Workplace size coeffs#18

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Merged
fscottfoti merged 4 commits into from
Feb 20, 2015
Merged

Workplace size coeffs #18

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a916134
adding the size terms to workplace location choice
fscottfoti Dec 18, 2014
0fce58c
Merge branch 'master' into workplace-size-coeffs
fscottfoti Feb 20, 2015
46b48c7
Merge branch 'master' into workplace-size-coeffs
fscottfoti Feb 20, 2015
24f770e
with the changes from the other branch, this branch ran with no edits
fscottfoti Feb 20, 2015
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1 change: 1 addition & 0 deletions activitysim/activitysim.py
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Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -110,6 +110,7 @@ def simple_simulate(choosers, alternatives, spec,
print "Failed with DataFrame eval:\n%s" % expr
raise e
vars[expr] = s
vars[expr] = vars[expr].astype('float') # explicit cast
model_design = pd.DataFrame(vars, index=df.index)

df = random_rows(model_design, min(100000, len(model_design)))\
Expand Down
2 changes: 1 addition & 1 deletion example/configs/workplace_location.csv
View file
Open in desktop
Original file line numberDiff line numberDiff line change
@@ -1 +1 @@
Description,Expression,Alt"Distance, piecewise linear from 0 to 1 miles",@df.distance.clip(1),-0.8428"Distance, piecewise linear from 1 to 2 miles","@(df.distance-1).clip(0,1)",-0.3104"Distance, piecewise linear from 2 to 5 miles","@(df.distance-2).clip(0,3)",-0.3783"Distance, piecewise linear from 5 to 15 miles","@(df.distance-5).clip(0,10)",-0.1285"Distance, piecewise linear for 15+ miles",@(df.distance-15.0).clip(0),-0.0917"Distance 0 to 5 mi, high and very high income",@(df.income_segment>=3)*df.distance.clip(upper=5),0.15"Distance 5+ mi, high and very high income",@(df.income_segment>=3)*(df.distance-5).clip(0),0.02"Size variable full-time worker, low income",(income_segment==1)*lnWorkLowDcSizeAlt,1"Size variable full-time worker, medium income",(income_segment==2)*lnWorkMedDcSizeAlt,1"Size variable full-time worker, high income",(income_segment==3)*lnWorkHighDcSizeAlt,1"Size variable full-time worker, very high income",(income_segment==4)*lnWorkVeryHighDcSizeAlt,1"No attractions full-time worker, low income",(income_segment==1)*lnWorkLowDcSizeAlt==0,-999"No attractions full-time worker, medium income",(income_segment==2)*lnWorkMedDcSizeAlt==0,-999"No attractions full-time worker, high income",(income_segment==3)*lnWorkHighDcSizeAlt==0,-999"No attractions full-time worker, very high income",(income_segment==4)*lnWorkVeryHighDcSizeAlt==0,-999Mode choice logsum,mcLogsum,0.3
Description,Expression,Alt"Distance, piecewise linear from 0 to 1 miles",@df.distance.clip(1),-0.8428"Distance, piecewise linear from 1 to 2 miles","@(df.distance-1).clip(0,1)",-0.3104"Distance, piecewise linear from 2 to 5 miles","@(df.distance-2).clip(0,3)",-0.3783"Distance, piecewise linear from 5 to 15 miles","@(df.distance-5).clip(0,10)",-0.1285"Distance, piecewise linear for 15+ miles",@(df.distance-15.0).clip(0),-0.0917"Distance 0 to 5 mi, high and very high income",@(df.income_segment>=3)*df.distance.clip(upper=5),0.15"Distance 5+ mi, high and very high income",@(df.income_segment>=3)*(df.distance-5).clip(0),0.02"Size variable full-time worker, low income",@(df.income_segment==1)*df.size_low,1"Size variable full-time worker, medium income",@(df.income_segment==2)*df.size_med,1"Size variable full-time worker, high income",@(df.income_segment==3)*df.size_high,1"Size variable full-time worker, very high income",@(df.income_segment==4)*df.size_veryhigh,1"No attractions full-time worker, low income",@(df.income_segment==1)&(df.size_low==0),-999"No attractions full-time worker, medium income",@(df.income_segment==2)&(df.size_med==0),-999"No attractions full-time worker, high income",@(df.income_segment==3)&(df.size_high==0),-999"No attractions full-time worker, very high income",@(df.income_segment==4)&(df.size_veryhigh==0),-999Mode choice logsum,mcLogsum,0.3
Expand Down
1 change: 1 addition & 0 deletions example/configs/workplace_location_size_terms.csv
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Open in desktop
Original file line numberDiff line numberDiff line change
@@ -0,0 +1 @@
purpose,segment,TOTHH,RETEMPN,FPSEMPN,HEREMPN,OTHEMPN,AGREMPN,MWTEMPN,AGE0519,HSENROLL,COLLFTE,COLLPTEwork,low,0,0.129,0.193,0.383,0.12,0.01,0.164,0,0,0,0work,med,0,0.12,0.197,0.325,0.139,0.008,0.21,0,0,0,0work,high,0,0.11,0.207,0.284,0.154,0.006,0.239,0,0,0,0work,veryhigh,0,0.093,0.27,0.241,0.146,0.004,0.246,0,0,0,0university,university,0,0,0,0,0,0,0,0,0,0.592,0.408school,grade,0,0,0,0,0,0,0,1,0,0,0school,high,0,0,0,0,0,0,0,0,1,0,0escort,kids,0,0.225,0,0.144,0,0,0,0.465,0.166,0,0escort,no kids,0,0.225,0,0.144,0,0,0,0.465,0.166,0,0shopping,shopping,0,1,0,0,0,0,0,0,0,0,0eatOut,eatOut,0,0.742,0,0.258,0,0,0,0,0,0,0othMaint,othMaint,0,0.482,0,0.518,0,0,0,0,0,0,0social,social,0,0.522,0,0.478,0,0,0,0,0,0,0othDiscr,othDiscr,0.252,0.212,0,0.272,0.165,0,0,0,0.098,0,0atwork,atwork,0,0.742,0,0.258,0,0,0,0,0,0,0
Expand Down
59 changes: 55 additions & 4 deletions example/models.py
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -37,7 +37,37 @@ def auto_ownership_spec():
@sim.injectable()
def workplace_location_spec():
f = os.path.join('configs', "workplace_location.csv")
return asim.read_model_spec(f).head(7)
return asim.read_model_spec(f).head(15)


@sim.table()
def workplace_size_spec():
f = os.path.join('configs', 'workplace_location_size_terms.csv')
return pd.read_csv(f)


@sim.table()
def workplace_size_terms(land_use, workplace_size_spec):
"""
This method takes the land use data and multiplies various columns of the
land use data by coefficients from the workplace_size_spec table in order
to yield a size term (a linear combination of land use variables) with
specified coefficients for different segments (like low, med, and high
income)
"""
land_use = land_use.to_frame()
df = workplace_size_spec.to_frame().query("purpose == 'work'")
df = df.drop("purpose", axis=1).set_index("segment")
new_df = {}
for index, row in df.iterrows():
missing = row[~row.index.isin(land_use.columns)]
if len(missing) > 0:
print "WARNING: missing columns in land use\n", missing.index
row = row[row.index.isin(land_use.columns)]
sparse = land_use[list(row.index)]
new_df["size_"+index] = np.dot(sparse.as_matrix(), row.values)
new_df = pd.DataFrame(new_df, index=land_use.index)
return new_df


@sim.model()
Expand DownExpand Up@@ -67,10 +97,11 @@ def workplace_location_simulate(persons,
households,
zones,
workplace_location_spec,
distance_matrix):
distance_matrix,
workplace_size_terms):

choosers = sim.merge_tables(persons.name, tables=[persons, households])
alternatives = zones.to_frame()
alternatives = zones.to_frame().join(workplace_size_terms.to_frame())

skims = {
"distance": distance_matrix
Expand All@@ -88,4 +119,24 @@ def workplace_location_simulate(persons,
print "Describe of hoices:\n", choices.describe()
sim.add_column("persons", "workplace_taz", choices)

return model_design
return model_design


@sim.column("land_use")
def total_households(land_use):
return land_use.local.TOTHH


@sim.column("land_use")
def total_employment(land_use):
return land_use.local.TOTEMP


@sim.column("land_use")
def total_acres(land_use):
return land_use.local.TOTACRE


@sim.column("land_use")
def county_id(land_use):
return land_use.local.COUNTY
6 changes: 1 addition & 5 deletions notebooks/data_mover.ipynb
View file
Open in desktop
Original file line numberDiff line numberDiff line change
@@ -1,7 +1,7 @@
{
"metadata": {
"name": "",
"signature": "sha256:d62442075d195d4893cdd65305dac0932aeb18464bb30a1d8aac6a89ad987ef1"
"signature": "sha256:afbc3e7040dd9e4a5b21433063f13a6a8abfcc04bcfc6574e7e43376c257cd33"
},
"nbformat": 3,
"nbformat_minor": 0,
Expand DownExpand Up@@ -43,10 +43,6 @@
"col_map = {\n",
" \"HHID\": \"household_id\",\n",
" \"AGE\": \"age\",\n",
" \"TOTHH\": \"total_households\",\n",
" \"TOTEMP\": \"total_employment\",\n",
" \"TOTACRE\": \"total_acres\",\n",
" \"COUNTY\": \"county_id\",\n",
" \"hworkers\": \"workers\",\n",
" \"HINC\": \"income\"\n",
"}"
Expand Down
Loading
, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
 blocks\n(function() {\n function addCopyButtons() {\n document.querySelectorAll('pre code').forEach(function(codeBlock) {\n if (codeBlock.parentElement.hasAttribute('data-copy-added')) return;\n codeBlock.parentElement.setAttribute('data-copy-added', 'true');\n \n var btn = document.createElement('button');\n btn.textContent = 'Copy';\n btn.style.cssText = 'position:absolute;top:4px;right:4px;padding:2px 8px;font-size:11px;background:#4ecdc4;border:none;border-radius:4px;color:#1a1a2e;cursor:pointer;opacity:0.7;transition:opacity 0.2s;';\n btn.onmouseover = function() { this.style.opacity = '1'; };\n btn.onmouseout = function() { this.style.opacity = '0.7'; };\n btn.onclick = function() {\n navigator.clipboard.writeText(codeBlock.textContent).then(function() {\n btn.textContent = 'Copied!';\n setTimeout(function() { btn.textContent = 'Copy'; }, 1500);\n });\n };\n codeBlock.parentElement.style.position = 'relative';\n codeBlock.parentElement.appendChild(btn);\n });\n }\n \n addCopyButtons();\n \n // Re-run on dynamic content\n var observer = new MutationObserver(addCopyButtons);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Add Copy Buttons to Code Blocks");
}
} catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); }
})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

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Workplace size coeffs#18

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 Merged

fscottfoti
merged 4 commits into

from

Feb 20, 2015
 Merged


Workplace size coeffs
#18
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a916134

adding the size terms to workplace location choice


fscottfoti Dec 18, 2014
0fce58c

Merge branch 'master' into workplace-size-coeffs


fscottfoti Feb 20, 2015
46b48c7

Merge branch 'master' into workplace-size-coeffs


fscottfoti Feb 20, 2015
24f770e

with the changes from the other branch, this branch ran with no edits


fscottfoti Feb 20, 2015

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1 change: 1 addition & 0 deletions
activitysim/activitysim.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -110,6 +110,7 @@ def simple_simulate(choosers, alternatives, spec,
 print "Failed with DataFrame eval:\n%s" % expr
 raise e
 vars[expr] = s
 vars[expr] = vars[expr].astype('float') # explicit cast
 model_design = pd.DataFrame(vars, index=df.index)

 df = random_rows(model_design, min(100000, len(model_design)))\
Expand Down

2 changes: 1 addition & 1 deletion
example/configs/workplace_location.csv

View file


Open in desktop

Original file line numberDiff line numberDiff line change
@@ -1 +1 @@
Description,Expression,Alt"Distance, piecewise linear from 0 to 1 miles",@df.distance.clip(1),-0.8428"Distance, piecewise linear from 1 to 2 miles","@(df.distance-1).clip(0,1)",-0.3104"Distance, piecewise linear from 2 to 5 miles","@(df.distance-2).clip(0,3)",-0.3783"Distance, piecewise linear from 5 to 15 miles","@(df.distance-5).clip(0,10)",-0.1285"Distance, piecewise linear for 15+ miles",@(df.distance-15.0).clip(0),-0.0917"Distance 0 to 5 mi, high and very high income",@(df.income_segment>=3)*df.distance.clip(upper=5),0.15"Distance 5+ mi, high and very high income",@(df.income_segment>=3)*(df.distance-5).clip(0),0.02"Size variable full-time worker, low income",(income_segment==1)*lnWorkLowDcSizeAlt,1"Size variable full-time worker, medium income",(income_segment==2)*lnWorkMedDcSizeAlt,1"Size variable full-time worker, high income",(income_segment==3)*lnWorkHighDcSizeAlt,1"Size variable full-time worker, very high income",(income_segment==4)*lnWorkVeryHighDcSizeAlt,1"No attractions full-time worker, low income",(income_segment==1)*lnWorkLowDcSizeAlt==0,-999"No attractions full-time worker, medium income",(income_segment==2)*lnWorkMedDcSizeAlt==0,-999"No attractions full-time worker, high income",(income_segment==3)*lnWorkHighDcSizeAlt==0,-999"No attractions full-time worker, very high income",(income_segment==4)*lnWorkVeryHighDcSizeAlt==0,-999Mode choice logsum,mcLogsum,0.3
Description,Expression,Alt"Distance, piecewise linear from 0 to 1 miles",@df.distance.clip(1),-0.8428"Distance, piecewise linear from 1 to 2 miles","@(df.distance-1).clip(0,1)",-0.3104"Distance, piecewise linear from 2 to 5 miles","@(df.distance-2).clip(0,3)",-0.3783"Distance, piecewise linear from 5 to 15 miles","@(df.distance-5).clip(0,10)",-0.1285"Distance, piecewise linear for 15+ miles",@(df.distance-15.0).clip(0),-0.0917"Distance 0 to 5 mi, high and very high income",@(df.income_segment>=3)*df.distance.clip(upper=5),0.15"Distance 5+ mi, high and very high income",@(df.income_segment>=3)*(df.distance-5).clip(0),0.02"Size variable full-time worker, low income",@(df.income_segment==1)*df.size_low,1"Size variable full-time worker, medium income",@(df.income_segment==2)*df.size_med,1"Size variable full-time worker, high income",@(df.income_segment==3)*df.size_high,1"Size variable full-time worker, very high income",@(df.income_segment==4)*df.size_veryhigh,1"No attractions full-time worker, low income",@(df.income_segment==1)&(df.size_low==0),-999"No attractions full-time worker, medium income",@(df.income_segment==2)&(df.size_med==0),-999"No attractions full-time worker, high income",@(df.income_segment==3)&(df.size_high==0),-999"No attractions full-time worker, very high income",@(df.income_segment==4)&(df.size_veryhigh==0),-999Mode choice logsum,mcLogsum,0.3
Expand Down

1 change: 1 addition & 0 deletions
example/configs/workplace_location_size_terms.csv

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Open in desktop

Original file line numberDiff line numberDiff line change
@@ -0,0 +1 @@
purpose,segment,TOTHH,RETEMPN,FPSEMPN,HEREMPN,OTHEMPN,AGREMPN,MWTEMPN,AGE0519,HSENROLL,COLLFTE,COLLPTEwork,low,0,0.129,0.193,0.383,0.12,0.01,0.164,0,0,0,0work,med,0,0.12,0.197,0.325,0.139,0.008,0.21,0,0,0,0work,high,0,0.11,0.207,0.284,0.154,0.006,0.239,0,0,0,0work,veryhigh,0,0.093,0.27,0.241,0.146,0.004,0.246,0,0,0,0university,university,0,0,0,0,0,0,0,0,0,0.592,0.408school,grade,0,0,0,0,0,0,0,1,0,0,0school,high,0,0,0,0,0,0,0,0,1,0,0escort,kids,0,0.225,0,0.144,0,0,0,0.465,0.166,0,0escort,no kids,0,0.225,0,0.144,0,0,0,0.465,0.166,0,0shopping,shopping,0,1,0,0,0,0,0,0,0,0,0eatOut,eatOut,0,0.742,0,0.258,0,0,0,0,0,0,0othMaint,othMaint,0,0.482,0,0.518,0,0,0,0,0,0,0social,social,0,0.522,0,0.478,0,0,0,0,0,0,0othDiscr,othDiscr,0.252,0.212,0,0.272,0.165,0,0,0,0.098,0,0atwork,atwork,0,0.742,0,0.258,0,0,0,0,0,0,0
Expand Down

59 changes: 55 additions & 4 deletions
example/models.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -37,7 +37,37 @@ def auto_ownership_spec():
@sim.injectable()
def workplace_location_spec():
 f = os.path.join('configs', "workplace_location.csv")
 return asim.read_model_spec(f).head(7)
 return asim.read_model_spec(f).head(15)


@sim.table()
def workplace_size_spec():
 f = os.path.join('configs', 'workplace_location_size_terms.csv')
 return pd.read_csv(f)


@sim.table()
def workplace_size_terms(land_use, workplace_size_spec):
 """
 This method takes the land use data and multiplies various columns of the
 land use data by coefficients from the workplace_size_spec table in order
 to yield a size term (a linear combination of land use variables) with
 specified coefficients for different segments (like low, med, and high
 income)
 """
 land_use = land_use.to_frame()
 df = workplace_size_spec.to_frame().query("purpose == 'work'")
 df = df.drop("purpose", axis=1).set_index("segment")
 new_df = {}
 for index, row in df.iterrows():
 missing = row[~row.index.isin(land_use.columns)]
 if len(missing) > 0:
 print "WARNING: missing columns in land use\n", missing.index
 row = row[row.index.isin(land_use.columns)]
 sparse = land_use[list(row.index)]
 new_df["size_"+index] = np.dot(sparse.as_matrix(), row.values)
 new_df = pd.DataFrame(new_df, index=land_use.index)
 return new_df


@sim.model()
Expand DownExpand Up@@ -67,10 +97,11 @@ def workplace_location_simulate(persons,
 households,
 zones,
 workplace_location_spec,
 distance_matrix):
 distance_matrix,
 workplace_size_terms):

 choosers = sim.merge_tables(persons.name, tables=[persons, households])
 alternatives = zones.to_frame()
 alternatives = zones.to_frame().join(workplace_size_terms.to_frame())

 skims = {
 "distance": distance_matrix
Expand All@@ -88,4 +119,24 @@ def workplace_location_simulate(persons,
 print "Describe of hoices:\n", choices.describe()
 sim.add_column("persons", "workplace_taz", choices)

 return model_design
 return model_design


@sim.column("land_use")
def total_households(land_use):
 return land_use.local.TOTHH


@sim.column("land_use")
def total_employment(land_use):
 return land_use.local.TOTEMP


@sim.column("land_use")
def total_acres(land_use):
 return land_use.local.TOTACRE


@sim.column("land_use")
def county_id(land_use):
 return land_use.local.COUNTY

6 changes: 1 addition & 5 deletions
notebooks/data_mover.ipynb

View file


Open in desktop

Original file line numberDiff line numberDiff line change
@@ -1,7 +1,7 @@
{
 "metadata": {
 "name": "",
 "signature": "sha256:d62442075d195d4893cdd65305dac0932aeb18464bb30a1d8aac6a89ad987ef1"
 "signature": "sha256:afbc3e7040dd9e4a5b21433063f13a6a8abfcc04bcfc6574e7e43376c257cd33"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
Expand DownExpand Up@@ -43,10 +43,6 @@
 "col_map = {\n",
 " \"HHID\": \"household_id\",\n",
 " \"AGE\": \"age\",\n",
 " \"TOTHH\": \"total_households\",\n",
 " \"TOTEMP\": \"total_employment\",\n",
 " \"TOTACRE\": \"total_acres\",\n",
 " \"COUNTY\": \"county_id\",\n",
 " \"hworkers\": \"workers\",\n",
 " \"HINC\": \"income\"\n",
 "}"
Expand Down
Loading

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix");
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} catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Workplace size coeffs#18

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 Merged

fscottfoti
merged 4 commits into

from

Feb 20, 2015
 Merged


Workplace size coeffs
#18
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a916134

adding the size terms to workplace location choice


fscottfoti Dec 18, 2014
0fce58c

Merge branch 'master' into workplace-size-coeffs


fscottfoti Feb 20, 2015
46b48c7

Merge branch 'master' into workplace-size-coeffs


fscottfoti Feb 20, 2015
24f770e

with the changes from the other branch, this branch ran with no edits


fscottfoti Feb 20, 2015

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1 change: 1 addition & 0 deletions
activitysim/activitysim.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -110,6 +110,7 @@ def simple_simulate(choosers, alternatives, spec,
 print "Failed with DataFrame eval:\n%s" % expr
 raise e
 vars[expr] = s
 vars[expr] = vars[expr].astype('float') # explicit cast
 model_design = pd.DataFrame(vars, index=df.index)

 df = random_rows(model_design, min(100000, len(model_design)))\
Expand Down

2 changes: 1 addition & 1 deletion
example/configs/workplace_location.csv

View file


Open in desktop

Original file line numberDiff line numberDiff line change
@@ -1 +1 @@
Description,Expression,Alt"Distance, piecewise linear from 0 to 1 miles",@df.distance.clip(1),-0.8428"Distance, piecewise linear from 1 to 2 miles","@(df.distance-1).clip(0,1)",-0.3104"Distance, piecewise linear from 2 to 5 miles","@(df.distance-2).clip(0,3)",-0.3783"Distance, piecewise linear from 5 to 15 miles","@(df.distance-5).clip(0,10)",-0.1285"Distance, piecewise linear for 15+ miles",@(df.distance-15.0).clip(0),-0.0917"Distance 0 to 5 mi, high and very high income",@(df.income_segment>=3)*df.distance.clip(upper=5),0.15"Distance 5+ mi, high and very high income",@(df.income_segment>=3)*(df.distance-5).clip(0),0.02"Size variable full-time worker, low income",(income_segment==1)*lnWorkLowDcSizeAlt,1"Size variable full-time worker, medium income",(income_segment==2)*lnWorkMedDcSizeAlt,1"Size variable full-time worker, high income",(income_segment==3)*lnWorkHighDcSizeAlt,1"Size variable full-time worker, very high income",(income_segment==4)*lnWorkVeryHighDcSizeAlt,1"No attractions full-time worker, low income",(income_segment==1)*lnWorkLowDcSizeAlt==0,-999"No attractions full-time worker, medium income",(income_segment==2)*lnWorkMedDcSizeAlt==0,-999"No attractions full-time worker, high income",(income_segment==3)*lnWorkHighDcSizeAlt==0,-999"No attractions full-time worker, very high income",(income_segment==4)*lnWorkVeryHighDcSizeAlt==0,-999Mode choice logsum,mcLogsum,0.3
Description,Expression,Alt"Distance, piecewise linear from 0 to 1 miles",@df.distance.clip(1),-0.8428"Distance, piecewise linear from 1 to 2 miles","@(df.distance-1).clip(0,1)",-0.3104"Distance, piecewise linear from 2 to 5 miles","@(df.distance-2).clip(0,3)",-0.3783"Distance, piecewise linear from 5 to 15 miles","@(df.distance-5).clip(0,10)",-0.1285"Distance, piecewise linear for 15+ miles",@(df.distance-15.0).clip(0),-0.0917"Distance 0 to 5 mi, high and very high income",@(df.income_segment>=3)*df.distance.clip(upper=5),0.15"Distance 5+ mi, high and very high income",@(df.income_segment>=3)*(df.distance-5).clip(0),0.02"Size variable full-time worker, low income",@(df.income_segment==1)*df.size_low,1"Size variable full-time worker, medium income",@(df.income_segment==2)*df.size_med,1"Size variable full-time worker, high income",@(df.income_segment==3)*df.size_high,1"Size variable full-time worker, very high income",@(df.income_segment==4)*df.size_veryhigh,1"No attractions full-time worker, low income",@(df.income_segment==1)&(df.size_low==0),-999"No attractions full-time worker, medium income",@(df.income_segment==2)&(df.size_med==0),-999"No attractions full-time worker, high income",@(df.income_segment==3)&(df.size_high==0),-999"No attractions full-time worker, very high income",@(df.income_segment==4)&(df.size_veryhigh==0),-999Mode choice logsum,mcLogsum,0.3
Expand Down

1 change: 1 addition & 0 deletions
example/configs/workplace_location_size_terms.csv

View file


Open in desktop

Original file line numberDiff line numberDiff line change
@@ -0,0 +1 @@
purpose,segment,TOTHH,RETEMPN,FPSEMPN,HEREMPN,OTHEMPN,AGREMPN,MWTEMPN,AGE0519,HSENROLL,COLLFTE,COLLPTEwork,low,0,0.129,0.193,0.383,0.12,0.01,0.164,0,0,0,0work,med,0,0.12,0.197,0.325,0.139,0.008,0.21,0,0,0,0work,high,0,0.11,0.207,0.284,0.154,0.006,0.239,0,0,0,0work,veryhigh,0,0.093,0.27,0.241,0.146,0.004,0.246,0,0,0,0university,university,0,0,0,0,0,0,0,0,0,0.592,0.408school,grade,0,0,0,0,0,0,0,1,0,0,0school,high,0,0,0,0,0,0,0,0,1,0,0escort,kids,0,0.225,0,0.144,0,0,0,0.465,0.166,0,0escort,no kids,0,0.225,0,0.144,0,0,0,0.465,0.166,0,0shopping,shopping,0,1,0,0,0,0,0,0,0,0,0eatOut,eatOut,0,0.742,0,0.258,0,0,0,0,0,0,0othMaint,othMaint,0,0.482,0,0.518,0,0,0,0,0,0,0social,social,0,0.522,0,0.478,0,0,0,0,0,0,0othDiscr,othDiscr,0.252,0.212,0,0.272,0.165,0,0,0,0.098,0,0atwork,atwork,0,0.742,0,0.258,0,0,0,0,0,0,0
Expand Down

59 changes: 55 additions & 4 deletions
example/models.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -37,7 +37,37 @@ def auto_ownership_spec():
@sim.injectable()
def workplace_location_spec():
 f = os.path.join('configs', "workplace_location.csv")
 return asim.read_model_spec(f).head(7)
 return asim.read_model_spec(f).head(15)


@sim.table()
def workplace_size_spec():
 f = os.path.join('configs', 'workplace_location_size_terms.csv')
 return pd.read_csv(f)


@sim.table()
def workplace_size_terms(land_use, workplace_size_spec):
 """
 This method takes the land use data and multiplies various columns of the
 land use data by coefficients from the workplace_size_spec table in order
 to yield a size term (a linear combination of land use variables) with
 specified coefficients for different segments (like low, med, and high
 income)
 """
 land_use = land_use.to_frame()
 df = workplace_size_spec.to_frame().query("purpose == 'work'")
 df = df.drop("purpose", axis=1).set_index("segment")
 new_df = {}
 for index, row in df.iterrows():
 missing = row[~row.index.isin(land_use.columns)]
 if len(missing) > 0:
 print "WARNING: missing columns in land use\n", missing.index
 row = row[row.index.isin(land_use.columns)]
 sparse = land_use[list(row.index)]
 new_df["size_"+index] = np.dot(sparse.as_matrix(), row.values)
 new_df = pd.DataFrame(new_df, index=land_use.index)
 return new_df


@sim.model()
Expand DownExpand Up@@ -67,10 +97,11 @@ def workplace_location_simulate(persons,
 households,
 zones,
 workplace_location_spec,
 distance_matrix):
 distance_matrix,
 workplace_size_terms):

 choosers = sim.merge_tables(persons.name, tables=[persons, households])
 alternatives = zones.to_frame()
 alternatives = zones.to_frame().join(workplace_size_terms.to_frame())

 skims = {
 "distance": distance_matrix
Expand All@@ -88,4 +119,24 @@ def workplace_location_simulate(persons,
 print "Describe of hoices:\n", choices.describe()
 sim.add_column("persons", "workplace_taz", choices)

 return model_design
 return model_design


@sim.column("land_use")
def total_households(land_use):
 return land_use.local.TOTHH


@sim.column("land_use")
def total_employment(land_use):
 return land_use.local.TOTEMP


@sim.column("land_use")
def total_acres(land_use):
 return land_use.local.TOTACRE


@sim.column("land_use")
def county_id(land_use):
 return land_use.local.COUNTY

6 changes: 1 addition & 5 deletions
notebooks/data_mover.ipynb

View file


Open in desktop

Original file line numberDiff line numberDiff line change
@@ -1,7 +1,7 @@
{
 "metadata": {
 "name": "",
 "signature": "sha256:d62442075d195d4893cdd65305dac0932aeb18464bb30a1d8aac6a89ad987ef1"
 "signature": "sha256:afbc3e7040dd9e4a5b21433063f13a6a8abfcc04bcfc6574e7e43376c257cd33"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
Expand DownExpand Up@@ -43,10 +43,6 @@
 "col_map = {\n",
 " \"HHID\": \"household_id\",\n",
 " \"AGE\": \"age\",\n",
 " \"TOTHH\": \"total_households\",\n",
 " \"TOTEMP\": \"total_employment\",\n",
 " \"TOTACRE\": \"total_acres\",\n",
 " \"COUNTY\": \"county_id\",\n",
 " \"hworkers\": \"workers\",\n",
 " \"HINC\": \"income\"\n",
 "}"
Expand Down
Loading

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms");
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Workplace size coeffs#18

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 Merged

fscottfoti
merged 4 commits into

from

Feb 20, 2015
 Merged


Workplace size coeffs
#18
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a916134

adding the size terms to workplace location choice


fscottfoti Dec 18, 2014
0fce58c

Merge branch 'master' into workplace-size-coeffs


fscottfoti Feb 20, 2015
46b48c7

Merge branch 'master' into workplace-size-coeffs


fscottfoti Feb 20, 2015
24f770e

with the changes from the other branch, this branch ran with no edits


fscottfoti Feb 20, 2015

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1 change: 1 addition & 0 deletions
activitysim/activitysim.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -110,6 +110,7 @@ def simple_simulate(choosers, alternatives, spec,
 print "Failed with DataFrame eval:\n%s" % expr
 raise e
 vars[expr] = s
 vars[expr] = vars[expr].astype('float') # explicit cast
 model_design = pd.DataFrame(vars, index=df.index)

 df = random_rows(model_design, min(100000, len(model_design)))\
Expand Down

2 changes: 1 addition & 1 deletion
example/configs/workplace_location.csv

View file


Open in desktop

Original file line numberDiff line numberDiff line change
@@ -1 +1 @@
Description,Expression,Alt"Distance, piecewise linear from 0 to 1 miles",@df.distance.clip(1),-0.8428"Distance, piecewise linear from 1 to 2 miles","@(df.distance-1).clip(0,1)",-0.3104"Distance, piecewise linear from 2 to 5 miles","@(df.distance-2).clip(0,3)",-0.3783"Distance, piecewise linear from 5 to 15 miles","@(df.distance-5).clip(0,10)",-0.1285"Distance, piecewise linear for 15+ miles",@(df.distance-15.0).clip(0),-0.0917"Distance 0 to 5 mi, high and very high income",@(df.income_segment>=3)*df.distance.clip(upper=5),0.15"Distance 5+ mi, high and very high income",@(df.income_segment>=3)*(df.distance-5).clip(0),0.02"Size variable full-time worker, low income",(income_segment==1)*lnWorkLowDcSizeAlt,1"Size variable full-time worker, medium income",(income_segment==2)*lnWorkMedDcSizeAlt,1"Size variable full-time worker, high income",(income_segment==3)*lnWorkHighDcSizeAlt,1"Size variable full-time worker, very high income",(income_segment==4)*lnWorkVeryHighDcSizeAlt,1"No attractions full-time worker, low income",(income_segment==1)*lnWorkLowDcSizeAlt==0,-999"No attractions full-time worker, medium income",(income_segment==2)*lnWorkMedDcSizeAlt==0,-999"No attractions full-time worker, high income",(income_segment==3)*lnWorkHighDcSizeAlt==0,-999"No attractions full-time worker, very high income",(income_segment==4)*lnWorkVeryHighDcSizeAlt==0,-999Mode choice logsum,mcLogsum,0.3
Description,Expression,Alt"Distance, piecewise linear from 0 to 1 miles",@df.distance.clip(1),-0.8428"Distance, piecewise linear from 1 to 2 miles","@(df.distance-1).clip(0,1)",-0.3104"Distance, piecewise linear from 2 to 5 miles","@(df.distance-2).clip(0,3)",-0.3783"Distance, piecewise linear from 5 to 15 miles","@(df.distance-5).clip(0,10)",-0.1285"Distance, piecewise linear for 15+ miles",@(df.distance-15.0).clip(0),-0.0917"Distance 0 to 5 mi, high and very high income",@(df.income_segment>=3)*df.distance.clip(upper=5),0.15"Distance 5+ mi, high and very high income",@(df.income_segment>=3)*(df.distance-5).clip(0),0.02"Size variable full-time worker, low income",@(df.income_segment==1)*df.size_low,1"Size variable full-time worker, medium income",@(df.income_segment==2)*df.size_med,1"Size variable full-time worker, high income",@(df.income_segment==3)*df.size_high,1"Size variable full-time worker, very high income",@(df.income_segment==4)*df.size_veryhigh,1"No attractions full-time worker, low income",@(df.income_segment==1)&(df.size_low==0),-999"No attractions full-time worker, medium income",@(df.income_segment==2)&(df.size_med==0),-999"No attractions full-time worker, high income",@(df.income_segment==3)&(df.size_high==0),-999"No attractions full-time worker, very high income",@(df.income_segment==4)&(df.size_veryhigh==0),-999Mode choice logsum,mcLogsum,0.3
Expand Down

1 change: 1 addition & 0 deletions
example/configs/workplace_location_size_terms.csv

View file


Open in desktop

Original file line numberDiff line numberDiff line change
@@ -0,0 +1 @@
purpose,segment,TOTHH,RETEMPN,FPSEMPN,HEREMPN,OTHEMPN,AGREMPN,MWTEMPN,AGE0519,HSENROLL,COLLFTE,COLLPTEwork,low,0,0.129,0.193,0.383,0.12,0.01,0.164,0,0,0,0work,med,0,0.12,0.197,0.325,0.139,0.008,0.21,0,0,0,0work,high,0,0.11,0.207,0.284,0.154,0.006,0.239,0,0,0,0work,veryhigh,0,0.093,0.27,0.241,0.146,0.004,0.246,0,0,0,0university,university,0,0,0,0,0,0,0,0,0,0.592,0.408school,grade,0,0,0,0,0,0,0,1,0,0,0school,high,0,0,0,0,0,0,0,0,1,0,0escort,kids,0,0.225,0,0.144,0,0,0,0.465,0.166,0,0escort,no kids,0,0.225,0,0.144,0,0,0,0.465,0.166,0,0shopping,shopping,0,1,0,0,0,0,0,0,0,0,0eatOut,eatOut,0,0.742,0,0.258,0,0,0,0,0,0,0othMaint,othMaint,0,0.482,0,0.518,0,0,0,0,0,0,0social,social,0,0.522,0,0.478,0,0,0,0,0,0,0othDiscr,othDiscr,0.252,0.212,0,0.272,0.165,0,0,0,0.098,0,0atwork,atwork,0,0.742,0,0.258,0,0,0,0,0,0,0
Expand Down

59 changes: 55 additions & 4 deletions
example/models.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -37,7 +37,37 @@ def auto_ownership_spec():
@sim.injectable()
def workplace_location_spec():
 f = os.path.join('configs', "workplace_location.csv")
 return asim.read_model_spec(f).head(7)
 return asim.read_model_spec(f).head(15)


@sim.table()
def workplace_size_spec():
 f = os.path.join('configs', 'workplace_location_size_terms.csv')
 return pd.read_csv(f)


@sim.table()
def workplace_size_terms(land_use, workplace_size_spec):
 """
 This method takes the land use data and multiplies various columns of the
 land use data by coefficients from the workplace_size_spec table in order
 to yield a size term (a linear combination of land use variables) with
 specified coefficients for different segments (like low, med, and high
 income)
 """
 land_use = land_use.to_frame()
 df = workplace_size_spec.to_frame().query("purpose == 'work'")
 df = df.drop("purpose", axis=1).set_index("segment")
 new_df = {}
 for index, row in df.iterrows():
 missing = row[~row.index.isin(land_use.columns)]
 if len(missing) > 0:
 print "WARNING: missing columns in land use\n", missing.index
 row = row[row.index.isin(land_use.columns)]
 sparse = land_use[list(row.index)]
 new_df["size_"+index] = np.dot(sparse.as_matrix(), row.values)
 new_df = pd.DataFrame(new_df, index=land_use.index)
 return new_df


@sim.model()
Expand DownExpand Up@@ -67,10 +97,11 @@ def workplace_location_simulate(persons,
 households,
 zones,
 workplace_location_spec,
 distance_matrix):
 distance_matrix,
 workplace_size_terms):

 choosers = sim.merge_tables(persons.name, tables=[persons, households])
 alternatives = zones.to_frame()
 alternatives = zones.to_frame().join(workplace_size_terms.to_frame())

 skims = {
 "distance": distance_matrix
Expand All@@ -88,4 +119,24 @@ def workplace_location_simulate(persons,
 print "Describe of hoices:\n", choices.describe()
 sim.add_column("persons", "workplace_taz", choices)

 return model_design
 return model_design


@sim.column("land_use")
def total_households(land_use):
 return land_use.local.TOTHH


@sim.column("land_use")
def total_employment(land_use):
 return land_use.local.TOTEMP


@sim.column("land_use")
def total_acres(land_use):
 return land_use.local.TOTACRE


@sim.column("land_use")
def county_id(land_use):
 return land_use.local.COUNTY

6 changes: 1 addition & 5 deletions
notebooks/data_mover.ipynb

View file


Open in desktop

Original file line numberDiff line numberDiff line change
@@ -1,7 +1,7 @@
{
 "metadata": {
 "name": "",
 "signature": "sha256:d62442075d195d4893cdd65305dac0932aeb18464bb30a1d8aac6a89ad987ef1"
 "signature": "sha256:afbc3e7040dd9e4a5b21433063f13a6a8abfcc04bcfc6574e7e43376c257cd33"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
Expand DownExpand Up@@ -43,10 +43,6 @@
 "col_map = {\n",
 " \"HHID\": \"household_id\",\n",
 " \"AGE\": \"age\",\n",
 " \"TOTHH\": \"total_households\",\n",
 " \"TOTEMP\": \"total_employment\",\n",
 " \"TOTACRE\": \"total_acres\",\n",
 " \"COUNTY\": \"county_id\",\n",
 " \"hworkers\": \"workers\",\n",
 " \"HINC\": \"income\"\n",
 "}"
Expand Down
Loading

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

Skip to content
Workplace size coeffs#18

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Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

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privacy statement. We’ll occasionally send you account related emails.

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to your account

 Merged

fscottfoti
merged 4 commits into

from

Feb 20, 2015
 Merged


Workplace size coeffs
#18
Show file tree
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4 commits


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a916134

adding the size terms to workplace location choice


fscottfoti Dec 18, 2014
0fce58c

Merge branch 'master' into workplace-size-coeffs


fscottfoti Feb 20, 2015
46b48c7

Merge branch 'master' into workplace-size-coeffs


fscottfoti Feb 20, 2015
24f770e

with the changes from the other branch, this branch ran with no edits


fscottfoti Feb 20, 2015

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Filter by extension
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1 change: 1 addition & 0 deletions
activitysim/activitysim.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -110,6 +110,7 @@ def simple_simulate(choosers, alternatives, spec,
 print "Failed with DataFrame eval:\n%s" % expr
 raise e
 vars[expr] = s
 vars[expr] = vars[expr].astype('float') # explicit cast
 model_design = pd.DataFrame(vars, index=df.index)

 df = random_rows(model_design, min(100000, len(model_design)))\
Expand Down

2 changes: 1 addition & 1 deletion
example/configs/workplace_location.csv

View file


Open in desktop

Original file line numberDiff line numberDiff line change
@@ -1 +1 @@
Description,Expression,Alt"Distance, piecewise linear from 0 to 1 miles",@df.distance.clip(1),-0.8428"Distance, piecewise linear from 1 to 2 miles","@(df.distance-1).clip(0,1)",-0.3104"Distance, piecewise linear from 2 to 5 miles","@(df.distance-2).clip(0,3)",-0.3783"Distance, piecewise linear from 5 to 15 miles","@(df.distance-5).clip(0,10)",-0.1285"Distance, piecewise linear for 15+ miles",@(df.distance-15.0).clip(0),-0.0917"Distance 0 to 5 mi, high and very high income",@(df.income_segment>=3)*df.distance.clip(upper=5),0.15"Distance 5+ mi, high and very high income",@(df.income_segment>=3)*(df.distance-5).clip(0),0.02"Size variable full-time worker, low income",(income_segment==1)*lnWorkLowDcSizeAlt,1"Size variable full-time worker, medium income",(income_segment==2)*lnWorkMedDcSizeAlt,1"Size variable full-time worker, high income",(income_segment==3)*lnWorkHighDcSizeAlt,1"Size variable full-time worker, very high income",(income_segment==4)*lnWorkVeryHighDcSizeAlt,1"No attractions full-time worker, low income",(income_segment==1)*lnWorkLowDcSizeAlt==0,-999"No attractions full-time worker, medium income",(income_segment==2)*lnWorkMedDcSizeAlt==0,-999"No attractions full-time worker, high income",(income_segment==3)*lnWorkHighDcSizeAlt==0,-999"No attractions full-time worker, very high income",(income_segment==4)*lnWorkVeryHighDcSizeAlt==0,-999Mode choice logsum,mcLogsum,0.3
Description,Expression,Alt"Distance, piecewise linear from 0 to 1 miles",@df.distance.clip(1),-0.8428"Distance, piecewise linear from 1 to 2 miles","@(df.distance-1).clip(0,1)",-0.3104"Distance, piecewise linear from 2 to 5 miles","@(df.distance-2).clip(0,3)",-0.3783"Distance, piecewise linear from 5 to 15 miles","@(df.distance-5).clip(0,10)",-0.1285"Distance, piecewise linear for 15+ miles",@(df.distance-15.0).clip(0),-0.0917"Distance 0 to 5 mi, high and very high income",@(df.income_segment>=3)*df.distance.clip(upper=5),0.15"Distance 5+ mi, high and very high income",@(df.income_segment>=3)*(df.distance-5).clip(0),0.02"Size variable full-time worker, low income",@(df.income_segment==1)*df.size_low,1"Size variable full-time worker, medium income",@(df.income_segment==2)*df.size_med,1"Size variable full-time worker, high income",@(df.income_segment==3)*df.size_high,1"Size variable full-time worker, very high income",@(df.income_segment==4)*df.size_veryhigh,1"No attractions full-time worker, low income",@(df.income_segment==1)&(df.size_low==0),-999"No attractions full-time worker, medium income",@(df.income_segment==2)&(df.size_med==0),-999"No attractions full-time worker, high income",@(df.income_segment==3)&(df.size_high==0),-999"No attractions full-time worker, very high income",@(df.income_segment==4)&(df.size_veryhigh==0),-999Mode choice logsum,mcLogsum,0.3
Expand Down

1 change: 1 addition & 0 deletions
example/configs/workplace_location_size_terms.csv

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Open in desktop

Original file line numberDiff line numberDiff line change
@@ -0,0 +1 @@
purpose,segment,TOTHH,RETEMPN,FPSEMPN,HEREMPN,OTHEMPN,AGREMPN,MWTEMPN,AGE0519,HSENROLL,COLLFTE,COLLPTEwork,low,0,0.129,0.193,0.383,0.12,0.01,0.164,0,0,0,0work,med,0,0.12,0.197,0.325,0.139,0.008,0.21,0,0,0,0work,high,0,0.11,0.207,0.284,0.154,0.006,0.239,0,0,0,0work,veryhigh,0,0.093,0.27,0.241,0.146,0.004,0.246,0,0,0,0university,university,0,0,0,0,0,0,0,0,0,0.592,0.408school,grade,0,0,0,0,0,0,0,1,0,0,0school,high,0,0,0,0,0,0,0,0,1,0,0escort,kids,0,0.225,0,0.144,0,0,0,0.465,0.166,0,0escort,no kids,0,0.225,0,0.144,0,0,0,0.465,0.166,0,0shopping,shopping,0,1,0,0,0,0,0,0,0,0,0eatOut,eatOut,0,0.742,0,0.258,0,0,0,0,0,0,0othMaint,othMaint,0,0.482,0,0.518,0,0,0,0,0,0,0social,social,0,0.522,0,0.478,0,0,0,0,0,0,0othDiscr,othDiscr,0.252,0.212,0,0.272,0.165,0,0,0,0.098,0,0atwork,atwork,0,0.742,0,0.258,0,0,0,0,0,0,0
Expand Down

59 changes: 55 additions & 4 deletions
example/models.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -37,7 +37,37 @@ def auto_ownership_spec():
@sim.injectable()
def workplace_location_spec():
 f = os.path.join('configs', "workplace_location.csv")
 return asim.read_model_spec(f).head(7)
 return asim.read_model_spec(f).head(15)


@sim.table()
def workplace_size_spec():
 f = os.path.join('configs', 'workplace_location_size_terms.csv')
 return pd.read_csv(f)


@sim.table()
def workplace_size_terms(land_use, workplace_size_spec):
 """
 This method takes the land use data and multiplies various columns of the
 land use data by coefficients from the workplace_size_spec table in order
 to yield a size term (a linear combination of land use variables) with
 specified coefficients for different segments (like low, med, and high
 income)
 """
 land_use = land_use.to_frame()
 df = workplace_size_spec.to_frame().query("purpose == 'work'")
 df = df.drop("purpose", axis=1).set_index("segment")
 new_df = {}
 for index, row in df.iterrows():
 missing = row[~row.index.isin(land_use.columns)]
 if len(missing) > 0:
 print "WARNING: missing columns in land use\n", missing.index
 row = row[row.index.isin(land_use.columns)]
 sparse = land_use[list(row.index)]
 new_df["size_"+index] = np.dot(sparse.as_matrix(), row.values)
 new_df = pd.DataFrame(new_df, index=land_use.index)
 return new_df


@sim.model()
Expand DownExpand Up@@ -67,10 +97,11 @@ def workplace_location_simulate(persons,
 households,
 zones,
 workplace_location_spec,
 distance_matrix):
 distance_matrix,
 workplace_size_terms):

 choosers = sim.merge_tables(persons.name, tables=[persons, households])
 alternatives = zones.to_frame()
 alternatives = zones.to_frame().join(workplace_size_terms.to_frame())

 skims = {
 "distance": distance_matrix
Expand All@@ -88,4 +119,24 @@ def workplace_location_simulate(persons,
 print "Describe of hoices:\n", choices.describe()
 sim.add_column("persons", "workplace_taz", choices)

 return model_design
 return model_design


@sim.column("land_use")
def total_households(land_use):
 return land_use.local.TOTHH


@sim.column("land_use")
def total_employment(land_use):
 return land_use.local.TOTEMP


@sim.column("land_use")
def total_acres(land_use):
 return land_use.local.TOTACRE


@sim.column("land_use")
def county_id(land_use):
 return land_use.local.COUNTY

6 changes: 1 addition & 5 deletions
notebooks/data_mover.ipynb

View file


Open in desktop

Original file line numberDiff line numberDiff line change
@@ -1,7 +1,7 @@
{
 "metadata": {
 "name": "",
 "signature": "sha256:d62442075d195d4893cdd65305dac0932aeb18464bb30a1d8aac6a89ad987ef1"
 "signature": "sha256:afbc3e7040dd9e4a5b21433063f13a6a8abfcc04bcfc6574e7e43376c257cd33"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
Expand DownExpand Up@@ -43,10 +43,6 @@
 "col_map = {\n",
 " \"HHID\": \"household_id\",\n",
 " \"AGE\": \"age\",\n",
 " \"TOTHH\": \"total_households\",\n",
 " \"TOTEMP\": \"total_employment\",\n",
 " \"TOTACRE\": \"total_acres\",\n",
 " \"COUNTY\": \"county_id\",\n",
 " \"hworkers\": \"workers\",\n",
 " \"HINC\": \"income\"\n",
 "}"
Expand Down
Loading

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default");
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Workplace size coeffs#18

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 Merged

fscottfoti
merged 4 commits into

from

Feb 20, 2015
 Merged


Workplace size coeffs
#18
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a916134

adding the size terms to workplace location choice


fscottfoti Dec 18, 2014
0fce58c

Merge branch 'master' into workplace-size-coeffs


fscottfoti Feb 20, 2015
46b48c7

Merge branch 'master' into workplace-size-coeffs


fscottfoti Feb 20, 2015
24f770e

with the changes from the other branch, this branch ran with no edits


fscottfoti Feb 20, 2015

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1 change: 1 addition & 0 deletions
activitysim/activitysim.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -110,6 +110,7 @@ def simple_simulate(choosers, alternatives, spec,
 print "Failed with DataFrame eval:\n%s" % expr
 raise e
 vars[expr] = s
 vars[expr] = vars[expr].astype('float') # explicit cast
 model_design = pd.DataFrame(vars, index=df.index)

 df = random_rows(model_design, min(100000, len(model_design)))\
Expand Down

2 changes: 1 addition & 1 deletion
example/configs/workplace_location.csv

View file


Open in desktop

Original file line numberDiff line numberDiff line change
@@ -1 +1 @@
Description,Expression,Alt"Distance, piecewise linear from 0 to 1 miles",@df.distance.clip(1),-0.8428"Distance, piecewise linear from 1 to 2 miles","@(df.distance-1).clip(0,1)",-0.3104"Distance, piecewise linear from 2 to 5 miles","@(df.distance-2).clip(0,3)",-0.3783"Distance, piecewise linear from 5 to 15 miles","@(df.distance-5).clip(0,10)",-0.1285"Distance, piecewise linear for 15+ miles",@(df.distance-15.0).clip(0),-0.0917"Distance 0 to 5 mi, high and very high income",@(df.income_segment>=3)*df.distance.clip(upper=5),0.15"Distance 5+ mi, high and very high income",@(df.income_segment>=3)*(df.distance-5).clip(0),0.02"Size variable full-time worker, low income",(income_segment==1)*lnWorkLowDcSizeAlt,1"Size variable full-time worker, medium income",(income_segment==2)*lnWorkMedDcSizeAlt,1"Size variable full-time worker, high income",(income_segment==3)*lnWorkHighDcSizeAlt,1"Size variable full-time worker, very high income",(income_segment==4)*lnWorkVeryHighDcSizeAlt,1"No attractions full-time worker, low income",(income_segment==1)*lnWorkLowDcSizeAlt==0,-999"No attractions full-time worker, medium income",(income_segment==2)*lnWorkMedDcSizeAlt==0,-999"No attractions full-time worker, high income",(income_segment==3)*lnWorkHighDcSizeAlt==0,-999"No attractions full-time worker, very high income",(income_segment==4)*lnWorkVeryHighDcSizeAlt==0,-999Mode choice logsum,mcLogsum,0.3
Description,Expression,Alt"Distance, piecewise linear from 0 to 1 miles",@df.distance.clip(1),-0.8428"Distance, piecewise linear from 1 to 2 miles","@(df.distance-1).clip(0,1)",-0.3104"Distance, piecewise linear from 2 to 5 miles","@(df.distance-2).clip(0,3)",-0.3783"Distance, piecewise linear from 5 to 15 miles","@(df.distance-5).clip(0,10)",-0.1285"Distance, piecewise linear for 15+ miles",@(df.distance-15.0).clip(0),-0.0917"Distance 0 to 5 mi, high and very high income",@(df.income_segment>=3)*df.distance.clip(upper=5),0.15"Distance 5+ mi, high and very high income",@(df.income_segment>=3)*(df.distance-5).clip(0),0.02"Size variable full-time worker, low income",@(df.income_segment==1)*df.size_low,1"Size variable full-time worker, medium income",@(df.income_segment==2)*df.size_med,1"Size variable full-time worker, high income",@(df.income_segment==3)*df.size_high,1"Size variable full-time worker, very high income",@(df.income_segment==4)*df.size_veryhigh,1"No attractions full-time worker, low income",@(df.income_segment==1)&(df.size_low==0),-999"No attractions full-time worker, medium income",@(df.income_segment==2)&(df.size_med==0),-999"No attractions full-time worker, high income",@(df.income_segment==3)&(df.size_high==0),-999"No attractions full-time worker, very high income",@(df.income_segment==4)&(df.size_veryhigh==0),-999Mode choice logsum,mcLogsum,0.3
Expand Down

1 change: 1 addition & 0 deletions
example/configs/workplace_location_size_terms.csv

View file


Open in desktop

Original file line numberDiff line numberDiff line change
@@ -0,0 +1 @@
purpose,segment,TOTHH,RETEMPN,FPSEMPN,HEREMPN,OTHEMPN,AGREMPN,MWTEMPN,AGE0519,HSENROLL,COLLFTE,COLLPTEwork,low,0,0.129,0.193,0.383,0.12,0.01,0.164,0,0,0,0work,med,0,0.12,0.197,0.325,0.139,0.008,0.21,0,0,0,0work,high,0,0.11,0.207,0.284,0.154,0.006,0.239,0,0,0,0work,veryhigh,0,0.093,0.27,0.241,0.146,0.004,0.246,0,0,0,0university,university,0,0,0,0,0,0,0,0,0,0.592,0.408school,grade,0,0,0,0,0,0,0,1,0,0,0school,high,0,0,0,0,0,0,0,0,1,0,0escort,kids,0,0.225,0,0.144,0,0,0,0.465,0.166,0,0escort,no kids,0,0.225,0,0.144,0,0,0,0.465,0.166,0,0shopping,shopping,0,1,0,0,0,0,0,0,0,0,0eatOut,eatOut,0,0.742,0,0.258,0,0,0,0,0,0,0othMaint,othMaint,0,0.482,0,0.518,0,0,0,0,0,0,0social,social,0,0.522,0,0.478,0,0,0,0,0,0,0othDiscr,othDiscr,0.252,0.212,0,0.272,0.165,0,0,0,0.098,0,0atwork,atwork,0,0.742,0,0.258,0,0,0,0,0,0,0
Expand Down

59 changes: 55 additions & 4 deletions
example/models.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -37,7 +37,37 @@ def auto_ownership_spec():
@sim.injectable()
def workplace_location_spec():
 f = os.path.join('configs', "workplace_location.csv")
 return asim.read_model_spec(f).head(7)
 return asim.read_model_spec(f).head(15)


@sim.table()
def workplace_size_spec():
 f = os.path.join('configs', 'workplace_location_size_terms.csv')
 return pd.read_csv(f)


@sim.table()
def workplace_size_terms(land_use, workplace_size_spec):
 """
 This method takes the land use data and multiplies various columns of the
 land use data by coefficients from the workplace_size_spec table in order
 to yield a size term (a linear combination of land use variables) with
 specified coefficients for different segments (like low, med, and high
 income)
 """
 land_use = land_use.to_frame()
 df = workplace_size_spec.to_frame().query("purpose == 'work'")
 df = df.drop("purpose", axis=1).set_index("segment")
 new_df = {}
 for index, row in df.iterrows():
 missing = row[~row.index.isin(land_use.columns)]
 if len(missing) > 0:
 print "WARNING: missing columns in land use\n", missing.index
 row = row[row.index.isin(land_use.columns)]
 sparse = land_use[list(row.index)]
 new_df["size_"+index] = np.dot(sparse.as_matrix(), row.values)
 new_df = pd.DataFrame(new_df, index=land_use.index)
 return new_df


@sim.model()
Expand DownExpand Up@@ -67,10 +97,11 @@ def workplace_location_simulate(persons,
 households,
 zones,
 workplace_location_spec,
 distance_matrix):
 distance_matrix,
 workplace_size_terms):

 choosers = sim.merge_tables(persons.name, tables=[persons, households])
 alternatives = zones.to_frame()
 alternatives = zones.to_frame().join(workplace_size_terms.to_frame())

 skims = {
 "distance": distance_matrix
Expand All@@ -88,4 +119,24 @@ def workplace_location_simulate(persons,
 print "Describe of hoices:\n", choices.describe()
 sim.add_column("persons", "workplace_taz", choices)

 return model_design
 return model_design


@sim.column("land_use")
def total_households(land_use):
 return land_use.local.TOTHH


@sim.column("land_use")
def total_employment(land_use):
 return land_use.local.TOTEMP


@sim.column("land_use")
def total_acres(land_use):
 return land_use.local.TOTACRE


@sim.column("land_use")
def county_id(land_use):
 return land_use.local.COUNTY

6 changes: 1 addition & 5 deletions
notebooks/data_mover.ipynb

View file


Open in desktop

Original file line numberDiff line numberDiff line change
@@ -1,7 +1,7 @@
{
 "metadata": {
 "name": "",
 "signature": "sha256:d62442075d195d4893cdd65305dac0932aeb18464bb30a1d8aac6a89ad987ef1"
 "signature": "sha256:afbc3e7040dd9e4a5b21433063f13a6a8abfcc04bcfc6574e7e43376c257cd33"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
Expand DownExpand Up@@ -43,10 +43,6 @@
 "col_map = {\n",
 " \"HHID\": \"household_id\",\n",
 " \"AGE\": \"age\",\n",
 " \"TOTHH\": \"total_households\",\n",
 " \"TOTEMP\": \"total_employment\",\n",
 " \"TOTACRE\": \"total_acres\",\n",
 " \"COUNTY\": \"county_id\",\n",
 " \"hworkers\": \"workers\",\n",
 " \"HINC\": \"income\"\n",
 "}"
Expand Down
Loading

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers");
}
} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
Workplace size coeffs#18

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 Merged

fscottfoti
merged 4 commits into

from

Feb 20, 2015
 Merged


Workplace size coeffs
#18
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a916134

adding the size terms to workplace location choice


fscottfoti Dec 18, 2014
0fce58c

Merge branch 'master' into workplace-size-coeffs


fscottfoti Feb 20, 2015
46b48c7

Merge branch 'master' into workplace-size-coeffs


fscottfoti Feb 20, 2015
24f770e

with the changes from the other branch, this branch ran with no edits


fscottfoti Feb 20, 2015

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1 change: 1 addition & 0 deletions
activitysim/activitysim.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -110,6 +110,7 @@ def simple_simulate(choosers, alternatives, spec,
 print "Failed with DataFrame eval:\n%s" % expr
 raise e
 vars[expr] = s
 vars[expr] = vars[expr].astype('float') # explicit cast
 model_design = pd.DataFrame(vars, index=df.index)

 df = random_rows(model_design, min(100000, len(model_design)))\
Expand Down

2 changes: 1 addition & 1 deletion
example/configs/workplace_location.csv

View file


Open in desktop

Original file line numberDiff line numberDiff line change
@@ -1 +1 @@
Description,Expression,Alt"Distance, piecewise linear from 0 to 1 miles",@df.distance.clip(1),-0.8428"Distance, piecewise linear from 1 to 2 miles","@(df.distance-1).clip(0,1)",-0.3104"Distance, piecewise linear from 2 to 5 miles","@(df.distance-2).clip(0,3)",-0.3783"Distance, piecewise linear from 5 to 15 miles","@(df.distance-5).clip(0,10)",-0.1285"Distance, piecewise linear for 15+ miles",@(df.distance-15.0).clip(0),-0.0917"Distance 0 to 5 mi, high and very high income",@(df.income_segment>=3)*df.distance.clip(upper=5),0.15"Distance 5+ mi, high and very high income",@(df.income_segment>=3)*(df.distance-5).clip(0),0.02"Size variable full-time worker, low income",(income_segment==1)*lnWorkLowDcSizeAlt,1"Size variable full-time worker, medium income",(income_segment==2)*lnWorkMedDcSizeAlt,1"Size variable full-time worker, high income",(income_segment==3)*lnWorkHighDcSizeAlt,1"Size variable full-time worker, very high income",(income_segment==4)*lnWorkVeryHighDcSizeAlt,1"No attractions full-time worker, low income",(income_segment==1)*lnWorkLowDcSizeAlt==0,-999"No attractions full-time worker, medium income",(income_segment==2)*lnWorkMedDcSizeAlt==0,-999"No attractions full-time worker, high income",(income_segment==3)*lnWorkHighDcSizeAlt==0,-999"No attractions full-time worker, very high income",(income_segment==4)*lnWorkVeryHighDcSizeAlt==0,-999Mode choice logsum,mcLogsum,0.3
Description,Expression,Alt"Distance, piecewise linear from 0 to 1 miles",@df.distance.clip(1),-0.8428"Distance, piecewise linear from 1 to 2 miles","@(df.distance-1).clip(0,1)",-0.3104"Distance, piecewise linear from 2 to 5 miles","@(df.distance-2).clip(0,3)",-0.3783"Distance, piecewise linear from 5 to 15 miles","@(df.distance-5).clip(0,10)",-0.1285"Distance, piecewise linear for 15+ miles",@(df.distance-15.0).clip(0),-0.0917"Distance 0 to 5 mi, high and very high income",@(df.income_segment>=3)*df.distance.clip(upper=5),0.15"Distance 5+ mi, high and very high income",@(df.income_segment>=3)*(df.distance-5).clip(0),0.02"Size variable full-time worker, low income",@(df.income_segment==1)*df.size_low,1"Size variable full-time worker, medium income",@(df.income_segment==2)*df.size_med,1"Size variable full-time worker, high income",@(df.income_segment==3)*df.size_high,1"Size variable full-time worker, very high income",@(df.income_segment==4)*df.size_veryhigh,1"No attractions full-time worker, low income",@(df.income_segment==1)&(df.size_low==0),-999"No attractions full-time worker, medium income",@(df.income_segment==2)&(df.size_med==0),-999"No attractions full-time worker, high income",@(df.income_segment==3)&(df.size_high==0),-999"No attractions full-time worker, very high income",@(df.income_segment==4)&(df.size_veryhigh==0),-999Mode choice logsum,mcLogsum,0.3
Expand Down

1 change: 1 addition & 0 deletions
example/configs/workplace_location_size_terms.csv

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Original file line numberDiff line numberDiff line change
@@ -0,0 +1 @@
purpose,segment,TOTHH,RETEMPN,FPSEMPN,HEREMPN,OTHEMPN,AGREMPN,MWTEMPN,AGE0519,HSENROLL,COLLFTE,COLLPTEwork,low,0,0.129,0.193,0.383,0.12,0.01,0.164,0,0,0,0work,med,0,0.12,0.197,0.325,0.139,0.008,0.21,0,0,0,0work,high,0,0.11,0.207,0.284,0.154,0.006,0.239,0,0,0,0work,veryhigh,0,0.093,0.27,0.241,0.146,0.004,0.246,0,0,0,0university,university,0,0,0,0,0,0,0,0,0,0.592,0.408school,grade,0,0,0,0,0,0,0,1,0,0,0school,high,0,0,0,0,0,0,0,0,1,0,0escort,kids,0,0.225,0,0.144,0,0,0,0.465,0.166,0,0escort,no kids,0,0.225,0,0.144,0,0,0,0.465,0.166,0,0shopping,shopping,0,1,0,0,0,0,0,0,0,0,0eatOut,eatOut,0,0.742,0,0.258,0,0,0,0,0,0,0othMaint,othMaint,0,0.482,0,0.518,0,0,0,0,0,0,0social,social,0,0.522,0,0.478,0,0,0,0,0,0,0othDiscr,othDiscr,0.252,0.212,0,0.272,0.165,0,0,0,0.098,0,0atwork,atwork,0,0.742,0,0.258,0,0,0,0,0,0,0
Expand Down

59 changes: 55 additions & 4 deletions
example/models.py

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Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -37,7 +37,37 @@ def auto_ownership_spec():
@sim.injectable()
def workplace_location_spec():
 f = os.path.join('configs', "workplace_location.csv")
 return asim.read_model_spec(f).head(7)
 return asim.read_model_spec(f).head(15)


@sim.table()
def workplace_size_spec():
 f = os.path.join('configs', 'workplace_location_size_terms.csv')
 return pd.read_csv(f)


@sim.table()
def workplace_size_terms(land_use, workplace_size_spec):
 """
 This method takes the land use data and multiplies various columns of the
 land use data by coefficients from the workplace_size_spec table in order
 to yield a size term (a linear combination of land use variables) with
 specified coefficients for different segments (like low, med, and high
 income)
 """
 land_use = land_use.to_frame()
 df = workplace_size_spec.to_frame().query("purpose == 'work'")
 df = df.drop("purpose", axis=1).set_index("segment")
 new_df = {}
 for index, row in df.iterrows():
 missing = row[~row.index.isin(land_use.columns)]
 if len(missing) > 0:
 print "WARNING: missing columns in land use\n", missing.index
 row = row[row.index.isin(land_use.columns)]
 sparse = land_use[list(row.index)]
 new_df["size_"+index] = np.dot(sparse.as_matrix(), row.values)
 new_df = pd.DataFrame(new_df, index=land_use.index)
 return new_df


@sim.model()
Expand DownExpand Up@@ -67,10 +97,11 @@ def workplace_location_simulate(persons,
 households,
 zones,
 workplace_location_spec,
 distance_matrix):
 distance_matrix,
 workplace_size_terms):

 choosers = sim.merge_tables(persons.name, tables=[persons, households])
 alternatives = zones.to_frame()
 alternatives = zones.to_frame().join(workplace_size_terms.to_frame())

 skims = {
 "distance": distance_matrix
Expand All@@ -88,4 +119,24 @@ def workplace_location_simulate(persons,
 print "Describe of hoices:\n", choices.describe()
 sim.add_column("persons", "workplace_taz", choices)

 return model_design
 return model_design


@sim.column("land_use")
def total_households(land_use):
 return land_use.local.TOTHH


@sim.column("land_use")
def total_employment(land_use):
 return land_use.local.TOTEMP


@sim.column("land_use")
def total_acres(land_use):
 return land_use.local.TOTACRE


@sim.column("land_use")
def county_id(land_use):
 return land_use.local.COUNTY

6 changes: 1 addition & 5 deletions
notebooks/data_mover.ipynb

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Open in desktop

Original file line numberDiff line numberDiff line change
@@ -1,7 +1,7 @@
{
 "metadata": {
 "name": "",
 "signature": "sha256:d62442075d195d4893cdd65305dac0932aeb18464bb30a1d8aac6a89ad987ef1"
 "signature": "sha256:afbc3e7040dd9e4a5b21433063f13a6a8abfcc04bcfc6574e7e43376c257cd33"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
Expand DownExpand Up@@ -43,10 +43,6 @@
 "col_map = {\n",
 " \"HHID\": \"household_id\",\n",
 " \"AGE\": \"age\",\n",
 " \"TOTHH\": \"total_households\",\n",
 " \"TOTEMP\": \"total_employment\",\n",
 " \"TOTACRE\": \"total_acres\",\n",
 " \"COUNTY\": \"county_id\",\n",
 " \"hworkers\": \"workers\",\n",
 " \"HINC\": \"income\"\n",
 "}"
Expand Down
Loading

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Universal Dark Mode - works on any site\n(function() {\n var enabled = true;\n \n function applyDarkMode() {\n if (!enabled) return;\n \n // Create style element if it doesn't exist\n var style = document.getElementById('universal-dark-mode-style');\n if (!style) {\n style = document.createElement('style');\n style.id = 'universal-dark-mode-style';\n document.head.appendChild(style);\n }\n \n // Dark mode CSS - inverts colors but preserves images/video\n style.textContent = '\n /* Invert everything except media */\n html {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #1a1a2e !important;\n }\n \n /* Restore images, videos, iframes, canvas */\n img, video, iframe, canvas, svg, picture, [style*=\"background-image\"] {\n filter: invert(1) hue-rotate(180deg) !important;\n }\n \n /* Preserve specific elements that should not be inverted */\n .no-dark-mode, .no-dark-mode *,\n [data-theme=\"light\"], [data-theme=\"light\"],\n .ace_editor, .ace_editor *,\n .CodeMirror, .CodeMirror *,\n .monaco-editor, .monaco-editor *,\n .markdown-body pre, .markdown-body pre *,\n .highlight, .highlight *,\n pre code, pre code * {\n filter: none !important;\n }\n \n /* Fix common UI elements */\n .modal, .popup, .dropdown-menu, .tooltip, .popover {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #2d2d44 !important;\n border-color: #444 !important;\n }\n \n /* Scrollbars */\n ::-webkit-scrollbar { background: #1a1a2e !important; }\n ::-webkit-scrollbar-thumb { background: #444 !important; }\n ::-webkit-scrollbar-thumb:hover { background: #555 !important; }\n \n /* Selection */\n ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ';\n }\n \n function removeDarkMode() {\n var style = document.getElementById('universal-dark-mode-style');\n if (style) style.remove();\n }\n \n // Toggle with Alt+Shift+D\n document.addEventListener('keydown', function(e) {\n if (e.altKey && e.shiftKey && e.key === 'D') {\n e.preventDefault();\n enabled = !enabled;\n if (enabled) {\n applyDarkMode();\n console.log('[Universal Dark Mode] Enabled');\n } else {\n removeDarkMode();\n console.log('[Universal Dark Mode] Disabled');\n }\n }\n });\n \n // Apply on load\n applyDarkMode();\n \n // Re-apply on dynamic content\n var observer = new MutationObserver(function(mutations) {\n if (enabled && !document.getElementById('universal-dark-mode-style')) {\n applyDarkMode();\n }\n });\n observer.observe(document.head, { childList: true });\n \n console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');\n})();", "Universal Dark Mode");
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();

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 Merged

fscottfoti
merged 4 commits into

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Feb 20, 2015
 Merged


Workplace size coeffs
#18
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a916134

adding the size terms to workplace location choice


fscottfoti Dec 18, 2014
0fce58c

Merge branch 'master' into workplace-size-coeffs


fscottfoti Feb 20, 2015
46b48c7

Merge branch 'master' into workplace-size-coeffs


fscottfoti Feb 20, 2015
24f770e

with the changes from the other branch, this branch ran with no edits


fscottfoti Feb 20, 2015

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1 change: 1 addition & 0 deletions
activitysim/activitysim.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -110,6 +110,7 @@ def simple_simulate(choosers, alternatives, spec,
 print "Failed with DataFrame eval:\n%s" % expr
 raise e
 vars[expr] = s
 vars[expr] = vars[expr].astype('float') # explicit cast
 model_design = pd.DataFrame(vars, index=df.index)

 df = random_rows(model_design, min(100000, len(model_design)))\
Expand Down

2 changes: 1 addition & 1 deletion
example/configs/workplace_location.csv

View file


Open in desktop

Original file line numberDiff line numberDiff line change
@@ -1 +1 @@
Description,Expression,Alt"Distance, piecewise linear from 0 to 1 miles",@df.distance.clip(1),-0.8428"Distance, piecewise linear from 1 to 2 miles","@(df.distance-1).clip(0,1)",-0.3104"Distance, piecewise linear from 2 to 5 miles","@(df.distance-2).clip(0,3)",-0.3783"Distance, piecewise linear from 5 to 15 miles","@(df.distance-5).clip(0,10)",-0.1285"Distance, piecewise linear for 15+ miles",@(df.distance-15.0).clip(0),-0.0917"Distance 0 to 5 mi, high and very high income",@(df.income_segment>=3)*df.distance.clip(upper=5),0.15"Distance 5+ mi, high and very high income",@(df.income_segment>=3)*(df.distance-5).clip(0),0.02"Size variable full-time worker, low income",(income_segment==1)*lnWorkLowDcSizeAlt,1"Size variable full-time worker, medium income",(income_segment==2)*lnWorkMedDcSizeAlt,1"Size variable full-time worker, high income",(income_segment==3)*lnWorkHighDcSizeAlt,1"Size variable full-time worker, very high income",(income_segment==4)*lnWorkVeryHighDcSizeAlt,1"No attractions full-time worker, low income",(income_segment==1)*lnWorkLowDcSizeAlt==0,-999"No attractions full-time worker, medium income",(income_segment==2)*lnWorkMedDcSizeAlt==0,-999"No attractions full-time worker, high income",(income_segment==3)*lnWorkHighDcSizeAlt==0,-999"No attractions full-time worker, very high income",(income_segment==4)*lnWorkVeryHighDcSizeAlt==0,-999Mode choice logsum,mcLogsum,0.3
Description,Expression,Alt"Distance, piecewise linear from 0 to 1 miles",@df.distance.clip(1),-0.8428"Distance, piecewise linear from 1 to 2 miles","@(df.distance-1).clip(0,1)",-0.3104"Distance, piecewise linear from 2 to 5 miles","@(df.distance-2).clip(0,3)",-0.3783"Distance, piecewise linear from 5 to 15 miles","@(df.distance-5).clip(0,10)",-0.1285"Distance, piecewise linear for 15+ miles",@(df.distance-15.0).clip(0),-0.0917"Distance 0 to 5 mi, high and very high income",@(df.income_segment>=3)*df.distance.clip(upper=5),0.15"Distance 5+ mi, high and very high income",@(df.income_segment>=3)*(df.distance-5).clip(0),0.02"Size variable full-time worker, low income",@(df.income_segment==1)*df.size_low,1"Size variable full-time worker, medium income",@(df.income_segment==2)*df.size_med,1"Size variable full-time worker, high income",@(df.income_segment==3)*df.size_high,1"Size variable full-time worker, very high income",@(df.income_segment==4)*df.size_veryhigh,1"No attractions full-time worker, low income",@(df.income_segment==1)&(df.size_low==0),-999"No attractions full-time worker, medium income",@(df.income_segment==2)&(df.size_med==0),-999"No attractions full-time worker, high income",@(df.income_segment==3)&(df.size_high==0),-999"No attractions full-time worker, very high income",@(df.income_segment==4)&(df.size_veryhigh==0),-999Mode choice logsum,mcLogsum,0.3
Expand Down

1 change: 1 addition & 0 deletions
example/configs/workplace_location_size_terms.csv

View file


Open in desktop

Original file line numberDiff line numberDiff line change
@@ -0,0 +1 @@
purpose,segment,TOTHH,RETEMPN,FPSEMPN,HEREMPN,OTHEMPN,AGREMPN,MWTEMPN,AGE0519,HSENROLL,COLLFTE,COLLPTEwork,low,0,0.129,0.193,0.383,0.12,0.01,0.164,0,0,0,0work,med,0,0.12,0.197,0.325,0.139,0.008,0.21,0,0,0,0work,high,0,0.11,0.207,0.284,0.154,0.006,0.239,0,0,0,0work,veryhigh,0,0.093,0.27,0.241,0.146,0.004,0.246,0,0,0,0university,university,0,0,0,0,0,0,0,0,0,0.592,0.408school,grade,0,0,0,0,0,0,0,1,0,0,0school,high,0,0,0,0,0,0,0,0,1,0,0escort,kids,0,0.225,0,0.144,0,0,0,0.465,0.166,0,0escort,no kids,0,0.225,0,0.144,0,0,0,0.465,0.166,0,0shopping,shopping,0,1,0,0,0,0,0,0,0,0,0eatOut,eatOut,0,0.742,0,0.258,0,0,0,0,0,0,0othMaint,othMaint,0,0.482,0,0.518,0,0,0,0,0,0,0social,social,0,0.522,0,0.478,0,0,0,0,0,0,0othDiscr,othDiscr,0.252,0.212,0,0.272,0.165,0,0,0,0.098,0,0atwork,atwork,0,0.742,0,0.258,0,0,0,0,0,0,0
Expand Down

59 changes: 55 additions & 4 deletions
example/models.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -37,7 +37,37 @@ def auto_ownership_spec():
@sim.injectable()
def workplace_location_spec():
 f = os.path.join('configs', "workplace_location.csv")
 return asim.read_model_spec(f).head(7)
 return asim.read_model_spec(f).head(15)


@sim.table()
def workplace_size_spec():
 f = os.path.join('configs', 'workplace_location_size_terms.csv')
 return pd.read_csv(f)


@sim.table()
def workplace_size_terms(land_use, workplace_size_spec):
 """
 This method takes the land use data and multiplies various columns of the
 land use data by coefficients from the workplace_size_spec table in order
 to yield a size term (a linear combination of land use variables) with
 specified coefficients for different segments (like low, med, and high
 income)
 """
 land_use = land_use.to_frame()
 df = workplace_size_spec.to_frame().query("purpose == 'work'")
 df = df.drop("purpose", axis=1).set_index("segment")
 new_df = {}
 for index, row in df.iterrows():
 missing = row[~row.index.isin(land_use.columns)]
 if len(missing) > 0:
 print "WARNING: missing columns in land use\n", missing.index
 row = row[row.index.isin(land_use.columns)]
 sparse = land_use[list(row.index)]
 new_df["size_"+index] = np.dot(sparse.as_matrix(), row.values)
 new_df = pd.DataFrame(new_df, index=land_use.index)
 return new_df


@sim.model()
Expand DownExpand Up@@ -67,10 +97,11 @@ def workplace_location_simulate(persons,
 households,
 zones,
 workplace_location_spec,
 distance_matrix):
 distance_matrix,
 workplace_size_terms):

 choosers = sim.merge_tables(persons.name, tables=[persons, households])
 alternatives = zones.to_frame()
 alternatives = zones.to_frame().join(workplace_size_terms.to_frame())

 skims = {
 "distance": distance_matrix
Expand All@@ -88,4 +119,24 @@ def workplace_location_simulate(persons,
 print "Describe of hoices:\n", choices.describe()
 sim.add_column("persons", "workplace_taz", choices)

 return model_design
 return model_design


@sim.column("land_use")
def total_households(land_use):
 return land_use.local.TOTHH


@sim.column("land_use")
def total_employment(land_use):
 return land_use.local.TOTEMP


@sim.column("land_use")
def total_acres(land_use):
 return land_use.local.TOTACRE


@sim.column("land_use")
def county_id(land_use):
 return land_use.local.COUNTY

6 changes: 1 addition & 5 deletions
notebooks/data_mover.ipynb

View file


Open in desktop

Original file line numberDiff line numberDiff line change
@@ -1,7 +1,7 @@
{
 "metadata": {
 "name": "",
 "signature": "sha256:d62442075d195d4893cdd65305dac0932aeb18464bb30a1d8aac6a89ad987ef1"
 "signature": "sha256:afbc3e7040dd9e4a5b21433063f13a6a8abfcc04bcfc6574e7e43376c257cd33"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
Expand DownExpand Up@@ -43,10 +43,6 @@
 "col_map = {\n",
 " \"HHID\": \"household_id\",\n",
 " \"AGE\": \"age\",\n",
 " \"TOTHH\": \"total_households\",\n",
 " \"TOTEMP\": \"total_employment\",\n",
 " \"TOTACRE\": \"total_acres\",\n",
 " \"COUNTY\": \"county_id\",\n",
 " \"hworkers\": \"workers\",\n",
 " \"HINC\": \"income\"\n",
 "}"
Expand Down
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