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@@ -7,8 +7,8 @@ You should have received a copy of the GNU General Public License along
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with DiaNA; if not, write to the Free Software Foundation, Inc., 51
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Franklin St, Fifth Floor, Boston, MA 02110-1301 USA
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"""
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-VERSION = "v0.1_beta"
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-RELEASE = "16032020"
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+VERSION = "v0.2_beta"
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+RELEASE = "17032020"
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SOURCE1 = "https://code.03c8.net/epsylon/diana"
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SOURCE2 = "https://github.com/epsylon/diana"
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CONTACT = "epsylon@riseup.net - (https://03c8.net)"
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@@ -149,6 +149,8 @@ def create_new_pattern(pattern): # append it to brain
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f=open(brain_path, 'a')
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f.write(str(repeats)+os.linesep) # add dict as str
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f.close()
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+ else:
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+ print("[ERROR] -> Invalid DNA pattern ... [EXITING!]\n")
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def teach_ai_automata_mode(): # search patterns by bruteforcing ranges & generate local database
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search_patterns_lesson_with_an_ai()
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@@ -392,13 +394,19 @@ def extract_pattern_most_present_local(memory):
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total_genomes = total_genomes + 1
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for m in memory:
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total_patterns = total_patterns + 1 # counter used for known patterns
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- max_size_pattern_name, less_size_pattern_name, biggest_pattern_name, biggest_pattern_size, smaller_pattern_name, smaller_pattern_size, total_patterns_all_genomes = extract_patterns_most_found_in_all_genomes(memory_dict)
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+ max_size_pattern_name, less_size_pattern_name, biggest_pattern_name, biggest_pattern_size, smaller_pattern_name, smaller_pattern_size, total_patterns_all_genomes, most_present_patterns_by_len_list, less_present_patterns_by_len_list = extract_patterns_most_found_in_all_genomes(memory_dict)
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print(" * Trying -[ "+str(total_patterns)+" ]- [PATTERNS LEARNED!] against -[ "+str(total_genomes)+ " ]- [DNA SECUENCES]:")
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print("\n + Total [PATTERNS FOUND!]: [ "+str(total_patterns_all_genomes)+" ]")
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- print("\n - [LARGEST] : [ "+str(max_size_pattern_name)+" ] -> [ "+str(len(max_size_pattern_name))+" bp linear RNA ]")
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- print(" - [SHORTEST]: [ "+str(less_size_pattern_name)+" ] -> [ "+str(len(less_size_pattern_name))+" bp linear RNA ]\n")
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- print(" - [MOST-PRESENT!]: [ "+str(biggest_pattern_name)+" ] -> [ "+str(biggest_pattern_size)+" ] time(s)")
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- print(" - [LESS-PRESENT!]: [ "+str(smaller_pattern_name)+" ] -> [ "+str(smaller_pattern_size)+" ] time(s)\n")
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+ print("\n - [MOST-PRESENT!]: [ "+str(biggest_pattern_size)+" ] time(s) -> [ "+str(biggest_pattern_name)+" ]\n")
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+ for k, v in most_present_patterns_by_len_list.items():
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+ print(" * [length = "+str(k)+"] : [ "+str(v[1])+" ] time(s) -> [ "+str(v[0])+" ]")
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+ print("\n - [LESS-PRESENT!]: [ "+str(smaller_pattern_size)+" ] time(s) -> [ "+str(smaller_pattern_name)+" ]\n")
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+ for n, m in less_present_patterns_by_len_list.items():
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+ print(" * [length = "+str(n)+"] : [ "+str(m[1])+" ] time(s) -> [ "+str(m[0])+" ]")
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+ max_size_pattern_name = max(most_present_patterns_by_len_list, key=most_present_patterns_by_len_list.get)
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+ less_size_pattern_name = min(most_present_patterns_by_len_list, key=most_present_patterns_by_len_list.get)
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+ print("\n - [LARGEST] : [ "+str(max_size_pattern_name)+" ] bp linear RNA")
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+ print(" - [SHORTEST]: [ "+str(less_size_pattern_name)+" ] bp linear RNA\n")
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def extract_patterns_most_found_in_all_genomes(memory_dict):
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present_patterns = []
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@@ -450,19 +458,51 @@ def extract_patterns_most_found_in_all_genomes(memory_dict):
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if z < smaller_pattern_size:
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smaller_pattern_name = r
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smaller_pattern_size = z
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- return max_size_pattern_name, less_size_pattern_name, biggest_pattern_name, biggest_pattern_size, smaller_pattern_name, smaller_pattern_size, total_patterns_all_genomes
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+ most_present_patterns_by_len_list = extract_most_present_pattern_by_len(list_total_patterns_by_pattern)
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+ less_present_patterns_by_len_list = extract_less_present_pattern_by_len(list_total_patterns_by_pattern)
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+ return max_size_pattern_name, less_size_pattern_name, biggest_pattern_name, biggest_pattern_size, smaller_pattern_name, smaller_pattern_size, total_patterns_all_genomes, most_present_patterns_by_len_list, less_present_patterns_by_len_list
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+
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+def extract_most_present_pattern_by_len(list_total_patterns_by_pattern):
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+ most_present_patterns_by_len_list = {}
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+ for k, v in list_total_patterns_by_pattern.items():
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+ pattern_len = len(k)
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+ if pattern_len in most_present_patterns_by_len_list.keys():
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+ if v > most_present_patterns_by_len_list[pattern_len][1]:
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+ most_present_patterns_by_len_list[pattern_len] = k, v
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+ else:
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+ most_present_patterns_by_len_list[pattern_len] = k, v
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+ return most_present_patterns_by_len_list
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+
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+def extract_less_present_pattern_by_len(list_total_patterns_by_pattern):
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+ less_present_patterns_by_len_list = {}
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+ for k, v in list_total_patterns_by_pattern.items():
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+ pattern_len = len(k)
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+ if pattern_len in less_present_patterns_by_len_list.keys():
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+ if v < less_present_patterns_by_len_list[pattern_len][1]:
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+ less_present_patterns_by_len_list[pattern_len] = k, v
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+ else:
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+ less_present_patterns_by_len_list[pattern_len] = k, v
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+ return less_present_patterns_by_len_list
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def extract_storage_sizes():
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total_dataset_size = 0
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total_files_size = 0
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total_list_size = 0
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- for file in glob.iglob(genomes_path + '**/*', recursive=True):
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+ for file in glob.iglob(genomes_path + '*/*/*', recursive=True): # extract datasets sizes
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if(file.endswith(".genome")):
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total_dataset_size = total_dataset_size + len(file)
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- elif(file.endswith(".in")):
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- total_brain_size = len(file)
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- elif(file.endswith(".list")):
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- total_list_size = len(file)
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+ try:
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+ f=open(brain_path, "r") # extract brain sizes
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+ total_brain_size = len(f.read())
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+ f.close()
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+ except:
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+ total_brain_size = 0
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+ try:
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+ f=open(genomes_list_path, "r") # extract genomes list sizes
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+ total_list_size = len(f.read())
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+ f.close()
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+ except:
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+ total_list_size = 0
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if total_dataset_size > 0:
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total_files_size = int(total_files_size) + int(total_dataset_size)
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dataset_s, dataset_size_name = convert_size(total_dataset_size)
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@@ -506,6 +546,67 @@ def extract_total_patterns_learned_from_local(memory):
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print(" + [PATTERNS LEARNED!]: [ "+str(total_patterns)+" ]\n")
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else:
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print(" + [PATTERNS LEARNED!]: [ "+str(total_patterns)+" ]")
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+ generate_pattern_len_report_structure(memory)
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+ return memory
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+
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+def list_genomes_on_database():
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+ print("[LIST] [REPORTING] [DNA SECUENCES] ... -> [STARTING!]\n")
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+ print("-"*15 + "\n")
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+ f=open(genomes_list_path, 'w')
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+ for k, v in genomes.items():
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+ print ("*"+str(k)+ "-> [ "+str(len(v))+" bp linear RNA ]")
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+ print (" + [A] Adenine :", str(v.count("A")))
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+ print (" + [G] Guanine :", str(v.count("G")))
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+ print (" + [C] Cytosine :", str(v.count("C")))
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+ print (" + [T] Thymine :", str(v.count("T")))
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+ f.write(str("*"+ str(k)+ " -> [ "+str(len(v))+"bp linear RNA ]\n"))
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+ f.write(str(" + [A] Adenine : " + str(v.count("A"))+"\n"))
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+ f.write(str(" + [G] Guanine : " + str(v.count("G"))+"\n"))
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+ f.write(str(" + [C] Cytosine : " + str(v.count("C"))+"\n"))
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+ f.write(str(" + [T] Thymine : " + str(v.count("T"))+"\n"))
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+ if v.count("N") > 0:
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+ print (" + [N] *ANY* :", str(v.count("N")))
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+ f.write(str(" + [N] *ANY* : "+ str(v.count("N"))+"\n"))
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+ print ("")
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+ f.write("\n")
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+ print("-"*15 + "\n")
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+ print ("[LIST] [INFO] [SAVED!] at: '"+str(genomes_list_path)+"'... -> [EXITING!]\n")
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+ f.close()
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+
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+def examine_stored_brain_memory():
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+ memory = [] # list used as hot-memory
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+ f=open(brain_path, 'r')
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+ for line in f.readlines():
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+ if line not in memory:
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+ memory.append(line)
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+ f.close()
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+ if memory == "": # first time run!
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+ print ("[LIBRE-AI] [INFO] Not any [BRAIN] present ... -> [BUILDING ONE!]\n")
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+ print("-"*15 + "\n")
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+ for i in range(2, 11+1):
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+ seed = [random.randrange(0, 4) for _ in range(i)] # generate "static" genesis seed
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+ if seed not in seeds_checked:
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+ seeds_checked.append(seed)
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+ pattern = ""
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+ for n in seed:
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+ if n == 0:
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+ pattern += "A"
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+ elif n == 1:
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+ pattern += "C"
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+ elif n == 2:
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+ pattern += "T"
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+ else:
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+ pattern += "G"
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+ print("[LIBRE-AI] [SEARCH] Generating [RANDOM] pattern: " + str(pattern) + "\n")
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+ create_new_pattern(pattern) # create new pattern
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+ print("-"*15 + "\n")
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+ print ("[LIBRE-AI] [INFO] A new [BRAIN] has been created !!! ... -> [ADVANCING!]\n")
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+ f=open(brain_path, 'r')
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+ memory = f.read().replace('\n',' ')
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+ f.close()
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+ return memory
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+
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+def generate_pattern_len_report_structure(memory):
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pattern_len_1 = 0 # related with [MAX. LENGTH] range
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pattern_len_2 = 0
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pattern_len_3 = 0
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@@ -866,163 +967,105 @@ def extract_total_patterns_learned_from_local(memory):
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else:
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progression_len_50 = 100 * "*+"+str(pattern_len_50-100)
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if pattern_len_1 > 0:
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- print(" - [length = 1] | "+progression_len_1 + " [ "+str(pattern_len_1)+" / "+str(estimated_quantity_per_pattern_for_library_completed)+" ]")
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+ print(" - [length = 1] : | "+progression_len_1 + " [ "+str(pattern_len_1)+" / "+str(estimated_quantity_per_pattern_for_library_completed)+" ]")
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if pattern_len_2 > 0:
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- print(" - [length = 2] | "+progression_len_2 + " [ "+str(pattern_len_2)+" / "+str(estimated_quantity_per_pattern_for_library_completed)+" ]")
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+ print(" - [length = 2] : | "+progression_len_2 + " [ "+str(pattern_len_2)+" / "+str(estimated_quantity_per_pattern_for_library_completed)+" ]")
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if pattern_len_3 > 0:
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- print(" - [length = 3] | "+progression_len_3 + " [ "+str(pattern_len_3)+" / "+str(estimated_quantity_per_pattern_for_library_completed)+" ]")
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+ print(" - [length = 3] : | "+progression_len_3 + " [ "+str(pattern_len_3)+" / "+str(estimated_quantity_per_pattern_for_library_completed)+" ]")
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if pattern_len_4 > 0:
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- print(" - [length = 4] | "+progression_len_4 + " [ "+str(pattern_len_4)+" / "+str(estimated_quantity_per_pattern_for_library_completed)+" ]")
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+ print(" - [length = 4] : | "+progression_len_4 + " [ "+str(pattern_len_4)+" / "+str(estimated_quantity_per_pattern_for_library_completed)+" ]")
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if pattern_len_5 > 0:
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- print(" - [length = 5] | "+progression_len_5 + " [ "+str(pattern_len_5)+" / "+str(estimated_quantity_per_pattern_for_library_completed)+" ]")
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+ print(" - [length = 5] : | "+progression_len_5 + " [ "+str(pattern_len_5)+" / "+str(estimated_quantity_per_pattern_for_library_completed)+" ]")
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if pattern_len_6 > 0:
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- print(" - [length = 6] | "+progression_len_6 + " [ "+str(pattern_len_6)+" / "+str(estimated_quantity_per_pattern_for_library_completed)+" ]")
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+ print(" - [length = 6] : | "+progression_len_6 + " [ "+str(pattern_len_6)+" / "+str(estimated_quantity_per_pattern_for_library_completed)+" ]")
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if pattern_len_7 > 0:
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- print(" - [length = 7] | "+progression_len_7 + " [ "+str(pattern_len_7)+" / "+str(estimated_quantity_per_pattern_for_library_completed)+" ]")
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+ print(" - [length = 7] : | "+progression_len_7 + " [ "+str(pattern_len_7)+" / "+str(estimated_quantity_per_pattern_for_library_completed)+" ]")
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if pattern_len_8 > 0:
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- print(" - [length = 8] | "+progression_len_8 + " [ "+str(pattern_len_8)+" / "+str(estimated_quantity_per_pattern_for_library_completed)+" ]")
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+ print(" - [length = 8] : | "+progression_len_8 + " [ "+str(pattern_len_8)+" / "+str(estimated_quantity_per_pattern_for_library_completed)+" ]")
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if pattern_len_9 > 0:
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- print(" - [length = 9] | "+progression_len_9 + " [ "+str(pattern_len_9)+" / "+str(estimated_quantity_per_pattern_for_library_completed)+" ]")
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+ print(" - [length = 9] : | "+progression_len_9 + " [ "+str(pattern_len_9)+" / "+str(estimated_quantity_per_pattern_for_library_completed)+" ]")
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if pattern_len_10 > 0:
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- print(" - [length = 10] | "+progression_len_10 + " [ "+str(pattern_len_10)+" / "+str(estimated_quantity_per_pattern_for_library_completed)+" ]")
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+ print(" - [length = 10]: | "+progression_len_10 + " [ "+str(pattern_len_10)+" / "+str(estimated_quantity_per_pattern_for_library_completed)+" ]")
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if pattern_len_11 > 0:
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- print(" - [length = 11] | "+progression_len_11 + " [ "+str(pattern_len_11)+" / "+str(estimated_quantity_per_pattern_for_library_completed)+" ]")
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+ print(" - [length = 11]: | "+progression_len_11 + " [ "+str(pattern_len_11)+" / "+str(estimated_quantity_per_pattern_for_library_completed)+" ]")
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if pattern_len_12 > 0:
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- print(" - [length = 12] | "+progression_len_12 + " [ "+str(pattern_len_12)+" / "+str(estimated_quantity_per_pattern_for_library_completed)+" ]")
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+ print(" - [length = 12]: | "+progression_len_12 + " [ "+str(pattern_len_12)+" / "+str(estimated_quantity_per_pattern_for_library_completed)+" ]")
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if pattern_len_13 > 0:
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- print(" - [length = 13] | "+progression_len_13 + " [ "+str(pattern_len_13)+" / "+str(estimated_quantity_per_pattern_for_library_completed)+" ]")
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+ print(" - [length = 13]: | "+progression_len_13 + " [ "+str(pattern_len_13)+" / "+str(estimated_quantity_per_pattern_for_library_completed)+" ]")
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if pattern_len_14 > 0:
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- print(" - [length = 14] | "+progression_len_14 + " [ "+str(pattern_len_14)+" / "+str(estimated_quantity_per_pattern_for_library_completed)+" ]")
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+ print(" - [length = 14]: | "+progression_len_14 + " [ "+str(pattern_len_14)+" / "+str(estimated_quantity_per_pattern_for_library_completed)+" ]")
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if pattern_len_15 > 0:
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- print(" - [length = 15] | "+progression_len_15 + " [ "+str(pattern_len_15)+" / "+str(estimated_quantity_per_pattern_for_library_completed)+" ]")
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+ print(" - [length = 15]: | "+progression_len_15 + " [ "+str(pattern_len_15)+" / "+str(estimated_quantity_per_pattern_for_library_completed)+" ]")
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if pattern_len_16 > 0:
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- print(" - [length = 16] | "+progression_len_16 + " [ "+str(pattern_len_16)+" / "+str(estimated_quantity_per_pattern_for_library_completed)+" ]")
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+ print(" - [length = 16]: | "+progression_len_16 + " [ "+str(pattern_len_16)+" / "+str(estimated_quantity_per_pattern_for_library_completed)+" ]")
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if pattern_len_17 > 0:
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- print(" - [length = 17] | "+progression_len_17 + " [ "+str(pattern_len_17)+" / "+str(estimated_quantity_per_pattern_for_library_completed)+" ]")
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+ print(" - [length = 17]: | "+progression_len_17 + " [ "+str(pattern_len_17)+" / "+str(estimated_quantity_per_pattern_for_library_completed)+" ]")
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if pattern_len_18 > 0:
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- print(" - [length = 18] | "+progression_len_18 + " [ "+str(pattern_len_18)+" / "+str(estimated_quantity_per_pattern_for_library_completed)+" ]")
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+ print(" - [length = 18]: | "+progression_len_18 + " [ "+str(pattern_len_18)+" / "+str(estimated_quantity_per_pattern_for_library_completed)+" ]")
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if pattern_len_19 > 0:
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- print(" - [length = 19] | "+progression_len_19 + " [ "+str(pattern_len_19)+" / "+str(estimated_quantity_per_pattern_for_library_completed)+" ]")
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+ print(" - [length = 19]: | "+progression_len_19 + " [ "+str(pattern_len_19)+" / "+str(estimated_quantity_per_pattern_for_library_completed)+" ]")
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if pattern_len_20 > 0:
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- print(" - [length = 20] | "+progression_len_20 + " [ "+str(pattern_len_20)+" / "+str(estimated_quantity_per_pattern_for_library_completed)+" ]")
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+ print(" - [length = 20]: | "+progression_len_20 + " [ "+str(pattern_len_20)+" / "+str(estimated_quantity_per_pattern_for_library_completed)+" ]")
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if pattern_len_21 > 0:
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- print(" - [length = 21] | "+progression_len_21 + " [ "+str(pattern_len_21)+" / "+str(estimated_quantity_per_pattern_for_library_completed)+" ]")
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+ print(" - [length = 21]: | "+progression_len_21 + " [ "+str(pattern_len_21)+" / "+str(estimated_quantity_per_pattern_for_library_completed)+" ]")
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if pattern_len_22 > 0:
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- print(" - [length = 22] | "+progression_len_22 + " [ "+str(pattern_len_22)+" / "+str(estimated_quantity_per_pattern_for_library_completed)+" ]")
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+ print(" - [length = 22]: | "+progression_len_22 + " [ "+str(pattern_len_22)+" / "+str(estimated_quantity_per_pattern_for_library_completed)+" ]")
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if pattern_len_23 > 0:
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- print(" - [length = 23] | "+progression_len_23 + " [ "+str(pattern_len_23)+" / "+str(estimated_quantity_per_pattern_for_library_completed)+" ]")
|
|
|
+ print(" - [length = 23]: | "+progression_len_23 + " [ "+str(pattern_len_23)+" / "+str(estimated_quantity_per_pattern_for_library_completed)+" ]")
|
|
|
if pattern_len_24 > 0:
|
|
|
- print(" - [length = 24] | "+progression_len_24 + " [ "+str(pattern_len_24)+" / "+str(estimated_quantity_per_pattern_for_library_completed)+" ]")
|
|
|
+ print(" - [length = 24]: | "+progression_len_24 + " [ "+str(pattern_len_24)+" / "+str(estimated_quantity_per_pattern_for_library_completed)+" ]")
|
|
|
if pattern_len_25 > 0:
|
|
|
- print(" - [length = 25] | "+progression_len_25 + " [ "+str(pattern_len_25)+" / "+str(estimated_quantity_per_pattern_for_library_completed)+" ]")
|
|
|
+ print(" - [length = 25]: | "+progression_len_25 + " [ "+str(pattern_len_25)+" / "+str(estimated_quantity_per_pattern_for_library_completed)+" ]")
|
|
|
if pattern_len_26 > 0:
|
|
|
- print(" - [length = 26] | "+progression_len_26 + " [ "+str(pattern_len_26)+" / "+str(estimated_quantity_per_pattern_for_library_completed)+" ]")
|
|
|
+ print(" - [length = 26]: | "+progression_len_26 + " [ "+str(pattern_len_26)+" / "+str(estimated_quantity_per_pattern_for_library_completed)+" ]")
|
|
|
if pattern_len_27 > 0:
|
|
|
- print(" - [length = 27] | "+progression_len_27 + " [ "+str(pattern_len_27)+" / "+str(estimated_quantity_per_pattern_for_library_completed)+" ]")
|
|
|
+ print(" - [length = 27]: | "+progression_len_27 + " [ "+str(pattern_len_27)+" / "+str(estimated_quantity_per_pattern_for_library_completed)+" ]")
|
|
|
if pattern_len_28 > 0:
|
|
|
- print(" - [length = 28] | "+progression_len_28 + " [ "+str(pattern_len_28)+" / "+str(estimated_quantity_per_pattern_for_library_completed)+" ]")
|
|
|
+ print(" - [length = 28]: | "+progression_len_28 + " [ "+str(pattern_len_28)+" / "+str(estimated_quantity_per_pattern_for_library_completed)+" ]")
|
|
|
if pattern_len_29 > 0:
|
|
|
- print(" - [length = 29] | "+progression_len_29 + " [ "+str(pattern_len_29)+" / "+str(estimated_quantity_per_pattern_for_library_completed)+" ]")
|
|
|
+ print(" - [length = 29]: | "+progression_len_29 + " [ "+str(pattern_len_29)+" / "+str(estimated_quantity_per_pattern_for_library_completed)+" ]")
|
|
|
if pattern_len_30 > 0:
|
|
|
- print(" - [length => 30] | "+progression_len_30 + " [ "+str(pattern_len_30)+" / "+str(estimated_quantity_per_pattern_for_library_completed)+" ]")
|
|
|
+ print(" - [length = 30]: | "+progression_len_30 + " [ "+str(pattern_len_30)+" / "+str(estimated_quantity_per_pattern_for_library_completed)+" ]")
|
|
|
if pattern_len_31 > 0:
|
|
|
- print(" - [length = 11] | "+progression_len_31 + " [ "+str(pattern_len_31)+" / "+str(estimated_quantity_per_pattern_for_library_completed)+" ]")
|
|
|
+ print(" - [length = 31]: | "+progression_len_31 + " [ "+str(pattern_len_31)+" / "+str(estimated_quantity_per_pattern_for_library_completed)+" ]")
|
|
|
if pattern_len_32 > 0:
|
|
|
- print(" - [length = 12] | "+progression_len_32 + " [ "+str(pattern_len_32)+" / "+str(estimated_quantity_per_pattern_for_library_completed)+" ]")
|
|
|
+ print(" - [length = 32]: | "+progression_len_32 + " [ "+str(pattern_len_32)+" / "+str(estimated_quantity_per_pattern_for_library_completed)+" ]")
|
|
|
if pattern_len_33 > 0:
|
|
|
- print(" - [length = 13] | "+progression_len_33 + " [ "+str(pattern_len_33)+" / "+str(estimated_quantity_per_pattern_for_library_completed)+" ]")
|
|
|
+ print(" - [length = 33]: | "+progression_len_33 + " [ "+str(pattern_len_33)+" / "+str(estimated_quantity_per_pattern_for_library_completed)+" ]")
|
|
|
if pattern_len_34 > 0:
|
|
|
- print(" - [length = 14] | "+progression_len_34 + " [ "+str(pattern_len_34)+" / "+str(estimated_quantity_per_pattern_for_library_completed)+" ]")
|
|
|
+ print(" - [length = 34]: | "+progression_len_34 + " [ "+str(pattern_len_34)+" / "+str(estimated_quantity_per_pattern_for_library_completed)+" ]")
|
|
|
if pattern_len_35 > 0:
|
|
|
- print(" - [length = 15] | "+progression_len_35 + " [ "+str(pattern_len_35)+" / "+str(estimated_quantity_per_pattern_for_library_completed)+" ]")
|
|
|
+ print(" - [length = 35]: | "+progression_len_35 + " [ "+str(pattern_len_35)+" / "+str(estimated_quantity_per_pattern_for_library_completed)+" ]")
|
|
|
if pattern_len_36 > 0:
|
|
|
- print(" - [length = 16] | "+progression_len_36 + " [ "+str(pattern_len_36)+" / "+str(estimated_quantity_per_pattern_for_library_completed)+" ]")
|
|
|
+ print(" - [length = 36]: | "+progression_len_36 + " [ "+str(pattern_len_36)+" / "+str(estimated_quantity_per_pattern_for_library_completed)+" ]")
|
|
|
if pattern_len_37 > 0:
|
|
|
- print(" - [length = 17] | "+progression_len_37 + " [ "+str(pattern_len_37)+" / "+str(estimated_quantity_per_pattern_for_library_completed)+" ]")
|
|
|
+ print(" - [length = 37]: | "+progression_len_37 + " [ "+str(pattern_len_37)+" / "+str(estimated_quantity_per_pattern_for_library_completed)+" ]")
|
|
|
if pattern_len_38 > 0:
|
|
|
- print(" - [length = 18] | "+progression_len_38 + " [ "+str(pattern_len_38)+" / "+str(estimated_quantity_per_pattern_for_library_completed)+" ]")
|
|
|
+ print(" - [length = 38]: | "+progression_len_38 + " [ "+str(pattern_len_38)+" / "+str(estimated_quantity_per_pattern_for_library_completed)+" ]")
|
|
|
if pattern_len_39 > 0:
|
|
|
- print(" - [length = 19] | "+progression_len_39 + " [ "+str(pattern_len_39)+" / "+str(estimated_quantity_per_pattern_for_library_completed)+" ]")
|
|
|
+ print(" - [length = 39]: | "+progression_len_39 + " [ "+str(pattern_len_39)+" / "+str(estimated_quantity_per_pattern_for_library_completed)+" ]")
|
|
|
if pattern_len_40 > 0:
|
|
|
- print(" - [length = 20] | "+progression_len_30 + " [ "+str(pattern_len_40)+" / "+str(estimated_quantity_per_pattern_for_library_completed)+" ]")
|
|
|
+ print(" - [length = 40]: | "+progression_len_30 + " [ "+str(pattern_len_40)+" / "+str(estimated_quantity_per_pattern_for_library_completed)+" ]")
|
|
|
if pattern_len_41 > 0:
|
|
|
- print(" - [length = 21] | "+progression_len_41 + " [ "+str(pattern_len_41)+" / "+str(estimated_quantity_per_pattern_for_library_completed)+" ]")
|
|
|
+ print(" - [length = 41]: | "+progression_len_41 + " [ "+str(pattern_len_41)+" / "+str(estimated_quantity_per_pattern_for_library_completed)+" ]")
|
|
|
if pattern_len_42 > 0:
|
|
|
- print(" - [length = 22] | "+progression_len_42 + " [ "+str(pattern_len_42)+" / "+str(estimated_quantity_per_pattern_for_library_completed)+" ]")
|
|
|
+ print(" - [length = 42]: | "+progression_len_42 + " [ "+str(pattern_len_42)+" / "+str(estimated_quantity_per_pattern_for_library_completed)+" ]")
|
|
|
if pattern_len_43 > 0:
|
|
|
- print(" - [length = 23] | "+progression_len_43 + " [ "+str(pattern_len_43)+" / "+str(estimated_quantity_per_pattern_for_library_completed)+" ]")
|
|
|
+ print(" - [length = 43]: | "+progression_len_43 + " [ "+str(pattern_len_43)+" / "+str(estimated_quantity_per_pattern_for_library_completed)+" ]")
|
|
|
if pattern_len_44 > 0:
|
|
|
- print(" - [length = 24] | "+progression_len_44 + " [ "+str(pattern_len_44)+" / "+str(estimated_quantity_per_pattern_for_library_completed)+" ]")
|
|
|
+ print(" - [length = 44]: | "+progression_len_44 + " [ "+str(pattern_len_44)+" / "+str(estimated_quantity_per_pattern_for_library_completed)+" ]")
|
|
|
if pattern_len_45 > 0:
|
|
|
- print(" - [length = 25] | "+progression_len_45 + " [ "+str(pattern_len_45)+" / "+str(estimated_quantity_per_pattern_for_library_completed)+" ]")
|
|
|
+ print(" - [length = 45]: | "+progression_len_45 + " [ "+str(pattern_len_45)+" / "+str(estimated_quantity_per_pattern_for_library_completed)+" ]")
|
|
|
if pattern_len_46 > 0:
|
|
|
- print(" - [length = 26] | "+progression_len_46 + " [ "+str(pattern_len_46)+" / "+str(estimated_quantity_per_pattern_for_library_completed)+" ]")
|
|
|
+ print(" - [length = 46]: | "+progression_len_46 + " [ "+str(pattern_len_46)+" / "+str(estimated_quantity_per_pattern_for_library_completed)+" ]")
|
|
|
if pattern_len_47 > 0:
|
|
|
- print(" - [length = 27] | "+progression_len_47 + " [ "+str(pattern_len_47)+" / "+str(estimated_quantity_per_pattern_for_library_completed)+" ]")
|
|
|
+ print(" - [length = 47]: | "+progression_len_47 + " [ "+str(pattern_len_47)+" / "+str(estimated_quantity_per_pattern_for_library_completed)+" ]")
|
|
|
if pattern_len_48 > 0:
|
|
|
- print(" - [length = 28] | "+progression_len_48 + " [ "+str(pattern_len_48)+" / "+str(estimated_quantity_per_pattern_for_library_completed)+" ]")
|
|
|
+ print(" - [length = 48]: | "+progression_len_48 + " [ "+str(pattern_len_48)+" / "+str(estimated_quantity_per_pattern_for_library_completed)+" ]")
|
|
|
if pattern_len_49 > 0:
|
|
|
- print(" - [length = 29] | "+progression_len_49 + " [ "+str(pattern_len_49)+" / "+str(estimated_quantity_per_pattern_for_library_completed)+" ]")
|
|
|
+ print(" - [length = 49]: | "+progression_len_49 + " [ "+str(pattern_len_49)+" / "+str(estimated_quantity_per_pattern_for_library_completed)+" ]")
|
|
|
if pattern_len_50 > 0:
|
|
|
- print(" - [length => 30] | "+progression_len_50 + " [ "+str(pattern_len_50)+" / "+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
|
|
|
+ print(" - [length => 50]: | "+progression_len_50 + " [ "+str(pattern_len_50)+" / "+str(estimated_quantity_per_pattern_for_library_completed)+" ]")
|
|
|
|
|
|
def print_banner():
|
|
|
print("\n"+"="*50)
|
