diff --git a/src/kst3000.cpp b/src/kst3000.cpp index 6dc2bab8..81b26352 100644 --- a/src/kst3000.cpp +++ b/src/kst3000.cpp @@ -252,21 +252,44 @@ int KST3000::get_waveform_data(char *data) { int num = get_waveform_points(); int data_length = 10 + num + 1; // 10 is the length of
, 1 is the end breakline(\n) char buffer[data_length]; -// memset(buffer, 0, num); exec(command, buffer, true, data_length); -// for (int i = 0; i < 10; i++) { -// cout << buffer[i]; -//// cout << (int) (unsigned char) buffer[i] << " "; -// } -// cout << endl; -// for (int i = 10; i < data_length; i++) { -// cout << std::hex << "0x" << (int) (unsigned char) buffer[i] << " "; -// } -// cout << endl; memcpy(data, buffer + 10, num); return 0; } +/** + * @brief convert a measurement data array to a 2d array: time array & voltage array + * */ +int KST3000::get_real_data(double **result) { + int points = get_waveform_points(); + char data[points]; + get_waveform_data(data); + char preamble[1024]; + get_waveform_preamble(preamble); + vector v_preamble = split(preamble, ","); + double x_increment = stod(v_preamble[4]); + double x_origin = stod(v_preamble[5]); + double x_reference = stod(v_preamble[6]); + double y_increment = stod(v_preamble[7]); + double y_origin = stod(v_preamble[8]); + double y_reference = stod(v_preamble[9]); + + for (int i = 0; i < points; i++) { + double time = ((i - x_reference) * x_increment) + x_origin; + result[0][i] = time; + + int voltage_data = (int) (unsigned char) data[i]; + if (voltage_data == 0) { + // Hole. Holes are locations where data has not yet been acquired. + continue; + } + double voltage = ((voltage_data - y_reference) * y_increment) + y_origin; + result[1][i] = voltage; + } + return 0; +} + + /** * @brief save waveform data to the target file * @details The file can be plotted, for example using python. @@ -283,30 +306,33 @@ int KST3000::save_waveform_data(string file_path) { char preamble[1024]; get_waveform_preamble(preamble); vector v_preamble = split(preamble, ","); - double x_increment = stod(v_preamble[4]); - double x_origin = stod(v_preamble[5]); - double x_reference = stod(v_preamble[6]); - double y_increment = stod(v_preamble[7]); - double y_origin = stod(v_preamble[8]); - double y_reference = stod(v_preamble[9]); int points = get_waveform_points(); - char data[points]; - get_waveform_data(data); + double *result[2]; + result[0] = new double[points]; + result[1] = new double[points]; + get_real_data(result); stringstream stream; stream << "time(ms)" << "," << "voltage(V)" << endl; for (int i = 0; i < points; i++) { - double time = ((i - x_reference) * x_increment) + x_origin; - int voltage_data = (int) (unsigned char) data[i]; - - if (voltage_data == 0) { - continue; - } - double voltage = ((voltage_data - y_reference) * y_increment) + y_origin; - stream << time * 1000 << "," << voltage << endl; + stream << result[0][i] * 1000 << "," << result[1][i] << endl; } - write_to_file(stream.str(), file_path); + +// stringstream stream; +// stream << "time(ms)" << "," << "voltage(V)" << endl; +// for (int i = 0; i < points; i++) { +// double time = ((i - x_reference) * x_increment) + x_origin; +// int voltage_data = (int) (unsigned char) data[i]; +// +// if (voltage_data == 0) { +// continue; +// } +// double voltage = ((voltage_data - y_reference) * y_increment) + y_origin; +// stream << time * 1000 << "," << voltage << endl; +// } +// +// write_to_file(stream.str(), file_path); return 0; } diff --git a/src/kst3000.h b/src/kst3000.h index b8b7814b..db2735bf 100644 --- a/src/kst3000.h +++ b/src/kst3000.h @@ -51,6 +51,8 @@ class KST3000 : public Device { int get_waveform_data(char *data); + int get_real_data(double **result); + int save_waveform_data(string file_path = "./buffer"); int set_waveform_source(int channel = 1); diff --git a/test/.ipynb_checkpoints/Untitled-checkpoint.ipynb b/test/.ipynb_checkpoints/Untitled-checkpoint.ipynb new file mode 100644 index 00000000..363fcab7 --- /dev/null +++ b/test/.ipynb_checkpoints/Untitled-checkpoint.ipynb @@ -0,0 +1,6 @@ +{ + "cells": [], + "metadata": {}, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/test/.ipynb_checkpoints/plot-checkpoint.ipynb b/test/.ipynb_checkpoints/plot-checkpoint.ipynb index 40ec761c..c641f542 100644 --- a/test/.ipynb_checkpoints/plot-checkpoint.ipynb +++ b/test/.ipynb_checkpoints/plot-checkpoint.ipynb @@ -2,13 +2,370 @@ "cells": [ { "cell_type": "code", - "execution_count": 65, + "execution_count": 212, + "id": "b5e66da9", + "metadata": {}, + "outputs": [], + "source": [ + "import xml.etree.ElementTree as ET\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import re" + ] + }, + { + "cell_type": "code", + "execution_count": 213, + "id": "7647d63c", + "metadata": {}, + "outputs": [], + "source": [ + "def plot_single_file(filename):\n", + " data = pd.read_csv(filename)\n", + " plt.plot(data['time'], data['input'], label=filename)\n", + " plt.plot(data['time'], data['output'], label=filename)" + ] + }, + { + "cell_type": "code", + "execution_count": 248, + "id": "f2f5ce11", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "def plot_file(filename):\n", + " data = pd.read_csv(filename) # input\n", + " plt.title(filename)\n", + " plt.scatter(data['input'], data['output'], s=1, label=filename)\n", + "\n", + "plot_file('data/1_4_100_12_1628855074')" + ] + }, + { + "cell_type": "code", + "execution_count": 215, + "id": "e1dd2cf0", + "metadata": {}, + "outputs": [], + "source": [ + "def plot_files(filenames, mix = False):\n", + " for filename in filenames:\n", + " if (not mix):\n", + " plt.figure()\n", + " plot_file(filename)\n", + " plt.legend()" + ] + }, + { + "cell_type": "code", + "execution_count": 216, "id": "bb2471c1", + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "tree = ET.parse('data/meta.xml')\n", + "tests = tree.getroot()\n", + "\n", + "def get_real_value(key, test):\n", + " text_value = test.find(key).text\n", + " if (key == 'voltage'):\n", + " return float(text_value)\n", + " elif (key == 'file'):\n", + " return text_value\n", + " else:\n", + " return int(text_value)\n", + "\n", + "def is_valid(dic, test):\n", + " v = False\n", + " if (test.find('frequency').text == '200' and float(test.find('voltage').text) == 0.5):\n", + " v = True\n", + " for key in dic.keys():\n", + " real_value = get_real_value(key, test)\n", + " if (key == 'file'):\n", + " if (not re.search(dic[key], real_value)):\n", + " return False\n", + " elif (real_value != dic[key]):\n", + " return False\n", + " return True\n", + " \n", + "def filter(dic):\n", + " keys = list(dic.keys())\n", + " filenames = []\n", + " for test in root.findall('test'):\n", + " if (is_valid(dic, test)):\n", + " filenames.append(test.find('file').text)\n", + " return filenames\n", + "\n", + "# filenames = filter({\"frequency\": 500, \"voltage\": 0.8})" + ] + }, + { + "cell_type": "code", + "execution_count": 268, + "id": "4cf8068c", + "metadata": { + "scrolled": false + }, + "outputs": [], + "source": [ + "import math\n", + "\n", + "def get_zero_points(filename):\n", + " data = pd.read_csv(filename)\n", + " input_data = data['input']\n", + " is_positive = input_data[0] > 0\n", + " zero_points = []\n", + " for i in range(len(data)):\n", + " new_is_positive = input_data[i] > 0\n", + " if (is_positive != new_is_positive):\n", + " # met a zero points\n", + " zero_points.append(i)\n", + " is_positive = new_is_positive\n", + " return zero_points\n", + "\n", + "cycles_per_file = 10\n", + "\n", + "def split_cycles(filename):\n", + " data = pd.read_csv(filename)\n", + " cycles = []\n", + "# cycles.append([list(data['input']), list(data['output'])])\n", + " data_len = len(data['input'])\n", + " points_per_cycle = math.ceil(data_len / cycles_per_file)\n", + " for i in range(0, data_len, points_per_cycle):\n", + " input_data = data['input'][i:i+points_per_cycle]\n", + " output_data = data['output'][i:i+points_per_cycle]\n", + " if (len(input_data) < 90):\n", + " continue\n", + " cycles.append([list(input_data), list(output_data)])\n", + " return cycles" + ] + }, + { + "cell_type": "code", + "execution_count": 275, + "id": "9af9415a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "800\n" + ] + } + ], + "source": [ + "from random import randint\n", + "\n", + "labels = []\n", + "train_data = []\n", + "\n", + "cells = 8\n", + "data_len = 90\n", + "\n", + "def parse_test(test):\n", + " dic = {}\n", + " for child in test:\n", + " dic[child.tag] = child.text\n", + " return dic\n", + "\n", + "def get_label(cell_number):\n", + " label = [0] * cells\n", + " label[cell_number - 1] = 1\n", + " return label\n", + "\n", + "def trim_cycle(cycle):\n", + " while (len(cycle[0]) > data_len):\n", + " pos = randint(0, len(cycle[0]) - 1)\n", + " cycle[0].pop(pos)\n", + " cycle[1].pop(pos)\n", + "\n", + "t_voltage = 1.2\n", + "t_frequency = 100\n", + "\n", + "for test in tests:\n", + " dic = parse_test(test)\n", + " cell_number = int(dic['cell'])\n", + " file = dic['file']\n", + " voltage = float(dic['voltage'])\n", + " frequency = int(dic['frequency'])\n", + " cycles = split_cycles(file)\n", + " if (len(cycles) == 0):\n", + " # measurement errors;\n", + " # nomarlly no more than 10 cycles in a test file;\n", + " # drop these error files\n", + " continue\n", + " if (cell_number > cells):\n", + " continue\n", + " if (voltage != t_voltage or frequency != t_frequency):\n", + " continue\n", + " for cycle in cycles:\n", + " trim_cycle(cycle)\n", + " labels.append(get_label(cell_number))\n", + " train_data.append(cycle)\n", + "\n", + "print(len(train_data))" + ] + }, + { + "cell_type": "code", + "execution_count": 276, + "id": "191c7ed0", + "metadata": {}, + "outputs": [], + "source": [ + "# print(labels[-1])\n", + "# print(train_data[-1])\n", + "# plt.scatter(train_data[-1][0], train_data[-1][1], s=1)\n", + "# plt.scatter(train_data[0][0], train_data[0][1], s=1)\n", + "# cycles = split_cycles('data/1_4_100_12_1628855077')\n", + "# # print(cycles[0])\n", + "# plt.scatter(cycles[1][0], cycles[1][1], s=1)\n", + "# data = pd.read_csv('data/1_4_100_12_1628855077') # input\n", + "# # plt.title(filename)\n", + "# print(len(data['input']))\n", + "# plt.scatter(data['time'][0:1000], data['output'][0:1000], s=1, label=filename)\n", + "# plt.scatter(data['input'][700:1000], data['output'][700:1000], s=1, label=filename)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 277, + "id": "acbfab48", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "tf.Tensor([800 2 90], shape=(3,), dtype=int32)\n", + "Model: \"sequential_31\"\n", + "_________________________________________________________________\n", + "Layer (type) Output Shape Param # \n", + "=================================================================\n", + "flatten_29 (Flatten) (None, 180) 0 \n", + "_________________________________________________________________\n", + "dense_58 (Dense) (None, 50) 9050 \n", + "_________________________________________________________________\n", + "dense_59 (Dense) (None, 8) 408 \n", + "=================================================================\n", + "Total params: 9,458\n", + "Trainable params: 9,458\n", + "Non-trainable params: 0\n", + "_________________________________________________________________\n", + "Epoch 1/20\n", + "90/90 [==============================] - 1s 5ms/step - loss: 1.9059 - accuracy: 0.2656 - val_loss: 1.6802 - val_accuracy: 0.4464\n", + "Epoch 2/20\n", + "90/90 [==============================] - 0s 2ms/step - loss: 1.5695 - accuracy: 0.4308 - val_loss: 1.5590 - val_accuracy: 0.4018\n", + "Epoch 3/20\n", + "90/90 [==============================] - 0s 2ms/step - loss: 1.3925 - accuracy: 0.5335 - val_loss: 1.3739 - val_accuracy: 0.5446\n", + "Epoch 4/20\n", + "90/90 [==============================] - 0s 2ms/step - loss: 1.2586 - accuracy: 0.5647 - val_loss: 1.1845 - val_accuracy: 0.6250\n", + "Epoch 5/20\n", + "90/90 [==============================] - 0s 2ms/step - loss: 1.1206 - accuracy: 0.6473 - val_loss: 1.1623 - val_accuracy: 0.5982\n", + "Epoch 6/20\n", + "90/90 [==============================] - 0s 2ms/step - loss: 1.0375 - accuracy: 0.6585 - val_loss: 1.0260 - val_accuracy: 0.6339\n", + "Epoch 7/20\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.9625 - accuracy: 0.6585 - val_loss: 0.9172 - val_accuracy: 0.6964\n", + "Epoch 8/20\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.8864 - accuracy: 0.6540 - val_loss: 0.9430 - val_accuracy: 0.6607\n", + "Epoch 9/20\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.8397 - accuracy: 0.6987 - val_loss: 0.8570 - val_accuracy: 0.6696\n", + "Epoch 10/20\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.8021 - accuracy: 0.6942 - val_loss: 0.8073 - val_accuracy: 0.7679\n", + "Epoch 11/20\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.7587 - accuracy: 0.7366 - val_loss: 0.7435 - val_accuracy: 0.7411\n", + "Epoch 12/20\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.7385 - accuracy: 0.7366 - val_loss: 0.7351 - val_accuracy: 0.7679\n", + "Epoch 13/20\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.6856 - accuracy: 0.7701 - val_loss: 0.7237 - val_accuracy: 0.7054\n", + "Epoch 14/20\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.6768 - accuracy: 0.7656 - val_loss: 0.7511 - val_accuracy: 0.6875\n", + "Epoch 15/20\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.6292 - accuracy: 0.7679 - val_loss: 0.7323 - val_accuracy: 0.7054\n", + "Epoch 16/20\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.6121 - accuracy: 0.7812 - val_loss: 0.6360 - val_accuracy: 0.7589\n", + "Epoch 17/20\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.6049 - accuracy: 0.8013 - val_loss: 0.6254 - val_accuracy: 0.8214\n", + "Epoch 18/20\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.5609 - accuracy: 0.8281 - val_loss: 0.6866 - val_accuracy: 0.7411\n", + "Epoch 19/20\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.5467 - accuracy: 0.8147 - val_loss: 0.6059 - val_accuracy: 0.8125\n", + "Epoch 20/20\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.5331 - accuracy: 0.8214 - val_loss: 0.6144 - val_accuracy: 0.7679\n", + "8/8 - 0s - loss: 0.7607 - accuracy: 0.7208\n", + "0.7606762647628784 0.7208333611488342\n" + ] + } + ], + "source": [ + "import tensorflow as tf\n", + "import numpy as np\n", + "from sklearn.model_selection import train_test_split\n", + "from tensorflow.keras import datasets, layers, models\n", + "from tensorflow.keras.models import Sequential\n", + "from tensorflow.keras.layers import Dense, Flatten\n", + "import matplotlib.pyplot as plt\n", + "\n", + "print(tf.shape(train_data))\n", + "input_shape = (2, 90)\n", + "\n", + "model = models.Sequential()\n", + "model.add(Flatten(input_shape=input_shape))\n", + "model.add(Dense(50,activation='relu'))\n", + "model.add(Dense(cells, activation='softmax'))\n", + "model.build(input_shape)\n", + "model.summary()\n", + "\n", + "# Split data into test and training set\n", + "X_train, X_test, y_train, y_test = train_test_split(np.array(train_data), np.array(labels), test_size=0.3, shuffle=True)\n", + "\n", + "# Set loss function and optimizer\n", + "model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n", + "\n", + "# Start training\n", + "history = model.fit(X_train, y_train, validation_split=0.2, epochs=20, batch_size=5, shuffle=True)\n", + "\n", + "\n", + "# Evaluate using the test data\n", + "test_loss, test_acc = model.evaluate(X_test, y_test, verbose=2)\n", + "print(test_loss, test_acc)" + ] + }, + { + "cell_type": "code", + "execution_count": 231, + "id": "824ce269", "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "execution_count": 231, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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\n", 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"text/plain": [ "
" ] @@ -32,36 +389,49 @@ } ], "source": [ - "import xml.etree.ElementTree as ET\n", - "import pandas as pd\n", - "import matplotlib.pyplot as plt\n", "\n", - "def plot_file(filename):\n", - " data_x = pd.read_csv(filename + \"_1\") # input\n", - " data_y = pd.read_csv(filename + \"_2\") # output\n", - " plt.figure()\n", - " plt.title(filename)\n", - " plt.scatter(data_x['voltage(V)'], data_y['voltage(V)'], s=1, label=filename)\n", - "# plt.legend()\n", - " \n", - "def plot_files(filenames):\n", - " for filename in filenames:\n", - " plot_file(filename)\n", - "\n", - "tree = ET.parse('data/meta.xml')\n", - "root = tree.getroot()\n", + "fig_loss, ax_loss = plt.subplots()\n", + "ax_loss.plot(history.history['loss'], label='loss')\n", + "ax_loss.plot(history.history['val_loss'], label='val_loss')\n", + "ax_loss.set_xlabel('Epoch')\n", + "ax_loss.set_ylabel('Loss')\n", + "ax_loss.legend(loc='lower right')\n", "\n", + "fig, ax = plt.subplots()\n", "\n", - "filenames = map(lambda file: file.text, files)\n", - "\n", - "plot_files(list(filenames))\n", - "\n", - "# for file in files:\n", - "# print(file.text)\n", - " \n", - "# for test in tests:\n", - "# print(test[0].text)" + "ax.plot(history.history['accuracy'], label='accuracy')\n", + "ax.plot(history.history['val_accuracy'], label = 'val_accuracy')\n", + "ax.set_xlabel('Epoch')\n", + "ax.set_ylabel('Accuracy')\n", + "ax.legend(loc='lower right')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5024b267", + "metadata": {}, + "outputs": [], + "source": [ + "predictions = model.predict(X_test)\n", + "for i in range(len(prediction)):" ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "77bf4d9f", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f9bee384", + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { diff --git a/test/data/meta.xml b/test/data/meta.xml index 434ab5dd..ceabb419 100644 --- 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/Users/lwh/CLionProjects/ce_device/test/data/1_7_100_12_1628863403 + + + 1 + 7 + 100 + 1.200000 + /Users/lwh/CLionProjects/ce_device/test/data/1_7_100_12_1628863404 + + + 1 + 7 + 100 + 1.200000 + /Users/lwh/CLionProjects/ce_device/test/data/1_7_100_12_1628863405 + + + 1 + 7 + 100 + 1.200000 + /Users/lwh/CLionProjects/ce_device/test/data/1_7_100_12_1628863407 diff --git a/test/main.cpp b/test/main.cpp index 23c64bd6..4ab2fa4a 100644 --- a/test/main.cpp +++ b/test/main.cpp @@ -1,6 +1,7 @@ #include #include #include +#include #include #include "pugixml.hpp" #include "kst3000.h" @@ -13,19 +14,21 @@ string PWD = "/Users/lwh/CLionProjects/ce_device/test/"; string DATA_PATH = "/Users/lwh/CLionProjects/ce_device/test/data/"; // the max voltage of the chip can be input, unit /10v, here is 1.2V -int MAX_VOLTAGE = 10; +int MAX_VOLTAGE = 12; // the min voltage of the chip can be input, unit /10v, here is 0.1V -int MIN_VOLTAGE = 4; +int MIN_VOLTAGE = 12; -int MAX_FREQUENCY = 500; +int MAX_FREQUENCY = 100; int MIN_FREQUENCY = 100; +double CYCLES_PER_FILE = 10.0; + int CHIP = 1; // how many time a cell need to be tested -int MAX_TIMES = 3; +int MAX_TIMES = 100; -void sleep(int secs) { +void sleep(double secs) { unsigned int microsecond = 1000000; usleep(secs * microsecond);//sleeps for 3 second } @@ -54,7 +57,7 @@ int set_params(KST33500 wg, KST3000 o, int frequency, int voltage) { wg.frequency(frequency); o.set_channel_range(d_voltage * 2, 1); // *2 for sin wave o.set_channel_range(d_voltage * 2, 2); // *2 for sin wave - o.set_time_range(10.0 / frequency); // collect 10 cycles + o.set_time_range(CYCLES_PER_FILE / frequency); // collect 1 cycle every time } /** @@ -112,6 +115,50 @@ int save_meta(string file_name, int frequency, double voltage, int cell, int chi return 0; } +int stream_triple(stringstream &stream, double time, double input, double output) { + stream << time << setw(20) << input << setw(20) << output << endl; + return 0; +} + +int write_to_file(string data, string file_path) { + string buffer(data); + ofstream file; + file.open(file_path); + if (file.is_open()) { + file << buffer; + file.close(); + return 0; + } + cout << "File open failed!\n"; + return 1; +} + +/** + * @brief save data into a file containing 3 columns: time; input; output; + * */ +int capture_data(string file_name, KST3000 o) { + o.digitize(); + int points = o.get_waveform_points(); + + o.set_waveform_source(1); + double *input[2]; + input[0] = new double[points]; + input[1] = new double[points]; + o.get_real_data(input); + + o.set_waveform_source(2); + double *output[2]; + output[0] = new double[points]; + output[1] = new double[points]; + o.get_real_data(output); + + stringstream stream; + stream << "time" << "," << "input" << "," << "output" << endl; + for (int i = 0; i < points; i++) { + stream << input[0][i] * 1000 << "," << input[1][i] << "," << output[1][i] << endl; + } + write_to_file(stream.str(), file_name); +} /** * @brief run a single test @@ -126,26 +173,20 @@ int save_meta(string file_name, int frequency, double voltage, int cell, int chi * 2, cell; * 3, frequency * 4, voltage(unit: /10 V); - * 5, data direction, 1 means input, 2 means output + * 5, timestamp * */ int single_test(int cell, KST33500 wg, KST3000 o, int frequency, int i_voltage) { double voltage = i_voltage / 10.0; + time_t t = time(nullptr); wg.voltage(voltage); wg.frequency(frequency); - string file_prefix = DATA_PATH + to_string(CHIP) - + "_" + to_string(cell) - + "_" + to_string(frequency) - + "_" + to_string(i_voltage); - string input_file = file_prefix + "_1"; - string output_file = file_prefix + "_2"; - - o.digitize(); - o.set_waveform_source(1); - o.save_waveform_data(input_file); - o.set_waveform_source(2); - o.save_waveform_data(output_file); - - save_meta(file_prefix, frequency, voltage, cell); + string file_name = DATA_PATH + to_string(CHIP) + + "_" + to_string(cell) + + "_" + to_string(frequency) + + "_" + to_string(i_voltage) + + "_" + to_string(t); + capture_data(file_name, o); + save_meta(file_name, frequency, voltage, cell); } /** @@ -161,21 +202,28 @@ int test_cell(int cell, KST33500 wg, KST3000 o) { for (int f = MIN_FREQUENCY; f <= MAX_FREQUENCY; f += 100) { for (int v = MIN_VOLTAGE; v <= MAX_VOLTAGE; v++) { set_params(wg, o, f, v); + sleep(0.5); single_test(cell, wg, o, f, v); - sleep(1); } } } int config(KST3000 o) { + o.set_timebase_mode(); o.set_waveform_points_mode("NORmal"); o.set_waveform_points(1000); } int main() { KST33500 wg = connect_wave_generator(); + wg.output(true); + sleep(1.0); KST3000 o = connect_oscilloscope(); config(o); - test_cell(1, wg, o); + int cell = 7; + for (int i = 0; i < MAX_TIMES; i++) { + test_cell(cell, wg, o); + } + wg.output(false); return 0; } diff --git a/test/plot.ipynb b/test/plot.ipynb index 67e9bb3e..8813e3f5 100644 --- a/test/plot.ipynb +++ b/test/plot.ipynb @@ -2,62 +2,48 @@ "cells": [ { "cell_type": "code", - "execution_count": 295, + "execution_count": 334, "id": "b5e66da9", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "import xml.etree.ElementTree as ET\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", - "import re\n", - "\n", - "fig = plt.gcf()\n", - "fig.set_size_inches(20, 5)" + "import re" ] }, { "cell_type": "code", - "execution_count": 296, + "execution_count": 335, "id": "7647d63c", "metadata": {}, "outputs": [], "source": [ "def plot_single_file(filename):\n", " data = pd.read_csv(filename)\n", - " plt.plot(data['time(ms)'], data['voltage(V)'], label=filename)\n", - "\n", - "# plot_single_file('data/1_4_100_1')\n", - "# plot_single_file('data/1_4_100_2')" + " plt.plot(data['time'], data['input'], label=filename)\n", + " plt.plot(data['time'], data['output'], label=filename)" ] }, { "cell_type": "code", - "execution_count": 297, + "execution_count": 336, "id": "f2f5ce11", "metadata": {}, "outputs": [], "source": [ "def plot_file(filename):\n", - " data_x = pd.read_csv(filename + \"_1\") # input\n", - " data_y = pd.read_csv(filename + \"_2\") # output\n", + " data = pd.read_csv(filename) # input\n", " plt.title(filename)\n", - " plt.scatter(data_x['voltage(V)'], data_y['voltage(V)'], s=1, label=filename)" + " plt.scatter(data['input'], data['output'], s=1, label=filename)\n", + "\n", + "# plot_file('data/1_4_100_12_1628855074')" ] }, { "cell_type": "code", - "execution_count": 298, + "execution_count": 337, "id": "e1dd2cf0", "metadata": {}, "outputs": [], @@ -72,82 +58,301 @@ }, { "cell_type": "code", - "execution_count": 332, + "execution_count": 338, "id": "bb2471c1", + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "tree = ET.parse('data/meta.xml')\n", + "tests = tree.getroot()\n", + "\n", + "def get_real_value(key, test):\n", + " text_value = test.find(key).text\n", + " if (key == 'voltage'):\n", + " return float(text_value)\n", + " elif (key == 'file'):\n", + " return text_value\n", + " else:\n", + " return int(text_value)\n", + "\n", + "def is_valid(dic, test):\n", + " v = False\n", + " if (test.find('frequency').text == '200' and float(test.find('voltage').text) == 0.5):\n", + " v = True\n", + " for key in dic.keys():\n", + " real_value = get_real_value(key, test)\n", + " if (key == 'file'):\n", + " if (not re.search(dic[key], real_value)):\n", + " return False\n", + " elif (real_value != dic[key]):\n", + " return False\n", + " return True\n", + " \n", + "def filter(dic):\n", + " keys = list(dic.keys())\n", + " filenames = []\n", + " for test in root.findall('test'):\n", + " if (is_valid(dic, test)):\n", + " filenames.append(test.find('file').text)\n", + " return filenames\n", + "\n", + "# filenames = filter({\"frequency\": 500, \"voltage\": 0.8})" + ] + }, + { + "cell_type": "code", + "execution_count": 339, + "id": "4cf8068c", "metadata": { "scrolled": false }, + "outputs": [], + "source": [ + "import math\n", + "\n", + "def get_zero_points(filename):\n", + " data = pd.read_csv(filename)\n", + " input_data = data['input']\n", + " is_positive = input_data[0] > 0\n", + " zero_points = []\n", + " for i in range(len(data)):\n", + " new_is_positive = input_data[i] > 0\n", + " if (is_positive != new_is_positive):\n", + " # met a zero points\n", + " zero_points.append(i)\n", + " is_positive = new_is_positive\n", + " return zero_points\n", + "\n", + "cycles_per_file = 10\n", + "\n", + "def split_cycles(filename):\n", + " data = pd.read_csv(filename)\n", + " cycles = []\n", + "# cycles.append([list(data['input']), list(data['output'])])\n", + " data_len = len(data['input'])\n", + " points_per_cycle = math.ceil(data_len / cycles_per_file)\n", + " for i in range(0, data_len, points_per_cycle):\n", + " input_data = data['input'][i:i+points_per_cycle]\n", + " output_data = data['output'][i:i+points_per_cycle]\n", + " if (len(input_data) < 90):\n", + " continue\n", + " cycles.append([list(input_data), list(output_data)])\n", + " return cycles" + ] + }, + { + "cell_type": "code", + "execution_count": 341, + "id": "9af9415a", + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "['/Users/lwh/CLionProjects/ce_device/test/data/1_1_200_4', '/Users/lwh/CLionProjects/ce_device/test/data/1_1_200_5', '/Users/lwh/CLionProjects/ce_device/test/data/1_1_200_6', '/Users/lwh/CLionProjects/ce_device/test/data/1_1_200_7', '/Users/lwh/CLionProjects/ce_device/test/data/1_1_200_8', '/Users/lwh/CLionProjects/ce_device/test/data/1_1_200_9', '/Users/lwh/CLionProjects/ce_device/test/data/1_1_200_10']\n" + "7000\n" ] - }, - { - "data": { - "image/png": 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\n", 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\n", 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, + } + ], + "source": [ + "from random import randint\n", + "\n", + "labels = []\n", + "train_data = []\n", + "\n", + "cells = 7\n", + "data_len = 90\n", + "\n", + "def parse_test(test):\n", + " dic = {}\n", + " for child in test:\n", + " dic[child.tag] = child.text\n", + " return dic\n", + "\n", + "def get_label(cell_number):\n", + " label = [0] * cells\n", + " label[cell_number - 1] = 1\n", + " return label\n", + "\n", + "def trim_cycle(cycle):\n", + " while (len(cycle[0]) > data_len):\n", + " pos = randint(0, len(cycle[0]) - 1)\n", + " cycle[0].pop(pos)\n", + " cycle[1].pop(pos)\n", + "\n", + "t_voltage = 1.2\n", + "t_frequency = 100\n", + "\n", + "for test in tests:\n", + " dic = parse_test(test)\n", + " cell_number = int(dic['cell'])\n", + " file = dic['file']\n", + " voltage = float(dic['voltage'])\n", + " frequency = int(dic['frequency'])\n", + " cycles = split_cycles(file)\n", + " if (len(cycles) == 0):\n", + " # measurement errors;\n", + " # nomarlly no more than 10 cycles in a test file;\n", + " # drop these error files\n", + " continue\n", + " if (cell_number > cells):\n", + " continue\n", + " if (voltage != t_voltage or frequency != t_frequency):\n", + " continue\n", + " for cycle in cycles:\n", + " trim_cycle(cycle)\n", + " labels.append(get_label(cell_number))\n", + " train_data.append(cycle)\n", + "\n", + "print(len(train_data))" + ] + }, + { + "cell_type": "code", + "execution_count": 342, + "id": "36f8817b", + "metadata": {}, + "outputs": [], + "source": [ + "# print(labels[-1])\n", + "# print(train_data[-1])\n", + "# plt.scatter(train_data[-1][0], train_data[-1][1], s=1)\n", + "# plt.scatter(train_data[0][0], train_data[0][1], s=1)\n", + "# cycles = split_cycles('data/1_4_100_12_1628855077')\n", + "# # print(cycles[0])\n", + "# plt.scatter(cycles[1][0], cycles[1][1], s=1)\n", + "# data = pd.read_csv('data/1_6_100_12_1628863259') # input\n", + "# # plt.title(filename)\n", + "# print(len(data['input']))\n", + "# plt.scatter(data['input'][0:100], data['output'][0:100], s=1, label=filename)\n", + "# # plt.scatter(data['input'][700:1000], data['output'][700:1000], s=1, label=filename)" + ] + }, + { + "cell_type": "code", + "execution_count": 343, + "id": "acbfab48", + "metadata": {}, + "outputs": [ { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "tf.Tensor([7000 2 90], shape=(3,), dtype=int32)\n", + "Model: \"sequential_37\"\n", + "_________________________________________________________________\n", + "Layer (type) Output Shape Param # \n", + "=================================================================\n", + "flatten_35 (Flatten) (None, 180) 0 \n", + "_________________________________________________________________\n", + "dense_70 (Dense) (None, 50) 9050 \n", + "_________________________________________________________________\n", + "dense_71 (Dense) (None, 7) 357 \n", + "=================================================================\n", + "Total params: 9,407\n", + "Trainable params: 9,407\n", + "Non-trainable params: 0\n", + "_________________________________________________________________\n", + "Epoch 1/20\n", + "784/784 [==============================] - 1s 1ms/step - loss: 1.2995 - accuracy: 0.4867 - val_loss: 0.8948 - val_accuracy: 0.7316\n", + "Epoch 2/20\n", + "784/784 [==============================] - 1s 1ms/step - loss: 0.7477 - accuracy: 0.7235 - val_loss: 0.6288 - val_accuracy: 0.8061\n", + "Epoch 3/20\n", + "784/784 [==============================] - 1s 971us/step - loss: 0.5289 - accuracy: 0.8342 - val_loss: 0.4552 - val_accuracy: 0.8816\n", + "Epoch 4/20\n", + "784/784 [==============================] - 1s 1ms/step - loss: 0.3912 - accuracy: 0.8982 - val_loss: 0.3623 - val_accuracy: 0.8939\n", + "Epoch 5/20\n", + "784/784 [==============================] - 1s 1ms/step - loss: 0.3068 - accuracy: 0.9268 - val_loss: 0.2659 - val_accuracy: 0.9367\n", + "Epoch 6/20\n", + "784/784 [==============================] - 1s 1ms/step - loss: 0.2583 - accuracy: 0.9372 - val_loss: 0.2147 - val_accuracy: 0.9541\n", + "Epoch 7/20\n", + "784/784 [==============================] - 1s 1ms/step - loss: 0.2003 - accuracy: 0.9559 - val_loss: 0.1910 - val_accuracy: 0.9571\n", + "Epoch 8/20\n", + "784/784 [==============================] - 1s 1ms/step - loss: 0.1744 - accuracy: 0.9594 - val_loss: 0.2513 - val_accuracy: 0.8959\n", + "Epoch 9/20\n", + "784/784 [==============================] - 1s 1ms/step - loss: 0.1610 - accuracy: 0.9597 - val_loss: 0.1285 - val_accuracy: 0.9765\n", + "Epoch 10/20\n", + "784/784 [==============================] - 1s 1ms/step - loss: 0.1329 - accuracy: 0.9653 - val_loss: 0.1351 - val_accuracy: 0.9704\n", + "Epoch 11/20\n", + "784/784 [==============================] - 1s 1ms/step - loss: 0.1222 - accuracy: 0.9686 - val_loss: 0.1027 - val_accuracy: 0.9704\n", + "Epoch 12/20\n", + "784/784 [==============================] - 1s 931us/step - loss: 0.1161 - accuracy: 0.9658 - val_loss: 0.1295 - val_accuracy: 0.9612\n", + "Epoch 13/20\n", + "784/784 [==============================] - 1s 960us/step - loss: 0.1114 - accuracy: 0.9684 - val_loss: 0.1075 - val_accuracy: 0.9714\n", + "Epoch 14/20\n", + "784/784 [==============================] - 1s 944us/step - loss: 0.0919 - accuracy: 0.9747 - val_loss: 0.0787 - val_accuracy: 0.9765\n", + "Epoch 15/20\n", + "784/784 [==============================] - 1s 938us/step - loss: 0.0905 - accuracy: 0.9730 - val_loss: 0.1007 - val_accuracy: 0.9684\n", + "Epoch 16/20\n", + "784/784 [==============================] - 1s 935us/step - loss: 0.0874 - accuracy: 0.9747 - val_loss: 0.0885 - val_accuracy: 0.9704\n", + "Epoch 17/20\n", + "784/784 [==============================] - 1s 931us/step - loss: 0.0916 - accuracy: 0.9709 - val_loss: 0.0785 - val_accuracy: 0.9704\n", + "Epoch 18/20\n", + "784/784 [==============================] - 1s 940us/step - loss: 0.0748 - accuracy: 0.9798 - val_loss: 0.2254 - val_accuracy: 0.8898\n", + "Epoch 19/20\n", + "784/784 [==============================] - 1s 1ms/step - loss: 0.0786 - accuracy: 0.9765 - val_loss: 0.0917 - val_accuracy: 0.9694\n", + "Epoch 20/20\n", + "784/784 [==============================] - 1s 1ms/step - loss: 0.0691 - accuracy: 0.9806 - val_loss: 0.0531 - val_accuracy: 0.9816\n", + "66/66 - 0s - loss: 0.0572 - accuracy: 0.9848\n", + "0.057217005640268326 0.9847618937492371\n" + ] + } + ], + "source": [ + "import tensorflow as tf\n", + "import numpy as np\n", + "from sklearn.model_selection import train_test_split\n", + "from tensorflow.keras import datasets, layers, models\n", + "from tensorflow.keras.models import Sequential\n", + "from tensorflow.keras.layers import Dense, Flatten\n", + "import matplotlib.pyplot as plt\n", + "\n", + "print(tf.shape(train_data))\n", + "input_shape = (2, 90)\n", + "\n", + "model = models.Sequential()\n", + "model.add(Flatten(input_shape=input_shape))\n", + "model.add(Dense(50,activation='relu'))\n", + "model.add(Dense(cells, activation='softmax'))\n", + "model.build(input_shape)\n", + "model.summary()\n", + "\n", + "# Split data into test and training set\n", + "X_train, X_test, y_train, y_test = train_test_split(np.array(train_data), np.array(labels), test_size=0.3, shuffle=True)\n", + "\n", + "# Set loss function and optimizer\n", + "model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n", + "\n", + "# Start training\n", + "history = model.fit(X_train, y_train, validation_split=0.2, epochs=20, batch_size=5, shuffle=True)\n", + "\n", + "\n", + "# Evaluate using the test data\n", + "test_loss, test_acc = model.evaluate(X_test, y_test, verbose=2)\n", + "print(test_loss, test_acc)" + ] + }, + { + "cell_type": "code", + "execution_count": 231, + "id": "824ce269", + "metadata": {}, + "outputs": [ { "data": { - "image/png": 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\n", 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\n", + "image/png": 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\n", 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\n", 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\n", 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" ] @@ -171,48 +376,49 @@ } ], "source": [ - "tree = ET.parse('data/meta.xml')\n", - "root = tree.getroot()\n", - "\n", - "def get_real_value(key, test):\n", - " text_value = test.find(key).text\n", - " if (key == 'voltage'):\n", - " return float(text_value)\n", - " elif (key == 'file'):\n", - " return text_value\n", - " else:\n", - " return int(text_value)\n", - "\n", - "def is_valid(dic, test):\n", - " v = False\n", - " if (test.find('frequency').text == '200' and float(test.find('voltage').text) == 0.5):\n", - " v = True\n", - " for key in dic.keys():\n", - " real_value = get_real_value(key, test)\n", - " if (key == 'file'):\n", - " if (not re.search(dic[key], real_value)):\n", - " return False\n", - " elif (real_value != dic[key]):\n", - " return False\n", - " return True\n", - " \n", - "def filter(dic):\n", - " keys = list(dic.keys())\n", - " filenames = []\n", - " for test in root.findall('test'):\n", - " if (is_valid(dic, test)):\n", - " filenames.append(test.find('file').text)\n", - " return filenames\n", "\n", + "fig_loss, ax_loss = plt.subplots()\n", + "ax_loss.plot(history.history['loss'], label='loss')\n", + "ax_loss.plot(history.history['val_loss'], label='val_loss')\n", + "ax_loss.set_xlabel('Epoch')\n", + "ax_loss.set_ylabel('Loss')\n", + "ax_loss.legend(loc='lower right')\n", "\n", - "# filenames = filter({\"frequency\": 200, \"voltage\": 0.5})\n", - "# filenames = filter({\"frequency\": 200})\n", - "filenames = filter({\"file\": \"1_200\"})\n", + "fig, ax = plt.subplots()\n", "\n", - "print(filenames)\n", - "plot_files(filenames)\n", - "# plot_files(filenames, True)" + "ax.plot(history.history['accuracy'], label='accuracy')\n", + "ax.plot(history.history['val_accuracy'], label = 'val_accuracy')\n", + "ax.set_xlabel('Epoch')\n", + "ax.set_ylabel('Accuracy')\n", + "ax.legend(loc='lower right')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5024b267", + "metadata": {}, + "outputs": [], + "source": [ + "predictions = model.predict(X_test)\n", + "for i in range(len(prediction)):" ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "77bf4d9f", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f9bee384", + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": {