Saravanan666/pypi-org huggingface dataset:
Package: jqqb
jQueryQueryBuilderPython Rule evaluator for jQuery-QueryBuilder. It evaluates rules agains provided objects.Website•autobotAI Cloud GovernanceInspired fromSixiS/jquery_query_builder-railsUsageInstall the package.pip install jqqbUsage Example:fromjqqbimportQueryBuilderrule_json={"condition":"AND","rules":[{"id":"tagname","field":"tags.name","type":"string","input":"text","operator":"not_contains","value":"production"},{"id":"tagname","field":"tags.name","type":"string","input":"text","operator":"begins_with","value":"development"},{"condition":"OR","rules":[{"id":"type","field":"type","type":"string","input":"text","operator":"equal","value":"ec2"},{"id":"type","field":"type","type":"string","input":"text","operator":"equal","value":"ami"}]}]}evaluator=QueryBuilder(rule_json)object_1={'type':"ec2","tags":[{"name":"hello"},{"name":"asdfasfproduction_instance"}]}object_2={'type':"ami","tags":[{"name":"development"},{"name":"asfdafdroduction_instance"},{"name":"proction"}]}objects=[object_1,object_2]print(evaluator.match_objects(objects))print(evaluator.inspect_objects(objects))Result:[{'type':'ami','tags':[{'name':'development'},{'name':'asfdafdroduction_instance'},{'name':'proction'}]}][{'object':{'type':'ec2','tags':[{'name':'hello'},{'name':'asdfasfproduction_instance'}]},'selected':False,'results':[({'id':'tagname','field':'tags.name','type':'string','input':'text','operator':'not_contains','value':'production'},(['hello','asdfasfproduction_instance'],'production',False)),({'id':'tagname','field':'tags.name','type':'string','input':'text','operator':'begins_with','value':'development'},(['hello','asdfasfproduction_instance'],'development',False)),({'condition':'OR','rules':[{'id':'type','field':'type','type':'string','input':'text','operator':'equal','value':'ec2'},{'id':'type','field':'type','type':'string','input':'text','operator':'equal','value':'ami'}]},[({'id':'type','field':'type','type':'string','input':'text','operator':'equal','value':'ec2'},('ec2','ec2',True)),({'id':'type','field':'type','type':'string','input':'text','operator':'equal','value':'ami'},('ec2','ami',False))])]},{'object':{'type':'ami','tags':[{'name':'development'},{'name':'asfdafdroduction_instance'},{'name':'proction'}]},'selected':True,'results':[({'id':'tagname','field':'tags.name','type':'string','input':'text','operator':'not_contains','value':'production'},(['development','asfdafdroduction_instance','proction'],'production',True)),({'id':'tagname','field':'tags.name','type':'string','input':'text','operator':'begins_with','value':'development'},(['development','asfdafdroduction_instance','proction'],'development',True)),({'condition':'OR','rules':[{'id':'type','field':'type','type':'string','input':'text','operator':'equal','value':'ec2'},{'id':'type','field':'type','type':'string','input':'text','operator':'equal','value':'ami'}]},[({'id':'type','field':'type','type':'string','input':'text','operator':'equal','value':'ec2'},('ami','ec2',False)),({'id':'type','field':'type','type':'string','input':'text','operator':'equal','value':'ami'},('ami','ami',True))])]}]
Package: jqqb-evaluator
jQuery-QueryBuilder-Python-EvaluatorPython Rule evaluator for jQuery-QueryBuilder. It evaluates rules agains provided objects.Website•autobotAI Cloud GovernanceInspired fromSixiS/jquery_query_builder-railsUsageInstall the package.pip install jqqb-evaluatorUsage Example:fromjqqb_evaluator.evaluatorimportEvaluatorrule_json={"condition":"AND","rules":[{"id":"tagname","field":"tags.name","type":"string","input":"text","operator":"not_contains","value":"production"},{"id":"tagname","field":"tags.name","type":"string","input":"text","operator":"begins_with","value":"development"},{"condition":"OR","rules":[{"id":"type","field":"type","type":"string","input":"text","operator":"equal","value":"ec2"},{"id":"type","field":"type","type":"string","input":"text","operator":"equal","value":"ami"}]}]}evaluator=Evaluator(rule_json)object_1={'type':"ec2","tags":[{"name":"hello"},{"name":"asdfasfproduction_instance"}]}object_2={'type':"ami","tags":[{"name":"development"},{"name":"asfdafdroduction_instance"},{"name":"proction"}]}objects=[object_1,object_2]print(evaluator.get_matching_objects(objects))Result:[{'type': 'ami', 'tags': [{'name': 'development'}, {'name': 'asfdafdroduction_instance'}, {'name': 'proction'}]}]
Package: jquants-algo
jquants-algojquants-algo is a python library for algorithmic trading with japanese stock trade using J-Quants on Python 3.8 and above.Installation$ pip install jquants-algoUsagebacktestfromjquants_algoimportAlgoimportpprintclassMyAlgo(Algo):defstrategy(self):fast_ma=self.sma(period=3)slow_ma=self.sma(period=5)# golden crossself.sell_exit=self.buy_entry=(fast_ma>slow_ma)&(fast_ma.shift()<=slow_ma.shift())# dead crossself.buy_exit=self.sell_entry=(fast_ma<slow_ma)&(fast_ma.shift()>=slow_ma.shift())algo=MyAlgo(mail_address="<your J-Quants mail address>",password="<your J-Quants password>",ticker="7203",# TOYOTAsize=100,# 100 shares)pprint.pprint(algo.backtest()){'long':{'average return':'0.156','maximum drawdown':'49350.000','profit':'11450.000','profit factor':'1.080','riskreward ratio':'1.455','sharpe ratio':'0.038','trades':'54.000','win rate':'0.426'},'short':{'average return':'-0.238','maximum drawdown':'42050.000','profit':'-31020.000','profit factor':'0.754','riskreward ratio':'1.319','sharpe ratio':'-0.091','trades':'55.000','win rate':'0.364'},'total':{'average return':'-0.043','maximum drawdown':'79950.000','profit':'-19570.000','profit factor':'0.927','riskreward ratio':'1.423','sharpe ratio':'-0.013','trades':'109.000','win rate':'0.394'}}predictfromjquants_algoimportAlgoimportpprintclassMyAlgo(Algo):defstrategy(self):fast_ma=self.sma(period=3)slow_ma=self.sma(period=5)# golden crossself.sell_exit=self.buy_entry=(fast_ma>slow_ma)&(fast_ma.shift()<=slow_ma.shift())# dead crossself.buy_exit=self.sell_entry=(fast_ma<slow_ma)&(fast_ma.shift()>=slow_ma.shift())algo=MyAlgo(mail_address="<your J-Quants mail address>",password="<your J-Quants password>",ticker="7203",# TOYOTAsize=100,# 100 shares)pprint.pprint(algo.predict()){'buy entry':True,'buy exit':False,'close':2416.5,'date':'2023-08-22','sell entry':False,'sell exit':True}advancedfromjquants_algoimportAlgoimportpprintclassMyAlgo(Algo):defstrategy(self):rsi=self.rsi(period=10)ema=self.ema(period=20)lower=ema-(ema*0.001)upper=ema+(ema*0.001)self.buy_entry=(rsi<30)&(self.df.Close<lower)self.sell_entry=(rsi>70)&(self.df.Close>upper)self.sell_exit=ema>self.df.Closeself.buy_exit=ema<self.df.Closealgo=MyAlgo(mail_address="<your J-Quants mail address>",password="<your J-Quants password>",ticker="7203",# TOYOTAsize=100,# 100 sharesoutputs_dir_path="outputs",data_dir_path="data",)pprint.pprint(algo.backtest())pprint.pprint(algo.predict())Supported indicatorsSimple Moving Average 'sma'Exponential Moving Average 'ema'Moving Average Convergence Divergence 'macd'Relative Strenght Index 'rsi'Bollinger Bands 'bbands'Market Momentum 'mom'Stochastic Oscillator 'stoch'Average True Range 'atr'Getting startedFor help getting started with J-Quants, view our onlinedocumentation.
Package: jquants-ml
jquants-mljquants-ml is a python library for machine learning with japanese stock trade using J-Quants on Python 3.8 and above.Installation$ pip install jquants-mlUsagebacktestfromjquants_mlimportMlimportpprintclassMyMl(Ml):deffeatures(self):self.X["close"]=self.df.Closeself.X["ror"]=self.df.Close.pct_change(1)self.X["sma"]=self.sma(period=5)ml=MyMl(mail_address="<your J-Quants mail address>",password="<your J-Quants password>",ticker="7203",# TOYOTAsize=100,# 100 shares)pprint.pprint(ml.backtest()){'long':{'maximum drawdown':'15250.000','profit':'12700.000','profit factor':'1.183','riskreward ratio':'1.213','sharpe ratio':'0.063','trades':'81.000','win rate':'0.494'},'short':{'maximum drawdown':'50100.000','profit':'-43800.000','profit factor':'0.413','riskreward ratio':'0.478','sharpe ratio':'0.298','trades':'41.000','win rate':'0.463'},'total':{'maximum drawdown':'47200.000','profit':'-31100.000','profit factor':'0.784','riskreward ratio':'0.837','sharpe ratio':'0.149','trades':'122.000','win rate':'0.484'}}predictfromjquants_mlimportMlimportpprintclassMyMl(Ml):deffeatures(self):self.X["close"]=self.df.Closeself.X["ror"]=self.df.Close.pct_change(1)self.X["sma"]=self.sma(period=5)ml=MyMl(mail_address="<your J-Quants mail address>",password="<your J-Quants password>",ticker="7203",# TOYOTAsize=100,# 100 shares)pprint.pprint(ml.predict()){'Date':'2023-08-01','Price':2445.5,'Sign':'short'}advancedfromjquants_mlimportMlclassMyMl(Ml):# Awesome Oscillatordefao(self,*,fast_period:int=5,slow_period:int=34):return((self.df.H+self.df.L)/2).rolling(fast_period).mean()-((self.df.H+self.df.L)/2).rolling(slow_period).mean()deffeatures(self):self.X["ao"]=self.ao(fast_period=5,slow_period=34)self.X["close"]=self.df.Closeself.X["ror"]=self.df.Close.pct_change(1)self.X["sma"]=self.sma(period=5)self.X["ema"]=self.ema(period=5)self.X["upper"],self.X["mid"],self.X["lower"]=self.bbands(period=20,band=2)self.X["macd"],self.X["signal"]=self.macd(fast_period=12,slow_period=26,signal_period=9)self.X["k"],self.X["d"]=self.stoch(k_period=5,d_period=3)self.X["rsi"]=self.rsi(period=14)self.X["atr"]=self.atr(period=14)self.X["mom"]=self.mom(period=10)ml=MyMl(mail_address="<your J-Quants mail address>",password="<your J-Quants password>",ticker="7203",# TOYOTAsize=100,# 100 sharesoutputs_dir_path="outputs",model_dir_path="model",data_dir_path="data",)pprint.pprint(ml.backtest())pprint.pprint(ml.predict())Supported indicatorsSimple Moving Average 'sma'Exponential Moving Average 'ema'Moving Average Convergence Divergence 'macd'Relative Strenght Index 'rsi'Bollinger Bands 'bbands'Market Momentum 'mom'Stochastic Oscillator 'stoch'Average True Range 'atr'Getting startedFor help getting started with J-Quants, view our onlinedocumentation.
Package: jquants-pairs-trading
jquants-pairs-tradingjquants-pairs-trading is a python library for backtest with japanese stock pairs trading using kalman filter, J-Quants on Python 3.8 and above.Installation$ pip install jquants-pairs-tradingUsagefind pairsfromjquants_pairs_tradingimportJquantsPairsTradingimportpprintjpt=JquantsPairsTrading(mail_address="<your J-Quants mail address>",password="<your J-Quants password>",)pprint.pprint(jpt.find_pairs([3382,4063,4502]))[('3382','4502')]backtestfromjquants_pairs_tradingimportJquantsPairsTradingimportpprintjpt=JquantsPairsTrading(mail_address="<your J-Quants mail address>",password="<your J-Quants password>",)pprint.pprint(jpt.backtest((3382,4502))){'cointegration':'0.016','correlation':'0.814','maximum_drawdown':'443.000','profit_factor':'1.654','riskreward_ratio':'1.081','sharpe_ratio':'0.183','total_profit':'2184.000','total_trades':'86.000','win_rate':'0.605'}latest signalfromjquants_pairs_tradingimportJquantsPairsTradingimportpprintjpt=JquantsPairsTrading(mail_address="<your J-Quants mail address>",password="<your J-Quants password>",)pprint.pprint(jpt.latest_signal((6954,6981))){'6954 buy':True,'6954 close':'4348.000','6954 long':False,'6954 sell':False,'6954 short':False,'6981 buy':False,'6981 close':'2775.000','6981 long':False,'6981 sell':True,'6981 short':False,'date':'2023-07-31'}advancedfromjquants_pairs_tradingimportJquantsPairsTradingimportpprintjpt=JquantsPairsTrading(mail_address="<your J-Quants mail address>",password="<your J-Quants password>",window=1,transition_covariance=0.01,pvalues=0.05,zscore=0.5,)pprint.pprint(jpt.find_pairs([3382,4063,4502]))pprint.pprint(jpt.backtest((3382,4502)))pprint.pprint(jpt.latest_signal((6954,6981)))Getting startedFor help getting started with J-Quants, view our onlinedocumentation.
Package: jque
j q u eQuery JSON in memory as though it were a Mongo databaseInstallationpip3installjqueUsageimportjquejqueaccepts a variety of inputs to the constructor.Pass a list of dicts:data=jque.jque([{"name":"john"},{"name":"paul"},{"name":"george"},{"name":"ringo"}])Pass a JSON filename:DATAFILE="~/my/big/data.json"data=jque.jque(DATAFILE)Now you can query this dataset using Mongo-like syntax:>>>data.query({"name":{"$neq":"paul"}})[{"name":"john"},{"name":"george"},{"name":"ringo"}]Arguments toquery:ArgDescriptionwrap(boolean:True)Whether to wrap the resultant dataset in a newjqueobject. This allows chaining, likejque.query(...).query(...), if you're the sort of person to do that. PassFalseto get back alistinstead.Another example!data=jque.jque([{"_id":"ABC","name":"Arthur Dent","age":42,"current_planet":"earth"},{"_id":"DE2","name":"Penny Lane","age":19,"current_planet":"earth"},{"_id":"123","name":"Ford Prefect","age":240,"current_planet":"Brontitall"}])teenage_earthlings=data.query({"current_planet":{"$eq":"earth"},"age":{"$lte":20,"$gte":10}})Which returns:[{"_id":"DE2","name":"Penny Lane","age":19,"current_planet":"earth"}]Use Python lambdas as a filter:>>>libraries=jque.jque([...{"name":"jque","language":"Python"},...{"name":"react","language":"node"}...])>>>list(libraries.query({...'language':lambdax:x[:2]=="Py"...}))[{"name":"jque","language":"Python"}]
Package: jquery
Featurejquery is a jquery javascript library wrapper and ajax helper for happy
TurboGears web designers.Available widgetsJquery (basic jquery libray wrapper for Turbogears)It also contains 3 extra ajax widgets based on jquery.addCallback / link_to_remote(target ,update, href, callback)addPeriodBack / periodically_call_remote(update, href, interval)addFormback / form_remote_tag(target, update, href)Which are inspired from Ruby on Rails/pquery and give them the twisted syntax.InstallUse setuptools to install:$easy_install jqueryUsagejqueryinclude in config/app.cfg:tg.include_widgets = ['jquery.jquery']jquery ajax usage:import in controllers.py:from jquery import addCallback
from jquery import addPeriodback
from jquery import addFormback
....
return dict(addCallback = addCallback,
        addPeriodback = addPeriodback,
        addFormback = addFormback)NoteUpdate notice form 1.1.2 jquery widget: you need return dict(link = addPeriodback) instead of
return dict(link = addPeriodback()) in the following versionsin template:[div id="timelink"][a href = "#"]get time[/a][/div]
[div id="timediv"][/div]
${addCallback(target="timelink" ,update="timediv", href="/time")}NoteWith genshi template, you should add ‘ET’ prefix before the callback, such as:${ET(addCallback(target="timelink" ,update="timediv", href="/time"))}or:[div id="timediv"][/div]
${addPeriodback(update="timediv", href="/time", interval="3000")}or:[form class="timelink" action="ajax"  method="get" ]
   Field : [input type="text" name="field" /][br /]
   [input type="submit" /]
[/form]
[div id="timediv"][/div]
${addFormback(target="timelink", update="timediv", href="ajax")}NoteThe addCallback/addPeriodback/addFormback call could be placed anywhere in your template.
Checkhttp://docs.turbogears.org/1.0/RemoteLinkfor detail.Referencejqueryhttp://jquery.compqueryhttp://www.ngcoders.com/pquery/SourceSource is available inhttp://svn.turbogears.org/projects/tgJquery/trunkHistory1.2.3:update to jquery 1.2.3host in TurboGears svn1.2.2:update to jquery 1.2.21.1.2w2:new twisted style ajax callnew addFormback/form_remote_tag callpassing ajax function no need extra ‘()’ at all.
Package: jqueryquerybuilder
jQuery Query BuilderGenerator and Parser for the jQuery Query Builder library.Free software: MIT licenseDocumentation:https://querybuilder.readthedocs.io.FeaturesTODOCreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.1.0 (2017-11-21)First release on PyPI.
Package: jquery.widget
This package contains a collection of JQuery-based widgets.* Rest Editor WidgetDetailed Documentation**********************==================Rest Editor Widget==================The jquery.resteditorre package provides a javascript which can render atoolbar above a testarea widget. This toolbar offers buttons which can be usedfor apply reStructuredText formatters to the text in the textarea.We can't demonstrate this here because it's all done in javascript. ThereStructuredText editor textarea looks exactly how a normal z3c.form textarealooks. Btw. any textarea can use this package, there is no need for using theimplemented RESTEditorWidget. But anyway such a textarea widget looks like:>>> import zope.schema>>> from z3c.form import widget>>> from z3c.form import testing>>> from jquery.widget.resteditor.browser import RESTEditorFieldWidgetWe have to define a text field and a test request for instantiate the widget:>>> text = zope.schema.Text(...     title=u'Text',...     description=u'Text field'...     )>>> widget = RESTEditorFieldWidget(text, testing.TestRequest())>>> widget<RESTEditorWidget ''>Before we can render the widget, we need to register a template This templateis normaly registered by the z3c.form framework. Let's do it here since we notuse a real site setup.>>> from zope.configuration import xmlconfig>>> import zope.component>>> import z3c.form>>> xmlconfig.XMLConfig('meta.zcml', zope.component)()>>> xmlconfig.XMLConfig('meta.zcml', z3c.form)()>>> xmlconfig.XMLConfig('configure.zcml', z3c.form)()Now we can render the widget:>>> widget.update()>>> print widget.render()<textarea id="" name="" class="restEditorWidget"></textarea>As you can see the reStructuredText editor widget uses a css class called``restEditorWidget``. this class can be used for apply a JQuery xpath ruleand load the JQuery method restEditor.This can be done like:$('.restEditorWidget').restEditor();Of corse you can do this inside a global JQuery dom onload event handler call.If so, you can use something like this:$(document).ready(function() {$('.restEditorWidget').restEditor();});Note, there is also a sample located at http://www.z3c.org/samples
Package: jr
jr 简介一个Python 语言开发的，基于textX和jinja的 DSL 研发平台Installationpipinstalljr目录结构说明├── demo                        jr 插件示例 demo 目录
│   └── pipeline                pipeline demo 目录
│       ├── input               待编译DSL文件目录
│       ├── output              编译输出的文件目录
│       ├── rule                DSL 语法描述文件目录
│       └── template            DSL 模板文件目录
├── doc                         jr 文档目录
├── jr                          jr 引擎所在的目录
├── requirements.txt            jr 项目依赖文件列表
├── setup.py                    setup 文件
├── LICENSE                     LICENSE 文件
├── MANIFEST.in                 MANIFEST 文件
├── README.md                   README 文件
└── test                        单元测试目录快速入门新建文件夹，做为应用根目录mkdir zion_demo
cd zion_demo创建应用描述文件appinfo.ini编写配置文件[appinfo]
app_name = zion_demo            # 应用名
app_version = 1.0.0             # 应用版本号
rule = python_tx                # DSL 语法描述文件, 默认支持 'python_tx', 'json_tx', 'pipeline_tx', 可自定义，指定自己的tx文件路径
input_path = input              # 待编译 DSL 文件存放路径
output_path = output            # 编译后输出文件存放路径
output_prefix = JR_             # 编译后输出文件前缀
output_ext = html               # 编译后输出文件后缀
template_path = template        # 模板文件路径
extension = jr:ZionExtension    # 使用的扩展类，默认支持 'jr:ZionExtension', 'jr:JsonExtension', 'jr:PipelineExtension', 可自定义，指定自己的扩展类, 格式 {module_name}:{class_name}创建对应的文件夹mkdir input output template创建模板文件...创建 DSL 文件submit_demo.py编写 DSL 文件from ..lib.textbox import *
from ..lib.submit import *

mer_id = Textbox()
mer_id.caption = "商户号"
mer_id.id = "mer_id"
mer_id.disabled = "true"

op_id = Textbox()
op_id.caption = "操作员号"
op_id.id = "login_operator_id"
op_id.disabled = "true"

test_info_1 = Textbox()
test_info_1.caption = "测试信息一"
test_info_1.id = "test_info_1"

test_info_2 = Textbox()
test_info_2.caption = "测试信息二"
test_info_2.id = "test_info_2"

test_info_3 = Textbox()
test_info_3.caption = "测试信息三"
test_info_3.id = "test_info_3"

btn_submit = Submit()
btn_submit.text = "提交信息"
btn_submit.callback = "showApiMsg"
btn_submit.func = "post"
btn_submit.url = "http://127.0.0.1/submit_demo"编译 DSL 文件jr compile
Package: jr100emulib
JR-100 Emulator LibraryJR-100 Emulator Library (jr100emulib)は、松下電子工業が1982年に発売した8ビットマイコンJR-100のエミュレータをサポートするPythonパッケージです。提供するモジュールFSK LoaderFSK Loaderは、JR-100のSAVE/MSAVEコマンドで出力された音声データを解析し、格納されているプログラムをファイルとして取り出すことができます。詳しくはfskloader.mdを参照してください。前提条件Python 3.7以上NumPySciPyインストールpipでjr100emulibパッケージをインストールしてください。$pipinstalljr100emulib不要になった場合はpipでアンインストールできます。$pipuninstalljr100emulib使い方モジュールとして使用する場合は、必要なモジュールをimportしてください。fromjr100emulibimportfskloaderまたコマンドラインツールが用意されているものはそれぞれのモジュールのドキュメントを参照してください。作者Kenichi Miyata (kemusiro@asamomiji.jp)ライセンスJR-100 Emulator Library is licensed under the MIT License.  See theLICENSEfile for details.JR-100 Emulator LibraryはMITライセンスの元にライセンスされています。詳細はLICENSEを参照してください。
Package: jrc
jrc 简介=======一个Python 语言开发的，基于`textX <https://github.com/igordejanovic/textX>`__ 和`jinja <https://github.com/pallets/jinja>`__ 的 DSL 编译器Installation============.. code:: pythonpip install jrc目录结构说明============.. code::├── demo                        jrc 插件示例 demo 目录│   └── pipeline                pipeline demo 目录│       ├── input               待编译DSL文件目录│       ├── output              编译输出的文件目录│       ├── parser              DSL 语法解析文件目录│       └── template            DSL 模板文件目录├── doc                         jrc 文档目录├── jrc                         jrc 引擎所在的目录├── requirements.txt            jrc 项目依赖文件列表├── setup.py                    setup 文件├── LICENSE                     LICENSE 文件├── MANIFEST.in                 MANIFEST 文件├── README.md                   README 文件└── test                        单元测试目录快速入门============新建文件夹，做为应用根目录.. code::mkdir zion_democd zion_demo创建应用描述文件.. code::appinfo.ini编写配置文件.. code::[appinfo]app_name = zion_demo            # 应用名app_version = 1.0.0             # 应用版本号parser = python_tx              # DSL 语法解析文件, 默认支持 'python_tx', 'json_tx', 'pipeline_tx', 可自定义，指定自己的tx文件路径input_path = input              # 待编译 DSL 文件存放路径output_path = output            # 编译后输出文件存放路径output_prefix = JR_             # 编译后输出文件前缀output_ext = html               # 编译后输出文件后缀template_path = template        # 模板文件路径extension = jrc:ZionExtension    # 使用的扩展类，默认支持 'jrc:ZionExtension', 'jrc:JsonExtension', 'jrc:PipelineExtension', 可自定义，指定自己的扩展类, 格式 {module_name}:{class_name}创建对应的文件夹.. code::mkdir input output template创建模板文件.. code::...创建 DSL 文件.. code::submit_demo.py编写 DSL 文件.. code::from ..lib.textbox import *from ..lib.submit import *mer_id = Textbox()mer_id.caption = "商户号"mer_id.id = "mer_id"mer_id.disabled = "true"op_id = Textbox()op_id.caption = "操作员号"op_id.id = "login_operator_id"op_id.disabled = "true"test_info_1 = Textbox()test_info_1.caption = "测试信息一"test_info_1.id = "test_info_1"test_info_2 = Textbox()test_info_2.caption = "测试信息二"test_info_2.id = "test_info_2"test_info_3 = Textbox()test_info_3.caption = "测试信息三"test_info_3.id = "test_info_3"btn_submit = Submit()btn_submit.text = "提交信息"btn_submit.callback = "showApiMsg"btn_submit.func = "post"btn_submit.url = "http://127.0.0.1/submit_demo"编译 DSL 文件.. code::jrc compile
Package: jr-data-science
data-scienceA set of useful functions for day-to-day python datascience project.DescriptionA few modules who work together to automatize some routine meet each day on a jr_data_science project.decouverte.py :Some functions to find informations around the files you are given.
You would find here some functions about paths, cleaning paths, and header extracts.extra_pandas.py :Never dreamed of a panda function able to convert anykind of time-encoded column ? Well, I didn't do it :-/ but I collected a rather long list of type of string I found on my way, and put it on one bigger-and-bigger convert-date columns ;)useful_functions.py:How to get all the csvs under a dir ? How to compile a multi-worksheet excel file into one big csv ? Here you have some tool to do so !import pandas as pd
import os
from collections import Counter
import numpy as np;import seaborn as sns;
import matplotlib.dates as mdates
import random
from functools import reducenp.random.seed(0)
sns.set()
%matplotlib inline
sns.set_style("white")
sns.set_palette("husl")
Package: jrdb-model
JRDB-ModelORM for JRDB with flask-sqlalchemy.You can store jrdb dataset to your database.This repository does not have fetching scripts.Setupset your environmental variable below.e.g.DB="sqlite:///tmp/jrdb.db"https://flask-sqlalchemy.palletsprojects.com/en/2.x/config/UsageInstallpip install jrdb_modelLoad datasetfrom  jrdb_model import KaisaiData  
kaisais = KaisaiData.query.filter_by(ymd='20181118').all()