def tailor_web_output(gt_dict):
if gt_dict["error"]:
return json.dumps(gt_dict)
gt_dict["summary"] = generate_summary(gt_dict)
gt_dict["pc"] = city_adapter.convert_readable(gt_dict["pc"])
del gt_dict["rname"]
gt_dict["tweets"] = gt_dict["tweets"][:10]
return json.dumps(gt_dict)
def tailor_web_output(gt_dict):
if gt_dict["error"]:
return json.dumps(gt_dict)
gt_dict["summary"] = generate_summary(gt_dict)
gt_dict["pc"] = city_adapter.convert_readable(gt_dict["pc"])
del gt_dict["rname"]
gt_dict["tweets"] = gt_dict["tweets"][:10]
return json.dumps(gt_dict)
def generate_summary(gt_dict):
summary = None
if gt_dict["oconf"] == 2:
summary = "Summary: <b>{0}</b> has <b>{1}</b> recent status updates. <b>{2}</b> of them are geotagged tweets and the home location is in <b>{3}</b>. Our prediction error distance is <b>{4}</b> kilometers.".format(
gt_dict["sname"],
len(gt_dict["tweets"]),
len(gt_dict["footprints"]),
city_adapter.convert_readable(gt_dict["oc"]),
gt_dict["errdist"],
)
elif gt_dict["oconf"] == 1:
summary = "Summary: <b>{0}</b> has <b>{1}</b> recent status updates. <b>{2}</b> of them are geotagged tweets and the most frequent location (<b>{3}</b>) is assumed to be the home location. Our prediction error distance is <b>{4}</b> kilometers.".format(
gt_dict["sname"],
len(gt_dict["tweets"]),
len(gt_dict["footprints"]),
city_adapter.convert_readable(gt_dict["oc"]),
gt_dict["errdist"],
)
else:
summary = "Summary: <b>{0}</b> has <b>{1}</b> recent status updates. None of them is geotagged.".format(gt_dict["sname"], len(gt_dict["tweets"]))
return summary
def generate_summary(gt_dict):
summary = None
if gt_dict["oconf"] == 2:
summary = "Summary: <b>{0}</b> has <b>{1}</b> recent status updates. <b>{2}</b> of them are geotagged tweets and the home location is in <b>{3}</b>. Our prediction error distance is <b>{4}</b> kilometers.".format(
gt_dict["sname"],
len(gt_dict["tweets"]),
len(gt_dict["footprints"]),
city_adapter.convert_readable(gt_dict["oc"]),
gt_dict["errdist"],
)
elif gt_dict["oconf"] == 1:
summary = "Summary: <b>{0}</b> has <b>{1}</b> recent status updates. <b>{2}</b> of them are geotagged tweets and the most frequent location (<b>{3}</b>) is assumed to be the home location. Our prediction error distance is <b>{4}</b> kilometers.".format(
gt_dict["sname"],
len(gt_dict["tweets"]),
len(gt_dict["footprints"]),
city_adapter.convert_readable(gt_dict["oc"]),
gt_dict["errdist"],
)
else:
summary = "Summary: <b>{0}</b> has <b>{1}</b> recent status updates. None of them is geotagged.".format(
gt_dict["sname"], len(gt_dict["tweets"]))
return summary
def tailor_bot_output(sname, gt_dict):
if gt_dict["error"]:
return None
lat = gt_dict["plat"]
lon = gt_dict["plon"]
pc = gt_dict["pc"]
oc = gt_dict["oc"]
rpc = city_adapter.convert_readable(pc)
msg = None
if oc:
if pc == oc:
msg = "Dear @{0}, I am confident that your home location is near {1}({2},{3})".format(sname, rpc, lat, lon)
else:
msg = "Dear @{0}, I think your home location is near {1}({2},{3}), but probably I am wrong".format(sname, rpc, lat, lon)
else:
msg = "Dear @{0}, I think your home location is near {1}({2},{3}). Please tell me if I am wrong.".format(sname, rpc, lat, lon)
return msg
def tailor_bot_output(sname, gt_dict):
if gt_dict["error"]:
return None
lat = gt_dict["plat"]
lon = gt_dict["plon"]
pc = gt_dict["pc"]
oc = gt_dict["oc"]
rpc = city_adapter.convert_readable(pc)
msg = None
if oc:
if pc == oc:
msg = "Dear @{0}, I am confident that your home location is near {1}({2},{3})".format(
sname, rpc, lat, lon)
else:
msg = "Dear @{0}, I think your home location is near {1}({2},{3}), but probably I am wrong".format(
sname, rpc, lat, lon)
else:
msg = "Dear @{0}, I think your home location is near {1}({2},{3}). Please tell me if I am wrong.".format(
sname, rpc, lat, lon)
return msg
