def report(line):
"""write the line enclosed in comment box"""
height, width = utils.getTerminalSize()
separatorStr = width*"#"
formatStr = "## %s %s"%(line, (width - len(line) - 4)*"#")
print "%s\n%s\n%s"%(separatorStr, formatStr, separatorStr)
return
def format_desc(indent, desc):
desc = " ".join(desc.split())
rows, columns = utils.getTerminalSize()
columns = int(columns)
if columns < 40: columns = 40
afterindent = columns - indent
output = ""
first = True
for line in textwrap.wrap(desc, afterindent):
if not first:
for i in range(0,indent):
output += " "
output += line
output += "\n"
first = False
return output.rstrip()
def printDatasets(self, datasets):
''' Given the heterogeneous dataset list, the method splits it in
categories and build the table per session. A unique id all over
the sessions permit the user to select univocally a dataset. All
the metadata will be printed with the parent chain: a parent dataset
is defined as the one used as input to create the child one. The
method is public but not exported in GPI'''
# check the term width
(width, height) = utils.getTerminalSize()
if width < 160:
logger.error("Your terminal's width is %d; must be at least 160\nYou can make your font size smaller" % width)
raise utils.QuitException()
# better a named tuple but available in Python 2.6 only
# fullsim: the table elements should be common to all the dataset keys in the list
# print table rules:
grouped_datasets = [
# 0
{'title': 'Fastsim Official Production',
'dataset': list(),
'order_by': ['prod_series', 'analysis', 'generator', 'dg', 'bkg_mixing', 'analysis_type'],
'columns': ['id', 'prod_series', 'analysis', 'generator', 'dg', 'bkg_mixing', 'analysis_type', 'status']
},
# 1
{'title': 'Fastsim Personal Production',
'dataset': list(),
'order_by': ['free_string', 'analysis', 'generator', 'dg', 'bkg_mixing', 'analysis_type'],
'columns': ['id', 'free_string', 'analysis', 'generator', 'dg', 'bkg_mixing', 'analysis_type', 'status'],
},
# 2
{'title': 'Fullsim Official Production',
'dataset': list(),
'order_by': ['prod_series', 'simtype', 'generator', 'dg', 'pl', 'g4ver', 'opt_photons'],
'columns': ['id', 'prod_series', 'simtype', 'generator', 'dg', 'pl', 'g4ver', 'opt_photons', 'status']
},
# 3
{'title': 'Fullsim Personal Production',
'dataset': list(),
'order_by': ['free_string', 'generator', 'dg', 'pl', 'g4ver', 'opt_photons'],
'columns': ['id', 'free_string', 'generator', 'dg', 'pl', 'g4ver', 'opt_photons', 'status']
},
# 4
{'title': 'Analysis',
'dataset': list(),
'order_by': ['free_string', 'creation_date'],
'columns': ['id', 'free_string', 'creation_date', 'status']
}
]
for dataset in datasets:
# put sub dictionary elements to level zero dictionary
for key, value in dataset.items():
if type(dataset[key]) is types.DictType:
for key1, value1 in dataset[key].iteritems():
dataset[key1] = value1
#del dataset[key]
# dataset selection
if dataset['session'] == 'fastsim':
if dataset['owner'] == 'Official':
grouped_datasets[0]['dataset'].append(dataset)
else:
grouped_datasets[1]['dataset'].append(dataset)
elif dataset['session'] == 'fullsim':
if dataset['owner'] == 'Official':
grouped_datasets[2]['dataset'].append(dataset)
else:
grouped_datasets[3]['dataset'].append(dataset)
elif dataset['session'] == 'analysis':
grouped_datasets[4]['dataset'].append(dataset)
else:
raise GangaException('session not recognized: %s' % dataset['session'])
i = 0
# field sort, adding id and print
for group in grouped_datasets:
if len(group['dataset']) > 0:
print('\n%s' % group['title'])
# dictionary sorting
group['dataset'] = sorted(group['dataset'], key=lambda elem: ('%s ' * len(group['order_by'])) % tuple([elem[d] for d in group['order_by']]))
# id adding
for dataset in group['dataset']:
dataset['id'] = i
i += 1
print(utils.format_dict_table(group['dataset'], group['columns']))
# ask for input and print dataset details
if i == 1:
index = 0
print('\nAutomatically selected the only entry')
else:
print('\nChoose the dataset:')
index = utils.getIndex(maxExclusive=i)
# Object oriented solution to investigate and/or binary search
# datasets have been grouped per print rules
for group in grouped_datasets:
for d in group['dataset']:
if d['id'] == index:
for dataset in datasets:
if dataset['dataset_id'] == d['dataset_id']:
return dataset
if input_line.find("http://") != -1 or input_line.find("https://") != -1:
input_line = get_url_title(input_line) + " - " + input_line
client.send(input_line)
if __name__ == '__main__':
if ID == None:
ID = raw_input("ID : ")
if PW == None:
PW = getpass.getpass("PW : ")
client = Client(ID,PW)
sz = getTerminalSize()
screen_width = sz[0]
# Print recent messages
for msg in client.get_recent_messages(15)[::-1]:
client.mark_read_message(msg.id)
if hasattr(msg,'content'):
if msg._from != client.user_id:
print(transColor(rjust(msg.content, screen_width,' '),lovers_color))
else:
print(msg.content)
else:
try:
sticker_id = msg.attachments[0][u'sticker'][u'sticker_id']
if msg._from != client.user_id:
def printDatasets(self, datasets):
''' Given the heterogeneous dataset list, the method splits it in
categories and build the table per session. A unique id all over
the sessions permit the user to select univocally a dataset. All
the metadata will be printed with the parent chain: a parent dataset
is defined as the one used as input to create the child one. The
method is public but not exported in GPI'''
# check the term width
(width, height) = utils.getTerminalSize()
if width < 160:
logger.error(
"Your terminal's width is %d; must be at least 160\nYou can make your font size smaller"
% width)
raise utils.QuitException()
# better a named tuple but available in Python 2.6 only
# fullsim: the table elements should be common to all the dataset keys in the list
# print table rules:
grouped_datasets = [
# 0
{
'title':
'Fastsim Official Production',
'dataset':
list(),
'order_by': [
'prod_series', 'analysis', 'generator', 'dg', 'bkg_mixing',
'analysis_type'
],
'columns': [
'id', 'prod_series', 'analysis', 'generator', 'dg',
'bkg_mixing', 'analysis_type', 'status'
]
},
# 1
{
'title':
'Fastsim Personal Production',
'dataset':
list(),
'order_by': [
'free_string', 'analysis', 'generator', 'dg', 'bkg_mixing',
'analysis_type'
],
'columns': [
'id', 'free_string', 'analysis', 'generator', 'dg',
'bkg_mixing', 'analysis_type', 'status'
],
},
# 2
{
'title':
'Fullsim Official Production',
'dataset':
list(),
'order_by': [
'prod_series', 'simtype', 'generator', 'dg', 'pl', 'g4ver',
'opt_photons'
],
'columns': [
'id', 'prod_series', 'simtype', 'generator', 'dg', 'pl',
'g4ver', 'opt_photons', 'status'
]
},
# 3
{
'title':
'Fullsim Personal Production',
'dataset':
list(),
'order_by': [
'free_string', 'generator', 'dg', 'pl', 'g4ver',
'opt_photons'
],
'columns': [
'id', 'free_string', 'generator', 'dg', 'pl', 'g4ver',
'opt_photons', 'status'
]
},
# 4
{
'title': 'Analysis',
'dataset': list(),
'order_by': ['free_string', 'creation_date'],
'columns': ['id', 'free_string', 'creation_date', 'status']
}
]
for dataset in datasets:
# put sub dictionary elements to level zero dictionary
for key, value in dataset.items():
if type(dataset[key]) is types.DictType:
for key1, value1 in dataset[key].iteritems():
dataset[key1] = value1
#del dataset[key]
# dataset selection
if dataset['session'] == 'fastsim':
if dataset['owner'] == 'Official':
grouped_datasets[0]['dataset'].append(dataset)
else:
grouped_datasets[1]['dataset'].append(dataset)
elif dataset['session'] == 'fullsim':
if dataset['owner'] == 'Official':
grouped_datasets[2]['dataset'].append(dataset)
else:
grouped_datasets[3]['dataset'].append(dataset)
elif dataset['session'] == 'analysis':
grouped_datasets[4]['dataset'].append(dataset)
else:
raise GangaException('session not recognized: %s' %
dataset['session'])
i = 0
# field sort, adding id and print
for group in grouped_datasets:
if len(group['dataset']) > 0:
print('\n%s' % group['title'])
# dictionary sorting
group['dataset'] = sorted(
group['dataset'],
key=lambda elem: ('%s ' * len(group['order_by'])) % tuple(
[elem[d] for d in group['order_by']]))
# id adding
for dataset in group['dataset']:
dataset['id'] = i
i += 1
print(
utils.format_dict_table(group['dataset'],
group['columns']))
# ask for input and print dataset details
if i == 1:
index = 0
print('\nAutomatically selected the only entry')
else:
print('\nChoose the dataset:')
index = utils.getIndex(maxExclusive=i)
# Object oriented solution to investigate and/or binary search
# datasets have been grouped per print rules
for group in grouped_datasets:
for d in group['dataset']:
if d['id'] == index:
for dataset in datasets:
if dataset['dataset_id'] == d['dataset_id']:
return dataset
