def __call__(self, cls):
model_name= os.path.basename(getmodule(cls).__file__).split('.')[0]
kwargs = make_name.DJANGO_MODEL
kwargs['remove_prefix'] = self.remove_prefix
model_name = make_name(model_name, **kwargs)
# self.db_name = re.sub(r'[.]pyc?$', '', self.db_name, flags=re.IGNORECASE)
# self.db_name = re.sub(r'[._-]+models$', '', self.db_name, flags=re.IGNORECASE)
# self.db_name = re.sub(r'^models[._-]+', '', self.db_name, flags=re.IGNORECASE)
setattr(cls, '__name__', model_name + self.sep + getattr(cls, '__name__'))
return cls
def __call__(self, cls):
model_name = os.path.basename(getmodule(cls).__file__).split('.')[0]
kwargs = make_name.DJANGO_MODEL
kwargs['remove_prefix'] = self.remove_prefix
model_name = make_name(model_name, **kwargs)
# self.db_name = re.sub(r'[.]pyc?$', '', self.db_name, flags=re.IGNORECASE)
# self.db_name = re.sub(r'[._-]+models$', '', self.db_name, flags=re.IGNORECASE)
# self.db_name = re.sub(r'^models[._-]+', '', self.db_name, flags=re.IGNORECASE)
setattr(cls, '__name__',
model_name + self.sep + getattr(cls, '__name__'))
return cls
def d3_plot_context(context, table=((0, 0),), title='Line Chart', xlabel='Time', ylabel='Value', header=None, limit=10001):
"""
Arguments:
table (list of lists of values): A CSV/Excel style table with an optional header row as the first list
title (str): String to display atop the plot
xlabel (str): Text to display along the bottom axis
ylabel (str): Text to display along the vertical axis
limit (int): Maximum number of points to include in context variable `data.d3data`
"""
if isinstance(table, pd.Series):
table = pd.DataFrame(table, columns=header or [ylabel]).sort_index()
if isinstance(table, pd.DataFrame):
df = table.sort_index()
table = list(df.to_records())
for i, row in enumerate(table):
d = row[0]
first_row = []
if isinstance(d, datetime.datetime):
# ISO 8601 date-time format is ECMA/javascript-friendly:
# YYYY-MM-DDTHH:mm:ss.sssZ
# `T` and `Z` are literal characters, alternatively `Z` (means UTC) can be replaced with timezone info like +/-HH:mm
table[i][0] = d.isoformat()
if not first_row:
first_row += ['Date-Time']
elif isinstance(d, datetime.date):
table[i][0] = "{0:02d}-{1:02d}-{2:02d}".format(d.year, d.month, d.day)
if not first_row:
first_row += ['Date']
else:
if not first_row:
first_row += ['Sample']
first_row += list(str(c).strip() for c in df.columns)
header = None
else:
first_row = list(table[0])
N, M = len(table), max(len(row) for row in table)
identifiers = header
descriptions = header
if not header and not all(isinstance(col, basestring) and col.strip() for col in first_row):
print first_row
if isinstance(header, bool):
header = []
else:
header = [('y{0}'.format(i-1) if i else 'x') for i in range(M)]
else:
header = first_row
table = table[1:]
# header should now be a list of one list of strings or an empty list,
# So now just need to make sure the names of the columns are valid javascript identifiers
if header:
identifiers = [util.make_name(h, language='javascript', space='') for h in header]
table = [header] + table
descriptions = [unicode(h) for h in header]
# print header, identifiers
if len(table) > limit:
new_table = [table[0]]
step = int(float(len(table))/limit)
print "step = {0}".format(step)
for i in range(1+limit, len(table), step):
new_table += [table[i]]
table = new_table
context['data'] = context.get('data', {})
context['data'].update({
#'lags_dict': {hist_type: lags},
'title': title,
'header': json.dumps(identifiers),
'descriptions': json.dumps(descriptions),
'xlabel': xlabel,
'ylabel': ylabel,
'd3data': json.dumps(util.transposed_lists(table)),
'form': {},
})
# print context['data']
return context
def clean_labels(df):
# in iPython Notebook print out df.columns to show that many of them contain dots
# rename the columns to be attribute-name friendly
df.columns = [label.replace('.', '_') for label in df.columns]
df.columns = [make_name(label) for label in df.columns]
return df
def clean_labels(df):
# in iPython Notebook print out df.columns to show that many of them contain dots
# rename the columns to be attribute-name friendly
df.columns = [label.replace('.', '_') for label in df.columns]
df.columns = [make_name(label) for label in df.columns]
return df
