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Adafruit_Learning_System_Gu.../Plotting_Offline_Data/Offline_Data.ipynb
Tyeth Gundry 0107361db8
Add assets for Plotting Offline Data guide
2025-04-02 16:13:04 +01:00

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{
"cells": [
{
"cell_type": "markdown",
"id": "ed1eb34b-22bb-4fc7-8810-4f247a78f95d",
"metadata": {},
"source": [
"### Offline data logging - JSONL data wrangling and plotting\n",
"\n",
"Upload your data file using the file browser on the left hand side, then update the variable below called `log_file_path` to match your uploaded filename (including file extension).\n",
"\n",
"Use the play button to run the currently selected cell (and move on to the next), or try the fast forward button to restart the kernel and then run all code cells."
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "f4161c1e-594a-45b4-83b1-6634fc4cbf63",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" i2c_address timestamp value si_unit pin\n",
"0 0x18 1742475983 78.12500 F NaN\n",
"1 0x18 1742475983 25.62500 C NaN\n",
"2 0x44 1742475984 30.02556 C NaN\n",
"3 0x44 1742475984 86.07004 F NaN\n",
"4 0x44 1742475984 31.18029 % NaN\n",
"5 NaN 1742476007 28.69000 C D13\n",
"6 NaN 1742476007 83.64000 F D13\n",
"7 NaN 1742476008 26.06000 C D14\n",
"8 NaN 1742476008 78.91000 F D14\n",
"9 0x18 1742476014 78.01250 F NaN\n"
]
}
],
"source": [
"import pandas as pd\n",
"import json\n",
"import os\n",
"records = None\n",
"# Path to your log file, update and comment out `records` beneath\n",
"log_file_path = 'log_1742475983.log' # Replace with your actual file path\n",
"\n",
"## Example dataset, comment out or delete to instead load log_file_path\n",
"# records = [\n",
"# {\"i2c_address\":\"0x18\",\"timestamp\":1742475983,\"value\":78.125,\"si_unit\":\"F\"},\n",
"# {\"i2c_address\":\"0x18\",\"timestamp\":1742475983,\"value\":25.625,\"si_unit\":\"C\"},\n",
"# {\"i2c_address\":\"0x44\",\"timestamp\":1742475984,\"value\":30.02556,\"si_unit\":\"C\"},\n",
"# {\"i2c_address\":\"0x44\",\"timestamp\":1742475984,\"value\":86.07004,\"si_unit\":\"F\"},\n",
"# {\"i2c_address\":\"0x44\",\"timestamp\":1742475984,\"value\":31.18029,\"si_unit\":\"%\"},\n",
"# {\"timestamp\":1742476007,\"pin\":\"D13\",\"value\":28.69,\"si_unit\":\"C\"},\n",
"# {\"timestamp\":1742476007,\"pin\":\"D13\",\"value\":83.64,\"si_unit\":\"F\"},\n",
"# {\"timestamp\":1742476008,\"pin\":\"D14\",\"value\":26.06,\"si_unit\":\"C\"},\n",
"# {\"timestamp\":1742476008,\"pin\":\"D14\",\"value\":78.91,\"si_unit\":\"F\"},\n",
"# {\"i2c_address\":\"0x18\",\"timestamp\":1742476014,\"value\":78.0125,\"si_unit\":\"F\"},\n",
"# {\"i2c_address\":\"0x18\",\"timestamp\":1742476014,\"value\":25.5625,\"si_unit\":\"C\"},\n",
"# {\"i2c_address\":\"0x44\",\"timestamp\":1742476014,\"value\":30.03891,\"si_unit\":\"C\"},\n",
"# {\"i2c_address\":\"0x44\",\"timestamp\":1742476014,\"value\":86.09407,\"si_unit\":\"F\"},\n",
"# {\"i2c_address\":\"0x44\",\"timestamp\":1742476014,\"value\":31.02464,\"si_unit\":\"%\"},\n",
"# {\"timestamp\":1742476038,\"pin\":\"D13\",\"value\":28.75,\"si_unit\":\"C\"},\n",
"# {\"timestamp\":1742476038,\"pin\":\"D13\",\"value\":83.75,\"si_unit\":\"F\"},\n",
"# {\"timestamp\":1742476039,\"pin\":\"D14\",\"value\":26.06,\"si_unit\":\"C\"},\n",
"# {\"timestamp\":1742476039,\"pin\":\"D14\",\"value\":78.91,\"si_unit\":\"F\"}\n",
"# ]\n",
"\n",
"# If not using fake data above, then load from file:\n",
"if records is None:\n",
" # Read the JSONL file\n",
" records = []\n",
" with open(log_file_path, 'r') as file:\n",
" for (i,line) in enumerate(file):\n",
" try:\n",
" # Parse each line as JSON to avoid any erroneous lines\n",
" record = json.loads(line.strip())\n",
" records.append(record)\n",
" except json.JSONDecodeError:\n",
" # Skip lines that aren't valid JSON\n",
" print(f\"Skipping invalid JSON line #{i}: {line[:50]}...\")\n",
" continue\n",
"\n",
"\n",
"# Create DataFrame from the records\n",
"df = pd.DataFrame(records)\n",
"\n",
"# Display the first 10 rows to verify\n",
"print(df.head(10))"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "ce6ab50b-dcab-433b-afda-c8ff95296d87",
"metadata": {},
"outputs": [],
"source": [
"# In this optional step, we add the component names from config.json\n",
"\n",
"config_file_path = 'config.json' # Replace with your actual config file path\n",
"selected_columns = []\n",
"\n",
"# 2. Load the config.json file if exists, otherwise do nothing\n",
"if os.path.exists(config_file_path):\n",
" with open(config_file_path, 'r') as config_file:\n",
" config = json.load(config_file)\n",
" \n",
" # 3. Create mapping dictionaries for I2C addresses and pins to component names\n",
" i2c_to_component = {}\n",
" pin_to_component = {}\n",
" \n",
" # Assuming config has a \"components\" section with entries containing \"i2c_address\" or \"pin\"\n",
" for component_info in config.get('components', []):\n",
" address = \"\"\n",
" component_name = component_info[\"name\"]\n",
" # Handle I2C addresses\n",
" if 'i2cDeviceAddress' in component_info:\n",
" # If there is an alternate set of bus pins, and/or mux then add that too\n",
" if 'i2cBusScl' in component_info:\n",
" address = f\"scl{component_info['i2cBusScl']}\"\n",
" if 'i2cBusSda' in component_info:\n",
" address = address + f\"sda{component_info['i2cBusSda']}_\"\n",
" if 'i2cMuxAddress' in component_info and 'i2cMuxChannel' in component_info:\n",
" address = address + \\\n",
" f\"mux{component_info['i2cMuxAddress']}ch{component_info['i2cMuxChannel']}_\" \n",
" # Convert to lowercase string for consistent matching\n",
" address = address + str(component_info['i2cDeviceAddress']).lower()\n",
" \n",
" i2c_to_component[address] = component_name\n",
"\n",
" # Handle pins\n",
" if 'pinName' in component_info:\n",
" pin = str(component_info['pinName'])\n",
" pin_to_component[pin] = component_name\n",
" \n",
" # 4. Create a function to map addresses/pins to component names\n",
" def get_component_name(row): \n",
" address = '' \n",
" # print(f\"About to return row:\\n**********************\\n{row}\\n***********************\")\n",
" if 'i2c_address' in row and not pd.isnull(row['i2c_address']):\n",
" if 'i2c_bus_scl' in row and not pd.isnull(row['i2c_bus_scl']):\n",
" address = f\"scl{row['i2c_bus_scl']}\"\n",
" if 'i2c_bus_sda' in row and not pd.isnull(row['i2c_bus_sda']):\n",
" address = address + f\"sda{row['i2c_bus_sda']}_\"\n",
" if 'i2c_mux_address' in row and 'i2c_mux_channel' in row:\n",
" address = address + \\\n",
" f\"mux{row['i2c_mux_address']}ch{row['i2c_mux_channel']}_\"\n",
" # Convert address to lowercase string for consistent matching\n",
" address = address + str(row['i2c_address']).lower()\n",
" \n",
" # print(f\"component addr {i2c_to_component[address]} ({address})\")\n",
" if address in i2c_to_component:\n",
" return f\"{i2c_to_component[address]} ({address})\"\n",
"\n",
" if 'pin' in row and not pd.isnull(row['pin']):\n",
" pin = str(row['pin'])\n",
" if pin in pin_to_component:\n",
" # print(f\"component addr {pin_to_component[pin]} ({row['pin']})\")\n",
" return f\"{pin_to_component[pin]} ({row['pin']})\"\n",
" return 'Unknown' # Default if no match found\n",
"\n",
"\n",
" # 5. Apply the function to create the new column\n",
" df['component_name'] = df.apply(get_component_name, axis=1)\n",
" \n",
" # Display the result\n",
" selected_columns = [\"component_name\",\"timestamp\",\"value\",\"si_unit\"]\n",
" print(df[selected_columns].head(10))"
]
},
{
"cell_type": "markdown",
"id": "586907df-bbb9-49b9-a2c6-77e7c6fc7802",
"metadata": {},
"source": [
"Here we confirm if we are using the component names and addresses from the `config.json` file, or just a column of automatically concatenated pin and i2c address information as a name."
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "8eaa64dd-0428-4469-b9b9-7e0b7c62c5cd",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"No component names added, using concatenation of I2C Address and Pin\n"
]
}
],
"source": [
"if not selected_columns:\n",
" print(\"No component names added, using concatenation of I2C Address and Pin\")\n",
" df[\"component_name\"] = (\n",
" (f'scl{df[\"i2c_bus_scl\"].fillna(\"\").astype(str)}' if \"i2c_bus_scl\" in df.columns else \"\") +\n",
" (f'sda{df[\"i2c_bus_sda\"].fillna(\"\").astype(str)}_' if \"i2c_bus_sda\" in df.columns else \"\") + \n",
" (f'mux{df[\"i2c_mux_address\"].fillna(\"\").astype(str)}' if \"i2c_mux_address\" in df.columns else \"\") +\n",
" (f'ch{df[\"i2c_mux_channel\"].fillna(\"\").astype(str)}_' if \"i2c_mux_channel\" in df.columns else \"\") +\n",
" (df[\"i2c_address\"].fillna(\"\").astype(str) if \"i2c_address\" in df.columns and not df[\"i2c_address\"].empty else \"\") + \n",
" (df[\"pin\"].fillna(\"\") if \"pin\" in df.columns and not df[\"pin\"].empty else \"\")\n",
" )\n",
"else:\n",
" print(\"Using component_name column generated from config.json\")"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "01818fe6-8fa1-4a96-ada4-ccac8918a009",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" i2c_address timestamp value si_unit pin component_name \\\n",
"0 0x18 1742475983 78.12500 F NaN 0x18 \n",
"1 0x18 1742475983 25.62500 C NaN 0x18 \n",
"2 0x44 1742475984 30.02556 C NaN 0x44 \n",
"3 0x44 1742475984 86.07004 F NaN 0x44 \n",
"4 0x44 1742475984 31.18029 % NaN 0x44 \n",
"5 NaN 1742476007 28.69000 C D13 D13 \n",
"6 NaN 1742476007 83.64000 F D13 D13 \n",
"7 NaN 1742476008 26.06000 C D14 D14 \n",
"8 NaN 1742476008 78.91000 F D14 D14 \n",
"9 0x18 1742476014 78.01250 F NaN 0x18 \n",
"\n",
" DateTime \n",
"0 2025-03-20 13:06:23 \n",
"1 2025-03-20 13:06:23 \n",
"2 2025-03-20 13:06:24 \n",
"3 2025-03-20 13:06:24 \n",
"4 2025-03-20 13:06:24 \n",
"5 2025-03-20 13:06:47 \n",
"6 2025-03-20 13:06:47 \n",
"7 2025-03-20 13:06:48 \n",
"8 2025-03-20 13:06:48 \n",
"9 2025-03-20 13:06:54 \n"
]
}
],
"source": [
"# Convert Unix timestamps to datetime format\n",
"df['DateTime'] = pd.to_datetime(df['timestamp'], unit='s')\n",
"\n",
"filtered_df = df\n",
"print(df.head(10))"
]
},
{
"cell_type": "markdown",
"id": "2919171b-415e-422c-815e-334631982e97",
"metadata": {},
"source": [
"Now that we've resolved the dates and components into a human readable format, we'll plot the data (using some widgets to filter it as desired).\n",
"\n",
"Go down to the end of this code cell to see the output, and alter the first dropdown to select the sensor to chart, and the second drop down to filter to just one metric / SI Unit."
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "d79be65d-28d2-4c03-ad28-94790e548124",
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "291dd9ffdf5d4806ba5b70042b324e8c",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"HBox(children=(Dropdown(description='Component:', options=('All', '0x18', '0x44', 'D13', 'D14'), value='All'),…"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "10ad05a432e94d1ab754033a4a3cc1d4",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"SelectionRangeSlider(description='Date Range:', index=(0, 1), options=(('2025-03-20', Timestamp('2025-03-20 00…"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "d5c15d1261094bf19a745467c7dcb55e",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Output()"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import pandas as pd\n",
"import plotly.express as px\n",
"from ipywidgets import widgets\n",
"from IPython.display import display, clear_output\n",
"from datetime import datetime, date\n",
"from typing import Optional, Tuple, Union, List\n",
"\n",
"# First, define the plot_data function\n",
"def plot_data(df, component, si_unit, start_date, end_date):\n",
" \"\"\"\n",
" Create a plot based on the selected filters.\n",
" \n",
" Args:\n",
" df (pd.DataFrame): The dataframe containing the data\n",
" component (str): Selected component name or 'All'\n",
" si_unit (str): Selected SI unit or 'All'\n",
" start_date (datetime): Start date for filtering\n",
" end_date (datetime): End date for filtering\n",
" \n",
" Returns:\n",
" plotly.graph_objects.Figure: The created plot\n",
" \"\"\"\n",
" # Filter the dataframe based on selections\n",
" plot_df = df.copy()\n",
" \n",
" # Filter by component if not 'All'\n",
" if component != 'All':\n",
" plot_df = plot_df[plot_df['component_name'] == component]\n",
" \n",
" # Filter by SI unit if not 'All'\n",
" if si_unit != 'All':\n",
" plot_df = plot_df[plot_df['si_unit'] == si_unit]\n",
" \n",
" # Filter by date range\n",
" plot_df = plot_df[(plot_df['DateTime'].dt.date >= start_date.date()) & \n",
" (plot_df['DateTime'].dt.date <= end_date.date())]\n",
" \n",
" # Create the plot (using plotly for interactive visualization)\n",
" fig = px.line(plot_df, x='DateTime', y='value', color='component_name',\n",
" title=f'Data for {component} ({si_unit if si_unit != \"All\" else \"All units\"})')\n",
" \n",
" return fig\n",
"\n",
"# Define the widgets\n",
"component_dropdown = widgets.Dropdown(\n",
" options=['All'] + list(filtered_df['component_name'].unique()),\n",
" description='Component:',\n",
" value='All'\n",
")\n",
"\n",
"si_unit_dropdown = widgets.Dropdown(\n",
" options=['All'] + list(filtered_df['si_unit'].unique()),\n",
" description='Unit:',\n",
" value='All'\n",
")\n",
"\n",
"# Get min and max dates from the dataframe\n",
"min_date = filtered_df['DateTime'].dt.date.min()\n",
"max_date = filtered_df['DateTime'].dt.date.max()\n",
"\n",
"# Use SelectionRangeSlider instead of DateRangeSlider\n",
"# Create a list of dates for the slider\n",
"dates = pd.date_range(min_date, max_date).tolist()\n",
"date_options = [(date.strftime('%Y-%m-%d'), date) for date in dates]\n",
"\n",
"date_range = widgets.SelectionRangeSlider(\n",
" options=date_options,\n",
" index=(0, len(dates)-1), # Select all dates initially\n",
" description='Date Range:',\n",
" orientation='horizontal'\n",
")\n",
"\n",
"# Create an output widget for the plot\n",
"plot_output = widgets.Output()\n",
"\n",
"# Display widgets first (outside the update function)\n",
"display(widgets.HBox([component_dropdown, si_unit_dropdown]))\n",
"display(date_range)\n",
"display(plot_output)\n",
"\n",
"def update_plot(change: Optional[dict] = None) -> None:\n",
" \"\"\"\n",
" Update the plot based on widget selections.\n",
" \n",
" Args:\n",
" change (Optional[dict]): Change event from ipywidgets. Defaults to None.\n",
" \"\"\"\n",
" # Get current values from widgets\n",
" component = component_dropdown.value\n",
" si_unit = si_unit_dropdown.value\n",
" start_date, end_date = date_range.value\n",
" \n",
" # Create plot with the current filter settings\n",
" fig = plot_data(filtered_df, component, si_unit, start_date, end_date)\n",
" \n",
" # Clear previous plot and display new one\n",
" with plot_output:\n",
" plot_output.clear_output(wait=True)\n",
" display(fig)\n",
"\n",
"# Connect the widgets to the update function\n",
"component_dropdown.observe(update_plot, names='value')\n",
"si_unit_dropdown.observe(update_plot, names='value')\n",
"date_range.observe(update_plot, names='value')\n",
"\n",
"# Initial plot\n",
"update_plot()"
]
},
{
"cell_type": "markdown",
"id": "daed8831-64d8-413d-8aa0-4b34a97348d1",
"metadata": {},
"source": [
"And that's about it. \n",
"\n",
"If you want to add additional columns for the difference over time, or any other such things, it's as simple as using the data frame object, usually `df`, or `filtered_df` in this case, and defining the column as a filter. \n",
"\n",
"For example to see the change in temperature between readings of the SHT30, we could define a filtered dataset for just the SHT30 using this code:\n",
"```python\n",
"sht_df = df[df['component_name'] == 'Weatherproof SHT30 (0x44)'].copy()\n",
"```"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "4e9a4003-e49a-47e6-81a2-cbe0dd862a6f",
"metadata": {},
"outputs": [
{
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" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>i2c_address</th>\n",
" <th>timestamp</th>\n",
" <th>value</th>\n",
" <th>si_unit</th>\n",
" <th>pin</th>\n",
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],
"text/plain": [
" i2c_address timestamp value si_unit pin component_name \\\n",
"2 0x44 1742475984 30.02556 C NaN 0x44 \n",
"3 0x44 1742475984 86.07004 F NaN 0x44 \n",
"4 0x44 1742475984 31.18029 % NaN 0x44 \n",
"\n",
" DateTime \n",
"2 2025-03-20 13:06:24 \n",
"3 2025-03-20 13:06:24 \n",
"4 2025-03-20 13:06:24 "
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"name_to_find = '0x44' # or if using config.json then 'Weatherproof SHT30 (0x44)'\n",
"sht_df = df[df['component_name'] == name_to_find].copy()\n",
"sht_df.head(3)"
]
},
{
"cell_type": "markdown",
"id": "fbec759b-104f-47fa-b0f2-a4dcabffd930",
"metadata": {},
"source": [
"__Or to only include temperature Celsius values for the SHT30:__"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "9ccbf03d-d404-4d14-b54a-a6d3f51eed85",
"metadata": {},
"outputs": [
{
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" i2c_address timestamp value si_unit pin component_name \\\n",
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"11 0x44 1742476014 30.03891 C NaN 0x44 \n",
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]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sht_df = df[(df['component_name'] == name_to_find) & (df['si_unit'] == 'C')].copy()\n",
"sht_df.head(3)"
]
},
{
"cell_type": "markdown",
"id": "df12bc88-672d-47e4-bd25-4fd2b0c4dfb7",
"metadata": {},
"source": [
"__Finally we then add three columns, one to indicate the difference in time between readings and one for the temperature difference, then `temp_diff` divided by `time_diff`:__"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "8c30c5ed-6e00-4d84-92c5-d15f5fccecc9",
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" i2c_address timestamp value si_unit pin component_name \\\n",
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],
"source": [
"sht_df['time_diff'] = sht_df['timestamp'].diff()\n",
"sht_df['temp_diff'] = sht_df['value'].diff()\n",
"sht_df['temp_diff_per_second'] = (sht_df['temp_diff'] / sht_df['time_diff'])\n",
"sht_df.head(5)"
]
},
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"id": "63811915-a242-4bd3-874e-aee8afd30548",
"metadata": {},
"source": [
"Then finally we plot the data to see how the temperature changed over time:"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "ff676f87-f167-4aa1-b98a-aa642a784d0b",
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",
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"source": [
"THE END - Why not try extending this notebook to plot both DS18b20 sensors on the same chart..."
]
}
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