def retrieve_user_mobility_data_single_user(dbcc, user_id, current_date, predicted_date):
history_date = calculate_mp_history_date(current_date)
current_week_date_range = support.unix_weeks_timestamp_to_date_range(
support.date_to_unix_weeks_timestamp(current_date)
)
history_week_date_range = support.unix_weeks_timestamp_to_date_range(
support.date_to_unix_weeks_timestamp(history_date)
)
begin_of_history_week = history_week_date_range.min_range
end_of_current_week = current_week_date_range.max_range
current_day = support.calc_day(current_date)
predicted_day = support.calc_day(predicted_date)
applicable_days = support.difference_of_days(current_day, predicted_day)
con = dbcc.retrieve_connection(db_layout.MPFetched.database_name)
select = "*"
table = db_layout.MPFetched.table_name
layout_dict = dict(db_layout.MPFetched.layout)
user_id_sql = layout_dict["user_id"].format_to_db_value(user_id)
end_date_sql = layout_dict["date"].format_to_db_value(end_of_current_week)
start_date_sql = layout_dict["date"].format_to_db_value(begin_of_history_week)
applicable_days_sql = map(layout_dict["day"].format_to_db_value, applicable_days)
applicable_days_query = " AND (day = " + " OR day = ".join(applicable_days_sql) + ")"
where_query = (
"WHERE user_id = "
+ user_id_sql
+ " AND date <= "
+ end_date_sql
+ " AND date >= "
+ start_date_sql
+ applicable_days_query
)
err.log_error(err.INFO, "Retrieving data for MP between " + str(history_date) + " and " + str(current_date))
mysql_rows = con.retrieve(select, table, where_query)
result = []
for mysql_row in mysql_rows:
result.append(db_layout.MPFetched.object_from_mysql_row(dbcc, mysql_row))
return result
def retrieve_user_mobility_data_single_user(dbcc, user_id, current_date,
predicted_date):
history_date = calculate_mp_history_date(current_date)
current_week_date_range = support.unix_weeks_timestamp_to_date_range(
support.date_to_unix_weeks_timestamp(current_date))
history_week_date_range = support.unix_weeks_timestamp_to_date_range(
support.date_to_unix_weeks_timestamp(history_date))
begin_of_history_week = history_week_date_range.min_range
end_of_current_week = current_week_date_range.max_range
current_day = support.calc_day(current_date)
predicted_day = support.calc_day(predicted_date)
applicable_days = support.difference_of_days(current_day, predicted_day)
con = dbcc.retrieve_connection(db_layout.MPFetched.database_name)
select = "*"
table = db_layout.MPFetched.table_name
layout_dict = dict(db_layout.MPFetched.layout)
user_id_sql = layout_dict["user_id"].format_to_db_value(user_id)
end_date_sql = layout_dict["date"].format_to_db_value(end_of_current_week)
start_date_sql = layout_dict["date"].format_to_db_value(
begin_of_history_week)
applicable_days_sql = map(layout_dict["day"].format_to_db_value,
applicable_days)
applicable_days_query = " AND (day = " + " OR day = ".join(
applicable_days_sql) + ")"
where_query = ("WHERE user_id = " + user_id_sql + " AND date <= " +
end_date_sql + " AND date >= " + start_date_sql +
applicable_days_query)
err.log_error(
err.INFO, "Retrieving data for MP between " + str(history_date) +
" and " + str(current_date))
mysql_rows = con.retrieve(select, table, where_query)
result = []
for mysql_row in mysql_rows:
result.append(
db_layout.MPFetched.object_from_mysql_row(dbcc, mysql_row))
return result
def prepare_pykov_chain_with_single_user_mobility_states(
grouped_by_date_grid_by_minute_of_day_mp_fetched, current_day_str, predicted_day_str
):
list_of_day_str_applicable = support.difference_of_days(current_day_str, predicted_day_str)
from_to_user_entry_dict = {}
for date, grid_by_minute_of_day_mp_fetched in grouped_by_date_grid_by_minute_of_day_mp_fetched.items():
if support.calc_day(date) in list_of_day_str_applicable:
# It seems this day is a contender in the prediction.
# For each minute of the day look at the cell id(source) and look at the next because that is the destination
# Each pair of source and destination should be added to the from_to user_entry_dict (date doesn't matter anymore as link is formed)
# Using the source as the key and adding the destination to the list
i = 0
for source_mp_fetched in grid_by_minute_of_day_mp_fetched:
if i == common_config.MINUTES_IN_A_DAY - 1:
# We have reached the end of the day, must look at the next if possible in the next day
next_date = date + support.Range.RESOLUTION_DATETIME_DAY
if support.calc_day(next_date) in list_of_day_str_applicable:
# It seems the adjoining day is also a contender in the prediction. Configure the destination correctly
destination_mp_fetched = grouped_by_date_grid_by_minute_of_day_mp_fetched[next_date][0]
else:
# There is no connection to the next day
destination_mp_fetched = None
else:
# In the middle of the day, destination is next in the grid
destination_mp_fetched = grid_by_minute_of_day_mp_fetched[i + 1]
if destination_mp_fetched:
source_user_entry = UserEntry(
source_mp_fetched.day, source_mp_fetched.minute_of_day, source_mp_fetched.cell_id
)
destination_user_entry = UserEntry(
destination_mp_fetched.day, destination_mp_fetched.minute_of_day, destination_mp_fetched.cell_id
)
# Add it to the from to user entry dict but with the source_user_entry as key
# Add it as a key if it doesn't exist yet
if source_user_entry in from_to_user_entry_dict:
from_to_user_entry_dict[source_user_entry].append(destination_user_entry)
else:
from_to_user_entry_dict[source_user_entry] = [destination_user_entry]
i += 1
# Calculate all percentages the same from and to user entry coincide
pykov_chain_entries = {}
for starting_user_entry, destinations in from_to_user_entry_dict.items():
starting_key = starting_user_entry.to_pykov_key()
total_amount = float(len(destinations))
grouped_by_cell_id = support.group_by(destinations, lambda user_entry: user_entry.cell_id)
for destination_cell_id, destinations_with_same_cell_id in grouped_by_cell_id.items():
destination_key = destinations_with_same_cell_id[0].to_pykov_key()
amount_of_destinations_with_same_cell_id = len(destinations_with_same_cell_id)
percentage = float(amount_of_destinations_with_same_cell_id) / total_amount
pykov_chain_entries[starting_key, destination_key] = percentage
return pykov.Chain(pykov_chain_entries)
def prepare_pykov_chain_with_single_user_mobility_states(
grouped_by_date_grid_by_minute_of_day_mp_fetched, current_day_str,
predicted_day_str):
list_of_day_str_applicable = support.difference_of_days(
current_day_str, predicted_day_str)
from_to_user_entry_dict = {}
for date, grid_by_minute_of_day_mp_fetched in grouped_by_date_grid_by_minute_of_day_mp_fetched.items(
):
if support.calc_day(date) in list_of_day_str_applicable:
#It seems this day is a contender in the prediction.
#For each minute of the day look at the cell id(source) and look at the next because that is the destination
#Each pair of source and destination should be added to the from_to user_entry_dict (date doesn't matter anymore as link is formed)
#Using the source as the key and adding the destination to the list
i = 0
for source_mp_fetched in grid_by_minute_of_day_mp_fetched:
if i == common_config.MINUTES_IN_A_DAY - 1:
#We have reached the end of the day, must look at the next if possible in the next day
next_date = date + support.Range.RESOLUTION_DATETIME_DAY
if support.calc_day(
next_date) in list_of_day_str_applicable:
#It seems the adjoining day is also a contender in the prediction. Configure the destination correctly
destination_mp_fetched = grouped_by_date_grid_by_minute_of_day_mp_fetched[
next_date][0]
else:
#There is no connection to the next day
destination_mp_fetched = None
else:
#In the middle of the day, destination is next in the grid
destination_mp_fetched = grid_by_minute_of_day_mp_fetched[
i + 1]
if destination_mp_fetched:
source_user_entry = UserEntry(
source_mp_fetched.day, source_mp_fetched.minute_of_day,
source_mp_fetched.cell_id)
destination_user_entry = UserEntry(
destination_mp_fetched.day,
destination_mp_fetched.minute_of_day,
destination_mp_fetched.cell_id)
#Add it to the from to user entry dict but with the source_user_entry as key
#Add it as a key if it doesn't exist yet
if source_user_entry in from_to_user_entry_dict:
from_to_user_entry_dict[source_user_entry].append(
destination_user_entry)
else:
from_to_user_entry_dict[source_user_entry] = [
destination_user_entry
]
i += 1
#Calculate all percentages the same from and to user entry coincide
pykov_chain_entries = {}
for starting_user_entry, destinations in from_to_user_entry_dict.items():
starting_key = starting_user_entry.to_pykov_key()
total_amount = float(len(destinations))
grouped_by_cell_id = support.group_by(
destinations, lambda user_entry: user_entry.cell_id)
for destination_cell_id, destinations_with_same_cell_id in grouped_by_cell_id.items(
):
destination_key = destinations_with_same_cell_id[0].to_pykov_key()
amount_of_destinations_with_same_cell_id = len(
destinations_with_same_cell_id)
percentage = float(
amount_of_destinations_with_same_cell_id) / total_amount
pykov_chain_entries[starting_key, destination_key] = percentage
return pykov.Chain(pykov_chain_entries)
