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,