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json.dump(s) & json.load(s)

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json.load(s) & json.dump(s)

python-json-load-loads-dump-dumps.png

There are two ways of reading in (load/loads) the following json file, in.json:

{"alpha": 1, "beta": 2}    

  1. string:
    import json
    
    io = open("in.json","r")
    string = io.read()
    # json.loads(str)
    dictionary = json.loads(string)
    
    # or one-liner
    # dictionary = json.loads(open("in.json","r").read())
    
    print(dictionary)
    

  2. dictionary:
    import json
    # json.load(_io)
    io = open("in.json","r")
    dictionary = json.load(io)
    
    # or one-liner
    # dictionary = json.load(open("in.json","r"))
    
    print(dictionary)
    

Both will print out:

{'alpha': 1, 'beta': 2}

Note that while the json.loads() requires string, load(s,...), the json.load() requires file descriptor, load(fp...).



Similarly, we can write a (dump/dumps) json file:

  1. string:
    import json
    d = {'alpha': 1, 'beta': 2}
    s = json.dumps(d)
    open("out.json","w").write(s)
    

  2. dictionary:
    import json
    d = {'alpha': 1, 'beta': 2}
    json.dump(d, open("out.json","w"))
    

Note that the json.dump() requires file descriptor as well as an obj, dump(obj, fp...).





In the following example, we'll convert Python dictionary to JSON and write it to a text file. Then, we'll read in back from the file and play with it.

Initially we'll construct Python dictionary like this:

# Four Fundamental Forces with JSON
d = {}

d ["gravity"] = {
"mediator":"gravitons",
"relative strength" : "1",
"range" : "infinity"
}
d ["weak"] = {
"mediator":"W/Z bosons",
"relative strength" : "10^25",
"range" : "10^-18"
}
d ["electromagnetic"] = {
"mediator":"photons",
"relative strength" : "10^36",
"range" : "infinity"
}
d ["strong"] = {
"mediator":"gluons",
"relative strength" : "10^38",
"range" : "10^-15"
}

print(d)

The output looks like this:

{'electromagnetic': {'relative strength': '10^36', 'range': 'infinity', 'mediator': 'photons'}, 'strong': {'relative strength': '10^38', 'range': '10^-15', 'mediator': 'gluons'}, 'weak': {'relative strength': '10^25', 'range': '10^-18', 'mediator': 'W/Z bosons'}, 'gravity': {'relative strength': '1', 'range': 'infinity', 'mediator': 'gravitons'}}

Now, we want to convert the dictionary to a string using json.dumps:

import json
data = json.dumps(d)
print(type(data))
print(data)

Output:

<type 'str'>
{"electromagnetic": {"relative strength": "10^36", "range": "infinity", "mediator": "photons"}, "strong": {"relative strength": "10^38", "range": "10^-15", "mediator": "gluons"}, "weak": {"relative strength": "10^25", "range": "10^-18", "mediator": "W/Z bosons"}, "gravity": {"relative strength": "1", "range": "infinity", "mediator": "gravitons"}}

Note that the "json.dumps()" returns a string as indicated by the "s" at the end of "dumps". This process is called encoding.

Let's write it to a file:

import json
data = json.dumps(d)
with open("4forces.json","w") as f:
  f.write(data)

Now that the file is written. Let's reads it back and decoding the JSON-encoded string back into a Python dictionary data structure:

# reads it back
with open("4forces.json","r") as f:
  data = f.read()

# decoding the JSON to dictionay
d = json.loads(data)

Let's play with the dictionary a little bit.

What's the relative strength of electromagnetic compared to gravity?

print(d["electromagnetic"]["relative strength"])
10^36

Who's the mediator for "strong" force?

print(d["strong"]["mediator"])
gluons

Ok, here is the full code:

# Four Fundamental Forces with JSON
d = {}

d ["gravity"] = {
"mediator":"gravitons",
"relative strength" : "1",
"range" : "infinity"
}
d ["weak"] = {
"mediator":"W/Z bosons",
"relative strength" : "10^25",
"range" : "10^-18"
}
d ["electromagnetic"] = {
"mediator":"photons",
"relative strength" : "10^36",
"range" : "infinity"
}
d ["strong"] = {
"mediator":"gluons",
"relative strength" : "10^38",
"range" : "10^-15"
}

import json

# encoding to JSON
data = json.dumps(d)

# write to a file
with open("4forces.json","w") as f:
  f.write(data)

# reads it back
with open("4forces.json","r") as f:
  data = f.read()

# decoding the JSON to dictionay
d = json.loads(data)

print(d)

If we prefer working with files instead of strings, we may want to use json.dump() / json.load() to encode / decode JSON data using the data from the previous example:

# write to a file
with open("4forces.json","w") as f:
  json.dump(d, f)

# reads it back
with open("4forces.json","r") as f:
  d = json.load(f)


Here is another example (json.dump()/json.load()) using simpler data:

import json

# in.json file - {"alpha":1, "beta":2}
with open("in.json","r") as fr:
  out_dict = json.load(fr)
print(out_dict)

in_dict = {"a":1,"b":2}
with open("out.json","w") as fw:
    json.dump(in_dict, fw)
# out.json file - {"a":1,"b":2}


Usage for string version: json.loads()/json.dumps():

import json

# string version of json load & dump

# in.json file - {"alpha":1, "beta":2}
with open("in.json", "r") as fr:
    out_str = fr.read()
out_dict = json.loads(out_str)

# in_dict = {"a":1,"b":2}
in_str = json.dumps(in_dict)
with open("out.json","w") as fw:
    fw.write(in_str)
# out.json file - {"a":1,"b":2}


Another example:

import json

# dict from a string : json.loads(string)
with open("bogo.json","r")  as f:
    a = f.read()
s_d = json.loads(a)
print(f"type(s_d) = {type(s_d)}, sd = {s_d}")

# dict from a file : json.load(file Pointer)
f_d = json.load(open("bogo.json","r"))
print(f"type(f_d) = {type(f_d)}, fd = {f_d}")
    
# dump dict as a string
d_s = json.dumps(s_d)
print(f"type(ds_) = {type(d_s)}, ds = {d_s}")

# dump dict as a file
json.dump(f_d, open("bogo_dumped.json","w"))   

bogo.json:

{
    "5-extinctions": 
    {
        "1st": "Ordovician extinction",
        "2nd": "Devonian extinction",
        "3rd": "Permian extinction",
        "4th": "Triassic extinction",
        "5th": "K-T extinction"
    }
}    


Output:

type(s_d) = , sd = {'5-extinctions': {'1st': 'Ordovician extinction', '2nd': 'Devonian extinction', '3rd': 'Permian extinction', '4th': 'Triassic extinction', '5th': 'K-T extinction'}}
type(f_d) = , fd = {'5-extinctions': {'1st': 'Ordovician extinction', '2nd': 'Devonian extinction', '3rd': 'Permian extinction', '4th': 'Triassic extinction', '5th': 'K-T extinction'}}
type(ds_) = , ds = {"5-extinctions": {"1st": "Ordovician extinction", "2nd": "Devonian extinction", "3rd": "Permian extinction", "4th": "Triassic extinction", "5th": "K-T extinction"}}    



The following example sends a syslog to logstash fargate containers behind AWS NLB:

import socket
import json
import sys

HOST = 'demo-NLB-.....elb.us-west-2.amazonaws.com'
PORT = 6514

try:
  sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
except socket.error as error:
  if error.errno == errno.ECONNREFUSED:
        print(os.strerror(error.errno))
  else:
        raise

try:
  sock.connect((HOST, PORT))
except socket.error as error:
  if error.errno == errno.ECONNREFUSED:
        print(os.strerror(error.errno))
  else:
        raise

msg = {'@message': 'May 11 10:40:48 scrooge disk-health-nurse[26783]: [ID 702911 user.error] m:SY-mon-full-500 c:H : partition health measures for /var did not suffice - still using 96% of partition space', '@tags': ['python', 'test']}

sock.send(json.dumps(msg).encode())

sock.close()
sys.exit(0)  

note that for the HOST, we can also use FQDN instead of the NLB's domain name.

Also, as usual, instead of the long line of code, we may want to use a simple linux command, nc:

$ echo "message at $(date) from khong" | nc demo-NLB-.....elb.us-west-2.amazonaws.com 6514    

If the NLB listener protocol is TLS, we can use openssl echo to the TLS NLB:

$ echo "message at $(date) from khong's mac" | openssl s_client -connect demo-TSL-NLB-.....elb.us-west-2.amazonaws.com:6514 -ign_eof    


Another example: AWS API response.

When we make an AWS API call, the response can be an invalid json due to datetime:

datetime.datetime(2021, 8, 25, 22, 45, 28, tzinfo = tzutc())    

We need to serialize it (ow to overcome “datetime.datetime not JSON serializable”?).

Here is a boto3 code for an API call to EC2 describe:

import boto3
import json

ec2 = boto3.client('ec2')
response = ec2.describe_instances()
s = json.dumps(response, default=str)
open("r.json","w").write(s)
print(response)    

The r.json with jq looks like this:

$ cat r.json | jq '.'
{
  "Reservations": [
    {
      "Groups": [],
      "Instances": [
        {
          "AmiLaunchIndex": 0,
          "ImageId": "ami-083ac7c7ecf9bb9b0",
          "InstanceId": "i-065ddf45930536083",
          "InstanceType": "t2.micro",
          "LaunchTime": "2021-08-25 22:45:28+00:00",
          "Monitoring": {
            "State": "disabled"
          },
          "Placement": {
            "AvailabilityZone": "us-west-2a",
            "GroupName": "",
            "Tenancy": "default"
          },
          "PrivateDnsName": "ip-10-99-101-164.us-west-2.compute.internal",
          "PrivateIpAddress": "10.99.101.164",
          "ProductCodes": [],
          "PublicDnsName": "ec2-34-219-168-233.us-west-2.compute.amazonaws.com",
          "PublicIpAddress": "34.219.168.233",
          "State": {
            "Code": 16,
            "Name": "running"
          },
          "StateTransitionReason": "",
          "SubnetId": "subnet-0c28e356543ecb34f",
          "VpcId": "vpc-02fda1ad9b61c51a2",
          "Architecture": "x86_64",
          "BlockDeviceMappings": [
            {
              "DeviceName": "/dev/xvda",
              "Ebs": {
                "AttachTime": "2021-08-25 22:45:29+00:00",
                "DeleteOnTermination": true,
                "Status": "attached",
                "VolumeId": "vol-0632c2b714a0cec83"
              }
            }
          ],
          "ClientToken": "",
          "EbsOptimized": false,
          "EnaSupport": true,
          "Hypervisor": "xen",
          "IamInstanceProfile": {
            "Arn": "arn:aws:iam::197828489041:instance-profile/AmazonSSMRoleForInstancesQuickSetup",
            "Id": "AIPAVPSFGBEENL5E6UYJ7"
          },
          "NetworkInterfaces": [
            {
              "Association": {
                "IpOwnerId": "amazon",
                "PublicDnsName": "ec2-34-219-168-233.us-west-2.compute.amazonaws.com",
                "PublicIp": "34.219.168.233"
              },
              "Attachment": {
                "AttachTime": "2021-08-25 22:45:28+00:00",
                "AttachmentId": "eni-attach-0e740740b080380ab",
                "DeleteOnTermination": true,
                "DeviceIndex": 0,
                "Status": "attached",
                "NetworkCardIndex": 0
              },
              "Description": "Primary network interface",
              "Groups": [
                {
                  "GroupName": "delete-me",
                  "GroupId": "sg-00bee859aca8c03ab"
                }
              ],
              "Ipv6Addresses": [],
              "MacAddress": "02:06:a7:41:c0:73",
              "NetworkInterfaceId": "eni-089753322166f05ab",
              "OwnerId": "197828489041",
              "PrivateDnsName": "ip-10-99-101-164.us-west-2.compute.internal",
              "PrivateIpAddress": "10.99.101.164",
              "PrivateIpAddresses": [
                {
                  "Association": {
                    "IpOwnerId": "amazon",
                    "PublicDnsName": "ec2-34-219-168-233.us-west-2.compute.amazonaws.com",
                    "PublicIp": "34.219.168.233"
                  },
                  "Primary": true,
                  "PrivateDnsName": "ip-10-99-101-164.us-west-2.compute.internal",
                  "PrivateIpAddress": "10.99.101.164"
                }
              ],
              "SourceDestCheck": true,
              "Status": "in-use",
              "SubnetId": "subnet-0c28e356543ecb34f",
              "VpcId": "vpc-02fda1ad9b61c51a2",
              "InterfaceType": "interface"
            }
          ],
          "RootDeviceName": "/dev/xvda",
          "RootDeviceType": "ebs",
          "SecurityGroups": [
            {
              "GroupName": "delete-me",
              "GroupId": "sg-00bee859aca8c03ab"
            }
          ],
          "SourceDestCheck": true,
          "VirtualizationType": "hvm",
          "CpuOptions": {
            "CoreCount": 1,
            "ThreadsPerCore": 1
          },
          "CapacityReservationSpecification": {
            "CapacityReservationPreference": "open"
          },
          "HibernationOptions": {
            "Configured": false
          },
          "MetadataOptions": {
            "State": "applied",
            "HttpTokens": "optional",
            "HttpPutResponseHopLimit": 1,
            "HttpEndpoint": "enabled",
            "HttpProtocolIpv6": "disabled"
          },
          "EnclaveOptions": {
            "Enabled": false
          }
        }
      ],
      "OwnerId": "197828489041",
      "ReservationId": "r-0b6752f9a69f3ba08"
    }
  ],
  "ResponseMetadata": {
    "RequestId": "5cd271e5-3631-4e4c-a07d-78d169514e39",
    "HTTPStatusCode": 200,
    "HTTPHeaders": {
      "x-amzn-requestid": "5cd271e5-3631-4e4c-a07d-78d169514e39",
      "cache-control": "no-cache, no-store",
      "strict-transport-security": "max-age=31536000; includeSubDomains",
      "content-type": "text/xml;charset=UTF-8",
      "content-length": "7803",
      "vary": "accept-encoding",
      "date": "Thu, 26 Aug 2021 00:02:15 GMT",
      "server": "AmazonEC2"
    },
    "RetryAttempts": 0
  }
}    




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Python Home

Introduction

Running Python Programs (os, sys, import)

Modules and IDLE (Import, Reload, exec)

Object Types - Numbers, Strings, and None

Strings - Escape Sequence, Raw String, and Slicing

Strings - Methods

Formatting Strings - expressions and method calls

Files and os.path

Traversing directories recursively

Subprocess Module

Regular Expressions with Python

Regular Expressions Cheat Sheet

Object Types - Lists

Object Types - Dictionaries and Tuples

Functions def, *args, **kargs

Functions lambda

Built-in Functions

map, filter, and reduce

Decorators

List Comprehension

Sets (union/intersection) and itertools - Jaccard coefficient and shingling to check plagiarism

Hashing (Hash tables and hashlib)

Dictionary Comprehension with zip

The yield keyword

Generator Functions and Expressions

generator.send() method

Iterators

Classes and Instances (__init__, __call__, etc.)

if__name__ == '__main__'

argparse

Exceptions

@static method vs class method

Private attributes and private methods

bits, bytes, bitstring, and constBitStream

json.dump(s) and json.load(s)

Python Object Serialization - pickle and json

Python Object Serialization - yaml and json

Priority queue and heap queue data structure

Graph data structure

Dijkstra's shortest path algorithm

Prim's spanning tree algorithm

Closure

Functional programming in Python

Remote running a local file using ssh

SQLite 3 - A. Connecting to DB, create/drop table, and insert data into a table

SQLite 3 - B. Selecting, updating and deleting data

MongoDB with PyMongo I - Installing MongoDB ...

Python HTTP Web Services - urllib, httplib2

Web scraping with Selenium for checking domain availability

REST API : Http Requests for Humans with Flask

Blog app with Tornado

Multithreading ...

Python Network Programming I - Basic Server / Client : A Basics

Python Network Programming I - Basic Server / Client : B File Transfer

Python Network Programming II - Chat Server / Client

Python Network Programming III - Echo Server using socketserver network framework

Python Network Programming IV - Asynchronous Request Handling : ThreadingMixIn and ForkingMixIn

Python Coding Questions I

Python Coding Questions II

Python Coding Questions III

Python Coding Questions IV

Python Coding Questions V

Python Coding Questions VI

Python Coding Questions VII

Python Coding Questions VIII

Python Coding Questions IX

Python Coding Questions X

Image processing with Python image library Pillow

Python and C++ with SIP

PyDev with Eclipse

Matplotlib

Redis with Python

NumPy array basics A

NumPy Matrix and Linear Algebra

Pandas with NumPy and Matplotlib

Celluar Automata

Batch gradient descent algorithm

Longest Common Substring Algorithm

Python Unit Test - TDD using unittest.TestCase class

Simple tool - Google page ranking by keywords

Google App Hello World

Google App webapp2 and WSGI

Uploading Google App Hello World

Python 2 vs Python 3

virtualenv and virtualenvwrapper

Uploading a big file to AWS S3 using boto module

Scheduled stopping and starting an AWS instance

Cloudera CDH5 - Scheduled stopping and starting services

Removing Cloud Files - Rackspace API with curl and subprocess

Checking if a process is running/hanging and stop/run a scheduled task on Windows

Apache Spark 1.3 with PySpark (Spark Python API) Shell

Apache Spark 1.2 Streaming

bottle 0.12.7 - Fast and simple WSGI-micro framework for small web-applications ...

Flask app with Apache WSGI on Ubuntu14/CentOS7 ...

Selenium WebDriver

Fabric - streamlining the use of SSH for application deployment

Ansible Quick Preview - Setting up web servers with Nginx, configure enviroments, and deploy an App

Neural Networks with backpropagation for XOR using one hidden layer

NLP - NLTK (Natural Language Toolkit) ...

RabbitMQ(Message broker server) and Celery(Task queue) ...

OpenCV3 and Matplotlib ...

Simple tool - Concatenating slides using FFmpeg ...

iPython - Signal Processing with NumPy

iPython and Jupyter - Install Jupyter, iPython Notebook, drawing with Matplotlib, and publishing it to Github

iPython and Jupyter Notebook with Embedded D3.js

Downloading YouTube videos using youtube-dl embedded with Python

Machine Learning : scikit-learn ...

Django 1.6/1.8 Web Framework ...


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Thank you.

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OpenCV 3 image and video processing with Python



OpenCV 3 with Python

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Image noise reduction : Non-local Means denoising algorithm

Image object detection : Face detection using Haar Cascade Classifiers

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scikit-learn : Features and feature extraction - iris dataset

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Data Compression via Dimensionality Reduction I - Principal component analysis (PCA)

scikit-learn : Data Compression via Dimensionality Reduction II - Linear Discriminant Analysis (LDA)

scikit-learn : Data Compression via Dimensionality Reduction III - Nonlinear mappings via kernel principal component (KPCA) analysis

scikit-learn : Logistic Regression, Overfitting & regularization

scikit-learn : Supervised Learning & Unsupervised Learning - e.g. Unsupervised PCA dimensionality reduction with iris dataset

scikit-learn : Unsupervised_Learning - KMeans clustering with iris dataset

scikit-learn : Linearly Separable Data - Linear Model & (Gaussian) radial basis function kernel (RBF kernel)

scikit-learn : Decision Tree Learning I - Entropy, Gini, and Information Gain

scikit-learn : Decision Tree Learning II - Constructing the Decision Tree

scikit-learn : Random Decision Forests Classification

scikit-learn : Support Vector Machines (SVM)

scikit-learn : Support Vector Machines (SVM) II

Flask with Embedded Machine Learning I : Serializing with pickle and DB setup

Flask with Embedded Machine Learning II : Basic Flask App

Flask with Embedded Machine Learning III : Embedding Classifier

Flask with Embedded Machine Learning IV : Deploy

Flask with Embedded Machine Learning V : Updating the classifier

scikit-learn : Sample of a spam comment filter using SVM - classifying a good one or a bad one




Machine learning algorithms and concepts

Batch gradient descent algorithm

Single Layer Neural Network - Perceptron model on the Iris dataset using Heaviside step activation function

Batch gradient descent versus stochastic gradient descent

Single Layer Neural Network - Adaptive Linear Neuron using linear (identity) activation function with batch gradient descent method

Single Layer Neural Network : Adaptive Linear Neuron using linear (identity) activation function with stochastic gradient descent (SGD)

Logistic Regression

VC (Vapnik-Chervonenkis) Dimension and Shatter

Bias-variance tradeoff

Maximum Likelihood Estimation (MLE)

Neural Networks with backpropagation for XOR using one hidden layer

minHash

tf-idf weight

Natural Language Processing (NLP): Sentiment Analysis I (IMDb & bag-of-words)

Natural Language Processing (NLP): Sentiment Analysis II (tokenization, stemming, and stop words)

Natural Language Processing (NLP): Sentiment Analysis III (training & cross validation)

Natural Language Processing (NLP): Sentiment Analysis IV (out-of-core)

Locality-Sensitive Hashing (LSH) using Cosine Distance (Cosine Similarity)




Artificial Neural Networks (ANN)

[Note] Sources are available at Github - Jupyter notebook files

1. Introduction

2. Forward Propagation

3. Gradient Descent

4. Backpropagation of Errors

5. Checking gradient

6. Training via BFGS

7. Overfitting & Regularization

8. Deep Learning I : Image Recognition (Image uploading)

9. Deep Learning II : Image Recognition (Image classification)

10 - Deep Learning III : Deep Learning III : Theano, TensorFlow, and Keras









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