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Working With Sessions

Import the LArray library:

[2]:

from larray import *

Three Kinds Of Sessions

They are three ways to group objects in LArray:

  • Session: is an ordered dict-like container with special I/O methods. Although the autocomplete* feature on the objects stored in the session is available in the larray-editor, it is not available in development tools like PyCharm making it cumbersome to use.

  • CheckedSession: provides the same methods as Session objects but are defined in a completely different way (see example below). The autocomplete* feature is both available in the larray-editor and in development tools (PyCharm). In addition, the type of each stored object is protected. Optionally, it is possible to constrain the axes and dtype of arrays using CheckedArray.

  • CheckedParameters: is a special version of CheckedSession in which the value of all stored objects (parameters) is frozen after initialization.

* Autocomplete is the feature in which development tools try to predict the variable or function a user intends to enter after only a few characters have been typed (like word completion in cell phones).

Creating Sessions

Session

Create a session:

[3]:

# define some scalars, axes and arrays
variant = 'baseline'

country = Axis('country=Belgium,France,Germany')
gender = Axis('gender=Male,Female')
time = Axis('time=2013..2017')

population = zeros([country, gender, time])
births = zeros([country, gender, time])
deaths = zeros([country, gender, time])

[4]:

# create an empty session and objects one by one after
s = Session()
s.variant = variant
s.country = country
s.gender = gender
s.time = time
s.population = population
s.births = births
s.deaths = deaths

print(s.summary())

variant: baseline
country: country ['Belgium' 'France' 'Germany'] (3)
gender: gender ['Male' 'Female'] (2)
time: time [2013 2014 2015 2016 2017] (5)
population: country, gender, time (3 x 2 x 5) [float64]
births: country, gender, time (3 x 2 x 5) [float64]
deaths: country, gender, time (3 x 2 x 5) [float64]

[5]:

# or create a session in one step by passing all objects to the constructor
s = Session(variant=variant, country=country, gender=gender, time=time,
            population=population, births=births, deaths=deaths)

print(s.summary())

variant: baseline
country: country ['Belgium' 'France' 'Germany'] (3)
gender: gender ['Male' 'Female'] (2)
time: time [2013 2014 2015 2016 2017] (5)
population: country, gender, time (3 x 2 x 5) [float64]
births: country, gender, time (3 x 2 x 5) [float64]
deaths: country, gender, time (3 x 2 x 5) [float64]

CheckedSession

The syntax to define a checked-session is a bit specific:

class MySession(CheckedSession):
    # Variables can be declared in two ways:
    # a) by specifying only the type of the variable (to be initialized later)
    var1: Type
    # b) by giving an initialization value.
    #    In that case, the type is deduced from the initialization value
    var2 = initialization value
    # Additionally, axes and dtype of Array variables can be constrained
    # using the special type CheckedArray
    arr1: CheckedArray([list, of, axes], dtype) = initialization value

Check the example below:

[6]:

class Demography(CheckedSession):
    # (convention is to declare parameters (read-only objects) in capital letters)
    # Declare 'VARIANT' parameter as of type string.
    # 'VARIANT' will be initialized when a 'Demography' session will be created
    VARIANT: str
    # declare variables with an initialization value.
    # Their type is deduced from their initialization value.
    COUNTRY = Axis('country=Belgium,France,Germany')
    GENDER = Axis('gender=Male,Female')
    TIME = Axis('time=2013..2017')
    population = zeros([COUNTRY, GENDER, TIME], dtype=int)
    births = zeros([COUNTRY, GENDER, TIME], dtype=int)
    # declare 'deaths' with constrained axes and dtype.
    # Its type (Array), axes and dtype are not modifiable.
    # It will be initialized with 0
    deaths: CheckedArray([COUNTRY, GENDER, TIME], int) = 0

d = Demography(VARIANT='baseline')

print(d.summary())

VARIANT: baseline
deaths: country, gender, time (3 x 2 x 5) [int64]
COUNTRY: country ['Belgium' 'France' 'Germany'] (3)
GENDER: gender ['Male' 'Female'] (2)
TIME: time [2013 2014 2015 2016 2017] (5)
population: country, gender, time (3 x 2 x 5) [int64]
births: country, gender, time (3 x 2 x 5) [int64]

Loading and Dumping Sessions

One of the main advantages of grouping arrays, axes and groups in session objects is that you can load and save all of them in one shot. Like arrays, it is possible to associate metadata to a session. These can be saved and loaded in all file formats.

Loading Sessions (CSV, Excel, HDF5)

To load the items of a session, you have two options:

  1. Instantiate a new session and pass the path to the Excel/HDF5 file or to the directory containing CSV files to the Session constructor:

[7]:

# create a new Session object and load all arrays, axes, groups and metadata
# from all CSV files located in the passed directory
csv_dir = get_example_filepath('demography_eurostat')
s = Session(csv_dir)

# create a new Session object and load all arrays, axes, groups and metadata
# stored in the passed Excel file
filepath_excel = get_example_filepath('demography_eurostat.xlsx')
s = Session(filepath_excel)

# create a new Session object and load all arrays, axes, groups and metadata
# stored in the passed HDF5 file
filepath_hdf = get_example_filepath('demography_eurostat.h5')
s = Session(filepath_hdf)

print(s.summary())

Metadata:
        title: Demographic datasets for a small selection of countries in Europe
        source: demo_jpan, demo_fasec, demo_magec and migr_imm1ctz tables from Eurostat
births: country, gender, time (3 x 2 x 5) [int32]
deaths: country, gender, time (3 x 2 x 5) [int32]
immigration: country, citizenship, gender, time (3 x 3 x 2 x 5) [int32]
population: country, gender, time (3 x 2 x 5) [int32]
population_5_countries: country, gender, time (5 x 2 x 5) [int32]
population_benelux: country, gender, time (3 x 2 x 5) [int32]
citizenship: citizenship ['Belgium' 'Luxembourg' 'Netherlands'] (3)
country: country ['Belgium' 'France' 'Germany'] (3)
country_benelux: country ['Belgium' 'Luxembourg' 'Netherlands'] (3)
gender: gender ['Male' 'Female'] (2)
time: time [2013 2014 2015 2016 2017] (5)
even_years: time[2014 2016] >> even_years (2)
odd_years: time[2013 2015 2017] >> odd_years (3)

  1. Call the load method on an existing session and pass the path to the Excel/HDF5 file or to the directory containing CSV files as first argument:

[8]:

# create a session containing 3 axes, 2 groups and one array 'population'
filepath = get_example_filepath('population_only.xlsx')
s = Session(filepath)

print(s.summary())

population: country, gender, time (3 x 2 x 3) [int64]

[9]:

# call the load method on the previous session and add the 'births' and 'deaths' arrays to it
filepath = get_example_filepath('births_and_deaths.xlsx')
s.load(filepath)

print(s.summary())

population: country, gender, time (3 x 2 x 3) [int64]
births: country, gender, time (3 x 2 x 3) [int64]
deaths: country, gender, time (3 x 2 x 3) [int64]

The load method offers some options:

  1. Using the names argument, you can specify which items to load:

[10]:

births_and_deaths_session = Session()

# use the names argument to only load births and deaths arrays
births_and_deaths_session.load(filepath_hdf, names=['births', 'deaths'])

print(births_and_deaths_session.summary())

Metadata:
        title: Demographic datasets for a small selection of countries in Europe
        source: demo_jpan, demo_fasec, demo_magec and migr_imm1ctz tables from Eurostat
births: country, gender, time (3 x 2 x 5) [int32]
deaths: country, gender, time (3 x 2 x 5) [int32]

  1. Setting the display argument to True, the load method will print a message each time a new item is loaded:

[11]:

s = Session()

# with display=True, the load method will print a message
# each time a new item is loaded
s.load(filepath_hdf, display=True)

opening /home/docs/checkouts/readthedocs.org/user_builds/larray/envs/latest/lib/python3.11/site-packages/larray/tests/data/demography_eurostat.h5
loading Array object births ... done
loading Array object deaths ... done
loading Array object immigration ... done
loading Array object population ... done
loading Array object population_5_countries ... done
loading Array object population_benelux ... done
loading Axis_Backward_Comp object citizenship ... done
loading Axis_Backward_Comp object country ... done
loading Axis_Backward_Comp object country_benelux ... done
loading Axis_Backward_Comp object gender ... done
loading Axis_Backward_Comp object time ... done
loading Group_Backward_Comp object even_years ... done
loading Group_Backward_Comp object odd_years ... done

Dumping Sessions (CSV, Excel, HDF5)

To save a session, you need to call the save method. The first argument is the path to a Excel/HDF5 file or to a directory if items are saved to CSV files:

[12]:

# save items of a session in CSV files.
# Here, the save method will create a 'demography' directory in which CSV files will be written
s.save('demography')

# save the session to an HDF5 file
s.save('demography.h5')

# save the session to an Excel file
s.save('demography.xlsx')

Note: Concerning the CSV and Excel formats, the metadata is saved in one Excel sheet (CSV file) named __metadata__(.csv). This sheet (CSV file) name cannot be changed.

The save method has several arguments:

  1. Using the names argument, you can specify which items to save:

[13]:

# use the names argument to only save births and deaths arrays
s.save('demography.h5', names=['births', 'deaths'])

# load session saved in 'demography.h5' to see its content
Session('demography.h5').names

[13]:

['births', 'deaths']

  1. By default, dumping a session to an Excel or HDF5 file will overwrite it. By setting the overwrite argument to False, you can choose to update the existing Excel or HDF5 file:

[14]:

population = read_csv('./demography/population.csv')
pop_ses = Session([('population', population)])

# by setting overwrite to False, the destination file is updated instead of overwritten.
# The items already stored in the file but not present in the session are left intact.
# On the contrary, the items that exist in both the file and the session are completely overwritten.
pop_ses.save('demography.h5', overwrite=False)

# load session saved in 'demography.h5' to see its content
Session('demography.h5').names

[14]:

['births', 'deaths', 'population']

  1. Setting the display argument to True, the save method will print a message each time an item is dumped:

[15]:

# with display=True, the save method will print a message
# each time an item is dumped
s.save('demography.h5', display=True)

dumping births ... done
dumping deaths ... done
dumping immigration ... done
dumping population ... done
dumping population_5_countries ... done
dumping population_benelux ... done
dumping citizenship ... done
dumping country ... done
dumping country_benelux ... done
dumping gender ... done
dumping time ... done
dumping even_years ... done
dumping odd_years ... done

Exploring Content

To get the list of items names of a session, use the names shortcut (be careful that the list is sorted alphabetically and does not follow the internal order!):

[16]:

# load a session representing the results of a demographic model
filepath_hdf = get_example_filepath('demography_eurostat.h5')
s = Session(filepath_hdf)

# print the content of the session
print(s.names)

['births', 'citizenship', 'country', 'country_benelux', 'deaths', 'even_years', 'gender', 'immigration', 'odd_years', 'population', 'population_5_countries', 'population_benelux', 'time']

To get more information of items of a session, the summary will provide not only the names of items but also the list of labels in the case of axes or groups and the list of axes, the shape and the dtype in the case of arrays:

[17]:

# print the content of the session
print(s.summary())

Metadata:
        title: Demographic datasets for a small selection of countries in Europe
        source: demo_jpan, demo_fasec, demo_magec and migr_imm1ctz tables from Eurostat
births: country, gender, time (3 x 2 x 5) [int32]
deaths: country, gender, time (3 x 2 x 5) [int32]
immigration: country, citizenship, gender, time (3 x 3 x 2 x 5) [int32]
population: country, gender, time (3 x 2 x 5) [int32]
population_5_countries: country, gender, time (5 x 2 x 5) [int32]
population_benelux: country, gender, time (3 x 2 x 5) [int32]
citizenship: citizenship ['Belgium' 'Luxembourg' 'Netherlands'] (3)
country: country ['Belgium' 'France' 'Germany'] (3)
country_benelux: country ['Belgium' 'Luxembourg' 'Netherlands'] (3)
gender: gender ['Male' 'Female'] (2)
time: time [2013 2014 2015 2016 2017] (5)
even_years: time[2014 2016] >> even_years (2)
odd_years: time[2013 2015 2017] >> odd_years (3)

Selecting And Filtering Items

Session objects work like ordinary dict Python objects. To select an item, use the usual syntax <session_var>['<item_name>']:

[18]:

s['population']

[18]:

country  gender\time      2013      2014      2015      2016      2017
Belgium         Male   5472856   5493792   5524068   5569264   5589272
Belgium       Female   5665118   5687048   5713206   5741853   5762455
 France         Male  31772665  32045129  32174258  32247386  32318973
 France       Female  33827685  34120851  34283895  34391005  34485148
Germany         Male  39380976  39556923  39835457  40514123  40697118
Germany       Female  41142770  41210540  41362080  41661561  41824535

A simpler way consists in the use the syntax <session_var>.<item_name>:

[19]:

s.population

[19]:

country  gender\time      2013      2014      2015      2016      2017
Belgium         Male   5472856   5493792   5524068   5569264   5589272
Belgium       Female   5665118   5687048   5713206   5741853   5762455
 France         Male  31772665  32045129  32174258  32247386  32318973
 France       Female  33827685  34120851  34283895  34391005  34485148
Germany         Male  39380976  39556923  39835457  40514123  40697118
Germany       Female  41142770  41210540  41362080  41661561  41824535

Warning: The syntax session_var.item_name will work as long as you don’t use any special character like , ; : in the item’s name.

To return a new session with selected items, use the syntax <session_var>[list, of, item, names]:

[20]:

s_selected = s['population', 'births', 'deaths']

s_selected.names

[20]:

['births', 'deaths', 'population']

Warning: The same selection as above can be applied on a checked-session but the returned object is a normal session and NOT a checked-session. This means that you will loose all the benefits (autocomplete, protection on type, axes and dtype) of checked-sessions.

[21]:

d_selected = d['births', 'deaths']

# test if v_selected is a checked-session
print('is still a check-session?', isinstance(d_selected, CheckedSession))
#test if v_selected is a normal session
print('is now a normal session?', isinstance(d_selected, Session))

is still a check-session? False
is now a normal session? True

The filter method allows you to select all items of the same kind (i.e. all axes, or groups or arrays) or all items with names satisfying a given pattern:

[22]:

# select only arrays of a session
s.filter(kind=Array)

[22]:

Session(births, deaths, immigration, population, population_5_countries, population_benelux)

[23]:

# selection all items with a name starting with a letter between a and k
s.filter(pattern='[a-k]*')

[23]:

Session(births, deaths, immigration, citizenship, country, country_benelux, gender, even_years)

Warning: Using the filter() method on a checked-session will return a normal session and NOT a checked-session. This means that you will loose all the benefits (autocomplete, protection on type, axes and dtype) of checked-sessions.

[24]:

d_filtered = d.filter(pattern='[a-k]*')

# test if v_selected is a checked-session
print('is still a check-session?', isinstance(d_filtered, CheckedSession))
#test if v_selected is a normal session
print('is now a normal session?', isinstance(d_filtered, Session))

is still a check-session? False
is now a normal session? True

Iterating over Items

Like the built-in Python dict objects, Session objects provide methods to iterate over items:

[25]:

# iterate over item names
for key in s.keys():
    print(key)

births
deaths
immigration
population
population_5_countries
population_benelux
citizenship
country
country_benelux
gender
time
even_years
odd_years

[26]:

# iterate over items
for value in s.values():
    if isinstance(value, Array):
        print(value.info)
    else:
        print(repr(value))
    print()

title: Live births by mother's age and newborn's sex
source: table demo_fasec from Eurostat
3 x 2 x 5
 country [3]: 'Belgium' 'France' 'Germany'
 gender [2]: 'Male' 'Female'
 time [5]: 2013 2014 2015 2016 2017
dtype: int32
memory used: 120 bytes

title: Deaths by age and sex
source: table demo_magec from Eurostat
3 x 2 x 5
 country [3]: 'Belgium' 'France' 'Germany'
 gender [2]: 'Male' 'Female'
 time [5]: 2013 2014 2015 2016 2017
dtype: int32
memory used: 120 bytes

title: Immigration by age group, sex and citizenship
source: table migr_imm1ctz from Eurostat
3 x 3 x 2 x 5
 country [3]: 'Belgium' 'Luxembourg' 'Netherlands'
 citizenship [3]: 'Belgium' 'Luxembourg' 'Netherlands'
 gender [2]: 'Male' 'Female'
 time [5]: 2013 2014 2015 2016 2017
dtype: int32
memory used: 360 bytes

title: Population on 1 January by age and sex
source: table demo_pjan from Eurostat
3 x 2 x 5
 country [3]: 'Belgium' 'France' 'Germany'
 gender [2]: 'Male' 'Female'
 time [5]: 2013 2014 2015 2016 2017
dtype: int32
memory used: 120 bytes

title: Population on 1 January by age and sex (Benelux + France + Germany)
source: table demo_pjan from Eurostat
5 x 2 x 5
 country [5]: 'Belgium' 'France' 'Germany' 'Luxembourg' 'Netherlands'
 gender [2]: 'Male' 'Female'
 time [5]: 2013 2014 2015 2016 2017
dtype: int32
memory used: 200 bytes

title: Population on 1 January by age and sex (Benelux)
source: table demo_pjan from Eurostat
3 x 2 x 5
 country [3]: 'Belgium' 'Luxembourg' 'Netherlands'
 gender [2]: 'Male' 'Female'
 time [5]: 2013 2014 2015 2016 2017
dtype: int32
memory used: 120 bytes

Axis(['Belgium', 'Luxembourg', 'Netherlands'], 'citizenship')

Axis(['Belgium', 'France', 'Germany'], 'country')

Axis(['Belgium', 'Luxembourg', 'Netherlands'], 'country')

Axis(['Male', 'Female'], 'gender')

Axis([2013, 2014, 2015, 2016, 2017], 'time')

time[2014, 2016] >> 'even_years'

time[2013, 2015, 2017] >> 'odd_years'


[27]:

# iterate over names and items
for key, value in s.items():
    if isinstance(value, Array):
        print(key, ':')
        print(value.info)
    else:
        print(key, ':', repr(value))
    print()

births :
title: Live births by mother's age and newborn's sex
source: table demo_fasec from Eurostat
3 x 2 x 5
 country [3]: 'Belgium' 'France' 'Germany'
 gender [2]: 'Male' 'Female'
 time [5]: 2013 2014 2015 2016 2017
dtype: int32
memory used: 120 bytes

deaths :
title: Deaths by age and sex
source: table demo_magec from Eurostat
3 x 2 x 5
 country [3]: 'Belgium' 'France' 'Germany'
 gender [2]: 'Male' 'Female'
 time [5]: 2013 2014 2015 2016 2017
dtype: int32
memory used: 120 bytes

immigration :
title: Immigration by age group, sex and citizenship
source: table migr_imm1ctz from Eurostat
3 x 3 x 2 x 5
 country [3]: 'Belgium' 'Luxembourg' 'Netherlands'
 citizenship [3]: 'Belgium' 'Luxembourg' 'Netherlands'
 gender [2]: 'Male' 'Female'
 time [5]: 2013 2014 2015 2016 2017
dtype: int32
memory used: 360 bytes

population :
title: Population on 1 January by age and sex
source: table demo_pjan from Eurostat
3 x 2 x 5
 country [3]: 'Belgium' 'France' 'Germany'
 gender [2]: 'Male' 'Female'
 time [5]: 2013 2014 2015 2016 2017
dtype: int32
memory used: 120 bytes

population_5_countries :
title: Population on 1 January by age and sex (Benelux + France + Germany)
source: table demo_pjan from Eurostat
5 x 2 x 5
 country [5]: 'Belgium' 'France' 'Germany' 'Luxembourg' 'Netherlands'
 gender [2]: 'Male' 'Female'
 time [5]: 2013 2014 2015 2016 2017
dtype: int32
memory used: 200 bytes

population_benelux :
title: Population on 1 January by age and sex (Benelux)
source: table demo_pjan from Eurostat
3 x 2 x 5
 country [3]: 'Belgium' 'Luxembourg' 'Netherlands'
 gender [2]: 'Male' 'Female'
 time [5]: 2013 2014 2015 2016 2017
dtype: int32
memory used: 120 bytes

citizenship : Axis(['Belgium', 'Luxembourg', 'Netherlands'], 'citizenship')

country : Axis(['Belgium', 'France', 'Germany'], 'country')

country_benelux : Axis(['Belgium', 'Luxembourg', 'Netherlands'], 'country')

gender : Axis(['Male', 'Female'], 'gender')

time : Axis([2013, 2014, 2015, 2016, 2017], 'time')

even_years : time[2014, 2016] >> 'even_years'

odd_years : time[2013, 2015, 2017] >> 'odd_years'

Manipulating Checked Sessions

Note: this section only concerns objects declared in checked-sessions.

Let’s create a simplified version of the Demography checked-session we have defined above:

[28]:

class Demography(CheckedSession):
    COUNTRY = Axis('country=Belgium,France,Germany')
    GENDER = Axis('gender=Male,Female')
    TIME = Axis('time=2013..2017')
    population = zeros([COUNTRY, GENDER, TIME], dtype=int)
    # declare the deaths array with constrained axes and dtype
    deaths: CheckedArray([COUNTRY, GENDER, TIME], int) = 0

d = Demography()

print(d.summary())

deaths: country, gender, time (3 x 2 x 5) [int64]
COUNTRY: country ['Belgium' 'France' 'Germany'] (3)
GENDER: gender ['Male' 'Female'] (2)
TIME: time [2013 2014 2015 2016 2017] (5)
population: country, gender, time (3 x 2 x 5) [int64]

One of the specificities of checked-sessions is that the type of the contained objects is protected (it cannot change). Any attempt to assign a value of different type will raise an error:

[29]:

# The population variable was initialized with the zeros() function which returns an Array object.
# The declared type of the population variable is Array and is protected
d.population = Axis('population=child,teenager,adult,elderly')

ArbitraryTypeError: instance of Array expected

The death array has been declared as a CheckedArray. As a consequence, its axes are protected. Trying to assign a value with incompatible axes raises an error:

[30]:

AGE = Axis('age=0..100')
d.deaths = zeros([d.COUNTRY, AGE, d.GENDER, d.TIME])

ValueError: Array 'deaths' was declared with axes {country, gender, time} but got array with axes {country, age, gender, time} (unexpected {age} axis)

The deaths array is also constrained by its declared dtype int. This means that if you try to assign a value of type float instead of int, the value will be converted to int if possible:

[31]:

d.deaths = 1.2
d.deaths

[31]:

country  gender\time  2013  2014  2015  2016  2017
Belgium         Male     1     1     1     1     1
Belgium       Female     1     1     1     1     1
 France         Male     1     1     1     1     1
 France       Female     1     1     1     1     1
Germany         Male     1     1     1     1     1
Germany       Female     1     1     1     1     1

or raise an error:

[32]:

d.deaths = 'undead'

ValueError: invalid literal for int() with base 10: 'undead'

It is possible to add a new variable after the checked-session has been initialized but in that case, a warning message is printed (in case you misspelled the name of variable while trying to modify it):

[33]:

# misspell population (forgot the 'a')
d.popultion = 0

/tmp/ipykernel_3291/1566890367.py:2: UserWarning: 'popultion' is not declared in 'Demography'
  d.popultion = 0

Arithmetic Operations On Sessions

Session objects accept binary operations with a scalar:

[34]:

# get population, births and deaths in millions
s_div = s / 1e6

s_div.population

[34]:

country  gender\time       2013       2014       2015       2016       2017
Belgium         Male   5.472856   5.493792   5.524068   5.569264   5.589272
Belgium       Female   5.665118   5.687048   5.713206   5.741853   5.762455
 France         Male  31.772665  32.045129  32.174258  32.247386  32.318973
 France       Female  33.827685  34.120851  34.283895  34.391005  34.485148
Germany         Male  39.380976  39.556923  39.835457  40.514123  40.697118
Germany       Female   41.14277   41.21054   41.36208  41.661561  41.824535

with an array (please read the documentation of the random.choice function first if you don’t know it):

[35]:

from larray import random
random_increment = random.choice([-1, 0, 1], p=[0.3, 0.4, 0.3], axes=s.population.axes) * 1000
random_increment

[35]:

country  gender\time  2013   2014   2015   2016   2017
Belgium         Male  1000      0  -1000   1000  -1000
Belgium       Female     0  -1000  -1000      0   1000
 France         Male     0   1000      0  -1000      0
 France       Female  1000  -1000      0   1000      0
Germany         Male     0   1000      0      0      0
Germany       Female  1000      0   1000      0   1000

[36]:

# add some variables of a session by a common array
s_rand = s['population', 'births', 'deaths'] + random_increment

s_rand.population

[36]:

country  gender\time      2013      2014      2015      2016      2017
Belgium         Male   5473856   5493792   5523068   5570264   5588272
Belgium       Female   5665118   5686048   5712206   5741853   5763455
 France         Male  31772665  32046129  32174258  32246386  32318973
 France       Female  33828685  34119851  34283895  34392005  34485148
Germany         Male  39380976  39557923  39835457  40514123  40697118
Germany       Female  41143770  41210540  41363080  41661561  41825535

with another session:

[37]:

# compute the difference between each array of the two sessions
s_diff = s - s_rand

s_diff.births

[37]:

country  gender\time   2013   2014   2015   2016   2017
Belgium         Male  -1000      0   1000  -1000   1000
Belgium       Female      0   1000   1000      0  -1000
 France         Male      0  -1000      0   1000      0
 France       Female  -1000   1000      0  -1000      0
Germany         Male      0  -1000      0      0      0
Germany       Female  -1000      0  -1000      0  -1000

Applying Functions On All Arrays

In addition to the classical arithmetic operations, the apply method can be used to apply the same function on all arrays. This function should take a single element argument and return a single value:

[38]:

# add the next year to all arrays
def add_next_year(array):
    if 'time' in array.axes.names:
        last_year = array.time.i[-1]
        return array.append('time', 0, last_year + 1)
    else:
        return array

s_with_next_year = s.apply(add_next_year)

print('population array before calling apply:')
print(s.population)
print()
print('population array after calling apply:')
print(s_with_next_year.population)

population array before calling apply:
country  gender\time      2013      2014      2015      2016      2017
Belgium         Male   5472856   5493792   5524068   5569264   5589272
Belgium       Female   5665118   5687048   5713206   5741853   5762455
 France         Male  31772665  32045129  32174258  32247386  32318973
 France       Female  33827685  34120851  34283895  34391005  34485148
Germany         Male  39380976  39556923  39835457  40514123  40697118
Germany       Female  41142770  41210540  41362080  41661561  41824535

population array after calling apply:
country  gender\time      2013      2014      2015      2016      2017  2018
Belgium         Male   5472856   5493792   5524068   5569264   5589272     0
Belgium       Female   5665118   5687048   5713206   5741853   5762455     0
 France         Male  31772665  32045129  32174258  32247386  32318973     0
 France       Female  33827685  34120851  34283895  34391005  34485148     0
Germany         Male  39380976  39556923  39835457  40514123  40697118     0
Germany       Female  41142770  41210540  41362080  41661561  41824535     0

It is possible to pass a function with additional arguments:

[39]:

# add the next year to all arrays.
# Use the 'copy_values_from_last_year flag' to indicate
# whether to copy values from the last year
def add_next_year(array, copy_values_from_last_year):
    if 'time' in array.axes.names:
        last_year = array.time.i[-1]
        value = array[last_year] if copy_values_from_last_year else 0
        return array.append('time', value, last_year + 1)
    else:
        return array

s_with_next_year = s.apply(add_next_year, True)

print('population array before calling apply:')
print(s.population)
print()
print('population array after calling apply:')
print(s_with_next_year.population)

population array before calling apply:
country  gender\time      2013      2014      2015      2016      2017
Belgium         Male   5472856   5493792   5524068   5569264   5589272
Belgium       Female   5665118   5687048   5713206   5741853   5762455
 France         Male  31772665  32045129  32174258  32247386  32318973
 France       Female  33827685  34120851  34283895  34391005  34485148
Germany         Male  39380976  39556923  39835457  40514123  40697118
Germany       Female  41142770  41210540  41362080  41661561  41824535

population array after calling apply:
country  gender\time      2013      2014      2015      2016      2017      2018
Belgium         Male   5472856   5493792   5524068   5569264   5589272   5589272
Belgium       Female   5665118   5687048   5713206   5741853   5762455   5762455
 France         Male  31772665  32045129  32174258  32247386  32318973  32318973
 France       Female  33827685  34120851  34283895  34391005  34485148  34485148
Germany         Male  39380976  39556923  39835457  40514123  40697118  40697118
Germany       Female  41142770  41210540  41362080  41661561  41824535  41824535

It is also possible to apply a function on non-Array objects of a session. Please refer the documentation of the apply method.

Comparing Sessions

Being able to compare two sessions may be useful when you want to compare two different models expected to give the same results or when you have updated your model and want to see what are the consequences of the recent changes.

Session objects provide the two methods to compare two sessions: equals and element_equals:

  • The equals method will return True if all items from both sessions are identical, False otherwise.

  • The element_equals method will compare items of two sessions one by one and return an array of boolean values.

[40]:

# load a session representing the results of a demographic model
filepath_hdf = get_example_filepath('demography_eurostat.h5')
s = Session(filepath_hdf)

# create a copy of the original session
s_copy = s.copy()

[41]:

# 'element_equals' compare arrays one by one
s.element_equals(s_copy)

[41]:

name  births  deaths  ...  time  even_years  odd_years
        True    True  ...  True        True       True

[42]:

# 'equals' returns True if all items of the two sessions have exactly the same items
s.equals(s_copy)

[42]:

True

[43]:

# slightly modify the 'population' array for some labels combination
s_copy.population += random_increment

[44]:

# the 'population' array is different between the two sessions
s.element_equals(s_copy)

[44]:

name  births  deaths  ...  time  even_years  odd_years
        True    True  ...  True        True       True

[45]:

# 'equals' returns False if at least one item of the two sessions are different in values or axes
s.equals(s_copy)

[45]:

False

[46]:

# reset the 'copy' session as a copy of the original session
s_copy = s.copy()

# add an array to the 'copy' session
s_copy.gender_ratio = s_copy.population.ratio('gender')

[47]:

# the 'gender_ratio' array is not present in the original session
s.element_equals(s_copy)

[47]:

name  births  deaths  ...  even_years  odd_years  gender_ratio
        True    True  ...        True       True         False

[48]:

# 'equals' returns False if at least one item is not present in the two sessions
s.equals(s_copy)

[48]:

False

The == operator return a new session with boolean arrays with elements compared element-wise:

[49]:

# reset the 'copy' session as a copy of the original session
s_copy = s.copy()

# slightly modify the 'population' array for some labels combination
s_copy.population += random_increment

[50]:

s_check_same_values = s == s_copy

s_check_same_values.population

[50]:

country  gender\time   2013   2014   2015   2016   2017
Belgium         Male  False   True  False  False  False
Belgium       Female   True  False  False   True  False
 France         Male   True  False   True  False   True
 France       Female  False  False   True  False   True
Germany         Male   True  False   True   True   True
Germany       Female  False   True  False   True  False

This also works for axes and groups:

[51]:

s_check_same_values.time

[51]:

time  2013  2014  2015  2016  2017
      True  True  True  True  True

The != operator does the opposite of == operator:

[52]:

s_check_different_values = s != s_copy

s_check_different_values.population

[52]:

country  gender\time   2013   2014   2015   2016   2017
Belgium         Male   True  False   True   True   True
Belgium       Female  False   True   True  False   True
 France         Male  False   True  False   True  False
 France       Female   True   True  False   True  False
Germany         Male  False   True  False  False  False
Germany       Female   True  False   True  False   True

A more visual way is to use the compare function which will open the Editor.

compare(s, s_alternative, names=['baseline', 'lower_birth_rate'])

compare two sessions