellWidget("U-TubeHeatExchanger"), 18, 1)
self.gl1.addWidget(self.createCellWidget("VacuumPump"), 18, 2)
self.gl1.addWidget(self.createCellWidget("VerticalPump"), 19, 0)
self.gl1.addWidget(self.createCellWidget("VerticalVessel"), 19, 1)
self.gl1.addWidget(self.createCellWidget("WastewaterTreatment"), 19, 2)
self.gl2.addWidget(self.createCellWidget("Air-blownCooler"), 1, 0)
self.gl2.addWidget(self.createCellWidget("Bag"), 1, 1)
self.gl2.addWidget(self.createCellWidget("Boiler"), 1, 2)
self.gl2.addWidget(self.createCellWidget("Tank"), 2, 0)
self.gl2.addWidget(self.createCellWidget("Breaker"), 2, 1)
self.gl2.addWidget(self.createCellWidget("BriquettingMachine"), 2, 2)
self.gl2.addWidget(self.createCellWidget("Centrifugal"), 3, 0)
self.gl2.addWidget(self.createCellWidget("CentrifugalCompressor"), 3, 1)
self.gl2.addWidget(self.createCellWidget("CentrifugalPump"), 3, 2)
self.gl2.addWidget(self.createCellWidget("CentrifugalPump2"), 4, 0)
self.gl2.addWidget(self.createCellWidget("CentrifugalPump3"), 4, 1)
self.gl2.addWidget(self.createCellWidget("Column"), 4, 2)
self.gl2.addWidget(self.createCellWidget("Compressor"), 5, 0)
self.gl2.addWidget(self.createCellWidget("CompressorSilencers"), 5, 1)
self.gl2.addWidget(self.createCellWidget("Condenser"), 5, 2)
self.gl2.addWidget(self.createCellWidget("Cooler"), 6, 0)
self.gl2.addWidget(self.createCellWidget("CoolingTower2"), 6, 1)
self.gl2.addWidget(self.createCellWidget("CoolingTower3"), 6, 2)
self.gl2.addWidget(self.createCellWidget("Crusher"), 7, 0)
self.gl2.addWidget(self.createCellWidget("DoublePipeHeat"), 7, 1)
self.gl2.addWidget(self.createCellWidget("ExtractorHood"), 7, 2)
self.gl2.addWidget(self.createCellWidget("FiredHeater"), 8, 0)
self.gl2.addWidget(self.createCellWidget("Forced-draftCooling"), 8, 1)
self.gl2.addWidget(self.createCellWidget("Forced-draftCoolingTower"), 8, 2)
self.gl2.addWidget(self.createCellWidget("Furnance"), 9, 0)
self.gl2.addWidget(self.createCellWidget("GasBottle"), 9, 1)
self.gl2.addWidget(self.createCellWidget("HalfPipeMixingVessel"), 9, 2)
self.gl2.addWidget(self.createCellWidget("Heater"), 10, 0)
self.gl2.addWidget(self.createCellWidget("HeatExchanger"), 10, 1)
self.gl2.addWidget(self.createCellWidget("HeatExchanger2"), 10, 2)
self.gl2.addWidget(self.createCellWidget("HorizontalVessel"), 11, 0)
self.gl2.addWidget(self.createCellWidget("JacketedMixingVessel"), 11, 1)
self.gl2.addWidget(self.createCellWidget("LiquidRingCompressor"), 11, 2)
self.gl2.addWidget(self.createCellWidget("Mixing"),12 , 0)
self.gl2.addWidget(self.createCellWidget("MixingReactor"), 12, 1)
self.gl2.addWidget(self.createCellWidget("OilBurner"), 12, 2)
self.gl2.addWidget(self.createCellWidget("OpenTank"), 13, 0)
self.gl2.addWidget(self.createCellWidget("ProportioningPump"), 13, 1)
self.gl2.addWidget(self.createCellWidget("Pump"), 13, 2)
self.gl2.addWidget(self.createCellWidget("Pump2"), 14, 0)
self.gl2.addWidget(self.createCellWidget("ReboilerHeatExchanger"), 14, 1)
self.gl2.addWidget(self.createCellWidget("ReciprocativeCompressor"), 14, 2)
self.gl2.addWidget(self.createCellWidget("RotaryCompressor"), 15, 0)
self.gl2.addWidget(self.createCellWidget("RotaryGearPump"), 15, 1)
self.gl2.addWidget(self.createCellWidget("ScrewPump"),15 , 2)
self.gl2.addWidget(self.createCellWidget("SelectableCompressor"), 16, 0)
self.gl2.addWidget(self.createCellWidget("SelectableFan"), 16, 1)
self.gl2.addWidget(self.createCellWidget("SinglePassHeat"), 16, 2)
self.gl2.addWidget(self.createCellWidget("SpiralHeatExchanger"), 17, 0)
self.gl2.addWidget(self.createCellWidget("StraightTubesHeat"), 17, 1)
self.gl2.addWidget(self.createCellWidget("Tank"), 17, 2)
self.gl2.addWidget(self.createCellWidget("TurbinePump"), 18, 0)
self.gl2.addWidget(self.createCellWidget("U-TubeHeatExchanger"), 18, 1)
self.gl2.addWidget(self.createCellWidget("VacuumPump"), 18, 2)
self.gl2.addWidget(self.createCellWidget("VerticalPump"), 19, 0)
self.gl2.addWidget(self.createCellWidget("VerticalVessel"), 19, 1)
self.gl2.addWidget(self.createCellWidget("WastewaterTreatment"), 19, 2)
def createCellWidget(self, text):
pic=QtGui.QPixmap("unitOp/type1/"+text+".png")
icon = QIcon(pic)
button = QToolButton()
button.setText(text)
button.setIcon(icon)
button.setIconSize(QSize(44, 44))
button.setToolTip(text)
layout = QGridLayout()
layout.addWidget(button, 0, 0, Qt.AlignHCenter)
#layout.addWidget(QLabel(text), 1, 0, Qt.AlignCenter)
widget = QWidget()
widget.setLayout(layout)
button.pressed.connect(lambda:self.component(text))
return widget
def btnState(self, b):
item = QGraphicsLineItem(QLineF(0,0,0,0))
self.scene.addItem(item)
#item.setPos(QPointF(2500,2500))
item.setPos(NodeItem.pos)
if b.text() == "Editing":
if b.isChecked() == True:
MainApp.flag = not MainApp.flag
Graphics.flag = MainApp.flag
for socket in socketLst:
NodeSocket.restoreSockets(socket)
for i in lst:
NodeSocket.removeSockets(i[1])
NodeSocket.removeSockets(i[2])
else:
if b.isChecked() == True:
MainApp.flag = not MainApp.flag
Graphics.flag = MainApp.flag
for socket in socketLst:
NodeSocket.removeSockets(socket)
def menuBar(self):
'''
MenuBar function handels all the all the operations of
menu bar like new,zoom,comounds selector, simulation options.
'''
self.actionNew.triggered.connect(self.new)
self.actionZoomIn.triggered.connect(self.zoomIn)
self.actionZoomOut.triggered.connect(self.zoomOut)
self.actionZoomReset.triggered.connect(self.zoomReset)
self.actionSave.triggered.connect(self.save)
self.actionInsertText.triggered.connect(self.insertText)
self.actionSocket.triggered.connect(self.socket)
self.actionNumber.triggered.connect(self.number)
def zoomReset(self):
'''
Resets the zoom level to default scaling
'''
if(self.zoomcount>0):
for i in range(self.zoomcount):
self.zoomOut()
elif(self.zoomcount<0):
for i in range(abs(self.zoomcount)):
self.zoomIn()
def zoomOut(self):
'''
ZoomOut the canvas
'''
self.graphicsView.scale(1.0/1.15,1.0/1.15)
self.zoomcount -=1
def zoomIn(self):
'''
ZoomIn the canvas
'''
if self.zoomcount < 0:
self.graphicsView.scale(1.15,1.15)
self.zoomcount +=1
def component(self,unitOpType):
'''
Instantiate a NodeItem object for selected type of
component and added that on canvas/flowsheeting area.
'''
if MainApp.flag == True:
self.type = unitOpType
self.obj = self.graphics.createNodeItem(self.type, self.graphicsView)
self.scene.addItem(self.obj)
self.obj.setPos(QPointF(2500-30, 2500-30))
def new(self):
'''
New is used to delete all the existing work.
'''
if MainApp.flag:
del self.graphics
self.graphics = Graphics()
self.scene = self.graphics.getScene()
self.graphicsView.setScene(self.scene)
self.graphicsView.setMouseTracking(True)
self.graphicsView.keyPressEvent=self.deleteCall
def deleteCall(self,event):
'''
Handels all the operations which will happen when delete button is pressed.
'''
try:
if event.key() == QtCore.Qt.Key_Delete:
l=self.scene.selectedItems()
self.delete(l)
except Exception as e:
print(e)
def delete(self,l):
'''
Deletes the selected item from the canvas and also the objects
created for that type.
'''
if MainApp.flag:
for item in l:
self.scene.removeItem(item)
if (item.typee == 'Graphics.NodeLine'):
for i in lst:
if i[0] == item:
NodeSocket.restoreSockets(i[1])
NodeSocket.restoreSockets(i[2])
lst.remove(i)
del item
def socket(self):
if MainApp.flag:
item = self.graphics.createNodeItem('none1',self.graphicsView)
self.scene.addItem(item)
item.setPos(QPointF(2020, 2200))
def number(self):
if MainApp.flag:
self.numberItem = NumberItem(self.graphicsView)
self.scene.addItem(self.numberItem)
self.numberItem.setPos(QPointF(2020, 2200))
def insertText(self):
if MainApp.flag:
self.textItem = TextItem(self.graphicsView)
self.scene.addItem(self.textItem)
self.textItem.setPos(QPointF(2020, 2200))
def save(self):
'''
Function for saving the current canvas items and compound_selected
'''
'''# Get region of scene to capture from somewhere.
#area = get_QRect_to_capture_from_somewhere()
area = QRectF(QPointF(0,0), QPointF(1500, 1500))
# Create a QImage to render to and fix up a QPainter for it.
image = QImage(area.width() , area.height(), QImage.Format_ARGB32_Premultiplied)
painter = QPainter(image)
# Render the region of interest to the QImage.
self.scene.render(painter)
painter.end()
fileFormat = 'png'
initialPath = QDir.currentPath() + 'image.' + fileFormat
fileName, _ = QFileDialog.getSaveFileName(self, "Save As",
initialPath, "%s Files (*.%s);; All Files (*)" %
(fileFormat.upper(), fileFormat))
if fileName != "":
with open(fileName, 'wb') as f:
image.save(fileName+fileFormat)
'''
fileFormat = 'png'
initialPath = QDir.currentPath() + 'untitled.' + fileFormat
fileName, _ = QFileDialog.getSaveFileName(self, "Save As",
initialPath, "%s Files (*.%s);; All Files (*)" %
(fileFormat.upper(), fileFormat))
screen = QApplication.primaryScreen()
screenshot = screen.grabWindow(self.graphicsView.winId())
screenshot.save(fileName, fileFormat)
button['label'] = 'Use Floodfill'
print('Search algorithm switched to A*')
# Program entry point
if __name__ == "__main__":
pathfinderAlgo = 'A*'
mapNodes = Graph.getNodes()
# Create car object
# carStartingNode = mapNodes[randint(0, len(mapNodes))] # Uncomment for random staring point
carStartingNode = mapNodes[153]
car = Entity.newEntity(carStartingNode['x'], carStartingNode['y'])
car.moveSpeed = 5
car.timeSpeedup = 4
renderEngine = Graphics.Init(overlayMap_width, overlayMap_height)
renderEngine.on_loop = update
renderEngine.on_render = draw
renderEngine.on_mouseDown = mouseDown
overlayMap = pygame.image.load('sample_map.png')
overlayMapRect = overlayMap.get_rect()
renderEngine.button(20, 20, 135, 35, "Use Floodfill", changeAlgorithm)
renderEngine.on_execute()
def load(self):
self.keyDown = {}
self.fps = 0
self.right_btn = False
#initialize the screen/window
self.size = self.width, self.height = Settings.Resolution
self.fullscreen = Settings.Fullscreen
self.resetVideoMode()
#init network variable thingy
self.network = None
#create console
self.console = Console.Console(
Graphics.Rectangle(20, 20, self.width - 20, self.height - 20))
self.console.setAsStdOut()
self.console.onInput = self.onConsoleInput
#initialize some basic opengl states
GraphicsCard.enable('depth_test')
GraphicsCard.setDepthFunc('less')
GraphicsCard.setShadeModel('smooth')
GraphicsCard.setScreenProjection(float(self.width / self.height), 0.1,
5000.0)
self.font = Font.TextureFont('../base/fonts/tahoma.fnt')
self.bigfont = Font.TextureFont('../base/fonts/tahoma_20.fnt')
FontManager.GetFont(Settings.ConsoleFont)
loadscreen = TextureManager.GetTexture(
'../base/art/ui/facehatlogo.png')
#Draw loading screen
GraphicsCard.clearDepth(1.0)
GraphicsCard.clearColor((1, 1, 1, 1))
GraphicsCard.clear()
#glBlendFunc(GL_SRC_ALPHA,GL_ONE_MINUS_SRC_ALPHA)
GraphicsCard.setBlendFunction('src_alpha', 'one_minus_src_alpha')
GraphicsCard.enable('blend', 'texture_2d')
gl2D.start2D()
gl2D.drawTexture2D(loadscreen, self.width / 2 - 256,
self.height / 2 - 256, 512, 512)
self.bigfont.draw(self.width / 2, self.height / 2 - 280, "Loading...")
gl2D.end2D()
pygame.display.flip()
#hide/show cursor
if Settings.GrabMouse:
pygame.mouse.set_visible(False)
pygame.event.set_grab(True)
else:
pygame.mouse.set_visible(True)
pygame.event.set_grab(False)
#Initialize OpenGL extensions
GraphicsCard.initExtensions()
#check for OpenGL 2.0
if Settings.UseShaders and GraphicsCard.hasShaders():
print "Shader Support Present"
Settings.UseShaders = True
else:
print "Warning: No shader support, or shaders disabled"
Settings.UseShaders = False
print "Max Anisotropy:", GraphicsCard.getMaxAnisotropy()
###load the map
if Settings.SinglePlayer:
self.world = World.World(Settings.DefaultMod,
Settings.DefaultMap,
is_server=True,
graphics_enabled=True)
else:
self.world = World.World(Settings.DefaultMod,
Settings.DefaultMap,
is_server=False,
graphics_enabled=True)
self.world.initGraphics()
self.lastjump = time_in_seconds()
#setup lighting
n = vec3(0, 1, 0)
glMaterialfv(GL_FRONT_AND_BACK, GL_DIFFUSE, [1.0, 1.0, 1.0, 1.0])
glMaterialfv(GL_FRONT_AND_BACK, GL_AMBIENT, [0.9, 0.9, 0.9, 1.0])
glLightfv(GL_LIGHT0, GL_AMBIENT, [0.0, 0.0, 0.0, 1.0])
glLightfv(GL_LIGHT0, GL_DIFFUSE, [1.0, 1.0, 1.0, 1.0])
glLightfv(GL_LIGHT0, GL_POSITION, [n.x, n.y, n.z, 0])
GraphicsCard.clearColor((0.0, 0.0, 1.0, 0.0))
GraphicsCard.enable('light0')
drug_names = [
drug for item in drug_names.itervalues() for drug in item['drugs']
if drug in filtered_list
]
drug_name_frequencies = Counter(drug_names)
cutoff = 20
name, frequency = zip(*drug_name_frequencies.most_common(cutoff))
idx = range(len(frequency))
fig = plt.figure()
ax = fig.add_subplot(111)
ax.bar(idx, frequency, color='k')
artist.adjust_spines(ax)
ax.set_ylabel(artist.format('No. of mentions'))
ax.set_xticks(np.array(range(len(name))) + 0.4)
ax.set_xticklabels(map(artist.format, name), rotation=90)
a = plt.axes([.65, .6, .2, .2])
_, full_frequencies = zip(*drug_name_frequencies.most_common())
a.hist(full_frequencies, color='k')
artist.adjust_spines(a)
a.set_xlabel(artist.format('No. of mentions'))
a.set_ylabel(artist.format('No. of substances'))
plt.yscale('log', nonposy='clip')
for label in a.get_xticklabels()[1::2]:
label.set_visible(False)
plt.tight_layout()
plt.savefig('drug-name-frequency-lycaeum.png')
class Game(SetupWindow):
def __init__(self):
super().__init__()
self.__players = ('W', 'B')
self.__turn = self.__players[0]
self.__graphics = Graphics()
self.__board = Board()
self.__board.initializePieces('g')
self.__selectedPiece = None
self.__jump = False
self.__buttonActions = ['Reset', 'Menu']
self.__backMenu = False
self.__buttonSize = [200, 50]
self.__endGame = False
def getBackMenu(self):
return self.__backMenu
def eventLoopPvP(self):
for event in pygame.event.get():
mousePos = self.__graphics.boardCoord(pygame.mouse.get_pos())
if event.type == pygame.QUIT:
self.terminate_game()
self.buttonFunc()
self.__board.jumpAvailable(self.__turn)
self.__graphics.message_display(self.turndisplay(self.__turn), 50,
self.getDisplayWidth() / 2,
self.getDisplayHeight() / 20,
colors["BRIGHT_SUMMER_SUN"])
self.__graphics.drawBoardSquares(self.__board)
self.__graphics.drawPieceCircles(self.__board)
self.clock.tick(60)
if self.__selectedPiece is not None and self.__jump is True and not self.__board.legalMoves(
self.__selectedPiece, self.__jump):
self.end_turn(self.__board)
if self.__selectedPiece is not None and not self.__board.legalMoves(
self.__selectedPiece):
if self.__board.location(
self.__selectedPiece).occupant is not None:
self.__board.location(
self.__selectedPiece).occupant.selected = False
self.__selectedPiece = None
self.invalidMove()
if self.__selectedPiece is not None and self.__board.jumpAvailablePieces:
if self.__selectedPiece not in self.__board.jumpAvailablePieces:
self.__board.location(
self.__selectedPiece).occupant.selected = False
self.__selectedPiece = None
self.invalidMove()
if self.__selectedPiece:
if self.__jump is False:
self.__graphics.highlightMoves(
self.__board.legalMoves(self.__selectedPiece))
else:
self.__graphics.highlightMoves(
self.__board.legalMoves(self.__selectedPiece,
self.__jump))
if event.type == pygame.MOUSEBUTTONDOWN and self.__board.isOnBoard(
mousePos):
if self.__jump is False:
if self.__board.location(
mousePos
).occupant is not None and self.__board.location(
mousePos
).occupant.color == self.__turn and self.__selectedPiece is None:
self.__selectedPiece = mousePos
self.__board.location(
mousePos).occupant.selected = True
elif self.__selectedPiece is not None:
if mousePos in self.__board.legalMoves(
self.__selectedPiece):
colisionMove = self.__board.checkColision(
self.__selectedPiece, mousePos)
self.__board.movePiece(self.__selectedPiece,
mousePos)
if mousePos not in self.__board.adjacent(
self.__selectedPiece) and isinstance(
self.__board.location(
mousePos).occupant, King) is False:
self.__board.removePiece(
(self.__selectedPiece[0] + int(
(mousePos[0] - self.__selectedPiece[0])
/ 2), self.__selectedPiece[1] + int(
(mousePos[1] -
self.__selectedPiece[1]) / 2)))
self.__jump = True
self.__selectedPiece = mousePos
self.__board.location(
mousePos).occupant.selected = True
elif isinstance(
self.__board.location(mousePos).occupant,
King) is True and colisionMove:
self.__board.removePiece(colisionMove)
self.__jump = True
self.__selectedPiece = mousePos
self.__board.location(
mousePos).occupant.selected = True
else:
self.end_turn(self.__board)
else:
self.invalidMove()
else:
if self.__selectedPiece is not None and mousePos in self.__board.legalMoves(
self.__selectedPiece, self.__jump):
self.__board.movePiece(self.__selectedPiece, mousePos)
self.__board.removePiece(
(self.__selectedPiece[0] + int(
(mousePos[0] - self.__selectedPiece[0]) / 2),
self.__selectedPiece[1] + int(
(mousePos[1] - self.__selectedPiece[1]) / 2)))
self.__board.location(
mousePos).occupant.selected = True
self.__selectedPiece = mousePos
if not self.__board.legalMoves(mousePos, self.__jump):
self.end_turn(self.__board)
else:
self.invalidMove()
if self.__endGame:
self.endGameDisplay()
pygame.display.update()
def invalidMove(self):
self.__graphics.message_display('Ruch', 40,
self.getDisplayWidth() / 2 + 270,
self.getDisplayHeight() / 1.5,
colors["CHERRY"])
self.__graphics.message_display('Nieprawidłowy!', 35,
self.getDisplayWidth() / 2 + 270,
self.getDisplayHeight() / 1.5 + 40,
colors["CHERRY"])
def terminate_game(self):
pygame.quit()
quit()
def end_turn(self, board):
if self.__turn == self.__players[0]:
self.__turn = self.__players[1]
else:
self.__turn = self.__players[0]
self.__selectedPiece = None
for x in range(8):
for y in range(8):
if board.matrix[x][y].occupant is not None:
board.matrix[x][y].occupant.selected = False
self.__jump = False
self.__board.jumpAvailablePieces = []
self.__board.jump = []
if self.checkEndgame():
self.__endGame = True
def turndisplay(self, turn):
if turn == 'W':
return 'Tura gracza 1'
else:
return 'Tura gracza 2'
def checkEndgame(self):
for x in range(8):
for y in range(8):
if self.__board.location(
(x, y)).color == '1' and self.__board.location(
(x, y)).occupant is not None and self.__board.location(
(x, y)).occupant.color == self.__turn:
if self.__board.legalMoves((x, y)):
return False
return True
def gameButtons(self, msg, msg_size, x, y, w, h, ic, ac, action=None):
mouse = pygame.mouse.get_pos()
click = pygame.mouse.get_pressed()
if x + w > mouse[0] > x and y + h > mouse[1] > y:
colour = ac
if click[0] == 1 and action is not None:
if action == self.__buttonActions[0]:
self.resetGame()
else:
self.__backMenu = True
else:
colour = ic
pygame.draw.rect(self.screen, colour, (x, y, w, h))
smallText = pygame.font.Font("freesansbold.ttf", msg_size)
textSurf, textRect = self.__graphics.text_objects(
msg, smallText, colors["MUSTARD"])
textRect.center = ((x + (w / 2)), (y + (h / 2)) + 5)
self.screen.blit(textSurf, textRect)
def buttonFunc(self):
for action, i in zip(self.__buttonActions, [540, 480]):
self.gameButtons(
action, 60,
self.getDisplayWidth() -
(self.getDisplayWidth() - self.__graphics.getBoardSize()) / 2 -
self.__buttonSize[0] / 2, i, self.__buttonSize[0],
self.__buttonSize[1], colors["SUMMER_SUN"],
colors["BRIGHT_SUMMER_SUN"], action)
def endGameDisplay(self):
if self.__turn == self.__players[0]:
self.__graphics.message_display('Wygrał 2', 100,
self.getDisplayWidth() / 2,
self.getDisplayHeight() / 2,
colors["CHERRY"])
else:
self.__graphics.message_display('Wygrał 1', 100,
self.getDisplayWidth() / 2,
self.getDisplayHeight() / 2,
colors["CHERRY"])
def resetGame(self):
self.__turn = self.__players[0]
self.__board = Board()
self.__board.initializePieces()
self.__selectedPiece = None
self.__endGame = False
def onInit(self):
self.console = Console.Console(Graphics.Rectangle(30, 30, 512, 160))
self.setRepeatRate(200, 50)
self.console.setAsStdOut()
print "WHATS UP BITCHES!!"
print "yeah, thought so!"
def checkDraw(self, mDrawX1, mDrawY1):
rowSwitch = self.y
switcher = 1
Graphics.tileSelectedClear(mDrawX1, mDrawY1)
for index in range(self.y):
for index in range(self.x):
if self.board[switcher - 1][index].emoji == 1:
Graphics.drawMoon(index, switcher)
if self.board[switcher - 1][index].emoji == 2:
Graphics.drawAvocado(index, switcher)
if self.board[switcher - 1][index].emoji == 3:
Graphics.drawFire(index, switcher)
if self.board[switcher - 1][index].emoji == 4:
Graphics.drawCoconut(index, switcher)
if self.board[switcher - 1][index].emoji == 5:
Graphics.drawJoy(index, switcher)
if self.board[switcher - 1][index].emoji == 6:
Graphics.drawHeart(index, switcher)
if self.board[switcher - 1][index].emoji == 0:
Graphics.drawEmpty(index, switcher)
rowSwitch -= 1
switcher += 1
Graphics.eraseScore(self.x, self.y)
Graphics.drawTotalScore(self.x, self.y, Score.score)
def testMaze(n_steps, learning_dbg_lvl=1, navigation_dbg_lvl=0):
nT = n_steps[0] # Training steps
nN = n_steps[1] # Navigation steps
ValueLearning.DBG_LVL = learning_dbg_lvl
move_distance = MOVE_DISTANCE
# Parameters describing the maze
# Build the Maze (add walls etc.)
# Maze with partition - 6 x 6 environment
# ----------------- (6,6)
# | |
# | (2,3) (4,3) |
# |----- -----| (6,3)
# | |
# | |
# (0,0) -----------------
nx = 6
ny = 6
lp_wall = Environment.Wall((0, 3), (2, 3))
rp_wall = Environment.Wall((4, 3), (6, 3))
maze = Environment.MazeWithWalls(nx, ny, [lp_wall, rp_wall], move_distance)
# 10 x 10 Maze with an L barrier
# (2,10)
# --------------------- (10,10)
# | | |
# | | |
# | | |
# | | (4,4) |
# (0,4) | |------ |
# | | |
# | (2,2) |
# (0,0) ---------------------
"""
nx = 10
ny = 10
h_wall = Environment.Wall((2,4), (4,4))
v_wall = Environment.Wall((2,2), (2,10))
maze = Environment.MazeWithWalls(nx, ny, [h_wall, v_wall])
"""
# Create a initial and goal location including the information about wall locations
# Object for plotting and visualization
canvas = Graphics.WallMazeCanvas(maze)
# Add Place fields and place cells
n_fields = round(1.0 * (nx + 3) * (ny + 3))
Hippocampus.N_CELLS_PER_FIELD = 4
n_cells = n_fields * Hippocampus.N_CELLS_PER_FIELD
place_fields = Hippocampus.setupPlaceFields(maze, n_fields)
place_cells = Hippocampus.assignPlaceCells(n_cells, place_fields)
if (learning_dbg_lvl > 2):
canvas.visualizePlaceFields(place_cells)
# Learn how to navigate this Environment
(actor, critic,
learning_steps) = ValueLearning.learnValueFunction(nT,
maze,
place_cells,
max_steps=4000)
# Try a single trial on the same Maze and see how we do
ValueLearning.DBG_LVL = navigation_dbg_lvl
navigation_steps = ValueLearning.navigate(nN,
maze,
place_cells,
actor,
critic,
max_steps=400)
return (learning_steps, navigation_steps)
def Simulate(Mol, Simulations):
if 'Graphics' in Simulations:
#Save a graph of the molecule and the 4 states around the Fermi Energy
Graph = Graphics()
Graph.SetMolecule(Mol)
Graph.Save(Mol.szOutputFolder + Mol.Name + " - Molecule.png",
part='Molecule')
Graph.UpdateAtomSel(
np.array([
np.real(Mol.eigvec[0][:, Mol.N / 2 - 2] *
np.conjugate(Mol.eigvec[0][:, Mol.N / 2 - 2])),
np.zeros(Mol.N)
]))
Graph.Save(Mol.szOutputFolder + Mol.Name + " - Orbital - H**O 2.png",
part='Molecule')
Graph.UpdateAtomSel(
np.array([
np.real(Mol.eigvec[0][:, Mol.N / 2 - 1] *
np.conjugate(Mol.eigvec[0][:, Mol.N / 2 - 1])),
np.zeros(Mol.N)
]))
Graph.Save(Mol.szOutputFolder + Mol.Name + " - Orbital - H**O 1.png",
part='Molecule')
Graph.UpdateAtomSel(
np.array([
np.real(Mol.eigvec[0][:, Mol.N / 2 - 0] *
np.conjugate(Mol.eigvec[0][:, Mol.N / 2 - 0])),
np.zeros(Mol.N)
]))
Graph.Save(Mol.szOutputFolder + Mol.Name + " - Orbital - LUMO 1.png",
part='Molecule')
Graph.UpdateAtomSel(
np.array([
np.real(Mol.eigvec[0][:, Mol.N / 2 + 1] *
np.conjugate(Mol.eigvec[0][:, Mol.N / 2 + 1])),
np.zeros(Mol.N)
]))
Graph.Save(Mol.szOutputFolder + Mol.Name + " - Orbital - LUMO 2.png",
part='Molecule')
if 'Lifting' in Simulations:
#Set up the plots
fig = plt.figure()
axLift, axOrbs = [fig.add_subplot(1, 2, 1), fig.add_subplot(1, 2, 2)]
#Load and plot the experimental data if available
for root, dirs, files in os.walk(Mol.szOutputFolder +
'Experimental Data/'):
for file in files:
print(file)
header, values = ImportData(Mol.szOutputFolder +
'Experimental Data/' + file)
axLift.plot(10 * values[:, 0],
values[:, 1],
'k',
marker='.',
lw=0,
label=file,
alpha=0.1)
#Create MD folder if it did not already exists
if not os.path.exists(Mol.szOutputFolder + 'MD'):
os.makedirs(Mol.szOutputFolder + 'MD')
#Perform lifting simulation
MD = MD_Lifting(Mol)
dz = np.linspace(0, np.max(Mol.Atom[:, 1]) - 0.1, 50)
# Mol.SetBias(0.01)
I = MD.PerformLifting(dz, export=['param', 'dzI', 'xyz'], axOrb=axOrbs)
#Plot tweaking and saving
axLift.plot(dz,
np.log(I),
color='midnightblue',
label='Simulated',
lw=2,
alpha=0.8)
axLift.plot(dz,
np.log(I),
color='midnightblue',
label='Simulated',
lw=0,
alpha=0.8,
marker='o',
markersize=10)
# axLift.legend()
# axLift.set_title('Lifting')
axLift.set_xlabel('$\Delta \ z [A]$')
axLift.set_ylabel('$ln(I)$')
axLift.set_xlim([np.min(dz), np.max(dz)])
ymin, ymax = np.min(np.log(I)), np.max(np.log(I))
axLift.set_ylim(
[ymin - 0.1 * (ymax - ymin), ymax + 0.1 * (ymax - ymin)])
# #Plot Beta factor
# axBeta = axLift.twinx()
# axBeta.plot((dz[0:-4]+dz[4:])/2, (np.log(I[0:-4])-np.log(I[4:]))/(dz[4]-dz[0])*10, c='r', alpha=0.5, lw=2)
# axBeta.set_ylabel('$Beta$')
axOrbs.set_title('Orbitals')
axOrbs.set_xlabel('$\Delta z [A]$')
axOrbs.set_ylabel('$E_{Fermi} [eV]$')
axOrbs.legend(['H**O 2', 'H**O 1', 'LUMO 1', 'LUMO 2'])
fig.set_size_inches(20.5, 6.5)
mpl.rcParams.update({'font.size': 20})
fig.savefig(Mol.szOutputFolder + Mol.Name +
' - Lifting vs Current.png',
dpi=fig.dpi)
if 'LiftingSpectroscopy' in Simulations:
#Set up parameters and perform simulation
MD = MD_Lifting(Mol)
dz = np.linspace(0, np.max(Mol.Atom[:, 1]) - 0.1, 50)
neg_bias = -np.linspace(0.015, 0.5, 100)[::-1]
pos_bias = np.linspace(0.005, 0.5, 100)
I = MD.PerformLiftingSpectroscopy(dz,
np.concatenate((neg_bias, pos_bias)),
export=['dz-V-I'])
I = [I[:, 0:len(neg_bias)], I[:, len(neg_bias):]]
#Set up the figure
c = mcolors.ColorConverter().to_rgb
fig = plt.figure()
axI, axdIdV, axBeta = [
fig.add_subplot(1, 3, 1),
fig.add_subplot(1, 3, 2),
fig.add_subplot(1, 3, 3)
]
#Plot ln(I) data
BIAS, DZ = [[None, None], [None, None]]
for i, bias in enumerate([neg_bias, pos_bias]):
BIAS[i], DZ[i] = np.meshgrid(bias, dz)
im = axI.contourf(np.concatenate([DZ[0], DZ[1]], axis=1),
np.concatenate([BIAS[0], BIAS[1]], axis=1),
np.log(abs(np.concatenate([I[0], I[1]], axis=1))),
100,
cmap=make_colormap([c('white'),
c('midnightblue')]))
axI.set_ylabel('$Bias$ [$V$]')
axI.set_xlabel('$\Delta z \ [\AA{}]$')
fig.colorbar(im, ax=axI, label='$ln(I)$')
#Plot dI/dV data
dIdV, mBIAS, mDZ = [[None, None], [None, None], [None, None]]
for i, bias in enumerate([neg_bias, pos_bias]):
dIdV[i] = (I[i][:, 1:] - I[i][:, 0:-1]) / (bias[1] - bias[0])
mBIAS[i], mDZ[i] = np.meshgrid(0.5 * (bias[1:] + bias[0:-1]), dz)
im = axdIdV.contourf(np.concatenate([mDZ[0], mDZ[1]], axis=1),
np.concatenate([mBIAS[0], mBIAS[1]], axis=1),
np.log(np.concatenate([dIdV[0], dIdV[1]],
axis=1)),
100,
cmap=make_colormap([c('white'),
c('darkgreen')]))
axdIdV.set_ylabel('$Bias$ [$V$]')
axdIdV.set_xlabel('$\Delta z \ [\AA{}]$')
fig.colorbar(im, ax=axdIdV, label='$ln(dI/dV)$')
#Plot beta factor
Beta, BIAS, DZ = [[None, None], [None, None], [None, None]]
for i, bias in enumerate([neg_bias, pos_bias]):
Beta[i] = (np.log(abs(I[i][1:, :])) -
np.log(abs(I[i][0:-1, :]))) / (dz[1] - dz[0])
BIAS[i], DZ[i] = np.meshgrid(bias, 0.5 * (dz[1:] + dz[0:-1]))
im = axBeta.contourf(np.concatenate([DZ[0], DZ[1]], axis=1),
np.concatenate([BIAS[0], BIAS[1]], axis=1),
np.concatenate([Beta[0], Beta[1]], axis=1),
100,
cmap=make_colormap([c('darkred'),
c('white')]))
axBeta.set_ylabel('$Bias$ [$V$]')
axBeta.set_xlabel('$\Delta \ z$ [$\AA{}]$')
fig.colorbar(im, ax=axBeta, label='$Beta factor$')
mpl.rcParams.update({'font.size': 42})
fig.set_size_inches(100.5, 13.5)
fig.savefig(Mol.szOutputFolder + Mol.Name +
' - LiftingSpectroscopy.png',
dpi=fig.dpi)
if 'MD' in Simulations:
#Perform a molecular dynamics simulation only
MD = MD_Lifting(Mol)
MD.PerformLiftingMD(np.linspace(0,
np.max(Mol1.Atom[:, 1]) - 0.1, 50),
export=['xyz'])
# For plotting the SEM measure, use this!
err_training_steps = stats.sem(training_steps, axis=1)
err_navigation_steps = stats.sem(navigation_steps, axis=1)
training_fig = pl.figure()
training_ax = training_fig.add_subplot(111)
training_ax.errorbar(range(n_training_trials),
mean_training_steps,
yerr=err_training_steps,
marker='d',
ecolor='black',
capsize=0.5)
training_ax.set_xlabel('Trials')
training_ax.set_ylabel('Distance Moved')
Graphics.cleanAxes(training_ax)
pl.show()
navigation_fig = pl.figure()
navigation_ax = navigation_fig.add_subplot(111)
navigation_ax.errorbar(range(n_navigation_trials),
mean_navigation_steps,
yerr=err_navigation_steps,
marker='o',
ecolor='black',
capsize=0.5)
navigation_ax.set_xlabel('Trials')
navigation_ax.set_ylabel('Distance Moved')
Graphics.cleanAxes(navigation_ax)
pl.show()
f = open("data.txt", 'w')
for i in range(len(p)):
pathString = str(p[i][0]) + str(p[i][1]) + ' '
f.write(pathString)
runString = "\n"
for i in range(len(runResult[1])):
if runResult[1][i] == "jump" or runResult[1][i] == "stay":
runString += "0"
else:
runString += "1"
f.write(runString)
f.close()
if "-na" not in sys.argv and "-g" not in sys.argv:
if "-g" not in sys.argv: print("Displaying best solution")
Graphics.main()
else:
if "-g" not in sys.argv: print("Skipping animation.")
# Plot the longest solutions
if "-np" not in sys.argv and "-g" not in sys.argv:
if "-g" not in sys.argv: print("Plotting solution lengths")
axes = plt.gca()
axes.set_xlim([0, len(longestSolutions)])
axes.set_ylim([0, pathlength])
xaxis = []
for i in range(len(longestSolutions)):
xaxis += [i]
plt.title("Solution Length over Time")
plt.xlabel("Generation")
plt.ylabel("Longest Solution")
def calculateStatistics(project, trace):
"""
Calculate derived OpenGL ES statistics instrumentation sensor data and trace events.
"""
if not "code" in project.targets:
raise ValueError("No code information in project file")
task = Task.startTask("gles-stats", "Calculating OpenGL ES statistics", len(trace.events))
library = project.targets["code"].library
constants = Collections.DictProxy(library.constants)
# Create the derived sensors
trace.sensors["average_triangle_size"] = Trace.InstrumentationSensor("Average triangle size", isAverage = True)
trace.sensors["texel_fetches_per_pixel"] = Trace.InstrumentationSensor("Texel fetches per pixel", isAverage = True)
trace.sensors["texel_uploads"] = Trace.InstrumentationSensor("Texel uploads")
trace.sensors["vertices_in"] = Trace.InstrumentationSensor("Vertices in")
trace.sensors["triangles_in"] = Trace.InstrumentationSensor("Triangles in")
trace.sensors["render_calls"] = Trace.InstrumentationSensor("Rendering calls")
trace.sensors["rasterizer_discarded_pixels"] = Trace.InstrumentationSensor("Rasterizer discarded pixels")
trace.sensors["draw_ratio"] = Trace.InstrumentationSensor("Draw ratio")
prevRenderEvent = None
depthMask = 1
for event in trace.events:
task.step()
func = library.functions[event.name]
if func.isRenderCall:
event.sensorData["render_calls"] = 1
if func.isRenderCall and "count" in event.values and "mode" in event.values:
m = event.values["mode"]
if m == constants.GL_TRIANGLES:
event.sensorData["triangles_in"] = int(event.values["count"] / 3)
elif m == constants.GL_TRIANGLE_STRIP:
event.sensorData["triangles_in"] = int(event.values["count"] - 2)
elif m == constants.GL_TRIANGLE_FAN:
event.sensorData["triangles_in"] = int(event.values["count"] - 2)
elif m == constants.GL_POINTS:
event.sensorData["triangles_in"] = int(event.values["count"] * 2)
elif m == constants.GL_LINES:
event.sensorData["triangles_in"] = int(event.values["count"])
elif m == constants.GL_LINE_STRIP:
event.sensorData["triangles_in"] = int(event.values["count"] * 2)
elif m == constants.GL_LINE_LOOP:
event.sensorData["triangles_in"] = int(event.values["count"] * 2 + 2)
fragments = event.sensorData.get("rasterizer_pixels", 0)
triangles = event.sensorData.get("triangles_in", 0)
texFetches = event.sensorData.get("rasterizer_texel_fetches", 0)
if triangles and fragments:
event.sensorData["average_triangle_size"] = fragments / float(triangles)
if fragments and texFetches:
event.sensorData["texel_fetches_per_pixel"] = fragments / float(texFetches)
if event.name in ["glTexImage2D", "glTexSubImage2D"]:
event.sensorData["texel_uploads"] = int(event.values["width"] * event.values["height"])
if func.isRenderCall and "count" in event.values:
event.sensorData["vertices_in"] = int(event.values["count"])
if event.name == "glDepthMask":
depthMask = event.values["flag"]
# If we have the depth buffers for this event and the previous draw event, see how many pixels
# actually changed and use that value to estimate overdraw
if func.isRenderCall and not "rasterizer_discarded_pixels" in event.sensorData:
if depthMask and prevRenderEvent and "depth_stride" in event.sensorData and "depth_mask" in event.sensorData:
f1 = player.Instrumentation.getBufferFileName(prevRenderEvent, "depth")
f2 = player.Instrumentation.getBufferFileName(event, "depth")
if f1 and f2:
diff = Graphics.compareDepthBuffers(f1, f2, event.sensorData["depth_stride"], event.sensorData["depth_mask"])
event.sensorData["rasterizer_discarded_pixels"] = fragments - diff
prevRenderEvent = event
discFragments = event.sensorData.get("rasterizer_discarded_pixels", 0)
width = event.sensorData.get("render_surface_width", 0)
height = event.sensorData.get("render_surface_height", 0)
if fragments and width and height:
event.sensorData["draw_ratio"] = (fragments - discFragments) / float(width * height)
def __init__(self):
self.graphics = Graphics()
self.board = Board()
import Graphics
import matrix
import DEBUG
import MatGraph
import math
from Mesh import *
import random
from typing import List
from dataclasses import dataclass
import threading
DEBUG.init()
Graphics.initDisplayHandler(DEBUG.window,(1920,1080),100,30*math.pi/180)
def PointAt(pos,target,up):
NewForward = target-pos
#print(NewForward.v)
try:
size = math.sqrt(NewForward.v[0]**2+NewForward.v[0]**2+NewForward.v[0]**2)
NewForward.v[0] /= size
NewForward.v[1] /= size
NewForward.v[2] /= size
except:
pass
a = NewForward * MatGraph.DotProduct(up,NewForward)
NewUp = up - a
def getOnscreenPos(self):
if self.currentState == CONST.CurrentPlayerState.Small:
return Graphics.getOnScreenPos(self.pos, self.cameraPos)
return Graphics.getOnScreenPos(
(self.pos[0], self.pos[1] +
(CONST.PlayerLargeSizeY - CONST.PlayerSizeY)), self.cameraPos)
])
TranslationMatrix2 = matrix.mat3x3([
[-0,0,0],
[-0,0,0],
[-0,0,0],
])
Cam = matrix.vector([0,0,0])
Light = matrix.vector([0,-0,-1])
Graphics.init()
theta = math.pi/3
spd = 0.01
Test = Mesh.Load3DElement("Gplanneur/Tests/Test 3D/3D rotations/GAMIIIING.obj")
#print(Test.v)
while Graphics.ProgramRunning :
cube2 = copy.deepcopy([Test])
Graphics.PrintFPS()
if(Graphics.SpaceToken):
theta += math.pi/8
else:
theta += math.pi/512
