diff --git a/examples/hist.rs b/examples/hist.rs
index f0e753ff00a9ce4888f4249e57c7696473e1a586..da204f8d165912e00100e7884a09a3036ae6824b 100644
--- a/examples/hist.rs
+++ b/examples/hist.rs
@@ -63,60 +63,60 @@ async fn main(_spawner: Spawner) {
// p.PIN_25.degrade().into_ref(),
// ];
- let mut gpios = [
- // serial
- // p.PIN_0.degrade().into_ref(),
- // p.PIN_1.degrade().into_ref(),
-
- p.PIN_2.degrade().into_ref(),
- p.PIN_3.degrade().into_ref(),
- p.PIN_4.degrade().into_ref(),
- p.PIN_5.degrade().into_ref(),
- p.PIN_6.degrade().into_ref(),
- p.PIN_7.degrade().into_ref(),
- p.PIN_8.degrade().into_ref(),
- p.PIN_9.degrade().into_ref(),
- p.PIN_10.degrade().into_ref(),
- p.PIN_11.degrade().into_ref(),
- p.PIN_12.degrade().into_ref(),
- p.PIN_13.degrade().into_ref(),
- p.PIN_14.degrade().into_ref(),
- p.PIN_15.degrade().into_ref(),
- p.PIN_16.degrade().into_ref(),
- p.PIN_17.degrade().into_ref(),
- p.PIN_18.degrade().into_ref(),
- p.PIN_19.degrade().into_ref(),
- p.PIN_20.degrade().into_ref(),
- p.PIN_21.degrade().into_ref(),
- p.PIN_22.degrade().into_ref(),
- // wl_on
- // p.PIN_23.degrade().into_ref(),
- // p.PIN_24.degrade().into_ref(),
- // wl_cs, gate of vsys adc mosfet
- p.PIN_25.degrade().into_ref(),
- p.PIN_26.degrade().into_ref(),
- p.PIN_27.degrade().into_ref(),
- p.PIN_28.degrade().into_ref(),
- // p.PIN_29.degrade().into_ref(),
- ];
// let mut gpios = [
+ // // serial
+ // // p.PIN_0.degrade().into_ref(),
+ // // p.PIN_1.degrade().into_ref(),
+
+ // p.PIN_2.degrade().into_ref(),
+ // p.PIN_3.degrade().into_ref(),
+ // p.PIN_4.degrade().into_ref(),
+ // p.PIN_5.degrade().into_ref(),
+ // p.PIN_6.degrade().into_ref(),
+ // p.PIN_7.degrade().into_ref(),
+ // p.PIN_8.degrade().into_ref(),
+ // p.PIN_9.degrade().into_ref(),
+ // p.PIN_10.degrade().into_ref(),
+ // p.PIN_11.degrade().into_ref(),
+ // p.PIN_12.degrade().into_ref(),
+ // p.PIN_13.degrade().into_ref(),
+ // p.PIN_14.degrade().into_ref(),
+ // p.PIN_15.degrade().into_ref(),
+ // p.PIN_16.degrade().into_ref(),
+ // p.PIN_17.degrade().into_ref(),
+ // p.PIN_18.degrade().into_ref(),
+ // p.PIN_19.degrade().into_ref(),
+ // p.PIN_20.degrade().into_ref(),
+ // p.PIN_21.degrade().into_ref(),
// p.PIN_22.degrade().into_ref(),
+ // // wl_on
+ // // p.PIN_23.degrade().into_ref(),
+ // // p.PIN_24.degrade().into_ref(),
+ // // wl_cs, gate of vsys adc mosfet
+ // p.PIN_25.degrade().into_ref(),
+ // p.PIN_26.degrade().into_ref(),
+ // p.PIN_27.degrade().into_ref(),
+ // p.PIN_28.degrade().into_ref(),
+ // // p.PIN_29.degrade().into_ref(),
// ];
+ let mut gpios = [
+ p.PIN_25.degrade().into_ref(),
+ ];
for gpio in gpios.iter_mut() {
- for low_delay in 0..1 {
+ // for low_cycles in 1..10 {
+ let low_cycles = caprand::best_low_time(gpio.reborrow(), 100).unwrap();
let pin = gpio.pin();
let mut n = 0;
- caprand::noise(gpio.reborrow(), low_delay,
- &mut cp.SYST,
- |v, overshoot| {
+ caprand::noise(gpio.reborrow(), low_cycles,
+ |v| {
// info!("{}", v);
let vu = v as usize;
hist[vu.min(hist.len()-1)] += 1;
n += 1;
if n % PRINT == 0 {
- info!("gpio {} delay {} iter {}", pin, low_delay, n);
+ info!("gpio {} delay {} iter {}", pin, low_cycles, n);
for (p, h) in hist.iter_mut().enumerate() {
if *h > 0 {
info!("{}: {}", p, h);
@@ -126,7 +126,12 @@ async fn main(_spawner: Spawner) {
}
n < PRINT
}).unwrap();
- }
+ // }
}
+
+ // for gpio in gpios.iter_mut() {
+ // let lt = caprand::best_low_time(gpio.reborrow(), 30).unwrap();
+ // info!("gpio {} lt {}", gpio.pin(), lt);
+ // }
cortex_m::asm::bkpt()
}
diff --git a/examples/noise.rs b/examples/noise.rs
index 09075cd137350ed8f08f3640360346f09a064aed..df70ee1ce477afd4cfd46e210ebfda9fc59e0854 100644
--- a/examples/noise.rs
+++ b/examples/noise.rs
@@ -50,7 +50,7 @@ async fn main(_spawner: Spawner) {
let mut hist = [0u32; 200];
- let PRINT = 20000;
+ let PRINT = 1000000;
// let PRINT = 50;
// let mut gpios = [
@@ -72,7 +72,7 @@ async fn main(_spawner: Spawner) {
// p.PIN_3.degrade().into_ref(),
// p.PIN_4.degrade().into_ref(),
// p.PIN_5.degrade().into_ref(),
- p.PIN_6.degrade().into_ref(),
+ // p.PIN_6.degrade().into_ref(),
// p.PIN_7.degrade().into_ref(),
// p.PIN_8.degrade().into_ref(),
// p.PIN_9.degrade().into_ref(),
@@ -82,36 +82,36 @@ async fn main(_spawner: Spawner) {
// p.PIN_13.degrade().into_ref(),
// p.PIN_14.degrade().into_ref(),
// p.PIN_15.degrade().into_ref(),
- p.PIN_16.degrade().into_ref(),
+ // p.PIN_16.degrade().into_ref(),
// p.PIN_17.degrade().into_ref(),
// p.PIN_18.degrade().into_ref(),
// p.PIN_19.degrade().into_ref(),
// p.PIN_20.degrade().into_ref(),
// p.PIN_21.degrade().into_ref(),
- p.PIN_22.degrade().into_ref(),
+ // p.PIN_22.degrade().into_ref(),
// p.PIN_23.degrade().into_ref(),
// p.PIN_24.degrade().into_ref(),
// wl_cs, gate of vsys adc mosfet
- // p.PIN_25.degrade().into_ref(),
+ p.PIN_25.degrade().into_ref(),
// p.PIN_26.degrade().into_ref(),
// p.PIN_27.degrade().into_ref(),
// p.PIN_28.degrade().into_ref(),
// p.PIN_29.degrade().into_ref(),
];
- info!("gpio,delay,time,n");
+ info!("gpio,delay,time");
for gpio in gpios.iter_mut() {
- for low_delay in 0..1{
+ let low_delay = 69;
+ // for low_delay in 0..1{
let pin = gpio.pin();
let mut n = 0;
caprand::noise(gpio.reborrow(), low_delay,
- &mut cp.SYST,
- |v, overshoot| {
- info!("{},{},{},{}", pin, low_delay, v, n);
+ |v| {
+ info!("{},{},{}", pin, low_delay, v);
n += 1;
n < PRINT
}).unwrap();
- }
+ // }
}
cortex_m::asm::bkpt()
}
diff --git a/signals/flip1.csv b/signals/flip1.csv
new file mode 100644
index 0000000000000000000000000000000000000000..8be435d46ba96240fb6ca6260eb2957396a5729d
--- /dev/null
+++ b/signals/flip1.csv
@@ -0,0 +1,60001 @@
+gpio,delay,time,n
+6,0,100,0
+6,0,109,1
+6,0,28,2
+6,0,91,3
+6,0,28,4
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diff --git a/signals/flip1.ipynb b/signals/flip1.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..da62b5dc9316d0f7d373975bbadd1dc05c803abd
--- /dev/null
+++ b/signals/flip1.ipynb
@@ -0,0 +1,365 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 43,
+ "id": "947e168c",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<style>.container { width:90% !important; }</style>"
+ ],
+ "text/plain": [
+ "<IPython.core.display.HTML object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "import pandas as pd\n",
+ "import numpy as np\n",
+ "import matplotlib.pyplot as plt\n",
+ "from matplotlib import cm\n",
+ "from pandas.plotting import lag_plot\n",
+ "\n",
+ "\n",
+ "from IPython.core.display import display, HTML\n",
+ "display(HTML(\"<style>.container { width:90% !important; }</style>\"))\n",
+ "plt.rcParams[\"figure.figsize\"] = (20,16)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 44,
+ "id": "626bd95d",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>gpio</th>\n",
+ " <th>delay</th>\n",
+ " <th>time</th>\n",
+ " <th>n</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>6</td>\n",
+ " <td>0</td>\n",
+ " <td>100</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>6</td>\n",
+ " <td>0</td>\n",
+ " <td>109</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>6</td>\n",
+ " <td>0</td>\n",
+ " <td>28</td>\n",
+ " <td>2</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>6</td>\n",
+ " <td>0</td>\n",
+ " <td>91</td>\n",
+ " <td>3</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>6</td>\n",
+ " <td>0</td>\n",
+ " <td>28</td>\n",
+ " <td>4</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>...</th>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>59995</th>\n",
+ " <td>22</td>\n",
+ " <td>0</td>\n",
+ " <td>42</td>\n",
+ " <td>19995</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>59996</th>\n",
+ " <td>22</td>\n",
+ " <td>0</td>\n",
+ " <td>42</td>\n",
+ " <td>19996</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>59997</th>\n",
+ " <td>22</td>\n",
+ " <td>0</td>\n",
+ " <td>42</td>\n",
+ " <td>19997</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>59998</th>\n",
+ " <td>22</td>\n",
+ " <td>0</td>\n",
+ " <td>42</td>\n",
+ " <td>19998</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>59999</th>\n",
+ " <td>22</td>\n",
+ " <td>0</td>\n",
+ " <td>42</td>\n",
+ " <td>19999</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "<p>60000 rows × 4 columns</p>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " gpio delay time n\n",
+ "0 6 0 100 0\n",
+ "1 6 0 109 1\n",
+ "2 6 0 28 2\n",
+ "3 6 0 91 3\n",
+ "4 6 0 28 4\n",
+ "... ... ... ... ...\n",
+ "59995 22 0 42 19995\n",
+ "59996 22 0 42 19996\n",
+ "59997 22 0 42 19997\n",
+ "59998 22 0 42 19998\n",
+ "59999 22 0 42 19999\n",
+ "\n",
+ "[60000 rows x 4 columns]"
+ ]
+ },
+ "execution_count": 44,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "d = pd.read_csv(\"flip1.csv\")\n",
+ "d\n",
+ "#down = pd.read_csv(\"l10const.down\")\n",
+ "#up = pd.read_csv(\"l10const.up\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 45,
+ "id": "e04ae03e",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "d.rename(columns = {'time': 'down'}, inplace = True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 46,
+ "id": "db331ebf",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[]"
+ ]
+ },
+ "execution_count": 46,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 1440x1152 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "d = d[d['gpio'] == 22]\n",
+ "x = d['down']\n",
+ "x.hist(bins=256).plot()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 47,
+ "id": "9959a4e8",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<AxesSubplot:xlabel='y(t)', ylabel='y(t + 1)'>"
+ ]
+ },
+ "execution_count": 47,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 1440x1440 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.figure(figsize=(20,20))\n",
+ "lag_plot(d['down'])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 48,
+ "id": "ca817abf",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 1440x1152 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "cut = d['down']\n",
+ "\n",
+ "\n",
+ "fig, ax = plt.subplots()\n",
+ "ax.stairs( cut)\n",
+ "\n",
+ "plt.show()\n",
+ "\n",
+ "\n",
+ "#.plot(kind = 'step', figsize=(30,16))\n",
+ "\n",
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 49,
+ "id": "3e2af103",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def colourplot(data, size = 300):\n",
+ " nx = size\n",
+ " ny = size\n",
+ " nm = data.name\n",
+ " data = data[:nx*ny].values.reshape(nx, ny)\n",
+ "\n",
+ " plt.xlim(0, nx)\n",
+ " plt.ylim(0, ny)\n",
+ "\n",
+ " plt.xlabel('samples')\n",
+ " plt.ylabel('samples')\n",
+ " plt.title(f'{nm}')\n",
+ "\n",
+ " #cax = plt.imshow(data, interpolation='nearest', cmap=cm.coolwarm)\n",
+ " cax = plt.imshow(data, interpolation='nearest', cmap=cm.afmhot)\n",
+ " cbar = plt.colorbar(cax, ticks=[255, 127, 0])\n",
+ " cbar.ax.set_yticklabels(['255', '127', '0'])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 50,
+ "id": "ddff7580",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 1440x1152 with 2 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "colourplot(d['down'][:141*141], 141)"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "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": "3.10.6"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/src/cap.rs b/src/cap.rs
index 9b01e32ee4cfc832113de577c556b89cbc5833ae..5523f13ff31765cd2108f303f41de28b358bb31b 100644
--- a/src/cap.rs
+++ b/src/cap.rs
@@ -107,41 +107,33 @@ impl<'t> SyTi<'t> {
Ok(self.t1 - t2)
}
}
-// Returns (ticks: u32, pos: u8)
-// `pos == 0` are less precise than other results
-fn time_rise(pin: PeripheralRef<AnyPin>, low_cycles: u32, syst: &mut SYST) -> Result<(u32, u8), ()> {
+
+/// Pulls a pin low for an exact number of cycles.
+/// Call with interrupts disabled if it's important.
+fn exact_low(pin: PeripheralRef<AnyPin>, low_cycles: u32) {
let pin_num = pin.pin() as usize;
let mask = 1u32 << pin_num;
-
- unsafe {
- pac::PADS_BANK0
- .gpio(pin_num)
- .modify(|s| {
- // Pullup
- s.set_pue(true);
- });
- };
-
- // bank 0 single cycle IO in
- let gpio_in = pac::SIO.gpio_in(0).ptr();
+ // pin low
let so = pac::SIO.gpio_out(0);
- let soe = pac::SIO.gpio_oe(0);
unsafe {
so.value_clr().write_value(1 << pin_num);
}
+ // output enable set/clear registers
+ let soe = pac::SIO.gpio_oe(0);
let soe_set = soe.value_set().ptr();
let soe_clr = soe.value_clr().ptr();
+ // We set output enable, wait a number of cycles, then clear output enable.
+ // Modulo 3 because the subs/bne loops take 3 cycles.
match (low_cycles, low_cycles % 3) {
(0, _) => {
- panic!("Don't use this");
// no pull low
},
(1, _) => unsafe {
asm!(
+ // one cycle for set
"str {mask}, [{soe_set}]",
- // low for 1 cycle
"str {mask}, [{soe_clr}]",
mask = in(reg) mask,
@@ -152,8 +144,9 @@ fn time_rise(pin: PeripheralRef<AnyPin>, low_cycles: u32, syst: &mut SYST) -> Re
},
(2, _) => unsafe {
asm!(
+ // one cycle for set
"str {mask}, [{soe_set}]",
- // low for 2 cycles
+ // one cycle
"nop",
"str {mask}, [{soe_clr}]",
@@ -166,13 +159,14 @@ fn time_rise(pin: PeripheralRef<AnyPin>, low_cycles: u32, syst: &mut SYST) -> Re
(_, 0) => unsafe {
// min low time of 3
asm!(
+ // one cycle for set
"str {mask}, [{soe_set}]",
"000:",
// one cycle
"subs {d}, 3",
// two cycles bne taken
- "bne 000b",
// one cycle for bne not taken
+ "bne 000b",
"str {mask}, [{soe_clr}]",
mask = in(reg) mask,
@@ -185,6 +179,7 @@ fn time_rise(pin: PeripheralRef<AnyPin>, low_cycles: u32, syst: &mut SYST) -> Re
(_, 1) => unsafe {
// min low time of 4
asm!(
+ // one cycle for set
"str {mask}, [{soe_set}]",
// one cycle
"subs {d}, 1",
@@ -192,8 +187,8 @@ fn time_rise(pin: PeripheralRef<AnyPin>, low_cycles: u32, syst: &mut SYST) -> Re
// one cycle
"subs {d}, 3",
// two cycles bne taken
- "bne 000b",
// one cycle for bne not taken
+ "bne 000b",
"str {mask}, [{soe_clr}]",
mask = in(reg) mask,
@@ -206,6 +201,7 @@ fn time_rise(pin: PeripheralRef<AnyPin>, low_cycles: u32, syst: &mut SYST) -> Re
(_, 2) => unsafe {
// min low time of 5
asm!(
+ // one cycle for set
"str {mask}, [{soe_set}]",
// one cycle
"subs {d}, 1",
@@ -215,8 +211,8 @@ fn time_rise(pin: PeripheralRef<AnyPin>, low_cycles: u32, syst: &mut SYST) -> Re
// one cycle
"subs {d}, 3",
// two cycles bne taken
- "bne 000b",
// one cycle for bne not taken
+ "bne 000b",
"str {mask}, [{soe_clr}]",
mask = in(reg) mask,
@@ -227,41 +223,26 @@ fn time_rise(pin: PeripheralRef<AnyPin>, low_cycles: u32, syst: &mut SYST) -> Re
);
},
// `match` doesn't understand modulo
- _ => unreachable!()
+ (_, 3..) => unreachable!()
}
+}
- let mut t = SyTi::new(syst);
- // unsafe {
- // soe.value_set().write_value(1 << pin_num);
- // }
- // // // unsafe {
- // // // asm!(
- // // // "nop",
- // // // )
- // // // }
- // // cortex_m::asm::delay(20);
- // // cortex_m::asm::delay(1000);
- // unsafe {
- // soe.value_clr().write_value(1 << pin_num);
- // }
-
- // for testing with logic analyzer
- // let mut out = Flex::new(unsafe { embassy_rp::peripherals::PIN_16::steal() });
- // out.set_as_output();
- // so.value_clr().write_value(1<<16);
-
- // pin.set_pull(Pull::Down);
- // cortex_m::asm::delay(900);
- // pin.set_pull(Pull::None);
+fn time_rise(pin: PeripheralRef<AnyPin>, low_cycles: u32) -> Result<u32, ()> {
+ let pin_num = pin.pin() as usize;
+ let mask = 1u32 << pin_num;
+ // Pull low for a number of cycles
+ exact_low(pin, low_cycles);
+ // bank 0 single cycle IO in
+ let gpio_in = pac::SIO.gpio_in(0).ptr();
let x0: u32;
let x1: u32;
let x2: u32;
let x3: u32;
let x4: u32;
let x5: u32;
-
+ // Time how long it takes to pull up
unsafe {
asm!(
// save
@@ -297,14 +278,7 @@ fn time_rise(pin: PeripheralRef<AnyPin>, low_cycles: u32, syst: &mut SYST) -> Re
);
}
- let tick = t.done()?;
-
- let result = (x0 & mask) << 0
- | (x1 & mask) << 1
- | (x2 & mask) << 2
- | (x3 & mask) << 3
- | (x4 & mask) << 4
- | (x5 & mask) << 5;
+ // let tick = t.done()?;
// let pos = if x0 & mask != 0 {
// 0
@@ -325,133 +299,170 @@ fn time_rise(pin: PeripheralRef<AnyPin>, low_cycles: u32, syst: &mut SYST) -> Re
// // first measurement is less precise.
// let precise = pos != 0;
- unsafe {
- // Turn off pullup when not used
- pac::PADS_BANK0
- .gpio(pin_num)
- .modify(|s| {
- s.set_pue(false);
- });
- };
-
- Ok((tick, pos as u8))
+ // Combine all measurements in a constant-time way
+ // TODO: Check it really is constant time - seems like it should be.
+ let result = (x0 & mask)
+ | (x1 & mask).rotate_left(1)
+ | (x2 & mask).rotate_left(2)
+ | (x3 & mask).rotate_left(3)
+ | (x4 & mask).rotate_left(4)
+ | (x5 & mask).rotate_left(5);
+ let result = result >> pin_num;
+
+ Ok(result)
}
// `f()` is called on each output `u32`.
pub fn noise<'d, F>(
mut pin: PeripheralRef<AnyPin>,
low_cycles: u32,
- syst: &mut SYST,
mut f: F,
) -> Result<(), ()>
where
- F: FnMut(u32, u32) -> bool,
+ F: FnMut(u32) -> bool,
{
let pin_num = pin.pin() as usize;
- syst.set_clock_source(cortex_m::peripheral::syst::SystClkSource::Core);
- // prescribed sequence for setup
- syst.set_reload(10_000_000 - 1);
- syst.clear_current();
- syst.enable_counter();
// Seemed to reduce noise (for investigating thermal noise vs other interference).
// XXX Somehow disabling ROSC breaks SWD, perhaps signal integrity issues?
// unsafe{ pac::ROSC.ctrl().modify(|s| s.set_enable(pac::rosc::vals::Enable::DISABLE)) };
-
let mut gpio = Flex::<AnyPin>::new(pin.reborrow());
- // Measure pullup time from 0 as a sanity check
- gpio.set_as_output();
- gpio.set_low();
- // Long enough to drive the capacitor low
- cortex_m::asm::delay(10000);
- gpio.set_as_input();
- // let del = critical_section::with(|_cs| {
- // gpio.set_pull(Pull::Up);
- // let t = SyTi::new(syst);
- // // Get it near the threshold to begin.
- // while gpio.is_low() {}
- // t.done()
- // })?;
+ // // Measure pullup time from 0 as a sanity check
+ // gpio.set_as_output();
+ // gpio.set_low();
+ // // Long enough to drive the capacitor low
+ // cortex_m::asm::delay(10000);
+ // gpio.set_as_input();
+ // // let del = critical_section::with(|_cs| {
+ // // gpio.set_pull(Pull::Up);
+ // // let t = SyTi::new(syst);
+ // // // Get it near the threshold to begin.
+ // // while gpio.is_low() {}
+ // // t.done()
+ // // })?;
drop(gpio);
- // info!("Initial pullup del is {}", del);
+ // // info!("Initial pullup del is {}", del);
- // if del < MIN_CAPACITOR_DEL {
- // error!("Capacitor seems small or missing?");
- // return Err(())
- // }
+ // // if del < MIN_CAPACITOR_DEL {
+ // // error!("Capacitor seems small or missing?");
+ // // return Err(())
+ // // }
- // The main loop
- let mut overshoot = 1u32;
-
let mut warming = WARMUP;
- let mut pos = 0;
- unsafe {
- pac::PADS_BANK0
- .gpio(pin_num)
- .modify(|s| {
- // // Disabling the Schmitt Trigger gives a clearer correlation between "overshoot"
- // // and measured values.
- // s.set_schmitt(false);
- // Input enable
- s.set_ie(true);
- });
-
- pac::SYSCFG
- .proc_in_sync_bypass()
- .modify(|s| {
- let val = s.proc_in_sync_bypass();
- s.set_proc_in_sync_bypass(val | 1 << pin_num);
- });
-
- // Use SIO
- pac::IO_BANK0
- .gpio(pin_num)
- .ctrl()
- .modify(|s| {
- s.set_funcsel(pac::io::vals::Gpio0ctrlFuncsel::SIO_0.0)
- });
- }
+ let _p = PinSetup::new(pin_num as u8);
- // After warmup we sample twice at each "overshoot" value.
- // One sample is returned as random output, the other is mixed
- // in to the overshoot value.
for (i, _) in core::iter::repeat(()).enumerate() {
+ let r = critical_section::with(|_cs| {
- let (meas, precise) = critical_section::with(|_cs| {
-
- let (r, pos) = time_rise(pin.reborrow(), low_cycles, syst)?;
- let precise = pos == 0;
+ let r = time_rise(pin.reborrow(), low_cycles)?;
+ Ok(r)
+ })?;
- // let mut gpio = Flex::<AnyPin>::new(pin.reborrow());
- // gpio.set_pull(Pull::None);
+ // real output
+ // if precise && warming == 0 {
+ if warming == 0 {
+ if !f(r) {
+ // no more output wanted
+ break
+ }
+ }
+ warming = warming.saturating_sub(1);
+ }
+ Ok(())
+}
- // if (r > )
+pub fn best_low_time(mut pin: PeripheralRef<AnyPin>, max_time: u32) -> Result<u32, ()> {
+ const ITERS: usize = 1000;
+ let mut best = None;
+ for t in 1..=max_time {
+ let mut hist = [0u32; 64];
+ let mut n = 0;
+ noise(pin.reborrow(), t,
+ |v| {
+ hist[v as usize] += 1;
+ n += 1;
+ n < ITERS
+ })?;
+ let hmax = *hist.iter().max().unwrap();
+ // trace!("t {} hmax {}", t, hmax);
+ if let Some((_, best_hmax)) = best {
+ if hmax < best_hmax {
+ best = Some((t, hmax));
+ }
+ } else {
+ best = Some((t, hmax));
+ }
+ }
+ // TODO unwrap
+ Ok(best.unwrap().0)
+}
+struct PinSetup(u8);
+
+impl PinSetup {
+ fn new(pin_num: u8) -> Self {
+ unsafe {
+ pac::PADS_BANK0
+ .gpio(pin_num as usize)
+ .modify(|s| {
+ // // Disabling the Schmitt Trigger gives a clearer correlation between "overshoot"
+ // // and measured values.
+ // s.set_schmitt(false);
+ // Input enable
+ s.set_ie(true);
+ // Pullup
+ s.set_pue(true);
+ s.set_pde(false);
+ });
+
+ // Use SIO
+ pac::IO_BANK0
+ .gpio(pin_num as usize)
+ .ctrl()
+ .modify(|s| {
+ s.set_funcsel(pac::io::vals::Gpio0ctrlFuncsel::SIO_0.0)
+ });
+
+ pac::SYSCFG
+ .proc_in_sync_bypass()
+ .modify(|s| {
+ let val = s.proc_in_sync_bypass();
+ s.set_proc_in_sync_bypass(val | 1 << pin_num);
+ });
- Ok((r, precise))
- })?;
+ }
+ PinSetup(pin_num)
+ }
+}
- if i % 2 == 0 {
- // real output
- // if precise && warming == 0 {
- if warming == 0 {
- if !f(meas, pos) {
- // no more output wanted
- break
- }
- }
- warming = warming.saturating_sub(1);
- } else {
- // don't produce output, mix measured sample in
- overshoot = overshoot * 2 + meas;
- // modulo to sensible range
- if overshoot > HIGH_OVER {
- overshoot = LOW_OVER + (overshoot % (HIGH_OVER - LOW_OVER + 1))
- }
+impl Drop for PinSetup {
+ fn drop(&mut self) {
+ let pin_num = self.0;
+ unsafe {
+ pac::PADS_BANK0
+ .gpio(pin_num as usize)
+ .modify(|s| {
+ // Pullup
+ s.set_pue(false);
+ });
+
+ pac::SYSCFG
+ .proc_in_sync_bypass()
+ .modify(|s| {
+ let val = s.proc_in_sync_bypass();
+ s.set_proc_in_sync_bypass(val & !(1 << pin_num));
+ });
+
+ // Use SIO
+ pac::IO_BANK0
+ .gpio(pin_num as usize)
+ .ctrl()
+ .modify(|s| {
+ s.set_funcsel(pac::io::vals::Gpio0ctrlFuncsel::NULL.0);
+ });
}
}
- Ok(())
}
diff --git a/src/lib.rs b/src/lib.rs
index f57abc60af347f87518199fe8f6f210e7fd3911c..8e09ade3eca0f9c67307faae6a7ac0d5d3eba40f 100644
--- a/src/lib.rs
+++ b/src/lib.rs
@@ -8,5 +8,5 @@ mod cap;
// mod numpin;
pub use rng::{setup, random};
-pub use cap::noise;
+pub use cap::{noise, best_low_time};
diff --git a/src/rng.rs b/src/rng.rs
index 00085373a9810792b1cd3a95cc7a58dbe0856a92..4c82d9e619991942961faafd841a4bc92595cd84 100644
--- a/src/rng.rs
+++ b/src/rng.rs
@@ -76,7 +76,7 @@ impl CapRng {
) -> Result<Self, getrandom::Error> {
let mut h = Sha256::new();
let mut count = 0;
- crate::cap::noise(pin, 0, syst, |v, _over| {
+ crate::cap::noise(pin, 0, |v| {
h.update(v.to_be_bytes());
count += 1;
count < Self::SEED_SAMPLES