sddk 3.9

A package to access sciencedata.dk

sddk

sddk is a Python package for writting and reading files to/from sciencedata.dk. Since version 3.0, it also supports owncloud.cesnet.cz. In the future, it will support other providers from the CS3MESH4EOSC inciative. It is especially designed for working with shared folders. It relies mainly upon Python requests library.

sciencedata.dk is a project managed by DEiC (Danish e-infrastrcture cooperation) aimed to offer a robust data storage, data management and data publication solution for researchers in Denmark and abroad (see docs and dev for more info). The storage is accessible either through (1) the web interface, (2) WebDAV clients, (3) OwnCloud/NextCloud desktop applications or (4) API relaying on the HTTP Protocol. One strength of sciencedata.dk is that it currently supports institutional login from 2976 research and educational institutions around the globe (using WAYF). That makes it a perfect tool for international research collaboration.

The main functionality of the package is in uploading any Python object (str, dict, list, dataframe or figure) as a file to a preselected personal or shared folder on the cloud platform and getting it back into Python as the original Python object. It uses sciencedata.dk API in combination with Python requests library.

Install and import

To install the package within within command line, run:

pip install sddk # # to have the latest version, use flag "--ignore-installed"

To install tje package within Jupyter environment, run:

!pip install sddk # to have the latest version, use flag "--ignore-installed"

Once installed, import the package in the following way:

import sddk

Authentification

To establish the cloud session, you have to know the following:

• name of the service provider; currently we support two options: sciencedata.dk or owncloud.cesnet.cz (sciencedata.dk by default)

• username/ID from the service provider (e.g. "[e-mail]" or "1fcd40da27c3573f1479718227a43e1a5426aac1"),

• password / token from the provider (has to be previously configured or generated manually using the web interface of the provider),

In the case you want to access a shared folder, you further need:

• name of the shared folder you want to access (e.g. "our_shared_folder"),
• username / id of the owner of the folder (if it is not yours)

cloudSession()

• parameters:
  • provider - default: "sciencedata.dk"; alternatively "owncloud.cesnet.cz"
  • shared_folder_name - name of the shared folder; default None
  • owner - username of the owner of the shared folder; default None

In the case of a shared folder, you might be either its owner, or it might be a folder which has been shared with you by someone else, who is its owner- one important feature of the package is that in both cases you use exactly the same syntax. That means all members of a team can configure the session and access the folder using the same piece of code, the rest is entered interactively.

Calling the cloudSession() class, you configure a an authorized session class object s, which supports an array of useful functions.

Establish personal session

s = cloudSession() # "sciencedata.dk by default for owncloud.cesnet.cz, run:
# s = cloudSession("owncloud.cesnet.cz")

Establish session with root in shared folder

To configure a session pointing to a shared folder, run:

s = sddk.cloudSession("sciencedata.dk", "our_shared_folder", "[e-mail]")

Subsequently, you can locate your files in relative path to this root folder ("our_shared_folder")

write_file()

The most important components of the package are two functions: write_file(path_and_filename, python_object) and read_file(path_and_filename, type_of_object).

So far these functions can be used with several different types of Python objects: str, list, dictionary, pandas' dataframe, geopandas geodataframe , matplotlib's figure, and plotly image object. These can be written either as .txt, .json, geojson , .png or .eps files, based upon type of the input object and filename's ending chosen by the user. Here are simple instances of these python objects to play with:

### Python "str" object
string_object =  "string content"
### Python "list" object
list_object = ['a', 'b', 'c', 'd']
### Python "dictionary" object
dict_object = {"a" : 1, "b" : 2, "c":3 }
### Pandas dataframe object
import pandas as pd
dataframe_object = pd.DataFrame([("a1", "b1", "c1"), ("a2", "b2", "c2")], columns=["a", "b", "c"]) 
### Matplotlib figure object
import matplotlib.pyplot as plt
figure_object = plt.figure() # generate object
plt.plot(range(10)) # fill it by plotted values
### (the same also works for plotly figures)

The simplest example is once we want to write a string object into a textfile located at our home folder (or shared folder)

s.write_file("test_string.txt", string_object)

In the case that everything is fine, you will receive following message:

> Your <class 'str'> object has been succefully written as "https://sciencedata.dk/files/test_string.txt"

However, there is a couple of things which might go wrong - You can choose an unsupported python object, a non-existent path or unsupported file format. The function captures some of these cases. For instance, once you run sddk.write_file("nonexistent_folder/filename.wtf", string_object, conf), you will be interactively asked for corrections. First: the function checks whether the path is correct. When corrected to an existent folder (here it is "personal_folder"), the function further inspect whether it has known ending (i.e. txt, json, feather, or png). If not, it asks you interactively for correction. Third, it checks whether the folder already contain a file of the same name (to avoid unintended overwritting), and if yes, asks you what to do. Finally, it prints out where you can find your file and what type of object it encapsulates.

>>> The path is not valid. Try different path and filename: textfile.wtf
>>> Unsupported file format. Type either "txt", "json", or "png"
>>> A file with the same name ("textfile.txt") already exists in this location.
Press Enter to overwrite it or choose different path and filename: textfile2.txt

The same function works with dictionaries, lists, Matplotlib's figures and especially Pandas' dataframes. Pandas' dataframe is our favorite. We send there and back 1GB+ dataframes as json or feather files on a daily basis. See examples below.

read_file()

On the other side, we have the function s.read_file(path_and_filename, object_type), which enables us to to read our files back to python as chosen python objects. Currently, the function can read textfiles as strings, json files as either dictionary, lists or Pandas's dataframes, and geojson files as geopandas GeoDataFrames. You have to specify the type of object as the second argument, the values are either "str", "list", "dict", "df" or "gdf" within quotation marks, like in these examples. If you omit this, the file is parsed as pandas DataFrame.

string_object = s.read_file("test_string.txt", "str")
string_object
>>> 'string content'

list_object = s.read_file("simple_list.json", "list")
list_object
>>> ['a', 'b', 'c', 'd']

dict_object = s.read_file("simple_dict.json", "dict")
dict_object
>>> {'a': 1, 'b': 2, 'c': 3}

dataframe_object = s.read_file("simple_df.json")
dataframe_object
>>>     a   b   c
0  a1  b1  c1
1  a2  b2  c2

Examples

pandas.DataFrame to .json and back

import pandas as pd
dataframe_object = pd.DataFrame([("a1", "b1", "c1"), ("a2", "b2", "c2")], columns=["a", "b", "c"])
dataframe_object

	a	b	c
0	a1	b1	c1
1	a2	b2	c2

s.write_file("simple_dataframe.json", dataframe_object)
> Your <class 'pandas.core.frame.DataFrame'> object has been succefully written as "https://sciencedata.dk/files/simple_dataframe.json"

# read the file back as a new object "df_back"
df_back = s.read_file("simple_dataframe.json")
df_back

	a	b	c
0	a1	b1	c1
1	a2	b2	c2

Reading a larger dataframe file from a public folder:

%%time
EDH_sample = s.read_file("https://sciencedata.dk/public/8fe7d59de1eafe5f8eaebc0044534606/EDH_sample.json")
EDH_sample.head(5)
# alternatively, you can use it by setting the three arguments (it is just a matter of taste):
# EDH_sample = sddk.read_file("EDH_sample.json", "df", public_folder="8fe7d59de1eafe5f8eaebc0044534606")
EDH_sample.head(5)
# this is an example usage of public folder, see below for explanation.

	diplomatic_text	literature	trismegistos_uri	id	findspot_ancient	not_before	type_of_inscription	work_status	edh_geography_uri	not_after	...	external_image_uris	religion	fotos	geography	military	social_economic_legal_history	coordinates	text_cleaned	origdate_text	objecttype
0	D M / NONIAE P F OPTATAE / ET C IVLIO ARTEMONI...	AE 1983, 0192.; M. Annecchino, Puteoli 4/5, 19...	https://www.trismegistos.org/text/251193	HD000001	Cumae, bei	0071	epitaph	provisional	https://edh-www.adw.uni-heidelberg.de/edh/geog...	0130	...	None	None	None	None	None	None	40.8471577,14.0550756	Dis Manibus Noniae Publi filiae Optatae et Cai...	71 AD – 130 AD	[Tafel, 257]
1	C SEXTIVS PARIS / QVI VIXIT / ANNIS LXX	AE 1983, 0080. (A); A. Ferrua, RAL 36, 1981, 1...	https://www.trismegistos.org/text/265631	HD000002	Roma	0051	epitaph	no image	https://edh-www.adw.uni-heidelberg.de/edh/geog...	0200	...	None	None	None	None	None	None	41.895466,12.482324	Caius Sextius Paris qui vixit annis LXX ...	51 AD – 200 AD	[Tafel, 257]
2	[ ]VMMIO [ ] / [ ]ISENNA[ ] / [ ] XV[ ] / [ ] / [	AE 1983, 0518. (B); J. González, ZPE 52, 1983,...	https://www.trismegistos.org/text/220675	HD000003	None	0131	honorific inscription	provisional	https://edh-www.adw.uni-heidelberg.de/edh/geog...	0170	...	None	None	None	None	None	None	37.37281,-6.04589	Publio Mummio Publi filio Galeria Sisennae Rut...	131 AD – 170 AD	[Statuenbasis, 57]
3	[ ]AVS[ ]LLA / M PORCI NIGRI SER / DOMINAE VEN...	AE 1983, 0533. (B); A.U. Stylow, Gerión 1, 198...	https://www.trismegistos.org/text/222102	HD000004	Ipolcobulcula	0151	votive inscription	checked with photo	https://edh-www.adw.uni-heidelberg.de/edh/geog...	0200	...	[http://cil-old.bbaw.de/test06/bilder/datenban...	names of pagan deities	None	None	None	None	37.4442,-4.27471	AVSLLA Marci Porci Nigri serva dominae Veneri ...	151 AD – 200 AD	[Altar, 29]
4	[ ] L SVCCESSVS / [ ] L L IRENAEVS / [ ] C L T...	AE 1983, 0078. (B); A. Ferrua, RAL 36, 1981, 1...	https://www.trismegistos.org/text/265629	HD000005	Roma	0001	epitaph	no image	https://edh-www.adw.uni-heidelberg.de/edh/geog...	0200	...	None	None	None	None	None	None	41.895466,12.482324	libertus Successus Luci libertus Irenaeus C...	1 AD – 200 AD	[Stele, 250]

5 rows × 40 columns

pandas.DataFrame to .feather and back

This might cause issues because of the way how pandas implements pyarrow and feather. To work with feather, check that you have installed a correct version of pyarrow package:

import pyarrow
pyarrow.__version__

You need 0.17.1 or higher. Google colab comes with 0.14.1 by default, so you have to upgrade:

!pip install pyarrow --upgrade

and restart your runtime.

Originally, sddk 1.9-2.4 specified the requirement pyarrow>=0.17.1 , but it produced a lot of conflicts during an installation on Google colab, since there many other packages requiring pyarrow==0.14.1. Therefore, pyarrow is currently bypassed.

s.write_file("simple_dataframe.feather", dataframe_object)
> Your <class 'pandas.core.frame.DataFrame'> object has been succefully written as "https://sciencedata.dk/files/simple_dataframe.feather"

s.read_file("simple_dataframe.feather")

	a	b	c
0	a1	b1	c1
1	a2	b2	c2

Reading a larger file from public folder

%%time
EDH_sample = s.read_file("https://sciencedata.dk/public/8fe7d59de1eafe5f8eaebc0044534606/EDH_sample.feather")
EDH_sample.head(5)
# alternative solution:
# EDH_sample = s.read_file("EDH_sample.feather", "df", "8fe7d59de1eafe5f8eaebc0044534606")
EDH_sample.head(5)

	diplomatic_text	literature	trismegistos_uri	id	findspot_ancient	not_before	type_of_inscription	work_status	edh_geography_uri	not_after	...	external_image_uris	religion	fotos	geography	military	social_economic_legal_history	coordinates	text_cleaned	origdate_text	objecttype
0	D M / NONIAE P F OPTATAE / ET C IVLIO ARTEMONI...	AE 1983, 0192.; M. Annecchino, Puteoli 4/5, 19...	https://www.trismegistos.org/text/251193	HD000001	Cumae, bei	0071	epitaph	provisional	https://edh-www.adw.uni-heidelberg.de/edh/geog...	0130	...	NaN	None	NaN	None	None	None	40.8471577,14.0550756	Dis Manibus Noniae Publi filiae Optatae et Cai...	71 AD – 130 AD	NaN
1	C SEXTIVS PARIS / QVI VIXIT / ANNIS LXX	AE 1983, 0080. (A); A. Ferrua, RAL 36, 1981, 1...	https://www.trismegistos.org/text/265631	HD000002	Roma	0051	epitaph	no image	https://edh-www.adw.uni-heidelberg.de/edh/geog...	0200	...	NaN	None	NaN	None	None	None	41.895466,12.482324	Caius Sextius Paris qui vixit annis LXX ...	51 AD – 200 AD	NaN
2	[ ]VMMIO [ ] / [ ]ISENNA[ ] / [ ] XV[ ] / [ ] / [	AE 1983, 0518. (B); J. González, ZPE 52, 1983,...	https://www.trismegistos.org/text/220675	HD000003	None	0131	honorific inscription	provisional	https://edh-www.adw.uni-heidelberg.de/edh/geog...	0170	...	NaN	None	NaN	None	None	None	37.37281,-6.04589	Publio Mummio Publi filio Galeria Sisennae Rut...	131 AD – 170 AD	NaN
3	[ ]AVS[ ]LLA / M PORCI NIGRI SER / DOMINAE VEN...	AE 1983, 0533. (B); A.U. Stylow, Gerión 1, 198...	https://www.trismegistos.org/text/222102	HD000004	Ipolcobulcula	0151	votive inscription	checked with photo	https://edh-www.adw.uni-heidelberg.de/edh/geog...	0200	...	NaN	names of pagan deities	NaN	None	None	None	37.4442,-4.27471	AVSLLA Marci Porci Nigri serva dominae Veneri ...	151 AD – 200 AD	NaN
4	[ ] L SVCCESSVS / [ ] L L IRENAEVS / [ ] C L T...	AE 1983, 0078. (B); A. Ferrua, RAL 36, 1981, 1...	https://www.trismegistos.org/text/265629	HD000005	Roma	0001	epitaph	no image	https://edh-www.adw.uni-heidelberg.de/edh/geog...	0200	...	NaN	None	NaN	None	None	None	41.895466,12.482324	libertus Successus Luci libertus Irenaeus C...	1 AD – 200 AD	NaN

5 rows × 40 columns

pandas.DataFrame to .csv and back

import pandas as pd
dataframe_object = pd.DataFrame([("a1", "b1", "c1"), ("a2", "b2", "c2")], columns=["a", "b", "c"]) 
dataframe_object

	a	b	c
0	a1	b1	c1
1	a2	b2	c2

s.write_file("simple_dataframe.csv", dataframe_object)
> Your <class 'pandas.core.frame.DataFrame'> object has been succefully written as "https://sciencedata.dk/files/simple_dataframe.csv"

s.read_file("simple_dataframe.csv")

	a	b	c
0	a1	b1	c1
1	a2	b2	c2

list_filenames()

This function enables you to list all files within a directory. You can specify the directory, type of the file you are interested in and the conf variable. For instance, the function belows returns all JSON files within your main directory.

 s.list_filenames(filetype="json")

Personal, shared and public folders

Shared in and out

One of the main strength of the sciencedata.dk are collaborative features, namely the way you can manage its shared and public folders.

Shared folders always have one of two forms: either (1) a shared folder you share with some users or (2) a shared folder someone else shares with you.

Each shared folder has its owner. The folders are located in their owner's personal space and can be easily accessed from there like from any other personal folder. However, in the case of shared folders you do not own (i.e. which were shared with you by someone else) you also need to know the username of their owner.

One of the key features of the sddk package is that it enables you to access both types of shared folders using exactly the same syntax, regardless you are their owner or not. This enables that all members of a team accessing a folder owned and shared by one member can you use the same code. The function just checks both options and chooses what works.

For instance, a project member with username [e-mail] created a folder in his personal space called team_folder, uploaded there a file called textfile.txt, and shared the folder with his teammates with usernames [e-mail] and [e-mail]. All of them can now access the file using the same series of commands:

Public files and folders

Sciencedata.dk also enables to produce public files and folders. These files and folders might be accessed using sddk.read_file() function even without having sciencedata.dk account. You just have to know share link code of the file or folder. To read a public file, you can use:

public_file_code = "3e0a55a4182de313e04523360cecd015"
gospels_cleaned = sddk.read_file("https://sciencedata.dk/public/" + public_file_code, "dict")
# of course, you can write it directly:
# gospels_cleaned = s.read_file("https://sciencedata.dk/public/3e0a55a4182de313e04523360cecd015", "dict")

Public files can be read even if you are not logged into a session at the moment (using sddk.read_file() instead of s.read_file())

gospels_cleaned = sddk.read_file("https://sciencedata.dk/public/" + public_file_code, "dict")

To read a specific file within a public folder, you can use the code below, i.e. you can replace the conf parameter by sharing code of the public folder.

public_folder_code = "31b393e2afe1ee96ce81869c7efe18cb"
c_aristotelicum = sddk.read_file("c_aristotelicum.json", "df", public_folder_code)

write_file()

The most important components of the package are two functions: write_file(path_and_filename, python_object, conf) and read_file(path_and_filename, type_of_object, conf).

So far these functions can be used with several different types of Python objects: str, list, dictionary, pandas' dataframe, geopandas geodataframe and matplotlib's figure. These can be written either as .txt, .json, geojson , .png or .eps files, based upin type of the input object a d filename's ending chosen by the user. Here are simple instances of these python objects to play with:

### Python "str" object
string_object =  "string content"
### Python "list" object
list_object = ['a', 'b', 'c', 'd']
### Python "dictionary" object
dict_object = {"a" : 1, "b" : 2, "c":3 }
### Pandas dataframe object
import pandas as pd
dataframe_object = pd.DataFrame([("a1", "b1", "c1"), ("a2", "b2", "c2")], columns=["a", "b", "c"]) 
### Matplotlib figure object
import matplotlib.pyplot as plt
figure_object = plt.figure() # generate object
plt.plot(range(10)) # fill it by plotted values
### (the same also works for plotly figures)

The simplest example is once we want to write a string object into a textfile located at our home folder (Remember, that since the configuration this home folder is contained within the sddk_url variable )

sddk.write_file("test_string.txt", string_object, conf)

In the case that everything is fine, you will receive following message:

> Your <class 'str'> object has been succefully written as "https://sciencedata.dk/files/test_string.txt"

However, there is a couple of things which might go wrong - You can choose an unsupported python object, a non-existent path or unsupported file format. The function captures some of these cases. For instance, once you run sddk.write_file("nonexistent_folder/filename.wtf", string_object, conf), you will be interactively asked for corrections. First: the function checks whether the path is correct. When corrected to an existent folder (here it is "personal_folder"), the function further inspect whether it has known ending (i.e. txt, json, feather, or png). If not, it asks you interactively for correction. Third, it checks whether the folder already contain a file of the same name (to avoid unintended overwritting), and if yes, asks you what to do. Finally, it prints out where you can find your file and what type of object it encapsulates.

>>> The path is not valid. Try different path and filename: textfile.wtf
>>> Unsupported file format. Type either "txt", "json", or "png": txt
>>> A file with the same name ("textfile.txt") already exists in this location.
Press Enter to overwrite it or choose different path and filename: textfile2.txt
>>> Your <class 'str'> object has been succefully written as "https://sciencedata.dk/files/textfile2.txt"

The same function works with dictionaries, lists, Matplotlib's figures and especially Pandas' dataframes. Pandas' dataframe is my favorite. I send there and back 1GB+ dataframes as json or feather files on a daily basis. See examples below

read_file()

On the other side, we have the function sddk.read_file(path_and_filename, object_type), which enables us to to read our files back to python as chosen python objects. Currently, the function can read only textfiles as strings, and json files as either dictionary, lists or Pandas's dataframes. You have to specify the type of object as the second argument, the values are either "str", "list", "dict", "df" or "gdf" within quotation marks, like in these examples:

string_object = read_file("test_string.txt", "str", conf)
string_object
>>> 'string content'

list_object = read_file("simple_list.json", "list", conf)
list_object
>>> ['a', 'b', 'c', 'd']

dict_object = read_file("simple_dict.json", "dict", conf)
dict_object
>>> {'a': 1, 'b': 2, 'c': 3}

dataframe_object = read_file("simple_df.json", "df", conf)
>>>     a   b   c
0  a1  b1  c1
1  a2  b2  c2

Examples

pandas.DataFrame to .json and back

import pandas as pd
dataframe_object = pd.DataFrame([("a1", "b1", "c1"), ("a2", "b2", "c2")], columns=["a", "b", "c"])
dataframe_object

	a	b	c
0	a1	b1	c1
1	a2	b2	c2

sddk.write_file("simple_dataframe.json", dataframe_object, conf)
> Your <class 'pandas.core.frame.DataFrame'> object has been succefully written as "https://sciencedata.dk/files/simple_dataframe.json"

sddk.read_file("simple_dataframe.json", "df", conf)

	a	b	c
0	a1	b1	c1
1	a2	b2	c2

Reading a larger file from a public folder

%%time
EDH_sample = sddk.read_file("EDH_sample.json", "df", "8fe7d59de1eafe5f8eaebc0044534606")
EDH_sample.head(5)
# this is an example usage of public folder, see below for explanation.

	diplomatic_text	literature	trismegistos_uri	id	findspot_ancient	not_before	type_of_inscription	work_status	edh_geography_uri	not_after	...	external_image_uris	religion	fotos	geography	military	social_economic_legal_history	coordinates	text_cleaned	origdate_text	objecttype
0	D M / NONIAE P F OPTATAE / ET C IVLIO ARTEMONI...	AE 1983, 0192.; M. Annecchino, Puteoli 4/5, 19...	https://www.trismegistos.org/text/251193	HD000001	Cumae, bei	0071	epitaph	provisional	https://edh-www.adw.uni-heidelberg.de/edh/geog...	0130	...	None	None	None	None	None	None	40.8471577,14.0550756	Dis Manibus Noniae Publi filiae Optatae et Cai...	71 AD – 130 AD	[Tafel, 257]
1	C SEXTIVS PARIS / QVI VIXIT / ANNIS LXX	AE 1983, 0080. (A); A. Ferrua, RAL 36, 1981, 1...	https://www.trismegistos.org/text/265631	HD000002	Roma	0051	epitaph	no image	https://edh-www.adw.uni-heidelberg.de/edh/geog...	0200	...	None	None	None	None	None	None	41.895466,12.482324	Caius Sextius Paris qui vixit annis LXX ...	51 AD – 200 AD	[Tafel, 257]
2	[ ]VMMIO [ ] / [ ]ISENNA[ ] / [ ] XV[ ] / [ ] / [	AE 1983, 0518. (B); J. González, ZPE 52, 1983,...	https://www.trismegistos.org/text/220675	HD000003	None	0131	honorific inscription	provisional	https://edh-www.adw.uni-heidelberg.de/edh/geog...	0170	...	None	None	None	None	None	None	37.37281,-6.04589	Publio Mummio Publi filio Galeria Sisennae Rut...	131 AD – 170 AD	[Statuenbasis, 57]
3	[ ]AVS[ ]LLA / M PORCI NIGRI SER / DOMINAE VEN...	AE 1983, 0533. (B); A.U. Stylow, Gerión 1, 198...	https://www.trismegistos.org/text/222102	HD000004	Ipolcobulcula	0151	votive inscription	checked with photo	https://edh-www.adw.uni-heidelberg.de/edh/geog...	0200	...	[http://cil-old.bbaw.de/test06/bilder/datenban...	names of pagan deities	None	None	None	None	37.4442,-4.27471	AVSLLA Marci Porci Nigri serva dominae Veneri ...	151 AD – 200 AD	[Altar, 29]
4	[ ] L SVCCESSVS / [ ] L L IRENAEVS / [ ] C L T...	AE 1983, 0078. (B); A. Ferrua, RAL 36, 1981, 1...	https://www.trismegistos.org/text/265629	HD000005	Roma	0001	epitaph	no image	https://edh-www.adw.uni-heidelberg.de/edh/geog...	0200	...	None	None	None	None	None	None	41.895466,12.482324	libertus Successus Luci libertus Irenaeus C...	1 AD – 200 AD	[Stele, 250]

5 rows × 40 columns

pandas.DataFrame to .feather and back

This might cause issues because of the way how pandas implements pyarrow and feather. To work with feather, check that you have installed a correct version of pyarrow package:

import pyarrow
pyarrow.__version__

You need 0.17.1 or higher. Google colab comes with 0.14.1 by default, so you have to upgrade:

!pip install pyarrow --upgrade

and restart your runtime.

Originally, sddk 1.9-2.4 specified the requirement pyarrow>=0.17.1 , but it produced a lot of conflicts during an installation on Google colab, since there many other packages requiring pyarrow==0.14.1. Therefore, pyarrow is currently bypassed.

sddk.write_file("simple_dataframe.feather", dataframe_object, conf)
> Your <class 'pandas.core.frame.DataFrame'> object has been succefully written as "https://sciencedata.dk/files/simple_dataframe.feather"

sddk.read_file("simple_dataframe.feather", "df", conf)

	a	b	c
0	a1	b1	c1
1	a2	b2	c2

Reading a larger file from public folder

%%time
EDH_sample = sddk.read_file("EDH_sample.feather", "df", "8fe7d59de1eafe5f8eaebc0044534606")
EDH_sample.head(5)

	diplomatic_text	literature	trismegistos_uri	id	findspot_ancient	not_before	type_of_inscription	work_status	edh_geography_uri	not_after	...	external_image_uris	religion	fotos	geography	military	social_economic_legal_history	coordinates	text_cleaned	origdate_text	objecttype
0	D M / NONIAE P F OPTATAE / ET C IVLIO ARTEMONI...	AE 1983, 0192.; M. Annecchino, Puteoli 4/5, 19...	https://www.trismegistos.org/text/251193	HD000001	Cumae, bei	0071	epitaph	provisional	https://edh-www.adw.uni-heidelberg.de/edh/geog...	0130	...	NaN	None	NaN	None	None	None	40.8471577,14.0550756	Dis Manibus Noniae Publi filiae Optatae et Cai...	71 AD – 130 AD	NaN
1	C SEXTIVS PARIS / QVI VIXIT / ANNIS LXX	AE 1983, 0080. (A); A. Ferrua, RAL 36, 1981, 1...	https://www.trismegistos.org/text/265631	HD000002	Roma	0051	epitaph	no image	https://edh-www.adw.uni-heidelberg.de/edh/geog...	0200	...	NaN	None	NaN	None	None	None	41.895466,12.482324	Caius Sextius Paris qui vixit annis LXX ...	51 AD – 200 AD	NaN
2	[ ]VMMIO [ ] / [ ]ISENNA[ ] / [ ] XV[ ] / [ ] / [	AE 1983, 0518. (B); J. González, ZPE 52, 1983,...	https://www.trismegistos.org/text/220675	HD000003	None	0131	honorific inscription	provisional	https://edh-www.adw.uni-heidelberg.de/edh/geog...	0170	...	NaN	None	NaN	None	None	None	37.37281,-6.04589	Publio Mummio Publi filio Galeria Sisennae Rut...	131 AD – 170 AD	NaN
3	[ ]AVS[ ]LLA / M PORCI NIGRI SER / DOMINAE VEN...	AE 1983, 0533. (B); A.U. Stylow, Gerión 1, 198...	https://www.trismegistos.org/text/222102	HD000004	Ipolcobulcula	0151	votive inscription	checked with photo	https://edh-www.adw.uni-heidelberg.de/edh/geog...	0200	...	NaN	names of pagan deities	NaN	None	None	None	37.4442,-4.27471	AVSLLA Marci Porci Nigri serva dominae Veneri ...	151 AD – 200 AD	NaN
4	[ ] L SVCCESSVS / [ ] L L IRENAEVS / [ ] C L T...	AE 1983, 0078. (B); A. Ferrua, RAL 36, 1981, 1...	https://www.trismegistos.org/text/265629	HD000005	Roma	0001	epitaph	no image	https://edh-www.adw.uni-heidelberg.de/edh/geog...	0200	...	NaN	None	NaN	None	None	None	41.895466,12.482324	libertus Successus Luci libertus Irenaeus C...	1 AD – 200 AD	NaN

5 rows × 40 columns

sddk.write_file("EDH_sample.feather", EDH_sample, conf)
> Your <class 'pandas.core.frame.DataFrame'> object has been succefully written as "https://sciencedata.dk/files/EDH_sample.feather"

pandas.DataFrame to .csv and back

import pandas as pd
dataframe_object = pd.DataFrame([("a1", "b1", "c1"), ("a2", "b2", "c2")], columns=["a", "b", "c"]) 
dataframe_object

	a	b	c
0	a1	b1	c1
1	a2	b2	c2

sddk.write_file("simple_dataframe.csv", dataframe_object, conf)
> Your <class 'pandas.core.frame.DataFrame'> object has been succefully written as "https://sciencedata.dk/files/simple_dataframe.csv"

sddk.read_file("simple_dataframe.csv", "df", conf)

	a	b	c
0	a1	b1	c1
1	a2	b2	c2

list_filenames()

This function enables you to list all files within a directory. You can specify the directory, type of the file you are interested in and the conf variable. For instance, the function belows returns all JSON files within your main directory.

 sddk.list_filenames(filetype="json", conf=conf)

Personal, shared and public folders

Shared in and out

One of the main strength of the sciencedata.dk are collaborative features, namely the way you can manage its shared and public folders.

Shared folders always have one of two forms: either (1) a shared folder you share with some users or (2) a shared folder someone else shares with you.

Each shared folder has its owner. The folders are located in their owner's personal space and can be easily accessed by them from there like any other personal folder.

However, in the case of shared folders you do not own (i.e. which were shared with you by someone else) you also need to know the username of their owner.

One of the key features of the sddk package is that it enables you to access both types of shared folders using exactly the same command, regardless you are their owner or not. This enables that all members of a team accessing a folder owned and shared by one member can you use the same code. The function just checks both options and chooses what works.

For instance, a project member with username [e-mail] created a folder in his personal space called team_folder, uploaded there a file called textfile.txt, and shared the folder with his teammates with usernames [e-mail] and [e-mail]. All of them can now access the file using the same series of commands:

# configure session with access to the shared folder:
conf = sddk.configure("team_folder", "[e-mail]")
# read the file located in this shared folder:
sddk.read_file("testfile.txt", "str", conf)

Public files and folders

Sciencedata.dk also enables to produce public files and folders. These files and folders might be accessed using sddk.read_file() function even without having sciencedata.dk account. You just have to know share link code of the file or folder. To read a public file, you can use:

public_file_code = "3e0a55a4182de313e04523360cecd015"
gospels_cleaned = sddk.read_file("https://sciencedata.dk/public/" + public_file_code, "dict")

To read a specific file within a public folder, you can use the code below, i.e. you can replace the conf parameter by sharing code of the public folder.

public_folder_code = "31b393e2afe1ee96ce81869c7efe18cb"
c_aristotelicum = sddk.read_file("c_aristotelicum.json", "df", public_folder_code)

Credit

The package is continuously develepod and maintained by Vojtěch Kaše as a part of the digital collaborative research workflow of the SDAM project at Aarhus University, Denmark. To cite this package, use:

Version history

• 3.9 - session with public folders (without auth)
• 3.8 - new function s.list_directories(); support for GeoDataFrames to .parquet and back
• 3.7 - fixing public files + version attribute
• 3.6 - fixing geojson and relative paths
• 3.5 - minor bugs
• 3.4 - fixing issues with feather
• 3.3 - fixing issues with missing plotly
• 3.2 - fixing an issue with nonfunctional "silo1" authentification & minor simplifications
• 3.1 - fixing an issue with nonfunctional "silo1" authentification & minor simplifications
• 3.0 - new way of authentification, based on cloudSession() class object; it also supports owncloud.cesnet.cz as service provider
• 2.10 - supports .geojson
• 2.9 - .eps file format for matplotlib figures support (plotly works only with .png)
• 2.8.2 - plotly support
• 2.7 - resolving issues #1 (reading public json files) & #2 (beautifulsoup import)
• 2.6 - pyarrow avoided
• 2.5 - pyarrow version changed back to unspecified
• 2.4 - json encoding bug removed
• 2.3 - json encoding
• 2.2 - setup.py update
• 2.1 - README.md update
• 2.0 - tested with .txt, .json, .feather and .png.
• 1.9 - supports public files and folders; supports .feather file format (utf.8 enforced)
• 1.8 - list_filenames() function and configure() alias added
• 1.7 - figures
• 1.6.1 - bug
• 1.6 - enables writing dataframes as csv
• 1.5 - reads individually shared files without necessary configuration
• 1.4 - json package dependency
• 1.3 - conf corrected
• 1.2 - conf variable added
• 1.1 - a simple correction
• 1.0 - functions write_file() and read_file() added
• 0.1.2 - redirection added
• 0.1.1 - added shared folder owner argument to the main configuration function; migration from test.pypi to real pypi
• 0.0.8 - shared folders reading&writing for ordinary users finally functional
• 0.0.7 - configuration of individual session by default
• 0.0.6 - first functional configuration