200 lines
6.1 KiB
Python
200 lines
6.1 KiB
Python
from matplotlib import pyplot as plt
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import pandas as pd
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import numpy as np
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import io
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import streamlit as st
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# import scienceplots
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# plt.style.use(['nature', 'no-latex',"cjk-sc-font"])
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plt.rcParams['font.family'] = 'sans-serif'
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plt.rcParams['font.sans-serif'] = ['SimHei'] # 设置中文字体
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plt.rcParams['axes.unicode_minus'] = False # 解决负号显示问题
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def cubic_bezier_with_zero_derivatives(p0, p1, t_array, influence_factor):
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"""
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创建三次贝塞尔曲线，确保起点和终点的导数为0
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参数:
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p0: 起点 [x0, y0]
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p1: 终点 [x1, y1]
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t_array: 参数数组 (0到1)
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influence_factor: 影响因子，控制控制点的位置
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返回:
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x_array, y_array: 贝塞尔曲线上的点
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"""
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x0, y0 = p0
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x1, y1 = p1
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# 计算控制点，确保起点和终点导数为0
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# 控制点位置基于影响因子和两点间距离
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dx = x1 - x0
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# 第一个控制点：在起点右侧，y坐标与起点相同（确保起点导数为0）
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p1_control = [x0 + dx * influence_factor[0], y0]
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# 第二个控制点：在终点左侧，y坐标与终点相同（确保终点导数为0）
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p2_control = [x1 - dx * influence_factor[1], y1]
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# 计算贝塞尔曲线
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x_bezier = ((1-t_array)**3 * x0 +
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3*(1-t_array)**2 * t_array * p1_control[0] +
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3*(1-t_array) * t_array**2 * p2_control[0] +
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t_array**3 * x1)
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y_bezier = ((1-t_array)**3 * y0 +
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3*(1-t_array)**2 * t_array * p1_control[1] +
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3*(1-t_array) * t_array**2 * p2_control[1] +
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t_array**3 * y1)
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return x_bezier, y_bezier
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# @st.cache_resource
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def plot_reaction_coordinate(changed=None, _lines=None):
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"""
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绘制反应坐标图
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"""
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lines = []
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fig,ax1 = plt.subplots(figsize=(9, 6))
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last=(-1,-1)
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maxy = data["Energy"].max()
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miny = data["Energy"].min()
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varyy = maxy - miny
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for i in range(data.shape[0]):
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line:pd.Series = data.loc[i]
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if last == (-1,-1):
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last = (1, line["Energy"])
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if not pd.isna(line["Name"]):
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ax1.annotate(str(line["Name"]), (1, line["Energy"]+varyy*K_POS[i]), ha='center')
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else:
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p1 = last[0]+2,line["Energy"]
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x,y = cubic_bezier_with_zero_derivatives(last,p1, np.linspace(0, 1, 300), INFLU_FACTORS[(i*2-2):i*2])
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l = ax1.plot(x, y, "-", color="black")[0]
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lines.append(l)
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if not pd.isna(line["Name"]):
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p = p1[0],p1[1]+varyy*K_POS[i]
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ax1.annotate(str(line["Name"]), p, ha='center')
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last = p1
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ax1.set_xlabel("Reaction Coordinate")
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ax1.xaxis.set_ticks([])
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ax1.set_ylabel("Energy (kcal/mol)")
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ax1.set_ylim(miny-varyy*0.1, maxy+varyy*0.1)
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return fig,lines
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# 创建图形和坐标轴
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def callback_gen(x,typ=0):
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if typ:
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def callback():
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global K_POS
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K_POS[x] = st.session_state.get(f'text_slider_{x}', 0.05)
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plot_reaction_coordinate(changed=x, _lines=lines)
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else:
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def callback():
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global INFLU_FACTORS
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INFLU_FACTORS[x-1] = st.session_state.get(f'slider_{x}', 0.5)
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plot_reaction_coordinate(changed=x, _lines=lines)
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return callback
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def on_save():
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global out_file
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# for slider in slides:
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# slider.ax.set_visible(False)
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plt.draw()
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plt.tight_layout()
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out_file = io.BytesIO()
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fig.savefig(out_file, format=st.session_state.get("export_format", ".tiff")[1:], dpi=300, bbox_inches='tight')
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out_file.seek(0)
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return out_file.getvalue()
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@st.cache_resource
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def load_data(file):
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# 读取数据文件
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if file is not None:
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try:
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data = pd.read_excel(file) if file.name.endswith((".xlsx", ".xls")) else pd.read_csv(file)
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except Exception as e:
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st.error(f"Error reading file: {e}")
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exit()
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else:
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exit()
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INFLU_FACTORS = [0.5] * data.shape[0] * 2 # 动态创建数组
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ene = data["Energy"].to_numpy()
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K_POS = np.where(ene[1:]>ene[:1],0.03,-0.05)
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K_POS = [-0.05] + K_POS.tolist()
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st.info(K_POS)
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data["Energy"] -= data["Energy"][0]
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data["Energy"]*=627.509
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return data, INFLU_FACTORS,K_POS
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out_file = io.BytesIO()
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st.set_page_config(
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page_title="反应坐标绘制",
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page_icon=":chart_with_upwards_trend:",
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layout="wide",
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initial_sidebar_state="expanded"
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)
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st.title("反应坐标绘制")
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st.write("---")
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col1,col2,col3 = st.columns([0.4,0.25,0.25],gap="medium")
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with col1:
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file = st.file_uploader("上传能量文件", type=["xlsx", "xls", "csv"])
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data, INFLU_FACTORS,K_POS = load_data(file)
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fig,lines = plot_reaction_coordinate()
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stfig = st.pyplot(fig,False)
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st.slider("字体大小",8,20, value=12, key="font_size",
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on_change=lambda: plt.rcParams.update({'font.size': st.session_state.get("font_size", 12)}))
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st.selectbox("导出文件拓展名",[".tiff",".pdf",".png",".pgf"],key="export_format")
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st.download_button(
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label="Download Plot",
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data=on_save(),
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file_name="reaction_coordinate"+st.session_state.get("export_format", ".tiff"),
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# mime="image/tiff"
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)
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with col2:
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st.write("调整滑块以改变反应坐标图曲线形状。")
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for i in range(data.shape[0]):
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if i!=0:
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st.slider(
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f'{data.loc[i,"Name"]} 左',
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0.0, 1.0, value=INFLU_FACTORS[i*2-1],
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key=f'slider_{i*2}',
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on_change=callback_gen(i*2)
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)
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if i!= data.shape[0] - 1:
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st.slider(
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f'{data.loc[i,"Name"]} 右',
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0.0, 1.0, value=INFLU_FACTORS[i*2],
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key=f'slider_{i*2+1}',
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on_change=callback_gen(i*2+1)
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)
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with col3:
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st.write("调整参数以改变文字位置。")
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for i in range(data.shape[0]):
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st.slider(
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f'{data.loc[i,"Name"]}',
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-0.1, 0.1, value=K_POS[i],
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key=f'text_slider_{i}',
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on_change=callback_gen(i,1)
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)
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st.write("---")
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st.dataframe(data)
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