question:**Objective**: Implement a custom asynchronous chat server using the `asynchat` module that can handle multiple clients concurrently. The server should accept client messages, process them, and send appropriate responses. **Problem Description**: You are to create a server class inheriting from `asynchat.async_chat` that can: 1. Accept incoming connections. 2. Read messages from clients. 3. Respond to clients with a modified message in uppercase. **Requirements**: 1. Implement a class `ChatServer` that handles async communications using `asynchat.async_chat`. 2. The server should: - Accept connections on a specified port. - Read messages from each connected client until a newline character (`n`) is found. - Respond back to the client with the received message transformed to uppercase. 3. Ensure your implementation handles multiple clients concurrently. 4. Your class should handle starting and stopping the server. **Implementation Details**: 1. **Class Definition**: Define `ChatServer` inheriting from `asynchat.async_chat`. 2. **Initialization**: Accept server socket and address in the constructor and call `asynchat.async_chat.__init__`. 3. **Methods**: - `collect_incoming_data(self, data)`: Append the incoming data to an instance buffer. - `found_terminator(self)`: Called when a newline character (`n`) is encountered in the data. Process the received message and prepare to respond with the uppercase version of it. - `start_server(self)`: Start the asynchronous loop to begin accepting and handling client connections. - `stop_server(self)`: Close any open connections and stop the async loop. **Constraints**: - The server should handle large messages, up to 4096 characters. - Use a FIFO queue for handling message delivery. **Example**: ```python import asynchat import asyncore import socket class ChatServer(asynchat.async_chat): def __init__(self, sock, addr): asynchat.async_chat.__init__(self, sock=sock) self.addr = addr self.ibuffer = [] self.set_terminator(b'n') def collect_incoming_data(self, data): Buffer the data self.ibuffer.append(data) def found_terminator(self): Process the complete message message = b"".join(self.ibuffer).decode('utf-8').strip() self.ibuffer = [] # Convert the message to uppercase and send it back response = message.upper() + 'n' self.push(response.encode('utf-8')) class Server(asyncore.dispatcher): def __init__(self, host, port): asyncore.dispatcher.__init__(self) self.create_socket(socket.AF_INET, socket.SOCK_STREAM) self.set_reuse_addr() self.bind((host, port)) self.listen(5) def handle_accepted(self, sock, addr): print(f"Incoming connection from {addr}") ChatServer(sock, addr) def start_server(self): Start the async loop asyncore.loop() def stop_server(self): Stop the server by closing all channels for channel in asyncore.socket_map.values(): channel.close() asyncore.close_all() # Usage example: if __name__ == "__main__": server = Server('localhost', 9090) server.start_server() ``` **Evaluation Criteria**: - Correct implementation of the `collect_incoming_data` and `found_terminator` methods. - Proper handling of multiple concurrent client connections. - Efficiently handling large messages up to 4096 characters. - Code readability and adherence to Python best practices.

question:Visualizing Health Expenditure with Seaborn Objective You are tasked with creating a visualization using the seaborn library that shows the health expenditure over time for different countries. You will load a dataset, preprocess it, and create an informative visualization that uses faceting and stacking to present the data clearly. Instructions 1. Import the necessary libraries: ```python import seaborn.objects as so from seaborn import load_dataset ``` 2. Load the `healthexp` dataset, preprocess it by pivoting, interpolating, stacking, renaming columns, resetting the index, and sorting by "Country": ```python healthexp = ( load_dataset("healthexp") .pivot(index="Year", columns="Country", values="Spending_USD") .interpolate() .stack() .rename("Spending_USD") .reset_index() .sort_values("Country") ) ``` 3. Create a plot that: - Uses area plots to show health expenditure over time. - Facets by "Country" using 3 columns for the subplots. - Colors the areas by "Country". - Stacks the areas to show part-whole relationships for each year. Expected Input and Output - **Input**: The input is provided within the question as the `healthexp` dataset. - **Output**: A visualization plot created using seaborn that meets the specified criteria. Constraints and Requirements - Your solution should leverage seaborn’s objects interface for creating and customizing the plot. - Use faceting with 3 columns to create subplots for each country. - Ensure areas are stacked to show part-whole relationships. Example Solution Here's an example of what your solution might look like: ```python import seaborn.objects as so from seaborn import load_dataset # Load and preprocess the dataset healthexp = ( load_dataset("healthexp") .pivot(index="Year", columns="Country", values="Spending_USD") .interpolate() .stack() .rename("Spending_USD") .reset_index() .sort_values("Country") ) # Create the plot with faceting and stacking p = so.Plot(healthexp, "Year", "Spending_USD").facet("Country", wrap=3) p.add(so.Area(color="Country", alpha=0.7), so.Stack()) p.show() ``` Ensure your plot closely follows the example provided and meets all specified criteria. Performance Requirements - The solution should be efficient and make good use of seaborn's plotting and data manipulation capabilities. - The final plot should be clear and informative, following the principles of good data visualization practice.

question:In this assessment, you will be required to create a custom probability distribution using PyTorch's `torch.distributions` module. Specifically, you will implement a mixed distribution that combines a Gamma and Normal distribution. The custom distribution will sample values from the Gamma distribution and then shift them based on samples from the Normal distribution. # Requirements 1. Create a new class `GammaNormal` that inherits from `torch.distributions.Distribution`. 2. Initialize `GammaNormal` with parameters of the Gamma and Normal distributions. 3. Implement a `sample` method that: - Samples a value from the Gamma distribution. - Samples a value from the Normal distribution. - Returns the sum of the two sampled values. 4. Implement a `log_prob` method that: - Calculates the log-probability of the resulting sample considering both the Gamma and Normal distributions. # Inputs and Outputs - **Input**: - Parameters for the Gamma distribution: `concentration` (alpha), `rate` (beta). - Parameters for the Normal distribution: `loc` (mean), `scale` (std deviation). - **Output**: - Generated samples using the combined Gamma and Normal distributions. - Log-probability of a given value. # Function Signature ```python import torch from torch.distributions import Distribution, Gamma, Normal class GammaNormal(Distribution): def __init__(self, concentration, rate, loc, scale): self.gamma = Gamma(concentration, rate) self.normal = Normal(loc, scale) def sample(self): gamma_sample = self.gamma.sample() normal_sample = self.normal.sample() return gamma_sample + normal_sample def log_prob(self, value): log_prob_gamma = self.gamma.log_prob(value) log_prob_normal = self.normal.log_prob(value) return log_prob_gamma + log_prob_normal # Example usage concentration = torch.tensor([1.0]) rate = torch.tensor([2.0]) loc = torch.tensor([0.0]) scale = torch.tensor([1.0]) gamma_normal_dist = GammaNormal(concentration, rate, loc, scale) sample = gamma_normal_dist.sample() log_prob = gamma_normal_dist.log_prob(sample) print("Sample:", sample) print("Log probability of sample:", log_prob) ``` # Constraints - Ensure that both Gamma and Normal distributions are properly parameterized. - Sampling should be efficient and handle batch dimensions if provided. - Log-probability should correctly reflect the combined probabilities of the two distributions. # Performance - The implementation should be efficient with respect to both time and space, leveraging PyTorch's built-in operations and tensor capabilities.

question:# Custom Browser Handler and URL Opener **Objective:** This task assesses your understanding of the `webbrowser` module, including registering new browser types, opening URLs using different methods, and handling potential browser unavailability. **Problem Statement:** You are required to implement a function `custom_open(url: str, browser_preference_list: List[str], fallback: str) -> bool`. This function should attempt to open a given URL in a browser specified by the `browser_preference_list`, which is a list of browser type names. If none of the preferred browsers are available, the function should open the URL in a browser specified by the `fallback` parameter. The function should return `True` if the URL was successfully opened in one of the specified browsers and `False` otherwise. **Detailed Constraints and Requirements:** 1. **Input:** - `url` (str): The URL to be opened. - `browser_preference_list` (List[str]): A list of browser type names to try, in order of preference. - `fallback` (str): A single browser type name to be used as a fallback if no preferred browsers are available. 2. **Output:** - Returns `True` if the URL is successfully opened in one of the browsers. - Returns `False` if all attempts to open the URL fail. 3. **Behavior and Requirements:** - Use the `webbrowser.get()` function to check for the specified browsers. - If a preferred browser is available, open the URL using `webbrowser.open_new_tab(url)` method of the corresponding browser controller. - If none of the preferred browsers are available, open the URL using the `fallback` browser. - Handle any exceptions that may occur when trying to open a URL and ensure the function does not terminate unexpectedly. 4. **Example Usage:** ```python url = "https://www.python.org" preferences = ["firefox", "chrome", "safari"] fallback_browser = "links" result = custom_open(url, preferences, fallback_browser) print(result) # Should print True if the URL is successfully opened in one of the browsers, otherwise False ``` 5. **Performance:** - The function should perform the checks and open the URL in a reasonable time frame, accounting for network and system performance limitations. **Note:** - Assume the environment and browsers are set up correctly for testing purposes. - Be mindful of platform-specific behaviors and handle them accordingly. # Function Signature: ```python from typing import List import webbrowser def custom_open(url: str, browser_preference_list: List[str], fallback: str) -> bool: # Your code here pass ``` You need to implement and test the `custom_open` function based on the provided specifications.