def test_concat(self):
p = Project()
p.data["a"] = Quantity("a")
p.data["a"].value = np.float_([1, 2, 3])
p.data["b"] = Quantity("b")
p.data["b"].value = np.float_(4)
p.concat("c", "a", "b")
self.assertTrue((p.data["c"].value == np.float_([1, 2, 3, 4])).all())
def test_plot(self):
p = Project()
p.data["x"]=Quantity("x")
p.data["y"]=Quantity("y")
p.data["y"].value_prefUnit = si["ms"]
p.data["x"].value = np.float_([0,1,2,3,4,5])
p.data["x"].error = np.float_([0.1,0.1,0.2,0.3,0.4,0.3])
p.data["x"].dim = Dimension(time=1)
p.data["y"].value = np.float_([1,2.3,1.8,3.4,4.5,8])
p.data["y"].dim = Dimension(time=1)
p.plot(("x","y"))
def test_formula(self):
p = Project()
p.data["U_I"] = Quantity("U_I")
p.data["I_I"] = Quantity("I_I")
u_err = Symbol("U_I_err", positive=True)
i_err = Symbol("I_I_err", positive=True)
p.data["Z_I"] = Quantity("Z_I", "Impedanz")
p.data["Z_I"].error_formula = u_err * i_err + p.data["U_I"] * p.data[
"I_I"]**2
self.assertEqual(
p.formula("Z_I", True),
r"\sigma{\left (Z_{I} \right )} = I_{I}^{2} U_{I} + \sigma{\left (I_{I} \right )} \sigma{\left (U_{I} \right )}"
)
def plot(expr_pairs, config, save=None, xunit=None, yunit=None, xrange=None, yrange=None, ignore_dim=False):
# TODO it should be possible to e.g. plot the function [t,2*r] if r depends on t
# one or multiple things to plot
if len(expr_pairs) > 1:
single_plot = False
else:
single_plot = True
x_dim = None
y_dim = None
data_sets = []
functions = []
if ignore_dim:
xunit = S.One
yunit = S.One
for expr_pair in expr_pairs:
x = expr_pair[0]
y = expr_pair[1]
if not ignore_dim:
# check dimensions
if x_dim is None:
x_dim = get_dimension(x)
else:
if not x_dim == get_dimension(x):
raise RuntimeError("dimension mismatch\n%s != %s" % (x_dim, get_dimension(x)))
if y_dim is None:
y_dim = get_dimension(y)
else:
if not y_dim == get_dimension(y):
raise RuntimeError("dimension mismatch\n%s != %s" % (y_dim, get_dimension(y)))
# if y contains x, it must be a function
dummy = Quantity()
rep_y = y.subs(x,dummy)
if rep_y.has(dummy):
# get titles
if isinstance(x, Quantity):
x_title = ((x.longname + " ") if x.longname else "") + str(x)
else:
x_title = str(x)
y_title = str(y)
# check if x not only quantity but more complicated expression
if not isinstance(x,Quantity):
# replace by Dummy
y = rep_y
x = dummy
if not ignore_dim:
# get factors
x_factor, xunit = convert_to_unit(x_dim, outputUnit=xunit)
y_factor, yunit = convert_to_unit(y_dim, outputUnit=yunit)
# scale function to units
y = y.subs(x,x*x_factor) / y_factor
# if only one thing on plot, write labels to x-axis and y-axis
if single_plot:
title = None
x_label = x_title + ("" if xunit == S.One else " [" + str(xunit) + "]")
y_label = y_title + ("" if yunit == S.One else " [" + str(yunit) + "]")
# if more than one thing, use legend for title
else:
title = y_title
functions.append({"x":x,"term":y,"title":title})
# if y doesn't contain x, it must be a data set
else:
# calculate expressions first if necessary
if isinstance(expr_pair[0], Quantity):
x = expr_pair[0]
x_title = ((x.longname + " ") if x.longname else "") + str(x)
else:
# dummy quantity for calculation
x = Quantity()
x.value = get_value(expr_pair[0])
x.error = get_error(expr_pair[0])[0]
x.dim = x_dim
x_title = str(expr_pair[0])
if isinstance(expr_pair[1], Quantity):
y = expr_pair[1]
y_title = ((y.longname + " ") if y.longname else "") + str(y)
else:
# dummy quantity for calculation
y = Quantity()
y.value = get_value(expr_pair[1])
y.error = get_error(expr_pair[1])[0]
y.dim = y_dim
y_title = str(expr_pair[1])
if ignore_dim:
x_values = x.value
x_errors = x.error
y_values = y.value
y_errors = y.error
else:
# get values and errors all in one unit
x_values, x_errors, xunit = adjust_to_unit(x, unit = xunit)
y_values, y_errors, yunit = adjust_to_unit(y, unit = yunit)
# if only one thing on plot, write labels to x-axis and y-axis
if single_plot:
title = None
x_label = x_title + ("" if xunit == S.One else " [" + str(xunit) + "]")
y_label = y_title + ("" if yunit == S.One else " [" + str(yunit) + "]")
# if more than one thing, use legend for title
else:
title = y_title
data_sets.append({"x_values": x_values, "y_values": y_values, "x_errors": x_errors, "y_errors":y_errors, "title": title})
# if more than one thing on plot, write only units to axes
if not single_plot:
x_label = ("" if xunit == S.One else "[" + str(xunit) + "]")
y_label = ("" if yunit == S.One else "[" + str(yunit) + "]")
# plot
if config["plot_module"] == "matplotlib":
from errorpro import plot_mat
return plot_mat.plot(data_sets, functions, save=save, xrange=xrange, yrange=yrange, x_label=x_label, y_label=y_label)
elif config["plot_module"] == "gnuplot":
from errorpro import plot_gnu
return plot_gnu.plot(data_sets, functions, save=save, xrange=xrange, yrange=yrange, x_label=x_label, y_label=y_label)
else:
raise ValueError("There is not plot module called '%s'" % config["plot_module"])
import errorpro.plot_mat as matplot
import numpy as np
from errorpro.dimensions.dimensions import Dimension
from errorpro.si import system as si
from errorpro.quantities import Quantity
# not suitable for automated testing, so must be tested manually
if __name__ == '__main__':
# single data set
data = {
"t": Quantity("t", "Zeit"),
"v": Quantity("v", "Geschwindigkeit"),
"t0": Quantity("t0", "Startzeit"),
"e": Quantity("e", "Bremsdings"),
"s": Quantity("s", "Startgeschwindigkeit")
}
data["t"].value = np.float_([0, 1, 2, 3, 4, 5])
data["t"].dim = Dimension(time=1)
data["v"].value = np.float_([33, 23, 14, 10, 7, 3])
data["v"].error = np.float_([1, 2, 1.3, 2.1, 0.5, 0.6])
data["v"].value_prefUnit = si["km"] / si["h"]
data["v"].dim = Dimension(length=1, time=-1)
matplot.plot([(data["t"], data["v"])], [], si)
def test_fit(self):
# test fit_scipy on its own
a = Quantity("a")
a.value = np.float_([1,3,5,6,7,9])
b = Quantity("b")
b.value = np.float_([2.3,4,5.5,6,9.2,10.5])
c = Quantity("c")
c.value = np.float_(1)
d = Quantity("d")
d.value = np.float_(1)
f = a*c+d
popt,perr = (sc.fit(a,b,f,[c,d]))
small = 0.0001
self.assertTrue(abs(popt[0] - 1.05184) < small)
self.assertTrue(abs(perr[0] - 0.1328) < small)
self.assertTrue(abs(popt[1] - 0.81551) < small)
self.assertTrue(abs(perr[1] - 0.7684) < small)
# test fit command
small = 0.001
p = Project()
g = commands.Assignment("x")
g.value = ["1","2","3"]
g.value_unit = "A"
g.execute(p)
g = commands.Assignment("y")
g.value = ["6","3","2.1"]
g.value_unit = "C"
g.error = ["1","1","2"]
g.error_unit = "1e-1*C"
g.execute(p)
h = commands.Assignment("m")
h.value = "1"
h.value_unit = "s"
h.execute(p)
i = commands.Assignment("b")
i.value = "1"
i.value_unit = "C"
i.execute(p)
p.fit("m*x+b",("x","y"),["m","b"])
self.assertTrue(abs(p.data["m"].value - (-2.3)) < small)
self.assertTrue(abs(p.data["m"].error - 0.7) < small)
self.assertTrue(abs(p.data["b"].value - 8.06667) < small)
self.assertTrue(abs(p.data["b"].error - 1.257) < small)
p.fit("m*x+b",("x","y"),["m","b"],weighted=False)
self.assertTrue(abs(p.data["m"].value - (-1.95)) < small)
self.assertTrue(abs(p.data["m"].error - 0.6062) < small)
self.assertTrue(abs(p.data["b"].value - 7.6) < small)
self.assertTrue(abs(p.data["b"].error - 1.31) < small)
p.data["y"].error = None
self.assertRaises(RuntimeError,p.fit,"m*x+b",("x","y"),["m","b"],weighted=True)
def assign(value, error=None, unit=None, name=None, longname=None, value_unit=None, error_unit=None, ignore_dim=False):
""" function to create a new quantity
Args:
value: number or string that can be parsed by numpy, or sympy
expression. If it's a sympy expression containing quantities, it
will perform the calculation, otherwise it just saves the value.
error: number that is saved as the value's uncertainty. this will replace
any error coming from a calculation.
unit: sympy expression of Unit objects. This is used to convert and save
value and error in base units. Replaces value_unit and error_unit if
specified.
name: short name of the quantity (usually one letter). If not specified,
quantity will get a dummy name.
longname: optional additional description of the quantity
value_unit: unit of value. Use this if value and error have different units.
error_unit: unit of error.
ignore_dim: bool. Keeps function from raising an error even if calculated
and given unit don't match. Then given unit is used instead.
"""
value_formula = None
value_factor = 1
value_dim = Dimension()
error_formula = None
error_factor = 1
error_dim = Dimension()
# parse units
if unit is not None:
# if one general unit is given
value_factor, value_dim, value_unit = parse_unit(unit)
error_factor = value_factor
error_dim = value_dim
error_unit = value_unit
else:
# if value unit is given
if value_unit is not None:
value_factor, value_dim, value_unit = parse_unit(value_unit)
# if error unit is given
if error_unit is not None:
error_factor, error_dim, error_unit = parse_unit(error_unit)
# check dimension consistency between value_dim and error_dim
if value_unit is not None and not value_dim == error_dim:
raise RuntimeError("dimension mismatch\n%s != %s" % (value_dim, error_dim))
# process value
# if it's a calculation
if isinstance(value, Expr) and not value.is_number:
value_formula = value
value = get_value(value_formula)
if ignore_dim:
# with ignore_dim=True, calculated value is converted to given unit
value = np.float_(value_factor)*np.float_(value)
else:
# calculate dimension from dependency
calculated_dim = get_dimension(value_formula)
if value_unit is None:
value_dim = calculated_dim
else:
if not calculated_dim == value_dim:
raise RuntimeError("dimension mismatch \n%s != %s" % (value_dim, calculated_dim))
# if it's a number
else:
value=np.float_(value_factor)*np.float_(value)
# process error
if error is not None:
error=np.float_(error_factor)*np.float_(error)
# check value and error shapes and duplicate error in case
if error.shape == () or value.shape[-len(error.shape):] == error.shape:
error = np.resize(error, value.shape)
else:
raise RuntimeError("length of value and error don't match and "\
"can't be adjusted by duplicating.\n"\
"%s and %s" % (value.shape, error.shape))
# if error can be calculated
elif value_formula is not None:
error, error_formula = get_error(value_formula)
if ignore_dim:
# with ignore_dim=True, calculated error is converted to given unit
error = np.float_(error_factor)*np.float_(error)
q = Quantity(name, longname)
q.value = value
q.value_formula = value_formula
q.error = error
q.error_formula = error_formula
if value_unit is not None:
q.prefer_unit = value_unit
else:
q.prefer_unit = error_unit
q.dim = value_dim
return q
def test_fit(self):
# test fit_scipy on its own
a = Quantity("a")
a.value = np.float_([1, 3, 5, 6, 7, 9])
b = Quantity("b")
b.value = np.float_([2.3, 4, 5.5, 6, 9.2, 10.5])
c = Quantity("c")
c.value = np.float_(1)
d = Quantity("d")
d.value = np.float_(1)
f = a * c + d
popt, perr = (sc.fit(a, b, f, [c, d]))
small = 0.0001
self.assertTrue(abs(popt[0] - 1.05184) < small)
self.assertTrue(abs(perr[0] - 0.1328) < small)
self.assertTrue(abs(popt[1] - 0.81551) < small)
self.assertTrue(abs(perr[1] - 0.7684) < small)
# test fit command
small = 0.001
p = Project()
g = commands.Assignment("x")
g.value = ["1", "2", "3"]
g.value_unit = "A"
g.execute(p)
g = commands.Assignment("y")
g.value = ["6", "3", "2.1"]
g.value_unit = "C"
g.error = ["1", "1", "2"]
g.error_unit = "1e-1*C"
g.execute(p)
h = commands.Assignment("m")
h.value = "1"
h.value_unit = "s"
h.execute(p)
i = commands.Assignment("b")
i.value = "1"
i.value_unit = "C"
i.execute(p)
p.fit("m*x+b", ("x", "y"), ["m", "b"])
self.assertTrue(abs(p.data["m"].value - (-2.3)) < small)
self.assertTrue(abs(p.data["m"].error - 0.7) < small)
self.assertTrue(abs(p.data["b"].value - 8.06667) < small)
self.assertTrue(abs(p.data["b"].error - 1.257) < small)
p.fit("m*x+b", ("x", "y"), ["m", "b"], weighted=False)
self.assertTrue(abs(p.data["m"].value - (-1.95)) < small)
self.assertTrue(abs(p.data["m"].error - 0.6062) < small)
self.assertTrue(abs(p.data["b"].value - 7.6) < small)
self.assertTrue(abs(p.data["b"].error - 1.31) < small)
p.data["y"].error = None
self.assertRaises(RuntimeError,
p.fit,
"m*x+b", ("x", "y"), ["m", "b"],
weighted=True)
def plot(expr_pairs,
config,
save=None,
xunit=None,
yunit=None,
xrange=None,
yrange=None,
ignore_dim=False):
# TODO it should be possible to e.g. plot the function [t,2*r] if r depends on t
# one or multiple things to plot
if len(expr_pairs) > 1:
single_plot = False
else:
single_plot = True
x_dim = None
y_dim = None
data_sets = []
functions = []
if ignore_dim:
xunit = S.One
yunit = S.One
for expr_pair in expr_pairs:
x = expr_pair[0]
y = expr_pair[1]
if not ignore_dim:
# check dimensions
if x_dim is None:
x_dim = get_dimension(x)
else:
if not x_dim == get_dimension(x):
raise RuntimeError("dimension mismatch\n%s != %s" %
(x_dim, get_dimension(x)))
if y_dim is None:
y_dim = get_dimension(y)
else:
if not y_dim == get_dimension(y):
raise RuntimeError("dimension mismatch\n%s != %s" %
(y_dim, get_dimension(y)))
# if y contains x, it must be a function
dummy = Quantity()
rep_y = y.subs(x, dummy)
if rep_y.has(dummy):
# get titles
if isinstance(x, Quantity):
x_title = ((x.longname + " ") if x.longname else "") + str(x)
else:
x_title = str(x)
y_title = str(y)
# check if x not only quantity but more complicated expression
if not isinstance(x, Quantity):
# replace by Dummy
y = rep_y
x = dummy
if not ignore_dim:
# get factors
x_factor, xunit = convert_to_unit(x_dim, outputUnit=xunit)
y_factor, yunit = convert_to_unit(y_dim, outputUnit=yunit)
# scale function to units
y = y.subs(x, x * x_factor) / y_factor
# if only one thing on plot, write labels to x-axis and y-axis
if single_plot:
title = None
x_label = x_title + ("" if xunit == S.One else " [" +
str(xunit) + "]")
y_label = y_title + ("" if yunit == S.One else " [" +
str(yunit) + "]")
# if more than one thing, use legend for title
else:
title = y_title
functions.append({"x": x, "term": y, "title": title})
# if y doesn't contain x, it must be a data set
else:
# calculate expressions first if necessary
if isinstance(expr_pair[0], Quantity):
x = expr_pair[0]
x_title = ((x.longname + " ") if x.longname else "") + str(x)
else:
# dummy quantity for calculation
x = Quantity()
x.value = get_value(expr_pair[0])
x.error = get_error(expr_pair[0])[0]
x.dim = x_dim
x_title = str(expr_pair[0])
if isinstance(expr_pair[1], Quantity):
y = expr_pair[1]
y_title = ((y.longname + " ") if y.longname else "") + str(y)
else:
# dummy quantity for calculation
y = Quantity()
y.value = get_value(expr_pair[1])
y.error = get_error(expr_pair[1])[0]
y.dim = y_dim
y_title = str(expr_pair[1])
if ignore_dim:
x_values = x.value
x_errors = x.error
y_values = y.value
y_errors = y.error
else:
# get values and errors all in one unit
x_values, x_errors, xunit = adjust_to_unit(x, unit=xunit)
y_values, y_errors, yunit = adjust_to_unit(y, unit=yunit)
# if only one thing on plot, write labels to x-axis and y-axis
if single_plot:
title = None
x_label = x_title + ("" if xunit == S.One else " [" +
str(xunit) + "]")
y_label = y_title + ("" if yunit == S.One else " [" +
str(yunit) + "]")
# if more than one thing, use legend for title
else:
title = y_title
data_sets.append({
"x_values": x_values,
"y_values": y_values,
"x_errors": x_errors,
"y_errors": y_errors,
"title": title
})
# if more than one thing on plot, write only units to axes
if not single_plot:
x_label = ("" if xunit == S.One else "[" + str(xunit) + "]")
y_label = ("" if yunit == S.One else "[" + str(yunit) + "]")
# plot
if config["plot_module"] == "matplotlib":
from errorpro import plot_mat
return plot_mat.plot(data_sets,
functions,
save=save,
xrange=xrange,
yrange=yrange,
x_label=x_label,
y_label=y_label)
elif config["plot_module"] == "gnuplot":
from errorpro import plot_gnu
return plot_gnu.plot(data_sets,
functions,
save=save,
xrange=xrange,
yrange=yrange,
x_label=x_label,
y_label=y_label)
else:
raise ValueError("There is not plot module called '%s'" %
config["plot_module"])
