joefutrelle

from datetime import datetime, timedelta

# util for converting MATLAB datenum to Python datetime
# as described in http://stackoverflow.com/questions/13965740/converting-matlabs-datenum-format-to-python
def datenum2datetime(matlab_datenum):
    dt = datetime.fromordinal(int(matlab_datenum)) + timedelta(days=matlab_datenum%1) - timedelta(days = 366)
    return dt

# util for converting datetime to MATLAB datenum
# as described in http://stackoverflow.com/questions/8776414/python-datetime-to-matlab-datenum

joefutrelle

joefutrelle

import numba as nb
from numba.typed import List

NO_ID = -1
DOESNT_FIT = 99999999
NO_SECTION = (0, 0, 0, 0, DOESNT_FIT)

@nb.jitclass([
    ('x', nb.int32),
    ('y', nb.int32),

joefutrelle

import h5py as h5

import numpy as np
from scipy.io import savemat

def save_class_scores(path, bin_id, scores, roi_numbers, class_labels):
    assert scores.shape[0] == len(roi_numbers), 'wrong number of ROI numbers'
    assert scores.shape[1] == len(class_labels), 'wrong number of class labels'
    with h5.File(path,'w') as f:
        ds = f.create_dataset('scores', data=scores, compression='gzip', dtype='f4')

joefutrelle

package S2z8N3;{
   $zyp=S2z8N3;use Socket;
       (S2z8N3+w1HC$zyp)&
   open SZzBN3,"<$0"
;while(<SZzBN3>){/\s\((.*p\))&/
    &&(@S2zBN3=unpack$age,$1)}foreach
  $zyp(@S2zBN3){
 while($S2z8M3++!=$zyp-
30){$_=<SZz8N3>}/^(.)/|print $1
      ;$S2z8M3=0}s/.*//|print}sub w1HC{$age=c17

joefutrelle

def wide_to_long(df, wide_cols_list, value_cols, long_col, long_labels):
    """converts selected columns from wide to long format. params:
    
    - df: the input dataframe
    - wide_cols_list: for each set of wide columns, a list of their names
    - value_cols: for each set of wide columns, the name of the long column to hold the values
    - long_col: the name of the column to indicate which set of wide columns the value comes from
    - long_labels: for each set of wide columns, what to call it in the long_col values.
    
    For example if I have the following DataFrame:

joefutrelle

# current as of 2018-12-10
# install docker-ce: https://docs.docker.com/install/linux/docker-ce/debian/#install-docker-ce-1
sudo apt-get update
sudo apt-get install -y apt-transport-https ca-certificates curl gnupg2 software-properties-common
curl -fsSL https://download.docker.com/linux/debian/gpg | sudo apt-key add -
sudo add-apt-repository \
   "deb [arch=amd64] https://download.docker.com/linux/debian \
   $(lsb_release -cs) \
   stable"
sudo apt-get update

joefutrelle

joefutrelle

<dataset type="EDDTableFromNcCFFiles" datasetID="lter-nutrient" active="true">
    <reloadEveryNMinutes>10080</reloadEveryNMinutes>
    <updateEveryNMillis>10000</updateEveryNMillis>
    <fileDir>/home/vagrant/lter-poc/output/</fileDir>
    <fileNameRegex>.*\.nc</fileNameRegex>
    <recursive>true</recursive>
    <pathRegex>.*</pathRegex>
    <metadataFrom>last</metadataFrom>
    <preExtractRegex></preExtractRegex>
    <postExtractRegex></postExtractRegex>

joefutrelle

import os
from scipy.io import loadmat
import pandas as pd
from pocean.dsg.timeseriesProfile.om import OrthogonalMultidimensionalTimeseriesProfile as OMTP

MAT_FILE = '/vagrant/nut_data_reps.mat'
OUT_DIR = './output'

mat = loadmat(MAT_FILE, squeeze_me=True)