def generatePacket(self, N = 0):
if N == 0:
N = int(round(n.rand()*290)*2) # max length is two
src = int(round(n.rand() * (2**6-1)))
typ = int(round(n.rand() * (2**2-1)))
id = self.ids[typ, src]
self.ids[typ, src] = self.ids[typ, src] + 1
data = n.zeros(N+6, dtype=n.uint8)
data[0] = (id >> 24) & 0xFF
data[1] = (id >> 16) & 0xFF
data[2] = (id >> 8) & 0xFF
data[3] = (id) & 0xFF
data[4] = typ
data[5] = src
# random data
for i in range(N):
data[i+6] = int(n.rand() * 255)
return (src, typ, data)
def epsilon_greedy(Q,
state,
all_actions,
current_total_steps=0,
epsilon_initial=1,
epsilon_final=0.2,
anneal_timesteps=10000,
eps_type="constant"):
if eps_type == 'constant':
epsilon = epsilon_final
# ADD YOUR CODE SNIPPET BETWEEN EX 3.1
# Implemenmt the epsilon-greedy algorithm for a constant epsilon value
# Use epsilon and all input arguments of epsilon_greedy you see fit
# It is recommended you use the np.random module
if np.rand() > epsilon:
index,_ = np.max(Q,axis=1)
else:
index = np.rand()
action = None
# ADD YOUR CODE SNIPPET BETWEEN EX 3.1
elif eps_type == 'linear':
# ADD YOUR CODE SNIPPET BETWEENEX 3.2
# Implemenmt the epsilon-greedy algorithm for a linear epsilon value
# Use epsilon and all input arguments of epsilon_greedy you see fit
# use the ScheduleLinear class
# It is recommended you use the np.random module
action = None
# ADD YOUR CODE SNIPPET BETWEENEX 3.2
else:
raise "Epsilon greedy type unknown"
return action
def test__as_json(test_client):
sample_document = {
"val": np.rand(20),
"val_2": np.rand(100)
}
sample_document_result = test_client._as_json(sample_document)
assert sample_document_result == sample_document
def load_array(input_config: Dict, prefix=None, shape=None):
"""
Load array from file or list into numpy array.
:param input_config: External data input file config.
:param prefix: Additional path to search for input file.
:return: Data stored in a numpy array.
"""
# get path to either source file or direct to the embedded array
data = input_config["data"]
dtype = input_config["data_type"].type
if isinstance(data, str): # source file or autogenerated
m = re.match(r"([^:]+):(.+)", data)
if m:
is_scalar = ("input_dims" in input_config
and input_config["input_dims"] is not None
and len(input_config["input_dims"]) <= 0)
if shape is None and not is_scalar:
raise ValueError(
"Must provide shape when using generated inputs")
if m.group(1) == "constant":
val = float(m.group(2))
if is_scalar:
return val
else:
arr = np.empty(shape, dtype=dtype)
arr[:] = val
return arr
elif m.group(1) == "random":
m1 = re.match(r"([0-9\.]+).+([0-9\.]+)", m.group(2))
rand_min = float(m1.group(1))
rand_max = float(m2.group(1))
if is_scalar:
return (rand_min + (rand_max - rand_min) * np.rand(1))[0]
else:
return rand_min + (rand_max - rand_min) * np.rand(*shape)
else:
raise ValueError("Unknown generation: " + m.group(1))
path = data
if not os.path.isfile(path):
if prefix is not None:
path = os.path.join(prefix, path)
if not os.path.isfile(path):
raise FileNotFoundError("File {} does not exists.".format(data))
if path.endswith(".csv"):
return np.genfromtxt(path, dtype, delimiter=',')
elif path.endswith(".dat"):
return np.fromfile(path, dtype)
else:
raise ValueError("Invalid file type: " + path)
elif shape is not None and len(shape) == 0:
return dtype(data)
elif isinstance(data, np.ndarray): # embedded array: already numpy array
return data
else:
# embedded array: collection item -> convert to np array
return np.array(data, dtype=input_config["data_type"].type)
def uniform_ics(nparticles, pscale=1, vscale=1, masses=None):
"""
Generates `nparticles` particles with uniformly distributed locations
(centered at the origin with box size `pscale`) and uniform velocities.
"""
from core import Particles
pos = pscale * (np.rand(3, nparticles) - .5)
vel = vscale * (np.rand(3, nparticles) - .5)
if masses is None:
return Particles(pos, vel)
else:
return Particles(pos, vel, masses)
def uniform_ics(nparticles,pscale=1,vscale=1,masses=None):
"""
Generates `nparticles` particles with uniformly distributed locations
(centered at the origin with box size `pscale`) and uniform velocities.
"""
from core import Particles
pos = pscale*(np.rand(3,nparticles)-.5)
vel = vscale*(np.rand(3,nparticles)-.5)
if masses is None:
return Particles(pos,vel)
else:
return Particles(pos,vel,masses)
def __init__(self, num_layers, num_neurons, num_inputs, num_outputs): #num_layers is the number of hidden layers + 1, num_neurons is the number of neurons in each layer
self.num_layers = num_layers #assuming atleast two hidden layer present
self.num_neurons = num_neurons #there will be one extra neuron to accomodate for the bias term
self.hidden_weights = np.rand(num_layers-2, num_neurons+1, num_neurons+1) # weights[i][j][k] gives weight between jth neuron of (i-1)th and kth neuron of ith layer, -1 corresponds to input
self.input_weights = np.rand(num_inputs + 1, num_neurons) # input_weights[i][j] gives weight b/w ith neuron of input and jth neurons of first output layer
self.output_weights = np.rand(num_neurons+1, num_outputs)
self.hidden_weights_derivatives = np.zeros(num_layers-2, num_neurons+1, num_neurons+1) # weights[i][j][k] gives weight between jth neuron of (i-1)th and kth neuron of ith layer, -1 corresponds to input
self.input_weights_derivatives = np.zeros(num_inputs + 1, num_neurons) # input_weights[i][j] gives weight b/w ith neuron of input and jth neurons of first output layer
self.output_weights_derivatives = np.zeros(num_neurons+1, num_outputs)
self.hidden_neuron_outputs = np.ones((num_neurons+1, num_layers-1))
self.output_layer_outputs = np.zeros(num_outputs)
self.hidden_neuron_output_derivatives = np.zeros((num_neurons+1, num_layers-1)) #Not much significance of using zeros
self.output_layer_output_derivatives = np.zeros(num_outputs)
self.num_inputs = num_inputs
self.num_outputs = num_outputs
def generatePacket(self, N = 0, src = -1, typ = -1):
if N == 0:
N = int(round(n.rand()*290)*2) # max length is two
if src < 0:
src = int(round(n.rand() * (2**6-1)))
if typ < 0:
typ = int(round(n.rand() * (2**2-1)))
id = self.ids[typ, src]
self.ids[typ, src] = self.ids[typ, src] + 1
return DataPacket(src, typ, id, N=N)
def train(self, num_batches, batch_size, burn_in_length, sequence_length,
online_replay_buffer=None, supervised_replay_buffer=None,
supervised_chance=0.25, writer=None):
"""
Trains R2D3 style with 2 replay buffers
"""
assert not online_replay_buffer == supervised_replay_buffer == None
for batch in range(1, num_batches + 1):
buff_choice = np.rand()
if(online_replay_buffer is None or buff_choice < supervised_chance):
replay_buffer = supervised_replay_buffer
else:
replay_buffer = online_replay_buffer
while(not replay_buffer.ready_to_sample(batch_size)):
pass
rollouts, idxs, is_weights = replay_buffer.sample(batch_size)
loss, new_errors = self.train_batch(rollouts, burn_in_length,
sequence_length)
replay_buffer.update_priorities(new_errors, idxs)
if(writer is not None):
if(buff_choice < supervised_chance):
writer.add_summary("Supervised Loss", loss, batch)
else:
writer.add_summary("Online Loss", loss, batch)
writer.add_summary("Loss", loss, batch)
def _initialization(data, k, useElements, useQuasiRandom):
if useElements:
if useQuasiRandom:
#k-means++
barycenters = _np.zeros((k, data.shape[1]), dtype=_np.float64)
lenData = data.shape[0]
available = _np.ones(lenData, bool)
for i in range(k):
if i == 0:
weights = _np.ones(lenData, dtype=_np.float64) / lenData
else:
weights = (
(barycenters[i - 1, :] - data) ** 2
).sum(1) * available
weights /= weights.sum()
choice = _np.random.choice(lenData, 1, False, weights)
available[choice] = False
barycenters[i, :] = data[choice]
else:
#default
barycenters = data[_np.random.choice(data.shape[0], k, False)]
else:
minValues, maxValues = data.min(0), data.max(0)
deltaValues = maxValues - minValues
if useQuasiRandom:
raise('Not Implemented Yet')
else:
#random values in value space
barycenters = (
_np.rand(k, data.shape[1], dtype=_np.float64) * deltaValues
) + minValues
return barycenters
def short_sim(self, T=500, batches=100):
# End time of simulation
Tend = T * self.Ts
time = np.linspace(0, Tend, T * Ts)
# input and function to sample input
u = self.random_bit_stream(T)
u_interp = interp.interp1d(time, u[:, 0])
# Construct function for the dynamcis of the system
# dyn = lambda t, x: self.dynamics(x, u_interp(t))
x0 = np.zeros((batches, 2 * self.N))
def dyn(t, x):
X = x.reshape(2 * self.N, -1)
dX = self.dynamics(X, u_interp(t)[None])
dx = dX.reshape(-1)
return dx
sol = integrate.solve_ivp(dyn, [0.0, Tend], x0, t_eval=time)
t = sol['t']
x = sol['y']
u = u.T
U = u[:, :-1].T
X = x[:, :-1].T
Y = x[:, 1:].T + self.v_sd * (np.rand(x[:, 1:].T.shape) - 0.5)
data = IO_data(U, Y)
loader = DataLoader(data,
batch_size=self.batchsize,
shuffle=False,
num_workers=1)
return loader
def initialize_weight_matrix(n_in,
n_out,
mag='xavier',
base_dist='normal',
rng=None):
"""
Initialize a weight matrix
:param n_in: Number of input units
:param n_out: Number of output units
:param mag: The magnitude, or a string identifying how to calculate the magnitude.
String options can be:
'xavier-forward' - Best for preserving variance of a linear, tanh, or sigmoidal network across layers.
'xavier-both': - A compromize between preserving the variance of the forward, backward pass
'xavier-relu': - Best for preserving variance on the forward pass in a ReLU net.
:param base_dist: 'normal' or 'uniform', or a function taking (n_in, n_out) and returning a (n_in, n_out) array
:param rng: Random number generator or seed
:return: A shape (n_in, n_out) initial weight matrix.
"""
rng = get_rng(rng)
w_base = rng.randn(n_in, n_out) if base_dist == 'normal' else \
(np.rand(n_in, n_out) - 0.5)*np.sqrt(12) if base_dist=='uniform' else \
bad_value(base_dist)
mag_number = \
np.sqrt(2./(n_in+n_out)) if mag in ('xavier', 'xavier-both') else \
np.sqrt(1./n_in) if mag=='xavier-forward' else \
np.sqrt(2./n_in) if mag=='xavier-relu' else \
mag if isinstance(mag, numbers.Real) else \
bad_value(mag)
return w_base * mag_number
def mi(x, y, k=3, base=2):
""" Mutual information of x and y
x, y should be a list of vectors, e.g. x = [[1.3], [3.7], [5.1], [2.4]]
if x is a one-dimensional scalar and we have four samples
"""
assert len(x) == len(y), "Lists should have same length"
assert k <= len(x) - 1, "Set k smaller than num. samples - 1"
intens = 1e-10 # small noise to break degeneracy, see doc.
x = [list(p + intens * np.rand(len(x[0]))) for p in x]
y = [list(p + intens * np.rand(len(y[0]))) for p in y]
points = zip2(x, y)
# Find nearest neighbors in joint space, p=inf means max-norm
tree = ss.cKDTree(points)
dvec = [tree.query(point, k + 1, p=float('inf'))[0][k] for point in points]
a, b, c, d = avgdigamma(x, dvec), avgdigamma(y, dvec), digamma(k), digamma(
len(x))
return (-a - b + c + d) / log(base)
def jitMCQ(self,x,y,jsig, mcp):
from PYME.Analysis.QuadTree import pointQT
Imc = numpy.rand(len(x)) < mcp
qt = pointQT.qtRoot(-250,250, 0, 500)
if type(jsig) == numpy.ndarray:
jsig = jsig[Imc]
for xi, yi in zip(x[Imc] + jsig*numpy.random.normal(size=Imc.sum()), y[Imc] + jsig*numpy.random.normal(size=Imc.sum())):
qt.insert(pointQT.qtRec(xi, yi, None))
self.setQuads(qt, 100, True)
def jitMCQ(self,x,y,jsig, mcp):
from PYME.Analysis.points.QuadTree import pointQT
Imc = numpy.rand(len(x)) < mcp
qt = pointQT.qtRoot(-250,250, 0, 500)
if type(jsig) == numpy.ndarray:
jsig = jsig[Imc]
for xi, yi in zip(x[Imc] + jsig*numpy.random.normal(size=Imc.sum()), y[Imc] + jsig*numpy.random.normal(size=Imc.sum())):
qt.insert(pointQT.qtRec(xi, yi, None))
self.setQuads(qt, 100, True)
def __init(self, input_size, output_size, hidden_size=64):
# init weights
self.W_f = np.randn(input_size + hidden_size,
hidden_size) / 1000 # forget gate weights
self.W_i = np.randn(input_size + hidden_size,
hidden_size) / 1000 # input gate weights
self.W_c = np.randn(input_size + hidden_size,
hidden_size) / 1000 # keep gate weights
self.W_o = np.rand(input_size + hidden_size,
hidden_size) / 1000 # output gate weights
self.W_y = np.rand(input_size + hidden_size, hidden_size) / 1000
self.b_f = np.zeros((hidden_size, 1)) # forget gate bias
self.b_i = np.zeros((hidden_size, 1)) # input gate bias
self.b_c = np.zeros((hidden_size, 1)) # candidate gate bias
self.b_o = np.zeros((hidden_size, 1)) # output gate
self.b_y = np.zeros((hidden_size, 1)) # y bias
def rnd_draw(p):
"""
To draw a sample using a probability distribution.
"""
a = [0]
a = np.append(a, np.cumsum(p[0:-1])) / np.sum(p)
b = cumsum(p) / np.sum(p)
toss = rand()
k = np.intersect1d(np.nonzero(a < toss)[0], np.nonzero(b >= toss)[0])
return k
def __init__(self, inputs, outputs, nTrainingExamples, stepSize = 0.1, nLayers = 3, regularizationParameter = 5, crossValidation = false, learningRate =1e-4): self.Xs = inputs self.Ys = outputs self.m = nTrainingExamples self.n = stepSize self.l = nLayers self.Lambda = regularizationParameter self.t = len(self.Xs[0]) + 1 self.theta = np.rand(self.t * self.t * self.l).reshape(self.l ,self.t, self.t) * 2 - 1 # Range [-1,1] self.nodesInInput = len(self.Xs[0]) + 1 self.nodesInOutput = len(self.Ys[0]) self.crossValidation = crossValidation self.learningRate = learningRate
def parse_param(self, n, maxiter=1e4, tol=1e-8, xtrue=[], randinit=False, x0=[]):
niter = maxiter
xcur = np.zeros(n) # this allows us to keep v = 1/n
xcur = xcur + self.v
if randinit:
xcur = np.rand(n, 1)
xcur = xcur / sum(xcur)
if x0:
xcur = np.zeros(n) + x0
hist = np.zeros((niter, 1))
ihist = np.zeros((n, niter))
return niter, xcur, hist, ihist
def visualize_protein_markers_tsne(vlm,
protein_markers,
pc_targets,
visualize_clusters=False,
colormap='inferno'):
"""
Visualizes an array of protein markers in protein principal component space. The components to plot are given by
the list pc_targets. If visualize_clusters is selected, an additional cluster-colored plot is generated.
Useful for iterative manual procedure to identify clusters based on characteristic markers.
"""
array_proteins = vlm.adt_names
pcs = vlm.prot_tsne
pc_zi = [pc_targets[0] - 1, pc_targets[1] - 1]
n_addit = int(visualize_clusters)
nrows = int(np.ceil((len(protein_markers) + n_addit) / 5))
# print(nrows)
f, ax = plt.subplots(nrows=nrows, ncols=5, figsize=(12, 0.25 + 2 * nrows))
ax = ax.flatten()
for j in range(len(protein_markers)):
prot_name = protein_markers[j]
sc = ax[j].scatter(pcs[:, pc_zi[0]],
pcs[:, pc_zi[1]],
s=3,
c=np.log(vlm.P[array_proteins == prot_name][0] + 1),
alpha=0.2,
cmap=colormap)
plt.colorbar(sc)
ax[j].set_title(prot_name)
if visualize_clusters:
if hasattr(vlm, 'cluster_ID') and hasattr(vlm, 'COLORS'):
col = vlm.COLORS[vlm.cluster_ID]
else:
COLORS = np.rand(np.amax(cluster_ID) + 1, 3)
col = COLORS[vlm.cluster_ID]
ax[-1].scatter(pcs[:, pc_zi[0]],
pcs[:, pc_zi[1]],
s=3,
c=col,
alpha=0.9)
for k in range(len(ax)):
ax[k].axis('off')
def init_params(options):
#全局(非lstm)参数, 这是对于嵌入行为和分类器而言的
params = OrderedDict()
#嵌入
randn = np.rand(options['n_words'], options['dim_proj'])
params['Wemb'] = (0.01 * randn).astype(config.floatX)
params = get_layer(options['encoder'])[0](options,
params,
prefix=options['encoder'])
#初始化分类器参数
params['U'] = 0.01 * numpy.random.randn(
options['dim_proj'], options['ydim']).astype(config.floadX)
params['b'] = np.zeros((options['ydim'], )).astype(config.floatX)
return params
def forward(self,source,target,teacher_force_ratio=0.5):
batch_size=source.shape[1]
target_len=target.shape[0]
#here we will save the outputs
outputs=torch.zeros(target_len,batch_size,target_vocab_size).to(device)
target_vocab_size=len(english.vocab)
hidden,cell=self.encoder(source)
#this is start token
x=target[0]
for i in range(1,len(target)):
output,(hidden,cell)=self.decoder(x,hidden,cell)
outputs[i]=output
best_guess=output.argmax[1]
random=np.rand()
x=target[i] if random > teacher_force_ratio else best_guess
return ouputs
def __init__(self, *args, **kwargs):
kwds["style"] = wx.DEFAULT_FRAME_STYLE
wx.Frame.__init__(self, *args, **kwargs)
self.panel_1 = wx.ScrolledWindow(self, -1, style=wx.TAB_TRAVERSAL)
self.ns = range(40)
self.ts = range(0, 200, 20)
self.bitmaps = {}
for ni, n in enumerate(self.ns):
self.bitmaps[n] = {}
for ti, t in enumerate(self.ts):
a = np.rand(32*3, 32)*255
a = a.round().astype(np.uint8)
im = wx.ImageFromData(32, 32, a.data)
im = im.Scale(64, 64)
self.bitmaps[n][t] = wx.StaticBitmap(parent=self.panel_1, bitmap=im.ConvertToBitmap())
#self.Bind(wx.EVT_PAINT, self.OnPaint)
self.__set_properties()
self.__do_layout()
def __init__(self, *args, **kwargs):
kwds["style"] = wx.DEFAULT_FRAME_STYLE
wx.Frame.__init__(self, *args, **kwargs)
self.panel_1 = wx.ScrolledWindow(self, -1, style=wx.TAB_TRAVERSAL)
self.ns = range(40)
self.ts = range(0, 200, 20)
self.bitmaps = {}
for ni, n in enumerate(self.ns):
self.bitmaps[n] = {}
for ti, t in enumerate(self.ts):
a = np.rand(32 * 3, 32) * 255
a = a.round().astype(np.uint8)
im = wx.ImageFromData(32, 32, a.data)
im = im.Scale(64, 64)
self.bitmaps[n][t] = wx.StaticBitmap(
parent=self.panel_1, bitmap=im.ConvertToBitmap())
#self.Bind(wx.EVT_PAINT, self.OnPaint)
self.__set_properties()
self.__do_layout()
def post(self, request, endpoint_name, format=None):
algorithm_status = self.request.query_params.get(
"status", "production")
algorithm_version = self.request.query_params.get("version")
algs = MLAlgorithm.objects.filter(parent_endpoint__name=endpoint_name,
status__status=algorithm_status,
status__active=True)
if algorithm_version is not None:
algs = algs.filter(version=algorithm_version)
if len(algs) == 0:
return Response(
{
"status":
'Error',
'message':
'ML algorithm selection is ambiguous. Please specify algorithm version'
},
status=status.HTTP_400_BAD_REQUEST)
alg_index = 0
if algorithm_status == 'ab_testing':
alg_index = 0 if np.rand() < 0.5 else 1
algorithm_object = registry.endpoints[algs[alg_index].id]
prediction = algorithm_object.compute_prediction(request.data)
label = prediction['label'] if 'label' in prediction else 'error'
ml_request = MLRequest(input_data=json.dumps(request.data),
full_response=prediction,
response=label,
feedback='',
parent_mlalgorithm=algs[alg_index])
ml_request.save()
prediction['request_id'] = ml_request.id
return Response(prediction)
def inititalize_stickbreaking_Z(customers, alpha=10, reducedprop=1.):
""" Simple implementation of the Indian Buffet Process. Generates a binary matrix with
customers rows and an expected number of columns of alpha * sum(1,1/2,...,1/customers).
This implementation uses a stick-breaking construction.
An additional parameter permits reducing the expected number of times a dish is tried. """
# max number of dishes is distributed according to Poisson(alpha*sum(1/i))
_lambda = alpha * np.sum(1. / np.array(list(range(1, customers + 1))))
alpha /= reducedprop
# we give it 2 standard deviations as cutoff
maxdishes = int(_lambda + np.sqrt(_lambda) * 2) + 1
res = np.zeros((customers, maxdishes), dtype=bool)
stickprops = np.beta(alpha, 1, maxdishes) # nu_i
currentstick = 1.
dishesskipped = 0
for i, nu in enumerate(stickprops):
currentstick *= nu
dishestaken = np.rand(customers) < currentstick * reducedprop
if np.sum(dishestaken) > 0:
res[:, i - dishesskipped] = dishestaken
else:
dishesskipped += 1
return res[:, :maxdishes - dishesskipped]
def writePacket(fid, packet):
""" randomly writes the packet at some random location inside
of the event cycle and fills in the other cycles
"""
indata = packet.getInputData()
N = len(indata) * 2
M = 980
startrange = M - N
p = int(n.rand()*startrange)
s = 1000 - N - p
for i in range(p):
fid.write("0 00\n")
for i in indata:
fid.write("1 %2.2X\n" % ((i >> 8) & 0xFF))
fid.write("1 %2.2X\n" % (i & 0xFF))
for i in range(s):
fid.write("0 00\n")
def writePacket(fid, packet):
""" randomly writes the packet at some random location inside
of the event cycle and fills in the other cycles.
We use an offset of 4 to strip off the calculated ids.
"""
os = 4
N = len(packet)-os
M = 980
startrange = M - N
p = int(n.rand()*startrange)
s = 1000 - N - p
for i in range(p):
fid.write("0 00\n")
for i in packet[os:]:
fid.write("1 %2.2X\n" % i)
for i in range(s):
fid.write("0 00\n")
# kernel = np.ones((3, 3))
# kernel[1, 1] = 0
next_img = np.zeros_like(img)
neighb = convolve2d(
img,
kernel,
mode='same',
)
next_img[:] = (~((neighb < 3) & (img == 1)))
# ==================================================================================================
input_data = [np.rand(1, 1, 10, 10) for _ in range(10000)]
target_data = [game(x) for x in range(100000)]
from torch.utils.data import Dataset, DataLoader
from torch import optim
def generate_data(img):
return img
class GameOfLifeData(Dataset):
"""Face Landmarks dataset."""
def __init__(self, ):
"""
Args:
from numpy import zeros, array, rand, linspace, random, Float, ones, arange, sum
from _ppssampler import set_rng_state, get_rng_state, local_irand, local_rand
from _ppssampler import equalprob, equalprobi
from population import *
from messages import restart, elapsedcpu
# Check passing RandomKit state back and forth with numpy.
print '*** Checking RandomKit state maintenance ***'
state0 = random.get_state()
id, key, pos = state0
set_rng_state(id, key, pos)
print 'Should be same:', rand(), local_rand()
for i in range(100):
local_rand()
print 'Should be dif: ', rand(), local_rand()
random.set_state(get_rng_state())
print 'Should be same:', rand(), local_rand()
print
# Check equal-weight samplers.
print '*** Checking equiprobability samplers ***'
pool = zeros(10) # Workspace
samp = equalprobi(5,10,pool)
print 'equalprobi:', samp
# Check equalprob with a list input.
pool = range(10,20)
samp = equalprob(5,pool)
print 'equalprob:', samp
# Check the original pool is unchanged.
def rand(shape, rg=[0, 1]):
if USE_GPU:
return gnp.rand(shape) * (rg[1] - rg[0]) + rg[0]
else:
return gnp.random.rand(*shape) * (rg[1] - rg[0]) + rg[0]
d = D[:,j]
alpha_update = alpha[j] + np.dot(d, x - np.dot(D, alpha)) / sqnorm(d)
alpha[j] = soft_threshold(alpha_update, lmbda)
obj_values.append(sqnorm(x - np.dot(D,alpha)))
return alpha, obj_values
# In[42]:
m = 30 # dimension of each atom
p = 100 # number of atoms
rand = np.random.rand
D = rand(m*p).reshape(m,p) # dictionary
x = rand(m)
# normalise by column
D = D / np.linalg.norm(D,axis=0)
x = x / np.linalg.norm(x)
alpha, obj_values = coordinate_descent(D, x, 0.01)
s = "sparsity: {}".format(len(np.nonzero(alpha)[0]))
plt.subplot(3, 1, 1)
plt.title(s)
plt.plot(np.array(obj_values))
alpha, obj_values = coordinate_descent(D, x, 0.5)
s2 = "sparsity: {}".format(len(np.nonzero(alpha)[0]))
plt.subplot(3, 1, 2)
def plot_subfaults(subfaults, plot_centerline=False, slip_color=False, \
cmap_slip=None, cmin_slip=None, cmax_slip=None, \
plot_rake=False, xylim=None, plot_box=True):
"""
Plot each subfault projected onto the surface.
Describe parameters...
"""
import matplotlib
import matplotlib.pyplot as plt
#figure(44,(6,12)) # For CSZe01
#clf()
# for testing purposes, make random slips:
test_random = False
max_slip = 0.
min_slip = 0.
for subfault in subfaults:
if test_random:
subfault['slip'] = 10.*numpy.rand() # for testing
#subfault['slip'] = 8. # uniform
slip = subfault['slip']
max_slip = max(abs(slip), max_slip)
min_slip = min(abs(slip), min_slip)
print "Max slip, Min slip: ",max_slip, min_slip
if slip_color:
if cmap_slip is None:
cmap_slip = matplotlib.cm.jet
#white_purple = colormaps.make_colormap({0.:'w', 1.:[.6,0.2,.6]})
#cmap_slip = white_purple
if cmax_slip is None:
cmax_slip = max_slip
if cmin_slip is None:
cmin_slip = 0.
if test_random:
print "*** test_random == True so slip and rake have been randomized"
y_ave = 0.
for subfault in subfaults:
set_fault_xy(subfault)
# unpack parameters:
paramlist = """x_top y_top x_bottom y_bottom x_centroid y_centroid
depth_top depth_bottom depth_centroid x_corners y_corners""".split()
for param in paramlist:
cmd = "%s = subfault['%s']" % (param,param)
exec(cmd)
y_ave += y_centroid
# Plot projection of planes to x-y surface:
if plot_centerline:
plt.plot([x_top],[y_top],'bo',label="Top center")
plt.plot([x_centroid],[y_centroid],'ro',label="Centroid")
plt.plot([x_top,x_centroid],[y_top,y_centroid],'r-')
if plot_rake:
if test_random:
subfault['rake'] = 90. + 30.*(rand()-0.5) # for testing
tau = (subfault['rake'] - 90) * numpy.pi/180.
plt.plot([x_centroid],[y_centroid],'go',markersize=5,label="Centroid")
dxr = x_top - x_centroid
dyr = y_top - y_centroid
x_rake = x_centroid + numpy.cos(tau)*dxr - numpy.sin(tau)*dyr
y_rake = y_centroid + numpy.sin(tau)*dxr + numpy.cos(tau)*dyr
plt.plot([x_rake,x_centroid],[y_rake,y_centroid],'g-',linewidth=1)
if slip_color:
slip = subfault['slip']
#c = cmap_slip(0.5*(cmax_slip + slip)/cmax_slip)
#c = cmap_slip(slip/cmax_slip)
s = min(1, max(0, (slip-cmin_slip)/(cmax_slip-cmin_slip)))
c = cmap_slip(s*.99) # since 1 does not map properly with jet
plt.fill(x_corners,y_corners,color=c,edgecolor='none')
if plot_box:
plt.plot(x_corners, y_corners, 'k-')
slipax = plt.gca()
y_ave = y_ave / len(subfaults)
slipax.set_aspect(1./numpy.cos(y_ave*numpy.pi/180.))
plt.ticklabel_format(format='plain',useOffset=False)
plt.xticks(rotation=80)
if xylim is not None:
plt.axis(xylim)
plt.title('Fault planes')
if slip_color:
cax,kw = matplotlib.colorbar.make_axes(slipax)
norm = matplotlib.colors.Normalize(vmin=cmin_slip,vmax=cmax_slip)
cb1 = matplotlib.colorbar.ColorbarBase(cax, cmap=cmap_slip, norm=norm)
#import pdb; pdb.set_trace()
plt.sca(slipax) # reset the current axis to the main figure
if a.ndim != 2:
raise ValueError("a must be 2-d")
code = r"""
int i,j;
for(i=1;i<Na[0]-1;i++) {
for(j=1;j<Na[1]-1;j++) {
B2(i,j) = A2(i,j) + A2(i-1,j)*0.5 +
A2(i+1,j)*0.5 + A2(i,j-1)*0.5
+ A2(i,j+1)*0.5
+ A2(i-1,j-1)*0.25
+ A2(i-1,j+1)*0.25
+ A2(i+1,j-1)*0.25
+ A2(i+1,j+1)*0.25;
}
}
"""
b = zeros_like(a)
weave.inline(code,['a','b'])
return b
a = [None]*10
print example1(a)
print a
a = rand(512,512)
b = arr(a)
h = [[0.25,0.5,0.25],[0.5,1,0.5],[0.25,0.5,0.25]]
import scipy.signal as ss
b2 = ss.convolve(h,a,'same')
import numpy as np import scipy.misc q_1 = np.zeros(8) q_2 = np.ones(7) q_3 = 5*np.ones(6) r_1 = np.arange(6) r_2 = np.arange(0,6,0.5) r_3 = np.arange(5,-1,-1) s_1 = [] t_1 = np.linspace(0,5,90) t_2 = np.linspace(5,0,80) u_1 = np.logspace(-2,2,9) u_2 = np.log10(u_1) v_1 = np.exp(np.arange(-2,4)) v_2 = np.log(v_1) w_1 = 2**np.arange(0,11) w_2 = 1 / 2**np.arange(0,6) x_1 = scipy.misc.factorial(np.arange(0,7)) y_1 = np.rand(10) y_2 = np.randn(10) y_3 = np.randint(5,15, size = 10)
def demo5 (*itest) :
"""demo5 () or demo5 (i)
Run examples of use of pl3d.i, plwf.i, and slice3.i. With
argument I = 1, 2, or 3, run that particular demonstration.
Read the source code to understand the details of how the
various effects are obtained.
demo5 (1) demonstrates the various effects which can be obtained
with the plwf (plot wire frame) function.
demo5 (2) demonstrates shading effects controlled by the light3
function
demo5 (3) demonstrates the slice3, slice2, and pl3tree functions,
as well as changing the orientation of the 3D object
"""
global making_movie
if len (itest) == 0 or itest [0] == 1 :
set_draw3_ (0)
x = span (-1, 1, 64, 64)
y = transpose (x)
z = (x + y) * exp (-6.*(x*x+y*y))
limits_(square = 1)
print "(plot wire frame) plwf,z,y,x"
orient3 ( )
light3 ( )
plwf (z, y, x)
[xmin, xmax, ymin, ymax] = draw3(1) # not necessary interactively
limits (xmin, xmax, ymin, ymax)
plt("opaque wire mesh", .30, .42)
paws ( )
print "plwf,z,y,x, shade=1,ecolor=\"red\""
plwf(z,y,x,shade=1,ecolor="red")
[xmin, xmax, ymin, ymax] = draw3(1) # not necessary interactively
limits (xmin, xmax, ymin, ymax)
paws()
print "plwf,z,y,x, shade=1,edges=0"
plwf(z,y,x,shade=1,edges=0)
[xmin, xmax, ymin, ymax] = draw3(1) # not necessary interactively
limits (xmin, xmax, ymin, ymax)
paws ( )
light3 ( diffuse=.1, specular=1., sdir=array([0,0,-1]))
[xmin, xmax, ymin, ymax] = draw3(1)
limits (xmin, xmax, ymin, ymax)
paws ( )
light3 ( diffuse=.5, specular=1., sdir=array([1,.5,1]))
[xmin, xmax, ymin, ymax] = draw3 (1)
limits (xmin, xmax, ymin, ymax)
paws ( )
light3 ( ambient=.1,diffuse=.1,specular=1.,
sdir=array([[0,0,-1],[1,.5,1]]),spower=array([4,2]))
[xmin, xmax, ymin, ymax] = draw3(1)
limits (xmin, xmax, ymin, ymax)
paws ( )
if len (itest) == 0 or itest [0] == 2 :
set_draw3_ (0)
x = span (-1, 1, 64, 64)
y = transpose (x)
z = (x + y) * exp (-6.*(x*x+y*y))
print "light3 function demo- default lighting"
orient3 ( )
light3 ( )
plwf (z,y,x,shade=1,edges=0)
[xmin, xmax, ymin, ymax] = draw3 (1) # not necessary interactively
limits (xmin, xmax, ymin, ymax)
paws( )
print "light3,diffuse=.2,specular=1"
light3(diffuse=.2,specular=1)
limits_(square = 1)
[xmin, xmax, ymin, ymax] = draw3(1) # not necessary interactively
limits (xmin, xmax, ymin, ymax)
paws()
print "light3,sdir=[cos(theta),.25,sin(theta)] -- movie"
making_movie = 1
movie(demo5_light, lims = [xmin, xmax, ymin, ymax])
making_movie = 0
fma()
demo5_light(1)
paws()
light3()
if len (itest) == 0 or itest [0] == 3 :
nx = demo5_n [0]
ny = demo5_n [1]
nz = demo5_n [2]
xyz = zeros ( (3, nx, ny, nz), Float)
xyz [0] = multiply.outer ( span (-1, 1, nx), ones ( (ny, nz), Float))
xyz [1] = multiply.outer ( ones (nx, Float),
multiply.outer ( span (-1, 1, ny), ones (nz, Float)))
xyz [2] = multiply.outer ( ones ( (nx, ny), Float), span (-1, 1, nz))
r = sqrt (xyz [0] ** 2 + xyz [1] **2 + xyz [2] **2)
theta = arccos (xyz [2] / r)
phi = arctan2 (xyz [1] , xyz [0] + logical_not (r))
y32 = sin (theta) ** 2 * cos (theta) * cos (2 * phi)
m3 = mesh3 (xyz, funcs = [r * (1. + y32)])
del r, theta, phi, xyz, y32
print " test uses " + `(nx - 1) * (ny - 1) * (nz - 1)` + " cells"
elapsed = [0., 0., 0.]
elapsed = timer_ (elapsed)
elapsed0 = elapsed
[nv, xyzv, dum] = slice3 (m3, 1, None, None, value = .50)
# (inner isosurface)
[nw, xyzw, dum] = slice3 (m3, 1, None, None, value = 1.)
# (outer isosurface)
pxy = plane3 ( array ([0, 0, 1], Float ), zeros (3, Float))
pyz = plane3 ( array ([1, 0, 0], Float ), zeros (3, Float))
[np, xyzp, vp] = slice3 (m3, pyz, None, None, 1)
# (pseudo-colored slice)
[np, xyzp, vp] = slice2 (pxy, np, xyzp, vp)
# (cut slice in half)
[nv, xyzv, d1, nvb, xyzvb, d2] = \
slice2x (pxy, nv, xyzv, None)
[nv, xyzv, d1] = \
slice2 (- pyz, nv, xyzv, None)
# (...halve one of those halves)
[nw, xyzw, d1, nwb, xyzwb, d2] = \
slice2x ( pxy , nw, xyzw, None)
# (split outer in halves)
[nw, xyzw, d1] = \
slice2 (- pyz, nw, xyzw, None)
elapsed = timer_ (elapsed)
timer_print ("slicing time", elapsed - elapsed0)
fma ()
print "split_palette,\"earth.gp\" -- generate palette for pl3tree"
split_palette ("earth.gp")
print "gnomon -- turn on gnomon"
gnomon (1)
print "pl3tree with 1 slicing plane, 2 isosurfaces"
clear3 ()
# Make sure we don't draw till ready
set_draw3_ (0)
pl3tree (np, xyzp, vp, pyz)
pl3tree (nvb, xyzvb)
pl3tree (nwb, xyzwb)
pl3tree (nv, xyzv)
pl3tree (nw, xyzw)
orient3 ()
light3 (diffuse = .2, specular = 1)
limits ()
limits (square=1)
demo5_light (1)
paws ()
hcp ()
print "spin3 animated rotation, use rot3 or orient3 for one frame"
# don't want limits to autoscale during animation
lims = limits ( )
spin3 ()
limits ( ) # back to autoscaling
demo5_light (1)
paws ()
light3 ()
gnomon (0)
limits (square = 1)
palette ("gray.gp")
if len (itest) == 0 or itest [0] == 4 :
f = PR ('./bills_plot')
n_nodes = f.NumNodes
n_z = f.NodesOnZones
x = f.XNodeCoords
y = f.YNodeCoords
z = f.ZNodeCoords
c = f.ZNodeVelocity
n_zones = f.NumZones
# Put vertices in right order for Gist
n_z = transpose (
take (transpose (n_z), array ( [0, 4, 3, 7, 1, 5, 2, 6]),axis=0))
m3 = mesh3 (x, y, z, funcs = [c], verts = n_z ) # [0:10])
[nv, xyzv, cv] = slice3 (m3, 1, None, None, 1, value = .9 * max (c) )
pyz = plane3 ( array ([1, 0, 0], Float ), zeros (3, Float))
pxz = plane3 ( array ([0, 1, 0], Float ), zeros (3, Float))
# draw a colored plane first
fma ()
clear3 ()
# Make sure we don't draw till ready
set_draw3_ (0)
[np, xyzp, vp] = slice3 (m3, pyz, None, None, 1)
pl3tree (np, xyzp, vp, pyz, split = 0)
palette ("rainbow.gp")
orient3 ()
demo5_light (1)
paws ()
# [nv, xyzv, d1] = \
# slice2 (- pyz, nv, xyzv, None)
[nw, xyzw, cw] = slice3 (m3, 1, None, None, 1, value = .9 * min (c) )
# [nw, xyzw, d1] = \
# slice2 (- pyz, nw, xyzw, None)
[nvi, xyzvi, cvi] = slice3 (m3, 1, None, None, 1, value = .5 * min (c) )
[nvi, xyzvi, cvi] = \
slice2 (- pyz, nvi, xyzvi, cvi)
[nvj, xyzvj, cvj] = slice3 (m3, 1, None, None, 1, value = .5 * max (c) )
[nvj, xyzvj, cvj] = \
slice2 (- pyz, nvj, xyzvj, cvj)
fma ()
print "gnomon -- turn on gnomon"
gnomon (1)
clear3 ()
# Make sure we don't draw till ready
set_draw3_ (0)
pl3tree (nv, xyzv) # , cv)
pl3tree (nw, xyzw) # , cw)
pl3tree (nvi, xyzvi) # , cvi)
pl3tree (nvj, xyzvj) # , cvi)
orient3 ()
light3 (ambient = 0, diffuse = .5, specular = 1, sdir = [0, 0, -1])
limits (square=1)
palette ("gray.gp")
demo5_light (1)
paws ()
print "spin3 animated rotation, use rot3 or orient3 for one frame"
# don't want limits to autoscale during animation
spin3 ()
limits ( ) # back to autoscaling
demo5_light (1)
paws ()
light3 ()
gnomon (0)
palette ("gray.gp")
draw3 ( 1 )
paws ()
clear3 ()
del nv, xyzv, cv, nw, xyzw, cw, nvi, xyzvi, cvi, nvj, xyzvj, cvj
# Make sure we don't draw till ready
set_draw3_ (0)
for i in range (8) :
[nv, xyzv, cv] = slice3 (m3, 1, None, None, 1, value = .9 * min (c) +
i * (.9 * max (c) - .9 * min (c)) / 8.)
[nv, xyzv, d1] = \
slice2 (pxz, nv, xyzv, None)
pl3tree (nv, xyzv)
orient3 ()
light3 (ambient = 0, diffuse = .5, specular = 1, sdir = [0, 0, -1])
limits (square=1)
palette ("heat.gp")
demo5_light (1)
paws ()
spin3 ()
limits ( ) # back to autoscaling
demo5_light (1)
paws ()
demo5_light (1)
paws ()
if len (itest) == 0 or itest [0] == 5 :
# Try bert's data
f = PR ('./berts_plot')
nums = array ( [63, 63, 49], Int)
dxs = array ( [2.5, 2.5, 10.], Float )
x0s = array ( [-80., -80., 0.0], Float )
c = f.c
m3 = mesh3 (nums, dxs, x0s, funcs = [transpose (c)])
[nv, xyzv, dum] = slice3 (m3, 1, None, None, value = 6.5)
fma ()
clear3 ()
print "gnomon -- turn on gnomon"
gnomon (1)
# Make sure we don't draw till ready
set_draw3_ (0)
palette ("rainbow.gp")
pl3tree (nv, xyzv)
orient3 ()
light3 (diffuse = .2, specular = 1)
limits (square=1)
demo5_light (1)
paws ()
spin3 ()
demo5_light (1)
paws ()
if len (itest) == 0 or itest [0] == 6 :
# Try Bill's irregular mesh
f = PR ("ball.s0001")
ZLss = f.ZLstruct_shapesize
ZLsc = f.ZLstruct_shapecnt
ZLsn = f.ZLstruct_nodelist
x = f.sap_mesh_coord0
y = f.sap_mesh_coord1
z = f.sap_mesh_coord2
c = f.W_vel_data
# Now we need to convert this information to avs-style data
istart = 0 # beginning index into ZLstruct_nodelist
NodeError = "NodeError"
ntet = 0
nhex = 0
npyr = 0
nprism = 0
nz_tet = []
nz_hex = []
nz_pyr = []
nz_prism = []
for i in range (4) :
if ZLss [i] == 4 : # TETRAHEDRON
nz_tet = reshape (ZLsn [istart: istart + ZLss [i] * ZLsc [i]],
(ZLsc [i], ZLss [i]))
ntet = ZLsc [i]
istart = istart + ZLss [i] * ZLsc [i]
elif ZLss[i] == 5 : # PYRAMID
nz_pyr = reshape (ZLsn [istart: istart + ZLss [i] * ZLsc [i]],
(ZLsc [i], ZLss [i]))
npyr = ZLsc [i]
# Now reorder the points (bill has the apex last instead of first)
nz_pyr = transpose (
take (transpose (nz_pyr), array ( [4, 0, 1, 2, 3]),axis=0))
istart = istart + ZLss [i] * ZLsc [i]
elif ZLss[i] == 6 : # PRISM
nz_prism = reshape (ZLsn [istart: istart + ZLss [i] * ZLsc [i]],
(ZLsc [i], ZLss [i]))
nprism = ZLsc [i]
# now reorder the points (bill goes around a square face
# instead of traversing the opposite sides in the same direction.
nz_prism = transpose (
take (transpose (nz_prism), array ( [0, 1, 3, 2, 4, 5]),axis=0))
istart = istart + ZLss [i] * ZLsc [i]
elif ZLss[i] == 8 : # HEXAHEDRON
nz_hex = reshape (ZLsn [istart: istart + ZLss [i] * ZLsc [i]],
(ZLsc [i], ZLss [i]))
# now reorder the points (bill goes around a square face
# instead of traversing the opposite sides in the same direction.
nz_hex = transpose (
take (transpose (nz_hex), array ( [0, 1, 3, 2, 4, 5, 7, 6]),axis=0))
nhex = ZLsc [i]
istart = istart + ZLss [i] * ZLsc [i]
else :
raise NodeError, `ZLss[i]` + "is an incorrect number of nodes."
m3 = mesh3 (x, y, z, funcs = [c], verts = [nz_tet, nz_pyr, nz_prism,
nz_hex])
[nv, xyzv, cv] = slice3 (m3, 1, None, None, 1, value = .9 * max (c) )
pyz = plane3 ( array ([1, 0, 0], Float ), zeros (3, Float))
pxz = plane3 ( array ([0, 1, 0], Float ), zeros (3, Float))
# draw a colored plane first
fma ()
clear3 ()
# Make sure we don't draw till ready
set_draw3_ (0)
[np, xyzp, vp] = slice3 (m3, pyz, None, None, 1)
pl3tree (np, xyzp, vp, pyz, split = 0)
palette ("rainbow.gp")
orient3 ()
limits (square=1)
demo5_light (1)
paws ()
[nw, xyzw, cw] = slice3 (m3, 1, None, None, 1, value = .9 * min (c) )
[nvi, xyzvi, cvi] = slice3 (m3, 1, None, None, 1, value = .1 * min (c) )
[nvi, xyzvi, cvi] = \
slice2 (- pyz, nvi, xyzvi, cvi)
[nvj, xyzvj, cvj] = slice3 (m3, 1, None, None, 1, value = .1 * max (c) )
[nvj, xyzvj, cvj] = \
slice2 (- pyz, nvj, xyzvj, cvj)
[nvii, xyzvii, cvii] = slice3 (m3, 1, None, None, 1,
value = 1.e-12 * min (c) )
[nvii, xyzvii, cvii] = \
slice2 (- pyz, nvii, xyzvii, cvii)
[nvjj, xyzvjj, cvjj] = slice3 (m3, 1, None, None, 1,
value = 1.e-12 * max (c) )
[nvjj, xyzvjj, cvjj] = \
slice2 (- pyz, nvjj, xyzvjj, cvjj)
fma ()
print "gnomon -- turn on gnomon"
gnomon (1)
clear3 ()
# Make sure we don't draw till ready
set_draw3_ (0)
pl3tree (nv, xyzv) # , cv)
pl3tree (nw, xyzw) # , cw)
pl3tree (nvi, xyzvi) # , cvi)
pl3tree (nvj, xyzvj) # , cvj)
pl3tree (nvii, xyzvii) # , cvii)
pl3tree (nvjj, xyzvjj) # , cvjj)
orient3 ()
light3 (ambient = 0, diffuse = .5, specular = 1, sdir = [0, 0, -1])
limits (square=1)
palette ("gray.gp")
demo5_light (1)
paws ()
palette ("heat.gp")
paws ()
if len (itest) == 0 or itest [0] == 7 :
# test plwf on the sombrero function
# compute sombrero function
x = arange (-20, 21, dtype = Float)
y = arange (-20, 21, dtype = Float)
z = zeros ( (41, 41), Float)
r = sqrt (add.outer ( x ** 2, y **2)) + 1e-6
z = sin (r) / r
fma ()
clear3 ()
gnomon (0)
# Make sure we don't draw till ready
set_draw3_ (0)
palette ("rainbow.gp")
limits (square=1)
orient3 ()
light3 ()
plwf (z, fill = z, ecolor = "black")
[xmin, xmax, ymin, ymax] = draw3 (1)
limits (xmin, xmax, ymin, ymax)
paws ()
##### Try smooth contours, log mode
[nv, xyzv, dum] = slice3mesh (x, y, z)
zmult = max (max (abs (x)), max (abs (y)))
plzcont (nv, xyzv, contours = 20, scale = "normal")
[xmin, xmax, ymin, ymax] = draw3 (1)
limits (xmin, xmax, ymin, ymax)
paws ()
plzcont (nv, xyzv, contours = 20, scale = "lin", edges=1)
[xmin, xmax, ymin, ymax] = draw3 (1)
limits (xmin, xmax, ymin, ymax)
paws ()
plwf (z, fill = z, shade = 1, ecolor = "black")
[xmin, xmax, ymin, ymax] = draw3 (1)
limits (xmin, xmax, ymin, ymax)
paws ()
plwf (z, fill = z, shade = 1, edges = 0)
[xmin, xmax, ymin, ymax] = draw3 (1)
limits (xmin, xmax, ymin, ymax)
paws ()
light3(diffuse=.2,specular=1)
print "light3,sdir=[cos(theta),.25,sin(theta)] -- movie"
making_movie = 1
movie(demo5_light, lims = [xmin, xmax, ymin, ymax])
making_movie = 0
fma()
demo5_light(1)
paws ()
plwf (z, fill = None, shade = 1, edges = 0)
[xmin, xmax, ymin, ymax] = draw3 (1)
palette("gray.gp")
limits (xmin, xmax, ymin, ymax)
paws ()
if len (itest) == 0 or itest [0] == 8 :
# test pl3surf on the sombrero function
# compute sombrero function
nc1 = 100
nv1 = nc1 + 1
br = - (nc1 / 2)
tr = nc1 / 2 + 1
x = arange (br, tr, dtype = Float) * 40. / nc1
y = arange (br, tr, dtype = Float) * 40. / nc1
z = zeros ( (nv1, nv1), Float)
r = sqrt (add.outer ( x ** 2, y **2)) + 1e-6
z = sin (r) / r
# In order to use pl3surf, we need to construct a mesh
# using mesh3. The way I am going to do that is to define
# a function on the 3d mesh so that the sombrero function
# is its 0-isosurface.
z0 = min (ravel (z))
z0 = z0 - .05 * abs (z0)
maxz = max (ravel (z))
maxz = maxz + .05 * abs (maxz)
zmult = max (max (abs (x)), max (abs (y)))
dz = (maxz - z0)
nxnynz = array ( [nc1, nc1, 1], Int)
dxdydz = array ( [1.0, 1.0, zmult*dz], Float )
x0y0z0 = array ( [float (br), float (br), z0*zmult], Float )
meshf = zeros ( (nv1, nv1, 2), Float )
meshf [:, :, 0] = zmult*z - (x0y0z0 [2])
meshf [:, :, 1] = zmult*z - (x0y0z0 [2] + dxdydz [2])
m3 = mesh3 (nxnynz, dxdydz, x0y0z0, funcs = [meshf])
fma ()
# Make sure we don't draw till ready
set_draw3_ (0)
pldefault(edges=0)
[nv, xyzv, col] = slice3 (m3, 1, None, None, value = 0.)
orient3 ()
pl3surf (nv, xyzv)
lim = draw3 (1)
limits (lim [0], lim [1], 1.5*lim [2], 1.5*lim [3])
palette ("gray.gp")
paws ()
# Try new slicing function to get color graph
[nv, xyzv, col] = slice3mesh (nxnynz [0:2], dxdydz [0:2], x0y0z0 [0:2],
zmult * z, color = zmult * z)
pl3surf (nv, xyzv, values = col)
lim = draw3 (1)
dif = 0.5 * (lim [3] - lim [2])
limits (lim [0], lim [1], lim [2] - dif, lim [3] + dif)
palette ("rainbow.gp")
paws ()
palette ("heat.gp")
# Try plzcont--see if smooth mode possible
plzcont (nv, xyzv)
draw3 (1)
paws ()
plzcont (nv, xyzv, contours = 20)
draw3 (1)
paws ()
plzcont (nv, xyzv, contours = 20, scale = "log")
draw3(1)
paws ()
plzcont (nv, xyzv, contours = 20, scale = "normal")
draw3(1)
paws ()
if len (itest) == 0 or itest [0] == 9 :
vsf = 0.
c = 1
s = 1000.
kmax = 25
lmax = 35
# The following computations define an interesting 3d surface.
xr = multiply.outer (
arange (1, kmax + 1, dtype = Float), ones (lmax, Float))
yr = multiply.outer (
ones (kmax, Float), arange (1, lmax + 1, dtype = Float))
zt = 5. + xr + .2 * rand (kmax, lmax) # ranf (xr)
rt = 100. + yr + .2 * rand (kmax, lmax) # ranf (yr)
z = s * (rt + zt)
z = z + .02 * z * rand (kmax, lmax) # ranf (z)
ut = rt/sqrt (rt ** 2 + zt ** 2)
vt = zt/sqrt (rt ** 2 + zt ** 2)
ireg = multiply.outer ( ones (kmax, Float), ones (lmax, Float))
ireg [0:1, 0:lmax]=0
ireg [0:kmax, 0:1]=0
ireg [1:15, 7:12]=2
ireg [1:15, 12:lmax]=3
ireg [3:7, 3:7]=0
freg=ireg + .2 * (1. - rand (kmax, lmax)) # ranf (ireg))
freg=array (freg, Float)
#rt [4:6, 4:6] = -1.e8
z [3:10, 3:12] = z [3:10, 3:12] * .9
z [5, 5] = z [5, 5] * .9
z [17:22, 15:18] = z [17:22, 15:18] * 1.2
z [16, 16] = z [16, 16] * 1.1
orient3 ()
plwf (freg, shade = 1, edges = 0)
[xmin, xmax, ymin, ymax] = draw3 (1)
limits (xmin, xmax, ymin, ymax)
paws ()
nxny = array ( [kmax - 1, lmax - 1])
x0y0 = array ( [0., 0.])
dxdy = array ( [1., 1.])
[nv, xyzv, col] = slice3mesh (nxny, dxdy, x0y0, freg)
[nw, xyzw, col] = slice3mesh (nxny, dxdy, x0y0, freg + ut)
pl3tree (nv, xyzv)
pl3tree (nw, xyzw)
draw3 (1)
limits ( )
paws ()
light3 (ambient = 0, diffuse = .5, specular = 1, sdir = [0, 0, -1])
demo5_light (1)
paws ()
[nv, xyzv, col] = slice3mesh (nxny, dxdy, x0y0, freg, color = freg)
pl3surf (nv, xyzv, values = col)
draw3 (1)
palette ("rainbow.gp")
paws ()
[nv, xyzv, col] = slice3mesh (nxny, dxdy, x0y0, freg, color = z)
pl3surf (nv, xyzv, values = col)
draw3 (1)
paws ()
palette ("stern.gp")
paws ()
[nv, xyzv, col] = slice3mesh (nxny, dxdy, x0y0, z, color = z)
pl3surf (nv, xyzv, values = col)
orient3(phi=0,theta=0)
draw3 (1)
paws ()
set_draw3_ (0)
palette ("gray.gp")
light3 ( diffuse=.1, specular=1., sdir=array([0,0,-1]))
pl3surf (nv, xyzv)
draw3 (1)
paws ()
# spin3 ()
# paws ()
hcp_finish ()
def tirage ( m ):
x = np.rand()
y = np.rand()
x = (x-0.5)*2*m
y = (y-0.5)*2*m
return x, y
import numpy as np import scipy.stats as sp x=np.rand(1000,1) y=np.rand(1000,1) print(sp.pearsonr(x, y))
def synthesize(self, tau=50, mode=None):
"""Synthesize obervations.
Parameters
----------
tau : int (default = 50)
Synthesize tau frames.
mode : Combination of ['s','q','r']
's' - Use the original states
'q' - Do NOT add state noise
'r' - Add observations noise
In case 's' is specified, 'tau' is ignored and the number of
frames equals the number of state time points.
Returns
-------
I : numpy array, shape = (D, tau)
Matrix with N D-dimensional column vectors as observations.
X : numpy array, shape = (N, tau)
Matrix with N tau-dimensional state vectors.
"""
if not self._ready:
raise ErrorDS("LDS not ready for synthesis!")
Bhat = None
Xhat = self._Xhat
Qhat = self._Qhat
Ahat = self._Ahat
Chat = self._Chat
Rhat = self._Rhat
Yavg = self._Yavg
initM0 = self._initM0
initS0 = self._initS0
nStates = self._nStates
if mode is None:
raise ErrorDS("No synthesis mode specified!")
# use original states -> tau is restricted
if mode.find('s') >= 0:
tau = Xhat.shape[1]
# data to be filled and returned
I = np.zeros((len(Yavg), tau))
X = np.zeros((nStates, tau))
if mode.find('r') >= 0:
stdR = np.sqrt(Rhat)
# add state noise, unless user explicitly decides against
if not mode.find('q') >= 0:
stdS = np.sqrt(initS0)
(U, S, V) = np.linalg.svd(Qhat, full_matrices=False)
Bhat = U*np.diag(np.sqrt(S))
t = 0
Xt = np.zeros((nStates, 1))
while (tau<0) or (t<tau):
# uses the original states
if mode.find('s') >= 0:
Xt1 = Xhat[:,t]
# first state
elif t == 0:
Xt1 = initM0;
if mode.find('q') < 0:
Xt1 += stdS*np.rand(nStates)
# any further states (if mode != 's')
else:
Xt1 = Ahat*Xt
if not mode.find('q') >= 0:
Xt1 = Xt1 + Bhat*np.rand(nStates)
# synthesizes image
It = Chat*Xt1 + np.reshape(Yavg,(len(Yavg),1))
# adds observation noise
if mode.find('r') >= 0:
It += stdR*np.randn(length(Yavg))
# save ...
Xt = Xt1;
I[:,t] = It.reshape(-1)
X[:,t] = Xt.reshape(-1)
t += 1
return (I, X)
def rand_init_weights(L_in, L_out, epsilon): """ """ return np.rand(L_out, 1 + L_in) * 2 * epsilon - epsilon
def step(self):
for _ in xrange(self.rows*self.cols*3):
data = np.rand() * 255
data
def fit(self, p, x, y):
plsq = leastsq(self.residuals, p0, args=(y, x))
return plsq
if __name__ == "__main__":
from scipy.optimize import leastsq
try:
data = pylab.csv2rec('data.csv')
except:
print "Error! You need to give some data to fit."
print ("I will generate some for you")
# data
data = np.rand(100)
pfh = FitHandler()
pfh.plot_data(data)
orders = [17]
poly_list = []
# Fitting
for order in orders:
pfit = pfh.fit_and_plot(data, order)
poly_list.append(pfit)
plt.title("Fitting the data")
plt.legend()
linestyle='--',
color='b',
marker='o',
label='Autoencoder(7,4)')
'''
BPSK ERROR RATE
'''
N = 5000000
EbNodB_range = range(0, 11)
itr = len(EbNodB_range)
ber = [None] * itr
for n in range(0, itr):
EbNodB = EbNodB_range[n]
EbNo = 10.0**(EbNodB / 10.0)
x = 2 * (np.rand(N) >= 0.5) - 1
noise_std = 1 / np.sqrt(2 * EbNo)
y = x + noise_std * np.randn(N)
y_d = 2 * (y >= 0) - 1
errors = (x != y_d).sum()
ber[n] = 1.0 * errors / N
print "EbNodB:", EbNodB
print "Error bits:", errors
print "Error probability:", ber[n]
plt.plot(EbNodB_range, ber, 'bo', EbNodB_range, ber, 'k')
plt.title('BPSK Modulation')
# plt.plot(EbNodB_range, ber, linestyle='', marker='o', color='r')
# plt.plot(EbNodB_range, ber, linestyle='-', color = 'b')
datapackets.append(data)
pktcnt = len(datapackets)
# write them in the event cycles:
pos = 0
fida = file('dataa.txt', 'w')
fidb = file('datab.txt', 'w')
while pos < pktcnt:
# flip two coins, for tx on A and B;
asend = n.rand() > 0.2
if asend :
writePacket(fida, datapackets[pos])
pos += 1
else:
writeEmpty(fida)
bsend = n.rand() > 0.2
if pos != pktcnt:
if bsend :
writePacket(fidb, datapackets[pos])
pos += 1
else:
writeEmpty(fidb)
# -*- coding: utf-8 -*-
import os
from numpy import rand
from matplotlib.pyplot import *
from random import random
rands = []
for i in range(100):
rands.append(random())
plot(rands)
from random import gauss
grands = []
for i in range(100):
grands.append(gauss(0, 1))
plot(grands)
plot(rand(100))
os.system("pause")
'''Main simulation loop '''
import numpy as np
import Bead
import physics
#Run parameters:
dim = 2
N_atoms = 100
atoms = []
x0 = np.rand(N_atoms)
y0 = np.rand(N-atoms)
for i in range(N_atoms):
atoms[i] = (x0[i], v0[i], a0[i], 1)
for i in range(atom_len_1): # relapce atom_len_1 with numer of atoms
force_vec=[0,0]
atom1=0 # get atom1 from Bead. It should be a number between 1 and 5
for j in range(atom_len_2): # replace atom_len_2 with the numer of atome atom1 interacts with
atom2=0 # get interacting atom2 from bead. It should be a number between 1 and 5
if ((atom1,atom2) in lipids) or ((atom2,atom1) in lipids:
force_exp=spring_force(atom1,atom2,inter[atom1-1][atom2-1])
angl=math.atan((atom2.x-atom1.x)[1]/(atom2.x-atom1.x)[0])
force_vec[0]+=force_exp*math.cos(angl)
force_vec[1]+=force_exp*math.sin(angl)
else:
force_exp=force(atom1,atom2,inter[atom1-1][atom2-1])
print "appears in the Narcisse GUI." paws () vsf = 0. c = 1 s = 1000. kmax = 25 lmax = 35 # The following computations define an interesting 3d surface. xr = multiply.outer ( arange (1, kmax + 1, dtype = Float), ones (lmax, Float)) yr = multiply.outer ( ones (kmax, Float), arange (1, lmax + 1, dtype = Float)) zt = 5. + xr + .2 * rand (kmax, lmax) # ranf (xr) rt = 100. + yr + .2 * rand (kmax, lmax) # ranf (yr) z = s * (rt + zt) z = z + .02 * z * rand (kmax, lmax) # ranf (z) ut = rt/sqrt (rt ** 2 + zt ** 2) vt = zt/sqrt (rt ** 2 + zt ** 2) ireg = multiply.outer ( ones (kmax), ones (lmax)) ireg [0:1, 0:lmax]=0 ireg [0:kmax, 0:1]=0 ireg [1:15, 7:12]=2 ireg [1:15, 12:lmax]=3 ireg [3:7, 3:7]=0 freg=ireg + .2 * (1. - rand (kmax, lmax)) # ranf (ireg)) freg=array (freg, Float) #rt [4:6, 4:6] = -1.e8 z [3:10, 3:12] = z [3:10, 3:12] * .9
def __init__(self, src, typ, id, data = None, N = 0):
if data == None:
self.data = (n.rand(N) * 2**16).astype(n.uint16)
self.typ = typ
self.src = src
self.id = id
import numpy as np
import buffermodule # buffer module reponsible for all interactions with the buffer
N=300
I=50
delay_ancien=0
steps=100
lr=0.001
batch_size = 10
decay=0.99
neurnet={}
neurnet["w1"]=np.rand(300,50)/np.sqrt(50)
neurnet["w2"]=np.rand(300)/np.sqrt(300)
wei_buff={i:np.zeros(j) for i,j in neurnet.items() }
rmsprop_cache = { k : np.zeros_like(v) for k,v in neurnet.items() }
def sigmoid(x):
return 1/(1+np.exp(-x))
def forwardp(i):
h=np.dot(neurnet["w1"],i)
h[h<0]=0
o=np.dot(neurnet["w1"],h)
o=sigmoid(o)
return h,o
def prepo(dl):
I[1:]=I[:-1]
I[0]=dl
for i in range(4):
for i in range(32):
writePacket(fida, datapackets[pos])
pos += 1
writePacket(fidb, datapackets[pos])
pos += 1
for i in range(50 - 32):
writeEmpty(fida)
writeEmpty(fidb)
while pos < pktcnt:
# flip two coins, for tx on A and B;
asend = n.rand() > 0.3
if asend :
writePacket(fida, datapackets[pos])
pos += 1
else:
writeEmpty(fida)
bsend = n.rand() > 0.3
if pos != pktcnt:
if bsend :
writePacket(fidb, datapackets[pos])
pos += 1
else:
writeEmpty(fidb)
host = "192.168.0.1"
port = 4400
buf = 1024
addr = (host,port)
UDPSock = socket(AF_INET,SOCK_DGRAM)
UDPSock.bind(("192.168.0.2", 40000))
UDPSock.setsockopt(SOL_SOCKET, SO_BROADCAST, 1)
UDPSock.sendto(data,addr)
fid = file('dataprops.txt', 'w')
for i in range(1000):
id = int(round(n.rand() * (2**32 - 1)))
src = int(round(n.rand() * 63))
typ = int(round(n.rand() * 3))
data = struct.pack(">BBI", typ, src, id)
fid.write("%2.2X %2.2X %8.8X \n" % (typ, src, id))
sendDataPacket(data)
def test(self, shape=(10, 1920, 1080, 3)):
return self.solve(np.rand(shape))
def GEMF_SIM(Para, Net, x0, StopCond, N, Directed=False):
"""
An event-driven approach to simulate the stochastic process.
"""
M = Para[0]
q = Para[1]
L = Para[2]
A_d = Para[3]
A_b = Para[4]
Neigh = Net[0]
I1 = Net[1]
I2 = Net[2]
NeighW = Net[4]
n_index = []
j_index = []
i_index = []
#------------------------------
bil = np.zeros((M, L))
for l in range(L):
bil[:, l] = A_b[l].sum(axis=1) #l'th column is row sum of l'th A_b
#------------------------------
bi = np.zeros((M, M, L))
for i in range(M):
for l in range(L):
bi[i, :, l] = A_b[l][i, :]
#------------------------------
di = A_d.sum(
axis=1
) #The rate that we leave compartment i, due to nodal transitions
#------------------------------
#X = copy(x0)
X = x0.astype(
int32
) #since compartments are just numbers we are using integer types. If
#------------------------------
Nq = np.zeros((L, N))
#------------------------------ver 2
for n in range(N):
for l in range(L):
Nln = Neigh[l][I1[l][n]:I2[l][n] + 1]
Nq[l][n] = sum((X[Nln] == q[l]) * NeighW[l][I1[l][n]:I2[l][n] + 1])
#------------------------------ver2
Rn = np.zeros(N)
for n in range(N):
# print 'di[X[n]]: '+str(di[X[n]])
# print 'Nq[:,n]: '+str(Nq[:,n])
# print 'bil[X[n],:]: '+str(bil[X[n],:])
# print 'np.dot(bil[X[n],:],Nq[:,n]): '+str(np.dot(bil[X[n],:],Nq[:,n]))
Rn[n] = di[X[n]] + np.dot(bil[X[n], :], Nq[:, n])
R = sum(Rn)
#------------------------------
EventNum = StopCond[1]
RunTime = StopCond[1]
ts = []
# #------------------------------
s = -1
Tf = 0
if len(Net) > 5:
NNeigh = Net[5]
NI1 = Net[6]
NI2 = Net[7]
NNeighW = Net[8]
while Tf < RunTime:
s += 1
ts.append(-log(rand()) / R)
#------------------------------ver 2
ns = rnd_draw(Rn)
iss = X[ns]
js = rnd_draw(np.ravel(A_d[iss, :].T + np.dot(bi[iss], Nq[:, ns])))
n_index.extend(ns)
j_index.extend(js)
i_index.extend(iss)
# -------------------- % Updateing ver2
X[ns] = js
R -= Rn[ns]
Rn[ns] = di[js] + np.dot(bil[js, :], Nq[:, ns])
R += Rn[ns]
infl = (q == js
).nonzero()[0] #inf is layers with influencer compartment
for l in infl:
Nln = NNeigh[l][
NI1[l][ns]:NI2[l][ns] +
1] #finding nodes that are adjacent to new infected
IncreasEff = NNeighW[l][NI1[l][ns]:NI2[l][ns] + 1]
Nq[l][Nln] += IncreasEff #add the new infection weight edges
k = 0
for n in Nln:
Rn[n] += bil[X[n], l] * IncreasEff[k]
R += bil[X[n], l] * IncreasEff[k]
k += 1
infl2 = (q == iss).nonzero()[
0] #infl2 is layers with influencer compartment
# print 'inf2: '+str(inf2)
for l in infl2: #finding influencer compartments
Nln = NNeigh[int(
l)][int(NI1[l][ns]):int(NI2[l][ns]) +
1] #finding nodes that are adjacent to new infected
reducEff = NNeighW[int(l)][int(NI1[l][ns]):int(NI2[l][ns]) + 1]
Nq[l][
Nln] -= reducEff #subtract the new infection weight edges
k = 0
for n in Nln:
Rn[n] -= bil[X[n], l] * reducEff[k]
R -= bil[X[n], l] * reducEff[k]
k += 1
if R < 1e-6:
break
Tf += ts[s]
else:
while Tf < RunTime:
s += 1
ts.append(-log(rand()) / R)
#------------------------------ver 2
ns = rnd_draw(Rn)
iss = X[ns]
js = rnd_draw(np.ravel(A_d[iss, :].T + np.dot(bi[iss], Nq[:, ns])))
n_index.extend(ns)
j_index.extend(js)
i_index.extend(iss)
# -------------------- % Updateing ver2
X[ns] = js
R -= Rn[ns]
Rn[ns] = di[js] + np.dot(bil[js, :], Nq[:, ns])
R += Rn[ns]
infl = (q == js
).nonzero()[0] #inf is layers with influencer compartment
for l in infl:
Nln = Neigh[int(
l)][int(I1[l][ns]):int(I2[l][ns]) +
1] #finding nodes that are adjacent to new infected
IncreasEff = NeighW[int(l)][int(I1[l][ns]):int(I2[l][ns]) + 1]
Nq[l][Nln] += IncreasEff #add the new infection weight edges
k = 0
for n in Nln:
Rn[n] += bil[X[n], l] * IncreasEff[k]
R += bil[X[n], l] * IncreasEff[k]
k += 1
infl2 = (q == iss).nonzero()[
0] #infl2 is layers with influencer compartment
# print 'inf2: '+str(inf2)
for l in infl2: #finding influencer compartments
Nln = Neigh[int(
l)][int(I1[l][ns]):int(I2[l][ns]) +
1] #finding nodes that are adjacent to new infected
reducEff = NeighW[int(l)][int(I1[l][ns]):int(I2[l][ns]) + 1]
Nq[l][
Nln] -= reducEff #subtract the new infection weight edges
k = 0
for n in Nln:
Rn[n] -= bil[X[n], l] * reducEff[k]
R -= bil[X[n], l] * reducEff[k]
k += 1
if R < 1e-6:
break
Tf += ts[s]
return ts, n_index, i_index, j_index
def __init__(self, inputs, outputs):
self.hidden = round(inputs * 0.6666, 0)
self.inputs = inputs
self.outputs = outputs
self.input_hidden_weights = numpy.rand((inputs.size, 1))
self.hidden_output_weights = numpy.rand((self.hidden, 1))
def rand_initialize_weights(l_in, l_out, epsilon_init=0.12):
return np.rand(l_in, l_out) * 2 * epsilon_init - epsilon_init
def min_err(train_data, d=D, reg=REG):
u = np.rand((100, d))
v = np.rand((d, 24983))
def plot_subfaults(subfaults, plot_centerline=False, slip_color=False, \
cmap_slip=None, cmin_slip=None, cmax_slip=None, \
plot_rake=False, xylim=None, plot_box=True):
"""
Plot each subfault projected onto the surface.
Describe parameters...
"""
import matplotlib
import matplotlib.pyplot as plt
#figure(44,(6,12)) # For CSZe01
#clf()
# for testing purposes, make random slips:
test_random = False
max_slip = 0.
min_slip = 0.
for subfault in subfaults:
if test_random:
subfault['slip'] = 10.*numpy.rand() # for testing
#subfault['slip'] = 8. # uniform
slip = subfault['slip']
max_slip = max(abs(slip), max_slip)
min_slip = min(abs(slip), min_slip)
print("Max slip, Min slip: ",max_slip, min_slip)
if slip_color:
if cmap_slip is None:
cmap_slip = matplotlib.cm.jet
#white_purple = colormaps.make_colormap({0.:'w', 1.:[.6,0.2,.6]})
#cmap_slip = white_purple
if cmax_slip is None:
cmax_slip = max_slip
if cmin_slip is None:
cmin_slip = 0.
if test_random:
print("*** test_random == True so slip and rake have been randomized")
y_ave = 0.
for subfault in subfaults:
set_fault_xy(subfault)
# unpack parameters:
paramlist = """x_top y_top x_bottom y_bottom x_centroid y_centroid
depth_top depth_bottom depth_centroid x_corners y_corners""".split()
for param in paramlist:
cmd = "%s = subfault['%s']" % (param,param)
exec(cmd)
y_ave += y_centroid
# Plot projection of planes to x-y surface:
if plot_centerline:
plt.plot([x_top],[y_top],'bo',label="Top center")
plt.plot([x_centroid],[y_centroid],'ro',label="Centroid")
plt.plot([x_top,x_centroid],[y_top,y_centroid],'r-')
if plot_rake:
if test_random:
subfault['rake'] = 90. + 30.*(rand()-0.5) # for testing
tau = (subfault['rake'] - 90) * numpy.pi/180.
plt.plot([x_centroid],[y_centroid],'go',markersize=5,label="Centroid")
dxr = x_top - x_centroid
dyr = y_top - y_centroid
x_rake = x_centroid + numpy.cos(tau)*dxr - numpy.sin(tau)*dyr
y_rake = y_centroid + numpy.sin(tau)*dxr + numpy.cos(tau)*dyr
plt.plot([x_rake,x_centroid],[y_rake,y_centroid],'g-',linewidth=1)
if slip_color:
slip = subfault['slip']
#c = cmap_slip(0.5*(cmax_slip + slip)/cmax_slip)
#c = cmap_slip(slip/cmax_slip)
s = min(1, max(0, (slip-cmin_slip)/(cmax_slip-cmin_slip)))
c = cmap_slip(s*.99) # since 1 does not map properly with jet
plt.fill(x_corners,y_corners,color=c,edgecolor='none')
if plot_box:
plt.plot(x_corners, y_corners, 'k-')
slipax = plt.gca()
y_ave = y_ave / len(subfaults)
slipax.set_aspect(1./numpy.cos(y_ave*numpy.pi/180.))
plt.ticklabel_format(format='plain',useOffset=False)
plt.xticks(rotation=80)
if xylim is not None:
plt.axis(xylim)
plt.title('Fault planes')
if slip_color:
cax,kw = matplotlib.colorbar.make_axes(slipax)
norm = matplotlib.colors.Normalize(vmin=cmin_slip,vmax=cmax_slip)
cb1 = matplotlib.colorbar.ColorbarBase(cax, cmap=cmap_slip, norm=norm)
#import pdb; pdb.set_trace()
plt.sca(slipax) # reset the current axis to the main figure
pool = zeros(10) samp = equalprobi(5,10,pool) print 'equalprobi:', samp pool = range(10,20) samp = equalprob(5,pool) print 'equalprob:', samp print pool pool = array(pool) samp = equalprob(5,pool) print 'equalprob:', samp print pool print wts = rand(6) ind, sorted, cum = prepwts(wts) print wts print ind print wts.take(ind) print cum print wts = linspace(.1, 1., 1000) ind, sorted, cumwt = prepwts(wts) nsamp = 10 cumrat = wtratios(nsamp, sorted, cumwt[-1]) ntry, samp = SampfordPPS(nsamp, cumwt, cumrat) state0 = random.get_state() id, key, pos = state0