def show_result(request):
settings = request.registry.settings
data_folder = settings['data.folder']
tool_definition = settings['tool_definition']
result_id = basename(request.matchdict['id'])
result_response_folder = join(
data_folder, 'results', result_id, 'response')
if not exists(result_response_folder):
raise HTTPNotFound
result_arguments, result_properties = load_result_configuration(
result_response_folder)
data_type_by_suffix = get_data_type_by_suffix()
tool_items = get_data_items(
result_arguments, tool_definition, data_type_by_suffix)
result_errors = get_data_items(merge_dictionaries(
result_properties.pop('standard_errors', {}),
result_properties.pop('type_errors', {}),
), tool_definition, data_type_by_suffix)
result_items = get_data_items(
result_properties.pop('standard_outputs', {}), tool_definition,
data_type_by_suffix)
result_properties = get_data_items(
result_properties, tool_definition, data_type_by_suffix)
return merge_dictionaries(
get_template_variables(tool_definition, 'tool', tool_items),
get_template_variables(tool_definition, 'result', result_items), {
'data_types': set(x.data_type for x in tool_items + result_items),
'tool_id': 1,
'result_id': result_id,
'result_errors': result_errors,
'result_properties': result_properties,
})
def prepare_cost_summary(cost_by_year, d, keywords, prefix):
"""
Summarize costs using the values provided in *d*
"""
discounted_cost = compute_discounted_cash_flow(
cost_by_year, keywords['financing_year'],
keywords['discount_rate_as_percent_of_cash_flow_per_year'])
levelized_cost = divide_safely(discounted_cost, keywords[
'discounted_consumption_in_kwh'], 0)
return merge_dictionaries(d, {
prefix + 'cost_by_year': cost_by_year,
prefix + 'initial_cost': sum([
sum_by_suffix(d, '_raw_cost'),
sum_by_suffix(d, '_installation_cost'),
]),
prefix + 'recurring_fixed_cost_per_year': sum([
sum_by_suffix(d, '_maintenance_cost_per_year'),
sum_by_suffix(d, '_replacement_cost_per_year'),
]),
prefix + 'recurring_variable_cost_per_year': sum([
d.get('final_electricity_production_cost_per_year', 0),
]),
prefix + 'discounted_cost': discounted_cost,
prefix + 'levelized_cost_per_kwh_consumed': levelized_cost,
})
def prepare_cost_summary(cost_by_year, d, keywords, prefix):
"""
Summarize costs using the values provided in *d*
"""
discounted_cost = compute_discounted_cash_flow(
cost_by_year, keywords['financing_year'],
keywords['discount_rate_as_percent_of_cash_flow_per_year'])
levelized_cost = divide_safely(discounted_cost, keywords[
'discounted_consumption_in_kwh'], 0)
return merge_dictionaries(d, {
prefix + 'cost_by_year': cost_by_year,
prefix + 'initial_cost': sum([
sum_by_suffix(d, '_raw_cost'),
sum_by_suffix(d, '_installation_cost'),
]),
prefix + 'recurring_fixed_cost_per_year': sum([
sum_by_suffix(d, '_maintenance_cost_per_year'),
sum_by_suffix(d, '_replacement_cost_per_year'),
]),
prefix + 'recurring_variable_cost_per_year': sum([
d.get('final_electricity_production_cost_per_year', 0),
]),
prefix + 'discounted_cost': discounted_cost,
prefix + 'levelized_cost_per_kwh_consumed': levelized_cost,
})
def get_result_template_variables(result, result_folder):
result_configuration = ResultConfiguration(result_folder)
tool_definition = result_configuration.tool_definition
result_arguments = result_configuration.result_arguments
result_properties = result_configuration.result_properties
tool_items = get_data_items(result_arguments, tool_definition)
result_items = get_data_items(
result_properties.pop('standard_outputs', {}), tool_definition)
result_errors = get_data_items(
merge_dictionaries(result_properties.pop('standard_errors', {}),
result_properties.pop('type_errors', {})),
tool_definition)
result_properties = get_data_items(result_properties, tool_definition)
tool = result.tool
tool.title, tool.template_parts = parse_template_from(
tool_definition, 'tool', tool_items)
result.title, result.template_parts = parse_template_from(
tool_definition, 'result', result_items)
return {
'data_types': set(x.data_type for x in tool_items + result_items),
'tool': tool,
'result': result,
'result_errors': result_errors,
'result_properties': result_properties,
}
def get_tool_template_variables(tool, tool_definition):
tool_arguments = get_tool_arguments(tool_definition)
tool_items = get_data_items(tool_arguments, tool_definition)
return merge_dictionaries(
get_template_variables(tool_definition, 'tool', tool_items), {
'data_types': set(x.data_type for x in tool_items),
'tool': tool,
})
def get_data_type_by_suffix(data_type_by_suffix=None):
d = {}
x_manager = ExtensionManager('crosscompute.types')
for x in x_manager.extensions:
data_type = x.plugin
for suffix in data_type.suffixes:
d[suffix] = data_type
return merge_dictionaries(d, data_type_by_suffix or {})
def show_tool(request):
settings = request.registry.settings
data_type_by_suffix = settings['data_type_by_suffix']
tool_definition = settings['tool_definition']
tool_arguments = get_tool_arguments(tool_definition)
tool_items = get_data_items(
tool_arguments, tool_definition, data_type_by_suffix)
return merge_dictionaries(
get_template_variables(tool_definition, 'tool', tool_items), {
'data_types': set(x.data_type for x in tool_items),
'tool_id': 1,
})
def run(main_functions, g):
g['infrastructure_graph'] = get_graph_from_table(g['demand_point_table'])
for f in main_functions:
print(f.func_name)
if '_total_' in f.func_name:
g.update(compute(f, g))
continue
for node_id, node_d in g['infrastructure_graph'].cycle_nodes():
v = merge_dictionaries(node_d, {
'node_id': node_id,
'local_overrides': dict(g['demand_point_table'].ix[node_id])})
node_d.update(compute(f, v, g))
return g
def get_result_template_variables(result, result_folder):
result_configuration = ResultConfiguration(result_folder)
tool_definition = result_configuration.tool_definition
result_arguments = result_configuration.result_arguments
result_properties = result_configuration.result_properties
tool_items = get_data_items(result_arguments, tool_definition)
result_errors = get_data_items(merge_dictionaries(
result_properties.pop('standard_errors', {}),
result_properties.pop('type_errors', {})), tool_definition)
result_items = get_data_items(
result_properties.pop('standard_outputs', {}), tool_definition)
result_properties = get_data_items(result_properties, tool_definition)
return merge_dictionaries(
get_template_variables(tool_definition, 'tool', tool_items),
get_template_variables(tool_definition, 'result', result_items), {
'data_types': set(x.data_type for x in tool_items + result_items),
'tool': result.tool,
'result': result,
'result_errors': result_errors,
'result_properties': result_properties,
})
def compute_raw(f, l, g=None):
if not g:
g = {}
# If the function wants every argument, provide every argument
argument_specification = inspect.getargspec(f)
if argument_specification.keywords:
return f(**merge_dictionaries(g, l))
# Otherwise, provide only requested arguments
keywords = {}
for argument_name in argument_specification.args:
argument_value = l.get(argument_name, g.get(argument_name))
if argument_value is None:
raise ValidationError(argument_name, 'required')
keywords[argument_name] = argument_value
return f(**keywords)
def run_script(tool_definition,
result_arguments,
result_folder,
target_folder=None,
environment=None):
timestamp, environment = time.time(), environment or {}
if 'target_folder' in tool_definition['argument_names']:
y = make_folder(abspath(target_folder or join(result_folder, 'y')))
result_arguments = OrderedDict(result_arguments, target_folder=y)
# Record
result_configuration = ResultConfiguration(result_folder)
result_configuration.save_tool_location(tool_definition)
result_configuration.save_result_arguments(result_arguments, environment)
# Run
command_terms = split_arguments(
render_command(tool_definition['command_template'],
result_arguments).replace('\n', ' '))
result_properties = OrderedDict()
try:
with cd(tool_definition['configuration_folder']):
command_process = subprocess.Popen(command_terms,
stdout=subprocess.PIPE,
stderr=subprocess.PIPE,
env=merge_dictionaries(
environment,
SCRIPT_ENVIRONMENT))
except OSError:
standard_output, standard_error = None, 'Command not found'
else:
standard_output, standard_error = [
x.rstrip().decode('utf-8') for x in command_process.communicate()
]
if command_process.returncode:
result_properties['return_code'] = command_process.returncode
# Save
result_properties.update(
_process_streams(standard_output, standard_error, result_folder,
tool_definition))
result_properties['execution_time_in_seconds'] = time.time() - timestamp
result_configuration.save_result_properties(result_properties)
result_configuration.save_result_script(tool_definition, result_arguments)
if 'target_folder' in tool_definition['argument_names']:
link_path(join(result_folder, 'y'), result_arguments['target_folder'])
return result_properties
def estimate_external_distribution_cost(node_id, latitude, longitude,
line_length_adjustment_factor,
grid_mv_line_cost_per_meter_by_year,
infrastructure_graph, **keywords):
d = defaultdict(float)
key = 'grid_mv_line_adjusted_length_in_meters'
relative_keys = [
'grid_mv_line_raw_cost_per_meter',
'grid_mv_line_installation_cost_per_meter',
'grid_mv_line_maintenance_cost_per_meter_per_year',
'grid_mv_line_replacement_cost_per_meter_per_year',
'grid_mv_line_final_cost_per_meter_per_year',
'grid_mv_line_discounted_cost_per_meter',
]
# Note that node_id is real but edge_node_id can be fake
for edge_node_id, edge_d in infrastructure_graph.edge[node_id].items():
edge_node_d = infrastructure_graph.node[edge_node_id]
edge_node_ll = edge_node_d['latitude'], edge_node_d['longitude']
line_length = get_distance((latitude, longitude), edge_node_ll).meters
if 'name' in edge_node_d:
# If both nodes are real, then the computation will reappear when
# we process the other node, so we halve it here
line_length /= 2.
line_adjusted_length = line_length * line_length_adjustment_factor
# Aggregate over each node that is connected to the edge
edge_d[key] = edge_d.get(key, 0) + line_adjusted_length
d[key] += line_adjusted_length
for relative_key in relative_keys:
cost_per_meter = keywords[relative_key]
x = cost_per_meter * line_adjusted_length
k = relative_key.replace('_per_meter', '')
edge_d[k] = edge_d.get(k, 0) + x
d[k] += x
line_adjusted_length = d.get(key, 0)
return merge_dictionaries(
d, {
'external_distribution_cost_by_year':
line_adjusted_length * grid_mv_line_cost_per_meter_by_year,
})
def estimate_external_distribution_cost(
node_id, latitude, longitude, line_length_adjustment_factor,
grid_mv_line_cost_per_meter_by_year, infrastructure_graph, **keywords):
d = defaultdict(float)
key = 'grid_mv_line_adjusted_length_in_meters'
relative_keys = [
'grid_mv_line_raw_cost_per_meter',
'grid_mv_line_installation_cost_per_meter',
'grid_mv_line_maintenance_cost_per_meter_per_year',
'grid_mv_line_replacement_cost_per_meter_per_year',
'grid_mv_line_final_cost_per_meter_per_year',
'grid_mv_line_discounted_cost_per_meter',
]
# Note that node_id is real but edge_node_id can be fake
for edge_node_id, edge_d in infrastructure_graph.edge[node_id].items():
edge_node_d = infrastructure_graph.node[edge_node_id]
edge_node_ll = edge_node_d['latitude'], edge_node_d['longitude']
line_length = get_distance((latitude, longitude), edge_node_ll).meters
if 'name' in edge_node_d:
# If both nodes are real, then the computation will reappear when
# we process the other node, so we halve it here
line_length /= 2.
line_adjusted_length = line_length * line_length_adjustment_factor
# Aggregate over each node that is connected to the edge
edge_d[key] = edge_d.get(key, 0) + line_adjusted_length
d[key] += line_adjusted_length
for relative_key in relative_keys:
cost_per_meter = keywords[relative_key]
x = cost_per_meter * line_adjusted_length
k = relative_key.replace('_per_meter', '')
edge_d[k] = edge_d.get(k, 0) + x
d[k] += x
line_adjusted_length = d.get(key, 0)
return merge_dictionaries(d, {
'external_distribution_cost_by_year':
line_adjusted_length * grid_mv_line_cost_per_meter_by_year,
})
def run_script(
tool_definition, result_arguments, result_folder, target_folder=None,
environment=None):
timestamp, environment = time.time(), environment or {}
if 'target_folder' in tool_definition['argument_names']:
y = make_folder(abspath(target_folder or join(result_folder, 'y')))
result_arguments = OrderedDict(result_arguments, target_folder=y)
# Record
result_configuration = ResultConfiguration(result_folder)
result_configuration.save_tool_location(tool_definition)
result_configuration.save_result_arguments(result_arguments, environment)
# Run
command_terms = split_arguments(render_command(tool_definition[
'command_template'], result_arguments).replace('\n', ' '))
result_properties = OrderedDict()
try:
with cd(tool_definition['configuration_folder']):
command_process = subprocess.Popen(
command_terms, stdout=subprocess.PIPE, stderr=subprocess.PIPE,
env=merge_dictionaries(environment, SCRIPT_ENVIRONMENT))
except OSError:
standard_output, standard_error = None, 'Command not found'
else:
standard_output, standard_error = [x.rstrip().decode(
'utf-8') for x in command_process.communicate()]
if command_process.returncode:
result_properties['return_code'] = command_process.returncode
# Save
result_properties.update(_process_streams(
standard_output, standard_error, result_folder, tool_definition))
result_properties['execution_time_in_seconds'] = time.time() - timestamp
result_configuration.save_result_properties(result_properties)
result_configuration.save_result_script(tool_definition, result_arguments)
if 'target_folder' in tool_definition['argument_names']:
link_path(join(result_folder, 'y'), result_arguments['target_folder'])
return result_properties
def load_files(g):
file_key_pattern = re.compile(r'(.*)_(\w+)_path')
file_value_by_name = {}
for k, v in g.items():
try:
key_base, key_type = file_key_pattern.match(k).groups()
except AttributeError:
continue
try:
if key_type == 'text':
name = key_base
value = load_text(v)
elif key_type == 'table':
name = key_base + '_table'
value = load_table(v)
elif key_type == 'geotable':
name = key_base + '_geotable'
value = load_geotable(v)
else:
continue
except UnsupportedFormat as e:
raise ValidationError(k, e)
file_value_by_name[name] = value
return merge_dictionaries(g, file_value_by_name)
def load_files(g):
file_key_pattern = re.compile(r'(.*)_(\w+)_path')
file_value_by_name = {}
for k, v in g.items():
try:
key_base, key_type = file_key_pattern.match(k).groups()
except AttributeError:
continue
try:
if key_type == 'text':
name = key_base
value = load_text(v)
elif key_type == 'table':
name = key_base + '_table'
value = load_table(v)
elif key_type == 'geotable':
name = key_base + '_geotable'
value = load_geotable(v)
else:
continue
except UnsupportedFormat as e:
raise ValidationError(k, e)
file_value_by_name[name] = value
return merge_dictionaries(g, file_value_by_name)