updating plot look for direct simulation

crnt4sbml/bistability_analysis.py

@@ -1225,15 +1225,18 @@ def mask(df, key, value):
         out = pandas.concat([xf, xr])
 
         g = (ggplot(out)
-             + aes(x='signal', y='s2', color='dir', alpha='dir', group='dir')
+             + aes(x='signal', y=str(self.__response), color='dir', alpha='dir', group='dir')
              + geom_path(size=4, arrow=arrow(), show_legend=False)
              + geom_point(size=2, shape='s', stroke=0.0)
              + geom_point(color="black", size=2, alpha=1.0)
              + scale_alpha_manual(values=[0.85, 0.35], guide=False)
-             # + scale_color_manual(values=["red", "blue"], labels=["d", "k"])
              + guides(color=guide_legend(override_aes={'size': 6, 'alpha': [0.85, 0.35]}))
-             + theme(legend_title=element_blank(),
-                     axis_text=element_text(size=0.8 * 20), legend_key=element_rect(color='None', fill='None'),
+             + theme(legend_title=element_blank(), text=element_text(size=14, weight='bold'),
+                     legend_key=element_rect(color='None', fill='None'),
+                     axis_text_x=element_line(color="black"),
+                     axis_text_y=element_line(color="black"),
+                     axis_title_x=element_text(size=19, weight='heavy'),
+                     axis_title_y=element_text(size=19, weight='heavy'),
                      panel_background=element_rect(fill='None'),
                      panel_border=element_rect(fill='None', color='#7f7f7f'),
                      panel_grid_major=element_line(color='#E5E5E5', size=0.8),

example_scripts/run_mass_conservation_quickstart.py

@@ -1,5 +1,3 @@
-import sys
-sys.path.insert(0, "..")
 import crnt4sbml
 
 network = crnt4sbml.CRNT("../sbml_files/Fig1Ci.xml")

example_scripts/run_paper_a_b.py

@@ -3,14 +3,14 @@
 import crnt4sbml
 import numpy
 
-# network = crnt4sbml.CRNT("../sbml_files/insulin_signaling_motifs/a_b.xml") # yes 10
-# signal = "C1"
-# response = "s5"
+network = crnt4sbml.CRNT("../sbml_files/insulin_signaling_motifs/a_b.xml") # yes 10
+signal = "C1"
+response = "s5"
 iters = 50
 
-network = crnt4sbml.CRNT("../sbml_files/Fig1Ci.xml")
-signal = "C3"
-response = "s15"
+# network = crnt4sbml.CRNT("../sbml_files/Fig1Ci.xml")
+# signal = "C3"
+# response = "s15"
 
 network.basic_report()
 network.print_c_graph()
@@ -40,24 +40,24 @@
 # # sympy.pprint(network.get_c_graph().get_a())
 # #
 print("")
-print(GA.get_independent_odes_subs())
-species = GA.get_independent_species()
-print(GA.get_independent_species())
+# print(GA.get_independent_odes_subs())
+# species = GA.get_independent_species()
+# print(GA.get_independent_species())
 # indp_subs = GA.get_independent_odes_subs()
 
-import itertools
-
-s1, s2, s3, s6, s7, s15, s16 = symbols('s1 s2 s3 s6 s7 s15 s16', real=True)
-
-species = [s1, s2, s3, s6, s7, s16, s15]
-all_spec_combos = list(itertools.permutations(species, 7))
+# import itertools
+#
+# s1, s2, s3, s6, s7, s15, s16 = symbols('s1 s2 s3 s6 s7 s15 s16', real=True)
+#
+# species = [s1, s2, s3, s6, s7, s16, s15]
+# all_spec_combos = list(itertools.permutations(species, 7))
 
 # print(all_spec_combos)
-for i in all_spec_combos:
-    if i[6] == s15:
-        print(i)
-
-sys.exit()
+# for i in all_spec_combos:
+#     if i[6] == s15:
+#         print(i)
+#
+# sys.exit()
 #
 # C1 = sympy.symbols('C1', real=True)
 #
@@ -127,9 +127,9 @@
 # print(GA.get_input_vector())
 # print(params_for_global_min[1])
 
-multistable_param_ind, plot_specifications = GA.run_greedy_continuity_analysis(species=response, parameters=params_for_global_min,
-                                                                               auto_parameters={'PrincipalContinuationParameter': signal},
-                                                                               dir_path="./basic_example_paper", plot_labels=["$B_{tot}$", "[A]", None])
+# multistable_param_ind, plot_specifications = GA.run_greedy_continuity_analysis(species=response, parameters=params_for_global_min,
+#                                                                                auto_parameters={'PrincipalContinuationParameter': signal},
+#                                                                                dir_path="./basic_example_paper", plot_labels=["$B_{tot}$", "[A]", None])
 
 # list_of_ggplots = GA.run_direct_simulation([params_for_global_min[1]], parallel_flag=True, print_flag=True)
 #
@@ -172,7 +172,7 @@ def jac_f(t, cs, ks, ode_lambda_functions, jacobian):
 param_index = 1
 
 print(params_for_global_min[param_index])
-sys.exit()
+# sys.exit()
 
 C1_val = params_for_global_min[param_index][7] + params_for_global_min[param_index][8]
 
@@ -182,6 +182,7 @@ def jac_f(t, cs, ks, ode_lambda_functions, jacobian):
 import scipy.integrate as itg
 import pandas
 from plotnine import ggplot, aes, geom_line, ylim, scale_color_distiller, facet_wrap, theme_bw, geom_path, geom_point, labs, annotate
+from plotnine import *
 from matplotlib import rc
 rc('text', usetex=True)
 
@@ -208,18 +209,35 @@ def sim_fun_rev(x):
 sim_res_rev = [numpy.transpose(sim_fun_rev(i).y) for i in numpy.flip(scan_vals)]
 
 out = pandas.DataFrame(columns=['dir', 'signal', 'time'] + network.get_c_graph().get_species())
-for i in range(len(sim_res_fwd)):
-    out_i = pandas.DataFrame(sim_res_fwd[i], columns=out.columns[3:])
-    out_i['time'] = t
-    out_i['signal'] = scan_vals[i]
-    out_i['dir'] = 'High [A]'
-    out = pandas.concat([out, out_i[out.columns]])
+
 for i in range(len(sim_res_rev)):
     out_i = pandas.DataFrame(sim_res_rev[i], columns=out.columns[3:])
     out_i['time'] = t
     out_i['signal'] = numpy.flip(scan_vals)[i]
     out_i['dir'] = 'Low [A]'
     out = pandas.concat([out, out_i[out.columns]])
+
+for i in range(len(sim_res_fwd)):
+    out_i = pandas.DataFrame(sim_res_fwd[i], columns=out.columns[3:])
+    out_i['time'] = t
+    out_i['signal'] = scan_vals[i]
+    out_i['dir'] = 'High [A]'
+    out = pandas.concat([out, out_i[out.columns]])
+# for i in range(len(sim_res_rev)):
+#     out_i = pandas.DataFrame(sim_res_rev[i], columns=out.columns[3:])
+#     out_i['time'] = t
+#     out_i['signal'] = numpy.flip(scan_vals)[i]
+#     out_i['dir'] = 'Low [A]'
+#     out = pandas.concat([out, out_i[out.columns]])
+
+# print(out.get_levels)
+
+# out.groupby(['IssueKey', 'User']).TimeSpent
+
+out['dir'] = out['dir'].astype('category')
+
+out['dir'].cat.reorder_categories(['Low [A]', 'High [A]'], ordered=True, inplace=True)
+
 # out.to_csv("./basic_example_paper/sim.txt", sep="\t", index=False)
 
 ###################### plotting ##################################
@@ -232,13 +250,62 @@ def sim_fun_rev(x):
      + theme_bw())
 g.save(filename="./basic_example_paper/sim_fwd_rev.png", format="png", width=8, height=4, units='in', verbose=False)
 
-eq = out[out.time == max(out.time)]
-g = (ggplot(eq)
-     + aes(x='signal', y=response, color='dir')
-     + labs(x="$B_{tot}$", y="[A]", color="")
-     + geom_path(size=2, alpha=0.5)
-     + geom_point(color="black")
-     + theme_bw()
-     + geom_point(color="black"))
 
-g.save(filename="./basic_example_paper/sim_bif_diag.png", format="png", width=6, height=4, units='in', verbose=False)
+out['dir'].cat.reorder_categories(['High [A]', 'Low [A]'], ordered=True, inplace=True)
+
+out = out[out.time == max(out.time)]
+
+
+def mask(df, key, value):
+    return df[df[key] == value]
+
+
+pandas.DataFrame.mask = mask
+
+# flip one of the directions
+xf = out.mask('dir', 'High [A]')
+xr = out.mask('dir', 'Low [A]')
+
+print(f"xf = {xf}")
+
+print("")
+print(f"xr = {xr}")
+
+# xr = xr.iloc[::-1]
+
+print("")
+print(f"xr = {xr}")
+
+out = pandas.concat([xf, xr])
+
+g = (ggplot(out)
+         + aes(x='signal', y='s5', color='dir', alpha='dir', group='dir')
+         + geom_path(size=4, arrow=arrow(), show_legend=False)
+         + geom_point(size=2, shape='s', stroke=0.0)
+         + geom_point(color="black", size=2, alpha=1.0)
+         + scale_alpha_manual(values=[0.85, 0.35], guide=False)
+         # + scale_color_manual(values=["red", "blue"], labels=["d", "k"])
+         + guides(color=guide_legend(override_aes={'size':6, 'alpha':[0.85, 0.35]}))
+         + theme(legend_title=element_blank(), text=element_text(size=14, weight='bold'),
+                 legend_key=element_rect(color='None', fill='None'),
+                 axis_text_x=element_line(color="black"),
+                 axis_text_y=element_line(color="black"),
+                 axis_title_x=element_text(size=19, weight='heavy'),
+                 axis_title_y=element_text(size=19, weight='heavy'),
+                 panel_background=element_rect(fill='None'), panel_border=element_rect(fill='None', color='#7f7f7f'),
+                 panel_grid_major=element_line(color='#E5E5E5', size=0.8),
+                 panel_grid_minor=element_line(color='#FAFAFA', size=1),
+                 strip_background=element_rect(fill='#CCCCCC', color='#7F7F7F', size=1)))
+
+
+g = (g + xlab("$B_{tot}$") + ylab("[A]")  + scale_color_manual(values=["red", "blue"], labels=["High [A]", "Low [A]"]))
+
+# g = (ggplot(eq)
+#      + aes(x='signal', y=response, color='dir')
+#      + labs(x="$B_{tot}$", y="[A]", color="")
+#      + geom_path(size=2, alpha=0.5)
+#      + geom_point(color="black")
+#      + theme_bw()
+#      + geom_point(color="black"))
+
+g.save(filename="./basic_example_paper/sim_bif_diag.png", format="png", width=8, height=5, units='in', verbose=False)

example_scripts/run_simple_biterminal.py

@@ -62,8 +62,8 @@
     from matplotlib import rc
     rc('text', usetex=True)
 
-    g = (g + p9.xlab("$E_{tot}$") + p9.ylab("$[S^{**}]$") + p9.scale_color_hue(labels=["High [$S^{**}$]", "Low [$S^{**}$]"]))
-    g.save(filename=path + f"/sim_bif_diag_0_0.png", format="png", width=6, height=4, units='in', verbose=False)
+    g = (g + p9.xlab("$E_{tot}$") + p9.ylab("$[S^{**}]$") + p9.scale_color_manual(values=["red", "blue"], labels=["High [$S^{**}$]", "Low [$S^{**}$]"]))
+    g.save(filename=path + f"/sim_bif_diag_0_0.png", format="png", width=8, height=5, units='in', verbose=False)
 
     print("")
 

example_scripts/run_song_model.py

@@ -27,29 +27,91 @@
 import numpy
 params = numpy.load('song_params.npy')
 params = [params[4]]
-#
-# print(len(approach.get_input_vector()))
-# print(approach.get_input_vector())
-# print(params)
+
+print(len(approach.get_input_vector()))
+print(approach.get_input_vector())
+print(params)
+
+# sys.exit()
 
 list_of_ggplots  = approach.run_direct_simulation(params, parallel_flag=True)
 
 if approach.get_my_rank() == 0:
 
-    print("list_of_ggplots ")
+    # print("list_of_ggplots ")
     g = list_of_ggplots[0]
     import plotnine as p9
     path = "./dir_sim_graphs"
     from matplotlib import rc
     rc('text', usetex=True)
 
-    g = (g + p9.xlab("$E_{tot}$") + p9.ylab("$[S^*]$") + p9.scale_color_manual(values=["red", "blue"], labels=["High [$S^*$]", "Low [$S^*$]"]))
-
-    g.save(filename=path + f"/song-bif-diag.png", format="png", width=8, height=5, units='in', verbose=False)
+    # signal = '$E_{tot}$'
+    # response = 's2'
+    #
+    # import pandas
+    # from plotnine import *
+    #
+    # out = pandas.read_csv(path + '/song_dir_sim_data.txt', sep="\t", index_col=False)
+    #
+    # out = pandas.DataFrame(data=out, columns=['dir', 'signal', 's2'])
+    #
+    # print(f"out = {out}")
+    #
+    #
+    # def mask(df, key, value):
+    #     return df[df[key] == value]
+    #
+    #
+    # pandas.DataFrame.mask = mask
+    #
+    # # flip one of the directions
+    # xf = out.mask('dir', 'Forward scan') #out[out.dir].filter(like="Forward scan", axis=0)
+    # xr = out.mask('dir', 'Reverse scan') #out.filter(like="Reverse scan", axis=0)
+    #
+    # print(f"xf = {xf}")
+    #
+    # print("")
+    # print(f"xr = {xr}")
+    #
+    # xr = xr.iloc[::-1]
+    #
+    # print("")
+    # print(f"xr = {xr}")
+    #
+    # # x2 = rbind(xf, xr[rev(seq_len(nrow(xr))),])
+    #
+    # out = pandas.concat([xf, xr])
+
+
+    # sys.exit()
+
+    # g = (ggplot(out)
+    #      + aes(x='signal', y='s2', color='dir', alpha='dir', group='dir')
+    #      + geom_path(size=4, arrow=arrow(), show_legend=False)
+    #      + geom_point(size=2, shape='s', stroke=0.0)
+    #      + geom_point(color="black", size=2, alpha=1.0)
+    #      + scale_alpha_manual(values=[0.85, 0.35], guide=False)
+    #      # + scale_color_manual(values=["red", "blue"], labels=["d", "k"])
+    #      + guides(color=guide_legend(override_aes={'size':6, 'alpha':[0.85, 0.35]}))
+    #      + theme(legend_title=element_blank(), text=element_text(size=14, weight='bold'),
+    #              legend_key=element_rect(color='None', fill='None'),
+    #              axis_text_x=element_line(color="black"),
+    #              axis_text_y=element_line(color="black"),
+    #              axis_title_x=element_text(size=19, weight='heavy'),
+    #              axis_title_y=element_text(size=19, weight='heavy'),
+    #              panel_background=element_rect(fill='None'), panel_border=element_rect(fill='None', color='#7f7f7f'),
+    #              panel_grid_major=element_line(color='#E5E5E5', size=0.8),
+    #              panel_grid_minor=element_line(color='#FAFAFA', size=1),
+    #              strip_background=element_rect(fill='#CCCCCC', color='#7F7F7F', size=1)))
+
+    g = (g + p9.xlab("$E_{tot}$") + p9.ylab("$[S^*]$")  + p9.scale_color_manual(values=["red", "blue"], labels=["High [$S^*$]", "Low [$S^*$]"]))
+
+
+    g.save(filename=path + f"/song-bif-diag_2.png", format="png", width=8, height=5, units='in', verbose=False)
 
 
 # multistable_param_ind, plot_specifications = approach.run_greedy_continuity_analysis(species="s2", parameters=params,
 #                                                                                      auto_parameters={'PrincipalContinuationParameter': "C1"})
 
-approach.generate_report()
+# approach.generate_report()