def scratch(date):
import toolkit as tk
mwf = 18.5101e9 * 2
print(tk.wlen_to_step(0.26 + 6, mwf))
print(tk.wlen_to_step(0.00824283455081 + 6.5, mwf))
print(tk.wlen_to_step(0.00824283455081 + 6, mwf))
return
def scratch(date):
from scipy.special import jv
import toolkit as tk
phase = 0.2
mwf = 18.5106e9 * 2
print(tk.wlen_to_step(phase + 6, mwf))
print(tk.wlen_to_step(phase + 6.5, mwf))
print(jv(0, 1)**2 * 944)
return
def find_delay_mins(date):
import os
import numpy as np
import matplotlib.pyplot as plt
import toolkit as tk
instrs = [
# ["3_delay_diode.txt", "MW = -10 dB", 0],
[ "4_delay_diode.txt", "MW = - 6 dB", 0],
[ "6_delay_diode.txt", "MW = - 8 dB", 1],
[ "7_delay_diode.txt", "MW = -10 dB", 2],
[ "5_delay_diode.txt", "MW = -12 dB", 3],
[ "8_delay_diode.txt", "MW = -16 dB", 4],
[ "9_delay_diode.txt", "MW = -20 dB", 5],
["10_delay_diode.txt", "MW = -26 dB", 6],
["11_delay_diode.txt", "MW = -32 dB", 7]
# ["12_delay_diode.txt", "MW = -38 dB", 8]
]
fig, axes = plt.subplots(nrows=len(instrs), sharex=True, sharey=True)
xkey = 'd'
ykey = 'nsig_rm'
ykey2 = 'nsig'
nave = 4
mwf = 18.5096e9*2 # Hz after doubler.
fold = 1
blink = False
smins = []
smaxs = []
for instr in instrs:
[fname, label, ax] = instr
ax = axes[ax]
fname = os.path.join("..", date, fname)
data = tk.delay_load(fname)
data, popt, pconv = tk.transform_delay(data, mwf, nave, xkey, fold,
blink)
data.plot(x='d', y='nsig', ax=ax, label=label)
data.plot(x='d', y='fit', ax=ax, label="fit")
step_min = tk.wlen_to_step(6 + popt[2], mwf)
smins = smins + [step_min]
step_max = tk.wlen_to_step(6.5 + popt[2], mwf)
smaxs = smaxs + [step_max]
print("\n", fname, "\t", label, "\n", step_min, "\t", step_max, "\n",
tk.step_to_wlen(step_min, mwf), "\t",
tk.step_to_wlen(step_max, mwf), "\n", popt[1])
for step in [step_min, step_max]:
ax.axvline(step, c='k', ls='solid', lw=2)
for ax in axes:
for step in smins:
ax.axvline(step, c='grey', ls='dashed')
for step in smaxs:
ax.axvline(step, c='grey', ls='dashed')
sm_m = np.mean(smins)
sm_s = np.std(smins)
print("\n", "mean min", "\n", sm_m, " +/- ", sm_s, "\n",
tk.step_to_wlen(sm_m, mwf), " +/- ",
tk.step_to_wlen(sm_s, mwf))
return
def delays(date):
import os
import matplotlib.pyplot as plt
import toolkit as tk
fig, ax = plt.subplots(nrows=1, sharex=True, sharey=True)
instrs = [["1_delay_diode.txt", "PM = 1.0", ax]]
xkey = 'd'
ykey = 'nsig_rm'
ykey2 = 'nsig'
nave = 4
mwf = 18.5117e9 * 2 # Hz after doubler.
fold = 1
blink = False
for instr in instrs:
[fname, label, ax] = instr
fname = os.path.join("..", date, fname)
data = tk.delay_load(fname)
data, popt, pconv = tk.transform_delay(data, mwf, nave, xkey, fold,
blink)
data.plot(x='d', y='nsig', ax=ax, label=label)
print(popt[2], tk.wlen_to_step(6 + popt[2], mwf))
# args = (fname, xkey, ykey, ykey2, mwf, nave, ax, label, fold, blink)
# data, popt, pcov = tk.delay_plot(*args)
# if popt[1] > 0:
# phi1 = (popt[2] + 0.5)%1.0
# else:
# phi1 = (popt[2])%1.0
# print("Step at minimum = ", 100e3 + tk.wlen_to_step(phi1, mwf))
# minima = mins_by_eye()
# for minimum in minima:
# ax.axvline(minimum, c='grey', linestyle='dashed')
return
def scratch(date):
mwf = 18511.0e6 * 2
steps = tk.wlen_to_step(1, mwf)
print(steps)
print(100e3 - 2 * steps, 100e3 + 2 * steps)
coeff = 1.0
print("j_0({0})^2 = ".format(coeff), jv(0, coeff)**2)
return
def delay_short(date):
import os
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import toolkit as tk
fname = "6_delay_diode.txt"
label = "MW = 9.0 dB, PM = 38.0 dB"
fig, ax = plt.subplots()
xkey = 'wlen_fold'
ykey = 'nsig_fold'
ykey2 = 'nsig'
nave = 4
mwf = 18.5119e9 * 2 # Hz after doubler.
fold = 1
blink = False
fname = os.path.join("..", date, fname)
data, popt, pcov = tk.delay_plot(fname,
xkey,
ykey,
ykey2,
mwf,
nave,
ax,
label,
fold=fold,
blink=blink)
obs = {
"mw": 9,
"pm": 38,
"a0": popt[0],
"a1": popt[1],
"a2": popt[3],
"a3": popt[5],
"a4": popt[7]
}
stepmin = tk.wlen_to_step(popt[2] + 6, mwf)
stepmax = tk.wlen_to_step(popt[2] + 6.5, mwf)
print("step_min = ", stepmin)
print("step_max = ", stepmax)
ax.axvline(tk.step_to_wlen(stepmin, mwf) % 1, c='grey', ls='dashed')
ax.axvline(tk.step_to_wlen(stepmax, mwf) % 1, c='grey', ls='dashed')
return