egpbos

#!/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

egpbos

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())

egpbos

#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;
}

egpbos

Installing xtensor-io on macOS high sierra

Do all of this in a conda env

conda create -n this_env
source activate this_env

egpbos

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

egpbos

egpbos

egpbos

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

egpbos

// 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)