diff --git a/.ipynb_checkpoints/data-processing-checkpoint.ipynb b/.ipynb_checkpoints/data-processing-checkpoint.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..82c92e9c55766499f6a377ea8600090936691693 --- /dev/null +++ b/.ipynb_checkpoints/data-processing-checkpoint.ipynb @@ -0,0 +1,115 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 22, + "id": "c44042ce-0838-4bde-a521-72c7e7d196d6", + "metadata": {}, + "outputs": [], + "source": [ + "import csv\n", + "\n", + "data = []\n", + "\n", + "with open('data/open-meteo-53.53N9.98E11m (3) (2).csv') as csvfile:\n", + " reader = csv.DictReader(csvfile, delimiter=',')\n", + " for row in reader:\n", + " datum = row['rain_sum']\n", + " data.append(float(datum))\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "c70207fe-f427-4e92-813c-698cf94855b6", + "metadata": {}, + "outputs": [], + "source": [ + "maximum = max(data)\n", + "normalized_data = [d/maximum for d in data]\n", + "\n", + "with open(\"data.h\", \"w\") as file:\n", + " file.write(\"#pragma once\\n\\n\")\n", + " file.write(f\"#define DATA_LENGTH {len(normalized_data)}\\n\\n\")\n", + " file.write(f\"float data[{len(normalized_data)}] = {{\\n\")\n", + " for d in normalized_data:\n", + " file.write(f\" {d},\\n\")\n", + " file.write(\"};\\n\")" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "id": "1fa46b19-0698-47bb-9f6d-e45ab7f2c96f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "3 days, 0:00:02.160000\n" + ] + } + ], + "source": [ + "from datetime import timedelta, datetime\n", + "t = 9.48 * 3\n", + "\n", + "duration = timedelta(seconds=(len(normalized_data)*t))\n", + "print(duration)" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "id": "20be4f8c-56b4-45c4-a7f5-8885a1345ea1", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "28.44" + ] + }, + "execution_count": 48, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "t" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "35be51de-976f-4f2a-a707-5dca0e213e18", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "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": "3.12.5" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/.ipynb_checkpoints/precipitationctl-checkpoint.ino b/.ipynb_checkpoints/precipitationctl-checkpoint.ino new file mode 100644 index 0000000000000000000000000000000000000000..67fcf4a1aa950b27e0a974d6c3e640f794d27801 --- /dev/null +++ b/.ipynb_checkpoints/precipitationctl-checkpoint.ino @@ -0,0 +1,41 @@ +#include "data.h" + +#define SERVO_MIN 30 +#define SERVO_MAX 480 +int servoPin = 23; +int pos = SERVO_MIN; + + +int data_index = 0; + +void setServoNormalized(float pos) { + pos = min(1.0f, max(0.0f, pos)); + int span = SERVO_MAX - SERVO_MIN; + int servo_pos = SERVO_MIN + span * pos; + analogWrite(servoPin, servo_pos); +} + +void setup() { + Serial.begin(115200); + // put your setup code here, to run once: + pinMode(servoPin, OUTPUT); + analogWriteFrequency(servoPin, 50); + analogWriteResolution(servoPin, 12); +} + +void loop() { + // put your main code here, to run repeatedly: + float datum = data[data_index]; + setServoNormalized(datum); + Serial.print("Aktueller Wert "); + Serial.print(datum * 67.2); + Serial.print(" mm --> "); + Serial.println(datum); + data_index++; + + if ( data_index >= DATA_LENGTH) { + data_index = 0; + Serial.println("Die daten sind durch, wir fangen wieder vorne an..."); + } + delay(28.44*1000); +} diff --git a/.jupyter/desktop-workspaces/default-37a8.jupyterlab-workspace b/.jupyter/desktop-workspaces/default-37a8.jupyterlab-workspace index b3583f54f4a317f29f0767dee23ecb640f0539ca..6014307243645c8d0e9eb577e75ed69656a27165 100644 --- a/.jupyter/desktop-workspaces/default-37a8.jupyterlab-workspace +++ b/.jupyter/desktop-workspaces/default-37a8.jupyterlab-workspace @@ -1 +1 @@ -{"data":{"layout-restorer:data":{"main":{"dock":{"type":"tab-area","currentIndex":0,"widgets":[]}},"down":{"size":0,"widgets":[]},"left":{"collapsed":false,"visible":true,"current":"filebrowser","widgets":["filebrowser","running-sessions","@jupyterlab/toc:plugin","extensionmanager.main-view"],"widgetStates":{"jp-running-sessions":{"sizes":[0.16666666666666666,0.16666666666666666,0.16666666666666666,0.16666666666666666,0.16666666666666666,0.16666666666666666],"expansionStates":[false,false,false,false,false,false]},"extensionmanager.main-view":{"sizes":[0.3333333333333333,0.3333333333333333,0.3333333333333333],"expansionStates":[false,false,false]}}},"right":{"collapsed":true,"visible":true,"widgets":["jp-property-inspector","debugger-sidebar"],"widgetStates":{"jp-debugger-sidebar":{"sizes":[0.2,0.2,0.2,0.2,0.2],"expansionStates":[false,false,false,false,false]}}},"relativeSizes":[0.26227795193312436,0.7377220480668757,0],"top":{"simpleVisibility":true}},"docmanager:recents":{"opened":[{"path":"","contentType":"directory","root":"~/Arduino/precipitationctl"},{"path":"Untitled.ipynb","contentType":"notebook","factory":"Notebook","root":"~/Arduino/precipitationctl"}],"closed":[{"path":"Untitled.ipynb","contentType":"notebook","factory":"Notebook","root":"~/Arduino/precipitationctl"}]}},"metadata":{"id":"default"}} \ No newline at end of file +{"data":{"layout-restorer:data":{"main":{"dock":{"type":"tab-area","currentIndex":1,"widgets":["notebook:data-processing.ipynb"]},"current":"notebook:data-processing.ipynb"},"down":{"size":0,"widgets":[]},"left":{"collapsed":false,"visible":true,"current":"filebrowser","widgets":["filebrowser","running-sessions","@jupyterlab/toc:plugin","extensionmanager.main-view"],"widgetStates":{"jp-running-sessions":{"sizes":[0.16666666666666666,0.16666666666666666,0.16666666666666666,0.16666666666666666,0.16666666666666666,0.16666666666666666],"expansionStates":[false,false,false,false,false,false]},"extensionmanager.main-view":{"sizes":[0.3333333333333333,0.3333333333333333,0.3333333333333333],"expansionStates":[false,false,false]}}},"right":{"collapsed":true,"visible":true,"widgets":["jp-property-inspector","debugger-sidebar"],"widgetStates":{"jp-debugger-sidebar":{"sizes":[0.2,0.2,0.2,0.2,0.2],"expansionStates":[false,false,false,false,false]}}},"relativeSizes":[0.26227795193312436,0.7377220480668757,0],"top":{"simpleVisibility":true}},"docmanager:recents":{"opened":[{"path":"","contentType":"directory","root":"~/Arduino/precipitationctl"},{"path":"data-processing.ipynb","contentType":"notebook","factory":"Notebook","root":"~/Arduino/precipitationctl"},{"path":"precipitationctl.ino","contentType":"file","factory":"Editor","root":"~/Arduino/precipitationctl"}],"closed":[{"path":"precipitationctl.ino","contentType":"file","factory":"Editor","root":"~/Arduino/precipitationctl"}]},"notebook:data-processing.ipynb":{"data":{"path":"data-processing.ipynb","factory":"Notebook"}}},"metadata":{"id":"default"}} \ No newline at end of file diff --git a/data-processing.ipynb b/data-processing.ipynb index 82c92e9c55766499f6a377ea8600090936691693..c1feb8a191fe503114fe2169fc14160fd6b5f315 100644 --- a/data-processing.ipynb +++ b/data-processing.ipynb @@ -63,30 +63,179 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 4, "id": "20be4f8c-56b4-45c4-a7f5-8885a1345ea1", "metadata": {}, "outputs": [ { "data": { + "image/png": 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", "text/plain": [ - "28.44" + "<Figure size 1000x600 with 1 Axes>" ] }, - "execution_count": 48, "metadata": {}, - "output_type": "execute_result" + "output_type": "display_data" } ], "source": [ - "t" + "import matplotlib.pyplot as plt\n", + "\n", + "# Sample input data\n", + "data = [0, 2, 1, 3, 0, 4, 1, 5, 0, 6, 2] # y-values\n", + "x_values = list(range(len(data))) # x-values are assumed as indices\n", + "\n", + "# Linear interpolation\n", + "def linear_interpolation(x, data):\n", + " for i in range(len(data) - 1):\n", + " if i <= x <= i + 1:\n", + " x0, x1 = i, i + 1\n", + " y0, y1 = data[i], data[i + 1]\n", + " return y0 + (y1 - y0) * (x - x0) / (x1 - x0)\n", + " return None # Out of bounds\n", + "\n", + "# Quadratic interpolation\n", + "def quadratic_interpolation(x, data):\n", + " for i in range(len(data) - 2):\n", + " if i <= x <= i + 2:\n", + " x0, x1, x2 = i, i + 1, i + 2\n", + " y0, y1, y2 = data[i], data[i + 1], data[i + 2]\n", + " \n", + " # Lagrange interpolation formula\n", + " term1 = y0 * (x - x1) * (x - x2) / ((x0 - x1) * (x0 - x2))\n", + " term2 = y1 * (x - x0) * (x - x2) / ((x1 - x0) * (x1 - x2))\n", + " term3 = y2 * (x - x0) * (x - x1) / ((x2 - x0) * (x2 - x1))\n", + " return term1 + term2 + term3\n", + " return None # Out of bounds\n", + "\n", + "def akima_interpolate(x_new, y):\n", + " \"\"\"\n", + " Perform Akima interpolation for a 1D array of y-values with implicit x-values as indices.\n", + "\n", + " Parameters:\n", + " y (list of float): 1D array of real values.\n", + " x_new (list of float): New x-values to interpolate at.\n", + "\n", + " Returns:\n", + " list of float: Interpolated y-values at x_new.\n", + " \"\"\"\n", + " n = len(y)\n", + " \n", + " if n < 3:\n", + " raise ValueError(\"Input array must have at least 3 elements.\")\n", + "\n", + " # Compute slopes (m) between consecutive points\n", + " m = [(y[i + 1] - y[i]) for i in range(n - 1)]\n", + "\n", + " # Extend slopes for boundary handling\n", + " mm = 2 * m[0] - m[1]\n", + " mmm = 2 * mm - m[0]\n", + " mp = 2 * m[-1] - m[-2]\n", + " mpp = 2 * mp - m[-1]\n", + " m_extended = [mmm, mm] + m + [mp, mpp]\n", + "\n", + " # Compute weights (dm)\n", + " dm = [abs(m_extended[i + 1] - m_extended[i]) for i in range(len(m_extended) - 1)]\n", + "\n", + " # Compute b coefficients\n", + " b = m[:]\n", + " for i in range(len(m)):\n", + " if i + 2 >= len(dm) or i >= len(dm) or dm[i] + dm[i + 2] == 0:\n", + " b[i] = m[i]\n", + " else:\n", + " b[i] = (dm[i + 2] * m_extended[i + 1] + dm[i] * m_extended[i + 2]) / (dm[i] + dm[i + 2])\n", + "\n", + " # Compute c and d coefficients\n", + " c = [(3 * m[i] - 2 * b[i] - b[i + 1]) for i in range(len(m))]\n", + " d = [(b[i] + b[i + 1] - 2 * m[i]) for i in range(len(m))]\n", + "\n", + " # Perform interpolation\n", + " results = []\n", + " for xi in x_new:\n", + " if xi < 0 or xi > n - 1:\n", + " raise ValueError(\"x_new values must be within the range of indices (0 to n-1).\")\n", + "\n", + " # Find the interval [j, j+1] containing xi\n", + " j = int(xi) if int(xi) < n - 1 else n - 2\n", + "\n", + " # Compute the interpolated value\n", + " dxi = xi - j\n", + " result = y[j] + b[j] * dxi + c[j] * (dxi ** 2) + d[j] * (dxi ** 3)\n", + " results.append(result)\n", + "\n", + " return results\n", + "\n", + "\n", + "# Generate fine-grained x-values for plotting\n", + "x_fine = [i * 0.1 for i in range(int(x_values[0] * 10), int(x_values[-1] * 10 + 1))]\n", + "\n", + "# Interpolate values\n", + "y_linear = [linear_interpolation(x, data) for x in x_fine]\n", + "y_quadratic = [quadratic_interpolation(x, data) for x in x_fine]\n", + "y_akima = [akima_interpolation(x, data) for x in x_fine]\n", + "\n", + "# Plot the results\n", + "plt.figure(figsize=(10, 6))\n", + "plt.plot(x_values, data, 'o', label='Original Data', markersize=8)\n", + "plt.plot(x_fine, y_linear, label='Linear Interpolation', linestyle='--')\n", + "plt.plot(x_fine, y_quadratic, label='Quadratic Interpolation', linestyle='-.')\n", + "plt.plot(x_fine, y_akima, label='Akima Interpolation', linestyle=':')\n", + "plt.xlabel('x (index)')\n", + "plt.ylabel('y (data)')\n", + "plt.title('Comparison of Interpolation Methods (Vanilla Python)')\n", + "plt.legend()\n", + "plt.grid(True)\n", + "plt.show()\n" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "id": "35be51de-976f-4f2a-a707-5dca0e213e18", "metadata": {}, + "outputs": [ + { + "ename": "IndexError", + "evalue": "list index out of range", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mIndexError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[5], line 6\u001b[0m\n\u001b[1;32m 3\u001b[0m x_new \u001b[38;5;241m=\u001b[39m [\u001b[38;5;241m0.5\u001b[39m, \u001b[38;5;241m1.5\u001b[39m, \u001b[38;5;241m2.5\u001b[39m, \u001b[38;5;241m3.5\u001b[39m, \u001b[38;5;241m4.5\u001b[39m]\n\u001b[1;32m 5\u001b[0m \u001b[38;5;66;03m# Perform interpolation\u001b[39;00m\n\u001b[0;32m----> 6\u001b[0m y_new \u001b[38;5;241m=\u001b[39m \u001b[43makima_interpolate\u001b[49m\u001b[43m(\u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mx_new\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 8\u001b[0m \u001b[38;5;66;03m# Print results\u001b[39;00m\n\u001b[1;32m 9\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mInterpolated values:\u001b[39m\u001b[38;5;124m\"\u001b[39m, y_new)\n", + "Cell \u001b[0;32mIn[4], line 68\u001b[0m, in \u001b[0;36makima_interpolate\u001b[0;34m(x_new, y)\u001b[0m\n\u001b[1;32m 65\u001b[0m b[i] \u001b[38;5;241m=\u001b[39m (dm[i \u001b[38;5;241m+\u001b[39m \u001b[38;5;241m2\u001b[39m] \u001b[38;5;241m*\u001b[39m m_extended[i \u001b[38;5;241m+\u001b[39m \u001b[38;5;241m1\u001b[39m] \u001b[38;5;241m+\u001b[39m dm[i] \u001b[38;5;241m*\u001b[39m m_extended[i \u001b[38;5;241m+\u001b[39m \u001b[38;5;241m2\u001b[39m]) \u001b[38;5;241m/\u001b[39m (dm[i] \u001b[38;5;241m+\u001b[39m dm[i \u001b[38;5;241m+\u001b[39m \u001b[38;5;241m2\u001b[39m])\n\u001b[1;32m 67\u001b[0m \u001b[38;5;66;03m# Compute c and d coefficients\u001b[39;00m\n\u001b[0;32m---> 68\u001b[0m c \u001b[38;5;241m=\u001b[39m [(\u001b[38;5;241m3\u001b[39m \u001b[38;5;241m*\u001b[39m m[i] \u001b[38;5;241m-\u001b[39m \u001b[38;5;241m2\u001b[39m \u001b[38;5;241m*\u001b[39m b[i] \u001b[38;5;241m-\u001b[39m \u001b[43mb\u001b[49m\u001b[43m[\u001b[49m\u001b[43mi\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m]\u001b[49m) \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;28mlen\u001b[39m(m))]\n\u001b[1;32m 69\u001b[0m d \u001b[38;5;241m=\u001b[39m [(b[i] \u001b[38;5;241m+\u001b[39m b[i \u001b[38;5;241m+\u001b[39m \u001b[38;5;241m1\u001b[39m] \u001b[38;5;241m-\u001b[39m \u001b[38;5;241m2\u001b[39m \u001b[38;5;241m*\u001b[39m m[i]) \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;28mlen\u001b[39m(m))]\n\u001b[1;32m 71\u001b[0m \u001b[38;5;66;03m# Perform interpolation\u001b[39;00m\n", + "\u001b[0;31mIndexError\u001b[0m: list index out of range" + ] + } + ], + "source": [ + "# Example data\n", + "y = [0, 2, 1, 3, 0, 4]\n", + "x_new = [0.5, 1.5, 2.5, 3.5, 4.5]\n", + "\n", + "# Perform interpolation\n", + "y_new = akima_interpolate(y, x_new)\n", + "\n", + "# Print results\n", + "print(\"Interpolated values:\", y_new)\n", + "\n", + "# Optional: Plot results\n", + "import matplotlib.pyplot as plt\n", + "x_indices = list(range(len(y))) # x values are just indices\n", + "plt.plot(x_indices, y, 'o-', label=\"Original Data\")\n", + "plt.plot(x_new, y_new, 'x', label=\"Interpolated Points\")\n", + "plt.legend()\n", + "plt.xlabel(\"x (index)\")\n", + "plt.ylabel(\"y\")\n", + "plt.title(\"Akima Interpolation (x = index of y)\")\n", + "plt.grid()\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "36b28310-d073-4898-9a65-5995bc742aa5", + "metadata": {}, "outputs": [], "source": [] } diff --git a/precipitationctl.ino b/precipitationctl.ino index 67fcf4a1aa950b27e0a974d6c3e640f794d27801..a04c731d394be142342d439b1dafd055b5d9e6f9 100644 --- a/precipitationctl.ino +++ b/precipitationctl.ino @@ -1,41 +1,103 @@ #include "data.h" +#include <Ticker.h> +#include <ESP32Servo.h> +#define SERVO_MIN 0 +#define SERVO_MAX 180 +#define ADC_MAX 4096 +#define INTERVAL 5 +// 28.44 -#define SERVO_MIN 30 -#define SERVO_MAX 480 int servoPin = 23; int pos = SERVO_MIN; +Servo servo; -int data_index = 0; +int data_index = 23; +double ptarget = 0.0f; +double target = 0.0f; +double current = 0.0f; +double blend = 0.0f; -void setServoNormalized(float pos) { - pos = min(1.0f, max(0.0f, pos)); - int span = SERVO_MAX - SERVO_MIN; - int servo_pos = SERVO_MIN + span * pos; - analogWrite(servoPin, servo_pos); +Ticker ticker; + +double lerp(double a, double b, double t) { + t = max(0.0, min(1.0, t)); + return a + t * (b - a); } -void setup() { - Serial.begin(115200); - // put your setup code here, to run once: - pinMode(servoPin, OUTPUT); - analogWriteFrequency(servoPin, 50); - analogWriteResolution(servoPin, 12); +void setServoNormalized(double pos) { + pos = min(1.0, max(0.0, pos)); + // + pos = sqrt(pos); + int span = abs(SERVO_MAX - SERVO_MIN); + int servo_pos = SERVO_MIN + span * pos; + // Serial.print(" --> "); + // Serial.print(pos); + // Serial.print(" --> "); + // Serial.println(servo_pos); + Serial.print(SERVO_MIN); // To freeze the lower limit + Serial.print(" "); + Serial.print(SERVO_MAX); // To freeze the upper limit + Serial.print(" "); + Serial.println(servo_pos); + servo.write(servo_pos); } -void loop() { - // put your main code here, to run repeatedly: - float datum = data[data_index]; - setServoNormalized(datum); - Serial.print("Aktueller Wert "); - Serial.print(datum * 67.2); - Serial.print(" mm --> "); - Serial.println(datum); +void setNewTarget() { + ptarget = target; + target = data[data_index]; + current = target; + // Serial.print("New Target is ["); + // Serial.print(data_index); + // Serial.print("]: "); + // Serial.print(target); + data_index++; - if ( data_index >= DATA_LENGTH) { + // Reset index when longer than the data (loop over) + if (data_index >= DATA_LENGTH) { data_index = 0; Serial.println("Die daten sind durch, wir fangen wieder vorne an..."); } - delay(28.44*1000); +} + +float testval = 0.0f; +float increment = 0.01f; + +void setup() { + Serial.begin(115200); + + // Allow allocation of all timers + ESP32PWM::allocateTimer(0); + ESP32PWM::allocateTimer(1); + ESP32PWM::allocateTimer(2); + ESP32PWM::allocateTimer(3); + + servo.setPeriodHertz(50);// Standard 50hz servo + servo.attach(servoPin, 1000, 2000); // attaches the servo on pin 18 to the servo object + // using SG90 servo min/max of 500us and 2400us + // for MG995 large servo, use 1000us and 2000us, + // which are the defaults, so this line could be + // "myservo.attach(servoPin);" + + // Set new Target every 28.44 seconds (leads to 3 days) + ticker.attach(INTERVAL, setNewTarget); // Interval in seconds +} + +void loop() { + unsigned long currentMillis = millis(); + blend = fmod(float(currentMillis), (INTERVAL * 1000.0f)) / (INTERVAL * 1000.0f); + current = lerp(ptarget, target, blend); + // Serial.print(ptarget); + // Serial.print(" ("); + // Serial.print(int(blend*100)); + // Serial.print("%) "); + // Serial.print(target); + // Serial.print(" ("); + // Serial.print(int((1.0-blend)*100)); + // Serial.print("%) = "); + // Serial.println(current); + + setServoNormalized(current); + delay(2); }