justinhchae

# how to pool processes for arima

# given chunked data from data_chunker.py
# https://gist.github.com/justinhchae/13d246e8e2e2d521a8d2cce20eb09a09

# given arima function from run_arima.py
# https://gist.github.com/justinhchae/d2a2dc8b71b5f5fbbb0f7eabf68b6850

# dependencies
from tqdm import tqdm

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from fbprophet import Prophet
import pandas as pd
import os

# https://stackoverflow.com/questions/2125702/how-to-suppress-console-output-in-python
# https://medium.com/spikelab/forecasting-multiples-time-series-using-prophet-in-parallel-2515abd1a245
# https://facebook.github.io/prophet/docs/quick_start.html#python-api


class suppress_stdout_stderr(object):

justinhchae

# how to pool processes for prophet

# given chunked data from data_chunker.py
# https://gist.github.com/justinhchae/13d246e8e2e2d521a8d2cce20eb09a09

# given prophet function wrapper
# https://gist.github.com/justinhchae/8ef78743f13f50051ad1aca2106eaa1a

# dependencies
from tqdm import tqdm

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# solution reference:
# https://stackoverflow.com/questions/41707229/tqdm-printing-to-newline

def fc(x, param):
  # inject some waiting to see progress unfold
  time.sleep(1)
  # set up pbar
  pbar = tqdm(x.items(), desc='Exp:', position=0)
  # init a variable for output
  yhat = 0

justinhchae

# given a function from fc.py
# https://gist.github.com/justinhchae/a2efee420330a985286e7b88fd049ce5

# in a file called main.py
import pandas as pd
# using the pytorch version of mp
import torch.multiprocessing as mp
# we need partial
from functools import partial
# progress bar

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def get_vars_main(filename):
    """
    a helper function for creating the code for the main table of an sql alchemy schema
    bases on a csv file, extract necessary column names and tables and print the code to terminal
    copy and paste the code into the schema section
    """
    logging.info('get_vars_main() Producing code to make main data table in alchemy.')

    class_name = f'class {db_table_name.title()}({db_class_name}):'
    full_path = os.sep.join([data_folder, filename])

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class Budget2021(BudgetDB.base):
    __tablename__ = "budget2021"
    # uncomment the following line to print out this class as text
    # df = get_vars_main('budget_main.csv')
    id = Column(Integer, primary_key=True)
    fund_type = Column(String)
    # the code is a value like 4355 that means "Department A" -> F key to the table the name of the dept.
    department_code = Column(Integer, ForeignKey("departmentDescription.department_code"))
    fund_code = Column(String, ForeignKey("fundDescription.fund_code"))
    organization_code = Column(Integer)

justinhchae

'''https://numpy.org/doc/stable/reference/generated/numpy.savez_compressed.html#numpy.savez_compressed
'''


import numpy as np
import os


# number of points in the point cloud
num_points = 10

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# install direct from our forked repository: https://github.com/Esri/pytorch3d/tree/multitexture-obj-high-precision
!pip install 'git+https://github.com/Esri/pytorch3d.git@multitexture-obj-high-precision'

# import the forked pytorch3d library and new functions
from pytorch3d.io import load_obj
from pytorch3d.ops import sample_points_from_obj
from pytorch3d.io import subset_obj, save_obj

justinhchae

# install direct from our forked repository: https://github.com/Esri/pytorch3d/tree/multitexture-obj-high-precision
!pip install 'git+https://github.com/Esri/pytorch3d.git@multitexture-obj-high-precision'

from pytorch3d.io import load_obj

# the current pytorch3d API for load_obj
obj = load_obj(
    path_to_obj_file,
    load_textures=True,
)