import numpy as np import matplotlib.pyplot as plt import re # Constants D = 0.1 mu = 0.1 kx = 6 x0 = 1 Fmax = 8 * kx * x0**3 / np.sqrt(27) L = D / (mu * Fmax) tau = L * L / D plt.rc('text.latex', preamble=r'\usepackage{amsmath} \usepackage{mathptmx}') # LaTeX-Unterstützung aktivieren und Schriftart setzen plt.rc('text', usetex=True) plt.rc('font', family='serif') # Set global font to Serif plt.rcParams['font.family'] = 'serif' # Function to read data from a file def readfile(file_path): return np.loadtxt(file_path) # Function to normalize and chunk the data def normalize_and_chunk(data, num_chunks=300): # Calculate the sum of all elements in the data array data_sum = np.sum(data) # Normalize the data by dividing each element by the sum normalized_data = data / data_sum # Chunk the data and calculate average frequency chunk_size = len(normalized_data) // num_chunks # Integer division to get chunk size chunked_data = [] x_values = [] for j in range(num_chunks): start_index = j * chunk_size end_index = (j + 1) * chunk_size chunk = normalized_data[start_index:end_index] chunk_avg = np.mean(chunk) chunked_data.append(chunk_avg * chunk_size) x_values.append((start_index + end_index) / 2 * 0.005 / tau) # Adjust x-values # Normalize the chunked data to maintain the area under the curve if len(x_values) > 2: # Ensure there are enough x-values for normalization chunked_data /= (x_values[3] - x_values[2]) return chunked_data, x_values # Function to extract tumblerate and runrate from filename def extract_rates(filename): match = re.search(r'tumblerate(\d+)runrate(\d+)', filename) if match: tumblerate = match.group(1) runrate = match.group(2) return tumblerate, runrate return None, None # Function to create a subplot for given MD and TPS file paths def create_subplot(ax, md_files, tps_files, colors, markers): # ax.plot([], [], marker='o', color='black', linestyle='None', label=r'MD') # ax.plot([], [], color='black', linestyle='-', label=r'TPS') for idx, (md_file, tps_file) in enumerate(zip(md_files, tps_files)): # Read data data_md = readfile(md_file) data_TPS = readfile(tps_file) # Normalize and chunk data chunked_data_md, x_values_md = normalize_and_chunk(data_md) chunked_data_TPS, x_values_tps = normalize_and_chunk(data_TPS) # Extract tumblerate and runrate for labels tumblerate, runrate = extract_rates(md_file) # Directly replace values as per your request if tumblerate == '2': tumblerate = '0.24' elif tumblerate == '10': tumblerate = '1.20' elif tumblerate == '6': tumblerate = '0.70' elif tumblerate == '1': tumblerate = '0.12' if runrate == '1': runrate = '0.12' elif runrate == '10': runrate = '1.20' elif runrate == '2': runrate = '0.24' # Plot MD data as dashed lines with unique markers and colors if tumblerate == '0': ax.plot(x_values_md, chunked_data_md, linestyle='none', color=colors[idx], marker=markers[idx], label=fr'ABP', linewidth=2, markersize=5) ax.plot(x_values_tps, chunked_data_TPS, color=colors[idx], alpha=0.5, linestyle = '--', #label=fr'$\lambda_{{1 \rightarrow 0}}={tumblerate}$, $\lambda_{{0 \rightarrow 1}}={runrate}$' ) elif runrate == '0': ax.plot(x_values_md, chunked_data_md, linestyle='none', color=colors[idx], marker=markers[idx], label=fr'BP', linewidth=2, markersize=5) ax.plot(x_values_tps, chunked_data_TPS, color=colors[idx], alpha=0.5, linestyle = ':', #label=fr'$\lambda_{{1 \rightarrow 0}}={tumblerate}$, $\lambda_{{0 \rightarrow 1}}={runrate}$' ) else: ax.plot(x_values_md, chunked_data_md, linestyle='none', color=colors[idx], marker=markers[idx], label=fr'$\lambda_{{1 \rightarrow 0}}={tumblerate}/\tau$, $\lambda_{{0 \rightarrow 1}}={runrate}/\tau$', linewidth=2, markersize=5) # Plot TPS data in the same color as MD ax.plot(x_values_tps, chunked_data_TPS, color=colors[idx], alpha=0.5, #label=fr'$\lambda_{{1 \rightarrow 0}}={tumblerate}$, $\lambda_{{0 \rightarrow 1}}={runrate}$' ) ax.set_xlim([0, 50]) # Adjust x-axis limit as necessary ax.set_ylim([0, 0.14]) # Adjust y-axis limit as necessary plt.rc('text', usetex=True) plt.rc('font', family='serif', weight='normal', size=10) # Enable ticks on both sides and inside ax.tick_params(axis='both', which='both', direction='in', top=True, right=True) ax.set_xlabel(fr'$t/\tau$', fontsize=10, fontweight='normal') ax.set_ylabel(fr'$P(t_\text{{TPT}})$', fontsize=10, fontweight='normal') ax.tick_params(axis='both', labelsize=8) # Set tick label font size ax.legend(fontsize=8, loc='upper center', bbox_to_anchor=(0.5, 1.)) # Set legend font size # Define specific colors and markers for each dataset in each subplot subplot_details = [ { "md": [ "data/length_distributions/length_distribution_histo_md_rtp_step0.005pe10tumblerate2runrate0samplesize1000000.dat", "data/length_distributions/length_distribution_histo_md_rtp_step0.005pe10tumblerate0runrate2samplesize1000000.dat", "data/length_distributions/length_distribution_histo_md_rtp_step0.005pe10tumblerate6runrate2samplesize1000000.dat", ], "TPS": [ "data/length_distributions/length_distribution_histo_TPS_rtp_step0.005pe10tumblerate2runrate0samplesize100000000.dat", "data/length_distributions/length_distribution_histo_TPS_rtp_step0.005pe10tumblerate0runrate2samplesize100000000.dat", "data/length_distributions/length_distribution_histo_TPS_rtp_step0.005pe10tumblerate6runrate2samplesize100000000.dat", ], "colors": ['blue', 'red', 'green'], "markers": ['o', 's', '^'] }, { "md": [ "data/length_distributions/length_distribution_histo_md_rtp_step0.005pe10tumblerate10runrate10samplesize1000000.dat", "data/length_distributions/length_distribution_histo_md_rtp_step0.005pe10tumblerate1runrate10samplesize1000000.dat" ], "TPS": [ "data/length_distributions/length_distribution_histo_TPS_rtp_step0.005pe10tumblerate10runrate10samplesize100000000.dat", "data/length_distributions/length_distribution_histo_TPS_rtp_step0.005pe10tumblerate1runrate10samplesize100000000.dat" ], "colors": ['cyan', 'red'], "markers": ['D', 'x'] }, { "md": [ "data/length_distributions/length_distribution_histo_md_rtp_step0.005pe10tumblerate1runrate1samplesize1000000.dat", "data/length_distributions/length_distribution_histo_md_rtp_step0.005pe10tumblerate10runrate1samplesize1000000.dat" ], "TPS": [ "data/length_distributions/length_distribution_histo_TPS_rtp_step0.005pe10tumblerate1runrate1samplesize100000000.dat", "data/length_distributions/length_distribution_histo_TPS_rtp_step0.005pe10tumblerate10runrate1samplesize100000000.dat" ], "colors": ['black', 'blue'], "markers": ['+', 'P'] } ] # Create the plot fig, axs = plt.subplots(1, 3, figsize=(7, 3.3), tight_layout=True) plt.rc('text', usetex=True) plt.rc('font', family='serif', weight='normal', size=10) for ax, data in zip(axs, subplot_details): create_subplot(ax, data["md"], data["TPS"], data["colors"], data["markers"]) # Hide the x-axis and x-ticks for axs[1] and axs[2] axs[1].set_ylabel('') axs[1].set_yticklabels([]) axs[1].tick_params(axis='y', which='both', direction='in', right=True, left=True) # Keep ticks on both sides axs[2].set_ylabel('') axs[2].set_yticklabels([]) axs[2].tick_params(axis='y', which='both', direction='in', right=True, left=True) # Keep ticks on both sides plt.rc('text.latex', preamble=r'\usepackage{amsmath} \usepackage{mathptmx}') # Set global font to Serif plt.rcParams['font.family'] = 'serif' plt.tight_layout() # Adjust layout to minimize whitespace plt.savefig('/home/tom/Ph.D./TPS_for_RTP/LaTex/paper/Figures/P(t_TPT)_TPS_MD_severalcases.pdf') plt.show()