### Installing latexify-py via pip

Source: https://github.com/google/latexify_py/blob/main/docs/getting_started.md

Installs the latexify-py library using the pip package manager. This is the standard and recommended way to install the library from PyPI.

```Shell
pip install latexify-py
```

--------------------------------

### Installing latexify-py Package (Python)

Source: https://github.com/google/latexify_py/blob/main/examples/latexify_examples.ipynb

Installs the required `latexify-py` package using pip within a notebook environment. Requires restarting the runtime after installation.

```python
# Restart the runtime before running the examples below.
%pip install latexify-py

```

--------------------------------

### Importing Libraries and Checking Version (Python)

Source: https://github.com/google/latexify_py/blob/main/examples/latexify_examples.ipynb

Imports the `math`, `numpy`, and `latexify` libraries. Optionally imports `math` and `numpy` for later examples. Prints the installed version of `latexify`.

```python
import math  # Optional
import numpy as np  # Optional
import latexify

latexify.__version__

```

--------------------------------

### Getting LaTeX string directly with latexify.get_latex

Source: https://github.com/google/latexify_py/blob/main/docs/getting_started.md

Illustrates how to use the `latexify.get_latex` function to obtain the LaTeX string for a given function directly, without needing to apply a decorator.

```Python
def solve(a, b, c):
    return (-b + math.sqrt(b**2 - 4 * a * c)) / (2 * a)

latexify.get_latex(solve)
```

--------------------------------

### Getting LaTeX source from decorated function

Source: https://github.com/google/latexify_py/blob/main/docs/getting_started.md

Shows how to retrieve the raw LaTeX string representation generated by `latexify` for a decorated function by printing the function object.

```Python
print(solve)
```

--------------------------------

### Invoking a latexify decorated function

Source: https://github.com/google/latexify_py/blob/main/docs/getting_started.md

Demonstrates that a function decorated with `latexify.function` remains callable and executes its original Python logic, returning the computed result.

```Python
solve(1, 2, 1)
```

--------------------------------

### Decorating a function with @latexify.function

Source: https://github.com/google/latexify_py/blob/main/docs/getting_started.md

Applies the `latexify.function` decorator to a Python function. This decorator enables the function to be automatically displayed as a LaTeX formula in environments that support rich output, such as Jupyter notebooks.

```Python
@latexify.function
def solve(a, b, c):
    return (-b + math.sqrt(b**2 - 4 * a * c)) / (2 * a)

solve
```

--------------------------------

### Decorating a function with @latexify.expression

Source: https://github.com/google/latexify_py/blob/main/docs/getting_started.md

Uses the `latexify.expression` decorator, which converts the function body into a LaTeX expression, similar to `latexify.function`, but typically omits the function signature in the output.

```Python
@latexify.expression
def solve(a, b, c):
    return (-b + math.sqrt(b**2 - 4 * a * c)) / (2 * a)

solve
```

--------------------------------

### Handling Conditional Logic with latexify.function (Python)

Source: https://github.com/google/latexify_py/blob/main/examples/latexify_examples.ipynb

Defines a function `sinc` with an `if/else` statement and applies the `@latexify.function` decorator. Shows how `latexify` translates conditional logic into LaTeX.

```python
@latexify.function
def sinc(x):
  if x == 0:
    return 1
  else:
    return math.sin(x) / x

sinc

```

--------------------------------

### Obtaining LaTeX Source with latexify.get_latex (Python)

Source: https://github.com/google/latexify_py/blob/main/examples/latexify_examples.ipynb

Shows how to use the `latexify.get_latex` function to directly obtain the LaTeX source string for a given Python function without using decorators.

```python
# latexify.get_latex obtains the underlying LaTeX expression directly.
def solve(a, b, c):
  return (-b + math.sqrt(b**2 - 4*a*c)) / (2*a)

latexify.get_latex(solve)

```

--------------------------------

### Generating LaTeX Equation with latexify.function (Python)

Source: https://github.com/google/latexify_py/blob/main/examples/latexify_examples.ipynb

Defines a Python function `solve` using the `@latexify.function` decorator to generate its LaTeX representation. Shows how to call the function, print its LaTeX source, display it, and write the source to a file.

```python
@latexify.function
def solve(a, b, c):
  return (-b + math.sqrt(b**2 - 4*a*c)) / (2*a)

print(solve(1, 4, 3))  # Invoking the function works as expected.
print(solve)  # Printing the function shows the underlying LaTeX source.
solve  # Displays the expression.

# Writes the underlying LaTeX source into a file.
with open("compiled.tex", "w") as fp:
  print(solve, file=fp)

```

--------------------------------

### Handling Elif/Nested Conditionals with latexify.function (Python)

Source: https://github.com/google/latexify_py/blob/main/examples/latexify_examples.ipynb

Defines a recursive Fibonacci function `fib` using `if/elif/else` and applies the `@latexify.function` decorator. Demonstrates how `latexify` handles and unrolls `elif` or nested `else-if` structures.

```python
# Elif or nested else-if are unrolled.
@latexify.function
def fib(x):
  if x == 0:
    return 0
  elif x == 1:
    return 1
  else:
    return fib(x-1) + fib(x-2)

fib

```

--------------------------------

### Handling Matrix Operations with latexify.function (Python)

Source: https://github.com/google/latexify_py/blob/main/examples/latexify_examples.ipynb

Defines a function `transform` that performs matrix operations using NumPy arrays and the `@` operator. Applies the `@latexify.function` decorator with `reduce_assignments=True` and `use_math_symbols=True` to generate LaTeX for the matrix transformation.

```python
# Matrix support.
@latexify.function(reduce_assignments=True, use_math_symbols=True)
def transform(x, y, a, b, theta, s, t):
  cos_t = math.cos(theta)
  sin_t = math.sin(theta)
  scale = np.array([[a, 0, 0], [0, b, 0], [0, 0, 1]])
  rotate = np.array([[cos_t, -sin_t, 0], [sin_t, cos_t, 0], [0, 0, 1]])
  move = np.array([[1, 0, s], [0, 1, t], [0, 0, 1]])
  return move @ rotate @ scale @ np.array([[x], [y], [1]])

transform

```

--------------------------------

### Generating LaTeX Expression with latexify.expression (Python)

Source: https://github.com/google/latexify_py/blob/main/examples/latexify_examples.ipynb

Uses the `@latexify.expression` decorator to generate a LaTeX expression from a Python function. Unlike `@latexify.function`, this decorator omits the function signature in the generated LaTeX.

```python
# latexify.expression works similarly, but does not output the signature.
@latexify.expression
def solve(a, b, c):
  return (-b + math.sqrt(b**2 - 4*a*c)) / (2*a)

solve

```

--------------------------------

### Generating Algorithmic Environment with latexify.algorithmic (Python)

Source: https://github.com/google/latexify_py/blob/main/examples/latexify_examples.ipynb

Defines a recursive Fibonacci function `fib` and applies the `@latexify.algorithmic` decorator. Shows how this decorator generates a LaTeX algorithmic environment representation of the function's control flow instead of a mathematical equation.

```python
# latexify.algorithmic generates an algorithmic environment instead of an equation.
@latexify.algorithmic
def fib(x):
  if x == 0:
    return 0
  elif x == 1:
    return 1
  else:
    return fib(x-1) + fib(x-2)

fib

```

--------------------------------

### Handling Loops and Conditionals with latexify.algorithmic (Python)

Source: https://github.com/google/latexify_py/blob/main/examples/latexify_examples.ipynb

Defines a function `collatz` implementing the Collatz conjecture using a `while` loop and `if/else` statements. Applies the `@latexify.algorithmic` decorator to show how it translates loops and control flow into a LaTeX algorithmic environment.

```python
# Another example: latexify.algorithmic supports usual control flows.
@latexify.algorithmic
def collatz(x):
  n = 0
  while x > 1:
    n = n + 1
    if x % 2 == 0:
      x = x // 2
    else:
      x = 3 * x + 1
  return n

collatz

```

--------------------------------

### Converting Math Symbols with latexify.function (Python)

Source: https://github.com/google/latexify_py/blob/main/examples/latexify_examples.ipynb

Defines a function `greek` using the `@latexify.function` decorator with `use_math_symbols=True`. Shows how this option automatically converts certain Python math symbols and variable names (like Greek letters) into their LaTeX equivalents.

```python
# Some math symbols are converted automatically.
@latexify.function(use_math_symbols=True)
def greek(alpha, beta, gamma, Omega):
  return alpha * beta + math.gamma(gamma) + Omega

greek

```

--------------------------------

### Reducing Assignments with latexify.function (Python)

Source: https://github.com/google/latexify_py/blob/main/examples/latexify_examples.ipynb

Defines a function `f` with intermediate variable assignments and applies the `@latexify.function` decorator with `reduce_assignments=True`. Shows how this option simplifies the generated LaTeX by substituting variables to produce a single expression.

```python
# Assignments can be reduced into one expression.
@latexify.function(reduce_assignments=True)
def f(a, b, c):
    discriminant = b**2 - 4 * a * c
    numerator = -b + math.sqrt(discriminant)
    denominator = 2 * a
    return numerator / denominator

f

```

--------------------------------

### Replacing Identifiers with latexify.function (Python)

Source: https://github.com/google/latexify_py/blob/main/examples/latexify_examples.ipynb

Defines a dictionary `identifiers` to map Python names to desired LaTeX names. Uses the `@latexify.function` decorator with the `identifiers` argument to replace function names, arguments, and variables in the generated LaTeX output.

```python
# Function names, arguments, variables can be replaced.
identifiers = {
    "my_function": "f",
    "my_inner_function": "g",
    "my_argument": "x",
}

@latexify.function(identifiers=identifiers)
def my_function(my_argument):
    return my_inner_function(my_argument)

my_function

```

--------------------------------

### Applying Identifiers with latexify.function (Python)

Source: https://github.com/google/latexify_py/blob/main/docs/parameters.md

Demonstrates how to use the `identifiers` parameter in `latexify.function` to replace variable and function names in the generated LaTeX output with custom strings defined in a dictionary.

```Python
identifiers = {
    "my_function": "f",
    "my_inner_function": "g",
    "my_argument": "x",
}

@latexify.function(identifiers=identifiers)
def my_function(my_argument):
    return my_inner_function(my_argument)

my_function
```

--------------------------------

### Using Set Symbols with latexify.function (Python)

Source: https://github.com/google/latexify_py/blob/main/docs/parameters.md

Demonstrates the `use_set_symbols=True` parameter, which converts standard Python bitwise operators (`&`, `|`, `-`, `^`, `<`, `<=`, `>`, `>=`) into their corresponding LaTeX set notation symbols.

```Python
@latexify.function(use_set_symbols=True)
def f(x, y):
    return x & y, x | y, x - y, x ^ y, x < y, x <= y, x > y, x >= y

f
```

--------------------------------

### Controlling Signature Output with latexify.function (Python)

Source: https://github.com/google/latexify_py/blob/main/docs/parameters.md

Shows how `use_signature=False` prevents `latexify.function` from including the function signature (`f(a, b, c) = ...`) in the generated LaTeX output, resulting in just the expression. Requires the `math` module for `sqrt`.

```Python
@latexify.function(use_signature=False)
def f(a, b, c):
    return (-b + math.sqrt(b**2 - 4 * a * c)) / (2 * a)

f
```

--------------------------------

### Reducing Assignments with latexify.function (Python)

Source: https://github.com/google/latexify_py/blob/main/docs/parameters.md

Shows how the `reduce_assignments=True` parameter simplifies the LaTeX output by composing intermediate variables defined before the return statement into a single expression. Requires the `math` module for `sqrt`.

```Python
@latexify.function(reduce_assignments=True)
def f(a, b, c):
    discriminant = b**2 - 4 * a * c
    numerator = -b + math.sqrt(discriminant)
    denominator = 2 * a
    return numerator / denominator

f
```

--------------------------------

### Using Math Symbols with latexify.function (Python)

Source: https://github.com/google/latexify_py/blob/main/docs/parameters.md

Illustrates the effect of `use_math_symbols=True`, which automatically converts variable names corresponding to mathematical symbols (like alpha, beta, gamma, Omega) into their LaTeX symbol representations. Requires the `math` module for `gamma`.

```Python
@latexify.function(use_math_symbols=True)
def greek(alpha, beta, gamma, Omega):
  return alpha * beta + math.gamma(gamma) + Omega

greek
```

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