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< \documentclass[unnumsec,webpdf,contemporary,large]{oup-authoring-template}%
< %\documentclass[unnumsec,webpdf,contemporary,large,namedate]{oup-authoring-template}% uncomment this line for author year citations and comment the above
---
> % \documentclass[unnumsec,webpdf,contemporary,large]{oup-authoring-template}%
> \documentclass[unnumsec,webpdf,contemporary,large,namedate]{oup-authoring-template}% uncomment this line for author year citations and comment the above
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< %\bibliographystyle{abbrvnat}
< %\bibliography{reference}
---

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"""
Purpose: Concatenate demultiplexed FASTQs by barcode for one or more ONT runs
Usage: python concat_by_barcode.py run1/fastq_pass run2/fastq_pass
Author: Bede Constantinides
"""

import subprocess
import sys

from collections import defaultdict

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from io import StringIO

import pandas as pd
import pandera as pa
import pandera.extensions as extensions
from pandera.typing import Index, Series

csv_string = """
sample_name,country,region
cDNA-VOC-1-v4-1,USA,Bretagne

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def split_summary_by_barcode(summary_path, out_dir, run_name):
    '''Given a sequencing summary file path, write per barcode summaries to an output directory'''
    
    dtypes = {
        'filename_fastq': 'object',
        'filename_fast5': 'object',
        'read_id': 'object',
        'run_id': 'category',
        'channel': 'int64',
        'mux': 'int64',

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import pandas as pd
from bokeh.models.widgets import Select
from bokeh.layouts import widgetbox
from bokeh.models import ColumnDataSource, DataTable, TableColumn, CustomJS
from bokeh.io import show, output_file, output_notebook, reset_output
from bokeh.layouts import row, column, layout


raw_data = {'ORG': ['APPLE', 'ORANGE', 'MELON'],
        'APPROVED': [5, 10, 15],

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import pandas as pd
from scipy.spatial.distance import squareform
from scipy.cluster.hierarchy import fcluster, linkage

def cluster_df(df, method='single', threshold=100):
    '''
    Accepts a square distance matrix as an indexed DataFrame and returns a dict of index keyed flat clusters 
    Performs single linkage clustering by default, see scipy.cluster.hierarchy.linkage docs for others
    '''
   

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FROM ubuntu:16.04

RUN apt-get update && \
	apt-get --yes install \
	kmc smalt python3-pip zlib1g-dev libncurses5-dev libncursesw5-dev mummer samtools 

RUN pip3 install iva

ENTRYPOINT ["iva"]

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$ cat build_freebayes.*
bgzf.c:44:1: warning: unused function 'kh_clear_cache' [-Wunused-function]
KHASH_MAP_INIT_INT64(cache, cache_t)
^
./khash.h:468:2: note: expanded from macro 'KHASH_MAP_INIT_INT64'
        KHASH_INIT(name, uint64_t, khval_t, 1, kh_int64_hash_func, kh_int64_hash_equal)
        ^
./khash.h:140:21: note: expanded from macro 'KHASH_INIT'
        static inline void kh_clear_##name(kh_##name##_t *h)                            \
                           ^