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https://github.com/SheffieldML/GPy.git
synced 2026-05-01 07:46:22 +02:00
Convert print to function for Python 3 compatibility
This commit is contained in:
Mike Croucher
parent
4b4e5d4901
commit
c5b91e543a
7 changed files with 48 additions and 48 deletions
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@ -75,7 +75,7 @@ def prompt_user(prompt):
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elif choice in no:
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return False
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else:
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print("Your response was a " + choice)
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print(("Your response was a " + choice))
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print("Please respond with 'yes', 'y' or 'no', 'n'")
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#return prompt_user()
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@ -99,7 +99,7 @@ def download_url(url, store_directory, save_name=None, messages=True, suffix='')
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"""Download a file from a url and save it to disk."""
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i = url.rfind('/')
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file = url[i+1:]
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print file
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print(file)
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dir_name = os.path.join(data_path, store_directory)
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if save_name is None: save_name = os.path.join(dir_name, file)
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@ -107,7 +107,7 @@ def download_url(url, store_directory, save_name=None, messages=True, suffix='')
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if suffix is None: suffix=''
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print "Downloading ", url, "->", save_name
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print("Downloading ", url, "->", save_name)
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if not os.path.exists(dir_name):
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os.makedirs(dir_name)
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try:
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@ -150,7 +150,7 @@ def download_url(url, store_directory, save_name=None, messages=True, suffix='')
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sys.stdout.write(status)
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sys.stdout.flush()
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sys.stdout.write(" "*(len(status)) + "\r")
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print status
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print(status)
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# if we wanted to get more sophisticated maybe we should check the response code here again even for successes.
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#with open(save_name, 'wb') as f:
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# f.write(response.read())
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@ -159,32 +159,32 @@ def download_url(url, store_directory, save_name=None, messages=True, suffix='')
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def authorize_download(dataset_name=None):
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"""Check with the user that the are happy with terms and conditions for the data set."""
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print('Acquiring resource: ' + dataset_name)
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print(('Acquiring resource: ' + dataset_name))
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# TODO, check resource is in dictionary!
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print('')
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dr = data_resources[dataset_name]
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print('Details of data: ')
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print(dr['details'])
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print((dr['details']))
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print('')
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if dr['citation']:
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print('Please cite:')
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print(dr['citation'])
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print((dr['citation']))
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print('')
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if dr['size']:
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print('After downloading the data will take up ' + str(dr['size']) + ' bytes of space.')
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print(('After downloading the data will take up ' + str(dr['size']) + ' bytes of space.'))
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print('')
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print('Data will be stored in ' + os.path.join(data_path, dataset_name) + '.')
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print(('Data will be stored in ' + os.path.join(data_path, dataset_name) + '.'))
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print('')
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if overide_manual_authorize:
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if dr['license']:
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print('You have agreed to the following license:')
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print(dr['license'])
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print((dr['license']))
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print('')
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return True
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else:
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if dr['license']:
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print('You must also agree to the following license:')
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print(dr['license'])
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print((dr['license']))
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print('')
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return prompt_user('Do you wish to proceed with the download? [yes/no]')
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@ -495,18 +495,18 @@ def google_trends(query_terms=['big data', 'machine learning', 'data science'],
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file = 'data.csv'
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file_name = os.path.join(dir_path,file)
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if not os.path.exists(file_name) or refresh_data:
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print "Accessing Google trends to acquire the data. Note that repeated accesses will result in a block due to a google terms of service violation. Failure at this point may be due to such blocks."
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print("Accessing Google trends to acquire the data. Note that repeated accesses will result in a block due to a google terms of service violation. Failure at this point may be due to such blocks.")
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# quote the query terms.
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quoted_terms = []
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for term in query_terms:
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quoted_terms.append(urllib2.quote(term))
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print "Query terms: ", ', '.join(query_terms)
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print("Query terms: ", ', '.join(query_terms))
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print "Fetching query:"
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print("Fetching query:")
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query = 'http://www.google.com/trends/fetchComponent?q=%s&cid=TIMESERIES_GRAPH_0&export=3' % ",".join(quoted_terms)
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data = urllib2.urlopen(query).read()
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print "Done."
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print("Done.")
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# In the notebook they did some data cleaning: remove Javascript header+footer, and translate new Date(....,..,..) into YYYY-MM-DD.
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header = """// Data table response\ngoogle.visualization.Query.setResponse("""
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data = data[len(header):-2]
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@ -520,8 +520,8 @@ def google_trends(query_terms=['big data', 'machine learning', 'data science'],
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df.to_csv(file_name)
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else:
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print "Reading cached data for google trends. To refresh the cache set 'refresh_data=True' when calling this function."
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print "Query terms: ", ', '.join(query_terms)
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print("Reading cached data for google trends. To refresh the cache set 'refresh_data=True' when calling this function.")
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print("Query terms: ", ', '.join(query_terms))
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df = pandas.read_csv(file_name, parse_dates=[0])
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@ -679,11 +679,11 @@ def ripley_synth(data_set='ripley_prnn_data'):
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def global_average_temperature(data_set='global_temperature', num_train=1000, refresh_data=False):
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path = os.path.join(data_path, data_set)
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if data_available(data_set) and not refresh_data:
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print 'Using cached version of the data set, to use latest version set refresh_data to True'
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print('Using cached version of the data set, to use latest version set refresh_data to True')
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else:
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download_data(data_set)
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data = np.loadtxt(os.path.join(data_path, data_set, 'GLBTS.long.data'))
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print 'Most recent data observation from month ', data[-1, 1], ' in year ', data[-1, 0]
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print('Most recent data observation from month ', data[-1, 1], ' in year ', data[-1, 0])
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allX = data[data[:, 3]!=-99.99, 2:3]
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allY = data[data[:, 3]!=-99.99, 3:4]
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X = allX[:num_train, 0:1]
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@ -695,11 +695,11 @@ def global_average_temperature(data_set='global_temperature', num_train=1000, re
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def mauna_loa(data_set='mauna_loa', num_train=545, refresh_data=False):
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path = os.path.join(data_path, data_set)
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if data_available(data_set) and not refresh_data:
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print 'Using cached version of the data set, to use latest version set refresh_data to True'
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print('Using cached version of the data set, to use latest version set refresh_data to True')
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else:
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download_data(data_set)
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data = np.loadtxt(os.path.join(data_path, data_set, 'co2_mm_mlo.txt'))
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print 'Most recent data observation from month ', data[-1, 1], ' in year ', data[-1, 0]
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print('Most recent data observation from month ', data[-1, 1], ' in year ', data[-1, 0])
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allX = data[data[:, 3]!=-99.99, 2:3]
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allY = data[data[:, 3]!=-99.99, 3:4]
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X = allX[:num_train, 0:1]
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@ -802,10 +802,10 @@ def hapmap3(data_set='hapmap3'):
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if not reduce(lambda a,b: a and b, map(os.path.exists, preprocessed_data_paths)):
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if not overide_manual_authorize and not prompt_user("Preprocessing requires ~25GB "
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"of memory and can take a (very) long time, continue? [Y/n]"):
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print "Preprocessing required for further usage."
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print("Preprocessing required for further usage.")
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return
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status = "Preprocessing data, please be patient..."
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print status
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print(status)
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def write_status(message, progress, status):
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stdout.write(" "*len(status)); stdout.write("\r"); stdout.flush()
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status = r"[{perc: <{ll}}] {message: <13s}".format(message=message, ll=20,
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@ -873,13 +873,13 @@ def hapmap3(data_set='hapmap3'):
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inandf = DataFrame(index=metadf.index, data=inan, columns=mapnp[:,1])
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inandf.to_pickle(preprocessed_data_paths[2])
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status=write_status('done :)', 100, status)
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print ''
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print('')
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else:
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print "loading snps..."
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print("loading snps...")
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snpsdf = read_pickle(preprocessed_data_paths[0])
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print "loading metainfo..."
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print("loading metainfo...")
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metadf = read_pickle(preprocessed_data_paths[1])
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print "loading nan entries..."
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print("loading nan entries...")
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inandf = read_pickle(preprocessed_data_paths[2])
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snps = snpsdf.values
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populations = metadf.population.values.astype('S3')
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@ -1001,7 +1001,7 @@ def singlecell_rna_seq_deng(dataset='singlecell_deng'):
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# Extract the tar file
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filename = os.path.join(dir_path, 'GSE45719_Raw.tar')
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with tarfile.open(filename, 'r') as files:
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print "Extracting Archive {}...".format(files.name)
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print("Extracting Archive {}...".format(files.name))
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data = None
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gene_info = None
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message = ''
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@ -1010,9 +1010,9 @@ def singlecell_rna_seq_deng(dataset='singlecell_deng'):
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for i, file_info in enumerate(members):
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f = files.extractfile(file_info)
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inner = read_csv(f, sep='\t', header=0, compression='gzip', index_col=0)
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print ' '*(len(message)+1) + '\r',
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print(' '*(len(message)+1) + '\r', end=' ')
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message = "{: >7.2%}: Extracting: {}".format(float(i+1)/overall, file_info.name[:20]+"...txt.gz")
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print message,
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print(message, end=' ')
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if data is None:
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data = inner.RPKM.to_frame()
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data.columns = [file_info.name[:-18]]
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@ -1035,8 +1035,8 @@ def singlecell_rna_seq_deng(dataset='singlecell_deng'):
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sys.stdout.write(' '*len(message) + '\r')
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sys.stdout.flush()
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print
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print "Read Archive {}".format(files.name)
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print()
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print("Read Archive {}".format(files.name))
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return data_details_return({'Y': data,
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'series_info': info,
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