chelseatroy

print("Timing cython multiplication:\n")
start = time.time()
python_result = multiplication.cython_multiplication(a, b, c, 2000, 2000)
end = time.time()
print(end - start)

chelseatroy

typedef struct {<-------------------------immutable key value data store to iterate over given array
       PyObject_HEAD
       int nd_m1;<------------------------number of dimensions in array minus 1
       npy_intp index;<-------------------counter for element that iterator is at
       npy_intp size;<--------------------number of elements in array       
       npy_intp coords[NPY_MAXDIMS];<-----the N-dimensional location of the current element
       npy_intp dims_m1[NPY_MAXDIMS];<----number of elements along each dimension minus 1
       npy_intp strides[NPY_MAXDIMS];<----number of bytes separating the elements in each dimension
       npy_intp backstrides[NPY_MAXDIMS];<number of bytes to subtract to return to beginning of dimension
       npy_intp factors[NPY_MAXDIMS];<----for converting from 1D index to N-dimensional coords in 1D arrays

chelseatroy

/*NUMPY_API
 * Get MultiIterator from array of Python objects and any additional
 *
 * PyObject **mps -- array of PyObjects
 * int n - number of PyObjects in the array
 * int nadd - number of additional arrays to include in the iterator.
 *
 * Returns a multi-iterator object.
 */
NPY_NO_EXPORT PyObject *

chelseatroy

def total_density(image):
    return image.sum(axis=1).sum()

chelseatroy

def horizontal_asymmetry(image):
    left, right = np.hsplit(image, 2)
    right_flipped = np.flip(right, axis=1)
    return np.sum(left - right_flipped) ** 2

def vertical_asymmetry(image):
    top, bottom = np.vsplit(image, 2)
    bottom_flipped = np.flip(bottom, axis=0)
    return np.sum(top - bottom_flipped) ** 2

chelseatroy

chelseatroy

chelseatroy

import numpy as np
import pandas as pd
from dask import dataframe as dd
from dask.multiprocessing import get
from multiprocessing import cpu_count
num_cores = cpu_count()
from datetime import date

def extract_procedure_features(physician_procedures):
    one_hot_procedures = pd.get_dummies(physician_procedures.procedure_code, prefix='procedure')

chelseatroy

import numpy as np
import pandas as pd
from datetime import date

def extract_procedure_features(physician_procedures):
            one_hot_procedures = pd.get_dummies(physician_procedures.procedure_code, prefix='procedure')
            dummied_procedures = pd.concat([physician_procedures.number_of_patients, one_hot_procedures], axis=1)
            
            def numerize(row):
                return np.asarray(row.number_of_patients) * np.asarray(row)

chelseatroy

echo "gem 'devise'" >> Gemfile         # Or edit Gemfile and add line: gem 'devise' 
bundle install                         # Fetch and install the gems 
rails generate devise:install          # Creates config file, etc. 
rails generate devise user             # Create model class, routes, etc. 
rake db:migrate                        # Create user table 
rails generate devise:views users      # Creates (minimal) views

# Code sample courtesy of launch school: https://launchschool.com/blog/how-to-use-devise-in-rails-for-authentication