def test_060_pow(cls):
cls.assertEqual(cls.m**2, Dimension({"L": 2}))
cls.assertEqual(cls.m**(1 / 2), Dimension({"L": 1 / 2}))
cls.assertEqual(cls.m**1.2, Dimension({"L": 1.2}))
cls.assertEqual(cls.m**Fraction(1 / 2),
Dimension({"L": Fraction(1 / 2)}))
def setUpClass(cls):
cls.m = Dimension("L")
cls.none = Dimension(None)
cls.dim_complexe = Dimension({"J": 1, "theta": -3})
cls.no_dimension_str = "no-dimension"
cls.times = []
cls.ids = []
cls.amp = Dimension("I")
def test_repr_latex(cls):
cls.assertEqual(Dimension(None)._repr_latex_(), "$1$")
cls.assertEqual(Dimension({"L": 1})._repr_latex_(), "$L$")
cls.assertEqual(Dimension({"L": 2})._repr_latex_(), "$L^{2}$")
cls.assertEqual(
Dimension({
"J": -1,
"L": 1,
"theta": 3
})._repr_latex_(), r"$\frac{L \theta^{3}}{J}$")
def test_latex_SI_unit(cls):
cls.assertEqual(Dimension(None).latex_SI_unit(), "$1$")
cls.assertEqual(Dimension({"L": 1}).latex_SI_unit(), "$m$")
cls.assertEqual(Dimension({"L": 2}).latex_SI_unit(), "$m^{2}$")
cls.assertEqual(
Dimension({
"J": -1,
"L": 1,
"theta": 3
}).latex_SI_unit(), r"$\frac{K^{3} m}{cd}$")
def test_050_div(cls):
cls.assertEqual(cls.m / cls.dim_complexe,
Dimension({
"J": -1,
"L": 1,
"theta": 3
}))
# Testing the inversion by dividing 1
cls.assertEqual(1 / cls.m, Dimension({"L": -1}))
# Dividing by a number, not a Dimension object
cls.assertRaises(TypeError, lambda: cls.m / 1.12)
cls.assertRaises(TypeError, lambda: 1.12 / cls.m)
def __init__(self, dimension=None, all_units=False, **kwargs):
super().__init__(**kwargs)
self.dimension = dimensionify(dimension)
self.units = units
self.units["-"] = Quantity(1, Dimension(None), symbol="-")
# list of available units
if self.dimension == Dimension(None) or all_units:
self.favunit_str_list = [
u_str for u_str, u_q in self.units.items()
]
else:
self.favunit_str_list = [
u_str for u_str, u_q in self.units.items()
if self.dimension == u_q.dimension
]
self.favunit_str_list.append("-")
self.value = self.units["-"]
# dropdown
self.favunit_dd = ipyw.Dropdown(
options=self.favunit_str_list,
# set init value
value=str(self.value.symbol),
description='Favunit:',
layout=Layout(width="30%",
margin="5px 5px 5px 5px",
border="solid black"),
)
self.children = [self.favunit_dd]
### 3. Change favunit
# selection of favunit
def update_favunit_on_favunit_dd_change(change):
# retrieve new favunit q
self.value = self.units[change.new]
self.favunit_dd.observe(update_favunit_on_favunit_dd_change,
names="value")
def update_dd_value(change):
self.favunit_dd.value = str(change.new.symbol)
self.observe(update_dd_value, names="value")
def test_100_expr_parsing(cls):
cls.assertEqual(cls.m, Dimension("L"))
cls.assertEqual(cls.m, Dimension("L**1"))
cls.assertEqual(cls.m * cls.m, Dimension("L**2"))
cls.assertEqual(cls.m * cls.dim_complexe, Dimension("L*J/theta**3"))
cls.assertEqual(cls.m, Dimension("m"))
cls.assertEqual(cls.m * cls.m, Dimension("m**2"))
cls.assertEqual(cls.m * cls.dim_complexe, Dimension("m*cd/K**3"))
# sympy was parsing "I" as complex number
cls.assertEqual(cls.amp * cls.m, Dimension("L*I"))
with cls.assertRaises(TypeError):
# sympy parsing not good with ^ char
Dimension("m^2")
def test_100_expr_parsing(cls):
cls.assertEqual(cls.m, Dimension("L"))
cls.assertEqual(cls.m, Dimension("L**1"))
cls.assertEqual(cls.m * cls.m, Dimension("L**2"))
cls.assertEqual(cls.m * cls.dim_complexe, Dimension("L*J/theta**3"))
cls.assertEqual(cls.m, Dimension("m"))
cls.assertEqual(cls.m * cls.m, Dimension("m**2"))
cls.assertEqual(cls.m * cls.dim_complexe, Dimension("m*cd/K**3"))
with cls.assertRaises(AttributeError):
# sympy parsing not good with ^ char
cls.assertEqual(cls.m * cls.m, Dimension("m^2"))
def test_010_init(cls):
metre_by_dict = Dimension({"L": 1})
cls.assertEqual(cls.m, metre_by_dict)
none_dimenion_dict = cls.none.dim_dict
dico_dimension_none = {
'L': 0,
'M': 0,
'T': 0,
'I': 0,
'theta': 0,
'N': 0,
'J': 0,
'RAD': 0,
'SR': 0
}
cls.assertEqual(none_dimenion_dict, dico_dimension_none)
cls.assertRaises(TypeError, lambda: Dimension({"m": 1}))
def test_040_mul(cls):
cls.assertEqual(cls.m * cls.dim_complexe,
Dimension({
"J": 1,
"L": 1,
"theta": -3
}))
# Multipliying by a number, not a Dimension object
cls.assertRaises(TypeError, lambda: cls.m * 1.12)
cls.assertRaises(TypeError, lambda: 1.12 * cls.m)
def setup(self):
self.length = Dimension("L")
self.mass = Dimension("M")
self.time = Dimension("T")
def test_070_eq_ne(cls):
cls.assertTrue(cls.m == Dimension({"L": 1}))
cls.assertTrue(cls.m != cls.none)
def decorated(x):
x = quantify(x)
if not x.dimension == Dimension(None):
raise DimensionError(x.dimension, Dimension(None))
return math_func(x.value)
np.iterable(m)
# %%
type(m.value)
# %%
iter(m)
# %% [markdown] tags=[]
# ## Array repr with 0 value
# Pick best favunit take the smallest when 0 is in the array with positiv and negativ values
# %%
from physipy import m, Quantity, Dimension
import numpy as np
Quantity(np.array([0, -1.2, 1.2]), Dimension("L"))
# %% [markdown]
# # Inplace change using asqarray
# %%
from physipy.quantity.utils import asqarray
print(type(m.value))
arrq_9 = np.array([m.__copy__()], dtype=object)
out = asqarray(arrq_9)
# this changes the type of m value
print(type(m.value))
# %% [markdown]
# # Numpy trapz implementaion not called when only x or dx is a quantity
# %timeit parse_str_to_dic("M*L**2/T**3*I**-1")
# %timeit new_parse_str_to_dict("M*L**2/T**3*I**-1")
# %%
# %timeit Dimension({"M":1, "L":2, "T":-3, "I":-1})
# %timeit Dimension("M*L**2/T**3*I**-1")
# %%
# %prun -D prunsum_file -s nfl new_parse_str_to_dict("M*L**2/T**3*I**-1")
# !snakeviz prunsum_file
# %%
from physipy import Dimension
# %%
d = Dimension("L")
# %%
# %prun -D prun d*d
# %%
# !snakeviz prun
# %% [markdown]
# We need operations on array-like objects.
# The solutions are :
# - a dict
# - list
# - numpy array
# - ordered dict
# - counter
"perm": (perm, a),
"pow": (math_pow, (a, 2)),
"prod": (prod, [a, a]),
"radians": (radians, a),
"remainder": (remainder, (a, b)),
"sin": (sin, a),
"sinh": (sinh, a),
"sqrt": (sqrt, a),
"tan": (tan, a),
"tanh": (tanh, a),
"trunc": (trunc, a),
}
q_rad = 0.4 * rad
q = 2.123 * m
q_dimless = Quantity(1, Dimension(None))
physipy_math_params = {
"acos": (physipy.math.acos, q_dimless),
"acosh": (physipy.math.acosh, q_dimless),
"asin": (physipy.math.asin, q_dimless),
"asinh": (physipy.math.asinh, q_dimless),
"atan": (physipy.math.atan, q_dimless),
"atan2": (physipy.math.atan2, (q, q)),
"atanh": (physipy.math.atanh, q_dimless),
"ceil": (physipy.math.ceil, q),
"coysign": (physipy.math.copysign, (q, q)),
#"comb" :(comb , 10, 3),
"cos": (physipy.math.cos, q),
"cosh": (physipy.math.cosh, q),
"degrees": (physipy.math.degrees, q),
return x + y + 1 * m
print(sum_length(1 * m, 1 * m))
@check_dimension((m, m), (m))
def sum_length(x, y):
"This function could be used on any Quantity, but here restricted to lengths."
return x + y
print(sum_length(1 * m, 1 * m))
@check_dimension((Dimension("L"), Dimension("L")), (Dimension("L")))
def sum_length(x, y):
return x + y
print(sum_length(1 * m, 1 * m))
# %% [markdown]
# ## Favunit setting
# This decorator simply sets the favunit of the outputs
# %%
mm = units["mm"]
@set_favunit(mm)
print(z / 2)
#print(2//z)
#print(z//2)
# %% [markdown]
# # Support with physipy
# %%
import physipy
from physipy import m, cd, s, Quantity, Dimension
# %% [markdown]
# ## Construction
# %%
x = Quantity(mcerp.Normal(1, 2), Dimension("L"))
x
# %%
# using multiplication
# x = mcerp.Normal(1, 2)*s : NotUpcast: <class 'physipy.quantity.quantity.Quantity'> cannot be converted to a number with uncertainty
x = m * mcerp.Normal(1, 2)
x
# %% [markdown]
# ## Basic Operation
# %%
# with scalar
scalar = 2 * m
print(x + scalar)
def test_080_pow_inverse(cls):
m_inverse = 1 / cls.m
cls.assertEqual(m_inverse, Dimension({"L": -1}))
# %% [markdown]
# ## Construction from class
# %% [markdown]
# Several ways are available constructing a Quantity object
# %%
from physipy import Quantity, m, sr, Dimension
# %% [markdown]
# Scalar Quantity
# %%
# from int
print(Quantity(1, Dimension("L")))
# from float
print(Quantity(1.123, Dimension("L")))
# from fraction.Fraction
from fractions import Fraction
print(Quantity(Fraction(1, 2), Dimension("L")))
# %% [markdown]
# Array-like Quantity
# %%
# from list
print(Quantity([1, 2, 3, 4], Dimension("L")))
def test_110_siunit_dict(cls):
cls.assertEqual(
Dimension(None).siunit_dict(), {
'm': 0,
'kg': 0,
's': 0,
'A': 0,
'K': 0,
'mol': 0,
'cd': 0,
'rad': 0,
'sr': 0
})
cls.assertEqual(
Dimension({
"L": 1
}).siunit_dict(), {
'm': 1,
'kg': 0,
's': 0,
'A': 0,
'K': 0,
'mol': 0,
'cd': 0,
'rad': 0,
'sr': 0
})
cls.assertEqual(
Dimension({
"L": 1.2
}).siunit_dict(), {
'm': 1.2,
'kg': 0,
's': 0,
'A': 0,
'K': 0,
'mol': 0,
'cd': 0,
'rad': 0,
'sr': 0
})
cls.assertEqual(
Dimension({
"L": 1 / 2
}).siunit_dict(), {
'm': 1 / 2,
'kg': 0,
's': 0,
'A': 0,
'K': 0,
'mol': 0,
'cd': 0,
'rad': 0,
'sr': 0
})
cls.assertEqual(
Dimension({
"J": -1,
"L": 1,
"theta": 3
}).siunit_dict(), {
'm': 1,
'kg': 0,
's': 0,
'A': 0,
'K': 3,
'mol': 0,
'cd': -1,
'rad': 0,
'sr': 0
})
import sys
#print(sys.path)
sys.path.insert(
0,
r"/Users/mocquin/Documents/CLE/Optique/Python/JUPYTER/MYLIB10/MODULES/physipy"
)
import matplotlib.pyplot as plt
import numpy as np
import physipy
from physipy import s
from physipy import Dimension
from IPython.display import display, Latex
# %%
a = Dimension(None)
b = Dimension("T")
c = Dimension({"T": 3})
d = Dimension({"T": -4})
e = Dimension({"T": 2, "L": -3})
# %%
print(a)
print(b)
print(c)
print(d)
print(e)
print()
# %%
print(str(a))
def setUp(cls):
cls.m = Dimension("L")
cls.none = Dimension(None)
cls.dim_complexe = Dimension({"J": 1, "theta": -3})
cls.no_dimension_str = "no-dimension"
#
# %% [markdown]
# ## Creation
# Basic creation of dimension-full arrays :
# %%
import matplotlib.pyplot as plt
import numpy as np
import physipy
from physipy import m, s, Quantity, Dimension, rad
# %%
x_samples = np.array([1, 2, 3, 4]) * m
y_samples = Quantity(np.array([1, 2, 3, 4]), Dimension("T"))
print(x_samples)
print(y_samples)
print(m * np.array([1, 2, 3, 4]) == x_samples) # multiplication is commutativ
# %% [markdown]
# ## Operation
# Basic array operation are handled the 'expected' way : note that the resulting dimension are consistent with the operation applied :
# %%
print(x_samples + 1 * m)
print(x_samples * 2)
print(x_samples**2)
print(1 / x_samples)
# %% [markdown]
def test_080_inverse(cls):
m_inverse = cls.m.inverse()
cls.assertEqual(m_inverse, Dimension({"L": -1}))
