<astroquery.utils.tap.model.taptable.TapTableMeta at 0x7eff7a9d80a0>
+<astroquery.utils.tap.model.taptable.TapTableMeta at 0x7f2e23f089d0>
obj_name
+Retrieving table 'gaiadr2.panstarrs1_original_valid'
+Parsing table 'gaiadr2.panstarrs1_original_valid'...
+Done.
+TAP Table name: gaiadr2.gaiadr2.panstarrs1_original_valid
+Description: The Panoramic Survey Telescope and Rapid Response System (Pan-STARRS) is
+a system for wide-field astronomical imaging developed and operated by
+the Institute for Astronomy at the University of Hawaii. Pan-STARRS1
+(PS1) is the first part of Pan-STARRS to be completed and is the basis
+for Data Release 1 (DR1). The PS1 survey used a 1.8 meter telescope and
+its 1.4 Gigapixel camera to image the sky in five broadband filters (g,
+r, i, z, y).
+
+The current table contains a filtered subsample of the 10 723 304 629
+entries listed in the original ObjectThin table.
+We used only ObjectThin and MeanObject tables to extract
+panstarrs1OriginalValid table, this means that objects detected only in
+stack images are not included here. The main reason for us to avoid the
+use of objects detected in stack images is that their astrometry is not
+as good as the mean objects astrometry: “The stack positions (raStack,
+decStack) have considerably larger systematic astrometric errors than
+the mean epoch positions (raMean, decMean).” The astrometry for the
+MeanObject positions uses Gaia DR1 as a reference catalog, while the
+stack positions use 2MASS as a reference catalog.
+
+In details, we filtered out all objects where:
+
+- nDetections = 1
+
+- no good quality data in Pan-STARRS, objInfoFlag 33554432 not set
+
+- mean astrometry could not be measured, objInfoFlag 524288 set
+
+- stack position used for mean astrometry, objInfoFlag 1048576 set
+
+- error on all magnitudes equal to 0 or to -999;
+
+- all magnitudes set to -999;
+
+- error on RA or DEC greater than 1 arcsec.
+
+The number of objects in panstarrs1OriginalValid is 2 264 263 282.
+
+The panstarrs1OriginalValid table contains only a subset of the columns
+available in the combined ObjectThin and MeanObject tables. A
+description of the original ObjectThin and MeanObjects tables can be
+found at:
+https://outerspace.stsci.edu/display/PANSTARRS/PS1+Database+object+and+detection+tables
+
+Download:
+http://mastweb.stsci.edu/ps1casjobs/home.aspx
+Documentation:
+https://outerspace.stsci.edu/display/PANSTARRS
+http://pswww.ifa.hawaii.edu/pswww/
+References:
+The Pan-STARRS1 Surveys, Chambers, K.C., et al. 2016, arXiv:1612.05560
+Pan-STARRS Data Processing System, Magnier, E. A., et al. 2016,
+arXiv:1612.05240
+Pan-STARRS Pixel Processing: Detrending, Warping, Stacking, Waters, C.
+Z., et al. 2016, arXiv:1612.05245
+Pan-STARRS Pixel Analysis: Source Detection and Characterization,
+Magnier, E. A., et al. 2016, arXiv:1612.05244
+Pan-STARRS Photometric and Astrometric Calibration, Magnier, E. A., et
+al. 2016, arXiv:1612.05242
+The Pan-STARRS1 Database and Data Products, Flewelling, H. A., et al.
+2016, arXiv:1612.05243
+
+Catalogue curator:
+SSDC - ASI Space Science Data Center
+https://www.ssdc.asi.it/
+Num. columns: 26
+obj_name
obj_id
ra
dec
@@ -816,7 +886,7 @@ quality_flag
-<astroquery.utils.tap.model.job.Job at 0x7eff7aa87d00>
+<astroquery.utils.tap.model.job.Job at 0x7f2e23f2afa0>
@@ -837,11 +907,11 @@ source_id int64 Unique source identifier (unique within a particular Data
ref_epoch float64 yr Reference epoch 0
ra float64 deg Right ascension 0
dec float64 deg Declination 0
- parallax float64 mas Parallax 5
+ parallax float64 mas Parallax 2
Jobid: None
Phase: COMPLETED
Owner: None
-Output file: sync_20201203154608.xml.gz
+Output file: sync_20201229114647.xml.gz
Results: None
@@ -877,20 +947,20 @@ Results: None
Table length=10
-
-
+
+
+
+
+
+
+
+
+
+
+
-
-
-
+
+
+
+
+
+
![](\"https://github.com/datacarpentry/astronomy-python/raw/gh-pages/fig/gd1-4.png\")
\n",
"\n",
- "If we select stars near this line, they are more likely to be in GD-1.\n",
+ "So stars near this line have the highest probability of being in GD-1.\n",
"\n",
- "We'll start by selecting the `phi2` column from the `DataFrame`:"
+ "To select them, we will use a \"Boolean mask\". We'll start by selecting the `phi2` column from the `DataFrame`:"
]
},
{
@@ -1421,7 +1394,7 @@
}
],
"source": [
- "phi2 = df['phi2']\n",
+ "phi2 = results_df['phi2']\n",
"type(phi2)"
]
},
@@ -1454,30 +1427,10 @@
"phi2_min = -1.0 * u.deg\n",
"phi2_max = 1.0 * u.deg\n",
"\n",
- "mask = (df['phi2'] > phi2_min)\n",
+ "mask = (phi2 > phi2_min)\n",
"type(mask)"
]
},
- {
- "cell_type": "code",
- "execution_count": 30,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "dtype('bool')"
- ]
- },
- "execution_count": 30,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "mask.dtype"
- ]
- },
{
"cell_type": "markdown",
"metadata": {},
@@ -1487,7 +1440,7 @@
},
{
"cell_type": "code",
- "execution_count": 31,
+ "execution_count": 30,
"metadata": {},
"outputs": [
{
@@ -1501,7 +1454,7 @@
"Name: phi2, dtype: bool"
]
},
- "execution_count": 31,
+ "execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
@@ -1514,7 +1467,30 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "A Boolean `Series` is sometimes called a \"mask\" because we can use it to mask out some of the rows in a `DataFrame` and select the rest, like this:"
+ "The `&` operator computes \"logical AND\", which means the result is true where elements from both Boolean `Series` are true."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 31,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "mask = (phi2 > phi2_min) & (phi2 < phi2_max)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Python note: We need the parentheses around the conditions; otherwise the order of operations is incorrect."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The sum of a Boolean `Series` is the number of `True` values, so we can use `sum` to see how many stars are in the selected region."
]
},
{
@@ -1525,7 +1501,7 @@
{
"data": {
"text/plain": [
- "pandas.core.frame.DataFrame"
+ "25084"
]
},
"execution_count": 32,
@@ -1534,96 +1510,56 @@
}
],
"source": [
- "subset = df[mask]\n",
- "type(subset)"
+ "mask.sum()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "`subset` is a `DataFrame` that contains only the rows from `df` that correspond to `True` values in `mask`.\n",
- "\n",
- "The previous mask selects all stars where `phi2` exceeds `phi2_min`; now we'll select stars where `phi2` falls between `phi2_min` and `phi2_max`."
+ "A Boolean `Series` is sometimes called a \"mask\" because we can use it to mask out some of the rows in a `DataFrame` and select the rest, like this:"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "pandas.core.frame.DataFrame"
+ ]
+ },
+ "execution_count": 33,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
- "phi_mask = ((df['phi2'] > phi2_min) & \n",
- " (df['phi2'] < phi2_max))"
+ "centerline_df = results_df[mask]\n",
+ "type(centerline_df)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "The `&` operator computes \"logical AND\", which means the result is true where elements from both Boolean `Series` are true.\n",
+ "`centerline_df` is a `DataFrame` that contains only the rows from `results_df` that correspond to `True` values in `mask`; that is, in contains the stars near the centerline of GD-1."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Plotting proper motion\n",
"\n",
- "The sum of a Boolean `Series` is the number of `True` values, so we can use `sum` to see how many stars are in the selected region."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 34,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "25084"
- ]
- },
- "execution_count": 34,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "phi_mask.sum()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "And we can use `phi1_mask` to select stars near the centerline, which are more likely to be in GD-1."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 35,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "25084"
- ]
- },
- "execution_count": 35,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "centerline = df[phi_mask]\n",
- "len(centerline)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
"Here's a scatter plot of proper motion for the selected stars."
]
},
{
"cell_type": "code",
- "execution_count": 36,
+ "execution_count": 34,
"metadata": {},
"outputs": [
{
@@ -1640,8 +1576,8 @@
}
],
"source": [
- "pm1 = centerline['pm_phi1']\n",
- "pm2 = centerline['pm_phi2']\n",
+ "pm1 = centerline_df['pm_phi1']\n",
+ "pm2 = centerline_df['pm_phi2']\n",
"\n",
"plt.plot(pm1, pm2, 'ko', markersize=0.1, alpha=0.1)\n",
" \n",
@@ -1660,7 +1596,7 @@
},
{
"cell_type": "code",
- "execution_count": 37,
+ "execution_count": 35,
"metadata": {},
"outputs": [
{
@@ -1677,8 +1613,8 @@
}
],
"source": [
- "pm1 = centerline['pm_phi1']\n",
- "pm2 = centerline['pm_phi2']\n",
+ "pm1 = centerline_df['pm_phi1']\n",
+ "pm2 = centerline_df['pm_phi2']\n",
"\n",
"plt.plot(pm1, pm2, 'ko', markersize=0.3, alpha=0.3)\n",
" \n",
@@ -1719,7 +1655,7 @@
},
{
"cell_type": "code",
- "execution_count": 44,
+ "execution_count": 36,
"metadata": {},
"outputs": [],
"source": [
@@ -1738,12 +1674,25 @@
},
{
"cell_type": "code",
- "execution_count": 45,
+ "execution_count": 37,
"metadata": {},
"outputs": [],
"source": [
- "pm1_rect = [pm1_min, pm1_min, pm1_max, pm1_max, pm1_min] * u.mas/u.yr\n",
- "pm2_rect = [pm2_min, pm2_max, pm2_max, pm2_min, pm2_min] * u.mas/u.yr"
+ "def make_rectangle(x1, x2, y1, y2):\n",
+ " \"\"\"Return the corners of a rectangle.\"\"\"\n",
+ " xs = [x1, x1, x2, x2, x1]\n",
+ " ys = [y1, y2, y2, y1, y1]\n",
+ " return xs, ys"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 38,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "pm1_rect, pm2_rect = make_rectangle(\n",
+ " pm1_min, pm1_max, pm2_min, pm2_max)"
]
},
{
@@ -1755,7 +1704,7 @@
},
{
"cell_type": "code",
- "execution_count": 46,
+ "execution_count": 39,
"metadata": {},
"outputs": [
{
@@ -1791,7 +1740,7 @@
},
{
"cell_type": "code",
- "execution_count": 41,
+ "execution_count": 40,
"metadata": {},
"outputs": [],
"source": [
@@ -1816,12 +1765,15 @@
},
{
"cell_type": "code",
- "execution_count": 42,
+ "execution_count": 41,
"metadata": {},
"outputs": [],
"source": [
- "pm_mask = (between(df['pm_phi1'], pm1_min, pm1_max) & \n",
- " between(df['pm_phi2'], pm2_min, pm2_max))"
+ "pm1 = results_df['pm_phi1']\n",
+ "pm2 = results_df['pm_phi2']\n",
+ "\n",
+ "pm_mask = (between(pm1, pm1_min, pm1_max) & \n",
+ " between(pm2, pm2_min, pm2_max))"
]
},
{
@@ -1831,6 +1783,33 @@
"Again, the sum of a Boolean series is the number of `True` values."
]
},
+ {
+ "cell_type": "code",
+ "execution_count": 42,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "1049"
+ ]
+ },
+ "execution_count": 42,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "pm_mask.sum()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now we can use this mask to select rows from `results_df`."
+ ]
+ },
{
"cell_type": "code",
"execution_count": 43,
@@ -1848,35 +1827,8 @@
}
],
"source": [
- "pm_mask.sum()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Now we can use this mask to select rows from `df`."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 44,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "1049"
- ]
- },
- "execution_count": 44,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "selected = df[pm_mask]\n",
- "len(selected)"
+ "selected_df = results_df[pm_mask]\n",
+ "len(selected_df)"
]
},
{
@@ -1888,12 +1840,12 @@
},
{
"cell_type": "code",
- "execution_count": 45,
+ "execution_count": 44,
"metadata": {},
"outputs": [
{
"data": {
- "image/png": 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\n",
+ "image/png": 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\n",
"text/plain": [
"![](\"https://github.com/datacarpentry/astronomy-python/raw/gh-pages/fig/gd1-2.png\")
\n",
"\n",
"![](\"https://github.com/datacarpentry/astronomy-python/raw/gh-pages/fig/gd1-3.png\")
\n",
"\n",
@@ -379,7 +380,7 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "-rw-rw-r-- 1 downey downey 396K Dec 10 11:33 candidate_df.xml\r\n"
+ "-rw-rw-r-- 1 downey downey 396K Dec 29 11:50 candidate_df.xml\r\n"
]
}
],
@@ -406,60 +407,39 @@
"\n",
"There's a gotcha here we want to warn you about. Why do you think we used double brackets to specify the column we wanted? What happens if you use single brackets?\n",
"\n",
- "Run these cells to find out."
+ "Run these code snippets to find out.\n",
+ "\n",
+ "```\n",
+ "table_id = candidate_table[['source_id']]\n",
+ "print(type(table_id))\n",
+ "```\n",
+ "\n",
+ "```\n",
+ "column = candidate_table['source_id']\n",
+ "print(type(column))\n",
+ "```\n",
+ "\n",
+ "```\n",
+ "# This one should cause an error\n",
+ "table_id.write('candidate_df.xml', \n",
+ " format='votable', \n",
+ " overwrite=True)\n",
+ "```"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "astropy.table.table.Table"
- ]
- },
- "execution_count": 9,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "table_id = candidate_table[['source_id']]\n",
- "type(table_id)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "astropy.table.column.Column"
- ]
- },
- "execution_count": 10,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "column = candidate_table['source_id']\n",
- "type(column)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 11,
- "metadata": {},
"outputs": [],
"source": [
- "# This line is commented out because it would cause an error\n",
+ "# Solution\n",
"\n",
- "# writeto(column, 'candidate_df.xml')"
+ "# table_id is a Table\n",
+ "\n",
+ "# column is a Column\n",
+ "\n",
+ "# Column does not provide `write`, so you get an AttributeError"
]
},
{
@@ -477,7 +457,7 @@
},
{
"cell_type": "code",
- "execution_count": 12,
+ "execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
@@ -499,7 +479,7 @@
},
{
"cell_type": "code",
- "execution_count": 13,
+ "execution_count": 11,
"metadata": {},
"outputs": [
{
@@ -541,7 +521,7 @@
},
{
"cell_type": "code",
- "execution_count": 14,
+ "execution_count": 12,
"metadata": {
"scrolled": true
},
@@ -550,7 +530,7 @@
"data": {
"text/html": [
"Table length=7346\n",
- "
](https://colab.research.google.com/github/AllenDowney/AstronomicalData/blob/main/01_query.ipynb)
+[Run Notebook 1 on Colab](https://colab.research.google.com/github/AllenDowney/AstronomicalData/blob/main/01_query.ipynb)
[or click here to read it on NBViewer](https://nbviewer.jupyter.org/github/AllenDowney/AstronomicalData/blob/main/01_query.ipynb)
@@ -68,7 +68,7 @@ Then, to select stars in the vicinity of GD-1, we:
Press this button to run this notebook on Colab:
-[