diff --git a/01_query.html b/01_query.html index cb385bb..d6d7f3c 100644 --- a/01_query.html +++ b/01_query.html @@ -617,7 +617,7 @@ We’ll use this clause to exclude nearby stars that are unlikely to be part of

We use launch_job_async to submit an asynchronous query.

-
job = Gaia.launch_job_async(query)
+
job = Gaia.launch_job_async(query2)
 job
 

 

diff --git a/02_coords.html b/02_coords.html
index 94d2f44..95aac6a 100644
--- a/02_coords.html
+++ b/02_coords.html
@@ -440,14 +440,9 @@ For example:

 
Exercise

 
Create a quantity that represents 5 arcminutes and assign it to a variable called radius.

 
Then convert it to degrees.

-

+

 

-
## Solution
-
-radius = 5 * u.arcmin
-print(radius)
-
-radius.to(u.degree)
+
# Solution goes here
 

 

 

diff --git a/03_motion.html b/03_motion.html
index f559892..c1fe0a6 100644
--- a/03_motion.html
+++ b/03_motion.html
@@ -385,7 +385,7 @@
 IN_COLAB = 'google.colab' in sys.modules
 
 if IN_COLAB:
-    !pip install astroquery astro-gala wget
+    !pip install astroquery astro-gala
diff --git a/04_select.html b/04_select.html index f9e4eb5..3f95a5f 100644 --- a/04_select.html +++ b/04_select.html @@ -344,7 +344,7 @@ We’ll also see how to write the results to a CSV file.

IN_COLAB = 'google.colab' in sys.modules if IN_COLAB: - !pip install astroquery astro-gala wget + !pip install astroquery astro-gala
@@ -352,7 +352,7 @@ We’ll also see how to write the results to a CSV file.

Reload the data

-

The following cells download the data from the previous lesson, if necessary, and load it into a Pandas DataFrame.

+

You can download the data from the previous lesson or run the following cell, which downloads it if necessary.

from os.path import basename, exists
@@ -370,6 +370,7 @@ We’ll also see how to write the results to a CSV file.
+

Now we can reload centerline_df and selected_df.

import pandas as pd
diff --git a/05_join.html b/05_join.html
index 0274bec..6b98ba7 100644
--- a/05_join.html
+++ b/05_join.html
@@ -702,27 +702,6 @@ The result should contain 490 rows and 9 columns.
-
-
-
# Solution goes here
-
-
-
-
-
-
-
# Solution goes here
-
-
-
-
-
-
-
# Solution goes here
-
-
-
-
@@ -800,27 +779,6 @@ Format the query base using the column names in -
-
# Solution goes here
-
-
-
-
-
-
-
# Solution goes here
-
-
-
-
-
-
-
# Solution goes here
-
-
-
-
diff --git a/06_photo.html b/06_photo.html index 7b25d9f..57657ec 100644 --- a/06_photo.html +++ b/06_photo.html @@ -243,11 +243,6 @@ Outline -
  • - - Installing libraries - -
  • Reload the data @@ -332,45 +327,32 @@ The following figure from the paper is a color-magnitude diagram showing the sta

  • Use Pandas to merge data from multiple DataFrames, much like a database JOIN operation.

    • -
    -

    Installing libraries

    -

    If you are running this notebook on Colab, you can run the following cell to install the libraries we’ll use.

    -

    If you are running this notebook on your own computer, you might have to install these libraries yourself. See the instructions in the preface.

    -
    -
    -
    # If we're running on Colab, install libraries
-
-import sys
-IN_COLAB = 'google.colab' in sys.modules
-
-if IN_COLAB:
-    !pip install wget
-
    -
    -
    -
    -

    Reload the data

    -

    The following cell downloads the photometry data we created in the previous notebook.

    +

    You can download the data from the previous lesson or run the following cell, which downloads it if necessary.

    -
    import os
-from wget import download
+
    from os.path import basename, exists
 
-filename = 'gd1_data.hdf'
-filepath = 'https://github.com/AllenDowney/AstronomicalData/raw/main/data/'
+def download(url):
+    filename = basename(url)
+    if not exists(filename):
+        from urllib.request import urlretrieve
+        local, _ = urlretrieve(url, filename)
+        print('Downloaded ' + local)
 
-if not os.path.exists(filename):
-    print(download(filepath+filename))
+download('https://github.com/AllenDowney/AstronomicalData/raw/main/' +
+         'data/gd1_data.hdf')
    +

    Now we can reload candidate_df.

    import pandas as pd
 
+filename = 'gd1_data.hdf'
 candidate_df = pd.read_hdf(filename, 'candidate_df')
    @@ -449,14 +431,8 @@ The input can be an Astropy The following cell downloads the results:

    -
    import os
-from wget import download
-
-filename = 'MIST_iso_5fd2532653c27.iso.cmd'
-filepath = 'https://github.com/AllenDowney/AstronomicalData/raw/main/data/'
-
-if not os.path.exists(filename):
-    print(download(filepath+filename))
+
    download('https://github.com/AllenDowney/AstronomicalData/raw/main/' +
+         'data/MIST_iso_5fd2532653c27.iso.cmd')
    @@ -464,14 +440,8 @@ The input can be an Astropy To read this file we’ll download a Python module from this repository.

    -
    import os
-from wget import download
-
-filename = 'read_mist_models.py'
-filepath = 'https://github.com/jieunchoi/MIST_codes/raw/master/scripts/'
-
-if not os.path.exists(filename):
-    print(download(filepath+filename))
+
    download('https://github.com/jieunchoi/MIST_codes/raw/master/scripts/' +
+         'read_mist_models.py')
    @@ -624,14 +594,8 @@ The input can be an Astropy The following cell downloads the isochrone we made in the previous section, if necessary.

    -
    import os
-from wget import download
-
-filename = 'gd1_isochrone.hdf5'
-filepath = 'https://github.com/AllenDowney/AstronomicalData/raw/main/data/'
-
-if not os.path.exists(filename):
-    print(download(filepath+filename))
+
    download('https://github.com/AllenDowney/AstronomicalData/raw/main/data/' +
+         'gd1_isochrone.hdf5')
    @@ -639,7 +603,8 @@ The input can be an Astropy Now we can read it back in.

    -
    iso_df = pd.read_hdf(filename, 'iso_df')
+
    filename = 'gd1_isochrone.hdf5'
+iso_df = pd.read_hdf(filename, 'iso_df')
 iso_df.head()
 
    
 
    
diff --git a/07_plot.html b/07_plot.html
index 5e73f9d..80e445f 100644
--- a/07_plot.html
+++ b/07_plot.html
@@ -242,11 +242,6 @@
    Outline
   
  
- 
  • 
-  
-   Installing libraries
-  
- 
    • 
  
  • 
   
    Making Figures That Tell a Story
@@ -389,24 +384,6 @@
 
  • Generate a figure with multiple subplots.
    •
    -
    -

    Installing libraries

    -

    If you are running this notebook on Colab, you can run the following cell to install the libraries we’ll use.

    -

    If you are running this notebook on your own computer, you might have to install these libraries yourself. See the instructions in the preface.

    -
    -
    -
    # If we're running on Colab, install libraries
-
-import sys
-IN_COLAB = 'google.colab' in sys.modules
-
-if IN_COLAB:
-    !pip install wget
-
    -
    -
    -
    -

    Making Figures That Tell a Story

    So far the figure we’ve made have been “quick and dirty”. Mostly we have used Matplotlib’s default style, although we have adjusted a few parameters, like markersize and alpha, to improve legibility.

    @@ -438,25 +415,30 @@

    Plotting GD-1

    -

    Let’s start with the panel in the lower left. The following cell reloads the data.

    +

    Let’s start with the panel in the lower left. You can download the data from the previous lesson or run the following cell, which downloads it if necessary.

    -
    import os
-from wget import download
+
    from os.path import basename, exists
 
-filename = 'gd1_data.hdf'
-path = 'https://github.com/AllenDowney/AstronomicalData/raw/main/data/'
+def download(url):
+    filename = basename(url)
+    if not exists(filename):
+        from urllib.request import urlretrieve
+        local, _ = urlretrieve(url, filename)
+        print('Downloaded ' + local)
 
-if not os.path.exists(filename):
-    print(download(path+filename))
+download('https://github.com/AllenDowney/AstronomicalData/raw/main/' +
+         'data/gd1_data.hdf')
    +

    Now we can reload winner_df

    import pandas as pd
 
+filename = 'gd1_data.hdf'
 winner_df = pd.read_hdf(filename, 'winner_df')
    @@ -587,17 +569,11 @@

    The style sheet you choose will affect the appearance of all figures you plot after calling use, unless you override any of the options or call use again.

    As an exercise, choose one of the styles on the list and select it by calling use. Then go back and plot one of the figures above and see what effect it has.

    If you can’t find a style sheet that’s exactly what you want, you can make your own. This repository includes a style sheet called az-paper-twocol.mplstyle, with customizations chosen by Azalee Bostroem for publication in astronomy journals.

    -

    The following cell downloads the style sheet.

    +

    You can download the style sheet or run the following cell, which downloads it if necessary.

    -
    import os
-from wget import download
-
-filename = 'az-paper-twocol.mplstyle'
-path = 'https://github.com/AllenDowney/AstronomicalData/raw/main/'
-
-if not os.path.exists(filename):
-    print(download(path+filename))
+
    download('https://github.com/AllenDowney/AstronomicalData/raw/main/' +
+         'az-paper-twocol.mplstyle')
    @@ -919,7 +895,7 @@

    Best practices

    • The most effective figures focus on telling a single story clearly and compellingly.

      • -

    • Consider using annotations to guide the readers attention to the most important elements of a figure.

      • +

    • Consider using annotations to guide the reader’s attention to the most important elements of a figure.

    • The default Matplotlib style generates good quality figures, but there are several ways you can override the defaults.

    • If you find yourself making the same customizations on several projects, you might want to create your own style sheet.

    diff --git a/_sources/01_query.ipynb b/_sources/01_query.ipynb index f7c10ac..7605cde 100644 --- a/_sources/01_query.ipynb +++ b/_sources/01_query.ipynb @@ -503,7 +503,7 @@ "metadata": {}, "outputs": [], "source": [ - "job = Gaia.launch_job_async(query)\n", + "job = Gaia.launch_job_async(query2)\n", "job" ] }, diff --git a/_sources/02_coords.ipynb b/_sources/02_coords.ipynb index 2056307..79dd724 100644 --- a/_sources/02_coords.ipynb +++ b/_sources/02_coords.ipynb @@ -210,15 +210,14 @@ { "cell_type": "code", "execution_count": 8, - "metadata": {}, + "metadata": { + "tags": [ + "hide-cell" + ] + }, "outputs": [], "source": [ - "## Solution\n", - "\n", - "radius = 5 * u.arcmin\n", - "print(radius)\n", - "\n", - "radius.to(u.degree)" + "# Solution goes here" ] }, { diff --git a/_sources/03_motion.ipynb b/_sources/03_motion.ipynb index 1adcbe7..308dbc9 100644 --- a/_sources/03_motion.ipynb +++ b/_sources/03_motion.ipynb @@ -57,7 +57,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 55, "metadata": { "tags": [] }, @@ -69,7 +69,7 @@ "IN_COLAB = 'google.colab' in sys.modules\n", "\n", "if IN_COLAB:\n", - " !pip install astroquery astro-gala wget" + " !pip install astroquery astro-gala" ] }, { @@ -87,7 +87,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 56, "metadata": {}, "outputs": [], "source": [ @@ -113,7 +113,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 57, "metadata": {}, "outputs": [], "source": [ @@ -134,7 +134,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 58, "metadata": {}, "outputs": [], "source": [ @@ -154,7 +154,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 59, "metadata": {}, "outputs": [], "source": [ @@ -170,7 +170,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 60, "metadata": {}, "outputs": [], "source": [ @@ -186,7 +186,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 61, "metadata": {}, "outputs": [], "source": [ @@ -202,7 +202,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 62, "metadata": {}, "outputs": [], "source": [ @@ -218,7 +218,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 63, "metadata": {}, "outputs": [], "source": [ @@ -237,7 +237,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 64, "metadata": {}, "outputs": [], "source": [ @@ -253,7 +253,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 65, "metadata": {}, "outputs": [], "source": [ @@ -281,7 +281,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 66, "metadata": {}, "outputs": [], "source": [ @@ -321,7 +321,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 67, "metadata": {}, "outputs": [], "source": [ @@ -364,7 +364,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 68, "metadata": { "tags": [ "hide-cell" @@ -395,7 +395,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 69, "metadata": {}, "outputs": [], "source": [ @@ -417,7 +417,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 70, "metadata": {}, "outputs": [], "source": [ @@ -447,7 +447,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 71, "metadata": {}, "outputs": [], "source": [ @@ -492,7 +492,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 72, "metadata": {}, "outputs": [], "source": [ @@ -516,7 +516,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 73, "metadata": { "scrolled": true }, @@ -548,7 +548,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 74, "metadata": {}, "outputs": [], "source": [ @@ -564,7 +564,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 75, "metadata": {}, "outputs": [], "source": [ @@ -590,7 +590,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 76, "metadata": {}, "outputs": [], "source": [ @@ -608,7 +608,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 77, "metadata": {}, "outputs": [], "source": [ @@ -624,7 +624,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 78, "metadata": {}, "outputs": [], "source": [ @@ -647,7 +647,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 79, "metadata": {}, "outputs": [], "source": [ @@ -665,7 +665,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 80, "metadata": {}, "outputs": [], "source": [ @@ -695,7 +695,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 81, "metadata": {}, "outputs": [], "source": [ @@ -717,7 +717,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 82, "metadata": { "tags": [ "hide-cell" @@ -757,7 +757,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 83, "metadata": {}, "outputs": [], "source": [ @@ -779,7 +779,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 84, "metadata": {}, "outputs": [], "source": [ @@ -821,7 +821,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 85, "metadata": {}, "outputs": [], "source": [ @@ -840,7 +840,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 86, "metadata": {}, "outputs": [], "source": [ @@ -860,7 +860,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 87, "metadata": {}, "outputs": [], "source": [ @@ -877,7 +877,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 88, "metadata": {}, "outputs": [], "source": [ @@ -902,7 +902,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 89, "metadata": {}, "outputs": [], "source": [ @@ -918,7 +918,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 90, "metadata": {}, "outputs": [], "source": [ @@ -938,7 +938,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 91, "metadata": {}, "outputs": [], "source": [ @@ -954,7 +954,7 @@ }, { "cell_type": "code", - "execution_count": 53, + "execution_count": 92, "metadata": {}, "outputs": [], "source": [ @@ -979,7 +979,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 93, "metadata": {}, "outputs": [], "source": [ @@ -1008,7 +1008,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 94, "metadata": {}, "outputs": [], "source": [ @@ -1045,7 +1045,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 95, "metadata": {}, "outputs": [], "source": [ @@ -1064,7 +1064,7 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 96, "metadata": {}, "outputs": [], "source": [ @@ -1077,7 +1077,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 97, "metadata": {}, "outputs": [], "source": [ @@ -1094,7 +1094,7 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 98, "metadata": {}, "outputs": [], "source": [ @@ -1113,7 +1113,7 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 99, "metadata": {}, "outputs": [], "source": [ @@ -1131,7 +1131,7 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 100, "metadata": {}, "outputs": [], "source": [ @@ -1151,7 +1151,7 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 101, "metadata": {}, "outputs": [], "source": [ @@ -1167,7 +1167,7 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 102, "metadata": {}, "outputs": [], "source": [ @@ -1184,7 +1184,7 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 103, "metadata": {}, "outputs": [], "source": [ @@ -1216,7 +1216,7 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 104, "metadata": {}, "outputs": [], "source": [ @@ -1257,7 +1257,7 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 105, "metadata": {}, "outputs": [], "source": [ @@ -1290,7 +1290,7 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 106, "metadata": { "tags": [ "hide-cell" @@ -1310,7 +1310,7 @@ }, { "cell_type": "code", - "execution_count": 56, + "execution_count": 107, "metadata": {}, "outputs": [], "source": [ @@ -1329,7 +1329,7 @@ }, { "cell_type": "code", - "execution_count": 53, + "execution_count": 108, "metadata": {}, "outputs": [], "source": [ diff --git a/_sources/04_select.ipynb b/_sources/04_select.ipynb index 9762e91..1baa845 100644 --- a/_sources/04_select.ipynb +++ b/_sources/04_select.ipynb @@ -52,7 +52,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 37, "metadata": { "tags": [] }, @@ -64,7 +64,7 @@ "IN_COLAB = 'google.colab' in sys.modules\n", "\n", "if IN_COLAB:\n", - " !pip install astroquery astro-gala wget" + " !pip install astroquery astro-gala" ] }, { @@ -73,12 +73,12 @@ "source": [ "## Reload the data\n", "\n", - "The following cells download the data from the previous lesson, if necessary, and load it into a Pandas `DataFrame`." + "You can [download the data from the previous lesson](https://github.com/AllenDowney/AstronomicalData/raw/main/data/gd1_data.hdf) or run the following cell, which downloads it if necessary." ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 38, "metadata": {}, "outputs": [], "source": [ @@ -95,9 +95,16 @@ " 'data/gd1_data.hdf')" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we can reload `centerline_df` and `selected_df`." + ] + }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 39, "metadata": {}, "outputs": [], "source": [ @@ -135,7 +142,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 40, "metadata": {}, "outputs": [], "source": [ @@ -147,7 +154,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 41, "metadata": {}, "outputs": [], "source": [ @@ -160,7 +167,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 42, "metadata": {}, "outputs": [], "source": [ @@ -177,7 +184,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 43, "metadata": {}, "outputs": [], "source": [ @@ -214,7 +221,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 44, "metadata": {}, "outputs": [], "source": [ @@ -236,7 +243,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 45, "metadata": {}, "outputs": [], "source": [ @@ -277,7 +284,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 46, "metadata": {}, "outputs": [], "source": [ @@ -303,7 +310,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 47, "metadata": {}, "outputs": [], "source": [ @@ -322,7 +329,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 48, "metadata": {}, "outputs": [], "source": [ @@ -338,7 +345,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 49, "metadata": {}, "outputs": [], "source": [ @@ -355,7 +362,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 50, "metadata": {}, "outputs": [], "source": [ @@ -373,7 +380,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 51, "metadata": {}, "outputs": [], "source": [ @@ -413,7 +420,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 52, "metadata": {}, "outputs": [], "source": [ @@ -442,7 +449,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 53, "metadata": {}, "outputs": [], "source": [ @@ -463,7 +470,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 54, "metadata": {}, "outputs": [], "source": [ @@ -480,7 +487,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 55, "metadata": {}, "outputs": [], "source": [ @@ -505,7 +512,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 56, "metadata": {}, "outputs": [], "source": [ @@ -518,7 +525,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 57, "metadata": {}, "outputs": [], "source": [ @@ -535,7 +542,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 58, "metadata": {}, "outputs": [], "source": [ @@ -551,7 +558,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 59, "metadata": {}, "outputs": [], "source": [ @@ -583,7 +590,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 60, "metadata": {}, "outputs": [], "source": [ @@ -602,7 +609,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 61, "metadata": { "scrolled": true }, @@ -623,7 +630,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 62, "metadata": { "tags": [ "hide-cell" @@ -645,7 +652,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 63, "metadata": { "tags": [ "hide-cell" @@ -665,7 +672,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 64, "metadata": { "scrolled": true }, @@ -686,7 +693,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 65, "metadata": {}, "outputs": [], "source": [ @@ -710,7 +717,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 66, "metadata": {}, "outputs": [], "source": [ @@ -727,7 +734,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 67, "metadata": {}, "outputs": [], "source": [ @@ -744,7 +751,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 68, "metadata": {}, "outputs": [], "source": [ @@ -763,7 +770,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 69, "metadata": {}, "outputs": [], "source": [ @@ -787,7 +794,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 70, "metadata": {}, "outputs": [], "source": [ @@ -829,7 +836,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 71, "metadata": {}, "outputs": [], "source": [ @@ -845,7 +852,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 72, "metadata": {}, "outputs": [], "source": [ diff --git a/_sources/05_join.ipynb b/_sources/05_join.ipynb index 0aebf97..094a8d2 100644 --- a/_sources/05_join.ipynb +++ b/_sources/05_join.ipynb @@ -56,7 +56,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 28, "metadata": { "tags": [] }, @@ -131,7 +131,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 29, "metadata": {}, "outputs": [], "source": [ @@ -142,7 +142,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 30, "metadata": {}, "outputs": [], "source": [ @@ -158,7 +158,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 31, "metadata": {}, "outputs": [], "source": [ @@ -189,7 +189,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 32, "metadata": {}, "outputs": [], "source": [ @@ -202,7 +202,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 33, "metadata": {}, "outputs": [], "source": [ @@ -211,7 +211,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 34, "metadata": { "scrolled": true }, @@ -232,7 +232,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 35, "metadata": {}, "outputs": [], "source": [ @@ -241,7 +241,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 36, "metadata": {}, "outputs": [], "source": [ @@ -257,7 +257,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 37, "metadata": {}, "outputs": [], "source": [ @@ -284,7 +284,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 38, "metadata": {}, "outputs": [], "source": [ @@ -297,7 +297,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 39, "metadata": {}, "outputs": [], "source": [ @@ -306,7 +306,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 40, "metadata": { "scrolled": true }, @@ -350,7 +350,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 41, "metadata": {}, "outputs": [], "source": [ @@ -373,7 +373,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 42, "metadata": {}, "outputs": [], "source": [ @@ -384,7 +384,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 43, "metadata": { "scrolled": true }, @@ -404,7 +404,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 44, "metadata": {}, "outputs": [], "source": [ @@ -426,7 +426,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 45, "metadata": {}, "outputs": [], "source": [ @@ -445,7 +445,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 46, "metadata": {}, "outputs": [], "source": [ @@ -454,7 +454,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 47, "metadata": { "scrolled": true }, @@ -484,7 +484,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 48, "metadata": {}, "outputs": [], "source": [ @@ -516,7 +516,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 49, "metadata": {}, "outputs": [], "source": [ @@ -536,7 +536,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 50, "metadata": {}, "outputs": [], "source": [ @@ -545,7 +545,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 51, "metadata": { "scrolled": true }, @@ -583,7 +583,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 52, "metadata": { "tags": [ "hide-cell" @@ -594,46 +594,6 @@ "# Solution goes here" ] }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": { - "tags": [ - "hide-cell" - ] - }, - "outputs": [], - "source": [ - "# Solution goes here" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": { - "tags": [ - "hide-cell" - ] - }, - "outputs": [], - "source": [ - "# Solution goes here" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": { - "scrolled": true, - "tags": [ - "hide-cell" - ] - }, - "outputs": [], - "source": [ - "# Solution goes here" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -647,7 +607,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 53, "metadata": {}, "outputs": [], "source": [ @@ -672,7 +632,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 54, "metadata": {}, "outputs": [], "source": [ @@ -692,7 +652,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 55, "metadata": {}, "outputs": [], "source": [ @@ -714,7 +674,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 56, "metadata": {}, "outputs": [], "source": [ @@ -723,7 +683,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 57, "metadata": { "scrolled": true }, @@ -749,46 +709,7 @@ }, { "cell_type": "code", - "execution_count": 34, - "metadata": { - "tags": [ - "hide-cell" - ] - }, - "outputs": [], - "source": [ - "# Solution goes here" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": { - "tags": [ - "hide-cell" - ] - }, - "outputs": [], - "source": [ - "# Solution goes here" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": { - "tags": [ - "hide-cell" - ] - }, - "outputs": [], - "source": [ - "# Solution goes here" - ] - }, - { - "cell_type": "code", - "execution_count": 37, + "execution_count": 58, "metadata": { "tags": [ "hide-cell" @@ -810,7 +731,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 59, "metadata": {}, "outputs": [], "source": [ @@ -828,7 +749,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 60, "metadata": {}, "outputs": [], "source": [ @@ -849,7 +770,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 61, "metadata": {}, "outputs": [], "source": [ @@ -877,7 +798,7 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 62, "metadata": {}, "outputs": [], "source": [ @@ -922,7 +843,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 63, "metadata": {}, "outputs": [], "source": [ @@ -938,7 +859,7 @@ }, { "cell_type": "code", - "execution_count": 53, + "execution_count": 64, "metadata": {}, "outputs": [], "source": [ @@ -971,7 +892,7 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 65, "metadata": {}, "outputs": [], "source": [ @@ -989,7 +910,7 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 66, "metadata": {}, "outputs": [], "source": [ @@ -1032,7 +953,7 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 67, "metadata": {}, "outputs": [], "source": [ @@ -1048,7 +969,7 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 68, "metadata": {}, "outputs": [], "source": [ @@ -1064,7 +985,7 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 69, "metadata": {}, "outputs": [], "source": [ @@ -1077,7 +998,7 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 70, "metadata": {}, "outputs": [], "source": [ @@ -1095,7 +1016,7 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 71, "metadata": {}, "outputs": [], "source": [ @@ -1111,7 +1032,7 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 72, "metadata": {}, "outputs": [], "source": [ @@ -1120,7 +1041,7 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": 73, "metadata": {}, "outputs": [], "source": [ diff --git a/_sources/06_photo.ipynb b/_sources/06_photo.ipynb index 9d68930..3200197 100644 --- a/_sources/06_photo.ipynb +++ b/_sources/06_photo.ipynb @@ -43,67 +43,48 @@ }, { "cell_type": "markdown", - "metadata": { - "tags": [] - }, + "metadata": {}, "source": [ - "## Installing libraries\n", + "## Reload the data\n", "\n", - "If you are running this notebook on Colab, you can run the following cell to install the libraries we'll use.\n", - "\n", - "If you are running this notebook on your own computer, you might have to install these libraries yourself. See the instructions in the preface." + "You can [download the data from the previous lesson](https://github.com/AllenDowney/AstronomicalData/raw/main/data/gd1_data.hdf) or run the following cell, which downloads it if necessary." ] }, { "cell_type": "code", - "execution_count": 1, - "metadata": { - "tags": [] - }, + "execution_count": 47, + "metadata": {}, "outputs": [], "source": [ - "# If we're running on Colab, install libraries\n", + "from os.path import basename, exists\n", "\n", - "import sys\n", - "IN_COLAB = 'google.colab' in sys.modules\n", + "def download(url):\n", + " filename = basename(url)\n", + " if not exists(filename):\n", + " from urllib.request import urlretrieve\n", + " local, _ = urlretrieve(url, filename)\n", + " print('Downloaded ' + local)\n", "\n", - "if IN_COLAB:\n", - " !pip install wget" + "download('https://github.com/AllenDowney/AstronomicalData/raw/main/' +\n", + " 'data/gd1_data.hdf')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Reload the data\n", - "\n", - "The following cell downloads the photometry data we created in the previous notebook." + "Now we can reload `candidate_df`." ] }, { "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "from wget import download\n", - "\n", - "filename = 'gd1_data.hdf'\n", - "filepath = 'https://github.com/AllenDowney/AstronomicalData/raw/main/data/'\n", - "\n", - "if not os.path.exists(filename):\n", - " print(download(filepath+filename))" - ] - }, - { - "cell_type": "code", - "execution_count": 3, + "execution_count": 48, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "\n", + "filename = 'gd1_data.hdf'\n", "candidate_df = pd.read_hdf(filename, 'candidate_df')" ] }, @@ -139,7 +120,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 49, "metadata": {}, "outputs": [], "source": [ @@ -195,7 +176,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 50, "metadata": {}, "outputs": [], "source": [ @@ -238,18 +219,12 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 51, "metadata": {}, "outputs": [], "source": [ - "import os\n", - "from wget import download\n", - "\n", - "filename = 'MIST_iso_5fd2532653c27.iso.cmd'\n", - "filepath = 'https://github.com/AllenDowney/AstronomicalData/raw/main/data/'\n", - "\n", - "if not os.path.exists(filename):\n", - " print(download(filepath+filename))" + "download('https://github.com/AllenDowney/AstronomicalData/raw/main/' +\n", + " 'data/MIST_iso_5fd2532653c27.iso.cmd')" ] }, { @@ -261,18 +236,12 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 52, "metadata": {}, "outputs": [], "source": [ - "import os\n", - "from wget import download\n", - "\n", - "filename = 'read_mist_models.py'\n", - "filepath = 'https://github.com/jieunchoi/MIST_codes/raw/master/scripts/'\n", - "\n", - "if not os.path.exists(filename):\n", - " print(download(filepath+filename))" + "download('https://github.com/jieunchoi/MIST_codes/raw/master/scripts/' +\n", + " 'read_mist_models.py')" ] }, { @@ -284,7 +253,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 53, "metadata": {}, "outputs": [], "source": [ @@ -303,7 +272,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 54, "metadata": {}, "outputs": [], "source": [ @@ -319,7 +288,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 55, "metadata": {}, "outputs": [], "source": [ @@ -335,7 +304,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 56, "metadata": {}, "outputs": [], "source": [ @@ -351,7 +320,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 57, "metadata": {}, "outputs": [], "source": [ @@ -367,7 +336,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 58, "metadata": {}, "outputs": [], "source": [ @@ -383,7 +352,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 59, "metadata": {}, "outputs": [], "source": [ @@ -399,7 +368,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 60, "metadata": {}, "outputs": [], "source": [ @@ -415,7 +384,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 61, "metadata": {}, "outputs": [], "source": [ @@ -425,7 +394,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 62, "metadata": {}, "outputs": [], "source": [ @@ -446,7 +415,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 63, "metadata": {}, "outputs": [], "source": [ @@ -467,7 +436,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 64, "metadata": {}, "outputs": [], "source": [ @@ -484,7 +453,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 65, "metadata": {}, "outputs": [], "source": [ @@ -505,7 +474,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 66, "metadata": {}, "outputs": [], "source": [ @@ -527,7 +496,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 67, "metadata": {}, "outputs": [], "source": [ @@ -546,18 +515,12 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 68, "metadata": {}, "outputs": [], "source": [ - "import os\n", - "from wget import download\n", - "\n", - "filename = 'gd1_isochrone.hdf5'\n", - "filepath = 'https://github.com/AllenDowney/AstronomicalData/raw/main/data/'\n", - "\n", - "if not os.path.exists(filename):\n", - " print(download(filepath+filename))" + "download('https://github.com/AllenDowney/AstronomicalData/raw/main/data/' +\n", + " 'gd1_isochrone.hdf5')" ] }, { @@ -569,10 +532,11 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 69, "metadata": {}, "outputs": [], "source": [ + "filename = 'gd1_isochrone.hdf5'\n", "iso_df = pd.read_hdf(filename, 'iso_df')\n", "iso_df.head()" ] @@ -586,7 +550,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 70, "metadata": {}, "outputs": [], "source": [ @@ -609,7 +573,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 71, "metadata": {}, "outputs": [], "source": [ @@ -628,7 +592,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 72, "metadata": {}, "outputs": [], "source": [ @@ -647,7 +611,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 73, "metadata": {}, "outputs": [], "source": [ @@ -667,7 +631,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 74, "metadata": {}, "outputs": [], "source": [ @@ -685,7 +649,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 75, "metadata": {}, "outputs": [], "source": [ @@ -712,7 +676,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 76, "metadata": {}, "outputs": [], "source": [ @@ -746,7 +710,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 77, "metadata": {}, "outputs": [], "source": [ @@ -763,7 +727,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 78, "metadata": {}, "outputs": [], "source": [ @@ -780,7 +744,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 79, "metadata": {}, "outputs": [], "source": [ @@ -797,7 +761,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 80, "metadata": {}, "outputs": [], "source": [ @@ -816,7 +780,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 81, "metadata": {}, "outputs": [], "source": [ @@ -837,7 +801,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 82, "metadata": {}, "outputs": [], "source": [ @@ -854,7 +818,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 83, "metadata": {}, "outputs": [], "source": [ @@ -889,7 +853,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 84, "metadata": {}, "outputs": [], "source": [ @@ -909,7 +873,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 85, "metadata": {}, "outputs": [], "source": [ @@ -930,7 +894,7 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 86, "metadata": {}, "outputs": [], "source": [ @@ -947,7 +911,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 87, "metadata": {}, "outputs": [], "source": [ @@ -963,7 +927,7 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 88, "metadata": {}, "outputs": [], "source": [ @@ -979,7 +943,7 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 89, "metadata": {}, "outputs": [], "source": [ @@ -1003,7 +967,7 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 90, "metadata": {}, "outputs": [], "source": [ @@ -1043,7 +1007,7 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 91, "metadata": {}, "outputs": [], "source": [ @@ -1053,7 +1017,7 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 92, "metadata": {}, "outputs": [], "source": [ diff --git a/_sources/07_plot.ipynb b/_sources/07_plot.ipynb index 3ca6b3c..d405af8 100644 --- a/_sources/07_plot.ipynb +++ b/_sources/07_plot.ipynb @@ -38,36 +38,6 @@ "* Generate a figure with multiple subplots." ] }, - { - "cell_type": "markdown", - "metadata": { - "tags": [] - }, - "source": [ - "## Installing libraries\n", - "\n", - "If you are running this notebook on Colab, you can run the following cell to install the libraries we'll use.\n", - "\n", - "If you are running this notebook on your own computer, you might have to install these libraries yourself. See the instructions in the preface." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "# If we're running on Colab, install libraries\n", - "\n", - "import sys\n", - "IN_COLAB = 'google.colab' in sys.modules\n", - "\n", - "if IN_COLAB:\n", - " !pip install wget" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -113,7 +83,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 30, "metadata": { "tags": [ "hide-cell" @@ -130,39 +100,50 @@ "source": [ "## Plotting GD-1\n", "\n", - "Let's start with the panel in the lower left. The following cell reloads the data." + "Let's start with the panel in the lower left. You can [download the data from the previous lesson](https://github.com/AllenDowney/AstronomicalData/raw/main/data/gd1_data.hdf) or run the following cell, which downloads it if necessary." ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 31, "metadata": {}, "outputs": [], "source": [ - "import os\n", - "from wget import download\n", + "from os.path import basename, exists\n", "\n", - "filename = 'gd1_data.hdf'\n", - "path = 'https://github.com/AllenDowney/AstronomicalData/raw/main/data/'\n", + "def download(url):\n", + " filename = basename(url)\n", + " if not exists(filename):\n", + " from urllib.request import urlretrieve\n", + " local, _ = urlretrieve(url, filename)\n", + " print('Downloaded ' + local)\n", "\n", - "if not os.path.exists(filename):\n", - " print(download(path+filename))" + "download('https://github.com/AllenDowney/AstronomicalData/raw/main/' +\n", + " 'data/gd1_data.hdf')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we can reload `winner_df`" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 32, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "\n", + "filename = 'gd1_data.hdf'\n", "winner_df = pd.read_hdf(filename, 'winner_df')" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 33, "metadata": {}, "outputs": [], "source": [ @@ -190,7 +171,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 34, "metadata": {}, "outputs": [], "source": [ @@ -232,7 +213,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 35, "metadata": { "tags": [ "hide-cell" @@ -286,7 +267,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 36, "metadata": { "tags": [ "hide-cell" @@ -310,7 +291,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 37, "metadata": {}, "outputs": [], "source": [ @@ -326,7 +307,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 38, "metadata": {}, "outputs": [], "source": [ @@ -364,7 +345,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 39, "metadata": {}, "outputs": [], "source": [ @@ -411,23 +392,17 @@ "source": [ "If you can't find a style sheet that's exactly what you want, you can make your own. This repository includes a style sheet called `az-paper-twocol.mplstyle`, with customizations chosen by Azalee Bostroem for publication in astronomy journals.\n", "\n", - "The following cell downloads the style sheet." + "You can [download the style sheet](https://github.com/AllenDowney/AstronomicalData/raw/main/az-paper-twocol.mplstyle) or run the following cell, which downloads it if necessary." ] }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 40, "metadata": {}, "outputs": [], "source": [ - "import os\n", - "from wget import download\n", - "\n", - "filename = 'az-paper-twocol.mplstyle'\n", - "path = 'https://github.com/AllenDowney/AstronomicalData/raw/main/'\n", - "\n", - "if not os.path.exists(filename):\n", - " print(download(path+filename))" + "download('https://github.com/AllenDowney/AstronomicalData/raw/main/' +\n", + " 'az-paper-twocol.mplstyle')" ] }, { @@ -495,7 +470,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 41, "metadata": {}, "outputs": [], "source": [ @@ -523,7 +498,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 42, "metadata": {}, "outputs": [], "source": [ @@ -563,7 +538,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 43, "metadata": {}, "outputs": [], "source": [ @@ -580,7 +555,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 44, "metadata": {}, "outputs": [], "source": [ @@ -604,7 +579,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 45, "metadata": {}, "outputs": [], "source": [ @@ -623,7 +598,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 46, "metadata": {}, "outputs": [], "source": [ @@ -657,7 +632,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 47, "metadata": {}, "outputs": [], "source": [ @@ -675,7 +650,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 48, "metadata": {}, "outputs": [], "source": [ @@ -692,7 +667,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 49, "metadata": {}, "outputs": [], "source": [ @@ -718,7 +693,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 50, "metadata": { "scrolled": true }, @@ -738,7 +713,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 51, "metadata": {}, "outputs": [], "source": [ @@ -771,7 +746,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 52, "metadata": {}, "outputs": [], "source": [ @@ -787,7 +762,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 53, "metadata": {}, "outputs": [], "source": [ @@ -809,7 +784,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 54, "metadata": { "tags": [ "hide-cell" @@ -841,7 +816,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 55, "metadata": {}, "outputs": [], "source": [ @@ -892,7 +867,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 56, "metadata": {}, "outputs": [], "source": [ @@ -935,7 +910,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 57, "metadata": { "tags": [ "hide-cell" @@ -965,7 +940,7 @@ "\n", "* The most effective figures focus on telling a single story clearly and compellingly.\n", "\n", - "* Consider using annotations to guide the readers attention to the most important elements of a figure.\n", + "* Consider using annotations to guide the reader's attention to the most important elements of a figure.\n", "\n", "* The default Matplotlib style generates good quality figures, but there are several ways you can override the defaults.\n", "\n", diff --git a/searchindex.js b/searchindex.js index 6e86dfb..64147d1 100644 --- a/searchindex.js +++ b/searchindex.js @@ -1 +1 @@ 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