--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/viz_functions.py	Wed Oct 13 11:19:52 2021 +0000
@@ -0,0 +1,230 @@
+import pandas as pd
+import matplotlib.patches as mpatches
+import matplotlib.font_manager as font_manager
+import matplotlib.pyplot as plt
+
+#########################################################################################
+
+# Read a file and return it as a list
+def read(path, flag):
+    if flag == 0:
+        with open(path) as fp:
+            file=fp.readlines()
+        fp.close()
+        return file
+
+    if flag == 1:
+        with open(path) as fp:
+            file = fp.read().splitlines()
+        fp.close()
+        return file
+
+# Write a list to a txt file
+def write(path, list):
+    with open(path,'w') as fp:
+        for x in list:
+            fp.write(str("\t".join(x[1:-1])))
+    fp.close()
+
+
+################################################################################################################################################################>
+
+def top_diff(miRNA_info, number,flag,l):
+
+    Kind=[]
+
+    miRNA_info.sort(key = lambda x: abs(x[1]),reverse=True)
+    miRNA_info = miRNA_info[:number]
+    miRNA_info.sort(key = lambda x: x[0])
+
+    for x in miRNA_info:
+        if x[1] > 0:
+           Kind.append(True)
+        elif x[1] < 0:
+           Kind.append(False)
+        else:
+           Kind.append("Zero")
+
+    top_miRNA = {"Names": [x[0] for x in miRNA_info],
+                  "Log2FC": [x[1] for x in miRNA_info],
+                  "Kind": Kind};
+
+    df_miRNA = pd.DataFrame(data=top_miRNA)
+    df_miRNA = df_miRNA.sort_values(by=['Names'])
+    if df_miRNA.empty==False:
+     h1=df_miRNA.plot.barh(x= 'Names',y='Log2FC',color=df_miRNA.Kind.map({True: 'g', False: 'r', 'Zero':'k'})) 
+     figure = plt.gcf()  # get current figure
+     figure.set_size_inches(5, 12) # set figure's size manually to your full screen (32x18)
+     up_reg = mpatches.Patch(color='green', label='Upregulated')
+     down_reg = mpatches.Patch(color='red', label='Downregulated')
+     font = font_manager.FontProperties(weight='bold', style='normal')
+     l3 = plt.legend(handles=[up_reg,down_reg],bbox_to_anchor=(1.04,0.5), loc="center left", borderaxespad=0)
+     h1.set_ylabel(" ", fontsize=3, fontweight='bold')
+     h1.set_xlabel("Log2FC", fontsize=12, fontweight='bold')
+     plt.axvline(x=0, color="k")
+
+     plt.grid(axis='y', linewidth=0.2)
+     plt.grid(axis='x', linewidth=0.2)
+     if flag=='t':
+        plt.savefig('tem.png', bbox_inches='tight', dpi=300)
+     if flag=='nt':
+        plt.savefig('non.png', bbox_inches='tight', dpi=300)
+
+
+################################################################################################################################################################>
+
+def unique(sequence):
+    seen = set()
+    return [x for x in sequence if not (x in seen or seen.add(x))]
+
+################################################################################################################################################################>
+
+def top_scatter_non(matures,isoforms,non_temp,uni_names,number):
+
+    mat_names=[]
+    mat_log2fc=[]
+
+    iso_names=[]
+    iso_log2fc=[]
+
+    non_temp_names=[]
+    non_temp_log2fc=[]
+
+    count=0
+    for x in uni_names:
+        flag = False
+        if count<number:
+          for y in matures:
+            if x in y[0]:
+               mat_log2fc.append(y[1])
+               mat_names.append(x)
+               flag=True
+          for y in isoforms:
+            if x in y[0]:
+               iso_log2fc.append(y[1])
+               iso_names.append(x)
+               flag=True
+          for y in non_temp:
+            if x in y[0]:
+               non_temp_log2fc.append(y[1])
+               non_temp_names.append(x)
+               flag=True
+          if flag==True:
+             count+=1
+
+    mat_df = pd.DataFrame(dict(names=mat_names, log2fc=mat_log2fc))
+    iso_df = pd.DataFrame(dict(names=iso_names, log2fc=iso_log2fc))
+    non_df = pd.DataFrame(dict(names=non_temp_names, log2fc= non_temp_log2fc))
+
+    iso_df.sort_values(by=['names'])
+    mat_df.sort_values(by=['names'])
+    non_df.sort_values(by=['names'])
+
+    fig, ax = plt.subplots()
+
+    h3=ax.scatter(iso_df['log2fc'],iso_df['names'],edgecolors='k',linewidth=1, marker='o', c='red',alpha=0.4)
+    h1=ax.scatter(mat_df['log2fc'],mat_df['names'],edgecolors='k',linewidth=1, marker='o', c='green',alpha=0.4)
+    h2=ax.scatter(non_df['log2fc'],non_df['names'],edgecolors='k',linewidth=1, marker='o', c='orange',alpha=0.4)
+
+    l3 = plt.legend([h1,h2,h3],["RefSeq miRNA","Non-templated isomiR","Templated isomiR"],bbox_to_anchor=(1.04,0.5), loc="center left", borderaxespad=0)
+    plt.axvline(x=0, color="k")
+    plt.grid(axis='y', linewidth=0.2)
+    plt.grid(axis='x', linewidth=0.2)
+    plt.xlabel("Log2FC", fontsize=12, fontweight='bold')
+    plt.yticks(rotation=0,ha="right", fontsize=10)
+    plt.xticks(rotation=0,ha="right", fontsize=10)
+    plt.tight_layout()
+    figure = plt.gcf()  # get current figure
+    figure.set_size_inches(16, 12) # set figure's size manually to your full screen (32x18)
+    plt.savefig('a2.png', bbox_inches='tight', dpi=300)
+
+#########################################################################################################################################################################################################################################
+
+def top_scatter_tem(matures,isoforms,uni_names,number):
+
+    mat_names=[]
+    mat_log2fc=[]
+
+    iso_names=[]
+    iso_log2fc=[]
+
+    count=0
+    for x in uni_names:
+        flag = False
+        if count<number:
+          for y in matures:
+            if x in y[0]:
+               mat_log2fc.append(y[1])
+               mat_names.append(x)
+               flag=True
+          for y in isoforms:
+            if x in y[0]:
+               iso_log2fc.append(y[1])
+               iso_names.append(x)
+               flag=True
+          if flag==True:
+             count+=1
+
+    mat_df = pd.DataFrame(dict(names=mat_names, log2fc=mat_log2fc))
+    iso_df = pd.DataFrame(dict(names=iso_names, log2fc=iso_log2fc))
+
+    iso_df.sort_values(by=['names'])
+    mat_df.sort_values(by=['names'])
+
+    fig, ax = plt.subplots()
+
+    h3=ax.scatter(iso_df['log2fc'],iso_df['names'],edgecolors='k',linewidth=1, marker='o', c='red',alpha=0.4)
+    h1=ax.scatter(mat_df['log2fc'],mat_df['names'],edgecolors='k',linewidth=1, marker='o', c='green',alpha=0.4)
+
+    l3 = plt.legend([h1,h3],["RefSeq miRNA","Templated isomiR"],bbox_to_anchor=(1.04,0.5), loc="center left", borderaxespad=0)
+    plt.axvline(x=0, color="k")
+    plt.grid(axis='y', linewidth=0.2)
+    plt.grid(axis='x', linewidth=0.2)
+    plt.xlabel("Log2FC", fontsize=12, fontweight='bold')
+    plt.yticks(rotation=0,ha="right", fontsize=10)
+    plt.xticks(rotation=0,ha="right", fontsize=10)
+    plt.tight_layout()
+    figure = plt.gcf()  # get current figure
+    figure.set_size_inches(16, 12) # set figure's size manually to your full screen (32x18)
+    plt.savefig('a2.png', bbox_inches='tight', dpi=300)
+
+
+##############################################################################################################################################################################################################################################
+
+def preproccess(non_templated,matures,isoforms,log2fc,pval,stat):
+
+       if stat=="3":
+          non_temp = [[x[0],float(x[1]),float(x[2])] for x in non_templated if abs(float(x[1]))>log2fc and float(x[2])>pval]
+          mat = [[x[0],float(x[1]),float(x[2])] for x in matures if abs(float(x[1]))>log2fc and float(x[2])>pval]
+          iso = [[x[0],float(x[1]),float(x[2])] for x in isoforms if abs(float(x[1]))>log2fc and float(x[2])>pval]
+       else:
+         non_temp = [[x[0],float(x[1]),float(x[2])] for x in non_templated if abs(float(x[1]))>log2fc and float(x[2])<pval]
+         mat = [[x[0],float(x[1]),float(x[2])] for x in matures if abs(float(x[1]))>log2fc and float(x[2])<pval]
+         iso = [[x[0],float(x[1]),float(x[2])] for x in isoforms if abs(float(x[1]))>log2fc and float(x[2])<pval]
+
+       mat_iso = mat+iso
+
+       if not non_temp and not mat and not iso:
+          sys.exit("There aren't entries which meet these criteria")
+
+       mat.sort(key = lambda x: abs(float(x[1])),reverse=True)
+       iso.sort(key = lambda x: abs(float(x[1])),reverse=True)
+       non_temp.sort(key = lambda x: abs(float(x[1])),reverse=True)
+
+       all=mat+iso+non_temp
+       all.sort(key = lambda x: abs(float(x[1])), reverse=True)
+       names=[x[0].split("_")[0] for x in all]
+       uni_names=unique(names)
+
+       diff_non_templated = [[x[0],float(x[1]),float(x[2])] for x in non_templated if abs(float(x[1]))>1 and float(x[2])<pval and x[0].split("_")[0] in uni_names]
+       diff_matures = [[x[0],float(x[1]),float(x[2])] for x in matures if abs(float(x[1]))>1 and float(x[2])<pval and x[0].split("_")[0] in uni_names]
+       diff_isoforms = [[x[0],float(x[1]),float(x[2])] for x in isoforms if abs(float(x[1]))>1 and float(x[2])<pval and x[0].split("_")[0] in uni_names]
+
+       diff_matures.sort(key = lambda x: abs(float(x[1])),reverse=True)
+       diff_isoforms.sort(key = lambda x: abs(float(x[1])),reverse=True)
+       diff_non_templated.sort(key = lambda x: abs(float(x[1])),reverse=True)
+
+       return diff_matures,diff_isoforms,diff_non_templated,uni_names,non_temp,mat_iso
+
+################################################################################################################################################################################################################################################>
+