initial commit

.gitattributes

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+*.conf text eol=lf
+*.md text eol=lf
+*.md5 text eol=lf
+*.py text eol=lf
+*.xml text eol=lf
+LICENSE text eol=lf
+COMMITMENT text eol=lf
+
+*_ binary
+*.dll binary
+*.pdf binary
+*.so binary
+*.wav binary
+*.zip binary
+*.x32 binary
+*.x64 binary
+*.exe binary
+*.sln binary
+*.vcproj binary
+

.github/CODE_OF_CONDUCT.md

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+# Contributor Covenant Code of Conduct
+
+## Our Pledge
+
+In the interest of fostering an open and welcoming environment, we as contributors and maintainers pledge to making participation in our project and our community a harassment-free experience for everyone, regardless of age, body size, disability, ethnicity, gender identity and expression, level of experience, nationality, personal appearance, race, religion, or sexual identity and orientation.
+
+## Our Standards
+
+Examples of behavior that contributes to creating a positive environment include:
+
+* Using welcoming and inclusive language
+* Being respectful of differing viewpoints and experiences
+* Gracefully accepting constructive criticism
+* Focusing on what is best for the community
+* Showing empathy towards other community members
+
+Examples of unacceptable behavior by participants include:
+
+* The use of sexualized language or imagery and unwelcome sexual attention or advances
+* Trolling, insulting/derogatory comments, and personal or political attacks
+* Public or private harassment
+* Publishing others' private information, such as a physical or electronic address, without explicit permission
+* Other conduct which could reasonably be considered inappropriate in a professional setting
+
+## Our Responsibilities
+
+Project maintainers are responsible for clarifying the standards of acceptable behavior and are expected to take appropriate and fair corrective action in response to any instances of unacceptable behavior.
+
+Project maintainers have the right and responsibility to remove, edit, or reject comments, commits, code, wiki edits, issues, and other contributions that are not aligned to this Code of Conduct, or to ban temporarily or permanently any contributor for other behaviors that they deem inappropriate, threatening, offensive, or harmful.
+
+## Scope
+
+This Code of Conduct applies both within project spaces and in public spaces when an individual is representing the project or its community. Examples of representing a project or community include using an official project e-mail address, posting via an official social media account, or acting as an appointed representative at an online or offline event. Representation of a project may be further defined and clarified by project maintainers.
+
+## Enforcement
+
+Instances of abusive, harassing, or otherwise unacceptable behavior may be reported by contacting the project leader at epsylon@riseup.net. The project team will review and investigate all complaints, and will respond in a way that it deems appropriate to the circumstances. The project team is obligated to maintain confidentiality with regard to the reporter of an incident. Further details of specific enforcement policies may be posted separately.
+
+Project maintainers who do not follow or enforce the Code of Conduct in good faith may face temporary or permanent repercussions as determined by other members of the project's leadership.
+
+## Attribution
+
+This Code of Conduct is adapted from the [Contributor Covenant][homepage], version 1.4, available at [http://contributor-covenant.org/version/1/4][version]
+
+[homepage]: http://contributor-covenant.org
+[version]: http://contributor-covenant.org/version/1/4/
+

.github/ISSUE_TEMPLATE/bug_report.md

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+---
+name: Bug report
+about: Create a report to help us improve
+title: ''
+labels: bug report
+assignees: ''
+
+---
+
+**Describe the bug**
+A clear and concise description of what the bug is.
+
+**To Reproduce**
+1. Run '...'
+2. See error
+
+**Expected behavior**
+A clear and concise description of what you expected to happen.
+
+**Screenshots**
+If applicable, add screenshots to help explain your problem.
+
+**Running environment:**
+ - DiaNA version [e.g. 0.1]
+ - Installation method [e.g. git]
+ - Operating system: [e.g. Debian 4.19.16-1~bpo9+1 (2019-02-07) ]
+ - Python version [e.g. 3.7]
+
+**Error details:**
+ - Relevant console output [if any]
+ - Exception traceback [if any]
+
+**Additional context**
+Add any other context about the problem here.
+
+---

.github/ISSUE_TEMPLATE/feature_request.md

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+---
+name: Feature request
+about: Suggest an idea for this project
+title: ''
+labels: feature request
+assignees: ''
+
+---
+
+**Is your feature request related to a problem? Please describe.**
+A clear and concise description of what the problem is. Ex. I'm always frustrated when [...]
+
+**Describe the solution you'd like**
+A clear and concise description of what you want to happen.
+
+**Describe alternatives you've considered**
+A clear and concise description of any alternative solutions or features you've considered.
+
+**Additional context**
+Add any other context or screenshots about the feature request here.
+
+---

.gitignore

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+__pycache__/
+*.py[cod]

README.md

@@ -1,3 +1,44 @@
-# DiaNA
 
-DiaNA can Search and Recognize patterns in DNA sequences.
+![c](https://03c8.net/images/diana.png)
+
+----------
+
+#### Info:
+ 
+ DiaNA is a free software tool for the search and recognition of patterns in DNA sequences.
+
+#### Installing:
+
+ This tool runs on many platforms and it requires Python (3.x.y).
+
+#### Executing:
+  
+  python diana.py (or python3 diana.py)
+
+----------
+
+#### License:
+
+ DiaNA is released under the GPLv3.
+
+#### Contact:
+
+      - psy (epsylon@riseup.net)
+
+#### Contribute: 
+
+ To make donations use the following hash:
+  
+     - Bitcoin: 19aXfJtoYJUoXEZtjNwsah2JKN9CK5Pcjw
+
+----------
+
+####  Screenshots:
+
+  ![c](https://03c8.net/images/diana2.png)
+  ![c](https://03c8.net/images/diana3.png)
+  ![c](https://03c8.net/images/diana4.png)
+  ![c](https://03c8.net/images/diana5.png)
+  ![c](https://03c8.net/images/diana6.png)
+  ![c](https://03c8.net/images/diana7.png)
+

diana.py

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+#!/usr/bin/env python 
+# -*- coding: utf-8 -*-"
+"""
+DiaNA - 2020 - by psy (epsylon@riseup.net)
+
+You should have received a copy of the GNU General Public License along
+with DiaNA; if not, write to the Free Software Foundation, Inc., 51
+Franklin St, Fifth Floor, Boston, MA  02110-1301  USA
+"""
+VERSION = "v0.1_beta"
+RELEASE = "16032020"
+SOURCE1 = "https://code.03c8.net/epsylon/diana"
+SOURCE2 = "https://github.com/epsylon/diana"
+CONTACT = "epsylon@riseup.net - (https://03c8.net)"
+"""
+DNA-equiv:
+ A <-> T
+ C <-> G
+"""
+import re, os, glob, random, time, math 
+
+brain_path = "datasets/brain.in" # in/out brain-tmp file
+genomes_path = 'datasets/' # genome datasets raw data
+genomes_list_path = "datasets/genome.list" # genome list
+dna_letters = ["A", "T", "G", "C", "N"] # dna alphabet [n for ANY nucl.]
+genomes = {} # main sources dict: genome_name
+seeds_checked = [] # list used for random checked patterns
+repeats = {} # repetitions 'tmp' dict: genome_name:(repets,pattern)
+known_patterns = [] # list used for known patterns
+estimated_max_range_for_library_completed = 20 # [MAX. LENGTH] for range [PATTERN]
+estimated_patterns_for_library_completed = 1466015503700 # x = y+4^z
+estimated_quantity_per_pattern_for_library_completed = int(estimated_patterns_for_library_completed / estimated_max_range_for_library_completed)
+
+def convert_size(size):
+    if (size == 0):
+        return '0 B'
+    size_name = ("B", "KB", "MB", "GB", "TB", "PB", "EB", "ZB", "YB")
+    i = int(math.floor(math.log(size,1024)))
+    p = math.pow(1024,i)
+    s = round(size/p,2)
+    return s, size_name[i]
+
+def search_pattern_with_human():
+    pattern = input("[HUMAN] [SEARCH] Pattern (ex: attacg): ").upper()
+    print("\n"+"-"*5 + "\n")
+    create_new_pattern(pattern) # create new pattern
+
+def try_pattern_against_all_genomes(pattern):
+    patterns_found = 0
+    for k, v in genomes.items():
+        if pattern in v:
+            t = len(re.findall(pattern, v))
+            print (" *", k +":", "-> [",t,"times ]")
+            repeats[k] = t, pattern
+            patterns_found = patterns_found + 1
+            print("")
+    if patterns_found == 0:
+        print("[INFO] -> Not any found! ... [EXITING!]\n")
+
+def sanitize_dna_pattern(pattern):
+    valid_pattern = True
+    for c in pattern:
+        if c == "A":
+            pass
+        elif c == "T":
+            pass
+        elif c == "G":
+            pass
+        elif c == "C":
+            pass
+        elif c == "N":
+            pass
+        else:
+            valid_pattern = False
+    return valid_pattern
+
+def teach_ai():
+    mode = input("[TRAIN-AI] MODE -> (H)uman, (A)utomata: ").upper()
+    if not os.path.isfile(brain_path):
+        create_initial_seed_file()
+    if mode == "H": # human mode
+        teach_ai_human_mode()
+    else: # libre AI
+        teach_ai_automata_mode() # automata mode
+
+def teach_ai_human_mode(): # search/discard patterns with human interaction & generate local database
+    search_patterns_lesson_with_a_human()
+
+def search_patterns_lesson_with_a_human():
+    print("\n"+"-"*30)
+    print("\n[TRAIN-AI] [HUMAN] [STOP] this mode; just entering whatever invalid pattern (ex: 'exit' or 'q').\n")
+    key = "K" # continue
+    while key == "K":
+        pattern = input("[TRAIN-AI] [HUMAN] [LOOP] [SEARCH] Pattern (ex: attacg): ").upper()
+        print("\n"+"-"*5 + "\n")
+        key = search_pattern_on_lesson(pattern)
+        if key == "Z": # stop
+            break
+
+def search_pattern_on_lesson(pattern):
+    valid_pattern = sanitize_dna_pattern(pattern)
+    if valid_pattern == True:
+        key = search_pattern_on_local_database(pattern) # search pattern on local database
+    else:
+        print("[ERROR] -> Invalid DNA pattern ... [EXITING!]\n")
+        key = "Z" # stop
+    return key
+
+def search_pattern_on_local_database(pattern):
+    f=open(brain_path, 'r')  
+    memory = f.read().replace('\n',' ')
+    f.close()
+    patterns_known = 0
+    if not "'"+pattern+"'" in memory: # always create new patterns
+        create_new_pattern(pattern) # create new pattern
+        patterns_known = patterns_known + 1
+    else:
+        for k, v in genomes.items(): # create patterns found for new genomes
+            if k not in memory:
+                create_new_pattern(pattern) # create new pattern
+                patterns_known = patterns_known + 1
+    if patterns_known == 0:
+        print("[TRAIN-AI] [AUTOMATA] [LOOP] [RESULTS] -ALREADY- [LEARNED!] ... -> [GOING FOR NEXT!]\n")
+    print("-"*5 + "\n")
+    key = "K" # continue
+    return key
+
+def create_initial_seed_file():
+    f=open(brain_path, 'w')
+    f.write("")
+    f.close()    
+
+def create_new_pattern(pattern): # append it to brain
+    valid_pattern = sanitize_dna_pattern(pattern)
+    if valid_pattern == True:
+        if pattern not in known_patterns:
+            known_patterns.append(pattern)
+            try_pattern_against_all_genomes(pattern)
+    f=open(brain_path, 'a')    
+    f.write(str(repeats)+os.linesep) # add dict as str
+    f.close()
+
+def teach_ai_automata_mode(): # search patterns by bruteforcing ranges & generate local database
+    search_patterns_lesson_with_an_ai()
+
+def search_patterns_lesson_with_an_ai():
+    print("\n"+"-"*30)
+    print("\n[TRAIN-AI] [AUTOMATA] [STOP] this mode; pressing 'CTRL+z'.\n")
+    ranges = input("[TRAIN-AI] [AUTOMATA] [SEARCH] Set range (x<y) for pattern deep searching (ex: 2-8): ")
+    print ("")
+    valid_range, ranged_permutations = check_for_deep_searching_ranges(ranges)
+    if str(valid_range) == "OK!":
+        ranged_ending = False
+        print("-"*15)
+        print("\n[TRAIN-AI] [AUTOMATA] [SEARCH] Number of [PERMUTATIONS] estimated: [ "+str(ranged_permutations)+" ]\n")
+        print("-"*15+"\n")
+        num_pat = 0
+        time.sleep(10)
+        while ranged_ending == False: # try to STOP it using: CTRL-z
+            try:
+                pattern, ranged_ending = generate_random_pattern(ranges, ranged_permutations) # generate random seed
+                if pattern:
+                    num_pat = num_pat + 1
+                    print("[TRAIN-AI] [AUTOMATA] [LOOP] [SEARCH] Generating [RANDOM!] ["+str(num_pat)+"/"+str(ranged_permutations)+"] pattern: [ " + str(pattern) + " ]\n")
+                    if not num_pat == ranged_permutations:
+                        search_pattern_on_lesson(pattern)
+                    else:
+                        search_pattern_on_lesson(pattern)
+                        print("[TRAIN-AI] [AUTOMATA] [RESULTS]: REVIEWED -> [ "+str(ranged_permutations)+" PERMUTATIONS ] ... -> [EXITING!]\n")
+                        ranged_ending = True
+            except:
+                pass
+    else:
+        print("-"*15+"\n")
+        print("[TRAIN-AI] [AUTOMATA] [ERROR] -> [INVALID!] Deep Learning [RANGE] -> "+valid_range+" ... [EXITING!]\n")
+
+def generate_random_pattern(ranges, ranged_permutations):
+    ranged_length = 0
+    try:
+        range_low = int(ranges.split("-")[0])
+        range_high = int(ranges.split("-")[1])
+        for i in range(range_low, range_high+1):
+            ranged_length = ranged_length + 1
+            if ranged_length == ranged_permutations: # all possible variables have been bruteforced/checked! -> exit
+                pattern = None
+                ranged_ending = True
+                return pattern, ranged_ending
+            else:
+                ranged_ending = False
+                seed = [random.randrange(0, 4) for _ in range(i)] # generate "random" seed
+                if seed not in seeds_checked:
+                    seeds_checked.append(seed)
+                    pattern = ""
+                    for n in seed:
+                        if n == 0:
+                            pattern += "A"
+                        elif n == 1:
+                            pattern += "C"
+                        elif n == 2:
+                            pattern += "T"
+                        else:
+                            pattern += "G"
+                    return pattern, ranged_ending
+    except:
+        print("[TRAIN-AI] [AUTOMATA] [ERROR] -> [INVALID!] Deep Learning [RANGE] ... [EXITING!]\n")
+        pattern = None
+        ranged_ending = True
+        return pattern, ranged_ending
+
+def check_for_deep_searching_ranges(ranges):
+    try:
+        range_low = ranges.split("-")[0]
+        range_high = ranges.split("-")[1]
+    except:
+        valid_range = "'bad format'"
+    try:
+        range_low = int(range_low)
+    except:
+        valid_range = "'low range' should be an integer"
+    try:
+        range_high = int(range_high)
+    except:
+        valid_range = "'high range' should be an integer"
+    try:
+        if range_low < range_high:
+            if range_low > 1: # always range > 1
+                valid_range = "OK!"
+            else:
+                valid_range = "'low range' should be > than 1"
+        else:
+            valid_range = "'low range' should be < than 'high range'"
+    except:
+        valid_range = "'bad format'"
+    try:
+        ranged_permutations = math_ranged_permutations(range_low, range_high)
+    except:
+        ranged_permutations = 0
+        valid_range = "'bad format'"
+    return valid_range, ranged_permutations
+
+def math_ranged_permutations(range_low, range_high): # calculate ranged_permutations
+    ranged_permutations = 0
+    for i in range(range_low, range_high+1):
+        ranged_permutations = ranged_permutations + (4**i)
+    return ranged_permutations
+
+def libre_ai(): # show statistics / download new genomes / keep crossing new genomes with local database / search for new patterns (non stop!)
+    if not os.path.isfile(brain_path):
+        create_initial_seed_file()
+    memory = examine_stored_brain_memory() 
+    if memory != "":
+        #print("[LIBRE-AI] [STOP] this mode; pressing 'CTRL+z'.\n")
+        libre_ai_show_statistics(memory) # show statistics
+
+def libre_ai_show_statistics(memory):
+    print("[LIBRE-AI] [REPORTING] [STATISTICS] ... -> [STARTING!]\n")
+    print("-"*15 + "\n")
+    total_genomes = 0
+    total_adenine = 0
+    total_guanine = 0
+    total_cytosine = 0
+    total_thymine = 0
+    total_any = 0
+    secuence_length = 0
+    secuences_list = {}
+    largest = None
+    largest_len = 0
+    shortest_len = 0
+    average = None
+    shortest = None
+    for k, v in genomes.items():
+        secuence_length = len(v)
+        secuences_list[k] = str(secuence_length)
+        total_genomes = total_genomes + 1
+        total_adenine = total_adenine + v.count("A")
+        total_guanine = total_guanine + v.count("G")
+        total_cytosine = total_cytosine + v.count("C")
+        total_thymine = total_thymine + v.count("T")
+        total_any = total_any + v.count("N")
+    largest = max(secuences_list, key=secuences_list.get)
+    shortest = min(secuences_list, key=secuences_list.get) 
+    for k, v in genomes.items():
+        if k == largest:
+            largest_len = len(v)
+        elif k == shortest:
+            shortest_len = len(v)
+        else:
+            pass
+    path = genomes_path # genome datasets raw data
+    l = glob.glob(genomes_path+"*") # black magic!
+    latest_collection_file = max(l, key=os.path.getctime)
+    latest_collection_date = time.ctime(os.path.getmtime(latest_collection_file))
+    total_nucleotids = [total_adenine, total_guanine, total_cytosine, total_thymine, total_any]
+    num_total_nucleotids = total_adenine + total_guanine + total_cytosine + total_thymine + total_any
+    nucleotid_more_present = max(total_nucleotids)
+    print("[LIBRE-AI] [REPORTING] -STORAGE- [STATISTICS]: \n")
+    extract_storage_sizes()
+    print(" * [LATEST UPDATE]: '"+str(latest_collection_date)+"'\n")
+    print("   + File: '"+str(latest_collection_file)+"'\n")
+    print("-"*5 + "\n")
+    print("[LIBRE-AI] [REPORTING] -COLLECTION- [STATISTICS]: \n")
+    extract_total_patterns_learned_from_local(memory)
+    print("\n"+"-"*5 + "\n")
+    print("[LIBRE-AI] [REPORTING] -ANALYSIS- [STATISTICS]: \n")
+    print(" * Total [DNA SECUENCES]: [ "+str(total_genomes)+" ]\n")
+    print("   + [LARGEST]: "+str(largest)+ " [ "+str(largest_len)+" bp linear RNA ]")
+    print("   + [SHORTEST]: "+str(shortest)+ " [ "+str(shortest_len)+" bp linear RNA ]\n")
+    print(" * Total [NUCLEOTIDS]: [ "+str(num_total_nucleotids)+" ]\n")
+    if nucleotid_more_present == total_adenine:
+        print("   + [A] Adenine  : "+str(total_adenine)+" <- [MAX]")
+    else:
+        print("   + [A] Adenine  : "+str(total_adenine))
+    if nucleotid_more_present == total_guanine:
+        print("   + [G] Guanine  : "+str(total_guanine)+" <- [MAX]")
+    else:
+        print("   + [G] Guanine  : "+str(total_guanine))
+    if nucleotid_more_present == total_cytosine:
+        print("   + [C] Cytosine : "+str(total_cytosine)+" <- [MAX]")
+    else:
+        print("   + [C] Cytosine : "+str(total_cytosine))
+    if nucleotid_more_present == total_thymine:
+        print("   + [T] Thymine  : "+str(total_thymine)+" <- [MAX]")
+    else:
+        print("   + [T] Thymine  : "+str(total_thymine))
+    if total_any > 0:
+        if nucleotid_more_present == total_any:
+            print("   + [N]  *ANY*   : "+str(total_any)+" <- [MAX]\n")
+        else:
+            print("   + [N]  *ANY*   : "+str(total_any)+"\n")
+    print("-"*5 + "\n")
+    extract_pattern_most_present_local(memory)
+
+def convert_memory_to_dict(memory): # [index] = genome_name, pattern, num_rep
+    memory_dict = {}
+    index = 0
+    for m in memory:
+        regex_record = "'(.+?)': (.+?), '(.+?)'" # regex magics! - extract first each record 
+        pattern_record = re.compile(regex_record)
+        record = re.findall(pattern_record, m)
+        for r in record: # now extract each field
+            index = index + 1
+            name = str(r).split("', '(")[0]
+            genome_name = str(name).split("'")[1]
+            repeats = str(r).split("', '(")[1]
+            genome_repeats = str(repeats).split("',")[0]
+            pattern = str(repeats).split("',")[1]
+            genome_pattern = pattern.replace(" ", "")
+            genome_pattern = genome_pattern.replace("'", "")
+            genome_pattern = genome_pattern.replace(")", "")  
+            memory_dict[index] = genome_name, genome_pattern, genome_repeats # generate memory_dict!
+    return memory_dict
+
+def extract_pattern_most_present_local(memory):
+    memory_dict = convert_memory_to_dict(memory)
+    if memory_dict:
+        print("[LIBRE-AI] [REPORTING] -RESEARCHING- [STATISTICS]: \n")
+        total_patterns_found = 0
+        total_genomes = 0
+        for k, v in memory_dict.items():
+            total_patterns_found = total_patterns_found + 1
+        for k, v in genomes.items():
+            total_genomes = total_genomes + 1
+        print(" * [ "+str(total_patterns_found)+" ] [PATTERNS FOUND!] in: [ "+str(total_genomes)+ " ] [DNA SECUENCES]\n")
+
+def extract_storage_sizes():
+    total_dataset_size = 0
+    total_files_size = 0
+    total_list_size = 0
+    for file in glob.iglob(genomes_path + '**/*', recursive=True):
+        if(file.endswith(".genome")):
+            total_dataset_size = total_dataset_size + len(file)
+        elif(file.endswith(".in")):
+            total_brain_size = len(file)
+        elif(file.endswith(".list")):
+            total_list_size = len(file)
+    if total_dataset_size > 0:
+        total_files_size = int(total_files_size) + int(total_dataset_size)
+        dataset_s, dataset_size_name = convert_size(total_dataset_size)
+        total_dataset_size = '%s %s' % (dataset_s,dataset_size_name)
+    if total_brain_size > 0:
+        total_files_size = int(total_files_size) + int(total_brain_size)
+        brain_s, brain_size_name = convert_size(total_brain_size)
+        total_brain_size = '%s %s' % (brain_s,brain_size_name)
+    if total_list_size > 0:
+        total_files_size = int(total_files_size) + int(total_list_size)
+        list_s, list_size_name = convert_size(total_list_size)
+        total_list_size = '%s %s' % (list_s,list_size_name)
+    total_s, total_size_name = convert_size(total_files_size)
+    total_files_size = '%s %s' % (total_s,total_size_name)
+    print(" * Total [FILE SIZES]: "+str(total_files_size)+"\n")
+    if total_dataset_size:
+        print("   + [DATASET]: "+str(total_dataset_size))
+    if total_list_size:
+        print("   + [LIST]: "+str(total_list_size))
+    if total_brain_size:
+        print("   + [BRAIN]: "+str(total_brain_size)+"\n")
+
+def extract_total_patterns_learned_from_local(memory):
+    total_patterns = 0
+    for m in memory:
+        total_patterns = total_patterns + 1
+    print(" * [SETTINGS] Using [MAX. LENGTH] for range [PATTERN] = "+str(estimated_max_range_for_library_completed)+"\n")
+    if total_patterns < estimated_patterns_for_library_completed:
+        library_completion = (total_patterns/estimated_patterns_for_library_completed)*100
+        print("   + [LIBRARY COMPLETED]: [ "+str('%.20f' % library_completion)+"% ]")
+        if total_patterns > 0:
+            print("   + [PATTERNS LEARNED!]: [ "+str(total_patterns)+" / "+str(estimated_patterns_for_library_completed)+" ] \n")
+        else:
+            print("   + [PATTERNS LEARNED!]: [ "+str(total_patterns)+" / "+str(estimated_patterns_for_library_completed)+" ]")
+    else:
+        total_current_library_completion = (total_patterns/estimated_patterns_for_library_completed)*100
+        library_completion = 100
+        print("   + [LIBRARY COMPLETED]: [ "+str(library_completion)+"% ]")
+        print("   + [CURRENT LIBRARY]  : [ "+str('%.00f' % total_current_library_completion)+"% ] -> [ATTENTION!]: INCREASED [MAX. LENGTH] for range [PATTERN] -> REQUIRED!")
+        if total_patterns > 0:
+            print("   + [PATTERNS LEARNED!]: [ "+str(total_patterns)+" ]\n")
+        else:
+            print("   + [PATTERNS LEARNED!]: [ "+str(total_patterns)+" ]")
+    pattern_len_1 = 0
+    pattern_len_2 = 0
+    pattern_len_3 = 0
+    pattern_len_4 = 0
+    pattern_len_5 = 0
+    pattern_len_6 = 0
+    pattern_len_7 = 0
+    pattern_len_8 = 0
+    pattern_len_9 = 0
+    pattern_len_10 = 0
+    pattern_len_11 = 0
+    pattern_len_12 = 0
+    pattern_len_13 = 0
+    pattern_len_14 = 0
+    pattern_len_15 = 0
+    pattern_len_16 = 0
+    pattern_len_17 = 0
+    pattern_len_18 = 0
+    pattern_len_19 = 0
+    pattern_len_20 = 0
+    for m in memory:
+        pattern_len = m.split(", '")[1]
+        pattern_len = pattern_len.split("')")[0]
+        pattern_len = len(pattern_len)
+        if pattern_len == 1:
+            pattern_len_1 = pattern_len_1 + 1
+        elif pattern_len == 2:
+            pattern_len_2 = pattern_len_2 + 1
+        elif pattern_len == 3:
+            pattern_len_3 = pattern_len_3 + 1
+        elif pattern_len == 4:
+            pattern_len_4 = pattern_len_4 + 1
+        elif pattern_len == 5:
+            pattern_len_5 = pattern_len_5 + 1
+        elif pattern_len == 6:
+            pattern_len_6 = pattern_len_6 + 1
+        elif pattern_len == 7:
+            pattern_len_7 = pattern_len_7 + 1
+        elif pattern_len == 8:
+            pattern_len_8 = pattern_len_8 + 1
+        elif pattern_len == 9:
+            pattern_len_9 = pattern_len_9 + 1
+        elif pattern_len == 10:
+            pattern_len_10 = pattern_len_10 + 1
+        elif pattern_len == 11:
+            pattern_len_11 = pattern_len_11 + 1
+        elif pattern_len == 12:
+            pattern_len_12 = pattern_len_12 + 1
+        elif pattern_len == 13:
+            pattern_len_13 = pattern_len_13 + 1
+        elif pattern_len == 14:
+            pattern_len_14 = pattern_len_14 + 1
+        elif pattern_len == 15:
+            pattern_len_15 = pattern_len_15 + 1
+        elif pattern_len == 16:
+            pattern_len_16 = pattern_len_16 + 1
+        elif pattern_len == 17:
+            pattern_len_17 = pattern_len_17 + 1
+        elif pattern_len == 18:
+            pattern_len_18 = pattern_len_18 + 1
+        elif pattern_len == 19:
+            pattern_len_19 = pattern_len_19 + 1
+        else:
+            pattern_len_20 = pattern_len_20 + 1
+    if pattern_len_1 < 101:
+        progression_len_1 = pattern_len_1 * "*"
+    else:
+        progression_len_1 = 100 * "*+"+str(pattern_len_1-100)
+    if pattern_len_2 < 101:
+        progression_len_2 = pattern_len_2 * "*"
+    else:
+        progression_len_2 = 100 * "*+"+str(pattern_len_2-100)
+    if pattern_len_3 < 101:
+        progression_len_3 = pattern_len_3 * "*"
+    else:
+        progression_len_3 = 100 * "*+"+str(pattern_len_3-100)
+    if pattern_len_4 < 101:
+        progression_len_4 = pattern_len_4 * "*"
+    else:
+        progression_len_4 = 100 * "*"+" 100+"+str(pattern_len_4-100)
+    if pattern_len_5 < 101:
+        progression_len_5 = pattern_len_5 * "*"
+    else:
+        progression_len_5 = 100 * "*+"+str(pattern_len_5-100)
+    if pattern_len_6 < 101:
+        progression_len_6 = pattern_len_6 * "*"
+    else:
+        progression_len_6 = 100 * "*+"+str(pattern_len_6-100)
+    if pattern_len_7 < 101:
+        progression_len_7 = pattern_len_7 * "*"
+    else:
+        progression_len_7 = 100 * "*+"+str(pattern_len_7-100)
+    if pattern_len_8 < 101:
+        progression_len_8 = pattern_len_8 * "*"
+    else:
+        progression_len_8 = 100 * "*+"+str(pattern_len_8-100)
+    if pattern_len_9 < 101:
+        progression_len_9 = pattern_len_9 * "*"
+    else:
+        progression_len_9 = 100 * "*+"+str(pattern_len_9-100)
+    if pattern_len_10 < 101:
+        progression_len_10 = pattern_len_10 * "*"
+    else:
+        progression_len_10 = 100 * "*+"+str(pattern_len_10-100)
+    if pattern_len_11 < 101:
+        progression_len_11 = pattern_len_11 * "*"
+    else:
+        progression_len_11 = 100 * "*+"+str(pattern_len_11-100)
+    if pattern_len_12 < 101:
+        progression_len_12 = pattern_len_12 * "*"
+    else:
+        progression_len_12 = 100 * "*+"+str(pattern_len_12-100)
+    if pattern_len_13 < 101:
+        progression_len_13 = pattern_len_13 * "*"
+    else:
+        progression_len_13 = 100 * "*+"+str(pattern_len_13-100)
+    if pattern_len_14 < 101:
+        progression_len_14 = pattern_len_14 * "*"
+    else:
+        progression_len_14 = 100 * "*+"+str(pattern_len_14-100)
+    if pattern_len_15 < 101:
+        progression_len_15 = pattern_len_15 * "*"
+    else:
+        progression_len_15 = 100 * "*+"+str(pattern_len_15-100)
+    if pattern_len_16 < 101:
+        progression_len_16 = pattern_len_16 * "*"
+    else:
+        progression_len_16 = 100 * "*+"+str(pattern_len_16-100)
+    if pattern_len_17 < 101:
+        progression_len_17 = pattern_len_17 * "*"
+    else:
+        progression_len_17 = 100 * "*+"+str(pattern_len_17-100)
+    if pattern_len_18 < 101:
+        progression_len_18 = pattern_len_18 * "*"
+    else:
+        progression_len_18 = 100 * "*+"+str(pattern_len_18-100)
+    if pattern_len_19 < 101:
+        progression_len_19 = pattern_len_19 * "*"
+    else:
+        progression_len_19 = 100 * "*+"+str(pattern_len_19-100)
+    if pattern_len_20 < 101:
+        progression_len_20 = pattern_len_20 * "*"
+    else:
+        progression_len_20 = 100 * "*+"+str(pattern_len_20-100)
+    if pattern_len_1 > 0:
+        print("     - [length = 1]  | "+progression_len_1 + " [ "+str(pattern_len_1)+" / "+str(estimated_quantity_per_pattern_for_library_completed)+" ]")
+    if pattern_len_2 > 0:
+        print("     - [length = 2]  | "+progression_len_2 + " [ "+str(pattern_len_2)+" / "+str(estimated_quantity_per_pattern_for_library_completed)+" ]")
+    if pattern_len_3 > 0:
+        print("     - [length = 3]  | "+progression_len_3 + " [ "+str(pattern_len_3)+" / "+str(estimated_quantity_per_pattern_for_library_completed)+" ]")
+    if pattern_len_4 > 0:
+        print("     - [length = 4]  | "+progression_len_4 + " [ "+str(pattern_len_4)+" / "+str(estimated_quantity_per_pattern_for_library_completed)+" ]")
+    if pattern_len_5 > 0:
+        print("     - [length = 5]  | "+progression_len_5 + " [ "+str(pattern_len_5)+" / "+str(estimated_quantity_per_pattern_for_library_completed)+" ]")
+    if pattern_len_6 > 0:
+        print("     - [length = 6]  | "+progression_len_6 + " [ "+str(pattern_len_6)+" / "+str(estimated_quantity_per_pattern_for_library_completed)+" ]")
+    if pattern_len_7 > 0:
+        print("     - [length = 7]  | "+progression_len_7 + " [ "+str(pattern_len_7)+" / "+str(estimated_quantity_per_pattern_for_library_completed)+" ]")
+    if pattern_len_8 > 0:
+        print("     - [length = 8]  | "+progression_len_8 + " [ "+str(pattern_len_8)+" / "+str(estimated_quantity_per_pattern_for_library_completed)+" ]")
+    if pattern_len_9 > 0:
+        print("     - [length = 9]  | "+progression_len_9 + " [ "+str(pattern_len_9)+" / "+str(estimated_quantity_per_pattern_for_library_completed)+" ]")
+    if pattern_len_10 > 0:
+        print("     - [length = 10] | "+progression_len_10 + " [ "+str(pattern_len_10)+" / "+str(estimated_quantity_per_pattern_for_library_completed)+" ]")
+    if pattern_len_11 > 0:
+        print("     - [length = 11] | "+progression_len_11 + " [ "+str(pattern_len_11)+" / "+str(estimated_quantity_per_pattern_for_library_completed)+" ]")
+    if pattern_len_12 > 0:
+        print("     - [length = 12] | "+progression_len_12 + " [ "+str(pattern_len_12)+" / "+str(estimated_quantity_per_pattern_for_library_completed)+" ]")
+    if pattern_len_13 > 0:
+        print("     - [length = 13] | "+progression_len_13 + " [ "+str(pattern_len_13)+" / "+str(estimated_quantity_per_pattern_for_library_completed)+" ]")
+    if pattern_len_14 > 0:
+        print("     - [length = 14] | "+progression_len_14 + " [ "+str(pattern_len_14)+" / "+str(estimated_quantity_per_pattern_for_library_completed)+" ]")
+    if pattern_len_15 > 0:
+        print("     - [length = 15] | "+progression_len_15 + " [ "+str(pattern_len_15)+" / "+str(estimated_quantity_per_pattern_for_library_completed)+" ]")
+    if pattern_len_16 > 0:
+        print("     - [length = 16] | "+progression_len_16 + " [ "+str(pattern_len_16)+" / "+str(estimated_quantity_per_pattern_for_library_completed)+" ]")
+    if pattern_len_17 > 0:
+        print("     - [length = 17] | "+progression_len_17 + " [ "+str(pattern_len_17)+" / "+str(estimated_quantity_per_pattern_for_library_completed)+" ]")
+    if pattern_len_18 > 0:
+        print("     - [length = 18] | "+progression_len_18 + " [ "+str(pattern_len_18)+" / "+str(estimated_quantity_per_pattern_for_library_completed)+" ]")
+    if pattern_len_19 > 0:
+        print("     - [length = 19] | "+progression_len_19 + " [ "+str(pattern_len_19)+" / "+str(estimated_quantity_per_pattern_for_library_completed)+" ]")
+    if pattern_len_20 > 0:
+        print("     - [length => 20] | "+progression_len_20 + " [ "+str(pattern_len_20)+" / "+str(estimated_quantity_per_pattern_for_library_completed)+" ]")
+    return memory
+
+def list_genomes_on_database():
+    print("[LIST] [REPORTING] [DNA SECUENCES] ... -> [STARTING!]\n")
+    print("-"*15 + "\n")
+    f=open(genomes_list_path, 'w')
+    for k, v in genomes.items():
+        print ("*"+str(k)+ "-> [ "+str(len(v))+" bp linear RNA ]")
+        print ("  + [A] Adenine  :", str(v.count("A")))
+        print ("  + [G] Guanine  :", str(v.count("G")))
+        print ("  + [C] Cytosine :", str(v.count("C")))
+        print ("  + [T] Thymine  :", str(v.count("T")))
+        f.write(str("*"+ str(k)+ " -> [ "+str(len(v))+"bp linear RNA ]\n"))
+        f.write(str("  + [A] Adenine  : " + str(v.count("A"))+"\n"))
+        f.write(str("  + [G] Guanine  : " + str(v.count("G"))+"\n"))
+        f.write(str("  + [C] Cytosine : " + str(v.count("C"))+"\n"))
+        f.write(str("  + [T] Thymine  : " + str(v.count("T"))+"\n"))
+        if v.count("N") > 0:
+            print ("  + [N]  *ANY*   :", str(v.count("N")))
+            f.write(str("  + [N]  *ANY*   : "+ str(v.count("N"))+"\n"))
+        print ("")
+        f.write("\n")
+    print("-"*15 + "\n")
+    print ("[LIST] [INFO] [SAVED!] at: '"+str(genomes_list_path)+"'... -> [EXITING!]\n")
+    f.close()
+
+def examine_stored_brain_memory():
+    memory = [] # list used as hot-memory
+    f=open(brain_path, 'r')
+    for line in f.readlines():
+        if line not in memory:
+            memory.append(line)
+    f.close()
+    if memory == "": # first time run!
+        print ("[LIBRE-AI] [INFO] Not any [BRAIN] present ... -> [BUILDING ONE!]\n")
+        print("-"*15 + "\n")
+        for i in range(2, 11+1):
+            seed = [random.randrange(0, 4) for _ in range(i)] # generate "static" genesis seed
+            if seed not in seeds_checked:
+                seeds_checked.append(seed)
+                pattern = ""
+                for n in seed:
+                    if n == 0:
+                        pattern += "A"
+                    elif n == 1:
+                        pattern += "C"
+                    elif n == 2:
+                        pattern += "T"
+                    else:
+                        pattern += "G"
+                print("[LIBRE-AI] [SEARCH] Generating [RANDOM] pattern: " + str(pattern) + "\n")
+                create_new_pattern(pattern) # create new pattern
+        print("-"*15 + "\n")
+        print ("[LIBRE-AI] [INFO] A new [BRAIN] has been created !!! ... -> [ADVANCING!]\n")
+        f=open(brain_path, 'r')
+        memory = f.read().replace('\n',' ')
+        f.close()
+    return memory
+
+def print_banner():
+    print("\n"+"="*50)
+    print(" ____  _       _   _    _     ")
+    print("|  _ \(_) __ _| \ | |  / \    ")
+    print("| | | | |/ _` |  \| | / _ \   ")
+    print("| |_| | | (_| | |\  |/ ___ \  ")
+    print("|____/|_|\__,_|_| \_/_/   \_\ by psy")
+    print('\n"Search and Recognize patterns in DNA sequences"')
+    print("\n"+"="*50)
+    print("+ GENOMES DETECTED:", str(num_files))
+    print("="*50)
+    print("\n"+"-"*15+"\n")
+    print(" * VERSION: ")
+    print("   + "+VERSION+" - (rev:"+RELEASE+")")
+    print("\n * SOURCES:")
+    print("   + "+SOURCE1)
+    print("   + "+SOURCE2)
+    print("\n * CONTACT: ")
+    print("   + "+CONTACT+"\n")
+    print("-"*15+"\n")
+    print("="*50)
+
+# sub_init #
+num_files=0
+for file in glob.iglob(genomes_path + '**/*', recursive=True):
+    if(file.endswith(".genome")): 
+        num_files = num_files + 1
+        f=open(file, 'r')  
+        genome =  f.read().replace('\n',' ')
+        genomes[file.replace("datasets/","")] = genome.upper() # add genome to main dict
+        f.close()
+print_banner() # show banner
+option = input("\n+ CHOOSE: (S)earch, (L)ist, (T)rain or (R)eport: ").upper()
+print("")
+print("="*50+"\n")
+if option == "S": # search pattern
+    search_pattern_with_human()
+elif option == "L": # list genomes
+    list_genomes_on_database()
+elif option == "T": # teach AI
+    teach_ai()
+else: # libre AI
+    libre_ai()
+print ("="*50+"\n")

docs/AUTHOR

@@ -0,0 +1,43 @@
+========================
+
+ nick: psy (epsylon)
+  
+  <epsylon@riseup.net> 
+
+ web: https://03c8.net
+
+=======================
+
+ code:
+
+ - https://code.03c8.net/epsylon
+ - https://github.com/epsylon
+
+=======================
+
+ software/projects:
+
+ - AnonTwi: Tool for OAuth2 applications (such as: GNUSocial, Twitter) that provides different layers of privacy/encryption.
+ - Bordercheck: Tool to visualize 'real-time' on a world map the geolocation of data when surfing the web.
+ - CIntruder: Tool to bypass captchas using OCR (Optical Character Recognition) bruteforcing methods.
+ - Collatz: Tool to simulate the Collatz's conjeture.
+ - DiaNA: Tool for the search and recognition of patterns in DNA sequences.
+ - DieKunstDerFuge: Video on different topics related to hacktivism recorded during 2013 from an intimate narrative perspective.
+ - ECOin: Decentralized key/value registration and transfer system based on Bitcoin technology (a cryptocurrency).
+ - Goldbach: Tool to simulate the Goldbach's conjeture.
+ - Lorea: Social networking autonomous project to build a distributed, encrypted and federated network.
+ - Orb: Tool for massive footprinting.
+ - pArAnoIA-Browser: Tool designed to surf the Internet using some "paranoic" methods.
+ - Propagare: Tool for extraction, organization and semantic analysis of newspapers.
+ - PyAISnake: Tool to train AI models on solve spatial problems through the classic video game "snake".
+ - PyDog4Apache: Tool to sneak logs from Apache web server.
+ - UFONet: Denial of Service [DDoS & DoS attacks] Toolkit (a botnet of botnets).
+ - XSSer: Automatic -framework- to detect, exploit and report XSS vulnerabilities.
+
+=======================
+
+ BTC: 
+
+  19aXfJtoYJUoXEZtjNwsah2JKN9CK5Pcjw
+
+========================

docs/COMMITMENT

@@ -0,0 +1,46 @@
+GPL Cooperation Commitment
+Version 1.0
+
+Before filing or continuing to prosecute any legal proceeding or claim
+(other than a Defensive Action) arising from termination of a Covered
+License, we commit to extend to the person or entity ('you') accused
+of violating the Covered License the following provisions regarding
+cure and reinstatement, taken from GPL version 3. As used here, the
+term 'this License' refers to the specific Covered License being
+enforced.
+
+    However, if you cease all violation of this License, then your
+    license from a particular copyright holder is reinstated (a)
+    provisionally, unless and until the copyright holder explicitly
+    and finally terminates your license, and (b) permanently, if the
+    copyright holder fails to notify you of the violation by some
+    reasonable means prior to 60 days after the cessation.
+
+    Moreover, your license from a particular copyright holder is
+    reinstated permanently if the copyright holder notifies you of the
+    violation by some reasonable means, this is the first time you
+    have received notice of violation of this License (for any work)
+    from that copyright holder, and you cure the violation prior to 30
+    days after your receipt of the notice.
+
+We intend this Commitment to be irrevocable, and binding and
+enforceable against us and assignees of or successors to our
+copyrights.
+
+Definitions
+
+'Covered License' means the GNU General Public License, version 2
+(GPLv2), the GNU Lesser General Public License, version 2.1
+(LGPLv2.1), or the GNU Library General Public License, version 2
+(LGPLv2), all as published by the Free Software Foundation.
+
+'Defensive Action' means a legal proceeding or claim that We bring
+against you in response to a prior proceeding or claim initiated by
+you or your affiliate.
+
+'We' means each contributor to this repository as of the date of
+inclusion of this file, including subsidiaries of a corporate
+contributor.
+
+This work is available under a Creative Commons Attribution-ShareAlike
+4.0 International license (https://creativecommons.org/licenses/by-sa/4.0/).