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jenkins-pi
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93
.jenkins/Jenkinsfile
vendored
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93
.jenkins/Jenkinsfile
vendored
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pipeline {
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agent {
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kubernetes {
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yamlFile '.jenkins/podTemplate.yaml'
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}
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}
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environment {
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ACR_NAME = 'marcin00'
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CLIENT_ID = 'c302726f-fafb-4143-94c1-67a70975574a'
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DOCKER_REGISTRY_URL = 'marcin00.azurecr.io'
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DOCKER_IMAGE = "${DOCKER_REGISTRY_URL}/user-microservice:${GIT_COMMIT}"
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DEPLOY_REPO = 'ssh://git@srv22.mikr.us:20343/pikram/user-microservice-deploy.git'
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}
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stages {
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stage('Code Tests') {
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steps {
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container('python') {
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dir('api') {
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sh '''
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python3 -m venv env
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source env/bin/activate
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pip install -r requirements.txt pytest
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python3 -m pytest --junit-xml=pytest_junit.xml
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'''
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}
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}
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}
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post {
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always {
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junit testResults: '**/*pytest_junit.xml'
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}
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}
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}
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stage('Build & Push Docker') {
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steps {
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container('docker') {
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sh '''
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docker build -t ${DOCKER_IMAGE} --build-arg APP_VERSION=${GIT_COMMIT} --build-arg BUILD_DATE=$(date -u +"%Y-%m-%dT%H:%M:%SZ") .
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az login --identity --client-id ${CLIENT_ID}
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az acr login --name ${ACR_NAME}
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docker push ${DOCKER_IMAGE}
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'''
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}
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}
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}
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stage('Commit new version to GitOps repo') {
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steps {
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container('git') {
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withCredentials([string(credentialsId: 'gitea-known-host', variable: 'GITEA_KNOWN_HOST')]) {
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sh '''
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mkdir -p ~/.ssh
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echo "$GITEA_KNOWN_HOST" >> ~/.ssh/known_hosts
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chmod 644 ~/.ssh/known_hosts
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git config --global user.name "jenkins[bot]"
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git config --global user.email "jenkins@marcin00.pl"
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'''
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sshagent(['gitea-deploy-key']) {
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sh 'git clone ${DEPLOY_REPO} --branch jenkins-kubernetes'
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}
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}
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dir('user-microservice-deploy') {
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sh '''
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# Podmień tag obrazu w pliku deploy.yaml
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awk -v commit="$GIT_COMMIT" '
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$0 ~ /name:[[:space:]]*api/ { in_api_container = 1; print; next }
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in_api_container && $0 ~ /^[[:space:]]*image:[[:space:]]*/ {
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sub(/:[^:[:space:]]+$/, ":" commit)
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in_api_container = 0
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print
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next
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}
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{ print }
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' deploy.yaml > deploy.tmp && mv deploy.tmp deploy.yaml
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'''
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sshagent(['gitea-deploy-key']) {
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sh '''
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git add deploy.yaml
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git diff-index --quiet HEAD || git commit -m "JENKINS: Changed deployed version to $GIT_COMMIT"
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git push origin jenkins-kubernetes
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'''
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}
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}
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}
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}
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}
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}
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}
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60
.jenkins/podTemplate.yaml
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60
.jenkins/podTemplate.yaml
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apiVersion: v1
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kind: Pod
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metadata:
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annotations:
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io.kubernetes.cri-o.userns-mode: "auto:size=65536"
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labels:
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jenkins: "slave"
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jenkins/label: "kubernetes-agent"
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spec:
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runtimeClassName: sysbox-runc
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containers:
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- name: jnlp
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image: jenkins/inbound-agent:alpine
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tty: false
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workingDir: /home/jenkins/agent
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volumeMounts:
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- name: workspace-volume
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mountPath: /home/jenkins/agent
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env:
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- name: JENKINS_WEB_SOCKET
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value: "true"
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- name: REMOTING_OPTS
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value: "-noReconnectAfter 1d"
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- name: python
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image: python:3.11.7-alpine
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command:
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- cat
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tty: true
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workingDir: /home/jenkins/agent
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volumeMounts:
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- name: workspace-volume
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mountPath: /home/jenkins/agent
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- name: docker
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image: marcin00.azurecr.io/azure-cli-docker:slim-bookworm
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tty: true
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workingDir: /home/jenkins/agent
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volumeMounts:
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- name: workspace-volume
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mountPath: /home/jenkins/agent
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- name: git
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image: alpine/git:latest
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command:
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- cat
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tty: true
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workingDir: /home/jenkins/agent
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volumeMounts:
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- name: workspace-volume
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mountPath: /home/jenkins/agent
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nodeSelector:
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kubernetes.io/os: linux
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restartPolicy: Never
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volumes:
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- name: workspace-volume
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emptyDir: {}
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import matplotlib.pyplot as plt # type: ignore
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import os
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import statistics
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# Dane
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data = {
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'Jenkins + Jenkins': (165, 158, 217, 164, 136, 135, 147, 145, 138, 134, 137, 129, 136, 142, 125, 138, 133, 136, 128, 131),
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'Jenkins + ArgoCD': (181, 111, 115, 121, 128, 105, 108, 119, 112, 109, 110, 108, 111, 106, 113, 117, 113, 120, 113, 107),
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'Jenkins + FluxCD' : (167, 119, 113, 110, 102, 126, 111, 113, 118, 106, 111, 104, 101, 105, 104, 106, 102, 105, 107, 103),
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'Woodpecker + Woodpecker': (340, 348, 334, 363, 350, 339, 331, 354, 357, 351, 356, 347, 354, 341, 357, 352, 368, 336, 331, 340),
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'Woodpecker + ArgoCD': (355, 360, 354, 344, 318, 353, 328, 305, 331, 324, 328, 349, 337, 328, 349, 350, 344, 344, 344, 341),
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'Woodpecker + FluxCD' : (326, 344, 325, 337, 343, 358, 339, 341, 335, 354, 342, 355, 345, 334, 356, 346, 338, 342, 330, 333),
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'Argo Workflows + Argo-Workflows': (190, 190, 169, 211, 172, 198, 207, 192, 212, 181, 168, 199, 216, 213, 220, 209, 192, 210, 196, 165),
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'Argo Workflows + ArgoCD': (145, 159, 163, 148, 169, 185, 153, 148, 139, 176, 133, 140, 161, 135, 161, 130, 139, 164, 183, 183),
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'Argo Workflows + FluxCD': (161, 136, 181, 157, 141, 139, 157, 149, 151, 139, 139, 148, 152, 142, 136, 149, 160, 145, 173, 161)
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}
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# Wyliczenie średnich
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labels = list(data.keys())
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means = [statistics.mean(data[k]) for k in labels]
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# Grupy indeksów do porównań
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groupings = [
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[0, 1, 2],
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[3, 4, 5],
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[6, 7, 8],
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[0, 3, 6],
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[1, 4, 7],
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[2, 5, 8]
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]
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# Folder wyjściowy (opcjonalnie)
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output_folder = "plots"
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os.makedirs(output_folder, exist_ok=True)
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# Generowanie wykresów
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for i, group in enumerate(groupings):
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group_labels = [labels[j] for j in group]
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group_means = [means[j] for j in group]
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plt.figure()
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plt.bar(group_labels, group_means)
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plt.ylabel("Średni czas wdrożenia (sek)")
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plt.title(f"Porównanie średnich czasów wdrożenia")
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plt.xticks(rotation=45)
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plt.tight_layout()
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plt.savefig(f"{output_folder}/mean_times_{i}.png")
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plt.close()
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print("Wykresy zapisane!")
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1
version_marker.txt
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1752168984
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