minor updates to utils

eshanking · Mar 8, 2024 · a287d14 · a287d14
1 parent f7fdf03
commit a287d14
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Showing 7 changed files with 45 additions and 75 deletions.
diff --git a/build/lib/fears/experiment.py b/build/lib/fears/experiment.py
@@ -101,14 +101,6 @@ def __init__(self,
         else:
             self.experiment_type = experiment_type
 
-        # if experiment_type == 'dose-survival' and len(inoculants) > 1:
-        #     raise Exception('The experiment type is set to dose-survival (default), but more than one inoculant is given.')
-        # elif experiment_type == 'inoculant-survival' and len(self.max_doses) > 1:
-        #     # print('here')
-        #     raise Exception('The experiment type is set to inoculant-survival, but more than one max dose is given.')
-
-        # if experiment_type == 'inoculant-survival' and inoculants is None:
-        #     raise Exception('The experiment type is set to inoculant-survival, but no inoculants are given.')
         self.ramp = ramp
         if self.experiment_type == 'ramp_up_down':
             self.p_landscape = Population(constant_pop = True,
@@ -151,19 +143,6 @@ def __init__(self,
             self.set_ramp_ud(self.p_landscape)
             self.set_ramp_ud(self.p_seascape)
 
-
-            # p_bottleneck = Population(max_dose = max_doses,
-            #                              n_sims = self.n_sims,
-            #                              curve_type = 'bottleneck'
-            #                              **self.population_options)
-            # self.populations.append(p_bottleneck)
-
-            # p_no_bottleneck = Population(max_dose = max_doses,
-            #                  n_sims = self.n_sims,
-            #                  curve_type = 'constant'
-            #                  **self.population_options)
-            # self.populations.append(p_no_bottleneck)
-
         if self.experiment_type == 'dose-survival':
             for curve_type in self.curve_types:
                 for max_dose in self.max_doses:
@@ -490,23 +469,14 @@ def run_experiment(self):
                             save_folder = 'k_abs=' + str(p.k_abs)
                             save_folder.replace('.',',')
                         else:
-                            save_folder = 'slope=' + str(p.slope)
+                            save_folder = 'slope=' + str(p.k_abs)
                             save_folder.replace('.',',')
                         # self.save_counts(counts,n,save_folder)
                         data_dict = {'counts':counts,
                                      'drug_curve':drug}
                         self.save_dict(data_dict,save_folder,num=n)
         if not self.debug:
             pickle.dump(self, open(self.experiment_info_path,"wb"))
-        # pickle.dump(self, open(self.experiment_info_path,"wb"))
-
-
-                # fig_savename = 'slope = ' + str(p.slope)
-                # self.figures = self.figures.append(fig)
-                # pbar.update()
-            # self.rate_survival_results.index = np.arange(len(self.rate_survival_results))
-
-        # pbar.close() # close progress bar
 
     # save counts as a csv in the given subfolder with the label 'num'
     def save_counts(self,counts,num,save_folder,prefix='sim_'):

diff --git a/build/lib/fears/utils/pharm.py b/build/lib/fears/utils/pharm.py
@@ -87,10 +87,18 @@ def gen_impulses(pop):
 
     # generate the drug dose regimen
 
+    if pop.dwell:
+        dwell_time = int(pop.dwell_time/pop.timestep_scale)
+    else:
+        dwell_time = 0
+
     if pop.regimen_length is None:
         regimen_length = pop.n_timestep
     else:
-        regimen_length = pop.regimen_length
+        regimen_length = pop.regimen_length + pop.dwell_time
+
+    if regimen_length > pop.n_timestep:
+        regimen_length = pop.n_timestep
 
     while impulse_indx[i] < regimen_length*pop.timestep_scale-pop.dose_schedule:
         impulse_indx.append(pop.dose_schedule*(i+1))
@@ -108,7 +116,6 @@ def gen_impulses(pop):
     u[impulse_indx]=1 # list of impulses at the simulated drug dosage times
 
     if pop.dwell:
-        dwell_time = int(pop.dwell_time/pop.timestep_scale)
         u[0:dwell_time] = 0
 
     return u

diff --git a/build/lib/fears/utils/plotter.py b/build/lib/fears/utils/plotter.py
@@ -598,6 +598,7 @@ def plot_landscape(p,conc=10**0,
                 plot_sub_network=False,
                 sub_network=None,
                 sub_network_color='white',
+                weight_list=None,
                 **kwargs):
     """
     Plots a graph representation of this landscape on the current matplotlib figure.
@@ -872,7 +873,11 @@ def plot_landscape(p,conc=10**0,
 
 
     if trajectory_list is not None:
-        for trajectory in trajectory_list:
+        for i,trajectory in enumerate(trajectory_list):
+
+                if weight_list is not None:
+                    weight = weight_list[i]
+                    arrowprops['lw'] = weight
 
                 trajectory_pairs = []
 
@@ -918,7 +923,7 @@ def plot_landscape(p,conc=10**0,
                             break
 
                 # Draw trajectory arrows
-
+                    
                     ax.annotate('', xy=end_pos, xytext=start_pos,
                             arrowprops=arrowprops)
 
@@ -1006,6 +1011,7 @@ def plot_kaplan_meier(pop,
                     event_times,
                     label=None,
                     t_max=None,
+                    t_vect = None,
                     n_sims=None,
                     ax=None,
                     mode='resistant',
@@ -1038,7 +1044,9 @@ def plot_kaplan_meier(pop,
     else:
         ylabel='% survival'
 
-    ax.plot(survival_curve,label=label,**kwargs)
+    if t_vect is None:
+        t_vect = np.arange(t_max)
+    ax.plot(t_vect,survival_curve,label=label,**kwargs)
 
     if errorband:
         # compute error bars
@@ -1053,7 +1061,7 @@ def plot_kaplan_meier(pop,
             err[t] = 100*((p*q)/n)**0.5 #
     t = np.arange(t_max)
 
-    ax.fill_between(t,survival_curve-err,survival_curve+err,alpha=0.4)
+    ax.fill_between(t_vect,survival_curve-err,survival_curve+err,alpha=0.4)
 
     xticks = ax.get_xticks()
     xlabels = xticks

diff --git a/fears/experiment.py b/fears/experiment.py
@@ -101,14 +101,6 @@ def __init__(self,
         else:
             self.experiment_type = experiment_type
 
-        # if experiment_type == 'dose-survival' and len(inoculants) > 1:
-        #     raise Exception('The experiment type is set to dose-survival (default), but more than one inoculant is given.')
-        # elif experiment_type == 'inoculant-survival' and len(self.max_doses) > 1:
-        #     # print('here')
-        #     raise Exception('The experiment type is set to inoculant-survival, but more than one max dose is given.')
-
-        # if experiment_type == 'inoculant-survival' and inoculants is None:
-        #     raise Exception('The experiment type is set to inoculant-survival, but no inoculants are given.')
         self.ramp = ramp
         if self.experiment_type == 'ramp_up_down':
             self.p_landscape = Population(constant_pop = True,
@@ -151,19 +143,6 @@ def __init__(self,
             self.set_ramp_ud(self.p_landscape)
             self.set_ramp_ud(self.p_seascape)
 
-
-            # p_bottleneck = Population(max_dose = max_doses,
-            #                              n_sims = self.n_sims,
-            #                              curve_type = 'bottleneck'
-            #                              **self.population_options)
-            # self.populations.append(p_bottleneck)
-
-            # p_no_bottleneck = Population(max_dose = max_doses,
-            #                  n_sims = self.n_sims,
-            #                  curve_type = 'constant'
-            #                  **self.population_options)
-            # self.populations.append(p_no_bottleneck)
-
         if self.experiment_type == 'dose-survival':
             for curve_type in self.curve_types:
                 for max_dose in self.max_doses:
@@ -490,23 +469,14 @@ def run_experiment(self):
                             save_folder = 'k_abs=' + str(p.k_abs)
                             save_folder.replace('.',',')
                         else:
-                            save_folder = 'slope=' + str(p.slope)
+                            save_folder = 'slope=' + str(p.k_abs)
                             save_folder.replace('.',',')
                         # self.save_counts(counts,n,save_folder)
                         data_dict = {'counts':counts,
                                      'drug_curve':drug}
                         self.save_dict(data_dict,save_folder,num=n)
         if not self.debug:
             pickle.dump(self, open(self.experiment_info_path,"wb"))
-        # pickle.dump(self, open(self.experiment_info_path,"wb"))
-
-
-                # fig_savename = 'slope = ' + str(p.slope)
-                # self.figures = self.figures.append(fig)
-                # pbar.update()
-            # self.rate_survival_results.index = np.arange(len(self.rate_survival_results))
-
-        # pbar.close() # close progress bar
 
     # save counts as a csv in the given subfolder with the label 'num'
     def save_counts(self,counts,num,save_folder,prefix='sim_'):

diff --git a/fears/utils/pharm.py b/fears/utils/pharm.py
@@ -87,10 +87,18 @@ def gen_impulses(pop):
 
     # generate the drug dose regimen
 
+    if pop.dwell:
+        dwell_time = int(pop.dwell_time/pop.timestep_scale)
+    else:
+        dwell_time = 0
+
     if pop.regimen_length is None:
         regimen_length = pop.n_timestep
     else:
-        regimen_length = pop.regimen_length
+        regimen_length = pop.regimen_length + pop.dwell_time
+
+    if regimen_length > pop.n_timestep:
+        regimen_length = pop.n_timestep
 
     while impulse_indx[i] < regimen_length*pop.timestep_scale-pop.dose_schedule:
         impulse_indx.append(pop.dose_schedule*(i+1))
@@ -108,7 +116,6 @@ def gen_impulses(pop):
     u[impulse_indx]=1 # list of impulses at the simulated drug dosage times
 
     if pop.dwell:
-        dwell_time = int(pop.dwell_time/pop.timestep_scale)
         u[0:dwell_time] = 0
 
     return u

diff --git a/fears/utils/plotter.py b/fears/utils/plotter.py
@@ -598,6 +598,7 @@ def plot_landscape(p,conc=10**0,
                 plot_sub_network=False,
                 sub_network=None,
                 sub_network_color='white',
+                weight_list=None,
                 **kwargs):
     """
     Plots a graph representation of this landscape on the current matplotlib figure.
@@ -872,7 +873,11 @@ def plot_landscape(p,conc=10**0,
 
 
     if trajectory_list is not None:
-        for trajectory in trajectory_list:
+        for i,trajectory in enumerate(trajectory_list):
+
+                if weight_list is not None:
+                    weight = weight_list[i]
+                    arrowprops['lw'] = weight
 
                 trajectory_pairs = []
 
@@ -918,7 +923,7 @@ def plot_landscape(p,conc=10**0,
                             break
 
                 # Draw trajectory arrows
-
+                    
                     ax.annotate('', xy=end_pos, xytext=start_pos,
                             arrowprops=arrowprops)
 
@@ -1006,6 +1011,7 @@ def plot_kaplan_meier(pop,
                     event_times,
                     label=None,
                     t_max=None,
+                    t_vect = None,
                     n_sims=None,
                     ax=None,
                     mode='resistant',
@@ -1038,7 +1044,9 @@ def plot_kaplan_meier(pop,
     else:
         ylabel='% survival'
 
-    ax.plot(survival_curve,label=label,**kwargs)
+    if t_vect is None:
+        t_vect = np.arange(t_max)
+    ax.plot(t_vect,survival_curve,label=label,**kwargs)
 
     if errorband:
         # compute error bars
@@ -1053,7 +1061,7 @@ def plot_kaplan_meier(pop,
             err[t] = 100*((p*q)/n)**0.5 #
     t = np.arange(t_max)
 
-    ax.fill_between(t,survival_curve-err,survival_curve+err,alpha=0.4)
+    ax.fill_between(t_vect,survival_curve-err,survival_curve+err,alpha=0.4)
 
     xticks = ax.get_xticks()
     xlabels = xticks

diff --git a/fears/utils/stats.py b/fears/utils/stats.py
@@ -164,7 +164,7 @@ def get_entropy_timetrace(sim_num=0,condition_num=0,exp=None,exp_info_path=None)
     sim = exp + os.sep + sim
     data_dict = results_manager.get_data(sim)
 
-    data = data_dict['counts']
+    data = data_dict['counts']/np.sum(data_dict['counts'],axis=1)
 
     return stats.entropy(data.T)