def __convert_other(self, other):
su = getattr(self, 'units', dimensionless)
ou = getattr(other, 'units', dimensionless)
if su == None and ou == None:
u = None
else:
if isinstance(other, units.unit.unit):
# Handles 5 * liters
ou = units.unit.unit(1, other.derivation)
other = units.convert(other.value, ou, su)
elif isinstance(other, UnitArray):
# Handles UnitArray or UnitScalar
other = units.convert(numpy.array(other), ou, su)
elif isinstance(other, numpy.ndarray):
if len(other.shape) > 0 and hasattr(other.item(0),
'derivation'):
# Handles array([1,2,3] * liters)
ou = units.unit.unit(1, other.item(0).derivation)
other = units.convert(other / ou, ou, su)
else:
#Everything else
other = units.convert(numpy.array(other), ou, su)
u = su
return other, u
def setUp(self):
unittest.TestCase.setUp(self)
# Put unit adapters on either side of a masking adapter to see if they
# cooperate. Store meters in the raw context, push fathoms through the
# mask, and expose feet to the outside world.
self.units = units = { 'in':meters, 'mid':fathom, 'out':feet }
# Set up data for the contexts
depth = Log(linspace(0.0, 100.0, 11), units=units['in'])
lith = array(['sand']*len(depth), dtype=object)
# Create the contexts
self.context = PassThruContext( ReductionContext( NumericContext() ) )
self.raw_context = self.context.context_base
# Add data (before creating the adapters)
self.context.update(depth=depth, lith=lith)
# (This simplifies creating UnitConversionAdapters)
def always(value):
class C(dict):
def get(self, key, default=None):
return value
def __repr__(self):
return '{*:%r}' % value
return C()
# Layer multiple adapters
self.mask = (15.0 < depth) & (depth < 55.0)
self.convert_out = lambda x: convert(x, units['in'], units['out'])
self.convert_in = lambda x: convert(x, units['out'], units['in'])
self.context.get_reduction_context(OpenContext).context_filter = \
MaskFilter( mask = self.mask )
def test_convert(self):
t1 = units.convert(60, angle.minutes, angle.degree)
self.assertAlmostEqual(t1, 1)
t1 = units.convert(3600, angle.seconds, angle.degree)
self.assertAlmostEqual(t1, 1)
t1 = units.convert(1, angle.degree, angle.radian)
self.assertAlmostEqual(t1, numpy.pi / 180.)
def _compute_force(self, mass, acc, mass_units=kg, acc_units=ms2, **unused_units):
"""
"""
# algorithm units are
mass_u = kg
acc_u = ms2
# convert into algorithm units
mass = units.convert(mass, mass_units, mass_u)
acc = units.convert(acc, acc_units, acc_u)
# do the math :-)
return mass * acc
def _compute_force(self, mass, acc, mass_units=kg,
acc_units=ms2, **unused_units):
"""
"""
# algorithm units are
mass_u = kg
acc_u = ms2
# convert into algorithm units
mass = units.convert(mass, mass_units, mass_u)
acc = units.convert(acc, acc_units, acc_u)
# do the math :-)
return mass * acc
def test_temperature(self):
k = 200
c = -73.15
f = -99.67
k2c = units.convert(k, kelvin, celsius)
self.assertAlmostEqual(k2c, c)
k2f = units.convert(k, kelvin, fahrenheit)
self.assertAlmostEqual(k2f, f)
f2c = units.convert(f, fahrenheit, celsius)
self.assertAlmostEqual(f2c, c)
t1 = units.convert(-40.0, temperature.fahrenheit, temperature.celsius)
self.assertAlmostEqual(t1, -40.0, 6)
t2 = units.convert(0.0, temperature.celsius, temperature.fahrenheit)
self.assertAlmostEqual(t2, 32.0, 6)
return
def test_convert(self):
t1 = units.convert(1, time.year, time.second)
self.assertAlmostEqual(t1, (365.25 * 24 * 60 * 60), 6)
t1 = units.convert(1, time.week, time.second)
self.assertAlmostEqual(t1, (7 * 24 * 60 * 60))
t1 = units.convert(1, time.day, time.second)
self.assertAlmostEqual(t1, (24 * 60 * 60))
t1 = units.convert(1, time.hour, time.second)
self.assertAlmostEqual(t1, 60 * 60)
t1 = units.convert(1, time.minute, time.second)
self.assertAlmostEqual(t1, 60)
def test_temperature(self):
k = 200
c = -73.15
f = -99.67
k2c = units.convert(k, kelvin, celsius)
self.assertAlmostEqual(k2c, c)
k2f = units.convert(k, kelvin, fahrenheit)
self.assertAlmostEqual(k2f, f)
f2c = units.convert(f, fahrenheit, celsius)
self.assertAlmostEqual(f2c, c)
t1 = units.convert(-40.0, temperature.fahrenheit, temperature.celsius)
self.assertAlmostEqual(t1, -40.0, 6)
t2 = units.convert(0.0, temperature.celsius, temperature.fahrenheit)
self.assertAlmostEqual(t2, 32.0, 6)
return
def as_units(self, new_units):
""" Convert UnitArray from its current units to a new set of units.
"""
result = self.__class__(units.convert(self.view(numpy.ndarray), self.units, new_units))
result.units = new_units
return result
def as_units(self, new_units):
""" Convert UnitArray from its current units to a new set of units.
"""
result = self.__class__(
units.convert(self.view(numpy.ndarray), self.units, new_units))
result.units = new_units
return result
def setUp(self):
unittest.TestCase.setUp(self)
# Put unit adapters on either side of a masking adapter to see if they
# cooperate. Store meters in the raw context, push fathoms through the
# mask, and expose feet to the outside world.
self.units = units = {'in': meters, 'mid': fathom, 'out': feet}
# Set up data for the contexts
depth = UnitArray(linspace(0.0, 100.0, 11), units=units['in'])
lith = array(['sand'] * len(depth), dtype=object)
# Create the contexts
self.context = AdaptedDataContext(subcontext=DataContext())
self.raw_context = self.context.subcontext
# Add data (before creating the adapters)
self.context.update(depth=depth, lith=lith)
# (This simplifies creating UnitConversionAdapters)
def always(value):
class C(dict):
def get(self, key, default=None):
return value
def __repr__(self):
return '{*:%r}' % value
return C()
# Layer multiple adapters
d = depth.view(ndarray)
self.mask = (15.0 < d) & (d < 55.0)
self.convert_out = lambda x: convert(x, units['in'], units['out'])
self.convert_in = lambda x: convert(x, units['out'], units['in'])
#self.context.push_adapter(
# UnitConversionAdapter(setitem_units=always(units['in']),
# getitem_units=always(units['mid'])))
self.context.push_adapter(MaskingAdapter(mask=self.mask))
self.context.push_adapter(
# UnitConversionAdapter(setitem_units=always(units['mid']),
UnitConversionAdapter(setitem_units=always(units['in']),
getitem_units=always(units['out'])))
def unit_array_units_converter(unit_array, new_units):
""" Convert a UnitArray from one set of units to another.
"""
if unit_array.units != new_units:
# Need conversion.
if isinstance(unit_array, ndarray) and unit_array.shape != ():
# this is an array
result = UnitArray(units.convert(unit_array.view(ndarray), unit_array.units,
new_units))
else:
# this is a scalar
result = UnitScalar(units.convert(unit_array.view(ndarray), unit_array.units,
new_units))
result.units = new_units
else:
# No conversion needed. Just return the unit_array.
result = unit_array
return result
def unit_array_units_converter(unit_array, new_units):
""" Convert a UnitArray from one set of units to another.
"""
if unit_array.units != new_units:
# Need conversion.
if isinstance(unit_array, ndarray) and unit_array.shape != ():
# this is an array
result = UnitArray(units.convert(unit_array.view(ndarray), unit_array.units,
new_units))
else:
# this is a scalar
result = UnitScalar(units.convert(unit_array.view(ndarray), unit_array.units,
new_units))
result.units = new_units
else:
# No conversion needed. Just return the unit_array.
result = unit_array
return result
def setUp(self):
unittest.TestCase.setUp(self)
# Put unit adapters on either side of a masking adapter to see if they
# cooperate. Store meters in the raw context, push fathoms through the
# mask, and expose feet to the outside world.
self.units = units = { 'in':meters, 'mid':fathom, 'out':feet }
# Set up data for the contexts
depth = UnitArray(linspace(0.0, 100.0, 11), units=units['in'])
lith = array(['sand']*len(depth), dtype=object)
# Create the contexts
self.context = AdaptedDataContext(subcontext=DataContext())
self.raw_context = self.context.subcontext
# Add data (before creating the adapters)
self.context.update(depth=depth, lith=lith)
# (This simplifies creating UnitConversionAdapters)
def always(value):
class C(dict):
def get(self, key, default=None):
return value
def __repr__(self):
return '{*:%r}' % value
return C()
# Layer multiple adapters
d = depth.view(ndarray)
self.mask = (15.0 < d) & (d < 55.0)
self.convert_out = lambda x: convert(x, units['in'], units['out'])
self.convert_in = lambda x: convert(x, units['out'], units['in'])
#self.context.push_adapter(
# UnitConversionAdapter(setitem_units=always(units['in']),
# getitem_units=always(units['mid'])))
self.context.push_adapter(MaskingAdapter(mask=self.mask))
self.context.push_adapter(
# UnitConversionAdapter(setitem_units=always(units['mid']),
UnitConversionAdapter(setitem_units=always(units['in']),
getitem_units=always(units['out'])))
def __convert_other(self, other):
su = getattr(self, 'units', dimensionless)
ou = getattr(other, 'units', dimensionless)
if su == None and ou == None:
u = None
else:
if isinstance(other, unit):
# Handles 5 * liters
ou = unit(1, other.derivation)
other = convert(other.value, ou, su)
elif isinstance(other, UnitArray):
# Handles UnitArray or UnitScalar
other = convert(numpy.array(other), ou, su)
elif isinstance(other, numpy.ndarray):
if len(other.shape) > 0 and hasattr(other.item(0), 'derivation'):
# Handles array([1,2,3] * liters)
ou = unit(1, other.item(0).derivation)
other = convert(other/ou, ou, su)
u = su
return other, u
def test_as_units(self):
""" Conversion from one unit system to another.
"""
ary = numpy.array((1,2,3))
unit_ary = UnitArray(ary, units=meters)
new_unit_ary = unit_ary.as_units(feet)
# Test the values are correct
desired_array = units.convert(ary, meters, feet)
self.assert_(numpy.all(desired_array==new_unit_ary))
# Also, make sure the units are correctly assigned.
self.assertEqual(new_unit_ary.units, feet)
def test_as_units(self):
""" Conversion from one unit system to another.
"""
ary = numpy.array((1, 2, 3))
unit_ary = UnitArray(ary, units=meters)
new_unit_ary = unit_ary.as_units(feet)
# Test the values are correct
desired_array = units.convert(ary, meters, feet)
self.assertTrue(numpy.all(desired_array == new_unit_ary))
# Also, make sure the units are correctly assigned.
self.assertEqual(new_unit_ary.units, feet)
def unit_array_units_converter(unit_array, new_units):
""" Convert a UnitArray from one set of units to another.
"""
if unit_array.units != new_units:
# A conversion is needed. Must pass in a real ndarray instead of
# a UnitArray since operations on it will also try to conversions that
# we don't want it to do.
result = units.convert(unit_array.view(numpy.ndarray),
unit_array.units, new_units).view(UnitArray)
result.units = new_units
else:
# No conversion needed. Just return the unit_array.
result = unit_array
return result
def setUp(self):
unittest.TestCase.setUp(self)
# Put unit adapters on either side of a masking adapter to see if they
# cooperate. Store meters in the raw context, push fathoms through the
# mask, and expose feet to the outside world.
self.units = units = {'in': meters, 'mid': fathom, 'out': feet}
# Set up data for the contexts
depth = Log(linspace(0.0, 100.0, 11), units=units['in'])
lith = array(['sand'] * len(depth), dtype=object)
# Create the contexts
self.context = PassThruContext(ReductionContext(NumericContext()))
self.raw_context = self.context.context_base
# Add data (before creating the adapters)
self.context.update(depth=depth, lith=lith)
# (This simplifies creating UnitConversionAdapters)
def always(value):
class C(dict):
def get(self, key, default=None):
return value
def __repr__(self):
return '{*:%r}' % value
return C()
# Layer multiple adapters
self.mask = (15.0 < depth) & (depth < 55.0)
self.convert_out = lambda x: convert(x, units['in'], units['out'])
self.convert_in = lambda x: convert(x, units['out'], units['in'])
self.context.get_reduction_context(OpenContext).context_filter = \
MaskFilter( mask = self.mask )
def unit_array_units_converter(unit_array, new_units):
""" Convert a UnitArray from one set of units to another.
"""
if unit_array.units != new_units:
# A conversion is needed. Must pass in a real ndarray instead of
# a UnitArray since operations on it will also try to conversions that
# we don't want it to do.
result = units.convert(unit_array.view(numpy.ndarray), unit_array.units,
new_units).view(UnitArray)
result.units = new_units
else:
# No conversion needed. Just return the unit_array.
result = unit_array
return result
def convert_units(unitted_data, tgt_unit, **kwargs):
""" Convert unitted data to the units specified by `tgt_unit`.
Parameters
----------
unitted_data : UnitScalar or UnitArray
The data to be converted to `tgt_unit`
tgt_unit : scimath.unit or str
The target units for `unitted_data`.
kwargs : dict
Additional arguments that may be needed by custom converters for
special units.
Returns
-------
UnitScalar or UnitArray
The converted data.
"""
if isinstance(tgt_unit, basestring):
tgt_unit = unit_parser.parse_unit(tgt_unit)
if isinstance(unitted_data, UnitScalar):
unit_klass = UnitScalar
elif isinstance(unitted_data, UnitArray):
unit_klass = UnitArray
else:
msg = "The `unitted_data` argument must be an instance of either " \
"scimath's UnitScalar or UnitArray but got {}."
msg = msg.format(unitted_data.__class__.__name__)
logger.exception(msg)
raise ValueError(msg)
src_unit = unitted_data.units
if (src_unit.label, tgt_unit.label) in CUSTOM_UNITS_CONVERTERS:
converter = CUSTOM_UNITS_CONVERTERS[(src_unit.label, tgt_unit.label)]
unitted_data = converter(unitted_data, **kwargs)
return unit_klass(unitted_data, units=tgt_unit)
else:
data = np.array(unitted_data.tolist())
data = convert(data, src_unit, tgt_unit)
return unit_klass(data, units=tgt_unit)
def add_processor(self, func, *args, **kwargs):
"""
Add a new processor and bind it to a set of parameters.
- func should be a function implementing the numpy ufunc class. If not,
then the signature and types keyword arguments will be necessary.
- args is a list of names and constants. The names link to internal
numpy array variables that will be bound to the function. Names can
be given in the form:
"varname(length, type)[range]"
The optional (length, type) field is used to initialize a new variable
if necessary. The optional [range] field copies binds only a subrange
of the array to the function. Non-string constant values will be
converted to the right type and bound to the function. Constants with
scimath time units will be converted to the internal clock unit.
- keyword arguments include:
- signature: broadcasting signature for a ufunc. By default, use
func.signature
- types: a list of strings defining the types of arrays needed for
the func. By default, use func.types
"""
# Get the signature and list of valid types for the function
signature = kwargs.get("signature", None)
if (signature == None): signature = func.signature
if (signature == None):
signature = '->'
for i in range(func.nin):
signature = ',()' + signature
for i in range(func.nout):
signature = signature + '(),'
signature = signature.strip(',')
types = kwargs.get("types", None)
if (types == None): types = func.types.copy()
if (types == None):
raise TypeError("Could not find a type signature list for " +
func.__name__ +
". Please supply a valid list of types.")
for i, typestr in enumerate(types):
types[i] = typestr.replace('->', '')
# make a list of parameters from *args. Replace any strings in the list
# with numpy objects from vars_dict, where able
params = []
for i, param in enumerate(args):
if (isinstance(param, str)):
param_val = self.get_variable(param)
if param_val is not None:
param = param_val
params.append(param)
# Make sure arrays obey the broadcasting rules, and make a dictionary
# of the correct dimensions
dims_list = re.findall("\((.*?)\)", signature)
dims_dict = {}
outerdims = []
# print('\nsetting param:', params[-1])
# print('dims list is:', dims_list)
for ipar, dims in enumerate(dims_list):
# print(ipar, dims, params[ipar])
if not isinstance(params[ipar], np.ndarray):
continue
fun_dims = outerdims + [d.strip() for d in dims.split(',') if d]
arr_dims = list(params[ipar].shape)
#check if arr_dims can be broadcast to match fun_dims
for i in range(max(len(fun_dims), len(arr_dims))):
fd = fun_dims[-i - 1] if i < len(fun_dims) else None
ad = arr_dims[-i - 1] if i < len(arr_dims) else None
if (isinstance(fd, str)):
# Define the dimension or make sure it is consistent
if not ad or dims_dict.setdefault(fd, ad) != ad:
raise ValueError(
"Failed to broadcast array dimensions for " +
func.__name__ +
". Could not find consistent value for dimension "
+ fd)
elif not fd:
# if we ran out of function dimensions, add a new outer dim
outerdims.insert(0, ad)
elif not ad:
continue
elif fd != ad:
# If dimensions disagree, either insert a broadcasted array dimension or raise an exception
if len(fun_dims) > len(arr_dims):
arr_dims.insert(len(arr_dims) - i, 1)
elif len(fun_dims) < len(arr_dims):
outerdims.insert(len(fun_dims) - i, ad)
fun_dims.insert(len(fun_dims) - i, ad)
else:
raise ValueError(
"Failed to broadcast array dimensions for " +
func.__name__ +
". Input arrays do not have consistent outer dimensions."
)
# find type signatures that match type of array
arr_type = params[ipar].dtype.char
types = [
type_sig for type_sig in types if arr_type == type_sig[ipar]
]
# Get the type signature we are using
if (not types):
raise TypeError(
"Could not find a type signature matching the types of the variables given for "
+ func.__name__)
elif (len(types) > 1):
self.__print(
1,
"Found multiple compatible type signatures for this function:",
types, "Using signature " + types[0] + ".")
types = [np.dtype(t) for t in types[0]]
# Reshape variable arrays to add broadcast dimensions and allocate new arrays as needed
for i, param, dims, dtype in zip(range(len(params)), params, dims_list,
types):
shape = outerdims + [
dims_dict[d.strip()] for d in dims.split(',') if d
]
if isinstance(param, str):
params[i] = self.__add_var(param, dtype, shape)
elif isinstance(param, np.ndarray):
arshape = list(param.shape)
for idim in range(-1, -1 - len(shape), -1):
if arshape[idim] != shape[idim]:
arshape.insert(len(arshape) + idim + 1, 1)
params[i] = param.reshape(tuple(arshape))
else:
if isinstance(param, unit):
param = convert(1, param, self._clk)
if np.issubdtype(dtype, np.integer):
params[i] = dtype.type(round(param))
else:
params[i] = dtype.type(param)
# Make strings of input variables/constants for later printing
proc_strs = []
for i, arg in enumerate(args):
if isinstance(arg, str):
proc_strs.append(arg)
else:
proc_strs.append(str(params[i]))
proc_strs = tuple(proc_strs)
self.__print(
2, 'Added processor: ' + func.__name__ +
str(proc_strs).replace("'", ""))
# Add the function and bound parameters to the list of processors
self.__proc_list.append((func, tuple(params)))
self.__proc_strs.append(proc_strs)
return None
def test_time(self):
t1 = units.convert(1, time.year, time.second)
self.assertAlmostEqual(t1, (365.25 * 24 * 60 * 60), 6)
def test_density(self):
d1 = units.convert(1, density.lb_per_gal, density.grams_per_cubic_centimeter)
self.assertAlmostEqual(d1, 0.1198284429, 6)
return
def test_density(self):
d1 = units.convert(1, density.lb_per_gal,
density.grams_per_cubic_centimeter)
self.assertAlmostEqual(d1, 0.1198284429, 6)
return
def __parse_expr(self, node):
"""
helper function for get_variable that recursively evaluates the AST tree
based on: https://stackoverflow.com/a/9558001.
"""
if node is None:
return None
elif isinstance(node, ast.Num):
return node.n
elif isinstance(node, ast.Str):
return node.s
elif isinstance(node, ast.Constant):
return node.val
# look for name in variable dictionary
elif isinstance(node, ast.Name):
# check if it is a unit
val = unit_parser.parse_unit(node.id)
if val.is_valid():
return convert(1, val, self._clk)
#check if it is a variable
val = self.__vars_dict.get(node.id, None)
return val
# define binary operators (+,-,*,/)
elif isinstance(node, ast.BinOp):
lhs = self.__parse_expr(node.left)
rhs = self.__parse_expr(node.right)
op = ast_ops_dict[type(node.op)]
if isinstance(lhs, np.ndarray) or isinstance(rhs, np.ndarray):
if not isinstance(lhs, np.ndarray):
if np.issubdtype(rhs.dtype, np.integer):
lhs = rhs.dtype.type(round(lhs))
else:
lhs = rhs.dtype.type(lhs)
if not isinstance(rhs, np.ndarray):
if np.issubdtype(lhs.dtype, np.integer):
rhs = lhs.dtype.type(round(rhs))
else:
rhs = lhs.dtype.type(rhs)
out = op(lhs, rhs)
self.__proc_list.append((op, (lhs, rhs, out)))
self.__proc_strs.append("Binary operator: " + op.__name__)
return out
return op(lhs, rhs)
# define unary operators (-)
elif isinstance(node, ast.UnaryOp):
operand = self.__parse_expr(node.operand)
op = ast_ops_dict[type(node.op)]
out = op(operand)
# if we have a np array, add to processor list
if isinstance(out, np.ndarray):
self.__proc_list.append((op, (operand, out)))
self.__proc_strs.append("Unary operator: " + op.__name__)
return out
elif isinstance(node, ast.Subscript):
# print(ast.dump(node))
val = self.__parse_expr(node.value)
if isinstance(node.slice, ast.Index):
if isinstance(val, np.ndarray):
return val[..., self.__parse_expr(node.slice.value)]
else:
return val[self.__parse_expr(node.slice.value)]
elif isinstance(node.slice, ast.Slice):
if isinstance(val, np.ndarray):
return val[...,
slice(self.__parse_expr(node.slice.lower),
self.__parse_expr(node.slice.upper),
self.__parse_expr(node.slice.step))]
else:
print(self.__parse_expr(node.slice.upper))
return val[slice(self.__parse_expr(node.slice.upper),
self.__parse_expr(node.slice.lower),
self.__parse_expr(node.slice.step))]
elif isinstance(node.slice, ast.ExtSlice):
slices = tuple(node.slice.dims)
for i, sl in enumerate(slices):
if isinstance(sl, ast.index):
slices[i] = self.__parse_expr(sl.value)
else:
slices[i] = slice(self.__parse_expr(sl.upper),
self.__parse_expr(sl.lower),
self.__parse_expr(sl.step))
return val[..., slices]
# for name.attribute
elif isinstance(node, ast.Attribute):
val = self.__parse_expr(node.value)
# get shape with buffer_len dimension removed
if node.attr == 'shape' and isinstance(val, np.ndarray):
return val.shape[1:]
# for func([args])
elif isinstance(node, ast.Call):
func = node.func.id
# get length of 1D array variable
if func == "len" and len(node.args) == 1 and isinstance(
node.args[0], ast.Name):
var = self.__parse_expr(node.args[0])
if isinstance(var, np.ndarray) and len(var.shape) == 2:
return var.shape[1]
else:
raise ValueError("len(): " + node.args[0].id +
"has wrong number of dims")
elif func == "round" and len(node.args) == 1:
var = self.__parse_expr(node.args[0])
return int(round(var))
# if this is a valid call to construct a new array, do so; otherwise raise an exception
else:
if len(node.args) == 2:
shape = self.__parse_expr(node.args[0])
if isinstance(shape, (int, np.int32, np.int64)):
shape = (self._block_width, shape)
elif isinstance(shape, tuple):
shape = (self._block_width, ) + shape
else:
raise ValueError(
"Do not recognize call to " + func +
" with arguments of types " +
str([arg.__dict__ for arg in node.args]))
try:
dtype = np.dtype(node.args[1].id)
except:
raise ValueError(
"Do not recognize call to " + func +
" with arguments of types " +
str([arg.__dict__ for arg in node.args]))
if func in self.__vars_dict:
var = self.__vars_dict[func]
if not var.shape == shape and var.dtype == dtype:
raise ValueError("Requested shape and type for " +
func +
" do not match existing values")
return var
else:
var = np.zeros(shape, dtype, 'F')
self.__vars_dict[func] = var
self.__print(
2, 'Added variable ' + func + ' with shape ' +
str(tuple(shape)) + ' and type ' + str(dtype))
return var
else:
raise ValueError(
"Do not recognize call to " + func +
" with arguments " +
str([str(arg.__dict__) for arg in node.args]))
raise ValueError("Cannot parse AST nodes of type " +
str(node.__dict__))
def test_frequency(self):
f1 = units.convert(1, frequency.hertz, frequency.khz)
self.assertAlmostEqual(f1, 0.001, 6)
return
def test_frequency(self):
f1 = units.convert(1, frequency.hertz, frequency.khz)
self.assertAlmostEqual(f1, 0.001, 6)
return
def test_time(self):
t1 = units.convert(1, time.year, time.second)
self.assertAlmostEqual(t1, (365.25 * 24 * 60 * 60), 6)
def test_speed(self):
s1 = units.convert(55, speed.miles_per_hour, speed.meters_per_second)
self.assertAlmostEqual(s1, 24.5872, 6)
def test_speed(self):
s1 = units.convert(55, speed.miles_per_hour, speed.meters_per_second)
self.assertAlmostEqual(s1, 24.5872, 6)
def __add_io_buffer(self, buff, varname, input, dtype, buffer_len, scale):
"""
append a tuple with the buffer and variable to either the input buffer
list (if input=true) or output buffer list (if input=false), making sure
that buffer shapes are compatible
"""
var = self.get_variable(varname)
if buff is not None and not isinstance(buff, np.ndarray):
raise ValueError("Buffers must be ndarrays.")
# if buffer length is not defined, figure out what it should be
if buffer_len is not None:
if self._buffer_len is None:
self._buffer_len = buffer_len
elif self._buffer_len != buffer_len:
raise ValueError(
"Buffer length was already set to a number different than the one provided. To change the buffer length, you must reset the buffers."
)
if not self._buffer_len:
if buff is not None: self._buffer_len = buff.shape[0]
else: self._buffer_len = self._block_width
self.__print(
1, "Setting i/o buffer length to " + str(self._buffer_len))
# if a unit is given, convert it to a scaling factor
if isinstance(scale, unit):
scale = convert(1, scale, self._clk)
# if no buffer was provided, make one
returnbuffer = False
if buff is None:
if var is None:
raise ValueError(
"Cannot make buffer for non-existent variable " + varname)
# deduce dtype. If scale is used, force float type
if not dtype:
if not scale:
dtype = var.dtype
else:
dtype = np.dtype('float' + str(var.dtype.itemsize * 8))
buff = np.zeros((self._buffer_len, ) + var.shape[1:], dtype)
returnbuffer = True
# Check that the buffer length is correct. For 1D buffer, reshape
# it if possible, assuming array of structure ordering
if not buff.shape[0] == self._buffer_len:
if buff.ndim == 1 and len(buff) % self._buffer_len == 0:
buff = buff.reshape(self._buffer_len,
len(buff) // self._buffer_len)
else:
raise ValueError("Buffer provided for " + varname +
" is the wrong length.")
# Check that shape of buffer is compatible with shape of variable.
# If variable does not yet exist, add it here
if var is None:
if dtype is None:
if not scale:
dtype = buff.dtype
else:
dtype = np.dtype('float' + str(buff.dtype.itemsize * 8))
var = self.__add_var(varname, dtype,
(self._block_width, ) + buff.shape[1:])
elif var.shape[1:] != buff.shape[1:]:
raise ValueError("Provided buffer has shape " + str(buff.shape) +
" which is not compatible with " + str(varname) +
" shape " + str(var.shape))
varname = re.search('(\w+)', varname).group(0)
if input:
self.__input_buffers[varname] = (buff, var, scale)
self.__print(
2, 'Binding input buffer of shape ' + str(buff.shape) +
' and type ' + str(buff.dtype) + ' to variable ' + varname +
' with shape ' + str(var.shape) + ' and type ' +
str(var.dtype))
else:
self.__output_buffers[varname] = (buff, var, scale)
self.__print(
2, 'Binding output buffer of shape ' + str(buff.shape) +
' and type ' + str(buff.dtype) + ' to variable ' + varname +
' with shape ' + str(var.shape) + ' and type ' +
str(var.dtype))
if returnbuffer: return buff
def length_label(meters):
convert = unitmap[parser.args.unit]
value = units.convert(meters, units.length.m, convert)
return "%i %s" % (value, convert.label)
