haversine | Calculate the distance bewteen 2 points on Earth | Map library
kandi X-RAY | haversine Summary
kandi X-RAY | haversine Summary
Calculate the distance (in various units) between two points on Earth using their latitude and longitude.
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- Computes the difference between two arrays .
- Calculate the distance between two points .
- Inverse of the inverse haversine .
- Gets the average earth radius .
haversine Key Features
haversine Examples and Code Snippets
Lines of Code : 47License : Permissive (MIT License)
Copydef haversine_distance(lat1: float, lon1: float, lat2: float, lon2: float) -> float:
"""
Calculate great circle distance between two points in a sphere,
given longitudes and latitudes https://en.wikipedia.org/wiki/Haversine_formula
import numpy as np
import itertools
from scipy.spatial.distance import pdist, squareform
from haversine import haversine # pip install haversine
# original approach
place_coordinates = [(x, y) for x in range(10) for y in range(10)]
d = p# Inspired by https://stackoverflow.com/a/4913653/15239951
def haversine_series(sr):
lon1 = sr['longitude']
lat1 = sr['latitude']
lon2 = sr['longitude'].shift(fill_value=sr['longitude'].iloc[0])
lat2 = sr['latitude'].shift(df2 = pd.concat([df.add_suffix('_pre').shift(), df], axis=1)
df2
duration_pre latitude_pre longitude_pre duration latitude longitude
0 NaN NaN NaN 5 11.923855 57.723843
1 5.0 @pandas_udf("float")
def udf_calc_distance(st_y_witness: pd.Series, st_x_witness: pd.Series, st_y_transmitter: pd.Series, st_x_transmitter: pd.Series) -> pd.Series:
distance_df = pd.DataFrame({'st_y_witness' : st_y_witness, 'st_x_wiimport random
import numpy as np
from haversine import haversine
def getNFacilities(n):
""" returns n random pairs of coordinates in the range [-90, +90]"""
for i in range(n):
yield random.random()*180 - 90, random.randomfrom haversine import haversine
cities = [
["Delhi", 28.7041, 77.1025],
["Noida", 28.5355, 77.3910],
["Gurgaon", 28.4595, 77.0266],
["Mumbai", 19.0760, 72.8777]
]
cities_crossed = [
[x[0], y[0], haversine((x[1], x[2])a=df.iloc[:,1::].values#Array of the Lat/Long
b=df2.iloc[:,1::].values##Array of the Lat/Long
df.join(pd.DataFrame(distance.cdist(a, b, 'euclidean')).rename(columns={0:75631,1:89210}))
City Latitude Longitude 75631 89In [25]: nsolve(eq, lat2, 0)
Out[25]: 0.661959292973035
In [26]: nsolve(eq, lat2, 1) val2 = pd.Series([v in ps2 for v in df2.geometry])
Community Discussions
Trending Discussions on haversine
QUESTION
Here's using how I use haversine library to calculate distance between two points
...ANSWER
Answered 2022-Apr-04 at 09:22Following the documentation and example found on: sklearn.metrics.haversine
QUESTION
Here's my dataset B
ANSWER
Answered 2022-Mar-29 at 08:59Check your output distances, what units are they? I converted mine to kilometers. You can check using an online distance calculator if you wanted. Let me know
QUESTION
First time Pyomo user here.
I am trying to build an optimization model that will maximize the quantity of waste collected in a waste recycling network consisting of customers i and recycling centres j. (i.e. maximize quantity waste Qij flowing from i to j). Here is the mathematical model:
I have coded a function in jupyter notebook that reads customer and recycling centre latitude and longitude coordinates from two seperate csv file using the read_csv function. The function called distance_from calculates the haversine distance between coordinates and runs a loop which will parse customers location one by one to distance_from function. This generates a dataframe of 80x16 RowsxColumns. Here is the code for this bit:
...ANSWER
Answered 2022-Mar-22 at 21:18Welcome to the site.
You are off to an "OK" start. Your model has quite a few errors in it.... did you look at the examples in the pyomo documentation?
A few suggestions:
Start with
ConcreteModeland manually initialize the data. I think it is easier to do, esp. with python's ability to handle .csv files either manually (as I show below) or with pandas or csv_reader.Throw out
pandasfor now. Use it from a separate file if need be to create the .csv files if you are comfortable with that, or just manually write them, or use csv_reader, but don't comminglepandasandpyomotill you get your feet on the ground. Same advice fornumpy.Use "flat file" format for your data, not tabular. It is easier to ingest. So, for example, create your distance table in a csv that has 3 columns as mine does and it is easier to read into a dictionary or, if you go to an
AbstractModelit is in an easy format.Use a small slice of your data and
pprint()your model to make sure it makes sense and complies with your math model.
QUESTION
from bs4 import BeautifulSoup
import requests
cont = requests.get("https://ichi.pro/tr/veri-biliminde-uzaklik-olculeri-159983401462266").content
soup = BeautifulSoup(cont,"html.parser")
metin = soup.text
import re
sonuç1 = re.search("1. Öklid Mesafesi",metin)
sonuç2 = re.search('Okuduğunuz için teşekkürler!',metin)
metin = metin[sonuç1.start():sonuç2.start()].split("\n")
from gensim.models import Word2Vec
model = Word2Vec(metin,size=200,window=15,min_count=5,sg=5)
model.wv["Jaccard mesafesi"]
ANSWER
Answered 2022-Mar-21 at 15:27There are multiple problems:
If you want a Turkish model, you can try to find a pretrained Word2Vec model for Turkish (e.g. check out this repository) or train a model for Turkish yourself. The way you use it now you seem to train a model but only from a single website, which will barely do anything because the model needs a large amount of sentences to learn anything (like at least 10.000, better much more). Also you set
min_count=5anyway, so any word appearing less than 5 times is ignored generally. Try something like training it on the Turkish Wikipedia, see the linked repository.Word2Vec by default is a unigram model, so the input is a single word. If you hand it a bigram consisting of two words like
"Jaccard mesafesi"it will not find anything. Also you should catch the case that the word is not in vocabulary, otherwise each unknown word will cause an error and your program to cancel. Search for the unigram representation of each token, then combine the two, e.g. by using the statistical mean of the vectors:
QUESTION
Here's my script, it takes to much time to give output
...ANSWER
Answered 2022-Mar-21 at 07:49To be faster, you should use compiled code.
pyproj allows to calculate a distance between 2 points: (Geod.line_length)
QUESTION
I have a huge Geo database that I frequently need to compare with real time Geo data in order to determine nearest location per Latitude:Longitude. The location table does hold a number of rows, but is rarely added with new records. Determining nearest location against millions of real time data is painfully costing us with super slow queries, even after implementing a rectangular distance comparison algorithm (than actually comparing by Haversine).
I want to convert this comparison with a DETERMINISTIC function that should really bring up a real performance boost with static results.
However, I want MySQL to reset/rebuild this deterministic result cache every week. Like, I want MySQL to return me the same result for a Latitude:Longitude pair comparing against my location table, but for 7 days. After 7 days, there is a good chance I might add new locations to that table, and I want MySQL to start rebuilding that deterministic function result cache considering new rows been added to that table, preferably without restarting MySQL server.
Note: A MariaDB compliant solution is a serious good to have :)
Correction: Please forgive me to use that term with MySQL. So far I could understand, the result does not change for a deterministic function where all input are the same, this allures me to think MySQL does not actually tend to execute or process the instruction inside the function, rather returns the previously calculated value for the same set of input values, so, definitely it does cache the values somewhere (I don't know where), thus behaving something like just looking up through a list or something like that. I think I overloaded the OPTIMIZER with CACHE here :(
======== EDIT FOR TECHNICAL CLARIFICATION ========
...ANSWER
Answered 2022-Mar-12 at 18:40I think you misunderstand what DETERMINISTIC means as an option to CREATE FUNCTION.
It does not mean the result of the function is memoized. There is no cache of function results. There is no command to refresh this cache, because the results are not kept.
The meaning of DETERMINISTIC mainly affects binary logging:
https://mariadb.com/kb/en/create-function/#not-deterministic
The
[NOT] DETERMINISTICclause also affects binary logging, because theSTATEMENTformat can not be used to store or replicate non-deterministic statements.
That is, a non-deterministic function may return different results if it is executed on a replica, so if you use the function in an SQL statement that modifies data, the binary log format for that event must be ROW to ensure the same change is applied on the replica.
There's also a vague reference:
The optimizer may choose a faster execution plan if it known that the function is deterministic.
But no example is given for such a case. This is likely to be a rarity for it to make a significant performance difference.
I don't think this will be an effective performance optimization for your use case.
QUESTION
What is the difference between the arango function - DISTANCE() and GE0_DISTANCE(). I know both of them calculates distance using haversines formula.
Thanks, Nilotpal
...ANSWER
Answered 2022-Feb-24 at 07:39Both are used for two different purposes
QUESTION
With this DataFrame I am trying to take the start lat/long and end lat/long to create a new column that shows the Haversine distance between the two
...ANSWER
Answered 2022-Feb-20 at 23:00You can use an apply with a lambda here to work on single rows. When you input df['start_coord'] you're using the whole series.
QUESTION
I am trying to tease out the dates that I was in a certain area (within a mile or so) using Google Location data and Python Pandas Dataframe.
First convert to latitude from latitudeE7:
...ANSWER
Answered 2022-Feb-19 at 15:53You cannot directly pass pd.Series to haversine function.
QUESTION
I want to order the list from less distance to more distance but I calculate the km inside the list and the data comes from a Future builder so Im not sure if I can order this list.
Here is the code
Widget build(BuildContext context) { final UserProvider userProvider = Provider.of(context);
...ANSWER
Answered 2022-Feb-16 at 21:38Instead of pulling the data straight up from Firestore and putting into the FutureBuilder, why don't you move this functionality inside your provided service UserProvider, for example (or another service), that way you can encapsulate your data fetching logic and your ordering logic there, as such:
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
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Install haversine
You can use haversine like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.
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