This week we were asked to read Chapter 4 of Search Inside Yourself and think about the relationship between mindfulness and self-awareness described in the book.
The book describes some ways of enhancing self-awareness. One of these is keeping a journal. I've done this before, and I always find it quite relaxing. Sometimes I start writing because I've got a problem that seems impossible to solve, and by the end I realise the whole thing's not as difficult as I thought. This metaphor from the book captures it nicely: "the moment you can see a raging river, it means you are already rising above it" (p. 78). By the time I've accurately captured something in words, I usually have a much better understanding of it and what needs to be done.
This heading sounds like something you'd mutter to yourself after discovering someone has used a metal spoon on your best non-stick pan. It's one of the key points in Chapter 4. Basically, it comes down to the idea that "emotions are simply what you feel, not who you are" (p. 90). I guess that means that when you find the pan, and notice your frustration levels rising, you should realise that what you're feeling is just a temporary state, and learn to move past it.


WITNESS LIST County Affairs Committee March 30, 2005-8:00A HB 348 For: Allison, Jim (County Judges & Commissioners Assn. of TX) Registering, but not testifying: For: Mendez, Mark (Tarrant County Commissioners Court) Smith, Bobby (Self) HB 647 Registering, but not testifying: For: Collie, Jr., Robert (Self) Flores, David (Williamson County) Henderson, Don (J.P. Morgan Securities) Kiley, Julie (Self) Lee, Donald (Texas Conference of Urban Counties) HB 1203 For: Brady, Kenneth (Fort Bend County Sheriff) Lopez, Freddie (Self) Registering, but not testifying: For: Cernosek, Larry (Self) Miller, Allan (Texas Towing & Storage Assn.) HB 1232 For: Smith, David (Bexar County Commissioners Court) HB 1455 For: Spaulding, Don (Maverick County Hosp. Dist.) HB 1456 For: Spaulding, Don (Maverick County Hospital District) Registering, but not testifying: For: Pearson, David (TX Organization of Rural & Community Hospitals) Welsh, Dinah (Texas Hospital Association) HB 1468 For: Allison, Jim (County Judges and Commissioners Assn. of TX) On: Bearden, Bob (State Comptroller) Registering, but not testifying: For: Smith, Bobby (Self) HB 1769 For: Clemson, Allen (Dallas County Commissioners Court) Pardue, Craig (Dallas County) Against: Charlton, Keith (Bexar County Sheriff's Office) Kerss, Thomas (Self and Sheriff's Association of Texas) Kirk, Chris (Self and Sheriff's Association of Texas) Registering, but not testifying: For: Carter, Jim (CUC Denton County) Gilmore, Scott (Keefe Commissary Network, LLC) Mendez, Mark (Tarrant County Commissioners Court) Whitley, Glen (Self and CUC Tarrant County) Against: Burnett, R.P. "Pat" (Self) Busby, Larry (Self and Sheriff's Association of Texas Legislative Committee) Flowers, George (Self) White, Mike (Orange County) HB 1915 For: Pardue, Craig (Dallas County) Registering, but not testifying: For: Sprinkle, G.K. (Daily Court Review) HB 2584 For: Jones, Marlene (Kimble Hospital District) Registering, but not testifying: For: Newton, David Ray (Self) HB 2587 For: Dietz, Sharon (Schleicher County Hospital) HB 2626 For: Harris, James (Linebarger, Goggan, Blair, & Sampson)


Documentation is available at md5.php
- <?php
- /*
- $s = '21232f297a57a5a743894a0e4a801fc3';
- $d= md5($s);
- print $d;
- */
- $str = 'apple';
- $p = md5(md5($str));
- print 'str='.$str;
- print 'p='.$p;
- $d = md5($p);
- print 'd='.$p;
- ?>
- //define constant variables
- // news articles
- var $writingarticle = "Writing an article "; //used when the use is writing an article
- var $editingarticle = "Editing an article "; //used when the user is editing an article
- var $savingarticle = "Saving an article "; //used when the ueer is saving an article
- var $deletingarticle = "Deleting an article ";
- var $archivingarticle = "Archiving an article ";
- var $unarchivingarticle = "Unarchiving an article ";
- var $publishingarticle = "Publishing an article "; //used when the user is publishing an article
- var $uploadingimg = "Uploading an image "; //used when the user uploads an image
- var $viewingimg = "Viewing an image ";
- var $editingimgdetails = "Editing image details ";
- var $deletingimg = "Deleting an image ";
- var $viewingarticle = "Viewing an article ";
- var $printingarticle = "Printing an article ";
- var $creatinguser = "Creating a user ";
- var $savinguser = "Saving a user ";
- var $editinguser = "Editing a user ";
- var $changingpw = "Changing a userpassword ";
- var $changingusername = "Changing a username ";
- var $loggedin = "Logged in ";
- var $logout = "Log-out ";
- var $editinguserpermissions = "Editing a user access permissions ";
- var $blockinguser = "Blocking a user ";
- var $unblockinguser = "Unblocking a user ";
- var $viewinguserguides = "Viewing a user guides ";
- var $viewingmsg = "Viewing a message ";
- var $deletingmsg = "Deleting a message ";
- var $sendingmsg = "Sending a message ";
- var $creatingmsg = "Creating a message ";
- var $creatingcategory = "Creating a category ";
- var $editingcategory = "Editing a category ";
- var $creatingbannerads = "Creating a banner ads ";
- var $editingbannerads = "Editing a banner ads ";
- var $changingtemplate = "Changing a site-themes ";
- var $searching = "Performing a search query ";
- var $generatingreport = "Generating a report "; //used when the user generated a report
- var $creatingpolltopic = "Creating a poll topic "; //used when the user created a poll topic
- var $editingpolltopic = "Editing a poll topic ";
- var $unknown_activity = "Unknown activity "; //used when the user is doing something fishy on the system.
Documentation generated on Wed, 5 Apr 2006 20:09:34 -0700 by phpDocumentor 1.3.0RC4


Information of a detected security incident.
Default constructor
public Void SecurityIncident()
Initializes a new instance of SecurityIncident.
public Void SecurityIncident(id)
| Name | Type | Summary |
|---|---|---|
| id | String | Security incident Id |
The Audit related to this security incident or null if not related audit is available.
public Audit Audit { get; set; }
Relates audit record Id (optional)
public String AuditId { get; set; }
Creation date and time on UTC.
public DateTime CreatedOnUtc { get; set; }
Security incident data
public Byte[] Data { get; set; }
Whether model validation must be enforced.
public Boolean EnforceModelValidation { get; }
Security incident Id
public String Id { get; set; }
Resolution opinion
public String ResolutionOpinion { get; set; }
Authority of the user that resolved the security incident.
public String ResolvedByUserAuthority { get; set; }
Display name of the user that revolves the incident.
public String ResolvedByUserDisplayName { get; set; }
User Id of the user that resolved the security incident.
public String ResolvedByUserId { get; set; }
Name of the user that resolved the security incident.
public String ResolvedByUserName { get; set; }
Data and time of the security incident resolution on Utc.
public Nullable<DateTime> ResolvedOnUtc { get; set; }
Id of the object that sources this security incident (optional)
public String SourceId { get; set; }
Protocol of the object that sources this security incident (optional)
public String SourceProtocol { get; set; }
Multi-tenant partition key.
public Int32 TenantId { get; set; }
User authority
public String UserAuthority { get; set; }
User display name
public String UserDisplayName { get; set; }
User Id
public String UserId { get; set; }
User name.
public String UserName { get; set; }
Checks if other is equals to this object.
public Boolean Equals(other)
| Name | Type | Summary |
|---|---|---|
| other | SecurityIncident |
Checks if obj is equals to this object.
public Boolean Equals(obj)
| Name | Type | Summary |
|---|---|---|
| obj | Object | An object to compare with this object. |
Not required, implemented to avoid warnings, returns base.GetHasCode().
public Int32 GetHashCode()
Gets incident internal data.
public SecurityIncidentInfo GetSecurityIncidentInfo()
The incident internal data or null if it is empty.
| Type | Summary |
|---|---|
| SecurityIncidentInfoDeserializationException | Thrown when problems decrypting or de-serializing the internal data. |
In this document


Workflows for analyzing single-cell RNA-seq data with R/Bioconductor
Aaron T. L. Lun
Cancer Research UK Cambridge Institute, Li Ka Shing Centre, Robinson Way, Cambridge CB2 0RE, United KingdomDavis J. McCarthy
EMBL European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, United KingdomJohn C. Marioni
Cancer Research UK Cambridge Institute, Li Ka Shing Centre, Robinson Way, Cambridge CB2 0RE, United Kingdom2018-10-25
Chapter 1 Intro
1.1 Workflow version information
R version: R version 3.5.0 (2018-04-23)
Bioconductor version: 3.7
Package: 1.2.1
1.2 Motivation
Single-cell RNA sequencing (scRNA-seq) is widely used to measure the genome-wide expression profile of individual cells. From each cell, mRNA is isolated and reverse transcribed to cDNA for high-throughput sequencing (Stegle, Teichmann, and Marioni 2015). This can be done using microfluidics platforms like the Fluidigm C1 (Pollen et al. 2014), protocols based on microtiter plates like Smart-seq2 (Picelli et al. 2014), or droplet-based technologies like inDrop (Klein et al. 2015; Macosko et al. 2015). The number of reads mapped to each gene is then used to quantify its expression in each cell. Alternatively, unique molecular identifiers (UMIs) can be used to directly measure the number of transcript molecules for each gene (Islam et al. 2014). Count data are analyzed to detect highly variable genes (HVGs) that drive heterogeneity across cells in a population, to find correlations between genes and cellular phenotypes, or to identify new subpopulations via dimensionality reduction and clustering. This provides biological insights at a single-cell resolution that cannot be achieved with conventional bulk RNA sequencing of cell populations.
Strategies for scRNA-seq data analysis differ markedly from those for bulk RNA-seq. One technical reason is that scRNA-seq data are much noisier than bulk data (Brennecke et al. 2013; Marinov et al. 2014). Reliable capture (i.e., conversion) of transcripts into cDNA for sequencing is difficult with the low quantity of RNA in a single cell. This increases the frequency of drop-out events where none of the transcripts for a gene are captured. Dedicated steps are required to deal with this noise during analysis, especially during quality control. In addition, scRNA-seq data can be used to study cell-to-cell heterogeneity, e.g., to identify new cell subtypes, to characterize differentiation processes, to assign cells into their cell cycle phases, or to identify HVGs driving variability across the population (Vallejos, Marioni, and Richardson 2015; Fan et al. 2016; Trapnell et al. 2014). This is simply not possible with bulk data, meaning that custom methods are required to perform these analyses.
1.3 scRNA-seq data analysis with Bioconductor
This package contains a set of computational workflows for basic analysis of scRNA-seq data, using software from the open-source Bioconductor project (Huber et al. 2015). The workflows start from a count matrix and describe a number of key steps for scRNA-seq data analysis, including quality control to remove problematic cells; normalization of cell-specific biases, with and without spike-ins; correction for batch effects; cell cycle phase classification from gene expression data; data exploration to identify putative subpopulations; and finally, HVG and marker gene identification to prioritize interesting genes. The application of these procedures will be demonstrated on several public scRNA-seq datasets involving immortalized myeloid progenitors, brain cells, haematopoietic stem cells, T-helper cells and mouse embryonic stem cells, generated with a range of experimental protocols and platforms (Lun et al. 2017; Wilson et al. 2015; Zeisel et al. 2015; Islam et al. 2011; Buettner et al. 2015; Zheng et al. 2017). The aim is to provide a variety of modular usage examples that can be applied to construct custom analysis pipelines.
See the simpleSingleCell landing page for links to individual workflows and for instructions on how to install the required packages. To cite any of these workflows, please refer to http://f1000research.com/articles/5-2122/v2 for instructions.
1.4 Obtaining a count matrix
All of these workflows start from a publicly available count matrix. For simplicity, we forego a description of the read processing steps required to generate the count matrix, i.e., read alignment and counting into features. These steps have been described in some detail elsewhere (Love et al. 2015; Chen, Lun, and Smyth 2016), and are largely the same for bulk and single-cell data. For users favouring an R-based approach to read alignment and counting, we suggest using the methods in the Rsubread package (Liao, Smyth, and Shi 2013, 2014).
Some considerations specific to single-cell data may also be relevant, depending on the experimental protocol used to generate the data:
- If spike-in RNA was added, the sequences of the spike-in transcripts can be included as additional FASTA files during genome index building prior to alignment. Similarly, genomic intervals for both spike-in transcripts and endogenous genes can be concatenated into a single GTF file prior to counting.
- If UMIs are present, these should be extracted from the read sequence prior to alignment, e.g., with the UMI-tools software (Smith, Heger, and Sudbery 2017). Reads with the same UMI mapping to the same gene represent a single underlying transcript molecule and only increment the count of that gene by one.
- Alignment-free methods such as kallisto (Bray et al. 2016) or Salmon (Patro, Duggal, and Kingsford 2015) may also be useful, due to their speed and ability to perform transcript quantification. This can be loaded into R using methods from the tximport package (Soneson, Love, and Robinson 2015).
References
Stegle, O., S. A. Teichmann, and J. C. Marioni. 2015. “Computational and analytical challenges in single-cell transcriptomics.” Nat. Rev. Genet. 16 (3): 133–45.
Pollen, A. A., T. J. Nowakowski, J. Shuga, X. Wang, A. A. Leyrat, J. H. Lui, N. Li, et al. 2014. “Low-coverage single-cell mRNA sequencing reveals cellular heterogeneity and activated signaling pathways in developing cerebral cortex.” Nat. Biotechnol. 32 (10): 1053–8.
Picelli, S., O. R. Faridani, A. K. Bjorklund, G. Winberg, S. Sagasser, and R. Sandberg. 2014. “Full-length RNA-seq from single cells using Smart-seq2.” Nat Protoc 9 (1): 171–81.
Klein, A. M., L. Mazutis, I. Akartuna, N. Tallapragada, A. Veres, V. Li, L. Peshkin, D. A. Weitz, and M. W. Kirschner. 2015. “Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells.” Cell 161 (5): 1187–1201.
Macosko, E. Z., A. Basu, R. Satija, J. Nemesh, K. Shekhar, M. Goldman, I. Tirosh, et al. 2015. “Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets.” Cell 161 (5): 1202–14.
Islam, S., A. Zeisel, S. Joost, G. La Manno, P. Zajac, M. Kasper, P. Lonnerberg, and S. Linnarsson. 2014. “Quantitative single-cell RNA-seq with unique molecular identifiers.” Nat. Methods 11 (2): 163–66.
Brennecke, P., S. Anders, J. K. Kim, A. A. Kołodziejczyk, X. Zhang, V. Proserpio, B. Baying, et al. 2013. “Accounting for technical noise in single-cell RNA-seq experiments.” Nat. Methods 10 (11): 1093–5.
Marinov, G. K., B. A. Williams, K. McCue, G. P. Schroth, J. Gertz, R. M. Myers, and B. J. Wold. 2014. “From single-cell to cell-pool transcriptomes: stochasticity in gene expression and RNA splicing.” Genome Res. 24 (3): 496–510.
Vallejos, C. A., J. C. Marioni, and S. Richardson. 2015. “BASiCS: Bayesian analysis of single-cell sequencing data.” PLoS Comput. Biol. 11 (6): e1004333.
Fan, J., N. Salathia, R. Liu, G. E. Kaeser, Y. C. Yung, J. L. Herman, F. Kaper, et al. 2016. “Characterizing transcriptional heterogeneity through pathway and gene set overdispersion analysis.” Nat. Methods 13 (3): 241–44.
Trapnell, C., D. Cacchiarelli, J. Grimsby, P. Pokharel, S. Li, M. Morse, N. J. Lennon, K. J. Livak, T. S. Mikkelsen, and J. L. Rinn. 2014. “The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells.” Nat. Biotechnol. 32 (4): 381–86.
Huber, W., V. J. Carey, R. Gentleman, S. Anders, M. Carlson, B. S. Carvalho, H. C. Bravo, et al. 2015. “Orchestrating high-throughput genomic analysis with Bioconductor.” Nat. Methods 12 (2): 115–21.
Lun, A. T. L., F. J. Calero-Nieto, L. Haim-Vilmovsky, B. Gottgens, and J. C. Marioni. 2017. “Assessing the reliability of spike-in normalization for analyses of single-cell RNA sequencing data.” Genome Res. 27 (11): 1795–1806.
Wilson, N. K., D. G. Kent, F. Buettner, M. Shehata, I. C. Macaulay, F. J. Calero-Nieto, M. Sanchez Castillo, et al. 2015. “Combined single-cell functional and gene expression analysis resolves heterogeneity within stem cell populations.” Cell Stem Cell 16 (6): 712–24.
Zeisel, A., A. B. Munoz-Manchado, S. Codeluppi, P. Lonnerberg, G. La Manno, A. Jureus, S. Marques, et al. 2015. “Brain structure. Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq.” Science 347 (6226): 1138–42.
Islam, S., U. Kjallquist, A. Moliner, P. Zajac, J. B. Fan, P. Lonnerberg, and S. Linnarsson. 2011. “Characterization of the single-cell transcriptional landscape by highly multiplex RNA-seq.” Genome Res. 21 (7): 1160–7.
Buettner, F., K. N. Natarajan, F. P. Casale, V. Proserpio, A. Scialdone, F. J. Theis, S. A. Teichmann, J. C. Marioni, and O. Stegle. 2015. “Computational analysis of cell-to-cell heterogeneity in single-cell RNA-sequencing data reveals hidden subpopulations of cells.” Nat. Biotechnol. 33 (2): 155–60.
Zheng, G. X., J. M. Terry, P. Belgrader, P. Ryvkin, Z. W. Bent, R. Wilson, S. B. Ziraldo, et al. 2017. “Massively parallel digital transcriptional profiling of single cells.” Nat Commun 8 (January): 14049.
Love, M. I., S. Anders, V. Kim, and W. Huber. 2015. “RNA-Seq workflow: gene-level exploratory analysis and differential expression.” F1000Res 4: 1070.
Chen, Y., A. T. Lun, and G. K. Smyth. 2016. “From reads to genes to pathways: differential expression analysis of RNA-Seq experiments using Rsubread and the edgeR quasi-likelihood pipeline.” F1000Res 5: 1438.
Liao, Y., G. K. Smyth, and W. Shi. 2013. “The Subread aligner: fast, accurate and scalable read mapping by seed-and-vote.” Nucleic Acids Res. 41 (10): e108.
Liao, Y., G. K. Smyth, and W. Shi. 2013. “The Subread aligner: fast, accurate and scalable read mapping by seed-and-vote.” Nucleic Acids Res. 41 (10): e108.
2014. “featureCounts: an efficient general purpose program for assigning sequence reads to genomic features.” Bioinformatics 30 (7): 923–30.Smith, T., A. Heger, and I. Sudbery. 2017. “UMI-tools: modeling sequencing errors in Unique Molecular Identifiers to improve quantification accuracy.” Genome Res. 27 (3): 491–99.
Bray, N. L., H. Pimentel, P. Melsted, and L. Pachter. 2016. “Near-optimal probabilistic RNA-seq quantification.” Nat. Biotechnol. 34 (5): 525–27.
Patro, Rob, Geet Duggal, and Carl Kingsford. 2015. “Accurate, Fast, and Model-Aware Transcript Expression Quantification with Salmon.” bioRxiv. Cold Spring Harbor Labs Journals. https://doi.org/10.1101/021592.
Soneson, C., M. I. Love, and M. D. Robinson. 2015. “Differential analyses for RNA-seq: transcript-level estimates improve gene-level inferences.” F1000Res 4: 1521.


The identifier of the moniker. The value is opaque in LSIF however schema owners are allowed to define the structure if they want.
The moniker kind if known.
The scheme of the moniker. For example tsc or .Net
The scope in which the moniker is unique
Generated using TypeDoc
Moniker definition to match LSIF 0.5 moniker definition.
3.16.0


Overview
Tracking and managing orders is an important part of an algorithmic trading strategy. Intelligent order management encourages discipline and a deep understanding of your algorithm. Through the QuantConnect API you can get order fields, update their values and cancel pending orders. This can be useful for lowering trading costs and improving order fills.
When you place a trade you receive an OrderTicket
for you to access the order. This allows you to safely (asynchronously) update and cancel the order while in live trading. In live trading you cannot assume order updates are processed successfully as the brokerage may have already filled the trade.
You can place several types of orders including:
| Supported Order Types |
| Market Order |
MarketOrder("SPY", 100); |
| Limit Order |
var ticket = LimitOrder("SPY", 100, 100.10m); |
| Stop Market Order |
var ticket = StopMarketOrder("SPY", 100, 100.10m); |
| Stop Limit Order |
var ticket = StopLimitOrder("SPY", 100, 100.12m, 99.5m); |
| Market On Open Order |
var ticket = MarketOnOpen("SPY", 100); |
| Market On Close Order |
var ticket = MarketOnClose("SPY", 100); |
Updating Orders
Once you have an order ticket you can use it to get order fields. In C# it looks like:
var currentStopPrice = _ticket.Get(OrderField.StopPrice);
Or update the order fields (LimitPrice, StopPrice, Tag or Quantity):
_ticket.Update(new UpdateOrderFields
{
LimitPrice = newLongLimit,
Tag = "Update #" + (longOrder.UpdateRequests.Count + 1)
});
You can also cancel your order if required:
_ticket.Cancel();
In Python, you can update order like this:
# Retrive the 'StopPrice' for an order from the ticket
currentStopPrice = _ticket.Get(OrderField.StopPrice)
# update the order fields (LimitPrice, StopPrice, Tag or Quantity)
updateOrderFields = UpdateOrderFields()
updateOrderFields.LimitPrice = newLongLimit
updateOrderFields.Tag = "Update #{0}".format(len(_ticket.UpdateRequests) + 1)
_ticket.Update(updateOrderFields)
# cancel your order if required
_ticket.Cancel()
In the video below we demonstrate putting it all together to create a moving take-profit order which gradually decreases its profit target as time passes.
https://www.youtube.com/watch?v=HykXfstdNW0
Full Python Example
This is a complete port from C# to Python of this Quick Start Lesson.
```Lesson 6 - Tracking and Managing Orders:
Immediately place 4 orders. Plot end of day price of each order in 'Order Tickets' plot. Debug log filled orders.
```
import numpy as np
from decimal import Decimal
class BasicTemplateAlgorithm(QCAlgorithm):
def Initialize(self):
self._limitTicket = None
self._stopMarketTicket = None
self._stopLimitTicket = None
self.SetCash(25000)
self.SetStartDate(2009,1,1)
self.SetEndDate(2009,6,1)
self.spy = self.AddEquity("SPY", Resolution.Minute).Symbol
def OnData(self, slice):
if self._limitTicket is None:
self.MarketOrder(self.spy, 100)
self._limitTicket = self.LimitOrder(
self.spy,
100,
slice["SPY"].Close * Decimal(0.9),
"limit order")
self._stopMarketTicket = self.StopMarketOrder(
self.spy,
-100,
slice["SPY"].Close * Decimal(0.95),
"stop market")
self._stopLimitTicket = self.StopLimitOrder(
self.spy,
-100,
slice["SPY"].Close * Decimal(0.9),
slice["SPY"].Close * Decimal(0.8),
"stop market")
def OnEndOfDay(self):
```End of each day, plot our asset & order prices```
if self._limitTicket is None:
return
self.Plot("Order Tickets", "SPY", self.Portfolio['SPY'].Price)
if self._stopMarketTicket.Status != OrderStatus.Filled:
self.Plot("Order Tickets", "Stop Price",
self._stopMarketTicket.Get(OrderField.StopPrice))
if self._stopLimitTicket.Status != OrderStatus.Filled:
self.Plot("Order Tickets", "Limit Price",
self._stopLimitTicket.Get(OrderField.LimitPrice))
def OnOrderEvent(self, OrderEvent):
```Event when the order is filled. Debug log the order fill. :OrderEvent:```
if OrderEvent.FillQuantity == 0:
return
fetched = self.Transactions.GetOrderById(OrderEvent.OrderId)
self.Debug("{} was filled. Symbol: {}. Quantity: {}. Direction: {}"
.format(str(fetched.Type),
str(OrderEvent.Symbol),
str(OrderEvent.FillQuantity),
str(OrderEvent.Direction)))


--- title: "More GAMs than you wanted" author: "Drew Tyre" date: "2017-09-07" output: html_document weight: 803081 ---
We’ll play with a few datasets from Mick Crawley’s “The R Book” 1 to see how to identify when a GAM can be useful, and when to stick with a GLM. The first dataset is of population sizes of Soay Sheep.
library(NRES803)
library(tidyverse)
library(mgcv)
library(broom)
library(GGally)
library(gridExtra)
# need this to make residual plots of gam models
bollocks.augment <- function(model) {
r <- model.frame(model)
r$.fitted <- fitted(model)
r$.resid <- resid(model)
r$.std.resid <- residuals(model, type = "scaled.pearson")
r$.hat <- model$hat
r$.cooksd <- cooks.distance(model)
return(r)
}
data(sheep, package = "NRES803data")
ggplot(sheep, aes(x = Year, y = Population)) + geom_point()
The goal is to identify a function relating growth rate Delta to population size. The simplest population model has this as a linear function, but there’s no a priori reason to suppose that it should be linear.
sheep.lm <- lm(Delta ~ Population, data = sheep)
sheep.gam <- gam(Delta ~ s(Population), data = sheep)
# get residual df for both models
stuff_lm <- glance(sheep.lm)
stuff_gam <- glance(sheep.gam)
nd <- data.frame(Population = seq(600, 2000, 10))
pred_lm <- augment(sheep.lm, newdata = nd, se_fit = TRUE) %>%
mutate(l95ci = .fitted - .se.fit * qt(0.975, stuff_lm$df.residual),
u95ci = .fitted + .se.fit * qt(0.975, stuff_lm$df.residual)) %>%
rename(Delta = .fitted)
pred.R <- predict(sheep.gam, newdata = nd, se.fit = TRUE)
pred.R <- as.data.frame(pred.R)
pred.R <- cbind(nd, pred.R)
names(pred.R)[names(pred.R) == "fit"] <- ".fitted"
names(pred.R)[names(pred.R) == "se.fit"] <- ".se.fit"
pred_gam <- pred.R %>%
mutate(l95ci = .fitted - .se.fit * qt(0.975, stuff_gam$df.residual),
u95ci = .fitted + .se.fit * qt(0.975, stuff_gam$df.residual)) %>%
rename(Delta = .fitted)
# make a plot
ggplot(sheep, aes(x = Population, y = Delta)) + geom_point() +
geom_line(data = pred_lm, color = "blue", size = 2) + geom_ribbon(aes(ymin = l95ci,
ymax = u95ci), data = pred_lm, color = "grey", alpha = 0.33) +
geom_line(data = pred_gam, color = "green", size = 2) + geom_ribbon(aes(ymin = l95ci,
ymax = u95ci), data = pred_gam, color = "grey", alpha = 0.33)
The gam fit is kind of interesting, looks like there are two broad regimes of population growth within which the growth rate is pretty constant with respect to population size. So which is the better model? This is actually quite important to get right, as the dynamics implied by the non-linear model will be much different. Recall that we can use gam() to fit the simpler model as well.
Check the assumptions graphically, and plot the residuals of both models against population size. Can you tell here which model is better? Would you use the linear or non-linear model, and why?
Fit the linear model using gam() and use anova() to compare the two models. Which one would you choose and why?
Next we’ll take a look at an air quality dataset. Not ecological, but a good test of your ability to see patterns in residuals.
The goal here is to predict ozone readings from solar radiation levels, temperature, and wind. You can see from the pairs plot that temperature in particular looks quite non-linear, as does radiation.
Set up a gam()
model with smooth terms for all three predictor variables. Evaluate this model graphically and interpret the results graphically (make a prediction plot) and in written form.
log transform Ozone, and repeat the model with three smooth terms. Compare these two models graphically and numerically, choose which one is better, and explain why.
Based on your conclusions from the initial model, fit one or more new models with either fewer smooth terms (possibly linear), or additional interaction terms. Evaluate and interpret these models graphically and numerically.


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Cloneswarm lets users interactively explore code clones in open source projects.It detects clones using NiCad a popular clone detection system.
Cloneswarm visualizes the project structure in a radial layout and uses heatmap style color variations to indicate the amount of cloning.


| DOSE: |
2.75 Tbsp |
oral |
Nutmeg |
(ground / crushed) |
My friends and I had been trying to get acid or mushrooms for a nice weekend trip, but the shit never came through. I had heard that if you take about 2-3 tablespoons of nutmeg, that you would trip. Well, everyone laughed at me, but they still tried it anyway.
3:45-4:30pm: Me and two friends ate our tablespoons of nutmeg. It was very nasty so we mixed it with water and took shots.
5:30pm: I started to feel a little weird but no big change yet. I went to the dinning hall to eat but wasn't interested in the food.
7:00pm: Chillin in my friends room playing video games when suddenly I dropped the controller and laid back on the bed. I felt really lazy and heavy. I didn't even have the energy to pick up the video game controller and play.
7:30pm: started to feel really really stoned.  I was just staring at the TV while other people in the room were laughing at me. My other friend said that he was seeing cool patterns and tracers when he moved his hand.
9:30pm: Still feeling the weed-like high, but its more intense. We decided to go to a party. When we get there I had a couple of beers and now I'm outta this world! I couldn't even talk right so I gave up trying.
12:00am: We left the party and went back to my friends room. I was still feeling the effects of the nutmeg. I sat there watching TV and staring into space while my friends roomate laughed his ass off at me.
2:30am: I left my friends room and went back to my room where I went to bed.
3:00-5:00am: I couldn't sleep cause everytime I closed my eyes, I would have weird visuals. It was like I was watching movie clips, but somehow I was in them. It was very unpleasant. It was sort of like an acid trip where you want to go to bed, but you keep thinking weird thoughts over and over again.
8:00am: Had to get up, take a piss and drink water. My mouth was so dry.  It seemed like I was still stoned.  I went back to bed.
3:00pm: Woke up and took a shower.  I was hoping taking a shower would wake me up, but it didn't work. Went back to bed.
7:00pm: Woke up. I slept like a baby. Still felt weird but it was over by 8pm.
Summary: I only recommend taking nutmeg if you are very desperate or poor. The next day 'hangover' was no fun at all. The basic effect of this drug on me was that I was really stoned for 24 hours. My two friends both agree that once was enough.
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.


You: If what you believe is not true, would you want to know?
You: Who is Jesus Christ? Isn't it interesting he said he was God? (Read the following Scriptures aloud.)
Read: John 10:30
I and my Father are one.
You: The literal translation of this means Jesus and the Father are of the same essence.
Read: John 14:7
If ye had known me, ye should have known my Father also: and from henceforth ye know him, and have seen him.
Read: Revelation 1:8
I am Alpha and Omega, the beginning and the ending, saith the Lord, which is, and which was, and which is to come, the Almighty.
You: We know this was Jesus speaking in Revelation 1:8 for the book closes with:
Read: Revelation 22:20
He which testifieth these things saith, Surely I come quickly. Amen. Even so, come, Lord Jesus.
Read: Colossians 1:15-16
Who is the image of the invisible God, the firstborn of every creature:
For by him were all things created, that are in heaven, and that are in earth, visible and invisible, whether they be thrones, or dominions, or principalities, or powers: all things were created by him, and for him:
Read: John 8:58
Jesus said unto them, Verily, verily, I say unto you, Before Abraham was, I am.
Read: Exodus 3:14
Jesus said unto them, Verily, verily, I say unto you, Before Abraham was, I am.
You: The Jewish people wanted to stone Jesus because they knew he was referring to himself as God when he called himself, “I AM!” He was using the divine name of God found in Exodus 3:14.
Read: John 5:18
Therefore the Jews sought the more to kill him, because he not only had broken the sabbath, but said also that God was his Father, making himself equal with God.
Read: John 20:28-29
And Thomas answered and said unto him, My Lord and my God.
Jesus saith unto him, Thomas, because thou hast seen me, thou hast believed: blessed are they that have not seen, and yet have believed.
You: Did Jesus ever sin?

You: Who can forgive sins but God alone? If Jesus were not God, how could he forgive sins and not sin himself?
Read: Matthew 9 2-6
2.
3. And, behold, certain of the scribes said within themselves, This man blasphemeth.
4. And Jesus knowing their thoughts said, Wherefore think ye evil in your hearts?
5. For whether is easier, to say, Thy sins be forgiven thee; or to say, Arise, and walk?
6.
Read: John 9:38
And he said, Lord, I believe. And he worshipped him.
You: May I show you a few Scriptures that changed my life? (show the “Share Jesus Scriptures”) or May I tell you how Christ changed my life?


Cheesecake Order Form
Quantities
1
2
3
4
Toppings
Plain
Cherry
Chocolate
Notes
Order
Orders for
Jan
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
11 Cherry
17 Chocolate
31 Plain


--- layout: doc_layout title: Creating a Custom Service Host Adapter category: Service Hosting prev: /docs/hosting/iis.html next: /docs/transports/introduction.html ---
It is possible to embed Wave into other hosting containers. To facilitate this, subclass Wave.BusHost
and override the Start
and Stop
methods:
It is important to note that you MUST call the base.Start
and base.Stop
methods, and that base.Start
will block forever, so if you need
to yield control back to the calling application, call base.Start
from a seperate thread.
You can enable the custom host using fluent configuration:
Or by using XML configuration in the host's configuration file:
{% highlight xml linenos %}


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Washington, D.C.
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Ontario, Canada
London, England
Sydney, Australia
Contact Us
General information
<EMAIL_ADDRESS>Business Inquiries
<EMAIL_ADDRESS>Corporate Office
143 Moon Landing Ave
Washington, D.C.
Phone:(333)456-789


Projects
This quarter we will have 4-5 projects that we designed to give you experience with system design and implementation. You will write programs that demonstrate the concepts that we cover in class. Broadly speaking, we will divide the projects in to two different categories. The first category will be relatively small and self-contained projects, but we will give three of them over the first four weeks of the class. The second category will be larger projects, and these will come in the second half of the class and will be spread out over several weeks.
Processes, system APIs, threads, and concurrency
During the first part of the quarter, we will give you three projects covering basic command line utilitites, processes, multi-threaded programming, and synchronization.
Network programming and distributed systems
In the second part of this quarter, we'll have 1-2 larger projects that will cover network programming, APIs and distributed systems, and file systems.
Group or individual?
You are welcome to work with other students in this class, but you must write and submit your own solutions. You may not copy any code and we will consider any copied code to be a violation of our cheating policy.


[Teleport to Hunting Grounds]
You may teleport to any of the following hunting locations. Each teleport requires a sum of AA.
Patriot's Necropolis (50-60) - 29000 AA
Necropolis of Devotion (60-70) - 39000 AA
Necropolis of Martyrdom (60-70) - 46000 AA
The Saint's Necropolis (70-80) - 73000 AA
Disciples Necropolis (70-80) - 35000 AA
Catacomb of the Witch (60-70) - 37000 AA
Catacomb of Dark Omens (70-80) - 25000 AA
Catacomb of the Forbidden Path (70-80) - 41000 AA


a.
When a comparison algorithm A is used to sort, there will be at least n! leaves reached in decision tree TA. This is because, there will a leaf for each possible input permutation and no two input permutations can reach the same leaf.
In a deterministic algorithm, the same leaf must be reached when a specific permutation is given as input so that not more than n! leaves are reached. Therefore there will be one leaf for each input permutation and exactly n! leaves are reached.
The probability of each of these n! leaves will be 1/n!. The probability of other leaves will be 0.
Hence it can be considered for rest of this problem that the paths that lead to the leaves with 0 probability are not present in the tree as they do not participate in the running time of the sort.
Thus, decision tree TA has only the n! leaves with probability 1/n!.
b.
When k > 1, then the root of decision tree T is not a leaf. All the leaves of T are leaves in LT and RT.
A leaf at depth h in left subtree LT or right subtree RT will be at depth h+ T.
Thus, D(T) must be equal to the sum of D(LT) , D(RT) and K where K is the total number of leaves.
Consider that = Depth of node x in tree T.
Then,
c.
In order to prove that, first prove that
and .
Proof of :
In order to prove that, it is sufficient if it is proved that for i=1,2,..,k-1.
For any i=1,2,..,k-1, there will be a RT with i leaves and LT with k-1 leaves. Then and .
Construct T such that RT and LT are the right and left sub trees of T’s root
respectively.
Then,
Proof of :
In order to prove that, it is sufficient if it is proved that for i=1,2,..,k-1.
Consider a tree T with k leaves such that D(T) = d(k). Let RT and LT be T’s right and left sub tree, respectively, and let i be the number of leaves in RT. Then k-i is the number of leaves in LT.
The values of i and k-1 cannot be 0 because if it happens then either the RT or LT will have all the k leaves of T. Also the subtree will have a which is a contradiction to the choice of T as the k- leaf tree with the minimum D.
Consider .
In order to find the value of i that minimizes fk , find i for which the derivative of fk with respect to i is 0:
is 0 at .
To confirm that is 0 at is a minimum, check that the second derivative of fk is positive at :
Proof of using substitution method:
Base case:
for any constant c.
Inductive step:
Assume for, where c is some constant to be determined.
and hence
e.
From part (d) and using the fact TA has n! leaves (part a), it can be concluded that
D(TA) is the sum of lengths of the decision- tree path for sorting all input permutations.
As n! permutations have equal probability 1/n!, the expected time is sort n random elements ( 1 input permutations) is the total time for all permutations divided by n!.
f.
Deterministic decision tree (algorithm) is as good as the randomized decision tree (algorithm) when comparison is made on average number of comparisons.
Following are the steps to modify a randomized decision tree ( algorithm ) to deterministic decision tree (algorithm ):
• At each randomized node, select the child with the smallest sub tree.
• Delete all the other children of the randomized node.
• Join the randomized node itself.
The average number of comparisons for the deterministic decision tree (algorithm) and a randomized decision tree ( algorithm ) are almost the same. This is because in each case, the higher- average sub trees are removed.
Each time a randomized node is joined to the tree, the average is being reduced.
Thus, the randomized algorithm and the corresponding deterministic algorithm will take almost the same number of comparisons.
Running time is the time taken by the algorithm for its execution. If all the elements are traced once and there are n elements, then it is said that the algorithm takes running time of.
In the similar way, if there are two loops which traces a matrix, then it takes the order of.
Stability of algorithm means that the order of entering the elements should be maintained in the output as well. This means that if any element is entered before another element, then it should be maintained in output as well.
Constant amount of storage space: The algorithm should take a constant amount of storage space and it should not vary.
a)
To sort an array of data records, each record’s key having value either 0 or 1, counting sort algorithm is used. Counting sort algorithm runs in time and is stable.
• Consider the array of n elements having data value from 0 to k and any element can repeat any time.
• For sorting array using counting sort algorithm, it requires two more arrays firstwhich stores the sorted elements of array L and other array for the temporary work.
• Counting sort algorithm is also known as integer sort algorithm.
The algorithm of counting sort is as follows:
COUNTING-SORT ( L , M , k )
// initializing the temporary array with 0.
1. P [0…k] is the array for temporary storage of array elements.
// for loop is used to traverse all element of loop.
2. for i=0 to k
3. P[i] = 0
// traversing the array to be sorted from initial index to its length.
4. for j=0 to L.length
//updating the temporary array
5. P[L[j]]= P[L[j]]+1
// travers the array P
6. for i=0 to k
// for integer i in each position of P finding the number of elements less than i
7 P[i] = P[i] +P [i-1]
// loop for putting the elements of array L in the array M in sorted order.
8. for j=0 to L.length down to 1
9. M [L[j]]] =L[j]
// updating the value in temporary array P
10. P [L[j]] =P [L[j]]-1
Explanation of algorithm:
• In the above algorithm, the loop in line 2-3 takes time for its execution.
• The loop in line 4-5 takes time as the.
• The loop in line 6-7 runs in and the loop in line 8-10 runs intime.
• So, the total time taken by the algorithm is. As the value of n is greater than the value of k for any array, it can be ignored.
Hence, the total time taken by the above algorithm is.
b)
Quick sort Partition algorithm runs in time and storage space should be constant.
QUICKSORT-PARTITION
//initialize the value of i
1. i=0
// for loop is used to traverse the all element of array
2. for j=1 to n
//if statement is used to check the value is less than or equal to 0.
3. if key[Array[j]] <=0
// increment the value of i
4. then i=i+1
//exchange the value at location i with j
5. exchange to
Explanation of Algorithm:
• In the above code of quick sort algorithm, single for loop is used to sort the data hence total time taken by algorithm is
• In the above algorithm in- place sorting method is used and stores the all sorted elements in the same array in which it is sorting So, it takes constant amount of space for the storage of its elements.
Insertion sort is a stable algorithm and it uses no more than a constant amount of storage space in addition to the original array.
INSERTION-SORT ( L )
// loop traversing each element of array starting with 2.
1. for j= 2 to L.length
// storing the jth element of array into the temporary variable key.
2. key=L[j]
// initializing the variable i with one less than j
3. i=j-1
// loop running from i down to 0 if ith element of array is greater than the key
4. while i>0 and l[i]>key
// shifting the array values one position to its right.
5. L[i+1]=L[i]
// decreasing the value of i by 1.
6.
// updating the array with key
7.
Explanation of Algorithm:
• In the above sorting algorithm, the finally sorted elements are stored in the same array in which they were sorted So, it is in-place sorting algorithm and takes the constant amount of space.
• In the above algorithm for loop is used to traverse the array from 2 to length of array so it takes time to execute the line 1.
• Line 2 and 3 of the above code is used to hold the value one at a time so it takes time.
• In line 4 of algorithm while loop is used for compression with all remaining value of array so its time complexity will be.
• In line 5, 6 and 7 of algorithm it exchanges the value of array to sort the data.
Hence the time complexity of algorithm will be.
d)
Radix-sort is a sorting algorithm that has been used to sort the numbers according to the orders of the digit.
• This Radix-sorting algorithm moves from least significant digit to most significant digit.
• Yes, counting sort algorithm (part a) is also used as a sorting method used in line 2 of RADIX-SORT.
• First sort the keys based on the least significant value using counting sort.
• Now sort the sorted array for each more significant digit using counting sort. Final output of this array will be a sorted array.
• Each iteration of the counting sort will take. Since, these records are -bit keys therefore total running time will be
e)
The modified algorithm of counting sort is as follow:
COUNTING-SORT ( L , M , k )
// initializing the temporary array with 0.
1. P1 [0…k] is the array for temporary storage of array elements.
// for loop is used to traverse all element of loop.
2. for i=0 to k
3. P1[i] = 0
// traversing the array to be sorted from initial index to its length.
4. for j=0 to L.length
//updating the temporary array
5. P1[L[j]]= P1[L[j]]+1
// travers the array P
6. for i=0 to k
// for integer i in each position of P finding the number of elements less than i
7. P1[i] = P1[i] +P1 [i-1]
//traverse the array
8. for i=0 to k
//copy the index into array.
9. P2[i]= i
// loop for putting the elements of array L in the array M in sorted order.
10. for j=0 to L.length down to 1
//if statement is used to check the index number with length of maximum index.
11. if i>P2 [L[i]]
//copy the content into variable a
12. a=L[i]
13. do
Copy the content of temporary array into variable k
14. k=P1[a]
//store the length of k.
15. c=L[k]
//copy the content of a into length of k .
16. L[k]=a
17. P1[a]=P1[a]-1
18. a=k
//compare the index number with temporary array
19. while P1[a]!= i
20. L[i]=a
• In the above algorithm, two arrays of length k is required.
• In the above modified algorithm P1 array act like a P array in the original counting sort algorithm.
• P2 array is used to store the necessary information. Array P2[i] is the maximum index that i can take-up.
• If L[i]> P2[L[i]] , it means L[i] must be put at wrong position then put L[i] at correct position by P1[L[i]]
• Finally each element put on correct position so no need to access anymore.
• Total running time is where k is the time that taken to set the all element in array P1 and P2.
a.
It is given that is placed in the location where should be placed. Hence, it is obvious that is also placed in the inappropriate location.
It is given that is the smallest value in array A that goes to the wrong array location. Therefore, it can be said that is strictly less than and hence,.
The array, B[1..n] is an array of Boolean values where is defined as shown below:
For, as
For, as
Thus proved that so that and .
b.
From the definition of the array B, it is clear that if then .
The illustration explains that the routine X commits the same series of exchanges on array B as it does on array A.
The output produced on array A is as and therefore the output produced on array B is of the form.
In array A, is not as large as. Therefore, the value would not be sorted in array B too.
Since and , therefore, replacing them will be resulting in it is obvious to see that array B is not sorted.
The implication of this is that the algorithm X does not succeed in sorting array B correctly.
c.
In the given algorithm, even steps perform fixed operations such as transposing the array, finding inverse, etc.
At the odd steps, the sorting of the columns are major factor. The sorting technique is not important as all the sorting algorithms provide the same output.
The odd steps sort each column by some sorting algorithm, which is not necessarily a compare-exchange algorithm. Even if compare-exchange algorithm is used, the result of the sorting each column would be same as that of any other column sort algorithm.
d.
Clean Area: The area of an array is considered as clean, if it contains either all 0s or all 1s. It is because all the elements are of same type and easy to identify.
Dirty Area: The Area of an array is considered as dirty, if it contains mixed 0s and 1s. It is due to the reason that it requires a lot of comparisons to sort the array.
Step 1 is sorting of each column. Therefore, after step 1, each column would have 0s on summit and 1s on the ground, with maximum of one transition between 0s and 1s, and that is 01 transition.
Step 2 is transposing the array with its shape. Therefore, after step 2, each upcoming group of rows, interpreted in row-major order, would have maximum of one transition, and this is 01 transition.
Now, All 10 transitions will appear at the end of a collection of rows.
Since there would be groups of rows, therefore, there will be at most dirty rows, and the remaining of them would be clean.
Step 3 is sorting of each column. Therefore, after step 3 all 0s will be at top of rows and all 1s will be at bottom rows. Hence dirty rows will be somewhere in the middle.
Therefore, it can be concluded that after steps 1-3, the array holds some genuine rows of 0s at the summit, some clean rows of 1s at the ground, and maximum of dirty rows in the rest of the area surrounded by them.
e.
From the above part d, it has been proved that after step 3, there will be at most of dirty rows. Hence, its area will be at most.
Step 4 is the inversion of the permutation performed in step 3. It will modify genuine 0s in the upper rows to form a clean span on the left. It will also modify the clean 1s in the lower rows to form a clean span on the right side, and the dirty span of size that lies in the span surrounded by clean areas.
Therefore, after step 4 is executed, the array, that was created by reading the elements in column-major order, starts with a clean area of 0s, ends with a clean area of 1s, and holds a dirty span of at most items in the middle.
Consider =number of rows and = total amount of columns.
Consider that … … (1)
Consider that if the dirty span after step 4 has the maximum size of , then Steps 5–8 can produce the order arrangement in the above part (e). It has been proved that after step 4, array has a dirty area of at most.
Now, from equation (1)
If the dirty area after step 4 has size maximum, then Steps 5–8 can produce the sorting.
Proof:
Given that the size of the dirty area is maximum . Then there are two possibilities.
Case1. It completely resides in one column.
Case2. It resides in bottom half of one column and the top half of next column.
Case 1:
If it completely resides in one column then step 5 will sort the column that is retaining the dirty area and step 6-8 will sort the array.
Case 2:
If it resides in bottom half of one column and the top half of next column then step 5 will not increase the dirty area span. Step 6 will move the complete dirty area in one column, step 7 will sort it, and step 8 will move it back.
In either case array is arranged in ascending order.
g.
If does not divide, then after step 2, the number of 01 transitions will be and number of 10 transitions will be. Also, there will be dirty rows.
After step 3, it should have dirty rows equal to dirty, for a dirty area spanning in the maximum of.
In part (e), it already proved that dirty area size .
Then,
Therefore, the value of r should be larger than or same as when s does not a divisor of r.
h.
In order to fulfill the task that states that even when does not divide r, reducing the necessary count of transitions committed to the rows following second step is executed to at most s by sorting every other column in reverse order in step 1.
Now if a transition (either 10 or 01) in the columns occurs after step 1, then either one of the columns had all 1s or the other had all 0. In both cases, no transition occurs within any of the columns.
Hence, the column-sort correctly sorts.


Gemunu Libre is the Unicode release of the popular Sinhala typeface ‘FM Gemunu.’ With 7 weights from ExtraLight to ExtraBold, each weight has complete Sinhala support, and includes a new Latin typeface for easy bilingual typesetting.
This new libre version has many small improvements and adjustments, made during the unicode adaptation process. More breathing space was introduced with bigger counters and smooth uniformity in the curves. The distinctive smooth curves and square edges set Gemunu Libre apart from all other Sinhala typefaces currently in use. This was perfected by completely redrawing each glyph.
This project is led by Mooniak, a foundry in Colombo, Sri Lanka. Mooniak collaborated with Pushpananda Ekanayake, who designed ‘FM Gemunu,’ and Sol Matas who designed the Latin typeface. To learn more, visit github.com/mooniak/gemunu-libre-font


The Story
I recently had a very annoying problem - some daemon failed, but ran
fine when told to run at foreground (not daemonize). Running at
foreground is the easiest way to debug processes, because that way you
get their input / output / error streams in your terminal.
Said daemon had no "log to file" option as well, so the only indication
I had that something was wrong is that the daemon didn't do what it's
supposed to do.
When processes daemonize, they create a sub process that isn't attached
to anything (so it won't be affected by the terminal closing, for
instance).
The originating process usually exists after creating the sub process,
and so you can't easily capture the output of the sub process.
I eventually realized that I need to "hijack" the stderr
stream from the
sub process. I did some stupid attempts, like this (NOT WORKING):
tail -f /proc/$(pidof $DAEMON)/ld/2
Eventually I wrote something using strace, which was OK:
sudo strace -ff -p $(pidof $DAEMON) -e write=1,2 -s 1024 2>&1 | grep "^ |"
It gave me the output I wanted, and I solved my issue (which was me
passing relative file locations, inaccessible to the sub process created
because it doesn't inherit the parent's working directory). However, I
wanted something more elegant. I found the commands in this
post,
which did something better - given a pid
, they redirect its input,
output and error streams to some tty
(terminal), giving you control over
the process.
However, it wasn't a fire-and forget script. I tried to fix that :-)
The Solution
My script will hook the process to your current terminal.
Please note: I don't think it's a good idea to leave the hijacked
daemon running after finishing troubleshooting. You should probably
restart it.
DAEMON=afuse
MYT=$(tty)
sudo gdb -p $(pidof $DAEMON) <<EOF
call close(0)
call close(1)
call close(2)
call open("$MYT", 2, 0)
call dup(0)
call dup(0)
detach
EOF
Notice the output - much better:
The way the script works is this:
First, it uses tty
to find the path to the current terminal.
gdb is then called to close streams 0,1,2 (which are almost always
stdin
, stdout
, stderr
) and open a new stream to the tty
found before.
The new stream opens at index 0 (because it's the lowest index
available, since we just closed 0,1,2) and copies it to 1 and 2 as well.
Now stdin
, stdout
and stderr
are all mapped to the current terminal -
success!


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Articles
-
RxJava Introduction
I had promised to publish one blog post every two weeks, but since I published my last blog post, I switched over to a new job and the last three months have been a lot of fun but busy adjusting to my new job and co workers. However, I feel like I may have settled now and will resume my blogging efforts. Today’s topic is not specific to Android but to reactive programming. As such, this post will be the first in a series of posts on RxJava for the foreseeable future. I want to really nail this topic down, because I see functional programming the way of the future, so let’s put in some topics around it.
-
Dagger
Dagger is a very popular dependency injection library created by the team at Square. Although Jake Wharton is associated with it, he has mentioned in his talks that the idea and initial development for Dagger was done by Jesse Wilson and Bob Lee (these two were also involved in the development of the another popular D.I. library - Guice at Google). Dagger uses a Directed Acyclic Graph(DAG) to map the different creational classes and hence the name DAGger. The folks at Google advocate against the use of DI frameworks on account of these frameworks using reflection and give example of Guice. However, Dagger was designed to avoid this issue and does not use reflection at all and instead uses static dependency to build the ObjectGraph. To be honest, I don’t know understand everything of what I have written in the last two sentences, but it just sounded so cool and so trustworthy that I felt inclined to use it. Once I started using it, I realized the awesome power especially as my app grew. We will be using Dagger 2 for this tutorial.
-
Handler Thread
If you have been an Android developer for sometime, no doubt you follow the gospel - move heavy duty processing stuff to a non-ui thread. This is because if you do heavy operations such as networking, image manipulation, the UI will be frozen, possibly leading to an ANR message…argggh. You will need to perform the heavy operations on some other thread and then communicate the result to the UI thread.
-
Sync Adapter
Without doubt, your app will communicate with the server and fetch some data for the the users to view, unless you have some extraordinary idea that is self sufficient within the app. Most likely, you will end up making frequent server calls to sync data. Here is where you will find the Sync adapter useful. For the Sync adapter you need an Account authenticator, which I have explained in my previous blog post. If you have not implemented an account authenticator, please do so. For someday when you end up sitting across Larry or Mark from you know where, you will thank me silently for the extra couple of billions that feature adds to your deal. I take 100% commission in the form of retweets and likes.
-
Account authenticator
Let us start at the beginning. You have an android app and want to allow users to create an account on your app. You might think about storing the user identifying token in shared preferences or sqllite DB, or just make a server call every time. There may be some solid implementations for these, but Android actually provides a mechanism for account creation and management. So let’s make use of it. But how? This is what grinds my gears. The documentation is sparse in my opinion and if not for some good blogs (mentioned at the bottom) I’d be stuck too. So this is one more attempt at demystifying the Account Authenticator.


How to stay inspired everyday!
Lets get right into it, I'll list the most important things you should be doing to stay inspired everyday
- Surround yourself with things that inspire you.   This is important, from the moment you wake up you should have that inspiration refueled, so when you wake up, the first thing on your mind or the first thing you see, should be something or someone that inspires you.
- Write down and/or find out 3 things that anger you. Most likely your dream or goal will be to overcome those things.  Well a goal will be for you to become a better person or to find someone better so that never happens to you again.  Find out how you can overcome them. Anger is not bad as long as you can control it and use it as fuel to drive you to become better.
- Write down 3 people that believe in you, need you, and/or look up to you. Everytime you feel like quitting I want you to look at that paper you wrote their names on or think of them, and remember how they believe in you. Think about them and remind yourself you cannot let them down.
- Rest. You need a day off, your body needs it, your mind needs it.  Slack off and do nothing.  Use that anger to pursue your goals once again and try to make up for that day.
- Write down your 3 strengths, 3 things that make you different from other people. I really like this one because it lets you just look at yourself and learn about yourself more. Everyday from the moment you wake up remind yourself of your strengths, what you're good at, and what you've accomplished. Everyone has at least 3 strengths and has accomplished something they're proud of.  Use this and remember you are unique!


Chapter 3 Assessment
In the Assessment stage, you will describe the available reproduction materials and assign a reproducibility score to the display items associated with your selected claims. You will also review reproducibility practices for the overall paper. This stage records rich information about each reproduction to allow future reproducers to pick up easily where others have left off.
In the previous two stages, you declared a paper and identified claims and their associated estimates (found in display items) that you intend to analyze in the remainder of your reproduction. In this stage, you will get to decide whether you are interested in assessing the reproducibility of entire display items (e.g., “Table 1”) or only specific estimates found in display items (e.g., “rows 3 and 4 of Table 1”). You can also include additional display items of interest.
The Assessment stage aims to analyze the reproduction package’s current reproducibility—before you suggest any improvements. By the end of this section, you will have created a highly detailed description of the reproduction package’s current reproducibility that you can use to implement improvements, potentially with the paper’s original authors’ help.
On the SSRP, you will first provide a detailed description of available inputs in the reproduction package. You will then connect the display items you’ve chosen to reproduce with their corresponding inputs. With these elements in place, you can assign a score of each display item’s reproducibility and record various paper-level dimensions of reproducibility.
Tip: We recommend that you first focus on just one display item (e.g., “Table 1”). After completing the assessment for this display item, you will have a much easier time assessing others.
3.1 Describe the inputs
This section explains how to record the input materials found (or referenced) in the reproduction package. At this point, it may be challenging to precisely identify the materials that correspond to your selected claims and display items, so we recommend listing all files in the reproduction packages (tip: using the command line, go to the directory of the reproduction package, and type file */*
on a Mac or dir /s /b /o:gn
on Windows to obtain a printout of all files within a folder). However, if the reproduction package is too extensive to record in its entirety, you can focus only on the materials required to reproduce a specific display item.
In section “Describe input” on the SSRP, you can record data sources and connect them with their raw data files (if available). You can then locate and provide a brief description of the analytic data files and then record the inputs and outpus of each script file.
Tip: For large volume of data sources, we recommend downloading the empty table as a CSV file, recording the information in the downloaded file, and uploading the final version of the document to populate the table.
The following concepts may be helpful as you work in the Assessment stage (also see the ACRe glossary here):
- Raw data: Unmodified data files obtained by the authors from the sources cited in the paper. Data from which personally identifiable information (PII) has been removed are still considered raw. All other modifications to raw data make it processed.
- Analysis/Analytic data: Data used as the final input in a workflow to produce a statistic displayed in the paper (including appendices).
- Cleaning code: A script associated primarily with data cleaning. Most of its content is dedicated to actions like deleting variables or observations, merging data sets, removing outliers, or reshaping the data structure (from long to wide, or vice versa).
- Analysis code: A script associated primarily with analysis. Most of its content is dedicated to generate some type of estimate, numerical or visual, to be presented in the paper (including the appendix). Examples of computations that lead to such estimates are: running regressions, running hypothesis tests, computing standard errors, and imputing missing values, generating a series and plotting it.
3.1.1 Describe the data sources and raw data
In the paper you chose, find references to all data sources used in the analysis. A data source is usually described in narrative form. For example, if in the body of the paper, or the appendix, you see text like “(…) for earnings in 2018 we use the Current Population Survey”, the data source should be recorded as “2018 Current Population Survey”. If the first reference to this data source is found on page 1 of the appendix, you should record its location as “A1”. Do this for all data sources mentioned in the paper. Each row represents a unique data source.
Data sources also vary by unit of analysis, with some sources matching the same unit of analysis used in the paper (as in previous examples). In contrast, others may be less clear, e.g., “our information on regional minimum wages comes from the Bureau of Labor Statistics.” You should record such data source as “regional minimum wages from the Bureau of Labor Statistics.”
Next, look at the reproduction package and map the data sources mentioned in the paper to the data files in the reproduction package. In the Location
column, record their folder locations relative to the main reproduction folder1. In addition to looking at the existing data files, we recommend that you also review the first lines of all code files (especially cleaning code), looking for lines that call the datasets. Inspecting these scripts may help you understand how different data sources are used, and possibly identify any missing files from the reproduction package. Whenever a data source contains multiple files, enter them in the same cell, separated by semicolon (;
).
If you cannot find the files names corresponding to a specific data source, type “Not available” in the Data Files
field. If you can identify the file name. Check the Provided
column if the data source was included in the original reproduction package. Check the Cited
column if the data source was explicitly cited in the paper. Record your work using the following structure:
| Data.Source | Page | Data.Files | Location | Provided | Cited |
|---|---|---|---|---|---|
| “Current Population Survey 2018” | A1 | cepr_march_2018.dta | data/ | TRUE | FALSE |
| “Provincial Administration Reports” | A4 | coast_simplepoint2.csv; rivers_simplepoint2.csv; … | Data/maps/ | TRUE | FALSE |
| “2017 SAT scores” | 4 | Not available | data/to_clean/ | FALSE | TRUE |
| … | … | … | … | … | … |
Note on data citations: Assessment of whether or not a data source has been cited should take into account the general guidelines for what is considered a complete data citation. In-text citations, mention of the source location (e.g. url to source location), description of the source or the data alone are examples of incomplete data citations. Please refer to the Guidance on Data Citations by Social Science Data Editors for a comprehensive overview and more reference material on data citation standards for the social sciences.
3.1.2 Describe the analytic data
First, identify all analytic data files in the reproduction package and record their names in the Analytic Data
column in Table 1.2. You will recognize analytic data files based on the documentation, their location folder, or if a code file either uses them to compute a statistic that is displayed in the paper (or appendix).
Second, record each analytic data file’s location relative to the main folder of the reproduction package in the Location
column.
Finally, provide a one-line description of each file in the Description
column (e.g., all_waves.csv can be “data for region-level analysis”). This will become easier as you progress through the reproduction, and you can always return to this table later on and add or modify your descriptions.
The resulting table may look like this:
| Analytic.Data | Location | Description |
|---|---|---|
| final_data.csv | analysis/fig1/ | data for figure1 |
| all_waves.csv | final_data/v1_april/ | data for region-level analysis |
| … | … | … |
3.1.3 Describe the code scripts
First, identify all code files in the reproduction package and record their names in the File Name
column and record their locations relative to the main folder in the Location
column.
Then, review the beginning and end of each code file to identify the inputs required to successfully run the file and the outputs it produces. Inputs are data sets or other code scripts that are typically found at the beginning of the script (e.g., load
, read
, source
, run
, do
). Outputs are other data sets, or plain text files typically found at the end of a script (e.g., save
, write
, export
). Record those in the Inputs and Outputs columns.
Finally, provide a brief description of the code’s function in the Description column and classify its function as analysis or cleaning and/or construction in the Primary Type column.
For each code file, record all outputs together and separate each item with (;
). Record all of this information in the SSRP using the following structure:
| file_name | location | inputs | outputs | description | primary_type |
|---|---|---|---|---|---|
| output_ table1.do | code/analysis/ | analysis_ data01.csv | output1_ part1.txt | produces first part of table 1 (unformatted) | analysis |
| data_ cleaning02.R | code/cleaning | admin_ 01raw.csv | analysis_ data02.csv | removes outliers and missing vals from raw admin data | cleaning |
| … | … | … | … | … | … |
Reproducers are encouraged to build on top of previous reproductions by exporting input descriptions of previous reproducers (by searching into previous reproductions and clicking “download table as csv” in the assessment section) and uploading them into their own reproduction (by clicking “upload csv”).
3.2 Connect display items to all its inputs usign the Diagram Builder
Using the information above, the SSRP will build a diagram that can help you visually trace your display items to the code and data files that produce them.
3.2.1 Complete workflow information
If you were able to identify all of the relevant components in the previous section, and if the code scripts are have the display items as final outputs, the Diagram Builder will produce a tree diagram that looks similar to the one below.
table1.tex
└──[code] analysis.R
└──analysis_data.dta
└──[code] final_merge.do
└──cleaned_1_2.dta
| └──[code] clean_merged_1_2.do
| └──merged_1_2.dta
| └──[code] merge_1_2.do
| └──cleaned_1.dta
| | └──[code] clean_raw_1.py
| | └──raw_1.dta
| └──cleaned_2.dta
| └──[code] clean_raw_2.py
| └──raw_2.dta
└──cleaned_3_4.dta
└──[code] clean_merged_3_4.do
└──merged_3_4.dta
└──[code] merge_3_4.do
└──cleaned_3.dta
| └──[code] clean_raw_3.py
| └──raw_3.dta
└──cleaned_4.dta
└──[code] clean_raw_4.py
└──raw_4.dta
This diagram, built with the information you provided, contributes to understanding the necessary components required to reproduce a specific display item. It also summarizes key information to allow for more constructive exchanges with original authors or other reproducers. For example, when contacting the authors for guidance, you can use the diagram to ask for specific files. Formulating your request this way makes it easier for authors to respond and demonstrates that you understand the reproduction package. You can also add this diagram to the readme of your revised reproduction package.
3.2.2 Incomplete workflow information
In some cases, some of the workflow components may not be easily identifiable in the reproduction package (or might be missing). Here the Diagram Builder will return a fagmented reproduction tree diagram. See below how you can work around such cases, but even if the reproduction tree is fragmented, you can still go on to the next step of the reproduction.
For example, here’s a simple complete reproduction tree:
table1.tex
└── analysis.R
└── analysis_data.dta
└── final_merge.do
└── cleaned_1_2.dta
└── clean_merged_1_2.do
└── merged_1_2.dta
For this case, if the the file final_merge.do
is missing, the Diagram Builder will produce the following reproduction tree:
table1.tex
└── analysis.R
└── analysis_data.dta
cleaned_1_2.dta
└── clean_merged_1_2.do
└── merged_1_2.dta
You can still manually combine this partial information with your knowledge from the paper and own (subjective) judgment to produce a “candidate” reproduction tree. This may look like the following:
table1.tex
└── analysis.R
└── cleaned_1_2.dta
└── MISSSING_CODE_FILE_1
└── cleaned_1_2.dta
└── clean_merged_1_2.do
└── merged_1_2.dta
If you notice that the reproduction tree is fragmented where it shouldn’t be, you may need to amend Table 1.3 above with placeholders. In the example above, that can look like this:
| file_name | location | inputs | outputs | description | primary_type |
|---|---|---|---|---|---|
| … | … | … | … | … | … |
| MISSSING_ CODE_FILE_1 | unknown | cleaned_1_2.dta | cleaned_1_2.dta | missing code | unknown |
As in the cases with complete workflows, these diagrams (fragmented or reconstructed trees) provide important information for assessing and improving the reproducibility of specific display items. For more examples of diagrams connecting final outputs to initial raw data, see here.
<IP_ADDRESS> Common issues that might occur when creating a reproduction tree
Script output is not a display item. Sometimes the reproduction package will not produce display items as its final outputs. In this situation, the final code script will generate some type of output (e.g.,
results1.log
,results2.csv
) that will require manual copying and pasting to reproduce the desired display item (e.g.Table 1
). In this case, we recommend adding one auxiliary line to the table linking the final output to the desired display item (e.g., File Name: aux1, Inputs:results1.log; results2.csv
, Outputs:Table 1
).Loops across files. If you notice that some files iterate between each other (e.g.,
file1.do
callsdata1.csv
to generatedata2.csv
, andfile2.R
callsdata2.csv
to generatedata1.csv
), look within the files to identify the one that contains some stopping criteria (e.g., stop when SSR is minimized). Then designate only one output as the final and record that in reproduction diagram in the next section.Large number of recurrent files. Some reproduction packages might have a large number of files that follow a recurrent structure (e.g.,
data_location1_year1, data_location2_year1, data_location1_year2, ...
). To make a more tractable tree we recommend that you record the names of these files only once and use a consistent place holder (e.g.,data_locationX_yearY
).Possible bugs due to missing
;
. If the reproduction tree shows several files under one input, make sure that each file is separated by a semi-colon (;
), that way the tree will separate each file into a separate input.
3.3 Assign a reproducibility score.
Once you have identified all possible inputs and have a clear understanding of the connection between the display items and their inputs, you can assign reproducibility scores to individual display items.
The following concepts may be helpful in this section:
Computationally Reproducible from Analytic data (CRA) — The output can be reproduced with minimal effort starting from the analytic datasets.
Computationally Reproducible from Raw data (CRR) — The output can be reproduced with minimal effort from the raw datasets.
**Standard of *minimal effort** — One hour or less is required to run the code, not including computing time.
3.3.1 Levels of Computational Reproducibility for a Specific Display Item
Each level of computational reproducibility is defined by the availability of data and materials, and whether the available materials faithfully reproduce the display item of interest. The description of each level also includes possible improvements that can help advance the display item’s reproducibility. You will learn in more detail about the possible improvements at the Improvement stage.
Note that the assessment is made at the level of individual display items—a paper can be highly reproducible for its main results, but its other display items may not be as reproducible. The assessment includes a 10-point scale, where 1 represents that, under the current circumstances, reproducers cannot access any reproduction package. At level 10, the reproducer can access all of the necessary materials to faithfully reproduce the target display item from the raw data.
- Level 1 (L1): No data or code are available. Possible improvements include adding: raw data, analysis data, cleaning code, and analysis code.
You may have detected papers without any reproduction materials at the Paper Selection stage, where you should have recorded them as abandoned candidate papers.
Level 2 (L2): Code scripts are available (partial or complete), but no data are available. Possible improvements include adding: raw data and analysis data.
Level 3 (L3): Analytic data and code are partially available, but raw data and cleaning code are missing. Possible improvements include: completing analysis data and/or code, adding raw data, and adding analysis code.
Level 4 (L4): All analytic data sets and analysis code are available, but the code fails to run or produces results inconsistent with the paper (not CRA). Possible improvements include: debugging the analysis code or obtaining raw data.
Level 5 (L5): Analytic data sets and analysis code are available and they produce the same results as presented in the paper (CRA). The reproducibility package may be improved by obtaining the original raw data.
Note: This is the highest level that most published research papers can attain currently. Computational reproducibility from raw data is required for papers that are reproducible at Level 6 and above.
Level 6 (L6): Cleaning code scripts are available (partial or complete), but raw data is missing. Possible improvements include: adding raw data.
Level 7 (L7): Cleaning code is available and complete, and raw data is partially available. Possible improvements: adding raw data.
Level 8 (L8): All the materials (raw data, analytic data, cleaning code, and analysis code) are available. However, the cleaning code fails to run or produces different results from those presented in the paper (not CRR) or the analysis code fails to run or produces results inconsistent with the paper (not CRA). Possible improvements: debugging the cleaning or analysis code.
Level 9 (L9): All the materials (raw data, analytic data, cleaning code, and analysis code) are available. The analysis code produces the same output as presented in the paper (CRA). However, the cleaning code fails to run or produces different results from those presented in the paper (not CRR). Possible improvements: debugging the cleaning code.
Level 10 (L10): All necessary materials are available and produce consistent results with those presented in the paper. The reproduction involves minimal effort and can be conducted starting from the analytic data ( CRA) and the raw data (CRR). Note that Level 10 is aspirational and may be unattainable for most research published today.
The following table summarizes the different levels of computational reproducibility (for any given display item). For each level, there are reproducibility components and practices that are present (✔
) or can be implemented to advance the level of reproducibility (-).
You may disagree with some of the levels outlined above, particularly wherever subjective judgment may be required. If so, you are welcome to interpret the levels as unordered categories (independent from their sequence) and suggest improvements using the “Edit” button above (top left corner if you are reading this document in your browser).
Note: The levels suggested here are to be used in the Assessment stage and, optionally, for the Improvement stage. These levels are not meant to be used in the Robustness stage.
3.3.2 Adjusting Levels To Account for Confidential/Proprietary Data
A large fraction of published research in the social sciences uses confidential or proprietary data (e.g., government data from tax records or service provision), generally referred to as administrative data. Since administrative data are rarely readily publicly available, some reproducibility levels presented above only apply once modified. The underlying theme of these modifications is that when data cannot be provided, you can assign a reproducibility score based on the level of detail in the instructions for accessing the data, typically provided in data availability statements (DAS). Use this checklist (.pdf, .md) to guide the assessment, and potential improvement, of a DAS.
When the reproducer cannot directly assess the reproducibility based on publicly available materials, the reproduction package should include certification that a third party (not involved in the paper’s production) faithfully reproduced the results.
Levels 1 and 2 can be applied as described above.
Adjusted Level 3 (L3*): All analysis code is provided, but only partial instructions to access the analysis data are available. This means that the original authors have provided some, but not all, of the following information:
- Contact information, including name of the organization(s) that provides access to at least one individual’s data and contact information.
- Terms of use, including licenses and eligibility criteria for accessing the data, if any.
- Information on data files (meta-data), including the name(s) and number of files, file size(s), relevant file version(s), and number of variables and observations in each file. Though not required, other relevant information may be included, including a dataset dictionary, summary statistics, and synthetic data (fake data with the same statistical properties as the original data).
- Estimated costs for access, including monetary costs such as fees and licenses required to access the data, and non-monetary costs such as wait times and specific geographical locations from where researchers need to access it.
Adjusted Level 4 (L4*): All analysis code is provided, and complete and detailed instructions on how to access the analysis data are available.
Adjusted Level 5 (L5*): All requirements for Level 4* are met, and the authors provide a certification that a third party was able to reproduce the display item (or the paper as a whole) from the analysis data (CRA). Such certification may include a signed letter by a disinterested reproducer or a certificate from a certification agency for data and code (e.g., see CASCaD).
Levels 6 can be applied as described above.
Adjusted Level 7 (7*): All requirements for Level 6* are met, but instructions for accessing the raw data are incomplete. Use the instructions described in Level 3 above to assess the instructions’ completeness.
Adjusted Level 8 (L8*): All requirements for Level 7* are met, and instructions for accessing the raw data are complete.
Adjusted Level 9 (L9*): All requirements for Level 8* are met, and a certification is provided that the display item can be reproduced from the analysis data (CRA).
Adjusted Level 10 (L10*): All requirements for Level 9* are met, and a certification is provided that the display item can be reproduced from the raw data (CRR).
3.3.3 Reproducibility dimensions at the paper-level
There are many tools and practices that facilitate the computational reproducibility of the paper as a whole. You can learn more about implementing such reproducibility tools and practices in the Improvements stage, but at this stage, you should only verify whether the current reproduction materials make use of any such tools and practices.
Note: The Assessment stage is the minimum requirement to submit your reproduction. To gain a better understanding of the paper and to help improve the reproducibility of social science research, however, we encourage you to also complete the Improvements and Robustness stages.
a relative location takes the form of
folder_in_rep_materials/sub_folder/file.txt
, in contrast to an absolute location that takes the form of/username/documents/projects/repros/folder_in_rep_materials/sub_folder/file.txt
↩


Suite: default
Feature: Collect all brands from CarDB and Service call and compare results
Feature: Collect all brands from CarDB and Service call and compare results
- And I go to "/login"
- Then I log in to FDMB CarDB
- And I go to "/admin/?entity=Car&action=list&menuIndex=1&submenuIndex=-1&sortField=id&sortDirection=ASC"
- And I save all brands
- And I call Brands service
-
Then I compare brands arrays
(There is a difference in Brands array )


The 50 best films of 2019 in the US: 10-50
Our countdown continues with a star-packed whodunnit, snappy stripper caper, riotous high-school comedy and CIA takedown. We’ll reveal more everyday
10
Pain and Glory
Antonio Banderas gives the performance of his career as a quasi-Pedro Almodóvar in this tricksy yet sincere late stage elegy. Read the full review.
11
Midsommar
Florence Pugh is plunged into a terrifying pagan bacchanal in a magnificent folk-horror tale from Hereditary director Ari Aster. Read the full review.
12
Monos
Our second Apocalypse Now nod in this list is Alejandro Landes’s deeply mad thriller about a wild cult of teenage bandits who have rituals, guns and a hostage – but no Colonel Kurtz. Read the full review.
13
Knives Out
Rian Johnson takes a breather from Star Wars to revisit his Brick roots with this wickedly entertaining Agatha Christie homage featuring a star-packed cast. Read the full review.
14
For Sama
One of the most extraordinary documentaries of recent years, this story of a baby in war-torn Syria, begun while her mother was still pregnant, is impossibly moving, upsetting and uplifting. Read the full review.
15
Booksmart
Olivia Wilde’s directorial debut is a riotous blast; a funny, filthy, female Superbad that’s also extremely smart and strangely sensitive. Read the full review.
16
Transit
A taut and elegant adaptation of Anna Seghers’s 1944 novel, this unfortunately topical tale of stolen identities, refugees and riot police is deeply and enduringly disturbing. Read the full review.
17
The Farewell
Awkwafina leads Lulu Wang’s moving and witty culture clash tale about a Chinese-American family. Read the full review.
18
Ash Is Purest White
Jia Zhangke’s melancholy epic stars Zhao Tao as a resilient gangster’s moll burning with misguided love in a shape-shifting China. Read the full review.
19
Hustlers
J-Lo delivers a standout turn in this snappy caper about a gang of strippers who scam Wall Street bankers. Read the full review.
20
The Report
Adam Driver and Annette Bening give excellent performances in an utterly absorbing film indicting the CIA’s post-9/11 interrogation practices. Read the full review.
21
All Is True
Kenneth Branagh and Judi Dench gave us a poignant insight into William Shakespeare and Anne Hathaway’s later life turmoil in this delicate biopic written by Ben Elton. Read the full review.
22
Honeyland
In this terrific documentary shot in North Macedonia, a female beekeeper is rattled by her new, disruptive neighbours. Read the full review.
23
A Beautiful Day in the Neighbourhood
Tom Hanks charms as beloved children’s TV figure Mister Rogers in a moving and engrossing departure from a traditional biopic. Read the full review.
24
Harriet
Kasi Lemmons’s belated but remarkable slavery biopic features Indiana Jones-style derring-do and a barnstorming central turn from Cynthia Erivo. Read the full review.
25
One Cut of the Dead
Noises Off meets George Romero in this lively and genre-revitalising metafictional horror show by Shin’ichirô Ueda. Read the full review.
26
A Hidden Life
Terrence Malick’s rhapsody to a conscientious objector – his second about the second world war – is a high-minded hymn to modern saint. Read the full review.
27
In Fabric
Set in an unearthly department store, Peter Strickland’s bizarre ghost story sees Marianne Jean-Baptiste battling a frock from another dimension. Read the full review.
28
Synonyms
Newcomer Tom Mercier excels as an ex-Israeli soldier who ups sticks to Paris in a big to ditch his national identification in Nadav Lapid’s latest.
29
The Good Liar
Ian McKellen and Helen Mirren are delicious foils in Bill Condon’s expertly paced and twisty story about an elderly conman who may have met his match. Read the full review.
30
Her Smell
Elisabeth Moss plays the monstrous, self-destructive singer of an all-girl band overtaken by younger, prettier rivals in Alex Ross Perry’s unsettling punk drama. Read the full review.
31
Loro
The film Paolo Sorrentino was born to direct and Toni Servillo born to star in didn’t quite live up to that billing, but this Silvio Berlusconi biopic is still a masterly and fascinating take. Read the full review.
32
Peterloo
Large-canvas history lesson from Mike Leigh, outlining the events surrounding the notorious Peterloo massacre in 1819 at a meeting calling for voting reform. Read the full review
33
Amazing Grace
Had Aretha Franklin approved of Sydney Pollack’s transcendent 1972 documentary, it doubtless would have shown up on our list closer to the time it was shot. Still, better late than never. Read the full review.
34
Clemency
Alfre Woodard is given a rare opportunity to lead in a tough-minded drama about a prison warden questioning the morality of death row. Read the full review.
35
Birds of Passage
The cost of the Columbian drugs trade to its indigenous people is uncovered in Ciro Guerra’s poetic and shocking drama. Read the full review.
36
The Eyes of Orson Welles
Mark Cousins’ whimsical but heartfelt love letter to the cinematic polymath connects the director’s films to his paintings and drawings. Read the full review.
37
Rafiki
Banned in Kenya until its director sued the government, Wauri Kahiu’s tale of two girls’ secret relationship is a fine depiction of the first flush of love. Read the full review.
38
Sunset
László Nemes follows Son of Saul with a cryptic and hyper-stylish study of the fracturing Austro-Hungarian empire on the eve of the first world war. Read the full review.
39
Diego Maradona
After Amy and Senna, Asif Kapadia tackles someone still alive in this gripping study of football, euphoria and catastrophe. Read the full review.
40
The Lighthouse
Willem Dafoe and Robert Pattinson are lonely masturbating lighthouse keepers in Robert Eggers’ triumphant follow-up to The Witch: a salty, black-and-white Steptoe and Son in hell. Read the full review.
41
Apollo 11
A front row seat for the moon landings? Few could resist this astonishing documentary featuring previously unseen footage, released for the 50th anniversary of Neil Armstrong’s lunar walk. Read the full review.
42
Ray & Liz
Richard Billingham mined his own family for this bleak debut, capturing the claustrophobic loneliness of a couple cut off from everyone, including each other. Read the full review.
43
Us
Jordan Peele’s follow-up to Get Out was a less obvious slam-dunk, but still an immensely skilful doppelganger satire with a gobstopping central turn from Lupita Nyong’o. Read the full review.
44
Dolemite Is My Name
Eddie Murphy’s glorious return is the richly entertaining tale of cult 70s blaxploitation star Rudy Ray Moore’s rise from nightclub standup to the movies. Read the full review.
45
The Image Book
Jean-Luc Godard’s latest essay film is a crazed mosaic with power and vitality of a horror movie and all the delight and joie de vivre of the French legend’s finest work. Read the full review.
46
Rojo
Benjamín Naishtat’s satire, set before the coup that installed a military junta in Argentina, is an enraging – and informative – parable of iniquity about the fate of the disappeared. Read the full review.
47
Ad Astra
Brad Pitt goes intergalactic in search of long-lost dad Tommy Lee Jones in James Gray’s thrilling Freudian mashup of Apocalypse Now and 2001: A Space Odyssey. Read the full review.
48
Atlantics
Mati Diop’s supernatural debut forces young Senegalese lovers to choose between love, duty and servitude, then adds a surreal twist. Read the full review.
49
The Nightingale
Jennifer Kent follows up The Babadook with some real-life monsters: the men who ran Tasmania’s penal colonies in the 1820s – one of whom gets some grisly, if just, comeuppance in this gothic thriller.


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Preparation time
less than 30 mins
Cooking time
1 to 2 hours
Serves
Serves 4
Consommé is one of those cheffy dishes that is hard to get right, but is so simple and beautiful: this version features a broth with intense lobster flavour served with lobster tail and claw.
To make the consommé, fry the lobster shells in olive oil for five minutes, deglaze the pan with a small splash of the brandy and drain the shells in a colander.
In a separate pan, fry the carrots, fennel, garlic, star anise, coriander seeds and shallots in a dash of olive oil for five minutes.
Add the tomato purée and cook for three minutes. Add the shells, chicken stock, brandy and tomatoes. Bring to a simmer and cook for 40 minutes.
Remove from the heat, cool slightly and strain through a muslin cloth into a clean bowl. Leave to cool completely.
To clarify the consommé, mix all the ingredients for the clarification in a blender and whisk it into the chilled lobster stock.
Place onto the heat and bring to a simmer.
Once a crust forms on the top, make a small hole with a ladle and simmer for 20 minutes.
Remove from the heat and carefully strain through double muslin, taste and season if necessary. Leave to chill in the fridge until ready to use.
Cut the cooked lobster tails into segments and place in a chilled bowl. Carefully arrange the garnish ingredients on top and then pour the chilled consommé over. Finish with a drizzle of lemon oil.