# Extracting rates by ward¶

One of the major applications of a custom extractor is to enable you to add your own live-analysis that is performed while the simulation is running. This is much faster than having metawards write all data to disk, and then running analysis in a post-processing step.

To analyse data, you need to understand the Workspace class.

## Workspace¶

The Workspace class provides a workspace in which data is accumulated and analysis performed. It is cleared between iterations, and holds the following information;

Data indexed by disease stage:
• inf_tot: The total number of workers in each disease stage, summed over all wards.

• pinf_tot: The total number of players in each disease stage, summed over all wards.

• n_inf_wards: The number of wards which have at least one individual in the disease stage,

Data indexed by ward:
• total_inf_ward: The total number of infections in each ward

• total_new_inf_ward: The total number of new infections in each ward

• incidence: The incidence of infection in each ward

• S_in_wards: The total population in the S state in each ward

• E_in_wards: The total population in the E state in each ward

• I_in_wards: The total population in the I state in each ward

• R_in_wards: The total population in the R state in each ward

Data indexed by disease stage and then by ward
• ward_inf_tot: The total population in each disease stage in each ward

## Extracting the population in I1¶

We can use a custom extractor to report the total number of individuals who are in the first I stage (I1) for each ward for each day.

To do this, create a new extractor called extract_i1.py and copy in the below;

from metawards.extractors import extract_default

def output_i1(population, workspace, output_dir, **kwargs):
# Open the file "total_i1.csv" in the output directory
FILE = output_dir.open("total_i1.csv")

ward_inf_tot = workspace.ward_inf_tot

# The I1 state is stage 2
I1_inf_tot = ward_inf_tot[2]

FILE.write(str(population.day) + ",")
FILE.write(",".join([str(x) for x in I1_inf_tot]) + "\n")

def extract_i1(**kwargs):
# return all of the functions from "extract_default"
# plus our new "output_i1"
funcs = extract_default(**kwargs)
funcs.append(output_i1)
return funcs


This defines a new output function called output_i1. This calls the open function of the OutputFiles object held in output_dir to open the file total_i1.csv in the output directory.

Note

The OutputFiles class is clever enough to only open the file once, and will automatically close it when needed. It will also ensure that the file is opened in the correct output directory for a model run and will compress the file using bz2 if the --auto-bzip command-line option has been passed (the default), or will disable automatic compression if --no-auto-bzip is set.

The function then gets I1_inf_tot from the third disease stage data held in workspace.ward_inf_tot. The third stage is the first I stage as the first stage (ward_inf_tot[0]) is a special * stage, used for advanced bookkeeping, while the second stage (ward_inf_tot[1]) is the latent, E stage.

Note

The * stage is used to help evaluate how many wards see new infections. Individuals are moved into the * stage at the point of infection, and are moved into the E stage on the day after infection. By default, individuals in the * stage are counted into R, which is why this does not appear to rise continuously. You can control how individuals in the * stage are counted using either --star-is-E to count them as E (additional latent stage), --star-is-R to count them as R (default behaviour) or --disable-star to remove the * stage and instead treat this first stage as the one and only E stage. Note that if you do this, you will need to update the disease files to remove the * stage, and to update your above extractor as now stage 1 will be the first I stage.

Now that we have the population in the I1 stage in each ward in I1_inf_tot, we write this as a comma-separated line to the file, starting each line with the day number obtained from the passed Population typed population object.

To use your extractor run metawards using;

metawards -d lurgy3 --extract extract_i1 -a ExtraSeedsLondon.dat --nsteps 30


Note

Note that we’ve set nsteps to 30 for illustration only, just to limit the runtime and the size of the file. In a real production run you wouldn’t need to set the number of steps.

You should see that your file called total_i1.csv.bz2 has been created in the output directory, and that this contains the populations of the I1 state for each ward.

## Calculating rates¶

As well as outputting raw data, you can also perform some simple analysis that is run live during the model run. For example, you may want to record the number of individuals entering each state, so that you can calculate the rate of progress across states.

To do this, you will need to save the ward_inf_tot data from the previous day’s state. You can do this by adding it as a custom attribute to the workspace.

Create a new extractor by creating extract_rate.py and copying in the below;

from metawards.extractors import extract_default
from copy import deepcopy

def output_rate(population, workspace, output_dir, **kwargs):
if not hasattr(workspace, "output_rate_previous"):
# This is the first day, so we cannot calculate the rate.
# Instead, just save today's data so that it can be
# be used tomorrow
workspace.output_rate_previous = deepcopy(workspace.ward_inf_tot)
return

# get yesterday's data
ward_inf_previous = workspace.output_rate_previous

# get today's data
ward_inf_tot = workspace.ward_inf_tot

# calculate and write the difference between the two to files for
# each disease stage...
for i in range(0, workspace.n_inf_classes):
FILE = output_dir.open(f"rate_{i}.csv")

FILE.write(str(population.day))

# loop over the data for each ward and write the
# difference to the file
for old, new in zip(ward_inf_previous[i], ward_inf_tot[i]):
FILE.write("," + str(new - old))

FILE.write("\n")

# save today's data so that it can be used tomorrow
workspace.output_rate_previous = deepcopy(ward_inf_tot)

def extract_rate(**kwargs):
funcs = extract_default(**kwargs)
funcs.append(output_rate)
return funcs


This extractor looks a little more complex, but it builds on what you have seen before. It defines output_rate, which if the function that will output the rates, and extract_rate which returns all of the functions from extract_default, plus your new output_rate function.

The first job of output_rate is to determine if it has been called on the first day of the model. If it has, then there is no previous data from “yesterday” that can be used to calculate the rate. The function detects if this is the case by checking for a new custom attribute that will be under the control of this function. We will call this attribute output_rate_previous, so to minimise the risk of a name collision. If this attribute doesn’t exist, then we must be on the first day. We this save today’s data so that it can be used tomorrow.

If the attribute does exist, then we can calculate a rate. We do that by getting yesterday’s data from output_rate_previous and todays data from workspace.ward_inf_tot. We then loop over all of the disease stages, and open an output file for each stage (called rate_{i}.csv). We then write into this file the day, then the difference between today’s and yesterday’s population in ward, for this ith disease stage.

Finally, we save today’s data into workspace.output_rate_previous, so that it can be used tomorrow as yesterday’s data.

Run this extractor in metawards using;

metawards -d lurgy3 --extract extract_rate -a ExtraSeedsLondon.dat --nsteps 30


(again, we are limiting this to 30 steps just for demonstration reasons)

You should see that you have files rate_0.csv.bz2, rate_1.csv.bz2 etc. now created in the output directory. If you look at these files you should see that they contain the differences between the populations in each ward for each disease stage between each day.

## Writing to a database¶

In the last example we wrote rates to a large number of files. While this has worked, the data is beginning to get so large and multi-dimensional that we are reaching the limits of what a CSV or other data file can reasonably support. As data sizes get larger, it is better to start writing data to a database.

The OutputFiles class has in-built support for opening connections to SQLite3 databases. To use this, we call the function open_db(). For example, let’s now create a new extractor that will output the size of the population at each disease stage for each day, and the change compared to the previous day. To do this, open a file called extract_db.py and copy in the below;

from metawards.extractors import extract_default

def create_tables(N_INF_CLASSES):
# return a function that creates the tables
# for the specified number of disease classes

def initialise(conn):
# create a table for the values...
values = ",".join([f"stage_{i} int" for i in range(0, N_INF_CLASSES)])
c = conn.cursor()
c.execute(f"create table totals(day int, S int, {values})")

# create a table for the rates...
c.execute(f"create table deltas(day int, S int, {values})")

conn.commit()

return initialise

def output_db(population, workspace, output_dir, **kwargs):
have_yesterday = hasattr(workspace, "output_rate_previous")

# get today's data
inf_tot = workspace.inf_tot
pinf_tot = workspace.pinf_tot
S = population.susceptibles

N_INF_CLASSES = workspace.n_inf_classes

# open a database to hold the data - call the 'create_tables'
# function on this database when it is first opened
conn = output_dir.open_db("stages.db",
initialise=create_tables(N_INF_CLASSES))

c = conn.cursor()

# get the values for today
today = [population.day, S] + [inf+pinf for inf, pinf in zip(inf_tot, pinf_tot)]

# convert this to a string
today_str = ",".join([str(t) for t in today])

# write these to the database
c.execute(f"insert into totals VALUES ({today_str})")

if hasattr(workspace, "output_rate_db"):
yesterday = workspace.output_rate_db

# calculate the difference in all columns of today and yesterday
deltas = [t - y for t, y in zip(today, yesterday)]
# (except for the day, which should be today)
deltas[0] = today[0]

delta_str = ",".join([str(d) for d in deltas])

# write this to the database
c.execute(f"insert into deltas values ({delta_str})")

conn.commit()

# save today's data so that it can be used tomorrow
workspace.output_rate_db = today

def extract_db(**kwargs):
funcs = extract_default(**kwargs)
funcs.append(output_db)
return funcs


Here, we have created a new function called create_tables that is called to create a function that is returned and passed to open_db(). This function creates two tables in the database; totals which contains the total population at each disease stage, and deltas, which contains the difference from the previous day.

Next, we have output_db. This function calls open_db() to create the connection, conn to the SQLite3 database. This connection is a standard Python sqlite3 connection object.

We calculate the total population in each stage as the sum of inf_tot (the workers at each stage) and pinf_tot (the players at each stage). We prepend the number of susceptibles and also the day number.

We then write this, as today’s data, to the database via a cursor.

Next, we check if there is any data from yesterday by looking for the custom attribute workspace.output_rate_db. If there is, then we get this data, and then calculate the difference from the previous day. This is then written to the deltas table via the cursor.

Finally, we commit the changes to the database, and then save today’s data to workspace.output_rate_db so that it can be used tomorrow.

Run this extractor by typing;

metawards -d lurgy3 --extract extract_db -a ExtraSeedsLondon.dat --nsteps 30


(again, we limit to 30 days just for illustration purposes)

Once this has finished, you should see a file called output/stages.db.bz2.

Uncompress this file and then examine it using any SQLite3 database viewer, e.g.

# sqlite3 output/stages.db
SQLite version 3.31.1 2020-01-27 19:55:54
Enter ".help" for usage hints.
sqlite> .dump
PRAGMA foreign_keys=OFF;
BEGIN TRANSACTION;
CREATE TABLE totals(day int, S int, stage_0 int,stage_1 int,stage_2 int,stage_3 int,stage_4 int,stage_5 int);
INSERT INTO totals VALUES(0,56082077,0,0,0,0,0,0);
INSERT INTO totals VALUES(1,56082072,0,5,0,0,0,0);
INSERT INTO totals VALUES(2,56082072,0,0,5,0,0,0);
INSERT INTO totals VALUES(3,56082067,5,0,3,2,0,0);
INSERT INTO totals VALUES(4,56082064,3,5,2,1,2,0);
INSERT INTO totals VALUES(5,56082061,3,3,6,1,2,1);
INSERT INTO totals VALUES(6,56082051,10,3,8,1,3,1);
INSERT INTO totals VALUES(7,56082039,12,10,9,3,2,2);
INSERT INTO totals VALUES(8,56082026,13,12,17,4,2,3);
INSERT INTO totals VALUES(9,56082002,24,13,26,4,4,4);
INSERT INTO totals VALUES(10,56081982,20,24,34,8,3,6);
INSERT INTO totals VALUES(11,56081950,32,20,55,5,8,7);
INSERT INTO totals VALUES(12,56081887,63,32,66,10,6,13);
INSERT INTO totals VALUES(13,56081827,60,63,76,30,6,15);
INSERT INTO totals VALUES(14,56081733,94,60,128,25,20,17);
INSERT INTO totals VALUES(15,56081588,145,94,169,31,21,29);
INSERT INTO totals VALUES(16,56081405,183,145,222,58,22,42);
INSERT INTO totals VALUES(17,56081131,274,183,317,78,42,52);
INSERT INTO totals VALUES(18,56080808,323,274,434,105,57,76);
INSERT INTO totals VALUES(19,56080318,490,323,618,144,82,102);
INSERT INTO totals VALUES(20,56079687,631,490,798,229,96,146);
INSERT INTO totals VALUES(21,56078886,801,631,1134,273,159,193);
INSERT INTO totals VALUES(22,56077748,1138,801,1539,356,224,271);
INSERT INTO totals VALUES(23,56076190,1558,1138,2028,488,301,374);
INSERT INTO totals VALUES(24,56074172,2018,1558,2759,652,388,530);
INSERT INTO totals VALUES(25,56071530,2642,2018,3760,883,525,719);
INSERT INTO totals VALUES(26,56067981,3549,2642,4997,1226,714,968);
INSERT INTO totals VALUES(27,56063321,4660,3549,6723,1522,961,1341);
INSERT INTO totals VALUES(28,56057020,6301,4660,8943,2095,1221,1837);
INSERT INTO totals VALUES(29,56048552,8468,6301,11808,2834,1631,2483);
CREATE TABLE deltas(day int, S int, stage_0 int,stage_1 int,stage_2 int,stage_3 int,stage_4 int,stage_5 int);
INSERT INTO deltas VALUES(1,-5,0,5,0,0,0,0);
INSERT INTO deltas VALUES(2,0,0,-5,5,0,0,0);
INSERT INTO deltas VALUES(3,-5,5,0,-2,2,0,0);
INSERT INTO deltas VALUES(4,-3,-2,5,-1,-1,2,0);
INSERT INTO deltas VALUES(5,-3,0,-2,4,0,0,1);
INSERT INTO deltas VALUES(6,-10,7,0,2,0,1,0);
INSERT INTO deltas VALUES(7,-12,2,7,1,2,-1,1);
INSERT INTO deltas VALUES(8,-13,1,2,8,1,0,1);
INSERT INTO deltas VALUES(9,-24,11,1,9,0,2,1);
INSERT INTO deltas VALUES(10,-20,-4,11,8,4,-1,2);
INSERT INTO deltas VALUES(11,-32,12,-4,21,-3,5,1);
INSERT INTO deltas VALUES(12,-63,31,12,11,5,-2,6);
INSERT INTO deltas VALUES(13,-60,-3,31,10,20,0,2);
INSERT INTO deltas VALUES(14,-94,34,-3,52,-5,14,2);
INSERT INTO deltas VALUES(15,-145,51,34,41,6,1,12);
INSERT INTO deltas VALUES(16,-183,38,51,53,27,1,13);
INSERT INTO deltas VALUES(17,-274,91,38,95,20,20,10);
INSERT INTO deltas VALUES(18,-323,49,91,117,27,15,24);
INSERT INTO deltas VALUES(19,-490,167,49,184,39,25,26);
INSERT INTO deltas VALUES(20,-631,141,167,180,85,14,44);
INSERT INTO deltas VALUES(21,-801,170,141,336,44,63,47);
INSERT INTO deltas VALUES(22,-1138,337,170,405,83,65,78);
INSERT INTO deltas VALUES(23,-1558,420,337,489,132,77,103);
INSERT INTO deltas VALUES(24,-2018,460,420,731,164,87,156);
INSERT INTO deltas VALUES(25,-2642,624,460,1001,231,137,189);
INSERT INTO deltas VALUES(26,-3549,907,624,1237,343,189,249);
INSERT INTO deltas VALUES(27,-4660,1111,907,1726,296,247,373);
INSERT INTO deltas VALUES(28,-6301,1641,1111,2220,573,260,496);
INSERT INTO deltas VALUES(29,-8468,2167,1641,2865,739,410,646);
COMMIT;