--- title: Philander 2014 keywords: fastai sidebar: home_sidebar summary: "Full replication" description: "Full replication" nb_path: "35_philander2014.ipynb" ---
This notebook shows how gamba can be used to reproduce findings from Philander's 2014 study on data mining methods for detecting high-risk gamblers.
It uses data available through the transaparency project above, and performs eight distinct supervised machine learning techniques.
Note: given the high dimensionality of this data (17), the sample size (530) doesn't meet the rule of thumb that 10x17 (or 1700) observations are required for learning to be generalisable. This means that the ouputs of the methods below may change drastically upon repeated executions, and comparison to the original may not be meaningful. That considered, this notebook shows you how to do this kind of analysis using identical methods.
To begin, import gamba as usual;
import gamba as gb
measures_table = gb.data.prepare_philander_data('AnalyticDataSet_HighRisk.txt', loud=True)
{% include note.html content='throughout this page the naming convention is an abbreviated version of the name for the test labels used, and the same name with a ’p’ on the end to denote the predicted labels.' %}
{% include note.html content='you’ll also notice a train_test_split parameter of 0.696 as a parameter to all of the methods, this is just to make sure that exactly the same train test split happens as in the paper (it defaults to 0.7)' %}
log_r, log_rp = gb.machine_learning.logistic_regression(measures_table, 'self_exclude', train_test_split=0.696)
lasso_l, lasso_lp = gb.machine_learning.lasso_logistic_regression(measures_table, 'self_exclude', train_test_split=0.696)
The following cell uses the Keras library to create and train some neural networks as described in the study. The original study uses the R nnet and caret packages, this stackoverflow post was helpful in understanding the original parameters.
Framing the self_exclude label (0 or 1) as a regression problem means creating a neural network which returns a continuous label. The classification version of the neural network used in the original analysis uses an identical network topology but passes two strings as values instead of a 1 or 0. This should in theory have no substantial difference on the performance of the network (given the sample size and identical architectures).
By contrast, the gamba library's neural network methods have subtly different topologies for classification and regression as described in Deep Learning with Python, which are used here.
nn_c, nn_cp = gb.machine_learning.neural_network_classification(measures_table, 'self_exclude', train_test_split=0.696)
nn_r, nn_rp = gb.machine_learning.neural_network_regression(measures_table, 'self_exclude', train_test_split=0.696)
The following cell uses scikit-learn's SVM methods to create and trains some SVM's. The original paper uses Dimitriadou et al's implementations in R described here.
svm_e, svm_ep = gb.machine_learning.svm_eps_regression(measures_table, 'self_exclude', train_test_split=0.696)
svm_c, svm_cp = gb.machine_learning.svm_c_classification(measures_table, 'self_exclude', train_test_split=0.696)
svm_o, svm_op = gb.machine_learning.svm_one_classification(measures_table, 'self_exclude', train_test_split=0.696)
This section implements scikit-learn's ensemble methods to create random forests for classification and regression
rf_r, rf_rp = gb.machine_learning.rf_regression(measures_table, 'self_exclude', train_test_split=0.696)
rf_c, rf_cp = gb.machine_learning.rf_classification(measures_table, 'self_exclude', train_test_split=0.696)
Finally lets present the performance of each of the machine learning techniques using a number of metrics. Not all of the metrics apply to all of the methods, but it's a good way to see roughly how they compare.
all_results = [
gb.machine_learning.compute_performance('Logistic Regression', log_r, log_rp),
gb.machine_learning.compute_performance('Lasso Logistic Regression', lasso_l, lasso_lp),
gb.machine_learning.compute_performance('NN Regression', nn_r, nn_rp),
gb.machine_learning.compute_performance('NN Classification', nn_c, nn_cp),
gb.machine_learning.compute_performance('SVM eps-Regression', svm_e, svm_ep),
gb.machine_learning.compute_performance('SVM c-Classification', svm_c, svm_cp),
gb.machine_learning.compute_performance('SVM one-Classification', svm_o, svm_op),
gb.machine_learning.compute_performance('RF Regression', rf_r, rf_rp),
gb.machine_learning.compute_performance('RF Classification', rf_c, rf_cp)
]
all_results_df = gb.concat(all_results)
display(all_results_df)


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New Zealand beat England by innings and 65 runs in first test
England had resumed on 55-3 and needing to bat the entire final day to stop NZ from taking a 1-0 lead
by ReutersNew Zealand wrapped up an impressive innings and 65-run victory over England shortly after tea on the fifth day of the first test at Bay Oval on November 25 with Neil Wagner running through the lower order.
Wagner took three wickets in 17 balls for just one run as England collapsed from 132-5 to 138-8 before tea, then broke a 59-run ninth-wicket partnership between Jofra Archer and Sam Curran after they had threatened to thwart the hosts.
The left armer then trapped Stuart Broad in front for a first ball duck to end England's resistance at 197 and end with figures of 5-44 from 19.2 overs.
England had resumed on November 25 on 55-3 and needing to bat the entire final day to stop New Zealand from taking a 1-0 lead into the second match of the two-test series that starts on November 22 in Hamilton.


Well here it goes... It was very random that I would goto this rave, scream 5 at that, and i was just hangin around doin my own thing and stuff when someone asked me if I wanted any AMT. i contemplated for a minute or two (literally), then i said what the hell, i copped a 20.. the dude who hooked it up said that there was enough for 2 doses there, i dont know how many mg or anything, but ill explain how it came
it was in a piece of paper, maybe a inch square folded intrequetely (sp.) with the amt on the innermost portion.. im gonna venture to guess between 40 and 70 mg.. and boy do i have to say it was one of the best 14 hours ive ever spent in my life. First of all, bieng at a rave is strange for me, i dont really like raves, or techno music that much, but my friend was in a bind so i went with her. this just made everything so much better. it was like candyflipping but sans-brain rot. now bieng 4:30 the day afetr, im still awake still feeling the trippyness of the amt.
I licked the line at around 2 AM, around 2:45 i started to feel the trip coming on, auras etc.., at about 3 i got naucious, my mouth was watering like i was going to throwup but i didnt, these went away after about 10 minutes of dealing with them.. by 3:30 i felt like i was rolling FACE, visuals were coming a lot more commonly now, everything was awsome.. it was the most peaceful thing ive ever experienced in my entire life.
We left the rave at 4:20, and started our 2 hour drive back home. the whole ride home was soooo intense.. I can handle my trips very well, never in my life had a bad trip *knock on wood*.. on the 4th of july i ate a 1/4 and a 1/8th of mushrooms and roamed around boston watching the fireworks and such, great time but anyway.. this was far more intense then even that amount of psilocybin had done to me.. not visually but as far as my thoughts and just thinking about random stuff that would only make sence to me..
we finally got back to my friends house at 6:30 in the morning, i was stillll tripping balls and everyone else was ready to goto sleep and crash out after the rave n stuff, i kept my friend J up all day with me hehe hope shes not mad ;) she had fun with the 'trippin kid' lol we talked about stuff and defenitly bonded a lot throughout the day.. and it wasnt that type of bond you get with soemone you rolled with the night before, thats a fake bond, this was actually meaningful and everything.. with me, every time i trip it is always a life changing experience.. for the better, i always learn something new about someone or myself.
It is now 4:40 (14 hours after initial ingestion) in the afternoon, im still awake from last nights party, still feeling mild effects from the AMT, things are blurring out of the corner of my eyes, the computer screen is wafting in the 'breeze', and my lizard (i have a bearded dragon) is pulsating. periodically, everything in my vision will get a tint of green to it.. some people experience that when they trip experience tints coloring everything around during the post-stages..
I would have to say, that now seemingly coming out of this wonderful experience, that the only thing that I want is to try it again. I have heard from a couple people that AMT isnt the same the second time around, but everyone reacts differently to everything. Ill just find out for myself thank you very much. :) One reccomendation is to not take AMT at a rave or any place that youre not familiar with or comfortable in, as with any trip setting is key, bad vibes can put you in bad place. Thats all i have to say at this moment, i feel very sleep deprived and ready for ni-nites..
Be safe everyone..
'Know your body, know your source, know your limits'
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


Tutorial on Microbiome Normalization
2021-08-17
Chapter 1 Introduction
1.1 The importance of normalization
Microbiome data must be normalized before any analysis can be performed. Following the process of sequencing and assigning raw reads into counts per observed and classified identified taxa classes/OTUs/ASVs, the data are in the form of a matrix of read counts. Normalization is the process of transforming raw read count data into data that can be compared between samples. Statistical analysis on this count matrix is then performed depending on the goal of the experiment. Common analysis goals include community-level analysis (alpha/beta diversity), differential abundance testing (the parallel of differential expression testing in gene expression studies), and network analysis.
Analysis of composition, differences, connections, etc. should be done based only on true biological aspects. However, technical variation in counts across samples is a given hurdle that must be accounted for. Biases can arise in the sequencing process, sample preparation, contamination, preferential amplification, and can manifest in differences in sparsity and unequal sequencing depths (Salter et al. 2014). An effective normalization strategy should put all samples on equal footing so interpretations are on biological signals, not technical signals such as sequencing depth. Currently, there is no known ‘best’ normalization method that removes all technical artifacts leaving only biological signals.
Due to the sequencing technology, samples will have different sequencing depths, or the sum of all the counts in a sample. Directly comparing raw counts between samples is not possible. To illustrate this, consider the counts of one taxon, labeled ASV1
, across two samples shown below. In Sample A, this taxon has a count of 230, and in Sample B, this taxon has a count of 23. Is this taxon differentially abundant between samples?
##
## Attaching package: 'gridExtra'
## The following object is masked from 'package:Biobase':
##
## combine
## The following object is masked from 'package:BiocGenerics':
##
## combine
## The following object is masked from 'package:dplyr':
##
## combine
Normalization for microbiome data often refers to standardizing sequencing depth across samples. One common approach to this is a scaling-based approach, where a scaling factor is calculated for every sample and the counts for each taxon are divided by the scaling factor for that sample. Figure B shows the same data as figure A, but where each sample has been transformed into proportions by dividing by the total counts for each sample. The difference in ASV1
between samples appears much smaller. However, now there appears to be a difference in ASV2
, even though the counts were originally the same. This is because in sample B ASV1
consists of a higher proportion of the total count than in sample A.
This demonstrates the importance of normalization, but also the artifacts that can occur depending on the method.
1.2 The compositional nature of microbiome data
Microbiome data are inherently compositional. The counts of the collection of taxa that make up each sample are constrained by the total sum, or sequencing depth for that sample. This means that the count of each sampled taxon is a portion of a larger whole. Each observed taxon is not independent. As we saw in the above example, before normalization, ASV2
was equal between samples. After converting to proportions, ASV2
no longer appears equal. If there is a difference between two samples it is unclear if that difference is because of a true difference on that taxon or that taxon is changing because of differences in another taxon.
Numerous traditional statistical methods rely on an independence assumption, which is not met with microbiome data. This can lead to spurious correlations that exist only because of the compositional nature and not any true signal.
With library size as the sum constraint for each sample, if we know in a biological system that after an event occurs (treatment), ASV1 decreases, this will change the composition of the sampled ASV2 regardless of its change or lack thereof in the underlying population.
Consider again two samples consisting of red and blue points. We can think of the samples as before and after treatment. In the second plot, the number of blue dots in the population and in the observed sample has decreased, but the red remains the same.
| Sample | blue | red |
|---|---|---|
| Before | 20 | 10 |
| After | 4 | 10 |
| Sample | blue | red |
|---|---|---|
| Before | 0.6666667 | 0.2857143 |
| After | 0.3333333 | 0.7142857 |
This observed increase in red is due to the compositional nature of the sampled points, and not any true difference in the red population.
1.2.1 Log ratio
The methodology developed by Aitchison in the 1980s is useful for analyzing compositional data. The theory developed there, however, is for data with different characteristics than microbiome data.
Microbiome data is high dimensional, and.
Taking the logarithm of ratios can be an appropriate transformation for compositional data, so standard statistical tests can be appropriate again. This transformation removes the issue of standardizing/normalizing different library sizes. The library size for a given sample will not distort the biological covariance or correlation structure.
This log-ratio method has a drawback, which is the decision of how to define the denominator. Two approaches to this problem can be to use one sample as the reference. This sample should be ‘representative’. The log-ratio transformation is then the ratio of every other taxon to that representative sample. Of course, knowledge of what makes a sample representative is hard to come by and often unknown, and subsequent results can be affected by this choice. This method is frequently called the additive log-ratio approach (alr). The alternative method is using the data to create a pseudo-reference sample. This pseudo-reference sample is the geometric mean of the counts of all taxa. This is called the centered log-ratio method.
While promising, these log-ratio methods have drawbacks in current metagenomic literature. Microbiome data are often incredibly sparse, with up to 80-90% of count matrices containing zero counts. For ratio transformations, if we have sparse data, the geometric mean can be zero. Then the ratio is undefined, and further, so is the log.
One solution to this is adding a small pseudo count to every element in the data. This removes problems occurring from having zero counts in the data, but there is not a clear best choice of what pseudo count to use, and it the choice can impact downstream results.
1.3 Zero-inflation of microbiome data
Include section on considerations that are necessary to account for zero-inflation.
References
Salter, Susannah J., Michael J. Cox, Elena M. Turek, Szymon T. Calus, William O. Cookson, Miriam F. Moffatt, Paul Turner, Julian Parkhill, Nicholas J. Loman, and Alan W. Walker. 2014. “Reagent and Laboratory Contamination Can Critically Impact Sequence-Based Microbiome Analyses.” BMC Biology 12 (1): 87. https://doi.org/10.1186/s12915-014-0087-z.


!Sir Gustav Athebaldt:
As most do.
But many are not aware of a great battle that bubbles under the surface, between our order and the Order of Dusk. We fight for control over the Seven Seals, the seals Shunaiman himself created to fight Shilen and her armies.
These seals are now buried deep in Aden's catacombs and necropolises, areas which have turned into hellish battlefields.
[Quest _194_SevenSignsMammonsContract sirgustavathebaldt_q194_2b.htm|"I see."]


NIF Objects | Compound Types | Enum Types | Basic Types | File Versions
| Number | Name | Description |
|---|---|---|
| 0x00000001 | SLSF1_Specular | Enables Specularity |
| 0x00000002 | SLSF1_Skinned | Required For Skinned Meshes. |
| 0x00000004 | SLSF1_Temp_Refraction | Unknown |
| 0x00000008 | SLSF1_Vertex_Alpha | Enables using alpha component of vertex colors. |
| 0x00000010 | SLSF1_Greyscale_To_PaletteColor | in EffectShaderProperty |
| 0x00000020 | SLSF1_Greyscale_To_PaletteAlpha | in EffectShaderProperty |
| 0x00000040 | SLSF1_Use_Falloff | Use Falloff value in EffectShaderProperty |
| 0x00000080 | SLSF1_Environment_Mapping | Environment mapping (uses Envmap Scale). |
| 0x00000100 | SLSF1_Recieve_Shadows | Object can recieve shadows. |
| 0x00000200 | SLSF1_Cast_Shadows | Can cast shadows |
| 0x00000400 | SLSF1_Facegen_Detail_Map | Use a face detail map in the 4th texture slot. |
| 0x00000800 | SLSF1_Parallax | Unused? |
| 0x00001000 | SLSF1_Model_Space_Normals | Use Model space normals and an external Specular Map. |
| 0x00002000 | SLSF1_Non_Projective_Shadows | Unknown. |
| 0x00004000 | SLSF1_Landscape | Unknown. |
| 0x00008000 | SLSF1_Refraction | Use normal map for refraction effect. |
| 0x00010000 | SLSF1_Fire_Refraction | SLSF1_Fire_Refraction |
| 0x00020000 | SLSF1_Eye_Environment_Mapping | Eye Environment Mapping (Must use the Eye shader and the model must be skinned) |
| 0x00040000 | SLSF1_Hair_Soft_Lighting | Keeps from going too bright under lights (hair shader only) |
| 0x00080000 | SLSF1_Screendoor_Alpha_Fade | SLSF1_Screendoor_Alpha_Fade |
| 0x00100000 | SLSF1_Localmap_Hide_Secret | Object and anything it is positioned above will not render on local map view. |
| 0x00200000 | SLSF1_FaceGen_RGB_Tint | Use tintmask for Face. |
| 0x00400000 | SLSF1_Own_Emit | Provides its own emittance color. (will not absorb light/ambient color?) |
| 0x00800000 | SLSF1_Projected_UV | Used for decalling? |
| 0x01000000 | SLSF1_Multiple_Textures | SLSF1_Multiple_Textures |
| 0x02000000 | SLSF1_Remappable_Textures | SLSF1_Remappable_Textures |
| 0x04000000 | SLSF1_Decal | SLSF1_Decal |
| 0x08000000 | SLSF1_Dynamic_Decal | SLSF1_Dynamic_Decal |
| 0x10000000 | SLSF1_Parallax_Occlusion | SLSF1_Parallax_Occlusion |
| 0x20000000 | SLSF1_External_Emittance | SLSF1_External_Emittance |
| 0x40000000 | SLSF1_Soft_Effect | SLSF1_Soft_Effect |
| 0x80000000 | SLSF1_ZBuffer_Test | ZBuffer Test (1=on) |


Small group of indie devs just trying to make stuff, ukno?
Our team is full of avid gamers, and we feel that through these experiences that we have a good grasp on what makes a game fun and rewarding to play. We hope to deliver engaging, quality games & share our journey with similar hobbyists.
Our group is composed of high school students, nearly all of which have taken or are taking nearly all of the computer science classes availible to us. We are currently tackling the complexities of game development.
Because some of us are starting out in game dev, we want to take our time when it comes to releasing content. Sign up to our emailing list in order to get information on new projects or games.


Kaipupu Wildlife sanctuary
Kaipupu Wildlife Sanctuary is a community project which aims to restore a 40 hectare 'mainland island' in Picton harbour. The Sanctuary offers amazing wildlife viewing, engaging school or group visits and volunteering opportunities.
click for more infoWine Tasting
Visit several wineries in the Marlborough wine region on a half-day tour from Picton. Enjoy wine tasting at each location.
click for more infoDay Cruise
When you have limited time, a delivery cruise is an ideal way to get around the Marlborough Sounds.
click for more info


- addComment(Comment) - Method in class com.example.meditrackr.models.CommentList
-
Add a comment to the list.
- addImage(byte[]) - Method in class com.example.meditrackr.models.record.PhotoList
-
Adds an image to the PhotoList.
- addImageTest() - Method in class com.example.meditrackr.ImageListTest
-
- addNewProfile(Context, Profile) - Static method in class com.example.meditrackr.utils.SaveLoad
-
attempts to add a new profile to the database
- addPatient(Context, Patient) - Static method in class com.example.meditrackr.controllers.model.PatientController
-
- addPatient(String) - Method in class com.example.meditrackr.models.CareProvider
-
Adds a patient to the care providers patient list, but only
if the user has not already been added.
- addPatient(String) - Method in class com.example.meditrackr.models.PatientList
-
this function will add a patient to the list
- addPatientTest() - Method in class com.example.meditrackr.CareProviderTest
-
- addPatientTest() - Method in class com.example.meditrackr.PatientListTest
-
- addProblem(Context, Problem) - Static method in class com.example.meditrackr.controllers.model.ProblemController
-
adds problem to database
- addProblem(Problem) - Method in class com.example.meditrackr.models.ProblemList
-
adds a problem to the list
- AddProblemFragment - Class in com.example.meditrackr.ui.patient
-
Allows user to add a title to the problem, change the date that was assigned and add a description.
- AddProblemFragment() - Constructor for class com.example.meditrackr.ui.patient.AddProblemFragment
-
- addProblemTest() - Method in class com.example.meditrackr.PatientTest
-
- addProblemTest() - Method in class com.example.meditrackr.ProblemListTest
-
- addProfile(Profile) - Method in class com.example.meditrackr.models.ProfileList
-
adds a profile in the profile list
- addProfile(Profile) - Static method in class com.example.meditrackr.utils.ElasticSearch
-
- addProfileTest() - Method in class com.example.meditrackr.ProfileListTest
-
- addRecord(Context, Record, int) - Static method in class com.example.meditrackr.controllers.model.RecordController
-
adds problem to elastic search and locally
- addRecord(Record) - Method in class com.example.meditrackr.models.record.RecordList
-
Appends a new record to the end of the RecordList.
- AddRecordFragment - Class in com.example.meditrackr.ui.patient
-
Allows user to add a title to the record, change the date that was assigned and add a description.
- AddRecordFragment() - Constructor for class com.example.meditrackr.ui.patient.AddRecordFragment
-
- addRecordTest() - Method in class com.example.meditrackr.RecordListTest
-


All Classes
And
ButtonsPanel
CircuitBuilder
DrawingPanel
FullAdder
HalfAdder
Input
Led
Link
LogicGate
LogicGateIO
Nand
Nor
Not
Or
Output
Switch
Xnor
Xor


--- layout: page status: publish published: true title: Planning author: display_name: Jessie Pechmann login: jpechmann email<EMAIL_ADDRESS>url: '' author_login: jpechmann author_email<EMAIL_ADDRESS>wordpress_id: 2867 wordpress_url: http://gis.utah.gov/?page_id=2867 date: '2011-10-21 15:15:11 -0600' date_gmt: '2011-10-21 21:15:11 -0600' categories: [] tags: - SDE - sgid - Data - utah - gis - map - mapping - dataset - download - agrc - layer - shapefile - geodatabase - metadata - shp - gdb - kml - lyr - digital - geographic - information - database - state - statewide - vector - overview - category - services - daas - arcgis ---
| Product Name | Type | Steward(s) | Description | |
|---|---|---|---|---|
| click a product name for more information and download links | ||||
| Water Related Land Use | map data | DNR | Water usage-related polygon map data, published annually that depicts the types and extent of irrigated crops as well as information concerning phreatophytes, wet/open water areas, dry land agriculture and residential/industrial areas | |
| Conservation Easements | map data | AGRC | Conservation Easements in Utah. | |
| Primitive Areas | map data | AGRC | Only in Emery County. | |
| Public Lands Initiative Proposal PLI | map data | BLM | Features in these data were produced by the BLM for HR 5780 to designate federal lands as wilderness and components of the National Wilderness Preservation System in Utah. The Public Lands Initiative (PLI) is a locally-driven effort to bring resolution and certainty to some of the most challenging land disputes in Utah. The initiative is rooted in the belief that conservation and economic development can coexist and make Utah a better place to live, work, and visit. | |
| Urban Interface Areas | map data | AGRC | Locations of Wildland Urban Interfaces. | |
| Wilderness, Proposals & Protected Areas | map data | USFS/BLM | Wilderness boundaries including BLM re-inventory and suitability, WSAs, roadless inventory and existing wilderness. | |
| Wetlands | map data | USFWS | Wetland areas in Utah. |


Edit AssetBeacon-Metadata for
UUID
Major
Minor
AssetId
AssetType
Icon URL
Register new AssetBeacon-Metadata
UUID
Major
Minor
AssetId
AssetType
Icon URL
uuid:
, major:
. minor:


|
|
|
|
Comparison table for the OSSs focused on the
project management.
| Company |
Cost |
Services |
File Access Method |
User # |
| eProject |
Express is free |
project calendar, team directory, tasks, project news, document sharing |
Web based |
N/A |
| MediaLot |
N/A |
On-Line Project Management Tool (multiple projects, secure access to
projects, automatic notifications...), Digital Assets Management
Tool (Group Annotation Tool, personal project folders, project
sharing, on-line reports), Project Primer |
Web based |
N/A |
| onProject |
1-3 projects - $29.95/mo, 20-50 projects - $249.95/mo, every +50 - $199.95/mo |
group calendar, online storage, resource tracking, tasks and timelines, discussion forums, management reports, automatic notifications |
WebDAV: No, FTP: No, Client software: No |
N/A |
| OpenItems |
2Mb free for up to 2 accounts and up to 2 projects, 25Mb
for about $9.95/month with additional disk storage around
$2.00/month for each additional 10 MB. |
online storage, projects, OpenItems and tasks, archive which contains the previous status of each OpenItem and task |
Web based |
N/A |
| TeamCenter |
$15,000 for the TeamCenter JDOT® server and $350 per user |
business collaboration, project management, document management, and executive views and reports |
Web based |
N/A |
|
|
|
|
|


At ND143.com, we're dedicated to helping our clients forge more valuable relationships with their constituents.
Learn more about a new, exciting company with tremendous opportunities for those that want to make an impact.
Copyright 2000-2001 ND143.com, Inc. All rights reserved.


This is the video version
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This is app for learning purposes
Please enter the value :
Which value do you want to calculate :
Result :
Gross value :
Net value :


The Art of Unit Testing: with Examples in .NET
ISBN-13:<PHONE_NUMBER>276
ISBN-10:<PHONE_NUMBER>
Author: Roy Osherove
Edition: 1
Binding: Paperback
Publisher: Manning Publications
Published: July 2009
List Price: $39.99
Best Rental Prices |
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|---|---|---|---|---|
| #1 |
Free shipping Ready to ship |
Notes |


My name is John and I am a made up person for this tutorial. I am 14 years old and have a blue shirt. Read More About Me
This is a made up website for a made up company. I built it using the amazing Python language and the Bootstrap framework.
I built this cool castle in the French Riviera which won a local competition.
I wrote a comprehensive essay on the conditions of Tigers in the wild and in captivity today.
I photo blogged my recent trip to San Fransico. During the trip I got to see all the major sites, including the Golden Gate bridge, Alcatraz Island, and much more.


{% extends 'panel.html' %} {% load utilities %} {% block head_title %} Server error {% endblock %} {% block panel_content %}
Sorry, your request resulted in a server error. The error has been logged and we'll investigate it.


You can often diagnose a problem with MultiMona by looking at the Messages window by selecting the 'View | Messages' menu option.
For a more detailed description of what has happened with MultiMona there is a log file to look at. This is stored in the 'log' directory in your user data directory and is called 'multimona.log'. Use a simple text viewer like Notepad or TextEdit to read it.
MultiMona stores user specific data such as logs and the default multimona.wallet in a "user data directory". This directory can be worked out as follows:
This section is for technical users proficient with accessing environment variables. These are enclosed in "[]" such that [APPDATA] references the APPDATA environment variable.
Try one of the following locations, remembering that you may need to enable hidden directories
[APPDATA]/MultiMona
. Windows (7,8): C:\Documents and Settings\example\AppData\Roaming\MultiMona
[APPDATA]/MultiMona
. Windows (XP and below): C:\Documents and Settings\example\Application Data\MultiMona
In OSX the user Library directory is normally hidden. Follow the show hidden files instructions to reveal it in the Finder.
[user.home]/Library/Application Support/MultiMona
. OSX 10.5+: /Users/example/Library/Application Support/MultiMona
In Linux the user home directory is normally visible.
[user.home]/MultiMona
. Ubuntu/Debian: /home/example/MultiMona
Here are some related articles


WITNESS LIST Subcommittee Lease of State Water Rights, Int Committee March 11, 2004 - 1:00P Lease of State Water Rights AGAINST: Addington, Bill (Sierra Club - El Paso Chapter), Sierra Blanca, TX Agan, Jerry County Judge (Presidio County), Marfa, TX Bales, Clementine (Self), Marfa, TX Davidson, Michael Consultant (Brewster County UGCD), Terlingua, TX Dowdey, Don (Sierra Club - Big Bend Chapter), Alpine, TX Magruder, Ed (McKnight Family Ranches), Odessa, TX McBride, Elizabeth (Self), Marfa, TX Medley, Bart (Self), Ft. Davis, TX Poindexter, John (Cibolo Creek Ranch) Ponton, Rod Attorney (City of Precidio) Rice Martinez, Oscar Mayor (City of Marfa), Marfa, TX Richardson, Sarah Student (Self), Marfa, TX Salmon, Stephen (Texas Sheep & Goat Raisers and Riverside), San Angelo, TX Tate, Rick (Self), Marfa, TX Todd, Teresa County Attorney (Presidio County), Marfa, TX Trammell, Barbara (Big Bend Realty & Big Bend Land and Cattle Co.), Terlingua, TX Watts, Tiffany Student (Self), Marfa, TX ON: Adams, Janet General Manager (Jeff Davis, Brewster, and Presidio Groundwater Districts) Cordero, Felipe County Commissioner (Presidio County) Hackler, Glen City Manager (City of Andrews), Andrews, TX Haley, J. Evetts (Self) Hartmann, Stephen Executive Director (University Lands, University of Texas System) Hartnett White, Kathleen Chair (Texas Commission on Environmental Quality), Austin, TX King, James (The Nature Conservancy), Ft. Davis, TX Nance, Tom City Administrator (City of Presidio), Presidio, TX Roberts, Travis Civil Engineer (Self), Marathon, TX Sage, Fran (Self), Alpine, TX Shelley, Howard (Self), Van Horn, TX Woodward, Lowell (Texas Sheep & Goat Raisers), Ft. Stockton, TX Registering, but not testifying: Against: Dorn, Fairfax (Self), Marfa, TX Halpern, Robert (Self), Marfa, TX Oliver, Gary (Self), Marfa, TX Roch, Michael (Self), Marfa, TX Scudday, Roy (Self), Ft. Davis, TX Small, B. Carolyn (Chisos Mining Co Motel), Terlingua, TX Wanstrom, Kate Family Nurse Practitioner (Self), Marfa, TX Wanstrom, Stefan (Self), Marfa, TX White, James (Presidio County UGCD), Marfa, TX White, Karen (Self), Marfa, TX Zbinden, Karen (Self), Marfa, TX On: Jenkins, Bill (Self), Marfa, TX Novovitch, Barbara (New York Times) Price, Kathryn Attorney (US Dept of Homeland Security & US Border Patrol), Marfa, TX Terry, Nick (Self), Marfa, TX Providing written testimony: Against: Beard, Val County Judge (Brewster County), Alpine, TX Butcher, Sterry (Big Bend Sentinel & Presidio International Newspapers), Marfa, TX


§16-29 Tally sheets. The tally sheets used in counting the ballots cast shall be marked and handled in a secure fashion prescribed in rules and regulations promulgated by the chief election officer in accordance with chapter 91. [L 1970, c 26, pt of §2]


Registration Form
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UNGA-DISEC
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LOK SABHA
UNHRC
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Applying as?
Single Delegation
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data_structure.Rmd
RtD3 has strict requirements for the format of input data. Below, The structure of each input dataset is described in detail.
summaryData should be a data.frame
that must contain the columns:
"Country", "New confirmed cases by infection date", "Expected change in daily cases", "Effective reproduction no.", "Rate of growth", "Doubling/halving time (days)"
rtData should be a JSON-like list object, containing either data.frame
values or NULL
values.
rtData can have an arbitrary number of entries, allowing for the visualization of rts from multiple data sources.
All data.frame
values in rtData must contain the columns:
"country","date","type","median","lower_90","upper_90","lower_50","upper_50"
All entries in the rtData list must contain the values:
"rtData", "casesInfectionData", "casesReportData", "obsCasesData"
"rtData"
- R estimates"casesInfectionData"
- Cases by date of infection estimates"casesReportData"
- Cases by date of report estimates"obsCasesData"
- Observed case data
An example of the overall structure of an rtData list from two data sources (“Cases” and “Deaths”):
rtData = list('Cases' = list('rtData' = rtData, 'casesInfectionData' = casesInfectionData, 'casesReportData' = casesReportData, 'obsCasesData' = obsCasesData), 'Deaths' = list('rtData' = rtData_death, 'casesInfectionData' = casesInfectionData_death, 'casesReportData' = casesReportData_death, 'obsCasesData' = obsCasesData_death))
An example of the overall structure of an rtData list with some NULL
values:


Replace this content with your own content. Content should be relevant, unique and written with the goal of providing.
Replace this content with your own content. Content should be relevant, unique and written with the goal of providing.
About Us
Programs
Doctors
Our Process
Location
Contact Us
It is a long established fact that a reader will be distracted by the readable content of a page when looking at its layout. The point of using Lorem Ipsum is that it has a more-or-less normal distribution of letters, as opposed to using 'Content here, content here', making it look like readable English. Many desktop publishing packages and web page editors now use Lorem Ipsum as their default model text, and a search for 'lorem ipsum' will uncover many web sites still in their infancy. Various versions have evolved over the years, sometimes by accident, sometimes on purpose.


Bad DogWalk
Create Account
Login
Deposit
Withdraw
Transactions
Balance
All Data
Land Owner Map
Dog Walker Map


Trump ‘Strongly Considering’ Testifying at ‘Phony Impeachment Witch Hunt’
President responds to Pelosi’s challenge
President Trump indicated he is giving serious thought to testifying during Democrat-led impeachment hearings against him.
Responding to a challenge issued by House Speaker Nancy Pelosi on Sunday, Trump fired back on twitter.
“Our Crazy, Do Nothing (where’s USMCA, infrastructure, lower drug pricing & much more?)
“She also said I could do it in writing. Even though I did nothing wrong, and don’t like giving credibility to this No Due Process Hoax, I like the idea & will, in order to get Congress focused again, strongly consider it!”
The Trump administration has now released transcripts of two conversations between the president and Ukrainian President Volodymyr Zelensky that do not clearly indicate any wrongdoing by President Trump.
Larry Nichols joins The Alex Jones Show to break down the corrupt establishment’s intentions to crush the will of the people.
The Black Friday sales have arrived early! Get up to 70% off our hottest products as well as free-shipping and double Patriot Points while the sale lasts!


Full data
here
Source:
WHO Situation Daily Reports Confirmed cases aren’t all cases. They only include people who tested
positive.
Testing rules
and availability vary by country.
This data changes rapidly, so what’s shown may be out of date. Table totals may not always
represent an accurate sum. Let me know if you think
there's any
typo/mistake in data < back to swapnilbhoite.dev


var EventEmitter = require('async-cancelable-events');
var q = require('queue-flow');
var util = require('util');
parallel-queue-flow Copyright (C) 2012 by David Ellis
Permission 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.
 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.
var EventEmitter = require('async-cancelable-events');
var q = require('queue-flow');
var util = require('util');
The parallel-queue-flow
constructor constructor function function :)
function parallel(parallelism) {
if(!parallelism) parallelism = Infinity;
function parallelQ(nameOrArray, q) {
EventEmitter.call(this);
this.qType = parallelQ;
this.namespace = q;
Private variables
var queue = [];
var handler = undefined;
var openQueue = [];
Priviledged methods
setHandler
defines the special function to call to process the queue
assumed to be ready initially, when called marked busy, call provided callback to
mark ready again.
this.setHandler = function setHandler(handlerFunc) {
handler = handlerFunc;
if(queue && queue.length > 0) this.push();
return this;
};
The handlerCallback
is provided to the handler along with the dequeued value.
If there is more work to be done, it continues, otherwise is marks the handler
as ready for when the data next arrives
var handlerCallback = function handlerCallback(openQueueStruct, done) {
openQueueStruct.done = done;
var currStruct = openQueue[0];
while(!!currStruct && currStruct.done instanceof Function) {
currStruct.done();
openQueue.shift();
currStruct = openQueue[0];
}
if(openQueue.length == 0 && (!queue || queue.length == 0)) {
this.emit('empty');
} else {
this.push();
}
}.bind(this);
var enqueueItems = function enqueueItems() {
var numToProcess = (parallelism - openQueue.length) > queue.length ? queue.length : parallelism - openQueue.length;
for(var i = 0; i < numToProcess; i++) {
var value = queue[0];
this.emit('pull', value, function(result) {
if(result) {
Javascript treats assignments of objects as pass-by-reference, so this seemingly unnecessary single-property object allows alteration of the correct value in the openQueue array without creating an explicit id
var openQueueStruct = {
done: false
};
openQueue.push(openQueueStruct);
process.nextTick(handler.bind(this, queue.shift(), handlerCallback.bind(this, openQueueStruct)));
}
}.bind(this));
}
}.bind(this);
Inserts a specified value into the queue, if allowed by the event handlers, and calls the special handler function, if it's ready.
this.push = function push() {
var values = Array.prototype.slice.call(arguments, 0);
this.emit('push', values, function(result) {
if(result === false) return;
if(!!handler && openQueue.length < parallelism) {
queue.push.apply(queue, values);
enqueueItems();
} else {
queue.push.apply(queue, values);
}
}.bind(this));
return this;
};
Signals that the queue is being destroyed and then, if allowed, destroys it
this.close = function close() {
this.emit('close', function(result) {
if(result) {
Stop accepting new items so the queue can actually close if processing time is slower than newly enqueued values come in
this.removeAllListeners('push');
this.on('push', function() { return false; });
Whatever made it into the queue at this point in time, allow it to be processed and de-queued.
var flushQueue = function() {
var tempHandler = handler;
handler = undefined;
eventHandlers = undefined;
queue = undefined;
if(this.namedQueues) Object.keys(this.namedQueues).forEach(function(queue) {
if(this.namedQueues[queue] === this) delete this.namedQueues[queue];
}.bind(this));
delete this;
if(tempHandler instanceof Function) {
process.nextTick(tempHandler.bind(null, 'close'));
}
}.bind(this);
if(openQueue && !openQueue.length && queue && !queue.length) process.nextTick(flushQueue.bind(this));
if((openQueue && openQueue.length) || (queue && queue.length)) {
this.removeAllListeners('empty');
this.on('empty', flushQueue.bind(this));
}
}
}.bind(this));
return this;
}.bind(this);
Kills the queue (and all sub-queues) immediately, no possibility of blocking with an event handler.
this.kill = function kill() {
this.emit('kill');
var tempHandler = handler;
handler = undefined;
eventHandlers = {};
queue = undefined;
if(this.namedQueues) Object.keys(this.namedQueues).forEach(function(queue) {
if(this.namedQueues[queue] === this) delete this.namedQueues[queue];
}.bind(this));
delete this;
if(tempHandler instanceof Function) {
tempHandler('kill');
}
};
Start processing the queue after the next JS event loop cycle and return the queue object to the remaining code.
if(nameOrArray instanceof Array) {
this.push.apply(this, nameOrArray);
if(queue.length > 0) this.on('empty', this.close.bind(this));
}
return this;
}
util.inherits(parallelQ, q.Q);
return parallelQ;
}
module.exports = parallel;


Ark Ecosystem
Pinned repositories
A Blockchain Explorer for ARK using Vue.js and Tailwind CSS.
A Blockchain Explorer for ARK hosted on GitHub Pages.
Library for interacting with an Ark Ecosystem Blockchain using the JVM.
A small deployment script for ARK Chains, that lets you create your own blockchain in minutes
arky
Forked from Moustikitos/arkyPythonic way to work with ARK.io
ark-net
Forked from kristjank/ark-netARK Client for .NET
An ARK API wrapper, written in TypeScript to interact with ARK blockchain.
A simple shell script to easily setup an ARK node
ARK Lite Client Template
Library for interacting with an Ark Ecosystem Blockchain using the JVM. Written in Kotlin and fully interoperable with Java & Android.
RPC server to connect to blockchain ARK
pythark
Forked from Highjhacker/pytharkArk API Wrapper in Python
Example app using ark-java
ark-go
Forked from kristjank/ark-goARK GO client for ARK.io blockchain ecosystem #golang #ark #blockchain
Ledger App for Nano S and Nano Blue
Universal npm package cli-wrapper
Monitoring ARK Tesnet


Cellular Automatas
Posted on April 27, 2020
Inspired by Stephen Wolfram's book, which I finally got around reading now under lockdown, I decided to spend my week trying an experiment with Cellular Automata (CA) and crypto markets. My hope was that there was some sort of pattern matching with a CA rule in the minute to minute movement. Markets are interesting because, while not quite Turing Complete, they do seem to follow the law of Computational Irreducibility, which states the only way to determine the answer to a computationally irreducible question is to perform, or simulate, the computation.
I won't dwell too much on what a CA is, there are other way better sources explaining that. But if you've ever seen one of those pictures or played Conway's Game of Life, those are CAs.
They're essentially abstract computational systems that can be used as models for complexity. A lot of CA results can be found in nature. For more info I'd greatly recommend reading a New Kind of Science, freely available on the internet. Back to the topic of markets, I had hoped that, as movement over larger periods of time can be attributed to rational actions and reactions, movement on a minute by minute basis could've matched some CA rule. Excerpt from A New Kind of Science
So I wrote some code to take various increments of the minute to minute movement and generate all CA rules with that size and find matches. As an example, taking 30 minutes of market data meant generating all CAs with a width of 30 and depth 10.000, changing the market data to 1s and 0s (1 if the price went up, 0 if the price went down), and then trying to identify matches. And while I've been able to find matches, especially in rule 45 that happened to contain all the market data if split in increments up to 20 minutes, they're completely random and follow no set pattern. I've tried plugging the results into https://oeis.org but still didn't get anything. For the most part I avoided using any of the rules that involved a high degree of randomness, such as rules part of class 4 (rule 110) and class 3 (rule 30, 90, 135, 184).
So, the results are that I didn't find anything. Or to be more scientific, I failed to reject the null hypothesis. And that's a good thing, because in retrospect I realized that my hypothesis was flawed, at least a degree. CAs are defined by the rules that instruct the cells how to act. I aimed for a low hanging fruit by simply attempting to find a CA that would match market data by brute forcing possibilities. Instead, as someone more observant than I am pointed out, I think I should've focused on formulating the market as a CA. To move away from the crypto space, let's consider that many raw materials are priced in dollars. U.S. Dollar depreciation typically increases the price of raw materials while a dollar appreciation decreases commodity prices. More details on investopedia.
Letss also add the S&P500 into the mix as a barometer for the health of the market. Here, the rising prices of commodities would lead people away from the stock market, and the inverse would apply too.
Let's put it into a CA format:
Appreciating dollar lowers material price and increases commodity price:
Probably not the best examples as all the values are too anchored to the dollar, but I hope it sketches out the idea a bit. I do intend to investigate this further at some point, but for now I'm taking a break to focus on my pentesting duties.
Regardless, let's move on to what I learned.
Lessons learned
Cellular Automata, Cellular Automaton and Multithreading
I did learn how to generate them and what makes them work, although I took a shortcut after generating the first 5 rules and used an already existing project on github: Cellpylib. In order to save time, I used that project to generate all the relevant CA rules in a 100 x 100.000 matrix. This took about 10 hours on an overclocked 8th gen i5 with multithreading turned to use all 6 cores. The global interpreter lock is a bit annoying but the multiprocessing lib helps to a large degree. It did, however, make me reconsider my skill set, as my main programming language - Python and my secondary one - JS, while immensely useful for my day to day job, have their drawbacks. Actually JS has a lot of drawbacks but I won't going to get into that now.Caching, GPU programming and numpy
I was fortunate enough that the cached_property library has been added to the functools standard library in python3.8. While I started by just reading and writing to the previously generated rules, this system stopped working once I attempted to move the code to the GPU with numba. Numba doesn't support i/o operations in the traditional sense, so I had to resort to caching. While numba still strives to maintain the python spirit of not having to explicitly declare data types and structures, this can severely affect performance or straight up prevent you from compiling your functions. To avoid those issues, I had to preload and cache all my rules, convert my parket data to numpy arrays and avoid any string operations. So instead of usingI had to use something like
To anyone used to working in lower level languages, this probably seems trivial, but to someone used to flinging code fast and optimizing later, this was a bit tedious.
Overall, I think I used my time to learn some useful concepts, and I'll definitely be returning to this topic in the future.


This is a link start of Test.
This is a link i2.
This is a link i4.
This is a link i6.
This is a link i8.
This is a link End of Test.
This tests that a media elements ie: <Audio>
or <video>
, without tabindex are able to be reached through keyboard access


Chiefs’ Khune Update As Semi Beckons
by Soccer LadumaWith Daniel Akpeyi suspended for Kaizer Chiefs’ Telkom Knockout semi-final this weekend, there were hopes that Itumeleng Khune might make his return – Ernst Middendorp has given an update…
Read: Inside Chiefs - Amakhosi With Tough Decisions To Make
Khune hasn’t played since September when he picked up a groin injury that saw Akpeyi stake his claim between the poles for the Glamour Boys in their impressive start to the season, but it had been hoped that the international break would give their captain time to recover.
Now though, Middendorp has explained that they can’t ‘try to push him’, revealing that it will be Bruce Bvuma who starts in goal against Maritzburg United on Sunday, with Khune not yet ready to take his place in the matchday squad, let alone the starting XI.
Middendorp told the media, "He’s training and trying to come closer. At a certain time, I see what he is delivering but I am waiting for feedback from the goalkeeper department, who can really say how far he is.
"But in the moment, we can’t try to push him. We have to manage now this one game, and that’s it. It’s not possible to think about Itu Khune."
Read: Key Chiefs Figure Resigns!
Meanwhile, Brylon Petersen, who was only recently brought back to the club after he left at the end of the season, is expected to be on the bench as he goes almost straight back into the matchday squad following his return.
Earn Airtime & Data With Soccer Laduma’s Social Media App - Download Now


{% extends "registration/registration_base.html" %} {% load i18n bootstrap %} {% block title %}{% trans "Account already exists" %}{% endblock %} {% block content %}
Welcome back! You have participated in an Open Debate before. If you can't remember your password, you can recover it below.


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Ponzu Docker Example
The reviews are being served from the Ponzu admin. See this
example project on github
.
loading


Elton John | Madman Across The Water
Tim McGraw | Love Story
Rockabye Baby! | Lullaby Renditions of Elton John
The White Raven | Tiny Dancer
Ben Folds | Ben Folds Live
Britney Spears | Oops!... I Did It Again
Whitney Houston | Whitney
Whitney Houston | My Love Is Your Love


By Aerostat to Hooting Yard
by Frank Key
Oh, I so wanted this to be a seafaring yarn. I wanted to hear the wind in the rigging, smell the salt tang in the breeze, roll with the creak and lurch of old wooden boards on the deck. I wanted to write a maritime tale, of a fabric woven of ships' cables and hawsers, an arctic wind blowing through it and birds of prey hovering over it. I wanted to prattle on about smacks, bilgewater, fo'c'sles, and splicing the mainbrace, whatever that means. Pirates would appear, cutlasses gleaming. Perched in the crow's nest, I would yell "land ahoy!", then scurry down the rigging to help tamp down the binnacles.
At first, things had looked hopeful. I had received my instructions from Dobson. As ever, he was precise: "Track down Burble. Beat him to death with a club.  I shall expect a full report upon your return. Dobson."
As soon as I had read his memo, I doused it in highly inflammable chemicals and took it down to the boiler room of my building. There, in solitude and gloom, I placed it in the roaring furnace. The flames licked up towards my face, and I turned away. I knew the tears would not be long in coming.
It had been two years since one of Dobson's communiques had uprooted me from my rut and catapulted me into frantic adventure; three years before that I had been sent on a mission, ranging over four continents; the year before that embroiled in a world-shattering plot; and there had been at least half a dozen earlier escapades. No doubt these Dobson-inspired excitements were meant to be wild and life-enhancing, yet I yearned for tedium and futility. Trudging out of the boiler room, I began to sob. It would be weeks, perhaps months, before I could once again wallow in monotony and ennui.
Despite my misgivings, I am very particular about my work. Dobson has even accused me of being finicky. I began to pack at once, having dusted down the enormous haversack which I always carry on my assignments. Within an hour of receiving my instructions, the packing was done, and I was perched on a wooden stool in my kitchen, wolfing down a bowl of slops. I had no idea when next I would eat. Having twice been struck by lightning while doing Dobson's bidding, I had taken the precaution of sprinkling finely-ground purslane on to my slops, and stashing a small pouch of the stuff in my haversack. Another half hour and I was out of the door and on my way. The weather was incomprehensible: I shall not record it here. I checked my watch by the gasworks' clock and spat into a hedgerow.
It was not for me to question why Dobson wanted Burble obliterated, yet I could not help feeling a small pang of surprise. I had always understood Burble to be a trusted agent, a man who could be relied upon. I knew for a fact that he had been invaluable when Perkins and Throwback contracted the dengue, and were ensnared by a band of repressed mahouts. I could only assume that he had lately committed some heinous offence for which Dobson could not forgive him. What on earth could he have done? But I could not allow myself to dwell on such matters. It would not get the dog bathed, the roof fed, or the baby mended, whatever that saying is. I spat into a hedgerow and lurched onwards.
Over the years, I have paid a great deal of attention to gaits, and have a repertoire of ambulatory techniques which the greatest of actors would envy. My usual form of locomotion is a sort of huffish lope. It should go without saying that, when engaged on one of Dobson's missions, I take diligent pains to disguise this. Prancing, hobbling, limping, trudging and scampering can all be brought into play. I am unusually adept at hurtling, stamping and scuttling, and have on occasion found it useful to bound, stride, plod, gad, or stalk. However, in the initial stages of any mission, I am an inveterate lurcher. I find that it aids the circulation, hones the brain, and abolishes any creeping panic. One day I shall write an essay explaining why.
I lurched to the docks, of course: all Dobson's assignments begin at the docks. While heating up the pan of slops in my kitchen, I had done a spot of preliminary research. The first thing I had to do was to find Burble, and he could be anywhere. Leafing through my card index, I traced his movements over the past year. He had spent Easter with Woodenberry in the Antarctic, swiftly moving on through Bloody Foreland, Concrete, Lake Disappointment, Elk, Flin Flon and Gubbio. I already knew that he had been in Hobart for most of July. I was unable to discover his whereabouts during the latter part of that month or for the first week in August. On the eighth, however, he turned up in Innsmouth, staying only a couple of days before a frantic spree through Jubbulpore, Kintap, Largs, Middle Tongue, Novi Bug, Orb, Pickle Crow, Quorn, Reindeer Depot and Splat. He spent Christmas in Teutoburger Wald and, typically, saw in the new year at Unst. Again there was a gap, then Burble turned up once more, in February, in Ventry, then in Weed. After that he travelled through Xacxicz, Yodd, and Zug. An intriguing fellow, I thought to myself. But was he still in Zug? I could only find out by going there: after all, I had to start somewhere.
I allowed half an hour for the slops to digest, using the time to trawl through the shipping timetables. I could make head nor tail of them.  I ingested a handful of crushed mugwort and immediately felt better. A damned good lurch, weighed down by my massive haversack, helped to dispel further the throbbing in my skull.
It took just four hours to arrive at the docks. They were splendid, but indescribable. I think Dobson attempted to write about them in one of his absurd pamphlets, but as usual I cannot remember a word of it. It hardly matters. I lurched past a group of stevedores, who were mucking about with torque wrenches, baskets, lengths of flax, that sort of equipment. One of them, resplendent in a toxic vest, hailed me.
"You there!" he cried, "Can you lend us a hand with these torque wrenches, baskets, and lengths of flax?"
"I'll do what I can," I replied, easing my haversack to the ground and rolling up my shirt sleeves. This was a break I could ill afford to ignore. My plan was to stow away on board a steamer bound for Zug's nearest port, wherever it was. This subterfuge was essential. It was quite impossible for me to buy a ticket, have my name appear on a passenger list, dine at the captain's table, take part in deck-board pingpong tournaments, and suchlike hooha. I could not risk incurring Dobson's wrath: look what lay in store for Burble. This was the perfect opportunity. If I could win the confidence of these stevedores, by dint of garrulity and bonhomie, they would hopefully assist me to board a ship in secret. I spat on my hands.
"How can I help?" I asked.
"Could you help us put these torque wrenches into these baskets and wrap these lengths of flax around them?" said the stevedore. I nodded my assent.
Everything went according to plan. Some weeks later, exhausted, my spirit virtually broken, I was holed up below the decks of the Milquetoast Jesuit, due to sail any day to a port near Zug. My stevedore pals had been magnificent. Having identified the ship, they bribed one of the crew to arrange a space for me, hidden among the stowage in the hold, where I could lie safely concealed for the duration of the voyage. This matelot, who was grimy and incoherent, was also to ensure that I did not starve or perish from thirst, although I had no worries on that count, as I had packed several bags of mush in my haversack.
At dead of night, I shared a last flagon of grog with the stevedores. We sat around a rusted oildrum in a particularly smelly corner of the dockyard, our only light a storm-lantern hanging from a nail rammed into the crumbling wall. At the appointed hour, the matelot arrived. We heard his snivelling before he came into view. This could not be tolerated: absolute silence would be necessary if we were to avoid discovery. The matelot apologised, with much slobbering, explaining that only seconds before his arrival he had developed a massive nosebleed. One of the stevedores was all for wrapping a tourniquet round his neck to staunch the flow, but I managed to intervene, unleashing from my pocket a medical dictionary and brandishing it above my head. I always carried this little booklet with me on my assignments. Dobson had written it many years ago, and forbade his agents to move so much as an inch without it. Although it is woefully lacking in diagrams, it is a boon to the amateur, shedding a Dobsonian light on all sorts of maladies and distempers, including agues, biting by mad dogs, choleric fluxes, dislocations, epidemics, frenzy, gall, hoarseness, inflammations, jaundice, knots in the flesh, loss of teeth, megrims, noise in the ears, obstructions of the spleen, pestilence, quinsy, ringworm, strangury, tetters, unmentionable horrors, vexation, whitlows, xenoglossic fury, yowling, and zombiedom. Where and when Dobson picked up all this information I cannot say. He certainly underwent no medical training to speak of, unless his autobiography is a pack of lies. That would not surprise me. Leaving the stevedores to bone up on nosebleeds, I rummaged in my haversack and took out some hound's-tongue and knapweed. Both had been crushed to a powder and stored in leathern pouches. I had only the grog to work with, but I made an infusion and commanded the matelot to drink it in one hurried slurp. Moments later, the bleeding stopped.  It was time to go.
Three weeks later, under cover of a pitch black night sky, I boarded the packet steamer Milquetoast Jesuit. The matelot and I had had innumerable adventures in the interim, none of which I shall record here. Dobson would only complain. We made our way down into the stowage without being seen. The matelot had prepared for me a rough wooden box, stacked among a heterogeneous jumble of crates, hampers, barrels and bales. Dribbling, he assured me that no member of the crew would ever come anywhere near this part of the hold. The air, what there was of it, was disgusting and fetid. In the stinking gloom, we shook hands. I understood that the ship was to set sail at any time in the next four days, and that I would not see my accomplice again until we were out at sea.
Left to myself, I lit my Curpin lantern and examined the wooden box which would be my home for the next few weeks - or so I thought. The matelot had done me proud. The box had been lined with plush crimplene. Cushions stuffed with natural sea-sponge were strewn about, as were little pot pourris of pimpernel and lavender. A rickety wooden shelf had been whacked up along one side of the box, containing a splendid library of books with which I could pass the time. Most of the titles were pulp science fiction or detective stories, including The Zinc Lozenge by Cupper, The Windy Quaker by Dawlish, The Case of the Tonsured Drunks by Kurt Pith, and The X-Ray-Eyed Galley-Slave by Sinbad Hoonjaw. I was pleased, however, to note that I had been provided with a couple of stern devotional tracts, Prayer and Imbecility by Monsignor Todge, and A Flask of Potage by Moop and Trellis. Underneath the shelf was a metal canister. I removed the lid, and found that it was a portable refrigerator, packed with highly expensive bottles of wine. Next to the canister was a dinky tray laden with a corkscrew, wineglass, and binary muffets. There was no sign of any food, but I had my bags of mush. Placing my haversack in the cleverly-designed cubby-hole of the box, I reclined on the straw pallet, arranged some of the cushions around me, and tried to shrug off the foul stench assaulting my nostrils. Within minutes, I was fast asleep. Meanwhile, up on deck and unbeknown to me, the purser was smearing all the lifeboats with a decoction of oakum and wormwood. According to Dobson, this sort of thing is quite common, although ships' crews are loth to admit it.
When I woke up in my dark and smelly box, my watch had stopped. Here was a predicament: I had no idea how long I had been asleep. As I took cognizance of my surroundings, it soon became apparent that the Milquetoast Jesuit was still in port. I cracked open one of the wine-bottles and swigged it through clenched teeth. Unfortunately, after a period of sleep my co-ordination was not at its best, and I managed to bash and dislodge one of my incisors. The wine swilled around in my gob, mingling with blood which was now pouring from my gums. Had the matelot thought of packing a first aid kit? I doubted it. I spat into one of the pot pourris and cursed heaven. As I did so, I was astonished to hear the sound of footsteps and snivelling coming from somewhere among the ramshackle stacks of stowage. Moments later, the matelot appeared, a torch clutched in his grimy fist. "What on earth are you doing here?" I hissed. The fool was meant to stay away from me until we were safely out to sea. If my presence were discovered now, I would without doubt be ejected from the ship and have my haversack impounded by the maritime authorities: the same fate that had befallen Perkins at the outset of the expropriated vinegar case. Dobson had been livid. The matelot did not reply. He merely handed me a scrunched up piece of paper and, turning on his heel, scurried off. I was bewildered, but only for a moment. Spitting out another gobbet, I unscrunched the piece of paper: it was blank. What inanity was this? The matelot must have taken leave of his senses. In a sudden fury, I tore the paper to shreds, then settled back on the pallet to eat a bag of mush. Only as the last mouthful slithered down my throat did it occur to me that I must have looked at the wrong side of the paper. I was about to gather the shreds from the floor when the Curpin lantern went out. I was plunged into complete darkness, unable to see my hand in front of my face. Hm.
Luck, as Dobson says, begins with L. Another thing that begins with L is lout's-skull, a rare and spiky herb found only on a few mountain slopes in Van Glubb's Land. If boiled in a little oil, it is good for shrunken sinews, and it helps to relieve the dropsy and diseases of the spleen. Among its more unlikely properties, lout's-skull will, under certain conditions, become incandescent and burst into an all-consuming conflagration like unto the fires of hell itself. As luck would have it, a crate of desiccated lout's-skull was among the cargo of the Milquetoast Jesuit, and the conditions in the hold were just right for the inferno to be provoked. My Curpin lantern did not, as I thought, go out: rather, I was temporarily blinded by the intense brilliance of explosive lout's-skull.  The water in the harbour evaporated. The ship itself simply vanished, blown to smithereens. I have no idea how I walked out of the devastation alive, clutching my haversack. But I did.
My first thought was to find the matelot, or at least to ascertain whether he was alive or dead. After all, he may have disembarked just before the explosion, or he may have survived it, as I had. I was about to prance into the nearest tavern when I was waylaid by a spangled monster. It was hilariously unkempt, and its provenance was dubious. Luring me into a shadowy nook, it drooled and growled, growled and drooled. My pulse raced. I tried to work out how I could reach the turpentine gun in my haversack before the monster did something unspeakable. I need not have worried. It was Perkins, of course.
"I bring news from Dobson," he said, unravelling himself from his stupendous disguise and lighting a cheroot.
"Gosh," I said. Then, pulling myself together, I asked Perkins as calmly as I could what Dobson had to say. It was almost unprecedented to receive a further message following initial instructions: Dobson trusted his agents to get on with the job. In truth, I felt indignant, and told Perkins as much.
"Ach!" he snarled, "Dobson, Dobson, Dobson. He is in his dotage, you know that. He hardly knows what he's doing anymore." He paused, seeming to agonise for a moment.
"Forgive me, I should not speak so. Dobson is Dobson, and that is enough. Let me tell you his message." Perkins spent about ten minutes rummaging through a variety of pockets, pouches, fobs, sockets, recesses and vanity bags before triumphantly brandishing a crumpled shoe-box in my face.
"Here it is!" he cried, tears of joy streaming down his face. Removing the lid from the box, he took out a little booklet, leafed through the pages, stopped at one and, wagging his finger in the air, read:
"Re: Burble. His travels over the last year are a series of lavish untruths. I was nearly taken in. He has been holed up in Hooting Yard all the time. I am not trying to interfere but I thought you ought to know. Dobson."
Bang went my chances of writing a seafaring yarn. Hooting Yard was only two hours away, by bus. Perkins and I vanished into the nearest tavern to get drunk. As we toped, we talked. And as we talked, we discovered a mutual interest in the more outré aspects of Dobson's history: his love of abnormal biscuits; his residence for eight years in a cod doss-house; the time he suffered from exposure in a foundry; his guilt about horses; the day he was injured by a jack-hammer; his insistence that a kraken had appeared in his latrine; his macabre napkin; his ounce of pebbles; his questionable rubric; his slabs of titanium; his illegal adoption of an unhinged vintner; his waterlogged xylographs; and his predilection for yellow zips. After innumerable pints of sour beer, we reeled out of the tavern, Perkins turning off towards the abandoned fireworks depot while I hurtled to the bus stop, where I discovered that all buses between the docks and Hooting Yard had been taken permanently out of service due to imponderable resin and pomposity. I lay prostrate on a paving slab and devoured another bag of mush. Then I returned to the tavern.
I eventually travelled to Hooting Yard by hot-air balloon, or aerostat. I met the pilot - if that is the word - in the tavern. Her name was Mopsa Blomqvist, and she agreed to carry me and my haversack purely as ballast. In other words, we would be hurled over the side should occasion arise. This was of no concern to me, as I had a parachute in my haversack: ha!  We got up safely, and I settled back to read some of the pamphlets. I was startled to discover that Dobson had written one of them. It was a tetchy little tract entitled Ninety-Two Uses and Abuses of Mustard. I barely got beyond the first few sentences: the prose was terse but doltish. Peering over the rim of the basket, I saw the sort of scene one sees from the basket of a balloon. We had been in the air for perhaps quarter of an hour when we were buffeted by an unearthly flock of spiny winged things. I am afraid that I screeched with fright. Mopsa Blomqvist showed rather more presence of mind, lashing at the malevolent beasts with a gleaming cutlass. But it was too late: the air-bag had already been punctured, and we began immediately to lose altitude. Mopsa carried out a number of exciting technical adjustments involving the gores, valves and flaps of the aerostat. Meanwhile, I searched desperately in my haversack for the parachute. Our descent quickened.
"We are plummeting towards the ground very fast," shouted Mopsa, "Hold on to your hat!"
"I am not wearing a hat," I replied, quaking with terror.
"Here!" she cried, tossing me a worm-eaten biretta. I thwacked it on to my skull in the nick of time. We crashed to earth, with much bumping, thumping, and a series of splats.
Miraculously, I was unharmed. Mopsa herself suffered only a lesion on her brow, although the spokes on her wheelchair were terribly mangled. We dragged ourselves clear of the silk and wicker wreckage and fetched up in a ditch. Mopsa had managed to salvage the biscuits and the useless rudder. I expelled a mouthful of mud and reached into my haversack for various bits and pieces of navigational equipment, including an extensively detailed map.
"Look!" gasped Mopsa, as she dallied with a biscuit, "There are the gates of Hooting Yard!"
What an old slowcoach I was! - so busy rooting around in my haversack that I did not use my eyes. Mopsa was right. I followed her pointing finger and saw a tremendous brickish portal, ornamented with lozenged carvings of quicklime, bilberry and glunt. I had reached my destination. Somewhere behind those gates lurked Burble, and I was coming for him. Mopsa's wheelchair was soon knocked back into shape, and she rattled off towards the outskirts of Hoon. Before she left, and despite my protestations, she insisted that I take half of her biscuits. As soon as her back was turned, I ground them into the muck beneath my boot. I could not bear the distractions of shortbread: my mush-bags would suffice.
I traced Burble easily, and carried out Dobson's instructions without a hitch. The flooded mineshaft was on a patch of waste ground four or five miles from Hooting Yard. Somewhat gratuitously, I wiped the blood off my hands with Burble's own cravat. It had been embroidered by hand - his sweetheart's? - depicting in intricate detail a selection of uproarious scenes from the Old Testament. For a moment, I considered keeping it as a souvenir. Pah! What a thought. I tossed it down the mineshaft, at the bottom of which lay the fresh corpse of Burble. Taking a blowtorch out of my haversack, I incinerated the wheelbarrow in which I had trundled his body to this detestable spot. When all that remained was a pile of ashes, I turned away and began the long plod back to Hooting Yard. The night air reeked of Stalinism. I pulled my cardigan tighter around my body and lit up one of Perkins' cheroots. As I plodded, I rehearsed in my head the opening lines of the report I would write for Dobson.
"Your instructions arrived at midnight. I was dog tired, having spent the evening slaughtering a colony of flies. Nevertheless, I took immediate steps. My first thought was to select the weapon. You specified a club, and I hoped I would not be straying too far from your intentions by using a leaden madge. I went up to the attic to fetch it. There was no battery in my torch, and I clambered - "
Such drivel! Would Dobson be interested in all the detail? But he had asked for a full report, and he was going to get one. " - and I clambered over the huge iron crates which were stacked in my loft. I could hardly see a thing. Eventually, I identified the correct crate, and blasted it open. Inside, underneath a pile of anti-matter, I found the madge. It had last been used to pound some ragwort into oblivion, and minute traces of the plant were embedded on the head. The madge would need a thorough cleaning. Descending from the attic, I filled the bath, donned a rather fetching kit of protective clothing, and hurled a glorious array of chemical preparations into the piping hot water. I then plunged the madge into the bath."
These not unpleasant - though wholly fictional - recollections were interrupted before I was even half-way back to Hooting Yard. A figure loomed before me in the darkness. As I closed upon it, I saw that it was Crunlop.
"Why, Crunlop!" I cried, "You have the appearance of a maniac. You are dishevelled and frowsty and pale. Your teeth are clenched and, although the light is not good, I can see that your features are distorted by an awful rictus.
Crunlop made no reply, but lunged towards me, aiming the dirk at my throat. If I were to avoid a grisly death, I would have to act fast. I stepped to one side, swivelled, ducked, and shimmied in an alarming fashion designed to bewilder my assailant. At the same time, I eased the haversack from my shoulders, lifted it above my head, and brought it crashing down on Crunlop's skull. He crumpled to the ground. Removing Dobson's medical dictionary from the haversack, I satisfied myself that Crunlop was dead. I then made a thorough search of his countless pockets. It was almost dawn before I finished emptying them, spreading their contents out on the ground around his body. Among the aniseed balls, bleach, corks, diuretics, ergot, floorcloths, gobstoppers, hurricane-lamps, ink, jam, kale, landing-nets, morris tubes, nard, orts, polio vaccines, quicksilver, rep, shears, tholes, ukulele strings, valves, wading, xanthene, yoghurt and zootomy diagrams, I found what I was looking for.  I spat into the bracken, and read them.
"Track down Scrimgeour. Waylay him, and slit his throat with a dirk. Then bury him under a geologically improbable pile of rocks. I shall expect your report as soon as possible. Dobson." Perkins had hinted that Dobson's brain was now a puny and curdled thing, but, still, I was crushed by this revelation. I slumped to the ground, scrunching a piece of Crunlop's rep in my fists. Tiny things with dozens of legs scurried around me, climbing on to my socks and into my boots. I left them to get on with it.
The dawn blasted me out of my stupor. I could not remain sitting around in the open country next to a dead man, the contents of his pockets strewn around me: I would look for all the world like a common robber. My first thought was to throw Crunlop down the flooded mineshaft, but I was a couple of miles away from it now, and I could hardly drag his body that far. His girth was vast. I think that was why Dobson had hired him. In his essay Triton Among The Minnows, Dobson expounded at length upon his loathing of all that was microscopic, elfin, or wee. He only employed me because I am almost seven feet tall, although I have been shrinking recently. It is most perplexing, the more so because my usual font of medical information, Dobson's dictionary, has absolutely no light to shed on the condition. I had been planning to tax him with this, perhaps in a little postscript to my report. Well, now there would be no report.
Aha! It was obvious, obvious. Dobson would assume I was dead, and who was I to gainsay him? Snorting with glee, I hurriedly buried Crunlop under a pile of rocks. The wasteland outside Hooting Yard could hardly have been bettered for providing a geologically improbable mound, as demanded in the instructions. I heaped great lumps and clodges of stuff on top of Crunlop. Bauxite rubbed shoulders with thop, chert with marl, wunt with calcium.  I gathered together Crunlop's pitiful agglomeration of odds and ends, including the dirk, and shoved the lot into my haversack.
When I got back to Hooting Yard it was lunchtime. I sat by the canal and devoured an enormous pot of boiling custard. One day I would be able to go home, but first I had to send Dobson a report: Crunlop's report. I stole a barge and spent a couple of years moored in an unspeakable fen. I assumed the air of a bargee, sprouting decisive mustachios and wearing a brocade mantle of some antiquity. I sold off the barge's cargo of rotting vegetable matter and made myself at home. I had wanted to write a seafaring yarn, but this would have to do. As I sat at my little escritoire, water rats gnawing at my boots, the stench of bilgewater wafting around me, I listened to the clanking of dredgers and the screeching flocks of beasts with wings. I had never been so happy. I took up my pencil and wrote:
"Dear Dobson. Scrimgeour has been obliterated. He did not put up a struggle. I am sending the dirk to you under separate cover. Farewell. Crunlop."


SMALL TIME CROOKS
Review by John Beachem
* * *
Directed by: Woody Allen Written by: Woody Allen
Ray Winkler (Woody Allen) is an ex-con who now works a menial job and dreams of a better life. His latest lame-brain idea is to rent space in a store across from a bank and tunnel under the street into the bank's vault. Ray brings three friends, Denny (Michael Rapaport), Tommy (Tony Darrow), and Benny (Jon Lovitz), into his scheme to help him dig the tunnel. His wife, Frenchy (Tracey Ullman), and her brain dead cousin, May (Elaine May), work a cookie shop in the space they have rented to hide Ray's activities. The plan doesn't work quite the way Ray intended. In fact, it doesn't work at all. The group has been so busy tunnelling they haven't noticed what a hit Frenchy's cookies are, and they decide to turn the little store into a huge franchise. Ray and Frenchy are now rich beyond their wildest dreams, but it affects them in two very different ways. Ray somehow turns into even more of a slob, wanting nothing to do with a rich lifestyle. Frenchy has become so completely infatuated with her new way of life that she hires an art dealer, named David (Hugh Grant), to teach her how to act like her new wealthy friends. Soon Ray and Frenchy are drifting down very different paths, and Frenchy starts to think she may have outgrown Ray.
Let me start by saying that I have never cared for Woody Allen's films, thus my opinion of this movie may be a tad biased. I've never understood Allen's humor - like when Frenchy says something sarcastic to Ray, and he responds with "I'm this close to popping ya Frenchy, just one more word." It wasn't funny back when Jackie Gleason said something similar, and it's somehow even less funny when Allen says it now. The only funny thing about it is that I think Tracey Ullman could probably beat Allen up. I will say this, "Small Time Crooks" actually starts off rather funny, with Ullman given a few amusing, witty comebacks to some of Allen's dumber statements. In fact, the first third or so of the film is really quite funny. Watching the four bumbling criminals try to carry out their plan is uproariously funny at times, especially when they break a pipe in the wall and flood the entire store. Unfortunately, once they make their fortunes, and the whole rags-to-riches story takes over, all the humor is sapped out of the movie. I suppose Allen was trying to show the benefits of a simple life compared to an extravagant one; but if that's the case, he failed miserably. Both lifestyles looked pretty horrendous.
Woody Allen has always seemed to be one of the more irritating actors in Hollywood.   This may be why he has surrounded himself by characters even dumber than Ray. Michael Rapaport ("True Romance") is notorious for playing brainless characters, and he plays more of the same here. Jon Lovitz ("Trapped in Paradise") is infamous for playing irritating characters, and Benny probably would have been fairly annoying if given more screen time.  The only exceptions are Tracey Ullman, in the film's first half, and Elaine May (you remember, she wrote and directed "Ishtar" way back when) as the hilariously dense May. If it hadn't been for Elaine May's presence, I probably would have rated this film quite a bit lower. She is so likeable and naive that she can't help but make the movie at least somewhat enjoyable. Last, we have Hugh Grant ("Mickey Blue Eyes"), who serves no purpose and seems to realize this judging by the performance he gives.
The thing which most bothered me about "Small Time Crooks" was that Allen leaves a dozen or so loose ends. Characters vanish half way through the film and are never heard from again; romantic angles arise but are never dealt with; and the ending doesn't tie things up quite the way I would have liked. There are numerous other faults, though they are fairly minor. There isn't much of a soundtrack; some of the scenes feel rushed (the first dinner party and the aftermath of the cookie company being created), while others seem to drag on forever (any scene with Hugh Grant); and the comic pacing is non-existent at the film's midpoint. The film's high points are Elaine May, Ullman's occasional smart-alec comments, and the attempted bank robbery. "Small Time Crooks" does run an admirable 94 minutes at a time when films running less than two hours are becoming a rare breed. Fans of the old Woody Allen films will probably enjoy it because he's back to his screw-ball antics rather than the dramatic pictures of the last few years. If, like me, you don't care for Allen's brand of humor, I'd suggest you skip this one. I give it three out of five stars.
Any past movies you want me to review? Send to<EMAIL_ADDRESS>Past reviews can be found at: http://www.dependentfilms.net, http://www.epinions.com/user-elerad?public3Dyes or http://us.imdb.com/ReviewsBy?John+Beachem
* * * * * - One of the best movies of the year. * * * * - Great flick, try and catch this one. * * * - Okay movie, hits and misses. * * - Pretty bad, see it at your own risk. * - See this one only if you enjoy pain.
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Based on judicial laws in California and your records, we have identified the following crimes are eligible for expungement.
| EXPUNGEMENT | CRIME NAME | LOCATION | DATE | |
|---|---|---|---|---|
| data | data | data | data | Details |
| data | data | data | data | Details |
| data | data | data | data | Details |
| data | data | data | data | Details |


Home
About me
Contact me
This is my first web page
Some content here for panel 1
Some content here for panel 2


Design Resources
Helpful tools I reference while designing
Models
Pace Layers Thinking by Paul Saffo and Stewart Brand
Behavior Wizard by BJ Fogg
Bloom’s Taxonomy 1,
Bloom’s Taxonomy 2 by Benjamin Bloom
Flow Diagram for Game Design by Mihaly Csikszentmihalyi
Four Stages of Competence
SECI Model for Knowledge Dimensions
Models by Hugh Dubberly
2x2 model for absolutely everything
The Only 10 Slides You Need In Your Pitch by Guy Kawasaki
Articles and essays
Clarke’s Three Laws
Up and Down the Ladder of Abstraction by Bret Victor
The Presumptions of Science by Robert Sinsheimer
Books
The Pattern Language by Christopher Alexander (digital book)
International Picture Language by Otto Neurath
Web
Chris Hamamoto’s data tracking resources from his class on cryptography Versus (popular competitors tool) by Jason Crawford of Flexport
Portfolios
Christian Leban
NIRLA
use this website for the tool tip code/idea to apply to your portfolio


Book Name: JavaScript: Novice to Ninja
Author: Darren Jones
Year: 2016
About the Author:
1Darren started using Sinatra in 2009 after falling in love with the direct simplicity it offers. At the start of 2010 he started the I Did It My Way blog, detailing his quest to master Sinatra, and at the end of that year used Sinatra to build the Cards in the Cloud website. Darren also writes Sinatra tutorials for RubySource.


Planner will help you to create different types of work-items, allows you to assign team members and iterations.
Total work items
View Planner


The RESUMEker
We the graduates need Resume like no other and building an impressive one is something that we
can not not give up on. The RESUMEker has now taken charge to make
impressive Resumes for you that are industry ready!  Right now ^_^
"Happy Resumekeration,
Happy Graduation"


mftrees package¶
Submodules¶
mftrees.features module¶
-
mftrees.features.
feature_vector
(sample, bins)¶ Compute a discretized, radially averaged Fourier power spectra for use as a feature vector.
- Parameters
sample (ndarray) – Input image as a 2-D ndarray
bins (ndarray) – ndarray with same dimensions as _sample_, containing the integer bin label for each element in _sample_.
- Returns
float64 1-D ndarray with length np.unique(bins).size
- Return type
ndarray
-
mftrees.features.
fmap
(block_size)¶ Construct a wavelength map for a FFT output with dimensions of block_size
-
mftrees.features.
full_feature_vector
(sample, bins)¶ Compute and concatenate feature vectors across all image bands in (c, h, w) form
- Parameters
sample (ndarray) – Input image as a 2-D ndarray
bins (ndarray) – ndarray with same dimensions as _sample_, containing the integer bin label for each element in _sample_.
-
mftrees.features.
spectrum
(arr)¶ Compute a 2-D Fourier power spectra of an image, based on a unitary forward FFT
- Parameters
arr (ndarray) – Input image as a 2-D ndarray
- Returns
float64 ndarray with same dimensions as _arr_ containing the Fourier power spectra. Low frequency components are shifted to the center of the image.
- Return type
ndarray
mftrees.util module¶
-
mftrees.util.
all_equal
(arg1, arg2)¶ Shortcut function to compute element-wise equality between two iterables
- Parameters
arg1 (iterable) – Any iterable sequence
arg2 (iterable) – Any iterable sequence that has the same length as arg1
- Returns
True if each pair of elements are equal. Otherwise, False
- Return type
bool
-
mftrees.util.
create_histmatcher
(img, ref)¶ Create a function that will histogram match pieces of a source image with a target image, incrementally
- Parameters
img (ndarray) – NumPy array containing the image data to transform
ref (ndarray) – Numpy array containing the reference image data for the transformation
- Returns
A function that takes a 2-D NumPy array to be transformed
- Return type
function
-
mftrees.util.
r2
(y_pred, y, w=1.0)¶ Compute generalized Pearson’s R^2, with optional weights
- Parameters
y_pred (ndarray) – NumPy array of predicted values
y (ndarray) – NumPy array of true values the same length and dimensionality as y_pred
w (float or ndarray, optional) – Weights for each sample (default is 1.0)


| augment.coxph {broom} | R Documentation |
Augment accepts a model object and a dataset and adds
information about each observation in the dataset. Most commonly, this
includes predicted values in the .fitted
column, residuals in the
.resid
column, and standard errors for the fitted values in a .se.fit
column. New columns always begin with a .
prefix to avoid overwriting
columns in the original dataset.
Users may pass data to augment via either the data
argument or the
newdata
argument. If the user passes data to the data
argument,
it must be exactly the data that was used to fit the model
object. Pass datasets to newdata
to augment data that was not used
during model fitting. This still requires that at least all predictor
variable columns used to fit the model are present. If the original outcome
variable used to fit the model is not included in newdata
, then no
.resid
column will be included in the output.
Augment will often behave differently depending on whether data
or
newdata
is given. This is because there is often information
associated with training observations (such as influences or related)
measures that is not meaningfully defined for new observations.
For convenience, many augment methods provide default data
arguments,
so that augment(fit)
will return the augmented training data. In these
cases, augment tries to reconstruct the original data based on the model
object with varying degrees of success.
The augmented dataset is always returned as a tibble::tibble with the
same number of rows as the passed dataset. This means that the
passed data must be coercible to a tibble. At this time, tibbles do not
support matrix-columns. This means you should not specify a matrix
of covariates in a model formula during the original model fitting
process, and that splines::ns()
, stats::poly()
and
survival::Surv()
objects are not supported in input data. If you
encounter errors, try explicitly passing a tibble, or fitting the original
model on data in a tibble.
We are in the process of defining behaviors for models fit with various
na.action
arguments, but make no guarantees about behavior when data is
missing at this time.
## S3 method for class 'coxph' augment( x, data = NULL, newdata = NULL, type.predict = "lp", type.residuals = "martingale", ... )
x |
A |
data |
A base::data.frame or |
newdata |
A |
type.predict |
Character indicating type of prediction to use. Passed
to the |
type.residuals |
Character indicating type of residuals to use. Passed
to the |
... |
Additional arguments. Not used. Needed to match generic
signature only. Cautionary note: Misspelled arguments will be
absorbed in |
When the modeling was performed with na.action = "na.omit"
(as is the typical default), rows with NA in the initial data are omitted
entirely from the augmented data frame. When the modeling was performed
with na.action = "na.exclude"
, one should provide the original data
as a second argument, at which point the augmented data will contain those
rows (typically with NAs in place of the new columns). If the original data
is not provided to augment()
and na.action = "na.exclude"
, a
warning is raised and the incomplete rows are dropped.
A tibble::tibble()
with columns:
.fitted |
Fitted or predicted value. |
.resid |
The difference between observed and fitted values. |
.se.fit |
Standard errors of fitted values. |
Other coxph tidiers:
glance.coxph()
,
tidy.coxph()
Other survival tidiers:
augment.survreg()
,
glance.aareg()
,
glance.cch()
,
glance.coxph()
,
glance.pyears()
,
glance.survdiff()
,
glance.survexp()
,
glance.survfit()
,
glance.survreg()
,
tidy.aareg()
,
tidy.cch()
,
tidy.coxph()
,
tidy.pyears()
,
tidy.survdiff()
,
tidy.survexp()
,
tidy.survfit()
,
tidy.survreg()
library(survival) cfit <- coxph(Surv(time, status) ~ age + sex, lung) tidy(cfit) tidy(cfit, exponentiate = TRUE) lp <- augment(cfit, lung) risks <- augment(cfit, lung, type.predict = "risk") expected <- augment(cfit, lung, type.predict = "expected") glance(cfit) # also works on clogit models resp <- levels(logan$occupation) n <- nrow(logan) indx <- rep(1:n, length(resp)) logan2 <- data.frame( logan[indx, ], id = indx, tocc = factor(rep(resp, each = n)) ) logan2$case <- (logan2$occupation == logan2$tocc) cl <- clogit(case ~ tocc + tocc:education + strata(id), logan2) tidy(cl) glance(cl) library(ggplot2) ggplot(lp, aes(age, .fitted, color = sex)) + geom_point() ggplot(risks, aes(age, .fitted, color = sex)) + geom_point() ggplot(expected, aes(time, .fitted, color = sex)) + geom_point()


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Kindness in the rain
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Helping an injured military person makes me feel patriotic for some reason
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I guess stray dogs are hungrier than we think they are
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Floods affect creatures other than ourselves as well, more so actually


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