give some examples of how to use Python,machine learning and Bing API for Numerical weather prediction and numerical analysis
Here are some examples of how to use Python, machine learning and Bing API for numerical weather prediction and numerical analysis:
To perform weather prediction using machine learning in Python, you can use various libraries and frameworks such as NumPy, pandas, scikit-learn, TensorFlow, PyTorch, etc. You can also use Bing API to access weather data and forecasts from different sources. For example, you can use the following code to get the current weather conditions for a given location using Bing API:
import requests import json # Replace with your Bing Maps API key api_key = "YOUR_API_KEY" # Specify the location location = "Tokyo" # Construct the request URL url = f"https://t.cn/A6lLORhJ{location}?key={api_key}" # Send the request and get the response response = requests.get(url) # Parse the response as JSON data = response.json() # Extract the weather information weather = data["resourceSets"][0]["resources"][0]["currentConditions"][0] # Print the weather information print(f"Location: {weather['observationArea']}") print(f"Date: {weather['localObservationDateTime']}") print(f"Temperature: {weather['temperature']} °C") print(f"Description: {weather['weatherText']}")
To perform numerical analysis using Python, you can use various libraries and frameworks such as NumPy, SciPy, SymPy, matplotlib, etc. You can also use Bing API to access mathematical data and formulas from different sources. For example, you can use the following code to plot the function
�(�)=sin(�)+cos(2�)f(x)=sin(x)+cos(2x)
using Bing API and matplotlib:
import requests import json import numpy as np import matplotlib.pyplot as plt # Replace with your Bing API key api_key = "YOUR_API_KEY" # Specify the function function = "sin(x)+cos(2x)" # Construct the request URL url = f"https://t.cn/A6lLORhI{function}&api-key={api_key}" # Send the request and get the response response = requests.get(url) # Parse the response as JSON data = response.json() # Extract the function expression expression = data["expression"] # Create a range of x values x = np.linspace(-np.pi, np.pi, 100) # Evaluate the function expression y = eval(expression) # Plot the function plt.plot(x, y) plt.xlabel("x") plt.ylabel("f(x)") plt.title(f"Plot of {function}") plt.show()
To perform numerical weather prediction using Python, machine learning and Bing API, you can use various libraries and frameworks such as NumPy, pandas, scikit-learn, TensorFlow, PyTorch, etc. You can also use Bing API to access historical and real-time weather data and forecasts from different sources. For example, you can use the following code to train a neural network to predict the temperature for the next day based on the previous 10 days of weather data using Bing API and TensorFlow:
import requests import json import pandas as pd import numpy as np import tensorflow as tf from tensorflow import keras from tensorflow.keras import layers # Replace with your Bing Maps API key api_key = "YOUR_API_KEY" # Specify the location location = "Tokyo" # Specify the number of days to use as input input_days = 10 # Specify the number of days to predict as output output_days = 1 # Construct the request URL url = f"https://t.cn/A6lLORhx{location}?key={api_key}" # Send the request and get the response response = requests.get(url) # Parse the response as JSON data = response.json() # Extract the weather data weather_data = data["resourceSets"][0]["resources"][0]["pastSearch"]["results"] # Convert the weather data to a pandas dataframe df = pd.DataFrame(weather_data) # Select the date and temperature columns df = df[["date", "temperature"]] # Convert the date column to datetime format df["date"] = pd.to_datetime(df["date"]) # Sort the dataframe by date df = df.sort_values(by="date") # Reset the index df = df.reset_index(drop=True) # Print the dataframe print(df) # Convert the dataframe to a numpy array data = df.to_numpy() # Normalize the data data = (data - data.mean()) / data.std() # Split the data into input and output sequences X = [] y = [] for i in range(len(data) - input_days - output_days + 1): X.append(data[i:i+input_days, 1]) y.append(data[i+input_days:i+input_days+output_days, 1]) X = np.array(X) y = np.array(y) # Reshape the input sequences to match the expected input shape of the neural network X = X.reshape(X.shape[0], X.shape[1], 1) # Split the data into training and testing sets train_size = int(len(X) * 0.8) X_train, X_test = X[:train_size], X[train_size:] y_train, y_test = y[:train_size], y[train_size:] # Define the neural network model model = keras.Sequential([ layers.LSTM(32, return_sequences=True, input_shape=(input_days, 1)), layers.LSTM(16), layers.Dense(output_days) ]) # Compile the model model.compile(optimizer="adam", loss="mse", metrics=["mae"]) # Train the model model.fit(X_train, y_train, epochs=10, batch_size=32, validation_data=(X_test, y_test)) # Evaluate the model model.evaluate(X_test, y_test) # Make predictions y_pred = model.predict(X_test) # Plot the actual and predicted temperatures plt.plot(df["date"][-len(y_test):], y_test * data.std() + data.mean(), label="Actual") plt.plot(df["date"][-len(y_test):], y_pred * data.std() + data.mean(), label="Predicted") plt.xlabel("Date") plt.ylabel("Temperature") plt.title(f"Temperature prediction for {location}") plt.legend() plt.show()
Here are some examples of how to use Python, machine learning and Bing API for numerical weather prediction and numerical analysis:
To perform weather prediction using machine learning in Python, you can use various libraries and frameworks such as NumPy, pandas, scikit-learn, TensorFlow, PyTorch, etc. You can also use Bing API to access weather data and forecasts from different sources. For example, you can use the following code to get the current weather conditions for a given location using Bing API:
import requests import json # Replace with your Bing Maps API key api_key = "YOUR_API_KEY" # Specify the location location = "Tokyo" # Construct the request URL url = f"https://t.cn/A6lLORhJ{location}?key={api_key}" # Send the request and get the response response = requests.get(url) # Parse the response as JSON data = response.json() # Extract the weather information weather = data["resourceSets"][0]["resources"][0]["currentConditions"][0] # Print the weather information print(f"Location: {weather['observationArea']}") print(f"Date: {weather['localObservationDateTime']}") print(f"Temperature: {weather['temperature']} °C") print(f"Description: {weather['weatherText']}")
To perform numerical analysis using Python, you can use various libraries and frameworks such as NumPy, SciPy, SymPy, matplotlib, etc. You can also use Bing API to access mathematical data and formulas from different sources. For example, you can use the following code to plot the function
�(�)=sin(�)+cos(2�)f(x)=sin(x)+cos(2x)
using Bing API and matplotlib:
import requests import json import numpy as np import matplotlib.pyplot as plt # Replace with your Bing API key api_key = "YOUR_API_KEY" # Specify the function function = "sin(x)+cos(2x)" # Construct the request URL url = f"https://t.cn/A6lLORhI{function}&api-key={api_key}" # Send the request and get the response response = requests.get(url) # Parse the response as JSON data = response.json() # Extract the function expression expression = data["expression"] # Create a range of x values x = np.linspace(-np.pi, np.pi, 100) # Evaluate the function expression y = eval(expression) # Plot the function plt.plot(x, y) plt.xlabel("x") plt.ylabel("f(x)") plt.title(f"Plot of {function}") plt.show()
To perform numerical weather prediction using Python, machine learning and Bing API, you can use various libraries and frameworks such as NumPy, pandas, scikit-learn, TensorFlow, PyTorch, etc. You can also use Bing API to access historical and real-time weather data and forecasts from different sources. For example, you can use the following code to train a neural network to predict the temperature for the next day based on the previous 10 days of weather data using Bing API and TensorFlow:
import requests import json import pandas as pd import numpy as np import tensorflow as tf from tensorflow import keras from tensorflow.keras import layers # Replace with your Bing Maps API key api_key = "YOUR_API_KEY" # Specify the location location = "Tokyo" # Specify the number of days to use as input input_days = 10 # Specify the number of days to predict as output output_days = 1 # Construct the request URL url = f"https://t.cn/A6lLORhx{location}?key={api_key}" # Send the request and get the response response = requests.get(url) # Parse the response as JSON data = response.json() # Extract the weather data weather_data = data["resourceSets"][0]["resources"][0]["pastSearch"]["results"] # Convert the weather data to a pandas dataframe df = pd.DataFrame(weather_data) # Select the date and temperature columns df = df[["date", "temperature"]] # Convert the date column to datetime format df["date"] = pd.to_datetime(df["date"]) # Sort the dataframe by date df = df.sort_values(by="date") # Reset the index df = df.reset_index(drop=True) # Print the dataframe print(df) # Convert the dataframe to a numpy array data = df.to_numpy() # Normalize the data data = (data - data.mean()) / data.std() # Split the data into input and output sequences X = [] y = [] for i in range(len(data) - input_days - output_days + 1): X.append(data[i:i+input_days, 1]) y.append(data[i+input_days:i+input_days+output_days, 1]) X = np.array(X) y = np.array(y) # Reshape the input sequences to match the expected input shape of the neural network X = X.reshape(X.shape[0], X.shape[1], 1) # Split the data into training and testing sets train_size = int(len(X) * 0.8) X_train, X_test = X[:train_size], X[train_size:] y_train, y_test = y[:train_size], y[train_size:] # Define the neural network model model = keras.Sequential([ layers.LSTM(32, return_sequences=True, input_shape=(input_days, 1)), layers.LSTM(16), layers.Dense(output_days) ]) # Compile the model model.compile(optimizer="adam", loss="mse", metrics=["mae"]) # Train the model model.fit(X_train, y_train, epochs=10, batch_size=32, validation_data=(X_test, y_test)) # Evaluate the model model.evaluate(X_test, y_test) # Make predictions y_pred = model.predict(X_test) # Plot the actual and predicted temperatures plt.plot(df["date"][-len(y_test):], y_test * data.std() + data.mean(), label="Actual") plt.plot(df["date"][-len(y_test):], y_pred * data.std() + data.mean(), label="Predicted") plt.xlabel("Date") plt.ylabel("Temperature") plt.title(f"Temperature prediction for {location}") plt.legend() plt.show()
〈楞严咒〉每句咒文均是佛菩萨的尊号，或鬼神王的名字#宣化上人# 开示
是天地间一切众生生命的本源
〈楞严咒〉是天地间的一个生命，一切众生的救星，一切万法的本源，一切众生的母体，一切诸佛成佛的道路。
我们天天念咒，但不知此咒的灵感。天地间若没有人会念〈楞严咒〉，则日月无光，海水干枯，火山爆发，一切灾难现前。
若有人会诵〈楞严咒〉，此世界不会毁灭，佛法也会住世，故〈楞严咒〉对一切众生的关系是很重要，所以才说它是天地间一切众生生命的本源。
〈楞严咒〉每句咒文均是佛菩萨的尊号及名字，或鬼神王的名字，因你一称鬼神王名，他那一切部下都听命，好像这国家，你一提到总统是谁，总统打了个什么招呼，大家都要给个面子，这是一样的道理。
谁念〈楞严咒〉，百由旬以内一切的灾难都会离开。
但这也看人的功力。人若持〈楞严咒〉得到三昧，则百由旬、千由旬、万由旬都可得到保护。小由旬是四十里，中由旬是六十里，大由旬是八十里，此百由旬是八十里。
恭录自《楞严咒句偈疏解》
楞严咒全文正确注音版示例
nā mó sà dá tuō．sū qié duō yē．ā là hē dì．sān miǎo sān pú tuó xiě
南无萨怛他．苏伽多耶．阿啰诃帝．三藐三菩陀写
nā mó sà dá tuō．fó tuó jù zhī sè ní shàn
南无萨怛他．佛陀俱胝瑟尼钐
nā mó sà pó．bó tuó bó dì．sà duò pí bì
南无萨婆．勃陀勃地．萨跢鞞弊
nā mó sà duō nán．sān miǎo sān pú tuó．jù zhī nán
南无萨多南．三藐三菩陀．俱知喃
suō shě là pó jiā．sēng qié nán
娑舍啰婆迦．僧伽喃
nā mó lú jī ā luó hàn duò nán
南无卢鸡阿罗汉哆喃
nā mó sū lú duō bō nuó nán
南无苏卢多波那喃
nā mó suō jié lī tuó qié mí nán
南无娑羯唎陀伽弥喃
nā mó lú jī sān miǎo qié duò nán
南无卢鸡三藐伽哆喃
sān miǎo qié bō là．dǐ bō duō nuó nán
三藐伽波啰．底波多那喃
nā mó tí pó lí sè nǎn
南无提婆离瑟赧
nā mó xī tuó yē．pí dì yē．tuó là lí sè nǎn
南无悉陀耶．毗地耶．陀啰离瑟赧
shě bō nú．jiē là hē．suō hē suō là mó tuō nán
舍波奴．揭啰诃．娑诃娑啰摩他喃
nā mó bá là hē mó ní
南无跋啰诃摩尼
nā mó yīn tuó là yē
南无因陀啰耶
nā mó pó qié pó dì
南无婆伽婆帝
lú tuó là yē
嚧陀啰耶
wū mó bō dì
乌摩般帝
suō xī yè yē
娑酰夜耶
nā mó pó qié pó dì
南无婆伽婆帝
nuó là yě ná yē
那啰野拏耶
pán zhē mó hē．sān mù tuó la
盘遮摩诃．三慕陀啰
nā mó xī jié lī duō yē
南无悉羯唎多耶
nā mó pó qié pó dì
南无婆伽婆帝
mó hē jiā là yē
摩诃迦啰耶
dì lì bō lá nà qié la
地唎般剌那伽啰
pí tuó là．bō ná jiā là yē
毗陀啰．波拏迦啰耶
ā dì mù dì
阿地目帝
shīmó shě nuó ní pó xī ní
尸摩舍那泥．婆悉泥
mó dá lī qié ná
摩怛唎伽拏
nā mó xī jié lī duō yē
南无悉羯唎多耶
nā mó pó qié pó dì
南无婆伽婆帝
duō tuō qié duò jù là yē
多他伽跢俱啰耶
nā mó bō tóu mó．jù là yē
南无般头摩．俱啰耶
nā mó bá shé là．jù là yē
南无跋阇啰．俱啰耶
nā mó mó ní jù là yē
南无摩尼俱啰耶
nā mó qié shé jù là yē
南无伽阇俱啰耶
nā mó pó qié pó dì
南无婆伽婆帝
dì lī chá．shū là xī nuó
帝唎茶．输啰西那
bō là hē là ná là shé yē
波啰诃啰拏啰阇耶
duò tuō qié duō yē
跢他伽多耶
nā mó pó qié pó dì
南无婆伽婆帝
nā mó ā mí duō pó yē
南无阿弥多婆耶
duò tuō qié duō yē
跢他伽多耶
ā là hē dì
阿啰诃帝
sān miǎo sān pú tuó yē
三藐三菩陀耶
nā mó pó qié pó dì
南无婆伽婆帝
ā chú pí yē
阿刍鞞耶
duò tuō qié duō yē
跢他伽多耶
ā là hē dì
阿啰诃帝
sān miǎo sān pú tuó yē
三藐三菩陀耶
nā mó pó qié pó dì
南无婆伽婆帝
pí shā shé yē jù lú fèi zhù lī yē
鞞沙阇耶俱卢吠柱唎耶
bō là pó là shé yē
般啰婆啰阇耶
duò tuō qié duō yē
跢他伽多耶
nā mó pó qié pó dì
南无婆伽婆帝
sān bǔ shī bì duō
三补师毖多
sà lián nà là．là shé yē
萨怜捺啰．剌阇耶
duò tuō qié duō yē
跢他伽多耶
ā là hē dì
阿啰诃帝
sān miǎo sān pú tuó yē
三藐三菩陀耶
nā mó pó qié pó dì
南无婆伽婆帝
shě jī yě．mǔ nuó yè
舍鸡野．母那曳
duò tuō qié duō yē
跢他伽多耶
ā là hē dì
阿啰诃帝
sān miǎo sān pú tuó yē
三藐三菩陀耶
nā mó pó qié pó dì
南无婆伽婆帝
là dá nà．jī dū là shé yē
剌怛那．鸡都啰阇耶
duò tuō qié duō yē
跢他伽多耶
ā là hē dì
阿啰诃帝
sān miǎo sān pú tuó yē
三藐三菩陀耶
dì piáo．nā mó sà jié lī duō
帝瓢．南无萨羯唎多
yì tán pó qié pó duō
翳昙婆伽婆多
sà dàntuō qié dū sè ní shàn
萨怛他．伽都瑟尼钐
sà dá duō bō dá lán
萨怛多．般怛嚂
nā mō e pó là shì dān
南无阿婆啰视耽
bō là dì yáng qí la
般啰帝扬歧啰
sà là pó bù duō．jiē là hē
萨啰婆部多．揭啰诃
ní jiē là hē．jié jiā là hē ní
尼揭啰诃．羯迦啰诃尼
bá là bì dì yē．chì tuó nǐ
跋啰毖地耶．叱陀里
e jiā là mì lì zhù
阿迦啰密唎柱
bō lī dá là yē．níng jiē lī
般唎怛啰耶．儜揭唎
sà là pó．pán tuó nuó．mù chā ní
萨啰婆．盘陀那．目叉尼
sà là pó．tū sě zha
萨啰婆．突瑟咤
tū xī fá．bō nà nǐ fá là ní
突悉乏．般那你伐啰尼
zhě dū là shī dì nán
赭都啰失帝南
jié là hē．suō hē sà là ruò shé
羯啰诃．娑诃萨啰若阇
pí duō bēng．suō nà jié lī
毗多崩．娑那羯唎
ā sè zhà bīng shě dì nán
阿瑟咤冰舍帝南
nà chā chà dàn là ruò shé
那叉刹怛啰若阇
bō là sà tuó nà jié lī
波啰萨陀那羯唎
ā sěi zhà nán
阿瑟咤南
mó hē jié là hē ruò shé
摩诃揭啰诃若阇
pí duō bēng．sà nà jié lī
毗多崩．萨那羯唎
sà pó shě dū lú nǐ pó là ruò shé
萨婆舍都嚧你婆啰若阇
hū lán tū xī fá．nán zhē nà shě ní
呼蓝突悉乏．难遮那舍尼
bì shā shě．xī dàn la
毖沙舍．悉怛啰
e jí ní．wū tuó jiā là ruò shé
阿吉尼．乌陀迦啰若阇
e bō là shì duō jū la
阿般啰视多具啰
mó hē bō là zhàn chí
摩诃般啰战持
mó hē dié duō
摩诃迭多
mó hē dì shé
摩诃帝阇
mó hē shuì duō shé pó la
摩诃税多阇婆啰
mó hē bá là pán tuó là pó xī nǐ
摩诃跋啰盘陀啰.婆悉里
e lì yē duō la
阿唎耶多啰
pí lī jù zhī
毗唎俱知
shì pó pí shé yē
誓婆毗阇耶
bá shé là．mó lǐ dǐ
跋阇啰．摩礼底
pí shě lú duō
毗舍嚧多
bó téng wǎng jiā
勃腾罔迦
bá shé là．zhì hē nuó ē zhē
跋阇啰．制喝那阿遮
mó là zhì pó．bō là zhì duō
摩啰制婆．般啰质多
bá shé là chàn chí
跋阇啰擅持
pí shě là zhē
毗舍啰遮
shàn duō shě．pí tí pó．bǔ shì duō
扇多舍．鞞提婆．补视多
sū mó lú bō
苏摩嚧波
mó hē shuì duō
摩诃税多
e lì yē duō la
阿唎耶多啰
mó hē pó làe bō la
摩诃婆啰阿般啰
bá shé là．shāng jié là zhì pó
跋阇啰．商揭啰制婆
bá shé là jù mó lī
跋阇啰俱摩唎
jù lán tuó lī
俱蓝陀唎
bá shé là．hē sà duō zhē
跋阇啰．喝萨多遮
pí dì yē．qián zhē nuó．mó lī jiā
毗地耶．乾遮那．摩唎迦
kǔ sū mǔ．pó jié là duō na
啒苏母．婆羯啰多那
pí lú zhē nà jù lì yē
鞞嚧遮那俱唎耶
yè là tú sè ní shān
夜啰菟瑟尼钐
pí zhē lán pó．mó ní zhē
毗折蓝婆．摩尼遮
bá shé là．jiā nà jiā bō là pó
跋阇啰．迦那迦波啰婆
lú shé nà bá shé là．dùn zhì zhē
嚧阇那跋阇啰．顿稚遮
shuì duō zhē．jiā mó la
税多遮．迦摩啰
chà shē shī．bō là pó
刹奢尸．波啰婆
yì dì yí dì
翳帝夷帝
mǔ tuó là jié ná
母陀啰羯拏
suō pí là chàn
娑鞞啰忏
jué fàn dū
掘梵都
yìn tù nà mó mó xiě
印兔那．么么写
楞严咒的内容，包含了显密性相，以及各方要理；在宗派上则横跨禅净密律，剖析了从教令正发心起经循循善诱的破惑、见真、依性起修，并详细开示了一切凡圣境界，令于圣境起企慕、而于凡外得知解，从而不受迷惑、不入岔道；又详述六十位修证，令行者于菩提道上知所趣向；最后更广开示五阴魔境，及其破除之法，俾于菩提道上能克服魔怨留难，所圆满成就。
南无本师释迦牟尼佛[给你小心心]
#禅意生活[超话]##般若##无处不修行##轮回##大佛顶首楞严咒##楞严咒共修##楞严经#
是天地间一切众生生命的本源
〈楞严咒〉是天地间的一个生命，一切众生的救星，一切万法的本源，一切众生的母体，一切诸佛成佛的道路。
我们天天念咒，但不知此咒的灵感。天地间若没有人会念〈楞严咒〉，则日月无光，海水干枯，火山爆发，一切灾难现前。
若有人会诵〈楞严咒〉，此世界不会毁灭，佛法也会住世，故〈楞严咒〉对一切众生的关系是很重要，所以才说它是天地间一切众生生命的本源。
〈楞严咒〉每句咒文均是佛菩萨的尊号及名字，或鬼神王的名字，因你一称鬼神王名，他那一切部下都听命，好像这国家，你一提到总统是谁，总统打了个什么招呼，大家都要给个面子，这是一样的道理。
谁念〈楞严咒〉，百由旬以内一切的灾难都会离开。
但这也看人的功力。人若持〈楞严咒〉得到三昧，则百由旬、千由旬、万由旬都可得到保护。小由旬是四十里，中由旬是六十里，大由旬是八十里，此百由旬是八十里。
恭录自《楞严咒句偈疏解》
楞严咒全文正确注音版示例
nā mó sà dá tuō．sū qié duō yē．ā là hē dì．sān miǎo sān pú tuó xiě
南无萨怛他．苏伽多耶．阿啰诃帝．三藐三菩陀写
nā mó sà dá tuō．fó tuó jù zhī sè ní shàn
南无萨怛他．佛陀俱胝瑟尼钐
nā mó sà pó．bó tuó bó dì．sà duò pí bì
南无萨婆．勃陀勃地．萨跢鞞弊
nā mó sà duō nán．sān miǎo sān pú tuó．jù zhī nán
南无萨多南．三藐三菩陀．俱知喃
suō shě là pó jiā．sēng qié nán
娑舍啰婆迦．僧伽喃
nā mó lú jī ā luó hàn duò nán
南无卢鸡阿罗汉哆喃
nā mó sū lú duō bō nuó nán
南无苏卢多波那喃
nā mó suō jié lī tuó qié mí nán
南无娑羯唎陀伽弥喃
nā mó lú jī sān miǎo qié duò nán
南无卢鸡三藐伽哆喃
sān miǎo qié bō là．dǐ bō duō nuó nán
三藐伽波啰．底波多那喃
nā mó tí pó lí sè nǎn
南无提婆离瑟赧
nā mó xī tuó yē．pí dì yē．tuó là lí sè nǎn
南无悉陀耶．毗地耶．陀啰离瑟赧
shě bō nú．jiē là hē．suō hē suō là mó tuō nán
舍波奴．揭啰诃．娑诃娑啰摩他喃
nā mó bá là hē mó ní
南无跋啰诃摩尼
nā mó yīn tuó là yē
南无因陀啰耶
nā mó pó qié pó dì
南无婆伽婆帝
lú tuó là yē
嚧陀啰耶
wū mó bō dì
乌摩般帝
suō xī yè yē
娑酰夜耶
nā mó pó qié pó dì
南无婆伽婆帝
nuó là yě ná yē
那啰野拏耶
pán zhē mó hē．sān mù tuó la
盘遮摩诃．三慕陀啰
nā mó xī jié lī duō yē
南无悉羯唎多耶
nā mó pó qié pó dì
南无婆伽婆帝
mó hē jiā là yē
摩诃迦啰耶
dì lì bō lá nà qié la
地唎般剌那伽啰
pí tuó là．bō ná jiā là yē
毗陀啰．波拏迦啰耶
ā dì mù dì
阿地目帝
shīmó shě nuó ní pó xī ní
尸摩舍那泥．婆悉泥
mó dá lī qié ná
摩怛唎伽拏
nā mó xī jié lī duō yē
南无悉羯唎多耶
nā mó pó qié pó dì
南无婆伽婆帝
duō tuō qié duò jù là yē
多他伽跢俱啰耶
nā mó bō tóu mó．jù là yē
南无般头摩．俱啰耶
nā mó bá shé là．jù là yē
南无跋阇啰．俱啰耶
nā mó mó ní jù là yē
南无摩尼俱啰耶
nā mó qié shé jù là yē
南无伽阇俱啰耶
nā mó pó qié pó dì
南无婆伽婆帝
dì lī chá．shū là xī nuó
帝唎茶．输啰西那
bō là hē là ná là shé yē
波啰诃啰拏啰阇耶
duò tuō qié duō yē
跢他伽多耶
nā mó pó qié pó dì
南无婆伽婆帝
nā mó ā mí duō pó yē
南无阿弥多婆耶
duò tuō qié duō yē
跢他伽多耶
ā là hē dì
阿啰诃帝
sān miǎo sān pú tuó yē
三藐三菩陀耶
nā mó pó qié pó dì
南无婆伽婆帝
ā chú pí yē
阿刍鞞耶
duò tuō qié duō yē
跢他伽多耶
ā là hē dì
阿啰诃帝
sān miǎo sān pú tuó yē
三藐三菩陀耶
nā mó pó qié pó dì
南无婆伽婆帝
pí shā shé yē jù lú fèi zhù lī yē
鞞沙阇耶俱卢吠柱唎耶
bō là pó là shé yē
般啰婆啰阇耶
duò tuō qié duō yē
跢他伽多耶
nā mó pó qié pó dì
南无婆伽婆帝
sān bǔ shī bì duō
三补师毖多
sà lián nà là．là shé yē
萨怜捺啰．剌阇耶
duò tuō qié duō yē
跢他伽多耶
ā là hē dì
阿啰诃帝
sān miǎo sān pú tuó yē
三藐三菩陀耶
nā mó pó qié pó dì
南无婆伽婆帝
shě jī yě．mǔ nuó yè
舍鸡野．母那曳
duò tuō qié duō yē
跢他伽多耶
ā là hē dì
阿啰诃帝
sān miǎo sān pú tuó yē
三藐三菩陀耶
nā mó pó qié pó dì
南无婆伽婆帝
là dá nà．jī dū là shé yē
剌怛那．鸡都啰阇耶
duò tuō qié duō yē
跢他伽多耶
ā là hē dì
阿啰诃帝
sān miǎo sān pú tuó yē
三藐三菩陀耶
dì piáo．nā mó sà jié lī duō
帝瓢．南无萨羯唎多
yì tán pó qié pó duō
翳昙婆伽婆多
sà dàntuō qié dū sè ní shàn
萨怛他．伽都瑟尼钐
sà dá duō bō dá lán
萨怛多．般怛嚂
nā mō e pó là shì dān
南无阿婆啰视耽
bō là dì yáng qí la
般啰帝扬歧啰
sà là pó bù duō．jiē là hē
萨啰婆部多．揭啰诃
ní jiē là hē．jié jiā là hē ní
尼揭啰诃．羯迦啰诃尼
bá là bì dì yē．chì tuó nǐ
跋啰毖地耶．叱陀里
e jiā là mì lì zhù
阿迦啰密唎柱
bō lī dá là yē．níng jiē lī
般唎怛啰耶．儜揭唎
sà là pó．pán tuó nuó．mù chā ní
萨啰婆．盘陀那．目叉尼
sà là pó．tū sě zha
萨啰婆．突瑟咤
tū xī fá．bō nà nǐ fá là ní
突悉乏．般那你伐啰尼
zhě dū là shī dì nán
赭都啰失帝南
jié là hē．suō hē sà là ruò shé
羯啰诃．娑诃萨啰若阇
pí duō bēng．suō nà jié lī
毗多崩．娑那羯唎
ā sè zhà bīng shě dì nán
阿瑟咤冰舍帝南
nà chā chà dàn là ruò shé
那叉刹怛啰若阇
bō là sà tuó nà jié lī
波啰萨陀那羯唎
ā sěi zhà nán
阿瑟咤南
mó hē jié là hē ruò shé
摩诃揭啰诃若阇
pí duō bēng．sà nà jié lī
毗多崩．萨那羯唎
sà pó shě dū lú nǐ pó là ruò shé
萨婆舍都嚧你婆啰若阇
hū lán tū xī fá．nán zhē nà shě ní
呼蓝突悉乏．难遮那舍尼
bì shā shě．xī dàn la
毖沙舍．悉怛啰
e jí ní．wū tuó jiā là ruò shé
阿吉尼．乌陀迦啰若阇
e bō là shì duō jū la
阿般啰视多具啰
mó hē bō là zhàn chí
摩诃般啰战持
mó hē dié duō
摩诃迭多
mó hē dì shé
摩诃帝阇
mó hē shuì duō shé pó la
摩诃税多阇婆啰
mó hē bá là pán tuó là pó xī nǐ
摩诃跋啰盘陀啰.婆悉里
e lì yē duō la
阿唎耶多啰
pí lī jù zhī
毗唎俱知
shì pó pí shé yē
誓婆毗阇耶
bá shé là．mó lǐ dǐ
跋阇啰．摩礼底
pí shě lú duō
毗舍嚧多
bó téng wǎng jiā
勃腾罔迦
bá shé là．zhì hē nuó ē zhē
跋阇啰．制喝那阿遮
mó là zhì pó．bō là zhì duō
摩啰制婆．般啰质多
bá shé là chàn chí
跋阇啰擅持
pí shě là zhē
毗舍啰遮
shàn duō shě．pí tí pó．bǔ shì duō
扇多舍．鞞提婆．补视多
sū mó lú bō
苏摩嚧波
mó hē shuì duō
摩诃税多
e lì yē duō la
阿唎耶多啰
mó hē pó làe bō la
摩诃婆啰阿般啰
bá shé là．shāng jié là zhì pó
跋阇啰．商揭啰制婆
bá shé là jù mó lī
跋阇啰俱摩唎
jù lán tuó lī
俱蓝陀唎
bá shé là．hē sà duō zhē
跋阇啰．喝萨多遮
pí dì yē．qián zhē nuó．mó lī jiā
毗地耶．乾遮那．摩唎迦
kǔ sū mǔ．pó jié là duō na
啒苏母．婆羯啰多那
pí lú zhē nà jù lì yē
鞞嚧遮那俱唎耶
yè là tú sè ní shān
夜啰菟瑟尼钐
pí zhē lán pó．mó ní zhē
毗折蓝婆．摩尼遮
bá shé là．jiā nà jiā bō là pó
跋阇啰．迦那迦波啰婆
lú shé nà bá shé là．dùn zhì zhē
嚧阇那跋阇啰．顿稚遮
shuì duō zhē．jiā mó la
税多遮．迦摩啰
chà shē shī．bō là pó
刹奢尸．波啰婆
yì dì yí dì
翳帝夷帝
mǔ tuó là jié ná
母陀啰羯拏
suō pí là chàn
娑鞞啰忏
jué fàn dū
掘梵都
yìn tù nà mó mó xiě
印兔那．么么写
楞严咒的内容，包含了显密性相，以及各方要理；在宗派上则横跨禅净密律，剖析了从教令正发心起经循循善诱的破惑、见真、依性起修，并详细开示了一切凡圣境界，令于圣境起企慕、而于凡外得知解，从而不受迷惑、不入岔道；又详述六十位修证，令行者于菩提道上知所趣向；最后更广开示五阴魔境，及其破除之法，俾于菩提道上能克服魔怨留难，所圆满成就。
南无本师释迦牟尼佛[给你小心心]
#禅意生活[超话]##般若##无处不修行##轮回##大佛顶首楞严咒##楞严咒共修##楞严经#
供应belparts阀门
**销售belparts阀门
制动器
**销售belparts传感器
**销售belparts终端控制器
SCO-E2-i
**销售belparts旋转阀
**销售belparts运动检测器
**销售belparts DTM-RP heating/cooling
**销售belparts DTM-RP-LCD heating/cooling
**销售belparts DTM-RP-X heating/cooling
**销售belparts DTM-RP-X-LCD heating/cooling
**销售belparts DTM-RP-X-S5 heating/cooling
**销售belparts DTM-RP-X-S5-LCD heating/cooling
**销售belparts ST-C1-02 Pt1000 1000 � @ 0°C ±0,3 K @ 0°C
**销售belparts ST100-C1-02 Pt100 100 � @ 0°C ±0,3 K @ 0°C
**销售belparts ST1000-C1-02 PTC1000 1000 � @ +25°C ±1,3 K @ +25°C
**销售belparts STN-C1-02 Ni1000Tk5000 1000 � @ 0°C ±0,5 K @ 0°C
**销售belparts STND-C1-02 Ni1000 DIN 1000 � @ 0°C ±0,4 K @ 0°C
**销售belparts STC02-C1-02 NTC 2k� 2252 � @ +25°C ±0,2 K @ +25°C
**销售belparts STC10-C1-02 NTC 10k� 10 k� @ +25°C ±0,2 K @ +25°C
**销售belparts STC10-C1-02 NTC 20k� 20 k� @ +25°C ±0,2 K @ +25°C
**销售belparts STP-C1-02 NTC 10k� Precon 10 k� @ +25°C ±0,25 K @ +25°C
Accessories
Type Description Remarks
**销售belparts ST.TR.010 T° transmitter 0..10Vdc to combine with Pt1000 (or Pt100) only !
**销售belparts ST.TR.420 T° transmitter 4..20mA to combine with Pt1000 (or Pt100) only !
20U42 � � � 200N 9mm 180s M30 x 1
20U42.RZ � � � 150N 9mm 140s M30 x 1.5
20M22.RZ � � 150N 9mm 140s M30 x 1.5
20U42.HE � � � 100N 3mm 45s M30 x 1.5
20M22.HE � � 100N 3mm 45s M30 x 1.5
20U42.DF1032 � � � 200N 9mm 180s ABQM DN10..DN32
Type 20M22.
RZ 20U42.
RZ 20U42
**销售belparts阀门
制动器
**销售belparts传感器
**销售belparts终端控制器
SCO-E2-i
**销售belparts旋转阀
**销售belparts运动检测器
**销售belparts DTM-RP heating/cooling
**销售belparts DTM-RP-LCD heating/cooling
**销售belparts DTM-RP-X heating/cooling
**销售belparts DTM-RP-X-LCD heating/cooling
**销售belparts DTM-RP-X-S5 heating/cooling
**销售belparts DTM-RP-X-S5-LCD heating/cooling
**销售belparts ST-C1-02 Pt1000 1000 � @ 0°C ±0,3 K @ 0°C
**销售belparts ST100-C1-02 Pt100 100 � @ 0°C ±0,3 K @ 0°C
**销售belparts ST1000-C1-02 PTC1000 1000 � @ +25°C ±1,3 K @ +25°C
**销售belparts STN-C1-02 Ni1000Tk5000 1000 � @ 0°C ±0,5 K @ 0°C
**销售belparts STND-C1-02 Ni1000 DIN 1000 � @ 0°C ±0,4 K @ 0°C
**销售belparts STC02-C1-02 NTC 2k� 2252 � @ +25°C ±0,2 K @ +25°C
**销售belparts STC10-C1-02 NTC 10k� 10 k� @ +25°C ±0,2 K @ +25°C
**销售belparts STC10-C1-02 NTC 20k� 20 k� @ +25°C ±0,2 K @ +25°C
**销售belparts STP-C1-02 NTC 10k� Precon 10 k� @ +25°C ±0,25 K @ +25°C
Accessories
Type Description Remarks
**销售belparts ST.TR.010 T° transmitter 0..10Vdc to combine with Pt1000 (or Pt100) only !
**销售belparts ST.TR.420 T° transmitter 4..20mA to combine with Pt1000 (or Pt100) only !
20U42 � � � 200N 9mm 180s M30 x 1
20U42.RZ � � � 150N 9mm 140s M30 x 1.5
20M22.RZ � � 150N 9mm 140s M30 x 1.5
20U42.HE � � � 100N 3mm 45s M30 x 1.5
20M22.HE � � 100N 3mm 45s M30 x 1.5
20U42.DF1032 � � � 200N 9mm 180s ABQM DN10..DN32
Type 20M22.
RZ 20U42.
RZ 20U42
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