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ROS-Kubernetes
Commit dbac465d authored by Larkin Heintzman's avatar Larkin Heintzman
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plotting tools update

parent b403c238
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Showing with 7480 additions and 46 deletions
clusterStartup.sh
View file @ dbac465d
......@@ -30,12 +30,12 @@ sudo swapoff -a # turn off swap memory
# --controlplane \
# --leaderElection | sudo tee /etc/kubernetes/manifests/vip.yaml
sudo docker run --network host --rm ghcr.io/kube-vip/kube-vip:main manifest pod \
--vip "$VIP" \
--interface "$INTERFACE" \
--arp \
--controlplane \
--leaderElection | sudo tee /etc/kubernetes/manifests/vip.yaml
# sudo docker run --network host --rm ghcr.io/kube-vip/kube-vip:main manifest pod \
# --vip "$VIP" \
# --interface "$INTERFACE" \
# --arp \
# --controlplane \
# --leaderElection | sudo tee /etc/kubernetes/manifests/vip.yaml
sudo sed -i 's/imagePullPolicy: Always/imagePullPolicy: IfNotPresent/g' /etc/kubernetes/manifests/vip.yaml
......
kube-flannel.yml 0 → 100644
View file @ dbac465d
---
kind: Namespace
apiVersion: v1
metadata:
name: kube-flannel
labels:
pod-security.kubernetes.io/enforce: privileged
---
kind: ClusterRole
apiVersion: rbac.authorization.k8s.io/v1
metadata:
name: flannel
rules:
- apiGroups:
- ""
resources:
- pods
verbs:
- get
- apiGroups:
- ""
resources:
- nodes
verbs:
- list
- watch
- apiGroups:
- ""
resources:
- nodes/status
verbs:
- patch
---
kind: ClusterRoleBinding
apiVersion: rbac.authorization.k8s.io/v1
metadata:
name: flannel
roleRef:
apiGroup: rbac.authorization.k8s.io
kind: ClusterRole
name: flannel
subjects:
- kind: ServiceAccount
name: flannel
namespace: kube-flannel
---
apiVersion: v1
kind: ServiceAccount
metadata:
name: flannel
namespace: kube-flannel
---
kind: ConfigMap
apiVersion: v1
metadata:
name: kube-flannel-cfg
namespace: kube-flannel
labels:
tier: node
app: flannel
data:
cni-conf.json: |
{
"name": "cbr0",
"cniVersion": "0.3.1",
"plugins": [
{
"type": "flannel",
"delegate": {
"hairpinMode": true,
"isDefaultGateway": true
}
},
{
"type": "portmap",
"capabilities": {
"portMappings": true
}
}
]
}
net-conf.json: |
{
"Network": "10.244.0.0/16",
"Backend": {
"Type": "vxlan"
}
}
---
apiVersion: apps/v1
kind: DaemonSet
metadata:
name: kube-flannel-ds
namespace: kube-flannel
labels:
tier: node
app: flannel
spec:
selector:
matchLabels:
app: flannel
template:
metadata:
labels:
tier: node
app: flannel
spec:
affinity:
nodeAffinity:
requiredDuringSchedulingIgnoredDuringExecution:
nodeSelectorTerms:
- matchExpressions:
- key: kubernetes.io/os
operator: In
values:
- linux
hostNetwork: true
priorityClassName: system-node-critical
tolerations:
- operator: Exists
effect: NoSchedule
serviceAccountName: flannel
initContainers:
- name: install-cni-plugin
#image: flannelcni/flannel-cni-plugin:v1.1.0 for ppc64le and mips64le (dockerhub limitations may apply)
image: docker.io/rancher/mirrored-flannelcni-flannel-cni-plugin:v1.1.0
command:
- cp
args:
- -f
- /flannel
- /opt/cni/bin/flannel
volumeMounts:
- name: cni-plugin
mountPath: /opt/cni/bin
- name: install-cni
#image: flannelcni/flannel:v0.19.2 for ppc64le and mips64le (dockerhub limitations may apply)
image: docker.io/rancher/mirrored-flannelcni-flannel:v0.19.2
command:
- cp
args:
- -f
- /etc/kube-flannel/cni-conf.json
- /etc/cni/net.d/10-flannel.conflist
volumeMounts:
- name: cni
mountPath: /etc/cni/net.d
- name: flannel-cfg
mountPath: /etc/kube-flannel/
containers:
- name: kube-flannel
#image: flannelcni/flannel:v0.19.2 for ppc64le and mips64le (dockerhub limitations may apply)
image: docker.io/rancher/mirrored-flannelcni-flannel:v0.19.2
command:
- /opt/bin/flanneld
args:
- --ip-masq
- --kube-subnet-mgr
resources:
requests:
cpu: "100m"
memory: "50Mi"
limits:
cpu: "100m"
memory: "50Mi"
securityContext:
privileged: false
capabilities:
add: ["NET_ADMIN", "NET_RAW"]
env:
- name: POD_NAME
valueFrom:
fieldRef:
fieldPath: metadata.name
- name: POD_NAMESPACE
valueFrom:
fieldRef:
fieldPath: metadata.namespace
- name: EVENT_QUEUE_DEPTH
value: "5000"
volumeMounts:
- name: run
mountPath: /run/flannel
- name: flannel-cfg
mountPath: /etc/kube-flannel/
- name: xtables-lock
mountPath: /run/xtables.lock
volumes:
- name: run
hostPath:
path: /run/flannel
- name: cni-plugin
hostPath:
path: /opt/cni/bin
- name: cni
hostPath:
path: /etc/cni/net.d
- name: flannel-cfg
configMap:
name: kube-flannel-cfg
- name: xtables-lock
hostPath:
path: /run/xtables.lock
type: FileOrCreate
kubeadm-config.yaml
View file @ dbac465d
......@@ -5,13 +5,13 @@
# bindPort: 6443
# ---
apiVersion: kubeadm.k8s.io/v1beta3
kubernetesVersion: v1.24.0
kubernetesVersion: v1.25.0
kind: ClusterConfiguration
# controlPlaneEndpoint: 192.168.111.25:6443
controlPlaneEndpoint: 192.168.111.200:6443
# controlPlaneEndpoint: 192.168.111.200:6443
networking:
podSubnet: "10.244.0.0/16"
# podSubnet: "192.168.222.0/16"
# podSubnet: "192.168.1.0/16"
---
kind: KubeletConfiguration
apiVersion: kubelet.config.k8s.io/v1beta1
......
kubernetes/ping-deployment.yaml
View file @ dbac465d
......@@ -26,7 +26,7 @@ spec:
effect: "NoSchedule"
operator: "Exists"
- key: "node.kubernetes.io/disk-pressure"
effect: "NoSchedule"
effect: "PreferNoSchedule"
operator: "Exists"
volumes:
- name: ping-storage
......@@ -40,21 +40,22 @@ spec:
volumeMounts:
- name: ping-storage
mountPath: /tmp/pingStats
image: doorbell
image: localhost:5000/doorbell:latest
imagePullPolicy: IfNotPresent
command: ["/bin/bash"]
args: ["-c", "/ping.sh"]
env:
- name: endpoints
# value: "192.168.1.5"
value: "192.168.111.200"
- name: interface
value: "eth0" # not strictly necessary
- name: filename
value: "/tmp/pingStats/pingStatsKubernetes.json"
- name: downtime # time to wait between pings
value: "1"
value: "3"
- name: gpstopic
value: "/rossettiModem/dji_sdk/gps_position"
value: "/marioModem/dji_sdk/gps_position"
- name: ROS_IP
value: "192.168.111.202"
- name: ROS_MASTER_URI
......@@ -63,6 +64,6 @@ spec:
- containerPort: 11311
name: pingport
nodeSelector:
kubernetes.io/hostname: rossetti
kubernetes.io/hostname: mario
# kubernetes.io/hostname: neruda
# kubernetes.io/hostname: sentinel
plottingTools/mapPlotter.py
View file @ dbac465d
......@@ -29,11 +29,11 @@ font = {'family' : 'DejaVu Sans',
'size' : 8}
matplotlib.rc('font', **font)
filenames = ["/home/llh/ros-kubernetes/plottingTools/pingStats_may_29th_2022/pingStats.json",
"/home/llh/ros-kubernetes/plottingTools/pingStats_may_29th_2022/pingStatsDocker.json"]
# filenames = ["/home/llh/ros-kubernetes/plottingTools/pingStats_june_3rd_2022/pingStats.json",
# "/home/llh/ros-kubernetes/plottingTools/pingStats_june_3rd_2022/pingStatsDocker.json",
# "/home/llh/ros-kubernetes/plottingTools/pingStats_june_3rd_2022/pingStatsKubernetes.json"]
filenames = ["/home/larkin/ros-kubernetes/plottingTools/pingStats_may_29th_2022/pingStats.json",
"/home/larkin/ros-kubernetes/plottingTools/pingStats_may_29th_2022/pingStatsDocker.json"]
# filenames = ["/home/larkin/ros-kubernetes/plottingTools/pingStats_june_3rd_2022/pingStats.json",
# "/home/larkin/ros-kubernetes/plottingTools/pingStats_june_3rd_2022/pingStatsDocker.json",
# "/home/larkin/ros-kubernetes/plottingTools/pingStats_june_3rd_2022/pingStatsKubernetes.json"]
plotColors = ["orange","slateblue","mediumspringgreen"]
lats = []
longs = []
......
plottingTools/marioPingStats/pingStats.json 0 → 100644
View file @ dbac465d
{
"192.168.111.200": [
{
"lat": 37.19694520511248,
"long": -80.57843266778926,
"alt": 464.33538818359375,
"min": 13.244,
"avg": 15.03,
"max": 20.598,
"std": 2.793,
"timedate": "16:20_2022-09-01",
"packetLoss": 0
},
{
"lat": 37.19694313413883,
"long": -80.57843428940932,
"alt": 464.333740234375,
"min": 14.285,
"avg": 16.024,
"max": 21.969,
"std": 2.986,
"timedate": "16:20_2022-09-01",
"packetLoss": 0
},
{
"lat": 37.19693730006559,
"long": -80.57843532351892,
"alt": 465.4197082519531,
"min": 13.633,
"avg": 14.344,
"max": 14.805,
"std": 0.464,
"timedate": "16:20_2022-09-01",
"packetLoss": 20
},
{
"lat": 37.19693473341801,
"long": -80.57843493571495,
"alt": 465.4223937988281,
"min": 12.793,
"avg": 15.495,
"max": 24.46,
"std": 4.507,
"timedate": "16:21_2022-09-01",
"packetLoss": 0
},
{
"lat": 37.19693447561253,
"long": -80.57843431823561,
"alt": 497.8923034667969,
"min": 13.057,
"avg": 20.581,
"max": 40.676,
"std": 10.422,
"timedate": "16:21_2022-09-01",
"packetLoss": 0
},
{
"lat": 37.19718016268259,
"long": -80.57852927936935,
"alt": 504.3514709472656,
"min": 13.232,
"avg": 15.64,
"max": 19.376,
"std": 2.545,
"timedate": "16:21_2022-09-01",
"packetLoss": 0
},
{
"lat": 37.19760167610952,
"long": -80.57869346529134,
"alt": 504.3047180175781,
"min": 13.556,
"avg": 13.937,
"max": 14.546,
"std": 0.338,
"timedate": "16:21_2022-09-01",
"packetLoss": 0
},
{
"lat": 37.19794470150608,
"long": -80.57864254970428,
"alt": 504.31341552734375,
"min": 13.58,
"avg": 16.586,
"max": 23.045,
"std": 3.66,
"timedate": "16:21_2022-09-01",
"packetLoss": 0
},
{
"lat": 37.19840836656752,
"long": -80.57848291293784,
"alt": 504.32763671875,
"min": 14.375,
"avg": 15.788,
"max": 19.698,
"std": 2.258,
"timedate": "16:22_2022-09-01",
"packetLoss": 20
},
{
"lat": 37.19864228449665,
"long": -80.57811203523464,
"alt": 504.3124694824219,
"min": 13.608,
"avg": 15.854,
"max": 22.492,
"std": 3.361,
"timedate": "16:22_2022-09-01",
"packetLoss": 0
},
{
"lat": 37.19876816035155,
"long": -80.57758234233314,
"alt": 504.3837890625,
"min": 13.065,
"avg": 16.018,
"max": 21.912,
"std": 3.545,
"timedate": "16:22_2022-09-01",
"packetLoss": 0
},
{
"lat": 37.1989041037944,
"long": -80.57700468190154,
"alt": 504.38427734375,
"min": 13.525,
"avg": 15.099,
"max": 18.485,
"std": 1.777,
"timedate": "16:22_2022-09-01",
"packetLoss": 0
},
{
"lat": 37.19893799356388,
"long": -80.5764887615473,
"alt": 504.2754821777344,
"min": 12.982,
"avg": 14.083,
"max": 17.718,
"std": 1.822,
"timedate": "16:23_2022-09-01",
"packetLoss": 0
},
{
"lat": 37.198754895802345,
"long": -80.57597242427916,
"alt": 504.33355712890625,
"min": 13.193,
"avg": 13.664,
"max": 14.158,
"std": 0.326,
"timedate": "16:23_2022-09-01",
"packetLoss": 0
},
{
"lat": 37.19857393226649,
"long": -80.57546482344758,
"alt": 504.348388671875,
"min": 13.619,
"avg": 14.208,
"max": 15.633,
"std": 0.778,
"timedate": "16:23_2022-09-01",
"packetLoss": 0
},
{
"lat": 37.19829567574165,
"long": -80.57499154342148,
"alt": 504.3603515625,
"min": 13.681,
"avg": 14.027,
"max": 14.436,
"std": 0.296,
"timedate": "16:23_2022-09-01",
"packetLoss": 0
},
{
"lat": 37.1979637254839,
"long": -80.57461240299864,
"alt": 504.3489074707031,
"min": 13.497,
"avg": 15.164,
"max": 21.27,
"std": 3.057,
"timedate": "16:23_2022-09-01",
"packetLoss": 0
},
{
"lat": 37.19762851411724,
"long": -80.57423241807999,
"alt": 504.3575744628906,
"min": 13.388,
"avg": 13.858,
"max": 14.516,
"std": 0.377,
"timedate": "16:24_2022-09-01",
"packetLoss": 0
},
{
"lat": 37.19729932007261,
"long": -80.57385664979019,
"alt": 504.3293762207031,
"min": 13.455,
"avg": 15.119,
"max": 21.177,
"std": 3.031,
"timedate": "16:24_2022-09-01",
"packetLoss": 0
},
{
"lat": 37.197007156404794,
"long": -80.57381724858871,
"alt": 504.4012145996094,
"min": 13.365,
"avg": 14.895,
"max": 18.61,
"std": 1.928,
"timedate": "16:24_2022-09-01",
"packetLoss": 0
},
{
"lat": 37.19672359899185,
"long": -80.57426967741434,
"alt": 504.3739929199219,
"min": 13.336,
"avg": 13.928,
"max": 14.478,
"std": 0.405,
"timedate": "16:24_2022-09-01",
"packetLoss": 0
},
{
"lat": 37.19644313528704,
"long": -80.5747092491869,
"alt": 504.142822265625,
"min": 14.443,
"avg": 16.12,
"max": 21.354,
"std": 2.634,
"timedate": "16:24_2022-09-01",
"packetLoss": 0
},
{
"lat": 37.19614353994472,
"long": -80.57513844955976,
"alt": 504.27679443359375,
"min": 13.801,
"avg": 14.424,
"max": 14.691,
"std": 0.32,
"timedate": "16:25_2022-09-01",
"packetLoss": 0
},
{
"lat": 37.195843283792854,
"long": -80.5755706232186,
"alt": 504.1975402832031,
"min": 13.457,
"avg": 13.591,
"max": 13.86,
"std": 0.158,
"timedate": "16:25_2022-09-01",
"packetLoss": 0
},
{
"lat": 37.19555233937037,
"long": -80.57599352944783,
"alt": 504.2591247558594,
"min": 13.793,
"avg": 15.854,
"max": 23.773,
"std": 3.96,
"timedate": "16:25_2022-09-01",
"packetLoss": 0
},
{
"lat": 37.19549444565894,
"long": -80.57651763786694,
"alt": 504.4447021484375,
"min": 13.486,
"avg": 13.825,
"max": 14.273,
"std": 0.282,
"timedate": "16:25_2022-09-01",
"packetLoss": 0
},
{
"lat": 37.19548535305298,
"long": -80.5770761770412,
"alt": 504.3387451171875,
"min": 13.532,
"avg": 15.447,
"max": 22.43,
"std": 3.493,
"timedate": "16:25_2022-09-01",
"packetLoss": 0
},
{
"lat": 37.19547622178125,
"long": -80.57764098592584,
"alt": 504.26202392578125,
"min": 13.642,
"avg": 16.885,
"max": 23.874,
"std": 3.969,
"timedate": "16:26_2022-09-01",
"packetLoss": 0
},
{
"lat": 37.19567974934979,
"long": -80.57808608230211,
"alt": 504.42352294921875,
"min": 13.128,
"avg": 14.533,
"max": 18.654,
"std": 2.088,
"timedate": "16:26_2022-09-01",
"packetLoss": 0
},
{
"lat": 37.19600300626999,
"long": -80.57854040967322,
"alt": 504.3788757324219,
"min": 13.148,
"avg": 14.615,
"max": 18.661,
"std": 2.048,
"timedate": "16:26_2022-09-01",
"packetLoss": 0
},
{
"lat": 37.196303379878266,
"long": -80.57896326918674,
"alt": 504.36553955078125,
"min": 13.598,
"avg": 15.115,
"max": 20.136,
"std": 2.518,
"timedate": "16:26_2022-09-01",
"packetLoss": 0
},
{
"lat": 37.19651091404436,
"long": -80.57925498459119,
"alt": 504.395751953125,
"min": 13.378,
"avg": 18.195,
"max": 29.433,
"std": 6.195,
"timedate": "16:26_2022-09-01",
"packetLoss": 0
},
{
"lat": 37.19653184614167,
"long": -80.57921693220851,
"alt": 504.23358154296875,
"min": 13.39,
"avg": 22.604,
"max": 51.731,
"std": 14.816,
"timedate": "16:27_2022-09-01",
"packetLoss": 0
},
{
"lat": 37.19693544005523,
"long": -80.57844374387378,
"alt": 504.4382019042969,
"min": 13.264,
"avg": 16.412,
"max": 23.343,
"std": 3.74,
"timedate": "16:27_2022-09-01",
"packetLoss": 0
},
{
"lat": 37.1969669531734,
"long": -80.5783851422811,
"alt": 484.4738464355469,
"min": 14.119,
"avg": 18.805,
"max": 37,
"std": 9.097,
"timedate": "16:27_2022-09-01",
"packetLoss": 0
},
{
"lat": 37.196945501241764,
"long": -80.57841736155262,
"alt": 464.57684326171875,
"min": 13.623,
"avg": 15.812,
"max": 23.296,
"std": 3.745,
"timedate": "16:27_2022-09-01",
"packetLoss": 0
},
{
"lat": 37.196946870277074,
"long": -80.57841604749434,
"alt": 464.1172180175781,
"min": 14.341,
"avg": 79.161,
"max": 328.125,
"std": 124.538,
"timedate": "16:28_2022-09-01",
"packetLoss": 0
}
]
}
plottingTools/marioPingStats/pingStatsBM10samples_cursed.json 0 → 100644
View file @ dbac465d
{
"192.168.111.200": [
{
"lat": 37.196928346889614,
"long": -80.57839536075299,
"alt": 479.7087707519531,
"min": 12.388,
"avg": 16.564,
"max": 29.63,
"std": 5.551,
"timedate": "14:30_2022-08-31",
"packetLoss": 30
},
{
"lat": 37.1969283192476,
"long": -80.578395320561,
"alt": 479.7126770019531,
"min": 12.226,
"avg": 15.09,
"max": 23.526,
"std": 3.117,
"timedate": "14:30_2022-08-31",
"packetLoss": 0
},
{
"lat": 37.19692664367643,
"long": -80.57839626083253,
"alt": 480.8296203613281,
"min": 12.907,
"avg": 16.996,
"max": 34.671,
"std": 6.386,
"timedate": "14:30_2022-08-31",
"packetLoss": 0
},
{
"lat": 37.1969267618176,
"long": -80.57839426613229,
"alt": 484.9986267089844,
"min": 12.745,
"avg": 21.995,
"max": 53.693,
"std": 12.368,
"timedate": "14:31_2022-08-31",
"packetLoss": 0
},
{
"lat": 37.1969639510882,
"long": -80.57841091673272,
"alt": 519.6161499023438,
"min": 12.29,
"avg": 169.24,
"max": 965.396,
"std": 302.283,
"timedate": "14:31_2022-08-31",
"packetLoss": 0
},
{
"lat": 37.19746468750461,
"long": -80.57862886087878,
"alt": 519.7149658203125,
"min": 12.726,
"avg": 17.813,
"max": 41.109,
"std": 8.674,
"timedate": "14:31_2022-08-31",
"packetLoss": 10
},
{
"lat": 37.197834568001035,
"long": -80.57867909730071,
"alt": 519.7515869140625,
"min": 13.783,
"avg": 24.735,
"max": 77.326,
"std": 19.139,
"timedate": "14:31_2022-08-31",
"packetLoss": 0
},
{
"lat": 37.19830019709621,
"long": -80.57852044583007,
"alt": 519.6963500976562,
"min": 12.285,
"avg": 19.228,
"max": 38.662,
"std": 8.191,
"timedate": "14:32_2022-08-31",
"packetLoss": 0
},
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plottingTools/marioPingStats/pingStatsBM3samples.json 0 → 100644
View file @ dbac465d
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plottingTools/marioPingStats/pingStatsDocker.json 0 → 100644
View file @ dbac465d
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plottingTools/marioPingStats/pingStatsDocker3samples.json 0 → 100644
View file @ dbac465d
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"packetLoss": 0
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{
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},
{
"lat": 37.19695100823632,
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},
{
"lat": 37.19695222813792,
"long": -80.57841288801434,
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"timedate": "20:30_2022-09-01",
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},
{
"lat": 37.19695205649911,
"long": -80.57841281371176,
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{
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"packetLoss": 0
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"long": -80.57550018008534,
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"packetLoss": 0
},
{
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"long": -80.57481607274563,
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"packetLoss": 0
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{
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"long": -80.57450969936812,
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"std": 0.338,
"timedate": "20:34_2022-09-01",
"packetLoss": 0
}
]
}
plottingTools/marioPingStats/pingStatsKubernetes.json 0 → 100644
View file @ dbac465d
{
"192.168.1.230": [
{
"min": 3.648,
"avg": 78.206,
"max": 156.886,
"std": 62.627,
"timedate": "19:21_2022-09-08",
"packetLoss": 0
},
{
"min": 3.413,
"avg": 27.454,
"max": 57.947,
"std": 22.725,
"timedate": "19:21_2022-09-08",
"packetLoss": 0
},
{
"min": 25.224,
"avg": 105.895,
"max": 186.485,
"std": 65.834,
"timedate": "19:21_2022-09-08",
"packetLoss": 0
}
]
}
plottingTools/packetFormatter.py 0 → 100755
View file @ dbac465d
import numpy as np
import json
if __name__ == "__main__":
filenames = ["/home/larkin/ros-kubernetes/plottingTools/pingStats_sept_20th_2022/pingStats.json",
"/home/larkin/ros-kubernetes/plottingTools/pingStats_sept_20th_2022/pingStatsDocker.json",
"/home/larkin/ros-kubernetes/plottingTools/pingStats_sept_20th_2022/pingStatsKubernetes.json"]
# reference point of the base station
anchorPoint = [37.196927, -80.578399, 452.7027587890625]
endpoint = "192.168.111.200"
packetCount = 10
'''
was supposed to format the packetLoss field but it's pretty messed up so
instead it just prints it out for now.
The issue with that field is that the decimal point got removed inside the
bash script so there's not a good way to convert it into a percentage
because sometimes it's 100 meaning 100% and sometimes it's 3333 meaning 33%
'''
for (i, fn) in enumerate(filenames):
with open(fn) as file:
data = json.load(file)
print([pt["packetLoss"] for pt in data[endpoint]])
plottingTools/pingStats_sept_20th_2022/pingStats.json 0 → 100644
View file @ dbac465d
{
"192.168.111.200": [
{
"lat": 37.19697569534258,
"long": -80.57842265167038,
"alt": 472.5221862792969,
"min": 13.183,
"avg": 14.441,
"max": 15.796,
"std": 0.834,
"timedate": "18:36_2022-09-20",
"packetLoss": 0
},
{
"lat": 37.196974076118835,
"long": -80.57842059293506,
"alt": 472.5301208496094,
"min": 14.255,
"avg": 16.226,
"max": 22.727,
"std": 3.255,
"timedate": "18:36_2022-09-20",
"packetLoss": 0
},
{
"lat": 37.1969712138077,
"long": -80.57841856553812,
"alt": 472.5417175292969,
"min": 14.316,
"avg": 21.906,
"max": 42.563,
"std": 10.671,
"timedate": "18:36_2022-09-20",
"packetLoss": 0
},
{
"lat": 37.19696453330185,
"long": -80.5784207441568,
"alt": 473.6070861816406,
"min": 12.302,
"avg": 32.615,
"max": 91.261,
"std": 29.757,
"timedate": "18:36_2022-09-20",
"packetLoss": 0
},
{
"lat": 37.19696411508323,
"long": -80.57842131856606,
"alt": 496.1601867675781,
"min": 14.296,
"avg": 32.654,
"max": 79.336,
"std": 24.247,
"timedate": "18:36_2022-09-20",
"packetLoss": 0
},
{
"lat": 37.19711041146627,
"long": -80.57848747557152,
"alt": 512.3334350585938,
"min": 14.106,
"avg": 15.908,
"max": 18.959,
"std": 2.147,
"timedate": "18:37_2022-09-20",
"packetLoss": 0
},
{
"lat": 37.19753808545317,
"long": -80.5786637829069,
"alt": 512.5341796875,
"min": 14.572,
"avg": 16.623,
"max": 22.518,
"std": 3.04,
"timedate": "18:37_2022-09-20",
"packetLoss": 0
},
{
"lat": 37.197870983960364,
"long": -80.57866675941891,
"alt": 512.57421875,
"min": 14.013,
"avg": 14.346,
"max": 14.711,
"std": 0.273,
"timedate": "18:37_2022-09-20",
"packetLoss": 0
},
{
"lat": 37.1982991271606,
"long": -80.57852002391532,
"alt": 512.400146484375,
"min": 14.045,
"avg": 17.396,
"max": 27.597,
"std": 5.135,
"timedate": "18:37_2022-09-20",
"packetLoss": 0
},
{
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"long": -80.57824568054126,
"alt": 512.788818359375,
"min": 12.564,
"avg": 19.617,
"max": 42.238,
"std": 11.353,
"timedate": "18:38_2022-09-20",
"packetLoss": 0
},
{
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"long": -80.57770797140991,
"alt": 512.4409790039062,
"min": 13.885,
"avg": 14.339,
"max": 14.695,
"std": 0.287,
"timedate": "18:38_2022-09-20",
"packetLoss": 0
},
{
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"alt": 512.4757080078125,
"min": 14.228,
"avg": 15.337,
"max": 18.807,
"std": 1.742,
"timedate": "18:38_2022-09-20",
"packetLoss": 0
},
{
"lat": 37.19899075332908,
"long": -80.57663564788946,
"alt": 512.6329956054688,
"min": 13.999,
"avg": 16.153,
"max": 23.158,
"std": 3.508,
"timedate": "18:38_2022-09-20",
"packetLoss": 0
},
{
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"long": -80.57612219395419,
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"max": 15.244,
"std": 0.476,
"timedate": "18:38_2022-09-20",
"packetLoss": 0
},
{
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"long": -80.57559634685445,
"alt": 512.446044921875,
"min": 14.208,
"avg": 17.653,
"max": 27.545,
"std": 5.077,
"timedate": "18:39_2022-09-20",
"packetLoss": 0
},
{
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"long": -80.57511307314647,
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"timedate": "18:39_2022-09-20",
"packetLoss": 0
},
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"timedate": "18:39_2022-09-20",
"packetLoss": 0
},
{
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"timedate": "18:39_2022-09-20",
"packetLoss": 0
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"timedate": "18:40_2022-09-20",
"packetLoss": 0
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plottingTools/pingStats_sept_20th_2022/pingStatsBM10samples_cursed.json 0 → 100644
View file @ dbac465d
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plottingTools/pingStats_sept_20th_2022/pingStatsBM3samples.json 0 → 100644
View file @ dbac465d
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plottingTools/pingStats_sept_20th_2022/pingStatsBM3samples_2.json 0 → 100644
View file @ dbac465d
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plottingTools/pingStats_sept_20th_2022/pingStatsDocker.json 0 → 100644
View file @ dbac465d
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plottingTools/pingStats_sept_20th_2022/pingStatsDocker3samples.json 0 → 100644
View file @ dbac465d
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plottingTools/pingStats_sept_20th_2022/pingStatsDocker3samples_2.json 0 → 100644
View file @ dbac465d
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plottingTools/pingStats_sept_20th_2022/pingStatsKubernetes.json 0 → 100644
View file @ dbac465d
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}
plottingTools/scatterPlotter.py 100644 → 100755
View file @ dbac465d
......@@ -28,22 +28,31 @@ def calculateDistance(pt1, pt2):
distance_3d = np.sqrt(distance_2d**2 + (pt2[2] - pt1[2])**2)
return distance_3d
def movingAverage(a, n=3):
ret = np.cumsum(a, dtype=float)
ret[n:] = ret[n:] - ret[:-n]
return ret[n - 1:] / n
def rejectOutliers(data, m = 2.):
d = np.abs(data - np.median(data))
mdev = np.median(d)
s = d/mdev if mdev else 0.
# return data[s<m]
return s<m
filenames = ["/home/llh/ros-kubernetes/plottingTools/pingStats_june_3rd_2022/pingStats.json",
"/home/llh/ros-kubernetes/plottingTools/pingStats_june_3rd_2022/pingStatsDocker.json",
"/home/llh/ros-kubernetes/plottingTools/pingStats_june_3rd_2022/pingStatsKubernetes.json"]
# filenames = ["/home/llh/ros-kubernetes/plottingTools/pingStats_may_29th_2022/pingStats.json",
# "/home/llh/ros-kubernetes/plottingTools/pingStats_may_29th_2022/pingStatsDocker.json"]
# "/home/llh/ros-kubernetes/plottingTools/pingStats_may_29th_2022/pingStatsKubernetes.json"]
font = {'family' : 'DejaVu Sans',
'weight' : 'medium',
'size' : 8}
matplotlib.rc('font', **font)
filenames = ["/home/larkin/ros-kubernetes/plottingTools/pingStats_sept_20th_2022/pingStats.json",
"/home/larkin/ros-kubernetes/plottingTools/pingStats_sept_20th_2022/pingStatsDocker.json",
"/home/larkin/ros-kubernetes/plottingTools/pingStats_sept_20th_2022/pingStatsKubernetes.json"]
plotColors = ["orange","slateblue","mediumspringgreen"]
# reference point of the base station
anchorPoint = [37.196927, -80.578399, 452.7027587890625]
fig, ax1 = plt.subplots()
ax2 = ax1.twinx()
# potentially the least python-ish way to handle this
for (i, fn) in enumerate(filenames):
......@@ -52,32 +61,58 @@ for (i, fn) in enumerate(filenames):
# need to iterate over endpoints as well
endpoint = "192.168.111.200"
# ignore time stamps for now
timelessData = []
for minuteData in data[endpoint].values():
for pt in minuteData:
if ("lat" in pt):
timelessData.append(pt)
dists = np.array([calculateDistance([dataPoint["lat"], dataPoint["long"], dataPoint["alt"]],anchorPoint) for dataPoint in timelessData])
avgLatencies = np.array([dataPoint["avg"] for dataPoint in timelessData])
endpointData = data[endpoint]
dists = []
for dataPoint in endpointData:
if ("lat" in dataPoint.keys()):
dataPointLocation = [dataPoint["lat"], dataPoint["long"], dataPoint["alt"]]
dists.append(calculateDistance(dataPointLocation,anchorPoint))
else:
dists.append(0.0) # might cause problems later
dists = np.array(dists)
avgLatencies = np.array([dataPoint["avg"] for dataPoint in endpointData])
packetLoss = np.array([dataPoint["packetLoss"] != 0 for dataPoint in endpointData])
maskArray = rejectOutliers(avgLatencies, m=2.0)
filteredLatencies = avgLatencies[maskArray]
filteredDistances = dists[maskArray]
maskArray = np.logical_and(maskArray, dists != 0)
# outlier rejection turned off for now
avgLatencies = avgLatencies[maskArray]
dists = dists[maskArray]
packetLoss = packetLoss[maskArray]
print("removed {outliers} outliers from the dataset".format(outliers=np.size(maskArray) - np.count_nonzero(maskArray)))
# calculate best fit line
func = np.poly1d(np.polyfit(filteredDistances, filteredLatencies, 1))
evenDists = np.linspace(np.min(filteredDistances),np.max(filteredDistances),10)
plt.scatter(filteredDistances, filteredLatencies, s=12, c=plotColors[i], label=[f.split('/') for f in filenames][i][-1])
plt.plot(evenDists, func(evenDists), c=plotColors[i], linewidth=1.5)
func = np.poly1d(np.polyfit(dists, avgLatencies, 1))
evenDists = np.linspace(np.min(dists),np.max(dists),10)
ax1.scatter(dists, avgLatencies, s=12, c=plotColors[i], label=filenames[i].split('/')[-1].split('.')[0])
ax1.plot(evenDists, func(evenDists), c=plotColors[i], linewidth=1.5)
# sort so distance always increases
sorter = np.argsort(dists)
# add moving average of packet losses
avgPacketLosses = movingAverage(packetLoss[sorter], n=5)
avgDists = movingAverage(dists[sorter], n=5)
ax2.plot(avgDists, avgPacketLosses, linewidth=1.0, dashes=[6,2], c=plotColors[i], label=filenames[i].split('/')[-1].split('.')[0] + " packets")
ax1.ticklabel_format(style='plain') # to prevent scientific notation
ax1.ticklabel_format(useOffset=False)
ax1.set_xlabel("Distance")
ax1.set_ylabel("Latency")
ax1.legend(loc = "upper left")
ax2.ticklabel_format(style='plain') # to prevent scientific notation
ax2.ticklabel_format(useOffset=False)
ax2.set_xlabel("Distance")
ax2.set_ylabel("Packet Loss \%")
ax2.legend(loc = "upper right")
plt.ticklabel_format(style='plain') # to prevent scientific notation
plt.ticklabel_format(useOffset=False)
plt.title('Network Latency vs Distance')
plt.xlabel("Distance")
plt.ylabel("Latency")
plt.legend()
# plt.xlabel("Distance")
# plt.ylabel("Latency")
# plt.legend()
# plt.show()
plt.savefig("scatterPlot.jpeg", dpi=400)
plottingTools/scatterPlotterOld.py 0 → 100755
View file @ dbac465d
# api key from gurgle AIzaSyDDFrIPAMv3HS6PXTDcWfiUys8si_-yZ-c
import numpy as np
from gmplot import GoogleMapPlotter
from random import random
import json
from pprint import PrettyPrinter
import matplotlib.pyplot as plt
import matplotlib
from matplotlib.ticker import FormatStrFormatter
import shutil
import requests
from geopy import distance
def getMapImage(bbox=[38.865,-77.072,38.876,-77.001], apiKey="GKvj5HV6DLjqJT8awam4KQL3d26GP1M1", mapType="sat", mapSize=[600,400]):
req = "https://www.mapquestapi.com/staticmap/v5/map?key={}&boundingBox={}&type={}&size={}".format(apiKey,','.join(map(str,bbox)),mapType,','.join(map(str,mapSize)))
response = requests.get(req, stream=True)
with open('mapImg.jpeg', 'wb') as out_file:
shutil.copyfileobj(response.raw, out_file)
del response
def inflateBounds(bbox=[38.865,-77.072,38.876,-77.001], amt=0.001):
return [bbox[0]-amt, bbox[1]-amt, bbox[2]+amt, bbox[3]+amt]
def calculateDistance(pt1, pt2):
# 2D geodesic distance in meters
distance_2d = distance.distance(pt1[:2], pt2[:2]).m
# 3D euclidean distance
distance_3d = np.sqrt(distance_2d**2 + (pt2[2] - pt1[2])**2)
return distance_3d
def rejectOutliers(data, m = 2.):
d = np.abs(data - np.median(data))
mdev = np.median(d)
s = d/mdev if mdev else 0.
# return data[s<m]
return s<m
# filenames = ["/home/larkin/ros-kubernetes/plottingTools/pingStats_june_3rd_2022/pingStats.json",
# "/home/larkin/ros-kubernetes/plottingTools/pingStats_june_3rd_2022/pingStatsDocker.json",
# "/home/larkin/ros-kubernetes/plottingTools/pingStats_june_3rd_2022/pingStatsKubernetes.json"]
filenames = ["/home/larkin/ros-kubernetes/plottingTools/marioPingStats/pingStats.json",
"/home/larkin/ros-kubernetes/plottingTools/marioPingStats/pingStatsDocker.json"]
# "/home/larkin/ros-kubernetes/plottingTools/marioPingStats/pingStatsKubernetes.json"]
plotColors = ["orange","slateblue","mediumspringgreen"]
# reference point of the base station
anchorPoint = [37.196927, -80.578399, 452.7027587890625]
# potentially the least python-ish way to handle this
for (i, fn) in enumerate(filenames):
with open(fn) as file:
data = json.load(file)
# need to iterate over endpoints as well
endpoint = "192.168.111.200"
# ignore time stamps for now
timelessData = []
for minuteData in data[endpoint].values():
for pt in minuteData:
if ("lat" in pt):
timelessData.append(pt)
dists = np.array([calculateDistance([dataPoint["lat"], dataPoint["long"], dataPoint["alt"]],anchorPoint) for dataPoint in timelessData])
avgLatencies = np.array([dataPoint["avg"] for dataPoint in timelessData])
maskArray = rejectOutliers(avgLatencies, m=2.0)
filteredLatencies = avgLatencies[maskArray]
filteredDistances = dists[maskArray]
print("removed {outliers} outliers from the dataset".format(outliers=np.size(maskArray) - np.count_nonzero(maskArray)))
# calculate best fit line
func = np.poly1d(np.polyfit(filteredDistances, filteredLatencies, 1))
evenDists = np.linspace(np.min(filteredDistances),np.max(filteredDistances),10)
plt.scatter(filteredDistances, filteredLatencies, s=12, c=plotColors[i], label=[f.split('/') for f in filenames][i][-1])
plt.plot(evenDists, func(evenDists), c=plotColors[i], linewidth=1.5)
plt.ticklabel_format(style='plain') # to prevent scientific notation
plt.ticklabel_format(useOffset=False)
plt.title('Network Latency vs Distance')
plt.xlabel("Distance")
plt.ylabel("Latency")
plt.legend()
# plt.show()
plt.savefig("scatterPlot.jpeg", dpi=400)
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