It enables one to:
- Add new data to a deeply nested dictionary via a path in form of an array of strings. If the defined structures do not exist, they are created on the fly.
- Read data from a deeply nested dictionary via a path in form of an array of strings.
- Export deeply nested dictionaries to CSV format for posterior data analysis.
- Return all values stored in a nested dictionary. This is just a
recursive
.values(). - Return all keys stored in a nested dictionary. This is just a
recursive
.keys(). - Filter/search deeply nested dictionaries via keywords (coming soon).
It is aimed at:
- Enabling scientific applications with Godot as nested data becomes analizable.
- Generally handling nested data more easily.
Download the DictCSV and/or DictIO scripts via
DownGit or clone this
repository to your local machine. Put the script(s) in your Godot
project folder and add them to the auto-load list. This procedure is
explained in more detail
here.
Remove the scripts from the auto-load list and delete them.
Running the following:
var dict = {}
var path = ["Hull", "Layer_1", "Layer_2", "Layer_3", 'Layer_4', "Core", "Temperature"]
var payload = 4500
var updated_dict = DictIO.manipulate_nested_dict(dict, path, 'write', payload, [])
print(updated_dict)Produces this dictionary:
{
"Hull":{
"Layer_1":{
"Layer_2":{
"Layer_3":{
"Layer_4":{
"Core":{
"Temperature":4500
}
}
}
}
}
}
}Note that the original outputs printed at the console in Godot look like
this javascript {Hull:{Layer_1:{Layer_2:{Layer_3:{Layer_4:{Core:{Sublevel_A:Inserted Info}}}}}}}. I have formatted the output for easier interpretation
here.
If one wishes to add more information to the dictionary, this is done exactly the same way as above. Adding the following to the above code:
var path2 = ["Hull", "Layer_1", "Layer_2", "Layer_3",
'New_Path_1', "New_Path_2", "Hidden_Core", "Radiation"]
var payload2 = 250
var updated_dict2 = DictIO.manipulate_nested_dict(updated_dict, path2, 'write', payload2, [])
print(updated_dict2)Output:
{
"Hull":{
"Layer_1":{
"Layer_2":{
"Layer_3":{
"Layer_4":{
"Core":{
"Temperature":4500
}
},
"New_Path_1":{
"New_Path_2":{
"Hidden_Core":{
"Radiation":250
}
}
}
}
}
}
}
}Observe how new entries are added only where the paths begin differing.
New_Path_1 is inserted at the level of Layer_4. Updating or changing
the values of a nested dictionary is performed in the same way as above.
Taking the last dictionary from above, reading is accomplished by
calling manipulate_nested_dict() with the “read”
argument:
path = ["Hull", "Layer_1", "Layer_2", "Layer_3", 'Layer_4', "Core", "Temperature"]
var looked_up = DictIO.manipulate_nested_dict(updated_dict2, path, 'read', [], [])
print(looked_up)Returns 4500.
Note that when writing and reading with the same path variable,
between the DictIO.manipulate_nested_dict function calls, the path
has to be re-written, like above. You cannot re-use the path
variable withou re-writing it between the function calls. The reasons
are unclear. My best guess is that the memory management injects some
information from one function-call to another. This persists even when
changing funcion names or sourcing from different scripts. Any pointers
would be great.
When the path does not point at an “endpoint” (a dictionary which does
contain a value), but another dictionary along the nesting hierarchy,
the function returns its contents. See here for an
example:
path = ["Hull", "Layer_1", "Layer_2", "Layer_3", 'Layer_4'] # Here, the path ends earlier than in the above example.
var looked_up = DictIO.manipulate_nested_dict(updated_dict2, path, 'read', [], [])
print(looked_up)The following warning is printed: “Warning: latest entry in path could not be used as key. Returning as is.” The function returns following:
{
"Core":{
"Temperature":4500
}
}For this example, consider the beginning of the following dictionary
TEST_DICT (below), where different data types are stored in
instances, belonging to different units. The stored data is
time-stamped and varies between types. This example is not far from
typical structures used in data collection in scientific experiments.
This format, while quite efficient, is not apt for analysis. In order
for it to be analizable, each datum (yes, even the missing values), have
to be individually identified with each key from the hierarchy it
belongs to. This is called tidy
data
or a data matrix.
{
"Data_Type_1":{
"Unit_A":{
"Instances":{
"Inst_01":{
"Type_A":{
"1":{
"01.01.2019":4.3,
"03.03.2003":"NAN"
},
"2":{
"01.02.2019":805
}
},
"Type_B":{
"3":{
"27.12.2017":"beta"
}
}
},
"Inst_02":{
"Type_B":{
"3":{
"23.04.2016":"gamma",
"12.11.2011":"NAN"
}
...Calling:
DictCSV.export_nested_dict_to_csv(TEST_DICT, '/home/username/somefolder', 'Filename', ",")will create a CSV file with the seperator ,, the name Filename at
the directory /home/username/somefolder. The contents look as follows:
Data_Type_1,Unit_A,Instances,Inst_01,Type_A,1,01.01.2019,4.3
Data_Type_1,Unit_A,Instances,Inst_01,Type_A,1,03.03.2003,NAN
Data_Type_1,Unit_A,Instances,Inst_01,Type_A,2,01.02.2019,805
Data_Type_1,Unit_A,Instances,Inst_01,Type_B,3,27.12.2017,beta
Data_Type_1,Unit_A,Instances,Inst_02,Type_B,3,23.04.2016,gamma
Data_Type_1,Unit_A,Instances,Inst_02,Type_B,3,12.11.2011,NAN
Data_Type_1,Unit_A,Instances,Inst_03,Type_D,1,30.02.2010,10
Data_Type_1,Unit_B,Instances,Inst_01,Type_D,1,30.02.2010,10
Data_Type_1,Unit_B,Instances,Inst_02,Type_A,1,01.01.2019,4.3
Data_Type_1,Unit_B,Instances,Inst_02,Type_A,1,03.03.2003,NAN
Data_Type_1,Unit_B,Instances,Inst_02,Type_A,2,01.02.2019,805
Data_Type_1,Unit_B,Instances,Inst_02,Type_B,1,03.01.2019,4.3
Data_Type_1,Unit_B,Instances,Inst_02,Type_B,1,25.03.2003,5
Data_Type_1,Unit_B,Instances,Inst_02,Type_B,2,01.01.2019,700
Data_Type_1,Unit_B,Instances,Inst_02,Type_B,2,03.03.2003,685
Data_Type_1,Unit_B,Instances,Inst_02,Type_B,3,21.11.2019,6f6e65
Data_Type_1,Unit_B,Instances,Inst_03,Type_D,1,31.03.2012,71
Data_Type_1,Unit_B,Instances,Inst_03,Type_D,1,05.05.2004,4
Data_Type_1,Unit_B,Instances,Inst_03,Type_D,2,24.11.2017,564
Data_Type_1,Unit_B,Instances,Inst_03,Type_D,2,23.09.2009,789
Data_Type_1,Unit_B,Instances,Inst_03,Type_D,2,10.05.2007,754The main limitation to this approach is that the length of the paths
where the data is stored (the endpoints) need to be at the same “depth”.
This means that empty dictionaries {} are also not an option and
forces the use of ‘NAN’/‘NA’/‘null’ when there are missing values or to
omit an entry alltogether. It is good statistical practice to consider
the occurence of missing data in your data analysis!
Calling:
DictCSV.flatten_dictionary(TEST_DICT, 'values')will return:
[4.3, NAN, 805, beta, gamma, NAN, 10, 10, 4.3, NAN, 805, 4.3, 5, 700, 685, 6f6e65, 71, 4, 564, 789, 754]Calling:
DictCSV.flatten_dictionary(TEST_DICT, 'paths')will return:
[[Data_Type_1, Unit_A, Instances, Inst_01, Type_A, 1, 01.01.2019],
[Data_Type_1, Unit_A, Instances, Inst_01, Type_A, 1, 03.03.2003],
[Data_Type_1, Unit_A, Instances, Inst_01, Type_A, 2, 01.02.2019],
[Data_Type_1, Unit_A, Instances, Inst_01, Type_B, 3, 27.12.2017],
[Data_Type_1, Unit_A, Instances, Inst_02, Type_B, 3, 23.04.2016],
[Data_Type_1, Unit_A, Instances, Inst_02, Type_B, 3, 12.11.2011],
[Data_Type_1, Unit_A, Instances, Inst_03, Type_D, 1, 30.02.2010],
[Data_Type_1, Unit_B, Instances, Inst_01, Type_D, 1, 30.02.2010],
[Data_Type_1, Unit_B, Instances, Inst_02, Type_A, 1, 01.01.2019],
[Data_Type_1, Unit_B, Instances, Inst_02, Type_A, 1, 03.03.2003],
[Data_Type_1, Unit_B, Instances, Inst_02, Type_A, 2, 01.02.2019],
[Data_Type_1, Unit_B, Instances, Inst_02, Type_B, 1, 03.01.2019],
[Data_Type_1, Unit_B, Instances, Inst_02, Type_B, 1, 25.03.2003],
[Data_Type_1, Unit_B, Instances, Inst_02, Type_B, 2, 01.01.2019],
[Data_Type_1, Unit_B, Instances, Inst_02, Type_B, 2, 03.03.2003],
[Data_Type_1, Unit_B, Instances, Inst_02, Type_B, 3, 21.11.2019],
[Data_Type_1, Unit_B, Instances, Inst_03, Type_D, 1, 31.03.2012],
[Data_Type_1, Unit_B, Instances, Inst_03, Type_D, 1, 05.05.2004],
[Data_Type_1, Unit_B, Instances, Inst_03, Type_D, 2, 24.11.2017],
[Data_Type_1, Unit_B, Instances, Inst_03, Type_D, 2, 23.09.2009],
[Data_Type_1, Unit_B, Instances, Inst_03, Type_D, 2, 10.05.2007]]The function flatten_dictionary delivers useful in-between products to
get to the CSV export. From here, one could easily write a filtering
function to make a dictionary filterable (coming soon).
I am open for any constructive input!
A work by Marco Wirthlin
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