In [1]:

    

%matplotlib inline


    

In [122]:

    

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd


    

In [3]:

    

a = np.random.random(10)


    

In [4]:

    

def make_viz(arr):
    def showit(width, height, cast_as, global_bounds = False):
        v = arr.view(cast_as)[:width*height].reshape((width, height))
        plt.clf()
        if global_bounds == True:
            mi = np.iinfo(cast_as).min
            ma = np.iinfo(cast_as).max
        elif global_bounds in ("u1", "u2", "u4", "u8"):
            print("Using ...")
            mi = np.iinfo(global_bounds).min
            ma = np.iinfo(global_bounds).max
            print("Using ...", mi, ma, v.min(), v.max())
        else:
            mi = v.min()
            ma = v.max()
        print(mi, ma)
        plt.imshow(v, vmin = mi, vmax = ma, cmap="viridis")
    return showit


    

In [5]:

    

arr = np.arange(256).reshape((16, 16), order="F").astype("uint8")


    

In [6]:

    

s = make_viz(arr)


    

In [7]:

    

s(16, 16, "uint8")


    

    




0 255






    

In [8]:

    

arr, _ = np.mgrid[0:1:256j, 0:1:256j]


    

In [9]:

    

s = make_viz(arr)


    

In [10]:

    

arr.size, arr.view("uint64").size


    

    
Out[10]:





(65536, 65536)

In [11]:

    

256*256*2


    

    
Out[11]:





131072

In [12]:

    

s(1024, 1024, "uint8", "u1")


    

    




---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-12-cc5a196ac45a> in <module>()
----> 1 s(1024, 1024, "uint8", "u1")

<ipython-input-4-385068e2483f> in showit(width, height, cast_as, global_bounds)
      1 def make_viz(arr):
      2     def showit(width, height, cast_as, global_bounds = False):
----> 3         v = arr.view(cast_as)[:width*height].reshape((width, height))
      4         plt.clf()
      5         if global_bounds == True:

ValueError: cannot reshape array of size 524288 into shape (1024,1024)

In [13]:

    

np.iinfo("u1")


    

    
Out[13]:





iinfo(min=0, max=255, dtype=uint8)

In [14]:

    

131072/256


    

    
Out[14]:





512.0

In [15]:

    

arr


    

    
Out[15]:





array([[ 0.        ,  0.        ,  0.        , ...,  0.        ,
         0.        ,  0.        ],
       [ 0.00392157,  0.00392157,  0.00392157, ...,  0.00392157,
         0.00392157,  0.00392157],
       [ 0.00784314,  0.00784314,  0.00784314, ...,  0.00784314,
         0.00784314,  0.00784314],
       ..., 
       [ 0.99215686,  0.99215686,  0.99215686, ...,  0.99215686,
         0.99215686,  0.99215686],
       [ 0.99607843,  0.99607843,  0.99607843, ...,  0.99607843,
         0.99607843,  0.99607843],
       [ 1.        ,  1.        ,  1.        , ...,  1.        ,
         1.        ,  1.        ]])

In [16]:

    

val = "390"
arr = np.array(val, dtype="c")


    

In [17]:

    

arr.view("uint8")


    

    
Out[17]:





array([51, 57, 48], dtype=uint8)

In [18]:

    

np.unpackbits(arr[0].view("uint8"))


    

    
Out[18]:





array([0, 0, 1, 1, 0, 0, 1, 1], dtype=uint8)

In [19]:

    

np.unpackbits(arr[1].view("uint8"))


    

    
Out[19]:





array([0, 0, 1, 1, 1, 0, 0, 1], dtype=uint8)

In [20]:

    

np.unpackbits(arr[2].view("uint8"))


    

    
Out[20]:





array([0, 0, 1, 1, 0, 0, 0, 0], dtype=uint8)

In [21]:

    

arr = np.array([390], dtype="f8")


    

In [22]:

    

arr


    

    
Out[22]:





array([ 390.])

In [23]:

    

np.unpackbits(arr.view("u1"))


    

    
Out[23]:





array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0,
       0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0], dtype=uint8)

In [24]:

    

np.array("1.83970e-003", dtype="c").view("uint8")


    

    
Out[24]:





array([ 49,  46,  56,  51,  57,  55,  48, 101,  45,  48,  48,  51], dtype=uint8)

In [25]:

    

_.size


    

    
Out[25]:





65536

In [29]:

    

a = np.array([390], dtype="f8")
a.view("u1")


    

    
Out[29]:





array([  0,   0,   0,   0,   0,  96, 120,  64], dtype=uint8)

In [32]:

    

np.array([1.83970e-003], dtype="f8").view("uint8")


    

    
Out[32]:





array([  2, 166, 103, 213,  66,  36,  94,  63], dtype=uint8)

In [33]:

    

!head data-readonly/IL_Building_Inventory.csv


    

    




address,agency_name,bldg_status,city,congress_dist,congressional_full_name,county,floors_above_grade,floors_below_grade,location_name,rep_dist,rep_full_name,senate_dist,senator_full_name,square_footage,total_floors,usage_description,usage_description_2,usage_description_3,year_acquired,year_constructed,zip_code
Anderson Lake C.a.,Department of Natural Resources,In Use,Astoria,17,Cheri Bustos,Fulton,1,0,Anderson Lake Conservation Area - Fulton County,93,Hammond Norine K.,47,Jil Tracy,144,1,Unusual,Unusual,Not provided,1975,1975,61501
Anderson Lake C.a.,Department of Natural Resources,In Use,Astoria,17,Cheri Bustos,Fulton,1,0,Anderson Lake Conservation Area - Fulton County,93,Hammond Norine K.,47,Jil Tracy,144,1,Unusual,Unusual,Not provided,2004,2004,61501
Anderson Lake C.a.,Department of Natural Resources,In Use,Astoria,17,Cheri Bustos,Fulton,1,0,Anderson Lake Conservation Area - Fulton County,93,Hammond Norine K.,47,Jil Tracy,144,1,Unusual,Unusual,Not provided,2004,2004,61501
Anderson Lake C.a.,Department of Natural Resources,In Use,Astoria,17,Cheri Bustos,Fulton,1,0,Anderson Lake Conservation Area - Fulton County,93,Hammond Norine K.,47,Jil Tracy,144,1,Unusual,Unusual,Not provided,2004,2004,61501
Anderson Lake C.a.,Department of Natural Resources,In Use,Astoria,17,Cheri Bustos,Fulton,1,0,Anderson Lake Conservation Area - Fulton County,93,Hammond Norine K.,47,Jil Tracy,144,1,Unusual,Unusual,Not provided,2004,2004,61501
Anderson Lake C.a.,Department of Natural Resources,In Use,Astoria,17,Cheri Bustos,Fulton,1,0,Anderson Lake Conservation Area - Fulton County,93,Hammond Norine K.,47,Jil Tracy,144,1,Unusual,Unusual,Not provided,2004,2004,61501
Rr #2,Department of Natural Resources,In Use,Golconda,15,John Shimkus,Pope,1,0,Dixon Springs State Park - Pope County,118,Phelps Brandon W.,59,Dale Fowler,120,1,Unusual,Unusual,Not provided,2000,2000,62938
Rr #2,Department of Natural Resources,In Use,Golconda,15,John Shimkus,Pope,1,0,Dixon Springs State Park - Pope County,118,Phelps Brandon W.,59,Dale Fowler,120,1,Unusual,Unusual,Not provided,2000,2000,62938
"R. R. 178, Box 509",Department of Natural Resources,In Use,Utica,16,Adam Kinzinger,LaSalle,1,0,Matthiessen State Park - LaSalle County,76,Long Jerry Lee,38,Sue Rezin,144,1,Unusual,Unusual,Not provided,2000,2000,61373

In [115]:

    

import pandas as pd
df = pd.read_csv("data-readonly/IL_Building_Inventory.csv", nrows=100000)


    

In [116]:

    

df.columns


    

    
Out[116]:





Index(['Agency Name', 'Location Name', 'Address', 'City', 'Zip code', 'County',
       'Congress Dist', 'Congressional Full Name', 'Rep Dist', 'Rep Full Name',
       'Senate Dist', 'Senator Full Name', 'Bldg Status', 'Year Acquired',
       'Year Constructed', 'Square Footage', 'Total Floors',
       'Floors Above Grade', 'Floors Below Grade', 'Usage Description',
       'Usage Description 2', 'Usage Description 3'],
      dtype='object')

In [117]:

    

df.shape


    

    
Out[117]:





(8862, 22)

In [118]:

    

df2 = df[ (df["Agency Name"] == "University of Illinois") &(df["Address"] == "501 E Daniel")]


    

In [119]:

    

print(df2.to_json(lines=True, orient="records"))


    

    




{"Agency Name":"University of Illinois","Location Name":"University of Illinois Urbana-Champaign","Address":"501 E Daniel","City":"Champaign","Zip code":61820,"County":"Champaign","Congress Dist":13,"Congressional Full Name":"Rodney L. Davis","Rep Dist":103,"Rep Full Name":"Ammons Carol","Senate Dist":52,"Senator Full Name":"Scott M. Bennett","Bldg Status":"In Use","Year Acquired":1992,"Year Constructed":1935,"Square Footage":21845,"Total Floors":5,"Floors Above Grade":4,"Floors Below Grade":1,"Usage Description":"Assembly","Usage Description 2":"Assembly","Usage Description 3":"Not provided"}

In [120]:

    

[_ for _ in list(df.Address) if "Daniel" in str(_)]


    

    
Out[120]:





['601 East Daniel Street', '501 E Daniel']

In [121]:

    

df.shape


    

    
Out[121]:





(8862, 22)

In [130]:

    

plt.style.use("ggplot")
plt.rcParams["xtick.labelsize"] = 20
plt.rcParams["ytick.labelsize"] = 20


    

In [ ]:

    

x = np.mgrid[0:2*np.pi:64j]


    

In [161]:

    

fig = plt.figure(figsize=(15, 12))
plt.plot(x, np.sin(x), '.-')
plt.ylim(-1.1, 1.1)
plt.xlim(-0.1, 2.0*np.pi + 0.1)


    

    
Out[161]:





(-0.1, 6.383185307179586)






    

In [162]:

    

fig = plt.figure(figsize=(15, 12))
for i, n in enumerate([1, 2, 8, 16]):
    ax = fig.add_subplot(2, 2, i + 1)
    ax.plot(x[::n], np.sin(x[::n]), '.-')
    ax.set_ylim(-1.1, 1.1)
    ax.set_xlim(-0.1, 2.0*np.pi + 0.1)
    ax.set_title("Step size %s" % n)


    

In [165]:

    

fig = plt.figure(figsize=(15, 12))
for i, n in enumerate([1, 2, 8, 16]):
    ind = np.arange(x.size)
    np.random.shuffle(ind)
    ind = ind[:x.size//n]
    ind.sort()
    ax = fig.add_subplot(2, 2, i + 1)
    ax.plot(x[ind], np.sin(x[ind]), '.-')
    ax.set_title("Random %s" % (x.size/n))
    ax.set_ylim(-1.1, 1.1)
    ax.set_xlim(-0.1, 2.0*np.pi + 0.1)


    

In [170]:

    

x = np.mgrid[0:2*np.pi:64j]


    

In [171]:

    

fig = plt.figure(figsize=(15, 12))
plt.plot(x, np.sin(3*x), '.-')
plt.ylim(-1.1, 1.1)
plt.xlim(-0.1, 2.0*np.pi + 0.1)


    

    
Out[171]:





(-0.1, 6.383185307179586)






    

In [172]:

    

fig = plt.figure(figsize=(15, 12))
for i, n in enumerate([1, 2, 8, 16]):
    ax = fig.add_subplot(2, 2, i + 1)
    ax.plot(x[::n], np.sin(3*x[::n]), '.-')
    ax.set_ylim(-1.1, 1.1)
    ax.set_xlim(-0.1, 2.0*np.pi + 0.1)
    ax.set_title("Step size %s" % n)


    

In [173]:

    

fig = plt.figure(figsize=(15, 12))
for i, n in enumerate([1, 2, 8, 16]):
    ind = np.arange(x.size)
    np.random.shuffle(ind)
    ind = ind[:x.size//n]
    ind.sort()
    ax = fig.add_subplot(2, 2, i + 1)
    ax.plot(x[ind], np.sin(3*x[ind]), '.-')
    ax.set_title("Random %s" % (x.size/n))
    ax.set_ylim(-1.1, 1.1)
    ax.set_xlim(-0.1, 2.0*np.pi + 0.1)


    

In [174]:

    

a = []
while len(a) < 5:
    a.append(input("Hello!"))


    

    




Hello!hi
Hello!hi
Hello!t
Hello!1
Hello!2

In [ ]: