create hw with classes

hw8/code/classes.py

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+from Bio.Seq import Seq
+from Bio import SeqIO
+from Bio.SeqUtils import GC
+import numpy as np
+import seaborn as sns
+import matplotlib.pyplot as plt
+
+
+class Employee:
+
+    def __init__(self, name, age, work_experience):
+        self.name = name
+        self.age = age
+        self.work_experience = work_experience
+
+    def __str__(self):
+        return 'Information about employee: {}'.format(self.name)
+
+    def proposed_position(self):
+        '''
+        Shows the position the candidate is applying for
+        :return: str, position name
+        '''
+        if self.work_experience <= 1:
+            position = 'Junior DS'
+        elif 1 < self.work_experience <= 3:
+            position = 'Middle DS'
+        else:
+            position = 'Senior DS'
+        return position
+
+    def proposed_salary(self, position):
+        '''
+        Shows salary the candidate wants to have
+        :param position: str, name of the position
+        :return: int, salary in dollars
+        '''
+        if position == 'Junior DS':
+            salary = 1000
+        elif position == 'Middle DS':
+            salary = 5000
+        else:
+            salary = 10000
+        return salary
+
+    def view_info(self):
+        '''
+        Shows basic information about candidate
+        :return: str with information
+        '''
+        position = self.proposed_position()
+        salary = self.proposed_salary(position)
+        return f"Name: {self.name}\nAge: {self.age} years\nPosition: {position}\nSalary {salary} $"
+
+
+class SetModified(set):
+
+    def __init__(self, *args):
+        self.positive_numbers = [arg for arg in args if isinstance(arg, (int, float)) and arg > 0]
+        super().__init__(self.positive_numbers)
+
+    def add(self, element):
+        if isinstance(element, (int, float)) and element > 0:
+            super().add(element)
+
+    def update(self, *elements):
+        for element in elements:
+            self.add(element)
+
+
+class RNA:
+
+    def __init__(self, rna):
+        self.rna = Seq(rna)
+
+    def translation(self):
+        '''
+        Translates the RNA sequence into protein
+        :return: protein sequence
+        '''
+        return self.rna.translate()
+
+    def back_transcription(self):
+        '''
+        Convert RNA sequence into DNA
+        :return: DNA sequence
+        '''
+        return self.rna.back_transcribe()
+
+
+class FastaStats:
+
+    def __init__(self, path):
+        self.path = path
+
+    def __str__(self):
+        return "Path to the file: {}".format(self.path)
+
+    def count_sequences(self):
+        '''
+        Counts number of sequences presented in the file
+        :return: int, number of sequences
+        '''
+        seq_numb = 0
+        with open(self.path) as f:
+            for record in SeqIO.parse(f, "fasta"):
+                seq_numb += 1
+        return seq_numb
+
+    def calc_sequences_length(self):
+        '''
+        Calculates length of sequences presented in the file
+        :return: list with lengths of sequences
+        '''
+        with open(self.path) as f:
+            length_ls = [len(record.seq) for record in SeqIO.parse(f, "fasta")]
+        return length_ls
+
+    def calc_gc_content_per_seq(self):
+        '''
+        Calculates GC content of sequences presented in the file
+        :return: list with GC content per each sequence
+        '''
+        with open(self.path) as f:
+            gc_content_ls = [GC(record.seq) for record in SeqIO.parse(f, "fasta")]
+        return gc_content_ls
+
+    def calc_mean_gc_content(self):
+        '''
+        Calculates mean GC content over all sequences presented in the file
+        :return: int, mean GC content (%)
+        '''
+        return np.mean(self.calc_gc_content_per_seq())
+
+    def calc_k_mers(self, kmer_size):
+        '''
+        Counts k-mer over all sequences in the file
+        :param kmer_size: int, size of k-mers
+        :return: dictionary, where keys - kmers, values - their counts
+        '''
+        k_mer_counts = {}
+        with open(self.path) as f:
+            for record in SeqIO.parse(f, "fasta"):
+                for i in range(len(record.seq)-kmer_size+1):
+                    kmer = str(record.seq[i:i+kmer_size])
+                    k_mer_counts[kmer] = k_mer_counts.get(kmer, 0) + 1
+        k_mer_counts = dict(sorted(k_mer_counts.items(), key=lambda item: item[1], reverse=True))
+        return k_mer_counts
+
+    def plot_length_distribution(self):
+        '''
+        Plots length distribution for sequences presented in the file
+        '''
+        length_ls = self.calc_sequences_length()
+        plt.figure(figsize=(10, 8))
+        sns.distplot(length_ls, bins=30)
+        plt.title('Length distribution', fontsize=15)
+        plt.xlabel('length', fontsize=15)
+        plt.ylabel('frequency', fontsize=15)
+
+    def plot_gc_content_distribution(self):
+        '''
+        Plots GC content distribution for sequences presented in the file
+        '''
+        gc_content_ls = self.calc_gc_content_per_seq()
+        mean_gc_content = self.calc_mean_gc_content()
+        plt.figure(figsize=(10, 8))
+        sns.distplot(gc_content_ls, bins=30)
+        plt.title('GC content distribution', fontsize=15)
+        plt.xlabel('GC content, %', fontsize=15)
+        plt.ylabel('frequency', fontsize=15)
+        plt.axvline(mean_gc_content, color='red', linestyle='--')
+
+    def plot_kmer_distribution(self, kmer_size):
+        '''
+        Plots frequency of k-mers over all sequences
+        :param kmer_size: int, size of k-mers
+        '''
+        k_mer_counts = self.calc_k_mers(kmer_size=kmer_size)
+        plt.figure(figsize=(12, 10))
+        plt.bar(k_mer_counts.keys(), k_mer_counts.values(), width=1)
+        plt.title('{}-mers distribution'.format(kmer_size), fontsize=15)
+        plt.xlabel('{}-mers'.format(kmer_size), fontsize=15)
+        plt.ylabel('counts', fontsize=15)
+        plt.xticks(fontsize=3, rotation=90)

hw8/code/requirements.txt

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+biopython==1.79
+matplotlib==3.1.3
+seaborn==0.10.0
+numpy==1.20.0