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adjugate | adjugateA package for calculating submatricies, minors, adjugate- and cofactor matricies.importnumpyasnpfromadjugateimportadjM=np.random.rand(4,4)adjM=adj(M)M,adjM(array([[0.46445212, 0.90776864, 0.5617845 , 0.46365445],
[0.23096052, 0.37817574, 0.19684724, 0.26416446],
[0.8157284 , 0.35109541, 0.29357478, 0.63978973],
[0.97616731, 0.71221494, 0.18073805, 0.7949908 ]]),
array([[ 0.04081465, -0.13855441, 0.00220198, 0.0204637 ],
[ 0.01427584, -0.02328107, -0.02248499, 0.01750541],
[ 0.01310092, -0.00671938, 0.02476545, -0.02533861],
[-0.06588407, 0.19251526, 0.01180968, -0.02407058]]))InstallationpipinstalladjugateUsageThis package provides four functions:submatrixsubmatrix(M, i, j)Removes thei-th row andj-th column ofM. Multiple indices or slices can be provided.fromadjugateimportsubmatrixM=np.arange(16).reshape(4,4)M,submatrix(M,1,2)(array([[ 0, 1, 2, 3],
[ 4, 5, 6, 7],
[ 8, 9, 10, 11],
[12, 13, 14, 15]]),
array([[ 0, 1, 3],
[ 8, 9, 11],
[12, 13, 15]]))minorminor(M, i, j)Calculates the (i,j) minor ofM.fromadjugateimportminorM=np.random.rand(4,4)minor(M,1,2)-0.2140487789380897adjadj(M)Calculates the adjugate ofM.M,adj(M)(array([[0.08665864, 0.99394655, 0.12535963, 0.26405097],
[0.5579125 , 0.86322768, 0.89414941, 0.568058 ],
[0.67013036, 0.84454395, 0.47131153, 0.19339756],
[0.66467323, 0.85295762, 0.13306573, 0.5569822 ]]),
array([[-0.23323852, -0.12057808, 0.24929258, 0.14698786],
[ 0.21400119, -0.06933021, 0.09234215, -0.06280701],
[-0.03500821, 0.21404878, 0.04025053, -0.21568468],
[-0.04102134, 0.19892593, -0.44852065, 0.29057721]]))cofcof(M)Calculates the cofactor matrix ofM.fromadjugateimportcofM,cof(M)(array([[0.08665864, 0.99394655, 0.12535963, 0.26405097],
[0.5579125 , 0.86322768, 0.89414941, 0.568058 ],
[0.67013036, 0.84454395, 0.47131153, 0.19339756],
[0.66467323, 0.85295762, 0.13306573, 0.5569822 ]]),
array([[-0.23323852, 0.21400119, -0.03500821, -0.04102134],
[-0.12057808, -0.06933021, 0.21404878, 0.19892593],
[ 0.24929258, 0.09234215, 0.04025053, -0.44852065],
[ 0.14698786, -0.06280701, -0.21568468, 0.29057721]]))SpeedIf you know that you matrix is invertible, thennp.linalg.det(M) * np.linalg.inv(M)might be a faster choice (O(3) instad of O(5)). But this is in general, especially as the determinant approaches zero, not possible or precise:importmatplotlib.pyplotaspltN=20dets,errs=[],[]for_inrange(10000):M=np.random.rand(N,N)dets+=[np.linalg.det(M)]errs+=[np.linalg.norm(adj(M)-np.linalg.det(M)*np.linalg.inv(M))]fig,ax=plt.subplots()ax.scatter(dets,errs,marker='o',s=(72./fig.dpi)**2)ax.set_yscale('log')ax.set_xlim(-2,+2)ax.set_ylim(1e-16,1e-12)ax.set_xlabel('det')ax.set_ylabel('err')plt.show() |
adjunct | No description available on PyPI. |
adjust | # adjustIn development. |
adjustable-random-package | More adjustable random functionsGenerating adjustable random values,
from min to max with numbers to adjust.For example, we have values from 1 to 10.If number 2 and 3 will be in numbers to adjust with k=5,
final array will look like this:[1,2,2,2,2,2,3,3,3,3,3,4,5,6,7,8,9,10]and chance of random.choice for those values
will be increasedExamplesRun basic functionsfrom adjustable_random import (
get_adjustable_random_list,
get_adjustable_random_value
)
if __name__ == '__main__':
adjustable_list: list[int] = get_adjustable_random_list(
min_value=0,
max_value=100,
numbers_to_adjust=[
10,15
]
)
adjustable_value: int = get_adjustable_random_value(
min_value=0,
max_value=100,
numbers_to_adjust=[
10,15
]
)
print(f"{adjustable_list=}")
print(f"{adjustable_value=}")Run tests to see how it worksfrom adjustable_random import (
init_random_values,
run_graph_test
)
if __name__ == '__main__':
values: list[int] = init_random_values()
run_graph_test(values) |
adjustor | AdjustorHome of the Adjustor TDP plugin for Handheld Daemon.
Adjustor currently allows for TDP control of all AMD Handhelds past generation
6### (support is added manually).
Since it integrates with Handheld Daemon, it is available throughDecky,
and throughhhd-ui.Adjustor implements a reversed engineered version of AMD's vendor function for
setting TDP on demand in Ryzen processors, through ACPI.
This means that it can be used regardless of the current memory policy
or secure-boot/lockdown status (provided the moduleacpi_callis installed.).In addition, it fully implements Lenovo's WMI protocol for the Legion Go, allowing
setting the TDP, including boosting behavior, without interference from
the Embedded Computer.
As part of the latest Lenovo bios, it also allows for setting a custom fan curve
for the Legion Go.AMD TDP ControlAdjustor controls TDP through the Dynamic Power and Thermal Configuration Interface
of AMD, which exposes a superset of the parameters that can be currently found inRyzenAdj, through ACPI.
This vendor interface is part of the ACPI ASL library, and provided through the
ALIB method 0x0C.
The underlying implementation of the interface is SMU calls.
This means that as long as the kernel moduleacpi_callis loaded, Adjustor
can control TDP in an equivalent way toRyzenAdj.Right now, Adjustor only implements a subset of useful ALIB parameters that are
well documented.
In addition, ALIB does not provide a way for reading the performance metrics table,
so Adjustor can only write (not read) TDP values.
From reverse engineering the Legion Go (seehere), and seeing how it
interacts with ALIB, it was found that there are at least 10 parameters which control
the method STTv2 and are not part of RyzenAdj or have been documented elsewhere.InstallationAdjustor is available onAURand provided Handheld Daemon has been installed throughAURtoo, it will load it automatically
on restart.
COPR coming soon.Alternatively, on a local install of Handheld Daemon you may:~/.local/share/hhd/venv/bin/pipinstall--upgradeadjustorHowever, the autoupdater in Handheld Daemon does not support updating yet.DevelopmentInstall to the same virtual environment as hhd to have Adjustor picked up
as a plugin upon restart, or to its own venv to use independently.pipinstall-e. |
adjust-precision-for-schema | No description available on PyPI. |
adjustpy | adjustpya simple utility python module to calculate adjusted p-values.IntroductionI was tired to copying over the same function between python scripts so
I decided to write this into a simple utilty you can install via pip.The computation is done with myadjustprust crate which I created to replicate the same p-value corrections done in R.InstallationYou can install this python module using pip.pipinstalladjustpyUsageThis is a single-function library which expects any dimension numpy arrays.1 Dimensional Inputimportnumpyasnpfromadjustpyimportadjustp_values=np.array([0.02,0.05,0.08,0.11,0.04])q_values=adjust(p_values,method="bh")# array([0.08333333, 0.08333333, 0.1 , 0.11 , 0.08333333])print(q_values)2 Dimensional InputThis works with multidimensional input as well, but will return a 1D output.
This is easy to work around though as you can just reshape to your original
input shape.importnumpyasnpfromadjustpyimportadjustp_values=np.random.random((10,10))q_values_flat=adjust(p_values,method="bh")q_values=adjust(p_values,method="bh").reshape(p_values.shape)# (100,)print(q_values_flat.shape)# (10, 10)print(q_values.shape) |
adjustText | adjustText - automatic label placement formatplotlibInspired byggrepelpackage for R/ggplot2 (https://github.com/slowkow/ggrepel)Alternative:textallochttps://github.com/ckjellson/textallocBrief descriptionThe idea is that often when we want to label multiple points on a graph the text will start heavily overlapping with both other labels and data points. This can be a major problem requiring manual solution. However this can be largely automatized by smart placing of the labels (difficult) or iterative adjustment of their positions to minimize overlaps (relatively easy). This library (well... script) implements the latter option to help with matplotlib graphs. Usage is very straightforward with usually pretty good results with no tweaking (most important is to just make text slightly smaller than default and maybe the figure a little larger). However the algorithm itself is highly configurable for complicated plots.Getting startedInstallationShould be installable from pypi:pip install adjustTextOr withconda:conda install -c conda-forge adjusttextFor the latest version from github:pip install https://github.com/Phlya/adjustText/archive/master.zipDocumentationWikihas some basic introduction, and more advanced usage examples can be foundhere.Thanks to Christophe Van Neste @beukueb,adjustTexthas a simple documentation:http://adjusttext.readthedocs.io/en/latest/CitingadjustTextTo cite the library if you use it in scientific publications (or anywhere else, if you wish), please use the link to the GitHub repository (https://github.com/Phlya/adjustText) and a zenodo doi (see top of this page). Thank you! |
adjusty | No description available on PyPI. |
adjutant | UNKNOWN |
adjutant-discord | AdjutantAdjutant is a package for managing ML experiments over Discord in conjunction with WandB.Adjutant allows users to run a Discord bot that provides updates on training jobs that have synced with WandB, and allows them to initiate new runs with different hyperparameters by posting in the Discord chat. Once the adjutant client is connected to Discord, users can start and get updates on training runs over Discord, from anywhere; no need to VPN andsshinto your office's servers, no need to open up your computer and try to remember where and in which files you need to set your hyperparameters. Just open up Discord and tell adjutant to start an experiment (or two, or three).Installationpipinstalladjutant-discordDiscord bot creationTo allow adjutant to post to Discord as a bot, first followthese instructionsfor creating a Discord bot and adding it to your Server. You then create anAdjutantobject with your bot token.Note: Be careful not to share your bot's token. Consider storing it in an environment variable or file that is not checked in to version control.WandB setupAdjutant is designed to work with WandB for ML experiment tracking. Create a WandB account atwandb.ai.Wherever you plan to run Adjutant, make sure you are either logged in to your WandB account, or have an API key populated in theWANDB_API_KEYenvironment variable. More information is available inthe WandB docs.Adjutant commandsOnce Adjutant is running and has connected to Discord (seethe Basic Adjutant example belowto get started), you can send it the following commands by posting in the chat.CommandEffectExample$helloGet a response from the bot$hello$experiment {hyperparams}Launch a new experiment with the given hyperparameters (must providerun_experiment_scriptin constructor)$experiment {"epochs": 10, "batch_size": 32}QuickstartFor more advanced examples, please seeexamples, starting withthe MNIST example.Basic AdjutantThe most basic formulation of Adjutant provides updates on WandB experiments under the given project name. Your WandB entity name is your account name, and the project title is the name of the project you have created (or will create) to store experiments.fromadjutantimportAdjutantclient=Adjutant('my-wandb-entity','my-wandb-project-title')client.run('my-discord-token')When you run the script, you will see your bot post to your Discord chat with information on the WandB runs it found for the project.Adjutant with experiment launchingBy providing arun_experiment_scriptconstructor argument, Adjutant will be able to respond to user requests on Discord to run a new experiment. Adjutant will executerun_experiment_scriptin a subprocess so that it can still respond to new requests.run_experiment_scriptmay also request another entity, e.g. Kubernetes, to initiate the experiment on its behalf rather than actually running the experiment itself.First, here are the contents ofrun_experiment.sh, which takes a JSON-formatted string as its command line argument. Adjutant will pass this script the hyperparameters with which to run the experiment. In this script,train_model.pytrains a new model with the supplied hyperparameters. For an example of what the training script might look like, seethe MNIST example.#!/bin/bashpythontrain_model.py"$1"Now we can create a client that referencesrun_experiment.sh.fromadjutantimportAdjutantclient=Adjutant('my-wandb-entity','my-wandb-project-title',run_experiment_script='./run_experiment.sh')client.run('my-discord-token')And we can run an experiment by posting in Discord with the$experimentcommand. Adjutant will start the experiment usingrun_experiment.shand post back on Discord when the run finishes. |
adjutant-odoo | Adjutant-Odoo is a plugin for Adjutant which adds a few actions and views
specific to the Odoo ERP system. These views can then be setup as active for
your users, and the actions can be used with your existing taskviews. Or just
as easily extend these views and actions for your own development.InstallingTo install:python setup.py installorpip install adjutant-odooAfter installation is complete addodoo_actionsandodoo_viewsto your
ADDITIONAL_APPS in the Adjutant conf.You can then use the Odoo actions as part of your Adjutant workflows, and
setup the Odoo views from this package in your ACTIVE_TASKVIEWS. For example
to introduce signups backed to Odoo you’d replace your other signup view in
ACTIVE_TASKVIEWS withOpenStackSignUp.You will also need to add some taskview settings for the new signups view:signup:
additional_actions:
- NewProjectDefaultNetworkAction
notifications:
standard:
EmailNotification:
emails:
- [email protected]
RTNotification:
queue: signups
error:
EmailNotification:
emails:
- [email protected]
RTNotification:
queue: signups
default_region: RegionOne
# If 'None' (null in yaml), will default to domain as parent.
# If domain isn't set explicity, will use Adjutant's admin user domain.
default_domain_id: default
default_parent_id: null
setup_network: TrueOnce active, and if debug is turned on, you can see the endpoint and test it
with the browsable django-rest-framework api.You will also need to add ‘adjutant-odoo’ plugin settings:PLUGIN_SETTINGS:
adjutant-odoo:
odoorpc:
odoo:
hostname: <odoo_hostname>
protocol: jsonrpc+ssl
port: 443
version: <odoo_version>
database: <odoo_db_name>
user: <odoo_username
password: <odoo_password> |
adjutant-ui | Adjutant DashboardFree software: Apache licenseSource:https://github.com/openstack/adjutant-uiThis is the Horizon plugin for the Adjutant service, and provides UI elements
to the core features that Adjutant adds.DocumentationDocumentation is stored in doc/, a sphinx build of the documentation can be
generated with the commandtox-edocs. |
adk | ADKA Develop Kit, for easy coding.Develop Tools:RPCConsole Commands: adk/adk.d config/start, details see "adk -h" for helpRPC config: adk.set_rpc_config, adk.get_rpc_config, adk.rpc_connect_argsadk.TaskManager, implemented methods ['set', 'get', 'clear', 'get_work_que', 'get_res_que', 'run']adk.start_rpc_server, base class is multiprocessing.managers.BaseManageradk.start_rpc_worker, get task from queue(get_work_que()), put result to queue(get_res_que())adk.rpc_client, get rpc client. ['set', 'get', 'clear'] for cache data, 'run' for submit task to serveradk.clt, a rpc client instance like above, for convenienceUtilsadk.TaskQue, Task Queue with key-value mapping, priority, speed limit, timeout and callbacks.adk.PriQue, Priority Queue with mutex lock and speed limit.adk.PriDict, Priority Dict with mutex lock and speed limit.Data types:Priority containersextension of heap, main methods are: push, pop, peekadk.PriorityQueue, Priority Queue, push(data, priority)adk.SimplePriorityQueue, Priority Queue, push(priority)adk.PriorityDict, Priority Dict, push(key, data, priority)adk.SimplePriorityDict, Priority Dict, push(key, priority) |
adkit | documentation can be found atthis url |
adk-testlab | # adk-testlab
> Sandbox area for my Python tests[]()
[]()A personal reference collection of test projects for my random development efforts.## Installation & Usage
TODO: Add more detailed instructions.## Release History0.0.1Initial project creation## Dependencies
TODO: Add future details here.## AboutDistributed under the MIT license. SeeLICENSEfor more information.[https://github.com/knightman/adk-testlab](https://github.com/knightman/adk-testlab) |
adl | No description available on PyPI. |
adl2pydm | Convert MEDM’s .adl files to PyDM’s .ui format. |
adl3 | This package wraps the AMD Display Library 3.0 [1] using ctypes. You donotneed to install the
SDK to use this package, and no compilation is required.Also included is a sample Python script using adl3 called _atitweak_. This script can list your
adapters and optionally sets core and memory clock speed and voltage per performance level.For example, to downlock your memory to 300MHz, you might use this command:$ atitweak -m 300
Setting performance level 0 on adapter 0: memory clock 300MHz
Setting performance level 1 on adapter 0: memory clock 300MHz
Setting performance level 2 on adapter 0: memory clock 300MHzWARNING! This software can damage or destroy your graphics card if used incorrectly.If this helps you squeeze out a few extra MHash/s, please consider throwing a few bitcoins my way:
1Kh3DsAhiu65EC7DFFHDGoGowAp5usQrCG[1]http://developer.amd.com/sdks/adlsdk/pages/default.aspxAMD Display Library 3.0 |
adleastcli | OverviewADLeastCLIprovides least operations for managing users/groups on Active
Directory(AD).When you want to use an AD for only authentication (i.e. AWS Client VPN with
Directory Service), it can manage users/groups with very simple procedure.And for changing password by each users, very simple web server is implemented.
For very limited usage, it can be used for changing password by users without
the administrator.Example:Create a user
$ adleastcli -S example.com user create testuser Password123#
Create a group
$ adleastcli -S example.com group create mygroup
Add the user to the group
$ adleastcli -S example.com user join testuser mygroup
Remove the user from the group
$ adleastcli -S example.com user leave testuser mygroup
Delete a group
$ adleastcli -S example.com group delete mygroup
Delete a user
$ adleastcli -S example.com user delete testuser
List users
$ adleastcli -S example.com user
Show user details
$ adleastcli -S example.com user info testuser
List groups
$ adleastcli -S example.com group
Show group details
$ adleastcli -S example.com group info mygroup
Set password(as administrator)
$ adleastcli -S example.com user setpw testuser NewPass123#
Change password(as a user)
$ adleastcli -S example.com -U testuser user passwd
Enter password for testuser:
Enter new password:
Confirm new password:
Start WebUI server for changing password by oneself
$ adleastcli -S example.com -b 0.0.0.0:8080 httpdInstallationInstall the latest release withpip install adleastclior simply downloadingadleastcli.py.Installing withpipstoreadleastclito your bin path, but it is only
copy ofadleastcli.py. If you getadleastcli.pywith downloading, you
can rename it toadleastcli. |
adler | No description available on PyPI. |
adlermanager | prometheus-adlermanagerWhat?A self-hostable status webpage that uses Prometheus alerts to create and
maintain service status and incident tracking / customer information flow.Why?Prometheusis awesome!
And AlertManager and Grafana are quite wonderful too, but they expose
too much information.This project allows you to choose which alerts/statistics get published in
a fashion suitable for user-facing status pages.I want it!DependenciesThe easiest way to manage dependencies for deployment and development is with
pipenv:On Debian-based systems:sudo apt install pipenvOn FreeBSD:pkg install py39-pipenv(ordevel/py-pipenvfrom ports)The actual dependencies are:attrstwisted[conch]service-identitypyyamlkleinjinja2markdownUsing Pipenv you can install them for development with:pipenvinstall--devAnd for deployment with:pipenvinstallConfigurationThis is done via environment variables, Pipenv will import them from a.envfile in the root of this repo.You can usedotenv.exampleas a base for your settings:cpdotenv.example.envReviewthe available settings and their descriptions, particularly you will
want to checkDATA_DIRandSSH_KEYS_DIR.SSH accessIn order to access AdlerManager via SSH, you will need to add your public SSH
key inauthorized_keysformat to:${SSH_KEYS_DIR:-data}/ssh/users/myuser.keyAnd give yourself access to the given site, by adding your username to itsssh_userslist.RunningTo run the server for development you can one of the following commands:# Using twistd# https://docs.twisted.org/en/stable/core/howto/basics.html#twistdpipenvruntwistd-nyapp.py# Running as a modulepython-madlermanagerAnd for deployment, you can use [twistd][twistd] itself to run the process
in the background or any other daemon watching strategy of your liking
(including e.g.runitorsystemd).
[twistd]:https://docs.twisted.org/en/stable/core/howto/basics.html#twistdUsingAfter that with the defaults you will have the public status web visible inhttp://localhost:8080and the ssh interface in localhost port 2222
which you can access withssh -p 2222 [email protected] does it work?We aim to solve that by using the same source of information to publish
only the desired state/statistics.1. Pretend to be an AlertManagerThis is done by acceptingPOSTrequests from Prometheus on/api/v1/alerts, seehttps://prometheus.io/docs/alerting/clients/docs.2. Structure Alerts into Services and Components3. Web only lists alerts configured for AdlerManager (public!)4. Keep track of incidents5. Allow for public updates / accountability (via SSH!) |
adlerpy | No description available on PyPI. |
adlframework | No description available on PyPI. |
adlfs | Filesystem interface to Azure-Datalake Gen1 and Gen2 StorageQuickstartThis package can be installed using:pip install adlfsorconda install -c conda-forge adlfsTheadl://andabfs://protocols are included in fsspec's known_implementations registry
in fsspec > 0.6.1, otherwise users must explicitly inform fsspec about the supported adlfs protocols.To use the Gen1 filesystem:importdask.dataframeasddstorage_options={'tenant_id':TENANT_ID,'client_id':CLIENT_ID,'client_secret':CLIENT_SECRET}dd.read_csv('adl://{STORE_NAME}/{FOLDER}/*.csv',storage_options=storage_options)To use the Gen2 filesystem you can use the protocolabfsoraz:importdask.dataframeasddstorage_options={'account_name':ACCOUNT_NAME,'account_key':ACCOUNT_KEY}ddf=dd.read_csv('abfs://{CONTAINER}/{FOLDER}/*.csv',storage_options=storage_options)ddf=dd.read_parquet('az://{CONTAINER}/folder.parquet',storage_options=storage_options)Acceptedprotocol/uriformatsinclude:'PROTOCOL://container/path-part/file''PROTOCOL://[email protected]/path-part/file'oroptionally,ifAZURE_STORAGE_ACCOUNT_NAMEandanAZURE_STORAGE_<CREDENTIAL>issetasanenvironmentalvariable,thenstorage_optionswillbereadfromtheenvironmentalvariablesTo read from a public storage blob you are required to specify the'account_name'.
For example, you can accessNYC Taxi & Limousine Commissionas:storage_options={'account_name':'azureopendatastorage'}ddf=dd.read_parquet('az://nyctlc/green/puYear=2019/puMonth=*/*.parquet',storage_options=storage_options)DetailsThe package includes pythonic filesystem implementations for both
Azure Datalake Gen1 and Azure Datalake Gen2, that facilitate
interactions between both Azure Datalake implementations and Dask. This is done leveraging theintake/filesystem_specbase class and Azure Python SDKs.Operations against both Gen1 Datalake currently only work with an Azure ServicePrincipal
with suitable credentials to perform operations on the resources of choice.Operations against the Gen2 Datalake are implemented by leveragingAzure Blob Storage Python SDK.Setting credentialsThestorage_optionscan be instantiated with a variety of keyword arguments depending on the filesystem. The most commonly used arguments are:connection_stringaccount_nameaccount_keysas_tokentenant_id,client_id, andclient_secretare combined for an Azure ServicePrincipal e.g.storage_options={'account_name': ACCOUNT_NAME, 'tenant_id': TENANT_ID, 'client_id': CLIENT_ID, 'client_secret': CLIENT_SECRET}anon:TrueorFalse. The default value for anon (i.e. anonymous) is Truelocation_mode: valid values are "primary" or "secondary" and apply to RA-GRS accountsFor more argument details see all arguments forAzureBlobFileSystemhereandAzureDatalakeFileSystemhere.The following environmental variables can also be set and picked up for authentication:"AZURE_STORAGE_CONNECTION_STRING""AZURE_STORAGE_ACCOUNT_NAME""AZURE_STORAGE_ACCOUNT_KEY""AZURE_STORAGE_SAS_TOKEN""AZURE_STORAGE_TENANT_ID""AZURE_STORAGE_CLIENT_ID""AZURE_STORAGE_CLIENT_SECRET"The filesystem can be instantiated for different use cases based on a variety ofstorage_optionscombinations. The following list describes some common use cases utilizingAzureBlobFileSystem, i.e. protocolsabfsoraz. Note that all cases require theaccount_nameargument to be provided:Anonymous connection to public container:storage_options={'account_name': ACCOUNT_NAME, 'anon': True}will assume theACCOUNT_NAMEpoints to a public container, and attempt to use an anonymous login. Note, the default value foranonis True.Auto credential solving using Azure's DefaultAzureCredential() library:storage_options={'account_name': ACCOUNT_NAME, 'anon': False}will useDefaultAzureCredentialto get valid credentials to the containerACCOUNT_NAME.DefaultAzureCredentialattempts to authenticate via themechanisms and order visualized here.Azure ServicePrincipal:tenant_id,client_id, andclient_secretare all used as credentials for an Azure ServicePrincipal: e.g.storage_options={'account_name': ACCOUNT_NAME, 'tenant_id': TENANT_ID, 'client_id': CLIENT_ID, 'client_secret': CLIENT_SECRET}.Append BlobTheAzureBlobFileSystemacceptsall of the Async BlobServiceClient arguments.By default, write operations create BlockBlobs in Azure, which, once written can not be appended. It is possible to create an AppendBlob usingmode="ab"when creating and operating on blobs. Currently, AppendBlobs are not available if hierarchical namespaces are enabled. |
adlib | No description available on PyPI. |
adlib27 | No description available on PyPI. |
adlinear | AdLinearUtility functions to manipulate data matricesWrapper classes around sklearn.PCA (Scikit-Learn), nmtf.NMF and nmtf.NTF (Paul Fogel) |
adlinear-dev | AdLinearUtility functions to manipulate data matricesWrapper classes around sklearn.PCA (Scikit-Learn), nmtf.NMF and nmtf.NTF (Paul Fogel) |
adlinear-staging | AdLinearUtility functions to manipulate data matricesWrapper classes around sklearn.PCA (Scikit-Learn), nmtf.NMF and nmtf.NTF (Paul Fogel) |
adlink-usb | Adlink USB is a fully open source implementation in python to
control Adlink USB DAQ devices. |
adlmagics | Azure Data Service Notebook (Alpha)Azure Data Service Notebook is a set of extentions for working with Azure Data Service (e.g. Azure Data Lake, HDIsight, CosmosDB, Azure SQL and Azure Data Warehouse etc.) usingJupyter Notebook.WARNING: This SDK/CLI is currently in very early stage of development. It can and will change in backwards incompatible ways.Latest Version:0.0.1a0FeatureAzure Data Service Notebook currently provides a set ofJupyter Magic Functionsfor users to access Azure Data Lake. Available magics are captured in the table below. Please click on the command name to see the syntax reference.CommandFunction%adl loginLine magic*to log in to Azure Data Lake.%adl listaccountsLine magic to list the Azure Data Lake Analytic accounts for current user.%adl listjobsLine magic to list the Azure Data Lake jobs for a given account.%%adl submitjobCell magic*to submit a USQL job to Azure Data Lake cluster.%adl viewjobLine magic to view detailed job info.%adl liststoreaccountsLine magic to list the Azure Data Lake Store accounts.%adl liststorefoldersLine magic to list the folders under a given directory.%adl liststorefilesLine magic to list the files under a given directory.%adl sampleLine magic to sample a given file, return results as Pandas DataFrame.%adl logoutLine magic to log out.*Please checkMagic Functionsfor detailed definiton ofLine magicandCell magics.InstallationDownload and Installpython 3.6+Install jupyter:pip install jupyterInstall adlmagic extention :pip install --no-cache-dir adlmagicsExamplesadlmagics_demo.ipynb, demo file ofadlmgicsfunctions for Azure Data Lake job control and data exploration.usql_samples.ipynb, samples code of common U-SQL scenarios, e.g. query a TSV file, create a database, populate table, query table and create rowset in script.FeedbackYou can submitbug reportorfeature requestdirectly in this repo. Our team will triage issues actively.Reference%adl loginLine magic to login to Azure Data Lake service.%adl login --tenant <tenant>Input ParametersNameTypeExampleDescriptiontenantrequiredstringmicrosoft.onmicrosoft.comThe value of this argument can either be an.onmicrosoft.comdomain or the Azure object ID for the tenant.%adl listaccountsLine magic to enumerate the Azure Data Lake Analytic accounts for current user. The account list will be returned as Pandas DataFrame, you can call Pandas funtions directly afterward.%adl listaccounts --page_index
--page_account_numberInput ParametersNameTypeExampleDescriptionpage_indexrequiredint0The result page number. This must be greater than 0. Default value is 0.page_account_numberrequiredint10The number of results per page.%adl listjobsLine magic to enumerate the Azure Data Lake jobs for a given account. The job list will be returned as Pandas DataFrame, you can call Pandas funtions directly afterward.%adl listjobs --account <azure data lake analytic account>
--page_index
--page_account_numberInput ParametersNameTypeExampleDescriptionaccountrequiredstringtelemetryadlaThe Azure Data Lake Analytics account to list the job from.page_indexrequiredint0The result page number. This must be greater than 0. Default value is 0.page_account_numberrequiredint10The number of results per page.%%adl submitjobCell magic to submit a U-SQL job to Azure Data Lake cluster.%%adl submitjob --account <zure data lake analytic account>
--name <job name>
--parallelism
--priority
--runtimeInput ParametersNameTypeExampleDescriptionaccountrequiredstringtelemetryadlathe Azure Data Lake Analytics account to execute job operations on.namerequiredstringmyscriptthe friendly name of the job to submit.parallelismint5the degree of parallelism used for this job. This must be greater than 0, if set to less than 0 it will default to 1.priorityint1000the priority value for the current job. Lower numbers have a higher priority. By default, a job has a priority of 1000. This must be greater than 0.runtimestringdefaultthe runtime version of the Data Lake Analytics engine to use for the specific type of job being run.%adl viewjobLine magic to view detailed job info.%adl view job --account <azure data lake analytic account>
--job_id <job GUID to be viewed>Input ParametersNameTypeExampleDescriptionaccountrequiredstringtelemetryadlathe Azure Data Lake Analytics account to execute job operations on.job_idrequiredGUID36a62f78-1881-1935-8a6a-9e37b497582djob identifier. uniquely identifies the job across all jobs submitted to the service.%adl liststoreaccontsLine magic to list the Azure Data Lake Store accounts.%adl liststoreaccounts%adl liststorefoldersLine magic to list the folders under a given directory.%adl liststorefolders --account <azure data lake store account>
--folder_pathInput ParametersNameTypeExampleDescriptionaccountrequiredstringtelemetryadlsthe name of the Data Lake Store account.folder_pathrequiredstringroot/datathe directory path under the Data Lake Store account.%adl liststorefilesLine magic to list the files under a given directory.%adl liststorefiles --account <azure data lake store account>
--folder_pathInput ParametersNameTypeExampleDescriptionaccountrequiredstringtelemetryadlsthe name of the Data Lake Store account.folder_pathrequiredstringroot/datathe directory path under the Data Lake Store account.%adl sampleLine magic to sample a given file, return results as Pandas DataFrame.%adl sample --account <azure data lake store account>
--file_path
--file_type
--encoding
--row_numberInput ParametersNameTypeExampleDescriptionaccountrequiredstringtelemetryadlsthe name of the Data Lake Store account.file_pathrequiredstringroot/data/sample.tsvthe file path to sample data from.file_typestringtsvthe type of the file to sample from.encodingstringUTF-8encoding type of the file.row_numberint10number of rows to read from the file.%adl logoutLine magic to log out.%adl logoutContributingThis project welcomes contributions and suggestions. Most contributions require you to agree to a
Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us
the rights to use your contribution. For details, visithttps://cla.microsoft.com.When you submit a pull request, a CLA-bot will automatically determine whether you need to provide
a CLA and decorate the PR appropriately (e.g., label, comment). Simply follow the instructions
provided by the bot. You will only need to do this once across all repos using our CLA.This project has adopted theMicrosoft Open Source Code of Conduct.
For more information see theCode of Conduct FAQor
[email protected] any additional questions or comments. |
ad-logger | Failed to fetch description. HTTP Status Code: 404 |
ad-logme | # logme“logme”はログ出力を簡単にするための自前ライブラリ
ファイル出力とシンプルなコンソール表示が可能。# DEMOnone# Featuresnone# RequirementPython 3.6.8`bash `# Usagenone`bash `# Notenone# AuthorToshiaki.Kosuga
Aderans Internal Information System Department# License“ecbeing-downloader” is under [MIT license](https://en.wikipedia.org/wiki/MIT_License).
“ecbeing-downloader” is Confidential. |
adl-recognition | AssistedLivingSystemRecognition of Activities of Daily Living(ADL) with privacy protection using depth and audio data.ActivitiesMaking a phone call, clapping, drinking, eating, entering from door, exiting from door, falling, lying down, opening pill container, picking object, reading, sit still, sitting down, sleeping, standing up, sweeping, using laptop, using phone, wake up, walking, washing hand, watching TV, water pouring and writingDependenciespython >= 2.7numpylibrosanoisereducecv2kerasThis project is developed as a FYP, UoM, 2019.LicenseMIT |
adls-management | IntroductionTODO: Give a short introduction of your project. Let this section explain the objectives or the motivation behind this project.Getting StartedTODO: Guide users through getting your code up and running on their own system. In this section you can talk about:Installation processSoftware dependenciesLatest releasesAPI referencesBuild and TestTODO: Describe and show how to build your code and run the tests.ContributeTODO: Explain how other users and developers can contribute to make your code better.If you want to learn more about creating good readme files then refer the followingguidelines. You can also seek inspiration from the below readme files:ASP.NET CoreVisual Studio CodeChakra Core |
adm | |
adman | admanADMan is a tool for performing automated Active Directory management.For more information,Read the Docs. |
admanagerplusclient | # admanagerplustclient
A python API client for Yahoo’s Ad Manager Plus (formerly Brightroll Ad DSP)# example for 1st time usefrom admanagerplusclient import ampclientbrc = ampclient.BrightRollClient()brc.cli_auth_dance()brc.traffic_types(‘advertisers’)# example for ETC or nightly job server
from admanagerplusclient import ampclientbrc = ampclient.BrightRollClient()brc.refresh_access_token()brc.traffic_types(‘advertisers’)# available methods# brc.traffic_types(traffic_type_object)brc.traffic_types(‘advertisers’)
brc.traffic_types(‘advertisers?page={page}&limit={limit}&sort={sort}&dir={dir}&query={query}’)Traffic_type_object can be:
* advertisers
* campaigns
* deals
* lines
* “dictionary”
* exchanges
* contextuals
* sitelists
* beacons (pixels)
* audiences (SRT & MRT)
* usergroups
* dictionarybrc.traffic_type_by_id(traffic_type_object, object_id)brc.dictionary(targetingTypes) |
ad-map-access | ad-map-access provides the python binding of a C++ implementation for accessing and operating on AD map data.ad-map-accesstransfers a-priori AD map information from a standardizedOpenDRIVEfile format into an internalrepresentation. Optionally, the internal representation can be stored/read in a proprietary binary format.On top of the internal representationad-map-accessprovides an API to access the map data(like road, lanes or landmarks and their semantics), perform coordinate transformations and otheroperations on geometry data. Furthermore, some higher level operations are available to performmap matching, route planning and other analysis operations required for automated driving, like e.g.right-of-way within intersections.Seeproject webpageordoxygen docufor a full interface description. |
admath | No description available on PyPI. |
admath1 | This is a intermediate Mathematics module with will provide you with these functions:sum()adds two numberssuminf()adds multiple numberssubtract()subtracts two numbersmultiply()multiplies two numbersdivide()divides two numbersaverage()finds average of two numbersprob()finds the probabilty for eventareaTri()finds area of triangle by 1/2*base*height methodareaRect()finds area of rectangle by l*bpythagoras()finds either base,perpendicular or hypotenuse of the basis of value given to it.
if “x” is given value for base, it will find base using pythagoras theoremangleTriangle()finds either of the angle of a triangle on the basis of given value
if “x” is given value for angle1, it will find angle1angleQuad()finds either of the angle of a rectangle on the basis of given value
if “x” is given value for angle1, it will find angle1max()finds the largest of the two numbersmin()finds the smallest of the two numbers |
adm-boundary-manager | Administrative Boundaries ManagerLoad, manage and visualize administrative boundaries for a country or more, in Wagtail Projects.OverviewThe Admin Boundary Manager is a Wagtail based application that enables to load boundary datasets from different sources,
at different administrative levels as provided by the source, for a country. The boundaries can then be served as vector
tiles and used in web maps, and across your GIS based application that needs reference to a country and its boundaries.A countryis at the core of the package. This means that you will load boundary data country by country. You can have
multiple countries. This is a design decision specifically for this package. If you need to load, say continental data
at once, this might not be the best solution for your case.FeaturesLoad administrative boundaries from different sources. Supported sources include:OCHA's Administrative Boundary Common Operational Datasets (COD-AB)Global Administrative Areas 4.1 (GADM 4.1)Load data from other sources. At the core, we define a boundary model schema that you can follow to load data from
other sources not currently implemented out of the boxPreview loaded boundary data to check that everything is correct as expectedServe boundary data as vector tiles for use in your web maps. Data is saved to a PostGIS Table.ST_AsMVTis used to
serve vector tilesInstallationPrerequisitesYou need a Wagtail Project, with a PostGIS database setup, to support a GeoDjango project.InstallationYou can install the package using pip:pipinstalladm-boundary-managerUsageMake sure the following are all added to yourINSTALLED_APPSin your Wagtail settingsINSTALLED_APPS=["adminboundarymanager","django_countries","wagtailcache","wagtail.contrib.modeladmin","wagtail.contrib.settings","django.contrib.gis",]Run app migrationspythonmanage.pymigrateadminboundarymanagerAdd the following to your project'surls.pyurlpatterns=[...path("",include("adminboundarymanager.urls")),...]Wagtail Cache SetupThe package uses thewagtail-cachepackage for caching vector tile
requests. Please have a look at thewagtail-cache documentationfor setup
instructions.Accessing the settings and data loader interfaceTheAdmin Boundary settingswill be added to theWagtail Settingspanel automatically. To access theBoundary Dataloader and preview interfaces, you can follow the following steps:In an existing or new Wagtail app, add the following to thewagtail_hooks.pyfile. Create one if it does not exist
yet.fromwagtail.contrib.modeladmin.optionsimportmodeladmin_registerfromadminboundarymanager.wagtail_hooksimportAdminBoundaryManagerAdminGroupmodeladmin_register(AdminBoundaryManagerAdminGroup)This will add aBoundary Managermenu to the Wagtail Admin side panel, as in the below screenshot:Click to expand and access the submenuBoundary SettingsTheBoundary Settingsallow to configure settings used by the package. This
usesWagtail's Site Settingscontrib module.The following are the available settings, as shown in the screenshot below:Access theBoundary Managermenu, as described in the previous sectionClick onAdmin Boundary SettingsBoundary Data Source- Select where you will be getting your boundary data from. See following sections for more
details on the database model and supported data sourcesCountries must share boundaries- Check this if you plan to add boundaries for more than one country, and want to
validate that all the added countriesshare a boundary at least with one other country.Countries- Here you can add multiple countries that you wish to load data for.Save buttonBoundary Model StructureTheAdminBoundarymodel is a simpleGeodjangomodel with the following fieldsname_0name_1name_2name_3name_4gid_0gid_1gid_2gid_3gid_4levelgeomAs you might have noticed, the model supports up to 4 administrative levels. Thelevelfield indicates the specific
level for a given model instance.The fields starting with prefixname_correspond to thenameof the admin boundary at a given level. For
examplename_0corresponds to the name of the boundary at admin level 0, which is usually the country level.The fields starting with prefixgid_correspond to theIDof the admin boundary at a given level. For
examplegid_0corresponds to the id of the boundary at admin level 0, which is usually the country code in
eitheralpha2(2-letter code)
oralpha3(3-letter code) format.Thegeomfield is aGeodjangofield that stores the boundary geometry as aMultipolygon. Every boundary instance
is saved as a Multipolygon, even if it was initially a Polygon. This is to ensure consistency across the Model.Using this schema, you can format any boundary data to follow this structure and easily load it.TheLayerMappingutility is used under the hood
to load the boundaries from a GIS formatted file, that can be a shapefile or Geopackage, depending on the source.Supported Data SourcesCurrently, the following data sources are supported out of the box:OCHA's Administrative Boundary Common Operational Datasets (COD-AB)Global Administrative Areas 4.1 (GADM 4.1)OCHA COD AB - Accessing, downloading and loading OCHA's COD-CAB dataAdministrative Boundary CODsare baseline geographical datasets that are used by humanitarian agencies during preparedness and response activities.They are preferably sourced from official government boundaries but when these are unavailable the IM network must
develop and agree to a process to develop an alternate dataset.Where available, werecommendusing this data source since most of the data is sourced from official Government
sources. The boundary source covers most of African Countries.Download and organize COD-AB dataBelow are steps to download and load the boundary data for a country of interest:First you will need to access the
OCHA'sHumanitarian Data ExchangePlatform.
Thislinkwill take you directly to the COD-AB data download page, similar to the below screenshot:Make sureCODSis checked on the filter panel, underFeaturedsectionStill on the filter panel, under theData Seriessection, make sureCOD - Subnational Administrative Boundariesis checkedYou can search for your country of interest hereThe results will be shown here. Click on the country result to go to the download page, that will look similar to the
screenshot below:Once in the dataset detail page, follow the steps below to download the country boundariesshapefilesSelect theData and Resourcestab if not selected by defaultLook for theshapefiledataset. The file name will usually end withSHP.zip. This is the correct file that you
should downloadOnce you identify the shapefile, click on theDOWNLOADbutton to download the shapefileOnce downloaded, extract the shapefile to an accessible location in your computer. Once extracted, you will notice a
large number of files. That is ok.The files are for all the administrative levels, usually from 0 to 3, and also all levels combined. We want the data at
different levels. We will need togroup and zip the files for the same levels, as in the sample screenshot below:Group similar admin level files and zip them. Look out for patterns, for example for admin level 0 files, they will
contain the charactersadm0or similar somewhere in the middle of the name.Make sure for each level you have at-least 3 files with the following extensions:.shp.shx.dbfThese are the necessary files for a valid shapefile. You should end up with zip files for at least 3 levels.You can name the admin level zip files with an easy name that you can identify later. For example for level one, you can
have<country_name>_level_0.zipLoading COD-AB DataBefore accessing the data loading interface, make sure you have
selectedOCHA Administrative Boundary Common Operational Datasets (COD-ABS)as theBoundary data sourceoption in
theAdmin Boundary SettingsAlso make sure you add the countries of interest under theCountriessection in theAdmin Boundary SettingsClick on the Boundary ManagerClick on Boundary Data. This will open the boundary preview interface, with aAdd Boundarybutton in the top right
cornerClick on theAdd Boundarybutton, to open an interface that looks like in the below screenshotThis is the link to the data source, as described in the previous sectionSelect the country you want to load the data for. Make sure you select the country corresponding to the country data
you want to upload. To add a new country, you can do so from theAdmin Boundary SettingsSelect theAdministrative levelfor the country boundary data you want to uploadSelect the zipped shapefile for the country boundary level data. Make sure you select the correct zipped file for the
correct country and level. The file must be azipped shapefileas described the previous section.If done correctly, you will be redirected to the preview page, and see the data loaded on the map.If an error was encountered during the upload process, an error message will show up with details of the error.Repeat the process for all the administrative levels datasets that you want to load.NOTE:If you load data for an already existing level and country, the existing boundary data for that level and
country will be deleted and the new one saved.GADM 4.1 - Accessing, downloading and loading GADM 4.1 DataTheGlobal Administrative Areas 4.1 (GADM)is a database of the location of the world's
administrative areas (boundaries). Administrative areas in this database include: countries, counties, districts etc.
and cover every country in the world. For each area it provides some attributes foremost being the name and in some
cases variant names.The most recent version is4.1and is the version currently supported by this package.Download GADM 4.1 dataAccess theGADM datapage. It should look similar to below screenshot:Click on theDatalink from the navigation bar. The data page, at the time of writing, looks like the above
screenshot.Click on thecountrylink that will take you to a download interface for specific countries. Remember we have to do
it country by country in our upload interface.TheCountrydownload interface will look like below:Select your country of interest. GADM data should available for all countries in the world.Look for theGeopackagedownload link. For GADM data, downloading a geopackage comes with all the data for all the
different levels. This allows to upload data for different levels of a country with one step.Once you download thecountry geopackageand saved it somewhere in your computer, you are ready to load it.Load GADM 4.1 dataBefore accessing the data loading interface, make sure you have selectedGlobal Administrative Areas 4.1 (GADM)as
theBoundary data sourceoption in theAdmin Boundary SettingsGo toBoundary Data>Add Boundary. The upload form should look like belowLink to download data for country, as explained in previous sectionSelect the country for the data you downloaded. To add a new country, you can do so from
theAdmin Boundary SettingsChoose the geopackage file for the country data you downloadedClick on uploadIf done correctly, you will be redirected to the preview page, and see the data loaded on the map.If an error was encountered during the upload process, an error message will show up with details of the error.Repeat the process for all the countries' data that you want to load.NOTE:If you load data for an already existing country, the existing boundary data for that country will be deleted
and the new one saved.Boundary data from other sourcesAs explained in theBoundary Model Structuresection, by following the defined model
structure, you can add data from other sources.You will need to separate your country boundary dataset into the different levels. For example, for level 0 of a
country, you will have onezipped shapefile, as so on for all the levels you want to upload.Your shapefile data should contain the following fields, for each level.Level 0name_0gid_0Level 1name_0gid_0name_1gid_1Level 2name_0gid_0name_1gid_1name_2gid_2Level 3name_0gid_0name_1gid_1name_2gid_2name_3gid_3NOTE: The field names are case-insensitive. They can be in lowercase or uppercase.Loading data from other sourcesBefore accessing the data loading interface, make sure you have selectedGeneric Data Sourceas
theBoundary data sourceoption in theAdmin Boundary SettingsGo toBoundary Data>Add Boundary. The upload form should look like belowSelect the country to upload data forSelect the admin levelChoose yourzipped shapefilefor the selected admin levelClick onUploadto loadIf done correctly, you will be redirected to the preview page, and see the data loaded on the map.If an error was encountered during the upload process, an error message will show up with details of the error.Repeat the process for all the administrative levels datasets that you want to load for a country.Important notesFor consistency, you should decide on one data source and use it for all your countriesRe-uploading data for a given country and or level, will delete any existing data for that country and or level, and
replace with the new uploaded oneDeleting a country from the settings will delete any previously loaded data for that countryCache InvalidationWagtail Cache is invalidated automatically at the following points:On UpdatingAdminBoundarySettingsOn uploading or overwriting boundary dataData APIAPI endpoints are provided for searching, retrieving and serving vector tiles for the boundary data.Search endpoint/api/admin-boundary/search?search=<name>The search endpoint allows searching Admin Boundaries byname_0,name_1,name_2andname_3. This will give you
results for all boundaries whose name match the search phrase.Retrieve endpoint/api/admin-boundary/<boundary_id>The retrieve endpoint allows to get an admin boundary by ID. This assumes you already know the ID for the boundary
instance you wish to retrieve.Usually you will use this endpoint in conjunction with the search API.A sample use case will be:Use theSearch Endpointto search for an admin level by name in an autocomplete search inputShow the matching results in the result dropdownOnce the user selects a result item, you can use the theRetrieve endpointto retrieve the detail of the boundary,
using the ID of the selected boundary item.Vector Tiles Endpoint/api/admin-boundary/tiles/<int:z>/<int:x>/<int:y>The vector tiles endpoint can be used to serve the vector tiles from the boundary data. Usually, you will use this with
a web mapping library that supports vector tiles, likeMapLibre GL JSBelow is a quick snippet on how you can add boundary vector tile layers to your MapLibre web map:<head><linkrel="stylesheet"href="https://unpkg.com/maplibre-gl/dist/maplibre-gl.css"></head><body><divid="map"></div><scriptsrc="https://unpkg.com/maplibre-gl/dist/maplibre-gl.js"></script><script>constmap=newmaplibregl.Map({container:'map',// container idstyle:'https://demotiles.maplibre.org/style.json',// style URLcenter:[0,0],// starting position [lng, lat]zoom:1// starting zoom});constboundaryTilesUrl="/api/admin-boundary/tiles/{z}/{x}/{y}"map.on("load",()=>{// add sourcemap.addSource("admin-boundary-source",{type:"vector",tiles:[boundaryTilesUrl],})// add fill layermap.addLayer({'id':'admin-boundary-fill','type':'fill','source':'admin-boundary-source',"source-layer":"default",'paint':{'fill-color':"#fff",'fill-opacity':0,}});// add line layermap.addLayer({'id':'admin-boundary-line','type':'line','source':'admin-boundary-source',"source-layer":"default",'paint':{"line-color":"#444444","line-width":0.7,}});})</script></body>Note: The vector tilesource-layeris named asdefaultAll the fields are available in the vector tile data. This means you can show, on a popup for example, the data for a
boundary on click.You can also use this to filter which data is shown on the map.For example if you only wanted to show data for admin level 0, (i.e Country Level), you can do it as in the example
below:constboundaryFilter=["==","level",0]// add fill layer, with custom filtermap.addLayer({'id':'admin-boundary-fill','type':'fill','source':'admin-boundary-source',"source-layer":"default",'paint':{'fill-color':"#fff",'fill-opacity':0,},"filter":boundaryFilter}); |
admcycles | admcyclesadmcycles is aSageMathmodule to compute with
the tautological ring of the moduli spaces of complex curves. You can install
it on top of a SageMath installation on your computer (see the instructions
below). You can alternatively use one of the online services below that have
admcycles already installed:SageMathCell: An online service for
SageMath computations that does not require authentification.CoCalc: A complete computation environment that
offers a free plan subscription (with limited resources).admcycles includes theSageMathmodule diffstrata
to compute with the tautological ring of the moduli space of multi-scale
differentials, a compactification of strata of flat surfaces. It is particularly
tailored towards computing Euler characteristics of these spaces.Detailed information on the package is contained in the documentsadmcycles – a Sage package for calculations in the tautological ring of the
moduli space of stable curves [arXiv:2002.01709]diffstrata – a Sage package for calculations in the tautological ring of the
moduli space of Abelian differentials [arXiv:2006.12815]online documentationNEW: Online database with tautological relations (Feb 2023)The latest version of admcycles includes an automatedonline lookupof pre-calculated tautological relations (seethe list of available cases).
By default, if you attempt a calculation that needs tautological relations, the program will first try to look them up in your local storage (in the folder.sage/admcycles). If not found there, the program will check the above database and download the relations to your computer in case they are available. If this fails as well, the program will then calculate the relations itself.If you would like to deactivate this online lookup, execute the following lines in a Sage session:sage: from admcycles.admcycles import set_online_lookup_default
sage: set_online_lookup_default(False)If you have calculated and stored relations that are not yet in our database, we would be happy if you reach out so we can integrate them!InstallationPrerequisitesMake sure thatSageMathversion 9.0 or later
is installed on your computer. Detailed installation instructions for
different operating systems are availablehereand on the
SageMath website. If you need to use admcycles with an older version of SageMath,
useadmcycles version 1.3.2.All the command below must be run inside a console (the last character of the
prompt is usally a$). On Windows this is calledSageMath Shellwhile
on linux and MacOS this is often referred to as aterminal.Inside the console, we assume that the commandsagelaunches a Sage
session (whose prompt is usuallysage:). To quit the Sage session
typequitand pressEnter.Installation with pip (the Python package manager)The most convenient way to use admcycles is to add the package to your
Sage installation. The exact procedure for this depends on your operating
system and how you installed Sage. If the pip installation fails, see
the sectionsManual installation with piporUse without installationbelow.If you manually installed Sage by downloading it from the website, then run
in a shell console:$ sage -pip install admcycles --userHere, the--useris an optional argument topipwhich, when
provided, will install admcycles not inside the Sage folder but in your home
directory.If you have a linux distribution and installed the sagemath package via your
package manager then run in a shell console:$ pip install admcycles --userThepipcommand above might have to be changed topip2orpip3depending on your system. Also, on Debian/Ubuntu systems, the following step
might be necessary before running the above command:$ source /usr/share/sagemath/bin/sage-envManual installation with pipThe automatic installation with pip from SectionInstallation with pipmight
fail. One common reason is the lack of SSL support of the SageMath binaries. In
such situation you can follow the procedure below that bypass the connection ofpipto the web.Download the admcycles package as atar.gz-file or.zipfile either fromPyPIor fromgitlab.Inside a shell console run:$ sage -pip install /where/is/the/package.tar.gz --userHere, the--useris an optional argument topipwhich, when
provided, will install admcycles not inside the Sage folder but in your home
directory.Installation of the development versionIf you want to install the development version, you need to have the
versioning softwaregitinstalled. The only change in the procedure
is to replaceadmcyclesin the any of the command above bygit+https://gitlab.com/modulispaces/admcycles.UpgradeIf you have already installed admcycles and a new version appears on PyPI, you
can update your installation by appending the option--upgradeabove.Use without installationTo use the package without installing, download the package as a.zipor.tar.gz-file either fromPyPIor
fromgitlab.
Unpack the.zipor.tar.gzfile. This creates a folder which should
contain a filesetup.py. In order to use the
module, you need to run Sage from this folder. For example, if the full path of
this folder is/u/You/Downloads/admcycles, you could do:$ cd /u/You/Downloads/admcycles
$ sageOr directly inside a Sage session:sage: cd /u/You/Downloads/admcyclesIf you run Sage in Windows using cygwin, the path above should be a cygwin path
and will looks something like/cygdrive/c/Users/You/Downloads/admcycles-master.ExampleTo start using admcycles, start a Sage session (either in the command line, or
a Jupyter notebook, or inside one of the online services). Then type:sage: from admcycles import *To try a first computation, you can compute the degree of the class kappa_1 on
Mbar_{1,1} by:sage: kappaclass(1,1,1).evaluate()
1/24You can have a look at the above computation directly inSageMathCell.Here is a more advanced computation:sage: t1 = 3*sepbdiv(1,(1,2),3,4) - psiclass(4,3,4)^2
sage: t1
Graph : [1, 2] [[1, 2, 5], [3, 4, 6]] [(5, 6)]
Polynomial : 3*
<BLANKLINE>
Graph : [3] [[1, 2, 3, 4]] []
Polynomial : (-1)*psi_4^2To use diffstrata, the package must be imported separately. Type:sage: from admcycles.diffstrata import *To try a first computation, you can compute the Euler characteristic of the
minimal stratum H(2) in genus 2:sage: X = Stratum((2,))
sage: X.euler_characteristic()
-1/40Here is a more advanced computation:sage: X = Stratum((1,1))
sage: (X.xi^2 * X.psi(1) * X.psi(2)).evaluate()
-1/720Building documentationThe documentation is available online athttps://modulispaces.gitlab.io/admcycles/You can alternatively build the documentation as follows. Go in the repository
docs/ and then run in a console:$ sage -sh
(sage-sh)$ make html
(sage-sh)$ exitThe documentation is then available in the folder docs/build/html/. Note that you
need the packagenbsphinxto compile
the full documentation including the example Jupyter notebooks. On most systems, you
should be able to install nbsphinx by typing:$ sage -pip install nbsphinxRunning doctestsTo run doctests, use the following command:$ sage -t --force-lib admcycles/ docs/sourceIf it succeeds, you should see a message:All tests passed!Licenseadmcycles is distributed under the terms of the GNU General Public License (GPL)
published by the Free Software Foundation; either version 2 of
the License, or (at your option) any later version. Seehttp://www.gnu.org/licenses/.AuthorsAaron PixtonJohannes SchmittVincent DelecroixJason van ZelmJonathan ZachhuberFundingJohannes Schmitt was supported by the grant SNF-200020162928 and has received funding
from the European Research Council (ERC) under the European Union Horizon 2020 research
and innovation programme (grant agreement No 786580). He also profited from the SNF Early
Postdoc.Mobility grant 184245 and also wants to thank the Max Planck Institute for Mathematics
in Bonn for its hospitality.
Vincent Delecroix was a guest of the Max-Planck Institut and then of the Hausdorff Institut
for Mathematics during the development of the project.
Jason van Zelm was supported by the Einstein Foundation Berlin during the course of this
work. |
admem | No description available on PyPI. |
adme-predict | Copyright (c) 2018 The Python Packaging AuthorityPermission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE. |
admesh | This module provides bindings for theADMeshlibrary. It lets you manipulate 3D models in binary or ASCII STL format and partially repair them if necessary.InstallationThere are wheels available for Linux and macOS X. All you need to do is:pipinstalladmeshIf you have a platform not supported by the above,
you’ll need to install the CADMeshlibrary.This release is designed for ADMesh 0.98.x. Follow the instructions there. Then you can install this as usual withoneof the following:./setup.pyinstallpython3setup.pyinstall# for Python 3pipinstalladmesh# install directly from PyPIIn case your ADMesh library is located in non-standard location, you’ll have to tell the compiler and linker where to look:LDFLAGS='-L/path/to/library'CFLAGS='-I/path/to/header'./setup.pyinstallUsageUse theStlclass provided.importadmesh# load an STL filestl=admesh.Stl('file.stl')# observe the available methodshelp(stl)# read the statsstl.stats# see how many facets are therelen(stl)# walk the facetsforfacetinstl:# get the normalfacet['normal']# walk the verticesforvertexinfacet['vertex']:# read the coordinatesvertex['x']vertex['y']vertex['z']# add another set of facets# every facet is a tuple (vertices, normal) or a dictstl.add_facets([(((0,0,0),(0,1,0),(0,0,1)),(1,0,0)),{'vertex':[{'x':0,'y':0,'z':0},{'x':1,'y':0,'z':0},{'x':0,'y':0,'z':1}],'normal':{'x':0,'y':1,'z':0}},])Note that all C ADMesh functions start withstl_prefix and the Python methods of this module do not. Also note that not all C ADMesh functions are provided, because some would require more complicated approach and are not considered worthy. In case you are missing some functions, createnew issue. |
admet-ai | ADMET-AIThis git repo contains the code for ADMET-AI, an ADMET prediction platform that usesChemprop-RDKitmodels trained on ADMET datasets from the Therapeutics Data Commons (TDC). ADMET-AI can be used to make ADMET predictions on new molecules via the command line, via the Python API, or via a web server. A live web server hosting ADMET-AI is atadmet.ai.greenstonebio.comADMET-AI will be described in a forthcoming paper.Instructions to reproduce the results in our paper are indocs/reproduce.md.InstallationPredicting ADMET propertiesCommand line toolPython moduleWeb serverInstallationADMET-AI can be installed in a few minutes on any operating system using pip (optionally within a conda environment). If a GPU is available, it will be used by default, but the code can also run on CPUs only.Optionally, create a conda environment.condacreate-y-nadmet_aipython=3.10
condaactivateadmet_aiInstall ADMET-AI via pip.pipinstalladmet_aiAlternatively, clone the repo and install ADMET-AI locally.gitclonehttps://github.com/swansonk14/admet_ai.gitcdadmet_ai
pipinstall-e.By default, the pip installation only includes dependencies required for making ADMET predictions, either via the command line or via the Python API. To install dependencies required for processing TDC data or plotting TDC results, runpip install admet_ai[tdc]. To install dependencies required for hosting the ADMET-AI web server, runpip install admet_ai[web].If there are version issues with the required packages, create a conda environment with specific working versions of the packages as follows.pipinstall-rrequirements.txt
pipinstall-e.Note: If you get the issueImportError: libXrender.so.1: cannot open shared object file: No such file or directory, runconda install -c conda-forge xorg-libxrender.Predicting ADMET propertiesADMET-AI can be used to make ADMET predictions in three ways: (1) as a command line tool, (2) as a Python module, or (3) as a web server.Command line toolADMET predictions can be made on the command line with theadmet_predictcommand, as illustrated below.admet_predict\--data_pathdata.csv\--save_pathpreds.csv\--smiles_columnsmilesThis command assumes that there exists a file calleddata.csvwith SMILES strings in the columnsmiles. The predictions will be saved to a file calledpreds.csv.Python moduleADMET predictions can be made using thepredictfunction in theadmet_aiPython module, as illustrated below.fromadmet_aiimportADMETModelmodel=ADMETModel()preds=model.predict(smiles="O(c1ccc(cc1)CCOC)CC(O)CNC(C)C")If a SMILES string is provided, thenpredsis a dictionary mapping property names to values. If a list of SMILES strings is provided, thenpredsis a Pandas DataFrame where the index is the SMILES and the columns are the properties.Web serverADMET predictions can be made using the ADMET-AI web server, as illustrated below. Note: Running the following command requires additional web dependencies (i.e.,pip install admet_ai[web]).admet_webThen navigate tohttp://127.0.0.1:5000to view the website. |
adminactionview | UNKNOWN |
adminapi | Eucalyptus Cloud Services and General System Administrative Utilities |
admin-auth0 | Django Auth0 project for AbexRequire ENV variables:BACKEND_HOSTDJANGO_ADMIN_AUTH0_AUDIENCEDJANGO_ADMIN_AUTH0_CLIENT_IDDJANGO_ADMIN_AUTH0_CLIENT_SECRETDJANGO_ADMIN_AUTH0_DOMAINDJANGO_ADMIN_AUTH0_SCOPEDJANGO_ADMIN_AUTH0_SUPER_ADMIN_ROLE |
admin_bootstrap | Django admin templates that are compatible with Twitter Bootstrap version 2.## UsageClone the repository into your Django project.Add the path to ‘admin_bootstrap/templates’ to yourTEMPLATE_DIRSsetting.Add ‘admin_bootstrap’ to yourINSTALLED_APPSsetting.Runpython manage.py collectstatic.Alternatively to #2, you can symlink the ‘admin’, ‘admin_doc’ and ‘registration’ folders in ‘admin_bootstrap/templates’ to your templates folder._This has only been tested in Django 1.4._## TODOTest more Django form field types and admin options.## LICENSEOffered under a [BSD 3 Clause License](https://github.com/dryan/django-admin-twitter-bootstrap/blob/master/LICENSE). |
adminbot | adminbot is a python package to set up a Telegram bot to admin a server.More information in theGitHub repository. |
adminbypasser | AdminbypasserAdminbypasser is a dork based admin panel finder and bypasser using SQL injection.$pipinstalladminbypasserUsage:$adminbypasser--help
usage:adminbypasser--snip<PAGE>--max<MAX_URL>--out<OUT_FILE>[OPTIONS]--help,-h:showhelp--snip,-s:URLsnippetparteg:admin.php
--country,-c:countrycodefortargetsites
--max,-m:maximumURLs
--out,-o:writebypassableURLtoafile
--user-agent,-u:user-agentforrequestsFinding bypassable admin panels:$adminbypasser-sadmin.php-clk-m20-oadmin_lk.txtDisclaimer: causing damage to unauthenticated sites is illegal, please use this tool on your own risk. |
admin-captcha | this a admin captcha package |
admincer | AdMincerAdMincer is a command line tool for enriching datasets of screenshots used in
ML-based ad detection. It can probably be used with other object-detection
datasets, but ad detection is the main use case we're after.InstallationClone this repository, then:$ cd admincer/
$ pip install .UsageFrom the command line, run$ admincer <command>to get information on command
options and usage. The current available options areplace,extract,slice,find, andconvert.PlaceThis command places fragment images into the regions of source images. It takes
a directory with source images that have regions marked on them and multiple
mappings of region type to fragment directory:$ admincer place -f ad=ads/dir -f label=labels:other/labels -n 5 source targetThis will take images with marked regions fromsource/, place images fromads/dir/into the regions of typeadand images fromlabels/andother/labels/into thelabelregions. It will generate 5 images and store
them intarget/.The placements are performed in the order of region types on the command line.
In the example above first alladregions will be placed and then alllabelregions.Region markingRegions of the images can be defined via a CSV file in the following format
(the numbers are X and Y coordinates of the top left corner followed by the
bottom right corner, and the headings in the first line are required):image,xmin,ymin,xmax,ymax,label
image1.png,50,50,80,90,region_type1
image2.gif,10,10,20,20,region_type2They can also be defined via TXT files of the same name as the image. The TXT
files should be in the format commonly used with YOLO object detector. The
numbers are:<object-class> <x> <y> <width> <height>Where:<object-class>is an integer representing the box's label<x> <y>are the float coordinates at thecenterof the rectangle<width> <height>are the ratio of the box's width/height relative to the
size of the whole image, from (0.0 to 1.0].E.g.<height> = <box_height> / <image_height>0 0.075 0.15 0.05 0.1
1 0.225 0.15 0.05 0.1It's possible to provide names for the region type numbers via placing a file
with.namesextension into the directory. It should simply contian the names
in the successive lines:region_type1
region_type2When the names file is provided it's also possible to mix CSV and TXT region
definitions but not for the same image.Note: regions that extend beyond the boundaries of the image will be clipped.Resize modesWhen the fragments placed into the regions are not of the same size as the
regions, there are several possible options for resizing them. The default
is to scale the fragment to match the size of the region. Another option is to
cut off the part of the fragment that doesn't fit and place the rest into the
part of the region that it would cover. Yet another approach is to cut off some
parts and pad the remaining image to the size of the region. These modes are
calledscale,cropandpadrespectively and they can be configured via--resize-modecommand line option. Example:$ admincer place -f ad=ads/dir -f label=labels -r pad -r label=crop ...Here the first-rsets the default resize mode and the second one overrides
it forlabelregion type.ExtractThis command extracts the contents of marked regions from source images. It
takes a directory with source images with marked regions (see above) and
multiple mappings of region type to target directory:$ admincer extract --target-dir ad=ads/dir -t label=labels sourceThis will load the images and region maps fromsourceand will extract the
contents of the regions labeledadandlabelintoads/dirandlabelsdirectories respectively.SliceThis command produces viewport-sized square screenshots from page-sized tall
rectangular screenshots. It remaps the regions of the original images to the
produced part (as long as sufficient part of the region is inside the part).$ admincer slice --step=10 --min-part=50 source targetIf additional--no-emptyoption is specified, slices that don't contain any
regions will not be produced.FindThis command finds source images that have regions of specific types and sizes.
For example the following command will find all images insourcedirectory
that have regions of the typead100 pixels wide by 50 pixels high.$ admincer find --region=ad:100x50 sourceThere's certain tolerance for size mismatches. Normally it's +25% and -20%.
Tolerance can be configured via an additional parameter of the region query:$ admincer find -r ad:100x50:100 sourceHere height and width can be up to 100% larger and up to 50% smaller. In
general the tolerance value X allows the region to be X% larger than specified
or the specification to be X% larger than the region.Multiple--region/-roptions can be given. In this case images that contain
at least one region matching each query will be found (i.e. multiple queries
are combined usingandoperator).ConvertThis command will convert annotations from a CVAT-format .xml file into YOLO-
format .txt files, placing the .txt files alongside their images:$ admincer convert source.xmlMultiple .xml files can also be provided, either as a list, or by using shell
expansions:$ admincer convert *.xmlOptionally, a--target-dircan be specified. This will place the .txt
annotations into the specified target directory, along with aclass.namesfile indicating the<object-class>order. If notarget-diris given,class.nameswill be written to the image directories.$ admincer convert *.xml --target-dir path/to/target/Additionally, the-mor-cflags may be given, which will either move or
copy the images to the--target-dir, respectively.Notes:Each image'snametag in the .xml file should contain the image's path,
relative to the xml file.A.namesfile may be provided. If multiple image folders are combined into
a--target-dir, their.namesfiles will be combined and written to<target_dir>/class.names. If new labels are found, the.namesfile will
be overwritten to include all labels.QuestionsFragment matching policy (current one allows scaling by 80% to 125%).What to do if there are no valid candidate fragments for placement? Right now
we bomb out with an exception.Do we want sampling with/without replacement? Or maybe some kind of
deterministic selection? Right now it's with replacement. |
admin-city | A xadmin city selector |
admin-cli-pkg-joemon.david | Failed to fetch description. HTTP Status Code: 404 |
admincontrol | No description available on PyPI. |
admincsvimport | UNKNOWN |
admindjango-ckeditor-blog | =====Blogs=====Blogs is a simple Django app to conduct Web-based Blogs. This Blog appallows authorized users to maintain a blog. Blogs are a series of poststhat are time stamped and are typically viewed by date. Blog entries canbe made depending on which roles have access to add blog.Quick start-----------1. Add "blog" to your INSTALLED_APPS setting like this:INSTALLED_APPS = [...'blog','ckeditor','ckeditor_uploader',]2. Add following lines to your settings.py:MEDIA_ROOT = os.path.join(BASE_DIR, "uploads")MEDIA_URL = "/uploads/"CKEDITOR_UPLOAD_SLUGIFY_FILENAME = FalseCKEDITOR_JQUERY_URL = '//ajax.googleapis.com/ajax/libs/jquery/2.1.1/jquery.min.js'CKEDITOR_IMAGE_BACKEND = "pillow"CKEDITOR_UPLOAD_SLUGIFY_FILENAME = TrueCKEDITOR_UPLOAD_PATH = "uploads/"CKEDITOR_CONFIGS = {'default': {'skin': 'moono','toolbar': 'full','height': 100,'allowedContent': True,},}STATIC_ROOT = os.path.join(BASE_DIR, 'static')STATIC_URL = '/static/'3. Add following lines in url.py filefrom django.conf.urls import url, includefrom django.conf import settingsfrom django.views.static import servefrom django.conf.urls.static import staticfrom django.core.urlresolvers import reverseadd the following url in urlpatterns:url(r'^ckeditor/', include('ckeditor_uploader.urls')),and at the end of urlpatterns:'+ static(settings.MEDIA_URL, document_root=settings.MEDIA_ROOT)'urlpatterns += [url(r'^media/(?P<path>.*)$', serve, {'document_root': settings.MEDIA_ROOT,}),]3. Run `python manage.py makemigrations` to create the blogs models.4. Run `python manage.py migrate` to create the blogs models.5. Start the development server and visit http://127.0.0.1:8000/admin/to create a blog (you'll need the Admin app enabled).6. Visit http://127.0.0.1:8000/blog/ to participate in the Blog. |
admindojo | admindojo.orgIntroductionadmindojo is a free, interactive Linux tutorial for (junior) sysadmins.
Using local virtual machines, admindojo provides real-world scenarios in real environments.This repository contains the client that is used inside each training VM.Visitadmindojo.orgfor more information.Basic Usageadmindojo start: Start trainingadmindojo check: Run check and calculate scoreadmindojo --help: helpLicenseFree software: MIT licenseCreditsPackage templating toolhttps://github.com/audreyr/cookiecutterPackage templatehttps://github.com/audreyr/cookiecutter-pypackage |
admin-extra-url | Failed to fetch description. HTTP Status Code: 404 |
admin-extra-urls | pluggable django application that offers one single mixin classExtraUrlMixinto easily add new url (and related buttons on the screen) to any ModelAdmin.It provides two decoratorslink()andaction().link()is intended to be used for multiple records. It will produce a button in the change list view.action()works on a single record. It will produce a button in the change form view.Installpipinstalladmin-extra-urlsAfter installation add it toINSTALLED_APPSINSTALLED_APPS=(...'admin_extra_urls',)How to use itfromadmin_extra_urls.extrasimportExtraUrlMixin,link,actionclassMyModelModelAdmin(ExtraUrlMixin,admin.ModelAdmin):@link()# /admin/myapp/mymodel/update_all/defupdate_all(self,request):......@action()# /admin/myapp/mymodel/update/10/defupdate(self,request,pk):obj=self.get_object(pk=pk)...You don’t need to return a HttpResponse. The default behavior is:withlink()button is displayed in the ‘list’ view and the browser will be redirected tochangelist_viewwithaction()button is displayed in the ‘update’ view and the browser will be redirected tochange_viewlink() / action() optionspathNonepath url path for the action. will be the url where the button will point to.labelNonelabel for the button. by default the “labelized” function nameicon‘’icon for the buttonpermissionNonepermission required to use the button. Can be a callable (current object as argument).css_class“btn btn-success”extra css classes to use for the buttonorder999in case of multiple button the order to usevisiblelambda o: o and o.pkcallable or bool. By default do not display “action” button if inaddmodeIntegration with other [email protected](Rule)classRuleAdmin(ExtraUrlMixin,ImportExportMixin,BaseModelAdmin):@link(label='Export')def_export(self,request):if'_changelist_filters'inrequest.GET:real_query=QueryDict(request.GET.get('_changelist_filters'))request.GET=real_queryreturnself.export_action(request)@link(label='Import')def_import(self,request):returnself.import_action(request)LinksStableDevelopmentProject home page:https://github.com/saxix/django-admin-extra-urlsIssue tracker:https://github.com/saxix/django-admin-extra-urls/issues?sortDownload:http://pypi.python.org/pypi/admin-extra-urls/ |
admin-fastapi | No description available on PyPI. |
admin-favorite | Mark most usable app as favorite in django adminhttps://admin-favorite.herokuapp.com/admin/Author :Achintya Ranjan Chaudhary
Steps to add this library to your projectStep 1 :Install library in your project by running command belowpip install admin-favoriteStep 2 :Add app name in your INSTALLED_APPS, make sure this is above ‘django.contrib.admin’ in your project‘admin_favorite’,Step 3 :Add the path in your django project urls.py filepath(‘’, include(‘admin_favorite.urls’)) |
adminfinder | A tool for finding admin panels and login pages. It uses a list of paths and a list of common admin panels and login pages to perform the search. |
admin-form-image-preivew | Insallationpip install admin-form-image-preivewAddadmin_image_previewin installed apps insettings.pyof django project
INSTALLED_APPS = [
'admin_image_preview',
# ...
] (at top)DescriptionIt show the preview of every image field in every admin change_form (add/edit) for all models and appsInstructions to run the sample usagegit clone https://github.com/humblesami/admin_form_image_preivew.gitcd admin_form_image_preivew/sample_usagepip install -r requirements.txtNot requiredbut if you want to reset the database, you can do this
3.1.python manage.py initsql.pypython manage.py runseverOpen following url in your browser and login with given username and passwordhttp://127.0.0.1:8000/admin/login/?next=/admin/loginusername
sa
password
123Go tohttp://127.0.0.1:8000/admin/admin_image_preview/model1/add/Add image to check previewMake you own pip packageSeehttps://github.com/humblesami/admin_form_image_preivew/blob/master/docs/make_pip.mdto make/build/test and uploadyour own pip package |
admin-frugal | No description available on PyPI. |
adminfuncs | Module to automatically build admin function runner |
adminish | UNKNOWN |
adminish-categories | UNKNOWN |
administrator | This is a high-level package that manages each of the APIs to infer
missing administrative data.The API currently handles the following fields:
1. Gender
2. Ethnicity
3. Race
4. Age
5. Political AffiliationThis package exposes methods to access each of these independently, but
also offers auto-detect and calls. |
administratum | No description available on PyPI. |
adminlettuce | Admin documentation for lettuce-generated integration tests.-------------------------------------This module integrates lettuce (http://www.lettuce.it) support for Django byprinting out the content of the features and scenarios to the Django admininterface.The goal is to make the final user aware of the specifications and requirementsof the application without having to browse the source code. |
admin-location | get lontion lation |
admin_logs | AboutThis small module for django allows you to store requests in your database(now supports only database) and then look at them in django admin.
This module was inspired by logs in Google Application EngineConfigureInclude this lines to your settings.py:INSTALLED_APPS += ('admin_logs', )
MIDDLEWARE_CLASSES = ('admin_logs.middleware.LogRequestMiddleware', ) + MIDDLEWARE_CLASSES # place middleware as early as possible
ADMIN_LOGS_BACKEND = 'admin_logs.backends.database.DatabaseBackend' # now supports only database
from admin_logs import setup_level
setup_level('INFO') # set minumum log level that will be written to logs
from admin_logs.decorators import log
# use full for celery tasks
@log(name='test')
def task_name():
logging.warning('logging in task')And this warning will be written to logs and you can check it later. |
adminlte-base | This is not a ready-to-use package, but a basic one - to simplify integration with other frameworks. |
adminlte-full | AdminLTE-Full- A metapackage that combines packages:adminlte-base- A basic package to simplify the integration of AdminLTE with other frameworks.flask-adminlte-full- This Flask extension is port the AdminLTE Template for easy integration into Flask Framework.django-adminlte-full- This Django application is port the AdminLTE Template for easy integration into Django Framework |
adminpy | adminpy - Python Admin access for most platformsThis module makes it easy to check system platform type and gain admin rights for scripts that require it.This utilises PyUAC on the Windows platform and requires the use of Sudo on *NIX systems for admin rights escalation.The purpose of this is to try and make people's life easier when needed more rights for python scripts.adminpy on GithubUsage of the moduleThere are 3 functions of this moduleThe sysinfo() function outputs whether the script recognises which type of system it's running on.Example: On a Windows system, the script will output,"You are running on a Windows-based system."The admincheck() function will output a "Yes" or "No", depending on if it has root or admin privilegeThe runadmin() function will utilise ctypes function or Sudo (for linux) to relaunch script if does not have root or admin privilegeRequirementsThis module requiresPyUACandPyWin32package to be installed on a Windows system.This module requires Python 3, and does not support Python 2. |
admin_reports | Easily define and show data analysis reports for django-admin. |
admin-rights | Admin Protected RouteThis is a function that takes an array of users as an argument and checks if the role is an admin.
If that isn't the case, the function returns an unauthorized status code and message.
Otherwise, the user is permitted to use the function. |
adminschUtils | file: README.rst |
admin-scripts | No description available on PyPI. |
admin-search-plus | Admin Search PlusAdmin Search Plus is a AdminMixin for Django that limits searches to specific fields which greatly enhances performance when working on large datasets.Installation$ pip install admin-search-plusOr through github:$ pip install -e git://github.com/Lenders-Cooperative/admin-search-plus#egg=admin-search-plusBuilding from source$ python -m build$ pip install admin_search_plus.whlUsageAddadmin_search_plusto yourINSTALLED_APPSbeforedjango.contrib.admin:INSTALLED_APPS = [
'app_to_be_overrided',
...
'admin_search_plus',
...
'django.contrib.admin',
'django.contrib.auth',
'django.contrib.contenttypes',
'django.contrib.sessions',
'django.contrib.messages',
'django.contrib.staticfiles',
]NOTE: To override a template, the app should be listed beforeadmin_search_plusInadmin.pyimportAdminSearchPlusMixinto add search functions toModelAdmin.from admin_search_plus import AdminSearchPlusMixin
class YourModelAdmin(AdminSearchPlusMixin, admin.ModelAdmin):
admin_search_plus = True
show_full_result_count = False
show_result_count = False
admin.site.register(YourModel, YourModelAdmin) |
admin-tool-button | django-admin-tool-buttonCustom tool buttons for Django adminRationaleDefine extra actions buttons for Django admin.SupportSupports: Python 3.9.Supports Django Versions: 4.2.7.Installation$pipinstalladmin_tool_buttonUsageAddadmin_tool_buttontoINSTALLED_APPS. In youradmin.pyadd your custom actions like so:fromadmin_tool_button.contrib.adminimportButtonActionAdminclassMyAdmin(ButtonActionAdmin):button_actions=['my_action']defmy_action(self,request):# Perform your custom action |
admin-tools | No description available on PyPI. |
admin-tools-db | Failed to fetch description. HTTP Status Code: 404 |
admin-tools-google-analytics | Google analytics modules for django admin tools dashboard. |
admin-tools-zinnia | Admin-tools-zinnia is package providing new dashboard modules related to
yourZinniaapplication fordjango-admin-tools. |
admin-torch | Admin-TorchTransformers Training **Stabilized**What's New?•Key Idea•How To Use•Docs•Examples•Citation•LicenseHere, we provide a plug-in-and-play implementation ofAdmin,
which stabilizes previously-diverged Transformer training and achieves better performance,without introducing additional hyper-parameters. The design of Admin is half-precision
friendly and can bereparameterized into the original Transformer.What's New?Beyond theoriginal admin implementation:admin-torchremoved the profilling stage and isplug-in-and-play.admin-torch's implementation ismore robust(see below).Comparison w. theDeepNet Initand theOriginal Admin Init(on WMT'17).Regular batch size (8x4096)Huge batch size (128x4096)Original Admin✅❌DeepNet❌✅admin-torch✅✅More details can be found inour example.Key IdeaWhat complicates Transformer training?For Transformer f, input x, randomly initialized weight w, we describe its stability (output_change_scale) asInour study, we show that, an original N-layer Transformer'soutput_change_scaleisO(n), which unstabilizes its training. Admin stabilize Transformer's
training by regulating this scale toO(logn)orO(1).More details can be found in ourpaper.How to use?installpip install admin-torchimportimport admin-torchenjoydef __init__(self, ...):
...+(residual = admin-torch.as_module(self, self.number_of_sub_layers))+...
def forward(self, ):
...-!x = x + f(x)!-+(x = residual(x, f(x)))+x = self.LN(x)
...An elaborated example can be found atour doc, and a real working example can be found atLiyuanLucasLiu/fairseq(training recipe is available atour example).CitationPlease cite the following papers if you found our model useful. Thanks!Liyuan Liu, Xiaodong Liu, Jianfeng Gao, Weizhu Chen, and Jiawei Han (2020). Understanding the Difficulty of Training Transformers. Proc. 2020 Conf. on Empirical Methods in Natural Language Processing (EMNLP'20).@inproceedings{liu2020admin,
title={Understanding the Difficulty of Training Transformers},
author = {Liu, Liyuan and Liu, Xiaodong and Gao, Jianfeng and Chen, Weizhu and Han, Jiawei},
booktitle = {Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020)},
year={2020}
}Xiaodong Liu, Kevin Duh, Liyuan Liu, and Jianfeng Gao (2020). Very Deep Transformers for Neural Machine Translation. arXiv preprint arXiv:2008.07772 (2020).@inproceedings{liu_deep_2020,
author = {Liu, Xiaodong and Duh, Kevin and Liu, Liyuan and Gao, Jianfeng},
booktitle = {arXiv:2008.07772 [cs]},
title = {Very Deep Transformers for Neural Machine Translation},
year = {2020}
} |
admin-totals | Django Admin TotalsModule to show totals in Django Admin List.Installationvirtualenv .
source bin/activate
pip install admin-totalsOrpip install git+https://github.com/douwevandermeij/admin-totals.gitUsageIn settings.pyINSTALLED_APPS=['admin_totals',]In admin.py:fromadmin_totals.adminimportModelAdminTotalsfromdjango.contribimportadminfromdjango.db.modelsimportSum,[email protected](MyModel)classMyModelAdmin(ModelAdminTotals):list_display=['col_a','col_b','col_c']list_totals=[('col_b',lambdafield:Coalesce(Sum(field),0)),('col_c',Avg)]Make sure to at least have the columns oflist_totalsinlist_display.Testspython runtests.pyContributingPlease make sure to run the following commands before pushing and making a PR:pip install -r requirements/test-ci.txt
isort --recursive admin_totals tests
flake8isortwill sort the imports andflake8will lint the code. Please fix any errors before committing. Also, make sure to write passing tests. |
adminui | Python AdminUIFull Readme at GithubDocumentation中文文档Write professional web interface with Python.If you need a simple web interface and you don't want to mess around with
HTML, CSS, React, Angular, Webpack or other fancy Javascript frontend stuff,
this project is for you. Now you can write web pages, forms, charts and dashboards with only Python.This library is good for: data projects, tools and scripts, small IT systems and management systems,
Hacker or Hackathon projects. Basically if you need an interface for your system and you don't
care much about customizing the style or performance for large traffic, consider this package.This project is based on Flask/FastApi and Ant Design Pro.FeaturesNo HTML, CSS, JS neededDatabase agnostic: feed content at your own, no matter it's MySql, Sqlite, Excel tables, Firebase or some IoT hardwareJWT based authentication/login system with a neat login pageForms and detail pagesLine and Bar ChartCreate decent looking menusData tables with paginationAdaptive to small screens and mobile devicesSupport both Flask and FastApiInstallation and quick startinstall the package with pip:pip install adminuiThe basic "form page" example:# example_form.py
from adminui import *
app = AdminApp()
def on_submit(form_data):
print(form_data)
@app.page('/', 'form')
def form_page():
return [
Form(on_submit = on_submit, content = [
TextField('Title', required_message="The title is required!"),
TextArea('Description'),
FormActions(content = [
SubmitButton('Submit')
])
])
]
if __name__ == '__main__':
app.run()Run the Python file:python example_form.pyThen visithttp://127.0.0.1:5000/to see the result.Use FastApi instead of FlaskSetuse_fastapi=Truewhen creating theapp; andprepare()instead ofrun()to expose the app to uvicorn. See python/example_fastapi.py for details# instead of app = AdminApp(), use
app = AdminApp(use_fastapi=True)
# ... other stuff you do with adminui app
# in the end of the main file, use
fastapi_app = app.prepare()
# in command line, run:
# uvicorn example_fastapi:fastapi_appDocumentationHosted onRead the DocsContributing and DevelopmentThis is a personal project. So please create issues to tell me what you need from this project.You may also give stars to let me know if this project is worthy to invest more time on.To work with the source code:This project has a Typescript front-end and a Python backend.The front-end is in the/srcfolder.The back-end is in the/pythonfolder.To start developing:cd into/pythonfolder and runpip install -r requirements.txtto install requirementsrun one of the example_xxx.py file in the/pythonfolderOpen another terminal, runnpm install&npm startat the root folder to start the frontend;Under this development mode, requests from front-end will forward to the backend.When you are done with developing:runnpm run buildwill build the project.The front-end is based on the amazingAnt Design Prolibrary, you may consult their documentation during the development.The Python backend is located in/python/adminui. It is Flask based. There are some examples in the/pythonfolder. |
admiral | No description available on PyPI. |
admiral-core | No description available on PyPI. |
admission-prediction-model | A sample model to predict your chances of admission in graduate universities |
admitad | UNKNOWN |
admitted | Admitted/ədˈmɪtɪd/ verb : allowed entry (as to a place, fellowship, or privilege)This project is very new. The API is very likely to change.This library aims to make automating tasks that require
authentication on web sites simpler. In general it would
be better to make HTTP requests using an appropriate
library, but at times it is not obvious how to replicate
the login process and you don't want to have to reverse
engineer the site just to get your task done. That is where
this library comes in.We use Selenium to automate a Chrome instance, and set the
user data directory to the Chrome default so that "remember
me" settings will persist to avoid 2FA on every instance.
We install ChromeDriver in a user binary location and manage
ensuring the ChromeDriver version is correct for the current
installed Chrome version.We expose afetchmethod to make HTTP requests to the site
with credentials through Chrome, eliminating the need to copy
cookies and headers; and adirect_requestmethod that usesurllib3(which is also a dependency of Selenium) to make
anonymous HTTP requests.We also introduce a couple of methods that support exploring
a site's Javascript environment from within Python:page.window.new_keys()lists non-default global variables,
andpage.window[key]will access global variables.page.browser.debug_show_pagewill dump a text version of
the current page to the console (ifhtml2textis
installed, otherwise the raw page source).Installationpippip install admitted, orpip install admitted[debug]to includehtml2textfor
exploratory work, orpip install admitted[dev]for the development environment.Requirement format for this GitHub repo as a dependencyadmitted @ git+https://[email protected]/Accounting-Data-Solutions/admitted@mainUsageGenerally, theadmittedAPI is intended to follow theencouraged practice of page object modelsby establishing a pattern of definingPageclasses each
with one initialization method that defines selectors for
all relevant elements on the page and one or more action
methods defining the desired interaction with the page.Define your SiteThe Site is a special version of a Page object that defines
your login page and the method to complete the login action.
All other Page objects will have a reference to this for
testing if you are authenticated and repeating the login
if necessary.The default behavior ofSite.__init__is to call theloginmethod; this can be avoided by passingpostpone=TruetoSite.fromadmittedimportSite,PageclassMySite(Site):def__init__(self):# get login credentials from secure locationcredentials={"username":"user","password":"do_not_actually_put_your_password_in_your_code",}super().__init__(login_url="https://www.example.com/login",credentials=credentials,)def_init_login(self):self.username_selector="input#username"self.password_selector="input#password"self.submit_selector="button#login"def_do_login(self)->"MySite":self.css(self.username_selector).clear().send_keys(self.credentials["username"])self.css(self.password_selector).clear().send_keys(self.credentials["password"])self.css(self.submit_selector).click()returnselfdefis_authenticated(self)->bool:returnself.window["localStorage.accessToken"]isnotNoneDefine a PageThe default behavior ofPage.navigateis to callself.site.loginon the first retry if navigation fails.classMyPage(Page):def__init__(self):super().__init__(MySite())self.navigate("https://www.example.com/interest")def_init_page(self)->None:self.element_of_interest="//div[@id='interest']"self.action_button="#action-btn"defget_interesting_text(self)->str:element=self.xpath(self.element_of_interest)returnelement.textdefdo_page_action(self)->None:self.css(self.action_button).click()Use your Page objectpage=MyPage()print(f"Received '{page.get_interesting_text()}'. Interesting!")page.do_page_action()print(f"Non-default global variables are{page.window.new_keys()}")print(f"The document title is '{page.window['document.title']}'.")response=page.window.fetch(method="post",url="/api/option",payload={"showInterest":True},headers={"x-snowflake":"example-option-header"})print(f"Fetch returned '{response.json}'.")response=page.direct_request(method="get",url="https://www.google.com")print(f"The length of Google's page source is{len(response.text)}characters.")HTTP Response APIThePage.window.fetchandPage.direct_requestmethods
both return aResponseobject with the following API.contentproperty: Response body asbytes.textproperty: Response body asstr, orNone.jsonproperty: JSON parsed response body, orNone.htmlproperty:lxmlparsed HTML element tree, orNone.write_streammethod: Stream response data to the provided file pointer ifdirect_requestmethod was called withstream=True, otherwise writesResponse.content.destinationargument: file pointer for a file opened with a write binary mode.chunck_sizeargument: (optional) number of bytes to write at a time.Returnsdestination.ReferencesSelenium Python bindings documentationSelenium project documentationlxml html parser documentationDevelopmentConfigure pre-commit hooks to format, lint, and test code before commit..git/hooks/pre-commit#!/bin/bashif[-z"${VIRTUAL_ENV}"];thenecho"Please activate your virtual environment before commit!"exit1firoot=$(gitrev-parse--show-toplevel)black${root}|whilereadline;doif[[${line}=="reformatted*"]];thenlen=$(($(wc-c<<<${line})-12))file=${line:12:len}gitadd${file}fidonepylint--rcfile${root}/pyproject.toml${root}/src/admitted
pytest-x-rN--no-cov--no-headerReleaseA release is published to PyPI by a GitHub Action when there
is a push tomainwith a tag. See.github/workflows/publish.yml. |
admix | Admix is a simple tool to calculate ancestry composition (admixture proportions) from SNP raw data provided by various DNA testing vendors (such as 23andme and AncestryDNA).For more information, please refer tohttps://github.com/stevenliuyi/admix. |
admix-kit | admix-kit: Toolkit for analyzing genetic data of admixed populationsThis package aims to facilitate analyses and methods development of genetics data from admixed populations. We welcomefeature requests or bug reports.Installationpipinstall-Uadmix-kitUsecondaff you wish to create separate environment to avoid potential conflicts with other packages with:gitclonehttps://github.com/KangchengHou/admix-kitcdadmix-kit
condaenvcreate--fileenvironment.yaml
condaactivateadmix-kitGet startedOverviewPython APICommand line interfaceUse casesThe following manuscripts contain more motivation and details for functionalities withinadmix-kit:Admix-kit: An Integrated Toolkit and Pipeline for Genetic Analyses of Admixed Populations. bioRxiv 2023Impact of cross-ancestry genetic architecture on GWASs in admixed populations. AJHG 2023Causal effects on complex traits are similar for common variants across segments of different continental ancestries within admixed individuals. Nature Genetics 2023On powerful GWAS in admixed populations. Nature Genetics 2021Upload to PyPI (for developers)python setup.py sdist
twine upload dist/* |