I am learning the Python tutorial on MIT, and I'm having a problem with one of the exercises. The exercise is,
Use the IPython prompt to calculate:
Positive root of the following equation: 34x2 + 68x - 510 = 0 Recall:
given ax2 + bx + c = 0 , then x = (-b +sqrt(b*b - 4ac))/(2*a)
I have entered
(-68)+(math.sqrt((68**2)-(4*34*510)))
in the iprompt, and I'm getting
Traceback (most recent call last):
File "", line 1, in
(-68)+(math.sqrt((68**2)-(434510)))
ValueError: math domain error
On calculating individually, I found that the value inside the square root is negative. Should I be adding some additional functions to enable negative root calculation, or is there something else that is wrong with my statement?
Can someone help me out?
Note: The solutions on the course page suggest making
(b**2-4ac)
positive before calculating the root. Will this not be mathematically inaccurate?
I am taking the same course, I did this way and got the right answers. Write the equation like this:
(-68+((68**2)-4*34*(-510))**0.5)/(2*34)
That will give you the 1st solution, just change the sign of plus to minus and you get the 2nd solution, as:
(-68-((68**2)-4*34*(-510))**0.5)/(2*34)
Check the photo I have attached, it is a screenshot of the solutions.
Screenshot of Spyder solving equation 34x^2+68x-510
Let f(x) = ax² + bx + c. If you want to solve f(x) = 0 in the field of real numbers, then you need to take care of the discriminant of f defined to be b² - 4ac. If the latter is negative, you do not have real solutions (and to have solutions you need to consider f as a complex polynomial, meaning that you want to find a solution to f(x) = 0 in the field of complex numbers).
Now, for your problem, it appears that you have a sign problem. The coefficient c is equal to -510 and not 510. Thus, 68² - 4*34*510 should be 68² - 4*34*(-510) = 68² + 4 * 34 * 510.
if f(x) = ax^2 + bx + c then its positive root will be
( -b + sqrt (b^2 - 4ac)) / 2a
According to your Question f(x) = 34x^2 + 68x - 510 = 0
So +ve root will be => ( -68 + sqrt (68^2 - 4*34*(-510)) ) / (2*34) = 3
In python code will be like -
from math import sqrt
print((-68 + sqrt(68**2 - 4*34*(-510)))/(2*34))
But what you did wrong is, you have taken co-efficient of 'C' is 510.
Instead It should be (-510).
I'm doing the course as well.
The question is from "getting started", so it shouldn't be hard.
And it should be related to the course material.
Therefore, here is what I have:
IDLE 1.2.4
>>> import math
>>> a=34
>>> b=68
>>> c=-510
>>> d = ( -b + math.sqrt ( b*b - 4*a*c))/(2 * a)
>>> print d
3.0
Related
I have several expressions involving the norm or norm squared of a vector u. I'd like to simplify these expressions by substituting a known value for the norm of u. However, it seems that obvious expressions involving even simple multiples of the norm are not simplified.
As an example, this code does what I would expect :
import sympy as sp
u1,u2,u3 = sp.symbols('u_1, u_2, u_3',real=True,positive=True)
utu = u1**2 + u2**2 + u3**2
print("Ex. 1")
print(utu.subs(utu,1))
This produces the expected output
Ex. 1
1
However, 2*utu does not simplify in the way I would expect :
print("Ex 2")
print((2*utu).subs(utu,1))
Ex 2
2*u_1**2 + 2*u_2**2 + 2*u_3**2
I can explicitly force the substitution with this :
print("Ex 3")
print((2*utu).subs(2*utu,2))
which produces the expected output :
Ex 3
2
Ideally, I'd like to substitute under a norm function, but the run into the same issue.
u = sp.Matrix(3, 1, [u1,u2,u3])
print("Ex 4")
print(u.norm().subs(utu,1))
print("Ex 5")
print((2*u).norm().subs(utu,1))
print("Ex 6")
print((2*u).norm().subs(4*utu,4))
which produces
Ex 4
1
Ex 5
sqrt(4*u_1**2 + 4*u_2**2 + 4*u_3**2)
Ex 6
2
Are there tricks I am missing that will catch these obvious (to me at least - maybe not to Sympy?) simplifications? I've tried factor and expand, without much luck.
Let's analyze this expression:
expr = 2*utu
# out: 2*u_1**2 + 2*u_2**2 + 2*u_3**2
The multiplication has been evaluated. This is SymPy's default behavior: it evaluates things. We can work with the expression manipulation functions to achieve our goal.
For example:
expr = collect_const(expr)
# out: 2*(u_1**2 + u_2**2 + u_3**2)
expr.subs(utu, 1)
# out: 2
Another example:
expr = (2 * u).norm()
# out: sqrt(4*u_1**2 + 4*u_2**2 + 4*u_3**2)
expr = expr.simplify() # Note that expr.factor() produces the same result with this expression
# out: 2*sqrt(u_1**2 + u_2**2 + u_3**2)
expr.subs(utu, 1)
# out: 2
If you play (and modify) with these examples, you will realize that the same result can be achieved with different functions (factor, simplify, collect, collect_const, ...), but even one little change in the expression might prevent one function from "doing its work", while others might be able to. Expression manipulation is kind of an art that one should practice (a lot).
For completeness, I'm going to show you UnevaluatedExpr, which allows a particular expression to remain unevaluated during expression manipulation, though it might not always be the best choice:
n = UnevaluatedExpr(utu)
# out: u_1**2 + u_2**2 + u_3**2
expr = 4 * n
# out: 4*(u_1**2 + u_2**2 + u_3**2)
Note that SymPy didn't proceed with the full evaluation. Now:
expr.subs(utu, 1)
# out: 4*1
Why is there a 4*1 instead of 4? The 1 refers to the UnevaluateExpr object that we created earlier: to evaluate it we can use the doit() method:
expr.subs(utu, 1).doit()
# 4
Keep in mind that while using UnevaluateExpr, the expression becomes non-commutative (I think it's a bug with SymPy), which will prevent other functions to produce the expected results.
Substituting compound expressions is problematic. For the most part you should only expect subs to work if the expression to be replaced is known to always appear literally as part of the expression tree that you are substituting into. When possible then it is better to rearrange for a single symbol like:
In [10]: utu
Out[10]:
2 2 2
u₁ + u₂ + u₃
In [11]: (2*utu).subs(u1**2, 1 - u2**2 - u3**2)
Out[11]: 2
Even here we are substituting for a power of a symbol (u1**2) which is potentially fragile if we can't be sure that exactly that power will always appear in the expression. More generally there are functions that can simplify expressions based on knowing some polynomial relation e.g. ratsimpmodprime:
In [16]: e = (1 - u1**2) / (u1**2 + u2**2 + u3**2)
In [17]: e
Out[17]:
2
1 - u₁
───────────────
2 2 2
u₁ + u₂ + u₃
In [18]: ratsimpmodprime(e, [u1**2 + u2**2 + u3**2 - 1])
Out[18]:
2 2
u₂ + u₃
Other possibilities could be using resultants or Groebner bases to do similar things. Note that u.norm() has a square root which is symbolically awkward so it is better to work with the square of the norm (same as when working on pen and paper):
In [20]: ratsimpmodprime((2*u).norm()**2, [u1**2 + u2**2 + u3**2 - 1])
Out[20]: 4
Also if you just want a more powerful version of subs then you can use replace but with patterns:
In [21]: a = Wild('a')
In [22]: p = a*u1**2 + a*u2**2 + a*u3**2
In [23]: (2*utu).replace(p, a)
Out[23]: 2
In [24]: (2*u).norm().replace(p, a)
Out[24]: 2
Both solid answers already. If you have an arbitrary expression that you expect to be a factor in another, factor_terms is what I try first to make that factor appear. It will collect common factors without doing factoring. But if this doesn't work and you know you have a factor, div is a nice way to check and see the expression with the factor removed:
>>> expr = 2*(x + y)
>>> factor_terms(expr)
2*(x + y)
>>> e2 = expand(expr*(x -y)) # 2*x**2 - y**2
>>> factor_terms(e2)
2*(x**2 - y**2)
>>> div(_,-x-y)
(-2*x + 2*y, 0)
>>> _[0]*z # if you wanted to replace factor -x-y with z
z*(-2*x + 2*y)
In sympy (python) it seems that, by default, terms in univarate polynomials are ordered according to decreasing degrees: highest degree first, then second to highest, and so on. So, for example, a polynomial like
x + 1 + x^3 + 3x^6
will be printed out as 3x^6 + x^3 + x + 1.
I would like to reverse this order of polynomial terms in sympy to be increasing in the degrees. For the same example, the print-out should read 1 + x + x^3 + 3x^6. A solution that globally changes some parameter in program preamble is preferred but other options are also welcome.
Here is an MWE to play around with. It is different from the actual program I am working with. One part of the actual program (not the MWE) is printing out a list of recursively defined polynomials, e.g., P_n(x) = P_(n-1)(x) + a_n * x^n. It is easier for me to compare them when they are ordered by increasing degree. This is the motivation to change the order; doing it globally would probably just keep the code more readable (aesthetically pleasing). But the MWE is just for the same simple polynomial given in example above.
import sympy as sym
from sympy import *
x = sym.Symbol('x')
polynomial = x + 1 + x**3 + 3*x**6
print(polynomial)
Output of MWE:
>>> 3*x**6 + x**3 + x + 1
Desired output for MWE:
>>> 1 + x + x**3 + 3*x**6
You can get the leading term using sympy.polys.polytools.LT:
LT(3x ** 6 + x ** 3 + x + 1) == 3x**6
So at least you can churn out terms recursively and print it in your own way.
Unfortunately I’ve been trying to find some way to print the terms in some fix order for a long while and find no solution better than this
It's seems that there isn't an explicit way to do that and I found this approach to the problem:
to modify the print-representation of the object you can subclass its type and override the corresponding printing method (for LaTeX, MathML, ...) see documentation.
In this case _sympystr is used to "generates readable representations of SymPy expressions."
Here a basic implementation:
from sympy import Poly, symbols, latex
class UPoly(Poly):
"""Modified univariative polynomial"""
def _sympystr(self, printer) -> str:
"""increasing order of powers"""
if self.is_multivariate: # or: not self.is_univariate
raise Exception('Error, Polynomial is not univariative')
x = next(iter(expr.free_symbols))
poly_print = ""
for deg, coef in sorted(self.terms()):
term = coef * x**deg[0]
if coef.is_negative:
term = -term # fix sign
poly_print += " - "
else:
poly_print += " + "
poly_print += printer._print(term)
return poly_print.lstrip(" +-")
def _latex(self, printer):
return latex(self._sympystr(printer)) # keep the order
x = symbols('x')
expr = 2*x + 6 - x**5
up = UPoly(expr)
print(up)
#6 + 2*x - x**5
print(latex(up))
#6 + 2 x - x^{5}
I am creating a program that calculates the optimum angles to fire a projectile from a range of heights and a set initial velocity. Within the final equation I need to utilise, there is an inverse sec function present that is causing some troubles.
I have imported math and attempted to use asec(whatever) however it seems math can not calculate inverse sec functions? I also understand that sec(x) = 1/cos(x) but when I sub 1/cos(x) into the equation instead and algebraically solve for x it becomes a non real result :/.
The code I have is as follows:
print("This program calculates the optimum angles to launch a projectile from a given range of heights and a initial speed.")
x = input("Input file name containing list of heights (m): ")
f = open(x, "r")
for line in f:
heights = line
print("the heights you have selected are : ", heights)
f.close()
speed = float(input("Input your initial speed (m/s): "))
print("The initial speed you have selected is : ", speed)
ran0 = speed*speed/9.8
print(ran0)
f = open(x, "r")
for line in f:
heights = (line)
import math
angle = (math.asec(1+(ran0/float(heights))))/2
print(angle)
f.close()
So my main question is, is there any way to find the inverse sec of anything in python without installing and importing something else?
I realise this may be more of a math based problem than a coding problem however any help is appreciated :).
Let's say we're looking for real number x whose arcsecant is angle θ. Then we have:
θ = arcsec(x)
sec(θ) = x
1 / cos(θ) = x
cos(θ) = 1 / x
θ = arccos(1/x)
So with this reasoning, you can write your arcsecant function as:
from math import acos
def asec(x):
return acos(1/x)
"I also understand that sec(x) = 1/cos(x) but when I sub 1/cos(x) ..." Do you have to use sec or asec ?
Because sec(x)= 1/cos(x) and asec(x) = acos(1/x). Be careful the notation ^-1 is ambiguous, cos^-1(x) = acos(x) is different of [cos(x)]^-1.
angle = (math.asec(1+(ran0/float(heights))))/2
asec is not defined from -1 to 1
If you have a height lower than zero, and so the result of (ran0/float(heights)) is between -2 and 0, your angle will be non real.
I don't really know if this is what you asked for, but I hope it helps.
If math is OK for you to import, then you can use:
import math
def asec(x):
if x == 0:
return 1j * math.inf
else:
return math.acos(1 / x)
For some other ways of of re-writing asec(x), feast your eyes on the relevant Wikipedia article.
Alternatively, you could use Taylor series expansions, which always come in polynomial form, so, although that is only an approximation in a neighborhood of a given point, it would not require math.
For asec(x), its Taylor expansion in a neighborhood of ±∞ (also known as Laurent series) is given by (without using math):
def asec_taylor(x, pi=3.14159265):
if x == 0:
return 1j * float('inf')
else:
return pi / 2 - 1 / x - 1 / (6 * x ** 3) - 3 / (40 * x ** 5)
You can quickly check that the farther you are from 0, the better the approximation holds:
for x in range(-10, 10):
print(x, asec(x), asec_taylor(x))
-10 1.6709637479564565 1.670963741666667
-9 1.6821373411358604 1.6821373299281108
-8 1.696124157962962 1.6961241346516926
-7 1.714143895700262 1.7141438389326868
-6 1.7382444060145859 1.7382442416666668
-5 1.7721542475852274 1.7721536583333335
-4 1.8234765819369754 1.823473733854167
-3 1.9106332362490186 1.910611139814815
-2 2.0943951023931957 2.0939734083333335
-1 3.141592653589793 2.8124629916666666
0 (nan+infj) (nan+infj)
1 0 0.32912965833333346
2 1.0471975511965979 1.0476192416666668
3 1.2309594173407747 1.2309815101851853
4 1.318116071652818 1.3181189161458333
5 1.369438406004566 1.3694389916666667
6 1.4033482475752073 1.4033484083333334
7 1.4274487578895312 1.4274488110673134
8 1.4454684956268313 1.4454685153483076
9 1.4594553124539327 1.4594553200718894
If you can try of inverse of sec then it will be same as
>>>from mpmath import *
>>> asec(-1)
mpf('3.1415926535897931')
Here are the link in where you can better understand - [http://omz-software.com/pythonista/sympy/modules/mpmath/functions/trigonometric.html]
I'm very new to python programming and to this site. I'm currently working on a problem and can't seem to understand the error.
import math
# Problem number 5.
A5 = 5
B5 = 0
C5 = 6.5
# Root1
x9 = (-B5 + math.sqrt(B5**2 - 4*A5*C5))/(2*A5)
# Root2
x10 = (-B5 + math.sqrt(B5**2 - 4*A5*C5))/(2*A5)
# Print solution
print()
print('Problem #5')
print('Root 1: ',x9)
print('Root 2: ',x10)
I get this after i run it:
x9 = (-B5 + math.sqrt(B5**2 - 4*A5*C5))/(2*A5)
ValueError: math domain error
I did the problem on paper and got an answer for both...
If you got an answer, it must have been a complex number (which are not included by default in Python). Look at the line math.sqrt(B5**2 - 4*A5*C5).
This evaluates as so:
math.sqrt(B5**2 - 4*A5*C5)
math.sqrt(0**2 - 4*5*6.5)
math.sqrt(0 - 130)
math.sqrt(-130)
The function math.sqrt doesn't find complex roots. You should use cmath.sqrt instead, as that does (this will require importing cmath at the start of your program).
Using cmath, I get this result:
Problem #5
Root 1: 1.1401754250991378j
Root 2: 1.1401754250991378j
(where j is the square root of -1).
I get this error when using a python script that calculates pi using the Gauss-Legendre algorithm. You can only use up to 1024 iterations before getting this:
C:\Users\myUsernameHere>python Desktop/piWriter.py
End iteration: 1025
Traceback (most recent call last):
File "Desktop/piWriter.py", line 15, in <module>
vars()['t' + str(sub)] = vars()['t' + str(i)] - vars()['p' + str(i)] * math.
pow((vars()['a' + str(i)] - vars()['a' + str(sub)]), 2)
OverflowError: long int too large to convert to float
Here is my code:
import math
a0 = 1
b0 = 1/math.sqrt(2)
t0 = .25
p0 = 1
finalIter = input('End iteration: ')
finalIter = int(finalIter)
for i in range(0, finalIter):
sub = i + 1
vars()['a' + str(sub)] = (vars()['a' + str(i)] + vars()['b' + str(i)])/ 2
vars()['b' + str(sub)] = math.sqrt((vars()['a' + str(i)] * vars()['b' + str(i)]))
vars()['t' + str(sub)] = vars()['t' + str(i)] - vars()['p' + str(i)] * math.pow((vars()['a' + str(i)] - vars()['a' + str(sub)]), 2)
vars()['p' + str(sub)] = 2 * vars()['p' + str(i)]
n = i
pi = math.pow((vars()['a' + str(n)] + vars()['b' + str(n)]), 2) / (4 * vars()['t' + str(n)])
print(pi)
Ideally, I want to be able to plug in a very large number as the iteration value and come back a while later to see the result.
Any help appreciated!
Thanks!
Floats can only represent numbers up to sys.float_info.max, or 1.7976931348623157e+308. Once you have an int with more than 308 digits (or so), you are stuck. Your iteration fails when p1024 has 309 digits:
179769313486231590772930519078902473361797697894230657273430081157732675805500963132708477322407536021120113879871393357658789768814416622492847430639474124377767893424865485276302219601246094119453082952085005768838150682342462881473913110540827237163350510684586298239947245938479716304835356329624224137216L
You'll have to find a different algorithm for pi, one that doesn't require such large values.
Actually, you'll have to be careful with floats all around, since they are only approximations. If you modify your program to print the successive approximations of pi, it looks like this:
2.914213562373094923430016933707520365715026855468750000000000
3.140579250522168575088244324433617293834686279296875000000000
3.141592646213542838751209274050779640674591064453125000000000
3.141592653589794004176383168669417500495910644531250000000000
3.141592653589794004176383168669417500495910644531250000000000
3.141592653589794004176383168669417500495910644531250000000000
3.141592653589794004176383168669417500495910644531250000000000
In other words, after only 4 iterations, your approximation has stopped getting better. This is due to inaccuracies in the floats you are using, perhaps starting with 1/math.sqrt(2). Computing many digits of pi requires a very careful understanding of the numeric representation.
As noted in previous answer, the float type has an upper bound on number size. In typical implementations, sys.float_info.max is 1.7976931348623157e+308, which reflects the use of 10 bits plus sign for the exponent field in a 64-bit floating point number. (Note that 1024*math.log(2)/math.log(10) is about 308.2547155599.)
You can add another half dozen decades to the exponent size by using the Decimal number type. Here is an example (snipped from an ipython interpreter session):
In [48]: import decimal, math
In [49]: g=decimal.Decimal('1e12345')
In [50]: g.sqrt()
Out[50]: Decimal('3.162277660168379331998893544E+6172')
In [51]: math.sqrt(g)
Out[51]: inf
This illustrates that decimal's sqrt() function performs correctly with larger numbers than does math.sqrt().
As noted above, getting lots of digits is going to be tricky, but looking at all those vars hurts my eyes. So here's a version of your code after (1) replacing your use of vars with dictionaries, and (2) using ** instead of the math functions:
a, b, t, p = {}, {}, {}, {}
a[0] = 1
b[0] = 2**-0.5
t[0] = 0.25
p[0] = 1
finalIter = 4
for i in range(finalIter):
sub = i + 1
a[sub] = (a[i] + b[i]) / 2
b[sub] = (a[i] * b[i])**0.5
t[sub] = t[i] - p[i] * (a[i] - a[sub])**2
p[sub] = 2 * p[i]
n = i
pi_approx = (a[n] + b[n])**2 / (4 * t[n])
Instead of playing games with vars, I've used dictionaries to store the values (the link there is to the official Python tutorial) which makes your code much more readable. You can probably even see an optimization or two now.
As noted in the comments, you really don't need to store all the values, only the last, but I think it's more important that you see how to do things without dynamically creating variables. Instead of a dict, you could also have simply appended the values to a list, but lists are always zero-indexed and you can't easily "skip ahead" and set values at arbitrary indices. That can occasionally be confusing when working with algorithms, so let's start simple.
Anyway, the above gives me
>>> print(pi_approx)
3.141592653589794
>>> print(pi_approx-math.pi)
8.881784197001252e-16
A simple solution is to install and use the arbitrary-precisionmpmath module which now supports Python 3. However, since I completely agree with DSM that your use ofvars()to create variables on the fly is an undesirable way to implement the algorithm, I've based my answer on his rewrite of your code and [trivially] modified it to make use ofmpmath to do the calculations.
If you insist on usingvars(), you could probably do something similar -- although I suspect it might be more difficult and the result would definitely harder to read, understand, and modify.
from mpmath import mpf # arbitrary-precision float type
a, b, t, p = {}, {}, {}, {}
a[0] = mpf(1)
b[0] = mpf(2**-0.5)
t[0] = mpf(0.25)
p[0] = mpf(1)
finalIter = 10000
for i in range(finalIter):
sub = i + 1
a[sub] = (a[i] + b[i]) / 2
b[sub] = (a[i] * b[i])**0.5
t[sub] = t[i] - p[i] * (a[i] - a[sub])**2
p[sub] = 2 * p[i]
n = i
pi_approx = (a[n] + b[n])**2 / (4 * t[n])
print(pi_approx) # 3.14159265358979