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"""An interactive front-end for Triplet Structures.
Call from runtime.py:interactive(...).
"""
# pylint: disable=import-error,no-name-in-module
from runtime.shadow_input import ShadowInput
class TSREPL:
"""An interactive interface for the TSRuntime.
"""
def __init__(self, runtime):
"""Initializes a new TSREPL.
"""
self.runtime = runtime
self.ts = runtime.ts
self.input = ShadowInput()
def run(self):
"""Run the REPL.
"""
while True:
if self.iteration() == "EXIT":
return
def iteration(self):
"""Prompt the user for an action and execute it.
"""
print("Starting a new iteration. Please pick an option:")
option = self.select_one([
"Manually apply individual rules.",
"Automatically apply all rules matching pattern.",
"Print structure.",
"Begin recording command sequence.",
"Save command sequence.",
"Load command sequence.",
"Set display name.",
"Prefer nodes.",
"Exit interactive session.",
])
if option == 0:
self.apply_manual()
elif option == 1:
self.to_fixedpoint()
elif option == 2:
print(self.ts)
elif option == 3:
self.input.begin_recording()
elif option == 4:
file_name = self.input("Save to file: ")
self.input.scrub_last(2)
self.input.save_recording(file_name)
elif option == 5:
file_name = self.input("Load from file: ")
self.input.load_recording(file_name)
elif option == 6:
self.set_display_name()
elif option == 7:
self.prefer_nodes()
elif option == 8:
return "EXIT"
else:
raise NotImplementedError
return None
def to_fixedpoint(self):
"""Apply rule(s) until fixedpoint is reached.
"""
rules = self.pick_rules()
search_term = self.input("Search proposals for term: ").lower()
n_matches = int(self.input("Apply if at least this many matches: "))
fixedpoint = False
while not fixedpoint:
fixedpoint = True
for _, delta in self.runtime.propose_all(rules):
if str(delta).lower().count(search_term) >= n_matches:
print("Applying proposal:")
print(delta)
delta.apply()
fixedpoint = False
break
def apply_manual(self):
"""Manually apply rule(s).
"""
rules = self.pick_rules()
search_term = self.input("Filter proposals for term: ").lower()
n_matches = int(self.input("With at least this many matches: ") or "0")
while True:
for _, delta in self.runtime.propose_all(rules):
if str(delta).lower().count(search_term) < n_matches:
continue
print(delta)
choice = self.input("Apply? (y/N/q): ").lower() or "n"
if choice[0] == "y":
delta.apply()
break
if choice[0] == "q":
return
else:
return
def set_display_name(self):
"""Allows the user to set a display name for a particular node.
"""
search_term = self.input("Existing display name: ")
matching = [
node for node, display_name in self.ts.display_names.items()
if search_term in display_name]
if matching:
node_name = min(
matching, key=lambda node: len(self.ts.display_names[node]))
new_name = self.input("Please select a new display name: ")
self.ts[node_name].display_name(new_name)
else:
print("No matching node.")
def prefer_nodes(self):
"""Tell the runtime to prefer particular nodes in matches.
"""
search_term = self.input("Prefer nodes containing string: ")
self.runtime.affinity.prefer_nodes(lambda node: search_term in node)
def pick_rules(self):
"""Helper to allow the user to select rules to apply.
"""
names = [rule.name for rule in self.runtime.rules]
print("Please select the rule(s):")
indices = self.select_multiple(names)
return [names[i] for i in indices]
def select_one(self, options):
"""Helper method for selecting an option.
"""
for i, option in enumerate(options):
print("{}: {}".format(i, option))
index = self.input("Selection: ").lower()
try:
option = options[int(index)]
return int(index)
except (ValueError, IndexError):
try:
# Treat it as a search term
return next(i for i, option in enumerate(options)
if index in str(option).lower())
except StopIteration:
print("Invalid choice, try again.")
return self.select_one(options)
def select_multiple(self, options):
"""Helper method for selecting multiple options.
"""
for i, option in enumerate(options):
print("{}: {}".format(i, option))
term = self.input("Selections: ").lower()
try:
if "-" in term:
from_index, to_index = map(int, term.split("-")[:2])
else:
from_index, to_index = int(term), int(term)
return list(range(from_index, to_index + 1))
except ValueError:
return [i for i, option in enumerate(options)
if term in str(option).lower()]
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