Skip to content

Instantly share code, notes, and snippets.

@Simon-L

Simon-L/foo.ipynb

Created March 13, 2025 00:47
Show Gist options

  • Select an option

    Learn more about clone URLs
  • Save Simon-L/0bfdd6abbc121bbe38270e9fdb151716 to your computer and use it in GitHub Desktop.

Select an option

Learn more about clone URLs
Save Simon-L/0bfdd6abbc121bbe38270e9fdb151716 to your computer and use it in GitHub Desktop.
Download ZIP
Display the source blob
Display the rendered blob
Raw
foo.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [],
"source": [
"import math\n"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [],
"source": [
"\n",
"L_L_low = 309\n",
"L_L_high = 437\n",
"\n",
"L_H_low = 400\n",
"L_H_high = 2340\n",
"\n",
"H_L_low = 2500\n",
"H_L_high = 3772\n",
"\n",
"H_H_low = 1000\n",
"H_H_high = 25151"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"| cutoff | envmod | low | high |\n",
"|--------|--------|------|-------|\n",
"| L | L | 309 | 437 |\n",
"| L | H | 400 | 2340 |\n",
"| H | L | 2500 | 3772 |\n",
"| H | H | 1000 | 25151 |"
]
},
{
"cell_type": "code",
"execution_count": 296,
"metadata": {},
"outputs": [],
"source": [
"def fton(f):\n",
" return 12.0 * math.log2(f/440.0)\n",
"\n",
"class Coeffs:\n",
" A: float = 4.87\n",
" B: float = 9.22\n",
" C: float = 0.315\n",
" D: float = 0.39\n",
" E: float = 4.020\n",
" exp_bias: float = 3.2\n",
"\n",
" minenvmod_from: float = 2.38\n",
" minenvmod_to: float = 3.9\n",
" maxenvmod_from: float = 1.86\n",
" maxenvmod_to: float = 10\n",
"\n",
" Vco = 11.84\n",
"\n",
"class Run:\n",
" c: Coeffs()\n",
" minlow: float\n",
" minhigh: float\n",
" maxlow: float\n",
" maxhigh: float\n",
"\n",
" def __init__(self, _c):\n",
" self.c = _c\n",
" self.minlow = (self.c.A * (self.c.Vco-self.c.exp_bias) + self.c.B) * math.exp(self.c.C * (self.c.minenvmod_from - self.c.exp_bias) + (self.c.D*0) + self.c.E)\n",
" self.minhigh = (self.c.A * (self.c.Vco-self.c.exp_bias) + self.c.B) * math.exp(self.c.C * (self.c.minenvmod_to - self.c.exp_bias) + (self.c.D*0) + self.c.E)\n",
" self.maxlow = (self.c.A * (self.c.Vco-self.c.exp_bias) + self.c.B) * math.exp(self.c.C * (self.c.maxenvmod_from - self.c.exp_bias) + (self.c.D*0) + self.c.E)\n",
" self.maxhigh = (self.c.A * (self.c.Vco-self.c.exp_bias) + self.c.B) * math.exp(self.c.C * (self.c.maxenvmod_to - self.c.exp_bias) + (self.c.D*0) + self.c.E)\n",
" self.score = self.assess()\n",
"\n",
" def print(self):\n",
" # print(\"Sweep range | A:{:3.3f}|B:{:3.3f}|C:{:3.3f}|D:{:3.3f}|E:{:3.3f} | @Vco={:3.3f}V Min Envmod {:3.3f} -> {:3.3f} | Max Envmod {:3.3f} -> {:3.3f}\"\\\n",
" # .format(c.A, c.B, c.C, c.D, c.E, c.Vco, minlow, minhigh, maxlow, maxhigh))\n",
" print(\"Score:{:3.6f}|A:{:3.6f}|B:{:3.6f}|C:{:3.6f}|D:{:3.6f}|E:{:3.6f}|maxenvmod_from:{:3.6f}|exp_bias:{:3.6f}\".format(self.score, self.c.A, self.c.B, self.c.C, self.c.D, self.c.E, self.c.maxenvmod_from, self.c.exp_bias))\n",
" print(\"\\t\\t{:.2f}->{:.2f}|{:.2f}->{:.2f}|{:.2f}->{:.2f}|{:.2f}->{:.2f}\".format(self.minlow, H_L_low,self.minhigh, H_L_high, self.maxlow, H_H_low, self.maxhigh, H_H_high))\n",
" \n",
" def assess(self):\n",
" self.H_L_low_delta = fton(self.minlow) - fton(H_L_low)\n",
" self.H_L_high_delta = fton(self.minhigh) - fton(H_L_high) \n",
" self.H_H_low_delta = fton(self.maxlow) - fton(H_H_low)\n",
" self.H_H_high_delta = fton(self.maxhigh) - fton(H_H_high)\n",
" score = abs(self.H_L_low_delta) + abs(self.H_L_high_delta) + abs(self.H_H_low_delta) + abs(self.H_H_high_delta)\n",
" # print(\"score:{} H_L_low_delta:{:2f} H_L_high_delta:{:2f} H_H_low_delta:{:2f} H_H_high_delta:{:2f}\".format(score,H_L_low_delta, H_L_high_delta, H_H_low_delta, H_H_high_delta))\n",
" return score\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 301,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Score:1.596496|A:2.641559|B:12.218335|C:0.305012|D:0.390000|E:4.294796|maxenvmod_from:-0.442149|exp_bias:2.675681\n",
"\t\t2440.37->2500.00|3879.74->3772.00|1031.86->1000.00|24936.75->25151.00\n"
]
}
],
"source": [
"import random\n",
"random.seed()\n",
"\n",
"c = Coeffs()\n",
"\n",
"\n",
"def run(c):\n",
" r = Run(c)\n",
" # r.print()\n",
"\n",
"def rrun():\n",
" c = Coeffs()\n",
" c.C += random.uniform(-5, 5)\n",
" c.A += random.uniform(-5, 5)\n",
" c.B += random.uniform(-5, 5)\n",
" c.exp_bias += random.uniform(-1, 1)\n",
" c.maxenvmod_from += random.uniform(-3, 3)\n",
" c.E += random.uniform(-5, 5)\n",
" return Run(c)\n",
"\n",
"runs = []\n",
"for i in range(10000000):\n",
" r = rrun()\n",
" runs.append(r)\n",
"\n",
"runs.sort(key=lambda r: r.score)\n",
"\n",
"runs[0].print()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": ".venv",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.12.6"
},
"orig_nbformat": 4,
"vscode": {
"interpreter": {
"hash": "fcabe5e7867ec6bb17dec7ce18a7d4db55bab62a70d42afb12c01f1841ed0749"
}
}
},
"nbformat": 4,
"nbformat_minor": 2
}
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment