import pandas as pd
import matplotlib.pyplot as plt

# ===== 可调参数 =====
FIG_WIDTH = 10
FIG_HEIGHT = 5
TITLE = "样本量分布"
X_LABEL = "样本量区间(例)"
Y_LABEL = "研究数量(篇)"
FILL_COLOR = "#0d6efd"
FILL_ALPHA = 0.4

# ===== 加载数据 =====
# 数据源（对应后端 CSV: /hub/DiseaseClinicalReport/demo/view/sampleSizeDistribution.csv）
DATA = [
    # [区间, 篇数]
    ["<100",     322],
    ["100~199",  183],
    ["200~299",   24],
    ["300~499",    3],
    ["500~999",    4],
    ["≥1000",      0],
]
df = pd.DataFrame(DATA, columns=["range", "count"])

# ===== 绘图 =====
plt.rcParams["font.sans-serif"] = ["SimHei"]
plt.figure(figsize=(FIG_WIDTH, FIG_HEIGHT))
plt.fill_between(df["range"], df["count"], alpha=FILL_ALPHA, color=FILL_COLOR)
plt.plot(df["range"], df["count"], marker="o", linewidth=2, color=FILL_COLOR)
for i, v in enumerate(df["count"]):
    plt.text(i, v + 8, str(v), ha="center", fontsize=10, color=FILL_COLOR)
plt.title(TITLE)
plt.xlabel(X_LABEL)
plt.ylabel(Y_LABEL)
plt.grid(alpha=0.3)
plt.tight_layout()
plt.show()

import pandas as pd
import matplotlib.pyplot as plt

# ===== 可调参数 =====
FIG_WIDTH = 12
FIG_HEIGHT = 7
TITLE = "中成药种类频次（按给药途径分组，每途径取 Top5）"
ROUTE_COLORS = {     # 各给药途径配色
    "口服": "#0d6efd",
    "注射": "#dc3545",
    "外用": "#198754",
    "其他": "#6c757d",
}

# ===== 加载数据 =====
# 数据源（对应后端 CSV: /hub/DiseaseClinicalReport/demo/view/drugByRoute.csv）
DATA = [
    # [给药途径, 中成药名称, 涉及研究数(篇)]
    ["口服", "脑心通胶囊", 48],
    ["口服", "复方丹参滴丸", 32],
    ["口服", "通心络胶囊", 28],
    ["口服", "步长脑心通", 15],
    ["口服", "血栓心脉宁", 12],
    ["注射", "疏血通注射液", 56],
    ["注射", "黄芪注射液", 42],
    ["注射", "舒血宁注射液", 38],
    ["注射", "丹参川芎嗪注射液", 25],
    ["注射", "醒脑静注射液", 18],
    ["外用", "通窍救心油", 4],
    ["外用", "云南白药", 3],
    ["其他", "其他少见给药途径", 5],
]
df = pd.DataFrame(DATA, columns=["route", "drug", "count"])

# ===== 绘图 =====
plt.rcParams["font.sans-serif"] = ["SimHei"]
fig, ax = plt.subplots(figsize=(FIG_WIDTH, FIG_HEIGHT))

# 按给药途径分组画气泡
routes = df["route"].unique()
x_offset = 0
xticks, xlabels = [], []
for route in routes:
    sub = df[df["route"] == route]
    xs = range(x_offset, x_offset + len(sub))
    ax.scatter(xs, sub["count"], s=sub["count"] * 30, alpha=0.6,
               color=ROUTE_COLORS.get(route, "#999"), label=route)
    for x, (_, row) in zip(xs, sub.iterrows()):
        ax.annotate(f"{row['drug']}\n({row['count']})", (x, row["count"]),
                    ha="center", va="bottom", fontsize=8)
    xticks.extend(xs)
    xlabels.extend(sub["drug"].tolist())
    x_offset += len(sub) + 1

ax.set_title(TITLE)
ax.set_ylabel("涉及研究数(篇)")
ax.set_xticks([])
ax.legend(title="给药途径")
ax.grid(alpha=0.3)
plt.tight_layout()
plt.show()

import pandas as pd
import matplotlib.pyplot as plt

# ===== 可调参数 =====
FIG_WIDTH = 10
FIG_HEIGHT = 6
TITLE = "组间对照方式分布"
X_LABEL = "研究数量(篇)"
BAR_COLOR = "#0d6efd"
TOP_N = 8            # 显示前 N 个对照方式

# ===== 加载数据 =====
# 数据源（对应后端 CSV: /hub/DiseaseClinicalReport/demo/view/comparisonDistribution.csv）
DATA = [
    # [对照方式, 篇数]
    ["中成药+常规治疗 vs. 常规治疗", 195],
    ["中成药 vs. 西药", 98],
    ["中成药+西药 vs. 西药", 87],
    ["中成药 vs. 中成药", 45],
    ["中成药+西药+常规治疗 vs. 西药+常规治疗", 32],
    ["中成药 vs. 安慰剂", 3],
    ["中成药 vs. 空白", 10],
    ["其他", 66],
]
df = pd.DataFrame(DATA, columns=["comparison", "count"]).head(TOP_N)

# ===== 绘图 =====
plt.rcParams["font.sans-serif"] = ["SimHei"]
plt.figure(figsize=(FIG_WIDTH, FIG_HEIGHT))
plt.barh(df["comparison"][::-1], df["count"][::-1], color=BAR_COLOR, alpha=0.7)
for i, v in enumerate(df["count"][::-1]):
    plt.text(v + 2, i, str(v), va="center", fontsize=10)
plt.title(TITLE)
plt.xlabel(X_LABEL)
plt.tight_layout()
plt.show()

import pandas as pd
import matplotlib.pyplot as plt

# ===== 可调参数 =====
FIG_WIDTH = 10
FIG_HEIGHT = 6
TITLE = "方法学质量评价（Cochrane RoB）"
X_LABEL = "占比(%)"
RISK_COLORS = {       # 风险等级配色
    "低风险": "#198754",
    "高风险": "#dc3545",
    "不清楚": "#ffc107",
}

# ===== 加载数据 =====
# 数据源（对应后端 CSV: /hub/DiseaseClinicalReport/demo/view/cochraneRisk.csv）
DATA = [
    # [条目, 低风险%, 高风险%, 不清楚%]
    ["① 随机序列生成方法",   52.61, 47.39,  0.00],
    ["② 分配隐藏",            2.80, 97.20,  0.00],
    ["③ 受试者盲法",          0.56, 99.44,  0.00],
    ["④ 结果评价盲法",       27.05, 72.95,  0.00],
    ["⑤ 结果数据的完整性",   92.35,  7.65,  0.00],
    ["⑥ 选择性报告研究结果", 93.10,  6.90,  0.00],
    ["⑦ 其他偏倚来源",        0.19, 99.81,  0.00],
]
df = pd.DataFrame(DATA, columns=["item", "低风险", "高风险", "不清楚"])

# ===== 绘图 =====
plt.rcParams["font.sans-serif"] = ["SimHei"]
fig, ax = plt.subplots(figsize=(FIG_WIDTH, FIG_HEIGHT))
left = pd.Series([0.0] * len(df))
for col in ["低风险", "高风险", "不清楚"]:
    ax.barh(df["item"], df[col], left=left, label=col,
            color=RISK_COLORS[col], alpha=0.8)
    left += df[col]
ax.set_title(TITLE)
ax.set_xlabel(X_LABEL)
ax.legend(loc="lower right")
ax.set_xlim(0, 100)
plt.tight_layout()
plt.show()