interpolation

问题 I am doing 1d interpolation using scipy on time-series. My x-axis data is in datetime format and y axis is in float like: 3/15/2012 16:00:00 32.94 3/16/2012 16:00:00 32.95 3/19/2012 16:00:00 32.61 Now during slope calculation slope = (y_hi-y_lo) / (x_hi-x_lo) i am getting the error TypeError: unsupported operand type(s) for /: 'float' and 'datetime.timedelta' which is an obvious error. Can someone point me toward the right direction, How to handle it ? 回答1: Your issue is that you are trying

问题 Relative programming newbie here. I have trouble figuring out how to plot interpolated functions over a series of iterations, where as the iteration index increases, the plot would go from black to gradually lighter shades of grey. For example, import numpy as np import matplotlib.pyplot as plt from scipy.interpolate import interp1d for t in np.arange(0.,2., 0.4): x = np.linspace(0.,4, 100) y = np.sin(x-2*t) + 0.01 * np.random.normal(size=x.shape) yint = interp1d(x, y) plt.plot(x, yint(x))

问题 This question already has answers here : Closed 8 years ago . Possible Duplicate: Akima interpolation of an array of doubles I'm searching for an algorithm for Akima interpolation, but I can't find one after googling for a while. I write my application in Objective-C, but the programming language doesn't matter. It would be very kind if you can provide any code or links. Thanks 回答1: edit: see this SO answer for an updated version of the list below. original answer: Akima's original paper http

问题 I have a NetCDF file of a probability surface. It's a 30x30 grid of 0.25 degree lat/lon intervals with a probability surface described in the z dimension. I can easily import this into Panoply, a NetCDF viewer: And it's then a breeze (checking one box) to interpolate/smooth the raw data to a finer grid size: However, I don't just want to visualize the data, I want to plot it in R along with bathymetry and point data. That all is no problem, but I have not found a straightforward way to

问题 I have a point dataset that I'm trying to interpolate on a grid. These points are aligned in grid fashion with some points missing see below: To complicate it, it's possible that other input pointsets may not align on a grid, so I'm trying to use scipy.interpolate.griddata to interpolate these values onto a regular grid. However, sometimes my underlying grid aligns perfectly with the sampling rate of the input point dataset and griddata hangs. According to this question scipy.interpolate

问题 I'm trying to use interp1d() from scipy to interpolate some data, but I keep hitting an out or range error. After hours of Googling, I now know that x values not in increasing order will cause the same error I'm getting but I've already made sure that's not the problem. As far as I can tell, it looks like interp1d() doesn't like decimals in the first value. Am I missing something? A simplified version of my problem: The following runs just fine. import numpy as np from scipy.interpolate

问题 Is there any decent Pythonic way to interpolate in a triangular mesh, or would I need to implement that myself? That is to say, given a (X,Y) point we'll call P, and a mesh (vertices at (X,Y) with value Z, forming triangular facets), estimate the value at P. So that means first find the facet that contains the point, then interpolate - ideally a higher order interpolation than just "linearly between the facet's vertices" (i.e., taking into account the neighboring facets)? I could implement it

问题 I've already looked at this, which was helpful to a point. Here's the problem. I have a list of users propagated into an element via user click; something like this: <div id="box"> joe-user page-joe-user someone-else page-someone-else </div> On click, I want to make sure that the user has not already been clicked into the div. So, I'm doing something like: if ( ! $('#box').html().match(rcpt) ) { update_div(); } else { alert(rcpt+' already exists.'); } However, with existing lack of

问题 I have data that looks like the following but I also have control of how it is formatted. Basically, I want to use Python with Numpy or Pandas to interpolate the dataset to achieve second by second interpolated data so that it is a much higher resolution. So I want to linearly interpolate and produce new values between each of the real values I currently have while keeping the original values as well. How can I accomplish this with Pandas or Numpy? As an example, I have this type of data:

问题 I'm running Nginx on Kubernetes. When I use the following proxy_pass directive it works as expected: proxy_pass "http://service-1.default"; However the following does not work: set $service "service-1"; proxy_pass "http://$service.default"; I get an error saying no resolver defined to resolve service-1.default As far as I can tell proxy_pass is receiving the exact same string so why is it behaving differently? I need to use a variable because I'm dynamically getting the service name from the