Have you ever found yourself coding up boilerplates to handle different scenarios? It's most likely that you have thought about using python's eval or exec in order to decouple some part of your code to be specified and evaluated later on at run-time. But, you have probably also come accross people who discourage you from using eval/exec. This is because programs with eval/exec are considered vulnarable and can be used to execute malicious code.
While this is true in general, but in many cases, you do not care about security concerns, and your highest priority is implementing a quick solution to a general problem which could be solved by using eval or exec. This is where dypy comes in. It allows you to use eval and exec effectively by providing utilities to dynamically compile code, lookup variables and lazily evaluate them.
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pip install dypyYou can use dypy.eval to combine the functionality of dypy.eval_function (see here) and dypy.get_value (see here). You can do as follows:
import dypy
dypy.eval("math.cos") # <function <lambda> at MEM_ADDRESS> (math is imported through get_value)
dypy.eval("math.cos", dynamic_args=True)(2, verbose=True) # 3, verbose is ignored (and math is imported through get_value)
dypy.eval("def my_function(x): return x + 1", function_of_interest="my_function") # <function my_function at MEM_ADDRESS>
dypy.eval("def my_function(x): return x + y", function_of_interest="my_function", context={"y": 2})(2) # 4You can use dypy.get_value to lookup a variable as a string and then evaluate it. This is useful when you want to use a variable that is not defined in the current scope. You can do as follows:
from dypy import get_value
get_value("math.pi") # 3.141592653589793
get_value("math.cos(0)") # won't evaluate, this is not a variable but a call to a variable
get_value("math.cos")(0) # 1.0
import math
get_value("cos", context=math) # math.cos
get_value("something_that_does_not_exist") # raises NameError
get_value("something_that_does_not_exist", strict=False) # Noneget_value supports looking up variables in a module or package in your current working directory as well (as opposed to python's import which only supports looking up variables in the python standard library and installed packages). This is useful when you want to create a script that can be run from anywhere and still be able to access variables in the current working directory.
For example, imagine you create your own python package with a runnable script that sorts files in a directory. You can use get_value to lookup a config.sort function in the current working directory. This way, you can create a config.py file in the current working directory and define your own sorting function. Or use a default sorting function if the config.py file does not exist.
Your code would look like this:
from dypy import get_value
def sort_files():
sort_function = get_value("config.sort", strict=False) or default_sort
# do something with sortAlthough this example is somewhat contrived, such a use case is very common in data science and machine learning. Imagine writing a package for training a Deep Learning model. You can use get_value to lookup custom Dataset classes and model implementations and this way, people can use your package without the need to modifying your code, because they can simply inject their own implementations in places where you have used get_value.
Another potential use case is defining Neural Network layers. You can use get_value to lookup custom layers and use them in your model. Such as:
from dypy import get_value
import torch
class MyLinearBlock(torch.nn.Module):
def __init__(self, in_features, out_features, activation="torch.nn.ReLU"):
super().__init__()
self.linear = torch.nn.Linear(in_features, out_features)
self.activation = get_value(activation, strict=False)
def forward(self, x):
x = self.linear(x)
x = self.activation(x) if self.activation else x
return xThis way, you can change the activation function by simply changing the activation argument. For example, you can use torch.nn.Sigmoid or torch.nn.Tanh or even a custom activation function that you have defined in the current working directory or use one from a 3rd party package.
What if you want to generate python programs dynamically? Meaning that you have a string that contains python code and you want to inject it into your program. You can use dypy.eval_function to evaluate a piece of code and retrieve a function. You can do as follows:
from dypy import eval_function
eval_function("lambda x: x + 1") # <function <lambda> at MEM_ADDRESS>
eval_function("def my_function(x): return x + 1") # wont work, this is not a function,
# but a code block and you need to mention your function_of_interest in that code block
eval_function("def my_function(x): return x + 1", function_of_interest="my_function") # <function my_function at MEM_ADDRESS>eval_function accepts three types of function descriptors:
- A lambda function, e.g.
lambda x: x + 1, or a code which evaluates to a callable object,math.cos(in this case, the values being looked up should be present in the evaluation context, more on this later). - A code block, which can contain multiple lines and functions, in which case you need to specify the name of the function of interest using the
function_of_interestargument.eval_functionwill evaluate the code block and retrieve your function of interest. - A dictionary of "code", ["context", "function_of_interest"] pairs. Useful when you are using
eval_functionon top of a configuration file, in which case you can specify the code and the context for each function of interest.
When evaluating a function descriptor, you can specify a context in which the code will be evaluated. This is useful when you want to use variables that are not defined in the current scope. dypy has a context registry that you can use as a global context for all your function evaluations.
from dypy import eval_function, register_context
import math
register_context(math, "math") # register math package as a context
# you can also use register(math), which will use the name of the package as the context name
eval_function("math.cos") # <function <lambda> at MEM_ADDRESS> (math is looked up through the context registry)You can also specify a context for each function evaluation using the context argument. The context is a dictionary of variable names and their values. You can do as follows:
eval_function("def my_function(x): return x + y", function_of_interest="my_function", context={"y": 1})(2) # 3You can also optionally set dynamic_args=True, when evaluating a function. This will create a function that intelligently evaluates its arguments, by wrapping it using dypy.dynamic_args_wrapper. Functions wrapped by dypy.dynamic_args_wrapper preprocess arguments passed to them, and ignore arguments that are not defined in the function signature. For instance:
eval_function("lambda x: x + 1", dynamic_args=True)(2, verbose=True) # 3, verbose is ignoredThere are times when you want to assign a variable in a dynamic manner. Meaning that you want to change a variable's value that is not necessarily defined in the current scope. You can use dypy.set_value to do so. You can do as follows:
from dypy import set_value
set_value("some_package.my_function", lambda x: x + 1)
# changing the value of pi in math package
set_value("math.pi", 3.14)
# now if you import math, math.pi will be 3.14
import math
math.pi # 3.14dypy is licensed under the MIT License. See LICENSE for the full license text.
If you use dypy in your research, please cite this repository as described in CITATION.cff.