docs: Build docs

docs-source/source/CHANGELOG.md

@@ -1,4 +1,20 @@
 # Changelog
+## v8.5.0 (2024-09-09)
+
+### Feat
+
+- functions operating on all elements in the time series (#274)
+- interpolation based on sequence data (#277)
+- mock scatter plot functionality (#279)
+
+### Fix
+
+- Add example units to guide users (#253)
+- **deps**: update dependency scikit-image to ^0.24.0 (#215)
+- **deps**: update dependency numba to ^0.60.0 (#261)
+- disable oscillation detection in charts (#240)
+- **deps**: update dependency scikit-image to ^0.22.0 (#42)
+
 ## v8.4.0 (2024-03-22)
 
 ### Feat

docs/_downloads/5de75731f01690b7ed8da2b559f04489/versioned_function.py

@@ -64,12 +64,12 @@ def abs_diff(a: pd.Series, b: pd.Series) -> pd.Series:
 # Next, we implement the new version of the `abs_diff` and mark it as version 1.1.
 #
 
-from indsl.resample import reindex  # noqa
+from indsl.resample import reindex_v1  # noqa
 
 
 @versioning.register(version="1.1")  # type: ignore
 def abs_diff(a: pd.Series, b: pd.Series) -> pd.Series:
-    a, b = reindex(a, b)
+    a, b = reindex_v1.reindex(a, b)
     return (a - b).abs()
 
 

docs/_downloads/6fcb0f672852d4c309d5b27535096a6d/plot_cumulative_co2.ipynb

@@ -1,16 +1,5 @@
 {
   "cells": [
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "%matplotlib inline"
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -26,7 +15,7 @@
       },
       "outputs": [],
       "source": [
-        "import os\n\nimport matplotlib.pyplot as plt\nimport pandas as pd\n\nfrom indsl.sustainability.co2_emissions_calculations import (\n    cumulative_co2_cost,\n    cumulative_co2_production,\n    rate_of_emissions,\n)\n\n\n# Load and pre-process data\nbase_path = os.path.dirname(\"\")\ndata = pd.read_csv(os.path.join(base_path, \"../../datasets/data/compressor_power_output.csv\"), index_col=0)\ndata.index = pd.to_datetime(data.index)\npower = data[data.columns[0]].resample(\"1h\").mean().ffill()  # Unit is in kW\n\n# Specify factors\nco2_cost_factor = 0.5  # NOK/kg CO2\nemissions_factor = 0.21  # kg CO2/kWh\n\n# Perform emissions calculations\nrate_co2_produced = rate_of_emissions(power, emissions_factor)\nco2_produced = cumulative_co2_production(rate_co2_produced)\nco2_cost = cumulative_co2_cost(power, co2_cost_factor=co2_cost_factor, emissions_factor=emissions_factor)\n\n\n# Plotting\nplt.subplots(2, 2, figsize=(10, 10))\n\nax = plt.subplot(2, 2, 1)\n(power).plot(ax=ax)\nplt.ylabel(\"Power (kW)\")\nplt.xlabel(\"Date\")\nplt.title(\"Compressor Power Output\")\n\nax = plt.subplot(2, 2, 2)\n(rate_co2_produced).plot(ax=ax)\nplt.ylabel(\"CO2 Production Rate (kg CO2/hr)\")\nplt.xlabel(\"Date\")\nplt.title(\"Rate of CO2 Production\")\n\n\nax = plt.subplot(2, 2, 3)\n(co2_produced / 1000).plot(ax=ax)\nplt.ylabel(\"Mass CO2 Emitted (tonnes)\")\nplt.xlabel(\"Date\")\nplt.title(\"Cumulative Sum of CO2 Production\")\n\n\nax = plt.subplot(2, 2, 4)\n(co2_cost / 1e6).plot(ax=ax)\nplt.ylabel(\"Cost (MNOK)\")\nplt.xlabel(\"Date\")\nplt.title(\"Cumulative Cost of CO2\")\n\nplt.show()"
+        "import os\n\nimport matplotlib.pyplot as plt\nimport pandas as pd\n\nfrom indsl.sustainability.co2_emissions_calculations import (\n    cumulative_co2_cost,\n    cumulative_co2_production,\n    rate_of_emissions,\n)\n\n\n# Load and pre-process data\nbase_path = os.path.dirname(\"\")\ndata = pd.read_csv(os.path.join(base_path, \"../../datasets/data/compressor_power_output.csv\"), index_col=0)\ndata.index = pd.to_datetime(data.index)\npower = data[data.columns[0]].resample(\"1h\").mean().ffill()  # Unit is in kW\n\n# Specify factors\nco2_cost_factor = 0.5  # NOK/kg CO2\nemissions_factor = 0.21  # kg CO2/kWh\n\n# Perform emissions calculations\nrate_co2_produced = rate_of_emissions(power, emissions_factor)\nco2_produced = cumulative_co2_production(rate_co2_produced, start_date=data.index[0])\nco2_cost = cumulative_co2_cost(\n    power, co2_cost_factor=co2_cost_factor, emissions_factor=emissions_factor, start_date=data.index[0]\n)\n\n# Plotting\nplt.subplots(2, 2, figsize=(10, 10))\n\nax = plt.subplot(2, 2, 1)\n(power).plot(ax=ax)\nplt.ylabel(\"Power (kW)\")\nplt.xlabel(\"Date\")\nplt.title(\"Compressor Power Output\")\n\nax = plt.subplot(2, 2, 2)\n(rate_co2_produced).plot(ax=ax)\nplt.ylabel(\"CO2 Production Rate (kg CO2/hr)\")\nplt.xlabel(\"Date\")\nplt.title(\"Rate of CO2 Production\")\n\n\nax = plt.subplot(2, 2, 3)\n(co2_produced / 1000).plot(ax=ax)\nplt.ylabel(\"Mass CO2 Emitted (tonnes)\")\nplt.xlabel(\"Date\")\nplt.title(\"Cumulative Sum of CO2 Production\")\n\n\nax = plt.subplot(2, 2, 4)\n(co2_cost / 1e6).plot(ax=ax)\nplt.ylabel(\"Cost (MNOK)\")\nplt.xlabel(\"Date\")\nplt.title(\"Cumulative Cost of CO2\")\n\nplt.show()"
       ]
     }
   ],
@@ -46,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.8.12"
+      "version": "3.11.5"
     }
   },
   "nbformat": 4,

docs/_downloads/7a4860042e79df99b950b949b8525ec2/plot_extreme_outlier.ipynb

@@ -1,16 +1,5 @@
 {
   "cells": [
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "%matplotlib inline"
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -46,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.8.12"
+      "version": "3.11.5"
     }
   },
   "nbformat": 4,

docs/_downloads/7ade8e29428581d09ee2e19053660e19/plot_datapoint_diff.ipynb

@@ -4,11 +4,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\n",
-        "# Threshold breach check for difference between two data points over a period of time\n",
-        "\n",
-        "Example of visualizing breach of threshold in hour count in a time series representing running hours of a piece of\n",
-        "equipment.\n"
+        "\n# Threshold breach check for difference between two data points over a period of time\n\nExample of visualizing breach of threshold in hour count in a time series representing running hours of a piece of\nequipment.\n"
       ]
     },
     {
@@ -19,63 +15,7 @@
       },
       "outputs": [],
       "source": [
-        "import os\n",
-        "\n",
-        "from datetime import datetime, timedelta\n",
-        "\n",
-        "import matplotlib.pyplot as plt\n",
-        "import numpy as np\n",
-        "import pandas as pd\n",
-        "\n",
-        "from indsl.data_quality.datapoint_diff import datapoint_diff_over_time_period\n",
-        "\n",
-        "\n",
-        "# import generated data\n",
-        "base_path = \"\" if __name__ == \"__main__\" else os.path.dirname(__file__)\n",
-        "data = pd.read_csv(os.path.join(base_path, \"../../datasets/data/hour_count.csv\"), index_col=0)\n",
-        "data = data.squeeze()\n",
-        "data.index = pd.to_datetime(data.index)\n",
-        "\n",
-        "# apply function to calculate difference between datapoint over a period of 1 day\n",
-        "hour_count_default_threshold = datapoint_diff_over_time_period(data, pd.Timedelta(\"1d\"), 24, pd.Timedelta(\"1h\"))\n",
-        "\n",
-        "# Resample and forward fill generated step series\n",
-        "resampled_step_series_default_threshold = hour_count_default_threshold.resample(\"60min\")\n",
-        "default_threshold_forward_filled = resampled_step_series_default_threshold.ffill()\n",
-        "\n",
-        "# Plot unchanged signal identification series against actual data\n",
-        "fig, ax1 = plt.subplots(figsize=(15, 5))\n",
-        "ax1.plot(data.index, data, label=\"Time series\", marker=\".\", color=\"blue\")\n",
-        "\n",
-        "values = np.arange(data.index[0], data.index[-1], timedelta(minutes=120)).astype(datetime)\n",
-        "\n",
-        "ax1.set_xticks(values)\n",
-        "ax1.set_xticklabels([ts.strftime(\"%d-%m \\n %H:%M\") for ts in values], fontsize=8)\n",
-        "\n",
-        "ax2 = ax1.twinx()\n",
-        "ax2.plot(\n",
-        "    data.index,\n",
-        "    default_threshold_forward_filled,\n",
-        "    label=\"Threshold breach indicator for datapoint diff over last 24 hours\",\n",
-        "    marker=\".\",\n",
-        "    color=\"red\",\n",
-        ")\n",
-        "\n",
-        "lines1, labels1 = ax1.get_legend_handles_labels()\n",
-        "lines2, labels2 = ax2.get_legend_handles_labels()\n",
-        "ax2.legend(lines1 + lines2, labels1 + labels2, loc=0)\n",
-        "\n",
-        "plt.xlabel(\"Timestamp\")\n",
-        "ax1.set_ylabel(\"Timeseries value\")\n",
-        "ax2.set_ylabel(\"Hour count threshold breach\")\n",
-        "\n",
-        "fig.suptitle(\n",
-        "    \"Check if difference between two datapoints at a distance of 24 hours exceeds the threshold\",\n",
-        "    fontsize=14,\n",
-        ")\n",
-        "fig.tight_layout()\n",
-        "\n",
-        "plt.show()"
+        "import os\n\nfrom datetime import datetime, timedelta\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport pandas as pd\n\nfrom indsl.data_quality.datapoint_diff import datapoint_diff_over_time_period\n\n\n# import generated data\nbase_path = \"\" if __name__ == \"__main__\" else os.path.dirname(__file__)\ndata = pd.read_csv(os.path.join(base_path, \"../../datasets/data/hour_count.csv\"), index_col=0)\ndata = data.squeeze()\ndata.index = pd.to_datetime(data.index)\n\n# apply function to calculate difference between datapoint over a period of 1 day\nhour_count_default_threshold = datapoint_diff_over_time_period(data, pd.Timedelta(\"1d\"), 24, pd.Timedelta(\"1h\"))\n\n# Resample and forward fill generated step series\nresampled_step_series_default_threshold = hour_count_default_threshold.resample(\"60min\")\ndefault_threshold_forward_filled = resampled_step_series_default_threshold.ffill()\n\n# Plot unchanged signal identification series against actual data\nfig, ax1 = plt.subplots(figsize=(15, 5))\nax1.plot(data.index, data, label=\"Time series\", marker=\".\", color=\"blue\")\n\nvalues = np.arange(data.index[0], data.index[-1], timedelta(minutes=120)).astype(datetime)\n\nax1.set_xticks(values)\nax1.set_xticklabels([ts.strftime(\"%d-%m \\n %H:%M\") for ts in values], fontsize=8)\n\nax2 = ax1.twinx()\nax2.plot(\n    data.index,\n    default_threshold_forward_filled,\n    label=\"Threshold breach indicator for datapoint diff over last 24 hours\",\n    marker=\".\",\n    color=\"red\",\n)\n\nlines1, labels1 = ax1.get_legend_handles_labels()\nlines2, labels2 = ax2.get_legend_handles_labels()\nax2.legend(lines1 + lines2, labels1 + labels2, loc=0)\n\nplt.xlabel(\"Timestamp\")\nax1.set_ylabel(\"Timeseries value\")\nax2.set_ylabel(\"Hour count threshold breach\")\n\nfig.suptitle(\n    \"Check if difference between two datapoints at a distance of 24 hours exceeds the threshold\",\n    fontsize=14,\n)\nfig.tight_layout()\n\nplt.show()"
       ]
     }
   ],
@@ -95,9 +35,9 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.11.6"
+      "version": "3.11.5"
     }
   },
   "nbformat": 4,
   "nbformat_minor": 0
-}
+}

docs/_downloads/845a3d2b5c7ab2a48d16aab704532d76/plot_cusum.ipynb

@@ -1,16 +1,5 @@
 {
   "cells": [
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "%matplotlib inline"
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -46,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.8.13"
+      "version": "3.11.5"
     }
   },
   "nbformat": 4,

docs/_downloads/9eb7de3db0c50f05854e6fab9ebbb5cf/plot_cumulative_co2.py

@@ -31,9 +31,10 @@
 
 # Perform emissions calculations
 rate_co2_produced = rate_of_emissions(power, emissions_factor)
-co2_produced = cumulative_co2_production(rate_co2_produced)
-co2_cost = cumulative_co2_cost(power, co2_cost_factor=co2_cost_factor, emissions_factor=emissions_factor)
-
+co2_produced = cumulative_co2_production(rate_co2_produced, start_date=data.index[0])
+co2_cost = cumulative_co2_cost(
+    power, co2_cost_factor=co2_cost_factor, emissions_factor=emissions_factor, start_date=data.index[0]
+)
 
 # Plotting
 plt.subplots(2, 2, figsize=(10, 10))

docs/_downloads/c1c97d00bcd89218402c6a300f3b4ca8/plot_group_by_region.ipynb

@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.11.6"
+      "version": "3.11.5"
     }
   },
   "nbformat": 4,