What would be the best approach to create a type that is a saturated integer in python ?
i.e.:
v = SaturatedInteger(0, 100)
# That should create an integer that will always be in between 0 and 100,
# and have all default operations
v += 68719
print v #Should print '100'.
I can think of inheriting int type, but where should the saturating logic be implemented then ?
If you need a new (quick and dirty) class for it, I would implement it as follows.
class SaturatedInteger:
def __init__(self, val, lo, hi):
self.real, self.lo, self.hi = val, lo, hi
def __add__(self, other):
return min(self.real + other.real, self.hi)
def __sub__(self, other):
return max(self.real - other.real, self.lo)
...
Add as many of the other operators in the docs as you feel you will need (and their 'r' variants).
By storing the value in the instance name real, you can do your arithmetic with regular integers, floats, etc. too:
a = SaturatedInteger(60, 0, 100)
print(a)
60
print(a+30)
90
print(a+40)
100
print(a+50.)
100
print(a-70.)
0
print(a+a)
100
Though, of course you only add the real part if you're adding a complex number to your SaturatedInteger, so watch out. (For a much more complete and robust version, #jonrsharpe's answer is the way to go).
In general, I would implement using a #property to protect an instance's value attribute, then emulate a numeric type, rather than inheriting from int:
class SaturatedInteger(object):
"""Emulates an integer, but with a built-in minimum and maximum."""
def __init__(self, min_, max_, value=None):
self.min = min_
self.max = max_
self.value = min_ if value is None else value
#property
def value(self):
return self._value
#value.setter
def value(self, new_val):
self._value = min(self.max, max(self.min, new_val))
#staticmethod
def _other_val(other):
"""Get the value from the other object."""
if hasattr(other, 'value'):
return other.value
return other
def __add__(self, other):
new_val = self.value + self._other_val(other)
return SaturatedInteger(self.min, self.max, new_val)
__radd__ = __add__
def __eq__(self, other):
return self.value == self._other_val(other)
if __name__ == '__main__':
v = SaturatedInteger(0, 100)
v += 68719
assert v == 100
assert 123 + v == 100
I've only implemented __add__, __radd__ and __eq__, but you can probably see how the rest could be built out as required. You might want to think about what happens when two SaturatedIntegers are used together - should the result have e.g. min(self.min, other.min) as its own min?
I wrote a sample class that has an add function:
class SatInt:
def __init__(self, low, up):
self.lower = low
self.upper = up
self.value = 0
def add(self, n):
if n+self.value > self.upper:
self.value = self.upper
else:
self.value = self.value + n
x = SatInt(0,100)
x.add(32718)
print(x.value)
100
Related
I need to allocate some values in 3 individual lists.
The values are generated on the fly but all included in the 0-6 range.
The point is that these values should be put in the three lists so that the average of each list does not differ so much from the others. The lists also need to be similar in length.
So the goal would be to progressively fill these lists to maintain, as much as possible, a uniform average value and size for all of them.
As I didn't found any built-in function to do this, I have implemented a code which keeps track of lists length and tries to keep them as close as possible in their average value. You can play with it and improve it to better fit your case.
class Data:
def __init__(self):
"""Init the three lists."""
self.a = []
self.b = []
self.c = []
#staticmethod
def get_average(data: list):
"""Get average value of a list."""
try:
return sum(data) / len(data)
except ZeroDivisionError:
return 0
def get_shortest(self):
"""Return list with the shortest length."""
shortest_length = min(len(self.a), len(self.b), len(self.c))
if len(self.a) == shortest_length:
return self.a
elif len(self.b) == shortest_length:
return self.b
else:
return self.c
def get_smallest(self):
"""Return list with the smallest average value."""
smallest_average = min(self.get_average(self.a), self.get_average(self.b), self.get_average(self.c))
if self.get_average(self.a) == smallest_average:
return self.a
elif self.get_average(self.b) == smallest_average:
return self.b
else:
return self.c
def get_highest(self):
"""Return list with the highest average value."""
highest_average = max(self.get_average(self.a), self.get_average(self.b), self.get_average(self.c))
if self.get_average(self.a) == highest_average:
return self.a
elif self.get_average(self.b) == highest_average:
return self.b
else:
return self.c
def add_number(self, num):
"""Add number to one of the lists."""
shortest = self.get_shortest()
smallest = self.get_smallest()
highest = self.get_highest()
# Lists must not differ by more than two elements
if len(smallest) - len(shortest) >= 2 or len(highest) - len(shortest) >= 2:
shortest.append(num)
else:
# Test if the number uppers the smallest average
initial_avg = self.get_average(smallest)
smallest.append(number)
final_avg = self.get_average(smallest)
if final_avg > initial_avg:
return
else:
smallest.pop()
# Test if the number lowers the highest average
initial_avg = self.get_average(highest)
highest.append(number)
final_avg = self.get_average(highest)
if final_avg < initial_avg:
return
else:
highest.pop()
# Last resort
shortest.append(num)
d = Data()
value = input("Add number: ")
while value != 'e':
try:
number = int(value)
except ValueError:
break
d.add_number(number)
print(f"List a: {d.a}, avg. {d.get_average(d.a)}")
print(f"List b: {d.b}, avg. {d.get_average(d.b)}")
print(f"List c: {d.c}, avg. {d.get_average(d.c)}")
value = input("Add number:")
I have one function where I am calculating the CPU usage of a test case. The function works, but I would like to append the result of the subtraction in a list for the further usage.
For example, first I subtract 10 and 15, which is -5. At this point the list looks like [-5]. Next I subtract 20 and 30, which is -10. Now I want the list to look like [-5, -10]. My current code is (python 2.7):
import psutil
class CPU():
def __init__(self):
self.cpu_start()
def cpu_start(self):
global a
a= psutil.cpu_percent(interval=1, percpu=False)
print a
def cpu_end(self):
global b
b = psutil.cpu_percent(interval=1, percpu=False)
print b
def diff(self):
c= a-b
list = []
list.append(c)
print list
def main():
CPU()
if __name__ == '__main__':
main()
Just make the diff function return a-b, and append that to an array:
import psutil
class CPU:
def __init__(self):
self.cpu_start()
self.list = []
self.a = 0
self.b = 0
self.c = 0
def cpu_start(self):
self.a = psutil.cpu_percent(interval=1, percpu=False)
return self.a
def cpu_end(self):
self.b = psutil.cpu_percent(interval=1, percpu=False)
return self.b
def diff(self):
self.c = self.cpu_start() - self.cpu_start()
return self.c
def main():
cpu = CPU()
results = []
while True:
results.append(cpu.diff())
print results
if __name__ == '__main__':
main()
Remember that when you're using a class function, you need to create an object of that class, such as cpu = CPU() - I'm creating an object called cpu of class CPU, initialised with nothing. Then the __init__ function will create a and b(created as self.a and self.b, because they're local) and store them locally in that class. The diff() function, takes no arguments, but returns the difference of a and b which are stored locally in that class. Then I create a list called results with no elements. I run cpu.diff(), which gets the difference from cpu_start() and cpu_end(), and append the result to the results array. This is run in a loop, constantly appending to the array and printing it.
Hope this helps.
I am defining two custom functions in Sympy, called phi and Phi. I know that Phi(x)+Phi(-x) == 1. How do I provide Sympy with this simplification rule? Can I specify this in my class definition?
Here is what I've done so far:
from sympy import Function
class phi(Function):
nargs = 1
def fdiff(self, argindex=1):
if argindex == 1:
return -1*self.args[0]*phi(self.args[0])
else:
raise ArgumentIndexError(self, argindex)
#classmethod
def eval(cls, arg):
# The function is even, so try to pull out factors of -1
if arg.could_extract_minus_sign():
return cls(-arg)
class Phi(Function):
nargs = 1
def fdiff(self, argindex=1):
if argindex == 1:
return phi(self.args[0])
else:
raise ArgumentIndexError(self, argindex)
For the curious, phi and Phi represent the Gaussian PDF and CDF, respectively. These are implemented in sympy.stats. But, in my case, it's easier to interpret results in terms of phi and Phi.
Based upon the comment by Stelios, Phi(x) should return 1-Phi(-x) if x is negative. Therefore, I modified Phi as follows:
class Phi(Function):
nargs = 1
def fdiff(self, argindex=1):
if argindex == 1:
return phi(self.args[0])
else:
raise ArgumentIndexError(self, argindex)
#classmethod
def eval(cls, arg):
# Phi(x) + Phi(-x) == 1
if arg.could_extract_minus_sign():
return 1-cls(-arg)
After having seen the nice implementation of the "ampl car example" in Pyomo repository, I would like to keep extending the problem with new features and constraints, but I have found the next problems during development. Is someone able of fix them?
1) Added new constraint "electric car": Now the acceleration is limited by adherence until a determined speed and then constant power model is used. I am not able of implement this constraint as i would think. It is commented in the, but Pyomo complains about that a constraint is related to a variable. (now Umax depends of the car speed).
2) Added new comfort acceleration and jerk constraints. It seems they are working right, but should be nice if a Pyomo guru supervise them and tell me if they are really implemented in the correct way.
3) About last one, in order of reducing verbosity. Is there any way of combine accelerationL and accelerationU in a unique constraint? Same for jerkL and jerkU.
4) The last feature is a speed limit constraint divided in two steps. Again, I am not able of getting it works, so it is commented in code. Does anybody dare to fix it?
# Ampl Car Example (Extended)
#
# Shows how to convert a minimize final time optimal control problem
# to a format pyomo.dae can handle by removing the time scaling from
# the ContinuousSet.
#
# min tf
# dx/dt = v
# dv/dt = u - R*v^2
# x(0)=0; x(tf)=L
# v(0)=0; v(tf)=0
# -3 <= u <= 1 (engine constraint)
#
# {v <= 7m/s ===> u < 1
# u <= { (electric car constraint)
# {v > 7m/s ===> u < 1*7/v
#
# -1.5 <= dv/dt <= 0.8 (comfort constraint -> smooth driving)
# -0.5 <= d2v/dt2 <= 0.5 (comfort constraint -> jerk)
# v <= Vmax (40 kmh[0-500m] + 25 kmh(500-1000m])
from pyomo.environ import *
from pyomo.dae import *
m = ConcreteModel()
m.R = Param(initialize=0.001) # Friction factor
m.L = Param(initialize=1000.0) # Final position
m.T = Param(initialize=50.0) # Estimated time
m.aU = Param(initialize=0.8) # Acceleration upper bound
m.aL = Param(initialize=-1.5) # Acceleration lower bound
m.jU = Param(initialize=0.5) # Jerk upper bound
m.jL = Param(initialize=-0.5) # Jerk lower bound
m.NFE = Param(initialize=100) # Number of finite elements
'''
def _initX(m, i):
return m.x[i] == i*m.L/m.NFE
def _initV(m):
return m.v[i] == m.L/50
'''
m.tf = Var()
m.tau = ContinuousSet(bounds=(0,1)) # Unscaled time
m.t = Var(m.tau) # Scaled time
m.x = Var(m.tau, bounds=(0,m.L))
m.v = Var(m.tau, bounds=(0,None))
m.u = Var(m.tau, bounds=(-3,1), initialize=0)
m.dt = DerivativeVar(m.t)
m.dx = DerivativeVar(m.x)
m.dv = DerivativeVar(m.v)
m.da = DerivativeVar(m.v, wrt=(m.tau, m.tau))
m.obj = Objective(expr=m.tf)
def _ode1(m, i):
if i==0:
return Constraint.Skip
return m.dt[i] == m.tf
m.ode1 = Constraint(m.tau, rule=_ode1)
def _ode2(m, i):
if i==0:
return Constraint.Skip
return m.dx[i] == m.tf * m.v[i]
m.ode2 = Constraint(m.tau, rule=_ode2)
def _ode3(m, i):
if i==0:
return Constraint.Skip
return m.dv[i] == m.tf*(m.u[i] - m.R*m.v[i]**2)
m.ode3 = Constraint(m.tau, rule=_ode3)
def _accelerationL(m, i):
if i==0:
return Constraint.Skip
return m.dv[i] >= m.aL*m.tf
m.accelerationL = Constraint(m.tau, rule=_accelerationL)
def _accelerationU(m, i):
if i==0:
return Constraint.Skip
return m.dv[i] <= m.aU*m.tf
m.accelerationU = Constraint(m.tau, rule=_accelerationU)
def _jerkL(m, i):
if i==0:
return Constraint.Skip
return m.da[i] >= m.jL*m.tf**2
m.jerkL = Constraint(m.tau, rule=_jerkL)
def _jerkU(m, i):
if i==0:
return Constraint.Skip
return m.da[i] <= m.jU*m.tf**2
m.jerkU = Constraint(m.tau, rule=_jerkU)
'''
def _electric(m, i):
if i==0:
return Constraint.Skip
elif value(m.v[i])<=7:
return m.a[i] <= 1
else:
return m.v[i] <= 1*7/m.v[i]
m.electric = Constraint(m.tau, rule=_electric)
'''
'''
def _speed(m, i):
if i==0:
return Constraint.Skip
elif value(m.x[i])<=500:
return m.v[i] <= 40/3.6
else:
return m.v[i] <= 25/3.6
m.speed = Constraint(m.tau, rule=_speed)
'''
def _initial(m):
yield m.x[0] == 0
yield m.x[1] == m.L
yield m.v[0] == 0
yield m.v[1] == 0
yield m.t[0] == 0
m.initial = ConstraintList(rule=_initial)
discretizer = TransformationFactory('dae.finite_difference')
discretizer.apply_to(m, nfe=value(m.NFE), wrt=m.tau, scheme='BACKWARD')
#discretizer = TransformationFactory('dae.collocation')
#discretizer.apply_to(m, nfe=value(m.NFE), ncp=4, wrt=m.tau, scheme='LAGRANGE-RADAU')
solver = SolverFactory('ipopt')
solver.solve(m,tee=True)
print("final time = %6.2f" %(value(m.tf)))
t = []
x = []
v = []
a = []
u = []
for i in m.tau:
t.append(value(m.t[i]))
x.append(value(m.x[i]))
v.append(3.6*value(m.v[i]))
a.append(10*value(m.u[i] - m.R*m.v[i]**2))
u.append(10*value(m.u[i]))
import matplotlib.pyplot as plt
plt.plot(x, v, label='v (km/h)')
plt.plot(x, a, label='a (dm/s2)')
plt.plot(x, u, label='u (dm/s2)')
plt.xlabel('distance')
plt.grid('on')
plt.legend()
plt.show()
Thanks a lot in advance,
Pablo
(1) You should not think of Pyomo constraint rules as callbacks that are used by the solver. You should think of them more as a function to generate a container of constraint objects that gets called once for each index when the model is constructed. Meaning it is invalid to use a variable in an if statement unless you are really only using its initial value to define the constraint expression. There are ways to express what I think you are trying to do, but they involve introducing binary variables into the problem, in which case you can no longer use Ipopt.
(2) Can't really provide any help. Syntax looks fine.
(3) Pyomo allows you to return double-sided inequality expressions (e.g., L <= f(x) <= U) from constraint rules, but they can not involve variable expressions in the L and U locations. It doesn't look like the constraints you are referring to can be combined into this form.
(4) See (1)
I am currently working on a small RPG in Pygame to get used to object oriented coding.
When looking into how to auto-update a property I came across the following:
class P:
def __init__(self,x):
self.x = x
#property
def x(self):
return self.__x
#x.setter
def x(self, x):
if x < 0:
self.__x = 0
elif x > 1000:
self.__x = 1000
else:
self.__x = x
I tried applying it to my code but I get the following error:
File "weapons.py", line 13, in __init__
self.name = '{} {}'.format(ammo, self.raw_name)
TypeError: name() takes exactly 3 arguments (2 given)
I understand what the error is but I don't get how to solve it since I need both the raw_name and the ammo attributes to auto-update my Arrow instance's name.
My class is as such:
class Projectile(Item):
def __init__(self, name, value, image, x, y, speed, dmg, dmg_modif, ammo):
super(Projectile, self).__init__(name, value, image, x, y)
self.dest = (self.rect[0],self.rect[1])
self.speed = speed
self.dmg_modif = dmg_modif
self.dmg = dmg
self.orientation = 0
self.ammo = ammo
#property
def name(self):
return self.___name
#name.setter
def name(self, raw_name, ammo):
if '{} {}'.format(raw_name,ammo) != self.___name:
self.___name = '{} {}'.format(raw_name,ammo)
The child class which returns the error is:
class Arrow(Projectile):
def __init__(self, ammo): #name, value, image, x, y, dmg
self.raw_name = 'Arrows'
self.name = '{} {}'.format(ammo, self.raw_name)
self.value = 5
self.image = variables.quiver_img
self.speed = 4
self.dmg = 2
self.dmg_modif = 1
super(Arrow, self).__init__(self.name, self.value, self.image, 200, 150, self.speed, self.dmg, self.dmg_modif, ammo)
And the parent classes are, Item and MySprite:
class Item(MySprite):
def __init__(self, name, value, image, x, y):
# Call the parent class (Sprite) constructor
super(Item, self).__init__(image, x, y)
self.name = name
self.value = value
self.inv_pos = -1
and
class MySprite(pygame.sprite.Sprite):
def __init__(self,image,x,y):
# Call the parent class (Sprite) constructor
super(MySprite, self).__init__()
self.image = image
self.rect = self.image.get_rect().move(x, y) #initial placement
self.top_cp = (self.rect[0]+self.rect[2]/2,self.rect[1])
self.bot_cp = (self.top_cp[0],self.rect[1]+self.rect[3])
self.left_cp = (self.rect[0],self.rect[1]+self.rect[3]/2)
self.right_cp = (self.left_cp[0]+self.rect[2],self.left_cp[1])
self.center = self.rect.center
self.pos = self.rect.topleft
self.blit_order = 1
self.level = variables.current_level #Level(1)#level to which sprite belongs
Any help would be welcome !
Please provide a minimal example next time.
Your code looks a little complicated. OK, the first problem is that you can't pass multiple arguments to a setter. You can either pass a tuple or just use a traditional setter method def set_name(self, name, ammo):.
Another problem is that you use the ___name attribute before it has been set, for example in the second line of the Arrows __init__ method.
Private attributes should have one underscore not three (that's just a convention to warn other programmers). If you have two or more underscores than the name gets mangled.
Also, it looks to me like you change the name in the setter only if the new name is equal to the old name (kinda pointless ;)). What do you actually want to do there? Maybe you don't need properties at all.
Here's a fixed (minimal) version of your code:
import pygame
class MySprite(pygame.sprite.Sprite):
def __init__(self):
super(MySprite, self).__init__()
class Item(MySprite):
def __init__(self, name):
super(Item, self).__init__()
self.name = name
class Projectile(Item):
def __init__(self, name):
super(Projectile, self).__init__(name)
#property
def name(self):
return self._name
#name.setter
def name(self, name):
self._name = name + ' foo'
class Arrow(Projectile):
def __init__(self):
super(Arrow, self).__init__(name='Arrows')
arrow = Arrow()
print(arrow.name)
arrow.name = 'New name'
print(arrow.name)
So I eventually managed to solve my problem:
class Projectile(object):
def __init__(self, raw_name, ammo):
self.raw_name = raw_name
self.name = self.raw_name
self.ammo = ammo
#property
def ammo(self):
return self._ammo
#ammo.setter
def ammo(self, ammo):
self.name = str(ammo) + self.raw_name
self._ammo = ammo
class Arrow(Projectile):
def __init__(self):
self.raw_name = ' Arrows'
self.name = self.raw_name
super(Arrow, self).__init__(self.raw_name, ammo
= 10)
arrow = Arrow()
print arrow.ammo
print arrow.name
arrow.ammo = 15
print arrow.ammo
print arrow.name
gives:
>>>10
>>>10 Arrows
>>>15
>>>15 Arrows