def draw_neurons(self):
if self.xs != self.xs_buffer:
if self.xs.ndim == 1:
self.xs = self.xs[np.newaxis, :]
elif self.xs.shape[0] != 1:
raise ValueError('Input data must only contain one sample')
if self._network.reg == 'dropout':
feed_dict = {self._network.data_placeholder: self.xs,
self._network.keep_prob_placeholder: self.keep_prob}
elif self._network.reg == 'l2':
feed_dict = {self._network.data_placeholder: self.xs,
self._network.l2_placeholder: self.l2_lambda}
else:
feed_dict = {self._network.data_placeholder: self.xs}
layers_final_output = self._network.sess.run(
self.network.layers_final_output, feed_dict)
i = 0
for neuron in range(self._network.n_inputs):
self.neurons_vertex_list[i].colors = cm.cool(neuron[0]) * 4
i += 1
for layer in layers_final_output:
for neuron in layer:
self.neurons_vertex_list[i].colors = cm.cool(neuron[0]) * 4
i += 1
self.xs_buffer = self.xs
else:
pass
def draw_neurons(self):
if self.xs != self.xs_buffer:
if self.xs.ndim == 1:
self.xs = self.xs[np.newaxis, :]
elif self.xs.shape[0] != 1:
raise ValueError('Input data must only contain one sample')
if self._network.reg == 'dropout':
feed_dict = {
self._network.data_placeholder: self.xs,
self._network.keep_prob_placeholder: self.keep_prob
}
elif self._network.reg == 'l2':
feed_dict = {
self._network.data_placeholder: self.xs,
self._network.l2_placeholder: self.l2_lambda
}
else:
feed_dict = {self._network.data_placeholder: self.xs}
layers_final_output = self._network.sess.run(
self.network.layers_final_output, feed_dict)
i = 0
for neuron in range(self._network.n_inputs):
self.neurons_vertex_list[i].colors = cm.cool(neuron[0]) * 4
i += 1
for layer in layers_final_output:
for neuron in layer:
self.neurons_vertex_list[i].colors = cm.cool(neuron[0]) * 4
i += 1
self.xs_buffer = self.xs
else:
pass
def __init__(self,position,radius):
self.x=asfarray(position)
self.v=zeros(2,dtype=float)
self.f=zeros(2,dtype=float)
self.m=1.
self.dt=0.03
self.damp=0.999
self.r=0.05 # scaling factor for random walk
self.direc=2*pi*rand() # wants to move in that direction
self.radius=radius
self.color=color_mpl2pg(cm.cool(0.5*rand()))
self.color2=color_mpl2pg(cm.cool(0.8+0.2*rand()))
self.world=None
def plot_timeVfreq(date_operator, collection):
if collection == "drop":
field = "pickup_time"
elif collection == "pickup":
field = "drop_time"
else:
raise Exception(
"Error: the collection arguments to timeVfreq are 'drop', or 'pickup'. Not",
collection)
graph_size = {"hour": (0, 23), "dayOfYear": (1, 366), "dayOfWeek": (1, 8)}
if date_operator not in graph_size.keys():
raise Exception(
"Error: the date_operator argument to timeVfreq must be 'hour', 'dayOfWeek', or 'dayOfYear'. Not",
date_operator)
tvf = get_timeVfreq(date_operator, collection, field)
min_freq = ndarray.min(tvf[1])
max_freq = ndarray.max(tvf[1])
fig = pyplot.figure(1, figsize=(7, 6))
myplot = fig.add_subplot(111)
myplot.set_xlim(graph_size[date_operator])
norm = colors.Normalize(min_freq, max_freq)
for i in range(len(tvf[0])):
color = colors.rgb2hex(cm.cool(norm(tvf[1][i])))
pyplot.bar(tvf[0][i], tvf[1][i], color=color, ec=color, align='edge')
pyplot.xlabel(date_operator)
pyplot.ylabel("# of rides")
pyplot.savefig(date_operator + "_" + collection, dpi=180)
def animate_bloch(states, duration=0.1, save_all=False):
b = Bloch()
b.vector_color = ['r']
b.view = [-40, 30]
images = []
try:
length = len(states)
except:
length = 1
states = [states]
## normalize colors to the length of data ##
nrm = mpl.colors.Normalize(0, length)
colors = cm.cool(nrm(
range(length))) # options: cool, summer, winter, autumn etc.
## customize sphere properties ##
b.point_color = list(colors) # options: 'r', 'g', 'b' etc.
b.point_marker = ['o']
b.point_size = [30]
for i in tqdm(range(length)):
b.clear()
b.add_states(states[i])
b.add_states(states[:(i + 1)], 'point')
if save_all:
b.save(dirc='tmp') #saving images to tmp directory
filename = "tmp/bloch_%01d.png" % i
else:
filename = 'temp_file.png'
b.save(filename)
images.append(imageio.imread(filename))
imageio.mimsave('bloch_anim.gif', images, duration=duration)
def annotate_linked_features(V, feats, i):
""" using the linked features dataframe feats, return an annotated image
from float 0-1 single channel pims video V.
* feats is a dataframe containing 'x','y','particle','frame'."""
import skimage as ski
from skimage import draw
import matplotlib.cm as cm
# define colors, get frame and features
colors = (cm.cool(np.linspace(0.0, 1.0, 10))[:, :3] * 255).astype('uint8')
ifeats = feats[feats['frame'] == i]
im = V[i]
im = (255 * np.stack((im, ) * 3, -1)).astype('uint8')
# draw on the frame
words = wordmask('time {} s'.format(round(i / 190.0, 3)), (20, 20),
40) #generate mask of the frame number
im[words] = [0, 0, 240]
# draw all features on the frame with circles..
for _, vals in ifeats.iterrows():
y = int(round(vals['x']))
x = int(round(vals['y']))
color = colors[int(vals['particle'] % 10)]
p = vals['particle']
row, col = ski.draw.circle_perimeter(x, y, 9, shape=(720, 1680))
im[row, col] = color
row, col = ski.draw.circle_perimeter(x, y, 10, shape=(720, 1680))
im[row, col] = color
row, col = ski.draw.circle_perimeter(x, y, 11, shape=(720, 1680))
im[row, col] = color
#also add the feature number of each object
words = wordmask(str(int(p)), (y + 5, x + 5), size=30)
im[words] = color
return im
def pmf_t(temp):
lam = [.1,.15,.2,.25,.3,.35,.4,.45,.5,.55]
colors = cm.cool(numpy.linspace(0,1,len(lam)))
plt.figure(8)
plt.rc('text',usetex=True)
direc = '/home/edz3fz/proteinmontecarlo/results/1PGB/surface'
for i,l in enumerate(lam):
f = open('%s/lambda%s/pmf_%i.pkl' %(direc,str(l)[1::],int(temp)),'rb')
temp = cPickle.load(f)
bin_centers = cPickle.load(f)
f_i = cPickle.load(f)
df_i = cPickle.load(f)
f.close()
idx = numpy.argmin(f_i[int(.7*len(f_i))::]) + int(.7*len(f_i))
f_i -= f_i[idx]
plt.errorbar(bin_centers[1::],f_i[1::],numpy.array(df_i[0,:])[0,1::],label=r'$\lambda$ = %s' % l, color=colors[i])
f = open('/home/edz3fz/proteinmontecarlo/results/1PGB/solution/pmf_%i.pkl' %(int(temp)),'rb')
temp = cPickle.load(f)
bin_centers = cPickle.load(f)
f_i = cPickle.load(f)
df_i = cPickle.load(f)
f.close()
idx = numpy.argmin(f_i[int(.7*len(f_i))::]) + int(.7*len(f_i))
f_i -= f_i[idx]
plt.errorbar(bin_centers[1::],f_i[1::],numpy.array(df_i[0,:])[0,1::],label='solution', color='k')
plt.xlabel('Q')
plt.ylabel('PMF (kcal/mol)')
plt.title('T = %s K' % temp)
plt.legend(loc=0, prop={'size':8})
def plot_timeVfreq(date_operator, collection):
if collection == "drop":
field = "pickup_time"
elif collection == "pickup":
field = "drop_time"
else:
raise Exception("Error: the collection arguments to timeVfreq are 'drop', or 'pickup'. Not", collection)
graph_size = {"hour":(0, 23), "dayOfYear":(1,366), "dayOfWeek":(1,8)}
if date_operator not in graph_size.keys():
raise Exception("Error: the date_operator argument to timeVfreq must be 'hour', 'dayOfWeek', or 'dayOfYear'. Not", date_operator)
tvf = get_timeVfreq(date_operator, collection, field)
min_freq = ndarray.min(tvf[1])
max_freq = ndarray.max(tvf[1])
fig = pyplot.figure(1, figsize=(7, 6))
myplot = fig.add_subplot(111)
myplot.set_xlim(graph_size[date_operator])
norm = colors.Normalize(min_freq, max_freq)
for i in range(len(tvf[0])):
color = colors.rgb2hex(cm.cool(norm(tvf[1][i])))
pyplot.bar(tvf[0][i], tvf[1][i], color=color, ec=color, align='edge')
pyplot.xlabel(date_operator)
pyplot.ylabel("# of rides")
pyplot.savefig(date_operator + "_" + collection, dpi=180)
def main_draw(self, ck, cks, alphas, points_F, points_coupe, F_label):
axe = self.fig.add_subplot(111, projection="3d")
axe.plot_surface(*points_F, alpha=0.4, color="gray")
alphas = alphas / alphas.max()
colors = cm.cool(alphas)
axe.scatter(ck[:, 0], ck[:, 1], cks, c=colors, s=15)
x, y, z = points_coupe
axe.plot(x,
y,
z * np.ones(x.shape),
color="green",
alpha=1,
label="$F(x) = y_{obs}$")
fake2Dline = matplotlib.lines.Line2D([0], [0],
linestyle="none",
c='gray',
marker='s',
alpha=0.4)
gray_patch = matplotlib.patches.Patch(color="gray", alpha=0.4)
fake2Dline2 = matplotlib.lines.Line2D([0], [0],
linestyle="none",
c='cyan',
marker='o')
axe.azim, axe.elev = 83, 21 # setup view for example
handle, label = axe.get_legend_handles_labels()
axe.legend([gray_patch, handle[0], fake2Dline2],
[f"$F$ : {F_label}", label[0], "$c_{k}^{*}$"],
bbox_to_anchor=[0.6, 0.15, 0.2, 0.3])
pyplot.show()
def conv1(model):
n1, n2, x, y, z = model.conv1.W.shape
fig = plt.figure()
for nn in range(0, n1):
ax = fig.add_subplot(4, 5, nn + 1, projection='3d')
ax.set_xlim(0.0, x)
ax.set_ylim(0.0, y)
ax.set_zlim(0.0, z)
ax.set_xticklabels([])
ax.set_yticklabels([])
ax.set_zticklabels([])
for xx in range(0, x):
for yy in range(0, y):
for zz in range(0, z):
max = np.max(model.conv1.W.data[nn, :])
min = np.min(model.conv1.W.data[nn, :])
step = (max - min) / 1.0
C = (model.conv1.W.data[nn, 0, xx, yy, zz] - min) / step
color = cm.cool(C)
C = abs(1.0 - C)
ax.plot(np.array([xx]),
np.array([yy]),
np.array([zz]),
"o",
color=color,
ms=7.0 * C,
mew=0.1)
plt.savefig("result/graph_conv1.png")
def __init__(self, I1, I2, positive=None, negative=None, **kwargs):
"""A visualizer for labelling positive and negative regions
Args:
I1, I2: The images to label
Keyword Args:
positive: The initial set of positive correspondences
negative: The initial set of background examples
"""
super(PNLabeller, self).__init__(I1, I2, **kwargs)
self.positive = []
self.negative = []
self.mode = 'P'
# initialize the colormaps
self.pmap = cycle([cm.cool(i) for i in range(0, cm.cool.N, 16)])
self.nmap = cycle([cm.autumn(i) for i in range(0, cm.autumn.N, 16)])
self.cmap = self.pmap
self.color = self.pmap.next()
# redraw the initial set
if positive is not None:
for left, right in positive:
left, right = tuple(left), tuple(right)
self.positive.append((left, right))
self.plot(left, right)
if negative is not None:
for left, self.color in zip(negative, self.nmap):
left = tuple(left)
self.negative.append(left)
self.plot(left)
def main():
lam = [.1, .2, .3, .35, .4, .45, .5, .6, .7]
lam = [.1, .2, .3, .4, .5, .675, .7]
lam = [.1, .15, .2, .25, .3, .35, .4, .45, .5] #, .55, .6, .65, .7]
files = [
'/home/edz3fz/proteinmontecarlo/results/1PGB/surface/umbrella_lambda%s/foldingcurve_umbrella.npy'
% str(x)[1::] for x in lam
]
colors = cm.cool(numpy.linspace(0, 1, len(lam)))
plt.figure(1)
plt.rc('text', usetex=True)
for i in range(len(lam)):
data = numpy.load(files[i])
x = 17
plt.errorbar(data[0, -x::],
data[1, -x::],
data[2, -x::],
label=r'$\lambda$ = %s' % lam[i],
color=colors[i])
soln = '/home/edz3fz/proteinmontecarlo/results/1PGB/solution/foldingcurve.npy'
data = numpy.load(soln)
plt.errorbar(data[0, :],
data[1, :],
data[2, :],
label='solution',
color='k')
plt.xlabel('temperature (K)')
plt.ylabel('Q')
plt.legend(prop={'size': 8})
plt.show()
def col_size(var,var_min=1.1):
import matplotlib.cm as cm
# Limit values in case they are not yet limited
var = np.clip(var, var_min, None)
color = cm.cool(np.log10(var)/np.max(np.log10(var)))
size = 100*np.log10(var)**2
return color, size
def scan(self,
propertyName,
minVal,
maxVal,
resolution,
function,
*args):
vprint("Scanning %s by %s..." % (self.fileName, propertyName))
#check that scanning range is valid and update if otherwise
minVal, maxVal = scanAxesClampHelper(self, propertyName, minVal, maxVal)
#Instantiate workbook for writing data from this function
workBookName = "%s %s scan of %s vs %s by %s.xlsx" % (TCG.SAVE_DIRECTORY, self.fileName, args[0], args[1], propertyName)
workbook = xlsxwriter.Workbook(workBookName, {'nan_inf_to_errors': True, 'in_memory': True})
bins = np.linspace(minVal, maxVal, resolution+1)
settings = args[2]
title = "%s, %s vs %s scanned by %s" % (self.fileName , args[0], args[1], propertyName)
legendStrings = []
colorIndices = np.linspace(0, 1, resolution)
fig = constructFig(self, title)
writeData(self, workbook, "binData")
keepOpen = True #flag for whether to close the excel workbook on the next write
for index, colorIdx in zip (range(0, len(bins)-1), colorIndices):
#get filter instance range
lowerVal = bins[index]
upperVal = bins[index+1]
#copy data
filtersCopy = {}
filtersCopy[propertyName] = [[lowerVal, upperVal]]
trackFileCopy = deepcopy(self)
trackFileCopy.selectData(filtersCopy)
#turn off settings new fig
settings["show"] = False
settings["newFig"] = False
settings["save"] = False
#get plot instance legend name
legendString = "%.1f to %.1f" % (lowerVal, upperVal)
legendStrings.append(legendString)
if (index == len(bins)-1):
keepOpen = False
#plot
P = function(trackFileCopy, *args, color = cm.cool(colorIdx), workbook = [workbook, legendString, keepOpen])
P.legend(legendStrings, title=(self.fields[propertyName]).axisLabel, loc = settings['legendLoc'], prop = {'size': 9})
savePlot(fig, title)
P.close()
def __init__(self, states, duration=0.1, save_all=False):
# Defining a Bloch
b = Bloch()
b.vector_color = ['r']
b.view = [-40, 30]
images = []
try:
length = len(states)
except:
length = 1
states = [states]
# Normalise Colours
norm = mpl.colors.Normalize(0, length)
colors = cm.cool(norm(range(length))) # cool, summer, winter, autumn
# Sphere Properties
b.point_color = list(colors)
b.point_marker = ['x']
b.point_size = [30]
# Defining save destination
dir = './blochsphere_images/'
for i in range(length):
b.clear()
b.add_states(states[i])
b.add_states(states[:(i + 1)], 'point')
if save_all:
b.save(dirc=dir)
filename = "temp/bloch_{i}01d.png".format(i=i)
else:
filename = "temp_file.png"
b.save(dir + filename)
images.append(imageio.imread(dir + filename))
imageio.mimsave(dir + 'gif/' + 'bloch_anim.gif',
images,
duration=duration)
im = Image.open(dir + 'gif/' + 'bloch_anim.gif')
animation = pyglet.image.load_animation(dir + 'gif/' +
'bloch_anim.gif')
sprite = pyglet.sprite.Sprite(animation)
w = sprite.width
h = sprite.height
window = pyglet.window.Window(width=w, height=h)
r, g, b, alpha = 0.5, 0.5, 0.8, 0.5
pyglet.gl.glClearColor(r, g, b, alpha)
@window.event
def on_draw():
window.clear()
sprite.draw()
pyglet.app.run()
def clustering(text_list):
k = 4
# see https://qiita.com/yuuki_1204_/items/3c0a298a521dc6e79615
# TF-IDFの処理クラス
vectorizer = TfidfVectorizer(min_df=1,
stop_words=STOP_WORDS,
tokenizer=mecab_analysis)
# データのベクトル化
tfidf_weighted_matrix = vectorizer.fit_transform(text_list)
# k-means
model = KMeans(n_clusters=k)
model.fit(tfidf_weighted_matrix)
# 2次元に圧縮 潜在意味解析(LSA)
dim = TruncatedSVD(2)
compressed_text_list = dim.fit_transform(tfidf_weighted_matrix)
compressed_center_list = dim.fit_transform(model.cluster_centers_)
# 描画
FP = fm.FontProperties(fname=FONT, size=7)
fig = plt.figure()
axes = fig.add_subplot(111)
# 3クラスタ
for label in range(k):
# ラベルごとに色を分ける
color = cm.cool(float(label) / k)
# ラベルの中心をプロット
xc, yc = compressed_center_list[label]
axes.plot(xc, yc, color=color, ms=6.0, zorder=3, marker='o')
# クラスタのラベルもプロット
axes.annotate(label, xy=(xc, yc), fontproperties=FP)
for text_num, text_label in enumerate(model.labels_):
if text_label == label:
# labelが一致するテキストをプロット
x, y = compressed_text_list[text_num]
axes.plot(x, y, color=color, ms=5.0, zorder=2, marker="x")
# テキストもプロット
axes.annotate(text_list[text_num][:8],
xy=(x, y),
fontproperties=FP)
# ラベルの中心点からの線をプロット
axes.plot([x, xc], [y, yc],
color=color,
linewidth=0.5,
zorder=1,
linestyle="--")
plt.axis('tight')
plt.show()
def plot_all_bootstrap(boot_direc, a, b):
colors = cm.cool(numpy.linspace(0, 1, len(boot_direc)))
for i, direc in enumerate(boot_direc):
f = open(direc, 'rb')
temp = cPickle.load(f)
bin = cPickle.load(f)
dG = cPickle.load(f)
ddG = cPickle.load(f)
dGf = dG[:, a] - dG[:, b]
ddGf = [numpy.sqrt(x[a, b]) for x in ddG]
plt.errorbar(temp, dGf, ddGf, color=colors[i])
def set_color(c,n):
if c == "rainbow":
color = cm.rainbow(n/8.)
elif c == "gnuplot":
color = cm.gnuplot(n/8.)
elif c == "autumn":
color = cm.autumn(n/8.)
elif c == "cool":
color = cm.cool(n/8.)
return color
def plot_all_bootstrap(boot_direc, a, b):
colors = cm.cool(numpy.linspace(0,1,len(boot_direc)))
for i,direc in enumerate(boot_direc):
f = open(direc,'rb')
temp = cPickle.load(f)
bin = cPickle.load(f)
dG = cPickle.load(f)
ddG = cPickle.load(f)
dGf = dG[:,a] - dG[:,b]
ddGf = [numpy.sqrt(x[a,b]) for x in ddG]
plt.errorbar(temp,dGf,ddGf,color=colors[i])
def plot_2D_labeled_data(X, y, fig_number, fig_title):
# put plt.ioff() and plt.show() at end
colors = cm.cool(y / max(y))
plt.ion()
f = plt.figure(fig_number)
plt.scatter(X[:, 0], X[:, 1], c=y)
# plt.axis('tight')
plt.title(fig_title)
f.show()
return fig_number + 1
def plot_state(state):
length = 1
b = Bloch()
b.vector_color = ['r']
b.view = [-40, 30]
nrm = mpl.colors.Normalize(0, length)
colors = cm.cool(nrm(range(length)))
b.point_color = list(colors) # options: 'r', 'g', 'b' etc.
b.point_marker = ['o']
b.point_size = [30]
b.add_states(state)
return b
def main():
lam = [.1, .15, .2, .25, .3, .35, .4, .45, .5, .55, .6, .65, .7]
Q_files = ['/home/edz3fz/proteinmontecarlo/results/1PGB/surface/umbrella_lambda%s/foldingcurve_umbrella.npy' % str(x)[1::] for x in lam]
Q_files[-3] = '/home/edz3fz/proteinmontecarlo/results/1PGB/surface/lambda.6/foldingcurve.npy'
Q_files[-2] = '/home/edz3fz/proteinmontecarlo/results/1PGB/surface/lambda.65/foldingcurve.npy'
Q_files[-1] = '/home/edz3fz/proteinmontecarlo/results/1PGB/surface/lambda.7/foldingcurve.npy'
Cv_files = ['/home/edz3fz/proteinmontecarlo/results/1PGB/surface/umbrella_lambda%s/heatcap_umbrella.npy' % str(x)[1::] for x in lam]
Cv_files[-2] = '/home/edz3fz/proteinmontecarlo/results/1PGB/surface/lambda.65/heatcap.npy'
Cv_files[-1] = '/home/edz3fz/proteinmontecarlo/results/1PGB/surface/lambda.7/heatcap.npy'
colors = cm.cool(numpy.linspace(0,1,len(lam)))
f = plt.figure(1,(7,8))
plt.rc('text',usetex=True)
matplotlib.rc('font', family = 'serif',size=20)
font = {'family' : 'serif',
'size' : 20}
ax1 = plt.subplot(211)
for i in range(len(lam)):
data = numpy.load(Q_files[i])
if i < 10:
x=30
ax1.errorbar(data[0,-x::],data[1,-x::],data[2,-x::],label=r'$\lambda$ = %s' % lam[i], color=colors[i])
else:
ax1.errorbar(data[0,:],data[1,:],data[2,:],label=r'$\lambda$ = %s' % lam[i], color=colors[i])
soln = '/home/edz3fz/proteinmontecarlo/results/1PGB/solution/foldingcurve.npy'
data = numpy.load(soln)
ax1.errorbar(data[0,:],data[1,:],data[2,:],label='solution', color='k')
plt.ylabel('Q')
plt.setp(ax1.get_xticklabels(), visible = False)
plt.legend(bbox_to_anchor=(1.37,.5),prop={'size':12})
plt.xlim((200,400))
ax2 = plt.subplot(212)
for i in range(len(lam)):
data = numpy.load(Cv_files[i])
if i < 11:
x=30
ax2.errorbar(data[0,-x::],data[1,-x::],data[2,-x::],label=r'$\lambda$ = %s' % lam[i], color=colors[i])
else:
ax2.errorbar(data[0,:],data[1,:],data[2,:],label=r'$\lambda$ = %s' % lam[i], color=colors[i])
soln = '/home/edz3fz/proteinmontecarlo/results/1PGB/solution/heatcap.npy'
data = numpy.load(soln)
ax2.errorbar(data[0,:],data[1,:],data[2,:],label='solution', color='k')
#plt.ylabel(r'$C_{v} \textnormal{ (kcal/mol$\cdot$K)}$')
f.text(.06,.3,r'$C_{v} \textnormal{ (kcal/mol$\cdot$K)}$',ha='center',va='center',rotation='vertical')
plt.xlabel('temperature (K)')
#plt.legend(prop={'size':8})
plt.xlim((200,400))
plt.yticks(numpy.arange(0,4.5,.5))
f.subplots_adjust(hspace=0, right=.75, left=.15)
plt.savefig('/home/edz3fz/proteinmontecarlo/manuscripts/figures/Fig6_foldingcurve.eps')
plt.show()
def main():
lam = [.1, .15, .2, .25, .3, .35, .45, .5, .55, .6] #, .65, .7]
PMF_files = [
'/home/edz3fz/proteinmontecarlo/results/1PGB/surface/umbrella_lambda%s/pmfQ_umbrella_325.pkl'
% str(x)[1::] for x in lam
]
PMF_files[
-2] = '/home/edz3fz/proteinmontecarlo/results/1PGB/surface/lambda.55/pmf_325.pkl'
#PMF_files[-1] = '/home/edz3fz/proteinmontecarlo/results/1PGB/surface/lambda.6/pmf_325.pkl'
colors = cm.cool(numpy.linspace(0, 1, len(lam)))
fig = plt.figure(1, (8.5, 6.5))
plt.rc('text', usetex=True)
matplotlib.rc('font', family='serif', size=20)
for i in range(len(lam)):
f = open(PMF_files[i], 'rb')
temp = cPickle.load(f)
bin_centers = cPickle.load(f)
f_i = cPickle.load(f)
df_i = cPickle.load(f)
f.close()
idx = numpy.argmin(f_i[int(.7 * len(f_i))::]) + int(.7 * len(f_i))
f_i -= f_i[idx]
plt.errorbar(bin_centers[1::],
f_i[1::],
numpy.array(df_i[0, :])[0, 1::],
label=r'$\lambda$ = %s' % lam[i],
color=colors[i])
soln = '/home/edz3fz/proteinmontecarlo/results/1PGB/solution/pmf_325.pkl'
f = open(soln, 'rb')
temp = cPickle.load(f)
bin_centers = cPickle.load(f)
f_i = cPickle.load(f)
df_i = cPickle.load(f)
f.close()
idx = numpy.argmin(f_i[int(.7 * len(f_i))::]) + int(.7 * len(f_i))
f_i -= f_i[idx]
plt.errorbar(bin_centers[1::],
f_i[1::],
numpy.array(df_i[0, :])[0, 1::],
label='solution',
color='k')
fig.subplots_adjust(right=.8)
plt.ylabel('PMF (kcal/mol)')
plt.xlabel('Q')
plt.legend(bbox_to_anchor=(1.29, .79), prop={'size': 14})
plt.savefig(
'/home/edz3fz/proteinmontecarlo/manuscripts/figures/Fig7_pmfQ.pdf')
plt.show()
def color_iter(num):
num = int(num / 3) + 1
x = np.arange(num)
ys = [i + x + (i * x)**2 for i in range(num)]
colormaps = cm.spring(np.linspace(0, 0.6, len(ys))), cm.plasma(
np.linspace(0.35, 0.8,
len(ys))), cm.cool(np.linspace(0.2, 0.9, len(ys)))
result = []
for colormap in colormaps:
for c in colormap:
result.append(c)
shuffle(result)
return iter(result)
def draw_many_trajectories(im, trajs, indices):
import matplotlib.cm as cm
# define colors, get frame and features
colors = (cm.cool(np.linspace(0.0, 1.0, 20))[:, :3] * 255).astype('uint8')
img = np.array(im)
if len(img.shape) != 3:
img = (np.stack((img, ) * 3, -1) * 255).astype('uint8')
j = 0
for i, c in zip(indices, colors):
j += 1
traj = trajs[i]
img = draw_trajectory(img, traj, color=colors[j % 20])
return img
def makePlots(arr):
clips = []
clipi = []
smooths = []
smoothi = []
for t in list(set(arr)):
subset = stars[arr == t]
clispec, clivar = sigclip_coadd(subset, sigLim=3.0)
clips.append(clispec)
clipi.append(clivar)
for s, i in zip(clips, clipi):
flux = Spectrum(lbin, s, ivar=i)
flux.smooth(width=5, filtertype="gaussian", replace=True)
smooths.append(flux.flux)
smoothi.append(flux.ivar)
uniqarr = list(set(arr))
sortInd = np.argsort(uniqarr)
n = len(uniqarr)
fig = py.figure(1, (9, 14))
ax = fig.add_subplot(111)
offset = 0
weight = np.ones(len(lbin))
weight[(7550 < lbin) & (lbin < 7700)] = np.nan
for i in sortInd[1:]:
color = cmp.autumn(i / np.float(n))
ax.plot(lbin, weight * smooths[i] + offset, lw=2.0, color=color)
offset += 1
ax.set_xlim(5500, 9500)
ax.set_ylim(0, 21)
ax.set_xlabel("$\lambda$")
ax.axes.get_yaxis().set_visible(False)
fig = py.figure(1, (15, 5))
ax = fig.add_subplot(111)
for i in sortInd:
color = cmp.cool(i / float(n))
ax.plot(lbin, weight * smooths[i], color=color)
ax.set_xlim(5500, 9500)
def solution_pmf(direc):
T = numpy.arange(300,355,5)
colors = cm.cool(numpy.linspace(0,1,len(T)))
plt.figure(7)
for i,t in enumerate(T):
f = open('%s/pmf_%i.pkl' %(direc,t),'rb')
temp = cPickle.load(f)
bin_centers = cPickle.load(f)
f_i = cPickle.load(f)
df_i = cPickle.load(f)
f.close()
idx = numpy.argmin(f_i[int(.7*len(f_i))::]) + int(.7*len(f_i))
f_i -= f_i[idx]
plt.errorbar(bin_centers[1::],f_i[1::],numpy.array(df_i[0,:])[0,1::],label='T = %i K' % t, color=colors[i])
plt.xlabel('Q')
plt.ylabel('PMF (kcal/mol)')
plt.legend(loc=0, prop={'size':8})
def plot_tracks_wind_speed(hurricane_list,
m=None,
color_max_wind=120.0,
linewidth=2):
if m == None:
m = create_default_map()
# color_max_wind is the highest wind speed which is displayed as its own color
for hurricane in hurricane_list:
for i in range(0, len(hurricane.track) - 1):
x = [hurricane.track[i][0], hurricane.track[i + 1][0]]
y = [hurricane.track[i][1], hurricane.track[i + 1][1]]
c = cm.cool(hurricane.data[i].max_wind / color_max_wind)
m.plot(x, y, latlon=True, color=c, linewidth=linewidth)
plt.show()
def EBL_plot(att_list,
zlist=0.1,
elist=100 * u.GeV,
tags="",
ax=None,
xsize=8,
ysize=8,
color=None,
ls=None,
debug=False,
**kwargs):
# If not a list but a single element
if not isinstance(att_list, list): att_list = [att_list]
if not isinstance(tags, list): tags = [tags]
if not isinstance(zlist, numpy.ndarray): zlist = [zlist]
if not isinstance(elist, numpy.ndarray): elist = [elist]
lines = ["-", "--", "-.", ":"] # A line style per attenuation model
linecycler = cycle(lines)
set_color = False
set_line = False
if ax == None: fig, ax = plt.subplots(figsize=(xsize, ysize))
if color == None: set_color = True
if ls == None: set_line = True
with quantity_support():
for att, tag in zip(att_list, tags):
if set_line: ls = next(linecycler)
for i, z in enumerate(zlist):
if set_color:
color = cm.cool(i / len(zlist)) # One color per z
ax.plot(elist,
att(elist, z),
label=tag + " z=" + str(round(z, 2)),
ls=ls,
color=color,
**kwargs)
ax.set_xscale("log")
ax.set_ylim(ymin=1e-2, ymax=2)
single_legend(ax)
return ax
def large_effect_mutations(myflu, ax=None, cols=None):
from matplotlib import cm
if ax is None:
plt.figure(figsize=(figheight, figheight))
ax = plt.subplot('111')
if cols is None:
cols = {}
cutoff_freq = 0.25
HI_cutoff = 0.3
pivots = myflu.tree.seed_node.pivots
dt = pivots[1] - pivots[0]
y3 = int(3.0 / dt)
y1 = int(1.0 / dt)
color_cycle = 0
for mut in myflu.mutation_effects:
HI = myflu.mutation_effects[mut]
if HI > HI_cutoff:
mutlabel = mut[0] + ':' + mut[1][1:]
if mutlabel in myflu.frequencies["mutations"]["global"]:
mut_freq = np.array(
myflu.frequencies["mutations"]["global"][mutlabel])
else:
print("no frequencies for ", mut, 'HI', HI)
continue
if mut_freq[0] < cutoff_freq:
print("Plotting ", mut, 'max: ', mut_freq.max(), 'HI', HI)
if mut not in cols:
cols[mut] = colors[color_cycle % len(colors)]
color_cycle += 1
c = cols[mut]
ax.plot(pivots,
mut_freq,
lw=2,
ls='--' if mut_freq.max() > 0.9 else '-',
c=cm.cool(
min(np.sqrt(HI - HI_cutoff * 0.8), 1.5) / 1.5))
ax.set_xlabel('time', fontsize=fs)
ax.set_ylabel('frequency', fontsize=fs)
#plt.xlim([-1,2])
ax.set_ylim([-0.01, 1.1])
ax.tick_params(axis='both', labelsize=fs)
return cols
def descent(self, vector_points, sample_point, depth=1):
"""
Recursively descent a Bezier curve for a given sample-point.
Args:
vector_points: A list of 2D-points defining the vectors a particular depth of the curve.
interval_point: A float defining which sampling point we are descending.
depth: Keeps track of the depth we're at.
Returns:
vector_points: A list of 2D-points that define the Bezier curve.
"""
# Use recursion as long as we can construct pairs of points between vectors.
if len(vector_points) > 1:
# First some variables for drawing the curve
c_code = ((float(depth) - 1) / (len(self.control_points) - 2))
zorder = depth - len(self.control_points) - 1
if __name__ == '__main__':
color = cm.cool(c_code) # @UndefinedVariable
else:
color = None
spline_draw = depth==1 and not self.plotter.draw_level=='spline' and sample_point==1
draw = True if self.plotter.draw_level=='bezier' or spline_draw else False
# Then the actual calculations
recursion_points = []
for i_line in xrange(len(vector_points) - 1):
(x1,y1,x2,y2) = self.calc_line(vector_points, i_line, draw, color, zorder)
dx = (x2 - x1) / self._params['interval']
dy = (y2 - y1) / self._params['interval']
x = x1 + dx * sample_point
y = y1 + dy * sample_point
recursion_points.append([x,y])
# Do the same stuff with the newly calculated points and recursively return appended.
return self.descent(recursion_points, sample_point, depth+1)
# If we ran out of vectors, simply return the endpoint of the curve.
else:
def plot_stretch(tt):
fig = plt.figure(figsize=onecolumn_figsize)
ax = plt.subplot(111)
vmin, vmax = 0.5, 1.5 # color branches according to the rate deviation
for n in tt.tree.find_clades():
if n.up:
n.color = [
int(x * 255) for x in cm.cool(
(min(max(vmin, n.branch_length_interpolator.gamma), vmax) -
vmin) / (vmax - vmin))[:3]
]
else:
n.color = [200, 200, 200]
ax.set_title("relaxed clock")
Phylo.draw(tt.tree,
axes=ax,
show_confidence=False,
label_func=lambda x: '')
def mcts_handicap_cdf_plot():
fig = plt.figure()
ax = plt.gca()
cdf_plot_file(ax, ROOT + "di.mcts_low_128.npy", "--k", lw=2.0)
labels = ["MCTS 128"]
flavours = ["noflow", "noq"]
B = len(flavours)
colors = [cm.cool(x) for x in np.linspace(0, 1, B)]
for (flavour, color) in zip(flavours, colors):
cdf_plot_file(ax, ROOT + "di.mcts_{0}_128.npy".format(flavour), lw=2.0, color=color)
labels.append("MCTS " + str(flavour))
ax.set_xlabel("Discounted Cost")
ax.set_title("MCTS with various handicaps")
ax.legend(labels, loc="best")
fig.savefig(IMAGES + "mcts_handicaps.png")
plt.close()
def pmf_t(temp):
lam = [.1, .15, .2, .25, .3, .35, .4, .45, .5, .55]
colors = cm.cool(numpy.linspace(0, 1, len(lam)))
plt.figure(8)
plt.rc('text', usetex=True)
direc = '/home/edz3fz/proteinmontecarlo/results/1PGB/surface'
for i, l in enumerate(lam):
f = open('%s/lambda%s/pmf_%i.pkl' % (direc, str(l)[1::], int(temp)),
'rb')
temp = cPickle.load(f)
bin_centers = cPickle.load(f)
f_i = cPickle.load(f)
df_i = cPickle.load(f)
f.close()
idx = numpy.argmin(f_i[int(.7 * len(f_i))::]) + int(.7 * len(f_i))
f_i -= f_i[idx]
plt.errorbar(bin_centers[1::],
f_i[1::],
numpy.array(df_i[0, :])[0, 1::],
label=r'$\lambda$ = %s' % l,
color=colors[i])
f = open(
'/home/edz3fz/proteinmontecarlo/results/1PGB/solution/pmf_%i.pkl' %
(int(temp)), 'rb')
temp = cPickle.load(f)
bin_centers = cPickle.load(f)
f_i = cPickle.load(f)
df_i = cPickle.load(f)
f.close()
idx = numpy.argmin(f_i[int(.7 * len(f_i))::]) + int(.7 * len(f_i))
f_i -= f_i[idx]
plt.errorbar(bin_centers[1::],
f_i[1::],
numpy.array(df_i[0, :])[0, 1::],
label='solution',
color='k')
plt.xlabel('Q')
plt.ylabel('PMF (kcal/mol)')
plt.title('T = %s K' % temp)
plt.legend(loc=0, prop={'size': 8})
def main():
lam = [.1, .2, .3, .35, .4, .45, .5, .6, .7]
lam = [.1, .2, .3, .4, .5, .675, .7]
lam = [.1, .15, .2, .25, .3, .35, .4, .45, .5, .55, .6, .65, .7]
files = ['/home/edz3fz/proteinmontecarlo/results/1PGB/surface/lambda%s/foldingcurve.npy' % str(x)[1::] for x in lam]
colors = cm.cool(numpy.linspace(0,1,len(lam)))
plt.figure(1)
plt.rc('text',usetex=True)
for i in range(len(lam)):
data = numpy.load(files[i])
plt.errorbar(data[0,:],data[1,:],data[2,:],label=r'$\lambda$ = %s' % lam[i], color=colors[i])
soln = '/home/edz3fz/proteinmontecarlo/results/1PGB/solution/foldingcurve.npy'
data = numpy.load(soln)
plt.errorbar(data[0,:],data[1,:],data[2,:],label='solution', color='k')
plt.xlabel('temperature (K)')
plt.ylabel('Q')
plt.legend(prop={'size':8})
plt.show()
def plot_resp_on_sort_shapes(ax, shapes, resp, top=25, fs=20, shrink=.5,):
c_imgs = np.zeros(np.shape(shapes) + (4,))
respsc = (resp - resp.min())
respsc = respsc/respsc.max()
scale = cm.cool(respsc)
resp_sort_inds = np.argsort(resp)[::-1]
for i, a_color in enumerate(scale):
c_imgs[i, np.nonzero(shapes[i])[0], np.nonzero(shapes[i])[1],:] = a_color
im = ax.imshow(np.tile(respsc,(2,1)), cmap=cm.cool, interpolation='nearest')
# cbar = ax.get_figure().colorbar(im, ax=ax, shrink=shrink,
# ticks=[np.min(respsc), np.max(respsc)], aspect=10)
# cbar.ax.set_yticklabels([])
#cbar.ax.set_ylabel('Normalized\nResponse', rotation='horizontal', fontsize=fs/1.5, ha='left')
data, im = vis_square(ax, c_imgs[resp_sort_inds][:top])
ax.imshow(data, interpolation='nearest')
#beautify(ax, ['top','right','left','bottom'])
return data
def mcts_noflow_cdf_plot():
fig = plt.figure()
ax = plt.gca()
cdf_plot_file(ax, ROOT + "di.rollout.npy", "--k", lw=2.0)
cdf_plot_file(ax, ROOT + "di.q_low.npy", "--b", lw=2.0)
cdf_plot_file(ax, ROOT + "di.q_ref.npy", "--r", lw=2.0)
labels = ["rollout", "16x16", "64x64"]
budgets = [4, 8, 16, 32, 64, 128, 256]
B = len(budgets)
colors = [cm.cool(x) for x in np.linspace(0, 1, B)]
for (budget, color) in zip(budgets, colors):
cdf_plot_file(ax, ROOT + "di.mcts_noflow_{0}.npy".format(budget), lw=2.0, color=color)
labels.append("MCTS " + str(budget))
ax.set_xlabel("Discounted Cost")
ax.set_title("No flow MCTS with various budgets")
ax.legend(labels, loc="best")
fig.savefig(IMAGES + "mcts_noflow.png")
plt.close()
def main():
lam = [.1, .15, .2, .25, .3, .35, .45, .5, .55, .6]#, .65, .7]
PMF_files = ['/home/edz3fz/proteinmontecarlo/results/1PGB/surface/umbrella_lambda%s/pmfQ_umbrella_325.pkl' % str(x)[1::] for x in lam]
PMF_files[-2] = '/home/edz3fz/proteinmontecarlo/results/1PGB/surface/lambda.55/pmf_325.pkl'
#PMF_files[-1] = '/home/edz3fz/proteinmontecarlo/results/1PGB/surface/lambda.6/pmf_325.pkl'
colors = cm.cool(numpy.linspace(0,1,len(lam)))
fig = plt.figure(1,(8.5,6.5))
plt.rc('text',usetex=True)
matplotlib.rc('font', family = 'serif', size=20)
for i in range(len(lam)):
f = open(PMF_files[i],'rb')
temp = cPickle.load(f)
bin_centers = cPickle.load(f)
f_i = cPickle.load(f)
df_i = cPickle.load(f)
f.close()
idx = numpy.argmin(f_i[int(.7*len(f_i))::]) + int(.7*len(f_i))
f_i -= f_i[idx]
plt.errorbar(bin_centers[1::],f_i[1::],numpy.array(df_i[0,:])[0,1::],label=r'$\lambda$ = %s' % lam[i], color=colors[i])
soln = '/home/edz3fz/proteinmontecarlo/results/1PGB/solution/pmf_325.pkl'
f = open(soln,'rb')
temp = cPickle.load(f)
bin_centers = cPickle.load(f)
f_i = cPickle.load(f)
df_i = cPickle.load(f)
f.close()
idx = numpy.argmin(f_i[int(.7*len(f_i))::]) + int(.7*len(f_i))
f_i -= f_i[idx]
plt.errorbar(bin_centers[1::],f_i[1::],numpy.array(df_i[0,:])[0,1::],label='solution', color='k')
fig.subplots_adjust(right=.8)
plt.ylabel('PMF (kcal/mol)')
plt.xlabel('Q')
plt.legend(bbox_to_anchor=(1.29,.79),prop={'size':14})
plt.savefig('/home/edz3fz/proteinmontecarlo/manuscripts/figures/Fig7_pmfQ.pdf')
plt.show()
def mcts_noflow_cdf_plot():
fig = plt.figure()
ax = plt.gca()
cdf_plot_file(ax,ROOT + 'hillcar.rollout.npy','--k',lw=2.0)
cdf_plot_file(ax,ROOT + 'hillcar.q_low.npy','--b',lw=2.0)
cdf_plot_file(ax,ROOT + 'hillcar.q_ref.npy','--r',lw=2.0)
labels = ['rollout','16x16','64x64']
budgets = [4,8,16,32,64,128,256,512]
B = len(budgets)
colors = [cm.cool(x) for x in np.linspace(0,1,B)]
for (budget,color) in zip(budgets,colors):
cdf_plot_file(ax,ROOT + 'hillcar.mcts_noflow_{0}.npy'.format(budget),
lw=2.,color=color)
labels.append('MCTS ' +str(budget))
ax.set_xlabel('Discounted Cost')
ax.set_title('Noflow MCTS with various budgets')
ax.legend(labels,loc='best')
fig.savefig(IMAGES + 'mcts_noflow.png')
plt.close()
def mcts_handicap_cdf_plot():
fig = plt.figure()
ax = plt.gca()
cdf_plot_file(ax,ROOT + 'hillcar.mcts_low_128.npy','--k',lw=2.0)
labels = ['MCTS 128']
flavours = ['noflow',
'noq_opt',
'noq_pes']
B = len(flavours)
colors = [cm.cool(x) for x in np.linspace(0,1,B)]
for (flavour,color) in zip(flavours,colors):
cdf_plot_file(ax,ROOT + 'hillcar.mcts_{0}_128.npy'.format(flavour),
lw=2.0,color=color)
labels.append('MCTS ' +str(flavour))
ax.set_xlabel('Discounted Cost')
ax.set_title('MCTS with various handicaps')
ax.legend(labels,loc='best')
fig.savefig(IMAGES + 'mcts_handicaps.png')
plt.close()
time,wave,step = squarepulse(0.0,1.0,3e-9,1e-12)
## Plot the results
fig = plt.figure(figsize=(10,8))
ax1 = fig.add_axes([0.1, 0.1, 0.7, 0.8])
ax1.plot(time, wave, "k--", label="$V(t)$")
l = ax1.legend()
l.draw_frame(True)
ax1.set_xlabel('Time $(s)$')
ax1.set_ylabel('Diode Voltage $(V_D)$')
## Set up a list of time steps
tlist = list(np.linspace(1e-12,4e-10,8))
for i,t in enumerate(tlist[::-1]):
time,wave,step = squarepulse(0.0,1.0,3e-9,t)
c = cm.cool(float(i)/len(tlist),1)
f = transient(wave,step,0.005)
f.trapezoid(0.0)
ax1.plot(time,f.result,color= c)
ax1.set_ylim(0,1.1)
ax1.set_title("Trapezoidal")
## Fancy colorbar
cmap = mpl.cm.cool_r
norm = mpl.colors.Normalize(min(tlist)/1e-12,max(tlist)/1e-12)
ax2 = fig.add_axes([0.85, 0.1, 0.03, 0.8])
cb1 = mpl.colorbar.ColorbarBase(ax2, cmap=cmap,
norm=norm,
orientation='vertical')
cb1.set_label('Time Step $(ps)$')
if (pop.generation % 200 == 0):
print "generation:", pop.generation, 'out of', maxgen
# update fitness function
selection_coefficients[m*np.random.randint(0,25)] = 0.01
pop.set_trait_additive(selection_coefficients)
# convert to an array to enable slicing
allele_frequencies = np.array(allele_frequencies)
# plot the allele frequency trajectories
plt.figure()
# plot the selected mutations
for locus in xrange(0,pop.L,m):
plt.plot(tp, allele_frequencies[:,locus], c=cm.cool(locus),lw=2, ls='--')
# plot some neutral sites
for locus in xrange(5,pop.L,50):
plt.plot(tp, allele_frequencies[:,locus], c=cm.cool(locus), lw=2)
plt.title('Drift and draft')
plt.xlabel('Time [generations]')
plt.ylabel('Allele frequencies')
plt.text(100,0.85, "neutral alleles: solid")
plt.text(100,0.9, "sweeping alleles: dashed")
plt.text(100,0.765, "color indicates position \non the genome")
plt.ion()
plt.show()
def plot_line(path, label, ratio):
x = []
y = []
x, y = load_fene(path)
plt.plot(x, y, lw=2, marker='o', label=label, color=cm.cool(ratio))
province = []
number_occurence = []
csvfile = open("/Users/leoniekruger/CencusData/Data/CrimeNumberProvince.csv",
"r")
csvReader = csv.reader(csvfile)
with open('/Users/leoniekruger/CencusData/Data/CrimeNumberProvince.csv',
'r') as csvfile:
plots = csv.reader(csvfile, delimiter=',')
for row in plots:
province.append(row[0])
type_of_crime.append(row[1])
number_occurence.append(int(row[2]) * 10)
# y.append(float(row[1]))
# create data
colors = cm.cool(np.random.rand(10))
# Use those colors as the color argument
plt.scatter(province, type_of_crime, s=number_occurence, color=colors)
# Add titles (main and on axis)
plt.xlabel("Province")
plt.ylabel("CrimeType")
plt.title("Crime South Africa")
plt.show()
def main():
lam = [.1, .15, .2, .25, .3, .35, .4, .45, .5, .55, .6]
#lam = [.1, .15, .2, .25, .3, .35, .4]
files = ['/home/edz3fz/proteinmontecarlo/results/1PGB/surface/umbrella_lambda%s/dG_raw_varz.pkl' % str(x)[1::] for x in lam]
files = ['/home/edz3fz/proteinmontecarlo/results/1PGB/surface/umbrella_lambda%s/dG_raw_noint_2.pkl' % str(x)[1::] for x in lam]
colors = cm.cool(numpy.linspace(0,1,len(lam)))
plt.rc('text',usetex=True)
matplotlib.rc('font', family = 'serif')
font = {'family' : 'serif',
'size' : 'larger'}
fig=plt.figure(1,(7,10))
soln = '/home/edz3fz/proteinmontecarlo/results/1PGB/solution/dG_raw.pkl'
plot_dG_solution.solution(soln)
for i in range(len(lam)):
print 'Reading %s' % files[i]
f = open(files[i],'rb')
target_temperatures = cPickle.load(f)
bin_centers = cPickle.load(f)
print 'The 4 states are centered at', bin_centers
dG = cPickle.load(f)
ddG = cPickle.load(f)
f.close()
# Free energies of folding
dGf = dG[:,1] - dG[:,0] # free energy of folding while adsorbed
dGf_des = dG[:,3] - dG[:,2] # free eneryg of folding while desorbed
# Entropies of folding
# dS(t) = -dG/dT = -(G[t+.5h] - G[t-.5h])/h
dSf = (dGf[2::] - dGf[0:-2])/-10 # finite difference
dSf_des = (dGf_des[2::] - dGf_des[0:-2])/-10
temp_sub = target_temperatures[1:-1]
# Enthalpies of folding
# dG = dH - TdS
# dH = dG + TdS
dHf = dGf[1:-1] + temp_sub*dSf
dHf_des = dGf_des[1:-1] + temp_sub*dSf_des
# Uncertainties - just propagation of error for now
ddGf = [numpy.sqrt(x[1,0]) for x in ddG]
ddGf = numpy.array(ddGf)
ddSf = (ddGf[0:-2]**2 + ddGf[2::]**2)**.5/10
ddHf = (ddGf[1:-1]**2 + (temp_sub*ddSf)**2)**.5
ddGf_des = [numpy.sqrt(x[3,2]) for x in ddG]
ddGf_des = numpy.array(ddGf_des)
ddSf_des = (ddGf_des[0:-2]**2 + ddGf_des[2::]**2)**.5/10
ddHf_des = (ddGf_des[1:-1]**2 + (temp_sub*ddSf_des)**2)**.5
fig.subplots_adjust(hspace=0)
ax1 = plt.subplot(311)
ax1.errorbar(target_temperatures,dGf,ddGf,label=r'$\lambda$ = %s' % lam[i], color=colors[i])
plt.xlim((300,350))
plt.ylabel(r'$\Delta$G_{folding}$')
box = ax1.get_position()
ax1.set_position([box.x0,box.y0,box.width*.82,box.height])
plt.setp(ax1.get_xticklabels(), visible=False)
ax3 = plt.subplot(313)
plt.xlim((300,350))
ax3.errorbar(temp_sub,dHf,ddHf,label=r'$\lambda$ = %s' % lam[i], color=colors[i])
plt.xlabel(r'temperature (K)')
plt.ylabel(r'$\Delta$H_{folding}$')
plt.yticks(numpy.arange(-100,-10,10))
box = ax3.get_position()
ax3.set_position([box.x0,box.y0,box.width*.82,box.height])
ax2 = plt.subplot(312)
plt.xlim((300,350))
ax2.errorbar(temp_sub,dSf,ddSf,label=r'$\lambda$ = %s' % lam[i], color=colors[i])
plt.setp(ax2.get_xticklabels(), visible=False)
plt.ylabel(r'$\Delta$S_{folding}$')
box = ax2.get_position()
ax2.set_position([box.x0,box.y0,box.width*.82,box.height])
lgd = ax2.legend(bbox_to_anchor=(1.29, .98), prop={'size':10})
fig.savefig('/home/edz3fz/proteinmontecarlo/manuscripts/figures/Fig8_dGf.pdf')
plt.show()
def large_effect_mutations(myflu, ax=None, cols = None):
from matplotlib import cm
if ax is None:
plt.figure(figsize=(figheight, figheight))
ax = plt.subplot('111')
if cols is None:
cols = {}
cutoff_freq = 0.25
HI_cutoff = 0.3
pivots = myflu.tree.seed_node.pivots
dt = pivots[1]-pivots[0]
y3 = int(3.0/dt)
y1 = int(1.0/dt)
color_cycle=0
for mut in myflu.mutation_effects:
HI = myflu.mutation_effects[mut]
if HI>HI_cutoff:
mutlabel = mut[0]+':'+mut[1][1:]
if mutlabel in myflu.frequencies["mutations"]["global"]:
mut_freq = np.array(myflu.frequencies["mutations"]["global"][mutlabel])
else:
print("no frequencies for ",mut, 'HI', HI)
continue
if mut_freq[0]<cutoff_freq:
print("Plotting ",mut, 'max: ',mut_freq.max(), 'HI', HI)
if mut not in cols:
cols[mut] = colors[color_cycle%len(colors)]
color_cycle+=1
c = cols[mut]
ax.plot(pivots, mut_freq, lw=2, ls = '--' if mut_freq.max()>0.9 else '-',c=cm.cool(min(np.sqrt(HI-HI_cutoff*0.8),1.5)/1.5))
ax.set_xlabel('time', fontsize=fs)
ax.set_ylabel('frequency', fontsize=fs)
#plt.xlim([-1,2])
ax.set_ylim([-0.01,1.1])
ax.tick_params(axis='both', labelsize=fs)
return cols
null = pd.concat([open_cnn_analysis(fns[3], layer_label)[-1], null_v4], axis=0)
cnn_an = pd.concat([alt, null, init, shuf ],
axis=0, keys=['resp', 's. resp', 'init. net', 's. layer wts'], names=['cond','layer_label','unit'])
#%%
plt.figure(figsize=(10, 10))
ax_dumb = plt.subplot(111)
ax = plt.subplot(111)
resp = v4_resp_apc[:,0]
#m, data = plot_resp_on_shapes(ax, no_blank_image, resp, image_square=20)
#beautify(ax, ['top','right','left','bottom'])
#cbar = plt.gcf().colorbar(im, ax=ax, shrink=0.5)
c_imgs = np.zeros(np.shape(no_blank_image) + (4,))
respsc = (resp - resp.min())
respsc = respsc/respsc.max()
scale = cm.cool(respsc)
for i, a_color in enumerate(scale):
c_imgs[i, np.nonzero(no_blank_image[i])[0], np.nonzero(no_blank_image[i])[1],:] = a_color
im = ax_dumb.imshow(np.tile(respsc,(2,1)), cmap=cm.cool, interpolation='nearest')
cbar = plt.gcf().colorbar(im, ax=ax, shrink=0.5, ticks=[np.min(respsc), np.mean(respsc), np.max(respsc)])
cbar.ax.set_yticklabels([str(np.round(np.min(respsc).values,1))
,'$\mu$',
str(np.round(np.max(respsc).values,1))], fontsize=20)
cbar.ax.set_ylabel('Normalized\nResponse', rotation='horizontal', fontsize=15, ha='left')
data, im = vis_square(ax, c_imgs)
ax.imshow(data, interpolation='nearest')
beautify(ax, ['top','right','left','bottom'])
def main():
lam = [.1, .15, .2, .25, .3, .35, .4, .45, .5, .55, .6, .65, .7]
Q_files = [
'/home/edz3fz/proteinmontecarlo/results/1PGB/surface/umbrella_lambda%s/foldingcurve_umbrella.npy'
% str(x)[1::] for x in lam
]
Q_files[
-3] = '/home/edz3fz/proteinmontecarlo/results/1PGB/surface/lambda.6/foldingcurve.npy'
Q_files[
-2] = '/home/edz3fz/proteinmontecarlo/results/1PGB/surface/lambda.65/foldingcurve.npy'
Q_files[
-1] = '/home/edz3fz/proteinmontecarlo/results/1PGB/surface/lambda.7/foldingcurve.npy'
Cv_files = [
'/home/edz3fz/proteinmontecarlo/results/1PGB/surface/umbrella_lambda%s/heatcap_umbrella.npy'
% str(x)[1::] for x in lam
]
Cv_files[
-2] = '/home/edz3fz/proteinmontecarlo/results/1PGB/surface/lambda.65/heatcap.npy'
Cv_files[
-1] = '/home/edz3fz/proteinmontecarlo/results/1PGB/surface/lambda.7/heatcap.npy'
colors = cm.cool(numpy.linspace(0, 1, len(lam)))
f = plt.figure(1, (7, 8))
plt.rc('text', usetex=True)
matplotlib.rc('font', family='serif', size=20)
font = {'family': 'serif', 'size': 20}
ax1 = plt.subplot(211)
for i in range(len(lam)):
data = numpy.load(Q_files[i])
if i < 10:
x = 30
ax1.errorbar(data[0, -x::],
data[1, -x::],
data[2, -x::],
label=r'$\lambda$ = %s' % lam[i],
color=colors[i])
else:
ax1.errorbar(data[0, :],
data[1, :],
data[2, :],
label=r'$\lambda$ = %s' % lam[i],
color=colors[i])
soln = '/home/edz3fz/proteinmontecarlo/results/1PGB/solution/foldingcurve.npy'
data = numpy.load(soln)
ax1.errorbar(data[0, :],
data[1, :],
data[2, :],
label='solution',
color='k')
plt.ylabel('Q')
plt.setp(ax1.get_xticklabels(), visible=False)
plt.legend(bbox_to_anchor=(1.37, .5), prop={'size': 12})
plt.xlim((200, 400))
ax2 = plt.subplot(212)
for i in range(len(lam)):
data = numpy.load(Cv_files[i])
if i < 11:
x = 30
ax2.errorbar(data[0, -x::],
data[1, -x::],
data[2, -x::],
label=r'$\lambda$ = %s' % lam[i],
color=colors[i])
else:
ax2.errorbar(data[0, :],
data[1, :],
data[2, :],
label=r'$\lambda$ = %s' % lam[i],
color=colors[i])
soln = '/home/edz3fz/proteinmontecarlo/results/1PGB/solution/heatcap.npy'
data = numpy.load(soln)
ax2.errorbar(data[0, :],
data[1, :],
data[2, :],
label='solution',
color='k')
#plt.ylabel(r'$C_{v} \textnormal{ (kcal/mol$\cdot$K)}$')
f.text(.06,
.3,
r'$C_{v} \textnormal{ (kcal/mol$\cdot$K)}$',
ha='center',
va='center',
rotation='vertical')
plt.xlabel('temperature (K)')
#plt.legend(prop={'size':8})
plt.xlim((200, 400))
plt.yticks(numpy.arange(0, 4.5, .5))
f.subplots_adjust(hspace=0, right=.75, left=.15)
plt.savefig(
'/home/edz3fz/proteinmontecarlo/manuscripts/figures/Fig6_foldingcurve.eps'
)
plt.show()
Vd = pa.sweepVoltage(0.0,1.0,0.05,"40E-3")
fig = plt.figure(figsize=(10,8))
ax1 = fig.add_axes([0.1, 0.1, 0.70, 0.8])
ax2 = ax1.twinx()
for n,vg in enumerate(vgx):
# time.sleep(1)
pa.stepVoltage(vg,0.0,1,"30E-3")
pa.setOutput()
pa.measureData()
Id = pa.getDataIdVd()
g0 = [xx/w for xx in list(ndfit.derivative(Id,Vd))]
print "%s %f"%("Vg =", vg)
c = cm.cool(float(n)/len(vgx),1)
ax1.plot(Vd, Id,color=c)
ax2.plot(Vd, g0,color=c,linestyle='--')
ax1.set_ylim(0.0, max(Id)+0.001)
ax2.set_ylim(0.0, max(g0)+100)
ax1.set_xlabel(r"Drain Voltage $(V_d)$")
ax1.set_ylabel(r"Drain Current $(I_d)$")
ax2.set_ylabel(r"Output Conductance $(g_0)$")
## Fancy Colorbar
cmap = mpl.cm.cool
norm = mpl.colors.Normalize(min(vgx),max(vgx))
ax2 = fig.add_axes([0.90, 0.1, 0.03, 0.8])
cb1 = mpl.colorbar.ColorbarBase(ax2, cmap=cmap,
norm=norm,
def main():
lam = [.1, .15, .2, .25, .3, .35, .4, .45, .5, .55, .6]
#lam = [.1, .15, .2, .25, .3, .35, .4]
files = ['/home/edz3fz/proteinmontecarlo/results/1PGB/surface/umbrella_lambda%s/dG_raw_varz.pkl' % str(x)[1::] for x in lam]
files = ['/home/edz3fz/proteinmontecarlo/results/1PGB/surface/umbrella_lambda%s/dG_raw_noint_2.pkl' % str(x)[1::] for x in lam]
colors = cm.cool(numpy.linspace(0,1,len(lam)))
matplotlib.rc('font', family = 'serif')
font = {'family' : 'serif',
'size' : 'larger'}
dGf = numpy.zeros((len(lam),13))
dHf = numpy.zeros((len(lam),11))
dSf = numpy.zeros((len(lam),11))
dGu = numpy.zeros((len(lam),13))
dHu = numpy.zeros((len(lam),11))
dSu = numpy.zeros((len(lam),11))
ddGf = numpy.zeros((len(lam),13))
ddHf = numpy.zeros((len(lam),11))
ddSf = numpy.zeros((len(lam),11))
ddGu = numpy.zeros((len(lam),13))
ddHu = numpy.zeros((len(lam),11))
ddSu = numpy.zeros((len(lam),11))
fig = plt.figure(10,(7,10))
for i in range(len(lam)):
print 'Reading %s' % files[i]
f = open(files[i],'rb')
target_temperatures = cPickle.load(f)
bin_centers = cPickle.load(f)
print 'The 4 states are centered at', bin_centers
dG = cPickle.load(f)
ddG = cPickle.load(f)
f.close()
# Free energies of adsorption
dGads_folded = dG[:,1] - dG[:,3] # free energy of adsorption while adsorbed
dGads_unfolded = dG[:,0] - dG[:,2] # free eneryg of adsorption while desorbed
# Entropies of adsorption
# dS(t) = -dG/dT = -(G[t+.5h] - G[t-.5h])/h
dSads_folded = (dGads_folded[2::] - dGads_folded[0:-2])/-10 # finite difference
dSads_unfolded = (dGads_unfolded[2::] - dGads_unfolded[0:-2])/-10
temp_sub = target_temperatures[1:-1]
# Enthalpies of adsorption
# dG = dH - TdS
# dH = dG + TdS
dHads_folded = dGads_folded[1:-1] + temp_sub*dSads_folded
dHads_unfolded = dGads_unfolded[1:-1] + temp_sub*dSads_unfolded
# Uncertainties - just propagation of error for now
ddGads_folded = [numpy.sqrt(x[1,3]) for x in ddG]
ddGads_folded = numpy.array(ddGads_folded)
ddSads_folded = (ddGads_folded[0:-2]**2 + ddGads_folded[2::]**2)**.5/10
ddHads_folded = (ddGads_folded[1:-1]**2 + (temp_sub*ddSads_folded)**2)**.5
ddGads_unfolded = [numpy.sqrt(x[0,2]) for x in ddG]
ddGads_unfolded = numpy.array(ddGads_unfolded)
ddSads_unfolded = (ddGads_unfolded[0:-2]**2 + ddGads_unfolded[2::]**2)**.5/10
ddHads_unfolded = (ddGads_unfolded[1:-1]**2 + (temp_sub*ddSads_unfolded)**2)**.5
dGf[i,:] = dGads_folded
dHf[i,:] = dHads_folded
dSf[i,:] = dSads_folded
dGu[i,:] = dGads_unfolded
dHu[i,:] = dHads_unfolded
dSu[i,:] = dSads_unfolded
ddGf[i,:] = ddGads_folded
ddHf[i,:] = ddHads_folded
ddSf[i,:] = ddSads_folded
ddGu[i,:] = ddGads_unfolded
ddHu[i,:] = ddHads_unfolded
ddSu[i,:] = ddSads_unfolded
plt.rc('text',usetex=True)
fig.subplots_adjust(hspace=0)
ax1 = plt.subplot(311)
ax1.errorbar(target_temperatures,dGads_folded-dGads_unfolded,ddGads_unfolded,label=r'$\lambda$ = %s' % lam[i], color=colors[i])
plt.xlim((300,350))
plt.ylabel(r'$\Delta$G_{adsorption}$')
box = ax1.get_position()
ax1.set_position([box.x0,box.y0,box.width*.82,box.height])
plt.setp(ax1.get_xticklabels(), visible=False)
ax3 = plt.subplot(313)
plt.xlim((300,350))
ax3.errorbar(temp_sub,dHads_folded-dHads_unfolded,ddHads_unfolded,label=r'$\lambda$ = %s' % lam[i], color=colors[i])
plt.xlabel(r'temperature (K)')
plt.ylabel(r'$\Delta$H_{adsorption}$')
plt.yticks(numpy.arange(-10,50,10))
box = ax3.get_position()
ax3.set_position([box.x0,box.y0,box.width*.82,box.height])
ax2 = plt.subplot(312)
plt.xlim((300,350))
ax2.errorbar(temp_sub,dSads_folded-dSads_unfolded,ddSads_unfolded,label=r'$\lambda$ = %s' % lam[i], color=colors[i])
plt.setp(ax2.get_xticklabels(), visible=False)
plt.ylabel(r'$\Delta$S_{adsorption}$')
plt.yticks(numpy.arange(-.05,.15,.05))
box = ax2.get_position()
ax2.set_position([box.x0,box.y0,box.width*.82,box.height])
lgd = ax2.legend(bbox_to_anchor=(1.29,.95),prop={'size':10})
plt.savefig('/home/edz3fz/proteinmontecarlo/manuscripts/figures/Fig10_ddGads.pdf')
f=plt.figure(9,(.85*7,10))
f.subplots_adjust(hspace=0)
plt.rc('text',usetex=True)
ax1 = plt.subplot(311)
i=6
ax1.errorbar(lam,dGf[:,i],ddGf[:,i],color='k',label='folded')
ax1.errorbar(lam,dGu[:,i],ddGu[:,i],color='k',ls='--',label='unfolded')
plt.ylabel(r'$\Delta$G_{adsorption}$')
plt.setp(ax1.get_xticklabels(), visible=False)
#plt.title('Thermodynamics of adsorption')
plt.legend(prop={'size':9})
# box = ax1.get_position()
# ax1.set_position([box.x0,box.y0,box.width*.82,box.height])
plt.xlim((.1,.6))
ax2 = plt.subplot(312)
i -= 1
ax2.errorbar(lam,dSf[:,i],ddSf[:,i],color='k',label='folded')
ax2.errorbar(lam,dSu[:,i],ddSu[:,i],color='k',ls='--',label='unfolded')
plt.setp(ax2.get_xticklabels(), visible=False)
plt.ylabel(r'$\Delta$S_{adsorption}$')
# plt.legend(prop={'size':9})
plt.yticks(numpy.arange(-.2,.05,.05))
# box = ax2.get_position()
# ax2.set_position([box.x0,box.y0,box.width*.82,box.height])
plt.xlim((.1,.6))
ax3 = plt.subplot(313)
ax3.errorbar(lam,dHf[:,i],ddHf[:,i],color='k',label='folded')
ax3.errorbar(lam,dHu[:,i],ddHu[:,i],color='k',ls='--',label='unfolded')
plt.xlabel(r'$\lambda$')
plt.ylabel(r'$\Delta$H_{adsorption}$')
# lgd = plt.legend(prop={'size':9})
plt.yticks(numpy.arange(-100,20,20))
# lgd = ax2.legend(bbox_to_anchor=(.5,-.1),prop={'size':10},ncol=2)
# box = ax3.get_position()
# ax3.set_position([box.x0,box.y0,box.width*.82,box.height])
plt.xlim((.1,.6))
plt.savefig('/home/edz3fz/proteinmontecarlo/manuscripts/figures/Fig9_dGads.pdf')
def main():
lam = [.1, .15, .2, .25, .3, .35, .4, .45, .5, .55, .6]
#lam = [.1, .15, .2, .25, .3, .35, .4]
files = [
'/home/edz3fz/proteinmontecarlo/results/1PGB/surface/umbrella_lambda%s/dG_raw_varz.pkl'
% str(x)[1::] for x in lam
]
files = [
'/home/edz3fz/proteinmontecarlo/results/1PGB/surface/umbrella_lambda%s/dG_raw_noint_2.pkl'
% str(x)[1::] for x in lam
]
colors = cm.cool(numpy.linspace(0, 1, len(lam)))
plt.rc('text', usetex=True)
matplotlib.rc('font', family='serif')
font = {'family': 'serif', 'size': 'larger'}
fig = plt.figure(1, (7, 10))
soln = '/home/edz3fz/proteinmontecarlo/results/1PGB/solution/dG_raw.pkl'
plot_dG_solution.solution(soln)
for i in range(len(lam)):
print 'Reading %s' % files[i]
f = open(files[i], 'rb')
target_temperatures = cPickle.load(f)
bin_centers = cPickle.load(f)
print 'The 4 states are centered at', bin_centers
dG = cPickle.load(f)
ddG = cPickle.load(f)
f.close()
# Free energies of folding
dGf = dG[:, 1] - dG[:, 0] # free energy of folding while adsorbed
dGf_des = dG[:, 3] - dG[:, 2] # free eneryg of folding while desorbed
# Entropies of folding
# dS(t) = -dG/dT = -(G[t+.5h] - G[t-.5h])/h
dSf = (dGf[2::] - dGf[0:-2]) / -10 # finite difference
dSf_des = (dGf_des[2::] - dGf_des[0:-2]) / -10
temp_sub = target_temperatures[1:-1]
# Enthalpies of folding
# dG = dH - TdS
# dH = dG + TdS
dHf = dGf[1:-1] + temp_sub * dSf
dHf_des = dGf_des[1:-1] + temp_sub * dSf_des
# Uncertainties - just propagation of error for now
ddGf = [numpy.sqrt(x[1, 0]) for x in ddG]
ddGf = numpy.array(ddGf)
ddSf = (ddGf[0:-2]**2 + ddGf[2::]**2)**.5 / 10
ddHf = (ddGf[1:-1]**2 + (temp_sub * ddSf)**2)**.5
ddGf_des = [numpy.sqrt(x[3, 2]) for x in ddG]
ddGf_des = numpy.array(ddGf_des)
ddSf_des = (ddGf_des[0:-2]**2 + ddGf_des[2::]**2)**.5 / 10
ddHf_des = (ddGf_des[1:-1]**2 + (temp_sub * ddSf_des)**2)**.5
fig.subplots_adjust(hspace=0)
ax1 = plt.subplot(311)
ax1.errorbar(target_temperatures,
dGf,
ddGf,
label=r'$\lambda$ = %s' % lam[i],
color=colors[i])
plt.xlim((300, 350))
plt.ylabel(r'$\Delta$G_{folding}$')
box = ax1.get_position()
ax1.set_position([box.x0, box.y0, box.width * .82, box.height])
plt.setp(ax1.get_xticklabels(), visible=False)
ax3 = plt.subplot(313)
plt.xlim((300, 350))
ax3.errorbar(temp_sub,
dHf,
ddHf,
label=r'$\lambda$ = %s' % lam[i],
color=colors[i])
plt.xlabel(r'temperature (K)')
plt.ylabel(r'$\Delta$H_{folding}$')
plt.yticks(numpy.arange(-100, -10, 10))
box = ax3.get_position()
ax3.set_position([box.x0, box.y0, box.width * .82, box.height])
ax2 = plt.subplot(312)
plt.xlim((300, 350))
ax2.errorbar(temp_sub,
dSf,
ddSf,
label=r'$\lambda$ = %s' % lam[i],
color=colors[i])
plt.setp(ax2.get_xticklabels(), visible=False)
plt.ylabel(r'$\Delta$S_{folding}$')
box = ax2.get_position()
ax2.set_position([box.x0, box.y0, box.width * .82, box.height])
lgd = ax2.legend(bbox_to_anchor=(1.29, .98), prop={'size': 10})
fig.savefig(
'/home/edz3fz/proteinmontecarlo/manuscripts/figures/Fig8_dGf.pdf')
plt.show()
def SpectroPlot(data,nx=100,ny=100,ylabel='incidence angle',zlabel='Amplification',yscale='lin',cmap='rainbow'):
import scipy.interpolate
from matplotlib.colors import LogNorm
from matplotlib.ticker import MaxNLocator
if data['sensitivity']==False:
raise IOError("Sensitivity calculation hasn't been performed! Please do so.")
x = np.asarray(data['x'])
y = np.asarray(data['y'])
z = np.asarray(data['z'])
xi,yi = np.logspace(np.log10(x.min()),np.log10(x.max()),nx), np.linspace(y.min(),y.max(),ny)
xi,yi = np.meshgrid(xi,yi)
# interpolation
zi = scipy.interpolate.griddata((x,y),z,(xi,yi),method='linear')
# plot the data
f = plt.figure(figsize=(10.,5.),dpi=300)
# plot velocity profile
a2= f.add_subplot(1,5,5)
if type(data['hl'][0])==float:
xmin = []
xmax = []
# plot vp
if data['modeID']>=5:
if type(data['vp'][0])==list:
lvp = len(data['vp'][0])
clistvp = cm.cool(np.arange(lvp))
for i in range(len(data['vp'][0])):
vptemp = np.concatenate(([data['vp'][0][i]],data['vp'][1:]))
depthtemp = np.concatenate(([0.],np.cumsum(data['hl'])))
depth = [0.]
vp = [vptemp[0]/1000.]
for j in range(1,len(depthtemp)-1):
depth.append(depthtemp[j])
vp.append(vptemp[j-1]/1000.)
depth.append(depthtemp[j])
vp.append(vptemp[j]/1000.)
depth.append(depth[-1]+0.1*depth[-1])
vp.append(vp[-1])
a2.plot(vp,depth,color=clistvp[i])
xmax.append(np.max(vp))
else:
vptemp = data['vp']
depthtemp = np.concatenate(([0.],np.cumsum(data['hl'])))
depth = [0.]
vp = [vptemp[0]/1000.]
for j in range(1,len(depthtemp)-1):
depth.append(depthtemp[j])
vp.append(vptemp[j-1]/1000.)
depth.append(depthtemp[j])
vp.append(vptemp[j]/1000.)
depth.append(depth[-1]+0.1*depth[-1])
vp.append(vp[-1])
a2.plot(vp,depth,color='b')
xmax.append(np.max(vp))
# plot vs
if type(data['vs'][0])==list:
lvs = len(data['vs'][0])
clistvs = cm.hot(np.arange(lvs))
for i in range(len(data['vs'][0])):
vstemp = np.concatenate(([data['vs'][0][i]],data['vs'][1:]))
depthtemp = np.concatenate(([0.],np.cumsum(data['hl'])))
depth = [0.]
vs = [vstemp[0]/1000.]
for j in range(1,len(depthtemp)-1):
depth.append(depthtemp[j])
vs.append(vstemp[j-1]/1000.)
depth.append(depthtemp[j])
vs.append(vstemp[j]/1000.)
depth.append(depth[-1]+0.1*depth[-1])
vs.append(vs[-1])
a2.plot(vs,depth,color=clistvs[i])
xmin.append(np.min(vs))
xmax.append(np.max(vs))
else:
vstemp = data['vs']
depthtemp = np.concatenate(([0.],np.cumsum(data['hl'])))
depth = [0.]
vs = [vstemp[0]/1000.]
for j in range(1,len(depthtemp)-1):
depth.append(depthtemp[j])
vs.append(vstemp[j-1]/1000.)
depth.append(depthtemp[j])
vs.append(vstemp[j]/1000.)
depth.append(depth[-1]+0.1*depth[-1])
vs.append(vs[-1])
a2.plot(vs,depth,color='r')
xmin.append(np.min(vs))
xmax.append(np.max(vs))
a2.set_xlim(np.min(xmin)-0.05,np.max(xmax)+0.05)
a2.set_ylim(np.min(depth),np.max(depth))
else:
if data['modeID']>=5:
ld = len(data['hl'][0])
clistvp = cm.cool(np.arange(ld))
clistvs = cm.hot(np.arange(ld))
for i in range(len(data['hl'][0])):
hl = np.concatenate(([data['hl'][0][i]],data['hl'][1:]))
depthtemp = np.concatenate(([0.],np.cumsum(hl)))
vptemp = data['vp']
vstemp = data['vs']
depth = [0.]
vs = [vstemp[0]/1000.]
vp = [vptemp[0]/1000.]
for j in range(1,len(hl)):
depth.append(depthtemp[j])
vs.append(vstemp[j-1]/1000.)
vp.append(vptemp[j-1]/1000.)
depth.append(depthtemp[j])
vs.append(vstemp[j]/1000.)
vp.append(vptemp[j]/1000.)
depth.append(depth[-1]+0.1*depth[-1])
vs.append(vs[-1])
vp.append(vp[-1])
a2.plot(vp,depth,color=clistvp[i])
a2.plot(vs,depth,color=clistvs[i])
a2.set_xlim(np.min(vs)-0.05,np.max(vp)+0.05)
a2.set_ylim(np.min(depth),np.max(depth))
else:
ld = len(data['hl'][0])
clistvs = cm.hot(np.arange(ld))
for i in range(len(data['hl'][0])):
hl = np.concatenate(([data['hl'][0][i]],data['hl'][1:]))
vstemp = data['vs']
depthtemp = np.concatenate(([0.],np.cumsum(hl)))
depth = [0.]
vs =[vstemp[0]/1000.]
for j in range(1,len(hl)):
depth.append(depthtemp[j])
vs.append(vstemp[j-1]/1000.)
depth.append(depthtemp[j])
vs.append(vstemp[j]/1000.)
depth.append(depth[-1]+0.1*depth[-1])
vs.append(vs[-1])
a2.plot(vs,depth,color=clistvs[i])
a2.set_xlim(np.min(vs)-0.05,np.max(vs)+0.05)
a2.set_ylim(np.min(depth),np.max(depth))
a2.invert_yaxis()
a2.set_xlabel('Velocity (km/s)')
a2.set_ylabel('Depth (m)')
a2.set_title('Velocity profile')
plt.xticks(rotation='vertical')
# plot data
a = f.add_subplot(1,5,(1,4))
#zi = np.log10(zi)
#z = np.log10(z)
am = a.imshow(zi, vmin=0.1, vmax=z.max(), origin='lower', extent=[x.min(), x.max(), y.min(), y.max()],
aspect = 'auto',norm=LogNorm())
a.set_xlabel('Frequency (Hz)')
a.set_ylabel(ylabel)
a.set_xscale('log')
if yscale=='log':
print yscale
a.set_yscale('log')
a.minorticks_on()
a.tick_params(axis='x', which='major', labelsize=11, labelcolor='k')
a.tick_params(axis='x', which='minor', labelsize=10, labelcolor='grey')
for axis in [a.xaxis]:
axis.set_major_formatter(FuncFormatter(majortickformat))
axis.set_minor_formatter(FuncFormatter(minortickformat))
cb = plt.colorbar(am,label=zlabel)
cb.locator = MaxNLocator(10)
# cb.formatter = ScalarFormatter()
cb.formatter = FuncFormatter(cbtickformat)
cb.update_ticks()
f.tight_layout()
def plot_all(phys_surf, calc_surf, phys_umat, calc_umat, E_b, E_s, normals, path = '', show = False):
phi_slice = 0
X_init, Y_init, Z_init = get_coord(phys_surf) #init_surf)
X, Y, Z = get_coord(phys_surf + phys_umat) #deform(init_surf, ue))
X_ss, Y_ss, Z_ss = get_coord(calc_surf + calc_umat) #deform(e_surf, ue))
# PLOT BEGINS!
if np.amax(E_s) != 0:
N_s = E_s/np.amax(E_s)
else:
N_s = E_s
if np.amax(E_b) != 0:
N_b = E_b/np.amax(E_b)
else:
N_b = E_b
x, y, nx, ny, nz, nr = [],[],[],[],[], []
r = np.zeros(len(calc_surf[:,phi_slice]))
for ik, k in enumerate(np.array(calc_surf[:,phi_slice] + calc_umat[:,phi_slice])):
r[ik] = k[0]
z = Z_ss[:,phi_slice]
for i in X_ss:
for j in i:
x.append(j)
for i in Y_ss:
for j in i:
y.append(j)
n = 0
for i in range(normals.shape[0]):
for j in range(normals.shape[1]):
normal = normals[i,j]
if abs(normal[0]**2 + normal[1]**2 + normal[2]**2 - 1) > 0.00001 :
print 'ALERT ALERT!1'
nx.append(normal[0])
ny.append(normal[1])
nnr = x[n]/np.sqrt(x[n]**2 + y[n]**2)*normal[0] + y[n]/np.sqrt(x[n]**2 + y[n]**2)*normal[1]
if j == phi_slice:
nr.append(nnr)
nz.append(normal[2])
n += 1
limits = [np.amin([np.amin(X),np.amin(Y),np.amin(Z)]), np.amax([np.amax(X),np.amax(Y),np.amax(Z)])]
fig = plt.figure(figsize=plt.figaspect(0.5)*1.5)
#1
ax = fig.add_subplot(121, projection='3d')
vv = max(int(len(X)/30), 1)
ax.plot_surface(X_init, Y_init, Z_init, alpha = 0.2, rstride = 4*vv, cstride = 4*vv)
ax.plot_surface(X, Y, Z, rstride = vv, cstride = vv, \
alpha = 1., facecolors=cm.cool(N_b), shade=False) #OrRd
ax.auto_scale_xyz(limits, limits, limits)
#2
ax1 = fig.add_subplot(122, projection='3d')
ax1.plot_surface(X_init, Y_init, Z_init, alpha = 0.2, rstride = 4*vv, cstride = 4*vv)
ax1.plot_surface(X, Y, Z, rstride = vv, cstride = vv, \
alpha = 1., facecolors=cm.cool(N_s), shade=False)
ax1.auto_scale_xyz(limits, limits, limits)
if path != '':
if not exists(path + 'pictures/'):
makedirs(path + 'pictures/')
plt.savefig(path + 'pictures/ener_surf.png')
fig2 = plt.figure(figsize=plt.figaspect(0.5)*1.5)
#3
ax2 = fig2.add_subplot(131)
ax2.quiver(x, y, nx, ny, scale=3.2) #, units='width')
ax2.axis('equal')
ax3 = fig2.add_subplot(132)
ax3.scatter(X, Y, marker = "+") #, units='width')
ax3.axis('equal')
#4
ax4 = fig2.add_subplot(133)
ax4.plot(r,z)
ax4.quiver(r, z, nr, nz, scale=2.8) #, units='width')
ax4.axis('equal')
if path != '':
plt.savefig(path + 'pictures/normals.png')
if show:
plt.show()
