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// run on ROOT 6.28+ built with -Droofit_multiprocess=ON
// root -l demo.cpp
// or call `demo(YOUR FAVORITE NUMBER OF CPUS)` inside root

#include "RooWorkspace.h"
#include "RooAbsPdf.h"
#include "RooDataSet.h"
#include "RooMinimizer.h"

void demo(int number_of_workers = 2)

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import pandas as pd
from sklearn.feature_extraction.text import TfidfVectorizer

data = pd.read_csv('google_api_frame_dec6.csv')

# We can look at unique labels for each video, or take into account that
# labels may occur multiple times in a single video (in different frames
# for instance). In this dataset, there are no separate frames, the labels
# are already unique, so this will work. In case there is per frame data
# for each video, groupby+unique should be replaced by something like

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struct plan_pkg {
  unsigned N1, N2, N3;
  ULONG N;
  ULONG Nhalf;
  complex_prec *C;
  real_prec *R;
  #ifdef SINGLE_PREC
  fftwf_plan plan;
  #endif
  #ifdef DOUBLE_PREC

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Installing xtensor-io on macOS high sierra

Do all of this in a conda env

conda create -n this_env
source activate this_env

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#include <complex>
#include <iostream>

template <typename T> using real_t = T;

template < typename precision, template <typename> class input_t, template <typename> class output_t >
void do_stuff(input_t<precision> a, output_t<precision> b) {
  std::cout << a + b << std::endl;
}

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import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt

def remove_text(figure):
    figure.suptitle("")
    for ax in figure.get_axes():
        ax.set_title("")
        ax.xaxis.set_major_formatter(mpl.ticker.NullFormatter())
        ax.xaxis.set_minor_formatter(mpl.ticker.NullFormatter())

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#!/usr/bin/env python
# coding: utf-8

import numpy as np
import matplotlib
import matplotlib.pyplot as plt
import seaborn as sns
import collections
import networkx as nx