ALASKA A film review by Alexander F. Relyea Copyright 1996 Alexander F. Relyea
Fraser Heston's new film ALASKA has scenery befitting an IMAX film with cinematography to match. Unfortunately, the horrid script and the sometimes laughable acting make you wish for more scenes without people. ALASKA is apparently an attempt to cross FLIPPER with CLIFFHANGER, featuring Vincent Kartheiser (THE INDIAN IN THE CUPBOARD) as the Stallone character. This works about as well as one might expect, and the result is too much bad acting, too many plot holes, and not nearly enough shots of the beautiful Alaskan scenery.
ALASKA features Dirk Benedict as Jake Barnes, a former jumbo jet pilot from Chicago who came to Quincy, Alaska with his two children following the death of his wife. Instead of flying for an airline, Jake now flies a supply run for the small towns of Alaska. His daughter Jessie (Thora Birch) is thrilled with the move because it helps her become one with nature, and she is quite adept at handling a kayak. His son Sean (Kartheiser) is absolutely miserable. There's nothing for him to do, and he's expected to clean up when he spills motor oil. He drowns his sorrows in video games and his passion for the Chicago Cubs (there is a pitiful attempt to create some cub symbolism throughout the movie which most audiences will mercifully miss). Charlton Heston also appears from time to time as a poacher called Perry. His role only ties in peripherally to the other characters, and although he's supposed to be evil incarnate, he comes across as scary as Paul Hogan in FLIPPER.
The film begins with brief introductions of all the major characters, and most of the minor ones. Soon after Sean tells Jake "I wish you had died instead of Mom!", Jake is compelled to fly emergency medical supplies to some small town. In the ensuing storm, Jessie loses radio contact with her father, and he subsequently crashes. The search and rescue helicopter finds a gum wrapper and an old shoe but can't find Jake's *bright yellow piper cub.* They also manage to miss the flare that Jake fires. After learning that the search is being aborted, Sean decides to search for his father himself. Jessie insists on coming along, and the search is afoot.
The kids quickly find a poacher's camp, and there they find an imprisoned polar bear cub. Naturally Jessie, our budding environmentalist, convinces Sean to help free the bear. For some reason the bear, cleverly named Cubby, decides to follow the kids, and is in turn pursued by Perry who wants him back.  Cubby effectively adopts Sean, and the relationship is consummated in a weird dream sequence which I did not recognize as such until the credits.
I won't bore you with the details of the trials and tribulations that the three face on their way to rescue Jake (without a travois or any form of radio), but Heston isn't as generous. The poorly written script treats Sean most harshly, but Kartheiser's overdone performance earns him no sympathy. Perry's function appears to be one of spouting platitudes, and his opening soliloquy is bound to cause titters. The soundtrack is a nightmare, written as if the bombastic score will enhance a flat story and fill the holes in the plot. ALASKA is only worth watching if you ignore the eminently forgettable characters, and consider the film as sponsored by the Alaska Department of Tourism, and it is on that basis that I give it *1/2 of ****.
-- Alex Relyea SMU Box 2575<EMAIL_ADDRESS>Dallas, TX 75275-2575<EMAIL_ADDRESS>Chair of SMU Student Branch of IEEE 2 John 12
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My goal with Sleepy Monkey was to make the lives of our digital designers better by simplifying their production process and providing them with new tools that promote learning and consistency. A side goal was to make our manager's lives easier by providing a level of logging and organization that allows them to hold designers accountable and troubleshoot errors more effectively.
Using Laravel as the backend, I was able to create a RESTful API for Sleepy Monkey backend so that, in the future, it can be integrated across other applications and systems if the need arises. This also keeps things simple for the AngularJS frontend, which uses the JSON response from the API to create views.
Learning AngularJS was the most challenging and rewarding part of this project for me. Coming from a jquery background, it took me while to develop that angular mindset of restricting all DOM manipulation to directives, but once I've since grown to love the workflow that comes with it.
After designing a few prototypes in Photoshop, I made the design come to life using a custom framework in SASS. I wanted to avoid bootstrap or any other large framework for my first application design so that I could learn how to create these core components myself. I initially used compass to compile my SASS in this project, but have since moved on to using Gulp and Libsass.
In the end, Sleepy Monkey was a great learning experience that led to happier designers and continues to save a lot of time for the Creative Services Center.


Copyright 2014-2015, James G. Willmore, LJ Computing
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.
Copyright © 2018 LJ Computing. All rights reserved.


Introduction to Python - Pivottable
introml.analyticsdojo.com
More Pivottables¶
!wget https://raw.githubusercontent.com/rpi-techfundamentals/spring2019-materials/master/input/train.csv
!wget https://raw.githubusercontent.com/rpi-techfundamentals/spring2019-materials/master/input/test.csv
--2019-09-13 15:30:05-- https://raw.githubusercontent.com/rpi-techfundamentals/spring2019-materials/master/input/train.csv
Resolving raw.githubusercontent.com (raw.githubusercontent.com)... <IP_ADDRESS>, <IP_ADDRESS>, <IP_ADDRESS>, ...
Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|<IP_ADDRESS>|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 61194 (60K) [text/plain]
Saving to: ‘train.csv’
train.csv 100%[===================>] 59.76K --.-KB/s in 0.05s
2019-09-13 15:30:05 (1.07 MB/s) - ‘train.csv’ saved [61194/61194]
--2019-09-13 15:30:05-- https://raw.githubusercontent.com/rpi-techfundamentals/spring2019-materials/master/input/test.csv
Resolving raw.githubusercontent.com (raw.githubusercontent.com)... <IP_ADDRESS>, <IP_ADDRESS>, <IP_ADDRESS>, ...
Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|<IP_ADDRESS>|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 28629 (28K) [text/plain]
Saving to: ‘test.csv’
test.csv 100%[===================>] 27.96K --.-KB/s in 0.03s
2019-09-13 15:30:05 (1012 KB/s) - ‘test.csv’ saved [28629/28629]
import numpy as np
import pandas as pd
# Input data files are available in the "../input/" directory.
# Let's input them into a Pandas DataFrame
train = pd.read_csv("train.csv")
test = pd.read_csv("test.csv")
train
| PassengerId | Survived | Pclass | Name | Sex | Age | SibSp | Parch | Ticket | Fare | Cabin | Embarked | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 0 | 3 | Braund, Mr. Owen Harris | male | 22.0 | 1 | 0 | A/5 21171 | 7.2500 | NaN | S |
| 1 | 2 | 1 | 1 | Cumings, Mrs. John Bradley (Florence Briggs Th... | female | 38.0 | 1 | 0 | PC 17599 | 71.2833 | C85 | C |
| 2 | 3 | 1 | 3 | Heikkinen, Miss. Laina | female | 26.0 | 0 | 0 | STON/O2. 3101282 | 7.9250 | NaN | S |
| 3 | 4 | 1 | 1 | Futrelle, Mrs. Jacques Heath (Lily May Peel) | female | 35.0 | 1 | 0 | 113803 | 53.1000 | C123 | S |
| 4 | 5 | 0 | 3 | Allen, Mr. William Henry | male | 35.0 | 0 | 0 | 373450 | 8.0500 | NaN | S |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 886 | 887 | 0 | 2 | Montvila, Rev. Juozas | male | 27.0 | 0 | 0 | 211536 | 13.0000 | NaN | S |
| 887 | 888 | 1 | 1 | Graham, Miss. Margaret Edith | female | 19.0 | 0 | 0 | 112053 | 30.0000 | B42 | S |
| 888 | 889 | 0 | 3 | Johnston, Miss. Catherine Helen "Carrie" | female | NaN | 1 | 2 | W./C. 6607 | 23.4500 | NaN | S |
| 889 | 890 | 1 | 1 | Behr, Mr. Karl Howell | male | 26.0 | 0 | 0 | 111369 | 30.0000 | C148 | C |
| 890 | 891 | 0 | 3 | Dooley, Mr. Patrick | male | 32.0 | 0 | 0 | 370376 | 7.7500 | NaN | Q |
891 rows × 12 columns
Pivot Tables¶
A pivot table is a data summarization tool.
It can be used to that sum, sort, averge, count, over a pandas dataframe.
Download and open data in excel to appreciate the ways that you can use Pivot Tables.
#Load it and create a pivot table.
from google.colab import files
files.download('train.csv')
pd.pivot_table?
pd.pivot_table(train,index=["Sex","Pclass"],values=["Survived"],aggfunc=['count','sum','mean',])
| count | sum | mean | ||
|---|---|---|---|---|
| Survived | Survived | Survived | ||
| Sex | Pclass | |||
| female | 1 | 94 | 91 | 0.968085 |
| 2 | 76 | 70 | 0.921053 | |
| 3 | 144 | 72 | 0.500000 | |
| male | 1 | 122 | 45 | 0.368852 |
| 2 | 108 | 17 | 0.157407 | |
| 3 | 347 | 47 | 0.135447 |
The above
#What does this tell us?
train.groupby(['Sex','Pclass']).Survived.mean()
#What does this tell us? Here it doesn't look so clear. We could separate by set age ranges.
train.groupby(['Sex','Age']).Survived.mean()
Combining Multiple¶
Splitting the data into groups based on some criteria
Applying a function to each group independently
Combining the results into a data structure
s = train.groupby(['Sex','Pclass'], as_index=False).Survived.sum()
s['PerSurv'] = train.groupby(['Sex','Pclass'], as_index=False).Survived.mean().Survived
s['PerSurv']=s['PerSurv']*100
s['Count'] = train.groupby(['Sex','Pclass'], as_index=False).Survived.count().Survived
survived =s.Survived
s
#What does this tell us?
spmean=train.groupby(['Sex','Pclass']).Survived.mean()
spcount=train.groupby(['Sex','Pclass']).Survived.sum()
spsum=train.groupby(['Sex','Pclass']).Survived.count()
spmean


Spring integration. The {@link solidbase.spring.UpgradeBean} makes it possible to upgrade the database with a Spring bean.


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{% extends "layout.html" %} {% block title %}Send a Postcard —{% endblock %} {% block content %}
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{% extends 'base/base.html' %} {% load static %} {% block exp_info_block %}
{% endblock %} {% block exp_block %} Below is a description of the search task that we would like you to undertake. The search task is a description of the topic, and the type of information within documents that would be relevant to this specific topic. {% if diversity < 3 %}
For this task you must find RELEVANT and DIFFERENT documents.
{% else %}For this task you must find RELEVANT documents.
{% endif %}{% if task == 0 %}Practice Task {% else %}Search Task: {{ task }}{% endif %}
{% autoescape off %} {{ taskdescription }} {% endautoescape %}
{% if diversity < 3 %}{% autoescape off %} {{ diversify }} {% endautoescape %}
{% endif %} {% endblock %} {% block exp_footer_block %} Participant: {{ participant }} - Condition: {{ condition }} - Task: {{ task }} {% endblock %}


Go to the source code of this file.
Typedefs | |
| typedef struct le_sem_t * | le_sem_Ref_t |
Functions | |
| le_sem_Ref_t | le_sem_Create (const char *name, int32_t initialCount) |
| le_sem_Ref_t | le_sem_CreateTraceable (const char *name, int32_t initialCount) |
| void | le_sem_Delete (le_sem_Ref_t semaphorePtr) |
| le_sem_Ref_t | le_sem_FindSemaphore (const char *name) |
| void | le_sem_Wait (le_sem_Ref_t semaphorePtr) |
| le_result_t | le_sem_TryWait (le_sem_Ref_t semaphorePtr) |
| le_result_t | le_sem_WaitWithTimeOut (le_sem_Ref_t semaphorePtr, le_clk_Time_t timeToWait) |
| void | le_sem_Post (le_sem_Ref_t semaphorePtr) |
| int | le_sem_GetValue (le_sem_Ref_t semaphorePtr) |
Legato Semaphore API include file.
Copyright (C) Sierra Wireless Inc. Use of this work is subject to license.
| typedef struct le_sem_t* le_sem_Ref_t |
Reference to Semaphore structure.
| le_sem_Ref_t le_sem_Create | ( | const char * | name, |
| int32_t | initialCount | ||
| ) |
Create a semaphore shared by threads within the same process.
| [in] | name | Name of the semaphore. |
| [in] | initialCount | Initial number of semaphore. |
| le_sem_Ref_t le_sem_CreateTraceable | ( | const char * | name, |
| int32_t | initialCount | ||
| ) |
Create a traceable semaphore shared by threads within the same process.
| [in] | name | Name of the semaphore. |
| [in] | initialCount | Initial number of semaphore. |
| void le_sem_Delete | ( | le_sem_Ref_t | semaphorePtr | ) |
Delete a semaphore.
| [in] | semaphorePtr | Pointer to the semaphore. |
| le_sem_Ref_t le_sem_FindSemaphore | ( | const char * | name | ) |
Finds a specified semaphore's name.
| [in] | name | Name of the semaphore. |
| int le_sem_GetValue | ( | le_sem_Ref_t | semaphorePtr | ) |
Get the value of a semaphore.
| [in] | semaphorePtr | Pointer to the semaphore. |
| void le_sem_Post | ( | le_sem_Ref_t | semaphorePtr | ) |
Post a semaphore.
| [in] | semaphorePtr | Pointer to the semaphore. |
| le_result_t le_sem_TryWait | ( | le_sem_Ref_t | semaphorePtr | ) |
Try to wait for a semaphore.
It's the same as le_sem_Wait, except if it can't be immediately performed, then returns an LE_WOULD_BLOCK instead of blocking it.
| [in] | semaphorePtr | Pointer to the semaphore. |
| void le_sem_Wait | ( | le_sem_Ref_t | semaphorePtr | ) |
Wait for a semaphore.
| [in] | semaphorePtr | Pointer to the semaphore. |
| le_result_t le_sem_WaitWithTimeOut | ( | le_sem_Ref_t | semaphorePtr, |
| le_clk_Time_t | timeToWait | ||
| ) |
Wait for a semaphore with a limit on how long to wait.
| [in] | semaphorePtr | Pointer to the semaphore. |
| [in] | timeToWait | Time to wait |


input type=password
element
with label
code
with label + placeholder
code
with label + placeholder + required
code
with label + placeholder + readonly
code
with label + placeholder + disabled
code
Code
<label>code <input id="input1" type="password"></label>
Reference
HTML5 input type=password element
HTML Accessibility API Mappings for input type=password element
Core Accessibility API Mappings for textbox role


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This is a method for Allele-specific DNA Copy Number profiling for whole-Exome sequencing data. Given the allele-specific coverage and site biases at the variant loci, this program segments the genome into regions of homogeneous allele-specific copy number. It requires, as input, the read counts for each variant allele in a pair of case and control samples, as well as the site biases. For detection of somatic mutations, the case and control samples can be the tumor and normal sample from the same individual. The implemented method is based on the paper: Chen, H., Jiang, Y., Maxwell, K., Nathanson, K. and Zhang, N. (under review). Allele-specific copy number estimation by whole Exome sequencing.
| Version: | 0.2 |
| Depends: | R (≥ 3.0.1) |
| Published: | 2017-02-24 |
| Author: | Hao Chen and Nancy R. Zhang |
| Maintainer: | Hao Chen <hxchen at ucdavis.edu> |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: | yes |
| CRAN checks: | falconx results |
| Reference manual: | falconx.pdf |
| Package source: | falconx_0.2.tar.gz |
| Windows binaries: | r-devel: falconx_0.2.zip, r-release: falconx_0.2.zip, r-oldrel: falconx_0.2.zip |
| OS X binaries: | r-release: falconx_0.2.tgz, r-oldrel: falconx_0.2.tgz |
| Old sources: | falconx archive |
Please use the canonical form https://CRAN.R-project.org/package=falconx to link to this page.


[gecode-users] Number of propagators
Gustavo Gutierrez
ggutierrez at cic.puj.edu.co
Tue May 23 23:08:14 CEST 2006
Hi Christian,
First of all thanks for the answer and the addition.
On 5/23/06, Christian Schulte <schulte at imit.kth.se> wrote:
> I just added two functions propagators() and branchings() to the trunk
> version that return the desired information but only if the space is stable.
I have a question about space stability and gecode. AFAIK, from mozart
and your PhD thesis, a space is stable when there are no
threads/propagators running. In the gecode case there are no threads
but only propagators (right?), so a space becomes stable after
performing propagation in it (i.e by calling the status method)? or
are there others situations when this can happen?
Thanks in advance,
--
Gustavo Gutierrez
More information about the gecode-users
mailing list


MY IDEA WAS TO CREATE THIS WEBSITE MOST EFFICIENTLY AND TO DESIGN BASIC UI AND WORK ON HOW THE WEBSITE LOOKS
MY IDEA IS TO CREATE THE WEBSITE AND THINK ABOUT HOW IT WORKS


--- layout: default title: Home --- {% for post in site.posts offset: 0 limit: 50 %}
{{ post.excerpt }} Read more
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This package contains classes that can be used by modules that need to work with Search in Projects dialog, or want to share information about what data was recently searched. There are also some utility classes for general usage.
If you want to get or store recently searched items, see
SearchHistory
,
SearchPattern
and ReplacePattern
To display Find in Project dialog, and start a search with
predefine criteria, use
SearchControl
with SearchScopeOptions
.
Class SearchRoot
defines
a location in a filesystem where the search should start.
RegexpUtil
can create a pattern
for matching file names. These classes can be useful when implementing some custom search infrastructure.


I am a professional specializing in lifestyle photography.
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tell us what tasq you need done cancel
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provide any extra details or requirements


To use SMSLib, you should install the following components:
You have two options:
The installation procedure for both the old Java Comm v2 and the new Java Comm v3 is identical.
Java Comm v2 is for Win32 systems and it is available for download at the Downloads section.
Java Comm v3 is for Linux systems and it is available for download directly from SUN downloads. Note that registration is required.
To install it, unzip the downloaded archive file in a temporary place and do the following copies:
JDKDIR/jre/lib/ext/
JDKDIR/jre/lib/
JDKDIR/jre/bin/
If you have a separate JRE directory, do the same copies for the JREDIR directory!
RxTx binaries' pack is available from here. The provided link will download the distro with some standard binaries (for linux 32bit, 64 bit, etc). If binaries for your specific operating system are not included, you may consider recompiling RxTx yourselves.
To install it, unzip the distribution file in a temporary place and do the following copies:
JDKDIR/jre/lib/ext/
JDKDIR/jre/bin/
If you have a separate JRE directory, do the same copies for the JREDIR directory!
Starting from v3.3, SMSLib will select and use whichever comm library is installed via reflection. So there is no need to change anything in the sources - SMSLib will detect and use the comm library you have already installed by itself.
Other issues that you may have to consider:
Download and install Apache Ant from here.
Download Apache log4j from here. You need to have the log4j-1.2.14.jar file (1.2.14 was the current version at the time of this writing) somewhere in your classpath or in Java's lib/ext directories.
Download Apache Jakarta Commons/NET from here. You need to have the commons-net-1.4.1.jar file (1.4.1 was the current version at the time of this writing) somewhere in your classpath or in Java's lib/ext directories.
Download JavaMail from here. You need to have the mail.jar file somewhere in your classpath or in Java's lib/ext directories.
Download JAF from here. You need to have the activation.jar file somewhere in your classpath or in Java's lib/ext directories.
The ANT build file provides the following targets:
Running
ant
(i.e. without a target) will give you a help screen.


Blistering Speed
Hugo is the fastest tool of its kind. At <1 ms per page, the average site builds in less than a second.
What's modern about waiting for your site to build?
Robust Content Management
Hugo supports unlimited content types, taxonomies, menus, dynamic API-driven content, and more, all without plugins.
Flexibility rules. Hugo is a content strategist's dream.
Shortcodes
We love the beautiful simplicity of markdown’s syntax, but there are times when we want more flexibility. Hugo shortcodes allow for both beauty and flexibility.
Hugo's shortcodes are Markdown's hidden superpower.
Built-in Templates
Hugo ships with pre-made templates to make quick work of SEO, commenting, analytics and other functions. One line of code, and you're done.
Hugo has common patterns to get your work done quickly.
Multilingual and i18n
Hugo provides full i18n support for multi-language sites with the same straightforward development experience Hugo users love in single-language sites.
Polyglot baked in.
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Capable Templating
Hugo's Go-based templating provides just the right amount of logic to build anything from the simple to complex. If you prefer Jade/Pug-like syntax, you can also use Amber, Ace, or any combination of the three.
We welcome all contributions
GitCourse is open-source and completely free.
Our contributors make GitCourse great.


rnalysis package¶
rnalysis.filtering module¶
This module can import HTCount and DESeq .csv files, perform various filtering operations on them, perform set operations (union, intersection, etc), run basic exploratory analyses and plots (such as PCA, clustergram, violin plots, scatter, etc), save the filtered files to the computer, and return set of features that appear in an imported DESeq file. When a filtered/modified DESeq/HTCount is saved back to disk, its new name will include by default all of the operations performed on it, in the order they were performed, to allow easy traceback of analyses.
-
class
rnalysis.filtering.
DESeqFilter
(fname: str) Bases:
rnalysis.filtering.Filter
A class that receives a DESeq output file and can filter it according to various characteristics.
- Parameters
fname (str or pathlib.Path) – name of the .csv DESeq file to be loaded.
-
describe
(percentiles: list = [0.01, 0.25, 0.5, 0.75, 0.99]) Generate descriptive statistics that summarize the central tendency, dispersion and shape of the dataset’s distribution, excluding NaN values. For more information see the documentation of pandas.DataFrame.describe.
- Parameters
percentiles (list-like of numbers, optional) – The percentiles to include in the output. All should fall between 0 and 1. The default is [.25, .5, .75], which returns the 25th, 50th, and 75th percentiles.
- Returns
Summary statistics of the dataset. :rtype: Series or DataFrame
-
difference
(other, return_type: str = 'set') Returns a set/string of the WBGene indices that exist in the first DESeqFilter object but NOT in the second.
- Parameters
other (DESeqFilter or set.) – a second DESeqFilter object or set to calculate difference with.
return_type – If ‘set’, returns a set of the WBGene indices that exist only in the first DESeqFilter. If ‘str’, returns a string of the WBGene indices that exist only in the first DESeqFilter, delimited by a comma.
- Returns
a set/string of the WBGene indices that that exist only in the first DESeqFilter object (set difference).
-
features_set
() → set Returns all of the features in the current DataFrame (which were not removed by previously used filter methods) as a set. Warning: if any duplicate features exist in the filter object (same WBGene appears more than once), the corresponding WBGene index will appear in the returned set ONLY ONCE.
- Returns
A set of WBGene names.
-
features_string
() → str Returns a string of all WBGene indices in the current DataFrame separated by newline (the WBGene indices which were not filtered out by previously-used filter methods). Warning: if any duplicate features exist in the filter object (same WBGene appears more than once), the corresponding WBGene index will appear in the returned string ONLY ONCE.
- return
A string of WBGene indices separated by newlines (
). For example, “WBGene00000001 WBGene00000003 WBGene12345678”.
-
filter_abs_fold_change
(abslog2fc: float = 2, opposite: bool = False, inplace: bool = True) Filters out all features whose absolute log2 fold change is below the indicated threshold. For example: if log2fc is 2.0, all features whose log2 fold change is between 2 and -2 (went up less than two-fold or went down less than two-fold) will be filtered out.
- Parameters
abslog2fc – The threshold absolute log2 fold change for filtering out a feature. Float or int. All features whose absolute log2 fold change is lower than log2fc will be filtered out.
opposite (bool) – If True, the output of the filtering will be the OPPOSITE of the specified (instead of filtering out X, the function will filter out anything BUT X). If False (default), the function will filter as expected.
inplace (bool) – If True (default), filtering will be applied to the current DESeqFilter object. If False, the function will return a new DESeqFilter instance and the current instance will not be affected.
- Returns
If ‘inplace’ is False, returns a new instance of DESeqFilter.
-
filter_biotype
(biotype='protein_coding', ref: str = 'C:\\Users\\Guy_Teichman\\PycharmProjects\\RNAlysis\\rnalysis\\extendedCelegansIDs_bigTable_39col_2019format_edit.csv', opposite: bool = False, inplace: bool = True) Filters out all features that do not match the indicated biotype. Legal inputs: ‘protein_coding’,’pseudogene’,’piRNA’,’miRNA’,’ncRNA’,’lincRNA’,’rRNA’,’snRNA’,’snoRNA’.
- Parameters
biotype – str
ref – Name of the reference file used to determine biotype. Default is the BigTable.
opposite (bool) – If True, the output of the filtering will be the OPPOSITE of the specified (instead of filtering out X, the function will filter out anything BUT X). If False (default), the function will filter as expected.
inplace (bool) – If True (default), filtering will be applied to the current Filter object. If False, the function will return a new Filter instance and the current instance will not be affected.
- Returns
If ‘inplace’ is False, returns a new instance of Filter object.
-
filter_by_bigtable_group
(attributes: list = None, mode='union', inclusion: bool = True, ref: str = 'C:\\Users\\Guy_Teichman\\PycharmProjects\\RNAlysis\\rnalysis\\extendedCelegansIDs_bigTable_39col_2019format_edit.csv', opposite: bool = False, inplace: bool = True) Filters features by inclusion in or exclusion from a Big Table attribute, or multiple Big Table attributes. When multiple attributes are given, filtering can be done in ‘union’ mode (where features that belong to at least one attribute are not filtered out), or in ‘intersection’ mode (where only features that belong to ALL attributes are not filtered out).
- Parameters
attributes – list of Big Table attributes to filter by.
mode ('union' or 'intersection'.) – ‘union’ or ‘intersection’. If ‘union’, filters out every feature that does not match at least one of the indicated Big Table attributes. If ‘intersection’, filters out every feature that does not match all of the indicated Big Table attributes.
inclusion – boolean. If True (default), features will be filtered by inclusion in the specified criteria (meaning features that belong to the attributes are kept and features that don’t belong to the attributes are filtered out). If False, features will be filtered out by exclusion from the specified criteria (meaning features that DON’T belong to the attributes are kept, and features that do belong to the attributes are filtered out).
ref – filename/path of the Big Table to be used as reference.
opposite (bool) – If True, the output of the filtering will be the OPPOSITE of the specified (instead of filtering out X, the function will filter out anything BUT X). If False (default), the function will filter as expected.
inplace (bool) – If True (default), filtering will be applied to the current Filter object. If False, the function will return a new Filter instance and the current instance will not be affected.
- Returns
If ‘inplace’ is False, returns a new instance of DESeqFilter.
-
filter_fold_change_direction
(direction: str = 'pos', opposite: bool = False, inplace: bool = True) Filters out features according to the direction in which they changed between the two conditions.
- Parameters
direction – ‘pos’ or ‘neg’. If ‘pos’, will keep only features that have positive log2foldchange. If ‘neg’, will keep only features that have negative log2foldchange.
opposite (bool) – If True, the output of the filtering will be the OPPOSITE of the specified (instead of filtering out X, the function will filter out anything BUT X). If False (default), the function will filter as expected.
inplace (bool) – If True (default), filtering will be applied to the current DESeqFilter object. If False, the function will return a new DESeqFilter instance and the current instance will not be affected.
- Returns
If ‘inplace’ is False, returns a new instance of DESeqFilter.
-
filter_significant
(alpha: float = 0.1, opposite: bool = False, inplace: bool = True) Removes all features which did not change significantly, according to the provided alpha.
- Parameters
alpha – the significance threshold to determine which genes will be filtered. between 0 and 1.
opposite (bool) – If True, the output of the filtering will be the OPPOSITE of the specified (instead of filtering out X, the function will filter out anything BUT X). If False (default), the function will filter as expected.
inplace (bool) – If True (default), filtering will be applied to the current DESeqFilter object. If False, the function will return a new DESeqFilter instance and the current instance will not be affected.
- Returns
If ‘inplace’ is False, returns a new instance of DESeqFilter.
-
filter_top_n
(n: int = 100, opposite: bool = False, inplace: bool = True) Removes all features except the ‘n’ most significantly-changed features.
- Parameters
n – an integer. How many genes to keep in the DESeqFilter instance
opposite (bool) – If True, the output of the filtering will be the OPPOSITE of the specified (instead of filtering out X, the function will filter out anything BUT X). If False (default), the function will filter as expected.
inplace (bool) – If True (default), filtering will be applied to the current DESeqFilter object. If False, the function will return a new DESeqFilter instance and the current instance will not be affected.
- Returns
If ‘inplace’ is False, returns a new instance of DESeqFilter.
-
intersection
(other, return_type: str = 'set') Returns a set/string of the WBGene indices that exist in BOTH of the DESeqFilter objects.
- Parameters
other (DESeqFilter or set.) – a second DESeqFilter object or set to calculate intersection with.
return_type – If ‘set’, returns a set of the intersecting WBGene indices. If ‘str’, returns a string of the intersecting WBGene indices, delimited by a comma.
- Returns
a set/string of the WBGene indices that intersect between two DESeqFilter objects.
-
save_csv
(alt_filename=None) Saves the current filtered data to a .csv file.
- Parameters
alt_filename – If None, file name will be generated automatically according to the filtering methods used. if string, will be the name of the saved file. Example input: ‘myfilename’
-
split_by_bigtable_group
(attributes: tuple = None, ref: str = 'C:\\Users\\Guy_Teichman\\PycharmProjects\\RNAlysis\\rnalysis\\extendedCelegansIDs_bigTable_39col_2019format_edit.csv') Splits the Filter object into multiple Filter objects, each corresponding to one of the specified Big Table attributes. Each object contains only features that match its indicated Big Table attribute.
- Parameters
attributes – list of Big Table attributes to filter by.
ref – filename/path of the Big Table to be used as reference.
- Returns
A list of Filter objects, each containing only features that match one Big Table attribute; the Filter objects appear in the list in the same order the Big Table attributes were given in.
-
split_fold_change_direction
() Splits the features in the current DESeqFilter object into two complementary, non-overlapping DESeqFilter objects, based on the direction of their log2foldchange. The first object will contain only features with a positive log2foldchange, the second object will contain only features with a negative log2foldchange.
- Returns
a tuple containing two DESeqFilter objects: the first has only features with positive log2 fold change, and the other has only features with negative log2 fold change.
-
symmetric_difference
(other, return_type: str = 'set') Returns a set/string of the WBGene indices that exist either in the first DESeqFilter object OR the second, but NOT in both (set symmetric difference).
- Parameters
other (DESeqFilter or set.) – a second DESeqFilter object or set to calculate symmetric difference with.
return_type – If ‘set’, returns a set of the WBGene indices that exist in exactly one DESeqFilter. If ‘str’, returns a string of the WBGene indices that exist in exactly one DESeqFilter., delimited by a comma.
- Returns
a set/string of the WBGene indices that that exist t in exactly one DESeqFilter. (set symmetric difference).
-
union
(other, return_type: str = 'set') Returns a set/string of the union of WBGene indices between two DESeqFilter objects (the indices that exist in at least one of the DESeqFilter objects).
- Parameters
other (DESeqFilter or set.) – a second DESeqFilter object or set to calculate union with.
return_type – If ‘set’, returns a set of the union WBGene indices. If ‘str’, returns a string of the union WBGene indices, delimited by a comma.
- Returns
a set/string of the WBGene indices that exist in at least one of the DESeqFilter objects.
-
class
rnalysis.filtering.
HTCountFilter
(fname: str) Bases:
rnalysis.filtering.Filter
A class that receives an HTSeq output file and can filter it according to various characteristics.
- Parameters
fname (str or pathlib.Path) – name of the .csv HTCount file to be loaded.
-
clustergram
(sample_names: list = 'all', metric: str = 'euclidean', linkage: str = 'average') Runs and plots a clustergram on the base-2 log of a given set of samples.
- Parameters
sample_names ('all' or list.) – the names of the relevant samples in a list. Example input: [“1A_N2_25”, “1B_N2_25”, “1C_N2_25”, “2A_rde4_25”, “2B_rde4_25”, “2C_rde4_25”]
metric – the distance metric to use in the clustergram. Example inputs: ‘euclidean’, ‘hamming’, ‘correlation’. For all possible inputs see scipy.spatial.distance.pdist documentation online.
linkage – the linkage method to use in the clustergram. Example inputs: ‘single’, ‘average’, ‘complete’, ‘ward’. For all possible inputs see scipy.cluster.hierarchy.linkage documentation online.
- Returns
A seaborn clustermap object.
-
describe
(percentiles: list = [0.01, 0.25, 0.5, 0.75, 0.99]) Generate descriptive statistics that summarize the central tendency, dispersion and shape of the dataset’s distribution, excluding NaN values. For more information see the documentation of pandas.DataFrame.describe.
- Parameters
percentiles (list-like of numbers, optional) – The percentiles to include in the output. All should fall between 0 and 1. The default is [.25, .5, .75], which returns the 25th, 50th, and 75th percentiles.
- Returns
Summary statistics of the dataset. :rtype: Series or DataFrame
-
features_set
() → set Returns all of the features in the current DataFrame (which were not removed by previously used filter methods) as a set. Warning: if any duplicate features exist in the filter object (same WBGene appears more than once), the corresponding WBGene index will appear in the returned set ONLY ONCE.
- Returns
A set of WBGene names.
-
features_string
() → str Returns a string of all WBGene indices in the current DataFrame separated by newline (the WBGene indices which were not filtered out by previously-used filter methods). Warning: if any duplicate features exist in the filter object (same WBGene appears more than once), the corresponding WBGene index will appear in the returned string ONLY ONCE.
- return
A string of WBGene indices separated by newlines (
). For example, “WBGene00000001 WBGene00000003 WBGene12345678”.
-
filter_biotype
(biotype='protein_coding', ref: str = 'C:\\Users\\Guy_Teichman\\PycharmProjects\\RNAlysis\\rnalysis\\extendedCelegansIDs_bigTable_39col_2019format_edit.csv', opposite: bool = False, inplace: bool = True) Filters out all features that do not match the indicated biotype. Legal inputs: ‘protein_coding’,’pseudogene’,’piRNA’,’miRNA’,’ncRNA’,’lincRNA’,’rRNA’,’snRNA’,’snoRNA’.
- Parameters
biotype – str
ref – Name of the reference file used to determine biotype. Default is the BigTable.
opposite (bool) – If True, the output of the filtering will be the OPPOSITE of the specified (instead of filtering out X, the function will filter out anything BUT X). If False (default), the function will filter as expected.
inplace (bool) – If True (default), filtering will be applied to the current Filter object. If False, the function will return a new Filter instance and the current instance will not be affected.
- Returns
If ‘inplace’ is False, returns a new instance of Filter object.
-
filter_by_bigtable_group
(attributes: list = None, mode='union', inclusion: bool = True, ref: str = 'C:\\Users\\Guy_Teichman\\PycharmProjects\\RNAlysis\\rnalysis\\extendedCelegansIDs_bigTable_39col_2019format_edit.csv', opposite: bool = False, inplace: bool = True) Filters features by inclusion in or exclusion from a Big Table attribute, or multiple Big Table attributes. When multiple attributes are given, filtering can be done in ‘union’ mode (where features that belong to at least one attribute are not filtered out), or in ‘intersection’ mode (where only features that belong to ALL attributes are not filtered out).
- Parameters
attributes – list of Big Table attributes to filter by.
mode ('union' or 'intersection'.) – ‘union’ or ‘intersection’. If ‘union’, filters out every feature that does not match at least one of the indicated Big Table attributes. If ‘intersection’, filters out every feature that does not match all of the indicated Big Table attributes.
inclusion – boolean. If True (default), features will be filtered by inclusion in the specified criteria (meaning features that belong to the attributes are kept and features that don’t belong to the attributes are filtered out). If False, features will be filtered out by exclusion from the specified criteria (meaning features that DON’T belong to the attributes are kept, and features that do belong to the attributes are filtered out).
ref – filename/path of the Big Table to be used as reference.
opposite (bool) – If True, the output of the filtering will be the OPPOSITE of the specified (instead of filtering out X, the function will filter out anything BUT X). If False (default), the function will filter as expected.
inplace (bool) – If True (default), filtering will be applied to the current Filter object. If False, the function will return a new Filter instance and the current instance will not be affected.
- Returns
If ‘inplace’ is False, returns a new instance of DESeqFilter.
-
filter_low_rpm
(threshold: float = 5, opposite: bool = False, inplace: bool = True) remove all features which have less then ‘threshold’ reads per million in all conditions.
- Parameters
threshold (float) – The minimal rpm a feature should have in at least one sample in order not to be filtered out.
opposite (bool) – If True, the output of the filtering will be the OPPOSITE of the specified (instead of filtering out X, the function will filter out anything BUT X). If False (default), the function will filter as expected.
inplace (bool) – If True (default), filtering will be applied to the current HTCountFilter object. If False, the function will return a new HTCountFilter instance and the current instance will not be affected.
- Returns
If ‘inplace’ is False, returns a new instance of HTCountFilter.
-
static
from_folder
(folder_path: str, save_csv: bool = True, norm_to_rpm: bool = True, save_reads_fname: str = None, save_not_counted_fname: str = None) Iterates over htcount files in a given folder and combines them into a single HTCountFilter table. Can also save the count data table and the uncounted data table to .csv files, and normalize the HTCountFilter table to reads per million (RPM). Note that the saved data will always be count data, and not normalized data, regardless if the HTCountFilter table was normalized or not.
- Parameters
folder_path – str or pathlib.Path. Full path of the folder that contains individual htcount .txt files.
save_csv – bool. If True (default), the joint DataFrame of count data and uncounted data will be saved to two separate .csv files. The files will be saved in ‘folder_path’, and named according to the parameters ‘save_reads_fname’ for the count data, and ‘save_not_counted_fname’ for the uncounted data (unaligned, alignment not unique, etc).
norm_to_rpm – bool. If True (default), the HTCountFilter table will be automatically normalized to reads per million (RPM). If False, the HTCountFilter object will not be normalized, and will instead contain absolute count data (as in the original htcount .txt files). Note that if save_csv is True, the saved .csv fill will contain ABSOLUTE COUNT DATA, as in the original htcount .txt files, and NOT normalized data.
save_reads_fname – str. Name under which to save the combined count data table. Does not need to include the ‘.csv’ suffix.
save_not_counted_fname – save_reads_fname: str. Name under which to save the combined uncounted data. Does not need to include the ‘.csv’ suffix.
- Returns
an HTCountFilter object containing the combined count data from all individual htcount .txt files in the specified folder.
-
norm_reads_to_rpm
(all_feature_fname: str, inplace: bool = True) Receives a dataframe of reads/counts and a dataframe of feature counts (ambiguous, no feature, not aligned, etc). Divides each column in the read dataframe by (total reads + ambiguous + no feature)*10^-6 in order to normalize the reads to RPM (reads per million).
- Parameters
all_feature_fname – the .csv file which contains feature information about the RNA library (ambiguous, no feature, not aligned, etc).
inplace – If True (default), filtering will be applied to the current HTCountFilter object. If False, the function will return a new HTCountFilter instance and the current instance will not be affected.
- Returns
a DataFrame normalized to RPM
-
pairplot
(sample_list: list) Plot pairwise relationships in the dataset. Can plot both single samples and average multiple replicates. For more information see the documentation of seaborn.pairplot.
- Parameters
sample_list ('all', list, or nested list.) – A list of the sample names and/or grouped sample names to be included in the pairplot. All specified samples must be present in the HTCountFilter object. To average multiple replicates of the same condition, they can be grouped in an inner list. Example input: [[‘SAMPLE1A’, ‘SAMPLE1B’, ‘SAMPLE1C’], [‘SAMPLE2A’, ‘SAMPLE2B’, ‘SAMPLE2C’],’SAMPLE3’ , ‘SAMPLE6’]
- Returns
An instance of seaborn.PairGrid.
-
run_pca
(sample_names: list = 'all', n_components=3, sample_grouping: list = None) runs and plots a PCA for a given set of samples.
- Parameters
sample_names ('all' or list.) – the names of the relevant samples in a list. Example input: [“1_REP_A”, “1_REP_B”, “1_REP_C”, “2_REP_A”, “2_REP_B”, “2_REP_C”, “2_REP_D”, “3_REP_A”]
n_components – number of PCA components to return have rpm lower than filter_low_rpm in all relevant samples.
sample_grouping – Optional. Indicates which samples are grouped together as replicates, so they will be colored similarly in the PCA plot. A list of indices from 0 and up, that indicates the sample grouping. For example, if sample_names is: [“1_REP_A”, “1_REP_B”, “1_REP_C”, “2_REP_A”, “2_REP_B”, “2_REP_C”, “2_REP_D”, “3_REP_A”], then the sample_grouping will be: [0, 0, 0, 1, 1, 1, 1, 2]
- Returns
A tuple whose first element is an sklearn.decomposition.pca object, and second element is a list of matplotlib.axis objects.
-
save_csv
(alt_filename=None) Saves the current filtered data to a .csv file.
- Parameters
alt_filename – If None, file name will be generated automatically according to the filtering methods used. if string, will be the name of the saved file. Example input: ‘myfilename’
-
scatter_rpm_vs_rpm
(sample1: str, sample2: str, xlabel: str = None, ylabel: str = None, deseq_highlight=None) Generate a scatter plot where every dot is a feature, the x value is log10 of rpm in sample1, the y value is log10 of rpm in sample2.
- Parameters
sample1 – str/list. Name of the first sample from the HTCountFilter object. If sample1 is a list, they will be avarged as replicates.
sample2 – str/list. Name of the second sample from the HTCountFilter object. If sample2 is a list, they will be averaged as replicates.
xlabel – optional. If not specified, sample1 will be used as xlabel.
ylabel – optional. If not specified, sample2 will be used as ylabel.
deseq_highlight – DESeqFilter object or iterable of WBGene indices(optional). If specified, the points in the scatter corresponding to the WBGene indices in deseq_highlight will be highlighted in red.
- Returns
a matplotlib axis object.
-
split_by_bigtable_group
(attributes: tuple = None, ref: str = 'C:\\Users\\Guy_Teichman\\PycharmProjects\\RNAlysis\\rnalysis\\extendedCelegansIDs_bigTable_39col_2019format_edit.csv') Splits the Filter object into multiple Filter objects, each corresponding to one of the specified Big Table attributes. Each object contains only features that match its indicated Big Table attribute.
- Parameters
attributes – list of Big Table attributes to filter by.
ref – filename/path of the Big Table to be used as reference.
- Returns
A list of Filter objects, each containing only features that match one Big Table attribute; the Filter objects appear in the list in the same order the Big Table attributes were given in.
-
split_by_rpm
(threshold: float = 5) Splits the features in the current HTCountFilter object into two complementary, non-overlapping HTCountFilter objects, based on the their maximum expression level. The first object will contain only highly-expressed features (which have RPM over the specified threshold in at least one sample). The second object will contain only lowly-expressed features (which have RPM below the specified threshold in all samples).
- Parameters
threshold – A float. The minimal rpm a feature needs to have in at least one sample in order to be included in the “highly expressed” object and no the “lowly expressed” object.
- Returns
A tuple containing two HTCountFilter objects: the first has only highly-expressed features, and the second has only lowly-expressed features.
-
violin_plot
(samples='all') Generates a violin plot of the specified samples in the HTCountFilter object. Can plot both single samples and average multiple replicates. It is recommended to use this function on normalized values and not on absolute read values. Box inside the violin plot indicates 25% and 75% percentiles, and the white dot indicates the median.
- Parameters
samples ('all' or list.) – A list of the sample names and/or grouped sample names to be plotted in the violin plot. All specified samples must be present in the HTCountFilter object. To average multiple replicates of the same condition, they can be grouped in an inner list. Example input: [[‘SAMPLE1A’, ‘SAMPLE1B’, ‘SAMPLE1C’], [‘SAMPLE2A’, ‘SAMPLE2B’, ‘SAMPLE2C’],’SAMPLE3’ , ‘SAMPLE6’]
- Returns
a seaborn violin object.
rnalysis.enrichment module¶
This module receives a set of feature names, and can perform various enrichment analyses on them, such as GO enrichment, tissue/phenotype enrichment, enrichment and depletion of big table columns, etc. Furthermore, set operations (union, intersect, difference, symmetric difference) can be performed on two EnrichmentProcessing objects. Results of enrichment analyses can be saved to .csv files.
-
class
rnalysis.enrichment.
EnrichmentProcessing
(gene_set: set = None) Bases:
object
receives a filtered gene set and preforms various enrichment analyses
-
difference
(other, inplace: bool = True) Calculates the set difference of the WBGene indices from two EnrichmentProcessing objects (the indices that appear in the first EnrichmentProcessing object but NOT in the second object).
- Parameters
other – A second EnrichmentProcessing object, or a set of WBGene indices , against which the current object will be compared.
inplace – boolean. If True (default), modifies the current instance of EnrichmentProcessing. If False, returns a new instance of EnrichmentProcessing.
- Returns
if inplace is False, returns a new instance of EnrichmentProcessing.
-
enrich_big_table
(attributes: list = None, fdr: float = 0.05, reps=10000, biotype: str = 'protein_coding', big_table_pth: str = 'C:\\Users\\Guy_Teichman\\PycharmProjects\\RNAlysis\\rnalysis\\extendedCelegansIDs_bigTable_39col_2019format_edit.csv', save_csv: bool = True, fname=None) Calculates enrichment scores, p-values and q-values for enrichment and depletion of selected attributes from the Big Table. P-values are calculated using a randomization test, and corrected for multiple comparisons using the Benjamini–Hochberg step-up procedure (original FDR method). Enrichment/depletion is determined automatically by the calculated enrichment score: if log2(enrichment score) is positive then enrichment is assumed, and if log2(enrichment score) is negative then depletion is assumed.
- param attributes
An iterable of attribute names (strings). If None, a manual input prompt will be raised.
- param fdr
float. Indicates the FDR threshold for significance.
- param reps
How many repetitions to run the randomization for. 10,000 is the default. Recommended 10,000 or higher.
- param big_table_pth
the path of the Big Table file to be used as reference.
- param biotype
the biotype you want your reference to have. ‘all’ will include all biotypes, ‘protein_coding’ will include only protein-coding genes in the reference, etc.
- param save_csv
bool. If True, will save the results to a .csv file, under the name specified in ‘fname’.
- param fname
str/Path. The full path and name of the file to which to save the results. For example: r’C:dir
- ile’. No ‘.csv’ suffix is required. If None (default), fname will be requested in a manual prompt.
- return
a pandas DataFrame with the indicated attribute names as rows/index, and the columns ‘log2_enrichment_score’ and ‘pvalue’.
-
go_enrichment
(mode: str = 'go', alpha: float = 0.05, save_csv: bool = False, fname: str = None) Analyzes GO, Tissue and/or Phenotype enrichment of the given group of features. Uses the the Anatomy, Phenotype and Gene Ontology annotations for C. elegans. Corrected p-values are calculated using hypergeometric statistics. For more details see GitHub page of the developers: https://github.com/dangeles/TissueEnrichmentAnalysis
- Parameters
mode – the enrichment you wish to perform. ‘go’ for gene ontology enrichment, ‘tissue’ for tissue enrichment, ‘phenotype’ for phenotype enrichment.
alpha – float. Significance threshold. Default is 0.05
save_csv – bool. False by default. If True, save the result to a csv.
fname – Name and path in which to save the results. Must be filled if save_csv is True.
- Returns
a DataFrame which contains the significant enrichmenet terms
-
intersect
(other, inplace: bool = True) Calculates the set intersection of the WBGene indices from two EnrichmentProcessing objects (the indices that exist in BOTH of the EnrichmentProcessing objects).
- Parameters
other – A second EnrichmentProcessing object, or a set of WBGene indices , against which the current object will be compared.
inplace – boolean. If True (default), modifies the current instance of EnrichmentProcessing. If False, returns a new instance of EnrichmentProcessing.
- Returns
if inplace is False, returns a new instance of EnrichmentProcessing.
-
symmetric_difference
(other, inplace: bool = True) Calculates the set symmetric difference of the WBGene indices from two EnrichmentProcessing objects (the indices that appear in EXACTLY ONE of the EnrichmentProcessing objects, and not both/neither). A-symmetric difference-B is equivalent to (A-difference-B)-union-(B-difference-A).
- Parameters
other – A second EnrichmentProcessing object, or a set of WBGene indices , against which the current object will be compared.
inplace – boolean. If True (default), modifies the current instance of EnrichmentProcessing. If False, returns a new instance of EnrichmentProcessing.
- Returns
if inplace is False, returns a new instance of EnrichmentProcessing.
-
union
(other, inplace: bool = True) Calculates the set union of the WBGene indices from two EnrichmentProcessing objects (the indices that exist in at least one of the EnrichmentProcessing objects).
- Parameters
other – A second EnrichmentProcessing object, or a set of WBGene indices, against which the current object will be compared.
inplace – boolean. If True (default), modifies the current instance of EnrichmentProcessing. If False, returns a new instance of EnrichmentProcessing.
- Returns
if inplace is False, returns a new instance of EnrichmentProcessing.
-
rnalysis.general module¶
This module contains general-purpose functions, such as loading and saving files, removing table rows with no WBGene index, identifying type of input variables, etc. This module is used mainly by other modules.
-
rnalysis.general.
check_is_df
(inp) checks whether an input file is a pandas DataFrame, a string that represent a path of a .csv file, a Path object of a .csv file, or an invalid input.
- Parameters
inp – the input we wish to test
- Returns
True if pandas DataFrame, False if a string/Path object that leads to a .csv file. Raises ValueError otherwise.
-
rnalysis.general.
filter_low_rpm
(df: pandas.core.frame.DataFrame, threshold: float = 5) remove all features which have less then ‘threshold’ reads per million in all conditions.
- Parameters
df – pandas DataFrame to be filtered
threshold – float. The minimal rpm a feature needs to have in at least one sample in order not to be filtered out.
- Returns
A filtered DataFrame
-
rnalysis.general.
load_csv
(filename: str, idx_col: int = None, drop_gene_names: bool = True) loads a csv df into a pandas dataframe.
- Parameters
filename – name of the csv file to be loaded
idx_col – number of column to be used as index. default is None, meaning no column will be used as index.
drop_gene_names – bool. If True, tries to drop the ‘genes’ column from the DataFrame (useful for filtering).
- Returns
a pandas dataframe of the csv file
-
rnalysis.general.
norm_reads_to_rpm
(htcount: pandas.core.frame.DataFrame, features: pandas.core.frame.DataFrame) Receives a dataframe of reads/counts and a dataframe of feature counts (ambiguous, no feature, not aligned, etc). Divides each column in the read dataframe by (total reads + ambiguous + no feature)*10^-6 in order to normalize the reads to RPM (reads per million).
- Parameters
htcount – DataFrame of reads/counts
features – the corresponding DataFrame of
- Returns
a DataFrame normalized to RPM
-
rnalysis.general.
normalize_file
(htcount_filename: str, feature_filename: str) receives an htcount filename and a feature filename, normalizes the htcount file to rpm, removes unindexed rows, and saves the file with the suffix ‘_rpm’.
- Parameters
htcount_filename – str or pathlib.Path object. Name of the htcount file to be normalized
feature_filename – str or pathlib.path object. Name of the corresponding feature file to be used for normalizing
-
rnalysis.general.
remove_unindexed_rows
(df: pandas.core.frame.DataFrame) removes rows which have no WBGene index.
- Parameters
df – a DataFrame with WBGene indices
- Returns
a new DataFrame in which all rows with no WBGene index are removed
-
rnalysis.general.
save_to_csv
(df: pandas.core.frame.DataFrame, filename: str, suffix: str) save a pandas DataFrame to csv.
- Parameters
df – pandas DataFrame to be saved
filename – a string or pathlib.Path object stating the original name of the file
suffix – str, a suffix to be added to the original name of the file
rnalysis.feature_name_to_wbgene module¶
This module can append WBGene indices to feature names in a DESeq/HTCount .csv file, according to a reference table (such as the Big Table).
-
class
rnalysis.feature_name_to_wbgene.
GeneNameTranslator
(target_filename, reference_filename: str = 'C:\Users\Guy_Teichman\PycharmProjects\RNAlysis\rnalysis\extendedCelegansIDs_bigTable_39col_2019format_edit.csv', ref_wbgene_col: str = 'gene', ref_gene_col: str = 'gene ID', ref_seq_col: str = 'Sequence ID', ref_other_col: str = 'Other IDs', target_gene_col: str = 'genes') Bases:
object
A class that takes a file with gene names that does not have WBGene indices, and a reference file that includes gene names and their corresponding WBGene indices, and adds WBGene indices to the original file.
-
generate_target_dict
() Generate the feature name to WBGene dictionary, and translate the imported pandas DataFrame feature names to WBGene.
-
import_target
() import the target filename into a pandas DataFrame
-
read_reference
() load the reference file into a pandas DataFrame
-
rewrite_target
() save the target file as a csv with the suffix “_WBGene”.
-
run
() load the specified file, translate its feature names to WBGenes, and save it as a csv with the “_WBGene” suffix.
-
-
rnalysis.feature_name_to_wbgene.
name_to_wbgene_folder
(pth: str) iterate over all files in a directory, translate their feature names to WBGene and save them as csv in the same folder. :param pth: path of the folder on which to iterate :return: a list of all GeneNameTranslator objects
-
rnalysis.feature_name_to_wbgene.
run_all
(path) run the translator on a list of files


Calculated Statistics
Player Statistics
Players With Shop Access:
Players With Shop Access and Multi-Accounting:
Players Without Shop Access:
Income Without Shop Access
Tial Farming Per Day:
Total Weekly Income:
Farm PSE Drops+Trials
Drop Income Per Day:
Total Weekly Income:
Farm Veteren Enemy Drops
Drop Income Per Day:
Total Weekly Income:
Farm Gigantix Drops
Drop Income Per Day:
Total Weekly Income:
Farm Gigantix+Veteren Drops
Drop Income Per Day:
Total Weekly Income:
Simulated Statistics
Assumptions
1) All players similar spending habits.
2) Player farming methods have little effect on meseta distribution between players with and without shop access in the long run.
Players With Shop Accesss
Held Meseta:
Spent Meseta:
Held Meseta Per Player:
Spent Meseta Per Player:
Players Without Shop Accesss
Held Meseta:
Spent Meseta:
Held Meseta Per Player:
Spent Meseta Per Player:


Albania: RTSH releases the Festivali i Këngës 58 entries
by Sanjay (Sergio) JiandaniRTSH, the Albanian national broadcaster, has released the Festivali i Këngës 58 competing entries.
The 58th edition of Festivali i Këngës is scheduled at the Congress Palace in Tirana on 19, 20 and 22 December. The 2020 Albanian Eurovision hopeful will be determined on Satuday 22 December. The winner of FiK58 will fly to Rotterdam.
A total of 20 acts will battle for the golden ticket to Eurovision 2020 at FiK58. Hence Albania will be holding the the first national final of the ESC 2020 season and thus paving the road to Rotterdam for the competing nations!
RTSH has announced that two former Eurovision celebrities will be gracing the forthcoming event in Tirana: Eleni Foureira (Cyprus 2018) and Mahmood ( Italy 2019). The Albanian broadcaster has released the 20 entries competing at FiK58 on its official Youtube Channel.
The 20 FiK 58 entries
- Wendi Mancaku – Ende
- Valon Shehu – Kutia e Pandorës
- Tiri Gjoci – Me gotën bosh
- Sara Bajraktari – Ajër
- Robert Berisha – Ajo nuk është unë
- Renis Gjoka – Loja
- Olta Boka – Shkrime në mur
- Nadia & Genc Tukiçi – Ju flet Tirana
- Kastro Zizo (né Kleviz Bega) – Asaj
- Kanita – Ankth
- Kamela Islamaj – Më ngjyros
- Gena – Shqiponja e lirë
- Era Rusi – Eja Merre
- Elvana Gjata – Me tana
- Eli Fara & Stresi – Bohem
- Devis Xherahu – Bisedoj me serenatën
- Bojken Lako – Malaseen
- Arilena Ara –Shaj
- Aldo Bardhi – Melodi
- Albërie Hadërgjonaj- Ku ta gjej dikë ta dua
Albania in Eurovision
Albania debuted at the Eurovision Song Contest in 2004 with Anjeza Shahini and is yet to win the competition. The country’s best result in the contest in the contest was achieved in 2012 when Rona Nishliu placed 5th in the Grand Final in Baku.
The Balkan country has participated in Europe’s favourite television show 16 times and has not missed a single contest since its debut in 2004.
In 2019 Jonida Maliqi won the Albanian national final and flew to Tel Aviv with his entry Ktheju Tokes, placing 17th in the Grand Final.


Freela
tor
First of all, we need some info regarding your freelance gig.
How many hours are you working per day?
How many days per week?
How many vacation days?
How much did the project cost?
Show me the money!
Your rate per hour is
Nice! 💵


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The Angel
The Angel is a movie starring Toby Kebbell, Marwan Kenzari, and Hannah Ware. Based on NYT bestselling book 'The Angel: The Egyptian Spy Who Saved Israel' by Uri Bar-Joseph.


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Logme - Python Logging Made Easy¶
Logme is a Python package that makes logging simple and robost. If you have found logging in Python not so straight forward, download this package and give it a try! :)
In a Nutshell¶
If you have a function you want to log, you can do this in your python file:
import logme
@logme.log
def my_awesome_function(my_arg, logger=None):
logger.info('this is my log message')
"""rest of the function"""
You can do the same with classes too:
import logme
@logme.log
class MyAwesomeClass:
def my_function(self, my_arg):
self.logger.info('this is my log message')
pretty nice right? :)