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retrieveDataCiteFacets.py
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retrieveDataCiteFacets.py
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import json
import datetime
from posix import CLD_CONTINUED
import pandas as pd
import sys
import argparse
import logging
import requests
import os
import numpy as np
import re
import itertools
import copy
def writeHTMLOutput(output:str, # output file name
df:pd.core.frame.DataFrame, # dataframe
simple:bool # flag for simple html
):
'''
Write a dataframe to an HTML file
'''
#
# Define html header and footer
#
startHTML = '''<!DOCTYPE html>
<html>
<head>
<meta charset="utf-8" />
<title>DataCite Facet Summary</title>
</head>
<style>body { font-family: "Calibri" }</style>
<body>
<h1>DataCite Facet Summary</h1>
'''
endHTML = f'<hr><i>Report created {dateStamp} by <a href="https://github.com/Metadata-Game-Changers/DataCiteFacets">retrieveDataCiteFacets</a> from <a href="https://metadatagamechangers.com">Metadata Game Changers</a></i></body></html>'
with open(output, 'w') as f:
f.write(startHTML)
addHTMLHeader(f)
html_output = dataframeToHTML(df,simple)
f.write(html_output)
f.write(endHTML)
def dataframeToHTML(df:pd.core.frame.DataFrame,
simple:bool):
#
# Dict used to format columns of the dataframe
#
t = dict(selector="table", props=[('border','1px solid black'),('width','100%')])
th = dict(selector="th", props=[('border','1px solid black'),('border-collapse','collapse'),('padding','5px'),('font-family','Century Gothic')])
td = dict(selector="td", props=[('border','1px solid black'),('border-collapse','collapse'),('padding','5px'),('font-family','Century Gothic')])
html_df = df.copy() # create a copy of df to be written to HTML to avoid changing the content of df
html_df.fillna(0, inplace=True) # replace nan values with 0's
if simple: # output simple HTML (no highlights)
for c in [x for x in html_df.columns if html_df[x].dtype == float]: # adjust column types (float > int)
html_df[c] = html_df[c].astype(int)
return html_df.style.set_table_styles([t,th,td]).hide(axis="index").to_html()
for c in html_df.columns: # adjust column types
if c.endswith(('NumberOfRecords','_number','_max','_total')): # integer columns
html_df[c] = html_df[c].astype(int)
if c.endswith(('_HI','_coverage')):
html_df[c] = html_df[c].astype(float) # float column
float_col_mask = html_df.columns.str.endswith(('_HI','_coverage'))
int_col_mask = html_df.columns.str.endswith(('NumberOfRecords','_number','_max','_total'))
color_col_mask = float_col_mask | int_col_mask
# define the styles and render
return html_df.style.set_properties(subset=html_df.columns[color_col_mask], # center the numeric columns
**{'text-align':'center'})\
.set_properties(subset=html_df.columns[~color_col_mask], # left-align the non-numeric columns
**{'text-align':'left'})\
.format(lambda x: '{:,.0f}'.format(x) if x > 1e3 else '{:.0%}'.format(x), # format _HI as %
subset=pd.IndexSlice[:,html_df.columns[float_col_mask]])\
.format(lambda x: '{0:d}'.format(x), subset=pd.IndexSlice[:,html_df.columns[int_col_mask]])\
.set_table_styles([t,th,td]).hide(axis="index")\
.apply(colorScale,subset=html_df.columns[float_col_mask])\
.apply(highlight_max,subset=html_df.columns[int_col_mask]).to_html()
def colorScale(s):
'''
Highlight table cells with floating point numbers red (<0.000005), lightGreen (> 0.99999), or yellow
'''
return ['' if not isinstance(v,float) \
else 'background-color: lightPink' if v < 0.000005 \
else 'background-color: lightGreen' if v > 0.99999 \
else 'background-color: yellow' for v in s]
def highlight_max(s):
'''
This function highlights the maximum value in a column of an HTML table lightGreen.
'''
is_max = s == s.max()
return ['background-color: lightGreen' if v else '' for v in is_max]
def addHTMLHeader(f):
'''
Write command line options to html header
'''
s = ''
if args.getRelations: # the target retrievals can be controlled by command line arguments:
s += f"<b>Relations:</b> {' '.join(parameters['relations']['data'])}<br>" # --affiliations --contributors --relations --resources retrieve all
if args.getResources: # items in each list, i.e. all relationTypes...
s += f"<b>Resources:</b> {' '.join(parameters['resources']['data'])}<br>"
if args.getContributorTypes:
s += f"<b>Contributor Types:</b> {' '.join(parameters['contributors']['data'])}<br>"
if args.getYears:
s += f"<b>Years:</b> {' '.join(parameters['years']['data'])}<br>"
if args.affiliationList:
s += f"<b>Affiliations:</b> {', '.join(parameters['affiliations']['data'])}<br>"
if args.itemList:
s += f"<b>Item list:</b> {' '.join(args.itemList)}<br>"
if args.facetList:
s += f"<b>Facet list:</b> {' '.join(args.facetList)}<br>"
else:
s += f"<b>Facet list:</b> {facets}<br>"
f.write(s)
def connectToDataCiteDatabase():
'''
make connection to sqlite database, a file defined as an environment variable
'''
database = os.environ['DATACITE_STATISTICS_DATABASE'] # environment defines database location
con = sqlite3.connect(database)
cur = con.cursor()
return(con,cur)
def createCountStringFromListOfDictionaries(l:list, # list of property dictionaries ({})
useID:bool # use repository id instead of name as title
)->str:
'''
Make a list of counts from DataCite list of property dictionaries (l)
The list has the form title1 (count1), title2 (count2), ...
'''
s = ''
if useID: # use repository id as column title
s = ", ".join([d['id'] + ' (' + str(d['count']) + ')' for d in l])
else: # use repository name as column title (default)
s = ", ".join([d['title'].replace(',',';') + ' (' + str(d['count']) + ')' for d in l])
return s
def createDictionaryFromCountString(s: str)-> dict:
'''
Convert a count string like Aalto University (69), University of Lapland (8)
into a dictionary like {'Aalto University':69, 'University of Lapland':8}
'''
if not isinstance(s,str):
return
d_ = {}
pc = re.compile('^(?P<value>.*?)\((?P<count>[0-9]+?)\)$')
items = s.replace(' ','').split(',')
for i in items:
m = re.match(pc, i)
if m is None:
print(f'No match: {i}')
continue
md = m.groupdict()
if md['value'] is not None:
d_[md['value']] = int(md['count'])
else:
d_[md['None']] = int(md['count'])
return d_
def retrieveMetadata(URL:str # DataCite API URL
)-> requests.models.Response: # query response
'''
retrieve and return DataCite metadata response from URL
'''
lggr.debug(f"Retrieving Metadata: {URL}")
try:
response = requests.get(URL)
response.raise_for_status()
except requests.exceptions.HTTPError as err:
lggr.debug(f'Error: {err}')
return None
except requests.exceptions.ConnectionError as err:
lggr.debug(f'Error: {err}')
return None
except requests.exceptions.Timeout as err:
lggr.debug(f'Error: {err}')
return None
except requests.exceptions.TooManyRedirects as err:
lggr.debug(f'Error: {err}')
return None
except requests.exceptions.MissingSchema as err:
lggr.debug(f'Error: {err}')
return None
lggr.debug(f'Response length: {len(response.text)}')
return response
def createFacetsDictionary(facetList: list, # list of facets e.g. ['states','resourceTypes','created'] see full list below
item:str, # item (resource, relation, contributorType) being retrieved,
dateStamp: str, # datestamp (YYYYMMDD_HH)
numberOfRecords:int, # number of records in query
item_json:dict)-> dict: # json retrieved for item
'''
Read DataCite json response and create a summary dictionary for facets in a list.
For the facet f, the dictionary includes the query item, the datestamp, the numberOfRecords and
the following statistics:
f_number: the number of facet values
f_max: the maximum facet value
f_common: the name of the facet with the largest value
f_total: the total of all of the facet values
f_HI: the homogeneity index (f_max / f_total)
f: the facet names and values written as a name (value) string
'''
d_list = []
#
# initialize facets dictionary
#
d_dict = {}
if len(item) == 1:
d_dict.update({'parameter':item[0]})
else:
for i in range(len(item)):
d_dict.update({'parameter ' + str(i+1): item[i]})
d_dict.update({'DateTime': dateStamp, 'NumberOfRecords': numberOfRecords})
numberOfFacets = 0
for f in facetList: # loop facets
try:
numberOfFacets = len(item_json['meta'][f])
except:
lggr.debug(f'No {f}')
finally:
lggr.debug(f'{numberOfFacets} {f}')
if numberOfFacets > 0: # populate dictionary for facet
d_dict[f + '_number'] = numberOfFacets # add count for facet
d_dict[f + '_max'] = max([d['count'] for d in item_json['meta'][f]]) # add max for facet
d_dict[f + '_common'] = ', '.join([d['id'] for d in item_json['meta'][f] \
if d['count'] == d_dict[f + '_max']]) # value with max count
d_dict[f + '_total'] = sum([d['count'] for d in item_json['meta'][f]]) # add total for facet
# d_dict[f + '_HI'] = d_dict[f + '_max'] / d_dict[f + '_total'] # add HI
d_dict[f + '_HI'] = d_dict[f + '_max'] / numberOfRecords # add HI (max/number of records) 20220708
d_dict[f + '_coverage'] = d_dict[f + '_total'] / numberOfRecords # %coverage of top 10 for facet
output = createCountStringFromListOfDictionaries(item_json['meta'][f], args.useIDAsTitle)
d_dict[f] = output # add count string to dictionary
return d_dict # return facet dictionary for item
parameters = {
"relations": {
"data": ['Collects', 'IsCollectedBy', 'IsCitedBy', 'Cites', 'IsSupplementTo', 'IsSupplementedBy', 'IsContinuedBy', 'Continues',\
'IsNewVersionOf', 'IsPreviousVersionOf', 'IsPartOf', 'HasPart', 'IsPublishedIn', 'IsReferencedBy',\
'References', 'IsDocumentedBy', 'Documents', 'IsCompiledBy', 'Compiles', 'IsVariantFormOf', \
'IsOriginalFormOf', 'IsIdenticalTo', 'HasMetadata', 'IsMetadataFor', 'Reviews', 'IsReviewedBy', \
'IsDerivedFrom', 'IsSourceOf', 'Describes', 'IsDescribedBy', 'HasVersion', 'IsVersionOf', 'Requires', \
'IsRequiredBy', 'Obsoletes', 'IsObsoletedBy'],
"queryString": 'query=relatedIdentifiers.relationType:',
"url": 'https://api.datacite.org/dois?&page[size]=1&query=relatedIdentifiers.relationType:'
},
"resources": {
"data": ['Audiovisual','Book','BookChapter','Collection','ComputationalNotebook','ConferencePaper',\
'ConferenceProceeding','DataPaper','Dataset','Dissertation','Event','Image','Instrument','InteractiveResource',\
'Journal','JournalArticle','Model','OutputManagementPlan','PeerReview','PhysicalObject','Preprint',\
'Report','Service','Software','Sound','Standard','StudyRegistration','Text','Workflow','Other'],
"queryString": 'resource-type-id=',
"url": 'https://api.datacite.org/dois?&page[size]=1&resource-type-id='
},
"contributors": {
"data": ['ContactPerson','DataCollector','DataCurator','DataManager','Distributor','Editor', 'Funder',
'HostingInstitution','Other','Producer','ProjectLeader','ProjectManager','ProjectMember',
'RegistrationAgency','RegistrationAuthority','RelatedPerson','ResearchGroup','RightsHolder',
'Researcher','Sponsor','Supervisor','WorkPackageLeader'],
"queryString": 'query=contributors.contributorType:',
"url": 'https://api.datacite.org/dois?query=contributors.contributorType:'
},
"affiliations": {
"data": [],
"queryString": 'query=creators.affiliation.name:*',
"url": 'https://api.datacite.org/dois?query=creators.affiliation.name:*'
},
"years": {
"data": [],
"queryString": 'registered=',
"url": 'https://api.datacite.org/dois?registered='
}
}
facets = ['states','resourceTypes','created','published','registered','providers','clients',
'affiliations','prefixes','certificates','licenses','schemaVersions','linkChecksStatus',
'subjects','fieldsOfScience','citations','views','downloads']
commandLine=argparse.ArgumentParser(prog='retrieveDataCiteFacets',
description='''Use DataCite API to retrieve metadata records for given relationType, resourceType,
contributorType, and affiliations from DataCite. Save the retrieved metadata into
json files (--jout) and facet data into csv or html file or database (defined in environment).'''
)
commandLine.add_argument("-al", "--affiliationList", nargs="*", type=str,
help='space separated list of affiliations to retrieve (affiliations with spaces in quotes)', default=[]
)
commandLine.add_argument("-il", "--itemList", nargs="*", type=str,
help='space separated list of items to retrieve', default=[]
)
commandLine.add_argument("-fl", "--facetList", nargs="*", type=str, default=[],
help='''Select space separated list of facets to retrieve from: states resourceTypes created published registered providers clients
affiliations prefixes certificates licenses schemaVersions linkChecksStatus
subjects fieldsOfScience citations views downloads. Default = all'''
)
commandLine.add_argument('--contributors', dest='getContributorTypes',
default=False, action='store_true',
help='''Retrieve facets for all contributorTypes: ContactPerson, DataCollector, DataCurator,
DataManager, Distributor, Editor, Funder, HostingInstitution, Other, Producer, ProjectLeader,
ProjectManager, ProjectMember, RegistrationAgency, RegistrationAuthority, RelatedPerson,
ResearchGroup, RightsHolder, Researcher, Sponsor, Supervisor, WorkPackageLeader'''
)
commandLine.add_argument('--relations', dest='getRelations',
default=False, action='store_true',
help='''Retrieve facets for all relations: IsCitedBy, Cites, IsSupplementTo, IsSupplementedBy, IsContinuedBy, Continues,
IsNewVersionOf, IsPreviousVersionOf, IsPartOf, HasPart, IsPublishedIn, IsReferencedBy, References, IsDocumentedBy,
Documents, IsCompiledBy, Compiles, IsVariantFormOf, IsOriginalFormOf, IsIdenticalTo, HasMetadata, IsMetadataFor,
Reviews, IsReviewedBy, IsDerivedFrom, IsSourceOf, Describes, IsDescribedBy, HasVersion, IsVersionOf, Requires,
IsRequiredBy, Obsoletes, IsObsoletedBy'''
)
commandLine.add_argument('--resources', dest='getResources',
default=False, action='store_true',
help='''Retrieve facets for all resource types: Audiovisual, Book, BookChapter, Collection, ComputationalNotebook,
ConferencePaper, ConferenceProceeding, DataPaper, Dataset, Dissertation, Event, Image,
InteractiveResource, Journal, JournalArticle, Model, OutputManagementPlan, PeerReview,
PhysicalObject, Preprint, Report, Service, Software, Sound, Standard, Text, Workflow, Other'''
)
commandLine.add_argument('--years', dest='getYears',
default=False, action='store_true',
help='''Retrieve facets for all years: 2004 to present'''
)
commandLine.add_argument('-minYear', dest='minYear', type=int,
default=2004,
help='''Minimum year for year queries'''
)
commandLine.add_argument('--showURLs', dest='showURLs',
default=False, action='store_true',
help='''Show URLs that will be retrieved but DO NOT retrieve metadata'''
)
commandLine.add_argument('--showtargets', dest='showTargetData',
default=False, action='store_true',
help='''Show target lists (e.g. all resourceTypes, relationTypes, contributorTypes)'''
)
commandLine.add_argument('--combineQueries', dest='combineQueries',
default=False, action='store_true',
help='Run all query parameter combinations'
)
commandLine.add_argument('--csvout', dest='csvout',
default=False, action='store_true',
help='Output results in CSV file'
)
commandLine.add_argument('--dbout', dest='dbout',
default=False, action='store_true',
help='Output results in database (requires sqlite3 package)'
)
commandLine.add_argument('--facetdata', dest='facetdata',
default=False, action='store_true',
help='Create dataframe from facet data'
)
commandLine.add_argument('--id', dest='useIDAsTitle',
default=False, action='store_true',
help='Use repository ID as column name instead of repository name'
)
commandLine.add_argument('--htmlout', dest='htmlout',
default=False, action='store_true',
help='Output results in HTML file'
)
commandLine.add_argument('--jout', dest='jout',
default=False, action='store_true',
help='Output retrieved metadata in json files'
)
commandLine.add_argument('--pout', dest='pout',
default=False, action='store_true',
help='Output facet counts to terminal (requires tabulate package https://pypi.org/project/tabulate/)'
)
commandLine.add_argument('--loglevel', default='info',
choices=['debug', 'info', 'warning'],
help='Logging level for logging module (https://docs.python.org/3/howto/logging.html#useful-handlers)'
)
commandLine.add_argument('--logto', metavar='FILE',
help='Log file (will append to file if exists)'
)
args = commandLine.parse_args() # parse the command line and define variables
if args.logto:
# Log to file
logging.basicConfig(
filename=args.logto, filemode='a',
format='%(asctime)s:%(levelname)s:%(name)s: %(message)s',
level=args.loglevel.upper(),
datefmt='%Y-%m-%d %H:%M:%S')
else:
# Log to stderr
logging.basicConfig(
format='%(asctime)s:%(levelname)s:%(name)s: %(message)s',
level=args.loglevel.upper(),
datefmt='%Y-%m-%d %H:%M:%S')
lggr = logging.getLogger('retrieveDataCiteFacets')
current_time = datetime.datetime.now()
dateStamp = f'{current_time.year}{current_time.month:02d}{current_time.day:02d}_{current_time.hour:02d}'
homeDir = os.path.expanduser('~')
lggr.info(f'*********************************** retrieveRelationandResourceCounts {dateStamp}')
urlCount = 0
urlInterval = 1
targets = []
if args.getRelations: # the target retrievals can be controlled by command line arguments:
targets.append('relations') # --affiliations --contributors --relations --resources retrieve all
if args.getResources: # items in each list, i.e. all relationTypes...
targets.append('resources')
if args.getContributorTypes:
targets.append('contributors')
if args.affiliationList: # read affiliations from the command line
targets.append('affiliations')
parameters['affiliations']['data'] = args.affiliationList # set affiliation list from -al argument
if args.getYears:
targets.append('years') # create a list of years from args.minYear to present
parameters['years']['data'] = list(map(str, range(args.minYear,current_time.year + 1)))
if args.showTargetData: # list items for each target
for t in list(parameters):
if len(parameters[t]['data']) > 0:
print(f"\nTarget {t} ({len(parameters[t]['data'])}) items:\n{parameters[t]['data']}")
print(f'\nFacets ({len(facets)}) items:\n{facets}')
exit()
for i in args.itemList: # targets can also be listed as items on the command line
for t in list(parameters): # if only a small number of targets are needed. For example, -il Workflow
if i in parameters[t]['data']: # will retrieve data for just Workflow (a resource type)
targets.append(t)
if len(targets) == 0:
lggr.warning('No targets specified')
exit()
else:
lggr.info(f'Targets: {targets}')
url_parameter_lists = []
parameter_lists = []
url_parameters = []
URL_List = []
param_List = []
if args.combineQueries:
#
# In some cases we need facet data from queries that combine multiple parameters, e.g. facets from
# resourceType = Dataset and affiliation = someUniversity. The combineQueries option can be used to
# generate these results. It takes items from args.itemList, finds the targets thay are in, and runs
# queries for all possible combinations.
#
# url_parameter_lists is a list of the url_parameters from each target,
# e.g [[relation url parameters], [resource url parameters], [years]]. All combinations of items in
# these lists are used to create combined queries.
#
if len(args.itemList) == 0:
lggr.warning('itemList must be defined for combinedQueries')
exit()
for target in ['relations', 'resources', 'contributors']:
# trim the parameter data lists for these targets so
# they only contain items in the itemList
parameters[target]['data'] = [x for x in parameters[target]['data'] if x in args.itemList]
if len(parameters[target]['data']) > 0: # if there are parameters from the itemList for this target
parameter_lists.append(parameters[target]['data']) # append the list of parameters to parameter_lists (a list of lists)
#
# convert the remaining parameters into URL parameters, i.e. Dataset (a resource) becomes resource-type-id=Dataset
# so that it can be included in a URL. url_parameters is a list of these parameters for this target
#
url_parameters = [parameters[target]['queryString'] + x for x in parameters[target]['data']]
url_parameter_lists.append(url_parameters) # append the list of url parameters to url_parameter_lists (a list of lists)
target = 'affiliations' # if affiliations are specified they are all included
if len(parameters[target]['data']) > 0: # in the combined queries, i.e. no trimming required
parameter_lists.append(parameters[target]['data']) # append list of affiliations to parameter_lists (list of lists)
# convert affiliations to URL parameters
url_parameters = [parameters[target]['queryString'] + x.replace(' ','*') + '*' for x in parameters[target]['data']]
url_parameter_lists.append(url_parameters) # append the list of affiliation url parameters to url_parameter_lists (a list of lists)
target = 'years' # if years are specified they are all included
if len(parameters[target]['data']) > 0: # in the combined queries, i.e. no trimming required
parameter_lists.append(parameters[target]['data']) # append list of years to parameter_lists (list of lists)
# convert years to URL parameters
url_parameters = [parameters[target]['queryString'] + x for x in parameters[target]['data']]
url_parameter_lists.append(url_parameters) # append the list of year url parameters to url_parameter_lists (a list of lists)
else: # simple queries, i.e. no combinations
for target in set(targets): # loop through unique targets (e.g. 'resources', 'contributors', 'relations')
lggr.debug(f'target')
for item in parameters[target]['data']: # loop items in target data (all specific items) to find items
# url_parameters are the strings used in the query URL to specify parameters for a
# particular kind of search. For example: "queryString": 'resource-type-id=' for
# resourceType queries.
if target == 'affiliations': # add wildcards to affiliation
url_parameters.append(parameters[target]['queryString'] + item.replace(' ','*') + '*')
else:
if len(args.itemList) > 0: # if args.itemList is specified, just get items on list
if item in args.itemList:
url_parameters.append(parameters[target]['queryString'] + item)
parameter_lists.append(item)
else:
url_parameters.append(parameters[target]['queryString'] + item)
if len(args.itemList) == 0: # parameter list is the list of requested parameter names
parameter_lists.extend(parameters[target]['data'])
url_parameter_lists = [url_parameters] # convert url_parameter_lists to list of lists
parameter_lists = [parameter_lists] # convert parameter_lists to list of lists
for u in list(itertools.product(*url_parameter_lists)): # create list of all combinations of items in url_parameter_lists
URL_List.append('https://api.datacite.org/dois?&page[size]=1&' + '&'.join(u)) # and create urls with &-separated parameters
for p in list(itertools.product(*parameter_lists)): # create a list of all combinations of the paramters used in
#param_List = list(itertools.product(*parameter_lists))
param_List.append(p) # each query. These are used to create the names of the query results
lggr.info(f"URL List: {len(URL_List)} items. Parameter List: {len(param_List)}")
#print('\n'.join(URL_List))
d_list = [] # initialize list of dictionaries
for u,p in zip(URL_List, param_List): # loop items in target data
if args.showURLs: # display URL and parameters to be retrieved without retrieving data
lggr.info(f'URL: {u} Parameters:{p}') # use this to test various command line arguments
continue
res = retrieveMetadata(u) # retrieve metadata from DataCite
item_json = res.json()
if args.jout: # write json to file in directory home/data/DataCite/metadata/
# the file name includes item__datestamp
jsonDirectory = homeDir + '/data/DataCite/metadata/' + '_'.join(p) + '__' + dateStamp + '/json'
os.makedirs(jsonDirectory, exist_ok = True)
jsonFile = jsonDirectory + '/' + '_'.join(p) + '.json'
lggr.info(f'{item} json output to {jsonFile}')
with open(jsonFile,'w') as outf:
json.dump(item_json, outf, ensure_ascii=False)
numberOfRecords = item_json.get('meta').get('total')
if numberOfRecords == 0:
lggr.info(f'Count: {urlCount} URL: {u} Parameters: {p} Number of records: {numberOfRecords}')
continue
urlCount += 1
if urlCount % urlInterval == 0:
lggr.info(f'Count: {urlCount} URL: {u} Parameters: {p} Number of records: {numberOfRecords}')
if args.facetList:
facetList = args.facetList
else:
facetList = facets
d_dict = createFacetsDictionary(facetList, p, dateStamp, numberOfRecords, item_json)
d_list.append(d_dict)
item_df = pd.DataFrame(d_list) # create dataframe
if args.facetdata: # create and output facet data
for facet in args.facetList:
data_l = []
for i in item_df.index:
data_d = {}
for p in [x for x in item_df.columns if x.startswith('parameter')]:
data_d.update({p: item_df.loc[i,p]})
# data_d = {'id':item_df.loc[i,'Id']}
data_d.update(createDictionaryFromCountString(item_df.loc[i,facet]))
data_l.append(copy.deepcopy(data_d))
facet_df = pd.DataFrame(data_l)
outputFile = 'DataCite_' + '_'.join(set(targets)) + '_' + facet + '__' + dateStamp + '.csv'
lggr.info(f'facet data output to {outputFile}')
facet_df.to_csv(outputFile,encoding='utf-8',sep=',',index=False)
if args.htmlout: # output data to html
htmlOutputFile = 'DataCite_' + '_'.join(set(targets)) + '_' + facet + '__' + dateStamp + '.html'
lggr.info(f'facet data output to {htmlOutputFile}')
writeHTMLOutput(htmlOutputFile,facet_df,True)
if args.csvout: # output data to csv
outputFile = 'DataCite_' + '_combined__' + dateStamp + '.csv'
lggr.info(f'facet count output to {outputFile}')
item_df.to_csv(outputFile,encoding='utf-8',sep=',',index=False)
if args.dbout: # add data to database
import sqlite3 # get sqlite3 package
con, cur = connectToDataCiteDatabase() # connect to database for output
databaseTableName = 'relationsAndResources'
item_df.to_sql(databaseTableName,con,if_exists='append',index=False)
if args.htmlout: # output data to html
htmlOutputFile = 'DataCite_' + '_'.join(set(targets)) + '__' + dateStamp + '.html'
lggr.info(f'facet count output to {htmlOutputFile}')
writeHTMLOutput(htmlOutputFile,item_df,False)
if args.pout: # print facet counts to screen
# this produces VERY UGLY screen output that may work
# for a quick look in some cases.
from tabulate import tabulate
print(tabulate(item_df, headers='keys', tablefmt='github', showindex=False))