sqlite Install sudo apt update sudo apt install sqlite3 Usage create/open a database sqlite3 data/sqlite.db commands # exit .exit # show tables .tables # show schema .schema

StandardLib Text Processing Services re 正则表达式 import re # 编译 datepat = re.compile(r'\d+/\d+/\d+') # 匹配 text1 = '11/27/2012' if datepat.match(text1): print('yes') # 搜索 text = 'Today is 11/27/2012. PyCon starts 3/13/2013.' datepat.findall(text) # ['11/27/2012', '3/13/2013'] # 通常会分组匹配 datepat = re.compile(r'(\d+)/(\d+)/(\d+)') m = datepat.match('11/27/2012') print(m.group(0), m.group(1), m.group(2), m.group(3), m.groups()) datepat.findall(text) # [('11', '27', '2012'), ('3', '13', '2013')] # 返回迭代 for m in datepat.finditer(text): print(m.groups()) # 只是一次匹配/搜索操作的话可以无需先编译 re.findall(r'(\d+)/(\d+)/(\d+)', text) # 替换 re.sub(r'(\d+)/(\d+)/(\d+)', r'\3-\1-\2', text) # 'Today is 2012-11-27. PyCon starts 2013-3-13.' re.sub(r'(?P<month>\d+)/(?P<day>\d+)/(?P<year>\d+)', r'\g<year>-\g<month>-\g<day>', text) # 命名分组 Data Types datetime from datetime import datetime a = datetime(2012, 9, 23) # 时间转字符串 a.strftime('%Y-%m-%d') # 字符串转时间 text = '2012-09-20' y = datetime.strptime(text, '%Y-%m-%d') zoneinfo (3.9+) from datetime import datetime from zoneinfo import ZoneInfo # Create a datetime object without timezone naive_dt = datetime.now() # Add the timezone to the datetime object aware_dt = naive_dt.replace(tzinfo=ZoneInfo('Asia/Shanghai')) print(aware_dt) collections nametuple from collections import nametuple # namedtuple(typename, field_names) Point = namedtuple('Point', ['x', 'y']) p = Point(x=11, y=22) print(p.x + p.y) deque from collections import deque d = deque(["a", "b", "c"]) d.append("f") # add to the right side d.appendleft("z") # add to the left side e = d.pop() # pop from the right side e = d.popleft() # pop from the left side d = deque(maxlen=10) # deque with max length, FIFO Counter collections — Container datatypes ...

Basic # define constant a = tf.constant(1) b = tf.constant(2, name='const', shape=(3,5), dtype=tf.float64) # define operation add = a + b mul = tf.square(tf.multiply(a, b)) # define placeholder p = tf.placeholder(tf.int32) # define variables x = tf.get_variable('x', [], dtype=tf.int32) y = tf.get_variable('y', shape=(3,5), initializer=tf.constant_initializer(0)) # session a = tf.constant(1) b = tf.constant(2) f = a + b sess = tf.Session() print(sess.run(f)) # constant do not need initialization x = tf.get_variable('x', [], dtype=tf.int32) f = x + a sess = tf.Session() sess.run(x.initializer) # variable must be initialized before run # return multiple items in a single sess.run() call, instead of making multiple calls print(sess.run([x, f])) p = tf.placeholder(tf.int32) sess = tf.Session() print(sess.run(p, feed_dict={p:2})) # placeholder must be feed in run # assignment # assign node is not connected to the variable, it change the variable's value by side effect x = tf.get_variable('x', [], dtype=tf.int32) a = tf.constant(1) assign = tf.assign(x, a) sess = tf.Session() sess.run(assign) print(sess.run(x)) # sess with block x = tf.get_variable('x', shape=(3,5), initializer=tf.constant_initializer(0)) y = tf.get_variable('y', shape=(3,5), initializer=tf.constant_initializer(0)) f = x + y with tf.Session() as sess: x.initializer.run() y.initializer.run() print(f.eval()) x = tf.get_variable('x', shape=(3,5), initializer=tf.constant_initializer(0)) y = tf.get_variable('y', shape=(3,5), initializer=tf.constant_initializer(0)) f = x * x g = f + y with tf.Session() as sess: x.initializer.run() y.initializer.run() f_val, g_val = sess.run([f, g]) # eval f and g in one run print(f_val, g_val) # interative session x = tf.get_variable('x', [], initializer=tf.constant_initializer(3)) f = x * x sess = tf.InteractiveSession() x.initializer.run() print(f.eval()) # global initializer x = tf.get_variable('x', shape=[], initializer=tf.constant_initializer(1)) y = tf.get_variable('y', shape=[], initializer=tf.constant_initializer(2)) f = x + y init = tf.global_variables_initializer() with tf.Session() as sess: init.run() print(f.eval()) # name scope x = tf.get_variable('x', [], , initializer=tf.constant_initializer(1)) with tf.variable_scope('scope'): y = tf.get_variable('x', [], , initializer=tf.constant_initializer(2)) print(x.name, y.name) # get graph default_graph = tf.get_default_graph() print(default_graph) new_graph = tf.Graph() with new_graph.as_default(): x = tf.Variable(2) print(x.graph) Save and Load a Model # save a model x = tf.get_variable('x', []) y = tf.get_variable('y', []) init = tf.global_variables_initializer() # define the saver after every variable is defined saver = tf.train.Saver() sess = tf.Session() sess.run(init) # save the model after run saver.save(sess, './models/test-model') # load a model x = tf.get_variable('x', []) y = tf.get_variable('y', []) saver = tf.train.Saver() sess = tf.Session() saver.restore(sess, './models/test-model') # no need to init sess.run([x, y]) Optimization # autodiff x = tf.get_variable('x', [], initializer=tf.constant_initializer(3.)) y = tf.get_variable('y', [], initializer=tf.constant_initializer(2.)) f = y*x + x*x + 3*x grad = tf.gradients(f, [x, y]) sess = tf.Session() sess.run(x.initializer) sess.run(y.initializer) print(sess.run(grad)) # return [df/dx, df/dy] # example k = tf.get_variable('k', [], initializer=tf.constant_initializer(0.)) b = tf.get_variable('b', [], initializer=tf.constant_initializer(0.)) init = tf.global_variables_initializer() x = tf.placeholder(tf.float32) y = tf.placeholder(tf.float32) y_pred = k * x + b loss = tf.square(y - y_pred) optimizer = tf.train.GradientDescentOptimizer(1e-3) train_op = optimizer.minimize(loss) sess = tf.Session() sess.run(init) import random true_k = random.random() true_b = random.random() for update_i in range(10000): input_data = random.random() output_data = random.random() _loss, _ = sess.run([loss, train_op], feed_dict={x: input_data, y: output_data}) print(update_i, _loss) print('True parameter: k={}, b={}'.format(true_k, true_b)) print('True parameter: k={}, b={}'.format(sess.run([m, b]))) Tensorboard from datetime import datetime now = datetime.now().strftime("%Y%m%d%H%M%S") root_logdir = "tf_logs" logdir = "{}/run-{}/".format(root_logdir, now) # end of construction phase mse_summary = tf.summary.scalar('MSE', mse) file_writer = tf.summary.FileWriter(logdir, tf.get_default_graph()) # execution phase for batch_index in range(n_batches): X_batch, y_batch = fetch_batch(epoch, batch_index, batch_size) if batch_index % 10 == 0: summary_str = mse_summary.eval(feed_dict={X: X_batch, y: y_batch}) step = epoch * n_batches + batch_index file_writer.add_summary(summary_str, step) sess.run(training_op, feed_dict={X: X_batch, y: y_batch}) # finally file_writer.close() # start the Tensorboard service !tensorboard --logdir tf_logs/ Keras sequential model import keras from keras.models import Sequential from keras.layers import Dense (x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data() y_train = keras.utils.to_categorical(y_train) model = Sequential() model.add(Dense(100, activation='relu', input_shape=(28*28,))) model.add(Dense(100, activation='relu')) model.add(Dense(10, activation='softmax')) model.compile(loss='categorical_crossentropy', optimizer='rmsprop', metrics=['acc']) history = model.fit(x_train.reshape(-1, 28*28), y_train, validation_split=0.1, batch_size=32, epochs=10) general model import keras from keras.layers import Input, Dense from keras.models import Model (x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data() y_train = keras.utils.to_categorical(y_train) input_tensor = Input(shape=(28*28, )) x = Dense(100, activation='relu')(input_tensor) x = Dense(100, activation='relu')(x) output_tensor = Dense(10, activation='softmax')(x) model = Model(input_tensor, output_tensor) model.compile(loss='categorical_crossentropy', optimizer='rmsprop', metrics=['acc']) history = model.fit(x_train.reshape(-1, 28*28), y_train, validation_split=0.1, batch_size=32, epochs=10) Use Keras Layers in Tensorflow import tensorflow as tf from tensorflow import keras from tensorflow.keras.layers import Dense x = tf.placeholder(name='x', shape=(None, 28*28), dtype=tf.float32) hidden1 = Dense(100, activation='relu')(x) hidden2 = Dense(100, activation='relu')(hidden1) output = Dense(10)(hidden2) y = tf.placeholder(tf.int64, shape=(None,)) xentropy = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=y, logits=output) loss = tf.reduce_mean(xentropy, name="loss") # train with tensorflow # ... Use Keras Model in Tensorflow import tensorflow as tf from tensorflow import keras from tensorflow.keras.models import Sequential from tensorflow.keras.layers import Dense model = Sequential() model.add(Dense(100, activation='relu', input_shape=(28*28,))) model.add(Dense(10, activation='softmax')) x = tf.placeholder(name='input', shape=(None, 28*28), dtype=tf.float32) y = model(x) Check GPU # check if tensorflow is gpu version pip list | grep tensorflow from keras import backend as K K.tensorflow_backend._get_available_gpus() from tensorflow.python.client import device_lib print(device_lib.list_local_devices()) import tensorflow as tf sess = tf.Session(config=tf.ConfigProto(log_device_placement=True)) with tf.device('/gpu:0'): a = tf.constant([1., 2., 3.], shape=[1,3], name='a') b = tf.constant([1., 2., 3.], shape=[3,1], name='b') c = tf.matmul(a, b) with tf.Session() as sess: print(sess.run(c))

References vimtutor Github - spf13-vim openvim - Tutorial Modes Normal Mode: ESC Insert Mode: i Replace Mode: R Visual Mode: v Command Mode: : Commands Quit: :q Save: :w Save and quit: :wq Quit without save: :q! Open file for editing: :e {filename} Show open buffers: :ls 切换不同buffer: :b {filename} :b1: 切换至第1个buffer 关闭当前buffer: :bw Help: :help {topic} opens help for the :w command: :help :w opens help for the w movement: :help w jump from one window to another: Ctrl-W+Ctrl-w Shell command: :! :!ls: list files Retrieve from file: :r {filename} Retrieve from command: :r !ls Set option: :set {option} Ignore case: :set ic 关掉选项，前面加’no’: :set noic Highlight search: :set hls/:nohlsearch 清除上次搜索结果: :noh Command completion: Ctrl-D, or TAB Normal mode Movement Basic movement: hjkl (left, down, up, right) Words: w (next word) b (beginning of word) e (end of word) Lines: 0 (beginning of line) ^ (first non-blank character) $ (end of line) Screen: H (top of screen) M (middle of screen) L (bottom of screen) Scroll: Ctrl-U (up)/Ctrl-D (down)：Scroll half a page Ctrl-B (up)/Ctrl-F (down)：Scroll a page Percentage jump 30% File: gg (beginning of file) G (end of file) Line numbers: :{number} or {number}G Find: f{character}, t{character}, F{character}, T{character} find/to forward/backward {character} on the current line , / ; for navigating matches Search: `/{regex}`` n / N for navigating matches ? : search in backward direction /ignore\c: ignore case 跳到对应括号: % (corresponding item) Edits insert: i: insert a: append A: append at the end of line insert line below / above: o/O delete: d{motion} dw: delete word d$/D: delete to end of line d0: delete to beginning of line dd: delete line change: c{motion} delete character: x substitude character: s :s/old/new/: substitute ’new’ for the first ‘old’ :s/old/new/g: substitute ’new’ for ‘old’ globally in the line :s/old/new/gc: with prompt :5,10s/old/new/g replace character: r R: 进入replace mode，连续替换字符 undo: u undo line: U redo: Ctrl-R copy (yank): y paste: p filp the case: ~ code complete Ctrl-N: Forward Ctrl-P: Backward Counts 3w move 3 words forward 5j move 5 lines down 7dw/d7w delete 7 words Misc 查看所在位置：Ctrl-G Visual Mode Visual: v Visual Line: V Visual Block: Ctrl-V Insert in Visual Mode: Shift-I Windows Split window: :sp/:vsp :sp {filename}: split window with file Ctrl-W+s/ Ctrl-W+v Move left/down/up/right: Ctrl-W+h/j/k/l, Move to next window: Ctrl-W+w Plugins Vim Awesome ctrlp.vim ctrlp.vim NERDTree NERDTree

Matplotlib Basic Import from matplotlib import pyplot as plt Build figure fig = plt.figure(1) fig = plt.figure(1, figsize=(10,10)) # set figure size Tighten the layout fig.tight_layout() Build subplots ax = plt.subplot(111) ax = plt.subplot(211) # build two subplots and select the left one ax = plt.subplot(111, projection='polar') # build polar subplot Draw graphs ax.plot() ax.bar() ax.hist() ax.scatter() ax.plot_date() Show figure fig.show() Clear figure fig.clf() Save figure plt.savefig('path/name.png') Legend & Label & Tick & Grid # title ax.set_title('plot', fontsize=20) # label ax.set_xlabel('Threshold (m/s)') ax.set_ylabel('Strom periods (hours)') # ticks ax.set_xticks(np.arange(0, 1.1, 0.1)) ax.set_yticks(np.arange(0, 1.1, 0.1)) ax.set_xticklabels(labels, size=9, rotation=15) # axis limits plt.xlim(0, 1) # or ax.set_xlim(0, 1) # grid ax.grid(True) ax.grid(False) ax.yaxis.grid(True) # legend ax.plot(xx, yy, label='plot1') ax.legend(loc='lower left', frameon=False, fontsize=12) # or ax.legend(['line1', 'line2']) Two y-axis ...

VSCode Shortcuts 设置-Settings: ⌘, 命令-Command Palette: ⇧⌘P Preference Color theme Git Format View Close All Editors 快速打开-Quick Open: ⌘P go to line: : 跳转-Go to Symbol: in file: ⇧⌘O in workspace: ⌘T 重命名-Rename Symbol: F2 移动到侧边栏-Move to Explorer Window: ⇧⌘E Markdown预览-Markdown Preview: ⇧⌘V Views Sidebar: ⌘B Panel: ⌘J Teminal: ⌃` Errors and Warnings: ⇧⌘M cycle through errors: F8/⇧F8 Extensions: ⇧⌘X Side by Side Editing: ⌘\ Editing Multi Cursor Selection ⌥⌘↑ or ⌥⌘↓ Option + Click / Option + Shift + Click Select All Occurences: ⇧⌘L Select Next Occurence: ⌘D Select Currnet Line: ⌘L Delete Current Line: ⇧⌘K Copy Line Up/Down: ⇧⌥↑ or ⇧⌥↓ Move Line Up/Down: ⌥↑ or ⌥↓ Shrink / Expand Selection: ⌃⇧⌘← or ⌃⇧⌘→ Rename Symbol: F2 Code Formatting: ⇧⌥F Refactor: ⌃⇧R Extract variable Extract method Goto Go to Definition F12 Cmd + Click Go to References ⇧F12 All references: ⇧⌥F12 Next Error F8 Navigate Back Ctrl + - Search Search: ⌘F Replace: ⌥⌘F Regular Expression: ⌥⌘R Commands Settings ...

Prerequisites Webpack 将 js 代码打包到一个文件中 安装：npm install webpack -g 使用：webpack <input> <output> vue cli（deprecated） Vue CLI the official webpack-based toolchain for Vue. It is now in maintenance mode and we recommend starting new projects with Vite unless you rely on specific webpack-only features. 安装：npm install -g @vue/cli 创建项目：vue create my-project # serve vue-cli-service serve --host 0.0.0.0 --port 8080 # build vue-cli-service build # lint vue-cli-service lint vite Vue.js - Tooling ...

Music Music for Programming Geo exping - Express anything on map NASA’s Eyes Earth Cam City Walks

n8n Install：Self-hosting n8n docker volume create n8n_data docker run -d \ --name n8n \ --network host \ -v n8n_data:/home/node/.n8n \ -e TZ="Asia/Shanghai" \ -e N8N_SECURE_COOKIE=false \ docker.n8n.io/n8nio/n8n Triggers Mannual Form Action Send Email Gmail SMTP 配置： User：<your_email>@gmail.com Password： 设置 app passwords：https://myaccount.google.com/u/3/apppasswords Host：smtp.gmail.com Port：465 SSL/TLS：true Code Python: 不支持发送 HTTP 请求 不支持访问文件系统 不支持自己安装第三方包，只支持 Pyodide 内置的包 windmill Github - windmill Perfect Github - Perfect

Zookeeper 配置 tickTime=2000 initLimit=10 syncLimit=5 dataDir=zookeeper/data dataLogDir=zookeeper/logs clientPort=2181 server.1=host1:2888:3888 server.2=host2:2888:3888 server.3=host3:2888:3888 #server.1 这个1是服务器的标识也可以是其他的数字， 表示这个是第几号服务器，用来标识服务器，这个标识要写到快照目录下面myid文件里 #host1为集群里的IP地址，第一个端口是master和slave之间的通信端口，默认是2888，第二个端口是leader选举的端口，集群刚启动的时候选举或者leader挂掉之后进行新的选举的端口默认是3888 Command Line Tools # start server bin/zkServer.sh start/start-foreground/stop/status # client bin/zkCli.sh -server 127.0.0.1:2181 create /MyFirstZNode ZNodeVal get /MyFirstZNode set /MyFirstZNode ZNodeValUpdated Maven <dependency> <groupId>org.apache.zookeeper</groupId> <artifactId>zookeeper</artifactId> <version>3.4.11</version> </dependency> Source Code Zookeeper框架设计及源码解读 References Getting Started with Java and Zookeeper Explaining Apache ZooKeeper