commit 814273d4d76dd2293c47d559540b7ec3e6e16285
Author: seung6lee <sj@seung6lee.com>
Date:   Fri Jul 17 20:07:01 2026 +0900

    archived

diff --git a/.gitignore b/.gitignore
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+.DS_Store
+.ipynb_checkpoints/
+node_modules/
diff --git a/README.md b/README.md
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+> This README is written by claude as I couldn't remember the project.
+
+# 비선형회귀법으로 분석한 서열 위치 효과
+
+> Pre-URP 연구 프로젝트 · 2024년 1월 · Serial Position Effect
+
+Murdock(1962)의 자유회상 서열위치효과 실험을 **한국어 단어**로 재현하되, 원 논문이 사용한 선형 회귀 대신 **2차(비선형) 회귀로 U자 곡선을 직접 적합**한 연구입니다. 자체 제작한 웹앱으로 실험을 실행하고, 참가자 약 18명 × 8조건의 자유회상 데이터를 수집해 조건별 2차 계수를 비교했습니다.
+
+최종 산출물은 발표자료 `SerialPositionEffect.pptx` (24장)입니다.
+
+---
+
+## 1. 실험 설계
+
+**2×2×2 = 8조건** 요인 설계. 각 조건은 세 글자 코드로 표기하며, 파일명·엑셀 시트명·그래프 번호가 모두 이 체계를 따릅니다.
+
+| 요인 | 수준 1 | 수준 2 |
+|---|---|---|
+| 단어 길이 | **A** = 1~3음절 (짧은 단어) | **B** = 4~6음절 (긴 단어) |
+| 단어 제시 시간 | **C** = 2초 | **D** = 3초 |
+| 배열의 총 단어 수 | **E** = 10개 | **F** = 20개 |
+
+조건 코드 순서: `ACE → ACF → ADE → ADF → BCE → BCF → BDE → BDF`
+(`Result graphs/1.png` ~ `8.png`이 이 순서에 대응합니다.)
+
+### 절차
+1. 단어를 1개씩 순차 제시 (제시 시간 2초 또는 3초, **각 단어 뒤 1초 공백**)
+2. 배열 종료 시 빨간색 `Finish` 표시
+3. 참가자가 기억나는 단어를 **순서와 무관하게** 종이 설문지에 작성 (`설문지 형식 초안.docx`)
+4. 위치별 회상 성공 여부를 0/1로 코딩해 엑셀에 입력
+
+### 자극
+표준국어대사전의 **명사** (한자어·외래어·혼종어 포함). 원본 단어 목록은 `단어 목록.xlsx`(1to3 / 4to6 각 145행)와 `pre_urp/korean_words.xlsx`(음절 수별 컬럼)에 있습니다.
+
+### 참가자
+조건당 **17~18명** (BCE만 17명, 나머지 7개 조건은 18명).
+
+---
+
+## 2. 파일 구조
+
+### 실험 실행 도구 — 3세대에 걸쳐 진화
+
+| 세대 | 위치 | 설명 |
+|---|---|---|
+| 1세대 | `pre_urp_contents/*.pptm` | **파워포인트 매크로판.** `ACE.pptm`~`BDF.pptm` 8개 파일. VBA `GetRandom()` 매크로가 단어 순서를 셔플해 슬라이드로 제시. `Random100Direct.pptm`은 이 셔플 알고리즘을 100회 돌려 검증한 파일 |
+| 2세대 | `Word View/` | **수동 웹앱.** Mix 버튼·제시 시간·단어 개수를 매번 직접 설정해 한 조건씩 실행 |
+| 3세대 | `Word View Auto/` | **자동 웹앱.** Express 서버 위에서 `ACE ACF ADE ADF BCE BCF BDE BDF`를 한 번에 입력하면 8조건 자동 연속 실행. 페이지 로드 시 자동 셔플 |
+
+### 데이터 · 분석
+
+| 파일 | 설명 |
+|---|---|
+| `exp_results1.xlsx` | **실제 수집 데이터 (최종본).** 8개 조건 시트 + `Dummy` 시트. 행 = 참가자, 열 = 서열 위치, 값 = 회상 성공 0/1 |
+| `pre_urp/exp_results.xlsx` | 데이터 수집 전 **빈 템플릿** (헤더만 존재) |
+| `pre_urp/korean_words.xlsx` | 음절 수별 단어 원본 |
+| `pre_urp.ipynb` | 분석 노트북 (sklearn 회귀 연습 → 단어 리스트 셔플/분할 → Dummy 적합) |
+| `pre_urp/comp_libraries.txt` | 필요 패키지: matplotlib, scikit-learn, pandas, openpyxl |
+
+### 산출물 · 문서
+
+| 파일 | 설명 |
+|---|---|
+| `SerialPositionEffect.pptx` | **최종 발표자료** (24장, 32MB) |
+| `Result graphs/1~8.png` | 조건별(ACE→BDF) 산점도 + 2차 적합 곡선 |
+| `wordlength.png` / `wordshown.png` / `wordnumber.png` | 요인별 비교 그래프 (단어 길이 / 제시 시간 / 단어 개수) |
+| `pre_urp_contents/배경지식.pptx` | 이론 정리 (감각기억, 단기기억, Sperling 1960, Miller의 매직넘버 7±2, 작업기억 음운루프) |
+| `pre_urp_contents/Experiment_Instruction.pptx` | 실험 진행 안내 (2024-01-08) |
+| `pre_urp_contents/exp_procedure.docx` | 실험 절차 초안 |
+| `기존연구.pdf`, `pre_urp_contents/murdock_serial_position_effect.pdf` | 선행연구 논문 (서로 다른 파일) |
+
+---
+
+## 3. 실행 방법
+
+### Word View Auto (3세대, 권장)
+```bash
+cd "Word View Auto"
+npm install        # node_modules는 이미 포함되어 있음
+node server.js     # → http://127.0.0.1:4000
+```
+`Test Type List`에 조건 코드를 공백으로 구분해 입력 (기본값 `ACE ACF ADE ADF BCE BCF BDE BDF`) → Start → **스페이스바**로 각 테스트 시작.
+
+### Word View (2세대)
+서버 불필요. `Word View/index.html`을 브라우저로 직접 열면 됩니다.
+`R` 키로 설정 화면 복귀.
+
+---
+
+## 4. 결과
+
+각 조건의 **위치별 평균 회상률**에 `y = ax² + bx + c` 를 최소제곱 적합한 결과입니다.
+
+| 조건 | 설명 | N | 평균 회상률 | a (x²) | b (x) | c | R² |
+|---|---|---:|---:|---:|---:|---:|---:|
+| ACE | 짧은 / 2초 / 10개 | 18 | 0.578 | 0.0105 | −0.1009 | 0.7278 | 0.398 |
+| ACF | 짧은 / 2초 / 20개 | 18 | 0.408 | 0.0033 | −0.0682 | 0.6444 | 0.447 |
+| ADE | 짧은 / 3초 / 10개 | 18 | 0.622 | 0.0141 | −0.1840 | 1.0917 | 0.566 |
+| ADF | 짧은 / 3초 / 20개 | 18 | 0.536 | 0.0040 | −0.0975 | 0.9822 | 0.771 |
+| BCE | 긴 / 2초 / 10개 | 17 | 0.535 | 0.0189 | −0.2201 | 1.0167 | 0.587 |
+| BCF | 긴 / 2초 / 20개 | 18 | 0.333 | 0.0038 | −0.0851 | 0.6881 | 0.599 |
+| BDE | 긴 / 3초 / 10개 | 18 | 0.467 | 0.0078 | −0.0782 | 0.5972 | 0.267 |
+| BDF | 긴 / 3초 / 20개 | 18 | 0.361 | 0.0040 | −0.0790 | 0.6152 | 0.428 |
+
+### 핵심 발견
+
+**8조건 모두 `a > 0`** — 아래로 볼록한 U자 곡선이 전 조건에서 재현되었습니다. 서열 위치 효과 자체는 성공적으로 확인되었습니다.
+
+**주효과** (조건 평균 회상률)
+
+| 요인 | 수준 1 | 수준 2 | 차이 |
+|---|---:|---:|---:|
+| 단어 개수 | E (10개) **0.550** | F (20개) **0.410** | **0.140** — 가장 큰 영향 |
+| 단어 길이 | A (짧은) **0.536** | B (긴) **0.424** | **0.112** — 두 번째 |
+| 제시 시간 | C (2초) **0.464** | D (3초) **0.497** | 0.033 — 거의 영향 없음 |
+
+**초두 효과 vs 최신 효과** — 발표자료 13쪽의 논점이지만, 데이터상 **일관된 우열이 나오지 않았습니다.** 첫 3개 위치 평균과 마지막 3개 위치 평균을 비교하면 8조건 중 4개는 최신 효과가, 4개는 초두 효과가 우세합니다.
+
+| 조건 | 초두(첫 3개) | 중간 | 최신(마지막 3개) | 우세 |
+|---|---:|---:|---:|---|
+| ACE | 0.611 | 0.514 | 0.630 | 최신 |
+| ACF | 0.519 | 0.349 | 0.574 | 최신 |
+| ADE | 0.815 | 0.514 | 0.574 | 초두 |
+| ADF | 0.778 | 0.472 | 0.593 | 초두 |
+| BCE | 0.647 | 0.441 | 0.549 | 초두 |
+| BCF | 0.537 | 0.262 | 0.463 | 초두 |
+| BDE | 0.426 | 0.472 | 0.500 | 최신 |
+| BDF | 0.500 | 0.282 | 0.593 | 최신 |
+
+### 위치별 평균 회상률 (원자료)
+
+```
+ACE (n=18, 10개): 0.61 0.61 0.61 0.28 0.50 0.61 0.67 0.44 0.61 0.83
+ADE (n=18, 10개): 0.83 0.78 0.83 0.50 0.67 0.44 0.44 0.50 0.39 0.83
+BCE (n=17, 10개): 0.76 0.65 0.53 0.59 0.41 0.24 0.53 0.41 0.35 0.88
+BDE (n=18, 10개): 0.67 0.33 0.28 0.44 0.56 0.39 0.50 0.39 0.50 0.61
+
+ACF (n=18, 20개): 0.67 0.39 0.50 0.39 0.22 0.28 0.50 0.44 0.44 0.39
+                  0.39 0.22 0.17 0.33 0.28 0.39 0.44 0.39 0.50 0.83
+ADF (n=18, 20개): 0.83 0.83 0.67 0.83 0.50 0.50 0.50 0.61 0.33 0.44
+                  0.44 0.33 0.39 0.33 0.50 0.39 0.50 0.56 0.50 0.72
+BCF (n=18, 20개): 0.72 0.39 0.50 0.28 0.44 0.28 0.28 0.33 0.28 0.22
+                  0.06 0.22 0.39 0.28 0.28 0.17 0.17 0.39 0.39 0.61
+BDF (n=18, 20개): 0.61 0.39 0.50 0.22 0.22 0.11 0.50 0.22 0.56 0.22
+                  0.11 0.33 0.28 0.33 0.22 0.33 0.28 0.28 0.67 0.83
+```
+
+---
+
+## 5. 알려진 문제
+
+프로젝트를 다시 열 때 알아둬야 할 사항들입니다.
+
+### 노트북의 셔플 버그
+`pre_urp.ipynb` 셀 11에서 4~6음절 단어를 섞어야 하는데 `np.random.shuffle(sw_arr)` 로 **짧은 단어 배열을 다시 섞고 있습니다** (`lw_arr` 이어야 함). 그 결과 `bce_list`~`bdf_list` 는 엑셀 원본 순서 그대로입니다.
+
+> 다만 실제 제시 순서는 PPT의 VBA 매크로가 한 번 더 셔플했으므로(`ACE.pptm`의 단어 순서가 노트북 `ace_list`와 다름을 확인) **실험 자체의 무작위성은 훼손되지 않았습니다.**
+
+### 최종 분석 코드 유실
+`pre_urp.ipynb` 셀 17이 **정의된 적 없는 `temp` 변수**를 참조합니다. 위 결과표의 8조건 회귀와 `Result graphs/` 의 그래프를 생성한 코드는 노트북에 남아있지 않습니다. 현재 노트북에는 sklearn 회귀 연습 → 단어 분할 → Dummy 시트 적합까지만 있습니다.
+
+> 위 결과표는 `exp_results1.xlsx` 로부터 독립적으로 재계산해 검증한 값입니다. 8개 조건 중 6개가 발표자료 14쪽 숫자와 소수점까지 일치합니다.
+
+### 발표자료 14쪽 오타 2건
+- **ADE의 b**: 발표자료에 `-0.2840` 으로 기재되어 있으나 실제 값은 `-0.1840` 입니다. (−0.2840을 쓰면 10번째 위치의 예측 회상률이 −0.34라는 불가능한 값이 나옵니다.)
+- **ACF의 c**: 발표자료에 `0.6881` 로 기재되어 있으나 실제 값은 `0.6444` 입니다. 기재된 값이 BCF의 c와 동일해 복사·붙여넣기 실수로 보입니다.
+
+### 중복 파일
+`pre_urp.ipynb`, `pre_urp (1).ipynb`, `pre_urp/pre_urp.ipynb` 세 파일은 **md5 해시가 모두 동일**합니다. 하나만 남겨도 됩니다.
+
+### 사소한 것들
+- `Word View/main.js` 의 단어 오타 2개(`고구마입벌레`, `고리대급업자`)는 Auto 버전에서 `고구마잎벌레`, `고리대금업자` 로 수정되었습니다.
+- `Word View/main.js` 의 `oneToThree` 배열에 `"칼"` 이 **중복**으로 들어있습니다.
+- `wordnumber.png` 의 제목이 `"Word bumber difference"` 오타입니다.
+- `Word View/history.json` (`{"Hello":"Me too"}`) 과 Auto 서버의 `/your-endpoint` 는 **미완성된 결과 자동저장 기능**의 흔적입니다. 최종적으로 응답은 종이 설문지로 수집했습니다.
+
+---
+
+## 6. 재현 방법
+
+`exp_results1.xlsx` 로부터 위 결과표를 다시 계산하려면:
+
+```python
+import pandas as pd, numpy as np
+from sklearn import linear_model
+
+for cond in ['ACE','ACF','ADE','ADF','BCE','BCF','BDE','BDF']:
+    df = pd.read_excel('exp_results1.xlsx', sheet_name=cond)
+    means = df.mean().to_numpy()                  # 위치별 평균 회상률
+    x = np.arange(1, len(means) + 1).reshape(-1, 1)
+    X = np.concatenate((x**2, x), axis=1)         # [x^2, x]
+    regr = linear_model.LinearRegression(fit_intercept=True).fit(X, means)
+    a, b = regr.coef_
+    print(f'{cond}: a={a:.4f} b={b:.4f} c={regr.intercept_:.4f}')
+```
+
+필요 패키지: `matplotlib`, `scikit-learn`, `pandas`, `openpyxl`
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diff --git a/Word View Auto/package-lock.json b/Word View Auto/package-lock.json
new file mode 100644
index 0000000..76ee0d3
--- /dev/null
+++ b/Word View Auto/package-lock.json	
@@ -0,0 +1,657 @@
+{
+  "name": "word-view-auto",
+  "version": "1.0.0",
+  "lockfileVersion": 3,
+  "requires": true,
+  "packages": {
+    "": {
+      "name": "word-view-auto",
+      "version": "1.0.0",
+      "license": "ISC",
+      "dependencies": {
+        "express": "^4.18.2"
+      }
+    },
+    "node_modules/accepts": {
+      "version": "1.3.8",
+      "resolved": "https://registry.npmjs.org/accepts/-/accepts-1.3.8.tgz",
+      "integrity": "sha512-PYAthTa2m2VKxuvSD3DPC/Gy+U+sOA1LAuT8mkmRuvw+NACSaeXEQ+NHcVF7rONl6qcaxV3Uuemwawk+7+SJLw==",
+      "dependencies": {
+        "mime-types": "~2.1.34",
+        "negotiator": "0.6.3"
+      },
+      "engines": {
+        "node": ">= 0.6"
+      }
+    },
+    "node_modules/array-flatten": {
+      "version": "1.1.1",
+      "resolved": "https://registry.npmjs.org/array-flatten/-/array-flatten-1.1.1.tgz",
+      "integrity": "sha512-PCVAQswWemu6UdxsDFFX/+gVeYqKAod3D3UVm91jHwynguOwAvYPhx8nNlM++NqRcK6CxxpUafjmhIdKiHibqg=="
+    },
+    "node_modules/body-parser": {
+      "version": "1.20.1",
+      "resolved": "https://registry.npmjs.org/body-parser/-/body-parser-1.20.1.tgz",
+      "integrity": "sha512-jWi7abTbYwajOytWCQc37VulmWiRae5RyTpaCyDcS5/lMdtwSz5lOpDE67srw/HYe35f1z3fDQw+3txg7gNtWw==",
+      "dependencies": {
+        "bytes": "3.1.2",
+        "content-type": "~1.0.4",
+        "debug": "2.6.9",
+        "depd": "2.0.0",
+        "destroy": "1.2.0",
+        "http-errors": "2.0.0",
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+          "url": "https://www.patreon.com/feross"
+        },
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+          "url": "https://feross.org/support"
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+        "send": "0.18.0"
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+    },
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+      },
+      "funding": {
+        "url": "https://github.com/sponsors/ljharb"
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+      "engines": {
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+}
diff --git a/Word View Auto/package.json b/Word View Auto/package.json
new file mode 100644
index 0000000..ab6f3b1
--- /dev/null
+++ b/Word View Auto/package.json	
@@ -0,0 +1,14 @@
+{
+  "name": "word-view-auto",
+  "version": "1.0.0",
+  "description": "",
+  "main": "server.js",
+  "scripts": {
+    "test": "echo \"Error: no test specified\" && exit 1"
+  },
+  "author": "",
+  "license": "ISC",
+  "dependencies": {
+    "express": "^4.18.2"
+  }
+}
diff --git a/Word View Auto/public/index.html b/Word View Auto/public/index.html
new file mode 100644
index 0000000..67bc005
--- /dev/null
+++ b/Word View Auto/public/index.html	
@@ -0,0 +1,37 @@
+<!DOCTYPE html>
+<html lang="en">
+
+<head>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <title>Word Display</title>
+    <script src="/public/main.js" defer></script>
+    <link rel="stylesheet" href="/public/main.css">
+</head>
+
+<body>
+    <div id="settings">
+        <h1>Settings</h1>
+        <label for="TypeList">Test Type List: </label>
+        <input type="text" id="TypeList" value="ACE ACF ADE ADF BCE BCF BDE BDF">
+        <button id="start">Start</button>
+    </div>
+
+
+    <!-- Introduction -->
+    <ul id="Intro">
+        <li id="wordNumberValue">20개의 짧은 무작위 단어들이 제시됩니다.</li>
+        <br>
+        <li id="intervalValue">단어당 제시되는 시간은 2초이며 후에 1초의 공백이 표시됩니다.</li>
+        <br>
+        <li>테스트 종료 시, 빨간색의 Finish가 표시됩니다.</li>
+        <br>
+        <li>종료 후에는 기억나는 단어를 종이에 작성하세요.</li>
+        <br>
+        <li>스페이스바를 누르면 테스트가 바로 시작됩니다.</li>
+    </ul>
+
+    <div id="Word">Ready</div>
+</body>
+
+</html>
\ No newline at end of file
diff --git a/Word View Auto/public/main.css b/Word View Auto/public/main.css
new file mode 100644
index 0000000..f812254
--- /dev/null
+++ b/Word View Auto/public/main.css	
@@ -0,0 +1,26 @@
+#Intro {
+    width: 1200px;
+    margin: 0;
+    position: absolute;
+    top: 50%;
+    left: 50%;
+    transform: translate(-50%, -50%);
+    font-size: 60px;
+    font-family: Arial, Helvetica, sans-serif;
+    display: none;
+}
+
+#Word {
+    margin: 0;
+    position: absolute;
+    top: 50%;
+    left: 50%;
+    transform: translate(-50%, -50%);
+    font-size: 150px;
+    font-family: Arial, Helvetica, sans-serif;
+    display: none;
+}
+
+/* #settings {
+    display: none;
+} */
\ No newline at end of file
diff --git a/Word View Auto/public/main.js b/Word View Auto/public/main.js
new file mode 100644
index 0000000..6d4c1dc
--- /dev/null
+++ b/Word View Auto/public/main.js	
@@ -0,0 +1,103 @@
+document.addEventListener('DOMContentLoaded', function () {
+    let oneToThree = ["낮", "논", "닭", "땀", "못", "빛", "솥", "짐", "턱", "코", "학", "회", "힘", "털", "칼", "춤", "칡", "죄", "값", "겁", "공", "너", "돌", "롤", "망", "벗", "실", "윷", "중", "초", "칼", "통", "폰", "홀", "검도", "결혼", "녹차", "나비", "행운", "등교", "루비", "만두", "무용", "박사", "보물", "사랑", "수리", "어깨", "역사", "윤곽", "자연", "저축", "졸업", "행복", "공간", "궁궐", "남자", "농심", "동심", "달력", "락스", "라임", "모집", "명동", "바람", "보정", "성종", "식기", "유행", "여우", "종점", "자리", "초코", "추위", "킬러", "콩밥", "틴더", "터널", "필적", "포도", "홍수", "형제", "자긍심", "전염병", "주인공", "칸막이", "친근감", "태양풍", "폐막식", "하반신", "황무지", "횡격막", "경기도", "농산물", "단백질", "대통령", "대장균", "동남아", "무기질", "오렌지", "캥거루", "칼국수", "거제도", "공유기", "네이버", "놀이터", "두리안", "달걀찜", "루시퍼", "롤리팝", "망아지", "메아리", "비비빅", "바밤바", "송중기", "성시경", "우리집", "아버지", "지킴이", "정관장", "청국장", "청록색", "큐피드", "코레일", "탕후루", "탕평책", "피해자", "폴라포", "헤이즈", "하르방"];
+    let fourToSix = ["어림짐작", "인문과학", "작심삼일", "착시효과", "중구난방", "천재지변", "팔방미인", "포화상태", "하루아침", "해수욕장", "허수아비", "홍익인간", "훈민정음", "거두절미", "대중교통", "두드러기", "만유인력", "물리화학", "가시광선", "고등학교", "가위눌림", "고기국수", "나무위키", "너비아니", "데리야끼", "도리토스", "라면사리", "롯데마트", "마음가짐", "모의재판", "보니하니", "바이올린", "세일즈맨", "삼단화환", "오징어회", "어버이날", "장례식장", "종종걸음", "초코머핀", "총알받이", "키득키득", "카이스트", "텔레토비", "토카토카", "피실험자", "페퍼로니", "호랑이굴", "하이마트", "우스갯소리", "청딱따구리", "크리스마스", "아르헨티나", "헌법재판소", "황소개구리", "게으름뱅이", "이데올로기", "순두부찌개", "대중목욕탕", "동음이의어", "프로그래밍", "정월대보름", "식기세척기", "지구온난화", "아이스크림", "오케스트라", "김치볶음밥", "최소공배수", "장수풍댕이", "괭이갈매기", "거짓말쟁이", "나이지리아", "나이팅게일", "담쟁이덩굴", "뒤치닥거리", "라티푼디움", "룩셈부르크", "막대그래프", "뮤직비디오", "바빌로니아", "바다코끼리", "산후조리원", "삼강행실도", "오므라이스", "오리온자리", "중모리장단", "증조할머니", "초코바나나", "초조대장경", "카트라이더", "카자흐스탄", "토이스토리", "타임스퀘어", "폴리에틸렌", "페이퍼타올", "하늘바라기", "하룻강아지", "어중이떠중이", "자이로스코프", "진인사대천명", "한해살이식물", "목도리도마뱀", "패러글라이딩", "자유의여신상", "콘트라베이스", "베이킹파우더", "에스컬레이터", "장대높이뛰기", "히말라야산맥", "나무아미타불", "스카치테이프", "아르키메데스", "조선왕조실록", "증조할아버지", "가시불가사리", "금메달리스트", "폭탄먼지벌레", "가슴지느러미", "가운뎃발가락", "고구마잎벌레", "고리대금업자", "네안데르탈인", "뉴펀들랜드주", "덩굴무늬매듭", "덧치마저고리", "라스베이거스", "라이스페이퍼", "마다가스카르", "마이크로미터", "빨강불가사리", "브레인스토밍", "상정고금예문", "샌프란시스코", "아메리칸드림", "알렉산드리아", "작은할아버지", "전민변정도감", "청분홍메뚜기", "콤팩트디스크", "코니아일랜드", "타이포그래피", "트리케라톱스", "파운드케이크", "프랑켄슈타인", "피보나치수열"];
+    let words = [];
+    let currentIndex = 0;
+    let onecurrentIndex = 0;
+    let fourcurrentIndex = 0;
+    let wordsNumber = 10;
+    let curEnd = 0;
+    let testType = [];
+    let curTest = 0;
+
+    // 사이트 로드시 리스트 섞기
+    oneToThree = oneToThree.sort(() => Math.random() - 0.5);
+    fourToSix = fourToSix.sort(() => Math.random() - 0.5);
+    console.log('Mixed')
+    console.log(oneToThree);
+    console.log(fourToSix);
+
+    const wordDisplay = document.getElementById('Word');
+    const startButton = document.getElementById('start');
+
+    startButton.addEventListener('click', () => {
+        for (i of document.getElementById('TypeList').value.split(' ')) {
+            let curType = [];
+            if (i.charAt(0) === 'A') { curType.push(13) } else { curType.push(46) }
+            if (i.charAt(1) === 'C') { curType.push(2000) } else { curType.push(3000) }
+            if (i.charAt(2) === 'E') { curType.push(10) } else { curType.push(20) }
+            testType.push(curType);
+        }
+        document.getElementById('settings').style.display = 'none';
+        newTest()
+    });
+
+    const newTest = () => {
+        if (testType[curTest][0] === 13) {
+            words = oneToThree;
+            curEnd = onecurrentIndex + testType[curTest][2];
+            currentIndex = onecurrentIndex;
+        } else {
+            words = fourToSix;
+            curEnd = fourcurrentIndex + testType[curTest][2];
+            currentIndex = fourcurrentIndex;
+        }
+
+        document.getElementById('wordNumberValue').textContent = `${testType[curTest][2]}개의 ${(testType[curTest][0] === 13) ? '짧은' : '긴'} 무작위 단어들이 제시됩니다.`;
+        document.getElementById('intervalValue').textContent = `단어당 제시되는 시간은 ${testType[curTest][1] / 1000}초이며 후에 1초의 공백이 표시됩니다.`;
+        document.getElementById('Word').style.display = 'none';
+        document.getElementById('Intro').style.display = 'block';
+        wordDisplay.style.color = '#000000';
+    }
+
+    document.addEventListener('keydown', function (event) {
+        if (event.code === 'Space') {
+            test()
+            document.getElementById('Intro').style.display = 'none';
+            document.getElementById('Word').style.display = 'block';
+            console.log(words.slice(currentIndex, curEnd));
+            sendDataToServer(document.getElementById('TypeList').value.split(' ')[currentIndex], words.slice(currentIndex, curEnd));
+        }
+    });
+
+    const test = () => {
+        if (currentIndex == curEnd) {
+            wordDisplay.textContent = 'Finish';
+            wordDisplay.style.color = '#D1080B';
+            if (testType[curTest][0] === 13) {
+                onecurrentIndex = currentIndex;
+            } else {
+                fourcurrentIndex = currentIndex;
+            }
+            if (curTest < 7) {
+                setTimeout(() => {
+                    curTest += 1;
+                    newTest()
+                }, 5000)
+            }
+            return
+        }
+        wordDisplay.textContent = words[currentIndex];
+        setTimeout(() => {
+            wordDisplay.textContent = '';
+            setTimeout(() => {
+                currentIndex += 1;
+                test();
+            }, 1000)
+        }, testType[curTest][1])
+    };
+});
+
+function sendDataToServer(ty ,li) {
+    console.log(JSON.stringify({ list: li }))
+    fetch('/your-endpoint', {
+        method: 'POST',
+        headers: {
+            'Content-Type': 'application/json',
+        },
+        body: JSON.stringify({ type: ty, list: li })
+    })
+        .then(response => response.json())
+        .then(data => console.log('Success:', data))
+        .catch((error) => console.error('Error:', error));
+}
\ No newline at end of file
diff --git a/Word View Auto/server.js b/Word View Auto/server.js
new file mode 100644
index 0000000..62754d0
--- /dev/null
+++ b/Word View Auto/server.js	
@@ -0,0 +1,20 @@
+const express = require("express");
+const path = require('path')
+const app = express();
+app.use(express.json()); // JSON 본문을 파싱하기 위해 필요합니다.
+
+
+app.use("/public", express.static(path.join(__dirname, 'public')));
+app.listen(4000, () => {
+    console.log(`Server running at http://127.0.0.1:4000`);
+});
+app.get("/", (req, res) => {
+    res.sendFile(__dirname + "/public/index.html")
+    //    res.send("hello world")
+});
+app.use(express.urlencoded({ extends: true }));
+
+app.post('/your-endpoint', (req, res) => {
+    console.log('Received data:', req.body); // 받은 데이터를 콘솔에 출력합니다.
+    res.json({ message: 'Data received successfully' }); // 응답을 보냅니다.
+});
\ No newline at end of file
diff --git a/Word View/history.json b/Word View/history.json
new file mode 100644
index 0000000..afa2149
--- /dev/null
+++ b/Word View/history.json	
@@ -0,0 +1 @@
+{"Hello":"Me too"}
\ No newline at end of file
diff --git a/Word View/index.html b/Word View/index.html
new file mode 100644
index 0000000..e215644
--- /dev/null
+++ b/Word View/index.html	
@@ -0,0 +1,49 @@
+<!DOCTYPE html>
+<html lang="en">
+
+<head>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <title>Word Display</title>
+    <script src="./main.js" defer></script>
+    <link rel="stylesheet" href="./main.css">
+</head>
+
+<body>
+    <div id="settings">
+        <h1>Settings</h1>
+        <button id="shuffleButton">Mix</button>
+        <label for="interval">Period: </label>
+        <input type="number" id="interval" value="2">
+        <select id="wordLength">
+            <option value="13">1to3</option>
+            <option value="46">4to6</option>
+        </select>
+        <label for="wordNumber">Words: </label>
+        <input type="number" id="wordNumber" value="10">
+        <button id="start">Start</button>
+
+        <h2>Rules: </h2>
+        <p>A: 짧은 단어 / B: 긴 단어</p>
+        <p>C: 2초 / D: 3초</p>
+        <p>E: 10개 / F: 20개</p>
+    </div>
+
+
+    <!-- Introduction -->
+    <ul id="Intro">
+        <li id="wordNumberValue">20개의 짧은 무작위 단어들이 제시됩니다.</li>
+        <br>
+        <li id="intervalValue">단어당 제시되는 시간은 2초이며 후에 1초의 공백이 표시됩니다.</li>
+        <br>
+        <li>테스트 종료 시, 빨간색의 Finish가 표시됩니다.</li>
+        <br>
+        <li>종료 후에는 기억나는 단어를 종이에 작성하세요.</li>
+        <br>
+        <li>스페이스바를 누르면 테스트가 바로 시작됩니다.</li>
+    </ul>
+
+    <div id="Word">Ready</div>
+</body>
+
+</html>
\ No newline at end of file
diff --git a/Word View/main.css b/Word View/main.css
new file mode 100644
index 0000000..f812254
--- /dev/null
+++ b/Word View/main.css	
@@ -0,0 +1,26 @@
+#Intro {
+    width: 1200px;
+    margin: 0;
+    position: absolute;
+    top: 50%;
+    left: 50%;
+    transform: translate(-50%, -50%);
+    font-size: 60px;
+    font-family: Arial, Helvetica, sans-serif;
+    display: none;
+}
+
+#Word {
+    margin: 0;
+    position: absolute;
+    top: 50%;
+    left: 50%;
+    transform: translate(-50%, -50%);
+    font-size: 150px;
+    font-family: Arial, Helvetica, sans-serif;
+    display: none;
+}
+
+/* #settings {
+    display: none;
+} */
\ No newline at end of file
diff --git a/Word View/main.js b/Word View/main.js
new file mode 100644
index 0000000..dcc23cd
--- /dev/null
+++ b/Word View/main.js	
@@ -0,0 +1,105 @@
+// fetch('history.json')
+//   .then(response => response.json())
+//   .then(data => {
+//     let jsonData = JSON.parse(data);
+//     console.log(jsonData.hello);
+//   })
+//   .catch(error => {
+//     console.error('Error reading the JSON file:', error);
+//   });
+
+
+
+document.addEventListener('DOMContentLoaded', function () {
+    let oneToThree = ["낮", "논", "닭", "땀", "못", "빛", "솥", "짐", "턱", "코", "학", "회", "힘", "털", "칼", "춤", "칡", "죄", "값", "겁", "공", "너", "돌", "롤", "망", "벗", "실", "윷", "중", "초", "칼", "통", "폰", "홀", "검도", "결혼", "녹차", "나비", "행운", "등교", "루비", "만두", "무용", "박사", "보물", "사랑", "수리", "어깨", "역사", "윤곽", "자연", "저축", "졸업", "행복", "공간", "궁궐", "남자", "농심", "동심", "달력", "락스", "라임", "모집", "명동", "바람", "보정", "성종", "식기", "유행", "여우", "종점", "자리", "초코", "추위", "킬러", "콩밥", "틴더", "터널", "필적", "포도", "홍수", "형제", "자긍심", "전염병", "주인공", "칸막이", "친근감", "태양풍", "폐막식", "하반신", "황무지", "횡격막", "경기도", "농산물", "단백질", "대통령", "대장균", "동남아", "무기질", "오렌지", "캥거루", "칼국수", "거제도", "공유기", "네이버", "놀이터", "두리안", "달걀찜", "루시퍼", "롤리팝", "망아지", "메아리", "비비빅", "바밤바", "송중기", "성시경", "우리집", "아버지", "지킴이", "정관장", "청국장", "청록색", "큐피드", "코레일", "탕후루", "탕평책", "피해자", "폴라포", "헤이즈", "하르방"];
+    let fourToSix = ["어림짐작", "인문과학", "작심삼일", "착시효과", "중구난방", "천재지변", "팔방미인", "포화상태", "하루아침", "해수욕장", "허수아비", "홍익인간", "훈민정음", "거두절미", "대중교통", "두드러기", "만유인력", "물리화학", "가시광선", "고등학교", "가위눌림", "고기국수", "나무위키", "너비아니", "데리야끼", "도리토스", "라면사리", "롯데마트", "마음가짐", "모의재판", "보니하니", "바이올린", "세일즈맨", "삼단화환", "오징어회", "어버이날", "장례식장", "종종걸음", "초코머핀", "총알받이", "키득키득", "카이스트", "텔레토비", "토카토카", "피실험자", "페퍼로니", "호랑이굴", "하이마트", "우스갯소리", "청딱따구리", "크리스마스", "아르헨티나", "헌법재판소", "황소개구리", "게으름뱅이", "이데올로기", "순두부찌개", "대중목욕탕", "동음이의어", "프로그래밍", "정월대보름", "식기세척기", "지구온난화", "아이스크림", "오케스트라", "김치볶음밥", "최소공배수", "장수풍댕이", "괭이갈매기", "거짓말쟁이", "나이지리아", "나이팅게일", "담쟁이덩굴", "뒤치닥거리", "라티푼디움", "룩셈부르크", "막대그래프", "뮤직비디오", "바빌로니아", "바다코끼리", "산후조리원", "삼강행실도", "오므라이스", "오리온자리", "중모리장단", "증조할머니", "초코바나나", "초조대장경", "카트라이더", "카자흐스탄", "토이스토리", "타임스퀘어", "폴리에틸렌", "페이퍼타올", "하늘바라기", "하룻강아지", "어중이떠중이", "자이로스코프", "진인사대천명", "한해살이식물", "목도리도마뱀", "패러글라이딩", "자유의여신상", "콘트라베이스", "베이킹파우더", "에스컬레이터", "장대높이뛰기", "히말라야산맥", "나무아미타불", "스카치테이프", "아르키메데스", "조선왕조실록", "증조할아버지", "가시불가사리", "금메달리스트", "폭탄먼지벌레", "가슴지느러미", "가운뎃발가락", "고구마입벌레", "고리대급업자", "네안데르탈인", "뉴펀들랜드주", "덩굴무늬매듭", "덧치마저고리", "라스베이거스", "라이스페이퍼", "마다가스카르", "마이크로미터", "빨강불가사리", "브레인스토밍", "상정고금예문", "샌프란시스코", "아메리칸드림", "알렉산드리아", "작은할아버지", "전민변정도감", "청분홍메뚜기", "콤팩트디스크", "코니아일랜드", "타이포그래피", "트리케라톱스", "파운드케이크", "프랑켄슈타인", "피보나치수열"];
+    let words = [];
+    let currentIndex = 0;
+    let onecurrentIndex = 0;
+    let fourcurrentIndex = 0;
+    let wordsNumber = 10;
+    let interval = 2000; // 기본 시간 간격 (밀리초 단위)
+    let curEnd = 0;
+
+    const wordDisplay = document.getElementById('Word');
+    const startButton = document.getElementById('start');
+    const shuffleButton = document.getElementById('shuffleButton');
+    const intervalInput = document.getElementById('interval');
+    const wordNumberInput = document.getElementById('wordNumber');
+    const exportButton = document.getElementById('export');
+
+    shuffleButton.addEventListener('click', () => {
+        oneToThree = oneToThree.sort(() => Math.random() - 0.5);
+        fourToSix = fourToSix.sort(() => Math.random() - 0.5);
+        console.log('Mixed')
+        console.log(oneToThree);
+        console.log(fourToSix);
+    });
+
+    intervalInput.addEventListener('change', (e) => {
+        interval = e.target.value * 1000;
+    });
+
+    wordNumberInput.addEventListener('change', (e) => {
+        wordsNumber = e.target.value;
+    });
+
+
+    startButton.addEventListener('click', () => {
+        let shortLong = '';
+        let wordLength = document.getElementById('wordLength');
+        if (wordLength.options[wordLength.selectedIndex].value == 13) {
+            words = oneToThree;
+            curEnd = onecurrentIndex + Number(wordsNumber);
+            currentIndex = onecurrentIndex;
+            shortLong = '짧은';
+        } else {
+            words = fourToSix;
+            curEnd = fourcurrentIndex + Number(wordsNumber);
+            currentIndex = fourcurrentIndex;
+            shortLong = '긴';
+        }
+
+        document.getElementById('wordNumberValue').textContent = `${wordsNumber}개의 ${shortLong} 무작위 단어들이 제시됩니다.`;
+        document.getElementById('intervalValue').textContent = `단어당 제시되는 시간은 ${interval / 1000}초이며 후에 1초의 공백이 표시됩니다.`;
+        document.getElementById('settings').style.display = 'none';
+        document.getElementById('Intro').style.display = 'block';
+        wordDisplay.style.color = '#000000';
+    });
+
+    document.addEventListener('keydown', function (event) {
+        if (event.code === 'Space') {
+            test()
+            document.getElementById('Intro').style.display = 'none';
+            document.getElementById('Word').style.display = 'block';
+            console.log(words.slice(currentIndex, curEnd))
+        }
+        if (event.code === 'KeyR') {
+            document.getElementById('Word').style.display = 'none';
+            document.getElementById('settings').style.display = 'block';
+        }
+    });
+
+    const test = () => {
+
+        if (currentIndex == curEnd) {
+            wordDisplay.textContent = 'Finish';
+            wordDisplay.style.color = '#D1080B';
+            let wordLength = document.getElementById('wordLength');
+            if (wordLength.options[wordLength.selectedIndex].value == 13) {
+                onecurrentIndex = currentIndex;
+            } else {
+                fourcurrentIndex = currentIndex;
+            }
+            return
+        }
+        wordDisplay.textContent = words[currentIndex];
+        setTimeout(() => {
+            wordDisplay.textContent = '';
+            setTimeout(() => {
+                currentIndex += 1;
+                test();
+            }, 1000)
+        }, interval)
+    };
+});
diff --git a/exp_results1.xlsx b/exp_results1.xlsx
new file mode 100644
index 0000000..9d0e486
Binary files /dev/null and b/exp_results1.xlsx differ
diff --git a/pre_urp (1).ipynb b/pre_urp (1).ipynb
new file mode 100644
index 0000000..8a59594
--- /dev/null
+++ b/pre_urp (1).ipynb	
@@ -0,0 +1,717 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "3cb83b7c-012e-4b0d-9e50-6816e319e14f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "from sklearn import datasets, linear_model\n",
+    "from sklearn.metrics import mean_squared_error\n",
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "3c76de60-2ad3-4edc-b24b-0d7d195050af",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "np.random.seed(0)\n",
+    "\n",
+    "x = np.arange(0,10,0.4).reshape(-1,1)\n",
+    "y = 3*x+4+2*np.random.randn(len(x)).reshape(-1,1)\n",
+    "\n",
+    "plt.scatter(x,y)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "0e548e9c-2399-49f5-81eb-c64fa9227aa6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "regr1 = linear_model.LinearRegression(fit_intercept=False)\n",
+    "regr1.fit(x, y)\n",
+    "\n",
+    "y_pred = regr1.predict(x)\n",
+    "\n",
+    "plt.scatter(x,y, color='black')\n",
+    "plt.plot(x, y_pred, color='red', linewidth=2)\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "578b5b13-8d28-4df1-b904-d9cd167a65eb",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "x_2d = np.concatenate((x,x**2),axis=1)\n",
+    "y_2d = x**2-10*x+25+2.5*np.random.randn(len(x)).reshape(-1,1)\n",
+    "\n",
+    "plt.scatter(x,y_2d)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "7afbc5f8-1c63-4486-8a60-6a1c7ce1cfc5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "regr2 = linear_model.LinearRegression(fit_intercept=True)\n",
+    "regr2.fit(x_2d, y_2d)\n",
+    "\n",
+    "y_pred2 = regr2.predict(x_2d)\n",
+    "\n",
+    "plt.scatter(x,y_2d, color='black')\n",
+    "plt.plot(x, y_pred2, color='red', linewidth=2)\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "81095c69-e1f1-4955-8290-fd8a90cc2e31",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>1음절</th>\n",
+       "      <th>2음절</th>\n",
+       "      <th>3음절</th>\n",
+       "      <th>4음절</th>\n",
+       "      <th>5음절</th>\n",
+       "      <th>6음절이상</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>낮</td>\n",
+       "      <td>검도</td>\n",
+       "      <td>자긍심</td>\n",
+       "      <td>어림짐작</td>\n",
+       "      <td>우스갯소리</td>\n",
+       "      <td>어중이떠중이</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>논</td>\n",
+       "      <td>결혼</td>\n",
+       "      <td>전염병</td>\n",
+       "      <td>인문과학</td>\n",
+       "      <td>청딱따구리</td>\n",
+       "      <td>자이로스코프</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>닭</td>\n",
+       "      <td>녹차</td>\n",
+       "      <td>주인공</td>\n",
+       "      <td>작심삼일</td>\n",
+       "      <td>크리스마스</td>\n",
+       "      <td>진인사대천명</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>땀</td>\n",
+       "      <td>나비</td>\n",
+       "      <td>칸막이</td>\n",
+       "      <td>착시효과</td>\n",
+       "      <td>아르헨티나</td>\n",
+       "      <td>한해살이식물</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>못</td>\n",
+       "      <td>행운</td>\n",
+       "      <td>친근감</td>\n",
+       "      <td>중구난방</td>\n",
+       "      <td>헌법재판소</td>\n",
+       "      <td>목도리도마뱀</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  1음절 2음절  3음절   4음절    5음절   6음절이상\n",
+       "0   낮  검도  자긍심  어림짐작  우스갯소리  어중이떠중이\n",
+       "1   논  결혼  전염병  인문과학  청딱따구리  자이로스코프\n",
+       "2   닭  녹차  주인공  작심삼일  크리스마스  진인사대천명\n",
+       "3   땀  나비  칸막이  착시효과  아르헨티나  한해살이식물\n",
+       "4   못  행운  친근감  중구난방  헌법재판소  목도리도마뱀"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = pd.read_excel(io='korean_words.xlsx')\n",
+    "\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "a011453e-dcfc-4002-8e4b-bacea37789e2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "np.random.seed(0)\n",
+    "\n",
+    "# 1음절 2음절 3음절 섞기\n",
+    "###########################\n",
+    "sw_arr = df.iloc[:,:3].to_numpy().reshape(-1,)\n",
+    "np.random.shuffle(sw_arr)\n",
+    "###########################\n",
+    "\n",
+    "\n",
+    "# 10 10 20 20으로 분할\n",
+    "###########################\n",
+    "ace_list = sw_arr[:10]\n",
+    "ade_list = sw_arr[10:20]\n",
+    "acf_list = sw_arr[20:40]\n",
+    "adf_list = sw_arr[40:]\n",
+    "###########################"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "022e0cbf-d626-4f29-b4bd-8c24c6c2603e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['황무지', '농산물', '칼국수', '박사', '칸막이', '자긍심', '사랑', '행복', '어깨', '만두'],\n",
+       "      dtype=object)"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ace_list"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "747a75cf-1a0e-474b-9359-4f1e6fed368e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['결혼', '나비', '학', '대통령', '회', '역사', '자연', '녹차', '친근감', '경기도'],\n",
+       "      dtype=object)"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ade_list"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "34e02025-b587-44b8-aeb3-095dfa4c84b4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['무기질', '횡경막', '칼', '값', '솥', '캥거루', '코', '빛', '전염병', '보물', '등교',\n",
+       "       '죄', '폐막식', '저축', '주인공', '행운', '무용', '수리', '태양풍', '춤'],\n",
+       "      dtype=object)"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "acf_list"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "8bf77f29-b502-4fc8-8f31-e678d52eb170",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['칡', '겁', '단백질', '검도', '못', '윤곽', '턱', '닭', '하반신', '힘', '짐', '루비',\n",
+       "       '땀', '털', '졸업', '논', '낮', '오렌지', '동남아', '대장균'], dtype=object)"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "adf_list"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "5972408d-1ec6-410b-b9ae-7f913999faac",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "np.random.seed(0)\n",
+    "\n",
+    "# 4음절 5음절 6음절 섞기\n",
+    "###########################\n",
+    "lw_arr = df.iloc[:,3:].to_numpy().reshape(-1,)\n",
+    "np.random.shuffle(sw_arr)\n",
+    "###########################\n",
+    "\n",
+    "# 10 10 20 20으로 분할\n",
+    "###########################\n",
+    "bce_list = lw_arr[:10]\n",
+    "bde_list = lw_arr[10:20]\n",
+    "bcf_list = lw_arr[20:40]\n",
+    "bdf_list = lw_arr[40:]\n",
+    "###########################"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "e441719c-bdb6-49af-af92-384415e10c6f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['어림짐작', '우스갯소리', '어중이떠중이', '인문과학', '청딱따구리', '자이로스코프', '작심삼일',\n",
+       "       '크리스마스', '진인사대천명', '착시효과'], dtype=object)"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bce_list"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "55131228-6f1d-4668-a8e6-d7a2c6b6839b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['아르헨티나', '한해살이식물', '중구난방', '헌법재판소', '목도리도마뱀', '천재지변', '황소개구리',\n",
+       "       '패러글라이딩', '팔방미인', '게으름뱅이'], dtype=object)"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bde_list"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "52e971c2-1f3b-4c0e-8160-0d2d6d43fa92",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['자유의여신상', '포화상태', '이데올로기', '콘트라베이스', '하루아침', '순두부찌개', '베이킹파우더',\n",
+       "       '해수욕장', '대중목욕탕', '에스컬레이터', '허수아비', '동음이의어', '장대높이뛰기', '홍익인간',\n",
+       "       '프로그래밍', '히말라야산맥', '훈민정음', '정월대보름', '나무아미타불', '거두절미'], dtype=object)"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bcf_list"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "fb29e630-d65e-4c23-a4dc-f5eea6d8e7f1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['식기세척기', '스카치테이프', '대중교통', '지구온난화', '아르키메데스', '두드러기', '아이스크림',\n",
+       "       '조선왕조실록', '만류인력', '오케스트라', '증조할아버지', '물리화학', '김치볶음밥', '가시불가사리',\n",
+       "       '가시광선', '최소공배수', '금메달리스트', '고등학교', '장수풍댕이', '폭탄먼지벌레'], dtype=object)"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bdf_list"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "6b315de9-031d-416f-9863-1e30018d3d8c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>1</th>\n",
+       "      <th>2</th>\n",
+       "      <th>3</th>\n",
+       "      <th>4</th>\n",
+       "      <th>5</th>\n",
+       "      <th>6</th>\n",
+       "      <th>7</th>\n",
+       "      <th>8</th>\n",
+       "      <th>9</th>\n",
+       "      <th>10</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   1   2   3   4   5   6   7   8   9   10\n",
+       "0   1   1   1   0   1   0   0   1   1   1\n",
+       "1   1   1   0   0   1   0   1   1   0   1\n",
+       "2   1   1   1   1   0   1   0   1   1   1\n",
+       "3   0   0   1   1   0   0   1   0   1   1\n",
+       "4   1   1   0   0   1   1   1   0   1   1"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dummy = pd.read_excel(io='exp_results.xlsx', sheet_name = 'Dummy')\n",
+    "\n",
+    "dummy.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "b1d4ad0f-6743-4901-a42d-6a1701ac76a7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "a = temp.mean().to_numpy()\n",
+    "x = np.arange(10).reshape(-1,1) + 1\n",
+    "\n",
+    "plt.scatter(x,a)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "8488ea94-c144-41ed-b25f-c83734891282",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "x_2d = np.concatenate((x**2,x),axis=1)\n",
+    "\n",
+    "regr3 = linear_model.LinearRegression(fit_intercept=True)\n",
+    "regr3.fit(x_2d, a)\n",
+    "\n",
+    "a_pred = regr3.predict(x_2d)\n",
+    "\n",
+    "plt.scatter(x,a, color='black')\n",
+    "plt.plot(x, a_pred, color='red', linewidth=2)\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "f3ee49bf-99e9-46e5-8328-12e9c9687e56",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 0.01116427, -0.12344498])"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "regr3.coef_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "1d2846b1-0451-4e65-85b6-40dec497e860",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.9649122807017545"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "regr3.intercept_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "01c7001f-6760-465a-a003-1d51dcf249b6",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/pre_urp.ipynb b/pre_urp.ipynb
new file mode 100644
index 0000000..8a59594
--- /dev/null
+++ b/pre_urp.ipynb
@@ -0,0 +1,717 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "3cb83b7c-012e-4b0d-9e50-6816e319e14f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "from sklearn import datasets, linear_model\n",
+    "from sklearn.metrics import mean_squared_error\n",
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "3c76de60-2ad3-4edc-b24b-0d7d195050af",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "np.random.seed(0)\n",
+    "\n",
+    "x = np.arange(0,10,0.4).reshape(-1,1)\n",
+    "y = 3*x+4+2*np.random.randn(len(x)).reshape(-1,1)\n",
+    "\n",
+    "plt.scatter(x,y)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "0e548e9c-2399-49f5-81eb-c64fa9227aa6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "regr1 = linear_model.LinearRegression(fit_intercept=False)\n",
+    "regr1.fit(x, y)\n",
+    "\n",
+    "y_pred = regr1.predict(x)\n",
+    "\n",
+    "plt.scatter(x,y, color='black')\n",
+    "plt.plot(x, y_pred, color='red', linewidth=2)\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "578b5b13-8d28-4df1-b904-d9cd167a65eb",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "x_2d = np.concatenate((x,x**2),axis=1)\n",
+    "y_2d = x**2-10*x+25+2.5*np.random.randn(len(x)).reshape(-1,1)\n",
+    "\n",
+    "plt.scatter(x,y_2d)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "7afbc5f8-1c63-4486-8a60-6a1c7ce1cfc5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "regr2 = linear_model.LinearRegression(fit_intercept=True)\n",
+    "regr2.fit(x_2d, y_2d)\n",
+    "\n",
+    "y_pred2 = regr2.predict(x_2d)\n",
+    "\n",
+    "plt.scatter(x,y_2d, color='black')\n",
+    "plt.plot(x, y_pred2, color='red', linewidth=2)\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "81095c69-e1f1-4955-8290-fd8a90cc2e31",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>1음절</th>\n",
+       "      <th>2음절</th>\n",
+       "      <th>3음절</th>\n",
+       "      <th>4음절</th>\n",
+       "      <th>5음절</th>\n",
+       "      <th>6음절이상</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>낮</td>\n",
+       "      <td>검도</td>\n",
+       "      <td>자긍심</td>\n",
+       "      <td>어림짐작</td>\n",
+       "      <td>우스갯소리</td>\n",
+       "      <td>어중이떠중이</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>논</td>\n",
+       "      <td>결혼</td>\n",
+       "      <td>전염병</td>\n",
+       "      <td>인문과학</td>\n",
+       "      <td>청딱따구리</td>\n",
+       "      <td>자이로스코프</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>닭</td>\n",
+       "      <td>녹차</td>\n",
+       "      <td>주인공</td>\n",
+       "      <td>작심삼일</td>\n",
+       "      <td>크리스마스</td>\n",
+       "      <td>진인사대천명</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>땀</td>\n",
+       "      <td>나비</td>\n",
+       "      <td>칸막이</td>\n",
+       "      <td>착시효과</td>\n",
+       "      <td>아르헨티나</td>\n",
+       "      <td>한해살이식물</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>못</td>\n",
+       "      <td>행운</td>\n",
+       "      <td>친근감</td>\n",
+       "      <td>중구난방</td>\n",
+       "      <td>헌법재판소</td>\n",
+       "      <td>목도리도마뱀</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  1음절 2음절  3음절   4음절    5음절   6음절이상\n",
+       "0   낮  검도  자긍심  어림짐작  우스갯소리  어중이떠중이\n",
+       "1   논  결혼  전염병  인문과학  청딱따구리  자이로스코프\n",
+       "2   닭  녹차  주인공  작심삼일  크리스마스  진인사대천명\n",
+       "3   땀  나비  칸막이  착시효과  아르헨티나  한해살이식물\n",
+       "4   못  행운  친근감  중구난방  헌법재판소  목도리도마뱀"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = pd.read_excel(io='korean_words.xlsx')\n",
+    "\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "a011453e-dcfc-4002-8e4b-bacea37789e2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "np.random.seed(0)\n",
+    "\n",
+    "# 1음절 2음절 3음절 섞기\n",
+    "###########################\n",
+    "sw_arr = df.iloc[:,:3].to_numpy().reshape(-1,)\n",
+    "np.random.shuffle(sw_arr)\n",
+    "###########################\n",
+    "\n",
+    "\n",
+    "# 10 10 20 20으로 분할\n",
+    "###########################\n",
+    "ace_list = sw_arr[:10]\n",
+    "ade_list = sw_arr[10:20]\n",
+    "acf_list = sw_arr[20:40]\n",
+    "adf_list = sw_arr[40:]\n",
+    "###########################"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "022e0cbf-d626-4f29-b4bd-8c24c6c2603e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['황무지', '농산물', '칼국수', '박사', '칸막이', '자긍심', '사랑', '행복', '어깨', '만두'],\n",
+       "      dtype=object)"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ace_list"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "747a75cf-1a0e-474b-9359-4f1e6fed368e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['결혼', '나비', '학', '대통령', '회', '역사', '자연', '녹차', '친근감', '경기도'],\n",
+       "      dtype=object)"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ade_list"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "34e02025-b587-44b8-aeb3-095dfa4c84b4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['무기질', '횡경막', '칼', '값', '솥', '캥거루', '코', '빛', '전염병', '보물', '등교',\n",
+       "       '죄', '폐막식', '저축', '주인공', '행운', '무용', '수리', '태양풍', '춤'],\n",
+       "      dtype=object)"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "acf_list"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "8bf77f29-b502-4fc8-8f31-e678d52eb170",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['칡', '겁', '단백질', '검도', '못', '윤곽', '턱', '닭', '하반신', '힘', '짐', '루비',\n",
+       "       '땀', '털', '졸업', '논', '낮', '오렌지', '동남아', '대장균'], dtype=object)"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "adf_list"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "5972408d-1ec6-410b-b9ae-7f913999faac",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "np.random.seed(0)\n",
+    "\n",
+    "# 4음절 5음절 6음절 섞기\n",
+    "###########################\n",
+    "lw_arr = df.iloc[:,3:].to_numpy().reshape(-1,)\n",
+    "np.random.shuffle(sw_arr)\n",
+    "###########################\n",
+    "\n",
+    "# 10 10 20 20으로 분할\n",
+    "###########################\n",
+    "bce_list = lw_arr[:10]\n",
+    "bde_list = lw_arr[10:20]\n",
+    "bcf_list = lw_arr[20:40]\n",
+    "bdf_list = lw_arr[40:]\n",
+    "###########################"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "e441719c-bdb6-49af-af92-384415e10c6f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['어림짐작', '우스갯소리', '어중이떠중이', '인문과학', '청딱따구리', '자이로스코프', '작심삼일',\n",
+       "       '크리스마스', '진인사대천명', '착시효과'], dtype=object)"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bce_list"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "55131228-6f1d-4668-a8e6-d7a2c6b6839b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['아르헨티나', '한해살이식물', '중구난방', '헌법재판소', '목도리도마뱀', '천재지변', '황소개구리',\n",
+       "       '패러글라이딩', '팔방미인', '게으름뱅이'], dtype=object)"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bde_list"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "52e971c2-1f3b-4c0e-8160-0d2d6d43fa92",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['자유의여신상', '포화상태', '이데올로기', '콘트라베이스', '하루아침', '순두부찌개', '베이킹파우더',\n",
+       "       '해수욕장', '대중목욕탕', '에스컬레이터', '허수아비', '동음이의어', '장대높이뛰기', '홍익인간',\n",
+       "       '프로그래밍', '히말라야산맥', '훈민정음', '정월대보름', '나무아미타불', '거두절미'], dtype=object)"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bcf_list"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "fb29e630-d65e-4c23-a4dc-f5eea6d8e7f1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['식기세척기', '스카치테이프', '대중교통', '지구온난화', '아르키메데스', '두드러기', '아이스크림',\n",
+       "       '조선왕조실록', '만류인력', '오케스트라', '증조할아버지', '물리화학', '김치볶음밥', '가시불가사리',\n",
+       "       '가시광선', '최소공배수', '금메달리스트', '고등학교', '장수풍댕이', '폭탄먼지벌레'], dtype=object)"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bdf_list"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "6b315de9-031d-416f-9863-1e30018d3d8c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>1</th>\n",
+       "      <th>2</th>\n",
+       "      <th>3</th>\n",
+       "      <th>4</th>\n",
+       "      <th>5</th>\n",
+       "      <th>6</th>\n",
+       "      <th>7</th>\n",
+       "      <th>8</th>\n",
+       "      <th>9</th>\n",
+       "      <th>10</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   1   2   3   4   5   6   7   8   9   10\n",
+       "0   1   1   1   0   1   0   0   1   1   1\n",
+       "1   1   1   0   0   1   0   1   1   0   1\n",
+       "2   1   1   1   1   0   1   0   1   1   1\n",
+       "3   0   0   1   1   0   0   1   0   1   1\n",
+       "4   1   1   0   0   1   1   1   0   1   1"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dummy = pd.read_excel(io='exp_results.xlsx', sheet_name = 'Dummy')\n",
+    "\n",
+    "dummy.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "b1d4ad0f-6743-4901-a42d-6a1701ac76a7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "a = temp.mean().to_numpy()\n",
+    "x = np.arange(10).reshape(-1,1) + 1\n",
+    "\n",
+    "plt.scatter(x,a)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "8488ea94-c144-41ed-b25f-c83734891282",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "x_2d = np.concatenate((x**2,x),axis=1)\n",
+    "\n",
+    "regr3 = linear_model.LinearRegression(fit_intercept=True)\n",
+    "regr3.fit(x_2d, a)\n",
+    "\n",
+    "a_pred = regr3.predict(x_2d)\n",
+    "\n",
+    "plt.scatter(x,a, color='black')\n",
+    "plt.plot(x, a_pred, color='red', linewidth=2)\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "f3ee49bf-99e9-46e5-8328-12e9c9687e56",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 0.01116427, -0.12344498])"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "regr3.coef_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "1d2846b1-0451-4e65-85b6-40dec497e860",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.9649122807017545"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "regr3.intercept_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "01c7001f-6760-465a-a003-1d51dcf249b6",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/pre_urp/Untitled.ipynb b/pre_urp/Untitled.ipynb
new file mode 100644
index 0000000..ba12916
--- /dev/null
+++ b/pre_urp/Untitled.ipynb
@@ -0,0 +1,3012 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "ae7516b2-3639-4a9f-95d2-dd15641765f6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Collecting package metadata (current_repodata.json): ...working... done\n",
+      "Solving environment: ...working... done\n",
+      "\n",
+      "## Package Plan ##\n",
+      "\n",
+      "  environment location: C:\\Users\\369rental\\anaconda3\\envs\\jupyter\n",
+      "\n",
+      "  added / updated specs:\n",
+      "    - matplotlib\n",
+      "\n",
+      "\n",
+      "The following packages will be downloaded:\n",
+      "\n",
+      "    package                    |            build\n",
+      "    ---------------------------|-----------------\n",
+      "    contourpy-1.2.0            |  py311h59b6b97_0         218 KB\n",
+      "    icu-73.1                   |       h6c2663c_0        29.5 MB\n",
+      "    intel-openmp-2023.1.0      |   h59b6b97_46320         2.7 MB\n",
+      "    libdeflate-1.17            |       h2bbff1b_1         153 KB\n",
+      "    matplotlib-3.8.0           |  py311haa95532_0           9 KB\n",
+      "    matplotlib-base-3.8.0      |  py311hf62ec03_0         7.6 MB\n",
+      "    mkl-2023.1.0               |   h6b88ed4_46358       155.9 MB\n",
+      "    numpy-1.26.3               |  py311hdab7c0b_0          11 KB\n",
+      "    numpy-base-1.26.3          |  py311hd01c5d8_0         7.3 MB\n",
+      "    pillow-10.0.1              |  py311h045eedc_0         937 KB\n",
+      "    pyqt-5.15.10               |  py311hd77b12b_0         4.1 MB\n",
+      "    pyqt5-sip-12.13.0          |  py311h2bbff1b_0          75 KB\n",
+      "    qt-main-5.15.2             |      h19c9488_10        59.4 MB\n",
+      "    sip-6.7.12                 |  py311hd77b12b_0         615 KB\n",
+      "    ------------------------------------------------------------\n",
+      "                                           Total:       268.4 MB\n",
+      "\n",
+      "The following NEW packages will be INSTALLED:\n",
+      "\n",
+      "  blas               pkgs/main/win-64::blas-1.0-mkl \n",
+      "  brotli             pkgs/main/win-64::brotli-1.0.9-h2bbff1b_7 \n",
+      "  brotli-bin         pkgs/main/win-64::brotli-bin-1.0.9-h2bbff1b_7 \n",
+      "  contourpy          pkgs/main/win-64::contourpy-1.2.0-py311h59b6b97_0 \n",
+      "  cycler             pkgs/main/noarch::cycler-0.11.0-pyhd3eb1b0_0 \n",
+      "  fonttools          pkgs/main/noarch::fonttools-4.25.0-pyhd3eb1b0_0 \n",
+      "  freetype           pkgs/main/win-64::freetype-2.12.1-ha860e81_0 \n",
+      "  giflib             pkgs/main/win-64::giflib-5.2.1-h8cc25b3_3 \n",
+      "  icu                pkgs/main/win-64::icu-73.1-h6c2663c_0 \n",
+      "  intel-openmp       pkgs/main/win-64::intel-openmp-2023.1.0-h59b6b97_46320 \n",
+      "  jpeg               pkgs/main/win-64::jpeg-9e-h2bbff1b_1 \n",
+      "  kiwisolver         pkgs/main/win-64::kiwisolver-1.4.4-py311hd77b12b_0 \n",
+      "  krb5               pkgs/main/win-64::krb5-1.20.1-h5b6d351_0 \n",
+      "  lerc               pkgs/main/win-64::lerc-3.0-hd77b12b_0 \n",
+      "  libbrotlicommon    pkgs/main/win-64::libbrotlicommon-1.0.9-h2bbff1b_7 \n",
+      "  libbrotlidec       pkgs/main/win-64::libbrotlidec-1.0.9-h2bbff1b_7 \n",
+      "  libbrotlienc       pkgs/main/win-64::libbrotlienc-1.0.9-h2bbff1b_7 \n",
+      "  libclang           pkgs/main/win-64::libclang-14.0.6-default_hb5a9fac_1 \n",
+      "  libclang13         pkgs/main/win-64::libclang13-14.0.6-default_h8e68704_1 \n",
+      "  libdeflate         pkgs/main/win-64::libdeflate-1.17-h2bbff1b_1 \n",
+      "  libpng             pkgs/main/win-64::libpng-1.6.39-h8cc25b3_0 \n",
+      "  libpq              pkgs/main/win-64::libpq-12.15-h906ac69_1 \n",
+      "  libtiff            pkgs/main/win-64::libtiff-4.5.1-hd77b12b_0 \n",
+      "  libwebp            pkgs/main/win-64::libwebp-1.3.2-hbc33d0d_0 \n",
+      "  libwebp-base       pkgs/main/win-64::libwebp-base-1.3.2-h2bbff1b_0 \n",
+      "  lz4-c              pkgs/main/win-64::lz4-c-1.9.4-h2bbff1b_0 \n",
+      "  matplotlib         pkgs/main/win-64::matplotlib-3.8.0-py311haa95532_0 \n",
+      "  matplotlib-base    pkgs/main/win-64::matplotlib-base-3.8.0-py311hf62ec03_0 \n",
+      "  mkl                pkgs/main/win-64::mkl-2023.1.0-h6b88ed4_46358 \n",
+      "  mkl-service        pkgs/main/win-64::mkl-service-2.4.0-py311h2bbff1b_1 \n",
+      "  mkl_fft            pkgs/main/win-64::mkl_fft-1.3.8-py311h2bbff1b_0 \n",
+      "  mkl_random         pkgs/main/win-64::mkl_random-1.2.4-py311h59b6b97_0 \n",
+      "  munkres            pkgs/main/noarch::munkres-1.1.4-py_0 \n",
+      "  numpy              pkgs/main/win-64::numpy-1.26.3-py311hdab7c0b_0 \n",
+      "  numpy-base         pkgs/main/win-64::numpy-base-1.26.3-py311hd01c5d8_0 \n",
+      "  openjpeg           pkgs/main/win-64::openjpeg-2.4.0-h4fc8c34_0 \n",
+      "  pillow             pkgs/main/win-64::pillow-10.0.1-py311h045eedc_0 \n",
+      "  ply                pkgs/main/win-64::ply-3.11-py311haa95532_0 \n",
+      "  pyparsing          pkgs/main/win-64::pyparsing-3.0.9-py311haa95532_0 \n",
+      "  pyqt               pkgs/main/win-64::pyqt-5.15.10-py311hd77b12b_0 \n",
+      "  pyqt5-sip          pkgs/main/win-64::pyqt5-sip-12.13.0-py311h2bbff1b_0 \n",
+      "  qt-main            pkgs/main/win-64::qt-main-5.15.2-h19c9488_10 \n",
+      "  sip                pkgs/main/win-64::sip-6.7.12-py311hd77b12b_0 \n",
+      "  tbb                pkgs/main/win-64::tbb-2021.8.0-h59b6b97_0 \n",
+      "  zstd               pkgs/main/win-64::zstd-1.5.5-hd43e919_0 \n",
+      "\n",
+      "\n",
+      "\n",
+      "Downloading and Extracting Packages\n",
+      "\n",
+      "icu-73.1             | 29.5 MB   |            |   0% \n",
+      "\n",
+      "pyqt5-sip-12.13.0    | 75 KB     |            |   0% \u001b[A\n",
+      "\n",
+      "\n",
+      "numpy-1.26.3         | 11 KB     |            |   0% \u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "numpy-base-1.26.3    | 7.3 MB    |            |   0% \u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "matplotlib-3.8.0     | 9 KB      |            |   0% \u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "sip-6.7.12           | 615 KB    |            |   0% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "pillow-10.0.1        | 937 KB    |            |   0% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "matplotlib-base-3.8. | 7.6 MB    |            |   0% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "mkl-2023.1.0         | 155.9 MB  |            |   0% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "intel-openmp-2023.1. | 2.7 MB    |            |   0% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "pyqt-5.15.10         | 4.1 MB    |            |   0% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "libdeflate-1.17      | 153 KB    |            |   0% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "qt-main-5.15.2       | 59.4 MB   |            |   0% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "contourpy-1.2.0      | 218 KB    |            |   0% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "pyqt5-sip-12.13.0    | 75 KB     | ##1        |  21% \u001b[A\n",
+      "icu-73.1             | 29.5 MB   |            |   0% \n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "matplotlib-3.8.0     | 9 KB      | ########## | 100% \u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "numpy-1.26.3         | 11 KB     | ########## | 100% \u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "numpy-base-1.26.3    | 7.3 MB    |            |   0% \u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "sip-6.7.12           | 615 KB    | 2          |   3% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "pillow-10.0.1        | 937 KB    | 1          |   2% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "matplotlib-base-3.8. | 7.6 MB    |            |   0% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "icu-73.1             | 29.5 MB   | 4          |   4% \n",
+      "\n",
+      "\n",
+      "\n",
+      "numpy-base-1.26.3    | 7.3 MB    | #2         |  13% \u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "matplotlib-3.8.0     | 9 KB      | ########## | 100% \u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "numpy-1.26.3         | 11 KB     | ########## | 100% \u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "pillow-10.0.1        | 937 KB    | #######1   |  72% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "sip-6.7.12           | 615 KB    | ########5  |  86% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "matplotlib-base-3.8. | 7.6 MB    | 6          |   7% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "pyqt5-sip-12.13.0    | 75 KB     | ########## | 100% \u001b[A\n",
+      "icu-73.1             | 29.5 MB   | 8          |   9% \n",
+      "\n",
+      "pyqt5-sip-12.13.0    | 75 KB     | ########## | 100% \u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "numpy-base-1.26.3    | 7.3 MB    | #9         |  20% \u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "mkl-2023.1.0         | 155.9 MB  |            |   0% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "intel-openmp-2023.1. | 2.7 MB    |            |   1% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "mkl-2023.1.0         | 155.9 MB  |            |   0% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "matplotlib-base-3.8. | 7.6 MB    | #8         |  19% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "icu-73.1             | 29.5 MB   | #2         |  13% \n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "intel-openmp-2023.1. | 2.7 MB    | #9         |  19% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "numpy-base-1.26.3    | 7.3 MB    | ##6        |  27% \u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "mkl-2023.1.0         | 155.9 MB  |            |   1% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "matplotlib-base-3.8. | 7.6 MB    | ##5        |  26% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "icu-73.1             | 29.5 MB   | #5         |  16% \n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "intel-openmp-2023.1. | 2.7 MB    | ###6       |  37% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "pillow-10.0.1        | 937 KB    | ########## | 100% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "sip-6.7.12           | 615 KB    | ########## | 100% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "icu-73.1             | 29.5 MB   | #8         |  19% \n",
+      "\n",
+      "\n",
+      "\n",
+      "numpy-base-1.26.3    | 7.3 MB    | ###5       |  36% \u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "mkl-2023.1.0         | 155.9 MB  |            |   1% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "numpy-base-1.26.3    | 7.3 MB    | ####4      |  44% \u001b[A\u001b[A\u001b[A\n",
+      "icu-73.1             | 29.5 MB   | ##3        |  23% \n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "mkl-2023.1.0         | 155.9 MB  | 1          |   1% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "matplotlib-base-3.8. | 7.6 MB    | ###2       |  33% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "matplotlib-base-3.8. | 7.6 MB    | ####9      |  49% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "matplotlib-base-3.8. | 7.6 MB    | #######3   |  73% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "matplotlib-base-3.8. | 7.6 MB    | #########8 |  98% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "pyqt-5.15.10         | 4.1 MB    |            |   0% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "pyqt-5.15.10         | 4.1 MB    | ##7        |  27% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "pyqt-5.15.10         | 4.1 MB    | #######6   |  77% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "libdeflate-1.17      | 153 KB    | #          |  10% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "libdeflate-1.17      | 153 KB    | ########## | 100% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "libdeflate-1.17      | 153 KB    | ########## | 100% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "qt-main-5.15.2       | 59.4 MB   |            |   0% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "qt-main-5.15.2       | 59.4 MB   |            |   1% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "qt-main-5.15.2       | 59.4 MB   | 2          |   2% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "qt-main-5.15.2       | 59.4 MB   | 5          |   5% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "qt-main-5.15.2       | 59.4 MB   | 6          |   7% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "qt-main-5.15.2       | 59.4 MB   | 9          |  10% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "matplotlib-base-3.8. | 7.6 MB    | ########## | 100% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "pyqt-5.15.10         | 4.1 MB    | ########## | 100% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "qt-main-5.15.2       | 59.4 MB   | #1         |  12% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "qt-main-5.15.2       | 59.4 MB   | #5         |  16% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "numpy-base-1.26.3    | 7.3 MB    | ####9      |  50% \u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "qt-main-5.15.2       | 59.4 MB   | #8         |  19% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "icu-73.1             | 29.5 MB   | ##6        |  26% \n",
+      "\n",
+      "\n",
+      "\n",
+      "numpy-base-1.26.3    | 7.3 MB    | #####4     |  54% \u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "qt-main-5.15.2       | 59.4 MB   | ##         |  20% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "icu-73.1             | 29.5 MB   | ##8        |  29% \n",
+      "\n",
+      "\n",
+      "\n",
+      "numpy-base-1.26.3    | 7.3 MB    | #####7     |  57% \u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "qt-main-5.15.2       | 59.4 MB   | ##1        |  22% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "icu-73.1             | 29.5 MB   | ###        |  31% \n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "mkl-2023.1.0         | 155.9 MB  | 1          |   1% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "numpy-base-1.26.3    | 7.3 MB    | ######6    |  66% \u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "qt-main-5.15.2       | 59.4 MB   | ##3        |  24% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "icu-73.1             | 29.5 MB   | ###3       |  34% \n",
+      "\n",
+      "\n",
+      "\n",
+      "numpy-base-1.26.3    | 7.3 MB    | #######    |  71% \u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "qt-main-5.15.2       | 59.4 MB   | ##4        |  25% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "mkl-2023.1.0         | 155.9 MB  | 1          |   2% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "icu-73.1             | 29.5 MB   | ###5       |  36% \n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "mkl-2023.1.0         | 155.9 MB  | 1          |   2% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "icu-73.1             | 29.5 MB   | ###7       |  37% \n",
+      "\n",
+      "\n",
+      "\n",
+      "numpy-base-1.26.3    | 7.3 MB    | #######9   |  80% \u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "qt-main-5.15.2       | 59.4 MB   | ##6        |  26% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "mkl-2023.1.0         | 155.9 MB  | 2          |   2% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "icu-73.1             | 29.5 MB   | ###9       |  40% \n",
+      "\n",
+      "\n",
+      "\n",
+      "numpy-base-1.26.3    | 7.3 MB    | ########8  |  89% \u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "qt-main-5.15.2       | 59.4 MB   | ##7        |  28% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "mkl-2023.1.0         | 155.9 MB  | 2          |   2% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "icu-73.1             | 29.5 MB   | ####1      |  41% \n",
+      "\n",
+      "\n",
+      "\n",
+      "numpy-base-1.26.3    | 7.3 MB    | #########3 |  94% \u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "qt-main-5.15.2       | 59.4 MB   | ##8        |  29% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "mkl-2023.1.0         | 155.9 MB  | 2          |   3% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "icu-73.1             | 29.5 MB   | ####4      |  45% \n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "contourpy-1.2.0      | 218 KB    | 7          |   7% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "qt-main-5.15.2       | 59.4 MB   | ##9        |  29% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "mkl-2023.1.0         | 155.9 MB  | 2          |   3% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "icu-73.1             | 29.5 MB   | ####8      |  49% \n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "contourpy-1.2.0      | 218 KB    | ########## | 100% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "contourpy-1.2.0      | 218 KB    | ########## | 100% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "icu-73.1             | 29.5 MB   | #####2     |  52% \n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "mkl-2023.1.0         | 155.9 MB  | 3          |   3% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "icu-73.1             | 29.5 MB   | #####5     |  56% \n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "qt-main-5.15.2       | 59.4 MB   | ###        |  30% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "mkl-2023.1.0         | 155.9 MB  | 3          |   4% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "icu-73.1             | 29.5 MB   | #####8     |  59% \n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "qt-main-5.15.2       | 59.4 MB   | ###        |  31% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "mkl-2023.1.0         | 155.9 MB  | 4          |   4% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "icu-73.1             | 29.5 MB   | ######1    |  62% \n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "qt-main-5.15.2       | 59.4 MB   | ###1       |  32% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "numpy-base-1.26.3    | 7.3 MB    | ########## | 100% \u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "numpy-base-1.26.3    | 7.3 MB    | ########## | 100% \u001b[A\u001b[A\u001b[A\n",
+      "icu-73.1             | 29.5 MB   | ######4    |  64% \n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "mkl-2023.1.0         | 155.9 MB  | 4          |   5% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "qt-main-5.15.2       | 59.4 MB   | ###2       |  32% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "icu-73.1             | 29.5 MB   | ######9    |  70% \n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "mkl-2023.1.0         | 155.9 MB  | 5          |   5% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "qt-main-5.15.2       | 59.4 MB   | ###3       |  33% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "icu-73.1             | 29.5 MB   | #######2   |  72% \n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "mkl-2023.1.0         | 155.9 MB  | 5          |   6% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "qt-main-5.15.2       | 59.4 MB   | ###4       |  34% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "qt-main-5.15.2       | 59.4 MB   | ###4       |  35% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "icu-73.1             | 29.5 MB   | #######6   |  76% \n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "mkl-2023.1.0         | 155.9 MB  | 5          |   6% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "qt-main-5.15.2       | 59.4 MB   | ###6       |  37% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "mkl-2023.1.0         | 155.9 MB  | 6          |   7% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "qt-main-5.15.2       | 59.4 MB   | ###7       |  38% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "icu-73.1             | 29.5 MB   | #######7   |  78% \n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "mkl-2023.1.0         | 155.9 MB  | 6          |   7% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "qt-main-5.15.2       | 59.4 MB   | ###8       |  39% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "qt-main-5.15.2       | 59.4 MB   | ###9       |  39% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "icu-73.1             | 29.5 MB   | ########3  |  83% \n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "mkl-2023.1.0         | 155.9 MB  | 7          |   7% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "qt-main-5.15.2       | 59.4 MB   | ####1      |  41% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "icu-73.1             | 29.5 MB   | ########6  |  87% \n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "mkl-2023.1.0         | 155.9 MB  | 7          |   7% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "qt-main-5.15.2       | 59.4 MB   | ####2      |  43% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "icu-73.1             | 29.5 MB   | ########8  |  89% \n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "mkl-2023.1.0         | 155.9 MB  | 7          |   8% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "qt-main-5.15.2       | 59.4 MB   | ####4      |  45% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "icu-73.1             | 29.5 MB   | #########1 |  92% \n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "mkl-2023.1.0         | 155.9 MB  | 7          |   8% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "qt-main-5.15.2       | 59.4 MB   | ####5      |  46% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "icu-73.1             | 29.5 MB   | #########4 |  95% \n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "mkl-2023.1.0         | 155.9 MB  | 8          |   8% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "qt-main-5.15.2       | 59.4 MB   | ####6      |  47% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "icu-73.1             | 29.5 MB   | #########6 |  96% \n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "mkl-2023.1.0         | 155.9 MB  | 8          |   9% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "qt-main-5.15.2       | 59.4 MB   | ####8      |  48% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "icu-73.1             | 29.5 MB   | #########9 |  99% \n",
+      "icu-73.1             | 29.5 MB   | ########## | 100% \n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "qt-main-5.15.2       | 59.4 MB   | ####9      |  50% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "mkl-2023.1.0         | 155.9 MB  | 8          |   9% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "qt-main-5.15.2       | 59.4 MB   | #####2     |  52% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "qt-main-5.15.2       | 59.4 MB   | #####3     |  54% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "mkl-2023.1.0         | 155.9 MB  | 9          |   9% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "qt-main-5.15.2       | 59.4 MB   | #####5     |  56% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "mkl-2023.1.0         | 155.9 MB  | 9          |  10% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "qt-main-5.15.2       | 59.4 MB   | #####7     |  57% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "mkl-2023.1.0         | 155.9 MB  | #          |  10% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "qt-main-5.15.2       | 59.4 MB   | #####9     |  59% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "qt-main-5.15.2       | 59.4 MB   | ######1    |  61% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "mkl-2023.1.0         | 155.9 MB  | #          |  11% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "qt-main-5.15.2       | 59.4 MB   | ######4    |  64% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "mkl-2023.1.0         | 155.9 MB  | #1         |  11% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "mkl-2023.1.0         | 155.9 MB  | #1         |  12% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "qt-main-5.15.2       | 59.4 MB   | ######6    |  66% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "qt-main-5.15.2       | 59.4 MB   | ######8    |  68% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "mkl-2023.1.0         | 155.9 MB  | #2         |  12% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "qt-main-5.15.2       | 59.4 MB   | #######    |  71% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "mkl-2023.1.0         | 155.9 MB  | #2         |  13% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "intel-openmp-2023.1. | 2.7 MB    | #####      |  50% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "intel-openmp-2023.1. | 2.7 MB    | #####1     |  51% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "mkl-2023.1.0         | 155.9 MB  | #3         |  13% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "qt-main-5.15.2       | 59.4 MB   | #######2   |  72% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "intel-openmp-2023.1. | 2.7 MB    | #####9     |  59% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "mkl-2023.1.0         | 155.9 MB  | #3         |  14% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "qt-main-5.15.2       | 59.4 MB   | #######3   |  73% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "mkl-2023.1.0         | 155.9 MB  | #4         |  14% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "qt-main-5.15.2       | 59.4 MB   | #######5   |  76% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "mkl-2023.1.0         | 155.9 MB  | #4         |  15% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "intel-openmp-2023.1. | 2.7 MB    | ######6    |  66% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "qt-main-5.15.2       | 59.4 MB   | #######7   |  78% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "intel-openmp-2023.1. | 2.7 MB    | #######8   |  78% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "mkl-2023.1.0         | 155.9 MB  | #5         |  15% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "qt-main-5.15.2       | 59.4 MB   | #######8   |  79% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "mkl-2023.1.0         | 155.9 MB  | #5         |  16% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "intel-openmp-2023.1. | 2.7 MB    | ########## | 100% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "intel-openmp-2023.1. | 2.7 MB    | ########## | 100% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "qt-main-5.15.2       | 59.4 MB   | ########   |  81% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "qt-main-5.15.2       | 59.4 MB   | ########3  |  83% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "mkl-2023.1.0         | 155.9 MB  | #5         |  16% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "qt-main-5.15.2       | 59.4 MB   | ########6  |  86% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "mkl-2023.1.0         | 155.9 MB  | #6         |  17% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "qt-main-5.15.2       | 59.4 MB   | ########8  |  89% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "mkl-2023.1.0         | 155.9 MB  | #7         |  17% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "qt-main-5.15.2       | 59.4 MB   | #########1 |  91% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "mkl-2023.1.0         | 155.9 MB  | #7         |  17% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "mkl-2023.1.0         | 155.9 MB  | #8         |  18% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "qt-main-5.15.2       | 59.4 MB   | #########3 |  93% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "mkl-2023.1.0         | 155.9 MB  | #9         |  19% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "mkl-2023.1.0         | 155.9 MB  | ##         |  20% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "mkl-2023.1.0         | 155.9 MB  | ##1        |  21% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "qt-main-5.15.2       | 59.4 MB   | #########4 |  94% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "mkl-2023.1.0         | 155.9 MB  | ##2        |  22% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "mkl-2023.1.0         | 155.9 MB  | ##2        |  23% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "qt-main-5.15.2       | 59.4 MB   | #########5 |  95% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "mkl-2023.1.0         | 155.9 MB  | ##4        |  25% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "qt-main-5.15.2       | 59.4 MB   | #########6 |  96% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "mkl-2023.1.0         | 155.9 MB  | ##5        |  25% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "qt-main-5.15.2       | 59.4 MB   | #########7 |  97% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "mkl-2023.1.0         | 155.9 MB  | ##6        |  26% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "qt-main-5.15.2       | 59.4 MB   | #########8 |  98% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "mkl-2023.1.0         | 155.9 MB  | ##7        |  27% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "mkl-2023.1.0         | 155.9 MB  | ##8        |  28% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "mkl-2023.1.0         | 155.9 MB  | ##9        |  30% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "mkl-2023.1.0         | 155.9 MB  | ###1       |  31% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "mkl-2023.1.0         | 155.9 MB  | ###1       |  32% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "mkl-2023.1.0         | 155.9 MB  | ###3       |  34% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "mkl-2023.1.0         | 155.9 MB  | ###5       |  35% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "mkl-2023.1.0         | 155.9 MB  | ###6       |  36% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "mkl-2023.1.0         | 155.9 MB  | ###8       |  38% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "mkl-2023.1.0         | 155.9 MB  | ###8       |  39% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "mkl-2023.1.0         | 155.9 MB  | ####1      |  41% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "mkl-2023.1.0         | 155.9 MB  | ####2      |  42% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "mkl-2023.1.0         | 155.9 MB  | ####3      |  44% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "mkl-2023.1.0         | 155.9 MB  | ####4      |  45% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "mkl-2023.1.0         | 155.9 MB  | ####6      |  46% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "mkl-2023.1.0         | 155.9 MB  | ####6      |  47% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "mkl-2023.1.0         | 155.9 MB  | ####8      |  49% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "mkl-2023.1.0         | 155.9 MB  | ####9      |  50% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "mkl-2023.1.0         | 155.9 MB  | #####1     |  52% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "mkl-2023.1.0         | 155.9 MB  | #####2     |  53% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "mkl-2023.1.0         | 155.9 MB  | #####3     |  53% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "mkl-2023.1.0         | 155.9 MB  | #####3     |  54% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "mkl-2023.1.0         | 155.9 MB  | #####4     |  55% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "mkl-2023.1.0         | 155.9 MB  | #####5     |  56% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "mkl-2023.1.0         | 155.9 MB  | #####6     |  57% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "mkl-2023.1.0         | 155.9 MB  | #####7     |  58% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "mkl-2023.1.0         | 155.9 MB  | #####8     |  59% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "mkl-2023.1.0         | 155.9 MB  | #####9     |  60% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "qt-main-5.15.2       | 59.4 MB   | ########## | 100% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "qt-main-5.15.2       | 59.4 MB   | ########## | 100% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "mkl-2023.1.0         | 155.9 MB  | ######     |  61% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "mkl-2023.1.0         | 155.9 MB  | ######1    |  62% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "mkl-2023.1.0         | 155.9 MB  | ######2    |  63% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "mkl-2023.1.0         | 155.9 MB  | ######3    |  64% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "mkl-2023.1.0         | 155.9 MB  | ######4    |  65% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "mkl-2023.1.0         | 155.9 MB  | ######6    |  66% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "mkl-2023.1.0         | 155.9 MB  | ######7    |  67% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "mkl-2023.1.0         | 155.9 MB  | ######8    |  68% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "mkl-2023.1.0         | 155.9 MB  | ######9    |  69% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "mkl-2023.1.0         | 155.9 MB  | #######    |  70% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "mkl-2023.1.0         | 155.9 MB  | #######    |  71% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "mkl-2023.1.0         | 155.9 MB  | #######2   |  72% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "mkl-2023.1.0         | 155.9 MB  | #######2   |  73% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "mkl-2023.1.0         | 155.9 MB  | #######3   |  74% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "mkl-2023.1.0         | 155.9 MB  | #######4   |  75% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "mkl-2023.1.0         | 155.9 MB  | #######5   |  75% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "mkl-2023.1.0         | 155.9 MB  | #######7   |  77% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "mkl-2023.1.0         | 155.9 MB  | #######8   |  78% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "mkl-2023.1.0         | 155.9 MB  | #######9   |  79% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "mkl-2023.1.0         | 155.9 MB  | ########   |  80% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "mkl-2023.1.0         | 155.9 MB  | ########2  |  82% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "mkl-2023.1.0         | 155.9 MB  | ########3  |  83% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "mkl-2023.1.0         | 155.9 MB  | ########4  |  85% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "mkl-2023.1.0         | 155.9 MB  | ########6  |  86% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "mkl-2023.1.0         | 155.9 MB  | ########7  |  87% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "mkl-2023.1.0         | 155.9 MB  | ########8  |  89% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "mkl-2023.1.0         | 155.9 MB  | #########  |  90% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "mkl-2023.1.0         | 155.9 MB  | #########1 |  91% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "mkl-2023.1.0         | 155.9 MB  | #########2 |  93% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "mkl-2023.1.0         | 155.9 MB  | #########3 |  94% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "mkl-2023.1.0         | 155.9 MB  | #########4 |  95% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "mkl-2023.1.0         | 155.9 MB  | #########6 |  96% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "mkl-2023.1.0         | 155.9 MB  | #########7 |  97% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "mkl-2023.1.0         | 155.9 MB  | #########8 |  99% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "mkl-2023.1.0         | 155.9 MB  | ########## | 100% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "mkl-2023.1.0         | 155.9 MB  | ########## | 100% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "                                                     \n",
+      "\n",
+      "\n",
+      "                                                     \u001b[A\n",
+      "\n",
+      "\n",
+      "                                                     \u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "                                                     \u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "                                                     \u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "                                                     \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "                                                     \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "                                                     \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "                                                     \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "                                                     \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "                                                     \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "                                                     \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "                                                     \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "                                                     \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "Preparing transaction: ...working... done\n",
+      "Verifying transaction: ...working... done\n",
+      "Executing transaction: ...working... done\n",
+      "\n",
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "==> WARNING: A newer version of conda exists. <==\n",
+      "  current version: 23.7.4\n",
+      "  latest version: 23.11.0\n",
+      "\n",
+      "Please update conda by running\n",
+      "\n",
+      "    $ conda update -n base -c defaults conda\n",
+      "\n",
+      "Or to minimize the number of packages updated during conda update use\n",
+      "\n",
+      "     conda install conda=23.11.0\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "conda install matplotlib"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "c4483730-3245-4f33-9965-882ecf773e7f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Collecting package metadata (current_repodata.json): ...working... done\n",
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "==> WARNING: A newer version of conda exists. <==\n",
+      "  current version: 23.7.4\n",
+      "  latest version: 23.11.0\n",
+      "\n",
+      "Please update conda by running\n",
+      "\n",
+      "    $ conda update -n base -c defaults conda\n",
+      "\n",
+      "Or to minimize the number of packages updated during conda update use\n",
+      "\n",
+      "     conda install conda=23.11.0\n",
+      "\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Solving environment: ...working... done\n",
+      "\n",
+      "## Package Plan ##\n",
+      "\n",
+      "  environment location: C:\\Users\\369rental\\anaconda3\\envs\\jupyter\n",
+      "\n",
+      "  added / updated specs:\n",
+      "    - scikit-learn\n",
+      "\n",
+      "\n",
+      "The following packages will be downloaded:\n",
+      "\n",
+      "    package                    |            build\n",
+      "    ---------------------------|-----------------\n",
+      "    scikit-learn-1.2.2         |  py311hd77b12b_1         7.6 MB\n",
+      "    scipy-1.11.4               |  py311hc1ccb85_0        20.9 MB\n",
+      "    ------------------------------------------------------------\n",
+      "                                           Total:        28.5 MB\n",
+      "\n",
+      "The following NEW packages will be INSTALLED:\n",
+      "\n",
+      "  icc_rt             pkgs/main/win-64::icc_rt-2022.1.0-h6049295_2 \n",
+      "  joblib             pkgs/main/win-64::joblib-1.2.0-py311haa95532_0 \n",
+      "  scikit-learn       pkgs/main/win-64::scikit-learn-1.2.2-py311hd77b12b_1 \n",
+      "  scipy              pkgs/main/win-64::scipy-1.11.4-py311hc1ccb85_0 \n",
+      "  threadpoolctl      pkgs/main/noarch::threadpoolctl-2.2.0-pyh0d69192_0 \n",
+      "\n",
+      "\n",
+      "\n",
+      "Downloading and Extracting Packages\n",
+      "\n",
+      "scikit-learn-1.2.2   | 7.6 MB    |            |   0% \n",
+      "\n",
+      "scipy-1.11.4         | 20.9 MB   |            |   0% \u001b[A\n",
+      "scikit-learn-1.2.2   | 7.6 MB    |            |   0% \n",
+      "\n",
+      "scipy-1.11.4         | 20.9 MB   |            |   0% \u001b[A\n",
+      "scikit-learn-1.2.2   | 7.6 MB    | ####       |  40% \n",
+      "\n",
+      "scipy-1.11.4         | 20.9 MB   | 2          |   2% \u001b[A\n",
+      "scikit-learn-1.2.2   | 7.6 MB    | #######3   |  73% \n",
+      "\n",
+      "scipy-1.11.4         | 20.9 MB   | 4          |   5% \u001b[A\n",
+      "\n",
+      "scipy-1.11.4         | 20.9 MB   | 7          |   8% \u001b[A\n",
+      "\n",
+      "scipy-1.11.4         | 20.9 MB   | #1         |  12% \u001b[A\n",
+      "\n",
+      "scipy-1.11.4         | 20.9 MB   | ##2        |  22% \u001b[A\n",
+      "\n",
+      "scipy-1.11.4         | 20.9 MB   | ###3       |  34% \u001b[A\n",
+      "\n",
+      "scipy-1.11.4         | 20.9 MB   | ####5      |  46% \u001b[A\n",
+      "scikit-learn-1.2.2   | 7.6 MB    | ########## | 100% \n",
+      "\n",
+      "scipy-1.11.4         | 20.9 MB   | #####2     |  52% \u001b[A\n",
+      "\n",
+      "scipy-1.11.4         | 20.9 MB   | ######3    |  64% \u001b[A\n",
+      "\n",
+      "scipy-1.11.4         | 20.9 MB   | #######2   |  72% \u001b[A\n",
+      "\n",
+      "scipy-1.11.4         | 20.9 MB   | #######8   |  79% \u001b[A\n",
+      "\n",
+      "scipy-1.11.4         | 20.9 MB   | #########3 |  93% \u001b[A\n",
+      "\n",
+      "scipy-1.11.4         | 20.9 MB   | ########## | 100% \u001b[A\n",
+      "                                                     \n",
+      "\n",
+      "\n",
+      "                                                     \u001b[A\n",
+      "\n",
+      "Preparing transaction: ...working... done\n",
+      "Verifying transaction: ...working... done\n",
+      "Executing transaction: ...working... \n",
+      "\n",
+      "    Windows 64-bit packages of scikit-learn can be accelerated using scikit-learn-intelex.\n",
+      "    More details are available here: https://intel.github.io/scikit-learn-intelex\n",
+      "\n",
+      "    For example:\n",
+      "\n",
+      "        $ conda install scikit-learn-intelex\n",
+      "        $ python -m sklearnex my_application.py\n",
+      "\n",
+      "\n",
+      "done\n"
+     ]
+    }
+   ],
+   "source": [
+    "conda install scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "54e43136-f123-4485-b2a0-4d51d828248e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Collecting package metadata (current_repodata.json): ...working... done\n",
+      "Note: you may need to restart the kernel to use updated packages.\n",
+      "\n",
+      "Solving environment: ...working... done\n",
+      "\n",
+      "## Package Plan ##\n",
+      "\n",
+      "  environment location: C:\\Users\\369rental\\anaconda3\\envs\\jupyter\n",
+      "\n",
+      "  added / updated specs:\n",
+      "    - pandas\n",
+      "\n",
+      "\n",
+      "The following packages will be downloaded:\n",
+      "\n",
+      "    package                    |            build\n",
+      "    ---------------------------|-----------------\n",
+      "    numexpr-2.8.7              |  py311h1fcbade_0         160 KB\n",
+      "    pandas-2.1.4               |  py311hf62ec03_0        13.6 MB\n",
+      "    ------------------------------------------------------------\n",
+      "                                           Total:        13.7 MB\n",
+      "\n",
+      "The following NEW packages will be INSTALLED:\n",
+      "\n",
+      "  bottleneck         pkgs/main/win-64::bottleneck-1.3.5-py311h5bb9823_0 \n",
+      "  numexpr            pkgs/main/win-64::numexpr-2.8.7-py311h1fcbade_0 \n",
+      "  pandas             pkgs/main/win-64::pandas-2.1.4-py311hf62ec03_0 \n",
+      "  python-tzdata      pkgs/main/noarch::python-tzdata-2023.3-pyhd3eb1b0_0 \n",
+      "\n",
+      "\n",
+      "\n",
+      "Downloading and Extracting Packages\n",
+      "\n",
+      "numexpr-2.8.7        | 160 KB    |            |   0% \n",
+      "\n",
+      "pandas-2.1.4         | 13.6 MB   |            |   0% \u001b[A\n",
+      "\n",
+      "pandas-2.1.4         | 13.6 MB   |            |   0% \u001b[A\n",
+      "numexpr-2.8.7        | 160 KB    | #          |  10% \n",
+      "\n",
+      "pandas-2.1.4         | 13.6 MB   | ##         |  20% \u001b[A\n",
+      "numexpr-2.8.7        | 160 KB    | ########## | 100% \n",
+      "numexpr-2.8.7        | 160 KB    | ########## | 100% \n",
+      "\n",
+      "pandas-2.1.4         | 13.6 MB   | ###3       |  34% \u001b[A\n",
+      "\n",
+      "pandas-2.1.4         | 13.6 MB   | #####      |  51% \u001b[A\n",
+      "\n",
+      "pandas-2.1.4         | 13.6 MB   | ######1    |  62% \u001b[A\n",
+      "\n",
+      "pandas-2.1.4         | 13.6 MB   | ########   |  80% \u001b[A\n",
+      "\n",
+      "pandas-2.1.4         | 13.6 MB   | ########## | 100% \u001b[A\n",
+      "\n",
+      "pandas-2.1.4         | 13.6 MB   | ########## | 100% \u001b[A\n",
+      "                                                     \n",
+      "\n",
+      "\n",
+      "                                                     \u001b[A\n",
+      "\n",
+      "Preparing transaction: ...working... done\n",
+      "Verifying transaction: ...working... done\n",
+      "Executing transaction: ...working... done\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "==> WARNING: A newer version of conda exists. <==\n",
+      "  current version: 23.7.4\n",
+      "  latest version: 23.11.0\n",
+      "\n",
+      "Please update conda by running\n",
+      "\n",
+      "    $ conda update -n base -c defaults conda\n",
+      "\n",
+      "Or to minimize the number of packages updated during conda update use\n",
+      "\n",
+      "     conda install conda=23.11.0\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "conda install pandas"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "70227bdb-c253-41e2-aaa3-614e53ae7d42",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Collecting package metadata (current_repodata.json): ...working... done\n",
+      "Note: you may need to restart the kernel to use updated packages.\n",
+      "\n",
+      "Solving environment: ...working... done\n",
+      "\n",
+      "## Package Plan ##\n",
+      "\n",
+      "  environment location: C:\\Users\\369rental\\anaconda3\\envs\\jupyter\n",
+      "\n",
+      "  added / updated specs:\n",
+      "    - openpyxl\n",
+      "\n",
+      "\n",
+      "The following NEW packages will be INSTALLED:\n",
+      "\n",
+      "  et_xmlfile         pkgs/main/win-64::et_xmlfile-1.1.0-py311haa95532_0 \n",
+      "  openpyxl           pkgs/main/win-64::openpyxl-3.0.10-py311h2bbff1b_0 \n",
+      "\n",
+      "\n",
+      "\n",
+      "Downloading and Extracting Packages\n",
+      "\n",
+      "\n",
+      "Preparing transaction: ...working... done\n",
+      "Verifying transaction: ...working... done\n",
+      "Executing transaction: ...working... done\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "==> WARNING: A newer version of conda exists. <==\n",
+      "  current version: 23.7.4\n",
+      "  latest version: 23.11.0\n",
+      "\n",
+      "Please update conda by running\n",
+      "\n",
+      "    $ conda update -n base -c defaults conda\n",
+      "\n",
+      "Or to minimize the number of packages updated during conda update use\n",
+      "\n",
+      "     conda install conda=23.11.0\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "conda install openpyxl"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "326f09bb-af05-4e79-b018-82399be66b27",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/pre_urp/comp_libraries.txt b/pre_urp/comp_libraries.txt
new file mode 100644
index 0000000..90a6e9f
--- /dev/null
+++ b/pre_urp/comp_libraries.txt
@@ -0,0 +1,4 @@
+matplotlib
+scikit-learn
+pandas
+openpyxl
\ No newline at end of file
diff --git a/pre_urp/exp_results.xlsx b/pre_urp/exp_results.xlsx
new file mode 100644
index 0000000..c66bbc0
Binary files /dev/null and b/pre_urp/exp_results.xlsx differ
diff --git a/pre_urp/korean_words.xlsx b/pre_urp/korean_words.xlsx
new file mode 100644
index 0000000..41f7520
Binary files /dev/null and b/pre_urp/korean_words.xlsx differ
diff --git a/pre_urp/pre_urp.ipynb b/pre_urp/pre_urp.ipynb
new file mode 100644
index 0000000..8a59594
--- /dev/null
+++ b/pre_urp/pre_urp.ipynb
@@ -0,0 +1,717 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "3cb83b7c-012e-4b0d-9e50-6816e319e14f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "from sklearn import datasets, linear_model\n",
+    "from sklearn.metrics import mean_squared_error\n",
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "3c76de60-2ad3-4edc-b24b-0d7d195050af",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "np.random.seed(0)\n",
+    "\n",
+    "x = np.arange(0,10,0.4).reshape(-1,1)\n",
+    "y = 3*x+4+2*np.random.randn(len(x)).reshape(-1,1)\n",
+    "\n",
+    "plt.scatter(x,y)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "0e548e9c-2399-49f5-81eb-c64fa9227aa6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "regr1 = linear_model.LinearRegression(fit_intercept=False)\n",
+    "regr1.fit(x, y)\n",
+    "\n",
+    "y_pred = regr1.predict(x)\n",
+    "\n",
+    "plt.scatter(x,y, color='black')\n",
+    "plt.plot(x, y_pred, color='red', linewidth=2)\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "578b5b13-8d28-4df1-b904-d9cd167a65eb",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "x_2d = np.concatenate((x,x**2),axis=1)\n",
+    "y_2d = x**2-10*x+25+2.5*np.random.randn(len(x)).reshape(-1,1)\n",
+    "\n",
+    "plt.scatter(x,y_2d)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "7afbc5f8-1c63-4486-8a60-6a1c7ce1cfc5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "regr2 = linear_model.LinearRegression(fit_intercept=True)\n",
+    "regr2.fit(x_2d, y_2d)\n",
+    "\n",
+    "y_pred2 = regr2.predict(x_2d)\n",
+    "\n",
+    "plt.scatter(x,y_2d, color='black')\n",
+    "plt.plot(x, y_pred2, color='red', linewidth=2)\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "81095c69-e1f1-4955-8290-fd8a90cc2e31",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>1음절</th>\n",
+       "      <th>2음절</th>\n",
+       "      <th>3음절</th>\n",
+       "      <th>4음절</th>\n",
+       "      <th>5음절</th>\n",
+       "      <th>6음절이상</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>낮</td>\n",
+       "      <td>검도</td>\n",
+       "      <td>자긍심</td>\n",
+       "      <td>어림짐작</td>\n",
+       "      <td>우스갯소리</td>\n",
+       "      <td>어중이떠중이</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>논</td>\n",
+       "      <td>결혼</td>\n",
+       "      <td>전염병</td>\n",
+       "      <td>인문과학</td>\n",
+       "      <td>청딱따구리</td>\n",
+       "      <td>자이로스코프</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>닭</td>\n",
+       "      <td>녹차</td>\n",
+       "      <td>주인공</td>\n",
+       "      <td>작심삼일</td>\n",
+       "      <td>크리스마스</td>\n",
+       "      <td>진인사대천명</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>땀</td>\n",
+       "      <td>나비</td>\n",
+       "      <td>칸막이</td>\n",
+       "      <td>착시효과</td>\n",
+       "      <td>아르헨티나</td>\n",
+       "      <td>한해살이식물</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>못</td>\n",
+       "      <td>행운</td>\n",
+       "      <td>친근감</td>\n",
+       "      <td>중구난방</td>\n",
+       "      <td>헌법재판소</td>\n",
+       "      <td>목도리도마뱀</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  1음절 2음절  3음절   4음절    5음절   6음절이상\n",
+       "0   낮  검도  자긍심  어림짐작  우스갯소리  어중이떠중이\n",
+       "1   논  결혼  전염병  인문과학  청딱따구리  자이로스코프\n",
+       "2   닭  녹차  주인공  작심삼일  크리스마스  진인사대천명\n",
+       "3   땀  나비  칸막이  착시효과  아르헨티나  한해살이식물\n",
+       "4   못  행운  친근감  중구난방  헌법재판소  목도리도마뱀"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = pd.read_excel(io='korean_words.xlsx')\n",
+    "\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "a011453e-dcfc-4002-8e4b-bacea37789e2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "np.random.seed(0)\n",
+    "\n",
+    "# 1음절 2음절 3음절 섞기\n",
+    "###########################\n",
+    "sw_arr = df.iloc[:,:3].to_numpy().reshape(-1,)\n",
+    "np.random.shuffle(sw_arr)\n",
+    "###########################\n",
+    "\n",
+    "\n",
+    "# 10 10 20 20으로 분할\n",
+    "###########################\n",
+    "ace_list = sw_arr[:10]\n",
+    "ade_list = sw_arr[10:20]\n",
+    "acf_list = sw_arr[20:40]\n",
+    "adf_list = sw_arr[40:]\n",
+    "###########################"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "022e0cbf-d626-4f29-b4bd-8c24c6c2603e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['황무지', '농산물', '칼국수', '박사', '칸막이', '자긍심', '사랑', '행복', '어깨', '만두'],\n",
+       "      dtype=object)"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ace_list"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "747a75cf-1a0e-474b-9359-4f1e6fed368e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['결혼', '나비', '학', '대통령', '회', '역사', '자연', '녹차', '친근감', '경기도'],\n",
+       "      dtype=object)"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ade_list"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "34e02025-b587-44b8-aeb3-095dfa4c84b4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['무기질', '횡경막', '칼', '값', '솥', '캥거루', '코', '빛', '전염병', '보물', '등교',\n",
+       "       '죄', '폐막식', '저축', '주인공', '행운', '무용', '수리', '태양풍', '춤'],\n",
+       "      dtype=object)"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "acf_list"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "8bf77f29-b502-4fc8-8f31-e678d52eb170",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['칡', '겁', '단백질', '검도', '못', '윤곽', '턱', '닭', '하반신', '힘', '짐', '루비',\n",
+       "       '땀', '털', '졸업', '논', '낮', '오렌지', '동남아', '대장균'], dtype=object)"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "adf_list"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "5972408d-1ec6-410b-b9ae-7f913999faac",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "np.random.seed(0)\n",
+    "\n",
+    "# 4음절 5음절 6음절 섞기\n",
+    "###########################\n",
+    "lw_arr = df.iloc[:,3:].to_numpy().reshape(-1,)\n",
+    "np.random.shuffle(sw_arr)\n",
+    "###########################\n",
+    "\n",
+    "# 10 10 20 20으로 분할\n",
+    "###########################\n",
+    "bce_list = lw_arr[:10]\n",
+    "bde_list = lw_arr[10:20]\n",
+    "bcf_list = lw_arr[20:40]\n",
+    "bdf_list = lw_arr[40:]\n",
+    "###########################"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "e441719c-bdb6-49af-af92-384415e10c6f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['어림짐작', '우스갯소리', '어중이떠중이', '인문과학', '청딱따구리', '자이로스코프', '작심삼일',\n",
+       "       '크리스마스', '진인사대천명', '착시효과'], dtype=object)"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bce_list"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "55131228-6f1d-4668-a8e6-d7a2c6b6839b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['아르헨티나', '한해살이식물', '중구난방', '헌법재판소', '목도리도마뱀', '천재지변', '황소개구리',\n",
+       "       '패러글라이딩', '팔방미인', '게으름뱅이'], dtype=object)"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bde_list"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "52e971c2-1f3b-4c0e-8160-0d2d6d43fa92",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['자유의여신상', '포화상태', '이데올로기', '콘트라베이스', '하루아침', '순두부찌개', '베이킹파우더',\n",
+       "       '해수욕장', '대중목욕탕', '에스컬레이터', '허수아비', '동음이의어', '장대높이뛰기', '홍익인간',\n",
+       "       '프로그래밍', '히말라야산맥', '훈민정음', '정월대보름', '나무아미타불', '거두절미'], dtype=object)"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bcf_list"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "fb29e630-d65e-4c23-a4dc-f5eea6d8e7f1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['식기세척기', '스카치테이프', '대중교통', '지구온난화', '아르키메데스', '두드러기', '아이스크림',\n",
+       "       '조선왕조실록', '만류인력', '오케스트라', '증조할아버지', '물리화학', '김치볶음밥', '가시불가사리',\n",
+       "       '가시광선', '최소공배수', '금메달리스트', '고등학교', '장수풍댕이', '폭탄먼지벌레'], dtype=object)"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bdf_list"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "6b315de9-031d-416f-9863-1e30018d3d8c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>1</th>\n",
+       "      <th>2</th>\n",
+       "      <th>3</th>\n",
+       "      <th>4</th>\n",
+       "      <th>5</th>\n",
+       "      <th>6</th>\n",
+       "      <th>7</th>\n",
+       "      <th>8</th>\n",
+       "      <th>9</th>\n",
+       "      <th>10</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   1   2   3   4   5   6   7   8   9   10\n",
+       "0   1   1   1   0   1   0   0   1   1   1\n",
+       "1   1   1   0   0   1   0   1   1   0   1\n",
+       "2   1   1   1   1   0   1   0   1   1   1\n",
+       "3   0   0   1   1   0   0   1   0   1   1\n",
+       "4   1   1   0   0   1   1   1   0   1   1"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dummy = pd.read_excel(io='exp_results.xlsx', sheet_name = 'Dummy')\n",
+    "\n",
+    "dummy.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "b1d4ad0f-6743-4901-a42d-6a1701ac76a7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "a = temp.mean().to_numpy()\n",
+    "x = np.arange(10).reshape(-1,1) + 1\n",
+    "\n",
+    "plt.scatter(x,a)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "8488ea94-c144-41ed-b25f-c83734891282",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "x_2d = np.concatenate((x**2,x),axis=1)\n",
+    "\n",
+    "regr3 = linear_model.LinearRegression(fit_intercept=True)\n",
+    "regr3.fit(x_2d, a)\n",
+    "\n",
+    "a_pred = regr3.predict(x_2d)\n",
+    "\n",
+    "plt.scatter(x,a, color='black')\n",
+    "plt.plot(x, a_pred, color='red', linewidth=2)\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "f3ee49bf-99e9-46e5-8328-12e9c9687e56",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 0.01116427, -0.12344498])"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "regr3.coef_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "1d2846b1-0451-4e65-85b6-40dec497e860",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.9649122807017545"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "regr3.intercept_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "01c7001f-6760-465a-a003-1d51dcf249b6",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
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