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Make a simulation of the system defined by functions f and G.
dy = f(y,t)dt + G(y,t).dW with initial condition y0
This helper function is for convenience, making it easy to define
one-off simulations interactively in ipython.
Args:
f: callable(y, t) (defined in global scope) returning (n,) array
Vector-valued function to define the deterministic part of the system
G: callable(y, t) (defined in global scope) returning (n,m) array
Optional matrix-valued function to define noise coefficients of an Ito
SDE system.
y0 (array): Initial condition
name (str): Optional class name for the new model
modelType (type): The type of model to simulate. Must be a subclass of
nsim.Model, for example nsim.ODEModel, nsim.ItoModel or
nsim.StratonovichModel. The default is nsim.ItoModel.
T: Total length of time to simulate, in seconds.
dt: Timestep for numerical integration.
repeat (int, optional)
identical (bool, optional)
Returns:
Simulation
Raises:
SimValueError, SimTypeError
def newsim(f, G, y0, name='NewModel', modelType=ItoModel, T=60.0, dt=0.005, repeat=1, identical=True):
"""Make a simulation of the system defined by functions f and G.
dy = f(y,t)dt + G(y,t).dW with initial condition y0
This helper function is for convenience, making it easy to define
one-off simulations interactively in ipython.
Args:
f: callable(y, t) (defined in global scope) returning (n,) array
Vector-valued function to define the deterministic part of the system
G: callable(y, t) (defined in global scope) returning (n,m) array
Optional matrix-valued function to define noise coefficients of an Ito
SDE system.
y0 (array): Initial condition
name (str): Optional class name for the new model
modelType (type): The type of model to simulate. Must be a subclass of
nsim.Model, for example nsim.ODEModel, nsim.ItoModel or
nsim.StratonovichModel. The default is nsim.ItoModel.
T: Total length of time to simulate, in seconds.
dt: Timestep for numerical integration.
repeat (int, optional)
identical (bool, optional)
Returns:
Simulation
Raises:
SimValueError, SimTypeError
"""
NewModel = newmodel(f, G, y0, name, modelType)
if repeat == 1:
return Simulation(NewModel(), T, dt)
else:
return RepeatedSim(NewModel, T, dt, repeat, identical) |
Use the functions f and G to define a new Model class for simulations.
It will take functions f and G from global scope and make a new Model class
out of them. It will automatically gather any globals used in the definition
of f and G and turn them into attributes of the new Model.
Args:
f: callable(y, t) (defined in global scope) returning (n,) array
Scalar or vector-valued function to define the deterministic part
G: callable(y, t) (defined in global scope) returning (n,m) array
Optional scalar or matrix-valued function to define noise coefficients
of a stochastic system. This should be ``None`` for an ODE system.
y0 (Number or array): Initial condition
name (str): Optional class name for the new model
modelType (type): The type of model to simulate. Must be a subclass of
nsim.Model, for example nsim.ODEModel, nsim.ItoModel or
nsim.StratonovichModel. The default is nsim.ItoModel.
Returns:
new class (subclass of Model)
Raises:
SimValueError, SimTypeError
def newmodel(f, G, y0, name='NewModel', modelType=ItoModel):
"""Use the functions f and G to define a new Model class for simulations.
It will take functions f and G from global scope and make a new Model class
out of them. It will automatically gather any globals used in the definition
of f and G and turn them into attributes of the new Model.
Args:
f: callable(y, t) (defined in global scope) returning (n,) array
Scalar or vector-valued function to define the deterministic part
G: callable(y, t) (defined in global scope) returning (n,m) array
Optional scalar or matrix-valued function to define noise coefficients
of a stochastic system. This should be ``None`` for an ODE system.
y0 (Number or array): Initial condition
name (str): Optional class name for the new model
modelType (type): The type of model to simulate. Must be a subclass of
nsim.Model, for example nsim.ODEModel, nsim.ItoModel or
nsim.StratonovichModel. The default is nsim.ItoModel.
Returns:
new class (subclass of Model)
Raises:
SimValueError, SimTypeError
"""
if not issubclass(modelType, Model):
raise SimTypeError('modelType must be a subclass of nsim.Model')
if not callable(f) or (G is not None and not callable(G)):
raise SimTypeError('f and G must be functions of y and t.')
if G is not None and f.__globals__ is not G.__globals__:
raise SimValueError('f and G must be defined in the same place')
# TODO: validate that f and G are defined at global scope.
# TODO: Handle nonlocals used in f,G so that we can lift this restriction.
if modelType is ODEModel and G is not None and not np.all(G == 0.0):
raise SimValueError('For an ODEModel, noise matrix G should be None')
if G is None or modelType is ODEModel:
newclass = type(name, (ODEModel,), dict())
setattr(newclass, 'f', staticmethod(__clone_function(f, 'f')))
else:
newclass = type(name, (modelType,), dict())
setattr(newclass, 'f', staticmethod(__clone_function(f, 'f')))
setattr(newclass, 'G', staticmethod(__clone_function(G, 'G')))
setattr(newclass, 'y0', copy.deepcopy(y0))
# For any global that is used by the functions f or G, create a
# corresponding attribute in our new class.
globals_used = [x for x in f.__globals__ if (x in f.__code__.co_names or
G is not None and x in G.__code__.co_names)]
for x in globals_used:
if G is None:
setattr(newclass, x, __AccessDict(x, newclass.f.__globals__))
else:
setattr(newclass, x, __AccessDicts(x, newclass.f.__globals__,
newclass.G.__globals__))
# Put the new class into namespace __main__ (to cause dill to pickle it)
newclass.__module__ = '__main__'
import __main__
__main__.__dict__[name] = newclass
return newclass |
Make a new version of a function that has its own independent copy
of any globals that it uses directly, and has its own name.
All other attributes are assigned from the original function.
Args:
f: the function to clone
name (str): the name for the new function (if None, keep the same name)
Returns:
A copy of the function f, having its own copy of any globals used
Raises:
SimValueError
def __clone_function(f, name=None):
"""Make a new version of a function that has its own independent copy
of any globals that it uses directly, and has its own name.
All other attributes are assigned from the original function.
Args:
f: the function to clone
name (str): the name for the new function (if None, keep the same name)
Returns:
A copy of the function f, having its own copy of any globals used
Raises:
SimValueError
"""
if not isinstance(f, types.FunctionType):
raise SimTypeError('Given parameter is not a function.')
if name is None:
name = f.__name__
newglobals = f.__globals__.copy()
globals_used = [x for x in f.__globals__ if x in f.__code__.co_names]
for x in globals_used:
gv = f.__globals__[x]
if isinstance(gv, types.FunctionType):
# Recursively clone any global functions used by this function.
newglobals[x] = __clone_function(gv)
elif isinstance(gv, types.ModuleType):
newglobals[x] = gv
else:
# If it is something else, deep copy it.
newglobals[x] = copy.deepcopy(gv)
newfunc = types.FunctionType(
f.__code__, newglobals, name, f.__defaults__, f.__closure__)
return newfunc |
Insert a new axis, at a given position in the array shape
Args:
axis (int): Position (amongst axes) where new axis is to be inserted.
def expand_dims(self, axis):
"""Insert a new axis, at a given position in the array shape
Args:
axis (int): Position (amongst axes) where new axis is to be inserted.
"""
if axis <= self._distaxis:
subaxis = axis
new_distaxis = self._distaxis + 1
else:
subaxis = axis - 1
new_distaxis = self._distaxis
new_subts = [rts.expand_dims(subaxis) for rts in self._subarrays]
if axis == 0:
# prepended an axis: no longer a Timeseries
return distob.DistArray(new_subts, new_distaxis)
else:
axislabels = self.labels[self._distaxis]
return DistTimeseries(new_subts, new_distaxis, axislabels) |
Calculate the absolute value element-wise.
Returns:
absolute (Timeseries):
Absolute value. For complex input (a + b*j) gives sqrt(a**a + b**2)
def absolute(self):
"""Calculate the absolute value element-wise.
Returns:
absolute (Timeseries):
Absolute value. For complex input (a + b*j) gives sqrt(a**a + b**2)
"""
da = distob.vectorize(np.absolute)(self)
return _dts_from_da(da, self.tspan, self.labels) |
Return the angle of a complex Timeseries
Args:
deg (bool, optional):
Return angle in degrees if True, radians if False (default).
Returns:
angle (Timeseries):
The counterclockwise angle from the positive real axis on
the complex plane, with dtype as numpy.float64.
def angle(self, deg=False):
"""Return the angle of a complex Timeseries
Args:
deg (bool, optional):
Return angle in degrees if True, radians if False (default).
Returns:
angle (Timeseries):
The counterclockwise angle from the positive real axis on
the complex plane, with dtype as numpy.float64.
"""
if self.dtype.str[1] != 'c':
warnings.warn('angle() is intended for complex-valued timeseries',
RuntimeWarning, 1)
da = distob.vectorize(np.angle)(self, deg)
return _dts_from_da(da, self.tspan, self.labels) |
How to couple the output of one subsystem to the input of another.
This is a fallback default coupling function that should usually be
replaced with your own.
This example coupling function takes the mean of all variables of the
source subsystem and uses that value weighted by the connection
strength to drive all variables of the target subsystem.
Arguments:
source_y (array of shape (d,)): State of the source subsystem.
target_y (array of shape (d,)): State of target subsystem.
weight (float): the connection strength for this connection.
Returns:
input (array of shape (d,)): Values to drive each variable of the
target system.
def coupling(self, source_y, target_y, weight):
"""How to couple the output of one subsystem to the input of another.
This is a fallback default coupling function that should usually be
replaced with your own.
This example coupling function takes the mean of all variables of the
source subsystem and uses that value weighted by the connection
strength to drive all variables of the target subsystem.
Arguments:
source_y (array of shape (d,)): State of the source subsystem.
target_y (array of shape (d,)): State of target subsystem.
weight (float): the connection strength for this connection.
Returns:
input (array of shape (d,)): Values to drive each variable of the
target system.
"""
return np.ones_like(target_y)*np.mean(source_y)*weight |
Deterministic term f of the complete network system
dy = f(y, t)dt + G(y, t).dot(dW)
(or for an ODE network system without noise, dy/dt = f(y, t))
Args:
y (array of shape (d,)): where d is the dimension of the overall
state space of the complete network system.
Returns:
f (array of shape (d,)): Defines the deterministic term of the
complete network system
def f(self, y, t):
"""Deterministic term f of the complete network system
dy = f(y, t)dt + G(y, t).dot(dW)
(or for an ODE network system without noise, dy/dt = f(y, t))
Args:
y (array of shape (d,)): where d is the dimension of the overall
state space of the complete network system.
Returns:
f (array of shape (d,)): Defines the deterministic term of the
complete network system
"""
coupling = self.coupling_function[0]
res = np.empty_like(self.y0)
for j, m in enumerate(self.submodels):
slicej = slice(self._si[j], self._si[j+1])
target_y = y[slicej] # target node state
res[slicej] = m.f(target_y, t) # deterministic part of submodel j
# get indices of all source nodes that provide input to node j:
sources = np.nonzero(self.network[:,j])[0]
for i in sources:
weight = self.network[i, j]
source_y = y[slice(self._si[i], self._si[i+1])] # source state
res[slicej] += coupling(source_y, target_y, weight)
return res |
Noise coefficient matrix G of the complete network system
dy = f(y, t)dt + G(y, t).dot(dW)
(for an ODE network system without noise this function is not used)
Args:
y (array of shape (d,)): where d is the dimension of the overall
state space of the complete network system.
Returns:
G (array of shape (d, m)): where m is the number of independent
Wiener processes driving the complete network system. The noise
coefficient matrix G defines the stochastic term of the system.
def G(self, y, t):
"""Noise coefficient matrix G of the complete network system
dy = f(y, t)dt + G(y, t).dot(dW)
(for an ODE network system without noise this function is not used)
Args:
y (array of shape (d,)): where d is the dimension of the overall
state space of the complete network system.
Returns:
G (array of shape (d, m)): where m is the number of independent
Wiener processes driving the complete network system. The noise
coefficient matrix G defines the stochastic term of the system.
"""
if self._independent_noise:
# then G matrix consists of submodel Gs diagonally concatenated:
res = np.zeros((self.dimension, self.nnoises))
offset = 0
for j, m in enumerate(self.submodels):
slicej = slice(self._si[j], self._si[j+1])
ix = (slicej, slice(offset, offset + self._nsubnoises[j]))
res[ix] = m.G(y[slicej], t) # submodel noise coefficient matrix
offset += self._nsubnoises[j]
else:
# identical driving: G consists of submodel Gs stacked vertically
res = np.empty((self.dimension, self.nnoises))
for j, m in enumerate(self.submodels):
slicej = slice(self._si[j], self._si[j+1])
ix = (slicej, slice(None))
res[ix] = m.G(y[slicej], t) # submodel noise coefficient matrix
return res |
Allow submodels with scalar equations. Convert to 1D vector systems.
Args:
m (Model)
def _scalar_to_vector(self, m):
"""Allow submodels with scalar equations. Convert to 1D vector systems.
Args:
m (Model)
"""
if not isinstance(m.y0, numbers.Number):
return m
else:
m = copy.deepcopy(m)
t0 = 0.0
if isinstance(m.y0, numbers.Integral):
numtype = np.float64
else:
numtype = type(m.y0)
y0_orig = m.y0
m.y0 = np.array([m.y0], dtype=numtype)
def make_vector_fn(fn):
def newfn(y, t):
return np.array([fn(y[0], t)], dtype=numtype)
newfn.__name__ = fn.__name__
return newfn
def make_matrix_fn(fn):
def newfn(y, t):
return np.array([[fn(y[0], t)]], dtype=numtype)
newfn.__name__ = fn.__name__
return newfn
def make_coupling_fn(fn):
def newfn(source_y, target_y, weight):
return np.array([fn(source_y[0], target_y[0], weight)])
newfn.__name__ = fn.__name__
return newfn
if isinstance(m.f(y0_orig, t0), numbers.Number):
m.f = make_vector_fn(m.f)
if hasattr(m, 'G') and isinstance(m.G(y0_orig,t0), numbers.Number):
m.G = make_matrix_fn(m.G)
if (hasattr(m, 'coupling') and
isinstance(m.coupling(y0_orig, y0_orig, 0.5),
numbers.Number)):
m.coupling = make_coupling_fn(m.coupling)
return m |
Introduce a new axis 2 that ranges across nodes of the network
def _reshape_timeseries(self, ts):
"""Introduce a new axis 2 that ranges across nodes of the network"""
if np.count_nonzero(np.diff(self._sublengths)) == 0:
# then all submodels have the same dimension, so can reshape array
# in place without copying data:
subdim = self.dimension // self._n
shp = list(ts.shape)
shp[1] = self._n
shp.insert(2, subdim)
ts = ts.reshape(tuple(shp)).swapaxes(1, 2)
# label variables only if all sub-models agree on the labels:
all_var_labels = [m.labels for m in self.submodels]
var_labels = all_var_labels[0]
if all(v == var_labels for v in all_var_labels[1:]):
ts.labels[1] = var_labels
ts.labels[2] = self._node_labels()
return ts
else:
# will pad with zeros for submodels with less variables
subdim = max(self._sublengths)
shp = list(ts.shape)
shp[1] = subdim
shp.insert(2, self._n)
ar = np.zeros(shp)
labels = ts.labels
labels.insert(2, self._node_labels())
for k in range(self._n):
sl = slice(self._si[k], self._si[k+1])
ar[:,:,k,...] = ts[:,sl,...]
return Timeseries(ar, ts.tspan, labels) |
Introduce a new axis 2 that ranges across nodes of the network
def _reshape_output(self, ts):
"""Introduce a new axis 2 that ranges across nodes of the network"""
subodim = len(self.submodels[0].output_vars)
shp = list(ts.shape)
shp[1] = subodim
shp.insert(2, self._n)
ts = ts.reshape(tuple(shp))
ts.labels[2] = self._node_labels()
return ts |
Simulated time series
def timeseries(self):
"""Simulated time series"""
if self._timeseries is None:
self.compute()
if isinstance(self.system, NetworkModel):
return self.system._reshape_timeseries(self._timeseries)
else:
return self._timeseries |
Simulated model output
def output(self):
"""Simulated model output"""
if self._timeseries is None:
self.compute()
output = self._timeseries[:, self.system.output_vars]
if isinstance(self.system, NetworkModel):
return self.system._reshape_output(output)
else:
return output |
Rank 3 array representing output time series. Axis 0 is time,
axis 1 ranges across output variables of a single simulation,
axis 2 ranges across different simulation instances.
def output(self):
"""Rank 3 array representing output time series. Axis 0 is time,
axis 1 ranges across output variables of a single simulation,
axis 2 ranges across different simulation instances."""
subts = [s.output for s in self.sims]
sub_ndim = subts[0].ndim
if sub_ndim is 1:
subts = [distob.expand_dims(ts, 1) for ts in subts]
sub_ndim += 1
nodeaxis = sub_ndim
subts = [distob.expand_dims(ts, nodeaxis) for ts in subts]
ts = subts[0].concatenate(subts[1:], axis=nodeaxis)
ts.labels[nodeaxis] = self._node_labels()
return ts |
Given an integer index ix into the list of sims, returns the pair
(s, m) where s is the relevant subsim and m is the subindex into s.
So self[ix] == self._subsims[s][m]
def _tosub(self, ix):
"""Given an integer index ix into the list of sims, returns the pair
(s, m) where s is the relevant subsim and m is the subindex into s.
So self[ix] == self._subsims[s][m]
"""
N = self._n
if ix >= N or ix < -N:
raise IndexError(
'index %d out of bounds for list of %d sims' % (ix, N))
if ix < 0:
ix += N
for s in range(0, self._n):
if self._si[s + 1] - 1 >= ix:
break
m = ix - self._si[s]
return s, m |
Maps a list of integer indices to sub-indices.
ixlist can contain repeated indices and does not need to be sorted.
Returns pair (ss, ms) where ss is a list of subsim numbers and ms is a
list of lists of subindices m (one list for each subsim in ss).
def _tosubs(self, ixlist):
"""Maps a list of integer indices to sub-indices.
ixlist can contain repeated indices and does not need to be sorted.
Returns pair (ss, ms) where ss is a list of subsim numbers and ms is a
list of lists of subindices m (one list for each subsim in ss).
"""
n = len(ixlist)
N = self._n
ss = []
ms = []
if n == 0:
return ss, ms
j = 0 # the position in ixlist currently being processed
ix = ixlist[j]
if ix >= N or ix < -N:
raise IndexError(
'index %d out of bounds for list of %d sims' % (ix, N))
if ix < 0:
ix += N
while j < n:
for s in range(0, self._n):
low = self._si[s]
high = self._si[s + 1]
if ix >= low and ix < high:
ss.append(s)
msj = [ix - low]
j += 1
while j < n:
ix = ixlist[j]
if ix >= N or ix < -N:
raise IndexError(
'index %d out of bounds for list of %d sims' % (
ix, N))
if ix < 0:
ix += N
if ix < low or ix >= high:
break
msj.append(ix - low)
j += 1
ms.append(msj)
if ix < low:
break
return ss, ms |
Rank 3 array representing output time series. Axis 0 is time,
axis 1 ranges across output variables of a single simulation, axis 2
ranges across different simulation instances.
def output(self):
"""Rank 3 array representing output time series. Axis 0 is time,
axis 1 ranges across output variables of a single simulation, axis 2
ranges across different simulation instances."""
subts = [rms.output for rms in self._subsims]
distaxis = subts[0].ndim - 1
return DistTimeseries(subts, distaxis, self._node_labels()) |
For a single variable timeseries, find the times at which the
value crosses ``c`` from above or below. Can optionally set a non-zero
``d`` to impose the condition that the value must wander at least ``d``
units away from ``c`` between crossings.
If the timeseries begins (or ends) exactly at ``c``, then time zero
(or the ending time) is also included as a crossing event,
so that the boundaries of the first and last excursions are included.
If the actual crossing time falls between two time steps, linear
interpolation is used to estimate the crossing time.
Args:
ts: Timeseries (single variable)
c (float): Critical value at which to report crossings.
d (float): Optional min distance from c to be attained between crossings.
Returns:
array of float
def crossing_times(ts, c=0.0, d=0.0):
"""For a single variable timeseries, find the times at which the
value crosses ``c`` from above or below. Can optionally set a non-zero
``d`` to impose the condition that the value must wander at least ``d``
units away from ``c`` between crossings.
If the timeseries begins (or ends) exactly at ``c``, then time zero
(or the ending time) is also included as a crossing event,
so that the boundaries of the first and last excursions are included.
If the actual crossing time falls between two time steps, linear
interpolation is used to estimate the crossing time.
Args:
ts: Timeseries (single variable)
c (float): Critical value at which to report crossings.
d (float): Optional min distance from c to be attained between crossings.
Returns:
array of float
"""
#TODO support multivariate time series
ts = ts.squeeze()
if ts.ndim is not 1:
raise ValueError('Currently can only use on single variable timeseries')
# Translate to put the critical value at zero:
ts = ts - c
tsa = ts[0:-1]
tsb = ts[1:]
# Time indices where phase crosses or reaches zero from below or above
zc = np.nonzero((tsa < 0) & (tsb >= 0) | (tsa > 0) & (tsb <= 0))[0] + 1
# Estimate crossing time interpolated linearly within a single time step
va = ts[zc-1]
vb = ts[zc]
ct = (np.abs(vb)*ts.tspan[zc-1] +
np.abs(va)*ts.tspan[zc]) / np.abs(vb - va) # denominator always !=0
# Also include starting time if we started exactly at zero
if ts[0] == 0.0:
zc = np.r_[np.array([0]), zc]
ct = np.r_[np.array([ts.tspan[0]]), ct]
if d == 0.0 or ct.shape[0] is 0:
return ct
# Time indices where value crosses c+d or c-d:
dc = np.nonzero((tsa < d) & (tsb >= d) | (tsa > -d) & (tsb <= -d))[0] + 1
# Select those zero-crossings separated by at least one d-crossing
splice = np.searchsorted(dc, zc)
which_zc = np.r_[np.array([0]), np.nonzero(splice[0:-1] - splice[1:])[0] +1]
return ct[which_zc] |
For a single variable time series, first wait until the time series
attains the value c for the first time. Then record the time intervals
between successive returns to c. If c is not given, the default is the mean
of the time series.
Args:
ts: Timeseries (single variable)
c (float): Optional target value (default is the mean of the time series)
d (float): Optional min distance from c to be attained between returns
Returns:
array of time intervals (Can take the mean of these to estimate the
expected first return time)
def first_return_times(ts, c=None, d=0.0):
"""For a single variable time series, first wait until the time series
attains the value c for the first time. Then record the time intervals
between successive returns to c. If c is not given, the default is the mean
of the time series.
Args:
ts: Timeseries (single variable)
c (float): Optional target value (default is the mean of the time series)
d (float): Optional min distance from c to be attained between returns
Returns:
array of time intervals (Can take the mean of these to estimate the
expected first return time)
"""
ts = np.squeeze(ts)
if c is None:
c = ts.mean()
if ts.ndim <= 1:
return np.diff(ts.crossing_times(c, d))
else:
return np.hstack(
[ts[..., i].first_return_times(c, d) for i in range(ts.shape[-1])]) |
Returns the discrete, linear convolution of a time series with itself,
optionally using unbiased normalization.
N.B. Autocorrelation estimates are necessarily inaccurate for longer lags,
as there are less pairs of points to convolve separated by that lag.
Therefore best to throw out the results except for shorter lags, e.g.
keep lags from tau=0 up to one quarter of the total time series length.
Args:
normalized (boolean): If True, the time series will first be normalized
to a mean of 0 and variance of 1. This gives autocorrelation 1 at
zero lag.
unbiased (boolean): If True, the result at each lag m will be scaled by
1/(N-m). This gives an unbiased estimation of the autocorrelation of a
stationary process from a finite length sample.
Ref: S. J. Orfanidis (1996) "Optimum Signal Processing", 2nd Ed.
def autocorrelation(ts, normalized=False, unbiased=False):
"""
Returns the discrete, linear convolution of a time series with itself,
optionally using unbiased normalization.
N.B. Autocorrelation estimates are necessarily inaccurate for longer lags,
as there are less pairs of points to convolve separated by that lag.
Therefore best to throw out the results except for shorter lags, e.g.
keep lags from tau=0 up to one quarter of the total time series length.
Args:
normalized (boolean): If True, the time series will first be normalized
to a mean of 0 and variance of 1. This gives autocorrelation 1 at
zero lag.
unbiased (boolean): If True, the result at each lag m will be scaled by
1/(N-m). This gives an unbiased estimation of the autocorrelation of a
stationary process from a finite length sample.
Ref: S. J. Orfanidis (1996) "Optimum Signal Processing", 2nd Ed.
"""
ts = np.squeeze(ts)
if ts.ndim <= 1:
if normalized:
ts = (ts - ts.mean())/ts.std()
N = ts.shape[0]
ar = np.asarray(ts)
acf = np.correlate(ar, ar, mode='full')
outlen = (acf.shape[0] + 1) / 2
acf = acf[(outlen - 1):]
if unbiased:
factor = np.array([1.0/(N - m) for m in range(0, outlen)])
acf = acf * factor
dt = (ts.tspan[-1] - ts.tspan[0]) / (len(ts) - 1.0)
lags = np.arange(outlen)*dt
return Timeseries(acf, tspan=lags, labels=ts.labels)
else:
# recursively handle arrays of dimension > 1
lastaxis = ts.ndim - 1
m = ts.shape[lastaxis]
acfs = [ts[...,i].autocorrelation(normalized, unbiased)[...,np.newaxis]
for i in range(m)]
res = distob.concatenate(acfs, axis=lastaxis)
res.labels[lastaxis] = ts.labels[lastaxis]
return res |
Verifies the value is between 1 and 9 inclusively.
def fan_speed(self, value):
"""Verifies the value is between 1 and 9 inclusively."""
if value not in range(1, 10):
raise exceptions.RoasterValueError
self._fan_speed.value = value |
Verifies that the heat setting is between 0 and 3.
def heat_setting(self, value):
"""Verifies that the heat setting is between 0 and 3."""
if value not in range(0, 4):
raise exceptions.RoasterValueError
self._heat_setting.value = value |
Verifies that the heater_level is between 0 and heater_segments.
Can only be called when freshroastsr700 object is initialized
with ext_sw_heater_drive=True. Will throw RoasterValueError
otherwise.
def heater_level(self, value):
"""Verifies that the heater_level is between 0 and heater_segments.
Can only be called when freshroastsr700 object is initialized
with ext_sw_heater_drive=True. Will throw RoasterValueError
otherwise."""
if self._ext_sw_heater_drive:
if value not in range(0, self._heater_bangbang_segments+1):
raise exceptions.RoasterValueError
self._heater_level.value = value
else:
raise exceptions.RoasterValueError |
THIS FUNCTION MUST BE CALLED BEFORE CALLING
freshroastsr700.auto_connect().
Set, or re-set, the state transition function callback.
The supplied function will be called from a separate thread within
freshroastsr700, triggered by a separate, internal child process.
This function will fail if the freshroastsr700 device is already
connected to hardware, because by that time, the timer process
and thread have already been spawned.
Args:
state_transition_func (func): the function to call for every
state transition. A state transition occurs whenever the
freshroastsr700's time_remaining value counts down to 0.
Returns:
nothing
def set_state_transition_func(self, func):
"""THIS FUNCTION MUST BE CALLED BEFORE CALLING
freshroastsr700.auto_connect().
Set, or re-set, the state transition function callback.
The supplied function will be called from a separate thread within
freshroastsr700, triggered by a separate, internal child process.
This function will fail if the freshroastsr700 device is already
connected to hardware, because by that time, the timer process
and thread have already been spawned.
Args:
state_transition_func (func): the function to call for every
state transition. A state transition occurs whenever the
freshroastsr700's time_remaining value counts down to 0.
Returns:
nothing
"""
if self._connected.value:
logging.error("freshroastsr700.set_state_transition_func must be "
"called before freshroastsr700.auto_connect()."
" Not registering func.")
return False
# no connection yet. so OK to set func pointer
self._create_state_transition_system(func)
return True |
This is the thread that listens to an event from
the comm process to execute the update_data_func callback
in the context of the main process.
def update_data_run(self, event_to_wait_on):
"""This is the thread that listens to an event from
the comm process to execute the update_data_func callback
in the context of the main process.
"""
# with the daemon=Turue setting, this thread should
# quit 'automatically'
while event_to_wait_on.wait():
event_to_wait_on.clear()
if self.update_data_callback_kill_event.is_set():
return
self.update_data_func() |
This is the thread that listens to an event from
the timer process to execute the state_transition_func callback
in the context of the main process.
def state_transition_run(self, event_to_wait_on):
"""This is the thread that listens to an event from
the timer process to execute the state_transition_func callback
in the context of the main process.
"""
# with the daemon=Turue setting, this thread should
# quit 'automatically'
while event_to_wait_on.wait():
event_to_wait_on.clear()
if self.state_transition_callback_kill_event.is_set():
return
self.state_transition_func() |
Do not call this directly - call auto_connect() or connect(),
which will call _connect() for you.
Connects to the roaster and creates communication thread.
Raises a RoasterLokkupError exception if the hardware is not found.
def _connect(self):
"""Do not call this directly - call auto_connect() or connect(),
which will call _connect() for you.
Connects to the roaster and creates communication thread.
Raises a RoasterLokkupError exception if the hardware is not found.
"""
# the following call raises a RoasterLookupException when the device
# is not found. It is
port = utils.find_device('1A86:5523')
# on some systems, after the device port is added to the device list,
# it can take up to 20 seconds after USB insertion for
# the port to become available... (!)
# let's put a safety timeout in here as a precaution
wait_timeout = time.time() + 40.0 # should be PLENTY of time!
# let's update the _connect_state while we're at it...
self._connect_state.value = self.CS_CONNECTING
connect_success = False
while time.time() < wait_timeout:
try:
self._ser = serial.Serial(
port=port,
baudrate=9600,
bytesize=8,
parity='N',
stopbits=1.5,
timeout=0.25,
xonxoff=False,
rtscts=False,
dsrdtr=False)
connect_success = True
break
except serial.SerialException:
time.sleep(0.5)
if not connect_success:
# timeout on attempts
raise exceptions.RoasterLookupError
self._initialize() |
Sends the initialization packet to the roaster.
def _initialize(self):
"""Sends the initialization packet to the roaster."""
self._header.value = b'\xAA\x55'
self._current_state.value = b'\x00\x00'
s = self._generate_packet()
self._ser.write(s)
self._header.value = b'\xAA\xAA'
self._current_state.value = b'\x02\x01'
return self._read_existing_recipe() |
Attempt to connect to hardware immediately. Will not retry.
Check freshroastsr700.connected or freshroastsr700.connect_state
to verify result.
Raises:
freshroastsr700.exeptions.RoasterLookupError
No hardware connected to the computer.
def connect(self):
"""Attempt to connect to hardware immediately. Will not retry.
Check freshroastsr700.connected or freshroastsr700.connect_state
to verify result.
Raises:
freshroastsr700.exeptions.RoasterLookupError
No hardware connected to the computer.
"""
self._start_connect(self.CA_SINGLE_SHOT)
while(self._connect_state.value == self.CS_ATTEMPTING_CONNECT or
self._connect_state.value == self.CS_CONNECTING):
time.sleep(0.1)
if self.CS_CONNECTED != self._connect_state.value:
raise exceptions.RoasterLookupError |
Starts the connection process, as called (internally)
from the user context, either from auto_connect() or connect().
Never call this from the _comm() process context.
def _start_connect(self, connect_type):
"""Starts the connection process, as called (internally)
from the user context, either from auto_connect() or connect().
Never call this from the _comm() process context.
"""
if self._connect_state.value != self.CS_NOT_CONNECTED:
# already done or in process, assume success
return
self._connected.value = 0
self._connect_state.value = self.CS_ATTEMPTING_CONNECT
# tell comm process to attempt connection
self._attempting_connect.value = connect_type
# EXTREMELY IMPORTANT - for this to work at all in Windows,
# where the above processes are spawned (vs forked in Unix),
# the thread objects (as sattributes of this object) must be
# assigned to this object AFTER we have spawned the processes.
# That way, multiprocessing can pickle the freshroastsr700
# successfully. (It can't pickle thread-related stuff.)
if self.update_data_func is not None:
# Need to launch the thread that will listen to the event
self._create_update_data_system(
None, setFunc=False, createThread=True)
self.update_data_thread.start()
if self.state_transition_func is not None:
# Need to launch the thread that will listen to the event
self._create_state_transition_system(
None, setFunc=False, createThread=True)
self.state_transition_thread.start() |
Attempts to connect to the roaster every quarter of a second.
def _auto_connect(self):
"""Attempts to connect to the roaster every quarter of a second."""
while not self._teardown.value:
try:
self._connect()
return True
except exceptions.RoasterLookupError:
time.sleep(.25)
return False |
Do not call this directly - call auto_connect(), which will spawn
comm() for you.
This is the main communications loop to the roaster.
whenever a valid packet is received from the device, if an
update_data_event is available, it will be signalled.
Args:
thermostat (bool): thermostat mode.
if set to True, turns on thermostat mode. In thermostat
mode, freshroastsr700 takes control of heat_setting and does
software PID control to hit the demanded target_temp.
ext_sw_heater_drive (bool): enable direct control over the internal
heat_controller object. Defaults to False. When set to True, the
thermostat field is IGNORED, and assumed to be False. Direct
control over the software heater_level means that the
PID controller cannot control the heater. Since thermostat and
ext_sw_heater_drive cannot be allowed to both be True, this arg
is given precedence over the thermostat arg.
kp (float): Kp value to use for PID control. Defaults to 0.06.
ki (float): Ki value to use for PID control. Defaults to 0.0075.
kd (float): Kd value to use for PID control. Defaults to 0.01.
heater_segments (int): the pseudo-control range for the internal
heat_controller object. Defaults to 8.
update_data_event (multiprocessing.Event): If set, allows the
comm_process to signal to the parent process that new device data
is available.
Returns:
nothing
def _comm(self, thermostat=False,
kp=0.06, ki=0.0075, kd=0.01,
heater_segments=8, ext_sw_heater_drive=False,
update_data_event=None):
"""Do not call this directly - call auto_connect(), which will spawn
comm() for you.
This is the main communications loop to the roaster.
whenever a valid packet is received from the device, if an
update_data_event is available, it will be signalled.
Args:
thermostat (bool): thermostat mode.
if set to True, turns on thermostat mode. In thermostat
mode, freshroastsr700 takes control of heat_setting and does
software PID control to hit the demanded target_temp.
ext_sw_heater_drive (bool): enable direct control over the internal
heat_controller object. Defaults to False. When set to True, the
thermostat field is IGNORED, and assumed to be False. Direct
control over the software heater_level means that the
PID controller cannot control the heater. Since thermostat and
ext_sw_heater_drive cannot be allowed to both be True, this arg
is given precedence over the thermostat arg.
kp (float): Kp value to use for PID control. Defaults to 0.06.
ki (float): Ki value to use for PID control. Defaults to 0.0075.
kd (float): Kd value to use for PID control. Defaults to 0.01.
heater_segments (int): the pseudo-control range for the internal
heat_controller object. Defaults to 8.
update_data_event (multiprocessing.Event): If set, allows the
comm_process to signal to the parent process that new device data
is available.
Returns:
nothing
"""
# since this process is started with daemon=True, it should exit
# when the owning process terminates. Therefore, safe to loop forever.
while not self._teardown.value:
# waiting for command to attempt connect
# print( "waiting for command to attempt connect")
while self._attempting_connect.value == self.CA_NONE:
time.sleep(0.25)
if self._teardown.value:
break
# if we're tearing down, bail now.
if self._teardown.value:
break
# we got the command to attempt to connect
# change state to 'attempting_connect'
self._connect_state.value = self.CS_ATTEMPTING_CONNECT
# attempt connection
if self.CA_AUTO == self._attempting_connect.value:
# this call will block until a connection is achieved
# it will also set _connect_state to CS_CONNECTING
# if appropriate
if self._auto_connect():
# when we unblock, it is an indication of a successful
# connection
self._connected.value = 1
self._connect_state.value = self.CS_CONNECTED
else:
# failure, normally due to a timeout
self._connected.value = 0
self._connect_state.value = self.CS_NOT_CONNECTED
# we failed to connect - start over from the top
# reset flag
self._attempting_connect.value = self.CA_NONE
continue
elif self.CA_SINGLE_SHOT == self._attempting_connect.value:
# try once, now, if failure, start teh big loop over
try:
self._connect()
self._connected.value = 1
self._connect_state.value = self.CS_CONNECTED
except exceptions.RoasterLookupError:
self._connected.value = 0
self._connect_state.value = self.CS_NOT_CONNECTED
if self._connect_state.value != self.CS_CONNECTED:
# we failed to connect - start over from the top
# reset flag
self._attempting_connect.value = self.CA_NONE
continue
else:
# shouldn't be here
# reset flag
self._attempting_connect.value = self.CA_NONE
continue
# We are connected!
# print( "We are connected!")
# reset flag right away
self._attempting_connect.value = self.CA_NONE
# Initialize PID controller if thermostat function was specified at
# init time
pidc = None
heater = None
if(thermostat):
pidc = pid.PID(kp, ki, kd,
Output_max=heater_segments,
Output_min=0
)
if thermostat or ext_sw_heater_drive:
heater = heat_controller(number_of_segments=heater_segments)
read_state = self.LOOKING_FOR_HEADER_1
r = []
write_errors = 0
read_errors = 0
while not self._disconnect.value:
start = datetime.datetime.now()
# write to device
if not self._write_to_device():
logging.error('comm - _write_to_device() failed!')
write_errors += 1
if write_errors > 3:
# it's time to consider the device as being "gone"
logging.error('comm - 3 successive write '
'failures, disconnecting.')
self._disconnect.value = 1
continue
else:
# reset write_errors
write_errors = 0
# read from device
try:
while self._ser.in_waiting:
_byte = self._ser.read(1)
read_state, r, err = (
self._process_reponse_byte(
read_state, _byte, r, update_data_event))
except IOError:
# typically happens when device is suddenly unplugged
logging.error('comm - read from device failed!')
read_errors += 1
if write_errors > 3:
# it's time to consider the device as being "gone"
logging.error('comm - 3 successive read '
'failures, disconnecting.')
self._disconnect.value = 1
continue
else:
read_errors = 0
# next, drive SW heater when using
# thermostat mode (PID controller calcs)
# or in external sw heater drive mode,
# when roasting.
if thermostat or ext_sw_heater_drive:
if 'roasting' == self.get_roaster_state():
if heater.about_to_rollover():
# it's time to use the PID controller value
# and set new output level on heater!
if ext_sw_heater_drive:
# read user-supplied value
heater.heat_level = self._heater_level.value
else:
# thermostat
output = pidc.update(
self.current_temp, self.target_temp)
heater.heat_level = output
# make this number visible to other processes...
self._heater_level.value = heater.heat_level
# read bang-bang heater output array element & apply it
if heater.generate_bangbang_output():
# ON
self.heat_setting = 3
else:
# OFF
self.heat_setting = 0
else:
# for all other states, heat_level = OFF
heater.heat_level = 0
# make this number visible to other processes...
self._heater_level.value = heater.heat_level
self.heat_setting = 0
# calculate sleep time to stick to 0.25sec period
comp_time = datetime.datetime.now() - start
sleep_duration = 0.25 - comp_time.total_seconds()
if sleep_duration > 0:
time.sleep(sleep_duration)
self._ser.close()
# reset disconnect flag
self._disconnect.value = 0
# reset connection values
self._connected.value = 0
self._connect_state.value = self.CS_NOT_CONNECTED |
Timer loop used to keep track of the time while roasting or
cooling. If the time remaining reaches zero, the roaster will call the
supplied state transistion function or the roaster will be set to
the idle state.
def _timer(self, state_transition_event=None):
"""Timer loop used to keep track of the time while roasting or
cooling. If the time remaining reaches zero, the roaster will call the
supplied state transistion function or the roaster will be set to
the idle state."""
while not self._teardown.value:
state = self.get_roaster_state()
if(state == 'roasting' or state == 'cooling'):
time.sleep(1)
self.total_time += 1
if(self.time_remaining > 0):
self.time_remaining -= 1
else:
if(state_transition_event is not None):
state_transition_event.set()
else:
self.idle()
else:
time.sleep(0.01) |
Returns a string based upon the current state of the roaster. Will
raise an exception if the state is unknown.
Returns:
'idle' if idle,
'sleeping' if sleeping,
'cooling' if cooling,
'roasting' if roasting,
'connecting' if in hardware connection phase,
'unknown' otherwise
def get_roaster_state(self):
"""Returns a string based upon the current state of the roaster. Will
raise an exception if the state is unknown.
Returns:
'idle' if idle,
'sleeping' if sleeping,
'cooling' if cooling,
'roasting' if roasting,
'connecting' if in hardware connection phase,
'unknown' otherwise
"""
value = self._current_state.value
if(value == b'\x02\x01'):
return 'idle'
elif(value == b'\x04\x04'):
return 'cooling'
elif(value == b'\x08\x01'):
return 'sleeping'
# handle null bytes as empty strings
elif(value == b'\x00\x00' or value == b''):
return 'connecting'
elif(value == b'\x04\x02'):
return 'roasting'
else:
return 'unknown' |
Generates a packet based upon the current class variables. Note that
current temperature is not sent, as the original application sent zeros
to the roaster for the current temperature.
def _generate_packet(self):
"""Generates a packet based upon the current class variables. Note that
current temperature is not sent, as the original application sent zeros
to the roaster for the current temperature."""
roaster_time = utils.seconds_to_float(self._time_remaining.value)
packet = (
self._header.value +
self._temp_unit.value +
self._flags.value +
self._current_state.value +
struct.pack(">B", self._fan_speed.value) +
struct.pack(">B", int(round(roaster_time * 10.0))) +
struct.pack(">B", self._heat_setting.value) +
b'\x00\x00' +
self._footer)
return packet |
Set the desired output level. Must be between 0 and
number_of_segments inclusive.
def heat_level(self, value):
"""Set the desired output level. Must be between 0 and
number_of_segments inclusive."""
if value < 0:
self._heat_level = 0
elif round(value) > self._num_segments:
self._heat_level = self._num_segments
else:
self._heat_level = int(round(value)) |
Generates the latest on or off pulse in
the string of on (True) or off (False) pulses
according to the desired heat_level setting. Successive calls
to this function will return the next value in the
on/off array series. Call this at control loop rate to
obtain the necessary on/off pulse train.
This system will not work if the caller expects to be able
to specify a new heat_level at every control loop iteration.
Only the value set at every number_of_segments iterations
will be picked up for output! Call about_to_rollover to determine
if it's time to set a new heat_level, if a new level is desired.
def generate_bangbang_output(self):
"""Generates the latest on or off pulse in
the string of on (True) or off (False) pulses
according to the desired heat_level setting. Successive calls
to this function will return the next value in the
on/off array series. Call this at control loop rate to
obtain the necessary on/off pulse train.
This system will not work if the caller expects to be able
to specify a new heat_level at every control loop iteration.
Only the value set at every number_of_segments iterations
will be picked up for output! Call about_to_rollover to determine
if it's time to set a new heat_level, if a new level is desired."""
if self._current_index >= self._num_segments:
# we're due to switch over to the next
# commanded heat_level
self._heat_level_now = self._heat_level
# reset array index
self._current_index = 0
# return output
out = self._output_array[self._heat_level_now][self._current_index]
self._current_index += 1
return out |
This is a method that will be called every time a packet is opened
from the roaster.
def update_data(self):
"""This is a method that will be called every time a packet is opened
from the roaster."""
time_elapsed = datetime.datetime.now() - self.start_time
crntTemp = self.roaster.current_temp
targetTemp = self.roaster.target_temp
heaterLevel = self.roaster.heater_level
# print(
# "Time: %4.6f, crntTemp: %d, targetTemp: %d, heaterLevel: %d" %
# (time_elapsed.total_seconds(), crntTemp, targetTemp, heaterLevel))
self.file.write(
"%4.6f,%d,%d,%d\n" %
(time_elapsed.total_seconds(), crntTemp, targetTemp, heaterLevel)) |
This is a method that will be called when the time remaining ends.
The current state can be: roasting, cooling, idle, sleeping, connecting,
or unkown.
def next_state(self):
"""This is a method that will be called when the time remaining ends.
The current state can be: roasting, cooling, idle, sleeping, connecting,
or unkown."""
self.active_recipe_item += 1
if self.active_recipe_item >= len(self.recipe):
# we're done!
return
# show state step on screen
print("--------------------------------------------")
print("Setting next process step: %d" % self.active_recipe_item)
print("time:%d, target: %ddegF, fan: %d, state: %s" %
(self.recipe[self.active_recipe_item]['time_remaining'],
self.recipe[self.active_recipe_item]['target_temp'],
self.recipe[self.active_recipe_item]['fan_speed'],
self.recipe[self.active_recipe_item]['state']
))
print("--------------------------------------------")
# set values for next state
self.roaster.time_remaining = (
self.recipe[self.active_recipe_item]['time_remaining'])
self.roaster.target_temp = (
self.recipe[self.active_recipe_item]['target_temp'])
self.roaster.fan_speed = (
self.recipe[self.active_recipe_item]['fan_speed'])
# set state
if(self.recipe[self.active_recipe_item]['state'] == 'roasting'):
self.roaster.roast()
elif(self.recipe[self.active_recipe_item]['state'] == 'cooling'):
self.roaster.cool()
elif(self.recipe[self.active_recipe_item]['state'] == 'idle'):
self.roaster.idle()
elif(self.recipe[self.active_recipe_item]['state'] == 'cooling'):
self.roaster.sleep() |
Process results by providers
def process_results(self):
""" Process results by providers """
for result in self._results:
provider = result.provider
self.providers.append(provider)
if result.error:
self.failed_providers.append(provider)
continue
if not result.response:
continue
# set blacklisted to True if ip is detected with at least one dnsbl
self.blacklisted = True
provider_categories = provider.process_response(result.response)
assert provider_categories.issubset(DNSBL_CATEGORIES)
self.categories = self.categories.union(provider_categories)
self.detected_by[provider.host] = list(provider_categories) |
Make lookup to dnsbl provider
Parameters:
* addr (string) - ip address to check
* provider (string) - dnsbl provider
Returns:
* DNSBLResponse object
Raises:
* ValueError
async def dnsbl_request(self, addr, provider):
"""
Make lookup to dnsbl provider
Parameters:
* addr (string) - ip address to check
* provider (string) - dnsbl provider
Returns:
* DNSBLResponse object
Raises:
* ValueError
"""
response = None
error = None
try:
socket.inet_aton(addr)
except socket.error:
raise ValueError('wrong ip format')
ip_reversed = '.'.join(reversed(addr.split('.')))
dnsbl_query = "%s.%s" % (ip_reversed, provider.host)
try:
async with self._semaphore:
response = await self._resolver.query(dnsbl_query, 'A')
except aiodns.error.DNSError as exc:
if exc.args[0] != 4: # 4: domain name not found:
error = exc
return DNSBLResponse(addr=addr, provider=provider, response=response, error=error) |
Async check ip with dnsbl providers.
Parameters:
* addr - ip address to check
Returns:
* DNSBLResult object
async def _check_ip(self, addr):
"""
Async check ip with dnsbl providers.
Parameters:
* addr - ip address to check
Returns:
* DNSBLResult object
"""
tasks = []
for provider in self.providers:
tasks.append(self.dnsbl_request(addr, provider))
results = await asyncio.gather(*tasks)
return DNSBLResult(addr=addr, results=results) |
sync check multiple ips
def check_ips(self, addrs):
"""
sync check multiple ips
"""
tasks = []
for addr in addrs:
tasks.append(self._check_ip(addr))
return self._loop.run_until_complete(asyncio.gather(*tasks)) |
A generator that will generate a range of floats.
def frange(start, stop, step, precision):
"""A generator that will generate a range of floats."""
value = start
while round(value, precision) < stop:
yield round(value, precision)
value += step |
Finds a connected device with the given VID:PID. Returns the serial
port url.
def find_device(vidpid):
"""Finds a connected device with the given VID:PID. Returns the serial
port url."""
for port in list_ports.comports():
if re.search(vidpid, port[2], flags=re.IGNORECASE):
return port[0]
raise exceptions.RoasterLookupError |
Calculate PID output value for given reference input and feedback.
def update(self, currentTemp, targetTemp):
"""Calculate PID output value for given reference input and feedback."""
# in this implementation, ki includes the dt multiplier term,
# and kd includes the dt divisor term. This is typical practice in
# industry.
self.targetTemp = targetTemp
self.error = targetTemp - currentTemp
self.P_value = self.Kp * self.error
# it is common practice to compute derivative term against PV,
# instead of de/dt. This is because de/dt spikes
# when the set point changes.
# PV version with no dPV/dt filter - note 'previous'-'current',
# that's desired, how the math works out
self.D_value = self.Kd * (self.Derivator - currentTemp)
self.Derivator = currentTemp
self.Integrator = self.Integrator + self.error
if self.Integrator > self.Integrator_max:
self.Integrator = self.Integrator_max
elif self.Integrator < self.Integrator_min:
self.Integrator = self.Integrator_min
self.I_value = self.Integrator * self.Ki
output = self.P_value + self.I_value + self.D_value
if output > self.Output_max:
output = self.Output_max
if output < self.Output_min:
output = self.Output_min
return(output) |
Initilize the setpoint of PID.
def setPoint(self, targetTemp):
"""Initilize the setpoint of PID."""
self.targetTemp = targetTemp
self.Integrator = 0
self.Derivator = 0 |
This is a method that will be called when the time remaining ends.
The current state can be: roasting, cooling, idle, sleeping, connecting,
or unkown.
def next_state(self):
"""This is a method that will be called when the time remaining ends.
The current state can be: roasting, cooling, idle, sleeping, connecting,
or unkown."""
if(self.roaster.get_roaster_state() == 'roasting'):
self.roaster.time_remaining = 20
self.roaster.cool()
elif(self.roaster.get_roaster_state() == 'cooling'):
self.roaster.idle() |
Load dict from file for random words.
:param str file: filename
def load_nouns(self, file):
"""
Load dict from file for random words.
:param str file: filename
"""
with open(os.path.join(main_dir, file + '.dat'), 'r') as f:
self.nouns = json.load(f) |
Load list from file for random mails
:param str file: filename
def load_dmails(self, file):
"""
Load list from file for random mails
:param str file: filename
"""
with open(os.path.join(main_dir, file + '.dat'), 'r') as f:
self.dmails = frozenset(json.load(f)) |
Load dict from file for random nicknames.
:param str file: filename
def load_nicknames(self, file):
"""
Load dict from file for random nicknames.
:param str file: filename
"""
with open(os.path.join(main_dir, file + '.dat'), 'r') as f:
self.nicknames = json.load(f) |
Returns list of random words.
:param str letter: letter
:param int count: how much words
:rtype: list
:returns: list of random words
:raises: ValueError
def random_words(self, letter=None, count=1):
"""
Returns list of random words.
:param str letter: letter
:param int count: how much words
:rtype: list
:returns: list of random words
:raises: ValueError
"""
self.check_count(count)
words = []
if letter is None:
all_words = list(
chain.from_iterable(self.nouns.values()))
try:
words = sample(all_words, count)
except ValueError:
len_sample = len(all_words)
raise ValueError('Param "count" must be less than {0}. \
(It is only {0} words)'.format(len_sample + 1, letter))
elif type(letter) is not str:
raise ValueError('Param "letter" must be string.')
elif letter not in self.available_letters:
raise ValueError(
'Param "letter" must be in {0}.'.format(
self.available_letters))
elif letter in self.available_letters:
try:
words = sample(self.nouns[letter], count)
except ValueError:
len_sample = len(self.nouns[letter])
raise ValueError('Param "count" must be less than {0}. \
(It is only {0} words for letter "{1}")'.format(len_sample + 1, letter))
return words |
Return list of random nicks.
:param str letter: letter
:param str gender: ``'f'`` for female, ``'m'`` for male and None for both
:param int count: how much nicks
:rtype: list
:returns: list of random nicks
:raises: ValueError
def random_nicks(self, letter=None, gender='u', count=1):
"""
Return list of random nicks.
:param str letter: letter
:param str gender: ``'f'`` for female, ``'m'`` for male and None for both
:param int count: how much nicks
:rtype: list
:returns: list of random nicks
:raises: ValueError
"""
self.check_count(count)
nicks = []
if gender not in ('f', 'm', 'u'):
raise ValueError('Param "gender" must be in (f, m, u)')
if letter is None:
all_nicks = list(
chain.from_iterable(self.nicknames[gender].values()))
try:
nicks = sample(all_nicks, count)
except ValueError:
len_sample = len(all_nicks)
raise ValueError('Param "count" must be less than {0}. \
(It is only {0} words.")'.format(len_sample + 1))
elif type(letter) is not str:
raise ValueError('Param "letter" must be string.')
elif letter not in self.available_letters:
raise ValueError(
'Param "letter" must be in "{0}".'.format(
self.available_letters))
elif letter in self.available_letters:
try:
nicks = sample(self.nicknames[gender][letter], count)
except ValueError:
len_sample = len(self.nicknames[gender][letter])
raise ValueError('Param "count" must be less than {0}. \
(It is only {0} nicks for letter "{1}")'.format(len_sample + 1, letter))
return nicks |
Return random e-mails.
:rtype: list
:returns: list of random e-mails
def randomMails(self, count=1):
"""
Return random e-mails.
:rtype: list
:returns: list of random e-mails
"""
self.check_count(count)
random_nicks = self.rn.random_nicks(count=count)
random_domains = sample(self.dmails, count)
return [
nick.lower() + "@" + domain for nick, domain in zip(random_nicks,
random_domains)
] |
Return sentences in list.
:param int sentences: how many sentences
:returns: list of strings with sentence
:rtype: list
def get_sentences_list(self, sentences=1):
"""
Return sentences in list.
:param int sentences: how many sentences
:returns: list of strings with sentence
:rtype: list
"""
if sentences < 1:
raise ValueError('Param "sentences" must be greater than 0.')
sentences_list = []
while sentences:
num_rand_words = random.randint(self.MIN_WORDS, self.MAX_WORDS)
random_sentence = self.make_sentence(
random.sample(self.words, num_rand_words))
sentences_list.append(random_sentence)
sentences -= 1
return sentences_list |
Return a sentence from list of words.
:param list list_words: list of words
:returns: sentence
:rtype: str
def make_sentence(list_words):
"""
Return a sentence from list of words.
:param list list_words: list of words
:returns: sentence
:rtype: str
"""
lw_len = len(list_words)
if lw_len > 6:
list_words.insert(lw_len // 2 + random.choice(range(-2, 2)), ',')
sentence = ' '.join(list_words).replace(' ,', ',')
return sentence.capitalize() + '.' |
Find the cover image path in the OPF file.
Returns a tuple: (image content in base64, file extension)
def _discover_cover_image(zf, opf_xmldoc, opf_filepath):
'''
Find the cover image path in the OPF file.
Returns a tuple: (image content in base64, file extension)
'''
content = None
filepath = None
extension = None
# Strategies to discover the cover-image path:
# e.g.: <meta name="cover" content="cover"/>
tag = find_tag(opf_xmldoc, 'meta', 'name', 'cover')
if tag and 'content' in tag.attributes.keys():
item_id = tag.attributes['content'].value
if item_id:
# e.g.: <item href="cover.jpg" id="cover" media-type="image/jpeg"/>
filepath, extension = find_img_tag(opf_xmldoc, 'item', 'id', item_id)
if not filepath:
filepath, extension = find_img_tag(opf_xmldoc, 'item', 'id', 'cover-image')
if not filepath:
filepath, extension = find_img_tag(opf_xmldoc, 'item', 'id', 'cover')
# If we have found the cover image path:
if filepath:
# The cover image path is relative to the OPF file
base_dir = os.path.dirname(opf_filepath)
# Also, normalize the path (ie opfpath/../cover.jpg -> cover.jpg)
coverpath = os.path.normpath(os.path.join(base_dir, filepath))
content = zf.read(coverpath)
content = base64.b64encode(content)
return content, extension |
Returns a list of objects: {title: str, src: str, level: int, index: int}
def _discover_toc(zf, opf_xmldoc, opf_filepath):
'''
Returns a list of objects: {title: str, src: str, level: int, index: int}
'''
toc = None
# ePub 3.x
tag = find_tag(opf_xmldoc, 'item', 'properties', 'nav')
if tag and 'href' in tag.attributes.keys():
filepath = unquote(tag.attributes['href'].value)
# The xhtml file path is relative to the OPF file
base_dir = os.path.dirname(opf_filepath)
# print('- Reading Nav file: {}/{}'.format(base_dir, filepath))
npath = os.path.normpath(os.path.join(base_dir, filepath))
nav_content = zf.read(npath)
toc_xmldoc = minidom.parseString(nav_content)
_toc = []
for n in toc_xmldoc.getElementsByTagName('a'):
if n.firstChild and ('href' in n.attributes.keys()):
href = unquote(n.attributes['href'].value)
# Discarding CFI links
if '.html' in href or '.xhtml' in href:
title = n.firstChild.nodeValue
# try the second node too (maybe the first child is an empty span)
if not title and n.firstChild.firstChild:
title = n.firstChild.firstChild.nodeValue
title = title.strip() if title else None
if title:
level = -1
parentNode = n.parentNode
avoid_infinite_loop = 0 # simple security issue to avoid infinite loop for bad epub files
while parentNode and parentNode.nodeName != 'nav' and avoid_infinite_loop < 50:
if parentNode.nodeName == 'ol': # count the depth of the a link related to ol items
level += 1
parentNode = parentNode.parentNode
avoid_infinite_loop += 1
level = max(level, 0) # root level is 0, not -1
_toc.append({'title': title, 'src': href, 'level': level})
if _toc:
toc = _toc
if not toc:
# ePub 2.x
tag = find_tag(opf_xmldoc, 'item', 'id', 'ncx')
if not tag:
tag = find_tag(opf_xmldoc, 'item', 'id', 'ncxtoc')
if tag and 'href' in tag.attributes.keys():
filepath = unquote(tag.attributes['href'].value)
# The ncx file path is relative to the OPF file
base_dir = os.path.dirname(opf_filepath)
# print('- Reading NCX file: {}/{}'.format(base_dir, filepath))
npath = os.path.normpath(os.path.join(base_dir, filepath))
ncx_content = zf.read(npath)
toc_xmldoc = minidom.parseString(ncx_content)
def read_nav_point(nav_point_node, level = 0):
items = []
item = {'title': None, 'src': None, 'level': level}
children_points = []
for item_node in nav_point_node.childNodes:
if item_node.nodeName in ('navLabel', 'ncx:navLabel'):
try:
text = item_node.getElementsByTagName('text')[0].firstChild
except IndexError:
try:
text = item_node.getElementsByTagName('ncx:text')[0].firstChild
except IndexError:
text = None
item['title'] = text.nodeValue.strip() if text and text.nodeValue else None
elif item_node.nodeName in ('content', 'ncx:content'):
if item_node.hasAttribute('src'):
item['src'] = item_node.attributes['src'].value
elif item_node.nodeName in ('navPoint', 'ncx:navPoint'):
children_points.append(item_node)
if item['title']:
items.append(item)
for child_node in children_points:
subitems = read_nav_point(child_node, level=level + 1)
items.extend(subitems)
return items
def read_nav_map(toc_xmldoc, level=0):
items = []
try:
nav_map_node = toc_xmldoc.getElementsByTagName('navMap')[0]
except IndexError:
# Some ebooks use the ncx: namespace so try that too
try:
nav_map_node = toc_xmldoc.getElementsByTagName('ncx:navMap')[0]
except IndexError:
print('Failed reading TOC')
return items
for nav_point in nav_map_node.childNodes:
if nav_point.nodeName in ('navPoint', 'ncx:navPoint'):
subitems = read_nav_point(nav_point, level=level)
items.extend(subitems)
return items
toc = read_nav_map(toc_xmldoc)
# add indexes
if toc:
for i, t in enumerate(toc):
t['index'] = i
return toc |
References: http://idpf.org/epub/201 and http://idpf.org/epub/301
1. Parse META-INF/container.xml file and find the .OPF file path.
2. In the .OPF file, find the metadata
def get_epub_metadata(filepath, read_cover_image=True, read_toc=True):
'''
References: http://idpf.org/epub/201 and http://idpf.org/epub/301
1. Parse META-INF/container.xml file and find the .OPF file path.
2. In the .OPF file, find the metadata
'''
if not zipfile.is_zipfile(filepath):
raise EPubException('Unknown file')
# print('Reading ePub file: {}'.format(filepath))
zf = zipfile.ZipFile(filepath, 'r', compression=zipfile.ZIP_DEFLATED, allowZip64=True)
container = zf.read('META-INF/container.xml')
container_xmldoc = minidom.parseString(container)
# e.g.: <rootfile full-path="content.opf" media-type="application/oebps-package+xml"/>
opf_filepath = container_xmldoc.getElementsByTagName('rootfile')[0].attributes['full-path'].value
opf = zf.read(os.path.normpath(opf_filepath))
opf_xmldoc = minidom.parseString(opf)
# This file is specific to the authors if it exists.
authors_html = None
try:
authors_html = minidom.parseString(zf.read('OEBPS/pr02.html'))
except KeyError:
# Most books store authors using epub tags, so no worries.
pass
# This file is specific to the publish date if it exists.
publish_date_html = None
try:
publish_date_html = minidom.parseString(zf.read('OEBPS/pr01.html'))
except KeyError:
# Most books store authors using epub tags, so no worries.
pass
file_size_in_bytes = os.path.getsize(filepath)
data = odict({
'epub_version': _discover_epub_version(opf_xmldoc),
'title': _discover_title(opf_xmldoc),
'language': _discover_language(opf_xmldoc),
'description': _discover_description(opf_xmldoc),
'authors': _discover_authors(opf_xmldoc, authors_html=authors_html),
'publisher': _discover_publisher(opf_xmldoc),
'publication_date': _discover_publication_date(opf_xmldoc,
date_html=publish_date_html),
'identifiers': _discover_identifiers(opf_xmldoc),
'subject': _discover_subject(opf_xmldoc),
'file_size_in_bytes': file_size_in_bytes,
})
if read_cover_image:
cover_image_content, cover_image_extension = _discover_cover_image(zf, opf_xmldoc, opf_filepath)
data.cover_image_content = cover_image_content
data.cover_image_extension = cover_image_extension
if read_toc:
data.toc = _discover_toc(zf, opf_xmldoc, opf_filepath)
return data |
Returns the file.OPF contents of the ePub file
def get_epub_opf_xml(filepath):
'''
Returns the file.OPF contents of the ePub file
'''
if not zipfile.is_zipfile(filepath):
raise EPubException('Unknown file')
# print('Reading ePub file: {}'.format(filepath))
zf = zipfile.ZipFile(filepath, 'r', compression=zipfile.ZIP_DEFLATED, allowZip64=True)
container = zf.read('META-INF/container.xml')
container_xmldoc = minidom.parseString(container)
# e.g.: <rootfile full-path="content.opf" media-type="application/oebps-package+xml"/>
opf_filepath = container_xmldoc.getElementsByTagName('rootfile')[0].attributes['full-path'].value
return zf.read(opf_filepath) |
u"""
Склонение имени
:param value: Значение для склонения
:param case: Падеж для склонения (значение из класса Case)
:param gender: Грамматический род
def firstname(self, value, case, gender=None):
u"""
Склонение имени
:param value: Значение для склонения
:param case: Падеж для склонения (значение из класса Case)
:param gender: Грамматический род
"""
if not value:
raise ValueError('Firstname cannot be empty.')
return self.__inflect(value, case, 'firstname', gender) |
u"""
Склонение фамилии
:param value: Значение для склонения
:param case: Падеж для склонения (значение из класса Case)
:param gender: Грамматический род
def lastname(self, value, case, gender=None):
u"""
Склонение фамилии
:param value: Значение для склонения
:param case: Падеж для склонения (значение из класса Case)
:param gender: Грамматический род
"""
if not value:
raise ValueError('Lastname cannot be empty.')
return self.__inflect(value, case, 'lastname', gender) |
u"""
Склонение отчества
:param value: Значение для склонения
:param case: Падеж для склонения (значение из класса Case)
:param gender: Грамматический род
def middlename(self, value, case, gender=None):
u"""
Склонение отчества
:param value: Значение для склонения
:param case: Падеж для склонения (значение из класса Case)
:param gender: Грамматический род
"""
if not value:
raise ValueError('Middlename cannot be empty.')
return self.__inflect(value, case, 'middlename', gender) |
u"""
Разделяет имя на сегменты по разделителям в self.separators
:param name: имя
:return: разделённое имя вместе с разделителями
def __split_name(self, name):
u"""
Разделяет имя на сегменты по разделителям в self.separators
:param name: имя
:return: разделённое имя вместе с разделителями
"""
def gen(name, separators):
if len(separators) == 0:
yield name
else:
segments = name.split(separators[0])
for subsegment in gen(segments[0], separators[1:]):
yield subsegment
for segment in segments[1:]:
for subsegment in gen(segment, separators[1:]):
yield separators[0]
yield subsegment
return gen(name, self.separators) |
Expand a reference expression to individual spans.
Also works on space-separated ID lists, although a sequence of space
characters will be considered a delimiter.
>>> expand('a1')
'a1'
>>> expand('a1[3:5]')
'a1[3:5]'
>>> expand('a1[3:5+6:7]')
'a1[3:5]+a1[6:7]'
>>> expand('a1 a2 a3')
'a1 a2 a3'
def expand(expression):
"""
Expand a reference expression to individual spans.
Also works on space-separated ID lists, although a sequence of space
characters will be considered a delimiter.
>>> expand('a1')
'a1'
>>> expand('a1[3:5]')
'a1[3:5]'
>>> expand('a1[3:5+6:7]')
'a1[3:5]+a1[6:7]'
>>> expand('a1 a2 a3')
'a1 a2 a3'
"""
tokens = []
for (pre, _id, _range) in robust_ref_re.findall(expression):
if not _range:
tokens.append('{}{}'.format(pre, _id))
else:
tokens.append(pre)
tokens.extend(
'{}{}[{}:{}]'.format(delim, _id, start, end)
for delim, start, end in span_re.findall(_range)
)
return ''.join(tokens) |
Compress a reference expression to group spans on the same id.
Also works on space-separated ID lists, although a sequence of space
characters will be considered a delimiter.
>>> compress('a1')
'a1'
>>> compress('a1[3:5]')
'a1[3:5]'
>>> compress('a1[3:5+6:7]')
'a1[3:5+6:7]'
>>> compress('a1[3:5]+a1[6:7]')
'a1[3:5+6:7]'
>>> compress('a1 a2 a3')
'a1 a2 a3'
def compress(expression):
"""
Compress a reference expression to group spans on the same id.
Also works on space-separated ID lists, although a sequence of space
characters will be considered a delimiter.
>>> compress('a1')
'a1'
>>> compress('a1[3:5]')
'a1[3:5]'
>>> compress('a1[3:5+6:7]')
'a1[3:5+6:7]'
>>> compress('a1[3:5]+a1[6:7]')
'a1[3:5+6:7]'
>>> compress('a1 a2 a3')
'a1 a2 a3'
"""
tokens = []
selection = []
last_id = None
for (pre, _id, _range) in robust_ref_re.findall(expression):
if _range and _id == last_id:
selection.extend([pre, _range])
continue
if selection:
tokens.extend(selection + [']'])
selection = []
tokens.extend([pre, _id])
if _range:
selection = ['[', _range]
last_id = _id
else:
last_id = None
if selection:
tokens.extend(selection + [']'])
return ''.join(tokens) |
Split the expression into individual selection expressions. The
delimiters will be kept as separate items if keep_delimters=True.
Also works on space-separated ID lists, although a sequence of space
characters will be considered a delimiter.
>>> selections('a1')
['a1']
>>> selections('a1[3:5]')
['a1[3:5]']
>>> selections('a1[3:5+6:7]')
['a1[3:5+6:7]']
>>> selections('a1[3:5+6:7]+a2[1:4]')
['a1[3:5+6:7]', '+', 'a2[1:4]']
>>> selections('a1[3:5+6:7]+a2[1:4]', keep_delimiters=False)
['a1[3:5+6:7]', 'a2[1:4]']
>>> selections('a1 a2 a3')
['a1', ' ', 'a2', ' ', 'a3']
def selections(expression, keep_delimiters=True):
"""
Split the expression into individual selection expressions. The
delimiters will be kept as separate items if keep_delimters=True.
Also works on space-separated ID lists, although a sequence of space
characters will be considered a delimiter.
>>> selections('a1')
['a1']
>>> selections('a1[3:5]')
['a1[3:5]']
>>> selections('a1[3:5+6:7]')
['a1[3:5+6:7]']
>>> selections('a1[3:5+6:7]+a2[1:4]')
['a1[3:5+6:7]', '+', 'a2[1:4]']
>>> selections('a1[3:5+6:7]+a2[1:4]', keep_delimiters=False)
['a1[3:5+6:7]', 'a2[1:4]']
>>> selections('a1 a2 a3')
['a1', ' ', 'a2', ' ', 'a3']
"""
tokens = []
for (pre, _id, _range) in robust_ref_re.findall(expression):
if keep_delimiters and pre:
tokens.append(pre)
if _id:
if _range:
tokens.append('{}[{}]'.format(_id, _range))
else:
tokens.append(_id)
return tokens |
Return the string that is the resolution of the alignment expression
`expression`, which selects ids from `container`.
def resolve(container, expression):
"""
Return the string that is the resolution of the alignment expression
`expression`, which selects ids from `container`.
"""
itemgetter = getattr(container, 'get_item', container.get)
tokens = []
expression = expression.strip()
for sel_delim, _id, _range in selection_re.findall(expression):
tokens.append(delimiters.get(sel_delim, ''))
item = itemgetter(_id)
if item is None:
raise XigtStructureError(
'Referred Item (id: {}) from reference "{}" does not '
'exist in the given container.'
.format(_id, expression)
)
# treat None values as empty strings for resolution
value = item.value() or ''
if _range:
for spn_delim, start, end in span_re.findall(_range):
start = int(start) if start else None
end = int(end) if end else None
tokens.extend([
delimiters.get(spn_delim, ''),
value[start:end]
])
else:
tokens.append(value)
return ''.join(tokens) |
Return a list of ids denoting objects (tiers or items) in `igt` that
are referred by the object denoted by `id` using a reference
attribute in `refattrs`. If `refattrs` is None, then consider all
known reference attributes for the type of object denoted by _id_.
In other words, if 'b1' refers to 'a1' using 'alignment', then
`referents(igt, 'b1', ['alignment'])` returns `['a1']`.
def referents(igt, id, refattrs=None):
"""
Return a list of ids denoting objects (tiers or items) in `igt` that
are referred by the object denoted by `id` using a reference
attribute in `refattrs`. If `refattrs` is None, then consider all
known reference attributes for the type of object denoted by _id_.
In other words, if 'b1' refers to 'a1' using 'alignment', then
`referents(igt, 'b1', ['alignment'])` returns `['a1']`.
"""
obj = igt.get_any(id)
if obj is None:
raise XigtLookupError(id)
if refattrs is None:
refattrs = obj.allowed_reference_attributes()
return {ra: ids(obj.attributes.get(ra, '')) for ra in refattrs} |
Return a list of ids denoting objects (tiers or items) in `igt` that
refer to the given `id`. In other words, if 'b1' refers to 'a1',
then `referrers(igt, 'a1')` returns `['b1']`.
def referrers(igt, id, refattrs=None):
"""
Return a list of ids denoting objects (tiers or items) in `igt` that
refer to the given `id`. In other words, if 'b1' refers to 'a1',
then `referrers(igt, 'a1')` returns `['b1']`.
"""
if refattrs is None:
result = {}
else:
result = {ra: [] for ra in refattrs}
# if the id is a tier, only look at tiers; otherwise only look at items
try:
obj = igt[id]
others = igt.tiers
except KeyError:
obj = igt.get_item(id)
others = [i for t in igt.tiers for i in t.items]
if obj is None:
raise XigtLookupError(id)
for other in others:
if other.id is None:
continue # raise a warning?
_refattrs = refattrs
if _refattrs is None:
_refattrs = other.allowed_reference_attributes()
attrget = other.attributes.get # just loop optimization
for ra in _refattrs:
result.setdefault(ra, [])
if id in ids(attrget(ra, '')):
result[ra].append(other.id)
return result |
>>> for anc in query.ancestors(igt.get_item('g1'), refattrs=(ALIGNMENT, SEGMENTATION)):
... print(anc)
(<Tier object (id: g type: glosses) at ...>, 'alignment', <Tier object (id: m type: morphemes) at ...>, [<Item object (id: m1) at ...>])
(<Tier object (id: m type: morphemes) at ...>, 'segmentation', <Tier object (id: w type: words) at ...>, [<Item object (id: w1) at ...>])
(<Tier object (id: w type: words) at ...>, 'segmentation', <Tier object (id: p type: phrases) at ...>, [<Item object (id: p1) at ...>])
def ancestors(obj, refattrs=(ALIGNMENT, SEGMENTATION)):
"""
>>> for anc in query.ancestors(igt.get_item('g1'), refattrs=(ALIGNMENT, SEGMENTATION)):
... print(anc)
(<Tier object (id: g type: glosses) at ...>, 'alignment', <Tier object (id: m type: morphemes) at ...>, [<Item object (id: m1) at ...>])
(<Tier object (id: m type: morphemes) at ...>, 'segmentation', <Tier object (id: w type: words) at ...>, [<Item object (id: w1) at ...>])
(<Tier object (id: w type: words) at ...>, 'segmentation', <Tier object (id: p type: phrases) at ...>, [<Item object (id: p1) at ...>])
"""
if hasattr(obj, 'tier'):
tier = obj.tier
items = [obj]
else:
tier = obj
items = tier.items
# a tier may be visited twice (e.g. A > B > A), but then it stops;
# this is to avoid cycles
visited = set([tier.id])
while True:
# get the first specified attribute
refattr = next((ra for ra in refattrs if ra in tier.attributes), None)
if not refattr:
break
reftier = ref.dereference(tier, refattr)
ids = set(chain.from_iterable(
ref.ids(item.attributes.get(refattr, '')) for item in items
))
refitems = [item for item in reftier.items if item.id in ids]
yield (tier, refattr, reftier, refitems)
# cycle detection; break if we've now encountered something twice
if reftier.id in visited:
break
visited.update(reftier.id)
tier = reftier
items = refitems |
>>> for des in query.descendants(igt.get_item('p1'), refattrs=(SEGMENTATION, ALIGNMENT)):
... print(des)
(<Tier object (id: p type: phrases) at ...>, 'segmentation', <Tier object (id: w type: words) at ...>, [<Item object (id: w1) at ...>])
(<Tier object (id: p type: phrases) at ...>, 'alignment', <Tier object (id: t type: translations) at ...>, [<Item object (id: t1) at ...>])
(<Tier object (id: w type: words) at ...>, 'segmentation', <Tier object (id: m type: morphemes) at ...>, [<Item object (id: m1) at ...>])
(<Tier object (id: m type: morphemes) at ...>, 'alignment', <Tier object (id: g type: glosses) at ...>, [<Item object (id: g1) at ...>])
def descendants(obj, refattrs=(SEGMENTATION, ALIGNMENT), follow='first'):
"""
>>> for des in query.descendants(igt.get_item('p1'), refattrs=(SEGMENTATION, ALIGNMENT)):
... print(des)
(<Tier object (id: p type: phrases) at ...>, 'segmentation', <Tier object (id: w type: words) at ...>, [<Item object (id: w1) at ...>])
(<Tier object (id: p type: phrases) at ...>, 'alignment', <Tier object (id: t type: translations) at ...>, [<Item object (id: t1) at ...>])
(<Tier object (id: w type: words) at ...>, 'segmentation', <Tier object (id: m type: morphemes) at ...>, [<Item object (id: m1) at ...>])
(<Tier object (id: m type: morphemes) at ...>, 'alignment', <Tier object (id: g type: glosses) at ...>, [<Item object (id: g1) at ...>])
"""
if hasattr(obj, 'tier'):
tier = obj.tier
items = [obj]
else:
tier = obj
items = tier.items
igt = tier.igt
visited = set()
agenda = deque([(tier, items)])
while agenda:
tier, items = agenda.popleft()
tier_refs = tier.referrers(refattrs)
item_ids = set(item.id for item in items)
# get followable refattrs with something on the referrers list
ras = [ra for ra in refattrs if tier_refs[ra]]
if follow == 'first' and ras:
ras = [ras[0]]
if not ras:
continue
# unlike ancestors, descendants for a refattr may have 1+ tiers
for refattr in ras:
# try to avoid cycles
if (tier.id, refattr) in visited:
continue
else:
visited.add((tier.id, refattr))
for reftier_id in tier_refs[refattr]:
reftier = igt[reftier_id]
refitems = [
item for item in reftier.items
if set(ref.ids(item.attributes.get(refattr,'')))
.intersection(item_ids)
]
yield (tier, refattr, reftier, refitems)
agenda.append((reftier, refitems)) |
Decode a XigtCorpus element.
def default_decode(events, mode='full'):
"""Decode a XigtCorpus element."""
event, elem = next(events)
root = elem # store root for later instantiation
while (event, elem.tag) not in [('start', 'igt'), ('end', 'xigt-corpus')]:
event, elem = next(events)
igts = None
if event == 'start' and elem.tag == 'igt':
igts = (
decode_igt(e)
for e in iter_elements(
'igt', events, root, break_on=[('end', 'xigt-corpus')]
)
)
xc = decode_xigtcorpus(root, igts=igts, mode=mode)
return xc |
Return a tuple, each value being a line of the source file.
Remove empty lines and comments (lines starting with a '#').
def _get_file_content(source):
"""Return a tuple, each value being a line of the source file.
Remove empty lines and comments (lines starting with a '#').
"""
filepath = os.path.join('siglists', source + '.txt')
lines = []
with resource_stream(__name__, filepath) as f:
for i, line in enumerate(f):
line = line.decode('utf-8', 'strict').strip()
if not line or line.startswith('#'):
continue
try:
re.compile(line)
except Exception as ex:
raise BadRegularExpressionLineError(
'Regex error: {} in file {} at line {}'.format(
str(ex),
filepath,
i
)
)
lines.append(line)
if source in _SPECIAL_EXTENDED_VALUES:
lines = lines + _SPECIAL_EXTENDED_VALUES[source]
return tuple(lines) |
Check quickly whether file is rar archive.
def _get_rar_version(xfile):
"""Check quickly whether file is rar archive.
"""
buf = xfile.read(len(RAR5_ID))
if buf.startswith(RAR_ID):
return 3
elif buf.startswith(RAR5_ID):
xfile.read(1)
return 5
return 0 |
Proceed to next volume.
def _open_next(self):
"""Proceed to next volume."""
# is the file split over archives?
if (self._cur.flags & rarfile.RAR_FILE_SPLIT_AFTER) == 0:
return False
if self._fd:
self._fd.close()
self._fd = None
# open next part
self._volfile = self._parser._next_volname(self._volfile)
fd = rarfile.XFile(self._volfile)
self._fd = fd
sig = fd.read(len(self._parser._expect_sig))
if sig != self._parser._expect_sig:
raise rarfile.BadRarFile("Invalid signature")
# loop until first file header
while 1:
cur = self._parser._parse_header(fd)
if not cur:
raise rarfile.BadRarFile("Unexpected EOF")
if cur.type in (rarfile.RAR_BLOCK_MARK, rarfile.RAR_BLOCK_MAIN):
if cur.add_size:
fd.seek(cur.add_size, 1)
continue
if cur.orig_filename != self._inf.orig_filename:
raise rarfile.BadRarFile("Did not found file entry")
self._cur = cur
self._cur_avail = cur.add_size
return True |
Do not check final CRC.
def _check(self): # TODO: fix?
"""Do not check final CRC."""
if self._returncode:
rarfile.check_returncode(self, '')
if self._remain != 0:
raise rarfile.BadRarFile("Failed the read enough data") |
rescales a numpy array, so that min is 0 and max is 255
def _normalize(mat: np.ndarray):
"""rescales a numpy array, so that min is 0 and max is 255"""
return ((mat - mat.min()) * (255 / mat.max())).astype(np.uint8) |
returns a matrix that contains RGB channels, and colors scaled
from 0 to 255
def to_24bit_gray(mat: np.ndarray):
"""returns a matrix that contains RGB channels, and colors scaled
from 0 to 255"""
return np.repeat(np.expand_dims(_normalize(mat), axis=2), 3, axis=2) |
returns an RGB matrix scaled by a matplotlib color map
def apply_color_map(name: str, mat: np.ndarray = None):
"""returns an RGB matrix scaled by a matplotlib color map"""
def apply_map(mat):
return (cm.get_cmap(name)(_normalize(mat))[:, :, :3] * 255).astype(np.uint8)
return apply_map if mat is None else apply_map(mat) |
Can be used to create a pygame.Surface from a 2d numpy array.
By default a grey image with scaled colors is returned, but using the
transformer argument any transformation can be used.
:param mat: the matrix to create the surface of.
:type mat: np.ndarray
:param transformer: function that transforms the matrix to a valid color
matrix, i.e. it must have 3dimension, were the 3rd dimension are the color
channels. For each channel a value between 0 and 255 is allowed
:type transformer: Callable[np.ndarray[np.ndarray]]
def mat_to_surface(mat: np.ndarray, transformer=to_24bit_gray):
"""Can be used to create a pygame.Surface from a 2d numpy array.
By default a grey image with scaled colors is returned, but using the
transformer argument any transformation can be used.
:param mat: the matrix to create the surface of.
:type mat: np.ndarray
:param transformer: function that transforms the matrix to a valid color
matrix, i.e. it must have 3dimension, were the 3rd dimension are the color
channels. For each channel a value between 0 and 255 is allowed
:type transformer: Callable[np.ndarray[np.ndarray]]"""
return pygame.pixelcopy.make_surface(transformer(mat.transpose())
if transformer is not None else mat.transpose()) |
Creates a random id with the given length and charset.
## Parameters
* length the number of characters in the id
* charset what character set to use (a list of characters)
* first_charset what character set for the first character
* sep='' what character to insert between groups
* group=0 how long the groups are (default 0 means no groups)
def random_id(length=16, charset=alphanum_chars, first_charset=alpha_chars, sep='', group=0):
"""Creates a random id with the given length and charset.
## Parameters
* length the number of characters in the id
* charset what character set to use (a list of characters)
* first_charset what character set for the first character
* sep='' what character to insert between groups
* group=0 how long the groups are (default 0 means no groups)
"""
t = []
first_chars = list(set(charset).intersection(first_charset))
if len(first_chars) == 0:
first_chars = charset
t.append(first_chars[random.randrange(len(first_chars))])
for i in range(len(t), length):
if (group > 0) and (i % group == 0) and (i < length):
t.append(sep)
t.append(charset[random.randrange(len(charset))])
return ''.join(t) |
Merge any number of dictionaries
def merge_dict(data, *args):
"""Merge any number of dictionaries
"""
results = {}
for current in (data,) + args:
results.update(current)
return results |
Joins individual URL strings together, and returns a single string.
def make_url(url, *paths):
"""Joins individual URL strings together, and returns a single string.
"""
for path in paths:
url = re.sub(r'/?$', re.sub(r'^/?', '/', path), url)
return url |
aggregate result
def aggregate_result(self, return_code, output, service_description='', specific_servers=None):
'''
aggregate result
'''
if specific_servers == None:
specific_servers = self.servers
else:
specific_servers = set(self.servers).intersection(specific_servers)
for server in specific_servers:
if not self.servers[server]['send_errors_only'] or return_code > 0:
self.servers[server]['results'].append({'return_code': return_code,
'output': output,
'service_description': service_description,
'return_status': STATUSES[return_code][0],
'custom_fqdn': self.servers[server]['custom_fqdn']})
LOG.info("[email][%s][%s]: Aggregate result: %r", service_description, server, self.servers[server]['results'][-1]) |
send results
def send_results(self):
'''
send results
'''
for server in self.servers:
if self.servers[server]['results']:
if len(self.servers[server]['results']) == 1:
msg = MIMEText('')
msg['Subject'] = '[%(custom_fqdn)s] [%(service_description)s] %(return_status)s: %(output)s' % self.servers[server]['results'][0]
else:
txt = ''
summary = [0, 0, 0, 0]
for results in self.servers[server]['results']:
txt += '[%(service_description)s] %(return_status)s: %(output)s\n' % results
summary[results['return_code']] += 1
msg = MIMEText(txt)
subject = '[%(custom_fqdn)s]' % self.servers[server]['results'][0]
for i, status in enumerate(STATUSES):
subject += ' %s:%s' % (status[0], summary[i])
msg['Subject'] = subject
msg['From'] = self.servers[server]['from']
msg['To'] = ', '.join(self.servers[server]['to'])
if self.servers[server]['tls']:
smtp_server = smtplib.SMTP_SSL(self.servers[server]['host'], self.servers[server]['port'])
else:
smtp_server = smtplib.SMTP(self.servers[server]['host'], self.servers[server]['port'])
if self.servers[server]['login'] and len(self.servers[server]['login']) > 0:
smtp_server.login(self.servers[server]['login'], self.servers[server]['password'])
smtp_server.sendmail(self.servers[server]['from'], self.servers[server]['to'], msg.as_string())
smtp_server.quit()
LOG.info("[email][%s]: e-mail sent from: %s to: %s", server, self.servers[server]['from'], self.servers[server]['to']) |
MAIN
def main():
"""MAIN"""
config = {
"api": {
"services": [
{
"name": "my_api",
"testkey": "testval",
},
],
"calls": {
"hello_world": {
"delay": 5,
"priority": 1,
"arguments": None,
},
"marco": {
"delay": 1,
"priority": 1,
},
"pollo": {
"delay": 1,
"priority": 1,
},
}
}
}
app = AppBuilder([MyAPI], Strategy(Print()), AppConf(config))
app.run() |
Parse the outgoing schema
def make_request(self, data):
"""Parse the outgoing schema"""
sch = MockItemSchema()
return Request(**{
"callname": self.context.get("callname"),
"payload": sch.dump(data),
}) |
Parse the outgoing schema
def populate_data(self, data):
"""Parse the outgoing schema"""
sch = MockItemSchema()
return Result(**{
"callname": self.context.get("callname"),
"result": sch.dump(data),
}) |
returns a handler that can be used with EventListener.listen()
and returns when a key in keys is pressed
def key_press(keys):
"""returns a handler that can be used with EventListener.listen()
and returns when a key in keys is pressed"""
return lambda e: e.key if e.type == pygame.KEYDOWN \
and e.key in keys else EventConsumerInfo.DONT_CARE |
returns a handler that listens for unicode characters
def unicode_char(ignored_chars=None):
"""returns a handler that listens for unicode characters"""
return lambda e: e.unicode if e.type == pygame.KEYDOWN \
and ((ignored_chars is None)
or (e.unicode not in ignored_chars))\
else EventConsumerInfo.DONT_CARE |
Adds a new MouseProxy for the given group to the
EventListener.mouse_proxies dict if it is not in there yet, and returns
the (new) MouseProxy. In listen() all entries in the current group of
mouse_proxies are used.
def mouse_area(self, handler, group=0, ident=None):
"""Adds a new MouseProxy for the given group to the
EventListener.mouse_proxies dict if it is not in there yet, and returns
the (new) MouseProxy. In listen() all entries in the current group of
mouse_proxies are used."""
key = ident or id(handler)
if key not in self.mouse_proxies[group]:
self.mouse_proxies[group][key] = MouseProxy(handler, ident)
return self.mouse_proxies[group][key] |
When listen() is called all queued pygame.Events will be passed to all
registered listeners. There are two ways to register a listener:
1. as a permanent listener, that is always executed for every event. These
are registered by passing the handler-functions during construction
2. as a temporary listener, that will only be executed during the current
call to listen(). These are registered by passing the handler functions
as arguments to listen()
When a handler is called it can provoke three different reactions through
its return value.
1. It can return EventConsumerInfo.DONT_CARE in which case the EventListener
will pass the event to the next handler in line, or go to the next event,
if the last handler was called.
2. It can return EventConsumerInfo.CONSUMED in which case the event will not
be passed to following handlers, and the next event in line will be
processed.
3. It can return anything else (including None, which will be returned if no
return value is specified) in this case the listen()-method will return
the result of the handler.
Therefore all permanent handlers should usually return
EventConsumerInfo.DONT_CARE
def listen(self, *temporary_handlers):
"""When listen() is called all queued pygame.Events will be passed to all
registered listeners. There are two ways to register a listener:
1. as a permanent listener, that is always executed for every event. These
are registered by passing the handler-functions during construction
2. as a temporary listener, that will only be executed during the current
call to listen(). These are registered by passing the handler functions
as arguments to listen()
When a handler is called it can provoke three different reactions through
its return value.
1. It can return EventConsumerInfo.DONT_CARE in which case the EventListener
will pass the event to the next handler in line, or go to the next event,
if the last handler was called.
2. It can return EventConsumerInfo.CONSUMED in which case the event will not
be passed to following handlers, and the next event in line will be
processed.
3. It can return anything else (including None, which will be returned if no
return value is specified) in this case the listen()-method will return
the result of the handler.
Therefore all permanent handlers should usually return
EventConsumerInfo.DONT_CARE
"""
funcs = tuple(itt.chain(self.permanent_handlers,
(proxy.listener for proxy in
self.mouse_proxies[self.proxy_group].values()),
temporary_handlers))
for event in self._get_q():
for func in funcs:
ret = func(event)
if ret == EventConsumerInfo.CONSUMED:
break
if ret == EventConsumerInfo.DONT_CARE:
continue
else:
return ret |
Calls listen repeatedly until listen returns something else than None.
Then returns listen's result. If timeout is not zero listen_until_return
stops after timeout seconds and returns None.
def listen_until_return(self, *temporary_handlers, timeout=0):
"""Calls listen repeatedly until listen returns something else than None.
Then returns listen's result. If timeout is not zero listen_until_return
stops after timeout seconds and returns None."""
start = time.time()
while timeout == 0 or time.time() - start < timeout:
res = self.listen(*temporary_handlers)
if res is not None:
return res |
Waits till one key was pressed n times.
:param key: the key to be pressed as defined by pygame. E.g.
pygame.K_LEFT for the left arrow key
:type key: int
:param n: number of repetitions till the function returns
:type n: int
def wait_for_n_keypresses(self, key, n=1):
"""Waits till one key was pressed n times.
:param key: the key to be pressed as defined by pygame. E.g.
pygame.K_LEFT for the left arrow key
:type key: int
:param n: number of repetitions till the function returns
:type n: int
"""
my_const = "key_consumed"
counter = 0
def keypress_listener(e): return my_const \
if e.type == pygame.KEYDOWN and e.key == key \
else EventConsumerInfo.DONT_CARE
while counter < n:
if self.listen(keypress_listener) == my_const:
counter += 1 |
Waits until one of the specified keys was pressed, and returns
which key was pressed.
:param keys: iterable of integers of pygame-keycodes, or simply
multiple keys passed via multiple arguments
:type keys: iterable
:param timeout: number of seconds to wait till the function returns
:type timeout: float
:returns: The keycode of the pressed key, or None in case of timeout
:rtype: int
def wait_for_keys(self, *keys, timeout=0):
"""Waits until one of the specified keys was pressed, and returns
which key was pressed.
:param keys: iterable of integers of pygame-keycodes, or simply
multiple keys passed via multiple arguments
:type keys: iterable
:param timeout: number of seconds to wait till the function returns
:type timeout: float
:returns: The keycode of the pressed key, or None in case of timeout
:rtype: int
"""
if len(keys) == 1 and _is_iterable(keys[0]):
keys = keys[0]
return self.listen_until_return(Handler.key_press(keys), timeout=timeout) |
The same as wait_for_keys, but returns a frozen_set which contains
the pressed key, and the modifier keys.
:param modifiers_to_check: iterable of modifiers for which the function
will check whether they are pressed
:type modifiers: Iterable[int]
def wait_for_keys_modified(self, *keys, modifiers_to_check=_mod_keys,
timeout=0):
"""The same as wait_for_keys, but returns a frozen_set which contains
the pressed key, and the modifier keys.
:param modifiers_to_check: iterable of modifiers for which the function
will check whether they are pressed
:type modifiers: Iterable[int]"""
set_mods = pygame.key.get_mods()
return frozenset.union(
frozenset([self.wait_for_keys(*keys, timeout=timeout)]),
EventListener._contained_modifiers(set_mods, modifiers_to_check)) |
Returns a str that contains the single character that was pressed.
This already respects modifier keys and keyboard layouts. If timeout is
not none and no key is pressed within the specified timeout, None is
returned. If a key is ingnored_chars it will be ignored. As argument for
irgnored_chars any object that has a __contains__ method can be used,
e.g. a string, a set, a list, etc
def wait_for_unicode_char(self, ignored_chars=None, timeout=0):
"""Returns a str that contains the single character that was pressed.
This already respects modifier keys and keyboard layouts. If timeout is
not none and no key is pressed within the specified timeout, None is
returned. If a key is ingnored_chars it will be ignored. As argument for
irgnored_chars any object that has a __contains__ method can be used,
e.g. a string, a set, a list, etc"""
return self.listen_until_return(Handler.unicode_char(ignored_chars),
timeout=timeout) |
Does not support the full range of ways rar can split
as it'd require reading the file to ensure you are using the
correct way.
def _find_all_first_files(self, item):
"""
Does not support the full range of ways rar can split
as it'd require reading the file to ensure you are using the
correct way.
"""
for listed_item in item.list():
new_style = re.findall(r'(?i)\.part(\d+)\.rar^', listed_item.id)
if new_style:
if int(new_style[0]) == 1:
yield 'new', listed_item
elif listed_item.id.lower().endswith('.rar'):
yield 'old', listed_item |
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