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spine | Lightweight MVC library for building JavaScript applications | Runtime Evironment library

 by   spine JavaScript Version: Current License: MIT

 by   spine JavaScript Version: Current License: MIT

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kandi X-RAY | spine Summary

spine is a JavaScript library typically used in Server, Runtime Evironment, Nodejs applications. spine has no bugs, it has a Permissive License and it has medium support. However spine has 2 vulnerabilities. You can install using 'npm i spine' or download it from GitHub, npm.
Spine is a lightweight MVC library for building JavaScript web applications. Spine gives you structure and then gets out of your way, allowing you to concentrate on the fun stuff: building awesome web applications. Spine is opinionated in its approach to web application architecture and design. Spine's architecture complements patterns such as de-coupled components and CommonJS modules, markedly helping with code quality and maintainability. The library is written in CoffeeScript, and though it doesn't necessarily require CoffeeScript to develop applications - you can use whichever language you're most familiar with or prefer - the documentation and some associated tools like Hem and spine.app cater to those who prefer CoffeeScript's syntax.
Support
Support
Quality
Quality
Security
Security
License
License
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kandi-support Support

  • spine has a medium active ecosystem.
  • It has 3573 star(s) with 447 fork(s). There are 114 watchers for this library.
  • It had no major release in the last 12 months.
  • There are 30 open issues and 269 have been closed. On average issues are closed in 292 days. There are 6 open pull requests and 0 closed requests.
  • It has a neutral sentiment in the developer community.
  • The latest version of spine is current.
spine Support
Best in #Runtime Evironment
Average in #Runtime Evironment
spine Support
Best in #Runtime Evironment
Average in #Runtime Evironment

quality kandi Quality

  • spine has 0 bugs and 0 code smells.
spine Quality
Best in #Runtime Evironment
Average in #Runtime Evironment
spine Quality
Best in #Runtime Evironment
Average in #Runtime Evironment

securitySecurity

  • spine has 2 vulnerability issues reported (0 critical, 1 high, 1 medium, 0 low).
  • spine code analysis shows 0 unresolved vulnerabilities.
  • There are 0 security hotspots that need review.
spine Security
Best in #Runtime Evironment
Average in #Runtime Evironment
spine Security
Best in #Runtime Evironment
Average in #Runtime Evironment

license License

  • spine is licensed under the MIT License. This license is Permissive.
  • Permissive licenses have the least restrictions, and you can use them in most projects.
spine License
Best in #Runtime Evironment
Average in #Runtime Evironment
spine License
Best in #Runtime Evironment
Average in #Runtime Evironment

buildReuse

  • spine releases are not available. You will need to build from source code and install.
  • Deployable package is available in npm.
  • Installation instructions are not available. Examples and code snippets are available.
spine Reuse
Best in #Runtime Evironment
Average in #Runtime Evironment
spine Reuse
Best in #Runtime Evironment
Average in #Runtime Evironment
Top functions reviewed by kandi - BETA

kandi has reviewed spine and discovered the below as its top functions. This is intended to give you an instant insight into spine implemented functionality, and help decide if they suit your requirements.

  • Initialize a controller
    • Initialize a new Route
      • Initialize Path .
        • Create a new Model
          • Initialize a new Collection .
            • Create an instance of Singleton .
              • Creates instance of an instance
                • List constructor .
                  • Initialize controllers .
                    • Create a new Module

                      Get all kandi verified functions for this library.

                      Get all kandi verified functions for this library.

                      spine Key Features

                      Lightweight MVC library for building JavaScript applications

                      Cloning master and running the test suite

                      copy iconCopydownload iconDownload
                      $ git clone git://github.com/spine/spine.git
                      $ cd spine
                      

                      CoffeeScript

                      copy iconCopydownload iconDownload
                      $ npm install
                      

                      Git

                      copy iconCopydownload iconDownload
                      $ git checkout dev
                      

                      checking if a string subset is in column values from a list and assign value in a new column in pandas

                      copy iconCopydownload iconDownload
                      import re
                      MAX = 3
                      procedure_name = ['brain', 'spine', 'abdomen', 'thorax']
                      regex = '|'.join(procedure_name)
                      
                      df2 = df.join(df['procedure']
                       .str.lower() # optional, to ensure output is lowercase
                       .str.extractall(f'({regex})', flags=re.I)[0]
                       .unstack('match')
                       .reindex(columns=range(MAX)) # optional (see below)
                       # alternative to reindex if you
                       # don't want to create empty columns
                       #.iloc[:, :MAX]
                       .rename(columns=lambda x: f'Anatomy{x+1}')
                       )
                      
                                   procedure Anatomy1 Anatomy2  Anatomy3
                      0      brain and spine    brain    spine       NaN
                      1          BRAIN+SPINE    brain    spine       NaN
                      2  Thorax limited cuts   thorax      NaN       NaN
                      3     Abdomen contrast  abdomen      NaN       NaN
                      4     Thorax + Abdomen   thorax  abdomen       NaN
                      
                      import re
                      MAX = 3
                      procedure_name = ['brain', 'spine', 'abdomen', 'thorax']
                      regex = '|'.join(procedure_name)
                      
                      df2 = df.join(df['procedure']
                       .str.lower() # optional, to ensure output is lowercase
                       .str.extractall(f'({regex})', flags=re.I)[0]
                       .unstack('match')
                       .reindex(columns=range(MAX)) # optional (see below)
                       # alternative to reindex if you
                       # don't want to create empty columns
                       #.iloc[:, :MAX]
                       .rename(columns=lambda x: f'Anatomy{x+1}')
                       )
                      
                                   procedure Anatomy1 Anatomy2  Anatomy3
                      0      brain and spine    brain    spine       NaN
                      1          BRAIN+SPINE    brain    spine       NaN
                      2  Thorax limited cuts   thorax      NaN       NaN
                      3     Abdomen contrast  abdomen      NaN       NaN
                      4     Thorax + Abdomen   thorax  abdomen       NaN
                      
                      def get_values(x):
                          v = x.dropna().to_numpy().tolist()
                          if len(v) >=3:
                              return v[:3]
                          else:
                              return v + [float('nan')] * (3 - len(v))
                      
                      tmp = df['procedure'].str.replace('+', ' ', regex=False).str.lower().str.get_dummies(' ')[procedure_name]
                      out = (pd.DataFrame(tmp.mul(tmp.columns).replace('', pd.NA)
                                          .apply(get_values, axis=1).tolist(), 
                                          columns=['Anatomy1', 'Anatomy2', 'Anatomy3']))
                      
                        Anatomy1 Anatomy2  Anatomy3
                      0    brain    spine       NaN
                      1    brain    spine       NaN
                      2   thorax      NaN       NaN
                      3  abdomen      NaN       NaN
                      4  abdomen   thorax       NaN
                      
                      def get_values(x):
                          v = x.dropna().to_numpy().tolist()
                          if len(v) >=3:
                              return v[:3]
                          else:
                              return v + [float('nan')] * (3 - len(v))
                      
                      tmp = df['procedure'].str.replace('+', ' ', regex=False).str.lower().str.get_dummies(' ')[procedure_name]
                      out = (pd.DataFrame(tmp.mul(tmp.columns).replace('', pd.NA)
                                          .apply(get_values, axis=1).tolist(), 
                                          columns=['Anatomy1', 'Anatomy2', 'Anatomy3']))
                      
                        Anatomy1 Anatomy2  Anatomy3
                      0    brain    spine       NaN
                      1    brain    spine       NaN
                      2   thorax      NaN       NaN
                      3  abdomen      NaN       NaN
                      4  abdomen   thorax       NaN
                      
                      import re
                      df = df.join(df.procedure.str.findall('|'.join(procedure_name),flags=re.IGNORECASE).apply(pd.Series).reindex(columns = [0,1,2]))
                      

                      How to draw a set of points using OpenCV Python

                      copy iconCopydownload iconDownload
                      import numpy as np
                      from math import sqrt
                      import matplotlib.pyplot as plt
                      import os
                      import matplotlib.image as mpimg
                      
                      
                      def onclick(event):
                          global ix, iy
                          ix, iy = event.xdata, event.ydata
                          plt.plot(x + ix, y1+ iy, 'r')
                          plt.plot(x + ix, y2+ iy, 'r')
                          plt.show()
                          fig.canvas.mpl_disconnect(cid)
                      
                          return 
                      
                      
                      B = 185
                      L = 250
                      W = (L-B)/6
                      D = (L/2)-L/4
                      
                      x = np.linspace(-L/2, L/2, 500)
                      y1 = []
                      y2 = []
                      
                      for X in x:
                          termo1 = sqrt((L**2 - 4*X**2) / (L**2 + 8*W*X + 4*W**2))
                          termo2 = ((sqrt(5.5*L**2 + 11*L*W + 4*W**2) * (sqrt(3)*B*B - 2*D*sqrt(L**2 + 2*W*L + 4*W**2))
                                     ) / (sqrt(3)*B*L*(sqrt(5.5*L**2 + 11*L*W + 4*W**2) - 2*sqrt(L**2 + 2*W*L + 4*W**2))))
                          termo3 = 1 - sqrt((L*(L**2 + 8*W*X + 4*W**2)) / (2*(L - 2*W)*X**2 +
                                            (L**2 + 8*L*W - 4*W**2)*X + 2*L*W**2 + L**2*W + L**2*W + L**3))
                      
                          calculo = B/2 * termo1 * (1-termo2 * termo3)
                          y1.append(calculo)
                      
                          calculo = -B/2 * termo1 * (1-termo2 * termo3)
                          y2.append(calculo)
                      
                      
                      fig = plt.figure()
                      ax = fig.add_subplot(1, 1, 1)
                      ax.set_xlim([-500,500])
                      ax.set_ylim([-500,500])
                      ax.spines['left'].set_position('center')
                      ax.spines['bottom'].set_position('zero')
                      ax.spines['right'].set_color('none')
                      ax.spines['top'].set_color('none')
                      ax.xaxis.set_ticks_position('bottom')
                      ax.yaxis.set_ticks_position('left')
                      
                      
                      coords = []
                      cid = fig.canvas.mpl_connect('button_press_event', onclick)
                      plt.show()
                      

                      matplotlib: How to plot a closed ring in a radar plot using fill_between? (My attempt leaves a gap.)

                      copy iconCopydownload iconDownload
                      ....
                      def fill_between(self, *args, **kwargs):
                          """
                          Override fill_between 
                          - CUSTOM ADDITION TO EXAMPLE - 
                          """
                          if len(args) and args[0][0] != args[0][-1]:
                              args = list(args)
                              for i, arg in enumerate(args):
                                  args[i] = np.append(arg, arg[0])            
                                                    
                          return super().fill_between(*args, **kwargs)
                      ....
                      
                      ....
                      if __name__ == '__main__':
                          N = 5
                          theta = radar_factory(N, frame='polygon')
                      
                          data1 = [2,1.5,3,3,2]
                          data2 = [1,0.5,2,2,1]
                          spoke_labels = ['A', 'B', 'C', 'D', 'E']
                      
                          fig, ax = plt.subplots(figsize=(9, 9), nrows=1, ncols=1,
                                                   subplot_kw=dict(projection='radar'))
                      
                          ax.plot(theta, data1, color='blue')
                          ax.plot(theta, data2, color='blue')
                          ax.fill_between(theta, data1, data2, color='red')
                          ax.fill(theta, data2, facecolor='blue', alpha=0.25)
                          ax.set_varlabels(spoke_labels)
                      
                          plt.show()
                      
                      #where array provided as arg of len N
                      ax.fill_between(theta, data1, data2, [True, True, False, True, True], color='red')    
                      #or as kwarg of len N+1
                      #ax.fill_between(theta, data1, data2, where=[True, True, False, True, True, True], color='red')
                      
                      ....
                      def fill_between(self, *args, **kwargs):
                          """
                          Override fill_between 
                          - CUSTOM ADDITION TO EXAMPLE - 
                          """
                          if len(args) and args[0][0] != args[0][-1]:
                              args = list(args)
                              for i, arg in enumerate(args):
                                  args[i] = np.append(arg, arg[0])            
                                                    
                          return super().fill_between(*args, **kwargs)
                      ....
                      
                      ....
                      if __name__ == '__main__':
                          N = 5
                          theta = radar_factory(N, frame='polygon')
                      
                          data1 = [2,1.5,3,3,2]
                          data2 = [1,0.5,2,2,1]
                          spoke_labels = ['A', 'B', 'C', 'D', 'E']
                      
                          fig, ax = plt.subplots(figsize=(9, 9), nrows=1, ncols=1,
                                                   subplot_kw=dict(projection='radar'))
                      
                          ax.plot(theta, data1, color='blue')
                          ax.plot(theta, data2, color='blue')
                          ax.fill_between(theta, data1, data2, color='red')
                          ax.fill(theta, data2, facecolor='blue', alpha=0.25)
                          ax.set_varlabels(spoke_labels)
                      
                          plt.show()
                      
                      #where array provided as arg of len N
                      ax.fill_between(theta, data1, data2, [True, True, False, True, True], color='red')    
                      #or as kwarg of len N+1
                      #ax.fill_between(theta, data1, data2, where=[True, True, False, True, True, True], color='red')
                      

                      How to set Y axis intersect to a specific date on X axis in matplotlib

                      copy iconCopydownload iconDownload
                      from matplotlib import pyplot as plt
                      ...
                      plt.xticks(rotation="vertical")
                      plt.plot(df)
                      
                      plt.margins(x=0)
                      plt.plot(df)
                      
                      from matplotlib import pyplot as plt
                      ...
                      plt.xticks(rotation="vertical")
                      plt.plot(df)
                      
                      plt.margins(x=0)
                      plt.plot(df)
                      

                      How to annotate a stackplot or area plot

                      copy iconCopydownload iconDownload
                      for i, c in df.iteritems():
                          v2 = 0
                          for v in c:
                              v2 += v
                              ax.text(i+1, v2, f'${v:.2f}')
                      
                      fig, ax = plt.subplots(figsize=(10,7))
                      ax.stackplot(years, y1,y2,y3,y4,y5,y6, labels=labels, colors = sns.color_palette("Blues")[::-1])
                      plt.legend(bbox_to_anchor=(1.1, 1), loc="upper left")
                      
                      df2 = df.cumsum()
                      
                      for id_col, col in df2.iteritems():
                          prev_val = 0
                          for val in col:
                              ax.annotate(text='${}'.format(round((val - prev_val),2)), xy=(years[id_col],(val)), weight='bold')        
                              prev_val = val
                      
                      plt.xticks(years)
                      
                      import matplotlib.pyplot as plt
                      import pandas as pd
                      import seaborn as sns
                      
                      y1=[26.8,24.97,25.69,24.07]
                      y2=[21.74,19.58,20.7,21.09]
                      y3=[13.1,12.45,12.75,10.79]
                      y4=[9.38,8.18,8.79,6.75]
                      y5=[12.1,10.13,10.76,8.03]
                      y6=[4.33,3.73,3.78,3.75]
                      labels = ["Medical", "Surgical", "Physician Services", 
                                "Newborn", "Maternity", "Mental Health"]
                      years = [2011,2012,2013,2014]
                      fig, ax = plt.subplots(figsize=(10,7))
                      plt.title("Overall, inpatient costs have decreased in 2011", weight='bold')
                      ax.spines['right'].set_visible(False);ax.spines['top'].set_visible(False)
                      ax.spines['bottom'].set_visible(False);ax.spines['left'].set_visible(False)
                      ax.stackplot(years, y1,y2,y3,y4,y5,y6, labels=labels, 
                                   colors = sns.color_palette("Blues")[::-1])
                      
                      df2 = pd.DataFrame([y1,y2,y3,y4,y5,y6]).cumsum()
                      for id_col, col in df2.iteritems():
                          prev_val = 0
                          for val in col:
                              # Base Matplotlib version use `text` or `s`
                              # ax.annotate(text='${}'.format(round((val - prev_val),2)),  xy=(years[id_col],(val)) , weight='bold')   
                              ax.annotate(s='${}'.format(round((val - prev_val),2)),  xy=(years[id_col],(val)) , weight='bold')        
                      
                              prev_val = val
                      
                      plt.xticks(years)
                      plt.xlabel('Year')
                      plt.ylabel('Cost (USD)')
                      plt.legend(bbox_to_anchor=(1.1, 1), loc="upper left")
                      plt.show()
                      
                      fig, ax = plt.subplots(figsize=(10,7))
                      ax.stackplot(years, y1,y2,y3,y4,y5,y6, labels=labels, colors = sns.color_palette("Blues")[::-1])
                      plt.legend(bbox_to_anchor=(1.1, 1), loc="upper left")
                      
                      df2 = df.cumsum()
                      
                      for id_col, col in df2.iteritems():
                          prev_val = 0
                          for val in col:
                              ax.annotate(text='${}'.format(round((val - prev_val),2)), xy=(years[id_col],(val)), weight='bold')        
                              prev_val = val
                      
                      plt.xticks(years)
                      
                      import matplotlib.pyplot as plt
                      import pandas as pd
                      import seaborn as sns
                      
                      y1=[26.8,24.97,25.69,24.07]
                      y2=[21.74,19.58,20.7,21.09]
                      y3=[13.1,12.45,12.75,10.79]
                      y4=[9.38,8.18,8.79,6.75]
                      y5=[12.1,10.13,10.76,8.03]
                      y6=[4.33,3.73,3.78,3.75]
                      labels = ["Medical", "Surgical", "Physician Services", 
                                "Newborn", "Maternity", "Mental Health"]
                      years = [2011,2012,2013,2014]
                      fig, ax = plt.subplots(figsize=(10,7))
                      plt.title("Overall, inpatient costs have decreased in 2011", weight='bold')
                      ax.spines['right'].set_visible(False);ax.spines['top'].set_visible(False)
                      ax.spines['bottom'].set_visible(False);ax.spines['left'].set_visible(False)
                      ax.stackplot(years, y1,y2,y3,y4,y5,y6, labels=labels, 
                                   colors = sns.color_palette("Blues")[::-1])
                      
                      df2 = pd.DataFrame([y1,y2,y3,y4,y5,y6]).cumsum()
                      for id_col, col in df2.iteritems():
                          prev_val = 0
                          for val in col:
                              # Base Matplotlib version use `text` or `s`
                              # ax.annotate(text='${}'.format(round((val - prev_val),2)),  xy=(years[id_col],(val)) , weight='bold')   
                              ax.annotate(s='${}'.format(round((val - prev_val),2)),  xy=(years[id_col],(val)) , weight='bold')        
                      
                              prev_val = val
                      
                      plt.xticks(years)
                      plt.xlabel('Year')
                      plt.ylabel('Cost (USD)')
                      plt.legend(bbox_to_anchor=(1.1, 1), loc="upper left")
                      plt.show()
                      
                      fig, ax = plt.subplots(figsize=(10,7))
                      ax.stackplot(years, y1,y2,y3,y4,y5,y6, labels=labels, colors = sns.color_palette("Blues")[::-1])
                      plt.legend(bbox_to_anchor=(1.1, 1), loc="upper left")
                      
                      df2 = df.cumsum()
                      
                      for id_col, col in df2.iteritems():
                          prev_val = 0
                          for val in col:
                              ax.annotate(text='${}'.format(round((val - prev_val),2)), xy=(years[id_col],(val)), weight='bold')        
                              prev_val = val
                      
                      plt.xticks(years)
                      
                      import matplotlib.pyplot as plt
                      import pandas as pd
                      import seaborn as sns
                      
                      y1=[26.8,24.97,25.69,24.07]
                      y2=[21.74,19.58,20.7,21.09]
                      y3=[13.1,12.45,12.75,10.79]
                      y4=[9.38,8.18,8.79,6.75]
                      y5=[12.1,10.13,10.76,8.03]
                      y6=[4.33,3.73,3.78,3.75]
                      labels = ["Medical", "Surgical", "Physician Services", 
                                "Newborn", "Maternity", "Mental Health"]
                      years = [2011,2012,2013,2014]
                      fig, ax = plt.subplots(figsize=(10,7))
                      plt.title("Overall, inpatient costs have decreased in 2011", weight='bold')
                      ax.spines['right'].set_visible(False);ax.spines['top'].set_visible(False)
                      ax.spines['bottom'].set_visible(False);ax.spines['left'].set_visible(False)
                      ax.stackplot(years, y1,y2,y3,y4,y5,y6, labels=labels, 
                                   colors = sns.color_palette("Blues")[::-1])
                      
                      df2 = pd.DataFrame([y1,y2,y3,y4,y5,y6]).cumsum()
                      for id_col, col in df2.iteritems():
                          prev_val = 0
                          for val in col:
                              # Base Matplotlib version use `text` or `s`
                              # ax.annotate(text='${}'.format(round((val - prev_val),2)),  xy=(years[id_col],(val)) , weight='bold')   
                              ax.annotate(s='${}'.format(round((val - prev_val),2)),  xy=(years[id_col],(val)) , weight='bold')        
                      
                              prev_val = val
                      
                      plt.xticks(years)
                      plt.xlabel('Year')
                      plt.ylabel('Cost (USD)')
                      plt.legend(bbox_to_anchor=(1.1, 1), loc="upper left")
                      plt.show()
                      
                      import pandas as pd
                      
                      # create the DataFrame
                      values = [y1, y2, y3, y4, y5, y6]
                      labels = ["Medical", "Surgical", "Physician Services", "Newborn", "Maternity", "Mental Health"]
                      years = [2011, 2012, 2013, 2014]
                      data = dict(zip(labels, values))
                      df = pd.DataFrame(data=data, index=years)
                      
                      # display(df)
                            Medical  Surgical  Physician Services  Newborn  Maternity  Mental Health
                      2011    26.80     21.74               13.10     9.38      12.10           4.33
                      2012    24.97     19.58               12.45     8.18      10.13           3.73
                      2013    25.69     20.70               12.75     8.79      10.76           3.78
                      2014    24.07     21.09               10.79     6.75       8.03           3.75
                      
                      # plot
                      ax = df.plot(kind='area', xticks=df.index, title='Overall, inpatient costs have decreased in 2011',
                                   color=sns.color_palette("Blues")[::-1], figsize=(10, 6), ylabel='Cost (USD)')
                      ax.legend(bbox_to_anchor=(1.07, 1.02), loc='upper left')  # move the legend
                      ax.set_frame_on(False)  # remove all the spines
                      ax.tick_params(left=False)  # remove the y tick marks
                      ax.set_yticklabels([])  # remove the y labels
                      ax.margins(x=0, y=0)  # remove the margin spacing
                      
                      # annotate
                      for x, v in df.iterrows():
                          cs = v.cumsum()[::-1]  # get the cumulative sum of the row and reverse it to provide the correct y position
                          for i, a in enumerate(v[::-1]):  # reverse the row values for the correct annotation
                              ax.annotate(text=f'${a:0.2f}', xy=(x, cs[i]))
                      
                      ax = df.plot(kind='bar', stacked=True, color=sns.color_palette("Blues")[::-1], rot=0,
                                   title='Overall, inpatient costs have decreased in 2011', ylabel='Cost (USD)', figsize=(10, 6))
                      ax.legend(bbox_to_anchor=(1, 0.5), loc='center left', frameon=False)
                      ax.set_frame_on(False)  # remove all the spines
                      ax.tick_params(left=False, bottom=False)  # remove the x and y tick marks
                      ax.set_yticklabels([])  # remove the y labels
                      
                      for c in ax.containers:
                          
                          # customize the label to account for cases when there might not be a bar section
                      #     labels = [f'${h:0.2f}' if (h := v.get_height()) > 0 else '' for v in c ]  # use this line with python >= 3.8
                          labels = [f'${v.get_height():0.2f}' if v.get_height() > 0 else '' for v in c ]
                          
                          # set the bar label
                          ax.bar_label(c, labels=labels, label_type='center', fontsize=8)
                      
                      import pandas as pd
                      
                      # create the DataFrame
                      values = [y1, y2, y3, y4, y5, y6]
                      labels = ["Medical", "Surgical", "Physician Services", "Newborn", "Maternity", "Mental Health"]
                      years = [2011, 2012, 2013, 2014]
                      data = dict(zip(labels, values))
                      df = pd.DataFrame(data=data, index=years)
                      
                      # display(df)
                            Medical  Surgical  Physician Services  Newborn  Maternity  Mental Health
                      2011    26.80     21.74               13.10     9.38      12.10           4.33
                      2012    24.97     19.58               12.45     8.18      10.13           3.73
                      2013    25.69     20.70               12.75     8.79      10.76           3.78
                      2014    24.07     21.09               10.79     6.75       8.03           3.75
                      
                      # plot
                      ax = df.plot(kind='area', xticks=df.index, title='Overall, inpatient costs have decreased in 2011',
                                   color=sns.color_palette("Blues")[::-1], figsize=(10, 6), ylabel='Cost (USD)')
                      ax.legend(bbox_to_anchor=(1.07, 1.02), loc='upper left')  # move the legend
                      ax.set_frame_on(False)  # remove all the spines
                      ax.tick_params(left=False)  # remove the y tick marks
                      ax.set_yticklabels([])  # remove the y labels
                      ax.margins(x=0, y=0)  # remove the margin spacing
                      
                      # annotate
                      for x, v in df.iterrows():
                          cs = v.cumsum()[::-1]  # get the cumulative sum of the row and reverse it to provide the correct y position
                          for i, a in enumerate(v[::-1]):  # reverse the row values for the correct annotation
                              ax.annotate(text=f'${a:0.2f}', xy=(x, cs[i]))
                      
                      ax = df.plot(kind='bar', stacked=True, color=sns.color_palette("Blues")[::-1], rot=0,
                                   title='Overall, inpatient costs have decreased in 2011', ylabel='Cost (USD)', figsize=(10, 6))
                      ax.legend(bbox_to_anchor=(1, 0.5), loc='center left', frameon=False)
                      ax.set_frame_on(False)  # remove all the spines
                      ax.tick_params(left=False, bottom=False)  # remove the x and y tick marks
                      ax.set_yticklabels([])  # remove the y labels
                      
                      for c in ax.containers:
                          
                          # customize the label to account for cases when there might not be a bar section
                      #     labels = [f'${h:0.2f}' if (h := v.get_height()) > 0 else '' for v in c ]  # use this line with python >= 3.8
                          labels = [f'${v.get_height():0.2f}' if v.get_height() > 0 else '' for v in c ]
                          
                          # set the bar label
                          ax.bar_label(c, labels=labels, label_type='center', fontsize=8)
                      

                      Unexpected token, expected "}" Error while passing object from FetchMovie to MovieList component at line 7 of MovieList.js

                      copy iconCopydownload iconDownload
                      props.newFilm.images.still["2"].medium.film_image
                      

                      Resize axes of top and right joint marginal plots to match central plot with matplotlib

                      copy iconCopydownload iconDownload
                      from matplotlib.transforms import Bbox
                      
                      # code added at the end, just before plt.show()
                      (x0m, y0m), (x1m, y1m) = axs[1, 0].get_position().get_points()  # main heatmap
                      (x0h, y0h), (x1h, y1h) = axs[0, 0].get_position().get_points()  # horizontal histogram
                      axs[0, 0].set_position(Bbox([[x0m, y0h], [x1m, y1h]]))
                      (x0v, y0v), (x1v, y1v) = axs[1, 1].get_position().get_points()  # vertical histogram
                      axs[1, 1].set_position(Bbox([[x0v, y0m], [x1v, y1m]]))
                      
                      plt.show()
                      

                      matplotlib graph plotting

                      copy iconCopydownload iconDownload
                      import matplotlib.pyplot as plt
                      import numpy as np
                      
                      #x_vals = np.linspace(-0.5,0.5,10)
                      #y_vals = np.linspace(-0.5,0.5,10)
                      
                      x_vals = [0.237, 0.415, -0.264, 0.142, -0.037, -0.088, -0.143, -0.332, 0.166, -0.079]
                      y_vals= [0.265, 0.325, -0.069, 0.086, -0.136, 0.124, -0.051, -0.052, 0.280, 0.121]
                      
                      x_vals_center = (np.max(x_vals) + np.min(x_vals)) / 2
                      y_vals_center = (np.max(y_vals) + np.min(y_vals)) / 2
                      
                      fig1 = plt.figure(figsize=[7,7])
                      axes1 = fig1.add_axes([-0.5,-0.5,0.5,0.5])
                      axes1.set_xlabel("xlabel", labelpad=0)
                      axes1.set_ylabel("ylabel", labelpad=110)
                      axes1.set_title("Emotions Quadrants")
                      axes1.spines[['top', 'right']].set_visible(False)
                      #axes1.spines[['left','bottom']].set_position(('data', 0))
                      
                      axes1.spines['left'].set_position(('data', x_vals_center))
                      axes1.spines['bottom'].set_position(('data', y_vals_center))
                      
                      axes1.xaxis.set_ticks_position('bottom')
                      axes1.yaxis.set_ticks_position('left')
                      
                      axes1.scatter(x_vals, y_vals, label="test")
                      
                      plt.show()
                      
                      x_vals = [0.237, 0.415, -0.264, 0.142, -0.037, -0.088, -0.143, -0.332, 0.166, -0.079] 
                      y_vals= [0.265, 0.325, -0.069, 0.086, -0.136, 0.124, -0.051, -0.052, 0.280, 0.121] 
                      
                      fig1 = plt.figure(figsize=[7,7])
                      axes1 = fig1.add_axes([-0.5,-0.5,0.5,0.5])
                      axes1.set_xlabel("xlabel", labelpad=110)
                      axes1.set_ylabel("ylabel", labelpad=110)
                      axes1.set_title("Emotions Quadrants")
                      axes1.spines['right'].set_visible(False)
                      axes1.spines['top'].set_visible(False)
                      
                      axes1.spines['left'].set_position(('axes', 0.5))
                      axes1.spines['bottom'].set_position(('axes', 0.5))
                      
                      axes1.xaxis.set_ticks_position('bottom')
                      axes1.yaxis.set_ticks_position('left')
                      
                      axes1.scatter(x_vals, y_vals, label="test")
                      axes1.set_ylabel('Arousal')
                      axes1.set_xlabel('Valence')
                      
                      axes1.set_ylim(-.5,.5)
                      axes1.set_xlim(-.5,.5)
                      
                      plt.show()
                      

                      Ansible save registered json variable to json file

                      copy iconCopydownload iconDownload
                        - name: VERIFICATION // SAVE BGP VARIABLE TO FILE
                          copy:
                            content: "{{ output_bgp_raw.msg | from_json | to_nice_json }}"
                            dest: "containers/verification/{{ instance_name }}-bgp-raw.json"
                          delegate_to: localhost
                      

                      python - matplot lib sub-plot grid: where to insert row/column arguments

                      copy iconCopydownload iconDownload
                      def top_word_comparison(axes, model, feature_names, n_top_words):
                          for topic_idx, topic in enumerate(model.components_):
                              top_features_ind = topic.argsort()[: -n_top_words - 1 : -1]
                              top_features = [feature_names[i] for i in top_features_ind]
                              weights = topic[top_features_ind]
                      
                              ax = axes[topic_idx]
                              ax.barh(top_features, weights, height=0.7)
                              ax.set_title(topic_map[f"Topic {topic_idx +1}"], fontdict={"fontsize": 30})
                              ax.invert_yaxis()
                              ax.tick_params(axis="both", which="major", labelsize=20)
                              for i in "top right left".split():
                                  ax.spines[i].set_visible(False)
                      
                      tf_list = [cm_array, xb_array]
                      fig, axes = plt.subplots(len(tf_list), 5, figsize=(30, 15), sharex=True)
                      fig.suptitle("Topics in LDA model", fontsize=40)
                      
                      for i in range(enumerate(tf_list)):
                          tf = tf_vectorizer.fit_transform(tf_list[i])
                          n_components = 1
                          lda.fit(tf)
                          n_top_words = 20
                          tf_feature_names = tf_vectorizer.get_feature_names_out()
                          top_word_comparison(axes[i], lda, tf_feature_names, n_top_words)
                      
                      plt.subplots_adjust(top=0.90, bottom=0.05, wspace=0.90, hspace=0.3)
                      plt.show()
                      
                      import matplotlib.pyplot as plt
                      import numpy as np
                      
                      
                      # dummy function
                      my_func = lambda x: np.random.random(x)
                      x_max = 100
                      
                      # fig properties
                      rows = 2
                      cols = 5
                      fig, axes = plt.subplots(rows,cols,figsize=(30, 15), sharex=True)
                      
                      for i in range(rows*cols):
                          ax_i = np.unravel_index(i,(rows,cols))
                          
                          axes[ax_i[0],ax_i[1]].barh(np.arange(x_max),my_func(x_max), height=0.7)
                      
                      plt.show()
                      

                      Community Discussions

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                      • Unexpected token, expected "}" Error while passing object from FetchMovie to MovieList component at line 7 of MovieList.js
                      • Resize axes of top and right joint marginal plots to match central plot with matplotlib
                      • matplotlib graph plotting
                      • Ansible save registered json variable to json file
                      • python - matplot lib sub-plot grid: where to insert row/column arguments
                      Trending Discussions on spine

                      QUESTION

                      checking if a string subset is in column values from a list and assign value in a new column in pandas

                      Asked 2022-Apr-01 at 03:11

                      I have list procedure_name = [brain, spine, abdomen, thorax]

                      actual list is longer.

                      I have a df where one of the columns is ['procedure'] has free text values (as its entered by human operator)

                      procedure
                      brain and spine
                      BRAIN+SPINE
                      Thorax limited cuts
                      Abdomen contrast
                      Thorax + Abdomen

                      and so on..

                      I want to check if each value in the column has one or more matching terms from the list(procedure_name) and add those terms to new columns ['Anatomy1']['Anatomy2'] ['Anatomy3'] for a max of 3 matches.

                      Expected output

                      procedure Anatomy1 Anatomy2 Anatomy3
                      brain and spine brain spine none
                      BRAIN+SPINE brain spine none
                      Thorax limited cuts thorax none none
                      Abdomen contrast abdomen none none
                      Thorax + Abdomen thorax abdomen none

                      I tried my best to explain the problem as logically as possible Thanks in advance

                      ANSWER

                      Answered 2022-Apr-01 at 02:58

                      You could use str.extractall and a custom regex, then unstack and join:

                      import re
                      MAX = 3
                      procedure_name = ['brain', 'spine', 'abdomen', 'thorax']
                      regex = '|'.join(procedure_name)
                      
                      df2 = df.join(df['procedure']
                       .str.lower() # optional, to ensure output is lowercase
                       .str.extractall(f'({regex})', flags=re.I)[0]
                       .unstack('match')
                       .reindex(columns=range(MAX)) # optional (see below)
                       # alternative to reindex if you
                       # don't want to create empty columns
                       #.iloc[:, :MAX]
                       .rename(columns=lambda x: f'Anatomy{x+1}')
                       )
                      

                      Output:

                                   procedure Anatomy1 Anatomy2  Anatomy3
                      0      brain and spine    brain    spine       NaN
                      1          BRAIN+SPINE    brain    spine       NaN
                      2  Thorax limited cuts   thorax      NaN       NaN
                      3     Abdomen contrast  abdomen      NaN       NaN
                      4     Thorax + Abdomen   thorax  abdomen       NaN
                      

                      Source https://stackoverflow.com/questions/71701072

                      Community Discussions, Code Snippets contain sources that include Stack Exchange Network

                      Vulnerabilities

                      Cross-site request forgery (CSRF) vulnerability in SPINE allows remote attackers to perform unauthorized actions as administrators via unspecified vectors. NOTE: some of these details are obtained from third party information.
                      Multiple unspecified vulnerabilities in SPINE before 1.2 have unknown impact and attack vectors, related to (1) "Placeholders in database handler" and (2) "Macro admin security."

                      Install spine

                      You can install using 'npm i spine' or download it from GitHub, npm.

                      Support

                      To file a bug report, please visit the GitHub issues page. It's great if you can attach code (test cases and fixes for bugs, and test cases and a proposed implementation for features), but reproducible bug reports are also welcome. For support or help with using spine please use the Spine Google Group and/or StackOverflow rather than opening an issue on Github. If you post in those places you are more likely to get more people to chime in, and others can benefit from it more readily.

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