Process inspire data

.gitignore

@@ -2,4 +2,6 @@
 _site/
 
 # I've had problems with deno.lock. It's in my bad books.
-deno.lock
+deno.lock
+
+/lcc-env/

scripts/lcip/transform.py

@@ -4,8 +4,8 @@
 from thefuzz import process
 
 OUT_DIR = os.path.join('src', '_data', 'viz', 'lcip')
-DATA_DIR = os.path.join('data', 'lcip', 'lcip_grant_stats.xlsx')
-INSPIRE_DATA = os.path.join('data', 'lcip', 'inspire.xlsx')
+# DATA_DIR = os.path.join('data', 'lcip', 'lcip_grant_stats.xlsx')
+INSPIRE_DATA = os.path.join('data', 'lcip', 'inspire-data.xlsx')
 WARD_DATA = os.path.join('data', 'leeds_wards.csv')
 
 # Function to fuzzy match and merge dataframes
@@ -24,9 +24,20 @@ def fuzzy_merge(df1, df2, key1, key2, threshold=90):
 
     # Process Inspire data
 
-    inspire_data = pd.read_excel(INSPIRE_DATA).drop(columns={'R2','R3','R4'})
+    inspire_data = pd.read_excel(INSPIRE_DATA).drop(columns={'R2 Q2','R3 Q3','R4 Q4'})
     ward_data = pd.read_csv(WARD_DATA)
 
+    # inspire_data = inspire_data.pivot(index='METRIC', columns='THEME', values='R1 Q1')
+
+    inspire_data_na = inspire_data.dropna().loc[inspire_data['R1 Q1'] != 0]
+    themes = inspire_data_na['THEME'].unique()
+
+    for theme in themes:
+        theme_df = inspire_data_na[inspire_data_na['THEME'] == theme]
+        theme_df = theme_df.pivot_table(index='THEME', columns='METRIC', values='R1 Q1')
+        theme_filename = theme.replace(" ", "_").replace("/", "_") + '.csv'
+        theme_df.to_csv(os.path.join(OUT_DIR, theme_filename), index=False)
+
     wards_applied = inspire_data.loc[inspire_data['THEME']== 'WARDS - APPLICANT BASED']
     wards_received = inspire_data.loc[inspire_data['THEME']== 'WARDS - RECEIVING ACTIVITY']
 
@@ -38,8 +49,8 @@ def fuzzy_merge(df1, df2, key1, key2, threshold=90):
             'WD21CD': 'ward_code',
             'THEME': 'metric',
             'METRIC': 'ward_name',
-            'R1': 'value'
-        })).to_csv(os.path.join(OUT_DIR, 'inspire', 'applications_by_ward.csv'))
+            'R1 Q1': 'value'
+        })).to_csv(os.path.join(OUT_DIR, 'inspire', 'APPLICATIONS_BY_WARD.csv'), index=False)
 
     funded = (
         fuzzy_merge(ward_data, wards_received, 'WD21NM', 'METRIC')
@@ -49,5 +60,5 @@ def fuzzy_merge(df1, df2, key1, key2, threshold=90):
             'WD21CD': 'ward_code',
             'THEME': 'metric',
             'METRIC': 'ward_name',
-            'R1': 'value'
-        })).to_csv(os.path.join(OUT_DIR, 'inspire', 'funding_received_by_ward.csv'))
+            'R1 Q1': 'value'
+        })).to_csv(os.path.join(OUT_DIR, 'inspire', 'RECEIVED_BY_WARD.csv'), index=False)

src/_components/dashboard/big_number.njk

@@ -1,2 +1,4 @@
-<div class='big-number'>{{ prefix | d('') }}{{ number }}{{ suffix | d('') }}</div>
+<div class='big-number' style="
+    {%- if fontsize %}font-size:{{ fontsize }};{% endif -%}
+">{{ prefix | d('') }}{{ number }}{{ suffix | d('') }}</div>
 

src/_components/dashboard/pane.njk

@@ -2,6 +2,8 @@
     {% if rows %}grid-row:auto/span {{ rows }};{% endif %}
     {% if columns %}grid-column:span {{ columns }};{% endif %}
     {% if bg %}background: {{ bg }}!important;{% endif %}
+    {% if color %}color: {{ color }}!important;{% endif %}
+    {% if height %}min-height: {{ height }}!important;{% endif %}
 ">
 {%-if link -%}<a class="impact-link" href="{{ link | url }}">{%- endif -%}
   {{ content | safe }}

src/_components/dashboard/text-panel.njk

@@ -0,0 +1,11 @@
+<li class="text-panel" style ="
+    {% if rows %}grid-row:auto/span {{ rows }};{% endif %}
+    {% if columns %}grid-column:span {{ columns }};{% endif %}
+    {% if bg %}background: {{ bg }}!important;{% endif %}
+    {% if color %}color: {{ color }}!important;{% endif %}
+    {% if height %}min-height: {{ height }}!important;{% endif %}
+">
+{%-if link -%}<a class="impact-link" href="{{ link | url }}">{%- endif -%}
+  {{ content | safe }}
+{%-if link -%}</a>{%- endif -%}
+</li>

src/_data/viz/lcip/AGE_(APPLIED).csv

@@ -0,0 +1,2 @@
+18 - 24,25 - 34,35 - 44,45 - 54,55 - 64,65 - 74,75 - 84,85+,Not known
+11.0,16.0,24.0,14.0,9.0,8.0,13.0,1.0,2.0

src/_data/viz/lcip/AGE_(FUNDED).csv

@@ -0,0 +1,2 @@
+18 - 24,25 - 34,35 - 44,45 - 54,55 - 64,65 - 74,75 - 84,85+
+1.0,3.0,17.0,3.0,7.0,4.0,8.0,1.0

src/_data/viz/lcip/APPLICATIONS_RECEIVED.csv

@@ -0,0 +1,2 @@
+No. of eligible applications ,No. of ineligible ,Total amount of funding requested (£),from an individual artist,from an organisation
+25.0,1.0,31695.0,17.0,8.0

src/_data/viz/lcip/ARTFORM_(APPLIED).csv

@@ -0,0 +1,2 @@
+Combined Arts,Crafts,Dance,Digital Media,Festival/Carnival ,Film,Literature / Poetry / Spoken Word,Music,Photography,Theatre,Visual Art 
+4.0,1.0,1.0,2.0,1.0,2.0,1.0,7.0,2.0,1.0,5.0

src/_data/viz/lcip/ARTFORM_(FUNDED).csv

@@ -0,0 +1,2 @@
+Crafts,Digital Media,Music,Photography,Visual Art 
+1.0,2.0,2.0,1.0,3.0

src/_data/viz/lcip/CARER_(APPLIED).csv

@@ -0,0 +1,2 @@
+No,Not known,Prefer not to say,Yes
+18.0,61.0,10.0,5.0

src/_data/viz/lcip/CARER_(FUNDED).csv

@@ -0,0 +1,2 @@
+No,Not known,Prefer not to say,Yes
+5.0,12.0,10.0,4.0

src/_data/viz/lcip/CONTRIBUTION_TO_WORKFORCE.csv

@@ -0,0 +1,2 @@
+Artists/Creatives,Volunteers
+390.0,48.0

src/_data/viz/lcip/DISABILITY_(APPLIED).csv

@@ -0,0 +1,2 @@
+Hearing impairment ,Learning disability,Long-standing illness or health condition,Mental health condition ,Neuro Diverse,Not known,Physical impairment ,Prefer not to say
+1.0,7.0,4.0,5.0,8.0,39.0,6.0,10.0

src/_data/viz/lcip/DISABILITY_(FUNDED).csv

@@ -0,0 +1,2 @@
+Hearing impairment ,Learning disability,Long-standing illness or health condition,Mental health condition ,Neuro Diverse,Not known,Physical impairment ,Prefer not to say
+1.0,2.0,2.0,2.0,3.0,4.0,6.0,10.0

src/_data/viz/lcip/ETHNIC_ORIGIN_(APPLIED).csv

@@ -0,0 +1,2 @@
+African,Any other white background,Caribbean,Chinese,Indian,Prefer not to say,White British,White English,White Scottish,White Welsh,White and Asian ,White and Black African,White and Black Caribbean 
+1.0,8.0,2.0,1.0,1.0,8.0,7.0,64.0,3.0,3.0,1.0,1.0,1.0

src/_data/viz/lcip/ETHNIC_ORIGIN_(FUNDED).csv

@@ -0,0 +1,2 @@
+Any other white background,Caribbean,White British,White English,White Welsh,White and Asian 
+3.0,2.0,1.0,31.0,1.0,1.0

src/_data/viz/lcip/FUNDING.csv

@@ -0,0 +1,2 @@
+Funding invested from Leeds City Council
+11401.0

src/_data/viz/lcip/FUNDING_ADVICE_SESSIONS.csv

@@ -0,0 +1,2 @@
+No. of funding surgeries booked,No. of funding surgeries offered ,No. of in person sessions,No. of online/Telephone sessions 
+20.0,26.0,6.0,14.0

src/_data/viz/lcip/LEEDS_CULTURE_PROGRAMMES_WEBSITE.csv

@@ -0,0 +1,2 @@
+No. applications started,No. of applications completed
+35.0,26.0

src/_data/viz/lcip/PROJECT_OUTPUTS_-_APPLIED.csv

@@ -0,0 +1,2 @@
+Days employment for freelance artists and creatives,Learning or participation sessions,New cultural activities/products,Performances or exhibition days,Training opportunities,Volunteer opportunities
+380.5,112.0,153.0,746.0,192.0,136.0

src/_data/viz/lcip/PROJECT_OUTPUTS_-_FUNDED.csv

@@ -0,0 +1,2 @@
+Days employment for freelance artists and creatives,Learning or participation sessions,New cultural activities/products,Performances or exhibition days,Training opportunities,Volunteer opportunities
+81.0,52.0,87.0,311.0,39.0,55.0

src/_data/viz/lcip/REACH.csv

@@ -0,0 +1,2 @@
+Audience - in person,Broadcast/online audiences,Participants - in person
+21710.0,75720.0,1147.0

src/_data/viz/lcip/RELIGIOUS_BELIEF_(APPLIED).csv

@@ -0,0 +1,2 @@
+Christian,Jewish,No religion/belief,Not known,Prefer not to say
+15.0,1.0,21.0,54.0,16.0

src/_data/viz/lcip/RELIGIOUS_BELIEF_(FUNDED).csv

@@ -0,0 +1,2 @@
+Christian,No religion/belief,Not known,Prefer not to say
+13.0,13.0,16.0,12.0

src/_data/viz/lcip/RESULTS_.csv

@@ -0,0 +1,2 @@
+No. of  withdrawn by applicant ,No. of funded individual artists,No. of funded organisations,No. of unsucessful artist applications ,No. of unsucessful organisation applications ,Total amount awarded to individual artists (£),Total amount awarded to organisations(£),Total amount not awarded to individual artists (£),Total amount not awarded to organisations(£)
+1.0,5.0,4.0,12.0,3.0,6473.0,3428.0,14897.0,3497.0

src/_data/viz/lcip/SEXUAL_ORIENTATION_(APPLIED).csv

@@ -0,0 +1,2 @@
+Bi-sexual,Gay ,Heterosexual or Straight ,Lesbian,Not known,Other,Prefer not to say
+7.0,3.0,38.0,2.0,54.0,1.0,3.0

src/_data/viz/lcip/SEXUAL_ORIENTATION_(FUNDED).csv

@@ -0,0 +1,2 @@
+Bi-sexual,Gay ,Heterosexual or Straight ,Lesbian,Not known,Other
+1.0,1.0,32.0,2.0,7.0,1.0

src/_data/viz/lcip/SEX_(APPLIED).csv

@@ -0,0 +1,2 @@
+Female,Male,Non Binary,Not known
+52.0,44.0,4.0,8.0

src/_data/viz/lcip/SEX_(FUNDED).csv

@@ -0,0 +1,2 @@
+Female,Male,Non Binary
+26.0,17.0,1.0

src/_data/viz/lcip/TARGET_GROUPS_-_APPLIED.csv

@@ -0,0 +1,2 @@
+Carers,Disabled people,Ethnically diverse people,LGBT+,Men,Non-binary people,Not targeted at a specific group,Older people (over 55),"People with challenging lives (e.g., those who are socially excluded, those from a low-income household, young people at risk, in care or NEET, carers)",Pupils in schools,Religious or Belief Communities,Women,Younger people (under 25)
+2.0,3.0,2.0,2.0,1.0,3.0,13.0,4.0,2.0,1.0,1.0,6.0,2.0

src/_data/viz/lcip/TARGET_GROUPS_-_FUNDED.csv

@@ -0,0 +1,2 @@
+Carers,Disabled people,Ethnically diverse people,LGBT+,Non-binary people,Not targeted at a specific group,Older people (over 55),"People with challenging lives (e.g., those who are socially excluded, those from a low-income household, young people at risk, in care or NEET, carers)",Pupils in schools,Religious or Belief Communities,Women
+2.0,3.0,2.0,1.0,1.0,2.0,3.0,2.0,1.0,1.0,3.0

src/_data/viz/lcip/WARDS_-_APPLICANT_BASED.csv

@@ -0,0 +1,2 @@
+Beeston & Holbeck ,Calverley & Farsley ,Chapel Allerton ,Farnley & Wortley ,Kirkstall ,Otley & Yeadon ,Pudsey ,Weetwood ,Wetherby 
+1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0

src/_data/viz/lcip/WARDS_-_RECEIVING_ACTIVITY.csv

@@ -0,0 +1,2 @@
+Beeston & Holbeck ,Calverley & Farsley ,Farnley & Wortley ,Gipton & Harehills ,Guiseley & Rawdon ,Headingley & Hyde Park ,Horsforth ,Kirkstall ,Little London & Woodhouse ,Otley & Yeadon ,Pudsey ,Roundhay ,Weetwood ,Wetherby 
+1.0,2.0,1.0,1.0,1.0,2.0,1.0,2.0,1.0,1.0,1.0,1.0,1.0,1.0

src/_data/viz/lcip/inspire/RECEIVED_BY_WARD.csv

@@ -0,0 +1,15 @@
+ward_code,metric,ward_name,value
+E05011395,WARDS - RECEIVING ACTIVITY,Guiseley & Rawdon ,1.0
+E05011549,WARDS - RECEIVING ACTIVITY,Otley & Yeadon ,1.0
+E05011414,WARDS - RECEIVING ACTIVITY,Wetherby ,1.0
+E05011547,WARDS - RECEIVING ACTIVITY,Horsforth ,1.0
+E05011413,WARDS - RECEIVING ACTIVITY,Weetwood ,1.0
+E05011411,WARDS - RECEIVING ACTIVITY,Roundhay ,1.0
+E05011389,WARDS - RECEIVING ACTIVITY,Calverley & Farsley ,2.0
+E05011402,WARDS - RECEIVING ACTIVITY,Kirkstall ,2.0
+E05011397,WARDS - RECEIVING ACTIVITY,Headingley & Hyde Park ,2.0
+E05011409,WARDS - RECEIVING ACTIVITY,Pudsey ,1.0
+E05011403,WARDS - RECEIVING ACTIVITY,Little London & Woodhouse ,1.0
+E05011394,WARDS - RECEIVING ACTIVITY,Gipton & Harehills ,1.0
+E05012648,WARDS - RECEIVING ACTIVITY,Farnley & Wortley ,1.0
+E05012647,WARDS - RECEIVING ACTIVITY,Beeston & Holbeck ,1.0

src/_data/viz/lcip/inspire/applications_by_ward.csv

@@ -1,33 +1,10 @@
-,ward_code,metric,ward_name,value
-0,E05011395,WARDS - APPLICANT BASED,Guiseley & Rawdon ,0.0
-1,E05011549,WARDS - APPLICANT BASED,Otley & Yeadon ,1.0
-3,E05012842,WARDS - APPLICANT BASED,Alwoodley ,1.0
-4,E05011396,WARDS - APPLICANT BASED,Harewood ,1.0
-5,E05011414,WARDS - APPLICANT BASED,Wetherby ,0.0
-6,E05011547,WARDS - APPLICANT BASED,Horsforth ,0.0
-7,E05011413,WARDS - APPLICANT BASED,Weetwood ,0.0
-8,E05011405,WARDS - APPLICANT BASED,Moortown ,0.0
-9,E05011411,WARDS - APPLICANT BASED,Roundhay ,0.0
-10,E05011389,WARDS - APPLICANT BASED,Calverley & Farsley ,1.0
-11,E05011387,WARDS - APPLICANT BASED,Bramley & Stanningley ,0.0
-12,E05011402,WARDS - APPLICANT BASED,Kirkstall ,0.0
-13,E05011397,WARDS - APPLICANT BASED,Headingley & Hyde Park ,0.0
-14,E05011390,WARDS - APPLICANT BASED,Chapel Allerton ,2.0
-15,E05011400,WARDS - APPLICANT BASED,Killingbeck & Seacroft ,0.0
-16,E05011409,WARDS - APPLICANT BASED,Pudsey ,0.0
-17,E05011385,WARDS - APPLICANT BASED,Armley ,1.0
-18,E05011403,WARDS - APPLICANT BASED,Little London & Woodhouse ,2.0
-19,E05011394,WARDS - APPLICANT BASED,Gipton & Harehills ,0.0
-20,E05011391,WARDS - APPLICANT BASED,Cross Gates & Whinmoor ,0.0
-21,E05012648,WARDS - APPLICANT BASED,Farnley & Wortley ,0.0
-22,E05012647,WARDS - APPLICANT BASED,Beeston & Holbeck ,2.0
-23,E05011399,WARDS - APPLICANT BASED,Hunslet & Riverside ,0.0
-24,E05011388,WARDS - APPLICANT BASED,Burmantofts & Richmond Hill ,0.0
-25,E05013831,WARDS - APPLICANT BASED,Temple Newsam ,0.0
-26,E05011406,WARDS - APPLICANT BASED,Morley North ,2.0
-27,E05011404,WARDS - APPLICANT BASED,Middleton Park ,0.0
-28,E05011410,WARDS - APPLICANT BASED,Rothwell ,0.0
-29,E05013830,WARDS - APPLICANT BASED,Garforth & Swillington ,0.0
-30,E05011407,WARDS - APPLICANT BASED,Morley South ,0.0
-31,E05011384,WARDS - APPLICANT BASED,Ardsley & Robin Hood ,0.0
-32,E05011401,WARDS - APPLICANT BASED,Kippax & Methley ,0.0
+ward_code,metric,ward_name,value
+E05011549,WARDS - APPLICANT BASED,Otley & Yeadon ,1.0
+E05011414,WARDS - APPLICANT BASED,Wetherby ,1.0
+E05011413,WARDS - APPLICANT BASED,Weetwood ,1.0
+E05011389,WARDS - APPLICANT BASED,Calverley & Farsley ,1.0
+E05011402,WARDS - APPLICANT BASED,Kirkstall ,1.0
+E05011390,WARDS - APPLICANT BASED,Chapel Allerton ,1.0
+E05011409,WARDS - APPLICANT BASED,Pudsey ,1.0
+E05012648,WARDS - APPLICANT BASED,Farnley & Wortley ,1.0
+E05012647,WARDS - APPLICANT BASED,Beeston & Holbeck ,1.0

src/_includes/css/oi.css

@@ -17,6 +17,7 @@ h1,h2,h3,h4, .title, .subtitle {
 h1, .title { 
 	font-size: 1.8em; 
 	margin-top: 0.5em; 
+	margin-bottom: 1em;
 	font-weight: 600;
 	text-decoration: underline;
 	color: #0a2f45; 
@@ -27,11 +28,20 @@ h1, .title {
 	text-align:center;
  }
 
-h2 { font-size: 1.7em; margin-top: 1.764706em; font-weight: 500; }
+h2 { font-size: 1.6em; 
+	margin-top: 1.5em; 
+	font-weight: 500; 
+	text-align: center;
+	text-decoration: underline; 
+	text-underline-offset: 10px; 
+	text-decoration-color: #0a2f45;
+	text-decoration-thickness: 3px;
+}
 h3 { 
 	font-size: 1.4em; 
 	margin-top: 1.428571em; 
 	font-weight: 600;
+	text-align: center;
 }
 h2:first-child, h3:first-child, .title { margin-top: 0; }
 h1 + *, h2 + *, h3 + * { margin-top: 2em; }
@@ -72,7 +82,7 @@ a.button:hover, button:hover, button:focus, a.button:focus { background-color: #
 .tallpadded { padding-top: 2em; padding-bottom: 2em; }
 section { padding: 2em 0; }
 
-.block { padding:2em; border-style: groove; margin-top: 2em; margin-bottom: 2em; border-width: 1px 1px; border-color: #bbbb}
+.block { padding:2em; border-style: solid; margin-top: 2em; margin-bottom: 2em; border-width: 1px 1px; border-color: #bbbb}
 
 .skip-to-content-link { position: absolute; top: -3rem; right: 0.5rem; z-index: 2000; transition: top 0.2s; padding: 0.5rem 1rem; }
 .skip-to-content-link:focus { top: 0.25rem; }
@@ -100,9 +110,9 @@ header nav ul li { display: inline-block; }
 header nav ul > li > a { padding: 0.5em 1em; display: inline-block; width: 100%; color: inherit; }
 .header-holder { max-width: 1300px; position: relative; margin: auto;}
 
-.about { padding: 2em; margin-bottom: 1em; border-style: groove; border-width: 1px; border-color: #bbbb;}
+.about { padding: 2em; margin-bottom: 1em; border-style: solid; border-width: 1px; border-color: #bbbb;}
 
-.summary { padding-top: 2em; padding-bottom: 1em;  }
+.summary { padding-top: 2em;  }
 
 .read-more { margin-top: 1em; padding-bottom: 0.5em; }
 .read-more a {  
@@ -112,7 +122,7 @@ header nav ul > li > a { padding: 0.5em 1em; display: inline-block; width: 100%;
 	text-underline-offset: 3px; 
 	text-decoration-color: #91cfce;
 	text-decoration-thickness: 2px;
-	border-style: groove;
+	border-style: solid;
 	border-width: 1px 1px 1px; 
 	border-color: #bbbb;
 	padding: 1em;
@@ -127,15 +137,13 @@ header nav ul > li > a { padding: 0.5em 1em; display: inline-block; width: 100%;
 /* Dashboard */
 .pane { 
 	padding: 2em; 
-	justify-content: space-evenly;
-
 }
 
-.grid { display: grid; grid-template-columns: 50% 50%; grid-gap: 0.5em; }
+.grid { display: grid; grid-template-columns: 50% 50%; grid-gap: 0.5em; margin-top: 0.5em; margin-bottom:0;}
 .grid .pane { 
 	background: #91cfce; 
 	color: #0a2f45;
-	border-right: groove; 
+	border-right: solid; 
 	border-width: 1px 1px; 
 	border-color: #7eb4b3;
     transition: color 0.15s linear, background-color 0.15s linear;
@@ -146,14 +154,44 @@ header nav ul > li > a { padding: 0.5em 1em; display: inline-block; width: 100%;
 	  }
 	}
 
+.grid .pane h2 {
+	text-decoration: none;
+	font-size: 1.4em;
+	margin:0;
+}
+
 ul.grid { list-style: none; margin-left: 0em; grid-template-columns: repeat(auto-fill, minmax(min(100%, 300px), 1fr));}
 ul.grid > li > a { display: block; text-decoration: none;}
 ul.grid > li > a > h3 { text-align: center; font-weight: 500; font-size: 1.2em; color: inherit; 
 	color:rgba(92, 176, 177, 0.881) 0%;
 	color: rgb(0, 50, 71) 100%;
+	line-height: 1.4em;
 }
 
+ul.grid > li > h3 {
+	text-align: center; 
+	font-weight: 500; 
+	font-size: 1.1em; 
+	color: inherit; 
+	color:rgba(92, 176, 177, 0.881) 0%;
+	color: rgb(0, 50, 71) 100%;
+	line-height: 1.4em;
+}
 
+.text-panel { 
+	padding: 5em; 
+	background: #91cfce; 
+	color: #0a2f45;
+	border-right: solid; 
+	border-width: 1px 1px; 
+	border-color: #7eb4b3;
+    transition: color 0.15s linear, background-color 0.15s linear;
+
+	&:hover {
+		color: white;
+		background: #002331;
+	  }
+}
 
 
 .impact-link:hover {