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Python Tips

File I/O!

f = open('workfile.b','r')
a = f.read() # or .readlines()
f.close()

f = open('workfile.b','w')

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# import numpy and set the printed precision to something humans can read
import numpy as np
np.set_printoptions(precision=2, suppress=True)
# set some prefs for matplotlib
import matplotlib.pyplot as plt
import matplotlib
matplotlib.rcParams.update({'text.usetex': True})
fig_width_pt = 700.  # Get this from LaTeX using \showthe\columnwidth
inches_per_pt = 1.0/72.27 # Convert pt to inches

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1. Log into AWS
2. Select N. California as location
3. Select AMI - cs224d_tensorflow - ami-16327176
4. 

OR go to EC2 > Instances > Start

ssh into machine
ssh -i cs224d_AWS.pem ubuntu@IP

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# create new repository (repo
git init

# check status of files
git status

# add file
git add <filename>

# commit files

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Start the saved VM

Open Docker Quickstart - docker run -it -p 8888:8888 gcr.io/tensorflow/tensorflow

or docker run -it -p 8888:8888 gcr.io/tensorflow/tensorflow bash

or docker run -it -p 8888:8888 gcr.io/tensorflow/tensorflow:0.12.1 bash

maybe install modules

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import boto3
import json

bucket = 'blah'
f = 'fileblah'

# get file from s3, read it, convert to json
s3 = boto3.resource('s3')
content_object = s3.Object(bucket, f)
file_content = content_object.get()['Body'].read().decode('utf-8')

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trainpct = 0.7
trainidx = int(np.round(len(df)*trainpct))
train_df = df.iloc[0:trainidx,:]

valpct = 0.2
validx = int(np.round(len(df)*(trainpct+valpct)))
val_df = df.iloc[trainidx:validx,:]
             
test_df = df.iloc[validx::,:]

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# scale data between 0 and 1
scaler = MinMaxScaler(feature_range=(0, 1))
scaler.fit(train_df.values)

train_scaled = scaler.transform(train_df.values)
val_scaled = scaler.transform(val_df.values)
test_scaled = scaler.transform(test_df.values)

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import json
with open('./data/generated/1127_22.json') as f:
  data = json.load(f)

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import cv2
fname = 'eye_detection.avi'
vidcap = cv2.VideoCapture(fname)
success,image = vidcap.read()
count = 0
while success:
  cv2.imwrite("frame%d.jpg" % count, image)     # save frame as JPEG file      
  success,image = vidcap.read()
  print('Read a new frame: ', success)
  count += 1