021 URLs

Introduction

Purpose

In this session, we will learn about files and similar resources. We will introduce the standard Python library pathlib which is how we deal with file paths. For URLs, we will use the Python packages: urlpath.

Prerequisites

You will need some understanding of the following:

001 Using Notebooks
002 Unix with a good familiarity with the UNIX commands we have been through.
003 Getting help
004_Accounts
005_Packages
010 Variables, comments and print()
011 Data types
012 String formatting
013_Python_string_methods

You will need a detailed understanding and familiarity with the Path package for dealing with files, as well asd the underlying concepts covered there.

020_Python_files

Test

In any public information (like these notebooks) we do not want to expose sensitive information such as usernames and passwords.

So here we will make use of stored passwords and usernames using the local cylog package that was covered in 004_Accounts.

We will be using the site https://e4ftl01.cr.usgs.gov today. You should have already tested that your NASA Earthdata login works for files on that site.

If you unsure about your login and/or password, test them on the Earthdata login page.

We should see that a call to Cylog(url).login() returns the username and password.

So, if you like, you can check what you have stored in cylog by using:

url='https://e4ftl01.cr.usgs.gov'
print(Cylog(url).login())

If you want to check it, run those commands in the cell below (uncomment the print line).

from geog0111.cylog import Cylog
url='https://e4ftl01.cr.usgs.gov'
#print(Cylog(url).login())

If you are prompted for a login and password, this means that you haven't previously entered one for this site (just enter them now, or go back and look at 004_Accounts.

Resources from a URL

`urlpath`

The library urlpath is designed to operate in a similar manner to pathlib for reading data from URLs. It is based on urllib.parse and requests. It doesn't quite have all of the functionality we will need, but it goes a long way to an object-oriented URL library that is similar to Pathlib.

The object in urlpath corresponding to Path is URL.

A text file example:

from urlpath import URL
site = 'https://www.metoffice.gov.uk/'
site_dir = 'hadobs/hadukp/data/monthly'
site_file = 'HadSEEP_monthly_totals.txt'

url = URL(site,site_dir,site_file)
print(f'remote file {url}')

remote file https://www.metoffice.gov.uk/hadobs/hadukp/data/monthly/HadSEEP_monthly_totals.txt

A binary file example:

from urlpath import URL

site = 'https://e4ftl01.cr.usgs.gov'
site_dir = 'MOTA/MCD15A3H.006/2020.01.01'
site_file = 'MCD15A3H.A2020001.h08v06.006.2020006032951.hdf'

url = URL(site,site_dir,site_file)
print(f'remote file {url}')

remote file https://e4ftl01.cr.usgs.gov/MOTA/MCD15A3H.006/2020.01.01/MCD15A3H.A2020001.h08v06.006.2020006032951.hdf

We have similar functionality in URL to Path for manipulating filenames, but more limited file information:

print(f'URL    : {url}')
print(f'name   : {url.name}')
print(f'parent : {url.parent}')

URL    : https://e4ftl01.cr.usgs.gov/MOTA/MCD15A3H.006/2020.01.01/MCD15A3H.A2020001.h08v06.006.2020006032951.hdf
name   : MCD15A3H.A2020001.h08v06.006.2020006032951.hdf
parent : https://e4ftl01.cr.usgs.gov/MOTA/MCD15A3H.006/2020.01.01

but also some other helpful ones on the URL:

from pathlib import Path

print(f'anchor   : {url.anchor}')
print(f'hostname : {url.hostinfo}')
print(f'scheme   : {url.scheme}')
print(f'name     : {url.name}')
print(f'netloc   : {url.netloc}')
print(f'parts    : {url.parent.parts}') # parent parts

anchor   : https://e4ftl01.cr.usgs.gov
hostname : e4ftl01.cr.usgs.gov
scheme   : https
name     : MCD15A3H.A2020001.h08v06.006.2020006032951.hdf
netloc   : e4ftl01.cr.usgs.gov
parts    : ('https://e4ftl01.cr.usgs.gov', 'MOTA', 'MCD15A3H.006', '2020.01.01')

Exercise 1

create a URL object for the file table.html in the directory psd/enso/mei/ on the site http://www.esrl.noaa.gov/.
print out the url name and check it is table.html

For accessing URLs, will mostly make use of the following functions in URL that you will see are very similar to those for Path:

function	purpose
`URL.name`	filename
`URL.parent`	parent
`URL.parts`	parts
`URL.as_posix()`	return URL as posix string
`URL.with_userinfo()`	add in username and password
`URL.get()`	URL get. Returns `requests.models.Response`
`URL.netloc`	network location e.g. `www.google.com`
`URL.path`	full pathname on server (including filename)

`URL.get()`

We can use URL.get() to access data. This returns a response (more formally, a type requests.models.Response):

r = URL.get()

requests.models.Response information table:

function	purpose
`r.status_code`	return code after request. You need to check this in case the call fails. It should be `200` if ok.
`r.text`	text content returned
`r.json`	text content interpreted as json
`r.content`	binary content returned

If the access has been successful, then r.status_code should return 200 (or requests.codes.ok). If it is 401 then there has been an access error. You can find the fuller list of possible http status codes on the web.

If the data type to be accessed is raw (ASCII) text, then you can access the data with r.text. Similarly, if it is json, r.json, or binary r.content

from urlpath import URL

site = 'https://covid.ourworldindata.org'
site_dir = 'data/archived/ecdc'
site_file = 'full_data.csv'
site_file = 'locations.csv'

url = URL(site,site_dir,site_file)
r = url.get()

# check error code: 200 is good
# 
if r.status_code == 200:
    print(f'data access good for {url.as_posix()}')

    # get data as text
    data = r.text
    # print first 500 characters
    print('\n==== data as text ====')
    print(data[:500])

    # get data as binary
    data = r.content
    # print first 500 characters
    print('\n==== data as binary ====')
    print(data[:500])

data access good for https://covid.ourworldindata.org/data/archived/ecdc/locations.csv

==== data as text ====
countriesAndTerritories,location,continent,population_year,population
Afghanistan,Afghanistan,Asia,2020,38928341
Albania,Albania,Europe,2020,2877800
Algeria,Algeria,Africa,2020,43851043
Andorra,Andorra,Europe,2020,77265
Angola,Angola,Africa,2020,32866268
Anguilla,Anguilla,North America,2020,15002
Antigua_and_Barbuda,Antigua and Barbuda,North America,2020,97928
Argentina,Argentina,South America,2020,45195777
Armenia,Armenia,Asia,2020,2963234
Aruba,Aruba,North America,2020,106766
Australia,Austral

==== data as binary ====
b'countriesAndTerritories,location,continent,population_year,population\nAfghanistan,Afghanistan,Asia,2020,38928341\nAlbania,Albania,Europe,2020,2877800\nAlgeria,Algeria,Africa,2020,43851043\nAndorra,Andorra,Europe,2020,77265\nAngola,Angola,Africa,2020,32866268\nAnguilla,Anguilla,North America,2020,15002\nAntigua_and_Barbuda,Antigua and Barbuda,North America,2020,97928\nArgentina,Argentina,South America,2020,45195777\nArmenia,Armenia,Asia,2020,2963234\nAruba,Aruba,North America,2020,106766\nAustralia,Austral'

Exercise 1

Use the URL.get() method to pull data from https://covid.ourworldindata.org/data/ecdc/archived/full_data.csv and store in a file called work/full_data.csv.
check the file size
show the first few lines of data

MODIS data|

Getting a MODIS product URL

If we need to add a username and password to access a URL, we can add this to the URL object with URL.with_userinfo(USERNAME,PASSWORD). We will demonstrate this with accessing data from NASA servers.

One of the deepest sources of geospatial information over the last two decades is that obtained from the NASA MODIS products. We will make use of various MODIS datasets in this course. We can access these via a URL.

The encoding for this is not very complex, but is a bit beyond what we have time to go through at this stage of the course. For that reason, we will be using the local utility geog0111.modisUtils.modisURL to access MODIS URLs. Further, accessing the URLs for these data needs a data file to vbe downloaded from the web. This can take some time, so in this code, we provide a cache of files that we have already downloaded. If you access a data product/date for the first time, it will take a minute to access the information. The next time you access, it will be immediate as you will use the cached version. See the help information for more details.

We use the example of the product MCD15A3H below. This is the MODIS 4-day Leaf Area Index product.

from geog0111.modisUtils import modisURL

# uncomment this to look at the help for this function
#help(modisURL)

from geog0111.modisUtils import modisURL

# set up control for which MODIS product/date/tile

modinfo = {  
    'product'  : 'MCD15A3H',
    'year'     : 2020,
    'month'    : 1,
    'day'      : 5,
    'tile'     : 'h08v06'
}

url = modisURL(**modinfo,verbose=False)
print(f'-> {url}')

-> https://e4ftl01.cr.usgs.gov/MOTA/MCD15A3H.006/2020.01.05/MCD15A3H.A2020005.h08v06.006.2020010210940.hdf

Pulling MODIS data with password

We add the username and password with URL.with_userinfo(USERNAME,PASSWORD).

Rather than type this information in here or expose it in stored notebooks, we retrieve it using Cylog:

from geog0111.modisUtils import modisURL
from geog0111.cylog import Cylog
from urlpath import URL
from pathlib import Path

# set up control for which MODIS product/date/tile

modinfo = {  
    'product'  : 'MCD15A3H',
    'year'     : 2020,
    'month'    : 1,
    'day'      : 1,
    'tile'     : 'h08v06'
}

url = modisURL(**modinfo,verbose=False)
if (url):
    print(f'anchor: {url.anchor}')
    print(f'-> {url}')

    # use url.anchor to get server name for Cylog
    # add the username and password
    username,password = Cylog(url.anchor).login()
    url = url.with_userinfo(username,password)

    # uncomment this line to see how username and password are inserted
    #print(f'-> {url}')
else:
    print(f'error with data request')

anchor: https://e4ftl01.cr.usgs.gov
-> https://e4ftl01.cr.usgs.gov/MOTA/MCD15A3H.006/2020.01.01/MCD15A3H.A2020001.h08v06.006.2020006032951.hdf

Because of the NASA login, we need to make two calls to the server. The first will get a new URL with encoded authentification information.

url.get() returns an object of type requests.models.Response. The new URL will be r.url, so we take that and add the username and password to it again.

# first call to URL to get auth
r = url.get()
url2 = URL(r.url).with_userinfo(username,password)

Now we can make a call to this with get() to retrieve the data from the URL. We need to check that the status_code returned is 200 to see if the call worked:

r2 = url2.get()
print(f'response: {r2.status_code}')

response: 200

If the response is not 200, then you have a problem of some sort. This could be that you don't have a correct password stored via cylog. If you suspect that might be the case, go back and check the test on 004_Accounts.md. If it is Wednesday afternoon, it is probably just that the NASA servers are down for maintenance.

If its ok (200), we can get the binary data as r2.content and use Path().write_bytes() to write this to a file:

if r2.status_code == 200:

    # setup Path object for output file
    filename = Path('work',url.name)

    # write binary data
    filename.write_bytes(r2.content)

    # check size:
    size_MB = filename.stat().st_size/(1024**2)

    # report
    print(f'file {filename} written: {size_MB :.1f} MB')
else:
    print(f'{url} status code {r2.status_code}')

file work/MCD15A3H.A2020001.h08v06.006.2020006032951.hdf written: 8.6 MB

Occasionally, you will find that the calls to get data from the server will not work, and may produce 401 errors.

This is typically because: * it is Wednesday, when the servers are down for maintenance * the load is too high on the servers, in which case, wait a minute and try again or set the timeout option URL.get(timeout=200).

Exercise 2

pull the MODIS dataset MCD15A3H for 9 January 2019 for tile h08v06 and confirm that the dataset size is 8.5 MB

Easier MODIS file access

In fact, we have a related MODIS utility modisFile that does all of this for you, and uses the cache to store files we have previously downloaded. You will generally be using that then, but you should know how to get the binary data from the URL directly as above to be a competent programmer.

from geog0111.modisUtils import modisFile

# set up control for which MODIS product/date/tile

modinfo = {  
    'product'  : 'MCD15A3H',
    'year'     : 2020,
    'month'    : 1,
    'day'      : 5,
    'tile'     : 'h08v06'
}

filename = modisFile(**modinfo,verbose=False)

if filename:
    size_MB = filename.stat().st_size/(1024**2)

    # report
    print(f'file {filename} is: {size_MB :.1f} MB')

file /Users/plewis/Documents/GitHub/geog0111/notebooks/.modis_cache/e4ftl01.cr.usgs.gov/MOTA/MCD15A3H.006/2020.01.05/MCD15A3H.A2020005.h08v06.006.2020010210940.hdf is: 8.9 MB

Exercise 3

pull the MODIS dataset MCD15A3H for 13 January 2019 for tile h08v06 using modisFile and confirm that the dataset size is 8.5 MB

`gdal`

The MODIS files are in hdf format, and as we have noted, we do not generally want direct access to the raw (byte) information. Instead we must use some package to interpret the data.

We can use the package gdal to access information from these and other geospatial files. We will explore the contents of MODIS files in a later session, but for now, we can note that each MODIS file contains a set of sub datasets.

Basic use of gdal in this context is:

g = gdal.Open(filename.as_posix())

where filename.as_posix() is a string of the filename we want open the file in gdal. If this returns None, there has been a problem opening the file, so we might check that.

Then

g.GetSubDatasets()

returns a list of sub-dataset information. Each item in the list is a tuple of two strings. In each, the first is the full name of the sub-dataset, and the second a text descriptor of the dataset. We call these filename,name below.

We read the dataset with:

gsub = gdal.Open(filename)
data = gsub.ReadAsArray()

In the illustration below, we will examine only the first sub-dataset g.GetSubDatasets()[0].

from osgeo import gdal
from geog0111.modisUtils import modisFile
# settings

modinfo = {  
    'product'  : 'MCD15A3H',
    'year'     : 2019,
    'month'    : 2,
    'day'      : 10,
    'tile'     : 'h08v06'
}

filename = modisFile(**modinfo,verbose=False)

if filename:
# open the local file associated with the dataset
    g = gdal.Open(filename.as_posix())
    if g:
        # get the first SDS only for illustration
        filename,name = g.GetSubDatasets()[0]
        print(f'dataset info is: {name}')
        # read the dataset
        gsub = gdal.Open(filename)
        if gsub:
            data = gsub.ReadAsArray()
        print(f'dataset read is shape {data.shape} and type {type(data)}')

dataset info is: [2400x2400] Fpar_500m MOD_Grid_MCD15A3H (8-bit unsigned integer)
dataset read is shape (2400, 2400) and type <class 'numpy.ndarray'>

Exercise 4

name = '[2400x2400] Fpar_500m MOD_Grid_MCD15A3H (8-bit unsigned integer)'

Take the string variable name above, split it to obtain the second field (Fpar_500m here) and store this in a variable sds_name
Write a function called getModisTiledata that reads an HDF (MODIS) filename, and returns a dictionary of all of the sub-datasets in the file, using ReadAsArray(). The dictionary keys should correspond to the items in sds_name above.
test the code by showing the keys in the dictionary returned and the shape of their dataset

You will need to recall how to split a string, that was covered in 013 Python string methods. You will also need to recall how to loop over a dictionary. We saw how to find the shape of the dataset returned above (.shape).

Summary

In this section, we have considered URLs in some detail in the same way as we did filenames, and made use of functions from urlpath to access them.

function	purpose
`URL.name`	filename
`URL.parent`	parent
`URL.parts`	parts
`URL.as_posix()`	return URL as posix string
`URL.with_userinfo()`	add in username and password
`URL.get()`	URL get. Returns `requests.models.Response`
`URL.netloc`	network location e.g. `www.google.com`
`URL.path`	full pathname on server (including filename)

We have seen how to access and download both text and binary files from the web using URL.get(). We have seen how to add username and password information to this, and have used that to access MODIS binary datasets. We know how to save the files that we download.

We have seen a simple MODIS file library to make downloading datasets easier. This includes caching of datasets that otherwise can take a long time to download. It also allows for sharing of downloaded datasets through a system-wide cache.

There will be times when a call to a URL doesn't return as expected. There can be several reasons for this, so you should check that you have formed the URL correctly and that the data you expect to download exists.

Functions in modisUtils:

function	purpose
`modisFile`	get a Path object for a file of the requested MODIS dataset, either from cache, or by downloading
`modisURL`	get the URL of a MODIS dataset, possibly using a cache for the filename

Even then, there can be times when the server load or network traffic mean that your request times out. In such cases, you should try again a short while afterwards, and consider setting a timeout on the URL.get() call. In any case, this point illustrates the need for comprehensive error checking: you cannot just assume that you download of data has worked, you must check it. This should also apply when you are using any files: check that any file you want to read exists and perhaps see if it is non-zero sized. You can trap errors, but it is best to try foresee them.

We have learned a little of how to use gdal to look at the sub-datasets in an HDF file and also how to read them.

gdal

Command	Comment
`g = gdal.Open(filename)`	Open geospatial file `filename` and return `gdal` object `g` (`None` if file not opened correctly)
`g.GetSubDatasets()`	Get list of sub-datasets from `gdal` object `g`
`g.ReadAsArray()`	Read dataset from `gdal` object `g` into array

You should now have some confidence in these matters, so that if you were set a task of downloading and saving datasets, as well as other tasks such as finding their size, whether the exists or not, you could do this.

Last update: December 6, 2022