<jupyter_start><jupyter_text>The following lines need to be used if data the from downloaded dataset should be used. The location of the ``Data`` folder needs to be specified by the parameter ``DATA_FOLDER_PATH`` in the file ``input_params.json``. If you want to analyse your own dataset you need to set the variable ``file_path`` to the folder where the simulation is located. Importantly, in this folder there should only be located exactly one simulation.<jupyter_code>file_path_input_params_json = '../input_params.json'
input_param_dict = mainClass.extract_variables_from_input_params_json(file_path_input_params_json)
root_path = input_param_dict["DATA_FOLDER_PATH"]
simulation_location = 'fig_1'
file_path = os.path.join(root_path, simulation_location)
print('file_path', file_path)
parameter_path = os.path.join(file_path, 'parameter_set.csv')
print('parameter_path', parameter_path)<jupyter_output>file_path /home/berger/Documents/Arbeit/PhD/data/UltrasensitivityCombined/NatCom/fig_1
parameter_path /home/berger/Documents/Arbeit/PhD/data/UltrasensitivityCombined/NatCom/fig_1/parameter_set.csv<jupyter_text>Make data frame from time traces<jupyter_code>data_frame = makeDataframe.make_dataframe(file_path)
time_traces_data_frame = pd.read_hdf(data_frame['path_dataset'].iloc[0], key='dataset_time_traces')

v_init_data_frame = pd.read_hdf(data_frame['path_dataset'].iloc[0], key='dataset_init_events')
v_init = v_init_data_frame.iloc[0]['v_init']
v_init
n_ori = np.array(time_traces_data_frame["n_ori"])
time = np.array(time_traces_data_frame["time"])
volume = np.array(time_traces_data_frame["volume"])
n_ori_density = n_ori / volume<jupyter_output><empty_output><jupyter_text>Color definitions<jupyter_code>pinkish_red = (247 / 255, 109 / 255, 109 / 255)
green = (0 / 255, 133 / 255, 86 / 255)
dark_blue = (36 / 255, 49 / 255, 94 / 255)
light_blue = (168 / 255, 209 / 255, 231 / 255)
blue = (55 / 255, 71 / 255, 133 / 255)
yellow = (247 / 255, 233 / 255, 160 / 255)<jupyter_output><empty_output><jupyter_text>Plot three figures<jupyter_code>label_list = [r'$V(t)$', r'$n_{ori}(t)$', r'$\rho_{ori}(t)$']
x_axes_list = [time, time, time]
y_axes_list = [volume, n_ori, n_ori_density]
color_list = [green, dark_blue, pinkish_red]
fig, ax = plt.subplots(3, figsize=(6,3))
plt.xlabel('time')
for item in range(0, len(label_list)):
    ax[item].set_ylabel(label_list[item])
    ax[item].plot(x_axes_list[item], y_axes_list[item], color=color_list[item])
    ax[item].set_ylim(ymin=0)
    ax[item].tick_params(
        axis='x',          # changes apply to the x-axis
        which='both',      # both major and minor ticks are affected
        bottom=False,      # ticks along the bottom edge are off
        top=False,         # ticks along the top edge are off
        labelbottom=False) # labels along the bottom edge are off
    ax[item].spines["top"].set_visible(False)
    ax[item].spines["right"].set_visible(False)
    ax[item].margins(0)
ax[0].set_yticks([v_init, 2*v_init])
ax[0].set_yticklabels([ r'$v^\ast$', r'$2 \, v^\ast$'])
ax[0].tick_params(axis='y', colors=green)
ax[0].axhline(y=v_init, color=green, linestyle='--')
ax[0].axhline(y=2*v_init, color=green, linestyle='--')

ax[1].set_yticks([2, 4])
ax[1].set_yticklabels([ r'2', r'4'])
ax[2].axhline(y=1/v_init, color=pinkish_red, linestyle='--')
ax[2].set_yticks([1/v_init])
ax[2].set_yticklabels([ r'$\rho^\ast$'])
ax[2].tick_params(axis='y', colors=pinkish_red)
plt.savefig(file_path + '/fig1_higher.pdf', format='pdf')<jupyter_output><empty_output>
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