def download(self, engine=None, debug=False):
Script.download(self, engine, debug)
engine = self.engine
# files are nested in another baad_data folder
# important files considered (baad_data.csv,baad_methods.csv)
# relevant files can be added in the same manner
file_names = ["baad_data/baad_data.csv", "baad_data/baad_methods.csv"]
engine.download_files_from_archive(self.urls["BAAD"], file_names)
# creating data from baad_data.csv
engine.auto_create_table(Table("data",
cleanup=Cleanup(correct_invalid_value,
nulls=['NA'])),
filename="baad_data.csv")
engine.insert_data_from_file(engine.format_filename("baad_data.csv"))
# creating methods from baad_methods.csv
engine.auto_create_table(Table("methods",
cleanup=Cleanup(correct_invalid_value,
nulls=['NA'])),
filename="baad_methods.csv")
engine.insert_data_from_file(
engine.format_filename("baad_methods.csv"))
def download(self, engine=None, debug=False):
Script.download(self, engine, debug)
# structure_plot_year table
self.engine.auto_create_table(Table("structure_plot_year"), url=self.urls["structure_plot_year"])
self.engine.insert_data_from_url(self.urls["structure_plot_year"])
# structure_plot_year table
self.engine.auto_create_table(Table("plots"), url=self.urls["plots"])
self.engine.insert_data_from_url(self.urls["plots"])
# species table
self.engine.download_file(self.urls["species"], "original_MSH_SPECIES_DESCRIPTORS.csv")
data_path = self.engine.format_filename("MSH_SPECIES_DESCRIPTORS.csv")
old_data = os.path.normpath(self.engine.find_file("original_MSH_SPECIES_DESCRIPTORS.csv"))
with open(old_data, 'rU') as infile, open(data_path, 'w')as new_data:
for line in infile:
line = str(line).encode('utf-8')
new_data.write(line)
infile.close()
new_data.close()
self.engine.auto_create_table(Table("species"),
filename="MSH_SPECIES_DESCRIPTORS.csv")
self.engine.insert_data_from_file(data_path)
# species_plot_year tables
table = Table("species_plot_year")
table.delimiter = ','
table.columns = [
('record_id', ('pk-auto',)),
('plot_id_year', ('char',)),
('plot_name', ('char',)),
('plot_number', ('int',)),
('year', ('int',)),
('species', ('ct_column',)),
('count', ('ct-double',))
]
table.ct_column = 'species'
table.ct_names = ['Abilas', 'Abipro', 'Achmil', 'Achocc', 'Agoaur', 'Agrexa', 'Agrpal', 'Agrsca', 'Alnvir',
'Anamar', 'Antmic', 'Antros', 'Aqifor', 'Arcnev', 'Arnlat', 'Astled', 'Athdis', 'Blespi',
'Brocar', 'Brosit', 'Carmer', 'Carmic', 'Carpac', 'Carpay', 'Carpha', 'Carros', 'Carspe',
'Casmin', 'Chaang', 'Cirarv', 'Cisumb', 'Crycas', 'Danint', 'Descae', 'Elyely', 'Epiana',
'Eriova', 'Eripyr', 'Fesocc', 'Fravir', 'Gencal', 'Hiealb', 'Hiegra', 'Hyprad', 'Junmer',
'Junpar', 'Juncom', 'Leppun', 'Lommar', 'Luepec', 'Luihyp', 'Luplat', 'Luplep', 'Luzpar',
'Maiste', 'Pencar', 'Pencon', 'Penser', 'Phahas', 'Phlalp', 'Phldif', 'Phyemp', 'Pincon',
'Poasec', 'Poldav', 'Polmin', 'Pollon', 'Poljun', 'Popbal', 'Potarg', 'Psemen', 'Raccan',
'Rumace', 'Salsit', 'Saxfer', 'Senspp', 'Sibpro', 'Sorsit', 'Spiden', 'Trispi', 'Tsumer',
'Vacmem', 'Vervir', 'Vioadu', 'Xerten']
self.engine.table = table
self.engine.create_table()
self.engine.insert_data_from_url(self.urls["species_plot_year"])
def download(self, engine=None, debug=False):
Script.download(self, engine, debug)
engine = self.engine
filename = 'Aquatic_animal_excretion_data.csv'
tablename = 'aquatic_animals'
table = Table(str(tablename), delimiter=',')
table.columns = [
("index", ("pk-int",)),
("sourcenumber", ("int",)),
("sourcename", ("char",)),
("speciesname", ("char",)),
("speciescode", ("char",)),
("invert/vert", ("char",)),
("phylum", ("char",)),
("class", ("char",)),
("order", ("char",)),
("family", ("char",)),
("trophicgild", ("char",)),
("drymass", ("double",)),
("logdrymass", ("double",)),
("ecosystemtype", ("char",)),
("energysource", ("char",)),
("habitat", ("char",)),
("residentecosystem", ("char",)),
("temperature", ("double",)),
("nexcretionrate", ("double",)),
("pexcretionrate", ("double",)),
("lognexcretionrate", ("double",)),
("logpexcretionrate", ("double",)),
("incubationtime", ("double",)),
("nform", ("char",)),
("pform", ("char",)),
("bodyc", ("double",)),
("bodyn", ("double",)),
("bodyp", ("double",)),
("bodyc:n", ("double",)),
("bodyc:p", ("double",)),
("bodyn:p", ("double",)),
("bodydatasource", ("char",)),
("datasource", ("char",)),
("dataproviders", ("char",))]
engine.table = table
if not os.path.isfile(engine.format_filename(filename)):
engine.download_files_from_archive(self.urls[tablename], [filename], filetype="zip")
engine.create_table()
engine.insert_data_from_file(engine.format_filename(str(filename)))
def download(self, engine=None, debug=False):
data_file_name = "eBird_Observation_Dataset_2013.csv"
Script.download(self, engine, debug)
self.engine.download_files_from_archive(self.urls["main"],
[data_file_name],
filetype='gz')
table = (Table("main", delimiter=","))
table.columns = [("BASISOFRECORD", ("char", )),
("INSTITUTIONCODE", ("char", )),
("COLLECTIONCODE", ("char", )),
("CATALOGNUMBER", ("char", )),
("OCCURRENCEID", ("char", )),
("RECORDEDBY", ("char", )), ("YEAR", ("int", )),
("MONTH", ("int", )), ("DAY", ("int", )),
("COUNTRY", ("char", )),
("STATEPROVINCE", ("char", )), ("COUNTY", ("char", )),
("DECIMALLATITUDE", ("double", )),
("DECIMALLONGITUDE", ("double", )),
("LOCALITY", ("char", )), ("KINGDOM", ("char", )),
("PHYLUM", ("char", )), ("CLASS", ("char", )),
("SPORDER", ("char", )), ("FAMILY", ("char", )),
("GENUS", ("char", )),
("SPECIFICEPITHET", ("char", )),
("SCIENTIFICNAME", ("char", )),
("VERNACULARNAME", ("char", )),
("INDIVIDUALCOUNT", ("int", ))]
engine.table = table
engine.create_table()
engine.insert_data_from_file(engine.format_filename(data_file_name))
return engine
def __init__(self, **kwargs):
Script.__init__(self, **kwargs)
self.title = "Gulf of Maine intertidal density/cover (Petraitis et al. 2008)"
self.citation = "Peter S. Petraitis, Harrison Liu, and " \
"Erika C. Rhile. 2008. Densities and cover " \
"data for intertidal organisms in the Gulf of " \
"Maine, USA, from 2003 to 2007. Ecology 89:588."
self.name = "intertidal-abund-me"
self.ref = "https://figshare.com/collections/DENSITIES_AND_COVER_DATA_FOR_INTERTIDAL_ORGANISMS_IN_THE_GULF_OF_MAINE_USA_FROM_2003_TO_2007/3300200"
self.description = "The data on densities and percent cover in the " \
"60 experimental plots from 2003 to 2007 and to " \
"update data from 1996 to 2002 that are already " \
"published in Ecological Archives." \
"Includes densities of mussels, " \
"herbivorous limpet, herbivorous snails, " \
"predatory snail, barnacle , fucoid algae and " \
"percent cover by mussels, barnacles, fucoids, " \
"and other sessile organisms."
self.retriever_minimum_version = '2.0.dev'
self.version = '1.5.3'
self.urls = {"main": "https://ndownloader.figshare.com/files/5600831"}
self.cleanup_func_table = Cleanup(correct_invalid_value,
missing_values=[-999.9])
if parse_version(VERSION) <= parse_version("2.0.0"):
self.shortname = self.name
self.name = self.title
self.cleanup_func_table = Cleanup(correct_invalid_value,
nulls=[-999.9])
self.tables = {"main": Table("main", cleanup=self.cleanup_func_table)}
def download(self, engine=None, debug=False):
Script.download(self, engine, debug)
for key in self.urls:
self.engine.download_file(self.urls[key],
self.urls[key].rpartition('/')[-1])
new_file_path = self.engine.format_filename("new" + key)
old_data = open_fr(
self.engine.find_file(self.urls[key].rpartition('/')[-1]))
new_data = open_fw(new_file_path)
with old_data as file_block:
# after the metadata lines, set data to True
data = False
for lines in file_block.readlines():
# meta data contins line with no ";" and may have "(;;;;)+" or empty lines
if not data and (";" not in lines or ";;;;" in lines):
pass
else:
data = True
new_data.write(lines)
file_block.close()
new_data.close()
self.engine.auto_create_table(Table(
key, cleanup=self.cleanup_func_table),
filename=str("new" + key))
self.engine.insert_data_from_file(new_file_path)
def download(self, engine=None, debug=False):
Script.download(self, engine, debug)
engine = self.engine
for key in self.urls:
original_file_name = "trade_prdct_{}.txt".format(key)
new_file_name = "trade_prdct_{}.csv".format(key)
engine.download_file(self.urls[key], original_file_name)
old_path = self.engine.format_filename(original_file_name)
new_path = self.engine.format_filename(new_file_name)
# Re-write the file with one delimeter
old_data = open_fr(old_path)
new_data = open_fw(new_path)
# Read header line and convert "," to "|"
line1 = old_data.readline().strip().replace(",", "|")
new_data.write(line1 + "\n")
for line in old_data:
# Remove leading "|" from the data
new_data.write(line.strip("|"))
new_data.close()
old_data.close()
table = Table(key, delimiter="|")
engine.auto_create_table(table, filename=new_file_name)
engine.insert_data_from_file(new_path)
def __init__(self):
Script.__init__(self,
tables={'trees': Table('trees', cleanup=Cleanup(correct_invalid_value, nulls=[-999]))},
name="Tree growth, mortality, physical condition - Clark, 2006",
tags=['Taxon > Plants'],
urls={'trees': 'http://esapubs.org/archive/ecol/E087/132/LS_trees_1983_2000.txt'},
shortname="Clark2006",
description="David B. Clark and Deborah A. Clark. 2006. Tree growth, mortality, physical condition, and microsite in an old-growth lowland tropical rain forest. Ecology 87:2132.")
def __init__(self, **kwargs):
Script.__init__(self, **kwargs)
self.name = "CRC Avian Body Masses"
self.shortname = "AvianBodyMass"
self.public = False
self.ref = "http://www.crcpress.com/ecommerce_product/product_detail.jsf?isbn=1420064444"
self.tables = {"mass": Table("mass", delimiter="~")}
self.urls = {"mass": ""}
self.tags = ["Taxon > Birds", "Data Type > Compilation"]
def __init__(self):
Script.__init__(self,
tables={'trees': Table('trees', cleanup=Cleanup(correct_invalid_value, nulls=[-999]))},
name="Tree growth, mortality, physical condition - Clark, 2006",
tags=['Taxon > Plants'],
urls={'trees': 'http://esapubs.org/archive/ecol/E087/132/LS_trees_1983_2000.txt'},
shortname="Clark2006",
description = "The data set helps to examine the post-establishment ecology of 10 species of tropical wet forest trees selected to span a range of predicted life history patterns at the La Selva Biological Station in Costa Rica.",
ref = "http://esapubs.org/archive/ecol/E087/132/",
citation="David B. Clark and Deborah A. Clark. 2006. Tree growth, mortality, physical condition, and microsite in an old-growth lowland tropical rain forest. Ecology 87:2132.")
def download(self, engine=None, debug=False):
Script.download(self, engine, debug)
engine = self.engine
taxa = ('Plant', 'Animal')
for tax in taxa:
table = Table(tax.lower() + 's', delimiter=',', header_rows = 3, pk='record_id', contains_pk=True)
columns = [("record_id" , ("pk-int",) ),
("station_id" , ("int",) ),
("obs_date" , ("char",) ),
("ind_id" , ("int",) ),
("sci_name" , ("char",) ),
("com_name" , ("char",) ),
("kingdom" , ("char",) ),
("pheno_cat" , ("char",) ),
("pheno_name" , ("char",) ),
("pheno_status" , ("char",) ),
("lat" , ("double",) ),
("lon" , ("double",) ),
("elevation" , ("int",) ),
("network_name" , ("char",) )]
table.columns = columns
engine.table = table
engine.create_table()
base_url = 'http://www.usanpn.org/getObs/observations/'
years = range(2009, 2013)
for year in years:
if year == 2009 and tax == 'Animal': continue
url = base_url + 'get%s%sDataNoDefinitions' % (year, tax)
filename = '%s_%s.csv' % (tax, year)
engine.download_file(url, filename)
engine.insert_data_from_file(engine.find_file(filename))
return engine
def download(self, engine=None, debug=False):
Script.download(self, engine, debug)
engine = self.engine
filename = 'Aquatic_animal_excretion_data.csv'
tablename = 'aquatic_animals'
table = Table(str(tablename), delimiter=',')
table.columns = [("index", ("pk-int", )), ("sourcenumber", ("int", )),
("sourcename", ("char", )),
("speciesname", ("char", )),
("speciescode", ("char", )),
("invert/vert", ("char", )), ("phylum", ("char", )),
("class", ("char", )), ("order", ("char", )),
("family", ("char", )), ("trophicgild", ("char", )),
("drymass", ("double", )),
("logdrymass", ("double", )),
("ecosystemtype", ("char", )),
("energysource", ("char", )), ("habitat", ("char", )),
("residentecosystem", ("char", )),
("temperature", ("double", )),
("nexcretionrate", ("double", )),
("pexcretionrate", ("double", )),
("lognexcretionrate", ("double", )),
("logpexcretionrate", ("double", )),
("incubationtime", ("double", )),
("nform", ("char", )), ("pform", ("char", )),
("bodyc", ("double", )), ("bodyn", ("double", )),
("bodyp", ("double", )), ("bodyc:n", ("double", )),
("bodyc:p", ("double", )), ("bodyn:p", ("double", )),
("bodydatasource", ("char", )),
("datasource", ("char", )),
("dataproviders", ("char", ))]
engine.table = table
if not os.path.isfile(engine.format_filename(filename)):
engine.download_files_from_archive(self.urls[tablename],
[filename],
filetype="zip")
engine.create_table()
engine.insert_data_from_file(engine.format_filename(str(filename)))
def download(self, engine=None, debug=False):
Script.download(self, engine, debug)
engine = self.engine
engine.download_files_from_archive(self.urls["data"],
["UPSP_Demo_data.txt",
"UPSP_Species_list2.txt"],
archive_type="zip")
# Create table sp_list(Species)
filename = "UPSP_Species_list2.txt"
engine.auto_create_table(
Table('sp_list', cleanup=self.cleanup_func_table),
filename=filename)
engine.insert_data_from_file(engine.format_filename(filename))
# Create table ind_loc_girth
filename = "UPSP_Demo_data.txt"
engine.auto_create_table(
Table('ind_loc_girth', cleanup=self.cleanup_func_table),
filename=filename)
engine.insert_data_from_file(engine.format_filename(filename))
def __init__(self, **kwargs):
Script.__init__(self, **kwargs)
self.name = "CRC Avian Body Masses"
self.shortname = "AvianBodyMass"
self.public = False
self.ref = "http://www.crcnetbase.com/isbn/9781420064452"
self.citation = "Robert B. Payne, CRC Handbook of Avian Body Masses. Second Edition. The Wilson Journal of Ornithology Sep 2009 : Vol. 121, Issue 3, pg(s) 661-662 doi: 10.1676/1559-4491-121.3.661."
self.description = "Body masses of birds of the world."
self.tables = {"mass": Table("mass", delimiter="~")}
self.urls = {"mass": ""}
self.tags = ["Taxon > Birds", "Data Type > Compilation"]
def download(self, engine=None, debug=False):
Script.download(self, engine, debug)
engine = self.engine
file_name = "PanTHERIA_1-0_WR05_Aug2008.txt"
engine.download_files_from_archive(self.urls["data"], [file_name],
"zip")
# Create table Species
engine.auto_create_table(Table('species',
cleanup=self.cleanup_func_table),
filename=file_name)
engine.insert_data_from_file(engine.format_filename(file_name))
def __init__(self):
Script.__init__(self,
tables={'trees': Table('trees', cleanup=Cleanup(correct_invalid_value, nulls=[-999]))},
name="Tree growth, mortality, physical condition - Clark, 2006",
tags=['plants', 'time-series'],
urls={'trees': 'https://ndownloader.figshare.com/files/5597693'},
shortname="la-selva-trees",
description="The data set helps to examine the post-establishment ecology of 10 species of tropical wet forest trees selected to span a range of predicted life history patterns at the La Selva Biological Station in Costa Rica.",
ref="https://doi.org/10.6084/m9.figshare.c.3299324.v1",
retriever_minimum_version= "2.0.dev",
version='1.3.0',
citation="David B. Clark and Deborah A. Clark. 2006. Tree growth, mortality, physical condition, and microsite in an old-growth lowland tropical rain forest. Ecology 87:2132.")
def download(self, engine=None, debug=False):
Script.download(self, engine, debug)
engine = self.engine
# files are nested in another baad_data folder
# important files considered (baad_data.csv,baad_methods.csv)
# relevant files can be added in the same manner
file_names = ["baad_data/baad_data.csv", "baad_data/baad_methods.csv"]
engine.download_files_from_archive(self.urls["BAAD"], file_names)
# creating data from baad_data.csv
if parse_version(VERSION).__str__() >= parse_version(
"2.1.dev").__str__():
filename = "baad_data/baad_data.csv"
engine.auto_create_table(Table("data",
cleanup=self.cleanup_func_table),
filename=filename)
engine.insert_data_from_file(engine.format_filename(filename))
else:
filename = "baad_data.csv"
engine.auto_create_table(Table("data",
cleanup=self.cleanup_func_table),
filename=filename)
engine.insert_data_from_file(engine.format_filename(filename))
# creating methods from baad_methods.csv
if parse_version(VERSION).__str__() >= parse_version(
"2.1.dev").__str__():
filename = "baad_data/baad_methods.csv"
engine.auto_create_table(Table("methods",
cleanup=self.cleanup_func_table),
filename=filename)
engine.insert_data_from_file(engine.format_filename(filename))
else:
filename = "baad_methods.csv"
engine.auto_create_table(Table("methods",
cleanup=self.cleanup_func_table),
filename=filename)
engine.insert_data_from_file(engine.format_filename(filename))
def download(self, engine=None, debug=False):
Script.download(self, engine, debug)
engine = self.engine
filenames = [
'Aquatic_animal_excretion_data.csv',
'Aquatic_animal_excretion_variable_descriptions.csv'
]
for file_paths in filenames:
if not os.path.isfile(engine.format_filename(file_paths)):
url = self.urls["aquatic_animals"]
engine.download_files_from_archive(url, filenames, "zip")
# processing Aquatic_animal_excretion_data.csv
filename = 'Aquatic_animal_excretion_data.csv'
tablename = 'aquatic_animals'
table = Table(str(tablename), delimiter=',')
table.columns = [("index", ("pk-int", )), ("sourcenumber", ("int", )),
("sourcename", ("char", )),
("speciesname", ("char", )),
("speciescode", ("char", )),
("invert/vert", ("char", )), ("phylum", ("char", )),
("class", ("char", )), ("order", ("char", )),
("family", ("char", )), ("trophicgild", ("char", )),
("drymass", ("double", )),
("logdrymass", ("double", )),
("ecosystemtype", ("char", )),
("energysource", ("char", )), ("habitat", ("char", )),
("residentecosystem", ("char", )),
("temperature", ("double", )),
("nexcretionrate", ("double", )),
("pexcretionrate", ("double", )),
("lognexcretionrate", ("double", )),
("logpexcretionrate", ("double", )),
("incubationtime", ("double", )),
("nform", ("char", )), ("pform", ("char", )),
("bodyc", ("double", )), ("bodyn", ("double", )),
("bodyp", ("double", )), ("bodyc:n", ("double", )),
("bodyc:p", ("double", )), ("bodyn:p", ("double", )),
("bodydatasource", ("char", )),
("datasource", ("char", )),
("dataproviders", ("char", ))]
engine.table = table
engine.create_table()
engine.insert_data_from_file(engine.format_filename(str(filename)))
# processing Aquatic_animal_excretion_variable_descriptions.csv
filename = 'Aquatic_animal_excretion_variable_descriptions.csv'
tablename = 'variable_descriptions'
table = Table(str(tablename), delimiter=',')
table.columns = [("Column", ("char", )), ("Variable", ("char", )),
("Description", ("char", )),
("Data Class", ("char", )), ("Units", ("char", )),
("Minimum_value", ("char", )),
("Maximum_value", ("char", )),
("Possible_values", ("char", )),
("Missing_data_symbol", ("char", )),
("Notes", ("char", ))]
engine.table = table
engine.create_table()
engine.insert_data_from_file(engine.format_filename(str(filename)))
def download(self, engine=None, debug=False):
Script.download(self, engine, debug)
engine = self.engine
filename = "Predator_and_prey_body_sizes_in_marine_food_webs_vsn4.txt"
engine.download_files_from_archive(self.urls["data"], [filename],
filetype="zip")
# Create table Species
engine.auto_create_table(Table('main',
cleanup=self.cleanup_func_table),
filename=filename)
engine.insert_data_from_file(engine.format_filename(filename))
def download(self, engine=None, debug=False):
Script.download(self, engine, debug)
engine = self.engine
file_names = [ ('isotopes.csv','isotopes'),
('sources.csv','sources'),
('diet.csv', 'diet')
]
engine.download_files_from_archive(self.urls["zip"], [i[0] for i in file_names], filetype="zip", archivename="ECOL_92_97")
for(filename,tablename) in file_names:
data_path = self.engine.format_filename(filename)
self.engine.auto_create_table(Table(str(tablename), cleanup=self.cleanup_func_table),filename=filename)
self.engine.insert_data_from_file(data_path)
def download(self, engine=None, debug=False):
Script.download(self, engine, debug)
engine = self.engine
engine.download_files_from_archive(self.urls["data"],
["PanTHERIA_1-0_WR05_Aug2008.txt"],
filetype="zip")
# Create table Species
engine.auto_create_table(Table('species',
cleanup=Cleanup(correct_invalid_value,
nulls=['NA'])),
filename="PanTHERIA_1-0_WR05_Aug2008.txt")
engine.insert_data_from_file(
engine.format_filename("PanTHERIA_1-0_WR05_Aug2008.txt"))
def __init__(self, **kwargs):
Script.__init__(self, **kwargs)
self.title = "Mammal Super Tree"
self.name = 'mammal-super-tree'
self.ref = 'http://doi.org/10.1111/j.1461-0248.2009.01307.x'
self.citation = "Fritz, S. A., Bininda-Emonds, O. R. P. and Purvis, A. (2009), Geographical variation in predictors of mammalian extinction risk: big is bad, but only in the tropics. Ecology Letters, 12: 538-549. doi:10.1111/j.1461-0248.2009.01307.x"
self.description = "Mammal Super Tree from Fritz, S.A., O.R.P Bininda-Emonds, and A. Purvis. 2009. Geographical variation in predictors of mammalian extinction risk: big is bad, but only in the tropics. Ecology Letters 12:538-549"
self.retriever_minimum_version = '2.0.dev'
self.version = "2.0.0"
self.urls = {
'mammal_super_tree_fritz2009.tre':
'http://onlinelibrary.wiley.com/store/10.1111/j.1461-0248.2009.01307.x/asset/supinfo/ELE_1307_sm_SA1.tre?v=1&s=366b28651a9b5d1a3148ef9a8620f8aa31a7df44'
}
self.downlaod_only = True
self.tables = {'trees': Table("trees")}
def __init__(self):
Script.__init__(self,
name="Gulf of Maine intertidal density/cover (Ecological Archives 2008)",
description="Peter S. Petraitis, Harrison Liu, and Erika C. Rhile. 2008. Densities and cover data for intertidal organisms in the Gulf of Maine, USA, from 2003 to 2007. Ecology 89:588.",
shortname="Petraitis2008",
ref="http://www.esapubs.org/archive/ecol/E089/032/",
urls = {
"main": "http://www.esapubs.org/archive/ecol/E089/032/Succession_sampling_03-07_data.txt",
},
tables = {
"main": Table("main",
cleanup=Cleanup(correct_invalid_value,
nulls=[-999.9])),
}
)
def download(self, engine=None, debug=False):
Script.download(self, engine, debug)
engine = self.engine
file_names = [('Flensburg_Data_Links.csv', 'links'),
('Flensburg_Data_Nodes.csv', 'nodes')]
engine.download_files_from_archive(self.urls["zip"],
[i[0] for i in file_names], "zip",
False, "ECOL_92_174")
for (filename, tablename) in file_names:
data_path = self.engine.format_filename(filename)
self.engine.auto_create_table(Table(
str(tablename), cleanup=self.cleanup_func_table),
filename=filename)
self.engine.insert_data_from_file(data_path)
def __init__(self):
Script.__init__(self,
name = "Gulf of Maine intertidal density/cover (Ecological Archives 2008)",
citation = "Peter S. Petraitis, Harrison Liu, and Erika C. Rhile. 2008. Densities and cover data for intertidal organisms in the Gulf of Maine, USA, from 2003 to 2007. Ecology 89:588.",
shortname = "Petraitis2008",
ref = "http://www.esapubs.org/archive/ecol/E089/032/",
description = "The data set provides access to data on densities and percent cover in the 60 experimental plots from 2003 to 2007 and to update data from 1996 to 2002 that are already published in Ecological Archives.It includes densities of mussels, an herbivorous limpet, herbivorous snails, a predatory snail, a barnacle , and fucoid algae and percent cover by mussels, barnacles, fucoids, and other sessile organisms.",
urls = {
"main": "http://www.esapubs.org/archive/ecol/E089/032/Succession_sampling_03-07_data.txt",
},
tables = {
"main": Table("main",
cleanup=Cleanup(correct_invalid_value,
nulls=[-999.9])),
}
)
def download(self, engine=None, debug=False):
Script.download(self, engine, debug)
engine = self.engine
files = ["Macroplot_data_Rev.txt", "Microplot_data.txt", "Site_variables.txt", "Species_list.txt"]
engine.download_files_from_archive(self.urls["data"], files, filetype="zip")
# Create table species
engine.auto_create_table(Table('species', cleanup=self.cleanup_func_table),
filename="Species_list.txt")
engine.insert_data_from_file(engine.format_filename("Species_list.txt"))
# Create table sites
engine.auto_create_table(Table('sites', cleanup=self.cleanup_func_table),
filename="Site_variables.txt")
engine.insert_data_from_file(engine.format_filename("Site_variables.txt"))
# Create table microplots
table = Table('microplots')
table.columns = [('record_id', ('pk-auto',)), ('SpCode', ('char', '30')), ('Count', ('ct-int',))]
table.ct_names = ['BSP1', 'BSP2', 'BSP3', 'BSP4', 'BSP5', 'BSP6', 'BSP7', 'BSP8', 'BSP9',
'BSP10', 'BSP11', 'BSP12', 'BSP13', 'BSP14', 'BSP15', 'BSP16', 'BSP17',
'BSP18', 'BSP20', 'BSP21', 'BSP22', 'BSP23', 'BSP24', 'BSP25', 'BSP26',
'BSP27', 'BSP28', 'BSP29', 'BSP30', 'BSP31', 'BSP33', 'BSP34', 'BSP35',
'BSP36', 'BSP37', 'BSP41', 'BSP42', 'BSP43', 'BSP44', 'BSP45', 'BSP46',
'BSP47', 'BSP48', 'BSP49', 'BSP50', 'BSP51', 'BSP52', 'BSP53', 'BSP54',
'BSP55', 'BSP56', 'BSP57', 'BSP58', 'BSP59', 'BSP60', 'BSP61', 'BSP62',
'BSP63', 'BSP64', 'BSP65', 'BSP66', 'BSP67', 'BSP68', 'BSP69', 'BSP70',
'BSP71', 'BSP72', 'BSP73', 'BSP74', 'BSP75', 'BSP76', 'BSP78', 'BSP79',
'BSP80', 'BSP82', 'BSP83', 'BSP84', 'BSP85', 'BSP86', 'BSP87', 'BSP88',
'BSP89', 'BSP90', 'BSP91', 'BSP92', 'BSP93', 'BSP94', 'BSP95', 'BSP96',
'BSP97', 'BSP98', 'BSP99', 'BSP100', 'BSP101', 'BSP102', 'BSP104']
table.ct_column = 'PlotID'
engine.auto_create_table(table, filename="Microplot_data.txt")
engine.insert_data_from_file(engine.format_filename("Microplot_data.txt"))
# Create table microplots
table = Table('macroplots')
table.ct_names = ['TreeGirth1', 'TreeGirth2', 'TreeGirth3', 'TreeGirth4', 'TreeGirth5']
table.ct_column = 'Tree'
table.columns = [('record_id', ('pk-auto',)), ('PlotID', ('char', '20')), ('SpCode', ('char', '30')),
('Girth', ('ct-int',))]
engine.auto_create_table(table, filename="Macroplot_data_Rev.txt")
engine.insert_data_from_file(engine.format_filename("Macroplot_data_Rev.txt"))
def __init__(self):
Script.__init__(
self,
name=
"Gulf of Maine intertidal density/cover (Petraitis et al. 2008)",
citation=
"Peter S. Petraitis, Harrison Liu, and Erika C. Rhile. 2008. Densities and cover data for intertidal organisms in the Gulf of Maine, USA, from 2003 to 2007. Ecology 89:588.",
shortname="intertidal-abund-me",
ref=
"https://figshare.com/collections/DENSITIES_AND_COVER_DATA_FOR_INTERTIDAL_ORGANISMS_IN_THE_GULF_OF_MAINE_USA_FROM_2003_TO_2007/3300200",
description=
"The data set provides access to data on densities and percent cover in the 60 experimental plots from 2003 to 2007 and to update data from 1996 to 2002 that are already published in Ecological Archives.It includes densities of mussels, an herbivorous limpet, herbivorous snails, a predatory snail, a barnacle , and fucoid algae and percent cover by mussels, barnacles, fucoids, and other sessile organisms.",
retriever_minimum_version='2.0.dev',
version='1.4.0',
urls={"main": "https://ndownloader.figshare.com/files/5600831"},
tables={
"main":
Table("main",
cleanup=Cleanup(correct_invalid_value, nulls=[-999.9]))
})
def create_resources(file, skip_lines):
"""Creates resources for the script or errors out if not possible"""
engine = Engine()
table = engine.auto_create_table(Table(str(file), header_rows=skip_lines),
filename=file,
make=False)
clean_table = table.__dict__
resource_dict = {}
path_to_table = os.path.basename(clean_table["name"])
resource_dict["name"] = os.path.splitext(path_to_table)[0]
resource_dict["schema"] = {}
resource_dict["dialect"] = {}
resource_dict["schema"]["fields"] = []
for cname, ctuple in clean_table["columns"]:
resource_dict["schema"]["fields"].append({
"name": cname,
"type": ctuple[0]
})
resource_dict["url"] = "FILL"
return resource_dict
def create_tabular_resources(self, file, skip_lines, encoding):
"""Create resources for tabular scripts"""
engine = Engine()
self.encoding = encoding
engine.encoding = encoding
table_val = Table(str(file), header_rows=skip_lines)
table = engine.auto_create_table(table_val, filename=file, make=False)
clean_table = table.__dict__
resource_dict = {}
path_to_table = os.path.basename(clean_table["name"])
print("Processing... {file_name}".format(file_name=path_to_table))
r_name = os.path.splitext(path_to_table)[0].lower()
resource_dict["name"] = clean_table_name(r_name)
resource_dict["path"] = path_to_table
resource_dict["schema"] = {}
resource_dict["dialect"] = {"delimiter": ","}
resource_dict["schema"]["fields"] = []
for cname, ctuple in clean_table["columns"]:
if len(ctuple) >= 2:
if ctuple[0] == "char":
# char sizes need quotes
char_size = "{a}".format(a=ctuple[1])
resource_dict["schema"]["fields"].append({
"name": cname,
"type": ctuple[0],
"size": char_size
})
else:
resource_dict["schema"]["fields"].append({
"name": cname,
"type": ctuple[0],
"size": ctuple[1]
})
else:
resource_dict["schema"]["fields"].append({
"name": cname,
"type": ctuple[0]
})
resource_dict["url"] = "fill"
return resource_dict
def create_resources(file, skip_lines):
"""Creates resources for the script or errors out if not possible"""
engine = Engine()
table = engine.auto_create_table(Table(str(file), header_rows=skip_lines),
filename=file,
make=False)
clean_table = table.__dict__
resource_dict = {}
path_to_table = os.path.basename(clean_table["name"])
print("Processing... {file_name}".format(file_name=path_to_table))
resource_dict["name"] = os.path.splitext(path_to_table)[0].lower()
resource_dict["path"] = path_to_table
resource_dict["schema"] = {}
resource_dict["dialect"] = {"delimiter": ","}
resource_dict["schema"]["fields"] = []
for cname, ctuple in clean_table["columns"]:
if len(ctuple) >= 2:
if ctuple[0] == 'char':
# char sizes need quotes
char_size = "{a}".format(a=ctuple[1])
resource_dict["schema"]["fields"].append({
"name": cname,
"type": ctuple[0],
"size": char_size
})
else:
resource_dict["schema"]["fields"].append({
"name": cname,
"type": ctuple[0],
"size": ctuple[1]
})
else:
resource_dict["schema"]["fields"].append({
"name": cname,
"type": ctuple[0]
})
resource_dict["url"] = "FILL"
return resource_dict
def __init__(self, **kwargs):
Script.__init__(self, **kwargs)
self.cleanup_func_table = Cleanup(correct_invalid_value,
missing_values=[-999])
self.title = "Tree growth, mortality, physical condition - Clark, 2006"
self.keywords = ['plants', 'time-series']
self.urls = {'trees': 'https://ndownloader.figshare.com/files/5597693'}
self.name = "la-selva-trees"
self.description = "The data set helps to examine the post-establishment ecology of 10 species of tropical wet forest trees selected to span a range of predicted life history patterns at the La Selva Biological Station in Costa Rica."
self.ref = "https://doi.org/10.6084/m9.figshare.c.3299324.v1"
self.retriever_minimum_version = "2.0.dev"
self.version = '1.4.1'
self.citation = "David B. Clark and Deborah A. Clark. 2006. Tree growth, mortality, physical condition, and microsite in an old-growth lowland tropical rain forest. Ecology 87:2132."
if parse_version(VERSION) <= parse_version("2.0.0"):
self.shortname = self.name
self.name = self.title
self.tags = self.keywords
self.cleanup_func_table = Cleanup(correct_invalid_value,
nulls=[-999])
self.tables = {
'trees': Table('trees', cleanup=self.cleanup_func_table)
}
def download(self, engine=None, debug=False):
Script.download(self, engine, debug)
engine = self.engine
filename = 'vertnet_latest_mammals.csv'
tablename = 'mammals'
table = Table(str(tablename), delimiter=',')
table.columns = [
("record_id", ("pk-auto",)),
("beginrecord", ("char",)),
("icode", ("char",)),
("title", ("char",)),
("citation", ("char",)),
("contact", ("char",)),
("email", ("char",)),
("emlrights", ("char",)),
("gbifdatasetid", ("char",)),
("gbifpublisherid", ("char",)),
("doi", ("char",)),
("migrator", ("char",)),
("networks", ("char",)),
("orgcountry", ("char",)),
("orgname", ("char",)),
("orgstateprovince", ("char",)),
("pubdate", ("char",)),
("source_url", ("char",)),
("iptrecordid", ("char",)),
("associatedmedia", ("char",)),
("associatedoccurrences", ("char",)),
("associatedorganisms", ("char",)),
("associatedreferences", ("char",)),
("associatedsequences", ("char",)),
("associatedtaxa", ("char",)),
("bed", ("char",)),
("behavior", ("char",)),
("catalognumber", ("char",)),
("continent", ("char",)),
("coordinateprecision", ("char",)),
("coordinateuncertaintyinmeters", ("char",)),
("country", ("char",)),
("countrycode", ("char",)),
("county", ("char",)),
("dateidentified", ("char",)),
("day", ("char",)),
("decimallatitude", ("char",)),
("decimallongitude", ("char",)),
("disposition", ("char",)),
("earliestageorloweststage", ("char",)),
("earliesteonorlowesteonothem", ("char",)),
("earliestepochorlowestseries", ("char",)),
("earliesteraorlowesterathem", ("char",)),
("earliestperiodorlowestsystem", ("char",)),
("enddayofyear", ("char",)),
("establishmentmeans", ("char",)),
("eventdate", ("char",)),
("eventid", ("char",)),
("eventremarks", ("char",)),
("eventtime", ("char",)),
("fieldnotes", ("char",)),
("fieldnumber", ("char",)),
("footprintspatialfit", ("char",)),
("footprintsrs", ("char",)),
("footprintwkt", ("char",)),
("formation", ("char",)),
("geodeticdatum", ("char",)),
("geologicalcontextid", ("char",)),
("georeferencedby", ("char",)),
("georeferenceddate", ("char",)),
("georeferenceprotocol", ("char",)),
("georeferenceremarks", ("char",)),
("georeferencesources", ("char",)),
("georeferenceverificationstatus", ("char",)),
("group", ("char",)),
("habitat", ("char",)),
("highergeography", ("char",)),
("highergeographyid", ("char",)),
("highestbiostratigraphiczone", ("char",)),
("identificationid", ("char",)),
("identificationqualifier", ("char",)),
("identificationreferences", ("char",)),
("identificationremarks", ("char",)),
("identificationverificationstatus", ("char",)),
("identifiedby", ("char",)),
("individualcount", ("char",)),
("island", ("char",)),
("islandgroup", ("char",)),
("latestageorhigheststage", ("char",)),
("latesteonorhighesteonothem", ("char",)),
("latestepochorhighestseries", ("char",)),
("latesteraorhighesterathem", ("char",)),
("latestperiodorhighestsystem", ("char",)),
("lifestage", ("char",)),
("lithostratigraphicterms", ("char",)),
("locality", ("char",)),
("locationaccordingto", ("char",)),
("locationid", ("char",)),
("locationremarks", ("char",)),
("lowestbiostratigraphiczone", ("char",)),
("materialsampleid", ("char",)),
("maximumdepthinmeters", ("char",)),
("maximumdistanceabovesurfaceinmeters", ("char",)),
("maximumelevationinmeters", ("char",)),
("member", ("char",)),
("minimumdepthinmeters", ("char",)),
("minimumdistanceabovesurfaceinmeters", ("char",)),
("minimumelevationinmeters", ("char",)),
("month", ("char",)),
("municipality", ("char",)),
("occurrenceid", ("char",)),
("occurrenceremarks", ("char",)),
("occurrencestatus", ("char",)),
("organismid", ("char",)),
("organismname", ("char",)),
("organismremarks", ("char",)),
("organismscope", ("char",)),
("othercatalognumbers", ("char",)),
("pointradiusspatialfit", ("char",)),
("preparations", ("char",)),
("previousidentifications", ("char",)),
("recordedby", ("char",)),
("recordnumber", ("char",)),
("reproductivecondition", ("char",)),
("samplingeffort", ("char",)),
("samplingprotocol", ("char",)),
("sex", ("char",)),
("startdayofyear", ("char",)),
("stateprovince", ("char",)),
("typestatus", ("char",)),
("verbatimcoordinates", ("char",)),
("verbatimcoordinatesystem", ("char",)),
("verbatimdepth", ("char",)),
("verbatimelevation", ("char",)),
("verbatimeventdate", ("char",)),
("verbatimlatitude", ("char",)),
("verbatimlocality", ("char",)),
("verbatimlongitude", ("char",)),
("verbatimsrs", ("char",)),
("waterbody", ("char",)),
("year", ("char",)),
("dctype", ("char",)),
("modified", ("char",)),
("language", ("char",)),
("license", ("char",)),
("rightsholder", ("char",)),
("accessrights", ("char",)),
("bibliographiccitation", ("char",)),
("dc_references", ("char",)),
("institutionid", ("char",)),
("collectionid", ("char",)),
("datasetid", ("char",)),
("institutioncode", ("char",)),
("collectioncode", ("char",)),
("datasetname", ("char",)),
("ownerinstitutioncode", ("char",)),
("basisofrecord", ("char",)),
("informationwithheld", ("char",)),
("datageneralizations", ("char",)),
("dynamicproperties", ("char",)),
("scientificnameid", ("char",)),
("namepublishedinid", ("char",)),
("scientificname", ("char",)),
("acceptednameusage", ("char",)),
("originalnameusage", ("char",)),
("namepublishedin", ("char",)),
("namepublishedinyear", ("char",)),
("higherclassification", ("char",)),
("kingdom", ("char",)),
("phylum", ("char",)),
("class", ("char",)),
("order", ("char",)),
("family", ("char",)),
("genus", ("char",)),
("subgenus", ("char",)),
("specificepithet", ("char",)),
("infraspecificepithet", ("char",)),
("taxonrank", ("char",)),
("verbatimtaxonrank", ("char",)),
("scientificnameauthorship", ("char",)),
("vernacularname", ("char",)),
("nomenclaturalcode", ("char",)),
("taxonomicstatus", ("char",)),
("keyname", ("char",)),
("haslicense", ("int",)),
("vntype", ("char",)),
("rank", ("int",)),
("mappable", ("int",)),
("hashid", ("char",)),
("hastypestatus", ("int",)),
("wascaptive", ("int",)),
("wasinvasive", ("int",)),
("hastissue", ("int",)),
("hasmedia", ("int",)),
("isfossil", ("int",)),
("haslength", ("int",)),
("haslifestage", ("int",)),
("hasmass", ("int",)),
("hassex", ("int",)),
("lengthinmm", ("double",)),
("massing", ("double",)),
("lengthunitsinferred", ("char",)),
("massunitsinferred", ("char",)),
("underivedlifestage", ("char",)),
("underivedsex", ("char",))]
engine.table = table
if not os.path.isfile(engine.format_filename(filename)):
engine.download_files_from_archive(self.urls[tablename], [filename], filetype="zip", archivename="vertnet_latest_" + str(tablename))
engine.create_table()
engine.insert_data_from_file(engine.format_filename(str(filename)))
def download(self, engine=None, debug=False):
try:
Script.download(self, engine, debug)
engine = self.engine
# Routes table
if not os.path.isfile(engine.format_filename("routes_new.csv")):
engine.download_files_from_archive(self.urls["routes"],
["routes.csv"])
read = open(engine.format_filename("routes.csv"), "rb")
write = open(engine.format_filename("routes_new.csv"), "wb")
print "Cleaning routes data..."
write.write(read.readline())
for line in read:
values = line.split(',')
v = Decimal(values[5])
if v > 0:
values[5] = str(v * Decimal("-1"))
write.write(','.join(str(value) for value in values))
write.close()
read.close()
engine.auto_create_table(Table("routes", cleanup=Cleanup()),
filename="routes_new.csv")
engine.insert_data_from_file(engine.format_filename("routes_new.csv"))
# Weather table
if not os.path.isfile(engine.format_filename("weather_new.csv")):
engine.download_files_from_archive(self.urls["weather"],
["weather.csv"])
read = open(engine.format_filename("weather.csv"), "rb")
write = open(engine.format_filename("weather_new.csv"), "wb")
print "Cleaning weather data..."
for line in read:
values = line.split(',')
newvalues = []
for value in values:
if ':' in value:
newvalues.append(value.replace(':', ''))
elif value == "N":
newvalues.append(None)
else:
newvalues.append(value)
write.write(','.join(str(value) for value in newvalues))
write.close()
read.close()
engine.auto_create_table(Table("weather", pk="RouteDataId", cleanup=Cleanup()),
filename="weather_new.csv")
engine.insert_data_from_file(engine.format_filename("weather_new.csv"))
# Species table
table = Table("species", pk=False, delimiter=',')
table.columns=[("species_id" , ("pk-auto",) ),
("AOU" , ("int",) ),
("genus" , ("char",30) ),
("species" , ("char",50) ),
("subspecies" , ("char",30) ),
("id_to_species" , ("bool",) )]
engine.table = table
engine.create_table()
engine.download_file(self.urls["species"], "SpeciesList.txt")
species_list = open(engine.format_filename("SpeciesList.txt"), "rb")
for n in range(8):
species_list.readline()
rows = []
for line in species_list:
if line and len(line) > 273:
latin_name = line[273:].split()
if len(latin_name) < 2:
# If there's no species given, add "None" value
latin_name.append("None")
subspecies = ' '.join(latin_name[2:]) if len(latin_name) > 2 else "None"
id_to_species = "1" if latin_name[1] != "None" else "0"
if latin_name[1] == "sp.":
latin_name[1] = "None"
id_to_species = "0"
if ("x" in latin_name or "/" in latin_name
or "/" in subspecies or "or" in latin_name):
# Hybrid species or only identified to a group of species
latin_name[1] = ' '.join(latin_name[1:])
subspecies = "None"
id_to_species = "0"
rows.append(','.join([
line.split()[1],
latin_name[0],
latin_name[1],
subspecies,
id_to_species
]))
engine.add_to_table(rows)
species_list.close()
# Region_codes table
table = Table("region_codes", pk=False, header_rows=11,
fixed_width=[11, 11, 30])
def regioncodes_cleanup(value, engine):
replace = {chr(225):"a", chr(233):"e", chr(237):"i", chr(243):"o"}
newvalue = str(value)
for key in replace.keys():
if key in newvalue:
newvalue = newvalue.replace(key, replace[key])
return newvalue
table.cleanup = Cleanup(regioncodes_cleanup)
table.columns=[("countrynum" , ("int",) ),
("regioncode" , ("int",) ),
("regionname" , ("char",30) )]
engine.table = table
engine.create_table()
engine.insert_data_from_url(self.urls["region_codes"])
# Counts table
table = Table("counts", delimiter=',')
table.columns=[("countrynum" , ("int",) ),
("statenum" , ("int",) ),
("Route" , ("int",) ),
("RPID" , ("int",) ),
("year" , ("int",) ),
("AOU" , ("int",) ),
("Stop1" , ("int",) ),
("Stop2" , ("int",) ),
("Stop3" , ("int",) ),
("Stop4" , ("int",) ),
("Stop5" , ("int",) ),
("Stop6" , ("int",) ),
("Stop7" , ("int",) ),
("Stop8" , ("int",) ),
("Stop9" , ("int",) ),
("Stop10" , ("int",) ),
("Stop11" , ("int",) ),
("Stop12" , ("int",) ),
("Stop13" , ("int",) ),
("Stop14" , ("int",) ),
("Stop15" , ("int",) ),
("Stop16" , ("int",) ),
("Stop17" , ("int",) ),
("Stop18" , ("int",) ),
("Stop19" , ("int",) ),
("Stop20" , ("int",) ),
("Stop21" , ("int",) ),
("Stop22" , ("int",) ),
("Stop23" , ("int",) ),
("Stop24" , ("int",) ),
("Stop25" , ("int",) ),
("Stop26" , ("int",) ),
("Stop27" , ("int",) ),
("Stop28" , ("int",) ),
("Stop29" , ("int",) ),
("Stop30" , ("int",) ),
("Stop31" , ("int",) ),
("Stop32" , ("int",) ),
("Stop33" , ("int",) ),
("Stop34" , ("int",) ),
("Stop35" , ("int",) ),
("Stop36" , ("int",) ),
("Stop37" , ("int",) ),
("Stop38" , ("int",) ),
("Stop39" , ("int",) ),
("Stop40" , ("int",) ),
("Stop41" , ("int",) ),
("Stop42" , ("int",) ),
("Stop43" , ("int",) ),
("Stop44" , ("int",) ),
("Stop45" , ("int",) ),
("Stop46" , ("int",) ),
("Stop47" , ("int",) ),
("Stop48" , ("int",) ),
("Stop49" , ("int",) ),
("Stop50" , ("int",) )]
part = ""
engine.table = table
engine.create_table()
for part in range(1,11):
part = str(part)
try:
print "Inserting data from part " + part + "..."
try:
engine.table.cleanup = Cleanup()
engine.insert_data_from_archive(self.urls["counts"] +
"Fifty" + part + ".exe",
["fifty" + part + ".csv"])
except:
print "Failed bulk insert on " + part + ", inserting manually."
engine.connection.rollback()
engine.table.cleanup = Cleanup(correct_invalid_value,
nulls=['*'])
engine.insert_data_from_archive(self.urls["counts"] +
"Fifty" + part + ".exe",
["fifty" + part + ".csv"])
except:
print "There was an error in part " + part + "."
raise
except zipfile.BadZipfile:
print "There was an unexpected error in the Breeding Bird Survey archives."
raise
return engine
def download(self, engine=None, debug=False):
try:
Script.download(self, engine, debug)
engine = self.engine
# Species table
table = Table("species", cleanup=Cleanup(), contains_pk=True,
header_rows=9)
table.columns=[("species_id", ("pk-int",) ),
("AOU", ("int",) ),
("english_common_name", ("char",50) ),
("french_common_name", ("char",50) ),
("spanish_common_name", ("char",50) ),
("sporder", ("char",30) ),
("family", ("char",30) ),
("genus", ("char",30) ),
("species", ("char",50) ),
]
table.fixed_width = [7,6,51,51,51,51,51,51,50]
engine.table = table
engine.create_table()
engine.insert_data_from_url(self.urls["species"])
# Routes table
engine.download_files_from_archive(self.urls["routes"], ["routes.csv"])
engine.auto_create_table(Table("routes", cleanup=Cleanup()),
filename="routes.csv")
engine.insert_data_from_file(engine.format_filename("routes.csv"))
# Weather table
if not os.path.isfile(engine.format_filename("weather_new.csv")):
engine.download_files_from_archive(self.urls["weather"],
["weather.csv"])
read = open_fr(engine.format_filename("weather.csv"))
write = open_fw(engine.format_filename("weather_new.csv"))
print("Cleaning weather data...")
for line in read:
values = line.split(',')
newvalues = []
for value in values:
if ':' in value:
newvalues.append(value.replace(':', ''))
elif value == "N":
newvalues.append(None)
else:
newvalues.append(value)
write.write(','.join(str(value) for value in newvalues))
write.close()
read.close()
engine.auto_create_table(Table("weather", pk="RouteDataId",
cleanup=Cleanup(correct_invalid_value, nulls=['NULL'])),
filename="weather_new.csv")
engine.insert_data_from_file(engine.format_filename("weather_new.csv"))
# Region_codes table
table = Table("region_codes", pk=False, header_rows=11,
fixed_width=[11, 11, 30])
def regioncodes_cleanup(value, engine):
replace = {chr(225):"a", chr(233):"e", chr(237):"i", chr(243):"o"}
newvalue = str(value)
for key in list(replace.keys()):
if key in newvalue:
newvalue = newvalue.replace(key, replace[key])
return newvalue
table.cleanup = Cleanup(regioncodes_cleanup)
table.columns=[("countrynum" , ("int",) ),
("regioncode" , ("int",) ),
("regionname" , ("char",30) )]
engine.table = table
engine.create_table()
engine.insert_data_from_url(self.urls["region_codes"])
# Counts table
table = Table("counts", delimiter=',')
table.columns=[("record_id" , ("pk-auto",) ),
("countrynum" , ("int",) ),
("statenum" , ("int",) ),
("Route" , ("int",) ),
("RPID" , ("int",) ),
("Year" , ("int",) ),
("Aou" , ("int",) ),
("Count10" , ("int",) ),
("Count20" , ("int",) ),
("Count30" , ("int",) ),
("Count40" , ("int",) ),
("Count50" , ("int",) ),
("StopTotal" , ("int",) ),
("SpeciesTotal" , ("int",) )]
stateslist = ["Alabama", "Alaska", "Arizona", "Arkansas", "California", "Colorado",
"Connecticut", "Delaware", "Florida", "Georgia", "Idaho",
"Illinois", "Indiana", "Iowa", "Kansas", "Kentucky", "Louisiana", "Maine",
"Maryland", "Massachusetts", "Michigan", "Minnesota", "Mississippi",
"Missouri", "Montana", "Nebraska", "Nevada",
["New Hampshire", "NHampsh"], ["New Jersey", "NJersey"],
["New Mexico", "NMexico"], ["New York", "NYork"],
["North Carolina", "NCaroli"], ["North Dakota", "NDakota"], "Ohio",
"Oklahoma", "Oregon", "Pennsylvania", ["Rhode Island", "RhodeIs"],
["South Carolina", "SCaroli"], ["South Dakota", "SDakota"], "Tennessee",
"Texas", "Utah", "Vermont", "Virginia", "Washington",
["West Virginia", "W_Virgi"], "Wisconsin", "Wyoming", "Alberta",
["British Columbia", "BritCol"], "Manitoba", ["New Brunswick", "NBrunsw"],
["Northwest Territories", "NWTerri"], "Newfoundland",
["Nova Scotia", "NovaSco"], "Nunavut", "Ontario",
["Prince Edward Island", "PEI"], "Quebec", "Saskatchewan", "Yukon"]
state = ""
shortstate = ""
engine.table = table
engine.create_table()
for state in stateslist:
try:
if len(state) > 2:
shortstate = state[0:7]
else:
state, shortstate = state[0], state[1]
print("Inserting data from " + state + "...")
try:
engine.table.cleanup = Cleanup()
engine.insert_data_from_archive(self.urls["counts"] + shortstate + ".zip",
[shortstate + ".csv"])
except:
print("Failed bulk insert on " + state + ", inserting manually.")
engine.connection.rollback()
engine.table.cleanup = Cleanup(correct_invalid_value,
nulls=['*'])
engine.insert_data_from_archive(self.urls["counts"] + shortstate + ".zip",
[shortstate + ".csv"])
except:
print("There was an error in " + state + ".")
raise
except zipfile.BadZipfile:
print("There was an unexpected error in the Breeding Bird Survey archives.")
raise
return engine
def download(self, engine=None, debug=False):
Script.download(self, engine, debug)
engine = self.engine
csv_files = []
request_src = "http://www.data-retriever.org/"
base_url = "http://www.usanpn.org/npn_portal/observations/getObservations.xml?start_date={startYear}&end_date={endYear_date}&request_src={request_src}"
header_values = ["observation_id",
"update_datetime",
"site_id",
"latitude",
"longitude",
"elevation_in_meters",
"state",
"species_id",
"genus",
"species",
"common_name",
"kingdom",
"individual_id",
"phenophase_id",
"phenophase_description",
"observation_date",
"day_of_year",
"phenophase_status",
"intensity_category_id",
"intensity_value",
"abundance_value"
]
columns = [("record_id", ("pk-auto",)),
("observation_id", ("int",)), # subsequently refered to as "status record"
("update_datetime", ("char",)),
("site_id", ("int",)),
("latitude", ("double",)),
("longitude", ("double",)),
("elevation_in_meters", ("char",)),
("state", ("char",)),
("species_id", ("int",)),
("genus", ("char",)),
("species", ("char",)),
("common_name", ("char",)),
("kingdom", ("char",)), # skip kingdom
("individual_id", ("char",)),
("phenophase_id", ("int",)),
("phenophase_description", ("char",)),
("observation_date", ("char",)),
("day_of_year", ("char",)),
("phenophase_status", ("char",)),
("intensity_category_id", ("char",)),
("intensity_value", ("char",)),
("abundance_value", ("char",))
]
start_date = datetime.date(2009, 1, 1)
end_date = datetime.date.today()
while start_date < end_date:
to_date = start_date + datetime.timedelta(90)
if to_date >= end_date:
data_url = base_url.format(startYear=str(start_date), endYear_date=str(end_date),
request_src=request_src)
else:
data_url = base_url.format(startYear=str(start_date), endYear_date=str(to_date),
request_src=request_src)
xml_file_name = '{}'.format(start_date) + ".xml"
engine.download_file(data_url, xml_file_name)
# Create csv files for 3 months
csv_observation = '{}'.format(start_date) + ".csv"
csv_files.append(csv_observation)
csv_buff = open_fw(engine.format_filename(csv_observation))
csv_writer = open_csvw(csv_buff)
csv_writer.writerow(header_values)
# Parse xml to read data
file_read = ""
fname = DATA_WRITE_PATH.strip('{dataset}') + 'NPN/' + xml_file_name
with open(fname, 'r') as fp1:
file_read = fp1.read()
root = ET.fromstring(file_read)
for elements in root:
index_map = {val: i for i, val in enumerate(header_values)}
diction = sorted(elements.attrib.items(), key=lambda pair: index_map[pair[0]])
csv_writer.writerow([x[1] for x in diction])
csv_buff.close()
start_date = to_date + datetime.timedelta(1)
# Create table
table = Table('obsercations', delimiter=',', pk='record_id', contains_pk=True)
table.columns = columns
engine.table = table
engine.create_table()
for data_file in csv_files:
engine.insert_data_from_file(engine.find_file(data_file))
return engine
def download(self, engine=None, debug=False):
Script.download(self, engine, debug)
engine = self.engine
engine.download_files_from_archive(self.urls["data"], ["Data_Files/Amniote_Database_Aug_2015.csv",
"Data_Files/Amniote_Database_References_Aug_2015.csv",
"Data_Files/Amniote_Range_Count_Aug_2015.csv"],
filetype="zip")
ct_column = 'trait' # all tables use the same ct_column name
# Create tables from Amniote_Database_Aug.csv and Amniote_Database_References_Aug_2015.csv
# Both reference and main have the same headers
ct_names = ['female_maturity_d', 'litter_or_clutch_size_n', 'litters_or_clutches_per_y', 'adult_body_mass_g',
'maximum_longevity_y', 'gestation_d', 'weaning_d', 'birth_or_hatching_weight_g', 'weaning_weight_g',
'egg_mass_g', 'incubation_d', 'fledging_age_d', 'longevity_y', 'male_maturity_d',
'inter_litter_or_interbirth_interval_y', 'female_body_mass_g', 'male_body_mass_g',
'no_sex_body_mass_g', 'egg_width_mm', 'egg_length_mm', 'fledging_mass_g', 'adult_svl_cm',
'male_svl_cm', 'female_svl_cm', 'birth_or_hatching_svl_cm', 'female_svl_at_maturity_cm',
'female_body_mass_at_maturity_g', 'no_sex_svl_cm', 'no_sex_maturity_d']
# Create table main from Amniote_Database_Aug_2015.csv
columns = [
('record_id', ('pk-auto',)), ('class', ('char', '20')), ('order', ('char', '20')),
('family', ('char', '20')), ('genus', ('char', '20')), ('species', ('char', '50')),
('subspecies', ('char', '20')), ('common_name', ('char', '400')), ('trait_value', ('ct-double',))]
table_main = Table('main', delimiter=',', cleanup=self.cleanup_func_table)
table_main.ct_column = ct_column
table_main.ct_names = ct_names
table_main.columns = columns
engine.auto_create_table(table_main,
filename="Amniote_Database_Aug_2015.csv")
engine.insert_data_from_file(engine.format_filename("Amniote_Database_Aug_2015.csv"))
# Create table reference from Amniote_Database_References_Aug_2015.csv
reference_columns = [
('record_id', ('pk-auto',)), ('class', ('char', '20')), ('order', ('char', '20')),
('family', ('char', '20')), ('genus', ('char', '20')), ('species', ('char', '50')),
('subspecies', ('char', '20')), ('common_name', ('char', '400')), ('reference', ('ct-char',))]
table_references = Table('references', delimiter=',', cleanup=self.cleanup_func_table)
table_references.ct_column = ct_column
table_references.ct_names = ct_names
table_references.columns = reference_columns
engine.auto_create_table(table_references,
filename="Amniote_Database_References_Aug_2015.csv")
engine.insert_data_from_file(engine.format_filename("Amniote_Database_References_Aug_2015.csv"))
# Create table Range
# This table has different values for headers from the above tables.
range_ct_names = ["min_female_maturity", "max_female_maturity", "count_female_maturity", "min_litter_clutch_size",
"max_litter_clutch_size", "count_litter_clutch_size", "min_litters_clutches",
"max_litters_clutches", "count_litters_clutches", "min_adult_body_mass", "max_adult_body_mass",
"count_adult_body_mass", "min_maximum_longevity", "max_maximum_longevity",
"count_maximum_longevity", "min_gestation", "max_gestation", "count_gestation", "min_weaning",
"max_weaning", "count_weaning", "min_birth_hatching_weight", "max_birth_hatching_weight",
"count_birth_hatching_weight", "min_weaning_weight", "max_weaning_weight", "count_weaning_weight",
"min_egg_mass", "max_egg_mass", "count_egg_mass", "min_incubation", "max_incubation",
"count_incubation", "min_fledging_age", "max_fledging_age", "count_fledging_age",
"min_male_maturity", "max_male_maturity", "count_male_maturity",
"min_inter_litter_interbirth_interval", "max_inter_litter_interbirth_interval",
"count_inter_litter_interbirth_interval", "min_female_body_mass", "max_female_body_mass",
"count_female_body_mass", "min_male_body_mass", "max_male_body_mass", "count_male_body_mass",
"min_no_sex_body_mass", "max_no_sex_body_mass", "count_no_sex_body_mass", "min_egg_width",
"max_egg_width", "count_egg_width", "min_egg_length", "max_egg_length", "count_egg_length",
"min_fledging_mass", "max_fledging_mass", "count_fledging_mass", "min_adult_svl", "max_adult_svl",
"count_adult_svl", "min_male_svl", "max_male_svl", "count_male_svl", "min_female_svl",
"max_female_svl", "count_female_svl", "min_hatching_svl", "max_hatching_svl", "count_hatching_svl",
"min_female_svl_at_maturity", "max_female_svl_at_maturity", "count_female_svl_at_maturity",
"min_female_body_mass_at_maturity", "max_female_body_mass_at_maturity",
"count_female_body_mass_at_maturity", "min_no_sex_svl", "max_no_sex_svl", "count_no_sex_svl",
"min_no_sex_maturity", "max_no_sex_maturity", "count_no_sex_maturity"]
range_columns = [
('record_id', ('pk-auto',)), ('classx', ('char', '20')), ('orderx', ('char', '20')),
('familyx', ('char', '20')), ('genus', ('char', '20')), ('species', ('char', '50')),
('subspecies', ('char', '20')), ('common_name', ('char', '400')), ('trait_value', ('ct-double',))]
table_range = Table('range', delimiter=',', cleanup=self.cleanup_func_table)
table_range.ct_column = ct_column
table_range.ct_names = range_ct_names
table_range.columns = range_columns
engine.auto_create_table(table_range,
filename="Amniote_Range_Count_Aug_2015.csv")
engine.insert_data_from_file(engine.format_filename("Amniote_Range_Count_Aug_2015.csv"))
def download(self, engine=None, debug=False):
Script.download(self, engine, debug)
self.engine.auto_create_table(Table("sites"), url=self.urls["sites"], filename='gentry_sites.csv')
self.engine.insert_data_from_url(self.urls["sites"])
self.engine.download_file(self.urls["stems"], "all_Excel.zip")
local_zip = zipfile.ZipFile(self.engine.format_filename("all_Excel.zip"))
filelist = local_zip.namelist()
local_zip.close()
self.engine.download_files_from_archive(self.urls["stems"], filelist)
filelist = [os.path.basename(filename) for filename in filelist]
# Currently all_Excel.zip is missing CURUYUQU.xls
# Download it separately and add it to the file list
if not self.engine.find_file('CURUYUQU.xls'):
self.engine.download_file("http://www.mobot.org/mobot/gentry/123/samerica/CURUYUQU.xls", "CURUYUQU.xls")
filelist.append('CURUYUQU.xls')
lines = []
tax = []
for filename in filelist:
print("Extracting data from " + filename + "...")
book = xlrd.open_workbook(self.engine.format_filename(filename))
sh = book.sheet_by_index(0)
rows = sh.nrows
cn = {'stems': []}
n = 0
for colnum, c in enumerate(sh.row(0)):
if not Excel.empty_cell(c):
cid = c.value.lower().strip()
# line number column is sometimes named differently
if cid in ["sub", "number"]:
cid = "line"
# the "number of individuals" column is named in various
# different ways; they always at least contain "nd"
if "nd" in cid:
cid = "count"
# in QUIAPACA.xls the "number of individuals" column is
# misnamed "STEMDBH" just like the stems columns, so weep
# for the state of scientific data and then fix manually
if filename == "QUIAPACA.xls" and colnum == 13:
cid = "count"
# if column is a stem, add it to the list of stems;
# otherwise, make note of the column name/number
if "stem" in cid or "dbh" in cid:
cn["stems"].append(n)
else:
cn[cid] = n
n += 1
# sometimes, a data file does not contain a liana or count column
if not "liana" in list(cn.keys()):
cn["liana"] = -1
if not "count" in list(cn.keys()):
cn["count"] = -1
for i in range(1, rows):
row = sh.row(i)
cellcount = len(row)
# make sure the row is real, not just empty cells
if not all(Excel.empty_cell(cell) for cell in row):
try:
this_line = {}
# get the following information from the appropriate columns
for i in ["line", "family", "genus", "species",
"liana", "count"]:
if cn[i] > -1:
if row[cn[i]].ctype != 2:
# if the cell type(ctype) is not a number
this_line[i] = row[cn[i]].value.lower().strip().replace("\\", "/").replace('"', '')
else:
this_line[i] = row[cn[i]].value
if this_line[i] == '`':
this_line[i] = 1
this_line["stems"] = [row[c]
for c in cn["stems"]
if not Excel.empty_cell(row[c])]
this_line["site"] = filename[0:-4]
# Manually correct CEDRAL data, which has a single line
# that is shifted by one to the left starting at Liana
if this_line["site"] == "CEDRAL" and type(this_line["liana"]) == float:
this_line["liana"] = ""
this_line["count"] = 3
this_line["stems"] = [2.5, 2.5, 30, 18, 25]
lines.append(this_line)
# Check how far the species is identified
full_id = 0
if len(this_line["species"]) < 3:
if len(this_line["genus"]) < 3:
id_level = "family"
else:
id_level = "genus"
else:
id_level = "species"
full_id = 1
tax.append((this_line["family"],
this_line["genus"],
this_line["species"],
id_level,
str(full_id)))
except:
raise
pass
tax = sorted(tax, key=lambda group: group[0] + " " + group[1] + " " + group[2])
unique_tax = []
tax_dict = {}
tax_count = 0
# Get all unique families/genera/species
print("\n")
for group in tax:
if not (group in unique_tax):
unique_tax.append(group)
tax_count += 1
tax_dict[group[0:3]] = tax_count
if tax_count % 10 == 0:
msg = "Generating taxonomic groups: " + str(tax_count) + " / " + str(TAX_GROUPS)
sys.stdout.flush()
sys.stdout.write(msg + "\b" * len(msg))
print("\n")
# Create species table
table = Table("species", delimiter=",")
table.columns=[("species_id" , ("pk-int",) ),
("family" , ("char", ) ),
("genus" , ("char", ) ),
("species" , ("char", ) ),
("id_level" , ("char", 10) ),
("full_id" , ("int",) )]
data = [[str(tax_dict[group[:3]])] + ['"%s"' % g for g in group]
for group in unique_tax]
table.pk = 'species_id'
table.contains_pk = True
self.engine.table = table
self.engine.create_table()
self.engine.add_to_table(data)
# Create stems table
table = Table("stems", delimiter=",")
table.columns=[("stem_id" , ("pk-auto",) ),
("line" , ("int",) ),
("species_id" , ("int",) ),
("site_code" , ("char", 12) ),
("liana" , ("char", 10) ),
("stem" , ("double",) )]
stems = []
counts = []
for line in lines:
try:
liana = line["liana"]
except KeyError:
liana = ""
species_info = [line["line"],
tax_dict[(line["family"],
line["genus"],
line["species"])],
line["site"],
liana
]
try:
counts.append([value for value in species_info + [line["count"]]])
except KeyError:
pass
for i in line["stems"]:
stem = species_info + [str(i)]
stems.append(stem)
self.engine.table = table
self.engine.create_table()
self.engine.add_to_table(stems)
# Create counts table
table = Table("counts", delimiter=",", contains_pk=False)
table.columns=[("count_id" , ("pk-auto",) ),
("line" , ("int",) ),
("species_id" , ("int",) ),
("site_code" , ("char", 12) ),
("liana" , ("char", 10) ),
("count" , ("double",) )]
self.engine.table = table
self.engine.create_table()
self.engine.add_to_table(counts)
return self.engine
def download(self, engine=None, debug=False):
try:
Script.download(self, engine, debug)
engine = self.engine
# Species table
table = Table("species", cleanup=Cleanup(), contains_pk=True,
header_rows=6)
table.columns=[("species_id", ("pk-int",) ),
("AOU", ("int",) ),
("english_common_name", ("char",50) ),
("french_common_name", ("char",50) ),
("spanish_common_name", ("char",50) ),
("sporder", ("char",30) ),
("family", ("char",30) ),
("genus", ("char",30) ),
("species", ("char",50) ),
]
table.fixed_width = [7,6,51,51,51,51,51,51,50]
engine.table = table
engine.create_table()
engine.insert_data_from_url(self.urls["species"])
# Routes table
if not os.path.isfile(engine.format_filename("routes_new.csv")):
engine.download_files_from_archive(self.urls["routes"],
["routes.csv"])
read = open(engine.format_filename("routes.csv"), "rb")
write = open(engine.format_filename("routes_new.csv"), "wb")
print "Cleaning routes data..."
write.write(read.readline())
for line in read:
values = line.split(',')
v = Decimal(values[5])
if v > 0:
values[5] = str(v * Decimal("-1"))
write.write(','.join(str(value) for value in values))
write.close()
read.close()
engine.auto_create_table(Table("routes", cleanup=Cleanup()),
filename="routes_new.csv")
engine.insert_data_from_file(engine.format_filename("routes_new.csv"))
# Weather table
if not os.path.isfile(engine.format_filename("weather_new.csv")):
engine.download_files_from_archive(self.urls["weather"],
["weather.csv"])
read = open(engine.format_filename("weather.csv"), "rb")
write = open(engine.format_filename("weather_new.csv"), "wb")
print "Cleaning weather data..."
for line in read:
values = line.split(',')
newvalues = []
for value in values:
if ':' in value:
newvalues.append(value.replace(':', ''))
elif value == "N":
newvalues.append(None)
else:
newvalues.append(value)
write.write(','.join(str(value) for value in newvalues))
write.close()
read.close()
engine.auto_create_table(Table("weather", pk="RouteDataId", cleanup=Cleanup()),
filename="weather_new.csv")
engine.insert_data_from_file(engine.format_filename("weather_new.csv"))
# Region_codes table
table = Table("region_codes", pk=False, header_rows=11,
fixed_width=[11, 11, 30])
def regioncodes_cleanup(value, engine):
replace = {chr(225):"a", chr(233):"e", chr(237):"i", chr(243):"o"}
newvalue = str(value)
for key in replace.keys():
if key in newvalue:
newvalue = newvalue.replace(key, replace[key])
return newvalue
table.cleanup = Cleanup(regioncodes_cleanup)
table.columns=[("countrynum" , ("int",) ),
("regioncode" , ("int",) ),
("regionname" , ("char",30) )]
engine.table = table
engine.create_table()
engine.insert_data_from_url(self.urls["region_codes"])
# Counts table
table = Table("counts", pk=False, delimiter=',')
table.columns=[("RouteDataID" , ("int",) ),
("countrynum" , ("int",) ),
("statenum" , ("int",) ),
("Route" , ("int",) ),
("RPID" , ("int",) ),
("year" , ("int",) ),
("AOU" , ("int",) ),
("Stop1" , ("int",) ),
("Stop2" , ("int",) ),
("Stop3" , ("int",) ),
("Stop4" , ("int",) ),
("Stop5" , ("int",) ),
("Stop6" , ("int",) ),
("Stop7" , ("int",) ),
("Stop8" , ("int",) ),
("Stop9" , ("int",) ),
("Stop10" , ("int",) ),
("Stop11" , ("int",) ),
("Stop12" , ("int",) ),
("Stop13" , ("int",) ),
("Stop14" , ("int",) ),
("Stop15" , ("int",) ),
("Stop16" , ("int",) ),
("Stop17" , ("int",) ),
("Stop18" , ("int",) ),
("Stop19" , ("int",) ),
("Stop20" , ("int",) ),
("Stop21" , ("int",) ),
("Stop22" , ("int",) ),
("Stop23" , ("int",) ),
("Stop24" , ("int",) ),
("Stop25" , ("int",) ),
("Stop26" , ("int",) ),
("Stop27" , ("int",) ),
("Stop28" , ("int",) ),
("Stop29" , ("int",) ),
("Stop30" , ("int",) ),
("Stop31" , ("int",) ),
("Stop32" , ("int",) ),
("Stop33" , ("int",) ),
("Stop34" , ("int",) ),
("Stop35" , ("int",) ),
("Stop36" , ("int",) ),
("Stop37" , ("int",) ),
("Stop38" , ("int",) ),
("Stop39" , ("int",) ),
("Stop40" , ("int",) ),
("Stop41" , ("int",) ),
("Stop42" , ("int",) ),
("Stop43" , ("int",) ),
("Stop44" , ("int",) ),
("Stop45" , ("int",) ),
("Stop46" , ("int",) ),
("Stop47" , ("int",) ),
("Stop48" , ("int",) ),
("Stop49" , ("int",) ),
("Stop50" , ("int",) )]
part = ""
engine.table = table
engine.create_table()
for part in range(1,11):
part = str(part)
try:
print "Inserting data from part " + part + "..."
try:
engine.table.cleanup = Cleanup()
engine.insert_data_from_archive(self.urls["counts"] +
"Fifty" + part + ".zip",
["fifty" + part + ".csv"])
except:
print "Failed bulk insert on " + part + ", inserting manually."
engine.connection.rollback()
engine.table.cleanup = Cleanup(correct_invalid_value,
nulls=['*'])
engine.insert_data_from_archive(self.urls["counts"] +
"Fifty" + part + ".zip",
["fifty" + part + ".csv"])
except:
print "There was an error in part " + part + "."
raise
except zipfile.BadZipfile:
print "There was an unexpected error in the Breeding Bird Survey archives."
raise
return engine
def download(self, engine=None, debug=False):
Script.download(self, engine, debug)
reload(sys)
if hasattr(sys, 'setdefaultencoding'):
sys.setdefaultencoding("utf-8")
self.engine.download_file(self.urls["GWDD"], "GlobalWoodDensityDatabase.xls")
filename = os.path.basename("GlobalWoodDensityDatabase.xls")
book = xlrd.open_workbook(self.engine.format_filename(filename))
sh = book.sheet_by_index(1)
rows = sh.nrows
# Creating data files
file_path = self.engine.format_filename("gwdd_data.csv")
gwdd_data = open_fw(file_path)
csv_writer = open_csvw(gwdd_data)
csv_writer.writerow(["Number", "Family", "Binomial", "Wood_Density", "Region", "Reference_Number"])
for index in range(1, rows):
row = sh.row(index)
# get each row and format the sell value.
row_as_list = [to_str(column_value.value) for column_value in row]
csv_writer.writerow(row_as_list)
gwdd_data.close()
table = Table("data", delimiter=",")
table.columns = [("Number", ("pk-int",)),
("Family", ("char",)),
("Binomial", ("char",)),
("Wood_Density", ("double",)),
("Region", ("char",)),
("Reference_Number", ("int",))]
table.pk = 'Number'
table.contains_pk = True
self.engine.table = table
self.engine.create_table()
self.engine.insert_data_from_file(engine.format_filename(file_path))
# Creating reference tale file
file_path = self.engine.format_filename("gwdd_ref.csv")
ref_file = open_fw(file_path)
csv_writerd = open_csvw(ref_file)
csv_writerd.writerow(["Reference_Number", "Reference"])
sh = book.sheet_by_index(2)
rows = sh.nrows
for index in range(1, rows):
row = sh.row(index)
# get each row and format the sell value.
row_as_list = [to_str(column_value.value, object_encoding=sys.stdout) for column_value in row]
csv_writerd.writerow(row_as_list)
ref_file.close()
table = Table("reference", delimiter=",")
table.columns = [("Reference_Number", ("pk-int",)), ("Reference", ("char",))]
table.pk = 'Reference_Number'
table.contains_pk = True
self.engine.table = table
self.engine.create_table()
self.engine.insert_data_from_file(engine.format_filename(file_path))
return self.engine
def download(self, engine=None, debug=False):
Script.download(self, engine, debug)
self.engine.auto_create_table(Table("sites"), url=self.urls["sites"])
self.engine.insert_data_from_url(self.urls["sites"])
self.engine.download_file(self.urls["stems"], "all_Excel.zip")
local_zip = zipfile.ZipFile(self.engine.format_filename("all_Excel.zip"))
filelist = local_zip.namelist()
local_zip.close()
self.engine.download_files_from_archive(self.urls["stems"], filelist)
filelist = [os.path.basename(filename) for filename in filelist]
lines = []
tax = []
for filename in filelist:
print "Extracting data from " + filename + "..."
book = xlrd.open_workbook(self.engine.format_filename(filename))
sh = book.sheet_by_index(0)
rows = sh.nrows
cn = {'stems': []}
n = 0
for c in sh.row(0):
if not Excel.empty_cell(c):
cid = Excel.cell_value(c).lower()
# line number column is sometimes named differently
if cid in ["sub", "number"]:
cid = "line"
# the "number of individuals" column is named in various
# different ways; they always at least contain "nd"
if "nd" in cid:
cid = "count"
# if column is a stem, add it to the list of stems;
# otherwise, make note of the column name/number
if "stem" in cid:
cn["stems"].append(n)
else:
cn[cid] = n
n += 1
# sometimes, a data file does not contain a liana or count column
if not "liana" in cn.keys():
cn["liana"] = -1
if not "count" in cn.keys():
cn["count"] = -1
for i in range(1, rows):
row = sh.row(i)
cellcount = len(row)
# make sure the row is real, not just empty cells
if cellcount > 4 and not Excel.empty_cell(row[0]):
try:
this_line = {}
def format_value(s):
s = Excel.cell_value(s)
return str(s).title().replace("\\", "/").replace('"', '')
# get the following information from the appropriate columns
for i in ["line", "family", "genus", "species",
"liana", "count"]:
if cn[i] > -1:
this_line[i] = format_value(row[cn[i]])
if this_line[i] == '`':
this_line[i] = 1
this_line["stems"] = [Excel.cell_value(row[c])
for c in cn["stems"]
if not Excel.empty_cell(row[c])]
this_line["site"] = filename[0:-4]
lines.append(this_line)
# Check how far the species is identified
full_id = 0
if len(this_line["species"]) < 3:
if len(this_line["genus"]) < 3:
id_level = "family"
else:
id_level = "genus"
else:
id_level = "species"
full_id = 1
tax.append((this_line["family"],
this_line["genus"],
this_line["species"].lower().replace('\\', '').replace('"', ''),
id_level,
str(full_id)))
except:
raise
pass
tax = sorted(tax, key=lambda group: group[0] + " " + group[1] + " " + group[2])
unique_tax = []
tax_dict = dict()
tax_count = 0
# Get all unique families/genera/species
for group in tax:
if not (group in unique_tax):
unique_tax.append(group)
tax_count += 1
tax_dict[group[0:3]] = tax_count
if tax_count % 10 == 0:
msg = "Generating taxonomic groups: " + str(tax_count) + " / " + str(TAX_GROUPS)
sys.stdout.write(msg + "\b" * len(msg))
print "Generating taxonomic groups: " + str(TAX_GROUPS) + " / " + str(TAX_GROUPS)
# Create species table
table = Table("species", delimiter=",")
table.columns=[("species_id" , ("pk-int",) ),
("family" , ("char", ) ),
("genus" , ("char", ) ),
("species" , ("char", ) ),
("id_level" , ("char", 10) ),
("full_id" , ("bool",) )]
data = [','.join([str(tax_dict[group[:3]])] + ['"%s"' % g for g in group])
for group in unique_tax]
table.pk = 'species_id'
table.contains_pk = True
self.engine.table = table
self.engine.create_table()
self.engine.add_to_table(data)
# Create stems table
table = Table("stems", delimiter=",", contains_pk=False)
table.columns=[("stem_id" , ("pk-auto",) ),
("line" , ("int",) ),
("species_id" , ("int",) ),
("site_code" , ("char", 12) ),
("liana" , ("char", 10) ),
("stem" , ("double",) )]
stems = []
counts = []
for line in lines:
try:
liana = line["liana"]
except KeyError:
liana = ""
species_info = [line["line"],
tax_dict[(line["family"],
line["genus"],
line["species"].lower())],
line["site"],
liana
]
try:
counts.append([str(value) for value in species_info + [line["count"]]])
except KeyError:
pass
for i in line["stems"]:
stem = species_info + [i]
stems.append([str(value) for value in stem])
data = [','.join(stem) for stem in stems]
self.engine.table = table
self.engine.create_table()
self.engine.add_to_table(data)
# Create counts table
table = Table("counts", delimiter=",", contains_pk=False)
table.columns=[("count_id" , ("pk-auto",) ),
("line" , ("int",) ),
("species_id" , ("int",) ),
("site_code" , ("char", 12) ),
("liana" , ("char", 10) ),
("count" , ("double",) )]
data = [','.join(count) for count in counts]
self.engine.table = table
self.engine.create_table()
self.engine.add_to_table(data)
return self.engine
def download(self, engine=None, debug=False):
Script.download(self, engine, debug)
engine = self.engine
filenames = ['Aquatic_animal_excretion_data.csv',
'Aquatic_animal_excretion_variable_descriptions.csv']
for file_paths in filenames:
if not os.path.isfile(engine.format_filename(file_paths)):
url = self.urls["aquatic_animals"]
engine.download_files_from_archive(url, filenames, "zip")
# processing Aquatic_animal_excretion_data.csv
filename = 'Aquatic_animal_excretion_data.csv'
tablename = 'aquatic_animals'
table = Table(str(tablename), delimiter=',')
table.columns = [
("index", ("pk-int",)),
("sourcenumber", ("int",)),
("sourcename", ("char",)),
("speciesname", ("char",)),
("speciescode", ("char",)),
("invert/vert", ("char",)),
("phylum", ("char",)),
("class", ("char",)),
("order", ("char",)),
("family", ("char",)),
("trophicgild", ("char",)),
("drymass", ("double",)),
("logdrymass", ("double",)),
("ecosystemtype", ("char",)),
("energysource", ("char",)),
("habitat", ("char",)),
("residentecosystem", ("char",)),
("temperature", ("double",)),
("nexcretionrate", ("double",)),
("pexcretionrate", ("double",)),
("lognexcretionrate", ("double",)),
("logpexcretionrate", ("double",)),
("incubationtime", ("double",)),
("nform", ("char",)),
("pform", ("char",)),
("bodyc", ("double",)),
("bodyn", ("double",)),
("bodyp", ("double",)),
("bodyc:n", ("double",)),
("bodyc:p", ("double",)),
("bodyn:p", ("double",)),
("bodydatasource", ("char",)),
("datasource", ("char",)),
("dataproviders", ("char",))]
engine.table = table
engine.create_table()
engine.insert_data_from_file(engine.format_filename(str(filename)))
# processing Aquatic_animal_excretion_variable_descriptions.csv
filename = 'Aquatic_animal_excretion_variable_descriptions.csv'
tablename = 'variable_descriptions'
table = Table(str(tablename), delimiter=',')
table.columns = [
("Column", ("char",)),
("Variable", ("char",)),
("Description", ("char",)),
("Data Class", ("char",)),
("Units", ("char",)),
("Minimum_value", ("char",)),
("Maximum_value", ("char",)),
("Possible_values", ("char",)),
("Missing_data_symbol", ("char",)),
("Notes", ("char",))]
engine.table = table
engine.create_table()
engine.insert_data_from_file(engine.format_filename(str(filename)))
def download(self, engine=None, debug=False):
Script.download(self, engine, debug)
self.engine.download_file(self.urls["GWDD"], "GlobalWoodDensityDatabase.xls")
filename = os.path.basename("GlobalWoodDensityDatabase.xls")
book = xlrd.open_workbook(self.engine.format_filename(filename))
sh = book.sheet_by_index(1)
rows = sh.nrows
#Creating data table
lines = []
for i in range(1, rows):
row = sh.row(i)
if not all(Excel.empty_cell(cell) for cell in row):
this_line = {}
def format_value(s):
s = Excel.cell_value(s)
return str(s).title().replace("\\", "/").replace('"', '')
for num, label in enumerate(["Number", "Family", "Binomial", "Wood_Density",
"Region", "Reference_Number"]):
this_line[label] = format_value(row[num])
lines.append(this_line)
table = Table("data", delimiter="\t")
table.columns=[("Number" , ("pk-int",) ),
("Family" , ("char",) ),
("Binomial" , ("char",) ),
("Wood_Density" , ("double",) ),
("Region" , ("char",) ),
("Reference_Number" , ("int",) )]
table.pk = 'Number'
table.contains_pk = True
gwdd = []
for line in lines:
gwdd_data = [line["Number"],
line["Family"],
line["Binomial"],
line["Wood_Density"],
line["Region"],
line["Reference_Number"]]
gwdd.append(gwdd_data)
data = ['\t'.join(gwdd_line) for gwdd_line in gwdd]
self.engine.table = table
self.engine.create_table()
self.engine.add_to_table(data)
#Creating reference table
lines = []
sh = book.sheet_by_index(2)
rows = sh.nrows
for i in range(1, rows):
row = sh.row(i)
if not all(Excel.empty_cell(cell) for cell in row):
this_line = {}
def format_value(s):
s = Excel.cell_value(s)
return str(s).title().replace("\\", "/").replace('"', '')
for num, label in enumerate(["Reference_Number", "Reference"]):
this_line[label] = format_value(row[num])
lines.append(this_line)
table = Table("reference", delimiter="\t")
table.columns=[("Reference_Number" , ("pk-int",) ),
("Reference" , ("char",) )]
table.pk = 'Reference_Number'
table.contains_pk = True
gwdd = []
for line in lines:
gwdd_ref = [line["Reference_Number"],
line["Reference"]]
gwdd.append(gwdd_ref)
data = ['\t'.join(gwdd_line) for gwdd_line in gwdd]
self.engine.table = table
self.engine.create_table()
self.engine.add_to_table(data)
return self.engine
def download(self, engine=None, debug=False):
Script.download(self, engine, debug)
engine = self.engine
filename = "database.csv"
tablename = "predicts_main"
table = Table(str(tablename), delimiter=',')
table.columns = [("Source_ID", ("char",)),
("Reference", ("char",)),
("Study_number", ("int",)),
("Study_name", ("char",)),
("SS", ("char",)),
("Diversity_metric", ("char",)),
("Diversity_metric_unit", ("char",)),
("Diversity_metric_type", ("char",)),
("Diversity_metric_is_effort_sensitive", ("char",)),
("Diversity_metric_is_suitable_for_Chao", ("char",)),
("Sampling_method", ("char",)),
("Sampling_effort_unit", ("char",)),
("Study_common_taxon", ("char",)),
("Rank_of_study_common_taxon", ("char",)),
("Site_number", ("int",)),
("Site_name", ("char",)),
("Block", ("char",)),
("SSS", ("char",)),
("SSB", ("char",)),
("SSBS", ("char",)),
("Sample_start_earliest", ("char",)),
("Sample_end_latest", ("char",)),
("Sample_midpoint", ("char",)),
("Sample_date_resolution", ("char",)),
("Max_linear_extent_metres", ("double",)),
("Habitat_patch_area_square_metres", ("double",)),
("Sampling_effort", ("double",)),
("Rescaled_sampling_effort", ("double",)),
("Habitat_as_described", ("char",)),
("Predominant_land_use", ("char",)),
("Source_for_predominant_land_use", ("char",)),
("Use_intensity", ("char",)),
("Km_to_nearest_edge_of_habitat", ("double",)),
("Years_since_fragmentation_or_conversion", ("double",)),
("Transect_details", ("char",)),
("Coordinates_method", ("char",)),
("Longitude", ("double",)),
("Latitude", ("double",)),
("Country_distance_metres", ("double",)),
("Country", ("char")),
("UN_subregion", ("char",)),
("UN_region", ("char",)),
("Ecoregion_distance_metres", ("double",)),
("Ecoregion", ("char",)),
("Biome", ("char",)),
("Realm", ("char",)),
("Hotspot", ("char",)),
("Wilderness_area", ("char",)),
("N_samples", ("double",)),
("Taxon_number", ("double",)),
("Taxon_name_entered", ("char",)),
("Indication", ("char",)),
("Parsed_name", ("char",)),
("Taxon", ("char",)),
("COL_ID", ("double",)),
("Name_status", ("char",)),
("Rank", ("char",)),
("Kingdom", ("char",)),
("Phylum", ("char",)),
("Class", ("char",)),
("Order", ("char",)),
("Family", ("char",)),
("Genus", ("char",)),
("Species", ("char",)),
("Best_guess_binomial", ("char",)),
("Higher_taxa", ("char",)),
("Higher_taxon", ("char",)),
("Measurement", ("double",)),
("Effort_corrected_measurement", ("double",))]
engine.table = table
if not os.path.isfile(engine.format_filename(filename)):
engine.download_files_from_archive(self.urls["PREDICTS"],
[filename],
"zip",
False,
"download.zip")
engine.create_table()
engine.insert_data_from_file(engine.format_filename(str(filename)))
def download(self, engine=None, debug=False):
Script.download(self, engine, debug)
engine = self.engine
filename = 'vertnet_latest_reptiles.csv'
tablename = 'reptiles'
table = Table(str(tablename), delimiter=',')
table.columns = [
("record_id", ("pk-auto",)),
("beginrecord", ("char",)),
("icode", ("char",)),
("title", ("char",)),
("citation", ("char",)),
("contact", ("char",)),
("email", ("char",)),
("emlrights", ("char",)),
("gbifdatasetid", ("char",)),
("gbifpublisherid", ("char",)),
("doi", ("char",)),
("migrator", ("char",)),
("networks", ("char",)),
("orgcountry", ("char",)),
("orgname", ("char",)),
("orgstateprovince", ("char",)),
("pubdate", ("char",)),
("source_url", ("char",)),
("iptrecordid", ("char",)),
("associatedmedia", ("char",)),
("associatedoccurrences", ("char",)),
("associatedorganisms", ("char",)),
("associatedreferences", ("char",)),
("associatedsequences", ("char",)),
("associatedtaxa", ("char",)),
("bed", ("char",)),
("behavior", ("char",)),
("catalognumber", ("char",)),
("continent", ("char",)),
("coordinateprecision", ("char",)),
("coordinateuncertaintyinmeters", ("char",)),
("country", ("char",)),
("countrycode", ("char",)),
("county", ("char",)),
("dateidentified", ("char",)),
("day", ("char",)),
("decimallatitude", ("char",)),
("decimallongitude", ("char",)),
("disposition", ("char",)),
("earliestageorloweststage", ("char",)),
("earliesteonorlowesteonothem", ("char",)),
("earliestepochorlowestseries", ("char",)),
("earliesteraorlowesterathem", ("char",)),
("earliestperiodorlowestsystem", ("char",)),
("enddayofyear", ("char",)),
("establishmentmeans", ("char",)),
("eventdate", ("char",)),
("eventid", ("char",)),
("eventremarks", ("char",)),
("eventtime", ("char",)),
("fieldnotes", ("char",)),
("fieldnumber", ("char",)),
("footprintspatialfit", ("char",)),
("footprintsrs", ("char",)),
("footprintwkt", ("char",)),
("formation", ("char",)),
("geodeticdatum", ("char",)),
("geologicalcontextid", ("char",)),
("georeferencedby", ("char",)),
("georeferenceddate", ("char",)),
("georeferenceprotocol", ("char",)),
("georeferenceremarks", ("char",)),
("georeferencesources", ("char",)),
("georeferenceverificationstatus", ("char",)),
("group", ("char",)),
("habitat", ("char",)),
("highergeography", ("char",)),
("highergeographyid", ("char",)),
("highestbiostratigraphiczone", ("char",)),
("identificationid", ("char",)),
("identificationqualifier", ("char",)),
("identificationreferences", ("char",)),
("identificationremarks", ("char",)),
("identificationverificationstatus", ("char",)),
("identifiedby", ("char",)),
("individualcount", ("char",)),
("island", ("char",)),
("islandgroup", ("char",)),
("latestageorhigheststage", ("char",)),
("latesteonorhighesteonothem", ("char",)),
("latestepochorhighestseries", ("char",)),
("latesteraorhighesterathem", ("char",)),
("latestperiodorhighestsystem", ("char",)),
("lifestage", ("char",)),
("lithostratigraphicterms", ("char",)),
("locality", ("char",)),
("locationaccordingto", ("char",)),
("locationid", ("char",)),
("locationremarks", ("char",)),
("lowestbiostratigraphiczone", ("char",)),
("materialsampleid", ("char",)),
("maximumdepthinmeters", ("char",)),
("maximumdistanceabovesurfaceinmeters", ("char",)),
("maximumelevationinmeters", ("char",)),
("member", ("char",)),
("minimumdepthinmeters", ("char",)),
("minimumdistanceabovesurfaceinmeters", ("char",)),
("minimumelevationinmeters", ("char",)),
("month", ("char",)),
("municipality", ("char",)),
("occurrenceid", ("char",)),
("occurrenceremarks", ("char",)),
("occurrencestatus", ("char",)),
("organismid", ("char",)),
("organismname", ("char",)),
("organismremarks", ("char",)),
("organismscope", ("char",)),
("othercatalognumbers", ("char",)),
("pointradiusspatialfit", ("char",)),
("preparations", ("char",)),
("previousidentifications", ("char",)),
("recordedby", ("char",)),
("recordnumber", ("char",)),
("reproductivecondition", ("char",)),
("samplingeffort", ("char",)),
("samplingprotocol", ("char",)),
("sex", ("char",)),
("startdayofyear", ("char",)),
("stateprovince", ("char",)),
("typestatus", ("char",)),
("verbatimcoordinates", ("char",)),
("verbatimcoordinatesystem", ("char",)),
("verbatimdepth", ("char",)),
("verbatimelevation", ("char",)),
("verbatimeventdate", ("char",)),
("verbatimlatitude", ("char",)),
("verbatimlocality", ("char",)),
("verbatimlongitude", ("char",)),
("verbatimsrs", ("char",)),
("waterbody", ("char",)),
("year", ("char",)),
("dctype", ("char",)),
("modified", ("char",)),
("language", ("char",)),
("license", ("char",)),
("rightsholder", ("char",)),
("accessrights", ("char",)),
("bibliographiccitation", ("char",)),
("dc_references", ("char",)),
("institutionid", ("char",)),
("collectionid", ("char",)),
("datasetid", ("char",)),
("institutioncode", ("char",)),
("collectioncode", ("char",)),
("datasetname", ("char",)),
("ownerinstitutioncode", ("char",)),
("basisofrecord", ("char",)),
("informationwithheld", ("char",)),
("datageneralizations", ("char",)),
("dynamicproperties", ("char",)),
("scientificnameid", ("char",)),
("namepublishedinid", ("char",)),
("scientificname", ("char",)),
("acceptednameusage", ("char",)),
("originalnameusage", ("char",)),
("namepublishedin", ("char",)),
("namepublishedinyear", ("char",)),
("higherclassification", ("char",)),
("kingdom", ("char",)),
("phylum", ("char",)),
("class", ("char",)),
("order", ("char",)),
("family", ("char",)),
("genus", ("char",)),
("subgenus", ("char",)),
("specificepithet", ("char",)),
("infraspecificepithet", ("char",)),
("taxonrank", ("char",)),
("verbatimtaxonrank", ("char",)),
("scientificnameauthorship", ("char",)),
("vernacularname", ("char",)),
("nomenclaturalcode", ("char",)),
("taxonomicstatus", ("char",)),
("keyname", ("char",)),
("haslicense", ("int",)),
("vntype", ("char",)),
("rank", ("int",)),
("mappable", ("int",)),
("hashid", ("char",)),
("hastypestatus", ("int",)),
("wascaptive", ("int",)),
("wasinvasive", ("int",)),
("hastissue", ("int",)),
("hasmedia", ("int",)),
("isfossil", ("int",)),
("haslength", ("int",)),
("haslifestage", ("int",)),
("hasmass", ("int",)),
("hassex", ("int",)),
("lengthinmm", ("double",)),
("massing", ("double",)),
("lengthunitsinferred", ("char",)),
("massunitsinferred", ("char",)),
("underivedlifestage", ("char",)),
("underivedsex", ("char",))]
engine.table = table
if not os.path.isfile(engine.format_filename(filename)):
engine.download_files_from_archive(self.urls[tablename],
[filename],
"zip",
False,
"vertnet_latest_" + str(tablename))
engine.create_table()
engine.insert_data_from_file(engine.format_filename(str(filename)))
def download(self, engine=None, debug=False):
Script.download(self, engine, debug)
engine = self.engine
engine.download_files_from_archive(self.urls["capture"], archive_type="zip")
# Convert xlsx to csv.
xlsx_file = self.engine.format_filename("DSD_FI_CAPTURE.xlsx")
file_path = self.engine.format_filename("DSD_CAPTURE.csv")
book = xlrd.open_workbook(xlsx_file)
sh = book.sheet_by_index(0)
rows = sh.nrows
# Creating data files
new_data = open_fw(file_path)
csv_writer = open_csvw(new_data)
csv_writer.writerow(["Order", "Concept_id",
"Role_Type", "Codelist_id",
"Codelist_Code_id", "Description"])
for index in range(2, rows):
row = sh.row(index)
# Get each row and format the sell value.
# Data starts at index 2
row_as_list = [to_str(column_value.value) for column_value in row]
csv_writer.writerow(row_as_list)
new_data.close()
file_names = [
('CL_FI_UNIT.csv', 'unit_data'),
('CL_FI_WATERAREA_GROUPS.csv', 'waterarea_groups'),
('DSD_CAPTURE.csv', 'dsd_capture_data'),
('CL_FI_SPECIES_GROUPS.csv', 'species_group')
]
for (filename, tablename) in file_names:
data_path = self.engine.format_filename(filename)
table = Table(tablename, delimiter=',', cleanup=self.cleanup_func_table)
self.engine.auto_create_table(table, filename=filename)
self.engine.insert_data_from_file(data_path)
# File CL_FI_COUNTRY_GROUPS.csv has multi encoding
file_names_encoded = [
('CL_FI_COUNTRY_GROUPS.csv', 'country_groups'),
]
for (filename, tablename) in file_names_encoded:
data_path = self.engine.format_filename(filename)
table = Table(tablename, delimiter=',', cleanup=self.cleanup_func_table)
table.columns = [('UN_Code', ('int', )),
('Identifier', ('int', )),
('ISO2_Code', ('char', '5')),
('ISO3_Code', ('char', '5')),
('Name_En', ('char', '50')),
('Name_Fr', ('char', '50')),
('Name_Es', ('char', '50')),
('Name_Ar', ('char', '120')),
('Name_Cn', ('char', '90')),
('Name_Ru', ('char', '150')),
('Official_Name_En', ('char', '70')),
('Official_Name_Fr', ('char', '70')),
('Official_Name_Es', ('char', '70')),
('Official_Name_Ar', ('char', '1100')),
('Official_Name_Cn', ('char', '70')),
('Official_Name_Ru', ('char', '130')),
('Continent_Group', ('char', '15')),
('EcoClass_Group', ('char', '50')),
('GeoRegion_Group', ('char', '30'))]
self.engine.auto_create_table(table, filename=filename)
self.engine.insert_data_from_file(data_path)
# TS_FI_CAPTURE is
file_names_encoded = [
('TS_FI_CAPTURE.csv', 'ts_capture_data',)
]
for (filename, tablename) in file_names_encoded:
data_path = self.engine.format_filename(filename)
table = Table(tablename, delimiter=',', cleanup=self.cleanup_func_table)
table.columns = [('COUNTRY', ('int', )),
('FISHING_AREA', ('int', )),
('SPECIES', ('char', '10')),
('YEAR', ('int', )),
('UNIT', ('char', '5')),
('QUANTITY', ('double', )),
('SYMBOL', ('char', '4'))]
self.engine.auto_create_table(table, filename=filename)
self.engine.insert_data_from_file(data_path)
