diff --git a/docs/source/example/_busy_fly_scan.ipynb b/docs/source/example/_busy_fly_scan.ipynb
index cfa63bac..120c5ed1 100644
--- a/docs/source/example/_busy_fly_scan.ipynb
+++ b/docs/source/example/_busy_fly_scan.ipynb
@@ -142,7 +142,7 @@
     "    poll_interval = 0.02\n",
     "\n",
     "    def run_one_phase(self, phase, busy):\n",
-    "        logger.info(\"%s phase start\", phase)\n",
+    "        logger.debug(\"%s phase start\", phase)\n",
     "        timeout=getattr(self, f\"{phase}_timeout\").get()\n",
     "\n",
     "        yield from bps.sleep(self.poll_interval)\n",
@@ -157,7 +157,7 @@
     "            if tNow > deadline:\n",
     "                raise TimeoutError(f\"{phase} not started after {tNow-t0:.3f} s\")\n",
     "            yield from bps.sleep(self.poll_interval)\n",
-    "        logger.info(\"%s busy started\", phase)\n",
+    "        logger.debug(\"%s busy started\", phase)\n",
     "\n",
     "        t0 = time.time()\n",
     "        deadline = t0 + timeout\n",
@@ -166,9 +166,9 @@
     "            if tNow > deadline:\n",
     "                raise TimeoutError(f\"{phase} not complete after {tNow-t0:.3g} s\")\n",
     "            yield from bps.sleep(self.poll_interval)\n",
-    "        logger.info(\"%s busy cleared\", phase)\n",
+    "        logger.debug(\"%s busy cleared\", phase)\n",
     "\n",
-    "        logger.info(\"%s phase end\", phase)\n",
+    "        logger.debug(\"%s phase end\", phase)\n",
     "\n",
     "    def taxi_fly_plan(self):\n",
     "        yield from self.run_one_phase(\"taxi\", self.taxi)\n",
@@ -265,9 +265,7 @@
    "source": [
     "### Connect with EPICS\n",
     "\n",
-    "Create local (ophyd-style) objects to connect with the EPICS IOC records.\n",
-    "\n",
-    "(The `SscanRecord` will be used in a different procedure.)"
+    "Create local (ophyd-style) objects to connect with the EPICS IOC records."
    ]
   },
   {
@@ -296,8 +294,8 @@
    "source": [
     "### Write the plan that prepares EPICS\n",
     "\n",
-    "The busy, swait, & sseq records for the taxi & fly phases are configured by the\n",
-    "following bluesky plan.\n",
+    "The *busy*, *swait*, & *sseq* records for the *taxi* & *fly* phases are\n",
+    "configured by the following bluesky plan.\n",
     "\n",
     "The plan uses predefined names for the ophyd objects, a pattern typical for\n",
     "beamline plans."
@@ -309,12 +307,12 @@
    "source": [
     "#### The `reset()` method\n",
     "\n",
-    "The *SseqRecord* support in apstools has a `reset()` method to clear any\n",
+    "The *SseqRecord* support in *apstools* has a `reset()` method to clear any\n",
     "previous settings of the EPICS PVs and ophyd object and return them to default\n",
     "settings.  Note: some of the other record support classes in *apstools.synApps*,\n",
     "including *SwaitRecord* and *SscanRecord*, have such `reset()` methods.\n",
     "\n",
-    "The `reset()` method is written as ophyd code, intended to be called from a\n",
+    "The `reset()` method is written as *ophyd* code, intended to be called from a\n",
     "command-line session.  The commands it contains that may take some time to\n",
     "complete and possibly block the normal execution of the RunEngine's callback\n",
     "thread. The\n",
@@ -401,10 +399,10 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2024-03-06 10:34:41,202 INFO bluesky - Executing plan <generator object prep_taxi_fly_simple_delay at 0x7fc0933f1000>\n",
-      "2024-03-06 10:34:41,204 INFO bluesky.RE.state - Change state on <bluesky.run_engine.RunEngine object at 0x7fc07ca39150> from 'idle' -> 'running'\n",
-      "2024-03-06 10:34:41,535 INFO bluesky.RE.state - Change state on <bluesky.run_engine.RunEngine object at 0x7fc07ca39150> from 'running' -> 'idle'\n",
-      "2024-03-06 10:34:41,536 INFO bluesky - Cleaned up from plan <generator object prep_taxi_fly_simple_delay at 0x7fc0933f1000>\n"
+      "2024-03-06 12:31:38,660 INFO bluesky - Executing plan <generator object prep_taxi_fly_simple_delay at 0x7fa6edab2e60>\n",
+      "2024-03-06 12:31:38,664 INFO bluesky.RE.state - Change state on <bluesky.run_engine.RunEngine object at 0x7fa6f01d6f50> from 'idle' -> 'running'\n",
+      "2024-03-06 12:31:39,009 INFO bluesky.RE.state - Change state on <bluesky.run_engine.RunEngine object at 0x7fa6f01d6f50> from 'running' -> 'idle'\n",
+      "2024-03-06 12:31:39,011 INFO bluesky - Cleaned up from plan <generator object prep_taxi_fly_simple_delay at 0x7fa6edab2e60>\n"
      ]
     },
     {
@@ -440,18 +438,10 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2024-03-06 10:34:41,545 INFO bluesky - Executing plan <generator object TaxiFlyScanDevice.taxi_fly_plan at 0x7fc0767f6f60>\n",
-      "2024-03-06 10:34:41,546 INFO bluesky.RE.state - Change state on <bluesky.run_engine.RunEngine object at 0x7fc07ca39150> from 'idle' -> 'running'\n",
-      "2024-03-06 10:34:41,547 INFO root - taxi phase start\n",
-      "2024-03-06 10:34:41,569 INFO root - taxi busy started\n",
-      "2024-03-06 10:34:43,578 INFO root - taxi busy cleared\n",
-      "2024-03-06 10:34:43,579 INFO root - taxi phase end\n",
-      "2024-03-06 10:34:43,579 INFO root - fly phase start\n",
-      "2024-03-06 10:34:43,602 INFO root - fly busy started\n",
-      "2024-03-06 10:34:47,613 INFO root - fly busy cleared\n",
-      "2024-03-06 10:34:47,614 INFO root - fly phase end\n",
-      "2024-03-06 10:34:47,616 INFO bluesky.RE.state - Change state on <bluesky.run_engine.RunEngine object at 0x7fc07ca39150> from 'running' -> 'idle'\n",
-      "2024-03-06 10:34:47,617 INFO bluesky - Cleaned up from plan <generator object TaxiFlyScanDevice.taxi_fly_plan at 0x7fc0767f6f60>\n"
+      "2024-03-06 12:31:39,025 INFO bluesky - Executing plan <generator object TaxiFlyScanDevice.taxi_fly_plan at 0x7fa6ed1fcad0>\n",
+      "2024-03-06 12:31:39,027 INFO bluesky.RE.state - Change state on <bluesky.run_engine.RunEngine object at 0x7fa6f01d6f50> from 'idle' -> 'running'\n",
+      "2024-03-06 12:31:45,085 INFO bluesky.RE.state - Change state on <bluesky.run_engine.RunEngine object at 0x7fa6f01d6f50> from 'running' -> 'idle'\n",
+      "2024-03-06 12:31:45,085 INFO bluesky - Cleaned up from plan <generator object TaxiFlyScanDevice.taxi_fly_plan at 0x7fa6ed1fcad0>\n"
      ]
     },
     {
@@ -678,10 +668,10 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2024-03-06 10:34:49,227 INFO bluesky - Executing plan <generator object prep_taxi_fly_step_scan at 0x7fc076f9f400>\n",
-      "2024-03-06 10:34:49,229 INFO bluesky.RE.state - Change state on <bluesky.run_engine.RunEngine object at 0x7fc07ca39150> from 'idle' -> 'running'\n",
-      "2024-03-06 10:34:49,671 INFO bluesky.RE.state - Change state on <bluesky.run_engine.RunEngine object at 0x7fc07ca39150> from 'running' -> 'idle'\n",
-      "2024-03-06 10:34:49,674 INFO bluesky - Cleaned up from plan <generator object prep_taxi_fly_step_scan at 0x7fc076f9f400>\n"
+      "2024-03-06 12:31:46,628 INFO bluesky - Executing plan <generator object prep_taxi_fly_step_scan at 0x7fa6edbc3400>\n",
+      "2024-03-06 12:31:46,629 INFO bluesky.RE.state - Change state on <bluesky.run_engine.RunEngine object at 0x7fa6f01d6f50> from 'idle' -> 'running'\n",
+      "2024-03-06 12:31:47,063 INFO bluesky.RE.state - Change state on <bluesky.run_engine.RunEngine object at 0x7fa6f01d6f50> from 'running' -> 'idle'\n",
+      "2024-03-06 12:31:47,064 INFO bluesky - Cleaned up from plan <generator object prep_taxi_fly_step_scan at 0x7fa6edbc3400>\n"
      ]
     },
     {
@@ -715,18 +705,10 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2024-03-06 10:34:49,708 INFO bluesky - Executing plan <generator object TaxiFlyScanDevice.taxi_fly_plan at 0x7fc0767f4110>\n",
-      "2024-03-06 10:34:49,711 INFO bluesky.RE.state - Change state on <bluesky.run_engine.RunEngine object at 0x7fc07ca39150> from 'idle' -> 'running'\n",
-      "2024-03-06 10:34:49,713 INFO root - taxi phase start\n",
-      "2024-03-06 10:34:49,736 INFO root - taxi busy started\n",
-      "2024-03-06 10:34:52,331 INFO root - taxi busy cleared\n",
-      "2024-03-06 10:34:52,334 INFO root - taxi phase end\n",
-      "2024-03-06 10:34:52,337 INFO root - fly phase start\n",
-      "2024-03-06 10:34:52,366 INFO root - fly busy started\n",
-      "2024-03-06 10:35:04,447 INFO root - fly busy cleared\n",
-      "2024-03-06 10:35:04,447 INFO root - fly phase end\n",
-      "2024-03-06 10:35:04,448 INFO bluesky.RE.state - Change state on <bluesky.run_engine.RunEngine object at 0x7fc07ca39150> from 'running' -> 'idle'\n",
-      "2024-03-06 10:35:04,449 INFO bluesky - Cleaned up from plan <generator object TaxiFlyScanDevice.taxi_fly_plan at 0x7fc0767f4110>\n"
+      "2024-03-06 12:31:47,071 INFO bluesky - Executing plan <generator object TaxiFlyScanDevice.taxi_fly_plan at 0x7fa6ec5d2b50>\n",
+      "2024-03-06 12:31:47,073 INFO bluesky.RE.state - Change state on <bluesky.run_engine.RunEngine object at 0x7fa6f01d6f50> from 'idle' -> 'running'\n",
+      "2024-03-06 12:32:00,947 INFO bluesky.RE.state - Change state on <bluesky.run_engine.RunEngine object at 0x7fa6f01d6f50> from 'running' -> 'idle'\n",
+      "2024-03-06 12:32:00,947 INFO bluesky - Cleaned up from plan <generator object TaxiFlyScanDevice.taxi_fly_plan at 0x7fa6ec5d2b50>\n"
      ]
     },
     {
@@ -820,11 +802,12 @@
     "            timestamps.put([])\n",
     "        else:\n",
     "            timestamps.put(timestamps.get() + [time.time() - t0])\n",
-    "            logger.info(f\"callback: {kwargs['value']}  {time.time()-t0:.4f}  {m1.position=}\")\n",
+    "            logger.debug(f\"callback: {kwargs['value']}  {time.time()-t0:.4f}  {m1.position=}\")\n",
     "    \n",
     "    scan1.current_point.subscribe(callback)\n",
     "\n",
-    "    yield from ifly.taxi_fly_plan()\n",
+    "    yield from ifly.taxi_fly_plan()  # <--- the taxi/fly scan\n",
+    "\n",
     "    t1 = time.time() - t0\n",
     "    logger.info(\"Fly time: %.3f s\", t1)\n",
     "    scan1.current_point.clear_sub(callback)\n",
@@ -841,7 +824,20 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Run the complete taxi/fly scan"
+    "### Run the complete taxi/fly scan\n",
+    "\n",
+    "Note: Includes data collection.  Plotting will follow.\n",
+    "\n",
+    "parameter | value | description\n",
+    "--- | --- | ---\n",
+    "start | -0.5 | first motor position for the step scan\n",
+    "finish | 0.5 | last motor position for the step scan\n",
+    "npts | 11 | number of data points to be collected\n",
+    "ct | 0.5 | scaler counting time per point\n",
+    "\n",
+    "The `m1` motor will be moved in constant size steps between `start` and\n",
+    "`finish`.  At each step of the scan, the scaler will be triggered to accumulate\n",
+    "counts for `ct` seconds in each of its detector channels."
    ]
   },
   {
@@ -853,40 +849,20 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2024-03-06 10:35:04,472 INFO bluesky - Executing plan <generator object taxi_fly_sscan_plan at 0x7fc076d10d50>\n",
-      "2024-03-06 10:35:04,473 INFO bluesky.RE.state - Change state on <bluesky.run_engine.RunEngine object at 0x7fc07ca39150> from 'idle' -> 'running'\n",
-      "2024-03-06 10:35:04,873 INFO root - callback: 11  0.0000  m1.position=1.2000000000000002\n",
-      "2024-03-06 10:35:04,874 INFO root - taxi phase start\n",
-      "2024-03-06 10:35:04,896 INFO root - taxi busy started\n",
-      "2024-03-06 10:35:06,841 INFO root - taxi busy cleared\n",
-      "2024-03-06 10:35:06,843 INFO root - taxi phase end\n",
-      "2024-03-06 10:35:06,844 INFO root - fly phase start\n",
-      "2024-03-06 10:35:06,867 INFO root - fly busy started\n",
-      "2024-03-06 10:35:07,633 INFO root - callback: 1  2.7594  m1.position=-0.5\n",
-      "2024-03-06 10:35:08,510 INFO root - callback: 2  3.6363  m1.position=-0.4\n",
-      "2024-03-06 10:35:09,411 INFO root - callback: 3  4.5376  m1.position=-0.30000000000000004\n",
-      "2024-03-06 10:35:10,313 INFO root - callback: 4  5.4398  m1.position=-0.2\n",
-      "2024-03-06 10:35:11,215 INFO root - callback: 5  6.3418  m1.position=-0.1\n",
-      "2024-03-06 10:35:12,112 INFO root - callback: 6  7.2392  m1.position=0.0\n",
-      "2024-03-06 10:35:13,015 INFO root - callback: 7  8.1418  m1.position=0.1\n",
-      "2024-03-06 10:35:13,916 INFO root - callback: 8  9.0429  m1.position=0.2\n",
-      "2024-03-06 10:35:14,817 INFO root - callback: 9  9.9439  m1.position=0.30000000000000004\n",
-      "2024-03-06 10:35:15,721 INFO root - callback: 10  10.8476  m1.position=0.4\n",
-      "2024-03-06 10:35:16,721 INFO root - callback: 11  11.8475  m1.position=0.5\n",
-      "2024-03-06 10:35:16,877 INFO root - fly busy cleared\n",
-      "2024-03-06 10:35:16,877 INFO root - fly phase end\n",
-      "2024-03-06 10:35:16,878 INFO root - Fly time: 12.005 s\n",
-      "2024-03-06 10:35:16,882 INFO bluesky.RE.state - Change state on <bluesky.run_engine.RunEngine object at 0x7fc07ca39150> from 'running' -> 'idle'\n",
-      "2024-03-06 10:35:16,882 INFO bluesky - Cleaned up from plan <generator object taxi_fly_sscan_plan at 0x7fc076d10d50>\n"
+      "2024-03-06 12:32:00,970 INFO bluesky - Executing plan <generator object taxi_fly_sscan_plan at 0x7fa6ed934d50>\n",
+      "2024-03-06 12:32:00,971 INFO bluesky.RE.state - Change state on <bluesky.run_engine.RunEngine object at 0x7fa6f01d6f50> from 'idle' -> 'running'\n",
+      "2024-03-06 12:32:13,392 INFO root - Fly time: 11.998 s\n",
+      "2024-03-06 12:32:13,396 INFO bluesky.RE.state - Change state on <bluesky.run_engine.RunEngine object at 0x7fa6f01d6f50> from 'running' -> 'idle'\n",
+      "2024-03-06 12:32:13,397 INFO bluesky - Cleaned up from plan <generator object taxi_fly_sscan_plan at 0x7fa6ed934d50>\n"
      ]
     },
     {
      "data": {
       "text/plain": [
        "BlueskyRun\n",
-       "  uid='15c8333a-9553-4c29-8563-c7c3e2ad3618'\n",
+       "  uid='0169d961-d823-4848-b62b-bbfe92d621a1'\n",
        "  exit_status='success'\n",
-       "  2024-03-06 10:35:04.474 -- 2024-03-06 10:35:16.881\n",
+       "  2024-03-06 12:32:00.972 -- 2024-03-06 12:32:13.395\n",
        "  Streams:\n",
        "    * primary\n"
       ]
@@ -1279,13 +1255,12 @@
        "  * time              (time) float64 1.71e+09\n",
        "Dimensions without coordinates: dim_0, dim_1, dim_2, dim_3\n",
        "Data variables:\n",
-       "    scan1_m1          (time, dim_0) float64 -1.1 -0.87 -0.64 ... 0.74 0.97 1.2\n",
-       "    scan1_I0          (time, dim_1) float32 2.0 4.0 3.0 2.0 ... 5.0 1.0 1.0 4.0\n",
-       "    scan1_scint       (time, dim_2) float32 2.0 1.0 2.0 2.0 ... 5.0 2.0 3.0 4.0\n",
-       "    scan1_timestamps  (time, dim_3) float64 2.759 3.636 4.538 ... 10.85 11.85</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-4fadb087-a070-43d1-98d0-ad48aba43d16' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-4fadb087-a070-43d1-98d0-ad48aba43d16' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 1</li><li><span>dim_0</span>: 11</li><li><span>dim_1</span>: 11</li><li><span>dim_2</span>: 11</li><li><span>dim_3</span>: 11</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-74b9b6cd-6bf4-4130-9609-d4fcf4d9f091' class='xr-section-summary-in' type='checkbox'  checked><label for='section-74b9b6cd-6bf4-4130-9609-d4fcf4d9f091' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.71e+09</div><input id='attrs-29511b67-c927-4955-91ea-c52921f9dc63' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-29511b67-c927-4955-91ea-c52921f9dc63' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7faa539b-6461-4c82-ba3a-4a4e6cf6b077' class='xr-var-data-in' type='checkbox'><label for='data-7faa539b-6461-4c82-ba3a-4a4e6cf6b077' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([1.709743e+09])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-c9b9eb17-9cba-450b-96d9-c0abd305b8a6' class='xr-section-summary-in' type='checkbox'  checked><label for='section-c9b9eb17-9cba-450b-96d9-c0abd305b8a6' class='xr-section-summary' >Data variables: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>scan1_m1</span></div><div class='xr-var-dims'>(time, dim_0)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-1.1 -0.87 -0.64 ... 0.74 0.97 1.2</div><input id='attrs-c81d1386-7b18-4705-b4c3-6cdd85f6601f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c81d1386-7b18-4705-b4c3-6cdd85f6601f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-67b17108-4e38-40b8-bce8-b39e9b5215be' class='xr-var-data-in' type='checkbox'><label for='data-67b17108-4e38-40b8-bce8-b39e9b5215be' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>object :</span></dt><dd>scan1</dd></dl></div><div class='xr-var-data'><pre>array([[-1.1 , -0.87, -0.64, -0.41, -0.18,  0.05,  0.28,  0.51,  0.74,\n",
-       "         0.97,  1.2 ]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>scan1_I0</span></div><div class='xr-var-dims'>(time, dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>2.0 4.0 3.0 2.0 ... 5.0 1.0 1.0 4.0</div><input id='attrs-1ade04ae-7227-48cd-ab84-887dff05d9d7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1ade04ae-7227-48cd-ab84-887dff05d9d7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8368006e-74d9-41de-a684-efc5779dbf86' class='xr-var-data-in' type='checkbox'><label for='data-8368006e-74d9-41de-a684-efc5779dbf86' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>object :</span></dt><dd>scan1</dd></dl></div><div class='xr-var-data'><pre>array([[2., 4., 3., 2., 4., 8., 8., 5., 1., 1., 4.]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>scan1_scint</span></div><div class='xr-var-dims'>(time, dim_2)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>2.0 1.0 2.0 2.0 ... 5.0 2.0 3.0 4.0</div><input id='attrs-fc79d4e5-2ce5-4327-96c6-6b9e59fae83f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fc79d4e5-2ce5-4327-96c6-6b9e59fae83f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-71b9b036-3fa0-44e2-b7d6-27bf774838ff' class='xr-var-data-in' type='checkbox'><label for='data-71b9b036-3fa0-44e2-b7d6-27bf774838ff' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>object :</span></dt><dd>scan1</dd></dl></div><div class='xr-var-data'><pre>array([[2., 1., 2., 2., 4., 8., 6., 5., 2., 3., 4.]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>scan1_timestamps</span></div><div class='xr-var-dims'>(time, dim_3)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.759 3.636 4.538 ... 10.85 11.85</div><input id='attrs-171be151-3a5d-48cd-a3d1-3fdf0b569469' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-171be151-3a5d-48cd-a3d1-3fdf0b569469' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c787f349-7a14-420b-89b3-06ea791623b0' class='xr-var-data-in' type='checkbox'><label for='data-c787f349-7a14-420b-89b3-06ea791623b0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>object :</span></dt><dd>scan1</dd></dl></div><div class='xr-var-data'><pre>array([[ 2.75930977,  3.63625598,  4.53758407,  5.43974614,  6.34175968,\n",
-       "         7.2391746 ,  8.14181399,  9.0429256 ,  9.9439249 , 10.84752321,\n",
-       "        11.84742022]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-dd553c92-22e4-46f5-8a00-3094046356a2' class='xr-section-summary-in' type='checkbox'  ><label for='section-dd553c92-22e4-46f5-8a00-3094046356a2' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-b21ba5c5-a7a2-4b6b-a243-e536e2514478' class='xr-index-data-in' type='checkbox'/><label for='index-b21ba5c5-a7a2-4b6b-a243-e536e2514478' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([1709742916.881003], dtype=&#x27;float64&#x27;, name=&#x27;time&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-7f517668-4981-4796-b725-0ead78d5b5b3' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-7f517668-4981-4796-b725-0ead78d5b5b3' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
+       "    scan1_m1          (time, dim_0) float64 -0.5 -0.4 -0.3 -0.2 ... 0.3 0.4 0.5\n",
+       "    scan1_I0          (time, dim_1) float32 2.0 2.0 1.0 1.0 ... 3.0 3.0 5.0 2.0\n",
+       "    scan1_scint       (time, dim_2) float32 3.0 2.0 2.0 2.0 ... 5.0 4.0 3.0 3.0\n",
+       "    scan1_timestamps  (time, dim_3) float64 2.729 3.7 4.601 ... 10.91 11.82</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-fcb62892-ecbf-4a3a-9e66-e306f95ae5a0' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-fcb62892-ecbf-4a3a-9e66-e306f95ae5a0' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 1</li><li><span>dim_0</span>: 11</li><li><span>dim_1</span>: 11</li><li><span>dim_2</span>: 11</li><li><span>dim_3</span>: 11</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-90347978-f1f9-48af-853c-1b05359a6b1e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-90347978-f1f9-48af-853c-1b05359a6b1e' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.71e+09</div><input id='attrs-0a9ac833-5926-41bd-928c-13ba87dd0369' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0a9ac833-5926-41bd-928c-13ba87dd0369' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0694234a-41ed-4d7c-bc88-34ba0dca5a05' class='xr-var-data-in' type='checkbox'><label for='data-0694234a-41ed-4d7c-bc88-34ba0dca5a05' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([1.70975e+09])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-aa4ed780-c5d8-4046-9963-c3022285aaea' class='xr-section-summary-in' type='checkbox'  checked><label for='section-aa4ed780-c5d8-4046-9963-c3022285aaea' class='xr-section-summary' >Data variables: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>scan1_m1</span></div><div class='xr-var-dims'>(time, dim_0)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-0.5 -0.4 -0.3 -0.2 ... 0.3 0.4 0.5</div><input id='attrs-1bddc7fe-dac6-4b6d-82e3-d049f1513ced' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1bddc7fe-dac6-4b6d-82e3-d049f1513ced' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-addec942-6c9e-4d34-8545-26bfdb12fa27' class='xr-var-data-in' type='checkbox'><label for='data-addec942-6c9e-4d34-8545-26bfdb12fa27' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>object :</span></dt><dd>scan1</dd></dl></div><div class='xr-var-data'><pre>array([[-0.5, -0.4, -0.3, -0.2, -0.1,  0. ,  0.1,  0.2,  0.3,  0.4,  0.5]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>scan1_I0</span></div><div class='xr-var-dims'>(time, dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>2.0 2.0 1.0 1.0 ... 3.0 3.0 5.0 2.0</div><input id='attrs-3b4c0461-6bba-4147-94c9-6b455ca89004' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3b4c0461-6bba-4147-94c9-6b455ca89004' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-eaa9a854-8e00-4d7a-b638-a0d74c19bdf3' class='xr-var-data-in' type='checkbox'><label for='data-eaa9a854-8e00-4d7a-b638-a0d74c19bdf3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>object :</span></dt><dd>scan1</dd></dl></div><div class='xr-var-data'><pre>array([[2., 2., 1., 1., 2., 8., 8., 3., 3., 5., 2.]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>scan1_scint</span></div><div class='xr-var-dims'>(time, dim_2)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>3.0 2.0 2.0 2.0 ... 5.0 4.0 3.0 3.0</div><input id='attrs-fbd1da12-6f4b-4fdf-97e2-21fce887708f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fbd1da12-6f4b-4fdf-97e2-21fce887708f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a4159ce0-4fa6-4d56-b99f-ac95077f6309' class='xr-var-data-in' type='checkbox'><label for='data-a4159ce0-4fa6-4d56-b99f-ac95077f6309' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>object :</span></dt><dd>scan1</dd></dl></div><div class='xr-var-data'><pre>array([[3., 2., 2., 2., 2., 7., 6., 5., 4., 3., 3.]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>scan1_timestamps</span></div><div class='xr-var-dims'>(time, dim_3)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.729 3.7 4.601 ... 10.91 11.82</div><input id='attrs-99b85f37-97ed-434c-8a0d-97122f7bfa6e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-99b85f37-97ed-434c-8a0d-97122f7bfa6e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b4c1d9f6-59b1-445b-b9a1-176296ee6283' class='xr-var-data-in' type='checkbox'><label for='data-b4c1d9f6-59b1-445b-b9a1-176296ee6283' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>object :</span></dt><dd>scan1</dd></dl></div><div class='xr-var-data'><pre>array([[ 2.72867441,  3.699718  ,  4.60067749,  5.49965453,  6.40006113,\n",
+       "         7.30474806,  8.20696402,  9.10958171, 10.00753117, 10.91087532,\n",
+       "        11.8152566 ]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-73c27559-610d-4c36-bd6e-07019eeee243' class='xr-section-summary-in' type='checkbox'  ><label for='section-73c27559-610d-4c36-bd6e-07019eeee243' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-87d08062-a999-4a28-86b6-62dc470cb9e2' class='xr-index-data-in' type='checkbox'/><label for='index-87d08062-a999-4a28-86b6-62dc470cb9e2' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([1709749933.3953764], dtype=&#x27;float64&#x27;, name=&#x27;time&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a63ba7b6-3387-40ab-a789-63ade9636c82' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-a63ba7b6-3387-40ab-a789-63ade9636c82' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
       ],
       "text/plain": [
        "<xarray.Dataset>\n",
@@ -1294,10 +1269,10 @@
        "  * time              (time) float64 1.71e+09\n",
        "Dimensions without coordinates: dim_0, dim_1, dim_2, dim_3\n",
        "Data variables:\n",
-       "    scan1_m1          (time, dim_0) float64 -1.1 -0.87 -0.64 ... 0.74 0.97 1.2\n",
-       "    scan1_I0          (time, dim_1) float32 2.0 4.0 3.0 2.0 ... 5.0 1.0 1.0 4.0\n",
-       "    scan1_scint       (time, dim_2) float32 2.0 1.0 2.0 2.0 ... 5.0 2.0 3.0 4.0\n",
-       "    scan1_timestamps  (time, dim_3) float64 2.759 3.636 4.538 ... 10.85 11.85"
+       "    scan1_m1          (time, dim_0) float64 -0.5 -0.4 -0.3 -0.2 ... 0.3 0.4 0.5\n",
+       "    scan1_I0          (time, dim_1) float32 2.0 2.0 1.0 1.0 ... 3.0 3.0 5.0 2.0\n",
+       "    scan1_scint       (time, dim_2) float32 3.0 2.0 2.0 2.0 ... 5.0 4.0 3.0 3.0\n",
+       "    scan1_timestamps  (time, dim_3) float64 2.729 3.7 4.601 ... 10.91 11.82"
       ]
      },
      "execution_count": 15,
@@ -1314,11 +1289,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Plot the data\n",
-    "\n",
-    "Follow the steps from the [plotting howto](https://bcda-aps.github.io/bluesky_training/howto/_plot_x_y_databroker.html#3.-Show-the-(primary)-data).\n",
+    "### Plot the data from the last scan\n",
     "\n",
-    "First, setup the plotting support in this notebook."
+    "Following the steps from the [plotting howto](https://bcda-aps.github.io/bluesky_training/howto/_plot_x_y_databroker.html#3.-Show-the-(primary)-data)..."
    ]
   },
   {
@@ -1329,7 +1302,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.legend.Legend at 0x7fc075ad17d0>"
+       "<matplotlib.legend.Legend at 0x7fa6eedd7d50>"
       ]
      },
      "execution_count": 16,
@@ -1338,7 +1311,7 @@
     },
     {
      "data": {
-      "image/png": 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27t2L4cOHo3z58khLS8Ps2bPRuXNn7N27F/Pnzzf4+CkpKTh9+nTG9o0bNxAeHo4iRYrgmWeeKXDsAPKMvyBCQ0ORkJCArVu3olatWvDw8DDd2jD5juQwMn0HfxCRdUi4laCkwElRACVyjwxEmzxZBuJVraooWq3KAerjjz8k4MqV1Y5EFYWdbvrpp7lPKZ04Ue43hdOnTytt27ZVSpQoobi6uioVK1ZUZs+enXH//PnzlUqVKinOzs5KYGCgMnz48Iz7Zs6cqQQGBiru7u5K27ZtlV9//VUBoNy/f19RlJwHO65YsULJetrUDZp88tKiRYtCx55f/Mhh8Obx48cz7r9//74CQNmeZRDy4MGDFT8/P5NPN9U8DtBs4uLi4OPjg9jYWHh7e5vz0ERkAsdnbked915AlGMwglIjAI0GsbFA6dJAXBywciXQpYvaUeYjNhYoUUKmoZ45A5h5sJvaHj16hCtXrqBs2bJwc3NTOxxSUV7vBX3P31zdlIgKJXbNbgDA1VLNMjrIfXyAIUPk/smTc558YVF8fGStEEAyISIqMCYWRFQoXv/uAQCkNs4+PfPddwFXVxm6sGuXGpEZSDcI3UIXdiLrEhERgSJFiuR6iYiIUDtEk+Gy6URUYGmP0lDx3n4AQGDPZtnuCwgA/u//gPnzpQpjixZqRGiALl2At9+WTCgqCggKUjsismJBQUEIDw/P835bxcSCiArswrITqIIExMIH5btUe+r+998Hvv8e2LABCA8Hatc2e4j6CwoCnn0WOHgQWLUKGDxY7YjIijk5OeU7M8RWsSuEiAosZrl0g5wv0QSOLk8XlCpXDnj5Zdn+6itzRlZAL74o1+wOISowJhZEVGAuhySxSKzdLNd9PvhArv/6C7h40RxRFYIusdi2TWaKEJHBmFgQUYEo6QrKRkliUbRz7olFrVpA+/ZAejowfbq5oiugypWBSpVk2un69WpHQ2SVmFgQUYFE7rqCgPRopMAZFfs1yHPfcePkesECK1iNnLNDiAqFiQURFUjEIqlfcc6rPtyL5b1oUrNmQJMmQEpK5vpfFkvXHbJunSx6QUQGYWJBRAWSvlu6Qe5WyX95cY0GGDtWtr/7DnjwwISBFVaDBkBgIBAfL2MtiMggTCyIqEBKXZHEwiMs9/EVWXXsCFSrJufrefNMGVkhOTjI4mQAu0MMpdUCO3YAixfLtZlW01TLqVOn0KNHD4SGhkKj0WDWrFkGPX7gwIF4UddC9th3332XUU67Xr162L17t/ECNhMmFkRksDtnbqN8ylkAwDOvNtHrMQ4Oma0Ws2YBDx+aKDhj0H3Yr1wpo04pf8uXA6GhUhq9b1+5Dg2V221UUlISypUrhylTpiAgIKDQz/fnn39ixIgR+Oijj3D8+HE0b94c7du3t7oqnUwsiMhgl37bBwC46FoVxSr46f24l18GypQBYmKAhQtNFJwxPP884O0N3LolBbMob8uXAz17AtevZ7/9xg253YTJxdKlS1GjRg24u7vDz88PrVu3RmJiIgDg559/RrVq1eDq6orAwEAMGzYs43EzZ85EjRo14OnpiZCQEAwZMgQJCQkZ9y9cuBC+vr7YuHEjqlSpgiJFiqBdu3aIzjL6uEGDBpg2bRp69+4NV1fXQv8uM2fOxOuvv4433ngDVapUwaxZsxASEoJ5Ft3E9zQmFkRksIebpRskqqx+3SA6zs7A6NGyPW0akJZm7MiMxMVF+m4A++wOURQgMVG/S1wc8M47Oa80p7vt3XdlP32ez4AV66Kjo9GnTx+89tprOHPmDHbs2IHu3btDURTMmzcPQ4cOxZtvvomTJ09i1apV2SphOjg44Ntvv8V///2HX375Bdu2bcOYMWOyPX9SUhKmT5+O3377Dbt27UJERARG697ARpaSkoKjR48iLCws2+1hYWHYt2+fSY5pKizpTUQG8zsriYVDC8MSCwB47TVg4kTgyhUpmtW3r7GjM5IXX5SxAitWyGInj1dutQtJSUCRIsZ5LkWRlgwfH/32T0gAPD312jU6OhppaWno3r07ypQpAwCoUaMGAODzzz/He++9h3fffTdj/wYNMqdFjxgxImO7bNmymDRpEt5++2189913GbenpqZi/vz5KF++PABg2LBhmDhxon6/h4Hu3LkDrVaLkiVLZru9ZMmSuHnzpkmOaSpssSAigyTdSUKlhKMAgDJ9DU8sPDzkCywg52uLXVK9XTtpubhwAThzRu1oKAe1atVCq1atUKNGDfTq1Qs//PAD7t+/j5iYGERFRaFVq1a5Pnb79u1o06YNSpUqBS8vL7z66qu4e/duRjcKAHh4eGQkFQAQGBiImJgYk/5OmicSWEVRnrrN0jGxICKDnP/9EFyQipsOQQhuFlqg5xgyRL4QnzxpwQUuvb0B3YnJ3rpDPDyk5UCfy7p1+j3nunX6PZ+Hh95hOjo6YvPmzVi/fj2qVq2K2bNno1KlSrh161aej7t27Ro6dOiA6tWrY9myZTh69Cjmzp0LQFopdJydnbM9TqPRQDFRJly8eHE4Ojo+1ToRExPzVCuGpWNiQUQGebBGukEul2oOjUPBvkkVLZq5eOjkycaKzATsdVEyjUa6I/S5hIUBwcG5dxVpNEBIiOynz/MZ+O1co9GgadOm+Oyzz3D8+HG4uLhg8+bNCA0NxdatW3N8zJEjR5CWloYZM2agUaNGqFixIqKiogz9KxmVi4sL6tWrh82bN2e7ffPmzWjSRL+ZV5aCiQURGaRIuCQWqc8a3g2S1ciR0tOwZ49cLFKXLnKiO3z46RkPJBwdM8upPpkU6H6eNUv2M7KDBw/iyy+/xJEjRxAREYHly5fj9u3bqFKlCiZMmIAZM2bg22+/xYULF3Ds2DHMnj0bAFC+fHmkpaVh9uzZuHz5Mn777TfMnz/f4OOnpKQgPDwc4eHhSElJwY0bNxAeHo6LBVxtb9SoUfjxxx/x888/48yZMxg5ciQiIiIwWJeFWwvFzGJjYxUASmxsrLkPTUSFlJacpsTCS1EA5eyS44V+vkGDFAVQlE6dCh+byTRpIkHOnat2JCbz8OFD5fTp08rDhw8L/iTLlilKcLD8rXSXkBC53UROnz6ttG3bVilRooTi6uqqVKxYUZk9e3bG/fPnz1cqVaqkODs7K4GBgcrw4cMz7ps5c6YSGBiouLu7K23btlV+/fVXBYBy//59RVEUZcGCBYqPj0+2461YsULJetq8cuWKAuCpS4sWLfSKf8CAAUrXrl2z3TZ37lylTJkyiouLi1K3bl1l586dBv1NCiuv94K+52+Noph36FRcXBx8fHwQGxsLb29vcx6aiArp3J/hqNS7DuLgBc/k+3B0Kdy30AsXZDFRRQFOnAAeD+i3LNOmAWPGAG3aAJs2qR2NSTx69AhXrlzJqPhYYFotsHu3rDQXGAg0b26SlgoynbzeC/qev9kVQkR6u7lU+izO+zUpdFIBABUqSP0kAPjqq0I/nWnoxlls3w7cv69qKBbP0RFo2RLo00eumVTYJSYWRKQ354OSWCTULtz4iqx0Zb6XLJHaFhanQgWgalWp5qXvDAiyexEREShSpEiuF2sr020IFsgiIr0o6QrK3pAFkXw7GS+xqFtXJgxs2gTMmAHMmWO0pzaeF18ETp+W2SH9+qkdDVmBoKAghIeH53m/reIYCyLSS+SuKwhpUQ4pcEba7QfwKK5/vYH8bN8OvPAC4OYGXLsG+Psb7amN4/BhoGFDmQ55544EakOMNsaCrB7HWBCR2Vz7Q7pBzhWpZ9SkApDu+IYNgUePMmcuWpT69YFSpWQti1xqIxCRYGJBRHpJ3ymJxd3KxusG0dFoMsdazJ0r61VZFI3GLoplpXOJeLtnjPcAx1gQkV6Crkhi4d7G+IkFAHTtClSuDJw9C/zvf8D775vkMAX34ouS9axcCcyfb1MzHlxcXODg4ICoqCiUKFECLi4uVrc+BRWOoihISUnB7du34eDgABcXlwI/F8dYEFG+7l24i2IViwMA7pyOQfEqJUxynIULgf/7PyAgQGaIWFR3f2oqUKIEEBsrtRqamSbBUktKSgqio6ORlJSkdiikIg8PDwQGBuaYWOh7/maLBRHl6+Kv+9AQwCWXyihvoqQCkCXUx4+X6tm//gq8+abJDmU4Z2egUydg0SLpDrGxxMLFxQWlS5dGWloatFqt2uGQChwdHeHk5FTo1iomFkSUr6RN0g1yo2wzlM9n38JwcQHee0/WEZk6FXj9dQvrcXjxxczEYto0gxfMsnQajQbOzs5PrepJZAgO3iSifBU7I4mFQ3PTf0sfNAgoVgy4dAlYtszkhzNMu3aAq6sEd+qU2tEQWSQmFkSUp4f3HqJy/GEAQOm+pk8sPD2Bd96R7SlTZB0Ri1GkiKwZAtj07BCiwmBiQUR5Or/oMFyQilsOAQhpUc4sxxw2DPDwAI4ft8B1v+xg2ilRYTCxIKI83V8t3SCXg5pD42CeMQV+fpkDN6dMMcsh9de5s4ytOHoUsOH1HogKiokFEeXJM1wSi5SG5p0F8d57MhFjxw7gwAGzHjpv/v5A06ayvXKlurEQWSCDEovQ0FBoNJqnLkOHDjVVfESkIm2KFhVv7wMA+Hc3b2IRHAy88opsW9yS6uwOIcqVQYnF4cOHER0dnXHZvHkzAKBXr14mCY6I1HVp1Sn4IBbxKIIKPWqa/fjvvy+9Dv/8I4uLWgxdYrFzJ3DvnqqhEFkagxKLEiVKICAgIOOyZs0alC9fHi1atDBVfESkouil0g1yvlhjOLmZv+xNlSqZ5/CpU81++NyVLw/UqAFotcDatWpHQ2RRCjzGIiUlBb///jtee+21PKt0JScnIy4uLtuFiKyD0wFJLOJrqVdlUrc42aJFFjZWkt0hRDkqcGLxzz//4MGDBxg4cGCe+02ePBk+Pj4Zl5CQkIIekojMLPS6JBY+HdVLLBo2BF54AUhLA2bOVC2Mp+kSiw0bgIcPVQ2FyJIUeBGytm3bwsXFBatXr85zv+TkZCQnJ2f8HBcXh5CQEC5CRmThru+9huBmoUiFE1JuPYCnv6dqsWzeDISFSW2La9eA4sVVCyWTogBlygCRkTI7pEsXtSMiMil9FyErUIvFtWvXsGXLFrzxxhv57uvq6gpvb+9sFyKyfNcWSWvFOc+6qiYVANC6NVC3LpCUBMyerWoomTQadocQ5aBAicWCBQvg7++Pjh07GjseIrIQ2p2SWNyprP4qnhoNMG6cbM+eDSQkqBtPBl1isWqV9NUQkeGJRXp6OhYsWIABAwbAyYmLoxLZqsBLkli4tVI/sQCAbt2AChWA+/eBH35QO5rHnnsOKFoUuHsX2LdP7WiILILBicWWLVsQERGB1157zRTxEJEFeHDlPiok/wcAKP9qU5WjEY6OwJgxsj1jBpBl6JZ6nJykxDfA7hCixwxOLMLCwqAoCipWrGiKeIjIAlz4Rb59X3GuiBLV/FWOJlP//kBQEHDjhkw/tQi67pAVKyxsKVYidXCtECJ6SuJG6Qa5HmoZ3SA6rq7AqFGyPXWq1KdSXVgY4OYGXL0KnDihdjREqmNiQURPKXpaEgs0s6zEApBVT319gXPnLGQNME9PSS4AdocQgYkFET3h0YNHqBx3CABQuq/lJRZeXsCwYbI9ebKF9D5w2ilRBiYWRJTN+T+OwBUpiHEoidIvPKN2ODl65x3A3R04cgTYtk3taCADOB0cgPBw6RIhsmNMLIgom3urpBvkUmAzaBxyXwdITSVKALr6fFOmqBsLACkF2ry5bFtE/wyRephYEFE2HsclsUhpYHndIFm9955MQd2yRVouVMfuECIATCyIKIv0tHRUvL0XAFCim2UnFmXKAH37yrZFtFp07SrXu3YBd+6oGwuRiphYEFGGS6tPw1d5gAR4ouJLtdUOJ18ffCDXy5fLLBFVlS0L1KoFpKcDa9aoHAyRephYEFGG6L+lG+R80UZwcrP8kv3VqsmioooCTJumdjRgdwgRmFgQURaO+yWxiKtp2d0gWY0dK9e//ioVOVXVrZtcb9okS7ES2SEmFkSUoUzkbgCAdwfrSSwaN5a1wFJTgZkzVQ6mZk0gNBR4+FCSCyI7xMSCiAAAN/ZHIFgbgTQ4ouKrjdQOxyC6Vov//Q+4d0/FQDQadoeQ3WNiQUQAgGt/yGyQ8x51UCSgiMrRGKZdOxk3mZgIzJ2rcjC6xGL1aiAtTdVQiNTAxIKIAACpO2R8xe1K1tMNoqPRZLZafPONJBiqadoU8POTppPdu1UMhEgdTCyICAAQcFESC5dW1pdYAEDPnkC5csDdu8BPP6kYiJOTlPgG2B1CdomJBREh9toDVHh0EgBQvn9TlaMpGCcn4P33ZXv6dBnMqRrd7JB//rGQVdKIzIeJBRHh/C/74QAFV52fgX/NALXDKbCBA4GSJYHISGDxYhUDadMG8PAAIiJkYTIiO8LEgoiQuFG6QSLLWGc3iI6bGzBypGx/9ZUUwVSFuzvQtq1sszuE7AwTCyJC0f8eDzJsat2JBQAMHgx4ewOnT8vEDNXoZoesWKFiEETmx8SCyM4lxyWjUtwhAEBwn+YqR1N4Pj7AkCGyPXmyikMcOnaU5VdPngQuXVIpCCLzY2JBZOfOLz4KNyTjtqYEQttUUDscoxgxAnB1BQ4elMVGVeHnJyVBAWDlSpWCIDI/JhZEdu7uShlfcSmgGTQOGpWjMY6SJYHXXpNtVZdUZxVOskNMLIjsnPsxSSweNbD+8RVZjR4NODgAGzYAx4+rFIQusdi7F4iJUSkIIvNiYkFkx9LT0lEhRkp5l3jRthKLcuWAl1+W7a++UimI0qWBunVlesqaNSoFQWReTCyI7NjldWdRTLmHJLij4st11A7H6HRlvv/+G7h4UaUgODuE7AwTCyI7FvWXdIOc820EZw9nlaMxvpo1gQ4dpMFg+nSVgtAlFps3AwkJKgVBZD5MLIjsmMN+SSxia9hWN0hWulaLBQuA6GgVAqheXfplkpOBjRtVCIDIvJhYENmxMhFSGMurve0mFs2aAU2aACkpwKxZKgSg0XB2CNkVJhZEdir68HWEpF2FFg6oOKCx2uGYjEYDjBsn2/PmAQ8eqBCEblGyNWtUXh2NyPSYWBDZqSu/y2yQ8+614RXkpXI0ptWhg/RIxMcD332nQgCNGwMlSkhWo1rFLiLzYGJBZKdSt8v4ipiKttsNouPgAHzwgWzPmgU8fGjmABwdgS5dZJvdIWTjmFgQ2amSFyWxcHnB9hMLQGpalCkD3L4tAznNLus4C9UWMCEyPSYWRHYoNiIWFR6eAACUfaWpytGYh7Mz8P77sj1tGpCWZuYAWrUCPD2B69eBo0fNfHAi82FiQWSHLvx2AI5IxzWncgioG6R2OGbzf/8nQx2uXgX+/NPMB3d3B9q1k212h5ANY2JBZIcSNkg3SGRp++gG0fHwAN59V7anTFGhR0I3O4SJBdkwJhZEdsj3pNSvUJraV2IBAEOGAEWKAP/9B6xbZ+aDd+gAODkBp04BFy6Y+eBE5sHEgsjOpCSkoFLsQQBAqd7NVY7G/IoWBd5+W7bNvqR60aJAy5ayvXKlmQ9OZB4GJxY3btzAK6+8Aj8/P3h4eKB27do4yoFIRFbj/JJjcMcj3NX4oWy7SmqHowqtVmaA7tkjl6wmTQImTDDhwbkoGdk4gxKL+/fvo2nTpnB2dsb69etx+vRpzJgxA76+viYKj4iM7c4/cia9WLIZNA4alaNRh6+vJBdA9laLSZOATz6RpMNkdPUs9u8Hbt404YGI1OFkyM5fffUVQkJCsCDLJPDQ0FBjx0REJuR+VBKLh/Xsb3yFzvjxwN27wDffAGvXAidOSM/EJ58AEyfK/SYTEgLUrw8cOQKsXg0MGmTCgxGZn0EtFqtWrUL9+vXRq1cv+Pv7o06dOvjhhx/yfExycjLi4uKyXYhIHUq6gmduSWJR/EX7TSwAqcBZrZps165tpqRCh7NDyIYZlFhcvnwZ8+bNQ4UKFbBx40YMHjwY77zzDn799ddcHzN58mT4+PhkXEJCQgodNBEVzJUN5+Cn3MVDuKFi77pqh6O6JUvkWlGk7PeHH5rpwLpxFlu2yAImRDbEoMQiPT0ddevWxZdffok6dergrbfewqBBgzBv3rxcHzNu3DjExsZmXCIjIwsdNBEVzI0/pbXinM+zcCnionI06ss6fjI9HXj2WTPVtqhSBahQQdZy37DBDAckMh+DEovAwEBUrVo1221VqlRBREREro9xdXWFt7d3tgsRqUOzR+pXPKhu390gQOZAzYkTpQqnRiOVtps3N0NyodFwdgjZLIMSi6ZNm+LcuXPZbjt//jzKlClj1KCIyDRCIqTFokh7+6tfkVXWpGL8eOCllwDdcLG9e4E2bcwQhC6xWLtWWi6IbIRBicXIkSNx4MABfPnll7h48SL++OMPfP/99xg6dKip4iMiI7l5LApl0i5DCwdUHNBY7XBUpdU+PVDz9deBGTNke+tWYM4cEwfRqBFQsiQQFwfs2GHigxGZj0GJRYMGDbBixQosXrwY1atXx6RJkzBr1iz069fPVPERkZFc+X0vAOCCe014B9t3l+SECTnP/hg1SloyAGD4cCCPcemF5+AAdO0q25wdQjbE4MqbnTp1wsmTJ/Ho0SOcOXMGgzgHm8gqpGyTbpBbz3B8RV4mTADeeUe2/+//TDwEQtcdsnKljB4lsgFcK4TITvifl8TC+XkmFnnRaICvvwYGDpRzfe/ewObNJjrYCy/IimhRUVIwi8gGMLEgsgPxUfGo+DAcAFD2labqBmMFHBxkMGePHjKu8sUXpQK30bm6yoqnALtDyGYwsSCyA+d/PQBHpCPSKRSBDYLVDscqODkBixYBYWFAUpKc///91wQH4rRTsjFMLIjsQPw6qV9xLYTdIIZwdQWWLweaNgUePJAk4/x5Ix+kQwfA2Rk4e1YuRFaOiQWRHfA5KeMr0pvYd/2KgvD0BNaskfVEYmKA1q2BPGoCGs7HR8ZaADKIk8jKMbEgsnGpSamo9OAAACDoJbZYFISvL7BxI1CpEhAZKQW0bt0y4gF03SEcZ0E2gIkFkY07/+dxeOAh7mmKoVyHymqHY7X8/WV2SOnS0h3Sti1w/76RnrxLF7k+cACIjjbSkxKpg4kFkY27/Y90g1zwbwoHJ/7LF0ZIiCxIWrKkDOTs2BFITDTCEwcFyQpoALBqlRGekEg9/JQhsnFuhyWxeFiX3SDGUKECsGmTdI/s3w906wYkJxvhiTk7hGwEEwsiG6akKyh/UxILv65MLIylZk1g/XoZ2Ll5M9CnD5CWVsgn1SUW27YBsbGFDZFINUwsiGzY1c0XUEK5jUdwRcU+9dQOx6Y0aiSTOFxcpJHhjTcKWZW7cmUZHZqaKlkLkZViYkFkw64vlvoV57wbwtXbVeVobE+rVsBffwGOjsAvvwAjRgCKUogn7NZNrjk7hKwYEwsiW7ZXukHuV2c3iKl07QosXCjbs2cDn35aiCfTdYesW2ekgRtE5sfEgsiGhVyTxMKzLQtjmdIrrwBz58r2pEnAjBkFfKIGDYDAQCA+Hti+3WjxEZkTEwsiGxVz4iZCUy8iHRpUHNBY7XBs3pAhwJdfyvbo0bKImcEcHKQJBODsELJaTCyIbNSl3/YCAC641YBPGV91g7ETY8cCY8bI9ltvAX/+WYAn0XWHrFxZyNGgROpgYkFko1K2SjfIzWc4vsJcNBpgyhRJKhRFukjWrTPwSZ5/HvD2lprhBw+aJE4iU2JiQWSjSpyTxMK5JRMLc9JoZLxF375S26JHD2DnTgOewMVFSnoCnB1CVomJBZENSriZgIpJxwEAoa8wsTA3R0eZKdKpE/DoEdC5M3DkiAFPkLUKZ6HmrxKZHxMLIht0/reDcIIW1x1LI+jZELXDsUvOzlLjomVLmeTRrh1w+rSeD27XTlouLlwAzp41ZZhERsfEgsgGxa2VwljXQthaoSZ3d1lTrEED4O5doHVr4PJlPR7o7S3VtwDODiGrw8SCyAZ5n5DxFdrGrF+hNi8vqdBdrZqsiN6mDRAVpccDdd0hHGdBVoaJBZGNSU1KRcX7BwAAgb3YYmEJ/PxksbJy5aTFIixMWjDy1KWLjAQ9fBi4ft0scRIZAxMLIhtzYem/KIJEPND4onznqmqHQ48FBgJbtgBBQcCpUzKMIi4ujwcEBACNHxc2W7XKLDESGQMTCyIbc3uFdIOcL9EUDk78F7ckZctKy4Wfn8wS6dIFePgwjwewO4SsED91iGyMy2FJLJLqsBvEElWtCmzcKGMvdu4EevWSldJzpEsstm8HHjwwU4REhcPEgsiGKOkKykdLYlGsCxMLS1WvHrBmDeDmBqxdC7z6KqDV5rBjhQqSiaSlFaCEJ5E6mFgQ2ZCI7Zfgn34LyXBBxb711Q6H8vDcc8Dy5YCTE7BkiSxilmMtrKzFsoisABMLIhsSsUjqV5zzbgA3XzeVo6H8tG8PLFoki5p+/z3wwQc5JBe6xGL9einjSWThmFgQ2ZI90g1yryrrV1iLl16SpAIApk0DJk9+Yof69YFSpYDERGDrVrPHR2QoJhZENiT4qiQWnm05vsKavP46MGOGbH/0ETBnTpY7NRrODiGrwsSCyEbcPhWDsqnnAQAVBjRRORoy1KhRwCefyPbw4cCvv2a5U5dYrFqVyyhPIsvBxILIRlz6dS8A4IJrdfiWLapyNFQQEyYA77wj26+9lmW8ZosWgI8PEBMDHDigVnhEemFiQWQjHm2VbpDo8uwGsVYaDfD118DAgdIw0bu3VOuEs7OswQ5wdghZPCYWRDai+FlJLBxbMLGwZg4OwA8/AD16ACkp0guyfz+yj7PIcV4qkWVgYkFkAxJjElEp8RgAoEw/JhbWzslJpqGGhclkkA4dgJOl2gGursClS7LYCJGFYmJBZAPO/3YQzkhDlGMwSjUurXY4ZASurlJAq2lTqebd+sUiSGjSRu78+mtg8WJgxw4O5iSLY1BiMWHCBGg0mmyXgIAAU8VGRHqKXSvdIFeCm0PjoFE5GjIWT08p/V27tozbXLI3RO74+Wegb1/g+eeB0FD83Wc5JkxQM1KiTE6GPqBatWrYsmVLxs+Ojo5GDYiIDOf1ryQWaY3YDWJrfH1l0bL3yy/HawnzoQDImjoq12+gx5KeQO+lALqrEyRRFgYnFk5OTmylILIgaY/SUPHefgBAYE8mFrbI30+Ln7zeBRIUPNkepXmcavTaOwLQdgX4ZY9UZvAYiwsXLiAoKAhly5ZF7969cfny5Tz3T05ORlxcXLYLERnPhWUn4IUExMIH5btUUzscMoXdu+EUfT3XD2wNFCAyEti926xhEeXEoMTi2Wefxa+//oqNGzfihx9+wM2bN9GkSRPcvXs318dMnjwZPj4+GZeQkJBCB01EmWKWSzfI+RJN4OjCb6s2KTrauPsRmZBBiUX79u3Ro0cP1KhRA61bt8batWsBAL/88kuujxk3bhxiY2MzLpGRkYWLmIiycTkkiUVibXaD2KzAQOPuR2RCBo+xyMrT0xM1atTAhQsXct3H1dUVrq6uhTkMEeVCSVdQNkoSi6KdmVjYrObNgeBgKNdvPB5TkZ0CDTQhwbIfkcoKVcciOTkZZ86cQSCzZCJVROy4jID0aKTAGRX7NVA7HDIVR0f83ewbKJAkIivlcarxd9NZHLhJFsGgxGL06NHYuXMnrly5goMHD6Jnz56Ii4vDgAEDTBUfEeUhcrG0Vpz1agD3Yu4qR0OmdKpSdyzrvRSa4FLZbn9YPBjLei/FqUqcakqWwaCukOvXr6NPnz64c+cOSpQogUaNGuHAgQMoU6aMqeIjojyk75bE4l4VdoPYOimA1V2mlG7bhrS2HeGkpOKLZuvxxeJq6KVyfEQ6BiUWS5YsMVUcRFQApa5IYuERxsTCbjg6Am3aIL5mExT9dycSN+9HWlo1OBVqxByR8XCtECIrdefMbZRPOQsAeObVJipHQ+bm00GSyTqJu7Fvn8rBEGXBxILISl36Tc4mF12rolgFP5WjIXNzeE4Si2bYg3/+UTcWoqyYWBBZqYebpRskqiy7QexSkyZQHBxQHpexb2kUlKdnoRKpgokFkZXyOyuJhUMLJhZ2ydsb6dVrAgBCIvfixAmV4yF6jIkFkRVKupOESglHAQBl+jKxsFeO7A4hC8TEgsgKnf/9EFyQipsOQQhuFqp2OKSWZkwsyPIwsSCyQg/WSDfI5VLNoXF4ciFtshtNmwIAaiMcF8PjcfWquuEQAUwsiKxSkXBJLFKfZTeIXQsOBkJD4Yh0NMIBrFypdkBETCyIrI42RYuKd2WqaUBPJhZ273F3SHPsZncIWQQmFkRW5uKKk/BGPOLghWe61VA7HFJblnEWu3YBd+6oHA/ZPSYWRFbm5lLpBjnv1wSOLlzN0u49Xiq9seYAHNJTsWaNyvGQ3WNiQWRlnA9KYpFQm90gBKByZaBYMbgrD1EHx9kdQqpjYkFkRZR0BWVv7AYA+HZiYkEAHBwyZoc0wx5s2gQkJakcE9k1JhZEVuT6nqsITI9CCpxR8ZWGaodDluLxOIswjz14+BDYtEnleMiuMbEgsiLX/pBukHNF6sGjuIfK0ZDFeJxYNFX2AFDYHUKqYmJBZEXSd0picbcyu0Eoi3r1AFdXeD28jQq4gNWrgbQ0tYMie8XEgsiKBF2RxMK9DRMLysLVFWgoXWPtPXfj3j1g926VYyK7xcSCyErcu3AXzySfBgCU799E5WjI4jzuDukZKMknu0NILUwsiKzExV+l2uYll8ooXqWEytGQxXlcz6JOYmZioSgqxkN2i4kFkZVI2iQnjBtl2Q1COWjcGNBoUCT6Isq630REBBAernZQZI+YWBBZiWJnJLFwaM7EgnLg6wvUkBLvg2vsBcDuEFIHEwsiK/Dw3kNUjj8MAAjpw8SCcvF4nEUnX46zIPUwsSCyAucXHYYLUnHTIRClW5ZTOxyyVI8Ti4oxe+DoCJw4AVy+rHJMZHeYWBBZgfur5RvolaBm0DhoVI6GLNbjxMLp5HGENUkAwFYLMj8mFkRWwDNcEouUhuwGoTyEhAClSwNaLd6seQAAEwsyPyYWRBZOm6JFxdsy1dS/OxMLysfjVovnnSUZ3bsXiIlRMyCyN0wsiCzcpVWn4INYxKMIKvSoqXY4ZOke17PwObkHdesC6enAmjUqx0R2hYkFkYWLXirfPM8XawwnNyeVoyGL97jFAgcOoHvnVADsDiHzYmJBZOGcDkhiEV+L3SCkh6pVpaZFYiJervwvAFlGPSFB3bDIfjCxILJgSrqCstdlNSmfjkwsSA8ODkDTpgCA8tF7UK4ckJwMbNyoclxkN5hYEFmwG/sjEKS9jlQ4oWL/Z9UOh6zF4+4Qzd496NZNbmJ3CJkLEwsiC3ZtkXSDnPOsC09/T5WjIauhG2exZw9e7Corka1ZA6SmqhgT2Q0mFkQWTLtTEos7ldkNQgaoXx9wcQFu3UJj/0soUQJ48ADYtUvtwMgeMLEgsmCBlySxcGvFxIIM4OYGNGgAAHDctxtdusjN7A4hc2BiQWShHly5jwrJ/wEAyr/aVOVoyOo8rmeBPXvw4ouy+c8/gKKoFRDZCyYWRBbqwi9SbfOKc0WUqOavcjRkdbKMs2jVCvD0BK5fB44eVTcssn1MLNSi1QI7dgCLF8u1Vqt2RGRJtFo4/7UIABDvX57vDzJckyZyff483ONj0K6d/MjuEBtmIeeVQiUWkydPhkajwYgRI4wUjp1YvhwIDQWefx7o21euQ0PldrJrEyYAf/eR90ftM4sBADVvrAdCQ/F3n+WYMEHV8MiaFC0KVK8u23v3ctqpDcv6ufHkeUWNz40CJxaHDx/G999/j5o1uXaBQZYvB3r2lDbJrG7ckNuZXNi1aueWo8eSnlCeeH8o12+gx5KeqHaO7w8yQJbukA4dACcn4NQp4MIFdcMi47K0z40CJRYJCQno168ffvjhBxQtWtTYMdkurRZ4992cR0/pbhsxgs3e9kqrRa8970IDBZon7tLd1mvvCL4/SH9ZEouiRYGWLeXHlStVi4iMzQI/NwqUWAwdOhQdO3ZE69at8903OTkZcXFx2S52a/fup1sqslIUIDJS9iP78/j98eSHg44GfH+QgXSJxbFjQGJixuyQFStUi4iMzQI/NwxOLJYsWYJjx45h8uTJeu0/efJk+Pj4ZFxCQkIMDtJmREcbdz+yLXx/kLGVLg0EBwNpacDBgxn1LPbvB27eVDc0MhIL/NwwKLGIjIzEu+++i99//x1ubm56PWbcuHGIjY3NuERGRhYoUJsQGGjc/ci28P1BxqbRZOsOCQmRopyKAqxerW5oZCQW+LlhUGJx9OhRxMTEoF69enBycoKTkxN27tyJb7/9Fk5OTtDm0Ifj6uoKb2/vbBe71by5fHvQ5NZoBSAgILOwDdmX5s2hlApGbvWLFGiAkBC+P8gwWQplAeDsEBuTWLc5YlyCkZ5LZ4ganxsGJRatWrXCyZMnER4ennGpX78++vXrh/DwcDg6OpoqTtvg6Ah8803epe+Sk4GICPPFRJbD0RFrivSGBngquVCkpxR/N50l7yMifelaLPbvB9LSMsZZbNkCxMerFhUZQXIy0L2XIwanfJPj/Wp9bhiUWHh5eaF69erZLp6envDz80N13XxpylujRjLn60mlSklWef8+0Lo1EBVl/thIVUq6gtKXtwMAkl2yt+xpQoKxrPdSnKrUXY3QyJpVqwb4+AAJCcCJE6hSBahQAUhJATZsUDs4Kqi0NKBfP2DTJmC1U3ccLt/nqX3U+tzI4QxHJjVrlrwjmjYFPv9cBtQEBkozVUyMfLu4fBkICwN27gT8/NSOmMzk2NQtqJd6FElwx8Pj5+EWcybb+6OXoyN6qR0kWR9HR6nCuX49sGcPNHXr4sUXgWnTZHZIL76prE56OjBoELBsmSxiu2YN8OxHF4BLAN57D6hXT9XPDY2imHdJmri4OPj4+CA2Ntb+xlvcvy+jtBMS5J3QsePT+1y5IslFVJSsTrh1K+DlZf5YyeyOFWuFuve3YWetd9AiPOemTaIC+fJL4KOPJIv46y/s2yffbby9gdu35eRE1kFRgJEjpVfd0RH4+2+gW4Pr0uKt0ciXkZIlTXJsfc/fXCvEnL77TpKK6tWBDh1y3qdsWWDzZmmpOHwY6NwZePjQvHGS2Z1acAh1729DKpzwzLz31A6HbE2WmSFQFDRqJOeeuDhZUoKsx2efSVIBAD///Hgwrq7iWZMmJksqDMHEwlySkjLfDWPH5j0zpGpVYONGaanYuRN46SUgNdU8cZIq4j/+CgBwsHw/lGpcWuVoyOY0aAA4O8u32cuX4eAAdO0qd3F2iPX4+mtJLABg9mzg1Vcf36F7EXUjc1XGxMJcFiyQNsfQUODll/Pfv1496S5xc5PrV19lKWcbdXndWTSMklKIATPHqBwN2SR3dylgAWRMO9Wdg1aulD57smw//wyMGiXbn38ODBv2+I779zObnXTZosqYWJhDaqqMlAKA99/PeVZITp57ThYlc3ICliwBhgzJe6oqWaUbI6bCAQoOBnTFM12qqh0O2aon6lm88AJQpIgM5zpyRMW4KF9//y2DNQFg9Gjgww+z3LlunUwIqFZNpvtYACYW5vDnn8C1a0CJEsD//Z9hj23fHli0CHBwAL7/HvjgAyYXNiTqYCSevfA7AMBz0liVoyGblnWcBQBX18yhXuwOsVwbNsi0Ut1MkKlTn+hJt7BuEICJhemlpwNTpsj2iBHSJGmol16SpAKQlg/d85HVOz/ka7ggFcd9W6L6G43UDodsWZMmcn32rHTLAlyUzMLt3g107y6N3i+/DMyb90RS8eiRTCMGmFjYlXXrgFOnZCDmkCEFf57XXwdmzJDtDz8E5s41TnykmnsX7qL+MUkYtaPZWkEm5ucnA8MBYN8+ANJi4ewsucbZsyrGRk85dgzo1EkmBXbsCPz2Ww7FM7dsARITZamIevVUiTMnTCxMSVEA3Sqwb78N+PoW7vlGjQI++US2hw0Dfv+9cM9Hqjrx5hwUQSLOutdGvXFhaodD9uCJ7hAfHxlrAWTOWCT1nTkDtG0r04FbtJAxFs7OOeyYtRskr5mGZsbEwpT27JFvBi4u0g1iDBMmAO+8I9sDB7Jz1EolxiSi5s5vAQD3Bo2FxsFyPhTIhj2RWACZLej8KLEMV68CbdoAd+7IRJ5Vq3LpQddq5U7AorpBACYWpqUbCzFwoPGWrNVoZDLzwIHyxnr5ZanOSVblyOAfUUy5h2tO5fHstJ5qh0P2QpdYHDkitXUAdOkiNx04IGUuSD3R0bJU1I0b0mu1YYNUR83R/v0yVsbXV2YQWhAmFqZy4oSMr3BwkCmmxuTgAPzwA9Cjh6wk1LWrvMnIKqQkpKDCqukAgGsvj4GjC1crJTMJDQWCgmR64qFDAOTHZ5+Vu3VfgMn87t2TJaIuXcpegDlXuiamTp1y6SdRDxMLU/lKKimiVy/gmWeM//xOTjINNSxMBu906CDJDFm8QyP+QJD2Om45BKDhnFfzfwCRsWg0T9WzADg7RG3x8VJZ4L//JNHbskWuc6UoFjnNVIeJhSlcviwFrQCpO2Eqrq5SQKtpU+DBA0kyLlww3fGo0NLT0hH4mySdZ9qOhJuvm8oRkd3JY5zFtm1AbKz5Q7Jnjx5Jo/OhQ9JCsXkzUK5cPg/67z9p2nBzA9q1M0uchmBiYQrTp0v9irZtgTp1THssT08p+V27NnDrlnTQRUaa9phUYIfHr0L5lLOIhQ/qfj9Y7XDIHukSi337MpYJqFwZqFRJ6iXoyiKQ6aWmSpmi7dulIsGGDZkzgvOka61o00bOARaGiYWx3bolRd0BYNw48xzT11cWLatUCYiIkOQiJsY8xya9KekKPGfL9OPjTYbCOzj3ZYeJTKZGDTmLxccDJ09m3Nytm1xzdoh5pKfLGPzVq6XhYfXqzOVc8mXB3SAAEwvj++YbIDkZaNTIvCN1/f2lDa10aeD8eWktefDAfMenfIXP2oHqiYfwEG6o9r931A6H7JWjY2YVzhy6Q9atk48wMh1FAYYOBf74Q4bLLV0q9Sr0EhEh1bMcHIDOnU0aZ0ExsTCm2NjMipj5LY1uCiEhMuqnZEkgPFzKtSUmmjcGypX2S5l+fKj6ayhRvaTK0ZBdy2GcRYMGMis+Pl6a5sl0PvwQmD9fThG//y4f1XrTVTJr2lTWn7JATCyMaf58KZVWpYp6mWSFCsCmTdI9sm+fFJrn1w/VnVl0DPXvbkIaHFFu7mi1wyF7p0ssdu/OWNTQwSFz1W3ODjGdKVMySxz9739SisggFt4NAjCxMJ5Hj6RwFSAzQRxU/NPWrCkjsDw9Jcno21fmrZNq7o+TmSAHQ3sj5LmyKkdDdq9hQ6l9EBUlpR4f052rVq6UMQBkXPPmZQ69mzYtcyl0vd29C+zcKdtMLOzAL7/IwM2QEDmRq61RI/l0cHGRKamDBvGTQiVXN1/As5FLAQAlpptw+jGRvjw8MhetytId8vzzUunx1i3g4EGVYrNRv/8u4yoA4OOPgdEFabhcu1Zm8tSsqcecVPUwsTCGtDRg6lTZHj3acqqgtWoF/PWXDNZauBAYOTKj2ZPMJ+KdaXBEOg75d0TFHjXUDodI5DDOwsUls7+fs0OMZ9UqmQGiKMDw4cDEiQV8IivoBgGYWBjH0qVSFMvPT5Y3tyRdu0pSAQDffiuLmJHZ3DwWhWfP/gIAcJtgpunHRPrIIbEAslfh5PeQwtu6VWpVaLXAq68Cs2YVcFx/UpIUugCYWNg8RckcifPOOxZZrASvvALMmSPbEycCM2eqG48dOTt4FlyRgn+9m6Hm203VDocok27K6enT0nf/WLt20nJx4QJw9qxKsdmIgwflu11ystQJ+emnQgy/27IFePhQSgrUrm3MMI2OiUVhbdwI/PuvJBTDhqkdTe6GDgW++EK233sP+PFHdeOxAw+u3Ee9w/MAACkjx6ocDdETSpSQkpuAzCB7zNtbelEBzg4pjJMnZf2PxEQpkLl4sdSsKLCs3SDmLmVgICYWhTVZKinirbeAYsXUjSU/48YBY8bI9ptvyvgLMpnwN7+DFxJwwbU66n/SQe1wiJ6WT3cIx1kUzMWLkkzcvw80biwJmqtrIZ4wLS1z6VldiVQLxsSiMPbtA3btksGaI0eqHU3+NBrptnnrLenC6ddPyuyR0SXdSUK1rd8AAGJeGwuNg2V/wyA7lUti0aWLfFwcPgxcv65CXFbs+nVZVeHWLaBWLfmILXQP+d690l1VrFjma2bBmFgUhm5p9P79geBgdWPRl0Yj1UH79JEsuEcPSY7IqA4PWYASym1EOoXi2ZmGVsAhMhPdSerwYem/fywgQL5pA5lflCl/t29LS8W1a0DFitJT7utrhCfWNR117lzI/hTzYGJRUKdOyX+cRpPZvWAtHB2l7kanTlLYq1Mn4OhRtaOyGalJqSi/fBoA4HKP9+HkZvkfBGSnypWTLCI1VZKLLNgdYpjYWFmi6exZKWe0ebOsrlBoimI100x1mFgUlK61ont3WVXU2jg7yxiLli1lcYC2bWV0OBXaoff+RLD2Gm5rSqDhd/+ndjhEudNogObNZTuXcRbbt3M9w/wkJcn3s+PHZT3ILVtk8oZRnDgh1VHd3YGwMCM9qWkxsSiIa9dkWTpAyndbK3d3aXVp0ED679q0Aa5cUTsqq5aelg7/BTL9+FTrEXAv5q5yRET5yGWcRYUKQNWq0mO6dq0KcVmJlBT5frlnD+DjI6soVKxoxAPoWivCwqRiqhVgYlEQM2ZItZNWreSkbM28vGRdkWrVZN2A1q2B6Gi1o7JaRyauQ4XkU4iDF+r8METtcIjyp0ss9u2Tz7Us2B2St7Q0GQO/caOc89etkwGbRqX741vBbBAdJhaGun07swbEOBuppOjnJx2C5cpJBdE2bbIVzCH9KOkK3GbJ9ONjDd+GTxlfdQMi0kfNmkCRIjJI4NSpbHfpEov162U4FmVKT5dZ+0uXSkGxf/7JrDlmNFeuAOHhUlWrUycjP7npMLEw1OzZMnq6fn3ghRfUjsZ4AgOlYzAoSD5c2reXsRektxPf7UHN+H1Ihguq/G+E2uEQ6cfJKXMKyBPdIfXrA6VKSZGnrVtViM1CKYrUGVywQMbCL1ki38eMbuVKuX7uOfkCaCWYWBgiPl4SCwAYO9biq58ZrGxZabnw85MR4l26ZJuCRnlLmShjKw5WHoiStQNVjobIALmMs9Bo2B2Sk4kTZc0PAPj5ZxP2UljZbBAdJhaG+P57GR5dsaLVvdB6q1pVOgy9vIAdO2T1nNRUtaOyeOeXnkCD2+ughQPKzHlf7XCIDKNLLHbvfmrlMd1H3apVTw3BsEuzZmWu5Th7tiwsZhJ37sjrAciCI1aEiYW+kpMzF+8aM0bav2xVvXrAmjWAm5tcDxjAT5R83B79uLUipBfKtHpG5WiIDPTss9Ilcv06EBGR7a4WLWS2Q0wMcOCASvFZiJ9/ziyy/PnnJl4eas0aGchRuzYQGmrCAxkfEwt9/f67zJooVUpWC7V1zz0HLF8uHzaLF8siZmlp0oqxeLFcM9kAAETsuIxG1/4EABT7yoqnH5P98vQE6taV7Se6Q5ydM8cN2vOiZEuXAoMGyfbo0cCHH5r4gFbaDQIYmFjMmzcPNWvWhLe3N7y9vdG4cWOsX7/eVLFZDq02syDWqFGFXE3GirRvDyxaJCOS//c/qVP//PNA375yHRoqyYeduzpsOhyRjiN+bVG5Tx21wyEqmFzGWQDZx1k80VNicyZMACZNyn7bhg3ysZeeLvnX1KkmHmKXmChd0oBVTTPVMSixCA4OxpQpU3DkyBEcOXIEL7zwArp27YpTT0xRsjkrVgAXLgBFi2amrPbipZdk0TLg6VkiN24APXvadXJx+79baHjqZwCA03gbmX5M9imPxKJdO/k+denSUzNSbY6jI/DJJ5nJxZ49UgBLN9RMt0CbSW3aJPN7y5YFatQw8cFMQCmkokWLKj/++KPe+8fGxioAlNjY2MIe2jzS0xWlXj1FARRl/Hi1ozG/tDRFCQ6W3z+ni0ajKCEhsp8d2tZ4nKIAyokijZR0bbra4RAV3K1bmf/X9+49dXenTnLXpEkqxGZmEyfK7zp4sKJ4e2f+WT791EwBvPqqHHDkSDMdUD/6nr8LPMZCq9ViyZIlSExMRGPdHOgcJCcnIy4uLtvFqmzdKgt0ubsD77yjdjTmt3t33usmKwoQGZk5etmOxEbEou7+uQCApOFcGp2snL9/Zi3qffueutuepp2OHw8MHw7Mnw/oTlnjx2fOBjGptDRg9WrZtsLxFUABBm+ePHkSRYoUgaurKwYPHowVK1agatWque4/efJk+Pj4ZFxCQkIKFbDZTZZKihg0CCheXN1Y1KBveW87LAN+/K358EEcLrpWRYOJndUOh6jw8ugO6dxZugCOHpXvErbs6tXsPbwuLlK7wix27wbu35fzjdFLeZqHwYlFpUqVEB4ejgMHDuDtt9/GgAEDcDqPVTHHjRuH2NjYjEukNb0jDx0Ctm2TmRGjRqkdjToC9Sz0pO9+NuLRg0eouulrAMDNVz+AgxMnWJENyFrP4gn+/kDTprJty60W0dGyZNKNG/Kzi4ssNPbkgE6T0f1xO3eWc48VMvjT0MXFBc888wzq16+PyZMno1atWvjmm29y3d/V1TVjFonuYjV0M0H69gXKlFE3FrU0bw4EB+c9WsnfP3PpZTtxaOgv8E+/hRuOIXh2Vh+1wyEyDl1icfhwjouD2Hp3yL17sojopUvy8+jRUsJo4sTsAzpNRlGsctGxJxX6a5aiKEhOTjZGLJbl7NnMSdvWvDR6YTk6ArrEMbfkIiHB9oeKZ5H2KA1l/poKALjYdTScPZxVjojISJ55BihZUr6iHzny1N26xGLnTjkJ25L4eJlh/99/8vOIEcC0abI9fryZkovjx6VAmYeHNJtYKYMSiw8//BC7d+/G1atXcfLkSXz00UfYsWMH+vXrZ6r41DN1qmSPXbtKmWt71r27VIcpVSr77cHBQOXKQFKSpPkXLqgTn5kdGrMUZdIu467GD/Xnva52OETGo9HkOc6ifHmZ/ajVAmvXmjk2E3r0SJKmQ4dknP6wYcDXX2ffR5dcmLQuoK61ol07CcRKGdSBc+vWLfTv3x/R0dHw8fFBzZo1sWHDBrQxybJuKoqMlEqbgCw2RpJcdO0qfa/R0TKmonlzSfOff16W9m3dWj6MrG2ArgGUdAV+P0j57pPPv4uW/p4qR0RkZM2aAcuW5ZhYAHICPnlSzoH9+5s1MpNITQVeflmG03l5yXX9+jnvO368iYOx4mqbWWkUxbx11OLi4uDj44PY2FjLHW8xapSkqy1aSOlqyltMjJQAP3dOpqvt3i3jLmzQkc83oP749kiAJ1IvRqBo+WJqh0RkXEeOAA0aAL6+wN27Unk3i2PHZDkhDw9ZJ8uKv1gjPV0WEVu0SJZG2rBBPvZVcemSdEU5OspnajHL+2zR9/zNoexPuntXVjEFgHGspKgXf39Zbr10aeD8eaBtW1kF1gY5TZfpx0fqvcWkgmxT7dqydsiDB0AOM/7q1JFGyaQk+be3VooiXR6LFsnki6VLVUwqAGDlSrlu0cIikwpDMLF40pw5Uqe9dm0ZN0D6CQkBtmyRgV/h4UDHjvJ3tCEn/7cPtWN3IQXOqDRvpNrhEJmGkxPQqJFs59AdotHYxuyQDz8E5s2T3+f33+UjS1U2MBtEh4lFVomJwLffyvbYsWYoCG9jKlSQGve+vlK5r3t3matlI5ImyPTjgxX6I7BBsMrREJlQHgM4gczEYtUqKRRpbaZMkQsg6yu+/LK68SAmJvNv3bWrurEYAROLrH78UeZQlS8P9OihdjTWqWZNYP16aUrdtElqgFjjJ88TLq48hWdvrkI6NCj1zRi1wyEyrTwKZQEypKpoUek5zqH6t0WbNy+zl3vaNAtZV3L1aumbqVfPJga/M7HQSUkBZsyQ7ffft9qKZxahUSPpL3Rxkbq4gwbJKCkrdnPU49aKUt1Rrn0llaMhMrFGjWQQYUSEXJ7g5CSFIQHr6g5ZtAgYOlS2P/5YCmBZBBuZDaLDxEJn8WKZZhoQAAwYoHY01q9VK+Cvv+TDaeFCYORIycit0PW919Do8h8AAJ8v7LhYGtmPIkVklCYA7N2b4y5Zx1lYw7/2qlXy0a4ossCY2db+yE9CQuYoWCYWNiQ9PbN898iRMu+ICq9rV0kqABm7YpalAY3v0pAZcIIWx4q2QtUBDdQOh8g88hlnERYmH5VXrgAnTpgxrgLYtg146SUpbvXqq8CsWRY0hG7jRhmLVr48UK2a2tEYBRMLQFLZM2cAHx9g8GC1o7Etr7wCzJWlxTFxIjBzprrxGOjOmdtocOJH+YHTj8me5JNYeHpmTpyz5O6QgweBLl3k3N2tG/DTT0+V5lBX1tkgFpPtFI4l/XnVoSiZS6MPGQJYatEuazZkCPDll7L93nsySNZK/Pfmt/DAQ5z2qI86772gdjhE5qNbyvTkyVzr0lj6tNOTJ2X9j8REoE0b6fG2qOFzqanAmjWybSPdIAATC1lN59AhadN79121o7FdY8cCYx7PpnjzTRl/YeHio+JRe+8cAEDskLHQONjGtwkivQQESCVIRQH2789xl86d5dt/eDhw9apZo8vXxYuSTNy/DzRuLGtKurqqHdUTdu6UpM3fP7N2iA1gYqFrrXjtNSnuRKah0cjE8bfekg+qfv2AdevUjipPR9/6Hr7KA1xxroiGX7yodjhE5pdPd0jx4rJkEJBZONISXL8uSxfdugXUqiUfNZ6WuKyPrqmnSxcZ6G4j7DuxOHZMai04OlrQvCMbptHIeIs+faS2RY8ewK5dakeVo+S4ZFRaJ+NBrvf7AI4utvNPT6S3fOpZAJbXHXL7trRUXLsmNfs2bpSafRZHUWxumqmOfScWupkgL78MlC2rbiz2wtER+OUXoFMnWau4UydZ9MjCHBz+OwLToxDtUAoNv+mndjhE6tA1Rxw6lGsVXV2hyF27ZFEyNcXGylJFZ89mX2XAIh09Cty4IU0prVqpHY1R2W9iceGCrDoDcGl0c3N2ljEWLVvKsuvt2uW42JFatClahCyWpPNcp1Fw9ba0jlkiM6lQAShRQpKKo0dz3KVsWeluSE/PHIeohqQk+Z5y/LiEvGWLrItosXStFe3b21yJA/tNLKZNk/+Ejh2BGjXUjsb+uLvLNN8GDaQucJs2MiHeAhwatwJlUy/gvqYo6s2zhHq/RCrRaPIdZwGo3x2SkiJLE+3ZI1UDNm0CKlZUJxa9rVgh1zaw6NiT7DOxiIqS5niArRVq8vKSdUWqVZPXpHVruVaRkq7AZ76sTvRv8+HwCvJSNR4i1emRWOjOjZs2ScuBOaWlyVjwjRsBDw8ZqFm7tnljMNj589JK6+QEdOigdjRGZ5+JxaxZkuI2bZr5T0Pq8POTcrblygGXL0vFnbt3VQvn2LStqJp0FElwR43vh6sWB5HF0H1G7t2b65o/NWsCoaHAw4eSXJhLerrMXl+6VJYm+ucfoEkT8x2/wHRTaJ5/3kJHlhaO/SUW9+/L8nYAKylaisBA6RANCgJOnZIxF3FxqoSi+UqmHx+uNQh+lYqrEgORRalTR7ou792TUZE50GjM3x2iKFJvb8ECqaWxZIn0qFoFG50NomN/icV338miL9Wr22QTlNUqW1ZaLvz8ZJZIly7y9ceMTi04hLr3tyEVTnhm3ntmPTaRxXJ2zizepMc4i9WrpXvC1CZOlMZnAPj5ZysaqnDzZmbBsS5d1I3FROwrsUhKAr75RrbHjrWZuuw2o2pV6Sj18pKKdL16SclbM4n/+PHS6OX7oVRjSx5OTmRmetSzaNpUvhfcu5fnbkYxa1bmmobffmtlC1KvXi3NLQ0aAMHBakdjEvaVWCxYINVTQkOldgVZnnr1ZM6amxuwdq0sRajVmvywl9edRcMoGaUdMHOMyY9HZFX0GMDp5CQlvgHTdof8/LMsQg0AkybJEuhWJeuiYzbKfhKL1FSZYgpIlU2LWomGsnnuOWD5cnmNliyRRcwUxaSHvDFiKhyg4GBAVzzTpapJj0VkdRo3loEMV69KvexcZB1nYYp/2b//BgY9ngE+ejTw0UfGP4ZJxcXJeDLAZsdXAPaUWPz1l9R4LVFC1gUhy9a+PbBokXyYff898MEHJksuog5G4tkLvwMAPCdx+jHRU7y8Mudw7t2b625hYTLlMyJCFiYzpg0bZFpperokF1OnWmFv9oYNMiOxYkWgcmW1ozEZ+0gs0tNlASwAGDFCRjiT5XvpJUkqAGlt0i0YZ2Tnh3wNF6TiuG9LVH/DdlYYJDIqPbpD3N2lpDZg3O6Q3bulAFZqqvRiz5tnhUkFkH02iFX+Avqxj8Ri3Trgv/8k6x4yRO1oyBCvvw7MmCHbH30EzJlj1Ke/d+Eu6h+T5EU7mq0VRLnSI7EAjD/t9NgxKdX98KFM5Pv1VytdCDQlRcaNATbdDQLYS2Kha60YPNgmi5HYvFGjgE8+ke3hw+WTxUhOvDkHRZCIs+61UW9cmNGel8jmNG0q1ydOyGpfuejYUU78J05IzbvCOHNGWkDi4mTola4QllXasUN+kYAA4Nln1Y7GpGw/sdizR/oEXVwyhxKT9ZkwAXjnHdl+7bXMOvuFkBiTiJo7vwUA3Bs0FhoH222aJCq0oCCpkJueDhw4kOtufn6SBACFa7W4elUKXt25A9SvL7M0rboXW/fH6NpVxo7ZMNv+7YDMfvmBA6XCI1knjQb4+mt5HbVaoHfvzNHVBXRk8I8optzDNafyaPhVD+PESWTL9KhnARS+OyQ6WpYOunFDytusXw94exfsuSxCerrNV9vMyrYTixMnZHyFgwPw/vtqR0OF5eAA/PAD0KOH9Fd27ZpZwc5AKQkpeGaVjN249vIYOLlx+jFRvgwcZ7F3LxATY9gh7t2T2SWXLmUW5C1u7dX1Dx+WbMnLS9YHsXG2nVh8JZUU0bMn8Mwz6sZCxuHkJNNQw8KkkmqHDsC//xr8NIdG/IFS2kjccghAwzmvmiBQIhvUvLlcHzwoyX0uSpcG6taVL+pr1uj/9PHxMtP8v/+yLyFk9XStFR06AK6uqoZiDrabWFy+LMWVAC6NbmtcXaWAVtOmwIMHkmScP6/3w9PT0hH4mySdZ9qOhJuvm4kCJbIxlSrJIIpHj2S6Rh4M7Q559EgaIQ8dyr7osU2wo24QwJYTi+nTJV1u21ZW5yPb4ukpX4Vq15a21tatpSqPHg6PX4XyKWcRCx/U/X6waeMksiUajcHdIZs2ybqPeUlNlbI127dLb8GGDUC1aoUP1yKcPSsXZ2dpjrEDtplY3LolBeUBtlbYMl9fWbSsUiUgMlKGkN+6ledDlHQFnrNlQO/xJkPhHWzNI8KIVKBnYlG9urQ4JCfLv2lu0tNlTPbq1bJE0OrVMgvEZqxcKdetWgE+PurGYia2mVh88428m599FmjRQu1oyJT8/aXNtHRp6Q5p21a6R3IRPmsHqicewkO4odr/3jFfnES2ImtikUeZfY0m/+4QRQGGDgX++EOGTy1daoMf2bqp8XbSDQLYYmIRGwvMnSvb48bZdNlUeiwkREZ5lSwpAzk7dgQSE3PcVfulFEs7VP01lKhe0pxREtmGunWlaeHuXeDcuTx31S3guWaNdHc86cMPgfnz5WP699/lX9emREXJQFcA6NJF3VjMyPYSi/nzpbpZlSqZa/iS7atQQTpzfX2BffvkEy05OdsuZxYdQ/27m5AGR5SbO1qdOImsnYtLZuXIfLpDGjeWdR8fPAB27cp+35QpmUWR//c/WQPE5qxaJdeNGtlVHSXbSCy0WimX+ssvmVNMP/jA5qub0RNq1pRKOp6e0j3Sp48kFzt2AIsXQzvyPQDAwdDeCHmurLqxElkzPQtlOTpmflHP2h0yb540KAOyvqBuKXSbY2ezQXQMOvNOnjwZDRo0gJeXF/z9/fHiiy/iXD5NYaY0YQLwd5/lQGioFB0ZOBC4fx9wdMTSte6YMEG10GzKhAnApEk53zdpEizr79yoEfDPP0hzdJG+zWLF5L3Rty+q394BALhZqp5lxUxkbXT1LPJpsZgwQaaRAnKOVRTp8hg6NPNpRttq42FsLLBtm2wzscjdzp07MXToUBw4cACbN29GWloawsLCkJhLf7apVTu3HD2W9IRy/Xq22xWtFt3/7o1q55arEpetcXSUNcCeTC4mTZLbLW6lwdatcbDxCCiAFNHKQgHQbe97fG8QFUbjxtIifPmyjCPIhaOj1LNzdgauX5dEY+DAzDGfrVubJVp1rF8vA0uqVJGZa3ZEoyh5DOvNx+3bt+Hv74+dO3fiOd2qM/mIi4uDj48PYmNj4V2Y4u9aLRAaCuX6deQ0PFOBBpqQYODKFQs881kfXRIxcWLmSuYzZ8rCo+9Y2uQKrRalmoXCMZrvDSKTqVMHCA8H/voL6NUr1910nx1P+uyznG+3GS+/LH+bceOAL79UOxqj0Pf8XagFEmIfL51brFixXPdJTk5GcpZBdHFxcYU5ZKbdu4FckgoA0ECR2ga7dwMtWxrnmHZs/HjJ5T75JPuHwcyZcrEkLbAbO3A91/v53iAygmbNJLHYsyfPxGL8eJmstWxZ5m0TJth4UpGcLOtUAXbXDQIUIrFQFAWjRo1Cs2bNUL169Vz3mzx5Mj777LOCHiZ30dHG3Y/ylJ4OXLyY/TY3C62EXUYbDeQwte0pfG8QFVyzZsCcOfmOswCABQukCr+iyKSSTz81Q3xq2rZNyo0GBdlYtS/9FDixGDZsGE6cOIE9+bypxo0bh1GjRmX8HBcXh5CQkIIeNpO+U3fsaIqPqSgKMGyY9JUCUsgmLU3moI8fr25sOdoRCOizgCDfG0QF17SpXIeHy+phXl657jprVmZSkZIi3SMW+dlhLLrZIF272uXsxAL9xsOHD8eqVauwfft2BAcH57mvq6srvL29s12MonlzIDgYSi6dIenQIC0wJHP0MhXYhx/K9DBAWjxTU2WsRU4DOi1CPu8NBRopqsX3BlHBBQfLjLz0dODAgVx3yzo+KznZwj87jCE9PbOMtx12gwAAFAOkp6crQ4cOVYKCgpTz588b8tAMsbGxCgAlNja2QI/P6q/eyxQtNEo6NIoiCbGiAIoWGkULjfJqkWXKrVuFPoxdmzw580/bpUv2+yZOlNsnTlQntrzk9t5If/ze+Kv3MrVDJLJ+r7wi/1vjx+d4d26fEZb82VFo+/bJL+fjoyjJyWpHY1T6nr8NarEYOnQofv/9d/zxxx/w8vLCzZs3cfPmTTx8+NA0WU8+TlXqjmW9l0ITXCrb7emBwRjgsRS/JnTPb+kIykPWIjZhYZlJuM748fLtQ6s1f2z5ye29oQkJxrLeS3GqUneVIiOyIfnUs9Bq5TPiyW4PS/7sKDRdN0jHjtL3Y4cMmm6qyWXdjQULFmDgwIF6PYfRpptmpdXKCP/oaOk3b94cFy47onlzWeyySROp9uzpaZzD2YNFi4D+/eVr/scfW3GzZQ7vDU4xJTKS06dlfXN3dykI5eysdkTqUhSpWXHhQr7TcK2RvufvQtWxKAiTJBa5OHFCVsp78EBW1F69GnB1NekhbcKqVUD37nJOHj5cFovlWm5E9JT0dFkM5N49WWyrYUO1I1KXLtFycQHu3MlzQKs10vf8bdPDVZ9cOqJvX5nNQLnbtg146SVJKl59VUZzM6kgohw5OGTODtFj2qnN03WDtG5tc0mFIWw6sQBk6YiVKyWBXL4ceOMNSbLpaQcPyoJBycmyOOhPP9nlTCkiMoRuQTImFna76NiT7OK00aqVdHc5OsoCqCNGZNaqJ3HyJNC+PZCYKN1GixdLvQoiojxlTSzs+YP1+nXg8GFp4tUt6Wqn7CKxAKROycKFsj17th1UfjPAxYuSTNy/L2sLrVjBsShEpKd69eQD4/ZtGbRor1atkusmTYCSJdWNRWV2k1gAwCuvAHPnyvakSbKQlr27fl26A2/dAmrVkvL2nD1DRHpzdc0ctLl7t7qxqGnFCrm2824QwM4SCwAYMiRzobnRo4Eff1Q3HjXdvi0tFdeuARUrAhs3Ar6+akdFRFbH3sdZ3L8P7Ngh2127qhqKJbC7xAIAxo4FxoyR7TffBP78U9141BAbC7RtC5w9K9WtN2+2+9Y7IiqofApl2bx162TKYbVqQIUKakejOrtMLDQaYMoU4K23ZKzRK69krnBrD5KSgE6dgOPHAX9/YMsWoHRptaMiIqvVuLF8sF68CNy8qXY05sfZINnYZWIByP/A3LmZtS169AB27VI7KtNLSZHiV3v2AD4+UpG0YkW1oyIiq+brC9SoIdt796oaitk9eiQFkwAmFo/ZbWIByPTThQvl2/ujR3J95IjaUZlOWhrQr5+MpfDwkFaaWrXUjoqIbIK9jrPYulXm6QcHywwZsu/EApDS9n/9BTz/PBAfD7RrJ1VZbU16uownWbpUioX984/MiiIiMgp7TSyyzgZhmWIATCwAyPo5K1cCDRoAd+/KTIkrV9SOyngUBXjvPWDBAmmlWbJEfkciIqPRJRbHjwMJCerGYi5abWb9CnaDZGBi8ZiXl3STVa8OREVJbYeoKLWjMo6JE2XNDwD4+Wcp101EZFQhITIKXKsFDhxQOxrz2L9f5u37+gLPPad2NBaDiUUWfn4ymLFcOeDyZSAsTFowrNmsWcCECbL97beysBgRkUnYW3eIbjZIp05cMj4LJhZPCAyU6ZelSgGnTsmYi7g4taMqmJ9/BkaOlO1Jk2QJdCIik7GnehaKwmmmuWBikYOyZaVglJ+fzBLp0gV4+FDtqAzz99/AoEGyPXo08NFH6sZDRHZA12Jx4ACQmqpuLKZ26hRw6RLg5ibfQCkDE4tcVKki0zK9vICdO4Fevazn/2TDBplWmp4uycXUqRysTERmULWqjDdITAT+/VftaExLNxukTRsusPQEJhZ5qFcPWLNGEtK1a2V8glardlR527NHCmClpgIvvwzMm8ekgojMxMEBaNpUtm29O4TdILliYpGP554Dli8HnJxkmuaQIdK1ZomOHQM6dpRumw4dgF9/lemlRERmYw8DOCMi5APXwQHo3FntaCwOEws9tG8PLFok76Hvvwc++MDykouzZ2VRsbg4SYZ0hbCIiMwqa2JhaR+UxrJypVw3bQqUKKFuLBaIiYWeXnpJkgoAmDYNmDxZ3XiyunpV6m7cuQPUrw+sXi1Fv4iIzK5+fflWc+uWDG60RewGyRMTCwO8/jowY4Zsf/QRMGeOuvEAQHS0JBU3bsi4qfXrAW9vtaMiIrvl5iZljAFg9251YzGFe/dkRD/AxCIXTCwMNGoU8Mknsj18uIxjUMu9e1LE69KlzCmyxYurFw8REQDbrmexdq2M4q9ZU6op0lOYWBTAhAnAO+/I9muvZbaKmVN8vIz9+O+/zKJeQUHmj4OI6Cm2PIAz66JjlCMmFgWg0QBffw0MHCiJ68svy4ndXB49kvf0oUNSxGvzZibORGRBdEsnnz8PxMSoG4sxJSVJoSCAiUUemFgUkIMD8MMPQI8eQEqKvMf27zf9cXX1KbZtk+JdGzYA1aqZ/rhERHorWlRWdASAvXvVjcWYtmyR+fylSwO1a6sdjcViYlEITk4yDTUsTArNdehg2mJz6enA//2frNLr5iazP+rXN93xiIgKzBa7Q7LOBmHlwVwxsSgkV1cpoNWkCfDggSQZFy4Y/ziKAgwbJomMk5PUqWjRwvjHISIyCltLLNLS5FsdwG6QfDCxMAJPTxkoXLu2dCe2bi2F2Yzpww8zy3P//rtU2CQisli6xOLYMWnStXb79gF37wLFimXOeqEcMbEwEl9fWbSsYkVJKtq0Md6YpSlT5AIA//ufjLEgIrJopUsDwcHyTf/gQbWjKTzdbJDOnaXZmHLFxMKI/P1lbE/p0jIYum1b6R4pjHnzgHHjZHvatMyl0ImILJpGYzv1LBSF1TYNwMTCyEJCJLkoWRIID5cui4K2Ai5aBAwdKtsffwyMHm20MImITM9WxlmcOCFrJ7i7y0A6yhMTCxOoUAHYtEm6R/btA7p1A5KTDXuOVauAAQMkUR4+HJg40SShEhGZji6x2L9fukSsla61IiwM8PBQNRRrwMTCRGrWlHU7PD2lgFXfvvr/X23bJoueabXAq68Cs2ZxZhMRWaFq1QAfHyAhQb71Wyt2gxiEiYUJNWok70cXF5mS+sYbUosiLwcPAl26SAtHt27ATz9JMS4iIqvj6JhZhdNau0OuXpV+bQcHGbhJ+eIpy8Ratwb+/FP+v375BRgxQro3cnLypKz/kZgos0oWL+bgYyKyctY+zkLXWvHcc7KGAuWLiYUZvPgisGCBbM+eDXz66dP7XLwoycT9+0DjxjKzydXVrGESERmfLrHYuhX44w9gxw7p57V0Wq3E+v338nOXLqqGY00MTix27dqFzp07IygoCBqNBv+osbSnFerfH5gzR7YnTQJmzMi87/p1adm4dUtmk6xdK2MziIis3o0bcn3vHtCvH/D880BoqPQPW6rlyyXG558HzpyR26ZOteyYLYjBiUViYiJq1aqFObqzJOlt6FCgVSvZHj0a+PFH4PZtaam4dk1uHzBA1u8hIrJ6y5dLMvGkGzeAnj0t80S9fLnEdv169ttv3bLcmC2MRlFy6/HX48EaDVasWIEXDRgpGxcXBx8fH8TGxsLb27ugh7ZaiiI1Y3QL/pUrB1y+LNujRmVvySAislparXzrf/IEraPRAAEBMvbC0dGsoeVKq5Wum+jonO/XaKSa6JUrlhOzGel7/jb50MDk5GQkZyniEBcXZ+pDWjSNBti9G2jYEDhyJDOpeOcdJhVEZEN27849qQDkW1Z0NFC+vPliKixFASIj5Xdr2VLtaCyWyROLyZMn47PPPjP1YayKRgMcOCDTUNPTAWdn4Jtv1I6KiMiIcvvW/yRnZ8v59q/VAqmp+e+n7+9mp0w+K2TcuHGIjY3NuERGRpr6kFbhyy8lqXBxkffxpElqR0REZESBgfrtt2kT8PChZVw2bTLu72anTJ5YuLq6wtvbO9vF3k2aBHzyiZTpTk6W608+YXJBRDakeXMZj5Bb2WCNRhZXsqQlyK0xZgvEOhZmljWpGD9ebhs/nskFEdkYR8fMPt4nT9S6n2fNspxuEMA6Y7ZABicWCQkJCA8PR3h4OADgypUrCA8PR0REhLFjs0labfakQkeXXFhD3RgiIr107w4sXQqUKpX99uBgub17d3Xiyos1xmxhDJ5uumPHDjz//PNP3T5gwAAsXLgw38fb+3RTIiK7o9XKTIroaBmf0Ly55X/rt8aYTUzf83eh6lgUBBMLIiIi66Pv+ZtjLIiIiMhomFgQERGR0TCxICIiIqNhYkFERERGw8SCiIiIjIaJBRERERkNEwsiIiIyGiYWREREZDRMLIiIiMhonMx9QF2hz7i4OHMfmoiIiApId97Or2C32ROL+Ph4AEBISIi5D01ERESFFB8fDx8fn1zvN/taIenp6YiKioKXlxc0ua15bwHi4uIQEhKCyMhIrmmiIr4OloGvg2Xg62AZ7PV1UBQF8fHxCAoKgoND7iMpzN5i4eDggODgYHMftsC8vb3t6o1jqfg6WAa+DpaBr4NlsMfXIa+WCh0O3iQiIiKjYWJBRERERsPEIheurq749NNP4erqqnYodo2vg2Xg62AZ+DpYBr4OeTP74E0iIiKyXWyxICIiIqNhYkFERERGw8SCiIiIjIaJBRERERkNE4vHvvjiCzRp0gQeHh7w9fXV6zGKomDChAkICgqCu7s7WrZsiVOnTpk2UBt3//599O/fHz4+PvDx8UH//v3x4MGDPB8zcOBAaDSabJdGjRqZJ2Ab8t1336Fs2bJwc3NDvXr1sHv37jz337lzJ+rVqwc3NzeUK1cO8+fPN1Okts2Q12HHjh1Pvfc1Gg3Onj1rxohty65du9C5c2cEBQVBo9Hgn3/+yfcx/F/IjonFYykpKejVqxfefvttvR8zdepUzJw5E3PmzMHhw4cREBCANm3aZKyHQobr27cvwsPDsWHDBmzYsAHh4eHo379/vo9r164doqOjMy7r1q0zQ7S2488//8SIESPw0Ucf4fjx42jevDnat2+PiIiIHPe/cuUKOnTogObNm+P48eP48MMP8c4772DZsmVmjty2GPo66Jw7dy7b+79ChQpmitj2JCYmolatWpgzZ45e+/N/IQcKZbNgwQLFx8cn3/3S09OVgIAAZcqUKRm3PXr0SPHx8VHmz59vwght1+nTpxUAyoEDBzJu279/vwJAOXv2bK6PGzBggNK1a1czRGi7GjZsqAwePDjbbZUrV1bGjh2b4/5jxoxRKleunO22t956S2nUqJHJYrQHhr4O27dvVwAo9+/fN0N09geAsmLFijz34f/C09hiUUBXrlzBzZs3ERYWlnGbq6srWrRogX379qkYmfXav38/fHx88Oyzz2bc1qhRI/j4+OT7N92xYwf8/f1RsWJFDBo0CDExMaYO12akpKTg6NGj2d7LABAWFpbr333//v1P7d+2bVscOXIEqampJovVlhXkddCpU6cOAgMD0apVK2zfvt2UYdIT+L/wNCYWBXTz5k0AQMmSJbPdXrJkyYz7yDA3b96Ev7//U7f7+/vn+Tdt3749Fi1ahG3btmHGjBk4fPgwXnjhBSQnJ5syXJtx584daLVag97LN2/ezHH/tLQ03Llzx2Sx2rKCvA6BgYH4/vvvsWzZMixfvhyVKlVCq1atsGvXLnOETOD/Qk7MvrqpOU2YMAGfffZZnvscPnwY9evXL/Axnlz6XVEUi14OXg36vg7A039PIP+/6csvv5yxXb16ddSvXx9lypTB2rVr0b179wJGbX8MfS/ntH9Ot5NhDHkdKlWqhEqVKmX83LhxY0RGRmL69Ol47rnnTBonZeL/QnY2nVgMGzYMvXv3znOf0NDQAj13QEAAAMlWAwMDM26PiYl5Knu1d/q+DidOnMCtW7eeuu/27dsG/U0DAwNRpkwZXLhwweBY7VHx4sXh6Oj41LfivN7LAQEBOe7v5OQEPz8/k8VqywryOuSkUaNG+P33340dHuWC/wtPs+nEonjx4ihevLhJnrts2bIICAjA5s2bUadOHQDSR7pz50589dVXJjmmtdL3dWjcuDFiY2Nx6NAhNGzYEABw8OBBxMbGokmTJnof7+7du4iMjMyW8FHuXFxcUK9ePWzevBndunXLuH3z5s3o2rVrjo9p3LgxVq9ene22TZs2oX79+nB2djZpvLaqIK9DTo4fP873vhnxfyEHao4ctSTXrl1Tjh8/rnz22WdKkSJFlOPHjyvHjx9X4uPjM/apVKmSsnz58oyfp0yZovj4+CjLly9XTp48qfTp00cJDAxU4uLi1PgVbEK7du2UmjVrKvv371f279+v1KhRQ+nUqVO2fbK+DvHx8cp7772n7Nu3T7ly5Yqyfft2pXHjxkqpUqX4OhhgyZIlirOzs/LTTz8pp0+fVkaMGKF4enoqV69eVRRFUcaOHav0798/Y//Lly8rHh4eysiRI5XTp08rP/30k+Ls7KwsXbpUrV/BJhj6Onz99dfKihUrlPPnzyv//fefMnbsWAWAsmzZMrV+BasXHx+f8fkPQJk5c6Zy/Phx5dq1a4qi8H9BH0wsHhswYIAC4KnL9u3bM/YBoCxYsCDj5/T0dOXTTz9VAgICFFdXV+W5555TTp48af7gbcjdu3eVfv36KV5eXoqXl5fSr1+/p6bSZX0dkpKSlLCwMKVEiRKKs7OzUrp0aWXAgAFKRESE+YO3cnPnzlXKlCmjuLi4KHXr1lV27tyZcd+AAQOUFi1aZNt/x44dSp06dRQXFxclNDRUmTdvnpkjtk2GvA5fffWVUr58ecXNzU0pWrSo0qxZM2Xt2rUqRG07dFN4n7wMGDBAURT+L+iDy6YTERGR0XC6KRERERkNEwsiIiIyGiYWREREZDRMLIiIiMhomFgQERGR0TCxICIiIqNhYkFERERGw8SCiIiIjIaJBREV2qlTp9CjRw+EhoZCo9Fg1qxZZjnuu+++i3r16sHV1RW1a9c2yzGJKG9MLIio0JKSklCuXDlMmTIlY+Vfc1AUBa+99hpefvllsx2TiPLGxILIRixduhQ1atSAu7s7/Pz80Lp1ayQmJgIAfv75Z1SrVg2urq4IDAzEsGHDMh43c+ZM1KhRA56enggJCcGQIUOQkJCQcf/ChQvh6+uLjRs3okqVKihSpAjatWuH6OjojH0aNGiAadOmoXfv3nB1dTU49pYtW2L48OEYMWIEihYtipIlS+L7779HYmIi/u///g9eXl4oX7481q9fn+1x3377LYYOHYpy5coZfEwiMg0mFkQ2IDo6Gn369MFrr72GM2fOYMeOHejevTsURcG8efMwdOhQvPnmmzh58iRWrVqFZ555JuOxDg4O+Pbbb/Hff//hl19+wbZt2zBmzJhsz5+UlITp06fjt99+w65duxAREYHRo0cb9Xf45ZdfULx4cRw6dAjDhw/H22+/jV69eqFJkyY4duwY2rZti/79+yMpKcmoxyUiI1N3DTQiMoajR48qADKW184qKChI+eijj/R+rr/++kvx8/PL+HnBggUKAOXixYsZt82dO1cpWbJkjo8vU6aM8vXXX+sfvKIoLVq0UJo1a5bxc1pamuLp6Zlteero6GgFgLJ///6nHv/pp58qtWrVMuiYRGQaTirnNURkBLVq1UKrVq1Qo0YNtG3bFmFhYejZsydSU1MRFRWFVq1a5frY7du348svv8Tp06cRFxeHtLQ0PHr0CImJifD09AQAeHh4oHz58hmPCQwMRExMjFF/h5o1a2ZsOzo6ws/PDzVq1Mi4rWTJkgBg9OMSkXGxK4TIBjg6OmLz5s1Yv349qlatitmzZ6NSpUq4detWno+7du0aOnTogOrVq2PZsmU4evQo5s6dCwBITU3N2M/Z2Tnb4zQaDRRFMervkNMxst6m0WgAAOnp6UY9LhEZFxMLIhuh0WjQtGlTfPbZZzh+/DhcXFywefNmhIaGYuvWrTk+5siRI0hLS8OMGTPQqFEjVKxYEVFRUWaOnIhsCbtCiGzAwYMHsXXrVoSFhcHf3x8HDx7E7du3UaVKFUyYMAGDBw+Gv78/2rdvj/j4eOzduxfDhw9H+fLlkZaWhtmzZ6Nz587Yu3cv5s+fb/DxU1JScPr06YztGzduIDw8HEWKFMk2UNTYLl68iISEBNy8eRMPHz5EeHg4AKBq1apwcXEx2XGJKHdMLIhsgLe3N3bt2oVZs2YhLi4OZcqUwYwZM9C+fXsAwKNHj/D1119j9OjRKF68OHr27AkAqF27NmbOnImvvvoK48aNw3PPPYfJkyfj1VdfNej4UVFRqFOnTsbP06dPx/Tp09GiRQvs2LHDaL/nk9544w3s3Lkz42ddDFeuXEFoaKjJjktEudMoxu4oJSIiIrvFMRZERERkNEwsiMikIiIiUKRIkVwvERERaodIREbErhAiMqm0tDRcvXo11/tDQ0Ph5MThXkS2gokFERERGQ27QoiIiMhomFgQERGR0TCxICIiIqNhYkFERERGw8SCiIiIjIaJBRERERkNEwsiIiIyGiYWREREZDT/DynnHysRdPgwAAAAAElFTkSuQmCC",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]