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# Assumes you've awk, jq, curl installed

calc() { awk "BEGIN{print $*}"; }
export PROPERTY_VALUE=1000000 
export LOAN_AMOUNT=`calc $PROPERTY_VALUE*0.8`
export PROPERTY_ZIP=94107

alias bankrate="curl 'https://mortgage-api.bankrate.com/rates/v4/?loanType=purchase&propertyValue=$PROPERTY_VALUE&propertyType=SingleFamily&propertyUse=PrimaryResidence&cashOutAmount=0&zipCode=$PROPERTY_ZIP&loanAmount=$LOAN_AMOUNT&creditScore=770&debtToIncomeRatio=0&pointsRange=Zero&productFamilies\[\]=conventional&loanTerms\[\]=30yr&loanTerms\[\]=7-1arm&loanTerms\[\]=7-6arm&loanTerms\[\]=10-1arm&loanTerms\[\]=10-6arm&defaultSearch=true&pid=br3&veteranStatus=NoMilitaryService&hadPriorVaLoan=false&hasVaDisabilities=false&firstTimeHomeBuyer=false&displayTargets\[\]=mobileRateTable&displayTargets\[\]=featuredRateTable&deviceTypes\[\]=mobile&e2eTestEnabled=false&clientId=MortgageRateTable&includeSponsored=true&includeEditorial=true'   -H 'authority: mortgage-api.bankrate.com'   -H 'accept: application/json, text/plain, */*'   -H 'accept

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!pip install cython==0.28.5
!pip install mmdet==2.10.0 requests
!pip install torch==1.7.0+cu110 torchvision==0.8.1+cu110 -f https://download.pytorch.org/whl/torch_stable.html
!pip install mmcv-full==1.2.7+torch1.7.0+cu110 -f https://openmmlab.oss-accelerate.aliyuncs.com/mmcv/dist/index.html
!pip install onnx onnxruntime onnxruntime-gpu onnxoptimizer 

!git clone --branch v2.10.0 https://github.com/open-mmlab/mmdetection.git

# Download conf and weights from
# https://github.com/iiLaurens/CascadeTabNet/blob/mmdet2x/Demo/Cascade_Tabnet_mmdet_v2_cpu_only_demo.ipynb

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# Execute this in a Jupyter notebook
import os
import json
import base64
import pandas as pd
from pprint import pprint


# Import USD 10B+, 1MM vol+, 25+ P/E, Buy/Strong Buy rated
buy_rated_tradingview = pd.read_csv('~/Downloads/america_2021-04-11.csv')

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Keybase proof

I hereby claim:

• I am pbamotra on github.
• I am benzene (https://keybase.io/benzene) on keybase.
• I have a public key ASCnWFSySbSBalyy4SCcjIzFTkb2gGmffCtnRewv0sLT2wo

To claim this, I am signing this object:

pbamotra

import datetime
import glob
from lxml import etree

import pandas as pd


def get_books(file):
    doc = etree.HTMLParser()
    tree = etree.parse(file, parser=doc)

pbamotra

Calendar: https://iclr.cc/virtual/calendar.html#tab-calendar
Paper search: https://iclr.cc/virtual/papers.html?filter=keywords

Papers:
1.  Title:
        Tree-Structured Attention with Hierarchical Accumulation
    Authority:
        Richard Socher
    Url:
        https://iclr.cc/virtual/poster_HJxK5pEYvr.html

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# Source: https://github.com/opencv/openvino_training_extensions/blob/develop/pytorch_toolkit/nncf/examples/common/utils.py#L86

import tarfile
from pathlib import Path


def create_code_snapshot(root, dst_path, extensions=(".py", ".json", ".cpp", ".cu")):
    """Creates tarball with the source code"""
    with tarfile.open(str(dst_path), "w:gz") as tar:
        for path in Path(root).rglob("*"):

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from nvidia.dali.plugin.pytorch import DALIGenericIterator

pipe = ExternalSourcePipeline(data_iterator=iterator, batch_size=16, num_threads=2, device_id=0)
pipe.build()

# first parameter is list of pipelines to run
# second pipeline is output_map that maps consecutive outputs to 
#   corresponding names
# last parameter is the number of iterations - number of examples you
# want to iterate on

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eii = ExternalInputIterator(batch_size=16, 
                            data_file=processed_data_file, 
                            image_dir=images_directory)
iterator = iter(eii)

class ExternalSourcePipeline(Pipeline):
    def __init__(self, data_iterator, batch_size, num_threads, device_id):
        super(ExternalSourcePipeline, self).__init__(batch_size,
                                      num_threads,
                                      device_id,

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import types
import numpy as np
import collections
import pandas as pd

from random import shuffle

import nvidia.dali.ops as ops
import nvidia.dali.types as types
from nvidia.dali.pipeline import Pipeline