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val data: Map<String, Any>
A mapping of a description to some statistic.
data - A mapping of a description to some statistic.
data


The crestr
package has been designed for two independent
but complementary modelling purposes [1]. The
probabilistic proxy-climate responses can be used to quantitatively
reconstruct climate from their statistical combination [2-3], or they can be
used in a more quantitative way to determine the (relative) climate
sensitivities of different taxa in a given area to characterise past
ecological changes [4-5].
In both cases, the difficulty of running a CREST analysis lies in the definition of the study-specific parameters of the algorithm, which can make the learning curve a bit steep at the beginning. However, default values are provided for all parameters to enable an easy and rapid generation of preliminary results that users can then use as a reasonable first estimate before adapting the model to their data. To guide users in this task and avoid the standard ‘black box’ criticism faced by many complex statistical tools, a large array of publication-ready, graphical diagnostic tools was designed to represent CREST data in a standardised way automatically. These tools include plots of the calibration data, the estimated climate responses, the reconstructions and many more. They can be used to look at the data from different perspectives to help understand why the results are what they are and possibly help identify potential issues or biases in the selected data and parameters. Such diagnostic tools are available for every step of the process, and, as exemplified by this example, they can be generated with a single line of R code.
This introductory tutorial presents the most important steps of a CREST reconstruction using pseudo-data. The different modelling assumptions are illustrated using the various graphical diagnostic tools proposed by the package. In the first part, the pseudo-data and their different characteristics are presented, the second part describes how to access the pseudo-data from crestr and in the last part, the data are used to reconstruct both temperature and precipitation. The R code used in this last part can be accessed here.
This example reconstructs temperature and precipitation from six taxa with different sensitivities to the observed climate gradients. For simplicity, the temperature data are constructed as an East-West climate gradient, while the precipitation data are designed as a North-South gradient. This results in, for instance, cold and rainy conditions in the bottom-left corner and warm and dry conditions in the opposite, top right corner (see Fig. 1). Such gradients are unrealistic, but they explain different concepts, such as the sensitivity of taxa to one or both variables.
In this environment, six different pseudo-species have been created, each one with specific climate preferences (Fig. 2):
Finally, we have also created a fossil record composed of 20 samples where the percentages of the different taxa vary (Fig. 3). We selected percentages so that the climate is relatively cold and dry in the first samples, then temperature and precipitation increase. A cold and dry reversal occurs around the middle of the records (samples 11 and 12), and stable humid and warm conditions prevail in the last samples. The top sample also contains a seventh taxon (Taxon7) that doesn’t grow across the study area. It was added to illustrate that crestr can deal with unknown taxa.
crestr
The pseudo-data are stored in crestr in different datasets and can be loaded as follow:
The fossil data are contained in crest_ex
(“crest
example”). The data frame comprises 20 fossil samples from which the
seven taxa have been identified. The data are already expressed in
percentages.
## the first 6 samples
head(crest_ex)
#> Age Taxon1 Taxon2 Taxon3 Taxon4 Taxon5 Taxon6 Taxon7
#> Sample_1 1 0 0 45 1 22 32 1
#> Sample_2 2 0 0 50 0 23 27 0
#> Sample_3 3 0 0 49 0 25 26 0
#> Sample_4 4 0 0 37 0 27 36 0
#> Sample_5 5 0 3 36 3 18 40 0
#> Sample_6 6 2 2 25 0 21 50 0
## the structure of the data frame
str(crest_ex)
#> 'data.frame': 20 obs. of 8 variables:
#> $ Age : int 1 2 3 4 5 6 7 8 9 10 ...
#> $ Taxon1: int 0 0 0 0 0 2 3 5 10 15 ...
#> $ Taxon2: int 0 0 0 0 3 2 5 5 12 8 ...
#> $ Taxon3: int 45 50 49 37 36 25 18 17 10 12 ...
#> $ Taxon4: int 1 0 0 0 3 0 0 6 15 14 ...
#> $ Taxon5: int 22 23 25 27 18 21 21 20 16 13 ...
#> $ Taxon6: int 32 27 26 36 40 50 53 47 37 38 ...
#> $ Taxon7: int 1 0 0 0 0 0 0 0 0 0 ...
A proxy-species equivalency (“pse”) table must be provided for each reconstruction. This table is used to associate distinct species to the fossil taxon. For example, in the case of pollen data, this step is used to link all the species producing undifferentiated pollen morphotypes in a single pollen-type. Here, the seven pseudo-taxa are ‘identified’ at the species level so that the table looks like the following:
crest_ex_pse
#> Level Family Genus Species ProxyName
#> 1 3 Randomaceae Randomus Taxon1 Taxon1
#> 2 3 Randomaceae Randomus Taxon2 Taxon2
#> 3 3 Randomaceae Randomus Taxon3 Taxon3
#> 4 3 Randomaceae Randomus Taxon4 Taxon4
#> 5 3 Randomaceae Randomus Taxon5 Taxon5
#> 6 3 Randomaceae Randomus Taxon6 Taxon6
#> 7 3 Randomaceae Randomus Taxon7 Taxon7
By default, all the identified taxa are used to produce the reconstruction. However, it is sometimes helpful to select a subset of taxa for each variable to improve the signal over noise ratio of the reconstructions. This identification is usually not straightforward and requires some expertise. Here, this information is known because the pseudo-data were generated with apparent sensitivities (see Fig. 2). In crestr, these known sensitivities are expressed in one single table containing 1s (meaning “use this taxon for this variable”) and 0s (meaning “do not use this taxon for this variable”). Based on the description of the sensitivities of the pseudo-taxa (see above), it translates as follow:
crest_ex_selection
#> bio1 bio12
#> Taxon1 1 1
#> Taxon2 1 0
#> Taxon3 1 0
#> Taxon4 0 1
#> Taxon5 0 1
#> Taxon6 0 0
#> Taxon7 1 1
Note that Taxon7 is indicated with 1s but, as no data are available to calibrate its relationship with climate, these values will later be transformed into “-1”.
To illustrate the process, we will reconstruct bio1 (mean annual temperature) and bio12 (annual precipitation) from these pseudo data. The complete description of the different parameters is available here.
The first step of any reconstruction is to extract the species
distribution data. This is done with the
crest.get_modern_data() function. This function creates a
nested R object reconstr
that contains all the information
that will be updated in the following steps. All the functions from the
package require such an object. For more details, check the
crestObj vignette. We can also verify that the
selectedTaxa table has been modified for Taxon7.
reconstr <- crest.get_modern_data(
df = crest_ex, # The fossil data
pse = crest_ex_pse, # The proxy-species equivalency table
taxaType = 0, # The type of taxa is 0 for the example
site_info = c(7.5, 7.5), # Coordinates to estimate modern values
site_name = 'crest-test', # A specific name for the dataset
climate = c("bio1", "bio12"), # The climate variables to reconstruct
selectedTaxa = crest_ex_selection, # The taxa to use for each variable
dbname = "crest_example", # The database to extract the data
verbose = FALSE # Print status messages
)
reconstr$inputs$selectedTaxa
#> bio1 bio12
#> Taxon1 1 1
#> Taxon2 1 0
#> Taxon3 1 0
#> Taxon4 0 1
#> Taxon5 0 1
#> Taxon6 0 0
#> Taxon7 -1 -1
At every step of the process, the content of the crestObj can be checked by printing the object. The information displayed is updated based on the reconstruction step.
print(reconstr, as='data_extracted')
#> *
#> * Summary of the crestObj named ``:
#> * x Calibration data formatted .. TRUE
#> * x PDFs fitted ................. FALSE
#> * x Climate reconstructed ....... FALSE
#> * x Leave-One-Out analysis ...... FALSE
#> *
#> * The dataset to be reconstructed (`df`) is composed of 20 samples with 7 taxa.
#> * Variables to analyse: bio1, bio12
#> *
#> * The calibration dataset was defined using the following set of parameters:
#> * x Proxy type ............ Example dataset
#> * x Longitude ............. [0 - 15]
#> * x Latitude .............. [0 - 15]
#> *
#> * Of the 7 taxa provided in `df` and `PSE`, 1 cannot be analysed.
#> * (This may be expected, but check `$misc$taxa_notes` for additional details.)
#> *
The next step consists in fitting the PDFs using the crest.calibrate() function. The climate space weighting is selected by default because it is essential to account for the possible heterogeneous distribution of the modern climate when fitting the PDFs. As can be seen in Fig. 5 for temperature, the coldest and warmest values are underrepresented compared to the intermediate values. The correction diminishes the effect of this distribution by upweighting rare values and down-weighting the most abundant ones.
reconstr <- crest.calibrate(
reconstr, # A crestObj produced at the last stage
climateSpaceWeighting = TRUE, # Correct the PDFs for the heteregenous
# distribution of the modern climate space
bin_width = c(2, 50), # The size of bins used for the correction
shape = c("normal", "lognormal"), # The shape of the species PDFs
verbose = FALSE # Print status messages
)
plot_climateSpace(reconstr)
Other graphical tools can be used to determine the specific sensitivities of each taxon to the selected climate variable. Fig. 6 below represents the characteristic responses of Taxon2 and Taxon7 to bio1. By construction, we know that Taxon2 is sensitive to bio1, while Taxon6 is not. This can be inferred from Fig. 6 by comparing the histograms that represent the proportion of the climate space occupied by each taxon, but also from the width of the PDFs. The PDFs of sensitive taxa are narrower. In the case of Taxon6, its presence in almost every grid cell of the study area indicates that its distribution is not constrained by either bio1 or bio12 at least in this study area. If the spatial scale of the study were changed, some trends in the distribution would ultimately appear.
plot_taxaCharacteristics(reconstr, taxanames='Taxon2', climate='bio1', h0=0.2)
plot_taxaCharacteristics(reconstr, taxanames='Taxon6', climate='bio1', h0=0.2)
The last step of the process is the reconstruction itself, which is
done with the crest.reconstruct() function. The results can be
accessed from reconstr
as follow:
reconstr <- crest.reconstruct(
reconstr, # A crestObj produced at the previous stage
verbose = FALSE # Print status messages
)
names(reconstr)
#> [1] "inputs" "parameters" "modelling" "reconstructions"
#> [5] "misc"
head(reconstr$reconstructions$bio1$optima)
#> Age optima mean
#> 1 1 15.71142 15.69949
#> 2 2 15.71142 15.69949
#> 3 3 15.71142 15.69949
#> 4 4 15.71142 15.69949
#> 5 5 17.31463 17.29054
#> 6 6 18.11623 18.12464
str(reconstr$reconstructions$bio1$optima)
#> 'data.frame': 20 obs. of 3 variables:
#> $ Age : num 1 2 3 4 5 6 7 8 9 10 ...
#> $ optima: num 15.7 15.7 15.7 15.7 17.3 ...
#> $ mean : num 15.7 15.7 15.7 15.7 17.3 ...
signif(reconstr$reconstructions$bio1$likelihood[1:6, 1:6], 3)
#> [,1] [,2] [,3] [,4] [,5] [,6]
#> [1,] 0.00e+00 8.02e-02 1.60e-01 2.40e-01 3.21e-01 4.01e-01
#> [2,] 8.41e-15 1.15e-14 1.57e-14 2.14e-14 2.92e-14 3.97e-14
#> [3,] 8.41e-15 1.15e-14 1.57e-14 2.14e-14 2.92e-14 3.97e-14
#> [4,] 8.41e-15 1.15e-14 1.57e-14 2.14e-14 2.92e-14 3.97e-14
#> [5,] 8.41e-15 1.15e-14 1.57e-14 2.14e-14 2.92e-14 3.97e-14
#> [6,] 1.45e-18 2.09e-18 3.01e-18 4.32e-18 6.19e-18 8.86e-18
str(reconstr$reconstructions$bio1$likelihood)
#> num [1:21, 1:500] 0.00 8.41e-15 8.41e-15 8.41e-15 8.41e-15 ...
print(reconstr, as='climate_reconstructed')
#> *
#> * Summary of the crestObj named ``:
#> * x Calibration data formatted .. TRUE
#> * x PDFs fitted ................. TRUE
#> * x Climate reconstructed ....... TRUE
#> * x Leave-One-Out analysis ...... FALSE
#> *
#> * The dataset to be reconstructed (`df`) is composed of 20 samples with 7 taxa.
#> * Variables to analyse: bio1, bio12
#> *
#> * The calibration dataset was defined using the following set of parameters:
#> * x Proxy type ............ Example dataset
#> * x Longitude ............. [0 - 15]
#> * x Latitude .............. [0 - 15]
#> *
#> * The PDFs were fitted using the following set of parameters:
#> * x Minimum distinct of distinct occurences .. 20
#> * x Weighted occurence data .................. FALSE
#> * x Number of points to fit the PDFs ......... 500
#> * x Geographical weighting ................... TRUE
#> * Using bins of width .................... bio1: 2
#> * ________________________________________ bio12: 50
#> * x Weighting of the climate space ........... TRUE
#> * Using a linear correction
#> * x Shape of the PDFs ........................ bio1: normal
#> * __________________________________________ bio12: lognormal
#> *
#> * Of the 7 taxa provided in `df` and `PSE`, 1 cannot be analysed.
#> * (This may be expected, but check `$misc$taxa_notes` for additional details.)
#> *
#> * The reconstructions were performed with the following set of parameters:
#> * x Minimum presence value .................. 0
#> * x Weighting of the taxa ................... normalisation
#> * x Calculated uncertainties ................ 0.5, 0.95
#> * x Number of taxa selected to reconstruct .. bio1: 3
#> * ----------------------------------------- bio12: 3
#> *
The results can also be visualised in different forms using the plot function (Fig. 7). As per the design, the first samples are rather cold and dry, while the last ones are relatively warm and humid. A cold and dry event interrupted the transition between the two states.
plot(reconstr, climate = 'bio1')
plot(reconstr, climate = 'bio12', simplify=TRUE,
uncertainties=c(0.4, 0.6, 0.8), as.anomaly=TRUE)
Interpretations can be refined using the built-in ‘leave-one-out’ (loo) approach. The loo approach iteratively removes one taxon at a time and measures the impact removing that taxon had on the ‘true’ reconstruction. The results can be plotted as a stratigraphic diagram.
reconstr <- loo(reconstr)
plot_loo(reconstr, climate = 'bio1')
If satisfying, the results can be directly exported from the R
environment in individual spreadsheets for each variable (or CSV files).
The reconstruct
object is exported as an RData file to
simplify future data reuse. In the following lines of code, replace
‘tempdir()’ with the location where the files should be saved.
export(reconstr, loc=tempdir(), dataname='crest-test')
list.files(file.path(tempdir(), 'crest-test'))
#> [1] "bio1" "bio12" "crest-test.RData"
[1] Chevalier, M., 2022. crestr an R package to perform probabilistic climate reconstructions from palaeoecological datasets. Clim. Past. doi:10.5194/cp-18-821-2022.
[2] Chevalier, M., Chase, B.M., Quick, L.J., Dupont, L.M. and Johnson, T.C., 2021. Temperature change in subtropical southeastern Africa during the past 790,000 yr. Geology 49, 71–75. 10.1130/G47841.1.
[3] Chevalier, M., Chase, B.M., 2015. Southeast African records reveal a coherent shift from high- to low-latitude forcing mechanisms along the east African margin across last glacial–interglacial transition. Quat. Sci. Rev. 125, 117–130. doi:10.1016/j.quascirev.2015.07.009.
[4] Quick, L.J., Chase, B.M., Carr, A.S., Chevalier, M., Grobler, B.A. and Meadows, M.E., 2021, A 25,000 year record of climate and vegetation change from the southwestern Cape coast, South Africa. Quaternary Research, pp. 1–18. 10.1017/qua.2021.31.
[5] Chevalier, M., Chase, B. M., Quick, L. J. and Scott, L.: An atlas of southern African pollen types and their climatic affinities, in Palaeoecology of Africa, vol. 35, pp. 239–258, CRC Press, London., 2021. 10.1201/9781003162766-15.


COMPUTER SCIENCE
[Programming Languages]
The programming languages that I've tried my hands on so far are: C++, PYTHON, C, JAVA, JAVASCRIPT, PHP, KOTLIN, SCALA . I really love coding in C++. I can go on endlessly learning about the new concepts, new features of the language. It is really fascinating to me to know how all of this "programming/coding" can do magic. To me, it is just another wonder of the world.
What did I do so far with these learnings...
Since it is been three years for me when I started to code. Now, I have learned about development. Since I have made this website and a few more so, I learned about HTML, CSS, JavaScript, PHP. In C++ I learned about data structures and with python, I am currently learning how to develop games. Java, in java I developed a desktop application, a basic calculator, and handling the exceptions. Kotlin, I used to develop mobile applications, so, I made a message share application, notes application, and a few more.
Where can you see my work?
You may visit my GitHub through this website and find all the work there, that I've mentioned over here.
What's Next?
I am currently exploring different possibilities on how can I integrate my knowledge with my imagination and make something meaningful. It is tough though, but, there is always a curiosity push for me, so, I am confident with what I'm learning and as of now, my main focus is on development and data structures. I'll explore these two fields for time being until my curiosity pushes me in some other direction.


Humanitarian Aid Worker Acquitted of Harboring Immigrants
by BRAD POOLETUCSON, Ariz. (CN) – Humanitarian aid worker Scott Warren emerged from the federal courthouse to cheers after a jury acquitted him on charges that he illegally shielded two undocumented immigrants from Border Patrol agents at a rural desert aid station.
Warren, 37, barely smiled when he heard the verdict that came back after less than three hours of deliberation. Outside the courthouse, flanked by attorneys, he thanked supporters.
“The government failed in its attempt to criminalize basic human kindness,” Warren said.
Border Patrol agents arrested Warren Jan. 17, 2018 at an aid station used by the non-profit No More Deaths. From a building known as the “barn,” Warren and other volunteers shuttle food and water into the Sonoran Desert where more than 3,000 bodies have been found – most identified by the Pima County Medical Examiner’s Office as undocumented immigrants.
Warren admitted he gave Kristian Gerardo Perez-Villanueva from El Salvador and Jose Arnaldo Sacaria-Godoy from Guatemala food, water, Tylenol and a place to rest for four days and three nights, but he claimed he was never trying to hide them.
“Being a good Samaritan isn’t against the law,” defense attorney Greg Kuykendall told the jury during closing arguments Wednesday morning. “Following the Golden Rule is not a felony.”
This was Warren’s second trial stemming from the 2018 arrest. In June, a jury deadlocked on the harboring charges and a charge that Warren conspired with Mexican aid worker Irineo Mujica and No More Deaths nurse Susannah Brown to help Perez-Villanueva and Sacaria-Godoy.
Kuykendall urged jurors in Wednesday’s closing argument to separate laws from morals. Some people consider it immoral to go outside without a head covering, but that’s not the law, he told the jury.
“Some people’s morality tells them that they should report undocumented migrants to the Border Patrol, but that’s not the law,” Kuykendall said. “We have to obey the law, but we don’t have to follow someone else’s moral code.”
Outside the courtroom after the verdict, Warren echoed that sentiment. He understands the moral stance of people who oppose No More Deaths’ work.
“I understand that they are following a moral compass that guides them to different conclusions about the border than me,” he said.
Kuykendall called the prosecution case a “superstore of reasonable doubt” based on assumptions about Warren’s intent and “head-spinning leaps of logic.”
Assistant U.S. Attorney Nathaniel Walters pointed out to jurors that if Warren were simply trying to keep the men safe, he could have told them how to find Arizona Route 85, which runs north toward Interstate 8, the men’s goal.
But Warren couldn’t offer them that route, Walters said.
“Because that route would lead them directly into the hands of the Border Patrol,” he told the jury.
The men didn’t need medical care or rest or food or water, Walters said.
“What they needed was a place to hide, and that is exactly what the defendant gave them, and that is intent to break the law,” he said.
After reading the verdict in this week’s trial, Judge Raner Collins also issued verdicts pending from a May bench trial stemming from a separate incident in which Warren and other volunteers were arrested during a water drop. He found Warren not guilty on one count of abandoning property – for leaving water in the desert.
That act was protected from prosecution because it was a religious act, as Warren had argued in his defense, but Collins convicted the aid worker on one charge of driving a vehicle in a wilderness area, deeming that not a religious act. Sentencing for that case is set for Feb. 18.
Outside the courthouse, Warren said the work will continue in the desert, where volunteers even today were out leaving food and water for immigrants.
“So let’s all take a deep breath, get some rest, and be ready for and open to whatever comes next,” he said.
Copyright © 2019 courthousenews.com


| DOSE: T+ 0:00 |
1 tablet |
oral |
MDMA |
(pill / tablet) |
| T+ 1:00 |
1 tablet |
oral |
MDMA |
(pill / tablet) |
| T+ 2:00 |
1 tablet |
oral |
MDMA |
(pill / tablet) |
| T+ 3:00 |
1 tablet |
oral |
MDMA |
(pill / tablet) |
| T+ 5:00 |
1 cig. |
smoked |
Cannabis |
(plant material) |
Iíve wanted to try Ecstasy (X) for a long time now. Iíve been researching the pros and cons of the drug and the personal experiences from many different sources. After a lot of research I decided X was worth it. That said I would like to contribute my own experience.
First off, Iíd like to say that Iíve never tried any ďseriousĒ drugs before this experience. Iíve had plenty of alcohol and some weed on certain occasions. This is mainly because of the lack of opportunity to try them. No contacts, no supply, living in Tampere (Finland), etcÖ So Iím pretty much a newbie as far as drugs go. To the storyÖ
My friend (Matt) and I were staying with a mutual friend (Max) in London while on vacation. It was a Monday. We had bought 10 small white pills with a heart imprinted on each of them. In London X costs about 2-5 pounds (£) per pill and we were told that they arenít as strong as the ones normally found in America. I was very excited to try these pills and I was very much hoping that I wouldnít be disappointed by an impure pill or one containing just caffeine, etcÖ Matt and I took our first pill around 21:30.
The setting is in an apartment building above a pub. The apartment is like a dorm. There is a general kitchen and bathroom with many bedrooms belonging to different tenants. My friend Max was one of these tenants. Other people who live in this apartment come into play later so itís important to know this.
T + 1:00: I donít feel anything and am very disappointed. I talk to some people who have tried X before and they say itís possibly because I had eaten a full meal 2 hours earlier or because they werenít X or were just really weak. I still have hope but not much. So Matt and I take another pill.
T + 1:30: Matt says he feels a little different. Kind of like heís feeling tiny waves of happiness and euphoria washing over him. Very mildly though. I feel nothing.
T + 2:00: I still feel nothing, but my friend feels the waves a little more but itís still mild. We both take another pill.
T + 2:30: I notice Iím becoming more and more restless and talkative. Matt is looking like he feels good and says he feels sorry for me that Iím not feeling what heís feeling. I decide that Iíll try and concentrate on what Iím feeling so Iíll feel it stronger. I have become quite disappointed with X as I am not feeling what the people said they felt in their experiences. I thought maybe its just not working with my body chemistry.
T + 3:00: Matt and I are in the same state as before but we decide to take another pill because basically we felt like it. Matt was definitely feeling good about something so I guess it was working for him. I was pondering why it feels good for him and why it doesnít feel much like anything to me. But then I notice I just really want to talk and open up to my 2 friends. We talk and I feel good.
T + 3:30: I feel damn good. I mean really really good. Max who has tried it before tells me to rub my feet on the floor. So Iím sitting there on the bed in Maxís room rubbing my bare feet against the carpet.  I was rubbing my feet on the carpet for an hour ďooohingĒ and ďaaaahingĒ in ecstasy.
I lose track of time so I can no longer tell you how long Iíve been doing what and when I did it but I can still remember what I did.
I go over to my friend Matt and we rub each others feet because it feels so good. We are very open and happy. We are both straight guys but we donít feel any need to feel weird about touching each other and rubbing each other. Normally this would never happen. We must have been rubbing each others feet for 30 minutes. It was so nice to touch and to be touched. There was nothing sexual about it, just feeling good.
After a while Max goes to another room. We wonder where heís disappeared to and go search for him. We find him in Eddieís room. Eddie is one of the tenants across from Maxí room. Eddie is an experienced X user as well as some other drugs. He is told what weíre on and is happy to let us in his room to listen to music and just be. I wiggle on the floor because it feels so good to rub my back against the floor.
So far we really hadnít listened to music during the X trip. And I love dance/trance music normally so this is a shame. Eddie decides to put on Faithless Ė Salva Mea for us. This music sounds absolutely wonderful. I get up and attempt to dance. I notice my coordination is lacking but I donít care and I eventually get with the beat. At that moment I wished I was at a club dancing with lots of people and this music and cool colored lights flashing. That would have been perfect at the moment.
After the song finishes Eddie offers us a spliff. OOOOOOOHHH! That was soooo smooth. The smoke didnít feel like anything in my lungs but my head sure felt it. It was like a little extra boost in the good feeling I was having. I feel very good.
After a while Eddie says we have to leave because he has to sleep so we do and thank him for his generosity and hospitality.
We go back to Maxí room. And just talk and rub our feet on the floor.
My friend has to go to the bathroom to piss. He comes back and says how hard it was to piss. This makes me have to piss. So I go to the bathroom and piss. I had no problem. In fact it felt good. It was the best piss Iíd ever taken. My legs shook because it felt so good. As Iím leaving the bathroom I happen to look into the mirror. WHOA! My pupils were freakiní huge. It was cool. It was fun to look at yourself with new eyes.
I go back to the room and lie on the bed and stare at the ceiling. The ceilings design is white with little bumps all over it like many other ceilings. I stare that this and the bumps seem to move around like a kaleidoscope. This is interesting because my friend Matt also sees this. After staring at it for a while I focus more on the ceiling light and squint my eyes not because itís bright but because I feel like it. In this squint-eyed vision I see colors pouring down onto me. From the top of my eyes a light pink color is pouring down like water onto me. From the bottom a light green is pouring down onto me. Right before they ďfall onto meĒ (only in a visual sense) they mix together and itís so pretty the green and pink mixing but not blending. Itís so beautiful. I describe it to my friends as ďlove is falling down on meĒ.
I get up and decide to sit by the open window. The cool breeze feels so good on me. I sit in a chair rubbing my feet on the ground with the cool breeze blowing against me and I ooh and aah in pleasure. It feels like a full body orgasm without the sex. It felt better than sex because it just lasts so long.
I look at the clock and its 5:30 in the morning. I was so surprised. Earlier it felt like 10 minutes was an hour and now it appears that short periods of time were in fact long.
Itís also worth mentioning that speech is very difficult. You want to talk and when you can remember what youíre talking about you talk so much. But short term memory is seriously disabled when it comes to speech. I would start a sentence and then half way through say ďWhat was I talking about?Ē. And I must have done that with every other sentence.
Before my X high was over I was told by my friends that it is cool to lie down and try to sleep. So I gave it a shot. At first I didnít feel like sleeping at all and I wanted to be up doing things, but then something strange happened. I started dreaming. I was dreaming very vividly while semi-awake. I was talking in my dream-like state. Every time I would say something to the people in my dreams I would wake up and my friends would look at me weird. I woke myself up at least 4 different times in this way until I finally fell asleep.
The next day I felt happy and very content to just lie in bed all day and that is just what we did.
This is my experience with MDMA. I hope you enjoyed it. Itís very hard to get all the necessary details to explain this trip. I donít think Iíve ever felt so good.
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.


SLANDER REFUTED!
AN EXTRACT FROM CHURCH PROCEEDINGS;
AND EXPULSION OF
MORMON APOSTATES, FROM THE CHURCH!
Solomon says, "..."
TO THE SAINTS SCATTERED ABROAD, GREETING:
Respected Friends and Brethren,
As this is a day of great developments, and wonderful explosions, it stands
every person in hand, to look well on all sides before they judge. In the
first place we should understand, that when a man has enemies that have sworn
to do him all the injury they can, out of malice and revenge, it will be
expected that all manner of lies will be put into circulation concerning him.
Now I will just suppose a case, and then leave the matter for you to judge.
Suppose you had an enemy, and he was to report five slanders concerning you,
to the injury of your character; and you were to call a council of eight men
of the first respectability, (Elders in Israel) and then prove four of the
slanderous reports to be false; what ought the people to think of the fifth
one? This is exactly the true statement of the case; only, the fifth one was
not known at the time of the council, and never would I presume, if it had not
have been to help these Mormon Apostates to carry out their plans of putting
me down, and that was manufactured out of whole cloth, -- a notorious
falsehood; and now you have to judge between the weight of testimony for me
and the testimony against me, and see which is the strongest, and entitled to
the most credit. I have herein inserted the names of the persons used against
me, and wish to place them along side of the names of the following
councilors, for your inspection and candid judgment.
No. 1. on the list is Zilph Hornor, with three, four or five illegitimate
children in her arms, (She speaks for herself.)
No. 2. Is Sarah Burtis, who was suspended from the fellowship of the church,
for disorderly conduct in meeting, as a mocker, frowner, and other unchristian
conduct, and a liar as the certificates of certain individuals plainly show,
in a pamphlet, entitled "..."
No. 3. A statement that Mr. Burtis says Hannah W. Allen has made, and as I
never saw the statement, and it has never been proven true, I do not know that
Hannah W. Allen, ever attached her name to any such statement, written or
verbal, therefore it amounts to no testimony at all, true or false.
No. 4. The forth and last is Elizabeth Hornor, sister to the first in this
train on the list, and a liar, as the following certificate will prove
Since the council, she makes a different statement, and is reporting
falsehoods concerning me.
The above are the weapons that A. Burtis fights with, worthy weapons indeed!
quite characteristic of his cause. Charles Asson is A. Burtis' right hand man,
that is enough for me, or any reasonable man; for what does he know about
church government, that he should dictate the Elders and condemn the decision
of a council, or A. Burtis either?
Not one of the statements, made by the above named persons, taken all
separately, has ever been proven true, by one, two or three witnesses, as the
law of God requires; consequently they are not entitled to the least credit
whatever. The witness of one person (if true) is not sufficient to condemn an
Elder, without that witness could prove his or her statement by two or three
witnesses and then the testimony is to be weighed and looked upon, according
to the character or credibility of the witness, and if every one of the above
named persons, were to swear to every word that Burtis has said, their
testimony would be entitled to no more credit than what their characters truly
merit; for my own part, I believe it would not amount to much in the minds of
reasonable and judicious men, with Zilph Hornor at the head of the train, and
Charless Asson and A. Burtis, as firemen, with their worthy confederates can
punch up the fire, and raise the steam, on the most approved plan of high
pressure locomotive, go ahead of modern times with every car chained to its
fate, they will (no doubt) soon arrive in port, unless the leading car should
run off the track; which depends very much on the management of the head
engineers.
But lest there be a bursting of boilers, I have concluded not to take passage
on this train, for the fare, 12 1/2 cents, is too cheap for the times.
 It would be a singular
thing, indeed, if A. Burtis could make the people believe that the above
council made a mistake, and cut off the wrong man, and that seven councilors
out of nine should all be wrong, and Abraham Burtis right. But all who please
may rely on the testimony of the enemies' row.
And I will risk my cause upon the decision of the councils of Heaven, composed
of the Elders of Israel here on earth.
Here follows a certificate given me by the council, signed by the President
and Clerk, in the name of the council.
...
Now all I have to say is, can a man do more than to prove himself innocent,
any more than this I shall not attempt to do One thing more I wish to mention,
which is, that Burtis is trying to make the people believe that he had not the
privilege of introducing his testimony against me before the council; this is
a mistake, he was called upon a number of times, to bring forth his testimony,
and did not, but got angry and left the council, who was to blame for this?
was the council to run after the man and catch him, and bind him hand and
foot, and bring him into council and compel him to talk? This is the true
statement of the case as the minutes of the council will shew, which will
shortly appear in due form, with a statement of the whole affair. And I wish
it distinctly understood that A. Burtis and Charles Asson, are cut off, for
lying and slandering the authorities of the Church, (and that their testimony
is no longer received against the members of the Church, according to the laws
of the same.) And that all who have lied about me, may soon expect to share
the same fate.
If any believe their lying tales hereafter, it is because they have not the
love of the truth, but take pleasure in unrighteousness and believe a lie that
they might be damned.
When an enemy like Burtis and others commence a warfare upon me without any
cause or provocation they must expect I will make my defence and if I hear any
more of their lying tales, I will give them their character in full.
It has been reported that I have spoken disrepectfully of the Burtis
connections; this is a falsehood! I never had any acquaintance with them,
consequently never had any occasion to say anything against them, and as this
is only one out of the hubdred and ninety and nine lies, that Burtis has told,
I shall take no further notice of it.


Introduction to PostgreSQL physical storage
November 9, 2015
I initially wrote this post for myself, I wanted to understand enough about the underlying physical storage to be able to grasp other concepts which can affect your database. This post is probably more aimed to people with similar interests, I will try to keep it accurate but the aim is to make it simpler to understand so I may overlook some details.
Should you know about how Postgres handle the physical storage? Maybe not, but knowing how it works can be useful if you need to investigate some problems.
PostgreSQL has a chapter on the subject but personally I had to dive in extra resources to get my head around it. I found difficult to navigate through multiple parts of the documentation and external resources, so this is my attempt to gather the essential in a single post. In this post, I didn't bother to rewrite everything so in some place I reused paragraphs from the official documentation.
At the time of this article, the current version of Postgres is 9.5, it's possible that the information in this post to be voided in a future release.
To illustrate the examples of this post, we will be assuming that we have a database called foo
which contain a table called bar
.
A bit of terminology
Once you dive into the documentation about storage then you start to hit some less common terms like relations, tuple, heap, block, page... Let me try to demystify what they mean:
- a
tuple
or anitem
is a synonym for a row - a
relation
is a synonym for a table - a
filenode
is an id which represent a reference to a table or an index. - a
block
andpage
are equals and they represent a 8kb segment information the file storing the table. - a
heap
refer toheap file
. Heap files are lists of unordered records of variable size. Although sharing a similar name, heap files are different from heap data structure. CTID
represent the physical location of the row version within its table.CTID
is also a special column available for every tables but not visible unless specifically mentioned. It consists of a page number and the index of an item identifier.OID
stands for Object Identifier.database cluster
, we call a database cluster the storage area on disk. A database cluster is a collection of databases that is managed by a single instance of a running database server.VACCUM
, PostgreSQL databases require periodic maintenance known as vacuuming
Don't get surprised if you hit this terms in the rest of this post. In few places, I simplified the content when it was possible.
Where is my database stored
The data files used by a database cluster are stored together within the cluster's data directory, commonly referred to as PGDATA (after the name of the environment variable that can be used to define it). PGDATA can have different location base on your OS or installation.
For each database in the cluster, there is a subdirectory within PGDATA/base
, named after the database's OID in pg_database
. This subdirectory is the default location for the database's files.
You can run the following query if you want to list the OID for each database in your cluster.
select oid, datname from pg_database;
oid | datname
-------+-------------
1 | template1
12398 | template0
12403 | postgres
17447 | foo
If you are looking for the OID of a specific database then you add a WHERE
clause to the query:
select oid, datname from pg_database WHERE datname = 'foo';
The other way to find the OID of a database is using the command line oid2name
. It's a utility program to examine the file structure used by PostgreSQL for more information on how to use it check the documentation
$> oid2name
All databases:
Oid Database Name Tablespace
----------------------------------
17447 foo pg_default
12398 template0 pg_default
1 template1 pg_default
The OID's above may be completely different in your system, but now we know that all the files concerning the database foo
are stored in PGDATA/base/17447
. If you ask yourself in which database, tables as pg_database
are stored then they are not in PGDATA/base
but in PGDATA/global
.
Now, we know where to find the database in our file system so let try to figure out where to find our table.
Where tables are stored
Each table is stored in a separate file. For ordinary relations, these files are named after the table or index's filenode number, which can be found in the column relfilenode
of the table pg_class
. pg_class
is system table which exist in the pg_catalog
schema.
When a table exceeds 1 GB, it is divided into gigabyte-sized segments. The first segment's file name is the same as the filenode; subsequent segments are named filenode.1, filenode.2, etc. This arrangement avoids problems on platforms that have file size limitations.
A table's filenode often matches its OID, this is not necessarily the case. To find table filenode path, you can run the following query when you connected to your database:
select pg_relation_filepath('bar');
pg_relation_filepath
----------------------
base/17447/27741
If you don't want the path but only the filenode then you can use the function pg_relation_filenode
with the same argument. As for the database OID, you can find the table filenode by using oid2name
> oid2name -d foo -t bar
From database "foo":
Filenode Table Name
----------------------
27741 bar
The System tables
In the previous section we talk a bit about system tables, it's interesting to know that there is a system catalog schema which contains tables that you may want to query to find extra information.
When you create a database, in addition to public
and user-created schemas, each database contains a pg_catalog
schema, which contains the system tables and all the built-in data types, functions, and operators. pg_catalog
is always effectively part of the search path, so you don't need to use the prefix when you query the system tables.
Once connected to your database via psql
, you can list the list the systems tables via:
\dt pg_catalog.*
How the rows are stored
Every table stored as an array of pages of a fixed size (usually 8Kb). In a table, all the pages are logically equivalent, so a particular item (row) can be stored in any page.
The structure used to store the table is a heap file. Heap files are lists of unordered records of variable size. The heap file is structured as a collection of pages (or block), each containing a collection of items. The term item refers to a row that is stored on a page.
A page structure looks like the following:
It contains some headers which we are not going to cover, but they provide info about checksum, start of free space, end of free space, ... Items after the headers is an array identifier composed of (offset, length) pairs pointing to the actual items.
Because an item identifier is never moved until it is freed, its index can be used on a long-term basis to reference an item, even when the item itself is moved around on the page to compact free space. A Pointer to an item is called CTID (ItemPointer), created by PostgreSQL, it consists of a page number and the index of an item identifier.
The items themselves are stored in space allocated backwards from the end of unallocated space. To summarize, inside a page the pointers to the row are stored at the starts and the tuples (rows) are stored at the end of the page.
You can access the CTID of a row by adding ctid
in the selected columns:
SELECT ctid, * from bar;
What are the limitations
According to About PostgreSQL, the max number of column is between 250 and 1600 depending on column types. The column types affect it because in PostgreSQL rows may be at most 8kb (one page) wide, they cannot span pages.
It doesn't mean that value of a column is limited to 8kb. Having a big value in columns is possible depending on the type because postgreSQL has a mechanism called TOAST which can handle that. There's still limit to how many columns you can fit in, that depends on how wide the data types used are. Even pointer to TOAST attribute still requires some bytes.
The Oversized-Attribute Storage Technique
In the previous section, we've said that some column can go beyond the size of a page if their types are TOAST-able. Let's do an overview of TOAST. First, TOAST stands for The Oversized-Attribute Storage Technique
, probably the best acronym in the history of Computer Science.
PostgreSQL uses a fixed page size (commonly 8 kB), and does not allow tuples to span multiple pages. Therefore, it is not possible to store very large field values directly. When a row is attempted to be stored that exceeds this size, TOAST basically breaks up the data of large columns into smaller "pieces" and stores them into a TOAST table. Each table you create has its own associated (unique) TOAST table, which may or may not ever end up being used, depending on the size of rows you insert. All of this is transparent to the user and enabled by default. The mechanism is accomplished by splitting up the large column entry into 2KB bytes and storing them as chunks in the TOAST tables. It then stores the length and a pointer to the TOAST entry back where the column is normally stored. Because of how the pointer system is implemented, most TOAST'able column types are limited to a max size of 1GB.
TOAST has a number of advantages compared to a more straightforward approach such as allowing row values to span pages. The big values of TOASTed attributes will only be pulled out (if selected at all) at the time the result set is sent to the client. The table itself will much smaller and more of its rows fit in the shared buffer cache than would be the case without any out-of-line storage (TOAST). It's also more likely that the sort sets get smaller which imply having sorts being done entirely in memory.
In our example, the table bar
with the OID of 27741
will have the TOAST table pg_toast_27741
. There is more to be said about TOAST like that it also provide compression, but I keep it to the essential. TOAST make it transparent to the user but keep in mind that TOAST-able column have a limit 1GB. You can store a lot of data in 1GB and it can be useful to denormalize data in an ARRAY type or store documents in JSONB type.
Free Space map
Each table has a free space map, which stores information about free space available in the relation. The free space map is stored in a file named with the filenode number plus the suffix _fsm.
The values stored in the free space map are not exact. They're rounded to a precision of 1/256th of page size (32 bytes with default 8kb), and they're not kept fully up-to-date as tuples are inserted and updated. Some operations update the freespace map as deleting all the rows, but the VACCUM will update the freespace map for the future row being able to know which page they can fit.
The freespace map is updated by running VACUUM because an UPDATE or DELETE of a row does not immediately remove the old version of the row. This approach is necessary to gain the benefits of multiversion concurrency control (MVCC): the row version must not be deleted while it is still potentially visible to other transactions. But eventually, an outdated or deleted row version is no longer of interest to any transaction. The space it occupies must then be reclaimed for reuse by new rows, to avoid unbounded growth of disk space requirements.
The standard form of VACUUM removes dead row versions in tables and marks the space available for future reuse. However, it will not return the space to the operating system, except in the special case where one or more pages at the end of a table become entirely free and an exclusive table lock can be obtained. It doesn't mean that the free space inside a page is fragmented, VACUUM rewrites the entire block, efficiently packing the remaining rows and leaving a single contiguous block of free space in a page.
In contrast, VACUUM FULL actively compacts tables by writing a complete new version of the table file with no dead space. This minimizes the size of the table, but can take a long time. It also requires extra disk space for the new copy of the table, until the operation completes. The goal of routine VACUUM is to avoid needing VACUUM FULL. The idea is not to keep tables at their minimum size, but to maintain steady-state usage of disk space: each table occupies space equivalent to its minimum size plus how much space gets used up between vacuumings.
In Past, when VACUUM ran, it had to look at every tuple in a table, because there was no information about which pages may not have been updated since the last VACUUM. PostgreSQL introduced the visibility map, VACUUM is now be able to perform partial scans of table data, skipping pages which are marked as fully visible. Partial scans mean fewer I/O operations for VACUUM.
You can query the frees pace available by pages in your table via following query:
CREATE EXTENSION pg_freespace;
SELECT * FROM pg_freespace('bar'::regclass);
Visibility map
Each table has also a Visibility Map (VM) to keep track of which pages contain only tuples that are known to be visible to all active transactions. In others words, to track which pages are known to have no dead row. The Visibility map is stored alongside the table file in a separate file, named after the filenode number of the relation, plus a _vm
suffix. For example, the filenode of a relation bar
is 27741, the VM is stored in a file called 12345_vm, in the same directory as the main relation file.
We've seen in the previous section, the dead rows are due to the MVCC mechanism.
The visibility map simply stores one bit per heap page. A set bit means that all tuples on the page are known to be visible to all transactions. This means that the page does not contain any tuples that need to be vacuumed. The map is conservative in the sense that we make sure that whenever a bit is set, we know the condition is true, but if a bit is not set, it might or might not be true. Visibility map bits are only set by vacuum but are cleared by any data-modifying operations on a page.
What about Indexes
Indexes are also stored as files too and in the same directory as the tables. Indexes are an other story, I didn't cover them as it will make this post much longer. Even if indexes also have pages, they follow a different structures as they are b-tree's (by default).
Want to work on challenging projects using Postgres?
If you are interested in working with Postgres on some interesting projects, come join me at Opendoor. We heavily use PostgreSQL for Data Processing, Web Development, Data Warehousing, ETLs, Data Science, and Geo Analysis. We are always looking for passionate Engineers and Data Scientists to join our team. Please reach out to our recruiter, Ryan or apply via our job page
Conclusion
I hope this post gave you enough information to have a better understand about how PostgreSQL may store your data. It has been useful for me to dive in the subject, it makes some other concepts or talks more easy to grasp but mainly fewer behaviors have to be accepted for granted. Much more could be said about PostgreSQL store and I hope that we will see more posts like this to make PostgreSQL internal parts understandable to beginners.


Usefull documentation for this section: developmentkitmanual.pdf
When you have power the RED light is on When you press the POWER ON button the STATE light turn to green
WARNING: The module will not start without the SIM card and the antenna
Usefull documentation for this section: documentationSIM5218ATcommands.pdf
First you need to find on wich port is attached the serial Link.
To do so you can type this command line in a terminal:
dmesg | grep tty
Write down the number of the USB port (e.g.:ttyUSB0)
Now you have two solutions to communicate with the module:
NOTE: In Putty you can save a session for next uses
python
import serial
ser=serial.Serial('/dev/ttyUSB0',115200)
ser.write('ati
')
NOTE: When using Python Serial you are not able to read the port
You can find a list of pdp configurations here or here
For instance if you are in Aland:
Alcom: APN: internet / no password needed Sonera: APN: internet / no password needed
at+cpin=1234 #enter your PIN code, where 1234 is YOUR PIN code
at+cgsockcont=1,"IP","internet" # define your APN regarding to the PDP configuration you use for the profile 1
at+csockauth=1,0 #configure authentification for profile 1, 0 = no password needed
at+csocksetpn=1 #select the profile 1 (up to 16 profiles can be used)
Now your card should be configured !
Open the internet connection (do not forget to plug the antenna):
at+netopen=,,1
NOTE: It can take some time for your first connection
at+csq #give the strongness of the 3G signal, the higher the better i.e:20
usefull documentation: sim52xxluaapplicationnotev0.04
at+cscriptstart="D:\script.lua" #where "script" is the name of your scipt
at+cscriptstop
at+fscopy="D:\script.lua","C:\script.lua"
NOTE: If you name your script as "autorun.lua" and put it in the memory of the SIMCOM, the script will automatically be launched when you start the module.


Celine Dion gets 'adrenaline' from new tour: 'It's addicting'
by Annie MartinNov. 14 (UPI) -- Celine Dion says performing on her new tour is an "addicting" experience.
The 51-year-old Canadian singer discussed her Courage world tour and her new involvement in the creative process during Thursday's episode of CBS This Morning.
Dion kicked off Courage, her first tour in 10 years, in September. She told host Gayle King that the shows have given her a new energy and excitement onstage.
"The adrenaline is like a drug. So that's why I keep doing it," she said. "It's addicting."
Dion said it's been great to connect with fans, whom King noted are "younger, older, male, female, black, white."
"I'm so proud. I look at those kids and they're, like, 17 years old.  And then I'm like, 'Wow.' What's happening in my career?'" Dion said. "That is the greatest gift."
"They look at me, like I said, beyond the songs," she added. "'Celine, are you okay? We got you, girl. We're here. we love you so much.'"
Courage takes its name from Dion's new album, scheduled for release Friday, and her single of the same name. Dion said she's found a new interest in production since her husband and manager René Angélil died in January 2016.
"I've been so much involved, not only in my album but into the production of this show," she said. "I say what I like, I have ideas. They can be tacky, wrong. I'm not asking to be right. I'm just asking to be heard."
Dion released a music video for "Courage" on Wednesday. Courage also includes the single "Imperfections," which Dion released a music video for in September. She said on Today in September that Courage represents losing Angelil.
Dion will next perform Monday in Montreal, Canada.
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https://docs.python.org/3/library/functions.html#open
Python distinguishes between binary and text I/O.
Files opened in binary mode ('mode' with 'b) return contents as bytes objects without any decoding.
Files opened in text mode ('mode' with 't', the default) return contents as string where bytes are decoded using 'encoding'. A text file content can be divided onto lines ending with '\n'. Note that the last line may be without '\n'.
'open()' returns a 'file object', and is most commonly used with two arguments: open(filename, mode). Files opened for processing should be closed as soon as possible.
# afile = open(filename, mode='r', buffering) # Py2 # afile = open(filename, mode='r', buffering=-1, encoding=None, errors=None, # newline=None, closefd=True, opener=None) # Py3 # filename : a path-like object giving the pathname # mode : a string that specifies the mode in which the file is opened # encoding : used in text mode, usually 'utf-8' # Available modes (character - meaning): # 'r' - open for reading (default) # 'w' - open for writing, truncating the file first # 'x' - open for exclusive creation, failing if the file already exists # 'a' - open for writing, appending to the end of the file if it exists # 'b' - binary mode # 't' - text mode (default) # '+' - open for updating (reading and writing)
infile = open("book.txt", "r") # default text mode, Py2 an Py3 #infile = open('book.txt', 'r', encoding='utf-8') # Py3 S = infile.read(3) # read 3 code points #S = infile.read() # read all, problems with large files! #S = infile.readline() # read a line with '\n' print(S) # print() adds '\n' infile.close()
infile = open("book.txt", "r") # default text mode #L = infile.readlines() # list of lines with '\n' L = infile.readlines(50) # list of lines, 50 or more code points print(L) infile.close()
infile = open("book.txt", "r") # default text mode for line in infile: # 'line' with '\n' print(line, len(line)) infile.close()
infile = open('book.txt', 'r') # default text mode while True: text = infile.read(3) print(text) if text == "": break infile.close()
# Using a context manager (recommended). with open('book.txt', 'r') as infile: # infile will be closed text = infile.read() print(text)
outfile = open('results.txt', 'w') # default text mode, Py2 an Py3 #outfile = open('results.txt', 'w', encoding='utf-8') # Py3 n = 53 word = "żarówka" # 7 code points ('bulb' in English) outfile.write("{}\n{}\n".format(n, word)) # 2 lines # outfile.write(str(n)) # outfile.write("\n") # outfile.write(word) # outfile.write("\n") outfile.close()
outfile = open('results.txt', 'w') n = 53 word = "żarówka" # 7 code points ('bulb' in English) L = ["{}\n".format(n), "{}\n".format(word)] # list of lines with '\n' outfile.writelines(L) outfile.close()
# Using a context manager (recommended). n = 53 word = "żarówka" # 7 code points ('bulb' in English) with open('results.txt', 'w') as outfile: # outfile will be closed outfile.write("{}\n{}\n".format(n, word)) # 2 lines


Official documentation for PalFinder App
Welcome to the official documentation page for PalFinder App!
We are really glad you chose PalFinder as your legacy project and we hope you'll enjoy working on it. To get you started we assembled the list of articles that you might find helpful. You'll also find a detailed list of functions (module, services, and controllers), as well as some side notes that could be userful down the road (note about https and SSL certificates).
We also created recommendations for future work that includes some things we started but didn't have time to complete, as well as features we wish we had time to start.
We also created recommendations for future work that includes some things we started but didn't have time to complete, as well as features we wish we had time to start.
If we missed anything, if you have any quesitons, or if you want to hang out - just look around you, or find us on Slack!
Good luck with your legacy project,
Anukul, Aprajita, Dan, and Hanyen
Anukul, Aprajita, Dan, and Hanyen


Mask RCNN on AWS Lambda
Drop your .jpg file here
Click an image above
or select here to add your own image
Object Mask Options
Show People
Show Dogs
Show All
Output Visualization Options
Confidence Level
Detect


Journal Writer
akka-persistence-journal-writer consists of an akka-streams Flow and Sink that makes it possible to write EventEnvelope , Seq[EventEnvelope], EventEnvelope2 or Seq[EventEnvelope2] to any akka-persistence jounal.
Artifacts
- sbt
-
libraryDependencies += "com.github.dnvriend" %% "akka-persistence-journal-writer" % "0.0.4"
- Maven
-
<dependency> <groupId>com.github.dnvriend</groupId> <artifactId>akka-persistence-journal-writer_2.11</artifactId> <version>0.0.4</version> </dependency>
- Gradle
-
dependencies { compile group: "com.github.dnvriend", name: "akka-persistence-journal-writer_2.11", version: "0.0.4" }
Basic Usage
The akka-persistence-journal-writer lets you write events to any journal. It accepts only two types of messages:
akka.persistence.query.EventEnvelope akka.persistence.query.EventEnvelope2 Of course, you can send immutable.Seq[EventEnvelope] or immutable.Seq[EventEnvelope2] of those too for bulk loading.
The basic use case would be loading one event store into another. In this example we will be loading events from the inmemory-journal, using akka-persistence-query and loading the events into the level-db journal:
import akka.stream.scaladsl._
import akka.persistence.query._
import akka.persistence.query.scaladsl._
val inMemoryReadJournal = PersistenceQuery(system).readJournalFor("inmemory-read-journal")
.asInstanceOf[ReadJournal with CurrentPersistenceIdsQuery with CurrentEventsByPersistenceIdQuery]
val result: Future[Done] =
inMemoryReadJournal.currentPersistenceIds().flatMapConcat { pid =>
inMemoryReadJournal.currentEventsByPersistenceId(pid, 0, Long.MaxValue)
}.grouped(100).runWith(JournalWriter.sink("akka.persistence.journal.leveldb"))
The fragment above reads all events from all persistenceIds from the inmemory-journal using akka-persistence-query and writes them directly into an empty level-db journal. Of course, any journal will work.
Converting serialization strategy
Some akka-persistence-query compatible plugins support using the event-adapters from the write-plugin, therefor converting the journal’s data-model to the application-model when querying the journal. Say for example that you have a journal that uses the Java-serialization strategy to store events and you would like to convert all those event using another serialization strategy, say Protobuf, then you can just configure some event adapters on the other write-plugin, say the level-db plugin and just load events from the in-memory plugin and store them into the level-db plugin. Akka-persistence will do all the work for you because you have configured the event-adapters on the plugin to do the serialization.
The solution is the configuration of the event-adapters on the write-plugins like eg:
inmemory-journal {
event-adapters {
adapter-a = "akka.stream.alpakka.persistence.writer.EventAdapterA"
}
event-adapter-bindings {
"akka.stream.alpakka.persistence.writer.MyMessage" = adapter-a
}
}
inmemory-read-journal {
write-plugin = "inmemory-journal"
}
akka.persistence.journal.leveldb {
dir = "target/journal"
event-adapters {
adapter-b = "akka.stream.alpakka.persistence.writer.EventAdapterB"
}
event-adapter-bindings {
"akka.stream.alpakka.persistence.writer.MyMessage" = adapter-b
}
}
akka.persistence.query.journal.leveldb {
write-plugin = "akka.persistence.journal.leveldb"
}
In the example above, all events will be written to the in-memory journal using the event-adapter AdapterA. The inmemory-read-journal has been configured to use the event-adapters as configured from the inmemory-journal so when reading events using akka-persistence-query it should return application-domain events and not the java-serialized byte arrays.
When asking the akka-persistence-journal-writer (JournalWriter) to write events to a write plugin with a certain journalPluginId, eg. the level-db plugin, that plugin has been configured with certain event-adapters. Imagine that those event adapters will convert application-domain events to Protobuf types, then the protobuf serializer will serialize events to byte arrays and store those events in the level-db journal.
Of course, all events in the inmemory-journal will stay untouched. All events in the level-db journal will be protobuf-encoded.
Bulk loading events
Say for a moment, that you have some business level entity with a stable identifier, and you have a lot of those. But those entities do not yet exist. They will exist only when they are created in the journal; ie. they have an initial state and a persistenceId of course.
Also imagine that the initial state is in some very large CSV file or JSON file that has been provided to you by an Apache Spark job for example.
You could do the following:
- read each record,
- convert to an event,
- send events to shard,
- persistent actor will be recovered,
- event will be stored,
- persistent actor will be passified,
- actor will be unloaded from memory.
- This is in the book…
You could also do the following:
- read each record with a FileIO source
- convert the CSV to a the known event, wrap into a Seq[EventEnvelope]
- directly store these events in the journal
- This will have no persistent-actor life cycle overhead and will be much faster.
Its only applicable in some use cases of course.
Running the example code
The code in this guide is part of runnable tests of this project. You are welcome to edit the code and run it in sbt.
- Scala
-
sbt > journalWriter/test
- Java
-
sbt > journalWriter/test
Changelog
0.0.4 (2016-11-22)
- Akka 2.4.13 -> 2.4.14
0.0.3 (2016-11-20)
- Akka 2.4.12 -> 2.4.13
0.0.2 (2016-11-03)
- cross scala 2.11.8 and 2.12.0 build against akka 2.4.12
0.0.1 (2016-11-03)
- First release


Catch mind example
Tools
Eraser
Erase All
Eraser
Color
Black
Blue
red
yellow
green
White
Thickness
Question
**
Players
Messages


Fifa
World Cup 2018 Russia
Home
Matches
Teams
Groups
Statistics
This page contains hyperlinks to different sections of the Fifa world cup page
Fifa World Cup 2018 is already over For more information on FIFA World Cup, come
here
Main Links
FIFA world cup official homepage
Matches
Teams
Groups
Players
Statistics
Destination
Fan zone
Awards
Ticketing
Classic
Other Links
News
Photos
Videos
Visual Stories
Organisation
Qualifiers
Store
Social Media Pages
Facebook
Twitter
Instagram
Youtube
Weibo


Item 9.
Web Browser and Miscellaneous Commands
Use enter with o to open a new url.
Space with i will display the name of the current web page.
Press enter to click a button.
Enter with f will go forward in a session history list.
Enter with b will go back a page in the session history list.
To refresh the current web page, press enter with the letter r.
To access the favorites menu, press enter with a low f (dots2 3 5).
-
Enter with m will go to the internet options menu. From the internet options menu, you may:
open an html file
set the current page as the home page
go to your home page
manage internet files
go to the display settings
print the current web page
check the error status
Enter with lower h (dots2 3 6 will go to the history list).
Space with dots1 3 will go to the previous input control. Space with dots4 and 6 will go to the next input control. Space with dots1 3 4 6 will read the current input control.
Space with b will take you to the block commands menu.
To access the spelling checker, use space with dots1 6.
Space with s will save a file.


Best Dental Clinic
Pain is a matter of hours
Don't waste your precious time and visit the clinic.
FMS VISION To be widely recognized as one of the world’s leading and most preferred Dental Care Service Provider in Asia, especially the Middle East Region through unmatched treatment outcomes, cutting edge technology & procedure delivery systems, and international standards of care.
Deliver multi-specialty treatment approach combined with professional care to resolve dental problems. To achieve excellence in dentistry through up-to-date technology and highly educated professionals. To provide outstanding dental care with a commitment of honesty,compassion, quality and integrity. To change lives everyday by creating healthy and beautiful smiles.
To raise the quality of clinical care through thought leadership, continual dental research and education. To collaborate and share our clinical expertise with dental colleagues in India and the region through workshops and conference lectures. To mentor young dentists in India and regionally through our sister concern, Sri Sai college of Dental Surgery, Vikarabad, Hyderabad. To serve the dental community in India through taking an active role in the various professional dental organizations.
FMS dental clinic in Secunderabad India constitutes senior team of qualified dental specialists in Hyderabad. These dental experts’ team includes Dental Surgeons, Pedodontists (Child Dentists), Prosthodontists, Oral & Maxilofacial Surgeons, Endodontists (Root Canal Specialists), Periodontists, Braces Specialists (Orthodontists), Dental Implantologists, Cosmetic Dentists etc.
We focus on using the latest standards and practices regarding dental development and strongly encourage our customers to follow us on this.
Here is the list of our awarded doctors:
Dr. Garima PhD
Dentist, specialist in orthodontics
Dr. Khushboo Maroo
Doctor of Dental Medicine
Dr. Afroz Ali
Dentist with wide experience in orthodontics, DMD
Dr. V. Gayathri
Dentist, oral surgeon, DMD
FMS Dental clinic clinical facilities are fully equipped with the most high end technology available in the field of dentistry.
Here is a list of the technologies available on our facilities:
Digital X-rays
Digital Radiography reduces radiation by 90%
Computerized software
Explain each and every dental procedure
Intra Oral Camera
Highest level of dental photography
Track Light Monitor
Enhances patient experience and improves efficiency and case
acceptance.
Invisible Aligners
Clear aligners are the future of orthodontic treatment.
Our team strives to achieve excellence in every aspect of the dental care while making sure that most FAQ of our customers can be answered by our detailed documentation.
To share knowledge on dental issues and current treatments so that patients can make more informed decisions regarding their dental health. To engage the public through social media, dental seminars /blogs, media interviews and other means to spread the message of prevention and rehabilitation to society at large. To conduct free dental camps for the needy, to increase awareness and address their immediate dental problems. To partner with leading oral care companies to provide relevant information to the public about their dental health.
Don't waste your precious time and visit the clinic.


To find out more about how to use the app and to
be safe during an earthquake, click here.
link toearthquakecountry.org
Change language: English/Spanish
For technical support for this app, refer to the APPLE STORE/GOOGLE PLAY? For information about earthquake early warning, visit the U.S. Geological Survey ShakeAlert site.
Please provide feedback on this app to help
improve earthquake safety in Los Angeles.
link to survey
The ShakeAlert™ Earthquake Early Warning
System (EEW) is under development by United
States Geological Survey (USGS) and its
academic partners at the California Institute of
Technology; the University of California, Berkeley;
the University of Oregon; and the University of
Washington. This development includes in close
partnership and collaboration with State Partners
from California, Oregon, and Washington
Emergency Management and Geological Surveys.
The USGS, in collaboration, with its academic partners run the seismic network which is a system of sensors that detects an earthquake when it starts. When an earthquake starts it produces two kinds of seismic waves that carry energy through the Earth. The waves are detected by the sensors which send the information to a computer system faster than the seismic waves travel. The system analyzes the ground motion data and provides an earthquake alert which includes rapid estimates of the magnitude, location, and expected intensity of shaking. Earthquake magnitude (Mag) represents the amount of energy released and depends on the surface area of the rupture. Seismic intensity (MMI) is the amount of shaking, and is a relevant measure for potential damage. When the earthquake reaches the pre-set threshold of magnitude and intensity of shaking an alert is triggered. The amount of warning time depends on the speed of the warning mechanism and your distance from the earthquake. You may receive as little as a few seconds to minutes of warning time.


EffectuationCausation
This is the documentation for the EffectuationCausation model...
The documentation for "EffectuationCausation.ModelInitializer" can be found
here
.


investigate - look around you!
talk ... - try to talk with someone
go ... - change location
pick ... - pick something off the ground
throw ... - permanently discard item from your backpack
equip ... - equip item from your backpack
off ... - take off item and put back in your backpack
backpack - show carried items
eq - show worn items
stats - print player stats
skills - print your abilities
fight ... - enter fight mode
attack - perform standard attack
block - increase your defence for next two incoming attacks, you can use items and abilities
during this time
use ... - use item
cast ... - use ability
suicide - commit suicide


|
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| SUMMARY: NESTED | FIELD | CONSTR | METHOD | DETAIL: FIELD | CONSTR | METHOD |
java.lang.Object com.hp.hpl.jena.sparql.engine.optimizer.probability.ProbabilityFactory
public class ProbabilityFactory
A factory for probabilistic models. There are currently three models: (1) the default model (2) the index model and (3) the data model. The default model is a wrapper around both the index and the data model. Essentially, it merges the *best* of both models. The index model requires a Jena model of the specialized index to support joined triple pattern probability calculation. The data model requires the Jena model of the queried ontology. Please note that the data model returns probabilities only if the corresponding graph implements the GraphStatisticsHandler. This is currently true only for non-inference in-memory models. The probabilities returned by a Jena data model which implements the GraphStatisticsHandler are the most accurate (i.e. more accurate than the probabilities returned by the index model. However, the data model allows probability calculation for triple patterns only (and only for non-inference in-memory Jena models) whereas the index model allows probability calculation for joined triple patterns and virtually for any Jena model. The index model is however less accurate than the data model for triple pattern probability calculation.
| Constructor Summary | |
|---|---|
ProbabilityFactory()
|
| Method Summary | |
|---|---|
static Probability |
createDefaultModel(com.hp.hpl.jena.graph.Graph dataGraph,
Config config)
Create a probabilistic default model by giving the Jena model of the queried ontology. |
static Probability |
createDefaultModel(com.hp.hpl.jena.rdf.model.Model dataModel,
Config config)
Create a probabilistic default model by giving the Jena model of the queried ontology. |
static com.hp.hpl.jena.rdf.model.Model |
createIndex(com.hp.hpl.jena.graph.Graph dataGraph)
Create and return the full specialized Jena model of the index required for the probabilistic framework. |
static com.hp.hpl.jena.rdf.model.Model |
createIndex(com.hp.hpl.jena.graph.Graph dataGraph,
Config config)
Create and return the full specialized Jena model of the index required for the probabilistic framework. |
static com.hp.hpl.jena.rdf.model.Model |
createIndex(com.hp.hpl.jena.rdf.model.Model dataModel)
Create and return the full specialized Jena model of the index required for the probabilistic framework. |
static com.hp.hpl.jena.rdf.model.Model |
createIndex(com.hp.hpl.jena.rdf.model.Model dataModel,
Config config)
Create and return the specialized Jena model of the index required for the probabilistic framework. |
static Probability |
createIndexModel(com.hp.hpl.jena.rdf.model.Model dataModel)
Create the probabilistic index model with the full index. |
static Probability |
createIndexModel(com.hp.hpl.jena.rdf.model.Model dataModel,
Config config)
Create a probabilistic index model by creating first the required index. |
static Probability |
loadDataModel(com.hp.hpl.jena.graph.Graph dataGraph,
Config config)
Load a probabilistic data model by giving the Jena graph of the queried ontology. |
static Probability |
loadDataModel(com.hp.hpl.jena.rdf.model.Model dataModel,
Config config)
Load a probabilistic data model by giving the Jena model of the queried ontology. |
static Probability |
loadDefaultModel(com.hp.hpl.jena.graph.Graph dataGraph,
com.hp.hpl.jena.rdf.model.Model indexModel,
Config config)
Load a probabilistic default model by giving the Jena graph of the queried ontology and the Jena model of the specialized index. |
static Probability |
loadDefaultModel(com.hp.hpl.jena.rdf.model.Model dataModel,
com.hp.hpl.jena.rdf.model.Model indexModel,
Config config)
Load a probabilistic default model by giving the Jena model of the queried ontology and the Jena model of the specialized index. |
static Probability |
loadIndexModel(com.hp.hpl.jena.graph.Graph dataGraph,
com.hp.hpl.jena.rdf.model.Model indexModel,
Config config)
Load a probabilistic index model by giving the Jena graph of the queried ontology and the Jena model of the specialized index. |
static Probability |
loadIndexModel(com.hp.hpl.jena.rdf.model.Model dataModel,
com.hp.hpl.jena.rdf.model.Model indexModel,
Config config)
Load a probabilistic index model by giving the Jena model of the queried ontology and the Jena model of the specialized index. |
| Methods inherited from class java.lang.Object |
|---|
equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait |
| Constructor Detail |
|---|
public ProbabilityFactory()
| Method Detail |
|---|
public static Probability createIndexModel(com.hp.hpl.jena.rdf.model.Model dataModel, Config config)
dataModel
- config
-
public static Probability createIndexModel(com.hp.hpl.jena.rdf.model.Model dataModel)
dataModel
-
public static com.hp.hpl.jena.rdf.model.Model createIndex(com.hp.hpl.jena.rdf.model.Model dataModel, Config config)
dataModel
- config
-
public static com.hp.hpl.jena.rdf.model.Model createIndex(com.hp.hpl.jena.rdf.model.Model dataModel)
dataModel
-
public static com.hp.hpl.jena.rdf.model.Model createIndex(com.hp.hpl.jena.graph.Graph dataGraph, Config config)
dataGraph
-
public static com.hp.hpl.jena.rdf.model.Model createIndex(com.hp.hpl.jena.graph.Graph dataGraph)
dataGraph
-
public static Probability loadDefaultModel(com.hp.hpl.jena.rdf.model.Model dataModel, com.hp.hpl.jena.rdf.model.Model indexModel, Config config)
dataModel
- indexModel
- config
-
public static Probability loadDefaultModel(com.hp.hpl.jena.graph.Graph dataGraph, com.hp.hpl.jena.rdf.model.Model indexModel, Config config)
dataGraph
- indexModel
- config
-
public static Probability createDefaultModel(com.hp.hpl.jena.rdf.model.Model dataModel, Config config)
dataModel
- config
-
public static Probability createDefaultModel(com.hp.hpl.jena.graph.Graph dataGraph, Config config)
dataGraph
- config
-
public static Probability loadIndexModel(com.hp.hpl.jena.rdf.model.Model dataModel, com.hp.hpl.jena.rdf.model.Model indexModel, Config config)
dataModel
- indexModel
- config
-
public static Probability loadIndexModel(com.hp.hpl.jena.graph.Graph dataGraph, com.hp.hpl.jena.rdf.model.Model indexModel, Config config)
dataGraph
- indexModel
- config
-
public static Probability loadDataModel(com.hp.hpl.jena.rdf.model.Model dataModel, Config config)
dataModel
- config
-
public static Probability loadDataModel(com.hp.hpl.jena.graph.Graph dataGraph, Config config)
dataGraph
- config
-
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| SUMMARY: NESTED | FIELD | CONSTR | METHOD | DETAIL: FIELD | CONSTR | METHOD |


Media Center, Agora and Municipal Archives / MU Architects
- Curated by Paula Pintos
- about 5 hours
- version="1.0" encoding="UTF-8"? Architects: MU Architects
- AreaArea of this architecture projectArea: 600.0 m2
- version="1.0" encoding="UTF-8"?YearCompletion year of this architecture project Year: 2018
- version="1.0" encoding="UTF-8"?PhotographsPhotographs: David Foessel
- Lead Architects Maïra Caldoncelli-Vidal, Ludovic Malbet, Grégoire Dubreux
- Client City of Epernon
- Landscape AC&T Paysage
- Collaborators Marc Vallet (sceno)
More Specs Less Specs
Text description provided by the architects. On the boardwalk that connects the medieval part of the city to the modern city center, the humble Media center stares at the horizon. Everyone is invited to enter this resourceful space open onto the exterior.
The Media center’s founding element is the landscape in which it is inserted. Placed on a frequently flooded ground, the plan is therefore raised on stilts. The modulations of the concrete screed, which covers the ground, calls upon the passerby, nests the spectator in the agora and raises the gaze towards the horizon.
Reception and consultation areas are inserted in a glass box bathed in sunlight. Two patios have been landscaped in order to bring the vegetation into the interior spaces: an airlock allows the visitor to briefly escape to an interior landscape. On the ceiling, a stretched canvas with the regional heritage patterns (public art project) punctuates the spaces.
By taking the wood path that leads to the city, under the awnings, the Media center floats like a lantern between two shores.


{% import 'macros.html' as macros %}
Music Nerds Recommendations
Source Code:
https://github.com/ravikiranj/music-nerds
YouTube
Spotify Tracks
Spotify Albums
{% for youtube_video in youtube_videos %}
{{ macros.render_youtube_video(youtube_video, loop.index) }}
{% endfor %}
{% for track in spotify_tracks %}
{{ macros.render_spotify_track(track, loop.index) }}
{% endfor %}
{% for album in spotify_albums %}
{{ macros.render_spotify_album(album, loop.index) }}
{% endfor %}


JV-1010 In:
JV-1010 Out:
Controller In:
User
Preset A
Preset B
Preset C
Preset D (GM)
Preset E
Session
Get Data
Get User Patch
#
Get All User Patches
Send Data
Drop .syx file here or click to browse
Misc SysEx
Identity Request
X
Send Patch "
PatchName
"
Upload to Temporary Patch
Upload to User Patch
#


Brazil vs South Korea result: Philippe Coutinho dazzles to help snap five-game winless streak
Brazil 3-0 South Korea: The Bayern Munich star scored a free-kick in a dominant display by the Selecao
by Jack RathbornBrazil snapped a five-game winless streak with Philippe Coutinho starring in a 3-0 win over South Korea in Abu Dhabi.
Lucas Paqueta brokes the deadlock when the AC Milan crouched low to head in Renan Lodi's delightful cross.
Coutinho, who was recalled to the starting line-up after a couple of matches on the bench, then doubled the lead nine minutes brefore the break, bending a super free-kick over the wall and into the top left corner past a helpless Jo.
Danilo got his first international goal on the hour with a crisp strike from outside the box.
The match extended South Korea’s poor run, with the Asian side now having gone three games without scoring.
The friendly was Brazil’s last before they begin their qualifying campaign for the 2022 World Cup in March next year.
Reuters contributed to this report


Career Development:
Biomedicine:
Engineering and MSE:
Economy and Business:
Chemistry/Physics:
Earth Science:
Math and Computer Science:
Social Science:
Specifications
Put weekly seminar links on each text box. Notice changes made here will be instantly reflected on www.uclac3s.org, so use it carefully!


|
KratosMultiphysics
KRATOS Multiphysics (Kratos) is a framework for building parallel, multi-disciplinary simulation software, aiming at modularity, extensibility, and high performance. Kratos is written in C++, and counts with an extensive Python interface.
|
Public Member Functions | |
| def | Info (self) |
| def | KratosPrintInfo (self, message) |
| def | __init__ (self, Model, path="", DEMStrategy=None) |
| def | AddMaterials (self) |
| def | AddProcesses (self) |
| def | Run (self) |
| def | Initialize (self) |
| def | Run (self) |
| def | Initialize (self) |
| def | RunMainTemporalLoop (self) |
| def | InitializeSolutionStep (self) |
| def | SolveSolutionStep (self) |
| def | FinalizeSolutionStep (self) |
| def | Finalize (self) |
| def | SetGraphicalOutput (self) |
| def | GraphicalOutputExecuteInitialize (self) |
| def | GraphicalOutputExecuteBeforeSolutionLoop (self) |
| def | GraphicalOutputExecuteInitializeSolutionStep (self) |
| def | GraphicalOutputExecuteFinalizeSolutionStep (self) |
| def | GraphicalOutputPrintOutput (self) |
| def | GraphicalOutputExecuteFinalize (self) |
| def | SetParallelSize (self, num_threads) |
| def | GetParallelSize (self) |
| def | StartTimeMeasuring (self) |
| def | StopTimeMeasuring (self, time_ip, process, report) |
| Public Member Functions inherited from MainSolidFEM.Solution | |
| def | __init__ (self) |
Public Attributes | |
| t0p | |
| TIME MONITORING START #### Time control starts. More... | |
| t0w | |
| ProjectParameters | |
| TIME MONITORING END ####. More... | |
| echo_level | |
| model | |
| Model_part settings start ####. More... | |
| main_model_part | |
| time_step | |
| start_time | |
| end_time | |
| Model | |
| replace this "model" for real one once available in kratos core More... | |
| solver | |
| problem_name | |
| Output settings start ####. More... | |
| model_processes | |
| computing_model_part | |
| START SOLUTION ####. More... | |
| step | |
| Sets strategies, builders, linear solvers, schemes and solving info, and fills the buffer. More... | |
| time | |
| delta_time | |
| clock_time | |
| output_settings | |
| graphical_output | |
| Public Attributes inherited from MainSolidFEM.Solution | |
| t0p | |
| TIME MONITORING START ####. More... | |
| t0w | |
| ProjectParameters | |
| TIME MONITORING END ####. More... | |
| echo_level | |
| main_model_part | |
| Model_part settings start ####. More... | |
| Model | |
| TODO replace this "model" for real one once available in kratos core. More... | |
| solver | |
| problem_path | |
| Output settings start ####. More... | |
| problem_name | |
| model_processes | |
| INITIALIZE ####. More... | |
| computing_model_part | |
| START SOLUTION ####. More... | |
| step | |
| Sets strategies, builders, linear solvers, schemes and solving info, and fills the buffer. More... | |
| time | |
| end_time | |
| delta_time | |
| clock_time | |
| output_settings | |
| graphical_output | |
| def MainFemDem.FEM_Solution.__init__ | ( | self, | |
| Model, | |||
path = "" , |
|||
DEMStrategy = None |
|||
| ) |
| def MainFemDem.FEM_Solution.AddMaterials | ( | self | ) |
Reimplemented from MainSolidFEM.Solution.
| def MainFemDem.FEM_Solution.AddProcesses | ( | self | ) |
Reimplemented from MainSolidFEM.Solution.
| def MainFemDem.FEM_Solution.Finalize | ( | self | ) |
Reimplemented from MainSolidFEM.Solution.
| def MainFemDem.FEM_Solution.FinalizeSolutionStep | ( | self | ) |
Reimplemented from MainSolidFEM.Solution.
| def MainFemDem.FEM_Solution.GetParallelSize | ( | self | ) |
Reimplemented from MainSolidFEM.Solution.
| def MainFemDem.FEM_Solution.GraphicalOutputExecuteBeforeSolutionLoop | ( | self | ) |
Reimplemented from MainSolidFEM.Solution.
| def MainFemDem.FEM_Solution.GraphicalOutputExecuteFinalize | ( | self | ) |
Reimplemented from MainSolidFEM.Solution.
| def MainFemDem.FEM_Solution.GraphicalOutputExecuteFinalizeSolutionStep | ( | self | ) |
Reimplemented from MainSolidFEM.Solution.
| def MainFemDem.FEM_Solution.GraphicalOutputExecuteInitialize | ( | self | ) |
Reimplemented from MainSolidFEM.Solution.
| def MainFemDem.FEM_Solution.GraphicalOutputExecuteInitializeSolutionStep | ( | self | ) |
Reimplemented from MainSolidFEM.Solution.
| def MainFemDem.FEM_Solution.GraphicalOutputPrintOutput | ( | self | ) |
Reimplemented from MainSolidFEM.Solution.
| def MainFemDem.FEM_Solution.Info | ( | self | ) |
| def MainFemDem.FEM_Solution.Initialize | ( | self | ) |
Reimplemented from MainSolidFEM.Solution.
Reimplemented in MainFEM_for_PFEM_coupling.FEM_for_PFEM_coupling_Solution, and MainFEM_for_coupling.FEM_for_coupling_Solution.
| def MainFemDem.FEM_Solution.Initialize | ( | self | ) |
Reimplemented from MainSolidFEM.Solution.
Reimplemented in MainFEM_for_PFEM_coupling.FEM_for_PFEM_coupling_Solution, and MainFEM_for_coupling.FEM_for_coupling_Solution.
| def MainFemDem.FEM_Solution.InitializeSolutionStep | ( | self | ) |
Reimplemented from MainSolidFEM.Solution.
| def MainFemDem.FEM_Solution.KratosPrintInfo | ( | self, | |
| message | |||
| ) |
| def MainFemDem.FEM_Solution.Run | ( | self | ) |
Reimplemented from MainSolidFEM.Solution.
| def MainFemDem.FEM_Solution.Run | ( | self | ) |
Reimplemented from MainSolidFEM.Solution.
| def MainFemDem.FEM_Solution.RunMainTemporalLoop | ( | self | ) |
Reimplemented from MainSolidFEM.Solution.
| def MainFemDem.FEM_Solution.SetGraphicalOutput | ( | self | ) |
Reimplemented from MainSolidFEM.Solution.
| def MainFemDem.FEM_Solution.SetParallelSize | ( | self, | |
| num_threads | |||
| ) |
Reimplemented from MainSolidFEM.Solution.
| def MainFemDem.FEM_Solution.SolveSolutionStep | ( | self | ) |
Reimplemented from MainSolidFEM.Solution.
| def MainFemDem.FEM_Solution.StartTimeMeasuring | ( | self | ) |
Reimplemented from MainSolidFEM.Solution.
| def MainFemDem.FEM_Solution.StopTimeMeasuring | ( | self, | |
| time_ip, | |||
| process, | |||
| report | |||
| ) |
Reimplemented from MainSolidFEM.Solution.
| MainFemDem.FEM_Solution.clock_time |
| MainFemDem.FEM_Solution.computing_model_part |
START SOLUTION ####.
| MainFemDem.FEM_Solution.delta_time |
| MainFemDem.FEM_Solution.echo_level |
| MainFemDem.FEM_Solution.end_time |
| MainFemDem.FEM_Solution.graphical_output |
| MainFemDem.FEM_Solution.main_model_part |
| MainFemDem.FEM_Solution.model |
Model_part settings start ####.
| MainFemDem.FEM_Solution.Model |
replace this "model" for real one once available in kratos core
| MainFemDem.FEM_Solution.model_processes |
| MainFemDem.FEM_Solution.output_settings |
| MainFemDem.FEM_Solution.problem_name |
Output settings start ####.
| MainFemDem.FEM_Solution.ProjectParameters |
TIME MONITORING END ####.
PARSING THE PARAMETERS ####
| MainFemDem.FEM_Solution.solver |
| MainFemDem.FEM_Solution.start_time |
| MainFemDem.FEM_Solution.step |
Sets strategies, builders, linear solvers, schemes and solving info, and fills the buffer.
| MainFemDem.FEM_Solution.t0p |
TIME MONITORING START #### Time control starts.
| MainFemDem.FEM_Solution.t0w |
| MainFemDem.FEM_Solution.time |
| MainFemDem.FEM_Solution.time_step |


#KogiDecides: With two LGs remaining, Dino Melaye trails Smart Adeyemi
by Yusuf AkinpeluElection results announced so far by the Independent National Electoral Commission in the Kogi West senatorial rerun election show that PDP’s Dino Melaye is poised to lose his seat in the Senate to his closest rival, Smart Adeyemi of the APC.
Of the seven local government areas in the senatorial district, five have been announced.
Of the five local government results announced, Mr Adeyemi has polled a total of 52,389 while Mr Melaye trails with 37,856.
The former won in all of Kaba Bunnu, Kogi Koton Karfe, Mopa Muro and Ijumu. Mr Melaye, however, scooped the highest votes in one LGA, Yagba East LGA.
Messrs Adeyemo and Delays are both from Ijumu
With the sail in Mr Adeyemi’s favour, Mr Melaye needs to cover up for the deficit of 14,533 votes in the two remaining local government areas that are yet to be announced by INEC.
These local government areas are Yabgba West and Lokoja LGAs.
The elections in Kogi West, like in a large part of the state, was characterized by violence.
Below is the breakdown of how both candidates fared in the five local governments announced so far.
Kaba Bunnu
APC — 15,037
PDP — 8,974
Kogi Koton Karfe
APC — 14,168
PDP — 9,786
Mopa Muro
APC — 4,874
PDP — 3,704
Ijumu
APC — 11,627
PDP — 7,647
Yagba East
APC — 6,683
PDP — 7,745


Hierarch Kekropus:
It may be that we have entered into an alliance with them, but they dare not enter our holy sanctuary.
(Only Kamael clans above level 10 may embark on this quest.)


§105-4 Enforcement; duties of sheriffs and police officers. The sheriff and the sheriff's deputies, or any police officer, within their respective jurisdictions, shall investigate any violation, or suspected violation of, and shall enforce section 105-1. [L 1949, c 389, pt of §1(d); RL 1955, §7-13; am L 1963, c 85, §3; HRS §105-4; gen ch 1985]


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This css has always wrong ratio
Doh!
Teaser 2
Always good! 🤠
Wee ha!
Teaser 3
Always good! 🤠
Wee ha!


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