def plot_hist(circuit_id, mode, sing_id, width, width2):
data = {"w0": [], "q": []}
sns.set_style("whitegrid")
for i in range(muestras):
tf = conseguir_tf(
ra1=disp(RA1[circuit_id], res_tol),
ra2=disp(RA2[circuit_id], res_tol),
r41=disp(R41[circuit_id], res_tol),
r42=disp(R42[circuit_id], res_tol),
r1=disp(R1[circuit_id], res_tol),
rb=disp(RB[circuit_id], res_tol),
c3=disp(C3[circuit_id], cap_tol),
c21=disp(C21[circuit_id], cap_tol),
c22=disp(C22[circuit_id], cap_tol)
)
if mode == "poles":
info = tf.poles[sing_id]
else:
info = tf.zeros[sing_id]
w0 = sqrt(info.real**2 + info.imag ** 2)
data["w0"].append(2*pi*w0)
data["q"].append(- w0 / (2 * info.real))
# g = sns.jointplot("w0", "q", data=data, kind="reg",
# color="m", height=7)
# plt.xlabel("Fo (Hz)")
#
# plt.ylabel("Q")
if str(mode) == "poles":
textmode = "Polo"
else:
textmode = "Cero"
plt.title(textmode + " " + str(sing_id+1) + ", etapa "+str(circuit_id+1))
make_histogram(variable="Fo",
unidad="Hz",
data=data["w0"],
filename="histograma_w0_"+str(mode)+"_"+str(sing_id) + str(circuit_id) +".png",
bar_width=width)
plt.title(textmode + " " + str(sing_id+1) + ", etapa "+str(circuit_id+1))
make_histogram(variable="Q",
unidad="adimensional",
data=data["q"],
filename="histograma_q_" + str(mode) + "_" + str(sing_id) + str(circuit_id) + ".png",
bar_width=width2)
def process_files(fnames):
for fname in fnames:
snap = Snapshot.read_snapshot(fname)
count = snap.get_bin_statistic("n")
percent = snap.get_species_statistic("percentage")
count[count == 0] = 1.0
energy_scale = 6.24150974e18 / count
make_histogram(snap, "temperature")
make_histogram(snap, "max_temperature")
make_histogram(snap, "ionization")
make_histogram(snap, "kinetic_energy", scalar=energy_scale)
make_histogram(snap,
"percentage",
scalar=snap.get_bin_statistic("density"))
import sys
from snapshot import Snapshot
from make_histogram import make_histogram
for fname in sys.argv[1:]:
snap = Snapshot.read_snapshot(fname)
make_histogram(snap, "temperature", fname)
make_histogram(snap, "max_temperature", fname)
make_histogram(snap, "ionization", fname)
make_histogram(snap, "kinetic_energy", fname, scalar=6.24150974e18)
import sys
from snapshot import Snapshot
from make_histogram import make_histogram
for fname in sys.argv[1:]:
snap = Snapshot.read_snapshot(fname)
make_histogram(snap, "temperature")
make_histogram(snap, "max_temperature")
make_histogram(snap, "ionization")
make_histogram(snap, "kinetic_energy", scalar=6.24150974e18)
data = read_file_spice("montecarlo.txt")
arr = {"notch_f": [], "min": [], "bw": []}
for i in range(len(data)):
info = computar_notch(data[i])
arr["notch_f"].append(info["notch_f"])
arr["min"].append(info["min"])
arr["bw"].append(info["f2"]-info["f1"])
#print(arr)
make_histogram(variable="Notch frecuency",
unidad="Hz",
data=arr["notch_f"],
filename="histograma1.png",
bar_width=50741-50225)
make_histogram(variable="Notch depth",
unidad="dB",
data=arr["min"],
filename="histograma_martu_notch_depth.png",
bar_width=6)
make_histogram(variable="Notch bandwidth",
unidad="Hz",
data=arr["bw"],
filename="histograma_martu_notch_bw.png",
bar_width=40169-39783)
import seaborn as sns
import matplotlib.pyplot as plt
from make_histogram import make_histogram
from computar_maximos import computar_maximos_bp
data = read_file_spice("montecarlo.txt")
arr = []
for i in range(len(data)):
act = conseguir_fp(data[i], -3)
arr.append(act)
make_histogram(variable="Frecuencia de corte",
unidad="Hz",
data=arr,
filename="histograma_marce_ej1_bessel.png",
bar_width=570.619 - 548.393)
data = read_file_spice(
"EJ1/Circuito con Legendre/Simulacion/BodeMontecarlo.txt")
arr = []
for i in range(len(data)):
act = conseguir_fp(data[i], -3)
arr.append(act)
make_histogram(variable="Frecuencia de corte",
unidad="Hz",
data=arr,
