def incidence_grabby(expname, hfca, rank, output_fname=None) :
calib_folder = calib_base + expname +"/"
if not output_fname:
output_fname = calib_folder + "rank{}_cases".format(rank)
LL_fname = calib_folder + "_plots/LL_all.csv"
LL_df = pd.read_csv(LL_fname)
LL_df.sort_values(by='total', ascending=False, inplace=True)
LL_df.reset_index(inplace=True)
sample = LL_df.loc[rank, 'sample']
iteration = LL_df.loc[rank, 'iteration']
am = AnalyzeManager()
# am.add_analyzer(IncidencePlotter(GriddedCalibSite(hfca),save_file=output_fname))
am.add_analyzer(IncidencePlotter(hfca, save_file=output_fname, save_csv=True))
with open(calib_folder+"iter{}/IterationState.json".format(iteration)) as fin:
iteration_state = json.loads(fin.read())
siminfo = OrderedDict(iteration_state['simulations'])
for item in list(siminfo.items()) :
if item[1]['__sample_index__'] == sample :
simid = item[0]
am.add_simulation(simid)
am.analyze()
def RDT_grabby(expname, rank, output_fname=None, plot_bairros=True) :
calib_folder = calib_base + expname +"/"
if not output_fname:
output_fname = calib_folder + "rank{}_rdt".format(rank)
LL_fname = calib_folder + "_plots/LL_all.csv"
LL_df = pd.read_csv(LL_fname)
LL_df.sort_values(by='total', ascending=False, inplace=True)
LL_df.reset_index(inplace=True)
sample = LL_df.loc[rank, 'sample']
iteration = LL_df.loc[rank, 'iteration']
start_date = "2009-01-01"
am = AnalyzeManager()
am.add_analyzer(PrevAnalyzer(start_date=start_date,
save_file=output_fname,
cait_output_mode=True,
plot_bairros=plot_bairros))
with open(calib_folder+"iter{}/IterationState.json".format(iteration)) as fin:
iteration_state = json.loads(fin.read())
siminfo = OrderedDict(iteration_state['simulations'])
for item in list(siminfo.items()) :
if item[1]['__sample_index__'] == sample :
simid = item[0]
# print("Sim ID: ",simid)
am.add_simulation(simid)
am.analyze()
def vector(expname, calib_stage, rank) :
calib_folder = calib_base + expname +"/"
output_fname = calib_folder + "rank{}_vectors".format(rank)
LL_fname = calib_folder + "_plots/LL_all.csv"
LL_df = pd.read_csv(LL_fname)
LL_df.sort_values(by='total', ascending=False, inplace=True)
LL_df.reset_index(inplace=True)
sample = LL_df.loc[rank, 'sample']
iteration = LL_df.loc[rank, 'iteration']
am = AnalyzeManager()
am.add_analyzer(VectorSpeciesReportAnalyzer(save_file=output_fname, channel='Daily HBR'))
with open(calib_folder+"iter{}/IterationState.json".format(iteration)) as fin:
iteration_state = json.loads(fin.read())
siminfo = OrderedDict(iteration_state['simulations'])
for item in list(siminfo.items()) :
if item[1]['__sample_index__'] == sample :
simid = item[0]
am.add_simulation(simid)
am.analyze()
selected = [
p.selected_data[id(self)] for p in parsers.values()
if id(self) in p.selected_data
]
self.data = pd.concat(selected)
def finalize(self):
import seaborn as sns
sns.set_style("darkgrid")
fig = plt.figure(figsize=(10, 6))
ax = fig.gca()
for a, adf in self.data.groupby('sim_id'):
for s, sdf in adf.groupby('species'):
ax.plot(sdf['date'], sdf[self.channel], label=s)
ax.legend()
plt.ylabel(self.channel)
if self.save_file:
plt.savefig(self.save_file + ".png")
plt.savefig(self.save_file + ".pdf")
else:
plt.show()
if __name__ == '__main__':
am = AnalyzeManager()
am.add_analyzer(VectorSpeciesReportAnalyzer())
am.add_simulation('4047a20f-b33d-e811-a2bf-c4346bcb7274')
am.analyze()