def plot_vectors(exp_id, sample, save_file=None):
am = AnalyzeManager()
am.add_experiment(retrieve_experiment(exp_id))
am.add_analyzer(
VectorSpeciesReportAnalyzer(sample,
save_file=save_file,
channel='Daily HBR'))
am.analyze()
# am.add_experiment(retrieve_experiment("15a20ddd-2a36-e811-a2bf-c4346bcb7274")) # facazissa iter5. best 0. 4/1 10:30pm
# am.add_experiment(retrieve_experiment("86413a54-0d36-e811-a2bf-c4346bcb7274")) # magude iter3. best 10. 4/1 10:30pm X
# am.add_experiment(retrieve_experiment("15a1d9fe-2f36-e811-a2bf-c4346bcb7274")) # mahel iter9. best 0. 4/1
# am.add_experiment(retrieve_experiment("0fc16f8f-2636-e811-a2bf-c4346bcb7274")) # mapulanguene iter9. best 10. 4/1 10:30pm
# am.add_experiment(retrieve_experiment("f5873afe-1336-e811-a2bf-c4346bcb7274")) # moine iter6. best 0 4/1
# am.add_experiment(retrieve_experiment("19794550-c135-e811-a2bf-c4346bcb7274")) # motaze iter1. best 15 4/1
# am.add_experiment(retrieve_experiment("e6f8c635-2d36-e811-a2bf-c4346bcb7274")) # panjane iter6. best 0 4/1
# am.add_experiment(retrieve_experiment("6fe0132a-c135-e811-a2bf-c4346bcb7274")) # faca stage1, iter1, best 9
# am.add_experiment(retrieve_experiment("86413a54-0d36-e811-a2bf-c4346bcb7274")) # m-s stage 1. iter3, best 12
# am.add_experiment(retrieve_experiment("eb30545d-e536-e811-a2bf-c4346bcb7274")) # m-s stage 2. ite3, best 6
# am.add_experiment(retrieve_experiment("d4b08d09-1835-e811-a2bf-c4346bcb7274")) #caputine iter12. best 8.
# am.add_experiment(retrieve_experiment("0fc97f4a-4634-e811-a2bf-c4346bcb7274")) # chichuco iter0. best 3
# am.add_experiment(retrieve_experiment("f67437d5-4e34-e811-a2bf-c4346bcb7274")) # chicutso iter2. best 3
# am.add_experiment(retrieve_experiment("d7d2a0be-a234-e811-a2bf-c4346bcb7274")) # facazissa iter3. best 12
# am.add_experiment(retrieve_experiment("3240a906-9e33-e811-a2bf-c4346bcb7274")) # magude iter0. best 21.
# am.add_experiment(retrieve_experiment("6cd7957f-cb34-e811-a2bf-c4346bcb7274")) # mahel iter6. best 11.
# am.add_experiment(retrieve_experiment("0dbd4e00-cc34-e811-a2bf-c4346bcb7274")) # mapulanguene iter8. best 3
# am.add_experiment(retrieve_experiment("777c34a8-dc34-e811-a2bf-c4346bcb7274")) # moine iter6. best 8
# am.add_experiment(retrieve_experiment("5171d868-4634-e811-a2bf-c4346bcb7274")) # motaze iter0. best 11
# am.add_experiment(retrieve_experiment("7a5ab67b-dc34-e811-a2bf-c4346bcb7274")) # panjane iter8. best 17
# am.add_experiment(retrieve_experiment("2ecf9cd7-9c35-e811-a2bf-c4346bcb7274")) #aggregate 2014. iter2, best 20
# am.add_experiment(retrieve_experiment("d8cb3061-ae35-e811-a2bf-c4346bcb7274")) #aggregate 2014,2015. iter2, best 5
am.add_experiment(
retrieve_experiment("2f76368f-bc57-e811-a2bf-c4346bcb7274"))
am.add_analyzer(PrevAnalyzer(cait_output_mode=True, gatesreview=True))
am.analyze()
if __name__=="__main__":
SetupParser.init('HPC')
am = AnalyzeManager()
# Corrected stepd
# am.add_experiment(retrieve_experiment("43cac760-cbd6-e711-9414-f0921c16b9e5")) # bbondo
# am.add_experiment(retrieve_experiment("a31b516a-cbd6-e711-9414-f0921c16b9e5")) # chabbobboma
# am.add_experiment(retrieve_experiment("1ecdf372-cbd6-e711-9414-f0921c16b9e5")) # chisanga
# am.add_experiment(retrieve_experiment("957e6159-32d6-e711-9414-f0921c16b9e5")) # chiyabi
# am.add_experiment(retrieve_experiment("9669907b-cbd6-e711-9414-f0921c16b9e5")) # luumbo
# am.add_experiment(retrieve_experiment("fbe40809-ccd6-e711-9414-f0921c16b9e5")) # munyumbwe
# am.add_experiment(retrieve_experiment("8aadd6a0-cbd6-e711-9414-f0921c16b9e5")) # nyanga chaamwe
# am.add_experiment(retrieve_experiment("d18a9aa8-cbd6-e711-9414-f0921c16b9e5")) # sinafala
am.add_experiment(retrieve_experiment("d28a9aa8-cbd6-e711-9414-f0921c16b9e5")) # sinamalima
# Old MBGSR
# am.add_experiment(retrieve_experiment("7f188957-2fe1-e711-9414-f0921c16b9e5")) # bbondo
# am.add_experiment(retrieve_experiment("f60d69eb-2fe1-e711-9414-f0921c16b9e5")) # chabbobboma
# am.add_experiment(retrieve_experiment("7aa30068-2fe1-e711-9414-f0921c16b9e5")) # chisanga
# am.add_experiment(retrieve_experiment("d57bccae-25e1-e711-9414-f0921c16b9e5")) # chiyabi
# am.add_experiment(retrieve_experiment("5d5cff6d-2fe1-e711-9414-f0921c16b9e5")) # luumbo
# am.add_experiment(retrieve_experiment("cf37cd7b-2fe1-e711-9414-f0921c16b9e5")) # munyumbwe
# am.add_experiment(retrieve_experiment("94aa85fb-2fe1-e711-9414-f0921c16b9e5")) # nyanga chaamwe
# am.add_experiment(retrieve_experiment("f5c0fb13-30e1-e711-9414-f0921c16b9e5")) # sinafala
# am.add_experiment(retrieve_experiment("33b92b39-30e1-e711-9414-f0921c16b9e5")) # sinamalima
am.add_analyzer(RDTPrevAnalyzer())
am.analyze()
def plot_RDT(exp_id, sample, save_file=None, **kwargs):
am = AnalyzeManager()
am.add_experiment(retrieve_experiment(exp_id))
am.add_analyzer(
prevalence_plot_analyzer(catch, sample, save_file=save_file, **kwargs))
am.analyze()
plt.tight_layout()
# if self.save_file:
# # if self.cait_output_mode:
# # MozambiqueExperiment.save_figs_for_caitlin(fig,self.save_file)
# # else:
if not self.save_file:
self.save_file = save_file = "figs/{}".format(self.catch)
# plt.savefig(self.save_file + ".pdf")
# plt.savefig(self.save_file + ".png")
# else:
plt.show()
print("Done!")
if __name__ == "__main__":
SetupParser.init('HPC')
am = AnalyzeManager()
# am.add_experiment(retrieve_experiment("0a373d77-1f93-e811-a2c0-c4346bcb7275")) # chichuco
# am.add_experiment(retrieve_experiment("0d801fc0-3c92-e811-a2c0-c4346bcb7275")) # chicutso
am.add_experiment(
retrieve_experiment(
"c5c3c5bb-a79c-e811-a2c0-c4346bcb7275")) # magude-sede-facazissa
# am.add_experiment(retrieve_experiment("210bcb89-e696-e811-a2c0-c4346bcb7275")) # mahel
# am.add_experiment(retrieve_experiment("10238aac-7593-e811-a2c0-c4346bcb7275")) # mapulanguene
# am.add_experiment(retrieve_experiment("85bef741-2d97-e811-a2c0-c4346bcb7275")) # moine
# am.add_experiment(retrieve_experiment("140fe8a7-1194-e811-a2c0-c4346bcb7275")) # motaze
# am.add_experiment(retrieve_experiment("b1c79146-6194-e811-a2c0-c4346bcb7275")) # panjane-caputine
am.add_analyzer(PrevAnalyzer())
am.analyze()
# Calibration experiments:
# am.add_experiment(retrieve_experiment("09829129-b00b-e811-9415-f0921c16b9e5")) #Mahel
# am.add_experiment(retrieve_experiment("11cb8543-e20b-e811-9415-f0921c16b9e5")) #Motaze
# am.add_experiment(retrieve_experiment("8853ca79-1c0c-e811-9415-f0921c16b9e5"))
# am.add_experiment(retrieve_experiment("171711d2-a010-e811-9415-f0921c16b9e5")) #Caputine
# am.add_experiment(retrieve_experiment("632dd6f5-a610-e811-9415-f0921c16b9e5")) # Chicutso
# am.add_experiment(retrieve_experiment("ef6564ad-a110-e811-9415-f0921c16b9e5")) # Mahel
# am.add_experiment(retrieve_experiment("fd4866f4-a310-e811-9415-f0921c16b9e5")) # Mapulanguene
# am.add_experiment(retrieve_experiment("da1bccd2-a910-e811-9415-f0921c16b9e5")) # Moine
# am.add_experiment(retrieve_experiment("7e10e1d1-a710-e811-9415-f0921c16b9e5")) # Panjane
# am.add_experiment(retrieve_experiment("7e10e1d1-a710-e811-9415-f0921c16b9e5")) # Panjane multi-dose
# am.add_experiment(retrieve_experiment("f335b9ab-1f12-e811-9415-f0921c16b9e5")) # Moine DONE
# am.add_experiment(retrieve_experiment("8731f656-2a12-e811-9415-f0921c16b9e5")) # Caputine iter6
# am.add_experiment(retrieve_experiment("f3ed1863-2b12-e811-9415-f0921c16b9e5")) # Mahel iter2
# am.add_experiment(retrieve_experiment("62454c29-1212-e811-9415-f0921c16b9e5")) # Panjane iter2
am.add_experiment(retrieve_experiment("354912fd-3612-e811-9415-f0921c16b9e5")) # Motaze iter4
# am.add_experiment(retrieve_experiment("169df5ae-2b12-e811-9415-f0921c16b9e5")) # Mapulanguene
# pbnb
# am.add_experiment(retrieve_experiment("002e8d2d-4e12-e811-9415-f0921c16b9e5")) # Caputine
am.add_analyzer(RDTPrevAnalyzer())
am.analyze()
plt.legend()
# plt.xlim([3000,7000])
plt.xlim([foo("2010-01-01"), foo("2019-01-01")])
plt.tight_layout()
plt.show()
# plt.savefig(self.base + "data/figs/{}_prev.png".format(catch))
if __name__ == "__main__":
SetupParser.init('HPC')
am = AnalyzeManager()
# Calibration experiments:
am.add_experiment(
retrieve_experiment("66f05adf-c10b-e811-9415-f0921c16b9e5"))
# hand-fudged Milen habitat params
# am.add_experiment(retrieve_experiment("4766b178-f5f4-e711-9414-f0921c16b9e5")) #bbondo
# am.add_experiment(retrieve_experiment("34213b5c-f8f4-e711-9414-f0921c16b9e5")) # chabbobboma
# am.add_experiment(retrieve_experiment("84d95a7a-faf4-e711-9414-f0921c16b9e5")) # chisanga
# am.add_experiment(retrieve_experiment("c6313998-faf4-e711-9414-f0921c16b9e5")) # chiyabi
# am.add_experiment(retrieve_experiment("69c0e4de-faf4-e711-9414-f0921c16b9e5")) # luumbo
# am.add_experiment(retrieve_experiment("4f045b1b-fbf4-e711-9414-f0921c16b9e5")) # munyumbwe
# am.add_experiment(retrieve_experiment("542b05fe-fbf4-e711-9414-f0921c16b9e5")) # nyanga chaamwe (x0.5)
# am.add_experiment(retrieve_experiment("b546a866-04f5-e711-9414-f0921c16b9e5")) # nyanga chaamwe (x0.25)
# am.add_experiment(retrieve_experiment("a938d951-06f5-e711-9414-f0921c16b9e5")) # nyanga chaamwe (x0.15)
# am.add_experiment(retrieve_experiment("47bc7d56-fcf4-e711-9414-f0921c16b9e5")) # sinafala
# am.add_experiment(retrieve_experiment("cd2853cf-fcf4-e711-9414-f0921c16b9e5")) # sinamalima
# Milen habitat params
# COMPS_experiment_name = suite_name # I want hover-over in COMPS to be the suite name
experiment_manager.run_simulations(exp_name=COMPS_experiment_name,
exp_builder=experiment_builder,
suite_id=suite_id)
experiments.append(experiment_manager)
experiments_ids.append(experiment_manager.experiment.exp_id)
# Dump the experiment ids for resume
with open('ids.json', 'w') as out:
json.dump(experiments_ids, out)
# Every experiments are created at this point -> Analyze
am = AnalyzeManager(verbose=False, create_dir_map=False)
for em in experiments:
am.add_experiment(em.experiment)
# am.add_analyzer(DownloadAnalyzerTPI(['output\\DemographicsSummary.json', 'config.json', 'output\\ReportHIVART.csv', 'output\\ReportHIVByAgeAndGender.csv'],
# output_dir='Test HIV 1'))
am.add_analyzer(
DownloadAnalyzerTPI(['output\\ReportHIVByAgeAndGender.csv'],
output_dir='Nyanza Base Case'))
# While the experiments are running, we are analyzing every 15 seconds
while not all([em.finished() for em in experiments]):
map(lambda e: e.refresh_experiment(), experiments)
print("Analyzing !")
am.analyze()
print("Waiting 15 seconds")
time.sleep(15)
# Analyze one last time when everything is complete
int(x["year"]) + 2010,
str(int(x["month"])).zfill(2)),
axis=1)
# print("mdate")
df["mdate"] = df.apply(lambda x: date_to_mdate(x["date"]), axis=1)
# print("plot")
# ax.plot(df["year"] * 12 + df["month"], df["cases"], *args, **kwargs)
ax.plot_date(df["mdate"], df["cases"], *args, **kwargs)
ax.set_xlabel("Date")
ax.set_ylabel("Cases")
ax.set_xlim([date_to_mdate("2010-01-01"), date_to_mdate("2017-01-01")])
# ax.tick_params(direction="inout")
def uid(self):
''' A unique identifier of site-name and analyzer-name. '''
return '_'.join([self.site.name, self.name])
if __name__ == "__main__":
SetupParser.init('HPC')
am = AnalyzeManager()
# Calibration experiments:
am.add_experiment(
retrieve_experiment("a0bee2bd-f8b5-e811-a2c0-c4346bcb7275"))
am.add_analyzer(incidence_likelihood(zambia_calib_site("bbondo")))
am.analyze()
for exp_id in self.pop_data.keys():
plt.plot(
np.array(self.raw_pop_data[exp_id][-2]) /
self.tot_pop[exp_id][-2])
ax.set_title("Late")
ax.set_xticks(range(24))
ax.set_xticklabels(self.age_bins)
plt.show()
#
# for exp_id in self.pop_data.keys():
# plt.plot_date(self.report_times, self.pop_data[exp_id],fmt='-',c=c,linewidth=lw,label=label,alpha=0.4)
# plt.plot_date(self.report_times, self.pop_data[exp_id], fmt='-', c=c, linewidth=lw, label=label, alpha=0.4)
# plt.plot_date(self.report_times, self.pop_data[exp_id], fmt='-', c=c, linewidth=lw, label=label, alpha=0.4)
# plt.plot_date(self.report_times, self.pop_data[exp_id], fmt='-', c=c, linewidth=lw, label=label, alpha=0.4)
# plt.legend([s['environment'] for s in self.metadata.values()])
if __name__ == "__main__":
SetupParser.init('HPC')
am = AnalyzeManager()
am.add_experiment(
retrieve_experiment("f4ecdcc6-768c-e711-9401-f0921c16849d")) # L1
# am.add_experiment(retrieve_experiment("001a9f44-758c-e711-9401-f0921c16849d")) # L5
am.add_experiment(
retrieve_experiment("4188b9de-e28c-e711-9401-f0921c16849d")) # L6
am.add_analyzer(AgeStratificationAnalyzer())
am.analyze()
# # c1 = green = IRS
# # c2 = red = MSAT
# # c3 = purple = MDA
plt.legend()
# plt.xlim([3000,7000])
plt.xlim([foo("2010-01-01"), foo("2019-01-01")])
# plt.show()
plt.tight_layout()
plt.savefig(base + "data/figs/{}_prev_node.png".format(catch))
if __name__ == "__main__":
SetupParser.init('HPC')
am = AnalyzeManager()
# am.add_experiment(retrieve_experiment("43cac760-cbd6-e711-9414-f0921c16b9e5")) # bbondo
# am.add_experiment(retrieve_experiment("a31b516a-cbd6-e711-9414-f0921c16b9e5")) # chabbobboma
# am.add_experiment(retrieve_experiment("1ecdf372-cbd6-e711-9414-f0921c16b9e5")) # chisanga
# am.add_experiment(retrieve_experiment("957e6159-32d6-e711-9414-f0921c16b9e5")) # chiyabi
# am.add_experiment(retrieve_experiment("9669907b-cbd6-e711-9414-f0921c16b9e5")) # luumbo
# am.add_experiment(retrieve_experiment("fbe40809-ccd6-e711-9414-f0921c16b9e5")) # munyumbwe
am.add_experiment(
retrieve_experiment(
"8aadd6a0-cbd6-e711-9414-f0921c16b9e5")) # nyanga chaamwe
# am.add_experiment(retrieve_experiment("d18a9aa8-cbd6-e711-9414-f0921c16b9e5")) # sinafala
# am.add_experiment(retrieve_experiment("d28a9aa8-cbd6-e711-9414-f0921c16b9e5")) # sinamalima
am.add_analyzer(RDTPrevAnalyzer())
am.analyze()
def finalize(self):
# print self.my_data
print("")
def plot(self):
import matplotlib.pyplot as plt
# Plot histogram of trips
for sim_id, data in self.n_trips.items():
# data only contains data for travellers. Need to add in "zero trips" for everyone who didn't travel.
n_couch = self.pop_init[sim_id] - self.n_travellers[sim_id]
full_data = np.append(data, np.zeros(int(n_couch)))
plt.hist(full_data,histtype='stepfilled',alpha=0.4,log=True, label=self.metadata[sim_id])
plt.legend()
plt.show()
if __name__=="__main__":
SetupParser.init('HPC')
am = AnalyzeManager()
am.add_experiment(retrieve_experiment("151f8b4b-867c-e711-9401-f0921c16849d"))
am.add_analyzer(MigrationAnalyzer())
am.analyze()
date_to_mdate("2013-01-01"),
date_to_mdate("2014-01-01"),
date_to_mdate("2015-01-01"),
date_to_mdate("2016-01-01"),
date_to_mdate("2017-01-01"),
date_to_mdate("2018-01-01"),
date_to_mdate("2019-01-01")
])
# plt.ylim([-0.01,0.25])
plt.ylabel("RDT Prevalence")
plt.legend(frameon=True)
plt.tight_layout()
if self.save_file:
plt.savefig(self.save_file + ".pdf")
plt.savefig(self.save_file + ".png")
else:
plt.show()
if __name__ == "__main__":
am = AnalyzeManager()
# am.add_experiment(retrieve_experiment("cdb12c2d-61c3-e811-a2bd-c4346bcb1555"))
am.add_experiment(
retrieve_experiment("9df3a55a-63c3-e811-a2bd-c4346bcb1555"))
# am.add_analyzer(custom_prev_plot_analyzer("chiyabi","C0", 3))
# am.add_analyzer(custom_prev_plot_analyzer("chiyabi","C1", 4))
am.add_analyzer(custom_prev_plot_analyzer("chiyabi", "C2", 5))
# am.add_analyzer(custom_prev_plot_analyzer("chiyabi","C3", 6))
am.analyze()