import directory import numpy as np import matplotlib.pyplot as plt import matplotlib.font_manager as fm from math import pi import scipy.stats as st import plotly.graph_objects as go from PIL import Image import os path1 = os.path.join(directory.FONT_DIR, 'Pretendard-SemiBold.ttf') fontprop_semibold = fm.FontProperties(fname=path1) path2 = os.path.join(directory.FONT_DIR, 'Pretendard-Bold.ttf') fontprop_bold = fm.FontProperties(fname=path2) path3 = os.path.join(directory.FONT_DIR, 'Font Awesome 6 Free-Solid-900.otf') fontprop_awesome = fm.FontProperties(fname=path3) BASE_DIR = directory.BASE_DIR def plot_EEG_ebsi_radar(values: list, RESULT_DIR: str): """ plot EEG EBSI radar chart Args: values (list) : ['Foggy', 'Talented', 'Tense', 'Mindful'] # 안개형, 영재형, 긴장형, 차분형 RESULT_DIR (str) : RESULT_DIR """ values += values[:1] # Because of the circulating Radar Chart personality. N = len(values) - 1 angles = [n / float(N) * 2 * pi for n in range(N)] angles += angles[:1] plt.figure(figsize=(7, 6)) ax = plt.subplot(111, polar=True) ax.yaxis.grid(linewidth=1, linestyle='--', color='#BFBFBF') ax.grid(alpha=0.7) ax.tick_params(axis='x', which='major', pad=25, labelsize=15) ax.set_rlabel_position(0) ax.scatter(angles, values, color='#1754A5', s=20) ax.plot(angles, values, color='#1754A5', linewidth=2, linestyle='solid') # Plot data ax.fill(angles, values, color='#0084E1', alpha=0.2) # Fill area circle = plt.Circle((0, 0), 100, transform=ax.transData._b, fill=False, edgecolor='#7D7D7D', linewidth=5, zorder=10) plt.gca().add_artist(circle) plt.ylim(0, 100) xticks = [" ", " ", " ", " "] plt.xticks(angles[:-1], xticks, color='black', size=15) plt.yticks(alpha=0.3, size=14) plt.savefig(os.path.join(RESULT_DIR, "eeg_ebsi_radar.png"), dpi=300) def plot_PPG_ebsi_radar(values: list, RESULT_DIR: str): """ plot PPG EBSI radar chart Args: values (list) : [Mind Weak, Depress, Anxious] # 심약형, 우울형, 불안형 RESULT_DIR (str) : RESULT_DIR """ values += values[:1] # Because of the circulating Radar Chart personality. N = len(values) - 1 angles = [n / float(N) * 2 * pi for n in range(N)] angles += angles[:1] plt.figure(figsize=(7, 6)) ax = plt.subplot(111, polar=True) ax.yaxis.grid(linewidth=1, linestyle='--', color='#BFBFBF') ax.grid(alpha=0.7) ax.tick_params(axis='x', which='major', pad=25, labelsize=15) ax.set_rlabel_position(0) ax.scatter(angles, values, color='#1754A5', s=20) ax.plot(angles, values, color='#1754A5', linewidth=2, linestyle='solid') # Plot data ax.fill(angles, values, color='#0084E1', alpha=0.2) # Fill area circle = plt.Circle((0, 0), 100, transform=ax.transData._b, fill=False, edgecolor='#7D7D7D', linewidth=5, zorder=10) plt.gca().add_artist(circle) plt.ylim(0, 100) xticks = [" ", " ", " "] plt.xticks(angles[:-1], xticks, color='black', size=15) plt.yticks(alpha=0.3, size=14) plt.savefig(os.path.join(RESULT_DIR, "ppg_ebsi_radar.png"), dpi=300) def plot_score(score: int, sort: str, RESULT_DIR: str, reportLanguage: str="kr"): ''' plot brain acitivity bar plot Args: score (int) : Maumgyeol Score sort (str) : eeg or ppg RESULT_DIR (str) : RESULT_DIR ''' fig, ax = plt.subplots(figsize=(6, 6)) ax = plt.subplot(projection='polar') data = [100, score] startangle = 90 colors = ['#121CA7', '#0053CC'] xs = [(i * pi * 2) / 100 for i in data] ys = [1, 1] left = (startangle * pi * 2) / 360 # this is to control where the bar starts # plot bars and points at the end to make them round ax.bar(10 * 2 * np.pi / 100, -4, width=2 * np.pi, color='#AAD2E6', edgecolor=None, alpha=0.2) ax.bar(10 * 2 * np.pi / 100, 1, width=2 * np.pi, color='#AAD2E6', edgecolor=None, alpha=0.2) ax.barh(ys[1], xs[1], left=left, height=0.8, color=colors[1], alpha=1, zorder=2) ax.barh(ys[0], xs[0], left=left, height=0.2, color=colors[0], alpha=0.2) ax.scatter(xs[1] + left, ys[1] - 0.005, s=250, color=colors[1], zorder=2) ax.scatter(left + 0.003, ys[1] + 0.005, s=250, color=colors[1], zorder=2) ax.fill(ys, xs, 'red') plt.ylim(-4, 4) if reportLanguage =="en": plt.text(-1.5, -3.5, score, ha='center', va='center', fontsize=90, fontproperties=fontprop_semibold) else: plt.text((10 * pi * 2)/ 100, -4, score, ha='center', va='center', fontsize=90, fontproperties=fontprop_semibold) plt.text(-1.54, -1.8, "점", ha='center', va='center', fontsize=25, fontproperties=fontprop_bold, color='#7D7D7D', alpha=0.6) plt.xticks([]) plt.yticks([]) ax.spines.clear() plt.savefig(os.path.join(RESULT_DIR, sort + '_score.png'), dpi=300) def plot_brain_activity(values: list, RESULT_DIR: str): ''' plot brain acitivity bar plot Args: values (list) : ['θ', 'Lα', 'Hα', 'Lβ', 'Mβ', 'Hβ', 'γ'], percent format RESULT_DIR (str) : RESULT_DIR ''' plt.figure(figsize=(6.7, 7.8)) # 7:5비율로 ax = plt.gca() ax.spines['bottom'].set_visible(False) ax.spines['left'].set_visible(False) ax.spines['right'].set_visible(False) ax.spines['top'].set_visible(False) x = ['θ', 'Lα', 'Hα', 'Lβ', 'Mβ', 'Hβ', 'γ'] colors = ['#0084E1', '#FFC200', '#F94C56', '#EA89E3', '#00BD53', '#9072DC', '#F27700'] plt.bar(x, values, color=colors, width=0.6) plt.gca().set_facecolor('#E4E4E4') for i, v in enumerate(values): plt.text(x[i], v + 3.5, int(values[i]), # 좌표 (x축 = v, y축 = y[0]..y[1], 표시 = y[0]..y[1]) fontsize=16, fontproperties=fontprop_bold, color='black', horizontalalignment='center', # horizontalalignment (left, center, right) verticalalignment='center') # verticalalignment (top, center, bottom) plt.axhspan(16, 84.2, color='#E6F7FF', zorder=0) plt.axhspan(100, 110, color='w', zorder=0) plt.axhline(16, zorder=0, linestyle='--', linewidth=0.8, color='#949595') plt.axhline(84.2, zorder=0, linestyle='--', linewidth=0.8, color='#949595') plt.gca().axes.yaxis.set_ticks([]) for label in ax.get_xticklabels(): label.set_fontproperties(fontprop_bold) plt.tick_params(labelsize=15, length=7, bottom=False) plt.ylim(0, 110) plt.savefig(os.path.join(RESULT_DIR, 'brain_activity.png'), dpi=300) def plot_brain_intelligence(apf_freq:float, apf_psd:float, RESULT_DIR:str): ''' plot brain intelligence Args: apf_freq (float) : IAPF frequency value apf_psd (float) : Percentage of subjects' apf_psd compared to normaltive data RESULT_DIR (str) : RESULT_DIR ''' fig = go.Figure() fig.add_layout_image( {'source': Image.open(os.path.join(BASE_DIR, "apf_base.png")), 'xref': "x", 'yref': "y", 'x': -1, 'y': 7.4, 'sizex': 8, 'sizey': 8, 'sizing': "stretch", 'layer': "below"}) fig.add_layout_image( {'source': Image.open(os.path.join(BASE_DIR, "apf_point.png")), 'xref': "x", 'yref': "y", 'x': (apf_freq -8) * 6.64/4 -0.24 , 'y': (apf_psd) * 6.76/100 +.25, 'sizex': .5, 'sizey': .5, 'sizing': "stretch", 'layer': "below"}) fig.update_layout( xaxis={'showgrid': False, 'zeroline': False, 'showline': False, 'showticklabels': False}, yaxis={'showgrid': False, 'zeroline': False, 'showline': False, 'showticklabels': False}, autosize=False, showlegend=False, plot_bgcolor='white', width=1200, height=1200) fig.update_layout(xaxis=dict(range=[-1, 10]), yaxis=dict(range=[-1, 10])) fig.write_image(os.path.join(RESULT_DIR, 'brain_intelligence.png')) def plot_brain_flexibility(ratio_com:float, RESULT_DIR:str, reportLanguage: str="kr"): ''' plot brain flexibility Args: ratio_com (float) : Percentage of subjects' complexity compared to normaltive data RESULT_DIR (str) : RESULT_DIR en (bool) ''' start_x = 0 if reportLanguage =="en": start_x = -0.15 fig = go.Figure() fig.add_layout_image( {'source': Image.open(os.path.join(BASE_DIR, "complexity_base.png")), 'xref': "x", 'yref': "y", 'x': start_x, 'y': 2, 'sizex': 71*0.15, 'sizey': 14*0.15, 'sizing': "contain", 'layer': "below"}) fig.add_layout_image( {'source': Image.open(os.path.join(BASE_DIR, "base_point.png")), 'xref': "x", 'yref': "y", 'x': 0.04 + (5.17-0.04)/100 * ratio_com, 'y': 3, 'sizex': 3.4*0.13, 'sizey': 5.6*0.13, 'sizing': "contain", 'layer': "below"}) fig.update_layout( xaxis={'showgrid': False, 'zeroline': False, 'showline': False, 'showticklabels': False}, yaxis={'showgrid': False, 'zeroline': False, 'showline': False, 'showticklabels': False}, autosize=False, showlegend=False, plot_bgcolor='white', width=1200, height=550) fig.update_layout(xaxis=dict(range=[-1, 6.5]), yaxis=dict(range=[-1, 4])) fig.write_image(os.path.join(RESULT_DIR, 'brain_flexibility.png')) def plot_brain_balance(ratio_fas: float, RESULT_DIR: str, reportLanguage: str="kr"): ''' plot brain balance Args: ratio_fas (float) : Percentage of subjects' FAA RESULT_DIR (str) : RESULT_DIR ''' fig = go.Figure() start_x = 0 start_y = 3 if reportLanguage =="en": start_x = -.08 start_y = 3 fig.add_layout_image( {'source': Image.open(os.path.join(BASE_DIR, "faa_base.png")), 'xref': "x", 'yref': "y", 'x': start_x, 'y': start_y, 'sizex': 7, 'sizey': 7, 'sizing': "contain", 'layer': "below"}) fig.update_layout( xaxis={'showgrid': False, 'zeroline': False, 'showline': True, 'showticklabels': False}, yaxis={'showgrid': False, 'zeroline': False, 'showline': True, 'showticklabels': False}, autosize=True, showlegend=False, plot_bgcolor='white', width=1200, height=700) theta = 180 - (ratio_fas * (9 / 5)) a = 2 * 0.95 b = 3 * 0.95 x_head = a * np.cos(np.radians(theta)) y_head = b * np.sin(np.radians(theta)) fig.add_trace( go.Scatter(mode='lines', x=[3.06, x_head + 3.06], y=[-1.9, y_head - 2], line=dict(color='black', width=5))) fig.add_trace( go.Scatter(mode='markers', x=[x_head + 3.06], y=[y_head - 2], marker={'line_color': 'black', 'line_width': 0, 'color': 'black', 'size': 4})) fig.update_layout(xaxis=dict(range=[-2, 7]), yaxis=dict(range=[-4, 4])) fig.write_image(os.path.join(RESULT_DIR, 'brain_balance.png')) def plot_ans_activity(values:list, RESULT_DIR:str): ''' plot ans acitivity bar plot Args: values (list) : ['TP', 'VLF', 'LF', 'HF'], percent format RESULT_DIR (str) : RESULT_DIR ''' plt.figure(figsize=(5, 4.5)) ax = plt.gca() ax.spines['bottom'].set_visible(False) ax.spines['left'].set_visible(False) ax.spines['right'].set_visible(False) ax.spines['top'].set_visible(False) x = ['TP', 'VLF', 'LF', 'HF'] color = ['#0084E1', '#FFC200', '#F94C56', '#00BD53'] plt.bar(x, values, color=color, width=0.45) plt.gca().set_facecolor('#E4E4E4') for i, v in enumerate(values): plt.text(x[i], v + 5, int(values[i]), # 좌표 (x축 = v, y축 = y[0]..y[1], 표시 = y[0]..y[1]) fontsize=16, fontproperties = fontprop_bold, color='black', horizontalalignment='center', # horizontalalignment (left, center, right) verticalalignment='center') # verticalalignment (top, center, bottom) plt.axhspan(16, 84.2, color='#E6F7FF', zorder=0) plt.axhspan(100, 110, color='w', zorder=0) plt.axhline(16, zorder=0, linestyle='--', linewidth=0.8, color='#949595') plt.axhline(84.2, zorder=0, linestyle='--', linewidth=0.8, color='#949595') plt.gca().axes.yaxis.set_ticks([]) for label in ax.get_xticklabels(): label.set_fontproperties(fontprop_semibold) plt.tick_params(labelsize=15, length=7, bottom=False) plt.ylim(0, 110) plt.savefig(os.path.join(RESULT_DIR, 'ANS_activity.png'), dpi=300) def plot_ans_balance(values: list, RESULT_DIR: str): ''' plot ans balance bar plot Args: values (list) : [SNS, PNS] RESULT_DIR (str) : RESULT_DIR ''' fig, axes = plt.subplots(nrows=2, ncols=1, sharex=True, figsize=(7, 3.5)) colors = ['#F30077', '#1194C8'] grp = [0, 0] label = ['SNS', 'PNS'] vline = np.linspace(10, 100, 10) for i, ax in enumerate(axes): height = .55 ax.barh(grp[i], values[i], color=colors[i], alpha=1, height=height) ax.barh(grp[i], 100, color='#E4E4E4', alpha=1,height=height, zorder=0) ax.set_ylabel(label[i], fontsize=23, fontproperties=fontprop_semibold, rotation='horizontal', labelpad=28, ha='center', va='center') for v in vline: ax.axvline(v, color='white', linewidth=2) ax.text(105, -.11, str(round(values[i], 1)), fontsize=28, fontproperties=fontprop_bold, color=colors[i]) ax.set_xticks([]) ax.set_yticks([]) ax.spines['bottom'].set_visible(False) ax.spines['right'].set_visible(False) ax.spines['top'].set_visible(False) ax.set_ylim(-.5, .5) ax.set_xlim(0, 130) plt.savefig(os.path.join(RESULT_DIR, 'ANS_balance.png'), dpi=300) def plot_stress_index(ratio_stress:float, RESULT_DIR:str, reportLanguage: str="kr"): ''' plot stree inedex Args: ratio_stress (float) : Percentage of subjects' stress index compared to normaltive data RESULT_DIR (str) : RESULT_DIR ''' fig = go.Figure() position_x = 0.2 + (5.22-0.2)/100 * ratio_stress if reportLanguage =="en": position_x = 0.22 + (5.13-0.22)/100 * ratio_stress fig.add_layout_image( {'source': Image.open(os.path.join(BASE_DIR, "si_base.png")), 'xref': "x", 'yref': "y", 'x': 0, 'y': 2, 'sizex': 73*0.15, 'sizey': 6*0.15, 'sizing': "contain", 'layer': "below"}) fig.add_layout_image( {'source': Image.open(os.path.join(BASE_DIR, "base_point.png")), 'xref': "x", 'yref': "y", 'x': position_x, 'y': 3, 'sizex': 3.4*0.13 , 'sizey': 5.5*0.13, 'sizing': "contain", 'layer': "below"}) fig.update_layout( xaxis={'showgrid': False, 'zeroline': False, 'showline': False, 'showticklabels': False}, yaxis={'showgrid': False, 'zeroline': False, 'showline': False, 'showticklabels': False}, autosize=False, showlegend=False, plot_bgcolor='white', width=1200, height=550) fig.update_layout(xaxis=dict(range=[-1, 6.5]), yaxis=dict(range=[-1, 4])) fig.write_image(os.path.join(RESULT_DIR, 'stress_index.png')) def plot_stress_resistance(ratio_stress_resistance:float, RESULT_DIR:str, reportLanguage: str="kr"): ''' plot stree inedex Args: ratio_stress_resistance (float) : Percentage of subjects' stress resistance compared to normaltive data RESULT_DIR (str) : RESULT_DIR ''' fig = go.Figure() position_x = 0.2 + (5.22-0.2)/100 * ratio_stress_resistance if reportLanguage =="en": position_x = 0.22 + (5.13-0.22)/100 * ratio_stress_resistance fig.add_layout_image( {'source': Image.open(os.path.join(BASE_DIR, "sr_base.png")), 'xref': "x", 'yref': "y", 'x': 0, 'y': 2, 'sizex': 73*0.15, 'sizey': 6*0.15, 'sizing': "contain", 'layer': "below"}) fig.add_layout_image( {'source': Image.open(os.path.join(BASE_DIR, "base_point.png")), 'xref': "x", 'yref': "y", 'x': position_x, 'y': 3, 'sizex': 3.4*0.13 , 'sizey': 5.5*0.13, 'sizing': "contain", 'layer': "below"}) fig.update_layout( xaxis={'showgrid': False, 'zeroline': False, 'showline': False, 'showticklabels': False}, yaxis={'showgrid': False, 'zeroline': False, 'showline': False, 'showticklabels': False}, autosize=False, showlegend=False, plot_bgcolor='white', width=1200, height=550) fig.update_layout(xaxis=dict(range=[-1, 6.5]), yaxis=dict(range=[-1, 4])) fig.write_image(os.path.join(RESULT_DIR, 'stress_resistance.png')) def plot_pse_14(x_val:float, sort:str, RESULT_DIR:str): ''' plot pse depress bar Args: x_val (float) : x value sort (str) : category value, 'depress' or 'unrest' RESULT_DIR (str) : RESULT_DIR ''' fig = go.Figure() fig.add_layout_image( {'source': Image.open(os.path.join(BASE_DIR, "pse_base_14.png")), 'xref': "x", 'yref': "y", 'x': 0.1, 'y': 2, 'sizex': 71*0.15, 'sizey': 6*0.15, 'sizing': "contain", 'layer': "below"}) fig.add_layout_image( {'source': Image.open(os.path.join(BASE_DIR, "base_point.png")), 'xref': "x", 'yref': "y", 'x': x_val, 'y': 3, #0 ~4.9 0, 0.95, 1.9, 2.95, 3.9, 4.9 'sizex': 3.4*0.13 , 'sizey': 5.5*0.13, 'sizing': "contain", 'layer': "below"}) fig.update_layout( xaxis={'showgrid': False, 'zeroline': False, 'showline': False, 'showticklabels': False}, yaxis={'showgrid': False, 'zeroline': False, 'showline': False, 'showticklabels': False}, autosize=False, showlegend=False, plot_bgcolor='white', width=1200, height=550) fig.update_layout(xaxis=dict(range=[-1, 6.5]), yaxis=dict(range=[-1, 4])) fig.write_image(os.path.join(RESULT_DIR, "pse_"+sort+".png")) def plot_pse(x_val:float, sort:str, RESULT_DIR:str): ''' plot pse depress bar Args: x_val (float) : x value sort (str) : category value, 'depress' or 'unrest' RESULT_DIR (str) : RESULT_DIR ''' fig = go.Figure() fig.add_layout_image( {'source': Image.open(os.path.join(BASE_DIR, "pse_base.png")), 'xref': "x", 'yref': "y", 'x': 0.1, 'y': 2, 'sizex': 71*0.15, 'sizey': 6*0.15, 'sizing': "contain", 'layer': "below"}) fig.add_layout_image( {'source': Image.open(os.path.join(BASE_DIR, "base_point.png")), 'xref': "x", 'yref': "y", 'x': x_val, 'y': 3, #0 ~4.9 0, 0.95, 1.9, 2.95, 3.9, 4.9 'sizex': 3.4*0.13 , 'sizey': 5.5*0.13, 'sizing': "contain", 'layer': "below"}) fig.update_layout( xaxis={'showgrid': False, 'zeroline': False, 'showline': False, 'showticklabels': False}, yaxis={'showgrid': False, 'zeroline': False, 'showline': False, 'showticklabels': False}, autosize=False, showlegend=False, plot_bgcolor='white', width=1200, height=550) fig.update_layout(xaxis=dict(range=[-1, 6.5]), yaxis=dict(range=[-1, 4])) fig.write_image(os.path.join(RESULT_DIR, "pse_"+sort+".png")) def plot_self_understanding(self_u:float, RESULT_DIR:str): ''' Args self_u (str) : self understanding ratio RESULT_DIR (str) : RESULT_DIR ''' fig = go.Figure() fig.add_layout_image( {'source': Image.open(os.path.join(BASE_DIR, "self_understanding_base.png")), 'xref': "x", 'yref': "y", 'x': 0, 'y': 2, 'sizex': 73*0.15, 'sizey': 6*0.15, 'sizing': "contain", 'layer': "below"}) fig.add_layout_image( {'source': Image.open(os.path.join(BASE_DIR, "base_point.png")), 'xref': "x", 'yref': "y", 'x': self_u / 100 * 5 + 0.2, 'y': 3, #0.2 5.2 'sizex': 3.4*0.13, 'sizey': 5.5*0.13, 'sizing': "contain", 'layer': "below"}) fig.update_layout( xaxis={'showgrid': False, 'zeroline': False, 'showline': False, 'showticklabels': False}, yaxis={'showgrid': False, 'zeroline': False, 'showline': False, 'showticklabels': False}, autosize=False, showlegend=False, plot_bgcolor='white', width=1200, height=550) fig.update_layout(xaxis=dict(range=[-1, 6.5]), yaxis=dict(range=[-1, 4])) fig.write_image(os.path.join(RESULT_DIR, "self_understanding.png")) def plot_statistic_mean(values, sort: str, RESULT_DIR: str, reportLanguage: str="kr"): ''' plot statistic mean bar Args: values : ['전체 평균', '내 연령평균', '내 성별평균', '내 점수'] sort (str) : category value, 'eeg' or 'ppg' RESULT_DIR (str) : RESULT_DIR en (bool) ''' colors = [(0.0, 0.5176470588235295, 0.8823529411764706, .35), (0.0, 0.5176470588235295, 0.8823529411764706, .35), (0.0, 0.5176470588235295, 0.8823529411764706, .35)] temp = [0, 1, 2, 3] label = ['전체 평균', '내 연령평균', '내 성별평균', '내 점수'] start_y = -17 if reportLanguage =="en": label = ['All Avg', 'My age Avg', 'My Gender Avg', 'My Score'] start_y = -19.5 vline = np.linspace(0, 100, 11) if values[-1] >= 80: # 건강 colors.insert(3, (0.0, 0.5176470588235295, 0.8823529411764706, 1)) elif (values[-1] >= 70) and (values[-1] < 80): # 보통 colors.insert(3, (0.0, 0.7411764705882353, 0.3254901960784314, 1)) elif (values[-1] >= 60) and (values[-1] < 70): # 경고 colors.insert(3, (0.9490196078431372, 0.4666666666666667, 0.0, 1)) elif values[-1] < 60: # 위험 colors.insert(3, (0.9764705882352941, 0.2980392156862745, 0.33725490196078434, 1)) plt.figure(figsize=(9.1, 3.1)) plt.barh(temp, values, color=colors, height=.4) for v in vline: # vline plt.axvline(v, color='white', linewidth=1) for i, v in enumerate(values): # value text if i == 3: plt.text(v + 3, temp[i] - 0.12, str(round(v, 1)), fontsize=16.5, fontproperties=fontprop_bold, color=colors[i]) else: plt.text(v + 3, temp[i] - 0.11, str(round(v, 1)), fontsize=14, fontproperties=fontprop_semibold, color='#717171') for i, yi in enumerate(np.arange(len(values))): # ytick text if i == 3: plt.gca().text(start_y, yi, label[i], horizontalalignment='left', verticalalignment='center', fontsize=12, fontproperties=fontprop_bold, color=colors[i]) else: plt.gca().text(start_y, yi + .01, label[i], horizontalalignment='left', verticalalignment='center', fontsize=12, fontproperties=fontprop_semibold, color='#717171') plt.xticks([]) plt.yticks([]) plt.gca().yaxis.set_ticks_position('none') # tick position 없음 plt.gca().spines['left'].set_linewidth(1) # 테두리 설정 plt.gca().spines['bottom'].set_visible(False) plt.gca().spines['right'].set_visible(False) plt.gca().spines['top'].set_visible(False) plt.ylim(-.7, 3.7) plt.xlim(0, 110) plt.tight_layout() plt.savefig(os.path.join(RESULT_DIR, sort + '_mean.png'), dpi=300) def plot_norm(sort: str, zscore: float, age: str, RESULT_DIR: str, reportLanguage: str="kr"): ''' plot norm Args: sort (str) : category value, 'eeg' or 'ppg' zscore (float) : zscore of subjects' sort score compared to normaltive data age (str) : subject's age format like '20세' ''' age = directory.age_modify(age, png=True) plt.figure(figsize=(11.7 * 1.4, 4.1 * 1.4)) percent = round(st.norm.cdf(zscore), 3) - 0.5 x = np.arange(-3, 3.01, 0.01) y = st.norm.pdf(x, 0, 1.1) plt.plot(x, y, linewidth=4, color='#0053CC', alpha=1) # 테두리 polygon = plt.fill_between(x, -0.04, y, alpha=.12, color='#0084E1', lw=0) # 안쪽 plt.gca().spines['bottom'].set_visible(False) plt.gca().spines['left'].set_visible(False) plt.gca().spines['right'].set_visible(False) plt.gca().spines['top'].set_visible(False) plt.xlim(-4, 3.2) plt.ylim(-0.15, 0.5) plt.gca().axes.xaxis.set_visible(False) plt.gca().axes.yaxis.set_visible(False) plt.vlines(percent * 6, -0.06, st.norm.pdf(percent * 6, 0, 1.1) - 0.02, linestyle="-", lw=2, color='#AAAAAB') position = '내 위치' low = "낮음" high = "높음" position_x = percent * 6 - 0.2 if reportLanguage =="en": position = 'My position' low = 'Low' high = 'High' position_x = percent * 6 - 0.38 plt.text(position_x, st.norm.pdf(percent * 6, 0, 1.1) + 0.12, position, fontproperties=fontprop_bold, fontsize=20, color='#0084E1') plt.text(percent * 6 - 0.005, st.norm.pdf(percent * 6, 0, 1.1) + 0.06, '\uf3c5', fontproperties=fontprop_awesome, size=35, va='center', ha='center', clip_on=True, c='#0084E1') a = str(round(100 - st.norm.cdf(zscore) * 100)) + "%" plt.text(percent * 6 - 0.14, -0.12, a, fontproperties=fontprop_bold, fontsize=25, color='#0084E1') plt.text(-3.35, -0.02, low, fontproperties=fontprop_semibold, fontsize=15, color='#717171') plt.text(3.15, -0.02, high, fontproperties=fontprop_semibold, fontsize=15, color='#717171') plt.savefig(os.path.join(RESULT_DIR, sort + '_norm.png'), dpi=300) def plot_4class( scores: dict, sort: str, RESULT_DIR: str): ''' plot brain acitivity bar plot Args: score (int) : Maumgyeol Score sort (str) : eeg or ppg RESULT_DIR (str) : RESULT_DIR ''' items = list(scores.items()) max_score = max(scores.values()) for idx, (label, score) in enumerate(items): fig, ax = plt.subplots(figsize=(6, 6)) ax = plt.subplot(projection='polar') # convert score (0–100) to radians xs = (score * 2 * pi) / 100 left = (90 * 2 * pi) / 360 bar_color = '#0053CC' # 기본 파란색 if score == max_score: if idx == 0: bar_color = '#6FAE3C' #연두 else: bar_color = '#E67E22' #주황 #colors = ['#121CA7', '#0053CC'] ys = [1, 1] # score bar bar = ax.barh( ys[1], xs, left=left, height=1.4, color=bar_color, zorder=2) # bars는 BarContainer, 내부 Rectangle patch들에 직접 스타일 적용 for p in bar.patches: p.set_edgecolor("white") # 배경색(흰색)과 동일하게 p.set_linewidth(5) ax.barh(ys[0], 2 * pi, left=left, height=1.1, color='#a2a9b0', alpha=0.2) # text plt.ylim(-4, 4) plt.text((10 * pi * 2) / 100, -4, f"{score}%", ha='center', va='center', fontsize=45, fontproperties=fontprop_semibold) # clean plt.xticks([]) plt.yticks([]) ax.spines.clear() # save save_path = os.path.join( RESULT_DIR, f"{sort}_{label}_score.png" ) plt.savefig(save_path, dpi=300, bbox_inches='tight') plt.close() def plot_mdd_score(score: float, sort: str, RESULT_DIR: str, colors=('#AAD2E6', "#191796"), reportLanguage: str = "kr"): ''' 우울증/정상 확률 도넛(원형 게이지) 플롯. plot_4class 와 동일한 각진(흰 테두리) 스타일이며 색상만 파라미터로 받는다 (우울증=파랑 계열, 정상=초록 계열). Args: score (float) : 0~100 확률(%) sort (str) : 저장 파일명 접두어 (예: 'mdd_dep', 'mdd_normal') RESULT_DIR (str) : RESULT_DIR colors (tuple) : (사용 안 함/호환용, 진행 바 색상) - colors[1]이 실제 바 색상 ''' bar_color = colors[1] fig, ax = plt.subplots(figsize=(6, 6)) ax = plt.subplot(projection='polar') xs = (score * 2 * pi) / 100 left = (90 * 2 * pi) / 360 ys = [1, 1] bar = ax.barh(ys[1], xs, left=left, height=1.4, color=bar_color, zorder=2) for p in bar.patches: p.set_edgecolor("white") # 각진 바 사이 흰 테두리 - plot_4class 와 동일 p.set_linewidth(5) ax.barh(ys[0], 2 * pi, left=left, height=1.1, color='#a2a9b0', alpha=0.2) plt.ylim(-4, 4) score_text = f"{score:.1f}%" plt.text((10 * pi * 2) / 100, -4, score_text, ha='center', va='center', fontsize=45, fontproperties=fontprop_semibold, color='black') plt.xticks([]) plt.yticks([]) ax.spines.clear() plt.savefig(os.path.join(RESULT_DIR, sort + '_score.png'), dpi=300, bbox_inches='tight') # plot_4class 와 동일 plt.close()