def fun2(arr, pname, guess_range, time, cond, cond_name):
out_arr = np.zeros_like(arr)
for i, beta_p in enumerate(arr):
cond = dict(cond)
cond[pname] = beta_p
out = norm_readout(cond_name, guess_range, time, cond)
print(out)
out_arr[i] = out
df = pd.DataFrame({"x": arr, "y": out_arr})
df["ynorm"] = df.y / float(df.y[df.x == 35.0])
df["ylog"] = np.log2(df.ynorm)
df["model"] = cond_name
return df
import module_models as models
import numpy as np
import seaborn as sns
import pandas as pd
sns.set(context="poster", style="ticks", rc={"lines.linewidth": 4})
colors = ["#3498db", "#95a5a6", "#e74c3c", "#34495e"]
sns.set_palette(colors)
time = np.arange(0, 200, 0.01)
pname = "rate_il7"
cond = d_il7
cond_names = ["timer"]
guess_range = (0, 5)
out = norm_readout(pname, guess_range, time, cond)
print(out)
def fun(arr, pname, guess_arr, cond_list, cond_names):
df_list = []
for cond, cond_name, guess_range in zip(cond_list, cond_names, guess_arr):
df = fun2(arr, pname, guess_range, time, cond, cond_name)
df_list.append(df)
df_cat = pd.concat(df_list)
return df_cat