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2024-10-23 19:14:35 -04:00
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.gitignore vendored Normal file
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backups/

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venv/lib/python3.11/site-packages/setuptools-66.1.1.dist-info/LICENSE → LICENSE
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@@ -1,19 +1,21 @@
Copyright Jason R. Coombs
MIT License
Copyright (c) [year] [fullname]
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to
deal in the Software without restriction, including without limitation the
rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
sell copies of the Software, and to permit persons to whom the Software is
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in
all copies or substantial portions of the Software.
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS
IN THE SOFTWARE.
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.

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README.md Normal file
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# Python Maze Solver
## Overview
This Python Maze Solver is a graphical maze generation and solving application built using `Tkinter` for the user interface. The program generates mazes with adjustable parameters such as maze size, branching factor, number of dead ends, wall density, and overall connectedness. Once generated, the user can solve the maze using various pathfinding algorithms including Breadth-First Search (BFS), Depth-First Search (DFS), and A* Search.
## Features
- **Maze Generation Algorithms**: Generate mazes using **Recursive Backtracker** and **Prim's Algorithm**.
- **Adjustable Maze Parameters**:
- **Rows and Columns**: Control the dimensions of the maze (supports strict 1:1 and 1:2 ratios).
- **Dead Ends**: Adjust the number of dead ends in the maze.
- **Branching Factor**: Increase or decrease the number of branches off the main path.
- **Connectedness**: Control how much of the maze is connected to the main path, from 10% to 90%.
- **Wall Density**: Add random walls to increase the maze's complexity.
- **Solvability**: All mazes are guaranteed to be solvable using one of the pathfinding algorithms.
- **Seed-Based Generation**: Allows users to input a seed for reproducibility or use an auto-generated seed.
- **Pathfinding Algorithms**:
- **Breadth-First Search (BFS)**: Explores the maze layer by layer, guaranteeing the shortest path.
- **Depth-First Search (DFS)**: Explores deep paths before backtracking, resulting in longer paths.
- **A* Search**: A heuristic-based algorithm that combines features of both BFS and DFS.
- **Real-Time Visualization**: Watch the maze being solved step-by-step, with visual indications of visited cells, the frontier, and the final path.
- **User-Controlled Timer**: The timer starts when the user initiates the solving process and stops once the solution is found.
- **Interactive Stop**: The solving process can be stopped at any time by pressing the stop button.
## Usage
### Getting Started
The program requires no external dependencies (except tkinter), so just clone the repository and run the `main.py` file:
```bash
python3 main.py
```
### Adjusting Maze Parameters
- **Rows/Columns**: Input the desired number of rows and columns for the maze. The program ensures a strict 1:1 or 1:2 ratio for proper visualization.
- **Dead Ends**: Adjust the number of dead ends in the maze using the slider.
- **Branching Factor**: Adjust the number of branches from the main path using the slider.
- **Connectedness**: Control the percentage of the maze connected to the main path. The slider ranges from 10% to 90%, with 70% being the default.
- **Wall Density**: Adjust how many walls are randomly added to the maze. Higher wall density increases the maze's complexity.
- **Seed**: If left blank, a random seed is generated and shown. If you provide a seed, it will generate the same maze every time.
### Pathfinding Algorithms
Once the maze is generated, select a pathfinding algorithm from the dropdown:
- **BFS**: Finds the shortest path by exploring all neighboring nodes evenly.
- **DFS**: Searches deep into the maze, often producing longer paths due to its depth-first nature.
- **A* Search**: Combines the exploration of BFS with heuristic guidance for faster results.
Click **Start** to begin solving the maze. The maze will be solved step by step, with different colors representing the visited cells, the frontier, and the final path.
### Stopping the Solver
The **Stop** button halts the solving process at any point. The solver will stop gracefully, and you can generate or solve a new maze afterward.
### Exiting the Program
You can exit the program by pressing `Q` or `Esc` to cleanly quit the application at any point.
## Code Overview
### `main.py`
This is the entry point for the application. It sets up the Tkinter user interface and connects it with the maze generation and solving functionalities.
Key features:
- **MazeSolverApp Class**: Manages the UI, timer, maze generation, and maze solving processes.
- **Maze Generation and Solving**: When you click **Generate Maze**, it spawns a new thread to avoid freezing the UI. Clicking **Start** initiates the solving process in a step-by-step manner.
- **Stop Functionality**: The `stop_requested` flag is used to gracefully interrupt the solving process.
### `maze_generator.py`
Handles maze generation using the **Recursive Backtracker** and **Prim's Algorithm**. Key parameters such as dead ends, branching factor, wall density, and connectedness are handled here to create custom mazes.
Key features:
- **Recursive Backtracker**: This is a depth-first maze generation algorithm that carves out a perfect maze (i.e., no loops) by backtracking whenever it hits a dead end.
- **Prim's Algorithm**: An algorithm that grows a maze from a starting point by adding walls around it, randomly selecting walls, and carving new paths.
- **Solvability Check**: Every generated maze is checked for solvability using BFS. If the maze is unsolvable, it is regenerated.
### `pathfinding_algorithms.py`
Implements three pathfinding algorithms for solving mazes:
- **BFS Generator**: Implements a breadth-first search that explores the maze layer by layer, ensuring the shortest path is found.
- **DFS Generator**: A depth-first search that dives deep into one path before backtracking, often producing longer paths.
- **A* Generator**: Uses the Manhattan distance heuristic to prioritize paths that are closer to the goal.
Each algorithm is implemented as a generator, which yields intermediate steps for real-time visualization.
### `render.py`
Handles the Tkinter UI and the canvas drawing logic. It updates the maze visualization, including marking visited cells, frontier cells, and the final path.
Key features:
- **Maze Visualization**: Displays the maze and highlights the solving process.
- **Controls**: Manages sliders, buttons, and input fields for configuring the maze generation and solving process.
## Algorithms Explained
### Recursive Backtracker
This algorithm works by starting at a random cell, then carving a path through the maze by choosing random directions. When it encounters a dead end, it backtracks to the last point where it has unvisited neighbors. The result is a "perfect" maze with no loops.
### Prim's Algorithm
Prim's algorithm starts from a single point and iteratively carves out a maze by adding walls to a list and randomly choosing walls to break through, creating paths as it grows the maze.
### Breadth-First Search (BFS)
BFS explores all possible paths equally in every direction, layer by layer. It guarantees the shortest path in an unweighted maze. BFS uses a queue to keep track of the cells to visit next.
### Depth-First Search (DFS)
DFS works by exploring one path as deeply as possible before backtracking to explore other paths. It uses a stack to keep track of the cells it needs to explore.
### A* Search
A* is a heuristic search algorithm that combines the features of BFS and DFS. It uses a heuristic (in this case, the Manhattan distance) to prioritize paths that are closer to the goal, making it faster and more efficient for finding optimal paths.

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activate_venv.sh
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#!/bin/bash
source venv/bin/activate

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main.py
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@@ -19,6 +19,7 @@ class MazeSolverApp:
self.maze = None
self.solving = False
self.autogenerated_seed = None # Track the last autogenerated seed
self.stop_requested = False # Add this flag for stopping the solver
# Bind keys to quit the application
self.root.bind('<Escape>', self.quit_application)
@@ -66,11 +67,11 @@ class MazeSolverApp:
# Get additional parameters from the new sliders
dead_ends = int(self.render.dead_ends_scale.get())
branching_factor = int(self.render.branching_factor_scale.get())
connectedness = int(self.render.connectedness_scale.get()) * 10
# Generate maze in a separate thread to avoid freezing the UI
threading.Thread(target=self._generate_maze_thread, args=(params, dead_ends, branching_factor)).start()
threading.Thread(target=self._generate_maze_thread, args=(params, dead_ends, branching_factor, connectedness)).start()
def _generate_maze_thread(self, params, dead_ends, branching_factor):
def _generate_maze_thread(self, params, dead_ends, branching_factor, connectedness):
try:
# Check if the seed input is empty or if it's the autogenerated seed
seed_input = params['seed']
@@ -82,6 +83,7 @@ class MazeSolverApp:
# Get the values from the new sliders
dead_ends = int(self.render.dead_ends_scale.get())
branching_factor = int(self.render.branching_factor_scale.get())
connectedness = int(self.render.connectedness_scale.get()) * 10 # Convert to percentage
# Call generate_maze with the seed
maze, used_seed = generate_maze(
@@ -90,8 +92,9 @@ class MazeSolverApp:
generation_algorithm=params['generation_algorithm'],
seed=seed,
wall_density=params['wall_density'],
dead_ends=dead_ends, # Pass the dead ends parameter
branching_factor=branching_factor # Pass the branching factor parameter
dead_ends=dead_ends,
branching_factor=branching_factor,
connectedness=connectedness
)
self.maze = maze
@@ -122,6 +125,7 @@ class MazeSolverApp:
return
self.solving = True
self.stop_requested = False # Reset the flag when starting
self.reset_timer()
self.start_time = time.time()
self.start_timer()
@@ -147,6 +151,11 @@ class MazeSolverApp:
self.root.after(0, self._process_solver_step)
def _process_solver_step(self):
if self.stop_requested:
self.solving = False
self.stop_timer()
print("Solver stopped by user.")
return
try:
action, cell, steps = next(self.solver_generator)
self.steps = steps
@@ -187,9 +196,8 @@ class MazeSolverApp:
def stop_solving(self):
if self.solving:
# Currently, our solving algorithms are not interruptible.
# Implementing a stoppable solving process would require more complex threading control.
messagebox.showinfo("Stop Solving", "Stopping the solver is not implemented yet.")
self.stop_requested = True # Set the flag to stop the solver
messagebox.showinfo("Stop Solving", "Maze Solver Should be stopped")
else:
messagebox.showinfo("Not Solving", "No solving process is currently running.")

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main.py.bak
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@@ -1,262 +0,0 @@
# main.py
import tkinter as tk
from render import Render
import time
import threading
from maze_generator import generate_maze
import numpy as np
from tkinter import messagebox
from pathfinding_algorithms import bfs_generator, dfs_generator, a_star_generator
class MazeSolverApp:
def __init__(self, root):
self.root = root
self.render = Render(root, self)
self.timer_running = False
self.start_time = None
self.elapsed_time = 0
self.timer_id = None
self.maze = None
self.solving = False
def start_timer(self):
if not self.timer_running:
self.timer_running = True
self.start_time = time.time() - self.elapsed_time
self.update_timer()
def stop_timer(self):
if self.timer_running:
self.timer_running = False
if self.timer_id:
self.root.after_cancel(self.timer_id)
def reset_timer(self):
self.stop_timer()
self.elapsed_time = 0
self.render.update_timer_label("00:00")
def update_timer(self):
if self.timer_running:
self.elapsed_time = time.time() - self.start_time
minutes = int(self.elapsed_time // 60)
seconds = int(self.elapsed_time % 60)
self.render.update_timer_label(f"{minutes:02d}:{seconds:02d}")
self.timer_id = self.root.after(1000, self.update_timer)
def generate_maze(self):
params = self.render.get_maze_parameters()
if params is None:
return # Invalid parameters, abort
self.reset_timer()
# Generate maze in a separate thread to avoid freezing the UI
threading.Thread(target=self._generate_maze_thread, args=(params,)).start()
def _generate_maze_thread(self, params):
try:
maze = generate_maze(
rows=params['rows'],
cols=params['cols'],
generation_algorithm=params['generation_algorithm'],
seed=params['seed'],
wall_density=params['wall_density']
)
self.maze = maze
# Update the UI in the main thread
self.root.after(0, self.render.draw_maze, self.maze)
self.root.after(0, self.start_timer)
print("Maze generated.")
except Exception as e:
print(f"Error generating maze: {e}")
messagebox.showerror("Error", f"An error occurred while generating the maze:\n{e}")
def solve_maze(self):
if not self.maze:
messagebox.showwarning("No Maze", "Please generate a maze first.")
return
if self.solving:
messagebox.showinfo("Solving", "Maze is already being solved.")
return
self.solving = True
self.reset_timer()
self.start_time = time.time()
self.start_timer()
self.steps = 0
# Get selected algorithm
algorithm = self.render.update_algorithm_selection()
# Initialize the solver generator
if algorithm == "BFS":
self.solver_generator = bfs_generator(self.maze, (1, 1), (self.maze.shape[0]-2, self.maze.shape[1]-2))
elif algorithm == "DFS":
self.solver_generator = dfs_generator(self.maze, (1, 1), (self.maze.shape[0]-2, self.maze.shape[1]-2))
elif algorithm == "A*":
self.solver_generator = a_star_generator(self.maze, (1, 1), (self.maze.shape[0]-2, self.maze.shape[1]-2))
else:
messagebox.showerror("Error", f"Unknown algorithm: {algorithm}")
self.solving = False
self.stop_timer()
return
# Start the solving process
self.root.after(0, self._process_solver_step)
def _process_solver_step(self):
try:
action, cell, steps = next(self.solver_generator)
self.steps = steps
if action == 'visit':
self.render.mark_visited(cell)
elif action == 'enqueue':
self.render.mark_frontier(cell)
elif action == 'path':
self.render.mark_path(cell)
elif action == 'done':
self.solving = False
self.stop_timer()
self.show_summary()
return
# Schedule the next step
self.root.after(10, self._process_solver_step) # Adjust delay as needed for visualization speed
except StopIteration:
self.solving = False
self.stop_timer()
self.show_summary()
def show_summary(self):
elapsed_time = time.time() - self.start_time
params = self.render.get_maze_parameters()
algorithm = self.render.update_algorithm_selection()
summary = (
f"Algorithm: {algorithm}\n"
f"Time Taken: {elapsed_time:.2f} seconds\n"
f"Steps: {self.steps}\n"
f"Maze Size: {params['rows']}x{params['cols']}\n"
f"Generation Algorithm: {params['generation_algorithm']}\n"
f"Wall Density: {params['wall_density']}\n"
f"Seed: {params['seed'] or 'None'}"
)
messagebox.showinfo("Maze Solved!", summary)
def stop_solving(self):
if self.solving:
# Currently, our solving algorithms are not interruptible.
# Implementing a stoppable solving process would require more complex threading control.
messagebox.showinfo("Stop Solving", "Stopping the solver is not implemented yet.")
else:
messagebox.showinfo("Not Solving", "No solving process is currently running.")
# Pathfinding algorithms
from collections import deque
import heapq
def get_adjacent_cells(cell, maze):
row, col = cell
directions = [(-1,0), (1,0), (0,-1), (0,1)]
neighbors = []
for dr, dc in directions:
r, c = row + dr, col + dc
if 0 <= r < maze.shape[0] and 0 <= c < maze.shape[1] and maze[r][c] == 0:
neighbors.append((r, c))
return neighbors
def bfs(maze, start, end):
queue = deque([start])
visited = set()
visited.add(start)
parent = {}
while queue:
current = queue.popleft()
if current == end:
break
for neighbor in get_adjacent_cells(current, maze):
if neighbor not in visited:
queue.append(neighbor)
visited.add(neighbor)
parent[neighbor] = current
# Reconstruct path
path = []
if end in parent:
cell = end
while cell != start:
path.append(cell)
cell = parent[cell]
path.append(start)
path.reverse()
return path
def dfs(maze, start, end):
stack = [start]
visited = set()
visited.add(start)
parent = {}
while stack:
current = stack.pop()
if current == end:
break
for neighbor in get_adjacent_cells(current, maze):
if neighbor not in visited:
stack.append(neighbor)
visited.add(neighbor)
parent[neighbor] = current
# Reconstruct path
path = []
if end in parent:
cell = end
while cell != start:
path.append(cell)
cell = parent[cell]
path.append(start)
path.reverse()
return path
def a_star(maze, start, end):
heap = []
heapq.heappush(heap, (0, start))
came_from = {}
g_score = {start: 0}
f_score = {start: heuristic(start, end)}
while heap:
current_f, current = heapq.heappop(heap)
if current == end:
break
for neighbor in get_adjacent_cells(current, maze):
tentative_g = g_score[current] + 1
if neighbor not in g_score or tentative_g < g_score[neighbor]:
came_from[neighbor] = current
g_score[neighbor] = tentative_g
f_score[neighbor] = tentative_g + heuristic(neighbor, end)
heapq.heappush(heap, (f_score[neighbor], neighbor))
# Reconstruct path
path = []
if end in came_from:
cell = end
while cell != start:
path.append(cell)
cell = came_from[cell]
path.append(start)
path.reverse()
return path
def heuristic(a, b):
# Use Manhattan distance as heuristic
return abs(a[0] - b[0]) + abs(a[1] - b[1])
if __name__ == "__main__":
root = tk.Tk()
root.title("Maze Solver")
app = MazeSolverApp(root)
root.mainloop()

53
maze_generator.py
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@@ -2,7 +2,7 @@ import numpy as np
import random
from collections import deque
def generate_maze(rows, cols, generation_algorithm="Recursive Backtracker", seed=None, wall_density=0.3, dead_ends=10, branching_factor=3):
def generate_maze(rows, cols, generation_algorithm="Recursive Backtracker", seed=None, wall_density=0.3, dead_ends=10, branching_factor=3, connectedness=70):
if seed is not None:
random.seed(seed)
np.random.seed(seed)
@@ -18,9 +18,9 @@ def generate_maze(rows, cols, generation_algorithm="Recursive Backtracker", seed
# Use a loop to regenerate maze if unsolvable
while not is_solved:
if generation_algorithm == "Recursive Backtracker":
maze = recursive_backtracker_maze(rows, cols, adjusted_wall_density, dead_ends, branching_factor)
maze = recursive_backtracker_maze(rows, cols, adjusted_wall_density, dead_ends, branching_factor, connectedness)
elif generation_algorithm == "Prim's":
maze = prim_maze(rows, cols, adjusted_wall_density, dead_ends, branching_factor)
maze = prim_maze(rows, cols, adjusted_wall_density, dead_ends, branching_factor, connectedness)
else:
raise ValueError(f"Unknown generation algorithm: {generation_algorithm}")
@@ -29,7 +29,7 @@ def generate_maze(rows, cols, generation_algorithm="Recursive Backtracker", seed
return maze, seed # Return the generated maze along with the seed
def recursive_backtracker_maze(rows, cols, wall_density, dead_ends, branching_factor):
def recursive_backtracker_maze(rows, cols, wall_density, dead_ends, branching_factor, connectedness):
maze = np.ones((rows, cols), dtype=int)
# Initialize the stack with the starting point
@@ -58,6 +58,19 @@ def recursive_backtracker_maze(rows, cols, wall_density, dead_ends, branching_fa
if not carved:
stack.pop() # Backtrack if no carving was possible
# Ensure connectedness to the main path
required_connections = int(len(main_path) * (connectedness / 100))
for _ in range(required_connections):
r, c = random.choice(main_path)
directions = [(2, 0), (-2, 0), (0, 2), (0, -2)]
random.shuffle(directions)
for dr, dc in directions:
nr, nc = r + dr, c + dc
if 0 < nr < rows - 1 and 0 < nc < cols - 1 and maze[nr][nc] == 1:
maze[nr - dr // 2][nc - dc // 2] = 0 # Remove wall between
maze[nr][nc] = 0 # Create a connection to the main path
break
# Create branches based on branching_factor
for _ in range(branching_factor):
if len(main_path) > 2:
@@ -85,7 +98,7 @@ def recursive_backtracker_maze(rows, cols, wall_density, dead_ends, branching_fa
return maze
def prim_maze(rows, cols, wall_density, dead_ends, branching_factor):
def prim_maze(rows, cols, wall_density, dead_ends, branching_factor, connectedness):
maze = np.ones((rows, cols), dtype=int)
start_r, start_c = 1, 1
maze[start_r][start_c] = 0
@@ -113,16 +126,30 @@ def prim_maze(rows, cols, wall_density, dead_ends, branching_factor):
main_path.append((opposite_r, opposite_c))
add_walls(opposite_r, opposite_c)
# Create branches based on branching_factor
for _ in range(branching_factor):
if len(main_path) > 2:
# Ensure connectedness to the main path
required_connections = int(len(main_path) * (connectedness / 100))
for _ in range(required_connections):
r, c = random.choice(main_path)
directions = [(-1,0), (1,0), (0,-1), (0,1)]
directions = [(2, 0), (-2, 0), (0, 2), (0, -2)]
random.shuffle(directions)
for dr, dc in directions:
nr, nc = r + dr, c + dc
if 0 < nr < rows - 1 and 0 < nc < cols - 1 and maze[nr][nc] == 1:
maze[nr][nc] = 0 # Create a false branch
maze[nr - dr // 2][nc - dc // 2] = 0 # Remove wall between
maze[nr][nc] = 0 # Create a connection to the main path
break
# Create branches based on branching_factor
for _ in range(branching_factor):
if len(main_path) > 2:
r, c = random.choice(main_path)
directions = [(2, 0), (-2, 0), (0, 2), (0, -2)]
random.shuffle(directions)
for dr, dc in directions:
nr, nc = r + dr, c + dc
if 0 < nr < rows-1 and 0 < nc < cols-1 and maze[nr][nc] == 1:
maze[nr - dr//2][nc - dc//2] = 0 # Remove wall between
maze[nr][nc] = 0 # Create a branch
break
# Create dead ends based on dead_ends
@@ -131,6 +158,12 @@ def prim_maze(rows, cols, wall_density, dead_ends, branching_factor):
if maze[r][c] == 0:
maze[r][c] = 1 # Add a dead end
# Add random walls based on wall_density
for r in range(1, rows-1):
for c in range(1, cols-1):
if maze[r][c] == 0 and random.random() < wall_density * 0.1:
maze[r][c] = 1 # Add wall
return maze
def is_solvable(maze, start, end):

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maze_generator.py.bak
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@@ -1,156 +0,0 @@
import numpy as np
import random
from collections import deque
def generate_maze(rows, cols, generation_algorithm="Recursive Backtracker", seed=None, wall_density=0.3, difficulty=5):
if seed is not None:
random.seed(seed)
np.random.seed(seed)
else:
seed = random.randint(0, 999999) # Generate a random seed if not provided
# Adjust wall density and complexity based on difficulty
adjusted_wall_density = wall_density + (difficulty * 0.08) # More severe increase in wall density
adjusted_wall_density = min(adjusted_wall_density, 0.7) # Cap wall density for solvability
maze = None
is_solved = False
# Use a loop to regenerate maze if unsolvable
while not is_solved:
if generation_algorithm == "Recursive Backtracker":
maze = recursive_backtracker_maze(rows, cols, adjusted_wall_density, difficulty)
elif generation_algorithm == "Prim's":
maze = prim_maze(rows, cols, adjusted_wall_density, difficulty)
else:
raise ValueError(f"Unknown generation algorithm: {generation_algorithm}")
# Check if the generated maze is solvable
is_solved = is_solvable(maze, (1, 1), (rows - 2, cols - 2))
return maze, seed # Return the generated maze along with the seed
def recursive_backtracker_maze(rows, cols, wall_density, difficulty):
maze = np.ones((rows, cols), dtype=int)
# Initialize the stack with the starting point
stack = [(1, 1)]
maze[1][1] = 0 # Start point
# Track the main path
main_path = []
# Iterative backtracker logic
while stack:
r, c = stack[-1] # Get the current cell from the stack
directions = [(2, 0), (-2, 0), (0, 2), (0, -2)]
random.shuffle(directions)
carved = False
for dr, dc in directions:
nr, nc = r + dr, c + dc
if 0 < nr < rows-1 and 0 < nc < cols-1 and maze[nr][nc] == 1:
maze[nr - dr//2][nc - dc//2] = 0 # Remove wall between
maze[nr][nc] = 0 # Mark the next cell as a passage
stack.append((nr, nc)) # Add the next cell to the stack
carved = True
main_path.append((nr, nc)) # Track the main path
break # Stop after carving one passage
if not carved:
stack.pop() # Backtrack if no carving was possible
# Add complexity based on difficulty - Branch off from the main path
branch_factor = difficulty * 2 # Scale with difficulty (higher means more branches)
for _ in range(branch_factor): # Use `_` to indicate we don't need the index
if len(main_path) > 2: # Only branch if there’s space
r, c = random.choice(main_path)
directions = [(2, 0), (-2, 0), (0, 2), (0, -2)]
random.shuffle(directions)
for dr, dc in directions:
nr, nc = r + dr, c + dc
if 0 < nr < rows-1 and 0 < nc < cols-1 and maze[nr][nc] == 1:
maze[nr - dr//2][nc - dc//2] = 0 # Remove wall between
maze[nr][nc] = 0 # Create false branch
break # Stop after adding one branch
# Add random walls based on wall_density (avoid adding walls along the main path)
for r in range(1, rows-1):
for c in range(1, cols-1):
if maze[r][c] == 0 and random.random() < wall_density * 0.05:
maze[r][c] = 1 # Add wall
return maze
def prim_maze(rows, cols, wall_density, difficulty):
maze = np.ones((rows, cols), dtype=int)
start_r, start_c = 1, 1
maze[start_r][start_c] = 0
walls = []
main_path = [(start_r, start_c)] # Track the main path
def add_walls(r, c):
directions = [(-1,0), (1,0), (0,-1), (0,1)]
for dr, dc in directions:
nr, nc = r + dr, c + dc
if 0 < nr < rows-1 and 0 < nc < cols-1 and maze[nr][nc] == 1:
walls.append((nr, nc, r, c))
add_walls(start_r, start_c)
while walls:
idx = random.randint(0, len(walls)-1)
wall = walls.pop(idx)
wr, wc, pr, pc = wall
opposite_r, opposite_c = wr + (wr - pr), wc + (wc - pc)
if 0 < opposite_r < rows-1 and 0 < opposite_c < cols-1:
if maze[opposite_r][opposite_c] == 1:
maze[wr][wc] = 0
maze[opposite_r][opposite_c] = 0
main_path.append((opposite_r, opposite_c)) # Track main path
add_walls(opposite_r, opposite_c)
# Add complexity based on difficulty - More branches from the main path
branch_factor = difficulty * 2 # More branches with higher difficulty
for _ in range(branch_factor): # Use `_` to indicate we don't need the index
if len(main_path) > 2:
r, c = random.choice(main_path)
directions = [(-1,0), (1,0), (0,-1), (0,1)]
random.shuffle(directions)
for dr, dc in directions:
nr, nc = r + dr, c + dc
if 0 < nr < rows-1 and 0 < nc < cols-1 and maze[nr][nc] == 1:
maze[nr][nc] = 0 # Create a false branch
break
# Add random walls based on wall_density
for r in range(1, rows-1):
for c in range(1, cols-1):
if maze[r][c] == 0 and random.random() < wall_density * 0.05:
maze[r][c] = 1 # Add wall
return maze
def is_solvable(maze, start, end):
queue = deque([start])
visited = set([start])
while queue:
current = queue.popleft()
if current == end:
return True
for neighbor in get_adjacent_cells(current, maze):
if neighbor not in visited:
visited.add(neighbor)
queue.append(neighbor)
return False
# Helper to get adjacent cells
def get_adjacent_cells(cell, maze):
row, col = cell
directions = [(-1,0), (1,0), (0,-1), (0,1)]
neighbors = []
for dr, dc in directions:
r, c = row + dr, col + dc
if 0 <= r < maze.shape[0] and 0 <= c < maze.shape[1] and maze[r][c] == 0:
neighbors.append((r, c))
return neighbors

8
render.py
View File

@@ -60,6 +60,12 @@ class Render:
self.branching_factor_scale.set(3) # Default value
self.branching_factor_scale.pack(fill='x', pady=5)
# Connectedness Scale
ttk.Label(self.sidebar, text="Connectedness (%):").pack(anchor='w')
self.connectedness_scale = ttk.Scale(self.sidebar, from_=1, to=9.9, orient='horizontal') # Scale ranges from 1 to 9 for 10% to 90%
self.connectedness_scale.set(7) # Default value is 7, representing 70%
self.connectedness_scale.pack(fill='x', pady=5)
# Maze Generation Algorithm Dropdown
ttk.Label(self.sidebar, text="Generation Algorithm:").pack(anchor='w', pady=(10, 0))
self.gen_algorithm_var = tk.StringVar()
@@ -113,6 +119,7 @@ class Render:
dead_ends = int(self.dead_ends_scale.get())
branching_factor = int(self.branching_factor_scale.get())
generation_algorithm = self.gen_algorithm_var.get()
connectedness = int(self.connectedness_scale.get()) * 10 # Convert the scale to percentage (e.g., 7 => 70%)
seed_input = self.seed_entry.get()
if seed_input == "":
seed = None
@@ -158,6 +165,7 @@ class Render:
'cols': cols,
'dead_ends': dead_ends,
'branching_factor': branching_factor,
'connectedness': connectedness,
'generation_algorithm': generation_algorithm,
'seed': seed,
'wall_density': wall_density

1
requirements.txt
View File

@@ -1 +0,0 @@
# Add your Python dependencies here

247
venv/bin/Activate.ps1
View File

@@ -1,247 +0,0 @@
<#
.Synopsis
Activate a Python virtual environment for the current PowerShell session.
.Description
Pushes the python executable for a virtual environment to the front of the
$Env:PATH environment variable and sets the prompt to signify that you are
in a Python virtual environment. Makes use of the command line switches as
well as the `pyvenv.cfg` file values present in the virtual environment.
.Parameter VenvDir
Path to the directory that contains the virtual environment to activate. The
default value for this is the parent of the directory that the Activate.ps1
script is located within.
.Parameter Prompt
The prompt prefix to display when this virtual environment is activated. By
default, this prompt is the name of the virtual environment folder (VenvDir)
surrounded by parentheses and followed by a single space (ie. '(.venv) ').
.Example
Activate.ps1
Activates the Python virtual environment that contains the Activate.ps1 script.
.Example
Activate.ps1 -Verbose
Activates the Python virtual environment that contains the Activate.ps1 script,
and shows extra information about the activation as it executes.
.Example
Activate.ps1 -VenvDir C:\Users\MyUser\Common\.venv
Activates the Python virtual environment located in the specified location.
.Example
Activate.ps1 -Prompt "MyPython"
Activates the Python virtual environment that contains the Activate.ps1 script,
and prefixes the current prompt with the specified string (surrounded in
parentheses) while the virtual environment is active.
.Notes
On Windows, it may be required to enable this Activate.ps1 script by setting the
execution policy for the user. You can do this by issuing the following PowerShell
command:
PS C:\> Set-ExecutionPolicy -ExecutionPolicy RemoteSigned -Scope CurrentUser
For more information on Execution Policies:
https://go.microsoft.com/fwlink/?LinkID=135170
#>
Param(
[Parameter(Mandatory = $false)]
[String]
$VenvDir,
[Parameter(Mandatory = $false)]
[String]
$Prompt
)
<# Function declarations --------------------------------------------------- #>
<#
.Synopsis
Remove all shell session elements added by the Activate script, including the
addition of the virtual environment's Python executable from the beginning of
the PATH variable.
.Parameter NonDestructive
If present, do not remove this function from the global namespace for the
session.
#>
function global:deactivate ([switch]$NonDestructive) {
# Revert to original values
# The prior prompt:
if (Test-Path -Path Function:_OLD_VIRTUAL_PROMPT) {
Copy-Item -Path Function:_OLD_VIRTUAL_PROMPT -Destination Function:prompt
Remove-Item -Path Function:_OLD_VIRTUAL_PROMPT
}
# The prior PYTHONHOME:
if (Test-Path -Path Env:_OLD_VIRTUAL_PYTHONHOME) {
Copy-Item -Path Env:_OLD_VIRTUAL_PYTHONHOME -Destination Env:PYTHONHOME
Remove-Item -Path Env:_OLD_VIRTUAL_PYTHONHOME
}
# The prior PATH:
if (Test-Path -Path Env:_OLD_VIRTUAL_PATH) {
Copy-Item -Path Env:_OLD_VIRTUAL_PATH -Destination Env:PATH
Remove-Item -Path Env:_OLD_VIRTUAL_PATH
}
# Just remove the VIRTUAL_ENV altogether:
if (Test-Path -Path Env:VIRTUAL_ENV) {
Remove-Item -Path env:VIRTUAL_ENV
}
# Just remove VIRTUAL_ENV_PROMPT altogether.
if (Test-Path -Path Env:VIRTUAL_ENV_PROMPT) {
Remove-Item -Path env:VIRTUAL_ENV_PROMPT
}
# Just remove the _PYTHON_VENV_PROMPT_PREFIX altogether:
if (Get-Variable -Name "_PYTHON_VENV_PROMPT_PREFIX" -ErrorAction SilentlyContinue) {
Remove-Variable -Name _PYTHON_VENV_PROMPT_PREFIX -Scope Global -Force
}
# Leave deactivate function in the global namespace if requested:
if (-not $NonDestructive) {
Remove-Item -Path function:deactivate
}
}
<#
.Description
Get-PyVenvConfig parses the values from the pyvenv.cfg file located in the
given folder, and returns them in a map.
For each line in the pyvenv.cfg file, if that line can be parsed into exactly
two strings separated by `=` (with any amount of whitespace surrounding the =)
then it is considered a `key = value` line. The left hand string is the key,
the right hand is the value.
If the value starts with a `'` or a `"` then the first and last character is
stripped from the value before being captured.
.Parameter ConfigDir
Path to the directory that contains the `pyvenv.cfg` file.
#>
function Get-PyVenvConfig(
[String]
$ConfigDir
) {
Write-Verbose "Given ConfigDir=$ConfigDir, obtain values in pyvenv.cfg"
# Ensure the file exists, and issue a warning if it doesn't (but still allow the function to continue).
$pyvenvConfigPath = Join-Path -Resolve -Path $ConfigDir -ChildPath 'pyvenv.cfg' -ErrorAction Continue
# An empty map will be returned if no config file is found.
$pyvenvConfig = @{ }
if ($pyvenvConfigPath) {
Write-Verbose "File exists, parse `key = value` lines"
$pyvenvConfigContent = Get-Content -Path $pyvenvConfigPath
$pyvenvConfigContent | ForEach-Object {
$keyval = $PSItem -split "\s*=\s*", 2
if ($keyval[0] -and $keyval[1]) {
$val = $keyval[1]
# Remove extraneous quotations around a string value.
if ("'""".Contains($val.Substring(0, 1))) {
$val = $val.Substring(1, $val.Length - 2)
}
$pyvenvConfig[$keyval[0]] = $val
Write-Verbose "Adding Key: '$($keyval[0])'='$val'"
}
}
}
return $pyvenvConfig
}
<# Begin Activate script --------------------------------------------------- #>
# Determine the containing directory of this script
$VenvExecPath = Split-Path -Parent $MyInvocation.MyCommand.Definition
$VenvExecDir = Get-Item -Path $VenvExecPath
Write-Verbose "Activation script is located in path: '$VenvExecPath'"
Write-Verbose "VenvExecDir Fullname: '$($VenvExecDir.FullName)"
Write-Verbose "VenvExecDir Name: '$($VenvExecDir.Name)"
# Set values required in priority: CmdLine, ConfigFile, Default
# First, get the location of the virtual environment, it might not be
# VenvExecDir if specified on the command line.
if ($VenvDir) {
Write-Verbose "VenvDir given as parameter, using '$VenvDir' to determine values"
}
else {
Write-Verbose "VenvDir not given as a parameter, using parent directory name as VenvDir."
$VenvDir = $VenvExecDir.Parent.FullName.TrimEnd("\\/")
Write-Verbose "VenvDir=$VenvDir"
}
# Next, read the `pyvenv.cfg` file to determine any required value such
# as `prompt`.
$pyvenvCfg = Get-PyVenvConfig -ConfigDir $VenvDir
# Next, set the prompt from the command line, or the config file, or
# just use the name of the virtual environment folder.
if ($Prompt) {
Write-Verbose "Prompt specified as argument, using '$Prompt'"
}
else {
Write-Verbose "Prompt not specified as argument to script, checking pyvenv.cfg value"
if ($pyvenvCfg -and $pyvenvCfg['prompt']) {
Write-Verbose " Setting based on value in pyvenv.cfg='$($pyvenvCfg['prompt'])'"
$Prompt = $pyvenvCfg['prompt'];
}
else {
Write-Verbose " Setting prompt based on parent's directory's name. (Is the directory name passed to venv module when creating the virtual environment)"
Write-Verbose " Got leaf-name of $VenvDir='$(Split-Path -Path $venvDir -Leaf)'"
$Prompt = Split-Path -Path $venvDir -Leaf
}
}
Write-Verbose "Prompt = '$Prompt'"
Write-Verbose "VenvDir='$VenvDir'"
# Deactivate any currently active virtual environment, but leave the
# deactivate function in place.
deactivate -nondestructive
# Now set the environment variable VIRTUAL_ENV, used by many tools to determine
# that there is an activated venv.
$env:VIRTUAL_ENV = $VenvDir
if (-not $Env:VIRTUAL_ENV_DISABLE_PROMPT) {
Write-Verbose "Setting prompt to '$Prompt'"
# Set the prompt to include the env name
# Make sure _OLD_VIRTUAL_PROMPT is global
function global:_OLD_VIRTUAL_PROMPT { "" }
Copy-Item -Path function:prompt -Destination function:_OLD_VIRTUAL_PROMPT
New-Variable -Name _PYTHON_VENV_PROMPT_PREFIX -Description "Python virtual environment prompt prefix" -Scope Global -Option ReadOnly -Visibility Public -Value $Prompt
function global:prompt {
Write-Host -NoNewline -ForegroundColor Green "($_PYTHON_VENV_PROMPT_PREFIX) "
_OLD_VIRTUAL_PROMPT
}
$env:VIRTUAL_ENV_PROMPT = $Prompt
}
# Clear PYTHONHOME
if (Test-Path -Path Env:PYTHONHOME) {
Copy-Item -Path Env:PYTHONHOME -Destination Env:_OLD_VIRTUAL_PYTHONHOME
Remove-Item -Path Env:PYTHONHOME
}
# Add the venv to the PATH
Copy-Item -Path Env:PATH -Destination Env:_OLD_VIRTUAL_PATH
$Env:PATH = "$VenvExecDir$([System.IO.Path]::PathSeparator)$Env:PATH"

69
venv/bin/activate
View File

@@ -1,69 +0,0 @@
# This file must be used with "source bin/activate" *from bash*
# you cannot run it directly
deactivate () {
# reset old environment variables
if [ -n "${_OLD_VIRTUAL_PATH:-}" ] ; then
PATH="${_OLD_VIRTUAL_PATH:-}"
export PATH
unset _OLD_VIRTUAL_PATH
fi
if [ -n "${_OLD_VIRTUAL_PYTHONHOME:-}" ] ; then
PYTHONHOME="${_OLD_VIRTUAL_PYTHONHOME:-}"
export PYTHONHOME
unset _OLD_VIRTUAL_PYTHONHOME
fi
# This should detect bash and zsh, which have a hash command that must
# be called to get it to forget past commands. Without forgetting
# past commands the $PATH changes we made may not be respected
if [ -n "${BASH:-}" -o -n "${ZSH_VERSION:-}" ] ; then
hash -r 2> /dev/null
fi
if [ -n "${_OLD_VIRTUAL_PS1:-}" ] ; then
PS1="${_OLD_VIRTUAL_PS1:-}"
export PS1
unset _OLD_VIRTUAL_PS1
fi
unset VIRTUAL_ENV
unset VIRTUAL_ENV_PROMPT
if [ ! "${1:-}" = "nondestructive" ] ; then
# Self destruct!
unset -f deactivate
fi
}
# unset irrelevant variables
deactivate nondestructive
VIRTUAL_ENV="/home/klein/codeWS/Python3/puzzle_solver/venv"
export VIRTUAL_ENV
_OLD_VIRTUAL_PATH="$PATH"
PATH="$VIRTUAL_ENV/bin:$PATH"
export PATH
# unset PYTHONHOME if set
# this will fail if PYTHONHOME is set to the empty string (which is bad anyway)
# could use `if (set -u; : $PYTHONHOME) ;` in bash
if [ -n "${PYTHONHOME:-}" ] ; then
_OLD_VIRTUAL_PYTHONHOME="${PYTHONHOME:-}"
unset PYTHONHOME
fi
if [ -z "${VIRTUAL_ENV_DISABLE_PROMPT:-}" ] ; then
_OLD_VIRTUAL_PS1="${PS1:-}"
PS1="(venv) ${PS1:-}"
export PS1
VIRTUAL_ENV_PROMPT="(venv) "
export VIRTUAL_ENV_PROMPT
fi
# This should detect bash and zsh, which have a hash command that must
# be called to get it to forget past commands. Without forgetting
# past commands the $PATH changes we made may not be respected
if [ -n "${BASH:-}" -o -n "${ZSH_VERSION:-}" ] ; then
hash -r 2> /dev/null
fi

26
venv/bin/activate.csh
View File

@@ -1,26 +0,0 @@
# This file must be used with "source bin/activate.csh" *from csh*.
# You cannot run it directly.
# Created by Davide Di Blasi <davidedb@gmail.com>.
# Ported to Python 3.3 venv by Andrew Svetlov <andrew.svetlov@gmail.com>
alias deactivate 'test $?_OLD_VIRTUAL_PATH != 0 && setenv PATH "$_OLD_VIRTUAL_PATH" && unset _OLD_VIRTUAL_PATH; rehash; test $?_OLD_VIRTUAL_PROMPT != 0 && set prompt="$_OLD_VIRTUAL_PROMPT" && unset _OLD_VIRTUAL_PROMPT; unsetenv VIRTUAL_ENV; unsetenv VIRTUAL_ENV_PROMPT; test "\!:*" != "nondestructive" && unalias deactivate'
# Unset irrelevant variables.
deactivate nondestructive
setenv VIRTUAL_ENV "/home/klein/codeWS/Python3/puzzle_solver/venv"
set _OLD_VIRTUAL_PATH="$PATH"
setenv PATH "$VIRTUAL_ENV/bin:$PATH"
set _OLD_VIRTUAL_PROMPT="$prompt"
if (! "$?VIRTUAL_ENV_DISABLE_PROMPT") then
set prompt = "(venv) $prompt"
setenv VIRTUAL_ENV_PROMPT "(venv) "
endif
alias pydoc python -m pydoc
rehash

69
venv/bin/activate.fish
View File

@@ -1,69 +0,0 @@
# This file must be used with "source <venv>/bin/activate.fish" *from fish*
# (https://fishshell.com/); you cannot run it directly.
function deactivate -d "Exit virtual environment and return to normal shell environment"
# reset old environment variables
if test -n "$_OLD_VIRTUAL_PATH"
set -gx PATH $_OLD_VIRTUAL_PATH
set -e _OLD_VIRTUAL_PATH
end
if test -n "$_OLD_VIRTUAL_PYTHONHOME"
set -gx PYTHONHOME $_OLD_VIRTUAL_PYTHONHOME
set -e _OLD_VIRTUAL_PYTHONHOME
end
if test -n "$_OLD_FISH_PROMPT_OVERRIDE"
set -e _OLD_FISH_PROMPT_OVERRIDE
# prevents error when using nested fish instances (Issue #93858)
if functions -q _old_fish_prompt
functions -e fish_prompt
functions -c _old_fish_prompt fish_prompt
functions -e _old_fish_prompt
end
end
set -e VIRTUAL_ENV
set -e VIRTUAL_ENV_PROMPT
if test "$argv[1]" != "nondestructive"
# Self-destruct!
functions -e deactivate
end
end
# Unset irrelevant variables.
deactivate nondestructive
set -gx VIRTUAL_ENV "/home/klein/codeWS/Python3/puzzle_solver/venv"
set -gx _OLD_VIRTUAL_PATH $PATH
set -gx PATH "$VIRTUAL_ENV/bin" $PATH
# Unset PYTHONHOME if set.
if set -q PYTHONHOME
set -gx _OLD_VIRTUAL_PYTHONHOME $PYTHONHOME
set -e PYTHONHOME
end
if test -z "$VIRTUAL_ENV_DISABLE_PROMPT"
# fish uses a function instead of an env var to generate the prompt.
# Save the current fish_prompt function as the function _old_fish_prompt.
functions -c fish_prompt _old_fish_prompt
# With the original prompt function renamed, we can override with our own.
function fish_prompt
# Save the return status of the last command.
set -l old_status $status
# Output the venv prompt; color taken from the blue of the Python logo.
printf "%s%s%s" (set_color 4B8BBE) "(venv) " (set_color normal)
# Restore the return status of the previous command.
echo "exit $old_status" | .
# Output the original/"old" prompt.
_old_fish_prompt
end
set -gx _OLD_FISH_PROMPT_OVERRIDE "$VIRTUAL_ENV"
set -gx VIRTUAL_ENV_PROMPT "(venv) "
end

8
venv/bin/f2py
View File

@@ -1,8 +0,0 @@
#!/home/klein/codeWS/Python3/puzzle_solver/venv/bin/python3
# -*- coding: utf-8 -*-
import re
import sys
from numpy.f2py.f2py2e import main
if __name__ == '__main__':
sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0])
sys.exit(main())

8
venv/bin/numpy-config
View File

@@ -1,8 +0,0 @@
#!/home/klein/codeWS/Python3/puzzle_solver/venv/bin/python3
# -*- coding: utf-8 -*-
import re
import sys
from numpy._configtool import main
if __name__ == '__main__':
sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0])
sys.exit(main())

8
venv/bin/pip
View File

@@ -1,8 +0,0 @@
#!/home/klein/codeWS/Python3/puzzle_solver/venv/bin/python3
# -*- coding: utf-8 -*-
import re
import sys
from pip._internal.cli.main import main
if __name__ == '__main__':
sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0])
sys.exit(main())

8
venv/bin/pip3
View File

@@ -1,8 +0,0 @@
#!/home/klein/codeWS/Python3/puzzle_solver/venv/bin/python3
# -*- coding: utf-8 -*-
import re
import sys
from pip._internal.cli.main import main
if __name__ == '__main__':
sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0])
sys.exit(main())

8
venv/bin/pip3.11
View File

@@ -1,8 +0,0 @@
#!/home/klein/codeWS/Python3/puzzle_solver/venv/bin/python3
# -*- coding: utf-8 -*-
import re
import sys
from pip._internal.cli.main import main
if __name__ == '__main__':
sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0])
sys.exit(main())

1
venv/bin/python
View File

@@ -1 +0,0 @@
python3

1
venv/bin/python3
View File

@@ -1 +0,0 @@
/usr/bin/python3

1
venv/bin/python3.11
View File

@@ -1 +0,0 @@
python3

222
venv/lib/python3.11/site-packages/_distutils_hack/__init__.py
View File

@@ -1,222 +0,0 @@
# don't import any costly modules
import sys
import os
is_pypy = '__pypy__' in sys.builtin_module_names
def warn_distutils_present():
if 'distutils' not in sys.modules:
return
if is_pypy and sys.version_info < (3, 7):
# PyPy for 3.6 unconditionally imports distutils, so bypass the warning
# https://foss.heptapod.net/pypy/pypy/-/blob/be829135bc0d758997b3566062999ee8b23872b4/lib-python/3/site.py#L250
return
import warnings
warnings.warn(
"Distutils was imported before Setuptools, but importing Setuptools "
"also replaces the `distutils` module in `sys.modules`. This may lead "
"to undesirable behaviors or errors. To avoid these issues, avoid "
"using distutils directly, ensure that setuptools is installed in the "
"traditional way (e.g. not an editable install), and/or make sure "
"that setuptools is always imported before distutils."
)
def clear_distutils():
if 'distutils' not in sys.modules:
return
import warnings
warnings.warn("Setuptools is replacing distutils.")
mods = [
name
for name in sys.modules
if name == "distutils" or name.startswith("distutils.")
]
for name in mods:
del sys.modules[name]
def enabled():
"""
Allow selection of distutils by environment variable.
"""
which = os.environ.get('SETUPTOOLS_USE_DISTUTILS', 'local')
return which == 'local'
def ensure_local_distutils():
import importlib
clear_distutils()
# With the DistutilsMetaFinder in place,
# perform an import to cause distutils to be
# loaded from setuptools._distutils. Ref #2906.
with shim():
importlib.import_module('distutils')
# check that submodules load as expected
core = importlib.import_module('distutils.core')
assert '_distutils' in core.__file__, core.__file__
assert 'setuptools._distutils.log' not in sys.modules
def do_override():
"""
Ensure that the local copy of distutils is preferred over stdlib.
See https://github.com/pypa/setuptools/issues/417#issuecomment-392298401
for more motivation.
"""
if enabled():
warn_distutils_present()
ensure_local_distutils()
class _TrivialRe:
def __init__(self, *patterns):
self._patterns = patterns
def match(self, string):
return all(pat in string for pat in self._patterns)
class DistutilsMetaFinder:
def find_spec(self, fullname, path, target=None):
# optimization: only consider top level modules and those
# found in the CPython test suite.
if path is not None and not fullname.startswith('test.'):
return
method_name = 'spec_for_{fullname}'.format(**locals())
method = getattr(self, method_name, lambda: None)
return method()
def spec_for_distutils(self):
if self.is_cpython():
return
import importlib
import importlib.abc
import importlib.util
try:
mod = importlib.import_module('setuptools._distutils')
except Exception:
# There are a couple of cases where setuptools._distutils
# may not be present:
# - An older Setuptools without a local distutils is
# taking precedence. Ref #2957.
# - Path manipulation during sitecustomize removes
# setuptools from the path but only after the hook
# has been loaded. Ref #2980.
# In either case, fall back to stdlib behavior.
return
class DistutilsLoader(importlib.abc.Loader):
def create_module(self, spec):
mod.__name__ = 'distutils'
return mod
def exec_module(self, module):
pass
return importlib.util.spec_from_loader(
'distutils', DistutilsLoader(), origin=mod.__file__
)
@staticmethod
def is_cpython():
"""
Suppress supplying distutils for CPython (build and tests).
Ref #2965 and #3007.
"""
return os.path.isfile('pybuilddir.txt')
def spec_for_pip(self):
"""
Ensure stdlib distutils when running under pip.
See pypa/pip#8761 for rationale.
"""
if self.pip_imported_during_build():
return
clear_distutils()
self.spec_for_distutils = lambda: None
@classmethod
def pip_imported_during_build(cls):
"""
Detect if pip is being imported in a build script. Ref #2355.
"""
import traceback
return any(
cls.frame_file_is_setup(frame) for frame, line in traceback.walk_stack(None)
)
@staticmethod
def frame_file_is_setup(frame):
"""
Return True if the indicated frame suggests a setup.py file.
"""
# some frames may not have __file__ (#2940)
return frame.f_globals.get('__file__', '').endswith('setup.py')
def spec_for_sensitive_tests(self):
"""
Ensure stdlib distutils when running select tests under CPython.
python/cpython#91169
"""
clear_distutils()
self.spec_for_distutils = lambda: None
sensitive_tests = (
[
'test.test_distutils',
'test.test_peg_generator',
'test.test_importlib',
]
if sys.version_info < (3, 10)
else [
'test.test_distutils',
]
)
for name in DistutilsMetaFinder.sensitive_tests:
setattr(
DistutilsMetaFinder,
f'spec_for_{name}',
DistutilsMetaFinder.spec_for_sensitive_tests,
)
DISTUTILS_FINDER = DistutilsMetaFinder()
def add_shim():
DISTUTILS_FINDER in sys.meta_path or insert_shim()
class shim:
def __enter__(self):
insert_shim()
def __exit__(self, exc, value, tb):
remove_shim()
def insert_shim():
sys.meta_path.insert(0, DISTUTILS_FINDER)
def remove_shim():
try:
sys.meta_path.remove(DISTUTILS_FINDER)
except ValueError:
pass

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@@ -1,966 +0,0 @@
Copyright (c) 2005-2024, NumPy Developers.
All rights reserved.
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are
met:
* Redistributions of source code must retain the above copyright
notice, this list of conditions and the following disclaimer.
* Redistributions in binary form must reproduce the above
copyright notice, this list of conditions and the following
disclaimer in the documentation and/or other materials provided
with the distribution.
* Neither the name of the NumPy Developers nor the names of any
contributors may be used to endorse or promote products derived
from this software without specific prior written permission.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
"AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
----
The NumPy repository and source distributions bundle several libraries that are
compatibly licensed. We list these here.
Name: lapack-lite
Files: numpy/linalg/lapack_lite/*
License: BSD-3-Clause
For details, see numpy/linalg/lapack_lite/LICENSE.txt
Name: dragon4
Files: numpy/_core/src/multiarray/dragon4.c
License: MIT
For license text, see numpy/_core/src/multiarray/dragon4.c
Name: libdivide
Files: numpy/_core/include/numpy/libdivide/*
License: Zlib
For license text, see numpy/_core/include/numpy/libdivide/LICENSE.txt
Note that the following files are vendored in the repository and sdist but not
installed in built numpy packages:
Name: Meson
Files: vendored-meson/meson/*
License: Apache 2.0
For license text, see vendored-meson/meson/COPYING
Name: spin
Files: .spin/cmds.py
License: BSD-3
For license text, see .spin/LICENSE
----
This binary distribution of NumPy also bundles the following software:
Name: OpenBLAS
Files: numpy.libs/libscipy_openblas*.so
Description: bundled as a dynamically linked library
Availability: https://github.com/OpenMathLib/OpenBLAS/
License: BSD-3-Clause
Copyright (c) 2011-2014, The OpenBLAS Project
All rights reserved.
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are
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1. Redistributions of source code must retain the above copyright
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2. Redistributions in binary form must reproduce the above copyright
notice, this list of conditions and the following disclaimer in
the documentation and/or other materials provided with the
distribution.
3. Neither the name of the OpenBLAS project nor the names of
its contributors may be used to endorse or promote products
derived from this software without specific prior written
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THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE
USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
Name: LAPACK
Files: numpy.libs/libscipy_openblas*.so
Description: bundled in OpenBLAS
Availability: https://github.com/OpenMathLib/OpenBLAS/
License: BSD-3-Clause-Attribution
Copyright (c) 1992-2013 The University of Tennessee and The University
of Tennessee Research Foundation. All rights
reserved.
Copyright (c) 2000-2013 The University of California Berkeley. All
rights reserved.
Copyright (c) 2006-2013 The University of Colorado Denver. All rights
reserved.
$COPYRIGHT$
Additional copyrights may follow
$HEADER$
Redistribution and use in source and binary forms, with or without
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met:
- Redistributions of source code must retain the above copyright
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- Redistributions in binary form must reproduce the above copyright
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contributors may be used to endorse or promote products derived from
this software without specific prior written permission.
The copyright holders provide no reassurances that the source code
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disclaim any liability to any recipient for claims brought against
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THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
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(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
Name: GCC runtime library
Files: numpy.libs/libgfortran*.so
Description: dynamically linked to files compiled with gcc
Availability: https://gcc.gnu.org/git/?p=gcc.git;a=tree;f=libgfortran
License: GPL-3.0-with-GCC-exception
Copyright (C) 2002-2017 Free Software Foundation, Inc.
Libgfortran is free software; you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation; either version 3, or (at your option)
any later version.
Libgfortran is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
Under Section 7 of GPL version 3, you are granted additional
permissions described in the GCC Runtime Library Exception, version
3.1, as published by the Free Software Foundation.
You should have received a copy of the GNU General Public License and
a copy of the GCC Runtime Library Exception along with this program;
see the files COPYING3 and COPYING.RUNTIME respectively. If not, see
<http://www.gnu.org/licenses/>.
----
Full text of license texts referred to above follows (that they are
listed below does not necessarily imply the conditions apply to the
present binary release):
----
GCC RUNTIME LIBRARY EXCEPTION
Version 3.1, 31 March 2009
Copyright (C) 2009 Free Software Foundation, Inc. <http://fsf.org/>
Everyone is permitted to copy and distribute verbatim copies of this
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This GCC Runtime Library Exception ("Exception") is an additional
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----
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Name: libquadmath
Files: numpy.libs/libquadmath*.so
Description: dynamically linked to files compiled with gcc
Availability: https://gcc.gnu.org/git/?p=gcc.git;a=tree;f=libquadmath
License: LGPL-2.1-or-later
GCC Quad-Precision Math Library
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This file is part of the libquadmath library.
Libquadmath is free software; you can redistribute it and/or
modify it under the terms of the GNU Library General Public
License as published by the Free Software Foundation; either
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but WITHOUT ANY WARRANTY; without even the implied warranty of
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https://www.gnu.org/licenses/old-licenses/lgpl-2.1.html

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Wheel-Version: 1.0
Generator: meson
Root-Is-Purelib: false
Tag: cp311-cp311-manylinux_2_17_x86_64
Tag: cp311-cp311-manylinux2014_x86_64

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[array_api]
numpy = numpy
[pyinstaller40]
hook-dirs = numpy:_pyinstaller_hooks_dir
[console_scripts]
f2py = numpy.f2py.f2py2e:main
numpy-config = numpy._configtool:main

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# This file is generated by numpy's build process
# It contains system_info results at the time of building this package.
from enum import Enum
from numpy._core._multiarray_umath import (
__cpu_features__,
__cpu_baseline__,
__cpu_dispatch__,
)
__all__ = ["show"]
_built_with_meson = True
class DisplayModes(Enum):
stdout = "stdout"
dicts = "dicts"
def _cleanup(d):
"""
Removes empty values in a `dict` recursively
This ensures we remove values that Meson could not provide to CONFIG
"""
if isinstance(d, dict):
return {k: _cleanup(v) for k, v in d.items() if v and _cleanup(v)}
else:
return d
CONFIG = _cleanup(
{
"Compilers": {
"c": {
"name": "gcc",
"linker": r"ld.bfd",
"version": "10.2.1",
"commands": r"cc",
"args": r"",
"linker args": r"",
},
"cython": {
"name": "cython",
"linker": r"cython",
"version": "3.0.11",
"commands": r"cython",
"args": r"",
"linker args": r"",
},
"c++": {
"name": "gcc",
"linker": r"ld.bfd",
"version": "10.2.1",
"commands": r"c++",
"args": r"",
"linker args": r"",
},
},
"Machine Information": {
"host": {
"cpu": "x86_64",
"family": "x86_64",
"endian": "little",
"system": "linux",
},
"build": {
"cpu": "x86_64",
"family": "x86_64",
"endian": "little",
"system": "linux",
},
"cross-compiled": bool("False".lower().replace("false", "")),
},
"Build Dependencies": {
"blas": {
"name": "scipy-openblas",
"found": bool("True".lower().replace("false", "")),
"version": "0.3.27",
"detection method": "pkgconfig",
"include directory": r"/opt/_internal/cpython-3.11.10/lib/python3.11/site-packages/scipy_openblas64/include",
"lib directory": r"/opt/_internal/cpython-3.11.10/lib/python3.11/site-packages/scipy_openblas64/lib",
"openblas configuration": r"OpenBLAS 0.3.27 USE64BITINT DYNAMIC_ARCH NO_AFFINITY Haswell MAX_THREADS=64",
"pc file directory": r"/project/.openblas",
},
"lapack": {
"name": "scipy-openblas",
"found": bool("True".lower().replace("false", "")),
"version": "0.3.27",
"detection method": "pkgconfig",
"include directory": r"/opt/_internal/cpython-3.11.10/lib/python3.11/site-packages/scipy_openblas64/include",
"lib directory": r"/opt/_internal/cpython-3.11.10/lib/python3.11/site-packages/scipy_openblas64/lib",
"openblas configuration": r"OpenBLAS 0.3.27 USE64BITINT DYNAMIC_ARCH NO_AFFINITY Haswell MAX_THREADS=64",
"pc file directory": r"/project/.openblas",
},
},
"Python Information": {
"path": r"/tmp/build-env-dv3lw3nl/bin/python",
"version": "3.11",
},
"SIMD Extensions": {
"baseline": __cpu_baseline__,
"found": [
feature for feature in __cpu_dispatch__ if __cpu_features__[feature]
],
"not found": [
feature for feature in __cpu_dispatch__ if not __cpu_features__[feature]
],
},
}
)
def _check_pyyaml():
import yaml
return yaml
def show(mode=DisplayModes.stdout.value):
"""
Show libraries and system information on which NumPy was built
and is being used
Parameters
----------
mode : {`'stdout'`, `'dicts'`}, optional.
Indicates how to display the config information.
`'stdout'` prints to console, `'dicts'` returns a dictionary
of the configuration.
Returns
-------
out : {`dict`, `None`}
If mode is `'dicts'`, a dict is returned, else None
See Also
--------
get_include : Returns the directory containing NumPy C
header files.
Notes
-----
1. The `'stdout'` mode will give more readable
output if ``pyyaml`` is installed
"""
if mode == DisplayModes.stdout.value:
try: # Non-standard library, check import
yaml = _check_pyyaml()
print(yaml.dump(CONFIG))
except ModuleNotFoundError:
import warnings
import json
warnings.warn("Install `pyyaml` for better output", stacklevel=1)
print(json.dumps(CONFIG, indent=2))
elif mode == DisplayModes.dicts.value:
return CONFIG
else:
raise AttributeError(
f"Invalid `mode`, use one of: {', '.join([e.value for e in DisplayModes])}"
)

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venv/lib/python3.11/site-packages/numpy/__init__.py
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@@ -1,542 +0,0 @@
"""
NumPy
=====
Provides
1. An array object of arbitrary homogeneous items
2. Fast mathematical operations over arrays
3. Linear Algebra, Fourier Transforms, Random Number Generation
How to use the documentation
----------------------------
Documentation is available in two forms: docstrings provided
with the code, and a loose standing reference guide, available from
`the NumPy homepage <https://numpy.org>`_.
We recommend exploring the docstrings using
`IPython <https://ipython.org>`_, an advanced Python shell with
TAB-completion and introspection capabilities. See below for further
instructions.
The docstring examples assume that `numpy` has been imported as ``np``::
>>> import numpy as np
Code snippets are indicated by three greater-than signs::
>>> x = 42
>>> x = x + 1
Use the built-in ``help`` function to view a function's docstring::
>>> help(np.sort)
... # doctest: +SKIP
For some objects, ``np.info(obj)`` may provide additional help. This is
particularly true if you see the line "Help on ufunc object:" at the top
of the help() page. Ufuncs are implemented in C, not Python, for speed.
The native Python help() does not know how to view their help, but our
np.info() function does.
Available subpackages
---------------------
lib
Basic functions used by several sub-packages.
random
Core Random Tools
linalg
Core Linear Algebra Tools
fft
Core FFT routines
polynomial
Polynomial tools
testing
NumPy testing tools
distutils
Enhancements to distutils with support for
Fortran compilers support and more (for Python <= 3.11)
Utilities
---------
test
Run numpy unittests
show_config
Show numpy build configuration
__version__
NumPy version string
Viewing documentation using IPython
-----------------------------------
Start IPython and import `numpy` usually under the alias ``np``: `import
numpy as np`. Then, directly past or use the ``%cpaste`` magic to paste
examples into the shell. To see which functions are available in `numpy`,
type ``np.<TAB>`` (where ``<TAB>`` refers to the TAB key), or use
``np.*cos*?<ENTER>`` (where ``<ENTER>`` refers to the ENTER key) to narrow
down the list. To view the docstring for a function, use
``np.cos?<ENTER>`` (to view the docstring) and ``np.cos??<ENTER>`` (to view
the source code).
Copies vs. in-place operation
-----------------------------
Most of the functions in `numpy` return a copy of the array argument
(e.g., `np.sort`). In-place versions of these functions are often
available as array methods, i.e. ``x = np.array([1,2,3]); x.sort()``.
Exceptions to this rule are documented.
"""
import os
import sys
import warnings
from ._globals import _NoValue, _CopyMode
from ._expired_attrs_2_0 import __expired_attributes__
# If a version with git hash was stored, use that instead
from . import version
from .version import __version__
# We first need to detect if we're being called as part of the numpy setup
# procedure itself in a reliable manner.
try:
__NUMPY_SETUP__
except NameError:
__NUMPY_SETUP__ = False
if __NUMPY_SETUP__:
sys.stderr.write('Running from numpy source directory.\n')
else:
# Allow distributors to run custom init code before importing numpy._core
from . import _distributor_init
try:
from numpy.__config__ import show as show_config
except ImportError as e:
msg = """Error importing numpy: you should not try to import numpy from
its source directory; please exit the numpy source tree, and relaunch
your python interpreter from there."""
raise ImportError(msg) from e
from . import _core
from ._core import (
False_, ScalarType, True_, _get_promotion_state, _no_nep50_warning,
_set_promotion_state, abs, absolute, acos, acosh, add, all, allclose,
amax, amin, any, arange, arccos, arccosh, arcsin, arcsinh,
arctan, arctan2, arctanh, argmax, argmin, argpartition, argsort,
argwhere, around, array, array2string, array_equal, array_equiv,
array_repr, array_str, asanyarray, asarray, ascontiguousarray,
asfortranarray, asin, asinh, atan, atanh, atan2, astype, atleast_1d,
atleast_2d, atleast_3d, base_repr, binary_repr, bitwise_and,
bitwise_count, bitwise_invert, bitwise_left_shift, bitwise_not,
bitwise_or, bitwise_right_shift, bitwise_xor, block, bool, bool_,
broadcast, busday_count, busday_offset, busdaycalendar, byte, bytes_,
can_cast, cbrt, cdouble, ceil, character, choose, clip, clongdouble,
complex128, complex64, complexfloating, compress, concat, concatenate,
conj, conjugate, convolve, copysign, copyto, correlate, cos, cosh,
count_nonzero, cross, csingle, cumprod, cumsum, cumulative_prod,
cumulative_sum, datetime64, datetime_as_string, datetime_data,
deg2rad, degrees, diagonal, divide, divmod, dot, double, dtype, e,
einsum, einsum_path, empty, empty_like, equal, errstate, euler_gamma,
exp, exp2, expm1, fabs, finfo, flatiter, flatnonzero, flexible,
float16, float32, float64, float_power, floating, floor, floor_divide,
fmax, fmin, fmod, format_float_positional, format_float_scientific,
frexp, from_dlpack, frombuffer, fromfile, fromfunction, fromiter,
frompyfunc, fromstring, full, full_like, gcd, generic, geomspace,
get_printoptions, getbufsize, geterr, geterrcall, greater,
greater_equal, half, heaviside, hstack, hypot, identity, iinfo, iinfo,
indices, inexact, inf, inner, int16, int32, int64, int8, int_, intc,
integer, intp, invert, is_busday, isclose, isdtype, isfinite,
isfortran, isinf, isnan, isnat, isscalar, issubdtype, lcm, ldexp,
left_shift, less, less_equal, lexsort, linspace, little_endian, log,
log10, log1p, log2, logaddexp, logaddexp2, logical_and, logical_not,
logical_or, logical_xor, logspace, long, longdouble, longlong, matmul,
matrix_transpose, max, maximum, may_share_memory, mean, memmap, min,
min_scalar_type, minimum, mod, modf, moveaxis, multiply, nan, ndarray,
ndim, nditer, negative, nested_iters, newaxis, nextafter, nonzero,
not_equal, number, object_, ones, ones_like, outer, partition,
permute_dims, pi, positive, pow, power, printoptions, prod,
promote_types, ptp, put, putmask, rad2deg, radians, ravel, recarray,
reciprocal, record, remainder, repeat, require, reshape, resize,
result_type, right_shift, rint, roll, rollaxis, round, sctypeDict,
searchsorted, set_printoptions, setbufsize, seterr, seterrcall, shape,
shares_memory, short, sign, signbit, signedinteger, sin, single, sinh,
size, sort, spacing, sqrt, square, squeeze, stack, std,
str_, subtract, sum, swapaxes, take, tan, tanh, tensordot,
timedelta64, trace, transpose, true_divide, trunc, typecodes, ubyte,
ufunc, uint, uint16, uint32, uint64, uint8, uintc, uintp, ulong,
ulonglong, unsignedinteger, unstack, ushort, var, vdot, vecdot, void,
vstack, where, zeros, zeros_like
)
# NOTE: It's still under discussion whether these aliases
# should be removed.
for ta in ["float96", "float128", "complex192", "complex256"]:
try:
globals()[ta] = getattr(_core, ta)
except AttributeError:
pass
del ta
from . import lib
from .lib import scimath as emath
from .lib._histograms_impl import (
histogram, histogram_bin_edges, histogramdd
)
from .lib._nanfunctions_impl import (
nanargmax, nanargmin, nancumprod, nancumsum, nanmax, nanmean,
nanmedian, nanmin, nanpercentile, nanprod, nanquantile, nanstd,
nansum, nanvar
)
from .lib._function_base_impl import (
select, piecewise, trim_zeros, copy, iterable, percentile, diff,
gradient, angle, unwrap, sort_complex, flip, rot90, extract, place,
vectorize, asarray_chkfinite, average, bincount, digitize, cov,
corrcoef, median, sinc, hamming, hanning, bartlett, blackman,
kaiser, trapezoid, trapz, i0, meshgrid, delete, insert, append,
interp, quantile
)
from .lib._twodim_base_impl import (
diag, diagflat, eye, fliplr, flipud, tri, triu, tril, vander,
histogram2d, mask_indices, tril_indices, tril_indices_from,
triu_indices, triu_indices_from
)
from .lib._shape_base_impl import (
apply_over_axes, apply_along_axis, array_split, column_stack, dsplit,
dstack, expand_dims, hsplit, kron, put_along_axis, row_stack, split,
take_along_axis, tile, vsplit
)
from .lib._type_check_impl import (
iscomplexobj, isrealobj, imag, iscomplex, isreal, nan_to_num, real,
real_if_close, typename, mintypecode, common_type
)
from .lib._arraysetops_impl import (
ediff1d, in1d, intersect1d, isin, setdiff1d, setxor1d, union1d,
unique, unique_all, unique_counts, unique_inverse, unique_values
)
from .lib._ufunclike_impl import fix, isneginf, isposinf
from .lib._arraypad_impl import pad
from .lib._utils_impl import (
show_runtime, get_include, info
)
from .lib._stride_tricks_impl import (
broadcast_arrays, broadcast_shapes, broadcast_to
)
from .lib._polynomial_impl import (
poly, polyint, polyder, polyadd, polysub, polymul, polydiv, polyval,
polyfit, poly1d, roots
)
from .lib._npyio_impl import (
savetxt, loadtxt, genfromtxt, load, save, savez, packbits,
savez_compressed, unpackbits, fromregex
)
from .lib._index_tricks_impl import (
diag_indices_from, diag_indices, fill_diagonal, ndindex, ndenumerate,
ix_, c_, r_, s_, ogrid, mgrid, unravel_index, ravel_multi_index,
index_exp
)
from . import matrixlib as _mat
from .matrixlib import (
asmatrix, bmat, matrix
)
# public submodules are imported lazily, therefore are accessible from
# __getattr__. Note that `distutils` (deprecated) and `array_api`
# (experimental label) are not added here, because `from numpy import *`
# must not raise any warnings - that's too disruptive.
__numpy_submodules__ = {
"linalg", "fft", "dtypes", "random", "polynomial", "ma",
"exceptions", "lib", "ctypeslib", "testing", "typing",
"f2py", "test", "rec", "char", "core", "strings",
}
# We build warning messages for former attributes
_msg = (
"module 'numpy' has no attribute '{n}'.\n"
"`np.{n}` was a deprecated alias for the builtin `{n}`. "
"To avoid this error in existing code, use `{n}` by itself. "
"Doing this will not modify any behavior and is safe. {extended_msg}\n"
"The aliases was originally deprecated in NumPy 1.20; for more "
"details and guidance see the original release note at:\n"
" https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations")
_specific_msg = (
"If you specifically wanted the numpy scalar type, use `np.{}` here.")
_int_extended_msg = (
"When replacing `np.{}`, you may wish to use e.g. `np.int64` "
"or `np.int32` to specify the precision. If you wish to review "
"your current use, check the release note link for "
"additional information.")
_type_info = [
("object", ""), # The NumPy scalar only exists by name.
("float", _specific_msg.format("float64")),
("complex", _specific_msg.format("complex128")),
("str", _specific_msg.format("str_")),
("int", _int_extended_msg.format("int"))]
__former_attrs__ = {
n: _msg.format(n=n, extended_msg=extended_msg)
for n, extended_msg in _type_info
}
# Some of these could be defined right away, but most were aliases to
# the Python objects and only removed in NumPy 1.24. Defining them should
# probably wait for NumPy 1.26 or 2.0.
# When defined, these should possibly not be added to `__all__` to avoid
# import with `from numpy import *`.
__future_scalars__ = {"str", "bytes", "object"}
__array_api_version__ = "2023.12"
from ._array_api_info import __array_namespace_info__
# now that numpy core module is imported, can initialize limits
_core.getlimits._register_known_types()
__all__ = list(
__numpy_submodules__ |
set(_core.__all__) |
set(_mat.__all__) |
set(lib._histograms_impl.__all__) |
set(lib._nanfunctions_impl.__all__) |
set(lib._function_base_impl.__all__) |
set(lib._twodim_base_impl.__all__) |
set(lib._shape_base_impl.__all__) |
set(lib._type_check_impl.__all__) |
set(lib._arraysetops_impl.__all__) |
set(lib._ufunclike_impl.__all__) |
set(lib._arraypad_impl.__all__) |
set(lib._utils_impl.__all__) |
set(lib._stride_tricks_impl.__all__) |
set(lib._polynomial_impl.__all__) |
set(lib._npyio_impl.__all__) |
set(lib._index_tricks_impl.__all__) |
{"emath", "show_config", "__version__", "__array_namespace_info__"}
)
# Filter out Cython harmless warnings
warnings.filterwarnings("ignore", message="numpy.dtype size changed")
warnings.filterwarnings("ignore", message="numpy.ufunc size changed")
warnings.filterwarnings("ignore", message="numpy.ndarray size changed")
def __getattr__(attr):
# Warn for expired attributes
import warnings
if attr == "linalg":
import numpy.linalg as linalg
return linalg
elif attr == "fft":
import numpy.fft as fft
return fft
elif attr == "dtypes":
import numpy.dtypes as dtypes
return dtypes
elif attr == "random":
import numpy.random as random
return random
elif attr == "polynomial":
import numpy.polynomial as polynomial
return polynomial
elif attr == "ma":
import numpy.ma as ma
return ma
elif attr == "ctypeslib":
import numpy.ctypeslib as ctypeslib
return ctypeslib
elif attr == "exceptions":
import numpy.exceptions as exceptions
return exceptions
elif attr == "testing":
import numpy.testing as testing
return testing
elif attr == "matlib":
import numpy.matlib as matlib
return matlib
elif attr == "f2py":
import numpy.f2py as f2py
return f2py
elif attr == "typing":
import numpy.typing as typing
return typing
elif attr == "rec":
import numpy.rec as rec
return rec
elif attr == "char":
import numpy.char as char
return char
elif attr == "array_api":
raise AttributeError("`numpy.array_api` is not available from "
"numpy 2.0 onwards", name=None)
elif attr == "core":
import numpy.core as core
return core
elif attr == "strings":
import numpy.strings as strings
return strings
elif attr == "distutils":
if 'distutils' in __numpy_submodules__:
import numpy.distutils as distutils
return distutils
else:
raise AttributeError("`numpy.distutils` is not available from "
"Python 3.12 onwards", name=None)
if attr in __future_scalars__:
# And future warnings for those that will change, but also give
# the AttributeError
warnings.warn(
f"In the future `np.{attr}` will be defined as the "
"corresponding NumPy scalar.", FutureWarning, stacklevel=2)
if attr in __former_attrs__:
raise AttributeError(__former_attrs__[attr], name=None)
if attr in __expired_attributes__:
raise AttributeError(
f"`np.{attr}` was removed in the NumPy 2.0 release. "
f"{__expired_attributes__[attr]}",
name=None
)
if attr == "chararray":
warnings.warn(
"`np.chararray` is deprecated and will be removed from "
"the main namespace in the future. Use an array with a string "
"or bytes dtype instead.", DeprecationWarning, stacklevel=2)
import numpy.char as char
return char.chararray
raise AttributeError("module {!r} has no attribute "
"{!r}".format(__name__, attr))
def __dir__():
public_symbols = (
globals().keys() | __numpy_submodules__
)
public_symbols -= {
"matrixlib", "matlib", "tests", "conftest", "version",
"compat", "distutils", "array_api"
}
return list(public_symbols)
# Pytest testing
from numpy._pytesttester import PytestTester
test = PytestTester(__name__)
del PytestTester
def _sanity_check():
"""
Quick sanity checks for common bugs caused by environment.
There are some cases e.g. with wrong BLAS ABI that cause wrong
results under specific runtime conditions that are not necessarily
achieved during test suite runs, and it is useful to catch those early.
See https://github.com/numpy/numpy/issues/8577 and other
similar bug reports.
"""
try:
x = ones(2, dtype=float32)
if not abs(x.dot(x) - float32(2.0)) < 1e-5:
raise AssertionError()
except AssertionError:
msg = ("The current Numpy installation ({!r}) fails to "
"pass simple sanity checks. This can be caused for example "
"by incorrect BLAS library being linked in, or by mixing "
"package managers (pip, conda, apt, ...). Search closed "
"numpy issues for similar problems.")
raise RuntimeError(msg.format(__file__)) from None
_sanity_check()
del _sanity_check
def _mac_os_check():
"""
Quick Sanity check for Mac OS look for accelerate build bugs.
Testing numpy polyfit calls init_dgelsd(LAPACK)
"""
try:
c = array([3., 2., 1.])
x = linspace(0, 2, 5)
y = polyval(c, x)
_ = polyfit(x, y, 2, cov=True)
except ValueError:
pass
if sys.platform == "darwin":
from . import exceptions
with warnings.catch_warnings(record=True) as w:
_mac_os_check()
# Throw runtime error, if the test failed Check for warning and error_message
if len(w) > 0:
for _wn in w:
if _wn.category is exceptions.RankWarning:
# Ignore other warnings, they may not be relevant (see gh-25433).
error_message = f"{_wn.category.__name__}: {str(_wn.message)}"
msg = (
"Polyfit sanity test emitted a warning, most likely due "
"to using a buggy Accelerate backend."
"\nIf you compiled yourself, more information is available at:"
"\nhttps://numpy.org/devdocs/building/index.html"
"\nOtherwise report this to the vendor "
"that provided NumPy.\n\n{}\n".format(error_message))
raise RuntimeError(msg)
del _wn
del w
del _mac_os_check
def hugepage_setup():
"""
We usually use madvise hugepages support, but on some old kernels it
is slow and thus better avoided. Specifically kernel version 4.6
had a bug fix which probably fixed this:
https://github.com/torvalds/linux/commit/7cf91a98e607c2f935dbcc177d70011e95b8faff
"""
use_hugepage = os.environ.get("NUMPY_MADVISE_HUGEPAGE", None)
if sys.platform == "linux" and use_hugepage is None:
# If there is an issue with parsing the kernel version,
# set use_hugepage to 0. Usage of LooseVersion will handle
# the kernel version parsing better, but avoided since it
# will increase the import time.
# See: #16679 for related discussion.
try:
use_hugepage = 1
kernel_version = os.uname().release.split(".")[:2]
kernel_version = tuple(int(v) for v in kernel_version)
if kernel_version < (4, 6):
use_hugepage = 0
except ValueError:
use_hugepage = 0
elif use_hugepage is None:
# This is not Linux, so it should not matter, just enable anyway
use_hugepage = 1
else:
use_hugepage = int(use_hugepage)
return use_hugepage
# Note that this will currently only make a difference on Linux
_core.multiarray._set_madvise_hugepage(hugepage_setup())
del hugepage_setup
# Give a warning if NumPy is reloaded or imported on a sub-interpreter
# We do this from python, since the C-module may not be reloaded and
# it is tidier organized.
_core.multiarray._multiarray_umath._reload_guard()
# TODO: Remove the environment variable entirely now that it is "weak"
_core._set_promotion_state(
os.environ.get("NPY_PROMOTION_STATE", "weak"))
# Tell PyInstaller where to find hook-numpy.py
def _pyinstaller_hooks_dir():
from pathlib import Path
return [str(Path(__file__).with_name("_pyinstaller").resolve())]
# Remove symbols imported for internal use
del os, sys, warnings

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"""
Array API Inspection namespace
This is the namespace for inspection functions as defined by the array API
standard. See
https://data-apis.org/array-api/latest/API_specification/inspection.html for
more details.
"""
from numpy._core import (
dtype,
bool,
intp,
int8,
int16,
int32,
int64,
uint8,
uint16,
uint32,
uint64,
float32,
float64,
complex64,
complex128,
)
class __array_namespace_info__:
"""
Get the array API inspection namespace for NumPy.
The array API inspection namespace defines the following functions:
- capabilities()
- default_device()
- default_dtypes()
- dtypes()
- devices()
See
https://data-apis.org/array-api/latest/API_specification/inspection.html
for more details.
Returns
-------
info : ModuleType
The array API inspection namespace for NumPy.
Examples
--------
>>> info = np.__array_namespace_info__()
>>> info.default_dtypes()
{'real floating': numpy.float64,
'complex floating': numpy.complex128,
'integral': numpy.int64,
'indexing': numpy.int64}
"""
__module__ = 'numpy'
def capabilities(self):
"""
Return a dictionary of array API library capabilities.
The resulting dictionary has the following keys:
- **"boolean indexing"**: boolean indicating whether an array library
supports boolean indexing. Always ``True`` for NumPy.
- **"data-dependent shapes"**: boolean indicating whether an array
library supports data-dependent output shapes. Always ``True`` for
NumPy.
See
https://data-apis.org/array-api/latest/API_specification/generated/array_api.info.capabilities.html
for more details.
See Also
--------
__array_namespace_info__.default_device,
__array_namespace_info__.default_dtypes,
__array_namespace_info__.dtypes,
__array_namespace_info__.devices
Returns
-------
capabilities : dict
A dictionary of array API library capabilities.
Examples
--------
>>> info = np.__array_namespace_info__()
>>> info.capabilities()
{'boolean indexing': True,
'data-dependent shapes': True}
"""
return {
"boolean indexing": True,
"data-dependent shapes": True,
# 'max rank' will be part of the 2024.12 standard
# "max rank": 64,
}
def default_device(self):
"""
The default device used for new NumPy arrays.
For NumPy, this always returns ``'cpu'``.
See Also
--------
__array_namespace_info__.capabilities,
__array_namespace_info__.default_dtypes,
__array_namespace_info__.dtypes,
__array_namespace_info__.devices
Returns
-------
device : str
The default device used for new NumPy arrays.
Examples
--------
>>> info = np.__array_namespace_info__()
>>> info.default_device()
'cpu'
"""
return "cpu"
def default_dtypes(self, *, device=None):
"""
The default data types used for new NumPy arrays.
For NumPy, this always returns the following dictionary:
- **"real floating"**: ``numpy.float64``
- **"complex floating"**: ``numpy.complex128``
- **"integral"**: ``numpy.intp``
- **"indexing"**: ``numpy.intp``
Parameters
----------
device : str, optional
The device to get the default data types for. For NumPy, only
``'cpu'`` is allowed.
Returns
-------
dtypes : dict
A dictionary describing the default data types used for new NumPy
arrays.
See Also
--------
__array_namespace_info__.capabilities,
__array_namespace_info__.default_device,
__array_namespace_info__.dtypes,
__array_namespace_info__.devices
Examples
--------
>>> info = np.__array_namespace_info__()
>>> info.default_dtypes()
{'real floating': numpy.float64,
'complex floating': numpy.complex128,
'integral': numpy.int64,
'indexing': numpy.int64}
"""
if device not in ["cpu", None]:
raise ValueError(
'Device not understood. Only "cpu" is allowed, but received:'
f' {device}'
)
return {
"real floating": dtype(float64),
"complex floating": dtype(complex128),
"integral": dtype(intp),
"indexing": dtype(intp),
}
def dtypes(self, *, device=None, kind=None):
"""
The array API data types supported by NumPy.
Note that this function only returns data types that are defined by
the array API.
Parameters
----------
device : str, optional
The device to get the data types for. For NumPy, only ``'cpu'`` is
allowed.
kind : str or tuple of str, optional
The kind of data types to return. If ``None``, all data types are
returned. If a string, only data types of that kind are returned.
If a tuple, a dictionary containing the union of the given kinds
is returned. The following kinds are supported:
- ``'bool'``: boolean data types (i.e., ``bool``).
- ``'signed integer'``: signed integer data types (i.e., ``int8``,
``int16``, ``int32``, ``int64``).
- ``'unsigned integer'``: unsigned integer data types (i.e.,
``uint8``, ``uint16``, ``uint32``, ``uint64``).
- ``'integral'``: integer data types. Shorthand for ``('signed
integer', 'unsigned integer')``.
- ``'real floating'``: real-valued floating-point data types
(i.e., ``float32``, ``float64``).
- ``'complex floating'``: complex floating-point data types (i.e.,
``complex64``, ``complex128``).
- ``'numeric'``: numeric data types. Shorthand for ``('integral',
'real floating', 'complex floating')``.
Returns
-------
dtypes : dict
A dictionary mapping the names of data types to the corresponding
NumPy data types.
See Also
--------
__array_namespace_info__.capabilities,
__array_namespace_info__.default_device,
__array_namespace_info__.default_dtypes,
__array_namespace_info__.devices
Examples
--------
>>> info = np.__array_namespace_info__()
>>> info.dtypes(kind='signed integer')
{'int8': numpy.int8,
'int16': numpy.int16,
'int32': numpy.int32,
'int64': numpy.int64}
"""
if device not in ["cpu", None]:
raise ValueError(
'Device not understood. Only "cpu" is allowed, but received:'
f' {device}'
)
if kind is None:
return {
"bool": dtype(bool),
"int8": dtype(int8),
"int16": dtype(int16),
"int32": dtype(int32),
"int64": dtype(int64),
"uint8": dtype(uint8),
"uint16": dtype(uint16),
"uint32": dtype(uint32),
"uint64": dtype(uint64),
"float32": dtype(float32),
"float64": dtype(float64),
"complex64": dtype(complex64),
"complex128": dtype(complex128),
}
if kind == "bool":
return {"bool": bool}
if kind == "signed integer":
return {
"int8": dtype(int8),
"int16": dtype(int16),
"int32": dtype(int32),
"int64": dtype(int64),
}
if kind == "unsigned integer":
return {
"uint8": dtype(uint8),
"uint16": dtype(uint16),
"uint32": dtype(uint32),
"uint64": dtype(uint64),
}
if kind == "integral":
return {
"int8": dtype(int8),
"int16": dtype(int16),
"int32": dtype(int32),
"int64": dtype(int64),
"uint8": dtype(uint8),
"uint16": dtype(uint16),
"uint32": dtype(uint32),
"uint64": dtype(uint64),
}
if kind == "real floating":
return {
"float32": dtype(float32),
"float64": dtype(float64),
}
if kind == "complex floating":
return {
"complex64": dtype(complex64),
"complex128": dtype(complex128),
}
if kind == "numeric":
return {
"int8": dtype(int8),
"int16": dtype(int16),
"int32": dtype(int32),
"int64": dtype(int64),
"uint8": dtype(uint8),
"uint16": dtype(uint16),
"uint32": dtype(uint32),
"uint64": dtype(uint64),
"float32": dtype(float32),
"float64": dtype(float64),
"complex64": dtype(complex64),
"complex128": dtype(complex128),
}
if isinstance(kind, tuple):
res = {}
for k in kind:
res.update(self.dtypes(kind=k))
return res
raise ValueError(f"unsupported kind: {kind!r}")
def devices(self):
"""
The devices supported by NumPy.
For NumPy, this always returns ``['cpu']``.
Returns
-------
devices : list of str
The devices supported by NumPy.
See Also
--------
__array_namespace_info__.capabilities,
__array_namespace_info__.default_device,
__array_namespace_info__.default_dtypes,
__array_namespace_info__.dtypes
Examples
--------
>>> info = np.__array_namespace_info__()
>>> info.devices()
['cpu']
"""
return ["cpu"]

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@@ -1,213 +0,0 @@
import sys
from typing import (
TYPE_CHECKING,
ClassVar,
Literal,
TypeAlias,
TypedDict,
TypeVar,
final,
overload,
)
import numpy as np
if sys.version_info >= (3, 11):
from typing import Never
elif TYPE_CHECKING:
from typing_extensions import Never
else:
# `NoReturn` and `Never` are equivalent (but not equal) for type-checkers,
# but are used in different places by convention
from typing import NoReturn as Never
_Device: TypeAlias = Literal["cpu"]
_DeviceLike: TypeAlias = None | _Device
_Capabilities = TypedDict(
"_Capabilities",
{
"boolean indexing": Literal[True],
"data-dependent shapes": Literal[True],
},
)
_DefaultDTypes = TypedDict(
"_DefaultDTypes",
{
"real floating": np.dtype[np.float64],
"complex floating": np.dtype[np.complex128],
"integral": np.dtype[np.intp],
"indexing": np.dtype[np.intp],
},
)
_KindBool: TypeAlias = Literal["bool"]
_KindInt: TypeAlias = Literal["signed integer"]
_KindUInt: TypeAlias = Literal["unsigned integer"]
_KindInteger: TypeAlias = Literal["integral"]
_KindFloat: TypeAlias = Literal["real floating"]
_KindComplex: TypeAlias = Literal["complex floating"]
_KindNumber: TypeAlias = Literal["numeric"]
_Kind: TypeAlias = (
_KindBool
| _KindInt
| _KindUInt
| _KindInteger
| _KindFloat
| _KindComplex
| _KindNumber
)
_T1 = TypeVar("_T1")
_T2 = TypeVar("_T2")
_T3 = TypeVar("_T3")
_Permute1: TypeAlias = _T1 | tuple[_T1]
_Permute2: TypeAlias = tuple[_T1, _T2] | tuple[_T2, _T1]
_Permute3: TypeAlias = (
tuple[_T1, _T2, _T3] | tuple[_T1, _T3, _T2]
| tuple[_T2, _T1, _T3] | tuple[_T2, _T3, _T1]
| tuple[_T3, _T1, _T2] | tuple[_T3, _T2, _T1]
)
class _DTypesBool(TypedDict):
bool: np.dtype[np.bool]
class _DTypesInt(TypedDict):
int8: np.dtype[np.int8]
int16: np.dtype[np.int16]
int32: np.dtype[np.int32]
int64: np.dtype[np.int64]
class _DTypesUInt(TypedDict):
uint8: np.dtype[np.uint8]
uint16: np.dtype[np.uint16]
uint32: np.dtype[np.uint32]
uint64: np.dtype[np.uint64]
class _DTypesInteger(_DTypesInt, _DTypesUInt):
...
class _DTypesFloat(TypedDict):
float32: np.dtype[np.float32]
float64: np.dtype[np.float64]
class _DTypesComplex(TypedDict):
complex64: np.dtype[np.complex64]
complex128: np.dtype[np.complex128]
class _DTypesNumber(_DTypesInteger, _DTypesFloat, _DTypesComplex):
...
class _DTypes(_DTypesBool, _DTypesNumber):
...
class _DTypesUnion(TypedDict, total=False):
bool: np.dtype[np.bool]
int8: np.dtype[np.int8]
int16: np.dtype[np.int16]
int32: np.dtype[np.int32]
int64: np.dtype[np.int64]
uint8: np.dtype[np.uint8]
uint16: np.dtype[np.uint16]
uint32: np.dtype[np.uint32]
uint64: np.dtype[np.uint64]
float32: np.dtype[np.float32]
float64: np.dtype[np.float64]
complex64: np.dtype[np.complex64]
complex128: np.dtype[np.complex128]
_EmptyDict: TypeAlias = dict[Never, Never]
@final
class __array_namespace_info__:
__module__: ClassVar[Literal['numpy']]
def capabilities(self) -> _Capabilities: ...
def default_device(self) -> _Device: ...
def default_dtypes(
self,
*,
device: _DeviceLike = ...,
) -> _DefaultDTypes: ...
def devices(self) -> list[_Device]: ...
@overload
def dtypes(
self,
*,
device: _DeviceLike = ...,
kind: None = ...,
) -> _DTypes: ...
@overload
def dtypes(
self,
*,
device: _DeviceLike = ...,
kind: _Permute1[_KindBool],
) -> _DTypesBool: ...
@overload
def dtypes(
self,
*,
device: _DeviceLike = ...,
kind: _Permute1[_KindInt],
) -> _DTypesInt: ...
@overload
def dtypes(
self,
*,
device: _DeviceLike = ...,
kind: _Permute1[_KindUInt],
) -> _DTypesUInt: ...
@overload
def dtypes(
self,
*,
device: _DeviceLike = ...,
kind: _Permute1[_KindFloat],
) -> _DTypesFloat: ...
@overload
def dtypes(
self,
*,
device: _DeviceLike = ...,
kind: _Permute1[_KindComplex],
) -> _DTypesComplex: ...
@overload
def dtypes(
self,
*,
device: _DeviceLike = ...,
kind: (
_Permute1[_KindInteger]
| _Permute2[_KindInt, _KindUInt]
),
) -> _DTypesInteger: ...
@overload
def dtypes(
self,
*,
device: _DeviceLike = ...,
kind: (
_Permute1[_KindNumber]
| _Permute3[_KindInteger, _KindFloat, _KindComplex]
),
) -> _DTypesNumber: ...
@overload
def dtypes(
self,
*,
device: _DeviceLike = ...,
kind: tuple[()],
) -> _EmptyDict: ...
@overload
def dtypes(
self,
*,
device: _DeviceLike = ...,
kind: tuple[_Kind, ...],
) -> _DTypesUnion: ...

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venv/lib/python3.11/site-packages/numpy/_configtool.py
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import argparse
from pathlib import Path
import sys
from .version import __version__
from .lib._utils_impl import get_include
def main() -> None:
parser = argparse.ArgumentParser()
parser.add_argument(
"--version",
action="version",
version=__version__,
help="Print the version and exit.",
)
parser.add_argument(
"--cflags",
action="store_true",
help="Compile flag needed when using the NumPy headers.",
)
parser.add_argument(
"--pkgconfigdir",
action="store_true",
help=("Print the pkgconfig directory in which `numpy.pc` is stored "
"(useful for setting $PKG_CONFIG_PATH)."),
)
args = parser.parse_args()
if not sys.argv[1:]:
parser.print_help()
if args.cflags:
print("-I" + get_include())
if args.pkgconfigdir:
_path = Path(get_include()) / '..' / 'lib' / 'pkgconfig'
print(_path.resolve())
if __name__ == "__main__":
main()

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venv/lib/python3.11/site-packages/numpy/_core/__init__.py
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"""
Contains the core of NumPy: ndarray, ufuncs, dtypes, etc.
Please note that this module is private. All functions and objects
are available in the main ``numpy`` namespace - use that instead.
"""
import os
from numpy.version import version as __version__
# disables OpenBLAS affinity setting of the main thread that limits
# python threads or processes to one core
env_added = []
for envkey in ['OPENBLAS_MAIN_FREE', 'GOTOBLAS_MAIN_FREE']:
if envkey not in os.environ:
os.environ[envkey] = '1'
env_added.append(envkey)
try:
from . import multiarray
except ImportError as exc:
import sys
msg = """
IMPORTANT: PLEASE READ THIS FOR ADVICE ON HOW TO SOLVE THIS ISSUE!
Importing the numpy C-extensions failed. This error can happen for
many reasons, often due to issues with your setup or how NumPy was
installed.
We have compiled some common reasons and troubleshooting tips at:
https://numpy.org/devdocs/user/troubleshooting-importerror.html
Please note and check the following:
* The Python version is: Python%d.%d from "%s"
* The NumPy version is: "%s"
and make sure that they are the versions you expect.
Please carefully study the documentation linked above for further help.
Original error was: %s
""" % (sys.version_info[0], sys.version_info[1], sys.executable,
__version__, exc)
raise ImportError(msg)
finally:
for envkey in env_added:
del os.environ[envkey]
del envkey
del env_added
del os
from . import umath
# Check that multiarray,umath are pure python modules wrapping
# _multiarray_umath and not either of the old c-extension modules
if not (hasattr(multiarray, '_multiarray_umath') and
hasattr(umath, '_multiarray_umath')):
import sys
path = sys.modules['numpy'].__path__
msg = ("Something is wrong with the numpy installation. "
"While importing we detected an older version of "
"numpy in {}. One method of fixing this is to repeatedly uninstall "
"numpy until none is found, then reinstall this version.")
raise ImportError(msg.format(path))
from . import numerictypes as nt
from .numerictypes import sctypes, sctypeDict
multiarray.set_typeDict(nt.sctypeDict)
from . import numeric
from .numeric import *
from . import fromnumeric
from .fromnumeric import *
from .records import record, recarray
# Note: module name memmap is overwritten by a class with same name
from .memmap import *
from . import function_base
from .function_base import *
from . import _machar
from . import getlimits
from .getlimits import *
from . import shape_base
from .shape_base import *
from . import einsumfunc
from .einsumfunc import *
del nt
from .numeric import absolute as abs
# do this after everything else, to minimize the chance of this misleadingly
# appearing in an import-time traceback
from . import _add_newdocs
from . import _add_newdocs_scalars
# add these for module-freeze analysis (like PyInstaller)
from . import _dtype_ctypes
from . import _internal
from . import _dtype
from . import _methods
acos = numeric.arccos
acosh = numeric.arccosh
asin = numeric.arcsin
asinh = numeric.arcsinh
atan = numeric.arctan
atanh = numeric.arctanh
atan2 = numeric.arctan2
concat = numeric.concatenate
bitwise_left_shift = numeric.left_shift
bitwise_invert = numeric.invert
bitwise_right_shift = numeric.right_shift
permute_dims = numeric.transpose
pow = numeric.power
__all__ = [
"abs", "acos", "acosh", "asin", "asinh", "atan", "atanh", "atan2",
"bitwise_invert", "bitwise_left_shift", "bitwise_right_shift", "concat",
"pow", "permute_dims", "memmap", "sctypeDict", "record", "recarray"
]
__all__ += numeric.__all__
__all__ += function_base.__all__
__all__ += getlimits.__all__
__all__ += shape_base.__all__
__all__ += einsumfunc.__all__
def _ufunc_reduce(func):
# Report the `__name__`. pickle will try to find the module. Note that
# pickle supports for this `__name__` to be a `__qualname__`. It may
# make sense to add a `__qualname__` to ufuncs, to allow this more
# explicitly (Numba has ufuncs as attributes).
# See also: https://github.com/dask/distributed/issues/3450
return func.__name__
def _DType_reconstruct(scalar_type):
# This is a work-around to pickle type(np.dtype(np.float64)), etc.
# and it should eventually be replaced with a better solution, e.g. when
# DTypes become HeapTypes.
return type(dtype(scalar_type))
def _DType_reduce(DType):
# As types/classes, most DTypes can simply be pickled by their name:
if not DType._legacy or DType.__module__ == "numpy.dtypes":
return DType.__name__
# However, user defined legacy dtypes (like rational) do not end up in
# `numpy.dtypes` as module and do not have a public class at all.
# For these, we pickle them by reconstructing them from the scalar type:
scalar_type = DType.type
return _DType_reconstruct, (scalar_type,)
def __getattr__(name):
# Deprecated 2022-11-22, NumPy 1.25.
if name == "MachAr":
import warnings
warnings.warn(
"The `np._core.MachAr` is considered private API (NumPy 1.24)",
DeprecationWarning, stacklevel=2,
)
return _machar.MachAr
raise AttributeError(f"Module {__name__!r} has no attribute {name!r}")
import copyreg
copyreg.pickle(ufunc, _ufunc_reduce)
copyreg.pickle(type(dtype), _DType_reduce, _DType_reconstruct)
# Unclutter namespace (must keep _*_reconstruct for unpickling)
del copyreg, _ufunc_reduce, _DType_reduce
from numpy._pytesttester import PytestTester
test = PytestTester(__name__)
del PytestTester

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# NOTE: The `np._core` namespace is deliberately kept empty due to it
# being private

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venv/lib/python3.11/site-packages/numpy/_core/_add_newdocs_scalars.py
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"""
This file is separate from ``_add_newdocs.py`` so that it can be mocked out by
our sphinx ``conf.py`` during doc builds, where we want to avoid showing
platform-dependent information.
"""
import sys
import os
from numpy._core import dtype
from numpy._core import numerictypes as _numerictypes
from numpy._core.function_base import add_newdoc
##############################################################################
#
# Documentation for concrete scalar classes
#
##############################################################################
def numeric_type_aliases(aliases):
def type_aliases_gen():
for alias, doc in aliases:
try:
alias_type = getattr(_numerictypes, alias)
except AttributeError:
# The set of aliases that actually exist varies between platforms
pass
else:
yield (alias_type, alias, doc)
return list(type_aliases_gen())
possible_aliases = numeric_type_aliases([
('int8', '8-bit signed integer (``-128`` to ``127``)'),
('int16', '16-bit signed integer (``-32_768`` to ``32_767``)'),
('int32', '32-bit signed integer (``-2_147_483_648`` to ``2_147_483_647``)'),
('int64', '64-bit signed integer (``-9_223_372_036_854_775_808`` to ``9_223_372_036_854_775_807``)'),
('intp', 'Signed integer large enough to fit pointer, compatible with C ``intptr_t``'),
('uint8', '8-bit unsigned integer (``0`` to ``255``)'),
('uint16', '16-bit unsigned integer (``0`` to ``65_535``)'),
('uint32', '32-bit unsigned integer (``0`` to ``4_294_967_295``)'),
('uint64', '64-bit unsigned integer (``0`` to ``18_446_744_073_709_551_615``)'),
('uintp', 'Unsigned integer large enough to fit pointer, compatible with C ``uintptr_t``'),
('float16', '16-bit-precision floating-point number type: sign bit, 5 bits exponent, 10 bits mantissa'),
('float32', '32-bit-precision floating-point number type: sign bit, 8 bits exponent, 23 bits mantissa'),
('float64', '64-bit precision floating-point number type: sign bit, 11 bits exponent, 52 bits mantissa'),
('float96', '96-bit extended-precision floating-point number type'),
('float128', '128-bit extended-precision floating-point number type'),
('complex64', 'Complex number type composed of 2 32-bit-precision floating-point numbers'),
('complex128', 'Complex number type composed of 2 64-bit-precision floating-point numbers'),
('complex192', 'Complex number type composed of 2 96-bit extended-precision floating-point numbers'),
('complex256', 'Complex number type composed of 2 128-bit extended-precision floating-point numbers'),
])
def _get_platform_and_machine():
try:
system, _, _, _, machine = os.uname()
except AttributeError:
system = sys.platform
if system == 'win32':
machine = os.environ.get('PROCESSOR_ARCHITEW6432', '') \
or os.environ.get('PROCESSOR_ARCHITECTURE', '')
else:
machine = 'unknown'
return system, machine
_system, _machine = _get_platform_and_machine()
_doc_alias_string = f":Alias on this platform ({_system} {_machine}):"
def add_newdoc_for_scalar_type(obj, fixed_aliases, doc):
# note: `:field: value` is rST syntax which renders as field lists.
o = getattr(_numerictypes, obj)
character_code = dtype(o).char
canonical_name_doc = "" if obj == o.__name__ else \
f":Canonical name: `numpy.{obj}`\n "
if fixed_aliases:
alias_doc = ''.join(f":Alias: `numpy.{alias}`\n "
for alias in fixed_aliases)
else:
alias_doc = ''
alias_doc += ''.join(f"{_doc_alias_string} `numpy.{alias}`: {doc}.\n "
for (alias_type, alias, doc) in possible_aliases if alias_type is o)
docstring = f"""
{doc.strip()}
:Character code: ``'{character_code}'``
{canonical_name_doc}{alias_doc}
"""
add_newdoc('numpy._core.numerictypes', obj, docstring)
_bool_docstring = (
"""
Boolean type (True or False), stored as a byte.
.. warning::
The :class:`bool` type is not a subclass of the :class:`int_` type
(the :class:`bool` is not even a number type). This is different
than Python's default implementation of :class:`bool` as a
sub-class of :class:`int`.
"""
)
add_newdoc_for_scalar_type('bool', [], _bool_docstring)
add_newdoc_for_scalar_type('bool_', [], _bool_docstring)
add_newdoc_for_scalar_type('byte', [],
"""
Signed integer type, compatible with C ``char``.
""")
add_newdoc_for_scalar_type('short', [],
"""
Signed integer type, compatible with C ``short``.
""")
add_newdoc_for_scalar_type('intc', [],
"""
Signed integer type, compatible with C ``int``.
""")
# TODO: These docs probably need an if to highlight the default rather than
# the C-types (and be correct).
add_newdoc_for_scalar_type('int_', [],
"""
Default signed integer type, 64bit on 64bit systems and 32bit on 32bit
systems.
""")
add_newdoc_for_scalar_type('longlong', [],
"""
Signed integer type, compatible with C ``long long``.
""")
add_newdoc_for_scalar_type('ubyte', [],
"""
Unsigned integer type, compatible with C ``unsigned char``.
""")
add_newdoc_for_scalar_type('ushort', [],
"""
Unsigned integer type, compatible with C ``unsigned short``.
""")
add_newdoc_for_scalar_type('uintc', [],
"""
Unsigned integer type, compatible with C ``unsigned int``.
""")
add_newdoc_for_scalar_type('uint', [],
"""
Unsigned signed integer type, 64bit on 64bit systems and 32bit on 32bit
systems.
""")
add_newdoc_for_scalar_type('ulonglong', [],
"""
Signed integer type, compatible with C ``unsigned long long``.
""")
add_newdoc_for_scalar_type('half', [],
"""
Half-precision floating-point number type.
""")
add_newdoc_for_scalar_type('single', [],
"""
Single-precision floating-point number type, compatible with C ``float``.
""")
add_newdoc_for_scalar_type('double', [],
"""
Double-precision floating-point number type, compatible with Python
:class:`float` and C ``double``.
""")
add_newdoc_for_scalar_type('longdouble', [],
"""
Extended-precision floating-point number type, compatible with C
``long double`` but not necessarily with IEEE 754 quadruple-precision.
""")
add_newdoc_for_scalar_type('csingle', [],
"""
Complex number type composed of two single-precision floating-point
numbers.
""")
add_newdoc_for_scalar_type('cdouble', [],
"""
Complex number type composed of two double-precision floating-point
numbers, compatible with Python :class:`complex`.
""")
add_newdoc_for_scalar_type('clongdouble', [],
"""
Complex number type composed of two extended-precision floating-point
numbers.
""")
add_newdoc_for_scalar_type('object_', [],
"""
Any Python object.
""")
add_newdoc_for_scalar_type('str_', [],
r"""
A unicode string.
This type strips trailing null codepoints.
>>> s = np.str_("abc\x00")
>>> s
'abc'
Unlike the builtin :class:`str`, this supports the
:ref:`python:bufferobjects`, exposing its contents as UCS4:
>>> m = memoryview(np.str_("abc"))
>>> m.format
'3w'
>>> m.tobytes()
b'a\x00\x00\x00b\x00\x00\x00c\x00\x00\x00'
""")
add_newdoc_for_scalar_type('bytes_', [],
r"""
A byte string.
When used in arrays, this type strips trailing null bytes.
""")
add_newdoc_for_scalar_type('void', [],
r"""
np.void(length_or_data, /, dtype=None)
Create a new structured or unstructured void scalar.
Parameters
----------
length_or_data : int, array-like, bytes-like, object
One of multiple meanings (see notes). The length or
bytes data of an unstructured void. Or alternatively,
the data to be stored in the new scalar when `dtype`
is provided.
This can be an array-like, in which case an array may
be returned.
dtype : dtype, optional
If provided the dtype of the new scalar. This dtype must
be "void" dtype (i.e. a structured or unstructured void,
see also :ref:`defining-structured-types`).
.. versionadded:: 1.24
Notes
-----
For historical reasons and because void scalars can represent both
arbitrary byte data and structured dtypes, the void constructor
has three calling conventions:
1. ``np.void(5)`` creates a ``dtype="V5"`` scalar filled with five
``\0`` bytes. The 5 can be a Python or NumPy integer.
2. ``np.void(b"bytes-like")`` creates a void scalar from the byte string.
The dtype itemsize will match the byte string length, here ``"V10"``.
3. When a ``dtype=`` is passed the call is roughly the same as an
array creation. However, a void scalar rather than array is returned.
Please see the examples which show all three different conventions.
Examples
--------
>>> np.void(5)
np.void(b'\x00\x00\x00\x00\x00')
>>> np.void(b'abcd')
np.void(b'\x61\x62\x63\x64')
>>> np.void((3.2, b'eggs'), dtype="d,S5")
np.void((3.2, b'eggs'), dtype=[('f0', '<f8'), ('f1', 'S5')])
>>> np.void(3, dtype=[('x', np.int8), ('y', np.int8)])
np.void((3, 3), dtype=[('x', 'i1'), ('y', 'i1')])
""")
add_newdoc_for_scalar_type('datetime64', [],
"""
If created from a 64-bit integer, it represents an offset from
``1970-01-01T00:00:00``.
If created from string, the string can be in ISO 8601 date
or datetime format.
When parsing a string to create a datetime object, if the string contains
a trailing timezone (A 'Z' or a timezone offset), the timezone will be
dropped and a User Warning is given.
Datetime64 objects should be considered to be UTC and therefore have an
offset of +0000.
>>> np.datetime64(10, 'Y')
np.datetime64('1980')
>>> np.datetime64('1980', 'Y')
np.datetime64('1980')
>>> np.datetime64(10, 'D')
np.datetime64('1970-01-11')
See :ref:`arrays.datetime` for more information.
""")
add_newdoc_for_scalar_type('timedelta64', [],
"""
A timedelta stored as a 64-bit integer.
See :ref:`arrays.datetime` for more information.
""")
add_newdoc('numpy._core.numerictypes', "integer", ('is_integer',
"""
integer.is_integer() -> bool
Return ``True`` if the number is finite with integral value.
.. versionadded:: 1.22
Examples
--------
>>> import numpy as np
>>> np.int64(-2).is_integer()
True
>>> np.uint32(5).is_integer()
True
"""))
# TODO: work out how to put this on the base class, np.floating
for float_name in ('half', 'single', 'double', 'longdouble'):
add_newdoc('numpy._core.numerictypes', float_name, ('as_integer_ratio',
"""
{ftype}.as_integer_ratio() -> (int, int)
Return a pair of integers, whose ratio is exactly equal to the original
floating point number, and with a positive denominator.
Raise `OverflowError` on infinities and a `ValueError` on NaNs.
>>> np.{ftype}(10.0).as_integer_ratio()
(10, 1)
>>> np.{ftype}(0.0).as_integer_ratio()
(0, 1)
>>> np.{ftype}(-.25).as_integer_ratio()
(-1, 4)
""".format(ftype=float_name)))
add_newdoc('numpy._core.numerictypes', float_name, ('is_integer',
f"""
{float_name}.is_integer() -> bool
Return ``True`` if the floating point number is finite with integral
value, and ``False`` otherwise.
.. versionadded:: 1.22
Examples
--------
>>> np.{float_name}(-2.0).is_integer()
True
>>> np.{float_name}(3.2).is_integer()
False
"""))
for int_name in ('int8', 'uint8', 'int16', 'uint16', 'int32', 'uint32',
'int64', 'uint64', 'int64', 'uint64', 'int64', 'uint64'):
# Add negative examples for signed cases by checking typecode
add_newdoc('numpy._core.numerictypes', int_name, ('bit_count',
f"""
{int_name}.bit_count() -> int
Computes the number of 1-bits in the absolute value of the input.
Analogous to the builtin `int.bit_count` or ``popcount`` in C++.
Examples
--------
>>> np.{int_name}(127).bit_count()
7""" +
(f"""
>>> np.{int_name}(-127).bit_count()
7
""" if dtype(int_name).char.islower() else "")))

135
venv/lib/python3.11/site-packages/numpy/_core/_asarray.py
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@@ -1,135 +0,0 @@
"""
Functions in the ``as*array`` family that promote array-likes into arrays.
`require` fits this category despite its name not matching this pattern.
"""
from .overrides import (
array_function_dispatch,
set_array_function_like_doc,
set_module,
)
from .multiarray import array, asanyarray
__all__ = ["require"]
POSSIBLE_FLAGS = {
'C': 'C', 'C_CONTIGUOUS': 'C', 'CONTIGUOUS': 'C',
'F': 'F', 'F_CONTIGUOUS': 'F', 'FORTRAN': 'F',
'A': 'A', 'ALIGNED': 'A',
'W': 'W', 'WRITEABLE': 'W',
'O': 'O', 'OWNDATA': 'O',
'E': 'E', 'ENSUREARRAY': 'E'
}
@set_array_function_like_doc
@set_module('numpy')
def require(a, dtype=None, requirements=None, *, like=None):
"""
Return an ndarray of the provided type that satisfies requirements.
This function is useful to be sure that an array with the correct flags
is returned for passing to compiled code (perhaps through ctypes).
Parameters
----------
a : array_like
The object to be converted to a type-and-requirement-satisfying array.
dtype : data-type
The required data-type. If None preserve the current dtype. If your
application requires the data to be in native byteorder, include
a byteorder specification as a part of the dtype specification.
requirements : str or sequence of str
The requirements list can be any of the following
* 'F_CONTIGUOUS' ('F') - ensure a Fortran-contiguous array
* 'C_CONTIGUOUS' ('C') - ensure a C-contiguous array
* 'ALIGNED' ('A') - ensure a data-type aligned array
* 'WRITEABLE' ('W') - ensure a writable array
* 'OWNDATA' ('O') - ensure an array that owns its own data
* 'ENSUREARRAY', ('E') - ensure a base array, instead of a subclass
${ARRAY_FUNCTION_LIKE}
.. versionadded:: 1.20.0
Returns
-------
out : ndarray
Array with specified requirements and type if given.
See Also
--------
asarray : Convert input to an ndarray.
asanyarray : Convert to an ndarray, but pass through ndarray subclasses.
ascontiguousarray : Convert input to a contiguous array.
asfortranarray : Convert input to an ndarray with column-major
memory order.
ndarray.flags : Information about the memory layout of the array.
Notes
-----
The returned array will be guaranteed to have the listed requirements
by making a copy if needed.
Examples
--------
>>> import numpy as np
>>> x = np.arange(6).reshape(2,3)
>>> x.flags
C_CONTIGUOUS : True
F_CONTIGUOUS : False
OWNDATA : False
WRITEABLE : True
ALIGNED : True
WRITEBACKIFCOPY : False
>>> y = np.require(x, dtype=np.float32, requirements=['A', 'O', 'W', 'F'])
>>> y.flags
C_CONTIGUOUS : False
F_CONTIGUOUS : True
OWNDATA : True
WRITEABLE : True
ALIGNED : True
WRITEBACKIFCOPY : False
"""
if like is not None:
return _require_with_like(
like,
a,
dtype=dtype,
requirements=requirements,
)
if not requirements:
return asanyarray(a, dtype=dtype)
requirements = {POSSIBLE_FLAGS[x.upper()] for x in requirements}
if 'E' in requirements:
requirements.remove('E')
subok = False
else:
subok = True
order = 'A'
if requirements >= {'C', 'F'}:
raise ValueError('Cannot specify both "C" and "F" order')
elif 'F' in requirements:
order = 'F'
requirements.remove('F')
elif 'C' in requirements:
order = 'C'
requirements.remove('C')
arr = array(a, dtype=dtype, order=order, copy=None, subok=subok)
for prop in requirements:
if not arr.flags[prop]:
return arr.copy(order)
return arr
_require_with_like = array_function_dispatch()(require)

41
venv/lib/python3.11/site-packages/numpy/_core/_asarray.pyi
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@@ -1,41 +0,0 @@
from collections.abc import Iterable
from typing import Any, TypeVar, overload, Literal
from numpy._typing import NDArray, DTypeLike, _SupportsArrayFunc
_ArrayType = TypeVar("_ArrayType", bound=NDArray[Any])
_Requirements = Literal[
"C", "C_CONTIGUOUS", "CONTIGUOUS",
"F", "F_CONTIGUOUS", "FORTRAN",
"A", "ALIGNED",
"W", "WRITEABLE",
"O", "OWNDATA"
]
_E = Literal["E", "ENSUREARRAY"]
_RequirementsWithE = _Requirements | _E
@overload
def require(
a: _ArrayType,
dtype: None = ...,
requirements: None | _Requirements | Iterable[_Requirements] = ...,
*,
like: _SupportsArrayFunc = ...
) -> _ArrayType: ...
@overload
def require(
a: object,
dtype: DTypeLike = ...,
requirements: _E | Iterable[_RequirementsWithE] = ...,
*,
like: _SupportsArrayFunc = ...
) -> NDArray[Any]: ...
@overload
def require(
a: object,
dtype: DTypeLike = ...,
requirements: None | _Requirements | Iterable[_Requirements] = ...,
*,
like: _SupportsArrayFunc = ...
) -> NDArray[Any]: ...

374
venv/lib/python3.11/site-packages/numpy/_core/_dtype.py
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@@ -1,374 +0,0 @@
"""
A place for code to be called from the implementation of np.dtype
String handling is much easier to do correctly in python.
"""
import numpy as np
_kind_to_stem = {
'u': 'uint',
'i': 'int',
'c': 'complex',
'f': 'float',
'b': 'bool',
'V': 'void',
'O': 'object',
'M': 'datetime',
'm': 'timedelta',
'S': 'bytes',
'U': 'str',
}
def _kind_name(dtype):
try:
return _kind_to_stem[dtype.kind]
except KeyError as e:
raise RuntimeError(
"internal dtype error, unknown kind {!r}"
.format(dtype.kind)
) from None
def __str__(dtype):
if dtype.fields is not None:
return _struct_str(dtype, include_align=True)
elif dtype.subdtype:
return _subarray_str(dtype)
elif issubclass(dtype.type, np.flexible) or not dtype.isnative:
return dtype.str
else:
return dtype.name
def __repr__(dtype):
arg_str = _construction_repr(dtype, include_align=False)
if dtype.isalignedstruct:
arg_str = arg_str + ", align=True"
return "dtype({})".format(arg_str)
def _unpack_field(dtype, offset, title=None):
"""
Helper function to normalize the items in dtype.fields.
Call as:
dtype, offset, title = _unpack_field(*dtype.fields[name])
"""
return dtype, offset, title
def _isunsized(dtype):
# PyDataType_ISUNSIZED
return dtype.itemsize == 0
def _construction_repr(dtype, include_align=False, short=False):
"""
Creates a string repr of the dtype, excluding the 'dtype()' part
surrounding the object. This object may be a string, a list, or
a dict depending on the nature of the dtype. This
is the object passed as the first parameter to the dtype
constructor, and if no additional constructor parameters are
given, will reproduce the exact memory layout.
Parameters
----------
short : bool
If true, this creates a shorter repr using 'kind' and 'itemsize',
instead of the longer type name.
include_align : bool
If true, this includes the 'align=True' parameter
inside the struct dtype construction dict when needed. Use this flag
if you want a proper repr string without the 'dtype()' part around it.
If false, this does not preserve the
'align=True' parameter or sticky NPY_ALIGNED_STRUCT flag for
struct arrays like the regular repr does, because the 'align'
flag is not part of first dtype constructor parameter. This
mode is intended for a full 'repr', where the 'align=True' is
provided as the second parameter.
"""
if dtype.fields is not None:
return _struct_str(dtype, include_align=include_align)
elif dtype.subdtype:
return _subarray_str(dtype)
else:
return _scalar_str(dtype, short=short)
def _scalar_str(dtype, short):
byteorder = _byte_order_str(dtype)
if dtype.type == np.bool:
if short:
return "'?'"
else:
return "'bool'"
elif dtype.type == np.object_:
# The object reference may be different sizes on different
# platforms, so it should never include the itemsize here.
return "'O'"
elif dtype.type == np.bytes_:
if _isunsized(dtype):
return "'S'"
else:
return "'S%d'" % dtype.itemsize
elif dtype.type == np.str_:
if _isunsized(dtype):
return "'%sU'" % byteorder
else:
return "'%sU%d'" % (byteorder, dtype.itemsize / 4)
elif dtype.type == str:
return "'T'"
elif not type(dtype)._legacy:
return f"'{byteorder}{type(dtype).__name__}{dtype.itemsize * 8}'"
# unlike the other types, subclasses of void are preserved - but
# historically the repr does not actually reveal the subclass
elif issubclass(dtype.type, np.void):
if _isunsized(dtype):
return "'V'"
else:
return "'V%d'" % dtype.itemsize
elif dtype.type == np.datetime64:
return "'%sM8%s'" % (byteorder, _datetime_metadata_str(dtype))
elif dtype.type == np.timedelta64:
return "'%sm8%s'" % (byteorder, _datetime_metadata_str(dtype))
elif np.issubdtype(dtype, np.number):
# Short repr with endianness, like '<f8'
if short or dtype.byteorder not in ('=', '|'):
return "'%s%c%d'" % (byteorder, dtype.kind, dtype.itemsize)
# Longer repr, like 'float64'
else:
return "'%s%d'" % (_kind_name(dtype), 8*dtype.itemsize)
elif dtype.isbuiltin == 2:
return dtype.type.__name__
else:
raise RuntimeError(
"Internal error: NumPy dtype unrecognized type number")
def _byte_order_str(dtype):
""" Normalize byteorder to '<' or '>' """
# hack to obtain the native and swapped byte order characters
swapped = np.dtype(int).newbyteorder('S')
native = swapped.newbyteorder('S')
byteorder = dtype.byteorder
if byteorder == '=':
return native.byteorder
if byteorder == 'S':
# TODO: this path can never be reached
return swapped.byteorder
elif byteorder == '|':
return ''
else:
return byteorder
def _datetime_metadata_str(dtype):
# TODO: this duplicates the C metastr_to_unicode functionality
unit, count = np.datetime_data(dtype)
if unit == 'generic':
return ''
elif count == 1:
return '[{}]'.format(unit)
else:
return '[{}{}]'.format(count, unit)
def _struct_dict_str(dtype, includealignedflag):
# unpack the fields dictionary into ls
names = dtype.names
fld_dtypes = []
offsets = []
titles = []
for name in names:
fld_dtype, offset, title = _unpack_field(*dtype.fields[name])
fld_dtypes.append(fld_dtype)
offsets.append(offset)
titles.append(title)
# Build up a string to make the dictionary
if np._core.arrayprint._get_legacy_print_mode() <= 121:
colon = ":"
fieldsep = ","
else:
colon = ": "
fieldsep = ", "
# First, the names
ret = "{'names'%s[" % colon
ret += fieldsep.join(repr(name) for name in names)
# Second, the formats
ret += "], 'formats'%s[" % colon
ret += fieldsep.join(
_construction_repr(fld_dtype, short=True) for fld_dtype in fld_dtypes)
# Third, the offsets
ret += "], 'offsets'%s[" % colon
ret += fieldsep.join("%d" % offset for offset in offsets)
# Fourth, the titles
if any(title is not None for title in titles):
ret += "], 'titles'%s[" % colon
ret += fieldsep.join(repr(title) for title in titles)
# Fifth, the itemsize
ret += "], 'itemsize'%s%d" % (colon, dtype.itemsize)
if (includealignedflag and dtype.isalignedstruct):
# Finally, the aligned flag
ret += ", 'aligned'%sTrue}" % colon
else:
ret += "}"
return ret
def _aligned_offset(offset, alignment):
# round up offset:
return - (-offset // alignment) * alignment
def _is_packed(dtype):
"""
Checks whether the structured data type in 'dtype'
has a simple layout, where all the fields are in order,
and follow each other with no alignment padding.
When this returns true, the dtype can be reconstructed
from a list of the field names and dtypes with no additional
dtype parameters.
Duplicates the C `is_dtype_struct_simple_unaligned_layout` function.
"""
align = dtype.isalignedstruct
max_alignment = 1
total_offset = 0
for name in dtype.names:
fld_dtype, fld_offset, title = _unpack_field(*dtype.fields[name])
if align:
total_offset = _aligned_offset(total_offset, fld_dtype.alignment)
max_alignment = max(max_alignment, fld_dtype.alignment)
if fld_offset != total_offset:
return False
total_offset += fld_dtype.itemsize
if align:
total_offset = _aligned_offset(total_offset, max_alignment)
return total_offset == dtype.itemsize
def _struct_list_str(dtype):
items = []
for name in dtype.names:
fld_dtype, fld_offset, title = _unpack_field(*dtype.fields[name])
item = "("
if title is not None:
item += "({!r}, {!r}), ".format(title, name)
else:
item += "{!r}, ".format(name)
# Special case subarray handling here
if fld_dtype.subdtype is not None:
base, shape = fld_dtype.subdtype
item += "{}, {}".format(
_construction_repr(base, short=True),
shape
)
else:
item += _construction_repr(fld_dtype, short=True)
item += ")"
items.append(item)
return "[" + ", ".join(items) + "]"
def _struct_str(dtype, include_align):
# The list str representation can't include the 'align=' flag,
# so if it is requested and the struct has the aligned flag set,
# we must use the dict str instead.
if not (include_align and dtype.isalignedstruct) and _is_packed(dtype):
sub = _struct_list_str(dtype)
else:
sub = _struct_dict_str(dtype, include_align)
# If the data type isn't the default, void, show it
if dtype.type != np.void:
return "({t.__module__}.{t.__name__}, {f})".format(t=dtype.type, f=sub)
else:
return sub
def _subarray_str(dtype):
base, shape = dtype.subdtype
return "({}, {})".format(
_construction_repr(base, short=True),
shape
)
def _name_includes_bit_suffix(dtype):
if dtype.type == np.object_:
# pointer size varies by system, best to omit it
return False
elif dtype.type == np.bool:
# implied
return False
elif dtype.type is None:
return True
elif np.issubdtype(dtype, np.flexible) and _isunsized(dtype):
# unspecified
return False
else:
return True
def _name_get(dtype):
# provides dtype.name.__get__, documented as returning a "bit name"
if dtype.isbuiltin == 2:
# user dtypes don't promise to do anything special
return dtype.type.__name__
if not type(dtype)._legacy:
name = type(dtype).__name__
elif issubclass(dtype.type, np.void):
# historically, void subclasses preserve their name, eg `record64`
name = dtype.type.__name__
else:
name = _kind_name(dtype)
# append bit counts
if _name_includes_bit_suffix(dtype):
name += "{}".format(dtype.itemsize * 8)
# append metadata to datetimes
if dtype.type in (np.datetime64, np.timedelta64):
name += _datetime_metadata_str(dtype)
return name

120
venv/lib/python3.11/site-packages/numpy/_core/_dtype_ctypes.py
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@@ -1,120 +0,0 @@
"""
Conversion from ctypes to dtype.
In an ideal world, we could achieve this through the PEP3118 buffer protocol,
something like::
def dtype_from_ctypes_type(t):
# needed to ensure that the shape of `t` is within memoryview.format
class DummyStruct(ctypes.Structure):
_fields_ = [('a', t)]
# empty to avoid memory allocation
ctype_0 = (DummyStruct * 0)()
mv = memoryview(ctype_0)
# convert the struct, and slice back out the field
return _dtype_from_pep3118(mv.format)['a']
Unfortunately, this fails because:
* ctypes cannot handle length-0 arrays with PEP3118 (bpo-32782)
* PEP3118 cannot represent unions, but both numpy and ctypes can
* ctypes cannot handle big-endian structs with PEP3118 (bpo-32780)
"""
# We delay-import ctypes for distributions that do not include it.
# While this module is not used unless the user passes in ctypes
# members, it is eagerly imported from numpy/_core/__init__.py.
import numpy as np
def _from_ctypes_array(t):
return np.dtype((dtype_from_ctypes_type(t._type_), (t._length_,)))
def _from_ctypes_structure(t):
for item in t._fields_:
if len(item) > 2:
raise TypeError(
"ctypes bitfields have no dtype equivalent")
if hasattr(t, "_pack_"):
import ctypes
formats = []
offsets = []
names = []
current_offset = 0
for fname, ftyp in t._fields_:
names.append(fname)
formats.append(dtype_from_ctypes_type(ftyp))
# Each type has a default offset, this is platform dependent
# for some types.
effective_pack = min(t._pack_, ctypes.alignment(ftyp))
current_offset = (
(current_offset + effective_pack - 1) // effective_pack
) * effective_pack
offsets.append(current_offset)
current_offset += ctypes.sizeof(ftyp)
return np.dtype(dict(
formats=formats,
offsets=offsets,
names=names,
itemsize=ctypes.sizeof(t)))
else:
fields = []
for fname, ftyp in t._fields_:
fields.append((fname, dtype_from_ctypes_type(ftyp)))
# by default, ctypes structs are aligned
return np.dtype(fields, align=True)
def _from_ctypes_scalar(t):
"""
Return the dtype type with endianness included if it's the case
"""
if getattr(t, '__ctype_be__', None) is t:
return np.dtype('>' + t._type_)
elif getattr(t, '__ctype_le__', None) is t:
return np.dtype('<' + t._type_)
else:
return np.dtype(t._type_)
def _from_ctypes_union(t):
import ctypes
formats = []
offsets = []
names = []
for fname, ftyp in t._fields_:
names.append(fname)
formats.append(dtype_from_ctypes_type(ftyp))
offsets.append(0) # Union fields are offset to 0
return np.dtype(dict(
formats=formats,
offsets=offsets,
names=names,
itemsize=ctypes.sizeof(t)))
def dtype_from_ctypes_type(t):
"""
Construct a dtype object from a ctypes type
"""
import _ctypes
if issubclass(t, _ctypes.Array):
return _from_ctypes_array(t)
elif issubclass(t, _ctypes._Pointer):
raise TypeError("ctypes pointers have no dtype equivalent")
elif issubclass(t, _ctypes.Structure):
return _from_ctypes_structure(t)
elif issubclass(t, _ctypes.Union):
return _from_ctypes_union(t)
elif isinstance(getattr(t, '_type_', None), str):
return _from_ctypes_scalar(t)
else:
raise NotImplementedError(
"Unknown ctypes type {}".format(t.__name__))

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