Added Mutant Cycle Analysis Scripts

PublicScripts/MutantCycleAnalysis/Step4_DoubleMutantCyclePlot.py

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+# Script for plotting the final results of the double mutant cycle analysis
+
+import pandas as pd
+import os
+import networkx as nx
+import numpy as np
+import matplotlib.pyplot as plt
+import matplotlib
+from matplotlib import rcParams
+
+matplotlib.use('WxAgg')
+
+rcParams['ps.useafm'] = True
+rcParams['ps.fonttype'] = 42
+rcParams['pdf.fonttype'] = 42
+rcParams['lines.linewidth'] = 0.75
+rcParams['errorbar.capsize'] = 3
+rcParams['patch.force_edgecolor'] = True
+rcParams['patch.facecolor'] = 'b'
+rcParams['lines.markersize'] = 7
+rcParams['font.size'] = 14
+rcParams['font.sans-serif'] = "Arial"
+
+
+def make_graph(df, m_name="G", s_name=None):
+    if s_name is None:
+        s_name=m_name+"E"
+    if m_name == "S":
+        r = 4
+    else:
+        r=2
+    print(df)
+    els = {}
+    edges = [("WT", "R224A"), ("WT", "R75A"), ("WT", "DM"), ("R224A", "DM"), ("R75A", "DM")]
+    G = nx.Graph()
+    weights = []
+    for i in range(0, 5):
+        e1 = edges[i][0]
+        e2 = edges[i][1]
+        mutant = e1 + "_" + e2
+        row = df[df["Mutant"] == mutant]
+        m = float(row[m_name])
+        s = np.abs(float(row[s_name]))
+
+        G.add_edge(e1, e2, weight=m)
+        label = str(round(m, r)) + "\n± " + str(round(s, r))
+        els.update({edges[i]: label})
+        sm = np.sqrt(3)
+        t = 3.182
+        cil = m - s #* t/sm
+        clh = m + s #* t/sm
+        if cil < 0 < clh:
+            weights.append(0)
+        else:
+            weights.append(m)
+    return G, els, weights
+
+
+path = "Z:\\Group Share\\Hiruni Jayasekera\\HSJ_MAE AqpZ CL\\Final2\\"
+os.chdir(path)
+df = pd.read_excel("CombinedResults2.xlsx")
+names=[ "H", "TS", "GA"]#["G", "GA", "S", "H", "TS"]
+for name in names:
+    #name = "G"
+    outstring = "Square_"+name+"_"
+    if name == "S":
+        minmax = [-0.01, 0.01]
+    elif name == "H" or name == "TS":
+        minmax = [-3, 3]
+    else:
+        minmax=[-0.75, 0.75]
+    if name in ["H", "S", "GA", "TS"]:
+        temps = [35]
+    else:
+        temps = [15, 20, 25, 30, 35, 40]
+    for j in range(0, len(temps)):
+        fig = plt.figure(figsize=(6, 8))
+        t = temps[j]
+        for i in range(0, 6):
+            df2 = df[df["# Lnum"] == i]
+            df2 = df2[df2["Temp"] == t]
+
+            G, els, weights = make_graph(df2, m_name=name)
+
+            p = {"WT": [0, 1], "DM": [1, 0], "R224A": [1, 1], "R75A": [0, 0]}
+            ax1 = plt.subplot(3, 2, i + 1, aspect="equal")
+            ax1.get_xaxis().set_visible(False)
+            ax1.get_yaxis().set_visible(False)
+            nx.draw(G, p)
+            ns = 1800
+            fs = 12
+            bbox = {"boxstyle": "round, pad=0.1", "ec": "None", "fc": "None", "alpha": 0.5}
+            # weights = list(nx.get_edge_attributes(G, 'weight').values())
+            nx.draw_networkx_nodes(G, p, margins=0.15, node_color="w", node_shape="o", node_size=ns, edgecolors="k")
+            nx.draw_networkx_edges(G, p, width=32, edge_color="k")
+            nx.draw_networkx_edges(G, p, width=30, edge_color=weights, edge_cmap=plt.cm.bwr,
+                                   node_size=ns, edge_vmin=minmax[0], edge_vmax=minmax[1])
+            nx.draw_networkx_labels(G, p, font_size=fs)
+            nx.draw_networkx_edge_labels(G, p, edge_labels=els, font_size=fs, bbox=bbox, font_color="k")
+
+            plt.title("Lipid " + str(i) + "→" + str(i + 1))
+        plt.tight_layout()
+        figfile = outstring + str(t)
+        fig.suptitle('ΔΔ' + name + " "+ str(t) + ' °C', fontsize=16)
+        plt.savefig(figfile + ".png")
+        plt.savefig(figfile + ".pdf")
+
+plt.show()