def aggregate(dataset, measures=['amount'], drilldowns=None, cuts=None,
page=1, pagesize=10000, order=None, inflate=None):
"""
Do an aggregation over a single dataset. This method calls the aggregation
method of the dataset but in case inflation is asked for, this method takes
care of those computations
"""
# If we have to inflate we need to add a year to the drilldowns
# (since inflation only supports years at the moment).
if inflate:
drilldowns.append('time.year')
# Aggregate the dataset via its own aggregate function
result = dataset.aggregate(measures=measures, drilldowns=drilldowns,
cuts=cuts, page=page,pagesize=pagesize,
order=order)
# If we have to inflate we do some inflation calculations
if inflate:
try:
# Since different parts of the drilldowns are going to change
# (differently) we need to recompute the sumamry total
summary_total = 0
# We collect adjusted drilldowns into a new dict since if
# inflation fails we can still serve the uninflated content
adjusted_drilldowns = {}
# We remove the time.year we added to the drilldowns
hash_key = drilldowns[:]
hash_key.remove('time.year')
for item in result['drilldown']:
# Get the inflated amount for this year (returns an inflation
# dictionary with values for reference and target dates along
# with original and inflated amounts)
adjustment = helpers.inflate(item['amount'],
inflate, item['time'],
dataset.territories)
# We make the reference and target datestrings with ISO format
# (ISO format is yyyy-mm-dd).
adjustment['reference'] = adjustment['reference'].isoformat()
adjustment['target'] = adjustment['target'].isoformat()
# Get the inflated amount into its own variable
inflated_amount = adjustment['inflated']
# Get the item key
item_key = unicode([get_value(k, item) for k in hash_key])
if item_key not in adjusted_drilldowns:
# We copy the item in case something happens (then we
# catch it and serve the original aggregation result)
adjusted_drilldown = item.copy()
remove_excessive_time(hash_key, adjusted_drilldown)
adjusted_drilldown['inflation_adjustment'] = [adjustment]
adjusted_drilldown['amount'] = inflated_amount
adjusted_drilldowns[item_key] = adjusted_drilldown
else:
adjusted_drilldowns[item_key]['inflation_adjustment']\
.append(adjustment)
adjusted_drilldowns[item_key]['amount'] += inflated_amount
summary_total += inflated_amount
result['drilldown'] = adjusted_drilldowns.values()
result['summary']['original'] = result['summary']['amount']
result['summary']['amount'] = summary_total
except KeyError, error:
# If inflation fails because a date wasn't found in the inflation
# data, then it raises a KeyError (returning the date in the
# exception message
# Note we do not remove time.year from the drilldown here since
# it can help resolve the warning (and it's bothersome)
result['warning'] = {'inflation':
'Unable to do inflation adjustment',
'error': 'Inflation error: %s' % error}
def view(self, dataset, id, format='html'):
"""
Get a specific entry in the dataset, identified by the id. Entry
can be return as html (default), json or csv.
"""
# Generate the dataset
self._get_dataset(dataset)
# Get the entry that matches the given id. c.dataset.entries is
# a generator so we create a list from it's responses based on the
# given constraint
entries = list(c.dataset.entries(c.dataset.alias.c.id == id))
# Since we're trying to get a single entry the list should only
# contain one entry, if not then we return an error
if not len(entries) == 1:
abort(404, _('Sorry, there is no entry %r') % id)
# Add urls to the dataset and assign assign it as a context variable
c.entry = entry_apply_links(dataset, entries.pop())
# Get and set some context variables from the entry
# This shouldn't really be necessary but it's here so nothing gets
# broken
c.id = c.entry.get('id')
c.from_ = c.entry.get('from')
c.to = c.entry.get('to')
c.currency = c.entry.get('currency', c.dataset.currency).upper()
c.time = c.entry.get('time')
# Get the amount for the entry
amount = c.entry.get('amount')
# We adjust for inflation if the user as asked for this to be inflated
if 'inflate' in request.params:
try:
# Inflate the amount. Target date is provided in request.params
# as value for inflate and reference date is the date of the
# entry. We also provide a list of the territories to extract
# a single country for which to do the inflation
c.inflation = h.inflate(amount, request.params['inflate'],
c.time, c.dataset.territories)
# The amount to show should be the inflated amount
# and overwrite the entry's amount as well
c.amount = c.inflation['inflated']
c.entry['amount'] = c.inflation['inflated']
# We include the inflation response in the entry's dict
# HTML description assumes every dict value for the entry
# includes a label so we include a default "Inflation
# adjustment" for it to work.
c.inflation['label'] = 'Inflation adjustment'
c.entry['inflation_adjustment'] = c.inflation
except:
# If anything goes wrong in the try clause (and there's a lot
# that can go wrong). We just say that we can't adjust for
# inflation and set the context amount as the original amount
h.flash_notice(_('Unable to adjust for inflation'))
c.amount = amount
else:
# If we haven't been asked to inflate then we just use the
# original amount
c.amount = amount
# Add custom html for the dataset entry if the dataset has some
# custom html
# 2013-11-17 disabled this as part of removal of genshi as depended on
# a genshi specific helper.
# TODO: reinstate if important
# c.custom_html = h.render_entry_custom_html(c.dataset, c.entry)
# Add the rest of the dimensions relating to this entry into a
# extras dictionary. We first need to exclude all dimensions that
# are already shown and then we can loop through the dimensions
excluded_keys = ('time', 'amount', 'currency', 'from', 'to', 'dataset',
'id', 'name', 'description')
c.extras = {}
if c.dataset:
# Create a dictionary of the dataset dimensions
c.desc = dict([(d.name, d) for d in c.dataset.dimensions])
# Loop through dimensions of the entry
for key in c.entry:
# Entry dimension must be a dataset dimension and not in
# the predefined excluded keys
if key in c.desc and \
key not in excluded_keys:
c.extras[key] = c.entry[key]
# Return entry based on
if format == 'json':
return to_jsonp(c.entry)
elif format == 'csv':
return write_csv([c.entry], response)
else:
return templating.render('entry/view.html')
def view(self, dataset, id, format='html'):
"""
Get a specific entry in the dataset, identified by the id. Entry
can be return as html (default), json or csv.
"""
# Generate the dataset
self._get_dataset(dataset)
# Get the entry that matches the given id. c.dataset.entries is
# a generator so we create a list from it's responses based on the
# given constraint
entries = list(c.dataset.entries(c.dataset.alias.c.id == id))
# Since we're trying to get a single entry the list should only
# contain one entry, if not then we return an error
if not len(entries) == 1:
abort(404, _('Sorry, there is no entry %r') % id)
# Add urls to the dataset and assign assign it as a context variable
c.entry = entry_apply_links(dataset, entries.pop())
# Get and set some context variables from the entry
# This shouldn't really be necessary but it's here so nothing gets
# broken
c.id = c.entry.get('id')
c.from_ = c.entry.get('from')
c.to = c.entry.get('to')
c.currency = c.entry.get('currency', c.dataset.currency).upper()
c.time = c.entry.get('time')
# Get the amount for the entry
amount = c.entry.get('amount')
# We adjust for inflation if the user as asked for this to be inflated
if request.params.has_key('inflate'):
try:
# Inflate the amount. Target date is provided in request.params
# as value for inflate and reference date is the date of the
# entry. We also provide a list of the territories to extract
# a single country for which to do the inflation
c.inflation = h.inflate(amount, request.params['inflate'],
c.time, c.dataset.territories)
# The amount to show should be the inflated amount
# and overwrite the entry's amount as well
c.amount = c.inflation['inflated']
c.entry['amount'] = c.inflation['inflated']
# We include the inflation response in the entry's dict
# HTML description assumes every dict value for the entry
# includes a label so we include a default "Inflation
# adjustment" for it to work.
c.inflation['label'] = 'Inflation adjustment'
c.entry['inflation_adjustment'] = c.inflation
except:
# If anything goes wrong in the try clause (and there's a lot
# that can go wrong). We just say that we can't adjust for
# inflation and set the context amount as the original amount
h.flash_notice(_('Unable to adjust for inflation'))
c.amount = amount
else:
# If we haven't been asked to inflate then we just use the
# original amount
c.amount = amount
# Add custom html for the dataset entry if the dataset has some
# custom html
# 2013-11-17 disabled this as part of removal of genshi as depended on
# a genshi specific helper.
# TODO: reinstate if important
# c.custom_html = h.render_entry_custom_html(c.dataset, c.entry)
# Add the rest of the dimensions relating to this entry into a
# extras dictionary. We first need to exclude all dimensions that
# are already shown and then we can loop through the dimensions
excluded_keys = ('time', 'amount', 'currency', 'from',
'to', 'dataset', 'id', 'name', 'description')
c.extras = {}
if c.dataset:
# Create a dictionary of the dataset dimensions
c.desc = dict([(d.name, d) for d in c.dataset.dimensions])
# Loop through dimensions of the entry
for key in c.entry:
# Entry dimension must be a dataset dimension and not in
# the predefined excluded keys
if key in c.desc and \
not key in excluded_keys:
c.extras[key] = c.entry[key]
# Return entry based on
if format == 'json':
return to_jsonp(c.entry)
elif format == 'csv':
return write_csv([c.entry], response)
else:
return templating.render('entry/view.html')
def aggregate(dataset,
measures=['amount'],
drilldowns=None,
cuts=None,
page=1,
pagesize=10000,
order=None,
inflate=None):
"""
Do an aggregation over a single dataset. This method calls the aggregation
method of the dataset but in case inflation is asked for, this method takes
care of those computations
"""
# If we have to inflate we need to add a year to the drilldowns
# (since inflation only supports years at the moment).
if inflate:
drilldowns.append('time.year')
# Aggregate the dataset via its own aggregate function
result = dataset.aggregate(measures=measures,
drilldowns=drilldowns,
cuts=cuts,
page=page,
pagesize=pagesize,
order=order)
# If we have to inflate we do some inflation calculations
if inflate:
try:
# Since different parts of the drilldowns are going to change
# (differently) we need to recompute the sumamry total
summary_total = 0
# We collect adjusted drilldowns into a new dict since if
# inflation fails we can still serve the uninflated content
adjusted_drilldowns = {}
# We remove the time.year we added to the drilldowns
hash_key = drilldowns[:]
hash_key.remove('time.year')
for item in result['drilldown']:
# Get the inflated amount for this year (returns an inflation
# dictionary with values for reference and target dates along
# with original and inflated amounts)
adjustment = helpers.inflate(item['amount'], inflate,
item['time'], dataset.territories)
# We make the reference and target datestrings with ISO format
# (ISO format is yyyy-mm-dd).
adjustment['reference'] = adjustment['reference'].isoformat()
adjustment['target'] = adjustment['target'].isoformat()
# Get the inflated amount into its own variable
inflated_amount = adjustment['inflated']
# Get the item key
item_key = unicode([get_value(k, item) for k in hash_key])
if item_key not in adjusted_drilldowns:
# We copy the item in case something happens (then we
# catch it and serve the original aggregation result)
adjusted_drilldown = item.copy()
remove_excessive_time(hash_key, adjusted_drilldown)
adjusted_drilldown['inflation_adjustment'] = [adjustment]
adjusted_drilldown['amount'] = inflated_amount
adjusted_drilldowns[item_key] = adjusted_drilldown
else:
adjusted_drilldowns[item_key]['inflation_adjustment'] \
.append(adjustment)
adjusted_drilldowns[item_key]['amount'] += inflated_amount
summary_total += inflated_amount
result['drilldown'] = adjusted_drilldowns.values()
result['summary']['original'] = result['summary']['amount']
result['summary']['amount'] = summary_total
except KeyError as error:
# If inflation fails because a date wasn't found in the inflation
# data, then it raises a KeyError (returning the date in the
# exception message
# Note we do not remove time.year from the drilldown here since
# it can help resolve the warning (and it's bothersome)
result['warning'] = {
'inflation': 'Unable to do inflation adjustment',
'error': 'Inflation error: %s' % error
}
return result
