def extract(raw_txt, logger):
c = Cleaner()
cleaned_text_list = c.clean(raw_txt)
logger.info('Done cleaning')
logger.debug(len(cleaned_text_list))
logger.debug(cleaned_text_list)
matrix_builder = MatrixBuilder()
matrix = matrix_builder.build_sim_matrix(cleaned_text_list, logger)
logger.info('Done building sim matrix')
logger.debug('Dimensions: {}'.format(matrix.shape))
logger.debug(matrix)
g = Grapher()
pageranks = g.graph(matrix)
logger.info('Generated graph and got pageranks')
logger.debug(pageranks)
total_doc_size = len(cleaned_text_list)
if total_doc_size in range(0, 300):
summary_length = int(0.4 * total_doc_size)
elif total_doc_size in range(301, 800):
summary_length = int(0.2 * total_doc_size)
elif total_doc_size in range(801, 1500):
summary_length = int(0.1 * total_doc_size)
else:
summary_length = int(0.05 * total_doc_size)
top_ranked = nlargest(summary_length, pageranks, key=pageranks.get)
top_ranked.sort()
cl = Cluster()
top_ranked = cl.splitIntoParagraph(top_ranked, 7.5)
logger.debug(top_ranked)
result = ''
for paragraph in top_ranked:
for key in paragraph:
top_ranked_sentence = cleaned_text_list[key]
result += '{}. '.format(top_ranked_sentence)
result += '\n\n'
try:
del c
del cleaned_text_list
del matrix_builder
del matrix
del g
del pageranks
del total_doc_size
del summary_length
del top_ranked
del cl
del raw_txt
except:
pass
return result
utils.exit_unless_accessible(args.calls)
utils.setup_logging(verbosity=args.verbose)
# Load graph database (remove duplicates)
df_all = df_from_csv_file(args.calls)
df = df_all.drop_duplicates()
from_fun, to_fun = args.from_function, args.to_function
left, right = search_settings(args.direction, args.cutoff)
merge_on = ["caller_filename", "caller_function", "callee_filename", "callee_function"]
chains_df_right = pd.DataFrame(columns=merge_on)
if right:
chains_df_right = find_chains_directed_df(df, from_fun, to_fun, right)
chains_df_left = pd.DataFrame(columns=merge_on)
if left:
chains_df_left = find_chains_directed_df(df, from_fun, to_fun, left)
_LOGGER.info("Generating the results...")
df_chains = pd.concat([chains_df_left, chains_df_right]).drop_duplicates()
df_chains = pd.merge(df_all, df_chains, on=merge_on, how='inner')
if args.out.endswith(".csv"):
df_to_csv_file(df_chains, args.out)
else:
grapher = Grapher(args.out)
grapher.graph(df_chains)
grapher.render(args.out)
_LOGGER.info("Done")
min_value=0.0)
assumptions.RRA = st.number_input('RRA',
value=2.0,
step=1.0,
min_value=0.1)
# re initialize strategies based on user defined parameters
for i in range(len(strategies)):
s = strategies[i].__class__(assumptions)
strategies[i] = s
sim = Simulator()
df = sim.simulate(assumptions, strategies, runs=400)
gr = Grapher()
chart = gr.graph(df)
st.altair_chart(chart)
st.title('Asset Sim')
st.markdown('''
Asset Sim is a tool to visualize long term investing strategies. Quickly simulate different strategies and market assumptions to see how they affect your finances.
## FAQ
### How is the graph generated?
A monte-carlo simulation simulates many runs with the given assumptions and aggegates them together.
The lines are the median amounts of assets at a point in time. The error bands show first and third quartiles.
### Why median?
Medians are less sensitive to outliers than means. A few lucky runs can blow up a mean.
### Where do the default values come from?
They are best guesses based on historical data of the S&P 500.
