somyamohanty

2020-07-02 11:27:12.313442: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1
2020-07-02 11:27:12.346151: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1618] Found device 0 with properties: 
name: GeForce GTX 1080 Ti major: 6 minor: 1 memoryClockRate(GHz): 1.582
pciBusID: 0000:05:00.0
2020-07-02 11:27:12.347723: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1618] Found device 1 with properties: 
name: GeForce GTX 1080 Ti major: 6 minor: 1 memoryClockRate(GHz): 1.582
pciBusID: 0000:06:00.0
2020-07-02 11:27:12.349257: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1618] Found device 2 with properties: 
name: GeForce GTX 1080 Ti major: 6 minor: 1 memoryClockRate(GHz): 1.582
pciBusID: 0000:09:00.0

somyamohanty

import tensorflow as tf
physical_devices = tf.config.experimental.list_physical_devices('GPU') 
for physical_device in physical_devices: 
    tf.config.experimental.set_memory_growth(physical_device, True)
gpus = tf.config.experimental.list_physical_devices('GPU')
if gpus:
    # Restrict TensorFlow to only allocate 1GB * 2 of memory on the first GPU
    try:
        tf.config.experimental.set_virtual_device_configuration(
            gpus[0],

somyamohanty

echo '>> Start of Script'
nodes=($( cat $PBS_NODEFILE | sort | uniq ))
nnodes=${#nodes[@]}
last=$(( $nnodes - 1 ))

export SPARK_HOME=/work/{user}/sparktest/spark/
ssh ${nodes[0]} "cd ${SPARK_HOME}; ./sbin/start-master.sh"
sparkmaster="spark://${nodes[0]}:7077"
echo 'master created'

somyamohanty

class FB_activity(models.Model):
    investigation = models.ForeignKey("investigation.Investigation")
    author = models.ForeignKey(FB_user)
    fbid = models.TextField()
    activity_type = models.TextField()
    body = models.TextField()
    created_time = models.DateTimeField(blank=True,null=True)
    updated_time = models.DateTimeField(blank=True,null=True)
    our_updated_time = models.DateTimeField(auto_now=True)
    place = models.TextField(blank=True)

somyamohanty

Environment:


Request Method: GET
Request URL: http://localhost:8000/admin/SocialNetworkData/fb_activity/4/

Django Version: 1.5
Python Version: 2.7.2
Installed Applications:
('django.contrib.auth',

somyamohanty

gnipclient.py
class PowertrackClient(object):
    """
    Auth attributes common to all gnip clients
    """

    def __init__(self, username, password, account):
        self.username = username
        self.password = password
        self.account = account

somyamohanty

import tornado
from tornado import autoreload, ioloop, web, options, escape, websocket
import re
import json

profane_dict = dict()
re_dict = dict()

def re_compile(word_list):
	exp = r'\b%s\b' %'|'.join(word_list)

somyamohanty

import nltk
docs = ['hi this is a test', 'testing done for now', 'today is a test']
docs_l = []
for sentence in docs:
  tokens = nltk.word_tokenize(sentence)
  docs_l.append(tokens)

finder = BigramCollocationFinder.from_documents(docs_l)
bigram_measures = nltk.collocations.BigramAssocMeasures()
print(finder.score_ngrams(bigram_measures.raw_freq))

somyamohanty

================================================================================
Document Title:  The rate of adaptation in a changing environment
Document Abstract:  Global warming is a major threat to biodiversity that goes beyond precedent. Historically, many species survived by shifting their range, but now human-induced habitat loss and fragmentation commonly restricts range shifts, forcing species to adapt in situ or face extinction. Simulations, using a model integrating population dynamics, mutation, environmental variance, and genetic change, examine the relationship between maximum phenotypic and genetic rates of change. Not surprisingly, small populations of range-bound species (103 or less) will be severely limited in their long-term response to very rapid climate change; however, linked populations can adapt effectively if gene flow between neighboring reserves is adequate.
Processed Document:  ['global/JJ', 'warming/NN', 'be/VB', 'major/JJ', 'threat/NN', 'biodiversity/NN', 'go/VB', 'precedent/NN

somyamohanty

from datetime import date
from dateutil.rrule import rrule, DAILY, HOURLY

a = date(2009, 5, 30)
b = date(2009, 6, 9)

for dt in rrule(DAILY, dtstart=a, until=b):
    print dt.strftime("%Y-%m-%d")