utilities.py

# -*- coding: utf-8 -*-
"""
Created on Sun Jun 14 11:44:03 2020

@author: Danish
"""


import time
import sys
import tensorflow as tf
import pandas as pd
from tqdm import tqdm

class Timer:
    def __init__(self):
        self.begin = 0
        self.total_time=0
    def restart(self):
        self.begin = time.time()
    def start(self):
        self.begin = time.time()
    def time_converter(self, time):
        m, s = divmod(time, 60)
        h, m = divmod(m, 60)
        return "%02d:%02d:%02d" % (h, m, s)
    
    def get_time_hhmmss(self, rem_batches):
        end = time.time()
        time_taken = end - self.begin
        reamin_time = time_taken*rem_batches
        time_str = self.time_converter(reamin_time)
        self.total_time += time_taken
        return time_str, time_taken
    def get_total_time(self):
        return self.time_converter(self.total_time)
    
#some utility methods    
def PrintInline(string):
    sys.stdout.write('\r'+string)
    sys.stdout.flush() 
    
def TF_GPUsetup(GB=4):
    """
    Restrict TensorFlow to only allocate 1*X GB of memory on the first GPU. Often Needed
    When GPU run out of memory. It would be one of the solution for the issue: Failed to 
    get convolution algorithm. This is probably because cuDNN failed to initialize,

    Parameters
    ----------
    GB : int, optional
        The amount of GPU memory you want to use. It is recommended to use  1 GB
        less than your total GPU memory. The default is 4.

    Returns
    -------
    None.

    """
    if type(GB)!=int:
        raise TypeError('Type of Parameter `GB` must be `int` and it should be 1 GB less than your GPU memory')
    gpus = tf.config.experimental.list_physical_devices('GPU')
    config = [tf.config.experimental.VirtualDeviceConfiguration(memory_limit=(1024*GB))]
    if gpus:
      # Restrict TensorFlow to only allocate 1*X GB of memory on the first GPU
      try:
        tf.config.experimental.set_virtual_device_configuration(gpus[0], config)
        logical_gpus = tf.config.experimental.list_logical_devices('GPU')
        print(len(gpus), "Physical GPUs,", len(logical_gpus), "Logical GPUs")
      except RuntimeError as e:
        # Virtual devices must be set before GPUs have been initialized
        print(e)
    print('\nTensorflow GPU installed: '+str(tf.test.is_built_with_cuda()))
    print('Is Tensorflow using GPU: '+str(tf.test.is_gpu_available()))
    
def to_weeks(obj, format_='%m%d%Y', splitter='/', convert=True, year_index=2, keep=None):   
    """
    

    Parameters
    ----------
    obj : TYPE
        DESCRIPTION.
    format_ : TYPE, optional
        DESCRIPTION. The default is '%m%d%Y'.
    splitter : TYPE, optional
        DESCRIPTION. The default is '/'.
    convert : TYPE, optional
        DESCRIPTION. The default is True.
    year_index : TYPE, optional
        DESCRIPTION. The default is 2.
    keep : TYPE, optional
        If you want to keep the certain amount of chracters in the given string and remove
        rest of them, then specify the amount of chracters you want to keep. The default is None.

    Returns
    -------
    TYPE
        DESCRIPTION.

    """
    if convert:
        for j in tqdm(range(len(obj))):
            if keep:
                split = obj[j][0:keep].split(splitter)
            else:
                split = obj[j].split(splitter)
            new = ['0'+i if len(i)<2  else i for i in split]
            if len(new[year_index])<4:
                new[year_index] = new[year_index]+'20'
            obj[j] = ''.join(new)
        obj = pd.to_datetime(obj, format=format_)
    return obj.dt.week

def extract_sub_df(df, idx): 
    if type(df.loc[idx]) == type(pd.Series([1])):
        return df.loc[[idx]]
    else:
        return df.loc[idx]