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add additional tutorials for android decimeter challenge
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Lines changed: 192 additions & 15 deletions

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‎notebooks/tutorials/parsers.ipynb‎

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"id": "76ee7e5e",
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"metadata": {},
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"source": [
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"# 2021 Google Android Derived Dataset"
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"# 2022 Google Android Derived Dataset"
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]
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},
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{
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"cell_type": "markdown",
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"id": "ceac98ce",
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"metadata": {},
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"source": [
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"This data comes from the 2021 Google Smartphone Decimeter Challenge and can be downloaded from [Kaggle](https://www.kaggle.com/c/google-smartphone-decimeter-challenge)."
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"This data comes from the 2022 Google Smartphone Decimeter Challenge and can be downloaded from [Kaggle](https://www.kaggle.com/c/smartphone-decimeter-2022)."
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]
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},
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{
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"metadata": {},
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"outputs": [],
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"source": [
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"from gnss_lib_py.parsers.android import AndroidDerived2021"
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"from gnss_lib_py.parsers.android import AndroidDerived2022"
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]
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},
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{
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"cell_type": "markdown",
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"id": "e2ec49c6",
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"metadata": {},
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"source": [
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"Loading the data into an instance of `NavData` is as easy as creating an instance of `AndroidDerived2021` with the relevant file path."
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"Loading the data into an instance of `NavData` is as easy as creating an instance of `AndroidDerived2022` with the relevant file path."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "c335608f",
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"metadata": {},
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"outputs": [],
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"source": [
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"# download Android data file\n",
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"!wget https://raw.githubusercontent.com/Stanford-NavLab/gnss_lib_py/main/data/unit_test/android_2022/device_gnss.csv --quiet -O \"device_gnss.csv\"\n",
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"# load Android Google Challenge data\n",
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"derived_data = AndroidDerived2022(\"device_gnss.csv\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "0e7c9e45",
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"id": "eb53fe00",
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"metadata": {},
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"source": [
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"**Note:** In this case, the data is filtered to be seconds apart, in the regular\n",
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"setting, such measurements would be removed. To prevent this from happening,\n",
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"we use the remove_timing_outliers flag here. For the full dataset, set this flag to True"
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"We can verify that the data loaded correctly by printing the shape and rows of the imported data."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "c335608f",
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"id": "01b5d56a",
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"metadata": {},
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"outputs": [],
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"source": [
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"derived_data.shape"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "cc2d26d9",
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"metadata": {},
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"outputs": [],
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"source": [
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"derived_data.rows"
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]
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},
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{
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"cell_type": "markdown",
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"id": "076d5ea5",
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"metadata": {},
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"source": [
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"# 2022 Google Android Ground Truth\n",
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"We can similarly load in the ground truth data from the same 2022 Google Smartphone Decimeter Challenge."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "9369f387",
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"metadata": {},
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"outputs": [],
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"source": [
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"from gnss_lib_py.parsers.android import AndroidGroundTruth2022"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "21581d69",
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"metadata": {},
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"outputs": [],
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"source": [
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"# download Android data file\n",
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"!wget https://raw.githubusercontent.com/Stanford-NavLab/gnss_lib_py/main/data/unit_test/android_2021/Pixel4XL_derived.csv --quiet -O \"Pixel4XL_derived.csv\"\n",
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"!wget https://raw.githubusercontent.com/Stanford-NavLab/gnss_lib_py/main/data/unit_test/android_2022/ground_truth.csv --quiet -O \"ground_truth.csv\"\n",
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"# load Android Google Challenge ground truth data\n",
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"gt_data = AndroidGroundTruth2022(\"ground_truth.csv\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "ef4a123a",
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"metadata": {},
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"outputs": [],
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"source": [
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"gt_data.shape"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "9f327d87",
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"metadata": {
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"scrolled": false
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},
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"outputs": [],
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"source": [
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"gt_data.rows"
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]
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},
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{
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"cell_type": "markdown",
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"id": "0f78bfdb",
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"metadata": {},
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"source": [
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"# 2021 Google Android Derived Dataset"
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]
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},
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{
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"cell_type": "markdown",
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"id": "b43a523e",
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"metadata": {},
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"source": [
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"This data comes from the 2021 Google Smartphone Decimeter Challenge and can be downloaded from [Kaggle](https://www.kaggle.com/c/google-smartphone-decimeter-challenge)."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "4da54a37",
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"metadata": {},
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"outputs": [],
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"source": [
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"from gnss_lib_py.parsers.android import AndroidDerived2021"
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]
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},
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{
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"cell_type": "markdown",
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"id": "e12cecb0",
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"metadata": {},
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"source": [
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"Loading the data into an instance of `NavData` is as easy as creating an instance of `AndroidDerived2021` with the relevant file path."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "6447cd99",
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"metadata": {},
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"outputs": [],
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"source": [
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"# download Android data file\n",
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"!wget https://raw.githubusercontent.com/Stanford-NavLab/gnss_lib_py/main/data/unit_test/android_2021/Pixel4_derived.csv --quiet -O \"Pixel4_derived.csv\"\n",
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"# load Android Google Challenge data\n",
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"derived_data = AndroidDerived2021(\"Pixel4XL_derived.csv\", remove_timing_outliers=False)"
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"derived_data = AndroidDerived2021(\"Pixel4_derived.csv\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "eb53fe00",
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"id": "00bc1b39",
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"metadata": {},
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"source": [
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"We can verify that the data loaded correctly by printing the shape and rows of the imported data."
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "01b5d56a",
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"id": "407270ad",
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"metadata": {},
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"outputs": [],
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"source": [
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "cc2d26d9",
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"id": "2ac2fc75",
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"metadata": {},
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"outputs": [],
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"source": [
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"derived_data.rows"
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]
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},
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{
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"cell_type": "markdown",
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"id": "602a7f1d",
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"metadata": {},
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"source": [
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"# 2021 Google Android Ground Truth\n",
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"We can similarly load in the ground truth data from the same 2021 Google Smartphone Decimeter Challenge."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "785f088b",
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"metadata": {},
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"outputs": [],
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"source": [
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"from gnss_lib_py.parsers.android import AndroidGroundTruth2021"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "8721297a",
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"metadata": {},
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"outputs": [],
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"source": [
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"# download Android data file\n",
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"!wget https://raw.githubusercontent.com/Stanford-NavLab/gnss_lib_py/main/data/unit_test/android_2021/Pixel4_ground_truth.csv --quiet -O \"Pixel4_ground_truth.csv\"\n",
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"# load Android Google Challenge ground truth data\n",
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"gt_data = AndroidGroundTruth2021(\"Pixel4_ground_truth.csv\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "35cba5a4",
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"metadata": {},
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"outputs": [],
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"source": [
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"gt_data.shape"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "a1b0e8b6",
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"metadata": {
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"scrolled": false
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},
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"outputs": [],
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"source": [
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"gt_data.rows"
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]
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},
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{
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"cell_type": "markdown",
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"id": "eb016e74",

‎notebooks/tutorials/utilities.ipynb‎

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"id": "952766c1",
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"metadata": {},
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"source": [
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"The `visualizations.py` file contains several plotting functionalities. We'll use some existing data to demonstrate their functionality."
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"The `visualizations.py` file contains several plotting functionalities. We'll use some existing data to demonstrate their functionality.\n",
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"\n",
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"**Note:** In this case, the example data is filtered to be seconds apart, in the regular\n",
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"setting, such measurements would be removed. To prevent this from happening,\n",
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"we set remove_timing_outliers to False here. For the full dataset, set this flag to True"
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]
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},
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{

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