shapely | Free multipurpose WordPress theme built using Bootstrap | Content Management System library
kandi X-RAY | shapely Summary
kandi X-RAY | shapely Summary
Shapely is a powerful and versatile one page WordPress theme with pixel perfect design and outstanding functionality. It is by far the most advanced free WordPress theme available today with loads of unmatched customization options. This theme comes with several homepage widgets that can be used to add portfolio, testimonials, parallax sections, your product or service information, call for action and much more. Shapely supports most free and premium WordPress plugins such as WooCommerce, Jetpack, Gravity Forms, Contact Form 7, Yoast SEO, Google Analytics by Yoast and much more. This theme is the best suited for business, landing page, portfolio, ecommerce, store, local business, personal websites but can be tweaked to be used as blog, magazine or any other awesome website while highlighting its unique one page setup. This is going to be the last WordPress theme you will ever want to use because it is so much better than anything you have seen. We promise.
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Top functions reviewed by kandi - BETA
- Start an element
- Render the template
- Handle the welcome screen AJAX AJAX .
- Ajax AJAX handler .
- Get sanitizer
- Generate action html .
- Parse field content
- Get CSS string .
- Adds control settings .
- Get repeated field
shapely Key Features
shapely Examples and Code Snippets
Community Discussions
Trending Discussions on shapely
QUESTION
I am taking data with latitude, longitude, and a z value and interpolating it using a cubic method. The values seem to be saved in a numpy array. Is there a way to save the interpolated results to a pandas dataframe? I'm trying to save it with a longitude, latitude, and z value column for the interpolated result.
The input file can be found here and is called nutrition.csv. Here is what I have done so far:
...ANSWER
Answered 2022-Mar-25 at 08:47You can construct a dataframe yourself from the data you obtained
QUESTION
I am trying to compute the area of an irregular polygon.
I used "ConvexHull" and "alpha_shape" but the output of the surface(area) is not the same.
any suggestions please?
you will find the data, and the code used to compute the area below:
...ANSWER
Answered 2022-Mar-24 at 14:30Your shapely polygon is not a hull figure (it is not even convex), so unsurprisingly its area is lower:
QUESTION
If I'm using Python Transforms in Palantir Foundry and I'm trying to run an algorithm which uses in-memory/non-spark libraries, and I want it automatically scale and work in Spark (not pandas). If I'm having a hard time writing the code and want to test and develop it locally, yet use the same code in pyspark later, how do I do this?
For a concrete example, I want to calculate the area of a geojson column which contains a polygon. Since I would need to use some libraries which arn't native to Spark (shapely and pyproj). I know that the best way (performance wise) is to use a pandas_udf (otherwise known as streaming udfs or vectorized udfs). But after reading a couple of guides, specifically Introducing Pandas UDF for PySpark, pandas user-defined functions
and Modeling at Scale with Pandas UDFs w/code examples, it's still challenging to debug and get working, and it seems like I can't use break statements and there isn't a first class way to log/print.
The actual dataframe would have millions of rows (relating to millions of polygons), but for simplicity I wanted to test locally with a simple dataframe, and it scale to larger dataset later:
...ANSWER
Answered 2022-Mar-22 at 19:01The way you can think about pandas_udfs is that you are writing your logic to be applied to a pandas series. This means that you would be applying an operation and it would automatically apply to every row.
If you want to develop this locally, you can actually take a much smaller sample of your data (like you did), and have it stored in a pandas series, and get it working there:
QUESTION
I installed Christoph Gohlke's prebuilt wheel Cartopy‑0.20.2‑cp39‑cp39‑win_amd64.whl using pip in an active virtual environment. The environment is using Python 3.9.5. When trying to import Cartopy I get the error message below. This used to work before and now it no longer works and I can't figure out why. Does anyone know what the issue could be or what I'm missing?
ANSWER
Answered 2022-Mar-22 at 12:20QUESTION
I have generated an interpolation map using the scipy.interpolate module. I am needing some help saving the map as a .tiff file and saving it to my directory. However, I'm not sure if I need to convert it to a numpy array or not, as it needs to have the latitude, longitude, and the interpolated data in each cell. Any help would be much appreciated!
Here is the data. The nutrition.csv file can be found here.
ANSWER
Answered 2022-Mar-16 at 20:18So this is the follow up of your question that I answered earlier. To save an array to a geotiff you need to determine the geotransform, which means you need to know the coordinates of the upper left corner of your array and the resolution in x and y.
For your data it might work like this:
QUESTION
I would like to determine the travel time of an object moving a certain distance along a parameterized curve. I already learned how to do this mathematically, but I think there should be a better way of implementing this in Python using scipy.optimize.minimize. However, for some reason, I cannot get it to work. The result always goes to +inf, even though my initial guess should be fairly close. What am I doing wrong?
The problem in more specific terms:
Given the curve (in 2d) parameterized by time, you choose an arbitrary point in time (t_end), which also specifies a specific point on the curve. From there you go back in time along the curve until the path traveled is equal to some arbitrary distance (d_min). What I want to know is the travel time along this segment of the curve, or in other words, given t_end and d_min, what is t_start so that the line integral along the curve from t_startto t_end is equal to d_min.
Below is an MWE that contains just the crucial part:
...ANSWER
Answered 2022-Mar-15 at 12:35I will only address your crucial part. Here are a few points that crossed my mind:
According to the comments, you basically want to solve an equation F(t) = d by minimizing the objective q(t) = F(t)-d. However, mathematically, this is not the same in general. To see why, let's consider the quadratic function F(t) = t^2 and d = 1. Then, t = 1 solves the equation F(t) = d. However, minimizing the objective q(t) = t^2 - 1 yields the local minimum t = 0 with objective function value q(0) = -1. And obviously, 0*0 ≠ 1. Note that F(t) = d if and only if q(t) = 0, i.e. the objective q has the objective value 0. Therefore, you need a minimum with an objective value of 0. And since any norm is non-negative by definition, we just minimize the euclidean norm of your function, i.e. we minimize p(t) = ||q(t)|| = ||F(t) - d||.
Because you have a (probably non-convex) scalar optimization problem of one variable, it's highly recommended to use the specialized algorithms behind
scipy.optimize.minimize_scalar:
QUESTION
Here's my starting point, I have the following two point datasets
...ANSWER
Answered 2022-Feb-17 at 18:57You can use the hull equations do determine if the point is inside the hull
QUESTION
i have a geoJSON
...ANSWER
Answered 2021-Aug-17 at 15:40Try this,
QUESTION
#!/usr/bin/env python
import os, sys
import pandas as pd
import cartopy
import cartopy.crs as ccrs
import matplotlib.pyplot as plt
import shapely.geometry as sgeom
import numpy as np
from cartopy.geodesic import Geodesic
if __name__ == '__main__':
stn = pd.read_csv('obs_station.csv')
gd = Geodesic()
lcc = ccrs.LambertConformal(central_longitude=126., central_latitude=38.)
fig = plt.figure(figsize=(7,7))
ax = fig.add_subplot(111, projection=lcc)
ax.coastlines(resolution='50m')
geoms = []
for lon, lat in zip(stn['longitude'], stn['latitude']):
cp = gd.circle(lon=lon, lat=lat, radius=250000.)
geoms.append(sgeom.Polygon(cp))
ax.add_geometries(geoms, crs=lcc, edgecolor='r')
ax.set_extent([120., 133., 30., 43.])
plt.show()
ANSWER
Answered 2022-Feb-13 at 02:26You did not get the plots of the circles because of wrong coordinate transformation you specifies in .add_geometries() statement.
To get it right, suppose I use this data file: 'obs_station.csv':
QUESTION
I have a geojson document 'streets' with a column "geometry" filled with data of type shapely Point. I have another geojson document 'quartiers' with a column 'geometry" with data of type shapely Polygon and in same document I have another column called "l_qu". What I am trying to achieve is looping through all streets['geometry] with a lambda in quartiers['geometry'] and assign the corresponding 'l_qu' match value in a new column 'matchquartier' in 'streets'. There are 150K lines in streets (the adresses) and 80 lines in quartiers (the neighboorhoods) The lambda for I built is :
...ANSWER
Answered 2022-Feb-07 at 20:50IIUC, you can just use a spatial join. This is working for me.
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