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  • Did Google Delete my index?

    - by Sei
    I have a website that I haven't had much time to take care of. I updated it 4 times since last October, the contents are all original and informative. I can see those cache from Way back Machine and the site was indexed in Yahoo, but not in google. Did I get my index on Google deleted because I did not update it often? Normally Google crawl often and index fast, I just got really worried if I did something wrong. Is it possible that I set something wrong in the hosting or something? Please help me

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  • Python regex to parse text file, get the items in list and count the list

    - by Nemo
    I have a text file which contains some data. I m particularly interested in finding the count of the number of items in v_dims v_dims pattern in my text file looks like this : v_dims={ "Sales", "Product Family", "Sales Organization", "Region", "Sales Area", "Sales office", "Sales Division", "Sales Person", "Sales Channel", "Sales Order Type", "Sales Number", "Sales Person", "Sales Quantity", "Sales Amount" } So I m thinking of getting all the elements in v_dims and dumping them out in a Python list. Then compute the len(mylist) to get the count of the items. The challenge is in getting all the elements of v_dims from my text file and putting them in an empty list. I m particularly interested in items in v_dims in my text file. The text file has data in the form of v_dims pattern i showed in my original post. Some data has nested patterns of v_dims. Thanks. Here's what I have tried and failed. Any help is appreciated. TIA. import re fname = "C:\Users\XXXX\Test.mrk" with open(fname, "r") as fo: content_as_string = fo.read() match = re.findall(r'v_dims={\"(.+?)\"}',content_as_string) Though I have a big text file, Here's a snippet of what's the structure of my text file version "1"; // Computer generated object language file object 'MRKR' "Main" { Data_Type=2, HeaderBlock={ Version_String="6.3 (25)" }, Printer_Info={ Orientation=0, Page_Width=8.50000000, Page_Height=11.00000000, Page_Header="", Page_Footer="", Margin_type=0, Top_Margin=0.50000000, Left_Margin=0.50000000, Bottom_Margin=0.50000000, Right_Margin=0.50000000 }, Marker_Options={ Close_All="TRUE", Hide_Console="FALSE", Console_Left="FALSE", Console_Width=217, Main_Style="Maximized", MDI_Rect={ 0, 0, 892, 1063 } }, Dives={ { Dive="A", Windows={ { View_Index=0, Window_Info={ Window_Rect={ 0, -288, 400, 1008 }, Window_Style="Maximized Front", Window_Name="Theater [Previous Qtr Diveplan-Dive A]" }, Dependent_bool="FALSE", Colset={ Dive_Type="Normal", Dimension_Name="Theater", Action_List={ Actions={ { Action_Type="Select", select_type=5 }, { Action_Type="Select", select_type=0, Key_Names={ "Theater" }, Key_Indexes={ { "AMERICAS" } } }, { Action_Type="Focus", Focus_Rows="True" }, { Action_Type="Dimensions", v_dims={ "Theater", "Product Family", "Division", "Region", "Install at Country Name", "Connect Home Type", "Connect In Type", "SymmConnect Enabled", "Connect Home Refusal Reason", "Sales Order Channel Type", "Maintained By Group", "PS Flag", "Avalanche Flag", "Product Item Family" }, Xtab_Bool="False", Xtab_Flip="False" }, { Action_Type="Select", select_type=5 }, { Action_Type="Select", select_type=0, Key_Names={ "Theater", "Product Family", "Division", "Region", "Install at Country Name", "Connect Home Type", "Connect In Type", "SymmConnect Enabled", "Connect Home Refusal Reason", "Sales Order Channel Type", "Maintained By Group", "PS Flag", "Avalanche Flag" }, Key_Indexes={ { "AMERICAS", "ATMOS", "Latin America CS Division", "37000 CS Region", "Mexico", "", "", "", "", "DIRECT", "EMC", "N", "0" } } } } }, Num_Palette_cols=0, Num_Palette_rows=0 }, Format={ Window_Type="Tabular", Tabular={ Num_row_labels=8 } } } } } }, Widget_Set={ Widget_Layout="Vertical", Go_Button=1, Picklist_Width=0, Sort_Subset_Dimensions="TRUE", Order={ } }, Views={ { Data_Type=1, dbname="Previous Qtr Diveplan", diveline_dbname="Current Qtr Diveplan", logical_name="Current Qtr Diveplan", cols={ { name="Total TSS installs", column_type="Calc[Total TSS installs]", output_type="Number", format_string="." }, { name="TSS Valid Connectivity Records", column_type="Calc[TSS Valid Connectivity Records]", output_type="Number", format_string="." }, { name="% TSS Connectivity Record", column_type="Calc[% TSS Connectivity Record]", output_type="Number" }, { name="TSS Not Applicable", column_type="Calc[TSS Not Applicable]", output_type="Number", format_string="." }, { name="TSS Customer Refusals", column_type="Calc[TSS Customer Refusals]", output_type="Number", format_string="." }, { name="% TSS Refusals", column_type="Calc[% TSS Refusals]", output_type="Number" }, { name="TSS Eligible for Physical Connectivity", column_type="Calc[TSS Eligible for Physical Connectivity]", output_type="Number", format_string="." }, { name="TSS Boxes with Physical Connectivty", column_type="Calc[TSS Boxes with Physical Connectivty]", output_type="Number", format_string="." }, { name="% TSS Physical Connectivity", column_type="Calc[% TSS Physical Connectivity]", output_type="Number" } }, dim_cols={ { name="Model", column_type="Dimension[Model]", output_type="None" }, { name="Model", column_type="Dimension[Model]", output_type="None" }, { name="Connect In Type", column_type="Dimension[Connect In Type]", output_type="None" }, { name="Connect Home Type", column_type="Dimension[Connect Home Type]", output_type="None" }, { name="SymmConnect Enabled", column_type="Dimension[SymmConnect Enabled]", output_type="None" }, { name="Theater", column_type="Dimension[Theater]", output_type="None" }, { name="Division", column_type="Dimension[Division]", output_type="None" }, { name="Region", column_type="Dimension[Region]", output_type="None" }, { name="Sales Order Number", column_type="Dimension[Sales Order Number]", output_type="None" }, { name="Product Item Family", column_type="Dimension[Product Item Family]", output_type="None" }, { name="Item Serial Number", column_type="Dimension[Item Serial Number]", output_type="None" }, { name="Sales Order Deal Number", column_type="Dimension[Sales Order Deal Number]", output_type="None" }, { name="Item Install Date", column_type="Dimension[Item Install Date]", output_type="None" }, { name="SYR Last Dial Home Date", column_type="Dimension[SYR Last Dial Home Date]", output_type="None" }, { name="Maintained By Group", column_type="Dimension[Maintained By Group]", output_type="None" }, { name="PS Flag", column_type="Dimension[PS Flag]", output_type="None" }, { name="Connect Home Refusal Reason", column_type="Dimension[Connect Home Refusal Reason]", output_type="None", col_width=177 }, { name="Cust Name", column_type="Dimension[Cust Name]", output_type="None" }, { name="Sales Order Channel Type", column_type="Dimension[Sales Order Channel Type]", output_type="None" }, { name="Sales Order Type", column_type="Dimension[Sales Order Type]", output_type="None" }, { name="Part Model Key", column_type="Dimension[Part Model Key]", output_type="None" }, { name="Ship Date", column_type="Dimension[Ship Date]", output_type="None" }, { name="Model Number", column_type="Dimension[Model Number]", output_type="None" }, { name="Item Description", column_type="Dimension[Item Description]", output_type="None" }, { name="Customer Classification", column_type="Dimension[Customer Classification]", output_type="None" }, { name="CS Customer Name", column_type="Dimension[CS Customer Name]", output_type="None" }, { name="Install At Customer Number", column_type="Dimension[Install At Customer Number]", output_type="None" }, { name="Install at Country Name", column_type="Dimension[Install at Country Name]", output_type="None" }, { name="TLA Serial Number", column_type="Dimension[TLA Serial Number]", output_type="None" }, { name="Product Version", column_type="Dimension[Product Version]", output_type="None" }, { name="Avalanche Flag", column_type="Dimension[Avalanche Flag]", output_type="None" }, { name="Product Family", column_type="Dimension[Product Family]", output_type="None" }, { name="Project Number", column_type="Dimension[Project Number]", output_type="None" }, { name="PROJECT_STATUS", column_type="Dimension[PROJECT_STATUS]", output_type="None" } }, Available_Columns={ "Total TSS installs", "TSS Valid Connectivity Records", "% TSS Connectivity Record", "TSS Not Applicable", "TSS Customer Refusals", "% TSS Refusals", "TSS Eligible for Physical Connectivity", "TSS Boxes with Physical Connectivty", "% TSS Physical Connectivity", "Total Installs", "All Boxes with Valid Connectivty Record", "% All Connectivity Record", "Overall Refusals", "Overall Refusals %", "All Eligible for Physical Connectivty", "Boxes with Physical Connectivity", "% All with Physical Conectivity" }, Remaining_columns={ { name="Total Installs", column_type="Calc[Total Installs]", output_type="Number", format_string="." }, { name="All Boxes with Valid Connectivty Record", column_type="Calc[All Boxes with Valid Connectivty Record]", output_type="Number", format_string="." }, { name="% All Connectivity Record", column_type="Calc[% All Connectivity Record]", output_type="Number" }, { name="Overall Refusals", column_type="Calc[Overall Refusals]", output_type="Number", format_string="." }, { name="Overall Refusals %", column_type="Calc[Overall Refusals %]", output_type="Number" }, { name="All Eligible for Physical Connectivty", column_type="Calc[All Eligible for Physical Connectivty]", output_type="Number" }, { name="Boxes with Physical Connectivity", column_type="Calc[Boxes with Physical Connectivity]", output_type="Number" }, { name="% All with Physical Conectivity", column_type="Calc[% All with Physical Conectivity]", output_type="Number" } }, calcs={ { name="Total TSS installs", definition="Total[Total TSS installs]", ts_flag="Not TS Calc" }, { name="TSS Valid Connectivity Records", definition="Total[PS Boxes w/ valid connectivity record (1=yes)]", ts_flag="Not TS Calc" }, { name="% TSS Connectivity Record", definition="Total[PS Boxes w/ valid connectivity record (1=yes)] /Total[Total TSS installs]", ts_flag="Not TS Calc" }, { name="TSS Not Applicable", definition="Total[Bozes w/ valid connectivity record (1=yes)]-Total[Boxes Eligible (1=yes)]-Total[TSS Refusals]", ts_flag="Not TS Calc" }, { name="TSS Customer Refusals", definition="Total[TSS Refusals]", ts_flag="Not TS Calc" }, { name="% TSS Refusals", definition="Total[TSS Refusals]/Total[PS Boxes w/ valid connectivity record (1=yes)]", ts_flag="Not TS Calc" }, { name="TSS Eligible for Physical Connectivity", definition="Total[TSS Eligible]-Total[Exception]", ts_flag="Not TS Calc" }, { name="TSS Boxes with Physical Connectivty", definition="Total[PS Physical Connectivity] - Total[PS Physical Connectivity, SymmConnect Enabled=\"Capable not enabled\"]", ts_flag="Not TS Calc" }, { name="% TSS Physical Connectivity", definition="Total[Boxes w/ phys conn]/Total[Boxes Eligible (1=yes)]", ts_flag="Not TS Calc" }, { name="Total Installs", definition="Total[Total Installs]", ts_flag="Not TS Calc" }, { name="All Boxes with Valid Connectivty Record", definition="Total[Bozes w/ valid connectivity record (1=yes)]", ts_flag="Not TS Calc" }, { name="% All Connectivity Record", definition="Total[Bozes w/ valid connectivity record (1=yes)]/Total[Total Installs]", ts_flag="Not TS Calc" }, { name="Overall Refusals", definition="Total[Overall Refusals]", ts_flag="Not TS Calc" }, { name="Overall Refusals %", definition="Total[Overall Refusals]/Total[Bozes w/ valid connectivity record (1=yes)]", ts_flag="Not TS Calc" }, { name="All Eligible for Physical Connectivty", definition="Total[Boxes Eligible (1=yes)]-Total[Exception]", ts_flag="Not TS Calc" }, { name="Boxes with Physical Connectivity", definition="Total[Boxes w/ phys conn]-Total[Boxes w/ phys conn,SymmConnect Enabled=\"Capable not enabled\"]", ts_flag="Not TS Calc" }, { name="% All with Physical Conectivity", definition="Total[Boxes w/ phys conn]/Total[Boxes Eligible (1=yes)]", ts_flag="Not TS Calc" } }, merge_type="consolidate", merge_dbs={ { dbname="connectivityallproducts.mdl", diveline_dbname="/DI_PSREPORTING/connectivityallproducts.mdl" } }, skip_constant_columns="FALSE", categories={ { name="Geography", dimensions={ "Theater", "Division", "Region", "Install at Country Name" } }, { name="Mappings and Flags", dimensions={ "Connect Home Type", "Connect In Type", "SymmConnect Enabled", "Connect Home Refusal Reason", "Sales Order Channel Type", "Maintained By Group", "Customer Installable", "PS Flag", "Top Level Flag", "Avalanche Flag" } }, { name="Product Information", dimensions={ "Product Family", "Product Item Family", "Product Version", "Item Description" } }, { name="Sales Order Info", dimensions={ "Sales Order Deal Number", "Sales Order Number", "Sales Order Type" } }, { name="Dates", dimensions={ "Item Install Date", "Ship Date", "SYR Last Dial Home Date" } }, { name="Details", dimensions={ "Item Serial Number", "TLA Serial Number", "Part Model Key", "Model Number" } }, { name="Customer Infor", dimensions={ "CS Customer Name", "Install At Customer Number", "Customer Classification", "Cust Name" } }, { name="Other Dimensions", dimensions={ "Model" } } }, Maintain_Category_Order="FALSE", popup_info="false" } } };

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  • Copy an entity in Google App Engine datastore in Python without knowing property names at 'compile'

    - by Gordon Worley
    In a Python Google App Engine app I'm writing, I have an entity stored in the datastore that I need to retrieve, make an exact copy of it (with the exception of the key), and then put this entity back in. How should I do this? In particular, are there any caveats or tricks I need to be aware of when doing this so that I get a copy of the sort I expect and not something else. ETA: Well, I tried it out and I did run into problems. I would like to make my copy in such a way that I don't have to know the names of the properties when I write the code. My thinking was to do this: #theThing = a particular entity we pull from the datastore with model Thing copyThing = Thing(user = user) for thingProperty in theThing.properties(): copyThing.__setattr__(thingProperty[0], thingProperty[1]) This executes without any errors... until I try to pull copyThing from the datastore, at which point I discover that all of the properties are set to None (with the exception of the user and key, obviously). So clearly this code is doing something, since it's replacing the defaults with None (all of the properties have a default value set), but not at all what I want. Suggestions?

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  • Filtering by entity key name in Google App Engine on Python

    - by Bemmu
    On Google App Engine to query the data store with Python, one can use GQL or Entity.all() and then filter it. So for example these are equivalent gql = "SELECT * FROM User WHERE age >= 18" db.GqlQuery(gql) and query = User.all() query.filter("age >=", 18) Now, it's also possible to query things by key name. I know that in GQL you do it like this gql = "SELECT * FROM User WHERE __key__ >= Key('User', 'abc')" db.GqlQuery(gql) But how would you now use filter to do the same? query = User.all() query.filter("__key__ >=", ?????)

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  • Python and App Engine project structure

    - by Joel
    Hello, I am relatively new to python and app engine, and I just finished my first project. It consists of several *.py files (usually py file for every page on the site) and respectively temple files for each py file. In addition, I have one big PY file that has many functions that are common to a lot of pages, in I also declared the classes of db.Model (that is the datastore kinds). My question is what is the convention (if there is one) of arranging these files. If I create a model.py with the datastore classes, should it be in different package? Where should I put my template files and all of the py files that handle every page (should they be in the same directory as the one big common PY file)? I have tried to look for MVC and such implementations online but there are very few. Thanks, Joel

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  • Google App Engine Email

    - by Frank
    I use the following method to send email in the Google App Engine servlet : void Send_Email(String From,String To,String Message_Text) { Properties props=new Properties(); Session session=Session.getDefaultInstance(props,null); try { Message msg=new MimeMessage(session); msg.setFrom(new InternetAddress(From,"nmjava.com Admin")); msg.addRecipient(Message.RecipientType.TO,new InternetAddress(To,"Ni , Min")); msg.setSubject("Servlet Message"); msg.setText(Message_Text); Transport.send(msg); } catch (Exception ex) { // ... } } But it doesn't work, have I missed anything ? Has anyone got the email function working ?

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  • appcfg.py upload_data entity kind problem

    - by Dingo
    Hi, I am developing application on app-engine-path and I would like to upload some data to datastore. For example I have a model models/places.py: class Place(db.Model): name = db.StringProperty() longitude = db.FloatProperty() latitude = db.FloatProperty() If I save this in view, kind() of this entity is "models_place". All is ok, Place.all() in view work fine. But: If I upload some next row using appcfg.py upload_data, the kind() of this entities is Place. loader.py look like this: import datetime, os, sys from google.appengine.ext import db from google.appengine.tools import bulkloader libs_path = os.path.join("/home/martin/myproject/src/") if libs_path not in sys.path: sys.path.insert(0, libs_path) from models import places class AlbumLoader(bulkloader.Loader): def __init__(self): bulkloader.Loader.__init__(self, 'Place', [('name', lambda x: x.decode('utf-8')), ('longitude', float), ('latitude', float), ]) loaders = [AlbumLoader] and command for uploading: python /usr/local/google_appengine/appcfg.py upload_data --config_file=places_loader.py --kind=models_place --filename=data/places.csv --url=http://localhost:8000/remote_api /home/martin/myproject/src/

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  • Custom keys for Google App Engine models (Python)

    - by Cameron
    First off, I'm relatively new to Google App Engine, so I'm probably doing something silly. Say I've got a model Foo: class Foo(db.Model): name = db.StringProperty() I want to use name as a unique key for every Foo object. How is this done? When I want to get a specific Foo object, I currently query the datastore for all Foo objects with the target unique name, but queries are slow (plus it's a pain to ensure that name is unique when each new Foo is created). There's got to be a better way to do this! Thanks.

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  • App Engine - Save response from an API in the data store as file (blob)

    - by herrherr
    Hi there, I'm banging my head against the wall with this one: What I want to do is store a file that is returned from an API in the data store as a blob. Here is the code that I use on my local machine (which of course works due to an existing file system): client.convertHtml(html, open('html.pdf', 'wb')) Since I cannot write to a file on App Engine I tried several ways to store the response, without success. Any hints on how to do this? I was trying to do it with StringIO and managed to store the response but then weren't able to store it as a blob in the data store. Thanks, Chris

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  • What is the the maximum time for a user to return to Google for the visit to be flagged up as a bounce in GA?

    - by Anonymous
    I know that Google measures bounce rates by how fast a user returns to the results page after clicking-through to a website. Roughly what is the maximum duration of the visit for the user to then return for it to be considered a bounce? i.e. <5 seconds, <30 seconds? I'm mainly interested as it appears a lot of users clicking through my PPC adverts (Adwords) are bouncing, despite my ads having a high quality score and the page's being entirely related to the adverts copy and at as best tied to what I think user's may be searching for from the key phrases I've selected so the high bounce rate (100% on some keywords) seems a bit strange. If a bounce isn't determined by time, but simply whether a user returns to the SERP after visiting my site or not after any amount of time that would make more sense but the average duration of visit for my keywords with a 100% bounce rate in GA is 00:00:00, which suggests a user immediately returned to the SERPs, which again, is odd. Is my GA data being skewed by https or anything like that? Scratching my head here.

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  • Monitor and Control Memory Usage in Google Chrome

    - by Asian Angel
    Do you want to know just how much memory Google Chrome and any installed extensions are using at a given moment? With just a few clicks you can see just what is going on under the hood of your browser. How Much Memory are the Extensions Using? Here is our test browser with a new tab and the Extensions Page open, five enabled extensions, and one disabled at the moment. You can access Chrome’s Task Manager using the Page Menu, going to Developer, and selecting Task manager… Or by right clicking on the Tab Bar and selecting Task manager. There is also a keyboard shortcut (Shift + Esc) available for the “keyboard ninjas”. Sitting idle as shown above here are the stats for our test browser. All of the extensions are sitting there eating memory even though some of them are not available/active for use on our new tab and Extensions Page. Not so good… If the default layout is not to your liking then you can easily modify the information that is available by right clicking and adding/removing extra columns as desired. For our example we added Shared Memory & Private Memory. Using the about:memory Page to View Memory Usage Want even more detail? Type about:memory into the Address Bar and press Enter. Note: You can also access this page by clicking on the Stats for nerds Link in the lower left corner of the Task Manager Window. Focusing on the four distinct areas you can see the exact version of Chrome that is currently installed on your system… View the Memory & Virtual Memory statistics for Chrome… Note: If you have other browsers running at the same time you can view statistics for them here too. See a list of the Processes currently running… And the Memory & Virtual Memory statistics for those processes. The Difference with the Extensions Disabled Just for fun we decided to disable all of the extension in our test browser… The Task Manager Window is looking rather empty now but the memory consumption has definitely seen an improvement. Comparing Memory Usage for Two Extensions with Similar Functions For our next step we decided to compare the memory usage for two extensions with similar functionality. This can be helpful if you are wanting to keep memory consumption trimmed down as much as possible when deciding between similar extensions. First up was Speed Dial”(see our review here). The stats for Speed Dial…quite a change from what was shown above (~3,000 – 6,000 K). Next up was Incredible StartPage (see our review here). Surprisingly both were nearly identical in the amount of memory being used. Purging Memory Perhaps you like the idea of being able to “purge” some of that excess memory consumption. With a simple command switch modification to Chrome’s shortcut(s) you can add a Purge Memory Button to the Task Manager Window as shown below.  Notice the amount of memory being consumed at the moment… Note: The tutorial for adding the command switch can be found here. One quick click and there is a noticeable drop in memory consumption. Conclusion We hope that our examples here will prove useful to you in managing the memory consumption in your own Google Chrome installation. If you have a computer with limited resources every little bit definitely helps out. Similar Articles Productive Geek Tips Stupid Geek Tricks: Compare Your Browser’s Memory Usage with Google ChromeMonitor CPU, Memory, and Disk IO In Windows 7 with Taskbar MetersFix for Firefox memory leak on WindowsHow to Purge Memory in Google ChromeHow to Make Google Chrome Your Default Browser TouchFreeze Alternative in AutoHotkey The Icy Undertow Desktop Windows Home Server – Backup to LAN The Clear & Clean Desktop Use This Bookmarklet to Easily Get Albums Use AutoHotkey to Assign a Hotkey to a Specific Window Latest Software Reviews Tinyhacker Random Tips Acronis Online Backup DVDFab 6 Revo Uninstaller Pro Registry Mechanic 9 for Windows iFixit Offers Gadget Repair Manuals Online Vista style sidebar for Windows 7 Create Nice Charts With These Web Based Tools Track Daily Goals With 42Goals Video Toolbox is a Superb Online Video Editor Fun with 47 charts and graphs

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  • importing modules in app engine

    - by tanky
    Ive asked this before, but it seems i wasnt clear/detailed enough and after a week of trying im still struggling so i will try again. i am trying to use, oauth2 and ply on app engine. i have tried copying their directories into my app engine project directory (in the form ply-3.4 or brosner-python-oauth2-82a05f9) and i have tried copying the specific sub directory contained within the aforemention one. (ply or oauth2) i have tried saying import oauth2, from brosner-oauth2_python-82a05f9 import oauth and other variations on the theme, but i still cant get it to work nothing has worked. i have tried including them in app.yaml, but that seemed to create an even bigger error as my entire project wouldnt even run when i tried that. and now i have run out of things to try. the error log i am getting is as follows. INFO 2012-10-20 22:33:29,358 dev_appserver.py:2884] "GET / HTTP/1.1" 500 - WARNING 2012-10-20 22:33:58,453 py_zipimport.py:139] Can't open zipfile C:\Python27\lib\site-packages\oauth2-1.0.2-py2.7.egg: IOError: [Errno 13] file not accessible: 'C:\Python27\lib\site-packages\oauth2-1.0.2-py2.7.egg' WARNING 2012-10-20 22:33:58,453 py_zipimport.py:139] Can't open zipfile C:\Python27\lib\site-packages\ply-3.4-py2.7.egg: IOError: [Errno 13] file not accessible: 'C:\Python27\lib\site-packages\ply-3.4-py2.7.egg' WARNING 2012-10-20 22:33:58,453 py_zipimport.py:139] Can't open zipfile C:\Python27\lib\site-packages\tweepy-1.11-py2.7.egg: IOError: [Errno 13] file not accessible: 'C:\Python27\lib\site-packages\tweepy-1.11-py2.7.egg' ERROR 2012-10-20 22:34:00,015 wsgi.py:189] Traceback (most recent call last): File "C:\Program Files\Google\google_appengine\google\appengine\runtime\wsgi.py", line 187, in Handle handler = _config_handle.add_wsgi_middleware(self._LoadHandler()) File "C:\Program Files\Google\google_appengine\google\appengine\runtime\wsgi.py", line 225, in _LoadHandler handler = import(path[0]) File "C:\Program Files\Google\google_appengine\google\appengine\tools\dev_appserver_import_hook.py", line 676, in Decorate return func(self, *args, **kwargs) File "C:\Program Files\Google\google_appengine\google\appengine\tools\dev_appserver_import_hook.py", line 1850, in load_module return self.FindAndLoadModule(submodule, fullname, search_path) File "C:\Program Files\Google\google_appengine\google\appengine\tools\dev_appserver_import_hook.py", line 676, in Decorate return func(self, *args, **kwargs) File "C:\Program Files\Google\google_appengine\google\appengine\tools\dev_appserver_import_hook.py", line 1722, in FindAndLoadModule description) File "C:\Program Files\Google\google_appengine\google\appengine\tools\dev_appserver_import_hook.py", line 676, in Decorate return func(self, *args, **kwargs) File "C:\Program Files\Google\google_appengine\google\appengine\tools\dev_appserver_import_hook.py", line 1665, in LoadModuleRestricted description) File "C:\Documents and Settings\ladds\My Documents\udacity\sigh\main.py", line 3, in import ply ImportError: No module named ply INFO 2012-10-20 22:34:00,030 dev_appserver.py:2884] "GET / HTTP/1.1" 500 - thanks for any help.

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  • Google I/O 2012 - Getting Started with Google+ History API [CONF]

    Google I/O 2012 - Getting Started with Google+ History API [CONF] Timothy Jordan, Daniel Dulitz Google+ history presents new opportunities to increase traffic to your site and engagement with your content by allowing users to connect their Google profile to your site. This session will explore the value of Google+ history and review basic implementation. Special guests will be on hand to describe their early success with this new service. For all I/O 2012 sessions, go to developers.google.com From: GoogleDevelopers Views: 92 6 ratings Time: 33:56 More in Science & Technology

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  • Google I/O 2010 - Data pipelines with Google App Engine

    Google I/O 2010 - Data pipelines with Google App Engine Google I/O 2010 - Building high-throughput data pipelines with Google App Engine App Engine 301 Brett Slatkin This session will cover how to build, test, and maintain large-scale data pipelines on Google App Engine. It will cover maximizing efficiency, productionization, and how to deal with changing requirements. For all I/O 2010 sessions, please go to code.google.com From: GoogleDevelopers Views: 5 0 ratings Time: 01:01:52 More in Science & Technology

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  • Google I/O 2010 - Google Buzz, location, and social gaming

    Google I/O 2010 - Google Buzz, location, and social gaming Google I/O 2010 - Surf the stream: Google Buzz, location, and social gaming Social Web 201 Bob Aman, Timothy Jordan Google Buzz has a feature-rich API that allows you to do all kinds of interesting things with conversations and location. In this session we'll build a Buzz-tastic mobile game using App Engine, HTML5, and the Buzz API for social awesomeness. For all I/O 2010 sessions, please go to code.google.com From: GoogleDevelopers Views: 2 0 ratings Time: 31:18 More in Science & Technology

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  • Google I/O 2010: Google TV Keynote - Developer And Partner Timeline

    Google I/O 2010: Google TV Keynote - Developer And Partner Timeline Due to licensing and permissions issues, we are unable to show the full Google TV demonstration from the Day 2 keynote at Google I/O. Until we are able to get these permissions, please check out these clips. For Google I/O session videos, presentations, developer interviews and more, go to: code.google.com/io From: GoogleDevelopers Views: 1 0 ratings Time: 04:47 More in Science & Technology

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  • Google I/O 2010 - OpenID-based SSO & OAuth for Google Apps

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  • Google I/O 2010 - Integrate apps w/ Google Apps Marketplace

    Google I/O 2010 - Integrate apps w/ Google Apps Marketplace Google I/O 2010 - Integrating your app with the Google Apps Marketplace: Navigation, SSO, Data APIs and manifests Enterprise 201 Ryan Boyd, Steve Bazyl In this fast-paced, demo-focused session, you'll learn how to build, integrate, and sell a web app on the Google Apps Marketplace. We'll go end-to-end in 40 minutes with time left for Q&A. For all I/O 2010 sessions, please go to code.google.com From: GoogleDevelopers Views: 5 0 ratings Time: 59:45 More in Science & Technology

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  • Google I/O 2010 - Sell your app on the Google Apps Marketplace

    Google I/O 2010 - Sell your app on the Google Apps Marketplace Google I/O 2010 - Reach new customers fast: Learn how to sell your cloud app on the Google Apps Marketplace Enterprise 201 Scott McMullan, Jay Simmons (Atlassian), Chuck Dietrich (Sliderocket), Amit Kulkarni (Manymoon) In this introductory session we'll provide an overview of the Google Apps Marketplace and learn product and marketing best practices directly from 3 Marketplace ISVs. For all I/O 2010 sessions, please go to code.google.com From: GoogleDevelopers Views: 12 0 ratings Time: 56:42 More in Science & Technology

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  • Google I/O 2010: Google TV Keynote - Under The Hood

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  • Google I/O 2010: Google TV Keynote - Push Android Apps From Web To TV

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  • Google I/O 2010: Google TV Keynote - Flinging From Phone To TV

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  • Google I/O 2010: Google TV Keynote - YouTube Leanback

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  • Google I/O 2010: Google TV Keynote - An Open Platform

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