more changes to prepare DemARKS for 0.10.6

notebooks/Chinese-Growth.py

@@ -174,7 +174,7 @@
 
 bottomDiscFac = 0.9800
 topDiscFac    = 0.9934 
-DiscFac_list  = approxUniform(N=num_consumer_types,bot=bottomDiscFac,top=topDiscFac)[1]
+DiscFac_list  = approxUniform(N=num_consumer_types,bot=bottomDiscFac,top=topDiscFac).X
 
 # Now, assign the discount factors we want to the ChineseConsumerTypes
 for j in range(num_consumer_types):

notebooks/IncExpectationExample.py

@@ -149,7 +149,7 @@ def runRoszypalSchlaffmanExperiment(CorrAct, CorrPcvd, DiscFac_center, DiscFac_s
     ThisDict['PrstIncCorr'] = CorrAct
 
     # Make a 7 point approximation to a uniform distribution of DiscFac
-    DiscFac_list = approxUniform(N=7,bot=DiscFac_center-DiscFac_spread,top=DiscFac_center+DiscFac_spread)[1]
+    DiscFac_list = approxUniform(N=7,bot=DiscFac_center-DiscFac_spread,top=DiscFac_center+DiscFac_spread).X
 
     type_list = []
     # Make a PersistentShockConsumerTypeX for each value of beta saved in DiscFac_list

notebooks/KrusellSmith.ipynb

@@ -740,13 +740,13 @@
    "outputs": [],
    "source": [
     "# Construct the distribution of types\n",
-    "from HARK.utilities import approxUniform\n",
+    "from HARK.distribution import approxUniform\n",
     "\n",
     "# Specify the distribution of the discount factor\n",
     "num_types = 3              # number of types we want;\n",
     "DiscFac_mean   = 0.9858    # center of beta distribution \n",
     "DiscFac_spread = 0.0085    # spread of beta distribution\n",
-    "DiscFac_dstn = approxUniform(num_types, DiscFac_mean-DiscFac_spread, DiscFac_mean+DiscFac_spread)[1]\n",
+    "DiscFac_dstn = approxUniform(num_types, DiscFac_mean-DiscFac_spread, DiscFac_mean+DiscFac_spread).X\n",
     "BaselineType = deepcopy(KSAgent)\n",
     "\n",
     "MyTypes = [] # initialize an empty list to hold our consumer types\n",

notebooks/KrusellSmith.py

@@ -465,13 +465,13 @@ def in_ipynb():
 
 # %% {"code_folding": []}
 # Construct the distribution of types
-from HARK.utilities import approxUniform
+from HARK.distribution import approxUniform
 
 # Specify the distribution of the discount factor
 num_types = 3              # number of types we want;
 DiscFac_mean   = 0.9858    # center of beta distribution 
 DiscFac_spread = 0.0085    # spread of beta distribution
-DiscFac_dstn = approxUniform(num_types, DiscFac_mean-DiscFac_spread, DiscFac_mean+DiscFac_spread)[1]
+DiscFac_dstn = approxUniform(num_types, DiscFac_mean-DiscFac_spread, DiscFac_mean+DiscFac_spread).X
 BaselineType = deepcopy(KSAgent)
 
 MyTypes = [] # initialize an empty list to hold our consumer types

notebooks/Micro-and-Macro-Implications-of-Very-Impatient-HHs.py

@@ -7,7 +7,7 @@
 #       extension: .py
 #       format_name: percent
 #       format_version: '1.2'
-#       jupytext_version: 1.2.1
+#       jupytext_version: 1.2.4
 #   kernelspec:
 #     display_name: Python 3
 #     language: python
@@ -186,14 +186,14 @@ def in_ipynb():
 
 # %%
 # This cell constructs seven instances of IndShockConsumerType with different discount factors
-from HARK.utilities import approxUniform
+from HARK.distribution import approxUniform
 BaselineType = IndShockConsumerType(**cstwMPC_calibrated_parameters)
 
 # Specify the distribution of the discount factor
 num_types = 7              # number of types we want
 DiscFac_mean   = 0.9855583 # center of beta distribution 
 DiscFac_spread = 0.0085    # spread of beta distribution
-DiscFac_dstn = approxUniform(num_types, DiscFac_mean-DiscFac_spread, DiscFac_mean+DiscFac_spread)[1]
+DiscFac_dstn = approxUniform(num_types, DiscFac_mean-DiscFac_spread, DiscFac_mean+DiscFac_spread).X
 
 MyTypes = [] # initialize an empty list to hold our consumer types
 for nn in range(num_types):

notebooks/Nondurables-During-Great-Recession.py

@@ -131,7 +131,7 @@
 # Calibrations from cstwMPC
 bottomDiscFac  = 0.9800
 topDiscFac     = 0.9934
-DiscFac_list   = approxUniform(N=num_consumer_types,bot=bottomDiscFac,top=topDiscFac)[1]
+DiscFac_list   = approxUniform(N=num_consumer_types,bot=bottomDiscFac,top=topDiscFac).X
 
 # Now, assign the discount factors
 for j in range(num_consumer_types):

notebooks/Structural-Estimates-From-Empirical-MPCs-Fagereng-et-al.ipynb

@@ -22,7 +22,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {
     "code_folding": [
      0
@@ -41,7 +41,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "metadata": {
     "code_folding": [
      0
@@ -61,7 +61,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {
     "code_folding": [
      0
@@ -81,7 +81,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {
     "code_folding": [
      0
@@ -104,7 +104,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {
     "code_folding": [
      0
@@ -123,7 +123,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {
     "code_folding": [
      0
@@ -138,7 +138,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {
     "code_folding": [
      0
@@ -158,7 +158,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "metadata": {
     "code_folding": [
      0
@@ -190,7 +190,7 @@
     "        Euclidean distance between simulated MPCs and (adjusted) Table 9 MPCs.\n",
     "    '''\n",
     "    # Give our consumer types the requested discount factor distribution\n",
-    "    beta_set = approxUniform(N=TypeCount,bot=center-spread,top=center+spread)[1]\n",
+    "    beta_set = approxUniform(N=TypeCount,bot=center-spread,top=center+spread).X\n",
     "    for j in range(TypeCount):\n",
     "        EstTypeList[j](DiscFac = beta_set[j])\n",
     "\n",
@@ -256,13 +256,117 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "metadata": {
     "code_folding": [
      0
     ]
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.92 0.03 1.1274854942184123\n",
+      "0.9660000000000001 0.03 1.8595059870322312\n",
+      "0.92 0.0315 1.1283488603479197\n",
+      "0.874 0.0315 0.778255871453904\n",
+      "0.8280000000000001 0.03225 0.6819825156919915\n",
+      "0.8280000000000002 0.03075 0.6835548812203114\n",
+      "0.7360000000000001 0.033 0.7922890441302506\n",
+      "0.782 0.03225 0.7016715189874164\n",
+      "0.8740000000000001 0.03075 0.7788170641661598\n",
+      "0.805 0.031875 0.6805742675552975\n",
+      "0.8049999999999998 0.033375 0.6787844220096427\n",
+      "0.7934999999999997 0.03468750000000001 0.6852355238234841\n",
+      "0.7819999999999998 0.033 0.700889842861641\n",
+      "0.8165 0.0324375 0.677362729820926\n",
+      "0.8164999999999997 0.033937499999999995 0.6757164456766519\n",
+      "0.8222499999999997 0.03496874999999999 0.6758511425548946\n",
+      "0.8279999999999997 0.03299999999999999 0.6811133592390257\n",
+      "0.8107499999999999 0.03328125 0.676894640632367\n",
+      "0.8107499999999996 0.03478124999999999 0.6750631689086809\n",
+      "0.8078749999999992 0.03595312499999999 0.6744239196038181\n",
+      "0.8136249999999989 0.036609374999999986 0.6724284249651289\n",
+      "0.8150624999999985 0.03827343749999998 0.6703322941257293\n",
+      "0.806437499999998 0.04028906249999997 0.6692403829292708\n",
+      "0.8014062499999974 0.043464843749999954 0.6673189410747502\n",
+      "0.8085937499999967 0.045785156249999945 0.6604269189061285\n",
+      "0.8089531249999955 0.05070117187499992 0.65272712055137\n",
+      "0.7952968749999945 0.0558925781249999 0.6531048166528524\n",
+      "0.8028437499999925 0.06312890624999987 0.6352275674142442\n",
+      "0.8035624999999902 0.07296093749999982 0.6174053756797356\n",
+      "0.8172187499999912 0.06776953124999985 0.623279644640036\n",
+      "0.8118281249999859 0.09002929687499975 0.5843314701962605\n",
+      "0.813265624999981 0.10969335937499966 0.5516622208832312\n",
+      "0.79960937499998 0.11488476562499964 0.5453880007399536\n",
+      "0.7908046874999743 0.13844238281249954 0.5163269956570604\n",
+      "0.8005078124999652 0.17517480468749938 0.5499092597796972\n",
+      "0.7780468749999585 0.20392382812499923 0.5813614142333464\n",
+      "0.8044609374999754 0.13325097656249957 0.5202086128131443\n",
+      "0.7947578124999846 0.09651855468749973 0.5805560040839527\n",
+      "0.7990703124999701 0.15551074218749947 0.5060419354103581\n",
+      "0.785414062499969 0.16070214843749947 0.5034386878700592\n",
+      "0.775890624999966 0.17442773437499942 0.5149023099777296\n",
+      "0.7936796874999648 0.17777050781249937 0.5315857186141032\n",
+      "0.791523437499972 0.1482744140624995 0.5061530602276895\n",
+      "0.7929609374999671 0.1679384765624994 0.5082715311300752\n",
+      "0.7918828124999708 0.15319042968749946 0.5033949436309413\n",
+      "0.7782265624999698 0.15838183593749947 0.512748835721533\n",
+      "0.7938593749999701 0.15622851562499945 0.5028877731574135\n",
+      "0.8003281249999719 0.14871679687499945 0.5068348206273852\n",
+      "0.7891425781249697 0.15770581054687446 0.502519441768042\n",
+      "0.7911191406249689 0.16074389648437443 0.5022974415782882\n",
+      "0.7907373046874677 0.1645206298828119 0.5032698407410588\n",
+      "0.7864023437499685 0.1622211914062494 0.5027664061147642\n",
+      "0.7882666015624689 0.16072302246093692 0.502198620174269\n",
+      "0.7902431640624681 0.16376110839843686 0.5026328874780666\n",
+      "0.7894177246093443 0.15921963500976505 0.5021703660917155\n",
+      "0.7865651855468443 0.15919876098632751 0.5030573631478745\n",
+      "0.7899806518554378 0.1603576126098627 0.502160531034932\n",
+      "0.7911317749023132 0.15885422515869083 0.5022769519932859\n",
+      "0.78898289489743 0.1602558231353754 0.502180579074867\n",
+      "0.7904154815673521 0.15932142448425235 0.5021833317477105\n",
+      "0.7893410415649105 0.16002222347259465 0.5021165086178974\n",
+      "0.7899039688110039 0.1611602010726923 0.5021112183082238\n",
+      "0.7901470909118338 0.16213048410415593 0.5022990867023371\n",
+      "0.7892643585204766 0.16082481193542425 0.502159403561549\n",
+      "0.7894434318542168 0.16070801210403385 0.5021404531991109\n",
+      "0.7898015785216975 0.1604744124412531 0.50213329203524\n",
+      "0.7897120418548274 0.16053281235694827 0.5021302497927074\n",
+      "0.789532968521087 0.16064961218833868 0.5021304652782825\n",
+      "0.7896672735213923 0.1605620123147959 0.502126210894908\n",
+      "0.789577736854522 0.16062041223049106 0.5021314791648644\n",
+      "0.7896448893546748 0.16057661229371967 0.5021356844024427\n",
+      "0.7896225051879572 0.16059121227264347 0.5021256061827057\n",
+      "0.7897856211661981 0.1608611066937441 0.5021324277978072\n",
+      "0.7897408528327629 0.16089030665159165 0.5021362355798749\n",
+      "0.7897744290828392 0.16086840668320598 0.5021389064710859\n",
+      "0.7897632369994805 0.16087570667266787 0.502129658550051\n",
+      "0.7898447949886009 0.1610106538832182 0.5021074951519786\n",
+      "0.7899855268001243 0.1612951482832426 0.5021245728056754\n",
+      "0.7899299543499633 0.16119028788059891 0.5021135412553295\n",
+      "0.7898188094496416 0.16098056707531155 0.5021025392131131\n",
+      "0.7897632369994805 0.16087570667266787 0.502129658550051\n",
+      "0.7897596356272387 0.16083101988583742 0.5021341894826656\n",
+      "0.7898678855150626 0.16107790577597858 0.5021133757712383\n",
+      "0.7898318022191213 0.16099561047926486 0.5021068384032057\n",
+      "0.7898613891303228 0.16107038407400193 0.5021036662483996\n",
+      "Optimization terminated successfully.\n",
+      "         Current function value: 0.502103\n",
+      "         Iterations: 41\n",
+      "         Function evaluations: 85\n",
+      "Time to estimate is 188.1969873905182 seconds.\n",
+      "Finished estimating for scaling factor of 1.0 and \"splurge amount\" of $0.0\n",
+      "Optimal (beta,nabla) is [0.78981881 0.16098057], simulated MPCs are:\n",
+      "[[0.77361336 0.68317127 0.56461082 0.40476962]\n",
+      " [0.74354975 0.66482752 0.55301552 0.39626053]\n",
+      " [0.70353353 0.63512154 0.5305429  0.3793119 ]\n",
+      " [0.5613238  0.50428804 0.4125933  0.29261249]]\n",
+      "Distance from Fagereng et al Table 9 is 0.5021025392131131\n"
+     ]
+    }
+   ],
    "source": [
     "# Conduct the estimation\n",
     "\n",
@@ -277,7 +381,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -286,7 +390,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [

notebooks/Structural-Estimates-From-Empirical-MPCs-Fagereng-et-al.py

@@ -120,7 +120,7 @@ def FagerengObjFunc(center,spread,verbose=False):
         Euclidean distance between simulated MPCs and (adjusted) Table 9 MPCs.
     '''
     # Give our consumer types the requested discount factor distribution
-    beta_set = approxUniform(N=TypeCount,bot=center-spread,top=center+spread)[1]
+    beta_set = approxUniform(N=TypeCount,bot=center-spread,top=center+spread).X
     for j in range(TypeCount):
         EstTypeList[j](DiscFac = beta_set[j])