nd | Framework for the analysis of n-dimensional , multivariate | Dataset library
kandi X-RAY | nd Summary
kandi X-RAY | nd Summary
The main goal of this library is to generalize methods that work in lower dimensions to higher-dimensional data. Multi-dimensional data often arise as spatio-temporal datacubes, e.g. climate data or time series of geospatial satellite data. Many data analysis methods are designed to work on single images or time series at a single point. nd makes it easy to broadcast these methods across a whole dataset, adding additional features such as automatic parallelization. nd is built on xarray. Internally, all data are passed around as xarray Datasets and all provided methods expect this format as inputs. An xarray.Dataset is essentially a Python representation of the NetCDF file format and as such easily reads/writes NetCDF files. nd is making heavy use of the xarray and rasterio libraries. The GDAL library is only used via rasterio as a compatibility layer to enable reading supported file formats. nd.open_dataset may be used to read any NetCDF file or any GDAL-readable file into an xarray.Dataset. Read the Documentation for detailed user guides.
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Top functions reviewed by kandi - BETA
- Rasterize a dataset
- Get the crs attribute of a dataset
- Parse a CRS
- Read a GeoDataFrame from a file
- Apply the filter
- Helper function to filter arrays
- Select a single item from a list
- Assemble a complex dataset
- Tile a dataset
- Product of a dict
- Write complex complex data to netcdf
- Disassemble complex variables into real and imaginary parts
- Wrap an Algorithm
- Extract arguments from fn
- Parse a docstring
- Assemble a docstring from a parsed docstring
- Decorator to require dependencies
- Merge multiple blocks
- Calculate common extent for datasets
- Apply projection to ds
- Return the path to the libgsl include directory
- Merge datasets
- Maps a function over a set of files
- Wrapper for parallelize functions
- Apply the given datasets
- Returns the library directory
nd Key Features
nd Examples and Code Snippets
Lines of Code : 12License : Strong Copyleft (CC BY-SA 4.0)
Copy[ "MR. JONES", "ACCT203", 2, 3.0, "CIS100", 3, 2.5 ]
>>> marks[0] # ...will give you...
[ "MR. JONES", "ACCT203", 2, 3.0, "CIS100", 3, 2.5 ]
>>> marks[0][2] + marks[0][5]
(((H|He|Li|Be|B|C|N|O|F|Ne|Na|Mg|Al|Si|P|S|Cl|Ar|K|Ca|Sc|Ti|V|Cr|Mn|Fe|Co|Ni|Cu|Zn|Ga|Ge|As|Se|Br|Kr|Rb|Sr|Y|Zr|Nb|Mo|Tc|Ru|Rh|Pd|Ag|Cd|In|Sn|Sb|Te|I|Xe|Cs|Ba|La|Ce|Pr|Nd|Pm|Sm|Eu|Gd|Tb|Dy|Ho|Er|Tm|Yb|Lu|Hf|Ta|W|Re|Os|Ir|Pt|Au|Hg|Tl|Pb|Bi|def all_key_sets_equal(dct: dict) -> bool:
key_sets = [set(nd) for nd in dct.values()]
return all(key_set == key_sets[0] for key_set in key_sets)
>>> import itertools
>>> list(itertools.chain(*[[1,2,3],[4,5,6],[7,8,9]]))
[1, 2, 3, 4, 5, 6, 7, 8, 9]
import re
df = pd.DataFrame(np.array([['Tom', 'apple1'], ['Tom', 'banana35'], ['Jeff', 'pear0']]),
columns=['customer', 'product'])
df1 = df.groupby(["customer"])["product"].unique().reset_index()
df1["product"] = df1["proddef main_fn(arr_1):
all_result_summary = []
for method in ["met_1", "met2", "met_3"]:
results: ndarray = np.array(main_fn(list(arr_1), method))
all_result_summary.append(
pd.DataFrame(
{
>>> pd.to_datetime(df['date'])
0 2021-10-01
1 2021-09-10
2 2020-10-02
3 2020-01-10
4 2019-11-01
5 2019-08-30
6 2019-05-10
7 2018-08-24
8 2017-09-01
9 2017-03-10
10 2017-02-10
11 2016-04-22
12 20arr = np.array([[[0, 1]]])
points = [(0, 0, 1), (0, 0, 0)]
values = []
for point in points:
value.append(arr[point])
# values -> [1, 0]
arr = np.array([[[0, 1]]])
points = (0, 0, slice(2) )
vals = arr[poinarr = np.array([[[0, 1]]])
points = np.array([[0, 0, 1], [0, 0, 0]])
x,y,z = np.split(points, 3, axis=1)
arr[x,y,z]
array([[1],
[0]])
arr[(*points.T,)]
arrayyield scrapy.Request(links, callback=self.parse_abstract_page)
yield scrapy.Request(response.urljoin(links), callback=self.parse_abstract_page)
yield response.follow(abstract_url, callbackCommunity Discussions
Trending Discussions on nd
QUESTION
I wish to move a large set of files from an AWS S3 bucket in one AWS account (source), having systematic filenames following this pattern:
...ANSWER
Answered 2021-Jun-15 at 15:28You can use sort -V command to consider the proper versioning of files and then invoke copy command on each file one by one or a list of files at a time.
ls | sort -V
If you're on a GNU system, you can also use ls -v. This won't work in MacOS.
QUESTION
Loading this XML works
...ANSWER
Answered 2021-Jun-14 at 12:12Just remove "//" from SelectNodes and SelectSingleNode. The double slash is parsing the complete xml
QUESTION
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision
from PIL import Image
import matplotlib.pyplot as plt
class Model_Down(nn.Module):
"""
Convolutional (Downsampling) Blocks.
nd = Number of Filters
kd = Kernel size
"""
def __init__(self,in_channels, nd = 128, kd = 3, padding = 1, stride = 2):
super(Model_Down,self).__init__()
self.padder = nn.ReflectionPad2d(padding)
self.conv1 = nn.Conv2d(in_channels = in_channels, out_channels = nd, kernel_size = kd, stride = stride)
self.bn1 = nn.BatchNorm2d(nd)
self.conv2 = nn.Conv2d(in_channels = nd, out_channels = nd, kernel_size = kd, stride = 1)
self.bn2 = nn.BatchNorm2d(nd)
self.relu = nn.LeakyReLU()
def forward(self, x):
x = self.padder(x)
x = self.conv1(x)
x = self.bn1(x)
x = self.relu(x)
x = self.padder(x)
x = self.conv2(x)
x = self.bn2(x)
x = self.relu(x)
return x
ANSWER
Answered 2021-Jun-11 at 17:50Here is a functional equivalent of the main Model forward(x) method. It is much more verbose, but it is "unravelling" the flow of operations, making it more easily understandable.
I assumed that the length of the list-arguments are always 5 (i is in the [0, 4] range, inclusive) so I could unpack properly (and it follows the default set of parameters).
QUESTION
hi i am trying to make category and then subcategory so if a category is deleted subcategory would also be deleted here what i have done so far created a model called category also created a subcategory model
run the migration for the category
...ANSWER
Answered 2021-Jun-10 at 08:30Just use
QUESTION
So I've written this, which is horrific:
...ANSWER
Answered 2021-Jun-09 at 17:13Whether you are using re or regex, you will have to fix your pattern, as it is catastrophic backtracking prone. Atomic groupings are not necessary here, you need optional groupings with obligatory patterns. Also, you need to fix your alternations that may start matching at the same location inside a string.
You can use
QUESTION
I have a list -cj1- with multiple data frames
...ANSWER
Answered 2021-Jun-06 at 20:40You can use the following solution. We use .x to refer to every individual element of your list. Here .x can be each of your data frames of which we would like to select only 2 columns c("individual","theta").
However, since only one of your data frames contains such column names I used keep function to actually keep only elements whose data frames contain the desired column name. Just bear in mind for this form of coding which is called purrr-style formula we need ~ before .x. So you use map function which is an equivalent to lapply from base R and use this syntax to apply whatever function on every individual elements (data frames here).
QUESTION
print('enter your age')
age = input()
print('this is how many days you have been alive')
time.sleep(1)
print('input the first three letters of the month you were born')
jan = 31
feb = 59
mar = 90
apr = 120
may = 151
jun = 181
jul = 212
aug = 243
sep = 273
oct = 304
nov = 334
dec = 365
month = input()
print('now for the day you were born. put this in number form without any "th"s or "nd"s ')
date = input()
print('your total age is:')
time.sleep(1)
print((int(age) * 365) + int(date) + int(month))
ANSWER
Answered 2021-Jun-05 at 20:38You're not using the month values anywhere, you're basically trying to convert a string to int and it raises an error. Here's a possible fix
QUESTION
I have a dataframe, df, where I would like to extract the end of a value and use this as a determining factor for a new column
Data
...ANSWER
Answered 2021-Jun-03 at 18:10Not very elegant but gets the work done.
QUESTION
I have a dataset where I would like to extract anything that is after the underscore
Data
...ANSWER
Answered 2021-Jun-03 at 17:02Using Regex.
Ex:
QUESTION
I have some trouble with UPDATE i use posgress in informix DB I have two table.
1-st table with call data (anslogin, grade_1, grade_2, grade_3, grade_4, grade_5) 2-nd data with agent id and count of grade for every agents there group by logins ID. I created 1-st table and copy there all logins ID from 2-nd table.
And then I want create another 5 request for every grade (1,2,3,4,5) but i have trouble with UPDATE:
...ANSWER
Answered 2021-Mar-21 at 14:56If I understand correctly, you can use 5 correlated subqueries:
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