answer:In Python, you can use **kwargs within a single function, but you cannot directly separate them into different sets for different tasks. You would need to either separate the kwargs into different ones listed in the function signature, or you could gather the kwargs into dictionaries and manually parse them to use. Here is how you can do it: ```python def smoothy(x, y, kind='cubic', order=3, scatter_kwargs={}, plot_kwargs={}): yn_cor = interp1d(x, y, kind=kind, assume_sorted=False) xn = np.linspace(np.min(x), np.max(x), len(x) * order) # Use the scatter_kwargs as you'd like inside `scatter` plt.scatter(x, y, **scatter_kwargs) # Use the plot_kwargs as you'd like inside `plot` plt.plot(xn, yn_cor(xn), **plot_kwargs) plt.show() ``` In this version of your function, we have two sets of argument variables `scatter_kwargs` and `plot_kwargs`. You can pass any keyword arguments to these, and then they are passed to "scatter" and "plot" respectively. This way, your "scatter" and "plot" functions can each use their own set of arguments without having to interact with each other's arguments. The downside of this solution is that you need to specify the argument keys on the function call, which can make your code less readable.,In Python, you cannot directly define a function with multiple **kwargs. However, you can pass in a dictionary for each set of kwargs you want to use. Here's how you can modify your function to accept two different dictionaries: ```python def smoothy(x, y, kind='cubic', order=3, kwargs_for_scatter=None, kwargs_for_plot=None): if kwargs_for_scatter is None: kwargs_for_scatter = {} if kwargs_for_plot is None: kwargs_for_plot = {} yn_cor = interp1d(x, y, kind=kind, assume_sorted = False) xn = np.linspace(np.min(x), np.max(x), len(x) * order) plt.scatter(x, y, **kwargs_for_scatter) plt.plot(xn, yn_cor(xn), **kwargs_for_plot) return ``` Now, you can pass the arguments to the functions in the form of dictionaries. Here's an example: ```python kwargs_for_scatter = {'color': 'red'} kwargs_for_plot = {'color': 'blue'} smoothy(x, y, kwargs_for_scatter=kwargs_for_scatter, kwargs_for_plot=kwargs_for_plot) ``` In this example, 'color' is just a placeholder for any argument you want to pass to the scatter or plot functions. You can replace 'color' with any argument that the scatter and plot functions in matplotlib can accept., In your current implementation, you are using `**kwargs_for_scatter` and `**kwargs_for_plot` in the function signature, which is not possible since a function can have only one set of `**kwargs`. One way to achieve your goal is to use a single `**kwargs` and then separate them manually inside the function using the `inspect` module. Here's how you can modify your function: ```python import inspect import matplotlib.pyplot as plt from scipy.interpolate import interp1d import numpy as np def smoothy(x, y, kind='cubic', order=3, **kwargs): yn_cor = interp1d(x, y, kind=kind, assume_sorted=False) xn = np.linspace(np.min(x), np.max(x), len(x) * order) # Separate **kwargs into kwargs_for_scatter and kwargs_for_plot argspec = inspect.getargspec(plt.scatter) kwargs_for_scatter = {k: v for k, v in kwargs.items() if k in argspec.args} kwargs_for_plot = {k: v for k, v in kwargs.items() if k not in argspec.args} plt.scatter(x, y, **kwargs_for_scatter) plt.plot(xn, yn_cor(xn), **kwargs_for_plot) return ``` This code will first call `inspect.getargspec(plt.scatter)` to get the names of the arguments for the `scatter` function. Then it uses a dictionary comprehension to separate the keyword arguments for `scatter` and `plot`. Please note that the `inspect` module is part of the Python Standard Library.

answer:You can use the `converters` parameter of the `pd.read_csv()` function to convert the UNIX timestamps to datetime objects during the DataFrame creation. Here's how you can modify your code to achieve that: ```python import pandas as pd from datetime import datetime data = 'RUN,UNIXTIME,VALUEn1,1447160702320,10n2,1447160702364,20n3,1447160722364,42' def convert_timestamp(x): return datetime.fromtimestamp(int(x) / 1000) df = pd.read_csv(StringIO(data), converters={'UNIXTIME': convert_timestamp}) print(df) ``` Now the DataFrame `df` will have the 'UNIXTIME' column in datetime format: ``` RUN UNIXTIME VALUE 0 1 2015-11-10 14:05:02 10 1 2 2015-11-10 14:05:02 20 2 3 2015-11-10 14:05:22 42 ```,You can directly apply the timestamp conversion to the UNIXTIME column in your dataframe. This will allow you to modify the original dataframe without needing to create a new one. Here is the modified version of your code: ```python import sys import pandas as pd import datetime if sys.version_info0 < 3: from StringIO import StringIO else: from io import StringIO data = 'RUN,UNIXTIME,VALUEn1,1447160702320,10n2,1447160702364,20n3,1447160722364,42' df = pd.read_csv(StringIO(data)) df'UNIXTIME' = pd.to_datetime(df'UNIXTIME', unit='ms') ``` In this code, we use the `pd.to_datetime()` function to convert the UNIX timestamps into datetime format. The `unit='ms'` argument specifies that the timestamps are in milliseconds. This will modify the UNIXTIME column in the original dataframe, df, without creating a new one.,Your existing code does not update the DataFrame in place, it creates a new DataFrame with the converted values. You could append your converted DataFrame to your original DataFrame, if you want it in your original DataFrame. But if you want to replace the old column with the converted data, you could use `df.loc` for index positioning or you can go with reassignment of the column value which could be done with a one-liner. Here's how you can do it: ```python import sys if sys.version_info0 < 3: from StringIO import StringIO else: from io import StringIO import pandas as pd data = 'RUN,UNIXTIME,VALUEn1,1447160702320,10n2,1447160702364,20n3,1447160722364,42' # Create the initial DataFrame df = pd.read_csv(StringIO(data)) # Function to convert strings to datetime def convert_to_datetime(x): return pd.to_datetime(x, unit='ms') # Apply the function to convert the 'UNIXTIME' column df'UNIXTIME' = df'UNIXTIME'.apply(convert_to_datetime) ``` Post this step, the 'UNIXTIME' column in `df` would be of type datetime64, as per your requirement. If you want to use `pd.to_datetime` directly, you could code it like this: ```python import sys if sys.version_info0 < 3: from StringIO import StringIO else: from io import StringIO import pandas as pd data = 'RUN,UNIXTIME,VALUEn1,1447160702320,10n2,1447160702364,20n3,1447160722364,42' # Create the initial DataFrame df = pd.read_csv(StringIO(data)) # Apply pd.to_datetime directly to convert the 'UNIXTIME' column df'UNIXTIME' = pd.to_datetime(df'UNIXTIME', unit='ms') ``` This will also meet your requirement of having the 'UNIXTIME' column as datetime64 in the DataFrame `df`.

answer:For each item on the canvas, you can bind an event such as a click or a mouse-over to a specific function call. This can be achieved using the `tag_bind` method on the Canvas object. The `tag_bind` method allows you to bind an event like a click to a function call for a specific tag. If you want to bind events to individual items instead of groups of items with the same tag, you can assign a unique tag to each item. Here is a sample code: ```python import tkinter as tk def on_object_click(event): # this function will be called whenever the canvas item is clicked print("You clicked on an object with id", event.widget.find_withtag("current")) root = tk.Tk() canvas = tk.Canvas(root, width=400, height=400) canvas.pack() oval = canvas.create_oval(10, 10, 100, 100, tags="mytag") rectangle = canvas.create_rectangle(110, 110, 210, 210, tags="anothertag") canvas.tag_bind("mytag", "<Button-1>", on_object_click) canvas.tag_bind("anothertag", "<Button-1>", on_object_click) root.mainloop() ``` In this example, we create an oval and a rectangle on the canvas, each with a unique tag. We then use `tag_bind` to bind a left mouse button click (`<Button-1>`) on each of these items to the function `on_object_click`. The function `on_object_click` will be called whenever either of these items is clicked. In the function, we use `find_withtag("current")` to get the id of the clicked item.,To bind events to individual items on a Canvas in Python, you can use the `bind` method from the tkinter library, which is used to create graphical user interfaces (GUI). The `bind` method allows you to register a command to be run when a given event occurs. Here's an example of how you can use the `bind` method to create a button widget and bind it to a function: ```python import tkinter as tk # Create a tkinter window and a canvas root = tk.Tk() canvas = tk.Canvas(root, bg='white', width=500, height=250) # Bind the click event to the canvas canvas.bind('<Button-1>', on_click) # Left mouse click # Create a button and bind it to a function def on_click(event): print('You clicked on ', event.widget) # Place the button somewhere on the canvas canvas.create_rectangle(100, 100, 400, 200) canvas.create_oval(200, 150, 350, 200) # Add the canvas to the window and start the event loop canvas.pack() root.mainloop() ``` In this example, when you click on any part of the canvas, the built-in function `<Button-1>` is triggered, and the `on_click` function is executed. You can replace this function with your own function that performs the desired actions. You can also bind separate events to different widgets or parts of the canvas, allowing for more complex interactions. Just remember that the parameters of the `bind` function depend on the type of event you're binding, so you'll need to check the documentation for the specific event you're interested in using., In Python, you can use the Tkinter library to create a canvas and bind events to individual items on it. While it's true that you can use tags to bind events, you can also bind events directly to individual items without using tags. Here's how you can do it: Suppose you have created a canvas `c` and added an item to it, like a rectangle: ```python from tkinter import Tk, Canvas root = Tk() c = Canvas(root, width=500, height=500) c.pack() item_id = c.create_rectangle(50, 50, 100, 100, fill="blue") ``` You can bind an event directly to this item by calling the `tag_bind` method on the canvas and passing the item's ID as the tag: ```python def on_click(event): print(f"You clicked on item {item_id}!") c.tag_bind(item_id, "<Button-1>", on_click) ``` In this example, `<Button-1>` is the event type, which represents a left mouse button click. The `on_click` function will be called whenever the user clicks on the rectangle. You can use different event types to bind different events to the same item. For example, you can bind a right-click event to the same item like this: ```python def on_right_click(event): print(f"You right-clicked on item {item_id}!") c.tag_bind(item_id, "<Button-3>", on_right_click) ``` In this case, the `on_right_click` function will be called whenever the user right-clicks on the rectangle. Note that you can also use other event types, such as mouse motion events (`<Motion>`), keyboard events (`<Key>`), and more. You can find a list of all available event types in the Tkinter documentation.

answer:To break this cycle, you can use a Python package called `setuptools_scm` to automatically generate a version number based on the git tags. This package is specifically designed for setuptools and can handle the use of git tags for versioning. Firstly, you need to install `setuptools_scm`. Do this using pip: ``` pip install setuptools_scm ``` This will automatically install `setuptools` if it's not already installed. Then, in your `setup.py` file, add the following lines: ```python import setuptools from setuptools_scm import get_version version = get_version() setuptools.setup( version=version, # Other arguments here ) ``` The `get_version()` function will retrieve the version number from git tags. After this change, you can generate a distribution using `setup.py` with the command: ``` python setup.py sdist ``` This will generate a source distribution with the version number derived from your git tags. For you to access the version number in your package for use in User-Agent HTTP header, you need to add an additional step. You need to add a `version.py` file to your package where you can import and use the version number. Do this in your package's directory: ```python # version.py VERSION = '{version}' ``` And in your `setup.py` add this line to install the `version.py` file: ```python setuptools.setup( ..., package_data={'': 'your-package/version.py'}, ... ) ``` Then you can import the version number in your package and use it in your User-Agent HTTP header, like this: ```python import your_package.version def your_function(): # Your code here ... headers = {'User-Agent': f'{your_package.version.VERSION} (+htt://your-site.com)'} ``` This way, you only need to make one commit for each version update, which will also update the version.py file automatically, without the need for an additional tag commit.,In order to resolve this issue, you can use versioneer which automates the process of keeping the version in sync with your tags. The first step is to install versioneer using pip: ```python pip install versioneer ``` After installing versioneer, you need to configure it. Create a `versioneer.cfg` in your project root directory with the following content: ```python versioneer VCS = git style = pep440 versionfile_source = your_package/_version.py versionfile_build = your_package/_version.py tag_prefix = v parentdir_prefix = your_package- ``` In your `setup.py`, import versioneer and use it's cmdclass: ```python import versioneer setup( name='your_package', version=versioneer.get_version(), cmdclass=versioneer.get_cmdclass(), ... ) ``` In your package's `__init__.py`, you can now import the version like so: ```python from ._version import get_versions __version__ = get_versions()'version' del get_versions ``` Now whenever you tag a release in the VCS, versioneer will update the version in your package. You can use the version in your User-Agent HTTP header as you wanted. This should break the cycle of dependencies., To break the cycle of dependencies and automatically generate a version number based on git tags for both the source distribution and the library itself, you can use the following approach: 1. Create a script that extracts the version number from the latest git tag and writes it to a version.py file in your library. Make sure this script is not included in the version control, and it should be executed before creating the source distribution. Here's a sample version.py file you can create using this script: ```python __version__ = '0.1-3-adcd' # replace this with a variable or function to get the version from the git tag ``` 2. Modify your setup.py script to load the version number from the version.py file, instead of defining it explicitly. This way, when you run `setup.py sdist`, the script will load the version number from version.py, which in turn reads the version number from the latest git tag. Here's an example of how to load the version number from version.py: ```ruby import version from setuptools import setup setup( version=version.__version__, # other options... ) ``` 3. Before creating the source distribution, execute the script to generate the version.py file. You can use the `git describe` command to extract the version number from the latest git tag. Here's a sample command you can use to generate the version.py file: ```bash git describe --tags --abbrev=0 > version.py ``` 4. Now you can run `setup.py sdist` to create the source distribution with the version number based on the git tag. This approach allows you to automatically generate the version number for the library and the source distribution without creating additional commits after making a version tag.