From 0198c0f4f3553280dd399b3e4419f4e67080fa31 Mon Sep 17 00:00:00 2001 From: Joe Ranalli Date: Thu, 4 Apr 2024 16:21:54 -0400 Subject: [PATCH] Automated CMV notebook demo, along with updates to the code-only version. --- README.md | 2 +- demos/automate_cmv_demo.ipynb | 446 ++++++++++++++++++++++++++++++++++ demos/automate_cmv_demo.py | 25 +- 3 files changed, 462 insertions(+), 11 deletions(-) create mode 100644 demos/automate_cmv_demo.ipynb diff --git a/README.md b/README.md index 8ea4279..cb7bde7 100644 --- a/README.md +++ b/README.md @@ -63,7 +63,7 @@ warranted. - Covers a single hour of data (9:15 - 10:15 UTC on Sept 8, 2013). - `hope_melpitz_10s` contains data sampled at 10s time resolution, acquired by temporally averaging time series data from the original dataset. - - Covers 3 full days, from Sept 8 - Sept 11, 2013. + - Covers 4 full days, from Sept 8 - Sept 11, 2013. - In both cases, data were first postprocessed using only removal of nulls (-9999) and linear interpolation to fill gaps left by the nulls, with a maximum interpolation window of 5s. See `dataio.hope_campaign` for details on diff --git a/demos/automate_cmv_demo.ipynb b/demos/automate_cmv_demo.ipynb new file mode 100644 index 0000000..43ec5c7 --- /dev/null +++ b/demos/automate_cmv_demo.ipynb @@ -0,0 +1,446 @@ +{ + "cells": [ + { + "metadata": {}, + "cell_type": "markdown", + "source": [ + "# Automation of CMV Detection for Large Time Series\n", + "\n", + "When applying the field analysis methodology, there is often an extensive dataset available showing, e.g. time series data for a plant over the course of an entire year or longer. In order to apply the method, it is necessary to obtain reduce the CMVs to a subset for which the method can be applied, because simply calculating for every possible cloud motion vector pair is computationally intractable. \n", + "\n", + "This notebook will demonstrate the process for downselecting a set of interesting CMVs according to the methodology discussed in the 2024 IEEE PVSC paper.\n", + "\n", + "\n", + "## Initialization\n", + "First we need to import relevant packages. " + ], + "id": "f25751d6420017ed" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2024-04-04T20:19:31.842693Z", + "start_time": "2024-04-04T20:19:29.837140Z" + } + }, + "cell_type": "code", + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import pvlib\n", + "\n", + "from solartoolbox import stats, spatial, cmv" + ], + "id": "f63c27a5889e67a3", + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\jar339\\AppData\\Local\\Temp\\ipykernel_19064\\3447369378.py:1: DeprecationWarning: \n", + "Pyarrow will become a required dependency of pandas in the next major release of pandas (pandas 3.0),\n", + "(to allow more performant data types, such as the Arrow string type, and better interoperability with other libraries)\n", + "but was not found to be installed on your system.\n", + "If this would cause problems for you,\n", + "please provide us feedback at https://github.com/pandas-dev/pandas/issues/54466\n", + " \n", + " import pandas as pd\n" + ] + } + ], + "execution_count": 1 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": [ + "## The Data\n", + "For this example, we will work on four days of 10s resolution data from the HOPE Melpitz campaign, included in the sample data package. We'll first read it in. Since the CMV routine is built to work on the clearsky index, we'll calculate that while we're at it. The plot shows a sample of the data for a single sensor over the four days. " + ], + "id": "2ad28bcd45de2c8e" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2024-04-04T20:19:34.439491Z", + "start_time": "2024-04-04T20:19:31.842693Z" + } + }, + "cell_type": "code", + "source": [ + "fn = 'data/hope_melpitz_10s.h5'\n", + "pos = pd.read_hdf(fn, mode=\"r\", key=\"latlon\")\n", + "pos_utm = spatial.latlon2utm(pos['lat'], pos['lon'])\n", + "ts = pd.read_hdf(fn, mode=\"r\", key=\"data\")\n", + "\n", + "loc = pvlib.location.Location(np.mean(pos['lat']), np.mean(pos['lon']))\n", + "cs_ghi = loc.get_clearsky(ts.index, model='simplified_solis')['ghi']\n", + "kt = ts.divide(cs_ghi, axis=0).clip(0,2)\n", + "\n", + "# Plot a single sensor's data\n", + "plt.plot(ts.index, ts[40])\n", + "plt.xticks(rotation=90)\n", + "plt.xlabel('Time')\n", + "plt.ylabel('Irradiance')\n", + "plt.show()" + ], + "id": "a40b8d0bce7985b7", + "outputs": [ + { + "data": { + "text/plain": [ + "
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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "execution_count": 2 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": [ + "## Deciding which Periods to Calculate CMVs For\n", + "I typically calculate CMVs using a one hour period of data. While that's viable for the whole four days here, it would rapidly become ornerous over e.g. an entire year, due to the computational intensity of the CMV method. One method to reduce the number of CMVs to calculate is to only calculate them for periods where the irradiance is changing rapidly, indicating the presence of clouds. Here we will use the Variability Score to quantify how variable each one hour period is in the data. \n", + "\n", + "Note that the variability score calculation has a slightly weird form using lambda functions, because doing so can allow the calculation to be performed using the vectorized form of the code. Other techniques might be possible. " + ], + "id": "cba49ff956de6bd6" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2024-04-04T20:19:39.254139Z", + "start_time": "2024-04-04T20:19:34.439491Z" + } + }, + "cell_type": "code", + "source": [ + "avg_interval = '1h'\n", + "\n", + "# Calc the VS for each 1 hour period\n", + "vs_all = ts.resample(avg_interval).apply(\n", + " lambda x: stats.variability_score(x[ts.columns]))\n", + "\n", + "fig, ax = plt.subplots()\n", + "ax2 = ax.twinx()\n", + "ax.plot(ts.index, ts[40])\n", + "plt.xlabel('Time')\n", + "plt.xticks(rotation=90)\n", + "ax.set_ylabel('Irradiance', color='blue')\n", + "ax.tick_params(axis='y', colors='blue')\n", + "ax2.plot(vs_all.index, vs_all[40], 'r')\n", + "ax2.set_ylabel('Variability Score', color='red')\n", + "ax2.tick_params(axis='y', colors='red')\n", + "plt.show()" + ], + "id": "50eecc1e62ada760", + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\jar339\\AppData\\Local\\Temp\\ipykernel_19064\\2407418665.py:5: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " lambda x: stats.variability_score(x[ts.columns]))\n" + ] + }, + { + "data": { + "text/plain": [ + "
" + ], + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "execution_count": 3 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "Once we have a metric of variability, we can downselect to retain only the N most variable periods. Here we'll use 20 just as an example.", + "id": "ecde5e3cad9e3f40" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2024-04-04T20:19:39.268061Z", + "start_time": "2024-04-04T20:19:39.254139Z" + } + }, + "cell_type": "code", + "source": [ + "n_var = 20\n", + "vs = vs_all.median(axis=1).sort_values(ascending=False)\n", + "vs = vs.iloc[0:n_var]\n", + "print(vs)" + ], + "id": "92f462cc466ac188", + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2013-09-08 11:00:00+00:00 36.827512\n", + "2013-09-10 10:00:00+00:00 17.346362\n", + "2013-09-09 10:00:00+00:00 16.861423\n", + "2013-09-08 10:00:00+00:00 14.032438\n", + "2013-09-10 08:00:00+00:00 11.659271\n", + "2013-09-10 12:00:00+00:00 7.277602\n", + "2013-09-08 07:00:00+00:00 4.998409\n", + "2013-09-10 14:00:00+00:00 3.878509\n", + "2013-09-08 06:00:00+00:00 3.846806\n", + "2013-09-08 12:00:00+00:00 3.722167\n", + "2013-09-09 12:00:00+00:00 3.016234\n", + "2013-09-09 16:00:00+00:00 2.515403\n", + "2013-09-10 16:00:00+00:00 2.369758\n", + "2013-09-11 12:00:00+00:00 2.250612\n", + "2013-09-09 11:00:00+00:00 2.169986\n", + "2013-09-11 08:00:00+00:00 1.778225\n", + "2013-09-09 13:00:00+00:00 1.750230\n", + "2013-09-09 09:00:00+00:00 1.719327\n", + "2013-09-10 13:00:00+00:00 1.264782\n", + "2013-09-08 09:00:00+00:00 1.185312\n", + "dtype: float64\n" + ] + } + ], + "execution_count": 4 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": [ + "## Calculating the CMVs\n", + "Now that we have a set of periods during which to calculate the CMV, we can loop over them to do the calculations. We also want to record some of the useful statistical and quality control information about the quality of the CMVs, because we'd later like to be able to determine which of these are of high quailty.\n", + "\n", + "We will assess the CMVs based on the following values:\n", + "- `ngood` - the number of point pairs that passed the Jamaly QC routines\n", + "- `r_corr` - the correlation coefficient of separation vs. delay for the good point pairs (see cmv_demo.ipynb). \n", + "- `flag` - the overall flag from the CMV QC process" + ], + "id": "2f9fbcc2bae8dc4d" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2024-04-04T20:19:41.668975Z", + "start_time": "2024-04-04T20:19:39.270073Z" + } + }, + "cell_type": "code", + "source": [ + "cmvs = pd.DataFrame(columns=[\"cld_spd\", \"cld_dir_rad\", \"df_p95\", \"ngood\", \"r_corr\", \"stderr_corr\", \"error_index\", \"flag\"])\n", + "for date in vs.index:\n", + " # Select the subset of data\n", + " hour = pd.date_range(date, date + pd.to_timedelta('1h'), freq='10s')\n", + " kt_hour = kt.loc[hour]\n", + " \n", + " hourlymax = ts.loc[hour].max().quantile(0.95)\n", + "\n", + " # Compute the CMV using the Jamaly method\n", + " cld_spd, cld_dir, dat = cmv.compute_cmv(kt_hour, pos_utm, method='jamaly', options={'minvelocity': 1})\n", + "\n", + " cmvs.loc[date] = [cld_spd, cld_dir, hourlymax, dat.method_data['ngood'], dat.method_data['r_corr'], dat.method_data['stderr_corr'], np.abs(dat.method_data[\"error_index\"]), dat.flag.name]\n", + "pd.options.display.width = 800\n", + "print(cmvs)" + ], + "id": "529475ecae6e313f", + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " cld_spd cld_dir_rad df_p95 ngood r_corr stderr_corr error_index flag\n", + "2013-09-08 11:00:00+00:00 17.253624 1.015742 1036.323712 306 0.990169 0.147371 0.102980 GOOD\n", + "2013-09-10 10:00:00+00:00 9.323374 0.446288 967.214252 624 0.970793 0.090337 0.128893 GOOD\n", + "2013-09-09 10:00:00+00:00 12.315270 5.789374 1105.664569 467 0.951945 0.197357 0.175704 GOOD\n", + "2013-09-08 10:00:00+00:00 15.790708 1.022334 1070.301172 781 0.872326 0.190603 0.202564 GOOD\n", + "2013-09-10 08:00:00+00:00 13.386957 0.736245 752.929056 581 0.989464 0.080910 0.082828 GOOD\n", + "2013-09-10 12:00:00+00:00 27.468773 0.515227 839.723816 412 0.978562 0.275288 0.122317 GOOD\n", + "2013-09-08 07:00:00+00:00 18.609486 0.871618 642.030344 797 0.992215 0.084133 0.081098 GOOD\n", + "2013-09-10 14:00:00+00:00 13.931439 0.698200 657.489758 756 0.943971 0.168528 0.195472 GOOD\n", + "2013-09-08 06:00:00+00:00 21.260689 0.833676 351.352144 680 0.993707 0.088246 0.086116 GOOD\n", + "2013-09-08 12:00:00+00:00 21.164760 0.558943 916.882816 90 0.873551 1.707730 0.241034 GOOD\n", + "2013-09-09 12:00:00+00:00 8.103066 5.511727 896.326129 623 0.966734 0.078114 0.110935 GOOD\n", + "2013-09-09 16:00:00+00:00 17.422306 0.081691 215.279455 491 0.806969 0.365211 0.133241 GOOD\n", + "2013-09-10 16:00:00+00:00 24.613327 0.378690 294.552469 578 0.960366 0.265352 0.146015 GOOD\n", + "2013-09-11 12:00:00+00:00 13.008833 0.057238 447.995486 772 0.983205 0.081499 0.100318 GOOD\n", + "2013-09-09 11:00:00+00:00 11.170620 0.140915 981.699747 933 0.919513 0.143933 0.240121 GOOD\n", + "2013-09-11 08:00:00+00:00 15.618439 1.117748 398.662215 760 0.825436 0.287277 0.173498 GOOD\n", + "2013-09-09 13:00:00+00:00 6.956777 5.610811 801.185779 860 0.879255 0.119534 0.306667 GOOD\n", + "2013-09-09 09:00:00+00:00 14.288631 5.987103 1088.490857 75 0.823863 1.168389 0.201482 GOOD\n", + "2013-09-10 13:00:00+00:00 31.678545 1.045541 675.928391 456 0.804433 0.710315 0.161430 GOOD\n", + "2013-09-08 09:00:00+00:00 18.401612 1.539226 1044.880005 689 0.970706 0.161717 0.092783 GOOD\n" + ] + } + ], + "execution_count": 5 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": [ + "# Downselect to High Quality CMVs\n", + "We can now downselect to only the highest quality CMVs. The limits shown here are just for reference based off this dataset. They probably need to be tuned to each specific dataset to appropriately limit the CMVs. The printout shows the ones that failed the QC." + ], + "id": "c379e843cd5efa35" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2024-04-04T20:19:41.683230Z", + "start_time": "2024-04-04T20:19:41.668975Z" + } + }, + "cell_type": "code", + "source": [ + "ngood_min = 200\n", + "rval_min = 0.85\n", + "bad_inds = []\n", + "for row in cmvs.itertuples():\n", + " if row.ngood < ngood_min:\n", + " bad_inds.append(row.Index)\n", + " elif row.r_corr < rval_min:\n", + " bad_inds.append(row.Index)\n", + " elif row.flag != 'GOOD':\n", + " bad_inds.append(row.Index)\n", + "print(cmvs.loc[bad_inds])\n", + "\n", + "cmvs = cmvs.drop(index=bad_inds)" + ], + "id": "642d3200bf9398f", + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " cld_spd cld_dir_rad df_p95 ngood r_corr stderr_corr error_index flag\n", + "2013-09-08 12:00:00+00:00 21.164760 0.558943 916.882816 90 0.873551 1.707730 0.241034 GOOD\n", + "2013-09-09 16:00:00+00:00 17.422306 0.081691 215.279455 491 0.806969 0.365211 0.133241 GOOD\n", + "2013-09-11 08:00:00+00:00 15.618439 1.117748 398.662215 760 0.825436 0.287277 0.173498 GOOD\n", + "2013-09-09 09:00:00+00:00 14.288631 5.987103 1088.490857 75 0.823863 1.168389 0.201482 GOOD\n", + "2013-09-10 13:00:00+00:00 31.678545 1.045541 675.928391 456 0.804433 0.710315 0.161430 GOOD\n" + ] + } + ], + "execution_count": 6 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": [ + "## Decide which CMVs make a representative set for Field Analysis\n", + "The field analysis requires vectors that are roughly perpendicular. So we will try to downselect to a set of CMVs that will give us a representative scatter across all directions. Since parallel and anti-parallel vectors are both disadvantageous here, we will rotate them all by 180 degrees. The function `cmv.optimum_subset` is capable of performing this optimization. For a real dataset, we might want to choose to keep around 10, but here we'll use 5 just to show the downselection process." + ], + "id": "c9d955d991ac2710" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2024-04-04T20:19:41.697237Z", + "start_time": "2024-04-04T20:19:41.683230Z" + } + }, + "cell_type": "code", + "source": [ + "nfinal = 5\n", + "# Compute the x and y components of the CMVs for optimum_subset\n", + "vx, vy = spatial.pol2rect(cmvs.cld_spd, cmvs.cld_dir_rad)\n", + "indices = cmv.optimum_subset(vx, vy, n=nfinal)\n", + "print(cmvs.iloc[indices])" + ], + "id": "2a76f4d6f99c3da", + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " cld_spd cld_dir_rad df_p95 ngood r_corr stderr_corr error_index flag\n", + "2013-09-10 16:00:00+00:00 24.613327 0.378690 294.552469 578 0.960366 0.265352 0.146015 GOOD\n", + "2013-09-08 07:00:00+00:00 18.609486 0.871618 642.030344 797 0.992215 0.084133 0.081098 GOOD\n", + "2013-09-08 09:00:00+00:00 18.401612 1.539226 1044.880005 689 0.970706 0.161717 0.092783 GOOD\n", + "2013-09-09 12:00:00+00:00 8.103066 5.511727 896.326129 623 0.966734 0.078114 0.110935 GOOD\n", + "2013-09-09 10:00:00+00:00 12.315270 5.789374 1105.664569 467 0.951945 0.197357 0.175704 GOOD\n" + ] + } + ], + "execution_count": 7 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": [ + "## Visualizing the result\n", + "Finally, we have a set of CMVs that are of high quality and represent a broad range of directions so that we are likely to have a good sampling of nearly perpendicular vectors that can be of use for the field analysis methodology. We'll plot the combination of vectors that passed QC and those that were selected for field analysis just to visualize that process." + ], + "id": "4e833173e929bceb" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2024-04-04T20:19:41.948803Z", + "start_time": "2024-04-04T20:19:41.697237Z" + } + }, + "cell_type": "code", + "source": [ + "# Plot the vectors visually\n", + "plt.figure()\n", + "for i, (dx, dy) in enumerate(zip(vx, vy)):\n", + " mylabel = 'All CMVs' if i == 0 else '_nolegend_'\n", + " plt.arrow(0, 0, dx, dy, head_width=1, head_length=1, fc='k', label=mylabel)\n", + "for i, (dx, dy) in enumerate(zip(vx.iloc[indices], vy.iloc[indices])):\n", + " mylabel = 'Selected CMVs' if i == 0 else '_nolegend_'\n", + " plt.arrow(0, 0, dx, dy, head_width=1, head_length=1, fc='r', ec='r', label=mylabel)\n", + "plt.xlabel('Eastward Velocity (m/s)')\n", + "plt.ylabel('Northward Velocity (m/s)')\n", + "plt.axis('equal')\n", + "plt.legend()\n", + "plt.tight_layout()\n", + "plt.show()" + ], + "id": "6369160a17047a45", + "outputs": [ + { + "data": { + "text/plain": [ + "
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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "execution_count": 8 + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/demos/automate_cmv_demo.py b/demos/automate_cmv_demo.py index d8d1e00..56b2995 100644 --- a/demos/automate_cmv_demo.py +++ b/demos/automate_cmv_demo.py @@ -1,6 +1,7 @@ import pandas as pd import numpy as np import matplotlib.pyplot as plt +import pvlib from solartoolbox import stats, spatial, cmv @@ -21,12 +22,18 @@ # # LOAD DATA # # ############# -# Load 3 days of data from the HOPE campaign. +# Load 4 days of data from the HOPE campaign. fn = 'data/hope_melpitz_10s.h5' pos = pd.read_hdf(fn, mode="r", key="latlon") pos_utm = spatial.latlon2utm(pos['lat'], pos['lon']) ts = pd.read_hdf(fn, mode="r", key="data") +# We will need the clear sky index for the CMV calculation, so let's compute it +# now. +loc = pvlib.location.Location(np.mean(pos['lat']), np.mean(pos['lon'])) +cs_ghi = loc.get_clearsky(ts.index, model='simplified_solis')['ghi'] +kt = ts.divide(cs_ghi, axis=0) + # ####################### # # FIND VARIABLE HOURS # # ####################### @@ -53,27 +60,25 @@ # Now we'll compute the CMVs for each of those 20 hours. # Build a holder for the output data -cmvs = pd.DataFrame(columns=["cld_spd", "cld_dir_rad", "df_p95", "ngood", "rval", "stderr", "error_index"]) +cmvs = pd.DataFrame(columns=["cld_spd", "cld_dir_rad", "df_p95", "ngood", "r_corr", "stderr_corr", "error_index", "flag"]) cmvs_flags = [] # Loop over hours for date in vs.index: # Select the subset of data hour = pd.date_range(date, date + pd.to_timedelta('1h'), freq='10s') + kt_hour = kt.loc[hour] hour = ts.loc[hour] - # Normalize it to yield something like kt (could actually calculate - # clearsky if available. Here, we're just using quantiles) - hourlymax = np.mean(hour.quantile(0.95)) - kt = hour / hourlymax + hourlymax = kt_hour.quantile(0.95).mean() # Compute the CMV using the Jamaly method - cld_spd, cld_dir, dat = cmv.compute_cmv(kt, pos_utm, method='jamaly', options={'minvelocity': 1}) + cld_spd, cld_dir, dat = cmv.compute_cmv(kt_hour, pos_utm, method='jamaly', options={'minvelocity': 1}) # Store the global flag cmvs_flags.append(dat.flag) - cmvs.loc[date] = [cld_spd, cld_dir, hourlymax, dat.method_data['ngood'], dat.method_data['r_corr'], dat.method_data['stderr_corr'], np.abs(dat.method_data["error_index"])] + cmvs.loc[date] = [cld_spd, cld_dir, hourlymax, dat.method_data['ngood'], dat.method_data['r_corr'], dat.method_data['stderr_corr'], np.abs(dat.method_data["error_index"]), dat.flag.name] # Display the 20 CMVs we just acquired pd.options.display.max_columns = None @@ -93,9 +98,9 @@ for row in cmvs.itertuples(): if row.ngood < ngood_min: bad_inds.append(row.Index) - elif row.rval < rval_min: + elif row.r_corr < rval_min: bad_inds.append(row.Index) - elif cmvs_flags[row.index(row.Index)] is not None: + elif cmvs_flags[row.index(row.Index)] is not cmv.Flag.GOOD: bad_inds.append(row.Index) # Drop the bad ones from the DF