def stackSimRes(simRes):
"""
input: a *list* of single steps
returns: an array that contains the complete gait (consecutive time & way)
"""
resDat = []
res_t = []
for part in simRes:
if len(resDat) == 0:
res_t.append(part['t'])
resDat.append(vstack( [ part['x'],
part['y'],
part['z'],
part['vx'],
part['vy'],
part['vz'],
]).T)
else:
res_t.append(part['t'][1:] + res_t[-1][-1])
# compensate x and z translation
resDat.append(vstack( [ part['x'][1:] + resDat[-1][-1,0],
part['y'][1:],
part['z'][1:] + resDat[-1][-1,2],
part['vx'][1:],
part['vy'][1:],
part['vz'][1:],
]).T)
return hstack(res_t), vstack(resDat)
def stackSimRes(simRes):
"""
input: a *list* of single steps
returns: an array that contains the complete gait (consecutive time & way)
"""
resDat = []
res_t = []
for part in simRes:
if len(resDat) == 0:
res_t.append(part['t'])
resDat.append(
vstack([
part['x'],
part['y'],
part['z'],
part['vx'],
part['vy'],
part['vz'],
]).T)
else:
res_t.append(part['t'][1:] + res_t[-1][-1])
# compensate x and z translation
resDat.append(
vstack([
part['x'][1:] + resDat[-1][-1, 0],
part['y'][1:],
part['z'][1:] + resDat[-1][-1, 2],
part['vx'][1:],
part['vy'][1:],
part['vz'][1:],
]).T)
return hstack(res_t), vstack(resDat)
def RecalcPhase(data):
"""
this function re-computes the phase of a given artificial Floquet system
analog to the way the phases are computed in the original systen.
Data must be given in a D x N-format,
D: number of Dimensions,
N: number of samples
"""
p1 = -1. * data[0, :] # R_Kne_y - L_kne_y
p2 = data[5, :] - data[2, :] # R_Trc_y - R_Anl_y
p3 = data[6, :] - data[1, :] # L_trc_y - L_Anl_y
p4 = data[2, :] - data[1, :] - data[0, :] # R_anl_y - L_anl_y
allPhrIn_TDR = [
vstack((p1, p2, p3, p4)),
]
psec_TDR = [
p4.copy(),
]
phrIn = vstack((p1, p2, p3, p4))
psecIn = p4.copy()
print 'building phaser ...\n'
Phaser = phaser2.Phaser2(y=allPhrIn_TDR, psecData=psec_TDR)
print 'computing phases ...\n'
return Phaser.phaserEval(phrIn, psecIn).squeeze()
def __init__(self, scanList, stateArray):
if len(scanList)!=stateArray.shape[0]:
raise Exception('number of scan and state should be the same')
times = [scan.timestamp for scan in scanList]
self.avgTime = times[int(pl.floor(len(times)/2))]
#self.avgTime = pl.mean([scan.timestamp for scan in scanList])
#transform the 3d coordinates of each scan
#and numpy.vstack all the output m*3 array together
#find average Lidar frame
avgBodyState = stateArray[int(pl.floor(len(stateArray)/2))]
#avgBodyState=pl.mean(stateArray, 0)
w_R_avg_L, w_T_avg_L = self._bodyState2LidarState(avgBodyState)
self.avgLidarState = self._matrix2State(w_R_avg_L, w_T_avg_L)
transform = self._transformPointsFromBodyToAvgLidar
#from data points with transformation to avgState
self.dataPoints = pl.vstack([transform(scan.dataArray, state, w_R_avg_L, w_T_avg_L) for scan, state in zip(scanList, stateArray) if scan.hasValidData()])
self.intensity = pl.vstack([scan.intensityArray for scan in scanList if scan.hasValidData()]).flatten()
if self.dataPoints.shape[0]!=self.intensity.shape[0]:
raise Exception('dist and intensity have different size')
def extrude_mesh(self, l, z_offset):
# accepts the number of layers and the length of extrusion
mesh = self.mesh
# Extrude vertices
all_coords = []
for i in linspace(0, z_offset, l):
all_coords.append(
hstack((mesh.coordinates(), i * ones((self.n_v2, 1)))))
self.global_vertices = vstack(all_coords)
# Extrude cells (tris to tetrahedra)
for i in range(l - 1):
for c in self.mesh.cells():
# Make a prism out of 2 stacked triangles
vertices = hstack((c + i * self.n_v2, c + (i + 1) * self.n_v2))
# Determine prism orientation
smallest_vertex_index = argmin(vertices)
# Map to I-ordering of Dompierre et al.
mapping = self.indirection_table[smallest_vertex_index]
# Determine which subdivision scheme to use.
if min(vertices[mapping][[1, 5]]) < min(
vertices[mapping][[2, 4]]):
local_tets = vstack((vertices[mapping][[0,1,2,5]],\
vertices[mapping][[0,1,5,4]],\
vertices[mapping][[0,4,5,3]]))
else:
local_tets = vstack((vertices[mapping][[0,1,2,4]],\
vertices[mapping][[0,4,2,5]],\
vertices[mapping][[0,4,5,3]]))
# Concatenate local tet to cell array
self.global_tets = vstack((self.global_tets, local_tets))
# Eliminate phantom initialization tet
self.global_tets = self.global_tets[1:, :]
# Query number of vertices and tets in new mesh
self.n_verts = self.global_vertices.shape[0]
self.n_tets = self.global_tets.shape[0]
# Initialize new dolfin mesh of dimension 3
self.new_mesh = Mesh()
m = MeshEditor()
m.open(self.new_mesh, 3, 3)
m.init_vertices(self.n_verts, self.n_verts)
m.init_cells(self.n_tets, self.n_tets)
# Copy vertex data into new mesh
for i, v in enumerate(self.global_vertices):
m.add_vertex(i, Point(*v))
# Copy cell data into new mesh
for j, c in enumerate(self.global_tets):
m.add_cell(j, *c)
m.close()
def __init__(self, scanList, stateArray):
if len(scanList) != stateArray.shape[0]:
raise Exception('number of scan and state should be the same')
times = [scan.timestamp for scan in scanList]
self.avgTime = times[int(pl.floor(len(times) / 2))]
#self.avgTime = pl.mean([scan.timestamp for scan in scanList])
#transform the 3d coordinates of each scan
#and numpy.vstack all the output m*3 array together
#find average Lidar frame
avgBodyState = stateArray[int(pl.floor(len(stateArray) / 2))]
#avgBodyState=pl.mean(stateArray, 0)
w_R_avg_L, w_T_avg_L = self._bodyState2LidarState(avgBodyState)
self.avgLidarState = self._matrix2State(w_R_avg_L, w_T_avg_L)
transform = self._transformPointsFromBodyToAvgLidar
#from data points with transformation to avgState
self.dataPoints = pl.vstack([
transform(scan.dataArray, state, w_R_avg_L, w_T_avg_L)
for scan, state in zip(scanList, stateArray)
if scan.hasValidData()
])
self.intensity = pl.vstack([
scan.intensityArray for scan in scanList if scan.hasValidData()
]).flatten()
if self.dataPoints.shape[0] != self.intensity.shape[0]:
raise Exception('dist and intensity have different size')
def extrude_mesh(self,l,z_offset):
# accepts the number of layers and the length of extrusion
# Extrude vertices
all_coords = []
for i in linspace(0,z_offset,l):
all_coords.append(hstack((mesh.coordinates(),i*ones((self.n_v2,1)))))
self.global_vertices = vstack(all_coords)
# Extrude cells (tris to tetrahedra)
for i in range(l-1):
for c in self.mesh.cells():
# Make a prism out of 2 stacked triangles
vertices = hstack((c+i*self.n_v2,c+(i+1)*self.n_v2))
# Determine prism orientation
smallest_vertex_index = argmin(vertices)
# Map to I-ordering of Dompierre et al.
mapping = self.indirection_table[smallest_vertex_index]
# Determine which subdivision scheme to use.
if min(vertices[mapping][[1,5]]) < min(vertices[mapping][[2,4]]):
local_tets = vstack((vertices[mapping][[0,1,2,5]],\
vertices[mapping][[0,1,5,4]],\
vertices[mapping][[0,4,5,3]]))
else:
local_tets = vstack((vertices[mapping][[0,1,2,4]],\
vertices[mapping][[0,4,2,5]],\
vertices[mapping][[0,4,5,3]]))
# Concatenate local tet to cell array
self.global_tets = vstack((self.global_tets,local_tets))
# Eliminate phantom initialization tet
self.global_tets = self.global_tets[1:,:]
# Query number of vertices and tets in new mesh
self.n_verts = self.global_vertices.shape[0]
self.n_tets = self.global_tets.shape[0]
# Initialize new dolfin mesh of dimension 3
self.new_mesh = Mesh()
m = MeshEditor()
m.open(self.new_mesh,3,3)
m.init_vertices(self.n_verts,self.n_verts)
m.init_cells(self.n_tets,self.n_tets)
# Copy vertex data into new mesh
for i,v in enumerate(self.global_vertices):
m.add_vertex(i,Point(*v))
# Copy cell data into new mesh
for j,c in enumerate(self.global_tets):
m.add_cell(j,*c)
m.close()
def color_by_level(current_data):
from pylab import vstack, contourf, plot, ones, arange, colorbar
fs = current_data.framesoln
pout, level = gridtools1.grid_output_1d(fs, 0, xout, return_level=True)
Xout = vstack((xout, xout))
Yout = vstack((-1.1 * ones(xout.shape), 1.1 * ones(xout.shape)))
L = vstack((level, level))
contourf(Xout, Yout, L, v_levels, colors=c_levels)
cb = colorbar(ticks=range(1, maxlevels + 1))
cb.set_label('AMR Level')
plot(xout, pout, 'k')
def getPeriodicOrbit(statesL,
T_L,
ymin_L,
statesR,
T_R,
ymin_R,
baseParams,
startParams=[14000, 1.16, 1, 0.]):
"""
returns a tuple of SLIP parameters, that result in the two-step periodic
solution defined by <statesL> -> <statesR> -> >statesL>,
with step time left (right) = <T_L> (<T_R>)
minimal vertical position left (right) = <ymin_L> (<ymin_R>)
statesL/R: a list of (left/right) apex states y, vx, vz
baseParams: dict of base SLIP parameters: g, m (gravity acceleration, mass)
returns: [SL, paramsL, dEL], [SR, paramsR, dER]
two tuples of initial apex states and corresponding SLIP
parameters that yield the two-step periodic solution
(dE: energy fluctuation)
"""
SL = mean(vstack(statesL), axis=0) if len(statesL) > 1 else statesL
SR = mean(vstack(statesR), axis=0) if len(statesR) > 1 else statesR
tr = mean(hstack(T_R))
tl = mean(hstack(T_L))
yminl = mean(hstack(ymin_L))
yminr = mean(hstack(ymin_R))
m = baseParams['m']
g = baseParams['g']
# energy input right (left) step
dER = (SL[0] - SR[0]) * m * abs(g) + .5 * m * (SL[1]**2 + SL[2]**2 -
SR[1]**2 - SR[2]**2)
dEL = -dER
# initialize parameters
PR = copy.deepcopy(baseParams)
PL = copy.deepcopy(baseParams)
PL['IC'] = SL
PL['dE'] = dEL
PR['IC'] = SR
PR['dE'] = dER
# define step params: (y_apex2, T, y_min, vz_apex2)
spL = (SR[0], tl, yminl, SR[2])
spR = (SL[0], tr, yminr, SL[2])
# compute necessary model parameters
paramsL = fl.calcSlipParams3D(spL, PL, startParams)
paramsR = fl.calcSlipParams3D(spR, PR, startParams)
return ([SL, paramsL, dEL], [SR, paramsR, dER])
def plot_finest(current_data):
from pylab import vstack,contourf,plot,ones,arange,colorbar,\
xlim,ylim,semilogy,figure,title,clf,subplot,show,draw,\
tight_layout,ylabel,grid
fs = current_data.framesoln
t = current_data.t
print('+++ plot_finest at t = %.4f' % t)
pout, level = gridtools1.grid_output_1d(fs, 0, xout, return_level=True)
err = abs(pout - p_true_fcn(xout, t))
Xout = vstack((xout, xout))
L = vstack((level, level))
figure(3, figsize=(12, 8))
clf()
subplot(311)
Yout = vstack((-1.1 * ones(xout.shape), 1.1 * ones(xout.shape)))
contourf(Xout, Yout, L, v_levels, colors=c_levels)
cb = colorbar(ticks=range(1, maxlevels + 1))
cb.set_label('AMR Level')
plot(xout, pout, 'k')
xlim(xlimits)
ylim(-1.1, 1.1)
title('Pressure at t = %.4f' % t)
subplot(312)
Yout = vstack((ylimits_error[0] * ones(xout.shape),
ylimits_error[1] * ones(xout.shape)))
contourf(Xout, Yout, L, v_levels, colors=c_levels)
cb = colorbar(ticks=range(1, maxlevels + 1))
cb.set_label('AMR Level')
semilogy(xout, err, 'k')
if tolerance is not None:
plot(xout, tolerance * ones(xout.shape), 'r--')
xlim(xlimits)
ylim(ylimits_error)
ylabel('abs(error)')
grid(True)
subplot(313)
Yout = vstack(
(0 * ones(xout.shape), (maxlevels + 1) * ones(xout.shape)))
contourf(Xout, Yout, L, v_levels, colors=c_levels)
cb = colorbar(ticks=range(1, maxlevels + 1))
cb.set_label('AMR Level')
plot(xout, level, 'k')
xlim(xlimits)
ylim(0, maxlevels + 1)
ylabel('AMR Level')
tight_layout()
grid(True)
draw()
def getPeriodicOrbit(statesL, T_L, ymin_L,
statesR, T_R, ymin_R,
baseParams ,
startParams=[14000, 1.16, 1, 0.] ):
"""
returns a tuple of SLIP parameters, that result in the two-step periodic
solution defined by <statesL> -> <statesR> -> >statesL>,
with step time left (right) = <T_L> (<T_R>)
minimal vertical position left (right) = <ymin_L> (<ymin_R>)
statesL/R: a list of (left/right) apex states y, vx, vz
baseParams: dict of base SLIP parameters: g, m (gravity acceleration, mass)
returns: [SL, paramsL, dEL], [SR, paramsR, dER]
two tuples of initial apex states and corresponding SLIP
parameters that yield the two-step periodic solution
(dE: energy fluctuation)
"""
SL = mean(vstack(statesL), axis=0) if len(statesL) > 1 else statesL
SR = mean(vstack(statesR), axis=0) if len(statesR) > 1 else statesR
tr = mean(hstack(T_R))
tl = mean(hstack(T_L))
yminl = mean(hstack(ymin_L))
yminr = mean(hstack(ymin_R))
m = baseParams['m']
g = baseParams['g']
# energy input right (left) step
dER = (SL[0]-SR[0])*m*abs(g) + .5*m*(SL[1]**2 + SL[2]**2
- SR[1]**2 - SR[2]**2)
dEL = -dER
# initialize parameters
PR = copy.deepcopy( baseParams )
PL = copy.deepcopy( baseParams )
PL['IC'] = SL
PL['dE'] = dEL
PR['IC'] = SR
PR['dE'] = dER
# define step params: (y_apex2, T, y_min, vz_apex2)
spL = (SR[0], tl, yminl, SR[2])
spR = (SL[0], tr, yminr, SL[2])
# compute necessary model parameters
paramsL = fl.calcSlipParams3D(spL, PL, startParams)
paramsR = fl.calcSlipParams3D(spR, PR, startParams)
return ([SL, paramsL, dEL],[SR, paramsR, dER])
def homog2D(xPrime, x):
"""
Compute the 3x3 homography matrix mapping a set of N 2D homogeneous
points (3xN) to another set (3xN)
"""
numPoints = xPrime.shape[1]
assert numPoints >= 4
A = None
for i in range(0, numPoints):
xiPrime = xPrime[:, i]
xi = x[:, i]
Ai_row0 = pl.concatenate((pl.zeros(3), -xiPrime[2] * xi, xiPrime[1] * xi))
Ai_row1 = pl.concatenate((xiPrime[2] * xi, pl.zeros(3), -xiPrime[0] * xi))
Ai = pl.row_stack((Ai_row0, Ai_row1))
if A is None:
A = Ai
else:
A = pl.vstack((A, Ai))
U, S, V = pl.svd(A)
V = V.T
h = V[:, -1]
H = pl.reshape(h, (3, 3))
return H
def homog3D(points2d, points3d):
"""
Compute a matrix relating homogeneous 3D points (4xN) to homogeneous
2D points (3xN)
Not sure why anyone would do this. Note that the returned transformation
*NOT* an isometry. But it's here... so deal with it.
"""
numPoints = points2d.shape[1]
assert numPoints >= 4
A = None
for i in range(0, numPoints):
xiPrime = points2d[:, i]
xi = points3d[:, i]
Ai_row0 = pl.concatenate((pl.zeros(4), -xiPrime[2] * xi, xiPrime[1] * xi))
Ai_row1 = pl.concatenate((xiPrime[2] * xi, pl.zeros(4), -xiPrime[0] * xi))
Ai = pl.row_stack((Ai_row0, Ai_row1))
if A is None:
A = Ai
else:
A = pl.vstack((A, Ai))
U, S, V = pl.svd(A)
V = V.T
h = V[:, -1]
P = pl.reshape(h, (3, 4))
return P
def main(): shifts = [ [-1, 1], [0, 1], [1, 1], [-1, 0], [1, 0], [-1, -1], [0, -1], [1, -1] ] num_atoms = 100 num_dims = 2 # dimensions coords = pl.random((num_atoms, num_dims)) chosen = pl.random_integers(num_atoms) # from 1 to num_atoms chosen -= 1 # from 0 to num_atoms - 1 for i in range(len(shifts)): coords = pl.vstack((coords, coords[:num_atoms] + shifts[i])) num_atoms *= 9 # after 8 shifts added max_distance = 0.9 for i in range(num_atoms): if i != chosen: dx = coords[chosen, 0] - coords[i, 0] dy = coords[chosen, 1] - coords[i, 1] distance = pl.sqrt(dx*dx + dy*dy) if distance < max_distance: pl.plot([coords[i, 0]], [coords[i, 1]], "bo") else: pl.plot([coords[i, 0]], [coords[i, 1]], "ko") # plot last for visibility pl.plot([coords[chosen, 0]], [coords[chosen, 1]], "ro") pl.grid(True) pl.show()
def homog3D (points2d, points3d):
"""
Compute a matrix relating homogeneous 3D points (4xN) to homogeneous
2D points (3xN)
Not sure why anyone would do this. Note that the returned transformation
*NOT* an isometry. But it's here... so deal with it.
"""
numPoints = points2d.shape[1]
assert (numPoints >= 4)
A = None
for i in range (0, numPoints):
xiPrime = points2d[:,i]
xi = points3d[:,i]
Ai_row0 = pl.concatenate ((pl.zeros (4,), -xiPrime[2]*xi, xiPrime[1]*xi))
Ai_row1 = pl.concatenate ((xiPrime[2]*xi, pl.zeros (4,), -xiPrime[0]*xi))
Ai = pl.row_stack ((Ai_row0, Ai_row1))
if A is None:
A = Ai
else:
A = pl.vstack ((A, Ai))
U, S, V = pl.svd (A)
V = V.T
h = V[:,-1]
P = pl.reshape (h, (3, 4))
return P
def calcJacobian(fun, x0, h=.0001):
"""
calculates the jacobian of a given function fun with respect to its
parameters at the point (array or list) x0.
:args:
fun (function): the function to calcualte the jacobian from
x0 (iterable, e.g. array): position to evaluate the jacobian at
h (float): step size
:returns:
J (n-by-n array): the jacobian of f at x0
"""
J = []
x = array(x0)
for elem, val in enumerate(x0):
ICp = x.copy()
ICp[elem] += h
resp = fun(ICp)
ICn = x.copy()
ICn[elem] -= h
resn = fun(ICn)
J.append((resp - resn) / (2. * h))
J = vstack(J).T
return J
def dS_dP(x0, PR, keys = [('k',750.),('alpha',0.05),('L0',0.05),
('beta',0.05), ('dE', 7.5) ], r_mag = .005):
"""
calculates the SLIP derivative with respect to 'keys'
keys is a list of tuples with the keys of PR that should be changed,
and the order of magnitude of deviation (i.e. something like std(x))
-- only for a single step --
"""
df = []
# r_mag = .005 # here: relative magnitude of disturbance in standrad dev's
for elem,mag in keys:
h = r_mag*mag
# positive direction
PRp = copy.deepcopy(PR)
PRp[elem] += h
resR = sl.SLIP_step3D(x0, PRp)
SRp = array([resR['y'][-1], resR['vx'][-1], resR['vz'][-1]])
#fhp = array(SR2 - x0)
# positive direction
PRn = copy.deepcopy(PR)
PRn[elem] -= h
resR = sl.SLIP_step3D(x0, PRn)
SRn = array([resR['y'][-1], resR['vx'][-1], resR['vz'][-1]])
#fhn = array(SR2 - x0)
# derivative: difference quotient
df.append( (SRp - SRn)/(2.*h) )
return vstack(df).T
def gen_std_devs(climate, multi_cities, years):
"""
For each year in years, compute the standard deviation over the averaged yearly
temperatures for each city in multi_cities.
Args:
climate: instance of Climate
multi_cities: the names of cities we want to use in our std dev calculation (list of str)
years: the range of years to calculate standard deviation for (list of int)
Returns:
a pylab 1-d array of floats with length = len(years). Each element in
this array corresponds to the standard deviation of the average annual
city temperatures for the given cities in a given year.
"""
std_dev = []
for year in years:
yearly_temps = None
for city in multi_cities:
if yearly_temps is None:
yearly_temps = climate.get_yearly_temp(city, year)
else:
yearly_temp = climate.get_yearly_temp(city, year)
yearly_temps = pylab.vstack((yearly_temps, yearly_temp))
if yearly_temps.ndim > 1:
yearly_temps = pylab.average(yearly_temps, axis=0)
std_dev.append(pylab.std(yearly_temps))
return pylab.array(std_dev)
def homog2D (xPrime, x):
"""
Compute the 3x3 homography matrix mapping a set of N 2D homogeneous
points (3xN) to another set (3xN)
"""
numPoints = xPrime.shape[1]
assert (numPoints >= 4)
A = None
for i in range (0, numPoints):
xiPrime = xPrime[:,i]
xi = x[:,i]
Ai_row0 = pl.concatenate ((pl.zeros (3,), -xiPrime[2]*xi, xiPrime[1]*xi))
Ai_row1 = pl.concatenate ((xiPrime[2]*xi, pl.zeros (3,), -xiPrime[0]*xi))
Ai = pl.row_stack ((Ai_row0, Ai_row1))
if A is None:
A = Ai
else:
A = pl.vstack ((A, Ai))
U, S, V = pl.svd (A)
V = V.T
h = V[:,-1]
H = pl.reshape (h, (3, 3))
return H
def plot_matches(im1, im2, locs1, locs2, matchscores, show_below=True):
"""
Show a figure with lines joining the accepted matches
input: im1, im2 (images as arrays), locs1, locs2 (feature locations),
matchscores (as output from the match() method)
show_below (if images should be shown below matches).
:param im1:
:param im2:
:param locs1:
:param locs2:
:param matchscores:
:param show_below:
:return:
"""
im3 = appendImages(im1, im2)
if show_below:
im3 = vstack((im3, im3))
imshow(im3)
cols1 = im1.shape[1]
for i,m in enumerate(matchscores):
if m>0:
plot([locs1[i][1], locs2[m][1]+cols1], [locs1[i][0],locs2[m][0]],'c')
axis('off')
def dS_dX(x0, PR, h_mag = .0005):
"""
calculates the Jacobian of the SLIP at the given point x0,
with PR beeing the parameters for that step
coordinates under consideration are:
y
vx
vz
only for a single step!
"""
df = []
for dim in range(len(x0)):
delta = zeros_like(x0)
delta[dim] = 1.
h = h_mag * delta
# in positive direction
resRp = sl.SLIP_step3D(x0 + h, PR)
SRp = array([resRp['y'][-1], resRp['vx'][-1], resRp['vz'][-1]])
#fhp = array(SR2 - x0)
# in negative direction
resRn = sl.SLIP_step3D(x0 - h, PR)
SRn = array([resRn['y'][-1], resRn['vx'][-1], resRn['vz'][-1]])
#fhn = array(SR2 - x0)
# derivative: difference quotient
df.append( (SRp - SRn)/(2.*h_mag) )
return vstack(df).T
def dS_dP(x0,
PR,
keys=[('k', 750.), ('alpha', 0.05), ('L0', 0.05), ('beta', 0.05),
('dE', 7.5)],
r_mag=.005):
"""
calculates the SLIP derivative with respect to 'keys'
keys is a list of tuples with the keys of PR that should be changed,
and the order of magnitude of deviation (i.e. something like std(x))
-- only for a single step --
"""
df = []
# r_mag = .005 # here: relative magnitude of disturbance in standrad dev's
for elem, mag in keys:
h = r_mag * mag
# positive direction
PRp = copy.deepcopy(PR)
PRp[elem] += h
resR = sl.SLIP_step3D(x0, PRp)
SRp = array([resR['y'][-1], resR['vx'][-1], resR['vz'][-1]])
#fhp = array(SR2 - x0)
# positive direction
PRn = copy.deepcopy(PR)
PRn[elem] -= h
resR = sl.SLIP_step3D(x0, PRn)
SRn = array([resR['y'][-1], resR['vx'][-1], resR['vz'][-1]])
#fhn = array(SR2 - x0)
# derivative: difference quotient
df.append((SRp - SRn) / (2. * h))
return vstack(df).T
def main():
shifts = [[-1, 1], [0, 1], [1, 1], [-1, 0], [1, 0], [-1, -1], [0, -1],
[1, -1]]
num_atoms = 100
num_dims = 2 # dimensions
coords = pl.random((num_atoms, num_dims))
chosen = pl.random_integers(num_atoms) # from 1 to num_atoms
chosen -= 1 # from 0 to num_atoms - 1
for i in range(len(shifts)):
coords = pl.vstack((coords, coords[:num_atoms] + shifts[i]))
num_atoms *= 9 # after 8 shifts added
max_distance = 0.9
for i in range(num_atoms):
if i != chosen:
dx = coords[chosen, 0] - coords[i, 0]
dy = coords[chosen, 1] - coords[i, 1]
distance = pl.sqrt(dx * dx + dy * dy)
if distance < max_distance:
pl.plot([coords[i, 0]], [coords[i, 1]], "bo")
else:
pl.plot([coords[i, 0]], [coords[i, 1]], "ko")
# plot last for visibility
pl.plot([coords[chosen, 0]], [coords[chosen, 1]], "ro")
pl.grid(True)
pl.show()
def dS_dX(x0, PR, h_mag=.0005):
"""
calculates the Jacobian of the SLIP at the given point x0,
with PR beeing the parameters for that step
coordinates under consideration are:
y
vx
vz
only for a single step!
"""
df = []
for dim in range(len(x0)):
delta = zeros_like(x0)
delta[dim] = 1.
h = h_mag * delta
# in positive direction
resRp = sl.SLIP_step3D(x0 + h, PR)
SRp = array([resRp['y'][-1], resRp['vx'][-1], resRp['vz'][-1]])
#fhp = array(SR2 - x0)
# in negative direction
resRn = sl.SLIP_step3D(x0 - h, PR)
SRn = array([resRn['y'][-1], resRn['vx'][-1], resRn['vz'][-1]])
#fhn = array(SR2 - x0)
# derivative: difference quotient
df.append((SRp - SRn) / (2. * h_mag))
return vstack(df).T
def calcJacobian(fun, x0, h=.0001):
"""
calculates the jacobian of a given function fun with respect to its
parameters at the point (array or list) x0.
:args:
fun (function): the function to calcualte the jacobian from
x0 (iterable, e.g. array): position to evaluate the jacobian at
h (float): step size
:returns:
J (n-by-n array): the jacobian of f at x0
"""
J = []
x = array(x0)
for elem, val in enumerate(x0):
ICp = x.copy()
ICp[elem] += h
resp = fun(ICp)
ICn = x.copy()
ICn[elem] -= h
resn = fun(ICn)
J.append((resp - resn) / (2. * h))
J = vstack(J).T
return J
def perspectiveTransform(xy, xaya):
M = array([], dtype=float64).reshape(0, 9)
# Make matrix M for arbritatry amount of points
for i in range(len(xy)):
xi = xy[i][0]
yi = xy[i][1]
xi_ = xaya[i][0]
yi_ = xaya[i][1]
M = vstack([M, array([xi, yi, 1, 0, 0, 0, -xi_*xi, -xi_*yi, -xi_])])
M = vstack([M, array([0, 0, 0, xi, yi, 1, -yi_*xi, -yi_*yi, -yi_])])
# Take SVD
U, S, Vt = sclin.svd(M, full_matrices=True)
# p = last column of V, and because Vt is V transposed, the last row is
# taken.
p = Vt[-1]
return p.reshape((3, 3))
def prepare_line(line, pad=16):
line = line * 1.0 / amax(line)
line = amax(line) - line
line = line.T
if pad > 0:
w = line.shape[1]
line = vstack([zeros((pad, w)), line, zeros((pad, w))])
return line
def get_kin(self, fps=250.):
"""
returns a list of the selected kinematics (one list item for each repetition)
:args:
self: kin object
fps (float, default 250): sampling frequency. Required to correctly compute the velocities.
:returns:
a list. Each element contains the selected (-> self.selection) data with corresponding
velocities (i.e. 2d x n elements per item)
"""
# walk through each element of "selection"
all_pos = []
all_vel = []
for raw in self.raw_dat:
curr_pos = []
curr_vel = []
for elem in self.selection:
items = [x.strip() for x in elem.split('-')] # 1 item if no "-" present
dims = []
markers = []
for item in items:
if item.endswith('_x'):
dims.append(0)
elif item.endswith('_y'):
dims.append(1)
elif item.endswith('_z'):
dims.append(2)
else:
print "invalid marker suffix: ", item
continue
markers.append(item[:-2])
if len(items) == 1: # single marker
curr_pos.append(raw[markers[0]][:, dims[0]])
curr_vel.append(gradient(raw[markers[0]][:, dims[0]]) * fps)
else: # difference between two markers
curr_pos.append(raw[markers[0]][:, dims[0]] - raw[markers[1]][:, dims[1]])
curr_vel.append(gradient(raw[markers[0]][:, dims[0]] - raw[markers[1]][:, dims[1]]) * fps)
all_pos.append(vstack(curr_pos + curr_vel))
all_vel.append(vstack(curr_vel))
return all_pos
def error_color_by_level(current_data):
from pylab import vstack,contourf,plot,ones,arange,colorbar,\
ylim,semilogy
fs = current_data.framesoln
t = current_data.t
pout, level = gridtools1.grid_output_1d(fs, 0, xout, return_level=True)
err = abs(pout - p_true_fcn(xout, t))
Xout = vstack((xout, xout))
Yout = vstack((ylimits_error[0] * ones(xout.shape),
ylimits_error[1] * ones(xout.shape)))
L = vstack((level, level))
contourf(Xout, Yout, L, v_levels, colors=c_levels)
cb = colorbar(ticks=range(1, maxlevels + 1))
cb.set_label('AMR Level')
semilogy(xout, err, 'k')
#semilogy(xout,level,'k')
if tolerance is not None:
plot(xout, tolerance * ones(xout.shape), 'r--')
def _generate_labeled_correlation_matrix(self, label):
""" Concatenates the feature names to the actual correlation matrices.
This is for better overview in stored txt files later on."""
labeled_corr_matrix = pylab.array([])
for i in pylab.array(self.corr_important_feats[label]):
if len(labeled_corr_matrix) == 0:
labeled_corr_matrix = [[('% .2f' % j).rjust(10) for j in i]]
else:
labeled_corr_matrix = pylab.vstack((labeled_corr_matrix,
[[('% .2f' % j).rjust(10) for j in i]]))
labeled_corr_matrix = pylab.c_[self.corr_important_feat_names,
labeled_corr_matrix]
labeled_corr_matrix = pylab.vstack((pylab.hstack((' ',
self.corr_important_feat_names)),
labeled_corr_matrix))
return labeled_corr_matrix
def getAllPrecNoise(self,timePreceedingSignal=-1):
#returns the concatenated Preceeding Noise
precNoise=py.array([])
for tdData in self._thzdata_raw:
tN=self.getPreceedingNoise(tdData,timePreceedingSignal)
if precNoise.shape[0]==0:
precNoise=tN
else:
precNoise=py.vstack((precNoise,tN))
return precNoise
def _generate_labeled_correlation_matrix(self, label):
""" Concatenates the feature names to the actual correlation matrices.
This is for better overview in stored txt files later on."""
labeled_corr_matrix = pylab.array([])
for i in pylab.array(self.corr_important_feats[label]):
if len(labeled_corr_matrix) == 0:
labeled_corr_matrix = [[('% .2f' % j).rjust(10) for j in i]]
else:
labeled_corr_matrix = pylab.vstack(
(labeled_corr_matrix, [[('% .2f' % j).rjust(10)
for j in i]]))
labeled_corr_matrix = pylab.c_[self.corr_important_feat_names,
labeled_corr_matrix]
labeled_corr_matrix = pylab.vstack((pylab.hstack(
(' ', self.corr_important_feat_names)),
labeled_corr_matrix))
return labeled_corr_matrix
def get_kin_apex(self, phases, return_times = False):
"""
returns the kinematic state at the apices which are close to the given phases. Apex is re-calculated.
:args:
self: kin object (-> later: "self")
phases (list): list of lists of apex phases. must match with length of "kin.raw_data".
The n'th list of apex phases will be assigned to the nth "<object>.raw_data" element.
return_times (bool): if true, return only the times at which apex occurred.
:returns:
if lr_split is True:
[[r_apices], [l_apices]]
else:
[[apices], ]
where apices is the kinematic (from <object>.selection at the apices *around* the given phases.
*NOTE* The apices themselves are re-computed for higher accuracy.
"""
all_kin = []
all_kin_orig = self.get_kin()
all_apex_times = []
if len(self.raw_dat) != len(phases):
raise ValueError("length of phases list does not match number of datasets")
for raw, phase, kin_orig in zip(self.raw_dat, phases, all_kin_orig):
kin_apex = []
kin_time = arange(len(raw['phi2'].squeeze()), dtype=float) / 250.
# 1st: compute apex *times*
apex_times = []
for phi_apex in phase:
# 1a: get "rough" estimate
idx_apex = argmin(abs(raw['phi2'].squeeze() - phi_apex))
# 1b: fit quadratic function to com_y
idx_low = max(0, idx_apex - 4)
idx_high = min(raw['com'].shape[0] - 1, idx_apex + 4)
com_y_pt = raw['com'][idx_low:idx_high + 1, 2]
tp = arange(idx_high - idx_low + 1) # improve numerical accuracy: do not take absolute time
p = polyfit(tp, com_y_pt, 2) # p: polynomial, highest power coeff first
t0 = -p[1] / (2.*p[0]) # "real" index of apex (offset is 2: a value of 2
# indicates that apex is exactly at the measured frame
t_apex = kin_time[idx_apex] + (t0 - 4.) / 250.
apex_times.append(t_apex)
if return_times:
all_apex_times.append(array(apex_times))
else:
# 2nd: interpolate data
dat = vstack([interp(apex_times, kin_time, kin_orig[row, :]) for row in arange(kin_orig.shape[0])])
all_kin.append(dat)
if return_times:
return all_apex_times
return all_kin
def sinDistort(data,twisting=1.):
"""
a distortion of the data:
x will be mapped to sin(x/max(abs(x))), for every coordinate
this is to distort a lower dimensional system so that it is not
restricted to a lower-dimensional subspace any longer
data must be given in NxD - Format
the optional twisting factor increases the twisting strength
"""
return vstack([sin( data[:,x]*twisting/max(abs(data[:,x])))*max(abs(data[:,x]))
for x in range(data.shape[1])]).T
def test_sim_data_2(self):
sims = 10000
return # skip for now
test1 = pl.zeros(3, dtype='f').view(pl.recarray)
for i in range(sims):
temp = data.sim_data(1, [0.1,0.1,0.8], [0.01,0.01,0.01])
test1 = pl.vstack((test1, temp))
test1 = test1[1:,]
test2 = data.sim_data(sims, [0.1,0.1,0.8], [0.01, 0.01, 0.01])
diff = (test1.mean(0) - test2.mean(0))/test1.mean(0)
assert pl.allclose(diff, 0, atol=0.01), 'should be close to zero, (%s found)' % str(diff)
def block_hankel(data, f):
"""
Create a block hankel matrix.
f : number of rows
"""
data = pl.matrix(data)
assert len(data.shape) == 2
n = data.shape[1] - f
return pl.matrix(pl.hstack([
pl.vstack([data[:, i+j] for i in range(f)])
for j in range(n)]))
def block_hankel(data, f):
"""
Create a block hankel matrix.
f : number of rows
"""
data = np.matrix(data)
assert len(data.shape) == 2
n = data.shape[1] - f
return np.matrix(pl.hstack([
pl.vstack([data[:, i+j] for i in range(f)])
for j in range(n)]))
def LS(self,X): """ estimate the connectivity kernel parameters and the time constant parameter using Least Square method Arguments ---------- X: list of matrix state vectors Returns --------- Least Square estimation of the the connectivity kernel parameters and the time constant parameter """ q=self.q_calc(X) Z=pb.vstack(X[1:]) X_t_1=pb.vstack(X[:-1]) q_t_1=pb.vstack(q[:-1]) X_ls=pb.hstack((q_t_1,X_t_1)) W=(X_ls.T*X_ls).I*X_ls.T*Z return [float( W[0]),float(W[1]),float(W[2]),float(W[3])]
def save_rsj_params(self):
mat = pl.vstack((pl.array(self.idx), pl.transpose(self.c),\
self.icarr, self.rnarr, self.ioarr, self.voarr,\
self.ic_err_arr, self.rn_err_arr, self.io_err_arr, self.vo_err_arr))
header_c = ''
for n in range(self.cdim):
header_c = header_c + 'Control%d '%(n+1)
header_ = 'Index ' + header_c + \
'Ic Rn Io Vo Ic_err Rn_err Io_err Vo_err'
pl.savetxt(self.get_fullpath(self.fn_rsjparams), pl.transpose(mat),\
header=header_)
def dewarp(self, img, cval=0, dtype=dtype('f')):
assert img.shape == self.shape
h, w = img.shape
hpadding = self.r
padded = vstack(
[cval * ones((hpadding, w)), img, cval * ones((hpadding, w))])
center = self.center + hpadding
dewarped = [
padded[center[i] - self.r:center[i] + self.r, i] for i in range(w)
]
dewarped = array(dewarped, dtype=dtype).T
return dewarped
def approximate(x,y):
"""
Linear approximation of y=f(x) using least square estimator.
In:
x : ndarray
y : ndarray
Out:
a, b : float, as in a*x+b=y
"""
assert pl.shape(x) == pl.shape(y)
A = pl.vstack([x, pl.ones(len(x))]).T
a, b = pl.lstsq(A, y)[0]
return a, b
def sinDistort(data, twisting=1.):
"""
a distortion of the data:
x will be mapped to sin(x/max(abs(x))), for every coordinate
this is to distort a lower dimensional system so that it is not
restricted to a lower-dimensional subspace any longer
data must be given in NxD - Format
the optional twisting factor increases the twisting strength
"""
return vstack([
sin(data[:, x] * twisting / max(abs(data[:, x]))) *
max(abs(data[:, x])) for x in range(data.shape[1])
]).T
def test_sim_data_2(self):
sims = 10000
return # skip for now
test1 = pl.zeros(3, dtype='f').view(pl.recarray)
for i in range(sims):
temp = data.sim_data(1, [0.1, 0.1, 0.8], [0.01, 0.01, 0.01])
test1 = pl.vstack((test1, temp))
test1 = test1[1:, ]
test2 = data.sim_data(sims, [0.1, 0.1, 0.8], [0.01, 0.01, 0.01])
diff = (test1.mean(0) - test2.mean(0)) / test1.mean(0)
assert pl.allclose(
diff, 0,
atol=0.01), 'should be close to zero, (%s found)' % str(diff)
def twoD_oneD(data2D,nps):
"""
transforms the 2D format into the 1D-format used here
1D-format:
a single row represents one stride;
the first (nps) frames represent coordinate 1, the second (nps)
frames represent coordinate 2, ...
2D-format:
a single row represents one coordinate.
The k-th stride is represented by the subsection [:,k*(nps):(k+1)*nps]
"""
data1D = vstack([hstack(data2D[:,x*nps:(x+1)*nps]) for x in range(data2D.shape[1]/nps)])
return data1D
def get_coords_for_frame(self, frame, root_offset=None):
coords = []
def draw_line_to_children(bone, ppos, P, rpos, ppindex):
for child in self.hierarchy[bone]:
cbone = self.bones[child]
C = self.__local_matrices[child + '__C']
Cinv = self.__local_matrices[child + '__Cinv']
B = self.__local_matrices[child + '__B']
#Motion matrix
M = eye(4)
try:
for dof, val in zip(cbone.dof, frame[child]):
val = pl.deg2rad(val)
R = eye(4)
if dof == 'rx':
R = Rx(val)
elif dof == 'ry':
R = Ry(val)
elif dof == 'rz':
R = Rz(val)
M = dot(R, M)
except: #We might not have dof data for the current bone
pass
L = C.dot(M).dot(Cinv).dot(B)
#Full transform
A = dot(P, L)
cpos = dot(A, [0, 0, 0, 1]) + rpos
coords.append(cpos[:3])
draw_line_to_children(child, cpos, A, rpos, len(coords) - 1)
#Root orientation and translation
transf = frame['root'].copy()
if root_offset is not None:
transf[0:3] += root_offset
R = dot(Rz(pl.deg2rad(transf[5])),
dot(Ry(pl.deg2rad(transf[4])), Rx(pl.deg2rad(transf[3]))))
B = T(transf[0:3])
rpos = dot(B, [0, 0, 0, 1]) / 0.45 * Skeleton.SCALE
coords.append(rpos[:3])
draw_line_to_children('root', rpos, R, rpos, 0)
return pl.vstack(coords).T
def predTest(idat, odat, out_of_sample=True, nboot=50, totvar=False, rcond=1e-7):
"""
.. note::
computes how well odat can be predicted (in terms of variance reduction)
using idat, using the bootstrap method
Some formatting test: :py:func:`mutils.statistics.vred`
Parameters
----------
idat : array_like
format: n x d , d-dimensional data in n rows
used to predict odat
odat : array_like
format: n x q, q-dimensional data in n rows
to be predicted from idat
out_of_sample : bool
if True, perform an out-of-sample prediction
nboot : int
the number of bootstrap repetitions to be performed for prediction
totvar : bool
if `True`, the total relative remaining variance will be computed,
otherwise the relative remaining variance for each coordinate
to be predict will be computed
Returns
-------
Return value and format depends on whether or not **totvar** is *True*
* if **totvar** is *True*:
returns an array of dimension nboot x 1, which contains
the relative remaining variance after prediciton
(in nboot bootstrap repetitions)
* if **totvar** is *False*:
returns an array of dimension nboot x q, which contains the
relative remaining variance after prediction for each
coordinate (in nboot bootstrap repetitions)
"""
_, mapsT, idcs = fitData(idat, odat, nps=1, nrep=nboot, rcond=rcond)
maps = [x.T for x in mapsT]
# indices will be "swapped" to "NOT(indices)" in vRedPartial
indices = idcs if out_of_sample else [otheridx(x, idat.shape[0]) for x in idcs]
vaxis = None if totvar else 0
res = vRedPartial(idat, odat, maps, indices, vaxis)
return vstack(res)
def estimate_kernel(self,X): """ estimate the ide parameters using least squares method Arguments ---------- X: list of ndarray state vectors Returns --------- least squares estimation of the IDE parameters """ Q=self.Q_calc(X) Z=pb.vstack(X[1:]) X_t_1=pb.vstack(X[:-1]) Q_t_1=pb.vstack(Q[:-1]) X_ls=pb.hstack((Q_t_1,X_t_1)) theta=dots(pb.inv(pb.dot(X_ls.T,X_ls)),X_ls.T,Z) parameters=[float(theta[i]) for i in range(theta.shape[0])] return parameters
def oneD_twoD(data1D,nps):
"""
transforms the 1D-format used here into the 2D-format
1D-format:
a single row represents one stride;
the first (nps) frames represent coordinate 1, the second (nps)
frames represent coordinate 2, ...
2D-format:
a single row represents one coordinate.
The k-th stride is represented by the subsection [:,k*(nps):(k+1)*nps]
"""
ncoords = data1D.shape[1]/nps
data2D = vstack([hstack(data1D[:,nps*x:nps*(x+1)]) for x in range(ncoords)])
return data2D
def combine_output(J, T, model, dir, reps, save=False):
"""
Combine output on absolute error, relative error, csmf_accuracy, and coverage from from
multiple runs of validate_once. Either saves the output to the disk, or returns arays
for each.
"""
cause = pl.zeros(J*T, dtype='f').view(pl.recarray)
time = pl.zeros(J*T, dtype='f').view(pl.recarray)
abs_err = pl.zeros(J*T, dtype='f').view(pl.recarray)
rel_err = pl.zeros(J*T, dtype='f').view(pl.recarray)
coverage = pl.zeros(J*T, dtype='f').view(pl.recarray)
csmf_accuracy = pl.zeros(J*T, dtype='f').view(pl.recarray)
for i in range(reps):
metrics = pl.csv2rec('%s/metrics_%s_%i.csv' % (dir, model, i))
cause = pl.vstack((cause, metrics.cause))
time = pl.vstack((time, metrics.time))
abs_err = pl.vstack((abs_err, metrics.abs_err))
rel_err = pl.vstack((rel_err, metrics.rel_err))
coverage = pl.vstack((coverage, metrics.coverage))
csmf_accuracy = pl.vstack((csmf_accuracy, metrics.csmf_accuracy))
cause = cause[1:,]
time = time[1:,]
abs_err = abs_err[1:,]
rel_err = rel_err[1:,]
coverage = coverage[1:,]
csmf_accuracy = csmf_accuracy[1:,]
mean_abs_err = abs_err.mean(0)
median_abs_err = pl.median(abs_err, 0)
mean_rel_err = rel_err.mean(0)
median_rel_err = pl.median(rel_err, 0)
mean_csmf_accuracy = csmf_accuracy.mean(0)
median_csmf_accuracy = pl.median(csmf_accuracy, 0)
mean_coverage_bycause = coverage.mean(0)
mean_coverage = coverage.reshape(reps, T, J).mean(0).mean(1)
percent_total_coverage = (coverage.reshape(reps, T, J).sum(2)==3).mean(0)
mean_coverage = pl.array([[i for j in range(J)] for i in mean_coverage]).ravel()
percent_total_coverage = pl.array([[i for j in range(J)] for i in percent_total_coverage]).ravel()
models = pl.array([[model for j in range(J)] for i in range(T)]).ravel()
true_cf = metrics.true_cf
true_std = metrics.true_std
std_bias = metrics.std_bias
all = pl.np.core.records.fromarrays([models, cause[0], time[0], true_cf, true_std, std_bias, mean_abs_err, median_abs_err, mean_rel_err, median_rel_err,
mean_csmf_accuracy, median_csmf_accuracy, mean_coverage_bycause, mean_coverage, percent_total_coverage],
names=['model', 'cause', 'time', 'true_cf', 'true_std', 'std_bias', 'mean_abs_err', 'median_abs_err',
'mean_rel_err', 'median_rel_err', 'mean_csmf_accuracy', 'median_csmf_accuracy',
'mean_covearge_bycause', 'mean_coverage', 'percent_total_coverage'])
if save:
pl.rec2csv(all, '%s/%s_summary.csv' % (dir, model))
else:
return all
def oneD_twoD(data1D, nps):
"""
transforms the 1D-format used here into the 2D-format
1D-format:
a single row represents one stride;
the first (nps) frames represent coordinate 1, the second (nps)
frames represent coordinate 2, ...
2D-format:
a single row represents one coordinate.
The k-th stride is represented by the subsection [:,k*(nps):(k+1)*nps]
"""
ncoords = data1D.shape[1] / nps
data2D = vstack(
[hstack(data1D[:, nps * x:nps * (x + 1)]) for x in range(ncoords)])
return data2D
def my_stats(node):
""" Convenience function to generate a stats dict even if the pymc.Node has no trace
:Parameters:
- `node` : pymc.PyMCObjects.Deterministic
:Results:
- dictionary of statistics
"""
try:
return node.stats()
except AttributeError:
return {'mean': node.value,
'95% HPD interval': pl.vstack((node.value, node.value)).T}
def my_stats(node):
""" Convenience function to generate a stats dict even if the pymc.Node has no trace
:Parameters:
- `node` : pymc.PyMCObjects.Deterministic
:Results:
- dictionary of statistics
"""
try:
return node.stats()
except AttributeError:
return {'mean': node.value,
'95% HPD interval': pl.vstack((node.value, node.value)).T}
def RecalcPhase(data):
"""
this function re-computes the phase of a given artificial Floquet system
analog to the way the phases are computed in the original systen.
Data must be given in a D x N-format,
D: number of Dimensions,
N: number of samples
"""
p1 = -1.*data[0,:] # R_Kne_y - L_kne_y
p2 = data[5,:] - data[2,:] # R_Trc_y - R_Anl_y
p3 = data[6,:] - data[1,:] # L_trc_y - L_Anl_y
p4 = data[2,:] - data[1,:] - data[0,:] # R_anl_y - L_anl_y
allPhrIn_TDR = [vstack((p1,p2,p3,p4)),]
psec_TDR = [p4.copy(),]
phrIn = vstack((p1,p2,p3,p4))
psecIn = p4.copy()
print 'building phaser ...\n'
Phaser = phaser2.Phaser2(y = allPhrIn_TDR, psecData = psec_TDR)
print 'computing phases ...\n'
return Phaser.phaserEval(phrIn,psecIn).squeeze()
def read_kistler(fname):
"""
reads the output text files from Kistler force plate data.
:args:
fname (str): file name of the data file (typically .txt export from
Kistler bioware)
:returns:
dat (dict): dictionary containing the data ("Matlab(r)"-workspace)
"""
data = []
fieldnames = []
desc_found = False
n_past_desc = 0
with open(fname) as f:
for line in f:
if desc_found:
n_past_desc += 1
elif 'description' in line.lower():
desc_found = True
if n_past_desc == 1:
fieldnames = [fn.strip() for fn in line.split('\t')]
if 'time' in fieldnames[0].lower(): # remove invalide name
fieldnames[0] = 'time'
elif n_past_desc == 2:
units = line.split('\t') # actually - this is ignored
elif n_past_desc > 2:
try:
numbers =[float(elem.replace(',','.')) for elem in
line.split('\t')]
data.append(numbers)
except ValueError:
pass
if not desc_found:
raise ValueError('Line with "Description" not found - does not appear'
' to be a valid file!')
data = vstack(data)
d = {}
for nr, fn in enumerate(fieldnames):
d[fn] = data[:, nr]
return d
def main():
pH, pMg, I, T = (7.0, 3, 0.1, 298.15)
db = SqliteDatabase('../res/gibbs.sqlite')
kegg = Kegg.getInstance()
alberty = PsuedoisomerTableThermodynamics(
'../data/thermodynamics/alberty_pseudoisomers.csv')
cids = alberty.get_all_cids()
dG0_f = pylab.zeros((len(cids), 1))
for i, cid in enumerate(cids):
dG0_f[i, 0] = alberty.cid2dG0_tag(cid, pH=pH, pMg=pMg, I=I, T=T)
S = pylab.zeros((0, len(cids)))
rids = []
ec_numbers = []
for rid in kegg.get_all_rids():
sparse = kegg.rid2sparse_reaction(rid)
if not set(cids).issuperset(sparse.keys()):
continue
rids.append(rid)
ec_numbers.append(kegg.rid2ec_list(rid))
S_row = pylab.zeros((1, len(cids)))
for cid, coeff in sparse.iteritems():
S_row[0, cids.index(cid)] = coeff
S = pylab.vstack([S, S_row])
dG0_r = pylab.dot(S, dG0_f)
util._mkdir('../res/arren')
s_writer = csv.writer(open('../res/arren/stoichiomety.csv', 'w'))
r_writer = csv.writer(open('../res/arren/reactions.csv', 'w'))
e_writer = csv.writer(open('../res/arren/ec_numbers.csv', 'w'))
r_writer.writerow(['rid', 'dG0_r'])
e_writer.writerow(['rid', 'ec0', 'ec1', 'ec2', 'ec3'])
for i in xrange(S.shape[0]):
s_writer.writerow(["%d" % x for x in S[i, :]])
for ec in ec_numbers[i].split(';'):
e_writer.writerow(['%d' % rids[i]] + ec.split('.'))
r_writer.writerow(["%d" % rids[i], '%.1f' % dG0_r[i, 0]])
c_writer = csv.writer(open('../res/arren/compounds.csv', 'w'))
c_writer.writerow(['cid', 'dG0_f'])
for j in xrange(len(cids)):
c_writer.writerow(['%d' % cids[j], '%.1f' % dG0_f[j, 0]])
def plot_color_gradients(cmap_category, cmap_list):
global ncolums, number, DBtable
axes[0][ncolums].set_title(cmap_category + ' Monitoring', fontsize=14)
for ax, name in zip(axes, cmap_list):
gradient_vstack = pylab.vstack((value[number], value[number]))
number+=1
ax[ncolums].imshow(gradient_vstack, aspect='auto', cmap='Blues')
pos = list(ax[ncolums].get_position().bounds)
x_text = pos[0] - 0.01
y_text = pos[1] + pos[3]/2.
fig.text(x_text, y_text, name, va='center', ha='right', fontsize=10)
# Turn off *all* ticks & spines, not just the ones with colormaps.
for ax in axes:
ax[ncolums].set_axis_off()
ncolums=ncolums+1
def main():
pH, pMg, I, T = (7.0, 3, 0.1, 298.15)
db = SqliteDatabase('../res/gibbs.sqlite')
kegg = Kegg.getInstance()
alberty = PsuedoisomerTableThermodynamics('../data/thermodynamics/alberty_pseudoisomers.csv')
cids = alberty.get_all_cids()
dG0_f = pylab.zeros((len(cids), 1))
for i, cid in enumerate(cids):
dG0_f[i, 0] = alberty.cid2dG0_tag(cid, pH=pH, pMg=pMg, I=I, T=T)
S = pylab.zeros((0, len(cids)))
rids = []
ec_numbers = []
for rid in kegg.get_all_rids():
sparse = kegg.rid2sparse_reaction(rid)
if not set(cids).issuperset(sparse.keys()):
continue
rids.append(rid)
ec_numbers.append(kegg.rid2ec_list(rid))
S_row = pylab.zeros((1, len(cids)))
for cid, coeff in sparse.iteritems():
S_row[0, cids.index(cid)] = coeff
S = pylab.vstack([S, S_row])
dG0_r = pylab.dot(S, dG0_f)
util._mkdir('../res/arren')
s_writer = csv.writer(open('../res/arren/stoichiomety.csv', 'w'))
r_writer = csv.writer(open('../res/arren/reactions.csv', 'w'))
e_writer = csv.writer(open('../res/arren/ec_numbers.csv', 'w'))
r_writer.writerow(['rid', 'dG0_r'])
e_writer.writerow(['rid', 'ec0', 'ec1', 'ec2', 'ec3'])
for i in xrange(S.shape[0]):
s_writer.writerow(["%d" % x for x in S[i,:]])
for ec in ec_numbers[i].split(';'):
e_writer.writerow(['%d' % rids[i]] + ec.split('.'))
r_writer.writerow(["%d" % rids[i], '%.1f' % dG0_r[i,0]])
c_writer = csv.writer(open('../res/arren/compounds.csv', 'w'))
c_writer.writerow(['cid', 'dG0_f'])
for j in xrange(len(cids)):
c_writer.writerow(['%d' % cids[j], '%.1f' % dG0_f[j, 0]])