def stats_for_i(i): from si_prefix import si from magnetics import air_coil_B_field as field brf = field(i, 50, 0.110) b1 = brf/2.0 omega = spectral_window(b1) t_90 = t90(b1) t_180 = t180(b1) n = 10 print 'brf:'.rjust(n), si(brf) print 'b1:'.rjust(n), si(b1) print 'omega:'.rjust(n), si(omega) print 't90:'.rjust(n), si(t_90) print 't180:'.rjust(n), si(t_180)
def nmr_loop2(target, l, cdamp, cset, printno=15):
'''
Evaluate best capacitor combinations.
target = target resonant frequency.
l = coil inductance.
cdamp = capacitance of damping circuit in parallel with coil.
this will become part of ca and so need to be
accounted for.
cset = list of tuples to evaluate of the form (ca, cb, cc).
printno = number of combinations to print.
'''
from si_prefix import si
results = []
for c in cset:
results = results + nmr_loop(target, l, c[0], c[1], c[2], cdamp)
results.sort(key=lambda x: abs(x[-1]))
for x in results[:printno]:
print '{0}+{1}, {2}+{3}, {4}+{5} = {6}, {7} ({8}%)'.format(*[si(i) for i in x])
return results
def nmr_loop2(target, l, cdamp, cset, printno=15):
'''
Evaluate best capacitor combinations.
target = target resonant frequency.
l = coil inductance.
cdamp = capacitance of damping circuit in parallel with coil.
this will become part of ca and so need to be
accounted for.
cset = list of tuples to evaluate of the form (ca, cb, cc).
printno = number of combinations to print.
'''
from si_prefix import si
results = []
for c in cset:
results = results + nmr_loop(target, l, c[0], c[1], c[2], cdamp)
results.sort(key=lambda x: abs(x[-1]))
for x in results[:printno]:
print '{0}+{1}, {2}+{3}, {4}+{5} = {6}, {7} ({8}%)'.format(
*[si(i) for i in x])
return results
def fr(f, l, d, t): ''' Calculate Fr, the ratio of AC to DC resistance for a winding dure to skin and proximity effects. f = frequency l = number of layers d = diameter of conductor t = temperature ''' from math import pi, sqrt, sinh, sin, cosh, cos from si_prefix import si h = 0.886*d # height of equivalent rectangular conductor w = 2*pi*f # angular frequency a = h * sqrt(w*u0*0.5/resistivity_cu(t)) mr = a*(sinh(2*a)+sin(2*a))/(cosh(2*a)-cos(2*a)) dr = 2*a*(sinh(a)-sin(a))/(cosh(a)+cos(a)) delta = sqrt(2*resistivity_cu(t)/(w*u0)) # skin depth print 'skin depth:', si(delta)+'m' fr = mr+(l**2-1)*dr/3 # Fr value return fr
def fr(f, l, d, t):
'''
Calculate Fr, the ratio of AC to DC
resistance for a winding dure to skin
and proximity effects.
f = frequency
l = number of layers
d = diameter of conductor
t = temperature
'''
from math import pi, sqrt, sinh, sin, cosh, cos
from si_prefix import si
h = 0.886 * d # height of equivalent rectangular conductor
w = 2 * pi * f # angular frequency
a = h * sqrt(w * u0 * 0.5 / resistivity_cu(t))
mr = a * (sinh(2 * a) + sin(2 * a)) / (cosh(2 * a) - cos(2 * a))
dr = 2 * a * (sinh(a) - sin(a)) / (cosh(a) + cos(a))
delta = sqrt(2 * resistivity_cu(t) / (w * u0)) # skin depth
print 'skin depth:', si(delta) + 'm'
fr = mr + (l**2 - 1) * dr / 3 # Fr value
return fr
# cxxx.
# .ll:
# .ll:
# .ll:
# .ll:
# .ll:
# .........,llc.........
# .cccccccccclllccccccccc.
# ......................
# ',,,,,,,,,,,,'
# .;;;;;;;;;;;;;;
# ......
# .cllllc
# ......
#
#
R1 = R3 = 1000 # ohm
R2 = R4 = R5 = 2000 # ohm
V1 = 3 # v
# a)
A = np.array([[R1 + R2, -R2, -R1], [-R2, R3 + R2 + R4, -R4],
[-R1, -R4, R4 + R1 + R5]])
b = np.array([V1, 0, 0])
X = np.linalg.solve(A, b)
print("8.\na)\ni1 = {}A\ni2 = {}A\ni3 = {}A".format(si(X[0]), si(X[1]),
si(X[2])))
print("a potencia fornecida é de {}W".format(si(X[0] * V1)))
# b)
# i1*R3/R3*R2 = i2
i3 = I1 * R3 / (R2 + R3)
v3 = V1 * R2R3 / Rreq
print("b) i3: {}A v3: {}V".format(si_format(i3), si_format(v3)))
print()
# c)
A = np.array([[1000, 2000, 0], [1, -1, -1], [0, -2000, -2000]])
b = np.array([1, 0, 0])
X = np.linalg.solve(A, b)
print(X)
print("solução do stor c) ir1: {}A ir2: {}A ir3: {}A".format(
si(X[0]), si(X[1]), si(X[2])))
print("c)")
print(
"como já sabemos i1 com a formula de ohm (V=I.R) sabemos que a corrente de R1 é {}A"
.format(si_format(i1)))
print("pelo teorema de kirchhof:")
print("V_R1 = R1*V1/Rreq")
V_R1 = R1 * V1 / Rreq
print("V_R1: {}V".format(V_R1))
print()
# ou como deve ser feito pelo sistema de equalções
# d) Calcule a potência dissipada na resistência R1.
# P=I.V
print("P_R1: {}W".format(si_format(V_R1 * i1)))
def print_comb(x):
from si_prefix import si
print 'ca=[{0}||{1}], cb=[{2}||{3}], cc=[{4}||{5}] = {6}, {7} ({8}%)'.format(*[si(i) for i in x])
R1 = 4e3
R2 = 6e3
R3 = 1e4
R4 = 1e4
I1 = 5e-3
V1 = 5
# R2
#
A = np.array([
# i1 i2 i3 currentes/malhas
[1, 0, 0], # I1
[-R2, R1 + R2 + R3, -R3], # I2
[-R4, -R3, R4 + R3]
]) #I3
b = np.array([I1, 0, -V1]) #voltagens
# como já sabemos o valor de i1/currente na
# malha I1 podemso meter uma resistemcia teorica
# de 1 para meter o valor de I1 no campo da voltagem
X = np.linalg.solve(A, b)
for i in range(X.size):
print("i{} : {}A".format(i + 1, si(X[i])))
V_R2 = (X[0] - X[1]) * R2
V_R4 = (X[0] - X[2]) * R4
V_I1 = (V_R4 + V_R2)
P_I1 = V_I1 * I1
print("V_R2: {}V ; V_R4: {}V ; V_I1: {}V ; P_I1: {}W".format(
si(V_R2), si(V_R4), si(V_I1), si(P_I1)))
def format(v):
from si_prefix import si
return si(v, space=0).replace('M', 'meg')
def format(v):
from si_prefix import si
return si(v, space=0).replace('M', 'meg')
def _finder(target, function, series, n_min=2, n_max=2, quiet=False, symetric=True):
'''
Combines the values listed in series using the function defined
by function and rates them by how close the combined value is
to the target value.
target = target value.
function = function with which to combine values.
series = series to search.
[n_min] = minimum number of combined values.
[m_max] = maximum number of combined values.
[quiet] = enables of disables printing.
[symetric] = enables or disables removal of equivalent input
combination for symetric functions.
'''
from si_prefix import si
# Wrap function to return function result and a comparason to target result.
def _calc(*x):
calc = function(*x)
within = ((calc*100.0)/target)-100.0
return (calc, within)
# Print target value.
if quiet == False:
print 'TARGET = ', si(target)
# Construct possible combinations of series.
x = [] # combinations
l = len(series)
for n in range(n_min, n_max+1):
for i in range(l**n):
xi = []
for j in range(n):
xi.append((i/(l**j))%l)
# If function inputs are symetric (can be aritrarily swapped) then
# the list of combinations can be minimised by not repeating equivalent
# combinations. For example if A,B = B,A.
if (len(xi) > 1) & (symetric==True):
if xi[-1] < xi[-2]:
break
else:
x.append(tuple([series[xii] for xii in xi]))
# Run all input combinations.
results = [_calc(*xi) + xi for xi in x]
# result of line above leaves the within variable in column 1 of each tuple.
within_index = 1
# Sort results by error from target.
results.sort(key=lambda x: abs(x[within_index]))
# Print top results.
if quiet == False:
for x in results[:min(15,len(results))]:
print si(x[0]), '{0:.3f}% | '.format(x[1]).rjust(12),
for y in x[2:]:
print si(y),
print ''
# Return top results.
return [x[2:] for x in results[:min(15,len(results))]]
def _finder(target,
function,
series,
n_min=2,
n_max=2,
quiet=False,
symetric=True):
'''
Combines the values listed in series using the function defined
by function and rates them by how close the combined value is
to the target value.
target = target value.
function = function with which to combine values.
series = series to search.
[n_min] = minimum number of combined values.
[m_max] = maximum number of combined values.
[quiet] = enables of disables printing.
[symetric] = enables or disables removal of equivalent input
combination for symetric functions.
'''
from si_prefix import si
# Wrap function to return function result and a comparason to target result.
def _calc(*x):
calc = function(*x)
within = ((calc * 100.0) / target) - 100.0
return (calc, within)
# Print target value.
if quiet == False:
print 'TARGET = ', si(target)
# Construct possible combinations of series.
x = [] # combinations
l = len(series)
for n in range(n_min, n_max + 1):
for i in range(l**n):
xi = []
for j in range(n):
xi.append((i / (l**j)) % l)
# If function inputs are symetric (can be aritrarily swapped) then
# the list of combinations can be minimised by not repeating equivalent
# combinations. For example if A,B = B,A.
if (len(xi) > 1) & (symetric == True):
if xi[-1] < xi[-2]:
break
else:
x.append(tuple([series[xii] for xii in xi]))
# Run all input combinations.
results = [_calc(*xi) + xi for xi in x]
# result of line above leaves the within variable in column 1 of each tuple.
within_index = 1
# Sort results by error from target.
results.sort(key=lambda x: abs(x[within_index]))
# Print top results.
if quiet == False:
for x in results[:min(15, len(results))]:
print si(x[0]), '{0:.3f}% | '.format(x[1]).rjust(12),
for y in x[2:]:
print si(y),
print ''
# Return top results.
return [x[2:] for x in results[:min(15, len(results))]]
def print_comb(x):
from si_prefix import si
print 'ca=[{0}||{1}], cb=[{2}||{3}], cc=[{4}||{5}] = {6}, {7} ({8}%)'.format(
*[si(i) for i in x])